commit 4f9296236a3cb46ccc1f9548ec6eba32eff8c906
Author: zhurui <274461951@qq.com>
Date:   Thu Jul 4 17:06:52 2024 +0800

    first commit

diff --git a/LICENSE b/LICENSE
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+MIT License
+
+Copyright (c) 2021 Or Patashnik, Zongze Wu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
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+# StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery (ICCV 2021 Oral)
+
+[Run this model on Replicate](https://replicate.ai/orpatashnik/styleclip)
+
+Optimization: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/orpatashnik/StyleCLIP/blob/main/notebooks/optimization_playground.ipynb)
+Mapper: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/orpatashnik/StyleCLIP/blob/main/notebooks/mapper_playground.ipynb)
+
+Global directions Torch: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/orpatashnik/StyleCLIP/blob/main/notebooks/StyleCLIP_global_torch.ipynb)
+Global directions TF1: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/orpatashnik/StyleCLIP/blob/main/notebooks/StyleCLIP_global.ipynb)
+
+
+<p align="center">
+  <a href="https://www.youtube.com/watch?v=5icI0NgALnQ"><img src='https://github.com/orpatashnik/StyleCLIP/blob/main/img/StyleCLIP_gif.gif' width=600 ></a>
+  
+Full Demo Video: <a href="https://www.youtube.com/watch?v=5icI0NgALnQ"><img src="https://img.shields.io/badge/-YouTube-red?&style=for-the-badge&logo=youtube&logoColor=white" height=20></a> &nbsp;&nbsp;&nbsp; ICCV Video <a href="https://www.youtube.com/watch?v=PhR1gpXDu0w"><img src="https://img.shields.io/badge/-YouTube-red?&style=for-the-badge&logo=youtube&logoColor=white" height=20></a>
+  
+</p>
+
+
+
+![](img/teaser.png)
+
+> **StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery**<br>
+> Or Patashnik*, Zongze Wu*, Eli Shechtman, Daniel Cohen-Or, Dani Lischinski <br>
+> *Equal contribution, ordered alphabetically <br>
+> https://arxiv.org/abs/2103.17249 <br>
+>
+>**Abstract:** Inspired by the ability of StyleGAN to generate highly realistic
+images in a variety of domains, much recent work has
+focused on understanding how to use the latent spaces of
+StyleGAN to manipulate generated and real images. However,
+discovering semantically meaningful latent manipulations
+typically involves painstaking human examination of
+the many degrees of freedom, or an annotated collection
+of images for each desired manipulation. In this work, we
+explore leveraging the power of recently introduced Contrastive
+Language-Image Pre-training (CLIP) models in order
+to develop a text-based interface for StyleGAN image
+manipulation that does not require such manual effort. We
+first introduce an optimization scheme that utilizes a CLIP-based
+loss to modify an input latent vector in response to a
+user-provided text prompt. Next, we describe a latent mapper
+that infers a text-guided latent manipulation step for
+a given input image, allowing faster and more stable textbased
+manipulation. Finally, we present a method for mapping
+a text prompts to input-agnostic directions in StyleGAN’s
+style space, enabling interactive text-driven image
+manipulation. Extensive results and comparisons demonstrate
+the effectiveness of our approaches.
+
+
+## Description
+Official Implementation of StyleCLIP, a method to manipulate images using a driving text. 
+Our method uses the generative power of a pretrained StyleGAN generator, and the visual-language power of CLIP.
+In the paper we present three methods: 
+- Latent vector optimization.
+- Latent mapper, trained to manipulate latent vectors according to a specific text description.
+- Global directions in the StyleSpace.
+
+
+## Updates
+**31/10/2022** Add support for global direction with torch implementation
+
+**15/8/2021** Add support for StyleSpace in optimization and latent mapper methods
+
+**6/4/2021** Add mapper training and inference (including a jupyter notebook) code
+
+**6/4/2021** Add support for custom StyleGAN2 and StyleGAN2-ada models, and also custom images
+
+**2/4/2021** Add the global directions code (a local GUI and a colab notebook)
+
+**31/3/2021** Upload paper to arxiv, and video to YouTube
+
+**14/2/2021** Initial version
+
+## Setup  (for all three methods)
+For all the methods described in the paper, is it required to have:
+- Anaconda
+- [CLIP](https://github.com/openai/CLIP)
+
+Specific requirements for each method are described in its section. 
+To install CLIP please run the following commands:
+  ```shell script
+conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=<CUDA_VERSION>
+pip install ftfy regex tqdm gdown
+pip install git+https://github.com/openai/CLIP.git
+```
+
+
+## Editing via Latent Vector Optimization
+
+### Setup
+
+Here, the code relies on the [Rosinality](https://github.com/rosinality/stylegan2-pytorch/) pytorch implementation of StyleGAN2.
+Some parts of the StyleGAN implementation were modified, so that the whole implementation is native pytorch.
+
+In addition to the requirements mentioned before, a pretrained StyleGAN2 generator will attempt to be downloaded, (or manually download from [here](https://drive.google.com/file/d/1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT/view?usp=sharing)).
+
+### Usage
+
+Given a textual description, one can both edit a given image, or generate a random image that best fits to the description.
+Both operations can be done through the `main.py` script, or the `optimization_playground.ipynb` notebook ([![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/orpatashnik/StyleCLIP/blob/main/notebooks/optimization_playground.ipynb)).
+
+#### Editing
+To edit an image set `--mode=edit`. Editing can be done on both provided latent vector, and on a random latent vector from StyleGAN's latent space.
+It is recommended to adjust the `--l2_lambda` according to the desired edit. 
+
+#### Generating Free-style Images
+To generate a free-style image set `--mode=free_generation`.
+
+## Editing via Latent Mapper
+Here, we provide the code for the latent mapper. The mapper is trained to learn *residuals* from a given latent vector, according to the driving text.
+The code for the mapper is in `mapper/`.
+
+### Setup
+As in the optimization, the code relies on [Rosinality](https://github.com/rosinality/stylegan2-pytorch/) pytorch implementation of StyleGAN2.
+In addition the the StyleGAN weights, it is neccessary to have weights for the facial recognition network used in the ID loss. 
+The weights can be downloaded from [here](https://drive.google.com/file/d/1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn/view?usp=sharing).
+
+The mapper is trained on latent vectors. It is recommended to train on *inverted real images*. 
+To this end, we provide the CelebA-HQ that was inverted by e4e:
+[train set](https://drive.google.com/file/d/1gof8kYc_gDLUT4wQlmUdAtPnQIlCO26q/view?usp=sharing), [test set](https://drive.google.com/file/d/1j7RIfmrCoisxx3t-r-KC02Qc8barBecr/view?usp=sharing).
+
+### Usage
+
+#### Training
+- The main training script is placed in `mapper/scripts/train.py`.
+- Training arguments can be found at `mapper/options/train_options.py`.
+- Intermediate training results are saved to opts.exp_dir. This includes checkpoints, train outputs, and test outputs.
+Additionally, if you have tensorboard installed, you can visualize tensorboard logs in opts.exp_dir/logs.
+Note that
+- To resume a training, please provide `--checkpoint_path`.
+- `--description` is where you provide the driving text.
+- If you perform an edit that is not supposed to change "colors" in the image, it is recommended to use the flag `--no_fine_mapper`.
+
+Example for training a mapper for the moahwk hairstyle:
+```bash
+cd mapper
+python train.py --exp_dir ../results/mohawk_hairstyle --no_fine_mapper --description "mohawk hairstyle"
+```
+All configurations for the examples shown in the paper are provided there.
+
+#### Inference
+- The main inferece script is placed in `mapper/scripts/inference.py`.
+- Inference arguments can be found at `mapper/options/test_options.py`.
+- Adding the flag `--couple_outputs` will save image containing the input and output images side-by-side.
+
+Pretrained models for variuos edits are provided. Please refer to `utils.py` for the complete links list.
+
+We also provide a notebook for performing inference with the mapper Mapper notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/orpatashnik/StyleCLIP/blob/main/notebooks/mapper_playground.ipynb)
+
+## Editing via Global Direction
+
+Here we provide GUI for editing images with the global directions. 
+We provide both a jupyter notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/orpatashnik/StyleCLIP/blob/main/notebooks/StyleCLIP_global.ipynb),
+and the GUI used in the [video](https://www.youtube.com/watch?v=5icI0NgALnQ).
+For both, the linear direction are computed in **real time**.
+The code is located at `global_directions/`.
+
+  
+### Setup
+Here, we rely on the [official](https://github.com/NVlabs/stylegan2) TensorFlow implementation of StyleGAN2.
+
+It is required to have TensorFlow, version 1.14 or 1.15 (`conda install -c anaconda tensorflow-gpu==1.14`).
+
+### Usage
+
+
+#### Local GUI
+
+To start the local GUI please run the following commands:
+
+```shell script
+cd global_directions
+
+# input dataset name 
+dataset_name='ffhq' 
+
+# pretrained StyleGAN2 model from standard [NVlabs implementation](https://github.com/NVlabs/stylegan2) will be download automatically.
+# pretrained StyleGAN2-ada model could be download from https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/ .
+# for custom StyleGAN2 or StyleGAN2-ada model, please place the model under ./StyleCLIP/global_directions/model/ folder.
+
+
+# input prepare data 
+python GetCode.py --dataset_name $dataset_name --code_type 'w'
+python GetCode.py --dataset_name $dataset_name --code_type 's'
+python GetCode.py --dataset_name $dataset_name --code_type 's_mean_std'
+
+# preprocess (this may take a few hours). 
+# we precompute the results for StyleGAN2 on ffhq, StyleGAN2-ada on afhqdog, afhqcat. For these model, we can skip the preprocess step.
+python SingleChannel.py --dataset_name $dataset_name
+
+# generated image to be manipulated 
+# this operation will generate and replace the w_plu.npy and .jpg images in './data/dataset_name/' folder. 
+# if you you want to keep the original data, please rename the original folder.
+# to use custom images, please use e4e encoder to generate latents.pt, and place it in './data/dataset_name/' folder, and add --real flag while running this function.
+# you may skip this step if you want to manipulate the real human faces we prepare in ./data/ffhq/ folder.   
+python GetGUIData.py --dataset_name $dataset_name
+
+# interactively manipulation 
+python PlayInteractively.py --dataset_name $dataset_name
+```
+
+As shown in the video, to edit an image it is requires to write a _neutral text_ and a _target text_. 
+To operate the GUI, please do the following:
+- Maximize the window size
+- Double click on the left square to choose an image. The images are taken from  `global_directions/data/ffhq`, and the corresponding latent vectors are in `global_directions/data/ffhq/w_plus.npy`.
+- Type a neutral text, then press enter
+- Modify the target text so that it will contain the target edit, then press enter.
+
+You can now play with:
+- *Manipulation strength* - positive values correspond to moving along the target direction.
+- *Disentanglement threshold* - large value means more disentangled edit, just a few channels will be manipulated so only the target attribute will change (for example, grey hair). Small value means less disentangled edit, a large number of channels will be manipulated, related attributes will also change (such as wrinkle, skin color, glasses).
+
+##### Examples:
+
+| Edit  | Neutral Text | Target Text |
+| --- | --- | --- |
+| Smile  | face  | smiling face |
+| Gender  | female face  | male face |
+| Blonde hair | face with hair | face with blonde hair |
+| Hi-top fade | face with hair | face with Hi-top fade hair |
+| Blue eyes | face with eyes | face with blue eyes |
+
+More examples could be found in the [video](https://www.youtube.com/watch?v=5icI0NgALnQ) and in the paper.
+
+
+##### Pratice Tips: 
+In the terminal, for every manipulation, the number of channels being manipulated is printed (the number is controlled by the attribute (neutral, target) and the disentanglement threshold).
+
+1. For color transformation, usually 10-20 channels is enough. For large structure change (for example, Hi-top fade), usually 100-200 channels are required.
+2. For an attribute (neutral, target), if you give a low disentanglement threshold, there are just few channels (<20) being manipulated, and usually it is not enough for performing the desired edit.
+
+
+#### Notebook
+Open the notebook in colab and run all the cells. In the last cell you can play with the image.
+
+`beta` corresponds to the *disentanglement threshold*, and `alpha` to the *manipulation strength*. 
+
+After you set the desired set of parameters, please run again the last cell to generate the image.
+
+## Editing Examples
+
+In the following, we show some results obtained with our methods. 
+All images are real, and were inverted into the StyleGAN's latent space using [e4e](https://github.com/omertov/encoder4editing). 
+The driving text that was used for each edit appears below or above each image.
+
+#### Latent Optimization
+
+![](img/me.png)
+![](img/ariana.png)
+![](img/federer.png)
+![](img/styles.png)
+
+#### Latent Mapper
+
+![](img/mapper_hairstyle.png)
+
+#### Global Directions
+
+![](img/global_example_1.png)
+![](img/global_example_2.png)
+![](img/global_example_3.png)
+![](img/global_example_4.png)
+
+## Related Works
+
+The global directions we find for editing are direction in the _S Space_, which was introduced and analyzed in [StyleSpace](https://arxiv.org/abs/2011.12799) (Wu et al).
+
+To edit real images, we inverted them to the StyleGAN's latent space using [e4e](https://arxiv.org/abs/2102.02766) (Tov et al.).
+
+The code strcuture of the mapper is heavily based on [pSp](https://github.com/eladrich/pixel2style2pixel). 
+
+## Citation
+
+If you use this code for your research, please cite our paper:
+
+```
+@InProceedings{Patashnik_2021_ICCV,
+    author    = {Patashnik, Or and Wu, Zongze and Shechtman, Eli and Cohen-Or, Daniel and Lischinski, Dani},
+    title     = {StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    month     = {October},
+    year      = {2021},
+    pages     = {2085-2094}
+}
+```
diff --git a/cog.yaml b/cog.yaml
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+++ b/cog.yaml
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+build:
+  gpu: true
+  system_packages:
+    - libgl1-mesa-glx
+    - libglib2.0-0
+    - cmake
+    - zip
+  python_version: 3.7
+  python_packages:
+    - torch==1.7.1
+    - tensorflow==1.15.0
+    - torchvision==0.8.2
+    - torchaudio==0.7.2
+    - ftfy==5.9
+    - regex==2021.4.4
+    - tqdm==4.59.0
+    - requests==2.25.1
+    - matplotlib==3.4.1
+    - opencv-python==4.3.0.38
+    - dlib==19.18.0
+    - scipy==1.6.3
+    - "git+git://github.com/openai/CLIP.git@8a665a683d791ed3491fedadcb3c91878f9eb78d"
+  pre_install:
+    - "mkdir /content"
+    - "git clone https://github.com/omertov/encoder4editing.git /content/encoder4editing"
+    - "wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip"
+    - "unzip ninja-linux.zip -d /usr/local/bin/"
+    - "update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force"
+    - "wget -O /content/shape_predictor_68_face_landmarks.dat.bz2 http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2"
+    - "cd /content && bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2"
+    - "echo > /content/encoder4editing/__init__.py"
+    - |
+      sed -i 's/img = PIL.Image.open(filepath)/img = PIL.Image.open(filepath).convert(\"RGB\")/' /content/encoder4editing/utils/alignment.py
+predict: cog_predict.py:Predictor
diff --git a/cog_predict.py b/cog_predict.py
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+++ b/cog_predict.py
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+import copy
+import os
+import pickle
+import sys
+import tempfile
+import time
+from argparse import Namespace
+from pathlib import Path
+
+import clip
+import cog
+import dlib
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+sys.path.insert(0, "/content")
+sys.path.insert(0, "/content/encoder4editing")
+
+from encoder4editing.models.psp import pSp
+from encoder4editing.utils.alignment import align_face
+from encoder4editing.utils.common import tensor2im
+
+os.chdir("global_directions")
+sys.path.insert(0, ".")
+
+from dnnlib import tflib
+from manipulate import Manipulator
+from MapTS import GetBoundary, GetDt, GetFs
+
+class Predictor(cog.Predictor):
+    def setup(self):
+
+        print("starting setup")
+
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model, self.preprocess = clip.load(
+            "ViT-B/32", device=self.device, jit=False
+        )
+
+        self.graph = tf.get_default_graph()
+        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
+        self.sess = tf.Session(
+            graph=self.graph, config=tf.ConfigProto(gpu_options=gpu_options)
+        )
+
+        self.experiment_args = {"model_path": "e4e_ffhq_encode.pt"}
+        self.experiment_args["transform"] = transforms.Compose(
+            [
+                transforms.Resize((256, 256)),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+            ]
+        )
+        self.resize_dims = (256, 256)
+
+        model_path = self.experiment_args["model_path"]
+
+        ckpt = torch.load(model_path, map_location="cpu")
+        opts = ckpt["opts"]
+        # pprint.pprint(opts)  # Display full options used
+        # update the training options
+        opts["checkpoint_path"] = model_path
+        opts = Namespace(**opts)
+
+        self.net = pSp(opts)
+        self.net.eval()
+        self.net.cuda()
+
+        self.shape_predictor = dlib.shape_predictor(
+            "/content/shape_predictor_68_face_landmarks.dat"
+        )
+
+        with self.graph.as_default(), self.sess.as_default():
+            #tflib.init_tf()
+
+            self.M = Manipulator(dataset_name="ffhq", sess=self.sess)
+            self.fs3 = np.load("npy/ffhq/fs3.npy")
+            np.set_printoptions(suppress=True)
+
+        print("setup complete")
+
+    @cog.input("input", type=Path, help="Input image")
+    @cog.input("neutral", type=str, help="Neutral image description")
+    @cog.input("target", type=str, help="Target image description")
+    @cog.input(
+        "manipulation_strength",
+        type=float,
+        min=-10,
+        max=10,
+        default=4.1,
+        help="The higher the manipulation strength, the closer the generated image becomes to the target description. Negative values moves the generated image further from the target description",
+    )
+    @cog.input(
+        "disentanglement_threshold",
+        type=float,
+        min=0.08,
+        max=0.3,
+        default=0.15,
+        help="The higher the disentanglement threshold, the more specific the changes are to the target attribute. Lower values mean that broader changes are made to the input image",
+    )
+    def predict(
+        self,
+        input,
+        neutral,
+        target,
+        manipulation_strength,
+        disentanglement_threshold,
+    ):
+
+        # @title Align image
+        #original_image = Image.open(str(input))
+        #original_image = original_image.convert("RGB")
+        input_image = self.run_alignment(str(input))
+        #input_image = original_image
+        input_image = input_image.resize(self.resize_dims)
+
+        img_transforms = self.experiment_args["transform"]
+        transformed_image = img_transforms(input_image)
+
+        with torch.no_grad():
+            images, latents = self.run_on_batch(transformed_image.unsqueeze(0))
+            result_image, latent = images[0], latents[0]
+
+            print("latents", latents)
+
+        print(transformed_image.shape, result_image.shape)
+
+        w_plus = latents.cpu().detach().numpy()
+        with self.graph.as_default(), self.sess.as_default():
+            dlatents_loaded = self.M.W2S(w_plus)
+
+        #print("w_plus, dlatents_loaded", w_plus, dlatents_loaded)
+
+        img_index = 0
+        w_plus=latents.cpu().detach().numpy()
+        with self.graph.as_default(), self.sess.as_default():
+            dlatents_loaded=self.M.W2S(w_plus)
+
+        img_indexs=[img_index]
+        dlatent_tmp=[tmp[img_indexs] for tmp in dlatents_loaded]
+        with self.graph.as_default(), self.sess.as_default():
+            self.M.num_images = len(img_indexs)
+            self.M.alpha = [0]
+            self.M.manipulate_layers = [0]
+
+        with self.graph.as_default(), self.sess.as_default():
+            codes, out = self.M.EditOneC(0, dlatent_tmp)
+
+        original = Image.fromarray(out[0, 0]).resize((512, 512))
+        with self.graph.as_default(), self.sess.as_default():
+            self.M.manipulate_layers = None
+
+        classnames = [target, neutral]
+        dt = GetDt(classnames, self.model)
+
+        with self.graph.as_default(), self.sess.as_default():
+            self.M.alpha = [manipulation_strength]
+            boundary_tmp2, c = GetBoundary(
+                self.fs3, dt, self.M, threshold=disentanglement_threshold
+            )
+            codes = self.M.MSCode(dlatent_tmp, boundary_tmp2)
+            out = self.M.GenerateImg(codes)
+        generated = Image.fromarray(out[0, 0])  # .resize((512,512))
+
+        out_path = Path(tempfile.mkdtemp()) / "out.jpg"
+        generated.save(str(out_path))
+
+        return out_path
+
+    def run_alignment(self, image_path):
+        aligned_image = align_face(filepath=image_path, predictor=self.shape_predictor)
+        print("Aligned image has shape: {}".format(aligned_image.size))
+        return aligned_image
+
+    def run_on_batch(self, inputs):
+        images, latents = self.net(
+            inputs.to("cuda").float(), randomize_noise=False, return_latents=True
+        )
+        return images, latents
+
+
+def concat_images(*images):
+    width = 0
+    for im in images:
+        width += im.width
+    height = max([im.height for im in images])
+    concat = Image.new("RGB", (width, height))
+    offset = 0
+    for im in images:
+        concat.paste(im, (offset, 0))
+        offset += im.width
+    return concat
diff --git a/criteria/__init__.py b/criteria/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/criteria/clip_loss.py b/criteria/clip_loss.py
new file mode 100644
index 0000000..18176ee
--- /dev/null
+++ b/criteria/clip_loss.py
@@ -0,0 +1,17 @@
+
+import torch
+import clip
+
+
+class CLIPLoss(torch.nn.Module):
+
+    def __init__(self, opts):
+        super(CLIPLoss, self).__init__()
+        self.model, self.preprocess = clip.load("ViT-B/32", device="cuda")
+        self.upsample = torch.nn.Upsample(scale_factor=7)
+        self.avg_pool = torch.nn.AvgPool2d(kernel_size=opts.stylegan_size // 32)
+
+    def forward(self, image, text):
+        image = self.avg_pool(self.upsample(image))
+        similarity = 1 - self.model(image, text)[0] / 100
+        return similarity
\ No newline at end of file
diff --git a/criteria/id_loss.py b/criteria/id_loss.py
new file mode 100644
index 0000000..2ca3501
--- /dev/null
+++ b/criteria/id_loss.py
@@ -0,0 +1,40 @@
+import torch
+from torch import nn
+
+from models.facial_recognition.model_irse import Backbone
+
+
+class IDLoss(nn.Module):
+    def __init__(self, opts):
+        super(IDLoss, self).__init__()
+        print('Loading ResNet ArcFace')
+        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
+        self.facenet.load_state_dict(torch.load(opts.ir_se50_weights))
+        self.pool = torch.nn.AdaptiveAvgPool2d((256, 256))
+        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
+        self.facenet.eval()
+        self.facenet.cuda()
+        self.opts = opts
+
+    def extract_feats(self, x):
+        if x.shape[2] != 256:
+            x = self.pool(x)
+        x = x[:, :, 35:223, 32:220]  # Crop interesting region
+        x = self.face_pool(x)
+        x_feats = self.facenet(x)
+        return x_feats
+
+    def forward(self, y_hat, y):
+        n_samples = y.shape[0]
+        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
+        y_hat_feats = self.extract_feats(y_hat)
+        y_feats = y_feats.detach()
+        loss = 0
+        sim_improvement = 0
+        count = 0
+        for i in range(n_samples):
+            diff_target = y_hat_feats[i].dot(y_feats[i])
+            loss += 1 - diff_target
+            count += 1
+
+        return loss / count, sim_improvement / count
diff --git a/global_torch/SingleChannel.py b/global_torch/SingleChannel.py
new file mode 100644
index 0000000..54b40b3
--- /dev/null
+++ b/global_torch/SingleChannel.py
@@ -0,0 +1,127 @@
+
+
+
+import numpy as np 
+import torch
+
+from PIL import Image
+import copy
+from manipulate import Manipulator
+import argparse
+
+import sys 
+sys.path.append('/cs/labs/danix/wuzongze/Tansformer_Manipulation/CLIP/')
+import clip
+
+def GetImgF(out,model,preprocess):
+    imgs=out
+    imgs1=imgs.reshape([-1]+list(imgs.shape[2:]))
+    
+    tmp=[]
+    for i in range(len(imgs1)):
+        
+        img=Image.fromarray(imgs1[i])
+        image = preprocess(img).unsqueeze(0).to(device)
+        tmp.append(image)
+    
+    image=torch.cat(tmp)
+    with torch.no_grad():
+        image_features = model.encode_image(image)
+    
+    image_features1=image_features.cpu().numpy()
+    image_features1=image_features1.reshape(list(imgs.shape[:2])+[512])
+    
+    return image_features1
+
+def GetFs(fs):
+    tmp=np.linalg.norm(fs,axis=-1)
+    fs1=fs/tmp[:,:,:,None]
+    fs2=fs1[:,:,1,:]-fs1[:,:,0,:]  # 5*sigma - (-5)* sigma
+    fs3=fs2/np.linalg.norm(fs2,axis=-1)[:,:,None]
+    fs3=fs3.mean(axis=1)
+    fs3=fs3/np.linalg.norm(fs3,axis=-1)[:,None]
+    return fs3
+
+#%%
+if __name__ == "__main__":
+    '''
+    parser = argparse.ArgumentParser(description='Process some integers.')
+    
+    parser.add_argument('--dataset_name',type=str,default='cat',
+                    help='name of dataset, for example, ffhq')
+    args = parser.parse_args()
+    dataset_name=args.dataset_name
+    '''
+    #%%
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model, preprocess = clip.load("ViT-B/32", device=device,jit=False)
+    #%%
+    
+    network_pkl='/cs/labs/danix/wuzongze/Gan_Manipulation/stylegan2/model/stylegan2-human-config-f.pkl'
+    device = torch.device('cuda')
+    M=Manipulator()
+    M.device=device
+    G=M.LoadModel(network_pkl,device)
+    M.G=G
+    M.SetGParameters()
+    num_img=100_000
+    M.GenerateS(num_img=num_img)
+    M.GetCodeMS()
+    
+    # M=Manipulator(dataset_name=dataset_name)
+    np.set_printoptions(suppress=True)
+    # print(M.dataset_name)
+    #%%
+    img_sindex=0
+    num_images=100
+    dlatents_o=[]
+    tmp=img_sindex*num_images
+    for i in range(len(M.dlatents)):
+        tmp1=M.dlatents[i][tmp:(tmp+num_images)]
+        dlatents_o.append(tmp1)
+    #%%
+    
+    all_f=[]
+    M.alpha=[-5,5] #ffhq 5
+    M.step=2
+    M.num_images=num_images
+    select=np.array(M.mindexs)<=16 #below or equal to 128 resolution 
+    mindexs2=np.array(M.mindexs)[select]
+    for lindex in mindexs2: #ignore ToRGB layers
+        print(lindex)
+        num_c=M.dlatents[lindex].shape[1]
+        for cindex in range(num_c):
+            
+            M.dlatents=copy.copy(dlatents_o)
+            M.dlatents[lindex][:,cindex]=M.code_mean[lindex][cindex]
+            
+            M.manipulate_layers=[lindex]
+            codes,out=M.EditOneC(cindex) 
+            image_features1=GetImgF(out,model,preprocess)
+            all_f.append(image_features1)
+    
+    all_f=np.array(all_f)
+    
+    fs3=GetFs(all_f)
+    
+    #%%
+    # file_path='./npy/'+M.dataset_name+'/'
+    file_path='/cs/labs/danix/wuzongze/Gan_Manipulation/stylegan2/results/npy/human/'
+    np.save(file_path+'fs3',fs3)
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
\ No newline at end of file
diff --git a/global_torch/StyleCLIP.py b/global_torch/StyleCLIP.py
new file mode 100644
index 0000000..5a97bbd
--- /dev/null
+++ b/global_torch/StyleCLIP.py
@@ -0,0 +1,246 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jun 14 09:40:28 2022
+
+@author: wuzongze
+"""
+
+import os
+
+import sys 
+import numpy as np 
+import torch
+
+from PIL import Image
+import pickle
+import copy
+import matplotlib.pyplot as plt
+from manipulate import Manipulator
+
+import clip
+
+
+def SplitS(ds_p,M,if_std):
+    all_ds=[]
+    start=0
+    for i in M.mindexs:
+        tmp=M.dlatents[i].shape[1]
+        end=start+tmp
+        tmp=ds_p[start:end]
+#        tmp=tmp*M.code_std[i]
+        
+        all_ds.append(tmp)
+        start=end
+    
+    all_ds2=[]
+    tmp_index=0
+    for i in range(len(M.s_names)):
+        if (not 'RGB' in M.s_names[i]) and (not len(all_ds[tmp_index])==0):
+            
+            if if_std:
+                tmp=all_ds[tmp_index]*M.code_std[i]
+            else:
+                tmp=all_ds[tmp_index]
+            
+            all_ds2.append(tmp)
+            tmp_index+=1
+        else:
+            tmp=np.zeros(len(M.dlatents[i][0]))
+            all_ds2.append(tmp)
+    return all_ds2
+
+
+imagenet_templates = [
+    'a bad photo of a {}.',
+#    'a photo of many {}.',
+    'a sculpture of a {}.',
+    'a photo of the hard to see {}.',
+    'a low resolution photo of the {}.',
+    'a rendering of a {}.',
+    'graffiti of a {}.',
+    'a bad photo of the {}.',
+    'a cropped photo of the {}.',
+    'a tattoo of a {}.',
+    'the embroidered {}.',
+    'a photo of a hard to see {}.',
+    'a bright photo of a {}.',
+    'a photo of a clean {}.',
+    'a photo of a dirty {}.',
+    'a dark photo of the {}.',
+    'a drawing of a {}.',
+    'a photo of my {}.',
+    'the plastic {}.',
+    'a photo of the cool {}.',
+    'a close-up photo of a {}.',
+    'a black and white photo of the {}.',
+    'a painting of the {}.',
+    'a painting of a {}.',
+    'a pixelated photo of the {}.',
+    'a sculpture of the {}.',
+    'a bright photo of the {}.',
+    'a cropped photo of a {}.',
+    'a plastic {}.',
+    'a photo of the dirty {}.',
+    'a jpeg corrupted photo of a {}.',
+    'a blurry photo of the {}.',
+    'a photo of the {}.',
+    'a good photo of the {}.',
+    'a rendering of the {}.',
+    'a {} in a video game.',
+    'a photo of one {}.',
+    'a doodle of a {}.',
+    'a close-up photo of the {}.',
+    'a photo of a {}.',
+    'the origami {}.',
+    'the {} in a video game.',
+    'a sketch of a {}.',
+    'a doodle of the {}.',
+    'a origami {}.',
+    'a low resolution photo of a {}.',
+    'the toy {}.',
+    'a rendition of the {}.',
+    'a photo of the clean {}.',
+    'a photo of a large {}.',
+    'a rendition of a {}.',
+    'a photo of a nice {}.',
+    'a photo of a weird {}.',
+    'a blurry photo of a {}.',
+    'a cartoon {}.',
+    'art of a {}.',
+    'a sketch of the {}.',
+    'a embroidered {}.',
+    'a pixelated photo of a {}.',
+    'itap of the {}.',
+    'a jpeg corrupted photo of the {}.',
+    'a good photo of a {}.',
+    'a plushie {}.',
+    'a photo of the nice {}.',
+    'a photo of the small {}.',
+    'a photo of the weird {}.',
+    'the cartoon {}.',
+    'art of the {}.',
+    'a drawing of the {}.',
+    'a photo of the large {}.',
+    'a black and white photo of a {}.',
+    'the plushie {}.',
+    'a dark photo of a {}.',
+    'itap of a {}.',
+    'graffiti of the {}.',
+    'a toy {}.',
+    'itap of my {}.',
+    'a photo of a cool {}.',
+    'a photo of a small {}.',
+    'a tattoo of the {}.',
+]
+
+
+def zeroshot_classifier(classnames, templates,model):
+    with torch.no_grad():
+        zeroshot_weights = []
+        for classname in classnames:
+            texts = [template.format(classname) for template in templates] #format with class
+            texts = clip.tokenize(texts).cuda() #tokenize
+            class_embeddings = model.encode_text(texts) #embed with text encoder
+            class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)
+            class_embedding = class_embeddings.mean(dim=0)
+            class_embedding /= class_embedding.norm()
+            zeroshot_weights.append(class_embedding)
+        zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()
+    return zeroshot_weights
+
+
+def GetDt(classnames,model):
+    text_features=zeroshot_classifier(classnames, imagenet_templates,model).t()
+    
+    dt=text_features[0]-text_features[1]
+    dt=dt.cpu().numpy()
+    
+
+    print(np.linalg.norm(dt))
+    dt=dt/np.linalg.norm(dt)
+    return dt
+
+
+def GetBoundary(fs3,dt,M,threshold):
+    tmp=np.dot(fs3,dt)
+    
+    ds_imp=copy.copy(tmp)
+    select=np.abs(tmp)<threshold
+    num_c=np.sum(~select)
+
+
+    ds_imp[select]=0
+    tmp=np.abs(ds_imp).max()
+    ds_imp/=tmp
+    
+    boundary_tmp2=SplitS(ds_imp,M,if_std=True)
+    print('num of channels being manipulated:',num_c)
+    return boundary_tmp2,num_c
+
+#%%
+if __name__ == "__main__":
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model, preprocess = clip.load("ViT-B/32", device=device,jit=False)
+    
+    # pls download the checkpoint from https://drive.google.com/file/d/1FlAb1rYa0r_--Zj_ML8e6shmaF28hQb5/view
+    network_pkl='/cs/labs/danix/wuzongze/Gan_Manipulation/stylegan2/model/stylegan2-human-config-f.pkl'
+    device = torch.device('cuda')
+    M=Manipulator()
+    M.device=device
+    G=M.LoadModel(network_pkl,device)
+    M.G=G
+    M.SetGParameters()
+    num_img=100_000
+    M.GenerateS(num_img=num_img)
+    M.GetCodeMS()
+    np.set_printoptions(suppress=True)
+    #%%
+    file_path='./npy/human/'
+    fs3=np.load(file_path+'fs3.npy')
+    #%%
+    img_indexs=np.arange(20)
+    
+    dlatent_tmp=[tmp[img_indexs] for tmp in M.dlatents]
+    M.num_images=len(img_indexs)
+    #%%
+
+    paras=[
+            ['person', 'original', 0, 0],
+            ['woman', 'man', 0.2, 3],
+            ['person', 'person with T-shirt', 0.15, 4],
+            ['person', 'person with jeans', 0.15, 4],
+            ['person', 'person with jacket', 0.15, 4],
+    ]
+    paras=np.array(paras)
+    #%%
+    
+    M.step=1
+    
+    
+    imgs=[]
+    all_b=[]
+    for i in range(len(paras)):
+        
+        neutral,target,beta,alpha=paras[i]
+        beta=np.float32(beta)
+        alpha=np.float32(alpha)
+        M.alpha=[alpha]
+        print()
+        print(target)
+        classnames=[target,neutral]
+        dt=GetDt(classnames,model)
+        boundary_tmp2,num_c=GetBoundary(fs3,dt,M,threshold=beta)
+        all_b.append(boundary_tmp2)
+        codes=M.MSCode(dlatent_tmp,boundary_tmp2)
+        
+        out=M.GenerateImg(codes)
+        imgs.append(out)
+    
+
+    imgs=np.concatenate(imgs,axis=1)
+    M.step=imgs.shape[1]
+    M.Vis('real','',imgs,colnames=list(paras[:,1]),rownames=img_indexs,viz_size=1024)
+    
+    
+    
diff --git a/global_torch/dnnlib/__init__.py b/global_torch/dnnlib/__init__.py
new file mode 100644
index 0000000..2f08cf3
--- /dev/null
+++ b/global_torch/dnnlib/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+from .util import EasyDict, make_cache_dir_path
diff --git a/global_torch/dnnlib/util.py b/global_torch/dnnlib/util.py
new file mode 100644
index 0000000..7672533
--- /dev/null
+++ b/global_torch/dnnlib/util.py
@@ -0,0 +1,477 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Miscellaneous utility classes and functions."""
+
+import ctypes
+import fnmatch
+import importlib
+import inspect
+import numpy as np
+import os
+import shutil
+import sys
+import types
+import io
+import pickle
+import re
+import requests
+import html
+import hashlib
+import glob
+import tempfile
+import urllib
+import urllib.request
+import uuid
+
+from distutils.util import strtobool
+from typing import Any, List, Tuple, Union
+
+
+# Util classes
+# ------------------------------------------------------------------------------------------
+
+
+class EasyDict(dict):
+    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
+
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        self[name] = value
+
+    def __delattr__(self, name: str) -> None:
+        del self[name]
+
+
+class Logger(object):
+    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
+
+    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
+        self.file = None
+
+        if file_name is not None:
+            self.file = open(file_name, file_mode)
+
+        self.should_flush = should_flush
+        self.stdout = sys.stdout
+        self.stderr = sys.stderr
+
+        sys.stdout = self
+        sys.stderr = self
+
+    def __enter__(self) -> "Logger":
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self.close()
+
+    def write(self, text: Union[str, bytes]) -> None:
+        """Write text to stdout (and a file) and optionally flush."""
+        if isinstance(text, bytes):
+            text = text.decode()
+        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
+            return
+
+        if self.file is not None:
+            self.file.write(text)
+
+        self.stdout.write(text)
+
+        if self.should_flush:
+            self.flush()
+
+    def flush(self) -> None:
+        """Flush written text to both stdout and a file, if open."""
+        if self.file is not None:
+            self.file.flush()
+
+        self.stdout.flush()
+
+    def close(self) -> None:
+        """Flush, close possible files, and remove stdout/stderr mirroring."""
+        self.flush()
+
+        # if using multiple loggers, prevent closing in wrong order
+        if sys.stdout is self:
+            sys.stdout = self.stdout
+        if sys.stderr is self:
+            sys.stderr = self.stderr
+
+        if self.file is not None:
+            self.file.close()
+            self.file = None
+
+
+# Cache directories
+# ------------------------------------------------------------------------------------------
+
+_dnnlib_cache_dir = None
+
+def set_cache_dir(path: str) -> None:
+    global _dnnlib_cache_dir
+    _dnnlib_cache_dir = path
+
+def make_cache_dir_path(*paths: str) -> str:
+    if _dnnlib_cache_dir is not None:
+        return os.path.join(_dnnlib_cache_dir, *paths)
+    if 'DNNLIB_CACHE_DIR' in os.environ:
+        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
+    if 'HOME' in os.environ:
+        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
+    if 'USERPROFILE' in os.environ:
+        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
+    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
+
+# Small util functions
+# ------------------------------------------------------------------------------------------
+
+
+def format_time(seconds: Union[int, float]) -> str:
+    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
+    s = int(np.rint(seconds))
+
+    if s < 60:
+        return "{0}s".format(s)
+    elif s < 60 * 60:
+        return "{0}m {1:02}s".format(s // 60, s % 60)
+    elif s < 24 * 60 * 60:
+        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
+    else:
+        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
+
+
+def ask_yes_no(question: str) -> bool:
+    """Ask the user the question until the user inputs a valid answer."""
+    while True:
+        try:
+            print("{0} [y/n]".format(question))
+            return strtobool(input().lower())
+        except ValueError:
+            pass
+
+
+def tuple_product(t: Tuple) -> Any:
+    """Calculate the product of the tuple elements."""
+    result = 1
+
+    for v in t:
+        result *= v
+
+    return result
+
+
+_str_to_ctype = {
+    "uint8": ctypes.c_ubyte,
+    "uint16": ctypes.c_uint16,
+    "uint32": ctypes.c_uint32,
+    "uint64": ctypes.c_uint64,
+    "int8": ctypes.c_byte,
+    "int16": ctypes.c_int16,
+    "int32": ctypes.c_int32,
+    "int64": ctypes.c_int64,
+    "float32": ctypes.c_float,
+    "float64": ctypes.c_double
+}
+
+
+def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
+    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
+    type_str = None
+
+    if isinstance(type_obj, str):
+        type_str = type_obj
+    elif hasattr(type_obj, "__name__"):
+        type_str = type_obj.__name__
+    elif hasattr(type_obj, "name"):
+        type_str = type_obj.name
+    else:
+        raise RuntimeError("Cannot infer type name from input")
+
+    assert type_str in _str_to_ctype.keys()
+
+    my_dtype = np.dtype(type_str)
+    my_ctype = _str_to_ctype[type_str]
+
+    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
+
+    return my_dtype, my_ctype
+
+
+def is_pickleable(obj: Any) -> bool:
+    try:
+        with io.BytesIO() as stream:
+            pickle.dump(obj, stream)
+        return True
+    except:
+        return False
+
+
+# Functionality to import modules/objects by name, and call functions by name
+# ------------------------------------------------------------------------------------------
+
+def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
+    """Searches for the underlying module behind the name to some python object.
+    Returns the module and the object name (original name with module part removed)."""
+
+    # allow convenience shorthands, substitute them by full names
+    obj_name = re.sub("^np.", "numpy.", obj_name)
+    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
+
+    # list alternatives for (module_name, local_obj_name)
+    parts = obj_name.split(".")
+    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
+
+    # try each alternative in turn
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(module_name) # may raise ImportError
+            get_obj_from_module(module, local_obj_name) # may raise AttributeError
+            return module, local_obj_name
+        except:
+            pass
+
+    # maybe some of the modules themselves contain errors?
+    for module_name, _local_obj_name in name_pairs:
+        try:
+            importlib.import_module(module_name) # may raise ImportError
+        except ImportError:
+            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
+                raise
+
+    # maybe the requested attribute is missing?
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(module_name) # may raise ImportError
+            get_obj_from_module(module, local_obj_name) # may raise AttributeError
+        except ImportError:
+            pass
+
+    # we are out of luck, but we have no idea why
+    raise ImportError(obj_name)
+
+
+def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
+    """Traverses the object name and returns the last (rightmost) python object."""
+    if obj_name == '':
+        return module
+    obj = module
+    for part in obj_name.split("."):
+        obj = getattr(obj, part)
+    return obj
+
+
+def get_obj_by_name(name: str) -> Any:
+    """Finds the python object with the given name."""
+    module, obj_name = get_module_from_obj_name(name)
+    return get_obj_from_module(module, obj_name)
+
+
+def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
+    """Finds the python object with the given name and calls it as a function."""
+    assert func_name is not None
+    func_obj = get_obj_by_name(func_name)
+    assert callable(func_obj)
+    return func_obj(*args, **kwargs)
+
+
+def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
+    """Finds the python class with the given name and constructs it with the given arguments."""
+    return call_func_by_name(*args, func_name=class_name, **kwargs)
+
+
+def get_module_dir_by_obj_name(obj_name: str) -> str:
+    """Get the directory path of the module containing the given object name."""
+    module, _ = get_module_from_obj_name(obj_name)
+    return os.path.dirname(inspect.getfile(module))
+
+
+def is_top_level_function(obj: Any) -> bool:
+    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
+    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
+
+
+def get_top_level_function_name(obj: Any) -> str:
+    """Return the fully-qualified name of a top-level function."""
+    assert is_top_level_function(obj)
+    module = obj.__module__
+    if module == '__main__':
+        module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
+    return module + "." + obj.__name__
+
+
+# File system helpers
+# ------------------------------------------------------------------------------------------
+
+def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
+    """List all files recursively in a given directory while ignoring given file and directory names.
+    Returns list of tuples containing both absolute and relative paths."""
+    assert os.path.isdir(dir_path)
+    base_name = os.path.basename(os.path.normpath(dir_path))
+
+    if ignores is None:
+        ignores = []
+
+    result = []
+
+    for root, dirs, files in os.walk(dir_path, topdown=True):
+        for ignore_ in ignores:
+            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
+
+            # dirs need to be edited in-place
+            for d in dirs_to_remove:
+                dirs.remove(d)
+
+            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
+
+        absolute_paths = [os.path.join(root, f) for f in files]
+        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
+
+        if add_base_to_relative:
+            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
+
+        assert len(absolute_paths) == len(relative_paths)
+        result += zip(absolute_paths, relative_paths)
+
+    return result
+
+
+def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
+    """Takes in a list of tuples of (src, dst) paths and copies files.
+    Will create all necessary directories."""
+    for file in files:
+        target_dir_name = os.path.dirname(file[1])
+
+        # will create all intermediate-level directories
+        if not os.path.exists(target_dir_name):
+            os.makedirs(target_dir_name)
+
+        shutil.copyfile(file[0], file[1])
+
+
+# URL helpers
+# ------------------------------------------------------------------------------------------
+
+def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
+    """Determine whether the given object is a valid URL string."""
+    if not isinstance(obj, str) or not "://" in obj:
+        return False
+    if allow_file_urls and obj.startswith('file://'):
+        return True
+    try:
+        res = requests.compat.urlparse(obj)
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+    except:
+        return False
+    return True
+
+
+def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
+    """Download the given URL and return a binary-mode file object to access the data."""
+    assert num_attempts >= 1
+    assert not (return_filename and (not cache))
+
+    # Doesn't look like an URL scheme so interpret it as a local filename.
+    if not re.match('^[a-z]+://', url):
+        return url if return_filename else open(url, "rb")
+
+    # Handle file URLs.  This code handles unusual file:// patterns that
+    # arise on Windows:
+    #
+    # file:///c:/foo.txt
+    #
+    # which would translate to a local '/c:/foo.txt' filename that's
+    # invalid.  Drop the forward slash for such pathnames.
+    #
+    # If you touch this code path, you should test it on both Linux and
+    # Windows.
+    #
+    # Some internet resources suggest using urllib.request.url2pathname() but
+    # but that converts forward slashes to backslashes and this causes
+    # its own set of problems.
+    if url.startswith('file://'):
+        filename = urllib.parse.urlparse(url).path
+        if re.match(r'^/[a-zA-Z]:', filename):
+            filename = filename[1:]
+        return filename if return_filename else open(filename, "rb")
+
+    assert is_url(url)
+
+    # Lookup from cache.
+    if cache_dir is None:
+        cache_dir = make_cache_dir_path('downloads')
+
+    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
+    if cache:
+        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
+        if len(cache_files) == 1:
+            filename = cache_files[0]
+            return filename if return_filename else open(filename, "rb")
+
+    # Download.
+    url_name = None
+    url_data = None
+    with requests.Session() as session:
+        if verbose:
+            print("Downloading %s ..." % url, end="", flush=True)
+        for attempts_left in reversed(range(num_attempts)):
+            try:
+                with session.get(url) as res:
+                    res.raise_for_status()
+                    if len(res.content) == 0:
+                        raise IOError("No data received")
+
+                    if len(res.content) < 8192:
+                        content_str = res.content.decode("utf-8")
+                        if "download_warning" in res.headers.get("Set-Cookie", ""):
+                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
+                            if len(links) == 1:
+                                url = requests.compat.urljoin(url, links[0])
+                                raise IOError("Google Drive virus checker nag")
+                        if "Google Drive - Quota exceeded" in content_str:
+                            raise IOError("Google Drive download quota exceeded -- please try again later")
+
+                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
+                    url_name = match[1] if match else url
+                    url_data = res.content
+                    if verbose:
+                        print(" done")
+                    break
+            except KeyboardInterrupt:
+                raise
+            except:
+                if not attempts_left:
+                    if verbose:
+                        print(" failed")
+                    raise
+                if verbose:
+                    print(".", end="", flush=True)
+
+    # Save to cache.
+    if cache:
+        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
+        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
+        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
+        os.makedirs(cache_dir, exist_ok=True)
+        with open(temp_file, "wb") as f:
+            f.write(url_data)
+        os.replace(temp_file, cache_file) # atomic
+        if return_filename:
+            return cache_file
+
+    # Return data as file object.
+    assert not return_filename
+    return io.BytesIO(url_data)
diff --git a/global_torch/html/[6]_501_c.html b/global_torch/html/[6]_501_c.html
new file mode 100644
index 0000000..4d0740c
--- /dev/null
+++ b/global_torch/html/[6]_501_c.html
@@ -0,0 +1,99 @@
+<script type="text/javascript">
+var column_idx;
+var sort_by_ascending = false;
+
+function sorting(tbody, column_idx){
+  this.column_idx = column_idx;
+  Array.from(tbody.rows)
+       .sort(compareCells)
+       .forEach(function(row) { tbody.appendChild(row); })
+  sort_by_ascending = !sort_by_ascending;
+}
+
+function compareCells(row_a, row_b) {
+  var val_a = row_a.cells[column_idx].innerText;
+  var val_b = row_b.cells[column_idx].innerText;
+  var flag = sort_by_ascending ? 1 : -1;
+  return flag * (val_a > val_b ? 1 : -1);
+}
+</script>
+
+<html>
+
+<head>
+<style>
+  table {
+    border-spacing: 0;
+    border: 1px solid black;
+  }
+  th {
+    cursor: pointer;
+  }
+  th, td {
+    text-align: left;
+    vertical-align: middle;
+    border-collapse: collapse;
+    border: 0.5px solid black;
+    padding: 8px;
+  }
+  tr:nth-child(even) {
+    background-color: #d2d2d2;
+  }
+</style>
+</head>
+
+<body>
+
+<table>
+<thead>
+<tr>
+  <th onclick="sorting(tbody, 0)">Name</th>
+  <th onclick="sorting(tbody, 1)">Step 01</th>
+</tr>
+</thead>
+<tbody id="tbody">
+<tr>
+  <td>0<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+<tr>
+  <td>1<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+<tr>
+  <td>2<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+<tr>
+  <td>3<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>4<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>5<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+<tr>
+  <td>6<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+<tr>
+  <td>7<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+<tr>
+  <td>8<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+<tr>
+  <td>9<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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"/></td>
+</tr>
+</tbody>
+</table>
+
+</body>
+</html>
diff --git a/global_torch/html/real_.html b/global_torch/html/real_.html
new file mode 100644
index 0000000..d32725e
--- /dev/null
+++ b/global_torch/html/real_.html
@@ -0,0 +1,223 @@
+<script type="text/javascript">
+var column_idx;
+var sort_by_ascending = false;
+
+function sorting(tbody, column_idx){
+  this.column_idx = column_idx;
+  Array.from(tbody.rows)
+       .sort(compareCells)
+       .forEach(function(row) { tbody.appendChild(row); })
+  sort_by_ascending = !sort_by_ascending;
+}
+
+function compareCells(row_a, row_b) {
+  var val_a = row_a.cells[column_idx].innerText;
+  var val_b = row_b.cells[column_idx].innerText;
+  var flag = sort_by_ascending ? 1 : -1;
+  return flag * (val_a > val_b ? 1 : -1);
+}
+</script>
+
+<html>
+
+<head>
+<style>
+  table {
+    border-spacing: 0;
+    border: 1px solid black;
+  }
+  th {
+    cursor: pointer;
+  }
+  th, td {
+    text-align: left;
+    vertical-align: middle;
+    border-collapse: collapse;
+    border: 0.5px solid black;
+    padding: 8px;
+  }
+  tr:nth-child(even) {
+    background-color: #d2d2d2;
+  }
+</style>
+</head>
+
+<body>
+
+<table>
+<thead>
+<tr>
+  <th onclick="sorting(tbody, 0)">Name</th>
+  <th onclick="sorting(tbody, 1)">original</th>
+  <th onclick="sorting(tbody, 2)">man</th>
+  <th onclick="sorting(tbody, 3)">person with T-shirt</th>
+  <th onclick="sorting(tbody, 4)">person with jeans</th>
+  <th onclick="sorting(tbody, 5)">person with jacket</th>
+</tr>
+</thead>
+<tbody id="tbody">
+<tr>
+  <td></td>
+  <td><img src="data:image/jpeg;base64, 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+<tr>
+  <td>1<br><br></td>
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+</tr>
+<tr>
+  <td>2<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>3<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>4<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>5<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>6<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>7<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>8<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>9<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>10<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>11<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>12<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>13<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>14<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>15<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>16<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>17<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>18<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+<tr>
+  <td>19<br><br></td>
+  <td><img src="data:image/jpeg;base64, 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+</tr>
+</tbody>
+</table>
+
+</body>
+</html>
diff --git a/global_torch/legacy.py b/global_torch/legacy.py
new file mode 100644
index 0000000..f7daee7
--- /dev/null
+++ b/global_torch/legacy.py
@@ -0,0 +1,326 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import click
+import pickle
+import re
+import copy
+import numpy as np
+import torch
+import dnnlib
+from torch_utils import misc
+
+#----------------------------------------------------------------------------
+
+def load_network_pkl(f, force_fp16=False):
+    data = _LegacyUnpickler(f).load()
+
+    # Legacy TensorFlow pickle => convert.
+    if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
+        tf_G, tf_D, tf_Gs = data
+        G = convert_tf_generator(tf_G)
+        D = convert_tf_discriminator(tf_D)
+        G_ema = convert_tf_generator(tf_Gs)
+        data = dict(G=G, D=D, G_ema=G_ema)
+
+    # Add missing fields.
+    if 'training_set_kwargs' not in data:
+        data['training_set_kwargs'] = None
+    if 'augment_pipe' not in data:
+        data['augment_pipe'] = None
+
+    # Validate contents.
+    assert isinstance(data['G'], torch.nn.Module)
+    assert isinstance(data['D'], torch.nn.Module)
+    assert isinstance(data['G_ema'], torch.nn.Module)
+    assert isinstance(data['training_set_kwargs'], (dict, type(None)))
+    assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
+
+    # Force FP16.
+    if force_fp16:
+        for key in ['G', 'D', 'G_ema']:
+            old = data[key]
+            kwargs = copy.deepcopy(old.init_kwargs)
+            if key.startswith('G'):
+                kwargs.synthesis_kwargs = dnnlib.EasyDict(kwargs.get('synthesis_kwargs', {}))
+                kwargs.synthesis_kwargs.num_fp16_res = 4
+                kwargs.synthesis_kwargs.conv_clamp = 256
+            if key.startswith('D'):
+                kwargs.num_fp16_res = 4
+                kwargs.conv_clamp = 256
+            if kwargs != old.init_kwargs:
+                new = type(old)(**kwargs).eval().requires_grad_(False)
+                misc.copy_params_and_buffers(old, new, require_all=True)
+                data[key] = new
+    return data
+
+#----------------------------------------------------------------------------
+
+class _TFNetworkStub(dnnlib.EasyDict):
+    pass
+
+class _LegacyUnpickler(pickle.Unpickler):
+    def find_class(self, module, name):
+        if module == 'dnnlib.tflib.network' and name == 'Network':
+            return _TFNetworkStub
+        return super().find_class(module, name)
+
+#----------------------------------------------------------------------------
+
+def _collect_tf_params(tf_net):
+    # pylint: disable=protected-access
+    tf_params = dict()
+    def recurse(prefix, tf_net):
+        for name, value in tf_net.variables:
+            tf_params[prefix + name] = value
+        for name, comp in tf_net.components.items():
+            recurse(prefix + name + '/', comp)
+    recurse('', tf_net)
+    return tf_params
+
+#----------------------------------------------------------------------------
+
+def _populate_module_params(module, *patterns):
+    for name, tensor in misc.named_params_and_buffers(module):
+        found = False
+        value = None
+        for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
+            match = re.fullmatch(pattern, name)
+            if match:
+                found = True
+                if value_fn is not None:
+                    value = value_fn(*match.groups())
+                break
+        try:
+            assert found
+            if value is not None:
+                tensor.copy_(torch.from_numpy(np.array(value)))
+        except:
+            print(name, list(tensor.shape))
+            raise
+
+#----------------------------------------------------------------------------
+
+def convert_tf_generator(tf_G):
+    if tf_G.version < 4:
+        raise ValueError('TensorFlow pickle version too low')
+
+    # Collect kwargs.
+    tf_kwargs = tf_G.static_kwargs
+    known_kwargs = set()
+    def kwarg(tf_name, default=None, none=None):
+        known_kwargs.add(tf_name)
+        val = tf_kwargs.get(tf_name, default)
+        return val if val is not None else none
+
+    # Convert kwargs.
+    kwargs = dnnlib.EasyDict(
+        z_dim                   = kwarg('latent_size',          512),
+        c_dim                   = kwarg('label_size',           0),
+        w_dim                   = kwarg('dlatent_size',         512),
+        img_resolution          = kwarg('resolution',           1024),
+        img_channels            = kwarg('num_channels',         3),
+        mapping_kwargs = dnnlib.EasyDict(
+            num_layers          = kwarg('mapping_layers',       8),
+            embed_features      = kwarg('label_fmaps',          None),
+            layer_features      = kwarg('mapping_fmaps',        None),
+            activation          = kwarg('mapping_nonlinearity', 'lrelu'),
+            lr_multiplier       = kwarg('mapping_lrmul',        0.01),
+            w_avg_beta          = kwarg('w_avg_beta',           0.995,  none=1),
+        ),
+        synthesis_kwargs = dnnlib.EasyDict(
+            channel_base        = kwarg('fmap_base',            16384) * 2,
+            channel_max         = kwarg('fmap_max',             512),
+            num_fp16_res        = kwarg('num_fp16_res',         0),
+            conv_clamp          = kwarg('conv_clamp',           None),
+            architecture        = kwarg('architecture',         'skip'),
+            resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
+            use_noise           = kwarg('use_noise',            True),
+            activation          = kwarg('nonlinearity',         'lrelu'),
+        ),
+    )
+
+    # Check for unknown kwargs.
+    kwarg('truncation_psi')
+    kwarg('truncation_cutoff')
+    kwarg('style_mixing_prob')
+    kwarg('structure')
+    if 'resolution_w' in tf_kwargs:
+        tf_kwargs.pop('resolution_w', None)
+        tf_kwargs.pop('resolution_h', None)
+    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
+    if len(unknown_kwargs) > 0:
+        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
+
+    # Collect params.
+    tf_params = _collect_tf_params(tf_G)
+    for name, value in list(tf_params.items()):
+        match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
+        if match:
+            r = kwargs.img_resolution // (2 ** int(match.group(1)))
+            tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
+            kwargs.synthesis.kwargs.architecture = 'orig'
+    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
+
+    # Convert params.
+    from training import networks
+    G = networks.Generator(**kwargs).eval().requires_grad_(False)
+    # pylint: disable=unnecessary-lambda
+    _populate_module_params(G,
+        r'mapping\.w_avg',                                  lambda:     tf_params[f'dlatent_avg'],
+        r'mapping\.embed\.weight',                          lambda:     tf_params[f'mapping/LabelEmbed/weight'].transpose(),
+        r'mapping\.embed\.bias',                            lambda:     tf_params[f'mapping/LabelEmbed/bias'],
+        r'mapping\.fc(\d+)\.weight',                        lambda i:   tf_params[f'mapping/Dense{i}/weight'].transpose(),
+        r'mapping\.fc(\d+)\.bias',                          lambda i:   tf_params[f'mapping/Dense{i}/bias'],
+        r'synthesis\.b4\.const',                            lambda:     tf_params[f'synthesis/4x4/Const/const'][0],
+        r'synthesis\.b4\.conv1\.weight',                    lambda:     tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
+        r'synthesis\.b4\.conv1\.bias',                      lambda:     tf_params[f'synthesis/4x4/Conv/bias'],
+        r'synthesis\.b4\.conv1\.noise_const',               lambda:     tf_params[f'synthesis/noise0'][0, 0],
+        r'synthesis\.b4\.conv1\.noise_strength',            lambda:     tf_params[f'synthesis/4x4/Conv/noise_strength'],
+        r'synthesis\.b4\.conv1\.affine\.weight',            lambda:     tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
+        r'synthesis\.b4\.conv1\.affine\.bias',              lambda:     tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.conv0\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
+        r'synthesis\.b(\d+)\.conv0\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
+        r'synthesis\.b(\d+)\.conv0\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
+        r'synthesis\.b(\d+)\.conv0\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
+        r'synthesis\.b(\d+)\.conv0\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
+        r'synthesis\.b(\d+)\.conv0\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.conv1\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
+        r'synthesis\.b(\d+)\.conv1\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
+        r'synthesis\.b(\d+)\.conv1\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
+        r'synthesis\.b(\d+)\.conv1\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
+        r'synthesis\.b(\d+)\.conv1\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
+        r'synthesis\.b(\d+)\.conv1\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.torgb\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
+        r'synthesis\.b(\d+)\.torgb\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
+        r'synthesis\.b(\d+)\.torgb\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
+        r'synthesis\.b(\d+)\.torgb\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
+        r'synthesis\.b(\d+)\.skip\.weight',                 lambda r:   tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
+        r'.*\.resample_filter',                             None,
+    )
+    return G
+
+#----------------------------------------------------------------------------
+
+def convert_tf_discriminator(tf_D):
+    if tf_D.version < 4:
+        raise ValueError('TensorFlow pickle version too low')
+
+    # Collect kwargs.
+    tf_kwargs = tf_D.static_kwargs
+    known_kwargs = set()
+    def kwarg(tf_name, default=None):
+        known_kwargs.add(tf_name)
+        return tf_kwargs.get(tf_name, default)
+
+    # Convert kwargs.
+    kwargs = dnnlib.EasyDict(
+        c_dim                   = kwarg('label_size',           0),
+        img_resolution          = kwarg('resolution',           1024),
+        img_channels            = kwarg('num_channels',         3),
+        architecture            = kwarg('architecture',         'resnet'),
+        channel_base            = kwarg('fmap_base',            16384) * 2,
+        channel_max             = kwarg('fmap_max',             512),
+        num_fp16_res            = kwarg('num_fp16_res',         0),
+        conv_clamp              = kwarg('conv_clamp',           None),
+        cmap_dim                = kwarg('mapping_fmaps',        None),
+        block_kwargs = dnnlib.EasyDict(
+            activation          = kwarg('nonlinearity',         'lrelu'),
+            resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
+            freeze_layers       = kwarg('freeze_layers',        0),
+        ),
+        mapping_kwargs = dnnlib.EasyDict(
+            num_layers          = kwarg('mapping_layers',       0),
+            embed_features      = kwarg('mapping_fmaps',        None),
+            layer_features      = kwarg('mapping_fmaps',        None),
+            activation          = kwarg('nonlinearity',         'lrelu'),
+            lr_multiplier       = kwarg('mapping_lrmul',        0.1),
+        ),
+        epilogue_kwargs = dnnlib.EasyDict(
+            mbstd_group_size    = kwarg('mbstd_group_size',     None),
+            mbstd_num_channels  = kwarg('mbstd_num_features',   1),
+            activation          = kwarg('nonlinearity',         'lrelu'),
+        ),
+    )
+
+    # Check for unknown kwargs.
+    kwarg('structure')
+    if 'resolution_w' in tf_kwargs:
+        tf_kwargs.pop('resolution_w', None)
+        tf_kwargs.pop('resolution_h', None)
+    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
+    if len(unknown_kwargs) > 0:
+        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
+
+    # Collect params.
+    tf_params = _collect_tf_params(tf_D)
+    for name, value in list(tf_params.items()):
+        match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
+        if match:
+            r = kwargs.img_resolution // (2 ** int(match.group(1)))
+            tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
+            kwargs.architecture = 'orig'
+    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
+
+    # Convert params.
+    from training import networks
+    D = networks.Discriminator(**kwargs).eval().requires_grad_(False)
+    # pylint: disable=unnecessary-lambda
+    _populate_module_params(D,
+        r'b(\d+)\.fromrgb\.weight',     lambda r:       tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
+        r'b(\d+)\.fromrgb\.bias',       lambda r:       tf_params[f'{r}x{r}/FromRGB/bias'],
+        r'b(\d+)\.conv(\d+)\.weight',   lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
+        r'b(\d+)\.conv(\d+)\.bias',     lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
+        r'b(\d+)\.skip\.weight',        lambda r:       tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
+        r'mapping\.embed\.weight',      lambda:         tf_params[f'LabelEmbed/weight'].transpose(),
+        r'mapping\.embed\.bias',        lambda:         tf_params[f'LabelEmbed/bias'],
+        r'mapping\.fc(\d+)\.weight',    lambda i:       tf_params[f'Mapping{i}/weight'].transpose(),
+        r'mapping\.fc(\d+)\.bias',      lambda i:       tf_params[f'Mapping{i}/bias'],
+        r'b4\.conv\.weight',            lambda:         tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
+        r'b4\.conv\.bias',              lambda:         tf_params[f'4x4/Conv/bias'],
+        r'b4\.fc\.weight',              lambda:         tf_params[f'4x4/Dense0/weight'].transpose(),
+        r'b4\.fc\.bias',                lambda:         tf_params[f'4x4/Dense0/bias'],
+        r'b4\.out\.weight',             lambda:         tf_params[f'Output/weight'].transpose(),
+        r'b4\.out\.bias',               lambda:         tf_params[f'Output/bias'],
+        r'.*\.resample_filter',         None,
+    )
+    return D
+
+#----------------------------------------------------------------------------
+
+@click.command()
+@click.option('--source', help='Input pickle', required=True, metavar='PATH')
+@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
+@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
+def convert_network_pickle(source, dest, force_fp16):
+    """Convert legacy network pickle into the native PyTorch format.
+
+    The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
+    It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
+
+    Example:
+
+    \b
+    python legacy.py \\
+        --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
+        --dest=stylegan2-cat-config-f.pkl
+    """
+    print(f'Loading "{source}"...')
+    with dnnlib.util.open_url(source) as f:
+        data = load_network_pkl(f, force_fp16=force_fp16)
+    print(f'Saving "{dest}"...')
+    with open(dest, 'wb') as f:
+        pickle.dump(data, f)
+    print('Done.')
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    convert_network_pickle() # pylint: disable=no-value-for-parameter
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/manipulate.py b/global_torch/manipulate.py
new file mode 100644
index 0000000..cc40ffc
--- /dev/null
+++ b/global_torch/manipulate.py
@@ -0,0 +1,383 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul 19 21:03:58 2021
+
+@author: wuzongze
+"""
+
+
+import sys
+
+import copy
+import os
+from time import perf_counter
+
+import click
+import imageio
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from PIL import Image
+
+import dnnlib
+import legacy
+import pickle
+from visualizer import HtmlPageVisualizer
+
+from torch_utils import misc
+import types
+from training.networks import SynthesisNetwork,SynthesisBlock,SynthesisLayer,ToRGBLayer
+
+
+def change_style_code(codes, layer, channel, step):
+    codes[layer][:, channel] += step
+    return codes
+
+def Vis(bname,suffix,out,rownames=None,colnames=None,save_path=None,viz_size=256):
+    
+    if save_path is None:
+        save_path='./html/'
+    
+    
+    num_images=out.shape[0]
+    step=out.shape[1]
+    
+    if colnames is None:
+        colnames=[f'Step {i:02d}' for i in range(1, step + 1)]
+    if rownames is None:
+        rownames=[str(i) for i in range(num_images)]
+    
+    
+    visualizer = HtmlPageVisualizer(
+      num_rows=num_images, num_cols=step + 1, viz_size=viz_size)
+    visualizer.set_headers(
+      ['Name'] +colnames)
+    
+    for i in range(num_images):
+        visualizer.set_cell(i, 0, text=rownames[i])
+    
+    for i in range(num_images):
+        for k in range(step):
+            image=out[i,k,:,:,:]
+            visualizer.set_cell(i, 1+k, image=image)
+    
+    visualizer.save(save_path+bname+'_'+suffix+'.html')
+
+def LoadModel(network_pkl,device):
+    with dnnlib.util.open_url(network_pkl) as fp:
+        G = legacy.load_network_pkl(fp)['G_ema'].requires_grad_(False).to(device) # type: ignore
+    
+    G.synthesis.forward=types.MethodType(SynthesisNetwork.forward,G.synthesis)
+    G.synthesis.W2S=types.MethodType(SynthesisNetwork.W2S,G.synthesis)
+    
+    for res in G.synthesis.block_resolutions:
+        block = getattr(G.synthesis, f'b{res}')
+        # print(block)
+        block.forward=types.MethodType(SynthesisBlock.forward,block)
+        
+        if res!=4:
+            layer=block.conv0
+            layer.forward=types.MethodType(SynthesisLayer.forward,layer)
+            layer.name='conv0_resolution_'+str(res)
+            
+        layer=block.conv1
+        layer.forward=types.MethodType(SynthesisLayer.forward,layer)
+        layer.name='conv1_resolution_'+str(res)
+        
+        layer=block.torgb
+        layer.forward=types.MethodType(ToRGBLayer.forward,layer)
+        layer.name='toRGB_resolution_'+str(res)
+        
+    
+    return G
+
+
+def S2List(encoded_styles):
+    all_s=[]
+    for name in encoded_styles.keys():
+        tmp=encoded_styles[name].cpu().numpy()
+        all_s.append(tmp)
+    return all_s
+
+
+
+class Manipulator():
+    def __init__(self,dataset_name='ffhq'):
+        
+        self.alpha=[0] #manipulation strength 
+        self.num_images=10
+        self.img_index=0  #which image to start 
+        # self.viz_size=256
+        self.manipulate_layers=None #which layer to manipulate, list
+        self.truncation_psi=0.7
+        self.truncation_cutoff=8
+        
+#        self.G=LoadModel(self.model_path,self.model_name)
+        
+        self.LoadModel=LoadModel
+        self.Vis=Vis
+        self.S2List=S2List
+        
+        fmaps=[512, 512, 512, 512, 512, 256, 128,  64, 32]
+        self.fmaps=np.repeat(fmaps,3)
+        
+    
+    def GetSName(self):
+        s_names=[]
+        for res in self.G.synthesis.block_resolutions:
+            if res==4: 
+                tmp=f'conv1_resolution_{res}'
+                s_names.append(tmp)
+                
+                tmp=f'toRGB_resolution_{res}'
+                s_names.append(tmp)
+            else:
+                tmp=f'conv0_resolution_{res}'
+                s_names.append(tmp)
+                
+                tmp=f'conv1_resolution_{res}'
+                s_names.append(tmp)
+                
+                tmp=f'toRGB_resolution_{res}'
+                s_names.append(tmp)
+                
+        return s_names
+    
+    def SL2D(self,tmp_code):
+        encoded_styles={}
+        for i in range(len(self.s_names)):
+            encoded_styles[self.s_names[i]]=torch.from_numpy(tmp_code[i]).to(self.device)
+        
+        return encoded_styles
+        
+    
+    
+    def GenerateS(self,num_img=100):
+        seed=5
+        with torch.no_grad(): 
+            z = torch.from_numpy(np.random.RandomState(seed).randn(num_img, self.G.z_dim)).to(self.device)
+            ws = self.G.mapping(z=z,c=None,truncation_psi=self.truncation_psi,truncation_cutoff=self.truncation_cutoff)
+            encoded_styles=self.G.synthesis.W2S(ws)
+#            encoded_styles=encoded_styles.cpu().numpy()
+            
+        self.dlatents=S2List(encoded_styles)
+    
+    def GenerateImg(self,codes):
+        
+        num_images,step=codes[0].shape[:2]
+        out=np.zeros((num_images,step,self.img_size,self.img_size,3),dtype='uint8')
+        for i in range(num_images):
+            for k in range(step):
+                
+                tmp_code=[]
+                for m in range(len(self.s_names)):
+                    tmp=codes[m][i,k][None,:]
+                    tmp_code.append(tmp)
+                    
+                encoded_styles=self.SL2D(tmp_code)
+                
+                with torch.no_grad(): 
+                    img = self.G.synthesis(None, encoded_styles=encoded_styles,noise_mode='const')
+                    img = (img + 1) * (255/2)
+                    img = img.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
+                
+                
+                
+                if img.shape[1]==img.shape[0]:
+                    out[i,k,:,:,:]=img
+                else:
+                    tmp=img.shape[1]
+                    tmp1=int((img.shape[0]-tmp)/2)
+                    out[i,k,:,tmp1:tmp1+tmp,:]=img
+        return out
+    
+    def ShowImg(self,num_img=10):
+        
+        codes=[]
+        for i in range(len(self.dlatents)):
+            # print(i)
+            tmp=self.dlatents[i][:num_img,None,:]
+            codes.append(tmp)
+        out=self.GenerateImg(codes)
+        return out
+    
+    def SetGParameters(self):
+        self.num_layers=self.G.synthesis.num_ws
+        self.img_size=self.G.synthesis.img_resolution
+        self.s_names=self.GetSName()
+        
+        self.img_size=self.G.synthesis.block_resolutions[-1]
+        
+        self.mindexs=[0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 20, 21,23,24]
+        
+        
+    
+    def MSCode(self,dlatent_tmp,boundary_tmp):
+        
+        step=len(self.alpha)
+        dlatent_tmp1=[tmp.reshape((self.num_images,-1)) for tmp in dlatent_tmp]
+        dlatent_tmp2=[np.tile(tmp[:,None],(1,step,1)) for tmp in dlatent_tmp1] # (10, 7, 512)
+
+        l=np.array(self.alpha)
+        l=l.reshape(
+                    [step if axis == 1 else 1 for axis in range(dlatent_tmp2[0].ndim)])
+        
+        if type(self.manipulate_layers)==int:
+            tmp=[self.manipulate_layers]
+        elif type(self.manipulate_layers)==list:
+            tmp=self.manipulate_layers
+        elif self.manipulate_layers is None:
+            tmp=np.arange(len(boundary_tmp))
+        else:
+            raise ValueError('manipulate_layers is wrong')
+            
+        for i in tmp:
+            dlatent_tmp2[i]+=l*boundary_tmp[i]
+        
+        codes=[]
+        for i in range(len(dlatent_tmp2)):
+            tmp=list(dlatent_tmp[i].shape)
+            tmp.insert(1,step)
+            codes.append(dlatent_tmp2[i].reshape(tmp))
+        return codes
+    
+    
+    def EditOne(self,bname,dlatent_tmp=None):
+        if dlatent_tmp==None:
+            dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents]
+        
+        boundary_tmp=[]
+        for i in range(len(self.boundary)):
+            tmp=self.boundary[i]
+            if len(tmp)<=bname:
+                boundary_tmp.append([])
+            else:
+                boundary_tmp.append(tmp[bname])
+        
+        codes=self.MSCode(dlatent_tmp,boundary_tmp)
+            
+        out=self.GenerateImg(codes)
+        return codes,out
+    
+    def EditOneC(self,cindex,dlatent_tmp=None): 
+        if dlatent_tmp==None:
+            dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents]
+        
+        boundary_tmp=[[] for i in range(len(self.dlatents))]
+        
+        #'only manipulate 1 layer and one channel'
+        assert len(self.manipulate_layers)==1 
+        
+        ml=self.manipulate_layers[0]
+        tmp=dlatent_tmp[ml].shape[1] #ada
+        tmp1=np.zeros(tmp)
+        tmp1[cindex]=self.code_std[ml][cindex]  #1
+        boundary_tmp[ml]=tmp1
+        
+        codes=self.MSCode(dlatent_tmp,boundary_tmp)
+        out=self.GenerateImg(codes)
+        return codes,out
+    
+    def GetFindex(self,lindex,cindex,ignore_RGB=False):
+        
+        if ignore_RGB:
+            tmp=np.array(self.mindexs)<lindex
+            tmp=np.sum(tmp)
+        else:
+            tmp=lindex
+        findex=np.sum(self.fmaps[:tmp])+cindex
+        return findex 
+    
+    def GetLCIndex(self,findex):
+        l_p=[]
+        cfmaps=np.cumsum(self.fmaps)
+        for i in range(len(findex)):
+            #    i=-2
+            tmp_index=findex[i]
+        #    importance_matrix.max(axis=0)
+        #    self.attrib_indices2
+            tmp=tmp_index-cfmaps
+            tmp=tmp[tmp>0]
+            lindex=len(tmp)
+            if lindex==0:
+                cindex=tmp_index
+            else:
+                cindex=tmp[-1]
+            
+            if cindex ==self.fmaps[lindex]:
+                cindex=0
+                lindex+=1
+    #        print(completeness.index[i],completeness.iloc[i,:].values,lindex,cindex)
+            l_p.append([lindex,cindex])
+        l_p=np.array(l_p)
+        return l_p
+    def GetLCIndex2(self,findex): #input findex without ToRGB
+        fmaps_o=copy.copy(self.fmaps)
+        mindexs=[0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 20, 21,23,24]
+        self.fmaps=fmaps_o[mindexs]
+        
+        l_p=self.GetLCIndex(findex)
+        
+        l=l_p[:,0]
+        l2=np.array(mindexs)[l]
+        l_p[:,0]=l2
+        self.fmaps=fmaps_o
+        return l_p
+    
+    def GetCodeMS(self):
+        m=[]
+        std=[]
+        for i in range(len(self.dlatents)):
+            tmp= self.dlatents[i] 
+            tmp_mean=tmp.mean(axis=0)
+            tmp_std=tmp.std(axis=0)
+            m.append(tmp_mean)
+            std.append(tmp_std)
+        
+        self.code_mean=m
+        self.code_std=std
+        # return m,std
+    
+    
+#%%
+if __name__ == "__main__":
+    network_pkl='/cs/labs/danix/wuzongze/Gan_Manipulation/stylegan2/model/stylegan2-ffhq-config-f.pkl'
+    device = torch.device('cuda')
+    M=Manipulator()
+    M.device=device
+    G=M.LoadModel(network_pkl,device)
+    M.G=G
+    M.SetGParameters()
+    num_img=100_000
+    M.GenerateS(num_img=num_img)
+    M.GetCodeMS()
+    np.set_printoptions(suppress=True)
+    
+    #%%
+    M.alpha=[24,16,8,0,-8,-16,-24]
+    M.step=len(M.alpha)
+    M.img_index=0
+    M.num_images=10
+    lindex,bname=6,501
+#    M.
+    M.manipulate_layers=[lindex]
+    codes,out=M.EditOneC(bname) #dlatent_tmp
+    tmp=str(M.manipulate_layers)+'_'+str(bname)
+    M.Vis(tmp,'c',out)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/global_torch/npy/ffhq/fs3.npy b/global_torch/npy/ffhq/fs3.npy
new file mode 100644
index 0000000..ef9dc0a
Binary files /dev/null and b/global_torch/npy/ffhq/fs3.npy differ
diff --git a/global_torch/npy/human/fs3.npy b/global_torch/npy/human/fs3.npy
new file mode 100644
index 0000000..5b9dd0d
Binary files /dev/null and b/global_torch/npy/human/fs3.npy differ
diff --git a/global_torch/torch_utils/__init__.py b/global_torch/torch_utils/__init__.py
new file mode 100644
index 0000000..ece0ea0
--- /dev/null
+++ b/global_torch/torch_utils/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+# empty
diff --git a/global_torch/torch_utils/custom_ops.py b/global_torch/torch_utils/custom_ops.py
new file mode 100644
index 0000000..4cc4e43
--- /dev/null
+++ b/global_torch/torch_utils/custom_ops.py
@@ -0,0 +1,126 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import os
+import glob
+import torch
+import torch.utils.cpp_extension
+import importlib
+import hashlib
+import shutil
+from pathlib import Path
+
+from torch.utils.file_baton import FileBaton
+
+#----------------------------------------------------------------------------
+# Global options.
+
+verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
+
+#----------------------------------------------------------------------------
+# Internal helper funcs.
+
+def _find_compiler_bindir():
+    patterns = [
+        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
+    ]
+    for pattern in patterns:
+        matches = sorted(glob.glob(pattern))
+        if len(matches):
+            return matches[-1]
+    return None
+
+#----------------------------------------------------------------------------
+# Main entry point for compiling and loading C++/CUDA plugins.
+
+_cached_plugins = dict()
+
+def get_plugin(module_name, sources, **build_kwargs):
+    assert verbosity in ['none', 'brief', 'full']
+
+    # Already cached?
+    if module_name in _cached_plugins:
+        return _cached_plugins[module_name]
+
+    # Print status.
+    if verbosity == 'full':
+        print(f'Setting up PyTorch plugin "{module_name}"...')
+    elif verbosity == 'brief':
+        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
+
+    try: # pylint: disable=too-many-nested-blocks
+        # Make sure we can find the necessary compiler binaries.
+        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
+            compiler_bindir = _find_compiler_bindir()
+            if compiler_bindir is None:
+                raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
+            os.environ['PATH'] += ';' + compiler_bindir
+
+        # Compile and load.
+        verbose_build = (verbosity == 'full')
+
+        # Incremental build md5sum trickery.  Copies all the input source files
+        # into a cached build directory under a combined md5 digest of the input
+        # source files.  Copying is done only if the combined digest has changed.
+        # This keeps input file timestamps and filenames the same as in previous
+        # extension builds, allowing for fast incremental rebuilds.
+        #
+        # This optimization is done only in case all the source files reside in
+        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
+        # environment variable is set (we take this as a signal that the user
+        # actually cares about this.)
+        source_dirs_set = set(os.path.dirname(source) for source in sources)
+        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
+            all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()))
+
+            # Compute a combined hash digest for all source files in the same
+            # custom op directory (usually .cu, .cpp, .py and .h files).
+            hash_md5 = hashlib.md5()
+            for src in all_source_files:
+                with open(src, 'rb') as f:
+                    hash_md5.update(f.read())
+            build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
+            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
+
+            if not os.path.isdir(digest_build_dir):
+                os.makedirs(digest_build_dir, exist_ok=True)
+                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
+                if baton.try_acquire():
+                    try:
+                        for src in all_source_files:
+                            shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src)))
+                    finally:
+                        baton.release()
+                else:
+                    # Someone else is copying source files under the digest dir,
+                    # wait until done and continue.
+                    baton.wait()
+            digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
+            torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
+                verbose=verbose_build, sources=digest_sources, **build_kwargs)
+        else:
+            torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
+        module = importlib.import_module(module_name)
+
+    except:
+        if verbosity == 'brief':
+            print('Failed!')
+        raise
+
+    # Print status and add to cache.
+    if verbosity == 'full':
+        print(f'Done setting up PyTorch plugin "{module_name}".')
+    elif verbosity == 'brief':
+        print('Done.')
+    _cached_plugins[module_name] = module
+    return module
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/misc.py b/global_torch/torch_utils/misc.py
new file mode 100644
index 0000000..7829f4d
--- /dev/null
+++ b/global_torch/torch_utils/misc.py
@@ -0,0 +1,262 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import re
+import contextlib
+import numpy as np
+import torch
+import warnings
+import dnnlib
+
+#----------------------------------------------------------------------------
+# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
+# same constant is used multiple times.
+
+_constant_cache = dict()
+
+def constant(value, shape=None, dtype=None, device=None, memory_format=None):
+    value = np.asarray(value)
+    if shape is not None:
+        shape = tuple(shape)
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+    if device is None:
+        device = torch.device('cpu')
+    if memory_format is None:
+        memory_format = torch.contiguous_format
+
+    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
+    tensor = _constant_cache.get(key, None)
+    if tensor is None:
+        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
+        if shape is not None:
+            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
+        tensor = tensor.contiguous(memory_format=memory_format)
+        _constant_cache[key] = tensor
+    return tensor
+
+#----------------------------------------------------------------------------
+# Replace NaN/Inf with specified numerical values.
+
+try:
+    nan_to_num = torch.nan_to_num # 1.8.0a0
+except AttributeError:
+    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
+        assert isinstance(input, torch.Tensor)
+        if posinf is None:
+            posinf = torch.finfo(input.dtype).max
+        if neginf is None:
+            neginf = torch.finfo(input.dtype).min
+        assert nan == 0
+        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
+
+#----------------------------------------------------------------------------
+# Symbolic assert.
+
+try:
+    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
+except AttributeError:
+    symbolic_assert = torch.Assert # 1.7.0
+
+#----------------------------------------------------------------------------
+# Context manager to suppress known warnings in torch.jit.trace().
+
+class suppress_tracer_warnings(warnings.catch_warnings):
+    def __enter__(self):
+        super().__enter__()
+        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
+        return self
+
+#----------------------------------------------------------------------------
+# Assert that the shape of a tensor matches the given list of integers.
+# None indicates that the size of a dimension is allowed to vary.
+# Performs symbolic assertion when used in torch.jit.trace().
+
+def assert_shape(tensor, ref_shape):
+    if tensor.ndim != len(ref_shape):
+        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
+    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
+        if ref_size is None:
+            pass
+        elif isinstance(ref_size, torch.Tensor):
+            with suppress_tracer_warnings(): # as_tensor results are registered as constants
+                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
+        elif isinstance(size, torch.Tensor):
+            with suppress_tracer_warnings(): # as_tensor results are registered as constants
+                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
+        elif size != ref_size:
+            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
+
+#----------------------------------------------------------------------------
+# Function decorator that calls torch.autograd.profiler.record_function().
+
+def profiled_function(fn):
+    def decorator(*args, **kwargs):
+        with torch.autograd.profiler.record_function(fn.__name__):
+            return fn(*args, **kwargs)
+    decorator.__name__ = fn.__name__
+    return decorator
+
+#----------------------------------------------------------------------------
+# Sampler for torch.utils.data.DataLoader that loops over the dataset
+# indefinitely, shuffling items as it goes.
+
+class InfiniteSampler(torch.utils.data.Sampler):
+    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
+        assert len(dataset) > 0
+        assert num_replicas > 0
+        assert 0 <= rank < num_replicas
+        assert 0 <= window_size <= 1
+        super().__init__(dataset)
+        self.dataset = dataset
+        self.rank = rank
+        self.num_replicas = num_replicas
+        self.shuffle = shuffle
+        self.seed = seed
+        self.window_size = window_size
+
+    def __iter__(self):
+        order = np.arange(len(self.dataset))
+        rnd = None
+        window = 0
+        if self.shuffle:
+            rnd = np.random.RandomState(self.seed)
+            rnd.shuffle(order)
+            window = int(np.rint(order.size * self.window_size))
+
+        idx = 0
+        while True:
+            i = idx % order.size
+            if idx % self.num_replicas == self.rank:
+                yield order[i]
+            if window >= 2:
+                j = (i - rnd.randint(window)) % order.size
+                order[i], order[j] = order[j], order[i]
+            idx += 1
+
+#----------------------------------------------------------------------------
+# Utilities for operating with torch.nn.Module parameters and buffers.
+
+def params_and_buffers(module):
+    assert isinstance(module, torch.nn.Module)
+    return list(module.parameters()) + list(module.buffers())
+
+def named_params_and_buffers(module):
+    assert isinstance(module, torch.nn.Module)
+    return list(module.named_parameters()) + list(module.named_buffers())
+
+def copy_params_and_buffers(src_module, dst_module, require_all=False):
+    assert isinstance(src_module, torch.nn.Module)
+    assert isinstance(dst_module, torch.nn.Module)
+    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
+    for name, tensor in named_params_and_buffers(dst_module):
+        assert (name in src_tensors) or (not require_all)
+        if name in src_tensors:
+            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
+
+#----------------------------------------------------------------------------
+# Context manager for easily enabling/disabling DistributedDataParallel
+# synchronization.
+
+@contextlib.contextmanager
+def ddp_sync(module, sync):
+    assert isinstance(module, torch.nn.Module)
+    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
+        yield
+    else:
+        with module.no_sync():
+            yield
+
+#----------------------------------------------------------------------------
+# Check DistributedDataParallel consistency across processes.
+
+def check_ddp_consistency(module, ignore_regex=None):
+    assert isinstance(module, torch.nn.Module)
+    for name, tensor in named_params_and_buffers(module):
+        fullname = type(module).__name__ + '.' + name
+        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
+            continue
+        tensor = tensor.detach()
+        other = tensor.clone()
+        torch.distributed.broadcast(tensor=other, src=0)
+        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
+
+#----------------------------------------------------------------------------
+# Print summary table of module hierarchy.
+
+def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
+    assert isinstance(module, torch.nn.Module)
+    assert not isinstance(module, torch.jit.ScriptModule)
+    assert isinstance(inputs, (tuple, list))
+
+    # Register hooks.
+    entries = []
+    nesting = [0]
+    def pre_hook(_mod, _inputs):
+        nesting[0] += 1
+    def post_hook(mod, _inputs, outputs):
+        nesting[0] -= 1
+        if nesting[0] <= max_nesting:
+            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
+            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
+            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
+    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
+    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
+
+    # Run module.
+    outputs = module(*inputs)
+    for hook in hooks:
+        hook.remove()
+
+    # Identify unique outputs, parameters, and buffers.
+    tensors_seen = set()
+    for e in entries:
+        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
+        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
+        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
+        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
+
+    # Filter out redundant entries.
+    if skip_redundant:
+        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
+
+    # Construct table.
+    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
+    rows += [['---'] * len(rows[0])]
+    param_total = 0
+    buffer_total = 0
+    submodule_names = {mod: name for name, mod in module.named_modules()}
+    for e in entries:
+        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
+        param_size = sum(t.numel() for t in e.unique_params)
+        buffer_size = sum(t.numel() for t in e.unique_buffers)
+        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
+        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
+        rows += [[
+            name + (':0' if len(e.outputs) >= 2 else ''),
+            str(param_size) if param_size else '-',
+            str(buffer_size) if buffer_size else '-',
+            (output_shapes + ['-'])[0],
+            (output_dtypes + ['-'])[0],
+        ]]
+        for idx in range(1, len(e.outputs)):
+            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
+        param_total += param_size
+        buffer_total += buffer_size
+    rows += [['---'] * len(rows[0])]
+    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
+
+    # Print table.
+    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
+    print()
+    for row in rows:
+        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
+    print()
+    return outputs
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/__init__.py b/global_torch/torch_utils/ops/__init__.py
new file mode 100644
index 0000000..ece0ea0
--- /dev/null
+++ b/global_torch/torch_utils/ops/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+# empty
diff --git a/global_torch/torch_utils/ops/bias_act.cpp b/global_torch/torch_utils/ops/bias_act.cpp
new file mode 100644
index 0000000..5d2425d
--- /dev/null
+++ b/global_torch/torch_utils/ops/bias_act.cpp
@@ -0,0 +1,99 @@
+// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "bias_act.h"
+
+//------------------------------------------------------------------------
+
+static bool has_same_layout(torch::Tensor x, torch::Tensor y)
+{
+    if (x.dim() != y.dim())
+        return false;
+    for (int64_t i = 0; i < x.dim(); i++)
+    {
+        if (x.size(i) != y.size(i))
+            return false;
+        if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
+            return false;
+    }
+    return true;
+}
+
+//------------------------------------------------------------------------
+
+static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
+{
+    // Validate arguments.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
+    TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
+    TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
+    TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
+    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
+    TORCH_CHECK(b.dim() == 1, "b must have rank 1");
+    TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
+    TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
+    TORCH_CHECK(grad >= 0, "grad must be non-negative");
+
+    // Validate layout.
+    TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
+    TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
+    TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
+    TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
+    TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
+
+    // Create output tensor.
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+    torch::Tensor y = torch::empty_like(x);
+    TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
+
+    // Initialize CUDA kernel parameters.
+    bias_act_kernel_params p;
+    p.x     = x.data_ptr();
+    p.b     = (b.numel()) ? b.data_ptr() : NULL;
+    p.xref  = (xref.numel()) ? xref.data_ptr() : NULL;
+    p.yref  = (yref.numel()) ? yref.data_ptr() : NULL;
+    p.dy    = (dy.numel()) ? dy.data_ptr() : NULL;
+    p.y     = y.data_ptr();
+    p.grad  = grad;
+    p.act   = act;
+    p.alpha = alpha;
+    p.gain  = gain;
+    p.clamp = clamp;
+    p.sizeX = (int)x.numel();
+    p.sizeB = (int)b.numel();
+    p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
+
+    // Choose CUDA kernel.
+    void* kernel;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
+    {
+        kernel = choose_bias_act_kernel<scalar_t>(p);
+    });
+    TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
+
+    // Launch CUDA kernel.
+    p.loopX = 4;
+    int blockSize = 4 * 32;
+    int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
+    void* args[] = {&p};
+    AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
+    return y;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("bias_act", &bias_act);
+}
+
+//------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/bias_act.cu b/global_torch/torch_utils/ops/bias_act.cu
new file mode 100644
index 0000000..dd8fc47
--- /dev/null
+++ b/global_torch/torch_utils/ops/bias_act.cu
@@ -0,0 +1,173 @@
+// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <c10/util/Half.h>
+#include "bias_act.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>     { typedef double scalar_t; };
+template <> struct InternalType<float>      { typedef float  scalar_t; };
+template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
+
+//------------------------------------------------------------------------
+// CUDA kernel.
+
+template <class T, int A>
+__global__ void bias_act_kernel(bias_act_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    int G                 = p.grad;
+    scalar_t alpha        = (scalar_t)p.alpha;
+    scalar_t gain         = (scalar_t)p.gain;
+    scalar_t clamp        = (scalar_t)p.clamp;
+    scalar_t one          = (scalar_t)1;
+    scalar_t two          = (scalar_t)2;
+    scalar_t expRange     = (scalar_t)80;
+    scalar_t halfExpRange = (scalar_t)40;
+    scalar_t seluScale    = (scalar_t)1.0507009873554804934193349852946;
+    scalar_t seluAlpha    = (scalar_t)1.6732632423543772848170429916717;
+
+    // Loop over elements.
+    int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x;
+    for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x)
+    {
+        // Load.
+        scalar_t x = (scalar_t)((const T*)p.x)[xi];
+        scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0;
+        scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0;
+        scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0;
+        scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one;
+        scalar_t yy = (gain != 0) ? yref / gain : 0;
+        scalar_t y = 0;
+
+        // Apply bias.
+        ((G == 0) ? x : xref) += b;
+
+        // linear
+        if (A == 1)
+        {
+            if (G == 0) y = x;
+            if (G == 1) y = x;
+        }
+
+        // relu
+        if (A == 2)
+        {
+            if (G == 0) y = (x > 0) ? x : 0;
+            if (G == 1) y = (yy > 0) ? x : 0;
+        }
+
+        // lrelu
+        if (A == 3)
+        {
+            if (G == 0) y = (x > 0) ? x : x * alpha;
+            if (G == 1) y = (yy > 0) ? x : x * alpha;
+        }
+
+        // tanh
+        if (A == 4)
+        {
+            if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); }
+            if (G == 1) y = x * (one - yy * yy);
+            if (G == 2) y = x * (one - yy * yy) * (-two * yy);
+        }
+
+        // sigmoid
+        if (A == 5)
+        {
+            if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one);
+            if (G == 1) y = x * yy * (one - yy);
+            if (G == 2) y = x * yy * (one - yy) * (one - two * yy);
+        }
+
+        // elu
+        if (A == 6)
+        {
+            if (G == 0) y = (x >= 0) ? x : exp(x) - one;
+            if (G == 1) y = (yy >= 0) ? x : x * (yy + one);
+            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one);
+        }
+
+        // selu
+        if (A == 7)
+        {
+            if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one);
+            if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha);
+            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha);
+        }
+
+        // softplus
+        if (A == 8)
+        {
+            if (G == 0) y = (x > expRange) ? x : log(exp(x) + one);
+            if (G == 1) y = x * (one - exp(-yy));
+            if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); }
+        }
+
+        // swish
+        if (A == 9)
+        {
+            if (G == 0)
+                y = (x < -expRange) ? 0 : x / (exp(-x) + one);
+            else
+            {
+                scalar_t c = exp(xref);
+                scalar_t d = c + one;
+                if (G == 1)
+                    y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d);
+                else
+                    y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d);
+                yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain;
+            }
+        }
+
+        // Apply gain.
+        y *= gain * dy;
+
+        // Clamp.
+        if (clamp >= 0)
+        {
+            if (G == 0)
+                y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp;
+            else
+                y = (yref > -clamp & yref < clamp) ? y : 0;
+        }
+
+        // Store.
+        ((T*)p.y)[xi] = (T)y;
+    }
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p)
+{
+    if (p.act == 1) return (void*)bias_act_kernel<T, 1>;
+    if (p.act == 2) return (void*)bias_act_kernel<T, 2>;
+    if (p.act == 3) return (void*)bias_act_kernel<T, 3>;
+    if (p.act == 4) return (void*)bias_act_kernel<T, 4>;
+    if (p.act == 5) return (void*)bias_act_kernel<T, 5>;
+    if (p.act == 6) return (void*)bias_act_kernel<T, 6>;
+    if (p.act == 7) return (void*)bias_act_kernel<T, 7>;
+    if (p.act == 8) return (void*)bias_act_kernel<T, 8>;
+    if (p.act == 9) return (void*)bias_act_kernel<T, 9>;
+    return NULL;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template void* choose_bias_act_kernel<double>       (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel<float>        (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel<c10::Half>    (const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/bias_act.h b/global_torch/torch_utils/ops/bias_act.h
new file mode 100644
index 0000000..a32187e
--- /dev/null
+++ b/global_torch/torch_utils/ops/bias_act.h
@@ -0,0 +1,38 @@
+// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct bias_act_kernel_params
+{
+    const void* x;      // [sizeX]
+    const void* b;      // [sizeB] or NULL
+    const void* xref;   // [sizeX] or NULL
+    const void* yref;   // [sizeX] or NULL
+    const void* dy;     // [sizeX] or NULL
+    void*       y;      // [sizeX]
+
+    int         grad;
+    int         act;
+    float       alpha;
+    float       gain;
+    float       clamp;
+
+    int         sizeX;
+    int         sizeB;
+    int         stepB;
+    int         loopX;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/bias_act.py b/global_torch/torch_utils/ops/bias_act.py
new file mode 100644
index 0000000..4bcb409
--- /dev/null
+++ b/global_torch/torch_utils/ops/bias_act.py
@@ -0,0 +1,212 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom PyTorch ops for efficient bias and activation."""
+
+import os
+import warnings
+import numpy as np
+import torch
+import dnnlib
+import traceback
+
+from .. import custom_ops
+from .. import misc
+
+#----------------------------------------------------------------------------
+
+activation_funcs = {
+    'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
+    'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
+    'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
+    'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
+    'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
+    'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
+    'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
+    'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
+    'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
+}
+
+#----------------------------------------------------------------------------
+
+_inited = False
+_plugin = None
+_null_tensor = torch.empty([0])
+
+def _init():
+    global _inited, _plugin
+    if not _inited:
+        _inited = True
+        sources = ['bias_act.cpp', 'bias_act.cu']
+        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
+        try:
+            _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
+        except:
+            warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
+    return _plugin is not None
+
+#----------------------------------------------------------------------------
+
+def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
+    r"""Fused bias and activation function.
+
+    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
+    and scales the result by `gain`. Each of the steps is optional. In most cases,
+    the fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports first and second order gradients,
+    but not third order gradients.
+
+    Args:
+        x:      Input activation tensor. Can be of any shape.
+        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
+                as `x`. The shape must be known, and it must match the dimension of `x`
+                corresponding to `dim`.
+        dim:    The dimension in `x` corresponding to the elements of `b`.
+                The value of `dim` is ignored if `b` is not specified.
+        act:    Name of the activation function to evaluate, or `"linear"` to disable.
+                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
+                See `activation_funcs` for a full list. `None` is not allowed.
+        alpha:  Shape parameter for the activation function, or `None` to use the default.
+        gain:   Scaling factor for the output tensor, or `None` to use default.
+                See `activation_funcs` for the default scaling of each activation function.
+                If unsure, consider specifying 1.
+        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
+                the clamping (default).
+        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+    Returns:
+        Tensor of the same shape and datatype as `x`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
+    return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
+    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert clamp is None or clamp >= 0
+    spec = activation_funcs[act]
+    alpha = float(alpha if alpha is not None else spec.def_alpha)
+    gain = float(gain if gain is not None else spec.def_gain)
+    clamp = float(clamp if clamp is not None else -1)
+
+    # Add bias.
+    if b is not None:
+        assert isinstance(b, torch.Tensor) and b.ndim == 1
+        assert 0 <= dim < x.ndim
+        assert b.shape[0] == x.shape[dim]
+        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
+
+    # Evaluate activation function.
+    alpha = float(alpha)
+    x = spec.func(x, alpha=alpha)
+
+    # Scale by gain.
+    gain = float(gain)
+    if gain != 1:
+        x = x * gain
+
+    # Clamp.
+    if clamp >= 0:
+        x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
+    return x
+
+#----------------------------------------------------------------------------
+
+_bias_act_cuda_cache = dict()
+
+def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
+    """Fast CUDA implementation of `bias_act()` using custom ops.
+    """
+    # Parse arguments.
+    assert clamp is None or clamp >= 0
+    spec = activation_funcs[act]
+    alpha = float(alpha if alpha is not None else spec.def_alpha)
+    gain = float(gain if gain is not None else spec.def_gain)
+    clamp = float(clamp if clamp is not None else -1)
+
+    # Lookup from cache.
+    key = (dim, act, alpha, gain, clamp)
+    if key in _bias_act_cuda_cache:
+        return _bias_act_cuda_cache[key]
+
+    # Forward op.
+    class BiasActCuda(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, b): # pylint: disable=arguments-differ
+            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format
+            x = x.contiguous(memory_format=ctx.memory_format)
+            b = b.contiguous() if b is not None else _null_tensor
+            y = x
+            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
+                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
+            ctx.save_for_backward(
+                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
+                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
+                y if 'y' in spec.ref else _null_tensor)
+            return y
+
+        @staticmethod
+        def backward(ctx, dy): # pylint: disable=arguments-differ
+            dy = dy.contiguous(memory_format=ctx.memory_format)
+            x, b, y = ctx.saved_tensors
+            dx = None
+            db = None
+
+            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
+                dx = dy
+                if act != 'linear' or gain != 1 or clamp >= 0:
+                    dx = BiasActCudaGrad.apply(dy, x, b, y)
+
+            if ctx.needs_input_grad[1]:
+                db = dx.sum([i for i in range(dx.ndim) if i != dim])
+
+            return dx, db
+
+    # Backward op.
+    class BiasActCudaGrad(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
+            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format
+            dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
+            ctx.save_for_backward(
+                dy if spec.has_2nd_grad else _null_tensor,
+                x, b, y)
+            return dx
+
+        @staticmethod
+        def backward(ctx, d_dx): # pylint: disable=arguments-differ
+            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
+            dy, x, b, y = ctx.saved_tensors
+            d_dy = None
+            d_x = None
+            d_b = None
+            d_y = None
+
+            if ctx.needs_input_grad[0]:
+                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
+
+            if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
+                d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
+
+            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
+                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
+
+            return d_dy, d_x, d_b, d_y
+
+    # Add to cache.
+    _bias_act_cuda_cache[key] = BiasActCuda
+    return BiasActCuda
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/conv2d_gradfix.py b/global_torch/torch_utils/ops/conv2d_gradfix.py
new file mode 100644
index 0000000..e95e10d
--- /dev/null
+++ b/global_torch/torch_utils/ops/conv2d_gradfix.py
@@ -0,0 +1,170 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom replacement for `torch.nn.functional.conv2d` that supports
+arbitrarily high order gradients with zero performance penalty."""
+
+import warnings
+import contextlib
+import torch
+
+# pylint: disable=redefined-builtin
+# pylint: disable=arguments-differ
+# pylint: disable=protected-access
+
+#----------------------------------------------------------------------------
+
+enabled = False                     # Enable the custom op by setting this to true.
+weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
+
+@contextlib.contextmanager
+def no_weight_gradients():
+    global weight_gradients_disabled
+    old = weight_gradients_disabled
+    weight_gradients_disabled = True
+    yield
+    weight_gradients_disabled = old
+
+#----------------------------------------------------------------------------
+
+def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
+    if _should_use_custom_op(input):
+        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
+    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
+
+def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
+    if _should_use_custom_op(input):
+        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
+    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
+
+#----------------------------------------------------------------------------
+
+def _should_use_custom_op(input):
+    assert isinstance(input, torch.Tensor)
+    if (not enabled) or (not torch.backends.cudnn.enabled):
+        return False
+    if input.device.type != 'cuda':
+        return False
+    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
+        return True
+    warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
+    return False
+
+def _tuple_of_ints(xs, ndim):
+    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
+    assert len(xs) == ndim
+    assert all(isinstance(x, int) for x in xs)
+    return xs
+
+#----------------------------------------------------------------------------
+
+_conv2d_gradfix_cache = dict()
+
+def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
+    # Parse arguments.
+    ndim = 2
+    weight_shape = tuple(weight_shape)
+    stride = _tuple_of_ints(stride, ndim)
+    padding = _tuple_of_ints(padding, ndim)
+    output_padding = _tuple_of_ints(output_padding, ndim)
+    dilation = _tuple_of_ints(dilation, ndim)
+
+    # Lookup from cache.
+    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
+    if key in _conv2d_gradfix_cache:
+        return _conv2d_gradfix_cache[key]
+
+    # Validate arguments.
+    assert groups >= 1
+    assert len(weight_shape) == ndim + 2
+    assert all(stride[i] >= 1 for i in range(ndim))
+    assert all(padding[i] >= 0 for i in range(ndim))
+    assert all(dilation[i] >= 0 for i in range(ndim))
+    if not transpose:
+        assert all(output_padding[i] == 0 for i in range(ndim))
+    else: # transpose
+        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
+
+    # Helpers.
+    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
+    def calc_output_padding(input_shape, output_shape):
+        if transpose:
+            return [0, 0]
+        return [
+            input_shape[i + 2]
+            - (output_shape[i + 2] - 1) * stride[i]
+            - (1 - 2 * padding[i])
+            - dilation[i] * (weight_shape[i + 2] - 1)
+            for i in range(ndim)
+        ]
+
+    # Forward & backward.
+    class Conv2d(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, input, weight, bias):
+            assert weight.shape == weight_shape
+            if not transpose:
+                output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
+            else: # transpose
+                output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
+            ctx.save_for_backward(input, weight)
+            return output
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            input, weight = ctx.saved_tensors
+            grad_input = None
+            grad_weight = None
+            grad_bias = None
+
+            if ctx.needs_input_grad[0]:
+                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
+                grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
+                assert grad_input.shape == input.shape
+
+            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
+                grad_weight = Conv2dGradWeight.apply(grad_output, input)
+                assert grad_weight.shape == weight_shape
+
+            if ctx.needs_input_grad[2]:
+                grad_bias = grad_output.sum([0, 2, 3])
+
+            return grad_input, grad_weight, grad_bias
+
+    # Gradient with respect to the weights.
+    class Conv2dGradWeight(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, grad_output, input):
+            op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
+            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
+            grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
+            assert grad_weight.shape == weight_shape
+            ctx.save_for_backward(grad_output, input)
+            return grad_weight
+
+        @staticmethod
+        def backward(ctx, grad2_grad_weight):
+            grad_output, input = ctx.saved_tensors
+            grad2_grad_output = None
+            grad2_input = None
+
+            if ctx.needs_input_grad[0]:
+                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
+                assert grad2_grad_output.shape == grad_output.shape
+
+            if ctx.needs_input_grad[1]:
+                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
+                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
+                assert grad2_input.shape == input.shape
+
+            return grad2_grad_output, grad2_input
+
+    _conv2d_gradfix_cache[key] = Conv2d
+    return Conv2d
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/conv2d_resample.py b/global_torch/torch_utils/ops/conv2d_resample.py
new file mode 100644
index 0000000..cd47507
--- /dev/null
+++ b/global_torch/torch_utils/ops/conv2d_resample.py
@@ -0,0 +1,156 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""2D convolution with optional up/downsampling."""
+
+import torch
+
+from .. import misc
+from . import conv2d_gradfix
+from . import upfirdn2d
+from .upfirdn2d import _parse_padding
+from .upfirdn2d import _get_filter_size
+
+#----------------------------------------------------------------------------
+
+def _get_weight_shape(w):
+    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
+        shape = [int(sz) for sz in w.shape]
+    misc.assert_shape(w, shape)
+    return shape
+
+#----------------------------------------------------------------------------
+
+def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
+    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
+    """
+    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
+
+    # Flip weight if requested.
+    if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
+        w = w.flip([2, 3])
+
+    # Workaround performance pitfall in cuDNN 8.0.5, triggered when using
+    # 1x1 kernel + memory_format=channels_last + less than 64 channels.
+    if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose:
+        if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
+            if out_channels <= 4 and groups == 1:
+                in_shape = x.shape
+                x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1])
+                x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
+            else:
+                x = x.to(memory_format=torch.contiguous_format)
+                w = w.to(memory_format=torch.contiguous_format)
+                x = conv2d_gradfix.conv2d(x, w, groups=groups)
+            return x.to(memory_format=torch.channels_last)
+
+    # Otherwise => execute using conv2d_gradfix.
+    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
+    return op(x, w, stride=stride, padding=padding, groups=groups)
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
+    r"""2D convolution with optional up/downsampling.
+
+    Padding is performed only once at the beginning, not between the operations.
+
+    Args:
+        x:              Input tensor of shape
+                        `[batch_size, in_channels, in_height, in_width]`.
+        w:              Weight tensor of shape
+                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
+        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
+                        calling upfirdn2d.setup_filter(). None = identity (default).
+        up:             Integer upsampling factor (default: 1).
+        down:           Integer downsampling factor (default: 1).
+        padding:        Padding with respect to the upsampled image. Can be a single number
+                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                        (default: 0).
+        groups:         Split input channels into N groups (default: 1).
+        flip_weight:    False = convolution, True = correlation (default: True).
+        flip_filter:    False = convolution, True = correlation (default: False).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
+    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
+    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
+    assert isinstance(up, int) and (up >= 1)
+    assert isinstance(down, int) and (down >= 1)
+    assert isinstance(groups, int) and (groups >= 1)
+    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
+    fw, fh = _get_filter_size(f)
+    px0, px1, py0, py1 = _parse_padding(padding)
+
+    # Adjust padding to account for up/downsampling.
+    if up > 1:
+        px0 += (fw + up - 1) // 2
+        px1 += (fw - up) // 2
+        py0 += (fh + up - 1) // 2
+        py1 += (fh - up) // 2
+    if down > 1:
+        px0 += (fw - down + 1) // 2
+        px1 += (fw - down) // 2
+        py0 += (fh - down + 1) // 2
+        py1 += (fh - down) // 2
+
+    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
+    if kw == 1 and kh == 1 and (down > 1 and up == 1):
+        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        return x
+
+    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
+    if kw == 1 and kh == 1 and (up > 1 and down == 1):
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
+        return x
+
+    # Fast path: downsampling only => use strided convolution.
+    if down > 1 and up == 1:
+        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
+        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
+        return x
+
+    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
+    if up > 1:
+        if groups == 1:
+            w = w.transpose(0, 1)
+        else:
+            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
+            w = w.transpose(1, 2)
+            w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
+        px0 -= kw - 1
+        px1 -= kw - up
+        py0 -= kh - 1
+        py1 -= kh - up
+        pxt = max(min(-px0, -px1), 0)
+        pyt = max(min(-py0, -py1), 0)
+        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
+        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
+        if down > 1:
+            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
+        return x
+
+    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
+    if up == 1 and down == 1:
+        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
+            return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
+
+    # Fallback: Generic reference implementation.
+    x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
+    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+    if down > 1:
+        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
+    return x
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/fma.py b/global_torch/torch_utils/ops/fma.py
new file mode 100644
index 0000000..2eeac58
--- /dev/null
+++ b/global_torch/torch_utils/ops/fma.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
+
+import torch
+
+#----------------------------------------------------------------------------
+
+def fma(a, b, c): # => a * b + c
+    return _FusedMultiplyAdd.apply(a, b, c)
+
+#----------------------------------------------------------------------------
+
+class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
+    @staticmethod
+    def forward(ctx, a, b, c): # pylint: disable=arguments-differ
+        out = torch.addcmul(c, a, b)
+        ctx.save_for_backward(a, b)
+        ctx.c_shape = c.shape
+        return out
+
+    @staticmethod
+    def backward(ctx, dout): # pylint: disable=arguments-differ
+        a, b = ctx.saved_tensors
+        c_shape = ctx.c_shape
+        da = None
+        db = None
+        dc = None
+
+        if ctx.needs_input_grad[0]:
+            da = _unbroadcast(dout * b, a.shape)
+
+        if ctx.needs_input_grad[1]:
+            db = _unbroadcast(dout * a, b.shape)
+
+        if ctx.needs_input_grad[2]:
+            dc = _unbroadcast(dout, c_shape)
+
+        return da, db, dc
+
+#----------------------------------------------------------------------------
+
+def _unbroadcast(x, shape):
+    extra_dims = x.ndim - len(shape)
+    assert extra_dims >= 0
+    dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
+    if len(dim):
+        x = x.sum(dim=dim, keepdim=True)
+    if extra_dims:
+        x = x.reshape(-1, *x.shape[extra_dims+1:])
+    assert x.shape == shape
+    return x
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/fused_act.py b/global_torch/torch_utils/ops/fused_act.py
new file mode 100644
index 0000000..9094954
--- /dev/null
+++ b/global_torch/torch_utils/ops/fused_act.py
@@ -0,0 +1,34 @@
+import os
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.autograd import Function
+
+
+module_path = os.path.dirname(__file__)
+
+
+
+class FusedLeakyReLU(nn.Module):
+    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
+        super().__init__()
+
+        self.bias = nn.Parameter(torch.zeros(channel))
+        self.negative_slope = negative_slope
+        self.scale = scale
+
+    def forward(self, input):
+        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
+
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
+    rest_dim = [1] * (input.ndim - bias.ndim - 1)
+    input = input.cuda()
+    return (
+        F.leaky_relu(
+            input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
+        )
+        * scale
+    )
+
diff --git a/global_torch/torch_utils/ops/grid_sample_gradfix.py b/global_torch/torch_utils/ops/grid_sample_gradfix.py
new file mode 100644
index 0000000..ca6b341
--- /dev/null
+++ b/global_torch/torch_utils/ops/grid_sample_gradfix.py
@@ -0,0 +1,83 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom replacement for `torch.nn.functional.grid_sample` that
+supports arbitrarily high order gradients between the input and output.
+Only works on 2D images and assumes
+`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
+
+import warnings
+import torch
+
+# pylint: disable=redefined-builtin
+# pylint: disable=arguments-differ
+# pylint: disable=protected-access
+
+#----------------------------------------------------------------------------
+
+enabled = False  # Enable the custom op by setting this to true.
+
+#----------------------------------------------------------------------------
+
+def grid_sample(input, grid):
+    if _should_use_custom_op():
+        return _GridSample2dForward.apply(input, grid)
+    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
+
+#----------------------------------------------------------------------------
+
+def _should_use_custom_op():
+    if not enabled:
+        return False
+    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
+        return True
+    warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
+    return False
+
+#----------------------------------------------------------------------------
+
+class _GridSample2dForward(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, grid):
+        assert input.ndim == 4
+        assert grid.ndim == 4
+        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
+        ctx.save_for_backward(input, grid)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input, grid = ctx.saved_tensors
+        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
+        return grad_input, grad_grid
+
+#----------------------------------------------------------------------------
+
+class _GridSample2dBackward(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, grad_output, input, grid):
+        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
+        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
+        ctx.save_for_backward(grid)
+        return grad_input, grad_grid
+
+    @staticmethod
+    def backward(ctx, grad2_grad_input, grad2_grad_grid):
+        _ = grad2_grad_grid # unused
+        grid, = ctx.saved_tensors
+        grad2_grad_output = None
+        grad2_input = None
+        grad2_grid = None
+
+        if ctx.needs_input_grad[0]:
+            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
+
+        assert not ctx.needs_input_grad[2]
+        return grad2_grad_output, grad2_input, grad2_grid
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/upfirdn2d.cpp b/global_torch/torch_utils/ops/upfirdn2d.cpp
new file mode 100644
index 0000000..2d7177f
--- /dev/null
+++ b/global_torch/torch_utils/ops/upfirdn2d.cpp
@@ -0,0 +1,103 @@
+// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "upfirdn2d.h"
+
+//------------------------------------------------------------------------
+
+static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
+{
+    // Validate arguments.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
+    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
+    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
+    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
+    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
+    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
+    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
+    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
+    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
+
+    // Create output tensor.
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
+    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
+    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
+    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
+    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
+
+    // Initialize CUDA kernel parameters.
+    upfirdn2d_kernel_params p;
+    p.x             = x.data_ptr();
+    p.f             = f.data_ptr<float>();
+    p.y             = y.data_ptr();
+    p.up            = make_int2(upx, upy);
+    p.down          = make_int2(downx, downy);
+    p.pad0          = make_int2(padx0, pady0);
+    p.flip          = (flip) ? 1 : 0;
+    p.gain          = gain;
+    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
+    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
+    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
+    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
+    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
+    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
+    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
+    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
+
+    // Choose CUDA kernel.
+    upfirdn2d_kernel_spec spec;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
+    {
+        spec = choose_upfirdn2d_kernel<scalar_t>(p);
+    });
+
+    // Set looping options.
+    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
+    p.loopMinor     = spec.loopMinor;
+    p.loopX         = spec.loopX;
+    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
+    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
+
+    // Compute grid size.
+    dim3 blockSize, gridSize;
+    if (spec.tileOutW < 0) // large
+    {
+        blockSize = dim3(4, 32, 1);
+        gridSize = dim3(
+            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
+            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
+            p.launchMajor);
+    }
+    else // small
+    {
+        blockSize = dim3(256, 1, 1);
+        gridSize = dim3(
+            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
+            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
+            p.launchMajor);
+    }
+
+    // Launch CUDA kernel.
+    void* args[] = {&p};
+    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
+    return y;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("upfirdn2d", &upfirdn2d);
+}
+
+//------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/upfirdn2d.cu b/global_torch/torch_utils/ops/upfirdn2d.cu
new file mode 100644
index 0000000..ebdd987
--- /dev/null
+++ b/global_torch/torch_utils/ops/upfirdn2d.cu
@@ -0,0 +1,350 @@
+// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <c10/util/Half.h>
+#include "upfirdn2d.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>     { typedef double scalar_t; };
+template <> struct InternalType<float>      { typedef float  scalar_t; };
+template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
+
+static __device__ __forceinline__ int floor_div(int a, int b)
+{
+    int t = 1 - a / b;
+    return (a + t * b) / b - t;
+}
+
+//------------------------------------------------------------------------
+// Generic CUDA implementation for large filters.
+
+template <class T> static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+
+    // Calculate thread index.
+    int minorBase = blockIdx.x * blockDim.x + threadIdx.x;
+    int outY = minorBase / p.launchMinor;
+    minorBase -= outY * p.launchMinor;
+    int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
+    int majorBase = blockIdx.z * p.loopMajor;
+    if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor)
+        return;
+
+    // Setup Y receptive field.
+    int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y;
+    int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y);
+    int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY;
+    int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y;
+    if (p.flip)
+        filterY = p.filterSize.y - 1 - filterY;
+
+    // Loop over major, minor, and X.
+    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
+    for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor)
+    {
+        int nc = major * p.sizeMinor + minor;
+        int n = nc / p.inSize.z;
+        int c = nc - n * p.inSize.z;
+        for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y)
+        {
+            // Setup X receptive field.
+            int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x;
+            int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x);
+            int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX;
+            int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x;
+            if (p.flip)
+                filterX = p.filterSize.x - 1 - filterX;
+
+            // Initialize pointers.
+            const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
+            const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y];
+            int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x;
+            int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y;
+
+            // Inner loop.
+            scalar_t v = 0;
+            for (int y = 0; y < h; y++)
+            {
+                for (int x = 0; x < w; x++)
+                {
+                    v += (scalar_t)(*xp) * (scalar_t)(*fp);
+                    xp += p.inStride.x;
+                    fp += filterStepX;
+                }
+                xp += p.inStride.y - w * p.inStride.x;
+                fp += filterStepY - w * filterStepX;
+            }
+
+            // Store result.
+            v *= p.gain;
+            ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
+        }
+    }
+}
+
+//------------------------------------------------------------------------
+// Specialized CUDA implementation for small filters.
+
+template <class T, int upx, int upy, int downx, int downy, int filterW, int filterH, int tileOutW, int tileOutH, int loopMinor>
+static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1;
+    const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1;
+    __shared__ volatile scalar_t sf[filterH][filterW];
+    __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor];
+
+    // Calculate tile index.
+    int minorBase = blockIdx.x;
+    int tileOutY = minorBase / p.launchMinor;
+    minorBase -= tileOutY * p.launchMinor;
+    minorBase *= loopMinor;
+    tileOutY *= tileOutH;
+    int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
+    int majorBase = blockIdx.z * p.loopMajor;
+    if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor)
+        return;
+
+    // Load filter (flipped).
+    for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x)
+    {
+        int fy = tapIdx / filterW;
+        int fx = tapIdx - fy * filterW;
+        scalar_t v = 0;
+        if (fx < p.filterSize.x & fy < p.filterSize.y)
+        {
+            int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx;
+            int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy;
+            v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y];
+        }
+        sf[fy][fx] = v;
+    }
+
+    // Loop over major and X.
+    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
+    {
+        int baseNC = major * p.sizeMinor + minorBase;
+        int n = baseNC / p.inSize.z;
+        int baseC = baseNC - n * p.inSize.z;
+        for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW)
+        {
+            // Load input pixels.
+            int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x;
+            int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y;
+            int tileInX = floor_div(tileMidX, upx);
+            int tileInY = floor_div(tileMidY, upy);
+            __syncthreads();
+            for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x)
+            {
+                int relC = inIdx;
+                int relInX = relC / loopMinor;
+                int relInY = relInX / tileInW;
+                relC -= relInX * loopMinor;
+                relInX -= relInY * tileInW;
+                int c = baseC + relC;
+                int inX = tileInX + relInX;
+                int inY = tileInY + relInY;
+                scalar_t v = 0;
+                if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z)
+                    v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
+                sx[relInY][relInX][relC] = v;
+            }
+
+            // Loop over output pixels.
+            __syncthreads();
+            for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x)
+            {
+                int relC = outIdx;
+                int relOutX = relC / loopMinor;
+                int relOutY = relOutX / tileOutW;
+                relC -= relOutX * loopMinor;
+                relOutX -= relOutY * tileOutW;
+                int c = baseC + relC;
+                int outX = tileOutX + relOutX;
+                int outY = tileOutY + relOutY;
+
+                // Setup receptive field.
+                int midX = tileMidX + relOutX * downx;
+                int midY = tileMidY + relOutY * downy;
+                int inX = floor_div(midX, upx);
+                int inY = floor_div(midY, upy);
+                int relInX = inX - tileInX;
+                int relInY = inY - tileInY;
+                int filterX = (inX + 1) * upx - midX - 1; // flipped
+                int filterY = (inY + 1) * upy - midY - 1; // flipped
+
+                // Inner loop.
+                if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z)
+                {
+                    scalar_t v = 0;
+                    #pragma unroll
+                    for (int y = 0; y < filterH / upy; y++)
+                        #pragma unroll
+                        for (int x = 0; x < filterW / upx; x++)
+                            v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx];
+                    v *= p.gain;
+                    ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
+                }
+            }
+        }
+    }
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p)
+{
+    int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y;
+
+    upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,1, 4}; // contiguous
+    if (s == 1)           spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,4, 1}; // channels_last
+
+    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous
+    {
+        if (fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,  64,16,1>, 64,16,1, 1};
+        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6,  64,16,1>, 64,16,1, 1};
+        if (fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5,  64,16,1>, 64,16,1, 1};
+        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  64,16,1>, 64,16,1, 1};
+        if (fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3,  64,16,1>, 64,16,1, 1};
+        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 20,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 12,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
+        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,20, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,12, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
+    }
+    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last
+    {
+        if (fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,  16,16,8>,  16,16,8,  1};
+        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
+        if (fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
+        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
+        if (fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
+        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 20,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 12,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
+        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,20, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,12, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
+    }
+    if (s != 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous
+    {
+        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 64,16,1>, 64,16,1, 1};
+        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 64,16,1>, 64,16,1, 1};
+        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 64,16,1>, 64,16,1, 1};
+        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 64,16,1>, 64,16,1, 1};
+    }
+    if (s == 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last
+    {
+        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 16,16,8>, 16,16,8, 1};
+        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 16,16,8>, 16,16,8, 1};
+        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 16,16,8>, 16,16,8, 1};
+        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 16,16,8>, 16,16,8, 1};
+    }
+    if (s != 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous
+    {
+        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 20,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 12,1, 128,8,1>, 128,8,1, 1};
+        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
+    }
+    if (s == 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last
+    {
+        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 20,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 12,1, 128,1,16>, 128,1,16, 1};
+        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
+    }
+    if (s != 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous
+    {
+        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,20, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,12, 32,32,1>, 32,32,1, 1};
+        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
+    }
+    if (s == 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last
+    {
+        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,20, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,12, 1,128,16>, 1,128,16, 1};
+        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
+    }
+    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // contiguous
+    {
+        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,  32,8,1>, 32,8,1, 1};
+        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,  32,8,1>, 32,8,1, 1};
+        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,  32,8,1>, 32,8,1, 1};
+        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,  32,8,1>, 32,8,1, 1};
+    }
+    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // channels_last
+    {
+        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,  8,8,8>, 8,8,8, 1};
+        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,  8,8,8>, 8,8,8, 1};
+        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,  8,8,8>, 8,8,8, 1};
+        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,  8,8,8>, 8,8,8, 1};
+    }
+    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // contiguous
+    {
+        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,8,1>, 64,8,1, 1};
+        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 20,1, 64,8,1>, 64,8,1, 1};
+        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,8,1>, 64,8,1, 1};
+        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 12,1, 64,8,1>, 64,8,1, 1};
+        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,8,1>, 64,8,1, 1};
+    }
+    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // channels_last
+    {
+        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,1,8>, 64,1,8, 1};
+        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 20,1, 64,1,8>, 64,1,8, 1};
+        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,1,8>, 64,1,8, 1};
+        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 12,1, 64,1,8>, 64,1,8, 1};
+        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,1,8>, 64,1,8, 1};
+    }
+    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // contiguous
+    {
+        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 32,16,1>, 32,16,1, 1};
+        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,20, 32,16,1>, 32,16,1, 1};
+        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 32,16,1>, 32,16,1, 1};
+        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,12, 32,16,1>, 32,16,1, 1};
+        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  32,16,1>, 32,16,1, 1};
+    }
+    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // channels_last
+    {
+        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 1,64,8>, 1,64,8, 1};
+        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,20, 1,64,8>, 1,64,8, 1};
+        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 1,64,8>, 1,64,8, 1};
+        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,12, 1,64,8>, 1,64,8, 1};
+        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  1,64,8>, 1,64,8, 1};
+    }
+    return spec;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<double>   (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<float>    (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<c10::Half>(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/upfirdn2d.h b/global_torch/torch_utils/ops/upfirdn2d.h
new file mode 100644
index 0000000..c9e2032
--- /dev/null
+++ b/global_torch/torch_utils/ops/upfirdn2d.h
@@ -0,0 +1,59 @@
+// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <cuda_runtime.h>
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct upfirdn2d_kernel_params
+{
+    const void*     x;
+    const float*    f;
+    void*           y;
+
+    int2            up;
+    int2            down;
+    int2            pad0;
+    int             flip;
+    float           gain;
+
+    int4            inSize;         // [width, height, channel, batch]
+    int4            inStride;
+    int2            filterSize;     // [width, height]
+    int2            filterStride;
+    int4            outSize;        // [width, height, channel, batch]
+    int4            outStride;
+    int             sizeMinor;
+    int             sizeMajor;
+
+    int             loopMinor;
+    int             loopMajor;
+    int             loopX;
+    int             launchMinor;
+    int             launchMajor;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel specialization.
+
+struct upfirdn2d_kernel_spec
+{
+    void*   kernel;
+    int     tileOutW;
+    int     tileOutH;
+    int     loopMinor;
+    int     loopX;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/ops/upfirdn2d.py b/global_torch/torch_utils/ops/upfirdn2d.py
new file mode 100644
index 0000000..ceeac2b
--- /dev/null
+++ b/global_torch/torch_utils/ops/upfirdn2d.py
@@ -0,0 +1,384 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom PyTorch ops for efficient resampling of 2D images."""
+
+import os
+import warnings
+import numpy as np
+import torch
+import traceback
+
+from .. import custom_ops
+from .. import misc
+from . import conv2d_gradfix
+
+#----------------------------------------------------------------------------
+
+_inited = False
+_plugin = None
+
+def _init():
+    global _inited, _plugin
+    if not _inited:
+        sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
+        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
+        try:
+            _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
+        except:
+            warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
+    return _plugin is not None
+
+def _parse_scaling(scaling):
+    if isinstance(scaling, int):
+        scaling = [scaling, scaling]
+    assert isinstance(scaling, (list, tuple))
+    assert all(isinstance(x, int) for x in scaling)
+    sx, sy = scaling
+    assert sx >= 1 and sy >= 1
+    return sx, sy
+
+def _parse_padding(padding):
+    if isinstance(padding, int):
+        padding = [padding, padding]
+    assert isinstance(padding, (list, tuple))
+    assert all(isinstance(x, int) for x in padding)
+    if len(padding) == 2:
+        padx, pady = padding
+        padding = [padx, padx, pady, pady]
+    padx0, padx1, pady0, pady1 = padding
+    return padx0, padx1, pady0, pady1
+
+def _get_filter_size(f):
+    if f is None:
+        return 1, 1
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    fw = f.shape[-1]
+    fh = f.shape[0]
+    with misc.suppress_tracer_warnings():
+        fw = int(fw)
+        fh = int(fh)
+    misc.assert_shape(f, [fh, fw][:f.ndim])
+    assert fw >= 1 and fh >= 1
+    return fw, fh
+
+#----------------------------------------------------------------------------
+
+def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
+    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
+
+    Args:
+        f:           Torch tensor, numpy array, or python list of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable),
+                     `[]` (impulse), or
+                     `None` (identity).
+        device:      Result device (default: cpu).
+        normalize:   Normalize the filter so that it retains the magnitude
+                     for constant input signal (DC)? (default: True).
+        flip_filter: Flip the filter? (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        separable:   Return a separable filter? (default: select automatically).
+
+    Returns:
+        Float32 tensor of the shape
+        `[filter_height, filter_width]` (non-separable) or
+        `[filter_taps]` (separable).
+    """
+    # Validate.
+    if f is None:
+        f = 1
+    f = torch.as_tensor(f, dtype=torch.float32)
+    assert f.ndim in [0, 1, 2]
+    assert f.numel() > 0
+    if f.ndim == 0:
+        f = f[np.newaxis]
+
+    # Separable?
+    if separable is None:
+        separable = (f.ndim == 1 and f.numel() >= 8)
+    if f.ndim == 1 and not separable:
+        f = f.ger(f)
+    assert f.ndim == (1 if separable else 2)
+
+    # Apply normalize, flip, gain, and device.
+    if normalize:
+        f /= f.sum()
+    if flip_filter:
+        f = f.flip(list(range(f.ndim)))
+    f = f * (gain ** (f.ndim / 2))
+    f = f.to(device=device)
+    return f
+
+#----------------------------------------------------------------------------
+
+def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Pad, upsample, filter, and downsample a batch of 2D images.
+
+    Performs the following sequence of operations for each channel:
+
+    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
+
+    2. Pad the image with the specified number of zeros on each side (`padding`).
+       Negative padding corresponds to cropping the image.
+
+    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
+       so that the footprint of all output pixels lies within the input image.
+
+    4. Downsample the image by keeping every Nth pixel (`down`).
+
+    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
+    The fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports gradients of arbitrary order.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the upsampled image. Can be a single number
+                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
+    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
+    """
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and x.ndim == 4
+    if f is None:
+        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    assert f.dtype == torch.float32 and not f.requires_grad
+    batch_size, num_channels, in_height, in_width = x.shape
+    upx, upy = _parse_scaling(up)
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+
+    # Upsample by inserting zeros.
+    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
+    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
+    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
+
+    # Pad or crop.
+    x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
+    x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]
+
+    # Setup filter.
+    f = f * (gain ** (f.ndim / 2))
+    f = f.to(x.dtype)
+    if not flip_filter:
+        f = f.flip(list(range(f.ndim)))
+
+    # Convolve with the filter.
+    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
+    if f.ndim == 4:
+        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
+    else:
+        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
+        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
+
+    # Downsample by throwing away pixels.
+    x = x[:, :, ::downy, ::downx]
+    return x
+
+#----------------------------------------------------------------------------
+
+_upfirdn2d_cuda_cache = dict()
+
+def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
+    """
+    # Parse arguments.
+    upx, upy = _parse_scaling(up)
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+
+    # Lookup from cache.
+    key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
+    if key in _upfirdn2d_cuda_cache:
+        return _upfirdn2d_cuda_cache[key]
+
+    # Forward op.
+    class Upfirdn2dCuda(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, f): # pylint: disable=arguments-differ
+            assert isinstance(x, torch.Tensor) and x.ndim == 4
+            if f is None:
+                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+            y = x
+            if f.ndim == 2:
+                y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
+            else:
+                y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain))
+                y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain))
+            ctx.save_for_backward(f)
+            ctx.x_shape = x.shape
+            return y
+
+        @staticmethod
+        def backward(ctx, dy): # pylint: disable=arguments-differ
+            f, = ctx.saved_tensors
+            _, _, ih, iw = ctx.x_shape
+            _, _, oh, ow = dy.shape
+            fw, fh = _get_filter_size(f)
+            p = [
+                fw - padx0 - 1,
+                iw * upx - ow * downx + padx0 - upx + 1,
+                fh - pady0 - 1,
+                ih * upy - oh * downy + pady0 - upy + 1,
+            ]
+            dx = None
+            df = None
+
+            if ctx.needs_input_grad[0]:
+                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)
+
+            assert not ctx.needs_input_grad[1]
+            return dx, df
+
+    # Add to cache.
+    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
+    return Upfirdn2dCuda
+
+#----------------------------------------------------------------------------
+
+def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Filter a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape matches the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        padding:     Padding with respect to the output. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + fw // 2,
+        padx1 + (fw - 1) // 2,
+        pady0 + fh // 2,
+        pady1 + (fh - 1) // 2,
+    ]
+    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Upsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a multiple of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the output. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    upx, upy = _parse_scaling(up)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw + upx - 1) // 2,
+        padx1 + (fw - upx) // 2,
+        pady0 + (fh + upy - 1) // 2,
+        pady1 + (fh - upy) // 2,
+    ]
+    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Downsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a fraction of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the input. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw - downx + 1) // 2,
+        padx1 + (fw - downx) // 2,
+        pady0 + (fh - downy + 1) // 2,
+        pady1 + (fh - downy) // 2,
+    ]
+    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/persistence.py b/global_torch/torch_utils/persistence.py
new file mode 100644
index 0000000..0186cfd
--- /dev/null
+++ b/global_torch/torch_utils/persistence.py
@@ -0,0 +1,251 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Facilities for pickling Python code alongside other data.
+
+The pickled code is automatically imported into a separate Python module
+during unpickling. This way, any previously exported pickles will remain
+usable even if the original code is no longer available, or if the current
+version of the code is not consistent with what was originally pickled."""
+
+import sys
+import pickle
+import io
+import inspect
+import copy
+import uuid
+import types
+import dnnlib
+
+#----------------------------------------------------------------------------
+
+_version            = 6         # internal version number
+_decorators         = set()     # {decorator_class, ...}
+_import_hooks       = []        # [hook_function, ...]
+_module_to_src_dict = dict()    # {module: src, ...}
+_src_to_module_dict = dict()    # {src: module, ...}
+
+#----------------------------------------------------------------------------
+
+def persistent_class(orig_class):
+    r"""Class decorator that extends a given class to save its source code
+    when pickled.
+
+    Example:
+
+        from torch_utils import persistence
+
+        @persistence.persistent_class
+        class MyNetwork(torch.nn.Module):
+            def __init__(self, num_inputs, num_outputs):
+                super().__init__()
+                self.fc = MyLayer(num_inputs, num_outputs)
+                ...
+
+        @persistence.persistent_class
+        class MyLayer(torch.nn.Module):
+            ...
+
+    When pickled, any instance of `MyNetwork` and `MyLayer` will save its
+    source code alongside other internal state (e.g., parameters, buffers,
+    and submodules). This way, any previously exported pickle will remain
+    usable even if the class definitions have been modified or are no
+    longer available.
+
+    The decorator saves the source code of the entire Python module
+    containing the decorated class. It does *not* save the source code of
+    any imported modules. Thus, the imported modules must be available
+    during unpickling, also including `torch_utils.persistence` itself.
+
+    It is ok to call functions defined in the same module from the
+    decorated class. However, if the decorated class depends on other
+    classes defined in the same module, they must be decorated as well.
+    This is illustrated in the above example in the case of `MyLayer`.
+
+    It is also possible to employ the decorator just-in-time before
+    calling the constructor. For example:
+
+        cls = MyLayer
+        if want_to_make_it_persistent:
+            cls = persistence.persistent_class(cls)
+        layer = cls(num_inputs, num_outputs)
+
+    As an additional feature, the decorator also keeps track of the
+    arguments that were used to construct each instance of the decorated
+    class. The arguments can be queried via `obj.init_args` and
+    `obj.init_kwargs`, and they are automatically pickled alongside other
+    object state. A typical use case is to first unpickle a previous
+    instance of a persistent class, and then upgrade it to use the latest
+    version of the source code:
+
+        with open('old_pickle.pkl', 'rb') as f:
+            old_net = pickle.load(f)
+        new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
+        misc.copy_params_and_buffers(old_net, new_net, require_all=True)
+    """
+    assert isinstance(orig_class, type)
+    if is_persistent(orig_class):
+        return orig_class
+
+    assert orig_class.__module__ in sys.modules
+    orig_module = sys.modules[orig_class.__module__]
+    orig_module_src = _module_to_src(orig_module)
+
+    class Decorator(orig_class):
+        _orig_module_src = orig_module_src
+        _orig_class_name = orig_class.__name__
+
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+            self._init_args = copy.deepcopy(args)
+            self._init_kwargs = copy.deepcopy(kwargs)
+            assert orig_class.__name__ in orig_module.__dict__
+            _check_pickleable(self.__reduce__())
+
+        @property
+        def init_args(self):
+            return copy.deepcopy(self._init_args)
+
+        @property
+        def init_kwargs(self):
+            return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
+
+        def __reduce__(self):
+            fields = list(super().__reduce__())
+            fields += [None] * max(3 - len(fields), 0)
+            if fields[0] is not _reconstruct_persistent_obj:
+                meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2])
+                fields[0] = _reconstruct_persistent_obj # reconstruct func
+                fields[1] = (meta,) # reconstruct args
+                fields[2] = None # state dict
+            return tuple(fields)
+
+    Decorator.__name__ = orig_class.__name__
+    _decorators.add(Decorator)
+    return Decorator
+
+#----------------------------------------------------------------------------
+
+def is_persistent(obj):
+    r"""Test whether the given object or class is persistent, i.e.,
+    whether it will save its source code when pickled.
+    """
+    try:
+        if obj in _decorators:
+            return True
+    except TypeError:
+        pass
+    return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck
+
+#----------------------------------------------------------------------------
+
+def import_hook(hook):
+    r"""Register an import hook that is called whenever a persistent object
+    is being unpickled. A typical use case is to patch the pickled source
+    code to avoid errors and inconsistencies when the API of some imported
+    module has changed.
+
+    The hook should have the following signature:
+
+        hook(meta) -> modified meta
+
+    `meta` is an instance of `dnnlib.EasyDict` with the following fields:
+
+        type:       Type of the persistent object, e.g. `'class'`.
+        version:    Internal version number of `torch_utils.persistence`.
+        module_src  Original source code of the Python module.
+        class_name: Class name in the original Python module.
+        state:      Internal state of the object.
+
+    Example:
+
+        @persistence.import_hook
+        def wreck_my_network(meta):
+            if meta.class_name == 'MyNetwork':
+                print('MyNetwork is being imported. I will wreck it!')
+                meta.module_src = meta.module_src.replace("True", "False")
+            return meta
+    """
+    assert callable(hook)
+    _import_hooks.append(hook)
+
+#----------------------------------------------------------------------------
+
+def _reconstruct_persistent_obj(meta):
+    r"""Hook that is called internally by the `pickle` module to unpickle
+    a persistent object.
+    """
+    meta = dnnlib.EasyDict(meta)
+    meta.state = dnnlib.EasyDict(meta.state)
+    for hook in _import_hooks:
+        meta = hook(meta)
+        assert meta is not None
+
+    assert meta.version == _version
+    module = _src_to_module(meta.module_src)
+
+    assert meta.type == 'class'
+    orig_class = module.__dict__[meta.class_name]
+    decorator_class = persistent_class(orig_class)
+    obj = decorator_class.__new__(decorator_class)
+
+    setstate = getattr(obj, '__setstate__', None)
+    if callable(setstate):
+        setstate(meta.state) # pylint: disable=not-callable
+    else:
+        obj.__dict__.update(meta.state)
+    return obj
+
+#----------------------------------------------------------------------------
+
+def _module_to_src(module):
+    r"""Query the source code of a given Python module.
+    """
+    src = _module_to_src_dict.get(module, None)
+    if src is None:
+        src = inspect.getsource(module)
+        _module_to_src_dict[module] = src
+        _src_to_module_dict[src] = module
+    return src
+
+def _src_to_module(src):
+    r"""Get or create a Python module for the given source code.
+    """
+    module = _src_to_module_dict.get(src, None)
+    if module is None:
+        module_name = "_imported_module_" + uuid.uuid4().hex
+        module = types.ModuleType(module_name)
+        sys.modules[module_name] = module
+        _module_to_src_dict[module] = src
+        _src_to_module_dict[src] = module
+        exec(src, module.__dict__) # pylint: disable=exec-used
+    return module
+
+#----------------------------------------------------------------------------
+
+def _check_pickleable(obj):
+    r"""Check that the given object is pickleable, raising an exception if
+    it is not. This function is expected to be considerably more efficient
+    than actually pickling the object.
+    """
+    def recurse(obj):
+        if isinstance(obj, (list, tuple, set)):
+            return [recurse(x) for x in obj]
+        if isinstance(obj, dict):
+            return [[recurse(x), recurse(y)] for x, y in obj.items()]
+        if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
+            return None # Python primitive types are pickleable.
+        if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']:
+            return None # NumPy arrays and PyTorch tensors are pickleable.
+        if is_persistent(obj):
+            return None # Persistent objects are pickleable, by virtue of the constructor check.
+        return obj
+    with io.BytesIO() as f:
+        pickle.dump(recurse(obj), f)
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/torch_utils/training_stats.py b/global_torch/torch_utils/training_stats.py
new file mode 100644
index 0000000..26f467f
--- /dev/null
+++ b/global_torch/torch_utils/training_stats.py
@@ -0,0 +1,268 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Facilities for reporting and collecting training statistics across
+multiple processes and devices. The interface is designed to minimize
+synchronization overhead as well as the amount of boilerplate in user
+code."""
+
+import re
+import numpy as np
+import torch
+import dnnlib
+
+from . import misc
+
+#----------------------------------------------------------------------------
+
+_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
+_reduce_dtype   = torch.float32 # Data type to use for initial per-tensor reduction.
+_counter_dtype  = torch.float64 # Data type to use for the internal counters.
+_rank           = 0             # Rank of the current process.
+_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
+_sync_called    = False         # Has _sync() been called yet?
+_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
+_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
+
+#----------------------------------------------------------------------------
+
+def init_multiprocessing(rank, sync_device):
+    r"""Initializes `torch_utils.training_stats` for collecting statistics
+    across multiple processes.
+
+    This function must be called after
+    `torch.distributed.init_process_group()` and before `Collector.update()`.
+    The call is not necessary if multi-process collection is not needed.
+
+    Args:
+        rank:           Rank of the current process.
+        sync_device:    PyTorch device to use for inter-process
+                        communication, or None to disable multi-process
+                        collection. Typically `torch.device('cuda', rank)`.
+    """
+    global _rank, _sync_device
+    assert not _sync_called
+    _rank = rank
+    _sync_device = sync_device
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def report(name, value):
+    r"""Broadcasts the given set of scalars to all interested instances of
+    `Collector`, across device and process boundaries.
+
+    This function is expected to be extremely cheap and can be safely
+    called from anywhere in the training loop, loss function, or inside a
+    `torch.nn.Module`.
+
+    Warning: The current implementation expects the set of unique names to
+    be consistent across processes. Please make sure that `report()` is
+    called at least once for each unique name by each process, and in the
+    same order. If a given process has no scalars to broadcast, it can do
+    `report(name, [])` (empty list).
+
+    Args:
+        name:   Arbitrary string specifying the name of the statistic.
+                Averages are accumulated separately for each unique name.
+        value:  Arbitrary set of scalars. Can be a list, tuple,
+                NumPy array, PyTorch tensor, or Python scalar.
+
+    Returns:
+        The same `value` that was passed in.
+    """
+    if name not in _counters:
+        _counters[name] = dict()
+
+    elems = torch.as_tensor(value)
+    if elems.numel() == 0:
+        return value
+
+    elems = elems.detach().flatten().to(_reduce_dtype)
+    moments = torch.stack([
+        torch.ones_like(elems).sum(),
+        elems.sum(),
+        elems.square().sum(),
+    ])
+    assert moments.ndim == 1 and moments.shape[0] == _num_moments
+    moments = moments.to(_counter_dtype)
+
+    device = moments.device
+    if device not in _counters[name]:
+        _counters[name][device] = torch.zeros_like(moments)
+    _counters[name][device].add_(moments)
+    return value
+
+#----------------------------------------------------------------------------
+
+def report0(name, value):
+    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
+    but ignores any scalars provided by the other processes.
+    See `report()` for further details.
+    """
+    report(name, value if _rank == 0 else [])
+    return value
+
+#----------------------------------------------------------------------------
+
+class Collector:
+    r"""Collects the scalars broadcasted by `report()` and `report0()` and
+    computes their long-term averages (mean and standard deviation) over
+    user-defined periods of time.
+
+    The averages are first collected into internal counters that are not
+    directly visible to the user. They are then copied to the user-visible
+    state as a result of calling `update()` and can then be queried using
+    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
+    internal counters for the next round, so that the user-visible state
+    effectively reflects averages collected between the last two calls to
+    `update()`.
+
+    Args:
+        regex:          Regular expression defining which statistics to
+                        collect. The default is to collect everything.
+        keep_previous:  Whether to retain the previous averages if no
+                        scalars were collected on a given round
+                        (default: True).
+    """
+    def __init__(self, regex='.*', keep_previous=True):
+        self._regex = re.compile(regex)
+        self._keep_previous = keep_previous
+        self._cumulative = dict()
+        self._moments = dict()
+        self.update()
+        self._moments.clear()
+
+    def names(self):
+        r"""Returns the names of all statistics broadcasted so far that
+        match the regular expression specified at construction time.
+        """
+        return [name for name in _counters if self._regex.fullmatch(name)]
+
+    def update(self):
+        r"""Copies current values of the internal counters to the
+        user-visible state and resets them for the next round.
+
+        If `keep_previous=True` was specified at construction time, the
+        operation is skipped for statistics that have received no scalars
+        since the last update, retaining their previous averages.
+
+        This method performs a number of GPU-to-CPU transfers and one
+        `torch.distributed.all_reduce()`. It is intended to be called
+        periodically in the main training loop, typically once every
+        N training steps.
+        """
+        if not self._keep_previous:
+            self._moments.clear()
+        for name, cumulative in _sync(self.names()):
+            if name not in self._cumulative:
+                self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
+            delta = cumulative - self._cumulative[name]
+            self._cumulative[name].copy_(cumulative)
+            if float(delta[0]) != 0:
+                self._moments[name] = delta
+
+    def _get_delta(self, name):
+        r"""Returns the raw moments that were accumulated for the given
+        statistic between the last two calls to `update()`, or zero if
+        no scalars were collected.
+        """
+        assert self._regex.fullmatch(name)
+        if name not in self._moments:
+            self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
+        return self._moments[name]
+
+    def num(self, name):
+        r"""Returns the number of scalars that were accumulated for the given
+        statistic between the last two calls to `update()`, or zero if
+        no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        return int(delta[0])
+
+    def mean(self, name):
+        r"""Returns the mean of the scalars that were accumulated for the
+        given statistic between the last two calls to `update()`, or NaN if
+        no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        if int(delta[0]) == 0:
+            return float('nan')
+        return float(delta[1] / delta[0])
+
+    def std(self, name):
+        r"""Returns the standard deviation of the scalars that were
+        accumulated for the given statistic between the last two calls to
+        `update()`, or NaN if no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
+            return float('nan')
+        if int(delta[0]) == 1:
+            return float(0)
+        mean = float(delta[1] / delta[0])
+        raw_var = float(delta[2] / delta[0])
+        return np.sqrt(max(raw_var - np.square(mean), 0))
+
+    def as_dict(self):
+        r"""Returns the averages accumulated between the last two calls to
+        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
+
+            dnnlib.EasyDict(
+                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
+                ...
+            )
+        """
+        stats = dnnlib.EasyDict()
+        for name in self.names():
+            stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
+        return stats
+
+    def __getitem__(self, name):
+        r"""Convenience getter.
+        `collector[name]` is a synonym for `collector.mean(name)`.
+        """
+        return self.mean(name)
+
+#----------------------------------------------------------------------------
+
+def _sync(names):
+    r"""Synchronize the global cumulative counters across devices and
+    processes. Called internally by `Collector.update()`.
+    """
+    if len(names) == 0:
+        return []
+    global _sync_called
+    _sync_called = True
+
+    # Collect deltas within current rank.
+    deltas = []
+    device = _sync_device if _sync_device is not None else torch.device('cpu')
+    for name in names:
+        delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
+        for counter in _counters[name].values():
+            delta.add_(counter.to(device))
+            counter.copy_(torch.zeros_like(counter))
+        deltas.append(delta)
+    deltas = torch.stack(deltas)
+
+    # Sum deltas across ranks.
+    if _sync_device is not None:
+        torch.distributed.all_reduce(deltas)
+
+    # Update cumulative values.
+    deltas = deltas.cpu()
+    for idx, name in enumerate(names):
+        if name not in _cumulative:
+            _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
+        _cumulative[name].add_(deltas[idx])
+
+    # Return name-value pairs.
+    return [(name, _cumulative[name]) for name in names]
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/training/__init__.py b/global_torch/training/__init__.py
new file mode 100644
index 0000000..e1e1a5b
--- /dev/null
+++ b/global_torch/training/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+# empty
diff --git a/global_torch/training/networks.py b/global_torch/training/networks.py
new file mode 100644
index 0000000..3abd4b1
--- /dev/null
+++ b/global_torch/training/networks.py
@@ -0,0 +1,809 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+
+import numpy as np
+import torch
+from torch_utils import misc
+from torch_utils import persistence
+from torch_utils.ops import conv2d_resample
+from torch_utils.ops import upfirdn2d
+from torch_utils.ops import bias_act
+from torch_utils.ops import fma
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def normalize_2nd_moment(x, dim=1, eps=1e-8):
+    return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def modulated_conv2d(
+    x,                          # Input tensor of shape [batch_size, in_channels, in_height, in_width].
+    weight,                     # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
+    styles,                     # Modulation coefficients of shape [batch_size, in_channels].
+    noise           = None,     # Optional noise tensor to add to the output activations.
+    up              = 1,        # Integer upsampling factor.
+    down            = 1,        # Integer downsampling factor.
+    padding         = 0,        # Padding with respect to the upsampled image.
+    resample_filter = None,     # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
+    demodulate      = True,     # Apply weight demodulation?
+    flip_weight     = True,     # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
+    fused_modconv   = True,     # Perform modulation, convolution, and demodulation as a single fused operation?
+):
+    batch_size = x.shape[0]
+    out_channels, in_channels, kh, kw = weight.shape
+    misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
+    misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
+    misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
+
+    # Pre-normalize inputs to avoid FP16 overflow.
+    if x.dtype == torch.float16 and demodulate:
+        weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk
+        styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I
+
+    # Calculate per-sample weights and demodulation coefficients.
+    w = None
+    dcoefs = None
+    if demodulate or fused_modconv:
+        w = weight.unsqueeze(0) # [NOIkk]
+        w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
+    if demodulate:
+        dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
+    if demodulate and fused_modconv:
+        w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
+
+    # Execute by scaling the activations before and after the convolution.
+    if not fused_modconv:
+        x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
+        x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
+        if demodulate and noise is not None:
+            x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype))
+        elif demodulate:
+            x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
+        elif noise is not None:
+            x = x.add_(noise.to(x.dtype))
+        return x
+
+    # Execute as one fused op using grouped convolution.
+    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
+        batch_size = int(batch_size)
+    misc.assert_shape(x, [batch_size, in_channels, None, None])
+    x = x.reshape(1, -1, *x.shape[2:])
+    w = w.reshape(-1, in_channels, kh, kw)
+    x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
+    x = x.reshape(batch_size, -1, *x.shape[2:])
+    if noise is not None:
+        x = x.add_(noise)
+    return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class FullyConnectedLayer(torch.nn.Module):
+    def __init__(self,
+        in_features,                # Number of input features.
+        out_features,               # Number of output features.
+        bias            = True,     # Apply additive bias before the activation function?
+        activation      = 'linear', # Activation function: 'relu', 'lrelu', etc.
+        lr_multiplier   = 1,        # Learning rate multiplier.
+        bias_init       = 0,        # Initial value for the additive bias.
+    ):
+        super().__init__()
+        self.activation = activation
+        self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier)
+        self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None
+        self.weight_gain = lr_multiplier / np.sqrt(in_features)
+        self.bias_gain = lr_multiplier
+
+    def forward(self, x):
+        w = self.weight.to(x.dtype) * self.weight_gain
+        b = self.bias
+        if b is not None:
+            b = b.to(x.dtype)
+            if self.bias_gain != 1:
+                b = b * self.bias_gain
+
+        if self.activation == 'linear' and b is not None:
+            x = torch.addmm(b.unsqueeze(0), x, w.t())
+        else:
+            x = x.matmul(w.t())
+            x = bias_act.bias_act(x, b, act=self.activation)
+        return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class Conv2dLayer(torch.nn.Module):
+    def __init__(self,
+        in_channels,                    # Number of input channels.
+        out_channels,                   # Number of output channels.
+        kernel_size,                    # Width and height of the convolution kernel.
+        bias            = True,         # Apply additive bias before the activation function?
+        activation      = 'linear',     # Activation function: 'relu', 'lrelu', etc.
+        up              = 1,            # Integer upsampling factor.
+        down            = 1,            # Integer downsampling factor.
+        resample_filter = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
+        conv_clamp      = None,         # Clamp the output to +-X, None = disable clamping.
+        channels_last   = False,        # Expect the input to have memory_format=channels_last?
+        trainable       = True,         # Update the weights of this layer during training?
+    ):
+        super().__init__()
+        self.activation = activation
+        self.up = up
+        self.down = down
+        self.conv_clamp = conv_clamp
+        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
+        self.padding = kernel_size // 2
+        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
+        self.act_gain = bias_act.activation_funcs[activation].def_gain
+
+        memory_format = torch.channels_last if channels_last else torch.contiguous_format
+        weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)
+        bias = torch.zeros([out_channels]) if bias else None
+        if trainable:
+            self.weight = torch.nn.Parameter(weight)
+            self.bias = torch.nn.Parameter(bias) if bias is not None else None
+        else:
+            self.register_buffer('weight', weight)
+            if bias is not None:
+                self.register_buffer('bias', bias)
+            else:
+                self.bias = None
+
+    def forward(self, x, gain=1):
+        w = self.weight * self.weight_gain
+        b = self.bias.to(x.dtype) if self.bias is not None else None
+        flip_weight = (self.up == 1) # slightly faster
+        x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
+
+        act_gain = self.act_gain * gain
+        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+        x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp)
+        return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class MappingNetwork(torch.nn.Module):
+    def __init__(self,
+        z_dim,                      # Input latent (Z) dimensionality, 0 = no latent.
+        c_dim,                      # Conditioning label (C) dimensionality, 0 = no label.
+        w_dim,                      # Intermediate latent (W) dimensionality.
+        num_ws,                     # Number of intermediate latents to output, None = do not broadcast.
+        num_layers      = 8,        # Number of mapping layers.
+        embed_features  = None,     # Label embedding dimensionality, None = same as w_dim.
+        layer_features  = None,     # Number of intermediate features in the mapping layers, None = same as w_dim.
+        activation      = 'lrelu',  # Activation function: 'relu', 'lrelu', etc.
+        lr_multiplier   = 0.01,     # Learning rate multiplier for the mapping layers.
+        w_avg_beta      = 0.995,    # Decay for tracking the moving average of W during training, None = do not track.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.num_ws = num_ws
+        self.num_layers = num_layers
+        self.w_avg_beta = w_avg_beta
+
+        if embed_features is None:
+            embed_features = w_dim
+        if c_dim == 0:
+            embed_features = 0
+        if layer_features is None:
+            layer_features = w_dim
+        features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
+
+        if c_dim > 0:
+            self.embed = FullyConnectedLayer(c_dim, embed_features)
+        for idx in range(num_layers):
+            in_features = features_list[idx]
+            out_features = features_list[idx + 1]
+            layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
+            setattr(self, f'fc{idx}', layer)
+
+        if num_ws is not None and w_avg_beta is not None:
+            self.register_buffer('w_avg', torch.zeros([w_dim]))
+
+    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False):
+        # Embed, normalize, and concat inputs.
+        x = None
+        with torch.autograd.profiler.record_function('input'):
+            if self.z_dim > 0:
+                misc.assert_shape(z, [None, self.z_dim])
+                x = normalize_2nd_moment(z.to(torch.float32))
+            if self.c_dim > 0:
+                misc.assert_shape(c, [None, self.c_dim])
+                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
+                x = torch.cat([x, y], dim=1) if x is not None else y
+
+        # Main layers.
+        for idx in range(self.num_layers):
+            layer = getattr(self, f'fc{idx}')
+            x = layer(x)
+
+        # Update moving average of W.
+        if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
+            with torch.autograd.profiler.record_function('update_w_avg'):
+                self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
+
+        # Broadcast.
+        if self.num_ws is not None:
+            with torch.autograd.profiler.record_function('broadcast'):
+                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
+
+        # Apply truncation.
+        if truncation_psi != 1:
+            with torch.autograd.profiler.record_function('truncate'):
+                assert self.w_avg_beta is not None
+                if self.num_ws is None or truncation_cutoff is None:
+                    x = self.w_avg.lerp(x, truncation_psi)
+                else:
+                    x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
+        return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class SynthesisLayer(torch.nn.Module):
+    def __init__(self,
+        in_channels,                    # Number of input channels.
+        out_channels,                   # Number of output channels.
+        w_dim,                          # Intermediate latent (W) dimensionality.
+        resolution,                     # Resolution of this layer.
+        kernel_size     = 3,            # Convolution kernel size.
+        up              = 1,            # Integer upsampling factor.
+        use_noise       = True,         # Enable noise input?
+        activation      = 'lrelu',      # Activation function: 'relu', 'lrelu', etc.
+        resample_filter = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
+        conv_clamp      = None,         # Clamp the output of convolution layers to +-X, None = disable clamping.
+        channels_last   = False,        # Use channels_last format for the weights?
+        name = ''
+    ):
+        super().__init__()
+        self.resolution = resolution
+        self.up = up
+        self.use_noise = use_noise
+        self.activation = activation
+        self.conv_clamp = conv_clamp
+        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
+        self.padding = kernel_size // 2
+        self.act_gain = bias_act.activation_funcs[activation].def_gain
+        self.name = name
+        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
+        memory_format = torch.channels_last if channels_last else torch.contiguous_format
+        self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
+        if use_noise:
+            self.register_buffer('noise_const', torch.randn([resolution, resolution]))
+            self.noise_strength = torch.nn.Parameter(torch.zeros([]))
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+        print(f"name:{name} Resolution: {resolution}, InC: {in_channels}, OutC:{out_channels}, w_dim: {w_dim}")
+
+    def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1, encoded_styles=None):
+        assert noise_mode in ['random', 'const', 'none']
+        in_resolution = self.resolution // self.up
+        # misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution]) # not need to be squre 
+        if encoded_styles is None:
+            styles = self.affine(w)
+        else:
+            styles = encoded_styles[self.name]
+
+        noise = None
+        if self.use_noise and noise_mode == 'random':
+            noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
+        if self.use_noise and noise_mode == 'const':
+            noise = self.noise_const * self.noise_strength
+
+        flip_weight = (self.up == 1) # slightly faster
+        x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
+            padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
+
+        act_gain = self.act_gain * gain
+        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+        x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
+        return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class ToRGBLayer(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False, name=''):
+        super().__init__()
+        self.conv_clamp = conv_clamp
+        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
+        memory_format = torch.channels_last if channels_last else torch.contiguous_format
+        self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
+        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
+        self.name = name
+        print(f"name:{name} InC: {in_channels}, OutC:{out_channels}, w_dim: {w_dim}")
+
+
+    def forward(self, x, w, fused_modconv=True, encoded_styles=None):
+        if encoded_styles is None:
+            styles = self.affine(w) #* self.weight_gain
+
+        else:
+            styles = encoded_styles[self.name]
+        tmp_s=styles* self.weight_gain  
+        
+        x = modulated_conv2d(x=x, weight=self.weight, styles=tmp_s, demodulate=False, fused_modconv=fused_modconv)
+        x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
+        return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class SynthesisBlock(torch.nn.Module):
+    def __init__(self,
+        in_channels,                        # Number of input channels, 0 = first block.
+        out_channels,                       # Number of output channels.
+        w_dim,                              # Intermediate latent (W) dimensionality.
+        resolution,                         # Resolution of this block.
+        img_channels,                       # Number of output color channels.
+        is_last,                            # Is this the last block?
+        architecture        = 'skip',       # Architecture: 'orig', 'skip', 'resnet'.
+        resample_filter     = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
+        conv_clamp          = None,         # Clamp the output of convolution layers to +-X, None = disable clamping.
+        use_fp16            = False,        # Use FP16 for this block?
+        fp16_channels_last  = False,        # Use channels-last memory format with FP16?
+        **layer_kwargs,                     # Arguments for SynthesisLayer.
+    ):
+        assert architecture in ['orig', 'skip', 'resnet']
+        super().__init__()
+        self.in_channels = in_channels
+        self.w_dim = w_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.is_last = is_last
+        self.architecture = architecture
+        self.use_fp16 = use_fp16
+        self.channels_last = (use_fp16 and fp16_channels_last)
+        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
+        self.num_conv = 0
+        self.num_torgb = 0
+
+
+        if in_channels == 0:
+            self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution]))
+
+        if in_channels != 0:
+            self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
+                resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, name=f'conv0_resolution_{resolution}', **layer_kwargs)
+            self.num_conv += 1
+
+        self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
+            conv_clamp=conv_clamp, channels_last=self.channels_last, name=f'conv1_resolution_{resolution}', **layer_kwargs)
+        self.num_conv += 1
+
+        if is_last or architecture == 'skip':
+            self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
+                conv_clamp=conv_clamp, channels_last=self.channels_last, name=f'toRGB_resolution_{resolution}')
+            self.num_torgb += 1
+
+        if in_channels != 0 and architecture == 'resnet':
+            self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
+                resample_filter=resample_filter, channels_last=self.channels_last)
+
+    def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, encoded_styles=None, **layer_kwargs):
+
+        class NoneIter:
+            def __init__(self):
+                pass
+            def __iter__(self):
+                return self
+            def __next__(self):
+                return None
+
+        if encoded_styles is None:
+            misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
+            w_iter = iter(ws.unbind(dim=1))
+        else:
+            w_iter = iter(NoneIter())
+
+        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
+        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
+        if fused_modconv is None:
+            with misc.suppress_tracer_warnings(): # this value will be treated as a constant
+                fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1)
+
+        # Input.
+        if self.in_channels == 0:
+            x = self.const.to(dtype=dtype, memory_format=memory_format)
+            if encoded_styles is None:
+                x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
+            else:
+                x = x.unsqueeze(0).repeat([encoded_styles['conv1_resolution_4'].shape[0], 1, 1, 1])
+        else:
+            # misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) # not need to be squre 
+            x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # Main layers.
+        if self.in_channels == 0:
+            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, encoded_styles=encoded_styles, **layer_kwargs)
+        elif self.architecture == 'resnet':
+            y = self.skip(x, gain=np.sqrt(0.5))
+            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, encoded_styles=encoded_styles,  **layer_kwargs)
+            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, encoded_styles=encoded_styles,  gain=np.sqrt(0.5), **layer_kwargs)
+            x = y.add_(x)
+        else:
+            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, encoded_styles=encoded_styles,  **layer_kwargs)
+            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, encoded_styles=encoded_styles,  **layer_kwargs)
+
+        # ToRGB.
+        if img is not None:
+            # misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) ## not need to be squre 
+            img = upfirdn2d.upsample2d(img, self.resample_filter)
+        if self.is_last or self.architecture == 'skip':
+            y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv, encoded_styles=encoded_styles,  )
+            y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
+            img = img.add_(y) if img is not None else y
+
+        assert x.dtype == dtype
+        assert img is None or img.dtype == torch.float32
+        return x, img
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class SynthesisNetwork(torch.nn.Module):
+    def __init__(self,
+        w_dim,                      # Intermediate latent (W) dimensionality.
+        img_resolution,             # Output image resolution.
+        img_channels,               # Number of color channels.
+        channel_base    = 32768,    # Overall multiplier for the number of channels.
+        channel_max     = 512,      # Maximum number of channels in any layer.
+        num_fp16_res    = 0,        # Use FP16 for the N highest resolutions.
+        **block_kwargs,             # Arguments for SynthesisBlock.
+    ):
+        assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
+        super().__init__()
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)]
+        channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions}
+        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
+
+        self.num_ws = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res // 2] if res > 4 else 0
+            out_channels = channels_dict[res]
+            use_fp16 = (res >= fp16_resolution)
+            is_last = (res == self.img_resolution)
+            block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
+                img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
+            self.num_ws += block.num_conv
+            if is_last:
+                self.num_ws += block.num_torgb
+            setattr(self, f'b{res}', block)
+
+    def forward(self, ws, encoded_styles=None, **block_kwargs):
+        if encoded_styles is None:
+            block_ws = []
+            with torch.autograd.profiler.record_function('split_ws'):
+                misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
+                ws = ws.to(torch.float32)
+                w_idx = 0
+                for res in self.block_resolutions:
+                    block = getattr(self, f'b{res}')
+                    block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
+                    w_idx += block.num_conv
+
+            x = img = None
+            for res, cur_ws in zip(self.block_resolutions, block_ws):
+                block = getattr(self, f'b{res}')
+                x, img = block(x, img, cur_ws, encoded_styles=encoded_styles, **block_kwargs)
+        else:
+            x = img = None
+            for res in self.block_resolutions:
+                block = getattr(self, f'b{res}')
+                x, img = block(x, img, None, encoded_styles=encoded_styles, **block_kwargs)
+        return img
+    
+    def W2S(self,ws):
+        
+        i=0
+        encoded_styles={}
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            if res==4: 
+                s=block.conv1.affine(ws[:,i])
+                encoded_styles[f'conv1_resolution_{res}'] =s
+                i+=1
+                s=block.torgb.affine(ws[:,i]) #* block.torgb.weight_gain
+                encoded_styles[f'toRGB_resolution_{res}'] =s
+#                i+=1
+            else:
+#                print(res,i)
+                s=block.conv0.affine(ws[:,i])
+                encoded_styles[f'conv0_resolution_{res}'] =s
+                i+=1
+#                print(res,i)
+                s=block.conv1.affine(ws[:,i])
+                encoded_styles[f'conv1_resolution_{res}'] =s
+                i+=1
+                # toRGB and next layer conv0 use the same w
+                s=block.torgb.affine(ws[:,i])#* block.torgb.weight_gain
+                encoded_styles[f'toRGB_resolution_{res}'] =s
+#                i+=1
+#        print(i)
+        
+
+            
+        
+        return encoded_styles
+    
+
+    
+        
+    
+    
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class Generator(torch.nn.Module):
+    def __init__(self,
+        z_dim,                      # Input latent (Z) dimensionality.
+        c_dim,                      # Conditioning label (C) dimensionality.
+        w_dim,                      # Intermediate latent (W) dimensionality.
+        img_resolution,             # Output resolution.
+        img_channels,               # Number of output color channels.
+        mapping_kwargs      = {},   # Arguments for MappingNetwork.
+        synthesis_kwargs    = {},   # Arguments for SynthesisNetwork.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
+        self.num_ws = self.synthesis.num_ws
+        self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
+
+    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, encoded_styles=None, **synthesis_kwargs):
+        if encoded_styles is None:
+            ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff)
+        else:
+            ws = None
+        img = self.synthesis(ws, encoded_styles=encoded_styles, **synthesis_kwargs)
+        return img
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class DiscriminatorBlock(torch.nn.Module):
+    def __init__(self,
+        in_channels,                        # Number of input channels, 0 = first block.
+        tmp_channels,                       # Number of intermediate channels.
+        out_channels,                       # Number of output channels.
+        resolution,                         # Resolution of this block.
+        img_channels,                       # Number of input color channels.
+        first_layer_idx,                    # Index of the first layer.
+        architecture        = 'resnet',     # Architecture: 'orig', 'skip', 'resnet'.
+        activation          = 'lrelu',      # Activation function: 'relu', 'lrelu', etc.
+        resample_filter     = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
+        conv_clamp          = None,         # Clamp the output of convolution layers to +-X, None = disable clamping.
+        use_fp16            = False,        # Use FP16 for this block?
+        fp16_channels_last  = False,        # Use channels-last memory format with FP16?
+        freeze_layers       = 0,            # Freeze-D: Number of layers to freeze.
+    ):
+        assert in_channels in [0, tmp_channels]
+        assert architecture in ['orig', 'skip', 'resnet']
+        super().__init__()
+        self.in_channels = in_channels
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.first_layer_idx = first_layer_idx
+        self.architecture = architecture
+        self.use_fp16 = use_fp16
+        self.channels_last = (use_fp16 and fp16_channels_last)
+        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
+
+        self.num_layers = 0
+        def trainable_gen():
+            while True:
+                layer_idx = self.first_layer_idx + self.num_layers
+                trainable = (layer_idx >= freeze_layers)
+                self.num_layers += 1
+                yield trainable
+        trainable_iter = trainable_gen()
+
+        if in_channels == 0 or architecture == 'skip':
+            self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
+                trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
+
+        self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
+            trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
+
+        self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
+            trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
+
+        if architecture == 'resnet':
+            self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
+                trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
+
+    def forward(self, x, img, force_fp32=False):
+        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
+        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
+
+        # Input.
+        if x is not None:
+            misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution])
+            x = x.to(dtype=dtype, memory_format=memory_format)
+
+        # FromRGB.
+        if self.in_channels == 0 or self.architecture == 'skip':
+            misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
+            img = img.to(dtype=dtype, memory_format=memory_format)
+            y = self.fromrgb(img)
+            x = x + y if x is not None else y
+            img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None
+
+        # Main layers.
+        if self.architecture == 'resnet':
+            y = self.skip(x, gain=np.sqrt(0.5))
+            x = self.conv0(x)
+            x = self.conv1(x, gain=np.sqrt(0.5))
+            x = y.add_(x)
+        else:
+            x = self.conv0(x)
+            x = self.conv1(x)
+
+        assert x.dtype == dtype
+        return x, img
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class MinibatchStdLayer(torch.nn.Module):
+    def __init__(self, group_size, num_channels=1):
+        super().__init__()
+        self.group_size = group_size
+        self.num_channels = num_channels
+
+    def forward(self, x):
+        N, C, H, W = x.shape
+        with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants
+            G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N
+        F = self.num_channels
+        c = C // F
+
+        y = x.reshape(G, -1, F, c, H, W)    # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
+        y = y - y.mean(dim=0)               # [GnFcHW] Subtract mean over group.
+        y = y.square().mean(dim=0)          # [nFcHW]  Calc variance over group.
+        y = (y + 1e-8).sqrt()               # [nFcHW]  Calc stddev over group.
+        y = y.mean(dim=[2,3,4])             # [nF]     Take average over channels and pixels.
+        y = y.reshape(-1, F, 1, 1)          # [nF11]   Add missing dimensions.
+        y = y.repeat(G, 1, H, W)            # [NFHW]   Replicate over group and pixels.
+        x = torch.cat([x, y], dim=1)        # [NCHW]   Append to input as new channels.
+        return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class DiscriminatorEpilogue(torch.nn.Module):
+    def __init__(self,
+        in_channels,                    # Number of input channels.
+        cmap_dim,                       # Dimensionality of mapped conditioning label, 0 = no label.
+        resolution,                     # Resolution of this block.
+        img_channels,                   # Number of input color channels.
+        architecture        = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
+        mbstd_group_size    = 4,        # Group size for the minibatch standard deviation layer, None = entire minibatch.
+        mbstd_num_channels  = 1,        # Number of features for the minibatch standard deviation layer, 0 = disable.
+        activation          = 'lrelu',  # Activation function: 'relu', 'lrelu', etc.
+        conv_clamp          = None,     # Clamp the output of convolution layers to +-X, None = disable clamping.
+    ):
+        assert architecture in ['orig', 'skip', 'resnet']
+        super().__init__()
+        self.in_channels = in_channels
+        self.cmap_dim = cmap_dim
+        self.resolution = resolution
+        self.img_channels = img_channels
+        self.architecture = architecture
+
+        if architecture == 'skip':
+            self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation)
+        self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
+        self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp)
+        self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation)
+        self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim)
+
+    def forward(self, x, img, cmap, force_fp32=False):
+        misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW]
+        _ = force_fp32 # unused
+        dtype = torch.float32
+        memory_format = torch.contiguous_format
+
+        # FromRGB.
+        x = x.to(dtype=dtype, memory_format=memory_format)
+        if self.architecture == 'skip':
+            misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
+            img = img.to(dtype=dtype, memory_format=memory_format)
+            x = x + self.fromrgb(img)
+
+        # Main layers.
+        if self.mbstd is not None:
+            x = self.mbstd(x)
+        x = self.conv(x)
+        x = self.fc(x.flatten(1))
+        x = self.out(x)
+
+        # Conditioning.
+        if self.cmap_dim > 0:
+            misc.assert_shape(cmap, [None, self.cmap_dim])
+            x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+        assert x.dtype == dtype
+        return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class Discriminator(torch.nn.Module):
+    def __init__(self,
+        c_dim,                          # Conditioning label (C) dimensionality.
+        img_resolution,                 # Input resolution.
+        img_channels,                   # Number of input color channels.
+        architecture        = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
+        channel_base        = 32768,    # Overall multiplier for the number of channels.
+        channel_max         = 512,      # Maximum number of channels in any layer.
+        num_fp16_res        = 0,        # Use FP16 for the N highest resolutions.
+        conv_clamp          = None,     # Clamp the output of convolution layers to +-X, None = disable clamping.
+        cmap_dim            = None,     # Dimensionality of mapped conditioning label, None = default.
+        block_kwargs        = {},       # Arguments for DiscriminatorBlock.
+        mapping_kwargs      = {},       # Arguments for MappingNetwork.
+        epilogue_kwargs     = {},       # Arguments for DiscriminatorEpilogue.
+    ):
+        super().__init__()
+        self.c_dim = c_dim
+        self.img_resolution = img_resolution
+        self.img_resolution_log2 = int(np.log2(img_resolution))
+        self.img_channels = img_channels
+        self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
+        channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
+        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
+
+        if cmap_dim is None:
+            cmap_dim = channels_dict[4]
+        if c_dim == 0:
+            cmap_dim = 0
+
+        common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
+        cur_layer_idx = 0
+        for res in self.block_resolutions:
+            in_channels = channels_dict[res] if res < img_resolution else 0
+            tmp_channels = channels_dict[res]
+            out_channels = channels_dict[res // 2]
+            use_fp16 = (res >= fp16_resolution)
+            block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
+                first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
+            setattr(self, f'b{res}', block)
+            cur_layer_idx += block.num_layers
+        if c_dim > 0:
+            self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
+        self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
+
+    def forward(self, img, c, **block_kwargs):
+        x = None
+        for res in self.block_resolutions:
+            block = getattr(self, f'b{res}')
+            x, img = block(x, img, **block_kwargs)
+
+        cmap = None
+        if self.c_dim > 0:
+            cmap = self.mapping(None, c)
+        x = self.b4(x, img, cmap)
+        return x
+
+#----------------------------------------------------------------------------
diff --git a/global_torch/visualizer.py b/global_torch/visualizer.py
new file mode 100644
index 0000000..8c4a1fb
--- /dev/null
+++ b/global_torch/visualizer.py
@@ -0,0 +1,605 @@
+# python 3.7
+"""Utility functions for visualizing results on html page."""
+
+import base64
+import os.path
+import cv2
+import numpy as np
+
+__all__ = [
+    'get_grid_shape', 'get_blank_image', 'load_image', 'save_image',
+    'resize_image', 'add_text_to_image', 'fuse_images', 'HtmlPageVisualizer',
+    'VideoReader', 'VideoWriter', 'adjust_pixel_range'
+]
+
+
+def adjust_pixel_range(images, min_val=-1.0, max_val=1.0, channel_order='NCHW'):
+  """Adjusts the pixel range of the input images.
+
+  This function assumes the input array (image batch) is with shape [batch_size,
+  channel, height, width] if `channel_order = NCHW`, or with shape [batch_size,
+  height, width] if `channel_order = NHWC`. The returned images are with shape
+  [batch_size, height, width, channel] and pixel range [0, 255].
+
+  NOTE: The channel order of output images will remain the same as the input.
+
+  Args:
+    images: Input images to adjust pixel range.
+    min_val: Min value of the input images. (default: -1.0)
+    max_val: Max value of the input images. (default: 1.0)
+    channel_order: Channel order of the input array. (default: NCHW)
+
+  Returns:
+    The postprocessed images with dtype `numpy.uint8` and range [0, 255].
+
+  Raises:
+    ValueError: If the input `images` are not with type `numpy.ndarray` or the
+      shape is invalid according to `channel_order`.
+  """
+  if not isinstance(images, np.ndarray):
+    raise ValueError(f'Images should be with type `numpy.ndarray`!')
+
+  channel_order = channel_order.upper()
+  if channel_order not in ['NCHW', 'NHWC']:
+    raise ValueError(f'Invalid channel order `{channel_order}`!')
+
+  if images.ndim != 4:
+    raise ValueError(f'Input images are expected to be with shape `NCHW` or '
+                     f'`NHWC`, but `{images.shape}` is received!')
+  if channel_order == 'NCHW' and images.shape[1] not in [1, 3]:
+    raise ValueError(f'Input images should have 1 or 3 channels under `NCHW` '
+                     f'channel order!')
+  if channel_order == 'NHWC' and images.shape[3] not in [1, 3]:
+    raise ValueError(f'Input images should have 1 or 3 channels under `NHWC` '
+                     f'channel order!')
+
+  images = images.astype(np.float32)
+  images = (images - min_val) * 255 / (max_val - min_val)
+  images = np.clip(images + 0.5, 0, 255).astype(np.uint8)
+  if channel_order == 'NCHW':
+    images = images.transpose(0, 2, 3, 1)
+
+  return images
+
+
+def get_grid_shape(size, row=0, col=0, is_portrait=False):
+  """Gets the shape of a grid based on the size.
+
+  This function makes greatest effort on making the output grid square if
+  neither `row` nor `col` is set. If `is_portrait` is set as `False`, the height
+  will always be equal to or smaller than the width. For example, if input
+  `size = 16`, output shape will be `(4, 4)`; if input `size = 15`, output shape
+  will be (3, 5). Otherwise, the height will always be equal to or larger than
+  the width.
+
+  Args:
+    size: Size (height * width) of the target grid.
+    is_portrait: Whether to return a portrait size of a landscape size.
+      (default: False)
+
+  Returns:
+    A two-element tuple, representing height and width respectively.
+  """
+  assert isinstance(size, int)
+  assert isinstance(row, int)
+  assert isinstance(col, int)
+  if size == 0:
+    return (0, 0)
+
+  if row > 0 and col > 0 and row * col != size:
+    row = 0
+    col = 0
+
+  if row > 0 and size % row == 0:
+    return (row, size // row)
+  if col > 0 and size % col == 0:
+    return (size // col, col)
+
+  row = int(np.sqrt(size))
+  while row > 0:
+    if size % row == 0:
+      col = size // row
+      break
+    row = row - 1
+
+  return (col, row) if is_portrait else (row, col)
+
+
+def get_blank_image(height, width, channels=3, is_black=True):
+  """Gets a blank image, either white of black.
+
+  NOTE: This function will always return an image with `RGB` channel order for
+  color image and pixel range [0, 255].
+
+  Args:
+    height: Height of the returned image.
+    width: Width of the returned image.
+    channels: Number of channels. (default: 3)
+    is_black: Whether to return a black image or white image. (default: True)
+  """
+  shape = (height, width, channels)
+  if is_black:
+    return np.zeros(shape, dtype=np.uint8)
+  return np.ones(shape, dtype=np.uint8) * 255
+
+
+def load_image(path):
+  """Loads an image from disk.
+
+  NOTE: This function will always return an image with `RGB` channel order for
+  color image and pixel range [0, 255].
+
+  Args:
+    path: Path to load the image from.
+
+  Returns:
+    An image with dtype `np.ndarray` or `None` if input `path` does not exist.
+  """
+  if not os.path.isfile(path):
+    return None
+
+  image = cv2.imread(path)
+  return image[:, :, ::-1]
+
+
+def save_image(path, image):
+  """Saves an image to disk.
+
+  NOTE: The input image (if colorful) is assumed to be with `RGB` channel order
+  and pixel range [0, 255].
+
+  Args:
+    path: Path to save the image to.
+    image: Image to save.
+  """
+  if image is None:
+    return
+
+  assert len(image.shape) == 3 and image.shape[2] in [1, 3]
+  cv2.imwrite(path, image[:, :, ::-1])
+
+
+def resize_image(image, *args, **kwargs):
+  """Resizes image.
+
+  This is a wrap of `cv2.resize()`.
+
+  NOTE: THe channel order of the input image will not be changed.
+
+  Args:
+    image: Image to resize.
+  """
+  if image is None:
+    return None
+
+  assert image.ndim == 3 and image.shape[2] in [1, 3]
+  image = cv2.resize(image, *args, **kwargs)
+  if image.ndim == 2:
+    return image[:, :, np.newaxis]
+  return image
+
+
+def add_text_to_image(image,
+                      text='',
+                      position=None,
+                      font=cv2.FONT_HERSHEY_TRIPLEX,
+                      font_size=1.0,
+                      line_type=cv2.LINE_8,
+                      line_width=1,
+                      color=(255, 255, 255)):
+  """Overlays text on given image.
+
+  NOTE: The input image is assumed to be with `RGB` channel order.
+
+  Args:
+    image: The image to overlay text on.
+    text: Text content to overlay on the image. (default: '')
+    position: Target position (bottom-left corner) to add text. If not set,
+      center of the image will be used by default. (default: None)
+    font: Font of the text added. (default: cv2.FONT_HERSHEY_TRIPLEX)
+    font_size: Font size of the text added. (default: 1.0)
+    line_type: Line type used to depict the text. (default: cv2.LINE_8)
+    line_width: Line width used to depict the text. (default: 1)
+    color: Color of the text added in `RGB` channel order. (default:
+      (255, 255, 255))
+
+  Returns:
+    An image with target text overlayed on.
+  """
+  if image is None or not text:
+    return image
+
+  cv2.putText(img=image,
+              text=text,
+              org=position,
+              fontFace=font,
+              fontScale=font_size,
+              color=color,
+              thickness=line_width,
+              lineType=line_type,
+              bottomLeftOrigin=False)
+
+  return image
+
+
+def fuse_images(images,
+                image_size=None,
+                row=0,
+                col=0,
+                is_row_major=True,
+                is_portrait=False,
+                row_spacing=0,
+                col_spacing=0,
+                border_left=0,
+                border_right=0,
+                border_top=0,
+                border_bottom=0,
+                black_background=True):
+  """Fuses a collection of images into an entire image.
+
+  Args:
+    images: A collection of images to fuse. Should be with shape [num, height,
+      width, channels].
+    image_size: Int or two-element tuple. This field is used to resize the image
+      before fusing. `None` disables resizing. (default: None)
+    row: Number of rows used for image fusion. If not set, this field will be
+      automatically assigned based on `col` and total number of images.
+      (default: None)
+    col: Number of columns used for image fusion. If not set, this field will be
+      automatically assigned based on `row` and total number of images.
+      (default: None)
+    is_row_major: Whether the input images should be arranged row-major or
+      column-major. (default: True)
+    is_portrait: Only active when both `row` and `col` should be assigned
+      automatically. (default: False)
+    row_spacing: Space between rows. (default: 0)
+    col_spacing: Space between columns. (default: 0)
+    border_left: Width of left border. (default: 0)
+    border_right: Width of right border. (default: 0)
+    border_top: Width of top border. (default: 0)
+    border_bottom: Width of bottom border. (default: 0)
+
+  Returns:
+    The fused image.
+
+  Raises:
+    ValueError: If the input `images` is not with shape [num, height, width,
+      width].
+  """
+  if images is None:
+    return images
+
+  if not images.ndim == 4:
+    raise ValueError(f'Input `images` should be with shape [num, height, '
+                     f'width, channels], but {images.shape} is received!')
+
+  num, image_height, image_width, channels = images.shape
+  if image_size is not None:
+    if isinstance(image_size, int):
+      image_size = (image_size, image_size)
+    assert isinstance(image_size, (list, tuple)) and len(image_size) == 2
+    width, height = image_size
+  else:
+    height, width = image_height, image_width
+  row, col = get_grid_shape(num, row=row, col=col, is_portrait=is_portrait)
+  fused_height = (
+      height * row + row_spacing * (row - 1) + border_top + border_bottom)
+  fused_width = (
+      width * col + col_spacing * (col - 1) + border_left + border_right)
+  fused_image = get_blank_image(
+      fused_height, fused_width, channels=channels, is_black=black_background)
+  images = images.reshape(row, col, image_height, image_width, channels)
+  if not is_row_major:
+    images = images.transpose(1, 0, 2, 3, 4)
+
+  for i in range(row):
+    y = border_top + i * (height + row_spacing)
+    for j in range(col):
+      x = border_left + j * (width + col_spacing)
+      if image_size is not None:
+        image = cv2.resize(images[i, j], image_size)
+      else:
+        image = images[i, j]
+      fused_image[y:y + height, x:x + width] = image
+
+  return fused_image
+
+
+def get_sortable_html_header(column_name_list, sort_by_ascending=False):
+  """Gets header for sortable html page.
+
+  Basically, the html page contains a sortable table, where user can sort the
+  rows by a particular column by clicking the column head.
+
+  Example:
+
+  column_name_list = [name_1, name_2, name_3]
+  header = get_sortable_html_header(column_name_list)
+  footer = get_sortable_html_footer()
+  sortable_table = ...
+  html_page = header + sortable_table + footer
+
+  Args:
+    column_name_list: List of column header names.
+    sort_by_ascending: Default sorting order. If set as `True`, the html page
+      will be sorted by ascending order when the header is clicked for the first
+      time.
+
+  Returns:
+    A string, which represents for the header for a sortable html page.
+  """
+  header = '\n'.join([
+      '<script type="text/javascript">',
+      'var column_idx;',
+      'var sort_by_ascending = ' + str(sort_by_ascending).lower() + ';',
+      '',
+      'function sorting(tbody, column_idx){',
+      '  this.column_idx = column_idx;',
+      '  Array.from(tbody.rows)',
+      '       .sort(compareCells)',
+      '       .forEach(function(row) { tbody.appendChild(row); })',
+      '  sort_by_ascending = !sort_by_ascending;',
+      '}',
+      '',
+      'function compareCells(row_a, row_b) {',
+      '  var val_a = row_a.cells[column_idx].innerText;',
+      '  var val_b = row_b.cells[column_idx].innerText;',
+      '  var flag = sort_by_ascending ? 1 : -1;',
+      '  return flag * (val_a > val_b ? 1 : -1);',
+      '}',
+      '</script>',
+      '',
+      '<html>',
+      '',
+      '<head>',
+      '<style>',
+      '  table {',
+      '    border-spacing: 0;',
+      '    border: 1px solid black;',
+      '  }',
+      '  th {',
+      '    cursor: pointer;',
+      '  }',
+      '  th, td {',
+      '    text-align: left;',
+      '    vertical-align: middle;',
+      '    border-collapse: collapse;',
+      '    border: 0.5px solid black;',
+      '    padding: 8px;',
+      '  }',
+      '  tr:nth-child(even) {',
+      '    background-color: #d2d2d2;',
+      '  }',
+      '</style>',
+      '</head>',
+      '',
+      '<body>',
+      '',
+      '<table>',
+      '<thead>',
+      '<tr>',
+      ''])
+  for idx, column_name in enumerate(column_name_list):
+    header += f'  <th onclick="sorting(tbody, {idx})">{column_name}</th>\n'
+  header += '</tr>\n'
+  header += '</thead>\n'
+  header += '<tbody id="tbody">\n'
+
+  return header
+
+
+def get_sortable_html_footer():
+  """Gets footer for sortable html page.
+
+  Check function `get_sortable_html_header()` for more details.
+  """
+  return '</tbody>\n</table>\n\n</body>\n</html>\n'
+
+
+def encode_image_to_html_str(image, image_size=None):
+  """Encodes an image to html language.
+
+  Args:
+    image: The input image to encode. Should be with `RGB` channel order.
+    image_size: Int or two-element tuple. This field is used to resize the image
+      before encoding. `None` disables resizing. (default: None)
+
+  Returns:
+    A string which represents the encoded image.
+  """
+  if image is None:
+    return ''
+
+  assert len(image.shape) == 3 and image.shape[2] in [1, 3]
+
+  # Change channel order to `BGR`, which is opencv-friendly.
+  image = image[:, :, ::-1]
+
+  # Resize the image if needed.
+  if image_size is not None:
+    if isinstance(image_size, int):
+      image_size = (image_size, image_size)
+    assert isinstance(image_size, (list, tuple)) and len(image_size) == 2
+    image = cv2.resize(image, image_size)
+
+  # Encode the image to html-format string.
+  encoded_image = cv2.imencode(".jpg", image)[1].tostring()
+  encoded_image_base64 = base64.b64encode(encoded_image).decode('utf-8')
+  html_str = f'<img src="data:image/jpeg;base64, {encoded_image_base64}"/>'
+
+  return html_str
+
+
+class HtmlPageVisualizer(object):
+  """Defines the html page visualizer.
+
+  This class can be used to visualize image results as html page. Basically, it
+  is based on an html-format sorted table with helper functions
+  `get_sortable_html_header()`, `get_sortable_html_footer()`, and
+  `encode_image_to_html_str()`. To simplify the usage, specifying the following
+  fields is enough to create a visualization page:
+
+  (1) num_rows: Number of rows of the table (header-row exclusive).
+  (2) num_cols: Number of columns of the table.
+  (3) header contents (optional): Title of each column.
+
+  NOTE: `grid_size` can be used to assign `num_rows` and `num_cols`
+  automatically.
+
+  Example:
+
+  html = HtmlPageVisualizer(num_rows, num_cols)
+  html.set_headers([...])
+  for i in range(num_rows):
+    for j in range(num_cols):
+      html.set_cell(i, j, text=..., image=...)
+  html.save('visualize.html')
+  """
+
+  def __init__(self,
+               num_rows=0,
+               num_cols=0,
+               grid_size=0,
+               is_portrait=False,
+               viz_size=None):
+    if grid_size > 0:
+      num_rows, num_cols = get_grid_shape(
+          grid_size, row=num_rows, col=num_cols, is_portrait=is_portrait)
+    assert num_rows > 0 and num_cols > 0
+
+    self.num_rows = num_rows
+    self.num_cols = num_cols
+    self.viz_size = viz_size
+    self.headers = ['' for _ in range(self.num_cols)]
+    self.cells = [[{
+        'text': '',
+        'image': '',
+    } for _ in range(self.num_cols)] for _ in range(self.num_rows)]
+
+  def set_header(self, column_idx, content):
+    """Sets the content of a particular header by column index."""
+    self.headers[column_idx] = content
+
+  def set_headers(self, contents):
+    """Sets the contents of all headers."""
+    if isinstance(contents, str):
+      contents = [contents]
+    assert isinstance(contents, (list, tuple))
+    assert len(contents) == self.num_cols
+    for column_idx, content in enumerate(contents):
+      self.set_header(column_idx, content)
+
+  def set_cell(self, row_idx, column_idx, text='', image=None):
+    """Sets the content of a particular cell.
+
+    Basically, a cell contains some text as well as an image. Both text and
+    image can be empty.
+
+    Args:
+      row_idx: Row index of the cell to edit.
+      column_idx: Column index of the cell to edit.
+      text: Text to add into the target cell.
+      image: Image to show in the target cell. Should be with `RGB` channel
+        order.
+    """
+    self.cells[row_idx][column_idx]['text'] = text
+    self.cells[row_idx][column_idx]['image'] = encode_image_to_html_str(
+        image, self.viz_size)
+
+  def save(self, save_path):
+    """Saves the html page."""
+    html = ''
+    for i in range(self.num_rows):
+      html += f'<tr>\n'
+      for j in range(self.num_cols):
+        text = self.cells[i][j]['text']
+        image = self.cells[i][j]['image']
+        if text:
+          html += f'  <td>{text}<br><br>{image}</td>\n'
+        else:
+          html += f'  <td>{image}</td>\n'
+      html += f'</tr>\n'
+
+    header = get_sortable_html_header(self.headers)
+    footer = get_sortable_html_footer()
+
+    with open(save_path, 'w') as f:
+      f.write(header + html + footer)
+
+
+class VideoReader(object):
+  """Defines the video reader.
+
+  This class can be used to read frames from a given video.
+  """
+
+  def __init__(self, path):
+    """Initializes the video reader by loading the video from disk."""
+    if not os.path.isfile(path):
+      raise ValueError(f'Video `{path}` does not exist!')
+
+    self.path = path
+    self.video = cv2.VideoCapture(path)
+    assert self.video.isOpened()
+    self.position = 0
+
+    self.length = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))
+    self.frame_height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    self.frame_width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH))
+    self.fps = self.video.get(cv2.CAP_PROP_FPS)
+
+  def __del__(self):
+    """Releases the opened video."""
+    self.video.release()
+
+  def read(self, position=None):
+    """Reads a certain frame.
+
+    NOTE: The returned frame is assumed to be with `RGB` channel order.
+
+    Args:
+      position: Optional. If set, the reader will read frames from the exact
+        position. Otherwise, the reader will read next frames. (default: None)
+    """
+    if position is not None and position < self.length:
+      self.video.set(cv2.CAP_PROP_POS_FRAMES, position)
+      self.position = position
+
+    success, frame = self.video.read()
+    self.position = self.position + 1
+
+    return frame[:, :, ::-1] if success else None
+
+
+class VideoWriter(object):
+  """Defines the video writer.
+
+  This class can be used to create a video.
+
+  NOTE: `.avi` and `DIVX` is the most recommended codec format since it does not
+  rely on other dependencies.
+  """
+
+  def __init__(self, path, frame_height, frame_width, fps=24, codec='DIVX'):
+    """Creates the video writer."""
+    self.path = path
+    self.frame_height = frame_height
+    self.frame_width = frame_width
+    self.fps = fps
+    self.codec = codec
+
+    self.video = cv2.VideoWriter(filename=path,
+                                 fourcc=cv2.VideoWriter_fourcc(*codec),
+                                 fps=fps,
+                                 frameSize=(frame_width, frame_height))
+
+  def __del__(self):
+    """Releases the opened video."""
+    self.video.release()
+
+  def write(self, frame):
+    """Writes a target frame.
+
+    NOTE: The input frame is assumed to be with `RGB` channel order.
+    """
+    self.video.write(frame[:, :, ::-1])
diff --git a/latents_test/example_celebs.pt b/latents_test/example_celebs.pt
new file mode 100644
index 0000000..bda9fb5
Binary files /dev/null and b/latents_test/example_celebs.pt differ
diff --git a/licenses/LICENSE-CLIP b/licenses/LICENSE-CLIP
new file mode 100644
index 0000000..c123b69
--- /dev/null
+++ b/licenses/LICENSE-CLIP
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 OpenAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/licenses/LICENSE-stylegan2-pytorch b/licenses/LICENSE-stylegan2-pytorch
new file mode 100644
index 0000000..81da3fc
--- /dev/null
+++ b/licenses/LICENSE-stylegan2-pytorch
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Kim Seonghyeon
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/models/__init__.py b/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/models/facial_recognition/__init__.py b/models/facial_recognition/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/models/facial_recognition/helpers.py b/models/facial_recognition/helpers.py
new file mode 100644
index 0000000..b51fdf9
--- /dev/null
+++ b/models/facial_recognition/helpers.py
@@ -0,0 +1,119 @@
+from collections import namedtuple
+import torch
+from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
+
+"""
+ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Flatten(Module):
+	def forward(self, input):
+		return input.view(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+	norm = torch.norm(input, 2, axis, True)
+	output = torch.div(input, norm)
+	return output
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+	""" A named tuple describing a ResNet block. """
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+	return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+	if num_layers == 50:
+		blocks = [
+			get_block(in_channel=64, depth=64, num_units=3),
+			get_block(in_channel=64, depth=128, num_units=4),
+			get_block(in_channel=128, depth=256, num_units=14),
+			get_block(in_channel=256, depth=512, num_units=3)
+		]
+	elif num_layers == 100:
+		blocks = [
+			get_block(in_channel=64, depth=64, num_units=3),
+			get_block(in_channel=64, depth=128, num_units=13),
+			get_block(in_channel=128, depth=256, num_units=30),
+			get_block(in_channel=256, depth=512, num_units=3)
+		]
+	elif num_layers == 152:
+		blocks = [
+			get_block(in_channel=64, depth=64, num_units=3),
+			get_block(in_channel=64, depth=128, num_units=8),
+			get_block(in_channel=128, depth=256, num_units=36),
+			get_block(in_channel=256, depth=512, num_units=3)
+		]
+	else:
+		raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
+	return blocks
+
+
+class SEModule(Module):
+	def __init__(self, channels, reduction):
+		super(SEModule, self).__init__()
+		self.avg_pool = AdaptiveAvgPool2d(1)
+		self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
+		self.relu = ReLU(inplace=True)
+		self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
+		self.sigmoid = Sigmoid()
+
+	def forward(self, x):
+		module_input = x
+		x = self.avg_pool(x)
+		x = self.fc1(x)
+		x = self.relu(x)
+		x = self.fc2(x)
+		x = self.sigmoid(x)
+		return module_input * x
+
+
+class bottleneck_IR(Module):
+	def __init__(self, in_channel, depth, stride):
+		super(bottleneck_IR, self).__init__()
+		if in_channel == depth:
+			self.shortcut_layer = MaxPool2d(1, stride)
+		else:
+			self.shortcut_layer = Sequential(
+				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+				BatchNorm2d(depth)
+			)
+		self.res_layer = Sequential(
+			BatchNorm2d(in_channel),
+			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
+			Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
+		)
+
+	def forward(self, x):
+		shortcut = self.shortcut_layer(x)
+		res = self.res_layer(x)
+		return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+	def __init__(self, in_channel, depth, stride):
+		super(bottleneck_IR_SE, self).__init__()
+		if in_channel == depth:
+			self.shortcut_layer = MaxPool2d(1, stride)
+		else:
+			self.shortcut_layer = Sequential(
+				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+				BatchNorm2d(depth)
+			)
+		self.res_layer = Sequential(
+			BatchNorm2d(in_channel),
+			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+			PReLU(depth),
+			Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+			BatchNorm2d(depth),
+			SEModule(depth, 16)
+		)
+
+	def forward(self, x):
+		shortcut = self.shortcut_layer(x)
+		res = self.res_layer(x)
+		return res + shortcut
diff --git a/models/facial_recognition/model_irse.py b/models/facial_recognition/model_irse.py
new file mode 100644
index 0000000..3fcd985
--- /dev/null
+++ b/models/facial_recognition/model_irse.py
@@ -0,0 +1,86 @@
+import sys
+sys.path.append('/home/ly/StyleCLIP-main/models/facial_recognition')
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
+from helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
+
+"""
+Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Backbone(Module):
+	def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
+		super(Backbone, self).__init__()
+		assert input_size in [112, 224], "input_size should be 112 or 224"
+		assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
+		assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
+		blocks = get_blocks(num_layers)
+		if mode == 'ir':
+			unit_module = bottleneck_IR
+		elif mode == 'ir_se':
+			unit_module = bottleneck_IR_SE
+		self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+									  BatchNorm2d(64),
+									  PReLU(64))
+		if input_size == 112:
+			self.output_layer = Sequential(BatchNorm2d(512),
+			                               Dropout(drop_ratio),
+			                               Flatten(),
+			                               Linear(512 * 7 * 7, 512),
+			                               BatchNorm1d(512, affine=affine))
+		else:
+			self.output_layer = Sequential(BatchNorm2d(512),
+			                               Dropout(drop_ratio),
+			                               Flatten(),
+			                               Linear(512 * 14 * 14, 512),
+			                               BatchNorm1d(512, affine=affine))
+
+		modules = []
+		for block in blocks:
+			for bottleneck in block:
+				modules.append(unit_module(bottleneck.in_channel,
+										   bottleneck.depth,
+										   bottleneck.stride))
+		self.body = Sequential(*modules)
+
+	def forward(self, x):
+		x = self.input_layer(x)
+		x = self.body(x)
+		x = self.output_layer(x)
+		return l2_norm(x)
+
+
+def IR_50(input_size):
+	"""Constructs a ir-50 model."""
+	model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_101(input_size):
+	"""Constructs a ir-101 model."""
+	model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_152(input_size):
+	"""Constructs a ir-152 model."""
+	model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_50(input_size):
+	"""Constructs a ir_se-50 model."""
+	model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_101(input_size):
+	"""Constructs a ir_se-101 model."""
+	model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_152(input_size):
+	"""Constructs a ir_se-152 model."""
+	model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
diff --git a/models/stylegan2/__init__.py b/models/stylegan2/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/models/stylegan2/model.py b/models/stylegan2/model.py
new file mode 100644
index 0000000..2a461d2
--- /dev/null
+++ b/models/stylegan2/model.py
@@ -0,0 +1,715 @@
+import math
+import random
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
+
+
+class PixelNorm(nn.Module):
+    def __init__(self):
+        super().__init__()
+    #normalizes了特征向量的每个元素到单位长度附近，阻止了信号幅度signal magnitudes导致的在训练过程中逐步失控的风险。
+    def forward(self, input):
+        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
+
+
+def make_kernel(k):
+    k = torch.tensor(k, dtype=torch.float32)
+
+    if k.ndim == 1:
+        k = k[None, :] * k[:, None]
+
+    k /= k.sum()
+
+    return k
+
+
+class Upsample(nn.Module):
+    def __init__(self, kernel, factor=2):
+        super().__init__()
+
+        self.factor = factor
+        kernel = make_kernel(kernel) * (factor ** 2)
+        self.register_buffer('kernel', kernel)
+
+        p = kernel.shape[0] - factor
+
+        pad0 = (p + 1) // 2 + factor - 1
+        pad1 = p // 2
+
+        self.pad = (pad0, pad1)
+
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
+
+        return out
+
+
+class Downsample(nn.Module):
+    def __init__(self, kernel, factor=2):
+        super().__init__()
+
+        self.factor = factor
+        kernel = make_kernel(kernel)
+        self.register_buffer('kernel', kernel)
+
+        p = kernel.shape[0] - factor
+
+        pad0 = (p + 1) // 2
+        pad1 = p // 2
+
+        self.pad = (pad0, pad1)
+
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
+
+        return out
+
+
+class Blur(nn.Module):
+    def __init__(self, kernel, pad, upsample_factor=1):
+        super().__init__()
+
+        kernel = make_kernel(kernel)
+
+        if upsample_factor > 1:
+            kernel = kernel * (upsample_factor ** 2)
+
+        self.register_buffer('kernel', kernel)
+
+        self.pad = pad
+
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, pad=self.pad)
+
+        return out
+
+
+class EqualConv2d(nn.Module):
+    def __init__(
+        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
+    ):
+        super().__init__()
+
+        self.weight = nn.Parameter(
+            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
+        )
+        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+
+        self.stride = stride
+        self.padding = padding
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_channel))
+
+        else:
+            self.bias = None
+
+    def forward(self, input):
+        out = F.conv2d(
+            input,
+            self.weight * self.scale,
+            bias=self.bias,
+            stride=self.stride,
+            padding=self.padding,
+        )
+
+        return out
+
+    def __repr__(self):
+        return (
+            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
+            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
+        )
+
+#定义了一个线性激活层
+class EqualLinear(nn.Module):
+    def __init__(
+        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
+    ):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+
+        else:
+            self.bias = None
+
+        self.activation = activation
+
+        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+        self.lr_mul = lr_mul
+
+    def forward(self, input):
+
+        if self.activation:
+            out = F.linear(input, self.weight * self.scale)
+            out = fused_leaky_relu(out, self.bias * self.lr_mul)
+
+        else:
+            out = F.linear(
+                input, self.weight * self.scale, bias=self.bias * self.lr_mul
+            )
+
+        return out
+
+    def __repr__(self):
+        return (
+            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
+        )
+
+
+class ScaledLeakyReLU(nn.Module):
+    def __init__(self, negative_slope=0.2):
+        super().__init__()
+
+        self.negative_slope = negative_slope
+
+    def forward(self, input):
+        out = F.leaky_relu(input, negative_slope=self.negative_slope)
+
+        return out * math.sqrt(2)
+
+
+class ModulatedConv2d(nn.Module):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        style_dim,
+        demodulate=True,
+        upsample=False,
+        #给卷积核乘以放缩参数
+        downsample=False,
+        blur_kernel=[1, 3, 3, 1],
+    ):
+        super().__init__()
+
+        self.eps = 1e-8
+        self.kernel_size = kernel_size
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+        self.upsample = upsample
+        self.downsample = downsample
+
+        if upsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) - (kernel_size - 1)
+            pad0 = (p + 1) // 2 + factor - 1
+            pad1 = p // 2 + 1
+
+            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            pad0 = (p + 1) // 2
+            pad1 = p // 2
+
+            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
+
+        fan_in = in_channel * kernel_size ** 2
+        self.scale = 1 / math.sqrt(fan_in)
+        self.padding = kernel_size // 2
+
+        self.weight = nn.Parameter(
+            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
+        )
+
+        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
+
+        self.demodulate = demodulate
+
+    def __repr__(self):
+    #返回了模型的各个参数的字符串
+        return (
+            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
+            f'upsample={self.upsample}, downsample={self.downsample})'
+        )
+
+    def forward(self, input, style, input_is_stylespace=False):
+        batch, in_channel, height, width = input.shape
+
+        if not input_is_stylespace:
+            style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
+        weight = self.scale * self.weight * style
+
+        #对权重进行解调
+        if self.demodulate:
+            #类似标准差计算，平方求和再反平方，目的是计算每个权重向量的解调因子
+            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
+            #demod是一个解调因子矩阵，通过demod.view()将其形状调整为与权重矩阵相同的形状，以便进行逐元素的相乘操作。
+            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
+
+        weight = weight.view(
+            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
+        )
+
+        if self.upsample:
+            input = input.view(1, batch * in_channel, height, width)
+            weight = weight.view(
+                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
+            )
+            weight = weight.transpose(1, 2).reshape(
+                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
+            )
+            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+            out = self.blur(out)
+
+        elif self.downsample:
+            input = self.blur(input)
+            _, _, height, width = input.shape
+            input = input.view(1, batch * in_channel, height, width)
+            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+
+        else:
+            input = input.view(1, batch * in_channel, height, width)
+            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+
+        return out, style
+
+# 用噪声 ( noise ) 来影响头发丝、皱纹、肤色等细节部分。
+class NoiseInjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.zeros(1))
+
+    def forward(self, image, noise=None):
+        if noise is None:
+            batch, _, height, width = image.shape
+            noise = image.new_empty(batch, 1, height, width).normal_()
+
+        return image + self.weight * noise
+
+
+class ConstantInput(nn.Module):
+    def __init__(self, channel, size=4):
+        super().__init__()
+
+        self.input = nn.Parameter(torch.randn(1, channel, size, size))
+
+    def forward(self, input):
+        batch = input.shape[0]
+        out = self.input.repeat(batch, 1, 1, 1)
+
+        return out
+
+
+class StyledConv(nn.Module):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        style_dim,
+        upsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        demodulate=True,
+    ):
+        super().__init__()
+
+        self.conv = ModulatedConv2d(
+            in_channel,
+            out_channel,
+            kernel_size,
+            style_dim,
+            upsample=upsample,
+            blur_kernel=blur_kernel,
+            demodulate=demodulate,
+        )
+
+        self.noise = NoiseInjection()
+        # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
+        # self.activate = ScaledLeakyReLU(0.2)
+        self.activate = FusedLeakyReLU(out_channel)
+
+    def forward(self, input, style, noise=None, input_is_stylespace=False):
+        out, style = self.conv(input, style, input_is_stylespace=input_is_stylespace)
+        out = self.noise(out, noise=noise)
+        # out = out + self.bias
+        out = self.activate(out)
+
+        return out, style
+
+
+class ToRGB(nn.Module):
+    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
+        super().__init__()
+
+        if upsample:
+            self.upsample = Upsample(blur_kernel)
+
+        #ToRGB层不进行demodulate处理
+        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
+        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+    def forward(self, input, style, skip=None, input_is_stylespace=False):
+        out, style = self.conv(input, style, input_is_stylespace=input_is_stylespace)
+        out = out + self.bias
+
+        if skip is not None:
+            skip = self.upsample(skip)
+
+            out = out + skip
+
+        return out, style
+
+
+class Generator(nn.Module):
+    def __init__(
+        self,
+        size,
+        style_dim,
+        n_mlp,
+        channel_multiplier=2,
+        blur_kernel=[1, 3, 3, 1],
+        lr_mlp=0.01,
+    ):
+        super().__init__()
+
+        self.size = size
+
+        self.style_dim = style_dim
+
+        layers = [PixelNorm()]
+
+        for i in range(n_mlp):
+            layers.append(
+                EqualLinear(
+                    style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
+                )
+            )
+
+        self.style = nn.Sequential(*layers)
+
+        self.channels = {
+            4: 512,
+            8: 512,
+            16: 512,
+            32: 512,
+            64: 256 * channel_multiplier,
+            128: 128 * channel_multiplier,
+            256: 64 * channel_multiplier,
+            512: 32 * channel_multiplier,
+            1024: 16 * channel_multiplier,
+        }
+
+        self.input = ConstantInput(self.channels[4])
+        self.conv1 = StyledConv(
+            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
+        )
+        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
+
+        self.log_size = int(math.log(size, 2))  #log(1024,2) = 10
+        self.num_layers = (self.log_size - 2) * 2 + 1
+
+        self.convs = nn.ModuleList()
+        self.upsamples = nn.ModuleList()
+        self.to_rgbs = nn.ModuleList()
+        self.noises = nn.Module()
+
+        in_channel = self.channels[4]
+
+        for layer_idx in range(self.num_layers):
+            res = (layer_idx + 5) // 2
+            shape = [1, 1, 2 ** res, 2 ** res]
+            self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape))
+
+        for i in range(3, self.log_size + 1):
+            out_channel = self.channels[2 ** i]
+
+            self.convs.append(
+                StyledConv(
+                    in_channel,
+                    out_channel,
+                    3,
+                    style_dim,
+                    upsample=True,
+                    blur_kernel=blur_kernel,
+                )
+            )
+
+            self.convs.append(
+                StyledConv(
+                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
+                )
+            )
+
+            self.to_rgbs.append(ToRGB(out_channel, style_dim))
+
+            in_channel = out_channel
+        # w+ repeat的倍数，例如1024计算为18,实际上就是上采样层1+8*2+1，因为第一层只需要一个style最后又多了一层to_rgb用了style，其中8个block每个上采样层之前均要加入两次style
+        self.n_latent = self.log_size * 2 - 2
+
+
+    def make_noise(self):
+        device = self.input.input.device
+
+        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
+
+        for i in range(3, self.log_size + 1):
+            for _ in range(2):
+                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
+
+        return noises
+
+    def mean_latent(self, n_latent):
+        latent_in = torch.randn(
+            n_latent, self.style_dim, device=self.input.input.device
+        )
+        latent = self.style(latent_in).mean(0, keepdim=True)
+
+        return latent
+
+    def get_latent(self, input):
+        return self.style(input)
+
+    def forward(
+        self,
+        styles,
+        return_latents=False,
+        inject_index=None,
+        truncation=1,
+        truncation_latent=None,
+        input_is_latent=False,
+        input_is_stylespace=False,
+        noise=None,
+        randomize_noise=True,
+    ):
+        if not input_is_latent and not input_is_stylespace:
+            styles = [self.style(s) for s in styles]
+
+        if noise is None:
+            if randomize_noise:
+                noise = [None] * self.num_layers
+            else:
+                noise = [
+                    getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
+                ]
+
+        if truncation < 1 and not input_is_stylespace:
+            style_t = []
+
+            for style in styles:
+                style_t.append(
+                    truncation_latent + truncation * (style - truncation_latent)
+                )
+
+            styles = style_t
+
+        if input_is_stylespace:
+            latent = styles[0]
+        elif len(styles) < 2:
+            inject_index = self.n_latent
+
+            if styles[0].ndim < 3:
+                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+
+            else:
+                latent = styles[0]
+
+        else:
+            if inject_index is None:
+                inject_index = random.randint(1, self.n_latent - 1)
+
+            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
+
+            latent = torch.cat([latent, latent2], 1)
+
+
+        style_vector = []
+
+        if not input_is_stylespace:
+            out = self.input(latent)
+            # print('laten:',latent.shape)   # torch.Size([1, 18, 512])
+            out, out_style = self.conv1(out, latent[:, 0], noise=noise[0])
+            style_vector.append(out_style)
+
+            skip, out_style = self.to_rgb1(out, latent[:, 1])
+            style_vector.append(out_style)
+
+            i = 1
+        else:
+            out = self.input(latent[0])
+            out, out_style = self.conv1(out, latent[0], noise=noise[0], input_is_stylespace=input_is_stylespace)
+            style_vector.append(out_style)
+
+            skip, out_style = self.to_rgb1(out, latent[1], input_is_stylespace=input_is_stylespace)
+            style_vector.append(out_style)
+
+            i = 2
+
+        for conv1, conv2, noise1, noise2, to_rgb in zip(
+            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
+        ):
+            if not input_is_stylespace:
+                out, out_style1 = conv1(out, latent[:, i], noise=noise1)
+                out, out_style2 = conv2(out, latent[:, i + 1], noise=noise2)
+                skip, rgb_style = to_rgb(out, latent[:, i + 2], skip)
+
+                style_vector.extend([out_style1, out_style2, rgb_style])
+
+                i += 2
+            else:
+                out, out_style1 = conv1(out, latent[i], noise=noise1, input_is_stylespace=input_is_stylespace)
+                out, out_style2 = conv2(out, latent[i + 1], noise=noise2, input_is_stylespace=input_is_stylespace)
+                skip, rgb_style = to_rgb(out, latent[i + 2], skip, input_is_stylespace=input_is_stylespace)
+
+                style_vector.extend([out_style1, out_style2, rgb_style])
+
+                i += 3
+
+        image = skip
+
+        if return_latents:
+            return image, latent, style_vector
+
+        else:
+            return image, None
+
+
+class ConvLayer(nn.Sequential):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        downsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        bias=True,
+        activate=True,
+    ):
+        layers = []
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            pad0 = (p + 1) // 2
+            pad1 = p // 2
+
+            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
+
+            stride = 2
+            self.padding = 0
+
+        else:
+            stride = 1
+            self.padding = kernel_size // 2
+
+        layers.append(
+            EqualConv2d(
+                in_channel,
+                out_channel,
+                kernel_size,
+                padding=self.padding,
+                stride=stride,
+                bias=bias and not activate,
+            )
+        )
+
+        if activate:
+            if bias:
+                layers.append(FusedLeakyReLU(out_channel))
+
+            else:
+                layers.append(ScaledLeakyReLU(0.2))
+
+        super().__init__(*layers)
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
+        super().__init__()
+
+        self.conv1 = ConvLayer(in_channel, in_channel, 3)
+        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
+
+        self.skip = ConvLayer(
+            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
+        )
+
+    def forward(self, input):
+        out = self.conv1(input)
+        out = self.conv2(out)
+
+        skip = self.skip(input)
+        out = (out + skip) / math.sqrt(2)
+
+        return out
+
+
+class Discriminator(nn.Module):
+    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
+        super().__init__()
+
+        channels = {
+            4: 512,
+            8: 512,
+            16: 512,
+            32: 512,
+            64: 256 * channel_multiplier,
+            128: 128 * channel_multiplier,
+            256: 64 * channel_multiplier,
+            512: 32 * channel_multiplier,
+            1024: 16 * channel_multiplier,
+        }
+
+        convs = [ConvLayer(3, channels[size], 1)]
+
+        log_size = int(math.log(size, 2))
+
+        in_channel = channels[size]
+
+        #这里代码是8个大残差block，让feature map大小从1024到4
+        for i in range(log_size, 2, -1):
+            out_channel = channels[2 ** (i - 1)]
+
+            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
+
+            in_channel = out_channel
+
+        self.convs = nn.Sequential(*convs)
+
+        self.stddev_group = 4
+        self.stddev_feat = 1
+
+        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
+        self.final_linear = nn.Sequential(
+            EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
+            EqualLinear(channels[4], 1),
+        )
+
+    def forward(self, input):
+        out = self.convs(input)
+
+        batch, channel, height, width = out.shape
+        group = min(batch, self.stddev_group)
+        stddev = out.view(
+            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
+        )
+        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+        stddev = stddev.repeat(group, 1, height, width)
+        out = torch.cat([out, stddev], 1)
+
+        out = self.final_conv(out)
+
+        out = out.view(batch, -1)
+        out = self.final_linear(out)
+
+        return out
+
diff --git a/models/stylegan2/op/__init__.py b/models/stylegan2/op/__init__.py
new file mode 100644
index 0000000..d0918d9
--- /dev/null
+++ b/models/stylegan2/op/__init__.py
@@ -0,0 +1,2 @@
+from .fused_act import FusedLeakyReLU, fused_leaky_relu
+from .upfirdn2d import upfirdn2d
diff --git a/models/stylegan2/op/__pycache__/__init__.cpython-310.pyc b/models/stylegan2/op/__pycache__/__init__.cpython-310.pyc
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index 0000000..3b27b09
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diff --git a/models/stylegan2/op/__pycache__/fused_act.cpython-310.pyc b/models/stylegan2/op/__pycache__/fused_act.cpython-310.pyc
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index 0000000..63cdef7
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diff --git a/models/stylegan2/op/__pycache__/upfirdn2d.cpython-310.pyc b/models/stylegan2/op/__pycache__/upfirdn2d.cpython-310.pyc
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diff --git a/models/stylegan2/op/fused_act.py b/models/stylegan2/op/fused_act.py
new file mode 100644
index 0000000..0eb2815
--- /dev/null
+++ b/models/stylegan2/op/fused_act.py
@@ -0,0 +1,40 @@
+import os
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+module_path = os.path.dirname(__file__)
+
+
+
+class FusedLeakyReLU(nn.Module):
+    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
+        super().__init__()
+
+        self.bias = nn.Parameter(torch.zeros(channel))
+        self.negative_slope = negative_slope
+        self.scale = scale
+
+    def forward(self, input):
+        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
+
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
+    rest_dim = [1] * (input.ndim - bias.ndim - 1)
+    input = input.cuda()
+    if input.ndim == 3:
+        return (
+            F.leaky_relu(
+                input + bias.view(1, *rest_dim, bias.shape[0]), negative_slope=negative_slope
+            )
+            * scale  #增益值，激活函数里的 gain（torch中scale） 是一个增益值，增益值是指的非线性函数稳态时输入幅度与输出幅度的比值，通常被用来乘在激活函数之后使激活函数更加稳定。
+        )
+    else:
+        return (
+            F.leaky_relu(
+                input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
+            )
+            * scale
+        )
+
diff --git a/models/stylegan2/op/upfirdn2d.py b/models/stylegan2/op/upfirdn2d.py
new file mode 100644
index 0000000..02fc25a
--- /dev/null
+++ b/models/stylegan2/op/upfirdn2d.py
@@ -0,0 +1,60 @@
+import os
+
+import torch
+from torch.nn import functional as F
+
+
+module_path = os.path.dirname(__file__)
+
+
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+    out = upfirdn2d_native(
+        input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
+    )
+
+    return out
+
+
+def upfirdn2d_native(
+    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
+):
+    _, channel, in_h, in_w = input.shape
+    input = input.reshape(-1, in_h, in_w, 1)
+
+    _, in_h, in_w, minor = input.shape
+    kernel_h, kernel_w = kernel.shape
+
+    out = input.view(-1, in_h, 1, in_w, 1, minor)
+    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
+    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
+
+    out = F.pad(
+        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
+    )
+    out = out[
+        :,
+        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
+        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
+        :,
+    ]
+
+    out = out.permute(0, 3, 1, 2)
+    out = out.reshape(
+        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
+    )
+    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+    out = F.conv2d(out, w)
+    out = out.reshape(
+        -1,
+        minor,
+        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
+        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
+    )
+    out = out.permute(0, 2, 3, 1)
+    out = out[:, ::down_y, ::down_x, :]
+
+    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+
+    return out.view(-1, channel, out_h, out_w)
\ No newline at end of file
diff --git a/models/stylegan3/dnnlib/__init__.py b/models/stylegan3/dnnlib/__init__.py
new file mode 100644
index 0000000..e7423bf
--- /dev/null
+++ b/models/stylegan3/dnnlib/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+from .util import EasyDict, make_cache_dir_path
diff --git a/models/stylegan3/dnnlib/__pycache__/__init__.cpython-310.pyc b/models/stylegan3/dnnlib/__pycache__/__init__.cpython-310.pyc
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index 0000000..99928c3
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diff --git a/models/stylegan3/dnnlib/__pycache__/util.cpython-310.pyc b/models/stylegan3/dnnlib/__pycache__/util.cpython-310.pyc
new file mode 100644
index 0000000..e3082a7
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diff --git a/models/stylegan3/dnnlib/util.py b/models/stylegan3/dnnlib/util.py
new file mode 100644
index 0000000..6bbdf3b
--- /dev/null
+++ b/models/stylegan3/dnnlib/util.py
@@ -0,0 +1,491 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Miscellaneous utility classes and functions."""
+
+import ctypes
+import fnmatch
+import importlib
+import inspect
+import numpy as np
+import os
+import shutil
+import sys
+import types
+import io
+import pickle
+import re
+import requests
+import html
+import hashlib
+import glob
+import tempfile
+import urllib
+import urllib.request
+import uuid
+
+from distutils.util import strtobool
+from typing import Any, List, Tuple, Union
+
+
+# Util classes
+# ------------------------------------------------------------------------------------------
+
+
+class EasyDict(dict):
+    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
+
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        self[name] = value
+
+    def __delattr__(self, name: str) -> None:
+        del self[name]
+
+
+class Logger(object):
+    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
+
+    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
+        self.file = None
+
+        if file_name is not None:
+            self.file = open(file_name, file_mode)
+
+        self.should_flush = should_flush
+        self.stdout = sys.stdout
+        self.stderr = sys.stderr
+
+        sys.stdout = self
+        sys.stderr = self
+
+    def __enter__(self) -> "Logger":
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self.close()
+
+    def write(self, text: Union[str, bytes]) -> None:
+        """Write text to stdout (and a file) and optionally flush."""
+        if isinstance(text, bytes):
+            text = text.decode()
+        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
+            return
+
+        if self.file is not None:
+            self.file.write(text)
+
+        self.stdout.write(text)
+
+        if self.should_flush:
+            self.flush()
+
+    def flush(self) -> None:
+        """Flush written text to both stdout and a file, if open."""
+        if self.file is not None:
+            self.file.flush()
+
+        self.stdout.flush()
+
+    def close(self) -> None:
+        """Flush, close possible files, and remove stdout/stderr mirroring."""
+        self.flush()
+
+        # if using multiple loggers, prevent closing in wrong order
+        if sys.stdout is self:
+            sys.stdout = self.stdout
+        if sys.stderr is self:
+            sys.stderr = self.stderr
+
+        if self.file is not None:
+            self.file.close()
+            self.file = None
+
+
+# Cache directories
+# ------------------------------------------------------------------------------------------
+
+_dnnlib_cache_dir = None
+
+def set_cache_dir(path: str) -> None:
+    global _dnnlib_cache_dir
+    _dnnlib_cache_dir = path
+
+def make_cache_dir_path(*paths: str) -> str:
+    if _dnnlib_cache_dir is not None:
+        return os.path.join(_dnnlib_cache_dir, *paths)
+    if 'DNNLIB_CACHE_DIR' in os.environ:
+        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
+    if 'HOME' in os.environ:
+        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
+    if 'USERPROFILE' in os.environ:
+        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
+    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
+
+# Small util functions
+# ------------------------------------------------------------------------------------------
+
+
+def format_time(seconds: Union[int, float]) -> str:
+    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
+    s = int(np.rint(seconds))
+
+    if s < 60:
+        return "{0}s".format(s)
+    elif s < 60 * 60:
+        return "{0}m {1:02}s".format(s // 60, s % 60)
+    elif s < 24 * 60 * 60:
+        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
+    else:
+        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
+
+
+def format_time_brief(seconds: Union[int, float]) -> str:
+    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
+    s = int(np.rint(seconds))
+
+    if s < 60:
+        return "{0}s".format(s)
+    elif s < 60 * 60:
+        return "{0}m {1:02}s".format(s // 60, s % 60)
+    elif s < 24 * 60 * 60:
+        return "{0}h {1:02}m".format(s // (60 * 60), (s // 60) % 60)
+    else:
+        return "{0}d {1:02}h".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24)
+
+
+def ask_yes_no(question: str) -> bool:
+    """Ask the user the question until the user inputs a valid answer."""
+    while True:
+        try:
+            print("{0} [y/n]".format(question))
+            return strtobool(input().lower())
+        except ValueError:
+            pass
+
+
+def tuple_product(t: Tuple) -> Any:
+    """Calculate the product of the tuple elements."""
+    result = 1
+
+    for v in t:
+        result *= v
+
+    return result
+
+
+_str_to_ctype = {
+    "uint8": ctypes.c_ubyte,
+    "uint16": ctypes.c_uint16,
+    "uint32": ctypes.c_uint32,
+    "uint64": ctypes.c_uint64,
+    "int8": ctypes.c_byte,
+    "int16": ctypes.c_int16,
+    "int32": ctypes.c_int32,
+    "int64": ctypes.c_int64,
+    "float32": ctypes.c_float,
+    "float64": ctypes.c_double
+}
+
+
+def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
+    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
+    type_str = None
+
+    if isinstance(type_obj, str):
+        type_str = type_obj
+    elif hasattr(type_obj, "__name__"):
+        type_str = type_obj.__name__
+    elif hasattr(type_obj, "name"):
+        type_str = type_obj.name
+    else:
+        raise RuntimeError("Cannot infer type name from input")
+
+    assert type_str in _str_to_ctype.keys()
+
+    my_dtype = np.dtype(type_str)
+    my_ctype = _str_to_ctype[type_str]
+
+    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
+
+    return my_dtype, my_ctype
+
+
+def is_pickleable(obj: Any) -> bool:
+    try:
+        with io.BytesIO() as stream:
+            pickle.dump(obj, stream)
+        return True
+    except:
+        return False
+
+
+# Functionality to import modules/objects by name, and call functions by name
+# ------------------------------------------------------------------------------------------
+
+def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
+    """Searches for the underlying module behind the name to some python object.
+    Returns the module and the object name (original name with module part removed)."""
+
+    # allow convenience shorthands, substitute them by full names
+    obj_name = re.sub("^np.", "numpy.", obj_name)
+    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
+
+    # list alternatives for (module_name, local_obj_name)
+    parts = obj_name.split(".")
+    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
+
+    # try each alternative in turn
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(module_name) # may raise ImportError
+            get_obj_from_module(module, local_obj_name) # may raise AttributeError
+            return module, local_obj_name
+        except:
+            pass
+
+    # maybe some of the modules themselves contain errors?
+    for module_name, _local_obj_name in name_pairs:
+        try:
+            importlib.import_module(module_name) # may raise ImportError
+        except ImportError:
+            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
+                raise
+
+    # maybe the requested attribute is missing?
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(module_name) # may raise ImportError
+            get_obj_from_module(module, local_obj_name) # may raise AttributeError
+        except ImportError:
+            pass
+
+    # we are out of luck, but we have no idea why
+    raise ImportError(obj_name)
+
+
+def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
+    """Traverses the object name and returns the last (rightmost) python object."""
+    if obj_name == '':
+        return module
+    obj = module
+    for part in obj_name.split("."):
+        obj = getattr(obj, part)
+    return obj
+
+
+def get_obj_by_name(name: str) -> Any:
+    """Finds the python object with the given name."""
+    module, obj_name = get_module_from_obj_name(name)
+    return get_obj_from_module(module, obj_name)
+
+
+def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
+    """Finds the python object with the given name and calls it as a function."""
+    assert func_name is not None
+    func_obj = get_obj_by_name(func_name)
+    assert callable(func_obj)
+    return func_obj(*args, **kwargs)
+
+
+def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
+    """Finds the python class with the given name and constructs it with the given arguments."""
+    return call_func_by_name(*args, func_name=class_name, **kwargs)
+
+
+def get_module_dir_by_obj_name(obj_name: str) -> str:
+    """Get the directory path of the module containing the given object name."""
+    module, _ = get_module_from_obj_name(obj_name)
+    return os.path.dirname(inspect.getfile(module))
+
+
+def is_top_level_function(obj: Any) -> bool:
+    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
+    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
+
+
+def get_top_level_function_name(obj: Any) -> str:
+    """Return the fully-qualified name of a top-level function."""
+    assert is_top_level_function(obj)
+    module = obj.__module__
+    if module == '__main__':
+        module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
+    return module + "." + obj.__name__
+
+
+# File system helpers
+# ------------------------------------------------------------------------------------------
+
+def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
+    """List all files recursively in a given directory while ignoring given file and directory names.
+    Returns list of tuples containing both absolute and relative paths."""
+    assert os.path.isdir(dir_path)
+    base_name = os.path.basename(os.path.normpath(dir_path))
+
+    if ignores is None:
+        ignores = []
+
+    result = []
+
+    for root, dirs, files in os.walk(dir_path, topdown=True):
+        for ignore_ in ignores:
+            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
+
+            # dirs need to be edited in-place
+            for d in dirs_to_remove:
+                dirs.remove(d)
+
+            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
+
+        absolute_paths = [os.path.join(root, f) for f in files]
+        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
+
+        if add_base_to_relative:
+            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
+
+        assert len(absolute_paths) == len(relative_paths)
+        result += zip(absolute_paths, relative_paths)
+
+    return result
+
+
+def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
+    """Takes in a list of tuples of (src, dst) paths and copies files.
+    Will create all necessary directories."""
+    for file in files:
+        target_dir_name = os.path.dirname(file[1])
+
+        # will create all intermediate-level directories
+        if not os.path.exists(target_dir_name):
+            os.makedirs(target_dir_name)
+
+        shutil.copyfile(file[0], file[1])
+
+
+# URL helpers
+# ------------------------------------------------------------------------------------------
+
+def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
+    """Determine whether the given object is a valid URL string."""
+    if not isinstance(obj, str) or not "://" in obj:
+        return False
+    if allow_file_urls and obj.startswith('file://'):
+        return True
+    try:
+        res = requests.compat.urlparse(obj)
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+    except:
+        return False
+    return True
+
+
+def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
+    """Download the given URL and return a binary-mode file object to access the data."""
+    assert num_attempts >= 1
+    assert not (return_filename and (not cache))
+
+    # Doesn't look like an URL scheme so interpret it as a local filename.
+    if not re.match('^[a-z]+://', url):
+        return url if return_filename else open(url, "rb")
+
+    # Handle file URLs.  This code handles unusual file:// patterns that
+    # arise on Windows:
+    #
+    # file:///c:/foo.txt
+    #
+    # which would translate to a local '/c:/foo.txt' filename that's
+    # invalid.  Drop the forward slash for such pathnames.
+    #
+    # If you touch this code path, you should test it on both Linux and
+    # Windows.
+    #
+    # Some internet resources suggest using urllib.request.url2pathname() but
+    # but that converts forward slashes to backslashes and this causes
+    # its own set of problems.
+    if url.startswith('file://'):
+        filename = urllib.parse.urlparse(url).path
+        if re.match(r'^/[a-zA-Z]:', filename):
+            filename = filename[1:]
+        return filename if return_filename else open(filename, "rb")
+
+    assert is_url(url)
+
+    # Lookup from cache.
+    if cache_dir is None:
+        cache_dir = make_cache_dir_path('downloads')
+
+    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
+    if cache:
+        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
+        if len(cache_files) == 1:
+            filename = cache_files[0]
+            return filename if return_filename else open(filename, "rb")
+
+    # Download.
+    url_name = None
+    url_data = None
+    with requests.Session() as session:
+        if verbose:
+            print("Downloading %s ..." % url, end="", flush=True)
+        for attempts_left in reversed(range(num_attempts)):
+            try:
+                with session.get(url) as res:
+                    res.raise_for_status()
+                    if len(res.content) == 0:
+                        raise IOError("No data received")
+
+                    if len(res.content) < 8192:
+                        content_str = res.content.decode("utf-8")
+                        if "download_warning" in res.headers.get("Set-Cookie", ""):
+                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
+                            if len(links) == 1:
+                                url = requests.compat.urljoin(url, links[0])
+                                raise IOError("Google Drive virus checker nag")
+                        if "Google Drive - Quota exceeded" in content_str:
+                            raise IOError("Google Drive download quota exceeded -- please try again later")
+
+                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
+                    url_name = match[1] if match else url
+                    url_data = res.content
+                    if verbose:
+                        print(" done")
+                    break
+            except KeyboardInterrupt:
+                raise
+            except:
+                if not attempts_left:
+                    if verbose:
+                        print(" failed")
+                    raise
+                if verbose:
+                    print(".", end="", flush=True)
+
+    # Save to cache.
+    if cache:
+        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
+        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
+        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
+        os.makedirs(cache_dir, exist_ok=True)
+        with open(temp_file, "wb") as f:
+            f.write(url_data)
+        os.replace(temp_file, cache_file) # atomic
+        if return_filename:
+            return cache_file
+
+    # Return data as file object.
+    assert not return_filename
+    return io.BytesIO(url_data)
diff --git a/models/stylegan3/model_3.py b/models/stylegan3/model_3.py
new file mode 100644
index 0000000..9a50d16
--- /dev/null
+++ b/models/stylegan3/model_3.py
@@ -0,0 +1,529 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Generator architecture from the paper
+"Alias-Free Generative Adversarial Networks"."""
+
+import numpy as np
+import scipy.signal
+import scipy.optimize
+import torch
+from torch_utils import misc
+from torch_utils import persistence
+from torch_utils.ops import conv2d_gradfix
+from torch_utils.ops import filtered_lrelu
+from torch_utils.ops import bias_act
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def modulated_conv2d(
+    x,                  # Input tensor: [batch_size, in_channels, in_height, in_width]
+    w,                  # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
+    s,                  # Style tensor: [batch_size, in_channels]
+    demodulate  = True, # Apply weight demodulation?
+    padding     = 0,    # Padding: int or [padH, padW]
+    input_gain  = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
+):
+    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
+        batch_size = int(x.shape[0])
+    out_channels, in_channels, kh, kw = w.shape
+    misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk]
+    misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
+    misc.assert_shape(s, [batch_size, in_channels]) # [NI]
+
+    # Pre-normalize inputs.
+    if demodulate:
+        w = w * w.square().mean([1,2,3], keepdim=True).rsqrt()
+        s = s * s.square().mean().rsqrt()
+
+    # Modulate weights.
+    w = w.unsqueeze(0) # [NOIkk]
+    w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
+
+    # Demodulate weights.
+    if demodulate:
+        dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
+        w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk]
+
+    # Apply input scaling.
+    if input_gain is not None:
+        input_gain = input_gain.expand(batch_size, in_channels) # [NI]
+        w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
+
+    # Execute as one fused op using grouped convolution.
+    x = x.reshape(1, -1, *x.shape[2:])
+    w = w.reshape(-1, in_channels, kh, kw)
+    x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size)
+    x = x.reshape(batch_size, -1, *x.shape[2:])
+    return x
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class FullyConnectedLayer(torch.nn.Module):
+    def __init__(self,
+        in_features,                # Number of input features.
+        out_features,               # Number of output features.
+        activation      = 'linear', # Activation function: 'relu', 'lrelu', etc.
+        bias            = True,     # Apply additive bias before the activation function?
+        lr_multiplier   = 1,        # Learning rate multiplier.
+        weight_init     = 1,        # Initial standard deviation of the weight tensor.
+        bias_init       = 0,        # Initial value of the additive bias.
+    ):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.activation = activation
+        self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier))
+        bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features])
+        self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None
+        self.weight_gain = lr_multiplier / np.sqrt(in_features)
+        self.bias_gain = lr_multiplier
+
+    def forward(self, x):
+        w = self.weight.to(x.dtype) * self.weight_gain
+        b = self.bias
+        if b is not None:
+            b = b.to(x.dtype)
+            if self.bias_gain != 1:
+                b = b * self.bias_gain
+        if self.activation == 'linear' and b is not None:
+            x = torch.addmm(b.unsqueeze(0), x, w.t())
+        else:
+            x = x.matmul(w.t())
+            x = bias_act.bias_act(x, b, act=self.activation)
+        return x
+
+    def extra_repr(self):
+        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class MappingNetwork(torch.nn.Module):
+    def __init__(self,
+        z_dim,                      # Input latent (Z) dimensionality.
+        c_dim,                      # Conditioning label (C) dimensionality, 0 = no labels.
+        w_dim,                      # Intermediate latent (W) dimensionality.
+        num_ws,                     # Number of intermediate latents to output.
+        num_layers      = 2,        # Number of mapping layers.
+        lr_multiplier   = 0.01,     # Learning rate multiplier for the mapping layers.
+        w_avg_beta      = 0.998,    # Decay for tracking the moving average of W during training.
+    ):
+        super().__init__()
+        self.z_dim = z_dim
+        self.c_dim = c_dim
+        self.w_dim = w_dim
+        self.num_ws = num_ws
+        self.num_layers = num_layers
+        self.w_avg_beta = w_avg_beta
+
+        # Construct layers.
+        self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None
+        features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers
+        for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]):
+            layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier)
+            setattr(self, f'fc{idx}', layer)
+        self.register_buffer('w_avg', torch.zeros([w_dim]))
+
+    def forward(self, z, c=0, truncation_psi=1, truncation_cutoff=None, update_emas=False):
+        #将传入的z由list改为tensor 好像改得不对，还是别改把
+        # z = torch.tensor( [item.cpu().detach().numpy() for item in z] )
+        misc.assert_shape(z, [None, self.z_dim])
+        if truncation_cutoff is None:
+            truncation_cutoff = self.num_ws
+
+        # Embed, normalize, and concatenate inputs.
+        x = z.to(torch.float32)
+        x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
+        if self.c_dim > 0:
+            misc.assert_shape(c, [None, self.c_dim])
+            y = self.embed(c.to(torch.float32))
+            y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
+            x = torch.cat([x, y], dim=1) if x is not None else y
+
+        # Execute layers.
+        for idx in range(self.num_layers):
+            x = getattr(self, f'fc{idx}')(x)
+
+        # Update moving average of W.
+        if update_emas:
+            self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
+
+        # Broadcast and apply truncation.
+        x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
+        if truncation_psi != 1:
+            x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
+        return x
+
+    def extra_repr(self):
+        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class SynthesisInput(torch.nn.Module):
+    def __init__(self,
+        w_dim,          # Intermediate latent (W) dimensionality.
+        channels,       # Number of output channels.
+        size,           # Output spatial size: int or [width, height].
+        sampling_rate,  # Output sampling rate.
+        bandwidth,      # Output bandwidth.
+    ):
+        super().__init__()
+        self.w_dim = w_dim
+        self.channels = channels
+        self.size = np.broadcast_to(np.asarray(size), [2])
+        self.sampling_rate = sampling_rate
+        self.bandwidth = bandwidth
+
+        # Draw random frequencies from uniform 2D disc.
+        freqs = torch.randn([self.channels, 2])
+        radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
+        freqs /= radii * radii.square().exp().pow(0.25)
+        freqs *= bandwidth
+        phases = torch.rand([self.channels]) - 0.5
+
+        # Setup parameters and buffers.
+        self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels]))
+        self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0])
+        self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image.
+        self.register_buffer('freqs', freqs)
+        self.register_buffer('phases', phases)
+
+    def forward(self, w):
+        # Introduce batch dimension.
+        transforms = self.transform.unsqueeze(0) # [batch, row, col]
+        freqs = self.freqs.unsqueeze(0) # [batch, channel, xy]
+        phases = self.phases.unsqueeze(0) # [batch, channel]
+
+        # Apply learned transformation.
+        t = self.affine(w) # t = (r_c, r_s, t_x, t_y)
+        t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y)
+        m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image.
+        m_r[:, 0, 0] = t[:, 0]  # r'_c
+        m_r[:, 0, 1] = -t[:, 1] # r'_s
+        m_r[:, 1, 0] = t[:, 1]  # r'_s
+        m_r[:, 1, 1] = t[:, 0]  # r'_c
+        m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image.
+        m_t[:, 0, 2] = -t[:, 2] # t'_x
+        m_t[:, 1, 2] = -t[:, 3] # t'_y
+        transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform.
+
+        # Transform frequencies.
+        phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
+        freqs = freqs @ transforms[:, :2, :2]
+
+        # Dampen out-of-band frequencies that may occur due to the user-specified transform.
+        amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
+
+        # Construct sampling grid.
+        theta = torch.eye(2, 3, device=w.device)
+        theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
+        theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
+        grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False)
+
+        # Compute Fourier features.
+        x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel]
+        x = x + phases.unsqueeze(1).unsqueeze(2)
+        x = torch.sin(x * (np.pi * 2))
+        x = x * amplitudes.unsqueeze(1).unsqueeze(2)
+
+        # Apply trainable mapping.
+        weight = self.weight / np.sqrt(self.channels)
+        x = x @ weight.t()
+
+        # Ensure correct shape.
+        x = x.permute(0, 3, 1, 2) # [batch, channel, height, width]
+        misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])])
+        return x
+
+    def extra_repr(self):
+        return '\n'.join([
+            f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
+            f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'])
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class SynthesisLayer(torch.nn.Module):
+    def __init__(self,
+        w_dim,                          # Intermediate latent (W) dimensionality.
+        is_torgb,                       # Is this the final ToRGB layer?
+        is_critically_sampled,          # Does this layer use critical sampling?
+        use_fp16,                       # Does this layer use FP16?
+
+        # Input & output specifications.
+        in_channels,                    # Number of input channels.
+        out_channels,                   # Number of output channels.
+        in_size,                        # Input spatial size: int or [width, height].
+        out_size,                       # Output spatial size: int or [width, height].
+        in_sampling_rate,               # Input sampling rate (s).
+        out_sampling_rate,              # Output sampling rate (s).
+        in_cutoff,                      # Input cutoff frequency (f_c).
+        out_cutoff,                     # Output cutoff frequency (f_c).
+        in_half_width,                  # Input transition band half-width (f_h).
+        out_half_width,                 # Output Transition band half-width (f_h).
+
+        # Hyperparameters.
+        conv_kernel         = 3,        # Convolution kernel size. Ignored for final the ToRGB layer.
+        filter_size         = 6,        # Low-pass filter size relative to the lower resolution when up/downsampling.
+        lrelu_upsampling    = 2,        # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
+        use_radial_filters  = False,    # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
+        conv_clamp          = 256,      # Clamp the output to [-X, +X], None = disable clamping.
+        magnitude_ema_beta  = 0.999,    # Decay rate for the moving average of input magnitudes.
+    ):
+        super().__init__()
+        self.w_dim = w_dim
+        self.is_torgb = is_torgb
+        self.is_critically_sampled = is_critically_sampled
+        self.use_fp16 = use_fp16
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.in_size = np.broadcast_to(np.asarray(in_size), [2])
+        self.out_size = np.broadcast_to(np.asarray(out_size), [2])
+        self.in_sampling_rate = in_sampling_rate
+        self.out_sampling_rate = out_sampling_rate
+        self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling)
+        self.in_cutoff = in_cutoff
+        self.out_cutoff = out_cutoff
+        self.in_half_width = in_half_width
+        self.out_half_width = out_half_width
+        self.conv_kernel = 1 if is_torgb else conv_kernel
+        self.conv_clamp = conv_clamp
+        self.magnitude_ema_beta = magnitude_ema_beta
+
+        # Setup parameters and buffers.
+        self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1)
+        self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel]))
+        self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
+        self.register_buffer('magnitude_ema', torch.ones([]))
+
+        # Design upsampling filter.
+        self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
+        assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
+        self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
+        self.register_buffer('up_filter', self.design_lowpass_filter(
+            numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate))
+
+        # Design downsampling filter.
+        self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
+        assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
+        self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
+        self.down_radial = use_radial_filters and not self.is_critically_sampled
+        self.register_buffer('down_filter', self.design_lowpass_filter(
+            numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial))
+
+        # Compute padding.
+        pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling.
+        pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling.
+        pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters.
+        pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3).
+        pad_hi = pad_total - pad_lo
+        self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])]
+
+    def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False):
+        assert noise_mode in ['random', 'const', 'none'] # unused
+        misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])])
+        misc.assert_shape(w, [x.shape[0], self.w_dim])
+
+        # Track input magnitude.
+        if update_emas:
+            with torch.autograd.profiler.record_function('update_magnitude_ema'):
+                magnitude_cur = x.detach().to(torch.float32).square().mean()
+                self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta))
+        input_gain = self.magnitude_ema.rsqrt()
+
+        # Execute affine layer.
+        styles = self.affine(w)
+        if self.is_torgb:
+            weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2))
+            styles = styles * weight_gain
+
+        # Execute modulated conv2d.
+        dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32
+        x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles,
+            padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain)
+
+        # Execute bias, filtered leaky ReLU, and clamping.
+        gain = 1 if self.is_torgb else np.sqrt(2)
+        slope = 1 if self.is_torgb else 0.2
+        x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype),
+            up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp)
+
+        # Ensure correct shape and dtype.
+        misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])])
+        assert x.dtype == dtype
+        return x
+
+    @staticmethod
+    def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
+        assert numtaps >= 1
+
+        # Identity filter.
+        if numtaps == 1:
+            return None
+
+        # Separable Kaiser low-pass filter.
+        if not radial:
+            f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)
+            return torch.as_tensor(f, dtype=torch.float32)
+
+        # Radially symmetric jinc-based filter.
+        x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
+        r = np.hypot(*np.meshgrid(x, x))
+        f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
+        beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
+        w = np.kaiser(numtaps, beta)
+        f *= np.outer(w, w)
+        f /= np.sum(f)
+        return torch.as_tensor(f, dtype=torch.float32)
+
+    def extra_repr(self):
+        return '\n'.join([
+            f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
+            f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
+            f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
+            f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
+            f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
+            f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
+            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'])
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class SynthesisNetwork(torch.nn.Module):
+    def __init__(self,
+        w_dim,                          # Intermediate latent (W) dimensionality. 512
+        img_resolution,                 # Output image resolution. 1024
+        img_channels,                   # Number of color channels. 3
+        channel_base        = 32768,    # Overall multiplier for the number of channels.通道总体倍增因子
+        channel_max         = 512,      # Maximum number of channels in any layer.
+        num_layers          = 14,       # Total number of layers, excluding Fourier features and ToRGB.
+        num_critical        = 2,        # Number of critically sampled layers at the end.
+        first_cutoff        = 2,        # Cutoff frequency of the first layer (f_{c,0}).
+        first_stopband      = 2**2.1,   # Minimum stopband of the first layer (f_{t,0}).
+        last_stopband_rel   = 2**0.3,   # Minimum stopband of the last layer, expressed relative to the cutoff.
+        margin_size         = 10,       # Number of additional pixels outside the image.
+        output_scale        = 0.25,     # Scale factor for the output image.
+        num_fp16_res        = 4,        # Use FP16 for the N highest resolutions.
+        **layer_kwargs,                 # Arguments for SynthesisLayer.
+    ):
+        super().__init__()
+        self.w_dim = w_dim
+        self.num_ws = num_layers + 2
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.num_layers = num_layers
+        self.num_critical = num_critical
+        self.margin_size = margin_size
+        self.output_scale = output_scale
+        self.num_fp16_res = num_fp16_res
+
+        # Geometric progression of layer cutoffs and min. stopbands.
+        last_cutoff = self.img_resolution / 2 # f_{c,N}
+        last_stopband = last_cutoff * last_stopband_rel # f_{t,N}
+        exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1)
+        cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i] [  2.           3.1748021    5.0396842    8.          12.69920842,  20.1587368   32.          50.79683366  80.63494719 128., 203.18733465 322.53978877 512.         512.         512.        ]
+        stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i]
+
+        # Compute remaining layer parameters.
+        sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i]
+        half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i]
+        sizes = sampling_rates + self.margin_size * 2
+        sizes[-2:] = self.img_resolution
+        channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max))
+        channels[-1] = self.img_channels
+
+        # Construct layers.
+        self.input = SynthesisInput(
+            w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]), #sizes:[  36.   36.   52.   52.   84.  148.  148.  276.  276.  532. 1044. 1044., 1044. 1024. 1024.]
+            sampling_rate=sampling_rates[0], bandwidth=cutoffs[0])  #sampling_rates ：[  16.   16.   32.   32.   64.  128.  128.  256.  256.  512. 1024. 1024., 1024. 1024. 1024.]
+        self.layer_names = []
+        for idx in range(self.num_layers + 1):
+            prev = max(idx - 1, 0)
+            is_torgb = (idx == self.num_layers)
+            is_critically_sampled = (idx >= self.num_layers - self.num_critical)
+            use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution)
+            layer = SynthesisLayer(
+                w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16,
+                in_channels=int(channels[prev]), out_channels= int(channels[idx]),
+                in_size=int(sizes[prev]), out_size=int(sizes[idx]),
+                in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]),
+                in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx],
+                in_half_width=half_widths[prev], out_half_width=half_widths[idx],
+                **layer_kwargs)
+            name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
+            setattr(self, name, layer)
+            self.layer_names.append(name)
+
+    def forward(self, ws, **layer_kwargs):
+        misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
+        ws = ws.to(torch.float32).unbind(dim=1)
+
+        # Execute layers.
+        x = self.input(ws[0])
+        for name, w in zip(self.layer_names, ws[1:]):
+            x = getattr(self, name)(x, w, **layer_kwargs)
+        if self.output_scale != 1:
+            x = x * self.output_scale
+
+        # Ensure correct shape and dtype.
+        misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution])
+        x = x.to(torch.float32)
+        return x
+
+    def extra_repr(self):
+        return '\n'.join([
+            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
+            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
+            f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
+            f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'])
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class Generator(torch.nn.Module):
+    def __init__(self,
+        z_dim,                      # Input latent (Z) dimensionality.
+        c_dim,                      # Conditioning label (C) dimensionality.
+        w_dim,                      # Intermediate latent (W) dimensionality.
+        img_resolution,             # Output resolution.
+        img_channels,               # Number of output color channels.
+        mapping_kwargs      = {},   # Arguments for MappingNetwork.
+        **synthesis_kwargs,         # Arguments for SynthesisNetwork.
+    ):
+        super().__init__()
+        self.z_dim = z_dim    #512
+        self.c_dim = c_dim    #0
+        self.w_dim = w_dim    #512
+        self.img_resolution = img_resolution
+        self.img_channels = img_channels
+        self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
+        self.num_ws = self.synthesis.num_ws   #16
+        self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
+
+    # def mean_latent(self, n_latent):
+    #     latent_in = torch.randn(
+    #         #此处的style_dim应与w_dim对应
+    #         n_latent, self.w_dim, device=self.synthesis.input.weight.device
+    #     )
+    #     latent = self.synthesis.styles(latent_in).mean(0, keepdim=True)
+    #
+    #     return latent
+
+    def forward(self, z, c=None, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
+        # print("-----------------------------------")
+        # print(z)
+        # print("-----------------------------------")
+        ws = self.mapping(z, c = None, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
+        img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
+        return img
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/run_optimization3.py b/models/stylegan3/run_optimization3.py
new file mode 100644
index 0000000..ac72fc4
--- /dev/null
+++ b/models/stylegan3/run_optimization3.py
@@ -0,0 +1,267 @@
+import argparse
+import math
+import os
+import pickle
+
+import torchvision
+from torch import optim
+from tqdm import tqdm
+
+import torch
+import clip
+
+
+class CLIPLoss(torch.nn.Module):
+
+    def __init__(self, opts):
+        super(CLIPLoss, self).__init__()
+        self.model, self.preprocess = clip.load("ViT-B/32", device="cuda")
+        self.upsample = torch.nn.Upsample(scale_factor=7)
+        self.avg_pool = torch.nn.AvgPool2d(kernel_size=opts.stylegan_size // 32)
+
+    def forward(self, image, text):
+        image = self.avg_pool(self.upsample(image))
+        similarity = 1 - self.model(image, text)[0] / 100
+        return similarity
+
+
+from torch import nn
+import sys
+sys.path.append('/home/ly/StyleCLIP-main/models/facial_recognition')
+from model_irse import Backbone
+
+
+class IDLoss(nn.Module):
+    def __init__(self, opts):
+        super(IDLoss, self).__init__()
+        print('Loading ResNet ArcFace')
+        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
+        self.facenet.load_state_dict(torch.load(opts.ir_se50_weights))
+        self.pool = torch.nn.AdaptiveAvgPool2d((256, 256))
+        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
+        self.facenet.eval()
+        self.facenet.cuda()
+        self.opts = opts
+
+    def extract_feats(self, x):
+        if x.shape[2] != 256:
+            x = self.pool(x)
+        x = x[:, :, 35:223, 32:220]  # Crop interesting region
+        x = self.face_pool(x)
+        x_feats = self.facenet(x)
+        return x_feats
+
+    def forward(self, y_hat, y):
+        n_samples = y.shape[0]
+        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
+        y_hat_feats = self.extract_feats(y_hat)
+        y_feats = y_feats.detach()
+        loss = 0
+        sim_improvement = 0
+        count = 0
+        for i in range(n_samples):
+            diff_target = y_hat_feats[i].dot(y_feats[i])
+            loss += 1 - diff_target
+            count += 1
+
+        return loss / count, sim_improvement / count
+sys.path.append('/home/ly/StyleCLIP-main/mapper/training')
+from train_utils import STYLESPACE_DIMENSIONS
+from model_3 import Generator
+from model_3 import SynthesisNetwork
+from model_3 import SynthesisLayer
+
+
+sys.path.append('/home/ly/StyleCLIP-main')
+from utils import ensure_checkpoint_exists
+
+STYLESPACE_INDICES_WITHOUT_TORGB = [i for i in range(len(STYLESPACE_DIMENSIONS)) if i not in list(range(1, len(STYLESPACE_DIMENSIONS), 3))]
+
+def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
+    lr_ramp = min(1, (1 - t) / rampdown)
+    lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
+    lr_ramp = lr_ramp * min(1, t / rampup)
+
+    return initial_lr * lr_ramp
+
+
+def main(args):
+    ensure_checkpoint_exists(args.ckpt)
+    # 把描述加载进clip预训练模型里面去
+    text_inputs = torch.cat([clip.tokenize(args.description)]).cuda()
+    # print('text_input是： ', text_inputs)
+    #tokenizer clip分词的机制 依据规则
+    #以及词汇表的总量
+    '''
+     --description "a person with purple hair" 
+     tensor([[49406,   320,  2533,   593,  5496,  2225, 49407,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0]], device='cuda:0',
+       dtype=torch.int32)
+       --description "a person with red hair" 
+        tensor([[49406,   320,  2533,   593,   736,  2225, 49407,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0]], device='cuda:0',
+       dtype=torch.int32)
+    '''
+
+    os.makedirs(args.results_dir, exist_ok=True)
+    #改成stylegan3的输入
+
+    # with open('/home/ly/StyleCLIP-main/models/stylegan3/torch_utils/stylegan3-r-afhqv2-512x512.pkl', 'rb') as f:
+    #     G = pickle.load(f)['G_ema'].cuda()  # torch.nn.Module
+    # z = torch.randn([1, G.z_dim]).cuda()  # latent codes
+    # c = None  # class labels (not used in this example)
+    # img = G(z, c)  # NCHW, float32, dynamic range [-1, +1], no truncation
+    
+    # g_ema = Generator(512, 0, 512,args.stylegan_size, 3)   #512,0,512,1024,3
+    # with open('/home/ly/StyleCLIP-main/models/stylegan3/torch_utils/stylegan3-r-afhqv2-512x512.pkl', 'rb') as f:
+    #stylegan3-r-ffhqu-1024x1024.pkl 生成图片的效果欠佳 别用
+    #stylegan3-t-ffhq-1024x1024.pkl 生成效果一般 loss值较好
+    #stylegan3-r-ffhq-1024x1024.pkl 折中
+    #stylegan3-t-ffhqu-1024x1024.pkl 生成图片可以 loss较差
+    with open('/home/ly/StyleCLIP-main/pretrained_models/stylegan3-t-ffhq-1024x1024.pkl', 'rb') as f:   #stylespace_dimensions [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 256, 256, 256, 128, 128, 128, 64, 64, 64, 32, 32]
+        # new_p = pickle.load(f)
+        # print(new_p)
+        # print("new_p")
+        # print(new_p.keys())
+        # G_ema.load_state_dict(pickle.load(f)['G_ema'].cuda(), strict=False) 这种方式模型加载不进来
+        g_ema = pickle.load(f)['G_ema'].cuda()  # torch.nn.Module 这种方式推演三百步的图片平均要4分钟
+    z = torch.randn([1, g_ema.z_dim]).cuda()  # latent codes
+    c = None  # class labels (not used in this example)
+    #g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)
+    # 将模型对象设置为评估模式
+    g_ema.eval()
+    #更改cuda卡号
+    g_ema = g_ema.cuda()
+    # device = torch.cuda.current_device()
+    # print('cuda:',device)
+    mean_latent = torch.randn([1, g_ema.z_dim]).cuda()
+    torch.save(mean_latent,'/home/ly/StyleCLIP-main/pretrained_models/latent_code/style3.pt')
+    # print('mean_latent: ', mean_latent)
+
+    if args.latent_path:
+        latent_code_init = torch.load(args.latent_path).cuda()
+    # elif args.mode == "edit":
+    #     latent_code_init_not_trunc = torch.randn(1, 512).cuda()
+    #     with torch.no_grad():
+    #         _, latent_code_init, _ = g_ema([latent_code_init_not_trunc], return_latents=True,
+    #                                     truncation=args.truncation, truncation_latent=mean_latent)
+    else:
+        # latent_code_init = mean_latent.detach().clone().repeat(1, 18, 1)  #在维度1上重复18次
+        latent_code_init = mean_latent.detach().clone()
+    # def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
+    with torch.no_grad():
+        print("mean_latent ", mean_latent.shape)
+        # img_orig, _ = g_ema([latent_code_init], c, input_is_latent=True, randomize_noise=False)
+        img_orig = g_ema(latent_code_init, c)
+
+    if args.work_in_stylespace:
+        with torch.no_grad():
+            _, _, latent_code_init = g_ema([latent_code_init], input_is_latent=True, return_latents=True)
+        latent = [s.detach().clone() for s in latent_code_init]
+        for c, s in enumerate(latent):
+            if c in STYLESPACE_INDICES_WITHOUT_TORGB:
+                s.requires_grad = True
+    else:
+        latent = latent_code_init.detach().clone()
+        latent.requires_grad = True
+
+    clip_loss = CLIPLoss(args)
+    id_loss = IDLoss(args)
+
+    if args.work_in_stylespace:
+        optimizer = optim.Adam(latent, lr=args.lr)
+    else:
+        optimizer = optim.Adam([latent], lr=args.lr)
+
+    pbar = tqdm(range(args.step))
+
+    for i in pbar:
+        t = i / args.step
+        lr = get_lr(t, args.lr)
+        optimizer.param_groups[0]["lr"] = lr
+
+        img_gen = g_ema(latent,c)
+
+        c_loss = clip_loss(img_gen, text_inputs)
+
+        if args.id_lambda > 0:
+            #身份损失
+            i_loss = id_loss(img_gen, img_orig)[0]
+        else:
+            i_loss = 0
+
+        if args.mode == "edit":
+            if args.work_in_stylespace:
+                l2_loss = sum([((latent_code_init[c] - latent[c]) ** 2).sum() for c in range(len(latent_code_init))])
+            else:
+                #与潜在空间的L2距离
+                l2_loss = ((latent_code_init - latent) ** 2).sum()
+            loss = c_loss + args.l2_lambda * l2_loss + args.id_lambda * i_loss
+        else:
+            loss = c_loss
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        pbar.set_description(
+            (
+                f"loss: {loss.item():.4f};"
+            )
+        )
+        if args.save_intermediate_image_every > 0 and i % args.save_intermediate_image_every == 0:
+            with torch.no_grad():
+                img_gen = g_ema(latent, c)
+
+            torchvision.utils.save_image(img_gen, f"results/stygan3Clip/{str(i).zfill(5)}.jpg", normalize=True, range=(-1, 1))
+
+    if args.mode == "edit":
+        final_result = torch.cat([img_orig, img_gen])
+    else:
+        final_result = img_gen
+
+    return final_result
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--description", type=str, default="a person with purple hair", help="the text that guides the editing/generation")
+    parser.add_argument("--ckpt", type=str, default="../pretrained_models/stylegan2-ffhq-config-f.pt", help="pretrained StyleGAN2 weights")
+    parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution")
+    parser.add_argument("--lr_rampup", type=float, default=0.05)
+    parser.add_argument("--lr", type=float, default=0.1)
+    parser.add_argument("--step", type=int, default=300, help="number of optimization steps")
+    parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"], help="choose between edit an image an generate a free one")
+    parser.add_argument("--l2_lambda", type=float, default=0.008, help="weight of the latent distance (used for editing only)")
+    parser.add_argument("--id_lambda", type=float, default=0.000, help="weight of id loss (used for editing only)")
+    parser.add_argument("--latent_path", type=str, default=None, help="starts the optimization from the given latent code if provided. Otherwose, starts from"
+                                                                      "the mean latent in a free generation, and from a random one in editing. "
+                                                                      "Expects a .pt format")
+    parser.add_argument("--truncation", type=float, default=1, help="used only for the initial latent vector, and only when a latent code path is"
+                                                                      "not provided")
+    parser.add_argument('--work_in_stylespace', default=False, action='store_true')
+    parser.add_argument("--save_intermediate_image_every", type=int, default=20, help="if > 0 then saves intermidate results during the optimization")
+    parser.add_argument("--results_dir", type=str, default="results")
+    parser.add_argument('--ir_se50_weights', default='../pretrained_models/model_ir_se50.pth', type=str,
+                             help="Path to facial recognition network used in ID loss")
+
+    args = parser.parse_args()
+
+    result_image = main(args)
+
+    torchvision.utils.save_image(result_image.detach().cpu(), os.path.join(args.results_dir, "final_result.jpg"), normalize=True, scale_each=True, range=(-1, 1))
+
+
diff --git a/models/stylegan3/show_pkl.py b/models/stylegan3/show_pkl.py
new file mode 100644
index 0000000..ae21b6d
--- /dev/null
+++ b/models/stylegan3/show_pkl.py
@@ -0,0 +1,194 @@
+# show_pkl.py
+
+import pickle
+import sys
+import torch
+sys.path.append('/home/ly/StyleCLIP-main/models/stylegan3/torch_utils')
+
+#
+# path = '/home/ly/StyleCLIP-main/models/stylegan3/torch_utils/stylegan3-r-afhqv2-512x512.pkl'  # path='/root/……/aus_openface.pkl'   pkl文件所在路径
+#
+# f = open(path, 'rb')
+# data = pickle.load(f)
+#
+# print(data)
+# print(len(data))
+# print(data.shape)
+
+with open('/home/ly/StyleCLIP-main/models/stylegan3/torch_utils/stylegan3-r-afhqv2-512x512.pkl', 'rb') as f:
+    G = pickle.load(f)['G_ema'].cuda()  # torch.nn.Module
+z = torch.randn([1, G.z_dim]).cuda()    # latent codes
+c = None                                # class labels (not used in this example)
+img = G(z, c)                           # NCHW, float32, dynamic range [-1, +1], no truncation
+print(G)
+
+
+#输出
+# Generator(
+#   (synthesis): SynthesisNetwork(
+#     w_dim=512, num_ws=16,
+#     img_resolution=512, img_channels=3,
+#     num_layers=14, num_critical=2,
+#     margin_size=10, num_fp16_res=4
+#     (input): SynthesisInput(
+#       w_dim=512, channels=1024, size=[36, 36],
+#       sampling_rate=16, bandwidth=2
+#       (affine): FullyConnectedLayer(in_features=512, out_features=4, activation=linear)
+#     )
+#     (L0_36_1024): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=False,
+#       in_sampling_rate=16, out_sampling_rate=16,
+#       in_cutoff=2, out_cutoff=2,
+#       in_half_width=6, out_half_width=6,
+#       in_size=[36, 36], out_size=[36, 36],
+#       in_channels=1024, out_channels=1024
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L1_36_1024): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=False,
+#       in_sampling_rate=16, out_sampling_rate=16,
+#       in_cutoff=2, out_cutoff=2.99661,
+#       in_half_width=6, out_half_width=5.00339,
+#       in_size=[36, 36], out_size=[36, 36],
+#       in_channels=1024, out_channels=1024
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L2_52_1024): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=False,
+#       in_sampling_rate=16, out_sampling_rate=32,
+#       in_cutoff=2.99661, out_cutoff=4.48985,
+#       in_half_width=5.00339, out_half_width=11.5102,
+#       in_size=[36, 36], out_size=[52, 52],
+#       in_channels=1024, out_channels=1024
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L3_52_1024): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=False,
+#       in_sampling_rate=32, out_sampling_rate=32,
+#       in_cutoff=4.48985, out_cutoff=6.72717,
+#       in_half_width=11.5102, out_half_width=9.27283,
+#       in_size=[52, 52], out_size=[52, 52],
+#       in_channels=1024, out_channels=1024
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L4_84_1024): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=32, out_sampling_rate=64,
+#       in_cutoff=6.72717, out_cutoff=10.0794,
+#       in_half_width=9.27283, out_half_width=21.9206,
+#       in_size=[52, 52], out_size=[84, 84],
+#       in_channels=1024, out_channels=1024
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L5_84_1024): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=64, out_sampling_rate=64,
+#       in_cutoff=10.0794, out_cutoff=15.102,
+#       in_half_width=21.9206, out_half_width=16.898,
+#       in_size=[84, 84], out_size=[84, 84],
+#       in_channels=1024, out_channels=1024
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L6_148_1024): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=64, out_sampling_rate=128,
+#       in_cutoff=15.102, out_cutoff=22.6274,
+#       in_half_width=16.898, out_half_width=41.3726,
+#       in_size=[84, 84], out_size=[148, 148],
+#       in_channels=1024, out_channels=1024
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L7_148_967): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=128, out_sampling_rate=128,
+#       in_cutoff=22.6274, out_cutoff=33.9028,
+#       in_half_width=41.3726, out_half_width=30.0972,
+#       in_size=[148, 148], out_size=[148, 148],
+#       in_channels=1024, out_channels=967
+#       (affine): FullyConnectedLayer(in_features=512, out_features=1024, activation=linear)
+#     )
+#     (L8_276_645): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=128, out_sampling_rate=256,
+#       in_cutoff=33.9028, out_cutoff=50.7968,
+#       in_half_width=30.0972, out_half_width=77.2032,
+#       in_size=[148, 148], out_size=[276, 276],
+#       in_channels=967, out_channels=645
+#       (affine): FullyConnectedLayer(in_features=512, out_features=967, activation=linear)
+#     )
+#     (L9_276_431): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=256, out_sampling_rate=256,
+#       in_cutoff=50.7968, out_cutoff=76.1093,
+#       in_half_width=77.2032, out_half_width=51.8907,
+#       in_size=[276, 276], out_size=[276, 276],
+#       in_channels=645, out_channels=431
+#       (affine): FullyConnectedLayer(in_features=512, out_features=645, activation=linear)
+#     )
+#     (L10_532_287): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=256, out_sampling_rate=512,
+#       in_cutoff=76.1093, out_cutoff=114.035,
+#       in_half_width=51.8907, out_half_width=141.965,
+#       in_size=[276, 276], out_size=[532, 532],
+#       in_channels=431, out_channels=287
+#       (affine): FullyConnectedLayer(in_features=512, out_features=431, activation=linear)
+#     )
+#     (L11_532_192): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=False, use_fp16=True,
+#       in_sampling_rate=512, out_sampling_rate=512,
+#       in_cutoff=114.035, out_cutoff=170.86,
+#       in_half_width=141.965, out_half_width=85.1405,
+#       in_size=[532, 532], out_size=[532, 532],
+#       in_channels=287, out_channels=192
+#       (affine): FullyConnectedLayer(in_features=512, out_features=287, activation=linear)
+#     )
+#     (L12_532_128): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=True, use_fp16=True,
+#       in_sampling_rate=512, out_sampling_rate=512,
+#       in_cutoff=170.86, out_cutoff=256,
+#       in_half_width=85.1405, out_half_width=59.173,
+#       in_size=[532, 532], out_size=[532, 532],
+#       in_channels=192, out_channels=128
+#       (affine): FullyConnectedLayer(in_features=512, out_features=192, activation=linear)
+#     )
+#     (L13_512_128): SynthesisLayer(
+#       w_dim=512, is_torgb=False,
+#       is_critically_sampled=True, use_fp16=True,
+#       in_sampling_rate=512, out_sampling_rate=512,
+#       in_cutoff=256, out_cutoff=256,
+#       in_half_width=59.173, out_half_width=59.173,
+#       in_size=[532, 532], out_size=[512, 512],
+#       in_channels=128, out_channels=128
+#       (affine): FullyConnectedLayer(in_features=512, out_features=128, activation=linear)
+#     )
+#     (L14_512_3): SynthesisLayer(
+#       w_dim=512, is_torgb=True,
+#       is_critically_sampled=True, use_fp16=True,
+#       in_sampling_rate=512, out_sampling_rate=512,
+#       in_cutoff=256, out_cutoff=256,
+#       in_half_width=59.173, out_half_width=59.173,
+#       in_size=[512, 512], out_size=[512, 512],
+#       in_channels=128, out_channels=3
+#       (affine): FullyConnectedLayer(in_features=512, out_features=128, activation=linear)
+#     )
+#   )
+#   (mapping): MappingNetwork(
+#     z_dim=512, c_dim=0, w_dim=512, num_ws=16
+#     (fc0): FullyConnectedLayer(in_features=512, out_features=512, activation=lrelu)
+#     (fc1): FullyConnectedLayer(in_features=512, out_features=512, activation=lrelu)
+#   )
+# )
diff --git a/models/stylegan3/test001_s3.py b/models/stylegan3/test001_s3.py
new file mode 100644
index 0000000..1df9193
--- /dev/null
+++ b/models/stylegan3/test001_s3.py
@@ -0,0 +1,37 @@
+import torchvision
+import argparse
+from argparse import Namespace
+from run_optimization3 import main
+
+parser = argparse.ArgumentParser()
+# parser.add_argument("--description", type=str, default="a person with purple hair",
+parser.add_argument("--description", type=str, default="a person with purple hair",
+                    help="the text that guides the editing/generation")
+parser.add_argument("--ckpt", type=str, default="/home/ly/StyleCLIP-main/pretrained_models/stylegan3-r-ffhqu-1024x1024.pkl",
+                    help="pretrained StyleGAN3 weights")
+parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution")
+parser.add_argument("--lr_rampup", type=float, default=0.05)
+parser.add_argument("--lr", type=float, default=0.1)
+parser.add_argument("--step", type=int, default=300, help="number of optimization steps")
+parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"],
+                    help="choose between edit an image an generate a free one")
+parser.add_argument("--l2_lambda", type=float, default=0.008,
+                    help="weight of the latent distance (used for editing only)")
+parser.add_argument("--latent_path", type=str, default=None,            #"/home/ly/StyleCLIP-main/latents_test/example_celebs.pt"
+                    help="starts the optimization from the given latent code if provided. Otherwise, starts from"
+                         "the mean latent in a free generation, and from a random one in editing. "
+                         "Expects a .pt format")
+parser.add_argument("--truncation", type=float, default=0.5,
+                    help="used only for the initial latent vector, and only when a latent code path is"
+                         "not provided")
+parser.add_argument("--save_intermediate_image_every", type=int, default=20,
+                    help="if > 0 then saves intermidate results during the optimization")
+parser.add_argument("--results_dir", type=str, default="/home/ly/StyleCLIP-main/results/stygan3Clip")
+parser.add_argument('--work_in_stylespace', default=False, action='store_true', help="trains a mapper in S instead of W+")
+parser.add_argument('--ir_se50_weights', default='/home/ly/StyleCLIP-main/pretrained_models/model_ir_se50.pth', type=str, help="Path to facial recognition network used in ID loss")
+parser.add_argument('--id_lambda', default=0.10, type=float, help='ID loss multiplier factor')
+
+args = vars(parser.parse_args())
+result_image = main(Namespace(**args))
+torchvision.utils.save_image(result_image.detach().cpu(), f"/home/ly/StyleCLIP-main/results/stygan3Clip/final_result.png", normalize=True, scale_each=True,
+                             range=(-1, 1))
\ No newline at end of file
diff --git a/models/stylegan3/torch_utils/__init__.py b/models/stylegan3/torch_utils/__init__.py
new file mode 100644
index 0000000..939e7c6
--- /dev/null
+++ b/models/stylegan3/torch_utils/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+# empty
diff --git a/models/stylegan3/torch_utils/custom_ops.py b/models/stylegan3/torch_utils/custom_ops.py
new file mode 100644
index 0000000..439e445
--- /dev/null
+++ b/models/stylegan3/torch_utils/custom_ops.py
@@ -0,0 +1,157 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import glob
+import hashlib
+import importlib
+import os
+import re
+import shutil
+import uuid
+
+import torch
+import torch.utils.cpp_extension
+from torch.utils.file_baton import FileBaton
+
+#----------------------------------------------------------------------------
+# Global options.
+
+verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
+
+#----------------------------------------------------------------------------
+# Internal helper funcs.
+
+def _find_compiler_bindir():
+    patterns = [
+        'C:/Program Files*/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files*/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files*/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
+        'C:/Program Files*/Microsoft Visual Studio */vc/bin',
+    ]
+    for pattern in patterns:
+        matches = sorted(glob.glob(pattern))
+        if len(matches):
+            return matches[-1]
+    return None
+
+#----------------------------------------------------------------------------
+
+def _get_mangled_gpu_name():
+    name = torch.cuda.get_device_name().lower()
+    out = []
+    for c in name:
+        if re.match('[a-z0-9_-]+', c):
+            out.append(c)
+        else:
+            out.append('-')
+    return ''.join(out)
+
+#----------------------------------------------------------------------------
+# Main entry point for compiling and loading C++/CUDA plugins.
+
+_cached_plugins = dict()
+
+def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs):
+    assert verbosity in ['none', 'brief', 'full']
+    if headers is None:
+        headers = []
+    if source_dir is not None:
+        sources = [os.path.join(source_dir, fname) for fname in sources]
+        headers = [os.path.join(source_dir, fname) for fname in headers]
+
+    # Already cached?
+    if module_name in _cached_plugins:
+        return _cached_plugins[module_name]
+
+    # Print status.
+    if verbosity == 'full':
+        print(f'Setting up PyTorch plugin "{module_name}"...')
+    elif verbosity == 'brief':
+        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
+    verbose_build = (verbosity == 'full')
+
+    # Compile and load.
+    try: # pylint: disable=too-many-nested-blocks
+        # Make sure we can find the necessary compiler binaries.
+        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
+            compiler_bindir = _find_compiler_bindir()
+            if compiler_bindir is None:
+                raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
+            os.environ['PATH'] += ';' + compiler_bindir
+
+        # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either
+        # break the build or unnecessarily restrict what's available to nvcc.
+        # Unset it to let nvcc decide based on what's available on the
+        # machine.
+        os.environ['TORCH_CUDA_ARCH_LIST'] = ''
+
+        # Incremental build md5sum trickery.  Copies all the input source files
+        # into a cached build directory under a combined md5 digest of the input
+        # source files.  Copying is done only if the combined digest has changed.
+        # This keeps input file timestamps and filenames the same as in previous
+        # extension builds, allowing for fast incremental rebuilds.
+        #
+        # This optimization is done only in case all the source files reside in
+        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
+        # environment variable is set (we take this as a signal that the user
+        # actually cares about this.)
+        #
+        # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work
+        # around the *.cu dependency bug in ninja config.
+        #
+        all_source_files = sorted(sources + headers)
+        all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files)
+        if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ):
+
+            # Compute combined hash digest for all source files.
+            hash_md5 = hashlib.md5()
+            for src in all_source_files:
+                with open(src, 'rb') as f:
+                    hash_md5.update(f.read())
+
+            # Select cached build directory name.
+            source_digest = hash_md5.hexdigest()
+            build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
+            cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}')
+
+            if not os.path.isdir(cached_build_dir):
+                tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}'
+                os.makedirs(tmpdir)
+                for src in all_source_files:
+                    shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src)))
+                try:
+                    os.replace(tmpdir, cached_build_dir) # atomic
+                except OSError:
+                    # source directory already exists, delete tmpdir and its contents.
+                    shutil.rmtree(tmpdir)
+                    if not os.path.isdir(cached_build_dir): raise
+
+            # Compile.
+            cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources]
+            torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir,
+                verbose=verbose_build, sources=cached_sources, **build_kwargs)
+        else:
+            torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
+
+        # Load.
+        module = importlib.import_module(module_name)
+
+    except:
+        if verbosity == 'brief':
+            print('Failed!')
+        raise
+
+    # Print status and add to cache dict.
+    if verbosity == 'full':
+        print(f'Done setting up PyTorch plugin "{module_name}".')
+    elif verbosity == 'brief':
+        print('Done.')
+    _cached_plugins[module_name] = module
+    return module
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/misc.py b/models/stylegan3/torch_utils/misc.py
new file mode 100644
index 0000000..23d9c95
--- /dev/null
+++ b/models/stylegan3/torch_utils/misc.py
@@ -0,0 +1,267 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import re
+import contextlib
+import numpy as np
+import torch
+import warnings
+import dnnlib
+
+#----------------------------------------------------------------------------
+# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
+# same constant is used multiple times.
+
+_constant_cache = dict()
+
+def constant(value, shape=None, dtype=None, device=None, memory_format=None):
+    value = np.asarray(value)
+    if shape is not None:
+        shape = tuple(shape)
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+    if device is None:
+        device = torch.device('cpu')
+    if memory_format is None:
+        memory_format = torch.contiguous_format
+
+    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
+    tensor = _constant_cache.get(key, None)
+    if tensor is None:
+        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
+        if shape is not None:
+            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
+        tensor = tensor.contiguous(memory_format=memory_format)
+        _constant_cache[key] = tensor
+    return tensor
+
+#----------------------------------------------------------------------------
+# Replace NaN/Inf with specified numerical values.
+
+try:
+    nan_to_num = torch.nan_to_num # 1.8.0a0
+except AttributeError:
+    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
+        assert isinstance(input, torch.Tensor)
+        if posinf is None:
+            posinf = torch.finfo(input.dtype).max
+        if neginf is None:
+            neginf = torch.finfo(input.dtype).min
+        assert nan == 0
+        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
+
+#----------------------------------------------------------------------------
+# Symbolic assert.
+
+try:
+    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
+except AttributeError:
+    symbolic_assert = torch.Assert # 1.7.0
+
+#----------------------------------------------------------------------------
+# Context manager to temporarily suppress known warnings in torch.jit.trace().
+# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672
+
+@contextlib.contextmanager
+def suppress_tracer_warnings():
+    flt = ('ignore', None, torch.jit.TracerWarning, None, 0)
+    warnings.filters.insert(0, flt)
+    yield
+    warnings.filters.remove(flt)
+
+#----------------------------------------------------------------------------
+# Assert that the shape of a tensor matches the given list of integers.
+# None indicates that the size of a dimension is allowed to vary.
+# Performs symbolic assertion when used in torch.jit.trace().
+
+def assert_shape(tensor, ref_shape):
+    #使用ndim报错：AttributeError: 'list' object has no attribute 'ndim'
+    if tensor.ndim != len(ref_shape):
+        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
+    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
+        if ref_size is None:
+            pass
+        elif isinstance(ref_size, torch.Tensor):
+            with suppress_tracer_warnings(): # as_tensor results are registered as constants
+                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
+        elif isinstance(size, torch.Tensor):
+            with suppress_tracer_warnings(): # as_tensor results are registered as constants
+                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
+        elif size != ref_size:
+            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
+
+#----------------------------------------------------------------------------
+# Function decorator that calls torch.autograd.profiler.record_function().
+
+def profiled_function(fn):
+    def decorator(*args, **kwargs):
+        with torch.autograd.profiler.record_function(fn.__name__):
+            return fn(*args, **kwargs)
+    decorator.__name__ = fn.__name__
+    return decorator
+
+#----------------------------------------------------------------------------
+# Sampler for torch.utils.data.DataLoader that loops over the dataset
+# indefinitely, shuffling items as it goes.
+
+class InfiniteSampler(torch.utils.data.Sampler):
+    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
+        assert len(dataset) > 0
+        assert num_replicas > 0
+        assert 0 <= rank < num_replicas
+        assert 0 <= window_size <= 1
+        super().__init__(dataset)
+        self.dataset = dataset
+        self.rank = rank
+        self.num_replicas = num_replicas
+        self.shuffle = shuffle
+        self.seed = seed
+        self.window_size = window_size
+
+    def __iter__(self):
+        order = np.arange(len(self.dataset))
+        rnd = None
+        window = 0
+        if self.shuffle:
+            rnd = np.random.RandomState(self.seed)
+            rnd.shuffle(order)
+            window = int(np.rint(order.size * self.window_size))
+
+        idx = 0
+        while True:
+            i = idx % order.size
+            if idx % self.num_replicas == self.rank:
+                yield order[i]
+            if window >= 2:
+                j = (i - rnd.randint(window)) % order.size
+                order[i], order[j] = order[j], order[i]
+            idx += 1
+
+#----------------------------------------------------------------------------
+# Utilities for operating with torch.nn.Module parameters and buffers.
+
+def params_and_buffers(module):
+    assert isinstance(module, torch.nn.Module)
+    return list(module.parameters()) + list(module.buffers())
+
+def named_params_and_buffers(module):
+    assert isinstance(module, torch.nn.Module)
+    return list(module.named_parameters()) + list(module.named_buffers())
+
+def copy_params_and_buffers(src_module, dst_module, require_all=False):
+    assert isinstance(src_module, torch.nn.Module)
+    assert isinstance(dst_module, torch.nn.Module)
+    src_tensors = dict(named_params_and_buffers(src_module))
+    for name, tensor in named_params_and_buffers(dst_module):
+        assert (name in src_tensors) or (not require_all)
+        if name in src_tensors:
+            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
+
+#----------------------------------------------------------------------------
+# Context manager for easily enabling/disabling DistributedDataParallel
+# synchronization.
+
+@contextlib.contextmanager
+def ddp_sync(module, sync):
+    assert isinstance(module, torch.nn.Module)
+    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
+        yield
+    else:
+        with module.no_sync():
+            yield
+
+#----------------------------------------------------------------------------
+# Check DistributedDataParallel consistency across processes.
+
+def check_ddp_consistency(module, ignore_regex=None):
+    assert isinstance(module, torch.nn.Module)
+    for name, tensor in named_params_and_buffers(module):
+        fullname = type(module).__name__ + '.' + name
+        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
+            continue
+        tensor = tensor.detach()
+        if tensor.is_floating_point():
+            tensor = nan_to_num(tensor)
+        other = tensor.clone()
+        torch.distributed.broadcast(tensor=other, src=0)
+        assert (tensor == other).all(), fullname
+
+#----------------------------------------------------------------------------
+# Print summary table of module hierarchy.
+
+def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
+    assert isinstance(module, torch.nn.Module)
+    assert not isinstance(module, torch.jit.ScriptModule)
+    assert isinstance(inputs, (tuple, list))
+
+    # Register hooks.
+    entries = []
+    nesting = [0]
+    def pre_hook(_mod, _inputs):
+        nesting[0] += 1
+    def post_hook(mod, _inputs, outputs):
+        nesting[0] -= 1
+        if nesting[0] <= max_nesting:
+            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
+            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
+            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
+    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
+    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
+
+    # Run module.
+    outputs = module(*inputs)
+    for hook in hooks:
+        hook.remove()
+
+    # Identify unique outputs, parameters, and buffers.
+    tensors_seen = set()
+    for e in entries:
+        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
+        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
+        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
+        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
+
+    # Filter out redundant entries.
+    if skip_redundant:
+        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
+
+    # Construct table.
+    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
+    rows += [['---'] * len(rows[0])]
+    param_total = 0
+    buffer_total = 0
+    submodule_names = {mod: name for name, mod in module.named_modules()}
+    for e in entries:
+        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
+        param_size = sum(t.numel() for t in e.unique_params)
+        buffer_size = sum(t.numel() for t in e.unique_buffers)
+        output_shapes = [str(list(t.shape)) for t in e.outputs]
+        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
+        rows += [[
+            name + (':0' if len(e.outputs) >= 2 else ''),
+            str(param_size) if param_size else '-',
+            str(buffer_size) if buffer_size else '-',
+            (output_shapes + ['-'])[0],
+            (output_dtypes + ['-'])[0],
+        ]]
+        for idx in range(1, len(e.outputs)):
+            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
+        param_total += param_size
+        buffer_total += buffer_size
+    rows += [['---'] * len(rows[0])]
+    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
+
+    # Print table.
+    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
+    print()
+    for row in rows:
+        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
+    print()
+    return outputs
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/__init__.py b/models/stylegan3/torch_utils/ops/__init__.py
new file mode 100644
index 0000000..939e7c6
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+# empty
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diff --git a/models/stylegan3/torch_utils/ops/bias_act.cpp b/models/stylegan3/torch_utils/ops/bias_act.cpp
new file mode 100644
index 0000000..3adaeee
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/bias_act.cpp
@@ -0,0 +1,99 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "bias_act.h"
+
+//------------------------------------------------------------------------
+
+static bool has_same_layout(torch::Tensor x, torch::Tensor y)
+{
+    if (x.dim() != y.dim())
+        return false;
+    for (int64_t i = 0; i < x.dim(); i++)
+    {
+        if (x.size(i) != y.size(i))
+            return false;
+        if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
+            return false;
+    }
+    return true;
+}
+
+//------------------------------------------------------------------------
+
+static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
+{
+    // Validate arguments.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
+    TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
+    TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
+    TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
+    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
+    TORCH_CHECK(b.dim() == 1, "b must have rank 1");
+    TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
+    TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
+    TORCH_CHECK(grad >= 0, "grad must be non-negative");
+
+    // Validate layout.
+    TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
+    TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
+    TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
+    TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
+    TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
+
+    // Create output tensor.
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+    torch::Tensor y = torch::empty_like(x);
+    TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
+
+    // Initialize CUDA kernel parameters.
+    bias_act_kernel_params p;
+    p.x     = x.data_ptr();
+    p.b     = (b.numel()) ? b.data_ptr() : NULL;
+    p.xref  = (xref.numel()) ? xref.data_ptr() : NULL;
+    p.yref  = (yref.numel()) ? yref.data_ptr() : NULL;
+    p.dy    = (dy.numel()) ? dy.data_ptr() : NULL;
+    p.y     = y.data_ptr();
+    p.grad  = grad;
+    p.act   = act;
+    p.alpha = alpha;
+    p.gain  = gain;
+    p.clamp = clamp;
+    p.sizeX = (int)x.numel();
+    p.sizeB = (int)b.numel();
+    p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
+
+    // Choose CUDA kernel.
+    void* kernel;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
+    {
+        kernel = choose_bias_act_kernel<scalar_t>(p);
+    });
+    TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
+
+    // Launch CUDA kernel.
+    p.loopX = 4;
+    int blockSize = 4 * 32;
+    int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
+    void* args[] = {&p};
+    AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
+    return y;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("bias_act", &bias_act);
+}
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/bias_act.cu b/models/stylegan3/torch_utils/ops/bias_act.cu
new file mode 100644
index 0000000..ed1d16f
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/bias_act.cu
@@ -0,0 +1,173 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <c10/util/Half.h>
+#include "bias_act.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>     { typedef double scalar_t; };
+template <> struct InternalType<float>      { typedef float  scalar_t; };
+template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
+
+//------------------------------------------------------------------------
+// CUDA kernel.
+
+template <class T, int A>
+__global__ void bias_act_kernel(bias_act_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    int G                 = p.grad;
+    scalar_t alpha        = (scalar_t)p.alpha;
+    scalar_t gain         = (scalar_t)p.gain;
+    scalar_t clamp        = (scalar_t)p.clamp;
+    scalar_t one          = (scalar_t)1;
+    scalar_t two          = (scalar_t)2;
+    scalar_t expRange     = (scalar_t)80;
+    scalar_t halfExpRange = (scalar_t)40;
+    scalar_t seluScale    = (scalar_t)1.0507009873554804934193349852946;
+    scalar_t seluAlpha    = (scalar_t)1.6732632423543772848170429916717;
+
+    // Loop over elements.
+    int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x;
+    for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x)
+    {
+        // Load.
+        scalar_t x = (scalar_t)((const T*)p.x)[xi];
+        scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0;
+        scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0;
+        scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0;
+        scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one;
+        scalar_t yy = (gain != 0) ? yref / gain : 0;
+        scalar_t y = 0;
+
+        // Apply bias.
+        ((G == 0) ? x : xref) += b;
+
+        // linear
+        if (A == 1)
+        {
+            if (G == 0) y = x;
+            if (G == 1) y = x;
+        }
+
+        // relu
+        if (A == 2)
+        {
+            if (G == 0) y = (x > 0) ? x : 0;
+            if (G == 1) y = (yy > 0) ? x : 0;
+        }
+
+        // lrelu
+        if (A == 3)
+        {
+            if (G == 0) y = (x > 0) ? x : x * alpha;
+            if (G == 1) y = (yy > 0) ? x : x * alpha;
+        }
+
+        // tanh
+        if (A == 4)
+        {
+            if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); }
+            if (G == 1) y = x * (one - yy * yy);
+            if (G == 2) y = x * (one - yy * yy) * (-two * yy);
+        }
+
+        // sigmoid
+        if (A == 5)
+        {
+            if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one);
+            if (G == 1) y = x * yy * (one - yy);
+            if (G == 2) y = x * yy * (one - yy) * (one - two * yy);
+        }
+
+        // elu
+        if (A == 6)
+        {
+            if (G == 0) y = (x >= 0) ? x : exp(x) - one;
+            if (G == 1) y = (yy >= 0) ? x : x * (yy + one);
+            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one);
+        }
+
+        // selu
+        if (A == 7)
+        {
+            if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one);
+            if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha);
+            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha);
+        }
+
+        // softplus
+        if (A == 8)
+        {
+            if (G == 0) y = (x > expRange) ? x : log(exp(x) + one);
+            if (G == 1) y = x * (one - exp(-yy));
+            if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); }
+        }
+
+        // swish
+        if (A == 9)
+        {
+            if (G == 0)
+                y = (x < -expRange) ? 0 : x / (exp(-x) + one);
+            else
+            {
+                scalar_t c = exp(xref);
+                scalar_t d = c + one;
+                if (G == 1)
+                    y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d);
+                else
+                    y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d);
+                yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain;
+            }
+        }
+
+        // Apply gain.
+        y *= gain * dy;
+
+        // Clamp.
+        if (clamp >= 0)
+        {
+            if (G == 0)
+                y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp;
+            else
+                y = (yref > -clamp & yref < clamp) ? y : 0;
+        }
+
+        // Store.
+        ((T*)p.y)[xi] = (T)y;
+    }
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p)
+{
+    if (p.act == 1) return (void*)bias_act_kernel<T, 1>;
+    if (p.act == 2) return (void*)bias_act_kernel<T, 2>;
+    if (p.act == 3) return (void*)bias_act_kernel<T, 3>;
+    if (p.act == 4) return (void*)bias_act_kernel<T, 4>;
+    if (p.act == 5) return (void*)bias_act_kernel<T, 5>;
+    if (p.act == 6) return (void*)bias_act_kernel<T, 6>;
+    if (p.act == 7) return (void*)bias_act_kernel<T, 7>;
+    if (p.act == 8) return (void*)bias_act_kernel<T, 8>;
+    if (p.act == 9) return (void*)bias_act_kernel<T, 9>;
+    return NULL;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template void* choose_bias_act_kernel<double>       (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel<float>        (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel<c10::Half>    (const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/bias_act.h b/models/stylegan3/torch_utils/ops/bias_act.h
new file mode 100644
index 0000000..60b81c6
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/bias_act.h
@@ -0,0 +1,38 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct bias_act_kernel_params
+{
+    const void* x;      // [sizeX]
+    const void* b;      // [sizeB] or NULL
+    const void* xref;   // [sizeX] or NULL
+    const void* yref;   // [sizeX] or NULL
+    const void* dy;     // [sizeX] or NULL
+    void*       y;      // [sizeX]
+
+    int         grad;
+    int         act;
+    float       alpha;
+    float       gain;
+    float       clamp;
+
+    int         sizeX;
+    int         sizeB;
+    int         stepB;
+    int         loopX;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/bias_act.py b/models/stylegan3/torch_utils/ops/bias_act.py
new file mode 100644
index 0000000..b2b53d7
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/bias_act.py
@@ -0,0 +1,209 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom PyTorch ops for efficient bias and activation."""
+
+import os
+import numpy as np
+import torch
+import dnnlib
+
+from .. import custom_ops
+from .. import misc
+
+#----------------------------------------------------------------------------
+
+activation_funcs = {
+    'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
+    'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
+    'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
+    'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
+    'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
+    'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
+    'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
+    'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
+    'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
+}
+
+#----------------------------------------------------------------------------
+
+_plugin = None
+_null_tensor = torch.empty([0])
+
+def _init():
+    global _plugin
+    if _plugin is None:
+        _plugin = custom_ops.get_plugin(
+            module_name='bias_act_plugin',
+            sources=['bias_act.cpp', 'bias_act.cu'],
+            headers=['bias_act.h'],
+            source_dir=os.path.dirname(__file__),
+            extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'],
+        )
+    return True
+
+#----------------------------------------------------------------------------
+
+def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
+    r"""Fused bias and activation function.
+
+    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
+    and scales the result by `gain`. Each of the steps is optional. In most cases,
+    the fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports first and second order gradients,
+    but not third order gradients.
+
+    Args:
+        x:      Input activation tensor. Can be of any shape.
+        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
+                as `x`. The shape must be known, and it must match the dimension of `x`
+                corresponding to `dim`.
+        dim:    The dimension in `x` corresponding to the elements of `b`.
+                The value of `dim` is ignored if `b` is not specified.
+        act:    Name of the activation function to evaluate, or `"linear"` to disable.
+                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
+                See `activation_funcs` for a full list. `None` is not allowed.
+        alpha:  Shape parameter for the activation function, or `None` to use the default.
+        gain:   Scaling factor for the output tensor, or `None` to use default.
+                See `activation_funcs` for the default scaling of each activation function.
+                If unsure, consider specifying 1.
+        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
+                the clamping (default).
+        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
+
+    Returns:
+        Tensor of the same shape and datatype as `x`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
+    return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
+    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert clamp is None or clamp >= 0
+    spec = activation_funcs[act]
+    alpha = float(alpha if alpha is not None else spec.def_alpha)
+    gain = float(gain if gain is not None else spec.def_gain)
+    clamp = float(clamp if clamp is not None else -1)
+
+    # Add bias.
+    if b is not None:
+        assert isinstance(b, torch.Tensor) and b.ndim == 1
+        assert 0 <= dim < x.ndim
+        assert b.shape[0] == x.shape[dim]
+        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
+
+    # Evaluate activation function.
+    alpha = float(alpha)
+    x = spec.func(x, alpha=alpha)
+
+    # Scale by gain.
+    gain = float(gain)
+    if gain != 1:
+        x = x * gain
+
+    # Clamp.
+    if clamp >= 0:
+        x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
+    return x
+
+#----------------------------------------------------------------------------
+
+_bias_act_cuda_cache = dict()
+
+def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
+    """Fast CUDA implementation of `bias_act()` using custom ops.
+    """
+    # Parse arguments.
+    assert clamp is None or clamp >= 0
+    spec = activation_funcs[act]
+    alpha = float(alpha if alpha is not None else spec.def_alpha)
+    gain = float(gain if gain is not None else spec.def_gain)
+    clamp = float(clamp if clamp is not None else -1)
+
+    # Lookup from cache.
+    key = (dim, act, alpha, gain, clamp)
+    if key in _bias_act_cuda_cache:
+        return _bias_act_cuda_cache[key]
+
+    # Forward op.
+    class BiasActCuda(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, b): # pylint: disable=arguments-differ
+            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format
+            x = x.contiguous(memory_format=ctx.memory_format)
+            b = b.contiguous() if b is not None else _null_tensor
+            y = x
+            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
+                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
+            ctx.save_for_backward(
+                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
+                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
+                y if 'y' in spec.ref else _null_tensor)
+            return y
+
+        @staticmethod
+        def backward(ctx, dy): # pylint: disable=arguments-differ
+            dy = dy.contiguous(memory_format=ctx.memory_format)
+            x, b, y = ctx.saved_tensors
+            dx = None
+            db = None
+
+            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
+                dx = dy
+                if act != 'linear' or gain != 1 or clamp >= 0:
+                    dx = BiasActCudaGrad.apply(dy, x, b, y)
+
+            if ctx.needs_input_grad[1]:
+                db = dx.sum([i for i in range(dx.ndim) if i != dim])
+
+            return dx, db
+
+    # Backward op.
+    class BiasActCudaGrad(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
+            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format
+            dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
+            ctx.save_for_backward(
+                dy if spec.has_2nd_grad else _null_tensor,
+                x, b, y)
+            return dx
+
+        @staticmethod
+        def backward(ctx, d_dx): # pylint: disable=arguments-differ
+            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
+            dy, x, b, y = ctx.saved_tensors
+            d_dy = None
+            d_x = None
+            d_b = None
+            d_y = None
+
+            if ctx.needs_input_grad[0]:
+                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
+
+            if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
+                d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
+
+            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
+                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
+
+            return d_dy, d_x, d_b, d_y
+
+    # Add to cache.
+    _bias_act_cuda_cache[key] = BiasActCuda
+    return BiasActCuda
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/conv2d_gradfix.py b/models/stylegan3/torch_utils/ops/conv2d_gradfix.py
new file mode 100644
index 0000000..156b6b2
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/conv2d_gradfix.py
@@ -0,0 +1,203 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom replacement for `torch.nn.functional.conv2d` that supports
+arbitrarily high order gradients with zero performance penalty."""
+
+import contextlib
+import torch
+from pkg_resources import parse_version
+
+# pylint: disable=redefined-builtin
+# pylint: disable=arguments-differ
+# pylint: disable=protected-access
+
+#----------------------------------------------------------------------------
+
+enabled = False                     # Enable the custom op by setting this to true.
+weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
+_use_pytorch_1_11_api = parse_version(torch.__version__) >= parse_version('1.11.0a') # Allow prerelease builds of 1.11
+
+@contextlib.contextmanager
+def no_weight_gradients(disable=True):
+    global weight_gradients_disabled
+    old = weight_gradients_disabled
+    if disable:
+        weight_gradients_disabled = True
+    yield
+    weight_gradients_disabled = old
+
+#----------------------------------------------------------------------------
+
+def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
+    if _should_use_custom_op(input):
+        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
+    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
+
+def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
+    if _should_use_custom_op(input):
+        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
+    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
+
+#----------------------------------------------------------------------------
+
+def _should_use_custom_op(input):
+    assert isinstance(input, torch.Tensor)
+    if (not enabled) or (not torch.backends.cudnn.enabled):
+        return False
+    if _use_pytorch_1_11_api:
+        # The work-around code doesn't work on PyTorch 1.11.0 onwards
+        return False
+    if input.device.type != 'cuda':
+        return False
+    return True
+
+def _tuple_of_ints(xs, ndim):
+    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
+    assert len(xs) == ndim
+    assert all(isinstance(x, int) for x in xs)
+    return xs
+
+#----------------------------------------------------------------------------
+
+_conv2d_gradfix_cache = dict()
+_null_tensor = torch.empty([0])
+
+def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
+    # Parse arguments.
+    ndim = 2
+    weight_shape = tuple(weight_shape)
+    stride = _tuple_of_ints(stride, ndim)
+    padding = _tuple_of_ints(padding, ndim)
+    output_padding = _tuple_of_ints(output_padding, ndim)
+    dilation = _tuple_of_ints(dilation, ndim)
+
+    # Lookup from cache.
+    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
+    if key in _conv2d_gradfix_cache:
+        return _conv2d_gradfix_cache[key]
+
+    # Validate arguments.
+    assert groups >= 1
+    assert len(weight_shape) == ndim + 2
+    assert all(stride[i] >= 1 for i in range(ndim))
+    assert all(padding[i] >= 0 for i in range(ndim))
+    assert all(dilation[i] >= 0 for i in range(ndim))
+    if not transpose:
+        assert all(output_padding[i] == 0 for i in range(ndim))
+    else: # transpose
+        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
+
+    # Helpers.
+    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
+    def calc_output_padding(input_shape, output_shape):
+        if transpose:
+            return [0, 0]
+        return [
+            input_shape[i + 2]
+            - (output_shape[i + 2] - 1) * stride[i]
+            - (1 - 2 * padding[i])
+            - dilation[i] * (weight_shape[i + 2] - 1)
+            for i in range(ndim)
+        ]
+
+    # Forward & backward.
+    class Conv2d(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, input, weight, bias):
+            assert weight.shape == weight_shape
+            ctx.save_for_backward(
+                input if weight.requires_grad else _null_tensor,
+                weight if input.requires_grad else _null_tensor,
+            )
+            ctx.input_shape = input.shape
+
+            # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere).
+            if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0):
+                a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1])
+                b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1)
+                c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2)
+                c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1)
+                c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3)
+                return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
+
+            # General case => cuDNN.
+            if transpose:
+                return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
+            return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            input, weight = ctx.saved_tensors
+            input_shape = ctx.input_shape
+            grad_input = None
+            grad_weight = None
+            grad_bias = None
+
+            if ctx.needs_input_grad[0]:
+                p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape)
+                op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
+                grad_input = op.apply(grad_output, weight, None)
+                assert grad_input.shape == input_shape
+
+            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
+                grad_weight = Conv2dGradWeight.apply(grad_output, input)
+                assert grad_weight.shape == weight_shape
+
+            if ctx.needs_input_grad[2]:
+                grad_bias = grad_output.sum([0, 2, 3])
+
+            return grad_input, grad_weight, grad_bias
+
+    # Gradient with respect to the weights.
+    class Conv2dGradWeight(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, grad_output, input):
+            ctx.save_for_backward(
+                grad_output if input.requires_grad else _null_tensor,
+                input if grad_output.requires_grad else _null_tensor,
+            )
+            ctx.grad_output_shape = grad_output.shape
+            ctx.input_shape = input.shape
+
+            # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere).
+            if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0):
+                a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
+                b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
+                c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape)
+                return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
+
+            # General case => cuDNN.
+            name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight'
+            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
+            return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
+
+        @staticmethod
+        def backward(ctx, grad2_grad_weight):
+            grad_output, input = ctx.saved_tensors
+            grad_output_shape = ctx.grad_output_shape
+            input_shape = ctx.input_shape
+            grad2_grad_output = None
+            grad2_input = None
+
+            if ctx.needs_input_grad[0]:
+                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
+                assert grad2_grad_output.shape == grad_output_shape
+
+            if ctx.needs_input_grad[1]:
+                p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape)
+                op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
+                grad2_input = op.apply(grad_output, grad2_grad_weight, None)
+                assert grad2_input.shape == input_shape
+
+            return grad2_grad_output, grad2_input
+
+    _conv2d_gradfix_cache[key] = Conv2d
+    return Conv2d
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/conv2d_resample.py b/models/stylegan3/torch_utils/ops/conv2d_resample.py
new file mode 100644
index 0000000..5eb5877
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/conv2d_resample.py
@@ -0,0 +1,143 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""2D convolution with optional up/downsampling."""
+
+import torch
+
+from .. import misc
+from . import conv2d_gradfix
+from . import upfirdn2d
+from .upfirdn2d import _parse_padding
+from .upfirdn2d import _get_filter_size
+
+#----------------------------------------------------------------------------
+
+def _get_weight_shape(w):
+    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
+        shape = [int(sz) for sz in w.shape]
+    misc.assert_shape(w, shape)
+    return shape
+
+#----------------------------------------------------------------------------
+
+def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
+    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
+    """
+    _out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w)
+
+    # Flip weight if requested.
+    # Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
+    if not flip_weight and (kw > 1 or kh > 1):
+        w = w.flip([2, 3])
+
+    # Execute using conv2d_gradfix.
+    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
+    return op(x, w, stride=stride, padding=padding, groups=groups)
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
+    r"""2D convolution with optional up/downsampling.
+
+    Padding is performed only once at the beginning, not between the operations.
+
+    Args:
+        x:              Input tensor of shape
+                        `[batch_size, in_channels, in_height, in_width]`.
+        w:              Weight tensor of shape
+                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
+        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
+                        calling upfirdn2d.setup_filter(). None = identity (default).
+        up:             Integer upsampling factor (default: 1).
+        down:           Integer downsampling factor (default: 1).
+        padding:        Padding with respect to the upsampled image. Can be a single number
+                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                        (default: 0).
+        groups:         Split input channels into N groups (default: 1).
+        flip_weight:    False = convolution, True = correlation (default: True).
+        flip_filter:    False = convolution, True = correlation (default: False).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
+    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
+    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
+    assert isinstance(up, int) and (up >= 1)
+    assert isinstance(down, int) and (down >= 1)
+    assert isinstance(groups, int) and (groups >= 1)
+    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
+    fw, fh = _get_filter_size(f)
+    px0, px1, py0, py1 = _parse_padding(padding)
+
+    # Adjust padding to account for up/downsampling.
+    if up > 1:
+        px0 += (fw + up - 1) // 2
+        px1 += (fw - up) // 2
+        py0 += (fh + up - 1) // 2
+        py1 += (fh - up) // 2
+    if down > 1:
+        px0 += (fw - down + 1) // 2
+        px1 += (fw - down) // 2
+        py0 += (fh - down + 1) // 2
+        py1 += (fh - down) // 2
+
+    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
+    if kw == 1 and kh == 1 and (down > 1 and up == 1):
+        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        return x
+
+    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
+    if kw == 1 and kh == 1 and (up > 1 and down == 1):
+        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
+        return x
+
+    # Fast path: downsampling only => use strided convolution.
+    if down > 1 and up == 1:
+        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
+        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
+        return x
+
+    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
+    if up > 1:
+        if groups == 1:
+            w = w.transpose(0, 1)
+        else:
+            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
+            w = w.transpose(1, 2)
+            w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
+        px0 -= kw - 1
+        px1 -= kw - up
+        py0 -= kh - 1
+        py1 -= kh - up
+        pxt = max(min(-px0, -px1), 0)
+        pyt = max(min(-py0, -py1), 0)
+        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
+        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
+        if down > 1:
+            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
+        return x
+
+    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
+    if up == 1 and down == 1:
+        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
+            return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
+
+    # Fallback: Generic reference implementation.
+    x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
+    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
+    if down > 1:
+        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
+    return x
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/filtered_lrelu.cpp b/models/stylegan3/torch_utils/ops/filtered_lrelu.cpp
new file mode 100644
index 0000000..ff4149b
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/filtered_lrelu.cpp
@@ -0,0 +1,300 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "filtered_lrelu.h"
+
+//------------------------------------------------------------------------
+
+static std::tuple<torch::Tensor, torch::Tensor, int> filtered_lrelu(
+    torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
+    int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
+{
+    // Set CUDA device.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+
+    // Validate arguments.
+    TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
+    TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
+    TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
+    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
+    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
+    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
+    TORCH_CHECK(x.numel() > 0, "x is empty");
+    TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
+    TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
+    TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
+    TORCH_CHECK(fu.numel() > 0, "fu is empty");
+    TORCH_CHECK(fd.numel() > 0, "fd is empty");
+    TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
+    TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
+
+    // Figure out how much shared memory is available on the device.
+    int maxSharedBytes = 0;
+    AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
+    int sharedKB = maxSharedBytes >> 10;
+
+    // Populate enough launch parameters to check if a CUDA kernel exists.
+    filtered_lrelu_kernel_params p;
+    p.up      = up;
+    p.down    = down;
+    p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
+    p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
+    filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel<float, int32_t, false, false>(p, sharedKB);
+    if (!test_spec.exec)
+    {
+        // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
+        return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
+    }
+
+    // Input/output element size.
+    int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
+
+    // Input sizes.
+    int64_t xw = (int)x.size(3);
+    int64_t xh = (int)x.size(2);
+    int64_t fut_w = (int)fu.size(-1) - 1;
+    int64_t fut_h = (int)fu.size(0)  - 1;
+    int64_t fdt_w = (int)fd.size(-1) - 1;
+    int64_t fdt_h = (int)fd.size(0)  - 1;
+
+    // Logical size of upsampled buffer.
+    int64_t cw = xw * up + (px0 + px1) - fut_w;
+    int64_t ch = xh * up + (py0 + py1) - fut_h;
+    TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
+    TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
+
+    // Compute output size and allocate.
+    int64_t yw = (cw - fdt_w + (down - 1)) / down;
+    int64_t yh = (ch - fdt_h + (down - 1)) / down;
+    TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
+    TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
+    torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
+
+    // Allocate sign tensor.
+    torch::Tensor so;
+    torch::Tensor s = si;
+    bool readSigns = !!s.numel();
+    int64_t sw_active = 0; // Active width of sign tensor.
+    if (writeSigns)
+    {
+        sw_active = yw * down - (down - 1) + fdt_w;     // Active width in elements.
+        int64_t sh = yh * down - (down - 1) + fdt_h;    // Height = active height.
+        int64_t sw = (sw_active + 15) & ~15;            // Width  = active width in elements, rounded up to multiple of 16.
+        TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
+        s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
+    }
+    else if (readSigns)
+        sw_active = s.size(3) << 2;
+
+    // Validate sign tensor if in use.
+    if (readSigns || writeSigns)
+    {
+        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
+        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
+        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
+        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
+        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
+        TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
+    }
+
+    // Populate rest of CUDA kernel parameters.
+    p.x         = x.data_ptr();
+    p.y         = y.data_ptr();
+    p.b         = b.data_ptr();
+    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
+    p.fu        = fu.data_ptr<float>();
+    p.fd        = fd.data_ptr<float>();
+    p.pad0      = make_int2(px0, py0);
+    p.gain      = gain;
+    p.slope     = slope;
+    p.clamp     = clamp;
+    p.flip      = (flip_filters) ? 1 : 0;
+    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
+    p.yShape    = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
+    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
+    p.sOfs      = make_int2(sx, sy);
+    p.swLimit   = (sw_active + 3) >> 2; // Rounded up to bytes.
+
+    // x, y, b strides are in bytes.
+    p.xStride   = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
+    p.yStride   = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
+    p.bStride   = sz * b.stride(0);
+
+    // fu, fd strides are in elements.
+    p.fuStride  = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
+    p.fdStride  = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
+
+    // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
+    bool index64b = false;
+    if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
+    if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
+    if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) >  INT_MAX) index64b = true;
+    if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
+    if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) >  INT_MAX) index64b = true;
+    if (s.numel() > INT_MAX) index64b = true;
+
+    // Choose CUDA kernel.
+    filtered_lrelu_kernel_spec spec = { 0 };
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
+    {
+        if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
+        {
+            // Choose kernel based on index type, datatype and sign read/write modes.
+            if      (!index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, true,  false>(p, sharedKB);
+            else if (!index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, true >(p, sharedKB);
+            else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, false>(p, sharedKB);
+            else if ( index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, true,  false>(p, sharedKB);
+            else if ( index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, true >(p, sharedKB);
+            else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, false>(p, sharedKB);
+        }
+    });
+    TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
+
+    // Launch CUDA kernel.
+    void* args[] = {&p};
+    int bx = spec.numWarps * 32;
+    int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
+    int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
+    int gz = p.yShape.z * p.yShape.w;
+
+    // Repeat multiple horizontal tiles in a CTA?
+    if (spec.xrep)
+    {
+        p.tilesXrep = spec.xrep;
+        p.tilesXdim = gx;
+
+        gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
+        std::swap(gx, gy);
+    }
+    else
+    {
+        p.tilesXrep = 0;
+        p.tilesXdim = 0;
+    }
+
+    // Launch filter setup kernel.
+    AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
+
+    // Copy kernels to constant memory.
+    if      ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<true,  false>(at::cuda::getCurrentCUDAStream())));
+    else if (!writeSigns &&  readSigns) AT_CUDA_CHECK((copy_filters<false, true >(at::cuda::getCurrentCUDAStream())));
+    else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<false, false>(at::cuda::getCurrentCUDAStream())));
+
+    // Set cache and shared memory configurations for main kernel.
+    AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
+    if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
+        AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
+    AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
+
+    // Launch main kernel.
+    const int maxSubGz = 65535; // CUDA maximum for block z dimension.
+    for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
+    {
+        p.blockZofs = zofs;
+        int subGz = std::min(maxSubGz, gz - zofs);
+        AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
+    }
+
+    // Done.
+    return std::make_tuple(y, so, 0);
+}
+
+//------------------------------------------------------------------------
+
+static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
+{
+    // Set CUDA device.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+
+    // Validate arguments.
+    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
+    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
+    TORCH_CHECK(x.numel() > 0, "x is empty");
+    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
+
+    // Output signs if we don't have sign input.
+    torch::Tensor so;
+    torch::Tensor s = si;
+    bool readSigns = !!s.numel();
+    if (writeSigns)
+    {
+        int64_t sw = x.size(3);
+        sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
+        s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
+    }
+
+    // Validate sign tensor if in use.
+    if (readSigns || writeSigns)
+    {
+        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
+        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
+        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
+        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
+        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
+        TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
+    }
+
+    // Initialize CUDA kernel parameters.
+    filtered_lrelu_act_kernel_params p;
+    p.x         = x.data_ptr();
+    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
+    p.gain      = gain;
+    p.slope     = slope;
+    p.clamp     = clamp;
+    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
+    p.xStride   = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
+    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
+    p.sOfs      = make_int2(sx, sy);
+
+    // Choose CUDA kernel.
+    void* func = 0;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
+    {
+        if (writeSigns)
+            func = choose_filtered_lrelu_act_kernel<scalar_t, true, false>();
+        else if (readSigns)
+            func = choose_filtered_lrelu_act_kernel<scalar_t, false, true>();
+        else
+            func = choose_filtered_lrelu_act_kernel<scalar_t, false, false>();
+    });
+    TORCH_CHECK(func, "internal error - CUDA kernel not found");
+
+    // Launch CUDA kernel.
+    void* args[] = {&p};
+    int bx = 128; // 4 warps per block.
+
+    // Logical size of launch = writeSigns ? p.s : p.x
+    uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
+    uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
+    uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
+    gx = (gx - 1) / bx + 1;
+
+    // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
+    const uint32_t gmax = 65535;
+    gy = std::min(gy, gmax);
+    gz = std::min(gz, gmax);
+
+    // Launch.
+    AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
+    return so;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("filtered_lrelu",      &filtered_lrelu);      // The whole thing.
+    m.def("filtered_lrelu_act_", &filtered_lrelu_act);  // Activation and sign tensor handling only. Modifies data tensor in-place.
+}
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/filtered_lrelu.cu b/models/stylegan3/torch_utils/ops/filtered_lrelu.cu
new file mode 100644
index 0000000..8e6f47f
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/filtered_lrelu.cu
@@ -0,0 +1,1284 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <c10/util/Half.h>
+#include "filtered_lrelu.h"
+#include <cstdint>
+
+//------------------------------------------------------------------------
+// Helpers.
+
+enum // Filter modes.
+{
+    MODE_SUSD = 0,  // Separable upsampling, separable downsampling.
+    MODE_FUSD = 1,  // Full upsampling, separable downsampling.
+    MODE_SUFD = 2,  // Separable upsampling, full downsampling.
+    MODE_FUFD = 3,  // Full upsampling, full downsampling.
+};
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>
+{
+    typedef double scalar_t; typedef double2 vec2_t; typedef double4 vec4_t;
+    __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_double2(0, 0); }
+    __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_double4(0, 0, 0, 0); }
+    __device__ __forceinline__ static double clamp(double x, double c) { return fmin(fmax(x, -c), c); }
+};
+template <> struct InternalType<float>
+{
+    typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t;
+    __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); }
+    __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); }
+    __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); }
+};
+template <> struct InternalType<c10::Half>
+{
+    typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t;
+    __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); }
+    __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); }
+    __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); }
+};
+
+#define MIN(A, B)       ((A) < (B) ? (A) : (B))
+#define MAX(A, B)       ((A) > (B) ? (A) : (B))
+#define CEIL_DIV(A, B) (((B)==1) ? (A) : \
+                        ((B)==2) ? ((int)((A)+1) >> 1) : \
+                        ((B)==4) ? ((int)((A)+3) >> 2) : \
+                        (((A) + ((A) > 0 ? (B) - 1 : 0)) / (B)))
+
+// This works only up to blocks of size 256 x 256 and for all N that are powers of two.
+template <int N> __device__ __forceinline__ void fast_div_mod(int& x, int& y, unsigned int i)
+{
+    if ((N & (N-1)) && N <= 256)
+        y = (i * ((1<<24)/N + 1)) >> 24; // Assumes N <= 256, i < N*256.
+    else
+        y = i/N;
+
+    x = i - y*N;
+}
+
+// Type cast stride before reading it.
+template <class T> __device__ __forceinline__ T get_stride(const int64_t& x)
+{
+    return *reinterpret_cast<const T*>(&x);
+}
+
+//------------------------------------------------------------------------
+// Filters, setup kernel, copying function.
+
+#define MAX_FILTER_SIZE 32
+
+// Combined up/down filter buffers so that transfer can be done with one copy.
+__device__              float g_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in global memory, written by setup kernel.
+__device__ __constant__ float c_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in constant memory, read by main kernel.
+
+// Accessors to combined buffers to index up/down filters individually.
+#define c_fu (c_fbuf)
+#define c_fd (c_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE)
+#define g_fu (g_fbuf)
+#define g_fd (g_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE)
+
+// Set up filters into global memory buffer.
+static __global__ void setup_filters_kernel(filtered_lrelu_kernel_params p)
+{
+    for (int idx = threadIdx.x; idx < MAX_FILTER_SIZE * MAX_FILTER_SIZE; idx += blockDim.x)
+    {
+        int x, y;
+        fast_div_mod<MAX_FILTER_SIZE>(x, y, idx);
+
+        int fu_x = p.flip ? x : (p.fuShape.x - 1 - x);
+        int fu_y = p.flip ? y : (p.fuShape.y - 1 - y);
+        if (p.fuShape.y > 0)
+            g_fu[idx] = (x >= p.fuShape.x || y >= p.fuShape.y) ? 0.0f : p.fu[fu_x * p.fuStride.x + fu_y * p.fuStride.y];
+        else
+            g_fu[idx] = (x >= p.fuShape.x || y > 0) ? 0.0f : p.fu[fu_x * p.fuStride.x];
+
+        int fd_x = p.flip ? x : (p.fdShape.x - 1 - x);
+        int fd_y = p.flip ? y : (p.fdShape.y - 1 - y);
+        if (p.fdShape.y > 0)
+            g_fd[idx] = (x >= p.fdShape.x || y >= p.fdShape.y) ? 0.0f : p.fd[fd_x * p.fdStride.x + fd_y * p.fdStride.y];
+        else
+            g_fd[idx] = (x >= p.fdShape.x || y > 0) ? 0.0f : p.fd[fd_x * p.fdStride.x];
+    }
+}
+
+// Host function to copy filters written by setup kernel into constant buffer for main kernel.
+template <bool, bool> static cudaError_t copy_filters(cudaStream_t stream)
+{
+    void* src = 0;
+    cudaError_t err = cudaGetSymbolAddress(&src, g_fbuf);
+    if (err) return err;
+    return cudaMemcpyToSymbolAsync(c_fbuf, src, 2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream);
+}
+
+//------------------------------------------------------------------------
+// Coordinate spaces:
+// - Relative to input tensor:      inX, inY, tileInX, tileInY
+// - Relative to input tile:        relInX, relInY, tileInW, tileInH
+// - Relative to upsampled tile:    relUpX, relUpY, tileUpW, tileUpH
+// - Relative to output tile:       relOutX, relOutY, tileOutW, tileOutH
+// - Relative to output tensor:     outX, outY, tileOutX, tileOutY
+//
+// Relationships between coordinate spaces:
+// - inX = tileInX + relInX
+// - inY = tileInY + relInY
+// - relUpX = relInX * up + phaseInX
+// - relUpY = relInY * up + phaseInY
+// - relUpX = relOutX * down
+// - relUpY = relOutY * down
+// - outX = tileOutX + relOutX
+// - outY = tileOutY + relOutY
+
+extern __shared__ char s_buf_raw[]; // When sharedKB <= 48, allocate shared memory statically inside the kernel, otherwise use the externally allocated shared memory buffer.
+
+template <class T, class index_t, int sharedKB, bool signWrite, bool signRead, int filterMode, int up, int fuSize, int down, int fdSize, int tileOutW, int tileOutH, int threadsPerBlock, bool enableXrep, bool enableWriteSkip>
+static __global__ void filtered_lrelu_kernel(filtered_lrelu_kernel_params p)
+{
+    // Check that we don't try to support non-existing filter modes.
+    static_assert(up   == 1 || up   == 2 || up   == 4, "only up=1, up=2, up=4 scales supported");
+    static_assert(down == 1 || down == 2 || down == 4, "only down=1, down=2, down=4 scales supported");
+    static_assert(fuSize >= up,   "upsampling filter size must be at least upsampling factor");
+    static_assert(fdSize >= down, "downsampling filter size must be at least downsampling factor");
+    static_assert(fuSize % up   == 0, "upsampling filter size must be divisible with upsampling factor");
+    static_assert(fdSize % down == 0, "downsampling filter size must be divisible with downsampling factor");
+    static_assert(fuSize <= MAX_FILTER_SIZE && fdSize <= MAX_FILTER_SIZE, "filter size greater than MAX_FILTER_SIZE");
+    static_assert(up   != 1 || (fuSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "up=1 supported only for 1x1 full filters");
+    static_assert(down != 1 || (fdSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "down=1 supported only for 1x1 full filters");
+    static_assert(!(up   == 4 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "full filters not supported for up=4");
+    static_assert(!(down == 4 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "full filters not supported for down=4");
+
+    // Static definitions.
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    typedef typename InternalType<T>::vec2_t vec2_t;
+    typedef typename InternalType<T>::vec4_t vec4_t;
+    const int tileUpW    = (tileOutW * down + (fdSize - 1) - (down - 1) + 3) & ~3;  // Upsampled tile width, rounded up to multiple of 4.
+    const int tileUpH    = tileOutH * down + (fdSize - 1) - (down - 1);             // Upsampled tile height.
+    const int tileInW    = CEIL_DIV(tileUpW  + (fuSize - 1), up);                   // Input tile width.
+    const int tileInH    = CEIL_DIV(tileUpH  + (fuSize - 1), up);                   // Input tile height.
+    const int tileUpH_up = CEIL_DIV(tileUpH, up) * up;                              // Upsampled tile height rounded up to a multiple of up.
+    const int tileInH_up = CEIL_DIV(tileUpH_up + (fuSize - 1), up);                 // For allocations only, to avoid shared memory read overruns with up=2 and up=4.
+
+    // Merge 1x1 downsampling into last upsampling step for upf1 and ups2.
+    const bool downInline = (down == 1) && ((up == 1 && filterMode == MODE_FUFD) || (up == 2 && filterMode == MODE_SUFD));
+
+    // Sizes of logical buffers.
+    const int szIn    = tileInH_up * tileInW;
+    const int szUpX   = tileInH_up * tileUpW;
+    const int szUpXY  = downInline ? 0 : (tileUpH * tileUpW);
+    const int szDownX = tileUpH * tileOutW;
+
+    // Sizes for shared memory arrays.
+    const int s_buf0_size_base =
+        (filterMode == MODE_SUSD) ? MAX(szIn, szUpXY) :
+        (filterMode == MODE_FUSD) ? MAX(szIn, szDownX) :
+        (filterMode == MODE_SUFD) ? MAX(szIn, szUpXY) :
+        (filterMode == MODE_FUFD) ? szIn :
+        -1;
+    const int s_buf1_size_base =
+        (filterMode == MODE_SUSD) ? MAX(szUpX, szDownX) :
+        (filterMode == MODE_FUSD) ? szUpXY :
+        (filterMode == MODE_SUFD) ? szUpX  :
+        (filterMode == MODE_FUFD) ? szUpXY :
+        -1;
+
+    // Ensure U128 alignment.
+    const int s_buf0_size = (s_buf0_size_base + 3) & ~3;
+    const int s_buf1_size = (s_buf1_size_base + 3) & ~3;
+
+    // Check at compile time that we don't use too much shared memory.
+    static_assert((s_buf0_size + s_buf1_size) * sizeof(scalar_t) <= (sharedKB << 10), "shared memory overflow");
+
+    // Declare shared memory arrays.
+    scalar_t* s_buf0;
+    scalar_t* s_buf1;
+    if (sharedKB <= 48)
+    {
+        // Allocate shared memory arrays here.
+        __shared__ scalar_t s_buf0_st[(sharedKB > 48) ? (1<<24) : (s_buf0_size + s_buf1_size)]; // Prevent launching if this isn't optimized away when unused.
+        s_buf0 = s_buf0_st;
+        s_buf1 = s_buf0 + s_buf0_size;
+    }
+    else
+    {
+        // Use the dynamically allocated shared memory array.
+        s_buf0 = (scalar_t*)s_buf_raw;
+        s_buf1 = s_buf0 + s_buf0_size;
+    }
+
+    // Pointers to the buffers.
+    scalar_t* s_tileIn;       // Input tile:                      [relInX * tileInH + relInY]
+    scalar_t* s_tileUpX;      // After horizontal upsampling:     [relInY * tileUpW + relUpX]
+    scalar_t* s_tileUpXY;     // After upsampling:                [relUpY * tileUpW + relUpX]
+    scalar_t* s_tileDownX;    // After horizontal downsampling:   [relUpY * tileOutW + relOutX]
+    if (filterMode == MODE_SUSD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpX   = s_buf1;
+        s_tileUpXY  = s_buf0;
+        s_tileDownX = s_buf1;
+    }
+    else if (filterMode == MODE_FUSD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpXY  = s_buf1;
+        s_tileDownX = s_buf0;
+    }
+    else if (filterMode == MODE_SUFD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpX   = s_buf1;
+        s_tileUpXY  = s_buf0;
+    }
+    else if (filterMode == MODE_FUFD)
+    {
+        s_tileIn    = s_buf0;
+        s_tileUpXY  = s_buf1;
+    }
+
+    // Allow large grids in z direction via per-launch offset.
+    int channelIdx = blockIdx.z + p.blockZofs;
+    int batchIdx = channelIdx / p.yShape.z;
+    channelIdx -= batchIdx * p.yShape.z;
+
+    // Offset to output feature map. In bytes.
+    index_t mapOfsOut = channelIdx * get_stride<index_t>(p.yStride.z) + batchIdx * get_stride<index_t>(p.yStride.w);
+
+    // Sign shift amount.
+    uint32_t signXo = ((threadIdx.x + p.sOfs.x) << 1) & 6;
+
+    // Inner tile loop.
+    #pragma unroll 1
+    for (int tileIdx = 0; !enableXrep || (tileIdx < MIN(p.tilesXrep, p.tilesXdim - p.tilesXrep * blockIdx.y)); tileIdx++)
+    {
+        // Locate output tile.
+        int tileX = enableXrep ? blockIdx.y * p.tilesXrep + tileIdx : blockIdx.x;
+        int tileOutX = tileX * tileOutW;
+        int tileOutY = (enableXrep ? blockIdx.x : blockIdx.y) * tileOutH;
+
+        // Locate input tile.
+        int tmpX = tileOutX * down - p.pad0.x;
+        int tmpY = tileOutY * down - p.pad0.y;
+        int tileInX = CEIL_DIV(tmpX, up);
+        int tileInY = CEIL_DIV(tmpY, up);
+        const int phaseInX = tileInX * up - tmpX;
+        const int phaseInY = tileInY * up - tmpY;
+
+        // Extra sync if input and output buffers are the same and we are not on first tile.
+        if (enableXrep && tileIdx > 0 && (filterMode == MODE_FUSD || (filterMode == MODE_SUFD && !downInline) || (filterMode == MODE_FUFD && downInline)))
+            __syncthreads();
+
+        // Load input tile & apply bias. Unrolled.
+        scalar_t b = (scalar_t)*(const T*)((const char*)p.b + (channelIdx * get_stride<index_t>(p.bStride)));
+        index_t mapOfsIn = channelIdx * get_stride<index_t>(p.xStride.z) + batchIdx * get_stride<index_t>(p.xStride.w);
+        int idx = threadIdx.x;
+        const int loopCountIN = CEIL_DIV(tileInW * tileInH, threadsPerBlock);
+        #pragma unroll
+        for (int loop = 0; loop < loopCountIN; loop++)
+        {
+            int relInX, relInY;
+            fast_div_mod<tileInW>(relInX, relInY, idx);
+            int inX = tileInX + relInX;
+            int inY = tileInY + relInY;
+            scalar_t v = 0;
+
+            if ((uint32_t)inX < p.xShape.x && (uint32_t)inY < p.xShape.y)
+                v = (scalar_t)*((const T*)((const char*)p.x + (inX * get_stride<index_t>(p.xStride.x) + inY * get_stride<index_t>(p.xStride.y) + mapOfsIn))) + b;
+
+            bool skip = (loop == loopCountIN-1) && (idx >= tileInW * tileInH);
+            if (!skip)
+                s_tileIn[idx] = v;
+
+            idx += threadsPerBlock;
+        }
+
+        if (filterMode == MODE_SUSD || filterMode == MODE_SUFD) // Separable upsampling filter.
+        {
+            // Horizontal upsampling.
+            __syncthreads();
+            if (up == 4)
+            {
+                for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up)
+                {
+                    int relUpX0, relInY;
+                    fast_div_mod<tileUpW>(relUpX0, relInY, idx);
+                    int relInX0 = relUpX0 / up;
+                    int src0 = relInX0 + tileInW * relInY;
+                    int dst = relInY * tileUpW + relUpX0;
+                    vec4_t v = InternalType<T>::zero_vec4();
+                    scalar_t a = s_tileIn[src0];
+                    if (phaseInX == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 3];
+                            v.z += a * (scalar_t)c_fu[step * up + 2];
+                            v.w += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else if (phaseInX == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.z += a * (scalar_t)c_fu[step * up + 3];
+                            v.w += a * (scalar_t)c_fu[step * up + 2];
+                        }
+                    }
+                    else if (phaseInX == 2)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 2];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                            v.z += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.w += a * (scalar_t)c_fu[step * up + 3];
+                        }
+                    }
+                    else // (phaseInX == 3)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 3];
+                            v.y += a * (scalar_t)c_fu[step * up + 2];
+                            v.z += a * (scalar_t)c_fu[step * up + 1];
+                            v.w += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                        }
+                    }
+                    s_tileUpX[dst+0] = v.x;
+                    s_tileUpX[dst+1] = v.y;
+                    s_tileUpX[dst+2] = v.z;
+                    s_tileUpX[dst+3] = v.w;
+                }
+            }
+            else if (up == 2)
+            {
+                bool p0 = (phaseInX == 0);
+                for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up)
+                {
+                    int relUpX0, relInY;
+                    fast_div_mod<tileUpW>(relUpX0, relInY, idx);
+                    int relInX0 = relUpX0 / up;
+                    int src0 = relInX0 + tileInW * relInY;
+                    int dst = relInY * tileUpW + relUpX0;
+                    vec2_t v = InternalType<T>::zero_vec2();
+                    scalar_t a = s_tileIn[src0];
+                    if (p0) // (phaseInX == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else // (phaseInX == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileIn[src0 + step + 1];
+                        }
+                    }
+                    s_tileUpX[dst+0] = v.x;
+                    s_tileUpX[dst+1] = v.y;
+                }
+            }
+
+            // Vertical upsampling & nonlinearity.
+
+            __syncthreads();
+            int groupMask = 15 << ((threadIdx.x & 31) & ~3);
+            int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs.
+            int sShapeMaxY = MIN(p.sShape.y, tileOutY * down + tileUpH); // Avoid out-of-tile sign writes.
+            if (up == 4)
+            {
+                minY -= 3; // Adjust according to block height.
+                for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x)
+                {
+                    int relUpX, relInY0;
+                    fast_div_mod<tileUpW>(relUpX, relInY0, idx);
+                    int relUpY0 = relInY0 * up;
+                    int src0 = relInY0 * tileUpW + relUpX;
+                    int dst = relUpY0 * tileUpW + relUpX;
+                    vec4_t v = InternalType<T>::zero_vec4();
+
+                    scalar_t a = s_tileUpX[src0];
+                    if (phaseInY == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.y += a * (scalar_t)c_fu[step * up + 3];
+                            v.z += a * (scalar_t)c_fu[step * up + 2];
+                            v.w += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else if (phaseInY == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.z += a * (scalar_t)c_fu[step * up + 3];
+                            v.w += a * (scalar_t)c_fu[step * up + 2];
+                        }
+                    }
+                    else if (phaseInY == 2)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 2];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                            v.z += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.w += a * (scalar_t)c_fu[step * up + 3];
+                        }
+                    }
+                    else // (phaseInY == 3)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 3];
+                            v.y += a * (scalar_t)c_fu[step * up + 2];
+                            v.z += a * (scalar_t)c_fu[step * up + 1];
+                            v.w += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                        }
+                    }
+
+                    int x = tileOutX * down + relUpX;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+                    index_t si1 = si0 + p.sShape.x;
+                    index_t si2 = si0 + p.sShape.x * 2;
+                    index_t si3 = si0 + p.sShape.x * 3;
+
+                    v.x *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.y *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.z *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.w *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write signs.
+                            int sx = __float_as_uint(v.x) >> 31 <<  0;
+                            int sy = __float_as_uint(v.y) >> 31 <<  8;
+                            int sz = __float_as_uint(v.z) >> 31 << 16;
+                            int sw = __float_as_uint(v.w) >> 31 << 24;
+                            if (sx) v.x *= p.slope;
+                            if (sy) v.y *= p.slope;
+                            if (sz) v.z *= p.slope;
+                            if (sw) v.w *= p.slope;
+                            if (fabsf(v.x) > p.clamp) { sx = 2 <<  0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                            if (fabsf(v.y) > p.clamp) { sy = 2 <<  8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                            if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                            if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                // Combine signs.
+                                uint32_t s = sx + sy + sw + sz;
+                                s <<= (signX & 3) << 1;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                                if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); }
+                                if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); }
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write signs.
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                int sx = __float_as_uint(v.x) >> 31 <<  0;
+                                int sy = __float_as_uint(v.y) >> 31 <<  8;
+                                int sz = __float_as_uint(v.z) >> 31 << 16;
+                                int sw = __float_as_uint(v.w) >> 31 << 24;
+                                if (sx) v.x *= p.slope;
+                                if (sy) v.y *= p.slope;
+                                if (sz) v.z *= p.slope;
+                                if (sw) v.w *= p.slope;
+                                if (fabsf(v.x) > p.clamp) { sx = 2 <<  0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                                if (fabsf(v.y) > p.clamp) { sy = 2 <<  8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                                if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                                if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                                // Combine signs.
+                                uint32_t s = sx + sy + sw + sz;
+                                s <<= (signX & 3) << 1;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                                if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); }
+                                if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); }
+                            }
+                            else
+                            {
+                                // Just compute the values.
+                                if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                                if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                                if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                                if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead) // Read signs and apply.
+                    {
+                        if ((uint32_t)signXb < p.swLimit)
+                        {
+                            int ss = (signX & 3) << 1;
+                            if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> ss; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; }
+                            if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> ss; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; }
+                            if ((uint32_t)(signY + 2) < p.sShape.y) { int s = p.s[si2] >> ss; if (s & 1) v.z *= p.slope; if (s & 2) v.z = 0.f; }
+                            if ((uint32_t)(signY + 3) < p.sShape.y) { int s = p.s[si3] >> ss; if (s & 1) v.w *= p.slope; if (s & 2) v.w = 0.f; }
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                        if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                        if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                        if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                    }
+
+                    s_tileUpXY[dst + 0 * tileUpW] = v.x;
+                    if (relUpY0 + 1 < tileUpH) s_tileUpXY[dst + 1 * tileUpW] = v.y;
+                    if (relUpY0 + 2 < tileUpH) s_tileUpXY[dst + 2 * tileUpW] = v.z;
+                    if (relUpY0 + 3 < tileUpH) s_tileUpXY[dst + 3 * tileUpW] = v.w;
+                }
+            }
+            else if (up == 2)
+            {
+                minY -= 1; // Adjust according to block height.
+                for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x)
+                {
+                    int relUpX, relInY0;
+                    fast_div_mod<tileUpW>(relUpX, relInY0, idx);
+                    int relUpY0 = relInY0 * up;
+                    int src0 = relInY0 * tileUpW + relUpX;
+                    int dst = relUpY0 * tileUpW + relUpX;
+                    vec2_t v = InternalType<T>::zero_vec2();
+
+                    scalar_t a = s_tileUpX[src0];
+                    if (phaseInY == 0)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                            v.y += a * (scalar_t)c_fu[step * up + 1];
+                        }
+                    }
+                    else // (phaseInY == 1)
+                    {
+                        #pragma unroll
+                        for (int step = 0; step < fuSize / up; step++)
+                        {
+                            v.x += a * (scalar_t)c_fu[step * up + 1];
+                            v.y += a * (scalar_t)c_fu[step * up + 0];
+                            a = s_tileUpX[src0 + (step + 1) * tileUpW];
+                        }
+                    }
+
+                    int x = tileOutX * down + relUpX;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+                    index_t si1 = si0 + p.sShape.x;
+
+                    v.x *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.y *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write signs.
+                            int sx = __float_as_uint(v.x) >> 31 << 0;
+                            int sy = __float_as_uint(v.y) >> 31 << 8;
+                            if (sx) v.x *= p.slope;
+                            if (sy) v.y *= p.slope;
+                            if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                            if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                // Combine signs.
+                                int s = sx + sy;
+                                s <<= signXo;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write signs.
+                            if ((uint32_t)signXb < p.swLimit && signY >= minY)
+                            {
+                                int sx = __float_as_uint(v.x) >> 31 << 0;
+                                int sy = __float_as_uint(v.y) >> 31 << 8;
+                                if (sx) v.x *= p.slope;
+                                if (sy) v.y *= p.slope;
+                                if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                                if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+
+                                // Combine signs.
+                                int s = sx + sy;
+                                s <<= signXo;
+                                s |= __shfl_xor_sync(groupMask, s, 1);
+                                s |= __shfl_xor_sync(groupMask, s, 2);
+
+                                // Write signs.
+                                if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >>  0); }
+                                if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >>  8); }
+                            }
+                            else
+                            {
+                                // Just compute the values.
+                                if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                                if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead) // Read signs and apply.
+                    {
+                        if ((uint32_t)signXb < p.swLimit)
+                        {
+                            if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> signXo; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; }
+                            if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> signXo; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; }
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                        if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                    }
+
+                    if (!downInline)
+                    {
+                        // Write into temporary buffer.
+                        s_tileUpXY[dst] = v.x;
+                        if (relUpY0 < tileUpH - 1)
+                            s_tileUpXY[dst + tileUpW] = v.y;
+                    }
+                    else
+                    {
+                        // Write directly into output buffer.
+                        if ((uint32_t)x < p.yShape.x)
+                        {
+                            int ymax = MIN(p.yShape.y, tileUpH + tileOutY * down);
+                            index_t ofs = x * get_stride<index_t>(p.yStride.x) + y * get_stride<index_t>(p.yStride.y) + mapOfsOut;
+                            if ((uint32_t)y + 0 < p.yShape.y) *((T*)((char*)p.y + ofs)) = (T)(v.x * (scalar_t)c_fd[0]);
+                            if ((uint32_t)y + 1 < ymax) *((T*)((char*)p.y + ofs + get_stride<index_t>(p.yStride.y))) = (T)(v.y * (scalar_t)c_fd[0]);
+                        }
+                    }
+                }
+            }
+        }
+        else if (filterMode == MODE_FUSD || filterMode == MODE_FUFD)
+        {
+            // Full upsampling filter.
+
+            if (up == 2)
+            {
+                // 2 x 2-wide.
+                __syncthreads();
+                int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH + p.sOfs.y : 0; // Skip already written signs.
+                for (int idx = threadIdx.x * 4; idx < tileUpW * tileUpH; idx += blockDim.x * 4)
+                {
+                    int relUpX0, relUpY0;
+                    fast_div_mod<tileUpW>(relUpX0, relUpY0, idx);
+                    int relInX0 = CEIL_DIV(relUpX0 - phaseInX, up);
+                    int relInY0 = CEIL_DIV(relUpY0 - phaseInY, up);
+                    int src0 = relInX0 + tileInW * relInY0;
+                    int tap0y = (relInY0 * up + phaseInY - relUpY0);
+
+                    #define X_LOOP(TAPY, PX) \
+                        for (int sx = 0; sx < fuSize / up; sx++) \
+                        { \
+                            v.x += a * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \
+                            v.z += b * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 0) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \
+                            v.y += a * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \
+                            v.w += b * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 1) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \
+                        }
+
+                    vec4_t v = InternalType<T>::zero_vec4();
+                    if (tap0y == 0 && phaseInX == 0)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(0, 0) }
+                    if (tap0y == 0 && phaseInX == 1)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(0, 1) }
+                    if (tap0y == 1 && phaseInX == 0)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(1, 0) }
+                    if (tap0y == 1 && phaseInX == 1)
+                        #pragma unroll
+                        for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1];
+                            #pragma unroll
+                            X_LOOP(1, 1) }
+
+                    #undef X_LOOP
+
+                    int x = tileOutX * down + relUpX0;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+
+                    v.x *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.y *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.z *= (scalar_t)((float)up * (float)up * p.gain);
+                    v.w *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write signs.
+                            int sx = __float_as_uint(v.x) >> 31;
+                            int sy = __float_as_uint(v.y) >> 31;
+                            int sz = __float_as_uint(v.z) >> 31;
+                            int sw = __float_as_uint(v.w) >> 31;
+                            if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                            if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                            if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                            if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6);
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write signs.
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                int sx = __float_as_uint(v.x) >> 31;
+                                int sy = __float_as_uint(v.y) >> 31;
+                                int sz = __float_as_uint(v.z) >> 31;
+                                int sw = __float_as_uint(v.w) >> 31;
+                                if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType<T>::clamp(v.x, p.clamp); }
+                                if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType<T>::clamp(v.y, p.clamp); }
+                                if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType<T>::clamp(v.z, p.clamp); }
+                                if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType<T>::clamp(v.w, p.clamp); }
+
+                                p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6);
+                            }
+                            else
+                            {
+                                // Just compute the values.
+                                if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                                if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                                if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                                if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead) // Read sign and apply.
+                    {
+                        if ((uint32_t)signY < p.sShape.y)
+                        {
+                            int s = 0;
+                            if ((uint32_t)signXb     < p.swLimit) s  = p.s[si];
+                            if ((uint32_t)signXb + 1 < p.swLimit) s |= p.s[si + 1] << 8;
+                            s >>= (signX & 3) << 1;
+                            if (s & 0x01) v.x *= p.slope; if (s & 0x02) v.x = 0.f;
+                            if (s & 0x04) v.y *= p.slope; if (s & 0x08) v.y = 0.f;
+                            if (s & 0x10) v.z *= p.slope; if (s & 0x20) v.z = 0.f;
+                            if (s & 0x40) v.w *= p.slope; if (s & 0x80) v.w = 0.f;
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v.x < 0.f) v.x *= p.slope; v.x = InternalType<T>::clamp(v.x, p.clamp);
+                        if (v.y < 0.f) v.y *= p.slope; v.y = InternalType<T>::clamp(v.y, p.clamp);
+                        if (v.z < 0.f) v.z *= p.slope; v.z = InternalType<T>::clamp(v.z, p.clamp);
+                        if (v.w < 0.f) v.w *= p.slope; v.w = InternalType<T>::clamp(v.w, p.clamp);
+                    }
+
+                    s_tileUpXY[idx + 0] = v.x;
+                    s_tileUpXY[idx + 1] = v.y;
+                    s_tileUpXY[idx + 2] = v.z;
+                    s_tileUpXY[idx + 3] = v.w;
+                }
+            }
+            else if (up == 1)
+            {
+                __syncthreads();
+                uint32_t groupMask = 15 << ((threadIdx.x & 31) & ~3);
+                int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs.
+                for (int idx = threadIdx.x; idx < tileUpW * tileUpH; idx += blockDim.x)
+                {
+                    int relUpX0, relUpY0;
+                    fast_div_mod<tileUpW>(relUpX0, relUpY0, idx);
+                    scalar_t v = s_tileIn[idx] * (scalar_t)c_fu[0]; // 1x1 filter.
+
+                    int x = tileOutX * down + relUpX0;
+                    int y = tileOutY * down + relUpY0;
+                    int signX = x + p.sOfs.x;
+                    int signY = y + p.sOfs.y;
+                    int signZ = blockIdx.z + p.blockZofs;
+                    int signXb = signX >> 2;
+                    index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ);
+                    v *= (scalar_t)((float)up * (float)up * p.gain);
+
+                    if (signWrite)
+                    {
+                        if (!enableWriteSkip)
+                        {
+                            // Determine and write sign.
+                            uint32_t s = 0;
+                            uint32_t signXbit = (1u << signXo);
+                            if (v < 0.f)
+                            {
+                                s = signXbit;
+                                v *= p.slope;
+                            }
+                            if (fabsf(v) > p.clamp)
+                            {
+                                s = signXbit * 2;
+                                v = InternalType<T>::clamp(v, p.clamp);
+                            }
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                s += __shfl_xor_sync(groupMask, s, 1);  // Coalesce.
+                                s += __shfl_xor_sync(groupMask, s, 2);  // Coalesce.
+                                p.s[si] = s;                            // Write.
+                            }
+                        }
+                        else
+                        {
+                            // Determine and write sign.
+                            if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY)
+                            {
+                                uint32_t s = 0;
+                                uint32_t signXbit = (1u << signXo);
+                                if (v < 0.f)
+                                {
+                                    s = signXbit;
+                                    v *= p.slope;
+                                }
+                                if (fabsf(v) > p.clamp)
+                                {
+                                    s = signXbit * 2;
+                                    v = InternalType<T>::clamp(v, p.clamp);
+                                }
+                                s += __shfl_xor_sync(groupMask, s, 1);  // Coalesce.
+                                s += __shfl_xor_sync(groupMask, s, 2);  // Coalesce.
+                                p.s[si] = s;                            // Write.
+                            }
+                            else
+                            {
+                                // Just compute the value.
+                                if (v < 0.f) v *= p.slope;
+                                v = InternalType<T>::clamp(v, p.clamp);
+                            }
+                        }
+                    }
+                    else if (signRead)
+                    {
+                        // Read sign and apply if within sign tensor bounds.
+                        if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y)
+                        {
+                            int s = p.s[si];
+                            s >>= signXo;
+                            if (s & 1) v *= p.slope;
+                            if (s & 2) v = 0.f;
+                        }
+                    }
+                    else // Forward pass with no sign write.
+                    {
+                        if (v < 0.f) v *= p.slope;
+                        v = InternalType<T>::clamp(v, p.clamp);
+                    }
+
+                    if (!downInline) // Write into temporary buffer.
+                        s_tileUpXY[idx] = v;
+                    else if ((uint32_t)x < p.yShape.x && (uint32_t)y < p.yShape.y) // Write directly into output buffer
+                        *((T*)((char*)p.y + (x * get_stride<index_t>(p.yStride.x) + y * get_stride<index_t>(p.yStride.y) + mapOfsOut))) = (T)(v * (scalar_t)c_fd[0]);
+                }
+            }
+        }
+
+        // Downsampling.
+        if (filterMode == MODE_SUSD || filterMode == MODE_FUSD)
+        {
+            // Horizontal downsampling.
+            __syncthreads();
+            if (down == 4 && tileOutW % 4 == 0)
+            {
+                // Calculate 4 pixels at a time.
+                for (int idx = threadIdx.x * 4; idx < tileOutW * tileUpH; idx += blockDim.x * 4)
+                {
+                    int relOutX0, relUpY;
+                    fast_div_mod<tileOutW>(relOutX0, relUpY, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int src0 = relUpY * tileUpW + relUpX0;
+                    vec4_t v = InternalType<T>::zero_vec4();
+                    #pragma unroll
+                    for (int step = 0; step < fdSize; step++)
+                    {
+                        v.x += s_tileUpXY[src0 +  0 + step] * (scalar_t)c_fd[step];
+                        v.y += s_tileUpXY[src0 +  4 + step] * (scalar_t)c_fd[step];
+                        v.z += s_tileUpXY[src0 +  8 + step] * (scalar_t)c_fd[step];
+                        v.w += s_tileUpXY[src0 + 12 + step] * (scalar_t)c_fd[step];
+                    }
+                    s_tileDownX[idx+0] = v.x;
+                    s_tileDownX[idx+1] = v.y;
+                    s_tileDownX[idx+2] = v.z;
+                    s_tileDownX[idx+3] = v.w;
+                }
+            }
+            else if ((down == 2 || down == 4) && (tileOutW % 2 == 0))
+            {
+                // Calculate 2 pixels at a time.
+                for (int idx = threadIdx.x * 2; idx < tileOutW * tileUpH; idx += blockDim.x * 2)
+                {
+                    int relOutX0, relUpY;
+                    fast_div_mod<tileOutW>(relOutX0, relUpY, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int src0 = relUpY * tileUpW + relUpX0;
+                    vec2_t v = InternalType<T>::zero_vec2();
+                    #pragma unroll
+                    for (int step = 0; step < fdSize; step++)
+                    {
+                        v.x += s_tileUpXY[src0 +    0 + step] * (scalar_t)c_fd[step];
+                        v.y += s_tileUpXY[src0 + down + step] * (scalar_t)c_fd[step];
+                    }
+                    s_tileDownX[idx+0] = v.x;
+                    s_tileDownX[idx+1] = v.y;
+                }
+            }
+            else
+            {
+                // Calculate 1 pixel at a time.
+                for (int idx = threadIdx.x; idx < tileOutW * tileUpH; idx += blockDim.x)
+                {
+                    int relOutX0, relUpY;
+                    fast_div_mod<tileOutW>(relOutX0, relUpY, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int src = relUpY * tileUpW + relUpX0;
+                    scalar_t v = 0.f;
+                    #pragma unroll
+                    for (int step = 0; step < fdSize; step++)
+                        v += s_tileUpXY[src + step] * (scalar_t)c_fd[step];
+                    s_tileDownX[idx] = v;
+                }
+            }
+
+            // Vertical downsampling & store output tile.
+            __syncthreads();
+            for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x)
+            {
+                int relOutX, relOutY0;
+                fast_div_mod<tileOutW>(relOutX, relOutY0, idx);
+                int relUpY0 = relOutY0 * down;
+                int src0 = relUpY0 * tileOutW + relOutX;
+                scalar_t v = 0;
+                #pragma unroll
+                for (int step = 0; step < fdSize; step++)
+                    v += s_tileDownX[src0 + step * tileOutW] * (scalar_t)c_fd[step];
+
+                int outX = tileOutX + relOutX;
+                int outY = tileOutY + relOutY0;
+
+                if (outX < p.yShape.x & outY < p.yShape.y)
+                    *((T*)((char*)p.y + (outX * get_stride<index_t>(p.yStride.x) + outY * get_stride<index_t>(p.yStride.y) + mapOfsOut))) = (T)v;
+            }
+        }
+        else if (filterMode == MODE_SUFD || filterMode == MODE_FUFD)
+        {
+            // Full downsampling filter.
+            if (down == 2)
+            {
+                // 2-wide.
+                __syncthreads();
+                for (int idx = threadIdx.x * 2; idx < tileOutW * tileOutH; idx += blockDim.x * 2)
+                {
+                    int relOutX0, relOutY0;
+                    fast_div_mod<tileOutW>(relOutX0, relOutY0, idx);
+                    int relUpX0 = relOutX0 * down;
+                    int relUpY0 = relOutY0 * down;
+                    int src0 = relUpY0 * tileUpW + relUpX0;
+                    vec2_t v = InternalType<T>::zero_vec2();
+                    #pragma unroll
+                    for (int sy = 0; sy < fdSize; sy++)
+                    #pragma unroll
+                    for (int sx = 0; sx < fdSize; sx++)
+                    {
+                        v.x += s_tileUpXY[src0 + 0 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE];
+                        v.y += s_tileUpXY[src0 + 2 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE];
+                    }
+
+                    int outX = tileOutX + relOutX0;
+                    int outY = tileOutY + relOutY0;
+                    if ((uint32_t)outY < p.yShape.y)
+                    {
+                        index_t ofs = outX * get_stride<index_t>(p.yStride.x) + outY * get_stride<index_t>(p.yStride.y) + mapOfsOut;
+                        if (outX + 0 < p.yShape.x) *((T*)((char*)p.y + ofs)) = (T)v.x;
+                        if (outX + 1 < p.yShape.x) *((T*)((char*)p.y + ofs + get_stride<index_t>(p.yStride.x))) = (T)v.y;
+                    }
+                }
+            }
+            else if (down == 1 && !downInline)
+            {
+                // Thread per pixel.
+                __syncthreads();
+                for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x)
+                {
+                    int relOutX0, relOutY0;
+                    fast_div_mod<tileOutW>(relOutX0, relOutY0, idx);
+                    scalar_t v = s_tileUpXY[idx] * (scalar_t)c_fd[0]; // 1x1 filter.
+
+                    int outX = tileOutX + relOutX0;
+                    int outY = tileOutY + relOutY0;
+                    if ((uint32_t)outX < p.yShape.x && (uint32_t)outY < p.yShape.y)
+                        *((T*)((char*)p.y + (outX * get_stride<index_t>(p.yStride.x) + outY * get_stride<index_t>(p.yStride.y) + mapOfsOut))) = (T)v;
+                }
+            }
+        }
+
+        if (!enableXrep)
+            break;
+    }
+}
+
+//------------------------------------------------------------------------
+// Compute activation function and signs for upsampled data tensor, modifying data tensor in-place. Used for accelerating the generic variant.
+// Sign tensor is known to be contiguous, and p.x and p.s have the same z, w dimensions. 64-bit indexing is always used.
+
+template <class T, bool signWrite, bool signRead>
+static __global__ void filtered_lrelu_act_kernel(filtered_lrelu_act_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+
+    // Indexing.
+    int32_t x = threadIdx.x + blockIdx.x * blockDim.x;
+    int32_t ymax = signWrite ? p.sShape.y : p.xShape.y;
+    int32_t qmax = p.xShape.z * p.xShape.w; // Combined minibatch*channel maximum index.
+
+    // Loop to accommodate oversized tensors.
+    for (int32_t q = blockIdx.z; q < qmax; q += gridDim.z)
+    for (int32_t y = blockIdx.y; y < ymax; y += gridDim.y)
+    {
+        // Extract z and w (channel, minibatch index).
+        int32_t w = q / p.xShape.z;
+        int32_t z = q - w * p.xShape.z;
+
+        // Choose behavior based on sign read/write mode.
+        if (signWrite)
+        {
+            // Process value if in p.x.
+            uint32_t s = 0;
+            if (x < p.xShape.x && y < p.xShape.y)
+            {
+                int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w;
+                T* pv = ((T*)p.x) + ix;
+                scalar_t v = (scalar_t)(*pv);
+
+                // Gain, LReLU, clamp.
+                v *= p.gain;
+                if (v < 0.f)
+                {
+                    v *= p.slope;
+                    s = 1; // Sign.
+                }
+                if (fabsf(v) > p.clamp)
+                {
+                    v = InternalType<T>::clamp(v, p.clamp);
+                    s = 2; // Clamp.
+                }
+
+                *pv = (T)v; // Write value.
+            }
+
+            // Coalesce into threads 0 and 16 of warp.
+            uint32_t m = (threadIdx.x & 16) ? 0xffff0000u : 0x0000ffffu;
+            s <<= ((threadIdx.x & 15) << 1); // Shift into place.
+            s |= __shfl_xor_sync(m, s, 1); // Distribute.
+            s |= __shfl_xor_sync(m, s, 2);
+            s |= __shfl_xor_sync(m, s, 4);
+            s |= __shfl_xor_sync(m, s, 8);
+
+            // Write signs if leader and in p.s.
+            if (!(threadIdx.x & 15) && x < p.sShape.x) // y is always in.
+            {
+                uint64_t is = x + p.sShape.x * (y + (int64_t)p.sShape.y * q); // Contiguous.
+                ((uint32_t*)p.s)[is >> 4] = s;
+            }
+        }
+        else if (signRead)
+        {
+            // Process value if in p.x.
+            if (x < p.xShape.x) // y is always in.
+            {
+                int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w;
+                T* pv = ((T*)p.x) + ix;
+                scalar_t v = (scalar_t)(*pv);
+                v *= p.gain;
+
+                // Apply sign buffer offset.
+                uint32_t sx = x + p.sOfs.x;
+                uint32_t sy = y + p.sOfs.y;
+
+                // Read and apply signs if we land inside valid region of sign buffer.
+                if (sx < p.sShape.x && sy < p.sShape.y)
+                {
+                    uint64_t is = (sx >> 2) + (p.sShape.x >> 2) * (sy + (uint64_t)p.sShape.y * q); // Contiguous.
+                    unsigned char s = p.s[is];
+                    s >>= (sx & 3) << 1; // Shift into place.
+                    if (s & 1) // Sign?
+                        v *= p.slope;
+                    if (s & 2) // Clamp?
+                        v = 0.f;
+                }
+
+                *pv = (T)v; // Write value.
+            }
+        }
+        else
+        {
+            // Forward pass with no sign write. Process value if in p.x.
+            if (x < p.xShape.x) // y is always in.
+            {
+                int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w;
+                T* pv = ((T*)p.x) + ix;
+                scalar_t v = (scalar_t)(*pv);
+                v *= p.gain;
+                if (v < 0.f)
+                    v *= p.slope;
+                if (fabsf(v) > p.clamp)
+                    v = InternalType<T>::clamp(v, p.clamp);
+                *pv = (T)v; // Write value.
+            }
+        }
+    }
+}
+
+template <class T, bool signWrite, bool signRead> void* choose_filtered_lrelu_act_kernel(void)
+{
+    return (void*)filtered_lrelu_act_kernel<T, signWrite, signRead>;
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T, class index_t, bool signWrite, bool signRead> filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB)
+{
+    filtered_lrelu_kernel_spec s = { 0 };
+
+    // Return the first matching kernel.
+#define CASE(SH, U, FU, D, FD, MODE, TW, TH, W, XR, WS) \
+    if (sharedKB >= SH) \
+    if ((p.fuShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_SUFD)) || (p.fuShape.y > 0 && (MODE == MODE_FUSD || MODE == MODE_FUFD))) \
+    if ((p.fdShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_FUSD)) || (p.fdShape.y > 0 && (MODE == MODE_SUFD || MODE == MODE_FUFD))) \
+    if (p.up == U && p.fuShape.x <= FU && p.fuShape.y <= FU && p.down == D && p.fdShape.x <= FD && p.fdShape.y <= FD) \
+    { \
+        static_assert((D*TW % 4) == 0, "down * tileWidth must be divisible by 4"); \
+        static_assert(FU % U == 0, "upscaling filter size must be multiple of upscaling factor"); \
+        static_assert(FD % D == 0, "downscaling filter size must be multiple of downscaling factor"); \
+        s.setup = (void*)setup_filters_kernel; \
+        s.exec = (void*)filtered_lrelu_kernel<T, index_t, SH, signWrite, signRead, MODE, U, FU, D, FD, TW, TH, W*32, !!XR, !!WS>; \
+        s.tileOut = make_int2(TW, TH); \
+        s.numWarps = W; \
+        s.xrep = XR; \
+        s.dynamicSharedKB = (SH == 48) ? 0 : SH; \
+        return s; \
+    }
+
+    // Launch parameters for various kernel specializations.
+    // Small filters must be listed before large filters, otherwise the kernel for larger filter will always match first.
+    // Kernels that use more shared memory must be listed before those that use less, for the same reason.
+
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/1,1,  /*mode*/MODE_FUFD, /*tw,th,warps,xrep,wskip*/64,  178, 32,  0,  0) // 1t-upf1-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/1,1,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/152, 95,  16,  0,  0) // 4t-ups2-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/2,8,  /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56,  22,  16,  0,  0) // 4t-upf1-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/2,8,  /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56,  29,  16,  11, 0) // 4t-ups2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/2,8,  /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/60,  28,  16,  0,  0) // 4t-upf2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/2,8,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56,  28,  16,  0,  0) // 4t-ups2-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8,  /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56,  31,  16,  11, 0) // 4t-ups4-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56,  36,  16,  0,  0) // 4t-ups4-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/4,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16,  22,  16,  12, 0) // 4t-ups2-downs4
+    CASE(/*sharedKB*/48, /*up,fu*/2,8,  /*down,fd*/4,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/29,  15,  16,  0,  0) // 4t-upf2-downs4
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/1,1,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/96,  150, 28,  0,  0) // 6t-ups2-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32,  35,  24,  0,  0) // 6t-upf1-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  46,  16,  10, 0) // 6t-ups2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/58,  28,  24,  8,  0) // 6t-upf2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/52,  28,  16,  0,  0) // 6t-ups2-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  51,  16,  5,  0) // 6t-ups4-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32,  56,  16,  6,  0) // 6t-ups4-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16,  18,  16,  12, 0) // 6t-ups2-downs4
+    CASE(/*sharedKB*/96, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27,  31,  32,  6,  0) // 6t-upf2-downs4 96kB
+    CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27,  13,  24,  0,  0) // 6t-upf2-downs4
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/1,1,  /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/148, 89,  24,  0,  0) // 8t-ups2-downf1
+    CASE(/*sharedKB*/48, /*up,fu*/1,1,  /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32,  31,  16,  5,  0) // 8t-upf1-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  41,  16,  9,  0) // 8t-ups2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56,  26,  24,  0,  0) // 8t-upf2-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32,  40,  16,  0,  0) // 8t-ups2-downf2
+    CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32,  46,  24,  5,  0) // 8t-ups4-downs2
+    CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32,  50,  16,  0,  0) // 8t-ups4-downf2
+    CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/24,  24,  32,  12, 1) // 8t-ups2-downs4 96kB
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16,  13,  16,  10, 1) // 8t-ups2-downs4
+    CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25,  28,  28,  4,  0) // 8t-upf2-downs4 96kB
+    CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25,  10,  24,  0,  0) // 8t-upf2-downs4
+
+    #undef CASE
+    return s; // No kernel found.
+}
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/filtered_lrelu.h b/models/stylegan3/torch_utils/ops/filtered_lrelu.h
new file mode 100644
index 0000000..2c403e3
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/filtered_lrelu.h
@@ -0,0 +1,90 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <cuda_runtime.h>
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct filtered_lrelu_kernel_params
+{
+    // These parameters decide which kernel to use.
+    int             up;         // upsampling ratio (1, 2, 4)
+    int             down;       // downsampling ratio (1, 2, 4)
+    int2            fuShape;    // [size, 1] | [size, size]
+    int2            fdShape;    // [size, 1] | [size, size]
+
+    int             _dummy;     // Alignment.
+
+    // Rest of the parameters.
+    const void*     x;          // Input tensor.
+    void*           y;          // Output tensor.
+    const void*     b;          // Bias tensor.
+    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
+    const float*    fu;         // Upsampling filter.
+    const float*    fd;         // Downsampling filter.
+
+    int2            pad0;       // Left/top padding.
+    float           gain;       // Additional gain factor.
+    float           slope;      // Leaky ReLU slope on negative side.
+    float           clamp;      // Clamp after nonlinearity.
+    int             flip;       // Filter kernel flip for gradient computation.
+
+    int             tilesXdim;  // Original number of horizontal output tiles.
+    int             tilesXrep;  // Number of horizontal tiles per CTA.
+    int             blockZofs;  // Block z offset to support large minibatch, channel dimensions.
+
+    int4            xShape;     // [width, height, channel, batch]
+    int4            yShape;     // [width, height, channel, batch]
+    int2            sShape;     // [width, height] - width is in bytes. Contiguous. Zeros if unused.
+    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
+    int             swLimit;    // Active width of sign tensor in bytes.
+
+    longlong4       xStride;    // Strides of all tensors except signs, same component order as shapes.
+    longlong4       yStride;    //
+    int64_t         bStride;    //
+    longlong3       fuStride;   //
+    longlong3       fdStride;   //
+};
+
+struct filtered_lrelu_act_kernel_params
+{
+    void*           x;          // Input/output, modified in-place.
+    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
+
+    float           gain;       // Additional gain factor.
+    float           slope;      // Leaky ReLU slope on negative side.
+    float           clamp;      // Clamp after nonlinearity.
+
+    int4            xShape;     // [width, height, channel, batch]
+    longlong4       xStride;    // Input/output tensor strides, same order as in shape.
+    int2            sShape;     // [width, height] - width is in elements. Contiguous. Zeros if unused.
+    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel specialization.
+
+struct filtered_lrelu_kernel_spec
+{
+    void*   setup;              // Function for filter kernel setup.
+    void*   exec;               // Function for main operation.
+    int2    tileOut;            // Width/height of launch tile.
+    int     numWarps;           // Number of warps per thread block, determines launch block size.
+    int     xrep;               // For processing multiple horizontal tiles per thread block.
+    int     dynamicSharedKB;    // How much dynamic shared memory the exec kernel wants.
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T, class index_t, bool signWrite, bool signRead> filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB);
+template <class T, bool signWrite, bool signRead> void* choose_filtered_lrelu_act_kernel(void);
+template <bool signWrite, bool signRead> cudaError_t copy_filters(cudaStream_t stream);
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/filtered_lrelu.py b/models/stylegan3/torch_utils/ops/filtered_lrelu.py
new file mode 100644
index 0000000..6701cd7
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/filtered_lrelu.py
@@ -0,0 +1,274 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import os
+import numpy as np
+import torch
+import warnings
+
+from .. import custom_ops
+from .. import misc
+from . import upfirdn2d
+from . import bias_act
+
+#----------------------------------------------------------------------------
+
+_plugin = None
+
+def _init():
+    global _plugin
+    if _plugin is None:
+        _plugin = custom_ops.get_plugin(
+            module_name='filtered_lrelu_plugin',
+            sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'],
+            headers=['filtered_lrelu.h', 'filtered_lrelu.cu'],
+            source_dir=os.path.dirname(__file__),
+            extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'],
+        )
+    return True
+
+def _get_filter_size(f):
+    if f is None:
+        return 1, 1
+    assert isinstance(f, torch.Tensor)
+    assert 1 <= f.ndim <= 2
+    return f.shape[-1], f.shape[0] # width, height
+
+def _parse_padding(padding):
+    if isinstance(padding, int):
+        padding = [padding, padding]
+    assert isinstance(padding, (list, tuple))
+    assert all(isinstance(x, (int, np.integer)) for x in padding)
+    padding = [int(x) for x in padding]
+    if len(padding) == 2:
+        px, py = padding
+        padding = [px, px, py, py]
+    px0, px1, py0, py1 = padding
+    return px0, px1, py0, py1
+
+#----------------------------------------------------------------------------
+
+def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'):
+    r"""Filtered leaky ReLU for a batch of 2D images.
+
+    Performs the following sequence of operations for each channel:
+
+    1. Add channel-specific bias if provided (`b`).
+
+    2. Upsample the image by inserting N-1 zeros after each pixel (`up`).
+
+    3. Pad the image with the specified number of zeros on each side (`padding`).
+       Negative padding corresponds to cropping the image.
+
+    4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it
+       so that the footprint of all output pixels lies within the input image.
+
+    5. Multiply each value by the provided gain factor (`gain`).
+
+    6. Apply leaky ReLU activation function to each value.
+
+    7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided.
+
+    8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking
+       it so that the footprint of all output pixels lies within the input image.
+
+    9. Downsample the image by keeping every Nth pixel (`down`).
+
+    The fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports gradients of arbitrary order.
+
+    Args:
+        x:           Float32/float16/float64 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        fu:          Float32 upsampling FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        fd:          Float32 downsampling FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        b:           Bias vector, or `None` to disable. Must be a 1D tensor of the same type
+                     as `x`. The length of vector must must match the channel dimension of `x`.
+        up:          Integer upsampling factor (default: 1).
+        down:        Integer downsampling factor. (default: 1).
+        padding:     Padding with respect to the upsampled image. Can be a single number
+                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        gain:        Overall scaling factor for signal magnitude (default: sqrt(2)).
+        slope:       Slope on the negative side of leaky ReLU (default: 0.2).
+        clamp:       Maximum magnitude for leaky ReLU output (default: None).
+        flip_filter: False = convolution, True = correlation (default: False).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0)
+    return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter)
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
+    """Slow and memory-inefficient reference implementation of `filtered_lrelu()` using
+    existing `upfirdn2n()` and `bias_act()` ops.
+    """
+    assert isinstance(x, torch.Tensor) and x.ndim == 4
+    fu_w, fu_h = _get_filter_size(fu)
+    fd_w, fd_h = _get_filter_size(fd)
+    if b is not None:
+        assert isinstance(b, torch.Tensor) and b.dtype == x.dtype
+        misc.assert_shape(b, [x.shape[1]])
+    assert isinstance(up, int) and up >= 1
+    assert isinstance(down, int) and down >= 1
+    px0, px1, py0, py1 = _parse_padding(padding)
+    assert gain == float(gain) and gain > 0
+    assert slope == float(slope) and slope >= 0
+    assert clamp is None or (clamp == float(clamp) and clamp >= 0)
+
+    # Calculate output size.
+    batch_size, channels, in_h, in_w = x.shape
+    in_dtype = x.dtype
+    out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down
+    out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down
+
+    # Compute using existing ops.
+    x = bias_act.bias_act(x=x, b=b) # Apply bias.
+    x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
+    x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp.
+    x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample.
+
+    # Check output shape & dtype.
+    misc.assert_shape(x, [batch_size, channels, out_h, out_w])
+    assert x.dtype == in_dtype
+    return x
+
+#----------------------------------------------------------------------------
+
+_filtered_lrelu_cuda_cache = dict()
+
+def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
+    """Fast CUDA implementation of `filtered_lrelu()` using custom ops.
+    """
+    assert isinstance(up, int) and up >= 1
+    assert isinstance(down, int) and down >= 1
+    px0, px1, py0, py1 = _parse_padding(padding)
+    assert gain == float(gain) and gain > 0
+    gain = float(gain)
+    assert slope == float(slope) and slope >= 0
+    slope = float(slope)
+    assert clamp is None or (clamp == float(clamp) and clamp >= 0)
+    clamp = float(clamp if clamp is not None else 'inf')
+
+    # Lookup from cache.
+    key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter)
+    if key in _filtered_lrelu_cuda_cache:
+        return _filtered_lrelu_cuda_cache[key]
+
+    # Forward op.
+    class FilteredLReluCuda(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ
+            assert isinstance(x, torch.Tensor) and x.ndim == 4
+
+            # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable).
+            if fu is None:
+                fu = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+            if fd is None:
+                fd = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+            assert 1 <= fu.ndim <= 2
+            assert 1 <= fd.ndim <= 2
+
+            # Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1.
+            if up == 1 and fu.ndim == 1 and fu.shape[0] == 1:
+                fu = fu.square()[None]
+            if down == 1 and fd.ndim == 1 and fd.shape[0] == 1:
+                fd = fd.square()[None]
+
+            # Missing sign input tensor.
+            if si is None:
+                si = torch.empty([0])
+
+            # Missing bias tensor.
+            if b is None:
+                b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device)
+
+            # Construct internal sign tensor only if gradients are needed.
+            write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad)
+
+            # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout.
+            strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1]
+            if any(a < b for a, b in zip(strides[:-1], strides[1:])):
+                warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning)
+
+            # Call C++/Cuda plugin if datatype is supported.
+            if x.dtype in [torch.float16, torch.float32]:
+                if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device):
+                    warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning)
+                y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs)
+            else:
+                return_code = -1
+
+            # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because
+            # only the bit-packed sign tensor is retained for gradient computation.
+            if return_code < 0:
+                warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning)
+
+                y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias.
+                y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
+                so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place.
+                y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample.
+
+            # Prepare for gradient computation.
+            ctx.save_for_backward(fu, fd, (si if si.numel() else so))
+            ctx.x_shape = x.shape
+            ctx.y_shape = y.shape
+            ctx.s_ofs = sx, sy
+            return y
+
+        @staticmethod
+        def backward(ctx, dy): # pylint: disable=arguments-differ
+            fu, fd, si = ctx.saved_tensors
+            _, _, xh, xw = ctx.x_shape
+            _, _, yh, yw = ctx.y_shape
+            sx, sy = ctx.s_ofs
+            dx  = None # 0
+            dfu = None; assert not ctx.needs_input_grad[1]
+            dfd = None; assert not ctx.needs_input_grad[2]
+            db  = None # 3
+            dsi = None; assert not ctx.needs_input_grad[4]
+            dsx = None; assert not ctx.needs_input_grad[5]
+            dsy = None; assert not ctx.needs_input_grad[6]
+
+            if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]:
+                pp = [
+                    (fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0,
+                    xw * up - yw * down + px0 - (up - 1),
+                    (fu.shape[0] - 1) + (fd.shape[0] - 1) - py0,
+                    xh * up - yh * down + py0 - (up - 1),
+                ]
+                gg = gain * (up ** 2) / (down ** 2)
+                ff = (not flip_filter)
+                sx = sx - (fu.shape[-1] - 1) + px0
+                sy = sy - (fu.shape[0]  - 1) + py0
+                dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy)
+
+            if ctx.needs_input_grad[3]:
+                db = dx.sum([0, 2, 3])
+
+            return dx, dfu, dfd, db, dsi, dsx, dsy
+
+    # Add to cache.
+    _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda
+    return FilteredLReluCuda
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/filtered_lrelu_ns.cu b/models/stylegan3/torch_utils/ops/filtered_lrelu_ns.cu
new file mode 100644
index 0000000..ef5d948
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/filtered_lrelu_ns.cu
@@ -0,0 +1,27 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include "filtered_lrelu.cu"
+
+// Template/kernel specializations for no signs mode (no gradients required).
+
+// Full op, 32-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int32_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Full op, 64-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int64_t, false, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Activation/signs only for generic variant. 64-bit indexing.
+template void* choose_filtered_lrelu_act_kernel<c10::Half, false, false>(void);
+template void* choose_filtered_lrelu_act_kernel<float,     false, false>(void);
+template void* choose_filtered_lrelu_act_kernel<double,    false, false>(void);
+
+// Copy filters to constant memory.
+template cudaError_t copy_filters<false, false>(cudaStream_t stream);
diff --git a/models/stylegan3/torch_utils/ops/filtered_lrelu_rd.cu b/models/stylegan3/torch_utils/ops/filtered_lrelu_rd.cu
new file mode 100644
index 0000000..9683478
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/filtered_lrelu_rd.cu
@@ -0,0 +1,27 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include "filtered_lrelu.cu"
+
+// Template/kernel specializations for sign read mode.
+
+// Full op, 32-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int32_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Full op, 64-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int64_t, false, true>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Activation/signs only for generic variant. 64-bit indexing.
+template void* choose_filtered_lrelu_act_kernel<c10::Half, false, true>(void);
+template void* choose_filtered_lrelu_act_kernel<float,     false, true>(void);
+template void* choose_filtered_lrelu_act_kernel<double,    false, true>(void);
+
+// Copy filters to constant memory.
+template cudaError_t copy_filters<false, true>(cudaStream_t stream);
diff --git a/models/stylegan3/torch_utils/ops/filtered_lrelu_wr.cu b/models/stylegan3/torch_utils/ops/filtered_lrelu_wr.cu
new file mode 100644
index 0000000..a4c6a24
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/filtered_lrelu_wr.cu
@@ -0,0 +1,27 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include "filtered_lrelu.cu"
+
+// Template/kernel specializations for sign write mode.
+
+// Full op, 32-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int32_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int32_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Full op, 64-bit indexing.
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<c10::Half, int64_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel<float,     int64_t, true, false>(const filtered_lrelu_kernel_params& p, int sharedKB);
+
+// Activation/signs only for generic variant. 64-bit indexing.
+template void* choose_filtered_lrelu_act_kernel<c10::Half, true, false>(void);
+template void* choose_filtered_lrelu_act_kernel<float,     true, false>(void);
+template void* choose_filtered_lrelu_act_kernel<double,    true, false>(void);
+
+// Copy filters to constant memory.
+template cudaError_t copy_filters<true, false>(cudaStream_t stream);
diff --git a/models/stylegan3/torch_utils/ops/fma.py b/models/stylegan3/torch_utils/ops/fma.py
new file mode 100644
index 0000000..51a45df
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/fma.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
+
+import torch
+
+#----------------------------------------------------------------------------
+
+def fma(a, b, c): # => a * b + c
+    return _FusedMultiplyAdd.apply(a, b, c)
+
+#----------------------------------------------------------------------------
+
+class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
+    @staticmethod
+    def forward(ctx, a, b, c): # pylint: disable=arguments-differ
+        out = torch.addcmul(c, a, b)
+        ctx.save_for_backward(a, b)
+        ctx.c_shape = c.shape
+        return out
+
+    @staticmethod
+    def backward(ctx, dout): # pylint: disable=arguments-differ
+        a, b = ctx.saved_tensors
+        c_shape = ctx.c_shape
+        da = None
+        db = None
+        dc = None
+
+        if ctx.needs_input_grad[0]:
+            da = _unbroadcast(dout * b, a.shape)
+
+        if ctx.needs_input_grad[1]:
+            db = _unbroadcast(dout * a, b.shape)
+
+        if ctx.needs_input_grad[2]:
+            dc = _unbroadcast(dout, c_shape)
+
+        return da, db, dc
+
+#----------------------------------------------------------------------------
+
+def _unbroadcast(x, shape):
+    extra_dims = x.ndim - len(shape)
+    assert extra_dims >= 0
+    dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
+    if len(dim):
+        x = x.sum(dim=dim, keepdim=True)
+    if extra_dims:
+        x = x.reshape(-1, *x.shape[extra_dims+1:])
+    assert x.shape == shape
+    return x
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/grid_sample_gradfix.py b/models/stylegan3/torch_utils/ops/grid_sample_gradfix.py
new file mode 100644
index 0000000..017f03a
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/grid_sample_gradfix.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom replacement for `torch.nn.functional.grid_sample` that
+supports arbitrarily high order gradients between the input and output.
+Only works on 2D images and assumes
+`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
+
+import torch
+from pkg_resources import parse_version
+
+# pylint: disable=redefined-builtin
+# pylint: disable=arguments-differ
+# pylint: disable=protected-access
+
+#----------------------------------------------------------------------------
+
+enabled = False  # Enable the custom op by setting this to true.
+_use_pytorch_1_11_api = parse_version(torch.__version__) >= parse_version('1.11.0a') # Allow prerelease builds of 1.11
+_use_pytorch_1_12_api = parse_version(torch.__version__) >= parse_version('1.12.0a') # Allow prerelease builds of 1.12
+
+#----------------------------------------------------------------------------
+
+def grid_sample(input, grid):
+    if _should_use_custom_op():
+        return _GridSample2dForward.apply(input, grid)
+    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
+
+#----------------------------------------------------------------------------
+
+def _should_use_custom_op():
+    return enabled
+
+#----------------------------------------------------------------------------
+
+class _GridSample2dForward(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, grid):
+        assert input.ndim == 4
+        assert grid.ndim == 4
+        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
+        ctx.save_for_backward(input, grid)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input, grid = ctx.saved_tensors
+        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
+        return grad_input, grad_grid
+
+#----------------------------------------------------------------------------
+
+class _GridSample2dBackward(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, grad_output, input, grid):
+        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
+        if _use_pytorch_1_12_api:
+            op = op[0]
+        if _use_pytorch_1_11_api:
+            output_mask = (ctx.needs_input_grad[1], ctx.needs_input_grad[2])
+            grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False, output_mask)
+        else:
+            grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
+        ctx.save_for_backward(grid)
+        return grad_input, grad_grid
+
+    @staticmethod
+    def backward(ctx, grad2_grad_input, grad2_grad_grid):
+        _ = grad2_grad_grid # unused
+        grid, = ctx.saved_tensors
+        grad2_grad_output = None
+        grad2_input = None
+        grad2_grid = None
+
+        if ctx.needs_input_grad[0]:
+            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
+
+        assert not ctx.needs_input_grad[2]
+        return grad2_grad_output, grad2_input, grad2_grid
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/upfirdn2d.cpp b/models/stylegan3/torch_utils/ops/upfirdn2d.cpp
new file mode 100644
index 0000000..44fa337
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/upfirdn2d.cpp
@@ -0,0 +1,107 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <torch/extension.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "upfirdn2d.h"
+
+//------------------------------------------------------------------------
+
+static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
+{
+    // Validate arguments.
+    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
+    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
+    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
+    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
+    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
+    TORCH_CHECK(x.numel() > 0, "x has zero size");
+    TORCH_CHECK(f.numel() > 0, "f has zero size");
+    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
+    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
+    TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large");
+    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
+    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
+    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
+
+    // Create output tensor.
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
+    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
+    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
+    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
+    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
+    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
+    TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large");
+
+    // Initialize CUDA kernel parameters.
+    upfirdn2d_kernel_params p;
+    p.x             = x.data_ptr();
+    p.f             = f.data_ptr<float>();
+    p.y             = y.data_ptr();
+    p.up            = make_int2(upx, upy);
+    p.down          = make_int2(downx, downy);
+    p.pad0          = make_int2(padx0, pady0);
+    p.flip          = (flip) ? 1 : 0;
+    p.gain          = gain;
+    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
+    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
+    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
+    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
+    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
+    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
+    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
+    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
+
+    // Choose CUDA kernel.
+    upfirdn2d_kernel_spec spec;
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
+    {
+        spec = choose_upfirdn2d_kernel<scalar_t>(p);
+    });
+
+    // Set looping options.
+    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
+    p.loopMinor     = spec.loopMinor;
+    p.loopX         = spec.loopX;
+    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
+    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
+
+    // Compute grid size.
+    dim3 blockSize, gridSize;
+    if (spec.tileOutW < 0) // large
+    {
+        blockSize = dim3(4, 32, 1);
+        gridSize = dim3(
+            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
+            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
+            p.launchMajor);
+    }
+    else // small
+    {
+        blockSize = dim3(256, 1, 1);
+        gridSize = dim3(
+            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
+            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
+            p.launchMajor);
+    }
+
+    // Launch CUDA kernel.
+    void* args[] = {&p};
+    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
+    return y;
+}
+
+//------------------------------------------------------------------------
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("upfirdn2d", &upfirdn2d);
+}
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/upfirdn2d.cu b/models/stylegan3/torch_utils/ops/upfirdn2d.cu
new file mode 100644
index 0000000..3a33e31
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/upfirdn2d.cu
@@ -0,0 +1,384 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <c10/util/Half.h>
+#include "upfirdn2d.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template <class T> struct InternalType;
+template <> struct InternalType<double>     { typedef double scalar_t; };
+template <> struct InternalType<float>      { typedef float  scalar_t; };
+template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
+
+static __device__ __forceinline__ int floor_div(int a, int b)
+{
+    int t = 1 - a / b;
+    return (a + t * b) / b - t;
+}
+
+//------------------------------------------------------------------------
+// Generic CUDA implementation for large filters.
+
+template <class T> static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+
+    // Calculate thread index.
+    int minorBase = blockIdx.x * blockDim.x + threadIdx.x;
+    int outY = minorBase / p.launchMinor;
+    minorBase -= outY * p.launchMinor;
+    int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
+    int majorBase = blockIdx.z * p.loopMajor;
+    if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor)
+        return;
+
+    // Setup Y receptive field.
+    int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y;
+    int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y);
+    int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY;
+    int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y;
+    if (p.flip)
+        filterY = p.filterSize.y - 1 - filterY;
+
+    // Loop over major, minor, and X.
+    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
+    for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor)
+    {
+        int nc = major * p.sizeMinor + minor;
+        int n = nc / p.inSize.z;
+        int c = nc - n * p.inSize.z;
+        for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y)
+        {
+            // Setup X receptive field.
+            int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x;
+            int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x);
+            int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX;
+            int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x;
+            if (p.flip)
+                filterX = p.filterSize.x - 1 - filterX;
+
+            // Initialize pointers.
+            const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
+            const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y];
+            int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x;
+            int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y;
+
+            // Inner loop.
+            scalar_t v = 0;
+            for (int y = 0; y < h; y++)
+            {
+                for (int x = 0; x < w; x++)
+                {
+                    v += (scalar_t)(*xp) * (scalar_t)(*fp);
+                    xp += p.inStride.x;
+                    fp += filterStepX;
+                }
+                xp += p.inStride.y - w * p.inStride.x;
+                fp += filterStepY - w * filterStepX;
+            }
+
+            // Store result.
+            v *= p.gain;
+            ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
+        }
+    }
+}
+
+//------------------------------------------------------------------------
+// Specialized CUDA implementation for small filters.
+
+template <class T, int upx, int upy, int downx, int downy, int filterW, int filterH, int tileOutW, int tileOutH, int loopMinor>
+static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p)
+{
+    typedef typename InternalType<T>::scalar_t scalar_t;
+    const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1;
+    const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1;
+    __shared__ volatile scalar_t sf[filterH][filterW];
+    __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor];
+
+    // Calculate tile index.
+    int minorBase = blockIdx.x;
+    int tileOutY = minorBase / p.launchMinor;
+    minorBase -= tileOutY * p.launchMinor;
+    minorBase *= loopMinor;
+    tileOutY *= tileOutH;
+    int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
+    int majorBase = blockIdx.z * p.loopMajor;
+    if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor)
+        return;
+
+    // Load filter (flipped).
+    for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x)
+    {
+        int fy = tapIdx / filterW;
+        int fx = tapIdx - fy * filterW;
+        scalar_t v = 0;
+        if (fx < p.filterSize.x & fy < p.filterSize.y)
+        {
+            int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx;
+            int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy;
+            v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y];
+        }
+        sf[fy][fx] = v;
+    }
+
+    // Loop over major and X.
+    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
+    {
+        int baseNC = major * p.sizeMinor + minorBase;
+        int n = baseNC / p.inSize.z;
+        int baseC = baseNC - n * p.inSize.z;
+        for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW)
+        {
+            // Load input pixels.
+            int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x;
+            int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y;
+            int tileInX = floor_div(tileMidX, upx);
+            int tileInY = floor_div(tileMidY, upy);
+            __syncthreads();
+            for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x)
+            {
+                int relC = inIdx;
+                int relInX = relC / loopMinor;
+                int relInY = relInX / tileInW;
+                relC -= relInX * loopMinor;
+                relInX -= relInY * tileInW;
+                int c = baseC + relC;
+                int inX = tileInX + relInX;
+                int inY = tileInY + relInY;
+                scalar_t v = 0;
+                if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z)
+                    v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
+                sx[relInY][relInX][relC] = v;
+            }
+
+            // Loop over output pixels.
+            __syncthreads();
+            for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x)
+            {
+                int relC = outIdx;
+                int relOutX = relC / loopMinor;
+                int relOutY = relOutX / tileOutW;
+                relC -= relOutX * loopMinor;
+                relOutX -= relOutY * tileOutW;
+                int c = baseC + relC;
+                int outX = tileOutX + relOutX;
+                int outY = tileOutY + relOutY;
+
+                // Setup receptive field.
+                int midX = tileMidX + relOutX * downx;
+                int midY = tileMidY + relOutY * downy;
+                int inX = floor_div(midX, upx);
+                int inY = floor_div(midY, upy);
+                int relInX = inX - tileInX;
+                int relInY = inY - tileInY;
+                int filterX = (inX + 1) * upx - midX - 1; // flipped
+                int filterY = (inY + 1) * upy - midY - 1; // flipped
+
+                // Inner loop.
+                if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z)
+                {
+                    scalar_t v = 0;
+                    #pragma unroll
+                    for (int y = 0; y < filterH / upy; y++)
+                        #pragma unroll
+                        for (int x = 0; x < filterW / upx; x++)
+                            v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx];
+                    v *= p.gain;
+                    ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
+                }
+            }
+        }
+    }
+}
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p)
+{
+    int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y;
+    upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,1, 4}; // contiguous
+    if (s == 1)           spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,4, 1}; // channels_last
+
+    // No up/downsampling.
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,24, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,16, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1,  128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1,  128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,   128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24,  32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16,  32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,   32,32,1>, 32,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,24, 32,32,1>,  32,32,1,  1};
+        if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,16, 32,32,1>,  32,32,1,  1};
+        if (s == 1 && fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3,   16,16,8>,  16,16,8,  1};
+        if (s == 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1,  128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1,  128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,   128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24,  1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16,  1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,   1,128,16>, 1,128,16, 1};
+    }
+
+    // 2x upsampling.
+    if (p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 24,24, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 16,16, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4,   64,16,1>, 64,16,1, 1};
+        if (s != 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2,   64,16,1>, 64,16,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 24,24, 32,32,1>, 32,32,1, 1};
+        if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 16,16, 32,32,1>, 32,32,1, 1};
+        if (s == 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8,   16,16,8>, 16,16,8, 1};
+        if (s == 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6,   16,16,8>, 16,16,8, 1};
+        if (s == 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4,   16,16,8>, 16,16,8, 1};
+        if (s == 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2,   16,16,8>, 16,16,8, 1};
+    }
+    if (p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
+    }
+
+    // 2x downsampling.
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 24,24, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 16,16, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,   32,8,1>,  32,8,1,  1};
+        if (s != 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,   32,8,1>,  32,8,1,  1};
+        if (s != 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,   32,8,1>,  32,8,1,  1};
+        if (s != 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,   32,8,1>,  32,8,1,  1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 24,24, 16,16,1>, 16,16,1, 1};
+        if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 16,16, 16,16,1>, 16,16,1, 1};
+        if (s == 1 && fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,   8,8,8>,   8,8,8,   1};
+        if (s == 1 && fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,   8,8,8>,   8,8,8,   1};
+        if (s == 1 && fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,   8,8,8>,   8,8,8,   1};
+        if (s == 1 && fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,   8,8,8>,   8,8,8,   1};
+    }
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,8,1>, 64,8,1, 1};
+        if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,8,1>, 64,8,1, 1};
+        if (s != 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,8,1>, 64,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,1,8>, 64,1,8, 1};
+        if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,1,8>, 64,1,8, 1};
+        if (s == 1 && fx <= 8  && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,1,8>, 64,1,8, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 32,16,1>, 32,16,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  32,16,1>, 32,16,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 1,64,8>, 1,64,8, 1};
+        if (s == 1 && fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 1,64,8>, 1,64,8, 1};
+        if (s == 1 && fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  1,64,8>, 1,64,8, 1};
+    }
+
+    // 4x upsampling.
+    if (p.up.x == 4 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 48,48, 64,32,1>, 64,32,1, 1};
+        if (s != 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 32,32, 64,32,1>, 64,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 48,48, 32,32,1>, 32,32,1, 1};
+        if (s == 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 4,4, 1,1, 32,32, 32,32,1>, 32,32,1, 1};
+    }
+    if (p.up.x == 4 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 48,1, 128,8,1>, 128,8,1, 1};
+        if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 32,1, 128,8,1>, 128,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 48,1, 128,1,16>, 128,1,16, 1};
+        if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 4,1, 1,1, 32,1, 128,1,16>, 128,1,16, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,48, 32,32,1>, 32,32,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,32, 32,32,1>, 32,32,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,48, 1,128,16>, 1,128,16, 1};
+        if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,4, 1,1, 1,32, 1,128,16>, 1,128,16, 1};
+    }
+
+    // 4x downsampling (inefficient).
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 4 && p.down.y == 1)
+    {
+        // contiguous
+        if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 48,1, 32,8,1>, 32,8,1, 1};
+        if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 32,1, 32,8,1>, 32,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 48,1, 32,1,8>, 32,1,8, 1};
+        if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 4,1, 32,1, 32,1,8>, 32,1,8, 1};
+    }
+    if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 4)
+    {
+        // contiguous
+        if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,48, 32,8,1>, 32,8,1, 1};
+        if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,32, 32,8,1>, 32,8,1, 1};
+        // channels_last
+        if (s == 1 && fx <= 1  && fy <= 48) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,48, 1,32,8>, 1,32,8, 1};
+        if (s == 1 && fx <= 1  && fy <= 32) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,4, 1,32, 1,32,8>, 1,32,8, 1};
+    }
+    return spec;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<double>   (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<float>    (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<c10::Half>(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/upfirdn2d.h b/models/stylegan3/torch_utils/ops/upfirdn2d.h
new file mode 100644
index 0000000..2793daf
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/upfirdn2d.h
@@ -0,0 +1,59 @@
+// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+#include <cuda_runtime.h>
+
+//------------------------------------------------------------------------
+// CUDA kernel parameters.
+
+struct upfirdn2d_kernel_params
+{
+    const void*     x;
+    const float*    f;
+    void*           y;
+
+    int2            up;
+    int2            down;
+    int2            pad0;
+    int             flip;
+    float           gain;
+
+    int4            inSize;         // [width, height, channel, batch]
+    int4            inStride;
+    int2            filterSize;     // [width, height]
+    int2            filterStride;
+    int4            outSize;        // [width, height, channel, batch]
+    int4            outStride;
+    int             sizeMinor;
+    int             sizeMajor;
+
+    int             loopMinor;
+    int             loopMajor;
+    int             loopX;
+    int             launchMinor;
+    int             launchMajor;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel specialization.
+
+struct upfirdn2d_kernel_spec
+{
+    void*   kernel;
+    int     tileOutW;
+    int     tileOutH;
+    int     loopMinor;
+    int     loopX;
+};
+
+//------------------------------------------------------------------------
+// CUDA kernel selection.
+
+template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/ops/upfirdn2d.py b/models/stylegan3/torch_utils/ops/upfirdn2d.py
new file mode 100644
index 0000000..394f746
--- /dev/null
+++ b/models/stylegan3/torch_utils/ops/upfirdn2d.py
@@ -0,0 +1,389 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Custom PyTorch ops for efficient resampling of 2D images."""
+
+import os
+import numpy as np
+import torch
+
+from .. import custom_ops
+from .. import misc
+from . import conv2d_gradfix
+
+#----------------------------------------------------------------------------
+
+_plugin = None
+
+def _init():
+    global _plugin
+    if _plugin is None:
+        _plugin = custom_ops.get_plugin(
+            module_name='upfirdn2d_plugin',
+            sources=['upfirdn2d.cpp', 'upfirdn2d.cu'],
+            headers=['upfirdn2d.h'],
+            source_dir=os.path.dirname(__file__),
+            extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'],
+        )
+    return True
+
+def _parse_scaling(scaling):
+    if isinstance(scaling, int):
+        scaling = [scaling, scaling]
+    assert isinstance(scaling, (list, tuple))
+    assert all(isinstance(x, int) for x in scaling)
+    sx, sy = scaling
+    assert sx >= 1 and sy >= 1
+    return sx, sy
+
+def _parse_padding(padding):
+    if isinstance(padding, int):
+        padding = [padding, padding]
+    assert isinstance(padding, (list, tuple))
+    assert all(isinstance(x, int) for x in padding)
+    if len(padding) == 2:
+        padx, pady = padding
+        padding = [padx, padx, pady, pady]
+    padx0, padx1, pady0, pady1 = padding
+    return padx0, padx1, pady0, pady1
+
+def _get_filter_size(f):
+    if f is None:
+        return 1, 1
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    fw = f.shape[-1]
+    fh = f.shape[0]
+    with misc.suppress_tracer_warnings():
+        fw = int(fw)
+        fh = int(fh)
+    misc.assert_shape(f, [fh, fw][:f.ndim])
+    assert fw >= 1 and fh >= 1
+    return fw, fh
+
+#----------------------------------------------------------------------------
+
+def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
+    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
+
+    Args:
+        f:           Torch tensor, numpy array, or python list of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable),
+                     `[]` (impulse), or
+                     `None` (identity).
+        device:      Result device (default: cpu).
+        normalize:   Normalize the filter so that it retains the magnitude
+                     for constant input signal (DC)? (default: True).
+        flip_filter: Flip the filter? (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        separable:   Return a separable filter? (default: select automatically).
+
+    Returns:
+        Float32 tensor of the shape
+        `[filter_height, filter_width]` (non-separable) or
+        `[filter_taps]` (separable).
+    """
+    # Validate.
+    if f is None:
+        f = 1
+    f = torch.as_tensor(f, dtype=torch.float32)
+    assert f.ndim in [0, 1, 2]
+    assert f.numel() > 0
+    if f.ndim == 0:
+        f = f[np.newaxis]
+
+    # Separable?
+    if separable is None:
+        separable = (f.ndim == 1 and f.numel() >= 8)
+    if f.ndim == 1 and not separable:
+        f = f.ger(f)
+    assert f.ndim == (1 if separable else 2)
+
+    # Apply normalize, flip, gain, and device.
+    if normalize:
+        f /= f.sum()
+    if flip_filter:
+        f = f.flip(list(range(f.ndim)))
+    f = f * (gain ** (f.ndim / 2))
+    f = f.to(device=device)
+    return f
+
+#----------------------------------------------------------------------------
+
+def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Pad, upsample, filter, and downsample a batch of 2D images.
+
+    Performs the following sequence of operations for each channel:
+
+    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
+
+    2. Pad the image with the specified number of zeros on each side (`padding`).
+       Negative padding corresponds to cropping the image.
+
+    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
+       so that the footprint of all output pixels lies within the input image.
+
+    4. Downsample the image by keeping every Nth pixel (`down`).
+
+    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
+    The fused op is considerably more efficient than performing the same calculation
+    using standard PyTorch ops. It supports gradients of arbitrary order.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the upsampled image. Can be a single number
+                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    assert isinstance(x, torch.Tensor)
+    assert impl in ['ref', 'cuda']
+    if impl == 'cuda' and x.device.type == 'cuda' and _init():
+        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
+    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
+    """
+    # Validate arguments.
+    assert isinstance(x, torch.Tensor) and x.ndim == 4
+    if f is None:
+        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+    assert f.dtype == torch.float32 and not f.requires_grad
+    batch_size, num_channels, in_height, in_width = x.shape
+    upx, upy = _parse_scaling(up)
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+
+    # Check that upsampled buffer is not smaller than the filter.
+    upW = in_width * upx + padx0 + padx1
+    upH = in_height * upy + pady0 + pady1
+    assert upW >= f.shape[-1] and upH >= f.shape[0]
+
+    # Upsample by inserting zeros.
+    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
+    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
+    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
+
+    # Pad or crop.
+    x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
+    x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]
+
+    # Setup filter.
+    f = f * (gain ** (f.ndim / 2))
+    f = f.to(x.dtype)
+    if not flip_filter:
+        f = f.flip(list(range(f.ndim)))
+
+    # Convolve with the filter.
+    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
+    if f.ndim == 4:
+        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
+    else:
+        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
+        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
+
+    # Downsample by throwing away pixels.
+    x = x[:, :, ::downy, ::downx]
+    return x
+
+#----------------------------------------------------------------------------
+
+_upfirdn2d_cuda_cache = dict()
+
+def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
+    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
+    """
+    # Parse arguments.
+    upx, upy = _parse_scaling(up)
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+
+    # Lookup from cache.
+    key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
+    if key in _upfirdn2d_cuda_cache:
+        return _upfirdn2d_cuda_cache[key]
+
+    # Forward op.
+    class Upfirdn2dCuda(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, f): # pylint: disable=arguments-differ
+            assert isinstance(x, torch.Tensor) and x.ndim == 4
+            if f is None:
+                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
+            if f.ndim == 1 and f.shape[0] == 1:
+                f = f.square().unsqueeze(0) # Convert separable-1 into full-1x1.
+            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
+            y = x
+            if f.ndim == 2:
+                y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
+            else:
+                y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0)
+                y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain)
+            ctx.save_for_backward(f)
+            ctx.x_shape = x.shape
+            return y
+
+        @staticmethod
+        def backward(ctx, dy): # pylint: disable=arguments-differ
+            f, = ctx.saved_tensors
+            _, _, ih, iw = ctx.x_shape
+            _, _, oh, ow = dy.shape
+            fw, fh = _get_filter_size(f)
+            p = [
+                fw - padx0 - 1,
+                iw * upx - ow * downx + padx0 - upx + 1,
+                fh - pady0 - 1,
+                ih * upy - oh * downy + pady0 - upy + 1,
+            ]
+            dx = None
+            df = None
+
+            if ctx.needs_input_grad[0]:
+                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)
+
+            assert not ctx.needs_input_grad[1]
+            return dx, df
+
+    # Add to cache.
+    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
+    return Upfirdn2dCuda
+
+#----------------------------------------------------------------------------
+
+def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Filter a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape matches the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        padding:     Padding with respect to the output. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + fw // 2,
+        padx1 + (fw - 1) // 2,
+        pady0 + fh // 2,
+        pady1 + (fh - 1) // 2,
+    ]
+    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Upsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a multiple of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        up:          Integer upsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the output. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    upx, upy = _parse_scaling(up)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw + upx - 1) // 2,
+        padx1 + (fw - upx) // 2,
+        pady0 + (fh + upy - 1) // 2,
+        pady1 + (fh - upy) // 2,
+    ]
+    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
+
+#----------------------------------------------------------------------------
+
+def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
+    r"""Downsample a batch of 2D images using the given 2D FIR filter.
+
+    By default, the result is padded so that its shape is a fraction of the input.
+    User-specified padding is applied on top of that, with negative values
+    indicating cropping. Pixels outside the image are assumed to be zero.
+
+    Args:
+        x:           Float32/float64/float16 input tensor of the shape
+                     `[batch_size, num_channels, in_height, in_width]`.
+        f:           Float32 FIR filter of the shape
+                     `[filter_height, filter_width]` (non-separable),
+                     `[filter_taps]` (separable), or
+                     `None` (identity).
+        down:        Integer downsampling factor. Can be a single int or a list/tuple
+                     `[x, y]` (default: 1).
+        padding:     Padding with respect to the input. Can be a single number or a
+                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
+                     (default: 0).
+        flip_filter: False = convolution, True = correlation (default: False).
+        gain:        Overall scaling factor for signal magnitude (default: 1).
+        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
+
+    Returns:
+        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
+    """
+    downx, downy = _parse_scaling(down)
+    padx0, padx1, pady0, pady1 = _parse_padding(padding)
+    fw, fh = _get_filter_size(f)
+    p = [
+        padx0 + (fw - downx + 1) // 2,
+        padx1 + (fw - downx) // 2,
+        pady0 + (fh - downy + 1) // 2,
+        pady1 + (fh - downy) // 2,
+    ]
+    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/persistence.py b/models/stylegan3/torch_utils/persistence.py
new file mode 100644
index 0000000..f90ce85
--- /dev/null
+++ b/models/stylegan3/torch_utils/persistence.py
@@ -0,0 +1,251 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Facilities for pickling Python code alongside other data.
+
+The pickled code is automatically imported into a separate Python module
+during unpickling. This way, any previously exported pickles will remain
+usable even if the original code is no longer available, or if the current
+version of the code is not consistent with what was originally pickled."""
+
+import sys
+import pickle
+import io
+import inspect
+import copy
+import uuid
+import types
+import dnnlib
+
+#----------------------------------------------------------------------------
+
+_version            = 6         # internal version number
+_decorators         = set()     # {decorator_class, ...}
+_import_hooks       = []        # [hook_function, ...]
+_module_to_src_dict = dict()    # {module: src, ...}
+_src_to_module_dict = dict()    # {src: module, ...}
+
+#----------------------------------------------------------------------------
+
+def persistent_class(orig_class):
+    r"""Class decorator that extends a given class to save its source code
+    when pickled.
+
+    Example:
+
+        from torch_utils import persistence
+
+        @persistence.persistent_class
+        class MyNetwork(torch.nn.Module):
+            def __init__(self, num_inputs, num_outputs):
+                super().__init__()
+                self.fc = MyLayer(num_inputs, num_outputs)
+                ...
+
+        @persistence.persistent_class
+        class MyLayer(torch.nn.Module):
+            ...
+
+    When pickled, any instance of `MyNetwork` and `MyLayer` will save its
+    source code alongside other internal state (e.g., parameters, buffers,
+    and submodules). This way, any previously exported pickle will remain
+    usable even if the class definitions have been modified or are no
+    longer available.
+
+    The decorator saves the source code of the entire Python module
+    containing the decorated class. It does *not* save the source code of
+    any imported modules. Thus, the imported modules must be available
+    during unpickling, also including `torch_utils.persistence` itself.
+
+    It is ok to call functions defined in the same module from the
+    decorated class. However, if the decorated class depends on other
+    classes defined in the same module, they must be decorated as well.
+    This is illustrated in the above example in the case of `MyLayer`.
+
+    It is also possible to employ the decorator just-in-time before
+    calling the constructor. For example:
+
+        cls = MyLayer
+        if want_to_make_it_persistent:
+            cls = persistence.persistent_class(cls)
+        layer = cls(num_inputs, num_outputs)
+
+    As an additional feature, the decorator also keeps track of the
+    arguments that were used to construct each instance of the decorated
+    class. The arguments can be queried via `obj.init_args` and
+    `obj.init_kwargs`, and they are automatically pickled alongside other
+    object state. A typical use case is to first unpickle a previous
+    instance of a persistent class, and then upgrade it to use the latest
+    version of the source code:
+
+        with open('old_pickle.pkl', 'rb') as f:
+            old_net = pickle.load(f)
+        new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
+        misc.copy_params_and_buffers(old_net, new_net, require_all=True)
+    """
+    assert isinstance(orig_class, type)
+    if is_persistent(orig_class):
+        return orig_class
+
+    assert orig_class.__module__ in sys.modules
+    orig_module = sys.modules[orig_class.__module__]
+    orig_module_src = _module_to_src(orig_module)
+
+    class Decorator(orig_class):
+        _orig_module_src = orig_module_src
+        _orig_class_name = orig_class.__name__
+
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+            self._init_args = copy.deepcopy(args)
+            self._init_kwargs = copy.deepcopy(kwargs)
+            assert orig_class.__name__ in orig_module.__dict__
+            _check_pickleable(self.__reduce__())
+
+        @property
+        def init_args(self):
+            return copy.deepcopy(self._init_args)
+
+        @property
+        def init_kwargs(self):
+            return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
+
+        def __reduce__(self):
+            fields = list(super().__reduce__())
+            fields += [None] * max(3 - len(fields), 0)
+            if fields[0] is not _reconstruct_persistent_obj:
+                meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2])
+                fields[0] = _reconstruct_persistent_obj # reconstruct func
+                fields[1] = (meta,) # reconstruct args
+                fields[2] = None # state dict
+            return tuple(fields)
+
+    Decorator.__name__ = orig_class.__name__
+    _decorators.add(Decorator)
+    return Decorator
+
+#----------------------------------------------------------------------------
+
+def is_persistent(obj):
+    r"""Test whether the given object or class is persistent, i.e.,
+    whether it will save its source code when pickled.
+    """
+    try:
+        if obj in _decorators:
+            return True
+    except TypeError:
+        pass
+    return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck
+
+#----------------------------------------------------------------------------
+
+def import_hook(hook):
+    r"""Register an import hook that is called whenever a persistent object
+    is being unpickled. A typical use case is to patch the pickled source
+    code to avoid errors and inconsistencies when the API of some imported
+    module has changed.
+
+    The hook should have the following signature:
+
+        hook(meta) -> modified meta
+
+    `meta` is an instance of `dnnlib.EasyDict` with the following fields:
+
+        type:       Type of the persistent object, e.g. `'class'`.
+        version:    Internal version number of `torch_utils.persistence`.
+        module_src  Original source code of the Python module.
+        class_name: Class name in the original Python module.
+        state:      Internal state of the object.
+
+    Example:
+
+        @persistence.import_hook
+        def wreck_my_network(meta):
+            if meta.class_name == 'MyNetwork':
+                print('MyNetwork is being imported. I will wreck it!')
+                meta.module_src = meta.module_src.replace("True", "False")
+            return meta
+    """
+    assert callable(hook)
+    _import_hooks.append(hook)
+
+#----------------------------------------------------------------------------
+
+def _reconstruct_persistent_obj(meta):
+    r"""Hook that is called internally by the `pickle` module to unpickle
+    a persistent object.
+    """
+    meta = dnnlib.EasyDict(meta)
+    meta.state = dnnlib.EasyDict(meta.state)
+    for hook in _import_hooks:
+        meta = hook(meta)
+        assert meta is not None
+
+    assert meta.version == _version
+    module = _src_to_module(meta.module_src)
+
+    assert meta.type == 'class'
+    orig_class = module.__dict__[meta.class_name]
+    decorator_class = persistent_class(orig_class)
+    obj = decorator_class.__new__(decorator_class)
+
+    setstate = getattr(obj, '__setstate__', None)
+    if callable(setstate):
+        setstate(meta.state) # pylint: disable=not-callable
+    else:
+        obj.__dict__.update(meta.state)
+    return obj
+
+#----------------------------------------------------------------------------
+
+def _module_to_src(module):
+    r"""Query the source code of a given Python module.
+    """
+    src = _module_to_src_dict.get(module, None)
+    if src is None:
+        src = inspect.getsource(module)
+        _module_to_src_dict[module] = src
+        _src_to_module_dict[src] = module
+    return src
+
+def _src_to_module(src):
+    r"""Get or create a Python module for the given source code.
+    """
+    module = _src_to_module_dict.get(src, None)
+    if module is None:
+        module_name = "_imported_module_" + uuid.uuid4().hex
+        module = types.ModuleType(module_name)
+        sys.modules[module_name] = module
+        _module_to_src_dict[module] = src
+        _src_to_module_dict[src] = module
+        exec(src, module.__dict__) # pylint: disable=exec-used
+    return module
+
+#----------------------------------------------------------------------------
+
+def _check_pickleable(obj):
+    r"""Check that the given object is pickleable, raising an exception if
+    it is not. This function is expected to be considerably more efficient
+    than actually pickling the object.
+    """
+    def recurse(obj):
+        if isinstance(obj, (list, tuple, set)):
+            return [recurse(x) for x in obj]
+        if isinstance(obj, dict):
+            return [[recurse(x), recurse(y)] for x, y in obj.items()]
+        if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
+            return None # Python primitive types are pickleable.
+        if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor', 'torch.nn.parameter.Parameter']:
+            return None # NumPy arrays and PyTorch tensors are pickleable.
+        if is_persistent(obj):
+            return None # Persistent objects are pickleable, by virtue of the constructor check.
+        return obj
+    with io.BytesIO() as f:
+        pickle.dump(recurse(obj), f)
+
+#----------------------------------------------------------------------------
diff --git a/models/stylegan3/torch_utils/training_stats.py b/models/stylegan3/torch_utils/training_stats.py
new file mode 100644
index 0000000..5de4134
--- /dev/null
+++ b/models/stylegan3/torch_utils/training_stats.py
@@ -0,0 +1,268 @@
+# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""Facilities for reporting and collecting training statistics across
+multiple processes and devices. The interface is designed to minimize
+synchronization overhead as well as the amount of boilerplate in user
+code."""
+
+import re
+import numpy as np
+import torch
+import dnnlib
+
+from . import misc
+
+#----------------------------------------------------------------------------
+
+_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
+_reduce_dtype   = torch.float32 # Data type to use for initial per-tensor reduction.
+_counter_dtype  = torch.float64 # Data type to use for the internal counters.
+_rank           = 0             # Rank of the current process.
+_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
+_sync_called    = False         # Has _sync() been called yet?
+_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
+_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
+
+#----------------------------------------------------------------------------
+
+def init_multiprocessing(rank, sync_device):
+    r"""Initializes `torch_utils.training_stats` for collecting statistics
+    across multiple processes.
+
+    This function must be called after
+    `torch.distributed.init_process_group()` and before `Collector.update()`.
+    The call is not necessary if multi-process collection is not needed.
+
+    Args:
+        rank:           Rank of the current process.
+        sync_device:    PyTorch device to use for inter-process
+                        communication, or None to disable multi-process
+                        collection. Typically `torch.device('cuda', rank)`.
+    """
+    global _rank, _sync_device
+    assert not _sync_called
+    _rank = rank
+    _sync_device = sync_device
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def report(name, value):
+    r"""Broadcasts the given set of scalars to all interested instances of
+    `Collector`, across device and process boundaries.
+
+    This function is expected to be extremely cheap and can be safely
+    called from anywhere in the training loop, loss function, or inside a
+    `torch.nn.Module`.
+
+    Warning: The current implementation expects the set of unique names to
+    be consistent across processes. Please make sure that `report()` is
+    called at least once for each unique name by each process, and in the
+    same order. If a given process has no scalars to broadcast, it can do
+    `report(name, [])` (empty list).
+
+    Args:
+        name:   Arbitrary string specifying the name of the statistic.
+                Averages are accumulated separately for each unique name.
+        value:  Arbitrary set of scalars. Can be a list, tuple,
+                NumPy array, PyTorch tensor, or Python scalar.
+
+    Returns:
+        The same `value` that was passed in.
+    """
+    if name not in _counters:
+        _counters[name] = dict()
+
+    elems = torch.as_tensor(value)
+    if elems.numel() == 0:
+        return value
+
+    elems = elems.detach().flatten().to(_reduce_dtype)
+    moments = torch.stack([
+        torch.ones_like(elems).sum(),
+        elems.sum(),
+        elems.square().sum(),
+    ])
+    assert moments.ndim == 1 and moments.shape[0] == _num_moments
+    moments = moments.to(_counter_dtype)
+
+    device = moments.device
+    if device not in _counters[name]:
+        _counters[name][device] = torch.zeros_like(moments)
+    _counters[name][device].add_(moments)
+    return value
+
+#----------------------------------------------------------------------------
+
+def report0(name, value):
+    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
+    but ignores any scalars provided by the other processes.
+    See `report()` for further details.
+    """
+    report(name, value if _rank == 0 else [])
+    return value
+
+#----------------------------------------------------------------------------
+
+class Collector:
+    r"""Collects the scalars broadcasted by `report()` and `report0()` and
+    computes their long-term averages (mean and standard deviation) over
+    user-defined periods of time.
+
+    The averages are first collected into internal counters that are not
+    directly visible to the user. They are then copied to the user-visible
+    state as a result of calling `update()` and can then be queried using
+    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
+    internal counters for the next round, so that the user-visible state
+    effectively reflects averages collected between the last two calls to
+    `update()`.
+
+    Args:
+        regex:          Regular expression defining which statistics to
+                        collect. The default is to collect everything.
+        keep_previous:  Whether to retain the previous averages if no
+                        scalars were collected on a given round
+                        (default: True).
+    """
+    def __init__(self, regex='.*', keep_previous=True):
+        self._regex = re.compile(regex)
+        self._keep_previous = keep_previous
+        self._cumulative = dict()
+        self._moments = dict()
+        self.update()
+        self._moments.clear()
+
+    def names(self):
+        r"""Returns the names of all statistics broadcasted so far that
+        match the regular expression specified at construction time.
+        """
+        return [name for name in _counters if self._regex.fullmatch(name)]
+
+    def update(self):
+        r"""Copies current values of the internal counters to the
+        user-visible state and resets them for the next round.
+
+        If `keep_previous=True` was specified at construction time, the
+        operation is skipped for statistics that have received no scalars
+        since the last update, retaining their previous averages.
+
+        This method performs a number of GPU-to-CPU transfers and one
+        `torch.distributed.all_reduce()`. It is intended to be called
+        periodically in the main training loop, typically once every
+        N training steps.
+        """
+        if not self._keep_previous:
+            self._moments.clear()
+        for name, cumulative in _sync(self.names()):
+            if name not in self._cumulative:
+                self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
+            delta = cumulative - self._cumulative[name]
+            self._cumulative[name].copy_(cumulative)
+            if float(delta[0]) != 0:
+                self._moments[name] = delta
+
+    def _get_delta(self, name):
+        r"""Returns the raw moments that were accumulated for the given
+        statistic between the last two calls to `update()`, or zero if
+        no scalars were collected.
+        """
+        assert self._regex.fullmatch(name)
+        if name not in self._moments:
+            self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
+        return self._moments[name]
+
+    def num(self, name):
+        r"""Returns the number of scalars that were accumulated for the given
+        statistic between the last two calls to `update()`, or zero if
+        no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        return int(delta[0])
+
+    def mean(self, name):
+        r"""Returns the mean of the scalars that were accumulated for the
+        given statistic between the last two calls to `update()`, or NaN if
+        no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        if int(delta[0]) == 0:
+            return float('nan')
+        return float(delta[1] / delta[0])
+
+    def std(self, name):
+        r"""Returns the standard deviation of the scalars that were
+        accumulated for the given statistic between the last two calls to
+        `update()`, or NaN if no scalars were collected.
+        """
+        delta = self._get_delta(name)
+        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
+            return float('nan')
+        if int(delta[0]) == 1:
+            return float(0)
+        mean = float(delta[1] / delta[0])
+        raw_var = float(delta[2] / delta[0])
+        return np.sqrt(max(raw_var - np.square(mean), 0))
+
+    def as_dict(self):
+        r"""Returns the averages accumulated between the last two calls to
+        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
+
+            dnnlib.EasyDict(
+                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
+                ...
+            )
+        """
+        stats = dnnlib.EasyDict()
+        for name in self.names():
+            stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
+        return stats
+
+    def __getitem__(self, name):
+        r"""Convenience getter.
+        `collector[name]` is a synonym for `collector.mean(name)`.
+        """
+        return self.mean(name)
+
+#----------------------------------------------------------------------------
+
+def _sync(names):
+    r"""Synchronize the global cumulative counters across devices and
+    processes. Called internally by `Collector.update()`.
+    """
+    if len(names) == 0:
+        return []
+    global _sync_called
+    _sync_called = True
+
+    # Collect deltas within current rank.
+    deltas = []
+    device = _sync_device if _sync_device is not None else torch.device('cpu')
+    for name in names:
+        delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
+        for counter in _counters[name].values():
+            delta.add_(counter.to(device))
+            counter.copy_(torch.zeros_like(counter))
+        deltas.append(delta)
+    deltas = torch.stack(deltas)
+
+    # Sum deltas across ranks.
+    if _sync_device is not None:
+        torch.distributed.all_reduce(deltas)
+
+    # Update cumulative values.
+    deltas = deltas.cpu()
+    for idx, name in enumerate(names):
+        if name not in _cumulative:
+            _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
+        _cumulative[name].add_(deltas[idx])
+
+    # Return name-value pairs.
+    return [(name, _cumulative[name]) for name in names]
+
+#----------------------------------------------------------------------------
diff --git a/optimization/run_optimization.py b/optimization/run_optimization.py
new file mode 100644
index 0000000..81543c6
--- /dev/null
+++ b/optimization/run_optimization.py
@@ -0,0 +1,181 @@
+import argparse
+import math
+import os
+
+import torch
+import torchvision
+from torch import optim
+from tqdm import tqdm
+
+from criteria.clip_loss import CLIPLoss
+from criteria.id_loss import IDLoss
+from mapper.training.train_utils import STYLESPACE_DIMENSIONS
+from models.stylegan2.model import Generator
+import clip
+from utils import ensure_checkpoint_exists
+
+STYLESPACE_INDICES_WITHOUT_TORGB = [i for i in range(len(STYLESPACE_DIMENSIONS)) if i not in list(range(1, len(STYLESPACE_DIMENSIONS), 3))]
+
+def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
+    lr_ramp = min(1, (1 - t) / rampdown)
+    lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
+    lr_ramp = lr_ramp * min(1, t / rampup)
+
+    return initial_lr * lr_ramp
+
+
+def main(args):
+    ensure_checkpoint_exists(args.ckpt)
+    # 把描述加载进clip预训练模型里面去
+    text_inputs = torch.cat([clip.tokenize(args.description)]).cuda()
+    # print('text_input是： ', text_inputs)
+    '''
+     --description "a person with purple hair" 
+     tensor([[49406,   320,  2533,   593,  5496,  2225, 49407,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0]], device='cuda:0',
+       dtype=torch.int32)
+       --description "a person with red hair" 
+        tensor([[49406,   320,  2533,   593,   736,  2225, 49407,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+             0,     0,     0,     0,     0,     0,     0]], device='cuda:0',
+       dtype=torch.int32)
+    '''
+
+    os.makedirs(args.results_dir, exist_ok=True)
+
+    g_ema = Generator(args.stylegan_size, 512, 8)
+    g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)
+    # 将模型对象设置为评估模式
+    g_ema.eval()
+    #更改cuda卡号
+    g_ema = g_ema.cuda()
+    # device = torch.cuda.current_device()
+    # print('cuda:',device)
+    mean_latent = g_ema.mean_latent(4096)
+    # print('mean_latent: ', mean_latent.shape )    #[1,512]
+
+
+    if args.latent_path:
+        latent_code_init = torch.load(args.latent_path).cuda()
+        with torch.no_grad():
+            _, latent_code_init, _ = g_ema([latent_code_init], return_latents=True,
+                                        truncation=args.truncation, truncation_latent=mean_latent)
+    elif args.mode == "edit":
+        latent_code_init_not_trunc = torch.randn(1, 512).cuda()
+        with torch.no_grad():
+            _, latent_code_init, _ = g_ema([latent_code_init_not_trunc], return_latents=True,
+                                        truncation=args.truncation, truncation_latent=mean_latent)
+    else:
+        latent_code_init = mean_latent.detach().clone().repeat(1, 18, 1)
+        print(latent_code_init)  #在维度1上重复18次 torch.Size([1, 18, 512])
+    with torch.no_grad():
+        img_orig, _ = g_ema([latent_code_init], input_is_latent=True, randomize_noise=False)
+
+    if args.work_in_stylespace:
+        with torch.no_grad():
+            _, _, latent_code_init = g_ema([latent_code_init], input_is_latent=True, return_latents=True)
+        latent = [s.detach().clone() for s in latent_code_init]
+        for c, s in enumerate(latent):
+            if c in STYLESPACE_INDICES_WITHOUT_TORGB:
+                s.requires_grad = True
+    else:
+        latent = latent_code_init.detach().clone()
+        latent.requires_grad = True
+
+    clip_loss = CLIPLoss(args)
+    id_loss = IDLoss(args)
+
+    if args.work_in_stylespace:
+        optimizer = optim.Adam(latent, lr=args.lr)
+    else:
+        optimizer = optim.Adam([latent], lr=args.lr)
+
+    pbar = tqdm(range(args.step))
+
+    for i in pbar:
+        t = i / args.step
+        lr = get_lr(t, args.lr)
+        optimizer.param_groups[0]["lr"] = lr
+
+        img_gen, _ = g_ema([latent], input_is_latent=True, randomize_noise=False, input_is_stylespace=args.work_in_stylespace)
+
+        c_loss = clip_loss(img_gen, text_inputs)
+
+        if args.id_lambda > 0:
+            i_loss = id_loss(img_gen, img_orig)[0]
+        else:
+            i_loss = 0
+
+        if args.mode == "edit":
+            if args.work_in_stylespace:
+                l2_loss = sum([((latent_code_init[c] - latent[c]) ** 2).sum() for c in range(len(latent_code_init))])
+            else:
+                l2_loss = ((latent_code_init - latent) ** 2).sum()
+            loss = c_loss + args.l2_lambda * l2_loss + args.id_lambda * i_loss
+        else:
+            loss = c_loss
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        pbar.set_description(
+            (
+                f"loss: {loss.item():.4f};"
+            )
+        )
+        if args.save_intermediate_image_every > 0 and i % args.save_intermediate_image_every == 0:
+            with torch.no_grad():
+                img_gen, _ = g_ema([latent], input_is_latent=True, randomize_noise=False, input_is_stylespace=args.work_in_stylespace)
+
+            torchvision.utils.save_image(img_gen, f"results/{str(i).zfill(5)}.jpg", normalize=True, range=(-1, 1))
+
+    if args.mode == "edit":
+        final_result = torch.cat([img_orig, img_gen])
+    else:
+        final_result = img_gen
+
+    return final_result
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--description", type=str, default="a person with purple hair", help="the text that guides the editing/generation")
+    parser.add_argument("--ckpt", type=str, default="../pretrained_models/stylegan2-ffhq-config-f.pt", help="pretrained StyleGAN2 weights")
+    parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution")
+    parser.add_argument("--lr_rampup", type=float, default=0.05)
+    parser.add_argument("--lr", type=float, default=0.1)
+    parser.add_argument("--step", type=int, default=300, help="number of optimization steps")
+    parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"], help="choose between edit an image an generate a free one")
+    parser.add_argument("--l2_lambda", type=float, default=0.008, help="weight of the latent distance (used for editing only)")
+    parser.add_argument("--id_lambda", type=float, default=0.000, help="weight of id loss (used for editing only)")
+    parser.add_argument("--latent_path", type=str, default=None, help="starts the optimization from the given latent code if provided. Otherwose, starts from"
+                                                                      "the mean latent in a free generation, and from a random one in editing. "
+                                                                      "Expects a .pt format")
+    parser.add_argument("--truncation", type=float, default=0.7, help="used only for the initial latent vector, and only when a latent code path is"
+                                                                      "not provided")
+    parser.add_argument('--work_in_stylespace', default=False, action='store_true')
+    parser.add_argument("--save_intermediate_image_every", type=int, default=20, help="if > 0 then saves intermidate results during the optimization")
+    parser.add_argument("--results_dir", type=str, default="results")
+    parser.add_argument('--ir_se50_weights', default='../pretrained_models/model_ir_se50.pth', type=str,
+                             help="Path to facial recognition network used in ID loss")
+
+    args = parser.parse_args()
+
+    result_image = main(args)
+
+    torchvision.utils.save_image(result_image.detach().cpu(), os.path.join(args.results_dir, "final_result.jpg"), normalize=True, scale_each=True, range=(-1, 1))
+
+
diff --git a/test001.py b/test001.py
new file mode 100644
index 0000000..ee7fde2
--- /dev/null
+++ b/test001.py
@@ -0,0 +1,36 @@
+import torchvision
+import argparse
+from argparse import Namespace
+from optimization.run_optimization import main
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--description", type=str, default="a person with purple hair",
+                    help="the text that guides the editing/generation")
+parser.add_argument("--ckpt", type=str, default="./pretrained_models/stylegan2-ffhq-config-f.pt",
+                    help="pretrained StyleGAN2 weights")
+parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution")
+parser.add_argument("--lr_rampup", type=float, default=0.05)
+parser.add_argument("--lr", type=float, default=0.1)
+parser.add_argument("--step", type=int, default=300, help="number of optimization steps")
+parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"],
+                    help="choose between edit an image an generate a free one")
+parser.add_argument("--l2_lambda", type=float, default=0.008,
+                    help="weight of the latent distance (used for editing only)")
+parser.add_argument("--latent_path", type=str, default="/home/ly/StyleCLIP-main/pretrained_models/latent_code/style3.pt",
+                    help="starts the optimization from the given latent code if provided. Otherwise, starts from"
+                         "the mean latent in a free generation, and from a random one in editing. "
+                         "Expects a .pt format")
+parser.add_argument("--truncation", type=float, default=0.7,
+                    help="used only for the initial latent vector, and only when a latent code path is"
+                         "not provided")
+parser.add_argument("--save_intermediate_image_every", type=int, default=20,
+                    help="if > 0 then saves intermidate results during the optimization")
+parser.add_argument("--results_dir", type=str, default="results")
+parser.add_argument('--work_in_stylespace', default=False, action='store_true', help="trains a mapper in S instead of W+")
+parser.add_argument('--ir_se50_weights', default='pretrained_models/model_ir_se50.pth', type=str, help="Path to facial recognition network used in ID loss")
+parser.add_argument('--id_lambda', default=0.1, type=float, help='ID loss multiplier factor')
+
+args = vars(parser.parse_args())
+result_image = main(Namespace(**args))
+torchvision.utils.save_image(result_image.detach().cpu(), f"results/final_result.png", normalize=True, scale_each=True,
+                             range=(-1, 1))
\ No newline at end of file
diff --git a/test002.py b/test002.py
new file mode 100644
index 0000000..f4f4c7a
--- /dev/null
+++ b/test002.py
@@ -0,0 +1,27 @@
+import torchvision
+import argparse
+from argparse import Namespace
+from PIL import Image
+
+from utils import ensure_checkpoint_exists
+from mapper.scripts.inference import run
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--exp_dir', default="./results", type=str, help='Path to experiment output directory')
+parser.add_argument('--checkpoint_path', default="./pretrained_models/mapper/purple_hair.pt", type=str,
+                    help='Path to model checkpoint')
+parser.add_argument('--couple_outputs', default=True, action='store_true',
+                    help='Whether to also save inputs + outputs side-by-side')
+parser.add_argument('--mapper_type', default='LevelsMapper', type=str, help='Which mapper to use')
+parser.add_argument('--no_coarse_mapper', default=False, action="store_true")
+parser.add_argument('--no_medium_mapper', default=False, action="store_true")
+parser.add_argument('--no_fine_mapper', default=False, action="store_true")
+parser.add_argument('--stylegan_size', default=1024, type=int)
+parser.add_argument('--test_batch_size', default=2, type=int, help='Batch size for testing and inference')
+parser.add_argument('--latents_test_path', default="./latents_test/example_celebs.pt", type=str,
+                    help="The latents for the validation")
+parser.add_argument('--test_workers', default=0, type=int, help='Number of test/inference dataloader workers')
+parser.add_argument('--n_images', type=int, default=None, help='Number of images to output. If None, run on all data')
+
+args = vars(parser.parse_args())
+run(Namespace(**args))
\ No newline at end of file
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..97e1bb9
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,49 @@
+import os
+
+
+google_drive_paths = {
+    "stylegan2-ffhq-config-f.pt": "https://drive.google.com/uc?id=1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT",
+
+    "mapper/pretrained/afro.pt": "https://drive.google.com/uc?id=1i5vAqo4z0I-Yon3FNft_YZOq7ClWayQJ",
+    "mapper/pretrained/angry.pt": "https://drive.google.com/uc?id=1g82HEH0jFDrcbCtn3M22gesWKfzWV_ma",
+    "mapper/pretrained/beyonce.pt": "https://drive.google.com/uc?id=1KJTc-h02LXs4zqCyo7pzCp0iWeO6T9fz",
+    "mapper/pretrained/bobcut.pt": "https://drive.google.com/uc?id=1IvyqjZzKS-vNdq_OhwapAcwrxgLAY8UF",
+    "mapper/pretrained/bowlcut.pt": "https://drive.google.com/uc?id=1xwdxI2YCewSt05dEHgkpmmzoauPjEnnZ",
+    "mapper/pretrained/curly_hair.pt": "https://drive.google.com/uc?id=1xZ7fFB12Ci6rUbUfaHPpo44xUFzpWQ6M",
+    "mapper/pretrained/depp.pt": "https://drive.google.com/uc?id=1FPiJkvFPG_y-bFanxLLP91wUKuy-l3IV",
+    "mapper/pretrained/hilary_clinton.pt": "https://drive.google.com/uc?id=1X7U2zj2lt0KFifIsTfOOzVZXqYyCWVll",
+    "mapper/pretrained/mohawk.pt": "https://drive.google.com/uc?id=1oMMPc8iQZ7dhyWavZ7VNWLwzf9aX4C09",
+    "mapper/pretrained/purple_hair.pt": "https://drive.google.com/uc?id=14H0CGXWxePrrKIYmZnDD2Ccs65EEww75",
+    "mapper/pretrained/surprised.pt": "https://drive.google.com/uc?id=1F-mPrhO-UeWrV1QYMZck63R43aLtPChI",
+    "mapper/pretrained/taylor_swift.pt": "https://drive.google.com/uc?id=10jHuHsKKJxuf3N0vgQbX_SMEQgFHDrZa",
+    "mapper/pretrained/trump.pt": "https://drive.google.com/uc?id=14v8D0uzy4tOyfBU3ca9T0AzTt3v-dNyh",
+    "mapper/pretrained/zuckerberg.pt": "https://drive.google.com/uc?id=1NjDcMUL8G-pO3i_9N6EPpQNXeMc3Ar1r",
+
+    "example_celebs.pt": "https://drive.google.com/uc?id=1VL3lP4avRhz75LxSza6jgDe-pHd2veQG"
+}
+
+
+def ensure_checkpoint_exists(model_weights_filename):
+    if not os.path.isfile(model_weights_filename) and (
+        model_weights_filename in google_drive_paths
+    ):
+        gdrive_url = google_drive_paths[model_weights_filename]
+        try:
+            from gdown import download as drive_download
+
+            drive_download(gdrive_url, model_weights_filename, quiet=False)
+        except ModuleNotFoundError:
+            print(
+                "gdown module not found.",
+                "pip3 install gdown or, manually download the checkpoint file:",
+                gdrive_url
+            )
+
+    if not os.path.isfile(model_weights_filename) and (
+        model_weights_filename not in google_drive_paths
+    ):
+        print(
+            model_weights_filename,
+            " not found, you may need to manually download the model weights."
+        )
+