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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import argparse | |
| import functools | |
| import os | |
| import pickle | |
| import sys | |
| sys.path.insert(0, 'stylegan_xl') | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from huggingface_hub import hf_hub_download | |
| ORIGINAL_REPO_URL = 'https://github.com/autonomousvision/stylegan_xl' | |
| TITLE = 'autonomousvision/stylegan_xl' | |
| DESCRIPTION = f'''This is a demo for {ORIGINAL_REPO_URL}. | |
| For class-conditional models, you can specify the class index. | |
| Index-to-label dictionaries for ImageNet and CIFAR-10 can be found [here](https://raw.githubusercontent.com/autonomousvision/stylegan_xl/main/misc/imagenet_idx2labels.txt) and [here](https://www.cs.toronto.edu/~kriz/cifar.html), respectively. | |
| ''' | |
| SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/StyleGAN-XL/resolve/main/samples' | |
| ARTICLE = f'''## Generated images | |
| - truncation: 0.7 | |
| ### ImageNet | |
| - size: 128x128 | |
| - class index: 0-999 | |
| - seed: 0 | |
|  | |
| ### CIFAR-10 | |
| - size: 32x32 | |
| - class index: 0-9 | |
| - seed: 0-9 | |
|  | |
| ### FFHQ | |
| - size: 256x256 | |
| - seed: 0-99 | |
|  | |
| ### Pokemon | |
| - size: 256x256 | |
| - seed: 0-99 | |
|  | |
| ''' | |
| TOKEN = os.environ['TOKEN'] | |
| def parse_args() -> argparse.Namespace: | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--device', type=str, default='cpu') | |
| parser.add_argument('--theme', type=str) | |
| parser.add_argument('--live', action='store_true') | |
| parser.add_argument('--share', action='store_true') | |
| parser.add_argument('--port', type=int) | |
| parser.add_argument('--disable-queue', | |
| dest='enable_queue', | |
| action='store_false') | |
| parser.add_argument('--allow-flagging', type=str, default='never') | |
| parser.add_argument('--allow-screenshot', action='store_true') | |
| return parser.parse_args() | |
| def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray: | |
| mat = np.eye(3) | |
| sin = np.sin(angle / 360 * np.pi * 2) | |
| cos = np.cos(angle / 360 * np.pi * 2) | |
| mat[0][0] = cos | |
| mat[0][1] = sin | |
| mat[0][2] = translate[0] | |
| mat[1][0] = -sin | |
| mat[1][1] = cos | |
| mat[1][2] = translate[1] | |
| return mat | |
| def generate_z(seed: int, device: torch.device) -> torch.Tensor: | |
| return torch.from_numpy(np.random.RandomState(seed).randn(1, | |
| 64)).to(device) | |
| def generate_image(model_name: str, class_index: int, seed: int, | |
| truncation_psi: float, tx: float, ty: float, angle: float, | |
| model_dict: dict[str, nn.Module], | |
| device: torch.device) -> np.ndarray: | |
| model = model_dict[model_name] | |
| seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max)) | |
| z = generate_z(seed, device) | |
| label = torch.zeros([1, model.c_dim], device=device) | |
| class_index = round(class_index) | |
| class_index = min(max(0, class_index), model.c_dim - 1) | |
| class_index = torch.tensor(class_index, dtype=torch.long) | |
| if class_index >= 0: | |
| label[:, class_index] = 1 | |
| mat = make_transform((tx, ty), angle) | |
| mat = np.linalg.inv(mat) | |
| model.synthesis.input.transform.copy_(torch.from_numpy(mat)) | |
| out = model(z, label, truncation_psi=truncation_psi) | |
| out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) | |
| return out[0].cpu().numpy() | |
| def load_model(model_name: str, device: torch.device) -> nn.Module: | |
| path = hf_hub_download('hysts/StyleGAN-XL', | |
| f'models/{model_name}.pkl', | |
| use_auth_token=TOKEN) | |
| with open(path, 'rb') as f: | |
| model = pickle.load(f)['G_ema'] | |
| model.eval() | |
| model.to(device) | |
| with torch.inference_mode(): | |
| z = torch.zeros((1, 64)).to(device) | |
| label = torch.zeros([1, model.c_dim], device=device) | |
| model(z, label) | |
| return model | |
| def main(): | |
| gr.close_all() | |
| args = parse_args() | |
| device = torch.device(args.device) | |
| model_names = [ | |
| 'imagenet16', | |
| 'imagenet32', | |
| 'imagenet64', | |
| 'imagenet128', | |
| 'cifar10', | |
| 'ffhq256', | |
| 'pokemon256', | |
| ] | |
| model_dict = {name: load_model(name, device) for name in model_names} | |
| func = functools.partial(generate_image, | |
| model_dict=model_dict, | |
| device=device) | |
| func = functools.update_wrapper(func, generate_image) | |
| gr.Interface( | |
| func, | |
| [ | |
| gr.inputs.Radio(model_names, | |
| type='value', | |
| default='imagenet128', | |
| label='Model'), | |
| gr.inputs.Number(default=284, label='Class index'), | |
| gr.inputs.Number(default=0, label='Seed'), | |
| gr.inputs.Slider( | |
| 0, 2, step=0.05, default=0.7, label='Truncation psi'), | |
| gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate X'), | |
| gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate Y'), | |
| gr.inputs.Slider(-180, 180, step=5, default=0, label='Angle'), | |
| ], | |
| gr.outputs.Image(type='numpy', label='Output'), | |
| theme=args.theme, | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| article=ARTICLE, | |
| allow_screenshot=args.allow_screenshot, | |
| allow_flagging=args.allow_flagging, | |
| live=args.live, | |
| ).launch( | |
| enable_queue=args.enable_queue, | |
| server_port=args.port, | |
| share=args.share, | |
| ) | |
| if __name__ == '__main__': | |
| main() | |