Add files

.gitmodules

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+[submodule "stylegan_xl"]
+	path = stylegan_xl
+	url = https://github.com/autonomousvision/stylegan_xl

app.py

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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.
+'''
+ARTICLE = None
+
+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)
+
+
+@torch.inference_mode()
+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()

requirements.txt

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+ftfy==6.1.1
+numpy==1.22.3
+Pillow==9.0.1
+scipy==1.8.0
+torch==1.11.0
+torchvision==0.12.0

stylegan_xl

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+Subproject commit 754f491583c96ff1c8ee9c05762aef1835b6d0b9