Add files

.gitmodules

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+[submodule "StyleGAN-Human"]
+	path = StyleGAN-Human
+	url = https://github.com/stylegan-human/StyleGAN-Human

StyleGAN-Human

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+Subproject commit d2514c145a451453804f60a12de8f13d40c9fe4f

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pickle
+import sys
+
+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+sys.path.insert(0, 'StyleGAN-Human')
+
+TITLE = 'StyleGAN-Human (Interpolation)'
+DESCRIPTION = 'This is a demo for https://github.com/stylegan-human/StyleGAN-Human.'
+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 load_model(file_name: str, device: torch.device) -> nn.Module:
+    path = hf_hub_download('hysts/StyleGAN-Human',
+                           f'models/{file_name}',
+                           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, model.z_dim)).to(device)
+        label = torch.zeros([1, model.c_dim], device=device)
+        model(z, label, force_fp32=True)
+    return model
+
+
+def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor:
+    return torch.from_numpy(np.random.RandomState(seed).randn(
+        1, z_dim)).to(device).float()
+
+
+@torch.inference_mode()
+def generate_interpolated_images(
+        seed0: int, psi0: float, seed1: int, psi1: float,
+        num_intermediate: int, model: nn.Module,
+        device: torch.device) -> tuple[list[np.ndarray], np.ndarray]:
+    seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
+    seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
+
+    z0 = generate_z(model.z_dim, seed0, device)
+    z1 = generate_z(model.z_dim, seed1, device)
+    vec = z1 - z0
+    dvec = vec / (num_intermediate + 1)
+    zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
+    dpsi = (psi1 - psi0) / (num_intermediate + 1)
+    psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]
+
+    label = torch.zeros([1, model.c_dim], device=device)
+
+    res = []
+    for z, psi in zip(zs, psis):
+        out = model(z, label, truncation_psi=psi, force_fp32=True)
+        out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
+            torch.uint8)
+        out = out[0].cpu().numpy()
+        res.append(out)
+    concatenated = np.hstack(res)
+    return res, concatenated
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    model = load_model('stylegan_human_v2_1024.pkl', device)
+
+    func = functools.partial(generate_interpolated_images,
+                             model=model,
+                             device=device)
+    func = functools.update_wrapper(func, generate_interpolated_images)
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Number(default=0, label='Seed 1'),
+            gr.inputs.Slider(
+                0, 2, step=0.05, default=0.7, label='Truncation psi 1'),
+            gr.inputs.Number(default=1, label='Seed 2'),
+            gr.inputs.Slider(
+                0, 2, step=0.05, default=0.7, label='Truncation psi 2'),
+            gr.inputs.Slider(0,
+                             21,
+                             step=1,
+                             default=7,
+                             label='Number of Intermediate Frames'),
+        ],
+        [
+            gr.outputs.Carousel(gr.outputs.Image(type='numpy'),
+                                label='Output Images'),
+            gr.outputs.Image(type='numpy', label='Concatenated'),
+        ],
+        title=TITLE,
+        description=DESCRIPTION,
+        article=ARTICLE,
+        theme=args.theme,
+        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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+numpy==1.22.3
+Pillow==9.1.0
+scipy==1.8.0
+torch==1.11.0
+torchvision==0.12.0