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 ccab82ad02088debe106872d8d73f9fc4b250ad1

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+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')
+
+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('--share', action='store_true')
+    parser.add_argument('--port', type=int)
+    parser.add_argument('--disable-queue',
+                        dest='enable_queue',
+                        action='store_false')
+    return parser.parse_args()
+
+
+class App:
+
+    def __init__(self, device: torch.device):
+        self.device = device
+        self.model = self.load_model('stylegan_human_v2_1024.pkl')
+
+    def load_model(self, file_name: str) -> 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(self.device)
+        with torch.inference_mode():
+            z = torch.zeros((1, model.z_dim)).to(self.device)
+            label = torch.zeros([1, model.c_dim], device=self.device)
+            model(z, label, force_fp32=True)
+        return model
+
+    def generate_z(self, z_dim: int, seed: int) -> torch.Tensor:
+        return torch.from_numpy(np.random.RandomState(seed).randn(
+            1, z_dim)).to(self.device).float()
+
+    @torch.inference_mode()
+    def generate_single_image(self, seed: int,
+                              truncation_psi: float) -> np.ndarray:
+        seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
+
+        z = self.generate_z(self.model.z_dim, seed)
+        label = torch.zeros([1, self.model.c_dim], device=self.device)
+
+        out = self.model(z,
+                         label,
+                         truncation_psi=truncation_psi,
+                         force_fp32=True)
+        out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
+            torch.uint8)
+        return out[0].cpu().numpy()
+
+    @torch.inference_mode()
+    def generate_interpolated_images(
+            self, seed0: int, psi0: float, seed1: int, psi1: float,
+            num_intermediate: int) -> 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 = self.generate_z(self.model.z_dim, seed0)
+        z1 = self.generate_z(self.model.z_dim, seed1)
+        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, self.model.c_dim], device=self.device)
+
+        res = []
+        for z, psi in zip(zs, psis):
+            out = self.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)
+        return res
+
+
+def main():
+    args = parse_args()
+    app = App(device=torch.device(args.device))
+
+    with gr.Blocks(theme=args.theme) as demo:
+        gr.Markdown('''<center><h1>StyleGAN-Human</h1></center>
+
+This is a Blocks version of [this app](https://huggingface.co/spaces/hysts/StyleGAN-Human) and [this app](https://huggingface.co/spaces/hysts/StyleGAN-Human-Interpolation).
+''')
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    seed1 = gr.Number(value=6876, label='Seed 1')
+                    psi1 = gr.Slider(0,
+                                     2,
+                                     value=0.7,
+                                     step=0.05,
+                                     label='Truncation psi 1')
+                with gr.Row():
+                    generate_button1 = gr.Button('Generate')
+                with gr.Row():
+                    generated_image1 = gr.Image(type='numpy',
+                                                label='Generated Image 1')
+
+            with gr.Column():
+                with gr.Row():
+                    seed2 = gr.Number(value=6886, label='Seed 2')
+                    psi2 = gr.Slider(0,
+                                     2,
+                                     value=0.7,
+                                     step=0.05,
+                                     label='Truncation psi 2')
+                with gr.Row():
+                    generate_button2 = gr.Button('Generate')
+                with gr.Row():
+                    generated_image2 = gr.Image(type='numpy',
+                                                label='Generated Image 2')
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    num_frames = gr.Slider(
+                        0,
+                        41,
+                        value=7,
+                        step=1,
+                        label='Number of Intermediate Frames')
+                with gr.Row():
+                    interpolate_button = gr.Button('Interpolate')
+                with gr.Row():
+                    interpolated_images = gr.Gallery(label='Output Images')
+
+        gr.Markdown(
+            '<center><img src="https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.stylegan-human" alt="visitor badge"/></center>'
+        )
+
+        generate_button1.click(app.generate_single_image,
+                               inputs=[seed1, psi1],
+                               outputs=generated_image1)
+        generate_button2.click(app.generate_single_image,
+                               inputs=[seed2, psi2],
+                               outputs=generated_image2)
+        interpolate_button.click(app.generate_interpolated_images,
+                                 inputs=[seed1, psi1, seed2, psi2, num_frames],
+                                 outputs=interpolated_images)
+
+    demo.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