Update

README.md

@@ -4,7 +4,7 @@ emoji: 🌍
 colorFrom: purple
 colorTo: yellow
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.36.0
 app_file: app.py
 pinned: false
 suggested_hardware: t4-small

app.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import functools
 import pickle
 import sys
 
@@ -18,22 +17,6 @@ TITLE = "StyleGAN-Human"
 DESCRIPTION = "https://github.com/stylegan-human/StyleGAN-Human"
 
 
-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_image(seed: int, truncation_psi: float, model: nn.Module, device: torch.device) -> np.ndarray:
-    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
-
-    z = generate_z(model.z_dim, seed, device)
-    label = torch.zeros([1, model.c_dim], device=device)
-
-    out = 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()
-
-
 def load_model(file_name: str, device: torch.device) -> nn.Module:
     path = hf_hub_download("public-data/StyleGAN-Human", f"models/{file_name}")
     with open(path, "rb") as f:
@@ -49,15 +32,36 @@ def load_model(file_name: str, device: torch.device) -> nn.Module:
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 model = load_model("stylegan_human_v2_1024.pkl", device)
-fn = functools.partial(generate_image, model=model, device=device)
 
-gr.Interface(
-    fn=fn,
+
+def generate_z(z_dim: int, seed: int) -> torch.Tensor:
+    return torch.from_numpy(np.random.RandomState(seed).randn(1, z_dim)).float()
+
+
+@torch.inference_mode()
+def generate_image(seed: int, truncation_psi: float) -> np.ndarray:
+    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
+
+    z = generate_z(model.z_dim, seed)
+    z = z.to(device)
+    label = torch.zeros([1, model.c_dim], device=device)
+
+    out = 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()
+
+
+demo = gr.Interface(
+    fn=generate_image,
     inputs=[
         gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, value=0),
         gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7),
     ],
-    outputs=gr.Image(label="Output", type="numpy"),
+    outputs=gr.Image(label="Output"),
     title=TITLE,
     description=DESCRIPTION,
-).queue(max_size=10).launch()
+)
+
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch()

requirements.txt

@@ -1,5 +1,5 @@
-numpy==1.23.5
-Pillow==10.0.0
-scipy==1.10.1
+numpy==1.26.4
+Pillow==10.3.0
+scipy==1.13.1
 torch==2.0.1
 torchvision==0.15.2