e

app.py

@@ -3,36 +3,40 @@ import gradio as gr
 import torch
 from diffusers import DiffusionPipeline
 
-# Read token from environment (configured as a Space secret)
+# Read token and optional model override from environment
 token = os.environ.get("HUGGINGFACE_TOKEN")
-if token is None:
+if not token:
     raise ValueError("Environment variable HUGGINGFACE_TOKEN is not set.")
 
-model_id = "Wan-AI/Wan2.1-I2V-14B-480P"
+# Use the Diffusers-ready model repository by default
+model_id = os.environ.get(
+    "WAN_MODEL_ID", "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+)
 
-# Load pipeline directly from the Hub, using the token
+# Load the pipeline with remote code support
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 pipe = DiffusionPipeline.from_pretrained(
     model_id,
-    torch_dtype=torch.float16,
+    torch_dtype=torch_dtype,
     trust_remote_code=True,
     use_auth_token=token
-).to("cuda")
+).to("cuda" if torch.cuda.is_available() else "cpu")
 
 # Enable memory-saving features
 pipe.enable_attention_slicing()
 
 # Generation function
 def generate_video(image, prompt, num_frames=16, steps=50, guidance_scale=7.5):
-    result = pipe(
+    output = pipe(
         prompt=prompt,
         init_image=image,
         num_inference_steps=steps,
         guidance_scale=guidance_scale,
         num_frames=num_frames
     )
-    return result.videos
+    return output.videos
 
-# Gradio UI definition
+# Gradio UI
 def main():
     with gr.Blocks() as demo:
         gr.Markdown("# Wan2.1 Image-to-Video Demo")