Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import AutoencoderKLWan, WanPipeline
+from diffusers.utils import export_to_video
+import spaces  # ZeroGPU integration
+
+@spaces.GPU  # This decorator requests a GPU when the function is called (ZeroGPU config)
+def generate_video(prompt, negative_prompt=""):
+    model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+    
+    # Load the VAE and pipeline with proper data types per model requirements
+    vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+    pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+    pipe.to("cuda")
+    
+    # Generate video frames at 480p resolution (480x832) with the desired settings
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=480,        # 480p height
+        width=832,         # width set to match 480p (adjust as needed)
+        num_frames=81,     # Number of frames (adjust for desired video length)
+        guidance_scale=5.0 # Recommended for the 1.3B model
+    ).frames[0]
+    
+    # Save the generated frames as a video file
+    video_path = "output.mp4"
+    export_to_video(output, video_path, fps=15)
+    return video_path
+
+# Create a Gradio interface for the video generation
+iface = gr.Interface(
+    fn=generate_video,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter your video prompt here"),
+        gr.Textbox(label="Negative Prompt", placeholder="Optional negative prompt", value="")
+    ],
+    outputs=gr.Video(label="Generated Video"),
+    title="Wan2.1-T2V-1.3B Video Generator",
+    description="Generate 480p videos using the Wan2.1-T2V-1.3B diffusers pipeline with ZeroGPU support."
+)
+
+if __name__ == "__main__":
+    iface.launch()