Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline
+
+# Load the model and move to GPU if available
+model_name = "ByteDance/AnimateDiff-Lightning"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = DiffusionPipeline.from_pretrained(model_name)
+pipe = pipe.to(device)
+
+def generate_video(prompt):
+    # Generate video (replace with actual method for your model)
+    result = pipe(prompt)
+    # Assuming the model returns a list of videos or a video object
+    # Adjust this based on the actual output
+    video = result.videos[0]  # or result['videos'][0]
+    # Save video to file
+    video_path = "generated_video.mp4"
+    video.save(video_path)
+    return video_path
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Text to Video Generation")
+    prompt_input = gr.Textbox(label="Enter your prompt", lines=2, placeholder="A spaceship in space, neon colors")
+    generate_button = gr.Button("Generate Video")
+    video_output = gr.Video(label="Generated Video")
+    
+    generate_button.click(
+        fn=generate_video,
+        inputs=prompt_input,
+        outputs=video_output
+    )
+
+demo.launch()