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app.py

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+import gradio as gr
+import requests
+
+NGROK_URL = "https://429e-35-192-148-148.ngrok-free.app"  # Update this with your actual ngrok URL
+
+def generate_and_save(prompt, negative_prompt, num_frames, guidance_scale, num_inference_steps, seed):
+    data = {
+        'prompt': prompt,
+        'negative_prompt': negative_prompt,
+        'num_frames': num_frames,
+        'guidance_scale': guidance_scale,
+        'num_inference_steps': num_inference_steps,
+        'seed': seed
+    }
+    response = requests.post(f'{NGROK_URL}/generate', json=data)
+    with open('output.gif', 'wb') as f:
+        f.write(response.content)
+    return 'output.gif'
+
+interface = gr.Interface(
+    fn=generate_and_save,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Textbox(label="Negative Prompt"),
+        gr.Slider(minimum=8, maximum=32, step=1, label="Number of Frames", value=16),
+        gr.Slider(minimum=1, maximum=10, step=0.1, label="Guidance Scale", value=2.0),
+        gr.Slider(minimum=1, maximum=50, step=1, label="Inference Steps", value=6),
+        gr.Number(label="Seed", value=0),
+    ],
+    outputs=gr.Image(type="filepath", label="Generated Animation"),
+    title="Text-to-Video Generation with AnimateLCM",
+    description="Generate short animations from text prompts using AnimateLCM model.",
+)
+
+if __name__ == "__main__":
+    interface.launch()
+

requirements.txt

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+torch
+diffusers
+transformers
+accelerate
+safetensors
+gradio
+peft
+flask
+pyngrok