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import gradio as gr | |
import requests | |
NGROK_URL = "https://f719-34-74-140-255.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() | |