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import gradio as gr | |
from diffusers import DiffusionPipeline | |
# Load the diffusion pipeline | |
pipe = DiffusionPipeline.from_pretrained("ByteDance/AnimateDiff-Lightning") | |
def generate_image(prompt): | |
# Generate the image from the prompt | |
image = pipe(prompt).images[0] | |
return image | |
# Create a Gradio interface | |
iface = gr.Interface( | |
fn=generate_image, | |
inputs=gr.Textbox(label="Enter your prompt", placeholder="e.g. Astronaut in a jungle"), | |
outputs=gr.Image(label="Generated Image"), | |
title="Diffusion Pipeline Image Generator", | |
description="Generate images using a diffusion pipeline based on your prompts." | |
) | |
if __name__ == "__main__": | |
iface.launch() | |