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import torch
from diffusers import StableDiffusionPipeline
import imageio
import os
import gradio as gr
# Ensure the output directory exists
OUTPUT_FOLDER = "generated_frames"
os.makedirs(OUTPUT_FOLDER, exist_ok=True)
# Load Stable Diffusion Model
print("Loading Stable Diffusion model...")
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe.to("cpu") # Force to run on CPU
def generate_images(prompt, num_images=5):
"""Generates images from a prompt and creates an animated GIF."""
images = []
for i in range(num_images):
print(f"Generating image {i+1}/{num_images}...")
image = pipe(prompt).images[0] # Generate image
image_path = os.path.join(OUTPUT_FOLDER, f"frame_{i}.png")
image.save(image_path)
images.append(imageio.v3.imread(image_path)) # Use v3 API
# Create GIF animation
gif_path = os.path.join(OUTPUT_FOLDER, "animation.gif")
imageio.mimsave(gif_path, images, duration=0.5)
return gif_path # Return GIF path for download
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# Stable Diffusion Animation Generator 🎨✨")
with gr.Row():
prompt_input = gr.Textbox(label="Enter your prompt")
generate_button = gr.Button("Generate Animation 🎥")
gif_output = gr.File(label="Download your GIF")
generate_button.click(fn=generate_images, inputs=prompt_input, outputs=gif_output)
# Launch Gradio App
if __name__ == "__main__":
demo.launch()
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