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import gradio as gr
from PIL import Image
from diffusers import DiffusionPipeline
import time
# Load model and scheduler
ldm = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
def generate_image(prompt, negative_prompt="Low quality", width=512, height=512):
# Run pipeline in inference (sample random noise and denoise)
start_time = time.time()
images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6, negative_prompts=[negative_prompt]).images
# Resize image to desired width and height
resized_images = [image.resize((int(width), int(height))) for image in images]
# Save images
for idx, image in enumerate(resized_images):
image.save(f"squirrel-{idx}.png")
end_time = time.time()
elapsed_time = round(end_time - start_time, 2)
return resized_images[0]
# Define the interface
iface = gr.Interface(
fn=generate_image,
inputs=["text", "text", "number", "number"],
outputs=gr.outputs.Image(type="pil", label="Generated Image"),
layout="vertical",
title="Image Generation",
description="Generate images based on prompts",
article="For more information, visit the documentation: [link](https://docs.gradio.app/)",
examples=[["A painting of a squirrel eating a burger", "Low quality", 512, 512]]
)
# Launch the interface
iface.launch()
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