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import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
from PIL import Image
# Load the Stable Diffusion XL model
def load_model():
pipe = StableDiffusionXLPipeline.from_pretrained(
"sd-community/sdxl-flash",
torch_dtype=torch.float16,
low_cpu_mem_usage=True
)#.to("cuda")
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(
pipe.scheduler.config,
timestep_spacing="trailing"
)
return pipe
# Function to generate images
def generate_image(prompt, num_inference_steps=3, guidance_scale=1):
pipe = load_model()
image = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0]
return image
# Create the Gradio interface
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Enter a prompt for the image"),
# gr.Slider(minimum=1, maximum=50, label="Number of Inference Steps"),
# gr.Slider(minimum=1, maximum=20, label="Guidance Scale")
],
outputs="image",
title="Stable Diffusion XL Text-to-Image",
description="Generate images from text prompts using Stable Diffusion XL."
)
# Launch the Gradio app
iface.launch()
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