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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
else:
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
pipe = pipe.to(device)
# Define the image generation function
def generate_image(prompt, steps, guidance):
image = pipe(prompt=prompt, num_inference_steps=steps, guidance_scale=guidance).images[0]
return image
# Create the Gradio interface
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt"),
gr.inputs.Slider(minimum=1, maximum=50, default=10, step=1, label="Inference Steps"),
gr.inputs.Slider(minimum=0.0, maximum=20.0, default=8.0, step=0.5, label="Guidance Scale")
],
outputs=gr.outputs.Image(type="pil", label="Generated Image"),
title="SDXL-Turbo Image Generator",
description="Generate images based on text prompts using the SDXL-Turbo model."
)
# Launch the interface
iface.launch()
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