app.py

import spaces
import gradio as gr
import torch
from diffusers import UNet2DConditionModel, StableDiffusionXLPipeline, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import rembg
from io import BytesIO
import PIL.Image as Image
import cv2
import numpy

base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.safetensors"

unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")

pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")

# Function to generate an image from text using diffusion
@spaces.GPU
def generate_image(prompt):
    prompt += "no background, side view, minimalist shot"
    
    image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]

    return image

_TITLE = "Shoe Generator"
with gr.Blocks(_TITLE) as ShoeGen:
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Enter a discription of a shoe")
            # neg_prompt = gr.Textbox(label="Enter a negative prompt", value="low quality, watermark, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft, closed eyes, text, logo")
            button_gen = gr.Button("Generate Image")
        with gr.Column():
            image = gr.Image(label="Generated Image", show_download_button=True)   
    
    button_gen.click(generate_image, inputs=[prompt], outputs=[image])

ShoeGen.launch()