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import streamlit as st | |
import torch | |
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler | |
from huggingface_hub import hf_hub_download | |
from safetensors.torch import load_file | |
# Model Path/Repo Information | |
base = "stabilityai/stable-diffusion-xl-base-1.0" | |
repo = "ByteDance/SDXL-Lightning" | |
ckpt = "sdxl_lightning_4step_unet.safetensors" | |
# Load model (Executed only once for efficiency) | |
def load_sdxl_pipeline(): | |
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") | |
return pipe | |
# Streamlit UI | |
st.title("Stable Diffusion XL Image Generation") | |
prompt = st.text_input("Enter your image prompt:") | |
if st.button("Generate Image"): | |
if not prompt: | |
st.warning("Please enter a prompt.") | |
else: | |
pipe = load_sdxl_pipeline() # Load the pipeline from cache | |
with torch.no_grad(): | |
image = pipe(prompt).images[0] | |
st.image(image) |