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import gradio as gr
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
# Load model.
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")
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# Load model.
@spaces.GPU
def generate(prompt, steps):
image = pipe(prompt, num_inference_steps=steps, guidance_scale=0).images[0]
return image
output_image = gr.Image(type="pil")
inputs=[
gr.Textbox(label="Prompt (What you want in the image)", value="Cinematic portrait of a handsome cat with a suit and sunglasses"),
gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Images(coming soon <3)")
]
demo = gr.Interface(fn=generate, inputs=inputs, outputs=output_image)
demo.launch() |