import gradio as gr import torch from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler from huggingface_hub import hf_hub_download import spaces # Constants base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" checkpoints = { "1-Step" : ["sdxl_lightning_1step_unet_x0.safetensors", 1], "2-Step" : ["sdxl_lightning_2step_unet.safetensors", 2], "4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4], "8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8], } # Ensure model and scheduler are initialized in GPU-enabled function if torch.cuda.is_available(): pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda") # Function @spaces.GPU(enable_queue=True) def generate_image(prompt, ckpt): checkpoint = checkpoints[ckpt][0] num_inference_steps = checkpoints[ckpt][1] if num_inference_steps==1: # Ensure sampler uses "trailing" timesteps and "sample" prediction type for 1-step inference. pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample") else: # Ensure sampler uses "trailing" timesteps. pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoint), map_location="cuda")) image = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0).images[0] return image # Gradio Interface description = """ This demo utilizes the SDXL-Lightning model by ByteDance, which is a fast text-to-image generative model capable of producing high-quality images in 4 steps. As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/ByteDance/SDXL-Lightning """ with gr.Blocks(css="style.css") as demo: gr.HTML("

Text-to-Image with SDXL Lightning ⚡

") gr.Markdown(description) with gr.Group(): with gr.Row(): prompt = gr.Textbox(label='Enter you image prompt:', scale=8) ckpt = gr.Dropdown(label='Select Inference Steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True) submit = gr.Button(scale=1, variant='primary') img = gr.Image(label='SDXL-Lightening Generate Image') prompt.submit(fn=generate_image, inputs=[prompt, ckpt], outputs=img, ) submit.click(fn=generate_image, inputs=[prompt, ckpt], outputs=img, ) demo.queue().launch()