import gradio as gr import torch from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler from huggingface_hub import hf_hub_download from safetensors.torch import load_file import spaces import os from PIL import Image SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1" # 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") if SAFETY_CHECKER: from safety_checker import StableDiffusionSafetyChecker from transformers import CLIPFeatureExtractor safety_checker = StableDiffusionSafetyChecker.from_pretrained( "CompVis/stable-diffusion-safety-checker" ).to("cuda") feature_extractor = CLIPFeatureExtractor.from_pretrained( "openai/clip-vit-base-patch32" ) def check_nsfw_images( images: list[Image.Image], ) -> tuple[list[Image.Image], list[bool]]: safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda") has_nsfw_concepts = safety_checker( images=[images], clip_input=safety_checker_input.pixel_values.to("cuda") ) return images, has_nsfw_concepts # 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(load_file(hf_hub_download(repo, checkpoint), device="cuda")) results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0) if SAFETY_CHECKER: images, has_nsfw_concepts = check_nsfw_images(results.images) if any(has_nsfw_concepts): gr.Warning("NSFW content detected.") return Image.new("RGB", (512, 512)) return images[0] return results.images[0] # 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()