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Running
on
Zero
| 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], | |
| } | |
| loaded = None | |
| # 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 | |
| def generate_image(prompt, ckpt): | |
| global loaded | |
| print(prompt, ckpt) | |
| checkpoint = checkpoints[ckpt][0] | |
| num_inference_steps = checkpoints[ckpt][1] | |
| if loaded != num_inference_steps: | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon") | |
| pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda")) | |
| loaded = num_inference_steps | |
| 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 lightning-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("<h1><center>Text-to-Image with SDXL-Lightning ⚡</center></h1>") | |
| gr.Markdown(description) | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Textbox(label='Enter your prompt (English)', 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-Lightning Generated Image') | |
| prompt.submit(fn=generate_image, | |
| inputs=[prompt, ckpt], | |
| outputs=img, | |
| ) | |
| submit.click(fn=generate_image, | |
| inputs=[prompt, ckpt], | |
| outputs=img, | |
| ) | |
| demo.queue().launch() |