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| import gradio as gr | |
| import torch | |
| import spaces | |
| from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| assert torch.cuda.is_available() | |
| device = "cuda" | |
| dtype = torch.float16 | |
| base = "stabilityai/stable-diffusion-xl-base-1.0" | |
| repo = "ByteDance/SDXL-Lightning" | |
| opts = { | |
| "1 Step" : ("sdxl_lightning_1step_unet_x0.safetensors", 1), | |
| "2 Steps" : ("sdxl_lightning_2step_unet.safetensors", 2), | |
| "4 Steps" : ("sdxl_lightning_4step_unet.safetensors", 4), | |
| "8 Steps" : ("sdxl_lightning_8step_unet.safetensors", 8), | |
| } | |
| # Default to load 4-step model. | |
| step_loaded = 4 | |
| unet = UNet2DConditionModel.from_config(base, subfolder="unet") | |
| unet.load_state_dict(load_file(hf_hub_download(repo, opts["4 Steps"][0]))) | |
| pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=dtype, variant="fp16").to(device, dtype) | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
| # Inference function. | |
| def generate(prompt, option, progress=gr.Progress()): | |
| global step_loaded | |
| print(prompt, option) | |
| ckpt, step = opts[option] | |
| progress((0, step)) | |
| if step != step_loaded: | |
| print(f"Switching checkpoint from {step_loaded} to {step}") | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if step == 1 else "epsilon") | |
| pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device)) | |
| step_loaded = step | |
| def inference_callback(p, i, t, kwargs): | |
| progress((i+1, step)) | |
| return kwargs | |
| return pipe(prompt, num_inference_steps=step, guidance_scale=0, callback_on_step_end=inference_callback).images[0] | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.HTML( | |
| "<h1><center>SDXL-Lightning</center></h1>" + | |
| "<p><center>Lightning-fast text-to-image generation</center></p>" + | |
| "<p><center><a href='https://huggingface.co/ByteDance/SDXL-Lightning'>https://huggingface.co/ByteDance/SDXL-Lightning</a></center></p>" | |
| ) | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| label="Text prompt", | |
| scale=8 | |
| ) | |
| option = gr.Dropdown( | |
| label="Inference steps", | |
| choices=["1 Step", "2 Steps", "4 Steps", "8 Steps"], | |
| value="4 Steps", | |
| interactive=True | |
| ) | |
| submit = gr.Button( | |
| scale=1, | |
| variant="primary" | |
| ) | |
| img = gr.Image(label="SDXL-Lighting Generated Image") | |
| prompt.submit( | |
| fn=generate, | |
| inputs=[prompt, option], | |
| outputs=img, | |
| ) | |
| submit.click( | |
| fn=generate, | |
| inputs=[prompt, option], | |
| outputs=img, | |
| ) | |
| gr.Examples( | |
| fn=generate, | |
| examples=[ | |
| ["An owl perches quietly on a twisted branch deep within an ancient forest.", "1 Step"], | |
| ["A lion in the galaxy, octane render", "2 Steps"], | |
| ["A dolphin leaps through the waves, set against a backdrop of bright blues and teal hues.", "2 Steps"], | |
| ["A girl smiling", "4 Steps"], | |
| ["An astronaut riding a horse", "4 Steps"], | |
| ["A fish on a bicycle, colorful art", "4 Steps"], | |
| ["A close-up of an Asian lady with sunglasses.", "4 Steps"], | |
| ["Man portrait, ethereal", "8 Steps"], | |
| ["Rabbit portrait in a forest, fantasy", "8 Steps"], | |
| ["A panda swimming", "8 Steps"], | |
| ], | |
| inputs=[prompt, option], | |
| outputs=img, | |
| cache_examples=True, | |
| ) | |
| gr.HTML( | |
| "<p><small><center>This demo is built together by the community</center></small></p>" + | |
| ) | |
| demo.queue().launch() |