import gradio as gr import spaces import torch from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler, AutoencoderKL from huggingface_hub import hf_hub_download from safetensors.torch import load_file vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) ### SDXL Turbo #### pipe_turbo = StableDiffusionXLPipeline.from_pretrained( "stabilityai/sdxl-turbo", vae=vae, torch_dtype=torch.float16, variant="fp16" ).to("cuda") ### SDXL Lightning ### base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" ckpt = "sdxl_lightning_1step_unet_x0.safetensors" unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.float16) unet.load_state_dict(load_file(hf_hub_download(repo, ckpt))) pipe_lightning = StableDiffusionXLPipeline.from_pretrained( base, unet=unet, vae=vae, text_encoder=pipe_turbo.text_encoder, text_encoder_2=pipe_turbo.text_encoder_2, tokenizer=pipe_turbo.tokenizer, tokenizer_2=pipe_turbo.tokenizer_2, torch_dtype=torch.float16, variant="fp16" )#.to("cuda") del unet pipe_lightning.scheduler = EulerDiscreteScheduler.from_config(pipe_lightning.scheduler.config, timestep_spacing="trailing", prediction_type="sample") pipe_lightning.to("cuda") ### Hyper SDXL ### repo_name = "ByteDance/Hyper-SD" ckpt_name = "Hyper-SDXL-1step-Unet.safetensors" unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.float16) unet.load_state_dict(load_file(hf_hub_download(repo_name, ckpt_name))) pipe_hyper = StableDiffusionXLPipeline.from_pretrained( base, unet=unet, vae=vae, text_encoder=pipe_turbo.text_encoder, text_encoder_2=pipe_turbo.text_encoder_2, tokenizer=pipe_turbo.tokenizer, tokenizer_2=pipe_turbo.tokenizer_2, torch_dtype=torch.float16, variant="fp16" )#.to("cuda") pipe_hyper.scheduler = LCMScheduler.from_config(pipe_hyper.scheduler.config) pipe_hyper.to("cuda") del unet @spaces.GPU def run_comparison(prompt, progress=gr.Progress(track_tqdm=True)): image_turbo=pipe_turbo(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0] yield image_turbo, None, None image_lightning=pipe_lightning(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0] yield image_turbo, image_lightning, None image_hyper=pipe_hyper(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[800]).images[0] yield image_turbo, image_lightning, image_hyper examples = [ "A dignified beaver wearing glasses, a vest, and colorful neck tie.", "The spirit of a tamagotchi wandering in the city of Barcelona", "an ornate, high-backed mahogany chair with a red cushion", "a sketch of a camel next to a stream", "a delicate porcelain teacup sits on a saucer, its surface adorned with intricate blue patterns", "a baby swan grafitti", "A bald eagle made of chocolate powder, mango, and whipped cream" ] with gr.Blocks() as demo: gr.Markdown("## One step SDXL comparison 🦶") gr.Markdown('Compare SDXL variants and distillations able to generate images in a single diffusion step') prompt = gr.Textbox(label="Prompt") run = gr.Button("Run") with gr.Row(): with gr.Column(): image_turbo = gr.Image(label="SDXL Turbo") gr.Markdown("## [SDXL Turbo](https://huggingface.co/stabilityai/sdxl-turbo)") with gr.Column(): image_lightning = gr.Image(label="SDXL Lightning") gr.Markdown("## [SDXL Lightning](https://huggingface.co/ByteDance/SDXL-Lightning)") with gr.Column(): image_hyper = gr.Image(label="Hyper SDXL") gr.Markdown("## [Hyper SDXL](https://huggingface.co/ByteDance/Hyper-SD)") image_outputs = [image_turbo, image_lightning, image_hyper] gr.on( triggers=[prompt.submit, run.click], fn=run_comparison, inputs=prompt, outputs=image_outputs ) gr.Examples( examples=examples, fn=run_comparison, inputs=prompt, outputs=image_outputs, cache_examples=False, run_on_click=True ) demo.launch()