app.py

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,
).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,
)#.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,
    )#.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()