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Running
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Zero
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import gradio as gr
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler, AutoencoderKL
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import spaces
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"
)
pipe_turbo.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):
image_turbo=pipe_turbo(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0]
image_lightning=pipe_lightning(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0]
image_hyper=pipe_hyper(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[800]).images[0]
return image_turbo, image_lightning, image_hyper
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)")
gr.on(
triggers=[prompt.submit, run.click],
fn=run_comparison,
inputs=prompt,
outputs=[image_turbo, image_lightning, image_hyper]
)
demo.launch() |