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import gradio as gr
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler, AutoencoderKL,DiffusionPipeline
import torch
import numpy as np
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import spaces
import os
import random
import uuid
def save_image(img):
unique_name = str(uuid.uuid4()) + ".png"
img.save(unique_name)
return unique_name
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
MAX_SEED = np.iinfo(np.int32).max
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
### RealVisXL V3 ###
RealVisXLv3_pipe = DiffusionPipeline.from_pretrained(
"SG161222/RealVisXL_V3.0",
torch_dtype=torch.float16,
use_safetensors=True,
add_watermarker=False,
variant="fp16"
)
RealVisXLv3_pipe.to("cuda")
### RealVisXL V4 ###
RealVisXLv4_pipe = DiffusionPipeline.from_pretrained(
"SG161222/RealVisXL_V4.0",
torch_dtype=torch.float16,
use_safetensors=True,
add_watermarker=False,
variant="fp16"
)
RealVisXLv4_pipe.to("cuda")
### 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: str,
negative_prompt: str = "",
use_negative_prompt: bool = False,
num_inference_steps: int = 30,
num_images_per_prompt: int = 2,
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale: float = 3,
randomize_seed: bool = False,
progress=gr.Progress(track_tqdm=True),
):
seed = int(randomize_seed_fn(seed, randomize_seed))
if not use_negative_prompt:
negative_prompt = ""
image_turbo=pipe_turbo(prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
cross_attention_kwargs={"scale": 0.65},
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
return image_paths, seed
yield image_turbo, None, None, None, None
image_lightning=pipe_lightning(prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
cross_attention_kwargs={"scale": 0.65},
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
return image_paths, seed
yield image_turbo, image_lightning, None, None, None
image_hyper=pipe_hyper(prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
cross_attention_kwargs={"scale": 0.65},
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
return image_paths, seed
yield image_turbo, image_lightning, image_hyper, None, None
image_r3=RealVisXLv3_pipe(prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
cross_attention_kwargs={"scale": 0.65},
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
return image_paths, seed
yield image_turbo, image_lightning, image_hyper,image_r3, None
image_r4=RealVisXLv4_pipe(prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
cross_attention_kwargs={"scale": 0.65},
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
return image_paths, seed
yield image_turbo, image_lightning, image_hyper,image_r3, image_r4
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.Accordion("Advanced options", open=False):
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
negative_prompt = gr.Text(
label="Negative prompt",
lines=4,
max_lines=6,
value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
placeholder="Enter a negative prompt",
visible=True,
)
with gr.Row():
num_inference_steps = gr.Slider(
label="Steps",
minimum=10,
maximum=60,
step=1,
value=30,
)
with gr.Row():
num_images_per_prompt = gr.Slider(
label="Images",
minimum=1,
maximum=5,
step=1,
value=2,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
visible=True
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row(visible=True):
width = gr.Slider(
label="Width",
minimum=512,
maximum=2048,
step=8,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=2048,
step=8,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.1,
maximum=20.0,
step=0.1,
value=6,
)
with gr.Row():
with gr.Column():
image_turbo = gr.Gallery(label="SDXL Turbo",columns=1, preview=True,)
gr.Markdown("## [SDXL Turbo](https://huggingface.co/stabilityai/sdxl-turbo)")
with gr.Column():
image_lightning = gr.Gallery(label="SDXL Lightning",columns=1, preview=True,)
gr.Markdown("## [SDXL Lightning](https://huggingface.co/ByteDance/SDXL-Lightning)")
with gr.Column():
image_hyper = gr.Gallery(label="Hyper SDXL",columns=1, preview=True,)
gr.Markdown("## [Hyper SDXL](https://huggingface.co/ByteDance/Hyper-SD)")
with gr.Column():
image_r3 = gr.Gallery(label="RealVisXL V3",columns=1, preview=True,)
gr.Markdown("## [RealVisXL V3](https://huggingface.co)")
with gr.Column():
image_r4 = gr.Gallery(label="RealVisXL V4",columns=1, preview=True,)
gr.Markdown("## [RealVisXL V3](https://huggingface.co)")
image_outputs = [image_turbo, image_lightning, image_hyper, image_r3, image_r4]
gr.on(
triggers=[
prompt.submit,
negative_prompt.submit,
run.click,
],
fn=run_comparison,
inputs=[
prompt,
negative_prompt,
use_negative_prompt,
num_inference_steps,
num_images_per_prompt,
seed,
width,
height,
guidance_scale,
randomize_seed,
],
outputs=[image_outputs, seed],
api_name="run",
)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
api_name=False,
)
gr.Examples(
examples=examples,
fn=run_comparison,
inputs=prompt,
outputs=[image_outputs, seed],
cache_examples=False,
run_on_click=True
)
demo.launch() |