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Support SDXL-Lightning and fix some errors for baseline.
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# pip install diffusers, transformers, accelerate, safetensors, huggingface_hub
import os
os.system("pip install -U peft")
import random
import gradio as gr
import numpy as np
import PIL.Image
import spaces
import torch
from diffusers import (
StableDiffusionXLPipeline,
UNet2DConditionModel,
EulerDiscreteScheduler,
)
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
DESCRIPTION = """
# Res-Adapter :Domain Consistent Resolution Adapter for Diffusion Models
**Demo by [ameer azam] - [Twitter](https://twitter.com/Ameerazam18) - [GitHub](https://github.com/AMEERAZAM08)) - [Hugging Face](https://huggingface.co/ameerazam08)**
This is a demo of https://huggingface.co/jiaxiangc/res-adapter ResAdapter by ByteDance.
ByteDance provide a demo of [ResAdapter](https://huggingface.co/jiaxiangc/res-adapter) with [SDXL-Lightning-Step4](https://huggingface.co/ByteDance/SDXL-Lightning) to expand resolution range from 1024-only to 256~1024.
"""
if not torch.cuda.is_available():
DESCRIPTION += (
"\n<h1>Running on CPU πŸ₯Ά This demo does not work on CPU.</a> instead</h1>"
)
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe = pipe.to(device)
# Load resadapter
pipe.load_lora_weights(
hf_hub_download(
repo_id="jiaxiangc/res-adapter",
subfolder="sdxl-i",
filename="resolution_lora.safetensors",
),
adapter_name="res_adapter",
)
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
@spaces.GPU(enable_queue=True)
def generate(
prompt: str,
negative_prompt: str = "",
prompt_2: str = "",
negative_prompt_2: str = "",
use_negative_prompt: bool = False,
use_prompt_2: bool = False,
use_negative_prompt_2: bool = False,
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale: float = 0,
num_inference_steps: int = 4,
progress=gr.Progress(track_tqdm=True),
) -> PIL.Image.Image:
print(f'** Generating image for: "{prompt}" **')
generator = torch.Generator().manual_seed(seed)
if not use_negative_prompt:
negative_prompt = None # type: ignore
if not use_prompt_2:
prompt_2 = None # type: ignore
if not use_negative_prompt_2:
negative_prompt_2 = None # type: ignore
pipe.set_adapters(["res_adapter"], adapter_weights=[0.0])
base_image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
output_type="pil",
generator=generator,
).images[0]
pipe.set_adapters(["res_adapter"], adapter_weights=[1.0])
res_adapt = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
output_type="pil",
generator=generator,
).images[0]
return [res_adapt, base_image]
examples = [
"A girl smiling",
"A boy smiling",
]
theme = gr.themes.Base(
font=[
gr.themes.GoogleFont("Libre Franklin"),
gr.themes.GoogleFont("Public Sans"),
"system-ui",
"sans-serif",
],
)
with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
with gr.Group():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
container=False,
placeholder="Enter your prompt",
)
run_button = gr.Button("Generate")
# result = gr.Gallery(label="Right is Res-Adapt-LORA and Left is Base"),
with gr.Accordion("Advanced options", open=False):
with gr.Row():
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
use_negative_prompt_2 = gr.Checkbox(
label="Use negative prompt 2", value=False
)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter your prompt",
visible=True,
)
prompt_2 = gr.Text(
label="Prompt 2",
max_lines=1,
placeholder="Enter your prompt",
visible=False,
)
negative_prompt_2 = gr.Text(
label="Negative prompt 2",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0,
maximum=20,
step=0.1,
value=0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
gr.Examples(
examples=examples,
inputs=prompt,
outputs=None,
fn=generate,
cache_examples=CACHE_EXAMPLES,
)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
queue=False,
api_name=False,
)
use_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_prompt_2,
outputs=prompt_2,
queue=False,
api_name=False,
)
use_negative_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt_2,
outputs=negative_prompt_2,
queue=False,
api_name=False,
)
gr.on(
triggers=[
prompt.submit,
negative_prompt.submit,
prompt_2.submit,
negative_prompt_2.submit,
run_button.click,
],
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=[
prompt,
negative_prompt,
prompt_2,
negative_prompt_2,
use_negative_prompt,
use_prompt_2,
use_negative_prompt_2,
seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=gr.Gallery(label="Left is ResAdapter and Right is Base"),
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20, api_open=False).launch(show_api=False)