app.py

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 🥶 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"
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: 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:
        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)
    # 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(
                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,
            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)