app.py

import os
import random
import gradio as gr
import torch
from diffusers import (
    DiffusionPipeline,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UniPCMultistepScheduler,
)
from diffusers.utils import make_image_grid

ACCESS_TOKEN = os.environ["ACCESS_TOKEN"]
pipeline = DiffusionPipeline.from_pretrained(
    "stabilityai/japanese-stable-diffusion-xl",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    use_auth_token=ACCESS_TOKEN
)
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline.to(device)
SCHEDULER_MAPPING = {
    "ddim": DDIMScheduler,
    "plms": PNDMScheduler,
    "lms": LMSDiscreteScheduler,
    "euler": EulerDiscreteScheduler,
    "euler_ancestral": EulerAncestralDiscreteScheduler,
    "dpm_solver++": DPMSolverMultistepScheduler,
    "unipc": UniPCMultistepScheduler,
}
noise_scheduler_name = "euler"
SD_XL_BASE_RATIOS = {
    "0.5": (704, 1408),
    "0.52": (704, 1344),
    "0.57": (768, 1344),
    "0.6": (768, 1280),
    "0.68": (832, 1216),
    "0.72": (832, 1152),
    "0.78": (896, 1152),
    "0.82": (896, 1088),
    "0.88": (960, 1088),
    "0.94": (960, 1024),
    "1.0": (1024, 1024),
    "1.07": (1024, 960),
    "1.13": (1088, 960),
    "1.21": (1088, 896),
    "1.29": (1152, 896),
    "1.38": (1152, 832),
    "1.46": (1216, 832),
    "1.67": (1280, 768),
    "1.75": (1344, 768),
    "1.91": (1344, 704),
    "2.0": (1408, 704),
    "2.09": (1472, 704),
    "2.4": (1536, 640),
    "2.5": (1600, 640),
    "2.89": (1664, 576),
    "3.0": (1728, 576),
    # "small": (512, 512),  # for testing
}


def set_noise_scheduler(name) -> None:
    pipeline.scheduler = SCHEDULER_MAPPING[name].from_config(pipeline.scheduler.config)


def infer(
    prompt,
    scale=7.5,
    steps=40,
    ratio="1.0",
    n_samples=1,
    seed="random",
    negative_prompt="",
    scheduler_name="euler",
):
    global noise_scheduler_name
    if noise_scheduler_name != scheduler_name:
        set_noise_scheduler(scheduler_name)
        noise_scheduler_name = scheduler_name
    scale = float(scale)
    steps = int(steps)
    W, H = SD_XL_BASE_RATIOS[ratio]
    n_samples = int(n_samples)
    if seed == "random":
        seed = random.randint(0, 2**32)
    seed = int(seed)

    images = pipeline(
        prompt=prompt,
        negative_prompt=negative_prompt if len(negative_prompt) > 0 else None,
        guidance_scale=scale,
        generator=torch.Generator(device=device).manual_seed(seed),
        num_images_per_prompt=n_samples,
        num_inference_steps=steps,
        height=H,
        width=W,
    ).images
    # grid = make_image_grid(images, 1, len(images))
    return (
        images,
        {
            "seed": seed,
        },
    )


examples = [
    ["柴犬、カラフルアート"],
    ["満面の笑みのお爺さん、スケッチ"],
    ["星空の中の1匹の鹿、アート"],
    ["ジャングルに立っている日本男性のポートレート"],
    ["茶色の猫のイラスト、アニメ"],
    ["舞妓さんのポートレート、デジタルアート"],
]
with gr.Blocks() as demo:
    gr.Markdown("# Japanese Stable Diffusion XL Demo")
    gr.Markdown(
        """[Japanese Stable Diffusion XL](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl) is a Japanese-version SDXL by [Stability AI](https://ja.stability.ai/).
                - Blog: https://ja.stability.ai/blog/japanese-stable-diffusion-xl
                - Twitter: https://twitter.com/StabilityAI_JP
                - Discord: https://discord.com/invite/StableJP"""
    )
    gr.Markdown(
        "### You can also try JSDXL on Google Colab [here](https://colab.research.google.com/github/Stability-AI/model-demo-notebooks/blob/main/japanese_stable_diffusion_xl.ipynb). "
    )
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(
                label="prompt",
                max_lines=1,
                show_label=False,
                placeholder="Enter your prompt",
                container=False,
            )
            btn = gr.Button("Run", scale=0)
        gallery = gr.Gallery(label="Generated images", show_label=False)
        with gr.Accordion(label="sampling info", open=False):
            info = gr.JSON(label="sampling_info")
    with gr.Accordion(open=False, label="Advanced options"):
        scale = gr.Number(value=7.5, label="cfg_scale")
        steps = gr.Number(value=25, label="steps", visible=False)
        size_ratio = gr.Dropdown(
            choices=list(SD_XL_BASE_RATIOS.keys()),
            value="1.0",
            label="size ratio",
            multiselect=False,
        )
        n_samples = gr.Slider(
            minimum=1,
            maximum=3,
            value=2,
            label="n_samples",
        )
        seed = gr.Text(
            value="random",
            label="seed (integer or 'random')",
        )
        negative_prompt = gr.Textbox(
            label="negative prompt",
            value="",
        )
        noise_scheduler = gr.Dropdown(
            list(SCHEDULER_MAPPING.keys()), value="euler", visible=False
        )

    inputs = [
        prompt,
        scale,
        steps,
        size_ratio,
        n_samples,
        seed,
        negative_prompt,
        noise_scheduler,
    ]
    outputs = [gallery, info]
    prompt.submit(infer, inputs=inputs, outputs=outputs)
    btn.click(infer, inputs=inputs, outputs=outputs)
    gr.Examples(examples=examples, inputs=inputs, outputs=outputs, fn=infer)

demo.queue().launch(debug=True, share=True, show_error=True)