app.py

import gradio as gr
import torch

from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
import diffusers
from share_btn import community_icon_html, loading_icon_html

pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE")

def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content

def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
    if negative_prompt == "":
        negative_prompt = None
    scheduler_class_name = scheduler.split("-")[0]

    add_kwargs = {}
    if len(scheduler.split("-")) > 1:
        add_kwargs["use_karras"] = True
    if len(scheduler.split("-")) > 2:
        add_kwargs["algorithm_type"] = "sde-dpmsolver++"

    scheduler = getattr(diffusers, scheduler_class_name)
    pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs)
    
    init_image = dict["image"].convert("RGB").resize((1024, 1024))
    mask = dict["mask"].convert("RGB").resize((1024, 1024))
    
    output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)
    
    return output.images[0], gr.update(visible=True)


css = '''
.gradio-container{max-width: 1100px !important}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
#share-btn-container:hover {background-color: #060606}
#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
#share-btn * {all: unset}
#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
#share-btn-container .wrap {display: none !important}
#share-btn-container.hidden {display: none!important}
#prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
#run_button{position:absolute;margin-top: 11px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px;
    border-top-left-radius: 0px;}
#prompt-container{margin-top:-18px;}
#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
#image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
'''

share_js = """async () => {
	async function uploadFile(file){
		const UPLOAD_URL = 'https://huggingface.co/uploads';
		const response = await fetch(UPLOAD_URL, {
			method: 'POST',
			headers: {
				'Content-Type': file.type,
				'X-Requested-With': 'XMLHttpRequest',
			},
			body: file, /// <- File inherits from Blob
		});
		const url = await response.text();
		return url;
	}
	async function getInputImgFile(imgCanvas){
        const blob = await new Promise(resolve => imgCanvas.toBlob(resolve));
        const imgId = Date.now() % 200;
        const fileName = `sd-inpainting-${{imgId}}.png`;
        return new File([blob], fileName, { type: 'image/png' });
	}
    async function getOutoutImgFile(imgEl){
        const res = await fetch(imgEl.src);
        const blob = await res.blob();
        const imgId = Date.now() % 200;
        const fileName = `sd-inpainting-${{imgId}}.png`;
        return new File([blob], fileName, { type: 'image/png' });
    }
    const gradioEl = document.querySelector('body > gradio-app');
    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
    const inputImgCanvas = gradioEl.querySelector('canvas[key="drawing"]');
    const outputImgEl = gradioEl.querySelector('#output-img img');
    const promptTxt = gradioEl.querySelector('#prompt textarea').value;
    let titleTxt = promptTxt;
    if(titleTxt.length > 100){
        titleTxt = titleTxt.slice(0, 100) + ' ...';
    }
    const shareBtnEl = gradioEl.querySelector('#share-btn');
    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
    if(!outputImgEl){
        return;
    };
    shareBtnEl.style.pointerEvents = 'none';
    shareIconEl.style.display = 'none';
    loadingIconEl.style.removeProperty('display');
    const inputImgFile = await getInputImgFile(inputImgCanvas);
    const outputImgFile = await getOutoutImgFile(outputImgEl);
    const files = [inputImgFile, outputImgFile];
    const urls = await Promise.all(files.map((f) => uploadFile(f)));
	const htmlImgs = urls.map(url => `<img src='${url}' style='max-width: 450px;'>`);
    const [inputImgUrl, outputImgUrl] = htmlImgs;
	const descriptionMd = `<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
<div>
${inputImgUrl}
${promptTxt}
</div>
<div>
${outputImgUrl}
</div>
</div>`;
    const params = new URLSearchParams({
        title: titleTxt,
        description: descriptionMd,
    });
	const paramsStr = params.toString();
 
	window.open(`https://huggingface.co/spaces/diffusers/stable-diffusion-xl-inpainting/discussions/new?${paramsStr}&preview=true`, '_blank');
    shareBtnEl.style.removeProperty('pointer-events');
    shareIconEl.style.removeProperty('display');
    loadingIconEl.style.display = 'none';
}"""


image_blocks = gr.Blocks(css=css, elem_id="total-container")
with image_blocks as demo:
    gr.HTML(read_content("header.html"))
    with gr.Row():
                with gr.Column():
                    image = gr.Image(elem_id="image_upload", type="pil", label="Upload",height=400,value="sketch")
                    with gr.Row(elem_id="prompt-container", equal_height=True):
                        with gr.Row():
                            prompt = gr.Textbox(placeholder="Your prompt (what you want in place of what is erased)", show_label=False, elem_id="prompt")
                            btn = gr.Button("Inpaint!", elem_id="run_button")
                    
                    with gr.Accordion(label="Advanced Settings", open=False):
                        with gr.Row(equal_height=True):
                            guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
                            steps = gr.Number(value=20, minimum=10, maximum=30, step=1, label="steps")
                            strength = gr.Number(value=0.99, minimum=0.01, maximum=1.0, step=0.01, label="strength")
                            negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
                        with gr.Row(equal_height=True):
                            schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
                            scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")
                        
                with gr.Column():
                    image_out = gr.Image(label="Output", elem_id="output-img", height=400)
                    with gr.Group(elem_id="share-btn-container", visible=False) as share_btn_container:
                        community_icon = gr.HTML(community_icon_html)
                        loading_icon = gr.HTML(loading_icon_html)
                        share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
            

    btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container], api_name='run')
    prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container])
    share_button.click(None, [], [], share_js)

    gr.Examples(
                examples=[
                    ["./imgs/aaa (8).png"],
                    ["./imgs/download (1).jpeg"],
                    ["./imgs/0_oE0mLhfhtS_3Nfm2.png"],
                    ["./imgs/02_HubertyBlog-1-1024x1024.jpg"],
                    ["./imgs/jdn_jacques_de_nuce-1024x1024.jpg"],
                    ["./imgs/c4ca473acde04280d44128ad8ee09e8a.jpg"],
                    ["./imgs/canam-electric-motorcycles-scaled.jpg"],
                    ["./imgs/e8717ce80b394d1b9a610d04a1decd3a.jpeg"],
                    ["./imgs/Nature___Mountains_Big_Mountain_018453_31.jpg"],
                    ["./imgs/Multible-sharing-room_ccexpress-2-1024x1024.jpeg"],
                ],
                fn=predict,
                inputs=[image],
                cache_examples=False,
    )
    gr.HTML(
        """
            <div class="footer">
                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
                </p>
            </div>
        """
    )

image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)