app.py

import gradio as gr
# import torch
# from torch import autocast
# from diffusers import StableDiffusionPipeline
from datasets import load_dataset
from PIL import Image
from io import BytesIO
# import base64
import re
import os
import requests
import json

from share_btn import community_icon_html, loading_icon_html, share_js


is_gpu_busy = False


def infer(prompt):
    global is_gpu_busy
    # generator = torch.Generator(device=device).manual_seed(seed)
    # print("Is GPU busy? ", is_gpu_busy)
    images = []
    # if(not is_gpu_busy):
    #    is_gpu_busy = True
    #    images_list = pipe(
    #        [prompt] * samples,
    #        num_inference_steps=steps,
    #        guidance_scale=scale,
    # generator=generator,
    #    )
    #    is_gpu_busy = False
    #    safe_image = Image.open(r"unsafe.png")
    #    for i, image in enumerate(images_list["sample"]):
    #       if(images_list["nsfw_content_detected"][i]):
    #           images.append(safe_image)
    #       else:
    #           images.append(image)
    # else:
    url = os.getenv('BACKEND_URL')
    response = requests.get(url + prompt)
    data = json.load(BytesIO(response.content))
    for image in data['output']['choices']:
        image_b64 = (f"data:image/jpeg;base64,{image['image_base64']}")
        images.append(image_b64)

    # payload = {'prompt': prompt}
    # images_request = requests.post(url, json=payload)
    # for image in images_request.json()["output"]['choices']:
    #    image_b64 = (f"data:image/jpeg;base64,{image['image_base64']}")
    #    images.append(image_b64)

    return images


css = """
        .gradio-container {
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: #3a669bff;
            background: #3a669bff;
        }
        input[type='range'] {
            accent-color: #3a669bff;
        }
        .dark input[type='range'] {
            accent-color: #3a669bff;
        }
        .container {
            max-width: 730px;
            margin: auto;
            padding-top: 1.5rem;
        }
        #gallery {
            min-height: 22rem;
            margin-bottom: 15px;
            margin-left: auto;
            margin-right: auto;
            border-bottom-right-radius: .5rem !important;
            border-bottom-left-radius: .5rem !important;
        }
        #gallery>div>.h-full {
            min-height: 20rem;
        }
        .details:hover {
            text-decoration: underline;
        }
        .gr-button {
            white-space: nowrap;
        }
        .gr-button:focus {
            border-color: rgb(147 197 253 / var(--tw-border-opacity));
            outline: none;
            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
            --tw-border-opacity: 1;
            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
            --tw-ring-opacity: .5;
        }
        #advanced-btn {
            display: none;
            font-size: .7rem !important;
            line-height: 19px;
            margin-top: 12px;
            margin-bottom: 12px;
            padding: 2px 8px;
            border-radius: 14px !important;
        }
        #advanced-options {
            display: none;
            margin-bottom: 20px;
        }
        .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%;
        }
        #container-advanced-btns{
            display: flex;
            flex-wrap: wrap;
            justify-content: space-between;
            align-items: center;
        }
        .animate-spin {
            animation: spin 1s linear infinite;
        }
        @keyframes spin {
            from {
                transform: rotate(0deg);
            }
            to {
                transform: rotate(360deg);
            }
        }
        #share-btn-container {
            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #3a669bff; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
        }
        #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: 0.25rem !important; padding-bottom: 0.25rem !important;
        }
        #share-btn * {
            all: unset;
        }
        .gr-form{
            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
        }
        #prompt-container{
            gap: 0;
        }
"""

block = gr.Blocks(css=css)

examples = [
    [
        'a gorgeous female photo, professionally retouched, soft lighting, torso, legs, feet, realistic, smooth face, perfect eyes, !! wide angle!!',
        #2,
        #7.5,
    ],
    [
        'the worst meme possible',
        #2,
        #7.5,
    ],
    [
        'padme amidala taking a bath artwork, safe for work, no nudity',
        #2,
        #7.5,
    ],
    [
        'a photograph by vanessa beecroft',
        #2,
        #7.5,
    ],
]

with block:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <img class="logo" src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1666181274838-62fa1d95e8c9c532aa75331c.png" alt="AIML Logo"
                    style="margin: auto; max-width: 7rem;">
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  Safe Stable Diffusion Demo
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Safe Stable Diffusion extends Stable Diffusion with safety guidance. In the case of NSFW images it returns the closest non-NSFW images instead of a black square.
                Stable Diffusion is a state of the art text-to-image model that generates
                images from text.
              </p>
            </div>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
                text = gr.Textbox(
                    label="Enter your prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    elem_id="prompt-text-input",
                ).style(
                    border=(True, False, True, True),
                    rounded=(True, False, False, True),
                    container=False,
                )
                btn = gr.Button("Generate image").style(
                    margin=False,
                    rounded=(False, True, True, False),
                    full_width=False,
                )

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(grid=[2], height="auto")

        with gr.Group(elem_id="container-advanced-btns"):
            advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
            with gr.Group(elem_id="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")

        with gr.Row(elem_id="advanced-options"):
            gr.Markdown("Advanced settings are temporarily unavailable")
            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
            steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
            scale = gr.Slider(
                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
            )
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=2147483647,
                step=1,
                randomize=True,
            )

        ex = gr.Examples(examples=examples, fn=infer, inputs=text,
                         outputs=[gallery, community_icon, loading_icon, share_button], cache_examples=False)
        ex.dataset.headers = [""]

        text.submit(infer, inputs=text, outputs=[gallery], postprocess=False)
        btn.click(infer, inputs=text, outputs=[gallery], postprocess=False)

        advanced_button.click(
            None,
            [],
            text,
            _js="""
            () => {
                const options = document.querySelector("body > gradio-app").querySelector("#advanced-options");
                options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none";
            }""",
        )
        share_button.click(
            None,
            [],
            [],
            _js=share_js,
        )
        gr.HTML(
            """
                <div class="footer">
                    <p>Model by <a href="https://huggingface.co/AIML-TUDA/" style="text-decoration: underline;" target="_blank">AIML Lab @TU Darmstadt</a> - backend provided through the generous support of <a href="https://www.together.xyz/" style="text-decoration: underline;" target="_blank">Together xyz program</a> - Gradio Demo by 🤗 Hugging Face
                    </p>
                </div>
                <div class="acknowledgments">
                    <p><h4>LICENSE</h4>
The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a>.</p>
                    <p><h4>Biases and content acknowledgment</h4>
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. While the applied safety guidance suppresses the majority of inappropriate content, this still could apply to Safe Stable Diffusion models. The original model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. Safety guidance suppresses potentially inappropriate content during inference. You can read more in the <a href="https://huggingface.co/AIML-TUDA/stable-diffusion-safe" style="text-decoration: underline;" target="_blank">model card</a>.</p>
               </div>
           """
        )

block.queue(concurrency_count=40, max_size=20).launch(max_threads=150)