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  
import re

model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda"

pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True, revision="fp16", torch_dtype=torch.float16)
pipe = pipe.to(device)
word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
word_list = word_list_dataset["train"]['text']

def infer(prompt, samples, steps, scale, seed):
    for filter in word_list:
        if re.search(rf"\b{filter}\b", prompt):
            raise Exception("Unsafe content found. Please try again with different prompts.")
        
    generator = torch.Generator(device=device).manual_seed(seed)
    with autocast("cuda"):
        images_list = pipe(
            [prompt] * samples,
            num_inference_steps=steps,
            guidance_scale=scale,
            generator=generator,
        )
    images = []
    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)
    return images


css = """
        .gradio-container {
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: black;
            background: black;
        }
        input[type='range'] {
            accent-color: black;
        }
        .dark input[type='range'] {
            accent-color: #dfdfdf;
        }
        .container {
            max-width: 1070px;
            margin: auto;
            padding-top: 2rem;
        }
        #gallery {
            min-height: 22rem;
            margin-bottom: 15px;
            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 {
            font-size: .7rem !important;
            line-height: 19px;
            margin-top: 24px;
            margin-bottom: 12px;
            padding: 2px 8px;
            border-radius: 14px !important;
        }
        #advanced-options {
            display: none;
            margin-bottom: 20px;
        }
        .footer {
            margin-bottom: 25px;
            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%;
        }
"""

block = gr.Blocks(css=css)

examples = [
    [
        'A high tech solarpunk utopia in the Amazon rainforest',
        3,
        40,
        7.5,
        1024,
    ],
    [
        'A pikachu fine dining with a view to the Eiffel Tower',
        3,
        40,
        7,
        1024,
    ],
    [
        'A mecha robot in a favela in expressionist style',
        3,
        40,
        7,
        1024,
    ],
    [
        'an insect robot preparing a delicious meal',
        3,
        40,
        7,
        1024,
    ],
    [
        "A small cabin on top of a snowy mountain in the style of disney, arstation",
        3,
        40,
        7,
        1024,
    ],
]

with block:
    gr.HTML(
        """
            <div style="text-align: center;">
                <div style="display: inline-flex; align-items: center; gap: .8rem; font-size: 1.75rem;">
                    <svg width="0.65em" height="0.65em" viewBox="0 0 115 115" fill="none" xmlns="http://www.w3.org/2000/svg">
                        <rect width="23" height="23" fill="white"/>
                        <rect y="69" width="23" height="23" fill="white"/>
                        <rect x="23" width="23" height="23" fill="#AEAEAE"/>
                        <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"/>
                        <rect x="46" width="23" height="23" fill="white"/>
                        <rect x="46" y="69" width="23" height="23" fill="white"/>
                        <rect x="69" width="23" height="23" fill="black"/>
                        <rect x="69" y="69" width="23" height="23" fill="black"/>
                        <rect x="92" width="23" height="23" fill="#D9D9D9"/>
                        <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"/>
                        <rect x="115" y="46" width="23" height="23" fill="white"/>
                        <rect x="115" y="115" width="23" height="23" fill="white"/>
                        <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"/>
                        <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"/>
                        <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"/>
                        <rect x="92" y="69" width="23" height="23" fill="white"/>
                        <rect x="69" y="46" width="23" height="23" fill="white"/>
                        <rect x="69" y="115" width="23" height="23" fill="white"/>
                        <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"/>
                        <rect x="46" y="46" width="23" height="23" fill="black"/>
                        <rect x="46" y="115" width="23" height="23" fill="black"/>
                        <rect x="46" y="69" width="23" height="23" fill="black"/>
                        <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"/>
                        <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"/>
                        <rect x="23" y="69" width="23" height="23" fill="black"/>
                    </svg>
                    <h1 style="font-weight: 900;">Stable Diffusion Spaces</h1>
                </div>
                <p style="margin-bottom: 20px;">Stable Diffusion is a state of the art text-to-image model that generates images from a text description.</p>
            </div>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                text = gr.Textbox(
                    label="Enter your prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                ).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),
                )

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(grid=[3], height="auto")

        advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")

        with gr.Row(elem_id="advanced-options"):
            samples = gr.Slider(label="Images", minimum=1, maximum=3, value=3, step=1)
            steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=40, step=1)
            scale = gr.Slider(
                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
            )
            seed = gr.Slider(
                label="Random seed",
                minimum=0,
                maximum=2147483647,
                step=1,
                randomize=True,
            )

        ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, steps, scale, seed], outputs=gallery, cache_examples=True)
        ex.dataset.headers = [""]

        
        text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
        btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
        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";
            }""",
        )
        gr.HTML(
            """
                <div class="footer">
                    <p>Model by <a href="https://huggingface.co/CompVis" style="text-decoration: underline;" target="_blank">CompVis</a> and <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">Stability AI</a> - Demo by 🤗 Hugging Face
                    </p>
                </div>
                <div class="acknowledgments">
                    <p><h4>LICENSE</h4>
The model is licensed with an <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The license states that the outputs that you make fully belong to you, and you are liable when sharing it. 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. The model was trained on the LAION-400M dataset, which scrapped non-curated image-text-pairs from the internet (the exception being the the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p>
               </div>
           """
        )

block.queue(max_size=40).launch()