app.py

import gradio as gr
import torch
import os
from diffusers import StableDiffusionPipeline

def dummy(images, **kwargs):
    return images, False

model_id = "timothepearce/gina-the-cat"

AUTH_TOKEN = os.environ.get('AUTH_TOKEN')
if not AUTH_TOKEN:
    with open('/root/.huggingface/token') as f:
        lines = f.readlines()
        AUTH_TOKEN = lines[0]

device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cuda":
    print('Nvidia GPU detected!')
    pipe = StableDiffusionPipeline.from_pretrained(
        model_id,
        use_auth_token=AUTH_TOKEN,
        torch_dtype=torch.float16,
    )
else:
    print('No Nvidia GPU in system!')
    pipe = StableDiffusionPipeline.from_pretrained(
        model_id,
        use_auth_token=AUTH_TOKEN
    )

pipe.to(device)
pipe.safety_checker = dummy
#torch.backends.cudnn.benchmark = True

def infer(prompt="", samples=4, steps=50, scale=7.5, seed=34354):
    generator = torch.Generator(device=device).manual_seed(seed)
    return pipe(
        [prompt] * samples,
        num_inference_steps=steps,
        guidance_scale=scale,
        generator=generator,
    ).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: 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 {
            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: #000000; 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 sqs cat facing the Eiffel Tower',
#        4,
#        45,
#        7.5,
#        1024,
    ],
    [
        'A sqs cat in the Acropolis',
#        4,
#        45,
#        7,
#        1024,
    ],
    [
        'A sqs cat close to the Taj Mahal',
#        4,
#        45,
#        7,
#        1024,
    ]
]


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;
                  margin-top: 3px;
                "
              >
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  😻 Gina the cat (Stable Diffusion v1-5 fine-tuned)
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Gina the cat (Stable Diffusion v1-5 fine-tuned) is a state of the art text-to-image model that generates
                images of Gina the cat 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 a Gina!").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.Row(elem_id="advanced-options"):
            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
            steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=50, 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, samples, steps, scale, seed], outputs=[gallery], cache_examples=False)
        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="""
            () => {
                var appDom = document.querySelector("body > gradio-app");
                var options = appDom.querySelector("#advanced-options")
                if (options == null) {options = appDom.shadowRoot.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/timothepearce" style="text-decoration: underline;" target="_blank">CompVis</a> and <a href="https://huggingface.co/timothepearce" style="text-decoration: underline;" target="_blank">Timothé Pearce</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. The 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. 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=10).launch(share=False, enable_queue=True)