app.py

import gradio as gr 
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
from PIL import Image
import io
import os
import warnings


theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
)

def infer(prompt, api_key):
    stability_api = client.StabilityInference(
    key=api_key, # API Key reference.
    verbose=True, # Print debug messages.
    engine="stable-diffusion-xl-beta-v2-2-2", # Set the engine to use for generation. 
    # Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0 stable-inpainting-v1-0 stable-inpainting-512-v2-0
)
    answers = stability_api.generate(
    prompt=prompt,
    seed=992446758, # If a seed is provided, the resulting generated image will be deterministic.
                    # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
                    # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
    steps=30, # Amount of inference steps performed on image generation. Defaults to 30. 
    cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt.
                   # Setting this value higher increases the strength in which it tries to match your prompt.
                   # Defaults to 7.0 if not specified.
    width=512, # Generation width, defaults to 512 if not included.
    height=512, # Generation height, defaults to 512 if not included.
    samples=2, # Number of images to generate, defaults to 1 if not included.
    sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with.
                                                 # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
                                                 # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m)
)
    for resp in answers:
        for artifact in resp.artifacts:
            if artifact.finish_reason == generation.FILTER:
                warnings.warn(
                "Your request activated the API's safety filters and could not be processed."
                "Please modify the prompt and try again.")
            if artifact.type == generation.ARTIFACT_IMAGE:
                img = Image.open(io.BytesIO(artifact.binary))
    
    return [img]

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%;
        }
        .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;
            margin-top: 10px;
            margin-left: auto;
        }
        #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;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;
        }
        
        .gr-form{
            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
        }
        #prompt-container{
            gap: 0;
        }
        #prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem}
        #component-16{border-top-width: 1px!important;margin-top: 1em}
        .image_duplication{position: absolute; width: 100px; left: 50px}
"""

with gr.Blocks(css = css) as demo:
    gr.Markdown("# Stable Diffusion XL")
    gr.Markdown("<p>This is an unoffical demo for Stable Diffusion XL, which is the latest stable diffusion model released by Stability AI. The main features include Next-level photorealism capabilities, image composition and face generation, use of shorter prompts to create descriptive imagery, greater capability to produce legible text and rich visuals and jaw-dropping aesthetics</p>")
    gr.Markdown(""" Please refer to [the official website](https://stability.ai/stable-diffusion) for further information""")
    api_key_input = gr.Textbox(type = "password", label = "Enter your StabilityAI API key here")
    
    with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
        with gr.Column():
            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")

    btn.click(infer, inputs=[text, api_key_input], outputs=[gallery])
    examples = [
        ["Vintage hot rod with custom flame paint job"],
        ["Ancient, mysterious temple in a mountain range, surrounded by misty clouds and tall peaks"],
        ["Glimpses of a herd of wild elephants crossing a savanna"],
        ["Beautiful waterfall in a lush jungle, with sunlight shining through the trees,"]
    ]
    ex = gr.Examples(examples=examples,inputs=[text], cache_examples=False)

    
demo.launch()