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import gradio as gr |
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import gradio.helpers |
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from datasets import load_dataset |
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import base64 |
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import re |
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import os |
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import requests |
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import time |
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from PIL import Image |
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from io import BytesIO |
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from typing import Tuple |
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import user_history |
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from share_btn import community_icon_html, loading_icon_html, share_js |
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style_list = [ |
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{ |
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"name": "(No style)", |
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"prompt": "{prompt}", |
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"negative_prompt": "", |
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}, |
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{ |
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"name": "Cinematic", |
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"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", |
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"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", |
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}, |
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{ |
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"name": "Photographic", |
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"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", |
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"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", |
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}, |
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{ |
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"name": "Anime", |
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"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", |
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"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", |
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}, |
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{ |
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"name": "Manga", |
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"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", |
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"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", |
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}, |
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{ |
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"name": "Digital Art", |
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"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", |
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"negative_prompt": "photo, photorealistic, realism, ugly", |
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}, |
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{ |
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"name": "Pixel art", |
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"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", |
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"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", |
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}, |
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{ |
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"name": "Fantasy art", |
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"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", |
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"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", |
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}, |
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{ |
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"name": "Neonpunk", |
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"prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", |
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"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", |
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}, |
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{ |
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"name": "3D Model", |
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"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", |
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"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", |
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}, |
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] |
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} |
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STYLE_NAMES = list(styles.keys()) |
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DEFAULT_STYLE_NAME = "(No style)" |
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def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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return p.replace("{prompt}", positive), n + negative |
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word_list_dataset = load_dataset("google/word-list-sd", data_files="list.txt", use_auth_token=True) |
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word_list = word_list_dataset["train"]['text'] |
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def infer(prompt, negative="low_quality", scale=7, style_name=None, profile: gr.OAuthProfile | None = None): |
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for filter in word_list: |
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if re.search(rf"\b{filter}\b", prompt): |
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raise gr.Error("Please try again with a different prompt") |
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prompt, negative = apply_style(style_name, prompt, negative) |
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images = [] |
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url = os.getenv('JAX_BACKEND_URL') |
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payload = {'prompt': prompt, 'negative_prompt': negative, 'guidance_scale': scale} |
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start_time = time.time() |
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images_request = requests.post(url, json = payload) |
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print(time.time() - start_time) |
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try: |
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json_data = images_request.json() |
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except requests.exceptions.JSONDecodeError: |
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raise gr.Error("SDXL did not return a valid result, try again") |
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for image in json_data["images"]: |
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image_b64 = (f"data:image/jpeg;base64,{image}") |
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images.append(image_b64) |
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if profile is not None: |
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pil_image = Image.open(BytesIO(base64.b64decode(image))) |
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user_history.save_image( |
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label=prompt, |
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image=pil_image, |
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profile=profile, |
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metadata={ |
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"prompt": prompt, |
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"negative_prompt": negative, |
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"guidance_scale": scale, |
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}, |
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) |
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return images, gr.update(visible=True) |
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css = """ |
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.gradio-container { |
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font-family: 'IBM Plex Sans', sans-serif; |
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} |
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.gr-button { |
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color: white; |
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border-color: black; |
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background: black; |
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} |
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input[type='range'] { |
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accent-color: black; |
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} |
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.dark input[type='range'] { |
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accent-color: #dfdfdf; |
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} |
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.gradio-container { |
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max-width: 730px !important; |
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margin: auto; |
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padding-top: 1.5rem; |
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} |
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#gallery { |
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min-height: 22rem; |
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margin-bottom: 15px; |
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margin-left: auto; |
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margin-right: auto; |
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border-bottom-right-radius: .5rem !important; |
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border-bottom-left-radius: .5rem !important; |
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} |
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#gallery>div>.h-full { |
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min-height: 20rem; |
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} |
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.details:hover { |
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text-decoration: underline; |
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} |
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.gr-button { |
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white-space: nowrap; |
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} |
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.gr-button:focus { |
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border-color: rgb(147 197 253 / var(--tw-border-opacity)); |
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outline: none; |
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box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); |
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--tw-border-opacity: 1; |
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--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); |
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--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); |
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--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); |
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--tw-ring-opacity: .5; |
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} |
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#advanced-btn { |
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font-size: .7rem !important; |
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line-height: 19px; |
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margin-top: 12px; |
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margin-bottom: 12px; |
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padding: 2px 8px; |
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border-radius: 14px !important; |
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} |
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#advanced-options { |
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display: none; |
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margin-bottom: 20px; |
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} |
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.footer { |
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margin-bottom: 45px; |
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margin-top: 35px; |
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text-align: center; |
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border-bottom: 1px solid #e5e5e5; |
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} |
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.footer>p { |
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font-size: .8rem; |
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display: inline-block; |
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padding: 0 10px; |
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transform: translateY(10px); |
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background: white; |
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} |
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.dark .footer { |
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border-color: #303030; |
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} |
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.dark .footer>p { |
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background: #0b0f19; |
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} |
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.acknowledgments h4{ |
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margin: 1.25em 0 .25em 0; |
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font-weight: bold; |
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font-size: 115%; |
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} |
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.animate-spin { |
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animation: spin 1s linear infinite; |
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} |
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@keyframes spin { |
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from { |
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transform: rotate(0deg); |
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} |
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to { |
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transform: rotate(360deg); |
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} |
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} |
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#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;} |
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div#share-btn-container > div {flex-direction: row;background: black;align-items: center} |
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#share-btn-container:hover {background-color: #060606} |
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#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;} |
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#share-btn * {all: unset} |
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#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;} |
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#share-btn-container .wrap {display: none !important} |
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#share-btn-container.hidden {display: none!important} |
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.gr-form{ |
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flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; |
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} |
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#prompt-container{ |
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gap: 0; |
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} |
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#prompt-container .form{ |
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border-top-right-radius: 0; |
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border-bottom-right-radius: 0; |
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} |
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#gen-button{ |
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border-top-left-radius:0; |
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border-bottom-left-radius:0; |
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} |
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#prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem} |
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#component-16{border-top-width: 1px!important;margin-top: 1em} |
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.image_duplication{position: absolute; width: 100px; left: 50px} |
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.tabitem{border: 0 !important} |
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""" |
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block = gr.Blocks() |
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examples = [ |
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[ |
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"A serious capybara at work, wearing a suit", |
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None, |
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None |
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], |
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[ |
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'A Squirtle fine dining with a view to the London Eye', |
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None, |
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None |
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], |
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[ |
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'A tamale food cart in front of a Japanese Castle', |
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None, |
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None |
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], |
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[ |
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'a graffiti of a robot serving meals to people', |
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None, |
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None |
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], |
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[ |
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'a beautiful cabin in Attersee, Austria, 3d animation style', |
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None, |
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None |
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], |
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] |
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with block: |
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gr.HTML( |
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""" |
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<div style="text-align: center; margin: 0 auto;"> |
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<div |
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style=" |
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display: inline-flex; |
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align-items: center; |
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gap: 0.8rem; |
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font-size: 1.75rem; |
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" |
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> |
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<svg |
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width="0.65em" |
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height="0.65em" |
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viewBox="0 0 115 115" |
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fill="none" |
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xmlns="http://www.w3.org/2000/svg" |
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> |
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<rect width="23" height="23" fill="white"></rect> |
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<rect y="69" width="23" height="23" fill="white"></rect> |
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<rect x="23" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="46" width="23" height="23" fill="white"></rect> |
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<rect x="46" y="69" width="23" height="23" fill="white"></rect> |
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<rect x="69" width="23" height="23" fill="black"></rect> |
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<rect x="69" y="69" width="23" height="23" fill="black"></rect> |
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<rect x="92" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="115" y="46" width="23" height="23" fill="white"></rect> |
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<rect x="115" y="115" width="23" height="23" fill="white"></rect> |
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<rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="92" y="69" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="46" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="115" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="46" y="46" width="23" height="23" fill="black"></rect> |
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<rect x="46" y="115" width="23" height="23" fill="black"></rect> |
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<rect x="46" y="69" width="23" height="23" fill="black"></rect> |
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<rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="23" y="69" width="23" height="23" fill="black"></rect> |
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</svg> |
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<h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px"> |
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Fast Stable Diffusion XL on TPU v5e ⚡ |
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</h1> |
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</div> |
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<p style="margin-bottom: 10px; font-size: 94%; line-height: 23px;"> |
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SDXL is a high quality text-to-image model from Stability AI. This demo is running on <a style="text-decoration: underline;" href="https://cloud.google.com/blog/products/compute/announcing-cloud-tpu-v5e-and-a3-gpus-in-ga">Google Cloud TPU v5e</a>, to achieve efficient and cost-effective inference of 1024×1024 images. <a href="https://hf.co/blog/sdxl_jax" target="_blank">How does it work?</a> |
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</p> |
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</div> |
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""" |
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) |
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with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): |
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text = gr.Textbox( |
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label="Enter your prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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elem_id="prompt-text-input", |
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) |
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btn = gr.Button("Generate", scale=0, elem_id="gen-button") |
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gallery = gr.Gallery( |
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label="Generated images", show_label=False, elem_id="gallery", grid=[2] |
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) |
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with gr.Group(elem_id="share-btn-container", visible=False) as community_group: |
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community_icon = gr.HTML(community_icon_html) |
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loading_icon = gr.HTML(loading_icon_html) |
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share_button = gr.Button("Share to community", elem_id="share-btn") |
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with gr.Accordion("Advanced settings", open=False): |
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style_selection = gr.Radio( |
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show_label=True, container=True, interactive=True, |
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choices=STYLE_NAMES, |
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value=DEFAULT_STYLE_NAME, |
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label='Image Style' |
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) |
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negative = gr.Textbox( |
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label="Enter your negative prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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elem_id="negative-prompt-text-input", |
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) |
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guidance_scale = gr.Slider( |
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label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1 |
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) |
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ex = gr.Examples(examples=examples, fn=infer, inputs=[text, negative, guidance_scale], outputs=[gallery, community_group], cache_examples=True, postprocess=False) |
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negative.submit(infer, inputs=[text, negative, guidance_scale, style_selection], outputs=[gallery, community_group], postprocess=False) |
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text.submit(infer, inputs=[text, negative, guidance_scale, style_selection], outputs=[gallery, community_group], postprocess=False) |
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btn.click(infer, inputs=[text, negative, guidance_scale, style_selection], outputs=[gallery, community_group], postprocess=False) |
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share_button.click( |
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None, |
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[], |
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[], |
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_js=share_js, |
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) |
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gr.HTML( |
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""" |
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<div class="footer"> |
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<p>Model by <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">StabilityAI</a> - backend running JAX on TPUs due to generous support of <a href="https://sites.research.google/trc/about/" style="text-decoration: underline;" target="_blank">Google TRC program</a> - Gradio Demo by 🤗 Hugging Face - this is not an official Google Product |
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</p> |
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</div> |
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""" |
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) |
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with gr.Accordion(label="License", open=True): |
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gr.HTML( |
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"""<div class="acknowledgments"> |
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<p><h4>LICENSE</h4> |
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The model is licensed with a <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md" style="text-decoration: underline;" target="_blank">Stability AI CreativeML Open RAIL++-M</a> license. The License allows users to take advantage of the model in a wide range of settings (including free use and redistribution) as long as they respect the specific use case restrictions outlined, which correspond to model applications the licensor deems ill-suited for the model or are likely to cause harm. For the full list of restrictions please <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p> |
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<p><h4>Biases and content acknowledgment</h4> |
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Despite how impressive being able to turn text into image is, beware that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. You can read more in the <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0" style="text-decoration: underline;" target="_blank">model card</a></p> |
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</div> |
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""" |
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) |
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with gr.Blocks(css=css) as block_with_history: |
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with gr.Tab("Demo"): |
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block.render() |
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with gr.Tab("Past generations"): |
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user_history.render() |
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block_with_history.queue(concurrency_count=8, max_size=10, api_open=False).launch(show_api=False) |
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