import gradio as gr from datasets import load_dataset import base64 import re import os import requests from PIL import Image from io import BytesIO import user_history from share_btn import community_icon_html, loading_icon_html, share_js # TODO #word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True) #word_list = word_list_dataset["train"]['text'] word_list = [] def infer(prompt, negative="low_quality", scale=7, profile: gr.OAuthProfile | None = None): for filter in word_list: if re.search(rf"\b{filter}\b", prompt): raise gr.Error("Unsafe content found. Please try again with different prompts.") images = [] url = os.getenv('JAX_BACKEND_URL') payload = {'prompt': prompt, 'negative_prompt': negative, 'guidance_scale': scale} images_request = requests.post(url, json = payload) for image in images_request.json()["images"]: image_b64 = (f"data:image/jpeg;base64,{image}") images.append(image_b64) if profile is not None: # avoid conversion on non-logged-in users pil_image = Image.open(BytesIO(base64.b64decode(image))) user_history.save_image( # save images + metadata to user history label=prompt, image=pil_image, profile=profile, metadata={ "prompt": prompt, "negative_prompt": negative, "guidance_scale": scale, }, ) return images, gr.update(visible=True) 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; } .gradio-container { max-width: 730px !important; 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 {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;} div#share-btn-container > div {flex-direction: row;background: black;align-items: center} #share-btn-container:hover {background-color: #060606} #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;} #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} #share-btn-container.hidden {display: none!important} .gr-form{ flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; } #prompt-container{ gap: 0; } #prompt-container .form{ border-top-right-radius: 0; border-bottom-right-radius: 0; } #gen-button{ border-top-left-radius:0; border-bottom-left-radius: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} .tabitem{border: 0} """ block = gr.Blocks() examples = [ [ "A serious capybara at work, wearing a suit", None, None ], [ 'A Squirtle fine dining with a view to the London Eye', None, None ], [ 'A tamale food cart in front of a Japanese Castle', None, None ], [ 'a graffiti of a robot serving meals to people', None, None ], [ 'a beautiful cabin in Attersee, Austria, 3d animation style', None, None ], ] with block: gr.HTML( """
SDXL is a high quality text-to-image model from Stability AI. This demo on a Google Cloud TPU v5e, to achieve efficient and cost-effective inference of 1024×1024 images. How does it work?.