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import gradio as gr |
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import torch |
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from torch import autocast |
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from diffusers import StableDiffusionPipeline |
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from datasets import load_dataset |
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from PIL import Image |
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from io import BytesIO |
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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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from share_btn import community_icon_html, loading_icon_html, share_js |
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model_id = "CompVis/stable-diffusion-v1-4" |
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device = "cuda" |
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word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True) |
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word_list = word_list_dataset["train"]['text'] |
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is_gpu_busy = False |
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def infer(prompt): |
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global is_gpu_busy |
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samples = 4 |
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steps = 50 |
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scale = 7.5 |
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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("Unsafe content found. Please try again with different prompts.") |
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generator = torch.Generator(device=device).manual_seed(seed) |
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print("Is GPU busy? ", is_gpu_busy) |
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images = [] |
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if(not is_gpu_busy): |
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is_gpu_busy = True |
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images_list = pipe( |
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[prompt] * samples, |
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num_inference_steps=steps, |
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guidance_scale=scale, |
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) |
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is_gpu_busy = False |
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safe_image = Image.open(r"unsafe.png") |
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for i, image in enumerate(images_list["sample"]): |
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if(images_list["nsfw_content_detected"][i]): |
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images.append(safe_image) |
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else: |
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images.append(image) |
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else: |
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url = os.getenv('JAX_BACKEND_URL') |
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payload = {'prompt': prompt} |
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images_request = requests.post(url, json = payload) |
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for image in images_request.json()["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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return images |
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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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.container { |
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max-width: 730px; |
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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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#container-advanced-btns{ |
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display: flex; |
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flex-wrap: wrap; |
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justify-content: space-between; |
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align-items: center; |
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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 { |
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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; |
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} |
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#share-btn { |
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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; |
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} |
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#share-btn * { |
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all: unset; |
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} |
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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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""" |
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block = gr.Blocks(css=css) |
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examples = [ |
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[ |
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'A high tech solarpunk utopia in the Amazon rainforest', |
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], |
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[ |
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'A pikachu fine dining with a view to the Eiffel Tower', |
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], |
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[ |
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'A mecha robot in a favela in expressionist style', |
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], |
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[ |
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'an insect robot preparing a delicious meal', |
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], |
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[ |
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"A small cabin on top of a snowy mountain in the style of Disney, artstation", |
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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; max-width: 650px; 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;"> |
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Stable Diffusion Demo |
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</h1> |
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</div> |
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<p style="margin-bottom: 10px; font-size: 94%"> |
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Stable Diffusion is a state of the art text-to-image model that generates |
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images from text.<br>For faster generation and API |
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access you can try |
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<a |
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href="http://beta.dreamstudio.ai/" |
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style="text-decoration: underline;" |
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target="_blank" |
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>DreamStudio Beta</a |
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> |
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</p> |
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</div> |
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""" |
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) |
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with gr.Group(): |
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with gr.Box(): |
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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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).style( |
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border=(True, False, True, True), |
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rounded=(True, False, False, True), |
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container=False, |
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) |
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btn = gr.Button("Generate image").style( |
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margin=False, |
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rounded=(False, True, True, False), |
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full_width=False, |
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) |
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gallery = gr.Gallery( |
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label="Generated images", show_label=False, elem_id="gallery" |
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).style(grid=[2], height="auto") |
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with gr.Group(elem_id="container-advanced-btns"): |
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advanced_button = gr.Button("Advanced options", elem_id="advanced-btn") |
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with gr.Group(elem_id="share-btn-container"): |
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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.Row(elem_id="advanced-options"): |
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gr.Markdown("Advanced settings are temporarily unavailable") |
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samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1) |
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1) |
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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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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=2147483647, |
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step=1, |
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randomize=True, |
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) |
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ex = gr.Examples(examples=examples, fn=infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button], cache_examples=False) |
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ex.dataset.headers = [""] |
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text.submit(infer, inputs=text, outputs=[gallery], postprocess=False) |
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btn.click(infer, inputs=text, outputs=[gallery], postprocess=False) |
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advanced_button.click( |
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None, |
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[], |
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text, |
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_js=""" |
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() => { |
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const options = document.querySelector("body > gradio-app").querySelector("#advanced-options"); |
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options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none"; |
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}""", |
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) |
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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/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> - 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 |
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</p> |
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</div> |
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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/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> |
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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 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> |
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</div> |
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""" |
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) |
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block.queue(concurrency_count=40, max_size=20).launch(max_threads=150) |
