Spaces:
Running
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CPU Upgrade
Running
on
CPU Upgrade
Commit
•
b3bea79
1
Parent(s):
d1e3269
Release
Browse files- app.py +270 -0
- requirements.txt +4 -0
- unsafe.png +0 -0
app.py
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1 |
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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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import re
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model_id = "CompVis/stable-diffusion-v1-4"
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device = "cuda"
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pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True, revision="fp16", torch_dtype=torch.float16)
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pipe = pipe.to(device)
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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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def infer(prompt, samples, steps, scale, seed):
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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 Exception("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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with autocast("cuda"):
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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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generator=generator,
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)
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images = []
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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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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: 1070px;
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margin: auto;
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padding-top: 2rem;
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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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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: 24px;
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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: 25px;
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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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"""
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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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3,
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40,
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7.5,
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1024,
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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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3,
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40,
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7,
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1024,
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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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3,
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40,
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7,
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1024,
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],
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[
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'an insect robot preparing a delicious meal',
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3,
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40,
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7,
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1024,
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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, arstation",
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3,
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40,
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7,
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1024,
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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;">
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<div style="display: inline-flex; align-items: center; gap: .8rem; font-size: 1.75rem;">
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<svg width="0.65em" height="0.65em" viewBox="0 0 115 115" fill="none" xmlns="http://www.w3.org/2000/svg">
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<rect width="23" height="23" fill="white"/>
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<rect y="69" width="23" height="23" fill="white"/>
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<rect x="23" width="23" height="23" fill="#AEAEAE"/>
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<rect x="23" y="69" width="23" height="23" fill="#AEAEAE"/>
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<rect x="46" width="23" height="23" fill="white"/>
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<rect x="46" y="69" width="23" height="23" fill="white"/>
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<rect x="69" width="23" height="23" fill="black"/>
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<rect x="69" y="69" width="23" height="23" fill="black"/>
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<rect x="92" width="23" height="23" fill="#D9D9D9"/>
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<rect x="92" y="69" width="23" height="23" fill="#AEAEAE"/>
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<rect x="115" y="46" width="23" height="23" fill="white"/>
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<rect x="115" y="115" width="23" height="23" fill="white"/>
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<rect x="115" y="69" width="23" height="23" fill="#D9D9D9"/>
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<rect x="92" y="46" width="23" height="23" fill="#AEAEAE"/>
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<rect x="92" y="115" width="23" height="23" fill="#AEAEAE"/>
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<rect x="92" y="69" width="23" height="23" fill="white"/>
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185 |
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<rect x="69" y="46" width="23" height="23" fill="white"/>
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<rect x="69" y="115" width="23" height="23" fill="white"/>
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<rect x="69" y="69" width="23" height="23" fill="#D9D9D9"/>
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188 |
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<rect x="46" y="46" width="23" height="23" fill="black"/>
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189 |
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<rect x="46" y="115" width="23" height="23" fill="black"/>
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<rect x="46" y="69" width="23" height="23" fill="black"/>
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<rect x="23" y="46" width="23" height="23" fill="#D9D9D9"/>
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<rect x="23" y="115" width="23" height="23" fill="#AEAEAE"/>
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193 |
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<rect x="23" y="69" width="23" height="23" fill="black"/>
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</svg>
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<h1 style="font-weight: 900;">Stable Diffusion Spaces</h1>
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</div>
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<p style="margin-bottom: 20px;">Stable Diffusion is a state of the art text-to-image model that generates images from a text description.</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().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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).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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)
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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=[3], height="auto")
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advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
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with gr.Row(elem_id="advanced-options"):
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samples = gr.Slider(label="Images", minimum=1, maximum=3, value=3, step=1)
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=40, 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="Random 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, samples, steps, scale, seed], outputs=gallery, cache_examples=True)
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ex.dataset.headers = [""]
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+
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text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
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btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
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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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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> - 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 an <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The license states that the outputs that you make fully belong to you, and you are liable when sharing it. 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 LAION-400M dataset, which scrapped non-curated image-text-pairs from the internet (the exception being the 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(max_size=40).launch()
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requirements.txt
ADDED
@@ -0,0 +1,4 @@
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1 |
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diffusers
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2 |
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transformers
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nvidia-ml-py3
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4 |
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--extra-index-url https://download.pytorch.org/whl/cu113 torch
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unsafe.png
ADDED
![]() |