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juancopi81
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Browse files- README.md +1 -1
- app.py +240 -0
- requirements.txt +3 -0
README.md
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---
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title: Multilingual Stable Diffusion
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colorFrom: red
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colorTo: pink
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sdk: gradio
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---
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title: Multilingual Stable Diffusion
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emoji: π₯
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colorFrom: red
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colorTo: pink
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sdk: gradio
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app.py
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import gradio as gr
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import torch
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from diffusers import DiffusionPipeline
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from transformers import (
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pipeline,
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MBart50TokenizerFast,
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MBartForConditionalGeneration,
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)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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device_dict = {"cuda": 0, "cpu": -1}
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# Add language detection pipeline
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language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
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language_detection_pipeline = pipeline("text-classification",
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model=language_detection_model_ckpt,
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device=device_dict[device])
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# Add model for language translation
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trans_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
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trans_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt").to(device)
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model_id = "CompVis/stable-diffusion-v1-4"
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pipe = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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custom_pipeline="multilingual_stable_diffusion",
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detection_pipeline=language_detection_pipeline,
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translation_model=trans_model,
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translation_tokenizer=trans_tokenizer,
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revision="fp16",
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torch_dtype=torch.float16,
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)
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pipe.enable_attention_slicing()
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pipe = pipe.to(device)
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#torch.backends.cudnn.benchmark = True
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num_samples = 2
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def infer(prompt):
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output = pipe([prompt] * num_samples)
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return output.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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#generated_id{
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min-height: 700px
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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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'Una casa en la playa en un atardecer lluvioso'
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],
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[
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'Ein Hund, der Orange isst'
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],
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[
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"Photo d'un restaurant parisien"
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],
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]
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with block as demo:
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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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<h1 style="font-weight: 900; margin-bottom: 7px;">
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Multilingual Stable Diffusion
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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 Pipeline that supports prompts in 50 different languages.
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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().style(mobile_collapse=False, equal_height=True):
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text = gr.Textbox(
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label="Enter your prompt", show_label=False, max_lines=1
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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("Run").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(label="Generated images", show_label=False, elem_id="generated_id").style(
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grid=[2], height="auto"
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)
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ex = gr.Examples(examples=examples, fn=infer, inputs=[text], outputs=gallery, cache_examples=True)
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ex.dataset.headers = [""]
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text.submit(infer, inputs=[text], outputs=gallery)
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btn.click(infer, inputs=[text], outputs=gallery)
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gr.HTML(
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"""
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<div class="footer">
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<p>Stable Diffusion model that supports multiple languages by <a href="https://huggingface.co/juancopi81" style="text-decoration: underline;" target="_blank">juancopi81</a>
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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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demo.queue(max_size=25).launch()
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requirements.txt
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diffusers==0.7
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transformers==4.24
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torch
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