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Create app.py
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app.py
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import gradio as gr
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import numpy as np
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import random
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from diffusers import DiffusionPipeline
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from safetensors.torch import load_file
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from huggingface_hub import hf_hub_download
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import torch
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from typing import Tuple
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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": "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": "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": "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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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
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pipe.to("cuda")
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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def infer(prompt, negative_prompt, width, height, guidance_scale, style_name=None):
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seed = random.randint(0,4294967295)
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generator = torch.Generator().manual_seed(seed)
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prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
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image = [pipe(
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prompt = prompt,
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negative_prompt = negative_prompt,
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guidance_scale = guidance_scale,
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width = width,
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height = height,
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generator = generator
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).images[0] for _ in range(4)]
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return image
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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"A delicious ceviche cheesecake slice",
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"A serious capybara at work, wearing a suit",
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'A Squirtle fine dining with a view to the London Eye',
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'a graffiti of a robot serving meals to people',
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'a beautiful cabin in Attersee, Austria, 3d animation style',
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]
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css="""
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#col-container {
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margin: 0 auto;
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max-width: 1000px;
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padding-top: 20px;
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text-align: center;
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}
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.header {
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margin: 10px auto 10px auto;
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text-align: center;
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max-width: 600px;
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}
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#example-container {
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max-width: 1000px;
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margin: 0 auto;
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}
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.footer {
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margin: 25px auto 45px auto;
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text-align: center;
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max-width: 600px;
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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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}
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"""
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if torch.cuda.is_available():
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power_device = "GPU"
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else:
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power_device = "CPU"
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="col-container"):
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gr.HTML(
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"""
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<div class="header">
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<h1>Welcome to Metamorph: Your Creative Gateway</h1>
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<h4>
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Transform your words into stunning visuals with our advanced AI-powered Text-to-Image generator
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</h4>
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</div>
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""")
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gr.Markdown(f"""
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Currently running on {power_device}.
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""")
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with gr.Row(elem_id="col-container"):
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# Left column
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with gr.Column(scale=1,elem_id="left-container"):
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with gr.Row():
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prompt = gr.Text(
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label="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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container=False,
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)
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run_button = gr.Button("Generate", scale=0)
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with gr.Accordion("Advanced Settings", open=True):
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negative_prompt = gr.Textbox(
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label="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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style_selection = gr.Radio(
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show_label=True,
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container=True,
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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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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=50.0,
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step=0.1,
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value=10,
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)
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# Right column
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with gr.Column(scale=1, elem_id="right-container"):
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result = gr.Gallery(label="Results", show_label=False, format="png", show_share_button=False, height=475)
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gr.Examples(
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elem_id="example-container",
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examples = examples,
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inputs = [prompt]
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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>
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This application harnesses the cutting-edge Stable Diffusion XL (SDXL) model by <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">StabilityAI</a>, offering unparalleled text-to-image generation, while acknowledging potential biases and content considerations outlined in the model card.</p>
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</p>
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</div>
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"""
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)
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run_button.click(
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fn = infer,
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inputs = [prompt, negative_prompt, width, height, guidance_scale, style_selection],
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outputs = [result]
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)
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demo.queue().launch()
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