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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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import spaces
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from diffusers import DiffusionPipeline
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "stabilityai/stable-diffusion-3.5-large"
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if torch.cuda.is_available():
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torch_dtype = torch.bfloat16
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else:
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torch_dtype = torch.float32
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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@spaces.GPU(duration=65)
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def infer(
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prompt,
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negative_prompt="",
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seed=42,
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randomize_seed=False,
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width=1024,
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height=1024,
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guidance_scale=4.5,
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num_inference_steps=40,
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progress=gr.Progress(track_tqdm=True),
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):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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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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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=generator,
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).images[0]
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return image, seed
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examples = [
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"A capybara wearing a suit holding a sign that reads Hello World",
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"A steampunk-style flying ship made of brass and wood, floating through cotton candy clouds",
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"A magical library where books are flying and glowing, with a wise owl librarian",
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"A cyberpunk street food vendor selling neon-colored dumplings in the rain",
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"A group of penguins having a formal tea party in the Antarctic",
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"A treehouse city at sunset with bioluminescent plants and floating lanterns"
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]
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css = """
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:root {
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--primary-color: #7B2CBF;
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--secondary-color: #9D4EDD;
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--background-color: #10002B;
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--text-color: #E0AAFF;
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--card-bg: #240046;
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}
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#col-container {
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max-width: 850px !important;
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margin: 0 auto;
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padding: 20px;
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background: var(--background-color);
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border-radius: 15px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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}
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.main-title {
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color: var(--text-color) !important;
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text-align: center;
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font-size: 2.5em !important;
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margin-bottom: 1em !important;
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text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3);
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}
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.gradio-container {
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background: var(--background-color) !important;
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color: var(--text-color) !important;
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}
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.gr-button {
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background: var(--primary-color) !important;
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border: none !important;
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color: white !important;
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transition: transform 0.2s !important;
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}
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.gr-button:hover {
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transform: translateY(-2px) !important;
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background: var(--secondary-color) !important;
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}
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.gr-input, .gr-box {
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background: var(--card-bg) !important;
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border: 1px solid var(--primary-color) !important;
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color: var(--text-color) !important;
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}
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.footer-custom a {
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color: var(--text-color);
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text-decoration: none;
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margin: 0 10px;
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transition: color 0.3s;
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}
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.footer-custom a:hover {
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color: var(--secondary-color);
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text-decoration: underline;
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}
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"""
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footer = """
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<div class="footer-custom" style="text-align: center; margin-top: 20px; color: #f8f8f2;">
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<a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
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<a href="https://github.com/arad1367" target="_blank">GitHub</a> |
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<a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a> |
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<a href="https://huggingface.co/stabilityai/stable-diffusion-3.5-large" target="_blank">stable-diffusion-3.5-large model</a> |
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<a href="https://huggingface.co/spaces/stabilityai/stable-diffusion-3.5-large-turbo" target="_blank">stable-diffusion-3.5-large-turbo</a> |
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<a href="https://stability.ai/license" target="_blank">Stability.ai licence</a>
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<br>
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<p style="margin-top: 10px;">Made with 💖 by Pejman Ebrahimi</p>
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</div>
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"""
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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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'<h1 class="main-title">Stable Diffusion 3.5 Large (8B)</h1>'
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'<div style="text-align: center; margin-bottom: 20px;">'
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'<a href="https://stability.ai" target="_blank" style="color: #E0AAFF;">Visit Stability.ai</a>'
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'</div>'
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)
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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, variant="primary")
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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visible=False,
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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=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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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=512,
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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=512,
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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=7.5,
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step=0.1,
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value=4.5,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=50,
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step=1,
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value=40,
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)
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gr.Examples(
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examples=examples,
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inputs=[prompt],
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outputs=[result, seed],
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fn=infer,
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cache_examples=True,
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cache_mode="lazy"
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)
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gr.HTML(footer)
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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inputs=[
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prompt,
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negative_prompt,
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seed,
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randomize_seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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],
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outputs=[result, seed],
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)
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if __name__ == "__main__":
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demo.launch() |
