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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 π 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() |