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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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import torch |
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from PIL import Image |
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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-turbo" |
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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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style_list = [ |
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{ |
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"name": "3840 x 2160", |
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"prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", |
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"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", |
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}, |
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{ |
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"name": "2560 x 1440", |
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"prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", |
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"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", |
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}, |
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{ |
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"name": "HD+", |
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"prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", |
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"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", |
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}, |
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{ |
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"name": "Style Zero", |
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"prompt": "{prompt}", |
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"negative_prompt": "", |
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}, |
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] |
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STYLE_NAMES = [style["name"] for style in style_list] |
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DEFAULT_STYLE_NAME = STYLE_NAMES[0] |
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grid_sizes = { |
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"2x1": (2, 1), |
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"1x2": (1, 2), |
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"2x2": (2, 2), |
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"2x3": (2, 3), |
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"3x2": (3, 2), |
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"1x1": (1, 1) |
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} |
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@spaces.GPU(duration=60) |
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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=0.0, |
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num_inference_steps=4, |
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style="Style Zero", |
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grid_size="1x1", |
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progress=gr.Progress(track_tqdm=True), |
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): |
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selected_style = next(s for s in style_list if s["name"] == style) |
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styled_prompt = selected_style["prompt"].format(prompt=prompt) |
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styled_negative_prompt = selected_style["negative_prompt"] |
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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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grid_size_x, grid_size_y = grid_sizes.get(grid_size, (1, 1)) |
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num_images = grid_size_x * grid_size_y |
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options = { |
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"prompt": styled_prompt, |
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"negative_prompt": styled_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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} |
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torch.cuda.empty_cache() |
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images = pipe(**options).images |
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grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y)) |
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for i, img in enumerate(images[:num_images]): |
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grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height)) |
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return grid_img, seed |
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examples = [ |
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"Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic oil --ar 2:3 --q 2 --s 750 --v 5 --ar 2:3 --q 2 --s 750 --v 5", |
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"3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)", |
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"Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K, Photo-Realistic", |
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"Food photography of a milk shake with flying strawberrys against a pink background, professionally studio shot with cinematic lighting. The image is in the style of a professional studio shot --ar 85:128 --v 6.0 --style raw" |
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] |
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css = ''' |
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.gradio-container{max-width: 585px !important} |
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h1{text-align:center} |
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footer { |
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visibility: hidden |
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} |
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''' |
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with gr.Blocks(css=css, theme="prithivMLmods/Minecraft-Theme") as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown("## SD3.5 TURBO") |
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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("Run", scale=0, variant="primary") |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Row(visible=True): |
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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="Quality Style", |
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) |
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with gr.Row(visible=True): |
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grid_size_selection = gr.Dropdown( |
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choices=["2x1", "1x2", "2x2", "2x3", "3x2", "1x1"], |
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value="1x1", |
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label="Grid Size" |
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) |
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with gr.Accordion("Advanced Settings", open=False, visible=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=0.0, |
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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=4, |
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) |
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gr.Examples(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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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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style_selection, |
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grid_size_selection, |
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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() |
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