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Update app.py
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app.py
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@@ -8,7 +8,6 @@ import numpy as np
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import torch
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from diffusers.pipelines.controlnet import StableDiffusionControlNetInpaintPipeline
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from diffusers import ControlNetModel, UniPCMultistepScheduler, AutoPipelineForText2Image
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# from transformers import AutoImageProcessor, UperNetForSemanticSegmentation, AutoModelForDepthEstimation
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from transformers import AutoImageProcessor, UperNetForSemanticSegmentation, \
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AutoModelForDepthEstimation, DetrForObjectDetection, DetrImageProcessor
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from colors import ade_palette
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@@ -397,18 +396,14 @@ def main():
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model = ControlNetDepthDesignModelMulti()
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print('Models uploaded successfully')
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title = "#
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description = """
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<p style='font-size: 14px; margin-bottom: 10px;'><a href='https://www.linkedin.com/in/mykola-lavreniuk/'>Mykola Lavreniuk</a>, <a href='https://www.linkedin.com/in/bartosz-ludwiczuk-a677a760/'>Bartosz Ludwiczuk</a></p>
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<p style='font-size: 16px; margin-bottom: 0px; margin-top=0px;'>Official demo for <strong>StableDesign:</strong> 2nd place solution for the Generative Interior Design 2024 <a href='https://www.aicrowd.com/challenges/generative-interior-design-challenge-2024/leaderboards?challenge_round_id=1314'>competition</a>. StableDesign is a deep learning model designed to harness the power of AI, providing innovative and creative tools for designers. Using our algorithms, images of empty rooms can be transformed into fully furnished spaces based on text descriptions. Please refer to our <a href='https://github.com/Lavreniuk/generative-interior-design'>GitHub</a> for more details.</p>
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"""
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with gr.Blocks() as demo:
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gr.Markdown(title)
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gr.Markdown(description)
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create_demo(model)
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gr.HTML('''
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<p><img src="https://visitor-badge.glitch.me/badge?page_id=MykolaL/StableDesign" alt="visitors"></p></center>''')
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demo.queue().launch(share=False)
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import torch
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from diffusers.pipelines.controlnet import StableDiffusionControlNetInpaintPipeline
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from diffusers import ControlNetModel, UniPCMultistepScheduler, AutoPipelineForText2Image
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from transformers import AutoImageProcessor, UperNetForSemanticSegmentation, \
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AutoModelForDepthEstimation, DetrForObjectDetection, DetrImageProcessor
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from colors import ade_palette
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model = ControlNetDepthDesignModelMulti()
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print('Models uploaded successfully')
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title = "# pi31415e2718"
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description = """ Broken Code """
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with gr.Blocks() as demo:
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gr.Markdown(title)
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gr.Markdown(description)
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create_demo(model)
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gr.HTML(''' ''')
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demo.queue().launch(share=False)
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