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Update app.py
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app.py
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@@ -63,7 +63,8 @@ title = r"""
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"""
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description = r"""
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<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/kongzhecn/OMG/' target='_blank'><b>OMG: Occlusion-friendly Personalized Multi-concept Generation In Diffusion Models</b></a>.<
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How to use:<br>
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1. Select two characters.
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2. Enter a text prompt as done in normal text-to-image models.
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@@ -366,7 +367,7 @@ def main(device, segment_type):
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image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
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return image
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@spaces.GPU(duration=
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def generate_image(prompt1, negative_prompt, man, woman, resolution, local_prompt1, local_prompt2, seed, condition, condition_img1, style):
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# try:
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path1 = lorapath_man[man]
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"""
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description = r"""
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<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/kongzhecn/OMG/' target='_blank'><b>OMG: Occlusion-friendly Personalized Multi-concept Generation In Diffusion Models</b></a>.<be>
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<a href='https://kongzhecn.github.io/omg-project/' target='_blank'><b>Project</b></a>.<a href='https://github.com/kongzhecn/OMG/' target='_blank'><b>Code</b></a>.<a href='https://arxiv.org/abs/2403.10983/' target='_blank'><b>Arxiv</b></a>.<br>
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How to use:<br>
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1. Select two characters.
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2. Enter a text prompt as done in normal text-to-image models.
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image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
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return image
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@spaces.GPU(duration=240)
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def generate_image(prompt1, negative_prompt, man, woman, resolution, local_prompt1, local_prompt2, seed, condition, condition_img1, style):
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# try:
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path1 = lorapath_man[man]
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