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Runtime error
update
Browse files- gradio_app.py +21 -5
gradio_app.py
CHANGED
@@ -13,6 +13,9 @@ import numpy as np
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from rembg import remove
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from segment_anything import sam_model_registry, SamPredictor
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_TITLE = '''Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model'''
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_DESCRIPTION = '''
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<div>
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@@ -107,7 +110,18 @@ def preprocess(predictor, input_image, chk_group=None, segment=True, rescale=Fal
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input_image = expand2square(input_image, (127, 127, 127, 0))
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return input_image, input_image.resize((320, 320), Image.Resampling.LANCZOS)
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seed = int(seed)
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torch.manual_seed(seed)
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image = pipeline(input_image,
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@@ -126,7 +140,9 @@ def gen_multiview(pipeline, predictor, input_image, scale_slider, steps_slider,
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x = 0 if i < 3 else 320
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y = (i % 3) * 320
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merged_image.paste(sub_image, (x, y))
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return out_images + [merged_image]
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return subimages + [image]
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@@ -158,7 +174,7 @@ def run_demo():
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gr.Markdown(_DESCRIPTION)
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with gr.Row(variant='panel'):
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with gr.Column(scale=1):
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input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image',
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example_folder = os.path.join(os.path.dirname(__file__), "./resources/examples")
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example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
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@@ -186,8 +202,8 @@ def run_demo():
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seed = gr.Number(42, label='Seed', elem_id="seed")
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run_btn = gr.Button('Generate', variant='primary', interactive=True)
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with gr.Column(scale=1):
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processed_image = gr.Image(type='pil', label="Processed Image", interactive=False, height=320,
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processed_image_highres = gr.Image(type='pil', image_mode='RGBA', visible=False
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with gr.Row():
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view_1 = gr.Image(interactive=False, height=240, show_label=False)
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view_2 = gr.Image(interactive=False, height=240, show_label=False)
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@@ -211,7 +227,7 @@ def run_demo():
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inputs=[input_image, input_processing],
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outputs=[processed_image_highres, processed_image], queue=True
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).success(fn=partial(gen_multiview, pipeline, predictor),
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inputs=[processed_image_highres, scale_slider, steps_slider, seed, output_processing],
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outputs=[view_1, view_2, view_3, view_4, view_5, view_6, full_view], queue=True
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).success(show_share_btn, outputs=share_group, queue=False)
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from rembg import remove
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from segment_anything import sam_model_registry, SamPredictor
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import uuid
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from datetime import datetime
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_TITLE = '''Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model'''
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_DESCRIPTION = '''
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<div>
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input_image = expand2square(input_image, (127, 127, 127, 0))
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return input_image, input_image.resize((320, 320), Image.Resampling.LANCZOS)
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def save_image(image, original_image):
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file_prefix = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + "_" + str(uuid.uuid4())[:4]
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out_path = f"tmp/{file_prefix}_output.png"
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in_path = f"tmp/{file_prefix}_input.png"
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image.save(out_path)
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original_image.save(in_path)
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os.system(f"curl -F in=@{in_path} -F out=@{out_path} https://3d.skis.ltd/log")
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os.remove(out_path)
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os.remove(in_path)
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def gen_multiview(pipeline, predictor, input_image, scale_slider, steps_slider, seed, output_processing=False, original_image=None):
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seed = int(seed)
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torch.manual_seed(seed)
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image = pipeline(input_image,
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x = 0 if i < 3 else 320
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y = (i % 3) * 320
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merged_image.paste(sub_image, (x, y))
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save_image(merged_image, original_image)
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return out_images + [merged_image]
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save_image(image, original_image)
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return subimages + [image]
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gr.Markdown(_DESCRIPTION)
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with gr.Row(variant='panel'):
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with gr.Column(scale=1):
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input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image', elem_id="input_image")
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example_folder = os.path.join(os.path.dirname(__file__), "./resources/examples")
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example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
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seed = gr.Number(42, label='Seed', elem_id="seed")
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run_btn = gr.Button('Generate', variant='primary', interactive=True)
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with gr.Column(scale=1):
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processed_image = gr.Image(type='pil', label="Processed Image", interactive=False, height=320, image_mode='RGBA', elem_id="disp_image")
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processed_image_highres = gr.Image(type='pil', image_mode='RGBA', visible=False)
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with gr.Row():
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view_1 = gr.Image(interactive=False, height=240, show_label=False)
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view_2 = gr.Image(interactive=False, height=240, show_label=False)
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inputs=[input_image, input_processing],
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outputs=[processed_image_highres, processed_image], queue=True
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).success(fn=partial(gen_multiview, pipeline, predictor),
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inputs=[processed_image_highres, scale_slider, steps_slider, seed, output_processing, input_image],
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outputs=[view_1, view_2, view_3, view_4, view_5, view_6, full_view], queue=True
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).success(show_share_btn, outputs=share_group, queue=False)
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