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Delete app.py

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  1. app.py +0 -85
app.py DELETED
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- import gradio as gr
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- import plotly.graph_objects as go
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-
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- import torch
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- from tqdm.auto import tqdm
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-
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- from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
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- from point_e.diffusion.sampler import PointCloudSampler
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- from point_e.models.download import load_checkpoint
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- from point_e.models.configs import MODEL_CONFIGS, model_from_config
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- from point_e.util.plotting import plot_point_cloud
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-
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- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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-
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- print('creating base model...')
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- base_name = 'base40M-textvec'
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- base_model = model_from_config(MODEL_CONFIGS[base_name], device)
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- base_model.eval()
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- base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
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-
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- print('creating upsample model...')
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- upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
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- upsampler_model.eval()
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- upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
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-
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- print('downloading base checkpoint...')
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- base_model.load_state_dict(load_checkpoint(base_name, device))
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-
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- print('downloading upsampler checkpoint...')
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- upsampler_model.load_state_dict(load_checkpoint('upsample', device))
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-
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- sampler = PointCloudSampler(
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- device=device,
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- models=[base_model, upsampler_model],
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- diffusions=[base_diffusion, upsampler_diffusion],
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- num_points=[1024, 4096 - 1024],
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- aux_channels=['R', 'G', 'B'],
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- guidance_scale=[3.0, 0.0],
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- model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
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- )
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-
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- def inference(prompt):
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- samples = None
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- for x in sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt])):
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- samples = x
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- pc = sampler.output_to_point_clouds(samples)[0]
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- pc = sampler.output_to_point_clouds(samples)[0]
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- colors=(238, 75, 43)
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- fig = go.Figure(
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- data=[
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- go.Scatter3d(
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- x=pc.coords[:,0], y=pc.coords[:,1], z=pc.coords[:,2],
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- mode='markers',
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- marker=dict(
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- size=2,
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- color=['rgb({},{},{})'.format(r,g,b) for r,g,b in zip(pc.channels["R"], pc.channels["G"], pc.channels["B"])],
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- )
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- )
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- ],
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- layout=dict(
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- scene=dict(
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- xaxis=dict(visible=False),
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- yaxis=dict(visible=False),
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- zaxis=dict(visible=False)
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- )
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- ),
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- )
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- return fig
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-
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- demo = gr.Interface(
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- fn=inference,
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- inputs="text",
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- outputs=gr.Plot(),
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- examples=[
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- ["a red motorcycle"],
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- ["a RED pumpkin"],
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- ["a yellow rubber duck"]
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- ],
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- title="Point-E demo: text to 3D",
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- description="""Generated 3D Point Cloiuds with [Point-E](https://github.com/openai/point-e/tree/main). This demo uses a small, worse quality text-to-3D model to produce 3D point clouds directly from text descriptions.
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- Check out the [notebook](https://github.com/openai/point-e/blob/main/point_e/examples/text2pointcloud.ipynb).
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- """
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- )
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- demo.queue(max_size=30)
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- demo.launch(debug=True)