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Duplicate from openai/point-e
Browse filesCo-authored-by: Omar Sanseviero <osanseviero@users.noreply.huggingface.co>
- .gitattributes +34 -0
- README.md +13 -0
- app.py +85 -0
- requirements.txt +2 -0
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README.md
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---
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title: Point E
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emoji: 🌖
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colorFrom: purple
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colorTo: red
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sdk: gradio
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sdk_version: 3.14.0
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app_file: app.py
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pinned: false
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duplicated_from: openai/point-e
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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import plotly.graph_objects as go
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import torch
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from tqdm.auto import tqdm
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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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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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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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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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print('downloading base checkpoint...')
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base_model.load_state_dict(load_checkpoint(base_name, device))
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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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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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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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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 Clouds 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)
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requirements.txt
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git+https://github.com/openai/point-e
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plotly
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