Duplicate from openai/point-e

Co-authored-by: Omar Sanseviero <osanseviero@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Point E
+emoji: 🌖
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 3.14.0
+app_file: app.py
+pinned: false
+duplicated_from: openai/point-e
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import plotly.graph_objects as go
+
+import torch
+from tqdm.auto import tqdm
+
+from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
+from point_e.diffusion.sampler import PointCloudSampler
+from point_e.models.download import load_checkpoint
+from point_e.models.configs import MODEL_CONFIGS, model_from_config
+from point_e.util.plotting import plot_point_cloud
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+print('creating base model...')
+base_name = 'base40M-textvec'
+base_model = model_from_config(MODEL_CONFIGS[base_name], device)
+base_model.eval()
+base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
+
+print('creating upsample model...')
+upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
+upsampler_model.eval()
+upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
+
+print('downloading base checkpoint...')
+base_model.load_state_dict(load_checkpoint(base_name, device))
+
+print('downloading upsampler checkpoint...')
+upsampler_model.load_state_dict(load_checkpoint('upsample', device))
+
+sampler = PointCloudSampler(
+    device=device,
+    models=[base_model, upsampler_model],
+    diffusions=[base_diffusion, upsampler_diffusion],
+    num_points=[1024, 4096 - 1024],
+    aux_channels=['R', 'G', 'B'],
+    guidance_scale=[3.0, 0.0],
+    model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
+)
+
+def inference(prompt):
+    samples = None
+    for x in sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt])):
+        samples = x
+    pc = sampler.output_to_point_clouds(samples)[0]
+    pc = sampler.output_to_point_clouds(samples)[0]
+    colors=(238, 75, 43)
+    fig = go.Figure(
+        data=[
+            go.Scatter3d(
+                x=pc.coords[:,0], y=pc.coords[:,1], z=pc.coords[:,2], 
+                mode='markers',
+                marker=dict(
+                  size=2,
+                  color=['rgb({},{},{})'.format(r,g,b) for r,g,b in zip(pc.channels["R"], pc.channels["G"], pc.channels["B"])],
+              )
+            )
+        ],
+        layout=dict(
+            scene=dict(
+                xaxis=dict(visible=False),
+                yaxis=dict(visible=False),
+                zaxis=dict(visible=False)
+            )
+        ),
+    )
+    return fig
+
+demo = gr.Interface(
+    fn=inference,
+    inputs="text",
+    outputs=gr.Plot(),
+    examples=[
+        ["a red motorcycle"],
+        ["a RED pumpkin"],
+        ["a yellow rubber duck"]
+    ],
+    title="Point-E demo: text to 3D",
+    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.
+    Skip the queue by duplicating this space and upgrading to GPU in settings
+<a href="https://huggingface.co/spaces/openai/point-e?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+"""
+)
+demo.queue(max_size=30)
+demo.launch(debug=True)

requirements.txt

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+git+https://github.com/openai/point-e
+plotly