Delete app.py

app.py

@@ -1,85 +0,0 @@
-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.
-    Check out the [notebook](https://github.com/openai/point-e/blob/main/point_e/examples/text2pointcloud.ipynb).
-"""
-)
-demo.queue(max_size=30)
-demo.launch(debug=True)