Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+
+
+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]
+    fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))
+    return fig 
+
+demo = gr.Interface(fn=inference, inputs="text", outputs=gr.Plot())
+demo.launch(debug=True)