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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 | |
| from point_e.util.pc_to_mesh import marching_cubes_mesh | |
| import trimesh | |
| 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)) | |
| print('creating SDF model...') | |
| name = 'sdf' | |
| sdf_model = model_from_config(MODEL_CONFIGS[name], device) | |
| sdf_model.eval() | |
| print('loading SDF model...') | |
| sdf_model.load_state_dict(load_checkpoint(name, 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) | |
| ) | |
| ), | |
| ) | |
| # Produce a mesh (with vertex colors) | |
| mesh = marching_cubes_mesh( | |
| pc=pc, | |
| model=sdf_model, | |
| batch_size=4096, | |
| grid_size=32, # increase to 128 for resolution used in evals | |
| progress=True, | |
| ) | |
| # Write the mesh to a PLY file to import into some other program. | |
| with open("mesh.ply", 'wb') as f: | |
| mesh.write_ply(f) | |
| obj_file = '3d_model.obj' | |
| mesh = trimesh.load('mesh.ply') | |
| mesh.export(obj_file) | |
| return fig, obj_file | |
| demo = gr.Interface( | |
| fn=inference, | |
| inputs="text", | |
| outputs=[gr.Plot(),gr.Model3D(value=None)], | |
| 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) | |