Is there any suggestion for sampling more points than 4096?

[easonnie]

Thank you for releasing point-e! I was wondering whether there would a way to sample more points than 4096?

I tried to do two-steps upsampling but it does not work.

sampler = PointCloudSampler(
    device=device,
    models=[base_model, upsampler_model, upsampler_diffusion],
    diffusions=[base_diffusion, upsampler_diffusion, upsampler_diffusion],
    num_points=[1024, 4096 - 1024, 4096 * 4 - 4096],
    aux_channels=['R', 'G', 'B'],
    guidance_scale=[3.0, 3.0, 3.0],
    use_karras = (True, True, True),
    karras_steps = (64, 64, 64),
    sigma_min = (1e-3, 1e-3, 1e-3),
    sigma_max = (120, 160, 160),
    s_churn = (3, 0, 0),
)

# Load an image to condition on.
# img = Image.open('example_data/cube_stack.jpg')
img = Image.open('example_data/render_img.png')

# Produce a sample from the model.
samples = None
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))):
    samples = x

It gives the following errors.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [48], line 7
      5 # Produce a sample from the model.
      6 samples = None
----> 7 for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))):
      8     samples = x

File ~/miniconda/envs/3d_cu116_py38/lib/python3.8/site-packages/tqdm/notebook.py:259, in tqdm_notebook.__iter__(self)
    257 try:
    258     it = super(tqdm_notebook, self).__iter__()
--> 259     for obj in it:
    260         # return super(tqdm...) will not catch exception
    261         yield obj
    262 # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt

File ~/miniconda/envs/3d_cu116_py38/lib/python3.8/site-packages/tqdm/std.py:1195, in tqdm.__iter__(self)
   1192 time = self._time
   1194 try:
-> 1195     for obj in iterable:
   1196         yield obj
   1197         # Update and possibly print the progressbar.
   1198         # Note: does not call self.update(1) for speed optimisation.

File ~/project/text2shape/repos/3DGen/de_package/point-e/point_e/diffusion/sampler.py:135, in PointCloudSampler.sample_batch_progressive(self, batch_size, model_kwargs)
    133 if stage_guidance_scale != 1 and stage_guidance_scale != 0:
    134     for k, v in stage_model_kwargs.copy().items():
--> 135         stage_model_kwargs[k] = torch.cat([v, torch.zeros_like(v)], dim=0)
    137 if stage_use_karras:
    138     samples_it = karras_sample_progressive(
    139         diffusion=diffusion,
    140         model=model,
   (...)
    149         guidance_scale=stage_guidance_scale,
    150     )

TypeError: zeros_like(): argument 'input' (position 1) must be Tensor, not list

[unixpickle]

The base model was trained to produce 1024 points and the upsampler was trained to produce the remaining 4096-1024 points. There is no upsampler trained for more points than that.

However, it may be possible to repurpose the current upsampler for this, using some kind of stochastic conditioning. I'll leave this open in case anybody wants to try it.

[Constipationpustule]

Are there any updates on this issue? I am currently working on using Point E to produce some design works, but I do not know how I can increase the number of points. On the homepage, the examples seem to have much more points than 1024