xinyun/ComfyUI-Hunyuan3DWrapper
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Update vae.py

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kijai 2025-02-05 17:55:03 +02:00
parent 44b9d0d676
commit 89536cbd42
1 changed files with 13 additions and 14 deletions
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23
hy3dgen/shapegen/models/vae.py
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@ -636,13 +636,22 @@ class ShapeVAE(nn.Module):
batch_logits = [] batch_logits = []
batch_size = latents.shape[0] batch_size = latents.shape[0]
comfy_pbar = ProgressBar(xyz_samples.shape[0]) comfy_pbar = ProgressBar(xyz_samples.shape[0])
for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
desc=f"MC Level {mc_level} Implicit Function:"):
if mc_algo == 'odc': if mc_algo == 'odc':
imp_func = lambda xyz: torch.flatten(self.geo_decoder(repeat(xyz, "p c -> b p c", b=batch_size).to(latents.dtype), latents)) imp_func = lambda xyz: torch.flatten(self.geo_decoder(repeat(xyz, "p c -> b p c", b=batch_size).to(latents.dtype), latents))
vertices, faces = odc.extract_mesh(imp_func, num_grid = octree_resolution, isolevel=mc_level, batch_size=num_chunks, min_coord=bbox_min, max_coord=bbox_max) vertices, faces = odc.extract_mesh(imp_func, num_grid = octree_resolution, isolevel=mc_level, batch_size=num_chunks, min_coord=bbox_min, max_coord=bbox_max)
comfy_pbar.update(num_chunks) comfy_pbar.update(num_chunks)
vertices = vertices.detach().cpu().numpy()
faces = faces.detach().cpu().numpy()
outputs = [
Latent2MeshOutput(
mesh_v=vertices.astype(np.float32),
mesh_f=np.ascontiguousarray(faces)
)]
return outputs
else: else:
for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
desc=f"MC Level {mc_level} Implicit Function:"):
queries = xyz_samples[start: start + num_chunks, :].to(device) queries = xyz_samples[start: start + num_chunks, :].to(device)
queries = queries.half() queries = queries.half()
batch_queries = repeat(queries, "p c -> b p c", b=batch_size) batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
@ -654,16 +663,6 @@ class ShapeVAE(nn.Module):
batch_logits.append(logits) batch_logits.append(logits)
comfy_pbar.update(num_chunks) comfy_pbar.update(num_chunks)
if mc_algo == 'odc':
vertices = vertices.detach().cpu().numpy()
faces = faces.detach().cpu().numpy()
outputs = [
Latent2MeshOutput(
mesh_v=vertices.astype(np.float32),
mesh_f=np.ascontiguousarray(faces)
)]
return outputs
else:
grid_logits = torch.cat(batch_logits, dim=1) grid_logits = torch.cat(batch_logits, dim=1)
grid_logits = grid_logits.view((batch_size, grid_size[0], grid_size[1], grid_size[2])).float() grid_logits = grid_logits.view((batch_size, grid_size[0], grid_size[1], grid_size[2])).float()