mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-08-17 12:43:32 +08:00
merged vaes and improved surface net
This commit is contained in:
Yousef Rafat
parent
ee65d6ea41
commit
491a49c828
@ -4,7 +4,9 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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import math
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from tqdm import tqdm
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from typing import Optional
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@ -13,6 +15,457 @@ import logging
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import comfy.ops
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ops = comfy.ops.disable_weight_init
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def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, start_random: bool = True):
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# manually create the pointer vector
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assert src.size(0) == batch.numel()
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batch_size = int(batch.max()) + 1
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deg = src.new_zeros(batch_size, dtype = torch.long)
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deg.scatter_add_(0, batch, torch.ones_like(batch))
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ptr_vec = deg.new_zeros(batch_size + 1)
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torch.cumsum(deg, 0, out=ptr_vec[1:])
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#return fps_sampling(src, ptr_vec, ratio)
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sampled_indicies = []
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for b in range(batch_size):
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# start and the end of each batch
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start, end = ptr_vec[b].item(), ptr_vec[b + 1].item()
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# points from the point cloud
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points = src[start:end]
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num_points = points.size(0)
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num_samples = max(1, math.ceil(num_points * sampling_ratio))
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selected = torch.zeros(num_samples, device = src.device, dtype = torch.long)
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distances = torch.full((num_points,), float("inf"), device = src.device)
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# select a random start point
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if start_random:
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farthest = torch.randint(0, num_points, (1,), device = src.device)
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else: farthest = torch.tensor([0], device = src.device, dtype = torch.long)
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for i in range(num_samples):
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selected[i] = farthest
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centroid = points[farthest].squeeze(0)
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dist = torch.norm(points - centroid, dim = 1) # compute euclidean distance
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distances = torch.minimum(distances, dist)
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farthest = torch.argmax(distances)
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sampled_indicies.append(torch.arange(start, end)[selected])
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return torch.cat(sampled_indicies, dim = 0)
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class PointCrossAttention(nn.Module):
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def __init__(self,
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num_latents: int,
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downsample_ratio: float,
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pc_size: int,
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pc_sharpedge_size: int,
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point_feats: int,
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width: int,
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heads: int,
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layers: int,
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fourier_embedder,
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normal_pe: bool = False,
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qkv_bias: bool = False,
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use_ln_post: bool = True,
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qk_norm: bool = True):
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super().__init__()
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self.fourier_embedder = fourier_embedder
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self.pc_size = pc_size
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self.normal_pe = normal_pe
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self.downsample_ratio = downsample_ratio
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self.pc_sharpedge_size = pc_sharpedge_size
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self.num_latents = num_latents
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self.point_feats = point_feats
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self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width)
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self.cross_attn = ResidualCrossAttentionBlock(
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width = width,
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heads = heads,
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qkv_bias = qkv_bias,
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qk_norm = qk_norm
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)
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self.self_attn = None
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if layers > 0:
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self.self_attn = Transformer(
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width = width,
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heads = heads,
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qkv_bias = qkv_bias,
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qk_norm = qk_norm,
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layers = layers
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)
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if use_ln_post:
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self.ln_post = nn.LayerNorm(width)
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else:
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self.ln_post = None
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def sample_points_and_latents(self, point_cloud: torch.Tensor, features: torch.Tensor):
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"""
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Subsample points randomly from the point cloud (input_pc)
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Further sample the subsampled points to get query_pc
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take the fourier embeddings for both input and query pc
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Mental Note: FPS-sampled points (query_pc) act as latent tokens that attend to and learn from the broader context in input_pc.
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Goal: get a smaller represenation (query_pc) to represent the entire scence structure by learning from a broader subset (input_pc).
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More computationally efficient.
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Features are additional information for each point in the cloud
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"""
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B, _, D = point_cloud.shape
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num_latents = int(self.num_latents)
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num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents
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num_sharpedge_query = num_latents - num_random_query
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# Split random and sharpedge surface points
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random_pc, sharpedge_pc = torch.split(point_cloud, [self.pc_size, self.pc_sharpedge_size], dim=1)
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# assert statements
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assert random_pc.shape[1] <= self.pc_size, "Random surface points size must be less than or equal to pc_size"
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assert sharpedge_pc.shape[1] <= self.pc_sharpedge_size, "Sharpedge surface points size must be less than or equal to pc_sharpedge_size"
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input_random_pc_size = int(num_random_query * self.downsample_ratio)
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random_query_pc, random_input_pc, random_idx_pc, random_idx_query = \
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self.subsample(pc = random_pc, num_query = num_random_query, input_pc_size = input_random_pc_size)
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input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio)
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if input_sharpedge_pc_size == 0:
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sharpedge_input_pc = torch.zeros(B, 0, D, dtype = random_input_pc.dtype).to(point_cloud.device)
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sharpedge_query_pc = torch.zeros(B, 0, D, dtype= random_query_pc.dtype).to(point_cloud.device)
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else: sharpedge_query_pc, sharpedge_input_pc, sharpedge_idx_pc, sharpedge_idx_query = \
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self.subsample(pc = sharpedge_pc, num_query = num_sharpedge_query, input_pc_size = input_sharpedge_pc_size)
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# concat the random and sharpedges
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query_pc = torch.cat([random_query_pc, sharpedge_query_pc], dim = 1)
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input_pc = torch.cat([random_input_pc, sharpedge_input_pc], dim = 1)
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query = self.fourier_embedder(query_pc)
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data = self.fourier_embedder(input_pc)
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if self.point_feats > 0:
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random_surface_features, sharpedge_surface_features = torch.split(features, [self.pc_size, self.pc_sharpedge_size], dim = 1)
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input_random_surface_features, query_random_features = \
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self.handle_features(features = random_surface_features, idx_pc = random_idx_pc, batch_size = B,
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input_pc_size = input_random_pc_size, idx_query = random_idx_query)
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if input_sharpedge_pc_size == 0:
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input_sharpedge_surface_features = torch.zeros(B, 0, self.point_feats,
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dtype = input_random_surface_features.dtype, device = point_cloud.device)
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query_sharpedge_features = torch.zeros(B, 0, self.point_feats,
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dtype = query_random_features.dtype, device = point_cloud.device)
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else:
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input_sharpedge_surface_features, query_sharpedge_features = \
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self.handle_features(idx_pc = sharpedge_idx_pc, features = sharpedge_surface_features,
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batch_size = B, idx_query = sharpedge_idx_query, input_pc_size = input_sharpedge_pc_size)
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query_features = torch.cat([query_random_features, query_sharpedge_features], dim = 1)
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input_features = torch.cat([input_random_surface_features, input_sharpedge_surface_features], dim = 1)
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if self.normal_pe:
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# apply the fourier embeddings on the first 3 dims (xyz)
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input_features_pe = self.fourier_embedder(input_features[..., :3])
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query_features_pe = self.fourier_embedder(query_features[..., :3])
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# replace the first 3 dims with the new PE ones
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input_features = torch.cat([input_features_pe, input_features[..., :3]], dim = -1)
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query_features = torch.cat([query_features_pe, query_features[..., :3]], dim = -1)
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# concat at the channels dim
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query = torch.cat([query, query_features], dim = -1)
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data = torch.cat([data, input_features], dim = -1)
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# don't return pc_info to avoid unnecessary memory usuage
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return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1])
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def forward(self, point_cloud: torch.Tensor, features: torch.Tensor):
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query, data = self.sample_points_and_latents(point_cloud = point_cloud, features = features)
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# apply projections
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query = self.input_proj(query)
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data = self.input_proj(data)
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# apply cross attention between query and data
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latents = self.cross_attn(query, data)
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if self.self_attn is not None:
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latents = self.self_attn(latents)
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if self.ln_post is not None:
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latents = self.ln_post(latents)
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return latents
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def subsample(self, pc, num_query, input_pc_size: int):
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"""
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num_query: number of points to keep after FPS
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input_pc_size: number of points to select before FPS
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"""
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B, _, D = pc.shape
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query_ratio = num_query / input_pc_size
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# random subsampling of points inside the point cloud
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idx_pc = torch.randperm(pc.shape[1], device = pc.device)[:input_pc_size]
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input_pc = pc[:, idx_pc, :]
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# flatten to allow applying fps across the whole batch
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flattent_input_pc = input_pc.view(B * input_pc_size, D)
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# construct a batch_down tensor to tell fps
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# which points belong to which batch
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N_down = int(flattent_input_pc.shape[0] / B)
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batch_down = torch.arange(B).to(pc.device)
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batch_down = torch.repeat_interleave(batch_down, N_down)
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idx_query = fps(flattent_input_pc, batch_down, sampling_ratio = query_ratio)
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query_pc = flattent_input_pc[idx_query].view(B, -1, D)
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return query_pc, input_pc, idx_pc, idx_query
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def handle_features(self, features, idx_pc, input_pc_size, batch_size: int, idx_query):
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B = batch_size
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input_surface_features = features[:, idx_pc, :]
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flattent_input_features = input_surface_features.view(B * input_pc_size, -1)
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query_features = flattent_input_features[idx_query].view(B, -1,
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flattent_input_features.shape[-1])
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return input_surface_features, query_features
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def forward(self, pc, feats):
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"""
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Args:
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pc (torch.FloatTensor): [B, N, 3]
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feats (torch.FloatTensor or None): [B, N, C]
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Returns:
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"""
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query, data = self.sample_points_and_latents(pc, feats)
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query = self.input_proj(query)
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query = query
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data = self.input_proj(data)
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data = data
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latents = self.cross_attn(query, data)
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if self.self_attn is not None:
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latents = self.self_attn(latents)
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if self.ln_post is not None:
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latents = self.ln_post(latents)
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return latents
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def normalize_mesh(mesh, scale = 0.9999):
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"""Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]"""
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bbox = mesh.bounds
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center = (bbox[1] + bbox[0]) / 2
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max_extent = (bbox[1] - bbox[0]).max()
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mesh.apply_translation(-center)
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mesh.apply_scale((2 * scale) / max_extent)
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return mesh
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def sample_pointcloud(mesh, num = 200000):
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""" Uniformly sample points from the surface of the mesh """
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points, face_idx = mesh.sample(num, return_index = True)
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normals = mesh.face_normals[face_idx]
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return torch.from_numpy(points.astype(np.float32)), torch.from_numpy(normals.astype(np.float32))
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def detect_sharp_edges(mesh, threshold=0.985):
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"""Return edge indices (a, b) that lie on sharp boundaries of the mesh."""
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V, F = mesh.vertices, mesh.faces
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VN, FN = mesh.vertex_normals, mesh.face_normals
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sharp_mask = np.ones(V.shape[0])
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for i in range(3):
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indices = F[:, i]
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alignment = np.einsum('ij,ij->i', VN[indices], FN)
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dot_stack = np.stack((sharp_mask[indices], alignment), axis=-1)
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sharp_mask[indices] = np.min(dot_stack, axis=-1)
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edge_a = np.concatenate([F[:, 0], F[:, 1], F[:, 2]])
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edge_b = np.concatenate([F[:, 1], F[:, 2], F[:, 0]])
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sharp_edges = (sharp_mask[edge_a] < threshold) & (sharp_mask[edge_b] < threshold)
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return edge_a[sharp_edges], edge_b[sharp_edges]
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def sharp_sample_pointcloud(mesh, num = 16384):
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""" Sample points preferentially from sharp edges in the mesh. """
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edge_a, edge_b = detect_sharp_edges(mesh)
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V, VN = mesh.vertices, mesh.vertex_normals
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va, vb = V[edge_a], V[edge_b]
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na, nb = VN[edge_a], VN[edge_b]
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edge_lengths = np.linalg.norm(vb - va, axis=-1)
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weights = edge_lengths / edge_lengths.sum()
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indices = np.searchsorted(np.cumsum(weights), np.random.rand(num))
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t = np.random.rand(num, 1)
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samples = t * va[indices] + (1 - t) * vb[indices]
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normals = t * na[indices] + (1 - t) * nb[indices]
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return samples.astype(np.float32), normals.astype(np.float32)
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def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = "cuda"):
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"""Load a surface with optional sharp-edge annotations from a trimesh mesh."""
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import trimesh
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try:
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mesh_full = trimesh.util.concatenate(mesh.dump())
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except Exception:
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mesh_full = trimesh.util.concatenate(mesh)
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mesh_full = normalize_mesh(mesh_full)
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faces = mesh_full.faces
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vertices = mesh_full.vertices
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origin_face_count = faces.shape[0]
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mesh_surface = trimesh.Trimesh(vertices=vertices, faces=faces[:origin_face_count])
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mesh_fill = trimesh.Trimesh(vertices=vertices, faces=faces[origin_face_count:])
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area_surface = mesh_surface.area
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area_fill = mesh_fill.area
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total_area = area_surface + area_fill
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sample_num = 499712 // 2
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fill_ratio = area_fill / total_area if total_area > 0 else 0
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num_fill = int(sample_num * fill_ratio)
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num_surface = sample_num - num_fill
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surf_pts, surf_normals = sample_pointcloud(mesh_surface, num_surface)
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fill_pts, fill_normals = (torch.zeros(0, 3), torch.zeros(0, 3)) if num_fill == 0 else sample_pointcloud(mesh_fill, num_fill)
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sharp_pts, sharp_normals = sharp_sample_pointcloud(mesh_surface, sample_num)
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def assemble_tensor(points, normals, label=None):
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data = torch.cat([points, normals], dim=1).half().to(device)
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if label is not None:
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label_tensor = torch.full((data.shape[0], 1), float(label), dtype=torch.float16).to(device)
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data = torch.cat([data, label_tensor], dim=1)
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return data
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surface = assemble_tensor(torch.cat([surf_pts.to(device), fill_pts.to(device)], dim=0),
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torch.cat([surf_normals.to(device), fill_normals.to(device)], dim=0),
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label = 0 if sharpedge_flag else None)
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sharp_surface = assemble_tensor(torch.from_numpy(sharp_pts), torch.from_numpy(sharp_normals),
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label = 1 if sharpedge_flag else None)
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rng = np.random.default_rng()
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surface = surface[rng.choice(surface.shape[0], num_points, replace = False)]
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sharp_surface = sharp_surface[rng.choice(sharp_surface.shape[0], num_sharp_points, replace = False)]
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full = torch.cat([surface, sharp_surface], dim = 0).unsqueeze(0)
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return full
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class SharpEdgeSurfaceLoader:
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""" Load mesh surface and sharp edge samples. """
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def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192):
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self.num_uniform_points = num_uniform_points
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self.num_sharp_points = num_sharp_points
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self.total_points = num_uniform_points + num_sharp_points
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def __call__(self, mesh_input, device = "cuda"):
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mesh = self._load_mesh(mesh_input)
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return load_surface_sharpedge(mesh, self.num_uniform_points, self.num_sharp_points, device = device)
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@staticmethod
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def _load_mesh(mesh_input):
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import trimesh
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if isinstance(mesh_input, str):
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mesh = trimesh.load(mesh_input, force="mesh", merge_primitives = True)
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else:
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mesh = mesh_input
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if isinstance(mesh, trimesh.Scene):
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combined = None
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for obj in mesh.geometry.values():
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combined = obj if combined is None else combined + obj
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return combined
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return mesh
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class FourierEmbedder(nn.Module):
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||||
def __init__(self, num_freq: int = 8, input_dim: int = 3, include_pi: bool = False):
|
||||
super().__init__()
|
||||
|
||||
frequencies = 2.0 ** torch.arange(
|
||||
num_freq,
|
||||
dtype = torch.float32
|
||||
)
|
||||
|
||||
if include_pi:
|
||||
frequencies *= torch.pi
|
||||
|
||||
self.register_buffer("frequencies", frequencies, persistent = False)
|
||||
|
||||
self.out_dim = input_dim * (num_freq * 2 + 1)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
|
||||
embed = (x[..., None].contiguous() * self.frequencies).view(*x.shape[:-1], -1)
|
||||
return torch.cat((x, embed.sin(), embed.cos()), dim = -1)
|
||||
|
||||
class DiagonalGaussianDistribution:
|
||||
def __init__(self, params: torch.Tensor, feature_dim: int = -1):
|
||||
|
||||
# divide quant channels (8) into mean and log variance
|
||||
self.mean, self.logvar = torch.chunk(params, 2, dim = feature_dim)
|
||||
|
||||
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
|
||||
self.std = torch.exp(0.5 * self.logvar)
|
||||
|
||||
def sample(self):
|
||||
|
||||
eps = torch.randn_like(self.std)
|
||||
z = self.mean + eps * self.std
|
||||
|
||||
return z
|
||||
|
||||
################################################
|
||||
# Volume Decoder
|
||||
################################################
|
||||
@ -20,7 +473,7 @@ ops = comfy.ops.disable_weight_init
|
||||
class VanillaVolumeDecoder():
|
||||
@torch.no_grad()
|
||||
def __call__(self, latents: torch.Tensor, geo_decoder: callable, octree_resolution: int, bounds = 1.01,
|
||||
num_chunks: int = 10_000):
|
||||
num_chunks: int = 10_000, enable_pbar: bool = True, **kwargs):
|
||||
|
||||
if isinstance(bounds, float):
|
||||
bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
|
||||
@ -36,7 +489,9 @@ class VanillaVolumeDecoder():
|
||||
grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1]
|
||||
|
||||
batch_logits = []
|
||||
for start in range(0, xyz.shape[0], num_chunks):
|
||||
for start in tqdm(range(0, xyz.shape[0], num_chunks), desc=f"Volume Decoding",
|
||||
disable=not enable_pbar):
|
||||
|
||||
chunk_queries = xyz[start: start + num_chunks, :]
|
||||
chunk_queries = chunk_queries.unsqueeze(0).repeat(latents.shape[0], 1, 1)
|
||||
logits = geo_decoder(queries = chunk_queries, latents = latents)
|
||||
@ -484,28 +939,44 @@ class CrossAttentionDecoder(nn.Module):
|
||||
|
||||
class ShapeVAE(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
embed_dim: int,
|
||||
width: int,
|
||||
heads: int,
|
||||
num_decoder_layers: int,
|
||||
geo_decoder_downsample_ratio: int = 1,
|
||||
geo_decoder_mlp_expand_ratio: int = 4,
|
||||
geo_decoder_ln_post: bool = True,
|
||||
num_freqs: int = 8,
|
||||
include_pi: bool = True,
|
||||
qkv_bias: bool = True,
|
||||
qk_norm: bool = False,
|
||||
label_type: str = "binary",
|
||||
drop_path_rate: float = 0.0,
|
||||
scale_factor: float = 1.0,
|
||||
self,
|
||||
*,
|
||||
num_latents: int = 4096,
|
||||
embed_dim: int = 64,
|
||||
width: int = 1024,
|
||||
heads: int = 16,
|
||||
num_decoder_layers: int = 16,
|
||||
num_encoder_layers: int = 8,
|
||||
pc_size: int = 81920,
|
||||
pc_sharpedge_size: int = 0,
|
||||
point_feats: int = 4,
|
||||
downsample_ratio: int = 20,
|
||||
geo_decoder_downsample_ratio: int = 1,
|
||||
geo_decoder_mlp_expand_ratio: int = 4,
|
||||
geo_decoder_ln_post: bool = True,
|
||||
num_freqs: int = 8,
|
||||
qkv_bias: bool = False,
|
||||
qk_norm: bool = True,
|
||||
drop_path_rate: float = 0.0,
|
||||
include_pi: bool = False,
|
||||
scale_factor: float = 1.0039506158752403,
|
||||
label_type: str = "binary",
|
||||
):
|
||||
super().__init__()
|
||||
self.geo_decoder_ln_post = geo_decoder_ln_post
|
||||
|
||||
self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
|
||||
|
||||
self.encoder = PointCrossAttention(layers = num_encoder_layers,
|
||||
num_latents = num_latents,
|
||||
downsample_ratio = downsample_ratio,
|
||||
heads = heads,
|
||||
pc_size = pc_size,
|
||||
width = width,
|
||||
point_feats = point_feats,
|
||||
fourier_embedder = self.fourier_embedder,
|
||||
pc_sharpedge_size = pc_sharpedge_size)
|
||||
|
||||
self.post_kl = ops.Linear(embed_dim, width)
|
||||
|
||||
self.transformer = Transformer(
|
||||
@ -534,7 +1005,7 @@ class ShapeVAE(nn.Module):
|
||||
self.scale_factor = scale_factor
|
||||
|
||||
def decode(self, latents, **kwargs):
|
||||
latents = self.post_kl(latents.movedim(-2, -1))
|
||||
latents = self.post_kl(latents)
|
||||
latents = self.transformer(latents)
|
||||
|
||||
bounds = kwargs.get("bounds", 1.01)
|
||||
@ -543,7 +1014,16 @@ class ShapeVAE(nn.Module):
|
||||
enable_pbar = kwargs.get("enable_pbar", True)
|
||||
|
||||
grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar)
|
||||
return grid_logits.movedim(-2, -1)
|
||||
return grid_logits
|
||||
|
||||
def encode(self, x):
|
||||
return None
|
||||
def encode(self, surface):
|
||||
|
||||
pc, feats = surface[:, :, :3], surface[:, :, 3:]
|
||||
latents = self.encoder(pc, feats)
|
||||
|
||||
moments = self.pre_kl(latents)
|
||||
posterior = DiagonalGaussianDistribution(moments, feature_dim = -1)
|
||||
|
||||
latents = posterior.sample()
|
||||
|
||||
return latents
|
||||
@ -1,629 +0,0 @@
|
||||
import torch
|
||||
from torch import Tensor
|
||||
import math
|
||||
import numpy as np
|
||||
from skimage import measure
|
||||
from dataclasses import dataclass
|
||||
import torch.nn as nn
|
||||
|
||||
import sys, os;
|
||||
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")))
|
||||
|
||||
from comfy.ldm.hunyuan3d.vae import (
|
||||
CrossAttentionDecoder, Transformer, ResidualCrossAttentionBlock, FourierEmbedder, VanillaVolumeDecoder
|
||||
)
|
||||
def fps(src: Tensor, batch: Tensor, sampling_ratio: float, start_random: bool = True):
|
||||
|
||||
# manually create the pointer vector
|
||||
assert src.size(0) == batch.numel()
|
||||
|
||||
batch_size = int(batch.max()) + 1
|
||||
deg = src.new_zeros(batch_size, dtype = torch.long)
|
||||
|
||||
deg.scatter_add_(0, batch, torch.ones_like(batch))
|
||||
|
||||
ptr_vec = deg.new_zeros(batch_size + 1)
|
||||
torch.cumsum(deg, 0, out=ptr_vec[1:])
|
||||
|
||||
#return fps_sampling(src, ptr_vec, ratio)
|
||||
sampled_indicies = []
|
||||
|
||||
for b in range(batch_size):
|
||||
# start and the end of each batch
|
||||
start, end = ptr_vec[b].item(), ptr_vec[b + 1].item()
|
||||
# points from the point cloud
|
||||
points = src[start:end]
|
||||
|
||||
num_points = points.size(0)
|
||||
num_samples = max(1, math.ceil(num_points * sampling_ratio))
|
||||
|
||||
selected = torch.zeros(num_samples, device = src.device, dtype = torch.long)
|
||||
distances = torch.full((num_points,), float("inf"), device = src.device)
|
||||
|
||||
# select a random start point
|
||||
if start_random:
|
||||
farthest = torch.randint(0, num_points, (1,), device = src.device)
|
||||
else: farthest = torch.tensor([0], device = src.device, dtype = torch.long)
|
||||
|
||||
for i in range(num_samples):
|
||||
selected[i] = farthest
|
||||
centroid = points[farthest].squeeze(0)
|
||||
dist = torch.norm(points - centroid, dim = 1) # compute euclidean distance
|
||||
distances = torch.minimum(distances, dist)
|
||||
farthest = torch.argmax(distances)
|
||||
|
||||
sampled_indicies.append(torch.arange(start, end)[selected])
|
||||
|
||||
return torch.cat(sampled_indicies, dim = 0)
|
||||
class PointCrossAttention(nn.Module):
|
||||
def __init__(self,
|
||||
num_latents: int,
|
||||
downsample_ratio: float,
|
||||
pc_size: int,
|
||||
pc_sharpedge_size: int,
|
||||
point_feats: int,
|
||||
width: int,
|
||||
heads: int,
|
||||
layers: int,
|
||||
fourier_embedder,
|
||||
normal_pe: bool = False,
|
||||
qkv_bias: bool = False,
|
||||
use_ln_post: bool = True,
|
||||
qk_norm: bool = True):
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.fourier_embedder = fourier_embedder
|
||||
|
||||
self.pc_size = pc_size
|
||||
self.normal_pe = normal_pe
|
||||
self.downsample_ratio = downsample_ratio
|
||||
self.pc_sharpedge_size = pc_sharpedge_size
|
||||
self.num_latents = num_latents
|
||||
self.point_feats = point_feats
|
||||
|
||||
self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width)
|
||||
|
||||
self.cross_attn = ResidualCrossAttentionBlock(
|
||||
width = width,
|
||||
heads = heads,
|
||||
qkv_bias = qkv_bias,
|
||||
qk_norm = qk_norm
|
||||
)
|
||||
|
||||
self.self_attn = None
|
||||
if layers > 0:
|
||||
self.self_attn = Transformer(
|
||||
width = width,
|
||||
heads = heads,
|
||||
qkv_bias = qkv_bias,
|
||||
qk_norm = qk_norm,
|
||||
layers = layers
|
||||
)
|
||||
|
||||
if use_ln_post:
|
||||
self.ln_post = nn.LayerNorm(width)
|
||||
else:
|
||||
self.ln_post = None
|
||||
|
||||
def sample_points_and_latents(self, point_cloud: torch.Tensor, features: torch.Tensor):
|
||||
|
||||
"""
|
||||
Subsample points randomly from the point cloud (input_pc)
|
||||
Further sample the subsampled points to get query_pc
|
||||
take the fourier embeddings for both input and query pc
|
||||
|
||||
Mental Note: FPS-sampled points (query_pc) act as latent tokens that attend to and learn from the broader context in input_pc.
|
||||
Goal: get a smaller represenation (query_pc) to represent the entire scence structure by learning from a broader subset (input_pc).
|
||||
More computationally efficient.
|
||||
|
||||
Features are additional information for each point in the cloud
|
||||
"""
|
||||
|
||||
B, _, D = point_cloud.shape
|
||||
|
||||
num_latents = int(self.num_latents)
|
||||
|
||||
num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents
|
||||
num_sharpedge_query = num_latents - num_random_query
|
||||
|
||||
# Split random and sharpedge surface points
|
||||
random_pc, sharpedge_pc = torch.split(point_cloud, [self.pc_size, self.pc_sharpedge_size], dim=1)
|
||||
|
||||
# assert statements
|
||||
assert random_pc.shape[1] <= self.pc_size, "Random surface points size must be less than or equal to pc_size"
|
||||
assert sharpedge_pc.shape[1] <= self.pc_sharpedge_size, "Sharpedge surface points size must be less than or equal to pc_sharpedge_size"
|
||||
|
||||
input_random_pc_size = int(num_random_query * self.downsample_ratio)
|
||||
random_query_pc, random_input_pc, random_idx_pc, random_idx_query = \
|
||||
self.subsample(pc = random_pc, num_query = num_random_query, input_pc_size = input_random_pc_size)
|
||||
|
||||
input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio)
|
||||
|
||||
if input_sharpedge_pc_size == 0:
|
||||
sharpedge_input_pc = torch.zeros(B, 0, D, dtype = random_input_pc.dtype).to(point_cloud.device)
|
||||
sharpedge_query_pc = torch.zeros(B, 0, D, dtype= random_query_pc.dtype).to(point_cloud.device)
|
||||
|
||||
else: sharpedge_query_pc, sharpedge_input_pc, sharpedge_idx_pc, sharpedge_idx_query = \
|
||||
self.subsample(pc = sharpedge_pc, num_query = num_sharpedge_query, input_pc_size = input_sharpedge_pc_size)
|
||||
|
||||
# concat the random and sharpedges
|
||||
query_pc = torch.cat([random_query_pc, sharpedge_query_pc], dim = 1)
|
||||
input_pc = torch.cat([random_input_pc, sharpedge_input_pc], dim = 1)
|
||||
|
||||
query = self.fourier_embedder(query_pc)
|
||||
data = self.fourier_embedder(input_pc)
|
||||
|
||||
if self.point_feats > 0:
|
||||
random_surface_features, sharpedge_surface_features = torch.split(features, [self.pc_size, self.pc_sharpedge_size], dim = 1)
|
||||
|
||||
input_random_surface_features, query_random_features = \
|
||||
self.handle_features(features = random_surface_features, idx_pc = random_idx_pc, batch_size = B,
|
||||
input_pc_size = input_random_pc_size, idx_query = random_idx_query)
|
||||
|
||||
if input_sharpedge_pc_size == 0:
|
||||
input_sharpedge_surface_features = torch.zeros(B, 0, self.point_feats,
|
||||
dtype = input_random_surface_features.dtype, device = point_cloud.device)
|
||||
|
||||
query_sharpedge_features = torch.zeros(B, 0, self.point_feats,
|
||||
dtype = query_random_features.dtype, device = point_cloud.device)
|
||||
else:
|
||||
|
||||
input_sharpedge_surface_features, query_sharpedge_features = \
|
||||
self.handle_features(idx_pc = sharpedge_idx_pc, features = sharpedge_surface_features,
|
||||
batch_size = B, idx_query = sharpedge_idx_query, input_pc_size = input_sharpedge_pc_size)
|
||||
|
||||
query_features = torch.cat([query_random_features, query_sharpedge_features], dim = 1)
|
||||
input_features = torch.cat([input_random_surface_features, input_sharpedge_surface_features], dim = 1)
|
||||
|
||||
if self.normal_pe:
|
||||
# apply the fourier embeddings on the first 3 dims (xyz)
|
||||
input_features_pe = self.fourier_embedder(input_features[..., :3])
|
||||
query_features_pe = self.fourier_embedder(query_features[..., :3])
|
||||
# replace the first 3 dims with the new PE ones
|
||||
input_features = torch.cat([input_features_pe, input_features[..., :3]], dim = -1)
|
||||
query_features = torch.cat([query_features_pe, query_features[..., :3]], dim = -1)
|
||||
|
||||
# concat at the channels dim
|
||||
query = torch.cat([query, query_features], dim = -1)
|
||||
data = torch.cat([data, input_features], dim = -1)
|
||||
|
||||
# don't return pc_info to avoid unnecessary memory usuage
|
||||
return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1])
|
||||
|
||||
def forward(self, point_cloud: torch.Tensor, features: torch.Tensor):
|
||||
|
||||
query, data = self.sample_points_and_latents(point_cloud = point_cloud, features = features)
|
||||
|
||||
# apply projections
|
||||
query = self.input_proj(query)
|
||||
data = self.input_proj(data)
|
||||
|
||||
# apply cross attention between query and data
|
||||
latents = self.cross_attn(query, data)
|
||||
|
||||
if self.self_attn is not None:
|
||||
latents = self.self_attn(latents)
|
||||
|
||||
if self.ln_post is not None:
|
||||
latents = self.ln_post(latents)
|
||||
|
||||
return latents
|
||||
|
||||
|
||||
def subsample(self, pc, num_query, input_pc_size: int):
|
||||
|
||||
"""
|
||||
num_query: number of points to keep after FPS
|
||||
input_pc_size: number of points to select before FPS
|
||||
"""
|
||||
|
||||
B, _, D = pc.shape
|
||||
query_ratio = num_query / input_pc_size
|
||||
|
||||
# random subsampling of points inside the point cloud
|
||||
idx_pc = torch.randperm(pc.shape[1], device = pc.device)[:input_pc_size]
|
||||
input_pc = pc[:, idx_pc, :]
|
||||
|
||||
# flatten to allow applying fps across the whole batch
|
||||
flattent_input_pc = input_pc.view(B * input_pc_size, D)
|
||||
|
||||
# construct a batch_down tensor to tell fps
|
||||
# which points belong to which batch
|
||||
N_down = int(flattent_input_pc.shape[0] / B)
|
||||
batch_down = torch.arange(B).to(pc.device)
|
||||
batch_down = torch.repeat_interleave(batch_down, N_down)
|
||||
|
||||
idx_query = fps(flattent_input_pc, batch_down, sampling_ratio = query_ratio)
|
||||
query_pc = flattent_input_pc[idx_query].view(B, -1, D)
|
||||
|
||||
return query_pc, input_pc, idx_pc, idx_query
|
||||
|
||||
def handle_features(self, features, idx_pc, input_pc_size, batch_size: int, idx_query):
|
||||
|
||||
B = batch_size
|
||||
|
||||
input_surface_features = features[:, idx_pc, :]
|
||||
flattent_input_features = input_surface_features.view(B * input_pc_size, -1)
|
||||
query_features = flattent_input_features[idx_query].view(B, -1,
|
||||
flattent_input_features.shape[-1])
|
||||
|
||||
return input_surface_features, query_features
|
||||
|
||||
def forward(self, pc, feats):
|
||||
"""
|
||||
|
||||
Args:
|
||||
pc (torch.FloatTensor): [B, N, 3]
|
||||
feats (torch.FloatTensor or None): [B, N, C]
|
||||
|
||||
Returns:
|
||||
|
||||
"""
|
||||
|
||||
query, data = self.sample_points_and_latents(pc, feats)
|
||||
|
||||
query = self.input_proj(query)
|
||||
query = query
|
||||
data = self.input_proj(data)
|
||||
data = data
|
||||
|
||||
latents = self.cross_attn(query, data)
|
||||
if self.self_attn is not None:
|
||||
latents = self.self_attn(latents)
|
||||
|
||||
if self.ln_post is not None:
|
||||
latents = self.ln_post(latents)
|
||||
|
||||
return latents
|
||||
|
||||
@dataclass
|
||||
class Latent2MeshOutput():
|
||||
# mesh for vertices and faces
|
||||
vertices: None
|
||||
faces: None
|
||||
|
||||
class SurfaceExtractor():
|
||||
def compute_box_stat(self, bounds, octree_resolution: int):
|
||||
|
||||
# if float, turn it into a cube
|
||||
if isinstance(bounds, float):
|
||||
bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
|
||||
|
||||
bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6])
|
||||
bbox_size = bbox_max - bbox_min
|
||||
grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1]
|
||||
return grid_size, bbox_min, bbox_size
|
||||
|
||||
def run(self, grid_logit, *, bounds, octree_resolution, level: float = 0.0, **kwargs):
|
||||
# grid_logit from volume decoder
|
||||
# use marching cube algo to turn an sdf to a mesh
|
||||
vertices, faces, _, _ = measure.marching_cubes(grid_logit.cpu().numpy(),
|
||||
level,
|
||||
method = "lewiner")
|
||||
|
||||
grid_size, bbox_min, bbox_size = self.compute_box_stat(bounds = bounds, octree_resolution = octree_resolution)
|
||||
vertices = vertices / grid_size * bbox_size + bbox_min
|
||||
|
||||
return vertices, faces
|
||||
|
||||
def __call__(self, grid_logits, **kwds):
|
||||
|
||||
outputs = []
|
||||
veritces_list = []
|
||||
faces_list = []
|
||||
# loop over the batches
|
||||
for i in range(grid_logits.shape[0]):
|
||||
try:
|
||||
# process each batch
|
||||
vertices, faces = self.run(grid_logits[i], **kwds)
|
||||
vertices = vertices.astype(np.float32)
|
||||
faces = np.ascontiguousarray(faces)
|
||||
#outputs.append(Latent2MeshOutput(vertices = vertices, faces = faces))
|
||||
veritces_list.append(vertices)
|
||||
faces_list.append(faces)
|
||||
|
||||
except Exception:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
outputs.append(None)
|
||||
|
||||
return outputs
|
||||
|
||||
def normalize_mesh(mesh, scale = 0.9999):
|
||||
"""Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]"""
|
||||
|
||||
bbox = mesh.bounds
|
||||
center = (bbox[1] + bbox[0]) / 2
|
||||
|
||||
max_extent = (bbox[1] - bbox[0]).max()
|
||||
mesh.apply_translation(-center)
|
||||
mesh.apply_scale((2 * scale) / max_extent)
|
||||
|
||||
return mesh
|
||||
|
||||
def sample_pointcloud(mesh, num = 200000):
|
||||
""" Uniformly sample points from the surface of the mesh """
|
||||
|
||||
points, face_idx = mesh.sample(num, return_index = True)
|
||||
normals = mesh.face_normals[face_idx]
|
||||
return torch.from_numpy(points.astype(np.float32)), torch.from_numpy(normals.astype(np.float32))
|
||||
|
||||
def detect_sharp_edges(mesh, threshold=0.985):
|
||||
"""Return edge indices (a, b) that lie on sharp boundaries of the mesh."""
|
||||
|
||||
V, F = mesh.vertices, mesh.faces
|
||||
VN, FN = mesh.vertex_normals, mesh.face_normals
|
||||
|
||||
sharp_mask = np.ones(V.shape[0])
|
||||
for i in range(3):
|
||||
indices = F[:, i]
|
||||
alignment = np.einsum('ij,ij->i', VN[indices], FN)
|
||||
dot_stack = np.stack((sharp_mask[indices], alignment), axis=-1)
|
||||
sharp_mask[indices] = np.min(dot_stack, axis=-1)
|
||||
|
||||
edge_a = np.concatenate([F[:, 0], F[:, 1], F[:, 2]])
|
||||
edge_b = np.concatenate([F[:, 1], F[:, 2], F[:, 0]])
|
||||
sharp_edges = (sharp_mask[edge_a] < threshold) & (sharp_mask[edge_b] < threshold)
|
||||
|
||||
return edge_a[sharp_edges], edge_b[sharp_edges]
|
||||
|
||||
|
||||
def sharp_sample_pointcloud(mesh, num = 16384):
|
||||
""" Sample points preferentially from sharp edges in the mesh. """
|
||||
|
||||
edge_a, edge_b = detect_sharp_edges(mesh)
|
||||
V, VN = mesh.vertices, mesh.vertex_normals
|
||||
|
||||
va, vb = V[edge_a], V[edge_b]
|
||||
na, nb = VN[edge_a], VN[edge_b]
|
||||
|
||||
edge_lengths = np.linalg.norm(vb - va, axis=-1)
|
||||
weights = edge_lengths / edge_lengths.sum()
|
||||
|
||||
indices = np.searchsorted(np.cumsum(weights), np.random.rand(num))
|
||||
t = np.random.rand(num, 1)
|
||||
|
||||
samples = t * va[indices] + (1 - t) * vb[indices]
|
||||
normals = t * na[indices] + (1 - t) * nb[indices]
|
||||
|
||||
return samples.astype(np.float32), normals.astype(np.float32)
|
||||
|
||||
def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = "cuda"):
|
||||
"""Load a surface with optional sharp-edge annotations from a trimesh mesh."""
|
||||
|
||||
import trimesh
|
||||
|
||||
try:
|
||||
mesh_full = trimesh.util.concatenate(mesh.dump())
|
||||
except Exception:
|
||||
mesh_full = trimesh.util.concatenate(mesh)
|
||||
|
||||
mesh_full = normalize_mesh(mesh_full)
|
||||
|
||||
faces = mesh_full.faces
|
||||
vertices = mesh_full.vertices
|
||||
origin_face_count = faces.shape[0]
|
||||
|
||||
mesh_surface = trimesh.Trimesh(vertices=vertices, faces=faces[:origin_face_count])
|
||||
mesh_fill = trimesh.Trimesh(vertices=vertices, faces=faces[origin_face_count:])
|
||||
|
||||
area_surface = mesh_surface.area
|
||||
area_fill = mesh_fill.area
|
||||
total_area = area_surface + area_fill
|
||||
|
||||
sample_num = 499712 // 2
|
||||
fill_ratio = area_fill / total_area if total_area > 0 else 0
|
||||
|
||||
num_fill = int(sample_num * fill_ratio)
|
||||
num_surface = sample_num - num_fill
|
||||
|
||||
surf_pts, surf_normals = sample_pointcloud(mesh_surface, num_surface)
|
||||
fill_pts, fill_normals = (torch.zeros(0, 3), torch.zeros(0, 3)) if num_fill == 0 else sample_pointcloud(mesh_fill, num_fill)
|
||||
|
||||
sharp_pts, sharp_normals = sharp_sample_pointcloud(mesh_surface, sample_num)
|
||||
|
||||
def assemble_tensor(points, normals, label=None):
|
||||
|
||||
data = torch.cat([points, normals], dim=1).half().to(device)
|
||||
|
||||
if label is not None:
|
||||
label_tensor = torch.full((data.shape[0], 1), float(label), dtype=torch.float16).to(device)
|
||||
data = torch.cat([data, label_tensor], dim=1)
|
||||
|
||||
return data
|
||||
|
||||
surface = assemble_tensor(torch.cat([surf_pts.to(device), fill_pts.to(device)], dim=0),
|
||||
torch.cat([surf_normals.to(device), fill_normals.to(device)], dim=0),
|
||||
label = 0 if sharpedge_flag else None)
|
||||
|
||||
sharp_surface = assemble_tensor(torch.from_numpy(sharp_pts), torch.from_numpy(sharp_normals),
|
||||
label = 1 if sharpedge_flag else None)
|
||||
|
||||
rng = np.random.default_rng()
|
||||
|
||||
surface = surface[rng.choice(surface.shape[0], num_points, replace = False)]
|
||||
sharp_surface = sharp_surface[rng.choice(sharp_surface.shape[0], num_sharp_points, replace = False)]
|
||||
|
||||
full = torch.cat([surface, sharp_surface], dim = 0).unsqueeze(0)
|
||||
|
||||
return full
|
||||
|
||||
class SharpEdgeSurfaceLoader:
|
||||
""" Load mesh surface and sharp edge samples. """
|
||||
|
||||
def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192):
|
||||
|
||||
self.num_uniform_points = num_uniform_points
|
||||
self.num_sharp_points = num_sharp_points
|
||||
self.total_points = num_uniform_points + num_sharp_points
|
||||
|
||||
def __call__(self, mesh_input, device = "cuda"):
|
||||
mesh = self._load_mesh(mesh_input)
|
||||
return load_surface_sharpedge(mesh, self.num_uniform_points, self.num_sharp_points, device = device)
|
||||
|
||||
@staticmethod
|
||||
def _load_mesh(mesh_input):
|
||||
import trimesh
|
||||
|
||||
if isinstance(mesh_input, str):
|
||||
mesh = trimesh.load(mesh_input, force="mesh", merge_primitives = True)
|
||||
else:
|
||||
mesh = mesh_input
|
||||
|
||||
if isinstance(mesh, trimesh.Scene):
|
||||
combined = None
|
||||
for obj in mesh.geometry.values():
|
||||
combined = obj if combined is None else combined + obj
|
||||
return combined
|
||||
|
||||
return mesh
|
||||
|
||||
class FourierEmbedder(nn.Module):
|
||||
def __init__(self, num_freq: int = 8, input_dim: int = 3, include_pi: bool = False):
|
||||
super().__init__()
|
||||
|
||||
frequencies = 2.0 ** torch.arange(
|
||||
num_freq,
|
||||
dtype = torch.float32
|
||||
)
|
||||
|
||||
if include_pi:
|
||||
frequencies *= torch.pi
|
||||
|
||||
self.register_buffer("frequencies", frequencies, persistent = False)
|
||||
|
||||
self.out_dim = input_dim * (num_freq * 2 + 1)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
|
||||
embed = (x[..., None].contiguous() * self.frequencies).view(*x.shape[:-1], -1)
|
||||
return torch.cat((x, embed.sin(), embed.cos()), dim = -1)
|
||||
|
||||
class DiagonalGaussianDistribution:
|
||||
def __init__(self, params: torch.Tensor, feature_dim: int = -1):
|
||||
|
||||
# divide quant channels (8) into mean and log variance
|
||||
self.mean, self.logvar = torch.chunk(params, 2, dim = feature_dim)
|
||||
|
||||
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
|
||||
self.std = torch.exp(0.5 * self.logvar)
|
||||
|
||||
def sample(self):
|
||||
|
||||
eps = torch.randn_like(self.std)
|
||||
z = self.mean + eps * self.std
|
||||
|
||||
return z
|
||||
|
||||
class VAE(nn.Module):
|
||||
def __init__(self,
|
||||
*,
|
||||
num_latents: int = 4096,
|
||||
embed_dim: int = 64,
|
||||
width: int = 1024,
|
||||
heads: int = 16,
|
||||
num_decoder_layers: int = 16,
|
||||
num_encoder_layers: int = 8,
|
||||
pc_size: int = 81920,
|
||||
pc_sharpedge_size: int = 0,
|
||||
point_feats: int = 4,
|
||||
downsample_ratio: int = 20,
|
||||
geo_decoder_downsample_ratio: int = 1,
|
||||
geo_decoder_mlp_expand_ratio: int = 4,
|
||||
geo_decoder_ln_post: bool = True,
|
||||
num_frequencies: int = 8,
|
||||
qkv_bias: bool = False,
|
||||
qk_norm: bool = True,
|
||||
drop_path_rate: float = 0.0,
|
||||
include_pi: bool = False,
|
||||
scale_factor: float = 1.0039506158752403
|
||||
):
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.latent_shape = (num_latents, embed_dim)
|
||||
self.scale_factor = scale_factor
|
||||
|
||||
self.fourier_embedder = FourierEmbedder(num_freq = num_frequencies, include_pi = include_pi)
|
||||
|
||||
self.encoder = PointCrossAttention(layers = num_encoder_layers,
|
||||
num_latents = num_latents,
|
||||
downsample_ratio = downsample_ratio,
|
||||
heads = heads,
|
||||
pc_size = pc_size,
|
||||
width = width,
|
||||
point_feats = point_feats,
|
||||
fourier_embedder = self.fourier_embedder,
|
||||
pc_sharpedge_size = pc_sharpedge_size)
|
||||
|
||||
self.transformer = Transformer(
|
||||
width=width,
|
||||
layers=num_decoder_layers,
|
||||
heads=heads,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_norm=qk_norm,
|
||||
drop_path_rate=drop_path_rate
|
||||
)
|
||||
|
||||
self.geo_decoder = CrossAttentionDecoder(
|
||||
fourier_embedder = self.fourier_embedder,
|
||||
out_channels = 1,
|
||||
mlp_expand_ratio = geo_decoder_mlp_expand_ratio,
|
||||
downsample_ratio = geo_decoder_downsample_ratio,
|
||||
enable_ln_post = geo_decoder_ln_post,
|
||||
width=width // geo_decoder_downsample_ratio,
|
||||
heads=heads // geo_decoder_downsample_ratio,
|
||||
qkv_bias = qkv_bias,
|
||||
qk_norm= qk_norm
|
||||
)
|
||||
|
||||
self.pre_kl = nn.Linear(width, embed_dim * 2)
|
||||
self.post_kl = nn.Linear(embed_dim, width)
|
||||
|
||||
self.volume_decoder = VanillaVolumeDecoder()
|
||||
self.surface_extractor = SurfaceExtractor()
|
||||
|
||||
|
||||
def forward(self):
|
||||
pass
|
||||
|
||||
def encode(self, surface):
|
||||
|
||||
pc, feats = surface[:, :, :3], surface[:, :, 3:]
|
||||
latents = self.encoder(pc, feats)
|
||||
|
||||
moments = self.pre_kl(latents)
|
||||
posterior = DiagonalGaussianDistribution(moments, feature_dim = -1)
|
||||
|
||||
latents = posterior.sample()
|
||||
|
||||
return latents
|
||||
|
||||
def decode(self, latents, **kwargs):
|
||||
to_mesh = kwargs.pop("to_mesh", True)
|
||||
latents = self.post_kl(latents)
|
||||
latents = self.transformer(latents)
|
||||
|
||||
if not to_mesh:
|
||||
return latents
|
||||
|
||||
grid_logits = self.volume_decoder(latents = latents, geo_decoder = self.geo_decoder, **kwargs)
|
||||
mesh = self.surface_extractor(grid_logits, **kwargs)
|
||||
|
||||
return mesh
|
||||
|
||||
def load_vae(vae):
|
||||
|
||||
DEBUG = False
|
||||
|
||||
checkpoint = "model.fp16.ckpt"
|
||||
missing, unexpected = vae.load_state_dict(torch.load(checkpoint), strict = not DEBUG)
|
||||
|
||||
if DEBUG:
|
||||
print(f"Missing {len(missing)}: ", missing)
|
||||
print(f"\nUnexpected {len(unexpected)}: ", unexpected)
|
||||
|
||||
return vae
|
||||
|
||||
|
||||
20
comfy/sd.py
20
comfy/sd.py
@ -432,8 +432,8 @@ class VAE:
|
||||
self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
|
||||
self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
|
||||
|
||||
# Hunyuan 3d v2 2.1
|
||||
elif 'geo_decoder.cross_attn_decoder.mlp.c_proj.weight' in sd:
|
||||
# Hunyuan 3d v2 2.0 & 2.1
|
||||
elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
|
||||
|
||||
self.latent_dim = 1
|
||||
|
||||
@ -450,20 +450,8 @@ class VAE:
|
||||
|
||||
self.memory_used_decode = lambda shape, dtype, num_layers = 16, kv_cache_multiplier = 2: \
|
||||
estimate_memory(shape, dtype, num_layers, kv_cache_multiplier)
|
||||
|
||||
self.first_stage_model = comfy.ldm.hunyuan3dv2_1.vae.VAE()
|
||||
self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
|
||||
|
||||
elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
|
||||
self.latent_dim = 1
|
||||
ln_post = "geo_decoder.ln_post.weight" in sd
|
||||
inner_size = sd["geo_decoder.output_proj.weight"].shape[1]
|
||||
downsample_ratio = sd["post_kl.weight"].shape[0] // inner_size
|
||||
mlp_expand = sd["geo_decoder.cross_attn_decoder.mlp.c_fc.weight"].shape[0] // inner_size
|
||||
self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) # TODO
|
||||
self.memory_used_decode = lambda shape, dtype: (1024 * 1024 * 1024 * 2.0) * model_management.dtype_size(dtype) # TODO
|
||||
ddconfig = {"embed_dim": 64, "num_freqs": 8, "include_pi": False, "heads": 16, "width": 1024, "num_decoder_layers": 16, "qkv_bias": False, "qk_norm": True, "geo_decoder_mlp_expand_ratio": mlp_expand, "geo_decoder_downsample_ratio": downsample_ratio, "geo_decoder_ln_post": ln_post}
|
||||
self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE(**ddconfig)
|
||||
|
||||
self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE()
|
||||
self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
|
||||
|
||||
|
||||
|
||||
@ -11,35 +11,16 @@ from comfy.cli_args import args
|
||||
class EmptyLatentHunyuan3Dv2:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}),
|
||||
"batch_size": ("INT", {
|
||||
"default": 1,
|
||||
"min": 1,
|
||||
"max": 4096,
|
||||
"tooltip": "The number of latent images in the batch."
|
||||
}),
|
||||
"version": (["2.0", "2.1"], {
|
||||
"default": "2.1",
|
||||
"tooltip": "Choose latent layout version. 2.0: (B, C, N), 2.1: (B, N, C)"
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
return {"required": {"resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}),
|
||||
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
|
||||
}}
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "generate"
|
||||
|
||||
CATEGORY = "latent/3d"
|
||||
|
||||
def generate(self, resolution, batch_size, version):
|
||||
embed_dim = 64
|
||||
if version == "2.0":
|
||||
latent = torch.zeros([batch_size, embed_dim, resolution],
|
||||
device = comfy.model_management.intermediate_device())
|
||||
else: # version = "2.1"
|
||||
latent = torch.zeros([batch_size, resolution, embed_dim],
|
||||
device = comfy.model_management.intermediate_device())
|
||||
|
||||
def generate(self, resolution, batch_size):
|
||||
latent = torch.zeros([batch_size, resolution, 64], device=comfy.model_management.intermediate_device())
|
||||
return ({"samples": latent, "type": "hunyuan3dv2"}, )
|
||||
|
||||
class Hunyuan3Dv2Conditioning:
|
||||
@ -97,53 +78,23 @@ class Hunyuan3Dv2ConditioningMultiView:
|
||||
class VOXEL:
|
||||
def __init__(self, data):
|
||||
self.data = data
|
||||
|
||||
class VAEDecodeHunyuan3D:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"samples": ("LATENT",),
|
||||
"vae": ("VAE",),
|
||||
"version": (["2.0", "2.1"], {
|
||||
"default": "2.1",
|
||||
"tooltip": "2.0 returns voxel grid; 2.1 returns implicit SDF function."
|
||||
}),
|
||||
"num_chunks": ("INT", {
|
||||
"default": 8000, "min": 1000, "max": 500000,
|
||||
}),
|
||||
"octree_resolution": ("INT", {
|
||||
"default": 256, "min": 16, "max": 512,
|
||||
}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("VOXEL", "MESH")
|
||||
RETURN_NAMES = ("voxel", "mesh")
|
||||
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"samples": ("LATENT", ),
|
||||
"vae": ("VAE", ),
|
||||
"num_chunks": ("INT", {"default": 8000, "min": 1000, "max": 500000}),
|
||||
"octree_resolution": ("INT", {"default": 256, "min": 16, "max": 512}),
|
||||
}}
|
||||
RETURN_TYPES = ("VOXEL",)
|
||||
FUNCTION = "decode"
|
||||
|
||||
CATEGORY = "latent/3d"
|
||||
|
||||
def decode(self, vae, samples, version, num_chunks, octree_resolution):
|
||||
|
||||
if version == "2.0":
|
||||
voxel = vae.decode(samples["samples"], vae_options={
|
||||
"num_chunks": num_chunks,
|
||||
"octree_resolution": octree_resolution
|
||||
})
|
||||
return (VOXEL(voxel), None)
|
||||
|
||||
mesh = vae.decode(samples["samples"],vae_options={
|
||||
"num_chunks": num_chunks,
|
||||
"octree_resolution": octree_resolution,
|
||||
"to_mesh": True
|
||||
})
|
||||
|
||||
# ensure batch dim
|
||||
if mesh.vertices.ndim == 2:
|
||||
mesh.vertices = mesh.vertices[np.newaxis, ...]
|
||||
mesh.faces = mesh.faces[np.newaxis, ...]
|
||||
|
||||
return (None, mesh)
|
||||
def decode(self, vae, samples, num_chunks, octree_resolution):
|
||||
voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution}))
|
||||
return (voxels, )
|
||||
|
||||
def voxel_to_mesh(voxels, threshold=0.5, device=None):
|
||||
if device is None:
|
||||
@ -275,13 +226,9 @@ def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None):
|
||||
[0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]
|
||||
], device=device)
|
||||
|
||||
corner_values = torch.zeros((cell_positions.shape[0], 8), device=device)
|
||||
for c, (dz, dy, dx) in enumerate(corner_offsets):
|
||||
corner_values[:, c] = padded[
|
||||
cell_positions[:, 0] + dz,
|
||||
cell_positions[:, 1] + dy,
|
||||
cell_positions[:, 2] + dx
|
||||
]
|
||||
pos = cell_positions.unsqueeze(1) + corner_offsets.unsqueeze(0)
|
||||
z_idx, y_idx, x_idx = pos.unbind(-1)
|
||||
corner_values = padded[z_idx, y_idx, x_idx]
|
||||
|
||||
corner_signs = corner_values > threshold
|
||||
has_inside = torch.any(corner_signs, dim=1)
|
||||
@ -512,12 +459,8 @@ def save_glb(vertices, faces, filepath, metadata=None):
|
||||
"""
|
||||
|
||||
# Convert tensors to numpy arrays
|
||||
if isinstance(vertices, torch.tensor) and isinstance(faces, torch.tensor):
|
||||
vertices_np = vertices.cpu().numpy().astype(np.float32)
|
||||
faces_np = faces.cpu().numpy().astype(np.uint32)
|
||||
else:
|
||||
vertices_np = vertices.astype(np.float32)
|
||||
faces_np = faces.astype(np.uint32)
|
||||
vertices_np = vertices.cpu().numpy().astype(np.float32)
|
||||
faces_np = faces.cpu().numpy().astype(np.uint32)
|
||||
|
||||
vertices_buffer = vertices_np.tobytes()
|
||||
indices_buffer = faces_np.tobytes()
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user