mirror of
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Depth anything 3 (Core-135) (#13853)
Co-authored-by: Alexis Rolland <alexisrolland@hotmail.com>
This commit is contained in:
Talmaj
GitHub
parent
f8e51b674c
commit
5ece24e73c
@ -1,7 +1,13 @@
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import torch
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import torch.nn.functional as F
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from comfy.text_encoders.bert import BertAttention
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import comfy.model_management
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from comfy.ldm.modules.attention import optimized_attention_for_device
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from comfy.ldm.depth_anything_3.reference_view_selector import (
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select_reference_view, reorder_by_reference, restore_original_order,
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THRESH_FOR_REF_SELECTION,
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)
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class Dino2AttentionOutput(torch.nn.Module):
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@ -14,13 +20,41 @@ class Dino2AttentionOutput(torch.nn.Module):
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class Dino2AttentionBlock(torch.nn.Module):
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def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
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def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations,
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qk_norm=False):
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super().__init__()
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self.heads = heads
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self.head_dim = embed_dim // heads
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self.attention = BertAttention(embed_dim, heads, dtype, device, operations)
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self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
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if qk_norm:
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self.q_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device)
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self.k_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device)
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else:
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self.q_norm = None
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self.k_norm = None
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def forward(self, x, mask, optimized_attention):
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return self.output(self.attention(x, mask, optimized_attention))
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def forward(self, x, mask, optimized_attention, pos=None, rope=None):
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# Fast path used by the existing CLIP-vision DINOv2 (no DA3 extensions).
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if self.q_norm is None and rope is None:
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return self.output(self.attention(x, mask, optimized_attention))
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# DA3 path: do QKV manually so we can apply per-head QK-norm and 2D RoPE.
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attn = self.attention
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B, N, C = x.shape
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h = self.heads
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d = self.head_dim
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q = attn.query(x).view(B, N, h, d).transpose(1, 2)
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k = attn.key(x).view(B, N, h, d).transpose(1, 2)
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v = attn.value(x).view(B, N, h, d).transpose(1, 2)
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if self.q_norm is not None:
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q = self.q_norm(q)
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k = self.k_norm(k)
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if rope is not None and pos is not None:
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q = rope(q, pos)
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k = rope(k, pos)
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out = optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
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return self.output(out)
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class LayerScale(torch.nn.Module):
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@ -64,9 +98,11 @@ class SwiGLUFFN(torch.nn.Module):
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class Dino2Block(torch.nn.Module):
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def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):
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def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn,
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qk_norm=False):
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super().__init__()
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self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
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self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations,
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qk_norm=qk_norm)
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self.layer_scale1 = LayerScale(dim, dtype, device, operations)
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self.layer_scale2 = LayerScale(dim, dtype, device, operations)
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if use_swiglu_ffn:
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@ -76,19 +112,90 @@ class Dino2Block(torch.nn.Module):
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self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
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self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
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def forward(self, x, optimized_attention):
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x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention))
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def forward(self, x, optimized_attention, pos=None, rope=None, attn_mask=None):
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x = x + self.layer_scale1(self.attention(self.norm1(x), attn_mask, optimized_attention,
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pos=pos, rope=rope))
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x = x + self.layer_scale2(self.mlp(self.norm2(x)))
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return x
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class Dino2Encoder(torch.nn.Module):
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def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):
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# -----------------------------------------------------------------------------
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# 2D Rotary position embedding (DA3 extension)
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# -----------------------------------------------------------------------------
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class _PositionGetter:
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"""Cache (h, w) -> flat (y, x) position grid used to feed ``rope``."""
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def __init__(self):
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self._cache: dict = {}
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def __call__(self, batch_size: int, height: int, width: int, device) -> torch.Tensor:
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key = (height, width, device)
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if key not in self._cache:
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y = torch.arange(height, device=device)
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x = torch.arange(width, device=device)
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self._cache[key] = torch.cartesian_prod(y, x)
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cached = self._cache[key]
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return cached.view(1, height * width, 2).expand(batch_size, -1, -1).clone()
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class RotaryPositionEmbedding2D(torch.nn.Module):
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"""2D RoPE used by DA3-Small/Base. No learnable parameters."""
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def __init__(self, frequency: float = 100.0):
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super().__init__()
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self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
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for _ in range(num_layers)])
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self.base_frequency = frequency
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self._freq_cache: dict = {}
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def _components(self, dim: int, seq_len: int, device, dtype):
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key = (dim, seq_len, device, dtype)
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if key not in self._freq_cache:
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exp = torch.arange(0, dim, 2, device=device).float() / dim
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inv_freq = 1.0 / (self.base_frequency ** exp)
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pos = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
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ang = torch.einsum("i,j->ij", pos, inv_freq)
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ang = ang.to(dtype)
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ang = torch.cat((ang, ang), dim=-1)
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self._freq_cache[key] = (ang.cos().to(dtype), ang.sin().to(dtype))
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return self._freq_cache[key]
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@staticmethod
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def _rotate(x: torch.Tensor) -> torch.Tensor:
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d = x.shape[-1]
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x1, x2 = x[..., : d // 2], x[..., d // 2:]
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return torch.cat((-x2, x1), dim=-1)
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def _apply_1d(self, tokens, positions, cos_c, sin_c):
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cos = F.embedding(positions, cos_c)[:, None, :, :]
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sin = F.embedding(positions, sin_c)[:, None, :, :]
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return (tokens * cos) + (self._rotate(tokens) * sin)
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def forward(self, tokens: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:
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feature_dim = tokens.size(-1) // 2
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max_pos = int(positions.max()) + 1
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cos_c, sin_c = self._components(feature_dim, max_pos, tokens.device, tokens.dtype)
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v, h = tokens.chunk(2, dim=-1)
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v = self._apply_1d(v, positions[..., 0], cos_c, sin_c)
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h = self._apply_1d(h, positions[..., 1], cos_c, sin_c)
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return torch.cat((v, h), dim=-1)
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class Dino2Encoder(torch.nn.Module):
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def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn,
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qknorm_start: int = -1):
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super().__init__()
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self.layer = torch.nn.ModuleList([
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Dino2Block(
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dim, num_heads, layer_norm_eps, dtype, device, operations,
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use_swiglu_ffn=use_swiglu_ffn,
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qk_norm=(qknorm_start != -1 and i >= qknorm_start),
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)
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for i in range(num_layers)
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])
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def forward(self, x, intermediate_output=None):
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# Backward-compat path used by ``ClipVisionModel`` (no DA3 extensions).
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optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
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if intermediate_output is not None:
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@ -122,16 +229,27 @@ class Dino2PatchEmbeddings(torch.nn.Module):
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class Dino2Embeddings(torch.nn.Module):
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def __init__(self, dim, dtype, device, operations):
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def __init__(self, dim, dtype, device, operations,
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patch_size: int = 14, image_size: int = 518,
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use_mask_token: bool = True,
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num_camera_tokens: int = 0):
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super().__init__()
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patch_size = 14
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image_size = 518
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self.patch_size = patch_size
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self.image_size = image_size
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self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
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self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
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self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key.
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self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
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if use_mask_token:
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self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
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else:
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self.mask_token = None
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if num_camera_tokens > 0:
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# DA3 stores (ref_token, src_token) pairs that get injected at the
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# alt-attn boundary; see ``Dinov2Model._inject_camera_token``.
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self.camera_token = torch.nn.Parameter(torch.empty(1, num_camera_tokens, dim, dtype=dtype, device=device))
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else:
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self.camera_token = None
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def interpolate_pos_encoding(self, x, h_pixels, w_pixels):
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pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32)
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@ -140,12 +258,22 @@ class Dino2Embeddings(torch.nn.Module):
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patch_pos = pos_embed[:, 1:]
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N = patch_pos.shape[1]
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M = int(N ** 0.5)
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assert N == M * M, f"DINOv2 position grid must be square, got N={N} patches (sqrt={M})"
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h0 = h_pixels // self.patch_size
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w0 = w_pixels // self.patch_size
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scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M) # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
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# +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
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# scale_factor is (height_scale, width_scale) -- height MUST come first;
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# swapping these only happens to work for square inputs and breaks
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# non-square paths like DA3-Small / DA3-Base multi-view.
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scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)
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patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2)
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patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False)
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assert (h0, w0) == patch_pos.shape[-2:], (
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f"Interpolated pos-embed grid {tuple(patch_pos.shape[-2:])} does not match "
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f"target patch grid ({h0}, {w0}) for input {h_pixels}x{w_pixels} (patch_size={self.patch_size}); "
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f"check scale_factor axis order and +0.1 rounding workaround"
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)
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patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2)
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return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype)
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@ -168,12 +296,51 @@ class Dinov2Model(torch.nn.Module):
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heads = config_dict["num_attention_heads"]
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layer_norm_eps = config_dict["layer_norm_eps"]
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use_swiglu_ffn = config_dict["use_swiglu_ffn"]
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patch_size = config_dict.get("patch_size", 14)
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image_size = config_dict.get("image_size", 518)
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use_mask_token = config_dict.get("use_mask_token", True)
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self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
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self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
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# DA3 extensions (all default to disabled).
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self.alt_start = config_dict.get("alt_start", -1)
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self.qknorm_start = config_dict.get("qknorm_start", -1)
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self.rope_start = config_dict.get("rope_start", -1)
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self.cat_token = config_dict.get("cat_token", False)
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rope_freq = config_dict.get("rope_freq", 100.0)
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self.embed_dim = dim
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self.patch_size = patch_size
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self.num_register_tokens = 0
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self.patch_start_idx = 1
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if self.rope_start != -1 and rope_freq > 0:
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self.rope = RotaryPositionEmbedding2D(frequency=rope_freq)
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self._position_getter = _PositionGetter()
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else:
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self.rope = None
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self._position_getter = None
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# camera_token shape: (1, 2, dim) -> (ref_token, src_token).
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num_cam_tokens = 2 if self.alt_start != -1 else 0
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self.embeddings = Dino2Embeddings(
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dim, dtype, device, operations,
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patch_size=patch_size, image_size=image_size,
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use_mask_token=use_mask_token, num_camera_tokens=num_cam_tokens,
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)
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self.encoder = Dino2Encoder(
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dim, heads, layer_norm_eps, num_layers, dtype, device, operations,
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use_swiglu_ffn=use_swiglu_ffn,
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qknorm_start=self.qknorm_start,
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)
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self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
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def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
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if self.alt_start != -1:
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raise RuntimeError(
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"Dinov2Model.forward() is the backward-compatible CLIP-vision path and does not "
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"apply DA3 extensions (RoPE, alternating attention, camera-token injection). "
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"Use get_intermediate_layers_da3() for Depth Anything 3 models."
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)
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x = self.embeddings(pixel_values)
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x, i = self.encoder(x, intermediate_output=intermediate_output)
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x = self.layernorm(x)
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@ -181,6 +348,7 @@ class Dinov2Model(torch.nn.Module):
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return x, i, pooled_output, None
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def get_intermediate_layers(self, pixel_values, indices, apply_norm=True):
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"""Single-view multi-layer feature extraction."""
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x = self.embeddings(pixel_values)
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optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
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n_layers = len(self.encoder.layer)
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@ -197,3 +365,132 @@ class Dinov2Model(torch.nn.Module):
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if i >= max_idx:
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break
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return [cache[i] for i in resolved]
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# ------------------------------------------------------------------
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# Depth Anything 3 forward
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# ------------------------------------------------------------------
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def _prepare_rope_positions(self, B, S, H, W, device):
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if self.rope is None:
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return None, None
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ph, pw = H // self.patch_size, W // self.patch_size
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pos = self._position_getter(B * S, ph, pw, device=device)
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# Shift so the cls/cam token at position 0 is reserved for "no diff".
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pos = pos + 1
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cls_pos = torch.zeros(B * S, self.patch_start_idx, 2, device=device, dtype=pos.dtype)
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# Per-view local: real grid positions for patches, 0 for cls token.
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pos_local = torch.cat([cls_pos, pos], dim=1)
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# Global (across views): same grid positions; cls token still at 0,
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# but patches share the same positions in every view.
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pos_global = torch.cat([cls_pos, torch.zeros_like(pos) + 1], dim=1)
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return pos_local, pos_global
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def _inject_camera_token(self, x: torch.Tensor, B: int, S: int, cam_token: "torch.Tensor | None") -> torch.Tensor:
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# x: (B, S, N, C). Replace token at index 0 with the camera token.
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if cam_token is not None:
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inj = cam_token
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else:
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ct = comfy.model_management.cast_to_device(self.embeddings.camera_token, x.device, x.dtype)
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ref_token = ct[:, :1].expand(B, -1, -1)
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src_token = ct[:, 1:].expand(B, max(S - 1, 0), -1)
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inj = torch.cat([ref_token, src_token], dim=1)
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x = x.clone()
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x[:, :, 0] = inj
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return x
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def get_intermediate_layers_da3(self, pixel_values, out_layers, cam_token=None, ref_view_strategy="saddle_balanced", export_feat_layers=None):
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"""Multi-view multi-layer feature extraction used by Depth Anything 3."""
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if pixel_values.ndim == 4:
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pixel_values = pixel_values.unsqueeze(1)
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assert pixel_values.ndim == 5 and pixel_values.shape[2] == 3, \
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f"expected (B,3,H,W) or (B,S,3,H,W); got {tuple(pixel_values.shape)}"
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B, S, _, H, W = pixel_values.shape
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# Patch + cls + (interpolated) pos embed for each view.
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x = pixel_values.reshape(B * S, 3, H, W)
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x = self.embeddings(x) # (B*S, 1+N, C)
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x = x.reshape(B, S, x.shape[-2], x.shape[-1]) # (B, S, 1+N, C)
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pos_local, pos_global = self._prepare_rope_positions(B, S, H, W, x.device)
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# optimized_attention is only used by blocks without QK-norm/RoPE
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# (vanilla DINOv2 path); enabling-aware blocks fall through to SDPA.
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optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
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out_set = set(out_layers)
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export_set = set(export_feat_layers) if export_feat_layers else set()
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outputs: list[torch.Tensor] = []
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aux_outputs: list[torch.Tensor] = []
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local_x = x
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b_idx = None
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for i, blk in enumerate(self.encoder.layer):
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apply_rope = self.rope is not None and i >= self.rope_start
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block_rope = self.rope if apply_rope else None
|
||||
l_pos = pos_local if apply_rope else None
|
||||
g_pos = pos_global if apply_rope else None
|
||||
|
||||
# Reference-view selection threshold: matches the upstream constant
|
||||
# THRESH_FOR_REF_SELECTION = 3. Skipped when a user-supplied
|
||||
# cam_token is provided (camera info already pins the geometry).
|
||||
if (self.alt_start != -1 and i == self.alt_start - 1 and S >= THRESH_FOR_REF_SELECTION and cam_token is None):
|
||||
b_idx = select_reference_view(x, strategy=ref_view_strategy)
|
||||
x = reorder_by_reference(x, b_idx)
|
||||
local_x = reorder_by_reference(local_x, b_idx)
|
||||
|
||||
if self.alt_start != -1 and i == self.alt_start:
|
||||
x = self._inject_camera_token(x, B, S, cam_token)
|
||||
|
||||
if self.alt_start != -1 and i >= self.alt_start and (i % 2 == 1):
|
||||
# Global attention across views: flatten S into the seq dim.
|
||||
t = x.reshape(B, S * x.shape[-2], x.shape[-1])
|
||||
p = g_pos.reshape(B, S * g_pos.shape[-2], g_pos.shape[-1]) if g_pos is not None else None
|
||||
t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope)
|
||||
x = t.reshape(B, S, x.shape[-2], x.shape[-1])
|
||||
else:
|
||||
# Per-view local attention.
|
||||
t = x.reshape(B * S, x.shape[-2], x.shape[-1])
|
||||
p = l_pos.reshape(B * S, l_pos.shape[-2], l_pos.shape[-1]) if l_pos is not None else None
|
||||
t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope)
|
||||
x = t.reshape(B, S, x.shape[-2], x.shape[-1])
|
||||
local_x = x
|
||||
|
||||
if i in out_set:
|
||||
if self.cat_token:
|
||||
out_x = torch.cat([local_x, x], dim=-1)
|
||||
else:
|
||||
out_x = x
|
||||
# Restore original view order on the way out so heads see views
|
||||
# in the user's expected order.
|
||||
if b_idx is not None and self.alt_start != -1:
|
||||
out_x = restore_original_order(out_x, b_idx)
|
||||
outputs.append(out_x)
|
||||
|
||||
if i in export_set:
|
||||
aux = x
|
||||
if b_idx is not None and self.alt_start != -1:
|
||||
aux = restore_original_order(aux, b_idx)
|
||||
aux_outputs.append(aux)
|
||||
|
||||
# Apply final norm. When cat_token is set, only the right half
|
||||
# ("global" features) is normalised; the left half is left as-is to
|
||||
# match the upstream DA3 head signature.
|
||||
normed: list[torch.Tensor] = []
|
||||
cls_tokens: list[torch.Tensor] = []
|
||||
for out_x in outputs:
|
||||
cls_tokens.append(out_x[:, :, 0])
|
||||
if out_x.shape[-1] == self.embed_dim:
|
||||
normed.append(self.layernorm(out_x))
|
||||
elif out_x.shape[-1] == self.embed_dim * 2:
|
||||
left = out_x[..., :self.embed_dim]
|
||||
right = self.layernorm(out_x[..., self.embed_dim:])
|
||||
normed.append(torch.cat([left, right], dim=-1))
|
||||
else:
|
||||
raise ValueError(f"Unexpected token width: {out_x.shape[-1]}")
|
||||
|
||||
# Drop cls/cam token from the patch sequence.
|
||||
normed = [o[..., 1 + self.num_register_tokens:, :] for o in normed]
|
||||
|
||||
# Final layernorm + drop cls token from auxiliary features too.
|
||||
aux_normed = [self.layernorm(o)[..., 1 + self.num_register_tokens:, :]
|
||||
for o in aux_outputs]
|
||||
return list(zip(normed, cls_tokens)), aux_normed
|
||||
|
||||
25
comfy/ldm/colormap.py
Normal file
25
comfy/ldm/colormap.py
Normal file
@ -0,0 +1,25 @@
|
||||
"""Colormap utilities for depth and geometry visualisation."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def turbo(x: torch.Tensor) -> torch.Tensor:
|
||||
"""Anton Mikhailov polynomial approximation of the Turbo colormap.
|
||||
|
||||
Args:
|
||||
x: Float tensor with values in [0, 1].
|
||||
|
||||
Returns:
|
||||
RGB tensor of the same shape as ``x`` with a trailing size-3 dimension.
|
||||
"""
|
||||
x = x.clamp(0.0, 1.0)
|
||||
x2 = x * x
|
||||
x3 = x2 * x
|
||||
x4 = x2 * x2
|
||||
x5 = x4 * x
|
||||
r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5
|
||||
g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 + 4.27729857*x4 + 2.82956604*x5
|
||||
b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5
|
||||
return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)
|
||||
177
comfy/ldm/depth_anything_3/camera.py
Normal file
177
comfy/ldm/depth_anything_3/camera.py
Normal file
@ -0,0 +1,177 @@
|
||||
"""Camera-token encoder and decoder for Depth Anything 3."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from comfy.ldm.modules.attention import optimized_attention_for_device
|
||||
from .transform import affine_inverse, extri_intri_to_pose_encoding
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Building blocks (mirror depth_anything_3.model.utils.{attention,block})
|
||||
# -----------------------------------------------------------------------
|
||||
|
||||
|
||||
class _Mlp(nn.Module):
|
||||
"""Standard 2-layer MLP with GELU. Matches upstream ``utils.attention.Mlp``."""
|
||||
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, *, device=None, dtype=None, operations=None):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = operations.Linear(in_features, hidden_features, bias=True, device=device, dtype=dtype)
|
||||
self.fc2 = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, x):
|
||||
return self.fc2(F.gelu(self.fc1(x)))
|
||||
|
||||
|
||||
class _LayerScale(nn.Module):
|
||||
"""Per-channel learnable scaling. Matches upstream LayerScale."""
|
||||
|
||||
def __init__(self, dim, *, device=None, dtype=None):
|
||||
super().__init__()
|
||||
self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
|
||||
|
||||
def forward(self, x):
|
||||
return x * self.gamma.to(dtype=x.dtype, device=x.device)
|
||||
|
||||
|
||||
class _Attention(nn.Module):
|
||||
""" Self-attention with fused QKV projection. Mirrors upstream utils.attention.Attention;
|
||||
Layout matches the HF safetensors (attn.qkv.{weight,bias} and attn.proj.{weight,bias})."""
|
||||
|
||||
def __init__(self, dim, num_heads, *, device=None, dtype=None, operations=None):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.qkv = operations.Linear(dim, dim * 3, bias=True, device=device, dtype=dtype)
|
||||
self.proj = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, x):
|
||||
B, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B, N, 3, C)
|
||||
q, k, v = qkv.unbind(2) # each (B, N, C)
|
||||
attn_fn = optimized_attention_for_device(x.device, small_input=True)
|
||||
out = attn_fn(q, k, v, heads=self.num_heads)
|
||||
return self.proj(out)
|
||||
|
||||
|
||||
class _Block(nn.Module):
|
||||
"""Pre-norm transformer block with LayerScale. Used by :class:CameraEnc. Layout follows upstream utils.block.Block."""
|
||||
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4, init_values=0.01, *, device=None, dtype=None, operations=None):
|
||||
super().__init__()
|
||||
self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
|
||||
self.attn = _Attention(dim, num_heads, device=device, dtype=dtype, operations=operations)
|
||||
self.ls1 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity()
|
||||
self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
|
||||
self.mlp = _Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), device=device, dtype=dtype, operations=operations)
|
||||
self.ls2 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
x = x + self.ls1(self.attn(self.norm1(x)))
|
||||
x = x + self.ls2(self.mlp(self.norm2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class CameraEnc(nn.Module):
|
||||
"""Encode per-view (extrinsics, intrinsics) into a camera token.
|
||||
|
||||
Maps a 9-D pose-encoding vector through a small MLP up to the backbone's
|
||||
``embed_dim``, then runs ``trunk_depth`` transformer blocks. The output
|
||||
has shape ``(B, S, embed_dim)`` and is injected at block ``alt_start``
|
||||
of the DINOv2 backbone in place of the cls token.
|
||||
|
||||
Parameters mirror the upstream ``cam_enc.py`` so HF weights load directly.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim_out: int = 1024,
|
||||
dim_in: int = 9,
|
||||
trunk_depth: int = 4,
|
||||
target_dim: int = 9,
|
||||
num_heads: int = 16,
|
||||
mlp_ratio: int = 4,
|
||||
init_values: float = 0.01,
|
||||
*,
|
||||
device=None, dtype=None, operations=None,
|
||||
**_kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
self.target_dim = target_dim
|
||||
self.trunk_depth = trunk_depth
|
||||
self.trunk = nn.Sequential(*[
|
||||
_Block(dim_out, num_heads=num_heads, mlp_ratio=mlp_ratio,
|
||||
init_values=init_values,
|
||||
device=device, dtype=dtype, operations=operations)
|
||||
for _ in range(trunk_depth)
|
||||
])
|
||||
self.token_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype)
|
||||
self.trunk_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype)
|
||||
self.pose_branch = _Mlp(
|
||||
in_features=dim_in,
|
||||
hidden_features=dim_out // 2,
|
||||
out_features=dim_out,
|
||||
device=device, dtype=dtype, operations=operations,
|
||||
)
|
||||
|
||||
def forward(self, extrinsics: torch.Tensor, intrinsics: torch.Tensor,
|
||||
image_size_hw) -> torch.Tensor:
|
||||
"""Encode camera parameters into ``(B, S, dim_out)`` tokens."""
|
||||
c2ws = affine_inverse(extrinsics)
|
||||
pose_encoding = extri_intri_to_pose_encoding(c2ws, intrinsics, image_size_hw)
|
||||
tokens = self.pose_branch(pose_encoding.to(self.pose_branch.fc1.weight.dtype))
|
||||
tokens = self.token_norm(tokens)
|
||||
tokens = self.trunk(tokens)
|
||||
tokens = self.trunk_norm(tokens)
|
||||
return tokens
|
||||
|
||||
|
||||
class CameraDec(nn.Module):
|
||||
"""Decode the final cam token into a 9-D pose encoding.
|
||||
|
||||
Output layout: ``[T(3), quat_xyzw(4), fov_h, fov_w]``. The translation is
|
||||
always predicted by the network; the quaternion and FoV can either be
|
||||
predicted or supplied via ``camera_encoding`` (used at training time
|
||||
when GT cameras are available -- not exercised at inference here).
|
||||
|
||||
Parameters mirror the upstream ``cam_dec.py`` so HF weights load directly.
|
||||
"""
|
||||
|
||||
def __init__(self, dim_in: int = 1536,
|
||||
*, device=None, dtype=None, operations=None, **_kwargs):
|
||||
super().__init__()
|
||||
d = dim_in
|
||||
self.backbone = nn.Sequential(
|
||||
operations.Linear(d, d, device=device, dtype=dtype),
|
||||
nn.ReLU(),
|
||||
operations.Linear(d, d, device=device, dtype=dtype),
|
||||
nn.ReLU(),
|
||||
)
|
||||
self.fc_t = operations.Linear(d, 3, device=device, dtype=dtype)
|
||||
self.fc_qvec = operations.Linear(d, 4, device=device, dtype=dtype)
|
||||
self.fc_fov = nn.Sequential(
|
||||
operations.Linear(d, 2, device=device, dtype=dtype),
|
||||
nn.ReLU(),
|
||||
)
|
||||
|
||||
def forward(self, feat: torch.Tensor,
|
||||
camera_encoding: "torch.Tensor | None" = None) -> torch.Tensor:
|
||||
"""Decode ``(B, N, dim_in)`` cam tokens into ``(B, N, 9)`` pose enc."""
|
||||
B, N = feat.shape[:2]
|
||||
feat = feat.reshape(B * N, -1)
|
||||
feat = self.backbone(feat)
|
||||
out_t = self.fc_t(feat.float()).reshape(B, N, 3)
|
||||
if camera_encoding is None:
|
||||
out_qvec = self.fc_qvec(feat.float()).reshape(B, N, 4)
|
||||
out_fov = self.fc_fov(feat.float()).reshape(B, N, 2)
|
||||
else:
|
||||
out_qvec = camera_encoding[..., 3:7]
|
||||
out_fov = camera_encoding[..., -2:]
|
||||
return torch.cat([out_t, out_qvec, out_fov], dim=-1)
|
||||
489
comfy/ldm/depth_anything_3/dpt.py
Normal file
489
comfy/ldm/depth_anything_3/dpt.py
Normal file
@ -0,0 +1,489 @@
|
||||
"""DPT / DualDPT heads for Depth Anything 3."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List, Optional, Sequence, Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class Permute(nn.Module):
|
||||
def __init__(self, dims: Tuple[int, ...]):
|
||||
super().__init__()
|
||||
self.dims = dims
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return x.permute(*self.dims)
|
||||
|
||||
|
||||
def _custom_interpolate(
|
||||
x: torch.Tensor,
|
||||
size: Optional[Tuple[int, int]] = None,
|
||||
scale_factor: Optional[float] = None,
|
||||
mode: str = "bilinear",
|
||||
align_corners: bool = True,
|
||||
) -> torch.Tensor:
|
||||
if size is None:
|
||||
assert scale_factor is not None
|
||||
size = (int(x.shape[-2] * scale_factor), int(x.shape[-1] * scale_factor))
|
||||
INT_MAX = 1610612736
|
||||
total = size[0] * size[1] * x.shape[0] * x.shape[1]
|
||||
if total > INT_MAX:
|
||||
chunks = torch.chunk(x, chunks=(total // INT_MAX) + 1, dim=0)
|
||||
outs = [F.interpolate(c, size=size, mode=mode, align_corners=align_corners) for c in chunks]
|
||||
return torch.cat(outs, dim=0).contiguous()
|
||||
return F.interpolate(x, size=size, mode=mode, align_corners=align_corners)
|
||||
|
||||
|
||||
def _create_uv_grid(width: int, height: int, aspect_ratio: float, dtype, device) -> torch.Tensor:
|
||||
"""Normalised UV grid spanning (-x_span, -y_span)..(x_span, y_span)."""
|
||||
diag_factor = (aspect_ratio ** 2 + 1.0) ** 0.5
|
||||
span_x = aspect_ratio / diag_factor
|
||||
span_y = 1.0 / diag_factor
|
||||
left_x = -span_x * (width - 1) / width
|
||||
right_x = span_x * (width - 1) / width
|
||||
top_y = -span_y * (height - 1) / height
|
||||
bottom_y = span_y * (height - 1) / height
|
||||
x_coords = torch.linspace(left_x, right_x, steps=width, dtype=dtype, device=device)
|
||||
y_coords = torch.linspace(top_y, bottom_y, steps=height, dtype=dtype, device=device)
|
||||
uu, vv = torch.meshgrid(x_coords, y_coords, indexing="xy")
|
||||
return torch.stack((uu, vv), dim=-1) # (H, W, 2)
|
||||
|
||||
|
||||
def _make_sincos_pos_embed(embed_dim: int, pos: torch.Tensor, omega_0: float = 100.0) -> torch.Tensor:
|
||||
omega = torch.arange(embed_dim // 2, dtype=torch.float32, device=pos.device)
|
||||
omega = 1.0 / omega_0 ** (omega / (embed_dim / 2.0))
|
||||
pos = pos.reshape(-1)
|
||||
out = torch.einsum("m,d->md", pos, omega)
|
||||
return torch.cat([out.sin(), out.cos()], dim=1).float()
|
||||
|
||||
|
||||
def _position_grid_to_embed(pos_grid: torch.Tensor, embed_dim: int, omega_0: float = 100.0) -> torch.Tensor:
|
||||
H, W, _ = pos_grid.shape
|
||||
pos_flat = pos_grid.reshape(-1, 2)
|
||||
emb_x = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 0], omega_0=omega_0)
|
||||
emb_y = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 1], omega_0=omega_0)
|
||||
emb = torch.cat([emb_x, emb_y], dim=-1)
|
||||
return emb.view(H, W, embed_dim)
|
||||
|
||||
|
||||
def _add_pos_embed(x: torch.Tensor, W: int, H: int, ratio: float = 0.1) -> torch.Tensor:
|
||||
"""Stateless UV positional embedding added to a feature map (B, C, h, w)."""
|
||||
pw, ph = x.shape[-1], x.shape[-2]
|
||||
pe = _create_uv_grid(pw, ph, aspect_ratio=W / H, dtype=x.dtype, device=x.device)
|
||||
pe = _position_grid_to_embed(pe, x.shape[1]) * ratio
|
||||
pe = pe.permute(2, 0, 1)[None].expand(x.shape[0], -1, -1, -1).to(dtype=x.dtype)
|
||||
return x + pe
|
||||
|
||||
|
||||
def _apply_activation(x: torch.Tensor, activation: str) -> torch.Tensor:
|
||||
act = (activation or "linear").lower()
|
||||
if act == "exp":
|
||||
return torch.exp(x)
|
||||
if act == "expp1":
|
||||
return torch.exp(x) + 1
|
||||
if act == "expm1":
|
||||
return torch.expm1(x)
|
||||
if act == "relu":
|
||||
return torch.relu(x)
|
||||
if act == "sigmoid":
|
||||
return torch.sigmoid(x)
|
||||
if act == "softplus":
|
||||
return F.softplus(x)
|
||||
if act == "tanh":
|
||||
return torch.tanh(x)
|
||||
return x
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Fusion building blocks
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class ResidualConvUnit(nn.Module):
|
||||
def __init__(self, features: int, device=None, dtype=None, operations=None):
|
||||
super().__init__()
|
||||
self.conv1 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype)
|
||||
self.conv2 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype)
|
||||
self.activation = nn.ReLU(inplace=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
out = self.activation(x)
|
||||
out = self.conv1(out)
|
||||
out = self.activation(out)
|
||||
out = self.conv2(out)
|
||||
return out + x
|
||||
|
||||
|
||||
class FeatureFusionBlock(nn.Module):
|
||||
def __init__(self, features: int, has_residual: bool = True, align_corners: bool = True, device=None, dtype=None, operations=None):
|
||||
super().__init__()
|
||||
self.align_corners = align_corners
|
||||
self.has_residual = has_residual
|
||||
if has_residual:
|
||||
self.resConfUnit1 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations)
|
||||
else:
|
||||
self.resConfUnit1 = None
|
||||
self.resConfUnit2 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations)
|
||||
self.out_conv = operations.Conv2d(features, features, 1, 1, 0, bias=True, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, *xs: torch.Tensor, size: Optional[Tuple[int, int]] = None) -> torch.Tensor:
|
||||
y = xs[0]
|
||||
if self.has_residual and len(xs) > 1 and self.resConfUnit1 is not None:
|
||||
y = y + self.resConfUnit1(xs[1])
|
||||
y = self.resConfUnit2(y)
|
||||
if size is None:
|
||||
up_kwargs = {"scale_factor": 2.0}
|
||||
else:
|
||||
up_kwargs = {"size": size}
|
||||
y = _custom_interpolate(y, **up_kwargs, mode="bilinear", align_corners=self.align_corners)
|
||||
y = self.out_conv(y)
|
||||
return y
|
||||
|
||||
|
||||
class _Scratch(nn.Module):
|
||||
"""Container that mirrors upstream ``scratch`` attribute layout."""
|
||||
|
||||
|
||||
def _make_scratch(in_shape: List[int], out_shape: int, device=None, dtype=None, operations=None) -> _Scratch:
|
||||
scratch = _Scratch()
|
||||
scratch.layer1_rn = operations.Conv2d(in_shape[0], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
|
||||
scratch.layer2_rn = operations.Conv2d(in_shape[1], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
|
||||
scratch.layer3_rn = operations.Conv2d(in_shape[2], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
|
||||
scratch.layer4_rn = operations.Conv2d(in_shape[3], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
|
||||
return scratch
|
||||
|
||||
|
||||
def _make_fusion_block(features: int, has_residual: bool = True, device=None, dtype=None, operations=None) -> FeatureFusionBlock:
|
||||
return FeatureFusionBlock(features, has_residual=has_residual, align_corners=True, device=device, dtype=dtype, operations=operations)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# DPT (single head + optional sky head) -- used by DA3Mono/Metric
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class DPT(nn.Module):
|
||||
"""Single-head DPT used by DA3Mono-Large and DA3Metric-Large."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim_in: int,
|
||||
patch_size: int = 14,
|
||||
output_dim: int = 1,
|
||||
activation: str = "exp",
|
||||
conf_activation: str = "expp1",
|
||||
features: int = 256,
|
||||
out_channels: Sequence[int] = (256, 512, 1024, 1024),
|
||||
pos_embed: bool = False,
|
||||
down_ratio: int = 1,
|
||||
head_name: str = "depth",
|
||||
use_sky_head: bool = True,
|
||||
sky_name: str = "sky",
|
||||
sky_activation: str = "relu",
|
||||
norm_type: str = "idt",
|
||||
device=None, dtype=None, operations=None,
|
||||
):
|
||||
super().__init__()
|
||||
self.patch_size = patch_size
|
||||
self.activation = activation
|
||||
self.conf_activation = conf_activation
|
||||
self.pos_embed = pos_embed
|
||||
self.down_ratio = down_ratio
|
||||
self.head_main = head_name
|
||||
self.sky_name = sky_name
|
||||
self.out_dim = output_dim
|
||||
self.has_conf = output_dim > 1
|
||||
self.use_sky_head = use_sky_head
|
||||
self.sky_activation = sky_activation
|
||||
self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
|
||||
|
||||
if norm_type == "layer":
|
||||
self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype)
|
||||
else:
|
||||
self.norm = nn.Identity()
|
||||
|
||||
out_channels = list(out_channels)
|
||||
self.projects = nn.ModuleList([
|
||||
operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
|
||||
for oc in out_channels
|
||||
])
|
||||
self.resize_layers = nn.ModuleList([
|
||||
operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype),
|
||||
operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype),
|
||||
nn.Identity(),
|
||||
operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype),
|
||||
])
|
||||
|
||||
self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
|
||||
|
||||
head_features_1 = features
|
||||
head_features_2 = 32
|
||||
self.scratch.output_conv1 = operations.Conv2d(
|
||||
head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1,
|
||||
device=device, dtype=dtype,
|
||||
)
|
||||
self.scratch.output_conv2 = nn.Sequential(
|
||||
operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
|
||||
nn.ReLU(inplace=False),
|
||||
operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
|
||||
)
|
||||
|
||||
if self.use_sky_head:
|
||||
self.scratch.sky_output_conv2 = nn.Sequential(
|
||||
operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
|
||||
nn.ReLU(inplace=False),
|
||||
operations.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
|
||||
)
|
||||
|
||||
def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict:
|
||||
# feats[i][0] is the patch-token tensor with shape (B, S, N_patch, C)
|
||||
B, S, N, C = feats[0][0].shape
|
||||
feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats]
|
||||
|
||||
ph, pw = H // self.patch_size, W // self.patch_size
|
||||
resized = []
|
||||
for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
|
||||
x = feats_flat[take_idx][:, patch_start_idx:]
|
||||
x = self.norm(x)
|
||||
x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw)
|
||||
x = self.projects[stage_idx](x)
|
||||
if self.pos_embed:
|
||||
x = _add_pos_embed(x, W, H)
|
||||
x = self.resize_layers[stage_idx](x)
|
||||
resized.append(x)
|
||||
|
||||
l1_rn = self.scratch.layer1_rn(resized[0])
|
||||
l2_rn = self.scratch.layer2_rn(resized[1])
|
||||
l3_rn = self.scratch.layer3_rn(resized[2])
|
||||
l4_rn = self.scratch.layer4_rn(resized[3])
|
||||
|
||||
out = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
|
||||
out = self.scratch.refinenet3(out, l3_rn, size=l2_rn.shape[2:])
|
||||
out = self.scratch.refinenet2(out, l2_rn, size=l1_rn.shape[2:])
|
||||
out = self.scratch.refinenet1(out, l1_rn)
|
||||
|
||||
h_out = int(ph * self.patch_size / self.down_ratio)
|
||||
w_out = int(pw * self.patch_size / self.down_ratio)
|
||||
|
||||
fused = self.scratch.output_conv1(out)
|
||||
fused = _custom_interpolate(fused, (h_out, w_out), mode="bilinear", align_corners=True)
|
||||
if self.pos_embed:
|
||||
fused = _add_pos_embed(fused, W, H)
|
||||
feat = fused
|
||||
|
||||
main_logits = self.scratch.output_conv2(feat)
|
||||
outs = {}
|
||||
if self.has_conf:
|
||||
fmap = main_logits.permute(0, 2, 3, 1)
|
||||
pred = _apply_activation(fmap[..., :-1], self.activation)
|
||||
conf = _apply_activation(fmap[..., -1], self.conf_activation)
|
||||
outs[self.head_main] = pred.squeeze(-1).view(B, S, *pred.shape[1:-1])
|
||||
outs[f"{self.head_main}_conf"] = conf.view(B, S, *conf.shape[1:])
|
||||
else:
|
||||
pred = _apply_activation(main_logits, self.activation)
|
||||
outs[self.head_main] = pred.squeeze(1).view(B, S, *pred.shape[2:])
|
||||
|
||||
if self.use_sky_head:
|
||||
sky_logits = self.scratch.sky_output_conv2(feat)
|
||||
if self.sky_activation.lower() == "sigmoid":
|
||||
sky = torch.sigmoid(sky_logits)
|
||||
elif self.sky_activation.lower() == "relu":
|
||||
sky = F.relu(sky_logits)
|
||||
else:
|
||||
sky = sky_logits
|
||||
outs[self.sky_name] = sky.squeeze(1).view(B, S, *sky.shape[2:])
|
||||
|
||||
return outs
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# DualDPT (depth + auxiliary "ray" head) -- used by DA3-Small / DA3-Base
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class DualDPT(nn.Module):
|
||||
"""Two-head DPT used by DA3-Small / DA3-Base."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim_in: int,
|
||||
patch_size: int = 14,
|
||||
output_dim: int = 2,
|
||||
activation: str = "exp",
|
||||
conf_activation: str = "expp1",
|
||||
features: int = 256,
|
||||
out_channels: Sequence[int] = (256, 512, 1024, 1024),
|
||||
pos_embed: bool = True,
|
||||
down_ratio: int = 1,
|
||||
aux_pyramid_levels: int = 4,
|
||||
aux_out1_conv_num: int = 5,
|
||||
head_names: Tuple[str, str] = ("depth", "ray"),
|
||||
device=None, dtype=None, operations=None,
|
||||
):
|
||||
super().__init__()
|
||||
self.patch_size = patch_size
|
||||
self.activation = activation
|
||||
self.conf_activation = conf_activation
|
||||
self.pos_embed = pos_embed
|
||||
self.down_ratio = down_ratio
|
||||
self.aux_levels = aux_pyramid_levels
|
||||
self.aux_out1_conv_num = aux_out1_conv_num
|
||||
self.head_main, self.head_aux = head_names
|
||||
self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
|
||||
# Toggle the auxiliary ray branch at runtime. Default off (mono path).
|
||||
# DepthAnything3Net flips this on when running multi-view + ray-pose.
|
||||
self.enable_aux: bool = False
|
||||
|
||||
self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype)
|
||||
out_channels = list(out_channels)
|
||||
self.projects = nn.ModuleList([
|
||||
operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
|
||||
for oc in out_channels
|
||||
])
|
||||
self.resize_layers = nn.ModuleList([
|
||||
operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype),
|
||||
operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype),
|
||||
nn.Identity(),
|
||||
operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype),
|
||||
])
|
||||
|
||||
self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations)
|
||||
# Main fusion chain
|
||||
self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
|
||||
# Auxiliary fusion chain (separate copies)
|
||||
self.scratch.refinenet1_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet2_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet3_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
|
||||
self.scratch.refinenet4_aux = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
|
||||
|
||||
head_features_1 = features
|
||||
head_features_2 = 32
|
||||
|
||||
# Main head neck + final projection
|
||||
self.scratch.output_conv1 = operations.Conv2d(
|
||||
head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1,
|
||||
device=device, dtype=dtype,
|
||||
)
|
||||
self.scratch.output_conv2 = nn.Sequential(
|
||||
operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
|
||||
nn.ReLU(inplace=False),
|
||||
operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
|
||||
)
|
||||
|
||||
# Aux pre-head per level (multi-level pyramid)
|
||||
self.scratch.output_conv1_aux = nn.ModuleList([
|
||||
self._make_aux_out1_block(head_features_1, device=device, dtype=dtype, operations=operations)
|
||||
for _ in range(self.aux_levels)
|
||||
])
|
||||
|
||||
# Aux final projection per level (includes LayerNorm permute path).
|
||||
ln_seq = [Permute((0, 2, 3, 1)),
|
||||
operations.LayerNorm(head_features_2, device=device, dtype=dtype),
|
||||
Permute((0, 3, 1, 2))]
|
||||
self.scratch.output_conv2_aux = nn.ModuleList([
|
||||
nn.Sequential(
|
||||
operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
|
||||
*ln_seq,
|
||||
nn.ReLU(inplace=False),
|
||||
operations.Conv2d(head_features_2, 7, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
|
||||
)
|
||||
for _ in range(self.aux_levels)
|
||||
])
|
||||
|
||||
@staticmethod
|
||||
def _make_aux_out1_block(in_ch: int, *, device=None, dtype=None, operations=None) -> nn.Sequential:
|
||||
# aux_out1_conv_num=5 in all Apache-2.0 variants.
|
||||
return nn.Sequential(
|
||||
operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
|
||||
operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype),
|
||||
operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
|
||||
operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype),
|
||||
operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
|
||||
)
|
||||
|
||||
def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict:
|
||||
B, S, N, C = feats[0][0].shape
|
||||
feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats]
|
||||
|
||||
ph, pw = H // self.patch_size, W // self.patch_size
|
||||
resized = []
|
||||
for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
|
||||
x = feats_flat[take_idx][:, patch_start_idx:]
|
||||
x = self.norm(x)
|
||||
x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw)
|
||||
x = self.projects[stage_idx](x)
|
||||
if self.pos_embed:
|
||||
x = _add_pos_embed(x, W, H)
|
||||
x = self.resize_layers[stage_idx](x)
|
||||
resized.append(x)
|
||||
|
||||
l1_rn = self.scratch.layer1_rn(resized[0])
|
||||
l2_rn = self.scratch.layer2_rn(resized[1])
|
||||
l3_rn = self.scratch.layer3_rn(resized[2])
|
||||
l4_rn = self.scratch.layer4_rn(resized[3])
|
||||
|
||||
# Main pyramid (output_conv1 is applied inside the upstream `_fuse`,
|
||||
# before interpolation -- replicate that order here).
|
||||
m = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
|
||||
if self.enable_aux:
|
||||
a4 = self.scratch.refinenet4_aux(l4_rn, size=l3_rn.shape[2:])
|
||||
aux_pyr = [a4]
|
||||
m = self.scratch.refinenet3(m, l3_rn, size=l2_rn.shape[2:])
|
||||
if self.enable_aux:
|
||||
aux_pyr.append(self.scratch.refinenet3_aux(aux_pyr[-1], l3_rn, size=l2_rn.shape[2:]))
|
||||
m = self.scratch.refinenet2(m, l2_rn, size=l1_rn.shape[2:])
|
||||
if self.enable_aux:
|
||||
aux_pyr.append(self.scratch.refinenet2_aux(aux_pyr[-1], l2_rn, size=l1_rn.shape[2:]))
|
||||
m = self.scratch.refinenet1(m, l1_rn)
|
||||
if self.enable_aux:
|
||||
aux_pyr.append(self.scratch.refinenet1_aux(aux_pyr[-1], l1_rn))
|
||||
m = self.scratch.output_conv1(m)
|
||||
|
||||
h_out = int(ph * self.patch_size / self.down_ratio)
|
||||
w_out = int(pw * self.patch_size / self.down_ratio)
|
||||
|
||||
m = _custom_interpolate(m, (h_out, w_out), mode="bilinear", align_corners=True)
|
||||
if self.pos_embed:
|
||||
m = _add_pos_embed(m, W, H)
|
||||
main_logits = self.scratch.output_conv2(m)
|
||||
fmap = main_logits.permute(0, 2, 3, 1)
|
||||
depth_pred = _apply_activation(fmap[..., :-1], self.activation)
|
||||
depth_conf = _apply_activation(fmap[..., -1], self.conf_activation)
|
||||
|
||||
outs = {
|
||||
self.head_main: depth_pred.squeeze(-1).view(B, S, *depth_pred.shape[1:-1]),
|
||||
f"{self.head_main}_conf": depth_conf.view(B, S, *depth_conf.shape[1:]),
|
||||
}
|
||||
|
||||
if self.enable_aux:
|
||||
# Auxiliary "ray" head (multi-level inside) -- only the last level
|
||||
# is returned. Mirrors upstream ``DualDPT._fuse`` + ``_forward_impl``:
|
||||
# each aux pyramid level goes through ``output_conv1_aux[i]``
|
||||
# (5-layer conv stack that ends at ``features // 2`` channels),
|
||||
# then the last level optionally gets a pos-embed and finally
|
||||
# ``output_conv2_aux[-1]``.
|
||||
aux_processed = [
|
||||
self.scratch.output_conv1_aux[i](a) for i, a in enumerate(aux_pyr)
|
||||
]
|
||||
last_aux = aux_processed[-1]
|
||||
if self.pos_embed:
|
||||
last_aux = _add_pos_embed(last_aux, W, H)
|
||||
last_aux_logits = self.scratch.output_conv2_aux[-1](last_aux)
|
||||
fmap_last = last_aux_logits.permute(0, 2, 3, 1)
|
||||
# Channels: [ray(6), ray_conf(1)]; ray uses 'linear' activation.
|
||||
aux_pred = fmap_last[..., :-1]
|
||||
aux_conf = _apply_activation(fmap_last[..., -1], self.conf_activation)
|
||||
outs[self.head_aux] = aux_pred.view(B, S, *aux_pred.shape[1:])
|
||||
outs[f"{self.head_aux}_conf"] = aux_conf.view(B, S, *aux_conf.shape[1:])
|
||||
|
||||
return outs
|
||||
236
comfy/ldm/depth_anything_3/model.py
Normal file
236
comfy/ldm/depth_anything_3/model.py
Normal file
@ -0,0 +1,236 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Dict, Optional, Sequence
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from comfy.image_encoders.dino2 import Dinov2Model
|
||||
|
||||
from .camera import CameraDec, CameraEnc
|
||||
from .dpt import DPT, DualDPT
|
||||
from .ray_pose import get_extrinsic_from_camray
|
||||
from .transform import affine_inverse, pose_encoding_to_extri_intri
|
||||
|
||||
|
||||
_HEAD_REGISTRY = {
|
||||
"dpt": DPT,
|
||||
"dualdpt": DualDPT,
|
||||
}
|
||||
|
||||
|
||||
# Backbone presets (mirror the upstream DINOv2 ViT variants).
|
||||
_BACKBONE_PRESETS = {
|
||||
"vits": dict(hidden_size=384, num_hidden_layers=12, num_attention_heads=6, use_swiglu_ffn=False),
|
||||
"vitb": dict(hidden_size=768, num_hidden_layers=12, num_attention_heads=12, use_swiglu_ffn=False),
|
||||
"vitl": dict(hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, use_swiglu_ffn=False),
|
||||
"vitg": dict(hidden_size=1536, num_hidden_layers=40, num_attention_heads=24, use_swiglu_ffn=True),
|
||||
}
|
||||
|
||||
|
||||
def _build_backbone_config(
|
||||
backbone_name: str,
|
||||
*,
|
||||
alt_start: int,
|
||||
qknorm_start: int,
|
||||
rope_start: int,
|
||||
cat_token: bool,
|
||||
) -> dict:
|
||||
if backbone_name not in _BACKBONE_PRESETS:
|
||||
raise ValueError(f"Unknown DINOv2 backbone variant: {backbone_name!r}")
|
||||
cfg = dict(_BACKBONE_PRESETS[backbone_name])
|
||||
cfg.update(dict(
|
||||
layer_norm_eps=1e-6,
|
||||
patch_size=14,
|
||||
image_size=518,
|
||||
# No mask_token in DA3 weights; omit param to avoid load warnings.
|
||||
use_mask_token=False,
|
||||
alt_start=alt_start,
|
||||
qknorm_start=qknorm_start,
|
||||
rope_start=rope_start,
|
||||
cat_token=cat_token,
|
||||
rope_freq=100.0,
|
||||
))
|
||||
return cfg
|
||||
|
||||
|
||||
class DepthAnything3Net(nn.Module):
|
||||
|
||||
PATCH_SIZE = 14
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
# --- Backbone ---
|
||||
backbone_name: str = "vitl",
|
||||
out_layers: Sequence[int] = (4, 11, 17, 23),
|
||||
alt_start: int = -1,
|
||||
qknorm_start: int = -1,
|
||||
rope_start: int = -1,
|
||||
cat_token: bool = False,
|
||||
# --- Head ---
|
||||
head_type: str = "dpt", # dpt or dualdpt
|
||||
head_dim_in: int = 1024,
|
||||
head_output_dim: int = 1, # 1 = depth only, 2 = depth+conf
|
||||
head_features: int = 256,
|
||||
head_out_channels: Sequence[int] = (256, 512, 1024, 1024),
|
||||
head_use_sky_head: bool = True, # ignored by DualDPT
|
||||
head_pos_embed: Optional[bool] = None, # default: True for DualDPT, False for DPT
|
||||
# --- Camera (multi-view) ---
|
||||
has_cam_enc: bool = False,
|
||||
has_cam_dec: bool = False,
|
||||
cam_dim_out: Optional[int] = None, # CameraEnc dim_out (defaults to embed_dim)
|
||||
cam_dec_dim_in: Optional[int] = None, # CameraDec dim_in (defaults to 2*embed_dim with cat_token)
|
||||
# ComfyUI plumbing
|
||||
device=None, dtype=None, operations=None,
|
||||
**_ignored,
|
||||
):
|
||||
super().__init__()
|
||||
head_cls = _HEAD_REGISTRY[head_type.lower()]
|
||||
self.head_type = head_type.lower()
|
||||
self.has_sky = (self.head_type == "dpt") and head_use_sky_head
|
||||
self.has_conf = head_output_dim > 1
|
||||
self.out_layers = list(out_layers)
|
||||
|
||||
backbone_cfg = _build_backbone_config(
|
||||
backbone_name,
|
||||
alt_start=alt_start,
|
||||
qknorm_start=qknorm_start,
|
||||
rope_start=rope_start,
|
||||
cat_token=cat_token,
|
||||
)
|
||||
self.backbone = Dinov2Model(backbone_cfg, dtype, device, operations)
|
||||
|
||||
head_kwargs = dict(
|
||||
dim_in=head_dim_in,
|
||||
patch_size=self.PATCH_SIZE,
|
||||
output_dim=head_output_dim,
|
||||
features=head_features,
|
||||
out_channels=tuple(head_out_channels),
|
||||
device=device, dtype=dtype, operations=operations,
|
||||
)
|
||||
if self.head_type == "dpt":
|
||||
head_kwargs.update(
|
||||
use_sky_head=head_use_sky_head,
|
||||
pos_embed=(False if head_pos_embed is None else head_pos_embed),
|
||||
)
|
||||
else: # dualdpt
|
||||
head_kwargs.update(
|
||||
pos_embed=(True if head_pos_embed is None else head_pos_embed),
|
||||
)
|
||||
self.head = head_cls(**head_kwargs)
|
||||
|
||||
# Built only if checkpoint has weights; cam_enc output dim == embed_dim.
|
||||
embed_dim = backbone_cfg["hidden_size"]
|
||||
if has_cam_enc:
|
||||
self.cam_enc = CameraEnc(
|
||||
dim_out=cam_dim_out if cam_dim_out is not None else embed_dim,
|
||||
num_heads=max(1, embed_dim // 64),
|
||||
device=device, dtype=dtype, operations=operations,
|
||||
)
|
||||
else:
|
||||
self.cam_enc = None
|
||||
if has_cam_dec:
|
||||
default_dim = embed_dim * (2 if cat_token else 1)
|
||||
self.cam_dec = CameraDec(
|
||||
dim_in=cam_dec_dim_in if cam_dec_dim_in is not None else default_dim,
|
||||
device=device, dtype=dtype, operations=operations,
|
||||
)
|
||||
else:
|
||||
self.cam_dec = None
|
||||
|
||||
self.dtype = dtype
|
||||
|
||||
def forward(
|
||||
self,
|
||||
image: torch.Tensor,
|
||||
extrinsics: Optional[torch.Tensor] = None,
|
||||
intrinsics: Optional[torch.Tensor] = None,
|
||||
*,
|
||||
use_ray_pose: bool = False,
|
||||
ref_view_strategy: str = "saddle_balanced",
|
||||
export_feat_layers: Optional[Sequence[int]] = None,
|
||||
**_unused,
|
||||
) -> Dict[str, torch.Tensor]:
|
||||
"""Run depth and optionally pose prediction."""
|
||||
if image.ndim == 4:
|
||||
image = image.unsqueeze(1) # (B, 1, 3, H, W)
|
||||
assert image.ndim == 5 and image.shape[2] == 3, \
|
||||
f"image must be (B,3,H,W) or (B,S,3,H,W); got {tuple(image.shape)}"
|
||||
|
||||
B, S, _, H, W = image.shape
|
||||
assert H % self.PATCH_SIZE == 0 and W % self.PATCH_SIZE == 0, \
|
||||
f"image H,W must be multiples of {self.PATCH_SIZE}; got {(H, W)}"
|
||||
|
||||
# Camera-token preparation (multi-view path).
|
||||
cam_token = None
|
||||
if extrinsics is not None and intrinsics is not None and self.cam_enc is not None:
|
||||
cam_token = self.cam_enc(extrinsics, intrinsics, (H, W))
|
||||
|
||||
# Toggle aux ray output on/off depending on what the caller asked for.
|
||||
if isinstance(self.head, DualDPT):
|
||||
self.head.enable_aux = bool(use_ray_pose)
|
||||
|
||||
feats, aux_feats = self.backbone.get_intermediate_layers_da3(
|
||||
image, self.out_layers, cam_token=cam_token,
|
||||
ref_view_strategy=ref_view_strategy,
|
||||
export_feat_layers=export_feat_layers,
|
||||
)
|
||||
head_out = self.head(feats, H=H, W=W, patch_start_idx=0)
|
||||
|
||||
# Pose prediction.
|
||||
out: Dict[str, torch.Tensor] = {}
|
||||
if use_ray_pose and "ray" in head_out and "ray_conf" in head_out:
|
||||
ray = head_out["ray"]
|
||||
ray_conf = head_out["ray_conf"]
|
||||
extr_c2w, focal, pp = get_extrinsic_from_camray(
|
||||
ray, ray_conf, ray.shape[-3], ray.shape[-2],
|
||||
)
|
||||
# Match the upstream output: w2c, drop the homogeneous row.
|
||||
extr_w2c = affine_inverse(extr_c2w)[:, :, :3, :]
|
||||
# Build pixel-space intrinsics from the normalised focal/pp output.
|
||||
intr = torch.eye(3, device=ray.device, dtype=ray.dtype)
|
||||
intr = intr[None, None].expand(extr_c2w.shape[0], extr_c2w.shape[1], 3, 3).clone()
|
||||
intr[:, :, 0, 0] = focal[:, :, 0] / 2 * W
|
||||
intr[:, :, 1, 1] = focal[:, :, 1] / 2 * H
|
||||
intr[:, :, 0, 2] = pp[:, :, 0] * W * 0.5
|
||||
intr[:, :, 1, 2] = pp[:, :, 1] * H * 0.5
|
||||
out["extrinsics"] = extr_w2c
|
||||
out["intrinsics"] = intr
|
||||
elif self.cam_dec is not None and S > 1:
|
||||
# Decode the cam-token of the final out_layer into a pose encoding.
|
||||
cam_feat = feats[-1][1] # (B, S, dim_in_to_cam_dec)
|
||||
pose_enc = self.cam_dec(cam_feat)
|
||||
c2w_3x4, intr = pose_encoding_to_extri_intri(pose_enc, (H, W))
|
||||
# Match the upstream output convention: w2c (world->camera), 3x4.
|
||||
c2w_4x4 = torch.cat([
|
||||
c2w_3x4,
|
||||
torch.tensor([0, 0, 0, 1], device=c2w_3x4.device, dtype=c2w_3x4.dtype)
|
||||
.view(1, 1, 1, 4).expand(B, S, 1, 4),
|
||||
], dim=-2)
|
||||
out["extrinsics"] = affine_inverse(c2w_4x4)[:, :, :3, :]
|
||||
out["intrinsics"] = intr
|
||||
|
||||
# Flatten the views axis for per-pixel outputs (depth/conf/sky) so the
|
||||
# per-image consumer keeps its (B*S, H, W) interface.
|
||||
for k, v in head_out.items():
|
||||
if k in ("ray", "ray_conf"):
|
||||
# Keep multi-view shape for downstream pose work.
|
||||
out[k] = v
|
||||
elif v.ndim >= 3 and v.shape[0] == B and v.shape[1] == S:
|
||||
out[k] = v.reshape(B * S, *v.shape[2:])
|
||||
else:
|
||||
out[k] = v
|
||||
|
||||
if export_feat_layers:
|
||||
out["aux_features"] = self._reshape_aux_features(aux_feats, H, W)
|
||||
return out
|
||||
|
||||
def _reshape_aux_features(self, aux_feats, H: int, W: int):
|
||||
"""Reshape (B, S, N, C) aux features into (B, S, h_p, w_p, C)."""
|
||||
ph, pw = H // self.PATCH_SIZE, W // self.PATCH_SIZE
|
||||
out = []
|
||||
for f in aux_feats:
|
||||
B, S, N, C = f.shape
|
||||
assert N == ph * pw, f"aux feature seq mismatch: {N} != {ph}*{pw}"
|
||||
out.append(f.reshape(B, S, ph, pw, C))
|
||||
return out
|
||||
128
comfy/ldm/depth_anything_3/preprocess.py
Normal file
128
comfy/ldm/depth_anything_3/preprocess.py
Normal file
@ -0,0 +1,128 @@
|
||||
"""Input/output preprocessing helpers for Depth Anything 3."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
|
||||
import comfy.utils
|
||||
|
||||
PATCH_SIZE = 14
|
||||
|
||||
# ImageNet normalization constants used during DA3 training.
|
||||
_IMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406])
|
||||
_IMAGENET_STD = torch.tensor([0.229, 0.224, 0.225])
|
||||
|
||||
|
||||
def _round_to_patch(x: int, patch: int = PATCH_SIZE) -> int:
|
||||
down = (x // patch) * patch
|
||||
up = down + patch
|
||||
return up if abs(up - x) <= abs(x - down) else down
|
||||
|
||||
|
||||
def compute_target_size(orig_h: int, orig_w: int, process_res: int, method: str = "upper_bound_resize") -> Tuple[int, int]:
|
||||
"""Compute (target_h, target_w) for a single image.
|
||||
upper_bound_resize: scale longest side to process_res, then round each dim to nearest multiple of 14 (default upstream method).
|
||||
lower_bound_resize: scale shortest side to process_res, then round."""
|
||||
|
||||
if method == "upper_bound_resize":
|
||||
longest = max(orig_h, orig_w)
|
||||
scale = process_res / float(longest)
|
||||
elif method == "lower_bound_resize":
|
||||
shortest = min(orig_h, orig_w)
|
||||
scale = process_res / float(shortest)
|
||||
else:
|
||||
raise ValueError(f"Unsupported process_res_method: {method}")
|
||||
|
||||
new_w = max(1, _round_to_patch(int(round(orig_w * scale))))
|
||||
new_h = max(1, _round_to_patch(int(round(orig_h * scale))))
|
||||
return new_h, new_w
|
||||
|
||||
|
||||
def preprocess_image(image: torch.Tensor, process_res: int = 504, method: str = "upper_bound_resize") -> torch.Tensor:
|
||||
assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
|
||||
B, H, W, _ = image.shape
|
||||
target_h, target_w = compute_target_size(H, W, process_res, method)
|
||||
|
||||
# (B, H, W, 3) -> (B, 3, H, W)
|
||||
x = image.movedim(-1, 1).contiguous()
|
||||
if (target_h, target_w) != (H, W):
|
||||
# Upstream uses cv2 INTER_CUBIC (upscale) / INTER_AREA (downscale).
|
||||
# Lanczos in ``common_upscale`` is anti-aliased and produces the
|
||||
# closest pixel-wise match in a sweep across {bilinear, bicubic,
|
||||
# area, lanczos, bislerp}. Used in both directions for simplicity.
|
||||
x = comfy.utils.common_upscale(x.float(), target_w, target_h, "lanczos", "disabled",)
|
||||
x = x.clamp(0.0, 1.0)
|
||||
|
||||
mean = _IMAGENET_MEAN.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
|
||||
std = _IMAGENET_STD.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
|
||||
x = (x - mean) / std
|
||||
return x
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Output post-processing (sky-aware clipping for Mono/Metric variants)
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def compute_non_sky_mask(sky_prediction: torch.Tensor, threshold: float = 0.3) -> torch.Tensor:
|
||||
"""Boolean mask: True for non-sky pixels (sky probability < threshold)."""
|
||||
return sky_prediction < threshold
|
||||
|
||||
|
||||
def apply_sky_aware_clip(depth: torch.Tensor, sky: torch.Tensor, threshold: float = 0.3, quantile: float = 0.99) -> torch.Tensor:
|
||||
"""Clips sky regions to the 99th percentile of non-sky depth. Returns a new depth tensor."""
|
||||
non_sky = compute_non_sky_mask(sky, threshold=threshold)
|
||||
if non_sky.sum() <= 10 or (~non_sky).sum() <= 10:
|
||||
return depth.clone()
|
||||
|
||||
non_sky_depth = depth[non_sky]
|
||||
if non_sky_depth.numel() > 100_000:
|
||||
idx = torch.randint(0, non_sky_depth.numel(), (100_000,), device=non_sky_depth.device)
|
||||
sampled = non_sky_depth[idx]
|
||||
else:
|
||||
sampled = non_sky_depth
|
||||
|
||||
max_depth = torch.quantile(sampled, quantile)
|
||||
out = depth.clone()
|
||||
out[~non_sky] = max_depth
|
||||
return out
|
||||
|
||||
|
||||
def normalize_depth_v2_style(depth: torch.Tensor, sky: torch.Tensor | None = None, low_quantile: float = 0.01, high_quantile: float = 0.99) -> torch.Tensor:
|
||||
"""V2-style normalization computes percentile bounds over non-sky pixels (when available), then maps depth into [0, 1] with near = white (1.0)."""
|
||||
if sky is not None:
|
||||
mask = compute_non_sky_mask(sky)
|
||||
if mask.any():
|
||||
valid = depth[mask]
|
||||
else:
|
||||
valid = depth.flatten()
|
||||
else:
|
||||
valid = depth.flatten()
|
||||
|
||||
if valid.numel() > 100_000:
|
||||
idx = torch.randint(0, valid.numel(), (100_000,), device=valid.device)
|
||||
sample = valid[idx]
|
||||
else:
|
||||
sample = valid
|
||||
|
||||
lo = torch.quantile(sample, low_quantile)
|
||||
hi = torch.quantile(sample, high_quantile)
|
||||
rng = (hi - lo).clamp(min=1e-6)
|
||||
norm = ((depth - lo) / rng).clamp(0.0, 1.0)
|
||||
# Nearer pixels are brighter (1.0)
|
||||
norm = 1.0 - norm
|
||||
if sky is not None:
|
||||
# Sky pixels become black (far / unknown)
|
||||
sky_mask = ~compute_non_sky_mask(sky)
|
||||
norm = torch.where(sky_mask, torch.zeros_like(norm), norm)
|
||||
return norm
|
||||
|
||||
|
||||
def normalize_depth_min_max(depth: torch.Tensor) -> torch.Tensor:
|
||||
"""Simple per-frame min/max normalization with near=1.0 convention."""
|
||||
lo = depth.amin(dim=(-2, -1), keepdim=True)
|
||||
hi = depth.amax(dim=(-2, -1), keepdim=True)
|
||||
rng = (hi - lo).clamp(min=1e-6)
|
||||
return 1.0 - ((depth - lo) / rng).clamp(0.0, 1.0)
|
||||
272
comfy/ldm/depth_anything_3/ray_pose.py
Normal file
272
comfy/ldm/depth_anything_3/ray_pose.py
Normal file
@ -0,0 +1,272 @@
|
||||
"""Ray-to-pose conversion for the multi-view path of Depth Anything 3."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
# qr/svd use fp32: CUDA often has no fp16/bf16 kernels for these ops.
|
||||
|
||||
|
||||
def _ql_decomposition(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Decompose A = Q @ L with Q orthogonal and L lower-triangular.
|
||||
Implemented in terms of QR by reversing the columns/rows; the standard
|
||||
trick from the upstream reference. Inputs A are (3, 3)."""
|
||||
P = torch.tensor([[0, 0, 1], [0, 1, 0], [1, 0, 0]], device=A.device, dtype=A.dtype)
|
||||
A_tilde = A @ P
|
||||
# CUDA QR is not implemented for fp16/bf16; upcast just for this call.
|
||||
Q_tilde, R_tilde = torch.linalg.qr(A_tilde.float())
|
||||
Q_tilde = Q_tilde.to(A.dtype)
|
||||
R_tilde = R_tilde.to(A.dtype)
|
||||
Q = Q_tilde @ P
|
||||
L = P @ R_tilde @ P
|
||||
d = torch.diag(L)
|
||||
sign = torch.sign(d)
|
||||
Q = Q * sign[None, :] # scale columns of Q
|
||||
L = L * sign[:, None] # scale rows of L
|
||||
return Q, L
|
||||
|
||||
|
||||
def _homogenize_points(points: torch.Tensor) -> torch.Tensor:
|
||||
return torch.cat([points, torch.ones_like(points[..., :1])], dim=-1)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Weighted-LSQ + RANSAC homography (batched)
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _find_homography_weighted_lsq(src_pts: torch.Tensor, dst_pts: torch.Tensor, confident_weight: torch.Tensor,) -> torch.Tensor:
|
||||
"""Solve a single H with weighted least-squares (DLT)."""
|
||||
N = src_pts.shape[0]
|
||||
if N < 4:
|
||||
raise ValueError("At least 4 points are required to compute a homography.")
|
||||
w = confident_weight.sqrt().unsqueeze(1) # (N, 1)
|
||||
x = src_pts[:, 0:1]
|
||||
y = src_pts[:, 1:2]
|
||||
u = dst_pts[:, 0:1]
|
||||
v = dst_pts[:, 1:2]
|
||||
zeros = torch.zeros_like(x)
|
||||
A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=1)
|
||||
A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=1)
|
||||
A = torch.cat([A1, A2], dim=0) # (2N, 9)
|
||||
# CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
|
||||
_, _, Vh = torch.linalg.svd(A.float())
|
||||
Vh = Vh.to(A.dtype)
|
||||
H = Vh[-1].reshape(3, 3)
|
||||
return H / H[-1, -1]
|
||||
|
||||
|
||||
def _find_homography_weighted_lsq_batched(src_pts_batch: torch.Tensor, dst_pts_batch: torch.Tensor, confident_weight_batch: torch.Tensor) -> torch.Tensor:
|
||||
"""Batched DLT solver. Inputs (B, K, 2) / (B, K); output (B, 3, 3)."""
|
||||
B, K, _ = src_pts_batch.shape
|
||||
w = confident_weight_batch.sqrt().unsqueeze(2)
|
||||
x = src_pts_batch[:, :, 0:1]
|
||||
y = src_pts_batch[:, :, 1:2]
|
||||
u = dst_pts_batch[:, :, 0:1]
|
||||
v = dst_pts_batch[:, :, 1:2]
|
||||
zeros = torch.zeros_like(x)
|
||||
A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=2)
|
||||
A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=2)
|
||||
A = torch.cat([A1, A2], dim=1) # (B, 2K, 9)
|
||||
# CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
|
||||
_, _, Vh = torch.linalg.svd(A.float())
|
||||
Vh = Vh.to(A.dtype)
|
||||
H = Vh[:, -1].reshape(B, 3, 3)
|
||||
return H / H[:, 2:3, 2:3]
|
||||
|
||||
|
||||
def _ransac_find_homography_weighted_batched(
|
||||
src_pts: torch.Tensor, # (B, N, 2)
|
||||
dst_pts: torch.Tensor, # (B, N, 2)
|
||||
confident_weight: torch.Tensor, # (B, N)
|
||||
n_sample: int,
|
||||
n_iter: int = 100,
|
||||
reproj_threshold: float = 3.0,
|
||||
num_sample_for_ransac: int = 8,
|
||||
max_inlier_num: int = 10000,
|
||||
rand_sample_iters_idx: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
"""Batched weighted-RANSAC homography estimator. Returns (B, 3, 3) homography matrices."""
|
||||
B, N, _ = src_pts.shape
|
||||
assert N >= 4
|
||||
device = src_pts.device
|
||||
|
||||
sorted_idx = torch.argsort(confident_weight, descending=True, dim=1)
|
||||
candidate_idx = sorted_idx[:, :n_sample] # (B, n_sample)
|
||||
|
||||
if rand_sample_iters_idx is None:
|
||||
rand_sample_iters_idx = torch.stack(
|
||||
[torch.randperm(n_sample, device=device)[:num_sample_for_ransac]
|
||||
for _ in range(n_iter)],
|
||||
dim=0,
|
||||
)
|
||||
|
||||
rand_idx = candidate_idx[:, rand_sample_iters_idx] # (B, n_iter, k)
|
||||
b_idx = (
|
||||
torch.arange(B, device=device)
|
||||
.view(B, 1, 1)
|
||||
.expand(B, n_iter, num_sample_for_ransac)
|
||||
)
|
||||
src_b = src_pts[b_idx, rand_idx]
|
||||
dst_b = dst_pts[b_idx, rand_idx]
|
||||
w_b = confident_weight[b_idx, rand_idx]
|
||||
|
||||
cB, cN = src_b.shape[:2]
|
||||
H_batch = _find_homography_weighted_lsq_batched(
|
||||
src_b.flatten(0, 1), dst_b.flatten(0, 1), w_b.flatten(0, 1),
|
||||
).unflatten(0, (cB, cN)) # (B, n_iter, 3, 3)
|
||||
|
||||
src_homo = torch.cat([src_pts, torch.ones(B, N, 1, device=device, dtype=src_pts.dtype)], dim=2)
|
||||
proj = torch.bmm(
|
||||
src_homo.unsqueeze(1).expand(B, n_iter, N, 3).reshape(-1, N, 3),
|
||||
H_batch.reshape(-1, 3, 3).transpose(1, 2),
|
||||
) # (B*n_iter, N, 3)
|
||||
proj_xy = (proj[:, :, :2] / proj[:, :, 2:3]).reshape(B, n_iter, N, 2)
|
||||
err = ((proj_xy - dst_pts.unsqueeze(1)) ** 2).sum(-1).sqrt() # (B, n_iter, N)
|
||||
inlier_mask = err < reproj_threshold
|
||||
score = (inlier_mask * confident_weight.unsqueeze(1)).sum(dim=2)
|
||||
best_idx = torch.argmax(score, dim=1)
|
||||
best_inlier_mask = inlier_mask[torch.arange(B, device=device), best_idx]
|
||||
|
||||
# Refit with the inlier set (per-batch, since the inlier counts vary).
|
||||
H_inlier_list = []
|
||||
for b in range(B):
|
||||
mask = best_inlier_mask[b]
|
||||
in_src = src_pts[b][mask]
|
||||
in_dst = dst_pts[b][mask]
|
||||
in_w = confident_weight[b][mask]
|
||||
if in_src.shape[0] < 4:
|
||||
# Fall back to identity when RANSAC fails to find enough inliers.
|
||||
H_inlier_list.append(torch.eye(3, device=device, dtype=src_pts.dtype))
|
||||
continue
|
||||
sorted_w = torch.argsort(in_w, descending=True)
|
||||
if len(sorted_w) > max_inlier_num:
|
||||
keep = max(int(len(sorted_w) * 0.95), max_inlier_num)
|
||||
sorted_w = sorted_w[:keep][torch.randperm(keep, device=device)[:max_inlier_num]]
|
||||
H_inlier_list.append(
|
||||
_find_homography_weighted_lsq(in_src[sorted_w], in_dst[sorted_w], in_w[sorted_w])
|
||||
)
|
||||
return torch.stack(H_inlier_list, dim=0)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Camera-ray utilities
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _unproject_identity(num_y: int, num_x: int, B: int, S: int, device, dtype) -> torch.Tensor:
|
||||
"""Camera-space unit rays for an identity intrinsic on a 2x2 image plane."""
|
||||
dx = 1.0 / num_x
|
||||
dy = 1.0 / num_y
|
||||
# Centered camera-space coords directly (skip the K^-1 step since it's
|
||||
# just a translation by -1 on x and y when K is identity-with-center=1).
|
||||
y = torch.linspace(-(1 - dy), (1 - dy), num_y, device=device, dtype=dtype)
|
||||
x = torch.linspace(-(1 - dx), (1 - dx), num_x, device=device, dtype=dtype)
|
||||
yy, xx = torch.meshgrid(y, x, indexing="ij")
|
||||
grid = torch.stack((xx, yy), dim=-1) # (h, w, 2)
|
||||
grid = grid.unsqueeze(0).unsqueeze(0).expand(B, S, num_y, num_x, 2)
|
||||
return torch.cat([grid, torch.ones_like(grid[..., :1])], dim=-1)
|
||||
|
||||
|
||||
def _camray_to_caminfo(
|
||||
camray: torch.Tensor, # (B, S, h, w, 6)
|
||||
confidence: Optional[torch.Tensor] = None, # (B, S, h, w)
|
||||
reproj_threshold: float = 0.2,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Convert per-pixel camera rays to per-view (R, T, focal, principal)."""
|
||||
if confidence is None:
|
||||
confidence = torch.ones_like(camray[..., 0])
|
||||
B, S, h, w, _ = camray.shape
|
||||
device = camray.device
|
||||
dtype = camray.dtype
|
||||
|
||||
rays_target = camray[..., :3] # (B, S, h, w, 3)
|
||||
rays_origin = _unproject_identity(h, w, B, S, device, dtype)
|
||||
|
||||
# Flatten (B*S, h*w, *) for the RANSAC routine.
|
||||
rays_target = rays_target.flatten(0, 1).flatten(1, 2)
|
||||
rays_origin = rays_origin.flatten(0, 1).flatten(1, 2)
|
||||
weights = confidence.flatten(0, 1).flatten(1, 2).clone()
|
||||
|
||||
# Project to 2D in homogeneous form (the upstream calls this "perspective division").
|
||||
z_thresh = 1e-4
|
||||
mask = (rays_target[:, :, 2].abs() > z_thresh) & (rays_origin[:, :, 2].abs() > z_thresh)
|
||||
weights = torch.where(mask, weights, torch.zeros_like(weights))
|
||||
src = rays_origin.clone()
|
||||
dst = rays_target.clone()
|
||||
src[..., 0] = torch.where(mask, src[..., 0] / src[..., 2], src[..., 0])
|
||||
src[..., 1] = torch.where(mask, src[..., 1] / src[..., 2], src[..., 1])
|
||||
dst[..., 0] = torch.where(mask, dst[..., 0] / dst[..., 2], dst[..., 0])
|
||||
dst[..., 1] = torch.where(mask, dst[..., 1] / dst[..., 2], dst[..., 1])
|
||||
src = src[..., :2]
|
||||
dst = dst[..., :2]
|
||||
|
||||
N = src.shape[1]
|
||||
n_iter = 100
|
||||
sample_ratio = 0.3
|
||||
num_sample_for_ransac = 8
|
||||
n_sample = max(num_sample_for_ransac, int(N * sample_ratio))
|
||||
rand_idx = torch.stack(
|
||||
[torch.randperm(n_sample, device=device)[:num_sample_for_ransac] for _ in range(n_iter)],
|
||||
dim=0,
|
||||
)
|
||||
|
||||
# Chunk along the view axis to keep peak memory predictable.
|
||||
chunk = 2
|
||||
A_list = []
|
||||
for i in range(0, src.shape[0], chunk):
|
||||
A = _ransac_find_homography_weighted_batched(
|
||||
src[i:i + chunk], dst[i:i + chunk], weights[i:i + chunk],
|
||||
n_sample=n_sample, n_iter=n_iter,
|
||||
num_sample_for_ransac=num_sample_for_ransac,
|
||||
reproj_threshold=reproj_threshold,
|
||||
rand_sample_iters_idx=rand_idx,
|
||||
max_inlier_num=8000,
|
||||
)
|
||||
# Flip sign on dets that come out < 0 (so that the QL produces a
|
||||
# right-handed rotation). ``det`` lacks fp16/bf16 CUDA kernels, so
|
||||
# do the comparison in fp32.
|
||||
flip = torch.linalg.det(A.float()) < 0
|
||||
A = torch.where(flip[:, None, None], -A, A)
|
||||
A_list.append(A)
|
||||
A = torch.cat(A_list, dim=0) # (B*S, 3, 3)
|
||||
|
||||
R_list, f_list, pp_list = [], [], []
|
||||
for i in range(A.shape[0]):
|
||||
R, L = _ql_decomposition(A[i])
|
||||
L = L / L[2][2]
|
||||
f_list.append(torch.stack((L[0][0], L[1][1])))
|
||||
pp_list.append(torch.stack((L[2][0], L[2][1])))
|
||||
R_list.append(R)
|
||||
R = torch.stack(R_list).reshape(B, S, 3, 3)
|
||||
focal = torch.stack(f_list).reshape(B, S, 2)
|
||||
pp = torch.stack(pp_list).reshape(B, S, 2)
|
||||
|
||||
# Translation: confidence-weighted average of camray direction(s).
|
||||
cf = confidence.flatten(0, 1).flatten(1, 2)
|
||||
T = (camray.flatten(0, 1).flatten(1, 2)[..., 3:] * cf.unsqueeze(-1)).sum(dim=1)
|
||||
T = T / cf.sum(dim=-1, keepdim=True)
|
||||
T = T.reshape(B, S, 3)
|
||||
|
||||
# Match upstream output convention: focal -> 1/focal, pp + 1.
|
||||
return R, T, 1.0 / focal, pp + 1.0
|
||||
|
||||
|
||||
def get_extrinsic_from_camray(
|
||||
camray: torch.Tensor, # (B, S, h, w, 6)
|
||||
conf: torch.Tensor, # (B, S, h, w, 1) or (B, S, h, w)
|
||||
patch_size_y: int,
|
||||
patch_size_x: int,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""Wrap a 4x4 extrinsic + per-view focal + principal-point output."""
|
||||
if conf.ndim == 5 and conf.shape[-1] == 1:
|
||||
conf = conf.squeeze(-1)
|
||||
R, T, focal, pp = _camray_to_caminfo(camray, confidence=conf)
|
||||
extr = torch.cat([R, T.unsqueeze(-1)], dim=-1) # (B, S, 3, 4)
|
||||
homo_row = torch.tensor([0, 0, 0, 1], dtype=R.dtype, device=R.device)
|
||||
homo_row = homo_row.view(1, 1, 1, 4).expand(R.shape[0], R.shape[1], 1, 4)
|
||||
extr = torch.cat([extr, homo_row], dim=-2) # (B, S, 4, 4)
|
||||
return extr, focal, pp
|
||||
87
comfy/ldm/depth_anything_3/reference_view_selector.py
Normal file
87
comfy/ldm/depth_anything_3/reference_view_selector.py
Normal file
@ -0,0 +1,87 @@
|
||||
"""Reference-view selection for the multi-view path of Depth Anything 3."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
RefViewStrategy = Literal["first", "middle", "saddle_balanced", "saddle_sim_range"]
|
||||
|
||||
|
||||
# Per the upstream constants module: ``THRESH_FOR_REF_SELECTION = 3``.
|
||||
# Reference selection only runs when there are at least this many views.
|
||||
THRESH_FOR_REF_SELECTION: int = 3
|
||||
|
||||
|
||||
def select_reference_view(x: torch.Tensor, strategy: RefViewStrategy = "saddle_balanced") -> torch.Tensor:
|
||||
"""Pick a reference view index per batch element."""
|
||||
B, S, _, _ = x.shape
|
||||
if S <= 1:
|
||||
return torch.zeros(B, dtype=torch.long, device=x.device)
|
||||
if strategy == "first":
|
||||
return torch.zeros(B, dtype=torch.long, device=x.device)
|
||||
if strategy == "middle":
|
||||
return torch.full((B,), S // 2, dtype=torch.long, device=x.device)
|
||||
|
||||
# Feature-based strategies: normalised cls/cam token per view.
|
||||
img_class_feat = x[:, :, 0] / x[:, :, 0].norm(dim=-1, keepdim=True) # (B,S,C)
|
||||
|
||||
if strategy == "saddle_balanced":
|
||||
sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2)) # (B,S,S)
|
||||
sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0)
|
||||
sim_score = sim_no_diag.sum(dim=-1) / (S - 1) # (B,S)
|
||||
feat_norm = x[:, :, 0].norm(dim=-1) # (B,S)
|
||||
feat_var = img_class_feat.var(dim=-1) # (B,S)
|
||||
|
||||
def _normalize(metric):
|
||||
mn = metric.min(dim=1, keepdim=True).values
|
||||
mx = metric.max(dim=1, keepdim=True).values
|
||||
return (metric - mn) / (mx - mn + 1e-8)
|
||||
|
||||
sim_n, norm_n, var_n = _normalize(sim_score), _normalize(feat_norm), _normalize(feat_var)
|
||||
balance = (sim_n - 0.5).abs() + (norm_n - 0.5).abs() + (var_n - 0.5).abs()
|
||||
return balance.argmin(dim=1)
|
||||
|
||||
if strategy == "saddle_sim_range":
|
||||
sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2))
|
||||
sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0)
|
||||
sim_max = sim_no_diag.max(dim=-1).values
|
||||
sim_min = sim_no_diag.min(dim=-1).values
|
||||
return (sim_max - sim_min).argmax(dim=1)
|
||||
|
||||
raise ValueError(
|
||||
f"Unknown reference view selection strategy: {strategy!r}. "
|
||||
f"Must be one of: 'first', 'middle', 'saddle_balanced', 'saddle_sim_range'"
|
||||
)
|
||||
|
||||
|
||||
def reorder_by_reference(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor:
|
||||
"""Reorder x so the reference view is at position 0 in axis S."""
|
||||
B, S = x.shape[0], x.shape[1]
|
||||
if S <= 1:
|
||||
return x
|
||||
positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1)
|
||||
b_idx_exp = b_idx.unsqueeze(1)
|
||||
reorder = torch.where(
|
||||
(positions > 0) & (positions <= b_idx_exp),
|
||||
positions - 1,
|
||||
positions,
|
||||
)
|
||||
reorder[:, 0] = b_idx
|
||||
batch = torch.arange(B, device=x.device).unsqueeze(1)
|
||||
return x[batch, reorder]
|
||||
|
||||
|
||||
def restore_original_order(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor:
|
||||
"""Inverse of reorder_by_reference."""
|
||||
B, S = x.shape[0], x.shape[1]
|
||||
if S <= 1:
|
||||
return x
|
||||
target_positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1)
|
||||
b_idx_exp = b_idx.unsqueeze(1)
|
||||
restore = torch.where(target_positions < b_idx_exp, target_positions + 1, target_positions)
|
||||
restore = torch.scatter(restore, dim=1, index=b_idx_exp, src=torch.zeros_like(b_idx_exp))
|
||||
batch = torch.arange(B, device=x.device).unsqueeze(1)
|
||||
return x[batch, restore]
|
||||
160
comfy/ldm/depth_anything_3/transform.py
Normal file
160
comfy/ldm/depth_anything_3/transform.py
Normal file
@ -0,0 +1,160 @@
|
||||
"""Geometry / camera transform helpers for Depth Anything 3."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Affine 4x4 helpers
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def as_homogeneous(ext: torch.Tensor) -> torch.Tensor:
|
||||
"""Promote (...,3,4) extrinsics to (...,4,4) homogeneous form. No-op when the input is already ``(...,4,4)``."""
|
||||
if ext.shape[-2:] == (4, 4):
|
||||
return ext
|
||||
if ext.shape[-2:] == (3, 4):
|
||||
ones = torch.zeros_like(ext[..., :1, :4])
|
||||
ones[..., 0, 3] = 1.0
|
||||
return torch.cat([ext, ones], dim=-2)
|
||||
raise ValueError(f"Invalid affine shape: {ext.shape}")
|
||||
|
||||
|
||||
def affine_inverse(A: torch.Tensor) -> torch.Tensor:
|
||||
"""Inverse of an affine matrix ``[R|T; 0 0 0 1]``."""
|
||||
R = A[..., :3, :3]
|
||||
T = A[..., :3, 3:]
|
||||
P = A[..., 3:, :]
|
||||
return torch.cat([torch.cat([R.mT, -R.mT @ T], dim=-1), P], dim=-2)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Quaternion <-> rotation matrix (xyzw / scalar-last)
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
|
||||
"""sqrt(max(0, x)) with a zero subgradient where x == 0."""
|
||||
ret = torch.zeros_like(x)
|
||||
positive_mask = x > 0
|
||||
if torch.is_grad_enabled():
|
||||
ret[positive_mask] = torch.sqrt(x[positive_mask])
|
||||
else:
|
||||
ret = torch.where(positive_mask, torch.sqrt(x), ret)
|
||||
return ret
|
||||
|
||||
|
||||
def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
|
||||
"""Force the real part of a unit quaternion (xyzw) to be non-negative."""
|
||||
return torch.where(quaternions[..., 3:4] < 0, -quaternions, quaternions)
|
||||
|
||||
|
||||
def quat_to_mat(quaternions: torch.Tensor) -> torch.Tensor:
|
||||
"""Convert quaternions (xyzw) to (...,3,3) rotation matrices."""
|
||||
i, j, k, r = torch.unbind(quaternions, -1)
|
||||
two_s = 2.0 / (quaternions * quaternions).sum(-1)
|
||||
o = torch.stack(
|
||||
(
|
||||
1 - two_s * (j * j + k * k),
|
||||
two_s * (i * j - k * r),
|
||||
two_s * (i * k + j * r),
|
||||
two_s * (i * j + k * r),
|
||||
1 - two_s * (i * i + k * k),
|
||||
two_s * (j * k - i * r),
|
||||
two_s * (i * k - j * r),
|
||||
two_s * (j * k + i * r),
|
||||
1 - two_s * (i * i + j * j),
|
||||
),
|
||||
-1,
|
||||
)
|
||||
return o.reshape(quaternions.shape[:-1] + (3, 3))
|
||||
|
||||
|
||||
def mat_to_quat(matrix: torch.Tensor) -> torch.Tensor:
|
||||
"""Convert (...,3,3) rotation matrices to quaternions (xyzw)."""
|
||||
if matrix.size(-1) != 3 or matrix.size(-2) != 3:
|
||||
raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
|
||||
|
||||
batch_dim = matrix.shape[:-2]
|
||||
m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
|
||||
matrix.reshape(batch_dim + (9,)), dim=-1
|
||||
)
|
||||
|
||||
q_abs = _sqrt_positive_part(
|
||||
torch.stack(
|
||||
[
|
||||
1.0 + m00 + m11 + m22,
|
||||
1.0 + m00 - m11 - m22,
|
||||
1.0 - m00 + m11 - m22,
|
||||
1.0 - m00 - m11 + m22,
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
)
|
||||
|
||||
quat_by_rijk = torch.stack(
|
||||
[
|
||||
torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
|
||||
torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
|
||||
torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
|
||||
torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
|
||||
],
|
||||
dim=-2,
|
||||
)
|
||||
|
||||
flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
|
||||
quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
|
||||
|
||||
out = quat_candidates[F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :].reshape(
|
||||
batch_dim + (4,)
|
||||
)
|
||||
# Reorder rijk -> xyzw (i.e. ijkr).
|
||||
out = out[..., [1, 2, 3, 0]]
|
||||
return standardize_quaternion(out)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Pose-encoding <-> extrinsics + intrinsics
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def extri_intri_to_pose_encoding(extrinsics: torch.Tensor, intrinsics: torch.Tensor, image_size_hw: Tuple[int, int]) -> torch.Tensor:
|
||||
"""Pack (extr, intr, image_size) into the 9-D pose-encoding vector.
|
||||
extrinsics: camera-to-world (c2w) (B,S,4,4) matrices,
|
||||
intrinsics: pixel-space (B,S,3,3) matrices,
|
||||
image_size_hw: is a (H, W) pair.
|
||||
"""
|
||||
R = extrinsics[..., :3, :3]
|
||||
T = extrinsics[..., :3, 3]
|
||||
quat = mat_to_quat(R)
|
||||
H, W = image_size_hw
|
||||
fov_h = 2 * torch.atan((H / 2) / intrinsics[..., 1, 1])
|
||||
fov_w = 2 * torch.atan((W / 2) / intrinsics[..., 0, 0])
|
||||
return torch.cat([T, quat, fov_h[..., None], fov_w[..., None]], dim=-1).float()
|
||||
|
||||
|
||||
def pose_encoding_to_extri_intri(pose_encoding: torch.Tensor, image_size_hw: Tuple[int, int]) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Inverse of extri_intri_to_pose_encoding."""
|
||||
T = pose_encoding[..., :3]
|
||||
quat = pose_encoding[..., 3:7]
|
||||
fov_h = pose_encoding[..., 7]
|
||||
fov_w = pose_encoding[..., 8]
|
||||
# Normalize to unit quaternion. CameraDec outputs raw values; a near-zero
|
||||
# quaternion causes two_s = 2/norm² → inf in quat_to_mat → NaN extrinsics.
|
||||
quat = quat / quat.norm(dim=-1, keepdim=True).clamp(min=1e-6)
|
||||
R = quat_to_mat(quat)
|
||||
extrinsics = torch.cat([R, T[..., None]], dim=-1)
|
||||
H, W = image_size_hw
|
||||
fy = (H / 2.0) / torch.clamp(torch.tan(fov_h / 2.0), 1e-6)
|
||||
fx = (W / 2.0) / torch.clamp(torch.tan(fov_w / 2.0), 1e-6)
|
||||
intrinsics = torch.zeros(pose_encoding.shape[:2] + (3, 3), device=pose_encoding.device, dtype=pose_encoding.dtype)
|
||||
intrinsics[..., 0, 0] = fx
|
||||
intrinsics[..., 1, 1] = fy
|
||||
intrinsics[..., 0, 2] = W / 2
|
||||
intrinsics[..., 1, 2] = H / 2
|
||||
intrinsics[..., 2, 2] = 1.0
|
||||
return extrinsics, intrinsics
|
||||
@ -65,6 +65,7 @@ import comfy.ldm.ernie.model
|
||||
import comfy.ldm.sam3.detector
|
||||
import comfy.ldm.hidream_o1.model
|
||||
from comfy.ldm.hidream_o1.conditioning import build_extra_conds
|
||||
import comfy.ldm.depth_anything_3.model
|
||||
|
||||
import comfy.model_management
|
||||
import comfy.patcher_extension
|
||||
@ -2319,6 +2320,12 @@ class RT_DETR_v4(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.rt_detr.rtdetr_v4.RTv4)
|
||||
|
||||
|
||||
class DepthAnything3(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device,
|
||||
unet_model=comfy.ldm.depth_anything_3.model.DepthAnything3Net)
|
||||
|
||||
class ErnieImage(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ernie.model.ErnieImageModel)
|
||||
|
||||
@ -862,6 +862,95 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
|
||||
return dit_config
|
||||
|
||||
# Depth Anything 3 (repackaged to ComfyUI's native Dinov2Model layout via scripts/convert_da3.py)
|
||||
if '{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix) in state_dict_keys:
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "DepthAnything3"
|
||||
|
||||
patch_w = state_dict['{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix)]
|
||||
embed_dim = patch_w.shape[0]
|
||||
depth = count_blocks(state_dict_keys, '{}backbone.encoder.layer.'.format(key_prefix) + '{}.')
|
||||
|
||||
# Backbone preset is determined by embed_dim (matches vits/vitb/vitl/vitg).
|
||||
backbone_name = {384: "vits", 768: "vitb", 1024: "vitl", 1536: "vitg"}.get(embed_dim)
|
||||
if backbone_name is None:
|
||||
return None
|
||||
dit_config["backbone_name"] = backbone_name
|
||||
|
||||
# Detect DA3 extensions on top of vanilla DINOv2.
|
||||
has_camera_token = '{}backbone.embeddings.camera_token'.format(key_prefix) in state_dict_keys
|
||||
# qk-norm shows up as `attention.q_norm.weight` on enabled blocks.
|
||||
qknorm_indices = [
|
||||
i for i in range(depth)
|
||||
if '{}backbone.encoder.layer.{}.attention.q_norm.weight'.format(key_prefix, i) in state_dict_keys
|
||||
]
|
||||
qknorm_start = qknorm_indices[0] if qknorm_indices else -1
|
||||
|
||||
# The DA3 main-series configs always set alt_start == qknorm_start == rope_start.
|
||||
# cat_token=True is implied by the presence of camera_token.
|
||||
if has_camera_token:
|
||||
dit_config["alt_start"] = qknorm_start
|
||||
dit_config["rope_start"] = qknorm_start
|
||||
dit_config["qknorm_start"] = qknorm_start
|
||||
dit_config["cat_token"] = True
|
||||
else:
|
||||
dit_config["alt_start"] = -1
|
||||
dit_config["rope_start"] = -1
|
||||
dit_config["qknorm_start"] = -1
|
||||
dit_config["cat_token"] = False
|
||||
|
||||
# Detect head type and config.
|
||||
has_aux = '{}head.scratch.refinenet1_aux.out_conv.weight'.format(key_prefix) in state_dict_keys
|
||||
dit_config["head_dim_in"] = state_dict['{}head.projects.0.weight'.format(key_prefix)].shape[1]
|
||||
dit_config["head_features"] = state_dict['{}head.scratch.refinenet1.out_conv.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["head_out_channels"] = [
|
||||
state_dict['{}head.projects.{}.weight'.format(key_prefix, i)].shape[0]
|
||||
for i in range(4)
|
||||
]
|
||||
if has_aux:
|
||||
# DualDPT: dim_in = 2 * embed_dim (because cat_token doubles token width).
|
||||
dit_config["head_type"] = "dualdpt"
|
||||
dit_config["head_output_dim"] = 2
|
||||
dit_config["head_use_sky_head"] = False
|
||||
else:
|
||||
dit_config["head_type"] = "dpt"
|
||||
dit_config["head_output_dim"] = state_dict[
|
||||
'{}head.scratch.output_conv2.2.weight'.format(key_prefix)
|
||||
].shape[0]
|
||||
dit_config["head_use_sky_head"] = (
|
||||
'{}head.scratch.sky_output_conv2.0.weight'.format(key_prefix) in state_dict_keys
|
||||
)
|
||||
|
||||
# out_layers: hard-coded per upstream YAML config (depth-aware default).
|
||||
if depth >= 24:
|
||||
# vitl: depths used vary between DA3-Large (DualDPT) and Mono/Metric (DPT).
|
||||
if has_aux:
|
||||
dit_config["out_layers"] = [11, 15, 19, 23]
|
||||
else:
|
||||
dit_config["out_layers"] = [4, 11, 17, 23]
|
||||
else:
|
||||
# vits/vitb: 12 blocks
|
||||
dit_config["out_layers"] = [5, 7, 9, 11]
|
||||
|
||||
# Camera encoder/decoder presence (multi-view + pose path).
|
||||
has_cam_enc = '{}cam_enc.token_norm.weight'.format(key_prefix) in state_dict_keys
|
||||
has_cam_dec = '{}cam_dec.fc_t.weight'.format(key_prefix) in state_dict_keys
|
||||
dit_config["has_cam_enc"] = has_cam_enc
|
||||
dit_config["has_cam_dec"] = has_cam_dec
|
||||
if has_cam_enc:
|
||||
cam_enc_w = state_dict.get(
|
||||
'{}cam_enc.pose_branch.fc2.weight'.format(key_prefix)
|
||||
)
|
||||
if cam_enc_w is not None:
|
||||
dit_config["cam_dim_out"] = cam_enc_w.shape[0]
|
||||
if has_cam_dec:
|
||||
cam_dec_w = state_dict.get(
|
||||
'{}cam_dec.fc_t.weight'.format(key_prefix)
|
||||
)
|
||||
if cam_dec_w is not None:
|
||||
dit_config["cam_dec_dim_in"] = cam_dec_w.shape[1]
|
||||
return dit_config
|
||||
|
||||
if '{}layers.0.mlp.linear_fc2.weight'.format(key_prefix) in state_dict_keys: # Ernie Image
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "ernie"
|
||||
|
||||
@ -2056,6 +2056,23 @@ class RT_DETR_v4(supported_models_base.BASE):
|
||||
return None
|
||||
|
||||
|
||||
class DepthAnything3(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "DepthAnything3",
|
||||
}
|
||||
|
||||
# Mono path: no num_heads / num_head_channels needed.
|
||||
unet_extra_config = {}
|
||||
|
||||
supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
return model_base.DepthAnything3(self, device=device)
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
return None
|
||||
|
||||
|
||||
class ErnieImage(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "ernie",
|
||||
@ -2298,4 +2315,5 @@ models = [
|
||||
CogVideoX_I2V,
|
||||
CogVideoX_T2V,
|
||||
SVD_img2vid,
|
||||
DepthAnything3,
|
||||
]
|
||||
|
||||
681
comfy_extras/nodes_depth_anything_3.py
Normal file
681
comfy_extras/nodes_depth_anything_3.py
Normal file
@ -0,0 +1,681 @@
|
||||
"""ComfyUI nodes for Depth Anything 3.
|
||||
Model capability matrix:
|
||||
|
||||
Variant head_type has_sky has_conf cam_dec
|
||||
DA3-Small dualdpt False True yes
|
||||
DA3-Base dualdpt False True yes
|
||||
DA3-Mono-Large dpt True False no
|
||||
DA3-Metric-Large dpt True False no (raw output is metres)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing_extensions import override
|
||||
|
||||
import torch
|
||||
|
||||
import comfy.model_management as mm
|
||||
import comfy.sd
|
||||
import folder_paths
|
||||
from comfy.ldm.colormap import turbo as _turbo
|
||||
from comfy.ldm.depth_anything_3 import preprocess as da3_preprocess
|
||||
from comfy_api.latest import ComfyExtension, Types, io
|
||||
from comfy.ldm.moge.geometry import triangulate_grid_mesh
|
||||
|
||||
DA3ModelType = io.Custom("DA3_MODEL")
|
||||
DA3Geometry = io.Custom("DA3_GEOMETRY")
|
||||
DA3PointCloud = io.Custom("DA3_POINT_CLOUD")
|
||||
|
||||
# DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them):
|
||||
#
|
||||
# Per-frame tensors - B = batch size in mono mode; B = S (number of views) in multi-view mode.
|
||||
# "depth": torch.Tensor (B, H, W) -- raw model depth (always present; matches MoGe convention)
|
||||
# "image": torch.Tensor (B, H, W, 3) -- source image in [0, 1], CPU (always present)
|
||||
# "mode": str -- "mono" or "multiview" (always present)
|
||||
# "sky": torch.Tensor (B, H, W) -- sky probability in [0, 1] (Mono/Metric variants only)
|
||||
# "confidence": torch.Tensor (B, H, W) -- raw model confidence output (Small/Base variants only)
|
||||
#
|
||||
# Multi-view only - S = number of views; the leading 1 is the scene dimension from the model.
|
||||
# "extrinsics": torch.Tensor (1, S, 3, 4) -- world-to-camera [R|t] matrices
|
||||
# "intrinsics": torch.Tensor (1, S, 3, 3) -- pixel-space intrinsics
|
||||
#
|
||||
# DA3_POINT_CLOUD is a dict:
|
||||
# "points": torch.Tensor (N, 3) -- 3-D coords in glTF convention (Y-up, Z-back)
|
||||
# "colors": torch.Tensor (N, 3) -- RGB in [0, 1], or None
|
||||
# "confidence": torch.Tensor (N,) -- raw confidence per point, or None
|
||||
|
||||
|
||||
def _da3_unproject(depth: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
|
||||
"""Pixel-space K⁻¹ unprojection: (H,W) depth → (H,W,3) point map in OpenCV space."""
|
||||
H, W = depth.shape
|
||||
u = torch.arange(W, dtype=torch.float32, device=depth.device)
|
||||
v = torch.arange(H, dtype=torch.float32, device=depth.device)
|
||||
u, v = torch.meshgrid(u, v, indexing='xy') # both (H, W)
|
||||
pix = torch.stack([u, v, torch.ones_like(u)], dim=-1) # (H, W, 3)
|
||||
rays = torch.einsum('ij,hwj->hwi', torch.linalg.inv(K.to(depth.device)), pix)
|
||||
return rays * depth.unsqueeze(-1) # (H, W, 3)
|
||||
|
||||
|
||||
def _da3_default_K(H: int, W: int) -> torch.Tensor:
|
||||
"""Fallback ~60° FOV pinhole K for mono-mode DA3 (no intrinsics in geometry)."""
|
||||
fx = fy = float(W) * 0.7
|
||||
return torch.tensor([[fx, 0.0, (W - 1) / 2.0],
|
||||
[0.0, fy, (H - 1) / 2.0],
|
||||
[0.0, 0.0, 1.0]], dtype=torch.float32)
|
||||
|
||||
|
||||
def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor:
|
||||
"""Return pixel-space K for batch element b, falling back to a default estimate."""
|
||||
if "intrinsics" in geometry:
|
||||
# shape (1, S, 3, 3) - leading scene dimension from the multiview head
|
||||
return geometry["intrinsics"][0, b].float()
|
||||
logging.getLogger("comfy").warning(
|
||||
"DA3_GEOMETRY has no intrinsics (mono-mode model). "
|
||||
"Using a ~60° FOV estimate; 3-D reconstruction may be inaccurate."
|
||||
)
|
||||
return _da3_default_K(H, W)
|
||||
|
||||
|
||||
def _da3_get_extrinsic(geometry: dict, b: int) -> torch.Tensor | None:
|
||||
"""Return the world-to-camera extrinsic for batch element b, or None in mono mode.
|
||||
|
||||
The model outputs (1, S, 3, 4) [R|t] matrices; the fallback identity is (4, 4).
|
||||
_da3_apply_extrinsic handles both shapes via [:3, :3] / [:3, 3] slicing.
|
||||
"""
|
||||
if "extrinsics" not in geometry:
|
||||
return None
|
||||
return geometry["extrinsics"][0, b].float()
|
||||
|
||||
|
||||
def _da3_apply_extrinsic(points_cam: torch.Tensor, E: torch.Tensor) -> torch.Tensor:
|
||||
"""Transform (H,W,3) OpenCV camera-space points to world space."""
|
||||
E = E.to(points_cam.device).float()
|
||||
if not torch.isfinite(E).all():
|
||||
logging.getLogger("comfy").warning(
|
||||
"DA3 extrinsic matrix contains non-finite values (pose estimation may have failed). "
|
||||
"Falling back to camera-space coordinates."
|
||||
)
|
||||
return points_cam
|
||||
H, W, _ = points_cam.shape
|
||||
R = E[:3, :3] # (3, 3) rotation
|
||||
t = E[:3, 3] # (3,) translation
|
||||
R_inv = R.T # rotation inverse = transpose for orthogonal R
|
||||
t_inv = -(R_inv @ t) # (3,)
|
||||
pts = points_cam.reshape(-1, 3) # (N, 3)
|
||||
pts_world = pts @ R_inv.T + t_inv # (N, 3)
|
||||
return pts_world.reshape(H, W, 3)
|
||||
|
||||
|
||||
def _normalize_confidence(conf: torch.Tensor) -> torch.Tensor:
|
||||
"""Map raw confidence to [0, 1] per image."""
|
||||
B = conf.shape[0]
|
||||
out = []
|
||||
for i in range(B):
|
||||
c = conf[i]
|
||||
c_min, c_max = c.min(), c.max()
|
||||
out.append((c - c_min) / (c_max - c_min) if c_max > c_min else torch.ones_like(c))
|
||||
return torch.stack(out, dim=0)
|
||||
|
||||
|
||||
def _da3_build_mask(geometry: dict, b: int, H: int, W: int, confidence_threshold: float, use_sky_mask: bool) -> torch.Tensor:
|
||||
"""Build (H,W) bool keep-mask from sky probability and confidence."""
|
||||
mask = torch.ones(H, W, dtype=torch.bool)
|
||||
if use_sky_mask and "sky" in geometry:
|
||||
mask = mask & (geometry["sky"][b] < 0.5)
|
||||
if "confidence" in geometry and confidence_threshold > 0.0:
|
||||
conf_norm = _normalize_confidence(geometry["confidence"][b:b + 1])[0]
|
||||
mask = mask & (conf_norm >= confidence_threshold)
|
||||
return mask
|
||||
|
||||
|
||||
class LoadDA3Model(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LoadDA3Model",
|
||||
display_name="Load Depth Anything 3",
|
||||
category="model/loaders",
|
||||
inputs=[
|
||||
io.Combo.Input(
|
||||
"model_name",
|
||||
options=folder_paths.get_filename_list("geometry_estimation"),
|
||||
),
|
||||
io.Combo.Input(
|
||||
"weight_dtype",
|
||||
options=["default", "fp16", "bf16", "fp32"],
|
||||
default="default",
|
||||
),
|
||||
],
|
||||
outputs=[DA3ModelType.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model_name, weight_dtype) -> io.NodeOutput:
|
||||
model_options = {}
|
||||
if weight_dtype == "fp16":
|
||||
model_options["dtype"] = torch.float16
|
||||
elif weight_dtype == "bf16":
|
||||
model_options["dtype"] = torch.bfloat16
|
||||
elif weight_dtype == "fp32":
|
||||
model_options["dtype"] = torch.float32
|
||||
|
||||
path = folder_paths.get_full_path_or_raise("geometry_estimation", model_name)
|
||||
model = comfy.sd.load_diffusion_model(path, model_options=model_options)
|
||||
return io.NodeOutput(model)
|
||||
|
||||
|
||||
def _run_da3(model_patcher, image: torch.Tensor, process_res: int, method: str = "upper_bound_resize"):
|
||||
"""Run DA3 on (B,H,W,3), returns depth/conf/sky at original resolution (or None)."""
|
||||
assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
|
||||
|
||||
B, H, W, _ = image.shape
|
||||
mm.load_model_gpu(model_patcher)
|
||||
diffusion = model_patcher.model.diffusion_model
|
||||
device = mm.get_torch_device()
|
||||
dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
|
||||
|
||||
depths, confs, skies = [], [], []
|
||||
for i in range(B):
|
||||
single = image[i:i + 1].to(device)
|
||||
x = da3_preprocess.preprocess_image(single, process_res=process_res, method=method)
|
||||
x = x.to(dtype=dtype)
|
||||
with torch.no_grad():
|
||||
out = diffusion(x)
|
||||
|
||||
depth_lr = out["depth"]
|
||||
depth_full = torch.nn.functional.interpolate(
|
||||
depth_lr.unsqueeze(1).float(), size=(H, W),
|
||||
mode="bilinear", align_corners=False,
|
||||
).squeeze(1).cpu()
|
||||
depths.append(depth_full)
|
||||
|
||||
if "depth_conf" in out:
|
||||
conf_full = torch.nn.functional.interpolate(
|
||||
out["depth_conf"].unsqueeze(1).float(), size=(H, W),
|
||||
mode="bilinear", align_corners=False,
|
||||
).squeeze(1).cpu()
|
||||
confs.append(conf_full)
|
||||
if "sky" in out:
|
||||
sky_full = torch.nn.functional.interpolate(
|
||||
out["sky"].unsqueeze(1).float(), size=(H, W),
|
||||
mode="bilinear", align_corners=False,
|
||||
).squeeze(1).cpu()
|
||||
skies.append(sky_full)
|
||||
|
||||
depth = torch.cat(depths, dim=0)
|
||||
confidence = torch.cat(confs, dim=0) if confs else None
|
||||
sky = torch.cat(skies, dim=0) if skies else None
|
||||
return depth, confidence, sky
|
||||
|
||||
|
||||
class DA3Inference(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="DA3Inference",
|
||||
search_aliases=["depth", "geometry", "da3", "depth anything", "monocular", "pointmap", "sky", "3d", "metric depth", "disparity"],
|
||||
display_name="Run Depth Anything 3",
|
||||
category="image/geometry estimation",
|
||||
description="Run Depth Anything 3 on an image. In multi-view mode each image is treated as a separate view of the same scene.",
|
||||
inputs=[
|
||||
DA3ModelType.Input("da3_model"),
|
||||
io.Image.Input("image"),
|
||||
io.Int.Input("resolution", default=504, min=140, max=2520, step=14,
|
||||
tooltip="Resolution the model runs at (longest side, multiple of 14).\n"
|
||||
"Lower = faster / less VRAM.\n"
|
||||
"Higher = more detail.\n"
|
||||
"Output is upsampled back to the original size."),
|
||||
io.Combo.Input("resize_method", options=["upper_bound_resize", "lower_bound_resize"], default="upper_bound_resize",
|
||||
tooltip="upper_bound_resize: scale so the longest side = resolution (caps memory, default).\n"
|
||||
"lower_bound_resize: scale so the shortest side = resolution (preserves more detail on tall/wide images, uses more memory)."),
|
||||
io.DynamicCombo.Input("mode", tooltip="mono: single view image (works with any model variant).\n"
|
||||
"multiview: all images processed together for geometric consistency + camera pose (for Small/Base models only).",
|
||||
options=[
|
||||
io.DynamicCombo.Option("mono", []),
|
||||
io.DynamicCombo.Option("multiview", [
|
||||
io.Combo.Input("ref_view_strategy", options=["saddle_balanced", "saddle_sim_range", "first", "middle"], default="saddle_balanced",
|
||||
tooltip="Which view acts as the geometric anchor.\n"
|
||||
"- saddle_balanced: the view most 'average' across all others (best general choice).\n"
|
||||
"- saddle_sim_range: the view most visually distinct from the others.\n"
|
||||
"- first / middle: fixed positional picks."),
|
||||
io.Combo.Input("pose_method", options=["cam_dec", "ray_pose"], default="cam_dec",
|
||||
tooltip="How the camera field-of-view is estimated (for Small/Base models only).\n"
|
||||
"- cam_dec: learned from image features.\n"
|
||||
"- ray_pose: derived geometrically from the model's 3D ray output.\n"
|
||||
"Affects perspective correctness of the 3D output. Try both if results look distorted."),
|
||||
]),
|
||||
]),
|
||||
],
|
||||
outputs=[
|
||||
DA3Geometry.Output("da3_geometry", tooltip="Dictionary of non-normalized tensors.\n"
|
||||
"Always has the keys: depth, image, mode.\n"
|
||||
"Optional keys: sky (for Mono/Metric), confidence (for Small/Base), extrinsics + intrinsics (for multi-view)."),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, da3_model, image, resolution, resize_method, mode) -> io.NodeOutput:
|
||||
mode_val = mode["mode"] # "mono" or "multiview"
|
||||
|
||||
if mode_val == "mono":
|
||||
return cls._execute_mono(da3_model, image, resolution, resize_method)
|
||||
|
||||
# Capability checks for multi-view mode.
|
||||
diffusion = da3_model.model.diffusion_model
|
||||
pose_method = mode["pose_method"]
|
||||
ref_view_strategy = mode["ref_view_strategy"]
|
||||
|
||||
has_cam_dec = diffusion.cam_dec is not None
|
||||
has_dualdpt = diffusion.head_type == "dualdpt"
|
||||
|
||||
if not has_cam_dec and not has_dualdpt:
|
||||
raise ValueError(
|
||||
"multi-view mode requires Small or Base model. The loaded model "
|
||||
f"(head_type='{diffusion.head_type}') does not support cross-view "
|
||||
"attention or camera pose estimation. Switch mode to 'mono', or "
|
||||
"load Small or Base model for mult-view."
|
||||
)
|
||||
|
||||
if pose_method == "cam_dec" and not has_cam_dec:
|
||||
raise ValueError(
|
||||
"pose_method='cam_dec' requires a camera decoder, but the loaded "
|
||||
f"model (head_type='{diffusion.head_type}') does not have one. "
|
||||
"Use pose_method='ray_pose' instead."
|
||||
)
|
||||
if pose_method == "ray_pose" and not has_dualdpt:
|
||||
raise ValueError(
|
||||
"pose_method='ray_pose' requires a DualDPT head, but the loaded "
|
||||
f"model has a '{diffusion.head_type}' head. "
|
||||
"Use pose_method='cam_dec' instead."
|
||||
)
|
||||
|
||||
return cls._execute_multiview(
|
||||
da3_model, image, resolution, resize_method,
|
||||
ref_view_strategy, pose_method,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _execute_mono(cls, model, image, resolution, resize_method) -> io.NodeOutput:
|
||||
depth, confidence, sky = _run_da3(model, image, resolution, method=resize_method)
|
||||
|
||||
geometry: dict = {
|
||||
"depth": depth.contiguous(),
|
||||
"image": image[..., :3].cpu(),
|
||||
"mode": "mono",
|
||||
}
|
||||
if sky is not None:
|
||||
geometry["sky"] = sky.contiguous()
|
||||
if confidence is not None:
|
||||
geometry["confidence"] = confidence.contiguous()
|
||||
return io.NodeOutput(geometry)
|
||||
|
||||
@classmethod
|
||||
def _execute_multiview(cls, model, image, resolution, resize_method, ref_view_strategy, pose_method) -> io.NodeOutput:
|
||||
assert image.ndim == 4 and image.shape[-1] == 3, \
|
||||
f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
|
||||
S, H, W, _ = image.shape
|
||||
|
||||
mm.load_model_gpu(model)
|
||||
diffusion = model.model.diffusion_model
|
||||
device = mm.get_torch_device()
|
||||
dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
|
||||
|
||||
# All views in a single forward pass: (1, S, 3, H', W').
|
||||
x = image.to(device)
|
||||
x = da3_preprocess.preprocess_image(x, process_res=resolution, method=resize_method)
|
||||
x = x.to(dtype=dtype).unsqueeze(0)
|
||||
|
||||
use_ray_pose = (pose_method == "ray_pose")
|
||||
with torch.no_grad():
|
||||
out = diffusion(x, use_ray_pose=use_ray_pose, ref_view_strategy=ref_view_strategy)
|
||||
|
||||
depth = torch.nn.functional.interpolate(
|
||||
out["depth"].float().unsqueeze(1), size=(H, W),
|
||||
mode="bilinear", align_corners=False,
|
||||
).squeeze(1).cpu()
|
||||
|
||||
sky = None
|
||||
if "sky" in out:
|
||||
sky = torch.nn.functional.interpolate(
|
||||
out["sky"].unsqueeze(1).float(), size=(H, W),
|
||||
mode="bilinear", align_corners=False,
|
||||
).squeeze(1).cpu()
|
||||
|
||||
if "extrinsics" in out and "intrinsics" in out:
|
||||
extrinsics = out["extrinsics"].float().cpu()
|
||||
intrinsics = out["intrinsics"].float().cpu()
|
||||
else:
|
||||
extrinsics = torch.eye(4)[None, None].expand(1, S, 4, 4).clone()
|
||||
intrinsics = torch.eye(3)[None, None].expand(1, S, 3, 3).clone()
|
||||
|
||||
geometry: dict = {
|
||||
"depth": depth.contiguous(),
|
||||
"image": image[..., :3].cpu(),
|
||||
"mode": "multiview",
|
||||
"extrinsics": extrinsics.contiguous(),
|
||||
"intrinsics": intrinsics.contiguous(),
|
||||
}
|
||||
if sky is not None:
|
||||
geometry["sky"] = sky.contiguous()
|
||||
if "depth_conf" in out:
|
||||
conf = torch.nn.functional.interpolate(
|
||||
out["depth_conf"].unsqueeze(1).float(), size=(H, W),
|
||||
mode="bilinear", align_corners=False,
|
||||
).squeeze(1).cpu()
|
||||
geometry["confidence"] = conf.contiguous()
|
||||
return io.NodeOutput(geometry)
|
||||
|
||||
|
||||
class DA3Render(io.ComfyNode):
|
||||
"""Render a visualization from a DA3_GEOMETRY packet."""
|
||||
|
||||
_DEPTH_RENDER_INPUTS = [
|
||||
io.Combo.Input("normalization",
|
||||
options=["v2_style", "min_max", "raw"],
|
||||
default="v2_style",
|
||||
tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n"
|
||||
"- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n"
|
||||
"- raw: no scaling,preserves metric units for Metric model."),
|
||||
io.Boolean.Input("apply_sky_clip", default=False,
|
||||
tooltip="Clip sky-region depth to the 99th percentile of foreground depth before normalisation. "
|
||||
"Requires a sky key in the da3_geometry input (for Mono/Metric models only)."),
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="DA3Render",
|
||||
display_name="Render Depth Anything 3",
|
||||
category="image/geometry estimation",
|
||||
description="Render a depth map, confidence map, or sky mask from Depth Anything 3 geometry data.",
|
||||
inputs=[
|
||||
DA3Geometry.Input("da3_geometry"),
|
||||
io.DynamicCombo.Input("output",
|
||||
tooltip="- depth: normalised greyscale depth image.\n"
|
||||
"- depth_colored: depth mapped through the Turbo colormap.\n"
|
||||
"- sky_mask: sky probability in [0, 1] (for Mono/Metric models only).\n"
|
||||
"- confidence: normalised depth confidence (for Small/Base models only).",
|
||||
options=[
|
||||
io.DynamicCombo.Option("depth", cls._DEPTH_RENDER_INPUTS),
|
||||
io.DynamicCombo.Option("depth_colored", cls._DEPTH_RENDER_INPUTS),
|
||||
io.DynamicCombo.Option("sky_mask", [
|
||||
io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the sky mask."),
|
||||
]),
|
||||
io.DynamicCombo.Option("confidence", [
|
||||
io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the confidence map."),
|
||||
]),
|
||||
]),
|
||||
],
|
||||
outputs=[io.Image.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, da3_geometry, output) -> io.NodeOutput:
|
||||
output_val = output["output"]
|
||||
|
||||
if output_val in ("depth", "depth_colored"):
|
||||
normalization = output["normalization"]
|
||||
apply_sky_clip = output["apply_sky_clip"]
|
||||
if apply_sky_clip and "sky" not in da3_geometry:
|
||||
raise ValueError(
|
||||
"apply_sky_clip=True requires a sky tensor in the da3_geometry input, but none is present. "
|
||||
"Run with Mono/Metric models or set apply_sky_clip=False."
|
||||
)
|
||||
depth = da3_geometry["depth"]
|
||||
sky = da3_geometry.get("sky")
|
||||
if apply_sky_clip and sky is not None:
|
||||
depth = torch.stack([
|
||||
da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
|
||||
for i in range(depth.shape[0])
|
||||
], dim=0)
|
||||
grey = cls._depth_to_image(depth, sky, normalization) # (B,H,W,3) greyscale
|
||||
result = _turbo(grey[..., 0]) if output_val == "depth_colored" else grey
|
||||
|
||||
elif output_val == "sky_mask":
|
||||
if "sky" not in da3_geometry:
|
||||
raise ValueError("geometry has no sky output; run with Mono/Metric models.")
|
||||
sky = da3_geometry["sky"]
|
||||
if output["colored"]:
|
||||
result = _turbo(sky)
|
||||
else:
|
||||
result = sky.unsqueeze(-1).expand(*sky.shape, 3).contiguous()
|
||||
|
||||
elif output_val == "confidence":
|
||||
if "confidence" not in da3_geometry:
|
||||
raise ValueError("da3_geometry has no confidence output; run with Small/Base models.")
|
||||
conf = _normalize_confidence(da3_geometry["confidence"])
|
||||
if output["colored"]:
|
||||
result = _turbo(conf)
|
||||
else:
|
||||
result = conf.unsqueeze(-1).expand(*conf.shape, 3).contiguous()
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown output mode: {output_val}")
|
||||
|
||||
return io.NodeOutput(result.float())
|
||||
|
||||
@staticmethod
|
||||
def _depth_to_image(depth: torch.Tensor, sky_for_norm: torch.Tensor | None, normalization: str) -> torch.Tensor:
|
||||
"""Normalise depth and pack as an (B,H,W,3) image tensor."""
|
||||
|
||||
N = depth.shape[0]
|
||||
if normalization == "v2_style":
|
||||
norm = torch.stack([
|
||||
da3_preprocess.normalize_depth_v2_style(
|
||||
depth[i], sky_for_norm[i] if sky_for_norm is not None else None)
|
||||
for i in range(N)
|
||||
], dim=0)
|
||||
elif normalization == "min_max":
|
||||
norm = da3_preprocess.normalize_depth_min_max(depth)
|
||||
else:
|
||||
norm = depth
|
||||
|
||||
out = norm.unsqueeze(-1).repeat(1, 1, 1, 3)
|
||||
if normalization != "raw":
|
||||
out = out.clamp(0.0, 1.0)
|
||||
return out.contiguous()
|
||||
|
||||
|
||||
class DA3GeometryToMesh(io.ComfyNode):
|
||||
"""Convert a DA3_GEOMETRY packet into a Types.MESH by unprojecting depth and triangulating."""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="DA3GeometryToMesh",
|
||||
search_aliases=["da3", "depth anything", "mesh", "geometry", "3d", "triangulate"],
|
||||
display_name="Convert DA3 Geometry to Mesh",
|
||||
category="image/geometry estimation",
|
||||
description="Convert a depth map into a triangulated 3D mesh.",
|
||||
inputs=[
|
||||
DA3Geometry.Input("da3_geometry"),
|
||||
io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert. Per-image vertex counts differ so batches cannot be stacked."),
|
||||
io.Int.Input("decimation", default=1, min=1, max=8, tooltip="Vertex stride. 1 = full resolution, 2 = half, etc."),
|
||||
io.Float.Input("discontinuity_threshold", default=0.04, min=0.0, max=1.0, step=0.01, tooltip="Drop triangles whose 3x3 depth span exceeds this fraction. 0 = off."),
|
||||
io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
|
||||
tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all, 1 = keep only the single most confident pixel). "
|
||||
"Used when the geometry has a confidence map (Small/Base models)."),
|
||||
io.Boolean.Input("use_sky_mask", default=True, tooltip="Exclude sky-probability pixels (sky >= 0.5) from the mesh. Used when the geometry has a sky map (Mono/Metric models)."),
|
||||
io.Boolean.Input("texture", default=True, tooltip="Use the source image as a base color texture."),
|
||||
],
|
||||
outputs=[io.Mesh.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, da3_geometry, batch_index, decimation, discontinuity_threshold, confidence_threshold, use_sky_mask, texture) -> io.NodeOutput:
|
||||
depth_all = da3_geometry["depth"] # (B, H, W)
|
||||
B = depth_all.shape[0]
|
||||
if batch_index >= B:
|
||||
raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
|
||||
|
||||
depth = depth_all[batch_index] # (H, W)
|
||||
H, W = depth.shape
|
||||
|
||||
# NaN/inf depth would propagate silently through unproject and produce an
|
||||
# empty mesh; replace them with 0 here so those pixels are later excluded
|
||||
# by the isfinite check inside triangulate_grid_mesh.
|
||||
depth = depth.clone()
|
||||
n_bad = (~torch.isfinite(depth)).sum().item()
|
||||
if n_bad:
|
||||
logging.getLogger("comfy").warning(
|
||||
f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels "
|
||||
f"({100*n_bad/(H*W):.1f}%) - zeroed before unproject."
|
||||
)
|
||||
depth[~torch.isfinite(depth)] = 0.0
|
||||
logging.getLogger("comfy").debug(
|
||||
f"DA3GeometryToMesh: depth[{batch_index}] range "
|
||||
f"[{depth.min():.4g}, {depth.max():.4g}], mean={depth.mean():.4g}"
|
||||
)
|
||||
|
||||
K = _da3_get_K(da3_geometry, batch_index, H, W)
|
||||
points = _da3_unproject(depth, K) # (H, W, 3) in OpenCV camera space
|
||||
|
||||
# Apply world-to-camera inverse so multi-view frames share a common world frame.
|
||||
E = _da3_get_extrinsic(da3_geometry, batch_index)
|
||||
if E is not None:
|
||||
points = _da3_apply_extrinsic(points, E)
|
||||
|
||||
# Mask invalid pixels by setting them to inf so triangulate_grid_mesh skips them.
|
||||
mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
|
||||
# Also exclude pixels where depth was invalid.
|
||||
mask = mask & (depth_all[batch_index] > 0) & torch.isfinite(depth_all[batch_index])
|
||||
points = points.clone()
|
||||
points[~mask] = float('inf')
|
||||
|
||||
verts, faces, uvs = triangulate_grid_mesh(
|
||||
points,
|
||||
decimation=decimation,
|
||||
discontinuity_threshold=discontinuity_threshold,
|
||||
depth=depth,
|
||||
)
|
||||
if verts.shape[0] == 0 or faces.shape[0] == 0:
|
||||
raise ValueError(
|
||||
"DA3GeometryToMesh produced an empty mesh. "
|
||||
"Try raising discontinuity_threshold, lowering confidence_threshold, "
|
||||
"or disabling use_sky_mask."
|
||||
)
|
||||
|
||||
# OpenCV (X right, Y down, Z forward) → glTF (X right, Y up, Z back).
|
||||
# Same transform as MoGePointMapToMesh perspective branch.
|
||||
verts = verts * torch.tensor([1.0, -1.0, -1.0], dtype=verts.dtype)
|
||||
faces = faces[:, [0, 2, 1]].contiguous()
|
||||
|
||||
tex = da3_geometry["image"][batch_index:batch_index + 1] if texture else None
|
||||
mesh = Types.MESH(
|
||||
vertices=verts.unsqueeze(0),
|
||||
faces=faces.unsqueeze(0),
|
||||
uvs=uvs.unsqueeze(0),
|
||||
texture=tex,
|
||||
)
|
||||
return io.NodeOutput(mesh)
|
||||
|
||||
|
||||
class DA3GeometryToPointCloud(io.ComfyNode):
|
||||
"""Unproject a DA3_GEOMETRY depth map into a filtered DA3_POINT_CLOUD."""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="DA3GeometryToPointCloud",
|
||||
search_aliases=["da3", "depth anything", "point cloud", "pointcloud", "3d", "geometry"],
|
||||
display_name="Convert DA3 Geometry to Point Cloud",
|
||||
category="image/geometry estimation",
|
||||
description="Convert a depth map into a 3D point cloud.",
|
||||
inputs=[
|
||||
DA3Geometry.Input("da3_geometry"),
|
||||
io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert."),
|
||||
io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
|
||||
tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all). Used when the geometry has a confidence map (Small/Base models)."),
|
||||
io.Boolean.Input("use_sky_mask", default=True,
|
||||
tooltip="Exclude sky-probability pixels (sky >= 0.5). Used when the geometry has a sky map (Mono/Metric models)."),
|
||||
io.Int.Input("downsample", default=1, min=1, max=16,
|
||||
tooltip="Take every Nth pixel (1 = full resolution). Higher values give fewer points and faster processing."),
|
||||
],
|
||||
# TODO: add a proper PointCloud output type
|
||||
outputs=[DA3PointCloud.Output(display_name="point_cloud")],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, da3_geometry, batch_index, confidence_threshold, use_sky_mask, downsample) -> io.NodeOutput:
|
||||
depth_all = da3_geometry["depth"] # (B, H, W)
|
||||
B = depth_all.shape[0]
|
||||
if batch_index >= B:
|
||||
raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
|
||||
|
||||
depth = depth_all[batch_index].clone() # (H, W)
|
||||
depth[~torch.isfinite(depth)] = 0.0
|
||||
H, W = depth.shape
|
||||
|
||||
K = _da3_get_K(da3_geometry, batch_index, H, W)
|
||||
|
||||
if downsample > 1:
|
||||
depth = depth[::downsample, ::downsample].contiguous()
|
||||
# Scale intrinsics to the downsampled grid.
|
||||
K = K.clone()
|
||||
K[0, :] /= downsample
|
||||
K[1, :] /= downsample
|
||||
|
||||
H_ds, W_ds = depth.shape
|
||||
points = _da3_unproject(depth, K) # (H_ds, W_ds, 3) in OpenCV camera space
|
||||
|
||||
# Apply world-to-camera inverse so multi-view frames share a common world frame.
|
||||
E = _da3_get_extrinsic(da3_geometry, batch_index)
|
||||
if E is not None:
|
||||
points = _da3_apply_extrinsic(points, E)
|
||||
|
||||
# Rebuild mask at downsampled resolution.
|
||||
mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
|
||||
if downsample > 1:
|
||||
mask = mask[::downsample, ::downsample]
|
||||
|
||||
mask = mask & torch.isfinite(depth)
|
||||
|
||||
# OpenCV → glTF: flip Y and Z.
|
||||
points_gltf = points.clone()
|
||||
points_gltf[..., 1] *= -1.0
|
||||
points_gltf[..., 2] *= -1.0
|
||||
|
||||
pts_flat = points_gltf.reshape(-1, 3)[mask.reshape(-1)]
|
||||
|
||||
colors_flat = None
|
||||
if "image" in da3_geometry:
|
||||
img = da3_geometry["image"][batch_index] # (H, W, 3)
|
||||
if downsample > 1:
|
||||
img = img[::downsample, ::downsample]
|
||||
colors_flat = img.reshape(-1, 3)[mask.reshape(-1)]
|
||||
|
||||
conf_flat = None
|
||||
if "confidence" in da3_geometry:
|
||||
conf = da3_geometry["confidence"][batch_index] # (H, W)
|
||||
if downsample > 1:
|
||||
conf = conf[::downsample, ::downsample]
|
||||
conf_flat = conf.reshape(-1)[mask.reshape(-1)]
|
||||
|
||||
if pts_flat.shape[0] == 0:
|
||||
raise ValueError(
|
||||
"DA3GeometryToPointCloud produced zero points after filtering. "
|
||||
"Try lowering confidence_threshold or disabling use_sky_mask."
|
||||
)
|
||||
|
||||
return io.NodeOutput({
|
||||
"points": pts_flat,
|
||||
"colors": colors_flat,
|
||||
"confidence": conf_flat,
|
||||
})
|
||||
|
||||
|
||||
class DA3Extension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [
|
||||
LoadDA3Model,
|
||||
DA3Inference,
|
||||
DA3Render,
|
||||
DA3GeometryToMesh,
|
||||
# DA3GeometryToPointCloud, # Keep this commented out for now until we have a proper PointCloud output type
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> DA3Extension:
|
||||
return DA3Extension()
|
||||
@ -8,6 +8,7 @@ import folder_paths
|
||||
from comfy_api.latest import ComfyExtension, Types, io
|
||||
from typing_extensions import override
|
||||
|
||||
from comfy.ldm.colormap import turbo as _turbo
|
||||
from comfy.ldm.moge.model import MoGeModel
|
||||
from comfy.ldm.moge.geometry import triangulate_grid_mesh
|
||||
from comfy.ldm.moge.panorama import get_panorama_cameras, split_panorama_image, merge_panorama_depth, spherical_uv_to_directions, _uv_grid
|
||||
@ -27,19 +28,6 @@ MoGeGeometry = io.Custom("MOGE_GEOMETRY")
|
||||
# "image": torch.Tensor (B, H, W, 3) in [0, 1], CPU (always present)
|
||||
|
||||
|
||||
def _turbo(x: torch.Tensor) -> torch.Tensor:
|
||||
"""Anton Mikhailov polynomial approximation of the turbo colormap."""
|
||||
x = x.clamp(0.0, 1.0)
|
||||
x2 = x * x
|
||||
x3 = x2 * x
|
||||
x4 = x2 * x2
|
||||
x5 = x4 * x
|
||||
r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5
|
||||
g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 + 4.27729857*x4 + 2.82956604*x5
|
||||
b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5
|
||||
return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)
|
||||
|
||||
|
||||
def _normals_from_points(points: torch.Tensor) -> torch.Tensor:
|
||||
"""Camera-space surface normals from a (B, H, W, 3) point map (v1 fallback)."""
|
||||
finite = torch.isfinite(points).all(dim=-1)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user