mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-04-27 19:02:31 +08:00
1174 lines
52 KiB
Python
1174 lines
52 KiB
Python
import torch
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import torch.nn.functional as F
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import torch.nn as nn
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from comfy.ldm.trellis2.vae import SparseTensor, SparseLinear, sparse_cat, VarLenTensor
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from typing import Optional, Tuple, Literal, Union, List
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from comfy.ldm.trellis2.attention import (
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sparse_windowed_scaled_dot_product_self_attention, sparse_scaled_dot_product_attention, scaled_dot_product_attention
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)
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from comfy.ldm.genmo.joint_model.layers import TimestepEmbedder
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from comfy.ldm.flux.math import apply_rope, apply_rope1
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class SparseGELU(nn.GELU):
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def forward(self, input: VarLenTensor) -> VarLenTensor:
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return input.replace(super().forward(input.feats))
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class SparseFeedForwardNet(nn.Module):
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def __init__(self, channels: int, mlp_ratio: float = 4.0, device=None, dtype=None, operations=None):
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super().__init__()
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self.mlp = nn.Sequential(
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SparseLinear(channels, int(channels * mlp_ratio), device=device, dtype=dtype, operations=operations),
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SparseGELU(approximate="tanh"),
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SparseLinear(int(channels * mlp_ratio), channels, device=device, dtype=dtype, operations=operations),
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)
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def forward(self, x: VarLenTensor) -> VarLenTensor:
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return self.mlp(x)
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def manual_cast(obj, dtype):
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return obj.to(dtype=dtype)
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class LayerNorm32(nn.LayerNorm):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x_dtype = x.dtype
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x = manual_cast(x, torch.float32)
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o = super().forward(x)
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return manual_cast(o, x_dtype)
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class SparseMultiHeadRMSNorm(nn.Module):
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def __init__(self, dim: int, heads: int, device, dtype):
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super().__init__()
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self.scale = dim ** 0.5
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self.gamma = nn.Parameter(torch.ones(heads, dim, device=device, dtype=dtype))
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def forward(self, x: Union[VarLenTensor, torch.Tensor]) -> Union[VarLenTensor, torch.Tensor]:
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x_type = x.dtype
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x = x.float()
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if isinstance(x, VarLenTensor):
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x = x.replace(F.normalize(x.feats, dim=-1) * self.gamma * self.scale)
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else:
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x = F.normalize(x, dim=-1) * self.gamma * self.scale
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return x.to(x_type)
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class SparseRotaryPositionEmbedder(nn.Module):
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def __init__(
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self,
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head_dim: int,
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dim: int = 3,
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rope_freq: Tuple[float, float] = (1.0, 10000.0),
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device=None
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):
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super().__init__()
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self.head_dim = head_dim
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self.dim = dim
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self.rope_freq = rope_freq
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self.freq_dim = head_dim // 2 // dim
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self.freqs = torch.arange(self.freq_dim, dtype=torch.float32, device=device) / self.freq_dim
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self.freqs = rope_freq[0] / (rope_freq[1] ** (self.freqs))
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def _get_freqs_cis(self, coords: torch.Tensor) -> torch.Tensor:
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phases_list = []
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for i in range(self.dim):
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phases_list.append(torch.outer(coords[..., i], self.freqs.to(coords.device)))
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phases = torch.cat(phases_list, dim=-1)
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if phases.shape[-1] < self.head_dim // 2:
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padn = self.head_dim // 2 - phases.shape[-1]
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phases = torch.cat([phases, torch.zeros(*phases.shape[:-1], padn, device=phases.device)], dim=-1)
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cos = torch.cos(phases)
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sin = torch.sin(phases)
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f_cis_0 = torch.stack([cos, sin], dim=-1)
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f_cis_1 = torch.stack([-sin, cos], dim=-1)
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freqs_cis = torch.stack([f_cis_0, f_cis_1], dim=-1)
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return freqs_cis
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def _get_phases(self, indices: torch.Tensor) -> torch.Tensor:
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self.freqs = self.freqs.to(indices.device)
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phases = torch.outer(indices, self.freqs)
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phases = torch.polar(torch.ones_like(phases), phases)
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return phases
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def forward(self, q, k=None):
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cache_name = f'rope_cis_{self.dim}d_f{self.rope_freq[1]}_hd{self.head_dim}'
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freqs_cis = q.get_spatial_cache(cache_name)
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if freqs_cis is None:
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coords = q.coords[..., 1:].to(torch.float32)
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freqs_cis = self._get_freqs_cis(coords)
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q.register_spatial_cache(cache_name, freqs_cis)
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if q.feats.ndim == 3:
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f_cis = freqs_cis.unsqueeze(1)
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else:
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f_cis = freqs_cis
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if k is None:
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return q.replace(apply_rope1(q.feats, f_cis))
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q_feats, k_feats = apply_rope(q.feats, k.feats, f_cis)
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return q.replace(q_feats), k.replace(k_feats)
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@staticmethod
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def apply_rotary_embedding(x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor:
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x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
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x_rotated = x_complex * phases.unsqueeze(-2)
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x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype)
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return x_embed
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class RotaryPositionEmbedder(SparseRotaryPositionEmbedder):
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def forward(self, indices: torch.Tensor) -> torch.Tensor:
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phases = self._get_phases(indices.reshape(-1)).reshape(*indices.shape[:-1], -1)
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if torch.is_complex(phases):
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phases = phases.to(torch.complex64)
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else:
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phases = phases.to(torch.float32)
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if phases.shape[-1] < self.head_dim // 2:
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padn = self.head_dim // 2 - phases.shape[-1]
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phases = torch.cat([phases, torch.polar(
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torch.ones(*phases.shape[:-1], padn, device=phases.device, dtype=torch.float32),
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torch.zeros(*phases.shape[:-1], padn, device=phases.device, dtype=torch.float32)
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)], dim=-1)
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return phases
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class SparseMultiHeadAttention(nn.Module):
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def __init__(
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self,
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channels: int,
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num_heads: int,
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ctx_channels: Optional[int] = None,
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type: Literal["self", "cross"] = "self",
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attn_mode: Literal["full", "windowed", "double_windowed"] = "full",
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window_size: Optional[int] = None,
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shift_window: Optional[Tuple[int, int, int]] = None,
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qkv_bias: bool = True,
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use_rope: bool = False,
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rope_freq: Tuple[int, int] = (1.0, 10000.0),
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qk_rms_norm: bool = False,
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device=None, dtype=None, operations=None
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):
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super().__init__()
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self.channels = channels
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self.head_dim = channels // num_heads
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self.ctx_channels = ctx_channels if ctx_channels is not None else channels
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self.num_heads = num_heads
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self._type = type
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self.attn_mode = attn_mode
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self.window_size = window_size
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self.shift_window = shift_window
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self.use_rope = use_rope
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self.qk_rms_norm = qk_rms_norm
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if self._type == "self":
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self.to_qkv = operations.Linear(channels, channels * 3, bias=qkv_bias, device=device, dtype=dtype)
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else:
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self.to_q = operations.Linear(channels, channels, bias=qkv_bias, device=device, dtype=dtype)
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self.to_kv = operations.Linear(self.ctx_channels, channels * 2, bias=qkv_bias, device=device, dtype=dtype)
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if self.qk_rms_norm:
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self.q_rms_norm = SparseMultiHeadRMSNorm(self.head_dim, num_heads, device=device, dtype=dtype)
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self.k_rms_norm = SparseMultiHeadRMSNorm(self.head_dim, num_heads, device=device, dtype=dtype)
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self.to_out = operations.Linear(channels, channels, device=device, dtype=dtype)
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if use_rope:
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self.rope = SparseRotaryPositionEmbedder(self.head_dim, rope_freq=rope_freq, device=device)
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@staticmethod
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def _linear(module: nn.Linear, x: Union[VarLenTensor, torch.Tensor]) -> Union[VarLenTensor, torch.Tensor]:
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if isinstance(x, VarLenTensor):
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return x.replace(module(x.feats))
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else:
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return module(x)
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@staticmethod
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def _reshape_chs(x: Union[VarLenTensor, torch.Tensor], shape: Tuple[int, ...]) -> Union[VarLenTensor, torch.Tensor]:
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if isinstance(x, VarLenTensor):
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return x.reshape(*shape)
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else:
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return x.reshape(*x.shape[:2], *shape)
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def _fused_pre(self, x: Union[VarLenTensor, torch.Tensor], num_fused: int) -> Union[VarLenTensor, torch.Tensor]:
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if isinstance(x, VarLenTensor):
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x_feats = x.feats.unsqueeze(0)
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else:
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x_feats = x
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x_feats = x_feats.reshape(*x_feats.shape[:2], num_fused, self.num_heads, -1)
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return x.replace(x_feats.squeeze(0)) if isinstance(x, VarLenTensor) else x_feats
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def forward(self, x: SparseTensor, context: Optional[Union[VarLenTensor, torch.Tensor]] = None) -> SparseTensor:
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if self._type == "self":
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dtype = next(self.to_qkv.parameters()).dtype
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x = x.to(dtype)
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qkv = self._linear(self.to_qkv, x)
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qkv = self._fused_pre(qkv, num_fused=3)
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if self.qk_rms_norm or self.use_rope:
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q, k, v = qkv.unbind(dim=-3)
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if self.qk_rms_norm:
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q = self.q_rms_norm(q)
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k = self.k_rms_norm(k)
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if self.use_rope:
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q, k = self.rope(q, k)
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qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1))
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if self.attn_mode == "full":
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h = sparse_scaled_dot_product_attention(qkv)
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elif self.attn_mode == "windowed":
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h = sparse_windowed_scaled_dot_product_self_attention(
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qkv, self.window_size, shift_window=self.shift_window
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)
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elif self.attn_mode == "double_windowed":
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qkv0 = qkv.replace(qkv.feats[:, :, self.num_heads//2:])
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qkv1 = qkv.replace(qkv.feats[:, :, :self.num_heads//2])
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h0 = sparse_windowed_scaled_dot_product_self_attention(
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qkv0, self.window_size, shift_window=(0, 0, 0)
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)
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h1 = sparse_windowed_scaled_dot_product_self_attention(
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qkv1, self.window_size, shift_window=tuple([self.window_size//2] * 3)
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)
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h = qkv.replace(torch.cat([h0.feats, h1.feats], dim=1))
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else:
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q = self._linear(self.to_q, x)
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q = self._reshape_chs(q, (self.num_heads, -1))
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dtype = next(self.to_kv.parameters()).dtype
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context = context.to(dtype)
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kv = self._linear(self.to_kv, context)
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kv = self._fused_pre(kv, num_fused=2)
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if self.qk_rms_norm:
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q = self.q_rms_norm(q)
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k, v = kv.unbind(dim=-3)
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k = self.k_rms_norm(k)
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h = sparse_scaled_dot_product_attention(q, k, v)
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else:
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h = sparse_scaled_dot_product_attention(q, kv)
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h = self._reshape_chs(h, (-1,))
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h = self._linear(self.to_out, h)
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return h
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class ModulatedSparseTransformerCrossBlock(nn.Module):
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"""
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Sparse Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning.
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"""
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def __init__(
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self,
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channels: int,
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ctx_channels: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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attn_mode: Literal["full", "swin"] = "full",
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window_size: Optional[int] = None,
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shift_window: Optional[Tuple[int, int, int]] = None,
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use_checkpoint: bool = False,
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use_rope: bool = False,
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rope_freq: Tuple[float, float] = (1.0, 10000.0),
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qk_rms_norm: bool = False,
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qk_rms_norm_cross: bool = False,
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qkv_bias: bool = True,
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share_mod: bool = False,
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device=None, dtype=None, operations=None
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):
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super().__init__()
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self.use_checkpoint = use_checkpoint
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self.share_mod = share_mod
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self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6, device=device)
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self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6, device=device)
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self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6, device=device)
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self.self_attn = SparseMultiHeadAttention(
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channels,
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num_heads=num_heads,
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type="self",
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attn_mode=attn_mode,
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window_size=window_size,
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shift_window=shift_window,
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qkv_bias=qkv_bias,
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use_rope=use_rope,
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rope_freq=rope_freq,
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qk_rms_norm=qk_rms_norm,
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device=device, dtype=dtype, operations=operations
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)
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self.cross_attn = SparseMultiHeadAttention(
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channels,
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ctx_channels=ctx_channels,
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num_heads=num_heads,
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type="cross",
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attn_mode="full",
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qkv_bias=qkv_bias,
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qk_rms_norm=qk_rms_norm_cross,
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device=device, dtype=dtype, operations=operations
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)
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self.mlp = SparseFeedForwardNet(
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channels,
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mlp_ratio=mlp_ratio,
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device=device, dtype=dtype, operations=operations
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)
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if not share_mod:
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operations.Linear(channels, 6 * channels, bias=True, device=device, dtype=dtype)
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)
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else:
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self.modulation = nn.Parameter(torch.randn(6 * channels, device=device, dtype=dtype) / channels ** 0.5)
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def _forward(self, x: SparseTensor, mod: torch.Tensor, context: Union[torch.Tensor, VarLenTensor]) -> SparseTensor:
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if self.share_mod:
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.modulation + mod).type(mod.dtype).chunk(6, dim=1)
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else:
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
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h = x.replace(self.norm1(x.feats))
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h = h * (1 + scale_msa) + shift_msa
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h = self.self_attn(h)
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h = h * gate_msa
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x = x + h
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h = x.replace(self.norm2(x.feats))
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h = self.cross_attn(h, context)
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x = x + h
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h = x.replace(self.norm3(x.feats))
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h = h * (1 + scale_mlp) + shift_mlp
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h = self.mlp(h)
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h = h * gate_mlp
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x = x + h
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return x
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def forward(self, x: SparseTensor, mod: torch.Tensor, context: Union[torch.Tensor, VarLenTensor]) -> SparseTensor:
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return self._forward(x, mod, context)
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|
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class SLatFlowModel(nn.Module):
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def __init__(
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self,
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resolution: int,
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in_channels: int,
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model_channels: int,
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cond_channels: int,
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out_channels: int,
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num_blocks: int,
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num_heads: Optional[int] = None,
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num_head_channels: Optional[int] = 64,
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mlp_ratio: float = 4,
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pe_mode: Literal["ape", "rope"] = "rope",
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rope_freq: Tuple[float, float] = (1.0, 10000.0),
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use_checkpoint: bool = False,
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share_mod: bool = False,
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initialization: str = 'vanilla',
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qk_rms_norm: bool = False,
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qk_rms_norm_cross: bool = False,
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dtype = None,
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device = None,
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operations = None,
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):
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super().__init__()
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self.resolution = resolution
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.cond_channels = cond_channels
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self.out_channels = out_channels
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self.num_blocks = num_blocks
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self.num_heads = num_heads or model_channels // num_head_channels
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self.mlp_ratio = mlp_ratio
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self.pe_mode = pe_mode
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self.use_checkpoint = use_checkpoint
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self.share_mod = share_mod
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self.initialization = initialization
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self.qk_rms_norm = qk_rms_norm
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self.qk_rms_norm_cross = qk_rms_norm_cross
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self.dtype = dtype
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self.t_embedder = TimestepEmbedder(model_channels, device=device, dtype=dtype, operations=operations)
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if share_mod:
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operations.Linear(model_channels, 6 * model_channels, bias=True, device=device, dtype=dtype)
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)
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self.input_layer = SparseLinear(in_channels, model_channels, device=device, dtype=dtype, operations=operations)
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self.blocks = nn.ModuleList([
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ModulatedSparseTransformerCrossBlock(
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model_channels,
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cond_channels,
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num_heads=self.num_heads,
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mlp_ratio=self.mlp_ratio,
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attn_mode='full',
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use_checkpoint=self.use_checkpoint,
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use_rope=(pe_mode == "rope"),
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rope_freq=rope_freq,
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share_mod=self.share_mod,
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qk_rms_norm=self.qk_rms_norm,
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qk_rms_norm_cross=self.qk_rms_norm_cross,
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device=device, dtype=dtype, operations=operations
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)
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for _ in range(num_blocks)
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])
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self.out_layer = SparseLinear(model_channels, out_channels, device=device, dtype=dtype, operations=operations)
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|
|
@property
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def device(self) -> torch.device:
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return next(self.parameters()).device
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|
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def forward(
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self,
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x: SparseTensor,
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t: torch.Tensor,
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cond: Union[torch.Tensor, List[torch.Tensor]],
|
|
concat_cond: Optional[SparseTensor] = None,
|
|
**kwargs
|
|
) -> SparseTensor:
|
|
if concat_cond is not None:
|
|
x = sparse_cat([x, concat_cond], dim=-1)
|
|
if isinstance(cond, list):
|
|
cond = VarLenTensor.from_tensor_list(cond)
|
|
|
|
dtype = next(self.input_layer.parameters()).dtype
|
|
x = x.to(dtype)
|
|
h = self.input_layer(x)
|
|
h = manual_cast(h, self.dtype)
|
|
t = t.to(dtype)
|
|
t_embedder = self.t_embedder.to(dtype)
|
|
t_emb = t_embedder(t, out_dtype = t.dtype)
|
|
if self.share_mod:
|
|
t_emb = self.adaLN_modulation(t_emb)
|
|
t_emb = manual_cast(t_emb, self.dtype)
|
|
cond = manual_cast(cond, self.dtype)
|
|
|
|
for block in self.blocks:
|
|
h = block(h, t_emb, cond)
|
|
|
|
h = manual_cast(h, x.dtype)
|
|
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
|
|
h = self.out_layer(h)
|
|
return h
|
|
|
|
class FeedForwardNet(nn.Module):
|
|
def __init__(self, channels: int, mlp_ratio: float = 4.0, device=None, dtype=None, operations=None):
|
|
super().__init__()
|
|
self.mlp = nn.Sequential(
|
|
operations.Linear(channels, int(channels * mlp_ratio), device=device, dtype=dtype),
|
|
nn.GELU(approximate="tanh"),
|
|
operations.Linear(int(channels * mlp_ratio), channels, device=device, dtype=dtype),
|
|
)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
return self.mlp(x)
|
|
|
|
class MultiHeadRMSNorm(nn.Module):
|
|
def __init__(self, dim: int, heads: int, device=None, dtype=None):
|
|
super().__init__()
|
|
self.scale = dim ** 0.5
|
|
self.gamma = nn.Parameter(torch.ones(heads, dim, device=device, dtype=dtype))
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
return (F.normalize(x.float(), dim = -1) * self.gamma * self.scale).to(x.dtype)
|
|
|
|
|
|
class MultiHeadAttention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
channels: int,
|
|
num_heads: int,
|
|
ctx_channels: Optional[int]=None,
|
|
type: Literal["self", "cross"] = "self",
|
|
attn_mode: Literal["full", "windowed"] = "full",
|
|
window_size: Optional[int] = None,
|
|
shift_window: Optional[Tuple[int, int, int]] = None,
|
|
qkv_bias: bool = True,
|
|
use_rope: bool = False,
|
|
rope_freq: Tuple[float, float] = (1.0, 10000.0),
|
|
qk_rms_norm: bool = False,
|
|
device=None, dtype=None, operations=None
|
|
):
|
|
super().__init__()
|
|
|
|
self.channels = channels
|
|
self.head_dim = channels // num_heads
|
|
self.ctx_channels = ctx_channels if ctx_channels is not None else channels
|
|
self.num_heads = num_heads
|
|
self._type = type
|
|
self.attn_mode = attn_mode
|
|
self.window_size = window_size
|
|
self.shift_window = shift_window
|
|
self.use_rope = use_rope
|
|
self.qk_rms_norm = qk_rms_norm
|
|
|
|
if self._type == "self":
|
|
self.to_qkv = operations.Linear(channels, channels * 3, bias=qkv_bias, dtype=dtype, device=device)
|
|
else:
|
|
self.to_q = operations.Linear(channels, channels, bias=qkv_bias, device=device, dtype=dtype)
|
|
self.to_kv = operations.Linear(self.ctx_channels, channels * 2, bias=qkv_bias, device=device, dtype=dtype)
|
|
|
|
if self.qk_rms_norm:
|
|
self.q_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads, device=device, dtype=dtype)
|
|
self.k_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads, device=device, dtype=dtype)
|
|
|
|
self.to_out = operations.Linear(channels, channels, device=device, dtype=dtype)
|
|
|
|
def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None, phases: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
B, L, C = x.shape
|
|
if self._type == "self":
|
|
x = x.to(next(self.to_qkv.parameters()).dtype)
|
|
qkv = self.to_qkv(x)
|
|
qkv = qkv.reshape(B, L, 3, self.num_heads, -1)
|
|
|
|
if self.attn_mode == "full":
|
|
if self.qk_rms_norm or self.use_rope:
|
|
q, k, v = qkv.unbind(dim=2)
|
|
if self.qk_rms_norm:
|
|
q = self.q_rms_norm(q)
|
|
k = self.k_rms_norm(k)
|
|
if self.use_rope:
|
|
assert phases is not None, "Phases must be provided for RoPE"
|
|
q = RotaryPositionEmbedder.apply_rotary_embedding(q, phases)
|
|
k = RotaryPositionEmbedder.apply_rotary_embedding(k, phases)
|
|
h = scaled_dot_product_attention(q, k, v)
|
|
else:
|
|
h = scaled_dot_product_attention(qkv)
|
|
else:
|
|
Lkv = context.shape[1]
|
|
q = self.to_q(x)
|
|
context = context.to(next(self.to_kv.parameters()).dtype)
|
|
kv = self.to_kv(context)
|
|
q = q.reshape(B, L, self.num_heads, -1)
|
|
kv = kv.reshape(B, Lkv, 2, self.num_heads, -1)
|
|
if self.qk_rms_norm:
|
|
q = self.q_rms_norm(q)
|
|
k, v = kv.unbind(dim=2)
|
|
k = self.k_rms_norm(k)
|
|
h = scaled_dot_product_attention(q, k, v)
|
|
else:
|
|
h = scaled_dot_product_attention(q, kv)
|
|
h = h.reshape(B, L, -1)
|
|
h = self.to_out(h)
|
|
return h
|
|
|
|
class ModulatedTransformerCrossBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
channels: int,
|
|
ctx_channels: int,
|
|
num_heads: int,
|
|
mlp_ratio: float = 4.0,
|
|
attn_mode: Literal["full", "windowed"] = "full",
|
|
window_size: Optional[int] = None,
|
|
shift_window: Optional[Tuple[int, int, int]] = None,
|
|
use_checkpoint: bool = False,
|
|
use_rope: bool = False,
|
|
rope_freq: Tuple[int, int] = (1.0, 10000.0),
|
|
qk_rms_norm: bool = False,
|
|
qk_rms_norm_cross: bool = False,
|
|
qkv_bias: bool = True,
|
|
share_mod: bool = False,
|
|
device=None, dtype=None, operations=None
|
|
):
|
|
super().__init__()
|
|
self.use_checkpoint = use_checkpoint
|
|
self.share_mod = share_mod
|
|
self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6, device=device)
|
|
self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6, device=device)
|
|
self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6, device=device)
|
|
self.self_attn = MultiHeadAttention(
|
|
channels,
|
|
num_heads=num_heads,
|
|
type="self",
|
|
attn_mode=attn_mode,
|
|
window_size=window_size,
|
|
shift_window=shift_window,
|
|
qkv_bias=qkv_bias,
|
|
use_rope=use_rope,
|
|
rope_freq=rope_freq,
|
|
qk_rms_norm=qk_rms_norm,
|
|
device=device, dtype=dtype, operations=operations
|
|
)
|
|
self.cross_attn = MultiHeadAttention(
|
|
channels,
|
|
ctx_channels=ctx_channels,
|
|
num_heads=num_heads,
|
|
type="cross",
|
|
attn_mode="full",
|
|
qkv_bias=qkv_bias,
|
|
qk_rms_norm=qk_rms_norm_cross,
|
|
device=device, dtype=dtype, operations=operations
|
|
)
|
|
self.mlp = FeedForwardNet(
|
|
channels,
|
|
mlp_ratio=mlp_ratio,
|
|
device=device, dtype=dtype, operations=operations
|
|
)
|
|
if not share_mod:
|
|
self.adaLN_modulation = nn.Sequential(
|
|
nn.SiLU(),
|
|
operations.Linear(channels, 6 * channels, bias=True, dtype=dtype, device=device)
|
|
)
|
|
else:
|
|
self.modulation = nn.Parameter(torch.randn(6 * channels, device=device, dtype=dtype) / channels ** 0.5)
|
|
|
|
def _forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor, phases: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
if self.share_mod:
|
|
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.modulation + mod).type(mod.dtype).chunk(6, dim=1)
|
|
else:
|
|
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
|
|
h = self.norm1(x)
|
|
h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
|
|
h = self.self_attn(h, phases=phases)
|
|
h = h * gate_msa.unsqueeze(1)
|
|
x = x + h
|
|
h = self.norm2(x)
|
|
h = self.cross_attn(h, context)
|
|
x = x + h
|
|
h = self.norm3(x)
|
|
h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
|
|
h = self.mlp(h)
|
|
h = h * gate_mlp.unsqueeze(1)
|
|
x = x + h
|
|
return x
|
|
|
|
def forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor, phases: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
return self._forward(x, mod, context, phases)
|
|
|
|
|
|
class SparseStructureFlowModel(nn.Module):
|
|
def __init__(
|
|
self,
|
|
resolution: int,
|
|
in_channels: int,
|
|
model_channels: int,
|
|
cond_channels: int,
|
|
out_channels: int,
|
|
num_blocks: int,
|
|
num_heads: Optional[int] = None,
|
|
num_head_channels: Optional[int] = 64,
|
|
mlp_ratio: float = 4,
|
|
pe_mode: Literal["ape", "rope"] = "rope",
|
|
rope_freq: Tuple[float, float] = (1.0, 10000.0),
|
|
use_checkpoint: bool = False,
|
|
share_mod: bool = False,
|
|
initialization: str = 'vanilla',
|
|
qk_rms_norm: bool = False,
|
|
qk_rms_norm_cross: bool = False,
|
|
operations=None,
|
|
device = None,
|
|
dtype = torch.float32,
|
|
**kwargs
|
|
):
|
|
super().__init__()
|
|
self.device = device
|
|
self.resolution = resolution
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
self.cond_channels = cond_channels
|
|
self.out_channels = out_channels
|
|
self.num_blocks = num_blocks
|
|
self.num_heads = num_heads or model_channels // num_head_channels
|
|
self.mlp_ratio = mlp_ratio
|
|
self.pe_mode = pe_mode
|
|
self.use_checkpoint = use_checkpoint
|
|
self.share_mod = share_mod
|
|
self.initialization = initialization
|
|
self.qk_rms_norm = qk_rms_norm
|
|
self.qk_rms_norm_cross = qk_rms_norm_cross
|
|
self.dtype = dtype
|
|
self.device = device
|
|
|
|
self.t_embedder = TimestepEmbedder(model_channels, dtype=dtype, device=device, operations=operations)
|
|
if share_mod:
|
|
self.adaLN_modulation = nn.Sequential(
|
|
nn.SiLU(),
|
|
operations.Linear(model_channels, 6 * model_channels, bias=True, device=device, dtype=dtype)
|
|
)
|
|
|
|
pos_embedder = RotaryPositionEmbedder(self.model_channels // self.num_heads, 3, device=device)
|
|
coords = torch.meshgrid(*[torch.arange(res, device=self.device, dtype=dtype) for res in [resolution] * 3], indexing='ij')
|
|
coords = torch.stack(coords, dim=-1).reshape(-1, 3)
|
|
rope_phases = pos_embedder(coords)
|
|
self.register_buffer("rope_phases", rope_phases, persistent=False)
|
|
|
|
if pe_mode != "rope":
|
|
self.rope_phases = None
|
|
|
|
self.input_layer = operations.Linear(in_channels, model_channels, device=device, dtype=dtype)
|
|
|
|
self.blocks = nn.ModuleList([
|
|
ModulatedTransformerCrossBlock(
|
|
model_channels,
|
|
cond_channels,
|
|
num_heads=self.num_heads,
|
|
mlp_ratio=self.mlp_ratio,
|
|
attn_mode='full',
|
|
use_checkpoint=self.use_checkpoint,
|
|
use_rope=(pe_mode == "rope"),
|
|
rope_freq=rope_freq,
|
|
share_mod=share_mod,
|
|
qk_rms_norm=self.qk_rms_norm,
|
|
qk_rms_norm_cross=self.qk_rms_norm_cross,
|
|
device=device, dtype=dtype, operations=operations
|
|
)
|
|
for _ in range(num_blocks)
|
|
])
|
|
|
|
self.out_layer = operations.Linear(model_channels, out_channels, device=device, dtype=dtype)
|
|
|
|
def forward(self, x: torch.Tensor, t: torch.Tensor, cond: torch.Tensor) -> torch.Tensor:
|
|
x = x.view(x.shape[0], self.in_channels, *[self.resolution] * 3)
|
|
|
|
h = x.view(*x.shape[:2], -1).permute(0, 2, 1).contiguous()
|
|
|
|
h = h.to(next(self.input_layer.parameters()).dtype)
|
|
h = self.input_layer(h)
|
|
t_emb = self.t_embedder(t, out_dtype = t.dtype)
|
|
if self.share_mod:
|
|
t_emb = self.adaLN_modulation(t_emb)
|
|
t_emb = manual_cast(t_emb, self.dtype)
|
|
h = manual_cast(h, self.dtype)
|
|
cond = manual_cast(cond, self.dtype)
|
|
for block in self.blocks:
|
|
h = block(h, t_emb, cond, self.rope_phases)
|
|
h = manual_cast(h, x.dtype)
|
|
h = F.layer_norm(h, h.shape[-1:])
|
|
h = h.to(next(self.out_layer.parameters()).dtype)
|
|
h = self.out_layer(h)
|
|
|
|
h = h.permute(0, 2, 1).view(h.shape[0], h.shape[2], *[self.resolution] * 3).contiguous()
|
|
|
|
return h
|
|
|
|
def timestep_reshift(t_shifted, old_shift=3.0, new_shift=5.0):
|
|
t_shifted = t_shifted / 1000.0
|
|
t_linear = t_shifted / (old_shift - t_shifted * (old_shift - 1))
|
|
t_new = (new_shift * t_linear) / (1 + (new_shift - 1) * t_linear)
|
|
t_new *= 1000.0
|
|
return t_new
|
|
|
|
class Trellis2(nn.Module):
|
|
def __init__(self, resolution,
|
|
in_channels = 32,
|
|
out_channels = 32,
|
|
model_channels = 1536,
|
|
cond_channels = 1024,
|
|
num_blocks = 30,
|
|
num_heads = 12,
|
|
mlp_ratio = 5.3334,
|
|
share_mod = True,
|
|
qk_rms_norm = True,
|
|
qk_rms_norm_cross = True,
|
|
init_txt_model=False, # for now
|
|
dtype=None, device=None, operations=None, **kwargs):
|
|
|
|
super().__init__()
|
|
self.dtype = dtype
|
|
operations = operations or nn
|
|
# for some reason it passes num_heads = -1
|
|
if num_heads == -1:
|
|
num_heads = 12
|
|
args = {
|
|
"out_channels":out_channels, "num_blocks":num_blocks, "cond_channels" :cond_channels,
|
|
"model_channels":model_channels, "num_heads":num_heads, "mlp_ratio": mlp_ratio, "share_mod": share_mod,
|
|
"qk_rms_norm": qk_rms_norm, "qk_rms_norm_cross": qk_rms_norm_cross, "device": device, "dtype": dtype, "operations": operations
|
|
}
|
|
self.img2shape = SLatFlowModel(resolution=resolution, in_channels=in_channels, **args)
|
|
self.shape2txt = None
|
|
if init_txt_model:
|
|
self.shape2txt = SLatFlowModel(resolution=resolution, in_channels=in_channels*2, **args)
|
|
self.img2shape_512 = SLatFlowModel(resolution=32, in_channels=in_channels, **args)
|
|
args.pop("out_channels")
|
|
self.structure_model = SparseStructureFlowModel(resolution=16, in_channels=8, out_channels=8, **args)
|
|
self.guidance_interval = [0.6, 1.0]
|
|
self.guidance_interval_txt = [0.6, 0.9]
|
|
|
|
def forward(self, x, timestep, context, **kwargs):
|
|
transformer_options = kwargs.get("transformer_options", {})
|
|
embeds = kwargs.get("embeds")
|
|
if embeds is None:
|
|
raise ValueError("Trellis2.forward requires 'embeds' in kwargs")
|
|
# img2shape.resolution is the latent-grid size, not the input pixel size:
|
|
# 32 -> 512px path, 64 -> 1024px path.
|
|
uses_1024_conditioning = self.img2shape.resolution == 64
|
|
coords = transformer_options.get("coords", None)
|
|
coord_counts = transformer_options.get("coord_counts")
|
|
mode = transformer_options.get("generation_mode", "structure_generation")
|
|
is_512_run = False
|
|
timestep = timestep.to(self.dtype)
|
|
if mode == "shape_generation_512":
|
|
is_512_run = True
|
|
mode = "shape_generation"
|
|
if coords is not None:
|
|
x = x.squeeze(-1).transpose(1, 2)
|
|
not_struct_mode = True
|
|
else:
|
|
mode = "structure_generation"
|
|
not_struct_mode = False
|
|
|
|
if uses_1024_conditioning and not_struct_mode and not is_512_run:
|
|
context = embeds
|
|
|
|
sigmas = transformer_options.get("sigmas")[0].item()
|
|
if sigmas < 1.00001:
|
|
timestep *= 1000.0
|
|
if context.size(0) > 1:
|
|
cond = context.chunk(2)[1]
|
|
else:
|
|
cond = context
|
|
shape_rule = sigmas < self.guidance_interval[0] or sigmas > self.guidance_interval[1]
|
|
txt_rule = sigmas < self.guidance_interval_txt[0] or sigmas > self.guidance_interval_txt[1]
|
|
dense_out = None
|
|
|
|
if not_struct_mode:
|
|
orig_bsz = x.shape[0]
|
|
rule = txt_rule if mode == "texture_generation" else shape_rule
|
|
|
|
logical_batch = coord_counts.shape[0] if coord_counts is not None else 1
|
|
if rule and orig_bsz > logical_batch:
|
|
half = orig_bsz // 2
|
|
x_eval = x[half:]
|
|
t_eval = timestep[half:] if timestep.shape[0] > 1 else timestep
|
|
c_eval = cond
|
|
else:
|
|
x_eval = x
|
|
t_eval = timestep
|
|
c_eval = context
|
|
|
|
x_eval_norms = [float(v) for v in x_eval.square().sum(dim=(1, 2)).detach().cpu().tolist()]
|
|
c_eval_norms = [float(v) for v in c_eval.square().sum(dim=(1, 2)).detach().cpu().tolist()]
|
|
print(
|
|
"TRELLIS2_NOT_STRUCT_INPUT_TRACE",
|
|
{
|
|
"mode": mode,
|
|
"orig_bsz": int(orig_bsz),
|
|
"logical_batch": int(logical_batch),
|
|
"rule": bool(rule),
|
|
"coord_counts": coord_counts.tolist() if coord_counts is not None else None,
|
|
"x_eval_norms": x_eval_norms,
|
|
"c_eval_norms": c_eval_norms,
|
|
},
|
|
)
|
|
|
|
B, N, C = x_eval.shape
|
|
|
|
if mode in ["shape_generation", "texture_generation"]:
|
|
if coord_counts is not None:
|
|
logical_batch = coord_counts.shape[0]
|
|
if B % logical_batch != 0:
|
|
raise ValueError(
|
|
f"Trellis2 coord_counts batch {logical_batch} doesn't divide latent batch {B}"
|
|
)
|
|
repeat_factor = B // logical_batch
|
|
sparse_outs = []
|
|
active_coord_counts = []
|
|
if mode == "shape_generation" and repeat_factor > 1:
|
|
grouped_outs = []
|
|
grouped_counts = []
|
|
for i in range(logical_batch):
|
|
count = int(coord_counts[i].item())
|
|
coords_i = coords[coords[:, 0] == i].clone()
|
|
if coords_i.shape[0] != count:
|
|
raise ValueError(
|
|
f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}"
|
|
)
|
|
|
|
feat_batches = []
|
|
coord_batches = []
|
|
index_batch = []
|
|
for rep in range(repeat_factor):
|
|
out_index = rep * logical_batch + i
|
|
feat_batches.append(x_eval[out_index, :count])
|
|
coords_rep = coords_i.clone()
|
|
coords_rep[:, 0] = rep
|
|
coord_batches.append(coords_rep)
|
|
index_batch.append(out_index)
|
|
|
|
print(
|
|
"TRELLIS2_GROUPED_INPUT_TRACE",
|
|
{
|
|
"mode": mode,
|
|
"sample_index": int(i),
|
|
"coord_count": int(count),
|
|
"feat_norms": [float(v.square().sum().detach().cpu().item()) for v in feat_batches],
|
|
},
|
|
)
|
|
|
|
x_st_i = SparseTensor(
|
|
feats=torch.cat(feat_batches, dim=0),
|
|
coords=torch.cat(coord_batches, dim=0).to(torch.int32),
|
|
)
|
|
index_tensor = torch.tensor(index_batch, device=x_eval.device, dtype=torch.long)
|
|
if t_eval.shape[0] > 1:
|
|
t_i = t_eval.index_select(0, index_tensor)
|
|
else:
|
|
t_i = t_eval
|
|
if c_eval.shape[0] > 1:
|
|
c_i = c_eval.index_select(0, index_tensor)
|
|
else:
|
|
c_i = c_eval
|
|
|
|
if is_512_run:
|
|
sparse_out = self.img2shape_512(x_st_i, t_i, c_i)
|
|
else:
|
|
sparse_out = self.img2shape(x_st_i, t_i, c_i)
|
|
|
|
feats_group, coords_group = sparse_out.to_tensor_list()
|
|
if len(feats_group) != repeat_factor:
|
|
raise ValueError(
|
|
f"Trellis2 expected {repeat_factor} sparse output groups for batch {i}, got {len(feats_group)}"
|
|
)
|
|
for rep, (feats_rep, coords_rep) in enumerate(zip(feats_group, coords_group)):
|
|
if feats_rep.shape[0] != count:
|
|
raise ValueError(
|
|
f"Trellis2 sparse output rows for batch {i} rep {rep} expected {count}, got {feats_rep.shape[0]}"
|
|
)
|
|
if coords_rep.shape[0] != count:
|
|
raise ValueError(
|
|
f"Trellis2 sparse output coords for batch {i} rep {rep} expected {count}, got {coords_rep.shape[0]}"
|
|
)
|
|
grouped_outs.append(feats_group)
|
|
grouped_counts.append(count)
|
|
|
|
for rep in range(repeat_factor):
|
|
for i in range(logical_batch):
|
|
sparse_outs.append(grouped_outs[i][rep])
|
|
active_coord_counts.append(grouped_counts[i])
|
|
else:
|
|
for rep in range(repeat_factor):
|
|
for i in range(logical_batch):
|
|
out_index = rep * logical_batch + i
|
|
count = int(coord_counts[i].item())
|
|
coords_i = coords[coords[:, 0] == i].clone()
|
|
if coords_i.shape[0] != count:
|
|
raise ValueError(
|
|
f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}"
|
|
)
|
|
coords_i[:, 0] = 0
|
|
feats_i = x_eval[out_index, :count]
|
|
x_st_i = SparseTensor(feats=feats_i, coords=coords_i.to(torch.int32))
|
|
t_i = t_eval[out_index].unsqueeze(0) if t_eval.shape[0] > 1 else t_eval
|
|
c_i = c_eval[out_index].unsqueeze(0) if c_eval.shape[0] > 1 else c_eval
|
|
|
|
if mode == "shape_generation":
|
|
if is_512_run:
|
|
sparse_out = self.img2shape_512(x_st_i, t_i, c_i)
|
|
else:
|
|
sparse_out = self.img2shape(x_st_i, t_i, c_i)
|
|
else:
|
|
slat = transformer_options.get("shape_slat")
|
|
if slat is None:
|
|
raise ValueError("shape_slat can't be None")
|
|
if slat.ndim == 3:
|
|
if slat.shape[0] != logical_batch:
|
|
raise ValueError(
|
|
f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
|
|
)
|
|
if slat.shape[1] < count:
|
|
raise ValueError(
|
|
f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
|
|
)
|
|
slat_feats = slat[i, :count].to(x_st_i.device)
|
|
else:
|
|
slat_feats = slat[:count].to(x_st_i.device)
|
|
x_st_i = x_st_i.replace(feats=torch.cat([x_st_i.feats, slat_feats], dim=-1))
|
|
sparse_out = self.shape2txt(x_st_i, t_i, c_i)
|
|
|
|
sparse_outs.append(sparse_out.feats)
|
|
active_coord_counts.append(count)
|
|
|
|
out_channels = sparse_outs[0].shape[-1]
|
|
sparse_out_norms = [float(feats.square().sum().detach().cpu().item()) for feats in sparse_outs]
|
|
print(
|
|
"TRELLIS2_SPARSE_OUT_TRACE",
|
|
{
|
|
"mode": mode,
|
|
"coords_rows": int(coords.shape[0]),
|
|
"active_coord_counts": active_coord_counts,
|
|
"sparse_out_norms": sparse_out_norms,
|
|
},
|
|
)
|
|
padded = sparse_outs[0].new_zeros((B, N, out_channels))
|
|
for out_index, (count, feats_i) in enumerate(zip(active_coord_counts, sparse_outs)):
|
|
padded[out_index, :count] = feats_i
|
|
dense_out = padded.transpose(1, 2).unsqueeze(-1)
|
|
elif coords.shape[0] == N:
|
|
feats_flat = x_eval.reshape(-1, C)
|
|
coords_list = []
|
|
for i in range(B):
|
|
c = coords.clone()
|
|
c[:, 0] = i
|
|
coords_list.append(c)
|
|
batched_coords = torch.cat(coords_list, dim=0)
|
|
elif coords.shape[0] == B * N:
|
|
feats_flat = x_eval.reshape(-1, C)
|
|
batched_coords = coords
|
|
else:
|
|
raise ValueError(
|
|
f"Trellis2 expected coords rows {N} or {B * N}, got {coords.shape[0]}"
|
|
)
|
|
else:
|
|
batched_coords = coords
|
|
feats_flat = x_eval
|
|
|
|
if dense_out is None:
|
|
x_st = SparseTensor(feats=feats_flat, coords=batched_coords.to(torch.int32))
|
|
|
|
if dense_out is not None:
|
|
out = dense_out
|
|
elif mode == "shape_generation":
|
|
if is_512_run:
|
|
out = self.img2shape_512(x_st, t_eval, c_eval)
|
|
else:
|
|
out = self.img2shape(x_st, t_eval, c_eval)
|
|
elif mode == "texture_generation":
|
|
if self.shape2txt is None:
|
|
raise ValueError("Checkpoint for Trellis2 doesn't include texture generation!")
|
|
slat = transformer_options.get("shape_slat")
|
|
if slat is None:
|
|
raise ValueError("shape_slat can't be None")
|
|
|
|
if slat.ndim == 3:
|
|
if coord_counts is not None:
|
|
logical_batch = coord_counts.shape[0]
|
|
if slat.shape[0] != logical_batch:
|
|
raise ValueError(
|
|
f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
|
|
)
|
|
if B % logical_batch != 0:
|
|
raise ValueError(
|
|
f"Trellis2 coord_counts batch {logical_batch} doesn't divide latent batch {B}"
|
|
)
|
|
repeat_factor = B // logical_batch
|
|
slat_list = []
|
|
for _ in range(repeat_factor):
|
|
for i in range(logical_batch):
|
|
count = int(coord_counts[i].item())
|
|
if slat.shape[1] < count:
|
|
raise ValueError(
|
|
f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
|
|
)
|
|
slat_list.append(slat[i, :count])
|
|
slat_feats_batched = torch.cat(slat_list, dim=0).to(x_st.device)
|
|
else:
|
|
if slat.shape[0] != B:
|
|
raise ValueError(f"shape_slat batch {slat.shape[0]} doesn't match latent batch {B}")
|
|
if slat.shape[1] != N:
|
|
raise ValueError(f"shape_slat tokens {slat.shape[1]} doesn't match latent tokens {N}")
|
|
slat_feats_batched = slat.reshape(B * N, -1).to(x_st.device)
|
|
else:
|
|
base_slat_feats = slat[:N]
|
|
slat_feats_batched = base_slat_feats.repeat(B, 1).to(x_st.device)
|
|
x_st = x_st.replace(feats=torch.cat([x_st.feats, slat_feats_batched], dim=-1))
|
|
out = self.shape2txt(x_st, t_eval, c_eval)
|
|
else: # structure
|
|
orig_bsz = x.shape[0]
|
|
cond_or_uncond = transformer_options.get("cond_or_uncond") or []
|
|
batch_groups = len(cond_or_uncond) if len(cond_or_uncond) > 0 and orig_bsz % len(cond_or_uncond) == 0 else 1
|
|
logical_batch = orig_bsz // batch_groups
|
|
print(
|
|
"TRELLIS2_STRUCTURE_INPUT_TRACE",
|
|
{
|
|
"orig_bsz": int(orig_bsz),
|
|
"batch_groups": int(batch_groups),
|
|
"logical_batch": int(logical_batch),
|
|
"cond_or_uncond": cond_or_uncond,
|
|
"x_norms": [float(v) for v in x.square().sum(dim=(1, 2, 3, 4)).detach().cpu().tolist()],
|
|
"x_sums": [float(v) for v in x.sum(dim=(1, 2, 3, 4)).detach().cpu().tolist()],
|
|
"c_norms": [float(v) for v in context.square().sum(dim=(1, 2)).detach().cpu().tolist()],
|
|
"c_sums": [float(v) for v in context.sum(dim=(1, 2)).detach().cpu().tolist()],
|
|
},
|
|
)
|
|
|
|
if logical_batch > 1:
|
|
x_groups = x.reshape(batch_groups, logical_batch, *x.shape[1:])
|
|
if timestep.shape[0] > 1:
|
|
t_groups = timestep.reshape(batch_groups, logical_batch, *timestep.shape[1:])
|
|
else:
|
|
t_groups = timestep
|
|
c_groups = context.reshape(batch_groups, logical_batch, *context.shape[1:])
|
|
|
|
if shape_rule and batch_groups > 1:
|
|
selected_group_indices = [batch_groups - 1]
|
|
else:
|
|
selected_group_indices = list(range(batch_groups))
|
|
|
|
out_groups = []
|
|
selected_x_norms = []
|
|
selected_x_sums = []
|
|
selected_c_norms = []
|
|
selected_c_sums = []
|
|
for sample_index in range(logical_batch):
|
|
if shape_rule and batch_groups > 1:
|
|
half = orig_bsz // 2
|
|
x_i = x[half + sample_index].unsqueeze(0)
|
|
if timestep.shape[0] > 1:
|
|
t_i = timestep[half + sample_index].unsqueeze(0)
|
|
else:
|
|
t_i = timestep
|
|
if cond.shape[0] > 1:
|
|
c_i = cond[sample_index].unsqueeze(0)
|
|
else:
|
|
c_i = cond
|
|
else:
|
|
x_i = x_groups[selected_group_indices, sample_index]
|
|
if timestep.shape[0] > 1:
|
|
t_i = t_groups[selected_group_indices, sample_index]
|
|
else:
|
|
t_i = timestep
|
|
c_i = c_groups[selected_group_indices, sample_index]
|
|
selected_x_norms.extend(float(v) for v in x_i.square().sum(dim=(1, 2, 3, 4)).detach().cpu().tolist())
|
|
selected_x_sums.extend(float(v) for v in x_i.sum(dim=(1, 2, 3, 4)).detach().cpu().tolist())
|
|
selected_c_norms.extend(float(v) for v in c_i.square().sum(dim=(1, 2)).detach().cpu().tolist())
|
|
selected_c_sums.extend(float(v) for v in c_i.sum(dim=(1, 2)).detach().cpu().tolist())
|
|
out_groups.append(self.structure_model(x_i, t_i, c_i))
|
|
|
|
print(
|
|
"TRELLIS2_STRUCTURE_SELECTED_TRACE",
|
|
{
|
|
"selected_group_indices": selected_group_indices,
|
|
"selected_x_norms": selected_x_norms,
|
|
"selected_x_sums": selected_x_sums,
|
|
"selected_c_norms": selected_c_norms,
|
|
"selected_c_sums": selected_c_sums,
|
|
},
|
|
)
|
|
|
|
out = out_groups[0].new_zeros((orig_bsz, *out_groups[0].shape[1:]))
|
|
for sample_index, out_sample in enumerate(out_groups):
|
|
if shape_rule and batch_groups > 1:
|
|
repeated = out_sample[0]
|
|
for group_index in range(batch_groups):
|
|
out[group_index * logical_batch + sample_index] = repeated
|
|
else:
|
|
for local_group_index, group_index in enumerate(selected_group_indices):
|
|
out[group_index * logical_batch + sample_index] = out_sample[local_group_index]
|
|
else:
|
|
if shape_rule and orig_bsz > 1:
|
|
half = orig_bsz // 2
|
|
x = x[half:]
|
|
timestep = timestep[half:] if timestep.shape[0] > 1 else timestep
|
|
out = self.structure_model(x, timestep, cond if shape_rule and orig_bsz > 1 else context)
|
|
if shape_rule and orig_bsz > 1:
|
|
out = out.repeat(2, 1, 1, 1, 1)
|
|
|
|
print(
|
|
"TRELLIS2_STRUCTURE_OUTPUT_TRACE",
|
|
{
|
|
"out_norms": [float(v) for v in out.square().sum(dim=(1, 2, 3, 4)).detach().cpu().tolist()],
|
|
"out_sums": [float(v) for v in out.sum(dim=(1, 2, 3, 4)).detach().cpu().tolist()],
|
|
},
|
|
)
|
|
|
|
if not_struct_mode:
|
|
if dense_out is None:
|
|
out = out.feats
|
|
out = out.view(B, N, -1).transpose(1, 2).unsqueeze(-1)
|
|
if rule and orig_bsz > B:
|
|
out = out.repeat(orig_bsz // B, 1, 1, 1)
|
|
print(
|
|
"TRELLIS2_DENSE_OUT_TRACE",
|
|
{
|
|
"mode": mode,
|
|
"coords_rows": int(coords.shape[0]) if coords is not None else None,
|
|
"output_shape": list(out.shape),
|
|
"output_norms": [float(v) for v in out.squeeze(-1).square().sum(dim=(1, 2)).detach().cpu().tolist()],
|
|
"coord_counts": coord_counts.tolist() if coord_counts is not None else None,
|
|
},
|
|
)
|
|
return out
|