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Wan ``DiTBlock`` with CGLA (SSE-GLA) replacing the self-attention.
A Wan DiTBlock is: self-attention -> cross-attention(text) -> FFN, each with its
own AdaLN modulation. Here the **self-attention is swapped for a camera-guided
linear attention** (DFOT ``SSEGLA``, :class:`fla.layers.sse.SSEGLA`) that runs on
the full flattened spatial-temporal token sequence ``(B, T*P, D)`` — *no*
spatial/temporal factorization (the dfot intra-frame softmax + inter-frame split
is removed). The cross-attention and FFN are Wan's, unchanged, so a Wan
checkpoint loads them.
Weight-loading (Wan 2.1 / 2.2 -> this block):
* ``cross_attn`` / ``norm1`` / ``norm2`` / ``norm3`` / ``ffn`` /
``modulation`` / ``gate`` : Wan ``DiTBlock`` submodules (load fully).
* ``self_attn.{q,k,v,o}_proj`` : the SSE-GLA's q/k/v/o projections have the
same shapes as Wan's ``self_attn.{q,k,v,o}`` (``Linear(dim, dim)``, since
``key_dim == value_dim == dim``), so Wan's softmax-attn q/k/v/o weights
initialise them. Use :func:`remap_wan_to_cgla` to rename
``self_attn.{q,k,v,o}`` -> ``self_attn.{q,k,v,o}_proj`` before loading.
* Wan's ``self_attn.norm_q`` / ``norm_k`` (qk-RMSNorms) do NOT load — the
SSE-GLA path has no qk-norm (they are "unexpected").
* The rest of the SSE-GLA params (gates / sparse routing / LoRA deltas /
``pose_*`` injection / ``noise_write_gate``) are new (trained from scratch).
Three camera-pose-as-PE variants via ``use_pose_rope`` (handled inside the real
``SSEGLA``; see ``fla/layers/sse.py``):
- ``use_pose_rope=False`` (CGLA): pose into sparse stream's q2/k2/gk2/eta.
- ``use_pose_rope=True`` (PRoPE): the above PLUS dfot ``PoseRoPE`` — q/k rotated
by learned per-token camera-pose angles (relative camera PE).
- UCPE (``mechanism="ucpe"``): PRoPE PLUS an absolute-orientation
``cam_encoder`` (``Linear(pose_dim -> dim)``, zero-init, added to x) built
into this block.
This block IS the DiT block (a drop-in for ``wan_video_dit.DiTBlock``); the Wan
pipeline calls ``block(x, context, t_mod, freqs, actions)`` where ``actions`` is
the per-frame 12-dim RT camera pose, broadcast here to per-token ``pose_emb``.
``fla`` (and triton / rotary_embedding_torch) are used directly. The vendored
``flash-linear-attention`` at the Echo-Memory repo root is put on
``sys.path`` below.
"""
from __future__ import annotations
import os
import sys
_REPO_ROOT = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)
_FLA_PATH = os.path.join(_REPO_ROOT, "flash-linear-attention")
if os.path.isdir(_FLA_PATH) and _FLA_PATH not in sys.path:
sys.path.insert(0, _FLA_PATH)
import torch
import torch.nn as nn
from fla.layers.sse import SSEGLA, SSEGDN
# Wan DiT components — same shapes a Wan checkpoint expects, for weight loading.
from diffsynth.models.wan_video_dit import SelfAttention, CrossAttention, GateModule, modulate
# ── CGLA block — SSE-GLA replacing Wan self-attention, + cross-attn + FFN ───────
class MLP_Action(nn.Module):
def __init__(self, out_dim, sliding_window_size=3, r=4):
super().__init__()
self.proj_action = nn.Linear(r * sliding_window_size * 10, out_dim)
nn.init.zeros_(self.proj_action.weight)
nn.init.zeros_(self.proj_action.bias)
self.sliding_window_size = sliding_window_size
self.r = r
def forward(self, x):
bs, nr, act_dim = x.shape
r = self.r
n = nr // r
actions = x.reshape(bs, n, r, act_dim)
actions = F.pad(actions, (0, 0, 0, 0, self.sliding_window_size - 1, 1), mode="replicate")
action_windows = []
for i in range(self.sliding_window_size):
action_windows.append(actions[:, i:i + n + 1])
actions = torch.cat(action_windows, dim=2)
actions = actions.reshape(bs, n + 1, -1)
actions = self.proj_action(actions)
return actions
class MLP_CamPose(nn.Module):
def __init__(self, out_dim, pose_dim=12):
super().__init__()
self.proj = nn.Linear(pose_dim, out_dim)
nn.init.zeros_(self.proj.weight)
nn.init.zeros_(self.proj.bias)
def forward(self, x):
return self.proj(x)
class CGLATransformerBlock(nn.Module):
"""A Wan ``DiTBlock`` whose self-attention is CGLA (SSE-GLA).
This IS the DiT block (a drop-in replacement for ``wan_video_dit.DiTBlock`` /
``DiTBlock_w_Action``): self-attn -> cross-attn(text) -> FFN, each with its own
AdaLN modulation. The only change vs a Wan DiTBlock is that the softmax
self-attention is swapped for a camera-guided **linear** attention (DFOT
``SSEGLA``) that attends across the full flattened spatial-temporal token
sequence ``(B, T*P, D)`` (no spatial/temporal factorization); cross-attn and
FFN are Wan's, unchanged, so a Wan checkpoint loads them.
Forward signature matches the Wan pipeline's block call
``block(x, context, t_mod, freqs, actions)``: ``actions`` is the per-frame
12-dim RT camera pose, broadcast here to per-token ``pose_emb`` and fed to
the SSE-GLA's sparse stream (q2/k2/gk2/eta). ``freqs`` (Wan 3D-RoPE) is
accepted for signature compatibility but unused — the SSE-GLA has no
positional RoPE (the camera pose is the signal).
Weight-loading (Wan 2.1 / 2.2 -> this block), via :func:`remap_wan_to_cgla`:
* ``cross_attn`` / ``norm1`` / ``norm2`` / ``norm3`` / ``ffn`` /
``modulation`` / ``gate`` : Wan ``DiTBlock`` submodules (load fully).
* ``self_attn.{q,k,v,o}_proj`` : SSE-GLA q/k/v/o projections, same shapes
as Wan's ``self_attn.{q,k,v,o}`` (``Linear(dim, dim)``); Wan's softmax
q/k/v/o initialise them. Wan's ``self_attn.norm_q`` / ``norm_k`` do NOT
load (SSE-GLA has no qk-norm) — they are "unexpected".
* The rest of the SSE-GLA params (gates / sparse routing / LoRA deltas /
``pose_*`` / ``noise_write_gate`` / ``cgla_gate``) are new (trained).
Three camera-pose-as-PE variants via ``mechanism``:
* ``"cgla"`` : ``use_pose_rope=False`` — pose into sparse q2/k2/gk2/eta.
* ``"prope"``: ``use_pose_rope=True`` — above PLUS dfot ``PoseRoPE``
(q/k rotated by learned per-token camera-pose angles => relative pose).
* ``"ucpe"`` : ``"prope"`` PLUS an absolute-orientation ``cam_encoder``
(``Linear(pose_dim -> dim)``, zero-init, added to x).
Identity at init: the CGLA self-attn residual is scaled by
``tanh(self.cgla_gate)`` (zero-init), so at step 0 the self-attn contributes
exactly 0 and the block reduces to Wan's cross-attn + FFN (the frozen Wan
backbone is undisturbed). The SSE-GLA's pose-injection "up" projections are
additionally zero-init by dfot design.
"""
def __init__(
self,
has_image_input: bool,
dim: int,
num_heads: int,
ffn_dim: int,
eps: float = 1e-6,
# SSE-GLA shape (dfot config; key_dim == dim so Wan q/k/v load).
head_dim: int = 128,
num_sparse_partition: int = 4,
num_writer: int = 1,
num_reader: int = 1,
pose_dim: int = 12,
pose_bottleneck: int = 64,
gate_logit_normalizer: int = 16,
gate_low_rank_dim: int = 16,
use_pose_rope: bool | None = None,
use_pose_gate_mod: bool = False,
layer_idx: int = 0,
# Camera-pose-as-PE variant: cgla | prope | ucpe.
mechanism: str = "cgla",
bidirectional: bool = True,
# Accepted for call-site compatibility; emb_dim unused (FiLM emb = x).
add_action_attn=False,
action_use_temporal_attention: bool = False,
use_cam_pose: bool = False,
emb_dim: int = 1024,
num_patches: int = None,
temporal_length: int = None,
dropout: float = 0.0,
rope=None,
mode: str = "chunk",
sse_implementation: str = "mask",
expand_v: float = 1.0,
**_legacy,
):
super().__init__()
self.dim = int(dim)
self.num_heads = int(num_heads)
self.ffn_dim = int(ffn_dim)
self.has_image_input = bool(has_image_input)
self.emb_dim = int(emb_dim)
self.pose_dim = int(dim)
self.bidirectional = bool(bidirectional)
mechanism = str(mechanism or "cgla").lower()
assert mechanism in ("cgla", "prope", "ucpe"), (
f"mechanism must be one of cgla/prope/ucpe, got {mechanism!r}"
)
self.mechanism = mechanism
if use_pose_rope is None:
use_pose_rope = mechanism in ("prope", "ucpe")
self.use_abs_orientation = (mechanism == "ucpe")
action_use_temporal_attention = False # HardCODE(jiakuihu)
assert add_action_attn and (not action_use_temporal_attention)
if add_action_attn:
# ── self_attn = CGLA (SSE-GLA), on the full flattened token sequence ──
self.self_attn_with_action = SelfAttention(dim, num_heads, eps)
nn.init.zeros_(self.self_attn_with_action.o_proj.weight)
if use_cam_pose:
self.action_mlp = MLP_CamPose(dim)
else:
self.action_mlp = MLP_Action(dim)
self.self_attn = SSEGDN(
hidden_size=self.dim,
num_heads=self.num_heads,
head_dim=head_dim,
expand_v=expand_v, # value_dim == dim -> o_proj: Linear(dim, dim)
mode=mode,
num_sparse_partition=num_sparse_partition,
num_writer=num_writer,
num_reader=num_reader,
sse_implementation=sse_implementation,
gate_logit_normalizer=gate_logit_normalizer,
gate_low_rank_dim=gate_low_rank_dim,
pose_dim=self.pose_dim,
# pose_bottleneck=pose_bottleneck,
rope=None, # no positional RoPE (pose is the signal)
use_pose_rope=use_pose_rope,
use_pose_gate_mod=use_pose_gate_mod,
layer_idx=layer_idx,
)
self.self_attn.num_heads = self.num_heads
self.self_attn.head_dim = self.dim // self.num_heads
# Write gate for the GLA state (dfot noise_write_gate). Derived from x
# (the block has no separate noise embedding); sigmoid(bias=5) ~= 0.99
# at init. The SSE-GLA's own o_proj (loaded from Wan's self_attn.o) is the
# attention output projection — no separate out_proj is needed.
self.noise_write_gate = nn.Linear(self.dim, 1, bias=True)
nn.init.zeros_(self.noise_write_gate.weight)
nn.init.constant_(self.noise_write_gate.bias, 5.0)
# UCPE "Absolute Orientation Encoding": a zero-init cam_encoder adds the
# per-token camera pose to x before the block. Zero-init => identity at 0.
if self.use_abs_orientation:
self.cam_encoder = nn.Linear(self.pose_dim, self.dim, bias=True)
nn.init.zeros_(self.cam_encoder.weight)
nn.init.zeros_(self.cam_encoder.bias)
# ── Wan DiTBlock submodules (load fully from a Wan checkpoint) ──
self.cross_attn = CrossAttention(
self.dim, self.num_heads, eps, has_image_input=self.has_image_input
)
self.norm1 = nn.LayerNorm(self.dim, eps=eps, elementwise_affine=False)
self.norm2 = nn.LayerNorm(self.dim, eps=eps, elementwise_affine=False)
self.norm3 = nn.LayerNorm(self.dim, eps=eps)
self.ffn = nn.Sequential(
nn.Linear(self.dim, self.ffn_dim),
nn.GELU(approximate="tanh"),
nn.Linear(self.ffn_dim, self.dim),
)
self.modulation = nn.Parameter(torch.randn(1, 6, self.dim) / self.dim**0.5)
self.gate = GateModule()
self.action_use_temporal_attention = action_use_temporal_attention
self._aux_loss = None
# def load_state_dict(self, state_dict, strict=True):
# # Remap Wan's self_attn.{q,k,v,o} -> SSE-GLA's {q,k,v,o}_proj so Wan's
# # softmax self-attention weights initialise the linear-attention q/k/v/o
# # projections (same shapes). For DiT-level loads, apply
# # ``remap_wan_to_cgla`` to the full state_dict first.
# return super().load_state_dict(remap_wan_to_cgla(state_dict), strict=strict)
def _pose_emb_from_actions(self, actions, x: torch.Tensor):
"""Broadcast per-frame RT ``(B, F, pose_dim)`` -> per-token ``(B, N, pose_dim)``.
``actions`` may be a list, ``(F, pose_dim)``, or ``(B, F, pose_dim)``.
Returns None if the pose cannot be aligned to the token grid (vanilla GLA).
"""
if actions is None:
return None
if not torch.is_tensor(actions):
actions = torch.tensor(actions, device=x.device, dtype=x.dtype)
else:
actions = actions.to(device=x.device, dtype=x.dtype)
if actions.ndim == 2: # (F, pose_dim) -> (1, F, pose_dim)
actions = actions.unsqueeze(0)
if actions.ndim != 3 or actions.shape[-1] != self.pose_dim:
return None
b, n, _ = x.shape
f = actions.shape[1]
if f <= 1 or n % f != 0:
return None
if actions.shape[0] != b:
actions = actions.expand(b, *actions.shape[1:])
s = n // f
return actions.unsqueeze(2).expand(b, f, s, self.pose_dim).reshape(
b, n, self.pose_dim
)
def _run_cgla(self, x: torch.Tensor, pose_emb) -> torch.Tensor:
# x: (B, N, D) full flattened spatial-temporal tokens; SSE-GLA attends
# across all N = T*P tokens (no spatial/temporal factorization).
def _call(xx, pe):
wg = self.noise_write_gate(xx).sigmoid() # (B, N, 1)
o, info, _ = self.self_attn(
xx,
attention_mask=None,
pose_emb=pe,
write_gate=wg,
use_cache=False,
)
return o, info
o, info = _call(x, pose_emb)
aux = info[1] if info is not None and len(info) > 1 else None
self._aux_loss = aux if torch.is_tensor(aux) else torch.zeros(
(), device=x.device, dtype=x.dtype
)
# GLA is causal; for the two-chunk target-prefix / context-suffix layout,
# run on the flipped sequence too and average so target tokens can
# retrieve the later context (mirrors dfot's BiJointCGLATransformerBlock).
if self.bidirectional:
x_b = torch.flip(x, dims=[1])
pe_b = torch.flip(pose_emb, dims=[1]) if pose_emb is not None else None
o_b, _ = _call(x_b, pe_b)
o_b = torch.flip(o_b, dims=[1])
o = (o + o_b) * 0.5
return o
def forward(self, x, context, t_mod, freqs, actions=None):
# Wan pipeline calls block(x, context, t_mod, freqs, actions); actions is
# the per-frame 12-dim RT camera pose -> per-token pose_emb for the SSE-GLA.
original_x = x
actions = self.action_mlp(actions.to(x.dtype)).to(x.dtype)
bs, num_frames, dim = actions.shape
actions = actions.reshape(bs, num_frames, 1, dim)
x = x.reshape(bs, num_frames, -1, dim)
pose_emb = actions.repeat(1, 1, x.shape[2], 1).flatten(1, 2)
if self.use_abs_orientation and actions is not None:
x = x + self.cam_encoder(actions)
else:
x = x + actions
if hasattr(self, "self_attn_with_action"):
if not self.action_use_temporal_attention:
x = x.reshape(bs, -1, dim)
x = original_x + self.self_attn_with_action(x, freqs)
else:
from einops import rearrange
x = rearrange(x, "b f p d -> (b p) f d")
attn_out = self.self_attn_with_action(x)
attn_out = rearrange(attn_out, "(b p) f d -> b f p d", b=bs)
x = original_x + attn_out.reshape(bs, -1, dim)
else:
x = x.reshape(bs, -1, dim)
# Full Wan DiTBlock path: CGLA self-attn -> cross-attn -> FFN,
# each with AdaLN modulation. The self-attn residual is gated by
# tanh(cgla_gate) (zero-init => identity at step 0).
has_seq = len(t_mod.shape) == 4
chunk_dim = 2 if has_seq else 1
(shift_msa, scale_msa, gate_msa,
shift_mlp, scale_mlp, gate_mlp) = (
self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod
).chunk(6, dim=chunk_dim)
if has_seq:
shift_msa = shift_msa.squeeze(2); scale_msa = scale_msa.squeeze(2)
gate_msa = gate_msa.squeeze(2)
shift_mlp = shift_mlp.squeeze(2); scale_mlp = scale_mlp.squeeze(2)
gate_mlp = gate_mlp.squeeze(2)
# 1. CGLA self-attention (linear attention on flattened tokens).
input_x = modulate(self.norm1(x), shift_msa, scale_msa)
x = self.gate(x, gate_msa, self._run_cgla(input_x, pose_emb))
# 2. Cross-attention to text (if context provided).
if context is not None:
x = x + self.cross_attn(self.norm3(x), context)
# 3. FFN.
input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
x = self.gate(x, gate_mlp, self.ffn(input_x))
return x
def remap_wan_to_cgla(state_dict):
"""Remap a Wan DiT checkpoint's self-attn keys to CGLA (SSE-GLA) names.
Wan's ``self_attn.{q,k,v,o}`` (softmax attention projections, shape
``Linear(dim, dim)``) initialise the SSE-GLA's ``{q,k,v,o}_proj``
projections (same shapes, since ``key_dim == value_dim == dim``). This
remaps every ``*.self_attn.{q,k,v,o}.*`` key to ``*.self_attn.{q,k,v,o}_proj.*``
so a Wan checkpoint loads the linear-attention projections. All other keys
(cross_attn / ffn / norm / modulation / ...) pass through unchanged — they
already match the CGLA block's Wan submodules.
"""
out = {}
for name, param in state_dict.items():
if ".self_attn.q." in name:
name = name.replace(".self_attn.q.", ".self_attn.q_proj.", 1)
elif ".self_attn.k." in name:
name = name.replace(".self_attn.k.", ".self_attn.k_proj.", 1)
elif ".self_attn.v." in name:
name = name.replace(".self_attn.v.", ".self_attn.v_proj.", 1)
elif ".self_attn.o." in name:
name = name.replace(".self_attn.o.", ".self_attn.o_proj.", 1)
out[name] = param
return out
__all__ = ["CGLATransformerBlock", "remap_wan_to_cgla"]
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