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Support Multi/InfiniteTalk #10179

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efe83f5
re-init
kijai Sep 30, 2025
460ce7f
Update model_multitalk.py
kijai Oct 3, 2025
6f6db12
whitespace...
kijai Oct 3, 2025
00c069d
Update model_multitalk.py
kijai Oct 3, 2025
57567bd
remove print
kijai Oct 3, 2025
9c5022e
this is redundant
kijai Oct 10, 2025
d0dce6b
Merge remote-tracking branch 'upstream/master' into multitalk
kijai Oct 10, 2025
7842a5c
remove import
kijai Oct 10, 2025
99dc959
Merge remote-tracking branch 'upstream/master' into multitalk
kijai Oct 19, 2025
4cbc1a6
Merge remote-tracking branch 'upstream/master' into multitalk
kijai Oct 23, 2025
f5d53f2
Restore preview functionality
kijai Oct 23, 2025
897ffeb
Merge remote-tracking branch 'upstream/master' into multitalk
kijai Oct 30, 2025
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22 changes: 17 additions & 5 deletions comfy/ldm/wan/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,7 +87,7 @@ def qkv_fn_k(x):
)

x = self.o(x)
return x
return x, q, k
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There is some uncertainty about whether returning this will in general increase the memory peak of WAN within native ComfyUI. Instead, comfy suggests that you add a patch to replace the x = optimized_attention(...) call on line 81 byreusing the ModelPatcher.set_model_attn1_replace functionality (in unet, attn1 is self, attn2 is cross), which can then do the optimized_attention call + the partial attention thing that happens inside the cross_attn patch. To get the q + k for the cross_attn patch, you can store the q and k values in transformer_options instead and then pop them out after usage.

The transformer_index can stay None (not given) since that was something unique to unet models.

It would probably be more optimal to not call optimized_attention anymore and just reuse the logic of hte slower partial attention thingy in this code, but comfy said he would be fine if you didn't go that far and just kept both within that attention replacement function.



class WanT2VCrossAttention(WanSelfAttention):
Expand Down Expand Up @@ -178,7 +178,8 @@ def __init__(self,
window_size=(-1, -1),
qk_norm=True,
cross_attn_norm=False,
eps=1e-6, operation_settings={}):
eps=1e-6, operation_settings={},
block_idx=None):
super().__init__()
self.dim = dim
self.ffn_dim = ffn_dim
Expand All @@ -187,6 +188,7 @@ def __init__(self,
self.qk_norm = qk_norm
self.cross_attn_norm = cross_attn_norm
self.eps = eps
self.block_idx = block_idx
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Instead of having the block_idx as a class parameter, can you make it an entry in transformer_options? This can be done within the for loop where the blocks are executed in the forward function. VACE already has the block_id variable, so the alternative would be to also call this block_id instead, but for some of the attention stuff I want to support (different attention in different blocks), adding block_idx to transformer_options instead makes the most sense I think.


# layers
self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
Expand Down Expand Up @@ -225,14 +227,16 @@ def forward(
"""
# assert e.dtype == torch.float32

patches = transformer_options.get("patches", {})

if e.ndim < 4:
e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
else:
e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
# assert e[0].dtype == torch.float32

# self-attention
y = self.self_attn(
y, q, k = self.self_attn(
torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
freqs, transformer_options=transformer_options)

Expand All @@ -241,6 +245,11 @@ def forward(

# cross-attention & ffn
x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)

if "cross_attn" in patches:
for p in patches["cross_attn"]:
x = x + p({"x": x, "q": q, "k": k, "block_idx": self.block_idx, "transformer_options": transformer_options})

y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
x = torch.addcmul(x, y, repeat_e(e[5], x))
return x
Expand All @@ -262,6 +271,7 @@ def __init__(
):
super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
self.block_id = block_id
self.block_idx = None
if block_id == 0:
self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
Expand Down Expand Up @@ -486,8 +496,8 @@ def __init__(self,
cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
self.blocks = nn.ModuleList([
wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads,
window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
for _ in range(num_layers)
window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings, block_idx=i)
for i in range(num_layers)
])

# head
Expand Down Expand Up @@ -540,6 +550,7 @@ def forward_orig(
# embeddings
x = self.patch_embedding(x.float()).to(x.dtype)
grid_sizes = x.shape[2:]
transformer_options["grid_sizes"] = grid_sizes
x = x.flatten(2).transpose(1, 2)

# time embeddings
Expand Down Expand Up @@ -722,6 +733,7 @@ def forward_orig(
# embeddings
x = self.patch_embedding(x.float()).to(x.dtype)
grid_sizes = x.shape[2:]
transformer_options["grid_sizes"] = grid_sizes
x = x.flatten(2).transpose(1, 2)

# time embeddings
Expand Down
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