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Support Multi/InfiniteTalk (#10179)

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* re-init

* Update model_multitalk.py

* whitespace...

* Update model_multitalk.py

* remove print

* this is redundant

* remove import

* Restore preview functionality

* Move block_idx to transformer_options

* Remove LoopingSamplerCustomAdvanced

* Remove looping functionality, keep extension functionality

* Update model_multitalk.py

* Handle ref_attn_mask with separate patch to avoid having to always return q and k from self_attn

* Chunk attention map calculation for multiple speakers to reduce peak VRAM usage

* Update model_multitalk.py

* Add ModelPatch type back

* Fix for latest upstream

* Use DynamicCombo for cleaner node

Basically just so that single_speaker mode hides mask inputs and 2nd audio input

* Update nodes_wan.py
This commit is contained in:
Jukka Seppänen
2026-01-22 06:09:48 +02:00
committed by GitHub
parent 245f6139b6
commit 16b9aabd52
5 changed files with 727 additions and 3 deletions
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17
comfy/ldm/wan/model.py
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@@ -62,6 +62,8 @@ class WanSelfAttention(nn.Module):
x(Tensor): Shape [B, L, num_heads, C / num_heads]
freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
"""
patches = transformer_options.get("patches", {})
b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
def qkv_fn_q(x):
@@ -86,6 +88,10 @@ class WanSelfAttention(nn.Module):
transformer_options=transformer_options,
)
if "attn1_patch" in patches:
for p in patches["attn1_patch"]:
x = p({"x": x, "q": q, "k": k, "transformer_options": transformer_options})
x = self.o(x)
return x
@@ -225,6 +231,8 @@ class WanAttentionBlock(nn.Module):
"""
# 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:
@@ -242,6 +250,11 @@ class WanAttentionBlock(nn.Module):
# cross-attention & ffn
x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
if "attn2_patch" in patches:
for p in patches["attn2_patch"]:
x = p({"x": x, "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
@@ -488,7 +501,7 @@ class WanModel(torch.nn.Module):
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)
for i in range(num_layers)
])
# head
@@ -541,6 +554,7 @@ class WanModel(torch.nn.Module):
# 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
@@ -738,6 +752,7 @@ class VaceWanModel(WanModel):
# 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