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Fix MultiTalk + SCAIL pose compatibility, expose audio_scale on SilentEmbeds #1931

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Fix MultiTalk + SCAIL pose compatibility, expose audio_scale on SilentEmbeds #1931

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f2a0df5
Fix MultiTalk audio_cross_attn compatibility with SCAIL pose conditio…
Li1177 Feb 7, 2026
db81a06
Expose audio_scale parameter on MultiTalkSilentEmbeds node
Li1177 Feb 7, 2026
3289178
Fix audio_cross_attn shape mismatch when grid_sizes temporal dim diff…
Li1177 Feb 7, 2026
1f09a3e
Polish model.py patch and align audio_scale parameter style
Li1177 Feb 7, 2026
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7 changes: 4 additions & 3 deletions multitalk/nodes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -352,6 +352,7 @@ class MultiTalkSilentEmbeds:
def INPUT_TYPES(s):
return {"required": {
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 1, "tooltip": "The total frame count to generate."}),
"audio_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "Strength of the audio conditioning"}),
},
}

Expand All @@ -360,17 +361,17 @@ def INPUT_TYPES(s):
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"

def process(self, num_frames):
def process(self, num_frames, audio_scale):
silence_path = os.path.join(script_directory, "encoded_silence.safetensors")
encoded_silence = load_torch_file(silence_path)["audio_emb"]

target_frames = num_frames
repeats = (target_frames + encoded_silence.shape[0] - 1) // encoded_silence.shape[0]
repeated = encoded_silence.repeat(repeats, 1, 1)
repeated = repeated[:target_frames]
multitalk_embeds = {
"audio_features": repeated,
"audio_scale": 1.0,
"audio_scale": audio_scale,
"audio_cfg_scale": 1.0,
"ref_target_masks": None
}
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22 changes: 20 additions & 2 deletions wanvideo/modules/model.py
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Expand Up @@ -1343,8 +1343,26 @@ def forward(
if audio_output_cond is not None:
x_audio = torch.cat([audio_output_cond, x_audio], dim=1).contiguous()
else:
x_audio = self.audio_cross_attn(self.norm_x(x.to(self.norm_x.weight.dtype)).to(input_dtype), encoder_hidden_states=multitalk_audio_embedding,
shape=grid_sizes[0], x_ref_attn_map=x_ref_attn_map, human_num=human_num)
# audio temporal frames may differ from grid_sizes when external conditioning
# (e.g. SCAIL pose ref_latent) modifies the spatial-temporal token layout
x_normed = self.norm_x(x.to(self.norm_x.weight.dtype)).to(input_dtype)
N_h, N_w = grid_sizes[0][1].item(), grid_sizes[0][2].item()
S = N_h * N_w
audio_N_t = multitalk_audio_embedding.shape[0]
audio_tokens = audio_N_t * S
if grid_sizes[0][0].item() != audio_N_t or x_normed.shape[1] != audio_tokens:
x_for_audio = x_normed[:, :audio_tokens]
audio_shape = grid_sizes[0].clone()
audio_shape[0] = audio_N_t
else:
x_for_audio = x_normed
audio_shape = grid_sizes[0]
x_audio = self.audio_cross_attn(x_for_audio, encoder_hidden_states=multitalk_audio_embedding,
shape=audio_shape, x_ref_attn_map=x_ref_attn_map, human_num=human_num)
if x_normed.shape[1] > audio_tokens:
x_audio_padded = torch.zeros(x.shape, dtype=x_audio.dtype, device=x_audio.device)
x_audio_padded[:, :audio_tokens] = x_audio
x_audio = x_audio_padded
x.add_(x_audio, alpha=audio_scale)
del x_audio

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