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Merged
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Feb 5, 2026
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[pull] master from Comfy-Org:master #591

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72 changes: 66 additions & 6 deletions comfy/ldm/ace/ace_step15.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,67 @@
import comfy.model_management
from comfy.ldm.flux.layers import timestep_embedding

def get_silence_latent(length, device):
head = torch.tensor([[[ 0.5707, 0.0982, 0.6909, -0.5658, 0.6266, 0.6996, -0.1365, -0.1291,
-0.0776, -0.1171, -0.2743, -0.8422, -0.1168, 1.5539, -4.6936, 0.7436,
-1.1846, -0.2637, 0.6933, -6.7266, 0.0966, -0.1187, -0.3501, -1.1736,
0.0587, -2.0517, -1.3651, 0.7508, -0.2490, -1.3548, -0.1290, -0.7261,
1.1132, -0.3249, 0.2337, 0.3004, 0.6605, -0.0298, -0.1989, -0.4041,
0.2843, -1.0963, -0.5519, 0.2639, -1.0436, -0.1183, 0.0640, 0.4460,
-1.1001, -0.6172, -1.3241, 1.1379, 0.5623, -0.1507, -0.1963, -0.4742,
-2.4697, 0.5302, 0.5381, 0.4636, -0.1782, -0.0687, 1.0333, 0.4202],
[ 0.3040, -0.1367, 0.6200, 0.0665, -0.0642, 0.4655, -0.1187, -0.0440,
0.2941, -0.2753, 0.0173, -0.2421, -0.0147, 1.5603, -2.7025, 0.7907,
-0.9736, -0.0682, 0.1294, -5.0707, -0.2167, 0.3302, -0.1513, -0.8100,
-0.3894, -0.2884, -0.3149, 0.8660, -0.3817, -1.7061, 0.5824, -0.4840,
0.6938, 0.1859, 0.1753, 0.3081, 0.0195, 0.1403, -0.0754, -0.2091,
0.1251, -0.1578, -0.4968, -0.1052, -0.4554, -0.0320, 0.1284, 0.4974,
-1.1889, -0.0344, -0.8313, 0.2953, 0.5445, -0.6249, -0.1595, -0.0682,
-3.1412, 0.0484, 0.4153, 0.8260, -0.1526, -0.0625, 0.5366, 0.8473],
[ 5.3524e-02, -1.7534e-01, 5.4443e-01, -4.3501e-01, -2.1317e-03,
3.7200e-01, -4.0143e-03, -1.5516e-01, -1.2968e-01, -1.5375e-01,
-7.7107e-02, -2.0593e-01, -3.2780e-01, 1.5142e+00, -2.6101e+00,
5.8698e-01, -1.2716e+00, -2.4773e-01, -2.7933e-02, -5.0799e+00,
1.1601e-01, 4.0987e-01, -2.2030e-02, -6.6495e-01, -2.0995e-01,
-6.3474e-01, -1.5893e-01, 8.2745e-01, -2.2992e-01, -1.6816e+00,
5.4440e-01, -4.9579e-01, 5.5128e-01, 3.0477e-01, 8.3052e-02,
-6.1782e-02, 5.9036e-03, 2.9553e-01, -8.0645e-02, -1.0060e-01,
1.9144e-01, -3.8124e-01, -7.2949e-01, 2.4520e-02, -5.0814e-01,
2.3977e-01, 9.2943e-02, 3.9256e-01, -1.1993e+00, -3.2752e-01,
-7.2707e-01, 2.9476e-01, 4.3542e-01, -8.8597e-01, -4.1686e-01,
-8.5390e-02, -2.9018e+00, 6.4988e-02, 5.3945e-01, 9.1988e-01,
5.8762e-02, -7.0098e-02, 6.4772e-01, 8.9118e-01],
[-3.2225e-02, -1.3195e-01, 5.6411e-01, -5.4766e-01, -5.2170e-03,
3.1425e-01, -5.4367e-02, -1.9419e-01, -1.3059e-01, -1.3660e-01,
-9.0984e-02, -1.9540e-01, -2.5590e-01, 1.5440e+00, -2.6349e+00,
6.8273e-01, -1.2532e+00, -1.9810e-01, -2.2793e-02, -5.0506e+00,
1.8818e-01, 5.0109e-01, 7.3546e-03, -6.8771e-01, -3.0676e-01,
-7.3257e-01, -1.6687e-01, 9.2232e-01, -1.8987e-01, -1.7267e+00,
5.3355e-01, -5.3179e-01, 4.4953e-01, 2.8820e-01, 1.3012e-01,
-2.0943e-01, -1.1348e-01, 3.3929e-01, -1.5069e-01, -1.2919e-01,
1.8929e-01, -3.6166e-01, -8.0756e-01, 6.6387e-02, -5.8867e-01,
1.6978e-01, 1.0134e-01, 3.3877e-01, -1.2133e+00, -3.2492e-01,
-8.1237e-01, 3.8101e-01, 4.3765e-01, -8.0596e-01, -4.4531e-01,
-4.7513e-02, -2.9266e+00, 1.1741e-03, 4.5123e-01, 9.3075e-01,
5.3688e-02, -1.9621e-01, 6.4530e-01, 9.3870e-01]]], device=device).movedim(-1, 1)

silence_latent = torch.tensor([[[-1.3672e-01, -1.5820e-01, 5.8594e-01, -5.7422e-01, 3.0273e-02,
2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
-2.7710e-02, -1.8066e-01, -2.9688e-01, 1.6016e+00, -2.6719e+00,
7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
2.4316e-01, 4.7266e-01, 4.6387e-02, -6.6406e-01, -2.1973e-01,
-6.7578e-01, -1.5723e-01, 9.5312e-01, -2.0020e-01, -1.7109e+00,
5.8984e-01, -5.7422e-01, 5.1562e-01, 2.8320e-01, 1.4551e-01,
-1.8750e-01, -5.9814e-02, 3.6719e-01, -1.0059e-01, -1.5723e-01,
2.0605e-01, -4.3359e-01, -8.2812e-01, 4.5654e-02, -6.6016e-01,
1.4844e-01, 9.4727e-02, 3.8477e-01, -1.2578e+00, -3.3203e-01,
-8.5547e-01, 4.3359e-01, 4.2383e-01, -8.9453e-01, -5.0391e-01,
-5.6152e-02, -2.9219e+00, -2.4658e-02, 5.0391e-01, 9.8438e-01,
7.2754e-02, -2.1582e-01, 6.3672e-01, 1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, length)
silence_latent[:, :, :head.shape[-1]] = head
return silence_latent


def get_layer_class(operations, layer_name):
if operations is not None and hasattr(operations, layer_name):
return getattr(operations, layer_name)
Expand Down Expand Up @@ -1040,22 +1101,21 @@ def prepare_condition(
lm_hints = self.detokenizer(lm_hints_5Hz)

lm_hints = lm_hints[:, :src_latents.shape[1], :]
if is_covers is None:
if is_covers is None or is_covers is True:
src_latents = lm_hints
else:
src_latents = torch.where(is_covers.unsqueeze(-1).unsqueeze(-1) > 0, lm_hints, src_latents)
elif is_covers is False:
src_latents = refer_audio_acoustic_hidden_states_packed

context_latents = torch.cat([src_latents, chunk_masks.to(src_latents.dtype)], dim=-1)

return encoder_hidden, encoder_mask, context_latents

def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, **kwargs):
def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, **kwargs):
text_attention_mask = None
lyric_attention_mask = None
refer_audio_order_mask = None
attention_mask = None
chunk_masks = None
is_covers = None
src_latents = None
precomputed_lm_hints_25Hz = None
lyric_hidden_states = lyric_embed
Expand All @@ -1067,7 +1127,7 @@ def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audi
if refer_audio_order_mask is None:
refer_audio_order_mask = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long)

if src_latents is None and is_covers is None:
if src_latents is None:
src_latents = x

if chunk_masks is None:
Expand Down
17 changes: 4 additions & 13 deletions comfy/model_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1560,29 +1560,20 @@ def extra_conds(self, **kwargs):

refer_audio = kwargs.get("reference_audio_timbre_latents", None)
if refer_audio is None or len(refer_audio) == 0:
refer_audio = torch.tensor([[[-1.3672e-01, -1.5820e-01, 5.8594e-01, -5.7422e-01, 3.0273e-02,
2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
-2.7710e-02, -1.8066e-01, -2.9688e-01, 1.6016e+00, -2.6719e+00,
7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
2.4316e-01, 4.7266e-01, 4.6387e-02, -6.6406e-01, -2.1973e-01,
-6.7578e-01, -1.5723e-01, 9.5312e-01, -2.0020e-01, -1.7109e+00,
5.8984e-01, -5.7422e-01, 5.1562e-01, 2.8320e-01, 1.4551e-01,
-1.8750e-01, -5.9814e-02, 3.6719e-01, -1.0059e-01, -1.5723e-01,
2.0605e-01, -4.3359e-01, -8.2812e-01, 4.5654e-02, -6.6016e-01,
1.4844e-01, 9.4727e-02, 3.8477e-01, -1.2578e+00, -3.3203e-01,
-8.5547e-01, 4.3359e-01, 4.2383e-01, -8.9453e-01, -5.0391e-01,
-5.6152e-02, -2.9219e+00, -2.4658e-02, 5.0391e-01, 9.8438e-01,
7.2754e-02, -2.1582e-01, 6.3672e-01, 1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, noise.shape[2])
refer_audio = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
pass_audio_codes = True
else:
refer_audio = refer_audio[-1][:, :, :noise.shape[2]]
out['is_covers'] = comfy.conds.CONDConstant(True)
pass_audio_codes = False

if pass_audio_codes:
audio_codes = kwargs.get("audio_codes", None)
if audio_codes is not None:
out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
refer_audio = refer_audio[:, :, :750]
else:
out['is_covers'] = comfy.conds.CONDConstant(False)

out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
return out
Expand Down
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