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Basic support for the ace step 1.5 model. (#12237)

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comfyanonymous
2026-02-02 21:06:18 -08:00
committed by GitHub
parent ba5bf3f1a8
commit 3c1a1a2df8
12 changed files with 1566 additions and 8 deletions
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42
comfy/model_base.py
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@@ -50,6 +50,7 @@ import comfy.ldm.omnigen.omnigen2
import comfy.ldm.qwen_image.model
import comfy.ldm.kandinsky5.model
import comfy.ldm.anima.model
import comfy.ldm.ace.ace_step15
import comfy.model_management
import comfy.patcher_extension
@@ -1540,6 +1541,47 @@ class ACEStep(BaseModel):
out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get("lyrics_strength", 1.0))
return out
class ACEStep15(BaseModel):
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.ace_step15.AceStepConditionGenerationModel)
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
device = kwargs["device"]
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
conditioning_lyrics = kwargs.get("conditioning_lyrics", None)
if cross_attn is not None:
out['lyric_embed'] = comfy.conds.CONDRegular(conditioning_lyrics)
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, 750)
else:
refer_audio = refer_audio[-1]
out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
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))
return out
class Omnigen2(BaseModel):
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel)