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Support the 4B ace step 1.5 lm model. (#12257)

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Can be used as an alternative to the 1.7B
This commit is contained in:
comfyanonymous
2026-02-03 16:01:38 -08:00
committed by GitHub
parent 3be0175166
commit fe2511468d
4 changed files with 101 additions and 32 deletions
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42
comfy/text_encoders/ace15.py
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@@ -162,14 +162,34 @@ class Qwen3_2B_ACE15(sd1_clip.SDClipModel):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_2B_ACE15_lm, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
class Qwen3_4B_ACE15(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
if llama_quantization_metadata is not None:
model_options = model_options.copy()
model_options["quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B_ACE15_lm, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
class ACE15TEModel(torch.nn.Module):
def __init__(self, device="cpu", dtype=None, dtype_llama=None, model_options={}):
def __init__(self, device="cpu", dtype=None, dtype_llama=None, lm_model=None, model_options={}):
super().__init__()
if dtype_llama is None:
dtype_llama = dtype
model = None
self.constant = 0.4375
if lm_model == "qwen3_4b":
model = Qwen3_4B_ACE15
self.constant = 0.5625
elif lm_model == "qwen3_2b":
model = Qwen3_2B_ACE15
self.lm_model = lm_model
self.qwen3_06b = Qwen3_06BModel(device=device, dtype=dtype, model_options=model_options)
self.qwen3_2b = Qwen3_2B_ACE15(device=device, dtype=dtype_llama, model_options=model_options)
if model is not None:
setattr(self, self.lm_model, model(device=device, dtype=dtype_llama, model_options=model_options))
self.dtypes = set([dtype, dtype_llama])
def encode_token_weights(self, token_weight_pairs):
@@ -182,17 +202,21 @@ class ACE15TEModel(torch.nn.Module):
lyrics_embeds, _, extra_l = self.qwen3_06b.encode_token_weights(token_weight_pairs_lyrics)
lm_metadata = token_weight_pairs["lm_metadata"]
audio_codes = generate_audio_codes(self.qwen3_2b, token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"])
audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"])
return base_out, None, {"conditioning_lyrics": lyrics_embeds[:, 0], "audio_codes": [audio_codes]}
def set_clip_options(self, options):
self.qwen3_06b.set_clip_options(options)
self.qwen3_2b.set_clip_options(options)
lm_model = getattr(self, self.lm_model, None)
if lm_model is not None:
lm_model.set_clip_options(options)
def reset_clip_options(self):
self.qwen3_06b.reset_clip_options()
self.qwen3_2b.reset_clip_options()
lm_model = getattr(self, self.lm_model, None)
if lm_model is not None:
lm_model.reset_clip_options()
def load_sd(self, sd):
if "model.layers.0.post_attention_layernorm.weight" in sd:
@@ -200,11 +224,11 @@ class ACE15TEModel(torch.nn.Module):
if shape[0] == 1024:
return self.qwen3_06b.load_sd(sd)
else:
return self.qwen3_2b.load_sd(sd)
return getattr(self, self.lm_model).load_sd(sd)
def memory_estimation_function(self, token_weight_pairs, device=None):
lm_metadata = token_weight_pairs["lm_metadata"]
constant = 0.4375
constant = self.constant
if comfy.model_management.should_use_bf16(device):
constant *= 0.5
@@ -213,11 +237,11 @@ class ACE15TEModel(torch.nn.Module):
num_tokens += lm_metadata['min_tokens']
return num_tokens * constant * 1024 * 1024
def te(dtype_llama=None, llama_quantization_metadata=None):
def te(dtype_llama=None, llama_quantization_metadata=None, lm_model="qwen3_2b"):
class ACE15TEModel_(ACE15TEModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
if llama_quantization_metadata is not None:
model_options = model_options.copy()
model_options["llama_quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, dtype_llama=dtype_llama, dtype=dtype, model_options=model_options)
super().__init__(device=device, dtype_llama=dtype_llama, lm_model=lm_model, dtype=dtype, model_options=model_options)
return ACE15TEModel_