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Support Ace Step 1.5 XL model. (#13317)

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comfyanonymous 2026-04-07 00:13:47 -07:00 committed by GitHub
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2 changed files with 20 additions and 7 deletions
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18
comfy/ldm/ace/ace_step15.py
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@ -611,6 +611,7 @@ class AceStepDiTModel(nn.Module):
intermediate_size,
patch_size,
audio_acoustic_hidden_dim,
condition_dim=None,
layer_types=None,
sliding_window=128,
rms_norm_eps=1e-6,
@ -640,7 +641,7 @@ class AceStepDiTModel(nn.Module):
self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)
self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device)
if layer_types is None:
layer_types = ["full_attention"] * num_layers
@ -1035,6 +1036,9 @@ class AceStepConditionGenerationModel(nn.Module):
fsq_dim=2048,
fsq_levels=[8, 8, 8, 5, 5, 5],
fsq_input_num_quantizers=1,
encoder_hidden_size=2048,
encoder_intermediate_size=6144,
encoder_num_heads=16,
audio_model=None,
dtype=None,
device=None,
@ -1054,24 +1058,24 @@ class AceStepConditionGenerationModel(nn.Module):
self.decoder = AceStepDiTModel(
in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim,
intermediate_size, patch_size, audio_acoustic_hidden_dim,
intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size,
layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps,
dtype=dtype, device=device, operations=operations
)
self.encoder = AceStepConditionEncoder(
text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers,
num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,
text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers,
encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps,
dtype=dtype, device=device, operations=operations
)
self.tokenizer = AceStepAudioTokenizer(
audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
dtype=dtype, device=device, operations=operations
)
self.detokenizer = AudioTokenDetokenizer(
hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
dtype=dtype, device=device, operations=operations
)
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device))
def prepare_condition(
self,

9
comfy/model_detection.py
View File

@ -696,6 +696,15 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:
dit_config = {}
dit_config["audio_model"] = "ace1.5"
head_dim = 128
dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0]
dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0]
dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.')
return dit_config
if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4