t2m

diffsynth/models/ace_step_lm.py

@@ -1,79 +0,0 @@
-import torch
-
-
-LM_CONFIGS = {
-    "acestep-5Hz-lm-0.6B": {
-        "hidden_size": 1024,
-        "intermediate_size": 3072,
-        "num_hidden_layers": 28,
-        "num_attention_heads": 16,
-        "layer_types": ["full_attention"] * 28,
-        "max_window_layers": 28,
-    },
-    "acestep-5Hz-lm-1.7B": {
-        "hidden_size": 2048,
-        "intermediate_size": 6144,
-        "num_hidden_layers": 28,
-        "num_attention_heads": 16,
-        "layer_types": ["full_attention"] * 28,
-        "max_window_layers": 28,
-    },
-    "acestep-5Hz-lm-4B": {
-        "hidden_size": 2560,
-        "intermediate_size": 9728,
-        "num_hidden_layers": 36,
-        "num_attention_heads": 32,
-        "layer_types": ["full_attention"] * 36,
-        "max_window_layers": 36,
-    },
-}
-
-
-class AceStepLM(torch.nn.Module):
-    """
-    Language model for ACE-Step.
-
-    Converts natural language prompts into structured parameters
-    (caption, lyrics, bpm, keyscale, duration, timesignature, etc.)
-    for ACE-Step music generation.
-
-    Wraps a Qwen3ForCausalLM transformers model. Config is manually
-    constructed based on variant type, and model weights are loaded
-    via DiffSynth's standard mechanism from safetensors files.
-    """
-
-    def __init__(
-        self,
-        variant: str = "acestep-5Hz-lm-1.7B",
-    ):
-        super().__init__()
-        from transformers import Qwen3Config, Qwen3ForCausalLM
-
-        config_params = LM_CONFIGS[variant]
-
-        config = Qwen3Config(
-            attention_bias=False,
-            attention_dropout=0.0,
-            bos_token_id=151643,
-            dtype="bfloat16",
-            eos_token_id=151645,
-            head_dim=128,
-            hidden_act="silu",
-            initializer_range=0.02,
-            max_position_embeddings=40960,
-            model_type="qwen3",
-            num_key_value_heads=8,
-            pad_token_id=151643,
-            rms_norm_eps=1e-06,
-            rope_scaling=None,
-            rope_theta=1000000,
-            sliding_window=None,
-            tie_word_embeddings=True,
-            use_cache=True,
-            use_sliding_window=False,
-            vocab_size=217204,
-            **config_params,
-        )
-
-        self.model = Qwen3ForCausalLM(config)
-        self.config = config