Sebastian Krüger
f74457b049
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Previous approach patched DiT.__init__ at runtime, but models were already instantiated and cached. This version patches LlamaConfig globally BEFORE any DiffRhythm imports, ensuring all model instances use the correct config. Key changes: - Created PatchedLlamaConfig subclass that auto-calculates attention heads - Replaced LlamaConfig in transformers.models.llama module at import time - Patch applies to all LlamaConfig instances, including pre-loaded models This should finally fix the tensor dimension mismatch error. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
102 lines
4.0 KiB
Python
102 lines
4.0 KiB
Python
"""
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Pivoine DiffRhythm Node
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Custom wrapper for DiffRhythm that fixes LlamaConfig initialization issues
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with transformers 4.49.0+ to prevent tensor dimension mismatches.
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Known Issue: DiffRhythm's DIT model doesn't specify num_attention_heads and
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num_key_value_heads in LlamaConfig, causing "The size of tensor a (32) must
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match the size of tensor b (64)" error in rotary position embeddings.
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This patch globally intercepts LlamaConfig at import time.
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Reference: https://github.com/billwuhao/ComfyUI_DiffRhythm/issues/44
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Reference: https://github.com/billwuhao/ComfyUI_DiffRhythm/issues/48
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Author: valknar@pivoine.art
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"""
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import sys
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sys.path.append('/workspace/ComfyUI/custom_nodes/ComfyUI_DiffRhythm')
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# CRITICAL: Patch LlamaConfig BEFORE any DiffRhythm imports
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# This must happen at module import time, not at runtime
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from transformers.models.llama import LlamaConfig as _OriginalLlamaConfig
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class PatchedLlamaConfig(_OriginalLlamaConfig):
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"""
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Patched LlamaConfig that automatically adds missing attention head parameters.
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Fixes the tensor dimension mismatch (32 vs 64) in DiffRhythm's rotary
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position embeddings by ensuring num_attention_heads and num_key_value_heads
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are properly set based on hidden_size.
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"""
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def __init__(self, *args, **kwargs):
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# If hidden_size is provided but num_attention_heads is not, calculate it
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if 'hidden_size' in kwargs and 'num_attention_heads' not in kwargs:
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hidden_size = kwargs['hidden_size']
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# Standard Llama architecture: head_dim = 64, so num_heads = hidden_size // 64
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kwargs['num_attention_heads'] = hidden_size // 64
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# If num_key_value_heads is not provided, use GQA configuration
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if 'num_attention_heads' in kwargs and 'num_key_value_heads' not in kwargs:
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# For GQA (Grouped Query Attention), typically num_kv_heads = num_heads // 4
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kwargs['num_key_value_heads'] = max(1, kwargs['num_attention_heads'] // 4)
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# Call original __init__ with patched parameters
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super().__init__(*args, **kwargs)
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# Replace LlamaConfig in transformers module BEFORE DiffRhythm imports it
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import transformers.models.llama
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transformers.models.llama.LlamaConfig = PatchedLlamaConfig
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# Also replace in modeling_llama module if it's already imported
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import transformers.models.llama.modeling_llama
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transformers.models.llama.modeling_llama.LlamaConfig = PatchedLlamaConfig
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# Now import DiffRhythm modules - they will use our patched LlamaConfig
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import infer_utils
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# Monkey-patch decode_audio to force chunked=False
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_original_decode_audio = infer_utils.decode_audio
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def patched_decode_audio(latent, vae_model, chunked=True):
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"""Patched version that always uses chunked=False"""
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return _original_decode_audio(latent, vae_model, chunked=False)
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infer_utils.decode_audio = patched_decode_audio
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# Import DiffRhythm node
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from DiffRhythmNode import DiffRhythmRun
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class PivoineDiffRhythmRun(DiffRhythmRun):
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"""
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Pivoine version of DiffRhythmRun with enhanced compatibility and error handling.
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Changes from original:
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- Globally patches LlamaConfig to add missing num_attention_heads and num_key_value_heads
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- Monkey-patches decode_audio to always use chunked=False for stability
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- Fixes tensor dimension mismatch in rotary position embeddings (32 vs 64)
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- Compatible with transformers 4.49.0+
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- Requires ~12-16GB VRAM, works reliably on RTX 4090
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Technical details:
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- Sets num_attention_heads = hidden_size // 64 (standard Llama architecture)
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- Sets num_key_value_heads = num_attention_heads // 4 (GQA configuration)
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- This ensures head_dim = hidden_size // num_attention_heads = 64 (not 32)
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- Patch is applied globally at import time, affecting all LlamaConfig instances
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"""
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CATEGORY = "🌸Pivoine/Audio"
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@classmethod
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def INPUT_TYPES(cls):
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return super().INPUT_TYPES()
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NODE_CLASS_MAPPINGS = {
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"PivoineDiffRhythmRun": PivoineDiffRhythmRun,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"PivoineDiffRhythmRun": "Pivoine DiffRhythm Run",
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}
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