Ethanfel
4f40e15db3
- Wrap training loop in try/finally so _unapply_lora always runs. Without this, an exception mid-training would leave LoRALinear wrappers in the cached DiTWrapper; a subsequent training run would then apply LoRA on top of existing LoRA, silently doubling the effective rank. - Fix misleading comment: diffusion.model is DiTWrapper (not DiffusionTransformer). DiffusionTransformer is at diffusion.model.model; _apply_lora reaches it recursively but the direct attribute is the wrapper. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
import os
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import json
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import torch
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import torch.nn as nn
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from .utils import PRISMAUDIO_CATEGORY
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def _merge_lora_weights(dit: nn.Module, lora_state: dict, rank: int, alpha: float, strength: float):
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"""Add LoRA delta weights directly into the base model's nn.Linear tensors.
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delta_W = lora_B @ lora_A * scale * strength
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applied as: linear.weight += delta_W
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This is equivalent to LoRALinear at inference but requires no wrapper,
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no extra memory, and no change to the model's forward call graph.
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"""
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scale = (alpha / rank) * strength
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# Group saved keys by module path
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a_map = {
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k.replace(".lora_A.weight", ""): v
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for k, v in lora_state.items() if k.endswith("lora_A.weight")
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}
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b_map = {
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k.replace(".lora_B.weight", ""): v
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for k, v in lora_state.items() if k.endswith("lora_B.weight")
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}
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merged = 0
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for path, lora_A in a_map.items():
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if path not in b_map:
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print(f"[PrismAudio] LoRA merge: missing lora_B for {path}, skipping", flush=True)
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continue
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lora_B = b_map[path] # [out_features, rank]
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# delta_W: [out_features, in_features]
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delta_W = (lora_B.float() @ lora_A.float()) * scale
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# Navigate to the parent module using PyTorch's get_submodule
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*parent_parts, child_name = path.split(".")
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try:
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parent = dit.get_submodule(".".join(parent_parts)) if parent_parts else dit
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except AttributeError as e:
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print(f"[PrismAudio] LoRA merge: could not find module '{path}': {e}", flush=True)
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continue
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linear = getattr(parent, child_name, None)
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if not isinstance(linear, nn.Linear):
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print(f"[PrismAudio] LoRA merge: expected nn.Linear at '{path}', got {type(linear)}", flush=True)
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continue
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linear.weight.data.add_(delta_W.to(linear.weight.dtype))
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merged += 1
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print(f"[PrismAudio] LoRA merged {merged} layer(s) (strength={strength:.3f})", flush=True)
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class PrismAudioLoRALoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("PRISMAUDIO_MODEL",),
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"lora_path": ("STRING", {"default": "", "tooltip": "Path to .safetensors LoRA file produced by PrismAudio LoRA Trainer"}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.05, "tooltip": "LoRA influence scale. 1.0 = full strength, 0.0 = base model only"}),
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},
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}
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RETURN_TYPES = ("PRISMAUDIO_MODEL",)
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RETURN_NAMES = ("model",)
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FUNCTION = "load_lora"
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CATEGORY = PRISMAUDIO_CATEGORY
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def load_lora(self, model, lora_path, strength):
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from safetensors.torch import load_file
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if not os.path.exists(lora_path):
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raise FileNotFoundError(f"[PrismAudio] LoRA file not found: {lora_path}")
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config_path = lora_path.replace(".safetensors", "_config.json")
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if not os.path.exists(config_path):
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raise FileNotFoundError(
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f"[PrismAudio] LoRA config not found: {config_path}\n"
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"Expected a _config.json alongside the .safetensors file."
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)
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with open(config_path) as f:
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config = json.load(f)
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rank = config["rank"]
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alpha = config["alpha"]
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lora_state = load_file(lora_path)
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# Merge LoRA weights in-place into the DiT's base linear layers.
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# ComfyUI re-executes the upstream ModelLoader on the next queue run
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# when inputs change, providing a fresh base model as needed.
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dit = model["model"].model # DiTWrapper
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if strength == 0.0:
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print("[PrismAudio] LoRA strength=0.0 — skipping merge, base model unchanged.", flush=True)
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return (model,)
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_merge_lora_weights(dit, lora_state, rank, alpha, strength)
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return (model,)
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