feat(assets): SA3 音效补齐 radio 11+1 与 dungeon 13源+15pan（token 走 hf-mirror 实测可用）

- 模型 17/17 文件经镜像全量缓存（gated+token，直连本机不通；sfx.md/setup.md/asset-pipeline.md 镜像结论同步更新） - 修复迁移遗留：stable_audio_3/models/ 源码子包被 .gitignore models/ 规则误伤丢失，从上游恢复并加白名单 - radio：arg_muffled/brake_screech 等叙事件落地（接线原有 L111/L154 零改动）+ phone_ring SYN→SA3 升级；dungeon 13 源 A/B 对齐（RMS 偏差 ≤0.3dB）全量同名替换，SYN 原版备份 tools/asset_fix_backup/sa3_ab_20260902/ - 关键玩法语义达标：echo 距离档 0.83<1.43<2.18<2.98s 严格递增；one-shot 真峰值全 ≤-3dBFS（解码回读法） - check_all_assets 8 游戏全绿（radio 254/254、dungeon 303/303）；playtest radio 221/dungeon 1610 全绿
2026-09-02 19:35:38 +08:00
parent d870a75a26
commit 24c526af68
181 changed files with 5324 additions and 12 deletions
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+"""Helpers for loading pre-trained LoRA checkpoints onto a live model."""
+import os
+from functools import partial
+
+import torch
+
+from .model import LoRAParametrization, add_lora
+from .utils import (
+    get_lora_layers,
+    infer_global_rank,
+    load_lora_checkpoint,
+    prepare_dora_state_dict,
+    remap_lora_state_dict,
+    resolve_adapter_type,
+)
+from ...verbose import vprint
+
+
+def load_and_apply_loras(model, lora_ckpt_paths, model_type, svd_bases_path=None):
+    """Load LoRA checkpoints from disk and attach them to `model`.
+
+    - Uses a two-pass approach: first resolves each LoRA's adapter type, then
+      loads SVD bases (only if any LoRA is -XS), then applies each LoRA.
+    - Handles legacy "dora" adapter_type via `resolve_adapter_type`.
+    - Only passes svd_bases to -XS LoRAs to avoid spurious "no -XS layers"
+      messages for non-XS LoRAs.
+    - Sets `model.use_lora = True` and `model.lora_names = [...]` as a
+      convenience for downstream UI code.
+
+    Args:
+        model: The loaded model. For `diffusion_cond` / `diffusion_cond_inpaint`
+            types, LoRAs are applied to `model.model` and `model.conditioner`;
+            otherwise they are applied to `model` directly.
+        lora_ckpt_paths: List of paths to `.ckpt` or `.safetensors` files.
+        model_type: String from the model config, e.g. "diffusion_cond".
+        svd_bases_path: Optional path to a precomputed SVD bases .pt file
+            (used only by -XS adapter types).
+
+    Returns:
+        List of display names (file stems) in the same order as the input paths.
+    """
+    if not lora_ckpt_paths:
+        model.use_lora = False
+        model.lora_names = []
+        return []
+
+    is_cond = model_type in ("diffusion_cond", "diffusion_cond_inpaint")
+
+    # Pass 1: load each checkpoint and resolve adapter type.
+    lora_entries = []  # (path, state_dict, config_dict, adapter_type)
+    for i, lora_path in enumerate(lora_ckpt_paths):
+        print(f"Loading LoRA {i} from {lora_path}")
+        state_dict, config_dict = load_lora_checkpoint(lora_path)
+        adapter_type_raw = config_dict.get("adapter_type", "lora")
+        adapter_type = resolve_adapter_type(adapter_type_raw, state_dict)
+        if adapter_type != adapter_type_raw:
+            vprint(f"Resolved legacy '{adapter_type_raw}' -> {adapter_type}")
+        lora_entries.append((lora_path, state_dict, config_dict, adapter_type))
+
+    # Load SVD bases only if at least one LoRA is -XS.
+    model_svd_bases = None
+    cond_svd_bases = None
+    any_xs = any(t.endswith("-xs") for _, _, _, t in lora_entries)
+    if any_xs:
+        if svd_bases_path is not None:
+            vprint(f"Loading SVD bases from {svd_bases_path}")
+            svd_bases_all = torch.load(svd_bases_path, map_location="cpu", weights_only=True)
+            model_svd_bases = {k[len("model."):]: v for k, v in svd_bases_all.items() if k.startswith("model.")}
+            cond_svd_bases = {k[len("conditioner."):]: v for k, v in svd_bases_all.items() if k.startswith("conditioner.")}
+            vprint(f"  model bases: {len(model_svd_bases)}, conditioner bases: {len(cond_svd_bases)}")
+        else:
+            print("WARNING: -XS adapter type present without svd_bases_path -- SVD will be computed on current device")
+
+    # Pass 2: apply each LoRA.
+    lora_names = []
+    for i, (lora_path, state_dict, config_dict, adapter_type) in enumerate(lora_entries):
+        rank = config_dict.get("rank", infer_global_rank(state_dict))
+        alpha = config_dict.get("alpha", rank)
+        include = config_dict.get("include", None)
+        exclude = config_dict.get("exclude", None)
+        is_xs = adapter_type.endswith("-xs")
+
+        lora_config = {
+            torch.nn.Linear: {
+                "weight": partial(LoRAParametrization.from_linear, rank=rank, lora_alpha=alpha, adapter_type=adapter_type, lora_index=i),
+            },
+            torch.nn.Conv1d: {
+                "weight": partial(LoRAParametrization.from_conv1d, rank=rank, lora_alpha=alpha, adapter_type=adapter_type, lora_index=i),
+            },
+        }
+        if is_cond:
+            add_lora(model.model, lora_config, include=include, exclude=exclude,
+                     svd_bases=model_svd_bases if is_xs else None)
+            add_lora(model.conditioner, lora_config, include=include, exclude=exclude,
+                     svd_bases=cond_svd_bases if is_xs else None)
+        else:
+            add_lora(model, lora_config, include=include, exclude=exclude,
+                     svd_bases=model_svd_bases if is_xs else None)
+
+        prepare_dora_state_dict(state_dict)
+        remapped_sd = remap_lora_state_dict(state_dict, i)
+        if is_cond:
+            model.model.load_state_dict(remapped_sd, strict=False)
+            model.conditioner.load_state_dict(remapped_sd, strict=False)
+        else:
+            model.load_state_dict(remapped_sd, strict=False)
+        lora_names.append(os.path.splitext(os.path.basename(lora_path))[0])
+
+    vprint("lora layers:", len(get_lora_layers(model)))
+    model.use_lora = True
+    model.lora_names = lora_names
+    return lora_names