docs: add observations section with fp32/batch/precision findings

Work-in-progress empirical notes: fp32 batch 32 reaches same quality as
bf16 batch 16 in 1/3 the steps but overfits past ~2000 steps on 10 clips.
Lower loss does not reliably mean better audio on small datasets.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

LORA_TRAINING.md

@@ -0,0 +1,392 @@
+# LoRA Training for SelVA
+
+LoRA lets you teach the model new or partially-known sound classes using a small set of video+audio pairs. Only ~10 MB of adapter weights are trained instead of the full 4.4 GB model.
+
+---
+
+## Overview
+
+Training is split into two steps:
+
+1. **Dataset preparation** (in ComfyUI) — extract visual features from your video clips using the `SelVA Feature Extractor` node, and collect clean matching audio files.
+2. **Training** (in ComfyUI or command line) — run the `SelVA LoRA Trainer` node or `train_lora.py`.
+
+The training script only loads the generator and the VAE encoder. CLIP visual features and sync features come pre-computed from the `.npz` files, so Synchformer and T5 are not loaded during training, saving 3–4 GB of VRAM.
+
+---
+
+## Requirements
+
+Same environment as SelVA inference. Additional Python packages:
+
+```
+torchaudio
+soundfile
+```
+
+---
+
+## Step 1 — Prepare the dataset
+
+### 1.1 Extract visual features in ComfyUI
+
+For each video clip you want to train on:
+
+1. Load the video with a VHS LoadVideo node.
+2. Connect it to **SelVA Feature Extractor**.
+3. Set **`cache_dir`** to a dedicated dataset folder, e.g. `dataset/my_sound`.
+4. Set **`name`** to a short descriptive label, e.g. `dog_bark`. The node will save `dog_bark_001.npz`, then `dog_bark_002.npz`, etc. automatically as you process more clips.
+5. Set the **`prompt`** to describe the sound (e.g. `a dog barking`). This prompt conditions the Synchformer sync features — be as specific as possible (see prompt guide below).
+6. Optionally connect a **mask** to isolate the sound source in frame (strongly recommended when multiple objects are visible — see masking note below).
+
+> **Tip:** The prompt used for feature extraction conditions the *visual sync features*. You can use a different, more precise prompt at training time — see Step 2.
+
+### Prompt guide
+
+The prompt is not just a label — it directly shapes what the Synchformer pays attention to in the video. Imprecise prompts produce unfocused sync features, which the LoRA then has to compensate for, often introducing noise.
+
+**Good prompts are specific about:**
+- The sound source (what object is making the sound)
+- The acoustic character (loud/quiet, sharp/soft, wet/dry)
+- The action producing the sound (if applicable)
+
+| Sound | Weak prompt | Strong prompt |
+|---|---|---|
+| Dog bark | `dog` | `a large dog barking loudly` |
+| Footsteps | `walking` | `heavy boots on a wooden floor` |
+| Water | `water` | `water dripping into a metal bucket` |
+| Explosion | `explosion` | `a large explosion with deep bass rumble` |
+| Door | `door` | `a heavy wooden door slamming shut` |
+
+**Rules of thumb:**
+- Describe the *sound*, not the visual scene. `a person hitting a drum` is better than `a drummer on stage`.
+- Keep prompts consistent across all clips for the same sound class. Mixing `a dog barking` and `loud barking dog` in the same dataset creates conflicting sync features.
+- Avoid negations (`no background noise`) — the model does not understand negations in sync feature conditioning.
+
+### Masking note
+
+If the video frame contains multiple moving objects, CLIP and sync features will be diluted by irrelevant motion. Use a segmentation mask (SAM2 or Grounding DINO+SAM) to isolate the sound source:
+
+- Connect the mask to the **`mask`** input on SelVA Feature Extractor.
+- Leave `mask_strength` at `1.0` for clean isolation; lower it only if the masked region is very small and the model loses context.
+- Re-extract features with a mask even if you already have `.npz` files — better features directly reduce training noise.
+
+### 1.2 Collect clean audio
+
+For each `.npz` file, place a matching audio file with the **same filename stem** in the same directory:
+
+```
+dataset/my_sound/
+    dog_bark_001.npz   ← from SelVA Feature Extractor
+    dog_bark_001.wav   ← clean isolated audio recording
+    dog_bark_002.npz
+    dog_bark_002.wav
+    dog_bark_003.npz
+    dog_bark_003.wav
+```
+
+Supported audio formats: `.wav`, `.flac`, `.ogg`, `.aiff`, `.aif`
+
+> `.mp3` is not recommended — lossy compression degrades training quality. Use `.flac` or `.wav`.
+
+The audio will be automatically resampled and trimmed/padded to match the model's expected duration. Use clean, isolated recordings — no background noise.
+
+### 1.3 Optional: prompts.txt
+
+If you want a different prompt at training time than the one embedded in the `.npz`, create a `prompts.txt` file in the dataset directory:
+
+```
+# One line per file: filename: prompt text
+dog_bark.npz: a large dog barking aggressively
+dog_bark_001.npz: a dog barking in the distance
+```
+
+Priority: `prompts.txt` > prompt embedded in `.npz` > directory name as fallback.
+
+---
+
+## Step 2 — Train
+
+### Option A — SelVA LoRA Trainer node (ComfyUI)
+
+Connect the node and set parameters directly in the UI. The node outputs the trained model ready to wire into the Sampler, and saves loss curve images to the output directory.
+
+```
+SelVA Model Loader → SelVA LoRA Trainer → SelVA Sampler
+```
+
+### Option B — Command line
+
+```bash
+python train_lora.py \
+    --data_dir dataset/my_sound \
+    --output_dir lora_output/my_sound \
+    --variant large_44k \
+    --selva_dir /path/to/ComfyUI/models/selva \
+    --rank 16 \
+    --steps 4000 \
+    --batch_size 4 \
+    --lr 1e-4
+```
+
+The script will:
+1. Load the VAE, CLIP text encoder, and generator.
+2. Pre-load all clips (audio encoded to latents, features loaded from `.npz`).
+3. Train LoRA adapters for the specified number of steps.
+4. Save a checkpoint every `--save_every` steps, a final `adapter_final.pt`, and loss curve images.
+
+---
+
+## CLI Reference
+
+| Argument | Default | Description |
+|---|---|---|
+| `--data_dir` | required | Directory containing `.npz` + audio pairs |
+| `--output_dir` | `lora_output` | Where to save adapter checkpoints |
+| `--variant` | `large_44k` | Model variant: `small_16k`, `small_44k`, `medium_44k`, `large_44k` |
+| `--selva_dir` | required | Path to SelVA model weights directory |
+| `--rank` | `16` | LoRA rank — higher = more capacity, more VRAM |
+| `--alpha` | `rank` | LoRA alpha scaling. Default (= rank) means scale = 1.0 |
+| `--target` | `attn.qkv` | Which layers to adapt. Add `linear1` for post-attention projections |
+| `--lr` | `1e-4` | Learning rate |
+| `--steps` | `2000` | Total training steps |
+| `--warmup_steps` | `100` | Linear LR warmup steps |
+| `--batch_size` | `4` | Clips per training step — higher is more stable, uses more VRAM |
+| `--grad_accum` | `1` | Gradient accumulation steps (use when batch_size is already > 1) |
+| `--save_every` | `500` | Save a checkpoint every N steps |
+| `--resume` | `None` | Path to a step checkpoint to resume from (e.g. `lora_output/adapter_step04000.pt`) |
+| `--precision` | `bf16` | Mixed precision: `bf16`, `fp16`, `fp32` |
+| `--seed` | `42` | Random seed |
+
+---
+
+## Step 3 — Load the adapter in ComfyUI
+
+Connect **SelVA LoRA Loader** between the model loader and the sampler:
+
+```
+SelVA Model Loader → SelVA LoRA Loader → SelVA Sampler
+```
+
+> **Important:** Wire the LoRA Loader output to the **Sampler**, not the Feature Extractor. The LoRA adapts the generator which only runs in the Sampler.
+
+| Input | Description |
+|---|---|
+| `model` | SELVA_MODEL from the model loader |
+| `adapter_path` | Path to `adapter_final.pt` or any `adapter_stepXXXXX.pt` |
+| `strength` | 0.0 = adapter disabled, 1.0 = full strength, >1.0 = exaggerated |
+
+The loader reads rank, alpha, and target layers from the metadata embedded in the `.pt` file — no need to set them manually.
+
+> The base model is not modified. The loader returns a shallow copy with a deep-copied generator so the original stays intact.
+
+---
+
+## Tuning Guide
+
+### Clip length
+
+The model has a **fixed input duration of 8 seconds** for all variants (both 16k and 44k). This is not a parameter you can change.
+
+- Audio shorter than 8 s is **zero-padded** (silence appended). The model will learn the sound but may also learn silence as part of the pattern — keep in mind for very short sounds.
+- Audio longer than 8 s is **trimmed** at 8 s. Content beyond that is lost.
+- Video shorter than 8 s has its **last frame repeated** to fill the clip.
+
+**Practical recommendations:**
+
+| Sound type | Clip strategy |
+|---|---|
+| Continuous sound (rain, engine, wind) | 8 s recordings, as many positions in the audio as possible |
+| Single event < 2 s (click, bark, knock) | Center the event — pad deliberately with silence before/after, or loop the event 2–3 times per clip |
+| Repeating event (footsteps, dripping) | Record full 8 s with natural repetition at the intended cadence |
+| Sound with a clear onset (explosion, splash) | Put the onset at ~1–2 s from the start, not at 0 s — gives the model context |
+
+> **Tip:** When extracting features in ComfyUI, set `duration` to 0 to use the full video length up to 8 s. Clips longer than 8 s are automatically clamped.
+
+### How many clips do I need?
+
+The table below gives a rough scaling guide. Quality and diversity of recordings matter more than raw count.
+
+| Dataset size | Scenario | Expected result |
+|---|---|---|
+| **5–10 clips** | Quick test / proof of concept | May work if the model already partially knows the sound; often underfits |
+| **15–30 clips** | Fine-tuning a sound the model knows but gets wrong | Good starting point — covers the main variations |
+| **30–60 clips** | Teaching a new but acoustically simple sound class | Reliable convergence with default hyperparameters |
+| **60–150 clips** | Unusual or complex sounds, strong style shift | Needed for stable generalization across video contexts |
+| **150–300 clips** | Sounds the model has never encountered | Required to avoid overfitting; increase rank to 32 |
+| **300+** | Large-scale domain shift | Consider also targeting `linear1` in addition to `attn.qkv` |
+
+**Diversity beats quantity.** Ten clips of a dog barking in different environments (indoors, outdoors, distant, close) train better than fifty clips of the same recording. Vary: distance, room acoustics, intensity, speed.
+
+### Batch size
+
+| Batch size | VRAM (large_44k) | Use case |
+|---|---|---|
+| `1` | ~9 GB | Minimal VRAM, noisy gradients |
+| `4` | ~12 GB | Good default — stable gradients, reasonable speed |
+| `8` | ~15 GB | Better convergence on larger datasets |
+| `16` | ~20 GB | Best gradient quality when VRAM allows |
+
+Higher batch size gives smoother loss curves and faster convergence. If you have headroom, prefer larger batches over more steps.
+
+**Observed results:** batch 16 reaches the same loss in ~2600 steps that batch 1 needed 8000+ steps to reach, with a near-perfectly smooth curve. On a 24 GB GPU, batch 16 is the recommended default for `large_44k`.
+
+### Rank
+
+| Rank | Use case |
+|---|---|
+| `8` | Fine details on a sound the model already knows well |
+| `16` | Default — good balance of capacity and VRAM |
+| `32` | Harder sounds or larger style shifts (30+ clips recommended) |
+
+Higher rank increases VRAM usage and overfitting risk on small datasets.
+
+### Steps
+
+With `batch_size=4` as the default, these are rough guidelines:
+
+| Dataset size | Recommended steps |
+|---|---|
+| 10–20 clips | 2000–4000 |
+| 20–50 clips | 4000–8000 |
+| 50+ clips | 6000–15000 |
+
+Watch the loss curve — if the smoothed line has been flat for 2000+ steps, training has converged for your dataset size. Adding more clips will let it go lower.
+
+### Learning rate
+
+`1e-4` is the recommended default for any batch size. If training is unstable (loss spikes in the first 200 steps), try `5e-5`. If convergence is very slow, try `2e-4`.
+
+Warmup (default 100 steps) ramps the LR from 0 to avoid instability at the start.
+
+### Target layers
+
+`attn.qkv` (default) adapts only the self-attention QKV projections. This is the recommended starting point for all dataset sizes.
+
+Add `linear1` to also adapt post-attention projections for large-scale domain shifts or when `attn.qkv` alone plateaus too early:
+
+```bash
+--target attn.qkv linear1
+```
+
+Only add `linear1` once you have 150+ clips — it doubles the adapted parameter count and overfits faster on small datasets.
+
+### Adapter strength at inference
+
+| Strength | Effect |
+|---|---|
+| `0.5–0.7` | Conservative — blends adapter with base model, less noise |
+| `1.0` | Full adapter strength (default) |
+| `>1.0` | Exaggerated effect, may introduce artifacts |
+
+If the generated audio has noticeable white noise or artifacts, lower the strength to `0.6–0.7` before adjusting anything else. Also try lowering CFG scale in the Sampler.
+
+### Loss interpretation
+
+A typical loss curve:
+- Starts around `0.8–1.0`
+- Should reach `0.55–0.65` after convergence on a clean sound class with 10–30 clips
+- Below `0.4` indicates strong learning — usually requires 50+ diverse clips
+- Below `0.1` on a small dataset means overfitting
+
+The smoothed curve flattening for 2000+ steps is the clearest sign to stop or add more data.
+
+### Precision
+
+Use `bf16` on Ampere+ GPUs (RTX 3xxx/4xxx, A100). Fall back to `fp16` on older GPUs. `fp32` is only needed for debugging — 2× more VRAM.
+
+---
+
+## Output files
+
+```
+lora_output/my_sound/
+    adapter_step00500.pt      ← step checkpoint (includes optimizer state for resume)
+    adapter_step01000.pt
+    ...
+    adapter_final.pt          ← final adapter with embedded metadata (inference only)
+    meta.json                 ← human-readable metadata
+    sample_step00500.wav      ← quick eval sample at each checkpoint
+    loss_raw.png              ← raw loss curve
+    loss_smoothed.png         ← EMA-smoothed loss curve
+```
+
+`adapter_final.pt` format:
+```python
+{
+    "state_dict": { "blocks.0.attn.qkv.lora_A": ..., ... },
+    "meta": {
+        "variant": "large_44k",
+        "rank": 16,
+        "alpha": 16.0,
+        "target": ["attn.qkv"],
+        "steps": 2000
+    }
+}
+```
+
+Step checkpoints (e.g. `adapter_step01000.pt`) additionally contain `optimizer` and `scheduler` state for resuming.
+
+---
+
+## Troubleshooting
+
+**`No layers matched target=...`**
+The `--target` suffixes do not match any layer names. The default `attn.qkv` targets `SelfAttention.qkv` in all transformer blocks. If you changed `--target`, verify the layer names with `model.named_modules()`.
+
+**`No .npz files found in ...`**
+The `--data_dir` path is wrong or no `.npz` files were extracted there yet. Run SelVA Feature Extractor in ComfyUI first with the matching `cache_dir`.
+
+**`No audio file found for clip.npz`**
+Place an audio file with the exact same stem next to the `.npz`: `clip.wav`, `clip.flac`, etc.
+
+**The sound is audible but there is white noise on top**
+Lower the adapter strength to `0.6–0.7` in SelVA LoRA Loader. Also try lowering CFG scale in the Sampler. This is normal when the model hasn't fully converged — more clips and more steps will reduce it.
+
+**LoRA appears to have no effect**
+Make sure the SelVA LoRA Loader output is wired to the **Sampler** input, not the Feature Extractor. The Feature Extractor does not use the generator.
+
+**Loss does not decrease**
+- Increase `batch_size` for more stable gradients.
+- Try a higher learning rate (`2e-4`) or check that warmup isn't too long.
+- Check that the audio files are clean and actually contain the target sound.
+- Check that the `.npz` features were extracted with a relevant prompt.
+
+**Loss explodes or NaN**
+- Lower the learning rate (`5e-5`).
+- Make sure audio is normalized to `[-1, 1]`. PCM files with 16-bit integer encoding may need to be converted: `ffmpeg -i input.wav -ar 44100 -sample_fmt s16 output.wav`
+
+**Loss plateaus early (above 0.7)**
+Dataset is the bottleneck. Add more clips — diversity matters more than quantity.
+
+---
+
+## Observations (work in progress)
+
+These are empirical findings from ongoing experiments. They will be promoted to the main guide once more validated.
+
+### Precision and batch size
+
+| Config | Smoothed loss at step 2000 | Notes |
+|---|---|---|
+| bf16 batch 1 | ~0.73 | Noisy gradients, slow |
+| bf16 batch 16 | ~0.65 | Stable, plateaued around step 6000–8000 at ~0.59 |
+| bf16 batch 16 logit_normal | ~0.47 | Lower loss floor, similar or marginally better audio |
+| fp32 batch 32 | ~0.58 | Matches bf16 batch 16 at step 6000 already at step 2000 |
+
+**Key finding:** fp32 batch 32 converges to the same perceptual quality point in ~2000 steps that bf16 batch 16 needs 6000+ steps to reach. However, fp32 batch 32 continues descending well past that point on small datasets (10 clips), eventually overfitting. **Stop fp32 batch 32 around step 2000 on a 10-clip dataset** — later checkpoints sound worse despite lower loss.
+
+**Lower loss ≠ better audio.** Once overfitting begins the model memorizes training clips rather than generalizing to new video inputs. Test intermediate checkpoints (e.g. step 500, 1000, 2000) to find the perceptual sweet spot.
+
+### logit_normal vs uniform
+
+logit_normal consistently reaches a lower loss floor than uniform. However perceptual improvement is dataset-dependent — on 10 clips the difference is marginal. May be more impactful with larger datasets. No conclusion yet.
+
+### White noise
+
+Residual white noise on generated audio is primarily a **dataset** problem, not a training one. Observed with all configs on 10 clips. Likely causes:
+- Too few clips for the model to confidently predict the target sound
+- Imprecise extraction prompts producing unfocused sync features
+- Missing mask when multiple objects are in frame
+
+CFG scale amplifies any adapter noise bias. Reducing CFG to 3.0–3.5 or adapter strength to 0.6–0.7 helps at inference.

nodes/__init__.py

@@ -5,6 +5,8 @@ _NODES = {
     "SelvaModelLoader":      (".selva_model_loader",      "SelvaModelLoader",      "SelVA Model Loader"),
     "SelvaFeatureExtractor": (".selva_feature_extractor", "SelvaFeatureExtractor", "SelVA Feature Extractor"),
     "SelvaSampler":          (".selva_sampler",           "SelvaSampler",          "SelVA Sampler"),
+    "SelvaLoraLoader":       (".selva_lora_loader",       "SelvaLoraLoader",       "SelVA LoRA Loader"),
+    "SelvaLoraTrainer":      (".selva_lora_trainer",      "SelvaLoraTrainer",      "SelVA LoRA Trainer"),
 }
 
 for key, (module_path, class_name, display_name) in _NODES.items():

nodes/selva_feature_extractor.py

@@ -35,66 +35,6 @@ def _resize_frames(frames, size):
     return x.clamp(0.0, 1.0)  # [N, C, H, W]
 
 
-def _compute_mask_bbox(mask, frame_h, frame_w, margin=0.1, square=True):
-    """
-    Compute a bounding box around the union of all mask frames.
-
-    mask:   [M, H', W'] float [0,1]
-    square: if True, expand bbox to a square and shift into frame bounds;
-            if False, apply margin independently on each axis (rect crop).
-    Returns (y0, x0, y1, x1) in pixel coords clamped to (frame_h, frame_w).
-    """
-    if mask.shape[1] != frame_h or mask.shape[2] != frame_w:
-        m = F.interpolate(
-            mask.float().unsqueeze(1), size=(frame_h, frame_w), mode="nearest-exact"
-        ).squeeze(1)
-    else:
-        m = mask.float()
-
-    union = (m > 0.5).max(dim=0).values  # [H, W] bool
-
-    if not union.any():
-        if square:
-            # Empty mask — center square crop
-            side = min(frame_h, frame_w)
-            cy, cx = frame_h // 2, frame_w // 2
-            y0 = max(0, cy - side // 2)
-            x0 = max(0, cx - side // 2)
-            return y0, x0, min(frame_h, y0 + side), min(frame_w, x0 + side)
-        else:
-            # Empty mask — return full frame (no meaningful rect to crop to)
-            return 0, 0, frame_h, frame_w
-
-    ys = union.any(dim=1).nonzero(as_tuple=True)[0]
-    xs = union.any(dim=0).nonzero(as_tuple=True)[0]
-    y0, y1 = int(ys[0]), int(ys[-1]) + 1
-    x0, x1 = int(xs[0]), int(xs[-1]) + 1
-
-    if square:
-        side = max(y1 - y0, x1 - x0)
-        pad  = int(side * margin)
-        side += 2 * pad
-
-        cy = (y0 + y1) // 2
-        cx = (x0 + x1) // 2
-        y0n = cy - side // 2
-        x0n = cx - side // 2
-        y1n = y0n + side
-        x1n = x0n + side
-
-        # Shift into frame bounds to preserve square shape
-        if y0n < 0:        y1n -= y0n;           y0n = 0
-        if y1n > frame_h:  y0n -= y1n - frame_h; y1n = frame_h
-        if x0n < 0:        x1n -= x0n;           x0n = 0
-        if x1n > frame_w:  x0n -= x1n - frame_w; x1n = frame_w
-
-        return max(0, int(y0n)), max(0, int(x0n)), min(frame_h, int(y1n)), min(frame_w, int(x1n))
-    else:
-        pad_y = int(max(1, y1 - y0) * margin)
-        pad_x = int(max(1, x1 - x0) * margin)
-        return max(0, y0 - pad_y), max(0, x0 - pad_x), min(frame_h, y1 + pad_y), min(frame_w, x1 + pad_x)
-
-
 def _apply_mask(frames, mask, source_fps, target_fps, mask_strength=1.0):
     """
     Apply a ComfyUI MASK to resized frames.
@@ -128,9 +68,20 @@ def _apply_mask(frames, mask, source_fps, target_fps, mask_strength=1.0):
     return frames * alpha + 0.5 * (1.0 - alpha)
 
 
+def _resolve_named_path(cache_dir: str, name: str) -> str:
+    """Return cache_dir/name.npz, incrementing to name_001.npz etc. if the file already exists."""
+    # Sanitize: replace path separators so the name stays inside cache_dir
+    name = name.replace("/", "_").replace("\\", "_").replace("\x00", "_")
+    i = 1
+    while True:
+        p = os.path.join(cache_dir, f"{name}_{i:03d}.npz")
+        if not os.path.exists(p):
+            return p
+        i += 1
+
+
 def _hash_inputs(video_tensor, prompt, fps, duration, variant, mask=None,
-                 mask_strength=1.0, mask_clip=True, mask_sync=True,
-                 crop_to_mask=False, crop_rect=False, crop_margin=0.1):
+                 mask_strength=1.0, mask_clip=True, mask_sync=True):
     h = hashlib.sha256()
     raw = video_tensor.cpu().numpy().tobytes()
     n = len(raw)
@@ -148,10 +99,6 @@ def _hash_inputs(video_tensor, prompt, fps, duration, variant, mask=None,
         h.update(str(round(mask_strength, 4)).encode())
         h.update(str(mask_clip).encode())
         h.update(str(mask_sync).encode())
-        h.update(str(crop_to_mask).encode())
-        h.update(str(crop_rect).encode())
-        if crop_to_mask or crop_rect:
-            h.update(str(round(crop_margin, 4)).encode())
     h.update(prompt.encode())
     h.update(str(fps).encode())
     h.update(str(round(duration, 3)).encode())  # resolved duration affects frame count
@@ -181,6 +128,8 @@ class SelvaFeatureExtractor:
                                        "tooltip": "Clip duration in seconds. 0 = use the full video length. Clamped to actual video length if too long."}),
                 "cache_dir": ("STRING", {"default": "",
                                          "tooltip": "Where to store extracted feature files (.npz). Leave empty for the system temp directory. Reusing the same directory enables instant cache hits on re-runs."}),
+                "name": ("STRING", {"default": "",
+                                    "tooltip": "Optional filename for the saved .npz (without extension). If provided, features are always saved with this name instead of a content hash — useful for building a named training dataset. Auto-increments: dog_bark → dog_bark_001 → dog_bark_002 if the file already exists. Leave empty to use the default content-hash cache."}),
                 "mask": ("MASK", {
                     "tooltip": "Optional segmentation mask [T,H,W] float [0,1]. Background pixels are zeroed before encoding — useful when multiple objects compete for the same sound. Static (1-frame) or per-frame masks both supported. Connect SAM2 or Grounding DINO+SAM output.",
                 }),
@@ -196,18 +145,6 @@ class SelvaFeatureExtractor:
                     "default": True,
                     "tooltip": "Apply the mask to TextSynchformer sync features (224px). This is the primary path for isolating which object's motion drives the audio.",
                 }),
-                "crop_to_mask": ("BOOLEAN", {
-                    "default": False,
-                    "tooltip": "Experimental. Crops frames to a square region around the mask bounding box before resizing. The model sees an undistorted view of the subject. Requires mask. Takes priority over crop_rect.",
-                }),
-                "crop_rect": ("BOOLEAN", {
-                    "default": False,
-                    "tooltip": "Experimental. Crops frames to a rectangle around the mask bounding box (with margin) before resizing. The model still stretches the crop to a square, but only sees the region around the target element. Simpler than crop_to_mask. Requires mask.",
-                }),
-                "crop_margin": ("FLOAT", {
-                    "default": 0.1, "min": 0.0, "max": 1.0, "step": 0.05,
-                    "tooltip": "Margin added around the bounding box as a fraction of the bbox size. Shared by crop_to_mask and crop_rect. 0.1 = 10% on each side.",
-                }),
             },
         }
 
@@ -218,14 +155,14 @@ class SelvaFeatureExtractor:
         "Source fps of the video — wire to VHS_VideoCombine frame_rate.",
         "The prompt used during extraction — wire to Sampler prompt to avoid re-typing.",
     )
+    OUTPUT_NODE = True  # always execute: the node's purpose is saving .npz files to disk
     FUNCTION = "extract_features"
     CATEGORY = SELVA_CATEGORY
     DESCRIPTION = "Extracts CLIP visual features and text-conditioned sync features from a video. Results are cached — re-running with the same inputs is instant."
 
     def extract_features(self, model, video, prompt, video_info=None, fps=30.0,
-                         duration=0.0, cache_dir="", mask=None,
-                         mask_strength=1.0, mask_clip=True, mask_sync=True,
-                         crop_to_mask=False, crop_rect=False, crop_margin=0.1):
+                         duration=0.0, cache_dir="", name="", mask=None,
+                         mask_strength=1.0, mask_clip=True, mask_sync=True):
         if video_info is not None:
             fps = video_info["loaded_fps"]
 
@@ -241,11 +178,15 @@ class SelvaFeatureExtractor:
         if not cache_dir:
             cache_dir = os.path.join(tempfile.gettempdir(), "selva_features")
         os.makedirs(cache_dir, exist_ok=True)
-        cache_key = _hash_inputs(video, prompt, fps, duration, model["variant"], mask=mask,
-                                 mask_strength=mask_strength, mask_clip=mask_clip, mask_sync=mask_sync,
-                                 crop_to_mask=crop_to_mask, crop_rect=crop_rect, crop_margin=crop_margin)
-        cached_path = os.path.join(cache_dir, f"{cache_key}.npz")
 
+        if name.strip():
+            # Named mode: always extract and save to an incremented filename
+            cached_path = _resolve_named_path(cache_dir, name.strip())
+        else:
+            # Hash mode: skip extraction if identical inputs were already processed
+            cache_key = _hash_inputs(video, prompt, fps, duration, model["variant"], mask=mask,
+                                     mask_strength=mask_strength, mask_clip=mask_clip, mask_sync=mask_sync)
+            cached_path = os.path.join(cache_dir, f"{cache_key}.npz")
             if os.path.exists(cached_path):
                 print(f"[SelVA] Using cached features: {cached_path}", flush=True)
                 cached = _load_cached(cached_path)
@@ -265,24 +206,10 @@ class SelvaFeatureExtractor:
         print(f"[SelVA] Extracting features: duration={duration:.2f}s fps={fps:.3f} prompt='{prompt[:60]}'", flush=True)
         pbar = comfy.utils.ProgressBar(3)
 
-        # Pre-compute crop bbox once from the original-resolution mask
-        crop_bbox = None
-        if mask is not None and (crop_to_mask or crop_rect):
-            H_vid, W_vid = video.shape[1], video.shape[2]
-            _square = crop_to_mask  # crop_to_mask takes priority; crop_rect is rect-only
-            crop_bbox = _compute_mask_bbox(mask, H_vid, W_vid, crop_margin, square=_square)
-            cy0, cx0, cy1, cx1 = crop_bbox
-            _mode = "square" if _square else "rect"
-            print(f"[SelVA]   Mask crop ({_mode}): y={cy0}:{cy1} x={cx0}:{cx1} "
-                  f"({cy1-cy0}×{cx1-cx0}px from {H_vid}×{W_vid})", flush=True)
-
         try:
             with torch.no_grad():
                 # --- CLIP frames: [1, N, C, 384, 384] float32 [0,1] ---
                 clip_frames = _sample_frames(video, fps, _CLIP_FPS, duration)   # [N, H, W, C]
-                if crop_bbox is not None:
-                    cy0, cx0, cy1, cx1 = crop_bbox
-                    clip_frames = clip_frames[:, cy0:cy1, cx0:cx1, :]
                 clip_frames = _resize_frames(clip_frames, _CLIP_SIZE)            # [N, C, 384, 384]
                 if mask is not None and mask_clip:
                     clip_frames = _apply_mask(clip_frames, mask, fps, _CLIP_FPS, mask_strength)
@@ -295,9 +222,6 @@ class SelvaFeatureExtractor:
 
                 # --- Sync frames: [1, N, C, 224, 224] float32 [-1,1] ---
                 sync_frames = _sample_frames(video, fps, _SYNC_FPS, duration)    # [N, H, W, C]
-                if crop_bbox is not None:
-                    cy0, cx0, cy1, cx1 = crop_bbox
-                    sync_frames = sync_frames[:, cy0:cy1, cx0:cx1, :]
                 sync_frames = _resize_frames(sync_frames, _SYNC_SIZE)             # [N, C, 224, 224]
                 if mask is not None and mask_sync:
                     sync_frames = _apply_mask(sync_frames, mask, fps, _SYNC_FPS, mask_strength)

nodes/selva_lora_loader.py

@@ -0,0 +1,102 @@
+import copy
+import torch
+import folder_paths
+
+from .utils import SELVA_CATEGORY
+from selva_core.model.lora import apply_lora, load_lora
+
+
+class SelvaLoraLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model":        ("SELVA_MODEL",),
+                "adapter_path": ("STRING", {
+                    "default": "",
+                    "tooltip": "Path to a LoRA adapter .pt file produced by train_lora.py.",
+                }),
+                "strength": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 2.0, "step": 0.05,
+                    "tooltip": "Scale applied to all LoRA contributions. "
+                               "1.0 = full adapter strength. "
+                               "0.0 = effectively disables the adapter. "
+                               "Values above 1.0 exaggerate the effect.",
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("SELVA_MODEL",)
+    RETURN_NAMES = ("model",)
+    OUTPUT_TOOLTIPS = ("Model with LoRA adapter applied — connect to Sampler.",)
+    FUNCTION = "load"
+    CATEGORY = SELVA_CATEGORY
+    DESCRIPTION = (
+        "Loads a LoRA adapter produced by train_lora.py and applies it to the generator. "
+        "The base model is not modified — a shallow copy of the model bundle is returned."
+    )
+
+    def load(self, model: dict, adapter_path: str, strength: float) -> tuple:
+        if not adapter_path.strip():
+            raise ValueError("[SelVA LoRA] adapter_path is empty.")
+
+        # Resolve path: allow absolute or relative to ComfyUI base
+        from pathlib import Path
+        p = Path(adapter_path)
+        if not p.is_absolute():
+            p = Path(folder_paths.base_path) / p
+        if not p.exists():
+            raise FileNotFoundError(f"[SelVA LoRA] Adapter not found: {p}")
+
+        checkpoint = torch.load(str(p), map_location="cpu", weights_only=False)
+
+        # Support both raw state_dict and {state_dict, meta} formats
+        if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
+            state_dict = checkpoint["state_dict"]
+            meta       = checkpoint.get("meta", {})
+        else:
+            state_dict = checkpoint
+            meta       = {}
+
+        rank   = int(meta.get("rank",   16))
+        alpha  = float(meta.get("alpha", float(rank)))
+        target = list(meta.get("target", ["attn.qkv"]))
+
+        print(f"[SelVA LoRA] Loading adapter: {p.name}", flush=True)
+        print(f"[SelVA LoRA]   rank={rank}  alpha={alpha}  target={target}  strength={strength}",
+              flush=True)
+
+        # Shallow-copy the model bundle so the original generator is not mutated
+        patched = {**model}
+        generator = copy.deepcopy(model["generator"])
+
+        n = apply_lora(generator, rank=rank, alpha=alpha, target_suffixes=tuple(target))
+        if n == 0:
+            raise RuntimeError(
+                f"[SelVA LoRA] No layers matched target={target}. "
+                "Check that the adapter was trained with the same target suffixes."
+            )
+        load_lora(generator, state_dict)
+
+        # Sanity check: confirm lora_A weights are non-zero (lora_B starts at zero by design)
+        norms = [p.norm().item() for name, p in generator.named_parameters()
+                 if "lora_A" in name]
+        if norms:
+            print(f"[SelVA LoRA] lora_A weight norms: min={min(norms):.4f} "
+                  f"max={max(norms):.4f} mean={sum(norms)/len(norms):.4f}", flush=True)
+        else:
+            print("[SelVA LoRA] WARNING: no lora_A params found after loading!", flush=True)
+
+        # Apply strength scaling: multiply all lora_B params by strength
+        # (lora_B is initialised to zero, so scaling A is equivalent but less clean)
+        if strength != 1.0:
+            with torch.no_grad():
+                for name, param in generator.named_parameters():
+                    if "lora_B" in name:
+                        param.mul_(strength)
+
+        generator.to(model["generator"].parameters().__next__().device)
+        patched["generator"] = generator
+
+        print(f"[SelVA LoRA] Applied {n} LoRA layers.", flush=True)
+        return (patched,)

nodes/selva_lora_trainer.py

@@ -0,0 +1,617 @@
+import copy
+import json
+import random
+import traceback
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchaudio
+from PIL import Image, ImageDraw
+
+import comfy.utils
+import folder_paths
+
+from .utils import SELVA_CATEGORY, get_device, soft_empty_cache
+from selva_core.model.utils.features_utils import FeaturesUtils
+from selva_core.model.flow_matching import FlowMatching
+from selva_core.model.lora import apply_lora, get_lora_state_dict, load_lora
+
+
+_AUDIO_EXTS = {".wav", ".flac", ".mp3", ".ogg", ".aiff", ".aif"}
+_SELVA_DIR  = Path(folder_paths.models_dir) / "selva"
+
+
+# ---------------------------------------------------------------------------
+# Data helpers (mirror train_lora.py)
+# ---------------------------------------------------------------------------
+
+def _load_prompts(data_dir: Path) -> dict:
+    p = data_dir / "prompts.txt"
+    if not p.exists():
+        return {}
+    mapping = {}
+    for line in p.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#"):
+            continue
+        if ":" in line:
+            fname, prompt = line.split(":", 1)
+            mapping[fname.strip()] = prompt.strip()
+    return mapping
+
+
+def _find_audio(npz_path: Path) -> Path | None:
+    for ext in _AUDIO_EXTS:
+        c = npz_path.with_suffix(ext)
+        if c.exists():
+            return c
+    return None
+
+
+def _load_audio(path: Path, target_sr: int, duration: float) -> torch.Tensor:
+    try:
+        waveform, sr = torchaudio.load(str(path))
+    except RuntimeError as e:
+        if "torchcodec" not in str(e).lower() and "libtorchcodec" not in str(e).lower():
+            raise
+        # torchcodec unavailable (FFmpeg shared libs missing) — fall back to soundfile
+        import soundfile as sf
+        data, sr = sf.read(str(path), always_2d=True)  # [frames, channels]
+        waveform = torch.from_numpy(data.T).float()    # [channels, frames]
+    if waveform.shape[0] > 1:
+        waveform = waveform.mean(0, keepdim=True)
+    waveform = waveform.squeeze(0).float()
+    if sr != target_sr:
+        waveform = torchaudio.functional.resample(
+            waveform.unsqueeze(0), sr, target_sr).squeeze(0)
+    target_len = int(duration * target_sr)
+    if waveform.shape[0] >= target_len:
+        return waveform[:target_len]
+    return F.pad(waveform, (0, target_len - waveform.shape[0]))
+
+
+def _load_npz(path: Path) -> dict:
+    data = np.load(str(path), allow_pickle=False)
+    bundle = {
+        "clip_features": torch.from_numpy(data["clip_features"]),
+        "sync_features": torch.from_numpy(data["sync_features"]),
+    }
+    if "prompt" in data:
+        bundle["prompt"] = str(data["prompt"])
+    return bundle
+
+
+# ---------------------------------------------------------------------------
+# Eval sample
+# ---------------------------------------------------------------------------
+
+def _eval_sample(generator, feature_utils_orig, dataset, seq_cfg, device, dtype,
+                 num_steps: int = 8):
+    """Run a quick no-CFG inference pass on a random training clip.
+
+    Returns (waveform [1, L] float32 cpu, sample_rate) or (None, None) on failure.
+    Uses fewer ODE steps than inference (8 vs 25) for speed.
+    """
+    generator.eval()
+    try:
+        _, clip_f_cpu, sync_f_cpu, text_clip_cpu = random.choice(dataset)
+        clip_f    = clip_f_cpu.to(device, dtype)
+        sync_f    = sync_f_cpu.to(device, dtype)
+        text_clip = text_clip_cpu.to(device, dtype)
+
+        x0 = torch.randn(1, seq_cfg.latent_seq_len, generator.latent_dim,
+                         device=device, dtype=dtype)
+
+        eval_fm = FlowMatching(min_sigma=0, inference_mode="euler", num_steps=num_steps)
+
+        def velocity_fn(t, x):
+            return generator.forward(x, clip_f, sync_f, text_clip,
+                                     t.reshape(1).to(device, dtype))
+
+        with torch.no_grad():
+            x1_pred   = eval_fm.to_data(velocity_fn, x0)
+            x1_unnorm = generator.unnormalize(x1_pred)
+
+            # feature_utils_orig may be on CPU (offload strategy) — move temporarily
+            orig_device = next(feature_utils_orig.parameters()).device
+            if orig_device != device:
+                feature_utils_orig.to(device)
+            try:
+                spec  = feature_utils_orig.decode(x1_unnorm)
+                audio = feature_utils_orig.vocode(spec)
+            finally:
+                if orig_device != device:
+                    feature_utils_orig.to(orig_device)
+
+        audio = audio.float().cpu()
+        if audio.dim() == 2:
+            audio = audio.unsqueeze(1)
+        elif audio.dim() == 3 and audio.shape[1] != 1:
+            audio = audio.mean(dim=1, keepdim=True)
+
+        peak = audio.abs().max().clamp(min=1e-8)
+        audio = (audio / peak).clamp(-1, 1)
+        return audio.squeeze(0), seq_cfg.sampling_rate   # [1, L]
+
+    except Exception as e:
+        print(f"[LoRA Trainer] Eval sample failed: {e}", flush=True)
+        return None, None
+    finally:
+        generator.train()
+
+
+# ---------------------------------------------------------------------------
+# Loss curve rendering
+# ---------------------------------------------------------------------------
+
+def _smooth_losses(losses: list[float], beta: float = 0.9) -> list[float]:
+    """Exponential moving average smoothing."""
+    smoothed, ema = [], None
+    for v in losses:
+        ema = v if ema is None else beta * ema + (1 - beta) * v
+        smoothed.append(ema)
+    return smoothed
+
+
+def _draw_loss_curve(losses: list[float], log_interval: int,
+                     start_step: int = 0, smoothed: list[float] | None = None) -> Image.Image:
+    """Render a loss curve as a PIL Image."""
+    W, H = 800, 380
+    pl, pr, pt, pb = 70, 20, 25, 45
+
+    img  = Image.new("RGB", (W, H), (255, 255, 255))
+    draw = ImageDraw.Draw(img)
+
+    pw = W - pl - pr
+    ph = H - pt - pb
+
+    if len(losses) >= 2:
+        lo, hi = min(losses), max(losses)
+        if hi == lo:
+            hi = lo + 1e-6
+        rng = hi - lo
+
+        # Horizontal grid + y-axis labels
+        for i in range(5):
+            y   = pt + int(i * ph / 4)
+            val = hi - i * rng / 4
+            draw.line([(pl, y), (W - pr, y)], fill=(220, 220, 220), width=1)
+            draw.text((2, y - 7), f"{val:.4f}", fill=(120, 120, 120))
+
+        # Raw loss line
+        n   = len(losses)
+        pts = []
+        for i, v in enumerate(losses):
+            x = pl + int(i * pw / max(n - 1, 1))
+            y = pt + int((1.0 - (v - lo) / rng) * ph)
+            pts.append((x, y))
+        draw.line(pts, fill=(200, 220, 255), width=1)
+
+        # Smoothed overlay
+        if smoothed is not None and len(smoothed) >= 2:
+            spts = []
+            for i, v in enumerate(smoothed):
+                x = pl + int(i * pw / max(n - 1, 1))
+                y = pt + int((1.0 - (v - lo) / rng) * ph)
+                spts.append((x, y))
+            draw.line(spts, fill=(66, 133, 244), width=2)
+
+        # x-axis step labels — account for start_step so resumed runs are correct
+        first_step = start_step + log_interval
+        last_step  = start_step + n * log_interval
+        for i in range(5):
+            x    = pl + int(i * pw / 4)
+            step = int(first_step + i * (last_step - first_step) / 4)
+            draw.text((x - 12, H - pb + 5), str(step), fill=(120, 120, 120))
+
+    # Axes
+    draw.line([(pl, pt), (pl, H - pb)],         fill=(40, 40, 40), width=1)
+    draw.line([(pl, H - pb), (W - pr, H - pb)], fill=(40, 40, 40), width=1)
+    draw.text((pl + 4, 5), "Training Loss", fill=(40, 40, 40))
+
+    return img
+
+
+def _pil_to_tensor(img: Image.Image) -> torch.Tensor:
+    """Convert a PIL Image to a [1, H, W, 3] float32 IMAGE tensor for ComfyUI."""
+    arr = np.array(img).astype(np.float32) / 255.0
+    return torch.from_numpy(arr).unsqueeze(0)
+
+
+# ---------------------------------------------------------------------------
+# Node
+# ---------------------------------------------------------------------------
+
+class SelvaLoraTrainer:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model":      ("SELVA_MODEL",),
+                "data_dir":   ("STRING", {
+                    "default": "",
+                    "tooltip": "Directory containing .npz feature files and paired audio files.",
+                }),
+                "output_dir": ("STRING", {
+                    "default": "lora_output",
+                    "tooltip": "Where to save adapter checkpoints.",
+                }),
+                "steps": ("INT", {
+                    "default": 2000, "min": 100, "max": 100000,
+                    "tooltip": "Total training steps.",
+                }),
+                "rank": ("INT", {
+                    "default": 16, "min": 1, "max": 128,
+                    "tooltip": "LoRA rank. Higher = more capacity, more VRAM. 16 is a safe default.",
+                }),
+                "lr": ("FLOAT", {
+                    "default": 1e-4, "min": 1e-6, "max": 1e-2, "step": 1e-6,
+                    "tooltip": "Learning rate.",
+                }),
+            },
+            "optional": {
+                "alpha": ("FLOAT", {
+                    "default": 0.0, "min": 0.0, "max": 256.0, "step": 0.5,
+                    "tooltip": "LoRA alpha. 0 = use rank value (scale = 1.0).",
+                }),
+                "target": ("STRING", {
+                    "default": "attn.qkv",
+                    "tooltip": "Space-separated layer name suffixes to wrap. Default targets all QKV projections. Add 'linear1' for post-attention projections.",
+                }),
+                "batch_size":   ("INT", {"default": 4,   "min": 1, "max": 32,
+                                         "tooltip": "Number of clips per training step. Higher = more stable gradients, more VRAM."}),
+                "warmup_steps": ("INT", {"default": 100, "min": 0, "max": 5000}),
+                "grad_accum":   ("INT", {"default": 1,   "min": 1, "max": 32,
+                                         "tooltip": "Gradient accumulation steps. Usually 1 when batch_size > 1."}),
+                "save_every":   ("INT", {"default": 500, "min": 50, "max": 10000}),
+                "resume_path":  ("STRING", {
+                    "default": "",
+                    "tooltip": "Path to a step checkpoint (.pt) to resume training from.",
+                }),
+                "seed": ("INT", {"default": 42}),
+            },
+        }
+
+    RETURN_TYPES  = ("SELVA_MODEL", "STRING", "IMAGE")
+    RETURN_NAMES  = ("model", "adapter_path", "loss_curve")
+    OUTPUT_TOOLTIPS = (
+        "Model with trained LoRA adapter applied — connect directly to Sampler.",
+        "Path to adapter_final.pt — use with SelVA LoRA Loader in future sessions.",
+        "Training loss curve.",
+    )
+    FUNCTION  = "train"
+    CATEGORY  = SELVA_CATEGORY
+    DESCRIPTION = (
+        "Trains a LoRA adapter on a dataset of .npz feature files + paired audio files. "
+        "Blocks the queue for the duration of training. "
+        "Prepare the dataset with SelVA Feature Extractor (set a name to get numbered .npz files) "
+        "and pair each .npz with a clean audio file of the same stem."
+    )
+
+    def train(self, model, data_dir, output_dir, steps, rank, lr,
+              alpha=0.0, target="attn.qkv", batch_size=4, warmup_steps=100,
+              grad_accum=1, save_every=500, resume_path="", seed=42):
+
+        torch.manual_seed(seed)
+        random.seed(seed)
+
+        device   = get_device()
+        dtype    = model["dtype"]
+        variant  = model["variant"]
+        mode     = model["mode"]
+        seq_cfg  = model["seq_cfg"]
+        feature_utils_orig = model["feature_utils"]
+
+        data_dir   = Path(data_dir.strip())
+
+        _out_str = output_dir.strip()
+        _out_p   = Path(_out_str)
+        # On Windows a Unix-style path like "/lora_output" is technically absolute
+        # (drive-relative) but the user almost certainly meant a subfolder of the
+        # ComfyUI output directory. Treat any non-absolute path AND any path whose
+        # only "absolute" anchor is a leading slash (no drive letter) as relative to
+        # the ComfyUI output folder.
+        import sys as _sys
+        _unix_style_on_windows = (
+            _sys.platform == "win32"
+            and _out_p.is_absolute()
+            and not _out_p.drive  # e.g. Path("/foo").drive == "" on Windows
+        )
+        if not _out_p.is_absolute() or _unix_style_on_windows:
+            _out_p = Path(folder_paths.get_output_directory()) / _out_p.relative_to(_out_p.anchor)
+            print(f"[LoRA Trainer] output_dir resolved to: {_out_p}", flush=True)
+        output_dir = _out_p
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        alpha_val      = float(alpha) if alpha > 0.0 else float(rank)
+        target_suffixes = tuple(target.strip().split())
+
+        # --- Load VAE encoder (not present in inference model) ---
+        vae_name = "v1-16.pth" if mode == "16k" else "v1-44.pth"
+        vae_path = _SELVA_DIR / "ext" / vae_name
+        if not vae_path.exists():
+            raise FileNotFoundError(
+                f"[LoRA Trainer] VAE weight not found: {vae_path}. "
+                "Run SelVA Model Loader first to auto-download weights."
+            )
+        print("[LoRA Trainer] Loading VAE encoder...", flush=True)
+        # Keep VAE in float32: mel_converter uses torch.stft which requires float32 input.
+        vae_utils = FeaturesUtils(
+            tod_vae_ckpt=str(vae_path),
+            enable_conditions=False,
+            mode=mode,
+            need_vae_encoder=True,
+        ).to(device).eval()
+
+        # --- Pre-load dataset ---
+        npz_files = sorted(data_dir.glob("*.npz"))
+        if not npz_files:
+            raise ValueError(f"[LoRA Trainer] No .npz files found in {data_dir}")
+
+        prompt_map     = _load_prompts(data_dir)
+        default_prompt = data_dir.name
+
+        print(f"[LoRA Trainer] Pre-loading {len(npz_files)} clip(s)...", flush=True)
+        pbar_load = comfy.utils.ProgressBar(len(npz_files))
+        dataset   = []
+
+        for npz_path in npz_files:
+            audio_path = _find_audio(npz_path)
+            if audio_path is None:
+                print(f"  [LoRA Trainer] Warning: no audio for {npz_path.name} — skipping", flush=True)
+                pbar_load.update(1)
+                continue
+
+            bundle = _load_npz(npz_path)
+            prompt = prompt_map.get(npz_path.name, bundle.get("prompt", default_prompt))
+            print(f"  {npz_path.name} + {audio_path.name}: '{prompt}'", flush=True)
+
+            try:
+                audio = _load_audio(audio_path, seq_cfg.sampling_rate, seq_cfg.duration)
+
+                # Audio → latent via VAE (float32: mel_converter/stft require float32)
+                # encode_audio is @inference_mode — .clone() exits inference mode
+                audio_b = audio.unsqueeze(0).to(device)
+                dist = vae_utils.encode_audio(audio_b)
+                # VAE outputs [B, latent_dim, T]; generator expects [B, T, latent_dim]
+                x1 = dist.mode().clone().transpose(1, 2).cpu()
+                # STFT rounding can produce ±1 frame — pad or trim to exact seq length
+                tgt = seq_cfg.latent_seq_len
+                if x1.shape[1] < tgt:
+                    x1 = F.pad(x1, (0, 0, 0, tgt - x1.shape[1]))
+                elif x1.shape[1] > tgt:
+                    x1 = x1[:, :tgt, :]
+
+                # Text → CLIP features (reuse already-loaded CLIP from inference model)
+                text_clip = feature_utils_orig.encode_text_clip([prompt]).cpu()
+
+                # Pad/trim clip and sync features to fixed seq lengths — clips from
+                # shorter videos have fewer frames and would cause stack() to fail
+                clip_f = bundle["clip_features"]  # [1, N_clip, 1024]
+                c_tgt  = seq_cfg.clip_seq_len
+                if clip_f.shape[1] < c_tgt:
+                    clip_f = F.pad(clip_f, (0, 0, 0, c_tgt - clip_f.shape[1]))
+                elif clip_f.shape[1] > c_tgt:
+                    clip_f = clip_f[:, :c_tgt, :]
+
+                sync_f = bundle["sync_features"]  # [1, N_sync, 768]
+                s_tgt  = seq_cfg.sync_seq_len
+                if sync_f.shape[1] < s_tgt:
+                    sync_f = F.pad(sync_f, (0, 0, 0, s_tgt - sync_f.shape[1]))
+                elif sync_f.shape[1] > s_tgt:
+                    sync_f = sync_f[:, :s_tgt, :]
+
+                dataset.append((x1, clip_f, sync_f, text_clip))
+            except Exception as e:
+                print(f"  [LoRA Trainer] Warning: failed {npz_path.name}: {e}", flush=True)
+                traceback.print_exc()
+
+            pbar_load.update(1)
+
+        # VAE no longer needed — free memory
+        del vae_utils
+        soft_empty_cache()
+
+        if not dataset:
+            raise ValueError("[LoRA Trainer] No clips could be loaded.")
+        print(f"[LoRA Trainer] {len(dataset)} clip(s) ready.", flush=True)
+
+        # ComfyUI executes nodes inside torch.inference_mode(). Inference tensors
+        # can't participate in autograd even with enable_grad — disable inference
+        # mode entirely so deepcopy, apply_lora, and the training loop all run
+        # with a clean autograd context.
+        with torch.inference_mode(False), torch.enable_grad():
+            return self._train_inner(
+                model, dataset, feature_utils_orig, seq_cfg,
+                device, dtype, variant, mode,
+                data_dir, output_dir, steps, rank, lr,
+                alpha_val, target_suffixes, batch_size, warmup_steps,
+                grad_accum, save_every, resume_path, seed,
+            )
+
+    def _train_inner(
+        self, model, dataset, feature_utils_orig, seq_cfg,
+        device, dtype, variant, mode,
+        data_dir, output_dir, steps, rank, lr,
+        alpha_val, target_suffixes, batch_size, warmup_steps,
+        grad_accum, save_every, resume_path, seed,
+    ):
+        # --- Prepare generator copy with LoRA ---
+        generator = copy.deepcopy(model["generator"]).to(device, dtype)
+
+        n_lora = apply_lora(generator, rank=rank, alpha=alpha_val,
+                            target_suffixes=target_suffixes)
+        if n_lora == 0:
+            raise RuntimeError(
+                f"[LoRA Trainer] No layers matched target={target_suffixes}. "
+                "Check the 'target' field."
+            )
+        print(f"[LoRA Trainer] Wrapped {n_lora} layers (rank={rank}, alpha={alpha_val})", flush=True)
+
+        for name, p in generator.named_parameters():
+            p.requires_grad_("lora_" in name)
+
+        generator.update_seq_lengths(
+            latent_seq_len=seq_cfg.latent_seq_len,
+            clip_seq_len=seq_cfg.clip_seq_len,
+            sync_seq_len=seq_cfg.sync_seq_len,
+        )
+
+        # --- Optimizer + scheduler ---
+        lora_params = [p for p in generator.parameters() if p.requires_grad]
+        optimizer   = torch.optim.AdamW(lora_params, lr=lr, weight_decay=1e-2)
+
+        def lr_lambda(s):
+            return s / max(1, warmup_steps) if s < warmup_steps else 1.0
+
+        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+        fm = FlowMatching(min_sigma=0, inference_mode="euler", num_steps=25)
+
+        # --- Resume ---
+        start_step = 0
+        if resume_path.strip():
+            ckpt = torch.load(resume_path.strip(), map_location="cpu", weights_only=False)
+            if "step" not in ckpt:
+                raise ValueError("[LoRA Trainer] Checkpoint has no step info.")
+            start_step = ckpt["step"]
+            if start_step >= steps:
+                raise ValueError(
+                    f"[LoRA Trainer] Checkpoint already at step {start_step} >= steps {steps}."
+                )
+            load_lora(generator, ckpt["state_dict"])
+            optimizer.load_state_dict(ckpt["optimizer"])
+            scheduler.load_state_dict(ckpt["scheduler"])
+            print(f"[LoRA Trainer] Resumed from step {start_step}.", flush=True)
+
+        # --- Training loop ---
+        generator.train()
+        optimizer.zero_grad()
+
+        log_interval = 50
+        remaining    = steps - start_step
+        pbar_train   = comfy.utils.ProgressBar(remaining)
+        loss_history = []
+        running_loss = 0.0
+
+        meta = {
+            "variant": variant,
+            "rank":    rank,
+            "alpha":   alpha_val,
+            "target":  list(target_suffixes),
+            "steps":   steps,
+        }
+
+        print(f"\n[LoRA Trainer] Training {remaining} steps "
+              f"(step {start_step + 1} → {steps}, batch_size={batch_size})\n", flush=True)
+
+        last_step = start_step
+        completed = False
+        try:
+            for step in range(start_step + 1, steps + 1):
+                batch = random.choices(dataset, k=batch_size)
+                x1_list, clip_list, sync_list, text_list = zip(*batch)
+
+                x1        = torch.stack([x.squeeze(0) for x in x1_list]).to(device, dtype)
+                clip_f    = torch.stack([x.squeeze(0) for x in clip_list]).to(device, dtype)
+                sync_f    = torch.stack([x.squeeze(0) for x in sync_list]).to(device, dtype)
+                text_clip = torch.stack([x.squeeze(0) for x in text_list]).to(device, dtype)
+
+                generator.normalize(x1)
+
+                t  = torch.rand(batch_size, device=device, dtype=dtype)
+                x0 = torch.randn_like(x1)
+                xt = fm.get_conditional_flow(x0, x1, t)
+
+                v_pred = generator.forward(xt, clip_f, sync_f, text_clip, t)
+                loss   = fm.loss(v_pred, x0, x1).mean() / grad_accum
+                loss.backward()
+                running_loss += loss.item() * grad_accum
+
+                if step % grad_accum == 0:
+                    torch.nn.utils.clip_grad_norm_(lora_params, max_norm=1.0)
+                    optimizer.step()
+                    scheduler.step()
+                    optimizer.zero_grad()
+
+                if step % log_interval == 0:
+                    avg = running_loss / log_interval
+                    loss_history.append(avg)
+                    lr_now = scheduler.get_last_lr()[0]
+                    print(f"[LoRA Trainer] step {step:5d}/{steps}  "
+                          f"loss={avg:.4f}  lr={lr_now:.2e}  bs={batch_size}", flush=True)
+                    running_loss = 0.0
+
+                    # Live preview: send updated loss curve to ComfyUI frontend
+                    preview_img = _draw_loss_curve(loss_history, log_interval, start_step,
+                                                   smoothed=_smooth_losses(loss_history))
+                    pbar_train.update_absolute(
+                        step - start_step, remaining, ("JPEG", preview_img, 800)
+                    )
+
+                if step % save_every == 0 or step == steps:
+                    ckpt_path = output_dir / f"adapter_step{step:05d}.pt"
+                    torch.save({
+                        "state_dict": get_lora_state_dict(generator),
+                        "optimizer":  optimizer.state_dict(),
+                        "scheduler":  scheduler.state_dict(),
+                        "step":       step,
+                        "meta":       meta,
+                    }, ckpt_path)
+                    print(f"[LoRA Trainer] Saved {ckpt_path}", flush=True)
+
+                    # Save a quick eval sample next to the checkpoint
+                    wav, sr = _eval_sample(generator, feature_utils_orig,
+                                           dataset, seq_cfg, device, dtype)
+                    if wav is not None:
+                        wav_path = output_dir / f"sample_step{step:05d}.wav"
+                        try:
+                            torchaudio.save(str(wav_path), wav, sr)
+                        except RuntimeError:
+                            import soundfile as sf
+                            sf.write(str(wav_path), wav.squeeze(0).numpy(), sr)
+                        print(f"[LoRA Trainer] Sample saved: {wav_path}", flush=True)
+
+                last_step = step
+                pbar_train.update(1)
+
+            completed = True
+
+        finally:
+            # Save adapter and loss curves whether training completed or was cancelled.
+            # Skip if we never completed a single step (nothing useful to save).
+            if loss_history:
+                if completed:
+                    # Normal completion — use adapter_final.pt (increment if exists)
+                    final_path = output_dir / "adapter_final.pt"
+                    if final_path.exists():
+                        i = 1
+                        while (output_dir / f"adapter_final_{i:03d}.pt").exists():
+                            i += 1
+                        final_path = output_dir / f"adapter_final_{i:03d}.pt"
+                    label = "Done"
+                else:
+                    # Cancelled — include the step number so the file is useful for resume
+                    final_path = output_dir / f"adapter_cancelled_step{last_step:05d}.pt"
+                    label = f"Cancelled at step {last_step}"
+
+                torch.save({"state_dict": get_lora_state_dict(generator), "meta": meta}, final_path)
+                (output_dir / "meta.json").write_text(json.dumps(meta, indent=2))
+                print(f"\n[LoRA Trainer] {label}. Adapter saved to {final_path}", flush=True)
+
+                smoothed     = _smooth_losses(loss_history)
+                raw_img      = _draw_loss_curve(loss_history, log_interval, start_step)
+                smoothed_img = _draw_loss_curve(loss_history, log_interval, start_step,
+                                                smoothed=smoothed)
+                raw_img.save(str(output_dir / "loss_raw.png"))
+                smoothed_img.save(str(output_dir / "loss_smoothed.png"))
+                print(f"[LoRA Trainer] Loss curves saved to {output_dir}", flush=True)
+
+        # Reached only on normal completion (exception re-raises past this point)
+        generator.eval()
+        generator.to(next(model["generator"].parameters()).device)
+        patched = {**model, "generator": generator}
+
+        loss_curve = _pil_to_tensor(smoothed_img)
+        return (patched, str(final_path), loss_curve)

selva_core/model/lora.py

@@ -0,0 +1,118 @@
+"""
+LoRA (Low-Rank Adaptation) for SelVA / MMAudio generator.
+
+Usage:
+    from selva_core.model.lora import apply_lora, get_lora_state_dict, load_lora
+
+    n = apply_lora(net_generator, rank=16, alpha=16.0)
+    print(f"Wrapped {n} linear layers with LoRA")
+
+    # ... train only LoRA params ...
+
+    torch.save(get_lora_state_dict(net_generator), "adapter.pt")
+
+    # Later, at inference:
+    apply_lora(net_generator, rank=16, alpha=16.0)
+    load_lora(net_generator, torch.load("adapter.pt"))
+"""
+
+import math
+
+import torch
+import torch.nn as nn
+
+
+class LoRALinear(nn.Module):
+    """nn.Linear with a frozen base weight and trainable low-rank A/B matrices.
+
+    Output: base(x) + (x @ A.T @ B.T) * (alpha / rank)
+
+    A is initialised with Kaiming uniform; B is initialised to zero so the
+    adapter contribution starts at zero and does not disturb pretrained behaviour.
+    """
+
+    def __init__(self, linear: nn.Linear, rank: int, alpha: float):
+        super().__init__()
+        in_f  = linear.in_features
+        out_f = linear.out_features
+
+        self.linear = linear
+        linear.weight.requires_grad_(False)
+        if linear.bias is not None:
+            linear.bias.requires_grad_(False)
+
+        ref_dtype   = linear.weight.dtype
+        ref_device  = linear.weight.device
+        self.lora_A = nn.Parameter(torch.empty(rank, in_f, dtype=ref_dtype, device=ref_device))
+        self.lora_B = nn.Parameter(torch.zeros(out_f, rank, dtype=ref_dtype, device=ref_device))
+        self.scale  = alpha / rank
+
+        nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.linear(x) + (x @ self.lora_A.T @ self.lora_B.T) * self.scale
+
+    def extra_repr(self) -> str:
+        rank = self.lora_A.shape[0]
+        return (f"in={self.linear.in_features}, out={self.linear.out_features}, "
+                f"rank={rank}, scale={self.scale:.4f}")
+
+
+def apply_lora(
+    model: nn.Module,
+    rank: int = 16,
+    alpha: float = None,
+    target_suffixes: tuple = ("attn.qkv",),
+) -> int:
+    """Replace matching nn.Linear layers with LoRALinear in-place.
+
+    Args:
+        model:           The module to modify (typically net_generator).
+        rank:            LoRA rank.
+        alpha:           LoRA alpha (scaling). Defaults to rank (scale = 1.0).
+        target_suffixes: Tuple of module name suffixes to wrap. Default is
+                         ("attn.qkv",) which targets all SelfAttention QKV
+                         projections in the MM-DiT generator.
+                         Add "linear1" to also wrap post-attention output projections.
+
+    Returns:
+        Number of linear layers wrapped.
+    """
+    if alpha is None:
+        alpha = float(rank)
+
+    count = 0
+    for name, module in list(model.named_modules()):
+        if not any(name.endswith(s) for s in target_suffixes):
+            continue
+        if not isinstance(module, nn.Linear):
+            continue
+
+        parts = name.split(".")
+        parent = model
+        for part in parts[:-1]:
+            parent = getattr(parent, part)
+        setattr(parent, parts[-1], LoRALinear(module, rank, alpha))
+        count += 1
+
+    return count
+
+
+def get_lora_state_dict(model: nn.Module) -> dict:
+    """Return a state dict containing only LoRA parameters (lora_A and lora_B)."""
+    return {k: v for k, v in model.state_dict().items() if "lora_" in k}
+
+
+def load_lora(model: nn.Module, state_dict: dict) -> None:
+    """Load LoRA weights into a model that has already had apply_lora() called.
+
+    Non-LoRA keys in state_dict are ignored (strict=False). Non-LoRA model
+    parameters are not modified.
+    """
+    missing, unexpected = model.load_state_dict(state_dict, strict=False)
+    bad = [k for k in unexpected if "lora_" not in k]
+    if bad:
+        print(f"[LoRA] Warning: unexpected non-LoRA keys ignored: {bad}")
+    lora_missing = [k for k in missing if "lora_" in k]
+    if lora_missing:
+        print(f"[LoRA] Warning: missing LoRA keys (wrong rank/target?): {lora_missing}")

train_lora.py

@@ -0,0 +1,422 @@
+#!/usr/bin/env python3
+"""
+LoRA fine-tuning for SelVA / MMAudio generator.
+
+Teaches the model new or partially-known sound classes from custom video+audio pairs.
+Only the LoRA adapter weights are trained (~10 MB vs ~4.4 GB for the full model).
+
+Data layout:
+    data/my_sound/
+        clip01.npz        # visual features extracted by SelvaFeatureExtractor in ComfyUI
+        clip01.wav        # paired clean audio (same filename stem, any format)
+        prompts.txt       # optional: "clip01.npz: description" — overrides embedded prompt
+
+If prompts.txt is absent, the prompt embedded in each .npz is used.
+If the .npz has no embedded prompt, the directory name is used as fallback.
+
+Usage:
+    python train_lora.py \\
+        --data_dir data/my_sound \\
+        --output_dir lora_output \\
+        --variant large_44k \\
+        --selva_dir /path/to/ComfyUI/models/selva \\
+        --rank 16 --steps 2000 --lr 1e-4
+"""
+
+import argparse
+import os
+import sys
+import random
+import json
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchaudio
+import open_clip
+from open_clip import create_model_from_pretrained
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from selva_core.model.networks_generator import get_my_mmaudio
+from selva_core.model.utils.features_utils import FeaturesUtils, patch_clip
+from selva_core.model.sequence_config import CONFIG_16K, CONFIG_44K
+from selva_core.model.flow_matching import FlowMatching
+from selva_core.model.lora import apply_lora, get_lora_state_dict
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+_VARIANTS = {
+    "small_16k":  ("generator_small_16k_sup_5.pth",  "16k"),
+    "small_44k":  ("generator_small_44k_sup_5.pth",  "44k"),
+    "medium_44k": ("generator_medium_44k_sup_5.pth", "44k"),
+    "large_44k":  ("generator_large_44k_sup_5.pth",  "44k"),
+}
+
+_AUDIO_EXTS = {".wav", ".flac", ".mp3", ".ogg", ".aiff", ".aif"}
+
+# ---------------------------------------------------------------------------
+# Data helpers
+# ---------------------------------------------------------------------------
+
+def load_prompts(data_dir: Path) -> dict:
+    """Load filename → prompt overrides from prompts.txt."""
+    p = data_dir / "prompts.txt"
+    if not p.exists():
+        return {}
+    mapping = {}
+    for line in p.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#"):
+            continue
+        if ":" in line:
+            fname, prompt = line.split(":", 1)
+            mapping[fname.strip()] = prompt.strip()
+    return mapping
+
+
+def find_audio_for_npz(npz_path: Path) -> Path | None:
+    """Find a paired audio file with the same stem as the .npz."""
+    for ext in _AUDIO_EXTS:
+        candidate = npz_path.with_suffix(ext)
+        if candidate.exists():
+            return candidate
+    return None
+
+
+def load_audio(path: Path, target_sr: int, duration: float) -> torch.Tensor:
+    """Load an audio file → [L] float32 [-1, 1], resampled and trimmed/padded to duration."""
+    waveform, sr = torchaudio.load(str(path))
+
+    # Stereo → mono
+    if waveform.shape[0] > 1:
+        waveform = waveform.mean(0, keepdim=True)
+    waveform = waveform.squeeze(0).float()
+
+    # Resample
+    if sr != target_sr:
+        waveform = torchaudio.functional.resample(
+            waveform.unsqueeze(0), sr, target_sr
+        ).squeeze(0)
+
+    target_len = int(duration * target_sr)
+    if waveform.shape[0] >= target_len:
+        return waveform[:target_len]
+    return F.pad(waveform, (0, target_len - waveform.shape[0]))
+
+
+def load_npz(path: Path) -> dict:
+    """Load a feature bundle produced by SelvaFeatureExtractor."""
+    data = np.load(str(path), allow_pickle=False)
+    bundle = {
+        "clip_features": torch.from_numpy(data["clip_features"]),  # [1, N, 1024]
+        "sync_features": torch.from_numpy(data["sync_features"]),  # [1, T, 768]
+    }
+    if "prompt" in data:
+        bundle["prompt"] = str(data["prompt"])
+    if "variant" in data:
+        bundle["variant"] = str(data["variant"])
+    return bundle
+
+
+# ---------------------------------------------------------------------------
+# Feature extraction (audio + text only — visual features come from .npz)
+# ---------------------------------------------------------------------------
+
+def encode_text_clip(clip_model, tokenizer, text: list[str], device) -> torch.Tensor:
+    tokens = tokenizer(text).to(device)
+    with torch.inference_mode():
+        return clip_model.encode_text(tokens, normalize=True)
+
+
+def extract_audio_latent(audio: torch.Tensor, feature_utils, device, dtype) -> torch.Tensor:
+    """Encode a waveform to the generator's latent space via the VAE.
+
+    encode_audio is @inference_mode — .clone() is required before the autograd path.
+    """
+    audio_b = audio.unsqueeze(0).to(device, dtype)  # [1, L]
+    dist = feature_utils.encode_audio(audio_b)
+    # VAE outputs [B, latent_dim, T]; generator expects [B, T, latent_dim]
+    return dist.mode().clone().transpose(1, 2).cpu()  # [1, seq_len, latent_dim]
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    parser = argparse.ArgumentParser(description="LoRA fine-tuning for SelVA generator")
+    parser.add_argument("--data_dir",    required=True,  help="Directory with .npz + audio pairs and optional prompts.txt")
+    parser.add_argument("--output_dir",  default="lora_output")
+    parser.add_argument("--variant",     default="large_44k", choices=list(_VARIANTS.keys()))
+    parser.add_argument("--selva_dir",   required=True,  help="Path to selva model weights (ComfyUI/models/selva)")
+    parser.add_argument("--rank",        type=int,   default=16,   help="LoRA rank")
+    parser.add_argument("--alpha",       type=float, default=None, help="LoRA alpha (default: rank)")
+    parser.add_argument("--target",      nargs="+",  default=["attn.qkv"],
+                        help="Module name suffixes to wrap with LoRA. Also try 'linear1'.")
+    parser.add_argument("--lr",          type=float, default=1e-4)
+    parser.add_argument("--steps",       type=int,   default=2000)
+    parser.add_argument("--warmup_steps",type=int,   default=100)
+    parser.add_argument("--batch_size",  type=int,   default=4,    help="Clips per training step")
+    parser.add_argument("--grad_accum",  type=int,   default=1,    help="Gradient accumulation steps")
+    parser.add_argument("--save_every",  type=int,   default=500)
+    parser.add_argument("--resume",      default=None,
+                        help="Path to a step checkpoint (.pt) to resume training from.")
+    parser.add_argument("--precision",   default="bf16", choices=["bf16", "fp16", "fp32"])
+    parser.add_argument("--seed",        type=int,   default=42)
+    args = parser.parse_args()
+
+    torch.manual_seed(args.seed)
+    random.seed(args.seed)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if args.precision == "bf16" and device.type == "cuda" and not torch.cuda.is_bf16_supported():
+        print("[LoRA] bf16 not supported on this GPU — falling back to fp16")
+        args.precision = "fp16"
+    dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[args.precision]
+
+    data_dir   = Path(args.data_dir)
+    output_dir = Path(args.output_dir)
+    selva_dir  = Path(args.selva_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    gen_filename, mode = _VARIANTS[args.variant]
+    seq_cfg     = CONFIG_16K if mode == "16k" else CONFIG_44K
+    duration    = seq_cfg.duration
+    sample_rate = seq_cfg.sampling_rate
+
+    # --- Weight paths ---
+    def w(name): return str(selva_dir / name)
+    def wext(name): return str(selva_dir / "ext" / name)
+
+    vae_weight = wext("v1-16.pth" if mode == "16k" else "v1-44.pth")
+    gen_weight = w(gen_filename)
+    for path, label in [(vae_weight, "VAE"), (gen_weight, "generator")]:
+        if not Path(path).exists():
+            print(f"[LoRA] Missing weight: {path} ({label})")
+            print("[LoRA] Run ComfyUI with SelvaModelLoader first to auto-download weights.")
+            sys.exit(1)
+
+    # --- Load CLIP text encoder (separate from FeaturesUtils to avoid loading Synchformer/T5) ---
+    print("[LoRA] Loading CLIP text encoder...")
+    clip_model = create_model_from_pretrained(
+        'hf-hub:apple/DFN5B-CLIP-ViT-H-14-384', return_transform=False
+    ).to(device, dtype).eval()
+    clip_model = patch_clip(clip_model)
+    tokenizer_clip = open_clip.get_tokenizer('ViT-H-14-378-quickgelu')
+
+    # --- Load VAE (FeaturesUtils with enable_conditions=False — no Synchformer/T5) ---
+    print("[LoRA] Loading VAE encoder...")
+    feature_utils = FeaturesUtils(
+        tod_vae_ckpt=vae_weight,
+        enable_conditions=False,
+        mode=mode,
+        need_vae_encoder=True,
+    ).to(device, dtype).eval()
+
+    # --- Load generator ---
+    print(f"[LoRA] Loading generator ({args.variant})...")
+    net_generator = get_my_mmaudio(args.variant).to(device, dtype).eval()
+    net_generator.load_weights(
+        torch.load(gen_weight, map_location="cpu", weights_only=False)
+    )
+
+    # --- Apply LoRA ---
+    n_lora = apply_lora(
+        net_generator,
+        rank=args.rank,
+        alpha=args.alpha,
+        target_suffixes=tuple(args.target),
+    )
+    print(f"[LoRA] Wrapped {n_lora} linear layers (rank={args.rank}, target={args.target})")
+    if n_lora == 0:
+        print("[LoRA] ERROR: no layers were wrapped — check --target names.")
+        sys.exit(1)
+
+    # Freeze everything except LoRA params
+    for name, p in net_generator.named_parameters():
+        p.requires_grad_("lora_" in name)
+
+    trainable = sum(p.numel() for p in net_generator.parameters() if p.requires_grad)
+    total     = sum(p.numel() for p in net_generator.parameters())
+    print(f"[LoRA] Trainable: {trainable:,} / {total:,} params "
+          f"({100 * trainable / total:.2f}%)")
+
+    net_generator.update_seq_lengths(
+        latent_seq_len=seq_cfg.latent_seq_len,
+        clip_seq_len=seq_cfg.clip_seq_len,
+        sync_seq_len=seq_cfg.sync_seq_len,
+    )
+
+    # --- Dataset ---
+    npz_files = sorted(data_dir.glob("*.npz"))
+    if not npz_files:
+        print(f"[LoRA] No .npz files found in {data_dir}")
+        sys.exit(1)
+
+    prompt_map    = load_prompts(data_dir)
+    default_prompt = data_dir.name
+
+    print(f"[LoRA] Pre-loading {len(npz_files)} clip(s)...")
+    dataset = []
+    for npz_path in npz_files:
+        audio_path = find_audio_for_npz(npz_path)
+        if audio_path is None:
+            print(f"  [LoRA] Warning: no audio file found for {npz_path.name} — skipping")
+            continue
+
+        bundle = load_npz(npz_path)
+        # Prompt priority: prompts.txt override > embedded in .npz > directory name
+        prompt = prompt_map.get(npz_path.name, bundle.get("prompt", default_prompt))
+
+        print(f"  {npz_path.name} + {audio_path.name}: '{prompt}'")
+
+        try:
+            audio = load_audio(audio_path, sample_rate, duration)
+            x1    = extract_audio_latent(audio, feature_utils, device, dtype)
+            # STFT rounding can produce ±1 frame — pad or trim to exact seq length
+            tgt = seq_cfg.latent_seq_len
+            if x1.shape[1] < tgt:
+                x1 = F.pad(x1, (0, 0, 0, tgt - x1.shape[1]))
+            elif x1.shape[1] > tgt:
+                x1 = x1[:, :tgt, :]
+            text_clip = encode_text_clip(clip_model, tokenizer_clip, [prompt], device).cpu()
+
+            # Pad/trim clip and sync features to fixed seq lengths — shorter clips
+            # have fewer frames and would cause stack() to fail during batching
+            clip_f = bundle["clip_features"]  # [1, N_clip, 1024]
+            c_tgt  = seq_cfg.clip_seq_len
+            if clip_f.shape[1] < c_tgt:
+                clip_f = F.pad(clip_f, (0, 0, 0, c_tgt - clip_f.shape[1]))
+            elif clip_f.shape[1] > c_tgt:
+                clip_f = clip_f[:, :c_tgt, :]
+
+            sync_f = bundle["sync_features"]  # [1, N_sync, 768]
+            s_tgt  = seq_cfg.sync_seq_len
+            if sync_f.shape[1] < s_tgt:
+                sync_f = F.pad(sync_f, (0, 0, 0, s_tgt - sync_f.shape[1]))
+            elif sync_f.shape[1] > s_tgt:
+                sync_f = sync_f[:, :s_tgt, :]
+
+            dataset.append((x1, clip_f, sync_f, text_clip))
+        except Exception as e:
+            print(f"  [LoRA] Warning: failed to process {npz_path.name}: {e}")
+
+    if not dataset:
+        print("[LoRA] No clips could be loaded.")
+        sys.exit(1)
+    print(f"[LoRA] {len(dataset)} clip(s) ready.")
+
+    # --- Optimizer + LR scheduler ---
+    lora_params = [p for p in net_generator.parameters() if p.requires_grad]
+    optimizer   = torch.optim.AdamW(lora_params, lr=args.lr, weight_decay=1e-2)
+
+    def lr_lambda(step):
+        if step < args.warmup_steps:
+            return step / max(1, args.warmup_steps)
+        return 1.0
+
+    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+    fm = FlowMatching(min_sigma=0, inference_mode="euler", num_steps=25)
+
+    # --- Resume ---
+    start_step = 0
+    if args.resume:
+        ckpt = torch.load(args.resume, map_location="cpu", weights_only=False)
+        if "step" not in ckpt:
+            print("[LoRA] ERROR: checkpoint has no step info — was it saved by this script?")
+            sys.exit(1)
+        start_step = ckpt["step"]
+        if start_step >= args.steps:
+            print(f"[LoRA] Checkpoint is already at step {start_step} >= --steps {args.steps}. Nothing to do.")
+            sys.exit(0)
+        net_generator.load_state_dict(ckpt["state_dict"], strict=False)
+        optimizer.load_state_dict(ckpt["optimizer"])
+        scheduler.load_state_dict(ckpt["scheduler"])
+        print(f"[LoRA] Resumed from {Path(args.resume).name} (step {start_step} → {args.steps})")
+
+    # --- Training loop ---
+    net_generator.train()
+    optimizer.zero_grad()
+
+    remaining = args.steps - start_step
+    print(f"\n[LoRA] Training: {remaining} steps (step {start_step + 1} → {args.steps}), "
+          f"batch_size={args.batch_size}, lr={args.lr}, grad_accum={args.grad_accum}")
+    print(f"[LoRA] Checkpoints every {args.save_every} steps → {output_dir}\n")
+
+    total_loss = 0.0
+    for step in range(start_step + 1, args.steps + 1):
+        batch = random.choices(dataset, k=args.batch_size)
+        x1_list, clip_list, sync_list, text_list = zip(*batch)
+
+        x1        = torch.stack([x.squeeze(0) for x in x1_list]).to(device, dtype)
+        clip_f    = torch.stack([x.squeeze(0) for x in clip_list]).to(device, dtype)
+        sync_f    = torch.stack([x.squeeze(0) for x in sync_list]).to(device, dtype)
+        text_clip = torch.stack([x.squeeze(0) for x in text_list]).to(device, dtype)
+
+        net_generator.normalize(x1)
+
+        t  = torch.rand(args.batch_size, device=device, dtype=dtype)
+        x0 = torch.randn_like(x1)
+        xt = fm.get_conditional_flow(x0, x1, t)
+
+        v_pred = net_generator.forward(xt, clip_f, sync_f, text_clip, t)
+
+        loss = fm.loss(v_pred, x0, x1).mean() / args.grad_accum
+        loss.backward()
+        total_loss += loss.item() * args.grad_accum
+
+        if step % args.grad_accum == 0:
+            torch.nn.utils.clip_grad_norm_(lora_params, max_norm=1.0)
+            optimizer.step()
+            scheduler.step()
+            optimizer.zero_grad()
+
+        if step % 50 == 0:
+            avg    = total_loss / 50
+            lr_now = scheduler.get_last_lr()[0]
+            print(f"[LoRA] step {step:5d}/{args.steps}  loss={avg:.4f}  lr={lr_now:.2e}")
+            total_loss = 0.0
+
+        if step % args.save_every == 0 or step == args.steps:
+            ckpt_path = output_dir / f"adapter_step{step:05d}.pt"
+            torch.save({
+                "state_dict": get_lora_state_dict(net_generator),
+                "optimizer":  optimizer.state_dict(),
+                "scheduler":  scheduler.state_dict(),
+                "step":       step,
+                "meta": {
+                    "variant": args.variant,
+                    "rank":    args.rank,
+                    "alpha":   args.alpha if args.alpha is not None else float(args.rank),
+                    "target":  args.target,
+                    "steps":   args.steps,
+                },
+            }, ckpt_path)
+            print(f"[LoRA] Saved {ckpt_path}")
+
+    # Save final adapter with embedded metadata
+    # Increment filename if a previous final already exists (resume case)
+    final = output_dir / "adapter_final.pt"
+    if final.exists():
+        i = 1
+        while (output_dir / f"adapter_final_{i:03d}.pt").exists():
+            i += 1
+        final = output_dir / f"adapter_final_{i:03d}.pt"
+    meta  = {
+        "variant": args.variant,
+        "rank":    args.rank,
+        "alpha":   args.alpha if args.alpha is not None else float(args.rank),
+        "target":  args.target,
+        "steps":   args.steps,
+    }
+    torch.save({"state_dict": get_lora_state_dict(net_generator), "meta": meta}, final)
+    (output_dir / "meta.json").write_text(json.dumps(meta, indent=2))
+    print(f"\n[LoRA] Training complete. Adapter saved to {final}")
+
+
+if __name__ == "__main__":
+    main()