chore: sanitize tooltips/comments + add experiment configs

- Replace all BJ references with generic "target style/audio" in
  activation steering, DITTO optimizer, and BigVGAN trainer
- Add latent_mixup_alpha/latent_noise_sigma to LoRA scheduler defaults
- Add bigvgan_disc_fm_retest.json and lora_optimized_dataset.json

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

experiments/bigvgan_disc_fm_retest.json

@@ -0,0 +1,31 @@
+{
+  "name": "bigvgan_disc_fm_retest",
+  "description": "Retest discriminator feature matching after bfloat16 dtype fix. Uses optimal config from overnight sweep (snake_alpha, GAFilter, lr=1e-4, phase=1.0, L2-SP=1e-3, 5000 steps).",
+  "data_dir": "/media/unraid/davinci/Selva/BJ/features",
+  "output_root": "/media/unraid/davinci/Selva/BJ/experiment/bigvgan_disc_fm_retest",
+  "base": {
+    "train_mode": "snake_alpha_only",
+    "steps": 5000,
+    "lr": 1e-4,
+    "batch_size": 8,
+    "segment_seconds": 0.5,
+    "lambda_l2sp": 1e-3,
+    "use_gafilter": true,
+    "gafilter_kernel_size": 9,
+    "lambda_phase": 1.0,
+    "save_every": 1000,
+    "seed": 42,
+    "lora_adapter": "/media/unraid/davinci/Selva/BJ/experiment/pissa_sweep/standard_baseline/adapter_final.pt"
+  },
+  "experiments": [
+    {
+      "id": "snake_5k_control",
+      "description": "Control: best config from overnight sweep without discriminator. Baseline for A/B comparison."
+    },
+    {
+      "id": "disc_fm_5k",
+      "description": "Discriminator feature matching at 5k steps. Tests if perceptual FM loss improves over mel+phase alone.",
+      "discriminator_path": "/media/unraid/davinci/Selva/BJ/experiment/bigvgan_discriminator_optimizer.pt"
+    }
+  ]
+}

experiments/lora_optimized_dataset.json

@@ -0,0 +1,64 @@
+{
+  "name": "lora_optimized_dataset",
+  "description": "LoRA training on optimized dataset (134 clips: resampled 44.1kHz, LUFS-normalized, spectral matched, HF smoothed, gain-augmented). Tests latent augmentation and schedule variants on top of known-best config (PiSSA, rank=128, lr=3e-4).",
+  "data_dir": "/media/unraid/davinci/Selva/BJ/features_v2_improved/",
+  "output_root": "/media/unraid/davinci/Selva/BJ/experiment/lora_optimized_dataset",
+  "base": {
+    "rank": 128,
+    "lr": 3e-4,
+    "steps": 5000,
+    "batch_size": 4,
+    "warmup_steps": 100,
+    "save_every": 1000,
+    "seed": 42,
+    "init_mode": "pissa",
+    "use_rslora": true,
+    "target": "attn.qkv",
+    "timestep_mode": "uniform",
+    "lr_schedule": "constant"
+  },
+  "experiments": [
+    {
+      "id": "baseline",
+      "description": "Control: known-best config (PiSSA r128 lr=3e-4) on the optimized dataset. No latent augmentation."
+    },
+    {
+      "id": "latent_mixup",
+      "description": "Latent mixup alpha=0.4 (MusicLDM). Tests if mixing training latents reduces memorization on 134 clips.",
+      "latent_mixup_alpha": 0.4
+    },
+    {
+      "id": "latent_noise",
+      "description": "Latent noise sigma=0.02. Mild Gaussian noise on training latents for regularization.",
+      "latent_noise_sigma": 0.02
+    },
+    {
+      "id": "mixup_and_noise",
+      "description": "Both latent mixup (0.4) and noise (0.02). Combined regularization.",
+      "latent_mixup_alpha": 0.4,
+      "latent_noise_sigma": 0.02
+    },
+    {
+      "id": "cosine_schedule",
+      "description": "Cosine LR decay. lr=3e-4 was stable with constant, but cosine may extract more from 5k steps.",
+      "lr_schedule": "cosine"
+    },
+    {
+      "id": "cosine_mixup",
+      "description": "Cosine LR + latent mixup. Best regularization combo candidate.",
+      "lr_schedule": "cosine",
+      "latent_mixup_alpha": 0.4
+    },
+    {
+      "id": "logit_normal",
+      "description": "Logit-normal timestep sampling (sigma=1.0). Concentrates training near t=0.5 where flow matching is hardest.",
+      "timestep_mode": "logit_normal"
+    },
+    {
+      "id": "curriculum_mixup",
+      "description": "Curriculum timesteps (logit_normal first 60%, then uniform) + latent mixup. Full regularization stack.",
+      "timestep_mode": "curriculum",
+      "latent_mixup_alpha": 0.4
+    }
+  ]
+}

nodes/selva_activation_steering_extractor.py

@@ -1,15 +1,15 @@
 """SelVA Activation Steering Extractor.
 
 Computes per-block steering vectors by running the frozen generator on the
-training dataset and recording how BJ's conditioning shifts the DiT hidden
+training dataset and recording how target style's conditioning shifts the DiT hidden
 states vs. empty/unconditional conditioning.
 
 For each block i:
-    steering[i] = mean(latent_hidden | BJ conditions)
+    steering[i] = mean(latent_hidden | target style conditions)
                 - mean(latent_hidden | empty conditions)
 
 The resulting vectors are injected at inference time (via SelVA Sampler's
-steering_strength input) to nudge the denoising trajectory toward BJ's
+steering_strength input) to nudge the denoising trajectory toward target style's
 activation patterns without modifying any model weights.
 """
 
@@ -58,7 +58,7 @@ class SelvaActivationSteeringExtractor:
     """Computes activation steering vectors from a training dataset.
 
     Runs the frozen generator on N clips at random timesteps with both
-    BJ-conditioned and empty-conditioned inputs, then saves the mean
+    target style-conditioned and empty-conditioned inputs, then saves the mean
     difference per DiT block to a .pt file.
     """
 
@@ -69,7 +69,7 @@ class SelvaActivationSteeringExtractor:
     RETURN_NAMES  = ("steering_path",)
     OUTPUT_TOOLTIPS = ("Path to saved steering_vectors.pt — load with SelVA Activation Steering Loader.",)
     DESCRIPTION = (
-        "Computes per-block activation steering vectors: mean(BJ activations) − "
+        "Computes per-block activation steering vectors: mean(target style activations) − "
         "mean(empty activations) at each DiT block. Load the result with "
         "SelVA Activation Steering Loader and connect to the Sampler."
     )
@@ -124,7 +124,7 @@ class SelvaActivationSteeringExtractor:
         indices = random.choices(range(len(dataset)), k=n_samples)
 
         n_blocks     = len(generator.joint_blocks) + len(generator.fused_blocks)
-        bj_sums      = [None] * n_blocks
+        style_sums      = [None] * n_blocks
         empty_sums   = [None] * n_blocks
         counts       = [0] * n_blocks
 
@@ -157,15 +157,15 @@ class SelvaActivationSteeringExtractor:
                 device=device, dtype=dtype,
             )
 
-            bj_acts    = _collect_activations(generator, conditions,       latent, t_tensor)
+            style_acts    = _collect_activations(generator, conditions,       latent, t_tensor)
             empty_acts = _collect_activations(generator, empty_conditions, latent, t_tensor)
 
-            for i, (bj, em) in enumerate(zip(bj_acts, empty_acts)):
+            for i, (st, em) in enumerate(zip(style_acts, empty_acts)):
-                if bj_sums[i] is None:
+                if style_sums[i] is None:
-                    bj_sums[i]    = bj.clone()
+                    style_sums[i]    = st.clone()
                     empty_sums[i] = em.clone()
                 else:
-                    bj_sums[i]    += bj
+                    style_sums[i]    += st
                     empty_sums[i] += em
                 counts[i] += 1
 
@@ -173,10 +173,10 @@ class SelvaActivationSteeringExtractor:
             if (sample_i + 1) % 4 == 0 or sample_i == n_samples - 1:
                 print(f"[Steering] Processed {sample_i + 1}/{n_samples} clips", flush=True)
 
-        # Steering vector per block: mean(BJ) - mean(empty)
+        # Steering vector per block: mean(target style) - mean(empty)
         steering_vectors = []
         for i in range(n_blocks):
-            vec = (bj_sums[i] - empty_sums[i]) / counts[i]   # [hidden]
+            vec = (style_sums[i] - empty_sums[i]) / counts[i]   # [hidden]
             steering_vectors.append(vec)
 
             norm = vec.norm().item()

nodes/selva_bigvgan_trainer.py

@@ -593,7 +593,7 @@ class SelvaBigvganTrainer:
     RETURN_NAMES  = ("checkpoint_path",)
     OUTPUT_TOOLTIPS = ("Path to saved vocoder checkpoint — load with SelVA BigVGAN Loader.",)
     DESCRIPTION = (
-        "Fine-tunes the BigVGAN vocoder (mel→waveform) on BJ audio clips. "
+        "Fine-tunes the BigVGAN vocoder (mel→waveform) on target audio clips. "
         "Default mode (snake_alpha_only) tunes only the ~5K Snake activation α "
         "parameters — cannot cause harmonic smearing. Add a discriminator path "
         "for perceptual feature matching loss. DiT and VAE stay frozen."
@@ -606,7 +606,7 @@ class SelvaBigvganTrainer:
                 "model": ("SELVA_MODEL",),
                 "data_dir": ("STRING", {
                     "default": "",
-                    "tooltip": "Directory with BJ audio files (.wav/.flac/.mp3). Searched recursively.",
+                    "tooltip": "Directory with target audio files (.wav/.flac/.mp3). Searched recursively.",
                 }),
                 "output_path": ("STRING", {
                     "default": "bigvgan_bj.pt",

nodes/selva_ditto_optimizer.py

@@ -1,14 +1,14 @@
 """SelVA DITTO Optimizer.
 
 Inference-time noise optimization: optimizes the initial noise latent x_0
-using a style loss against BJ reference clips, backpropagating through the
+using a style loss against target style reference clips, backpropagating through the
 ODE solver. All model weights remain frozen — only x_0 changes.
 
 Based on DITTO: Diffusion Inference-Time T-Optimization (arXiv:2401.12179,
 ICML 2024 Oral). Adapted for SelVA's flow-matching Euler ODE.
 
 Style loss: mel-spectrogram statistics matching (mean spectrum + Gram matrix)
-against BJ reference clips. Runs entirely before the vocoder — optimization
+against target style reference clips. Runs entirely before the vocoder — optimization
 only requires the DiT + VAE decoder, not BigVGAN.
 
 Memory strategy: gradient checkpointing at each ODE step — stores O(1 DiT
@@ -97,7 +97,7 @@ class SelvaDittoOptimizer:
     """DITTO inference-time noise optimization.
 
     Freezes all model weights and optimizes only the initial noise latent x_0
-    to make the generated audio sound like the BJ reference clips.
+    to make the generated audio sound like the target style reference clips.
     No training data or gradient updates to the model — per-video per-run.
     """
 
@@ -116,7 +116,7 @@ class SelvaDittoOptimizer:
                 }),
                 "reference_dir": ("STRING", {
                     "default": "",
-                    "tooltip": "Directory with BJ reference audio files (.wav/.flac/.mp3). "
+                    "tooltip": "Directory with target style reference audio files (.wav/.flac/.mp3). "
                                "Reference mel statistics are precomputed from these once.",
                 }),
                 "n_opt_steps": ("INT", {
@@ -143,8 +143,8 @@ class SelvaDittoOptimizer:
                 }),
                 "style_weight": ("FLOAT", {
                     "default": 0.1, "min": 0.0, "max": 10.0, "step": 0.05,
-                    "tooltip": "Weight of the BJ style loss. High values push harder toward "
+                    "tooltip": "Weight of the target style style loss. High values push harder toward "
-                               "BJ style but add noise. Start at 0.1 and increase slowly.",
+                               "target style style but add noise. Start at 0.1 and increase slowly.",
                 }),
                 "gram_weight": ("FLOAT", {
                     "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01,
@@ -176,12 +176,12 @@ class SelvaDittoOptimizer:
 
     RETURN_TYPES = ("AUDIO",)
     RETURN_NAMES = ("audio",)
-    OUTPUT_TOOLTIPS = ("DITTO-optimized audio — x_0 steered toward BJ style.",)
+    OUTPUT_TOOLTIPS = ("DITTO-optimized audio — x_0 steered toward target style style.",)
     FUNCTION = "optimize"
     CATEGORY = SELVA_CATEGORY
     DESCRIPTION = (
         "DITTO inference-time noise optimization (arXiv:2401.12179). "
-        "Optimizes the initial noise latent x_0 to match BJ reference clips "
+        "Optimizes the initial noise latent x_0 to match target style reference clips "
         "via mel statistics style loss, backpropagating through the ODE. "
         "All model weights frozen — zero quality degradation risk."
     )

nodes/selva_lora_scheduler.py

@@ -81,6 +81,8 @@ _PARAM_DEFAULTS = {
     "lr_schedule":         "constant",
     "init_mode":           "pissa",
     "use_rslora":          True,
+    "latent_mixup_alpha":  0.0,
+    "latent_noise_sigma":  0.0,
 }
 
 # Palette for comparison chart: one color per experiment (cycles if > 8)