apply_lora() is called after generator.to(device), so lora_A/lora_B were
being created on CPU while the rest of the model was on CUDA.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- LoRALinear now creates lora_A/lora_B with dtype matching the base
linear's weight, preventing a float32/bf16 mismatch at forward time
when the generator is loaded in bf16 or fp16.
- Remove unused `import math` from train_lora.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Teaches the model new/partial sound classes from custom video+audio pairs.
Only ~10 MB of adapter weights are trained vs ~4.4 GB for the full model.
selva_core/model/lora.py
LoRALinear: wraps nn.Linear with frozen base + trainable A/B matrices.
B initialised to zero → zero adapter contribution at init.
apply_lora(): walks named_modules, replaces matching nn.Linear in-place.
Default target: "attn.qkv" (all 21 SelfAttention QKV projections in
large_44k). Add "linear1" to also wrap post-attention output projections.
get_lora_state_dict() / load_lora() for ~10 MB save/load.
train_lora.py (standalone script, no ComfyUI dependency)
Data format: directory of video files + optional prompts.txt
("filename: description"). Falls back to directory name as prompt.
Pre-extracts features for all clips into RAM, then trains from those.
Training loop: encode audio→latent (need_vae_encoder=True), flow
matching MSE loss on velocity prediction, backward on LoRA params only.
Saves adapter_stepNNNNN.pt checkpoints + adapter_final.pt with metadata.
Key verified interfaces used:
encode_audio() → DiagonalGaussianDistribution; .mode().clone() required
normalize() is in-place
forward(latent, clip_f, sync_f, text_f, t) takes raw tensors
nodes/selva_lora_loader.py (SelVA LoRA Loader ComfyUI node)
Loads .pt adapter, deep-copies the generator, applies LoRA, loads weights.
strength param scales lora_B to adjust adapter contribution at inference.
Reads rank/alpha/target from embedded metadata if present.
Returns a patched SELVA_MODEL bundle for use with the existing Sampler.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Newer hf_hub stopped passing proxies/resume_download/local_files_only/token
to _from_pretrained(). Give them defaults so the call doesn't fail when
these kwargs are omitted.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
PyTorch 2.6 changed the default to weights_only=True. SelVA checkpoints
contain non-tensor types (numpy scalars etc.) that fail strict unpickling.
All weights come from trusted sources (jnwnlee/selva HF repo).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
find_pruneable_heads_and_indices and prune_linear_layer were removed from
both pytorch_utils and modeling_utils in some transformers builds. Provide
minimal inline implementations as final fallback — prune_heads() is never
called at inference time so correctness is only needed for completeness.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Some transformers builds removed these from pytorch_utils. Fall back to
modeling_utils which exposes them in all known versions.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pure PyTorch SelVA source for SelvaModelLoader/FeatureExtractor/Sampler nodes.
Imports rewritten from selva.* to selva_core.*. mel_converter.py: replaced
librosa.filters.mel with pure-numpy implementation to avoid librosa→numba→NumPy
version incompatibility in some ComfyUI environments.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ethanfel