Setting duration to 0 in PrismAudioSampler now reads the duration
stored in the PRISMAUDIO_FEATURES dict (set by the feature extractor).
Default changed from 10.0 to 0.0 so V2A workflows are wired up
automatically.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Remove debug_zero_video/debug_zero_sync inputs from PrismAudioSampler,
DIT velocity diagnostics, conditioner stats logging, and feature stats
prints from both sampler.py and text_only.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add per-key conditioning output stats (after Cond_MLP/Sync_MLP, after
_substitute_empty_features) to both sampler and text_only nodes. Also
add raw T5 text feature stats in T2A before conditioning.
This lets us directly compare:
- T2A vs V2A conditioning outputs to find which path differs
- T2A vs npz text feature ranges
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Match the diagnostic output already in text_only.py to compare
V2A vs T2A latent distributions and diagnose conditioning issues.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Zero features through bias-free Cond_MLP produce near-zero activations,
not the learned null signal the model was trained with. Use empty_clip_feat
(the learned null video embedding) just like empty_sync_feat for sync.
Also improve text_prompt tooltip to encourage detailed CoT descriptions.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Allows isolating which feature set causes quality issues:
- debug_zero_video: zero video_features → text+sync only
- debug_zero_sync: zero sync_features → text+video only
Also logs mean/std/shape for all three feature tensors on every run.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>