v0.2.69 turned the dense backend OFF when enabling BSA, reasoning that
_process_attn tests enable_bsa first so anything else would be a silent second
opinion. That is true of SELF-attention and wrong about the model:

    attention.py:248, _process_cross_attn
      elif self.enable_xformers: ...
      else: raise RuntimeError("Unsupported attention operations.")

Cross-attention has no enable_bsa branch at all. With the dense backend off,
every cross-attention call in all 48 blocks raised — the first real BSA run died
on step 0 of the first generation.

So enable_bsa is now set alongside the chosen dense backend: BSA wins for
self-attention on video because it is tested first, and cross-attention keeps a
path to run on.

Measured on the 3090, 29 frames, warm steady-state per step:
  dense  480x832 (30 latent rows)  8.13 s/it
  BSA    448x832 (28 latent rows)  6.34 s/it
Adjusting for the 6.7% token difference the two geometries force (BSA needs
sides divisible by 64), that is roughly 10-15% per step — well short of the 16x
the 0.9375 sparsity suggests, because at this size attention is not the
dominant cost; the weight-only INT8 linears are. BSA should matter more at 93
frames and 720p, where attention grows quadratically. It also pays a one-time
Triton compile of about four minutes per service.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0185zEBiy3KWB37xNGSrxL7w