video_speedmem_bench.py drives the real video-loader lever functions
(quantize_transformer / quantize_text_encoders / quantize_vae / apply_speed_optims
/ apply_attention_backend / apply_step_cache) with the loader's own defaults, so each
measured config reflects a real load. It decomposes the video speed/memory stack
(compile, cuDNN attention, First-Block-Cache, DiT/TE/VAE quant) with per-step latency,
peak resident GB, and per-frame LPIPS vs a bit-exact reference. This is the harness that
surfaced and validated the Wan fp8 black-frame fix.
quant_speedmem_bench.py gains the DiT-quant mode (dense vs fp8/int8/mxfp8 speed, peak
memory, and LPIPS vs the dense render) plus the shared LPIPS(AlexNet) helper.
A B200 speed/memory sweep (new scripts/quant_speedmem_bench.py) shows the VAE quant
win is a video story. Image AutoencoderKLs are ~0.15-0.26 GB, so fp8 saves ~0.1 GB
and only slows their tiny decode (+6-16%); the video Conv3d VAEs are ~2.5 GB and
halve to ~1.2 GB at ~2% decode cost. So VAE auto now only engages above a ~1 GB size
floor: small image VAEs stay dense (faster decode, no quant quality risk), video VAEs
still quantize. An explicit fp8 / fp8_dynamic request skips the gate (opted in).
The same sweep confirmed the text-encoder default is already right: fp8_dynamic is
E2E-neutral (denoise per-step unchanged; +2% one-time encode) and, by hidden-state
cosine vs bf16, marginally more accurate than layerwise fp8 -- so that default is left
as is. Tests cover the gate (small skipped, large quantized, explicit bypasses).
