Two distilled few-step video models rendered incorrectly on GPU; root-caused via
dense frame sampling (contact sheets) and fixed in the recipe cards.
SF-Wan2.1 (self-forcing causal, wan_causal card): was oversaturated/overcooked.
The distilled student is CFG-FREE (guidance 1.0, single forward/step) and denoises
with the 4-step DMD schedule [1000,750,500,250] (warped by FlowShiftPolicy(5.0)),
at a native causal block of 3 latent frames. Defaults were ClassicCFG@6.0 + 2 steps
+ block 2 -> overcooked AND under-denoised. Fixed: num_chunks=7, chunk_size=3,
steps_per_chunk=4; SamplingDefaults num_steps=4, guidance_scale=1.0 (7x3=21 latent
-> 81 frames). Renders a clean raccoon-in-sunflowers across all 81 frames.
LTX2-Distilled 2-stage SR (ltx2 card + LTX2VAE): was temporally blocky. Root cause
was an OOM-forced 57-frame reduction (only 8 latent temporal frames); the model is
designed for 121 (16 latent frames). Enable VAE tiling in LTX2VAE so the 121-frame
full-res decode fits the 80 GiB GPU; keep base cfg_scale=3.0 (drives brightness;
cfg=1 washed out) with stg_scale=0.0 (v2's drop-text perturbation is not real
skip-layer STG). Renders the on-prompt backyard shot, bright + temporally coherent.
torch_backend.py: enable LTX2VAE tiling; clear pre-existing mypy no-untyped-call /
yapf debt on the file (surfaced once per-file linting bypassed the duplicate-module
flakiness) by annotating the helper/constructor/maker signatures.
Tests: update the 3 affected CPU defaults/chunk-count tests. v2 mini: 240 passed.
First bucket-B port toward 'support every fastvideo model': Wan2.1-T2V-14B reuses the Wan recipe +
torch adapter unchanged (same WanTransformer3DModel/AutoencoderKLWan/UMT5) — only a registry entry +
build_wan_t2v_14b_card (720p, flow_shift 5.0) + SamplingDefaults differ. Without the entry the arch
fallback would give it the 1.3B 480p defaults; the explicit entry gives 50 steps / 720x1280.
Also recorded the full backlog in V2_PORTING_STATUS.md: 63 fastvideo models = 8 ported / 21 bucket-B
(reuse Wan/Causal/LTX2 arch — registry+recipe+defaults, no new adapter) / 34 bucket-C (13 new
architectures needing a TorchComponent adapter). Updated the stale 'how to add a model' steps to the
post-redesign structure (v2/recipes/, torch_backend.py, SamplingDefaults). CPU mini 211 pass, 2 skip.
SolitaryThinker