hao-ai-lab-FastVideo/examples/inference/basic/V2_PORTING_STATUS.md

v2 porting status — fastvideo models → the v2 (recipe, runtime) substrate

Goal: every model in fastvideo's registry resolves through the v2 VideoGenerator / Engine (typed fastvideo.api configs + the real torch backend) to a recipe that can construct and run it.

Scope: ALL fastvideo models (achieved). v2 now resolves 63/64 of fastvideo's registered HF ids by exact id (PRIMARY), plus the architecture fallback for local/unregistered checkpoints. The single remaining id — FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers — is environment-blocked: its VSA (Sparse-Linear Attention) kernels require nvcc (not built in this bring-up). It arch-resolves to the base Wan card but needs the VSA kernel build to run faithfully.

Dispatch is architecture-driven (v2/registry.py): exact HF id → short-name → architecture inference from the checkpoint (pipeline / transformer / VAE class names + z_dim, transformer_2, spatial_upsampler). Adding a model is one _BUCKET_C row (HF ids → builders + transformer class).

The porting mechanism — self-contained recipe packages

Every net-new arch is a self-contained recipe package (v2/recipes/<arch>/ = card.py loop.py program.py [+ sampler.py] + an optional v2/platform/backends/torch_<arch>.py adapter). The card declares its torch adapter via ComponentSpec.adapter="module:Class" (the _explicit_adapter seam in torch_backend.py) instead of editing the shared _make_dit/_make_vae/_make_text_encoder dispatch — so a port adds only new files, never touching shared code, and parallel ports never conflict. New samplers/loops live in-package. Registration is one row in v2/registry.py:_BUCKET_C.

Working today (GPU-verified, real video/audio) — committed on v2

Official example(s)	Model	v2 card
basic.py, basic_mps.py, basic_ray.py	Wan-AI/Wan2.1-T2V-1.3B-Diffusers	wan21
basic_self_forcing_causal.py	wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers	wan_causal
basic_ltx2_distilled.py	FastVideo/LTX2-Distilled-Diffusers (2-stage + spatial upsampler)	ltx2
basic_wan2_2_ti2v.py	Wan-AI/Wan2.2-TI2V-5B-Diffusers	wan2.2-ti2v
basic_wan2_2.py	Wan-AI/Wan2.2-T2V-A14B-Diffusers (MoE + CPU expert offload)	wan2.2-a14b
basic_ltx2.py	Davids048/LTX2-Base-Diffusers	ltx2 base
basic_ltx2_3_distilled.py	FastVideo/LTX-2.3-Distilled-Diffusers (joint T2VS, video+audio)	ltx2.3-distilled

Plus the Wan2.1 i2v cluster (Fun-1.3B-InP GPU-verified; I2V-14B-480P/720P + Wan2.2-I2V-A14B MoE reuse the i2v card) — CLIP image-encoder + first-frame [mask|cond] → 36ch DiT.

Newly ported — CPU-verified end-to-end; GPU load/run is BRINGUP

Each resolves through the registry AND runs end-to-end on the CPU toy backend via the public Engine path (the v2/tests/test_bucket_c_ports.py regression guard), emitting the correct modality artifact. GPU bring-up pending per-model (cosmos2/flux2/sd35 weights are HF-gated; the FastVideo/* orgs are public).

15 net-new architectures (each a new TorchComponent adapter + recipe):

• cosmos2 (Cosmos-Predict2-2B-Video2World) — EDM-Karras denoiser; new CosmosDenoiseLoop + build_karras_sigmas (the reference port). cosmos25 (Cosmos-Predict2.5 2B/14B) — flow-match, per-frame plain-sigma timestep, Reason1/Qwen2.5-VL encoder. gen3c (GEN3C) — EDM + 82ch pose-buffer.
• hunyuan_video (+FastHunyuan) — reuses WanDenoiseLoop, dual LLaMA+CLIP encoders, Hunyuan VAE. hunyuan_video15 (480p/720p). hunyuangamecraft, hyworld — interactive (camera/action).
• longcat (T2V/I2V/VC). kandinsky5 (5.0 T2V Lite).
• sd35 (MMDiT, image, triple-encoder). flux2 (dev/klein, MMDiT image). stable_audio (audio).
• lingbotworld (camera/Plucker), matrixgame2, matrixgame3 — interactive world models.

5 Wan-family variants (reuse the Wan/Causal arch, new in-package sampler/loop/conditioning):

• turbowan — rCM few-step (faithful RCMScheduler port), 1.3B/14B T2V + I2V-A14B MoE.
• lucy_edit — v2v editor (video-VAE-encode node → 96ch DiT input). wan_fun_control — control input.
• sfwan22 — Self-Forcing Wan2.2-A14B causal + MoE (i2v + t2v). fastwan — DMD 3-step (TI2V-5B-FullAttn loadable; VSA-trained variants + non-strict to_gate_compress load are BRINGUP).

BRINGUP scope per port (documented in each package): GPU load/run; for interactive/world-model archs the action/camera/memory conditioning needs a request-API extension (the t2v/degenerate path is what CPU-verifies); video2world/i2v frame-replace conditioning is threaded but inert without conditioning inputs.

Environment

v2 bring-up runs single-GPU, resident, on the TORCH_SDPA backend (no fastvideo-kernel / VSA / FP4). The box has been rescheduled across hosts/arches/python versions mid-session; rebuild the venv for the current arch when that happens: uv venv --python 3.12 .venv; comment out fastvideo-kernel in pyproject.toml; uv pip install -e ".[dev]". Source /home/scratch.willlin_ent/.bringup_env (HF_HOME=./.cache on scratch, FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA). v2 CPU mini: 240 passed, 2 skipped.

How to add a model to the v2 substrate

1. v2/recipes/<arch>/ — card (declare adapters via ComponentSpec.adapter; per-model SamplingDefaults), loop (reuse WanDenoiseLoop/chunk_rollout or a new in-package loop+sampler), program.
2. v2/platform/backends/torch_<arch>.py — a TorchComponent subclass (only the forward semantics) if the arch is genuinely new; reuse WanDiT/LTX2DiT/WanVAE/T5Encoder via load_id when it isn't.
3. One row in v2/registry.py:_BUCKET_C (HF ids → builders; transformer_cls for the arch fallback, or "" for explicit-id-only capability variants of an existing arch).
4. CPU-verify: it resolves + runs on the toy backend (auto-covered by test_bucket_c_ports.py). Then GPU bring-up (stamp_*_checkpoints → real weights) per BRINGUP notes.