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

Goal: drive each `examples/inference/basic/` model through the **v2 `VideoGenerator`** (typed
`fastvideo.api` configs + the real torch backend) and generate video, with a matching `v2_*` example.

**Active scope (per request): the Wan family + LTX-2 only.** The Cosmos/Hunyuan/SD3.5/LongCat/Flux2/
audio/interactive families below are kept as an upstream reference map but are out of the current scope.

Dispatch is **architecture-driven** (`v2/video_generator.py`): `from_config` reads the checkpoint's
pipeline / transformer / VAE class names (+ `z_dim`, `transformer_2`) and picks the v2 card — mirroring
fastvideo's `get_pipeline_config_cls_from_name`, with no hardcoded repo-id table.

The v2 mini implements a small set of loops/samplers (**flow-match Euler**, causal **chunk_rollout**,
**FlowGRPO SDE**) and adapters (Wan DiT/VAE/UMT5, causal Wan, LTX-2 DiT/VAE/Gemma/**spatial upsampler**).
A model ports **cleanly** only when its architecture *and* sampler match what v2 implements; otherwise it
needs new per-model work (a card, an adapter for the DiT/VAE/encoder forward, and/or a new sampler), and
some are blocked by the environment (gated weights, local-only weights, non-video modality, no VSA).

The v2 bring-up runs **single-GPU, resident, on the `TORCH_SDPA` backend** (no fastvideo-kernel / VSA /
FP4), so distilled models that depend on VSA/SLA/FP4 kernels or non-flow-match samplers won't match.
(Wan2.2-A14B MoE is the exception to "resident": its two 14B experts are CPU-offloaded and swapped onto
the GPU one at a time at the boundary-timestep transition, so it fits a single 80GB GPU.)

**Environment status:** the box was rescheduled from an aarch64 host to an x86_64 host mid-session
(`ipp1a1…` → `ipp2-0493`). The aarch64 `.venv` couldn't execute on x86, so the venv was **rebuilt for
x86** (torch 2.11.0+cu128 x86 + numpy 2.5.0rc1 x86 + fastvideo[dev]) and re-validated: the v2 CPU suite
is green and wan21 + LTX-2 base both generate real video on the x86 stack. (If the box flips arch again,
rebuild the venv for the current arch — system python `/usr/bin/python3.12`, reinstall the matching
`uv`, `uv pip install -e ".[dev]"` with `fastvideo-kernel` commented out.)

## Working today (verified, real video) — committed on `v2`
| Official example(s) | Model | v2 card | v2 example |
|---|---|---|---|
| `basic.py`, `basic_mps.py`*, `basic_ray.py`* | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | wan21 | `v2_basic.py`, `v2_basic_new_api.py` |
| `basic_self_forcing_causal.py` | `wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers` | wan_causal | `v2_basic_self_forcing_causal.py`, `v2_basic_new_api.py` |
| `basic_ltx2_distilled.py`, `basic_ltx2_distilled_fast_profile.py` | `FastVideo/LTX2-Distilled-Diffusers` | ltx2 — real `LTX2LatentUpsampler` between base/refine (un_normalize→learned 2× upsample→normalize) | `v2_basic_new_api.py` |
| `basic_wan2_2_ti2v.py` (T2V branch) | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | wan2.2-ti2v | `v2_basic_wan2_2_ti2v.py` |
| `basic_wan2_2.py` | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` (MoE) | wan2.2-a14b + **CPU expert offload** (one 14B expert resident at a time, swapped at the boundary → 60GB peak) | `v2_basic_wan2_2.py` |
| `basic_ltx2.py` | `Davids048/LTX2-Base-Diffusers` | ltx2 base (single-stage, request-driven many-step) | `v2_basic_ltx2.py` |
| `basic_ltx2_3_distilled.py` | `FastVideo/LTX-2.3-Distilled-Diffusers` (18.99B) | ltx2.3-distilled card — single-stage **joint T2VS** (video **+** audio): SEPARATE video/audio text connectors + gated attention, joint DiT forward (video↔audio cross-attention), then video VAE + AudioDecoder→Vocoder | `v2_basic_ltx2_3_distilled.py` |

All seven rows GPU-verified to generate real video on the rebuilt x86 stack. LTX-2.3 is the only **A/V**
model: GPU-verified T2VS produces video (3,33,256,384) **and** stereo audio (2×61920 @ 24 kHz, nonzero,
std 0.059) in one joint pass — the dual-connector / joint-cross-attention path is exercised end-to-end.
\* `basic_mps.py` (Apple MPS) and `basic_ray.py` (ray executor) are device/executor variants of the same
Wan2.1 model — covered by wan21; those runtime modes are out of scope.

## Needs per-model work (architecture/sampler matches partially)
| Official example(s) | Model | What v2 needs |
|---|---|---|
| `basic_dmd.py`, `basic_dmd_new_api.py` | FastWan2.1-T2V-1.3B (WanDMDPipeline) | **Blocked (env):** loading needs a *non-strict* load to skip the VSA-only `to_gate_compress` (root cause: the checkpoint key mis-maps under the generic Wan path, and `TransformerLoader` is strict except Cosmos2.5 — `component_loader.py:1005`). Even loaded, the DMD model is trained for VSA, which is **not built here (no nvcc)**, so SDPA output would be degraded. Needs the fastvideo-kernel VSA build + a non-strict WanDMD load. |
| `basic_turbodiffusion*.py` | TurboWan2.1/2.2 (RCM) | **New sampler:** Reparameterized Consistency Model (`fastvideo/models/schedulers/scheduling_rcm.py:RCMScheduler`) — v2 has only flow-match/chunk/SDE loops; needs a new RCM consistency loop. |
| `basic_wan2_2_i2v.py`, `basic_wan2_2_Fun.py` | Wan2.2-I2V / Fun | **I2V mechanism (identified, `pipelines/stages/image_encoding.py`):** SigLIP `image_encoder`+`image_processor` → `image_embeds` (the DiT's `encoder_hidden_states_image`, already accepted) **and** VAE-encode the first frame, concat `[noisy_latent, image_latent, mask]` (first-frame mask=1) → an i2v DiT (larger in_channels). v2 needs: image_encoder/image_processor components+adapters, a VAE-encode-image node, the concat-mask conditioning in the wan loop, the i2v DiT in_channels, `image_path` handling (reuse A14B offload). Fun adds a control input. |
| `basic_lucy_edit.py` | `decart-ai/Lucy-Edit-Dev` (Wan v2v) | **V2V mechanism (identified):** `WanVideoToVideoPipeline` + `VideoVAEEncodingStage` — VAE-encode the input video to a conditioning latent. v2 needs input-video loading + a VAE-encode-video node + the v2v conditioning (no image_encoder). |
| `basic_ltx2_3_distilled_i2v*.py` | LTX-2.3-Distilled I2V | Image conditioning on the ltx2 single-stage program (encode the input image + condition the denoise). |

## Backlog — goal: port ALL fastvideo models (63 total: 8 ported · 21 reuse-arch · 34 new-arch)
Ported (12): the 7 above + **Wan2.1-T2V-14B** + **Wan2.2-I2V-A14B** (MoE i2v, structural) + the
**Wan2.1 i2v cluster** — Wan2.1-Fun-1.3B-InP
(GPU-verified: CLIP encoder + first-frame [mask|cond] -> 36ch DiT) and Wan2.1-I2V-14B-480P/720P (reuse the
same i2v card; 14B weights GPU-pending). i2v required a real sub-system: the image-encoder adapter, the
loop's i2v conditioning hooks, and the Wan adapter's 36ch concat (all landed + T2V-unregressed).

⚠️ Not as trivial as the bucket split implies: the `FastVideo/*` distilled checkpoints (FastWan DMD, VSA,
Turbo) carry **gated-attention params** (`to_gate_compress`) the generic `WanTransformer3DModel` loader
can't map (`select_by_architecture` already rejects `WanDMDPipeline` for this) — they need a param-mapping
fix (as LTX-2.3 did) on top of the few-step schedule, so they're really bucket-C-effort. GPU-verify each
port before claiming support.

**Bucket B (21 left) — reuse a v2 architecture** (`WanTransformer3DModel` / `CausalWanTransformer3DModel` /
`LTX2Transformer3DModel`): a `ModelEntry` in `v2/registry.py` + a recipe (parameterize the wan21/wan_causal/
ltx2 builders) + `SamplingDefaults`, NO new adapter. Includes other Wan T2V sizes; i2v variants (need an
image-encoder component + i2v program); FastWan/Turbo (DMD/RCM schedules → a loop variant); Wan2.2-I2V-A14B;
SF-Wan2.2; LTX-2 base/2.3 repo aliases (already arch-fallback-resolvable, so they work without an entry).

**Bucket C (34 models, ~13 new architectures) — need a new `TorchComponent` adapter + recipe per arch:**
`basic_cosmos2_5_*` (Cosmos-Predict2.5), `basic_gen3c.py` (GEN3C — **local `converted_weights/` only**),
`basic_hy15*.py` / `basic_gamecraft.py` / `basic_hyworld.py` (Hunyuan family),
`basic_longcat_*.py` (LongCat + LoRA), `basic_lingbotworld_base_cam.py`,
`basic_sd35_t2i.py` (SD3.5 image), `basic_flux2*.py` (**gated** black-forest-labs).

## Different modality / interactive (out of the t2v VideoGenerator surface)
`basic_stable_audio*.py` (audio), `basic_matrixgame2*.py` / `basic_matrixgame3.py`
(interactive mouse/keyboard-conditioned world models).

## How to add a model to the v2 VideoGenerator
1. Add a v2 recipe under `v2/recipes/<family>/` (card/loop/program) whose `load_id`s name the real models;
   set per-model `SamplingDefaults` on the card (`v2/card/specs.py`) — see `wan21`/`ltx2` for the pattern.
2. Register it in the shared `v2/registry.py` (every entrypoint resolves through it): add a `ModelEntry`
   mapping its HF id(s) → builders (the PRIMARY path — the only way to split same-arch capability variants,
   e.g. T2V vs the i2v "InP"), and/or a branch in `select_by_architecture` for the arch-inference FALLBACK.
3. **Bucket B (reuse an existing arch):** nothing more — the redesigned `torch_backend.py` already builds
   Wan/LTX components. **Bucket C (new arch):** add a `TorchComponent` subclass + a `_MAKERS`/class-detect
   branch in `torch_backend.py`, vendor the new model code as marked re-export stubs under `v2/models/...`
   (see `v2-vendoring-approach`), and register any new component kind in `torch_cuda.py`.
4. Add `examples/inference/basic/v2_<name>.py` mirroring the official example; generate to confirm.