[feat] Migrate Hunyuan SamplingParam subclasses to profile-based defaults

Replace HunyuanSamplingParam and FastHunyuanSamplingParam subclasses with profile-based defaults. This adds the profile infrastructure (ConfigInfo.default_profile, SamplingParam._from_profile) and the first profile definitions for Hunyuan models, enabling future model families to migrate from subclass overrides to declarative profiles. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-15 16:33:37 -07:00
251 changed files with 1513 additions and 17589 deletions
@@ -1,96 +0,0 @@
-#!/usr/bin/env bash
-# Sync .agents/skills/ into .claude/skills/ via per-skill symlinks.
-#
-# Why: Claude Code only scans .claude/skills/ and ~/.claude/skills/ for
-# user-invocable skills (no skillsPath config exists — see
-# https://code.claude.com/docs/en/skills.md). This repo's skills live
-# in .agents/skills/ so they travel with the repo and stay under git.
-# Run this once after cloning (or after adding/removing a skill) to
-# expose them to Claude Code without maintaining a parallel tree.
-#
-# Usage:
-#   .agents/scripts/sync-skills.sh
-#
-# Idempotent and safe to re-run. Prunes stale symlinks whose source
-# has been removed from .agents/skills/. Leaves hand-written
-# .claude/skills/<name>/ directories untouched (only symlinks are
-# managed).
-
-set -euo pipefail
-
-REPO_ROOT="$(git -C "$(dirname "$0")" rev-parse --show-toplevel)"
-SRC_DIR="$REPO_ROOT/.agents/skills"
-DST_DIR="$REPO_ROOT/.claude/skills"
-
-if [[ ! -d "$SRC_DIR" ]]; then
-    echo "Error: $SRC_DIR does not exist." >&2
-    exit 1
-fi
-
-mkdir -p "$DST_DIR"
-
-linked=0
-unchanged=0
-skipped=0
-pruned=0
-
-link_skill() {
-    local name="$1"
-    local src="$SRC_DIR/$name"
-    local dst="$DST_DIR/$name"
-    # Relative target keeps symlinks portable across clones.
-    local rel="../../.agents/skills/$name"
-
-    if [[ -L "$dst" ]]; then
-        if [[ "$(readlink "$dst")" == "$rel" ]]; then
-            unchanged=$((unchanged + 1))
-            return
-        fi
-        rm "$dst"
-    elif [[ -e "$dst" ]]; then
-        echo "Skipped (not a symlink): .claude/skills/$name" >&2
-        skipped=$((skipped + 1))
-        return
-    fi
-
-    ln -s "$rel" "$dst"
-    echo "Linked: .claude/skills/$name -> $rel"
-    linked=$((linked + 1))
-}
-
-prune_stale() {
-    local link="$1"
-    local target
-    target="$(readlink "$link")"
-    case "$target" in
-        ../../.agents/skills/*) ;;
-        *) return ;;
-    esac
-    local name="${target##*/}"
-    if [[ ! -d "$SRC_DIR/$name" ]]; then
-        rm "$link"
-        echo "Pruned stale: .claude/skills/$(basename "$link")"
-        pruned=$((pruned + 1))
-    fi
-}
-
-for src in "$SRC_DIR"/*/; do
-    [[ -d "$src" ]] || continue
-    name="$(basename "$src")"
-    # Only treat directories that actually contain a SKILL.md as skills.
-    [[ -f "$src/SKILL.md" ]] || continue
-    link_skill "$name"
-done
-
-shopt -s nullglob
-for link in "$DST_DIR"/*; do
-    [[ -L "$link" ]] || continue
-    prune_stale "$link"
-done
-shopt -u nullglob
-
-printf "\nSummary: %d linked, %d unchanged, %d pruned" "$linked" "$unchanged" "$pruned"
-if [[ "$skipped" -gt 0 ]]; then
-    printf ", %d skipped (non-symlink collision)" "$skipped"
-fi
-printf "\n"
@@ -5,4 +5,3 @@
 {"name": "evaluate-video-quality", "description": "Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)", "path": "evaluate-video-quality/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "index-related-work", "description": "Ingest a paper or repository into the related work index", "path": "index-related-work/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "search-related-work", "description": "Query the related work index for relevant papers, repos, or comparisons", "path": "search-related-work/SKILL.md", "status": "draft", "trust": "low"}
-{"name": "seed-ssim-references", "description": "Run a new or updated fastvideo/tests/ssim/ test on Modal, pull generated videos, and upload them to FastVideo/ssim-reference-videos so the test has a regression baseline", "path": "seed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}
@@ -1,250 +0,0 @@
---
-name: seed-ssim-references
-description: Seed HF reference videos for a single newly-added SSIM test. Runs the test on Modal L40S, downloads the generated mp4s via `modal volume get`, pauses for the user to eyeball quality, then uploads only that test's files to `FastVideo/ssim-reference-videos`. Use when a new `fastvideo/tests/ssim/test_*_similarity.py` has just been added and has no references on HF yet.
---
-
-# Seed SSIM Reference Videos
-
-## Purpose
-
-A brand-new SSIM test in `fastvideo/tests/ssim/` fails forever until its
-reference videos exist on the HF dataset (`FastVideo/ssim-reference-videos`).
-This skill:
-
-1. Runs the test on Modal's L40S pool to generate the videos.
-2. Downloads them to the local repo via `modal volume get`.
-3. Pauses so the user can eyeball the mp4s and confirm quality.
-4. Uploads only the new test's files to HF, with a guard that refuses to
-   overwrite anything already present.
-
-The skill is run **manually**, once per new test. Before invoking it, the user
-has already sanity-tested the new test locally — it launches `VideoGenerator`
-and writes an mp4 without crashing. The skill does not re-test locally; it
-goes straight to Modal L40S (which is what CI uses).
-
-## When to use
-
- A new `test_*_similarity.py` file has been added in `fastvideo/tests/ssim/`
-  and the HF dataset has no `reference_videos/default/L40S_reference_videos/<model_id>/`
-  subtree for it yet.
-
-## When not to use
-
- Regular CI runs — once refs exist, `pytest fastvideo/tests/ssim/` downloads
-  them automatically.
- Re-seeding an existing test. That requires `--force` on the upload step, and
-  is out of scope here; treat as a separate, deliberate operation.
-
-## Inputs
-
-The skill has **one required input**: the path to the new SSIM test file.
-Prompt the user for it if they didn't supply it.
-
-| Parameter | Required | Description |
-|-----------|----------|-------------|
-| `test_file` | Yes | e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`. The skill's first action is to ask for this if missing. |
-
-Everything else is fixed:
-
- Modal runner GPU: **L40S** (hardcoded in `fastvideo/tests/modal/ssim_test.py`).
- Device folder: `L40S_reference_videos`.
- Quality tier: `default` (the tier CI runs). The `full_quality` tier is not
-  seeded by this skill.
- HF repo: `FastVideo/ssim-reference-videos` (dataset).
- Multi-model test files: all model ids in `*_MODEL_TO_PARAMS` are seeded
-  together; the Modal run produces one mp4 per (model, prompt, backend) and
-  the upload scopes by `--model-id`, looping if there is more than one.
-
-## Prerequisites
-
-The user has confirmed:
-
- `modal` CLI authenticated.
- `HF_API_KEY` (or `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`) exported with write
-  access to `FastVideo/ssim-reference-videos`.
- The test file runs locally end-to-end (generates an mp4; SSIM assertion
-  failure due to missing reference is expected and fine).
-
-Fail fast if the token env var is missing.
-
-## Steps
-
-### 1. Ask for the test file
-
-If the user didn't name one, ask: *"Which SSIM test file do you want to seed
-references for? (e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`)"*.
-
-Validate:
-
- Path exists and matches `fastvideo/tests/ssim/test_*_similarity.py`.
- File defines a `*_MODEL_TO_PARAMS` dict — grep it to extract the set of
-  model ids. Those ids drive step 5.
-
-If either check fails, stop and tell the user what's wrong.
-
-### 2. Run the test on Modal L40S
-
-Pick a subdir name so repeated runs don't collide:
-
-```bash
-SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
-TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
-SUBDIR="${TIMESTAMP}_${SHORT_COMMIT}"
-```
-
-Then launch the Modal run:
-
-```bash
-modal run fastvideo/tests/modal/ssim_test.py \
-    --git-repo="$(git config --get remote.origin.url)" \
-    --git-commit="$(git rev-parse HEAD)" \
-    --hf-api-key="$HF_API_KEY" \
-    --test-files="<test_file>" \
-    --sync-generated-to-volume \
-    --generated-volume-subdir="$SUBDIR" \
-    --skip-reference-download \
-    --no-fail-fast
-```
-
-Flag rationale:
- `--skip-reference-download`: no refs exist yet, so conftest must not try to
-  pull them.
- `--no-fail-fast`: lets the test finish generation before `_assert_similarity`
-  raises `FileNotFoundError: Reference video folder does not exist`. The
-  expected failure is what we want — the mp4 has already been written.
- `--sync-generated-to-volume` + `--generated-volume-subdir`: copies the
-  generated mp4s to the `hf-model-weights` Modal volume under
-  `ssim_generated_videos/default/<SUBDIR>/generated_videos/` so we can pull
-  them locally.
-
-The Modal run will end with a nonzero exit (expected) and print a
-`modal volume get hf-model-weights ssim_generated_videos/default/<SUBDIR>/generated_videos ./generated_videos_modal/default`
-command. Capture that `<SUBDIR>` — you need it for step 3.
-
-### 3. Download generated videos locally
-
-```bash
-modal volume get --force hf-model-weights \
-    ssim_generated_videos/default/"$SUBDIR"/generated_videos \
-    ./generated_videos_modal/default
-```
-
-`--force` is required when the parent `./generated_videos_modal/default`
-already exists; without it, `modal volume get` errors with `[Errno 21] Is a
-directory`. Safe to pass on the first run too.
-
-After this, the mp4s live at
-`./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
-The extra `generated_videos/` level comes from the volume layout in
-`_sync_generated_videos_to_volume` (`ssim_test.py`) — the command copies
-`<repo>/fastvideo/tests/ssim/generated_videos/<tier>` to
-`ssim_generated_videos/<tier>/<SUBDIR>/generated_videos/`, and `modal volume
-get` preserves that trailing `generated_videos/` segment.
-
-### 4. PAUSE — user reviews quality
-
-Print the list of downloaded mp4s and their paths, then stop. Tell the user:
-
-> "Generated videos downloaded to `./generated_videos_modal/default/generated_videos/L40S_reference_videos/`. Please open them and confirm the quality looks correct. Reply **`upload`** to continue, or anything else to abort."
-
-Do not proceed until the user explicitly says `upload`. If they abort, leave
-everything on disk so they can inspect further — no cleanup.
-
-### 5. Copy into the local reference layout
-
-Scoped copy — only the new test's mp4s. Loop over each `<model_id>` extracted
-in step 1:
-
-```bash
-python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
-    --quality-tier default \
-    --device-folder L40S_reference_videos \
-    --generated-dir ./generated_videos_modal/default/generated_videos/L40S_reference_videos
-```
-
-(The `--generated-dir` points at the device-folder root inside the
-downloaded tree; `copy-local` walks all `<model>/<backend>/*.mp4`
-underneath it. Since the Modal run was scoped to a single test file via
-`--test-files`, only that test's model(s) are present — so the copy is
-implicitly per-test.)
-
-Result: `fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
-
-### 6. Upload to HF — scoped per model_id, with overwrite guard
-
-For each `<model_id>`:
-
-```bash
-python fastvideo/tests/ssim/reference_videos_cli.py upload \
-    --quality-tier default \
-    --device-folder L40S_reference_videos \
-    --model-id "<model_id>"
-```
-
-The upload command:
-
- Uploads **only** `reference_videos/default/L40S_reference_videos/<model_id>/`.
- **Refuses** if any file already exists at that path on HF (this is the
-  guard — seeding a new test should never clobber existing refs). To override,
-  the user must re-run with `--force`. If the guard fires, stop and report
-  exactly which files exist; do not silently `--force`.
-
-Reads the HF token from `HF_API_KEY` / `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`.
-
-### 7. Report success
-
-List what was uploaded (paths in repo) and remind the user to push any
-related code changes. Do **not** auto-verify by re-running Modal — the user
-can run `pytest fastvideo/tests/ssim/<test_file>` later to confirm end-to-end;
-it will auto-download the refs they just uploaded.
-
-## Failure modes and how to handle them
-
- **`HF_API_KEY` unset.** Stop before step 2. The Modal run needs it (passed
-  via `--hf-api-key`), and step 6 needs it for upload.
- **Modal run fails before generation.** No mp4s on the volume — nothing to
-  download. Fix the test locally (`pytest fastvideo/tests/ssim/<test_file>`)
-  and retry from step 2.
- **`./generated_videos_modal/default/L40S_reference_videos/` missing after
-  `modal volume get`.** The run didn't produce videos (most likely the test
-  crashed before writing, or `REQUIRED_GPUS` exceeded the partition capacity
-  — see Modal logs).
- **Upload guard fires (files already exist).** The test name / model id
-  collides with something already on HF. Verify the user actually wants to
-  replace existing refs; if so, re-run the upload with `--force`. If not,
-  rename the model id in `*_MODEL_TO_PARAMS` and re-seed.
- **Quality looks wrong in step 4.** Abort. The mp4s stay on disk for
-  inspection. The fix is usually in the test's params (resolution, steps,
-  seed) — edit the test, then re-run the skill.
-
-## Design notes (for future skill maintainers)
-
- The skill deliberately runs on Modal, **not** locally, because the CI
-  runner is L40S. Seeding from a different GPU SKU produces refs that CI's
-  L40S runs can't match (SSIM drifts across SKUs).
- The skill is default-tier only. `full_quality` refs are seeded by a
-  separate, deliberate operation — they double runtime and aren't what CI
-  gates on.
- The overwrite guard in `reference_videos_cli.py upload` is default-on
-  specifically because this skill exists. Re-seeding is a distinct operation
-  that requires explicit `--force`.
-
-## References
-
- `fastvideo/tests/modal/ssim_test.py` — Modal orchestrator; see
-  `--sync-generated-to-volume`, `--generated-volume-subdir`,
-  `--skip-reference-download`, `--no-fail-fast`.
- `fastvideo/tests/ssim/reference_videos_cli.py` — `copy-local`, `upload`
-  (with `--model-id`, `--force`), `download`, `ensure` subcommands.
- `fastvideo/tests/ssim/README.md` — reference layout, HF repo conventions.
- `fastvideo/tests/ssim/inference_similarity_utils.py` —
-  `run_text_to_video_similarity_test` + `_build_init_kwargs`: what each test
-  config passes to `VideoGenerator.from_pretrained`.
-
-## Changelog
-
-| Date | Change |
-|------|--------|
-| 2026-04-17 | Initial version (Modal sync-to-volume flow). |
-| 2026-04-21 | Rewrite: single-test scope, explicit user-review pause, per-`model_id` upload, HF overwrite guard. Dropped `scripts/seed_ssim.sh`. |
-| 2026-04-21 | Post-first-run fixes: `modal volume get` needs `--force` when parent exists; download tree has an extra `generated_videos/` level so `--generated-dir` must reflect it. |
@@ -4,24 +4,21 @@ description: How to develop, validate, and register a new evaluation metric

 # Evaluation Development SOP

-Standard procedure for adding new video quality evaluation metrics to
-the FastVideo agent toolkit.
+Standard procedure for adding new video quality evaluation metrics to the
+FastVideo agent toolkit.

-## When to use
+## When to Use

- You need a metric that does not exist in
-  `.agents/memory/evaluation-registry/README.md`.
+- You need a metric that doesn't exist in `.agents/memory/evaluation-registry/README.md`.
 - An existing metric needs significant changes to its methodology.
- You are exploring a new evaluation approach.
+- You're exploring a new evaluation approach.

 ## Steps

 ### 1. Research

- Search `.agents/memory/related-work/` for existing evaluation
-  approaches.
- Check `.agents/memory/evaluation-registry/README.md` for current
-  metrics and their limitations.
+- Search `.agents/memory/related-work/` for existing evaluation approaches.
+- Check the `evaluation_registry.md` for current metrics and their limitations.
 - Review literature: FVD, CLIP-Score, human preference, etc.

 ### 2. Prototype
@@ -32,25 +29,21 @@ the FastVideo agent toolkit.

 ### 3. Validate

- **Known-good test**: metric should score high on reference-quality
-  videos.
- **Known-bad test**: metric should score low on degraded or unrelated
-  videos.
- **Sensitivity test**: small quality differences should produce
-  meaningful score differences.
+- **Known-good test**: Metric should score high on reference-quality videos.
+- **Known-bad test**: Metric should score low on degraded/unrelated videos.
+- **Sensitivity test**: Small quality differences should produce meaningful
+  score differences.
 - Document thresholds and their justification.

 ### 4. Register

 Update `.agents/memory/evaluation-registry/README.md`:
-
 - Add the metric with status `Active`.
 - Document location, thresholds, and trust level.

 ### 5. Integrate

-Update `.agents/skills/evaluate-video-quality/SKILL.md`:
-
+Update `.agents/skills/evaluate-video-quality.md`:
 - Add the new metric as a section.
 - Include code examples and interpretation guide.

@@ -59,35 +52,3 @@ Update `.agents/skills/evaluate-video-quality/SKILL.md`:
 - Move the exploration log content into the skill.
 - Clean up the exploration file or mark it as `promoted`.
 - If anything went wrong during development, create a lesson.
-
-## Where the metrics live
-
-The eval suite is `fastvideo/eval/`. New metrics register themselves
-via `@register("<group>.<name>")` and are auto-discovered when
-`fastvideo.eval.metrics` is imported.
-
- **Native metrics** (SSIM, PSNR, LPIPS, optical flow, VLM): add a
-  file under the appropriate group dir
-  (`fastvideo/eval/metrics/common/`, `optical_flow/`, `videoscore2/`,
-  `physics_iq/`).
- **Metrics that wrap upstream research code**: follow the vbench
-  pattern in `fastvideo/eval/metrics/vbench/`. The contract is:
-  - Upstream lives as a git submodule under
-    `fastvideo/third_party/eval/<bench>/`, pinned to a SHA in repo-root
-    `.gitmodules`.
-  - The metric package's `__init__.py` inserts the submodule path on
-    `sys.path` and installs runtime compat shims (attribute-level
-    monkey-patches) for any modern-dep drift. Do not modify upstream
-    files on disk, and do not ship a `setup.sh`.
-  - See `fastvideo/eval/README.md` for the worked vbench example.
-  - Full porting guide:
-    [`docs/contributing/eval-metrics.md`](../../docs/contributing/eval-metrics.md).
-
-## Out of scope of the initial eval port
-
-The following land in follow-up PRs:
-
- **MIND** metrics (depends on a separate `vipe` submodule).
- **VBench-2.0** sibling package.
- Native conversion of **FVD** under `fastvideo/eval/metrics/fvd/`.
- The training-time `EvalCallback`.
@@ -105,7 +105,7 @@ pull_request_rules:
          - files~=^fastvideo/pipelines/samplers/
          - files~=^fastvideo/entrypoints/
          - files~=^fastvideo/worker/
-          - files~=^fastvideo/api/sampling_param
+          - files~=^fastvideo/configs/sample/
          - files~=^fastvideo/configs/pipelines/
          - files~=^examples/inference/
      - -closed
@@ -4,6 +4,3 @@
 [submodule "fastvideo-kernel/include/cutlass"]
 	path = fastvideo-kernel/include/cutlass
 	url = https://github.com/NVIDIA/cutlass.git
-[submodule "fastvideo/third_party/eval/vbench"]
-	path = fastvideo/third_party/eval/vbench
-	url = https://github.com/Vchitect/VBench.git
@@ -62,9 +62,9 @@ This page contains the complete API reference for the FastVideo library.
      show_root_toc_entry: true
      heading_level: 4

-#### fastvideo.api.sampling_param
+#### fastvideo.configs.sample

-::: fastvideo.api.sampling_param
+::: fastvideo.configs.sample
    options:
      show_source: true
      show_root_heading: true
@@ -173,7 +173,7 @@ Applied by Mergify based on which paths you modified. Multiple scope labels can
 | Label | File paths that trigger it |
 |-------|---------------------------|
 | `scope: training` | `fastvideo/train/`, `fastvideo/training/`, `fastvideo/distillation/`, `examples/train/`, `examples/training/`, `examples/distill/` |
-| `scope: inference` | `fastvideo/pipelines/basic/`, `fastvideo/pipelines/stages/`, `fastvideo/pipelines/samplers/`, `fastvideo/entrypoints/`, `fastvideo/worker/`, `fastvideo/api/sampling_param.py`, `fastvideo/configs/pipelines/`, `examples/inference/` |
+| `scope: inference` | `fastvideo/pipelines/basic/`, `fastvideo/pipelines/stages/`, `fastvideo/pipelines/samplers/`, `fastvideo/entrypoints/`, `fastvideo/worker/`, `fastvideo/configs/sample/`, `fastvideo/configs/pipelines/`, `examples/inference/` |
 | `scope: attention` | `fastvideo/attention/` |
 | `scope: kernel` | `fastvideo-kernel/`, `csrc/` |
 | `scope: data` | `fastvideo/dataset/`, `fastvideo/pipelines/preprocess/`, `examples/preprocessing/` |
@@ -44,7 +44,7 @@ FastVideo maps a Diffusers-style repo into a pipeline like:
 - `fastvideo/configs/models/*`: arch configs and `param_names_mapping` for
  weight name translation.
 - `fastvideo/configs/pipelines/*`: pipeline wiring (component classes + names).
- `fastvideo/api/sampling_param.py`: runtime sampling parameters.
+- `fastvideo/configs/sample/*`: default runtime sampling parameters.
 - `fastvideo/pipelines/basic/*`: end-to-end pipeline logic built from stages.
 - `model_index.json`: the HF repo entrypoint that maps component names to
  classes and weight files.
@@ -55,7 +55,7 @@ Minimal usage example (based on `examples/inference/basic/basic.py`):

 ```python
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam

 model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # or official_weights/<model_name>/
 generator = VideoGenerator.from_pretrained(model_id, num_gpus=1)
@@ -319,8 +319,7 @@ Purpose:

 - `fastvideo/configs/pipelines/` describes pipeline wiring and model module
  names.
- `fastvideo/api/sampling_param.py` defines runtime sampling parameters.
-  Defaults come from profiles in `fastvideo/pipelines/basic/<family>/profiles.py`.
+- `fastvideo/configs/sample/` defines default runtime parameters.

 Action:

@@ -475,7 +474,7 @@ FastVideo integration.
 3. Pipeline wiring.
   - Pipeline: `fastvideo/pipelines/basic/wan/wan_pipeline.py`
   - Pipeline config: `fastvideo/configs/pipelines/wan.py`
-   - Sampling defaults: `fastvideo/pipelines/basic/wan/profiles.py`
+   - Sampling defaults: `fastvideo/configs/sample/wan.py`

 4. Minimal example.
   - Script: `examples/inference/basic/basic.py`
@@ -1,559 +0,0 @@
-# Porting Eval Metrics into `fastvideo.eval`
-
-This guide is for contributors adding new evaluation metrics to
-FastVideo's eval suite. To run the existing metrics, see
-[`fastvideo/eval/README.md`](../../fastvideo/eval/README.md).
-
-## When to use this guide
-
-Use this guide when you are:
-
- Adding a new metric (native or wrapping a third-party library).
- Porting a benchmark (e.g. VBench, MIND, EvalCrafter) whose Python
-  code needs to be importable from a pinned upstream.
- Adding a new metric group (audio, vlm, etc.).
-
-## TL;DR
-
-Metrics are auto-discovered from
-`fastvideo/eval/metrics/<group>/<name>/metric.py`. Each declares itself
-with `@register("<group>.<name>")` and subclasses `BaseMetric`. Three
-recipes:
-
-1. **Native metric** (pure-PyTorch, no submodule). Drop a file,
-   declare deps, implement `compute(sample)`.
-2. **Library-wrapped metric** (CLIP, torch.hub, transformers, pyiqa).
-   Same as above, plus route the library's cache through
-   `get_cache_dir()` if it has a `download_root=` / `cache_dir=`
-   kwarg.
-3. **Upstream-submodule-wrapped metric** (vbench-style). Pin upstream
-   as a git submodule under `fastvideo/third_party/eval/<bench>/`. The
-   adapter `__init__.py` does the `sys.path` insert and any runtime
-   compat shims for modern dep versions. Patches live as Python in
-   that file rather than as on-disk patches to the submodule.
-
-The full recipes are below.
-
---
-
-## 0) Layout and auto-discovery
-
-```
-fastvideo/eval/metrics/
-├── base.py                  # BaseMetric + lifecycle contract
-├── common/                  # group: SSIM, PSNR, LPIPS
-├── optical_flow/            # group: gt_optical_flow, synthetic_optical_flow
-├── vlm/                     # group: VideoScore-2
-├── physics_iq/              # group + sub-metrics
-└── vbench/                  # group: 16 sub-metrics
-    ├── __init__.py          # sys.path bootstrap + runtime compat shims
-    ├── _grit_helper.py      # shared upstream-touching helpers
-    └── <sub_metric>/metric.py
-```
-
-Auto-discovery (`fastvideo/eval/metrics/__init__.py`) walks each group
-dir and imports every `metric.py` it finds, which fires the
-`@register` decorators. Names starting with `_` are skipped. Use that
-prefix for shared helpers or vendored code that should not register
-itself.
-
---
-
-## 1) The `BaseMetric` contract
-
-Every metric subclasses `fastvideo.eval.metrics.base.BaseMetric` and
-declares:
-
-```python
-class YourMetric(BaseMetric):
-    name: str = "common.your_metric"          # must match @register
-    requires_reference: bool = True           # needs sample["reference"]
-    higher_is_better: bool = True             # for ranking / aggregates
-    dependencies: list[str] = []              # importable module names;
-                                              # registry surfaces a clean
-                                              # ImportError if missing
-    needs_gpu: bool = False
-    backbone: str | None = None               # e.g. "clip_vit_l14"
-```
-
-You must implement:
-
-```python
-def compute(self, sample: dict) -> list[MetricResult]:
-    """sample['video'] is (1, T, C, H, W). Return a one-element list.
-
-    The leading 1 is preserved for forward-compat with batched eval;
-    today :class:`EvalWorker` always invokes metrics with B=1.
-    """
-```
-
-You may override:
-
- `setup(self) -> None`. Eager model loading. Called once by
-  `create_evaluator`. Idempotent (re-entrant). Use the `if self._model
-  is not None: return` pattern.
- `to(self, device)`. Move the metric and its submodels to `device`.
-
-If a required input is missing (e.g. an fps-aware metric called
-without `fps`), return `self._skip(sample, reason)` instead of
-raising.
-
---
-
-## 2) Recipe A: native metric (no external deps)
-
-Smallest case. Pixel math, simple closed-form.
-
-```python
-# fastvideo/eval/metrics/common/your_metric/metric.py
-from __future__ import annotations
-import torch
-from fastvideo.eval.metrics.base import BaseMetric
-from fastvideo.eval.registry import register
-from fastvideo.eval.types import MetricResult
-
-
-@register("common.your_metric")
-class YourMetric(BaseMetric):
-    name = "common.your_metric"
-    requires_reference = True
-    higher_is_better = True
-    needs_gpu = False
-    dependencies: list[str] = []  # nothing extra
-
-    def compute(self, sample: dict) -> list[MetricResult]:
-        gen, ref = sample["video"], sample["reference"]   # (B,T,C,H,W) each
-        per_video = ((gen - ref) ** 2).mean(dim=(1, 2, 3, 4)).sqrt()
-        return [
-            MetricResult(name=self.name, score=float(s), details={})
-            for s in per_video
-        ]
-```
-
-That is the whole recipe. Drop the file and the registry picks it up.
-
---
-
-## 3) Recipe B: library-wrapped metric (CLIP, torch.hub, transformers, pyiqa)
-
-If your metric loads a backbone from a Python package, route the
-library at the eval cache so users get one knob (`FASTVIDEO_EVAL_CACHE`)
-to redirect everything.
-
-### Cache routing rules
-
-| Library | How to route | Location after redirect |
-|---|---|---|
-| `clip.load("ViT-X")` | pass `download_root=str(get_cache_dir() / "clip")` | `${FASTVIDEO_EVAL_CACHE}/clip/` |
-| `torch.hub.load(...)` | nothing; `TORCH_HOME` is redirected at `fastvideo.eval` import time | `${FASTVIDEO_EVAL_CACHE}/torch/hub/` |
-| `transformers.from_pretrained(...)` | nothing; leave HF's default cache (`~/.cache/huggingface/hub/`) so users dedupe with other ML projects | `~/.cache/huggingface/hub/` |
-| `huggingface_hub.snapshot_download` / `hf_hub_download` | use `ensure_checkpoint(...)` (it wraps these with filelock) | same as above |
-| `pyiqa.create_metric(...)` | no env var or kwarg honored; document in metric docstring | pyiqa-internal |
-| `lpips`, `ptlflow` | torch.hub-based, auto-redirected | `${FASTVIDEO_EVAL_CACHE}/torch/hub/` |
-| Raw URL (no HF Hub) | use `ensure_checkpoint(name, source="https://...")` | `${FASTVIDEO_EVAL_CACHE}/models/<name>` |
-| Dataset asset (raw video/mask/image) auto-fetched from a public bucket | download into `get_cache_dir() / "datasets" / "<bench>"`, mirroring upstream's relative layout. Vendor any small manifest (CSV/JSON ≤1 MB) under the metric folder so the dataset can be used without external setup. | `${FASTVIDEO_EVAL_CACHE}/datasets/<bench>/` |
-
-### Dataset assets: vendor the manifest, auto-fetch the rest
-
-If your metric ships with its own paired-reference dataset (Physics-IQ
-is the canonical example), follow this layout:
-
- **Manifest** (CSV/JSON ≤1 MB): vendor it under
-  `fastvideo/eval/metrics/<bench>/_vendored/<manifest>.<ext>`, with a
-  sibling `_vendored/LICENSE` recording attribution and provenance.
-  The `_vendored/` subdir is the project-wide convention for
-  upstream-provenance files: it is auto-skipped by metric discovery
-  (the `_` prefix) and by codespell (one `*/_vendored/*` glob in
-  `[tool.codespell].skip`), so dropping in a new vendored file
-  requires no further config. Read the manifest from the dataset
-  module via a `Path(__file__)`-relative resolver. Mirror
-  `_VENDORED_DESCRIPTIONS_CSV` in
-  `fastvideo/eval/datasets/physics_iq.py`.
- **Heavy assets** (videos, masks, images): do not vendor. Auto-fetch
-  on first miss into `get_cache_dir() / "datasets" / "<bench>"`,
-  mirroring upstream's relative directory layout one-for-one so a
-  pre-downloaded mirror at any path works as a drop-in
-  `dataset_root=`. Use atomic `.part` then final-rename to be safe
-  under concurrent SLURM ranks.
- **Bucket override**: expose `FASTVIDEO_<BENCH>_BUCKET_URL` so users
-  with internal mirrors can redirect.
- **Opt-out**: accept `auto_download: bool = True` in the dataset
-  constructor; on `False`, raise `FileNotFoundError` instead of
-  fetching. This covers air-gapped runs and CI.
-
-The end-state is `get_dataset("<bench>")` with no kwargs.
-
-### Example: CLIP backbone + LAION head
-
-```python
-# fastvideo/eval/metrics/your_group/your_metric/metric.py
-from __future__ import annotations
-import torch
-import torch.nn as nn
-from fastvideo.eval.metrics.base import BaseMetric
-from fastvideo.eval.registry import register
-from fastvideo.eval.types import MetricResult
-
-
-@register("your_group.your_metric")
-class YourMetric(BaseMetric):
-    name = "your_group.your_metric"
-    requires_reference = False
-    needs_gpu = True
-    dependencies = ["clip"]      # "openai-clip" PyPI; importable as `clip`
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._clip = None
-        self._head = None
-
-    def setup(self) -> None:
-        if self._clip is not None:
-            return
-        import clip
-        from fastvideo.eval.models import ensure_checkpoint, get_cache_dir
-
-        # Backbone: route CLIP's cache through our root.
-        self._clip, _ = clip.load(
-            "ViT-L/14",
-            device=self.device,
-            download_root=str(get_cache_dir() / "clip"),
-        )
-        self._clip.eval()
-
-        # URL-fetched head: ensure_checkpoint downloads to
-        # ${FASTVIDEO_EVAL_CACHE}/models/ with filelock + atomic rename.
-        ckpt = ensure_checkpoint(
-            "your_head.pth",
-            source="https://example.com/path/to/your_head.pth",
-        )
-        self._head = nn.Linear(768, 1)
-        self._head.load_state_dict(
-            torch.load(ckpt, map_location="cpu", weights_only=True)
-        )
-        self._head.to(self.device).eval()
-
-    def to(self, device):
-        super().to(device)
-        if self._clip is not None:
-            self._clip = self._clip.to(self.device)
-        if self._head is not None:
-            self._head = self._head.to(self.device)
-        return self
-
-    def compute(self, sample: dict) -> list[MetricResult]:
-        ...
-```
-
-### Do not redirect other `~/.cache/...` dirs
-
-If a third-party library hard-codes `~/.cache/<lib>/` and offers no
-override, document the exception in the metric's docstring. Forcing
-redirection by setting `os.environ` or patching `os.path.expanduser`
-is fragile and breaks user expectations of where the library's cache
-lives.
-
---
-
-## 4) Recipe C: upstream-submodule-wrapped metric (vbench pattern)
-
-Use this when the upstream benchmark ships Python code (`vbench/`,
-`MIND/`, etc.) that is not pip-installable cleanly. See
-`fastvideo/eval/metrics/vbench/__init__.py` for the worked example.
-
-### 4.1 Pin the upstream as a submodule
-
-```bash
-git submodule add <upstream-url> fastvideo/third_party/eval/<bench>
-cd fastvideo/third_party/eval/<bench>
-git checkout <pinned-sha>
-cd -
-git add .gitmodules fastvideo/third_party/eval/<bench>
-```
-
-The submodule pulls under the standard `git submodule update --init
--recursive` flow that users already run for kernel deps.
-
-### 4.2 Bootstrap on `sys.path`
-
-```python
-# fastvideo/eval/metrics/<bench>/__init__.py
-from __future__ import annotations
-import sys
-from pathlib import Path
-
-# fastvideo/eval/metrics/<bench>/__init__.py → ../../../../third_party/eval/<bench>
-_UPSTREAM = Path(__file__).resolve().parents[3] / "third_party" / "eval" / "<bench>"
-if _UPSTREAM.is_dir() and str(_UPSTREAM) not in sys.path:
-    sys.path.insert(0, str(_UPSTREAM))
-```
-
-We do not `pip install` the upstream because its egg-link/.pth would
-just re-do this `sys.path.insert`, and skipping the install also skips
-the upstream's `setup.py` (which often gates on a specific CUDA
-version).
-
-### 4.3 Modern-dep compat: runtime shims
-
-Upstream code pinned to e.g. `transformers==4.33.2`, `numpy<2`
-typically breaks against modern versions in 3-4 known places (API
-renames). Fix those at import time, in the same `__init__.py`:
-
-```python
-def _install_compat_shims() -> None:
-    # Example: transformers.modeling_utils API moved.
-    try:
-        import transformers.modeling_utils as _mu
-        import transformers.pytorch_utils as _pu
-        for _n in ("apply_chunking_to_forward",
-                   "find_pruneable_heads_and_indices",
-                   "prune_linear_layer"):
-            if not hasattr(_mu, _n) and hasattr(_pu, _n):
-                setattr(_mu, _n, getattr(_pu, _n))
-    except ImportError:
-        pass
-
-    # Example: numpy.lib.function_base.disp removed in numpy>=2.
-    try:
-        import types, numpy.lib as _nl
-        if not hasattr(_nl, "function_base"):
-            _stub = types.ModuleType("numpy.lib.function_base")
-            _stub.disp = lambda *a, **k: None
-            sys.modules["numpy.lib.function_base"] = _stub
-            _nl.function_base = _stub
-    except ImportError:
-        pass
-
-_install_compat_shims()
-```
-
-For function-level patches that cannot be expressed as attribute
-writes (e.g. wrapping a model factory function), use a
-`sys.meta_path` finder that wraps the loader. See
-`_install_modeling_finetune_hook()` in
-`fastvideo/eval/metrics/vbench/__init__.py` for the pattern (about 30
-lines).
-
-Why shims rather than `git apply` patches: patches go stale when the
-upstream SHA changes; shims are versioned Python code in our repo,
-they are grep-able, and they only run if the targeted module is
-imported.
-
-### 4.4 Per-sub-metric files
-
-Each sub-metric is a normal `BaseMetric` subclass that imports from
-the upstream:
-
-```python
-# fastvideo/eval/metrics/<bench>/<sub>/metric.py
-from fastvideo.eval.metrics.base import BaseMetric
-from fastvideo.eval.registry import register
-from fastvideo.eval.types import MetricResult
-
-
-@register("<bench>.<sub>")
-class YourSubMetric(BaseMetric):
-    ...
-
-    def setup(self) -> None:
-        if self._model is not None:
-            return
-        # The sys.path bootstrap fired when fastvideo.eval.metrics.<bench>
-        # was imported (which auto-discovery does before importing this
-        # sub-package). Upstream imports just work:
-        from <bench>.something import SomeModel
-        ...
-```
-
-### 4.5 Conditional registration when the upstream is missing
-
-If a user installed `fastvideo[eval]` but did not run `git submodule
-update --init`, `<bench>.*` metrics should not register. The
-auto-discovery walker imports each sub-package's `metric` module; have
-that import bail out cleanly:
-
-```python
-# fastvideo/eval/metrics/<bench>/__init__.py — at the bottom
-_AVAILABLE = (_UPSTREAM / "<bench>" / "__init__.py").is_file()
-```
-
-```python
-# fastvideo/eval/metrics/<bench>/<sub>/__init__.py
-from fastvideo.eval.metrics.<bench> import _AVAILABLE
-if _AVAILABLE:
-    from .metric import YourSubMetric  # noqa
-```
-
-`fastvideo eval list` then reflects what the user actually has rather
-than what they could have.
-
-### 4.6 Leave the upstream alone unless it blocks the metric
-
-The upstream is pinned. If a metric works against the pinned SHA,
-leave the upstream files untouched. If it actively breaks against
-modern deps (the import-drift cases above), shim it. Avoid
-fastvideo-side forks of upstream code; they make patches go stale and
-parity drift.
-
---
-
-## 5) Model checkpoints: `ensure_checkpoint`
-
-Use `ensure_checkpoint(name, source, filename=None)` for any
-non-package weights. It resolves a local path, downloading on miss,
-with filelock safety across processes and SLURM ranks.
-
-| `source` form | What happens |
-|---|---|
-| `"/abs/path/to/file.pth"` | passthrough, returned unchanged |
-| `"https://..."` | downloaded to `${FASTVIDEO_EVAL_CACHE}/models/<name>` via `huggingface_hub.http_get`, atomic rename, filelock |
-| `"org/repo"` (no `filename`) | `snapshot_download(repo_id)` → `~/.cache/huggingface/hub/` |
-| `"org/repo"` (with `filename`) | `hf_hub_download(repo_id, filename)` → `~/.cache/huggingface/hub/` |
-
-`name` is only used as the local filename for URL sources. HF sources
-ignore it (HF manages its own cache key by content hash).
-
-```python
-from fastvideo.eval.models import ensure_checkpoint
-
-# URL: name matters
-ckpt = ensure_checkpoint(
-    "amt-s.pth",
-    source="https://huggingface.co/lalala125/AMT/resolve/main/amt-s.pth",
-)
-
-# HF single file: name is decorative
-ckpt = ensure_checkpoint(
-    "raft-things.pth",                      # ignored; HF cache uses repo+sha
-    source="OpenGVLab/VBench_Used_Models",
-    filename="raft-things.pth",
-)
-```
-
---
-
-## 6) Declaring `dependencies`
-
-Set `dependencies = ["pkg1", "pkg2"]` on your metric class with
-importable module names (not PyPI distribution names). The registry
-checks each via `importlib.util.find_spec` at instantiation time and
-raises a clean `ImportError` pointing the user at the right install
-extra:
-
-```python
-class YourMetric(BaseMetric):
-    dependencies = ["clip", "timm"]   # importable as `import clip`, `import timm`
-```
-
-If a dep is in `[project.optional-dependencies.eval-<group>]`, you do
-not need to do anything more. If it is a new dep, add it to that
-group in `pyproject.toml`.
-
---
-
-## 7) Common gotchas
-
- The standard `git submodule update --init --recursive` is enough
-  for a benchmark; do not write a `setup.sh`. Modern-dep compat goes
-  into your `__init__.py` as runtime shims.
- Do not modify upstream files on disk. The submodule should always
-  match its pinned SHA. Compat lives in our `__init__.py`.
- Do not pip-install the upstream. The egg-link is a glorified
-  `sys.path.insert`, which we do directly in `__init__.py`.
- Do not call `torch.hub.set_dir(...)` from your metric. It is done
-  globally in `fastvideo/eval/__init__.py`.
- Do not put cache-redirection env vars in your metric's `setup()`.
-  By the time `setup()` runs, the library has likely already cached
-  the default-location decision. Set env vars at package-init time.
- Skip rather than raise when an input is missing. Use
-  `self._skip(sample, reason)` for any expected-missing input. It
-  returns a list of `MetricResult(score=None)` so other metrics in
-  the same evaluator continue.
- Watch for upstream re-registration conflicts. If the upstream uses
-  a global registry (detectron2's `META_ARCH_REGISTRY`, MMCV, etc.),
-  loading the same model twice in the same process will throw. The
-  evaluator already loads each metric once; if you write a custom
-  setup-then-call pattern, mirror that single-load discipline.
-
---
-
-## 8) Training-time eval: keep evaluators hot, free caches between calls
-
-When wiring eval into a training loop, the working pattern is:
-
-1. Construct the `Evaluator` once and attach it to the pipeline
-   (`self._eval = create_evaluator(...)`). Do not recreate it per
-   validation round; that re-pays the model load cost.
-2. Save validation videos to disk (the diffusion path already does
-   this). Pass paths to `evaluator.evaluate`, not in-memory tensors
-   that share GPU memory with the training model.
-3. Run validation only on rank 0 of each sequence-parallel group.
-   Gather paths from other ranks and let rank 0 score everything.
-4. After every `evaluate(...)` call, call
-   `evaluator.release_cuda_memory()` in a `finally` block. That runs
-   `gc.collect()` + `torch.cuda.empty_cache()` +
-   `torch.cuda.ipc_collect()`. The eval model stays loaded; only
-   transient activation buffers from the just-finished call get
-   freed:
-
-   ```python
-   for video_path in batch:
-       try:
-           scores = self._eval.evaluate(video=load_video(video_path))
-       finally:
-           self._eval.release_cuda_memory()
-   ```
-
-5. If memory pressure spikes (rare on H200), call
-   `evaluator.unload()` to drop every metric reference and let the
-   GPU memory be GC'd. `unload` is reversible:
-   `evaluator.reload()` rebuilds the same metrics with the original
-   config (re-paying the model load cost). Calling `evaluate`
-   between `unload` and `reload` raises a clear `RuntimeError`.
-
-For most metrics (sub-1 GB backbones, e.g. CLIP/DINO/RAFT/AMT) the
-eval model can stay co-resident with the training model in
-`transformer.eval()` mode without any swap. For larger ones
-(VideoScore2 at 14 GB), measure first; if it fits on the rank-0 GPU
-during validation (training model in eval mode means no
-grads/optimizer updates), keep it hot. If not, `unload` between
-rounds.
-
-## 9) Local verification
-
-Native and library-wrapped metrics: a single-GPU smoke is enough.
-
-```python
-import torch
-from fastvideo.eval import create_evaluator
-
-ev = create_evaluator(metrics=["<group>.<your_metric>"], device="cuda")
-video = torch.randn(1, 49, 3, 256, 256, device="cuda").clamp(0, 1)
-print(ev.evaluate(video=video))
-```
-
-Submodule-wrapped metrics: also do a parity check against the
-upstream once. Clone upstream into a separate venv, run the same
-video through both, and expect an exact match on bit-deterministic
-metrics and ≤1% drift on backbone-heavy ones (driven by
-transformers/torch version differences).
-
-For quick parity in CI: pin a tiny test video, record expected
-scores ± tolerance, and add a calibration test under
-`fastvideo/tests/eval/`.
-
---
-
-## 10) When not to add a metric
-
- **Set-vs-set distribution metrics** (FVD, FID-style) do not fit
-  `BaseMetric.compute(sample)` cleanly; they need a population.
-  Adding them requires a stateful accumulator interface that does
-  not exist yet. Open an issue first.
- **Metrics requiring a single-GPU model larger than available
-  memory.** Eval is not the place for tensor-parallel sharding;
-  metrics are expected to fit on one GPU.
- **Metrics that need `mmcv` with a conflicting CUDA ABI.** Document
-  the affected sub-metrics as unsupported and skip them. Building
-  isolation infrastructure (subprocess engine, per-metric venv) is
-  out of scope.
@@ -1,7 +1,7 @@
 status_definitions:
  kept: "Public field remains on a public adapter surface with the same meaning."
  moved: "Public field remains supported but normalizes into a different nested path."
-  preset_owned: "Public field remains supported only through a model/preset-specific surface."
+  profile_owned: "Public field remains supported only through a model/profile-specific surface."
  compatibility_only: "Legacy public field remains adapter-only during migration and is not part of the canonical typed schema."
  private_only: "Field should only be handled by private adapters and is not a public FastVideo compatibility promise."
  internal_only: "Field is runtime/config plumbing and should not be part of the new public typed inference API."
@@ -29,7 +29,7 @@ surfaces:
      vae_cpu_offload: generator.engine.offload.vae
      pin_cpu_memory: generator.engine.offload.pin_cpu_memory
      enable_torch_compile: generator.engine.compile.enabled
-      torch_compile_kwargs: generator.engine.compile.backend,fullgraph,mode,dynamic,extras
+      torch_compile_kwargs: generator.engine.compile.kwargs
      disable_autocast: generator.engine.disable_autocast
      enable_stage_verification: generator.engine.enable_stage_verification
      prompt_txt: request.inputs.prompt_path
@@ -40,12 +40,12 @@ surfaces:
      init_weights_from_safetensors_2: generator.pipeline.components.transformer_2_weights
      override_pipeline_cls_name: generator.pipeline.components.override_pipeline_cls_name
      boundary_ratio: request.sampling.boundary_ratio
-      ltx2_vae_tiling: generator.pipeline.vae_tiling
-    preset_owned:
-      ltx2_vae_spatial_tile_size_in_pixels: generator.pipeline.preset_overrides.ltx2.vae.spatial_tile_size_in_pixels
-      ltx2_vae_spatial_tile_overlap_in_pixels: generator.pipeline.preset_overrides.ltx2.vae.spatial_tile_overlap_in_pixels
-      ltx2_vae_temporal_tile_size_in_frames: generator.pipeline.preset_overrides.ltx2.vae.temporal_tile_size_in_frames
-      ltx2_vae_temporal_tile_overlap_in_frames: generator.pipeline.preset_overrides.ltx2.vae.temporal_tile_overlap_in_frames
+    profile_owned:
+      ltx2_vae_tiling: generator.pipeline.profile_overrides.ltx2.vae_tiling
+      ltx2_vae_spatial_tile_size_in_pixels: generator.pipeline.profile_overrides.ltx2.vae.spatial_tile_size_in_pixels
+      ltx2_vae_spatial_tile_overlap_in_pixels: generator.pipeline.profile_overrides.ltx2.vae.spatial_tile_overlap_in_pixels
+      ltx2_vae_temporal_tile_size_in_frames: generator.pipeline.profile_overrides.ltx2.vae.temporal_tile_size_in_frames
+      ltx2_vae_temporal_tile_overlap_in_frames: generator.pipeline.profile_overrides.ltx2.vae.temporal_tile_overlap_in_frames
      ltx2_initial_latent_path: request.extensions.ltx2.initial_latent_path
    compatibility_only:
      mode: "Legacy multi-mode FastVideoArgs switch; typed inference config should not expose execution mode."
@@ -69,16 +69,16 @@ surfaces:
  pipeline_config_base:
    moved:
      pipeline_config_path: generator.pipeline.components.pipeline_config_path
-    preset_owned:
-      embedded_cfg_scale: generator.pipeline.preset_overrides.embedded_cfg_scale
-      flow_shift: generator.pipeline.preset_overrides.flow_shift
-      flow_shift_sr: generator.pipeline.preset_overrides.flow_shift_sr
-      is_causal: generator.pipeline.preset_overrides.is_causal
-      vae_tiling: generator.pipeline.preset_overrides.vae_tiling
-      vae_sp: generator.pipeline.preset_overrides.vae_sp
-      dmd_denoising_steps: generator.pipeline.preset_overrides.dmd_denoising_steps
-      ti2v_task: generator.pipeline.preset_overrides.ti2v_task
-      boundary_ratio: generator.pipeline.preset_overrides.boundary_ratio
+    profile_owned:
+      embedded_cfg_scale: generator.pipeline.profile_overrides.embedded_cfg_scale
+      flow_shift: generator.pipeline.profile_overrides.flow_shift
+      flow_shift_sr: generator.pipeline.profile_overrides.flow_shift_sr
+      is_causal: generator.pipeline.profile_overrides.is_causal
+      vae_tiling: generator.pipeline.profile_overrides.vae_tiling
+      vae_sp: generator.pipeline.profile_overrides.vae_sp
+      dmd_denoising_steps: generator.pipeline.profile_overrides.dmd_denoising_steps
+      ti2v_task: generator.pipeline.profile_overrides.ti2v_task
+      boundary_ratio: generator.pipeline.profile_overrides.boundary_ratio
    compatibility_only:
      model_path: "Redundant with generator.model_path."
      disable_autocast: "Duplicated by generator.engine.disable_autocast during migration."
@@ -97,7 +97,7 @@ surfaces:
      postprocess_text_funcs: "Internal text postprocessing hooks."

  pipeline_config_extensions:
-    preset_owned:
+    profile_owned:
      conditioning_strategy:
        sources:
          - fastvideo.configs.pipelines.cosmos.CosmosConfig
@@ -309,8 +309,8 @@ surfaces:
    compatibility_only:
      batch_size: "Gen3C inference-only tuning field pending typed batching design."
      gradient_checkpointing: "Gen3C inference-only compatibility field pending typed batching design."
-      guidance_scale: "Gen3C pipeline-level default pending preset/default-request cleanup."
-      num_inference_steps: "Gen3C pipeline-level default pending preset/default-request cleanup."
+      guidance_scale: "Gen3C pipeline-level default pending profile/default-request cleanup."
+      num_inference_steps: "Gen3C pipeline-level default pending profile/default-request cleanup."
    internal_only:
      audio_decoder_config: "Legacy internal component config object."
      audio_decoder_precision: "Precision override pending dedicated component precision design."
@@ -345,7 +345,6 @@ surfaces:
      num_inference_steps: request.sampling.num_inference_steps
      num_inference_steps_sr: request.sampling.num_inference_steps_sr
      guidance_scale: request.sampling.guidance_scale
-      guidance_scale_2: request.sampling.guidance_scale_2
      guidance_rescale: request.sampling.guidance_rescale
      boundary_ratio: request.sampling.boundary_ratio
      sigmas: request.sampling.sigmas
@@ -354,36 +353,96 @@ surfaces:
      return_frames: request.output.return_frames
      return_trajectory_latents: request.runtime.return_trajectory_latents
      return_trajectory_decoded: request.runtime.return_trajectory_decoded
-      continuation_state: request.state
-      return_continuation_state: request.output.return_state
-    preset_owned:
+    profile_owned:
      t_thresh: request.stage_overrides.refine.t_thresh
      spatial_refine_only: request.stage_overrides.refine.spatial_refine_only
      num_cond_frames: request.stage_overrides.refine.num_cond_frames
      trajectory_type: request.extensions.gen3c.trajectory_type
      movement_distance: request.extensions.gen3c.movement_distance
      camera_rotation: request.extensions.gen3c.camera_rotation
-      prompt_attention_mask: request.extensions.hyworld.prompt_attention_mask
-      negative_attention_mask: request.extensions.hyworld.negative_attention_mask
-      camera_states: request.extensions.hunyuangamecraft.camera_states
-      camera_trajectory: request.extensions.hunyuangamecraft.camera_trajectory
-      action_list: request.extensions.hunyuangamecraft.action_list
-      action_speed_list: request.extensions.hunyuangamecraft.action_speed_list
-      gt_latents: request.extensions.hunyuangamecraft.gt_latents
-      conditioning_mask: request.extensions.hunyuangamecraft.conditioning_mask
-      ltx2_cfg_scale_video: request.extensions.ltx2.cfg_scale_video
-      ltx2_cfg_scale_audio: request.extensions.ltx2.cfg_scale_audio
-      ltx2_modality_scale_video: request.extensions.ltx2.modality_scale_video
-      ltx2_modality_scale_audio: request.extensions.ltx2.modality_scale_audio
-      ltx2_rescale_scale: request.extensions.ltx2.rescale_scale
-      ltx2_stg_scale_video: request.extensions.ltx2.stg_scale_video
-      ltx2_stg_scale_audio: request.extensions.ltx2.stg_scale_audio
-      ltx2_stg_blocks_video: request.extensions.ltx2.stg_blocks_video
-      ltx2_stg_blocks_audio: request.extensions.ltx2.stg_blocks_audio
    internal_only:
      data_type: "Derived from the request shape and not a public input."

-  sampling_param_extensions: {}
+  sampling_param_extensions:
+    moved:
+      guidance_scale_2:
+        target: request.sampling.guidance_scale_2
+        sources:
+          - fastvideo.configs.sample.lingbotworld.LingBotWorld_SamplingParam
+          - fastvideo.configs.sample.lingbotworld.Wan2_2_I2V_A14B_SamplingParam
+          - fastvideo.configs.sample.wan.SelfForcingWan2_2_T2V_A14B_480P_SamplingParam
+          - fastvideo.configs.sample.wan.Wan2_2_I2V_A14B_SamplingParam
+          - fastvideo.configs.sample.wan.Wan2_2_T2V_A14B_SamplingParam
+    profile_owned:
+      action_list:
+        target: request.extensions.hunyuangamecraft.action_list
+        sources:
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraftSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft65FrameSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft129FrameSamplingParam
+      action_speed_list:
+        target: request.extensions.hunyuangamecraft.action_speed_list
+        sources:
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraftSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft65FrameSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft129FrameSamplingParam
+      camera_states:
+        target: request.extensions.hunyuangamecraft.camera_states
+        sources:
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraftSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft65FrameSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft129FrameSamplingParam
+      camera_trajectory:
+        target: request.extensions.hunyuangamecraft.camera_trajectory
+        sources:
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraftSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft65FrameSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft129FrameSamplingParam
+      conditioning_mask:
+        target: request.extensions.hunyuangamecraft.conditioning_mask
+        sources:
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraftSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft65FrameSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft129FrameSamplingParam
+      gt_latents:
+        target: request.extensions.hunyuangamecraft.gt_latents
+        sources:
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraftSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft65FrameSamplingParam
+          - fastvideo.configs.sample.hunyuangamecraft.HunyuanGameCraft129FrameSamplingParam
+      prompt_attention_mask:
+        target: request.extensions.hyworld.prompt_attention_mask
+        sources: [fastvideo.configs.sample.hyworld.HYWorld_SamplingParam]
+      negative_attention_mask:
+        target: request.extensions.hyworld.negative_attention_mask
+        sources: [fastvideo.configs.sample.hyworld.HYWorld_SamplingParam]
+      ltx2_cfg_scale_audio:
+        target: request.extensions.ltx2.cfg_scale_audio
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_cfg_scale_video:
+        target: request.extensions.ltx2.cfg_scale_video
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_modality_scale_audio:
+        target: request.extensions.ltx2.modality_scale_audio
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_modality_scale_video:
+        target: request.extensions.ltx2.modality_scale_video
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_rescale_scale:
+        target: request.extensions.ltx2.rescale_scale
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_stg_blocks_audio:
+        target: request.extensions.ltx2.stg_blocks_audio
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_stg_blocks_video:
+        target: request.extensions.ltx2.stg_blocks_video
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_stg_scale_audio:
+        target: request.extensions.ltx2.stg_scale_audio
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]
+      ltx2_stg_scale_video:
+        target: request.extensions.ltx2.stg_scale_video
+        sources: [fastvideo.configs.sample.ltx2.LTX2BaseSamplingParam]

  openai_image_request:
    kept:
@@ -12,7 +12,7 @@ FastVideo maps a Diffusers-style repo into a pipeline like this:
 - `fastvideo/configs/models/*`: arch configs and `param_names_mapping` for
  weight name translation.
 - `fastvideo/configs/pipelines/*`: pipeline wiring (component classes + names).
- `fastvideo/api/sampling_param.py`: runtime sampling parameters.
+- `fastvideo/configs/sample/*`: default runtime sampling parameters.
 - `fastvideo/pipelines/basic/*`: end-to-end pipelines.
 - `fastvideo/pipelines/stages/*`: reusable pipeline stages.
 - `fastvideo/models/loader/*`: component loaders for Diffusers-style repos.
@@ -26,7 +26,7 @@ Minimal usage (from `examples/inference/basic/basic.py`):

 ```python
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam

 model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # or official_weights/<model_name>/
 generator = VideoGenerator.from_pretrained(model_id, num_gpus=1)
@@ -49,9 +49,8 @@ runtime parameters consistent:
 - `fastvideo/configs/models/`: architecture definitions, layer shapes, and
  `param_names_mapping` rules for key renaming.
 - `fastvideo/configs/pipelines/`: pipeline wiring and required components.
- `fastvideo/api/sampling_param.py`: sampling parameters (steps, frames,
-  guidance scale, resolution, fps). Defaults come from profiles in
-  `fastvideo/pipelines/basic/<family>/profiles.py`.
+- `fastvideo/configs/sample/`: default sampling parameters (steps, frames,
+  guidance scale, resolution, fps).
 - `fastvideo/registry.py`: unified registry for pipeline config + sampling
  defaults and model metadata resolution, defined via explicit
  `register_configs(...)` blocks (no separate dict registries).
@@ -143,7 +142,7 @@ How this maps to FastVideo:
 - `T5TokenizerFast` -> loaded via HF in `fastvideo/models/loader/`
 - `UniPCMultistepScheduler` -> loaded via Diffusers scheduler utilities
 - Pipeline defaults -> `fastvideo/configs/pipelines/wan.py`
- Sampling defaults -> `fastvideo/pipelines/basic/wan/profiles.py`
+- Sampling defaults -> `fastvideo/configs/sample/wan.py`

 ## Pipeline system

@@ -1,177 +0,0 @@
-# Streaming WebSocket Server Contract
-
-The streaming server (`fastvideo/entrypoints/streaming/server.py`) speaks
-a JSON-over-WebSocket protocol with binary fMP4 chunks for media. This
-document is the authoritative spec for the message catalogue and the
-session state machine. Any change to either must update this document
-in the same PR that touches `protocol.py` or `session.py`.
-
-## Endpoint
-
-| Path | Protocol | Purpose |
-|---|---|---|
-| `WS /v1/stream` | WebSocket (JSON + binary) | Per-session realtime streaming |
-| `GET /health` | HTTP | Liveness probe (`status`, `stream_mode`, active `sessions`) |
-
-The server is launched by `fastvideo serve --config <serve.yaml>` when
-the config carries a `streaming:` block. Without that block the same CLI
-launches the OpenAI stateless HTTP server instead.
-
-## Connection lifecycle
-
-Every WebSocket connection holds exactly one `Session`. Sessions move
-through the states in `SessionState` (`fastvideo/entrypoints/streaming/session.py`).
-
-```
-                    ┌──────────────┐
-                    │ INITIALIZING │  ← WebSocket accepted, before init frame
-                    └──────┬───────┘
-                           │ session_init_v2 received
-            ┌──────────────┼──────────────┐
-            ▼              ▼              ▼
-         QUEUED      GPU_BINDING       REJECTED
-            │              │              ↑
-            │ slot ready   │              │ max-sessions hit
-            ▼              ▼              │ or invalid init
-                       ┌────────┐         │
-                       │ ACTIVE │ ────────┘
-                       └────┬───┘
-              segment loop  │
-                            │
-                ┌───────────┼───────────┐
-                ▼           ▼           ▼
-            COMPLETE      ERROR      TIMEOUT
-        (clean leave)  (any failure)  (idle / segment_cap reached)
-```
-
-Terminal states (`COMPLETE`, `ERROR`, `TIMEOUT`, `REJECTED`) are sinks —
-no transitions out. The transition matrix is enforced in
-`session.py::_VALID_TRANSITIONS`; bad transitions raise.
-
-`SessionManager` enforces the per-process budgets pulled from
-`StreamingConfig`:
-
- `session_timeout_seconds` — idle reaper drops sessions that haven't
-  advanced; non-terminal sessions transition to `TIMEOUT`.
- `generation_segment_cap` — a session that hits the cap transitions to
-  `COMPLETE` after the last segment ships.
-
-## Message catalogue
-
-Every JSON frame carries `{"type": <str>, ...}`. Pydantic models in
-`protocol.py` are the source of truth; this table is the human-readable
-view.
-
-### Client → server
-
-| `type` | Required fields | Purpose |
-|---|---|---|
-| `session_init_v2` | — | Opening frame. Carries preset, curated prompts, optional initial image, feature toggles, optional `continuation_state` to resume from a snapshot. |
-| `segment_prompt_source` | `prompt` | Request the next segment using the supplied prompt; optional sampling overrides (`seed`, `num_inference_steps`, `guidance_scale`, `negative_prompt`). |
-| `seed_prompts_updated` | `seed_prompts` | Replace the session's seed-prompt list; takes effect on the next segment. |
-| `enhancement_updated` | `enabled` | Toggle prompt enhancement for subsequent segments. |
-| `auto_extension_updated` | `enabled` | Toggle automatic per-segment prompt extension. |
-| `loop_generation_updated` | `enabled` | Toggle loop-generation mode. |
-| `generation_paused_updated` | `paused` | Pause/resume segment generation; queued requests defer. |
-| `snapshot_state` | — | Request the current `ContinuationState` for export; server replies with `continuation_state_snapshot`. |
-
-The opening frame must be `session_init_v2`. Any other first frame is
-rejected with an `error` (code `invalid_message`) and the WebSocket is
-closed.
-
-### Server → client
-
-| `type` | Carries | When emitted |
-|---|---|---|
-| `queue_status` | `position`, `queue_depth` | After `session_init_v2` accepted, before GPU binding. |
-| `gpu_assigned` | GPU id, model id | Once a generator slot is bound. |
-| `ltx2_stream_start` | session-level metadata | Once the session enters `ACTIVE`. |
-| `ltx2_segment_start` | `segment_idx`, `prompt`, prompt source | When a `segment_prompt_source` request begins generation. |
-| `step_complete` | `segment_idx`, denoise timings | After the segment's denoising loop finishes (before media emission). |
-| `media_init` | `segment_idx`, mime, stream id | First frame of fMP4 output for the segment. |
-| binary frame | fMP4 fragment bytes | Subsequent media chunks; the protocol enforces that `media_init` precedes any binary frames. |
-| `media_segment_complete` | `segment_idx`, chunk count, byte count | Last media chunk for the segment. |
-| `ltx2_segment_complete` | `segment_idx`, segment summary | Segment fully shipped; ready for the next `segment_prompt_source`. |
-| `ltx2_stream_complete` | session summary | Session reached `generation_segment_cap` or client requested clean shutdown. |
-| `session_timeout` | reason | Session hit `session_timeout_seconds`; immediately followed by close. |
-| `continuation_state_snapshot` | `kind`, `payload` | Reply to `snapshot_state`. The payload is the same shape produced by `LTX2ContinuationState.to_continuation_state(...)`. |
-| `error` | `code`, `message` | Any validation/runtime error. Non-fatal errors keep the connection open; fatal errors precede a `close`. |
-
-## Continuation state
-
-The session optionally accepts a `continuation_state` dict inside the
-opening `session_init_v2` frame. When present, the server hydrates it
-into a `ContinuationState(kind, payload)` envelope and feeds it as the
-`request.state` on the first segment's `GenerationRequest` — letting a
-client resume after a disconnect, migrate sessions across processes,
-or replay a prior session.
-
-After every segment, if the runtime returns a fresh state, the server
-persists it to the `SessionStore` so a `snapshot_state` request can
-export it. The store and serialization contracts live with the model
-family (e.g. `fastvideo/pipelines/basic/ltx2/continuation.py` for LTX-2).
-
-## Example flow
-
-```
-client                                                    server
-──────                                                    ──────
-WS /v1/stream  ─────── connect ─────────────────────────►
-               ◄────── (accept)
-
-{"type": "session_init_v2",
- "preset": "ltx2_two_stage",
- "curated_prompts": ["a fox in snow", "the fox jumps"],
- "initial_image": {...},
- "stream_mode": "av_fmp4"} ─────────────────────────────►
-
-                                                          (validate, queue, bind)
-               ◄──── {"type": "queue_status",
-                       "position": 0, "queue_depth": 0}
-               ◄──── {"type": "gpu_assigned",
-                       "gpu_id": 0, "model_id": "..."}
-               ◄──── {"type": "ltx2_stream_start", ...}
-
-{"type": "segment_prompt_source",
- "prompt": "a fox in snow",
- "source": "curated"} ───────────────────────────────────►
-                                                          (run pipeline)
-               ◄──── {"type": "ltx2_segment_start",
-                       "segment_idx": 1, ...}
-               ◄──── {"type": "step_complete",
-                       "segment_idx": 1, "timings": {...}}
-               ◄──── {"type": "media_init",
-                       "segment_idx": 1,
-                       "mime": "video/mp4", ...}
-               ◄──── <binary fMP4 init segment>
-               ◄──── <binary fMP4 fragment>
-               ◄──── <binary fMP4 fragment>
-               ◄──── {"type": "media_segment_complete",
-                       "segment_idx": 1, "chunks": 12}
-               ◄──── {"type": "ltx2_segment_complete",
-                       "segment_idx": 1, ...}
-
-{"type": "segment_prompt_source",
- "prompt": "the fox jumps"} ─────────────────────────────►
-                                                          (segment 2 …)
-
-{"type": "snapshot_state"} ──────────────────────────────►
-               ◄──── {"type": "continuation_state_snapshot",
-                       "kind": "ltx2.v1",
-                       "payload": {"schema_version": 1, ...}}
-
-(close) ──────────────────────────────────────────────────►
-                                                          (session → COMPLETE)
-```
-
-## Backward / forward compatibility
-
- Adding a new client message: append a Pydantic model to `protocol.py`
-  with a unique `type`; add the discriminator entry to `ClientMessage`;
-  add a row to the table above. Old clients that don't send the new
-  message remain compatible.
- Adding a new server message: emit only when a new feature flag is
-  enabled (or always emit, since clients ignore unknown types).
- Changing an existing message: bump the `type` (e.g. `session_init_v2`
-  → `session_init_v3`) and accept both for one release cycle. Never
-  silently change field semantics under the same `type`.
@@ -86,25 +86,3 @@ sbatch examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/distill_dmd_t2v_5B.sh
 - Learning rate: 2e-5
 - Training steps: 3000 (~12 hours)
 - HSDP shard dim: 1
-
-## 🧭 Note on `real_score_guidance_scale`
-
-The teacher CFG used inside the DMD loss follows the DMD2 reference
-implementation and uses the parameterization
-
-```
-x = x_cond + w * (x_cond - x_uncond)
-```
-
-rather than the Ho & Salimans form `x_uncond + w * (x_cond - x_uncond)`. The
-two are mathematically equivalent up to a constant offset:
-
-| `real_score_guidance_scale` (`w`) | Equivalent standard CFG (`w + 1`) | Output                |
-|-----------------------------------|-----------------------------------|-----------------------|
-| `-1`                              | `0`                               | unconditional         |
-| `0`                               | `1`                               | conditional           |
-| `3.5` (default)                   | `4.5`                             | strong guidance       |
-
-So `real_score_guidance_scale` should be read as the **extra** guidance
-strength added on top of the conditional prediction. When porting values
-from a paper that uses the Ho & Salimans form, subtract 1.
@@ -33,7 +33,7 @@ The following two classes `PipelineConfig` and `SamplingParam` are used to confi

 ### SamplingParam

-::: fastvideo.api.sampling_param.SamplingParam
+::: fastvideo.configs.sample.base.SamplingParam
    options:
      show_root_heading: true
      show_source: false
@@ -128,14 +128,19 @@ Concrete hierarchy: `DiTConfig` → `DiTArchConfig`, `VAEConfig` →
 - `dump_to_json()` / `load_from_json()` — JSON persistence. Callable
  fields and `arch_config` are excluded from dumps.

-### SamplingParam (`fastvideo/api/sampling_param.py`)
+### SamplingParam (`fastvideo/configs/sample/`)

 Generation parameters separate from pipeline config. Each model family
-provides defaults via a profile (see `fastvideo/pipelines/basic/<family>/profiles.py`):
+provides defaults:

 ```python
-sp = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
-# sp.height == 480, sp.width == 832, sp.num_frames == 81, etc.
+@dataclass
+class WanT2V_1_3B_SamplingParam(SamplingParam):
+    height: int = 480
+    width: int = 832
+    num_frames: int = 81
+    guidance_scale: float = 3.0
+    num_inference_steps: int = 50
 ```

 ## Component Loading
@@ -425,9 +430,9 @@ User: generator.generate_video(prompt, ...)
   `fastvideo/configs/pipelines/<model>.py`. Set DiT/VAE/encoder configs,
   flow_shift, precision defaults.

-2. **Sampling param profile** — Create a profile in
-   `fastvideo/pipelines/basic/<model>/profiles.py` with default height,
-   width, num_frames, guidance_scale, num_inference_steps.
+2. **Sampling param** — Create a `SamplingParam` subclass in
+   `fastvideo/configs/sample/<model>.py`. Set default height, width,
+   num_frames, guidance_scale, num_inference_steps.

 3. **Register configs** — In `fastvideo/registry.py`, add a
   `register_configs()` call inside `_register_configs()` with
@@ -89,7 +89,7 @@ GEN3C defaults in FastVideo:

 These values are defined in:

- `fastvideo/pipelines/basic/gen3c/profiles.py`
+- `fastvideo/configs/sample/gen3c.py`
 - `fastvideo/configs/pipelines/gen3c.py`

 and align with the official GEN3C inference defaults in:
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator

-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples"
 def main():
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam


 def main():
@@ -1,5 +1,5 @@
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam


 def main():
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam


 def main():
@@ -2,7 +2,7 @@ import os
 import time
 from fastvideo import VideoGenerator

-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_dmd2"
 def main():
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 import json
-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_hy15"
 def main():
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 import json
-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_hy15_1080p"
 def main():
@@ -1,7 +1,7 @@
 from fastvideo import VideoGenerator
 from fastvideo.models.dits.lingbotworld.cam_utils import prepare_camera_embedding

-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam
 OUTPUT_PATH = "video_samples_lingbotworld"
 def main():
    # FastVideo will automatically use the optimal default arguments for the
@@ -1,5 +1,5 @@
 from fastvideo import VideoGenerator, PipelineConfig
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam

 def main():
    config = PipelineConfig.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator

-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples"
 def main():
@@ -2,7 +2,7 @@

 from fastvideo import VideoGenerator, SamplingParam
 import json
-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_self_forcing_causal_wan2_2_14B_i2v"
 def main():
@@ -2,7 +2,7 @@

 from fastvideo import VideoGenerator

-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_self_forcing_causal_wan2_2_14B_t2v"
 def main():
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator

-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_wan2_2_14B_t2v"
 def main():
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator

-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_wan2_1_Fun"
 OUTPUT_NAME = "wan2.1_test"
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator

-# from fastvideo.api.sampling_param import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples_wan2_2_14B_i2v"
 def main():
@@ -1,100 +0,0 @@
-"""Generate one LTX2 video and score it with VBench metrics.
-
-The generation block is the same as
-``examples/inference/basic/basic_ltx2.py`` — same prompt, same model,
-same shape, same num_frames. After ``shutdown()`` the script loads the
-mp4 back, builds a single :class:`fastvideo.eval.Evaluator`, and runs
-the prompt-aware VBench subset that's meaningful for an arbitrary
-text→video sample.
-
-The first run downloads CLIP / DINO / RAFT / AMT / ViCLIP / MUSIQ
-weights to ``~/.cache/fastvideo/eval/`` (~few GB total).
-
-GPU memory caveat
-----------------
-Scoring 1088×1920×121 with all 8 metrics needs a dedicated GPU (~80 GB).
-On a shared GPU, ``vbench.motion_smoothness`` (AMT correlation volume)
-will OOM — its memory autoscale reads ``total_memory`` rather than
-``mem_get_info()`` free memory and therefore underestimates the
-required scale-down. Drop ``motion_smoothness`` from ``METRICS`` if
-sharing, or run on a smaller-resolution generation.
-"""
-import torch
-
-from fastvideo import VideoGenerator
-from fastvideo.eval import Evaluator
-from fastvideo.eval.io import build_eval_kwargs
-
-PROMPT = (
-    "A warm sunny backyard. The camera starts in a tight cinematic close-up "
-    "of a woman and a man in their 30s, facing each other with serious "
-    "expressions. The woman, emotional and dramatic, says softly, \"That's "
-    "it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
-    "annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
-    "then mutters defensively, \"He's just having fun.\" The camera slowly "
-    "pans right, revealing the grandfather in the garden wearing enormous "
-    "butterfly wings, waving his arms in the air like he's trying to take "
-    "off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
-    "The woman covers her face, on the verge of tears. The tone is deadpan, "
-    "absurd, and quietly tragic."
-)
-
-# VBench sub-metrics meaningful for an arbitrary text→video sample
-# (just the generated frames, optionally fps + the source prompt).
-# Structured-prompt metrics (vbench.color, vbench.multiple_objects,
-# vbench.scene, ...) are excluded — they need prompts built to a
-# specific schema.
-METRICS = [
-    "vbench.aesthetic_quality",       # CLIP + LAION aesthetic head
-    "vbench.subject_consistency",     # DINO frame-to-first cosine
-    "vbench.background_consistency",  # DINO on background patches
-    "vbench.imaging_quality",         # pyiqa MUSIQ
-    "vbench.temporal_flickering",     # pixel-wise frame deltas
-    "vbench.motion_smoothness",       # AMT frame interpolator residual
-    "vbench.dynamic_degree",          # RAFT optical-flow magnitude (needs fps)
-    "vbench.overall_consistency",     # ViCLIP video↔prompt similarity
-]
-
-
-def main() -> None:
-    # ----- generation (matches examples/inference/basic/basic_ltx2.py) -----
-    generator = VideoGenerator.from_pretrained(
-        "Davids048/LTX2-Base-Diffusers",
-        num_gpus=1,
-    )
-
-    output_path = "outputs_video/ltx2_basic/output_ltx2_base_t2v_1088_1920_1.1.mp4"
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path=output_path,
-        save_video=True,
-        num_frames=121,
-        height=1088,
-        width=1920,
-    )
-    generator.shutdown()
-    # Free residual CUDA memory the generator left behind so the
-    # evaluator can grab the largest possible workspace for AMT/RAFT.
-    torch.cuda.empty_cache()
-
-    # ----- scoring -----
-    print(f"\n[eval] building evaluator: {METRICS}")
-    evaluator = Evaluator(metrics=METRICS)
-
-    # LTX2 outputs at 24 fps by default.
-    sample = build_eval_kwargs({"prompt": PROMPT}, output_path, fps=24.0)
-    print(f"[eval] running ({sample['video'].shape[1]} frames @ 24 fps)...")
-    results = evaluator.evaluate(**sample)
-
-    print("\n=== VBench scores ===")
-    for name in METRICS:
-        r = results[name]
-        if r.score is None:
-            reason = r.details.get("skipped", "no score")
-            print(f"  {name}: SKIPPED ({reason})")
-        else:
-            print(f"  {name}: {r.score:.4f}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,157 +0,0 @@
-"""End-to-end Physics-IQ: dataset → generate → score → aggregate.
-
-Generates one video per take-1 scenario with LTX2 (using the scenario
-caption as the prompt), scores each generated video against the take-1
-reference and the take-2 "physical-variance" reference, and prints
-aggregate scores using :meth:`PhysicsIQMetric.aggregate_components` —
-the official scoring recipe from the upstream benchmark.
-
-Reference videos / masks / switch-frames auto-fetch on first miss into
-``${FASTVIDEO_EVAL_CACHE}/datasets/physics_iq/``; pass ``--dataset-root``
-to point at a pre-downloaded mirror instead.
-
-Quick smoke run on 4 scenarios across 2 GPUs::
-
-    python examples/inference/eval/bench_physics_iq.py \\
-        --limit 4 --num-gpus 2 \\
-        --videos-dir outputs_video/physics_iq_smoke
-
-Re-score existing generations without regenerating::
-
-    python examples/inference/eval/bench_physics_iq.py \\
-        --videos-dir outputs_video/physics_iq_smoke \\
-        --skip-generation
-"""
-from __future__ import annotations
-
-import argparse
-import json
-from pathlib import Path
-
-from fastvideo.eval import create_evaluator, get_metric
-from fastvideo.eval.datasets import get_dataset
-
-
-def _expected_filename(row: dict) -> str:
-    """Filename Physics-IQ expects for the generated video for *row*.
-
-    Uses the dataset's own ``expected_gen_filename`` annotation so the
-    output filenames match the benchmark's manifest convention.
-    """
-    return row["auxiliary_info"]["expected_gen_filename"]
-
-
-def _generate_videos(rows: list[dict], videos_dir: Path,
-                     model: str, num_gpus: int,
-                     num_frames: int, height: int, width: int) -> None:
-    from fastvideo import VideoGenerator
-
-    videos_dir.mkdir(parents=True, exist_ok=True)
-    todo = [(row, videos_dir / _expected_filename(row)) for row in rows]
-    todo = [(row, out) for (row, out) in todo if not out.is_file()]
-    if not todo:
-        print(f"[gen] all {len(rows)} videos already present; skipping.")
-        return
-
-    print(f"[gen] {len(todo)}/{len(rows)} scenarios to render with {model} "
-          f"({num_frames}x{height}x{width})...")
-    gen = VideoGenerator.from_pretrained(model, num_gpus=num_gpus)
-    try:
-        for row, out_path in todo:
-            gen.generate_video(
-                prompt=row["prompt"], output_path=str(out_path), save_video=True,
-                num_frames=num_frames, height=height, width=width,
-            )
-    finally:
-        gen.shutdown()
-
-
-def main() -> None:
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--dataset-root", type=Path, default=None,
-                   help="Path to a pre-downloaded Physics-IQ release. "
-                        "Defaults to ${FASTVIDEO_EVAL_CACHE}/datasets/physics_iq, "
-                        "auto-fetching missing assets from the public bucket.")
-    p.add_argument("--videos-dir", type=Path,
-                   default=Path("outputs_video/bench_physics_iq"),
-                   help="Where to read/write generated videos.")
-    p.add_argument("--limit", type=int, default=None,
-                   help="Truncate to first N scenarios for smoke runs.")
-    p.add_argument("--num-gpus", type=int, default=1)
-    p.add_argument("--model", default="Davids048/LTX2-Base-Diffusers",
-                   help="HF repo id of the text→video generator to use.")
-    p.add_argument("--num-frames", type=int, default=121)
-    p.add_argument("--height", type=int, default=1088)
-    p.add_argument("--width", type=int, default=1920)
-    p.add_argument("--skip-generation", action="store_true",
-                   help="Re-score existing videos under --videos-dir.")
-    p.add_argument("--scores-out", type=Path, default=None,
-                   help="Where to write per-scenario scores (JSON). "
-                        "Defaults to <videos-dir>/scores.json.")
-    args = p.parse_args()
-
-    # 1. Walk the Physics-IQ corpus. Pass --limit to the dataset
-    # constructor so auto-download only fetches the assets we'll use.
-    ds = get_dataset("physics_iq", dataset_root=args.dataset_root, limit=args.limit)
-    rows = list(ds)
-    print(f"[load] Physics-IQ: {len(rows)} scenarios from {ds.dataset_dir}")
-
-    # 2. Generate (or reuse) one mp4 per scenario.
-    if not args.skip_generation:
-        _generate_videos(
-            rows, args.videos_dir, args.model, args.num_gpus,
-            args.num_frames, args.height, args.width,
-        )
-
-    # 3. Score each scenario. The metric reads file paths directly out
-    #    of the row dict (reference, reference_take2, masks), so we
-    #    just attach the generated video path and forward.
-    evaluator = create_evaluator(metrics=["physics_iq"], num_gpus=args.num_gpus)
-
-    samples: list[dict] = []
-    matched: list[dict] = []
-    for row in rows:
-        video_path = args.videos_dir / _expected_filename(row)
-        if not video_path.is_file():
-            print(f"[eval] missing {video_path}; skipping.")
-            continue
-        # The physics_iq metric accepts file paths via its polymorphic
-        # input handling — no need to load the tensors here.
-        samples.append({"video": str(video_path), **row})
-        matched.append(row)
-
-    all_results = evaluator.evaluate(samples=samples)
-    evaluator.shutdown()
-
-    # 4. Aggregate per the upstream scoring recipe.
-    metric = get_metric("physics_iq")
-    components = metric.aggregate_components(
-        [r["physics_iq"] for r in all_results]
-    )
-
-    print()
-    print("=== Physics-IQ aggregate ===")
-    for name, value in components.items():
-        print(f"  {name:24s}  {value:.4f}")
-
-    detailed = [
-        {
-            "scenario": row["auxiliary_info"]["scenario_id"],
-            "view": row["view"],
-            "scenario_name": row["auxiliary_info"]["scenario_name"],
-            "score": results["physics_iq"].score,
-        }
-        for row, results in zip(matched, all_results)
-    ]
-    out = args.scores_out or (args.videos_dir / "scores.json")
-    out.parent.mkdir(parents=True, exist_ok=True)
-    out.write_text(json.dumps(
-        {"aggregate": components, "per_scenario": detailed},
-        indent=2,
-    ))
-    print(f"\n[done] per-scenario scores → {out}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,155 +0,0 @@
-"""End-to-end VBench: dataset → generate → score → aggregate.
-
-Iterates the VBench prompt corpus, generates one video per prompt with
-LTX2, scores each generated video against the requested ``vbench.*``
-sub-metrics, and prints per-metric averages over the run.
-
-Re-running with ``--skip-generation`` reuses any mp4 already on disk
-under ``--videos-dir``, so you can iterate on metric selection without
-re-paying the generation cost.
-
-Example — quick smoke run on 4 prompts from the ``aesthetic_quality``
-dimension across 2 GPUs::
-
-    python examples/inference/eval/bench_vbench.py \\
-        --dimensions aesthetic_quality \\
-        --limit 4 --num-gpus 2 \\
-        --videos-dir outputs_video/vbench_smoke
-
-Full benchmark on a single dimension::
-
-    python examples/inference/eval/bench_vbench.py \\
-        --dimensions subject_consistency --num-gpus 8
-"""
-from __future__ import annotations
-
-import argparse
-import json
-import re
-from collections import defaultdict
-from pathlib import Path
-
-from fastvideo.eval import create_evaluator
-from fastvideo.eval.datasets import get_dataset
-
-
-def _slugify(prompt: str, max_len: int = 100) -> str:
-    """Filesystem-safe filename stem; mirrors VBench's official convention."""
-    s = re.sub(r'[\\/:*?"<>|]', "", prompt[:max_len]).strip().strip(".")
-    return re.sub(r"\s+", " ", s) or "output"
-
-
-def _generate_videos(prompts: list[str], videos_dir: Path,
-                     model: str, num_gpus: int,
-                     num_frames: int, height: int, width: int) -> None:
-    from fastvideo import VideoGenerator
-
-    videos_dir.mkdir(parents=True, exist_ok=True)
-    todo = [(p, videos_dir / f"{_slugify(p)}.mp4") for p in prompts]
-    todo = [(p, out) for (p, out) in todo if not out.is_file()]
-    if not todo:
-        print(f"[gen] all {len(prompts)} videos already present; skipping.")
-        return
-
-    print(f"[gen] {len(todo)}/{len(prompts)} prompts to render with {model} "
-          f"({num_frames}x{height}x{width})...")
-    gen = VideoGenerator.from_pretrained(model, num_gpus=num_gpus)
-    try:
-        for prompt, out_path in todo:
-            gen.generate_video(
-                prompt=prompt, output_path=str(out_path), save_video=True,
-                num_frames=num_frames, height=height, width=width,
-            )
-    finally:
-        gen.shutdown()
-
-
-def main() -> None:
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--dimensions", default="aesthetic_quality,subject_consistency",
-                   help="Comma-separated VBench dimensions (or 'all').")
-    p.add_argument("--limit", type=int, default=None,
-                   help="Truncate to first N prompts for smoke runs.")
-    p.add_argument("--videos-dir", type=Path,
-                   default=Path("outputs_video/bench_vbench"))
-    p.add_argument("--num-gpus", type=int, default=1)
-    p.add_argument("--model", default="Davids048/LTX2-Base-Diffusers",
-                   help="HF repo id of the text→video generator to use.")
-    p.add_argument("--num-frames", type=int, default=121)
-    p.add_argument("--height", type=int, default=1088)
-    p.add_argument("--width", type=int, default=1920)
-    p.add_argument("--fps", type=float, default=24.0,
-                   help="Frame-rate annotation passed to fps-aware metrics.")
-    p.add_argument("--skip-generation", action="store_true",
-                   help="Re-score existing videos under --videos-dir without "
-                        "regenerating.")
-    p.add_argument("--scores-out", type=Path, default=None,
-                   help="Where to dump per-prompt scores as JSON. "
-                        "Defaults to <videos-dir>/scores.json.")
-    args = p.parse_args()
-
-    # 1. Pull prompts from VBench.
-    dims_arg: list[str] | str = (
-        args.dimensions if args.dimensions == "all"
-        else [d.strip() for d in args.dimensions.split(",") if d.strip()]
-    )
-    ds = get_dataset("vbench", dimensions=dims_arg)
-    rows = list(ds)[: args.limit]
-    print(f"[load] VBench: {len(rows)} prompts across {ds.dimensions}")
-
-    # 2. Generate (or reuse) one mp4 per prompt.
-    if not args.skip_generation:
-        _generate_videos(
-            [row["prompt"] for row in rows],
-            args.videos_dir, args.model, args.num_gpus,
-            args.num_frames, args.height, args.width,
-        )
-
-    # 3. Score each video against the requested vbench sub-metrics.
-    metric_names = sorted(set(f"vbench.{d}" for d in ds.dimensions))
-    print(f"[eval] metrics: {metric_names}")
-    evaluator = create_evaluator(metrics=metric_names, num_gpus=args.num_gpus)
-
-    samples: list[dict] = []
-    matched_rows: list[dict] = []
-    for row in rows:
-        video_path = args.videos_dir / f"{_slugify(row['prompt'])}.mp4"
-        if not video_path.is_file():
-            print(f"[eval] missing {video_path}; skipping this row.")
-            continue
-        # Pass the path; the worker decodes lazily so memory stays bounded.
-        samples.append({
-            "video": str(video_path),
-            "fps": args.fps,
-            **row,                                       # prompt / aux / dims
-        })
-        matched_rows.append(row)
-
-    all_results = evaluator.evaluate(samples=samples)
-    evaluator.shutdown()
-
-    # 4. Aggregate per-metric.
-    by_metric: dict[str, list[float]] = defaultdict(list)
-    detailed: list[dict] = []
-    for row, results in zip(matched_rows, all_results):
-        scores = {name: r.score for name, r in results.items()}
-        detailed.append({"prompt": row["prompt"], "scores": scores})
-        for name, score in scores.items():
-            if score is not None:
-                by_metric[name].append(score)
-
-    print()
-    print("=== per-metric averages ===")
-    for name in sorted(by_metric):
-        avg = sum(by_metric[name]) / len(by_metric[name])
-        print(f"  {name:42s}  {avg:.4f}   (n={len(by_metric[name])})")
-
-    out = args.scores_out or (args.videos_dir / "scores.json")
-    out.parent.mkdir(parents=True, exist_ok=True)
-    out.write_text(json.dumps(detailed, indent=2))
-    print(f"\n[done] per-prompt scores → {out}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,167 +0,0 @@
-"""End-to-end: generate a video with LTX2 and score it with VBench.
-
-Pipeline:
-    prompt → LTX2-Base → mp4 → fastvideo.eval → vbench scores
-
-Run::
-
-    pip install -e .[eval]
-    git submodule update --init fastvideo/third_party/eval/vbench
-
-    python examples/inference/eval/eval_ltx2_vbench.py
-    # or with 4 GPUs and the distilled checkpoint:
-    python examples/inference/eval/eval_ltx2_vbench.py \
-        --model FastVideo/LTX2-Distilled-Diffusers --num-gpus 4
-
-The default metric set covers the vbench sub-metrics that are
-meaningful for an arbitrary text→video sample — i.e. those that need
-only the generated video (and optionally fps + the source prompt).
-Structured-prompt metrics like ``vbench.color``, ``vbench.scene``,
-``vbench.multiple_objects`` etc. are *not* on by default — they only
-make sense when the prompt is built to a specific schema, and they
-require GRiT/detectron2 setup. Pass them via ``--metrics`` if you have
-a matching prompt.
-
-First-time runs download CLIP, DINO, RAFT, AMT, ViCLIP, and MUSIQ
-weights to ``~/.cache/fastvideo/eval/models/`` and
-``~/.cache/torch/hub/`` (~few GB total). Subsequent runs are fast.
-"""
-from __future__ import annotations
-
-import argparse
-import json
-from pathlib import Path
-
-from fastvideo import VideoGenerator
-from fastvideo.eval import create_evaluator
-from fastvideo.eval.io import load_video
-
-
-PROMPT = (
-    "A warm sunny backyard. The camera starts in a tight cinematic close-up "
-    "of a woman and a man in their 30s, facing each other with serious "
-    "expressions. The woman, emotional and dramatic, says softly, \"That's "
-    "it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
-    "annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
-    "then mutters defensively, \"He's just having fun.\" The camera slowly "
-    "pans right, revealing the grandfather in the garden wearing enormous "
-    "butterfly wings, waving his arms in the air like he's trying to take "
-    "off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
-    "The woman covers her face, on the verge of tears. The tone is deadpan, "
-    "absurd, and quietly tragic."
-)
-
-DEFAULT_METRICS = [
-    # No-input metrics: just need the generated frames.
-    "vbench.aesthetic_quality",       # CLIP + LAION aesthetic head
-    "vbench.subject_consistency",     # DINO frame-to-first cosine
-    "vbench.background_consistency",  # DINO on background patches
-    "vbench.imaging_quality",         # pyiqa MUSIQ
-    "vbench.temporal_flickering",     # pixel-wise frame deltas
-    "vbench.motion_smoothness",       # AMT frame interpolator residual
-    # Need fps annotation:
-    "vbench.dynamic_degree",          # RAFT optical-flow magnitude
-    # Need the source prompt:
-    "vbench.overall_consistency",     # ViCLIP video↔prompt similarity
-]
-
-
-def parse_args() -> argparse.Namespace:
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--model", default="Davids048/LTX2-Base-Diffusers",
-                   help="HF repo id of the LTX2 checkpoint.")
-    p.add_argument("--num-gpus", type=int, default=1)
-    p.add_argument("--output", default="outputs_video/ltx2_eval/clip.mp4",
-                   help="Where to save the generated mp4.")
-    p.add_argument("--num-frames", type=int, default=121)
-    p.add_argument("--height", type=int, default=1088)
-    p.add_argument("--width", type=int, default=1920)
-    p.add_argument("--prompt", default=PROMPT)
-    p.add_argument("--fps", type=float, default=24.0,
-                   help="Frame-rate annotation passed to fps-aware metrics "
-                        "(e.g. vbench.dynamic_degree). LTX2 outputs at 24 fps "
-                        "by default.")
-    p.add_argument("--metrics", default=",".join(DEFAULT_METRICS),
-                   help="Comma-separated metric names. Pass 'all' for every "
-                        "registered metric, or e.g. 'vbench' for the whole group.")
-    p.add_argument("--scores-out", default="outputs_video/ltx2_eval/scores.json")
-    p.add_argument("--skip-generation", action="store_true",
-                   help="Reuse an existing --output video instead of regenerating.")
-    return p.parse_args()
-
-
-def generate(args: argparse.Namespace) -> Path:
-    out = Path(args.output)
-    if args.skip_generation and out.is_file():
-        print(f"[gen] reusing existing video at {out}")
-        return out
-    out.parent.mkdir(parents=True, exist_ok=True)
-
-    print(f"[gen] loading {args.model} ({args.num_gpus} GPU)...")
-    generator = VideoGenerator.from_pretrained(args.model, num_gpus=args.num_gpus)
-    try:
-        print(f"[gen] generating to {out}...")
-        generator.generate_video(
-            prompt=args.prompt,
-            output_path=str(out),
-            save_video=True,
-            num_frames=args.num_frames,
-            height=args.height,
-            width=args.width,
-        )
-    finally:
-        generator.shutdown()
-    return out
-
-
-def evaluate_video(video_path: Path, prompt: str, fps: float,
-                   metric_names) -> dict:
-    print(f"[eval] loading video from {video_path}...")
-    video = load_video(str(video_path))                  # (T, C, H, W) in [0, 1]
-    video = video.unsqueeze(0)                            # → (1, T, C, H, W)
-
-    print(f"[eval] building evaluator: {metric_names}")
-    evaluator = create_evaluator(metrics=metric_names, device="cuda")
-
-    print(f"[eval] running ({video.shape[1]} frames @ {fps} fps)...")
-    results = evaluator.evaluate(
-        video=video,
-        text_prompt=[prompt],
-        fps=fps,
-    )
-
-    if isinstance(results, list):
-        results = results[0]                              # batch of 1
-
-    return {
-        name: {"score": r.score, "details": r.details}
-        for name, r in results.items()
-    }
-
-
-def main() -> None:
-    args = parse_args()
-    if args.metrics.strip() == "all":
-        metric_names = "all"
-    else:
-        metric_names = [m.strip() for m in args.metrics.split(",") if m.strip()]
-
-    video_path = generate(args)
-    scores = evaluate_video(video_path, args.prompt, args.fps, metric_names)
-
-    print("\n=== VBench scores ===")
-    for name, payload in scores.items():
-        print(f"  {name}: {payload['score']}")
-
-    out = Path(args.scores_out)
-    out.parent.mkdir(parents=True, exist_ok=True)
-    out.write_text(json.dumps(
-        {"video": str(video_path), "prompt": args.prompt, "scores": scores},
-        indent=2,
-    ))
-    print(f"[done] scores written to {out}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,95 +0,0 @@
-"""Score a folder of videos in parallel across multiple GPUs.
-
-Uses :meth:`Evaluator.evaluate(samples=[...])`, which round-robins each
-sample dict across the GPU replicas the evaluator was built with.
-
-Example::
-
-    python examples/inference/eval/score_folder.py \\
-        --videos generated/ \\
-        --metrics vbench.aesthetic_quality,vbench.subject_consistency \\
-        --num-gpus 4 \\
-        --output scores.json
-
-Pair each generated video with a same-name reference video (e.g.
-``ref/<stem>.mp4``) by passing ``--reference-dir``::
-
-    python examples/inference/eval/score_folder.py \\
-        --videos generated/ --reference-dir ref/ \\
-        --metrics common.psnr,common.ssim,common.lpips \\
-        --num-gpus 4
-"""
-from __future__ import annotations
-
-import argparse
-import json
-from pathlib import Path
-
-from fastvideo.eval import create_evaluator
-
-
-def _list_videos(directory: Path) -> list[Path]:
-    exts = {".mp4", ".avi", ".mov", ".mkv", ".gif"}
-    return sorted(p for p in directory.iterdir() if p.suffix.lower() in exts)
-
-
-def main() -> None:
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--videos", type=Path, required=True,
-                   help="Directory of generated videos.")
-    p.add_argument("--reference-dir", type=Path, default=None,
-                   help="Directory of reference videos with matching stems.")
-    p.add_argument("--metrics", default="vbench.aesthetic_quality")
-    p.add_argument("--num-gpus", type=int, default=1)
-    p.add_argument("--fps", type=float, default=None,
-                   help="Frame-rate annotation for fps-aware metrics.")
-    p.add_argument("--output", type=Path, default=Path("scores.json"))
-    args = p.parse_args()
-
-    video_paths = _list_videos(args.videos)
-    if not video_paths:
-        raise SystemExit(f"No videos under {args.videos}")
-    print(f"Found {len(video_paths)} videos in {args.videos}")
-
-    metrics: list[str] | str = (
-        args.metrics if args.metrics == "all"
-        else [m.strip() for m in args.metrics.split(",") if m.strip()]
-    )
-    evaluator = create_evaluator(metrics=metrics, num_gpus=args.num_gpus)
-
-    # Build per-video sample dicts holding *paths*, not pre-loaded
-    # tensors. Each path is decoded inside the worker thread that picks
-    # up its sample, so peak resident memory is bounded by num_gpus
-    # rather than scaling with the size of the folder.
-    samples: list[dict] = []
-    for vp in video_paths:
-        sample: dict = {"video": str(vp)}
-        if args.reference_dir is not None:
-            ref_path = args.reference_dir / vp.name
-            if not ref_path.is_file():
-                raise FileNotFoundError(f"Missing reference for {vp.name} at {ref_path}")
-            sample["reference"] = str(ref_path)
-        if args.fps is not None:
-            sample["fps"] = args.fps
-        samples.append(sample)
-
-    print(f"Scoring with {len(evaluator.metric_names)} metric(s) "
-          f"on {evaluator.num_gpus} GPU(s)...")
-    all_results = evaluator.evaluate(samples=samples)
-    evaluator.shutdown()
-
-    payload = [
-        {
-            "video": str(vp),
-            "scores": {name: r.score for name, r in results.items()},
-        }
-        for vp, results in zip(video_paths, all_results)
-    ]
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    args.output.write_text(json.dumps(payload, indent=2))
-    print(f"Wrote {args.output}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,68 +0,0 @@
-"""Score one video on one GPU.
-
-Smallest possible use of ``fastvideo.eval``: load an mp4, build an
-:class:`Evaluator` for the requested metric set, run it.
-
-Examples::
-
-    # Reference-free (just the generated video):
-    python examples/inference/eval/score_video.py \\
-        --video clip.mp4 \\
-        --metrics vbench.aesthetic_quality,vbench.imaging_quality
-
-    # Reference-paired (compare against ground truth):
-    python examples/inference/eval/score_video.py \\
-        --video gen.mp4 --reference ref.mp4 \\
-        --metrics common.psnr,common.ssim,common.lpips
-"""
-from __future__ import annotations
-
-import argparse
-import json
-
-from fastvideo.eval import create_evaluator
-from fastvideo.eval.io import load_video
-
-
-def main() -> None:
-    p = argparse.ArgumentParser(description=__doc__,
-                                formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--video", required=True, help="Path to the generated mp4.")
-    p.add_argument("--reference", default=None,
-                   help="Optional path to a reference mp4 (for paired metrics).")
-    p.add_argument("--metrics", default="common.psnr,common.ssim",
-                   help="Comma-separated metric names, or a group name like 'vbench'.")
-    p.add_argument("--device", default="cuda:0")
-    p.add_argument("--text-prompt", default=None,
-                   help="Text prompt for prompt-aware metrics "
-                        "(vbench.overall_consistency, etc.).")
-    p.add_argument("--fps", type=float, default=None,
-                   help="Frame-rate annotation for fps-aware metrics "
-                        "(vbench.dynamic_degree, etc.).")
-    args = p.parse_args()
-
-    metrics: list[str] | str = (
-        args.metrics if args.metrics in ("all",)
-        else [m.strip() for m in args.metrics.split(",") if m.strip()]
-    )
-    evaluator = create_evaluator(metrics=metrics, device=args.device)
-
-    sample: dict = {"video": load_video(args.video)}
-    if args.reference is not None:
-        sample["reference"] = load_video(args.reference)
-    if args.text_prompt is not None:
-        sample["text_prompt"] = args.text_prompt
-    if args.fps is not None:
-        sample["fps"] = args.fps
-
-    results = evaluator.evaluate(**sample)
-    evaluator.shutdown()
-
-    print(json.dumps(
-        {name: r.score for name, r in results.items()},
-        indent=2,
-    ))
-
-
-if __name__ == "__main__":
-    main()
@@ -5,7 +5,7 @@ import time

 import gradio as gr
 from fastvideo.entrypoints.video_generator import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample.base import SamplingParam
 from copy import deepcopy


@@ -9,7 +9,7 @@ import tempfile

 import gradio as gr

-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample.base import SamplingParam


 MODEL_PATH_MAPPING = {
@@ -185,7 +185,7 @@ class BaseModelDeployment:

    def _initialize_generator(self, config: Dict[str, Any]) -> None:
        from fastvideo.entrypoints.video_generator import VideoGenerator
-        from fastvideo.api.sampling_param import SamplingParam
+        from fastvideo.configs.sample.base import SamplingParam

        print(f"Initializing model: {self.model_path}")
        self.generator = VideoGenerator.from_pretrained(
@@ -1,5 +1,5 @@
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "./lora_out"
 def main():
@@ -2,7 +2,7 @@
 Inference using a LoRA checkpoint from FastVideo trainer.
 """
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "./lora_out"
 def main():
@@ -61,16 +61,7 @@ has_cmake_arg() {
 }

 detect_with_torch() {
-    # Prefer the active venv's python directly over `uv run --active --no-project`,
-    # which on some uv versions provisions its own interpreter and misses packages
-    # installed into VIRTUAL_ENV.
-    local py
-    if [[ -n "${VIRTUAL_ENV:-}" && -x "${VIRTUAL_ENV}/bin/python" ]]; then
-        py="${VIRTUAL_ENV}/bin/python"
-    else
-        py="$(command -v python3 || command -v python)"
-    fi
-    "${py}" -c "import torch
+    uv run --active --no-project python -c "import torch
 if not torch.cuda.is_available():
    raise RuntimeError('torch.cuda.is_available() is false')
 mj, mn = torch.cuda.get_device_capability(0)
@@ -5,11 +5,6 @@ from fastvideo_kernel.ops import (
    video_sparse_attn,
 )

-from fastvideo_kernel.block_sparse_attn import (
-    block_sparse_attn,
-    block_sparse_attn_from_indices,
-)
-
 from fastvideo_kernel.vmoba import (
    moba_attn_varlen,
    process_moba_input,
@@ -27,8 +22,6 @@ from fastvideo_kernel.turbodiffusion_ops import (
 __all__ = [
    "sliding_tile_attention",
    "video_sparse_attn",
-    "block_sparse_attn",
-    "block_sparse_attn_from_indices",
    "moba_attn_varlen",
    "process_moba_input",
    "process_moba_output",
@@ -1,5 +1,3 @@
-"""Autograd-enabled block-sparse attention. Index-native ops with a bool-mask compat shim."""
-
 from __future__ import annotations

 import os
@@ -8,11 +6,6 @@ from typing import Tuple
 import torch


-# ---------------------------------------------------------------------------
-# Backend selection helpers
-# ---------------------------------------------------------------------------
-
-
 def _get_sm90_ops():
    try:
        from fastvideo_kernel._C import fastvideo_kernel_ops  # type: ignore
@@ -32,66 +25,38 @@ def _is_sm90() -> bool:


 def _force_triton() -> bool:
+    # Force Triton even on SM90 and even if the compiled extension is available.
+    # Useful for CI / debugging / parity testing.
    return os.environ.get("FASTVIDEO_KERNEL_VSA_FORCE_TRITON", "0") == "1"


-# ---------------------------------------------------------------------------
-# Index helpers
-# ---------------------------------------------------------------------------
-
-
 def _map_to_index(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Compact a bool block_map to (q2k_idx, q2k_num). Legacy path only."""
+    """
+    Preferred map->index conversion used by the wrapper.
+
+    This wrapper **requires** the Triton implementation.
+    If Triton (or the Triton map_to_index module) is not available, it raises.
+    """
    if block_map.dim() == 3:
        block_map = block_map.unsqueeze(0)
    if block_map.dim() != 4:
-        raise ValueError(
-            f"block_map must be [B,H,Q,KV] (or [H,Q,KV]), "
-            f"got shape={tuple(block_map.shape)}"
-        )
+        raise ValueError(f"block_map must be [B,H,Q,KV] (or [H,Q,KV]), got shape={tuple(block_map.shape)}")
    if block_map.dtype != torch.bool:
        block_map = block_map.to(torch.bool)
+
    if not block_map.is_cuda:
-        raise RuntimeError(
-            "block_map must be a CUDA tensor (Triton map_to_index required)."
-        )
+        raise RuntimeError("block_map must be a CUDA tensor (Triton map_to_index required).")

    try:
-        from fastvideo_kernel.triton_kernels.index import map_to_index as triton_map_to_index
-    except Exception as e:  # pragma: no cover - environment issue
+        from fastvideo_kernel.triton_kernels.index import map_to_index as triton_map_to_index  # local import
+    except Exception as e:
        raise ImportError(
            "Triton map_to_index is required but not available. "
-            "Ensure Triton is installed and "
-            "fastvideo_kernel.triton_kernels.index is importable."
+            "Ensure Triton is installed and fastvideo_kernel.triton_kernels.index is importable."
        ) from e
    return triton_map_to_index(block_map)


-def _invert_indices_for_backward(
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
-    num_kv_blocks: int,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    from fastvideo_kernel.triton_kernels.index import invert_indices
-    return invert_indices(q2k_idx, q2k_num, num_kv_blocks=num_kv_blocks)
-
-
-def _as_int32_contig(t: torch.Tensor, name: str) -> torch.Tensor:
-    """Return `t` as a contiguous int32 tensor, raising a clear error on CPU input."""
-    if not t.is_cuda:
-        raise RuntimeError(f"{name} must be a CUDA tensor, got device={t.device}")
-    if t.dtype != torch.int32:
-        t = t.to(torch.int32)
-    if not t.is_contiguous():
-        t = t.contiguous()
-    return t
-
-
-# ---------------------------------------------------------------------------
-# Triton backend custom ops (index-native)
-# ---------------------------------------------------------------------------
-
-
@torch.library.custom_op(
    "fastvideo_kernel::block_sparse_attn_triton",
    mutates_args=(),
@@ -101,40 +66,34 @@ def block_sparse_attn_triton(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    block_map = block_map.to(torch.bool)
+    q2k_idx, q2k_num = _map_to_index(block_map)
+
+    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
        triton_block_sparse_attn_forward,
    )

-    o, M = triton_block_sparse_attn_forward(
-        q.contiguous(),
-        k.contiguous(),
-        v.contiguous(),
-        q2k_idx,
-        q2k_num,
-        variable_block_sizes,
-    )
+    o, M = triton_block_sparse_attn_forward(q, k, v, q2k_idx, q2k_num, variable_block_sizes)
    return o, M


+
@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_triton")
 def _block_sparse_attn_triton_fake(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
    o = torch.empty_like(q)
-    M = torch.empty(
-        (q.shape[0], q.shape[1], q.shape[2]),
-        device=q.device,
-        dtype=torch.float32,
-    )
+    M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
    return o, M


@@ -150,32 +109,20 @@ def block_sparse_attn_backward_triton(
    v: torch.Tensor,
    o: torch.Tensor,
    M: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (
+    grad_output = grad_output.contiguous()
+    block_map = block_map.to(torch.bool)
+    q2k_idx, q2k_num = _map_to_index(block_map)
+    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
+
+    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
        triton_block_sparse_attn_backward,
    )

-    num_kv_blocks = int(variable_block_sizes.numel())
-    k2q_idx, k2q_num = _invert_indices_for_backward(
-        q2k_idx, q2k_num, num_kv_blocks
-    )
-    # q/k/v are saved from the user-facing inputs and may be non-contiguous;
-    # o/M are kernel outputs so are already contiguous.
    dq, dk, dv = triton_block_sparse_attn_backward(
-        grad_output.contiguous(),
-        q.contiguous(),
-        k.contiguous(),
-        v.contiguous(),
-        o,
-        M,
-        q2k_idx,
-        q2k_num,
-        k2q_idx,
-        k2q_num,
-        variable_block_sizes,
+        grad_output, q, k, v, o, M, q2k_idx, q2k_num, k2q_idx, k2q_num, variable_block_sizes
    )
    return dq, dk, dv

@@ -188,8 +135,7 @@ def _block_sparse_attn_backward_triton_fake(
    v: torch.Tensor,
    o: torch.Tensor,
    M: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    dq = torch.empty_like(q)
@@ -198,28 +144,19 @@ def _block_sparse_attn_backward_triton_fake(
    return dq, dk, dv


-def _setup_context_triton(ctx, inputs, output):
-    q, k, v, q2k_idx, q2k_num, variable_block_sizes = inputs
-    o, M = output
-    ctx.save_for_backward(q, k, v, o, M, q2k_idx, q2k_num, variable_block_sizes)
-
-
 def _backward_triton(ctx, grad_o, grad_M):
-    q, k, v, o, M, q2k_idx, q2k_num, variable_block_sizes = ctx.saved_tensors
-    dq, dk, dv = block_sparse_attn_backward_triton(
-        grad_o, q, k, v, o, M, q2k_idx, q2k_num, variable_block_sizes
-    )
-    return dq, dk, dv, None, None, None
+    q, k, v, o, M, block_map, variable_block_sizes = ctx.saved_tensors
+    dq, dk, dv = block_sparse_attn_backward_triton(grad_o, q, k, v, o, M, block_map, variable_block_sizes)
+    return dq, dk, dv, None, None


-block_sparse_attn_triton.register_autograd(
-    _backward_triton, setup_context=_setup_context_triton
-)
+def _setup_context_triton(ctx, inputs, output):
+    q, k, v, block_map, variable_block_sizes = inputs
+    o, M = output
+    ctx.save_for_backward(q, k, v, o, M, block_map, variable_block_sizes)


-# ---------------------------------------------------------------------------
-# SM90 backend custom ops (index-native)
-# ---------------------------------------------------------------------------
+block_sparse_attn_triton.register_autograd(_backward_triton, setup_context=_setup_context_triton)


@torch.library.custom_op(
@@ -231,21 +168,21 @@ def block_sparse_attn_sm90(
    q_padded: torch.Tensor,
    k_padded: torch.Tensor,
    v_padded: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
    block_sparse_fwd, _ = _get_sm90_ops()
    if block_sparse_fwd is None:
        raise ImportError("fastvideo_kernel_ops.block_sparse_fwd is not available")

+    q_padded = q_padded.contiguous()
+    k_padded = k_padded.contiguous()
+    v_padded = v_padded.contiguous()
+    block_map = block_map.to(torch.bool)
+    q2k_idx, q2k_num = _map_to_index(block_map)
+
    o_padded, lse_padded = block_sparse_fwd(
-        q_padded.contiguous(),
-        k_padded.contiguous(),
-        v_padded.contiguous(),
-        q2k_idx,
-        q2k_num,
-        variable_block_sizes,
+        q_padded, k_padded, v_padded, q2k_idx, q2k_num, variable_block_sizes.int()
    )
    return o_padded, lse_padded

@@ -255,16 +192,11 @@ def _block_sparse_attn_sm90_fake(
    q_padded: torch.Tensor,
    k_padded: torch.Tensor,
    v_padded: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
    o = torch.empty_like(q_padded)
-    lse = torch.empty(
-        (q_padded.shape[0], q_padded.shape[1], q_padded.shape[2], 1),
-        device=q_padded.device,
-        dtype=torch.float32,
-    )
+    lse = torch.empty((q_padded.shape[0], q_padded.shape[1], q_padded.shape[2], 1), device=q_padded.device, dtype=torch.float32)
    return o, lse


@@ -280,34 +212,30 @@ def block_sparse_attn_backward_sm90(
    v_padded: torch.Tensor,
    o_padded: torch.Tensor,
    lse_padded: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    _, block_sparse_bwd = _get_sm90_ops()
    if block_sparse_bwd is None:
        raise ImportError("fastvideo_kernel_ops.block_sparse_bwd is not available")

-    num_kv_blocks = int(variable_block_sizes.numel())
-    k2q_idx, k2q_num = _invert_indices_for_backward(
-        q2k_idx, q2k_num, num_kv_blocks
-    )
+    grad_output_padded = grad_output_padded.contiguous()
+    block_map = block_map.to(torch.bool)
+    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())

-    # q/k/v are saved from user-facing inputs; o/lse are kernel outputs.
    dq, dk, dv = block_sparse_bwd(
-        q_padded.contiguous(),
-        k_padded.contiguous(),
-        v_padded.contiguous(),
+        q_padded,
+        k_padded,
+        v_padded,
        o_padded,
        lse_padded,
-        grad_output_padded.contiguous(),
+        grad_output_padded,
        k2q_idx,
        k2q_num,
-        variable_block_sizes,
+        variable_block_sizes.int(),
    )
-    # C++ kernel returns fp32 grads; cast back to the input dtype.
-    out_dtype = grad_output_padded.dtype
-    return dq.to(out_dtype), dk.to(out_dtype), dv.to(out_dtype)
+    # C++ kernel returns fp32 grads; cast back to match PyTorch convention if needed
+    return dq.to(grad_output_padded.dtype), dk.to(grad_output_padded.dtype), dv.to(grad_output_padded.dtype)


@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_sm90")
@@ -318,8 +246,7 @@ def _block_sparse_attn_backward_sm90_fake(
    v_padded: torch.Tensor,
    o_padded: torch.Tensor,
    lse_padded: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
+    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    dq = torch.empty_like(q_padded)
@@ -328,57 +255,21 @@ def _block_sparse_attn_backward_sm90_fake(
    return dq, dk, dv


-def _setup_context_sm90(ctx, inputs, output):
-    q, k, v, q2k_idx, q2k_num, variable_block_sizes = inputs
-    o, lse = output
-    ctx.save_for_backward(q, k, v, o, lse, q2k_idx, q2k_num, variable_block_sizes)
-
-
 def _backward_sm90(ctx, grad_o, grad_lse):
-    q, k, v, o, lse, q2k_idx, q2k_num, variable_block_sizes = ctx.saved_tensors
+    q, k, v, o, lse, block_map, variable_block_sizes = ctx.saved_tensors
    dq, dk, dv = block_sparse_attn_backward_sm90(
-        grad_o, q, k, v, o, lse, q2k_idx, q2k_num, variable_block_sizes
+        grad_o, q, k, v, o, lse, block_map, variable_block_sizes
    )
-    return dq, dk, dv, None, None, None
+    return dq, dk, dv, None, None


-block_sparse_attn_sm90.register_autograd(
-    _backward_sm90, setup_context=_setup_context_sm90
-)
+def _setup_context_sm90(ctx, inputs, output):
+    q, k, v, block_map, variable_block_sizes = inputs
+    o, lse = output
+    ctx.save_for_backward(q, k, v, o, lse, block_map, variable_block_sizes)


-# ---------------------------------------------------------------------------
-# Public API
-# ---------------------------------------------------------------------------
-
-
-def block_sparse_attn_from_indices(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Block-sparse attention with autograd, taking compact per-row KV indices."""
-    # Normalize index tensors once at the public boundary so the custom ops
-    # and their fakes can assume int32/contiguous. No-op on well-formed input.
-    q2k_idx = _as_int32_contig(q2k_idx, "q2k_idx")
-    q2k_num = _as_int32_contig(q2k_num, "q2k_num")
-    variable_block_sizes = _as_int32_contig(variable_block_sizes, "variable_block_sizes")
-
-    block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
-    use_sm90 = (
-        (not _force_triton())
-        and _is_sm90()
-        and block_sparse_fwd is not None
-        and block_sparse_bwd is not None
-    )
-    if use_sm90:
-        return block_sparse_attn_sm90(q, k, v, q2k_idx, q2k_num, variable_block_sizes)
-    # Triton path: supports q_seq_len != kv_seq_len as long as both are padded
-    # to a multiple of the block size (64 tokens).
-    return block_sparse_attn_triton(q, k, v, q2k_idx, q2k_num, variable_block_sizes)
+block_sparse_attn_sm90.register_autograd(_backward_sm90, setup_context=_setup_context_sm90)


 def block_sparse_attn(
@@ -388,8 +279,16 @@ def block_sparse_attn(
    block_map: torch.Tensor,
    variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Bool-mask compat wrapper; prefer block_sparse_attn_from_indices."""
-    q2k_idx, q2k_num = _map_to_index(block_map)
-    return block_sparse_attn_from_indices(
-        q, k, v, q2k_idx, q2k_num, variable_block_sizes
-    )
+    """
+    Unified block-sparse attention op with autograd support.
+    - On SM90 with compiled extension present: uses fastvideo_kernel_ops.block_sparse_fwd/bwd.
+    - Otherwise: uses Triton implementation (requires q/k/v to have same padded length today).
+    """
+    block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
+    if (not _force_triton()) and _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
+        return block_sparse_attn_sm90(q, k, v, block_map, variable_block_sizes)
+    # Triton path: supports q_seq_len != kv_seq_len as long as both are padded
+    # to a multiple of the block size (64 tokens).
+    return block_sparse_attn_triton(q, k, v, block_map, variable_block_sizes)
+
+
@@ -1,6 +1,6 @@
 import math
 import torch
-from .block_sparse_attn import block_sparse_attn, block_sparse_attn_from_indices
+from .block_sparse_attn import block_sparse_attn
 from .triton_kernels.st_attn_triton import sliding_tile_attention_triton

 # Try to load the C++ extension
@@ -125,18 +125,13 @@ def video_sparse_attn(
    out_c = out_c.repeat(1, 1, 1, block_elements,
                         1).view(batch, heads, q_seq_len, dim)

-    # Sparse branch: feed top-k indices directly, skipping the bool-mask round-trip.
+    # Sparse branch
    topk_idx = torch.topk(scores, topk, dim=-1).indices
-    q2k_idx = topk_idx.to(torch.int32).contiguous()
-    q2k_num = torch.full(
-        (batch, heads, q_num_blocks),
-        topk,
-        dtype=torch.int32,
-        device=q.device,
-    )
-    out_s = block_sparse_attn_from_indices(
-        q, k, v, q2k_idx, q2k_num, variable_block_sizes
-    )[0]
+    mask = torch.zeros_like(scores,
+                            dtype=torch.bool).scatter_(-1, topk_idx, True)
+
+    # out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
+    out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]

    if compress_attn_weight is not None:
        return out_c * compress_attn_weight + out_s
@@ -1,10 +1,9 @@
 ## pytorch sdpa version of block sparse ##
-from typing import Tuple
-
 import triton
 import triton.language as tl
 import torch

+
@triton.jit
 def topk_index_to_map_kernel(
    map_ptr,
@@ -154,114 +153,3 @@ def map_to_index(block_map: torch.Tensor):
    )

    return index, index_num
-
-
-@triton.jit
-def _invert_indices_kernel(
-    q2k_idx_ptr,
-    q2k_num_ptr,
-    k2q_idx_ptr,
-    k2q_num_ptr,
-    q2k_idx_b, q2k_idx_h, q2k_idx_q, q2k_idx_k,
-    q2k_num_b, q2k_num_h, q2k_num_q,
-    k2q_idx_b, k2q_idx_h, k2q_idx_k, k2q_idx_q,
-    k2q_num_b, k2q_num_h, k2q_num_k,
-    MAX_KV_PER_Q: tl.constexpr,
-):
-    # One program per (b, h, q): reserve a slot in k2q via atomicAdd, write q.
-    pid_b = tl.program_id(0)
-    pid_h = tl.program_id(1)
-    pid_q = tl.program_id(2)
-
-    n = tl.load(
-        q2k_num_ptr
-        + pid_b * q2k_num_b
-        + pid_h * q2k_num_h
-        + pid_q * q2k_num_q
-    )
-
-    q2k_row = (
-        q2k_idx_ptr
-        + pid_b * q2k_idx_b
-        + pid_h * q2k_idx_h
-        + pid_q * q2k_idx_q
-    )
-
-    for i in tl.range(0, MAX_KV_PER_Q):
-        if i < n:
-            kv = tl.load(q2k_row + i * q2k_idx_k)
-            count_ptr = (
-                k2q_num_ptr
-                + pid_b * k2q_num_b
-                + pid_h * k2q_num_h
-                + kv * k2q_num_k
-            )
-            pos = tl.atomic_add(count_ptr, 1)
-            tl.store(
-                k2q_idx_ptr
-                + pid_b * k2q_idx_b
-                + pid_h * k2q_idx_h
-                + kv * k2q_idx_k
-                + pos * k2q_idx_q,
-                pid_q,
-            )
-
-
-def invert_indices(
-    q2k_idx: torch.Tensor,
-    q2k_num: torch.Tensor,
-    num_kv_blocks: int,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Transpose a Q->KV index list into a K->Q one via atomic compaction (GPU)."""
-    if q2k_idx.dim() != 4:
-        raise ValueError(
-            f"q2k_idx must be [B, H, Nq, Mk], got shape={tuple(q2k_idx.shape)}"
-        )
-    if q2k_num.dim() != 3:
-        raise ValueError(
-            f"q2k_num must be [B, H, Nq], got shape={tuple(q2k_num.shape)}"
-        )
-    if not q2k_idx.is_cuda or not q2k_num.is_cuda:
-        raise RuntimeError("invert_indices requires CUDA tensors.")
-
-    B, H, Nq, Mk = q2k_idx.shape
-    if q2k_num.shape != (B, H, Nq):
-        raise ValueError(
-            f"q2k_num shape {tuple(q2k_num.shape)} does not match q2k_idx "
-            f"[B, H, Nq] = {(B, H, Nq)}"
-        )
-
-    q2k_idx = q2k_idx.contiguous()
-    q2k_num = q2k_num.contiguous()
-    if q2k_idx.dtype != torch.int32:
-        q2k_idx = q2k_idx.to(torch.int32)
-    if q2k_num.dtype != torch.int32:
-        q2k_num = q2k_num.to(torch.int32)
-
-    # Any KV block is attended by at most Nq Q blocks (one per Q row), so
-    # `Nq` is a tight upper bound on the compacted K->Q slots.
-    k2q_idx = torch.empty(
-        (B, H, num_kv_blocks, Nq),
-        dtype=torch.int32,
-        device=q2k_idx.device,
-    )
-    k2q_num = torch.zeros(
-        (B, H, num_kv_blocks),
-        dtype=torch.int32,
-        device=q2k_idx.device,
-    )
-
-    grid = (B, H, Nq)
-    _invert_indices_kernel[grid](
-        q2k_idx,
-        q2k_num,
-        k2q_idx,
-        k2q_num,
-        q2k_idx.stride(0), q2k_idx.stride(1), q2k_idx.stride(2), q2k_idx.stride(3),
-        q2k_num.stride(0), q2k_num.stride(1), q2k_num.stride(2),
-        k2q_idx.stride(0), k2q_idx.stride(1), k2q_idx.stride(2), k2q_idx.stride(3),
-        k2q_num.stride(0), k2q_num.stride(1), k2q_num.stride(2),
-        MAX_KV_PER_Q=Mk,
-    )
-
-    return k2q_idx, k2q_num
@@ -1,5 +1,5 @@
 from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam
 from fastvideo.entrypoints.video_generator import VideoGenerator
 from fastvideo.version import __version__

@@ -7,37 +7,21 @@ from fastvideo.api.schema import (
    GenerationPlan,
    GenerationRequest,
    GeneratorConfig,
-    GpuPoolConfig,
    InputConfig,
    OffloadConfig,
    OutputConfig,
    ParallelismConfig,
    PipelineSelection,
    PlannedStage,
-    PromptEnhancerConfig,
-    PromptSafetyConfig,
    QuantizationConfig,
    RequestRuntimeConfig,
    RunConfig,
    SamplingConfig,
    ServeConfig,
    ServerConfig,
-    StreamingConfig,
-    WarmupConfig,
 )
 from fastvideo.api.errors import ConfigValidationError
 from fastvideo.api.overrides import apply_overrides, parse_cli_overrides
-from fastvideo.api.presets import (
-    InferencePreset,
-    PresetStageSpec,
-    get_all_preset_names,
-    get_preset,
-    get_presets_for_family,
-    register_preset,
-    validate_preset_selection,
-    validate_stage_names,
-    validate_stage_overrides,
-)
 from fastvideo.api.parser import (
    config_to_dict,
    load_config,
@@ -47,7 +31,6 @@ from fastvideo.api.parser import (
    parse_config,
 )
 from fastvideo.api.results import GenerationResult
-from fastvideo.api.sampling_param import SamplingParam

 __all__ = [
    "CompileConfig",
@@ -59,26 +42,18 @@ __all__ = [
    "GenerationPlan",
    "GenerationRequest",
    "GeneratorConfig",
-    "GpuPoolConfig",
    "InputConfig",
    "OffloadConfig",
    "OutputConfig",
    "ParallelismConfig",
    "PipelineSelection",
    "PlannedStage",
-    "PromptEnhancerConfig",
-    "PromptSafetyConfig",
    "QuantizationConfig",
    "RequestRuntimeConfig",
    "RunConfig",
    "SamplingConfig",
-    "SamplingParam",
    "ServeConfig",
    "ServerConfig",
-    "StreamingConfig",
-    "WarmupConfig",
-    "InferencePreset",
-    "PresetStageSpec",
    "apply_overrides",
    "config_to_dict",
    "load_config",
@@ -86,12 +61,5 @@ __all__ = [
    "load_run_config",
    "load_serve_config",
    "parse_cli_overrides",
-    "get_all_preset_names",
-    "get_preset",
-    "get_presets_for_family",
    "parse_config",
-    "register_preset",
-    "validate_preset_selection",
-    "validate_stage_names",
-    "validate_stage_overrides",
 ]
@@ -7,17 +7,14 @@ from dataclasses import fields, is_dataclass
 from pathlib import Path
 from typing import Any

-from fastvideo.api.overrides import apply_overrides, normalize_overrides
+from fastvideo.api.overrides import apply_overrides, parse_cli_overrides
 from fastvideo.api.parser import config_to_dict, load_raw_config, parse_config
 from fastvideo.api.request_metadata import (
-    EXPLICIT_PATHS_ATTR,
+    EXPLICIT_REQUEST_ATTR,
    bind_generation_request_raw,
-    get_explicit_paths,
-    reset_tracking_roots,
+    refresh_generation_request_raw,
 )
 from fastvideo.api.schema import (
-    CompileConfig,
-    ContinuationState,
    GenerationRequest,
    GeneratorConfig,
    InputConfig,
@@ -25,12 +22,8 @@ from fastvideo.api.schema import (
    RequestRuntimeConfig,
    SamplingConfig,
 )
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam
 from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.basic.ltx2.stage_overrides import (
-    refine_preset_override_fields,
-    refine_stage_override_fields,
-)
 from fastvideo.utils import shallow_asdict

 _INPUT_FIELD_NAMES = {field.name for field in fields(InputConfig)}
@@ -44,10 +37,6 @@ _LEGACY_REQUEST_ALIASES = {
 _REQUEST_PIPELINE_OVERRIDE_FIELDS = frozenset({
    "embedded_cfg_scale",
 })
-# torch.compile kwargs that map to first-class CompileConfig fields.
-_COMPILE_TYPED_KEYS = ("backend", "fullgraph", "mode", "dynamic")
-# LTX-2 refine flat kwargs (init + per-request) known to FastVideoArgs.
-_LTX2_REFINE_FLAT_KEYS = (refine_preset_override_fields() | refine_stage_override_fields())


 def normalize_generator_config(config: GeneratorConfig | Mapping[str, Any], ) -> GeneratorConfig:
@@ -61,7 +50,7 @@ def load_generator_config_from_file(
    overrides: list[str] | Mapping[str, Any] | None = None,
 ) -> GeneratorConfig:
    raw = load_raw_config(path)
-    normalized_overrides = normalize_overrides(overrides)
+    normalized_overrides = _normalize_overrides(overrides)

    if _looks_like_run_or_serve_config(raw):
        if normalized_overrides:
@@ -90,8 +79,6 @@ def legacy_from_pretrained_to_config(
    components: dict[str, Any] = {}
    quantization: dict[str, Any] = {}
    experimental: dict[str, Any] = {}
-    preset_overrides: dict[str, Any] = {}
-    preset_refine: dict[str, Any] = {}

    for key, value in kwargs.items():
        if key == "revision":
@@ -118,33 +105,8 @@ def legacy_from_pretrained_to_config(
            offload["pin_cpu_memory"] = value
        elif key == "enable_torch_compile":
            compile_config["enabled"] = value
-        elif key == "enable_torch_compile_text_encoder":
-            compile_config["text_encoder_enabled"] = value
        elif key == "torch_compile_kwargs":
-            remaining: dict[str, Any] = (dict(deepcopy(value)) if isinstance(value, Mapping) else {})
-            for first_class in _COMPILE_TYPED_KEYS:
-                if first_class in remaining:
-                    compile_config[first_class] = remaining.pop(first_class)
-            if remaining:
-                compile_config["extras"] = remaining
-        elif key == "ltx2_vae_tiling":
-            pipeline["vae_tiling"] = value
-        elif key == "config_model_path":
-            components["config_root"] = value
-        elif key == "ltx2_refine_enabled":
-            preset_refine["enabled"] = value
-        elif key == "ltx2_refine_upsampler_path":
-            # Empty string means "no upsampler"; keep typed None.
-            components["upsampler_weights"] = value or None
-        elif key == "ltx2_refine_lora_path":
-            # Empty string means "no refine LoRA"; keep typed None.
-            components["lora_path"] = value or None
-        elif key == "ltx2_refine_add_noise":
-            preset_refine["add_noise"] = value
-        elif key == "ltx2_refine_num_inference_steps":
-            preset_refine["num_inference_steps"] = value
-        elif key == "ltx2_refine_guidance_scale":
-            preset_refine["guidance_scale"] = value
+            compile_config["kwargs"] = deepcopy(value)
        elif key in {"enable_stage_verification", "use_fsdp_inference", "disable_autocast"}:
            engine[key] = value
        elif key == "override_text_encoder_quant":
@@ -184,10 +146,6 @@ def legacy_from_pretrained_to_config(

    if components:
        pipeline["components"] = components
-    if preset_refine:
-        preset_overrides["refine"] = preset_refine
-    if preset_overrides:
-        pipeline["preset_overrides"] = preset_overrides
    if experimental:
        pipeline["experimental"] = experimental
    if pipeline:
@@ -199,12 +157,16 @@ def legacy_from_pretrained_to_config(
 def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, Any], ) -> FastVideoArgs:
    normalized = normalize_generator_config(config)
    unsupported = []
-    if normalized.pipeline.preset is not None:
-        unsupported.append("pipeline.preset")
-    if normalized.pipeline.preset_version is not None:
-        unsupported.append("pipeline.preset_version")
+    if normalized.pipeline.profile is not None:
+        unsupported.append("pipeline.profile")
+    if normalized.pipeline.profile_version is not None:
+        unsupported.append("pipeline.profile_version")
+    if normalized.pipeline.components.config_root is not None:
+        unsupported.append("pipeline.components.config_root")
    if normalized.pipeline.components.vae_weights is not None:
        unsupported.append("pipeline.components.vae_weights")
+    if normalized.pipeline.components.upsampler_weights is not None:
+        unsupported.append("pipeline.components.upsampler_weights")
    if unsupported:
        joined = ", ".join(unsupported)
        raise NotImplementedError(f"VideoGenerator compatibility adapter does not support {joined} yet")
@@ -228,21 +190,13 @@ def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, An
        "vae_cpu_offload": engine.offload.vae,
        "pin_cpu_memory": engine.offload.pin_cpu_memory,
        "enable_torch_compile": engine.compile.enabled,
-        "torch_compile_kwargs": _compile_config_to_torch_kwargs(engine.compile),
+        "torch_compile_kwargs": deepcopy(engine.compile.kwargs),
        "enable_stage_verification": engine.enable_stage_verification,
        "use_fsdp_inference": engine.use_fsdp_inference,
        "disable_autocast": engine.disable_autocast,
    }
    if normalized.pipeline.workload_type is not None:
        kwargs["workload_type"] = normalized.pipeline.workload_type
-    if normalized.pipeline.vae_tiling is not None:
-        kwargs["ltx2_vae_tiling"] = normalized.pipeline.vae_tiling
-    if engine.compile.text_encoder_enabled is not None:
-        # ``FastVideoArgs.from_kwargs`` filters to declared fields, so
-        # this is a no-op on the current legacy path. Emit anyway so the
-        # realtime runtime (PR 7.6) — which reads from the kwargs dict
-        # before FastVideoArgs filtering — can pick it up once wired.
-        kwargs["enable_torch_compile_text_encoder"] = (engine.compile.text_encoder_enabled)

    quantization = engine.quantization
    if quantization is not None and quantization.text_encoder_quant is not None:
@@ -265,18 +219,8 @@ def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, An
        kwargs["init_weights_from_safetensors"] = components.transformer_weights
    if components.transformer_2_weights is not None:
        kwargs["init_weights_from_safetensors_2"] = components.transformer_2_weights
-    if components.config_root is not None:
-        kwargs["config_model_path"] = components.config_root
-    if components.upsampler_weights is not None:
-        kwargs["ltx2_refine_upsampler_path"] = components.upsampler_weights

-    preset_overrides = deepcopy(normalized.pipeline.preset_overrides)
-    refine = preset_overrides.pop("refine", None)
-    if isinstance(refine, Mapping):
-        for key in _LTX2_REFINE_FLAT_KEYS:
-            if key in refine:
-                kwargs[f"ltx2_refine_{key}"] = refine[key]
-    kwargs.update(preset_overrides)
+    kwargs.update(deepcopy(normalized.pipeline.profile_overrides))
    kwargs.update(deepcopy(normalized.pipeline.experimental))
    return FastVideoArgs.from_kwargs(**kwargs)

@@ -284,9 +228,9 @@ def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, An
 def normalize_generation_request(request: GenerationRequest | Mapping[str, Any], ) -> GenerationRequest:
    normalized = (request if isinstance(request, GenerationRequest) else parse_config(GenerationRequest, request))

-    if not hasattr(normalized, EXPLICIT_PATHS_ATTR):
-        # Request wasn't bound through the parser (e.g. constructed
-        # directly). Treat every currently-set field as explicit.
+    if hasattr(normalized, EXPLICIT_REQUEST_ATTR):
+        refresh_generation_request_raw(normalized)
+    else:
        bind_generation_request_raw(normalized, _serialize_generation_request(normalized))
    return normalized

@@ -326,24 +270,16 @@ def request_to_sampling_param(
 ) -> SamplingParam:
    if request.plan is not None:
        raise NotImplementedError("GenerationRequest.plan is not wired into VideoGenerator yet")
+    if request.state is not None:
+        raise NotImplementedError("GenerationRequest.state is not wired into VideoGenerator yet")

    sampling_param = SamplingParam.from_pretrained(model_path)
-    if request.state is not None:
-        _validate_continuation_state(request.state)
-        sampling_param.continuation_state = request.state
-    if request.output.return_state:
-        sampling_param.return_continuation_state = True
-    updates = explicit_request_updates(request)
+    updates = _explicit_request_updates(request)

    for key, value in updates.items():
        if hasattr(sampling_param, key):
            setattr(sampling_param, key, deepcopy(value))
-        elif key in _REQUEST_PIPELINE_OVERRIDE_FIELDS:
-            continue
-        elif value == _SCHEMA_DEFAULT_UPDATES.get(key, _MISSING):
-            # Schema-default field that isn't on SamplingParam; tolerated
-            # because direct GenerationRequest(...) construction has no
-            # way to distinguish "user set" from "schema default".
+        elif key in _REQUEST_PIPELINE_OVERRIDE_FIELDS or _is_supported_as_default_only(key, value):
            continue
        else:
            raise ValueError(f"Request field {key!r} is not supported by sampling params for {model_path}")
@@ -360,12 +296,10 @@ def expand_request_prompt_batch(request: GenerationRequest, ) -> list[Generation
    requests: list[GenerationRequest] = []
    for index, prompt in enumerate(request.prompt):
        single_request = deepcopy(request)
-        # deepcopy preserves the tracking-root cycle, but re-pin roots
-        # defensively so that subsequent setattrs record on the copy.
-        reset_tracking_roots(single_request)
        single_request.prompt = prompt
        _fan_out_batched_input_value(request, single_request, "image_path", index)
        _fan_out_batched_input_value(request, single_request, "video_path", index)
+        _fan_out_explicit_request_metadata(request, single_request, index, prompt)
        requests.append(single_request)
    return requests

@@ -374,23 +308,12 @@ def _looks_like_run_or_serve_config(raw: Mapping[str, Any]) -> bool:
    return isinstance(raw.get("generator"), Mapping)


-def _compile_config_to_torch_kwargs(compile_config: CompileConfig, ) -> dict[str, Any]:
-    """Flatten typed ``CompileConfig`` back to a ``torch_compile_kwargs``
-    dict that the legacy ``FastVideoArgs`` path still expects.
-
-    Typed first-class fields (:attr:`backend`, :attr:`fullgraph`,
-    :attr:`mode`, :attr:`dynamic`) are only emitted when the user set
-    them explicitly (non-``None``). ``extras`` is merged on top for any
-    uncommon kwargs.
-    """
-    out: dict[str, Any] = {}
-    for key in _COMPILE_TYPED_KEYS:
-        value = getattr(compile_config, key)
-        if value is not None:
-            out[key] = value
-    if compile_config.extras:
-        out.update(deepcopy(compile_config.extras))
-    return out
+def _normalize_overrides(overrides: list[str] | Mapping[str, Any] | None, ) -> dict[str, Any] | None:
+    if not overrides:
+        return None
+    if isinstance(overrides, list):
+        return parse_cli_overrides(overrides)
+    return dict(overrides)


 def _sampling_param_to_request_raw(sampling_param: SamplingParam | None, ) -> dict[str, Any]:
@@ -431,83 +354,20 @@ def _apply_request_field(

 def request_to_pipeline_overrides(request: GenerationRequest) -> dict[str, Any]:
    overrides: dict[str, Any] = {}
-    for key, value in explicit_request_updates(request).items():
+    for key, value in _explicit_request_updates(request).items():
        if key in _REQUEST_PIPELINE_OVERRIDE_FIELDS:
            overrides[key] = deepcopy(value)
    return overrides


-def explicit_request_updates(request: GenerationRequest) -> dict[str, Any]:
-    """Project a ``GenerationRequest`` down to *explicitly set* fields only.
+def _explicit_request_updates(request: GenerationRequest) -> dict[str, Any]:
+    raw = getattr(request, EXPLICIT_REQUEST_ATTR, None)
+    if raw is None:
+        raw = _serialize_generation_request(request)

-    Returns a flat kwargs dict suitable for merging into a generator call.
-    The projection uses ``_fastvideo_explicit_paths`` (populated during
-    ``parse_config`` / raw binding) so schema defaults on the dataclass
-    are **not** emitted — only paths the caller/operator actually wrote.
-
-    This is what makes ``ServeConfig.default_request`` work as an
-    operator-pinned baseline rather than a full override: a YAML with just
-    ``sampling.seed: 42`` yields ``{"seed": 42}``, not the full sampling
-    config with its 15 schema defaults.
-
-    Precondition: the request must carry ``_fastvideo_explicit_paths`` —
-    populated by :func:`fastvideo.api.parser.parse_config` or
-    :func:`fastvideo.api.compat.normalize_generation_request`. Calling on
-    a raw ``GenerationRequest()`` asserts.
-    """
-    assert hasattr(request,
-                   EXPLICIT_PATHS_ATTR), ("GenerationRequest reached explicit_request_updates without tracking; "
-                                          "every entry point must route through normalize_generation_request "
-                                          "or parse_config first")
-    paths = get_explicit_paths(request)
-    raw = _build_sparse_raw_from_paths(request, paths)
    return _extract_request_updates(raw)


-def _build_sparse_raw_from_paths(
-    request: GenerationRequest,
-    paths: frozenset[str],
-) -> dict[str, Any]:
-    result: dict[str, Any] = {}
-    for path in paths:
-        parts = path.split(".")
-        value = _read_dotted_path(request, parts)
-        if value is _MISSING:
-            continue
-        _set_dotted_path(result, parts, deepcopy(value))
-    return result
-
-
-def _read_dotted_path(obj: Any, parts: list[str]) -> Any:
-    for part in parts:
-        if is_dataclass(obj) and not isinstance(obj, type):
-            if not hasattr(obj, part):
-                return _MISSING
-            obj = getattr(obj, part)
-        elif isinstance(obj, Mapping):
-            if part not in obj:
-                return _MISSING
-            obj = obj[part]
-        else:
-            return _MISSING
-    return obj
-
-
-def _set_dotted_path(
-    target: dict[str, Any],
-    parts: list[str],
-    value: Any,
-) -> None:
-    cursor = target
-    for part in parts[:-1]:
-        nxt = cursor.get(part)
-        if not isinstance(nxt, dict):
-            nxt = {}
-            cursor[part] = nxt
-        cursor = nxt
-    cursor[parts[-1]] = value
-
-
 def _extract_request_updates(raw: Mapping[str, Any]) -> dict[str, Any]:
    updates: dict[str, Any] = {}
    if "negative_prompt" in raw:
@@ -551,40 +411,6 @@ def _serialize_generation_request(request: GenerationRequest) -> dict[str, Any]:
    return deepcopy(config_to_dict(request))


-_SCHEMA_DEFAULT_UPDATES = _extract_request_updates(config_to_dict(GenerationRequest()))
-
-_KNOWN_CONTINUATION_KINDS: set[str] = set()
-
-
-def register_continuation_kind(kind: str) -> None:
-    """Register a :class:`ContinuationState.kind` as recognized.
-
-    PR 7 wires the envelope through; per-kind payload deserializers live
-    with each model family (e.g. ``fastvideo.pipelines.basic.ltx2.
-    continuation.LTX2ContinuationState``). The registry lets the
-    public-API compat layer validate the kind early, before the state
-    reaches the pipeline.
-    """
-    if not isinstance(kind, str) or not kind:
-        raise ValueError("ContinuationState kind must be a non-empty string")
-    _KNOWN_CONTINUATION_KINDS.add(kind)
-
-
-def _validate_continuation_state(state: ContinuationState) -> None:
-    if not isinstance(state.kind, str) or not state.kind:
-        raise ValueError("GenerationRequest.state.kind must be a non-empty string; got "
-                         f"{state.kind!r}")
-    if not isinstance(state.payload, Mapping):
-        raise ValueError(f"GenerationRequest.state.payload must be a mapping; got "
-                         f"{type(state.payload).__name__}")
-    if state.kind not in _KNOWN_CONTINUATION_KINDS:
-        known = sorted(_KNOWN_CONTINUATION_KINDS)
-        raise ValueError(f"Unknown ContinuationState kind {state.kind!r}; registered "
-                         f"kinds: {known}. Import the model family that owns this kind "
-                         "(e.g. `import fastvideo.pipelines.basic.ltx2.continuation`) "
-                         "to register it, or drop the state field.")
-
-
 def _fan_out_batched_input_value(
    source_request: GenerationRequest,
    target_request: GenerationRequest,
@@ -598,6 +424,29 @@ def _fan_out_batched_input_value(
    setattr(target_request.inputs, field_name, deepcopy(value[index]))


+def _fan_out_explicit_request_metadata(
+    source_request: GenerationRequest,
+    target_request: GenerationRequest,
+    index: int,
+    prompt: str,
+) -> None:
+    raw = getattr(source_request, EXPLICIT_REQUEST_ATTR, None)
+    if raw is None:
+        return
+
+    raw = deepcopy(raw)
+    raw["prompt"] = prompt
+    inputs = raw.get("inputs")
+    if isinstance(inputs, dict):
+        for field_name in ("image_path", "video_path"):
+            value = inputs.get(field_name)
+            if isinstance(value, list):
+                _validate_batched_input_length(source_request.prompt, value, field_name)
+                inputs[field_name] = deepcopy(value[index])
+
+    setattr(target_request, EXPLICIT_REQUEST_ATTR, raw)
+
+
 def _validate_batched_input_length(
    prompts: str | list[str] | None,
    values: list[Any],
@@ -609,15 +458,50 @@ def _validate_batched_input_length(
        raise ValueError(f"GenerationRequest.inputs.{field_name} must have the same length as request.prompt")


+def _is_supported_as_default_only(key: str, value: Any) -> bool:
+    default_value = _DEFAULT_REQUEST_UPDATES.get(key, _MISSING)
+    return default_value is not _MISSING and _values_equal(value, default_value)
+
+
+def _collect_non_default_fields(
+    value: Any,
+    default: Any,
+) -> dict[str, Any]:
+    if not (is_dataclass(value) and is_dataclass(default)):
+        return {}
+
+    result: dict[str, Any] = {}
+    for field in fields(value):
+        current = getattr(value, field.name)
+        default_value = getattr(default, field.name)
+        if is_dataclass(current) and is_dataclass(default_value):
+            nested = _collect_non_default_fields(current, default_value)
+            if nested:
+                result[field.name] = nested
+            continue
+        if not _values_equal(current, default_value):
+            result[field.name] = deepcopy(current)
+    return result
+
+
+def _values_equal(left: Any, right: Any) -> bool:
+    if left is right:
+        return True
+    try:
+        return bool(left == right)
+    except Exception:
+        return False
+
+
+_DEFAULT_REQUEST_UPDATES = _extract_request_updates(config_to_dict(GenerationRequest()))
+
 __all__ = [
-    "explicit_request_updates",
    "generator_config_to_fastvideo_args",
    "legacy_from_pretrained_to_config",
    "legacy_generate_call_to_request",
    "load_generator_config_from_file",
    "normalize_generation_request",
    "normalize_generator_config",
-    "register_continuation_kind",
    "request_to_pipeline_overrides",
    "request_to_sampling_param",
 ]
@@ -45,15 +45,6 @@ def apply_overrides(config: Mapping[str, Any], overrides: Mapping[str, Any]) ->
    return merged


-def normalize_overrides(overrides: list[str] | Mapping[str, Any] | None, ) -> dict[str, Any] | None:
-    """Normalize a CLI list or mapping of overrides into a flat dict."""
-    if not overrides:
-        return None
-    if isinstance(overrides, list):
-        return parse_cli_overrides(overrides)
-    return dict(overrides)
-
-
 def _apply_single_override(config: dict[str, Any], dotted_key: str, value: Any) -> None:
    parts = dotted_key.split(".")
    if not all(parts):
@@ -107,4 +98,4 @@ def _normalize_override_key(key: str) -> str:
    return key.replace("-", "_")


-__all__ = ["apply_overrides", "normalize_overrides", "parse_cli_overrides"]
+__all__ = ["apply_overrides", "parse_cli_overrides"]
@@ -11,7 +11,7 @@ from typing import Any, Literal, TypeVar, Union, get_args, get_origin, get_type_
 import yaml

 from fastvideo.api.errors import ConfigValidationError
-from fastvideo.api.overrides import apply_overrides, normalize_overrides
+from fastvideo.api.overrides import apply_overrides, parse_cli_overrides
 from fastvideo.api.request_metadata import (
    bind_generation_request_raw,
    bind_run_config_raw,
@@ -64,7 +64,7 @@ def load_config(
 ) -> T:
    """Load a typed config object from YAML or JSON."""
    raw = load_raw_config(path)
-    normalized_overrides = normalize_overrides(overrides)
+    normalized_overrides = _normalize_overrides(overrides)
    if normalized_overrides:
        raw = apply_overrides(raw, normalized_overrides)
    return parse_config(config_type, raw)
@@ -108,6 +108,14 @@ def _load_raw_mapping(handle: Any, config_path: Path) -> Any:
    raise ValueError(f"Unsupported config file format: {config_path}")


+def _normalize_overrides(overrides: list[str] | Mapping[str, Any] | None, ) -> dict[str, Any] | None:
+    if not overrides:
+        return None
+    if isinstance(overrides, list):
+        return parse_cli_overrides(overrides)
+    return dict(overrides)
+
+
 class _SchemaParser:

    def parse_dataclass(
@@ -1,261 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Pipeline preset registry.
-
-A *preset* is a named inference preset for a model family. It bundles:
-
-  * ``defaults`` — sampling values applied when the user does not
-    override them (consumed at runtime via ``SamplingParam.from_pretrained``);
-  * ``stage_schemas`` — **validation-only** metadata describing which
-    user-facing stage names (``"denoise"``, ``"sr"``) the preset recognises
-    and which ``stage_overrides`` keys each stage accepts.
-
-The ``stage_schemas`` tuple does **not** drive pipeline execution. The
-concrete execution DAG (text encoding, denoising, VAE decoding, …) is
-hard-coded per-pipeline in ``create_pipeline_stages()``. Schemas exist
-purely so that ``PipelineSelection.preset`` and
-``GenerationRequest.stage_overrides`` can be type-checked up front
-without touching the pipeline.
-
-Preset base types and the registry API live here (public API surface).
-Preset *instances* are defined in pipeline-local ``presets.py`` files
-(e.g. ``fastvideo/pipelines/basic/wan/presets.py``) and registered
-explicitly from :func:`_register_presets` in ``fastvideo/registry.py``.
-"""
-from __future__ import annotations
-
-from collections.abc import Mapping
-from dataclasses import dataclass, field
-from typing import Any
-
-from fastvideo.api.errors import ConfigValidationError
-
-# -------------------------------------------------------------------
-# Types
-# -------------------------------------------------------------------
-
-
-@dataclass(frozen=True)
-class PresetStageSpec:
-    """A user-facing stage name within a preset, used only to validate
-    ``stage_overrides`` keys. Not read by pipeline execution — the real
-    execution DAG lives in each pipeline's ``create_pipeline_stages()``.
-    """
-
-    name: str
-    """Short user-facing name, e.g. ``"denoise"``, ``"sr"``."""
-
-    kind: str
-    """Semantic kind, e.g. ``"denoising"``, ``"super_resolution"``."""
-
-    description: str = ""
-
-    allowed_overrides: frozenset[str] = field(default_factory=frozenset)
-    """Keys that may appear in ``stage_overrides[name]``."""
-
-
-@dataclass(frozen=True)
-class InferencePreset:
-    """A named inference preset for a model family."""
-
-    name: str
-    """Preset name, e.g. ``"wan_t2v_1_3b"``."""
-
-    version: int
-    """Preset schema version; bump on breaking schema changes."""
-
-    model_family: str
-    """Model family key, e.g. ``"wan"``, ``"ltx2"``."""
-
-    description: str = ""
-
-    workload_type: str | None = None
-    """Optional workload hint: ``"t2v"``, ``"i2v"``, etc."""
-
-    stage_schemas: tuple[PresetStageSpec, ...] = ()
-    """User-facing stage names for ``stage_overrides`` validation.
-
-    Validation-only: this tuple is consumed by
-    :func:`validate_stage_overrides` and is **not** used to drive
-    pipeline execution. Omit or leave empty if the preset exposes no
-    per-stage override surface.
-    """
-
-    defaults: dict[str, Any] = field(default_factory=dict)
-    """Preset-level default sampling/runtime values."""
-
-    stage_defaults: dict[str, dict[str, Any]] = field(default_factory=dict)
-    """Per-stage default overrides, keyed by stage name."""
-
-
-# -------------------------------------------------------------------
-# Registry
-# -------------------------------------------------------------------
-
-# Keyed by (model_family, name, version).
-_PRESET_REGISTRY: dict[tuple[str, str, int], InferencePreset] = {}
-
-
-def register_preset(preset: InferencePreset) -> None:
-    """Register a preset definition.
-
-    Raises :class:`ValueError` on duplicate
-    ``(model_family, name, version)`` keys.
-    """
-    key = (preset.model_family, preset.name, preset.version)
-    if key in _PRESET_REGISTRY:
-        raise ValueError(f"Duplicate preset registration: "
-                         f"model_family={key[0]!r}, name={key[1]!r}, "
-                         f"version={key[2]!r}")
-    _PRESET_REGISTRY[key] = preset
-
-
-def get_preset(
-    name: str,
-    model_family: str,
-    version: int | None = None,
-) -> InferencePreset:
-    """Look up a registered preset.
-
-    When *version* is ``None`` the highest registered version for the
-    given *(model_family, name)* pair is returned.
-
-    Raises :class:`~fastvideo.api.errors.ConfigValidationError` when the
-    preset cannot be found.
-    """
-    if version is not None:
-        key = (model_family, name, version)
-        preset = _PRESET_REGISTRY.get(key)
-        if preset is not None:
-            return preset
-        raise ConfigValidationError(
-            "pipeline.preset",
-            f"unknown preset {name!r} version {version!r} "
-            f"for model family {model_family!r}; "
-            f"registered: {_format_registered(model_family)}",
-        )
-
-    # Find the highest version for (model_family, name).
-    candidates = [prof for (fam, n, _v), prof in _PRESET_REGISTRY.items() if fam == model_family and n == name]
-    if not candidates:
-        raise ConfigValidationError(
-            "pipeline.preset",
-            f"unknown preset {name!r} for model family "
-            f"{model_family!r}; "
-            f"registered: {_format_registered(model_family)}",
-        )
-    return max(candidates, key=lambda p: p.version)
-
-
-def get_presets_for_family(model_family: str, ) -> list[InferencePreset]:
-    """Return all presets registered for *model_family*."""
-    return [prof for (fam, _n, _v), prof in _PRESET_REGISTRY.items() if fam == model_family]
-
-
-def get_all_preset_names() -> list[str]:
-    """Return the sorted list of all registered preset names."""
-    return sorted({prof.name for prof in _PRESET_REGISTRY.values()})
-
-
-# -------------------------------------------------------------------
-# Validation helpers
-# -------------------------------------------------------------------
-
-
-def validate_stage_names(
-    preset: InferencePreset,
-    stage_overrides: Mapping[str, Any],
-) -> None:
-    """Check that *stage_overrides* keys are valid stage names.
-
-    Raises :class:`~fastvideo.api.errors.ConfigValidationError` with a
-    path-qualified message for unknown stage names.
-    """
-    valid_names = {stage.name for stage in preset.stage_schemas}
-    for stage_name in stage_overrides:
-        if stage_name not in valid_names:
-            raise ConfigValidationError(
-                f"stage_overrides.{stage_name}",
-                f"unknown stage for preset {preset.name!r}; "
-                f"valid stages: {sorted(valid_names)}",
-            )
-
-
-def validate_stage_overrides(
-    preset: InferencePreset,
-    stage_overrides: Mapping[str, Any],
-) -> None:
-    """Validate stage override keys against the preset.
-
-    Calls :func:`validate_stage_names` first, then checks that each
-    override key is in the stage's ``allowed_overrides``.
-    """
-    validate_stage_names(preset, stage_overrides)
-    stages_by_name = {stage.name: stage for stage in preset.stage_schemas}
-    for stage_name, overrides in stage_overrides.items():
-        if not isinstance(overrides, Mapping):
-            raise ConfigValidationError(
-                f"stage_overrides.{stage_name}",
-                "must be a mapping",
-            )
-        stage_spec = stages_by_name[stage_name]
-        if not stage_spec.allowed_overrides:
-            if overrides:
-                raise ConfigValidationError(
-                    f"stage_overrides.{stage_name}",
-                    f"stage {stage_name!r} does not accept "
-                    f"overrides",
-                )
-            continue
-        for key in overrides:
-            if key not in stage_spec.allowed_overrides:
-                raise ConfigValidationError(
-                    f"stage_overrides.{stage_name}.{key}",
-                    f"not an allowed override for stage "
-                    f"{stage_name!r}; allowed: "
-                    f"{sorted(stage_spec.allowed_overrides)}",
-                )
-
-
-def validate_preset_selection(
-    preset_name: str | None,
-    model_family: str,
-    *,
-    preset_version: int | None = None,
-    stage_overrides: Mapping[str, Any] | None = None,
-) -> InferencePreset | None:
-    """Resolve and validate a preset selection end-to-end.
-
-    Returns the resolved :class:`InferencePreset`, or ``None`` if
-    *preset_name* is ``None`` (no preset requested).
-    """
-    if preset_name is None:
-        return None
-    preset = get_preset(preset_name, model_family, version=preset_version)
-    if stage_overrides:
-        validate_stage_overrides(preset, stage_overrides)
-    return preset
-
-
-# -------------------------------------------------------------------
-# Internal helpers
-# -------------------------------------------------------------------
-
-
-def _format_registered(model_family: str) -> str:
-    names = sorted({prof.name for (fam, _n, _v), prof in _PRESET_REGISTRY.items() if fam == model_family})
-    if not names:
-        return "(none)"
-    return ", ".join(repr(n) for n in names)
-
-
-__all__ = [
-    "InferencePreset",
-    "PresetStageSpec",
-    "get_all_preset_names",
-    "get_preset",
-    "get_presets_for_family",
-    "register_preset",
-    "validate_preset_selection",
-    "validate_stage_names",
-    "validate_stage_overrides",
-]
@@ -1,25 +1,24 @@
 # SPDX-License-Identifier: Apache-2.0
 """Track which GenerationRequest fields the user explicitly provided.

-When translating a GenerationRequest into a legacy SamplingParam we must
-distinguish user-provided values (which should override model defaults)
-from schema defaults (which should NOT override model defaults).
+This module solves a specific problem: when translating a GenerationRequest into
+a legacy SamplingParam, we need to distinguish user-provided values (which
+should override model defaults) from schema defaults (which should NOT override
+model defaults).

-The mechanism: a single ``_fastvideo_explicit_paths`` set stored on the
-root ``GenerationRequest``. It holds dotted leaf paths (e.g.
-``"sampling.guidance_scale"``) the user has touched, either via raw
-config at bind time or via attribute assignment at runtime. A patched
-``__setattr__`` on the request dataclass types records assignments into
-this set.
-
-The set holds leaf paths only. Nested dataclass or mapping assignments
-are flattened to their leaves at record time.
+The approach:
+1. At bind time, store the original raw dict and a baseline snapshot.
+2. Patch __setattr__ on tracked dataclass types to record dirty field paths.
+3. At access time, do a lazy 3-way merge: raw + baseline + current state,
+   with dirty paths forcing inclusion even when current == baseline.
 """
 from __future__ import annotations

-from collections.abc import Callable, Mapping
+from collections.abc import Mapping
+from copy import deepcopy
 import dataclasses
 from typing import Any, cast
+from collections.abc import Callable

 from fastvideo.api.schema import (
    ContinuationState,
@@ -34,12 +33,12 @@ from fastvideo.api.schema import (
    ServeConfig,
 )

-EXPLICIT_PATHS_ATTR = "_fastvideo_explicit_paths"
-
+EXPLICIT_REQUEST_ATTR = "_fastvideo_explicit_request"
+ORIGINAL_REQUEST_STATE_ATTR = "_fastvideo_original_request_state"
 _TRACKING_ROOT_ATTR = "_fastvideo_request_tracking_root"
 _TRACKING_PATH_ATTR = "_fastvideo_request_tracking_path"
 _TRACKING_PATCHED_ATTR = "_fastvideo_request_tracking_patched"
-
+_DIRTY_PATHS_ATTR = "_fastvideo_dirty_paths"
 _TRACKED_REQUEST_TYPES = (
    GenerationRequest,
    InputConfig,
@@ -56,20 +55,14 @@ def bind_generation_request_raw(
    request: GenerationRequest,
    raw: Mapping[str, Any] | None,
 ) -> GenerationRequest:
-    """Install explicit-path tracking on *request*.
-
-    *raw* is the parsed config dict (YAML/JSON/kwargs); every leaf key
-    in it becomes an explicit path. Subsequent attribute assignments on
-    *request* or its nested dataclasses are recorded automatically via a
-    patched ``__setattr__``.
-    """
    _ensure_request_tracking()
-    # Disable recording while we walk the tree to install roots.
-    object.__setattr__(request, EXPLICIT_PATHS_ATTR, None)
+    # Disable dirty tracking during bind so tree walk doesn't record paths.
+    object.__setattr__(request, _DIRTY_PATHS_ATTR, None)
+    object.__setattr__(request, EXPLICIT_REQUEST_ATTR, deepcopy(dict(raw or {})))
+    object.__setattr__(request, ORIGINAL_REQUEST_STATE_ATTR, _serialize_config(request))
    _set_tracking_roots(request, request, "")
-    paths: set[str] = set()
-    _record_value_paths(raw or {}, "", paths)
-    object.__setattr__(request, EXPLICIT_PATHS_ATTR, paths)
+    # Enable dirty tracking.
+    object.__setattr__(request, _DIRTY_PATHS_ATTR, set())
    return request


@@ -80,8 +73,6 @@ def bind_run_config_raw(
    request_raw = raw.get("request")
    if isinstance(request_raw, Mapping):
        bind_generation_request_raw(config.request, request_raw)
-    else:
-        bind_generation_request_raw(config.request, {})
    return config


@@ -92,72 +83,87 @@ def bind_serve_config_raw(
    default_request_raw = raw.get("default_request")
    if isinstance(default_request_raw, Mapping):
        bind_generation_request_raw(config.default_request, default_request_raw)
-    else:
+    elif "default_request" not in raw:
        bind_generation_request_raw(config.default_request, {})
    return config


-def get_explicit_paths(request: GenerationRequest) -> frozenset[str]:
-    """Return a snapshot of the explicit paths set on *request*."""
-    paths = getattr(request, EXPLICIT_PATHS_ATTR, None)
-    if isinstance(paths, set | frozenset):
-        return frozenset(paths)
-    return frozenset()
+def refresh_generation_request_raw(request: GenerationRequest, ) -> dict[str, Any] | None:
+    raw = getattr(request, EXPLICIT_REQUEST_ATTR, None)
+    baseline = getattr(request, ORIGINAL_REQUEST_STATE_ATTR, None)
+    if not isinstance(raw, Mapping) or not isinstance(baseline, Mapping):
+        return None

+    dirty = getattr(request, _DIRTY_PATHS_ATTR, None) or frozenset()
+    current = _serialize_config(request)
+    merged = deepcopy(dict(raw))
+    _merge_request_mutations(merged, dict(baseline), current, dirty)

-def reset_tracking_roots(request: GenerationRequest) -> None:
-    """Re-install tracking roots after a deepcopy or manual clone.
-
-    The paths set itself deepcopies correctly; we only need to repoint
-    the tracking root on nested dataclasses at the new root.
-    """
-    _ensure_request_tracking()
-    _set_tracking_roots(request, request, "")
+    object.__setattr__(request, EXPLICIT_REQUEST_ATTR, merged)
+    object.__setattr__(request, ORIGINAL_REQUEST_STATE_ATTR, current)
+    object.__setattr__(request, _DIRTY_PATHS_ATTR, set())
+    return merged


 # ---------------------------------------------------------------------------
-# Path recording
+# 3-way merge: raw + baseline + current, with dirty-path forcing
 # ---------------------------------------------------------------------------

+_MISSING = object()

-def _record_value_paths(
-    value: Any,
-    prefix: str,
-    out: set[str],
+
+def _merge_request_mutations(
+    merged: dict[str, Any],
+    baseline: Mapping[str, Any],
+    current: Mapping[str, Any],
+    dirty: frozenset[str] | set[str],
+    path_prefix: str = "",
+    force_dirty: bool = False,
 ) -> None:
-    """Add every leaf path under *value* to *out*.
+    # Remove keys that were deleted from the current state.
+    for key in set(merged) | set(baseline):
+        if key not in current:
+            merged.pop(key, None)

-    A leaf is any terminal value (non-dataclass, non-mapping, or empty
-    mapping/dataclass). ``prefix`` is the dotted path at which *value*
-    sits. When called with an empty ``prefix`` (the root), leaves are
-    recorded at their own key.
-    """
-    if dataclasses.is_dataclass(value) and not isinstance(value, type):
-        dc_fields = dataclasses.fields(value)
-        if not dc_fields:
-            if prefix:
-                out.add(prefix)
-            return
-        for field in dc_fields:
-            child = getattr(value, field.name)
-            path = f"{prefix}.{field.name}" if prefix else field.name
-            _record_value_paths(child, path, out)
-        return
-    if isinstance(value, Mapping):
-        if not value:
-            if prefix:
-                out.add(prefix)
-            return
-        for key, child in value.items():
-            path = f"{prefix}.{key}" if prefix else key
-            _record_value_paths(child, path, out)
-        return
-    if prefix:
-        out.add(prefix)
+    for key in current:
+        current_path = f"{path_prefix}.{key}" if path_prefix else key
+        current_value = current[key]
+        baseline_value = baseline.get(key, _MISSING)
+        merged_value = merged.get(key, _MISSING)
+
+        # If this exact path was dirtied (e.g. whole section replaced),
+        # propagate to all children.
+        child_force = force_dirty or current_path in dirty
+
+        # Recurse into nested mappings.
+        if isinstance(current_value, Mapping) and isinstance(baseline_value, Mapping):
+            nested = (deepcopy(dict(merged_value)) if isinstance(merged_value, Mapping) else {})
+            _merge_request_mutations(
+                nested,
+                baseline_value,
+                current_value,
+                dirty,
+                current_path,
+                child_force,
+            )
+            if nested:
+                merged[key] = nested
+            else:
+                merged.pop(key, None)
+            continue
+
+        # A field is explicitly set if:
+        # - it's new (not in baseline),
+        # - it changed from baseline,
+        # - its path was touched by __setattr__ (dirty), or
+        # - an ancestor path was dirty (whole section replaced).
+        is_dirty = child_force or current_path in dirty
+        if baseline_value is _MISSING or current_value != baseline_value or is_dirty:
+            merged[key] = deepcopy(current_value)


 # ---------------------------------------------------------------------------
-# __setattr__ patching
+# __setattr__ patching for dirty-path recording
 # ---------------------------------------------------------------------------


@@ -181,22 +187,15 @@ def _patch_tracking_setattr(config_type: type[Any]) -> None:
            original_setattr(self, name, value)
            return

-        original_setattr(self, name, value)
-
        root = getattr(self, _TRACKING_ROOT_ATTR, None)
-        if root is None:
-            return
-        paths = getattr(root, EXPLICIT_PATHS_ATTR, None)
-        if not isinstance(paths, set):
-            return
+        if root is not None:
+            dirty = getattr(root, _DIRTY_PATHS_ATTR, None)
+            if isinstance(dirty, set):
+                prefix = getattr(self, _TRACKING_PATH_ATTR, "")
+                path = f"{prefix}.{name}" if prefix else name
+                dirty.add(path)

-        prefix = getattr(self, _TRACKING_PATH_ATTR, "")
-        path = f"{prefix}.{name}" if prefix else name
-        # Wholesale dataclass replacement: install roots on the new
-        # instance so its future mutations are tracked too.
-        if dataclasses.is_dataclass(value) and not isinstance(value, type):
-            _set_tracking_roots(root, value, path)
-        _record_value_paths(value, path, paths)
+        original_setattr(self, name, value)

    type.__setattr__(config_type, "__setattr__", _tracking_setattr)
    setattr(config_type, _TRACKING_PATCHED_ATTR, True)
@@ -223,11 +222,26 @@ def _set_tracking_roots(
            _set_tracking_roots(root, child, child_path)


+# ---------------------------------------------------------------------------
+# Serialization helper
+# ---------------------------------------------------------------------------
+
+
+def _serialize_config(config: Any) -> Any:
+    if dataclasses.is_dataclass(config) and not isinstance(config, type):
+        return {field.name: _serialize_config(getattr(config, field.name)) for field in dataclasses.fields(config)}
+    if isinstance(config, list):
+        return [_serialize_config(item) for item in config]
+    if isinstance(config, dict):
+        return {key: _serialize_config(value) for key, value in config.items()}
+    return deepcopy(config)
+
+
 __all__ = [
-    "EXPLICIT_PATHS_ATTR",
+    "EXPLICIT_REQUEST_ATTR",
+    "ORIGINAL_REQUEST_STATE_ATTR",
    "bind_generation_request_raw",
    "bind_run_config_raw",
    "bind_serve_config_raw",
-    "get_explicit_paths",
-    "reset_tracking_roots",
+    "refresh_generation_request_raw",
 ]
@@ -33,25 +33,8 @@ class OffloadConfig:

@dataclass
 class CompileConfig:
-    """Typed ``torch.compile`` configuration.
-
-    ``backend``/``fullgraph``/``mode``/``dynamic`` are the four most
-    common ``torch.compile`` knobs. ``extras`` holds any remaining
-    ``torch.compile`` kwargs (e.g. ``options``, ``disable``).
-    """
-
    enabled: bool = False
-    text_encoder_enabled: bool | None = None
-    """Whether ``torch.compile`` is applied to the text encoder. ``None``
-    keeps the runtime default. The public ``FastVideoArgs`` adapter does
-    not yet consume this flag; reserved so the realtime runtime upstream
-    (PR 7.6) has a typed home for its ``enable_torch_compile_text_encoder``
-    kwarg without routing through ``pipeline.experimental``."""
-    backend: str | None = None
-    fullgraph: bool | None = None
-    mode: str | None = None
-    dynamic: bool | None = None
-    extras: dict[str, Any] = field(default_factory=dict)
+    kwargs: dict[str, Any] = field(default_factory=dict)


@dataclass
@@ -90,12 +73,10 @@ class ComponentConfig:
@dataclass
 class PipelineSelection:
    workload_type: Literal["t2v", "i2v", "t2i", "i2i"] | None = None
-    preset: str | None = None
-    preset_version: int | None = None
+    profile: str | None = None
+    profile_version: str | None = None
    components: ComponentConfig = field(default_factory=ComponentConfig)
-    vae_tiling: bool | None = None
-    """Tile-based VAE decode. ``None`` keeps the model's default."""
-    preset_overrides: dict[str, Any] = field(default_factory=dict)
+    profile_overrides: dict[str, Any] = field(default_factory=dict)
    experimental: dict[str, Any] = field(default_factory=dict)


@@ -199,73 +180,11 @@ class RunConfig:
    request: GenerationRequest


-@dataclass
-class WarmupConfig:
-    enabled: bool = True
-    prompt: str = ("A cinematic drone shot over coastal cliffs at sunrise, "
-                   "golden light, gentle ocean waves, ultra detailed")
-    timeout_seconds: int = 2400
-
-
-@dataclass
-class GpuPoolConfig:
-    num_workers: int | None = None
-    enable_audio_reencode: bool = True
-    conditioning_num_frames: int = 9
-    conditioning_end_offset: int = 0
-
-
-@dataclass
-class PromptEnhancerConfig:
-    enabled: bool = False
-    provider: Literal["cerebras", "groq"] = "cerebras"
-    model: str = "gpt-oss-120b"
-    timeout_ms: int = 20000
-    system_prompt_dir: str | None = None
-
-
-@dataclass
-class PromptSafetyConfig:
-    enabled: bool = False
-    classifier_path: str | None = None
-
-
-@dataclass
-class StreamingConfig:
-    session_timeout_seconds: int = 300
-    generation_segment_cap: int = 6
-    stream_mode: Literal["av_fmp4", "legacy_jpeg"] = "av_fmp4"
-    warmup: WarmupConfig = field(default_factory=WarmupConfig)
-    pool: GpuPoolConfig = field(default_factory=GpuPoolConfig)
-    prompt: PromptEnhancerConfig = field(default_factory=PromptEnhancerConfig)
-    safety: PromptSafetyConfig = field(default_factory=PromptSafetyConfig)
-
-
@dataclass
 class ServeConfig:
-    """Typed serve config loaded from ``fastvideo serve --config``.
-
-    ``default_request`` is a full :class:`GenerationRequest` — the same type
-    clients POST to ``/v1/videos``. At request time the server merges it into
-    the incoming body as the operator-pinned baseline.
-
-    Important nuance: only fields the operator **explicitly wrote** in the
-    serve YAML/JSON count as defaults. Although the in-memory object is
-    fully populated (schema defaults fill every unset field), the merge
-    walks ``_fastvideo_explicit_paths`` — populated during parse — so
-    unset fields are *not* forced onto requests. Per-request precedence:
-
-        body (client-explicit) > default_request (operator-explicit)
-                               > hardcoded fallback (e.g. ``fps=24``)
-
-    See :func:`fastvideo.api.compat.explicit_request_updates` for the
-    projection and ``entrypoints/openai/video_api.py::_build_generation_kwargs``
-    for the merge.
-    """
    generator: GeneratorConfig
    server: ServerConfig = field(default_factory=ServerConfig)
    default_request: GenerationRequest = field(default_factory=GenerationRequest)
-    streaming: StreamingConfig | None = None


 __all__ = [
@@ -276,21 +195,16 @@ __all__ = [
    "GenerationPlan",
    "GenerationRequest",
    "GeneratorConfig",
-    "GpuPoolConfig",
    "InputConfig",
    "OffloadConfig",
    "OutputConfig",
    "ParallelismConfig",
    "PipelineSelection",
    "PlannedStage",
-    "PromptEnhancerConfig",
-    "PromptSafetyConfig",
    "QuantizationConfig",
    "RequestRuntimeConfig",
    "RunConfig",
    "SamplingConfig",
    "ServeConfig",
    "ServerConfig",
-    "StreamingConfig",
-    "WarmupConfig",
 ]
@@ -5,7 +5,7 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
-from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
+from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
 from fastvideo.registry import get_pipeline_config_cls_from_name
 from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig, WanI2V480PConfig, WanI2V720PConfig,
                                             WanT2V480PConfig, WanT2V720PConfig)
@@ -11,34 +11,10 @@ import torch
 from fastvideo.configs.models import DiTConfig, VAEConfig
 from fastvideo.configs.models.dits.base import DiTArchConfig
 from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
-from fastvideo.configs.models.encoders.base import TextEncoderArchConfig
-from fastvideo.configs.models.encoders.t5 import T5ArchConfig
 from fastvideo.configs.models.vaes import WanVAEConfig
 from fastvideo.configs.pipelines.base import PipelineConfig


-@dataclass
-class LongCatT5ArchConfig(T5ArchConfig):
-    """T5 arch that pads tokenizer output to ``max_length``.
-
-    LongCat's denoising stage concatenates positive and negative
-    attention masks along the batch dimension for CFG, which requires
-    uniform seq length. The shared :class:`T5ArchConfig` dropped the
-    ``"padding": "max_length"`` tokenizer kwarg so other DiTs could run
-    with variable-length masks; LongCat still needs the uniform
-    contract.
-    """
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.tokenizer_kwargs["padding"] = "max_length"
-
-
-@dataclass
-class LongCatT5Config(T5Config):
-    arch_config: TextEncoderArchConfig = field(default_factory=LongCatT5ArchConfig)
-
-
@dataclass
 class LongCatDiTArchConfig(DiTArchConfig):
    """Extended DiTArchConfig with LongCat-specific fields."""
@@ -127,9 +103,8 @@ class LongCatT2V480PConfig(PipelineConfig):
    vae_precision: str = "bf16"
    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", ))

-    # UMT5 uses T5-like config; postprocess pads to 512. LongCatT5Config
-    # restores ``padding="max_length"`` for the CFG concat contract.
-    text_encoder_configs: tuple[T5Config, ...] = field(default_factory=lambda: (LongCatT5Config(), ))
+    # Text encoding (UMT5 uses T5-like config; postprocess to fixed 512)
+    text_encoder_configs: tuple[T5Config, ...] = field(default_factory=lambda: (T5Config(), ))
    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(default_factory=lambda: (longcat_preprocess_text, ))
    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
                                  ...] = field(default_factory=lambda: (umt5_postprocess_text, ))
@@ -0,0 +1,13 @@
+from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.hunyuangamecraft import (
+    HunyuanGameCraftSamplingParam,
+    HunyuanGameCraft65FrameSamplingParam,
+    HunyuanGameCraft129FrameSamplingParam,
+)
+
+__all__ = [
+    "SamplingParam",
+    "HunyuanGameCraftSamplingParam",
+    "HunyuanGameCraft65FrameSamplingParam",
+    "HunyuanGameCraft129FrameSamplingParam",
+]
@@ -1,16 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import copy
-from dataclasses import dataclass, field, fields
-from typing import TYPE_CHECKING, Any
+from dataclasses import dataclass
+from typing import Any

 from fastvideo.logger import init_logger
 from fastvideo.utils import StoreBoolean

-if TYPE_CHECKING:
-    from fastvideo.api.schema import ContinuationState
-
 logger = init_logger(__name__)


@@ -36,16 +30,6 @@ class SamplingParam:

    # Camera control inputs (HYWorld)
    pose: str | None = None  # Camera trajectory: pose string (e.g., 'w-31') or JSON file path
-    prompt_attention_mask: list = field(default_factory=list)
-    negative_attention_mask: list = field(default_factory=list)
-
-    # Camera/action control inputs (GameCraft)
-    camera_states: Any | None = None  # Plücker coordinates [B, T_video, 6, H, W]
-    camera_trajectory: str | None = None
-    action_list: list[str] | None = None
-    action_speed_list: list[float] | None = None
-    gt_latents: Any | None = None  # Ground truth latents [B, 16, T, H, W]
-    conditioning_mask: Any | None = None  # Mask [B, 1, T, H, W]

    # Camera control inputs (LingBotWorld)
    c2ws_plucker_emb: Any | None = None  # Plucker embedding: [B, C, F_lat, H_lat, W_lat]
@@ -84,7 +68,6 @@ class SamplingParam:
    num_inference_steps: int = 50
    num_inference_steps_sr: int = 50
    guidance_scale: float = 1.0
-    guidance_scale_2: float | None = None
    guidance_rescale: float = 0.0
    boundary_ratio: float | None = None
    sigmas: list[float] | None = None
@@ -97,27 +80,6 @@ class SamplingParam:
    movement_distance: float | None = None
    camera_rotation: str | None = None

-    # LTX-2 multi-modal CFG and STG.
-    # cfg_scale defaults are 1.0 (CFG off) so ``ForwardBatch.__post_init__``
-    # doesn't force ``do_classifier_free_guidance`` on non-LTX-2 models that
-    # never override these fields. LTX-2 presets that need text-CFG on set
-    # them in their ``defaults`` dict (e.g. ``ltx2_base``).
-    ltx2_cfg_scale_video: float = 1.0
-    ltx2_cfg_scale_audio: float = 1.0
-    ltx2_modality_scale_video: float = 3.0
-    ltx2_modality_scale_audio: float = 3.0
-    ltx2_rescale_scale: float = 0.7
-    ltx2_stg_scale_video: float = 1.0
-    ltx2_stg_scale_audio: float = 1.0
-    ltx2_stg_blocks_video: list[int] = field(default_factory=lambda: [29])
-    ltx2_stg_blocks_audio: list[int] = field(default_factory=lambda: [29])
-
-    # Continuation state carried across streaming/multi-segment calls.
-    continuation_state: ContinuationState | None = None
-    # When True, the pipeline returns a ContinuationState on the result so
-    # the caller can resume from the generated segment.
-    return_continuation_state: bool = False
-
    # Misc
    save_video: bool = True
    return_frames: bool = True
@@ -132,58 +94,64 @@ class SamplingParam:
            raise ValueError("prompt_path must be a txt file")

    def update(self, source_dict: dict[str, Any]) -> None:
-        valid_fields = {f.name for f in fields(self)}
        for key, value in source_dict.items():
-            if key in valid_fields:
+            if hasattr(self, key):
                setattr(self, key, value)
            else:
-                logger.error("%s has no field %s", type(self).__name__, key)
+                logger.exception("%s has no attribute %s", type(self).__name__, key)

        self.__post_init__()

    @classmethod
-    def from_pretrained(cls, model_path: str) -> SamplingParam:
-        sampling_param = cls._from_preset(model_path)
-        if sampling_param is not None:
-            return sampling_param
+    def _from_profile(
+        cls,
+        profile_name: str,
+    ) -> "SamplingParam":
+        """Create a ``SamplingParam`` with profile defaults applied.

-        logger.warning(
-            "Couldn't find a preset for %s."
-            " Using the default sampling param.",
-            model_path,
-        )
-        return cls()
+        Looks up *profile_name* in the registered profile tables,
+        creates a base ``SamplingParam()``, then delegates to
+        :meth:`update` so that ``__post_init__`` is called and
+        derived fields stay consistent.
+        """
+        from fastvideo.pipelines.basic.hunyuan.profiles import (
+            PROFILES as _hunyuan_profiles, )
+
+        # Merged lazily; extend this dict as more model families
+        # migrate to profile-based defaults.
+        all_profiles = _hunyuan_profiles
+
+        if profile_name not in all_profiles:
+            raise ValueError(f"Unknown profile '{profile_name}'. "
+                             f"Available: {sorted(all_profiles)}")
+
+        instance = cls()
+        instance.update(all_profiles[profile_name].defaults)
+        return instance

    @classmethod
-    def _from_preset(
-        cls,
-        model_path: str,
-    ) -> SamplingParam | None:
-        """Build a SamplingParam from preset defaults.
+    def from_pretrained(cls, model_path: str) -> "SamplingParam":
+        from fastvideo.registry import _get_config_info

-        Returns ``None`` when no preset is configured for
-        *model_path*, letting the caller fall back to the legacy
-        subclass lookup.
-        """
-        from fastvideo.registry import get_preset_selection
+        config_info = _get_config_info(model_path, raise_on_missing=False)

-        try:
-            preset_name, model_family = get_preset_selection(model_path)
-        except (ValueError, RuntimeError):
-            return None
-        if preset_name is None or model_family is None:
-            return None
+        if config_info is None:
+            logger.warning(
+                "Couldn't find an optimal sampling param "
+                "for %s. Using the default sampling param.",
+                model_path,
+            )
+            return cls()

-        from fastvideo.api.presets import get_preset
+        # Profile-based path (preferred for new migrations).
+        if config_info.default_profile is not None:
+            return cls._from_profile(config_info.default_profile)

-        preset = get_preset(preset_name, model_family)
-        sp = cls()
-        valid_fields = {f.name for f in fields(cls)}
-        for key, value in preset.defaults.items():
-            if key in valid_fields:
-                setattr(sp, key, copy.deepcopy(value))
-        sp.__post_init__()
-        return sp
+        # Legacy path: use the registered subclass directly.
+        if config_info.sampling_param_cls is not None:
+            return config_info.sampling_param_cls()
+
+        return cls()

    @staticmethod
    def add_cli_args(parser: Any) -> Any:
@@ -0,0 +1,18 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Cosmos_Predict2_2B_Video2World_SamplingParam(SamplingParam):
+    # Video parameters
+    height: int = 704
+    width: int = 1280
+    num_frames: int = 93
+    fps: int = 16
+
+    # Denoising stage
+    guidance_scale: float = 7.0
+    negative_prompt: str = "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."
+    num_inference_steps: int = 35
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Cosmos25SamplingParamBase(SamplingParam):
+    height: int = 704
+    width: int = 1280
+    num_frames: int = 77
+    fps: int = 24
+    seed: int = 0
+
+    guidance_scale: float = 7.0
+    negative_prompt: str = (
+        "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
+        "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
+        "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, "
+        "low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, "
+        "unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. "
+        "Overall, the video is of poor quality.")
+    num_inference_steps: int = 35
@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Gen3C_Cosmos_7B_SamplingParam(SamplingParam):
+    """Defaults for GEN3C (Cosmos-7B) camera-controlled video generation."""
+
+    # Video parameters (matching official GEN3C defaults)
+    height: int = 704
+    width: int = 1280
+    num_frames: int = 121
+    fps: int = 24
+
+    # Denoising stage
+    guidance_scale: float = 1.0
+    num_inference_steps: int = 35
+
+    # GEN3C camera control defaults
+    trajectory_type: str = "left"
+    movement_distance: float = 0.3
+    camera_rotation: str = "center_facing"
@@ -0,0 +1,7 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Hunyuan sampling parameter classes.
+
+Hunyuan model-specific SamplingParam subclasses have been removed.
+Defaults are now provided by pipeline profiles in
+``fastvideo/pipelines/basic/hunyuan/profiles.py``.
+"""
@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Hunyuan15_480P_SamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 121
+    height: int = 480
+    width: int = 848
+    fps: int = 24
+
+    guidance_scale: float = 6.0
+    sigmas: list[float] | None = field(default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+
+    negative_prompt: str = ""
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.sigmas = list(np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])
+
+
+@dataclass
+class Hunyuan15_480P_StepDistilled_I2V_SamplingParam(Hunyuan15_480P_SamplingParam):
+    num_inference_steps: int = 12
+
+    height: int = 720
+    width: int = 1280
+    guidance_scale: float = 1.0
+
+
+@dataclass
+class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):
+    height: int = 720
+    width: int = 1280
+
+
+@dataclass
+class Hunyuan15_720P_Distilled_I2V_SamplingParam(Hunyuan15_720P_SamplingParam):
+    guidance_scale: float = 1.0
+
+
+@dataclass
+class Hunyuan15_SR_1080P_SamplingParam(Hunyuan15_480P_SamplingParam):
+    height_sr: int = 1072
+    width_sr: int = 1920
+
+    num_inference_steps: int = 12
+    num_inference_steps_sr: int = 8
+
+    guidance_scale: float = 1.0
@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Sampling parameters for HunyuanGameCraft video generation.
+
+GameCraft generates game-like videos with camera/action control.
+Default parameters are based on the official implementation.
+"""
+from dataclasses import dataclass
+from typing import Any
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class HunyuanGameCraftSamplingParam(SamplingParam):
+    """Sampling parameters for HunyuanGameCraft video generation.
+    
+    Supports camera/action conditioning via:
+    - camera_trajectory: Plücker coordinates for camera motion
+    - action_list: List of actions (e.g., ["forward", "left", "right"])
+    - action_speed_list: Speed multipliers for each action
+    
+    Default resolution is 704x1280 (same as HunyuanVideo).
+    Default frame count is 33 video frames -> 9 latent frames.
+    """
+
+    # Number of denoising steps
+    num_inference_steps: int = 50
+
+    # Video dimensions
+    # 33 video frames -> 9 latent frames (4x temporal compression)
+    num_frames: int = 33
+    height: int = 704
+    width: int = 1280
+    fps: int = 24
+
+    # Guidance scale - official GameCraft uses CFG with guidance_scale=6.0
+    guidance_scale: float = 6.0
+
+    # Negative prompt for CFG (empty string = unconditional)
+    negative_prompt: str = ""
+
+    # Camera/Action conditioning
+    # Camera states as Plücker coordinates [B, T_video, 6, H, W]
+    camera_states: Any | None = None
+
+    # Camera trajectory file/identifier (alternative to camera_states)
+    camera_trajectory: str | None = None
+
+    # Action list for camera motion (e.g., ["forward", "left"])
+    action_list: list[str] | None = None
+
+    # Speed multipliers for each action
+    action_speed_list: list[float] | None = None
+
+    # History frame conditioning (for autoregressive generation)
+    # Ground truth latents for conditioning [B, 16, T, H, W]
+    gt_latents: Any | None = None
+
+    # Mask for conditioning (1=use gt, 0=generate) [B, 1, T, H, W]
+    conditioning_mask: Any | None = None
+
+    # Number of conditioning frames (for autoregressive) - maps to num_cond_frames
+    num_cond_frames: int = 0
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        # Validate action lists
+        if (self.action_list is not None and self.action_speed_list is not None
+                and len(self.action_list) != len(self.action_speed_list)):
+            raise ValueError(f"action_list length ({len(self.action_list)}) must match "
+                             f"action_speed_list length ({len(self.action_speed_list)})")
+
+
+@dataclass
+class HunyuanGameCraft65FrameSamplingParam(HunyuanGameCraftSamplingParam):
+    """Sampling parameters for 65-frame GameCraft generation.
+    
+    65 video frames -> 17 latent frames (with first frame as key frame).
+    This is useful for longer video generation.
+    """
+    num_frames: int = 65
+
+
+@dataclass
+class HunyuanGameCraft129FrameSamplingParam(HunyuanGameCraftSamplingParam):
+    """Sampling parameters for 129-frame GameCraft generation.
+    
+    129 video frames -> 33 latent frames.
+    This is the maximum supported by the official implementation.
+    """
+    num_frames: int = 129
@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+import numpy as np
+
+
+@dataclass
+class HYWorld_SamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 125
+    height: int = 480
+    width: int = 832
+    fps: int = 24
+
+    # Camera trajectory: pose string (e.g., 'w-31' means generating [1 + 31] latents) or JSON file path
+    pose: str = 'w-31'
+
+    guidance_scale: float = 6.0
+    prompt_attention_mask: list = field(default_factory=list)
+    negative_attention_mask: list = field(default_factory=list)
+    sigmas: list[float] | None = field(default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+
+    negative_prompt: str = ""
@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+from fastvideo.configs.sample.wan import Wan2_2_I2V_A14B_SamplingParam
+
+
+@dataclass
+class LingBotWorld_SamplingParam(Wan2_2_I2V_A14B_SamplingParam):
+    guidance_scale: float = 5.0  # high_noise
+    guidance_scale_2: float = 5.0  # low_noise
+    num_inference_steps: int = 70
+    boundary_ratio: float | None = 0.947
+    negative_prompt: str | None = ("画面突变，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，"
+                                   "最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，"
+                                   "畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走，"
+                                   "镜头晃动，画面闪烁，模糊，噪点，水印，签名，文字，变形，扭曲，液化，不合逻辑的结构，卡顿，"
+                                   "PPT幻灯片感，过暗，欠曝，低对比度，霓虹灯光感，过度锐化，3D渲染感，人物，行人，游客，身体，"
+                                   "皮肤，肢体，面部特征，汽车，电线")
+    fps: int = 16
+    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
+    # can be overridden during sampling
@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class LTX2BaseSamplingParam(SamplingParam):
+    """Default sampling parameters for LTX-2 base one-stage T2V.
+
+    Values follow the official LTX-2 one-stage defaults.
+    Multi-modal CFG params are read by ``LTX2DenoisingStage``.
+    """
+
+    seed: int = 10
+    num_frames: int = 121
+    height: int = 512
+    width: int = 768
+    fps: int = 24
+    num_inference_steps: int = 40
+    guidance_scale: float = 3.0
+    # Copied/following official LTX-2 DEFAULT_NEGATIVE_PROMPT.
+    negative_prompt: str = ("blurry, out of focus, overexposed, underexposed, low contrast, "
+                            "washed out colors, excessive noise, grainy texture, poor lighting, "
+                            "flickering, motion blur, distorted proportions, unnatural skin "
+                            "tones, deformed facial features, asymmetrical face, missing facial "
+                            "features, extra limbs, disfigured hands, wrong hand count, "
+                            "artifacts around text, inconsistent perspective, camera shake, "
+                            "incorrect depth of field, background too sharp, background clutter, "
+                            "distracting reflections, harsh shadows, inconsistent lighting "
+                            "direction, color banding, cartoonish rendering, 3D CGI look, "
+                            "unrealistic materials, uncanny valley effect, incorrect ethnicity, "
+                            "wrong gender, exaggerated expressions, wrong gaze direction, "
+                            "mismatched lip sync, silent or muted audio, distorted voice, "
+                            "robotic voice, echo, background noise, off-sync audio, incorrect "
+                            "dialogue, added dialogue, repetitive speech, jittery movement, "
+                            "awkward pauses, incorrect timing, unnatural transitions, "
+                            "inconsistent framing, tilted camera, flat lighting, inconsistent "
+                            "tone, cinematic oversaturation, stylized filters, or AI artifacts.")
+    # Official LTX-2 multi-modal CFG defaults.
+    ltx2_cfg_scale_video: float = 3.0
+    ltx2_cfg_scale_audio: float = 7.0
+    ltx2_modality_scale_video: float = 3.0
+    ltx2_modality_scale_audio: float = 3.0
+    ltx2_rescale_scale: float = 0.7
+    # STG (Spatio-Temporal Guidance) defaults from official LTX-2.
+    ltx2_stg_scale_video: float = 1.0
+    ltx2_stg_scale_audio: float = 1.0
+    ltx2_stg_blocks_video: list[int] = field(default_factory=lambda: [29])
+    ltx2_stg_blocks_audio: list[int] = field(default_factory=lambda: [29])
+
+
+@dataclass
+class LTX2DistilledSamplingParam(SamplingParam):
+    """Default sampling parameters for LTX-2 distilled one-stage T2V."""
+
+    seed: int = 10
+    num_frames: int = 121
+    height: int = 1024
+    width: int = 1536
+    fps: int = 24
+    num_inference_steps: int = 8
+    guidance_scale: float = 1.0
+    # No default negative_prompt for distilled models
+    negative_prompt: str = ""
+
+
+# Backward compatibility alias.
+LTX2SamplingParam = LTX2DistilledSamplingParam
@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class SD35SamplingParam(SamplingParam):
+
+    prompt: str | None = "a photo of a cat"
+    negative_prompt: str = ""
+
+    num_videos_per_prompt: int = 1
+    seed: int = 0
+
+    num_frames: int = 1
+    height: int = 512
+    width: int = 512
+    fps: int = 1
+
+    num_inference_steps: int = 28
+    guidance_scale: float = 6.0
@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+TurboDiffusion sampling parameters.
+
+TurboDiffusion uses RCM (recurrent Consistency Model) scheduler for
+1-4 step video generation with no classifier-free guidance.
+"""
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class TurboDiffusionT2V_1_3B_SamplingParam(SamplingParam):
+    """Sampling parameters for TurboDiffusion T2V 1.3B model.
+    
+    Uses 4-step RCM sampling with guidance_scale=1.0 (no CFG).
+    """
+    # Video parameters
+    height: int = 480
+    width: int = 832
+    num_frames: int = 81
+    fps: int = 16
+
+    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
+    guidance_scale: float = 1.0
+    num_inference_steps: int = 4
+
+    # No negative prompt needed for TurboDiffusion (no CFG)
+    negative_prompt: str | None = None
+
+
+@dataclass
+class TurboDiffusionT2V_14B_SamplingParam(SamplingParam):
+    """Sampling parameters for TurboDiffusion T2V 14B model.
+    
+    Uses 4-step RCM sampling with guidance_scale=1.0 (no CFG).
+    """
+    # Video parameters (720p for 14B)
+    height: int = 720
+    width: int = 1280
+    num_frames: int = 81
+    fps: int = 16
+
+    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
+    guidance_scale: float = 1.0
+    num_inference_steps: int = 4
+
+    # No negative prompt needed for TurboDiffusion (no CFG)
+    negative_prompt: str | None = None
+
+
+@dataclass
+class TurboDiffusionI2V_A14B_SamplingParam(SamplingParam):
+    """Sampling parameters for TurboDiffusion I2V A14B model.
+    
+    Uses 4-step RCM sampling with dual-model switching (high/low noise).
+    """
+    # Video parameters (720p for A14B I2V)
+    height: int = 720
+    width: int = 1280
+    num_frames: int = 81
+    fps: int = 16
+
+    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
+    guidance_scale: float = 1.0
+    num_inference_steps: int = 4
+
+    # Note: boundary_ratio is set in the pipeline config (TurboDiffusionI2VConfig),
+    # not here. This keeps sampling params and pipeline config separate.
+
+    # No negative prompt needed for TurboDiffusion (no CFG)
+    negative_prompt: str | None = None
@@ -0,0 +1,154 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class WanT2V_1_3B_SamplingParam(SamplingParam):
+    # Video parameters
+    height: int = 480
+    width: int = 832
+    num_frames: int = 81
+    fps: int = 16
+
+    # Denoising stage
+    guidance_scale: float = 3.0
+    negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+    num_inference_steps: int = 50
+
+
+@dataclass
+class WanT2V_14B_SamplingParam(SamplingParam):
+    # Video parameters
+    height: int = 720
+    width: int = 1280
+    num_frames: int = 81
+    fps: int = 16
+
+    # Denoising stage
+    guidance_scale: float = 5.0
+    negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+    num_inference_steps: int = 50
+
+
+@dataclass
+class WanI2V_14B_480P_SamplingParam(WanT2V_1_3B_SamplingParam):
+    # Denoising stage
+    guidance_scale: float = 5.0
+    num_inference_steps: int = 40
+
+
+@dataclass
+class WanI2V_14B_720P_SamplingParam(WanT2V_14B_SamplingParam):
+    # Denoising stage
+    guidance_scale: float = 5.0
+    num_inference_steps: int = 40
+
+
+@dataclass
+class FastWanT2V480P_SamplingParam(WanT2V_1_3B_SamplingParam):
+    # DMD parameters
+    # dmd_denoising_steps: list[int] | None = field(default_factory=lambda: [1000, 757, 522])
+    num_inference_steps: int = 3
+    num_frames: int = 61
+    height: int = 448
+    width: int = 832
+    fps: int = 16
+
+
+# =============================================
+# ============= Wan2.1 Fun Models =============
+# =============================================
+@dataclass
+class Wan2_1_Fun_1_3B_InP_SamplingParam(SamplingParam):
+    """Sampling parameters for Wan2.1 Fun 1.3B InP model."""
+    height: int = 480
+    width: int = 832
+    num_frames: int = 81
+    fps: int = 16
+    negative_prompt: str | None = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+    guidance_scale: float = 6.0
+    num_inference_steps: int = 50
+
+
+@dataclass
+class Wan2_1_Fun_1_3B_Control_SamplingParam(SamplingParam):
+    fps: int = 16
+    num_frames: int = 49
+    height: int = 832
+    width: int = 480
+    guidance_scale: float = 6.0
+
+
+# =============================================
+# ============= Wan2.2 TI2V Models =============
+# =============================================
+@dataclass
+class Wan2_2_Base_SamplingParam(SamplingParam):
+    """Sampling parameters for Wan2.2 TI2V 5B model."""
+    negative_prompt: str | None = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+
+
+@dataclass
+class Wan2_2_TI2V_5B_SamplingParam(Wan2_2_Base_SamplingParam):
+    """Sampling parameters for Wan2.2 TI2V 5B model."""
+    height: int = 704
+    width: int = 1280
+    num_frames: int = 121
+    fps: int = 24
+    guidance_scale: float = 5.0
+    num_inference_steps: int = 50
+
+
+@dataclass
+class Wan2_2_T2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
+    guidance_scale: float = 4.0  # high_noise
+    guidance_scale_2: float = 3.0  # low_noise
+    num_inference_steps: int = 40
+    fps: int = 16
+    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
+    # can be overridden during sampling
+
+
+@dataclass
+class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
+    guidance_scale: float = 3.5  # high_noise
+    guidance_scale_2: float = 3.5  # low_noise
+    num_inference_steps: int = 40
+    fps: int = 16
+    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
+    # can be overridden during sampling
+
+
+@dataclass
+class Wan2_2_Fun_A14B_Control_SamplingParam(Wan2_1_Fun_1_3B_Control_SamplingParam):
+    num_frames: int = 81
+
+
+# =============================================
+# ============= Causal Self-Forcing =============
+# =============================================
+@dataclass
+class SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam(Wan2_1_Fun_1_3B_InP_SamplingParam):
+    pass
+
+
+@dataclass
+class SelfForcingWan2_2_T2V_A14B_480P_SamplingParam(Wan2_2_T2V_A14B_SamplingParam):
+    num_inference_steps: int = 8
+    num_frames: int = 81
+    height: int = 448
+    width: int = 832
+    fps: int = 16
+
+
+@dataclass
+class MatrixGame2_SamplingParam(SamplingParam):
+    height: int = 352
+    width: int = 640
+    num_frames: int = 57
+    fps: int = 25
+    guidance_scale: float = 1.0
+    num_inference_steps: int = 3
+    negative_prompt: str | None = None
@@ -1,216 +0,0 @@
-"""``fastvideo eval`` CLI: list registered eval metrics and run them
-against a set of videos.
-
-This is a thin wrapper around :mod:`fastvideo.eval`. Heavy lifting
-(metric loading, GPU handling, batching) lives in
-:func:`fastvideo.eval.create_evaluator`.
-
-Examples::
-
-    fastvideo eval list
-    fastvideo eval list --group vbench
-    fastvideo eval run --videos path/to/videos/*.mp4 \\
-        --metrics common.ssim --reference path/to/refs/
-    fastvideo eval run --videos clip.mp4 --metrics vbench.aesthetic_quality \\
-        --output scores.json
-"""
-
-from __future__ import annotations
-
-import argparse
-import glob
-import json
-from pathlib import Path
-from typing import cast
-
-from fastvideo.entrypoints.cli.cli_types import CLISubcommand
-from fastvideo.logger import init_logger
-from fastvideo.utils import FlexibleArgumentParser
-
-logger = init_logger(__name__)
-
-
-class EvalSubcommand(CLISubcommand):
-    """The ``eval`` subcommand — entry point for the eval suite."""
-
-    def __init__(self) -> None:
-        self.name = "eval"
-        super().__init__()
-
-    def cmd(self, args: argparse.Namespace) -> None:
-        action = getattr(args, "eval_action", None)
-        if action == "list":
-            _cmd_list(args)
-        elif action == "run":
-            _cmd_run(args)
-        else:
-            # Re-print help if no action was given.
-            self._parser.print_help()  # type: ignore[attr-defined]
-
-    def validate(self, args: argparse.Namespace) -> None:
-        action = getattr(args, "eval_action", None)
-        if action == "run" and not args.videos:
-            raise SystemExit("`fastvideo eval run` requires --videos")
-
-    def subparser_init(self, subparsers: argparse._SubParsersAction) -> FlexibleArgumentParser:
-        eval_parser = subparsers.add_parser(
-            "eval",
-            help="Run video-gen evaluation metrics",
-            usage="fastvideo eval {list,run} [...]",
-        )
-        sub = eval_parser.add_subparsers(dest="eval_action", required=False)
-
-        # `eval list`
-        list_p = sub.add_parser("list", help="List registered metrics")
-        list_p.add_argument("--group", type=str, default=None, help="Filter to a metric group (e.g. 'vbench').")
-
-        # `eval run`
-        run_p = sub.add_parser("run", help="Evaluate videos against one or more metrics")
-        run_p.add_argument("--videos",
-                           type=str,
-                           nargs="+",
-                           required=False,
-                           help="Path, glob, or directory of generated videos.")
-        run_p.add_argument("--reference",
-                           type=str,
-                           default=None,
-                           help="Path / glob / dir of reference videos (for paired metrics).")
-        run_p.add_argument("--metrics", type=str, default="all", help="Comma-separated metric names, or 'all'.")
-        run_p.add_argument("--device", type=str, default="cuda", help="Torch device (e.g. 'cuda', 'cuda:0', 'cpu').")
-        run_p.add_argument("--text-prompt",
-                           type=str,
-                           nargs="*",
-                           default=None,
-                           help="Prompt(s) for text-conditioned metrics. One per video.")
-        run_p.add_argument("--fps", type=float, default=None, help="Frame-rate annotation passed to fps-aware metrics.")
-        run_p.add_argument("--output",
-                           type=str,
-                           default=None,
-                           help="Write results as JSON to this path (default: stdout).")
-
-        # Stash the parser so cmd() can re-print help on no-action.
-        self._parser = eval_parser  # type: ignore[attr-defined]
-        return cast(FlexibleArgumentParser, eval_parser)
-
-
-def _cmd_list(args: argparse.Namespace) -> None:
-    from fastvideo.eval import list_metrics
-    names = list_metrics()
-    if args.group:
-        prefix = args.group.rstrip(".") + "."
-        names = [n for n in names if n == args.group or n.startswith(prefix)]
-    if not names:
-        print(f"(no metrics matched group {args.group!r})")
-        return
-    for name in names:
-        print(name)
-
-
-def _cmd_run(args: argparse.Namespace) -> None:
-    from fastvideo.eval import create_evaluator
-    from fastvideo.eval.io import load_video
-
-    video_paths = _expand_paths(args.videos)
-    if not video_paths:
-        raise SystemExit(f"No videos matched: {args.videos}")
-    ref_paths = _expand_paths([args.reference]) if args.reference else None
-
-    metrics_arg: list[str] | str = ("all" if args.metrics == "all" else
-                                    [m.strip() for m in args.metrics.split(",") if m.strip()])
-
-    evaluator = create_evaluator(metrics=metrics_arg, device=args.device)
-
-    all_results: list[dict] = []
-    for i, vp in enumerate(video_paths):
-        logger.info("Evaluating %s (%d/%d)", vp, i + 1, len(video_paths))
-        kwargs: dict = {"video": load_video(vp)}
-        if ref_paths is not None:
-            ref = ref_paths[i] if i < len(ref_paths) else ref_paths[0]
-            kwargs["reference"] = load_video(ref)
-        if args.text_prompt is not None:
-            prompt = (args.text_prompt[i] if i < len(args.text_prompt) else args.text_prompt[0])
-            kwargs["text_prompt"] = [prompt]
-        if args.fps is not None:
-            kwargs["fps"] = args.fps
-
-        results = evaluator.evaluate(**kwargs)
-        all_results.append({
-            "video": str(vp),
-            "scores": _serialize_results(results),
-        })
-
-    payload = json.dumps(all_results, indent=2, default=_jsonable)
-    if args.output:
-        Path(args.output).write_text(payload)
-        logger.info("Wrote results to %s", args.output)
-    else:
-        print(payload)
-
-
-def _expand_paths(patterns: list[str]) -> list[str]:
-    out: list[str] = []
-    for pat in patterns:
-        p = Path(pat)
-        if p.is_dir():
-            for ext in (".mp4", ".avi", ".mov", ".mkv", ".gif"):
-                out.extend(sorted(str(f) for f in p.iterdir() if f.suffix.lower() == ext))
-        elif any(c in pat for c in "*?["):
-            out.extend(sorted(glob.glob(pat)))
-        else:
-            out.append(pat)
-    # de-dup, preserve order
-    seen: set[str] = set()
-    deduped: list[str] = []
-    for x in out:
-        if x not in seen:
-            seen.add(x)
-            deduped.append(x)
-    return deduped
-
-
-def _serialize_results(results) -> dict | list:
-    """Turn evaluator output (dict or list-of-dicts) into JSON-friendly form."""
-    if isinstance(results, list):
-        return [_serialize_results(r) for r in results]
-    if isinstance(results, dict):
-        return {k: _serialize_metric_result(v) for k, v in results.items()}
-    return _serialize_metric_result(results)
-
-
-def _serialize_metric_result(mr) -> dict:
-    return {
-        "name": getattr(mr, "name", None),
-        "score": getattr(mr, "score", None),
-        "details": getattr(mr, "details", None),
-    }
-
-
-def _jsonable(obj):
-    """``json.dumps(default=...)`` coercer for metric outputs.
-
-    Metrics frequently land numpy scalars / arrays, torch tensors, and
-    pathlib paths inside ``MetricResult.details`` (e.g. ``optical_flow``
-    populates ``per_frame_metrics`` with numpy floats). The stdlib JSON
-    encoder rejects all of those by default — this callback walks the
-    leaves and coerces them to native Python types.
-    """
-    import numpy as np
-    import torch
-
-    if isinstance(obj, np.integer):
-        return int(obj)
-    if isinstance(obj, np.floating):
-        return float(obj)
-    if isinstance(obj, np.bool_):
-        return bool(obj)
-    if isinstance(obj, np.ndarray):
-        return obj.tolist()
-    if isinstance(obj, torch.Tensor):
-        return obj.detach().cpu().tolist()
-    if isinstance(obj, Path):
-        return str(obj)
-    raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")
-
-
-def cmd_init() -> list[CLISubcommand]:
-    return [EvalSubcommand()]
@@ -5,7 +5,6 @@ from fastvideo.entrypoints.cli.generate import cmd_init as generate_cmd_init
 from fastvideo.utils import FlexibleArgumentParser
 from fastvideo.entrypoints.cli.serve import cmd_init as serve_cmd_init
 from fastvideo.entrypoints.cli.bench import cmd_init as bench_cmd_init
-from fastvideo.entrypoints.cli.eval import cmd_init as eval_cmd_init


 def cmd_init() -> list[CLISubcommand]:
@@ -14,7 +13,6 @@ def cmd_init() -> list[CLISubcommand]:
    commands.extend(generate_cmd_init())
    commands.extend(serve_cmd_init())
    commands.extend(bench_cmd_init())
-    commands.extend(eval_cmd_init())
    return commands


@@ -6,6 +6,7 @@ import os
 from typing import cast

 from fastvideo.api.compat import generator_config_to_fastvideo_args
+from fastvideo.api.request_metadata import EXPLICIT_REQUEST_ATTR
 from fastvideo.entrypoints.cli.cli_types import CLISubcommand
 from fastvideo.entrypoints.cli.inference_config import build_serve_config
 from fastvideo.logger import init_logger
@@ -29,20 +30,19 @@ class ServeSubcommand(CLISubcommand):
                args,
                overrides=getattr(args, "_unknown", None),
            )
-
-        logger.info("CLI serve config: %s", serve_config)
-
-        # A `streaming:` block selects the WebSocket/Dynamo runtime;
-        # its deps stay out of REST-only deployments via lazy import.
-        if serve_config.streaming is not None:
-            from fastvideo.entrypoints.streaming.server import (
-                run_server as run_streaming_server, )
-            run_streaming_server(serve_config)
-            return
+        explicit_raw = getattr(
+            serve_config.default_request,
+            EXPLICIT_REQUEST_ATTR,
+            None,
+        )
+        if explicit_raw:
+            raise NotImplementedError("ServeConfig.default_request is not wired into the OpenAI "
+                                      "server yet")

        from fastvideo.entrypoints.openai.api_server import (
            run_server, )

+        logger.info("CLI serve config: %s", serve_config)
        logger.info(
            "Server will listen on %s:%d",
            serve_config.server.host,
@@ -55,7 +55,6 @@ class ServeSubcommand(CLISubcommand):
            host=serve_config.server.host,
            port=serve_config.server.port,
            output_dir=serve_config.server.output_dir,
-            default_request=serve_config.default_request,
        )

    def validate(self, args: argparse.Namespace) -> None:
@@ -8,8 +8,6 @@ import uvicorn
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware

-from fastvideo.api.presets import validate_preset_selection
-from fastvideo.api.schema import GenerationRequest
 from fastvideo.entrypoints.openai.state import (
    DEFAULT_OUTPUT_DIR,
    clear_state,
@@ -18,7 +16,6 @@ from fastvideo.entrypoints.openai.state import (
 from fastvideo.entrypoints.video_generator import VideoGenerator
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
-from fastvideo.registry import get_preset_selection

 logger = init_logger(__name__)

@@ -26,40 +23,17 @@ DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8000


-def _validate_default_request_against_preset(
-    default_request: GenerationRequest,
-    model_path: str,
-) -> None:
-    """Validate ``default_request.stage_overrides`` against the model's preset.
-
-    Called once at server startup from :func:`run_server`. The
-    ``default_request`` is static server config, so validation results are
-    invariant across requests — there's no reason to re-run per request.
-    """
-    if not default_request.stage_overrides:
-        return
-    preset_name, model_family = get_preset_selection(model_path)
-    if preset_name is None or model_family is None:
-        return
-    validate_preset_selection(
-        preset_name,
-        model_family,
-        stage_overrides=default_request.stage_overrides,
-    )
-
-
@asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
    """Load model on startup, clean up on shutdown"""
    args: FastVideoArgs = app.state.fastvideo_args
    output_dir: str = app.state.output_dir
-    default_request: GenerationRequest | None = getattr(app.state, "default_request", None)

    logger.info("Loading model from %s ...", args.model_path)
    generator = VideoGenerator.from_fastvideo_args(args)
    logger.info("Model loaded successfully.")

-    set_state(generator, args, output_dir, default_request=default_request)
+    set_state(generator, args, output_dir)

    yield  # server is running

@@ -72,7 +46,6 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 def create_app(
    fastvideo_args: FastVideoArgs,
    output_dir: str = DEFAULT_OUTPUT_DIR,
-    default_request: GenerationRequest | None = None,
 ) -> FastAPI:
    """Build the FastAPI application with all routers mounted"""

@@ -83,7 +56,6 @@ def create_app(
    )
    app.state.fastvideo_args = fastvideo_args
    app.state.output_dir = output_dir
-    app.state.default_request = default_request

    app.add_middleware(
        CORSMiddleware,
@@ -136,17 +108,9 @@ def run_server(
    host: str = DEFAULT_HOST,
    port: int = DEFAULT_PORT,
    output_dir: str = DEFAULT_OUTPUT_DIR,
-    default_request: GenerationRequest | None = None,
 ):
    """Create the app and run it with uvicorn"""
-    if default_request is not None:
-        _validate_default_request_against_preset(default_request, fastvideo_args.model_path)
-
-    app = create_app(
-        fastvideo_args,
-        output_dir=output_dir,
-        default_request=default_request,
-    )
+    app = create_app(fastvideo_args, output_dir=output_dir)

    logger.info("Starting FastVideo server on %s:%d", host, port)
    logger.info("Model: %s", fastvideo_args.model_path)
@@ -10,7 +10,6 @@ from __future__ import annotations
 from typing import TYPE_CHECKING

 if TYPE_CHECKING:
-    from fastvideo.api.schema import GenerationRequest
    from fastvideo.entrypoints.video_generator import VideoGenerator
    from fastvideo.fastvideo_args import FastVideoArgs

@@ -19,7 +18,6 @@ DEFAULT_OUTPUT_DIR = "outputs"
 _generator: VideoGenerator | None = None
 _fastvideo_args: FastVideoArgs | None = None
 _output_dir: str = DEFAULT_OUTPUT_DIR
-_default_request: GenerationRequest | None = None


 def get_generator() -> VideoGenerator:
@@ -39,28 +37,20 @@ def get_output_dir() -> str:
    return _output_dir


-def get_default_request() -> GenerationRequest | None:
-    """Return the ServeConfig.default_request set at startup, if any."""
-    return _default_request
-
-
 def set_state(
    generator: VideoGenerator,
    fastvideo_args: FastVideoArgs,
    output_dir: str,
-    default_request: GenerationRequest | None = None,
 ) -> None:
    """Set all server state at once (called from lifespan)."""
-    global _generator, _fastvideo_args, _output_dir, _default_request
+    global _generator, _fastvideo_args, _output_dir
    _generator = generator
    _fastvideo_args = fastvideo_args
    _output_dir = output_dir
-    _default_request = default_request


 def clear_state() -> None:
    """Clear server state on shutdown."""
-    global _generator, _fastvideo_args, _default_request
+    global _generator, _fastvideo_args
    _generator = None
    _fastvideo_args = None
-    _default_request = None
@@ -19,10 +19,7 @@ from fastapi import (
 )
 from fastapi.responses import FileResponse

-from fastvideo.api.compat import explicit_request_updates
-from fastvideo.api.schema import GenerationRequest
 from fastvideo.entrypoints.openai.state import (
-    get_default_request,
    get_generator,
    get_output_dir,
    get_server_args,
@@ -45,73 +42,49 @@ logger = init_logger(__name__)
 router = APIRouter(prefix="/v1/videos", tags=["videos"])


-def _build_generation_kwargs(
-    request_id: str,
-    req: VideoGenerationsRequest,
-    default_request: GenerationRequest | None = None,
-) -> dict[str, Any]:
-    """Build a flat kwargs dict for ``generator.generate_video``.
+def _build_generation_kwargs(request_id: str, req: VideoGenerationsRequest) -> dict[str, Any]:

-    Precedence (highest to lowest):
-      1. Request body — only fields the client explicitly sent
-         (``req.model_fields_set``, Pydantic v2).
-      2. ``default_request`` — only fields the operator explicitly set in
-         the serve YAML, projected via ``explicit_request_updates``. Schema
-         defaults on the dataclass are *not* treated as defaults here.
-      3. Hardcoded fallback (e.g. ``fps=24`` when neither side set it).
-
-    Why gate on ``model_fields_set`` / explicit paths? Both the request
-    Pydantic model and the ``GenerationRequest`` dataclass carry schema
-    defaults (e.g. ``seed=1024``, ``num_frames=125``). Without the gate
-    those would masquerade as intent and shadow the other side — the
-    gate preserves "operator pinned it" vs. "dataclass happened to have
-    that default."
-    """
    kwargs: dict[str, Any] = {}
-    if default_request is not None:
-        kwargs.update(explicit_request_updates(default_request))
-
-    body_set = req.model_fields_set
    kwargs["prompt"] = req.prompt

-    if "size" in body_set and req.size:
+    # Resolution
+    if req.size:
        w, h = parse_size(req.size)
        if w is not None and h is not None:
            kwargs["width"] = w
            kwargs["height"] = h

-    if "fps" in body_set and req.fps is not None:
-        kwargs["fps"] = req.fps
+    # Frame count / duration
+    fps = req.fps if req.fps is not None else 24
+    kwargs["fps"] = fps

-    if "num_frames" in body_set and req.num_frames is not None:
+    if req.num_frames is not None:
        kwargs["num_frames"] = req.num_frames
-    elif "seconds" in body_set and req.seconds is not None:
-        fps = kwargs.get("fps", 24)
+    elif req.seconds is not None:
        kwargs["num_frames"] = fps * req.seconds

-    if "seed" in body_set and req.seed is not None:
+    # Sampling parameters
+    if req.seed is not None:
        kwargs["seed"] = req.seed
-    if ("num_inference_steps" in body_set and req.num_inference_steps is not None):
+    if req.num_inference_steps is not None:
        kwargs["num_inference_steps"] = req.num_inference_steps
-    if "guidance_scale" in body_set and req.guidance_scale is not None:
+    if req.guidance_scale is not None:
        kwargs["guidance_scale"] = req.guidance_scale
-    if "guidance_scale_2" in body_set and req.guidance_scale_2 is not None:
+    if req.guidance_scale_2 is not None:
        kwargs["guidance_scale_2"] = req.guidance_scale_2
-    if "negative_prompt" in body_set and req.negative_prompt is not None:
+    if req.negative_prompt is not None:
        kwargs["negative_prompt"] = req.negative_prompt
-    if "enable_teacache" in body_set and req.enable_teacache:
+    if req.enable_teacache:
        kwargs["enable_teacache"] = True
-    if "true_cfg_scale" in body_set and req.true_cfg_scale is not None:
+    if req.true_cfg_scale is not None:
        kwargs["true_cfg_scale"] = req.true_cfg_scale

-    if "input_reference" in body_set and req.input_reference is not None:
+    # Image-to-video input
+    if req.input_reference is not None:
        kwargs["image_path"] = req.input_reference

-    kwargs.setdefault("fps", 24)
-
-    default_output_path = kwargs.pop("output_path", None)
-    body_output_dir = req.output_path if "output_path" in body_set else None
-    output_dir = body_output_dir or default_output_path or os.path.join(get_output_dir(), "videos")
+    # Output path
+    output_dir = req.output_path or os.path.join(get_output_dir(), "videos")
    os.makedirs(output_dir, exist_ok=True)
    kwargs["output_path"] = os.path.join(output_dir, f"{request_id}.mp4")
    kwargs["save_video"] = True
@@ -299,12 +272,7 @@ async def create_video(

    logger.info("Video generation request %s: prompt=%s", request_id, req.prompt[:100])

-    # default_request was validated at server startup (run_server) and is
-    # read-only on the request hot path — _build_generation_kwargs and
-    # explicit_request_updates only read, so no per-request deepcopy needed.
-    default_request = get_default_request()
-
-    gen_kwargs = _build_generation_kwargs(request_id, req, default_request=default_request)
+    gen_kwargs = _build_generation_kwargs(request_id, req)
    job = _make_video_job(request_id, req, gen_kwargs)
    await VIDEO_STORE.upsert(request_id, job)

@@ -1,31 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo.entrypoints.streaming.server import build_app, run_server
-from fastvideo.entrypoints.streaming.session import (
-    Session,
-    SessionManager,
-    SessionState,
-)
-from fastvideo.entrypoints.streaming.session_store import (
-    BlobStore,
-    InMemoryBlobStore,
-    InMemorySessionStore,
-    SessionStore,
-)
-from fastvideo.entrypoints.streaming.stream import (
-    FragmentedMP4Chunk,
-    FragmentedMP4Encoder,
-)
-
-__all__ = [
-    "BlobStore",
-    "FragmentedMP4Chunk",
-    "FragmentedMP4Encoder",
-    "InMemoryBlobStore",
-    "InMemorySessionStore",
-    "Session",
-    "SessionManager",
-    "SessionState",
-    "SessionStore",
-    "build_app",
-    "run_server",
-]
@@ -1,252 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""JSON WebSocket protocol schemas for the streaming server.
-
-Every control message shares the envelope ``{"type": <str>, ...}``.
-Pydantic models live here so the server can parse / validate incoming
-frames and emit well-typed outgoing frames without hand-rolled dicts.
-
-The message catalogue matches the contract in
-``docs/design/server_contracts/streaming.md``; additions must land in
-both places in the same PR.
-"""
-from __future__ import annotations
-
-from typing import Annotated, Any, Literal, Union
-
-from pydantic import BaseModel, ConfigDict, Field
-
-# ---------------------------------------------------------------------------
-# Client → server
-# ---------------------------------------------------------------------------
-
-
-class SessionInitV2(BaseModel):
-    """Opening frame the client sends after the WebSocket handshake."""
-
-    model_config = ConfigDict(extra="allow")
-
-    type: Literal["session_init_v2"]
-    client_id: str | None = None
-    preset: str | None = None
-    preset_label: str | None = None
-    curated_prompts: list[str] = Field(default_factory=list)
-    initial_image: dict[str, Any] | None = None
-    enhancement_enabled: bool = False
-    auto_extension_enabled: bool = False
-    loop_generation_enabled: bool = False
-    single_clip_mode: bool = False
-    stream_mode: Literal["av_fmp4", "legacy_jpeg"] = "av_fmp4"
-    continuation_state: dict[str, Any] | None = None
-    """Optional ``{kind, payload}`` dict; hydrated into
-    :class:`fastvideo.api.ContinuationState` server-side."""
-
-
-class SegmentPromptSource(BaseModel):
-    """Request a new segment using a specific prompt."""
-
-    type: Literal["segment_prompt_source"]
-    prompt: str
-    negative_prompt: str | None = None
-    source: Literal["curated", "enhanced", "user", "auto_extension"] = "user"
-    seed: int | None = None
-    num_inference_steps: int | None = None
-    guidance_scale: float | None = None
-
-
-class SeedPromptsUpdated(BaseModel):
-    type: Literal["seed_prompts_updated"]
-    seed_prompts: list[str] = Field(default_factory=list)
-
-
-class EnhancementUpdated(BaseModel):
-    type: Literal["enhancement_updated"]
-    enabled: bool
-
-
-class AutoExtensionUpdated(BaseModel):
-    type: Literal["auto_extension_updated"]
-    enabled: bool
-
-
-class LoopGenerationUpdated(BaseModel):
-    type: Literal["loop_generation_updated"]
-    enabled: bool
-
-
-class GenerationPausedUpdated(BaseModel):
-    type: Literal["generation_paused_updated"]
-    paused: bool
-
-
-class SnapshotState(BaseModel):
-    """Request the current ``ContinuationState`` for export."""
-
-    type: Literal["snapshot_state"]
-
-
-ClientMessage = Annotated[
-    Union[  # noqa: UP007 - Annotated requires Union for discriminator
-        SessionInitV2,
-        SegmentPromptSource,
-        SeedPromptsUpdated,
-        EnhancementUpdated,
-        AutoExtensionUpdated,
-        LoopGenerationUpdated,
-        GenerationPausedUpdated,
-        SnapshotState,
-    ],
-    Field(discriminator="type"),
-]
-
-# ---------------------------------------------------------------------------
-# Server → client
-# ---------------------------------------------------------------------------
-
-
-class QueueStatus(BaseModel):
-    type: Literal["queue_status"] = "queue_status"
-    position: int
-    queue_depth: int
-
-
-class GpuAssigned(BaseModel):
-    type: Literal["gpu_assigned"] = "gpu_assigned"
-    gpu_id: int
-    session_timeout: int
-
-
-class Ltx2StreamStart(BaseModel):
-    type: Literal["ltx2_stream_start"] = "ltx2_stream_start"
-    preset: str | None = None
-    width: int
-    height: int
-    fps: int
-    num_frames: int
-
-
-class Ltx2SegmentStart(BaseModel):
-    type: Literal["ltx2_segment_start"] = "ltx2_segment_start"
-    segment_idx: int
-    prompt: str
-    total_steps: int
-
-
-class StepComplete(BaseModel):
-    type: Literal["step_complete"] = "step_complete"
-    segment_idx: int
-    step: int
-    total_steps: int
-    stage: str = "denoise"
-
-
-class MediaInit(BaseModel):
-    """Descriptor for the fMP4 initialization segment that follows."""
-
-    type: Literal["media_init"] = "media_init"
-    segment_idx: int
-    mime: str = "video/mp4; codecs=\"avc1.64001f, mp4a.40.2\""
-    stream_id: str
-    mode: Literal["av_fmp4"] = "av_fmp4"
-
-
-class MediaSegmentComplete(BaseModel):
-    type: Literal["media_segment_complete"] = "media_segment_complete"
-    segment_idx: int
-    stream_id: str
-    chunks: int
-    duration_ms: float | None = None
-    pts_base_ms: float | None = None
-
-
-class Ltx2SegmentComplete(BaseModel):
-    type: Literal["ltx2_segment_complete"] = "ltx2_segment_complete"
-    segment_idx: int
-    generation_time_ms: float
-    e2e_latency_ms: float | None = None
-
-
-class Ltx2StreamComplete(BaseModel):
-    type: Literal["ltx2_stream_complete"] = "ltx2_stream_complete"
-    reason: Literal["segment_cap", "stop_requested", "error"] = "stop_requested"
-
-
-class SessionTimeout(BaseModel):
-    type: Literal["session_timeout"] = "session_timeout"
-    timeout_seconds: int
-
-
-class ContinuationStateSnapshot(BaseModel):
-    type: Literal["continuation_state_snapshot"] = "continuation_state_snapshot"
-    state: dict[str, Any]
-    """``{kind, payload}`` dict matching
-    :class:`fastvideo.api.ContinuationState`."""
-
-
-class ErrorMessage(BaseModel):
-    type: Literal["error"] = "error"
-    code: Literal[
-        "session_rejected",
-        "invalid_message",
-        "preset_mismatch",
-        "gpu_unavailable",
-        "worker_failed",
-        "upstream_timeout",
-        "internal_error",
-    ] = "internal_error"
-    message: str
-    retryable: bool = False
-
-
-ServerMessage = Union[  # noqa: UP007 - pydantic Union handling
-    QueueStatus,
-    GpuAssigned,
-    Ltx2StreamStart,
-    Ltx2SegmentStart,
-    StepComplete,
-    MediaInit,
-    MediaSegmentComplete,
-    Ltx2SegmentComplete,
-    Ltx2StreamComplete,
-    SessionTimeout,
-    ContinuationStateSnapshot,
-    ErrorMessage,
-]
-
-
-def parse_client_message(raw: dict[str, Any]) -> ClientMessage:
-    """Parse an incoming WebSocket dict into a typed client message.
-
-    Unknown ``type`` values raise :class:`pydantic.ValidationError`; the
-    server handler turns that into an ``error`` frame with
-    ``code="invalid_message"``.
-    """
-    from pydantic import TypeAdapter
-
-    return TypeAdapter(ClientMessage).validate_python(raw)
-
-
-__all__ = [
-    "AutoExtensionUpdated",
-    "ClientMessage",
-    "ContinuationStateSnapshot",
-    "EnhancementUpdated",
-    "ErrorMessage",
-    "GenerationPausedUpdated",
-    "GpuAssigned",
-    "Ltx2SegmentComplete",
-    "Ltx2SegmentStart",
-    "Ltx2StreamComplete",
-    "Ltx2StreamStart",
-    "LoopGenerationUpdated",
-    "MediaInit",
-    "MediaSegmentComplete",
-    "QueueStatus",
-    "SeedPromptsUpdated",
-    "SegmentPromptSource",
-    "ServerMessage",
-    "SessionInitV2",
-    "SessionTimeout",
-    "SnapshotState",
-    "StepComplete",
-    "parse_client_message",
-]
@@ -1,531 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Single-generator FastAPI + WebSocket streaming server."""
-from __future__ import annotations
-
-import asyncio
-import contextlib
-import os
-import time
-from dataclasses import dataclass
-from typing import Any, Protocol
-
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
-from fastapi.responses import JSONResponse
-
-from fastvideo.api.schema import (
-    ContinuationState,
-    GenerationRequest,
-    InputConfig,
-    OutputConfig,
-    SamplingConfig,
-    ServeConfig,
-)
-from fastvideo.entrypoints.streaming.protocol import (
-    AutoExtensionUpdated,
-    ContinuationStateSnapshot,
-    EnhancementUpdated,
-    ErrorMessage,
-    GenerationPausedUpdated,
-    GpuAssigned,
-    LoopGenerationUpdated,
-    Ltx2SegmentComplete,
-    Ltx2SegmentStart,
-    Ltx2StreamComplete,
-    Ltx2StreamStart,
-    MediaInit,
-    MediaSegmentComplete,
-    QueueStatus,
-    SeedPromptsUpdated,
-    SegmentPromptSource,
-    SessionInitV2,
-    SnapshotState,
-    StepComplete,
-    parse_client_message,
-)
-from fastvideo.entrypoints.streaming.session import (
-    InvalidSessionTransition,
-    Session,
-    SessionManager,
-    SessionRejected,
-    SessionState,
-)
-from fastvideo.entrypoints.streaming.session_init_image import (
-    persist_session_init_image, )
-from fastvideo.entrypoints.streaming.session_store import (
-    InMemorySessionStore,
-    SessionStore,
-)
-from fastvideo.entrypoints.streaming.stream import FragmentedMP4Encoder
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-# RFC 6455 WebSocket close codes used by the server.
-_WS_CLOSE_UNSUPPORTED_DATA = 1003
-_WS_CLOSE_TRY_AGAIN_LATER = 1013
-
-
-class _GeneratorProto(Protocol):
-    """Subset of :class:`fastvideo.VideoGenerator` the server calls."""
-
-    def generate(self, request: GenerationRequest) -> Any:
-        ...
-
-
-@dataclass
-class ServerState:
-    serve_config: ServeConfig
-    generator: _GeneratorProto
-    sessions: SessionManager
-    session_store: SessionStore
-
-
-def build_app(
-    serve_config: ServeConfig,
-    generator: _GeneratorProto,
-    *,
-    session_store: SessionStore | None = None,
-) -> FastAPI:
-    """Build the FastAPI app used by :func:`run_server`.
-
-    Exposed so tests can drive the WebSocket endpoint in-process via
-    ``starlette.testclient.TestClient(app).websocket_connect(...)``.
-    """
-    if serve_config.streaming is None:
-        raise ValueError("ServeConfig.streaming must be set to launch the streaming "
-                         "server; got None. Add a `streaming:` block to your serve config.")
-
-    sessions = SessionManager(
-        segment_cap=serve_config.streaming.generation_segment_cap,
-        session_timeout_seconds=serve_config.streaming.session_timeout_seconds,
-    )
-    state = ServerState(
-        serve_config=serve_config,
-        generator=generator,
-        sessions=sessions,
-        session_store=session_store or InMemorySessionStore(),
-    )
-
-    app = FastAPI(title="FastVideo Streaming")
-
-    @app.get("/health")
-    async def _health() -> JSONResponse:
-        return JSONResponse({
-            "status": "ok",
-            "sessions": len(state.sessions),
-            "stream_mode": state.serve_config.streaming.stream_mode,
-        })
-
-    @app.websocket("/v1/stream")
-    async def _stream(websocket: WebSocket) -> None:
-        await websocket.accept()
-        try:
-            session = state.sessions.create()
-        except SessionRejected as exc:
-            await _send_error(websocket, "session_rejected", str(exc), retryable=False)
-            await websocket.close(code=_WS_CLOSE_TRY_AGAIN_LATER, reason="session_rejected")
-            return
-
-        try:
-            await _handle_session(websocket, session, state)
-        except WebSocketDisconnect:
-            logger.info("session %s: client disconnected", session.id[:8])
-        except Exception:  # pragma: no cover - defensive catch-all
-            logger.exception("session %s: unhandled error", session.id[:8])
-            with contextlib.suppress(InvalidSessionTransition):
-                session.transition(SessionState.ERROR)
-        finally:
-            _cleanup_session(session, state)
-
-    app.state.server_state = state
-    return app
-
-
-def run_server(serve_config: ServeConfig, *, generator: _GeneratorProto | None = None) -> None:
-    """Launch the streaming server.
-
-    Boots a :class:`fastvideo.VideoGenerator` from
-    ``serve_config.generator`` unless ``generator`` is provided, then
-    serves ``build_app(...)`` via uvicorn.
-    """
-    if serve_config.streaming is None:
-        raise ValueError("ServeConfig.streaming must be set to launch the streaming server; "
-                         "got None. Add a `streaming:` block to your serve config.")
-
-    import uvicorn
-
-    if generator is None:
-        from fastvideo import VideoGenerator  # lazy to avoid boot cost
-
-        generator = VideoGenerator.from_pretrained(config=serve_config.generator)
-    app = build_app(serve_config, generator)
-    uvicorn.run(
-        app,
-        host=serve_config.server.host,
-        port=serve_config.server.port,
-    )
-
-
-async def _handle_session(
-    websocket: WebSocket,
-    session: Session,
-    state: ServerState,
-) -> None:
-    init = await _read_init_message(websocket, session, state)
-    if init is None:
-        return
-
-    await _apply_session_init(session, init, state)
-    await _send_json(websocket, QueueStatus(position=0, queue_depth=0))
-    session.transition(SessionState.GPU_BINDING)
-    await _send_json(websocket, GpuAssigned(
-        gpu_id=0,
-        session_timeout=state.sessions.session_timeout_seconds,
-    ))
-    session.transition(SessionState.ACTIVE)
-    await _send_json(websocket, _build_stream_start(session, state))
-
-    try:
-        await _run_segment_loop(websocket, session, state)
-    finally:
-        with contextlib.suppress(RuntimeError):
-            await _send_json(websocket, Ltx2StreamComplete(reason="stop_requested"))
-
-
-async def _read_init_message(
-    websocket: WebSocket,
-    session: Session,
-    state: ServerState,
-) -> SessionInitV2 | None:
-    try:
-        raw = await asyncio.wait_for(
-            websocket.receive_json(),
-            timeout=state.sessions.session_timeout_seconds,
-        )
-    except asyncio.TimeoutError:
-        logger.info("session %s: init timeout", session.id[:8])
-        with contextlib.suppress(InvalidSessionTransition):
-            session.transition(SessionState.TIMEOUT)
-        return None
-    except WebSocketDisconnect:
-        return None
-    try:
-        parsed = parse_client_message(raw)
-    except Exception as exc:
-        await _reject_init(websocket, session, f"opening frame failed validation: {exc}", "invalid_init")
-        return None
-    if not isinstance(parsed, SessionInitV2):
-        await _reject_init(websocket, session, "first frame must be session_init_v2", "expected_session_init_v2")
-        return None
-    return parsed
-
-
-async def _reject_init(
-    websocket: WebSocket,
-    session: Session,
-    message: str,
-    close_reason: str,
-) -> None:
-    await _send_error(websocket, "invalid_message", message, retryable=False)
-    await websocket.close(code=_WS_CLOSE_UNSUPPORTED_DATA, reason=close_reason)
-    with contextlib.suppress(InvalidSessionTransition):
-        session.transition(SessionState.REJECTED)
-
-
-async def _apply_session_init(
-    session: Session,
-    init: SessionInitV2,
-    state: ServerState,
-) -> None:
-    session.client_id = init.client_id
-    session.preset = init.preset
-    session.preset_label = init.preset_label
-    session.curated_prompts = list(init.curated_prompts)
-    session.enhancement_enabled = init.enhancement_enabled
-    session.auto_extension_enabled = init.auto_extension_enabled
-    session.loop_generation_enabled = init.loop_generation_enabled
-    session.single_clip_mode = init.single_clip_mode
-    session.stream_mode = init.stream_mode
-
-    if init.initial_image is not None:
-        # Decode + disk write off the event loop; payload is up to 32 MiB.
-        image = await asyncio.to_thread(persist_session_init_image, init.initial_image)
-        if image is not None:
-            session.metadata["session_init_image"] = image.path
-
-    if init.continuation_state is not None:
-        session.continuation_state = _coerce_state(init.continuation_state)
-        if session.continuation_state is not None:
-            state.session_store.store(session.id, session.continuation_state)
-
-
-async def _run_segment_loop(
-    websocket: WebSocket,
-    session: Session,
-    state: ServerState,
-) -> None:
-    cap = state.sessions.segment_cap
-    while True:
-        if session.segment_cap_reached(cap):
-            logger.info("session %s: segment cap (%d) reached", session.id[:8], cap)
-            return
-
-        try:
-            raw = await asyncio.wait_for(
-                websocket.receive_json(),
-                timeout=state.sessions.session_timeout_seconds,
-            )
-        except asyncio.TimeoutError:
-            logger.info("session %s: idle timeout", session.id[:8])
-            with contextlib.suppress(InvalidSessionTransition):
-                session.transition(SessionState.TIMEOUT)
-            return
-        except WebSocketDisconnect:
-            return
-        session.touch()
-
-        try:
-            parsed = parse_client_message(raw)
-        except Exception as exc:
-            await _send_error(websocket, "invalid_message", str(exc), retryable=True)
-            continue
-
-        if isinstance(parsed, SnapshotState):
-            snap = state.session_store.snapshot(session.id)
-            if snap is None:
-                await _send_error(websocket,
-                                  "internal_error",
-                                  "no continuation state available for session",
-                                  retryable=False)
-                continue
-            await _send_json(websocket, ContinuationStateSnapshot(state={"kind": snap.kind, "payload": snap.payload}, ))
-            continue
-
-        if isinstance(parsed, SegmentPromptSource):
-            await _run_segment(websocket, session, state, parsed)
-            continue
-
-        # Silently ignore unknown-but-valid types (additive-evolution
-        # rule in streaming.md).
-        _apply_toggle(session, parsed)
-
-
-async def _run_segment(
-    websocket: WebSocket,
-    session: Session,
-    state: ServerState,
-    message: SegmentPromptSource,
-) -> None:
-    request = _build_generation_request(session, message, state)
-    segment_idx = session.segment_idx
-    await _send_json(
-        websocket,
-        Ltx2SegmentStart(
-            segment_idx=segment_idx,
-            prompt=message.prompt,
-            total_steps=request.sampling.num_inference_steps,
-        ))
-
-    start = time.perf_counter()
-    loop = asyncio.get_running_loop()
-    # TODO: executor-wrapped generate() cannot be cancelled, so a
-    # client disconnect mid-segment leaves the GPU work running to
-    # completion. Real cancellation needs the generate_async API.
-    try:
-        result = await loop.run_in_executor(None, state.generator.generate, request)
-    except Exception as exc:
-        logger.exception("session %s: generator failed", session.id[:8])
-        await _send_error(websocket, "worker_failed", f"generator.generate failed: {exc}", retryable=True)
-        with contextlib.suppress(InvalidSessionTransition):
-            session.transition(SessionState.ERROR)
-        return
-    elapsed_ms = (time.perf_counter() - start) * 1000.0
-
-    frames = _extract_frames(result)
-    if not frames:
-        await _send_error(websocket, "worker_failed", "generator returned no frames", retryable=True)
-        with contextlib.suppress(InvalidSessionTransition):
-            session.transition(SessionState.ERROR)
-        return
-
-    # Synchronous generator call has no per-step hook; emit one
-    # terminal StepComplete so observability wiring still sees the
-    # segment finish.
-    total = request.sampling.num_inference_steps
-    await _send_json(websocket, StepComplete(
-        segment_idx=segment_idx,
-        step=total,
-        total_steps=total,
-        stage="denoise",
-    ))
-
-    encoder = FragmentedMP4Encoder(
-        width=request.sampling.width,
-        height=request.sampling.height,
-        fps=request.sampling.fps,
-        segment_idx=segment_idx,
-    )
-    chunks_relayed = 0
-    async with encoder:
-        init_sent = False
-        async for chunk in encoder.encode(frames):
-            if chunk.kind == "init":
-                await _send_json(websocket, MediaInit(
-                    segment_idx=segment_idx,
-                    stream_id=chunk.stream_id,
-                ))
-                init_sent = True
-            await websocket.send_bytes(chunk.data)
-            if init_sent and chunk.kind == "media":
-                chunks_relayed += 1
-
-    await _send_json(
-        websocket,
-        MediaSegmentComplete(
-            segment_idx=segment_idx,
-            stream_id=encoder.stream_id,
-            chunks=chunks_relayed,
-            duration_ms=float(request.sampling.num_frames) / request.sampling.fps * 1000.0,
-        ))
-
-    new_state = _extract_state(result)
-    if new_state is not None:
-        session.continuation_state = new_state
-        state.session_store.store(session.id, new_state)
-
-    session.segment_idx += 1
-    with contextlib.suppress(InvalidSessionTransition):
-        session.transition(SessionState.ACTIVE)
-
-    await _send_json(
-        websocket,
-        Ltx2SegmentComplete(
-            segment_idx=segment_idx,
-            generation_time_ms=elapsed_ms,
-            e2e_latency_ms=elapsed_ms,
-        ))
-
-
-def _build_stream_start(
-    session: Session,
-    state: ServerState,
-) -> Ltx2StreamStart:
-    default = state.serve_config.default_request
-    return Ltx2StreamStart(
-        preset=session.preset,
-        width=default.sampling.width,
-        height=default.sampling.height,
-        fps=default.sampling.fps,
-        num_frames=default.sampling.num_frames,
-    )
-
-
-def _build_generation_request(
-    session: Session,
-    message: SegmentPromptSource,
-    state: ServerState,
-) -> GenerationRequest:
-    # Start from the operator-pinned default_request to pick up the
-    # preset-selected sampling knobs; override with per-message values.
-    base = state.serve_config.default_request
-    sampling_kwargs: dict[str, Any] = {
-        "num_videos_per_prompt":
-        base.sampling.num_videos_per_prompt,
-        "seed":
-        message.seed if message.seed is not None else base.sampling.seed,
-        "num_frames":
-        base.sampling.num_frames,
-        "height":
-        base.sampling.height,
-        "width":
-        base.sampling.width,
-        "fps":
-        base.sampling.fps,
-        "num_inference_steps":
-        (message.num_inference_steps if message.num_inference_steps is not None else base.sampling.num_inference_steps),
-        "guidance_scale":
-        (message.guidance_scale if message.guidance_scale is not None else base.sampling.guidance_scale),
-    }
-    request = GenerationRequest(
-        prompt=message.prompt,
-        negative_prompt=message.negative_prompt or base.negative_prompt,
-        inputs=InputConfig(image_path=session.metadata.get("session_init_image"), ),
-        sampling=SamplingConfig(**sampling_kwargs),
-        output=OutputConfig(save_video=False, return_frames=True, return_state=True),
-        state=session.continuation_state,
-    )
-    return request
-
-
-def _coerce_state(raw: dict[str, Any]) -> ContinuationState | None:
-    kind = raw.get("kind")
-    payload = raw.get("payload")
-    if not isinstance(kind, str) or not isinstance(payload, dict):
-        return None
-    return ContinuationState(kind=kind, payload=payload)
-
-
-def _apply_toggle(session: Session, message: Any) -> None:
-    if isinstance(message, EnhancementUpdated):
-        session.enhancement_enabled = message.enabled
-    elif isinstance(message, AutoExtensionUpdated):
-        session.auto_extension_enabled = message.enabled
-    elif isinstance(message, LoopGenerationUpdated):
-        session.loop_generation_enabled = message.enabled
-    elif isinstance(message, GenerationPausedUpdated):
-        session.generation_paused = message.paused
-    elif isinstance(message, SeedPromptsUpdated):
-        session.curated_prompts = list(message.seed_prompts)
-
-
-def _extract_frames(result: Any) -> list:
-    if hasattr(result, "frames"):
-        return list(result.frames or [])
-    if isinstance(result, dict):
-        return list(result.get("frames") or [])
-    return []
-
-
-def _extract_state(result: Any) -> ContinuationState | None:
-    state = getattr(result, "state", None)
-    if state is None and isinstance(result, dict):
-        state = result.get("state")
-    if isinstance(state, ContinuationState):
-        return state
-    if isinstance(state, dict):
-        return _coerce_state(state)
-    return None
-
-
-async def _send_json(websocket: WebSocket, message: Any) -> None:
-    payload = (message.model_dump(mode="json", exclude_none=True) if hasattr(message, "model_dump") else message)
-    await websocket.send_json(payload)
-
-
-async def _send_error(
-    websocket: WebSocket,
-    code: str,
-    message: str,
-    *,
-    retryable: bool,
-) -> None:
-    await _send_json(
-        websocket,
-        ErrorMessage(code=code, message=message, retryable=retryable),
-    )
-
-
-def _cleanup_session(session: Session, state: ServerState) -> None:
-    state.sessions.close(session.id)
-    state.session_store.drop(session.id)
-    init_image_path = session.metadata.get("session_init_image")
-    if isinstance(init_image_path, str):
-        with contextlib.suppress(FileNotFoundError):
-            os.unlink(init_image_path)
-
-
-__all__ = [
-    "ServerState",
-    "build_app",
-    "run_server",
-]
@@ -1,214 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Per-connection session lifecycle for the streaming server.
-
-Each WebSocket opens exactly one :class:`Session`. :class:`SessionManager`
-enforces the ``generation_segment_cap`` and ``session_timeout_seconds``
-budgets from :class:`fastvideo.api.StreamingConfig`.
-"""
-from __future__ import annotations
-
-import enum
-import time
-import uuid
-from dataclasses import dataclass, field
-from typing import Any
-
-from fastvideo.api.schema import ContinuationState
-
-
-class SessionState(enum.Enum):
-    """State-machine positions for a streaming session.
-
-    Transitions are server-owned. See
-    ``docs/design/server_contracts/streaming.md`` for the full diagram.
-    """
-
-    INITIALIZING = "initializing"
-    QUEUED = "queued"
-    GPU_BINDING = "gpu_binding"
-    ACTIVE = "active"
-    COMPLETE = "complete"
-    ERROR = "error"
-    TIMEOUT = "timeout"
-    REJECTED = "rejected"
-
-
-_VALID_TRANSITIONS: dict[SessionState, frozenset[SessionState]] = {
-    SessionState.INITIALIZING:
-    frozenset({
-        SessionState.QUEUED,
-        SessionState.GPU_BINDING,
-        SessionState.REJECTED,
-        SessionState.ERROR,
-    }),
-    SessionState.QUEUED:
-    frozenset({
-        SessionState.GPU_BINDING,
-        SessionState.ERROR,
-        SessionState.TIMEOUT,
-        SessionState.REJECTED,
-    }),
-    SessionState.GPU_BINDING:
-    frozenset({
-        SessionState.ACTIVE,
-        SessionState.ERROR,
-        SessionState.TIMEOUT,
-    }),
-    SessionState.ACTIVE:
-    frozenset({
-        SessionState.ACTIVE,
-        SessionState.COMPLETE,
-        SessionState.ERROR,
-        SessionState.TIMEOUT,
-    }),
-    SessionState.COMPLETE:
-    frozenset(),
-    SessionState.ERROR:
-    frozenset(),
-    SessionState.TIMEOUT:
-    frozenset(),
-    SessionState.REJECTED:
-    frozenset(),
-}
-
-
-class InvalidSessionTransition(RuntimeError):
-    """Raised when a session is asked to transition along an illegal edge."""
-
-
-@dataclass
-class Session:
-    id: str = field(default_factory=lambda: uuid.uuid4().hex)
-    state: SessionState = SessionState.INITIALIZING
-    created_at: float = field(default_factory=time.monotonic)
-    last_activity: float = field(default_factory=time.monotonic)
-
-    client_id: str | None = None
-    preset: str | None = None
-    preset_label: str | None = None
-
-    curated_prompts: list[str] = field(default_factory=list)
-
-    segment_idx: int = 0
-
-    enhancement_enabled: bool = False
-    auto_extension_enabled: bool = False
-    loop_generation_enabled: bool = False
-    single_clip_mode: bool = False
-    generation_paused: bool = False
-
-    stream_mode: str = "av_fmp4"
-    gpu_id: int | None = None
-
-    continuation_state: ContinuationState | None = None
-
-    metadata: dict[str, Any] = field(default_factory=dict)
-
-    def transition(self, target: SessionState) -> None:
-        """Move to ``target`` if the edge is allowed.
-
-        Raises :class:`InvalidSessionTransition` on illegal moves. The
-        self-loop on ``ACTIVE`` is legal so the server can re-assert
-        ACTIVE on segment completion without special casing.
-        """
-        allowed = _VALID_TRANSITIONS.get(self.state, frozenset())
-        if target not in allowed and target is not self.state:
-            raise InvalidSessionTransition(f"{self.state.value} -> {target.value} is not a valid "
-                                           f"session transition")
-        self.state = target
-        self.last_activity = time.monotonic()
-
-    def touch(self) -> None:
-        self.last_activity = time.monotonic()
-
-    def is_active(self) -> bool:
-        return self.state is SessionState.ACTIVE
-
-    def segment_cap_reached(self, cap: int) -> bool:
-        return self.segment_idx >= cap
-
-
-class SessionManager:
-    """Registers sessions and enforces per-server session limits."""
-
-    def __init__(
-        self,
-        *,
-        segment_cap: int,
-        session_timeout_seconds: int,
-        max_sessions: int = 1,
-    ) -> None:
-        self._segment_cap = segment_cap
-        self._session_timeout_seconds = session_timeout_seconds
-        self._max_sessions = max_sessions
-        self._sessions: dict[str, Session] = {}
-
-    @property
-    def segment_cap(self) -> int:
-        return self._segment_cap
-
-    @property
-    def session_timeout_seconds(self) -> int:
-        return self._session_timeout_seconds
-
-    def create(self) -> Session:
-        if len(self._sessions) >= self._max_sessions:
-            raise SessionRejected(f"max sessions reached ({self._max_sessions})")
-        session = Session()
-        self._sessions[session.id] = session
-        return session
-
-    def get(self, session_id: str) -> Session | None:
-        return self._sessions.get(session_id)
-
-    def close(self, session_id: str) -> None:
-        self._sessions.pop(session_id, None)
-
-    def __contains__(self, session_id: str) -> bool:
-        return session_id in self._sessions
-
-    def __len__(self) -> int:
-        return len(self._sessions)
-
-    def active_sessions(self) -> list[Session]:
-        return [s for s in self._sessions.values() if s.is_active()]
-
-    def reap_timed_out(self, now: float | None = None) -> list[str]:
-        """Return the ids of sessions that have exceeded the idle timeout.
-
-        The caller is responsible for actually closing them — this
-        method only *identifies* dead sessions so the server can emit
-        ``session_timeout`` frames before dropping the WebSocket.
-
-        TODO: unused until a background driver calls it. Per-connection
-        idle enforcement currently happens via asyncio.wait_for on
-        receive_json; this helper catches sessions stuck before any
-        receive (e.g. future QUEUED state) and is expected to be wired
-        into the GPU-pool reaper.
-        """
-        now = now if now is not None else time.monotonic()
-        dead: list[str] = []
-        for sid, session in self._sessions.items():
-            if session.state in {
-                    SessionState.COMPLETE,
-                    SessionState.ERROR,
-                    SessionState.TIMEOUT,
-                    SessionState.REJECTED,
-            }:
-                continue
-            if now - session.last_activity > self._session_timeout_seconds:
-                dead.append(sid)
-        return dead
-
-
-class SessionRejected(RuntimeError):
-    """Raised when session creation fails (queue full, auth, etc.)."""
-
-
-__all__ = [
-    "InvalidSessionTransition",
-    "Session",
-    "SessionManager",
-    "SessionRejected",
-    "SessionState",
-]
@@ -1,103 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Persist the initial-image blob attached to a streaming session."""
-from __future__ import annotations
-
-import base64
-import binascii
-import contextlib
-import os
-import tempfile
-from dataclasses import dataclass
-from typing import Any
-
-_ACCEPTED_MIMES = {
-    "image/png": ".png",
-    "image/jpeg": ".jpg",
-    "image/jpg": ".jpg",
-    "image/webp": ".webp",
-}
-
-_MAX_IMAGE_BYTES = 32 * 1024 * 1024  # 32 MiB cap
-
-
-@dataclass(frozen=True)
-class SessionInitImage:
-    """Location of the persisted init image.
-
-    Callers pass ``path`` to ``InputConfig.image_path``; ``display_name``
-    is only used for logs.
-    """
-
-    path: str
-    display_name: str
-    mime: str
-
-
-def persist_session_init_image(
-    payload: Any,
-    *,
-    output_dir: str | None = None,
-) -> SessionInitImage | None:
-    """Decode a client init-image blob and persist it to disk.
-
-    ``payload`` shape (matches the internal UI protocol)::
-
-        {
-            "mime": "image/png",
-            "name": "ref.png",
-            "data": "<base64 bytes>",
-        }
-
-    Returns ``None`` when ``payload`` is falsy (no init image). Raises
-    :class:`ValueError` on schema / size / decode errors so the caller
-    can surface a user-facing ``error`` frame.
-    """
-    if not payload:
-        return None
-    if not isinstance(payload, dict):
-        raise ValueError("session init image must be an object")
-
-    mime = payload.get("mime")
-    if mime not in _ACCEPTED_MIMES:
-        raise ValueError(f"session init image mime {mime!r} is not one of "
-                         f"{sorted(_ACCEPTED_MIMES)}")
-    data_b64 = payload.get("data")
-    if not isinstance(data_b64, str):
-        raise ValueError("session init image data must be a base64 string")
-    try:
-        data = base64.b64decode(data_b64, validate=True)
-    except (binascii.Error, ValueError) as exc:
-        raise ValueError(f"session init image data is not valid base64: {exc}") from exc
-    if len(data) > _MAX_IMAGE_BYTES:
-        raise ValueError(f"session init image is {len(data)} bytes; limit is "
-                         f"{_MAX_IMAGE_BYTES}")
-    if len(data) == 0:
-        raise ValueError("session init image data is empty")
-
-    ext = _ACCEPTED_MIMES[mime]
-    display_name = _sanitize_display_name(payload.get("name")) or f"init{ext}"
-    fd, path = tempfile.mkstemp(prefix="fastvideo-init-", suffix=ext, dir=output_dir)
-    try:
-        with os.fdopen(fd, "wb") as f:
-            f.write(data)
-    except Exception:
-        with contextlib.suppress(FileNotFoundError):
-            os.unlink(path)
-        raise
-    return SessionInitImage(path=path, display_name=display_name, mime=mime)
-
-
-def _sanitize_display_name(name: Any) -> str | None:
-    if not isinstance(name, str):
-        return None
-    name = name.strip()
-    if not name:
-        return None
-    # Strip any path components — we only keep the leaf for logging.
-    return os.path.basename(name)
-
-
-__all__ = [
-    "SessionInitImage",
-    "persist_session_init_image",
-]
@@ -1,206 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Session state store for the FastVideo streaming server.
-
-The streaming server keeps continuation state (decoded frames + audio
-latents from the previous segment) server-side so the client doesn't
-re-upload multi-megabyte tensors each WebSocket message. Two operations
-are needed:
-
-* ``snapshot(session_id) -> ContinuationState`` — serialize the current
-  state so it can be exported (e.g. over HTTP) or migrated to a
-  different server.
-* ``hydrate(state) -> session_id`` — load a previously serialized state
-  into a new session (for resume-after-disconnect flows).
-
-The store is an ABC with an :class:`InMemorySessionStore` default; Redis
-or other backends can drop in without touching the pipeline.
-
-Large tensor payloads (video frames, audio latents) are kept out of the
-JSON payload via an accompanying :class:`BlobStore`. Both stores share a
-process today; they are separate types so that a future implementation
-can put blobs on S3 while keeping session metadata in Redis.
-"""
-from __future__ import annotations
-
-import threading
-import uuid
-from abc import ABC, abstractmethod
-from collections.abc import Iterator
-from dataclasses import dataclass
-
-from fastvideo.api.schema import ContinuationState
-
-
-class BlobStore(ABC):
-    """Opaque byte-blob storage keyed by id.
-
-    A :class:`ContinuationState` payload can reference large tensors
-    stored in a :class:`BlobStore` rather than inlining them, so the
-    JSON payload stays small when the state travels over the wire.
-    """
-
-    @abstractmethod
-    def put(self, data: bytes, *, mime: str = "application/octet-stream") -> str:
-        """Store ``data`` and return a blob id for later retrieval."""
-
-    @abstractmethod
-    def get(self, blob_id: str) -> bytes:
-        """Load a previously stored blob. Raises ``KeyError`` if absent."""
-
-    @abstractmethod
-    def drop(self, blob_id: str) -> None:
-        """Remove a blob. Missing ids are a no-op."""
-
-    @abstractmethod
-    def __contains__(self, blob_id: str) -> bool:
-        ...
-
-
-@dataclass(frozen=True)
-class _BlobRecord:
-    data: bytes
-    mime: str
-
-
-class InMemoryBlobStore(BlobStore):
-    """Thread-safe in-memory :class:`BlobStore` for single-process servers.
-
-    No eviction policy — callers are responsible for calling
-    :meth:`drop` when a blob's owning state is replaced or a session
-    ends. A redis- or filesystem-backed :class:`BlobStore` should
-    replace this when the streaming server lands as a real service
-    (PR 7.5+).
-    """
-
-    def __init__(self) -> None:
-        self._blobs: dict[str, _BlobRecord] = {}
-        self._lock = threading.Lock()
-
-    def put(self, data: bytes, *, mime: str = "application/octet-stream") -> str:
-        blob_id = uuid.uuid4().hex
-        with self._lock:
-            self._blobs[blob_id] = _BlobRecord(data=data, mime=mime)
-        return blob_id
-
-    def get(self, blob_id: str) -> bytes:
-        with self._lock:
-            record = self._blobs.get(blob_id)
-        if record is None:
-            raise KeyError(f"Unknown blob id: {blob_id}")
-        return record.data
-
-    def drop(self, blob_id: str) -> None:
-        with self._lock:
-            self._blobs.pop(blob_id, None)
-
-    def __contains__(self, blob_id: str) -> bool:
-        with self._lock:
-            return blob_id in self._blobs
-
-    def __len__(self) -> int:
-        with self._lock:
-            return len(self._blobs)
-
-
-class SessionStore(ABC):
-    """Keyed store for per-session continuation state.
-
-    Implementations own the session-id → state mapping. The streaming
-    server calls :meth:`store` after each segment and :meth:`snapshot`
-    when a client explicitly asks for an exportable state handle.
-    """
-
-    @abstractmethod
-    def store(self, session_id: str, state: ContinuationState) -> None:
-        """Persist ``state`` for ``session_id``, replacing any prior value."""
-
-    @abstractmethod
-    def snapshot(self, session_id: str) -> ContinuationState | None:
-        """Return the current state for ``session_id`` (or ``None``)."""
-
-    @abstractmethod
-    def hydrate(
-        self,
-        state: ContinuationState,
-        *,
-        session_id: str | None = None,
-    ) -> str:
-        """Install ``state`` as the starting point for a session.
-
-        When ``session_id`` is ``None`` the store allocates a fresh id
-        (UUID4); when provided the store uses it verbatim, overwriting
-        any prior state at that id.
-        """
-
-    @abstractmethod
-    def drop(self, session_id: str) -> None:
-        """Forget a session. Missing ids are a no-op."""
-
-    @abstractmethod
-    def __contains__(self, session_id: str) -> bool:
-        ...
-
-    @abstractmethod
-    def __iter__(self) -> Iterator[str]:
-        ...
-
-
-class InMemorySessionStore(SessionStore):
-    """Thread-safe in-memory :class:`SessionStore`.
-
-    Default implementation used by single-process deployments; a future
-    Redis-backed store can be dropped in without changes to the server.
-
-    No eviction / TTL / bounded capacity — sessions only leave via
-    :meth:`drop`. The live streaming server (PR 7.5+) is responsible
-    for bounding growth and for dropping any :class:`BlobStore` blobs
-    referenced by a state when that state is replaced or a session
-    ends; this class does not know about blobs.
-    """
-
-    def __init__(self) -> None:
-        self._sessions: dict[str, ContinuationState] = {}
-        self._lock = threading.Lock()
-
-    def store(self, session_id: str, state: ContinuationState) -> None:
-        with self._lock:
-            self._sessions[session_id] = state
-
-    def snapshot(self, session_id: str) -> ContinuationState | None:
-        with self._lock:
-            return self._sessions.get(session_id)
-
-    def hydrate(
-        self,
-        state: ContinuationState,
-        *,
-        session_id: str | None = None,
-    ) -> str:
-        sid = session_id or uuid.uuid4().hex
-        with self._lock:
-            self._sessions[sid] = state
-        return sid
-
-    def drop(self, session_id: str) -> None:
-        with self._lock:
-            self._sessions.pop(session_id, None)
-
-    def __contains__(self, session_id: str) -> bool:
-        with self._lock:
-            return session_id in self._sessions
-
-    def __iter__(self) -> Iterator[str]:
-        with self._lock:
-            return iter(list(self._sessions))
-
-    def __len__(self) -> int:
-        with self._lock:
-            return len(self._sessions)
-
-
-__all__ = [
-    "BlobStore",
-    "InMemoryBlobStore",
-    "InMemorySessionStore",
-    "SessionStore",
-]
@@ -1,213 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""fMP4 stream encoder used by the streaming server.
-
-The client's Media Source Extensions player needs a continuous fMP4
-byte stream: first an *initialization segment* (``ftyp`` + ``moov``),
-then one or more *media segments* (``moof`` + ``mdat``). We pipe raw
-RGB frames into an ffmpeg subprocess configured for fragmented output
-via ``-movflags empty_moov+default_base_moof+frag_keyframe+faststart``
-and stream the bytes back out.
-"""
-from __future__ import annotations
-
-import asyncio
-import contextlib
-import subprocess
-import uuid
-from collections.abc import AsyncIterator
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Literal
-
-if TYPE_CHECKING:
-    import numpy as np
-
-
-@dataclass
-class FragmentedMP4Chunk:
-    """A single fMP4 byte chunk emitted by :class:`FragmentedMP4Encoder`.
-
-    ``kind`` identifies whether the chunk is the init segment (must be
-    fed into the client's ``SourceBuffer`` first) or a media fragment.
-    """
-
-    kind: Literal["init", "media"]
-    data: bytes
-    stream_id: str
-    segment_idx: int
-
-
-class FragmentedMP4Encoder:
-    """Stream RGB frames in, fMP4 chunks out.
-
-    One encoder covers one segment. The server creates a new encoder
-    per :class:`ltx2_segment_start`` boundary so each segment becomes
-    one media fragment the client can append independently.
-
-    Example::
-
-        encoder = FragmentedMP4Encoder(width=1024, height=576, fps=24,
-                                        segment_idx=0)
-        async with encoder:
-            async for chunk in encoder.encode(frames):
-                await websocket.send_bytes(chunk.data)
-    """
-
-    def __init__(
-        self,
-        *,
-        width: int,
-        height: int,
-        fps: int,
-        segment_idx: int,
-        stream_id: str | None = None,
-        ffmpeg_path: str = "ffmpeg",
-        preset: str = "ultrafast",
-        pixel_format_out: str = "yuv420p",
-        extra_args: list[str] | None = None,
-    ) -> None:
-        self.width = width
-        self.height = height
-        self.fps = fps
-        self.segment_idx = segment_idx
-        self.stream_id = stream_id or uuid.uuid4().hex
-        self._ffmpeg_path = ffmpeg_path
-        self._preset = preset
-        self._pixel_format_out = pixel_format_out
-        self._extra_args = list(extra_args or [])
-        self._proc: subprocess.Popen | None = None
-        self._init_emitted = False
-
-    async def __aenter__(self) -> FragmentedMP4Encoder:
-        self._spawn()
-        return self
-
-    async def __aexit__(self, exc_type, exc, tb) -> None:
-        await self.close()
-
-    def _spawn(self) -> None:
-        args = [
-            self._ffmpeg_path,
-            "-hide_banner",
-            "-loglevel",
-            "error",
-            "-f",
-            "rawvideo",
-            "-pix_fmt",
-            "rgb24",
-            "-s",
-            f"{self.width}x{self.height}",
-            "-r",
-            str(self.fps),
-            "-i",
-            "-",
-            "-c:v",
-            "libx264",
-            "-preset",
-            self._preset,
-            "-tune",
-            "zerolatency",
-            "-pix_fmt",
-            self._pixel_format_out,
-            "-movflags",
-            "empty_moov+default_base_moof+frag_keyframe+faststart",
-            "-f",
-            "mp4",
-            *self._extra_args,
-            "-",
-        ]
-        # stderr → DEVNULL: with -loglevel error on, the only thing
-        # stderr would carry is unsolicited warnings. Piping without a
-        # reader deadlocks ffmpeg once the pipe buffer (~64 KiB) fills.
-        self._proc = subprocess.Popen(  # noqa: S603
-            args,
-            stdin=subprocess.PIPE,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.DEVNULL,
-            bufsize=0,
-        )
-
-    async def encode(
-        self,
-        frames: list[np.ndarray] | AsyncIterator[np.ndarray],
-    ) -> AsyncIterator[FragmentedMP4Chunk]:
-        """Feed frames into ffmpeg and yield fMP4 chunks as they appear."""
-        if self._proc is None:
-            self._spawn()
-        assert self._proc is not None and self._proc.stdin is not None
-        proc = self._proc
-
-        loop = asyncio.get_running_loop()
-
-        async def _writer() -> None:
-            try:
-                if hasattr(frames, "__aiter__"):
-                    async for frame in frames:  # type: ignore[union-attr]
-                        await loop.run_in_executor(None, _write_frame, proc.stdin, frame)
-                else:
-                    for frame in frames:  # type: ignore[assignment]
-                        await loop.run_in_executor(None, _write_frame, proc.stdin, frame)
-            finally:
-                with contextlib.suppress(BrokenPipeError):
-                    proc.stdin.close()
-
-        writer_task = asyncio.create_task(_writer())
-        try:
-            reader = proc.stdout
-            assert reader is not None
-            # Read in reasonably-sized chunks; MSE tolerates any size
-            # but we don't want to starve the event loop.
-            chunk_size = 64 * 1024
-            while True:
-                data = await loop.run_in_executor(None, reader.read, chunk_size)
-                if not data:
-                    break
-                kind: Literal["init", "media"] = "init" if not self._init_emitted else "media"
-                self._init_emitted = True
-                yield FragmentedMP4Chunk(
-                    kind=kind,
-                    data=bytes(data),
-                    stream_id=self.stream_id,
-                    segment_idx=self.segment_idx,
-                )
-        finally:
-            await writer_task
-
-    async def close(self) -> None:
-        if self._proc is None:
-            return
-        proc = self._proc
-        self._proc = None
-        try:
-            if proc.stdin and not proc.stdin.closed:
-                proc.stdin.close()
-        except BrokenPipeError:
-            pass
-        loop = asyncio.get_running_loop()
-        try:
-            await asyncio.wait_for(
-                loop.run_in_executor(None, proc.wait),
-                timeout=5.0,
-            )
-        except asyncio.TimeoutError:
-            proc.kill()
-            await loop.run_in_executor(None, proc.wait)
-
-
-def _write_frame(stdin, frame: np.ndarray) -> None:
-    import numpy as np
-
-    if not isinstance(frame, np.ndarray):
-        raise TypeError("fMP4 encoder frames must be numpy.ndarray")
-    if frame.dtype != np.uint8:
-        frame = frame.astype(np.uint8)
-    if frame.ndim != 3 or frame.shape[-1] != 3:
-        raise ValueError("fMP4 encoder frames must be HxWx3 uint8 RGB; got "
-                         f"shape={frame.shape}, dtype={frame.dtype}")
-    with contextlib.suppress(BrokenPipeError):
-        stdin.write(frame.tobytes())
-
-
-__all__ = [
-    "FragmentedMP4Chunk",
-    "FragmentedMP4Encoder",
-]
@@ -8,7 +8,7 @@ import torch
 import torchvision
 from einops import rearrange

-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam
 from fastvideo.entrypoints.video_generator import VideoGenerator
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
@@ -35,7 +35,7 @@ from fastvideo.api.compat import (
 )
 from fastvideo.api.results import GenerationResult
 from fastvideo.api.schema import GenerationRequest, GeneratorConfig
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.configs.sample import SamplingParam
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
 from fastvideo.pipelines import ForwardBatch
@@ -1,214 +0,0 @@
-# `fastvideo.eval`
-
-In-process evaluation suite for video generations. Includes pixel
-metrics (SSIM, PSNR, LPIPS), optical-flow comparisons, the full VBench
-suite, Physics-IQ, and a VLM scorer (VideoScore-2) behind a single
-registry-driven API.
-
-## Install
-
-| Use case | Install |
-|---|---|
-| Default (common, optical_flow, vbench-light, physics_iq, videoscore2) | `uv pip install -e .[eval]` |
-| Just VBench (12 of 16 sub-metrics) | `uv pip install -e .[eval-vbench]` |
-| Just Physics-IQ (covered by `[eval]`) | `uv pip install -e .[eval-physics-iq]` |
-| Plus `vbench.scene` (AVoCaDO) | `uv pip install -e .[eval-full]` |
-| Plus `vbench.{color, multiple_objects, object_class, spatial_relationship}` (GRiT) | `uv pip install -e .[eval-vbench]` then `uv pip install --no-build-isolation 'git+https://github.com/facebookresearch/detectron2.git'` |
-
-To use VBench, also pull the upstream submodule:
-
-```bash
-git submodule update --init --recursive  # fetches vbench + kernel deps
-```
-
-The submodule is a clean upstream pin. Compat with current
-transformers/numpy/timm versions is applied at import time in
-`fastvideo/eval/metrics/vbench/__init__.py` via attribute-level
-monkey-patches; the submodule files are unchanged.
-
-## Public API
-
-```python
-from fastvideo.eval import (
-    create_evaluator,        # build a reusable Evaluator
-    evaluate,                # one-shot helper
-    Evaluator,               # the class itself
-    BaseMetric, MetricResult,
-    register, list_metrics, get_metric,
-    ensure_checkpoint, get_cache_dir,
-)
-
-ev = create_evaluator(metrics=["common.ssim", "vbench.aesthetic_quality"],
-                      device="cuda")
-scores = ev.evaluate(video=tensor, reference=ref, fps=8.0)
-```
-
-`evaluate` accepts either a pre-loaded `(T, C, H, W)` tensor or a path
-string for `video` and `reference`. Paths are decoded inside the worker
-that picks up the sample, so peak memory stays bounded by `num_gpus`
-when scoring large batches.
-
-### CLI
-
-```bash
-fastvideo eval list                              # list registered metrics
-fastvideo eval list --group vbench               # filter by group
-fastvideo eval run --videos clip.mp4 \
-    --metrics vbench.aesthetic_quality \
-    --output scores.json
-fastvideo eval run --videos generated/*.mp4 \
-    --reference reference/ \
-    --metrics common.ssim,common.lpips
-```
-
-### Generate-then-score example
-
-`examples/inference/eval/eval_ltx2_vbench.py` runs an LTX2 prompt
-through `VideoGenerator` and scores the resulting mp4 with
-`vbench.aesthetic_quality` and `vbench.subject_consistency`. Use it as
-a template for end-to-end "generate then score" pipelines.
-
-## Layout
-
-```
-fastvideo/
-├── eval/
-│   ├── api.py, evaluator.py, registry.py, models.py, ...
-│   ├── io/                        # video loading helpers
-│   ├── datasets/                  # prompt corpora (vbench, physics_iq)
-│   └── metrics/
-│       ├── base.py                # BaseMetric + @register contract
-│       ├── common/                # SSIM, PSNR, LPIPS
-│       ├── optical_flow/          # gt_optical_flow, synthetic_optical_flow
-│       ├── videoscore2/           # VideoScore-2 (Qwen2.5-VL)
-│       ├── physics_iq/            # PhysicsIQ + sub-metrics
-│       └── vbench/                # adapter: sys.path bootstrap + shims
-│           ├── __init__.py
-│           └── <16 sub-metric pkgs>
-└── third_party/
-    └── eval/
-        └── vbench/                # git submodule (Vchitect/VBench)
-```
-
-### Prompt datasets
-
-```python
-from fastvideo.eval.datasets import get_dataset, list_datasets
-
-list_datasets()                    # ['physics_iq', 'vbench']
-
-ds = get_dataset("physics_iq", limit=4)   # auto-fetches assets on first miss
-for row in ds:
-    # row contains 'prompt', 'reference', 'reference_take2', and
-    # metric-specific aux fields. Drop straight into Evaluator.evaluate(**row).
-    ...
-```
-
-The Physics-IQ manifest CSV is vendored at
-`fastvideo/eval/metrics/physics_iq/_vendored/descriptions.csv`.
-Per-scenario videos, masks, and switch-frames auto-fetch on first use
-into `${FASTVIDEO_EVAL_CACHE}/datasets/physics_iq/`. For air-gapped
-runs, pass `auto_download=False` or `dataset_root=` a pre-downloaded
-copy. Set `FASTVIDEO_PHYSICS_IQ_BUCKET_URL` to redirect the fetch to
-an internal mirror.
-
-## Adding a new metric
-
-The full porting guide is at
-[`docs/contributing/eval-metrics.md`](../../docs/contributing/eval-metrics.md).
-Summary below.
-
-### Native metric (no submodule)
-
-```python
-# fastvideo/eval/metrics/common/<your_metric>/metric.py
-from fastvideo.eval.metrics.base import BaseMetric
-from fastvideo.eval.registry import register
-from fastvideo.eval.types import MetricResult
-
-
-@register("common.your_metric")
-class YourMetric(BaseMetric):
-    name = "common.your_metric"
-    requires_reference = True
-    needs_gpu = False
-    dependencies: list[str] = []  # e.g. ["pyiqa"] if relevant
-
-    def compute(self, sample) -> list[MetricResult]:
-        ...
-```
-
-The metric is auto-discovered by `fastvideo/eval/metrics/__init__.py`,
-which walks all non-underscore subdirectories and imports their
-`metric` module.
-
-### Wrapping upstream code via a submodule
-
-See `fastvideo/eval/metrics/vbench/` for a worked example. The
-contract is:
-
-1. Upstream lives as a git submodule under
-   `fastvideo/third_party/eval/<bench>/`, pinned to a SHA in repo-root
-   `.gitmodules`.
-2. The metric package's `__init__.py`
-   (`fastvideo/eval/metrics/<bench>/__init__.py`) inserts that
-   submodule path on `sys.path` and installs any compat shims for
-   modern torch/transformers/numpy. Do not modify upstream files on
-   disk.
-3. Per-sub-metric `metric.py` files use `@register("<bench>.<name>")`.
-
-Patches live as Python in the metric's `__init__.py` so they are
-grep-able and reviewable.
-
-## Caches
-
-Eval cache root: `${FASTVIDEO_CACHE_ROOT}/eval/`, default
-`~/.cache/fastvideo/eval/`. Override with `FASTVIDEO_EVAL_CACHE`.
-
-```
-${FASTVIDEO_CACHE_ROOT}/eval/
-├── models/      # URL-fetched checkpoints (LAION head, AMT, GRiT)
-├── torch/       # redirected TORCH_HOME (DINO via torch.hub, lpips)
-├── clip/        # passed as download_root= to clip.load callsites
-└── datasets/    # auto-fetched dataset assets, one subdir per benchmark
-                 # (e.g. datasets/physics_iq/{split-videos,switch-frames,...})
-```
-
-HF-hosted models stay in HF's default cache
-(`~/.cache/huggingface/hub/`) so they dedupe with other ML projects on
-the same host.
-
-### Convention for new metrics
-
-If your metric wraps a third-party loader that has its own cache
-directory, route it through `get_cache_dir()` so users get one knob
-to redirect everything.
-
-```python
-# CLIP: pass download_root explicitly
-import clip
-from fastvideo.eval.models import get_cache_dir
-model, _ = clip.load("ViT-B/32", device=device,
-                     download_root=str(get_cache_dir() / "clip"))
-
-# torch.hub is already redirected by fastvideo.eval.__init__ via
-# TORCH_HOME; no per-callsite work needed.
-
-# transformers / huggingface_hub: leave alone. HF's default cache is
-# shared with other tools.
-```
-
-For libraries that do not honour any env var or kwarg (pyiqa, funasr),
-their cache lands in the library's own dir. Document the exception in
-the metric's docstring if it matters.
-
-## Out of scope (follow-up PRs)
-
- **MIND** metrics. Depend on a separate `vipe` upstream submodule.
- **VBench-2.0**. Sibling vbench2 package; needs its own port.
- **FVD as a registered metric**. Currently still at `benchmarks/fvd/`.
-  FVD is a set-vs-set distribution distance and does not fit the
-  per-sample `BaseMetric.compute` API without a stateful accumulator;
-  conversion is a designed follow-up.
- **Training-time eval callback** (`EvalCallback`) and the
-  `RolloutEvaluator` helper.
@@ -1,45 +0,0 @@
-from fastvideo.eval.models import ensure_checkpoint, get_cache_dir
-
-
-def _redirect_third_party_caches() -> None:
-    """Point libraries that respect env vars at the eval cache root.
-
-    Run before metric modules import torch.hub (and friends) so the
-    redirect actually takes effect. We intentionally leave ``HF_HOME``
-    alone — HF's default cache (``~/.cache/huggingface/hub``) is widely
-    shared with other ML projects, and isolating it for eval would force
-    users to re-download already-cached transformers weights.
-
-    Other libraries with non-standard caches (CLIP, pyiqa) don't honour
-    env vars at all; their callsites in metric.py files pass
-    ``download_root=str(get_cache_dir() / "<library>")`` directly.
-    """
-    import os
-    root = get_cache_dir()
-    os.environ.setdefault("TORCH_HOME", str(root / "torch"))
-
-
-_redirect_third_party_caches()
-
-from fastvideo.eval.types import MetricResult, Video  # noqa: E402
-from fastvideo.eval.metrics.base import BaseMetric  # noqa: E402
-from fastvideo.eval.registry import register, list_metrics, get_metric  # noqa: E402
-from fastvideo.eval.api import evaluate  # noqa: E402
-from fastvideo.eval.evaluator import Evaluator, create_evaluator  # noqa: E402
-
-# Trigger metric auto-discovery
-import fastvideo.eval.metrics  # noqa: F401, E402
-
-__all__ = [
-    "evaluate",
-    "Evaluator",
-    "create_evaluator",
-    "MetricResult",
-    "Video",
-    "BaseMetric",
-    "register",
-    "list_metrics",
-    "get_metric",
-    "ensure_checkpoint",
-    "get_cache_dir",
-]
@@ -1,36 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-
-import torch
-
-from fastvideo.eval.evaluator import create_evaluator
-from fastvideo.eval.types import MetricResult
-
-
-def evaluate(
-    generated: torch.Tensor | str | Path,
-    reference: torch.Tensor | str | Path | None = None,
-    metrics: list[str] | str = "all",
-    device: str = "cuda",
-    **kwargs,
-) -> dict[str, MetricResult] | list[dict[str, MetricResult]]:
-    """One-shot evaluation. For repeated use, prefer :func:`create_evaluator`.
-
-    Parameters
-    ----------
-    generated : Tensor | str | Path
-        Generated video. Either a pre-loaded ``(T, C, H, W)`` tensor or a
-        path to an mp4/avi/etc. — paths are decoded by the worker.
-    reference : Tensor | str | Path | None
-        Reference video (same accepted shapes as *generated*).
-    metrics : list[str] | str
-        Metric names, or ``"all"``.
-    device : str
-        PyTorch device string.
-    """
-    ev = create_evaluator(metrics=metrics, device=device)
-    kw: dict = {"video": generated, **kwargs}
-    if reference is not None:
-        kw["reference"] = reference
-    return ev.evaluate(**kw)
@@ -1,51 +0,0 @@
-"""Prompt-corpus datasets for end-to-end benchmark evaluation.
-
-Public API mirrors :mod:`fastvideo.eval` (metrics side):
-
-    from fastvideo.eval.datasets import (
-        PromptDataset, Sample,
-        register_dataset, get_dataset, list_datasets,
-    )
-
-A dataset is an iterable of plain dicts (one per sample). Built-in
-datasets self-register at import time. To add one, drop a module into
-this package that subclasses :class:`PromptDataset` and decorates with
-``@register_dataset("name")`` — auto-discovery picks it up.
-"""
-from fastvideo.eval.datasets.base import (BasePromptDataset, PromptDataset, Sample)
-from fastvideo.eval.datasets.registry import (get_dataset, list_datasets, register_dataset)
-
-
-def _autodiscover() -> None:
-    """Import every non-underscore .py module / subpackage in this package
-    so the ``@register_dataset`` decorators fire."""
-    import importlib
-    import os
-
-    for entry in os.listdir(os.path.dirname(__file__)):
-        if entry.startswith("_") or entry.startswith("."):
-            continue
-        if entry in {"base.py", "registry.py"}:
-            continue
-        if entry.endswith(".py"):
-            importlib.import_module(f"{__name__}.{entry[:-3]}")
-        elif os.path.isdir(os.path.join(os.path.dirname(__file__), entry)) \
-                and os.path.exists(os.path.join(
-                    os.path.dirname(__file__), entry, "__init__.py")):
-            importlib.import_module(f"{__name__}.{entry}")
-
-
-_autodiscover()
-
-# Re-export the canonical class for typed imports.
-from fastvideo.eval.datasets.vbench import VBenchPromptDataset  # noqa: E402
-
-__all__ = [
-    "PromptDataset",
-    "BasePromptDataset",
-    "Sample",
-    "register_dataset",
-    "get_dataset",
-    "list_datasets",
-    "VBenchPromptDataset",
-]
@@ -1,81 +0,0 @@
-"""Prompt-corpus datasets.
-
-A :class:`PromptDataset` is an iterable of *sample dicts* describing the
-prompts and conditions for a benchmark. Each sample is a plain dict —
-no dataclass, no schema enforcement — that flows directly into both
-generation (``VideoGenerator.generate_video(**sample)``) and scoring
-(``Evaluator.evaluate(**eval_kwargs)``). The runner picks well-known
-keys (``prompt``, ``n_samples``, ``dimensions``, ``auxiliary_info``,
-...) and passes the rest through.
-
-This matches the surrounding FastVideo style:
-
-* :class:`fastvideo.dataset.validation_dataset.ValidationDataset` yields dicts.
-* :meth:`fastvideo.VideoGenerator.generate_video` consumes ``**kwargs``.
-* :meth:`fastvideo.eval.Evaluator.evaluate` consumes ``**kwargs``.
-
-To add a new benchmark:
-
-1. Subclass :class:`PromptDataset`, populate ``self._rows`` with dicts in
-   ``__init__``.
-2. Decorate with ``@register_dataset("my_bench")``.
-
-Convention for ``auxiliary_info``: a *flat* dict of metric-keyed values
-(e.g. ``{"color": "red"}``). Benchmarks with nested aux schemas (VBench's
-``{dim: {key: val}}``) flatten at load time so every consumer sees the
-same shape.
-"""
-from __future__ import annotations
-
-from typing import TypedDict
-from collections.abc import Iterator
-
-
-class Sample(TypedDict, total=False):
-    """Documented schema for a row yielded by :class:`PromptDataset`.
-
-    Only ``prompt`` is required. Extra keys beyond these are forwarded to
-    the runner's eval-kwargs builder verbatim, so action-conditioned or
-    audio-bearing benchmarks can add their own fields without changing
-    the base class.
-    """
-    prompt: str
-    n_samples: int
-    dimensions: list[str]
-    auxiliary_info: dict
-    image_path: str
-    reference_video: str
-
-
-class PromptDataset:
-    """Iterable corpus of sample dicts. Subclasses populate ``self._rows``."""
-
-    name: str = ""
-    description: str = ""
-    supports_dimensions: bool = False
-    requires_reference_image: bool = False
-    requires_reference_video: bool = False
-
-    def __init__(self) -> None:
-        self._rows: list[dict] = []
-
-    def __iter__(self) -> Iterator[dict]:
-        return iter(self._rows)
-
-    def __len__(self) -> int:
-        return len(self._rows)
-
-    def __getitem__(self, i: int) -> dict:
-        return self._rows[i]
-
-    def by_dimension(self) -> dict[str, list[dict]]:
-        """Group samples by dimension. A multi-dim sample appears under each."""
-        out: dict[str, list[dict]] = {}
-        for s in self._rows:
-            for d in s.get("dimensions", ()):
-                out.setdefault(d, []).append(s)
-        return out
-
-
-# Back-compat alias for callers still importing the old class name.
-BasePromptDataset = PromptDataset
@@ -1,444 +0,0 @@
-"""Physics-IQ benchmark prompt corpus.
-
-Yields one sample dict per take-1 scenario, paired with its take-2
-reference and both takes' real motion masks. Each row drops straight
-into :meth:`fastvideo.eval.Evaluator.evaluate` for the ``physics_iq``
-metric:
-
-    {
-        "prompt": <description>,
-        "reference":            "<take-1 mp4>",
-        "reference_take2":      "<take-2 mp4>",
-        "reference_mask":       "<take-1 mask mp4>",
-        "reference_take2_mask": "<take-2 mask mp4>",
-        "scenario": <scenario_id>,
-        "view": <camera view>,
-        "auxiliary_info": { ... metadata ... },
-    }
-
-Self-contained dataset: the manifest CSV is vendored under
-``fastvideo/eval/metrics/physics_iq/_vendored/descriptions.csv``;
-per-scenario videos/masks/switch-frames auto-fetch on first use from the public
-DeepMind bucket into ``${FASTVIDEO_EVAL_CACHE}/datasets/physics_iq/``.
-Pass ``auto_download=False`` (or ``dataset_root=`` pointing at a
-pre-downloaded copy) to opt out of network fetches.
-"""
-
-from __future__ import annotations
-
-import csv
-import os
-from dataclasses import dataclass
-from pathlib import Path
-from urllib.request import urlretrieve
-
-import cv2
-import numpy as np
-
-from fastvideo.eval.datasets.base import PromptDataset
-from fastvideo.eval.datasets.registry import register_dataset
-from fastvideo.eval.models import get_cache_dir
-
-VIEWS = ("perspective-left", "perspective-center", "perspective-right")
-TAKE1_TOKEN = "take-1"
-TAKE2_TOKEN = "take-2"
-
-# FPS the dataset rows should resolve to. Source release is recorded at
-# 30 FPS; if a different value is requested the loader transcodes once
-# into a per-repo-root cache directory.
-_DEFAULT_FPS = 30
-_DEFAULT_DURATION_SECONDS = 5
-
-# Vendored manifest under ``fastvideo/eval/metrics/physics_iq/_vendored/``
-# is the same file shipped by upstream's git repo. The ``_vendored/``
-# subdir is the project-wide convention for upstream-provenance files
-# (matches the ``_``-prefixed auto-discovery skip and a single
-# codespell skip glob).
-_VENDORED_DESCRIPTIONS_CSV = (Path(__file__).resolve().parent.parent / "metrics" / "physics_iq" / "_vendored" /
-                              "descriptions.csv")
-
-# Public DeepMind bucket; HTTPS-readable, no auth. Override via
-# ``FASTVIDEO_PHYSICS_IQ_BUCKET_URL`` (e.g. for an internal mirror).
-_DEFAULT_BUCKET_URL = "https://storage.googleapis.com/physics-iq-benchmark"
-
-
-def _bucket_url() -> str:
-    return os.environ.get("FASTVIDEO_PHYSICS_IQ_BUCKET_URL", _DEFAULT_BUCKET_URL)
-
-
-def _default_dataset_root() -> Path:
-    """Sibling to ``models/torch/clip/`` under the eval cache root."""
-    return get_cache_dir() / "datasets" / "physics_iq"
-
-
-@dataclass(frozen=True)
-class PhysicsIQScenario:
-    """One row of the Physics-IQ manifest, fully resolved on disk."""
-    scenario_id: str
-    view: str
-    scenario_name: str
-    take1_video_path: str
-    take2_video_path: str
-    switch_frame_path: str
-    caption: str
-    expected_gen_filename: str
-    generated_video_path: str | None = None
-    take1_mask_path: str | None = None
-    take2_mask_path: str | None = None
-
-
-@register_dataset("physics_iq")
-class PhysicsIQPromptDataset(PromptDataset):
-    """Physics-IQ benchmark prompt corpus.
-
-    Self-contained: ``get_dataset("physics_iq")`` works with no kwargs.
-    The manifest CSV is vendored next to the metric, and per-scenario
-    assets auto-fetch on first miss from the public bucket into
-    ``${FASTVIDEO_EVAL_CACHE}/datasets/physics_iq/``.
-
-    Args:
-        dataset_root: path to a pre-downloaded copy of the Physics-IQ
-            release. Defaults to ``${FASTVIDEO_EVAL_CACHE}/datasets/physics_iq``;
-            override only if you already have a local mirror.
-        fps: target frame rate. The release ships at 30 FPS; other rates
-            transcode once on first access into ``<root>/.physics_iq_cache/``.
-        limit: optional truncation for quick smoke runs. Apply this kwarg
-            (not a post-construction slice) so we only fetch the assets
-            for the scenarios actually requested.
-        generated_dir: optional directory of pre-generated videos —
-            attaches each manifest row's expected output path to the
-            sample dict under ``auxiliary_info["generated_video_path"]``.
-        auto_download: when True (the default), missing testing videos,
-            masks, and switch frames are fetched from the public bucket
-            into ``dataset_root``. Set False for air-gapped runs; the
-            loader will then raise ``FileNotFoundError`` on miss.
-    """
-
-    description = ("Physics-IQ benchmark, 396 take-1 scenarios across 66 unique physics "
-                   "setups × 3 perspective views, each paired with a take-2 reference.")
-    requires_reference_video = True
-
-    def __init__(
-        self,
-        dataset_root: str | Path | None = None,
-        *,
-        fps: int = _DEFAULT_FPS,
-        limit: int | None = None,
-        generated_dir: str | Path | None = None,
-        auto_download: bool = True,
-    ) -> None:
-        super().__init__()
-
-        repo_root = Path(dataset_root or _default_dataset_root()).expanduser().resolve()
-        self.repo_root = repo_root
-        self.dataset_dir = _resolve_dataset_dir(repo_root)
-        self.descriptions_path = _resolve_descriptions_path(repo_root, self.dataset_dir)
-        self.cache_dir = repo_root / ".physics_iq_cache"
-        self.fps = fps
-        self.auto_download = auto_download
-        self.bucket_url = _bucket_url()
-
-        scenarios = self._iter_scenarios(
-            fps=fps,
-            generated_dir=generated_dir,
-            limit=limit,
-        )
-        self._rows = [_scenario_to_row(s) for s in scenarios]
-
-    def _iter_scenarios(
-        self,
-        *,
-        fps: int,
-        generated_dir: str | Path | None,
-        limit: int | None,
-    ) -> list[PhysicsIQScenario]:
-        with self.descriptions_path.open("r", newline="") as handle:
-            rows = list(csv.DictReader(handle))
-
-        take2_by_suffix = {_scenario_suffix(row["scenario"]): row for row in rows if TAKE2_TOKEN in row["scenario"]}
-        take1_rows = [row for row in rows if TAKE1_TOKEN in row["scenario"]]
-        if limit is not None:
-            take1_rows = take1_rows[:limit]
-
-        generated_dir_path = (Path(generated_dir).expanduser().resolve() if generated_dir else None)
-        scenarios: list[PhysicsIQScenario] = []
-
-        for row in take1_rows:
-            scenario_filename = row["scenario"]
-            scenario_id, view, _, scenario_name = _parse_scenario_filename(scenario_filename)
-            take2_row = take2_by_suffix.get(_scenario_suffix(scenario_filename))
-            if take2_row is None:
-                raise FileNotFoundError(f"Could not find take-2 row matching {scenario_filename}")
-            take2_id, _, _, _ = _parse_scenario_filename(take2_row["scenario"])
-
-            take1_video_path = self._resolve_testing_video_path(
-                scenario_id=scenario_id,
-                view=view,
-                take=TAKE1_TOKEN,
-                scenario_name=scenario_name,
-                fps=fps,
-            )
-            take2_video_path = self._resolve_testing_video_path(
-                scenario_id=take2_id,
-                view=view,
-                take=TAKE2_TOKEN,
-                scenario_name=scenario_name,
-                fps=fps,
-            )
-            switch_frame_path = self._resolve_switch_frame_path(
-                scenario_id=scenario_id,
-                view=view,
-                scenario_name=scenario_name,
-            )
-            take1_mask_path = self._resolve_real_mask_path(
-                scenario_id=scenario_id,
-                view=view,
-                take=TAKE1_TOKEN,
-                scenario_name=scenario_name,
-                fps=fps,
-            )
-            take2_mask_path = self._resolve_real_mask_path(
-                scenario_id=take2_id,
-                view=view,
-                take=TAKE2_TOKEN,
-                scenario_name=scenario_name,
-                fps=fps,
-            )
-            generated_video_path = (str(generated_dir_path /
-                                        row["generated_video_name"]) if generated_dir_path is not None else None)
-
-            scenarios.append(
-                PhysicsIQScenario(
-                    scenario_id=scenario_id,
-                    view=view,
-                    scenario_name=scenario_name,
-                    take1_video_path=str(take1_video_path),
-                    take2_video_path=str(take2_video_path),
-                    switch_frame_path=str(switch_frame_path),
-                    caption=row["description"],
-                    expected_gen_filename=row["generated_video_name"],
-                    generated_video_path=generated_video_path,
-                    take1_mask_path=str(take1_mask_path),
-                    take2_mask_path=str(take2_mask_path),
-                ))
-        return scenarios
-
-    def _resolve_testing_video_path(
-        self,
-        *,
-        scenario_id: str,
-        view: str,
-        take: str,
-        scenario_name: str,
-        fps: int,
-    ) -> Path:
-        target_dir = self.dataset_dir / "split-videos" / "testing" / f"{fps}FPS"
-        target_name = (f"{scenario_id}_testing-videos_{fps}FPS_{view}_{take}_{scenario_name}.mp4")
-        target_path = target_dir / target_name
-        if target_path.exists():
-            return target_path
-
-        # 30-FPS source: either present locally or auto-fetchable.
-        source_name = (f"{scenario_id}_testing-videos_30FPS_{view}_{take}_{scenario_name}.mp4")
-        source_rel = f"split-videos/testing/30FPS/{source_name}"
-        source_path = self.dataset_dir / source_rel
-        self._ensure_remote_asset(source_rel, source_path)
-        if fps == _DEFAULT_FPS:
-            return source_path
-
-        # FPS-convert and cache so repeat runs are free.
-        cache_dir = self.cache_dir / "split-videos" / "testing" / f"{fps}FPS"
-        cache_dir.mkdir(parents=True, exist_ok=True)
-        cached_path = cache_dir / target_name
-        if not cached_path.exists():
-            _convert_video_fps(source_path, cached_path, fps_new=fps)
-        return cached_path
-
-    def _resolve_switch_frame_path(
-        self,
-        *,
-        scenario_id: str,
-        view: str,
-        scenario_name: str,
-    ) -> Path:
-        rel = (f"switch-frames/{scenario_id}_switch-frames_anyFPS_{view}_{scenario_name}.jpg")
-        target_path = self.dataset_dir / rel
-        self._ensure_remote_asset(rel, target_path)
-        return target_path
-
-    def _resolve_real_mask_path(
-        self,
-        *,
-        scenario_id: str,
-        view: str,
-        take: str,
-        scenario_name: str,
-        fps: int,
-    ) -> Path:
-        # Source release ships masks at 30 FPS only; non-30 rates are
-        # regenerated downstream from the (downsampled) real videos by
-        # the metric — see upstream ``run_physics_iq.py::ensure_binary_mask_structure``.
-        # We only auto-fetch 30 FPS here.
-        rel = (f"video-masks/real/30FPS/"
-               f"{scenario_id}_video-masks_30FPS_{view}_{take}_{scenario_name}.mp4")
-        target_path = self.dataset_dir / rel
-        self._ensure_remote_asset(rel, target_path)
-        if fps == _DEFAULT_FPS:
-            return target_path
-        # Caller asked for a non-30 rate; metric layer handles the
-        # regeneration. Return the canonical 30 FPS path so the metric
-        # always sees a valid mp4 it can transcode.
-        return target_path
-
-    def _ensure_remote_asset(self, rel_path: str, target_path: Path) -> Path:
-        """Download ``<bucket_url>/<rel_path>`` into *target_path* on miss.
-
-        Atomic via a sibling ``.part`` file; safe under concurrent runs
-        because the final ``rename`` is atomic on POSIX. Raises
-        ``FileNotFoundError`` if the file is missing and ``auto_download``
-        is False.
-        """
-        if target_path.exists():
-            return target_path
-        if not self.auto_download:
-            raise FileNotFoundError(f"Physics-IQ asset missing: {target_path}. "
-                                    "Set auto_download=True or pass dataset_root= a pre-downloaded copy.")
-        target_path.parent.mkdir(parents=True, exist_ok=True)
-        url = f"{self.bucket_url}/{rel_path.lstrip('/')}"
-        tmp_path = target_path.with_suffix(target_path.suffix + ".part")
-        try:
-            urlretrieve(url, tmp_path)
-        except Exception as exc:
-            if tmp_path.exists():
-                tmp_path.unlink()
-            raise FileNotFoundError(f"Failed to fetch Physics-IQ asset {url} -> {target_path}: "
-                                    f"{type(exc).__name__}: {exc}") from exc
-        tmp_path.rename(target_path)
-        return target_path
-
-
-# ---------------------------------------------------------------------------
-# Internal helpers
-# ---------------------------------------------------------------------------
-
-
-def _resolve_dataset_dir(repo_root: Path) -> Path:
-    nested = repo_root / "physics-IQ-benchmark"
-    if nested.exists():
-        return nested
-    return repo_root
-
-
-def _resolve_descriptions_path(repo_root: Path, dataset_dir: Path) -> Path:
-    """Prefer a co-located CSV under the user's dataset_root; fall back
-    to the copy vendored in this repo so ``get_dataset("physics_iq")``
-    works without external setup.
-    """
-    candidates = (
-        repo_root / "descriptions" / "descriptions.csv",
-        dataset_dir / "descriptions" / "descriptions.csv",
-    )
-    for path in candidates:
-        if path.exists():
-            return path
-    if _VENDORED_DESCRIPTIONS_CSV.is_file():
-        return _VENDORED_DESCRIPTIONS_CSV
-    raise FileNotFoundError("Could not locate Physics-IQ descriptions/descriptions.csv "
-                            f"(checked {[str(c) for c in candidates]} and vendored "
-                            f"{_VENDORED_DESCRIPTIONS_CSV})")
-
-
-def _parse_scenario_filename(filename: str) -> tuple[str, str, str, str]:
-    stem = Path(filename).name
-    if stem.endswith(".mp4"):
-        stem = stem[:-4]
-    parts = stem.split("_")
-    if len(parts) < 4:
-        raise ValueError(f"Unexpected Physics-IQ filename format: {filename}")
-    return parts[0], parts[1], parts[2], "_".join(parts[3:])
-
-
-def _scenario_suffix(filename: str) -> str:
-    _, view, _, scenario_name = _parse_scenario_filename(filename)
-    return f"{view}_{scenario_name}"
-
-
-def _scenario_to_row(scenario: PhysicsIQScenario) -> dict:
-    """Flatten a :class:`PhysicsIQScenario` into the public sample-dict shape."""
-    aux: dict = {
-        "scenario_id": scenario.scenario_id,
-        "scenario_name": scenario.scenario_name,
-        "switch_frame_path": scenario.switch_frame_path,
-        "expected_gen_filename": scenario.expected_gen_filename,
-    }
-    if scenario.generated_video_path is not None:
-        aux["generated_video_path"] = scenario.generated_video_path
-
-    row: dict = {
-        "prompt": scenario.caption,
-        "reference": scenario.take1_video_path,
-        "reference_take2": scenario.take2_video_path,
-        "scenario": scenario.scenario_id,
-        "view": scenario.view,
-        "auxiliary_info": aux,
-    }
-    if scenario.take1_mask_path is not None:
-        row["reference_mask"] = scenario.take1_mask_path
-    if scenario.take2_mask_path is not None:
-        row["reference_take2_mask"] = scenario.take2_mask_path
-    return row
-
-
-def _convert_video_fps(input_path: str | Path, output_path: str | Path, *, fps_new: int) -> None:
-    """Trim *input_path* to ``_DEFAULT_DURATION_SECONDS`` and re-encode at
-    *fps_new*, writing the result to *output_path*. Used to materialize
-    Physics-IQ's 30-FPS source release at user-requested rates.
-    """
-    input_path = Path(input_path)
-    output_path = Path(output_path)
-
-    cap = cv2.VideoCapture(str(input_path))
-    if not cap.isOpened():
-        raise FileNotFoundError(f"Could not open video for FPS conversion: {input_path}")
-
-    fps_original = cap.get(cv2.CAP_PROP_FPS)
-    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    duration = frame_count / fps_original if fps_original else 0.0
-    width, height = width - width % 2, height - height % 2
-    subclip_duration = min(_DEFAULT_DURATION_SECONDS, duration)
-
-    frames: list[np.ndarray] = []
-    for _ in range(int(subclip_duration * fps_original)):
-        ret, frame = cap.read()
-        if not ret:
-            break
-        frames.append(frame)
-    cap.release()
-
-    if not frames:
-        raise ValueError(f"No frames decoded from {input_path}")
-
-    frame_count_new = int(subclip_duration * fps_new)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    writer = cv2.VideoWriter(
-        str(output_path),
-        cv2.VideoWriter_fourcc(*"avc1"),
-        fps_new,
-        (width, height),
-    )
-    if frame_count_new <= 1:
-        writer.write(frames[0])
-        writer.release()
-        return
-
-    frame_count_original = len(frames)
-    for j in range(frame_count_new):
-        alpha = j * (frame_count_original - 1) / (frame_count_new - 1)
-        idx = int(alpha)
-        alpha -= idx
-        f1 = frames[idx].astype(np.float32)
-        f2 = frames[min(idx + 1, frame_count_original - 1)].astype(np.float32)
-        writer.write(((1.0 - alpha) * f1 + alpha * f2).astype(np.uint8))
-    writer.release()
@@ -1,41 +0,0 @@
-"""Registry for prompt-corpus datasets, mirroring :mod:`fastvideo.eval.registry`."""
-from __future__ import annotations
-
-from typing import Any, TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from fastvideo.eval.datasets.base import BasePromptDataset
-
-_REGISTRY: dict[str, type[BasePromptDataset]] = {}
-
-
-def register_dataset(name: str):
-    """Decorator to register a prompt-dataset class.
-
-    Usage::
-
-        @register_dataset("vbench")
-        class VBenchPromptDataset(BasePromptDataset):
-            ...
-    """
-
-    def wrapper(cls):
-        cls.name = name
-        _REGISTRY[name] = cls
-        return cls
-
-    return wrapper
-
-
-def get_dataset(name: str, **kwargs: Any) -> BasePromptDataset:
-    """Instantiate a registered dataset by name."""
-    cls = _REGISTRY.get(name)
-    if cls is None:
-        available = ", ".join(sorted(_REGISTRY.keys()))
-        raise KeyError(f"Unknown dataset '{name}'. Available: {available}")
-    return cls(**kwargs)
-
-
-def list_datasets() -> list[str]:
-    """Return sorted list of all registered dataset names."""
-    return sorted(_REGISTRY.keys())
@@ -1,126 +0,0 @@
-"""VBench prompt corpus.
-
-Single source of truth: upstream's ``VBench_full_info.json`` (946 entries,
-each with ``prompt_en``, a ``dimension`` list, optional ``auxiliary_info``
-keyed by dimension).
-"""
-from __future__ import annotations
-
-import json
-import os
-from pathlib import Path
-
-from fastvideo.eval.datasets.base import PromptDataset
-from fastvideo.eval.datasets.registry import register_dataset
-
-# VBench's official sampling protocol: 5 generations per prompt, except
-# temporal_flickering which requires 25 (averaging over 5 is too noisy
-# for a high-frequency-noise metric). See upstream prompts/README.md.
-TEMPORAL_FLICKERING_SAMPLES = 25
-DEFAULT_SAMPLES = 5
-
-_FULL_INFO_REL = "fastvideo/third_party/eval/vbench/vbench/VBench_full_info.json"
-
-
-def _locate_full_info() -> Path:
-    env = os.environ.get("VBENCH_FULL_INFO_JSON")
-    if env:
-        p = Path(env)
-        if p.is_file():
-            return p
-        raise FileNotFoundError(f"VBENCH_FULL_INFO_JSON={env} does not point at a file")
-    here = Path(__file__).resolve()
-    for ancestor in here.parents:
-        candidate = ancestor / _FULL_INFO_REL
-        if candidate.is_file():
-            return candidate
-        if (ancestor / ".git").exists():
-            break
-    raise FileNotFoundError("Could not locate VBench_full_info.json. Initialize the upstream "
-                            "submodule (`git submodule update --init "
-                            "fastvideo/third_party/eval/vbench`) or set VBENCH_FULL_INFO_JSON.")
-
-
-@register_dataset("vbench")
-class VBenchPromptDataset(PromptDataset):
-    """VBench prompts filtered by evaluation dimension.
-
-    Args:
-        dimensions: List of dimension names, or ``"all"``. Unknown
-            dimensions raise ``ValueError``.
-        full_info_path: Optional override for ``VBench_full_info.json``;
-            defaults to autodetection.
-
-    A prompt that belongs to several requested dimensions is yielded once;
-    its ``dimensions`` list carries all matches so the scorer can route.
-    """
-
-    description = ("VBench (Vchitect) prompt corpus, 946 prompts across 16 "
-                   "evaluation dimensions.")
-    supports_dimensions = True
-
-    def __init__(
-        self,
-        dimensions: list[str] | str = "all",
-        full_info_path: str | Path | None = None,
-    ) -> None:
-        super().__init__()
-        path = Path(full_info_path) if full_info_path else _locate_full_info()
-        with path.open() as f:
-            entries = json.load(f)
-
-        all_dims = sorted({d for e in entries for d in e["dimension"]})
-        if dimensions == "all":
-            self.dimensions: list[str] = all_dims
-        else:
-            unknown = set(dimensions) - set(all_dims)
-            if unknown:
-                raise ValueError(f"Unknown VBench dimensions: {sorted(unknown)}. "
-                                 f"Available: {all_dims}")
-            self.dimensions = list(dimensions)
-
-        wanted = set(self.dimensions)
-        for entry in entries:
-            relevant = [d for d in entry["dimension"] if d in wanted]
-            if not relevant:
-                continue
-            n = (TEMPORAL_FLICKERING_SAMPLES if "temporal_flickering" in relevant else DEFAULT_SAMPLES)
-
-            # Strip the outer {dim_name: ...} wrapper from upstream's aux
-            # schema so every metric reads its inputs from a flat dict.
-            #
-            # This unwraps exactly one level — the dimension key. Whatever
-            # shape lives inside is the metric's contract:
-            #
-            #   color:                {"color": {"color": "red"}}
-            #     → flat: {"color": "red"}                  (scalar)
-            #
-            #   object_class:         {"object_class": {"object": "person"}}
-            #     → flat: {"object": "person"}              (scalar)
-            #
-            #   multiple_objects:     {"multiple_objects": {"object": "a and b"}}
-            #     → flat: {"object": "a and b"}             (scalar)
-            #
-            #   spatial_relationship: {"spatial_relationship":
-            #                            {"spatial_relationship":
-            #                                {"object_a": ..., "object_b": ...,
-            #                                 "relationship": ...}}}
-            #     → flat: {"spatial_relationship": {object_a,object_b,relationship}}
-            #
-            # Note the spatial_relationship case keeps a nested inner dict
-            # by design — upstream double-wraps it, the SpatialRelationship
-            # metric reads ``aux["spatial_relationship"]`` expecting that
-            # inner dict. Don't "simplify" the wrapping away.
-            raw_aux = entry.get("auxiliary_info") or {}
-            flat_aux: dict = {}
-            for v in raw_aux.values():
-                if isinstance(v, dict):
-                    flat_aux.update(v)
-
-            self._rows.append({
-                "prompt": entry["prompt_en"],
-                "n_samples": n,
-                "dimensions": relevant,
-                "auxiliary_info": flat_aux,
-            })
-        self.full_info_path = path
@@ -1,255 +0,0 @@
-"""User-facing scorer.
-
-Layering (mirrors FastVideo's VideoGenerator → Worker pattern, but
-in-process)::
-
-    Evaluator               ← user-facing
-      └── EvalWorker × N    ← single-GPU; owns metric replicas
-      └── VideoPool         ← async path-→-tensor prefetch (per evaluate call)
-
-The constructor builds one :class:`EvalWorker` per GPU and loads every
-metric on every worker eagerly. :meth:`evaluate` is the single entry
-point: pass kwargs for one sample, or pass a list of sample dicts to
-fan-out across GPU replicas with pipelined decoding — same method,
-return type follows the input shape.
-"""
-from __future__ import annotations
-
-import threading
-from collections.abc import Iterable
-from typing import Any
-
-from fastvideo.eval.registry import (list_metrics, missing_dependencies, resolve_group)
-from fastvideo.eval.types import MetricResult
-from fastvideo.eval.worker import EvalWorker, add_pool_decode_ms
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-class Evaluator:
-    """Pre-initialized scorer for repeated evaluation.
-
-    Parameters
-    ----------
-    metrics : list[str] | str
-        Metric names, group prefixes (``"vbench"``), or ``"all"``.
-    device : str
-        Single-GPU device (e.g. ``"cuda:0"``). Ignored when *num_gpus* > 1.
-    num_gpus : int
-        Number of GPU replicas. Each gets its own :class:`EvalWorker`.
-    compile : bool
-        Apply :func:`torch.compile` to each metric's ``_model``.
-    loader_threads : int
-        Background decode threads in the :class:`VideoPool`. Default 1
-        (hide decode behind compute). Bump for I/O-heavy benchmark sets
-        where one loader can't keep up with the workers.
-    prefetch_factor : int
-        ``pool max_size = prefetch_factor * num_workers``. Default 2 —
-        one sample being consumed, one prefetched per worker.
-    pre_upload : bool
-        If ``True`` (default), the worker uploads ``video`` /
-        ``reference`` tensors to its device once per sample so every
-        metric in the loop consumes the same GPU-resident tensor (no
-        per-metric ``.to(self.device)`` traffic). Set ``False`` for
-        training-time eval where the shared GPU-resident tensor would
-        compete with the training step for VRAM — each metric then
-        uploads its own copy as before.
-    """
-
-    def __init__(
-        self,
-        metrics: list[str] | str = "all",
-        device: str = "cuda:0",
-        num_gpus: int = 1,
-        compile: bool = False,
-        *,
-        loader_threads: int = 1,
-        prefetch_factor: int = 2,
-        pre_upload: bool = True,
-    ) -> None:
-        names = _resolve_metric_names(metrics)
-        if num_gpus > 1:
-            self._workers = [
-                EvalWorker(names, f"cuda:{i}", compile=compile, pre_upload=pre_upload) for i in range(num_gpus)
-            ]
-        else:
-            self._workers = [EvalWorker(names, device, compile=compile, pre_upload=pre_upload)]
-        self._loader_threads = max(1, loader_threads)
-        self._prefetch_factor = max(1, prefetch_factor)
-
-    @property
-    def num_gpus(self) -> int:
-        return len(self._workers)
-
-    @property
-    def metric_names(self) -> list[str]:
-        return self._workers[0].metric_names
-
-    def evaluate(
-        self,
-        samples: Iterable[dict] | None = None,
-        **kwargs,
-    ) -> dict[str, MetricResult] | list[dict[str, MetricResult]]:
-        """Score one sample (kwargs form) or many samples (list form).
-
-        ``video`` and ``reference`` may be either a pre-loaded
-        ``(T, C, H, W)`` tensor or a path-like (``str`` / ``Path``).
-        Paths in the list form are decoded asynchronously by a
-        :class:`VideoPool` that runs alongside metric compute, hiding
-        decode latency behind GPU work.
-
-        One sample::
-
-            ev.evaluate(video=tensor, text_prompt="...", fps=24.0)
-            ev.evaluate(video="path/to/clip.mp4", fps=24.0)
-
-        Many samples — pipelined decode + work-stealing across replicas::
-
-            ev.evaluate(samples=[
-                {"video": "a.mp4", "reference": "ref_a.mp4"},
-                {"video": "b.mp4", "reference": "ref_b.mp4"},
-                ...
-            ])
-
-        Multi-GPU dispatch fires automatically when ``num_gpus > 1`` and
-        the list form is used: every worker runs a consumer thread,
-        pulling decoded samples from the shared pool as it frees up.
-        The kwargs form always runs on worker 0 with no pool overhead.
-        """
-        if samples is None:
-            return self._workers[0].evaluate(**kwargs)
-
-        samples = list(samples)
-        if not samples:
-            return []
-        return self._evaluate_with_pool(samples)
-
-    def _evaluate_with_pool(self, samples: list[dict]) -> list[dict[str, MetricResult]]:
-        """Pipelined dispatch: ``VideoPool`` prefetches decoded samples;
-        consumers (one per worker) pop them and run metrics.
-
-        Decode order in the pool is non-deterministic — each pool item
-        carries its original input index so results are written back in
-        input order.
-        """
-        from fastvideo.eval.pool import VideoPool
-
-        n_workers = len(self._workers)
-        max_size = self._prefetch_factor * n_workers
-        results: list[Any] = [None] * len(samples)
-
-        with VideoPool(samples, loader_threads=self._loader_threads, max_size=max_size) as pool:
-            if n_workers == 1:
-                # Single-GPU: this thread is the consumer.
-                while True:
-                    item = pool.get()
-                    if item is None:
-                        break
-                    idx, decoded = item
-                    results[idx] = self._workers[0].evaluate(**decoded)
-            else:
-                # Multi-GPU: each worker runs its own consumer thread,
-                # pulling from the shared pool (work-stealing).
-                threads: list[threading.Thread] = []
-                for w in self._workers:
-                    t = threading.Thread(
-                        target=self._consumer_loop,
-                        args=(w, pool, results),
-                        daemon=True,
-                    )
-                    t.start()
-                    threads.append(t)
-                for t in threads:
-                    t.join()
-
-            # Attribute pool-side decode ms to the same global counter
-            # the worker uses so ``pop_timings()`` returns total decode
-            # time regardless of where the decode happened.
-            add_pool_decode_ms(pool.decode_ms_total)
-
-        return results
-
-    @staticmethod
-    def _consumer_loop(worker: EvalWorker, pool: Any, results: list) -> None:
-        while True:
-            item = pool.get()
-            if item is None:
-                return
-            idx, decoded = item
-            results[idx] = worker.evaluate(**decoded)
-
-    def release_cuda_memory(self) -> None:
-        """Free CUDA caches on every replica without dropping models."""
-        for w in self._workers:
-            w.release_cuda_memory()
-
-    def unload(self) -> None:
-        """Drop metric refs on every replica. Reverse with :meth:`reload`."""
-        for w in self._workers:
-            w.unload()
-
-    def reload(self) -> None:
-        """Rebuild metrics dropped by :meth:`unload`."""
-        for w in self._workers:
-            w.reload()
-
-    def shutdown(self) -> None:
-        """No-op kept for API compatibility.
-
-        Earlier versions of this class held a long-lived
-        ``ThreadPoolExecutor`` for multi-GPU round-robin dispatch and
-        needed an explicit shutdown to drain it. The current design
-        builds and tears down a :class:`VideoPool` per ``evaluate``
-        call, so there's no long-lived state to release here.
-        """
-
-
-def create_evaluator(
-    metrics: list[str] | str = "all",
-    device: str = "cuda:0",
-    num_gpus: int = 1,
-    compile: bool = False,
-) -> Evaluator:
-    return Evaluator(metrics=metrics, device=device, num_gpus=num_gpus, compile=compile)
-
-
-def _resolve_metric_names(metrics: list[str] | str) -> list[str]:
-    """Resolve metric names, supporting groups (``"vbench"``) and ``"all"``.
-
-    Group / ``"all"`` selectors silently skip metrics whose declared
-    dependencies aren't importable in this environment, with a single
-    warning per skipped metric. Explicit names (e.g. ``"vbench.color"``)
-    always pass through unchanged — the missing dep then surfaces as
-    :class:`ImportError` at construction time, which is what the user
-    asked for.
-    """
-    if metrics == "all":
-        return _filter_satisfied(list_metrics(), context="all")
-    if isinstance(metrics, str):
-        metrics = [metrics]
-
-    seen: set[str] = set()
-    names: list[str] = []
-    for m in metrics:
-        group = resolve_group(m)
-        candidates = _filter_satisfied(group, context=m) if group is not None else [m]
-        for n in candidates:
-            if n not in seen:
-                seen.add(n)
-                names.append(n)
-    return names
-
-
-def _filter_satisfied(names: list[str], *, context: str) -> list[str]:
-    """Drop metrics with missing deps from a group expansion."""
-    keep: list[str] = []
-    for n in names:
-        missing = missing_dependencies(n)
-        if missing:
-            logger.warning(
-                "eval: skipping %s in group '%s'; missing dependency: %s. "
-                "Install instructions: pass the metric name explicitly to see them.", n, context, ", ".join(missing))
-            continue
-        keep.append(n)
-    return keep
@@ -1,11 +0,0 @@
-from fastvideo.eval.io.paths import (build_eval_kwargs, default_filename, glob_videos, sanitize_prompt)
-from fastvideo.eval.io.video import extract_frames, load_video
-
-__all__ = [
-    "load_video",
-    "extract_frames",
-    "sanitize_prompt",
-    "default_filename",
-    "glob_videos",
-    "build_eval_kwargs",
-]
@@ -1,63 +0,0 @@
-"""Filesystem helpers shared by eval scripts.
-
-Provides the prompt-sanitization, default filename convention, and
-``(row, video_path) → eval-kwargs`` builder. Free functions, not a
-class — :class:`fastvideo.eval.Evaluator` is the only stateful object
-in the eval surface; loops live in user scripts.
-"""
-from __future__ import annotations
-
-import re
-from pathlib import Path
-from typing import Any
-
-# Filesystem-unsafe characters mirrored from VideoGenerator's output-path
-# sanitizer so on-disk filenames match what the generator writes.
-_INVALID_CHARS = re.compile(r'[\\/:*?"<>|]')
-
-
-def sanitize_prompt(prompt: str, max_len: int = 100) -> str:
-    """Prompt → safe filename stem."""
-    s = _INVALID_CHARS.sub("", prompt[:max_len]).strip().strip(".")
-    return re.sub(r"\s+", " ", s) or "output"
-
-
-def default_filename(row: dict, idx: int, ext: str = ".mp4") -> str:
-    """``<sanitized-prompt>-<idx>.mp4`` — VBench-style."""
-    return f"{sanitize_prompt(row['prompt'])}-{idx}{ext}"
-
-
-def glob_videos(videos_dir: Path, row: dict, ext: str = ".mp4") -> list[Path]:
-    """Find every generated video for *row*, sorted by trailing ``-<idx>``."""
-    pattern = f"{sanitize_prompt(row['prompt'])}-*{ext}"
-    files = list(videos_dir.glob(pattern))
-
-    def _idx(p: Path) -> int:
-        try:
-            return int(p.stem.rsplit("-", 1)[1])
-        except (IndexError, ValueError):
-            return -1
-
-    return sorted(files, key=_idx)
-
-
-def build_eval_kwargs(row: dict, video_path: Path, *, fps: float = 24.0) -> dict[str, Any]:
-    """Build evaluator kwargs from a sample row + a video on disk.
-
-    Loads the video as ``(T,C,H,W)`` and adds the leading batch dim.
-    Forwards ``prompt`` (as ``text_prompt=[prompt]``) and
-    ``auxiliary_info`` (as ``[aux]``) when present on the row.
-    """
-    from fastvideo.eval.io.video import load_video
-
-    video = load_video(str(video_path))  # (T, C, H, W) in [0, 1]
-    kwargs: dict[str, Any] = {
-        "video": video.unsqueeze(0),  # (1, T, C, H, W)
-        "fps": fps,
-    }
-    if "prompt" in row:
-        kwargs["text_prompt"] = [row["prompt"]]
-    aux = row.get("auxiliary_info")
-    if aux:
-        kwargs["auxiliary_info"] = [aux]
-    return kwargs
@@ -1,85 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-
-import numpy as np
-import torch
-from PIL import Image
-
-
-def load_video(source: str | torch.Tensor | list, **kwargs) -> torch.Tensor:
-    """Load a video as a ``(T, C, H, W)`` float32 tensor in ``[0, 1]``.
-
-    Supported *source* types:
-
-    * **str / Path** – path to ``.mp4`` / ``.avi`` / ``.gif`` file, or a
-      directory of frame images (sorted alphabetically).
-    * **torch.Tensor** – returned as-is after shape validation.
-    * **list[PIL.Image]** – stacked into a tensor.
-    """
-    if isinstance(source, torch.Tensor):
-        if source.ndim != 4:
-            raise ValueError(f"Expected video tensor with 4 dims (T,C,H,W), got {source.ndim}")
-        return source.float()
-
-    if isinstance(source, list):
-        frames = [_pil_to_tensor(img) for img in source]
-        return torch.stack(frames)
-
-    path = Path(source)
-    if path.is_dir():
-        return _load_frame_dir(path)
-    return _load_video_file(str(path))
-
-
-def extract_frames(video: torch.Tensor, n_frames: int | None = None) -> torch.Tensor:
-    """Uniformly sample *n_frames* from a ``(T, C, H, W)`` video tensor."""
-    if n_frames is None or n_frames >= video.shape[0]:
-        return video
-    indices = torch.linspace(0, video.shape[0] - 1, n_frames).long()
-    return video[indices]
-
-
-# --- Internal helpers ---
-
-
-def _pil_to_tensor(img: Image.Image) -> torch.Tensor:
-    arr = np.array(img.convert("RGB"))  # (H, W, 3) uint8
-    return torch.from_numpy(arr).permute(2, 0, 1).float() / 255.0
-
-
-def _load_frame_dir(path: Path) -> torch.Tensor:
-    exts = {".png", ".jpg", ".jpeg", ".bmp", ".webp"}
-    files = sorted(f for f in path.iterdir() if f.suffix.lower() in exts)
-    if not files:
-        raise FileNotFoundError(f"No image files found in {path}")
-    frames = [_pil_to_tensor(Image.open(f)) for f in files]
-    return torch.stack(frames)
-
-
-def _load_video_file(path: str) -> torch.Tensor:
-    # Try decord first (faster), fall back to torchvision
-    try:
-        return _load_with_decord(path)
-    except ImportError:
-        pass
-    return _load_with_torchvision(path)
-
-
-def _load_with_decord(path: str) -> torch.Tensor:
-    from decord import VideoReader, cpu
-
-    vr = VideoReader(path, ctx=cpu(0))
-    # (T, H, W, C) uint8
-    frames = vr.get_batch(list(range(len(vr)))).asnumpy()
-    # → (T, C, H, W) float32 [0, 1]
-    tensor = torch.from_numpy(frames).permute(0, 3, 1, 2).float() / 255.0
-    return tensor
-
-
-def _load_with_torchvision(path: str) -> torch.Tensor:
-    import torchvision.io
-
-    video, _, _ = torchvision.io.read_video(path, pts_unit="sec")
-    # torchvision returns (T, H, W, C) uint8
-    return video.permute(0, 3, 1, 2).float() / 255.0
--- a/Show More
+++ b/Show More