[ci] Add /test pre-commit slash command

- /test pre-commit triggers pre-commit via workflow_call - Posts commit status to PR head SHA so check appears on PR page - Mergify check-success~=pre-commit condition can now be satisfied
2026-03-31 08:18:06 -04:00
510 changed files with 2767 additions and 57508 deletions
@@ -119,7 +119,7 @@ FastVideo-WorldModel/
 ## Build & Test Commands

 ```bash
-uv pip install -e ".[dev]"                        # Editable install
+uv pip install -e .[dev]                          # Editable install
 pre-commit run --all-files                        # Lint/format/spell
 pytest tests/                                     # Top-level tests
 pytest fastvideo/tests/ -v                        # Package tests
@@ -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"
@@ -5,6 +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"}
-{"name": "reseed-ssim-references", "description": "Re-seed (overwrite) HF reference videos for an existing fastvideo/tests/ssim/ test and a single model id on Modal L40S. Always backs up current refs first, regenerates on Modal, pauses for the user to eyeball before-vs-after, then uploads with --force scoped to --model-id. Sister skill to seed-ssim-references; use when intentional code change has invalidated existing refs", "path": "reseed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}
-{"name": "add-model", "description": "Add a new model (or variant) to FastVideo: DiT + configs + pipeline + presets + registry + tests. Walks through FastVideo's single stage-based pipeline architecture with exact file paths and registration hooks.", "path": "add-model/SKILL.md", "status": "draft", "trust": "low"}
@@ -12,7 +12,7 @@ automates the boilerplate of setting environment variables, picking the right
 entrypoint, and applying defaults from the closest example script.

 ## Prerequisites
- The repo is cloned and `fastvideo` is installed (`uv pip install -e ".[dev]"`).
+- The repo is cloned and `fastvideo` is installed (`uv pip install -e .[dev]`).
 - Dataset is preprocessed (see `docs/training/data_preprocess.md`).
 - `WANDB_API_KEY` is set in the environment (or `WANDB_MODE=offline` for local).
 - GPU resources are available (multi-GPU requires NCCL).
@@ -1,343 +0,0 @@
---
-name: reseed-ssim-references
-description: Re-seed HF reference videos for a single existing SSIM test on Modal L40S. Always backs up current refs locally first, regenerates on Modal, pauses for the user to eyeball before-vs-after quality, then overwrites the targeted `<model_id>` subtree on `FastVideo/ssim-reference-videos` with `--force`. Use when an intentional code change (model port fix, attention backend swap, kernel upgrade, hyperparameter change) has invalidated existing refs and they need to be regenerated. Pairs with `seed-ssim-references`, which is for first-time seeding only.
---
-
-# Re-seed SSIM Reference Videos
-
-## Purpose
-
-Replace the existing SSIM reference videos for a single `(test_file, model_id)`
-pair on the HF dataset (`FastVideo/ssim-reference-videos`). This is **destructive**
-on HF — the old refs are overwritten — so the skill always:
-
-1. Confirms intent with a one-liner the user has to type.
-2. Downloads the existing refs as a local, timestamped backup.
-3. Regenerates on Modal L40S (same code path that CI uses).
-4. Pauses for a side-by-side eyeball of backup vs new mp4s.
-5. Uploads with `--force`, scoped to the single `--model-id`.
-6. Reminds the user to keep the backup until the PR lands.
-
-Pairs with `seed-ssim-references`, which is the inverse (first-time seeding
-only, refuses to overwrite). Re-seeding is intentionally a separate, more
-ceremonial operation because mistakenly clobbering production refs is much
-harder to recover from than failing closed.
-
-## When to use
-
- An intentional code change (model port fix, kernel upgrade, attention
-  backend swap, hyperparameter change in the test itself) has shifted the
-  expected SSIM output and the existing refs no longer represent the new
-  ground truth.
- A test is failing in CI **for the right reason** (the new code is correct,
-  the old refs are stale).
-
-## When not to use
-
- A test is failing for the **wrong** reason (the port is buggy, not the
-  refs). Fix the port; re-seeding hides the bug.
- A brand-new test that has no refs on HF yet. Use `seed-ssim-references`.
- "Just to clean up drift" without a concrete code change to point at. The
-  PR description has to justify *why* refs changed; without a concrete
-  change, there's nothing to write.
-
-## Inputs
-
-| Parameter | Required | Description |
-|-----------|----------|-------------|
-| `test_file` | Yes | Path to the SSIM test, e.g. `fastvideo/tests/ssim/test_matrixgame_similarity.py`. Validated against `fastvideo/tests/ssim/test_*_similarity.py`. |
-| `model_id` | Yes | Single model id from the test's `*_MODEL_TO_PARAMS`, e.g. `Matrix-Game-2.0-Diffusers-Base`. Re-seed runs are **per model**. For multi-model tests, invoke the skill once per model. |
-| `intent_rationale` | Yes | One-line explanation of *why* refs are being regenerated (e.g. "Relax FA-2 head_size whitelist to include 80 — matrix_game now uses FLASH_ATTN instead of TORCH_SDPA"). Recorded in the backup directory and reused in the PR description. |
-
-Hardcoded:
-
- Modal GPU: **L40S** (matches CI; re-seeding from another SKU produces refs
-  that L40S CI cannot match).
- Quality tier: **`default`**. `full_quality` is a separate, deliberate
-  operation.
- HF repo: `FastVideo/ssim-reference-videos` (override via
-  `FASTVIDEO_SSIM_REFERENCE_HF_REPO`).
- Device folder: `L40S_reference_videos`.
-
-## Prerequisites
-
-The user has confirmed:
-
- `modal` CLI authenticated.
- `hf` CLI authenticated, **and** `HF_API_KEY` (or `HUGGINGFACE_HUB_TOKEN` /
-  `HF_TOKEN`) exported with **write** access to
-  `FastVideo/ssim-reference-videos`.
- The current branch's code is the change that motivated the re-seed (i.e.
-  `git rev-parse HEAD` is the commit that intentionally invalidated refs).
-
-Fail fast if any of these are missing.
-
-## Steps
-
-### 1. Validate inputs and confirm intent
-
- Verify `test_file` exists and matches `fastvideo/tests/ssim/test_*_similarity.py`.
- Grep the file for `*_MODEL_TO_PARAMS` and assert `model_id` is one of its
-  keys. If the file has only a single hardcoded model, accept that model id
-  as the only valid value.
- Print the rationale and ask the user to type **`confirm reseed`** (not just
-  `y` — make it deliberate):
-
-  > About to RE-SEED references for model `<model_id>` from test `<test_file>`.
-  > This will OVERWRITE existing refs on
-  > `FastVideo/ssim-reference-videos/reference_videos/default/L40S_reference_videos/<model_id>/`
-  > after backup + Modal regen + eyeball.
-  >
-  > Reason: `<intent_rationale>`
-  > HEAD: `<git rev-parse --short=12 HEAD>`
-  >
-  > Reply `confirm reseed` to proceed, anything else to abort.
-
-  Stop until the user types exactly `confirm reseed`. Anything else aborts
-  with no side effects.
-
-### 2. Back up existing refs
-
-Always required. The backup is the only graceful path back if anything goes
-wrong later.
-
-```bash
-SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
-TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
-MODEL_SAFE=$(echo "<model_id>" | tr '/' '_')
-BACKUP_DIR="ssim_reseed_backup/${TIMESTAMP}_${SHORT_COMMIT}_${MODEL_SAFE}"
-mkdir -p "$BACKUP_DIR"
-
-hf download \
-    --repo-type dataset FastVideo/ssim-reference-videos \
-    --include "reference_videos/default/L40S_reference_videos/<model_id>/**" \
-    --local-dir "$BACKUP_DIR"
-
-mp4_count=$(find "$BACKUP_DIR" -name "*.mp4" | wc -l)
-echo "Backup mp4 count: $mp4_count"
-[ "$mp4_count" -gt 0 ] || {
-    echo "ERROR: backup is empty for <model_id>. Either the model id is wrong"
-    echo "or there are no existing refs (use seed-ssim-references instead)."
-    exit 1
-}
-
-# Provenance — used in the PR description
-cat > "$BACKUP_DIR/PROVENANCE.txt" <<EOF
-test_file: <test_file>
-model_id: <model_id>
-head_commit: $(git rev-parse HEAD)
-timestamp_utc: $(date -u +%FT%TZ)
-reason: <intent_rationale>
-EOF
-```
-
-If the `hf download` produces zero mp4s, abort — the user has either picked a
-non-existent `model_id` or there are no refs yet (in which case
-`seed-ssim-references` is the right tool).
-
-### 3. Regenerate on Modal L40S
-
-Mirror CI's exact env recipe so the regenerated refs are byte-comparable to
-what CI will produce on the same commit. Two differences from CI:
-
-1. **Pass the same env prefix CI uses** (`IMAGE_VERSION`, `BUILDKITE_*`) — see
-   `.buildkite/pipeline.yml:1-3` and `.buildkite/scripts/pr_test.sh:62-83`.
-   Without this, `ssim_test.py:17-18` resolves a different GHCR image tag
-   (default is `latest`, CI is `py3.12-latest`), and `ssim_test.py:38-46`
-   bakes different values into the image's frozen env block. **Mismatched
-   image or env is the most common source of SSIM drift between reseed and
-   CI runs.**
-2. **Do not pass `--skip-reference-download`**. Letting the test fetch the
-   existing refs and run the full SSIM compare gives "before" SSIM numbers
-   for the PR description, and the test still produces the new mp4s
-   regardless of whether the comparison passes or fails.
-
-```bash
-SUBDIR="${TIMESTAMP}_${SHORT_COMMIT}"
-
-IMAGE_VERSION="py3.12-latest" \
-BUILDKITE_REPO="$(git config --get remote.origin.url)" \
-BUILDKITE_COMMIT="$(git rev-parse HEAD)" \
-BUILDKITE_PULL_REQUEST="${BUILDKITE_PULL_REQUEST:-false}" \
-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" \
-    --no-fail-fast
-```
-
-Capture the printed `modal volume get ...` hint — its `<SUBDIR>` matches
-`$SUBDIR` and is needed for step 4. Capture the SSIM numbers from the test
-output (or from the JSON next to the generated mp4) for the PR description.
-
-### 4. Download generated videos
-
-```bash
-modal volume get --force hf-model-weights \
-    ssim_generated_videos/default/"$SUBDIR"/generated_videos \
-    ./generated_videos_modal/default
-```
-
-After this, the new mp4s live at:
-
-```
-./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4
-```
-
-`--force` is required when `./generated_videos_modal/default` already exists
-from a prior run; safe on the first run too.
-
-### 5. PAUSE — user reviews quality side-by-side
-
-Print the diff and the comparison:
-
-```bash
-echo "=== File list diff (backup vs new) ==="
-diff -u \
-    <(find "$BACKUP_DIR/reference_videos/default/L40S_reference_videos/<model_id>" -name "*.mp4" \
-        | sed "s|$BACKUP_DIR/reference_videos/default/L40S_reference_videos/||" | sort) \
-    <(find ./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id> -name "*.mp4" \
-        | sed "s|./generated_videos_modal/default/generated_videos/L40S_reference_videos/||" | sort) \
-    || true
-
-echo
-echo "=== SSIM numbers from this run (paste into PR) ==="
-find ./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id> -name "*_ssim.json" -exec cat {} \;
-```
-
-Then stop and tell the user:
-
-> Old refs backed up to `$BACKUP_DIR`.
-> New videos in `./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/`.
->
-> Open both in a video player. Confirm the new videos:
->   1. Look correct (no obvious artifacts, no black/static frames).
->   2. Are *intentionally* different from the backup in the way described
->      in `<intent_rationale>` (e.g. slight numerical drift only, not a
->      different scene / different motion / corrupted output).
->
-> Reply **`upload`** to overwrite HF, anything else to abort.
-> Aborting leaves the backup and new videos on disk for inspection — nothing
-> on HF changes.
-
-Do not proceed until the user types exactly `upload`. If they abort, leave
-everything on disk and stop here.
-
-### 6. Copy into the local reference layout
-
-Same as `seed-ssim-references` step 5:
-
-```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
-```
-
-Result: `fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
-
-### 7. Upload with `--force`, scoped to `--model-id`
-
-The `--force` flag is what makes this skill different from `seed-ssim-references`.
-Always pair it with `--model-id` so a typo cannot accidentally overwrite a
-neighboring model's refs.
-
-```bash
-python fastvideo/tests/ssim/reference_videos_cli.py upload \
-    --quality-tier default \
-    --device-folder L40S_reference_videos \
-    --model-id "<model_id>" \
-    --force
-```
-
-The CLI's overwrite guard refuses without `--force`; with `--force` it
-overwrites only files under
-`reference_videos/default/L40S_reference_videos/<model_id>/`.
-
-### 8. Report success and retention guidance
-
-Print:
-
- The HF path that was overwritten (`<repo>/reference_videos/default/L40S_reference_videos/<model_id>/`).
- The local backup directory path.
- The new SSIM numbers from step 5.
- This restore command, in case the PR review surfaces a problem after
-  upload:
-
-  ```bash
-  python fastvideo/tests/ssim/reference_videos_cli.py upload \
-      --quality-tier default \
-      --device-folder L40S_reference_videos \
-      --model-id "<model_id>" \
-      --reference-dir "$BACKUP_DIR/reference_videos/default/L40S_reference_videos" \
-      --force
-  ```
-
- This PR-description checklist (see `fastvideo/tests/ssim/AGENTS.md` →
-  *Updating Reference Videos*):
-  1. Source commit that produced the new refs (HEAD at re-seed time).
-  2. Test command and GPU SKU (`L40S`).
-  3. Before/after SSIM numbers.
-  4. The `<intent_rationale>` from step 1.
-  5. A note that the backup lives at `$BACKUP_DIR` and should be retained
-     until CI on the PR is green.
-
-Do **not** auto-rerun the SSIM test — the user does that as part of the PR.
-
-## Failure modes and how to handle them
-
- **`HF_API_KEY` unset.** Stop before step 2.
- **Backup is empty (zero mp4s).** Stop before step 3 — the model id is
-  wrong or the refs don't exist yet (use `seed-ssim-references`).
- **Modal run fails before generation.** No mp4s on the volume. Don't
-  upload. Investigate the failure (test crash, OOM, partition exhaustion),
-  fix, then retry from step 3. Backup is still intact.
- **Quality regressed (visual or metric).** User aborts at step 5. Backup
-  retained. New videos retained on disk for inspection. Nothing on HF
-  changed. Either fix the underlying code change or abandon the re-seed.
- **User confirmed `upload` but later realized the new refs are wrong.**
-  Run the restore command from step 8 with the backup `--reference-dir`.
-  This is exactly why the backup exists.
- **Multi-model test, only one model is being re-seeded.** Run the skill
-  once per model id. The `--model-id` scope on upload guarantees the others
-  are untouched.
-
-## Design notes (for future skill maintainers)
-
- Per-`model_id` scope is mandatory. The dataset houses many model subtrees;
-  re-seeding the wrong one is hard to undo without backup.
- `default` tier only; `full_quality` is a separate, deliberate operation
-  with different params and ~doubled runtime, and isn't what CI gates on.
- The skill deliberately does **not** pass `--skip-reference-download` to
-  Modal so we get pre-reseed SSIM numbers for the PR. The `seed`-skill
-  passes it because no refs exist yet; for re-seed, refs do exist and
-  exposing the comparison is informative.
- The two-token confirm (`confirm reseed`, then `upload`) is intentional.
-  Re-seeding is high-blast-radius and should not be one-keystroke.
- The backup directory is plain mp4s + `PROVENANCE.txt`. No HF metadata is
-  preserved; the restore path uses `reference_videos_cli.py upload
-  --reference-dir` which doesn't need it.
-
-## References
-
- `.agents/skills/seed-ssim-references/SKILL.md` — the first-time seed
-  skill this one parallels. Read it for the Modal flag rationale shared
-  between the two flows.
- `fastvideo/tests/ssim/AGENTS.md` — directory rules, including the PR
-  expectations for any reference-video change (rationale, before/after
-  SSIM, source commit/model/backend).
- `fastvideo/tests/ssim/reference_videos_cli.py` — `copy-local`, `upload`
-  (with `--model-id`, `--force`), `download`. The overwrite guard at
-  `upload_reference_videos` is the safety net this skill leans on.
- `fastvideo/tests/modal/ssim_test.py` — Modal orchestrator;
-  `--sync-generated-to-volume`, `--generated-volume-subdir`,
-  `--skip-reference-download`, `--no-fail-fast`.
-
-## Changelog
-
-| Date | Change |
-|------|--------|
-| 2026-05-02 | Initial version. Sister skill to `seed-ssim-references`, scoped to single `(test_file, model_id)` re-seeds, with mandatory backup and two-token confirm. |
@@ -1,376 +0,0 @@
---
-name: seed-ssim-references
-description: Seed HF reference artefacts for a single newly-added SSIM test (pixel `.mp4` for `run_text_to_video_similarity_test`-style tests, or latent `.pt` for `run_text_to_latent_similarity_test`-style tests). Runs the test on Modal L40S, downloads the generated artefacts via `modal volume get`, pauses for the user to verify (visual eyeball for mp4, numerics dump for pt), 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 Artefacts (mp4 or pt)
-
-## Purpose
-
-A brand-new SSIM test in `fastvideo/tests/ssim/` fails forever until its
-reference artefacts exist on the HF dataset
-(`FastVideo/ssim-reference-videos`). The dataset hosts two kinds of artefacts
-side-by-side per `(model_id, backend, prompt)`:
-
- **`.mp4`** — pixel ground-truth for tests that call
-  `run_text_to_video_similarity_test` / `run_image_to_video_similarity_test`
-  in `inference_similarity_utils.py`. Compared via SSIM.
- **`.pt`** — pre-VAE latent bundle (fp16 full latent + fp32 slice +
-  metadata + `slice_spec` + `format_version`) for tests that call
-  `run_text_to_latent_similarity_test` in `latent_similarity_utils.py`.
-  Compared via cosine distance on the slice and the full tensor.
-
-This skill:
-
-1. Detects which artefact type the test produces (pixel vs latent).
-2. Runs the test on Modal's L40S pool to generate the artefacts.
-3. Downloads them to the local repo via `modal volume get`.
-4. Pauses so the user can verify quality:
-   - **mp4**: visual eyeball in a video player.
-   - **pt**: numerics dump (shape, slice stats, NaN/Inf check, metadata).
-5. 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 artefact without crashing (the missing-reference assertion at
-the end is expected). 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, then detect artefact type
-
-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.
-
-Detect artefact type by inspecting the file's imports / helper call:
-
- **latent** (`.pt`) — file imports `run_text_to_latent_similarity_test`
-  from `fastvideo.tests.ssim.latent_similarity_utils` (or any other helper
-  that ends with `_latent_similarity_test`).
- **pixel** (`.mp4`) — file imports
-  `run_text_to_video_similarity_test` / `run_image_to_video_similarity_test`
-  from `fastvideo.tests.ssim.inference_similarity_utils`, OR uses the
-  legacy custom-inline helper pattern (see `test_gamecraft`,
-  `test_longcat`, etc.). Default to pixel when both heuristics fail.
-
-Record `ARTEFACT_TYPE ∈ {pixel, latent}` for use in step 4. Steps 2, 3, 5,
-and 6 are artefact-type-agnostic — `_iter_reference_files`,
-`copy_generated_to_reference`, and `upload_reference_videos` already walk
-both `.mp4` and `.pt` (see `reference_videos_cli.py`).
-
-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}_${SHORT_COMMIT}"
-```
-
-Then launch the Modal run. The `IMAGE_VERSION` and `BUILDKITE_*` env-prefix
-**must** match what CI exports in `.buildkite/scripts/pr_test.sh`, otherwise
-`fastvideo/tests/modal/ssim_test.py` resolves a different GHCR image tag
-(default is `latest`, CI is `py3.12-latest`) and bakes different values into
-the image's frozen env block (`ssim_test.py:17-18, 38-46`). Mismatched image
-or env produces SSIM drift that doesn't show up until the same commit runs
-in CI.
-
-```bash
-IMAGE_VERSION="py3.12-latest" \
-BUILDKITE_REPO="$(git config --get remote.origin.url)" \
-BUILDKITE_COMMIT="$(git rev-parse HEAD)" \
-BUILDKITE_PULL_REQUEST="${BUILDKITE_PULL_REQUEST:-false}" \
-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
-```
-
-Env prefix rationale (parity with CI; see `.buildkite/pipeline.yml:1-3` and
-`.buildkite/scripts/pr_test.sh:62-83`):
- `IMAGE_VERSION=py3.12-latest`: pins the Modal image tag to the same one CI
-  uses. Without this, `ssim_test.py:17` falls back to `latest`, which on
-  GHCR is built from `Dockerfile.python3.10` — different Python, torch, and
-  flash-attn wheel than CI's `py3.12-latest` (`infra-build-image.yml:51-67`,
-  `_template-build-image.yml:65-101`).
- `BUILDKITE_REPO`/`BUILDKITE_COMMIT`/`BUILDKITE_PULL_REQUEST`: mirror what
-  Buildkite exports. `ssim_test.py:38-46` bakes these into the image's
-  `.env(...)` block; mismatched values can perturb in-container code paths
-  that branch on PR-vs-non-PR. `false` for `BUILDKITE_PULL_REQUEST` matches
-  Buildkite's "non-PR build" sentinel.
-
-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
-
-Type-aware verification.
-
-**For `ARTEFACT_TYPE = pixel`** — list the downloaded mp4s and ask the user to
-open them in a video player:
-
-> "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."
-
-**For `ARTEFACT_TYPE = latent`** — `.pt` files are not human-watchable. Print
-a numerics dump for each `.pt` so the user can sanity-check shape, distribution,
-and metadata:
-
-```python
-import torch
-from pathlib import Path
-ROOT = Path("./generated_videos_modal/default/generated_videos/L40S_reference_videos")
-for p in sorted(ROOT.rglob("*.pt")):
-    d = torch.load(p, map_location="cpu", weights_only=False)
-    s = d["expected_slice"]
-    L = d["latent"].float()
-    print(f"=== {p.relative_to(ROOT)} ===")
-    print(f"  format_version: {d['format_version']}")
-    print(f"  shape:          {d['shape']}")
-    print(f"  dtype_original: {d['dtype_original']}")
-    print(f"  slice_spec:     {d['slice_spec']}")
-    print(f"  slice  shape={tuple(s.shape)}  mean={s.mean():+.4f}  std={s.std():.4f}  min={s.min():+.4f}  max={s.max():+.4f}")
-    print(f"  latent shape={tuple(L.shape)}  mean={L.mean():+.4f}  std={L.std():.4f}  min={L.min():+.4f}  max={L.max():+.4f}")
-    print(f"  finite: latent NaN={torch.isnan(L).any().item()} Inf={torch.isinf(L).any().item()}; "
-          f"slice NaN={torch.isnan(s).any().item()} Inf={torch.isinf(s).any().item()}")
-    print(f"  metadata: {d['metadata']}\n")
-```
-
-Sanity criteria:
- `format_version == 1` (matches `LATENT_REFERENCE_FORMAT_VERSION`).
- `shape` matches what the model produces (e.g. LTX-2 distilled =
-  `[1, 128, T_lat, H_lat, W_lat]`; Stable Audio Open 1.0 = `[1, 64, 1024]`).
- `slice_spec.kind` matches a registered kind (`corner_3x3_first_frame`
-  for video, `audio_first_8_timesteps` for audio).
- No `NaN`/`Inf`. `mean ≈ 0`, `std ≈ 1` (denoised latents stay close to
-  the initial Gaussian distribution; very wide deviations suggest
-  numerical drift).
- `metadata.prompt` matches the test's prompt.
-
-Then ask:
-
-> "Numerics look right? 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 artefacts. Single command works for both
-artefact types because `_iter_reference_files` walks `.mp4` and `.pt`:
-
-```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,pt}`
-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 for pixel: `fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
-Result for latent: same path with `.pt` extension.
-
-### 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. If the user
-  ran `hf auth login` instead of exporting an env var, read the cached
-  token via `huggingface_hub.get_token()` and forward it to Modal as
-  `--hf-api-key="$CACHED_TOKEN"`.
- **Modal run fails before generation.** No artefacts 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 artefacts (most likely the
-  test crashed before writing, or `REQUIRED_GPUS` exceeded the partition
-  capacity — see Modal logs).
- **Latent test crashed with FSDP / inference_mode error
-  (`RuntimeError: Inference tensors do not track version counter`).** The
-  test must pass `init_kwargs_override={"use_fsdp_inference": False}` when
-  `sp_size == 1` — see `test_stable_audio_similarity.py` for the pattern.
-  Fix in the test, push, retry.
- **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 artefacts 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.
-  - For latent: also check `slice_spec.kind` matches the latent rank
-    (`corner_3x3_first_frame` requires 5-D, `audio_first_8_timesteps`
-    requires 3-D); a rank/kind mismatch raises in `_extract_expected_slice`.
-
-## 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 (pixel SSIM drifts across SKUs; latent cosine has
-  tighter cross-SKU bf16 drift but the configured tolerances assume
-  same-SKU seed → same-SKU verify).
- 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`.
- Both artefact types share the same Modal flow: the orchestrator sets
-  `--skip-reference-download` + `--no-fail-fast`, runs pytest, the test's
-  helper writes the artefact (`.mp4` via `imageio` for pixel,
-  `save_latent_reference` → `torch.save` for latent) BEFORE the
-  missing-reference assertion raises. `_sync_generated_videos_to_volume` in
-  `ssim_test.py` does a `shutil.copytree` of the whole `generated_videos/`
-  tree, picking up `.mp4`, `.pt`, and the `*_ssim.json` / `*_latent.json`
-  metric files alongside.
-
-## 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.
-  Extension allowlist is `REFERENCE_EXTENSIONS = VIDEO_EXTENSIONS +
-  LATENT_EXTENSIONS` (`.pt`).
- `fastvideo/tests/ssim/README.md` — reference layout, HF repo conventions.
- `fastvideo/tests/ssim/inference_similarity_utils.py` — pixel helpers
-  (`run_text_to_video_similarity_test`,
-  `run_image_to_video_similarity_test`, `build_init_kwargs`).
- `fastvideo/tests/ssim/latent_similarity_utils.py` — latent helper
-  (`run_text_to_latent_similarity_test`), slice spec dispatch
-  (`_extract_expected_slice`), reference schema
-  (`save_latent_reference` / `load_latent_reference`),
-  `LATENT_REFERENCE_FORMAT_VERSION`.
-
-## 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. |
-| 2026-05-01 | Latent (`*.pt`) artefact support: artefact-type detection in step 1, type-aware verification (visual eyeball for mp4, numerics dump for pt) in step 4, FSDP+inference_mode failure-mode added, design notes for the unified Modal flow. Triggered by PR #1253 (LTX-2 latent migration + Stable Audio latent test). |
@@ -29,8 +29,8 @@
    "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting."
  ],
  "run_config": {
-    "num_warmup_runs": 2,
-    "num_measurement_runs": 5,
+    "num_warmup_runs": 1,
+    "num_measurement_runs": 3,
    "required_gpus": 2
  },
  "thresholds": {
@@ -9,183 +9,11 @@ notify:
  - github_commit_status:
      context: "full-suite-passed"
    if: build.env("TEST_SCOPE") == "full"
-  - github_commit_status:
-      context: "direct-test-completed"
-    if: build.env("TEST_SCOPE") == "direct"

 steps:
  # ============================================================
-  # Direct test: triggered by /test <name> slash command.
-  # Labels match fastcheck/full-suite counterparts so the GitHub
-  # check status overwrites the original failed check.
-  # Only ONE step executes per build (gated by TEST_TYPE).
-  # ============================================================
-
-  # --- Fastcheck-scope direct tests ---
-    - label: ":microscope: Encoder Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "encoder"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: VAE Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "vae"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: Transformer Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "transformer"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: Kernel Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "kernel_tests"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: Unit Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "unit_test"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-
-  # --- Full-suite-scope direct tests ---
-    - label: ":bar_chart: SSIM Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "ssim"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-          - exit_status: 1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: LoRA Inference Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "inference_lora"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Training Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Distillation DMD Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "distillation_dmd"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Self-Forcing Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "self_forcing"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: LoRA Training Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training_lora"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-          - exit_status: 1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Training Tests VSA"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training_vsa"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-          - exit_status: 1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Inference Tests VMoBA"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "inference_vmoba"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Performance Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "performance"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: API Server Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "api_server"
+    - label: ":dart: Direct Test (${TEST_TYPE})"
+      if: build.env("TEST_SCOPE") == "direct"
      command: "timeout 90m .buildkite/scripts/pr_test.sh"
      retry:
        automatic:
@@ -307,10 +135,6 @@ steps:
                label: ":bar_chart: SSIM Tests"
                env:
                  - TEST_TYPE=ssim
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
                agents:
                  queue: "default"
            - path:
@@ -371,10 +195,6 @@ steps:
                label: ":test_tube: LoRA Training Tests"
                env:
                  - TEST_TYPE=training_lora
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
                agents:
                  queue: "default"
            - path:
@@ -387,10 +207,6 @@ steps:
                label: ":test_tube: Training Tests VSA"
                env:
                  - TEST_TYPE=training_vsa
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
                agents:
                  queue: "default"
            - path:
@@ -15,21 +15,8 @@ log "Project root: $PROJECT_ROOT"
 # Install Modal if not available
 if ! python3 -m modal --version &> /dev/null; then
    log "Modal not found, installing..."
-    if ! command -v uv &> /dev/null; then
-        log "uv not found, bootstrapping..."
-        if ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
-            log "Error: Failed to bootstrap uv via astral.sh installer."
-            exit 1
-        fi
-        export PATH="$HOME/.local/bin:$PATH"
-        if ! command -v uv &> /dev/null; then
-            log "Error: uv still not on PATH after bootstrap."
-            exit 1
-        fi
-    fi
-    # --break-system-packages preserves prior `pip install --user` semantics on PEP 668 agents.
-    uv pip install --system --break-system-packages modal
-
+    python3 -m pip install modal
+    
    # Verify installation
    if ! python3 -m modal --version &> /dev/null; then
        log "Error: Failed to install modal. Please install it manually."
@@ -76,72 +63,7 @@ EFFECTIVE_PR=${BUILDKITE_PULL_REQUEST:-false}
 if [ "$EFFECTIVE_PR" = "false" ] && [ -n "${PR_NUMBER:-}" ]; then
    EFFECTIVE_PR=$PR_NUMBER
 fi
-MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR BUILDKITE_BRANCH=${BUILDKITE_BRANCH:-} TEST_SCOPE=${TEST_SCOPE:-} IMAGE_VERSION=$IMAGE_VERSION"
-
-POST_RUN_HOOK=""
-
-upload_performance_artifacts() {
-    SHORT_SHA=${BUILDKITE_COMMIT:0:7}
-    LOCAL_DIR="downloaded_reports"
-
-    _download_reports() {
-        log "Downloading perf_reports/ from Modal Volume..."
-        mkdir -p "$LOCAL_DIR"
-        if ! modal volume get hf-model-weights "perf_reports/" "$LOCAL_DIR"; then
-            log "Error: Failed to download perf_reports/ from Modal Volume."
-            return 1
-        fi
-    }
-
-    _upload_dashboard() {
-        local target
-        target=$(find "$LOCAL_DIR" -name "dashboard_${SHORT_SHA}_*" | head -n 1)
-        log "TARGET dashboard: '$target'"
-
-        if [ -n "$target" ]; then
-            log "Found dashboard: $target. Uploading to Buildkite..."
-            buildkite-agent artifact upload "$target"
-            buildkite-agent annotate --style info --context "perf-dashboard" < "$target"
-        else
-            log "Warning: Could not find a dashboard file matching $SHORT_SHA"
-        fi
-    }
-
-    _upload_perf_summary() {
-        local target
-        target=$(find "$LOCAL_DIR" -name "perf_${SHORT_SHA}_*" | head -n 1)
-        log "TARGET perf summary: '$target'"
-
-        if [ -n "$target" ]; then
-            log "Found perf summary: $target. Uploading to Buildkite..."
-            buildkite-agent artifact upload "$target"
-            buildkite-agent annotate --style info --context "perf-summary" < "$target"
-        else
-            log "Warning: Could not find a perf summary file matching $SHORT_SHA"
-        fi
-    }
-
-    _cleanup_modal_volume() {
-        log "Cleaning up perf_reports/ from Modal Volume..."
-        if modal volume rm hf-model-weights "perf_reports/" --recursive; then
-            log "Successfully deleted perf_reports/ from Modal Volume."
-        else
-            log "Warning: Failed to delete perf_reports/ from Modal Volume. Manual cleanup may be required."
-        fi
-    }
-
-    _cleanup_local() {
-        log "Cleaning up local download directory..."
-        rm -rf "$LOCAL_DIR"
-    }
-
-    # --- Main flow ---
-    _download_reports || { _cleanup_local; return 1; }
-    _upload_dashboard
-    _upload_perf_summary
-    _cleanup_modal_volume
-    _cleanup_local
-}
+MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR IMAGE_VERSION=$IMAGE_VERSION"

 case "$TEST_TYPE" in
    "encoder")
@@ -202,9 +124,8 @@ case "$TEST_TYPE" in
        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_lora_extraction_tests"
        ;;
    "performance")
-        log "Running performance tests on Modal..."
+        log "Running performance tests..."
        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_performance_tests"
-        POST_RUN_HOOK="upload_performance_artifacts"
        ;;
    "api_server")
        log "Running API server integration tests..."
@@ -226,10 +147,5 @@ else
    log "Error: Modal test failed with exit code: $TEST_EXIT_CODE"
 fi

-if [ -n "$POST_RUN_HOOK" ]; then
-    log "Executing post-run hook: $POST_RUN_HOOK"
-    "$POST_RUN_HOOK"
-fi
-
 log "=== Test execution completed with exit code: $TEST_EXIT_CODE ==="
 exit $TEST_EXIT_CODE
@@ -13,21 +13,8 @@ log "Project root: $PROJECT_ROOT"

 if ! python3 -m pre_commit --version &> /dev/null; then
    log "pre-commit not found, installing..."
-    if ! command -v uv &> /dev/null; then
-        log "uv not found, bootstrapping..."
-        if ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
-            log "Error: Failed to bootstrap uv via astral.sh installer."
-            exit 1
-        fi
-        export PATH="$HOME/.local/bin:$PATH"
-        if ! command -v uv &> /dev/null; then
-            log "Error: uv still not on PATH after bootstrap."
-            exit 1
-        fi
-    fi
-    # --break-system-packages preserves prior `pip install --user` semantics on PEP 668 agents.
-    uv pip install --system --break-system-packages pre-commit==4.0.1
-
+    python3 -m pip install --user pre-commit==4.0.1
+    
    if ! python3 -m pre_commit --version &> /dev/null; then
        log "Error: Failed to install pre-commit."
        exit 1
@@ -4,10 +4,8 @@ merge_protections:
      - base = main
    success_conditions:
      - "title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model)\\]"
-      - "#approved-reviews-by>=1"
      - check-success~=pre-commit
      - check-success=fastcheck-passed
-      - check-success=full-suite-passed

 pull_request_rules:

@@ -105,7 +103,7 @@ 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
@@ -274,15 +272,24 @@ pull_request_rules:
      merge:
        method: squash

-  - name: auto-update when ready
+  - name: auto-rebase when ready and Full Suite passed
    conditions:
      - label=ready
      - "#approved-reviews-by>=1"
+      - check-success=full-suite-passed
      - -conflict
      - -closed
      - -draft
    actions:
-      update: {}
+      rebase: {}
+
+  - name: remove ready label on Full Suite failure
+    conditions:
+      - label=ready
+      - check-failure=full-suite-passed
+    actions:
+      label:
+        remove: [ready]

  # ============================================================
  # PR title format help
@@ -312,5 +319,3 @@ pull_request_rules:

          Please update your PR title and the merge protection check will pass automatically.

-merge_protections_settings:
-  reporting_method: check-runs
@@ -1,80 +0,0 @@
-name: Aggregate Test Status
-
-on:
-  status:
-
-permissions:
-  statuses: write
-
-jobs:
-  aggregate:
-    if: >-
-      github.event.context == 'direct-test-completed'
-      && github.event.state == 'success'
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check and update aggregate status
-        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
-        with:
-          script: |
-            const sha = context.payload.sha;
-
-            const { data } = await github.rest.repos.getCombinedStatusForRef({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              ref: sha,
-              per_page: 100,
-            });
-
-            const bkStatuses = data.statuses.filter(
-              s => s.context.startsWith('buildkite/ci/')
-            );
-
-            const FASTCHECK_PREFIX = 'buildkite/ci/microscope-';
-            const FULL_SUITE_PREFIXES = [
-              'buildkite/ci/test-tube-',
-              'buildkite/ci/bar-chart-',
-            ];
-
-            const fastcheck = bkStatuses.filter(
-              s => s.context.startsWith(FASTCHECK_PREFIX)
-            );
-            const fullSuite = bkStatuses.filter(
-              s => FULL_SUITE_PREFIXES.some(p => s.context.startsWith(p))
-            );
-
-            if (
-              fastcheck.length > 0
-              && fastcheck.every(s => s.state === 'success')
-            ) {
-              core.info(
-                `All ${fastcheck.length} fastcheck tests passed — updating fastcheck-passed`
-              );
-              await github.rest.repos.createCommitStatus({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                sha,
-                state: 'success',
-                context: 'fastcheck-passed',
-                description:
-                  `All ${fastcheck.length} fastcheck tests passed`,
-              });
-            }
-
-            if (
-              fullSuite.length > 0
-              && fullSuite.every(s => s.state === 'success')
-            ) {
-              core.info(
-                `All ${fullSuite.length} full suite tests passed — updating full-suite-passed`
-              );
-              await github.rest.repos.createCommitStatus({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                sha,
-                state: 'success',
-                context: 'full-suite-passed',
-                description:
-                  `All ${fullSuite.length} full suite tests passed`,
-              });
-            }
@@ -4,11 +4,10 @@ on:
  pull_request:
    branches: [main]
  workflow_call:
-    inputs:
-      ref:
-        description: 'Git ref to checkout (defaults to github.ref)'
-        required: false
-        type: string
+
+concurrency:
+  group: pre-commit-${{ github.ref }}
+  cancel-in-progress: ${{ github.event_name == 'pull_request' }}

 permissions:
  contents: read
@@ -19,8 +18,6 @@ jobs:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
-      with:
-        ref: ${{ inputs.ref || '' }}
    - uses: actions/setup-python@v5
      with:
        python-version: "3.12"
@@ -7,7 +7,6 @@ on:
 permissions:
  contents: read
  pull-requests: write
-  statuses: write

 jobs:
  handle-merge:
@@ -33,7 +32,6 @@ jobs:
            core.setOutput('has_write', String(hasWrite));

      - name: Add ready label and react
-        id: label
        if: steps.perm.outputs.has_write == 'true'
        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
        with:
@@ -41,6 +39,7 @@ jobs:
            const owner = context.repo.owner;
            const repo = context.repo.repo;
            const prNumber = context.payload.issue.number;
+            // Remove ready first to allow re-trigger (labeled event fires on add, not if already present)
            try { await github.rest.issues.removeLabel({ owner, repo, issue_number: prNumber, name: 'ready' }); } catch {}
            await github.rest.issues.addLabels({ owner, repo, issue_number: prNumber, labels: ['ready'] });
            await github.rest.reactions.createForIssueComment({
@@ -48,44 +47,6 @@ jobs:
              comment_id: context.payload.comment.id,
              content: 'rocket',
            });
-            const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-            core.setOutput('pr_sha', pr.head.sha);
-            core.setOutput('pr_branch', pr.head.ref);
-            core.setOutput('pr_number', String(prNumber));
-
-      - name: Trigger Full Suite
-        if: steps.perm.outputs.has_write == 'true'
-        env:
-          BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
-          PR_SHA: ${{ steps.label.outputs.pr_sha }}
-          PR_BRANCH: ${{ steps.label.outputs.pr_branch }}
-          PR_NUMBER: ${{ steps.label.outputs.pr_number }}
-          BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
-          BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
-        run: |
-          curl -sS --fail-with-body -X POST \
-            "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
-            -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
-            -H "Content-Type: application/json" \
-            --data-raw "$(jq -n \
-              --arg commit "$PR_SHA" \
-              --arg branch "$PR_BRANCH" \
-              --arg message "Full Suite for PR #${PR_NUMBER} (via /merge)" \
-              --argjson pr_id "$PR_NUMBER" \
-              '{
-                commit: $commit,
-                branch: $branch,
-                message: $message,
-                ignore_pipeline_branch_filters: true,
-                pull_request_id: $pr_id,
-                pull_request_base_branch: "main",
-                env: {
-                  TEST_SCOPE: "full",
-                  FULL_SUITE: "true",
-                  PR_NUMBER: ($pr_id | tostring)
-                }
-              }')"
-
  parse-command:
    if: >-
      github.event.issue.pull_request != null
@@ -181,33 +142,20 @@ jobs:
            core.setOutput('sha', pr.head.sha);
            core.setOutput('branch', pr.head.ref);

-      - name: React to comment
-        if: steps.perm.outputs.has_write == 'true'
-        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
-        with:
-          script: |
-            await github.rest.reactions.createForIssueComment({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              comment_id: context.payload.comment.id,
-              content: 'rocket',
-            });
-
  pre-commit:
    needs: parse-command
    if: >-
      needs.parse-command.outputs.has_write == 'true'
      && needs.parse-command.outputs.test_scope == 'precommit'
    uses: ./.github/workflows/ci-precommit.yml
-    with:
-      ref: refs/pull/${{ github.event.issue.number }}/merge

  post-precommit-status:
    needs: [parse-command, pre-commit]
    if: always() && needs.parse-command.outputs.test_scope == 'precommit'
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
+      - name: Post commit status
+        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
        env:
          PR_SHA: ${{ needs.parse-command.outputs.pr_sha }}
          RESULT: ${{ needs.pre-commit.result }}
@@ -230,6 +178,17 @@ jobs:
      && needs.parse-command.outputs.test_type != ''
    runs-on: ubuntu-latest
    steps:
+      - name: React to comment
+        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
+        with:
+          script: |
+            await github.rest.reactions.createForIssueComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              comment_id: context.payload.comment.id,
+              content: 'rocket',
+            });
+
      - name: Trigger Buildkite
        env:
          BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
@@ -1,7 +1,7 @@
 name: Trigger Full Suite

 on:
-  pull_request_target:
+  pull_request:
    types: [labeled, synchronize]

 permissions:
@@ -10,7 +10,7 @@ permissions:

 concurrency:
  group: full-suite-${{ github.event.pull_request.number }}
-  cancel-in-progress: false
+  cancel-in-progress: true

 jobs:
  trigger:
@@ -42,7 +42,7 @@ jobs:
          # Find running builds for this branch with TEST_SCOPE=full and cancel them
          builds=$(curl -sS -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
            "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds?branch=${PR_BRANCH}&state=running,scheduled" \
-            | jq -r '.[] | select(try (.env.TEST_SCOPE == "full") catch false) | .number')
+            | jq -r '.[] | select(.env.TEST_SCOPE == "full") | .number')
          for build_num in $builds; do
            echo "Cancelling Buildkite build #$build_num"
            curl -sS -X PUT -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
@@ -37,11 +37,10 @@ jobs:
        with:
          python-version: '3.12'

-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-
      - name: Install dependencies
-        run: uv pip install --system -r requirements-mkdocs.txt
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements-mkdocs.txt

      - name: Setup Pages
        uses: actions/configure-pages@v4
@@ -56,11 +56,10 @@ jobs:
        with:
          python-version: '3.10'

-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-
      - name: Install build dependencies
-        run: uv pip install --system build twine wheel
+        run: |
+          python -m pip install --upgrade pip
+          pip install build twine wheel

      - name: Build package
        run: |
@@ -131,13 +131,11 @@ jobs:
          clang-11 --version
          nvcc --version

-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-
      - name: Install PyTorch ${{ matrix.torch-cuda.torch-version }}+cu${{ matrix.torch-cuda.cuda-version }}
        run: |
-          uv pip install --system typing-extensions==4.12.2
-          uv pip install --system --no-cache-dir torch==${{ matrix.torch-cuda.torch-version }} --index-url https://download.pytorch.org/whl/${{matrix.torch-cuda.torch-cuda-short}}
+          pip install --upgrade pip
+          pip install typing-extensions==4.12.2
+          pip install --no-cache-dir torch==${{ matrix.torch-cuda.torch-version }} --index-url https://download.pytorch.org/whl/${{matrix.torch-cuda.torch-cuda-short}}
          nvcc --version
          python --version
          python -c "import torch; print('PyTorch:', torch.__version__)"
@@ -147,20 +145,20 @@ jobs:
      - name: Build wheel
        run: |
          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
-
-          uv pip install --system setuptools ninja packaging wheel triton scikit-build-core cmake build
-
+          
+          pip install setuptools ninja packaging wheel triton scikit-build-core cmake build
+          
          cd fastvideo-kernel
          git submodule update --init --recursive  # Ensure ThunderKittens submodule is initialized
          # Release builds are produced on GPU-less runners, so force-enable TK and target Hopper.
          export TORCH_CUDA_ARCH_LIST="9.0a"
          export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
-
+          
          # Build standard wheel (no local version suffix) for PyPI
          python -m build --wheel --outdir dist
-
+          
          # Fix the wheel to be manylinux compliant
-          uv pip install --system auditwheel
+          pip install auditwheel
          # Point auditwheel at torch libs, but do not vendor them into the wheel.
          TORCH_LIB_DIR=$(python - <<'PY'
          import os
@@ -213,13 +211,10 @@ jobs:
          pattern: 'fastvideo_kernel-py*'
          merge-multiple: true

-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-
      - name: Build source distribution
        run: |
-          uv pip install --system build scikit-build-core cmake ninja
-
+          pip install build scikit-build-core cmake ninja
+          
          cd fastvideo-kernel
          # We don't need full CUDA/Torch to just package the source (sdist)
          python -m build --sdist --outdir dist
@@ -85,18 +85,9 @@ docs/distillation/examples/
 dmd_t2v_output/
 preprocess_output_text/

-# Next.js / Node artifacts under ui/: see ui/.gitignore

 .claude/
 .codex/
 .sisyphus/
 openspec/
 fastvideo/tests/ssim/reference_videos/**
-
-# Local clones of upstream repos used only for parity testing.
-/stable-audio-tools/
-/daVinci-MagiHuman/
-
-# Converted model weights (produced by scripts/checkpoint_conversion/*).
-# Tens of GB; should live on HF, not in git.
-/converted_weights/
@@ -1 +0,0 @@
-WRN 2026-03-26T13:46:33.469 ?.19646    server_start:193: Failed to start server: operation not permitted: /var/folders/z_/h_6myyk14d1b7z87z3vy4mjh0000gn/T/nvim.dsynkd/iSe0el/nvim.19646.0
@@ -7,13 +7,20 @@ exclude: |
    fastvideo-kernel/.*|
    assets/.*|
    tests/.*|
+    demo/.*|
+    predict\.py|
    scripts/.*|
+    assets/prompts/.*|
+    fastvideo/data_preprocess/.*|
    fastvideo/dataset/.*|
    fastvideo/models/.*|
+    fastvideo/sample/.*|
+    fastvideo/train\.py|
+    fastvideo/utils/.*|
    examples/.*|
-    \.agents/.*|
    .github/workflows/publish-fastvideo.yml|
-    .github/workflows/_template-build-image.yml
+    .github/workflows/_template-build-image.yml|
+    docs/source/inference/support_matrix.md
  )
 repos:
 - repo: https://github.com/google/yapf
@@ -1 +0,0 @@
-3.12
@@ -11,7 +11,7 @@
 - Static assets: `assets/` (including `assets/images/`, `assets/videos/`, and `assets/prompts/`) and `comfyui/assets/`.

 ## Build, Test, and Development Commands
- `uv pip install -e ".[dev]"`: editable install with lint/test extras.
+- `uv pip install -e .[dev]`: editable install with lint/test extras.
 - `pre-commit install --hook-type pre-commit --hook-type commit-msg`: enable local hooks.
 - `pre-commit run --all-files`: run formatter/lint/type/spelling checks.
 - `pytest tests/`: run top-level test suite.
@@ -23,8 +23,7 @@
 - Python 3.10+; 4-space indentation; keep code and imports readable and explicit.
 - Style tools are configured in `pyproject.toml` and `.pre-commit-config.yaml`:
  - `yapf` (format), `ruff` (lint, auto-fix), `mypy` (typing), `codespell`.
- Lint via `pre-commit run --files <changed paths>` (or `pre-commit run --all-files` for a full sweep) before committing. Do not shell out to `yapf`/`ruff`/`codespell`/`mypy` directly — pre-commit chains them with the project's config and respects the `.pre-commit-config.yaml` excludes (e.g. `fastvideo/tests/` is intentionally skipped). If pre-commit reports `(no files to check)` for your paths, that exclude is deliberate — don't bypass it.
- Target line length is 120 (configured in `pyproject.toml` for ruff, yapf, and isort).
+- Target line length is 80.
 - Naming: `snake_case` for functions/files, `PascalCase` for classes, `UPPER_SNAKE_CASE` for constants.

 ## Testing Guidelines
@@ -55,31 +54,3 @@ This repository is agent-friendly. Before doing any work, read:
 If you are exploring a new procedure that has no existing SOP, document your
 progress in `.agents/exploration/` and flag it for review at the end of your
 session.
-
-## Per-Directory AGENTS.md
-
-Local guidance lives next to the code. Read the in-scope file before editing:
-
-| Directory | What it covers |
-|-----------|----------------|
-| `fastvideo/AGENTS.md` | Core package map, public API, registry-driven model dispatch |
-| `fastvideo/configs/AGENTS.md` | Arch + pipeline config dataclasses, `param_names_mapping` |
-| `fastvideo/models/AGENTS.md` | DiT / VAE / encoder / scheduler / loader layout (pre-commit excluded) |
-| `fastvideo/layers/AGENTS.md` | Tensor-parallel linear/attention layer rules for ports |
-| `fastvideo/attention/AGENTS.md` | Backend registry + env-var override |
-| `fastvideo/pipelines/AGENTS.md` | Stage ABC, `basic/<model>/`, `preprocess/`, presets |
-| `fastvideo/training/AGENTS.md` | Legacy monolithic pipelines (frozen for existing models) |
-| `fastvideo/train/AGENTS.md` | New modular trainer (methods × models × callbacks, YAML) |
-| `fastvideo/tests/AGENTS.md` | Test taxonomy, conftest, pre-commit-excluded path |
-| `fastvideo/tests/ssim/AGENTS.md` | GPU SSIM regression authoring + reference video sync |
-| `scripts/checkpoint_conversion/AGENTS.md` | Adding a converter for a new HF/official checkpoint |
-
-## Critical: Two Training Stacks Coexist
-
- `fastvideo/training/` — legacy, monolithic per-model `*_training_pipeline.py` and
-  `*_distillation_pipeline.py`. Still authoritative for shipped models.
- `fastvideo/train/` — new modular framework (composable methods × models × callbacks
-  driven by YAML). Preferred for new training work.
-
-Pick the matching stack before editing. Do not migrate a pipeline between them
-without an explicit ask — the conventions and config surfaces differ.
@@ -128,7 +128,7 @@ class CLIPFeatureExtractor(BaseFeatureExtractor):

    def __init__(self, device: str = 'cuda', model_name: str = "openai/clip-vit-base-patch32"):
        if not TRANSFORMERS_AVAILABLE:
-            raise ImportError("Please install transformers: uv pip install transformers")
+            raise ImportError("Please install transformers: pip install transformers")
        super().__init__(device)
        self.processor = CLIPProcessor.from_pretrained(model_name)
        self.model = CLIPModel.from_pretrained(model_name).to(self.device)
@@ -171,7 +171,7 @@ class VideoMAEFeatureExtractor(BaseFeatureExtractor):

    def __init__(self, device: str = 'cuda', model_name: str = "MCG-NJU/videomae-base"):
        if not TRANSFORMERS_AVAILABLE:
-            raise ImportError("Please install transformers: uv pip install transformers")
+            raise ImportError("Please install transformers: pip install transformers")
        super().__init__(device)
        self.model = VideoMAEModel.from_pretrained(model_name).to(self.device)
        self.model.eval()
@@ -57,7 +57,7 @@ class I3DFeatureExtractor(nn.Module):
        except Exception as e:
            raise RuntimeError(f"Failed to load I3D model from Hugging Face Hub. Error: {e}\n"
                               f"Ensure you have internet connection and huggingface_hub installed:\n"
-                               f"uv pip install huggingface_hub") from e
+                               f"pip install huggingface_hub") from e

    def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
        """
@@ -1,7 +1,7 @@
 #!/bin/bash

 # 1. Install missing dependency
-uv pip install -q opencv-python-headless transformers huggingface_hub
+pip install -q opencv-python-headless transformers huggingface_hub

 # 2. Run FVD script
 python benchmarks/fvd/run_fvd.py
@@ -1,4 +1,4 @@
 #!/bin/bash

 # 1. Install missing dependency
-uv pip install -q opencv-python-headless
+pip install -q opencv-python-headless
@@ -38,10 +38,10 @@ cp -r /path/to/FastVideo/comfyui /path/to/ComfyUI/custom_nodes/FastVideo

 #### Install dependencies:

-Currently, the only dependency is `fastvideo`, which can be installed with `uv`.
+Currently, the only dependency is `fastvideo`, which can be installed using pip.

 ```bash
-uv pip install fastvideo
+pip install fastvideo
 ```

 #### Install missing custom nodes:
@@ -42,15 +42,15 @@ RUN source $HOME/.local/bin/env && \
    uv venv --python 3.10 --seed /opt/venv && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
-    uv pip install --no-cache-dir ".[dev]" && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.4/flash_attn-2.8.3+cu128torch2.11-cp310-cp310-linux_x86_64.whl
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp310-cp310-linux_x86_64.whl

 COPY . .

 # Install dependencies using uv and set up shell configuration
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir -e ".[dev]" && \
+    uv pip install --no-cache-dir -e .[dev] && \
    git config --unset-all http.https://github.com/.extraheader || true && \
    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
@@ -42,15 +42,15 @@ RUN source $HOME/.local/bin/env && \
    uv venv --python 3.11 --seed /opt/venv && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
-    uv pip install --no-cache-dir ".[dev]" && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.4/flash_attn-2.8.3+cu128torch2.11-cp311-cp311-linux_x86_64.whl
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp311-cp311-linux_x86_64.whl

 COPY . .

 # Install dependencies using uv and set up shell configuration
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir -e ".[dev]" && \
+    uv pip install --no-cache-dir -e .[dev] && \
    git config --unset-all http.https://github.com/.extraheader || true && \
    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
@@ -42,15 +42,15 @@ RUN source $HOME/.local/bin/env && \
    uv venv --python 3.12 --seed /opt/venv && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
-    uv pip install --no-cache-dir ".[dev]" && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.4/flash_attn-2.8.3+cu128torch2.11-cp312-cp312-linux_x86_64.whl
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp312-cp312-linux_x86_64.whl

 COPY . .

 # Install dependencies using uv and set up shell configuration
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir -e ".[dev]" && \
+    uv pip install --no-cache-dir -e .[dev] && \
    git config --unset-all http.https://github.com/.extraheader || true && \
    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
@@ -42,7 +42,7 @@ RUN source $HOME/.local/bin/env && \
    uv venv --python 3.12 --seed /opt/venv && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
-    uv pip install --no-cache-dir ".[dev]" && \
+    uv pip install --no-cache-dir .[dev] && \
    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation

 COPY . .
@@ -50,7 +50,7 @@ COPY . .
 # Install dependencies using uv and set up shell configuration
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir -e ".[dev]" && \
+    uv pip install --no-cache-dir -e .[dev] && \
    git config --unset-all http.https://github.com/.extraheader || true && \
    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
@@ -43,7 +43,7 @@ COPY . .
 # Install dependencies using uv and set up shell configuration
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir -e ".[rocm]" && \
+    uv pip install --no-cache-dir -e .[rocm] && \
    git config --unset-all http.https://github.com/.extraheader || true && \
    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
@@ -6,7 +6,7 @@ This directory contains the FastVideo documentation built with MkDocs.

 ```bash
 # Install dependencies
-uv pip install -r requirements-mkdocs.txt
+pip install -r requirements-mkdocs.txt

 # Serve docs with live reload (recommended for development)
 mkdocs serve
@@ -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
@@ -1,210 +0,0 @@
-# Activation Trace Mode
-
-!!! note
-    This page covers Extension 0 (module forward hooks), which is the implemented
-    tracing mechanism. Extensions 1-3 are design sketches for future work and are
-    **not yet implemented**.
-
-## Overview
-
-Activation trace mode is a zero-overhead-when-off, env-gated mechanism for
-dumping per-layer activation statistics during FastVideo inference. Its primary
-use case is **parity debugging across model ports**: enable tracing on both
-FastVideo and the upstream reference implementation, then `diff` the resulting
-JSONL files to find the first divergent layer.
-
-The mechanism is intentionally narrow. It doesn't replace general logging,
-profiling, or function tracing. It answers one question: "at which layer do
-FastVideo and the reference model first produce different numbers?"
-
-## When to use
-
- Investigating numerical drift between FastVideo and an upstream reference.
- Debugging mid-pipeline divergence (e.g., one block produces wrong output while earlier blocks match).
- Validating that a refactor preserves bf16 noise-floor behavior across many layers.
-
-## When NOT to use
-
-| Goal | Use instead |
-|---|---|
-| General logging | `init_logger(__name__)` |
-| Per-stage timing | `FASTVIDEO_STAGE_LOGGING` |
-| Profiling kernel timings | `FASTVIDEO_TORCH_PROFILER_DIR` (see [Profiling](profiling.md)) |
-| Function-call tracing | `FASTVIDEO_TRACE_FUNCTION` (heavy) |
-
-## Quickstart
-
-```bash
-FASTVIDEO_TRACE_ACTIVATIONS=1 \
-FASTVIDEO_TRACE_LAYERS="^block\.layers\.[0-9]+$" \
-FASTVIDEO_TRACE_STATS="abs_mean,sum,max,shape" \
-FASTVIDEO_TRACE_OUTPUT="/tmp/fv_trace.jsonl" \
-python examples/inference/basic/basic_magi_human.py
-```
-
-Each line in `/tmp/fv_trace.jsonl` is a JSON record:
-
-```json
-{"module": "block.layers.0", "tensor": "out", "step": 0, "abs_mean": 1.234, "sum": -5.678, "max": 9.012, "shape": [1, 4096, 5120]}
-```
-
-## Configuration
-
-| Env var | Default | Description |
-|---|---|---|
-| `FASTVIDEO_TRACE_ACTIVATIONS` | `False` | Master toggle. When unset or false, **zero overhead** in the production hot path. |
-| `FASTVIDEO_TRACE_LAYERS` | `""` (all) | Python regex filter applied to `model.named_modules()` names. Empty string matches all modules. |
-| `FASTVIDEO_TRACE_STATS` | `"abs_mean,sum"` | Comma-separated stats to compute. Available: `abs_mean`, `sum`, `min`, `max`, `mean`, `std`, `shape`, `dtype`. |
-| `FASTVIDEO_TRACE_OUTPUT` | `"/tmp/fv_trace_<pid>.jsonl"` | Output file path. `<pid>` is replaced with the process ID at runtime. |
-| `FASTVIDEO_TRACE_STEPS` | `""` (all) | Comma-separated denoising step indices to capture. Empty string captures all steps. |
-
-## Workflow: parity-debug a model port
-
-1. Set up a tightly-controlled comparison: a parity test or a small standalone
-   script that loads both the FastVideo model and the upstream reference with
-   identical inputs and seeds.
-
-2. Run the FastVideo side with tracing on:
-
-   ```bash
-   FASTVIDEO_TRACE_ACTIVATIONS=1 \
-   FASTVIDEO_TRACE_LAYERS="<your regex>" \
-   FASTVIDEO_TRACE_OUTPUT="/tmp/fv_trace_fv.jsonl" \
-   python <fv_runner.py>
-   ```
-
-3. Run the upstream side. The upstream repo needs separate instrumentation. See
-   "Hooking the upstream side" below.
-
-4. Sort both files by `(module, step)` if needed, then diff:
-
-   ```bash
-   diff /tmp/fv_trace_fv.jsonl /tmp/fv_trace_upstream.jsonl
-   ```
-
-5. The first divergent line identifies the first layer where FastVideo and the
-   upstream produce different outputs. Start debugging there.
-
-## Architecture (Extension 0: module forward hooks)
-
-At pipeline initialization, `attach_activation_trace()` reads the env vars once.
-If `FASTVIDEO_TRACE_ACTIVATIONS` is unset or false, the function returns
-immediately and no hooks are registered. If tracing is on, it walks
-`model.named_modules()`, filters by the layer regex, and registers an
-`ActivationStatHook` on each matching module.
-
-During the forward pass, each hook fires after its module completes, computes
-the requested stats on the output tensor, and appends a JSON record to the
-output file.
-
-```
-ComposedPipelineBase
-  └─ attach_activation_trace()
-       ├─ reads env vars (once at startup)
-       ├─ if off: returns None immediately
-       └─ if on: walks named_modules()
-            └─ registers ActivationStatHook on matching modules
-                 └─ on each forward: compute stats → append JSONL
-```
-
-### Zero-overhead-when-off guarantee
-
- The env var check happens **once at startup** inside `attach_activation_trace()`.
- If the env var is unset or false, the function returns `None` immediately.
- No hooks are registered. No branches are added to the production forward path.
- The only cost when tracing is off is one env var lookup at pipeline
-  initialization, which takes under a microsecond.
-
-### Hooking the upstream side
-
-The upstream reference repo isn't part of FastVideo, so it can't read FastVideo
-env vars directly. Two options:
-
-**Option 1: Inline patch** in your local clone of the upstream repo. Add
-`register_forward_hook` calls in the same shape as `ActivationStatHook`. Clean
-up afterward with `git stash` or `git checkout HEAD -- <file>`.
-
-**Option 2: Wrapper script**. Write a small Python harness that imports the
-upstream model, walks its `named_modules()`, and attaches hooks externally.
-This is the same pattern used in
-`tests/local_tests/transformers/_debug_magi_human_block_parity.py`.
-
-The `add-model-trace` skill at `~/.config/opencode/skill/add-model-trace/`
-provides a script template for this purpose.
-
-## Future extensions (design only, not yet implemented)
-
-### Extension 1: FX/Dynamo backend graph rewrite
-
-**Granularity**: per-FX-node (every matmul, every add).
-
-**Mechanism**: a `torch.compile` backend that takes the captured `GraphModule`
-and inserts logger nodes after each op. Compiles into a separate artifact from
-the production graph.
-
-**Off semantics**: zero overhead. The production compile path is untouched.
-
-**When to add**: if you need to trace inside a `torch.compile`'d graph and
-Extension 0 is too coarse.
-
-**Build cost**: roughly 1-2 days. Reference:
-`torchao.quantization.pt2e._numeric_debugger`.
-
-### Extension 2: AST source injection at import time
-
-**Granularity**: per-line (between any two Python statements).
-
-**Mechanism**: an importlib loader hook rewrites Python source AST at module
-import time, inserting `if TRACE: dump(...)` statements. The decision is made
-once at import.
-
-**Off semantics**: zero overhead. If the env var is off at import time, source
-is loaded as-is.
-
-**When to add**: if you need per-line granularity that even FX-node-level can't
-provide. This is almost never the right choice.
-
-**Build cost**: roughly 1 week. Brittle and hard to debug.
-
-### Extension 3: `__torch_dispatch__` / `TorchDispatchMode`
-
-**Granularity**: per-op (every dispatcher call: matmul, add, view, etc.).
-
-**Mechanism**: a `TorchDispatchMode` context manager that intercepts all ops at
-the dispatcher level.
-
-**Off semantics**: zero overhead. PyTorch's dispatcher only invokes mode hooks
-when a mode is active.
-
-**When on**: significant overhead. Every op pays a Python callback cost. Triton
-kernels bypass it.
-
-**When to add**: useful for quantization or dtype debugging where module-level
-granularity isn't enough.
-
-**Build cost**: roughly 1 day. Reference:
-`torch.utils._python_dispatch.TorchDispatchMode`.
-
-## Comparison with similar tools
-
-| Tool | Pattern | FastVideo equivalent |
-|---|---|---|
-| SGLang `--debug-tensor-dump-output-folder` | env-gated forward hooks at startup | Extension 0 (this) |
-| TransformerEngine `DumpTensors` | config-driven selective dumps | Extension 0 (env-driven) |
-| HuggingFace `output_hidden_states=True` | source-level boolean gating | Not used; Extension 0 avoids model code edits |
-| torchao numeric debugger | FX pass + node-level loggers | Extension 1 (future) |
-| W&B `wandb.watch()` | runtime forward hooks (always on once registered) | Extension 0 has a similar mechanism, but gated off by default |
-
-## Implementation references
-
- Module: `fastvideo/hooks/activation_trace.py`
- Env vars: `fastvideo/envs.py` (`FASTVIDEO_TRACE_ACTIVATIONS` and friends)
- Pipeline integration: `fastvideo/pipelines/composed_pipeline_base.py`
- Tests: `fastvideo/tests/hooks/test_activation_trace.py`
- Companion skill (for ad-hoc port investigations): `~/.config/opencode/skill/add-model-trace/`
-
-## Changelog
-
-| Date | Change |
-|---|---|
-| 2026-05-01 | Initial Extension 0 (module forward hooks) implementation. Extensions 1-3 designed but not implemented. |
@@ -24,7 +24,7 @@ PR push
          Runs on the PR branch directly
              │
          pass ──► Mergify auto-squash-merges to main, branch deleted
-          fail ──► fix the regression, push, and /merge again
+          fail ──► Mergify removes 'ready' label; fix and /merge again
 ```

 ---
@@ -102,8 +102,8 @@ failing test's output.
 | Performance Tests | `performance` | 30 min |
 | API Server Tests | `api_server` | 30 min |

-If a Full Suite test fails, check the Buildkite build log for the failing step's output.
-Fix the regression, push, and comment `/merge` again to re-trigger.
+A Full Suite failure removes the `ready` label automatically. A Mergify comment links to
+the Buildkite build. Fix the regression, push, and comment `/merge` again.

 ---

@@ -129,8 +129,8 @@ Suite passing directly on the PR branch.
   - No merge conflicts
 5. If all conditions pass, Mergify squash-merges to `main` automatically. The branch is
   deleted after merge.
-6. If the Full Suite fails, the developer fixes the issue, pushes, and comments `/merge`
-   again to re-trigger.
+6. If the Full Suite fails, Mergify removes the `ready` label and posts a comment linking to
+   the Buildkite build. The developer fixes the issue, pushes, and comments `/merge` again.

 **Merge conditions summary:**

@@ -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/` |
@@ -279,30 +279,6 @@ Triggers a specific Buildkite test or suite on the current PR branch.
 | `/test api` | API server integration tests | `api_server` |
 | `/test full` | Entire Full Suite | all (with `TEST_SCOPE=full`) |
 | `/test fastcheck` | Entire Fastcheck suite | fastcheck (with `TEST_SCOPE=fastcheck`) |
-| `/test pre-commit` | Pre-commit checks on PR code | — (runs `ci-precommit.yml` via `workflow_call`) |
-
-**Re-running failed tests:** When you use `/test <name>` to re-run a specific failed test,
-the resulting Buildkite check uses the same name as the original (e.g., `/test encoder`
-creates `buildkite/ci/microscope-encoder-tests`). This overwrites the failed check status.
-Once all tests in a tier pass, the aggregate status (`fastcheck-passed` or
-`full-suite-passed`) is automatically updated to `success` by the `ci-aggregate-status.yml`
-workflow.
-
-**How aggregate status refresh works:**
-
-1. `/test <name>` triggers a Buildkite build with `TEST_SCOPE=direct`. The test step uses
-   the same label as its fastcheck/full-suite counterpart, so the resulting GitHub check
-   overwrites the original.
-2. When the build completes, Buildkite's `notify` posts a `direct-test-completed` commit
-   status. This is the only signal that triggers the aggregate workflow — intermediate step
-   status updates do not trigger it.
-3. `ci-aggregate-status.yml` fires, calls `getCombinedStatusForRef` to fetch the latest
-   status for every context on that commit (each context returns only its most recent
-   state), groups them by prefix (`microscope-*` → fastcheck, `test-tube-*`/`bar-chart-*`
-   → full suite), and posts `fastcheck-passed: success` or `full-suite-passed: success` if
-   all entries in the group are `success`.
-4. Tests that were never triggered (skipped by monorepo-diff) have no status entry and do
-   not block the aggregate.

 ---

@@ -320,7 +296,6 @@ Protected branches (`main`, `master`, `release/*`) are never deleted.
 | `ci-precommit.yml` | Every push / PR against `main` | Runs pre-commit hooks (yapf, ruff, mypy, codespell, pymarkdown, actionlint, check-filenames) |
 | `ci-trigger-full-suite.yml` | `ready` label added to a PR | Calls Buildkite API to run Full Suite on the PR branch |
 | `ci-slash-commands.yml` | PR comment starting with `/merge` or `/test` | Handles slash commands; adds `ready` label or triggers Buildkite |
-| `ci-aggregate-status.yml` | Any Buildkite commit status update | Checks if all tests in a tier passed; updates `fastcheck-passed` or `full-suite-passed` |
 | `community-issue-labeler.yml` | Issue opened or edited | Auto-labels issues by keyword matching against title and body |
 | `community-welcome.yml` | First contribution | Posts a welcome comment for first-time contributors |
 | `community-stale.yml` | Scheduled | Marks and closes stale issues and PRs |
@@ -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)
@@ -296,10 +296,8 @@ Action:

 - Add or reuse a numerical parity test that loads the official model and the
  FastVideo model and compares outputs.
- See examples in `tests/local_tests/` organized by model family
-  (e.g., `tests/local_tests/sd35/`, `tests/local_tests/ltx2/`,
-  `tests/local_tests/stable_audio/`) and the navigation index in
-  `tests/local_tests/README.md`.
+- See examples in `tests/local_tests/` (e.g., `tests/local_tests/upsamplers/`)
+  and the commands in `tests/local_tests/README.md`.
 - If there are discrepancies, add opt‑in logging to both models and compare
  activation summaries (layer output sums, per‑stage logs).
 - First align the loaded weights (validate `param_names_mapping`).
@@ -321,8 +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:

@@ -350,8 +347,7 @@ Purpose:

 Action:

- Add a pipeline parity test under `tests/local_tests/<family>/`
-  (e.g., `tests/local_tests/<family>/test_<family>_pipeline_parity.py`).
+- Add a pipeline parity test under `tests/local_tests/pipelines/`.
 - See the [Testing Guide](testing.md) for test conventions.

 ### 7) Add user‑facing examples
@@ -478,7 +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`
@@ -99,7 +99,7 @@ cd /FastVideo
 **Install the package**

 ```bash
-uv pip install -e ".[dev]"
+uv pip install -e .[dev]
 ```

 The Docker image already includes Flash Attention and most heavy dependencies, so this is fast.
@@ -49,7 +49,7 @@ git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
 Install FastVideo in editable mode and set up hooks:

 ```bash
-uv pip install -e ".[dev]"
+uv pip install -e .[dev]

 # Optional: FlashAttention (builds native kernels)
 uv pip install flash-attn --no-build-isolation -v
@@ -104,9 +104,8 @@ distillation, self-forcing, VSA, VMoBA, performance benchmarks, and API server t
 8. If all Full Suite tests pass and all merge conditions are met (approval, valid title,
   pre-commit green, fastcheck green, no draft, no conflicts), Mergify squash-merges to
   `main` automatically. Your branch is deleted.
-9. If a Full Suite test fails, check the Buildkite build log for the failing step. Fix the
-   issue, push, and comment `/merge` again. You can also re-run individual failed tests
-   with `/test <name>` — see below.
+9. If a Full Suite test fails, Mergify removes the `ready` label and posts a comment with a
+   link to the Buildkite build. Fix the issue, push, and comment `/merge` again.

 !!! note
    Only contributors with write permission to the repository can trigger slash commands.
@@ -150,15 +149,10 @@ Comment on your PR to trigger specific tests independently of the auto-merge flo
 /test vmoba            # VMoBA inference tests
 /test performance      # Performance benchmarks
 /test api              # API server integration tests
-/test pre-commit       # Pre-commit checks on PR code
 ```

 The workflow reacts with a 🚀 emoji to confirm the command was received.

-When you re-run an individual test with `/test <name>`, the new result overwrites the
-original failed check (same Buildkite check name). Once all tests in a tier pass, the
-`fastcheck-passed` or `full-suite-passed` status is automatically updated.
-
 ---

 ## Troubleshooting
@@ -205,8 +199,9 @@ Mergify removes the `needs-rebase` label automatically once conflicts are resolv

 ### Full Suite failed after `/merge`

-The Full Suite found a regression. Check the failing Buildkite step's output for assertion
-errors or tracebacks.
+The Full Suite found a regression. Mergify removes the `ready` label and posts a comment
+linking to the Buildkite build. Check the failing step's output for assertion errors or
+tracebacks.

 Common causes:

@@ -1,480 +0,0 @@
-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."
-  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."
-
-surfaces:
-  fastvideo_args:
-    moved:
-      model_path: generator.model_path
-      workload_type: generator.pipeline.workload_type
-      distributed_executor_backend: generator.engine.execution_backend
-      trust_remote_code: generator.trust_remote_code
-      revision: generator.revision
-      num_gpus: generator.engine.num_gpus
-      tp_size: generator.engine.parallelism.tp_size
-      sp_size: generator.engine.parallelism.sp_size
-      hsdp_replicate_dim: generator.engine.parallelism.hsdp_replicate_dim
-      hsdp_shard_dim: generator.engine.parallelism.hsdp_shard_dim
-      dist_timeout: generator.engine.parallelism.dist_timeout
-      lora_path: generator.pipeline.components.lora_path
-      dit_cpu_offload: generator.engine.offload.dit
-      use_fsdp_inference: generator.engine.use_fsdp_inference
-      dit_layerwise_offload: generator.engine.offload.dit_layerwise
-      text_encoder_cpu_offload: generator.engine.offload.text_encoder
-      image_encoder_cpu_offload: generator.engine.offload.image_encoder
-      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
-      disable_autocast: generator.engine.disable_autocast
-      enable_stage_verification: generator.engine.enable_stage_verification
-      prompt_txt: request.inputs.prompt_path
-      override_text_encoder_safetensors: generator.pipeline.components.text_encoder_weights
-      override_text_encoder_quant: generator.engine.quantization.text_encoder_quant
-      override_transformer_cls_name: generator.pipeline.components.override_transformer_cls_name
-      init_weights_from_safetensors: generator.pipeline.components.transformer_weights
-      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
-      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."
-      inference_mode: "Legacy boolean mirror of mode; kept only through adapters while FastVideoArgs remains."
-      lora_nickname: "Legacy adapter-selection surface pending LoRA API cleanup."
-      lora_target_modules: "Legacy LoRA configuration surface pending dedicated component API."
-      output_type: "Legacy output formatting surface pending GenerationResult cleanup."
-      VSA_sparsity: "Model-specific inference optimization not yet represented in the typed public schema."
-      moba_config_path: "Model-specific MoBA optimization surface not yet represented in the typed public schema."
-      master_port: "Executor/bootstrap compatibility field; not part of the canonical inference schema."
-    private_only:
-      ray_placement_group: "Ray deployment-only field."
-      ray_runtime_env: "Ray deployment-only field."
-    internal_only:
-      pipeline_config: "Legacy internal carrier object."
-      preprocess_config: "Legacy preprocess carrier object."
-      moba_config: "Derived runtime config loaded from moba_config_path."
-      model_paths: "Runtime bookkeeping."
-      model_loaded: "Runtime bookkeeping."
-
-  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
-    compatibility_only:
-      model_path: "Redundant with generator.model_path."
-      disable_autocast: "Duplicated by generator.engine.disable_autocast during migration."
-      dit_precision: "Precision override pending dedicated typed component precision design."
-      upsampler_precision: "Precision override pending dedicated typed component precision design."
-      vae_precision: "Precision override pending dedicated typed component precision design."
-      image_encoder_precision: "Precision override pending dedicated typed component precision design."
-      text_encoder_precisions: "Precision override pending dedicated typed component precision design."
-    internal_only:
-      dit_config: "Legacy internal component config object."
-      upsampler_config: "Legacy internal component config object."
-      vae_config: "Legacy internal component config object."
-      image_encoder_config: "Legacy internal component config object."
-      text_encoder_configs: "Legacy internal component config object."
-      preprocess_text_funcs: "Internal text preprocessing hooks."
-      postprocess_text_funcs: "Internal text postprocessing hooks."
-
-  pipeline_config_extensions:
-    preset_owned:
-      conditioning_strategy:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      max_num_conditional_frames:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      min_num_conditional_frames:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      sigma_conditional:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      sigma_data:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      state_ch:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      state_t:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      text_encoder_class:
-        sources:
-          - fastvideo.configs.pipelines.cosmos.CosmosConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      autoregressive_chunk_frames:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      autoregressive_overlap_frames:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      cfg_behavior:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      default_camera_rotation:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      default_movement_distance:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      default_negative_prompt:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      default_trajectory_type:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      filter_points_threshold:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      fps:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      frame_buffer_max:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      moge_model_name:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      noise_aug_strength:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      num_frames:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      offload_moge_after_depth:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      use_moge_depth:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      video_resolution:
-        sources:
-          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
-          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      text_encoder_crop_start:
-        sources:
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V480PStepDistilledConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V720PConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15SR1080PConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V480PConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V720PConfig
-          - fastvideo.configs.pipelines.hyworld.HYWorldConfig
-          - fastvideo.configs.pipelines.hyworld.Hunyuan15T2V480PConfig
-      text_encoder_max_lengths:
-        sources:
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V480PStepDistilledConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V720PConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15SR1080PConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V480PConfig
-          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V720PConfig
-          - fastvideo.configs.pipelines.hyworld.HYWorldConfig
-          - fastvideo.configs.pipelines.hyworld.Hunyuan15T2V480PConfig
-      precision:
-        sources:
-          - fastvideo.configs.pipelines.lingbotworld.LingBotWorldI2V480PConfig
-          - fastvideo.configs.pipelines.lingbotworld.Wan2_2_I2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_1_3B_Config
-          - fastvideo.configs.pipelines.wan.FastWan2_1_T2V_480P_Config
-          - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
-          - fastvideo.configs.pipelines.wan.MatrixGameBaseI2V480PConfig
-          - fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig
-          - fastvideo.configs.pipelines.wan.SelfForcingWan2_2_T2V480PConfig
-          - fastvideo.configs.pipelines.wan.SelfForcingWanT2V480PConfig
-          - fastvideo.configs.pipelines.wan.WANV2VConfig
-          - fastvideo.configs.pipelines.wan.Wan2_2_I2V_A14B_Config
-          - fastvideo.configs.pipelines.wan.Wan2_2_T2V_A14B_Config
-          - fastvideo.configs.pipelines.wan.Wan2_2_TI2V_5B_Config
-          - fastvideo.configs.pipelines.wan.WanI2V480PConfig
-          - fastvideo.configs.pipelines.wan.WanI2V720PConfig
-          - fastvideo.configs.pipelines.wan.WanT2V480PConfig
-          - fastvideo.configs.pipelines.wan.WanT2V720PConfig
-      warp_denoising_step:
-        sources:
-          - fastvideo.configs.pipelines.lingbotworld.LingBotWorldI2V480PConfig
-          - fastvideo.configs.pipelines.lingbotworld.Wan2_2_I2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_1_3B_Config
-          - fastvideo.configs.pipelines.wan.FastWan2_1_T2V_480P_Config
-          - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
-          - fastvideo.configs.pipelines.wan.MatrixGameBaseI2V480PConfig
-          - fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig
-          - fastvideo.configs.pipelines.wan.SelfForcingWan2_2_T2V480PConfig
-          - fastvideo.configs.pipelines.wan.SelfForcingWanT2V480PConfig
-          - fastvideo.configs.pipelines.wan.WANV2VConfig
-          - fastvideo.configs.pipelines.wan.Wan2_2_I2V_A14B_Config
-          - fastvideo.configs.pipelines.wan.Wan2_2_T2V_A14B_Config
-          - fastvideo.configs.pipelines.wan.Wan2_2_TI2V_5B_Config
-          - fastvideo.configs.pipelines.wan.WanI2V480PConfig
-          - fastvideo.configs.pipelines.wan.WanI2V720PConfig
-          - fastvideo.configs.pipelines.wan.WanT2V480PConfig
-          - fastvideo.configs.pipelines.wan.WanT2V720PConfig
-      bsa_cdf_threshold:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_chunk_k:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_chunk_q:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_params:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_sparsity:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      enable_bsa:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      enable_kv_cache:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      enhance_hf:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      offload_kv_cache:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      t_thresh:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      use_distill:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      scheduler_arch:
-        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
-      text_encoder_archs:
-        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
-      tokenizer_archs:
-        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
-      transformer_arch:
-        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
-      vae_arch:
-        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
-      expand_timesteps:
-        sources:
-          - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
-          - fastvideo.configs.pipelines.wan.Wan2_2_TI2V_5B_Config
-      context_noise:
-        sources: [fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig]
-      num_frames_per_block:
-        sources: [fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig]
-      audio_channels:
-        sources:
-          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
-          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
-      audio_end_in_s:
-        sources:
-          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
-          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
-      audio_start_in_s:
-        sources:
-          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
-          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
-      max_audio_duration_s:
-        sources:
-          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
-          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
-      sample_size:
-        sources:
-          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
-          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
-      sampling_rate:
-        sources:
-          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
-          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
-    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."
-    internal_only:
-      audio_decoder_config: "Legacy internal component config object."
-      audio_decoder_precision: "Precision override pending dedicated component precision design."
-      vocoder_config: "Legacy internal component config object."
-      vocoder_precision: "Precision override pending dedicated component precision design."
-
-  sampling_param_base:
-    moved:
-      image_path: request.inputs.image_path
-      pil_image: request.inputs.pil_image
-      video_path: request.inputs.video_path
-      mouse_cond: request.inputs.mouse_cond
-      keyboard_cond: request.inputs.keyboard_cond
-      grid_sizes: request.inputs.grid_sizes
-      pose: request.inputs.pose
-      c2ws_plucker_emb: request.inputs.c2ws_plucker_emb
-      refine_from: request.inputs.refine_from
-      stage1_video: request.inputs.stage1_video
-      prompt: request.prompt
-      negative_prompt: request.negative_prompt
-      prompt_path: request.inputs.prompt_path
-      output_path: request.output.output_path
-      output_video_name: request.output.output_video_name
-      num_videos_per_prompt: request.sampling.num_videos_per_prompt
-      seed: request.sampling.seed
-      num_frames: request.sampling.num_frames
-      height: request.sampling.height
-      width: request.sampling.width
-      height_sr: request.sampling.height_sr
-      width_sr: request.sampling.width_sr
-      fps: request.sampling.fps
-      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
-      enable_teacache: request.runtime.enable_teacache
-      save_video: request.output.save_video
-      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:
-      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
-      audio_start_in_s: request.extensions.stable_audio.audio_start_in_s
-      audio_end_in_s: request.extensions.stable_audio.audio_end_in_s
-      init_audio: request.extensions.stable_audio.init_audio
-      init_audio_strength: request.extensions.stable_audio.init_audio_strength
-      init_noise_level: request.extensions.stable_audio.init_noise_level
-      inpaint_audio: request.extensions.stable_audio.inpaint_audio
-      inpaint_mask: request.extensions.stable_audio.inpaint_mask
-    internal_only:
-      data_type: "Derived from the request shape and not a public input."
-
-  sampling_param_extensions: {}
-
-  openai_image_request:
-    kept:
-      model: "HTTP adapter model-routing field."
-      response_format: "HTTP adapter response formatting field."
-      output_format: "HTTP adapter output-format field."
-      background: "HTTP adapter output-format field."
-      quality: "Compatibility field currently accepted by the adapter."
-      style: "Compatibility field currently accepted by the adapter."
-      user: "Compatibility field currently accepted by the adapter."
-    moved:
-      prompt: request.prompt
-      n: request.sampling.num_videos_per_prompt
-      size:
-        target: request.sampling.width,height
-        note: "Adapter parses OpenAI size strings as WIDTHxHEIGHT and forwards width then height."
-      num_inference_steps: request.sampling.num_inference_steps
-      guidance_scale: request.sampling.guidance_scale
-      true_cfg_scale: request.sampling.true_cfg_scale
-      seed: request.sampling.seed
-      negative_prompt: request.negative_prompt
-      enable_teacache: request.runtime.enable_teacache
-
-  openai_video_request:
-    kept:
-      model: "HTTP adapter model-routing field."
-    moved:
-      prompt: request.prompt
-      input_reference: request.inputs.image_path
-      reference_url: request.inputs.image_path
-      size:
-        target: request.sampling.width,height
-        note: "Adapter parses OpenAI size strings as WIDTHxHEIGHT and forwards width then height."
-      fps: request.sampling.fps
-      num_frames: request.sampling.num_frames
-      seed: request.sampling.seed
-      num_inference_steps: request.sampling.num_inference_steps
-      guidance_scale: request.sampling.guidance_scale
-      guidance_scale_2: request.sampling.guidance_scale_2
-      true_cfg_scale: request.sampling.true_cfg_scale
-      negative_prompt: request.negative_prompt
-      enable_teacache: request.runtime.enable_teacache
-      output_path: request.output.output_path
-    compatibility_only:
-      seconds:
-        target: request.sampling.num_frames
-        note: "HTTP adapter duration convenience field. If num_frames is omitted, the adapter computes num_frames = fps * seconds."
-
-cli:
-  notes:
-    - "CLI parity is checked against the actual generate/serve parser dest sets."
-    - "The inventory tracks parser dest names, excluding argparse's implicit help action."
-    - "The refactored inference CLI is config-only: subcommands expose only --config, and any additional CLI input must use dotted override paths."
-  generate:
-    explicit_local_fields:
-      - config
-    expected_dests:
-      - config
-  serve:
-    explicit_local_fields:
-      - config
-    expected_dests:
-      - config
@@ -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`.
@@ -243,7 +243,7 @@ for step in range(start_step, max_steps):

 ```bash
 # Install
-uv pip install -e ".[dev]"
+uv pip install -e .[dev]

 # Run DMD2 distillation on Wan 2.1
 torchrun --nproc_per_node=8 -m fastvideo.train.entrypoint.train \
@@ -16,8 +16,7 @@ Both models are trained on **61×448×832** resolution but support generating vi
 First install [VSA](../attention/vsa/index.md). Set `MODEL_BASE` to your own model path and run:

 ```bash
-FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN \
-  fastvideo generate --config scripts/inference/inference_wan_VSA_DMD_1_3B.yaml
+bash scripts/inference/v1_inference_wan_dmd.sh
 ```

 ## 🗂️ Dataset
@@ -86,25 +85,3 @@ 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.
@@ -27,7 +27,7 @@ uv pip install fastvideo
 conda create -n fastvideo python=3.12 -y
 conda activate fastvideo

-uv pip install fastvideo
+pip install fastvideo
 ```

 ### From source
@@ -41,11 +41,11 @@ uv pip install -e .
 uv pip install flash-attn --no-build-isolation -v
 ```

-Alternative with Conda environment (still drives installs through `uv`):
+Alternative with Conda environment:

 ```bash
-uv pip install -e .
-uv pip install flash-attn --no-build-isolation -v
+pip install -e .
+pip install flash-attn --no-build-isolation -v
 ```

 ## Hardware Requirements
@@ -58,16 +58,14 @@ uv pip install flash-attn --no-build-isolation -v

 #### With Conda environment (alternative)

-`uv` works inside an active conda env too, so prefer `uv pip` for the actual install:
-
 ```bash
-uv pip install fastvideo
+pip install fastvideo
 ```

 Also optionally install FlashAttention:

 ```bash
-uv pip install flash-attn --no-build-isolation -v
+pip install flash-attn --no-build-isolation -v
 ```

 ### Installation from Source
@@ -89,7 +87,7 @@ uv pip install -e .
 Alternative with Conda environment:

 ```bash
-uv pip install -e .
+pip install -e .
 ```

 ### Optional Dependencies
@@ -103,7 +101,7 @@ uv pip install flash-attn --no-build-isolation -v
 Alternative with Conda environment:

 ```bash
-uv pip install flash-attn --no-build-isolation -v
+pip install flash-attn --no-build-isolation -v
 ```

 ## Set up using Docker
@@ -57,10 +57,8 @@ uv pip install fastvideo

 #### With Conda environment (alternative)

-`uv` works inside an active conda env too, so prefer `uv pip` for the actual install:
-
 ```bash
-uv pip install fastvideo
+pip install fastvideo
 ```

 ### Installation from Source
@@ -82,7 +80,7 @@ uv pip install -e .
 Alternative with Conda environment:

 ```bash
-uv pip install -e .
+pip install -e .
 ```

 ## Development Environment Setup
@@ -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
@@ -450,6 +455,6 @@ User: generator.generate_video(prompt, ...)
   `fastvideo/pipelines/stages/`, implement `forward()`, optionally
   implement `verify_input()`/`verify_output()`.

-7. **Verify** — Run `fastvideo generate --config <config.yaml>` with a
-   minimal nested config to confirm the pipeline loads and generates
-   output.
+7. **Verify** — Run `fastvideo generate --model-path <path> --prompt
+   "test" --num-inference-steps 2` to confirm the pipeline loads and
+   generates output.
@@ -1,29 +1,71 @@
 # FastVideo CLI Inference

-The FastVideo CLI is config-first. Inference runs are driven by a nested JSON or
-YAML config, with optional dotted-path overrides on the command line. The
-contract matches training: use an explicit subcommand plus `--config`, then add
-any dotted overrides you need.
+The FastVideo CLI exposes the same core inference controls as the Python API.

 ## Basic Usage

+Use either:
+
+1. `--model-path` + `--prompt`
+2. `--model-path` + `--prompt-txt` (batch prompts, one line per prompt)
+3. `--config` (JSON/YAML)
+
 ```bash
-fastvideo generate --config config.yaml
-fastvideo serve --config serve.yaml
+fastvideo generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --prompt "A cat playing with a ball of yarn"
 ```

+```bash
+fastvideo generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --prompt-txt prompts.txt
+```
+
+You cannot provide both `--prompt` and `--prompt-txt` in the same run.
+
 ## View All Arguments

 ```bash
 fastvideo generate --help
 ```

-The subcommands intentionally expose only `--config`. Any per-run CLI changes
-must use dotted override paths such as:
+Arguments come from:

- `--generator.engine.num_gpus 2`
- `--request.sampling.seed 42`
- `--server.port 9000`
+- FastVideo runtime args (`FastVideoArgs`)
+- Sampling args (`SamplingParam`)
+- Pipeline config args (`PipelineConfig`)
+
+## Common Arguments
+
+### Parallelism
+
+- `--num-gpus`
+- `--sp-size`
+- `--tp-size`
+
+### Sampling
+
+- `--num-frames`
+- `--height` / `--width`
+- `--num-inference-steps`
+- `--guidance-scale`
+- `--seed`
+- `--negative-prompt`
+
+### Output
+
+- `--output-path`
+- `--save-video` / `--no-save-video`
+- `--return-frames`
+
+### Offloading and Performance
+
+- `--dit-layerwise-offload`
+- `--use-fsdp-inference`
+- `--text-encoder-cpu-offload`
+- `--image-encoder-cpu-offload`
+- `--vae-cpu-offload`
+- `--enable-torch-compile`
+- `--torch-compile-kwargs`

 ## Using Config Files

@@ -31,53 +73,50 @@ must use dotted override paths such as:
 fastvideo generate --config config.yaml
 ```

-Config files can be JSON or YAML. Dotted CLI overrides take precedence over
-config-file values.
+Config files can be JSON or YAML. CLI flags override config-file values.

 Example `config.yaml`:

 ```yaml
-generator:
-  model_path: FastVideo/FastHunyuan-diffusers
-  engine:
-    num_gpus: 2
-    parallelism:
-      sp_size: 2
-      tp_size: 1
-request:
-  prompt: A capybara lounging in a hammock
-  sampling:
-    num_frames: 45
-    height: 720
-    width: 1280
-    num_inference_steps: 6
-    seed: 1024
-  output:
-    output_path: outputs/
+model_path: "FastVideo/FastHunyuan-diffusers"
+prompt: "A capybara lounging in a hammock"
+output_path: "outputs/"
+num_gpus: 2
+sp_size: 2
+tp_size: 1
+num_frames: 45
+height: 720
+width: 1280
+num_inference_steps: 6
+seed: 1024
+dit_precision: "bf16"
+vae_precision: "fp16"
+vae_tiling: true
+vae_sp: true
+enable_torch_compile: false
 ```

 Notes:

- `generator` and `request` are the top-level keys for generation configs.
- `serve` configs use `generator`, `server`, and optional `default_request`.
- Prompt text files belong under `request.inputs.prompt_path`.
+- Use `dit_precision` / `vae_precision` (not `precision`).
+- Nested config objects are supported, for example `vae_config` and
+  `dit_config`.

 ## Examples

 Simple generation:

 ```bash
-fastvideo generate --config config.yaml
+fastvideo generate \
+  --model-path FastVideo/FastHunyuan-diffusers \
+  --prompt "A cat playing with a ball of yarn" \
+  --num-frames 45 --height 720 --width 1280 \
+  --num-inference-steps 6 --seed 1024 \
+  --output-path outputs/
 ```

-Config + dotted override:
+Config + CLI override:

 ```bash
-fastvideo generate --config config.yaml --request.prompt "A panda skiing at sunset"
-```
-
-Helper wrapper with positional config path:
-
-```bash
-bash scripts/inference/run.sh scripts/inference/inference_wan.yaml
+fastvideo generate --config config.yaml --prompt "A panda skiing at sunset"
 ```
@@ -73,40 +73,32 @@ if __name__ == '__main__':

 ## JSON/YAML Config Files (CLI)

-The inference CLI is config-first. Use an explicit subcommand with `--config`,
-then apply optional dotted overrides on top, matching the training CLI style.
-By default, CLI generation uses `return_frames=false` unless you set
-`request.output.return_frames: true` in config or via a dotted override.
+The CLI supports `--config` with JSON or YAML. Command-line arguments override
+config file values.
+By default, `fastvideo generate` uses `return_frames=false` unless you set
+`--return-frames` (or `return_frames: true` in config).

 ```bash
 fastvideo generate --config config.yaml
 ```

-Example nested config:
+Use CLI argument names as keys (underscore or hyphen is accepted). Example:

 ```yaml
-generator:
-  model_path: FastVideo/FastHunyuan-diffusers
-  engine:
-    num_gpus: 2
-    parallelism:
-      sp_size: 2
-request:
-  prompt: A capybara relaxing in a hammock
-  sampling:
-    num_frames: 45
-    height: 720
-    width: 1280
-    num_inference_steps: 6
-    seed: 1024
-  output:
-    output_path: outputs/
-```
-
-Override individual values from the CLI with dotted paths:
-
-```bash
-fastvideo generate --config config.yaml --request.sampling.seed 42
+model_path: "FastVideo/FastHunyuan-diffusers"
+prompt: "A capybara relaxing in a hammock"
+num_gpus: 2
+sp_size: 2
+num_frames: 45
+height: 720
+width: 1280
+num_inference_steps: 6
+seed: 1024
+dit_precision: "bf16"
+vae_precision: "fp16"
+vae_tiling: true
+vae_sp: true
+enable_torch_compile: false
 ```

 ## Performance Optimization
@@ -1,129 +0,0 @@
-# GEN3C: 3D-Informed Camera-Controlled Video Generation
-
-[GEN3C](https://arxiv.org/abs/2503.03751) is NVIDIA's Cosmos-7B-based video model for camera-controlled generation from a single image. The FastVideo integration supports the GEN3C I2V workflow, including 3D cache conditioning and tokenizer-based conditioning latents.
-
-## Key Features
-
- **Camera trajectory control**: `left/right/up/down/zoom_in/zoom_out/clockwise/counterclockwise`
- **3D cache conditioning**: depth prediction -> point cloud cache -> forward warping -> latent conditioning
- **Single-image to video generation**: 121-frame generation with camera motion
- **Official raw checkpoint conversion**: `model.pt` -> Diffusers/FastVideo layout
-
-## Model Sources
-
- Official raw checkpoint (not Diffusers): `nvidia/GEN3C-Cosmos-7B`
- Diffusers-format checkpoint: `FastVideo/GEN3C-Cosmos-7B-Diffusers`
-
-## Prerequisites
-
- Install MoGe:
-
-```bash
-uv pip install git+https://github.com/microsoft/MoGe.git
-```
-
- If you hit `ImportError: libGL.so.1` (common on Ubuntu/headless nodes), you can try installing OpenCV runtime libs:
-
-```bash
-sudo apt-get update
-sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxext6 libxrender1
-```
-
-## Quick Start
-
-### Option A: Use Diffusers-format weights directly
-
-```bash
-python examples/inference/basic/basic_gen3c.py \
-  --model_path FastVideo/GEN3C-Cosmos-7B-Diffusers \
-  --image_path /path/to/input.png \
-  --prompt "" \
-  --trajectory left \
-  --movement_distance 0.3 \
-  --camera_rotation center_facing \
-  --num_inference_steps 35 \
-  --guidance_scale 1.0 \
-  --output_path outputs_video/gen3c_output.mp4
-```
-
-### Option B: Convert official raw checkpoint locally
-
-1. Download:
-
-```bash
-huggingface-cli download nvidia/GEN3C-Cosmos-7B --local-dir official_weights/GEN3C-Cosmos-7B
-```
-
-1. Convert:
-
-```bash
-python scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py \
-  --source official_weights/GEN3C-Cosmos-7B/model.pt \
-  --output converted_weights/GEN3C-Cosmos-7B
-```
-
-1. Run:
-
-```bash
-python examples/inference/basic/basic_gen3c.py \
-  --model_path converted_weights/GEN3C-Cosmos-7B \
-  --image_path /path/to/input.png \
-  --prompt "" \
-  --trajectory left \
-  --movement_distance 0.3 \
-  --camera_rotation center_facing \
-  --num_inference_steps 35 \
-  --guidance_scale 1.0 \
-  --output_path outputs_video/gen3c_output.mp4
-```
-
-## FastVideo Defaults
-
-GEN3C defaults in FastVideo:
-
- `height=704`, `width=1280`
- `num_frames=121`
- `num_inference_steps=35`
- `guidance_scale=1.0`
- `fps=24`
-
-These values are defined in:
-
- `fastvideo/pipelines/basic/gen3c/profiles.py`
- `fastvideo/configs/pipelines/gen3c.py`
-
-and align with the official GEN3C inference defaults in:
-
- `tmp/GEN3C/cosmos_predict1/diffusion/inference/inference_utils.py`
-
-## Scheduler Note
-
-The converted GEN3C Diffusers layout may include a FlowMatch scheduler config, but GEN3C denoising uses EDM preconditioning behavior. FastVideo's GEN3C pipeline enforces an EDM scheduler at runtime for parity with official inference behavior.
-
-Implementation path:
-
- `fastvideo/pipelines/basic/gen3c/gen3c_pipeline.py`
-
-## 3D Cache Conditioning Path
-
-FastVideo GEN3C conditioning stage performs:
-
-1. MoGe depth estimation from input image
-2. 3D cache initialization
-3. Camera trajectory generation
-4. Forward rendering of warped frames + masks
-5. VAE/tokenizer encoding of conditioning buffers
-6. Denoising with condition mask + condition pose channels
-
-Main implementation:
-
- `fastvideo/pipelines/basic/gen3c/gen3c_pipeline.py`
- `fastvideo/pipelines/basic/gen3c/cache_3d.py`
- `fastvideo/pipelines/basic/gen3c/depth_estimation.py`
- `fastvideo/models/vaes/gen3c_tokenizer_vae.py`
-
-## References
-
- [GEN3C Paper](https://arxiv.org/abs/2503.03751)
- [Official Repository](https://github.com/nv-tlabs/GEN3C)
- [Official Checkpoint (raw)](https://huggingface.co/nvidia/GEN3C-Cosmos-7B)
@@ -54,7 +54,7 @@ FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN python example.py
 We recommend always installing [Flash Attention 2](https://github.com/Dao-AILab/flash-attention):

 ```bash
-uv pip install flash-attn==2.7.4.post1 --no-build-isolation
+pip install flash-attn==2.7.4.post1 --no-build-isolation
 ```

 And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#flashattention-3-beta-release) by compiling it from source (takes about 10 minutes for me):
@@ -63,7 +63,7 @@ And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://git
 git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention

 cd hopper
-uv pip install ninja
+pip install ninja
 python setup.py install
 ```

@@ -98,7 +98,7 @@ To use [SageAttention](https://github.com/thu-ml/SageAttention) 2.1.1, please co
 ```bash
 git clone https://github.com/thu-ml/SageAttention.git
 cd sageattention
-python setup.py install  # or uv pip install -e .
+python setup.py install  # or pip install -e .
 ```

 ### Sage Attention 3
@@ -73,7 +73,6 @@ pipeline initialization and sampling.
 | Matrix Game 2.0 Base | `FastVideo/Matrix-Game-2.0-Base-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Matrix Game 2.0 GTA | `FastVideo/Matrix-Game-2.0-GTA-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Matrix Game 2.0 TempleRun | `FastVideo/Matrix-Game-2.0-TempleRun-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| GEN3C Cosmos 7B | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | 704px1280p | ❌ | ❌ | ❌ | ⭕ | ⭕ |

 **Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.

@@ -86,11 +85,6 @@ The authoritative source for model-ID recognition is
 `fastvideo/registry.py`. If a model ID is registered there, FastVideo can
 resolve default pipeline and sampling configuration for it.

-**Note (GEN3C)**: The official `nvidia/GEN3C-Cosmos-7B` repo provides a raw
-`model.pt` checkpoint. Use a Diffusers-format repo (for example,
-`FastVideo/GEN3C-Cosmos-7B-Diffusers`) or convert locally with
-`scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py`.
-
 ## Special requirements

 ### Sliding Tile Attention
@@ -4,7 +4,7 @@ These are end-to-end example scripts for distilling Wan2.1 T2V 1.3B model using
 ### 0. Make sure you have installed VSA

 ```bash
-uv pip install vsa
+pip install vsa
 ```

 ### 1. Download dataset:
@@ -4,7 +4,7 @@ These are end-to-end example scripts for distilling Wan2.2 TI2V 5B model DMD+VSA
 ### 0. Make sure you have installed VSA

 ```bash
-uv pip install vsa
+pip install vsa
 ```

 ### Data-free Distillation
@@ -4,7 +4,7 @@ These are end-to-end example scripts for distilling Wan2.2 TI2V 5B model DMD+VSA
 ### 0. Make sure you have installed VSA

 ```bash
-uv pip install vsa
+pip install vsa
 ```

 ### 1. Download dataset:
@@ -28,11 +28,6 @@ For an example running DMD+VSA inference:
 python examples/inference/basic/basic_dmd.py
 ```

-For the typed config/request path added during the inference API refactor:
-```
-python examples/inference/basic/basic_dmd_new_api.py
-```
-
 ## Basic Walkthrough

 All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!
@@ -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():
@@ -1,98 +0,0 @@
-import os
-import time
-
-from fastvideo import VideoGenerator
-from fastvideo.api import (
-    EngineConfig,
-    GenerationRequest,
-    GeneratorConfig,
-    OffloadConfig,
-    OutputConfig,
-    PipelineSelection,
-)
-
-OUTPUT_PATH = "video_samples_dmd2_typed"
-
-
-def main():
-    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
-
-    model_name = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers"
-    generator_config = GeneratorConfig(
-        model_path=model_name,
-        engine=EngineConfig(
-            num_gpus=1,
-            use_fsdp_inference=False,
-            offload=OffloadConfig(
-                text_encoder=True,
-                pin_cpu_memory=True,
-                dit=False,
-                vae=False,
-            ),
-        ),
-        # PR 2 still routes a few advanced inference knobs through the
-        # compatibility bridge until they get first-class typed fields.
-        pipeline=PipelineSelection(
-            experimental={
-                "VSA_sparsity": 0.8,
-            },
-        ),
-    )
-
-    load_start_time = time.perf_counter()
-    generator = VideoGenerator.from_config(generator_config)
-    load_end_time = time.perf_counter()
-    load_time = load_end_time - load_start_time
-
-    prompt = (
-        "A neon-lit alley in futuristic Tokyo during a heavy rainstorm at night. "
-        "The puddles reflect glowing signs in kanji, advertising ramen, karaoke, "
-        "and VR arcades. A woman in a translucent raincoat walks briskly with an "
-        "LED umbrella. Steam rises from a street food cart, and a cat darts "
-        "across the screen. Raindrops are visible on the camera lens, creating "
-        "a cinematic bokeh effect."
-    )
-    request = GenerationRequest(
-        prompt=prompt,
-        output=OutputConfig(
-            output_path=OUTPUT_PATH,
-            save_video=True,
-            return_frames=False,
-        ),
-    )
-
-    start_time = time.perf_counter()
-    result = generator.generate(request)
-    end_time = time.perf_counter()
-    gen_time = end_time - start_time
-
-    prompt2 = (
-        "A majestic lion strides across the golden savanna, its powerful frame "
-        "glistening under the warm afternoon sun. The tall grass ripples gently "
-        "in the breeze, enhancing the lion's commanding presence. The tone is "
-        "vibrant, embodying the raw energy of the wild. Low angle, steady "
-        "tracking shot, cinematic."
-    )
-    request2 = GenerationRequest(
-        prompt=prompt2,
-        output=OutputConfig(
-            output_path=OUTPUT_PATH,
-            save_video=True,
-            return_frames=False,
-        ),
-    )
-
-    start_time = time.perf_counter()
-    result2 = generator.generate(request2)
-    end_time = time.perf_counter()
-    gen_time2 = end_time - start_time
-
-    print(f"Time taken to load model: {load_time} seconds")
-    print(f"Time taken to generate video: {gen_time} seconds")
-    print(f"First output written to: {result.video_path}")
-    print(f"Time taken to generate video2: {gen_time2} seconds")
-    print(f"Second output written to: {result2.video_path}")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,109 +0,0 @@
-"""
-GEN3C: 3D-aware camera-controlled video generation.
-
-This example generates a video from a single input image with camera control.
-The pipeline uses MoGe depth estimation, 3D point cloud forward warping,
-and the GEN3C diffusion model.
-
-Requirements:
-  1. Install MoGe:
-     uv pip install git+https://github.com/microsoft/MoGe.git
-     If you hit `ImportError: libGL.so.1`, install:
-     sudo apt-get update && sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxext6 libxrender1
-  2. Download and convert weights:
-     huggingface-cli download nvidia/GEN3C-Cosmos-7B --local-dir official_weights/GEN3C-Cosmos-7B
-     python scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py \
-       --source ./official_weights/GEN3C-Cosmos-7B/model.pt \
-       --output ./converted_weights/GEN3C-Cosmos-7B \
-       --components-source nvidia/Cosmos-Predict2-2B-Video2World
-  3. Provide an input image for 3D-conditioned generation.
-"""
-
-import argparse
-
-from fastvideo import VideoGenerator
-
-
-def main():
-    parser = argparse.ArgumentParser(description="GEN3C video generation")
-    parser.add_argument("--model_path",
-                        type=str,
-                        default="converted_weights/GEN3C-Cosmos-7B")
-    parser.add_argument("--image_path",
-                        type=str,
-                        default=None,
-                        help="Input image for 3D cache conditioning")
-    parser.add_argument("--prompt",
-                        type=str,
-                        default="A slow camera pan over a sunlit landscape.")
-    parser.add_argument(
-        "--negative_prompt",
-        type=str,
-        default=(
-            "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."
-        ),
-    )
-    parser.add_argument("--trajectory",
-                        type=str,
-                        default="left",
-                        choices=[
-                            "left", "right", "up", "down", "zoom_in",
-                            "zoom_out", "clockwise", "counterclockwise", "none"
-                        ])
-    parser.add_argument("--movement_distance", type=float, default=0.3)
-    parser.add_argument("--camera_rotation",
-                        type=str,
-                        default="center_facing",
-                        choices=[
-                            "center_facing", "no_rotation",
-                            "trajectory_aligned"
-                        ])
-    parser.add_argument("--height", type=int, default=704)
-    parser.add_argument("--width", type=int, default=1280)
-    parser.add_argument("--num_frames", type=int, default=121)
-    parser.add_argument("--num_inference_steps", type=int, default=35)
-    parser.add_argument("--guidance_scale", type=float, default=1.0)
-    parser.add_argument("--output_path",
-                        type=str,
-                        default="outputs_video/gen3c.mp4")
-    parser.add_argument("--seed", type=int, default=42)
-    args = parser.parse_args()
-
-    generator = VideoGenerator.from_pretrained(
-        args.model_path,
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-    )
-
-    video = generator.generate_video(
-        args.prompt,
-        negative_prompt=args.negative_prompt,
-        image_path=args.image_path,
-        trajectory_type=args.trajectory,
-        movement_distance=args.movement_distance,
-        camera_rotation=args.camera_rotation,
-        height=args.height,
-        width=args.width,
-        num_frames=args.num_frames,
-        num_inference_steps=args.num_inference_steps,
-        guidance_scale=args.guidance_scale,
-        fps=24,
-        seed=args.seed,
-        output_path=args.output_path,
-        save_video=True,
-    )
-
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    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
@@ -1,51 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Minimal user-runnable example for the daVinci-MagiHuman base AV pipeline.
-
-Produces an mp4 with both video (Wan 2.2 TI2V-5B VAE) and audio (Stable
-Audio Open 1.0 VAE, first-class FastVideo port in
-`fastvideo/models/vaes/oobleck.py`) muxed together via PyAV.
-
-Prerequisites (one-off):
-
-  # Accept terms of use on the gated HF repos with your HF_TOKEN:
-  #   - https://huggingface.co/google/t5gemma-9b-9b-ul2
-  #   - https://huggingface.co/stabilityai/stable-audio-open-1.0
-  # All four cross-variant shared components (Wan 2.2 VAE, T5-Gemma
-  # encoder + tokenizer, Stable Audio VAE) are lazy-loaded from their
-  # canonical upstream HF repos on first build, so a single ~25 GB
-  # cache is shared across every MagiHuman variant.
-
-The umbrella HF repo `FastVideo/MagiHuman-Diffusers` holds all four
-variants (base / distill / sr_540p / sr_1080p) under sibling subfolders
-and FastVideo will download just the requested subfolder. Local
-conversion via `scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py`
-is also supported.
-"""
-from fastvideo import VideoGenerator
-
-
-PROMPT = (
-    "A warm afternoon scene: a person sits on a park bench reading a book, "
-    "surrounded by softly swaying trees."
-)
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/base",
-        num_gpus=1,
-    )
-    output_path = "outputs_video/magi_human_basic/output_magi_human.mp4"
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path=output_path,
-        save_video=True,
-        # Defaults pulled from the registered preset (magi_human_base):
-        # height=256, width=448, fps=25, num_inference_steps=32, seed=42.
-        # Override here only if you have a specific QA scenario.
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,53 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Minimal user-runnable example for the daVinci-MagiHuman DMD-2 distilled
-text-to-AV pipeline.
-
-Same arch as the base model (`basic_magi_human.py`) but with DMD-2 distilled
-weights: 8 denoising steps, no classifier-free guidance. ~4x faster than
-base at the same 256x480 resolution. Mirrors upstream
-`daVinci-MagiHuman/example/distill/run_T2V.sh`.
-
-Prerequisites (one-off):
-
-  # 1) Accept terms on the gated HF repos with your HF_TOKEN:
-  #    - https://huggingface.co/google/t5gemma-9b-9b-ul2
-  #    - https://huggingface.co/stabilityai/stable-audio-open-1.0
-  #    Cross-variant shared components (Wan 2.2 VAE + T5-Gemma + Stable
-  #    Audio VAE) are lazy-loaded from their canonical upstream HF repos
-  #    and shared with the base variant cache.
-  # 2) Convert the distill subfolder of GAIR/daVinci-MagiHuman:
-  python scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py \\
-      --source GAIR/daVinci-MagiHuman \\
-      --subfolder distill \\
-      --output converted_weights/magi_human_distill \\
-      --cast-bf16
-  # `--cast-bf16` is recommended (61 GB fp32 -> 30 GB bf16); the FV pipeline
-  # loads bf16 anyway, and the conversion keeps norms / RoPE bands fp32.
-"""
-from fastvideo import VideoGenerator
-
-
-PROMPT = (
-    "A warm afternoon scene: a person sits on a park bench reading a book, "
-    "surrounded by softly swaying trees."
-)
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/distill",
-        num_gpus=1,
-    )
-    output_path = "outputs_video/magi_human_basic/output_magi_human_distill.mp4"
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path=output_path,
-        save_video=True,
-        # Defaults pulled from the registered preset (magi_human_distill):
-        # height=256, width=480, fps=25, num_inference_steps=8, cfg=1, seed=42.
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,34 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Minimal daVinci-MagiHuman DMD-2 distilled text+image-to-AV example."""
-from fastvideo import VideoGenerator
-from fastvideo.pipelines.basic.magi_human.pipeline_configs import (
-    MagiHumanDistillI2VConfig,
-)
-
-
-PROMPT = (
-    "A cheerful saxophonist performs a short line with expressive facial "
-    "motion, natural head movement, and synchronized audio in a small jazz club."
-)
-IMAGE_PATH = "assets/images/saxophonist.jpg"
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/distill",
-        num_gpus=1,
-        workload_type="i2v",
-        override_pipeline_cls_name="MagiHumanI2VPipeline",
-        pipeline_config=MagiHumanDistillI2VConfig(),
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        image_path=IMAGE_PATH,
-        output_path="outputs_video/magi_human_distill_ti2v/output_magi_human_distill_ti2v.mp4",
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,45 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Run daVinci-MagiHuman SR-1080p text-to-AV in FastVideo.
-
-Build the converted repo on large local storage, then symlink it into the
-workspace:
-
-    python scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py \
-        --source GAIR/daVinci-MagiHuman \
-        --subfolder base \
-        --sr-source GAIR/daVinci-MagiHuman \
-        --sr-subfolder 1080p_sr \
-        --output /raid/william5lin_converted_weights/magi_human_sr_1080p \
-        --cast-bf16
-    ln -s /raid/william5lin_converted_weights/magi_human_sr_1080p \
-        converted_weights/magi_human_sr_1080p
-"""
-from fastvideo import VideoGenerator
-from fastvideo.pipelines.basic.magi_human.pipeline_configs import (
-    MagiHumanSR1080pConfig,
-)
-
-
-PROMPT = (
-    "A warm afternoon scene: a person sits on a park bench reading a book, "
-    "surrounded by softly swaying trees."
-)
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/sr_1080p",
-        num_gpus=1,
-        override_pipeline_cls_name="MagiHumanSR1080pPipeline",
-        pipeline_config=MagiHumanSR1080pConfig(),
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path="outputs_video/magi_human_sr1080p/output_magi_human_sr1080p.mp4",
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,34 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Run daVinci-MagiHuman SR-1080p text+image-to-AV in FastVideo."""
-from fastvideo import VideoGenerator
-from fastvideo.pipelines.basic.magi_human.pipeline_configs import (
-    MagiHumanSR1080pI2VConfig,
-)
-
-
-PROMPT = (
-    "A cheerful saxophonist performs a short line with expressive facial "
-    "motion, natural head movement, and synchronized audio in a small jazz club."
-)
-IMAGE_PATH = "assets/images/saxophonist.jpg"
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/sr_1080p",
-        num_gpus=1,
-        workload_type="i2v",
-        override_pipeline_cls_name="MagiHumanSR1080pI2VPipeline",
-        pipeline_config=MagiHumanSR1080pI2VConfig(),
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        image_path=IMAGE_PATH,
-        output_path="outputs_video/magi_human_sr1080p_ti2v/output_magi_human_sr1080p_ti2v.mp4",
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,37 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Run daVinci-MagiHuman SR-540p text-to-AV in FastVideo.
-
-The converted repo must contain both ``transformer/`` (base DiT) and
-``sr_transformer/`` (540p SR DiT). Build it with:
-
-    python scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py \
-        --source GAIR/daVinci-MagiHuman \
-        --subfolder base \
-        --sr-source GAIR/daVinci-MagiHuman \
-        --sr-subfolder 540p_sr \
-        --output converted_weights/magi_human_sr_540p
-"""
-from fastvideo import VideoGenerator
-
-
-PROMPT = (
-    "A warm afternoon scene: a person sits on a park bench reading a book, "
-    "surrounded by softly swaying trees."
-)
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/sr_540p",
-        num_gpus=1,
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path="outputs_video/magi_human_sr540p/output_magi_human_sr540p.mp4",
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,34 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Run daVinci-MagiHuman SR-540p text+image-to-AV in FastVideo."""
-from fastvideo import VideoGenerator
-from fastvideo.pipelines.basic.magi_human.pipeline_configs import (
-    MagiHumanSR540pI2VConfig,
-)
-
-
-PROMPT = (
-    "A cheerful saxophonist performs a short line with expressive facial "
-    "motion, natural head movement, and synchronized audio in a small jazz club."
-)
-IMAGE_PATH = "assets/images/saxophonist.jpg"
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/sr_540p",
-        num_gpus=1,
-        workload_type="i2v",
-        override_pipeline_cls_name="MagiHumanSRI2VPipeline",
-        pipeline_config=MagiHumanSR540pI2VConfig(),
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        image_path=IMAGE_PATH,
-        output_path="outputs_video/magi_human_sr540p_ti2v/output_magi_human_sr540p_ti2v.mp4",
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,34 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Minimal daVinci-MagiHuman base text+image-to-AV example."""
-from fastvideo import VideoGenerator
-from fastvideo.pipelines.basic.magi_human.pipeline_configs import (
-    MagiHumanBaseI2VConfig,
-)
-
-
-PROMPT = (
-    "A cheerful saxophonist performs a short line with expressive facial "
-    "motion, natural head movement, and synchronized audio in a small jazz club."
-)
-IMAGE_PATH = "assets/images/saxophonist.jpg"
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/MagiHuman-Diffusers/base",
-        num_gpus=1,
-        workload_type="i2v",
-        override_pipeline_cls_name="MagiHumanI2VPipeline",
-        pipeline_config=MagiHumanBaseI2VConfig(),
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        image_path=IMAGE_PATH,
-        output_path="outputs_video/magi_human_ti2v/output_magi_human_ti2v.mp4",
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -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():
@@ -1,77 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Stable Audio Open 1.0 — text-to-audio (baseline) example.
-
-User story (game-audio designer, prototyping):
-    "I'm prototyping a level and I need 6 seconds of background
-    ambience — gentle wind, distant thunder, a hint of birdsong. I
-    don't want to dig through a sound library; I want to type what I
-    hear in my head and get a wav back. If it's wrong I'll iterate
-    on the prompt. This is the first stop."
-
-User story (musician sketching ideas):
-    "I want to bounce a 30s lo-fi drum loop to use as a placeholder
-    bed while I build the rest of the track. Type prompt, get audio,
-    drop into the DAW. The actual production beat I'll record
-    myself, but I need *something* to write the chords against."
-
-User story (researcher exploring the model):
-    "First time touching Stable Audio Open — what does it sound
-    like at default settings? This is the smallest amount of code
-    that goes from prompt to mp4."
-
-How it works:
-    Pure text-to-audio (T2A). The pipeline runs:
-        T5 + NumberConditioner -> StableAudioDiT -> Oobleck VAE
-    via the `dpmpp-3m-sde` k-diffusion sampler. All components are
-    FastVideo-native — no diffusers / transformers model imports at
-    runtime (see REVIEW item 30). Mirrors upstream
-    `stable_audio_tools.inference.generation.generate_diffusion_cond`
-    bit-for-bit (~0.2% abs_mean drift on 25 steps).
-
-Tunable knobs (the "creative dials"):
-    audio_end_in_s
-        1–6   — quick ideation (sub-10s wall clock at 100 steps)
-        10–30 — full musical phrase / loop length (the README example
-                uses 30s)
-        47.5  — model maximum (full sample_size = 2097152 / 44100 Hz)
-    num_inference_steps
-        25  — fast preview, occasional artifacts
-        100 — preset default (matches the HF model card)
-        250 — diminishing returns past here
-    guidance_scale
-        3   — looser, more variation per seed
-        7   — preset default; matches README
-        12+ — sharper but can sound "fried"
-
-Prerequisites:
-  1. Accept the terms on https://huggingface.co/stabilityai/stable-audio-open-1.0
-     and export your HF token in the shell:
-         export HF_TOKEN=hf_...
-  2. Install optional inference deps (one-time):
-         uv pip install k_diffusion einops_exts alias_free_torch torchsde
-"""
-from fastvideo import VideoGenerator
-
-PROMPT = "Lo-fi hip hop instrumental with vinyl crackle and gentle piano."
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/stable-audio-open-1.0-Diffusers",
-        num_gpus=1,
-    )
-    output_path = "outputs_audio/stable_audio_basic/output_stable_audio.wav"
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path=output_path,
-        save_video=True,
-        # 6-second clip; the model max is ~47.5s.
-        audio_end_in_s=6.0,
-        # The registered preset gives 100 steps + CFG=7.0 by default;
-        # override num_inference_steps / guidance_scale here for QA.
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,77 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Stable Audio Open 1.0 — audio-to-audio variation example.
-
-User story (musician, late at night):
-    "I generated this 12-second lo-fi loop earlier and I love the chord
-    progression and overall vibe, but the snare hit at 0:08 sounds wrong
-    and the rhythm feels stiff. I don't want to start over from scratch
-    and lose what's working — I want the model to keep the harmony and
-    mood but reroll the percussion + groove."
-
-User story (sound designer, on a deadline):
-    "I have one good 'sword clang' SFX. The art director wants 8 sibling
-    variations that all feel like the same sword from different angles —
-    same metal, same weight, slightly different impact. I'd rather
-    refine my one good take than text-prompt my way through 50 misses."
-
-Pass `init_audio=path/to/clip` (any wav/mp3/mp4/m4a/flac the standard
-deps decode) and the model will use it as a starting point for the
-text prompt instead of pure noise.
-
-Picking `init_audio_strength` (0.0 to 1.0):
-
-    Higher = closer to the source clip. Lower = more transformation.
-    (Same convention as the "Input Audio Strength" slider in
-    Stability's commercial Stable Audio web UI, so values transfer
-    directly.)
-
-      | strength | what you get                                       |
-      |----------|----------------------------------------------------|
-      |   1.00   | Output ≈ reference. No transformation.             |
-      |   0.85   | Texture micro-variation only.                      |
-      |   0.70   | Light reroll, same instruments.                    |
-      |   0.60   | Default. Instrument identity is replaceable        |
-      |          | (cello can take over from piano on the same notes).|
-      |   0.50   | Heavy — only melody / chord progression survives.  |
-      |   0.30   | Reference acts as a loose mood prompt.             |
-      |   0.00   | Plain T2A — reference ignored.                     |
-
-    Rule of thumb by intent:
-      * "Fix one part of this clip"            -> 0.75 .. 0.85
-      * "Same notes, different instrument"     -> 0.55 .. 0.65
-      * "Same chord progression, new content"  -> 0.40 .. 0.55
-      * "Use this as a loose mood prompt"      -> 0.20 .. 0.35
-
-    If the reference timbre is bleeding through more than you want,
-    lower it; if the structure is gone, raise it.
-
-Prerequisites: same as `basic_stable_audio.py`.
-"""
-from fastvideo import VideoGenerator
-
-PROMPT = "Change the piano to a cello playing the same notes"
-# Path to any audio-bearing file (wav, mp3, mp4, m4a, flac, ...).
-# Set to `None` to skip A2A and run plain T2A.
-INIT_AUDIO_PATH: str | None = None
-# Reference fidelity in [0, 1] -- higher = closer to source.
-INIT_AUDIO_STRENGTH = 0.6
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/stable-audio-open-1.0-Diffusers",
-        num_gpus=1,
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path="outputs_audio/stable_audio_a2a/output_a2a.wav",
-        save_video=True,
-        audio_end_in_s=6.0,
-        init_audio=INIT_AUDIO_PATH,
-        init_audio_strength=INIT_AUDIO_STRENGTH,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,84 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Stable Audio Open 1.0 — inpainting / outpainting (loop extension) example.
-
-User story (loop extension — the killer app):
-    "I have a 6-second drum loop my client likes. They want it as
-    background bed for a 30-second ad. I need it to loop seamlessly,
-    but a hard cut every 6s sounds bad. Let me extend it to 30s,
-    keeping the first 6s exactly as-is and letting the model continue
-    the groove for the remaining 24s."
-
-User story (audio repair):
-    "There's a microphone bump at 0:14 in this 30-second field
-    recording — really obvious in headphones. Mask out 0:13 to 0:15
-    and let the model regenerate plausible ambience that blends in.
-    Everything else stays exactly as I recorded it."
-
-User story (transition smoothing):
-    "I have two 10-second clips I want to crossfade. Mask out a 1s
-    overlap region in the middle and let the model invent a coherent
-    transition between the two."
-
-How it works (RePaint-style blending):
-    Stable Audio Open 1.0 wasn't trained as an inpainting model
-    (`model_type=diffusion_cond`, not `diffusion_cond_inpaint`), so we
-    can't use the upstream's mask-conditioned approach directly. We
-    use the RePaint trick instead, which works on any v-prediction
-    diffusion model:
-
-      1. Encode the reference clip into latent space.
-      2. At every denoising step `i`, replace the kept region of the
-         in-flight latent (where mask == 1) with the reference
-         re-noised to the next timestep's sigma. Only the unkept
-         region (mask == 0) is freely denoised.
-      3. After the loop, the kept region is exactly the reference;
-         the unkept region is freshly generated content.
-
-    This is approximate compared to a properly trained inpainting
-    checkpoint — the seam between kept/unkept can have slight EQ
-    discontinuity — but it works on the existing public model.
-
-Tunable: the mask is a 1-D tensor in {0, 1} at the model's sample
-rate. Conventions:
-    1.0 = keep this sample from the reference
-    0.0 = regenerate this sample
-
-Prerequisites: same as `basic_stable_audio.py`.
-"""
-import os
-
-from fastvideo import VideoGenerator
-
-PROMPT = "Steady lo-fi hip hop drum loop with vinyl crackle."
-# Required: path to the reference audio file (wav, mp3, mp4, m4a, flac,
-# ...) you want to extend or repair. The pipeline raises if a mask is
-# passed without a reference, so this must be a real path.
-REFERENCE_AUDIO_PATH = "path/to/your/loop.wav"
-KEEP_SECONDS = 6.0       # first KEEP_SECONDS preserved exactly
-TOTAL_SECONDS = 12.0     # extend the loop to this duration
-
-
-def main() -> None:
-    if not os.path.isfile(REFERENCE_AUDIO_PATH):
-        raise FileNotFoundError(
-            f"REFERENCE_AUDIO_PATH={REFERENCE_AUDIO_PATH!r} does not exist. "
-            "Edit this script to point at a real audio file (wav/mp3/mp4/"
-            "m4a/flac) before running.")
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/stable-audio-open-1.0-Diffusers",
-        num_gpus=1,
-    )
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path="outputs_audio/stable_audio_inpaint/output_inpaint.wav",
-        save_video=True,
-        audio_end_in_s=TOTAL_SECONDS,
-        inpaint_audio=REFERENCE_AUDIO_PATH,
-        # Tuple form: keep first KEEP_SECONDS, regenerate the rest.
-        inpaint_mask=(KEEP_SECONDS, TOTAL_SECONDS),
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,53 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Stable Audio Open Small — fast / lightweight T2A example.
-
-User story (interactive UI builder):
-    "I'm building a sound-design UI where the user types a prompt and
-    we want sub-2-second feedback so the experience feels like
-    autocomplete, not a render queue. The full Stable Audio Open 1.0
-    takes ~8s on a single GPU; the small variant takes a fraction of
-    that — quality is lower but completely usable for real-time
-    iteration."
-
-User story (overnight batch jobs):
-    "I'm generating 10,000 short SFX variants for a procedural game.
-    Wall-clock matters more than per-clip polish — give me the small
-    model so I can fit the run in one night instead of a week."
-
-How it works:
-    The small variant is a separate Stability AI checkpoint
-    (`stabilityai/stable-audio-open-small`) that ships the same Oobleck
-    VAE as the 1.0 base model but a smaller / faster DiT (`embed_dim=1024`,
-    `depth=16`, `qk_norm="ln"`) and only one duration conditioner
-    (`seconds_total`, no `seconds_start`). FastVideo loads from the
-    converted Diffusers-format repo `FastVideo/stable-audio-open-small-Diffusers`
-    via the standard component loader; per-variant arch fields come
-    from `transformer/config.json` and `conditioner/config.json`.
-
-Prerequisites: same as `basic_stable_audio.py`. The converted repo is
-public so no gated-access flow is required.
-"""
-from fastvideo import VideoGenerator
-
-PROMPT = "Lo-fi hip hop instrumental with vinyl crackle and gentle piano."
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/stable-audio-open-small-Diffusers",
-        num_gpus=1,
-    )
-    output_path = "outputs_audio/stable_audio_small/output_stable_audio_small.wav"
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path=output_path,
-        save_video=True,
-        # Small variant trains on a ~11.9s window — keep `audio_end_in_s`
-        # at or below that.
-        audio_end_in_s=6.0,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    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():
@@ -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():
@@ -1,73 +0,0 @@
-# Cosmos Predict2 2B T2V finetune config.
-#
-# Data must be preprocessed with Cosmos VAE + T5 text encoder
-# into parquet format before training.
-
-models:
-  student:
-    _target_: fastvideo.train.models.cosmos.CosmosModel
-    init_from: nvidia/Cosmos-Predict2-2B-Video2World
-    trainable: true
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
-
-training:
-  distributed:
-    num_gpus: 8
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 8
-    hsdp_shard_dim: 1
-
-  data:
-    data_path: data/cosmos_preprocessed
-    dataloader_num_workers: 4
-    train_batch_size: 1
-    training_cfg_rate: 0.0
-    seed: 1000
-    # Cosmos VAE: 4x temporal, 8x spatial compression.
-    # 93 frames -> 24 latent frames, 480x832 -> 60x104
-    num_latent_t: 24
-    num_height: 480
-    num_width: 832
-    num_frames: 93
-
-  optimizer:
-    learning_rate: 1.0e-5
-    betas: [0.9, 0.999]
-    weight_decay: 0.01
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    max_train_steps: 5000
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/cosmos_finetune
-    training_state_checkpointing_steps: 500
-    checkpoints_total_limit: 3
-    resume_from_checkpoint: latest
-
-  tracker:
-    project_name: fastvideo_cosmos
-    run_name: cosmos_finetune
-
-  model:
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
-    max_grad_norm: 1.0
-  validation:
-    _target_: fastvideo.train.callbacks.validation.ValidationCallback
-    pipeline_target: fastvideo.pipelines.basic.cosmos.cosmos_pipeline.Cosmos2VideoToWorldPipeline
-    dataset_file: data/cosmos_preprocessed/validation_prompts.json
-    every_steps: 100
-    sampling_steps: [50]
-    guidance_scale: 6.0
-
-pipeline:
-  flow_shift: 1.0
@@ -1,79 +0,0 @@
-# Cosmos-Predict2.5-2B Text-to-World overfitting test config.
-#
-# Overfits on a few short videos (480x832, 93 frames) to verify the
-# Cosmos 2.5 training plugin works end-to-end.
-#
-# Preprocess data first:
-#   CUDA_VISIBLE_DEVICES=0 python fastvideo/pipelines/preprocess/preprocess_cosmos25_overfit.py
-#
-# Run:
-#   bash examples/train/run.sh examples/train/configs/overfit_cosmos25_t2w.yaml
-
-models:
-  student:
-    _target_: fastvideo.train.models.cosmos.CosmosModel
-    init_from: KyleShao/Cosmos-Predict2.5-2B-Diffusers
-    trainable: true
-    enable_gradient_checkpointing_type: full
-    flow_shift: 1.0
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
-
-training:
-  distributed:
-    num_gpus: 1
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 1
-
-  data:
-    data_path: data/cosmos25_overfit_preprocessed
-    dataloader_num_workers: 0
-    train_batch_size: 1
-    training_cfg_rate: 0.0
-    seed: 42
-    num_latent_t: 24
-    num_height: 480
-    num_width: 832
-    num_frames: 93
-
-  optimizer:
-    learning_rate: 5.0e-5
-    betas: [0.9, 0.999]
-    weight_decay: 0.0
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    max_train_steps: 300
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/cosmos25_overfit
-    training_state_checkpointing_steps: 50
-    checkpoints_total_limit: 2
-
-  tracker:
-    project_name: fastvideo_cosmos25
-    run_name: cosmos25_overfit
-
-  model:
-    precondition_outputs: false
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
-    max_grad_norm: 1.0
-  validation:
-    _target_: fastvideo.train.callbacks.validation.ValidationCallback
-    pipeline_target: fastvideo.pipelines.basic.cosmos.cosmos2_5_pipeline.Cosmos2_5Pipeline
-    dataset_file: data/cosmos25_overfit_preprocessed/validation_prompts.json
-    every_steps: 150
-    sampling_steps: [35]
-    guidance_scale: 7.0
-
-pipeline:
-  flow_shift: 1.0
@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.26 FATAL_ERROR)
 project(fastvideo-kernel LANGUAGES CXX)

-# Prefer environment variable (used by CI or uv pip install git+repo_addr) if CMake var is not explicitly set.
+# Prefer environment variable (used by CI or pip install git+repo_addr) if CMake var is not explicitly set.
 if(NOT DEFINED GPU_BACKEND AND DEFINED ENV{GPU_BACKEND})
    set(GPU_BACKEND "$ENV{GPU_BACKEND}")
 endif()
@@ -10,35 +10,6 @@ set -ex

 echo "Building fastvideo-kernel..."

-# ---------------------------------------------------------------------------
-# Neutralise conda-injected compiler toolchains.
-#
-# Conda compiler packages (gcc_linux-aarch64, gxx_linux-64, etc.) set
-# CMAKE_ARGS, CFLAGS, CXXFLAGS, and LDFLAGS on activation.  When multiple
-# toolchains are installed the variables can reference a *cross*-compiler
-# that doesn't match the host (e.g. aarch64-conda-linux-gnu-c++ on x86_64).
-# Even when the correct toolchain is active, the flags it injects
-# (-march=nocona, -mtune=haswell, …) can conflict with nvcc's host-compiler
-# expectations.  Clear them so CMake discovers the system compiler instead.
-# ---------------------------------------------------------------------------
-if [[ -n "${CONDA_PREFIX:-}" ]]; then
-    _need_clean=0
-    # Detect conda cross-compiler that doesn't match the host.
-    _host_arch="$(uname -m)"
-    if [[ "${CXX:-}" == *"conda"* ]] || [[ "${CC:-}" == *"conda"* ]]; then
-        _need_clean=1
-    fi
-    if [[ "${CMAKE_ARGS:-}" == *"conda"* ]]; then
-        _need_clean=1
-    fi
-    if (( _need_clean )); then
-        echo "NOTE: Clearing conda-injected compiler settings (CC/CXX/CMAKE_ARGS/CFLAGS/...)"
-        echo "      to use the system compiler for CUDA extension builds."
-        unset CC CXX CMAKE_ARGS CFLAGS CXXFLAGS LDFLAGS
-    fi
-    unset _need_clean _host_arch
-fi
-
 # Ensure submodules are initialized if needed (tk)
 git submodule update --init --recursive

@@ -61,16 +32,7 @@ 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)
@@ -23,7 +23,7 @@ classifiers = [
 ]
 dependencies = [
    "torch>=2.5.0",
-    "triton>=2.0.0; sys_platform == 'linux'",
+    "triton>=2.0.0",
 ]

 [project.urls]
@@ -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",
--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
				`WRN 2026-03-26T13:46:33.469 ?.19646 server_start:193: Failed to start server: operation not permitted: /var/folders/z_/h_6myyk14d1b7z87z3vy4mjh0000gn/T/nvim.dsynkd/iSe0el/nvim.19646.0`