[ci] Post pre-commit status to PR commit SHA

2026-03-31 08:28:30 -04:00
390 changed files with 2643 additions and 41639 deletions
@@ -1,96 +0,0 @@
-#!/usr/bin/env bash
-# Sync .agents/skills/ into .claude/skills/ via per-skill symlinks.
-#
-# Why: Claude Code only scans .claude/skills/ and ~/.claude/skills/ for
-# user-invocable skills (no skillsPath config exists — see
-# https://code.claude.com/docs/en/skills.md). This repo's skills live
-# in .agents/skills/ so they travel with the repo and stay under git.
-# Run this once after cloning (or after adding/removing a skill) to
-# expose them to Claude Code without maintaining a parallel tree.
-#
-# Usage:
-#   .agents/scripts/sync-skills.sh
-#
-# Idempotent and safe to re-run. Prunes stale symlinks whose source
-# has been removed from .agents/skills/. Leaves hand-written
-# .claude/skills/<name>/ directories untouched (only symlinks are
-# managed).
-
-set -euo pipefail
-
-REPO_ROOT="$(git -C "$(dirname "$0")" rev-parse --show-toplevel)"
-SRC_DIR="$REPO_ROOT/.agents/skills"
-DST_DIR="$REPO_ROOT/.claude/skills"
-
-if [[ ! -d "$SRC_DIR" ]]; then
-    echo "Error: $SRC_DIR does not exist." >&2
-    exit 1
-fi
-
-mkdir -p "$DST_DIR"
-
-linked=0
-unchanged=0
-skipped=0
-pruned=0
-
-link_skill() {
-    local name="$1"
-    local src="$SRC_DIR/$name"
-    local dst="$DST_DIR/$name"
-    # Relative target keeps symlinks portable across clones.
-    local rel="../../.agents/skills/$name"
-
-    if [[ -L "$dst" ]]; then
-        if [[ "$(readlink "$dst")" == "$rel" ]]; then
-            unchanged=$((unchanged + 1))
-            return
-        fi
-        rm "$dst"
-    elif [[ -e "$dst" ]]; then
-        echo "Skipped (not a symlink): .claude/skills/$name" >&2
-        skipped=$((skipped + 1))
-        return
-    fi
-
-    ln -s "$rel" "$dst"
-    echo "Linked: .claude/skills/$name -> $rel"
-    linked=$((linked + 1))
-}
-
-prune_stale() {
-    local link="$1"
-    local target
-    target="$(readlink "$link")"
-    case "$target" in
-        ../../.agents/skills/*) ;;
-        *) return ;;
-    esac
-    local name="${target##*/}"
-    if [[ ! -d "$SRC_DIR/$name" ]]; then
-        rm "$link"
-        echo "Pruned stale: .claude/skills/$(basename "$link")"
-        pruned=$((pruned + 1))
-    fi
-}
-
-for src in "$SRC_DIR"/*/; do
-    [[ -d "$src" ]] || continue
-    name="$(basename "$src")"
-    # Only treat directories that actually contain a SKILL.md as skills.
-    [[ -f "$src/SKILL.md" ]] || continue
-    link_skill "$name"
-done
-
-shopt -s nullglob
-for link in "$DST_DIR"/*; do
-    [[ -L "$link" ]] || continue
-    prune_stale "$link"
-done
-shopt -u nullglob
-
-printf "\nSummary: %d linked, %d unchanged, %d pruned" "$linked" "$unchanged" "$pruned"
-if [[ "$skipped" -gt 0 ]]; then
-    printf ", %d skipped (non-symlink collision)" "$skipped"
-fi
-printf "\n"
@@ -5,4 +5,3 @@
 {"name": "evaluate-video-quality", "description": "Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)", "path": "evaluate-video-quality/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "index-related-work", "description": "Ingest a paper or repository into the related work index", "path": "index-related-work/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "search-related-work", "description": "Query the related work index for relevant papers, repos, or comparisons", "path": "search-related-work/SKILL.md", "status": "draft", "trust": "low"}
-{"name": "seed-ssim-references", "description": "Run a new or updated fastvideo/tests/ssim/ test on Modal, pull generated videos, and upload them to FastVideo/ssim-reference-videos so the test has a regression baseline", "path": "seed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}
@@ -1,250 +0,0 @@
---
-name: seed-ssim-references
-description: Seed HF reference videos for a single newly-added SSIM test. Runs the test on Modal L40S, downloads the generated mp4s via `modal volume get`, pauses for the user to eyeball quality, then uploads only that test's files to `FastVideo/ssim-reference-videos`. Use when a new `fastvideo/tests/ssim/test_*_similarity.py` has just been added and has no references on HF yet.
---
-
-# Seed SSIM Reference Videos
-
-## Purpose
-
-A brand-new SSIM test in `fastvideo/tests/ssim/` fails forever until its
-reference videos exist on the HF dataset (`FastVideo/ssim-reference-videos`).
-This skill:
-
-1. Runs the test on Modal's L40S pool to generate the videos.
-2. Downloads them to the local repo via `modal volume get`.
-3. Pauses so the user can eyeball the mp4s and confirm quality.
-4. Uploads only the new test's files to HF, with a guard that refuses to
-   overwrite anything already present.
-
-The skill is run **manually**, once per new test. Before invoking it, the user
-has already sanity-tested the new test locally — it launches `VideoGenerator`
-and writes an mp4 without crashing. The skill does not re-test locally; it
-goes straight to Modal L40S (which is what CI uses).
-
-## When to use
-
- A new `test_*_similarity.py` file has been added in `fastvideo/tests/ssim/`
-  and the HF dataset has no `reference_videos/default/L40S_reference_videos/<model_id>/`
-  subtree for it yet.
-
-## When not to use
-
- Regular CI runs — once refs exist, `pytest fastvideo/tests/ssim/` downloads
-  them automatically.
- Re-seeding an existing test. That requires `--force` on the upload step, and
-  is out of scope here; treat as a separate, deliberate operation.
-
-## Inputs
-
-The skill has **one required input**: the path to the new SSIM test file.
-Prompt the user for it if they didn't supply it.
-
-| Parameter | Required | Description |
-|-----------|----------|-------------|
-| `test_file` | Yes | e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`. The skill's first action is to ask for this if missing. |
-
-Everything else is fixed:
-
- Modal runner GPU: **L40S** (hardcoded in `fastvideo/tests/modal/ssim_test.py`).
- Device folder: `L40S_reference_videos`.
- Quality tier: `default` (the tier CI runs). The `full_quality` tier is not
-  seeded by this skill.
- HF repo: `FastVideo/ssim-reference-videos` (dataset).
- Multi-model test files: all model ids in `*_MODEL_TO_PARAMS` are seeded
-  together; the Modal run produces one mp4 per (model, prompt, backend) and
-  the upload scopes by `--model-id`, looping if there is more than one.
-
-## Prerequisites
-
-The user has confirmed:
-
- `modal` CLI authenticated.
- `HF_API_KEY` (or `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`) exported with write
-  access to `FastVideo/ssim-reference-videos`.
- The test file runs locally end-to-end (generates an mp4; SSIM assertion
-  failure due to missing reference is expected and fine).
-
-Fail fast if the token env var is missing.
-
-## Steps
-
-### 1. Ask for the test file
-
-If the user didn't name one, ask: *"Which SSIM test file do you want to seed
-references for? (e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`)"*.
-
-Validate:
-
- Path exists and matches `fastvideo/tests/ssim/test_*_similarity.py`.
- File defines a `*_MODEL_TO_PARAMS` dict — grep it to extract the set of
-  model ids. Those ids drive step 5.
-
-If either check fails, stop and tell the user what's wrong.
-
-### 2. Run the test on Modal L40S
-
-Pick a subdir name so repeated runs don't collide:
-
-```bash
-SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
-TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
-SUBDIR="${TIMESTAMP}_${SHORT_COMMIT}"
-```
-
-Then launch the Modal run:
-
-```bash
-modal run fastvideo/tests/modal/ssim_test.py \
-    --git-repo="$(git config --get remote.origin.url)" \
-    --git-commit="$(git rev-parse HEAD)" \
-    --hf-api-key="$HF_API_KEY" \
-    --test-files="<test_file>" \
-    --sync-generated-to-volume \
-    --generated-volume-subdir="$SUBDIR" \
-    --skip-reference-download \
-    --no-fail-fast
-```
-
-Flag rationale:
- `--skip-reference-download`: no refs exist yet, so conftest must not try to
-  pull them.
- `--no-fail-fast`: lets the test finish generation before `_assert_similarity`
-  raises `FileNotFoundError: Reference video folder does not exist`. The
-  expected failure is what we want — the mp4 has already been written.
- `--sync-generated-to-volume` + `--generated-volume-subdir`: copies the
-  generated mp4s to the `hf-model-weights` Modal volume under
-  `ssim_generated_videos/default/<SUBDIR>/generated_videos/` so we can pull
-  them locally.
-
-The Modal run will end with a nonzero exit (expected) and print a
-`modal volume get hf-model-weights ssim_generated_videos/default/<SUBDIR>/generated_videos ./generated_videos_modal/default`
-command. Capture that `<SUBDIR>` — you need it for step 3.
-
-### 3. Download generated videos locally
-
-```bash
-modal volume get --force hf-model-weights \
-    ssim_generated_videos/default/"$SUBDIR"/generated_videos \
-    ./generated_videos_modal/default
-```
-
-`--force` is required when the parent `./generated_videos_modal/default`
-already exists; without it, `modal volume get` errors with `[Errno 21] Is a
-directory`. Safe to pass on the first run too.
-
-After this, the mp4s live at
-`./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
-The extra `generated_videos/` level comes from the volume layout in
-`_sync_generated_videos_to_volume` (`ssim_test.py`) — the command copies
-`<repo>/fastvideo/tests/ssim/generated_videos/<tier>` to
-`ssim_generated_videos/<tier>/<SUBDIR>/generated_videos/`, and `modal volume
-get` preserves that trailing `generated_videos/` segment.
-
-### 4. PAUSE — user reviews quality
-
-Print the list of downloaded mp4s and their paths, then stop. Tell the user:
-
-> "Generated videos downloaded to `./generated_videos_modal/default/generated_videos/L40S_reference_videos/`. Please open them and confirm the quality looks correct. Reply **`upload`** to continue, or anything else to abort."
-
-Do not proceed until the user explicitly says `upload`. If they abort, leave
-everything on disk so they can inspect further — no cleanup.
-
-### 5. Copy into the local reference layout
-
-Scoped copy — only the new test's mp4s. Loop over each `<model_id>` extracted
-in step 1:
-
-```bash
-python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
-    --quality-tier default \
-    --device-folder L40S_reference_videos \
-    --generated-dir ./generated_videos_modal/default/generated_videos/L40S_reference_videos
-```
-
-(The `--generated-dir` points at the device-folder root inside the
-downloaded tree; `copy-local` walks all `<model>/<backend>/*.mp4`
-underneath it. Since the Modal run was scoped to a single test file via
-`--test-files`, only that test's model(s) are present — so the copy is
-implicitly per-test.)
-
-Result: `fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
-
-### 6. Upload to HF — scoped per model_id, with overwrite guard
-
-For each `<model_id>`:
-
-```bash
-python fastvideo/tests/ssim/reference_videos_cli.py upload \
-    --quality-tier default \
-    --device-folder L40S_reference_videos \
-    --model-id "<model_id>"
-```
-
-The upload command:
-
- Uploads **only** `reference_videos/default/L40S_reference_videos/<model_id>/`.
- **Refuses** if any file already exists at that path on HF (this is the
-  guard — seeding a new test should never clobber existing refs). To override,
-  the user must re-run with `--force`. If the guard fires, stop and report
-  exactly which files exist; do not silently `--force`.
-
-Reads the HF token from `HF_API_KEY` / `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`.
-
-### 7. Report success
-
-List what was uploaded (paths in repo) and remind the user to push any
-related code changes. Do **not** auto-verify by re-running Modal — the user
-can run `pytest fastvideo/tests/ssim/<test_file>` later to confirm end-to-end;
-it will auto-download the refs they just uploaded.
-
-## Failure modes and how to handle them
-
- **`HF_API_KEY` unset.** Stop before step 2. The Modal run needs it (passed
-  via `--hf-api-key`), and step 6 needs it for upload.
- **Modal run fails before generation.** No mp4s on the volume — nothing to
-  download. Fix the test locally (`pytest fastvideo/tests/ssim/<test_file>`)
-  and retry from step 2.
- **`./generated_videos_modal/default/L40S_reference_videos/` missing after
-  `modal volume get`.** The run didn't produce videos (most likely the test
-  crashed before writing, or `REQUIRED_GPUS` exceeded the partition capacity
-  — see Modal logs).
- **Upload guard fires (files already exist).** The test name / model id
-  collides with something already on HF. Verify the user actually wants to
-  replace existing refs; if so, re-run the upload with `--force`. If not,
-  rename the model id in `*_MODEL_TO_PARAMS` and re-seed.
- **Quality looks wrong in step 4.** Abort. The mp4s stay on disk for
-  inspection. The fix is usually in the test's params (resolution, steps,
-  seed) — edit the test, then re-run the skill.
-
-## Design notes (for future skill maintainers)
-
- The skill deliberately runs on Modal, **not** locally, because the CI
-  runner is L40S. Seeding from a different GPU SKU produces refs that CI's
-  L40S runs can't match (SSIM drifts across SKUs).
- The skill is default-tier only. `full_quality` refs are seeded by a
-  separate, deliberate operation — they double runtime and aren't what CI
-  gates on.
- The overwrite guard in `reference_videos_cli.py upload` is default-on
-  specifically because this skill exists. Re-seeding is a distinct operation
-  that requires explicit `--force`.
-
-## References
-
- `fastvideo/tests/modal/ssim_test.py` — Modal orchestrator; see
-  `--sync-generated-to-volume`, `--generated-volume-subdir`,
-  `--skip-reference-download`, `--no-fail-fast`.
- `fastvideo/tests/ssim/reference_videos_cli.py` — `copy-local`, `upload`
-  (with `--model-id`, `--force`), `download`, `ensure` subcommands.
- `fastvideo/tests/ssim/README.md` — reference layout, HF repo conventions.
- `fastvideo/tests/ssim/inference_similarity_utils.py` —
-  `run_text_to_video_similarity_test` + `_build_init_kwargs`: what each test
-  config passes to `VideoGenerator.from_pretrained`.
-
-## Changelog
-
-| Date | Change |
-|------|--------|
-| 2026-04-17 | Initial version (Modal sync-to-volume flow). |
-| 2026-04-21 | Rewrite: single-test scope, explicit user-review pause, per-`model_id` upload, HF overwrite guard. Dropped `scripts/seed_ssim.sh`. |
-| 2026-04-21 | Post-first-run fixes: `modal volume get` needs `--force` when parent exists; download tree has an extra `generated_videos/` level so `--generated-dir` must reflect it. |
@@ -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 (${TEST_TYPE})"
+      if: build.env("TEST_SCOPE") == "direct"
      command: "timeout 90m .buildkite/scripts/pr_test.sh"
      retry:
        automatic:
@@ -307,10 +135,6 @@ steps:
                label: ":bar_chart: SSIM Tests"
                env:
                  - TEST_TYPE=ssim
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
                agents:
                  queue: "default"
            - path:
@@ -371,10 +195,6 @@ steps:
                label: ":test_tube: LoRA Training Tests"
                env:
                  - TEST_TYPE=training_lora
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
                agents:
                  queue: "default"
            - path:
@@ -387,10 +207,6 @@ steps:
                label: ":test_tube: Training Tests VSA"
                env:
                  - TEST_TYPE=training_vsa
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
                agents:
                  queue: "default"
            - path:
@@ -63,72 +63,7 @@ EFFECTIVE_PR=${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 "$TEST_TYPE" in
    "encoder")
@@ -189,9 +124,8 @@ case "$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..."
@@ -213,10 +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
@@ -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"
@@ -33,7 +33,6 @@ 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 +40,7 @@ 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 +48,6 @@ 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
@@ -181,26 +143,12 @@ 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]
@@ -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" \
@@ -85,7 +85,6 @@ docs/distillation/examples/
 dmd_t2v_output/
 preprocess_output_text/

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

 .claude/
 .codex/
@@ -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
@@ -18,7 +18,6 @@ exclude: |
    fastvideo/train\.py|
    fastvideo/utils/.*|
    examples/.*|
-    \.agents/.*|
    .github/workflows/publish-fastvideo.yml|
    .github/workflows/_template-build-image.yml|
    docs/source/inference/support_matrix.md
@@ -1 +0,0 @@
-3.12
@@ -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
@@ -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`
@@ -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:

@@ -1,449 +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]
-    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
-    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`.
@@ -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.
@@ -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
-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)
@@ -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
@@ -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:
-     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
@@ -1,5 +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):
-         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
@@ -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",
@@ -1,5 +1,3 @@
-"""Autograd-enabled block-sparse attention. Index-native ops with a bool-mask compat shim."""
-
 from __future__ import annotations

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


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


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


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

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


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

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


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


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

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

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


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


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


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


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

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

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


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

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

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


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


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


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


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


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

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

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

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

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

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

@@ -1,97 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo.api.schema import (
-    CompileConfig,
-    ComponentConfig,
-    ContinuationState,
-    EngineConfig,
-    GenerationPlan,
-    GenerationRequest,
-    GeneratorConfig,
-    GpuPoolConfig,
-    InputConfig,
-    OffloadConfig,
-    OutputConfig,
-    ParallelismConfig,
-    PipelineSelection,
-    PlannedStage,
-    PromptEnhancerConfig,
-    PromptSafetyConfig,
-    QuantizationConfig,
-    RequestRuntimeConfig,
-    RunConfig,
-    SamplingConfig,
-    ServeConfig,
-    ServerConfig,
-    StreamingConfig,
-    WarmupConfig,
-)
-from fastvideo.api.errors import ConfigValidationError
-from fastvideo.api.overrides import apply_overrides, parse_cli_overrides
-from fastvideo.api.presets import (
-    InferencePreset,
-    PresetStageSpec,
-    get_all_preset_names,
-    get_preset,
-    get_presets_for_family,
-    register_preset,
-    validate_preset_selection,
-    validate_stage_names,
-    validate_stage_overrides,
-)
-from fastvideo.api.parser import (
-    config_to_dict,
-    load_config,
-    load_raw_config,
-    load_run_config,
-    load_serve_config,
-    parse_config,
-)
-from fastvideo.api.results import GenerationResult
-from fastvideo.api.sampling_param import SamplingParam
-
-__all__ = [
-    "CompileConfig",
-    "ComponentConfig",
-    "ContinuationState",
-    "ConfigValidationError",
-    "EngineConfig",
-    "GenerationResult",
-    "GenerationPlan",
-    "GenerationRequest",
-    "GeneratorConfig",
-    "GpuPoolConfig",
-    "InputConfig",
-    "OffloadConfig",
-    "OutputConfig",
-    "ParallelismConfig",
-    "PipelineSelection",
-    "PlannedStage",
-    "PromptEnhancerConfig",
-    "PromptSafetyConfig",
-    "QuantizationConfig",
-    "RequestRuntimeConfig",
-    "RunConfig",
-    "SamplingConfig",
-    "SamplingParam",
-    "ServeConfig",
-    "ServerConfig",
-    "StreamingConfig",
-    "WarmupConfig",
-    "InferencePreset",
-    "PresetStageSpec",
-    "apply_overrides",
-    "config_to_dict",
-    "load_config",
-    "load_raw_config",
-    "load_run_config",
-    "load_serve_config",
-    "parse_cli_overrides",
-    "get_all_preset_names",
-    "get_preset",
-    "get_presets_for_family",
-    "parse_config",
-    "register_preset",
-    "validate_preset_selection",
-    "validate_stage_names",
-    "validate_stage_overrides",
-]
@@ -1,623 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from collections.abc import Mapping
-from copy import deepcopy
-from dataclasses import fields, is_dataclass
-from pathlib import Path
-from typing import Any
-
-from fastvideo.api.overrides import apply_overrides, normalize_overrides
-from fastvideo.api.parser import config_to_dict, load_raw_config, parse_config
-from fastvideo.api.request_metadata import (
-    EXPLICIT_PATHS_ATTR,
-    bind_generation_request_raw,
-    get_explicit_paths,
-    reset_tracking_roots,
-)
-from fastvideo.api.schema import (
-    CompileConfig,
-    ContinuationState,
-    GenerationRequest,
-    GeneratorConfig,
-    InputConfig,
-    OutputConfig,
-    RequestRuntimeConfig,
-    SamplingConfig,
-)
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.basic.ltx2.stage_overrides import (
-    refine_preset_override_fields,
-    refine_stage_override_fields,
-)
-from fastvideo.utils import shallow_asdict
-
-_INPUT_FIELD_NAMES = {field.name for field in fields(InputConfig)}
-_SAMPLING_FIELD_NAMES = {field.name for field in fields(SamplingConfig)}
-_RUNTIME_FIELD_NAMES = {field.name for field in fields(RequestRuntimeConfig)}
-_OUTPUT_FIELD_NAMES = {field.name for field in fields(OutputConfig)}
-_MISSING = object()
-_LEGACY_REQUEST_ALIASES = {
-    "neg_prompt": "negative_prompt",
-}
-_REQUEST_PIPELINE_OVERRIDE_FIELDS = frozenset({
-    "embedded_cfg_scale",
-})
-# torch.compile kwargs that map to first-class CompileConfig fields.
-_COMPILE_TYPED_KEYS = ("backend", "fullgraph", "mode", "dynamic")
-# LTX-2 refine flat kwargs (init + per-request) known to FastVideoArgs.
-_LTX2_REFINE_FLAT_KEYS = (refine_preset_override_fields() | refine_stage_override_fields())
-
-
-def normalize_generator_config(config: GeneratorConfig | Mapping[str, Any], ) -> GeneratorConfig:
-    if isinstance(config, GeneratorConfig):
-        return config
-    return parse_config(GeneratorConfig, config)
-
-
-def load_generator_config_from_file(
-    path: str | Path,
-    overrides: list[str] | Mapping[str, Any] | None = None,
-) -> GeneratorConfig:
-    raw = load_raw_config(path)
-    normalized_overrides = normalize_overrides(overrides)
-
-    if _looks_like_run_or_serve_config(raw):
-        if normalized_overrides:
-            raw = apply_overrides(raw, normalized_overrides)
-        return parse_config(GeneratorConfig, raw["generator"])
-
-    if normalized_overrides:
-        adjusted = normalized_overrides
-        if all(key.startswith("generator.") for key in adjusted):
-            adjusted = {key[len("generator."):]: value for key, value in adjusted.items()}
-        raw = apply_overrides(raw, adjusted)
-
-    return parse_config(GeneratorConfig, raw)
-
-
-def legacy_from_pretrained_to_config(
-    model_path: str,
-    kwargs: Mapping[str, Any],
-) -> GeneratorConfig:
-    raw: dict[str, Any] = {"model_path": model_path}
-    engine: dict[str, Any] = {}
-    parallelism: dict[str, Any] = {}
-    offload: dict[str, Any] = {}
-    compile_config: dict[str, Any] = {}
-    pipeline: dict[str, Any] = {}
-    components: dict[str, Any] = {}
-    quantization: dict[str, Any] = {}
-    experimental: dict[str, Any] = {}
-    preset_overrides: dict[str, Any] = {}
-    preset_refine: dict[str, Any] = {}
-
-    for key, value in kwargs.items():
-        if key == "revision":
-            raw["revision"] = value
-        elif key == "trust_remote_code":
-            raw["trust_remote_code"] = value
-        elif key == "num_gpus":
-            engine["num_gpus"] = value
-        elif key == "distributed_executor_backend":
-            engine["execution_backend"] = value
-        elif key in {"tp_size", "sp_size", "hsdp_replicate_dim", "hsdp_shard_dim", "dist_timeout"}:
-            parallelism[key] = value
-        elif key == "dit_cpu_offload":
-            offload["dit"] = value
-        elif key == "dit_layerwise_offload":
-            offload["dit_layerwise"] = value
-        elif key == "text_encoder_cpu_offload":
-            offload["text_encoder"] = value
-        elif key == "image_encoder_cpu_offload":
-            offload["image_encoder"] = value
-        elif key == "vae_cpu_offload":
-            offload["vae"] = value
-        elif key == "pin_cpu_memory":
-            offload["pin_cpu_memory"] = value
-        elif key == "enable_torch_compile":
-            compile_config["enabled"] = value
-        elif key == "enable_torch_compile_text_encoder":
-            compile_config["text_encoder_enabled"] = value
-        elif key == "torch_compile_kwargs":
-            remaining: dict[str, Any] = (dict(deepcopy(value)) if isinstance(value, Mapping) else {})
-            for first_class in _COMPILE_TYPED_KEYS:
-                if first_class in remaining:
-                    compile_config[first_class] = remaining.pop(first_class)
-            if remaining:
-                compile_config["extras"] = remaining
-        elif key == "ltx2_vae_tiling":
-            pipeline["vae_tiling"] = value
-        elif key == "config_model_path":
-            components["config_root"] = value
-        elif key == "ltx2_refine_enabled":
-            preset_refine["enabled"] = value
-        elif key == "ltx2_refine_upsampler_path":
-            # Empty string means "no upsampler"; keep typed None.
-            components["upsampler_weights"] = value or None
-        elif key == "ltx2_refine_lora_path":
-            # Empty string means "no refine LoRA"; keep typed None.
-            components["lora_path"] = value or None
-        elif key == "ltx2_refine_add_noise":
-            preset_refine["add_noise"] = value
-        elif key == "ltx2_refine_num_inference_steps":
-            preset_refine["num_inference_steps"] = value
-        elif key == "ltx2_refine_guidance_scale":
-            preset_refine["guidance_scale"] = value
-        elif key in {"enable_stage_verification", "use_fsdp_inference", "disable_autocast"}:
-            engine[key] = value
-        elif key == "override_text_encoder_quant":
-            quantization["text_encoder_quant"] = value
-        elif key == "workload_type":
-            pipeline["workload_type"] = value
-        elif key == "lora_path":
-            components["lora_path"] = value
-        elif key == "override_pipeline_cls_name":
-            components["override_pipeline_cls_name"] = value
-        elif key == "override_transformer_cls_name":
-            components["override_transformer_cls_name"] = value
-        elif key == "pipeline_config":
-            if isinstance(value, str):
-                components["pipeline_config_path"] = value
-            else:
-                experimental[key] = deepcopy(value)
-        elif key == "override_text_encoder_safetensors":
-            components["text_encoder_weights"] = value
-        elif key == "init_weights_from_safetensors":
-            components["transformer_weights"] = value
-        elif key == "init_weights_from_safetensors_2":
-            components["transformer_2_weights"] = value
-        else:
-            experimental[key] = deepcopy(value)
-
-    if parallelism:
-        engine["parallelism"] = parallelism
-    if offload:
-        engine["offload"] = offload
-    if compile_config:
-        engine["compile"] = compile_config
-    if quantization:
-        engine["quantization"] = quantization
-    if engine:
-        raw["engine"] = engine
-
-    if components:
-        pipeline["components"] = components
-    if preset_refine:
-        preset_overrides["refine"] = preset_refine
-    if preset_overrides:
-        pipeline["preset_overrides"] = preset_overrides
-    if experimental:
-        pipeline["experimental"] = experimental
-    if pipeline:
-        raw["pipeline"] = pipeline
-
-    return parse_config(GeneratorConfig, raw)
-
-
-def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, Any], ) -> FastVideoArgs:
-    normalized = normalize_generator_config(config)
-    unsupported = []
-    if normalized.pipeline.preset is not None:
-        unsupported.append("pipeline.preset")
-    if normalized.pipeline.preset_version is not None:
-        unsupported.append("pipeline.preset_version")
-    if normalized.pipeline.components.vae_weights is not None:
-        unsupported.append("pipeline.components.vae_weights")
-    if unsupported:
-        joined = ", ".join(unsupported)
-        raise NotImplementedError(f"VideoGenerator compatibility adapter does not support {joined} yet")
-
-    engine = normalized.engine
-    kwargs: dict[str, Any] = {
-        "model_path": normalized.model_path,
-        "revision": normalized.revision,
-        "trust_remote_code": normalized.trust_remote_code,
-        "num_gpus": engine.num_gpus,
-        "distributed_executor_backend": engine.execution_backend,
-        "tp_size": engine.parallelism.tp_size,
-        "sp_size": engine.parallelism.sp_size,
-        "hsdp_replicate_dim": engine.parallelism.hsdp_replicate_dim,
-        "hsdp_shard_dim": engine.parallelism.hsdp_shard_dim,
-        "dist_timeout": engine.parallelism.dist_timeout,
-        "dit_cpu_offload": engine.offload.dit,
-        "dit_layerwise_offload": engine.offload.dit_layerwise,
-        "text_encoder_cpu_offload": engine.offload.text_encoder,
-        "image_encoder_cpu_offload": engine.offload.image_encoder,
-        "vae_cpu_offload": engine.offload.vae,
-        "pin_cpu_memory": engine.offload.pin_cpu_memory,
-        "enable_torch_compile": engine.compile.enabled,
-        "torch_compile_kwargs": _compile_config_to_torch_kwargs(engine.compile),
-        "enable_stage_verification": engine.enable_stage_verification,
-        "use_fsdp_inference": engine.use_fsdp_inference,
-        "disable_autocast": engine.disable_autocast,
-    }
-    if normalized.pipeline.workload_type is not None:
-        kwargs["workload_type"] = normalized.pipeline.workload_type
-    if normalized.pipeline.vae_tiling is not None:
-        kwargs["ltx2_vae_tiling"] = normalized.pipeline.vae_tiling
-    if engine.compile.text_encoder_enabled is not None:
-        # ``FastVideoArgs.from_kwargs`` filters to declared fields, so
-        # this is a no-op on the current legacy path. Emit anyway so the
-        # realtime runtime (PR 7.6) — which reads from the kwargs dict
-        # before FastVideoArgs filtering — can pick it up once wired.
-        kwargs["enable_torch_compile_text_encoder"] = (engine.compile.text_encoder_enabled)
-
-    quantization = engine.quantization
-    if quantization is not None and quantization.text_encoder_quant is not None:
-        kwargs["override_text_encoder_quant"] = quantization.text_encoder_quant
-    if quantization is not None and quantization.transformer_quant is not None:
-        kwargs["transformer_quant"] = quantization.transformer_quant
-
-    components = normalized.pipeline.components
-    if components.pipeline_config_path is not None:
-        kwargs["pipeline_config"] = components.pipeline_config_path
-    if components.lora_path is not None:
-        kwargs["lora_path"] = components.lora_path
-    if components.override_pipeline_cls_name is not None:
-        kwargs["override_pipeline_cls_name"] = components.override_pipeline_cls_name
-    if components.override_transformer_cls_name is not None:
-        kwargs["override_transformer_cls_name"] = components.override_transformer_cls_name
-    if components.text_encoder_weights is not None:
-        kwargs["override_text_encoder_safetensors"] = components.text_encoder_weights
-    if components.transformer_weights is not None:
-        kwargs["init_weights_from_safetensors"] = components.transformer_weights
-    if components.transformer_2_weights is not None:
-        kwargs["init_weights_from_safetensors_2"] = components.transformer_2_weights
-    if components.config_root is not None:
-        kwargs["config_model_path"] = components.config_root
-    if components.upsampler_weights is not None:
-        kwargs["ltx2_refine_upsampler_path"] = components.upsampler_weights
-
-    preset_overrides = deepcopy(normalized.pipeline.preset_overrides)
-    refine = preset_overrides.pop("refine", None)
-    if isinstance(refine, Mapping):
-        for key in _LTX2_REFINE_FLAT_KEYS:
-            if key in refine:
-                kwargs[f"ltx2_refine_{key}"] = refine[key]
-    kwargs.update(preset_overrides)
-    kwargs.update(deepcopy(normalized.pipeline.experimental))
-    return FastVideoArgs.from_kwargs(**kwargs)
-
-
-def normalize_generation_request(request: GenerationRequest | Mapping[str, Any], ) -> GenerationRequest:
-    normalized = (request if isinstance(request, GenerationRequest) else parse_config(GenerationRequest, request))
-
-    if not hasattr(normalized, EXPLICIT_PATHS_ATTR):
-        # Request wasn't bound through the parser (e.g. constructed
-        # directly). Treat every currently-set field as explicit.
-        bind_generation_request_raw(normalized, _serialize_generation_request(normalized))
-    return normalized
-
-
-def legacy_generate_call_to_request(
-    prompt: str | None,
-    sampling_param: SamplingParam | None,
-    *,
-    mouse_cond: Any | None = None,
-    keyboard_cond: Any | None = None,
-    grid_sizes: Any | None = None,
-    legacy_kwargs: Mapping[str, Any] | None = None,
-) -> GenerationRequest:
-    raw = _sampling_param_to_request_raw(sampling_param)
-    if prompt is not None:
-        raw["prompt"] = prompt
-
-    for key, value in (legacy_kwargs or {}).items():
-        _apply_request_field(raw, key, value)
-
-    if mouse_cond is not None:
-        raw.setdefault("inputs", {})["mouse_cond"] = mouse_cond
-    if keyboard_cond is not None:
-        raw.setdefault("inputs", {})["keyboard_cond"] = keyboard_cond
-    if grid_sizes is not None:
-        raw.setdefault("inputs", {})["grid_sizes"] = grid_sizes
-
-    normalized = parse_config(GenerationRequest, raw)
-    bind_generation_request_raw(normalized, raw)
-    return normalized
-
-
-def request_to_sampling_param(
-    request: GenerationRequest,
-    *,
-    model_path: str,
-) -> SamplingParam:
-    if request.plan is not None:
-        raise NotImplementedError("GenerationRequest.plan is not wired into VideoGenerator yet")
-
-    sampling_param = SamplingParam.from_pretrained(model_path)
-    if request.state is not None:
-        _validate_continuation_state(request.state)
-        sampling_param.continuation_state = request.state
-    if request.output.return_state:
-        sampling_param.return_continuation_state = True
-    updates = explicit_request_updates(request)
-
-    for key, value in updates.items():
-        if hasattr(sampling_param, key):
-            setattr(sampling_param, key, deepcopy(value))
-        elif key in _REQUEST_PIPELINE_OVERRIDE_FIELDS:
-            continue
-        elif value == _SCHEMA_DEFAULT_UPDATES.get(key, _MISSING):
-            # Schema-default field that isn't on SamplingParam; tolerated
-            # because direct GenerationRequest(...) construction has no
-            # way to distinguish "user set" from "schema default".
-            continue
-        else:
-            raise ValueError(f"Request field {key!r} is not supported by sampling params for {model_path}")
-
-    sampling_param.__post_init__()
-    sampling_param.check_sampling_param()
-    return sampling_param
-
-
-def expand_request_prompt_batch(request: GenerationRequest, ) -> list[GenerationRequest]:
-    if not isinstance(request.prompt, list):
-        return [request]
-
-    requests: list[GenerationRequest] = []
-    for index, prompt in enumerate(request.prompt):
-        single_request = deepcopy(request)
-        # deepcopy preserves the tracking-root cycle, but re-pin roots
-        # defensively so that subsequent setattrs record on the copy.
-        reset_tracking_roots(single_request)
-        single_request.prompt = prompt
-        _fan_out_batched_input_value(request, single_request, "image_path", index)
-        _fan_out_batched_input_value(request, single_request, "video_path", index)
-        requests.append(single_request)
-    return requests
-
-
-def _looks_like_run_or_serve_config(raw: Mapping[str, Any]) -> bool:
-    return isinstance(raw.get("generator"), Mapping)
-
-
-def _compile_config_to_torch_kwargs(compile_config: CompileConfig, ) -> dict[str, Any]:
-    """Flatten typed ``CompileConfig`` back to a ``torch_compile_kwargs``
-    dict that the legacy ``FastVideoArgs`` path still expects.
-
-    Typed first-class fields (:attr:`backend`, :attr:`fullgraph`,
-    :attr:`mode`, :attr:`dynamic`) are only emitted when the user set
-    them explicitly (non-``None``). ``extras`` is merged on top for any
-    uncommon kwargs.
-    """
-    out: dict[str, Any] = {}
-    for key in _COMPILE_TYPED_KEYS:
-        value = getattr(compile_config, key)
-        if value is not None:
-            out[key] = value
-    if compile_config.extras:
-        out.update(deepcopy(compile_config.extras))
-    return out
-
-
-def _sampling_param_to_request_raw(sampling_param: SamplingParam | None, ) -> dict[str, Any]:
-    if sampling_param is None:
-        return {}
-
-    raw: dict[str, Any] = {}
-    for key, value in shallow_asdict(sampling_param).items():
-        if key == "prompt":
-            continue
-        _apply_request_field(raw, key, deepcopy(value))
-    return raw
-
-
-def _apply_request_field(
-    raw: dict[str, Any],
-    key: str,
-    value: Any,
-) -> None:
-    key = _LEGACY_REQUEST_ALIASES.get(key, key)
-    if key == "negative_prompt":
-        raw["negative_prompt"] = value
-        return
-    if key in _INPUT_FIELD_NAMES:
-        raw.setdefault("inputs", {})[key] = value
-        return
-    if key in _SAMPLING_FIELD_NAMES:
-        raw.setdefault("sampling", {})[key] = value
-        return
-    if key in _RUNTIME_FIELD_NAMES:
-        raw.setdefault("runtime", {})[key] = value
-        return
-    if key in _OUTPUT_FIELD_NAMES:
-        raw.setdefault("output", {})[key] = value
-        return
-    raw.setdefault("extensions", {})[key] = value
-
-
-def request_to_pipeline_overrides(request: GenerationRequest) -> dict[str, Any]:
-    overrides: dict[str, Any] = {}
-    for key, value in explicit_request_updates(request).items():
-        if key in _REQUEST_PIPELINE_OVERRIDE_FIELDS:
-            overrides[key] = deepcopy(value)
-    return overrides
-
-
-def explicit_request_updates(request: GenerationRequest) -> dict[str, Any]:
-    """Project a ``GenerationRequest`` down to *explicitly set* fields only.
-
-    Returns a flat kwargs dict suitable for merging into a generator call.
-    The projection uses ``_fastvideo_explicit_paths`` (populated during
-    ``parse_config`` / raw binding) so schema defaults on the dataclass
-    are **not** emitted — only paths the caller/operator actually wrote.
-
-    This is what makes ``ServeConfig.default_request`` work as an
-    operator-pinned baseline rather than a full override: a YAML with just
-    ``sampling.seed: 42`` yields ``{"seed": 42}``, not the full sampling
-    config with its 15 schema defaults.
-
-    Precondition: the request must carry ``_fastvideo_explicit_paths`` —
-    populated by :func:`fastvideo.api.parser.parse_config` or
-    :func:`fastvideo.api.compat.normalize_generation_request`. Calling on
-    a raw ``GenerationRequest()`` asserts.
-    """
-    assert hasattr(request,
-                   EXPLICIT_PATHS_ATTR), ("GenerationRequest reached explicit_request_updates without tracking; "
-                                          "every entry point must route through normalize_generation_request "
-                                          "or parse_config first")
-    paths = get_explicit_paths(request)
-    raw = _build_sparse_raw_from_paths(request, paths)
-    return _extract_request_updates(raw)
-
-
-def _build_sparse_raw_from_paths(
-    request: GenerationRequest,
-    paths: frozenset[str],
-) -> dict[str, Any]:
-    result: dict[str, Any] = {}
-    for path in paths:
-        parts = path.split(".")
-        value = _read_dotted_path(request, parts)
-        if value is _MISSING:
-            continue
-        _set_dotted_path(result, parts, deepcopy(value))
-    return result
-
-
-def _read_dotted_path(obj: Any, parts: list[str]) -> Any:
-    for part in parts:
-        if is_dataclass(obj) and not isinstance(obj, type):
-            if not hasattr(obj, part):
-                return _MISSING
-            obj = getattr(obj, part)
-        elif isinstance(obj, Mapping):
-            if part not in obj:
-                return _MISSING
-            obj = obj[part]
-        else:
-            return _MISSING
-    return obj
-
-
-def _set_dotted_path(
-    target: dict[str, Any],
-    parts: list[str],
-    value: Any,
-) -> None:
-    cursor = target
-    for part in parts[:-1]:
-        nxt = cursor.get(part)
-        if not isinstance(nxt, dict):
-            nxt = {}
-            cursor[part] = nxt
-        cursor = nxt
-    cursor[parts[-1]] = value
-
-
-def _extract_request_updates(raw: Mapping[str, Any]) -> dict[str, Any]:
-    updates: dict[str, Any] = {}
-    if "negative_prompt" in raw:
-        updates["negative_prompt"] = deepcopy(raw["negative_prompt"])
-
-    for section_name in ("inputs", "sampling", "runtime", "output"):
-        section = raw.get(section_name)
-        if not isinstance(section, Mapping):
-            continue
-        for key, value in section.items():
-            updates[key] = deepcopy(value)
-
-    stage_overrides = raw.get("stage_overrides")
-    if stage_overrides:
-        updates.update(_flatten_stage_overrides(stage_overrides))
-
-    extensions = raw.get("extensions")
-    if isinstance(extensions, Mapping):
-        for key, value in extensions.items():
-            updates[key] = deepcopy(value)
-
-    return updates
-
-
-def _flatten_stage_overrides(stage_overrides: Any) -> dict[str, Any]:
-    if not isinstance(stage_overrides, Mapping):
-        raise ValueError("GenerationRequest.stage_overrides must be a mapping")
-
-    flattened: dict[str, Any] = {}
-    for stage_name, overrides in stage_overrides.items():
-        if not isinstance(overrides, Mapping):
-            raise ValueError(f"GenerationRequest.stage_overrides.{stage_name} must be a mapping")
-        for key, value in overrides.items():
-            if key in flattened and flattened[key] != value:
-                raise ValueError(f"Conflicting stage override for {key!r} across stages")
-            flattened[key] = deepcopy(value)
-    return flattened
-
-
-def _serialize_generation_request(request: GenerationRequest) -> dict[str, Any]:
-    return deepcopy(config_to_dict(request))
-
-
-_SCHEMA_DEFAULT_UPDATES = _extract_request_updates(config_to_dict(GenerationRequest()))
-
-_KNOWN_CONTINUATION_KINDS: set[str] = set()
-
-
-def register_continuation_kind(kind: str) -> None:
-    """Register a :class:`ContinuationState.kind` as recognized.
-
-    PR 7 wires the envelope through; per-kind payload deserializers live
-    with each model family (e.g. ``fastvideo.pipelines.basic.ltx2.
-    continuation.LTX2ContinuationState``). The registry lets the
-    public-API compat layer validate the kind early, before the state
-    reaches the pipeline.
-    """
-    if not isinstance(kind, str) or not kind:
-        raise ValueError("ContinuationState kind must be a non-empty string")
-    _KNOWN_CONTINUATION_KINDS.add(kind)
-
-
-def _validate_continuation_state(state: ContinuationState) -> None:
-    if not isinstance(state.kind, str) or not state.kind:
-        raise ValueError("GenerationRequest.state.kind must be a non-empty string; got "
-                         f"{state.kind!r}")
-    if not isinstance(state.payload, Mapping):
-        raise ValueError(f"GenerationRequest.state.payload must be a mapping; got "
-                         f"{type(state.payload).__name__}")
-    if state.kind not in _KNOWN_CONTINUATION_KINDS:
-        known = sorted(_KNOWN_CONTINUATION_KINDS)
-        raise ValueError(f"Unknown ContinuationState kind {state.kind!r}; registered "
-                         f"kinds: {known}. Import the model family that owns this kind "
-                         "(e.g. `import fastvideo.pipelines.basic.ltx2.continuation`) "
-                         "to register it, or drop the state field.")
-
-
-def _fan_out_batched_input_value(
-    source_request: GenerationRequest,
-    target_request: GenerationRequest,
-    field_name: str,
-    index: int,
-) -> None:
-    value = getattr(source_request.inputs, field_name)
-    if not isinstance(value, list):
-        return
-    _validate_batched_input_length(source_request.prompt, value, field_name)
-    setattr(target_request.inputs, field_name, deepcopy(value[index]))
-
-
-def _validate_batched_input_length(
-    prompts: str | list[str] | None,
-    values: list[Any],
-    field_name: str,
-) -> None:
-    if not isinstance(prompts, list):
-        return
-    if len(values) != len(prompts):
-        raise ValueError(f"GenerationRequest.inputs.{field_name} must have the same length as request.prompt")
-
-
-__all__ = [
-    "explicit_request_updates",
-    "generator_config_to_fastvideo_args",
-    "legacy_from_pretrained_to_config",
-    "legacy_generate_call_to_request",
-    "load_generator_config_from_file",
-    "normalize_generation_request",
-    "normalize_generator_config",
-    "register_continuation_kind",
-    "request_to_pipeline_overrides",
-    "request_to_sampling_param",
-]
@@ -1,16 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-
-class ConfigValidationError(ValueError):
-    """Validation error that keeps track of the nested config path."""
-
-    def __init__(self, path: str, message: str):
-        self.path = path
-        self.message = message
-        super().__init__(str(self))
-
-    def __str__(self) -> str:
-        if self.path:
-            return f"{self.path}: {self.message}"
-        return self.message
@@ -1,110 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from copy import deepcopy
-from typing import Any
-from collections.abc import Mapping
-
-import yaml
-
-from fastvideo.api.errors import ConfigValidationError
-
-
-def parse_cli_overrides(overrides: list[str]) -> dict[str, Any]:
-    """Parse ``--dotted.key value`` style overrides into a flat mapping."""
-    parsed: dict[str, Any] = {}
-    index = 0
-    while index < len(overrides):
-        token = overrides[index]
-        if not token.startswith("--"):
-            raise ValueError(f"Expected --dotted.key, got {token!r}")
-
-        key = token[2:]
-        if not key:
-            raise ValueError("Override key cannot be empty")
-
-        if "=" in key:
-            key, raw_value = key.split("=", 1)
-        else:
-            index += 1
-            if index >= len(overrides):
-                raise ValueError(f"Missing value for override {token!r}")
-            raw_value = overrides[index]
-
-        parsed[_normalize_override_key(key)] = _cast_override_value(raw_value)
-        index += 1
-
-    return parsed
-
-
-def apply_overrides(config: Mapping[str, Any], overrides: Mapping[str, Any]) -> dict[str, Any]:
-    """Return a copy of ``config`` with dotted-key overrides applied."""
-    merged = deepcopy(dict(config))
-    for dotted_key, value in overrides.items():
-        _apply_single_override(merged, dotted_key, value)
-    return merged
-
-
-def normalize_overrides(overrides: list[str] | Mapping[str, Any] | None, ) -> dict[str, Any] | None:
-    """Normalize a CLI list or mapping of overrides into a flat dict."""
-    if not overrides:
-        return None
-    if isinstance(overrides, list):
-        return parse_cli_overrides(overrides)
-    return dict(overrides)
-
-
-def _apply_single_override(config: dict[str, Any], dotted_key: str, value: Any) -> None:
-    parts = dotted_key.split(".")
-    if not all(parts):
-        raise ValueError(f"Invalid override path {dotted_key!r}")
-
-    cursor = config
-    for depth, part in enumerate(parts[:-1]):
-        existing = cursor.get(part)
-        if existing is None:
-            existing = {}
-            cursor[part] = existing
-        elif not isinstance(existing, dict):
-            raise ConfigValidationError(
-                ".".join(parts[:depth + 1]),
-                "cannot apply nested override through a non-mapping value",
-            )
-        cursor = existing
-
-    cursor[parts[-1]] = value
-
-
-def _cast_override_value(raw: str) -> Any:
-    lowered = raw.lower()
-    if lowered == "true":
-        return True
-    if lowered == "false":
-        return False
-    if lowered in {"none", "null"}:
-        return None
-
-    try:
-        return int(raw)
-    except ValueError:
-        pass
-
-    try:
-        return float(raw)
-    except ValueError:
-        pass
-
-    if raw.startswith("[") or raw.startswith("{"):
-        try:
-            return yaml.safe_load(raw)
-        except yaml.YAMLError:
-            pass
-
-    return raw
-
-
-def _normalize_override_key(key: str) -> str:
-    return key.replace("-", "_")
-
-
-__all__ = ["apply_overrides", "normalize_overrides", "parse_cli_overrides"]
@@ -1,324 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import dataclasses
-import json
-import types
-from pathlib import Path
-from collections.abc import Mapping
-from typing import Any, Literal, TypeVar, Union, get_args, get_origin, get_type_hints
-
-import yaml
-
-from fastvideo.api.errors import ConfigValidationError
-from fastvideo.api.overrides import apply_overrides, normalize_overrides
-from fastvideo.api.request_metadata import (
-    bind_generation_request_raw,
-    bind_run_config_raw,
-    bind_serve_config_raw,
-)
-from fastvideo.api.schema import GenerationRequest, RunConfig, ServeConfig
-
-T = TypeVar("T")
-_UNION_ORIGINS = {types.UnionType, Union}
-
-
-@dataclasses.dataclass(frozen=True)
-class _DataclassSpec:
-    cls: type[Any]
-    type_hints: dict[str, Any]
-    fields_by_name: dict[str, dataclasses.Field[Any]]
-
-
-def parse_config(config_type: type[T], raw: Mapping[str, Any] | T) -> T:
-    """Parse a nested mapping into a typed inference config object."""
-    if isinstance(raw, config_type):
-        return raw
-    if not isinstance(raw, Mapping):
-        raise ConfigValidationError("", f"expected mapping for {config_type.__name__}")
-    parsed = _SchemaParser().parse_dataclass(config_type, raw, "")
-    if config_type is GenerationRequest:
-        return bind_generation_request_raw(parsed, raw)
-    if config_type is RunConfig:
-        return bind_run_config_raw(parsed, raw)
-    if config_type is ServeConfig:
-        return bind_serve_config_raw(parsed, raw)
-    return parsed
-
-
-def config_to_dict(config: Any) -> Any:
-    """Serialize a typed config object into plain Python containers."""
-    if dataclasses.is_dataclass(config) and not isinstance(config, type):
-        return {field.name: config_to_dict(getattr(config, field.name)) for field in dataclasses.fields(config)}
-    if isinstance(config, list):
-        return [config_to_dict(item) for item in config]
-    if isinstance(config, dict):
-        return {key: config_to_dict(value) for key, value in config.items()}
-    return config
-
-
-def load_config(
-    config_type: type[T],
-    path: str | Path,
-    overrides: list[str] | Mapping[str, Any] | None = None,
-) -> T:
-    """Load a typed config object from YAML or JSON."""
-    raw = load_raw_config(path)
-    normalized_overrides = normalize_overrides(overrides)
-    if normalized_overrides:
-        raw = apply_overrides(raw, normalized_overrides)
-    return parse_config(config_type, raw)
-
-
-def load_run_config(
-    path: str | Path,
-    overrides: list[str] | Mapping[str, Any] | None = None,
-) -> RunConfig:
-    return load_config(RunConfig, path, overrides)
-
-
-def load_serve_config(
-    path: str | Path,
-    overrides: list[str] | Mapping[str, Any] | None = None,
-) -> ServeConfig:
-    return load_config(ServeConfig, path, overrides)
-
-
-def load_raw_config(path: str | Path) -> dict[str, Any]:
-    config_path = Path(path)
-    if not config_path.exists():
-        raise FileNotFoundError(f"Config file not found: {config_path}")
-
-    with config_path.open(encoding="utf-8") as handle:
-        raw = _load_raw_mapping(handle, config_path)
-
-    if raw is None:
-        return {}
-    if not isinstance(raw, Mapping):
-        raise ConfigValidationError("", f"{config_path} must contain a top-level mapping")
-    return dict(raw)
-
-
-def _load_raw_mapping(handle: Any, config_path: Path) -> Any:
-    suffix = config_path.suffix.lower()
-    if suffix in {".yaml", ".yml"}:
-        return yaml.safe_load(handle)
-    if suffix == ".json":
-        return json.load(handle)
-    raise ValueError(f"Unsupported config file format: {config_path}")
-
-
-class _SchemaParser:
-
-    def parse_dataclass(
-        self,
-        config_type: type[T],
-        raw: Mapping[str, Any],
-        path: str,
-    ) -> T:
-        if not isinstance(raw, Mapping):
-            raise ConfigValidationError(path, f"expected mapping for {config_type.__name__}")
-
-        spec = _get_dataclass_spec(config_type)
-        self._validate_keys(raw, spec, path)
-
-        values: dict[str, Any] = {}
-        for name, field in spec.fields_by_name.items():
-            field_path = _join_path(path, name)
-            if name in raw:
-                values[name] = self.parse_value(spec.type_hints[name], raw[name], field_path)
-                continue
-            if _field_is_required(field):
-                raise ConfigValidationError(field_path, "missing required field")
-
-        return config_type(**values)
-
-    def parse_value(self, annotation: Any, value: Any, path: str) -> Any:
-        if annotation is Any:
-            return value
-
-        origin = get_origin(annotation)
-        if origin in _UNION_ORIGINS:
-            return self._parse_union(annotation, value, path)
-        if origin is Literal:
-            return self._parse_literal(annotation, value, path)
-        if origin is list:
-            return self._parse_list(annotation, value, path)
-        if origin is dict:
-            return self._parse_dict(annotation, value, path)
-        if origin is tuple:
-            return self._parse_tuple(annotation, value, path)
-        if isinstance(annotation, type) and dataclasses.is_dataclass(annotation):
-            return self.parse_dataclass(annotation, value, path)
-
-        scalar_parser = _SCALAR_PARSERS.get(annotation)
-        if scalar_parser is not None:
-            return scalar_parser(value, path)
-
-        return self._parse_instance(annotation, value, path)
-
-    def _validate_keys(
-        self,
-        raw: Mapping[str, Any],
-        spec: _DataclassSpec,
-        path: str,
-    ) -> None:
-        for key in raw:
-            if not isinstance(key, str):
-                raise ConfigValidationError(path, "expected mapping keys to be strings")
-            if key not in spec.fields_by_name:
-                raise ConfigValidationError(_join_path(path, key), "unknown field")
-
-    def _parse_union(self, annotation: Any, value: Any, path: str) -> Any:
-        candidates = [candidate for candidate in get_args(annotation) if candidate is not type(None)]
-        if value is None and len(candidates) != len(get_args(annotation)):
-            return None
-        if len(candidates) == 1:
-            return self.parse_value(candidates[0], value, path)
-
-        errors: list[str] = []
-        for candidate in candidates:
-            try:
-                return self.parse_value(candidate, value, path)
-            except ConfigValidationError as exc:
-                errors.append(exc.message)
-
-        expected = ", ".join(_type_name(candidate) for candidate in candidates)
-        detail = errors[0] if errors else f"expected one of ({expected})"
-        raise ConfigValidationError(path, detail)
-
-    def _parse_literal(self, annotation: Any, value: Any, path: str) -> Any:
-        allowed = get_args(annotation)
-        if value not in allowed:
-            raise ConfigValidationError(path, f"expected one of {sorted(allowed)!r}")
-        return value
-
-    def _parse_list(self, annotation: Any, value: Any, path: str) -> list[Any]:
-        if not isinstance(value, list):
-            raise ConfigValidationError(path, "expected list")
-        item_type = get_args(annotation)[0] if get_args(annotation) else Any
-        return [self.parse_value(item_type, item, f"{path}[{index}]") for index, item in enumerate(value)]
-
-    def _parse_dict(self, annotation: Any, value: Any, path: str) -> dict[Any, Any]:
-        if not isinstance(value, Mapping):
-            raise ConfigValidationError(path, "expected mapping")
-
-        key_type, value_type = (get_args(annotation) + (Any, Any))[:2]
-        parsed: dict[Any, Any] = {}
-        for key, item in value.items():
-            parsed_key = self._parse_dict_key(key_type, key, path)
-            item_path = _join_path(path, str(key))
-            parsed[parsed_key] = self.parse_value(value_type, item, item_path)
-        return parsed
-
-    def _parse_tuple(self, annotation: Any, value: Any, path: str) -> tuple[Any, ...]:
-        if not isinstance(value, list | tuple):
-            raise ConfigValidationError(path, "expected tuple")
-
-        item_types = get_args(annotation)
-        if len(item_types) == 2 and item_types[1] is Ellipsis:
-            return tuple(self.parse_value(item_types[0], item, f"{path}[{index}]") for index, item in enumerate(value))
-
-        if len(value) != len(item_types):
-            raise ConfigValidationError(path, f"expected tuple of length {len(item_types)}")
-
-        return tuple(
-            self.parse_value(item_type, item, f"{path}[{index}]")
-            for index, (item_type, item) in enumerate(zip(item_types, value, strict=True)))
-
-    def _parse_dict_key(self, annotation: Any, value: Any, path: str) -> Any:
-        if annotation is Any:
-            return value
-        if annotation is str:
-            if not isinstance(value, str):
-                raise ConfigValidationError(path, "expected string dictionary keys")
-            return value
-        if annotation is int:
-            if not isinstance(value, int) or isinstance(value, bool):
-                raise ConfigValidationError(path, "expected integer dictionary keys")
-            return value
-        return value
-
-    def _parse_instance(self, annotation: Any, value: Any, path: str) -> Any:
-        if isinstance(annotation, type) and not isinstance(value, annotation):
-            raise ConfigValidationError(path, f"expected {annotation.__name__}")
-        return value
-
-
-def _parse_bool(value: Any, path: str) -> bool:
-    if type(value) is not bool:
-        raise ConfigValidationError(path, "expected bool")
-    return value
-
-
-def _parse_int(value: Any, path: str) -> int:
-    if not isinstance(value, int) or isinstance(value, bool):
-        raise ConfigValidationError(path, "expected int")
-    return value
-
-
-def _parse_float(value: Any, path: str) -> float:
-    if not isinstance(value, int | float) or isinstance(value, bool):
-        raise ConfigValidationError(path, "expected float")
-    return float(value)
-
-
-def _parse_str(value: Any, path: str) -> str:
-    if not isinstance(value, str):
-        raise ConfigValidationError(path, "expected str")
-    return value
-
-
-_SCALAR_PARSERS: dict[Any, Any] = {
-    bool: _parse_bool,
-    int: _parse_int,
-    float: _parse_float,
-    str: _parse_str,
-}
-
-
-def _field_is_required(field: dataclasses.Field[Any]) -> bool:
-    return (field.default is dataclasses.MISSING and field.default_factory is dataclasses.MISSING)
-
-
-def _get_dataclass_spec(config_type: type[Any]) -> _DataclassSpec:
-    spec = _DATACLASS_SPEC_CACHE.get(config_type)
-    if spec is not None:
-        return spec
-
-    spec = _DataclassSpec(
-        cls=config_type,
-        type_hints=get_type_hints(config_type),
-        fields_by_name={field.name: field
-                        for field in dataclasses.fields(config_type)},
-    )
-    _DATACLASS_SPEC_CACHE[config_type] = spec
-    return spec
-
-
-_DATACLASS_SPEC_CACHE: dict[type[Any], _DataclassSpec] = {}
-
-
-def _join_path(prefix: str, suffix: str) -> str:
-    if not prefix:
-        return suffix
-    return f"{prefix}.{suffix}"
-
-
-def _type_name(annotation: Any) -> str:
-    origin = get_origin(annotation)
-    if origin is not None:
-        return str(annotation)
-    if hasattr(annotation, "__name__"):
-        return annotation.__name__
-    return str(annotation)
-
-
-__all__ = [
-    "config_to_dict",
-    "load_config",
-    "load_raw_config",
-    "load_run_config",
-    "load_serve_config",
-    "parse_config",
-]
@@ -1,261 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Pipeline preset registry.
-
-A *preset* is a named inference preset for a model family. It bundles:
-
-  * ``defaults`` — sampling values applied when the user does not
-    override them (consumed at runtime via ``SamplingParam.from_pretrained``);
-  * ``stage_schemas`` — **validation-only** metadata describing which
-    user-facing stage names (``"denoise"``, ``"sr"``) the preset recognises
-    and which ``stage_overrides`` keys each stage accepts.
-
-The ``stage_schemas`` tuple does **not** drive pipeline execution. The
-concrete execution DAG (text encoding, denoising, VAE decoding, …) is
-hard-coded per-pipeline in ``create_pipeline_stages()``. Schemas exist
-purely so that ``PipelineSelection.preset`` and
-``GenerationRequest.stage_overrides`` can be type-checked up front
-without touching the pipeline.
-
-Preset base types and the registry API live here (public API surface).
-Preset *instances* are defined in pipeline-local ``presets.py`` files
-(e.g. ``fastvideo/pipelines/basic/wan/presets.py``) and registered
-explicitly from :func:`_register_presets` in ``fastvideo/registry.py``.
-"""
-from __future__ import annotations
-
-from collections.abc import Mapping
-from dataclasses import dataclass, field
-from typing import Any
-
-from fastvideo.api.errors import ConfigValidationError
-
-# -------------------------------------------------------------------
-# Types
-# -------------------------------------------------------------------
-
-
-@dataclass(frozen=True)
-class PresetStageSpec:
-    """A user-facing stage name within a preset, used only to validate
-    ``stage_overrides`` keys. Not read by pipeline execution — the real
-    execution DAG lives in each pipeline's ``create_pipeline_stages()``.
-    """
-
-    name: str
-    """Short user-facing name, e.g. ``"denoise"``, ``"sr"``."""
-
-    kind: str
-    """Semantic kind, e.g. ``"denoising"``, ``"super_resolution"``."""
-
-    description: str = ""
-
-    allowed_overrides: frozenset[str] = field(default_factory=frozenset)
-    """Keys that may appear in ``stage_overrides[name]``."""
-
-
-@dataclass(frozen=True)
-class InferencePreset:
-    """A named inference preset for a model family."""
-
-    name: str
-    """Preset name, e.g. ``"wan_t2v_1_3b"``."""
-
-    version: int
-    """Preset schema version; bump on breaking schema changes."""
-
-    model_family: str
-    """Model family key, e.g. ``"wan"``, ``"ltx2"``."""
-
-    description: str = ""
-
-    workload_type: str | None = None
-    """Optional workload hint: ``"t2v"``, ``"i2v"``, etc."""
-
-    stage_schemas: tuple[PresetStageSpec, ...] = ()
-    """User-facing stage names for ``stage_overrides`` validation.
-
-    Validation-only: this tuple is consumed by
-    :func:`validate_stage_overrides` and is **not** used to drive
-    pipeline execution. Omit or leave empty if the preset exposes no
-    per-stage override surface.
-    """
-
-    defaults: dict[str, Any] = field(default_factory=dict)
-    """Preset-level default sampling/runtime values."""
-
-    stage_defaults: dict[str, dict[str, Any]] = field(default_factory=dict)
-    """Per-stage default overrides, keyed by stage name."""
-
-
-# -------------------------------------------------------------------
-# Registry
-# -------------------------------------------------------------------
-
-# Keyed by (model_family, name, version).
-_PRESET_REGISTRY: dict[tuple[str, str, int], InferencePreset] = {}
-
-
-def register_preset(preset: InferencePreset) -> None:
-    """Register a preset definition.
-
-    Raises :class:`ValueError` on duplicate
-    ``(model_family, name, version)`` keys.
-    """
-    key = (preset.model_family, preset.name, preset.version)
-    if key in _PRESET_REGISTRY:
-        raise ValueError(f"Duplicate preset registration: "
-                         f"model_family={key[0]!r}, name={key[1]!r}, "
-                         f"version={key[2]!r}")
-    _PRESET_REGISTRY[key] = preset
-
-
-def get_preset(
-    name: str,
-    model_family: str,
-    version: int | None = None,
-) -> InferencePreset:
-    """Look up a registered preset.
-
-    When *version* is ``None`` the highest registered version for the
-    given *(model_family, name)* pair is returned.
-
-    Raises :class:`~fastvideo.api.errors.ConfigValidationError` when the
-    preset cannot be found.
-    """
-    if version is not None:
-        key = (model_family, name, version)
-        preset = _PRESET_REGISTRY.get(key)
-        if preset is not None:
-            return preset
-        raise ConfigValidationError(
-            "pipeline.preset",
-            f"unknown preset {name!r} version {version!r} "
-            f"for model family {model_family!r}; "
-            f"registered: {_format_registered(model_family)}",
-        )
-
-    # Find the highest version for (model_family, name).
-    candidates = [prof for (fam, n, _v), prof in _PRESET_REGISTRY.items() if fam == model_family and n == name]
-    if not candidates:
-        raise ConfigValidationError(
-            "pipeline.preset",
-            f"unknown preset {name!r} for model family "
-            f"{model_family!r}; "
-            f"registered: {_format_registered(model_family)}",
-        )
-    return max(candidates, key=lambda p: p.version)
-
-
-def get_presets_for_family(model_family: str, ) -> list[InferencePreset]:
-    """Return all presets registered for *model_family*."""
-    return [prof for (fam, _n, _v), prof in _PRESET_REGISTRY.items() if fam == model_family]
-
-
-def get_all_preset_names() -> list[str]:
-    """Return the sorted list of all registered preset names."""
-    return sorted({prof.name for prof in _PRESET_REGISTRY.values()})
-
-
-# -------------------------------------------------------------------
-# Validation helpers
-# -------------------------------------------------------------------
-
-
-def validate_stage_names(
-    preset: InferencePreset,
-    stage_overrides: Mapping[str, Any],
-) -> None:
-    """Check that *stage_overrides* keys are valid stage names.
-
-    Raises :class:`~fastvideo.api.errors.ConfigValidationError` with a
-    path-qualified message for unknown stage names.
-    """
-    valid_names = {stage.name for stage in preset.stage_schemas}
-    for stage_name in stage_overrides:
-        if stage_name not in valid_names:
-            raise ConfigValidationError(
-                f"stage_overrides.{stage_name}",
-                f"unknown stage for preset {preset.name!r}; "
-                f"valid stages: {sorted(valid_names)}",
-            )
-
-
-def validate_stage_overrides(
-    preset: InferencePreset,
-    stage_overrides: Mapping[str, Any],
-) -> None:
-    """Validate stage override keys against the preset.
-
-    Calls :func:`validate_stage_names` first, then checks that each
-    override key is in the stage's ``allowed_overrides``.
-    """
-    validate_stage_names(preset, stage_overrides)
-    stages_by_name = {stage.name: stage for stage in preset.stage_schemas}
-    for stage_name, overrides in stage_overrides.items():
-        if not isinstance(overrides, Mapping):
-            raise ConfigValidationError(
-                f"stage_overrides.{stage_name}",
-                "must be a mapping",
-            )
-        stage_spec = stages_by_name[stage_name]
-        if not stage_spec.allowed_overrides:
-            if overrides:
-                raise ConfigValidationError(
-                    f"stage_overrides.{stage_name}",
-                    f"stage {stage_name!r} does not accept "
-                    f"overrides",
-                )
-            continue
-        for key in overrides:
-            if key not in stage_spec.allowed_overrides:
-                raise ConfigValidationError(
-                    f"stage_overrides.{stage_name}.{key}",
-                    f"not an allowed override for stage "
-                    f"{stage_name!r}; allowed: "
-                    f"{sorted(stage_spec.allowed_overrides)}",
-                )
-
-
-def validate_preset_selection(
-    preset_name: str | None,
-    model_family: str,
-    *,
-    preset_version: int | None = None,
-    stage_overrides: Mapping[str, Any] | None = None,
-) -> InferencePreset | None:
-    """Resolve and validate a preset selection end-to-end.
-
-    Returns the resolved :class:`InferencePreset`, or ``None`` if
-    *preset_name* is ``None`` (no preset requested).
-    """
-    if preset_name is None:
-        return None
-    preset = get_preset(preset_name, model_family, version=preset_version)
-    if stage_overrides:
-        validate_stage_overrides(preset, stage_overrides)
-    return preset
-
-
-# -------------------------------------------------------------------
-# Internal helpers
-# -------------------------------------------------------------------
-
-
-def _format_registered(model_family: str) -> str:
-    names = sorted({prof.name for (fam, _n, _v), prof in _PRESET_REGISTRY.items() if fam == model_family})
-    if not names:
-        return "(none)"
-    return ", ".join(repr(n) for n in names)
-
-
-__all__ = [
-    "InferencePreset",
-    "PresetStageSpec",
-    "get_all_preset_names",
-    "get_preset",
-    "get_presets_for_family",
-    "register_preset",
-    "validate_preset_selection",
-    "validate_stage_names",
-    "validate_stage_overrides",
-]
@@ -1,233 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Track which GenerationRequest fields the user explicitly provided.
-
-When translating a GenerationRequest into a legacy SamplingParam we must
-distinguish user-provided values (which should override model defaults)
-from schema defaults (which should NOT override model defaults).
-
-The mechanism: a single ``_fastvideo_explicit_paths`` set stored on the
-root ``GenerationRequest``. It holds dotted leaf paths (e.g.
-``"sampling.guidance_scale"``) the user has touched, either via raw
-config at bind time or via attribute assignment at runtime. A patched
-``__setattr__`` on the request dataclass types records assignments into
-this set.
-
-The set holds leaf paths only. Nested dataclass or mapping assignments
-are flattened to their leaves at record time.
-"""
-from __future__ import annotations
-
-from collections.abc import Callable, Mapping
-import dataclasses
-from typing import Any, cast
-
-from fastvideo.api.schema import (
-    ContinuationState,
-    GenerationPlan,
-    GenerationRequest,
-    InputConfig,
-    OutputConfig,
-    PlannedStage,
-    RequestRuntimeConfig,
-    RunConfig,
-    SamplingConfig,
-    ServeConfig,
-)
-
-EXPLICIT_PATHS_ATTR = "_fastvideo_explicit_paths"
-
-_TRACKING_ROOT_ATTR = "_fastvideo_request_tracking_root"
-_TRACKING_PATH_ATTR = "_fastvideo_request_tracking_path"
-_TRACKING_PATCHED_ATTR = "_fastvideo_request_tracking_patched"
-
-_TRACKED_REQUEST_TYPES = (
-    GenerationRequest,
-    InputConfig,
-    SamplingConfig,
-    RequestRuntimeConfig,
-    OutputConfig,
-    ContinuationState,
-    PlannedStage,
-    GenerationPlan,
-)
-
-
-def bind_generation_request_raw(
-    request: GenerationRequest,
-    raw: Mapping[str, Any] | None,
-) -> GenerationRequest:
-    """Install explicit-path tracking on *request*.
-
-    *raw* is the parsed config dict (YAML/JSON/kwargs); every leaf key
-    in it becomes an explicit path. Subsequent attribute assignments on
-    *request* or its nested dataclasses are recorded automatically via a
-    patched ``__setattr__``.
-    """
-    _ensure_request_tracking()
-    # Disable recording while we walk the tree to install roots.
-    object.__setattr__(request, EXPLICIT_PATHS_ATTR, None)
-    _set_tracking_roots(request, request, "")
-    paths: set[str] = set()
-    _record_value_paths(raw or {}, "", paths)
-    object.__setattr__(request, EXPLICIT_PATHS_ATTR, paths)
-    return request
-
-
-def bind_run_config_raw(
-    config: RunConfig,
-    raw: Mapping[str, Any],
-) -> RunConfig:
-    request_raw = raw.get("request")
-    if isinstance(request_raw, Mapping):
-        bind_generation_request_raw(config.request, request_raw)
-    else:
-        bind_generation_request_raw(config.request, {})
-    return config
-
-
-def bind_serve_config_raw(
-    config: ServeConfig,
-    raw: Mapping[str, Any],
-) -> ServeConfig:
-    default_request_raw = raw.get("default_request")
-    if isinstance(default_request_raw, Mapping):
-        bind_generation_request_raw(config.default_request, default_request_raw)
-    else:
-        bind_generation_request_raw(config.default_request, {})
-    return config
-
-
-def get_explicit_paths(request: GenerationRequest) -> frozenset[str]:
-    """Return a snapshot of the explicit paths set on *request*."""
-    paths = getattr(request, EXPLICIT_PATHS_ATTR, None)
-    if isinstance(paths, set | frozenset):
-        return frozenset(paths)
-    return frozenset()
-
-
-def reset_tracking_roots(request: GenerationRequest) -> None:
-    """Re-install tracking roots after a deepcopy or manual clone.
-
-    The paths set itself deepcopies correctly; we only need to repoint
-    the tracking root on nested dataclasses at the new root.
-    """
-    _ensure_request_tracking()
-    _set_tracking_roots(request, request, "")
-
-
-# ---------------------------------------------------------------------------
-# Path recording
-# ---------------------------------------------------------------------------
-
-
-def _record_value_paths(
-    value: Any,
-    prefix: str,
-    out: set[str],
-) -> None:
-    """Add every leaf path under *value* to *out*.
-
-    A leaf is any terminal value (non-dataclass, non-mapping, or empty
-    mapping/dataclass). ``prefix`` is the dotted path at which *value*
-    sits. When called with an empty ``prefix`` (the root), leaves are
-    recorded at their own key.
-    """
-    if dataclasses.is_dataclass(value) and not isinstance(value, type):
-        dc_fields = dataclasses.fields(value)
-        if not dc_fields:
-            if prefix:
-                out.add(prefix)
-            return
-        for field in dc_fields:
-            child = getattr(value, field.name)
-            path = f"{prefix}.{field.name}" if prefix else field.name
-            _record_value_paths(child, path, out)
-        return
-    if isinstance(value, Mapping):
-        if not value:
-            if prefix:
-                out.add(prefix)
-            return
-        for key, child in value.items():
-            path = f"{prefix}.{key}" if prefix else key
-            _record_value_paths(child, path, out)
-        return
-    if prefix:
-        out.add(prefix)
-
-
-# ---------------------------------------------------------------------------
-# __setattr__ patching
-# ---------------------------------------------------------------------------
-
-
-def _ensure_request_tracking() -> None:
-    for config_type in _TRACKED_REQUEST_TYPES:
-        _patch_tracking_setattr(config_type)
-
-
-def _patch_tracking_setattr(config_type: type[Any]) -> None:
-    if getattr(config_type, _TRACKING_PATCHED_ATTR, False):
-        return
-
-    original_setattr = cast(
-        Callable[[Any, str, Any], None],
-        config_type.__setattr__,
-    )
-    field_names = {field.name for field in dataclasses.fields(config_type)}
-
-    def _tracking_setattr(self: Any, name: str, value: Any) -> None:
-        if name.startswith("_fastvideo_") or name not in field_names:
-            original_setattr(self, name, value)
-            return
-
-        original_setattr(self, name, value)
-
-        root = getattr(self, _TRACKING_ROOT_ATTR, None)
-        if root is None:
-            return
-        paths = getattr(root, EXPLICIT_PATHS_ATTR, None)
-        if not isinstance(paths, set):
-            return
-
-        prefix = getattr(self, _TRACKING_PATH_ATTR, "")
-        path = f"{prefix}.{name}" if prefix else name
-        # Wholesale dataclass replacement: install roots on the new
-        # instance so its future mutations are tracked too.
-        if dataclasses.is_dataclass(value) and not isinstance(value, type):
-            _set_tracking_roots(root, value, path)
-        _record_value_paths(value, path, paths)
-
-    type.__setattr__(config_type, "__setattr__", _tracking_setattr)
-    setattr(config_type, _TRACKING_PATCHED_ATTR, True)
-
-
-# ---------------------------------------------------------------------------
-# Tree walk to set tracking root/path on nested dataclasses
-# ---------------------------------------------------------------------------
-
-
-def _set_tracking_roots(
-    root: GenerationRequest,
-    obj: Any,
-    prefix: str,
-) -> None:
-    if not dataclasses.is_dataclass(obj) or isinstance(obj, type):
-        return
-    object.__setattr__(obj, _TRACKING_ROOT_ATTR, root)
-    object.__setattr__(obj, _TRACKING_PATH_ATTR, prefix)
-    for field in dataclasses.fields(obj):
-        child = getattr(obj, field.name)
-        child_path = f"{prefix}.{field.name}" if prefix else field.name
-        if dataclasses.is_dataclass(child) and not isinstance(child, type):
-            _set_tracking_roots(root, child, child_path)
-
-
-__all__ = [
-    "EXPLICIT_PATHS_ATTR",
-    "bind_generation_request_raw",
-    "bind_run_config_raw",
-    "bind_serve_config_raw",
-    "get_explicit_paths",
-    "reset_tracking_roots",
-]
@@ -1,105 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any
-from collections.abc import Mapping
-
-from fastvideo.api.schema import ContinuationState
-
-
-@dataclass
-class GenerationResult:
-    prompt: str | None = None
-    prompt_index: int | None = None
-    samples: Any | None = None
-    frames: Any | None = None
-    audio: Any | None = None
-    audio_sample_rate: int | None = None
-    size: tuple[int, int, int] | None = None
-    generation_time: float | None = None
-    logging_info: Any | None = None
-    trajectory: Any | None = None
-    trajectory_timesteps: Any | None = None
-    trajectory_decoded: Any | None = None
-    video_path: str | None = None
-    peak_memory_mb: float | None = None
-    state: ContinuationState | None = None
-    extra: dict[str, Any] = field(default_factory=dict)
-
-    @classmethod
-    def from_legacy_result(
-        cls,
-        result: Mapping[str, Any],
-    ) -> GenerationResult:
-        prompt = result.get("prompt")
-        if prompt is None:
-            prompt = result.get("prompts")
-
-        extra = {
-            key: value
-            for key, value in result.items() if key not in {
-                "prompt",
-                "prompt_index",
-                "prompts",
-                "samples",
-                "frames",
-                "audio",
-                "audio_sample_rate",
-                "size",
-                "generation_time",
-                "logging_info",
-                "trajectory",
-                "trajectory_timesteps",
-                "trajectory_decoded",
-                "video_path",
-                "peak_memory_mb",
-                "state",
-            }
-        }
-
-        return cls(
-            prompt=prompt,
-            prompt_index=result.get("prompt_index"),
-            samples=result.get("samples"),
-            frames=result.get("frames"),
-            audio=result.get("audio"),
-            audio_sample_rate=result.get("audio_sample_rate"),
-            size=result.get("size"),
-            generation_time=result.get("generation_time"),
-            logging_info=result.get("logging_info"),
-            trajectory=result.get("trajectory"),
-            trajectory_timesteps=result.get("trajectory_timesteps"),
-            trajectory_decoded=result.get("trajectory_decoded"),
-            video_path=result.get("video_path"),
-            peak_memory_mb=result.get("peak_memory_mb"),
-            state=result.get("state"),
-            extra=extra,
-        )
-
-    def to_legacy_dict(self) -> dict[str, Any]:
-        result = {
-            "prompts": self.prompt,
-            "samples": self.samples,
-            "frames": self.frames,
-            "audio": self.audio,
-            "audio_sample_rate": self.audio_sample_rate,
-            "size": self.size,
-            "generation_time": self.generation_time,
-            "logging_info": self.logging_info,
-            "trajectory": self.trajectory,
-            "trajectory_timesteps": self.trajectory_timesteps,
-            "trajectory_decoded": self.trajectory_decoded,
-            "video_path": self.video_path,
-            "peak_memory_mb": self.peak_memory_mb,
-        }
-        if self.prompt_index is not None:
-            result["prompt_index"] = self.prompt_index
-            result["prompt"] = self.prompt
-        if self.state is not None:
-            result["state"] = self.state
-        result.update(self.extra)
-        return result
-
-
-__all__ = ["GenerationResult"]
@@ -1,296 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, Literal
-
-
-@dataclass
-class ServerConfig:
-    host: str = "0.0.0.0"
-    port: int = 8000
-    output_dir: str = "outputs/"
-
-
-@dataclass
-class ParallelismConfig:
-    tp_size: int = -1
-    sp_size: int = -1
-    hsdp_replicate_dim: int = 1
-    hsdp_shard_dim: int = -1
-    dist_timeout: int | None = None
-
-
-@dataclass
-class OffloadConfig:
-    dit: bool = True
-    dit_layerwise: bool = True
-    text_encoder: bool = True
-    image_encoder: bool = True
-    vae: bool = True
-    pin_cpu_memory: bool = True
-
-
-@dataclass
-class CompileConfig:
-    """Typed ``torch.compile`` configuration.
-
-    ``backend``/``fullgraph``/``mode``/``dynamic`` are the four most
-    common ``torch.compile`` knobs. ``extras`` holds any remaining
-    ``torch.compile`` kwargs (e.g. ``options``, ``disable``).
-    """
-
-    enabled: bool = False
-    text_encoder_enabled: bool | None = None
-    """Whether ``torch.compile`` is applied to the text encoder. ``None``
-    keeps the runtime default. The public ``FastVideoArgs`` adapter does
-    not yet consume this flag; reserved so the realtime runtime upstream
-    (PR 7.6) has a typed home for its ``enable_torch_compile_text_encoder``
-    kwarg without routing through ``pipeline.experimental``."""
-    backend: str | None = None
-    fullgraph: bool | None = None
-    mode: str | None = None
-    dynamic: bool | None = None
-    extras: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass
-class QuantizationConfig:
-    text_encoder_quant: str | None = None
-    transformer_quant: str | None = None
-
-
-@dataclass
-class EngineConfig:
-    num_gpus: int = 1
-    execution_backend: Literal["mp", "ray"] = "mp"
-    parallelism: ParallelismConfig = field(default_factory=ParallelismConfig)
-    offload: OffloadConfig = field(default_factory=OffloadConfig)
-    compile: CompileConfig = field(default_factory=CompileConfig)
-    enable_stage_verification: bool = True
-    use_fsdp_inference: bool = False
-    disable_autocast: bool = False
-    quantization: QuantizationConfig | None = None
-
-
-@dataclass
-class ComponentConfig:
-    config_root: str | None = None
-    pipeline_config_path: str | None = None
-    text_encoder_weights: str | None = None
-    transformer_weights: str | None = None
-    transformer_2_weights: str | None = None
-    vae_weights: str | None = None
-    upsampler_weights: str | None = None
-    lora_path: str | None = None
-    override_pipeline_cls_name: str | None = None
-    override_transformer_cls_name: str | None = None
-
-
-@dataclass
-class PipelineSelection:
-    workload_type: Literal["t2v", "i2v", "t2i", "i2i"] | None = None
-    preset: str | None = None
-    preset_version: int | None = None
-    components: ComponentConfig = field(default_factory=ComponentConfig)
-    vae_tiling: bool | None = None
-    """Tile-based VAE decode. ``None`` keeps the model's default."""
-    preset_overrides: dict[str, Any] = field(default_factory=dict)
-    experimental: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass
-class GeneratorConfig:
-    model_path: str
-    revision: str | None = None
-    trust_remote_code: bool = False
-    engine: EngineConfig = field(default_factory=EngineConfig)
-    pipeline: PipelineSelection = field(default_factory=PipelineSelection)
-
-
-@dataclass
-class InputConfig:
-    prompt_path: str | None = None
-    image_path: str | list[str] | None = None
-    video_path: str | list[str] | None = None
-    pil_image: Any | None = None
-    pose: str | None = None
-    mouse_cond: Any | None = None
-    keyboard_cond: Any | None = None
-    grid_sizes: Any | None = None
-    c2ws_plucker_emb: Any | None = None
-    refine_from: str | None = None
-    stage1_video: Any | None = None
-
-
-@dataclass
-class SamplingConfig:
-    num_videos_per_prompt: int = 1
-    seed: int = 1024
-    num_frames: int = 125
-    height: int = 720
-    width: int = 1280
-    height_sr: int = 1072
-    width_sr: int = 1920
-    fps: int = 24
-    num_inference_steps: int = 50
-    num_inference_steps_sr: int = 50
-    guidance_scale: float = 1.0
-    guidance_scale_2: float | None = None
-    guidance_rescale: float = 0.0
-    true_cfg_scale: float | None = None
-    boundary_ratio: float | None = None
-    sigmas: list[float] | None = None
-
-
-@dataclass
-class RequestRuntimeConfig:
-    enable_teacache: bool = False
-    return_trajectory_latents: bool = False
-    return_trajectory_decoded: bool = False
-
-
-@dataclass
-class OutputConfig:
-    output_path: str = "outputs/"
-    output_video_name: str | None = None
-    save_video: bool = True
-    return_frames: bool = True
-    return_state: bool = False
-
-
-@dataclass
-class ContinuationState:
-    kind: str
-    payload: dict[str, Any]
-
-
-@dataclass
-class PlannedStage:
-    name: str
-    kind: str
-    source: str | None = None
-    overrides: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass
-class GenerationPlan:
-    stages: list[PlannedStage]
-    final_stage: str | None = None
-
-
-@dataclass
-class GenerationRequest:
-    prompt: str | list[str] | None = None
-    negative_prompt: str | None = None
-    inputs: InputConfig = field(default_factory=InputConfig)
-    sampling: SamplingConfig = field(default_factory=SamplingConfig)
-    runtime: RequestRuntimeConfig = field(default_factory=RequestRuntimeConfig)
-    output: OutputConfig = field(default_factory=OutputConfig)
-    stage_overrides: dict[str, Any] = field(default_factory=dict)
-    state: ContinuationState | None = None
-    plan: GenerationPlan | None = None
-    extensions: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass
-class RunConfig:
-    generator: GeneratorConfig
-    request: GenerationRequest
-
-
-@dataclass
-class WarmupConfig:
-    enabled: bool = True
-    prompt: str = ("A cinematic drone shot over coastal cliffs at sunrise, "
-                   "golden light, gentle ocean waves, ultra detailed")
-    timeout_seconds: int = 2400
-
-
-@dataclass
-class GpuPoolConfig:
-    num_workers: int | None = None
-    enable_audio_reencode: bool = True
-    conditioning_num_frames: int = 9
-    conditioning_end_offset: int = 0
-
-
-@dataclass
-class PromptEnhancerConfig:
-    enabled: bool = False
-    provider: Literal["cerebras", "groq"] = "cerebras"
-    model: str = "gpt-oss-120b"
-    timeout_ms: int = 20000
-    system_prompt_dir: str | None = None
-
-
-@dataclass
-class PromptSafetyConfig:
-    enabled: bool = False
-    classifier_path: str | None = None
-
-
-@dataclass
-class StreamingConfig:
-    session_timeout_seconds: int = 300
-    generation_segment_cap: int = 6
-    stream_mode: Literal["av_fmp4", "legacy_jpeg"] = "av_fmp4"
-    warmup: WarmupConfig = field(default_factory=WarmupConfig)
-    pool: GpuPoolConfig = field(default_factory=GpuPoolConfig)
-    prompt: PromptEnhancerConfig = field(default_factory=PromptEnhancerConfig)
-    safety: PromptSafetyConfig = field(default_factory=PromptSafetyConfig)
-
-
-@dataclass
-class ServeConfig:
-    """Typed serve config loaded from ``fastvideo serve --config``.
-
-    ``default_request`` is a full :class:`GenerationRequest` — the same type
-    clients POST to ``/v1/videos``. At request time the server merges it into
-    the incoming body as the operator-pinned baseline.
-
-    Important nuance: only fields the operator **explicitly wrote** in the
-    serve YAML/JSON count as defaults. Although the in-memory object is
-    fully populated (schema defaults fill every unset field), the merge
-    walks ``_fastvideo_explicit_paths`` — populated during parse — so
-    unset fields are *not* forced onto requests. Per-request precedence:
-
-        body (client-explicit) > default_request (operator-explicit)
-                               > hardcoded fallback (e.g. ``fps=24``)
-
-    See :func:`fastvideo.api.compat.explicit_request_updates` for the
-    projection and ``entrypoints/openai/video_api.py::_build_generation_kwargs``
-    for the merge.
-    """
-    generator: GeneratorConfig
-    server: ServerConfig = field(default_factory=ServerConfig)
-    default_request: GenerationRequest = field(default_factory=GenerationRequest)
-    streaming: StreamingConfig | None = None
-
-
-__all__ = [
-    "CompileConfig",
-    "ComponentConfig",
-    "ContinuationState",
-    "EngineConfig",
-    "GenerationPlan",
-    "GenerationRequest",
-    "GeneratorConfig",
-    "GpuPoolConfig",
-    "InputConfig",
-    "OffloadConfig",
-    "OutputConfig",
-    "ParallelismConfig",
-    "PipelineSelection",
-    "PlannedStage",
-    "PromptEnhancerConfig",
-    "PromptSafetyConfig",
-    "QuantizationConfig",
-    "RequestRuntimeConfig",
-    "RunConfig",
-    "SamplingConfig",
-    "ServeConfig",
-    "ServerConfig",
-    "StreamingConfig",
-    "WarmupConfig",
-]
@@ -1,738 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Bidirectional Sparse Attention (BSA) backend for FastVideo.
-
-Pure-PyTorch reference implementation from:
-"Bidirectional Sparse Attention for Faster Video Diffusion Training"
-(arXiv:2509.01085)
-
-BSA sparsifies both queries (pruning redundant tokens per block) and
-key-value pairs (keeping only relevant KV blocks per query block).
-
-This is a training-free inference backend: it works with any model
-trained with full attention by applying BSA sparsity at inference time.
-"""
-
-import functools
-import math
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-import torch.nn.functional as F
-
-from fastvideo.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionMetadata,
-    AttentionMetadataBuilder,
-)
-from fastvideo.distributed import get_sp_group
-from fastvideo.logger import init_logger
-
-try:
-    from fastvideo.attention.utils.flash_attn_no_pad import (
-        flash_attn_varlen_func_impl, )
-    FLASH_ATTN_AVAILABLE = True
-except ImportError:
-    try:
-        from flash_attn import flash_attn_varlen_func as flash_attn_varlen_func_impl
-        FLASH_ATTN_AVAILABLE = True
-    except ImportError:
-        FLASH_ATTN_AVAILABLE = False
-
-logger = init_logger(__name__)
-
-BSA_TILE_SIZE = (4, 4, 4)
-
-# ---------------------------------------------------------------------------
-# Cached index helpers (same pattern as VSA)
-# ---------------------------------------------------------------------------
-
-
-@functools.lru_cache(maxsize=10)
-def get_tile_partition_indices(
-    dit_seq_shape: tuple[int, int, int],
-    tile_size: tuple[int, int, int],
-    device: torch.device,
-) -> torch.LongTensor:
-    """Map raster-order tokens to tile-contiguous order."""
-    T, H, W = dit_seq_shape
-    ts, hs, ws = tile_size
-    indices = torch.arange(T * H * W, device=device, dtype=torch.long).reshape(T, H, W)
-    ls = []
-    for t in range(math.ceil(T / ts)):
-        for h in range(math.ceil(H / hs)):
-            for w in range(math.ceil(W / ws)):
-                ls.append(indices[
-                    t * ts:min(t * ts + ts, T),
-                    h * hs:min(h * hs + hs, H),
-                    w * ws:min(w * ws + ws, W),
-                ].flatten())
-    return torch.cat(ls, dim=0)
-
-
-@functools.lru_cache(maxsize=10)
-def get_reverse_tile_partition_indices(
-    dit_seq_shape: tuple[int, int, int],
-    tile_size: tuple[int, int, int],
-    device: torch.device,
-) -> torch.LongTensor:
-    """Inverse mapping: tile-contiguous order back to raster order."""
-    return torch.argsort(get_tile_partition_indices(dit_seq_shape, tile_size, device))
-
-
-# ---------------------------------------------------------------------------
-# BSA core operations
-# ---------------------------------------------------------------------------
-
-
-def _prune_queries(
-    q_blocks: torch.Tensor,
-    keep_ratio: float,
-) -> tuple[torch.Tensor, torch.Tensor, int]:
-    """
-    Prune redundant query tokens within each block.
-
-    Scores tokens by cosine similarity to the block center.
-    Keeps the LEAST similar (most informative) tokens.
-
-    Args:
-        q_blocks: [B, N_heads, N_blocks, block_size, D]
-        keep_ratio: fraction of tokens to keep
-
-    Returns:
-        sparse_q: [B, N_heads, N_blocks, keep_size, D]
-        keep_indices: [B, N_heads, N_blocks, keep_size]
-        keep_size: int
-    """
-    B, H, N, S, D = q_blocks.shape
-    keep_size = max(1, int(S * keep_ratio))
-
-    if keep_size >= S:
-        idx = torch.arange(S, device=q_blocks.device)
-        idx = idx.view(1, 1, 1, S).expand(B, H, N, S)
-        return q_blocks, idx, S
-
-    center_idx = S // 2
-    center = q_blocks[:, :, :, center_idx:center_idx + 1, :]
-
-    q_norm = F.normalize(q_blocks, dim=-1)
-    c_norm = F.normalize(center, dim=-1)
-    similarity = (q_norm * c_norm).sum(dim=-1)  # [B, H, N, S]
-
-    # lowest similarity = most distinctive = keep
-    _, indices = similarity.topk(keep_size, dim=-1, largest=False)
-    indices, _ = indices.sort(dim=-1)
-
-    idx_expand = indices.unsqueeze(-1).expand(-1, -1, -1, -1, D)
-    sparse_q = torch.gather(q_blocks, 3, idx_expand)
-
-    return sparse_q, indices, keep_size
-
-
-def _select_kv_blocks(
-    sparse_q: torch.Tensor,
-    k_blocks: torch.Tensor,
-    cumulative_threshold: float,
-    min_kv_blocks: int,
-) -> torch.Tensor:
-    """
-    Dynamically select KV blocks for each query block.
-
-    Mean-pools to block level, computes block attention scores,
-    admits blocks in descending order until cumulative mass
-    exceeds threshold.
-
-    Args:
-        sparse_q: [B, H, N, Sq, D]
-        k_blocks:  [B, H, N, Sk, D]
-        cumulative_threshold: e.g. 0.9
-        min_kv_blocks: minimum blocks to keep
-
-    Returns:
-        kv_mask: [B, H, N, N] boolean
-    """
-    B, H, N, _, D = sparse_q.shape
-
-    q_repr = sparse_q.mean(dim=3)
-    k_repr = k_blocks.mean(dim=3)
-
-    scores = torch.matmul(q_repr, k_repr.transpose(-1, -2)) / (D**0.5)
-    block_attn = F.softmax(scores, dim=-1)
-
-    sorted_attn, sorted_idx = block_attn.sort(dim=-1, descending=True)
-    cumsum = sorted_attn.cumsum(dim=-1)
-
-    keep_sorted = torch.ones_like(cumsum, dtype=torch.bool)
-    keep_sorted[..., 1:] = cumsum[..., :-1] < cumulative_threshold
-
-    min_mask = torch.zeros_like(keep_sorted)
-    min_mask[..., :min(min_kv_blocks, N)] = True
-    keep_sorted = keep_sorted | min_mask
-
-    kv_mask = torch.zeros_like(block_attn, dtype=torch.bool)
-    kv_mask.scatter_(-1, sorted_idx, keep_sorted)
-
-    return kv_mask
-
-
-def _compute_sparse_attention(
-    sparse_q: torch.Tensor,
-    k_blocks: torch.Tensor,
-    v_blocks: torch.Tensor,
-    kv_mask: torch.Tensor,
-) -> torch.Tensor:
-    """
-    Compute attention for each query block against selected KV blocks.
-
-    Handles per-batch and per-head KV masks correctly.
-    Uses flash_attn_varlen_func when available on GPU.
-    Falls back to pure-PyTorch reference on CPU.
-
-    Args:
-        sparse_q: [B, H, N, Sq, D]
-        k_blocks:  [B, H, N, Sk, D]
-        v_blocks:  [B, H, N, Sk, D]
-        kv_mask:   [B, H, N, N] boolean (per-batch, per-head)
-
-    Returns:
-        output: [B, H, N, Sq, D]
-    """
-    if FLASH_ATTN_AVAILABLE and sparse_q.is_cuda:
-        return _compute_sparse_attention_flash(sparse_q, k_blocks, v_blocks, kv_mask)
-    else:
-        return _compute_sparse_attention_reference(sparse_q, k_blocks, v_blocks, kv_mask)
-
-
-def _compute_sparse_attention_reference(
-    sparse_q: torch.Tensor,
-    k_blocks: torch.Tensor,
-    v_blocks: torch.Tensor,
-    kv_mask: torch.Tensor,
-) -> torch.Tensor:
-    """Pure-PyTorch fallback with per-batch, per-head mask support."""
-    B, H, N, Sq, D = sparse_q.shape
-    output = torch.zeros_like(sparse_q)
-
-    for b in range(B):
-        for h in range(H):
-            for qb in range(N):
-                selected = kv_mask[b, h, qb]  # [N] boolean
-                sel_idx = selected.nonzero(as_tuple=True)[0]
-
-                if sel_idx.shape[0] == 0:
-                    continue
-
-                # [num_sel * Sk, D]
-                sel_k = k_blocks[b, h, sel_idx].reshape(-1, D)
-                sel_v = v_blocks[b, h, sel_idx].reshape(-1, D)
-
-                q = sparse_q[b, h, qb]  # [Sq, D]
-                scores = torch.matmul(q, sel_k.transpose(-1, -2)) / (D**0.5)
-                weights = F.softmax(scores, dim=-1)
-                output[b, h, qb] = torch.matmul(weights, sel_v)
-
-    return output
-
-
-def _compute_sparse_attention_flash(
-    sparse_q: torch.Tensor,
-    k_blocks: torch.Tensor,
-    v_blocks: torch.Tensor,
-    kv_mask: torch.Tensor,
-) -> torch.Tensor:
-    """
-    FlashAttention implementation with per-batch, per-head mask support.
-
-    Strategy: check if all heads share the same mask. If so, use a single
-    FlashAttention call per batch (fast path). If not, process each head
-    separately (correct path).
-
-    Args:
-        sparse_q: [B, H, N, Sq, D]
-        k_blocks:  [B, H, N, Sk, D]
-        v_blocks:  [B, H, N, Sk, D]
-        kv_mask:   [B, H, N, N] boolean
-
-    Returns:
-        output: [B, H, N, Sq, D]
-    """
-    B, H, N, Sq, D = sparse_q.shape
-    Sk = k_blocks.shape[3]
-    device = sparse_q.device
-    output = torch.zeros_like(sparse_q)
-
-    for b in range(B):
-        # Check if all heads share the same mask for this batch element
-        # Compare each head's mask to head 0's mask
-        head0_mask = kv_mask[b, 0]  # [N, N]
-        all_heads_same = all(torch.equal(kv_mask[b, h], head0_mask) for h in range(1, H))
-
-        if all_heads_same:
-            # Fast path: all heads share the same mask, single FA call
-            _flash_attn_single_mask(
-                sparse_q[b],
-                k_blocks[b],
-                v_blocks[b],
-                head0_mask,
-                output[b],
-                H,
-                N,
-                Sq,
-                Sk,
-                D,
-                device,
-            )
-        else:
-            # Per-head path: process each head individually
-            for h in range(H):
-                head_mask = kv_mask[b, h]  # [N, N]
-                # Process single head: squeeze head dim, run FA, put back
-                _flash_attn_single_head(
-                    sparse_q[b, h],
-                    k_blocks[b, h],
-                    v_blocks[b, h],
-                    head_mask,
-                    output,
-                    b,
-                    h,
-                    N,
-                    Sq,
-                    Sk,
-                    D,
-                    device,
-                )
-
-    return output
-
-
-def _flash_attn_single_mask(
-    sparse_q_b: torch.Tensor,  # [H, N, Sq, D]
-    k_blocks_b: torch.Tensor,  # [H, N, Sk, D]
-    v_blocks_b: torch.Tensor,  # [H, N, Sk, D]
-    mask: torch.Tensor,  # [N, N] boolean
-    output_b: torch.Tensor,  # [H, N, Sq, D] (modified in-place)
-    H: int,
-    N: int,
-    Sq: int,
-    Sk: int,
-    D: int,
-    device: torch.device,
-) -> None:
-    """Run FlashAttention for all heads sharing the same KV mask."""
-    q_list = []
-    k_list = []
-    v_list = []
-    cu_seqlens_q = [0]
-    cu_seqlens_k = [0]
-    active_blocks = []
-
-    for qb in range(N):
-        selected = mask[qb]  # [N] boolean
-        sel_idx = selected.nonzero(as_tuple=True)[0]
-
-        if sel_idx.shape[0] == 0:
-            continue
-
-        active_blocks.append(qb)
-        num_kv_tokens = sel_idx.shape[0] * Sk
-
-        # [H, Sq, D] -> [Sq, H, D]
-        q_block = sparse_q_b[:, qb].permute(1, 0, 2)
-        q_list.append(q_block)
-
-        # [H, num_sel, Sk, D] -> [num_kv_tokens, H, D]
-        sel_k = k_blocks_b[:, sel_idx].permute(1, 2, 0, 3).reshape(num_kv_tokens, H, D)
-        sel_v = v_blocks_b[:, sel_idx].permute(1, 2, 0, 3).reshape(num_kv_tokens, H, D)
-        k_list.append(sel_k)
-        v_list.append(sel_v)
-
-        cu_seqlens_q.append(cu_seqlens_q[-1] + Sq)
-        cu_seqlens_k.append(cu_seqlens_k[-1] + num_kv_tokens)
-
-    if not q_list:
-        return
-
-    flat_q = torch.cat(q_list, dim=0)
-    flat_k = torch.cat(k_list, dim=0)
-    flat_v = torch.cat(v_list, dim=0)
-
-    cu_seqlens_q_t = torch.tensor(cu_seqlens_q, dtype=torch.int32, device=device)
-    cu_seqlens_k_t = torch.tensor(cu_seqlens_k, dtype=torch.int32, device=device)
-
-    max_seqlen_q = Sq
-    max_seqlen_k = int((cu_seqlens_k_t[1:] - cu_seqlens_k_t[:-1]).max().item())
-
-    orig_dtype = flat_q.dtype
-    compute_dtype = orig_dtype
-    if compute_dtype not in (torch.float16, torch.bfloat16):
-        compute_dtype = torch.bfloat16
-        flat_q = flat_q.to(compute_dtype)
-        flat_k = flat_k.to(compute_dtype)
-        flat_v = flat_v.to(compute_dtype)
-
-    flat_out = flash_attn_varlen_func_impl(
-        flat_q,
-        flat_k,
-        flat_v,
-        cu_seqlens_q_t,
-        cu_seqlens_k_t,
-        max_seqlen_q,
-        max_seqlen_k,
-        causal=False,
-    )
-
-    if compute_dtype != orig_dtype:
-        flat_out = flat_out.to(orig_dtype)
-
-    idx = 0
-    for qb in active_blocks:
-        block_out = flat_out[idx:idx + Sq]  # [Sq, H, D]
-        output_b[:, qb] = block_out.permute(1, 0, 2)  # [H, Sq, D]
-        idx += Sq
-
-
-def _flash_attn_single_head(
-    sparse_q_bh: torch.Tensor,  # [N, Sq, D]
-    k_blocks_bh: torch.Tensor,  # [N, Sk, D]
-    v_blocks_bh: torch.Tensor,  # [N, Sk, D]
-    mask: torch.Tensor,  # [N, N] boolean
-    output: torch.Tensor,  # [B, H, N, Sq, D] (modified in-place)
-    b: int,
-    h: int,
-    N: int,
-    Sq: int,
-    Sk: int,
-    D: int,
-    device: torch.device,
-) -> None:
-    """Run FlashAttention for a single head with its own KV mask."""
-    q_list = []
-    k_list = []
-    v_list = []
-    cu_seqlens_q = [0]
-    cu_seqlens_k = [0]
-    active_blocks = []
-
-    for qb in range(N):
-        selected = mask[qb]
-        sel_idx = selected.nonzero(as_tuple=True)[0]
-
-        if sel_idx.shape[0] == 0:
-            continue
-
-        active_blocks.append(qb)
-        num_kv_tokens = sel_idx.shape[0] * Sk
-
-        # [Sq, D] -> [Sq, 1, D] (single head)
-        q_block = sparse_q_bh[qb].unsqueeze(1)
-        q_list.append(q_block)
-
-        # [num_sel, Sk, D] -> [num_kv_tokens, 1, D]
-        sel_k = k_blocks_bh[sel_idx].reshape(num_kv_tokens, 1, D)
-        sel_v = v_blocks_bh[sel_idx].reshape(num_kv_tokens, 1, D)
-        k_list.append(sel_k)
-        v_list.append(sel_v)
-
-        cu_seqlens_q.append(cu_seqlens_q[-1] + Sq)
-        cu_seqlens_k.append(cu_seqlens_k[-1] + num_kv_tokens)
-
-    if not q_list:
-        return
-
-    flat_q = torch.cat(q_list, dim=0)
-    flat_k = torch.cat(k_list, dim=0)
-    flat_v = torch.cat(v_list, dim=0)
-
-    cu_seqlens_q_t = torch.tensor(cu_seqlens_q, dtype=torch.int32, device=device)
-    cu_seqlens_k_t = torch.tensor(cu_seqlens_k, dtype=torch.int32, device=device)
-
-    max_seqlen_q = Sq
-    max_seqlen_k = int((cu_seqlens_k_t[1:] - cu_seqlens_k_t[:-1]).max().item())
-
-    orig_dtype = flat_q.dtype
-    compute_dtype = orig_dtype
-    if compute_dtype not in (torch.float16, torch.bfloat16):
-        compute_dtype = torch.bfloat16
-        flat_q = flat_q.to(compute_dtype)
-        flat_k = flat_k.to(compute_dtype)
-        flat_v = flat_v.to(compute_dtype)
-
-    flat_out = flash_attn_varlen_func_impl(
-        flat_q,
-        flat_k,
-        flat_v,
-        cu_seqlens_q_t,
-        cu_seqlens_k_t,
-        max_seqlen_q,
-        max_seqlen_k,
-        causal=False,
-    )
-
-    if compute_dtype != orig_dtype:
-        flat_out = flat_out.to(orig_dtype)
-
-    idx = 0
-    for qb in active_blocks:
-        block_out = flat_out[idx:idx + Sq]  # [Sq, 1, D]
-        output[b, h, qb] = block_out.squeeze(1)  # [Sq, D]
-        idx += Sq
-
-
-def _reconstruct_pruned(
-    sparse_output: torch.Tensor,
-    keep_indices: torch.Tensor,
-    block_size: int,
-) -> torch.Tensor:
-    """
-    Scatter sparse output back to full block size.
-    Pruned positions get nearest kept token's output.
-
-    Handles per-batch, per-head indices correctly.
-
-    Args:
-        sparse_output: [B, H, N, keep_size, D]
-        keep_indices:  [B, H, N, keep_size]
-        block_size: original tokens per block
-
-    Returns:
-        full_output: [B, H, N, block_size, D]
-    """
-    B, H, N, keep_size, D = sparse_output.shape
-    device = sparse_output.device
-
-    if keep_size >= block_size:
-        return sparse_output
-
-    full_output = torch.zeros(B, H, N, block_size, D, device=device, dtype=sparse_output.dtype)
-
-    # Scatter kept tokens
-    idx_expand = keep_indices.unsqueeze(-1).expand(-1, -1, -1, -1, D)
-    full_output.scatter_(3, idx_expand, sparse_output)
-
-    # Fill pruned positions with nearest kept token (vectorized)
-    all_pos = torch.arange(block_size, device=device)
-
-    for b in range(B):
-        for h in range(H):
-            for n in range(N):
-                kept = keep_indices[b, h, n]  # [keep_size]
-
-                # Distance from every position to every kept position
-                dists = (all_pos.view(-1, 1) - kept.view(1, -1)).abs()
-                nearest_local_idx = dists.argmin(dim=1)  # [block_size]
-
-                # Identify pruned positions
-                is_pruned = torch.ones(block_size, dtype=torch.bool, device=device)
-                is_pruned[kept] = False
-                pruned_indices = is_pruned.nonzero(as_tuple=True)[0]
-
-                if pruned_indices.numel() > 0:
-                    src_indices = nearest_local_idx[pruned_indices]
-                    full_output[b, h, n, pruned_indices] = sparse_output[b, h, n, src_indices]
-
-    return full_output
-
-
-# ---------------------------------------------------------------------------
-# FastVideo backend classes
-# ---------------------------------------------------------------------------
-
-
-class BSAAttentionBackend(AttentionBackend):
-
-    accept_output_buffer: bool = False
-
-    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        return [64, 128]
-
-    @staticmethod
-    def get_name() -> str:
-        return "BSA_ATTN"
-
-    @staticmethod
-    def get_impl_cls() -> type["BSAAttentionImpl"]:
-        return BSAAttentionImpl
-
-    @staticmethod
-    def get_metadata_cls() -> type["BSAAttentionMetadata"]:
-        return BSAAttentionMetadata
-
-    @staticmethod
-    def get_builder_cls() -> type["BSAAttentionMetadataBuilder"]:
-        return BSAAttentionMetadataBuilder
-
-
-@dataclass
-class BSAAttentionMetadata(AttentionMetadata):
-    current_timestep: int
-    dit_seq_shape: tuple[int, int, int]
-    total_seq_length: int
-    num_blocks: int
-    block_size: int
-    tile_partition_indices: torch.LongTensor
-    reverse_tile_partition_indices: torch.LongTensor
-    # BSA-specific config
-    query_keep_ratio: float
-    kv_cumulative_threshold: float
-    min_kv_blocks: int
-
-
-class BSAAttentionMetadataBuilder(AttentionMetadataBuilder):
-
-    def __init__(self):
-        pass
-
-    def prepare(self):
-        pass
-
-    def build(
-        self,
-        current_timestep: int,
-        raw_latent_shape: tuple[int, int, int],
-        patch_size: tuple[int, int, int],
-        device: torch.device,
-        bsa_query_keep_ratio: float = 0.5,
-        bsa_kv_cumulative_threshold: float = 0.9,
-        bsa_min_kv_blocks: int = 4,
-        **kwargs: dict[str, Any],
-    ) -> "BSAAttentionMetadata":
-        # Ensure patching does not drop tokens silently.
-        assert all(r % p == 0 for r, p in zip(raw_latent_shape, patch_size, strict=False)), (
-            "raw_latent_shape must be divisible by patch_size for BSA", )
-
-        dit_seq_shape = (
-            raw_latent_shape[0] // patch_size[0],
-            raw_latent_shape[1] // patch_size[1],
-            raw_latent_shape[2] // patch_size[2],
-        )
-
-        total_seq_length = math.prod(dit_seq_shape)
-        block_size = math.prod(BSA_TILE_SIZE)
-        # Require exact tiling to avoid reshape failures later.
-        assert all(d % t == 0 for d, t in zip(dit_seq_shape, BSA_TILE_SIZE, strict=False)), (
-            "dit_seq_shape must be divisible by BSA_TILE_SIZE", )
-        num_blocks = total_seq_length // block_size
-
-        tile_partition_indices = get_tile_partition_indices(dit_seq_shape, BSA_TILE_SIZE, device)
-        reverse_tile_partition_indices = get_reverse_tile_partition_indices(dit_seq_shape, BSA_TILE_SIZE, device)
-
-        return BSAAttentionMetadata(
-            current_timestep=current_timestep,
-            dit_seq_shape=dit_seq_shape,
-            total_seq_length=total_seq_length,
-            num_blocks=num_blocks,
-            block_size=block_size,
-            tile_partition_indices=tile_partition_indices,
-            reverse_tile_partition_indices=reverse_tile_partition_indices,
-            query_keep_ratio=bsa_query_keep_ratio,
-            kv_cumulative_threshold=bsa_kv_cumulative_threshold,
-            min_kv_blocks=bsa_min_kv_blocks,
-        )
-
-
-class BSAAttentionImpl(AttentionImpl):
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        causal: bool,
-        softmax_scale: float,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        **extra_impl_args,
-    ) -> None:
-        self.prefix = prefix
-        self.num_heads = num_heads
-        self.head_size = head_size
-        if num_kv_heads is not None and num_kv_heads != num_heads:
-            raise ValueError("BSA backend does not support grouped-query attention")
-        if causal:
-            raise ValueError("BSA backend is bidirectional; causal=True is unsupported")
-        if softmax_scale is not None:
-            expected_scale = 1.0 / math.sqrt(self.head_size)
-            if not math.isclose(softmax_scale, expected_scale, rel_tol=1e-4, abs_tol=1e-5):
-                raise ValueError("softmax_scale must be default (1/sqrt(d)) for BSA")
-        try:
-            sp_group = get_sp_group()
-            self.sp_size = sp_group.world_size
-        except (AssertionError, RuntimeError):
-            self.sp_size = 1
-
-    def preprocess_qkv(
-        self,
-        qkv: torch.Tensor,
-        attn_metadata: BSAAttentionMetadata,
-    ) -> torch.Tensor:
-        """Reorder tokens from raster order to tile-contiguous order."""
-        # qkv: [B, L, num_heads, D]
-        return qkv[:, attn_metadata.tile_partition_indices]
-
-    def postprocess_output(
-        self,
-        output: torch.Tensor,
-        attn_metadata: BSAAttentionMetadata,
-    ) -> torch.Tensor:
-        """Reorder tokens from tile-contiguous order back to raster order."""
-        return output[:, attn_metadata.reverse_tile_partition_indices]
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: BSAAttentionMetadata,
-    ) -> torch.Tensor:
-        """
-        BSA attention forward pass.
-
-        Input tensors are already in tile-contiguous order from preprocess_qkv.
-
-        Args:
-            query: [B, L, num_heads, D] (tile-ordered)
-            key:   [B, L, num_heads, D] (tile-ordered)
-            value: [B, L, num_heads, D] (tile-ordered)
-            attn_metadata: BSA metadata
-
-        Returns:
-            output: [B, L, num_heads, D] (tile-ordered)
-        """
-        B, L, H, D = query.shape
-        block_size = attn_metadata.block_size
-        num_blocks = attn_metadata.num_blocks
-        assert num_blocks * block_size == L, "Sequence length must match tiling"
-
-        # Reshape to [B, H, L, D] for attention computation
-        q = query.transpose(1, 2).contiguous()  # [B, H, L, D]
-        k = key.transpose(1, 2).contiguous()
-        v = value.transpose(1, 2).contiguous()
-
-        # Reshape into blocks: [B, H, num_blocks, block_size, D]
-        q_blocks = q.view(B, H, num_blocks, block_size, D)
-        k_blocks = k.view(B, H, num_blocks, block_size, D)
-        v_blocks = v.view(B, H, num_blocks, block_size, D)
-
-        # --- Query sparsification ---
-        sparse_q, keep_indices, keep_size = _prune_queries(q_blocks, attn_metadata.query_keep_ratio)
-
-        # --- KV block selection ---
-        kv_mask = _select_kv_blocks(
-            sparse_q,
-            k_blocks,
-            attn_metadata.kv_cumulative_threshold,
-            attn_metadata.min_kv_blocks,
-        )
-
-        # --- Sparse attention ---
-        sparse_output = _compute_sparse_attention(sparse_q, k_blocks, v_blocks, kv_mask)
-
-        # --- Reconstruct pruned positions ---
-        full_output = _reconstruct_pruned(sparse_output, keep_indices, block_size)
-
-        # Reshape back: [B, H, num_blocks, block_size, D] -> [B, H, L, D] -> [B, L, H, D]
-        hidden_states = full_output.view(B, H, L, D).transpose(1, 2)
-
-        return hidden_states
@@ -48,6 +48,8 @@ class ModelConfig:
        for key, value in source_model_dict.items():
            if key in valid_fields:
                setattr(arch_config, key, value)
+            else:
+                raise AttributeError(f"{type(arch_config).__name__} has no field '{key}'")

        if hasattr(arch_config, "__post_init__"):
            arch_config.__post_init__()
@@ -5,13 +5,11 @@ from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
 from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
 from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
-from fastvideo.configs.models.dits.stable_audio import StableAudioConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
 from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 from fastvideo.configs.models.dits.kandinsky5 import Kandinsky5VideoConfig

 __all__ = [
    "HunyuanVideoConfig", "HunyuanVideo15Config", "HunyuanGameCraftConfig", "WanVideoConfig", "CosmosVideoConfig",
-    "Cosmos25VideoConfig", "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig", "Kandinsky5VideoConfig",
-    "StableAudioConfig"
+    "Cosmos25VideoConfig", "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig", "Kandinsky5VideoConfig"
 ]
@@ -50,8 +50,7 @@ class CosmosArchConfig(DiTArchConfig):
        })

    # Cosmos-specific config parameters based on transformer_cosmos.py
-    # in_channels includes the condition_mask channel (16 latent + 1 cond = 17)
-    in_channels: int = 17
+    in_channels: int = 16
    out_channels: int = 16
    num_attention_heads: int = 16
    attention_head_dim: int = 128
@@ -1,188 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-
-
-def is_transformer_blocks(n: str, m) -> bool:
-    return "transformer_blocks" in n and str.isdigit(n.split(".")[-1])
-
-
-@dataclass
-class Gen3CArchConfig(DiTArchConfig):
-    """Configuration for GEN3C architecture (VideoExtendGeneralDIT)."""
-
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_transformer_blocks])
-
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            # Official GEN3C checkpoint key naming to FastVideo mapping.
-            # The official checkpoint uses nn.Sequential patterns like attn.to_q.0 (Linear)
-            # and attn.to_q.1 (RMSNorm), and layer1/layer2 for MLP.
-            #
-            # Patch embedding: net.x_embedder.proj.1.weight -> patch_embed.proj.weight
-            r"^net\.x_embedder\.proj\.1\.(.*)$": r"patch_embed.proj.\1",
-
-            # Time embedding: net.t_embedder.1.linear_*.weight -> time_embed.t_embedder.linear_*.weight
-            r"^net\.t_embedder\.0\.(.*)$": r"time_embed.time_proj.\1",
-            r"^net\.t_embedder\.1\.linear_1\.(.*)$": r"time_embed.t_embedder.linear_1.\1",
-            r"^net\.t_embedder\.1\.linear_2\.(.*)$": r"time_embed.t_embedder.linear_2.\1",
-
-            # Augment sigma embedding (GEN3C-specific)
-            r"^net\.augment_sigma_embedder\.0\.(.*)$": r"augment_sigma_embed.time_proj.\1",
-            r"^net\.augment_sigma_embedder\.1\.linear_1\.(.*)$": r"augment_sigma_embed.t_embedder.linear_1.\1",
-            r"^net\.augment_sigma_embedder\.1\.linear_2\.(.*)$": r"augment_sigma_embed.t_embedder.linear_2.\1",
-
-            # Affine embedding norm: net.affline_norm.weight -> affine_norm.weight
-            # Note: "affline" is a typo in the official GEN3C checkpoint (should be "affine")
-            r"^net\.affline_norm\.(.*)$": r"affine_norm.\1",
-
-            # Extra positional embeddings (learnable per-axis)
-            r"^net\.extra_pos_embedder\.pos_emb_t$": r"learnable_pos_embed.pos_emb_t",
-            r"^net\.extra_pos_embedder\.pos_emb_h$": r"learnable_pos_embed.pos_emb_h",
-            r"^net\.extra_pos_embedder\.pos_emb_w$": r"learnable_pos_embed.pos_emb_w",
-
-            # Transformer blocks: net.blocks.blockN -> transformer_blocks.N
-            # Official uses: block.attn.to_q.0 (Linear), block.attn.to_q.1 (QK RMSNorm)
-            #
-            # Self-attention (block index 0)
-            r"^net\.blocks\.block(\d+)\.blocks\.0\.block\.attn\.to_q\.0\.(.*)$": r"transformer_blocks.\1.attn1.to_q.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.0\.block\.attn\.to_q\.1\.(.*)$":
-            r"transformer_blocks.\1.attn1.norm_q.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.0\.block\.attn\.to_k\.0\.(.*)$": r"transformer_blocks.\1.attn1.to_k.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.0\.block\.attn\.to_k\.1\.(.*)$":
-            r"transformer_blocks.\1.attn1.norm_k.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.0\.block\.attn\.to_v\.0\.(.*)$": r"transformer_blocks.\1.attn1.to_v.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.0\.block\.attn\.to_out\.0\.(.*)$":
-            r"transformer_blocks.\1.attn1.to_out.\2",
-            # AdaLN modulation for self-attention
-            r"^net\.blocks\.block(\d+)\.blocks\.0\.adaLN_modulation\.(.*)$":
-            r"transformer_blocks.\1.adaln_modulation_self_attn.\2",
-
-            # Cross-attention (block index 1)
-            r"^net\.blocks\.block(\d+)\.blocks\.1\.block\.attn\.to_q\.0\.(.*)$": r"transformer_blocks.\1.attn2.to_q.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.1\.block\.attn\.to_q\.1\.(.*)$":
-            r"transformer_blocks.\1.attn2.norm_q.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.1\.block\.attn\.to_k\.0\.(.*)$": r"transformer_blocks.\1.attn2.to_k.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.1\.block\.attn\.to_k\.1\.(.*)$":
-            r"transformer_blocks.\1.attn2.norm_k.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.1\.block\.attn\.to_v\.0\.(.*)$": r"transformer_blocks.\1.attn2.to_v.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.1\.block\.attn\.to_out\.0\.(.*)$":
-            r"transformer_blocks.\1.attn2.to_out.\2",
-            # AdaLN modulation for cross-attention
-            r"^net\.blocks\.block(\d+)\.blocks\.1\.adaLN_modulation\.(.*)$":
-            r"transformer_blocks.\1.adaln_modulation_cross_attn.\2",
-
-            # MLP (block index 2): layer1 -> fc_in, layer2 -> fc_out
-            r"^net\.blocks\.block(\d+)\.blocks\.2\.block\.layer1\.(.*)$": r"transformer_blocks.\1.mlp.fc_in.\2",
-            r"^net\.blocks\.block(\d+)\.blocks\.2\.block\.layer2\.(.*)$": r"transformer_blocks.\1.mlp.fc_out.\2",
-            # AdaLN modulation for MLP
-            r"^net\.blocks\.block(\d+)\.blocks\.2\.adaLN_modulation\.(.*)$":
-            r"transformer_blocks.\1.adaln_modulation_mlp.\2",
-
-            # Final layer: net.final_layer.linear -> final_layer.proj_out
-            r"^net\.final_layer\.linear\.(.*)$": r"final_layer.proj_out.\1",
-            # Final layer AdaLN: net.final_layer.adaLN_modulation -> final_layer.adaln_modulation
-            r"^net\.final_layer\.adaLN_modulation\.(.*)$": r"final_layer.adaln_modulation.\1",
-
-            # Note: The following keys from official checkpoint are NOT mapped and can be safely ignored:
-            # - net.pos_embedder.* (rope position embeddings computed dynamically)
-            # - net.accum_* keys (training metadata)
-            # - logvar.* (training-only module, not used in inference)
-        })
-
-    lora_param_names_mapping: dict = field(
-        default_factory=lambda: {
-            r"^transformer_blocks\.(\d+)\.attn1\.to_q\.(.*)$": r"transformer_blocks.\1.attn1.to_q.\2",
-            r"^transformer_blocks\.(\d+)\.attn1\.to_k\.(.*)$": r"transformer_blocks.\1.attn1.to_k.\2",
-            r"^transformer_blocks\.(\d+)\.attn1\.to_v\.(.*)$": r"transformer_blocks.\1.attn1.to_v.\2",
-            r"^transformer_blocks\.(\d+)\.attn1\.to_out\.(.*)$": r"transformer_blocks.\1.attn1.to_out.\2",
-            r"^transformer_blocks\.(\d+)\.attn2\.to_q\.(.*)$": r"transformer_blocks.\1.attn2.to_q.\2",
-            r"^transformer_blocks\.(\d+)\.attn2\.to_k\.(.*)$": r"transformer_blocks.\1.attn2.to_k.\2",
-            r"^transformer_blocks\.(\d+)\.attn2\.to_v\.(.*)$": r"transformer_blocks.\1.attn2.to_v.\2",
-            r"^transformer_blocks\.(\d+)\.attn2\.to_out\.(.*)$": r"transformer_blocks.\1.attn2.to_out.\2",
-            r"^transformer_blocks\.(\d+)\.mlp\.(.*)$": r"transformer_blocks.\1.mlp.\2",
-        })
-
-    # GEN3C architecture parameters
-    # Base VAE latent channels
-    in_channels: int = 16
-    out_channels: int = 16
-
-    # Channels per 3D cache buffer: 16 (warped frame latent) + 16 (warped mask latent)
-    CHANNELS_PER_BUFFER: int = 32
-
-    # Number of 3D cache buffers
-    frame_buffer_max: int = 2
-
-    # Attention configuration (7B model: 32 heads x 128 dim = 4096 hidden)
-    num_attention_heads: int = 32
-    attention_head_dim: int = 128  # 4096 / 32
-    num_layers: int = 28
-    mlp_ratio: float = 4.0
-
-    # Text encoder configuration
-    text_embed_dim: int = 1024
-
-    # AdaLN-LoRA configuration
-    adaln_lora_dim: int = 256
-    use_adaln_lora: bool = True
-
-    # GEN3C-specific: augment sigma embedding for conditioning noise augmentation
-    # Note: The official GEN3C-Cosmos-7B checkpoint was trained without this
-    add_augment_sigma_embedding: bool = False
-
-    # Position embedding configuration
-    max_size: tuple[int, int, int] = (128, 240, 240)  # T, H, W
-    patch_size: tuple[int, int, int] = (1, 2, 2)
-    rope_scale: tuple[float, float, float] = (2.0, 1.0, 1.0)  # T, H, W scaling
-
-    # GEN3C uses learnable positional embeddings in addition to RoPE
-    extra_pos_embed_type: str = "learnable"
-
-    # Padding mask handling
-    concat_padding_mask: bool = True
-
-    # Cross-attention projection (not used in GEN3C 7B)
-    use_crossattn_projection: bool = False
-
-    # RoPE FPS modulation
-    rope_enable_fps_modulation: bool = True
-
-    # QK normalization
-    qk_norm: str = "rms_norm"
-    eps: float = 1e-6
-
-    # Affine embedding normalization
-    affine_emb_norm: bool = True
-
-    # Block format (THWBD for GEN3C compatibility)
-    block_x_format: str = "THWBD"
-
-    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])
-
-    def __post_init__(self):
-        super().__post_init__()
-        self.out_channels = self.out_channels or self.in_channels
-        self.hidden_size = self.num_attention_heads * self.attention_head_dim
-        self.num_channels_latents = self.in_channels
-
-        # Calculate total input channels for patch embedding:
-        # - in_channels (16): VAE latent
-        # - condition_video_input_mask (1): Binary mask for conditioning frames
-        # - condition_video_pose (frame_buffer_max * 32): 3D cache buffers
-        # - padding_mask (1 if concat_padding_mask): Padding mask
-        self.buffer_channels = self.frame_buffer_max * self.CHANNELS_PER_BUFFER
-        self.total_input_channels = (
-            self.in_channels +  # 16: VAE latent
-            1 +  # 1: condition_video_input_mask
-            self.buffer_channels  # 64: 3D cache buffers (2 * 32)
-        )
-        # padding_mask is added in build_patch_embed if concat_padding_mask=True
-
-
-@dataclass
-class Gen3CVideoConfig(DiTConfig):
-    """Configuration for GEN3C video generation model."""
-    arch_config: DiTArchConfig = field(default_factory=Gen3CArchConfig)
-    prefix: str = "Gen3C"
@@ -1,76 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Config for the Stable Audio Open 1.0 DiT.
-
-Note: the SA pipeline bypasses the standard `ComposedPipelineBase`
-component loader because the published HF repo ships a single monolithic
-`model.safetensors` (no Diffusers-style `model_index.json` or
-per-subfolder layout). The arch fields and `param_names_mapping` here
-document the architecture and key remap so the same conventions used by
-the rest of the DiT family apply (FSDP shard conditions, supported
-attention backends, future loader integrations) — they are not currently
-consumed by `fastvideo/models/loader/fsdp_load.py` for SA.
-"""
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-from fastvideo.platforms import AttentionBackendEnum
-
-
-def _is_transformer_layer(n: str, m) -> bool:
-    # Matches `transformer.layers.{i}` in the SA DiT module tree.
-    parts = n.split(".")
-    return (len(parts) >= 3 and parts[-3] == "transformer" and parts[-2] == "layers" and parts[-1].isdigit())
-
-
-@dataclass
-class StableAudioArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [_is_transformer_layer])
-
-    # SA's checkpoint is `stable_audio_tools` raw format (not Diffusers),
-    # so the only remaps are: strip the `model.model.` host-pipeline
-    # prefix, and rename `nn.LayerNorm`'s `gamma`/`beta` to torch's
-    # canonical `weight`/`bias`. Linear / cross-attention naming already
-    # matches FastVideo's conventions, so no further remap is needed.
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            r"^model\.model\.(.*?)\.gamma$": r"\1.weight",
-            r"^model\.model\.(.*?)\.beta$": r"\1.bias",
-            r"^model\.model\.(.*)$": r"\1",
-        })
-
-    # SA only supports backends compatible with single-GPU LocalAttention.
-    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.FLASH_ATTN,
-        AttentionBackendEnum.TORCH_SDPA,
-    )
-
-    # Architecture constants (from the published `model_config.json` for
-    # `stabilityai/stable-audio-open-1.0`).
-    io_channels: int = 64
-    embed_dim: int = 1536
-    depth: int = 24
-    num_attention_heads: int = 24
-    cond_token_dim: int = 768
-    global_cond_dim: int = 1536
-    project_cond_tokens: bool = False
-    project_global_cond: bool = True
-    # Set to "ln" to wrap attention Q/K in LayerNorm (used by
-    # `stable-audio-open-small`; absent in the 1.0 base).
-    qk_norm: str | None = None
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.hidden_size = self.embed_dim
-        self.in_channels = self.io_channels
-        self.out_channels = self.io_channels
-        self.num_channels_latents = self.io_channels
-        self.attention_head_dim = self.embed_dim // self.num_attention_heads
-
-
-@dataclass
-class StableAudioConfig(DiTConfig):
-    arch_config: DiTArchConfig = field(default_factory=StableAudioArchConfig)
-
-    prefix: str = "StableAudio"
@@ -7,12 +7,9 @@ from fastvideo.configs.models.encoders.qwen2_5 import Qwen2_5_VLConfig
 from fastvideo.configs.models.encoders.siglip import SiglipVisionConfig
 from fastvideo.configs.models.encoders.reason1 import Reason1ArchConfig, Reason1Config
 from fastvideo.configs.models.encoders.gemma import LTX2GemmaConfig
-from fastvideo.configs.models.encoders.stable_audio_conditioner import (StableAudioConditionerArchConfig,
-                                                                        StableAudioConditionerConfig)

 __all__ = [
    "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig", "BaseEncoderOutput", "CLIPTextConfig",
    "CLIPVisionConfig", "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig", "Qwen2_5_VLConfig",
-    "Reason1ArchConfig", "Reason1Config", "LTX2GemmaConfig", "SiglipVisionConfig", "StableAudioConditionerArchConfig",
-    "StableAudioConditionerConfig"
+    "Reason1ArchConfig", "Reason1Config", "LTX2GemmaConfig", "SiglipVisionConfig"
 ]
@@ -1,80 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Config for the Stable Audio Open 1.0 multi-conditioner.
-
-The conditioner bundles three sub-conditioners — a T5 text encoder
-(prompt) and two NumberConditioners (`seconds_start` / `seconds_total`)
-— into the (cross_attn_cond, cross_attn_mask, global_embed) triple the
-DiT consumes. The architecture is fully specified by the official
-`stable_audio_tools` `MultiConditioner` config; the constants here
-mirror that.
-"""
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.base import ArchConfig
-from fastvideo.configs.models.encoders.base import (EncoderArchConfig, EncoderConfig)
-
-
-def _default_configs() -> list[dict]:
-    """Default = `stable-audio-open-1.0`'s three sub-conditioners."""
-    return [
-        {
-            "id": "prompt",
-            "type": "t5",
-            "config": {
-                "t5_model_name": "t5-base",
-                "max_length": 128
-            }
-        },
-        {
-            "id": "seconds_start",
-            "type": "number",
-            "config": {
-                "min_val": 0,
-                "max_val": 512
-            }
-        },
-        {
-            "id": "seconds_total",
-            "type": "number",
-            "config": {
-                "min_val": 0,
-                "max_val": 512
-            }
-        },
-    ]
-
-
-@dataclass
-class StableAudioConditionerArchConfig(EncoderArchConfig):
-    architectures: list[str] = field(default_factory=lambda: ["StableAudioMultiConditioner"])
-
-    # Shared embedding width across all sub-conditioners (T5 last-hidden
-    # dim and NumberEmbedder feature dim both = `cond_dim`).
-    cond_dim: int = 768
-
-    # Sub-conditioner identifiers. Order in `cross_attention_cond_ids`
-    # is the concat order for the cross-attn token sequence; order in
-    # `global_cond_ids` is the concat order for the global FiLM-style
-    # embedding.
-    cross_attention_cond_ids: tuple[str, ...] = ("prompt", "seconds_start", "seconds_total")
-    global_cond_ids: tuple[str, ...] = ("seconds_start", "seconds_total")
-
-    # Per-sub-conditioner spec list (mirrors upstream
-    # `model_config.json.model.conditioning.configs`). Each entry is
-    # `{"id": ..., "type": "t5"|"number", "config": {...}}`. The default
-    # matches `stable-audio-open-1.0`; SA-small overrides via the
-    # `conditioner/config.json` shipped in the converted repo.
-    configs: list = field(default_factory=_default_configs)
-
-    # Match official `stable_audio_tools/models/conditioners.py:334`:
-    # T5 is loaded directly in fp16.
-    t5_dtype: str = "float16"
-
-
-@dataclass
-class StableAudioConditionerConfig(EncoderConfig):
-    arch_config: ArchConfig = field(default_factory=StableAudioConditionerArchConfig)
-
-    prefix: str = "stable_audio_conditioner"
@@ -41,14 +41,6 @@ class T5ArchConfig(TextEncoderArchConfig):
    text_len: int = 512
    dtype: str | None = None
    gradient_checkpointing: bool = False
-    # Extra fields present in upstream HF T5Config but unused by FastVideo's
-    # encoder. Declared here so `update_model_arch` doesn't reject them when
-    # loading repos like `stabilityai/stable-audio-open-1.0` that ship the
-    # full HF config.
-    n_positions: int = 512
-    decoder_start_token_id: int = 0
-    output_past: bool = True
-    task_specific_params: dict | None = None
    stacked_params_mapping: list[tuple[str, str, str]] = field(default_factory=lambda: [
        # (param_name, shard_name, shard_id)
        (".qkv_proj", ".q", "q"),
@@ -1,11 +1,9 @@
 from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
 from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
 from fastvideo.configs.models.vaes.gamecraftvae import GameCraftVAEConfig
-from fastvideo.configs.models.vaes.gen3cvae import Gen3CVAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
 from fastvideo.configs.models.vaes.ltx2vae import LTX2VAEConfig
-from fastvideo.configs.models.vaes.oobleck import OobleckVAEArchConfig, OobleckVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEConfig

 __all__ = [
@@ -14,9 +12,6 @@ __all__ = [
    "WanVAEConfig",
    "CosmosVAEConfig",
    "Cosmos25VAEConfig",
-    "Gen3CVAEConfig",
    "Hunyuan15VAEConfig",
    "LTX2VAEConfig",
-    "OobleckVAEArchConfig",
-    "OobleckVAEConfig",
 ]
@@ -1,14 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass
-
-from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
-
-
-@dataclass
-class Gen3CVAEConfig(CosmosVAEConfig):
-    """
-    GEN3C VAE config placeholder.
-
-    GEN3C uses tokenizer-backed VAE loading logic at runtime, but we keep a
-    model-specific config class so pipeline/model configs stay model-scoped.
-    """
@@ -1,68 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Config for the Stable Audio Open 1.0 "Oobleck" VAE.
-
-Mirrors the per-channel `vae/config.json` shipped in
-`stabilityai/stable-audio-open-1.0` 1:1 (see
-`fastvideo/models/vaes/oobleck.py::OobleckVAE.from_pretrained`, which
-constructs the VAE from these fields). Inherits the FastVideo VAEConfig
-base so the standard `load_encoder` / `load_decoder` flags + tiling
-knobs apply.
-
-Naming: the VAE architecture is officially "Oobleck" (per Stability
-AI's stable-audio-tools) — the surrounding model family is "Stable
-Audio Open 1.0". This config is named after the architecture
-(`OobleckVAEConfig`) since the same VAE is shared across Stable Audio
-checkpoints; downstream pipelines reference it by its arch name, not
-by a host-pipeline name.
-"""
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
-
-
-@dataclass
-class OobleckVAEArchConfig(VAEArchConfig):
-    """Stable Audio Open 1.0 VAE architecture constants."""
-
-    architectures: list[str] = field(default_factory=lambda: ["AutoencoderOobleck"])
-
-    # From stabilityai/stable-audio-open-1.0/vae/config.json.
-    encoder_hidden_size: int = 128
-    downsampling_ratios: list[int] = field(default_factory=lambda: [2, 4, 4, 8, 8])
-    channel_multiples: list[int] = field(default_factory=lambda: [1, 2, 4, 8, 16])
-    decoder_channels: int = 128
-    decoder_input_channels: int = 64
-    audio_channels: int = 2  # stereo
-    sampling_rate: int = 44100
-
-
-@dataclass
-class OobleckVAEConfig(VAEConfig):
-    """FastVideo VAE config wrapping the Oobleck arch.
-
-    Audio VAEs don't use the temporal/spatial tiling defaults that the
-    base VAEConfig is shaped for (those exist for video VAEs); they are
-    retained but irrelevant for audio.
-    """
-
-    arch_config: VAEArchConfig = field(default_factory=OobleckVAEArchConfig)
-
-    # Audio is 1-D, so the video-VAE tiling defaults are inert. Disable
-    # them so callers don't accidentally trip on tile-stride math built
-    # for spatial tensors.
-    use_tiling: bool = False
-    use_temporal_tiling: bool = False
-    use_parallel_tiling: bool = False
-
-    # Where the FastVideo loader / pipeline-glue wrapper should fetch
-    # weights from when no local path is supplied. Gated repo — caller's
-    # HF token must have accepted terms on
-    # https://huggingface.co/stabilityai/stable-audio-open-1.0.
-    pretrained_path: str = "stabilityai/stable-audio-open-1.0"
-    pretrained_subfolder: str = "vae"
-    # Match official `stable_audio_tools`: VAE runs in fp16 (the
-    # `pretransform.model_half` path in
-    # `stable_audio_tools/models/pretransforms.py`).
-    pretrained_dtype: str = "float16"
@@ -5,7 +5,7 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
-from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
+from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
 from fastvideo.registry import get_pipeline_config_cls_from_name
 from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig, WanI2V480PConfig, WanI2V720PConfig,
                                             WanT2V480PConfig, WanT2V720PConfig)
@@ -1,171 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits.gen3c import Gen3CVideoConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput
-from fastvideo.configs.models.encoders.base import TextEncoderArchConfig
-from fastvideo.configs.models.encoders.t5 import (T5LargeArchConfig, T5LargeConfig)
-from fastvideo.configs.models.vaes import Gen3CVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-
-
-@dataclass
-class _Gen3CT5LargeArchConfig(T5LargeArchConfig):
-    """T5 Large arch config that pads inputs to max_length.
-
-    GEN3C requires padded text encoder inputs, while the base 
-    T5 config no longer pads by default after the SP mask 
-    refactor [PR#1142](https://github.com/hao-ai-lab/FastVideo/pull/1142).
-    """
-
-    def __post_init__(self):
-        super().__post_init__()
-        self.tokenizer_kwargs["padding"] = "max_length"
-
-
-@dataclass
-class _Gen3CT5LargeConfig(T5LargeConfig):
-    arch_config: TextEncoderArchConfig = field(default_factory=_Gen3CT5LargeArchConfig)
-    prefix: str = "t5"
-
-
-def t5_large_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
-    """Postprocess T5 Large text encoder outputs for GEN3C pipeline.
-    
-    Return raw last_hidden_state without truncation/padding.
-    """
-    hidden_state = outputs.last_hidden_state
-
-    if hidden_state is None:
-        raise ValueError("T5 Large outputs missing last_hidden_state")
-
-    nan_count = torch.isnan(hidden_state).sum()
-    if nan_count > 0:
-        hidden_state = hidden_state.masked_fill(torch.isnan(hidden_state), 0.0)
-
-    # Zero out embeddings beyond actual sequence length (vectorized)
-    if outputs.attention_mask is not None:
-        attention_mask = outputs.attention_mask
-        lengths = attention_mask.sum(dim=1)
-        max_len = hidden_state.shape[1]
-        mask = torch.arange(max_len, device=hidden_state.device)[None, :] >= lengths[:, None]
-        hidden_state[mask] = 0.0
-
-    return hidden_state
-
-
-@dataclass
-class Gen3CConfig(PipelineConfig):
-    """Configuration for GEN3C Video Generation Pipeline.
-    
-    GEN3C extends Cosmos with 3D cache for camera-controlled video generation.
-    Key parameters:
-    - frame_buffer_max: Number of 3D cache buffers (default: 2)
-    - noise_aug_strength: Strength of noise augmentation per buffer
-    - filter_points_threshold: Threshold for filtering unreliable depth points
-    """
-
-    dit_config: DiTConfig = field(default_factory=Gen3CVideoConfig)
-
-    vae_config: VAEConfig = field(default_factory=Gen3CVAEConfig)
-
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (_Gen3CT5LargeConfig(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (t5_large_postprocess_text, ))
-
-    dit_precision: str = "bf16"
-    vae_precision: str = "bf16"
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", ))
-
-    # GEN3C-specific conditioning parameters
-    conditioning_strategy: str = "frame_replace"
-    min_num_conditional_frames: int = 1
-    max_num_conditional_frames: int = 2
-    # Match official GEN3C/Cosmos inference defaults.
-    sigma_conditional: float = 0.001
-    sigma_data: float = 0.5
-    state_ch: int = 16
-    state_t: int = 16  # GEN3C uses 16 latent frames (121 pixel frames)
-    text_encoder_class: str = "T5"
-
-    # Flow matching parameters
-    embedded_cfg_scale: int = 6
-    flow_shift: float = 1.0
-
-    # GEN3C 3D Cache parameters
-    frame_buffer_max: int = 2
-    noise_aug_strength: float = 0.0
-    filter_points_threshold: float = 0.05
-
-    # Depth estimation settings
-    use_moge_depth: bool = True
-    moge_model_name: str = "Ruicheng/moge-vitl"
-    offload_moge_after_depth: bool = True
-
-    # Camera trajectory settings (matching NVIDIA inference defaults)
-    default_trajectory_type: str = "left"
-    default_movement_distance: float = 0.3
-    default_camera_rotation: str = "center_facing"
-
-    # Video generation settings
-    # Match official GEN3C defaults (height=704, width=1280).
-    video_resolution: tuple[int, int] = (704, 1280)  # H, W
-    num_frames: int = 121  # Default number of frames to generate
-
-    # Generation frame rate
-    fps: int = 24
-
-    # Explicit CFG behavior policy:
-    # - "legacy": CFG branch only when guidance_scale > 1.0
-    # - "official_uncond_at_unity": also run uncond branch at guidance_scale == 1.0
-    cfg_behavior: str = "legacy"
-    default_negative_prompt: str = (
-        "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
-        "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
-        "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, "
-        "jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special "
-        "effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and "
-        "flickering. Overall, the video is of poor quality.")
-
-    # Autoregressive generation settings
-    autoregressive_chunk_frames: int = 121  # Frames per chunk
-    autoregressive_overlap_frames: int = 1  # Overlap between chunks
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-
-        self._vae_latent_dim = 16
-
-        # Validate frame buffer configuration matches DiT
-        if hasattr(self.dit_config, 'arch_config'):
-            arch_config = self.dit_config.arch_config
-            if (hasattr(arch_config, 'frame_buffer_max') and arch_config.frame_buffer_max != self.frame_buffer_max):
-                raise ValueError(f"frame_buffer_max mismatch: pipeline config has {self.frame_buffer_max}, "
-                                 f"DiT config has {arch_config.frame_buffer_max}")
-
-        allowed_cfg_behavior = {"legacy", "official_uncond_at_unity"}
-        if self.cfg_behavior not in allowed_cfg_behavior:
-            raise ValueError(f"cfg_behavior must be one of {sorted(allowed_cfg_behavior)}, got {self.cfg_behavior!r}")
-
-
-@dataclass
-class Gen3CInferenceConfig(Gen3CConfig):
-    """Configuration for GEN3C inference with optimized defaults."""
-
-    # Use smaller batch sizes for inference
-    batch_size: int = 1
-
-    # Enable gradient checkpointing for memory efficiency
-    gradient_checkpointing: bool = False
-
-    # Inference-specific parameters
-    guidance_scale: float = 1.0
-    num_inference_steps: int = 35
-
-    # Disable noise augmentation during inference
-    noise_aug_strength: float = 0.0
@@ -11,34 +11,10 @@ import torch
 from fastvideo.configs.models import DiTConfig, VAEConfig
 from fastvideo.configs.models.dits.base import DiTArchConfig
 from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
-from fastvideo.configs.models.encoders.base import TextEncoderArchConfig
-from fastvideo.configs.models.encoders.t5 import T5ArchConfig
 from fastvideo.configs.models.vaes import WanVAEConfig
 from fastvideo.configs.pipelines.base import PipelineConfig


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

-    # UMT5 uses T5-like config; postprocess pads to 512. LongCatT5Config
-    # restores ``padding="max_length"`` for the CFG concat contract.
-    text_encoder_configs: tuple[T5Config, ...] = field(default_factory=lambda: (LongCatT5Config(), ))
+    # Text encoding (UMT5 uses T5-like config; postprocess to fixed 512)
+    text_encoder_configs: tuple[T5Config, ...] = field(default_factory=lambda: (T5Config(), ))
    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(default_factory=lambda: (longcat_preprocess_text, ))
    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
                                  ...] = field(default_factory=lambda: (umt5_postprocess_text, ))
@@ -1,67 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""`PipelineConfig` for Stable Audio Open 1.0."""
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models import DiTConfig, VAEConfig
-from fastvideo.configs.models.dits import StableAudioConfig
-from fastvideo.configs.models.vaes import OobleckVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-
-
-@dataclass
-class StableAudioT2AConfig(PipelineConfig):
-    """Stable Audio Open 1.0 pipeline config."""
-
-    dit_config: DiTConfig = field(default_factory=StableAudioConfig)
-    # Standard `TransformerLoader` reads `dit_precision`; default in
-    # `PipelineConfig` is bf16, but we want fp16 to match official.
-    dit_precision: str = "fp16"
-
-    vae_config: VAEConfig = field(default_factory=OobleckVAEConfig)
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    # `StableAudioMultiConditioner` owns its own T5; zero out the
-    # parent's text-encoder slots so the length-equality validator passes.
-    text_encoder_configs: tuple = field(default_factory=tuple)
-    preprocess_text_funcs: tuple = field(default_factory=tuple)
-    postprocess_text_funcs: tuple = field(default_factory=tuple)
-
-    num_inference_steps: int = 100
-    guidance_scale: float = 7.0
-    audio_end_in_s: float = 10.0  # short-clip default
-    audio_start_in_s: float = 0.0
-    sampling_rate: int = 44100
-    audio_channels: int = 2
-    # Stable Audio Open 1.0 was trained at a fixed 2,097,152-sample
-    # window (= 2097152 / 44100 ≈ 47.55s). Anything past this is
-    # silently truncated by the post-decode slice — validate up-front.
-    sample_size: int = 2097152
-    max_audio_duration_s: float = 2097152 / 44100
-
-    # Match the official `stable_audio_tools` defaults (`model_half=True`
-    # in `run_gradio.py`), which loads the DiT, VAE, and T5 in fp16 and
-    # wraps T5 forward in `autocast(fp16)`. fp16 is also a hard
-    # requirement for FlashAttention-2 / FA-3.
-    precision: str = "fp16"
-    vae_precision: str = "fp16"
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=tuple)
-
-    def __post_init__(self) -> None:
-        # A2A needs encode; load both halves for either path.
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-
-
-@dataclass
-class StableAudioOpenSmallConfig(StableAudioT2AConfig):
-    """`stable-audio-open-small` overrides: shorter training window
-    (524288 samples ≈ 11.89s @ 44.1 kHz) and a faster default sampler
-    config carried by the small preset.
-    """
-
-    sample_size: int = 524288
-    max_audio_duration_s: float = 524288 / 44100
-    audio_end_in_s: float = 6.0  # short-clip default suitable for the small window
@@ -0,0 +1,13 @@
+from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.hunyuangamecraft import (
+    HunyuanGameCraftSamplingParam,
+    HunyuanGameCraft65FrameSamplingParam,
+    HunyuanGameCraft129FrameSamplingParam,
+)
+
+__all__ = [
+    "SamplingParam",
+    "HunyuanGameCraftSamplingParam",
+    "HunyuanGameCraft65FrameSamplingParam",
+    "HunyuanGameCraft129FrameSamplingParam",
+]
@@ -1,16 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import copy
-from dataclasses import dataclass, field, fields
-from typing import TYPE_CHECKING, Any
+from dataclasses import dataclass
+from typing import Any

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

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


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

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

    # Camera control inputs (LingBotWorld)
    c2ws_plucker_emb: Any | None = None  # Plucker embedding: [B, C, F_lat, H_lat, W_lat]
@@ -84,67 +68,10 @@ class SamplingParam:
    num_inference_steps: int = 50
    num_inference_steps_sr: int = 50
    guidance_scale: float = 1.0
-    guidance_scale_2: float | None = None
    guidance_rescale: float = 0.0
    boundary_ratio: float | None = None
    sigmas: list[float] | None = None

-    # TeaCache parameters
-    enable_teacache: bool = False
-
-    # GEN3C camera control
-    trajectory_type: str | None = None
-    movement_distance: float | None = None
-    camera_rotation: str | None = None
-
-    # LTX-2 multi-modal CFG and STG.
-    # cfg_scale defaults are 1.0 (CFG off) so ``ForwardBatch.__post_init__``
-    # doesn't force ``do_classifier_free_guidance`` on non-LTX-2 models that
-    # never override these fields. LTX-2 presets that need text-CFG on set
-    # them in their ``defaults`` dict (e.g. ``ltx2_base``).
-    ltx2_cfg_scale_video: float = 1.0
-    ltx2_cfg_scale_audio: float = 1.0
-    ltx2_modality_scale_video: float = 3.0
-    ltx2_modality_scale_audio: float = 3.0
-    ltx2_rescale_scale: float = 0.7
-    ltx2_stg_scale_video: float = 1.0
-    ltx2_stg_scale_audio: float = 1.0
-    ltx2_stg_blocks_video: list[int] = field(default_factory=lambda: [29])
-    ltx2_stg_blocks_audio: list[int] = field(default_factory=lambda: [29])
-
-    # Stable Audio (T2A): clip start/end in seconds. Honored by
-    # `StableAudioConditioningStage` + `StableAudioDecodingStage`. Other
-    # families ignore them.
-    audio_start_in_s: float | None = None
-    audio_end_in_s: float | None = None
-
-    # Stable Audio audio-to-audio (variation):
-    #   `init_audio` -- a path or `[B, C, samples]` waveform at the model
-    #                   sample rate; the pipeline encodes it via the VAE
-    #                   and uses it as the starting latent.
-    #   `init_audio_strength` -- 0..1, higher = closer to the reference
-    #                            (matches the convention of Stability's
-    #                            commercial Stable Audio 2.0 UI). 1.0 ~=
-    #                            VAE round-trip, 0.0 ~= plain T2A.
-    #   `init_noise_level` -- legacy raw `sigma_max` override (0.3..500,
-    #                         higher = more freedom). Kept for callers
-    #                         that already use it; prefer `init_audio_strength`.
-    init_audio: Any = None
-    init_audio_strength: float | None = None
-    init_noise_level: float | None = None
-
-    # Stable Audio inpainting (RePaint-style): `inpaint_audio` is the
-    # reference clip, `inpaint_mask` is a [samples] tensor in {0, 1} where
-    # 1 means *keep the reference* and 0 means *regenerate*.
-    inpaint_audio: Any = None
-    inpaint_mask: Any = None
-
-    # Continuation state carried across streaming/multi-segment calls.
-    continuation_state: ContinuationState | None = None
-    # When True, the pipeline returns a ContinuationState on the result so
-    # the caller can resume from the generated segment.
-    return_continuation_state: bool = False
-
    # Misc
    save_video: bool = True
    return_frames: bool = True
@@ -159,58 +86,26 @@ class SamplingParam:
            raise ValueError("prompt_path must be a txt file")

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

        self.__post_init__()

    @classmethod
-    def from_pretrained(cls, model_path: str) -> SamplingParam:
-        sampling_param = cls._from_preset(model_path)
-        if sampling_param is not None:
-            return sampling_param
+    def from_pretrained(cls, model_path: str) -> "SamplingParam":
+        from fastvideo.registry import get_sampling_param_cls_for_name
+        sampling_cls = get_sampling_param_cls_for_name(model_path)
+        if sampling_cls is not None:
+            sampling_param: SamplingParam = sampling_cls()
+        else:
+            logger.warning("Couldn't find an optimal sampling param for %s. Using the default sampling param.",
+                           model_path)
+            sampling_param = cls()

-        logger.warning(
-            "Couldn't find a preset for %s."
-            " Using the default sampling param.",
-            model_path,
-        )
-        return cls()
-
-    @classmethod
-    def _from_preset(
-        cls,
-        model_path: str,
-    ) -> SamplingParam | None:
-        """Build a SamplingParam from preset defaults.
-
-        Returns ``None`` when no preset is configured for
-        *model_path*, letting the caller fall back to the legacy
-        subclass lookup.
-        """
-        from fastvideo.registry import get_preset_selection
-
-        try:
-            preset_name, model_family = get_preset_selection(model_path)
-        except (ValueError, RuntimeError):
-            return None
-        if preset_name is None or model_family is None:
-            return None
-
-        from fastvideo.api.presets import get_preset
-
-        preset = get_preset(preset_name, model_family)
-        sp = cls()
-        valid_fields = {f.name for f in fields(cls)}
-        for key, value in preset.defaults.items():
-            if key in valid_fields:
-                setattr(sp, key, copy.deepcopy(value))
-        sp.__post_init__()
-        return sp
+        return sampling_param

    @staticmethod
    def add_cli_args(parser: Any) -> Any:
@@ -0,0 +1,18 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Cosmos_Predict2_2B_Video2World_SamplingParam(SamplingParam):
+    # Video parameters
+    height: int = 704
+    width: int = 1280
+    num_frames: int = 93
+    fps: int = 16
+
+    # Denoising stage
+    guidance_scale: float = 7.0
+    negative_prompt: str = "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."
+    num_inference_steps: int = 35
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Cosmos25SamplingParamBase(SamplingParam):
+    height: int = 704
+    width: int = 1280
+    num_frames: int = 77
+    fps: int = 24
+    seed: int = 0
+
+    guidance_scale: float = 7.0
+    negative_prompt: str = (
+        "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
+        "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
+        "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, "
+        "low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, "
+        "unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. "
+        "Overall, the video is of poor quality.")
+    num_inference_steps: int = 35
@@ -0,0 +1,21 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class HunyuanSamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 125
+    height: int = 720
+    width: int = 1280
+    fps: int = 24
+
+    guidance_scale: float = 1.0
+
+
+@dataclass
+class FastHunyuanSamplingParam(HunyuanSamplingParam):
+    num_inference_steps: int = 6
@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Hunyuan15_480P_SamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 121
+    height: int = 480
+    width: int = 848
+    fps: int = 24
+
+    guidance_scale: float = 6.0
+    sigmas: list[float] | None = field(default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+
+    negative_prompt: str = ""
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.sigmas = list(np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])
+
+
+@dataclass
+class Hunyuan15_480P_StepDistilled_I2V_SamplingParam(Hunyuan15_480P_SamplingParam):
+    num_inference_steps: int = 12
+
+    height: int = 720
+    width: int = 1280
+    guidance_scale: float = 1.0
+
+
+@dataclass
+class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):
+    height: int = 720
+    width: int = 1280
+
+
+@dataclass
+class Hunyuan15_720P_Distilled_I2V_SamplingParam(Hunyuan15_720P_SamplingParam):
+    guidance_scale: float = 1.0
+
+
+@dataclass
+class Hunyuan15_SR_1080P_SamplingParam(Hunyuan15_480P_SamplingParam):
+    height_sr: int = 1072
+    width_sr: int = 1920
+
+    num_inference_steps: int = 12
+    num_inference_steps_sr: int = 8
+
+    guidance_scale: float = 1.0
@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Sampling parameters for HunyuanGameCraft video generation.
+
+GameCraft generates game-like videos with camera/action control.
+Default parameters are based on the official implementation.
+"""
+from dataclasses import dataclass
+from typing import Any
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class HunyuanGameCraftSamplingParam(SamplingParam):
+    """Sampling parameters for HunyuanGameCraft video generation.
+    
+    Supports camera/action conditioning via:
+    - camera_trajectory: Plücker coordinates for camera motion
+    - action_list: List of actions (e.g., ["forward", "left", "right"])
+    - action_speed_list: Speed multipliers for each action
+    
+    Default resolution is 704x1280 (same as HunyuanVideo).
+    Default frame count is 33 video frames -> 9 latent frames.
+    """
+
+    # Number of denoising steps
+    num_inference_steps: int = 50
+
+    # Video dimensions
+    # 33 video frames -> 9 latent frames (4x temporal compression)
+    num_frames: int = 33
+    height: int = 704
+    width: int = 1280
+    fps: int = 24
+
+    # Guidance scale - official GameCraft uses CFG with guidance_scale=6.0
+    guidance_scale: float = 6.0
+
+    # Negative prompt for CFG (empty string = unconditional)
+    negative_prompt: str = ""
+
+    # Camera/Action conditioning
+    # Camera states as Plücker coordinates [B, T_video, 6, H, W]
+    camera_states: Any | None = None
+
+    # Camera trajectory file/identifier (alternative to camera_states)
+    camera_trajectory: str | None = None
+
+    # Action list for camera motion (e.g., ["forward", "left"])
+    action_list: list[str] | None = None
+
+    # Speed multipliers for each action
+    action_speed_list: list[float] | None = None
+
+    # History frame conditioning (for autoregressive generation)
+    # Ground truth latents for conditioning [B, 16, T, H, W]
+    gt_latents: Any | None = None
+
+    # Mask for conditioning (1=use gt, 0=generate) [B, 1, T, H, W]
+    conditioning_mask: Any | None = None
+
+    # Number of conditioning frames (for autoregressive) - maps to num_cond_frames
+    num_cond_frames: int = 0
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        # Validate action lists
+        if (self.action_list is not None and self.action_speed_list is not None
+                and len(self.action_list) != len(self.action_speed_list)):
+            raise ValueError(f"action_list length ({len(self.action_list)}) must match "
+                             f"action_speed_list length ({len(self.action_speed_list)})")
+
+
+@dataclass
+class HunyuanGameCraft65FrameSamplingParam(HunyuanGameCraftSamplingParam):
+    """Sampling parameters for 65-frame GameCraft generation.
+    
+    65 video frames -> 17 latent frames (with first frame as key frame).
+    This is useful for longer video generation.
+    """
+    num_frames: int = 65
+
+
+@dataclass
+class HunyuanGameCraft129FrameSamplingParam(HunyuanGameCraftSamplingParam):
+    """Sampling parameters for 129-frame GameCraft generation.
+    
+    129 video frames -> 33 latent frames.
+    This is the maximum supported by the official implementation.
+    """
+    num_frames: int = 129
@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+import numpy as np
+
+
+@dataclass
+class HYWorld_SamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 125
+    height: int = 480
+    width: int = 832
+    fps: int = 24
+
+    # Camera trajectory: pose string (e.g., 'w-31' means generating [1 + 31] latents) or JSON file path
+    pose: str = 'w-31'
+
+    guidance_scale: float = 6.0
+    prompt_attention_mask: list = field(default_factory=list)
+    negative_attention_mask: list = field(default_factory=list)
+    sigmas: list[float] | None = field(default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+
+    negative_prompt: str = ""
@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+from fastvideo.configs.sample.wan import Wan2_2_I2V_A14B_SamplingParam
+
+
+@dataclass
+class LingBotWorld_SamplingParam(Wan2_2_I2V_A14B_SamplingParam):
+    guidance_scale: float = 5.0  # high_noise
+    guidance_scale_2: float = 5.0  # low_noise
+    num_inference_steps: int = 70
+    boundary_ratio: float | None = 0.947
+    negative_prompt: str | None = ("画面突变，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，"
+                                   "最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，"
+                                   "畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走，"
+                                   "镜头晃动，画面闪烁，模糊，噪点，水印，签名，文字，变形，扭曲，液化，不合逻辑的结构，卡顿，"
+                                   "PPT幻灯片感，过暗，欠曝，低对比度，霓虹灯光感，过度锐化，3D渲染感，人物，行人，游客，身体，"
+                                   "皮肤，肢体，面部特征，汽车，电线")
+    fps: int = 16
+    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
+    # can be overridden during sampling
--- 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`