[ci] Cherrypick perf regression skills to component time commit

[ci] Add performance baseline reseed skill and failed-run artifacts

.agents/memory/codebase-map/README.md

@@ -119,7 +119,7 @@ FastVideo-WorldModel/
 ## Build & Test Commands
 
 ```bash
-uv pip install -e .[dev]                          # Editable install
+uv pip install -e ".[dev]"                        # Editable install
 pre-commit run --all-files                        # Lint/format/spell
 pytest tests/                                     # Top-level tests
 pytest fastvideo/tests/ -v                        # Package tests

.agents/scripts/sync-skills.sh

@@ -0,0 +1,96 @@
+#!/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"

.agents/skills/index.jsonl

@@ -5,3 +5,6 @@
 {"name": "evaluate-video-quality", "description": "Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)", "path": "evaluate-video-quality/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "index-related-work", "description": "Ingest a paper or repository into the related work index", "path": "index-related-work/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "search-related-work", "description": "Query the related work index for relevant papers, repos, or comparisons", "path": "search-related-work/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "seed-ssim-references", "description": "Run a new or updated fastvideo/tests/ssim/ test on Modal, pull generated videos, and upload them to FastVideo/ssim-reference-videos so the test has a regression baseline", "path": "seed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "reseed-ssim-references", "description": "Re-seed (overwrite) HF reference videos for an existing fastvideo/tests/ssim/ test and a single model id on Modal L40S. Always backs up current refs first, regenerates on Modal, pauses for the user to eyeball before-vs-after, then uploads with --force scoped to --model-id. Sister skill to seed-ssim-references; use when intentional code change has invalidated existing refs", "path": "reseed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "reseed-performance-baseline", "description": "Re-seed the HF performance-tracking baseline for an intentional runtime, dependency, or environment-caused benchmark shift. Use when performance CI fails because metrics such as latency, throughput, component time, or peak memory changed for an accepted reason and the rolling median baseline must be advanced by replicating one reviewed shifted source result into three success=true records, or five records when explicitly requested", "path": "reseed-performance-baseline/SKILL.md", "status": "draft", "trust": "low"}

.agents/skills/launch-experiment/SKILL.md

@@ -12,7 +12,7 @@ automates the boilerplate of setting environment variables, picking the right
 entrypoint, and applying defaults from the closest example script.
 
 ## Prerequisites
-- The repo is cloned and `fastvideo` is installed (`uv pip install -e .[dev]`).
+- The repo is cloned and `fastvideo` is installed (`uv pip install -e ".[dev]"`).
 - Dataset is preprocessed (see `docs/training/data_preprocess.md`).
 - `WANDB_API_KEY` is set in the environment (or `WANDB_MODE=offline` for local).
 - GPU resources are available (multi-GPU requires NCCL).

.agents/skills/reseed-performance-baseline/SKILL.md

@@ -0,0 +1,426 @@
+---
+name: reseed-performance-baseline
+description: Re-seed the HF performance-tracking baseline for an intentional runtime, dependency, or environment-caused benchmark shift. Use when performance CI fails because metrics such as latency, throughput, component time, or peak memory changed for an accepted reason and the rolling median baseline in FastVideo/performance-tracking must be advanced by replicating one reviewed shifted source result into three success=true records, or five records when explicitly requested.
+---
+
+# Re-seed Performance Baseline
+
+## Purpose
+
+Replace or advance the rolling performance baseline for a single
+`(model_id, gpu_type)` pair in the HF dataset
+`FastVideo/performance-tracking`.
+
+Performance comparison uses the median of up to the last 5 successful records
+for the same model and GPU. Failed records are useful audit history, but they
+do not move the future baseline because `compare_baseline.py` loads records
+with `successful_only=True`.
+
+For a 5-record median, one shifted record is not enough to move the median if
+the other four records are from the old runtime. This skill therefore creates
+3 reviewed `success=true` records from one accepted shifted source result by
+default. If the user explicitly asks for a full reset, create 5 records.
+
+These replicated records are an intentional operator-approved baseline reset,
+not independent measurements. Mark them clearly with provenance fields so the
+HF history remains auditable.
+
+Use this skill when a performance test fails for an intentional and reviewed
+reason, such as a torch/runtime/container upgrade that legitimately increases
+peak memory or changes timings. This is the performance equivalent of
+`reseed-ssim-references`: backup first, scope tightly, require explicit human
+approval, then upload reviewed accepted baseline records.
+
+## When to use
+
+- A PR or main run failed the rolling performance comparison by more than the
+  allowed regression threshold, and maintainers agree the shift is caused by
+  an intentional runtime, dependency, hardware image, or benchmark environment
+  change rather than a FastVideo logic regression.
+- One shifted source result has been reviewed and accepted, and the operator
+  wants to replicate it into 3 successful records so the rolling median moves
+  immediately. Use 5 records only when the user explicitly asks to fully reset
+  the last-5 window.
+
+## When not to use
+
+- The benchmark failure might be a real code regression. Fix or investigate
+  the code path first.
+- The fixed benchmark thresholds in
+  `.buildkite/performance-benchmarks/tests/*.json` are too low. Those are a
+  separate gate from the rolling HF baseline and may need a code review change.
+- There is no clear source run, commit, and rationale. Baseline history is a
+  production signal; do not edit it without provenance.
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `model_id` | Yes | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. |
+| `gpu_type` | Yes | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. Baselines are GPU-specific. |
+| `source_result` | Yes | Path or Buildkite artifact URL for one accepted shifted performance JSON. Prefer the normalized `normalized_perf_*.json` artifact emitted by `compare_baseline.py`. |
+| `replica_count` | No | Number of success records to create from `source_result`. Default: `3`. Only use `5` if the user explicitly asks for a full reset. |
+| `intent_rationale` | Yes | One-line explanation for why the baseline shift is legitimate. This is written into provenance and should be reused in the PR. |
+
+Hardcoded defaults:
+
+- HF repo: `FastVideo/performance-tracking` (`HF_REPO_ID` override is
+  supported by the code, but use the default unless the user explicitly asks).
+- Local sync root: `/tmp/perf-tracking` or a timestamped local backup under
+  `performance_reseed_backup/`.
+- Baseline window: last 5 `success=true` records for the same
+  `(model_id, gpu_type)`.
+- Default reseed count: 3 replicated `success=true` records from one reviewed
+  source result. Explicit full-reset count: 5.
+
+## Steps
+
+### 1. Validate the target and source result
+
+If `source_result` is a Buildkite artifact URL, download it first into a
+local scratch directory such as `performance_reseed_source/` and use that
+downloaded JSON path for the rest of the workflow. If the agent cannot access
+the artifact because Buildkite authentication is missing, ask the user to
+download the artifact manually and provide the local path.
+
+Prefer the normalized Buildkite artifact emitted by `compare_baseline.py`:
+
+```text
+perf_reports/results/normalized_perf_*.json
+```
+
+That file is already in the HF tracking schema. Load it directly and confirm
+it has the expected baseline fields:
+
+```python
+import json
+
+with open(source_result, encoding="utf-8") as f:
+    record = json.load(f)
+```
+
+If only the older raw `fastvideo/tests/performance/results/perf_*.json`
+artifact is available, normalize it with `compare_baseline.py`'s shared helper
+before continuing. Run this from the repository root with
+`PYTHONPATH=fastvideo/tests/performance` so the script-local `hf_store` import
+resolves the same way it does in CI:
+
+```python
+import json
+from compare_baseline import normalize_performance_result
+
+with open(source_result, encoding="utf-8") as f:
+    record = normalize_performance_result(json.load(f))
+```
+
+The raw-to-normalized helper maps:
+
+- `model_id` comes from `benchmark_id`.
+- `gpu_type` comes from `device`.
+- `memory` comes from `max_peak_memory_mb`.
+- `latency` comes from `avg_generation_time_s`.
+- `throughput` comes from `throughput_fps`.
+- component timings come from the raw `text_encoder_time_s`, `dit_time_s`,
+  and `vae_decode_time_s` fields when present. If an older raw artifact lacks
+  those keys, they normalize to `None`; that source can still reseed latency,
+  throughput, and memory, but it cannot move component-time baselines.
+
+Stop if the normalized record's `model_id` or `gpu_type` does not match the
+requested `model_id` and `gpu_type`.
+
+The source record may have `success: false` when it came from a failed rolling
+baseline comparison. That is expected; only the reviewed reseed replicas become
+new `success: true` baseline records after explicit approval.
+
+Set `replica_count` to `3` by default. Set it to `5` only when the user
+explicitly asks to upload the same shifted source result 5 times for a full
+last-5 reset. Reject other counts unless the user gives a concrete reason.
+
+Check that `HF_API_KEY` is exported. The sync path may be public, but the
+upload path requires write access.
+
+### 1a. How to obtain `source_result` from CI
+
+The performance CI exports normalized source results for failed rolling
+baseline comparisons when `compare_baseline.py` ran. The preferred artifact
+comes from:
+
+```text
+perf_reports/results/normalized_perf_*.json
+```
+
+and is uploaded by Buildkite with the performance reports. The normal operator
+flow is:
+
+1. Open the failed Buildkite performance job.
+2. Download the `normalized_perf_*.json` artifact for the failed benchmark.
+3. Pass the local path or artifact URL as `source_result`.
+
+Do not scrape the Markdown performance summary to reconstruct the JSON. The
+normalized JSON artifact is the source of truth for reseed metrics and
+provenance. If only a raw `fastvideo/tests/performance/results/perf_*.json`
+artifact is present, normalize it with `normalize_performance_result()` before
+continuing. If no JSON artifact is present, the benchmark likely failed before
+writing results, so that run is not a valid source for baseline reseeding.
+
+### 2. Sync and back up existing HF records
+
+Use `fastvideo/tests/performance/hf_store.py` helpers directly. Do **not** use
+`compare_baseline.py` as a sync shortcut; on full main runs it can persist
+records, while this step must only fetch and back up existing history.
+
+The sync command pattern is:
+
+```bash
+export PERFORMANCE_TRACKING_ROOT="${PERFORMANCE_TRACKING_ROOT:-/tmp/perf-tracking}"
+export HF_REPO_ID="${HF_REPO_ID:-FastVideo/performance-tracking}"
+PYTHONPATH=fastvideo/tests/performance python -c 'from hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
+```
+
+Then back up only the sanitized model directory:
+
+```bash
+SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
+TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
+MODEL_SAFE=$(python - <<'PY'
+from fastvideo.tests.performance.hf_store import sanitize
+print(sanitize("<model_id>"))
+PY
+)
+BACKUP_DIR="performance_reseed_backup/${TIMESTAMP}_${SHORT_COMMIT}_${MODEL_SAFE}"
+mkdir -p "$BACKUP_DIR"
+cp -R "${PERFORMANCE_TRACKING_ROOT}/${MODEL_SAFE}" "$BACKUP_DIR/" 2>/dev/null || true
+```
+
+Write provenance next to the backup:
+
+```bash
+cat > "$BACKUP_DIR/PROVENANCE.txt" <<EOF
+model_id: <model_id>
+gpu_type: <gpu_type>
+source_result: <source_result>
+replica_count: <3_or_5>
+head_commit: $(git rev-parse HEAD)
+timestamp_utc: $(date -u +%FT%TZ)
+reason: <intent_rationale>
+EOF
+```
+
+If the backup has no prior records, this is not a destructive reseed; it is a
+first baseline seed. Continue, but report that baseline history was empty.
+
+### 3. Compute old baseline and candidate shift
+
+Load the last 5 successful records for the target:
+
+```python
+from fastvideo.tests.performance.hf_store import load_records_for_model
+
+records = load_records_for_model(
+    "/tmp/perf-tracking",
+    "<model_id>",
+    "<gpu_type>",
+    last_n=5,
+    successful_only=True,
+)
+```
+
+Print a small table showing the source result metrics, the replicated
+candidate median, and the old medians for:
+
+- `latency`
+- `throughput`
+- `memory`
+- `text_encoder_time_s`
+- `dit_time_s`
+- `vae_decode_time_s`
+
+Also print how many successful old records exist. Make clear:
+
+- 1 shifted record only seeds audit history and usually does not move the
+  median.
+- 3 replicated shifted records in a 5-record window move the median
+  immediately.
+- 5 replicated shifted records fully reset the rolling window to the source
+  result's runtime profile.
+- Replicated records are not independent measurements; they are an intentional
+  approved baseline reset and must be labeled that way.
+
+### 4. Confirm intent
+
+Require an explicit confirmation phrase before preparing the upload:
+
+> About to RE-SEED performance baseline for `<model_id>` on `<gpu_type>`.
+> This will upload `<N>` new `success=true` records to
+> `FastVideo/performance-tracking/<sanitize(model_id)>/`.
+>
+> Reason: `<intent_rationale>`
+> Source result: `<source_result>`
+> Replica count: `<replica_count>`
+> Note: these records replicate one reviewed measurement to force the rolling
+> median to the accepted runtime profile.
+> HEAD: `<git rev-parse --short=12 HEAD>`
+> Backup: `<BACKUP_DIR>`
+>
+> Reply `confirm performance reseed` to proceed, anything else to abort.
+
+Do not continue unless the user types exactly `confirm performance reseed`.
+
+### 5. Create the accepted seed records
+
+Create `replica_count` normalized records from the single source result. Use
+an explicit allowlist; do not copy the raw result JSON wholesale.
+
+Each record must include only these baseline fields plus the reseed provenance
+fields below:
+
+- `model_id`
+- `timestamp`
+- `commit_sha`
+- `gpu_type`
+- `latency`
+- `throughput`
+- `memory`
+- `text_encoder_time_s`
+- `dit_time_s`
+- `vae_decode_time_s`
+- `success: true`
+
+For normalized `normalized_perf_*.json` sources, these fields already exist.
+For older raw `perf_*.json` sources, map the raw fields exactly as
+`normalize_performance_result()` in `compare_baseline.py` does:
+
+| Normalized field | Raw source field |
+|------------------|------------------|
+| `model_id` | `benchmark_id` |
+| `gpu_type` | `device` |
+| `latency` | `avg_generation_time_s` |
+| `throughput` | `throughput_fps` |
+| `memory` | `max_peak_memory_mb` |
+| `text_encoder_time_s` | `text_encoder_time_s` |
+| `dit_time_s` | `dit_time_s` |
+| `vae_decode_time_s` | `vae_decode_time_s` |
+| `commit_sha` | `commit` |
+
+Do not upload raw-only fields such as `model_short_name`, `num_gpus`,
+`num_warmup_runs`, `num_measurement_runs`, `individual_times_s`,
+`individual_peak_memories_mb`, `thresholds`, or `pr_number`.
+
+Optional provenance fields are allowed and useful:
+
+- `baseline_reseed: true`
+- `baseline_reseed_reason`
+- `baseline_reseed_source_result`
+- `baseline_reseed_source_timestamp`
+- `baseline_reseed_replicated_source: true`
+- `baseline_reseed_batch_size`
+- `baseline_reseed_batch_index`
+- `baseline_reseed_operator`
+
+Use a fresh reseed timestamp for each replicated record, not the original
+source result timestamp. This is required because
+`load_records_for_model(..., last_n=5)` keeps the last records after loading
+the model directory; stale filenames/timestamps may not enter the last-5
+window and therefore may not move the median. Preserve the original source
+timestamp in `baseline_reseed_source_timestamp`.
+
+Use the existing filename convention from `_write_tracking_record()`:
+`<sanitize(timestamp)>_<sanitize(commit_sha)>.json` under the sanitized model
+directory, but include a deterministic suffix such as `_reseed_01`,
+`_reseed_02`, and `_reseed_03` before `.json` so the replicated files do not
+overwrite each other. For a 5-record full reset, continue through
+`_reseed_05`.
+
+If the source record already exists on HF with `success=false`, do not edit it
+in place unless the user explicitly asked for an audit-preserving correction.
+Prefer uploading new accepted seed records so failed history remains visible.
+
+### 6. Pause before upload
+
+Print:
+
+- Backup directory path.
+- HF paths that will receive the new records.
+- Old rolling medians.
+- Source metrics, replica count, and candidate median.
+- Rationale.
+
+Ask the user to reply exactly `upload`. Anything else aborts and leaves the
+prepared records plus backup on disk.
+
+### 7. Upload only the scoped records
+
+Use the shared storage helper so the path and repo type match CI:
+
+```python
+from fastvideo.tests.performance.hf_store import upload_record
+
+upload_record("<local_record_path>", record, strict=True)
+```
+
+Run it once per prepared record. Each upload goes to:
+
+```text
+FastVideo/performance-tracking/<sanitize(model_id)>/<record_filename>.json
+```
+
+Never bulk upload the whole tracking root. Never modify another model's
+directory in the same operation.
+
+### 8. Report outcome
+
+Report:
+
+- Uploaded HF paths.
+- Backup directory.
+- Old baseline window count and medians.
+- Source metrics, replica count, and candidate median.
+- Expected effect: 3 replicated shifted records move the 5-record median; 5
+  replicated shifted records fully reset the window to the accepted source
+  result.
+- Any separate threshold changes still needed in
+  `.buildkite/performance-benchmarks/tests/*.json`.
+
+Include the `intent_rationale` in the PR or follow-up comment so reviewers can
+distinguish an accepted baseline shift from a hidden regression.
+
+## Failure modes and handling
+
+- **`HF_API_KEY` unset.** Stop before upload. Do not create an untracked
+  process that appears to have reseeded but never reached HF.
+- **Source result does not match target.** Stop. The wrong benchmark or GPU
+  would poison a separate baseline.
+- **`replica_count` is 5 but the user did not explicitly ask for a full
+  reset.** Stop and use the default count of 3.
+- **The source result is noisy or suspicious.** Stop. Replicating one result
+  amplifies that measurement into the baseline, so it must be reviewed first.
+- **HF sync fails.** Stop for destructive reseeds. A stale or empty sync can
+  make the old baseline look missing.
+- **Candidate still violates fixed thresholds.** Report that this skill only
+  handles the rolling HF baseline; update benchmark JSON thresholds in code
+  review if maintainers accept the new absolute limit.
+- **The user aborts at either confirmation.** Leave the backup and prepared
+  records on disk. Nothing should be uploaded.
+- **A bad seed was uploaded.** Use the backup and HF history to identify the
+  uploaded file, then remove or supersede it with an explicitly reviewed
+  corrective record. Do not silently rewrite unrelated history.
+
+## References
+
+- `.agents/skills/reseed-ssim-references/SKILL.md` — safety pattern for
+  intentional baseline replacement.
+- `fastvideo/tests/performance/compare_baseline.py` — normalization, rolling
+  median comparison, and persistence rules.
+- `fastvideo/tests/performance/hf_store.py` — HF sync, record loading,
+  `sanitize()`, and `upload_record()`.
+- `fastvideo/tests/performance/test_inference_performance.py` — source result
+  JSON schema.
+- `.buildkite/performance-benchmarks/tests/*.json` — fixed absolute benchmark
+  thresholds, separate from rolling baseline comparisons.
+
+## Changelog
+
+| Date | Change |
+|------|--------|
+| 2026-05-03 | Initial version. Sister workflow to `reseed-ssim-references`, scoped to one performance `(model_id, gpu_type)` baseline seed with backup, confirmation, provenance, and `success=true` upload. |
+| 2026-05-03 | Current policy: replicate one approved shifted source result into 3 success records by default, or 5 only when explicitly requested. Add provenance marker for replicated-source reseeds. |

.agents/skills/reseed-ssim-references/SKILL.md

@@ -0,0 +1,343 @@
+---
+name: reseed-ssim-references
+description: Re-seed HF reference videos for a single existing SSIM test on Modal L40S. Always backs up current refs locally first, regenerates on Modal, pauses for the user to eyeball before-vs-after quality, then overwrites the targeted `<model_id>` subtree on `FastVideo/ssim-reference-videos` with `--force`. Use when an intentional code change (model port fix, attention backend swap, kernel upgrade, hyperparameter change) has invalidated existing refs and they need to be regenerated. Pairs with `seed-ssim-references`, which is for first-time seeding only.
+---
+
+# Re-seed SSIM Reference Videos
+
+## Purpose
+
+Replace the existing SSIM reference videos for a single `(test_file, model_id)`
+pair on the HF dataset (`FastVideo/ssim-reference-videos`). This is **destructive**
+on HF — the old refs are overwritten — so the skill always:
+
+1. Confirms intent with a one-liner the user has to type.
+2. Downloads the existing refs as a local, timestamped backup.
+3. Regenerates on Modal L40S (same code path that CI uses).
+4. Pauses for a side-by-side eyeball of backup vs new mp4s.
+5. Uploads with `--force`, scoped to the single `--model-id`.
+6. Reminds the user to keep the backup until the PR lands.
+
+Pairs with `seed-ssim-references`, which is the inverse (first-time seeding
+only, refuses to overwrite). Re-seeding is intentionally a separate, more
+ceremonial operation because mistakenly clobbering production refs is much
+harder to recover from than failing closed.
+
+## When to use
+
+- An intentional code change (model port fix, kernel upgrade, attention
+  backend swap, hyperparameter change in the test itself) has shifted the
+  expected SSIM output and the existing refs no longer represent the new
+  ground truth.
+- A test is failing in CI **for the right reason** (the new code is correct,
+  the old refs are stale).
+
+## When not to use
+
+- A test is failing for the **wrong** reason (the port is buggy, not the
+  refs). Fix the port; re-seeding hides the bug.
+- A brand-new test that has no refs on HF yet. Use `seed-ssim-references`.
+- "Just to clean up drift" without a concrete code change to point at. The
+  PR description has to justify *why* refs changed; without a concrete
+  change, there's nothing to write.
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `test_file` | Yes | Path to the SSIM test, e.g. `fastvideo/tests/ssim/test_matrixgame_similarity.py`. Validated against `fastvideo/tests/ssim/test_*_similarity.py`. |
+| `model_id` | Yes | Single model id from the test's `*_MODEL_TO_PARAMS`, e.g. `Matrix-Game-2.0-Diffusers-Base`. Re-seed runs are **per model**. For multi-model tests, invoke the skill once per model. |
+| `intent_rationale` | Yes | One-line explanation of *why* refs are being regenerated (e.g. "Relax FA-2 head_size whitelist to include 80 — matrix_game now uses FLASH_ATTN instead of TORCH_SDPA"). Recorded in the backup directory and reused in the PR description. |
+
+Hardcoded:
+
+- Modal GPU: **L40S** (matches CI; re-seeding from another SKU produces refs
+  that L40S CI cannot match).
+- Quality tier: **`default`**. `full_quality` is a separate, deliberate
+  operation.
+- HF repo: `FastVideo/ssim-reference-videos` (override via
+  `FASTVIDEO_SSIM_REFERENCE_HF_REPO`).
+- Device folder: `L40S_reference_videos`.
+
+## Prerequisites
+
+The user has confirmed:
+
+- `modal` CLI authenticated.
+- `hf` CLI authenticated, **and** `HF_API_KEY` (or `HUGGINGFACE_HUB_TOKEN` /
+  `HF_TOKEN`) exported with **write** access to
+  `FastVideo/ssim-reference-videos`.
+- The current branch's code is the change that motivated the re-seed (i.e.
+  `git rev-parse HEAD` is the commit that intentionally invalidated refs).
+
+Fail fast if any of these are missing.
+
+## Steps
+
+### 1. Validate inputs and confirm intent
+
+- Verify `test_file` exists and matches `fastvideo/tests/ssim/test_*_similarity.py`.
+- Grep the file for `*_MODEL_TO_PARAMS` and assert `model_id` is one of its
+  keys. If the file has only a single hardcoded model, accept that model id
+  as the only valid value.
+- Print the rationale and ask the user to type **`confirm reseed`** (not just
+  `y` — make it deliberate):
+
+  > About to RE-SEED references for model `<model_id>` from test `<test_file>`.
+  > This will OVERWRITE existing refs on
+  > `FastVideo/ssim-reference-videos/reference_videos/default/L40S_reference_videos/<model_id>/`
+  > after backup + Modal regen + eyeball.
+  >
+  > Reason: `<intent_rationale>`
+  > HEAD: `<git rev-parse --short=12 HEAD>`
+  >
+  > Reply `confirm reseed` to proceed, anything else to abort.
+
+  Stop until the user types exactly `confirm reseed`. Anything else aborts
+  with no side effects.
+
+### 2. Back up existing refs
+
+Always required. The backup is the only graceful path back if anything goes
+wrong later.
+
+```bash
+SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
+TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
+MODEL_SAFE=$(echo "<model_id>" | tr '/' '_')
+BACKUP_DIR="ssim_reseed_backup/${TIMESTAMP}_${SHORT_COMMIT}_${MODEL_SAFE}"
+mkdir -p "$BACKUP_DIR"
+
+hf download \
+    --repo-type dataset FastVideo/ssim-reference-videos \
+    --include "reference_videos/default/L40S_reference_videos/<model_id>/**" \
+    --local-dir "$BACKUP_DIR"
+
+mp4_count=$(find "$BACKUP_DIR" -name "*.mp4" | wc -l)
+echo "Backup mp4 count: $mp4_count"
+[ "$mp4_count" -gt 0 ] || {
+    echo "ERROR: backup is empty for <model_id>. Either the model id is wrong"
+    echo "or there are no existing refs (use seed-ssim-references instead)."
+    exit 1
+}
+
+# Provenance — used in the PR description
+cat > "$BACKUP_DIR/PROVENANCE.txt" <<EOF
+test_file: <test_file>
+model_id: <model_id>
+head_commit: $(git rev-parse HEAD)
+timestamp_utc: $(date -u +%FT%TZ)
+reason: <intent_rationale>
+EOF
+```
+
+If the `hf download` produces zero mp4s, abort — the user has either picked a
+non-existent `model_id` or there are no refs yet (in which case
+`seed-ssim-references` is the right tool).
+
+### 3. Regenerate on Modal L40S
+
+Mirror CI's exact env recipe so the regenerated refs are byte-comparable to
+what CI will produce on the same commit. Two differences from CI:
+
+1. **Pass the same env prefix CI uses** (`IMAGE_VERSION`, `BUILDKITE_*`) — see
+   `.buildkite/pipeline.yml:1-3` and `.buildkite/scripts/pr_test.sh:62-83`.
+   Without this, `ssim_test.py:17-18` resolves a different GHCR image tag
+   (default is `latest`, CI is `py3.12-latest`), and `ssim_test.py:38-46`
+   bakes different values into the image's frozen env block. **Mismatched
+   image or env is the most common source of SSIM drift between reseed and
+   CI runs.**
+2. **Do not pass `--skip-reference-download`**. Letting the test fetch the
+   existing refs and run the full SSIM compare gives "before" SSIM numbers
+   for the PR description, and the test still produces the new mp4s
+   regardless of whether the comparison passes or fails.
+
+```bash
+SUBDIR="${TIMESTAMP}_${SHORT_COMMIT}"
+
+IMAGE_VERSION="py3.12-latest" \
+BUILDKITE_REPO="$(git config --get remote.origin.url)" \
+BUILDKITE_COMMIT="$(git rev-parse HEAD)" \
+BUILDKITE_PULL_REQUEST="${BUILDKITE_PULL_REQUEST:-false}" \
+modal run fastvideo/tests/modal/ssim_test.py \
+    --git-repo="$(git config --get remote.origin.url)" \
+    --git-commit="$(git rev-parse HEAD)" \
+    --hf-api-key="$HF_API_KEY" \
+    --test-files="<test_file>" \
+    --sync-generated-to-volume \
+    --generated-volume-subdir="$SUBDIR" \
+    --no-fail-fast
+```
+
+Capture the printed `modal volume get ...` hint — its `<SUBDIR>` matches
+`$SUBDIR` and is needed for step 4. Capture the SSIM numbers from the test
+output (or from the JSON next to the generated mp4) for the PR description.
+
+### 4. Download generated videos
+
+```bash
+modal volume get --force hf-model-weights \
+    ssim_generated_videos/default/"$SUBDIR"/generated_videos \
+    ./generated_videos_modal/default
+```
+
+After this, the new mp4s live at:
+
+```
+./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4
+```
+
+`--force` is required when `./generated_videos_modal/default` already exists
+from a prior run; safe on the first run too.
+
+### 5. PAUSE — user reviews quality side-by-side
+
+Print the diff and the comparison:
+
+```bash
+echo "=== File list diff (backup vs new) ==="
+diff -u \
+    <(find "$BACKUP_DIR/reference_videos/default/L40S_reference_videos/<model_id>" -name "*.mp4" \
+        | sed "s|$BACKUP_DIR/reference_videos/default/L40S_reference_videos/||" | sort) \
+    <(find ./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id> -name "*.mp4" \
+        | sed "s|./generated_videos_modal/default/generated_videos/L40S_reference_videos/||" | sort) \
+    || true
+
+echo
+echo "=== SSIM numbers from this run (paste into PR) ==="
+find ./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id> -name "*_ssim.json" -exec cat {} \;
+```
+
+Then stop and tell the user:
+
+> Old refs backed up to `$BACKUP_DIR`.
+> New videos in `./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/`.
+>
+> Open both in a video player. Confirm the new videos:
+>   1. Look correct (no obvious artifacts, no black/static frames).
+>   2. Are *intentionally* different from the backup in the way described
+>      in `<intent_rationale>` (e.g. slight numerical drift only, not a
+>      different scene / different motion / corrupted output).
+>
+> Reply **`upload`** to overwrite HF, anything else to abort.
+> Aborting leaves the backup and new videos on disk for inspection — nothing
+> on HF changes.
+
+Do not proceed until the user types exactly `upload`. If they abort, leave
+everything on disk and stop here.
+
+### 6. Copy into the local reference layout
+
+Same as `seed-ssim-references` step 5:
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
+    --quality-tier default \
+    --device-folder L40S_reference_videos \
+    --generated-dir ./generated_videos_modal/default/generated_videos/L40S_reference_videos
+```
+
+Result: `fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
+
+### 7. Upload with `--force`, scoped to `--model-id`
+
+The `--force` flag is what makes this skill different from `seed-ssim-references`.
+Always pair it with `--model-id` so a typo cannot accidentally overwrite a
+neighboring model's refs.
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py upload \
+    --quality-tier default \
+    --device-folder L40S_reference_videos \
+    --model-id "<model_id>" \
+    --force
+```
+
+The CLI's overwrite guard refuses without `--force`; with `--force` it
+overwrites only files under
+`reference_videos/default/L40S_reference_videos/<model_id>/`.
+
+### 8. Report success and retention guidance
+
+Print:
+
+- The HF path that was overwritten (`<repo>/reference_videos/default/L40S_reference_videos/<model_id>/`).
+- The local backup directory path.
+- The new SSIM numbers from step 5.
+- This restore command, in case the PR review surfaces a problem after
+  upload:
+
+  ```bash
+  python fastvideo/tests/ssim/reference_videos_cli.py upload \
+      --quality-tier default \
+      --device-folder L40S_reference_videos \
+      --model-id "<model_id>" \
+      --reference-dir "$BACKUP_DIR/reference_videos/default/L40S_reference_videos" \
+      --force
+  ```
+
+- This PR-description checklist (see `fastvideo/tests/ssim/AGENTS.md` →
+  *Updating Reference Videos*):
+  1. Source commit that produced the new refs (HEAD at re-seed time).
+  2. Test command and GPU SKU (`L40S`).
+  3. Before/after SSIM numbers.
+  4. The `<intent_rationale>` from step 1.
+  5. A note that the backup lives at `$BACKUP_DIR` and should be retained
+     until CI on the PR is green.
+
+Do **not** auto-rerun the SSIM test — the user does that as part of the PR.
+
+## Failure modes and how to handle them
+
+- **`HF_API_KEY` unset.** Stop before step 2.
+- **Backup is empty (zero mp4s).** Stop before step 3 — the model id is
+  wrong or the refs don't exist yet (use `seed-ssim-references`).
+- **Modal run fails before generation.** No mp4s on the volume. Don't
+  upload. Investigate the failure (test crash, OOM, partition exhaustion),
+  fix, then retry from step 3. Backup is still intact.
+- **Quality regressed (visual or metric).** User aborts at step 5. Backup
+  retained. New videos retained on disk for inspection. Nothing on HF
+  changed. Either fix the underlying code change or abandon the re-seed.
+- **User confirmed `upload` but later realized the new refs are wrong.**
+  Run the restore command from step 8 with the backup `--reference-dir`.
+  This is exactly why the backup exists.
+- **Multi-model test, only one model is being re-seeded.** Run the skill
+  once per model id. The `--model-id` scope on upload guarantees the others
+  are untouched.
+
+## Design notes (for future skill maintainers)
+
+- Per-`model_id` scope is mandatory. The dataset houses many model subtrees;
+  re-seeding the wrong one is hard to undo without backup.
+- `default` tier only; `full_quality` is a separate, deliberate operation
+  with different params and ~doubled runtime, and isn't what CI gates on.
+- The skill deliberately does **not** pass `--skip-reference-download` to
+  Modal so we get pre-reseed SSIM numbers for the PR. The `seed`-skill
+  passes it because no refs exist yet; for re-seed, refs do exist and
+  exposing the comparison is informative.
+- The two-token confirm (`confirm reseed`, then `upload`) is intentional.
+  Re-seeding is high-blast-radius and should not be one-keystroke.
+- The backup directory is plain mp4s + `PROVENANCE.txt`. No HF metadata is
+  preserved; the restore path uses `reference_videos_cli.py upload
+  --reference-dir` which doesn't need it.
+
+## References
+
+- `.agents/skills/seed-ssim-references/SKILL.md` — the first-time seed
+  skill this one parallels. Read it for the Modal flag rationale shared
+  between the two flows.
+- `fastvideo/tests/ssim/AGENTS.md` — directory rules, including the PR
+  expectations for any reference-video change (rationale, before/after
+  SSIM, source commit/model/backend).
+- `fastvideo/tests/ssim/reference_videos_cli.py` — `copy-local`, `upload`
+  (with `--model-id`, `--force`), `download`. The overwrite guard at
+  `upload_reference_videos` is the safety net this skill leans on.
+- `fastvideo/tests/modal/ssim_test.py` — Modal orchestrator;
+  `--sync-generated-to-volume`, `--generated-volume-subdir`,
+  `--skip-reference-download`, `--no-fail-fast`.
+
+## Changelog
+
+| Date | Change |
+|------|--------|
+| 2026-05-02 | Initial version. Sister skill to `seed-ssim-references`, scoped to single `(test_file, model_id)` re-seeds, with mandatory backup and two-token confirm. |

.agents/skills/seed-ssim-references/SKILL.md

@@ -0,0 +1,376 @@
+---
+name: seed-ssim-references
+description: Seed HF reference artefacts for a single newly-added SSIM test (pixel `.mp4` for `run_text_to_video_similarity_test`-style tests, or latent `.pt` for `run_text_to_latent_similarity_test`-style tests). Runs the test on Modal L40S, downloads the generated artefacts via `modal volume get`, pauses for the user to verify (visual eyeball for mp4, numerics dump for pt), then uploads only that test's files to `FastVideo/ssim-reference-videos`. Use when a new `fastvideo/tests/ssim/test_*_similarity.py` has just been added and has no references on HF yet.
+---
+
+# Seed SSIM Reference Artefacts (mp4 or pt)
+
+## Purpose
+
+A brand-new SSIM test in `fastvideo/tests/ssim/` fails forever until its
+reference artefacts exist on the HF dataset
+(`FastVideo/ssim-reference-videos`). The dataset hosts two kinds of artefacts
+side-by-side per `(model_id, backend, prompt)`:
+
+- **`.mp4`** — pixel ground-truth for tests that call
+  `run_text_to_video_similarity_test` / `run_image_to_video_similarity_test`
+  in `inference_similarity_utils.py`. Compared via SSIM.
+- **`.pt`** — pre-VAE latent bundle (fp16 full latent + fp32 slice +
+  metadata + `slice_spec` + `format_version`) for tests that call
+  `run_text_to_latent_similarity_test` in `latent_similarity_utils.py`.
+  Compared via cosine distance on the slice and the full tensor.
+
+This skill:
+
+1. Detects which artefact type the test produces (pixel vs latent).
+2. Runs the test on Modal's L40S pool to generate the artefacts.
+3. Downloads them to the local repo via `modal volume get`.
+4. Pauses so the user can verify quality:
+   - **mp4**: visual eyeball in a video player.
+   - **pt**: numerics dump (shape, slice stats, NaN/Inf check, metadata).
+5. Uploads only the new test's files to HF, with a guard that refuses to
+   overwrite anything already present.
+
+The skill is run **manually**, once per new test. Before invoking it, the user
+has already sanity-tested the new test locally — it launches `VideoGenerator`
+and writes an artefact without crashing (the missing-reference assertion at
+the end is expected). The skill does not re-test locally; it goes straight
+to Modal L40S (which is what CI uses).
+
+## When to use
+
+- A new `test_*_similarity.py` file has been added in `fastvideo/tests/ssim/`
+  and the HF dataset has no `reference_videos/default/L40S_reference_videos/<model_id>/`
+  subtree for it yet.
+
+## When not to use
+
+- Regular CI runs — once refs exist, `pytest fastvideo/tests/ssim/` downloads
+  them automatically.
+- Re-seeding an existing test. That requires `--force` on the upload step, and
+  is out of scope here; treat as a separate, deliberate operation.
+
+## Inputs
+
+The skill has **one required input**: the path to the new SSIM test file.
+Prompt the user for it if they didn't supply it.
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `test_file` | Yes | e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`. The skill's first action is to ask for this if missing. |
+
+Everything else is fixed:
+
+- Modal runner GPU: **L40S** (hardcoded in `fastvideo/tests/modal/ssim_test.py`).
+- Device folder: `L40S_reference_videos`.
+- Quality tier: `default` (the tier CI runs). The `full_quality` tier is not
+  seeded by this skill.
+- HF repo: `FastVideo/ssim-reference-videos` (dataset).
+- Multi-model test files: all model ids in `*_MODEL_TO_PARAMS` are seeded
+  together; the Modal run produces one mp4 per (model, prompt, backend) and
+  the upload scopes by `--model-id`, looping if there is more than one.
+
+## Prerequisites
+
+The user has confirmed:
+
+- `modal` CLI authenticated.
+- `HF_API_KEY` (or `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`) exported with write
+  access to `FastVideo/ssim-reference-videos`.
+- The test file runs locally end-to-end (generates an mp4; SSIM assertion
+  failure due to missing reference is expected and fine).
+
+Fail fast if the token env var is missing.
+
+## Steps
+
+### 1. Ask for the test file, then detect artefact type
+
+If the user didn't name one, ask: *"Which SSIM test file do you want to seed
+references for? (e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`)"*.
+
+Validate:
+
+- Path exists and matches `fastvideo/tests/ssim/test_*_similarity.py`.
+- File defines a `*_MODEL_TO_PARAMS` dict — grep it to extract the set of
+  model ids. Those ids drive step 5.
+
+Detect artefact type by inspecting the file's imports / helper call:
+
+- **latent** (`.pt`) — file imports `run_text_to_latent_similarity_test`
+  from `fastvideo.tests.ssim.latent_similarity_utils` (or any other helper
+  that ends with `_latent_similarity_test`).
+- **pixel** (`.mp4`) — file imports
+  `run_text_to_video_similarity_test` / `run_image_to_video_similarity_test`
+  from `fastvideo.tests.ssim.inference_similarity_utils`, OR uses the
+  legacy custom-inline helper pattern (see `test_gamecraft`,
+  `test_longcat`, etc.). Default to pixel when both heuristics fail.
+
+Record `ARTEFACT_TYPE ∈ {pixel, latent}` for use in step 4. Steps 2, 3, 5,
+and 6 are artefact-type-agnostic — `_iter_reference_files`,
+`copy_generated_to_reference`, and `upload_reference_videos` already walk
+both `.mp4` and `.pt` (see `reference_videos_cli.py`).
+
+If either check fails, stop and tell the user what's wrong.
+
+### 2. Run the test on Modal L40S
+
+Pick a subdir name so repeated runs don't collide:
+
+```bash
+SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
+TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
+SUBDIR="${TIMESTAMP}_${SHORT_COMMIT}"
+```
+
+Then launch the Modal run. The `IMAGE_VERSION` and `BUILDKITE_*` env-prefix
+**must** match what CI exports in `.buildkite/scripts/pr_test.sh`, otherwise
+`fastvideo/tests/modal/ssim_test.py` resolves a different GHCR image tag
+(default is `latest`, CI is `py3.12-latest`) and bakes different values into
+the image's frozen env block (`ssim_test.py:17-18, 38-46`). Mismatched image
+or env produces SSIM drift that doesn't show up until the same commit runs
+in CI.
+
+```bash
+IMAGE_VERSION="py3.12-latest" \
+BUILDKITE_REPO="$(git config --get remote.origin.url)" \
+BUILDKITE_COMMIT="$(git rev-parse HEAD)" \
+BUILDKITE_PULL_REQUEST="${BUILDKITE_PULL_REQUEST:-false}" \
+modal run fastvideo/tests/modal/ssim_test.py \
+    --git-repo="$(git config --get remote.origin.url)" \
+    --git-commit="$(git rev-parse HEAD)" \
+    --hf-api-key="$HF_API_KEY" \
+    --test-files="<test_file>" \
+    --sync-generated-to-volume \
+    --generated-volume-subdir="$SUBDIR" \
+    --skip-reference-download \
+    --no-fail-fast
+```
+
+Env prefix rationale (parity with CI; see `.buildkite/pipeline.yml:1-3` and
+`.buildkite/scripts/pr_test.sh:62-83`):
+- `IMAGE_VERSION=py3.12-latest`: pins the Modal image tag to the same one CI
+  uses. Without this, `ssim_test.py:17` falls back to `latest`, which on
+  GHCR is built from `Dockerfile.python3.10` — different Python, torch, and
+  flash-attn wheel than CI's `py3.12-latest` (`infra-build-image.yml:51-67`,
+  `_template-build-image.yml:65-101`).
+- `BUILDKITE_REPO`/`BUILDKITE_COMMIT`/`BUILDKITE_PULL_REQUEST`: mirror what
+  Buildkite exports. `ssim_test.py:38-46` bakes these into the image's
+  `.env(...)` block; mismatched values can perturb in-container code paths
+  that branch on PR-vs-non-PR. `false` for `BUILDKITE_PULL_REQUEST` matches
+  Buildkite's "non-PR build" sentinel.
+
+Flag rationale:
+- `--skip-reference-download`: no refs exist yet, so conftest must not try to
+  pull them.
+- `--no-fail-fast`: lets the test finish generation before `_assert_similarity`
+  raises `FileNotFoundError: Reference video folder does not exist`. The
+  expected failure is what we want — the mp4 has already been written.
+- `--sync-generated-to-volume` + `--generated-volume-subdir`: copies the
+  generated mp4s to the `hf-model-weights` Modal volume under
+  `ssim_generated_videos/default/<SUBDIR>/generated_videos/` so we can pull
+  them locally.
+
+The Modal run will end with a nonzero exit (expected) and print a
+`modal volume get hf-model-weights ssim_generated_videos/default/<SUBDIR>/generated_videos ./generated_videos_modal/default`
+command. Capture that `<SUBDIR>` — you need it for step 3.
+
+### 3. Download generated videos locally
+
+```bash
+modal volume get --force hf-model-weights \
+    ssim_generated_videos/default/"$SUBDIR"/generated_videos \
+    ./generated_videos_modal/default
+```
+
+`--force` is required when the parent `./generated_videos_modal/default`
+already exists; without it, `modal volume get` errors with `[Errno 21] Is a
+directory`. Safe to pass on the first run too.
+
+After this, the mp4s live at
+`./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
+The extra `generated_videos/` level comes from the volume layout in
+`_sync_generated_videos_to_volume` (`ssim_test.py`) — the command copies
+`<repo>/fastvideo/tests/ssim/generated_videos/<tier>` to
+`ssim_generated_videos/<tier>/<SUBDIR>/generated_videos/`, and `modal volume
+get` preserves that trailing `generated_videos/` segment.
+
+### 4. PAUSE — user reviews quality
+
+Type-aware verification.
+
+**For `ARTEFACT_TYPE = pixel`** — list the downloaded mp4s and ask the user to
+open them in a video player:
+
+> "Generated videos downloaded to `./generated_videos_modal/default/generated_videos/L40S_reference_videos/`. Please open them and confirm the quality looks correct. Reply **`upload`** to continue, or anything else to abort."
+
+**For `ARTEFACT_TYPE = latent`** — `.pt` files are not human-watchable. Print
+a numerics dump for each `.pt` so the user can sanity-check shape, distribution,
+and metadata:
+
+```python
+import torch
+from pathlib import Path
+ROOT = Path("./generated_videos_modal/default/generated_videos/L40S_reference_videos")
+for p in sorted(ROOT.rglob("*.pt")):
+    d = torch.load(p, map_location="cpu", weights_only=False)
+    s = d["expected_slice"]
+    L = d["latent"].float()
+    print(f"=== {p.relative_to(ROOT)} ===")
+    print(f"  format_version: {d['format_version']}")
+    print(f"  shape:          {d['shape']}")
+    print(f"  dtype_original: {d['dtype_original']}")
+    print(f"  slice_spec:     {d['slice_spec']}")
+    print(f"  slice  shape={tuple(s.shape)}  mean={s.mean():+.4f}  std={s.std():.4f}  min={s.min():+.4f}  max={s.max():+.4f}")
+    print(f"  latent shape={tuple(L.shape)}  mean={L.mean():+.4f}  std={L.std():.4f}  min={L.min():+.4f}  max={L.max():+.4f}")
+    print(f"  finite: latent NaN={torch.isnan(L).any().item()} Inf={torch.isinf(L).any().item()}; "
+          f"slice NaN={torch.isnan(s).any().item()} Inf={torch.isinf(s).any().item()}")
+    print(f"  metadata: {d['metadata']}\n")
+```
+
+Sanity criteria:
+- `format_version == 1` (matches `LATENT_REFERENCE_FORMAT_VERSION`).
+- `shape` matches what the model produces (e.g. LTX-2 distilled =
+  `[1, 128, T_lat, H_lat, W_lat]`; Stable Audio Open 1.0 = `[1, 64, 1024]`).
+- `slice_spec.kind` matches a registered kind (`corner_3x3_first_frame`
+  for video, `audio_first_8_timesteps` for audio).
+- No `NaN`/`Inf`. `mean ≈ 0`, `std ≈ 1` (denoised latents stay close to
+  the initial Gaussian distribution; very wide deviations suggest
+  numerical drift).
+- `metadata.prompt` matches the test's prompt.
+
+Then ask:
+
+> "Numerics look right? Reply **`upload`** to continue, or anything else to abort."
+
+Do not proceed until the user explicitly says `upload`. If they abort, leave
+everything on disk so they can inspect further — no cleanup.
+
+### 5. Copy into the local reference layout
+
+Scoped copy — only the new test's artefacts. Single command works for both
+artefact types because `_iter_reference_files` walks `.mp4` and `.pt`:
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
+    --quality-tier default \
+    --device-folder L40S_reference_videos \
+    --generated-dir ./generated_videos_modal/default/generated_videos/L40S_reference_videos
+```
+
+(The `--generated-dir` points at the device-folder root inside the
+downloaded tree; `copy-local` walks all `<model>/<backend>/*.{mp4,pt}`
+underneath it. Since the Modal run was scoped to a single test file via
+`--test-files`, only that test's model(s) are present — so the copy is
+implicitly per-test.)
+
+Result for pixel: `fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
+Result for latent: same path with `.pt` extension.
+
+### 6. Upload to HF — scoped per model_id, with overwrite guard
+
+For each `<model_id>`:
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py upload \
+    --quality-tier default \
+    --device-folder L40S_reference_videos \
+    --model-id "<model_id>"
+```
+
+The upload command:
+
+- Uploads **only** `reference_videos/default/L40S_reference_videos/<model_id>/`.
+- **Refuses** if any file already exists at that path on HF (this is the
+  guard — seeding a new test should never clobber existing refs). To override,
+  the user must re-run with `--force`. If the guard fires, stop and report
+  exactly which files exist; do not silently `--force`.
+
+Reads the HF token from `HF_API_KEY` / `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`.
+
+### 7. Report success
+
+List what was uploaded (paths in repo) and remind the user to push any
+related code changes. Do **not** auto-verify by re-running Modal — the user
+can run `pytest fastvideo/tests/ssim/<test_file>` later to confirm end-to-end;
+it will auto-download the refs they just uploaded.
+
+## Failure modes and how to handle them
+
+- **`HF_API_KEY` unset.** Stop before step 2. The Modal run needs it (passed
+  via `--hf-api-key`), and step 6 needs it for upload. If the user
+  ran `hf auth login` instead of exporting an env var, read the cached
+  token via `huggingface_hub.get_token()` and forward it to Modal as
+  `--hf-api-key="$CACHED_TOKEN"`.
+- **Modal run fails before generation.** No artefacts on the volume — nothing
+  to download. Fix the test locally (`pytest fastvideo/tests/ssim/<test_file>`)
+  and retry from step 2.
+- **`./generated_videos_modal/default/L40S_reference_videos/` missing after
+  `modal volume get`.** The run didn't produce artefacts (most likely the
+  test crashed before writing, or `REQUIRED_GPUS` exceeded the partition
+  capacity — see Modal logs).
+- **Latent test crashed with FSDP / inference_mode error
+  (`RuntimeError: Inference tensors do not track version counter`).** The
+  test must pass `init_kwargs_override={"use_fsdp_inference": False}` when
+  `sp_size == 1` — see `test_stable_audio_similarity.py` for the pattern.
+  Fix in the test, push, retry.
+- **Upload guard fires (files already exist).** The test name / model id
+  collides with something already on HF. Verify the user actually wants to
+  replace existing refs; if so, re-run the upload with `--force`. If not,
+  rename the model id in `*_MODEL_TO_PARAMS` and re-seed.
+- **Quality looks wrong in step 4.** Abort. The artefacts stay on disk for
+  inspection. The fix is usually in the test's params (resolution, steps,
+  seed) — edit the test, then re-run the skill.
+  - For latent: also check `slice_spec.kind` matches the latent rank
+    (`corner_3x3_first_frame` requires 5-D, `audio_first_8_timesteps`
+    requires 3-D); a rank/kind mismatch raises in `_extract_expected_slice`.
+
+## Design notes (for future skill maintainers)
+
+- The skill deliberately runs on Modal, **not** locally, because the CI
+  runner is L40S. Seeding from a different GPU SKU produces refs that CI's
+  L40S runs can't match (pixel SSIM drifts across SKUs; latent cosine has
+  tighter cross-SKU bf16 drift but the configured tolerances assume
+  same-SKU seed → same-SKU verify).
+- The skill is default-tier only. `full_quality` refs are seeded by a
+  separate, deliberate operation — they double runtime and aren't what CI
+  gates on.
+- The overwrite guard in `reference_videos_cli.py upload` is default-on
+  specifically because this skill exists. Re-seeding is a distinct operation
+  that requires explicit `--force`.
+- Both artefact types share the same Modal flow: the orchestrator sets
+  `--skip-reference-download` + `--no-fail-fast`, runs pytest, the test's
+  helper writes the artefact (`.mp4` via `imageio` for pixel,
+  `save_latent_reference` → `torch.save` for latent) BEFORE the
+  missing-reference assertion raises. `_sync_generated_videos_to_volume` in
+  `ssim_test.py` does a `shutil.copytree` of the whole `generated_videos/`
+  tree, picking up `.mp4`, `.pt`, and the `*_ssim.json` / `*_latent.json`
+  metric files alongside.
+
+## References
+
+- `fastvideo/tests/modal/ssim_test.py` — Modal orchestrator; see
+  `--sync-generated-to-volume`, `--generated-volume-subdir`,
+  `--skip-reference-download`, `--no-fail-fast`.
+- `fastvideo/tests/ssim/reference_videos_cli.py` — `copy-local`, `upload`
+  (with `--model-id`, `--force`), `download`, `ensure` subcommands.
+  Extension allowlist is `REFERENCE_EXTENSIONS = VIDEO_EXTENSIONS +
+  LATENT_EXTENSIONS` (`.pt`).
+- `fastvideo/tests/ssim/README.md` — reference layout, HF repo conventions.
+- `fastvideo/tests/ssim/inference_similarity_utils.py` — pixel helpers
+  (`run_text_to_video_similarity_test`,
+  `run_image_to_video_similarity_test`, `build_init_kwargs`).
+- `fastvideo/tests/ssim/latent_similarity_utils.py` — latent helper
+  (`run_text_to_latent_similarity_test`), slice spec dispatch
+  (`_extract_expected_slice`), reference schema
+  (`save_latent_reference` / `load_latent_reference`),
+  `LATENT_REFERENCE_FORMAT_VERSION`.
+
+## Changelog
+
+| Date | Change |
+|------|--------|
+| 2026-04-17 | Initial version (Modal sync-to-volume flow). |
+| 2026-04-21 | Rewrite: single-test scope, explicit user-review pause, per-`model_id` upload, HF overwrite guard. Dropped `scripts/seed_ssim.sh`. |
+| 2026-04-21 | Post-first-run fixes: `modal volume get` needs `--force` when parent exists; download tree has an extra `generated_videos/` level so `--generated-dir` must reflect it. |
+| 2026-05-01 | Latent (`*.pt`) artefact support: artefact-type detection in step 1, type-aware verification (visual eyeball for mp4, numerics dump for pt) in step 4, FSDP+inference_mode failure-mode added, design notes for the unified Modal flow. Triggered by PR #1253 (LTX-2 latent migration + Stable Audio latent test). |

.buildkite/performance-benchmarks/tests/wan-t2v-1.3b.json

@@ -29,18 +29,24 @@
     "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": 1,
-    "num_measurement_runs": 3,
+    "num_warmup_runs": 2,
+    "num_measurement_runs": 5,
     "required_gpus": 2
   },
   "thresholds": {
     "L40S": {
       "max_generation_time_s": 34.0,
-      "max_peak_memory_mb": 11000.0
+      "max_peak_memory_mb": 11000.0,
+      "max_text_encoder_time_s": 5.0,
+      "max_dit_time_s": 10.0,
+      "max_vae_decode_time_s": 10.0
     },
     "default": {
       "max_generation_time_s": 120.0,
-      "max_peak_memory_mb": 30000.0
+      "max_peak_memory_mb": 30000.0,
+      "max_text_encoder_time_s": 5.0,
+      "max_dit_time_s": 10.0,
+      "max_vae_decode_time_s": 10.0
     }
   }
 }

.buildkite/pipeline.yml

@@ -9,11 +9,183 @@ 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:
   # ============================================================
-    - label: ":dart: Direct Test (${TEST_TYPE})"
-      if: build.env("TEST_SCOPE") == "direct"
+  # 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"
       command: "timeout 90m .buildkite/scripts/pr_test.sh"
       retry:
         automatic:
@@ -135,6 +307,10 @@ steps:
                 label: ":bar_chart: SSIM Tests"
                 env:
                   - TEST_TYPE=ssim
+                retry:
+                  automatic:
+                    - exit_status: 1
+                      limit: 2
                 agents:
                   queue: "default"
             - path:
@@ -195,6 +371,10 @@ steps:
                 label: ":test_tube: LoRA Training Tests"
                 env:
                   - TEST_TYPE=training_lora
+                retry:
+                  automatic:
+                    - exit_status: 1
+                      limit: 2
                 agents:
                   queue: "default"
             - path:
@@ -207,6 +387,10 @@ steps:
                 label: ":test_tube: Training Tests VSA"
                 env:
                   - TEST_TYPE=training_vsa
+                retry:
+                  automatic:
+                    - exit_status: 1
+                      limit: 2
                 agents:
                   queue: "default"
             - path:

.buildkite/scripts/pr_test.sh

@@ -15,8 +15,21 @@ log "Project root: $PROJECT_ROOT"
 # Install Modal if not available
 if ! python3 -m modal --version &> /dev/null; then
     log "Modal not found, installing..."
-    python3 -m pip install modal
-    
+    if ! command -v uv &> /dev/null; then
+        log "uv not found, bootstrapping..."
+        if ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
+            log "Error: Failed to bootstrap uv via astral.sh installer."
+            exit 1
+        fi
+        export PATH="$HOME/.local/bin:$PATH"
+        if ! command -v uv &> /dev/null; then
+            log "Error: uv still not on PATH after bootstrap."
+            exit 1
+        fi
+    fi
+    # --break-system-packages preserves prior `pip install --user` semantics on PEP 668 agents.
+    uv pip install --system --break-system-packages modal
+
     # Verify installation
     if ! python3 -m modal --version &> /dev/null; then
         log "Error: Failed to install modal. Please install it manually."
@@ -63,7 +76,86 @@ EFFECTIVE_PR=${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 IMAGE_VERSION=$IMAGE_VERSION"
+MODAL_ENV="BUILDKITE_REPO=$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
+    }
+
+    _upload_normalized_perf_results() {
+        local found=0
+        while IFS= read -r -d '' target; do
+            found=1
+            log "Found normalized performance result: $target. Uploading to Buildkite..."
+            buildkite-agent artifact upload "$target"
+        done < <(find "$LOCAL_DIR" -path "*/results/normalized_perf_*.json" -print0)
+
+        if [ "$found" -eq 0 ]; then
+            log "No normalized performance result artifacts found. This is expected when the rolling performance comparison did not run."
+        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
+    _upload_normalized_perf_results
+    _cleanup_modal_volume
+    _cleanup_local
+}
 
 case "$TEST_TYPE" in
     "encoder")
@@ -124,8 +216,9 @@ case "$TEST_TYPE" in
         MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_lora_extraction_tests"
         ;;
     "performance")
-        log "Running performance tests..."
+        log "Running performance tests on Modal..."
         MODAL_COMMAND="$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..."
@@ -147,5 +240,10 @@ 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

.buildkite/scripts/pre_commit.sh

@@ -13,8 +13,21 @@ log "Project root: $PROJECT_ROOT"
 
 if ! python3 -m pre_commit --version &> /dev/null; then
     log "pre-commit not found, installing..."
-    python3 -m pip install --user pre-commit==4.0.1
-    
+    if ! command -v uv &> /dev/null; then
+        log "uv not found, bootstrapping..."
+        if ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
+            log "Error: Failed to bootstrap uv via astral.sh installer."
+            exit 1
+        fi
+        export PATH="$HOME/.local/bin:$PATH"
+        if ! command -v uv &> /dev/null; then
+            log "Error: uv still not on PATH after bootstrap."
+            exit 1
+        fi
+    fi
+    # --break-system-packages preserves prior `pip install --user` semantics on PEP 668 agents.
+    uv pip install --system --break-system-packages pre-commit==4.0.1
+
     if ! python3 -m pre_commit --version &> /dev/null; then
         log "Error: Failed to install pre-commit."
         exit 1

.github/mergify.yml

@@ -4,8 +4,10 @@ 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:
 
@@ -103,7 +105,7 @@ pull_request_rules:
           - files~=^fastvideo/pipelines/samplers/
           - files~=^fastvideo/entrypoints/
           - files~=^fastvideo/worker/
-          - files~=^fastvideo/configs/sample/
+          - files~=^fastvideo/api/sampling_param
           - files~=^fastvideo/configs/pipelines/
           - files~=^examples/inference/
       - -closed
@@ -272,24 +274,15 @@ pull_request_rules:
       merge:
         method: squash
 
-  - name: auto-rebase when ready and Full Suite passed
+  - name: auto-update when ready
     conditions:
       - label=ready
       - "#approved-reviews-by>=1"
-      - check-success=full-suite-passed
       - -conflict
       - -closed
       - -draft
     actions:
-      rebase: {}
-
-  - name: remove ready label on Full Suite failure
-    conditions:
-      - label=ready
-      - check-failure=full-suite-passed
-    actions:
-      label:
-        remove: [ready]
+      update: {}
 
   # ============================================================
   # PR title format help
@@ -319,3 +312,5 @@ pull_request_rules:
 
           Please update your PR title and the merge protection check will pass automatically.
 
+merge_protections_settings:
+  reporting_method: check-runs

.github/workflows/ci-aggregate-status.yml

@@ -0,0 +1,80 @@
+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`,
+              });
+            }

.github/workflows/ci-precommit.yml

@@ -4,20 +4,23 @@ on:
   pull_request:
     branches: [main]
   workflow_call:
-
-concurrency:
-  group: pre-commit-${{ github.ref }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
+    inputs:
+      ref:
+        description: 'Git ref to checkout (defaults to github.ref)'
+        required: false
+        type: string
 
 permissions:
   contents: read
 
 jobs:
   pre-commit:
-    if: github.event.pull_request.draft != true
+    if: github.event_name == 'workflow_call' || github.event.pull_request.draft != true
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
+      with:
+        ref: ${{ inputs.ref || '' }}
     - uses: actions/setup-python@v5
       with:
         python-version: "3.12"

.github/workflows/ci-slash-commands.yml

@@ -7,6 +7,7 @@ on:
 permissions:
   contents: read
   pull-requests: write
+  statuses: write
 
 jobs:
   handle-merge:
@@ -32,6 +33,7 @@ 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:
@@ -39,7 +41,6 @@ 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({
@@ -47,6 +48,44 @@ 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
@@ -86,7 +125,7 @@ jobs:
           set -euo pipefail
           TEST_NAME=$(echo "$COMMENT" | grep -oP '(?<=/test\s)\S+' | head -1 || true)
 
-          VALID="encoder vae transformer kernel unit ssim training lora-inference lora-training distillation self-forcing vsa vmoba performance api full fastcheck"
+          VALID="encoder vae transformer kernel unit ssim training lora-inference lora-training distillation self-forcing vsa vmoba performance api full fastcheck pre-commit"
           if [ -z "$TEST_NAME" ] || ! echo "$VALID" | grep -qw "$TEST_NAME"; then
             echo "Unknown test: '$TEST_NAME'. Valid: $VALID"
             exit 1
@@ -114,6 +153,12 @@ jobs:
               echo "test_scope=fastcheck"
               echo "full_suite=false"
             } >> "$GITHUB_OUTPUT"
+          elif [ "$TEST_NAME" = "pre-commit" ]; then
+            {
+              echo "test_type="
+              echo "test_scope=precommit"
+              echo "full_suite=false"
+            } >> "$GITHUB_OUTPUT"
           else
             {
               echo "test_type=${MAP[$TEST_NAME]}"
@@ -136,14 +181,8 @@ jobs:
             core.setOutput('sha', pr.head.sha);
             core.setOutput('branch', pr.head.ref);
 
-  trigger-buildkite:
-    needs: parse-command
-    if: >-
-      needs.parse-command.outputs.has_write == 'true'
-      && needs.parse-command.outputs.test_type != ''
-    runs-on: ubuntu-latest
-    steps:
       - name: React to comment
+        if: steps.perm.outputs.has_write == 'true'
         uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
         with:
           script: |
@@ -154,6 +193,43 @@ jobs:
               content: 'rocket',
             });
 
+  pre-commit:
+    needs: parse-command
+    if: >-
+      needs.parse-command.outputs.has_write == 'true'
+      && needs.parse-command.outputs.test_scope == 'precommit'
+    uses: ./.github/workflows/ci-precommit.yml
+    with:
+      ref: refs/pull/${{ github.event.issue.number }}/merge
+
+  post-precommit-status:
+    needs: [parse-command, pre-commit]
+    if: always() && needs.parse-command.outputs.test_scope == 'precommit'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
+        env:
+          PR_SHA: ${{ needs.parse-command.outputs.pr_sha }}
+          RESULT: ${{ needs.pre-commit.result }}
+        with:
+          script: |
+            const state = process.env.RESULT === 'success' ? 'success' : 'failure';
+            await github.rest.repos.createCommitStatus({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              sha: process.env.PR_SHA,
+              state,
+              context: 'pre-commit',
+              description: `Triggered via /test pre-commit (${state})`,
+            });
+
+  trigger-buildkite:
+    needs: parse-command
+    if: >-
+      needs.parse-command.outputs.has_write == 'true'
+      && needs.parse-command.outputs.test_type != ''
+    runs-on: ubuntu-latest
+    steps:
       - name: Trigger Buildkite
         env:
           BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}

.github/workflows/ci-trigger-full-suite.yml

@@ -1,7 +1,7 @@
 name: Trigger Full Suite
 
 on:
-  pull_request:
+  pull_request_target:
     types: [labeled, synchronize]
 
 permissions:
@@ -10,7 +10,7 @@ permissions:
 
 concurrency:
   group: full-suite-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
+  cancel-in-progress: false
 
 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(.env.TEST_SCOPE == "full") | .number')
+            | jq -r '.[] | select(try (.env.TEST_SCOPE == "full") catch false) | .number')
           for build_num in $builds; do
             echo "Cancelling Buildkite build #$build_num"
             curl -sS -X PUT -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \

.github/workflows/infra-docs.yml

@@ -37,10 +37,11 @@ jobs:
         with:
           python-version: '3.12'
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+
       - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements-mkdocs.txt
+        run: uv pip install --system -r requirements-mkdocs.txt
 
       - name: Setup Pages
         uses: actions/configure-pages@v4

.github/workflows/publish-fastvideo.yml

@@ -56,10 +56,11 @@ jobs:
         with:
           python-version: '3.10'
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+
       - name: Install build dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install build twine wheel
+        run: uv pip install --system build twine wheel
 
       - name: Build package
         run: |

.github/workflows/publish-kernel.yml

@@ -131,11 +131,13 @@ jobs:
           clang-11 --version
           nvcc --version
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+
       - name: Install PyTorch ${{ matrix.torch-cuda.torch-version }}+cu${{ matrix.torch-cuda.cuda-version }}
         run: |
-          pip install --upgrade pip
-          pip install typing-extensions==4.12.2
-          pip install --no-cache-dir torch==${{ matrix.torch-cuda.torch-version }} --index-url https://download.pytorch.org/whl/${{matrix.torch-cuda.torch-cuda-short}}
+          uv pip install --system typing-extensions==4.12.2
+          uv pip install --system --no-cache-dir torch==${{ matrix.torch-cuda.torch-version }} --index-url https://download.pytorch.org/whl/${{matrix.torch-cuda.torch-cuda-short}}
           nvcc --version
           python --version
           python -c "import torch; print('PyTorch:', torch.__version__)"
@@ -145,20 +147,20 @@ jobs:
       - name: Build wheel
         run: |
           export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
-          
-          pip install setuptools ninja packaging wheel triton scikit-build-core cmake build
-          
+
+          uv pip install --system setuptools ninja packaging wheel triton scikit-build-core cmake build
+
           cd fastvideo-kernel
           git submodule update --init --recursive  # Ensure ThunderKittens submodule is initialized
           # Release builds are produced on GPU-less runners, so force-enable TK and target Hopper.
           export TORCH_CUDA_ARCH_LIST="9.0a"
           export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
-          
+
           # Build standard wheel (no local version suffix) for PyPI
           python -m build --wheel --outdir dist
-          
+
           # Fix the wheel to be manylinux compliant
-          pip install auditwheel
+          uv pip install --system auditwheel
           # Point auditwheel at torch libs, but do not vendor them into the wheel.
           TORCH_LIB_DIR=$(python - <<'PY'
           import os
@@ -211,10 +213,13 @@ jobs:
           pattern: 'fastvideo_kernel-py*'
           merge-multiple: true
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+
       - name: Build source distribution
         run: |
-          pip install build scikit-build-core cmake ninja
-          
+          uv pip install --system build scikit-build-core cmake ninja
+
           cd fastvideo-kernel
           # We don't need full CUDA/Torch to just package the source (sdist)
           python -m build --sdist --outdir dist

.gitignore

@@ -85,6 +85,7 @@ docs/distillation/examples/
 dmd_t2v_output/
 preprocess_output_text/
 
+# Next.js / Node artifacts under ui/: see ui/.gitignore
 
 .claude/
 .codex/

.nvimlog

@@ -0,0 +1 @@
+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

.pre-commit-config.yaml

@@ -7,20 +7,13 @@ exclude: |
     fastvideo-kernel/.*|
     assets/.*|
     tests/.*|
-    demo/.*|
-    predict\.py|
     scripts/.*|
-    assets/prompts/.*|
-    fastvideo/data_preprocess/.*|
     fastvideo/dataset/.*|
     fastvideo/models/.*|
-    fastvideo/sample/.*|
-    fastvideo/train\.py|
-    fastvideo/utils/.*|
     examples/.*|
+    \.agents/.*|
     .github/workflows/publish-fastvideo.yml|
-    .github/workflows/_template-build-image.yml|
-    docs/source/inference/support_matrix.md
+    .github/workflows/_template-build-image.yml
   )
 repos:
 - repo: https://github.com/google/yapf

.python-version

@@ -0,0 +1 @@
+3.12

AGENTS.md

@@ -11,7 +11,7 @@
 - Static assets: `assets/` (including `assets/images/`, `assets/videos/`, and `assets/prompts/`) and `comfyui/assets/`.
 
 ## Build, Test, and Development Commands
-- `uv pip install -e .[dev]`: editable install with lint/test extras.
+- `uv pip install -e ".[dev]"`: editable install with lint/test extras.
 - `pre-commit install --hook-type pre-commit --hook-type commit-msg`: enable local hooks.
 - `pre-commit run --all-files`: run formatter/lint/type/spelling checks.
 - `pytest tests/`: run top-level test suite.
@@ -23,7 +23,8 @@
 - Python 3.10+; 4-space indentation; keep code and imports readable and explicit.
 - Style tools are configured in `pyproject.toml` and `.pre-commit-config.yaml`:
   - `yapf` (format), `ruff` (lint, auto-fix), `mypy` (typing), `codespell`.
-- Target line length is 80.
+- Lint via `pre-commit run --files <changed paths>` (or `pre-commit run --all-files` for a full sweep) before committing. Do not shell out to `yapf`/`ruff`/`codespell`/`mypy` directly — pre-commit chains them with the project's config and respects the `.pre-commit-config.yaml` excludes (e.g. `fastvideo/tests/` is intentionally skipped). If pre-commit reports `(no files to check)` for your paths, that exclude is deliberate — don't bypass it.
+- Target line length is 120 (configured in `pyproject.toml` for ruff, yapf, and isort).
 - Naming: `snake_case` for functions/files, `PascalCase` for classes, `UPPER_SNAKE_CASE` for constants.
 
 ## Testing Guidelines
@@ -54,3 +55,31 @@ This repository is agent-friendly. Before doing any work, read:
 If you are exploring a new procedure that has no existing SOP, document your
 progress in `.agents/exploration/` and flag it for review at the end of your
 session.
+
+## Per-Directory AGENTS.md
+
+Local guidance lives next to the code. Read the in-scope file before editing:
+
+| Directory | What it covers |
+|-----------|----------------|
+| `fastvideo/AGENTS.md` | Core package map, public API, registry-driven model dispatch |
+| `fastvideo/configs/AGENTS.md` | Arch + pipeline config dataclasses, `param_names_mapping` |
+| `fastvideo/models/AGENTS.md` | DiT / VAE / encoder / scheduler / loader layout (pre-commit excluded) |
+| `fastvideo/layers/AGENTS.md` | Tensor-parallel linear/attention layer rules for ports |
+| `fastvideo/attention/AGENTS.md` | Backend registry + env-var override |
+| `fastvideo/pipelines/AGENTS.md` | Stage ABC, `basic/<model>/`, `preprocess/`, presets |
+| `fastvideo/training/AGENTS.md` | Legacy monolithic pipelines (frozen for existing models) |
+| `fastvideo/train/AGENTS.md` | New modular trainer (methods × models × callbacks, YAML) |
+| `fastvideo/tests/AGENTS.md` | Test taxonomy, conftest, pre-commit-excluded path |
+| `fastvideo/tests/ssim/AGENTS.md` | GPU SSIM regression authoring + reference video sync |
+| `scripts/checkpoint_conversion/AGENTS.md` | Adding a converter for a new HF/official checkpoint |
+
+## Critical: Two Training Stacks Coexist
+
+- `fastvideo/training/` — legacy, monolithic per-model `*_training_pipeline.py` and
+  `*_distillation_pipeline.py`. Still authoritative for shipped models.
+- `fastvideo/train/` — new modular framework (composable methods × models × callbacks
+  driven by YAML). Preferred for new training work.
+
+Pick the matching stack before editing. Do not migrate a pipeline between them
+without an explicit ask — the conventions and config surfaces differ.

benchmarks/fvd/feature_extractors.py

@@ -128,7 +128,7 @@ class CLIPFeatureExtractor(BaseFeatureExtractor):
 
     def __init__(self, device: str = 'cuda', model_name: str = "openai/clip-vit-base-patch32"):
         if not TRANSFORMERS_AVAILABLE:
-            raise ImportError("Please install transformers: pip install transformers")
+            raise ImportError("Please install transformers: uv pip install transformers")
         super().__init__(device)
         self.processor = CLIPProcessor.from_pretrained(model_name)
         self.model = CLIPModel.from_pretrained(model_name).to(self.device)
@@ -171,7 +171,7 @@ class VideoMAEFeatureExtractor(BaseFeatureExtractor):
 
     def __init__(self, device: str = 'cuda', model_name: str = "MCG-NJU/videomae-base"):
         if not TRANSFORMERS_AVAILABLE:
-            raise ImportError("Please install transformers: pip install transformers")
+            raise ImportError("Please install transformers: uv pip install transformers")
         super().__init__(device)
         self.model = VideoMAEModel.from_pretrained(model_name).to(self.device)
         self.model.eval()

benchmarks/fvd/i3d_model.py

@@ -57,7 +57,7 @@ class I3DFeatureExtractor(nn.Module):
         except Exception as e:
             raise RuntimeError(f"Failed to load I3D model from Hugging Face Hub. Error: {e}\n"
                                f"Ensure you have internet connection and huggingface_hub installed:\n"
-                               f"pip install huggingface_hub") from e
+                               f"uv pip install huggingface_hub") from e
 
     def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
         """

benchmarks/scripts/run.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # 1. Install missing dependency
-pip install -q opencv-python-headless transformers huggingface_hub
+uv pip install -q opencv-python-headless transformers huggingface_hub
 
 # 2. Run FVD script
 python benchmarks/fvd/run_fvd.py

benchmarks/scripts/setup_fvd.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 
 # 1. Install missing dependency
-pip install -q opencv-python-headless
+uv pip install -q opencv-python-headless

comfyui/README.md

@@ -38,10 +38,10 @@ cp -r /path/to/FastVideo/comfyui /path/to/ComfyUI/custom_nodes/FastVideo
 
 #### Install dependencies:
 
-Currently, the only dependency is `fastvideo`, which can be installed using pip.
+Currently, the only dependency is `fastvideo`, which can be installed with `uv`.
 
 ```bash
-pip install fastvideo
+uv pip install fastvideo
 ```
 
 #### Install missing custom nodes:

docker/Dockerfile.python3.10

@@ -42,15 +42,15 @@ RUN source $HOME/.local/bin/env && \
     uv venv --python 3.10 --seed /opt/venv && \
     source /opt/venv/bin/activate && \
     uv pip install --no-cache-dir --upgrade pip && \
-    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp310-cp310-linux_x86_64.whl
+    uv pip install --no-cache-dir ".[dev]" && \
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.4/flash_attn-2.8.3+cu128torch2.11-cp310-cp310-linux_x86_64.whl
 
 COPY . .
 
 # Install dependencies using uv and set up shell configuration
 RUN source $HOME/.local/bin/env && \
     source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir -e .[dev] && \
+    uv pip install --no-cache-dir -e ".[dev]" && \
     git config --unset-all http.https://github.com/.extraheader || true && \
     echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
     echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile

docker/Dockerfile.python3.11

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

docker/Dockerfile.python3.12

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

docker/Dockerfile.python3.12.cuda12.9.1

@@ -42,7 +42,7 @@ RUN source $HOME/.local/bin/env && \
     uv venv --python 3.12 --seed /opt/venv && \
     source /opt/venv/bin/activate && \
     uv pip install --no-cache-dir --upgrade pip && \
-    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir ".[dev]" && \
     uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation
 
 COPY . .
@@ -50,7 +50,7 @@ COPY . .
 # Install dependencies using uv and set up shell configuration
 RUN source $HOME/.local/bin/env && \
     source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir -e .[dev] && \
+    uv pip install --no-cache-dir -e ".[dev]" && \
     git config --unset-all http.https://github.com/.extraheader || true && \
     echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
     echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile

docker/rocm7_1.Dockerfile

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

docs/README.md

@@ -6,7 +6,7 @@ This directory contains the FastVideo documentation built with MkDocs.
 
 ```bash
 # Install dependencies
-pip install -r requirements-mkdocs.txt
+uv pip install -r requirements-mkdocs.txt
 
 # Serve docs with live reload (recommended for development)
 mkdocs serve

docs/api/fastvideo.md

@@ -62,9 +62,9 @@ This page contains the complete API reference for the FastVideo library.
       show_root_toc_entry: true
       heading_level: 4
 
-#### fastvideo.configs.sample
+#### fastvideo.api.sampling_param
 
-::: fastvideo.configs.sample
+::: fastvideo.api.sampling_param
     options:
       show_source: true
       show_root_heading: true

docs/contributing/ci_architecture.md

@@ -24,7 +24,7 @@ PR push
           Runs on the PR branch directly
               │
           pass ──► Mergify auto-squash-merges to main, branch deleted
-          fail ──► Mergify removes 'ready' label; fix and /merge again
+          fail ──► fix the regression, push, and /merge again
 ```
 
 ---
@@ -102,8 +102,8 @@ failing test's output.
 | Performance Tests | `performance` | 30 min |
 | API Server Tests | `api_server` | 30 min |
 
-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.
+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.
 
 ---
 
@@ -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, Mergify removes the `ready` label and posts a comment linking to
-   the Buildkite build. The developer fixes the issue, pushes, and comments `/merge` again.
+6. If the Full Suite fails, the developer fixes the issue, pushes, and comments `/merge`
+   again to re-trigger.
 
 **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/configs/sample/`, `fastvideo/configs/pipelines/`, `examples/inference/` |
+| `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: attention` | `fastvideo/attention/` |
 | `scope: kernel` | `fastvideo-kernel/`, `csrc/` |
 | `scope: data` | `fastvideo/dataset/`, `fastvideo/pipelines/preprocess/`, `examples/preprocessing/` |
@@ -279,6 +279,30 @@ 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.
 
 ---
 
@@ -296,6 +320,7 @@ 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 |

docs/contributing/coding_agents.md

@@ -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/configs/sample/*`: default runtime sampling parameters.
+- `fastvideo/api/sampling_param.py`: 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.configs.sample import SamplingParam
+from fastvideo.api.sampling_param 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)
@@ -296,8 +296,10 @@ 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/` (e.g., `tests/local_tests/upsamplers/`)
-  and the commands in `tests/local_tests/README.md`.
+- 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`.
 - 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`).
@@ -319,7 +321,8 @@ Purpose:
 
 - `fastvideo/configs/pipelines/` describes pipeline wiring and model module
   names.
-- `fastvideo/configs/sample/` defines default runtime parameters.
+- `fastvideo/api/sampling_param.py` defines runtime sampling parameters.
+  Defaults come from profiles in `fastvideo/pipelines/basic/<family>/profiles.py`.
 
 Action:
 
@@ -347,7 +350,8 @@ Purpose:
 
 Action:
 
-- Add a pipeline parity test under `tests/local_tests/pipelines/`.
+- Add a pipeline parity test under `tests/local_tests/<family>/`
+  (e.g., `tests/local_tests/<family>/test_<family>_pipeline_parity.py`).
 - See the [Testing Guide](testing.md) for test conventions.
 
 ### 7) Add user‑facing examples
@@ -474,7 +478,7 @@ FastVideo integration.
 3. Pipeline wiring.
    - Pipeline: `fastvideo/pipelines/basic/wan/wan_pipeline.py`
    - Pipeline config: `fastvideo/configs/pipelines/wan.py`
-   - Sampling defaults: `fastvideo/configs/sample/wan.py`
+   - Sampling defaults: `fastvideo/pipelines/basic/wan/profiles.py`
 
 4. Minimal example.
    - Script: `examples/inference/basic/basic.py`

docs/contributing/developer_env/runpod.md

@@ -99,7 +99,7 @@ cd /FastVideo
 **Install the package**
 
 ```bash
-uv pip install -e .[dev]
+uv pip install -e ".[dev]"
 ```
 
 The Docker image already includes Flash Attention and most heavy dependencies, so this is fast.

docs/contributing/overview.md

@@ -49,7 +49,7 @@ git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
 Install FastVideo in editable mode and set up hooks:
 
 ```bash
-uv pip install -e .[dev]
+uv pip install -e ".[dev]"
 
 # Optional: FlashAttention (builds native kernels)
 uv pip install flash-attn --no-build-isolation -v

docs/contributing/pull_requests.md

@@ -104,8 +104,9 @@ 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, Mergify removes the `ready` label and posts a comment with a
-   link to the Buildkite build. Fix the issue, push, and comment `/merge` again.
+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.
 
 !!! note
     Only contributors with write permission to the repository can trigger slash commands.
@@ -149,10 +150,15 @@ 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
@@ -199,9 +205,8 @@ Mergify removes the `needs-rebase` label automatically once conflicts are resolv
 
 ### Full Suite failed after `/merge`
 
-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.
+The Full Suite found a regression. Check the failing Buildkite step's output for assertion
+errors or tracebacks.
 
 Common causes:
 

docs/design/inference_schema_parity_inventory.yaml

@@ -0,0 +1,480 @@
+status_definitions:
+  kept: "Public field remains on a public adapter surface with the same meaning."
+  moved: "Public field remains supported but normalizes into a different nested path."
+  preset_owned: "Public field remains supported only through a model/preset-specific surface."
+  compatibility_only: "Legacy public field remains adapter-only during migration and is not part of the canonical typed schema."
+  private_only: "Field should only be handled by private adapters and is not a public FastVideo compatibility promise."
+  internal_only: "Field is runtime/config plumbing and should not be part of the new public typed inference API."
+
+surfaces:
+  fastvideo_args:
+    moved:
+      model_path: generator.model_path
+      workload_type: generator.pipeline.workload_type
+      distributed_executor_backend: generator.engine.execution_backend
+      trust_remote_code: generator.trust_remote_code
+      revision: generator.revision
+      num_gpus: generator.engine.num_gpus
+      tp_size: generator.engine.parallelism.tp_size
+      sp_size: generator.engine.parallelism.sp_size
+      hsdp_replicate_dim: generator.engine.parallelism.hsdp_replicate_dim
+      hsdp_shard_dim: generator.engine.parallelism.hsdp_shard_dim
+      dist_timeout: generator.engine.parallelism.dist_timeout
+      lora_path: generator.pipeline.components.lora_path
+      dit_cpu_offload: generator.engine.offload.dit
+      use_fsdp_inference: generator.engine.use_fsdp_inference
+      dit_layerwise_offload: generator.engine.offload.dit_layerwise
+      text_encoder_cpu_offload: generator.engine.offload.text_encoder
+      image_encoder_cpu_offload: generator.engine.offload.image_encoder
+      vae_cpu_offload: generator.engine.offload.vae
+      pin_cpu_memory: generator.engine.offload.pin_cpu_memory
+      enable_torch_compile: generator.engine.compile.enabled
+      torch_compile_kwargs: generator.engine.compile.backend,fullgraph,mode,dynamic,extras
+      disable_autocast: generator.engine.disable_autocast
+      enable_stage_verification: generator.engine.enable_stage_verification
+      prompt_txt: request.inputs.prompt_path
+      override_text_encoder_safetensors: generator.pipeline.components.text_encoder_weights
+      override_text_encoder_quant: generator.engine.quantization.text_encoder_quant
+      override_transformer_cls_name: generator.pipeline.components.override_transformer_cls_name
+      init_weights_from_safetensors: generator.pipeline.components.transformer_weights
+      init_weights_from_safetensors_2: generator.pipeline.components.transformer_2_weights
+      override_pipeline_cls_name: generator.pipeline.components.override_pipeline_cls_name
+      boundary_ratio: request.sampling.boundary_ratio
+      ltx2_vae_tiling: generator.pipeline.vae_tiling
+    preset_owned:
+      ltx2_vae_spatial_tile_size_in_pixels: generator.pipeline.preset_overrides.ltx2.vae.spatial_tile_size_in_pixels
+      ltx2_vae_spatial_tile_overlap_in_pixels: generator.pipeline.preset_overrides.ltx2.vae.spatial_tile_overlap_in_pixels
+      ltx2_vae_temporal_tile_size_in_frames: generator.pipeline.preset_overrides.ltx2.vae.temporal_tile_size_in_frames
+      ltx2_vae_temporal_tile_overlap_in_frames: generator.pipeline.preset_overrides.ltx2.vae.temporal_tile_overlap_in_frames
+      ltx2_initial_latent_path: request.extensions.ltx2.initial_latent_path
+    compatibility_only:
+      mode: "Legacy multi-mode FastVideoArgs switch; typed inference config should not expose execution mode."
+      inference_mode: "Legacy boolean mirror of mode; kept only through adapters while FastVideoArgs remains."
+      lora_nickname: "Legacy adapter-selection surface pending LoRA API cleanup."
+      lora_target_modules: "Legacy LoRA configuration surface pending dedicated component API."
+      output_type: "Legacy output formatting surface pending GenerationResult cleanup."
+      VSA_sparsity: "Model-specific inference optimization not yet represented in the typed public schema."
+      moba_config_path: "Model-specific MoBA optimization surface not yet represented in the typed public schema."
+      master_port: "Executor/bootstrap compatibility field; not part of the canonical inference schema."
+    private_only:
+      ray_placement_group: "Ray deployment-only field."
+      ray_runtime_env: "Ray deployment-only field."
+    internal_only:
+      pipeline_config: "Legacy internal carrier object."
+      preprocess_config: "Legacy preprocess carrier object."
+      moba_config: "Derived runtime config loaded from moba_config_path."
+      model_paths: "Runtime bookkeeping."
+      model_loaded: "Runtime bookkeeping."
+
+  pipeline_config_base:
+    moved:
+      pipeline_config_path: generator.pipeline.components.pipeline_config_path
+    preset_owned:
+      embedded_cfg_scale: generator.pipeline.preset_overrides.embedded_cfg_scale
+      flow_shift: generator.pipeline.preset_overrides.flow_shift
+      flow_shift_sr: generator.pipeline.preset_overrides.flow_shift_sr
+      is_causal: generator.pipeline.preset_overrides.is_causal
+      vae_tiling: generator.pipeline.preset_overrides.vae_tiling
+      vae_sp: generator.pipeline.preset_overrides.vae_sp
+      dmd_denoising_steps: generator.pipeline.preset_overrides.dmd_denoising_steps
+      ti2v_task: generator.pipeline.preset_overrides.ti2v_task
+      boundary_ratio: generator.pipeline.preset_overrides.boundary_ratio
+    compatibility_only:
+      model_path: "Redundant with generator.model_path."
+      disable_autocast: "Duplicated by generator.engine.disable_autocast during migration."
+      dit_precision: "Precision override pending dedicated typed component precision design."
+      upsampler_precision: "Precision override pending dedicated typed component precision design."
+      vae_precision: "Precision override pending dedicated typed component precision design."
+      image_encoder_precision: "Precision override pending dedicated typed component precision design."
+      text_encoder_precisions: "Precision override pending dedicated typed component precision design."
+    internal_only:
+      dit_config: "Legacy internal component config object."
+      upsampler_config: "Legacy internal component config object."
+      vae_config: "Legacy internal component config object."
+      image_encoder_config: "Legacy internal component config object."
+      text_encoder_configs: "Legacy internal component config object."
+      preprocess_text_funcs: "Internal text preprocessing hooks."
+      postprocess_text_funcs: "Internal text postprocessing hooks."
+
+  pipeline_config_extensions:
+    preset_owned:
+      conditioning_strategy:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      max_num_conditional_frames:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      min_num_conditional_frames:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      sigma_conditional:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      sigma_data:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      state_ch:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      state_t:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      text_encoder_class:
+        sources:
+          - fastvideo.configs.pipelines.cosmos.CosmosConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      autoregressive_chunk_frames:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      autoregressive_overlap_frames:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      cfg_behavior:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      default_camera_rotation:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      default_movement_distance:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      default_negative_prompt:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      default_trajectory_type:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      filter_points_threshold:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      fps:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      frame_buffer_max:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      moge_model_name:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      noise_aug_strength:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      num_frames:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      offload_moge_after_depth:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      use_moge_depth:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      video_resolution:
+        sources:
+          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
+          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      text_encoder_crop_start:
+        sources:
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V480PStepDistilledConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V720PConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15SR1080PConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V480PConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V720PConfig
+          - fastvideo.configs.pipelines.hyworld.HYWorldConfig
+          - fastvideo.configs.pipelines.hyworld.Hunyuan15T2V480PConfig
+      text_encoder_max_lengths:
+        sources:
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V480PStepDistilledConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V720PConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15SR1080PConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V480PConfig
+          - fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V720PConfig
+          - fastvideo.configs.pipelines.hyworld.HYWorldConfig
+          - fastvideo.configs.pipelines.hyworld.Hunyuan15T2V480PConfig
+      precision:
+        sources:
+          - fastvideo.configs.pipelines.lingbotworld.LingBotWorldI2V480PConfig
+          - fastvideo.configs.pipelines.lingbotworld.Wan2_2_I2V_A14B_Config
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2VConfig
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2V_A14B_Config
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2VConfig
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_14B_Config
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_1_3B_Config
+          - fastvideo.configs.pipelines.wan.FastWan2_1_T2V_480P_Config
+          - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
+          - fastvideo.configs.pipelines.wan.MatrixGameBaseI2V480PConfig
+          - fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig
+          - fastvideo.configs.pipelines.wan.SelfForcingWan2_2_T2V480PConfig
+          - fastvideo.configs.pipelines.wan.SelfForcingWanT2V480PConfig
+          - fastvideo.configs.pipelines.wan.WANV2VConfig
+          - fastvideo.configs.pipelines.wan.Wan2_2_I2V_A14B_Config
+          - fastvideo.configs.pipelines.wan.Wan2_2_T2V_A14B_Config
+          - fastvideo.configs.pipelines.wan.Wan2_2_TI2V_5B_Config
+          - fastvideo.configs.pipelines.wan.WanI2V480PConfig
+          - fastvideo.configs.pipelines.wan.WanI2V720PConfig
+          - fastvideo.configs.pipelines.wan.WanT2V480PConfig
+          - fastvideo.configs.pipelines.wan.WanT2V720PConfig
+      warp_denoising_step:
+        sources:
+          - fastvideo.configs.pipelines.lingbotworld.LingBotWorldI2V480PConfig
+          - fastvideo.configs.pipelines.lingbotworld.Wan2_2_I2V_A14B_Config
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2VConfig
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2V_A14B_Config
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2VConfig
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_14B_Config
+          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_1_3B_Config
+          - fastvideo.configs.pipelines.wan.FastWan2_1_T2V_480P_Config
+          - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
+          - fastvideo.configs.pipelines.wan.MatrixGameBaseI2V480PConfig
+          - fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig
+          - fastvideo.configs.pipelines.wan.SelfForcingWan2_2_T2V480PConfig
+          - fastvideo.configs.pipelines.wan.SelfForcingWanT2V480PConfig
+          - fastvideo.configs.pipelines.wan.WANV2VConfig
+          - fastvideo.configs.pipelines.wan.Wan2_2_I2V_A14B_Config
+          - fastvideo.configs.pipelines.wan.Wan2_2_T2V_A14B_Config
+          - fastvideo.configs.pipelines.wan.Wan2_2_TI2V_5B_Config
+          - fastvideo.configs.pipelines.wan.WanI2V480PConfig
+          - fastvideo.configs.pipelines.wan.WanI2V720PConfig
+          - fastvideo.configs.pipelines.wan.WanT2V480PConfig
+          - fastvideo.configs.pipelines.wan.WanT2V720PConfig
+      bsa_cdf_threshold:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      bsa_chunk_k:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      bsa_chunk_q:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      bsa_params:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      bsa_sparsity:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      enable_bsa:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      enable_kv_cache:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      enhance_hf:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      offload_kv_cache:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      t_thresh:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      use_distill:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+      scheduler_arch:
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
+      text_encoder_archs:
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
+      tokenizer_archs:
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
+      transformer_arch:
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
+      vae_arch:
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
+      expand_timesteps:
+        sources:
+          - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
+          - fastvideo.configs.pipelines.wan.Wan2_2_TI2V_5B_Config
+      context_noise:
+        sources: [fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig]
+      num_frames_per_block:
+        sources: [fastvideo.configs.pipelines.wan.MatrixGameI2V480PConfig]
+      audio_channels:
+        sources:
+          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
+          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
+      audio_end_in_s:
+        sources:
+          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
+          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
+      audio_start_in_s:
+        sources:
+          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
+          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
+      max_audio_duration_s:
+        sources:
+          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
+          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
+      sample_size:
+        sources:
+          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
+          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
+      sampling_rate:
+        sources:
+          - fastvideo.configs.pipelines.stable_audio.StableAudioT2AConfig
+          - fastvideo.configs.pipelines.stable_audio.StableAudioOpenSmallConfig
+    compatibility_only:
+      batch_size: "Gen3C inference-only tuning field pending typed batching design."
+      gradient_checkpointing: "Gen3C inference-only compatibility field pending typed batching design."
+      guidance_scale: "Gen3C pipeline-level default pending preset/default-request cleanup."
+      num_inference_steps: "Gen3C pipeline-level default pending preset/default-request cleanup."
+    internal_only:
+      audio_decoder_config: "Legacy internal component config object."
+      audio_decoder_precision: "Precision override pending dedicated component precision design."
+      vocoder_config: "Legacy internal component config object."
+      vocoder_precision: "Precision override pending dedicated component precision design."
+
+  sampling_param_base:
+    moved:
+      image_path: request.inputs.image_path
+      pil_image: request.inputs.pil_image
+      video_path: request.inputs.video_path
+      mouse_cond: request.inputs.mouse_cond
+      keyboard_cond: request.inputs.keyboard_cond
+      grid_sizes: request.inputs.grid_sizes
+      pose: request.inputs.pose
+      c2ws_plucker_emb: request.inputs.c2ws_plucker_emb
+      refine_from: request.inputs.refine_from
+      stage1_video: request.inputs.stage1_video
+      prompt: request.prompt
+      negative_prompt: request.negative_prompt
+      prompt_path: request.inputs.prompt_path
+      output_path: request.output.output_path
+      output_video_name: request.output.output_video_name
+      num_videos_per_prompt: request.sampling.num_videos_per_prompt
+      seed: request.sampling.seed
+      num_frames: request.sampling.num_frames
+      height: request.sampling.height
+      width: request.sampling.width
+      height_sr: request.sampling.height_sr
+      width_sr: request.sampling.width_sr
+      fps: request.sampling.fps
+      num_inference_steps: request.sampling.num_inference_steps
+      num_inference_steps_sr: request.sampling.num_inference_steps_sr
+      guidance_scale: request.sampling.guidance_scale
+      guidance_scale_2: request.sampling.guidance_scale_2
+      guidance_rescale: request.sampling.guidance_rescale
+      boundary_ratio: request.sampling.boundary_ratio
+      sigmas: request.sampling.sigmas
+      enable_teacache: request.runtime.enable_teacache
+      save_video: request.output.save_video
+      return_frames: request.output.return_frames
+      return_trajectory_latents: request.runtime.return_trajectory_latents
+      return_trajectory_decoded: request.runtime.return_trajectory_decoded
+      continuation_state: request.state
+      return_continuation_state: request.output.return_state
+    preset_owned:
+      t_thresh: request.stage_overrides.refine.t_thresh
+      spatial_refine_only: request.stage_overrides.refine.spatial_refine_only
+      num_cond_frames: request.stage_overrides.refine.num_cond_frames
+      trajectory_type: request.extensions.gen3c.trajectory_type
+      movement_distance: request.extensions.gen3c.movement_distance
+      camera_rotation: request.extensions.gen3c.camera_rotation
+      prompt_attention_mask: request.extensions.hyworld.prompt_attention_mask
+      negative_attention_mask: request.extensions.hyworld.negative_attention_mask
+      camera_states: request.extensions.hunyuangamecraft.camera_states
+      camera_trajectory: request.extensions.hunyuangamecraft.camera_trajectory
+      action_list: request.extensions.hunyuangamecraft.action_list
+      action_speed_list: request.extensions.hunyuangamecraft.action_speed_list
+      gt_latents: request.extensions.hunyuangamecraft.gt_latents
+      conditioning_mask: request.extensions.hunyuangamecraft.conditioning_mask
+      ltx2_cfg_scale_video: request.extensions.ltx2.cfg_scale_video
+      ltx2_cfg_scale_audio: request.extensions.ltx2.cfg_scale_audio
+      ltx2_modality_scale_video: request.extensions.ltx2.modality_scale_video
+      ltx2_modality_scale_audio: request.extensions.ltx2.modality_scale_audio
+      ltx2_rescale_scale: request.extensions.ltx2.rescale_scale
+      ltx2_stg_scale_video: request.extensions.ltx2.stg_scale_video
+      ltx2_stg_scale_audio: request.extensions.ltx2.stg_scale_audio
+      ltx2_stg_blocks_video: request.extensions.ltx2.stg_blocks_video
+      ltx2_stg_blocks_audio: request.extensions.ltx2.stg_blocks_audio
+      audio_start_in_s: request.extensions.stable_audio.audio_start_in_s
+      audio_end_in_s: request.extensions.stable_audio.audio_end_in_s
+      init_audio: request.extensions.stable_audio.init_audio
+      init_audio_strength: request.extensions.stable_audio.init_audio_strength
+      init_noise_level: request.extensions.stable_audio.init_noise_level
+      inpaint_audio: request.extensions.stable_audio.inpaint_audio
+      inpaint_mask: request.extensions.stable_audio.inpaint_mask
+    internal_only:
+      data_type: "Derived from the request shape and not a public input."
+
+  sampling_param_extensions: {}
+
+  openai_image_request:
+    kept:
+      model: "HTTP adapter model-routing field."
+      response_format: "HTTP adapter response formatting field."
+      output_format: "HTTP adapter output-format field."
+      background: "HTTP adapter output-format field."
+      quality: "Compatibility field currently accepted by the adapter."
+      style: "Compatibility field currently accepted by the adapter."
+      user: "Compatibility field currently accepted by the adapter."
+    moved:
+      prompt: request.prompt
+      n: request.sampling.num_videos_per_prompt
+      size:
+        target: request.sampling.width,height
+        note: "Adapter parses OpenAI size strings as WIDTHxHEIGHT and forwards width then height."
+      num_inference_steps: request.sampling.num_inference_steps
+      guidance_scale: request.sampling.guidance_scale
+      true_cfg_scale: request.sampling.true_cfg_scale
+      seed: request.sampling.seed
+      negative_prompt: request.negative_prompt
+      enable_teacache: request.runtime.enable_teacache
+
+  openai_video_request:
+    kept:
+      model: "HTTP adapter model-routing field."
+    moved:
+      prompt: request.prompt
+      input_reference: request.inputs.image_path
+      reference_url: request.inputs.image_path
+      size:
+        target: request.sampling.width,height
+        note: "Adapter parses OpenAI size strings as WIDTHxHEIGHT and forwards width then height."
+      fps: request.sampling.fps
+      num_frames: request.sampling.num_frames
+      seed: request.sampling.seed
+      num_inference_steps: request.sampling.num_inference_steps
+      guidance_scale: request.sampling.guidance_scale
+      guidance_scale_2: request.sampling.guidance_scale_2
+      true_cfg_scale: request.sampling.true_cfg_scale
+      negative_prompt: request.negative_prompt
+      enable_teacache: request.runtime.enable_teacache
+      output_path: request.output.output_path
+    compatibility_only:
+      seconds:
+        target: request.sampling.num_frames
+        note: "HTTP adapter duration convenience field. If num_frames is omitted, the adapter computes num_frames = fps * seconds."
+
+cli:
+  notes:
+    - "CLI parity is checked against the actual generate/serve parser dest sets."
+    - "The inventory tracks parser dest names, excluding argparse's implicit help action."
+    - "The refactored inference CLI is config-only: subcommands expose only --config, and any additional CLI input must use dotted override paths."
+  generate:
+    explicit_local_fields:
+      - config
+    expected_dests:
+      - config
+  serve:
+    explicit_local_fields:
+      - config
+    expected_dests:
+      - config

docs/design/overview.md

@@ -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/configs/sample/*`: default runtime sampling parameters.
+- `fastvideo/api/sampling_param.py`: 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.configs.sample import SamplingParam
+from fastvideo.api.sampling_param 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)
@@ -49,8 +49,9 @@ 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/configs/sample/`: default sampling parameters (steps, frames,
-  guidance scale, resolution, fps).
+- `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/registry.py`: unified registry for pipeline config + sampling
   defaults and model metadata resolution, defined via explicit
   `register_configs(...)` blocks (no separate dict registries).
@@ -142,7 +143,7 @@ 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/configs/sample/wan.py`
+- Sampling defaults -> `fastvideo/pipelines/basic/wan/profiles.py`
 
 ## Pipeline system
 

docs/design/server_contracts/streaming.md

@@ -0,0 +1,177 @@
+# 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`.

docs/design/training_architecture.md

@@ -243,7 +243,7 @@ for step in range(start_step, max_steps):
 
 ```bash
 # Install
-uv pip install -e .[dev]
+uv pip install -e ".[dev]"
 
 # Run DMD2 distillation on Wan 2.1
 torchrun --nproc_per_node=8 -m fastvideo.train.entrypoint.train \

docs/distillation/dmd.md

@@ -16,7 +16,8 @@ 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
-bash scripts/inference/v1_inference_wan_dmd.sh
+FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN \
+  fastvideo generate --config scripts/inference/inference_wan_VSA_DMD_1_3B.yaml
 ```
 
 ## 🗂️ Dataset
@@ -85,3 +86,25 @@ 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.

docs/getting_started/installation.md

@@ -27,7 +27,7 @@ uv pip install fastvideo
 conda create -n fastvideo python=3.12 -y
 conda activate fastvideo
 
-pip install fastvideo
+uv pip install fastvideo
 ```
 
 ### From source
@@ -41,11 +41,11 @@ uv pip install -e .
 uv pip install flash-attn --no-build-isolation -v
 ```
 
-Alternative with Conda environment:
+Alternative with Conda environment (still drives installs through `uv`):
 
 ```bash
-pip install -e .
-pip install flash-attn --no-build-isolation -v
+uv pip install -e .
+uv pip install flash-attn --no-build-isolation -v
 ```
 
 ## Hardware Requirements

docs/getting_started/installation/gpu.md

@@ -58,14 +58,16 @@ uv pip install flash-attn --no-build-isolation -v
 
 #### With Conda environment (alternative)
 
+`uv` works inside an active conda env too, so prefer `uv pip` for the actual install:
+
 ```bash
-pip install fastvideo
+uv pip install fastvideo
 ```
 
 Also optionally install FlashAttention:
 
 ```bash
-pip install flash-attn --no-build-isolation -v
+uv pip install flash-attn --no-build-isolation -v
 ```
 
 ### Installation from Source
@@ -87,7 +89,7 @@ uv pip install -e .
 Alternative with Conda environment:
 
 ```bash
-pip install -e .
+uv pip install -e .
 ```
 
 ### Optional Dependencies
@@ -101,7 +103,7 @@ uv pip install flash-attn --no-build-isolation -v
 Alternative with Conda environment:
 
 ```bash
-pip install flash-attn --no-build-isolation -v
+uv pip install flash-attn --no-build-isolation -v
 ```
 
 ## Set up using Docker

docs/getting_started/installation/mps.md

@@ -57,8 +57,10 @@ uv pip install fastvideo
 
 #### With Conda environment (alternative)
 
+`uv` works inside an active conda env too, so prefer `uv pip` for the actual install:
+
 ```bash
-pip install fastvideo
+uv pip install fastvideo
 ```
 
 ### Installation from Source
@@ -80,7 +82,7 @@ uv pip install -e .
 Alternative with Conda environment:
 
 ```bash
-pip install -e .
+uv pip install -e .
 ```
 
 ## Development Environment Setup

docs/getting_started/v1_api.md

@@ -33,7 +33,7 @@ The following two classes `PipelineConfig` and `SamplingParam` are used to confi
 
 ### SamplingParam
 
-::: fastvideo.configs.sample.base.SamplingParam
+::: fastvideo.api.sampling_param.SamplingParam
     options:
       show_root_heading: true
       show_source: false

docs/inference/architecture.md

@@ -128,19 +128,14 @@ Concrete hierarchy: `DiTConfig` → `DiTArchConfig`, `VAEConfig` →
 - `dump_to_json()` / `load_from_json()` — JSON persistence. Callable
   fields and `arch_config` are excluded from dumps.
 
-### SamplingParam (`fastvideo/configs/sample/`)
+### SamplingParam (`fastvideo/api/sampling_param.py`)
 
 Generation parameters separate from pipeline config. Each model family
-provides defaults:
+provides defaults via a profile (see `fastvideo/pipelines/basic/<family>/profiles.py`):
 
 ```python
-@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
+sp = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
+# sp.height == 480, sp.width == 832, sp.num_frames == 81, etc.
 ```
 
 ## Component Loading
@@ -430,9 +425,9 @@ User: generator.generate_video(prompt, ...)
    `fastvideo/configs/pipelines/<model>.py`. Set DiT/VAE/encoder configs,
    flow_shift, precision defaults.
 
-2. **Sampling param** — Create a `SamplingParam` subclass in
-   `fastvideo/configs/sample/<model>.py`. Set default height, width,
-   num_frames, guidance_scale, num_inference_steps.
+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.
 
 3. **Register configs** — In `fastvideo/registry.py`, add a
    `register_configs()` call inside `_register_configs()` with
@@ -455,6 +450,6 @@ User: generator.generate_video(prompt, ...)
    `fastvideo/pipelines/stages/`, implement `forward()`, optionally
    implement `verify_input()`/`verify_output()`.
 
-7. **Verify** — Run `fastvideo generate --model-path <path> --prompt
-   "test" --num-inference-steps 2` to confirm the pipeline loads and
-   generates output.
+7. **Verify** — Run `fastvideo generate --config <config.yaml>` with a
+   minimal nested config to confirm the pipeline loads and generates
+   output.

docs/inference/cli.md

@@ -1,71 +1,29 @@
 # FastVideo CLI Inference
 
-The FastVideo CLI exposes the same core inference controls as the Python API.
+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.
 
 ## 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 --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
-  --prompt "A cat playing with a ball of yarn"
+fastvideo generate --config config.yaml
+fastvideo serve --config serve.yaml
 ```
 
-```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
 ```
 
-Arguments come from:
+The subcommands intentionally expose only `--config`. Any per-run CLI changes
+must use dotted override paths such as:
 
-- 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`
+- `--generator.engine.num_gpus 2`
+- `--request.sampling.seed 42`
+- `--server.port 9000`
 
 ## Using Config Files
 
@@ -73,50 +31,53 @@ Arguments come from:
 fastvideo generate --config config.yaml
 ```
 
-Config files can be JSON or YAML. CLI flags override config-file values.
+Config files can be JSON or YAML. Dotted CLI overrides take precedence over
+config-file values.
 
 Example `config.yaml`:
 
 ```yaml
-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
+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/
 ```
 
 Notes:
 
-- Use `dit_precision` / `vae_precision` (not `precision`).
-- Nested config objects are supported, for example `vae_config` and
-  `dit_config`.
+- `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`.
 
 ## Examples
 
 Simple generation:
 
 ```bash
-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/
+fastvideo generate --config config.yaml
 ```
 
-Config + CLI override:
+Config + dotted override:
 
 ```bash
-fastvideo generate --config config.yaml --prompt "A panda skiing at sunset"
+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
 ```

docs/inference/configuration.md

@@ -73,32 +73,40 @@ if __name__ == '__main__':
 
 ## JSON/YAML Config Files (CLI)
 
-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).
+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.
 
 ```bash
 fastvideo generate --config config.yaml
 ```
 
-Use CLI argument names as keys (underscore or hyphen is accepted). Example:
+Example nested config:
 
 ```yaml
-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
+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
 ```
 
 ## Performance Optimization

docs/inference/gen3c.md

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

docs/inference/optimizations.md

@@ -54,7 +54,7 @@ FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN python example.py
 We recommend always installing [Flash Attention 2](https://github.com/Dao-AILab/flash-attention):
 
 ```bash
-pip install flash-attn==2.7.4.post1 --no-build-isolation
+uv pip install flash-attn==2.7.4.post1 --no-build-isolation
 ```
 
 And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#flashattention-3-beta-release) by compiling it from source (takes about 10 minutes for me):
@@ -63,7 +63,7 @@ And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://git
 git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention
 
 cd hopper
-pip install ninja
+uv pip install ninja
 python setup.py install
 ```
 
@@ -98,7 +98,7 @@ To use [SageAttention](https://github.com/thu-ml/SageAttention) 2.1.1, please co
 ```bash
 git clone https://github.com/thu-ml/SageAttention.git
 cd sageattention
-python setup.py install  # or pip install -e .
+python setup.py install  # or uv pip install -e .
 ```
 
 ### Sage Attention 3

docs/inference/support_matrix.md

@@ -73,6 +73,7 @@ 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.
 
@@ -85,6 +86,11 @@ 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

examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/README.md

@@ -4,7 +4,7 @@ These are end-to-end example scripts for distilling Wan2.1 T2V 1.3B model using
 ### 0. Make sure you have installed VSA
 
 ```bash
-pip install vsa
+uv pip install vsa
 ```
 
 ### 1. Download dataset:

examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/README.md

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

examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/README.md

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

examples/inference/basic/README.md

@@ -28,6 +28,11 @@ 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!

examples/inference/basic/basic.py

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

examples/inference/basic/basic_cosmos2_5_i2w.py

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

examples/inference/basic/basic_cosmos2_5_t2w.py

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

examples/inference/basic/basic_cosmos2_5_v2w.py

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

examples/inference/basic/basic_dmd.py

@@ -2,7 +2,7 @@ import os
 import time
 from fastvideo import VideoGenerator
 
-from fastvideo.configs.sample import SamplingParam
+from fastvideo.api.sampling_param import SamplingParam
 
 OUTPUT_PATH = "video_samples_dmd2"
 def main():

examples/inference/basic/basic_dmd_new_api.py

@@ -0,0 +1,98 @@
+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()

examples/inference/basic/basic_gen3c.py

@@ -0,0 +1,109 @@
+"""
+GEN3C: 3D-aware camera-controlled video generation.
+
+This example generates a video from a single input image with camera control.
+The pipeline uses MoGe depth estimation, 3D point cloud forward warping,
+and the GEN3C diffusion model.
+
+Requirements:
+  1. Install MoGe:
+     uv pip install git+https://github.com/microsoft/MoGe.git
+     If you hit `ImportError: libGL.so.1`, install:
+     sudo apt-get update && sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxext6 libxrender1
+  2. Download and convert weights:
+     huggingface-cli download nvidia/GEN3C-Cosmos-7B --local-dir official_weights/GEN3C-Cosmos-7B
+     python scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py \
+       --source ./official_weights/GEN3C-Cosmos-7B/model.pt \
+       --output ./converted_weights/GEN3C-Cosmos-7B \
+       --components-source nvidia/Cosmos-Predict2-2B-Video2World
+  3. Provide an input image for 3D-conditioned generation.
+"""
+
+import argparse
+
+from fastvideo import VideoGenerator
+
+
+def main():
+    parser = argparse.ArgumentParser(description="GEN3C video generation")
+    parser.add_argument("--model_path",
+                        type=str,
+                        default="converted_weights/GEN3C-Cosmos-7B")
+    parser.add_argument("--image_path",
+                        type=str,
+                        default=None,
+                        help="Input image for 3D cache conditioning")
+    parser.add_argument("--prompt",
+                        type=str,
+                        default="A slow camera pan over a sunlit landscape.")
+    parser.add_argument(
+        "--negative_prompt",
+        type=str,
+        default=(
+            "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
+            "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
+            "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, "
+            "jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special "
+            "effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and "
+            "flickering. Overall, the video is of poor quality."
+        ),
+    )
+    parser.add_argument("--trajectory",
+                        type=str,
+                        default="left",
+                        choices=[
+                            "left", "right", "up", "down", "zoom_in",
+                            "zoom_out", "clockwise", "counterclockwise", "none"
+                        ])
+    parser.add_argument("--movement_distance", type=float, default=0.3)
+    parser.add_argument("--camera_rotation",
+                        type=str,
+                        default="center_facing",
+                        choices=[
+                            "center_facing", "no_rotation",
+                            "trajectory_aligned"
+                        ])
+    parser.add_argument("--height", type=int, default=704)
+    parser.add_argument("--width", type=int, default=1280)
+    parser.add_argument("--num_frames", type=int, default=121)
+    parser.add_argument("--num_inference_steps", type=int, default=35)
+    parser.add_argument("--guidance_scale", type=float, default=1.0)
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs_video/gen3c.mp4")
+    parser.add_argument("--seed", type=int, default=42)
+    args = parser.parse_args()
+
+    generator = VideoGenerator.from_pretrained(
+        args.model_path,
+        num_gpus=1,
+        use_fsdp_inference=False,
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+    )
+
+    video = generator.generate_video(
+        args.prompt,
+        negative_prompt=args.negative_prompt,
+        image_path=args.image_path,
+        trajectory_type=args.trajectory,
+        movement_distance=args.movement_distance,
+        camera_rotation=args.camera_rotation,
+        height=args.height,
+        width=args.width,
+        num_frames=args.num_frames,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        fps=24,
+        seed=args.seed,
+        output_path=args.output_path,
+        save_video=True,
+    )
+
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_hy15.py

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

examples/inference/basic/basic_hy15_1080p.py

@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 import json
-# from fastvideo.configs.sample import SamplingParam
+# from fastvideo.api.sampling_param import SamplingParam
 
 OUTPUT_PATH = "video_samples_hy15_1080p"
 def main():

examples/inference/basic/basic_lingbotworld_base_cam.py

@@ -1,7 +1,7 @@
 from fastvideo import VideoGenerator
 from fastvideo.models.dits.lingbotworld.cam_utils import prepare_camera_embedding
 
-# from fastvideo.configs.sample import SamplingParam
+# from fastvideo.api.sampling_param import SamplingParam
 OUTPUT_PATH = "video_samples_lingbotworld"
 def main():
     # FastVideo will automatically use the optimal default arguments for the

examples/inference/basic/basic_mps.py

@@ -1,5 +1,5 @@
 from fastvideo import VideoGenerator, PipelineConfig
-from fastvideo.configs.sample import SamplingParam
+from fastvideo.api.sampling_param import SamplingParam
 
 def main():
     config = PipelineConfig.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")

examples/inference/basic/basic_ray.py

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

examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py

@@ -2,7 +2,7 @@
 
 from fastvideo import VideoGenerator, SamplingParam
 import json
-# from fastvideo.configs.sample import SamplingParam
+# from fastvideo.api.sampling_param import SamplingParam
 
 OUTPUT_PATH = "video_samples_self_forcing_causal_wan2_2_14B_i2v"
 def main():

examples/inference/basic/basic_self_forcing_causal_wan2_2_t2v.py

@@ -2,7 +2,7 @@
 
 from fastvideo import VideoGenerator
 
-# from fastvideo.configs.sample import SamplingParam
+# from fastvideo.api.sampling_param import SamplingParam
 
 OUTPUT_PATH = "video_samples_self_forcing_causal_wan2_2_14B_t2v"
 def main():

examples/inference/basic/basic_stable_audio.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Stable Audio Open 1.0 — text-to-audio (baseline) example.
+
+User story (game-audio designer, prototyping):
+    "I'm prototyping a level and I need 6 seconds of background
+    ambience — gentle wind, distant thunder, a hint of birdsong. I
+    don't want to dig through a sound library; I want to type what I
+    hear in my head and get a wav back. If it's wrong I'll iterate
+    on the prompt. This is the first stop."
+
+User story (musician sketching ideas):
+    "I want to bounce a 30s lo-fi drum loop to use as a placeholder
+    bed while I build the rest of the track. Type prompt, get audio,
+    drop into the DAW. The actual production beat I'll record
+    myself, but I need *something* to write the chords against."
+
+User story (researcher exploring the model):
+    "First time touching Stable Audio Open — what does it sound
+    like at default settings? This is the smallest amount of code
+    that goes from prompt to mp4."
+
+How it works:
+    Pure text-to-audio (T2A). The pipeline runs:
+        T5 + NumberConditioner -> StableAudioDiT -> Oobleck VAE
+    via the `dpmpp-3m-sde` k-diffusion sampler. All components are
+    FastVideo-native — no diffusers / transformers model imports at
+    runtime (see REVIEW item 30). Mirrors upstream
+    `stable_audio_tools.inference.generation.generate_diffusion_cond`
+    bit-for-bit (~0.2% abs_mean drift on 25 steps).
+
+Tunable knobs (the "creative dials"):
+    audio_end_in_s
+        1–6   — quick ideation (sub-10s wall clock at 100 steps)
+        10–30 — full musical phrase / loop length (the README example
+                uses 30s)
+        47.5  — model maximum (full sample_size = 2097152 / 44100 Hz)
+    num_inference_steps
+        25  — fast preview, occasional artifacts
+        100 — preset default (matches the HF model card)
+        250 — diminishing returns past here
+    guidance_scale
+        3   — looser, more variation per seed
+        7   — preset default; matches README
+        12+ — sharper but can sound "fried"
+
+Prerequisites:
+  1. Accept the terms on https://huggingface.co/stabilityai/stable-audio-open-1.0
+     and export your HF token in the shell:
+         export HF_TOKEN=hf_...
+  2. Install optional inference deps (one-time):
+         uv pip install k_diffusion einops_exts alias_free_torch torchsde
+"""
+from fastvideo import VideoGenerator
+
+PROMPT = "Lo-fi hip hop instrumental with vinyl crackle and gentle piano."
+
+
+def main() -> None:
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/stable-audio-open-1.0-Diffusers",
+        num_gpus=1,
+    )
+    output_path = "outputs_audio/stable_audio_basic/output_stable_audio.wav"
+    generator.generate_video(
+        prompt=PROMPT,
+        output_path=output_path,
+        save_video=True,
+        # 6-second clip; the model max is ~47.5s.
+        audio_end_in_s=6.0,
+        # The registered preset gives 100 steps + CFG=7.0 by default;
+        # override num_inference_steps / guidance_scale here for QA.
+    )
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_stable_audio_a2a.py

@@ -0,0 +1,77 @@
+# 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()

examples/inference/basic/basic_stable_audio_inpaint.py

@@ -0,0 +1,84 @@
+# 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()

examples/inference/basic/basic_stable_audio_small.py

@@ -0,0 +1,53 @@
+# 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()

examples/inference/basic/basic_wan2_2.py

@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 
-# from fastvideo.configs.sample import SamplingParam
+# from fastvideo.api.sampling_param import SamplingParam
 
 OUTPUT_PATH = "video_samples_wan2_2_14B_t2v"
 def main():

examples/inference/basic/basic_wan2_2_Fun.py

@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 
-# from fastvideo.configs.sample import SamplingParam
+# from fastvideo.api.sampling_param import SamplingParam
 
 OUTPUT_PATH = "video_samples_wan2_1_Fun"
 OUTPUT_NAME = "wan2.1_test"

examples/inference/basic/basic_wan2_2_i2v.py

@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 
-# from fastvideo.configs.sample import SamplingParam
+# from fastvideo.api.sampling_param import SamplingParam
 
 OUTPUT_PATH = "video_samples_wan2_2_14B_i2v"
 def main():

examples/inference/gradio/local/gradio_local_demo.py

@@ -5,7 +5,7 @@ import time
 
 import gradio as gr
 from fastvideo.entrypoints.video_generator import VideoGenerator
-from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.api.sampling_param import SamplingParam
 from copy import deepcopy
 
 

examples/inference/gradio/serving/gradio_frontend.py

@@ -9,7 +9,7 @@ import tempfile
 
 import gradio as gr
 
-from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.api.sampling_param import SamplingParam
 
 
 MODEL_PATH_MAPPING = {

examples/inference/gradio/serving/ray_serve_backend.py

@@ -185,7 +185,7 @@ class BaseModelDeployment:
 
     def _initialize_generator(self, config: Dict[str, Any]) -> None:
         from fastvideo.entrypoints.video_generator import VideoGenerator
-        from fastvideo.configs.sample.base import SamplingParam
+        from fastvideo.api.sampling_param import SamplingParam
 
         print(f"Initializing model: {self.model_path}")
         self.generator = VideoGenerator.from_pretrained(

examples/inference/lora/wan_lora_inference.py

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

examples/inference/lora/wan_lora_inference_from_ckpt.py

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

examples/train/configs/fine_tuning/cosmos/t2v.yaml

@@ -0,0 +1,73 @@
+# 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

examples/train/configs/overfit_cosmos25_t2w.yaml

@@ -0,0 +1,79 @@
+# 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

fastvideo-kernel/CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.26 FATAL_ERROR)
 project(fastvideo-kernel LANGUAGES CXX)
 
-# Prefer environment variable (used by CI or pip install git+repo_addr) if CMake var is not explicitly set.
+# Prefer environment variable (used by CI or uv pip install git+repo_addr) if CMake var is not explicitly set.
 if(NOT DEFINED GPU_BACKEND AND DEFINED ENV{GPU_BACKEND})
     set(GPU_BACKEND "$ENV{GPU_BACKEND}")
 endif()

fastvideo-kernel/build.sh

@@ -10,6 +10,35 @@ 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
 
@@ -32,7 +61,16 @@ has_cmake_arg() {
 }
 
 detect_with_torch() {
-    uv run --active --no-project python -c "import 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
 if not torch.cuda.is_available():
     raise RuntimeError('torch.cuda.is_available() is false')
 mj, mn = torch.cuda.get_device_capability(0)

fastvideo-kernel/pyproject.toml

@@ -23,7 +23,7 @@ classifiers = [
 ]
 dependencies = [
     "torch>=2.5.0",
-    "triton>=2.0.0",
+    "triton>=2.0.0; sys_platform == 'linux'",
 ]
 
 [project.urls]

fastvideo-kernel/python/fastvideo_kernel/__init__.py

@@ -5,6 +5,11 @@ 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,
@@ -22,6 +27,8 @@ 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",

fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py

@@ -1,3 +1,5 @@
+"""Autograd-enabled block-sparse attention. Index-native ops with a bool-mask compat shim."""
+
 from __future__ import annotations
 
 import os
@@ -6,6 +8,11 @@ from typing import Tuple
 import torch
 
 
+# ---------------------------------------------------------------------------
+# Backend selection helpers
+# ---------------------------------------------------------------------------
+
+
 def _get_sm90_ops():
     try:
         from fastvideo_kernel._C import fastvideo_kernel_ops  # type: ignore
@@ -25,38 +32,66 @@ 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"
 
 
-def _map_to_index(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Preferred map->index conversion used by the wrapper.
+# ---------------------------------------------------------------------------
+# Index helpers
+# ---------------------------------------------------------------------------
 
-    This wrapper **requires** the Triton implementation.
-    If Triton (or the Triton map_to_index module) is not available, it raises.
-    """
+
+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."""
     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]), got shape={tuple(block_map.shape)}")
+        raise ValueError(
+            f"block_map must be [B,H,Q,KV] (or [H,Q,KV]), "
+            f"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  # local import
-    except Exception as e:
+        from fastvideo_kernel.triton_kernels.index import map_to_index as triton_map_to_index
+    except Exception as e:  # pragma: no cover - environment issue
         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=(),
@@ -66,34 +101,40 @@ def block_sparse_attn_triton(
     q: torch.Tensor,
     k: torch.Tensor,
     v: torch.Tensor,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: torch.Tensor,
     variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    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
+    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (
         triton_block_sparse_attn_forward,
     )
 
-    o, M = triton_block_sparse_attn_forward(q, k, v, q2k_idx, q2k_num, variable_block_sizes)
+    o, M = triton_block_sparse_attn_forward(
+        q.contiguous(),
+        k.contiguous(),
+        v.contiguous(),
+        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,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: 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
 
 
@@ -109,20 +150,32 @@ def block_sparse_attn_backward_triton(
     v: torch.Tensor,
     o: torch.Tensor,
     M: torch.Tensor,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: torch.Tensor,
     variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    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
+    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton 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, q, k, v, o, M, q2k_idx, q2k_num, k2q_idx, k2q_num, variable_block_sizes
+        grad_output.contiguous(),
+        q.contiguous(),
+        k.contiguous(),
+        v.contiguous(),
+        o,
+        M,
+        q2k_idx,
+        q2k_num,
+        k2q_idx,
+        k2q_num,
+        variable_block_sizes,
     )
     return dq, dk, dv
 
@@ -135,7 +188,8 @@ def _block_sparse_attn_backward_triton_fake(
     v: torch.Tensor,
     o: torch.Tensor,
     M: torch.Tensor,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: torch.Tensor,
     variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     dq = torch.empty_like(q)
@@ -144,19 +198,28 @@ def _block_sparse_attn_backward_triton_fake(
     return dq, dk, dv
 
 
-def _backward_triton(ctx, grad_o, grad_M):
-    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
-
-
 def _setup_context_triton(ctx, inputs, output):
-    q, k, v, block_map, variable_block_sizes = inputs
+    q, k, v, q2k_idx, q2k_num, variable_block_sizes = inputs
     o, M = output
-    ctx.save_for_backward(q, k, v, o, M, block_map, variable_block_sizes)
+    ctx.save_for_backward(q, k, v, o, M, q2k_idx, q2k_num, variable_block_sizes)
 
 
-block_sparse_attn_triton.register_autograd(_backward_triton, setup_context=_setup_context_triton)
+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
+
+
+block_sparse_attn_triton.register_autograd(
+    _backward_triton, setup_context=_setup_context_triton
+)
+
+
+# ---------------------------------------------------------------------------
+# SM90 backend custom ops (index-native)
+# ---------------------------------------------------------------------------
 
 
 @torch.library.custom_op(
@@ -168,21 +231,21 @@ def block_sparse_attn_sm90(
     q_padded: torch.Tensor,
     k_padded: torch.Tensor,
     v_padded: torch.Tensor,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: 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, k_padded, v_padded, q2k_idx, q2k_num, variable_block_sizes.int()
+        q_padded.contiguous(),
+        k_padded.contiguous(),
+        v_padded.contiguous(),
+        q2k_idx,
+        q2k_num,
+        variable_block_sizes,
     )
     return o_padded, lse_padded
 
@@ -192,11 +255,16 @@ def _block_sparse_attn_sm90_fake(
     q_padded: torch.Tensor,
     k_padded: torch.Tensor,
     v_padded: torch.Tensor,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: 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
 
 
@@ -212,30 +280,34 @@ def block_sparse_attn_backward_sm90(
     v_padded: torch.Tensor,
     o_padded: torch.Tensor,
     lse_padded: torch.Tensor,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: 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")
 
-    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())
+    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 user-facing inputs; o/lse are kernel outputs.
     dq, dk, dv = block_sparse_bwd(
-        q_padded,
-        k_padded,
-        v_padded,
+        q_padded.contiguous(),
+        k_padded.contiguous(),
+        v_padded.contiguous(),
         o_padded,
         lse_padded,
-        grad_output_padded,
+        grad_output_padded.contiguous(),
         k2q_idx,
         k2q_num,
-        variable_block_sizes.int(),
+        variable_block_sizes,
     )
-    # 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)
+    # 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)
 
 
 @torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_sm90")
@@ -246,7 +318,8 @@ def _block_sparse_attn_backward_sm90_fake(
     v_padded: torch.Tensor,
     o_padded: torch.Tensor,
     lse_padded: torch.Tensor,
-    block_map: torch.Tensor,
+    q2k_idx: torch.Tensor,
+    q2k_num: torch.Tensor,
     variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     dq = torch.empty_like(q_padded)
@@ -255,21 +328,57 @@ def _block_sparse_attn_backward_sm90_fake(
     return dq, dk, dv
 
 
-def _backward_sm90(ctx, grad_o, grad_lse):
-    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, block_map, variable_block_sizes
-    )
-    return dq, dk, dv, None, None
-
-
 def _setup_context_sm90(ctx, inputs, output):
-    q, k, v, block_map, variable_block_sizes = inputs
+    q, k, v, q2k_idx, q2k_num, variable_block_sizes = inputs
     o, lse = output
-    ctx.save_for_backward(q, k, v, o, lse, block_map, variable_block_sizes)
+    ctx.save_for_backward(q, k, v, o, lse, q2k_idx, q2k_num, variable_block_sizes)
 
 
-block_sparse_attn_sm90.register_autograd(_backward_sm90, setup_context=_setup_context_sm90)
+def _backward_sm90(ctx, grad_o, grad_lse):
+    q, k, v, o, lse, q2k_idx, q2k_num, 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
+    )
+    return dq, dk, dv, None, None, None
+
+
+block_sparse_attn_sm90.register_autograd(
+    _backward_sm90, setup_context=_setup_context_sm90
+)
+
+
+# ---------------------------------------------------------------------------
+# 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)
 
 
 def block_sparse_attn(
@@ -279,16 +388,8 @@ def block_sparse_attn(
     block_map: torch.Tensor,
     variable_block_sizes: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    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)
-
-
+    """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
+    )

fastvideo-kernel/python/fastvideo_kernel/ops.py

@@ -1,6 +1,6 @@
 import math
 import torch
-from .block_sparse_attn import block_sparse_attn
+from .block_sparse_attn import block_sparse_attn, block_sparse_attn_from_indices
 from .triton_kernels.st_attn_triton import sliding_tile_attention_triton
 
 # Try to load the C++ extension
@@ -125,13 +125,18 @@ def video_sparse_attn(
     out_c = out_c.repeat(1, 1, 1, block_elements,
                          1).view(batch, heads, q_seq_len, dim)
 
-    # Sparse branch
+    # Sparse branch: feed top-k indices directly, skipping the bool-mask round-trip.
     topk_idx = torch.topk(scores, topk, dim=-1).indices
-    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]
+    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]
 
     if compress_attn_weight is not None:
         return out_c * compress_attn_weight + out_s

fastvideo-kernel/python/fastvideo_kernel/triton_kernels/index.py

@@ -1,9 +1,10 @@
 ## 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,
@@ -153,3 +154,114 @@ 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

fastvideo-kernel/python/fastvideo_kernel/vmoba.py

@@ -11,7 +11,7 @@ except ImportError:
 
     def _unsupported(*args, **kwargs):
         raise ImportError(
-            "flash-attn is not installed. Please install it, e.g., `pip install flash-attn`."
+            "flash-attn is not installed. Please install it, e.g., `uv pip install flash-attn`."
         )
 
     _flash_attn_varlen_forward = _unsupported

fastvideo/AGENTS.md

@@ -0,0 +1,67 @@
+# `fastvideo/` — Core Package
+
+**Generated:** 2026-05-02
+
+Inference + training framework for video DiTs. Public API entry: `from fastvideo import VideoGenerator, PipelineConfig, SamplingParam`.
+
+## Public Surface (`__init__.py`)
+
+```python
+VideoGenerator   # entrypoints/video_generator.py — high-level inference handle
+PipelineConfig   # configs/pipelines/base.py — pipeline wiring dataclass
+SamplingParam    # api/sampling_param.py — runtime sampling knobs
+```
+
+CLI entry: `fastvideo` script → `entrypoints/cli/main.py` (subcommands: `generate`, `serve`, `bench`).
+
+## Layout
+
+```
+fastvideo/
+├── api/             # Schema + presets for the OpenAI-compatible serving layer
+├── attention/       # Backends + selector (FlashAttn / SageAttn / SDPA / VSA / VMoBA / SLA)
+├── configs/         # Per-model arch configs + per-pipeline configs (registry-driven)
+├── dataset/         # Dataloaders (pre-commit excluded — minimal lint surface)
+├── distributed/     # SP/TP groups, device communicators, init helpers
+├── entrypoints/     # cli/, openai/, streaming/, video_generator.py
+├── hooks/           # Runtime hook system for pipelines
+├── layers/          # Tensor-parallel linears + attention wrappers (port targets)
+├── models/          # DiT / VAE / encoder / scheduler / loader (pre-commit excluded)
+├── pipelines/       # basic/<model>/, preprocess/, stages/, training/
+├── platforms/       # CUDA/ROCm capability + AttentionBackendEnum
+├── third_party/     # Vendored externals (lint excluded; do not reformat)
+├── train/           # NEW modular trainer — methods × models × callbacks
+├── training/        # LEGACY monolithic *_training/distillation_pipeline.py
+├── worker/          # Multi-process / Ray executors
+├── workflow/        # Preprocessing workflow base class
+├── registry.py      # Pipeline-config + model-class lookup (canonical)
+├── envs.py          # Env-var declarations
+├── fastvideo_args.py# Runtime arg dataclass passed through pipelines
+└── utils.py         # FlexibleArgumentParser, qualname resolver, etc.
+```
+
+## Where to Look
+
+| Task | Location |
+|------|----------|
+| Add a new pipeline class | `pipelines/basic/<model>/` + `configs/pipelines/<model>.py` + register in `registry.py` |
+| Add a new model component | `models/<role>/<model>.py` + `configs/models/<role>/<model>.py` |
+| Wire an existing model into a new pipeline | `pipelines/basic/<model>/presets.py` + reuse stages from `pipelines/stages/` |
+| Add a converter | `scripts/checkpoint_conversion/<model>_to_*.py` (separate dir, separate AGENTS.md) |
+| Add an attention backend | `attention/backends/<name>.py` + register in selector |
+| Add a runtime CLI flag | `fastvideo_args.py` (avoid `argparse` ad-hoc inside stages) |
+
+## Conventions Specific Here
+
+- `PipelineStage` subclasses (`pipelines/stages/`) own one verb each (encode, schedule, denoise, decode). Compose, don't fork.
+- Every pipeline reads from a `PipelineConfig` subclass and a `SamplingParam`. Never read raw env vars inside a stage — go through `fastvideo.envs`.
+- Logger setup: `from fastvideo.logger import init_logger; logger = init_logger(__name__)`. Do not call `logging.getLogger` directly.
+- Imports between `train/` and `training/` are **forbidden** — they are independent stacks.
+
+## Pre-Commit Exclusions (do not assume linted)
+
+These dirs are listed in `.pre-commit-config.yaml` `exclude`:
+
+- `fastvideo/third_party/`, `fastvideo/dataset/`, `fastvideo/models/`
+
+Editing files there will NOT trigger yapf/ruff/mypy/codespell. Format manually if a sibling file shows clear style; do not introduce new violations.

fastvideo/__init__.py

@@ -1,5 +1,5 @@
 from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.configs.sample import SamplingParam
+from fastvideo.api.sampling_param import SamplingParam
 from fastvideo.entrypoints.video_generator import VideoGenerator
 from fastvideo.version import __version__
 

fastvideo/api/__init__.py

@@ -0,0 +1,97 @@
+# 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",
+]

fastvideo/api/compat.py

@@ -0,0 +1,623 @@
+# 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",
+]