[fix] dreamverse: steering session-flow, mock parity, and init-image validation

Steering protocol/UI fixes that interlock via one contract change — the frontend now sends initial_rollout_prompt_id (its own prompt id for the opening scene) in session-init and reset payloads, and both backend pre-seed sites use it, so prompt lifecycle events (enhancing/ready/fallback) finally match the frontend's records instead of no-oping against a backend uuid. - controller: suppress prompt_sources_blocked while a submission is queued or enhancing (fixes the blank player at typed-prompt session start); segment prompt logging is now opt-in via DREAMVERSE_SEGMENT_PROMPT_LOG, written off the event loop, warning on failure (drops the hardcoded author-machine path and bare except). - web: 'Generating next scene' overlay driven by explicit generation state instead of waitingForSegmentPrompt prop edges, so auto_extension_updated while idle can't strand it over the video; steering scene history is no longer truncated by the 24-event prompt feed cap and keeps stable numbers for 30+ scene sessions. - mock server: manual_continuation_mode parity (no rewrite-flow wait, honors initial_rollout_prompt_id, no segment cap in manual mode) so the GPU-less dev backend works with the steering-only frontend. - init image: client-side type/size validation on picker and paste paths (png/jpeg/webp, 15MB) with a visible error, and ws_max_size raised to 32MiB on both servers so a legitimate ~15MB image reaches the backend's own validation instead of tripping uvicorn's 16MiB frame cap.
[fix] ltx2: drop unreachable video_position_offset_sec kwargs fallback
2026-07-16 20:39:32 -07:00 · 2026-07-16 20:39:32 -07:00 · 2026-07-16 20:39:13 -07:00 · 2026-07-16 20:38:57 -07:00 · 2026-07-15 23:20:23 +00:00 · 2026-07-15 23:20:23 +00:00
327 changed files with 34656 additions and 1704 deletions
@@ -1,20 +1,23 @@
 ---
 name: reseed-performance-baseline
-description: Re-seed the HF performance-tracking baseline for an intentional runtime, dependency, or environment-caused benchmark shift using one or more reviewed normalized performance JSONs. 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 from a consistent batch of reviewed source results. The workflow backs up existing history under /tmp, validates all source JSONs for the same (model_id, gpu_type), rejects internally inconsistent source batches, uploads one success=true reseed record per accepted source JSON, and offers to clean local temp state after a successful upload.
+description: Re-seed the HF performance-tracking baseline for an intentional runtime, dependency, environment-caused benchmark shift, or reviewed v2 calibration using one or more reviewed normalized performance JSONs. 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, or when a new v2 exact comparable identity needs its first approved baseline. The workflow backs up existing history under /tmp, validates all source JSONs for the same legacy (model_id, gpu_type) target or the same v2 exact identity, rejects internally inconsistent source batches, uploads one success=true baseline record per accepted source JSON, and offers to clean local temp state after a successful upload.
 ---

 # 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`.
+Replace or advance the rolling performance baseline in the HF dataset
+`FastVideo/performance-tracking`. Legacy targets are scoped by
+`(model_id, gpu_type)`. V2 targets are scoped by exact comparable identity:
+`workload_id`, `variant_id`, `benchmark_version`, `hardware_profile_id`,
+`software_profile_id`, and `recipe_fingerprint`.

-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`.
+Performance comparison uses the median of up to the last 5 successful,
+baseline-eligible records for the same target. Failed or calibration-only
+records are useful audit history, but they do not move the future baseline
+because `compare_baseline.py` loads records with `successful_only=True` and
+`baseline_eligible_only=True`.

 This skill now reseeds from a reviewed batch of one or more source performance
 JSONs. It uploads one new `success=true` record per accepted source JSON; it
@@ -22,11 +25,13 @@ does not blindly replicate one measurement into 3 or 5 records. The effective
 reseed size is therefore dynamic and equals the number of provided, validated,
 internally consistent source JSONs.

-If the operator provides fewer than 3 records, call out that the last-5 rolling
-median may not move immediately. If the operator provides 3 consistent shifted
-records, the rolling median usually moves immediately. If the operator provides
-5 consistent shifted records, the last-5 window is effectively reset to the new
-runtime profile.
+For baseline shifts with existing history, if the operator provides fewer than
+3 records, call out that the last-5 rolling median may not move immediately. If
+the operator provides 3 consistent shifted records, the rolling median usually
+moves immediately. If the operator provides 5 consistent shifted records, the
+last-5 window is effectively reset to the new runtime profile. For the first
+approved v2 baseline of a new exact identity, one reviewed calibration seed is
+enough for the next comparable run to leave `CALIBRATION_NEEDED`.

 These records are intentional operator-approved baseline resets, not ordinary
 independent main-branch persistence. Mark them clearly with provenance fields
@@ -66,10 +71,10 @@ approval, then upload reviewed accepted baseline records.

 | 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. |
+| `model_id` | Legacy required; v2 inferred | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. For v2 records, use the `model_id` from each source artifact only as the upload directory; comparison is by exact identity. |
+| `gpu_type` | Legacy required; v2 inferred | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. V2 hardware matching uses `hardware_profile_id`; preserve `gpu_type` as display metadata. |
 | `source_results` | Yes | One or more local paths or Buildkite artifact URLs for accepted shifted performance JSONs. Prefer normalized `normalized_perf_*.json` artifacts emitted by `compare_baseline.py`. Accept `source_result` as an alias only for a single JSON. |
-| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `PERF_MAX_REGRESSION` if set, otherwise `0.05` (5%). |
+| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `0.05` (5%). |
 | `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:
@@ -78,10 +83,14 @@ Hardcoded defaults:
  supported by the code, but use the default unless the user explicitly asks).
 - Local sync root: `/tmp/perf-tracking` (`PERFORMANCE_TRACKING_ROOT` override
  is supported).
+- Prepared-record staging root: `/tmp/performance_reseed_prepared`
+  (`PERFORMANCE_RESEED_STAGING_ROOT` override is supported). Keep it separate
+  and non-nested from the sync root.
 - Backup root: `/tmp/performance_reseed_backup`.
 - Download scratch root for source artifact URLs: `/tmp/performance_reseed_source`.
- Baseline window: last 5 `success=true` records for the same
-  `(model_id, gpu_type)`.
+- Baseline window: last 5 `success=true`, `baseline_eligible=true` records
+  for the same legacy `(model_id, gpu_type)` target or the same v2 exact
+  comparable identity.
 - Reseed count: dynamic. Upload exactly one accepted seed record per validated
  source JSON.

@@ -115,12 +124,24 @@ with open(source_result, encoding="utf-8") as f:
    record = json.load(f)
 ```

-Stop if any normalized record's `model_id` or `gpu_type` does not match the
-requested `model_id` and `gpu_type`.
+Classify the source batch before continuing:
+
+- **Legacy source records** have no v2 exact identity fields. Stop if any
+  normalized record's `model_id` or `gpu_type` does not match the requested
+  `model_id` and `gpu_type`.
+- **V2 source records** have exact identity fields. Stop unless every source
+  record has all six comparable identity fields and they are identical across
+  the batch: `workload_id`, `variant_id`, `benchmark_version`,
+  `hardware_profile_id`, `software_profile_id`, and `recipe_fingerprint`.
+  Do not fall back to legacy `(model_id, gpu_type)` matching for v2 records.

 The source records may have `success: false` when they came from failed
 rolling baseline comparisons. That is expected; only the reviewed reseed
 records become new `success: true` baseline records after explicit approval.
+For a first v2 baseline seed, the source records must instead be successful
+scheduled-main full-suite `CALIBRATION_NEEDED` normalized artifacts. Reject PR,
+local, direct-run, non-main-branch, or non-full-suite calibration artifacts as
+seed sources.

 Sort validated source records by their original `timestamp` ascending before
 preparing the seed records. If a source timestamp is missing or unparsable,
@@ -148,8 +169,7 @@ For each metric with at least two non-null source values:
 4. Stop if any source record regresses against the batch median by more than
   `max_intra_batch_regression`.

-Default `max_intra_batch_regression` to `PERF_MAX_REGRESSION` when set,
-otherwise `0.05`. Print a table with per-source values, batch median, and
+Default `max_intra_batch_regression` to `0.05`. Print a table with per-source values, batch median, and
 worst intra-batch regression.

 This check prevents uploading a mixed batch where one JSON is materially
@@ -183,7 +203,7 @@ present, that run is not a valid source for baseline reseeding.

 ### 2. Sync and back up existing HF records under /tmp

-Use `fastvideo/tests/performance/hf_store.py` helpers directly. Do **not** use
+Use `fastvideo/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.

@@ -192,16 +212,16 @@ 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)'
+python -c 'from fastvideo.performance.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 under `/tmp`:
+For legacy records, back up the sanitized model directory under `/tmp`:

 ```bash
 SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
 TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
-MODEL_SAFE=$(PYTHONPATH=fastvideo/tests/performance python - <<'PY'
-from hf_store import sanitize
+MODEL_SAFE=$(python - <<'PY'
+from fastvideo.performance.hf_store import sanitize
 print(sanitize("<model_id>"))
 PY
 )
@@ -210,6 +230,16 @@ mkdir -p "$BACKUP_DIR"
 cp -R "${PERFORMANCE_TRACKING_ROOT}/${MODEL_SAFE}" "$BACKUP_DIR/" 2>/dev/null || true
 ```

+For v2 records, back up the full local tracking root after sync. Exact identity
+lookup scans across model directories, so a benchmark rename may have relevant
+history outside the current source artifact's `model_id` directory:
+
+```bash
+BACKUP_DIR="/tmp/performance_reseed_backup/${TIMESTAMP}_${SHORT_COMMIT}_v2_exact_identity"
+mkdir -p "$BACKUP_DIR"
+cp -R "${PERFORMANCE_TRACKING_ROOT}" "$BACKUP_DIR/tracking-root"
+```
+
 Write provenance next to the backup:

 ```bash
@@ -232,10 +262,12 @@ 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:
+Load the last 5 successful baseline records for the target.
+
+For legacy targets:

 ```python
-from hf_store import load_records_for_model
+from fastvideo.performance.hf_store import load_records_for_model

 records = load_records_for_model(
    "/tmp/perf-tracking",
@@ -243,6 +275,28 @@ records = load_records_for_model(
    "<gpu_type>",
    last_n=5,
    successful_only=True,
+    baseline_eligible_only=True,
+)
+```
+
+For v2 exact-identity targets:
+
+```python
+from fastvideo.performance.hf_store import load_records_for_identity
+
+records = load_records_for_identity(
+    "/tmp/perf-tracking",
+    {
+        "workload_id": "<workload_id>",
+        "variant_id": "<variant_id>",
+        "benchmark_version": "<benchmark_version>",
+        "hardware_profile_id": "<hardware_profile_id>",
+        "software_profile_id": "<software_profile_id>",
+        "recipe_fingerprint": "<recipe_fingerprint>",
+    },
+    last_n=5,
+    successful_only=True,
+    baseline_eligible_only=True,
 )
 ```

@@ -258,7 +312,8 @@ medians after appending the proposed seed records, and source batch spread for:

 Also print how many successful old records exist. Make clear:

- 1 seed record usually does not move a last-5 median by itself.
+- 1 seed record usually does not move an existing last-5 median by itself, but
+  it is enough to establish the first v2 baseline for a new exact identity.
 - 3 consistent seed records usually move the last-5 median immediately.
 - 5 consistent seed records effectively reset the last-5 window.
 - The records are intentional approved baseline resets and must be labeled
@@ -268,10 +323,10 @@ Also print how many successful old records exist. Make clear:

 Require an explicit confirmation phrase before preparing the upload:

-> About to RE-SEED performance baseline for `<model_id>` on `<gpu_type>`.
+> About to RE-SEED performance baseline for `<target description>`.
 > This will upload `<N>` new `success=true` records to
-> `FastVideo/performance-tracking/<sanitize(model_id)>/`, one per accepted
-> source JSON.
+> `FastVideo/performance-tracking/<sanitize(model_id)>/` or the source
+> artifact's v2 model directory, one per accepted source JSON.
 >
 > Reason: `<intent_rationale>`
 > Source results: `<source_results>`
@@ -289,25 +344,63 @@ Do not continue unless the user types exactly `confirm performance reseed`.

 ### 5. Create the accepted seed records

-Create one seed record from each normalized source result. Do not copy the
+Create one seed record from each normalized source result.
+
+For first v2 baseline seeds, use the scoped utility. It validates exact
+identity, requires successful scheduled-main full-suite `CALIBRATION_NEEDED`
+source artifacts, preserves the normalized v2 identity and metadata fields,
+and writes seed records with `success=true`, `baseline_eligible=true`, and
+`comparison_status=PASS`:
+
+```bash
+python fastvideo/tests/performance/seed_baseline.py \
+  --source-result <normalized_perf_1.json> \
+  --source-result <normalized_perf_2.json> \
+  --intent-rationale "<intent_rationale>" \
+  --max-intra-batch-regression 0.05 \
+  --tracking-root "${PERFORMANCE_TRACKING_ROOT}" \
+  --staging-root "${PERFORMANCE_RESEED_STAGING_ROOT:-/tmp/performance_reseed_prepared}"
+```
+
+The utility is prepare-only and intentionally has no upload option. Upload the
+scoped records only after the separate confirmation in step 6.
+
+The utility validates against an isolated fresh HF snapshot and leaves
+`PERFORMANCE_TRACKING_ROOT` untouched; that argument only proves the staging
+root is separate from the operator's tracking mirror. Before writing, it stops
+if the exact identity already has a successful baseline-eligible record or if
+the workload/variant/version already trusts another recipe. It atomically
+reserves the exact identity and writes a digest-protected upload manifest bound
+to the current HF endpoint, repository id, and repository type. Keep the
+prepared records, manifest, source files, and reservation unchanged until the
+operation is uploaded or explicitly cleaned up.
+
+If the prepared seed records look correct, upload only those scoped records in
+step 7. Do not rerun the utility with a different source list after approval.
+
+For legacy reseeds or accepted v2 baseline shifts from regression artifacts,
+create one seed record from each normalized source result. Do not copy the
 source JSON wholesale.

 Infer the baseline field allowlist from all existing HF records for the target
-`(model_id, gpu_type)` after syncing, including both `success=true` and
-`success=false` records. Use the union of non-provenance keys present in those
-target records, preserving only fields that also exist in the normalized
-source record or are explicitly set by the reseed workflow. Always include
-`model_id`, `timestamp`, and `success` because the upload path and baseline
-loader depend on them. Always set `timestamp` to a fresh reseed timestamp and
-`success` to `true`. Do not include unrelated source-only fields that are
-absent from existing HF records.
+after syncing, including both `success=true` and `success=false` records. For
+legacy targets the target is `(model_id, gpu_type)`. For v2 baseline-shift
+reseeds the target is the exact comparable identity. Use the union of
+non-provenance keys present in those target records, preserving only fields
+that also exist in the normalized source record or are explicitly set by the
+reseed workflow. Always include `model_id`, `timestamp`, `success`,
+`baseline_eligible`, and `comparison_status` because the upload path and
+baseline loader depend on them. Always set `timestamp` to a fresh reseed
+timestamp, `success` to `true`, `baseline_eligible` to `true`, and
+`comparison_status` to `PASS`. Do not include unrelated source-only fields
+that are absent from existing HF records.

 Exclude existing provenance or operator metadata from the inferred baseline
 field allowlist. At minimum, exclude keys prefixed with `baseline_reseed` and
 any fields known to be local-only audit metadata.

-If there are no previous HF records for the target model/GPU, fall back to this
-default baseline field list:
+If there are no previous HF records for the target, fall back to this default
+baseline field list:

 - `model_id`
 - `timestamp`
@@ -320,6 +413,22 @@ default baseline field list:
 - `dit_time_s`
 - `vae_decode_time_s`
 - `success`
+- `baseline_eligible`
+- `comparison_status`
+
+For v2 baseline-shift reseeds with no previous HF records for the exact
+identity, also preserve:
+
+- `workload_id`
+- `variant_id`
+- `benchmark_version`
+- `recipe_fingerprint`
+- `hardware_profile_id`
+- `software_profile_id`
+- `recipe`
+- `hardware_profile`
+- `software_profile`
+- `software_comparison_profile`

 Do not upload extra fields from the source artifact.

@@ -335,6 +444,22 @@ Optional provenance fields are allowed and useful:
 - `baseline_reseed_operator`
 - `baseline_reseed_max_intra_batch_regression`

+The v2 calibration seed utility writes analogous first-seed provenance:
+
+- `baseline_seed: true`
+- `baseline_seed_reason`
+- `baseline_seed_source_result`
+- `baseline_seed_source_status`
+- `baseline_seed_source_timestamp`
+- `baseline_seed_source_success`
+- `baseline_seed_source_run_source`
+- `baseline_seed_source_branch`
+- `baseline_seed_source_test_scope`
+- `baseline_seed_source_pr_number`
+- `baseline_seed_batch_size`
+- `baseline_seed_batch_index`
+- `baseline_seed_operator`
+
 Use a fresh reseed timestamp for each seed record, not the original source
 result timestamp. This is required because
 `load_records_for_model(..., last_n=5)` keeps the last records after loading
@@ -357,7 +482,8 @@ Prefer uploading new accepted seed records so failed history remains visible.
 Print:

 - Backup directory path under `/tmp`.
- Prepared local record paths under `PERFORMANCE_TRACKING_ROOT`.
+- Prepared local record paths under `PERFORMANCE_RESEED_STAGING_ROOT`.
+- Prepared upload-manifest path under the identity reservation.
 - HF paths that will receive the new records.
 - Old rolling medians.
 - Source batch medians, source batch spread, reseed count, and candidate
@@ -369,22 +495,36 @@ 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:
+For a first v2 calibration seed, use the manifest uploader after the user
+replies exactly `upload`:

-```python
-from hf_store import upload_record
-
-upload_record("<local_record_path>", record, strict=True)
+```bash
+python -c 'from fastvideo.tests.performance.seed_baseline import upload_prepared_seed_manifest; print(upload_prepared_seed_manifest("<prepared_manifest>"))'
 ```

-Run it once per prepared record. Each upload goes to:
+The uploader verifies the source and prepared-record digests, pins and scans
+the current HF revision, rechecks exact-identity and recipe-cohort conflicts,
+and writes the entire batch in one commit whose `parent_commit` must still be
+current. A concurrent Hub update makes the commit fail. Do not retry
+automatically: preserve staging, refresh/review remote state, and request a new
+explicit `upload` after the conflict is understood. Each record 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.
+Never call `upload_record()` once per first-seed record: that can partially
+land the batch and has no compare-and-swap guard.
+
+For a legacy reseed or an accepted v2 baseline shift, the first-seed manifest
+validator does not apply because an eligible baseline already exists. Upload
+only the individually reviewed records prepared in step 5 with the shared
+`upload_record(local_path, record, strict=True)` helper. Stop on the first
+failure and report exactly which records reached HF; do not silently rerun or
+replicate the remainder.
+
+Never bulk upload the tracking or staging root, and never modify another
+model's directory in the same operation.

 ### 8. Report outcome and offer cleanup

@@ -406,9 +546,14 @@ distinguish an accepted baseline shift from a hidden regression.
 After the upload is verified, ask whether the user wants to clear temporary
 local state. Explain what each directory is for:

- `PERFORMANCE_TRACKING_ROOT`, usually `/tmp/perf-tracking`: local synced
-  mirror of `FastVideo/performance-tracking` plus the prepared local seed
-  records used for scoped upload.
+- `PERFORMANCE_TRACKING_ROOT`, usually `/tmp/perf-tracking`: read-only local
+  synced mirror used for operator review and reporting. First-v2 preparation
+  independently proves remote state from a fresh temporary HF snapshot.
+- `PERFORMANCE_RESEED_STAGING_ROOT`, usually
+  `/tmp/performance_reseed_prepared`: prepared local seed records used for the
+  scoped upload, plus the identity reservation and digest manifest. Keeping
+  this separate prevents aborted preparations from appearing in later
+  baseline reads.
 - `/tmp/performance_reseed_backup/<...>`: local backup of the target model's
  pre-reseed HF history plus `PROVENANCE.txt`, kept so a bad reseed can be
  audited or corrected.
@@ -418,14 +563,18 @@ local state. Explain what each directory is for:
 Ask:

 > Reseed succeeded. Do you want me to delete the local temp tracking mirror,
-> source downloads, and reseed backup under `/tmp`? These files are local
-> safety/audit artifacts only; HF already has the uploaded records.
+> this reseed's prepared staging records, source downloads, and reseed backup
+> under `/tmp`? These files are local safety/audit artifacts only; HF already
+> has the uploaded records.
 >
 > Reply `cleanup reseed temp` to delete them, anything else to keep them.

 Do not delete anything unless the user replies exactly
 `cleanup reseed temp`. If cleanup is requested, remove only the specific
-directories created for this reseed. Never remove unrelated `/tmp` contents.
+directories and prepared record paths created for this reseed. Do not remove
+the shared staging root when it contains other records. Remove this operation's
+identity reservation only with its prepared records and manifest, and never
+remove unrelated `/tmp` contents.

 ## Failure modes and handling

@@ -437,19 +586,34 @@ directories created for this reseed. Never remove unrelated `/tmp` contents.
  against the source batch median by more than `max_intra_batch_regression`.
  Ask for cleaner sources or a reviewed explanation before continuing.
 - **Too few source records to move the median.** Continue only after making
-  clear that one or two records may not immediately move the last-5 median.
+  clear that one or two records may not immediately move an existing last-5
+  median. This warning does not block a first v2 calibration seed for an exact
+  identity with no eligible baseline yet.
 - **The source results are noisy or suspicious.** Stop. Reseeding amplifies
  those measurements into the baseline, so they must be reviewed first.
 - **HF sync fails.** Stop for destructive reseeds. A stale or empty sync can
  make the old baseline look missing.
+- **The exact v2 identity already has an eligible baseline.** Stop. The
+  `CALIBRATION_NEEDED` artifact is stale; use the reviewed baseline-shift path
+  instead of the first-seed utility.
+- **The workload/variant/version trusts another recipe.** Stop. The source is
+  stale relative to the current recipe cohort and must not bypass
+  `RECIPE_MISMATCH` by creating a second trusted recipe.
+- **The staging root already has a prepared seed for the exact identity.**
+  Stop and reuse, upload, or explicitly clean that preparation. Do not prepare
+  another copy of the same measurement.
+- **The conditional Hub commit loses its parent race.** Stop without retrying.
+  Keep the preparation, refresh and review the new remote state, then request
+  a new explicit `upload` only if the seed is still valid.
 - **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.
- **The user declines cleanup.** Keep `/tmp/perf-tracking`, the source
-  download directory if any, and `/tmp/performance_reseed_backup/<...>` in
-  place for audit/debugging.
+- **The user declines cleanup.** Keep `/tmp/perf-tracking`, the prepared seed
+  records under `/tmp/performance_reseed_prepared`, the source download
+  directory if any, and `/tmp/performance_reseed_backup/<...>` in place for
+  audit/debugging.
 - **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.
@@ -460,8 +624,9 @@ directories created for this reseed. Never remove unrelated `/tmp` contents.
  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/performance/hf_store.py` — HF sync and record loading helpers.
+- `fastvideo/tests/performance/seed_baseline.py` — first-seed preparation,
+  staging reservation, manifest validation, and conditional batch upload.
 - `fastvideo/tests/performance/test_inference_performance.py` — source result
  JSON schema.
 - `.buildkite/performance-benchmarks/tests/*.json` — fixed absolute benchmark
@@ -474,3 +639,4 @@ directories created for this reseed. Never remove unrelated `/tmp` contents.
 | 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 | Previous 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. Superseded by the 2026-05-08 dynamic multi-source policy. |
 | 2026-05-08 | Replace fixed 3/5 replication with dynamic multi-source reseeding: upload one seed record per reviewed source JSON, validate intra-batch consistency, move backup/source scratch under `/tmp`, and ask whether to clean temp state after successful upload. |
+| 2026-07-13 | Keep first-v2-seed preparation outside the canonical mirror, reserve staging identities atomically, reject stale or replayed calibration seeds, and upload reviewed manifests with a single parent-guarded Hub commit. |
@@ -1,5 +1,9 @@
 {
  "benchmark_id": "wan-t2v-1.3b-2gpu",
+  "config_schema_version": 2,
+  "workload_id": "wan-t2v",
+  "variant_id": "1.3b-sp2",
+  "benchmark_version": 3,
  "description": "Wan2.1 T2V 1.3B inference performance",
  "model": {
    "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
@@ -114,6 +114,17 @@ steps:
            limit: 2
      agents:
        queue: "default"
+    - label: ":test_tube: LoRA Extraction Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "lora_extraction"
+      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"
@@ -371,6 +382,21 @@ steps:
                  - TEST_TYPE=inference_lora
                agents:
                  queue: "default"
+            - path:
+                - "scripts/lora_extraction/**"
+                - "fastvideo/tests/lora_extraction/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/training/training_utils.py"
+                - "fastvideo/layers/lora/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile"
+              config:
+                command: "timeout 90m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: LoRA Extraction Tests"
+                env:
+                  - TEST_TYPE=lora_extraction
+                agents:
+                  queue: "default"
            - path:
                - "fastvideo/**"
                - "pyproject.toml"
@@ -410,7 +436,7 @@ steps:
                - "pyproject.toml"
                - "docker/Dockerfile"
              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                command: "timeout 25m .buildkite/scripts/pr_test.sh"
                label: ":test_tube: LoRA Training Tests"
                env:
                  - TEST_TYPE=training_lora
@@ -455,6 +481,7 @@ steps:
                - "fastvideo/layers/**"
                - "fastvideo/worker/**"
                - "fastvideo/entrypoints/**"
+                - "fastvideo/performance/**"
                - "fastvideo/tests/performance/**"
                - ".buildkite/performance-benchmarks/**"
                - "pyproject.toml"
@@ -76,10 +76,27 @@ EFFECTIVE_PR=${BUILDKITE_PULL_REQUEST:-false}
 if [ "$EFFECTIVE_PR" = "false" ] && [ -n "${PR_NUMBER:-}" ]; then
    EFFECTIVE_PR=$PR_NUMBER
 fi
-MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR BUILDKITE_BRANCH=${BUILDKITE_BRANCH:-} TEST_SCOPE=${TEST_SCOPE:-} BUILDKITE_BUILD_URL=${BUILDKITE_BUILD_URL:-} BUILDKITE_BUILD_ID=${BUILDKITE_BUILD_ID:-} BUILDKITE_JOB_ID=${BUILDKITE_JOB_ID:-} IMAGE_VERSION=$IMAGE_VERSION"
+MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR BUILDKITE_BRANCH=${BUILDKITE_BRANCH:-} BUILDKITE_SOURCE=${BUILDKITE_SOURCE:-} TEST_SCOPE=${TEST_SCOPE:-} BUILDKITE_BUILD_URL=${BUILDKITE_BUILD_URL:-} BUILDKITE_BUILD_ID=${BUILDKITE_BUILD_ID:-} BUILDKITE_JOB_ID=${BUILDKITE_JOB_ID:-} IMAGE_VERSION=$IMAGE_VERSION"

 POST_RUN_HOOK=""

+is_truthy() {
+    case "${1:-}" in
+        1|true|TRUE|yes|YES|on|ON) return 0 ;;
+        *) return 1 ;;
+    esac
+}
+
+ssim_bootstrap_args() {
+    local title="${PR_TITLE:-}"
+    local message="${BUILDKITE_MESSAGE:-}"
+    if is_truthy "${FASTVIDEO_SSIM_BOOTSTRAP_MODE:-}" \
+        || [[ "$title" == *"[new-model]"* ]] \
+        || [[ "$message" == *"[new-model]"* ]]; then
+        printf ' --bootstrap-mode'
+    fi
+}
+
 upload_performance_artifacts() {
    SHORT_SHA=${BUILDKITE_COMMIT:0:7}
    LOCAL_DIR="downloaded_reports"
@@ -172,7 +189,12 @@ case "$TEST_TYPE" in
        ;;
    "ssim")
        log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_SSIM_TEST_FILE::run_ssim_tests"
+        SSIM_BOOTSTRAP_ARGS=$(ssim_bootstrap_args)
+        if [ -n "$SSIM_BOOTSTRAP_ARGS" ]; then
+            log "SSIM bootstrap mode enabled for new-model reference draft generation"
+        fi
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run "
+        MODAL_COMMAND+="$MODAL_SSIM_TEST_FILE::run_ssim_tests$SSIM_BOOTSTRAP_ARGS"
        ;;
    "training")
        log "Running training tests..."
@@ -0,0 +1,133 @@
+#!/usr/bin/env bash
+# Gate the expensive Buildkite full suite on the cheap GitHub checks.
+#
+# Polls the workflow runs for the PR head commit and only exits 0 once the
+# watched cheap workflows (pre-commit, docs build) have succeeded, so the
+# 'ready' label cannot burn ~20 GPU lanes on a head that a cheap check has
+# already doomed.
+#
+# Semantics:
+#   - watched run completed with a bad conclusion  -> exit 1 (fail CLOSED:
+#     no full suite; the next push re-arms via the 'synchronize' trigger)
+#   - watched run cancelled                         -> still pending: the docs
+#     workflow's repo-global 'pages' concurrency group cancels runs superseded
+#     by unrelated pushes, so 'cancelled' is not a verdict on this PR
+#   - watched runs pending                          -> poll until done
+#   - docs run absent                               -> not applicable after a
+#     short grace period ('Deploy Documentation' is path-filtered on PRs)
+#   - pre-commit run absent                         -> keep polling: pre-commit
+#     is never path-filtered, so its absence is always anomalous
+#   - 'ready' label removed while waiting           -> exit 1 (fail CLOSED:
+#     un-labeling is a deliberate maintainer action)
+#   - GitHub API unreachable or timeout             -> exit 0 (fail OPEN,
+#     loud warning: never brick CI on a GitHub outage)
+#
+# Required env: PR_SHA (PR head commit), PR_NUMBER, GITHUB_REPOSITORY, GH_TOKEN.
+set -euo pipefail
+
+: "${PR_SHA:?PR_SHA (PR head commit) is required}"
+: "${PR_NUMBER:?PR_NUMBER (pull request number) is required}"
+: "${GITHUB_REPOSITORY:?GITHUB_REPOSITORY is required}"
+
+# Workflow-level `name:` values that must be green before the full suite
+# may start. "Deploy Documentation" is path-filtered on PRs, so its run may
+# legitimately never exist; pre-commit always runs, so it must appear.
+WATCHED_NAMES='["pre-commit", "Deploy Documentation"]'
+WATCHED_REGEX='^(pre-commit|Deploy Documentation)$'
+POLL_SECS="${POLL_SECS:-20}"
+GRACE_SECS="${GRACE_SECS:-60}"
+MAX_WAIT_SECS="${MAX_WAIT_SECS:-1500}"
+
+# Bound each API call so a hung connection hits the 3-strike fail-open path
+# instead of pinning the loop until the job timeout (which would fail closed
+# on exactly the GitHub-outage case this script is meant to survive).
+if command -v timeout >/dev/null 2>&1; then
+  gh_api() { timeout 30 gh api "$@"; }
+else
+  gh_api() { gh api "$@"; }  # macOS dev boxes; CI always has coreutils timeout
+fi
+
+# The workflow checked the label before starting the gate, but the wait can
+# last ~25 min: re-check once before any exit 0 and fail closed if 'ready'
+# was removed in the meantime. An API error here proceeds (the label was
+# present when the gate started; never brick CI on an outage).
+recheck_ready_label() {
+  local pr_json
+  if pr_json=$(gh_api "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}" 2>/dev/null); then
+    if ! jq -e '[.labels[]?.name] | index("ready")' <<<"$pr_json" >/dev/null 2>&1; then
+      echo "::error::PR #${PR_NUMBER} no longer has the 'ready' label —" \
+        "NOT triggering the Buildkite full suite. Re-add the label to re-arm."
+      exit 1
+    fi
+  else
+    echo "::warning::Could not re-check the 'ready' label on PR #${PR_NUMBER}; proceeding (it was present when the gate started)."
+  fi
+}
+
+start=$(date +%s)
+api_fails=0
+missing=""
+
+while true; do
+  elapsed=$(( $(date +%s) - start ))
+
+  if runs_json=$(gh_api "repos/${GITHUB_REPOSITORY}/actions/runs?head_sha=${PR_SHA}&per_page=100" 2>/dev/null) \
+     && state=$(jq --arg re "$WATCHED_REGEX" '
+          [.workflow_runs[]? | select(.name // "" | test($re))]
+          | group_by(.name) | map(max_by(.id))
+          | map({name, status, conclusion})' <<<"$runs_json" 2>/dev/null); then
+    api_fails=0
+    echo "t+${elapsed}s watched checks: $(jq -c . <<<"$state")"
+
+    failed=$(jq -r '[.[] | select(.status == "completed"
+          and (.conclusion | IN("success", "skipped", "neutral", "cancelled") | not))]
+        | map(.name) | join(", ")' <<<"$state")
+    if [ -n "$failed" ]; then
+      echo "::error::Cheap check(s) failed on ${PR_SHA}: ${failed}." \
+        "NOT triggering the Buildkite full suite. Push a fix (the 'ready'" \
+        "label re-arms on every push), or re-run the failed check and then" \
+        "re-run this workflow."
+      exit 1
+    fi
+
+    # 'cancelled' counts as pending: wait for a re-run to reach a real verdict
+    # (bounded by MAX_WAIT, then the fail-open below).
+    pending=$(jq '[.[] | select(.status != "completed" or .conclusion == "cancelled")] | length' <<<"$state")
+    missing=$(jq -r --argjson watched "$WATCHED_NAMES" '($watched - map(.name)) | join(", ")' <<<"$state")
+    if [ "$pending" -eq 0 ]; then
+      if [ -z "$missing" ]; then
+        recheck_ready_label
+        echo "All watched cheap checks are green — full suite may proceed."
+        exit 0
+      fi
+      case "$missing" in
+        *pre-commit*)
+          echo "pre-commit run not found for ${PR_SHA} yet; waiting (pre-commit is never path-filtered, so its absence is anomalous)."
+          ;;
+        *)
+          if [ "$elapsed" -ge "$GRACE_SECS" ]; then
+            recheck_ready_label
+            echo "::warning::Watched run(s) never appeared for ${PR_SHA}: ${missing} (path-filtered, likely not applicable). Proceeding on the checks that did run."
+            exit 0
+          fi
+          echo "Waiting up to ${GRACE_SECS}s grace for path-filtered run(s) to appear: ${missing}."
+          ;;
+      esac
+    fi
+  else
+    api_fails=$(( api_fails + 1 ))
+    echo "::warning::GitHub API error querying workflow runs for ${PR_SHA} (attempt ${api_fails}/3)."
+    if [ "$api_fails" -ge 3 ]; then
+      recheck_ready_label
+      echo "::warning::FAILING OPEN: cannot query GitHub check status — triggering the full suite WITHOUT the cheap-check gate."
+      exit 0
+    fi
+  fi
+
+  if [ "$elapsed" -ge "$MAX_WAIT_SECS" ]; then
+    recheck_ready_label
+    echo "::warning::FAILING OPEN: watched checks still pending after $(( MAX_WAIT_SECS / 60 )) min${missing:+ (never appeared: ${missing})} — triggering the full suite anyway."
+    exit 0
+  fi
+  sleep "$POLL_SECS"
+done
@@ -0,0 +1,122 @@
+#!/usr/bin/env bash
+# Self-test for gate_full_suite.sh using a mocked `gh`. No network, runs on
+# any dev box: bash .github/scripts/test_gate_full_suite.sh
+set -u
+here=$(cd "$(dirname "$0")" && pwd)
+tmp=$(mktemp -d)
+trap 'rm -rf "$tmp"' EXIT
+
+# Mock gh. Asserts the exact endpoint (including head_sha) it is called
+# with — an endpoint typo in the gate script fails the test rather than
+# silently serving canned data. On the runs endpoint it serves
+# $MOCK_DIR/response_<call#>.json, sticking on the highest existing file,
+# and exits 1 if none exist (simulates a GitHub API outage). On the pulls
+# endpoint it serves $MOCK_DIR/pr.json, defaulting to a 'ready'-labeled PR.
+cat > "$tmp/gh" <<'EOF'
+#!/usr/bin/env bash
+if [ "${1:-}" != "api" ]; then
+  echo "unexpected gh invocation: $*" >> "$MOCK_DIR/endpoint_error"
+  exit 2
+fi
+case "${2:-}" in
+  "repos/o/r/actions/runs?head_sha=deadbeef&per_page=100")
+    n=$(( $(cat "$MOCK_DIR/count" 2>/dev/null || echo 0) + 1 ))
+    echo "$n" > "$MOCK_DIR/count"
+    while [ "$n" -gt 0 ]; do
+      if [ -f "$MOCK_DIR/response_$n.json" ]; then
+        cat "$MOCK_DIR/response_$n.json"
+        exit 0
+      fi
+      n=$(( n - 1 ))
+    done
+    echo "api outage" >&2
+    exit 1
+    ;;
+  "repos/o/r/pulls/42")
+    if [ -f "$MOCK_DIR/pr.json" ]; then
+      cat "$MOCK_DIR/pr.json"
+    else
+      echo '{"labels": [{"name": "ready"}]}'
+    fi
+    ;;
+  *)
+    echo "unexpected gh endpoint: $2" >> "$MOCK_DIR/endpoint_error"
+    exit 2
+    ;;
+esac
+EOF
+chmod +x "$tmp/gh"
+
+PC_OK='{"name": "pre-commit", "id": 1, "status": "completed", "conclusion": "success"}'
+PC_BAD='{"name": "pre-commit", "id": 1, "status": "completed", "conclusion": "failure"}'
+PC_PENDING='{"name": "pre-commit", "id": 1, "status": "in_progress", "conclusion": null}'
+DOCS_OK='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "success"}'
+DOCS_BAD='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "failure"}'
+DOCS_CANCELLED='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "cancelled"}'
+OTHER='{"name": "Trigger Full Suite", "id": 3, "status": "in_progress", "conclusion": null}'
+NULL_NAME='{"name": null, "id": 4, "status": "completed", "conclusion": "failure"}'
+PC_OK_RERUN='{"name": "pre-commit", "id": 5, "status": "completed", "conclusion": "success"}'
+
+fails=0
+want_log=""  # optional: expect() also greps out.log for this regex, then resets
+pr_json=""   # optional: served for the pulls (label re-check) endpoint, then resets
+raw_body=""  # optional: serve responses verbatim instead of wrapping in workflow_runs
+expect() { # <name> <expected-exit> <response json>...
+  local name=$1 want=$2 dir i=1
+  shift 2
+  dir=$(mktemp -d "$tmp/test_XXXXXX")
+  for body in "$@"; do
+    if [ -n "$raw_body" ]; then
+      printf '%s' "$body" > "$dir/response_$i.json"
+    else
+      printf '{"workflow_runs": [%s]}' "$body" > "$dir/response_$i.json"
+    fi
+    i=$(( i + 1 ))
+  done
+  [ -n "$pr_json" ] && printf '%s' "$pr_json" > "$dir/pr.json"
+  ( export PATH="$tmp:$PATH" MOCK_DIR="$dir" PR_SHA=deadbeef PR_NUMBER=42 \
+      GITHUB_REPOSITORY=o/r POLL_SECS=0 GRACE_SECS=1 MAX_WAIT_SECS=3
+    bash "$here/gate_full_suite.sh" > "$dir/out.log" 2>&1 )
+  local rc=$?
+  if [ "$rc" -ne "$want" ]; then
+    echo "FAIL: $name (exit $rc, want $want)"
+    cat "$dir/out.log"
+    fails=1
+  elif [ -f "$dir/endpoint_error" ]; then
+    echo "FAIL: $name (mock gh got an unexpected call)"
+    cat "$dir/endpoint_error"
+    fails=1
+  elif [ -n "$want_log" ] && ! grep -Eq "$want_log" "$dir/out.log"; then
+    echo "FAIL: $name (log does not match: $want_log)"
+    cat "$dir/out.log"
+    fails=1
+  else
+    echo "ok: $name"
+  fi
+  want_log="" pr_json="" raw_body=""
+}
+
+expect "both green -> proceed" 0 "$PC_OK, $DOCS_OK, $OTHER, $NULL_NAME"
+expect "docs build failed -> blocked" 1 "$PC_OK, $DOCS_BAD"
+expect "pre-commit failed -> blocked" 1 "$PC_BAD"
+expect "pending then green -> proceed" 0 "$PC_PENDING" "$PC_OK, $DOCS_OK"
+want_log="never appeared.*Deploy Documentation"
+expect "docs run absent (path-filtered) -> proceed after grace" 0 "$PC_OK"
+expect "API outage -> fail open" 0
+want_log="FAILING OPEN"
+expect "pending past MAX_WAIT -> fail open" 0 "$PC_PENDING"
+want_log="FAILING OPEN"
+expect "unrelated runs only -> no grace, fail open at MAX_WAIT" 0 "$OTHER"
+expect "cancelled docs then green -> proceed" 0 \
+  "$PC_OK, $DOCS_CANCELLED" "$PC_OK, $DOCS_OK"
+want_log="FAILING OPEN"
+expect "cancelled docs forever -> fail open at MAX_WAIT" 0 "$PC_OK, $DOCS_CANCELLED"
+want_log="FAILING OPEN"
+expect "pre-commit absent -> no grace, fail open at MAX_WAIT" 0 "$DOCS_OK"
+expect "duplicate run names -> latest wins" 0 "$PC_BAD, $PC_OK_RERUN, $DOCS_OK"
+raw_body=1
+expect "garbage response body -> fail open" 0 "this is not json"
+pr_json='{"labels": [{"name": "other"}]}'
+expect "ready label removed mid-gate -> blocked" 1 "$PC_OK, $DOCS_OK"
+
+exit "$fails"
@@ -1,7 +1,11 @@
 name: pre-commit

 on:
-  pull_request:
+  # pull_request_target instead of pull_request: the workflow definition and
+  # the hook config are always taken from the BASE branch, so fork /
+  # first-time-contributor PRs run immediately without a maintainer clicking
+  # "Approve and run". The PR head is checked out as data only.
+  pull_request_target:
    branches: [main]
  workflow_call:
    inputs:
@@ -15,12 +19,25 @@ permissions:

 jobs:
  pre-commit:
-    if: github.event_name == 'workflow_call' || github.event.pull_request.draft != true
+    if: github.event.pull_request.draft != true
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
      with:
        ref: ${{ inputs.ref || '' }}
+    # For PR events, lint the PR head — but keep the hook definitions from
+    # the base branch so an untrusted PR cannot alter what gets executed.
+    - name: Save trusted hook config
+      if: github.event_name == 'pull_request_target'
+      run: cp .pre-commit-config.yaml "$RUNNER_TEMP/trusted-pre-commit-config.yaml"
+    - uses: actions/checkout@v4
+      if: github.event_name == 'pull_request_target'
+      with:
+        ref: ${{ github.event.pull_request.head.sha }}
+        persist-credentials: false
+    - name: Restore trusted hook config
+      if: github.event_name == 'pull_request_target'
+      run: cp "$RUNNER_TEMP/trusted-pre-commit-config.yaml" .pre-commit-config.yaml
    - uses: actions/setup-python@v5
      with:
        python-version: "3.12"
@@ -30,3 +47,6 @@ jobs:
    - uses: pre-commit/action@v3.0.1
      with:
        extra_args: --all-files --hook-stage manual
+    # After pre-commit so a self-test failure cannot mask lint failures.
+    - name: Full-suite gate self-test
+      run: bash .github/scripts/test_gate_full_suite.sh
@@ -52,6 +52,7 @@ jobs:
            core.setOutput('pr_sha', pr.head.sha);
            core.setOutput('pr_branch', pr.head.ref);
            core.setOutput('pr_number', String(prNumber));
+            core.setOutput('pr_title', pr.title);

      - name: Trigger Full Suite
        if: steps.perm.outputs.has_write == 'true'
@@ -60,6 +61,7 @@ jobs:
          PR_SHA: ${{ steps.label.outputs.pr_sha }}
          PR_BRANCH: ${{ steps.label.outputs.pr_branch }}
          PR_NUMBER: ${{ steps.label.outputs.pr_number }}
+          PR_TITLE: ${{ steps.label.outputs.pr_title }}
          BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
          BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
        run: |
@@ -71,6 +73,7 @@ jobs:
              --arg commit "$PR_SHA" \
              --arg branch "$PR_BRANCH" \
              --arg message "Full Suite for PR #${PR_NUMBER} (via /merge)" \
+              --arg pr_title "$PR_TITLE" \
              --argjson pr_id "$PR_NUMBER" \
              '{
                commit: $commit,
@@ -80,11 +83,12 @@ jobs:
                pull_request_id: $pr_id,
                pull_request_base_branch: "main",
                env: {
-                  TEST_SCOPE: "full",
-                  FULL_SUITE: "true",
-                  PR_NUMBER: ($pr_id | tostring)
-                }
-              }')"
+                    TEST_SCOPE: "full",
+                    FULL_SUITE: "true",
+                    PR_NUMBER: ($pr_id | tostring),
+                    PR_TITLE: $pr_title
+                  }
+                }')"

  parse-command:
    if: >-
@@ -125,7 +129,7 @@ jobs:
          set -euo pipefail
          TEST_NAME=$(echo "$COMMENT" | grep -oP '(?<=/test\s)\S+' | head -1 || true)

-          VALID="encoder vae transformer kernel unit dreamverse ssim training lora-inference lora-training distillation self-forcing vsa vmoba performance api train-framework eval full fastcheck pre-commit"
+          VALID="encoder vae transformer kernel unit dreamverse ssim training lora-inference lora-training lora-extraction distillation self-forcing vsa vmoba performance api train-framework eval full fastcheck pre-commit"
          if [ -z "$TEST_NAME" ] || ! echo "$VALID" | grep -qw "$TEST_NAME"; then
            echo "Unknown test: '$TEST_NAME'. Valid: $VALID"
            exit 1
@@ -136,6 +140,7 @@ jobs:
            [kernel]=kernel_tests [unit]=unit_test [dreamverse]=dreamverse_app
            [ssim]=ssim [training]=training
            [lora-inference]=inference_lora [lora-training]=training_lora
+            [lora-extraction]=lora_extraction
            [distillation]=distillation_dmd [self-forcing]=self_forcing
            [vsa]=training_vsa [vmoba]=inference_vmoba
            [performance]=performance [api]=api_server
@@ -240,6 +245,7 @@ jobs:
          TEST_SCOPE: ${{ needs.parse-command.outputs.test_scope }}
          FULL_SUITE: ${{ needs.parse-command.outputs.full_suite }}
          TEST_TYPE: ${{ needs.parse-command.outputs.test_type }}
+          PR_TITLE: ${{ github.event.issue.title }}
          BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
          BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
        run: |
@@ -256,6 +262,7 @@ jobs:
              --arg full_suite "$FULL_SUITE" \
              --arg test_type "$TEST_TYPE" \
              --arg pr_number "$PR_NUMBER" \
+              --arg pr_title "$PR_TITLE" \
              '{
                commit: $commit,
                branch: $branch,
@@ -265,8 +272,9 @@ jobs:
                pull_request_base_branch: "main",
                env: {
                  TEST_SCOPE: $test_scope,
-                  FULL_SUITE: $full_suite,
-                  TEST_TYPE: $test_type,
-                  PR_NUMBER: $pr_number
-                }
-              }')"
+                    FULL_SUITE: $full_suite,
+                    TEST_TYPE: $test_type,
+                    PR_NUMBER: $pr_number,
+                    PR_TITLE: $pr_title
+                  }
+                }')"
@@ -7,6 +7,7 @@ on:
 permissions:
  contents: read
  pull-requests: read
+  actions: read

 concurrency:
  group: full-suite-${{ github.event.pull_request.number }}
@@ -18,6 +19,8 @@ jobs:
      (github.event.action == 'labeled' && github.event.label.name == 'ready')
      || github.event.action == 'synchronize'
    runs-on: ubuntu-latest
+    # Gate below may wait for cheap checks (up to MAX_WAIT_SECS = 25 min).
+    timeout-minutes: 35
    steps:
      - name: Check ready label
        id: check
@@ -49,6 +52,20 @@ jobs:
              "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds/${build_num}/cancel"
          done

+      # Checks out the BASE branch (default for pull_request_target), so PR
+      # authors cannot tamper with the gate script.
+      - name: Checkout gate script
+        if: steps.check.outputs.has_ready == 'true'
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Wait for pre-commit and docs build
+        if: steps.check.outputs.has_ready == 'true'
+        env:
+          GH_TOKEN: ${{ github.token }}
+          PR_SHA: ${{ github.event.pull_request.head.sha }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+        run: bash .github/scripts/gate_full_suite.sh
+
      - name: Trigger Buildkite Full Suite
        if: steps.check.outputs.has_ready == 'true'
        env:
@@ -56,6 +73,7 @@ jobs:
          PR_SHA: ${{ github.event.pull_request.head.sha }}
          PR_BRANCH: ${{ github.event.pull_request.head.ref }}
          PR_NUMBER: ${{ github.event.pull_request.number }}
+          PR_TITLE: ${{ github.event.pull_request.title }}
          BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
          BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
        run: |
@@ -67,6 +85,7 @@ jobs:
              --arg commit "$PR_SHA" \
              --arg branch "$PR_BRANCH" \
              --arg message "Full Suite for PR #${PR_NUMBER}" \
+              --arg pr_title "$PR_TITLE" \
              --argjson pr_id "$PR_NUMBER" \
              '{
                commit: $commit,
@@ -78,6 +97,7 @@ jobs:
                env: {
                  TEST_SCOPE: "full",
                  FULL_SUITE: "true",
-                  PR_NUMBER: ($pr_id | tostring)
+                  PR_NUMBER: ($pr_id | tostring),
+                  PR_TITLE: $pr_title
                }
              }')"
@@ -13,12 +13,33 @@ on:
        required: false
        default: false
        type: boolean
+  # Auto-rebuild the CUDA images when their Dockerfile changes on main. The CUDA
+  # matrix is the only lane that builds from docker/Dockerfile, so a path-scoped
+  # push trigger is a sufficient change detector on its own -- no separate
+  # detect-changes/paths-filter job is needed now that there is a single
+  # in-scope Dockerfile. Dreamverse (apps/dreamverse/docker/Dockerfile) and the
+  # rocm Dockerfile stay manual-dispatch only.
+  push:
+    branches: [main]
+    paths:
+      - 'docker/Dockerfile'


 permissions:
  contents: read
  packages: write

+# One static group, no cancellation: every run of this workflow writes the same
+# mutable registry tags (latest, py3.12-latest, ...), so runs must serialize —
+# concurrent push/dispatch runs would race on those tags, and cancelling a run
+# mid-publish can strand the cu126/cu130 tag families at different commits. An
+# in-flight superseded build wastes its runner time, but its tags are then
+# overwritten by the newer queued run. GitHub keeps a single pending run per
+# group: the newest queued run replaces any older queued one.
+concurrency:
+  group: infra-build-image
+  cancel-in-progress: false
+
 jobs:
  # CUDA matrix: Python 3.12 x {12.6.3, 13.0.0} x {amd64, arm64}. Each architecture
  # builds natively and pushes only by digest; publish-cuda-manifests is the sole
@@ -28,7 +49,11 @@ jobs:
  # aliases; 13.0.0/cu130 is published under explicit versioned tags. Flash-attn
  # 2.8.3 comes from the architecture-specific prebuilt releases.
  build-cuda-images:
-    if: ${{ github.event.inputs.build_cuda_matrix == 'true' }}
+    # Runs on a manual dispatch when build_cuda_matrix is set, or automatically
+    # on a push that changed docker/Dockerfile (inputs are null on push). The
+    # repository guard keeps fork syncs from auto-building; manual dispatch
+    # still works in forks.
+    if: ${{ (github.event_name == 'push' && github.repository == 'hao-ai-lab/FastVideo') || github.event.inputs.build_cuda_matrix == 'true' }}
    strategy:
      fail-fast: false
      matrix:
@@ -75,10 +100,10 @@ jobs:
    secrets: inherit

  publish-cuda-manifests:
-    # !cancelled(): a failed sibling build leg must not skip the manifests for a
-    # CUDA lane whose own digests all exist; the digest-count check below fails
-    # the incomplete lane loudly instead.
-    if: ${{ !cancelled() && github.event.inputs.build_cuda_matrix == 'true' }}
+    # !cancelled(): publish lanes whose digests exist even if a sibling build
+    # leg failed (the digest-count check fails incomplete lanes); it also
+    # bypasses skipped-needs propagation, hence the explicit skipped check.
+    if: ${{ !cancelled() && needs.build-cuda-images.result != 'skipped' }}
    needs: build-cuda-images
    runs-on: ubuntu-latest
    permissions:
@@ -72,8 +72,7 @@ docs/distillation/examples/
 # Python pickle files
 *.pkl

-# Reference videos
-!fastvideo/tests/ssim/reference_videos/**/*.mp4
+# Reference videos (negations must come after the catch-all on line below)

 # Static images
 !docs/assets/images/**/*.png
@@ -127,6 +126,8 @@ apps/dreamverse/web/.env.production.local
 .sisyphus/
 openspec/
 fastvideo/tests/ssim/reference_videos/**
+!fastvideo/tests/ssim/reference_videos/**/*.mp4
+!fastvideo/tests/ssim/reference_videos/**/*.png

 # Editor logs and local Python version pins (accidentally committed)
 *.nvimlog
@@ -84,9 +84,12 @@ RUN source /opt/venv/bin/activate \
       FFMPEG_NATIVE_CXX=/usr/bin/g++ \
       bash /opt/FastVideo/apps/dreamverse/scripts/install_native_ffmpeg.sh

+# FASTVIDEO_FA4: FA4 (flash_attn.cute) is opt-in; this image installs it via
+# the dreamverse extra and is validated with it, so enable it here.
 ENV FASTVIDEO_DREAMVERSE_HOME=/var/lib/dreamverse \
    STREAM_MODE=av_fmp4 \
    FASTVIDEO_ENABLE_PROMPT_SAFETY=0 \
+    FASTVIDEO_FA4=1 \
    HF_HOME=/root/.cache/huggingface

 RUN mkdir -p /var/lib/dreamverse
@@ -67,7 +67,7 @@ MODEL_REGISTRY = {
    },
 }

-DEFAULT_MODEL_ID = "fast-ltx2"
+DEFAULT_MODEL_ID = "fast-ltx23"

 ACTIVE_MODEL_ID = (os.getenv("DREAMVERSE_MODEL_ID", "").strip() or DEFAULT_MODEL_ID)
 if ACTIVE_MODEL_ID not in MODEL_REGISTRY:
@@ -76,14 +76,11 @@ if ACTIVE_MODEL_ID not in MODEL_REGISTRY:
 # Active model configuration
 MODEL_CONFIG = MODEL_REGISTRY[ACTIVE_MODEL_ID]

-# Generation limits
-SESSION_TIMEOUT_SECONDS = 300
-
 # Frame settings
 NUM_FRAMES = 121
 FRAME_HEIGHT = 1088
 FRAME_WIDTH = 1920
-NUM_INFERENCE_STEPS = 5
+NUM_INFERENCE_STEPS = 6
 JPEG_QUALITY = 100
 BATCH_SIZE = 3

@@ -168,6 +165,9 @@ def _optional_env(*names: str) -> str | None:
    return None


+# Generation limits
+SESSION_TIMEOUT_SECONDS = _env_int("DREAMVERSE_SESSION_TIMEOUT_SECONDS", 300)
+
 DEVTOOLS_ENABLED = _env_bool("FASTVIDEO_ENABLE_DEVTOOLS", False)
 PROMPT_SAFETY_ENABLED = _env_bool("FASTVIDEO_ENABLE_PROMPT_SAFETY", False)
 DREAMVERSE_MAX_AUTOTUNE = _env_bool("DREAMVERSE_MAX_AUTOTUNE", True)
@@ -1023,7 +1023,11 @@ def get_available_gpus() -> list[int]:
    """Get list of available GPU IDs from environment or auto-detect."""
    cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES", "")
    if cuda_visible:
-        visible_gpu_ids = [int(x.strip()) for x in cuda_visible.split(",") if x.strip()]
+        try:
+            visible_gpu_ids = [int(x.strip()) for x in cuda_visible.split(",") if x.strip()]
+        except ValueError as exc:
+            raise RuntimeError("CUDA_VISIBLE_DEVICES must be a comma-separated list of integer GPU "
+                               f"indices (got {cuda_visible!r}); GPU UUIDs are not supported.") from exc
        return _limit_gpu_ids(visible_gpu_ids)

    # Auto-detect available GPUs
@@ -206,7 +206,9 @@ def cli() -> None:
    args = parser.parse_args()

    _install_heartbeat_log_filter()
-    uvicorn.run(app, host=args.host, port=args.port)
+    # A 15MB init image (session_init_image.MAX_SESSION_INIT_IMAGE_BYTES) is ~20MB
+    # as a base64 ws message, above uvicorn's default 16MiB frame cap.
+    uvicorn.run(app, host=args.host, port=args.port, ws_max_size=32 * 1024 * 1024)


 if __name__ == "__main__":
@@ -30,11 +30,11 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles

 from dreamverse._deps import require_dreamverse_runtime_deps
-from dreamverse.config import FRONTEND_STATIC_DIR_CANDIDATES, GENERATION_SEGMENT_CAP
+from dreamverse.config import FRONTEND_STATIC_DIR_CANDIDATES, GENERATION_SEGMENT_CAP, SESSION_TIMEOUT_SECONDS
 from dreamverse.session_init_image import cleanup_session_init_image, persist_session_init_image
+from dreamverse.utils import _resolve_generation_segment_cap

 LATENCY_MS = 200
-SESSION_TIMEOUT_SECONDS = 300
 MOCK_FRAME_WIDTH = 640
 MOCK_FRAME_HEIGHT = 352
 MOCK_FPS = 24
@@ -334,6 +334,7 @@ async def websocket_endpoint(websocket: WebSocket):
        auto_extension_enabled = bool(init_data.get("auto_extension_enabled", False))
        loop_generation_enabled = bool(init_data.get("loop_generation_enabled", False))
        single_clip_mode = bool(init_data.get("single_clip_mode", False))
+        manual_continuation_mode = bool(init_data.get("manual_continuation_mode", False))
        generation_paused = False

        if init_type == "session_init_v2":
@@ -344,7 +345,8 @@ async def websocket_endpoint(websocket: WebSocket):
            incoming_prompts = []

        curated_prompts = [prompt.strip() for prompt in incoming_prompts if isinstance(prompt, str) and prompt.strip()]
-        generation_paused = bool(initial_rollout_prompt and not single_clip_mode and len(curated_prompts) == 0)
+        generation_paused = bool(not manual_continuation_mode and initial_rollout_prompt and not single_clip_mode
+                                 and len(curated_prompts) == 0)

        try:
            session_init_image = persist_session_init_image(init_data.get("initial_image"))
@@ -373,7 +375,10 @@ async def websocket_endpoint(websocket: WebSocket):
        prompt_sources_blocked = False
        pending_seed_reset = False
        pending_seed_reset_reason = ""
-        pending_simple_submission: PromptSubmission | None = None
+        pending_simple_submission: PromptSubmission | None = (PromptSubmission(
+            prompt_id=str(init_data.get("initial_rollout_prompt_id") or uuid.uuid4()),
+            raw_prompt=initial_rollout_prompt,
+        ) if manual_continuation_mode and initial_rollout_prompt else None)
        single_clip_waiting_for_request = False
        rollout_waiting_for_rewrite = False
        initial_rollout_waiting_for_rewrite = generation_paused
@@ -393,14 +398,26 @@ async def websocket_endpoint(websocket: WebSocket):

        async def send_stream_start(seed_reason: str) -> None:
            await ws_send_json({
-                "type": "ltx2_stream_start",
-                "total_segments": len(curated_prompts),
-                "preset_id": preset_id,
-                "stream_mode": "av_fmp4",
-                "live_mode": True,
-                "loop_generation_enabled": loop_generation_enabled,
-                "loop_iteration": loop_iteration,
-                "generation_segment_cap": 0,
+                "type":
+                "ltx2_stream_start",
+                "total_segments":
+                len(curated_prompts),
+                "preset_id":
+                preset_id,
+                "stream_mode":
+                "av_fmp4",
+                "live_mode":
+                True,
+                "loop_generation_enabled":
+                loop_generation_enabled,
+                "loop_iteration":
+                loop_iteration,
+                "generation_segment_cap":
+                _resolve_generation_segment_cap(
+                    single_clip_mode=single_clip_mode,
+                    cap=GENERATION_SEGMENT_CAP,
+                    manual_continuation_mode=manual_continuation_mode,
+                ),
            })
            if seed_reason == "init":
                await ws_send_json({
@@ -493,6 +510,7 @@ async def websocket_endpoint(websocket: WebSocket):
            nonlocal auto_extension_enabled
            nonlocal loop_generation_enabled
            nonlocal single_clip_mode
+            nonlocal manual_continuation_mode
            nonlocal generation_paused
            nonlocal seed_prompt_memory
            nonlocal curated_prompts
@@ -537,6 +555,7 @@ async def websocket_endpoint(websocket: WebSocket):
            auto_extension_enabled = bool(payload.get("auto_extension_enabled", False))
            loop_generation_enabled = bool(payload.get("loop_generation_enabled", False))
            single_clip_mode = bool(payload.get("single_clip_mode", False))
+            manual_continuation_mode = bool(payload.get("manual_continuation_mode", False))

            seed_prompt_memory = list(next_curated_prompts)
            curated_prompts = list(seed_prompt_memory)
@@ -544,10 +563,14 @@ async def websocket_endpoint(websocket: WebSocket):
            segment_idx = 0
            pending_seed_reset = False
            pending_seed_reset_reason = ""
-            pending_simple_submission = None
+            pending_simple_submission = (PromptSubmission(
+                prompt_id=str(payload.get("initial_rollout_prompt_id") or uuid.uuid4()),
+                raw_prompt=initial_rollout_prompt,
+            ) if manual_continuation_mode and initial_rollout_prompt else None)
            single_clip_waiting_for_request = False
            rollout_waiting_for_rewrite = False
-            generation_paused = bool(initial_rollout_prompt and not single_clip_mode and len(curated_prompts) == 0)
+            generation_paused = bool(not manual_continuation_mode and initial_rollout_prompt and not single_clip_mode
+                                     and len(curated_prompts) == 0)
            initial_rollout_waiting_for_rewrite = generation_paused
            rewrite_restart_pending = False
            loop_iteration = 0
@@ -968,10 +991,11 @@ async def websocket_endpoint(websocket: WebSocket):
                    project_stream_started = True
                    await send_stream_start(pending_seed_reset_reason)
                    pending_seed_reset_reason = ""
-                    if pending_simple_submission is not None:
-                        submission = pending_simple_submission
-                        pending_simple_submission = None
-                        await promote_submission_to_ready(submission)
+
+                if pending_simple_submission is not None:
+                    submission = pending_simple_submission
+                    pending_simple_submission = None
+                    await promote_submission_to_ready(submission)

                if generation_paused:
                    await asyncio.sleep(0.05)
@@ -981,8 +1005,8 @@ async def websocket_endpoint(websocket: WebSocket):
                    await asyncio.sleep(0.05)
                    continue

-                if (not single_clip_mode and not rollout_waiting_for_rewrite and GENERATION_SEGMENT_CAP > 0
-                        and segment_idx >= GENERATION_SEGMENT_CAP):
+                if (not single_clip_mode and not manual_continuation_mode and not rollout_waiting_for_rewrite
+                        and GENERATION_SEGMENT_CAP > 0 and segment_idx >= GENERATION_SEGMENT_CAP):
                    rollout_waiting_for_rewrite = True
                    loop_generation_enabled = False
                    project_stream_started = False
@@ -1217,7 +1241,9 @@ def cli() -> None:
    print(f"Starting mock server with {LATENCY_MS}ms latency on port {args.port}")

    _install_heartbeat_log_filter()
-    uvicorn.run(app, host="0.0.0.0", port=args.port)
+    # A 15MB init image (session_init_image.MAX_SESSION_INIT_IMAGE_BYTES) is ~20MB
+    # as a base64 ws message, above uvicorn's default 16MiB frame cap.
+    uvicorn.run(app, host="0.0.0.0", port=args.port, ws_max_size=32 * 1024 * 1024)


 if __name__ == "__main__":
@@ -313,6 +313,33 @@ def _extract_content_or_empty(response_json: dict[str, Any]) -> str:
        return ""


+def _find_balanced_object_end(text: str, start: int) -> int:
+    """Return the index just past the brace-balanced span opening at
+    ``text[start] == '{'``, honoring JSON string literals and escapes, or -1
+    if the braces never balance (i.e. the object was truncated)."""
+    depth = 0
+    in_string = False
+    escaped = False
+    for i in range(start, len(text)):
+        ch = text[i]
+        if in_string:
+            if escaped:
+                escaped = False
+            elif ch == "\\":
+                escaped = True
+            elif ch == '"':
+                in_string = False
+        elif ch == '"':
+            in_string = True
+        elif ch == "{":
+            depth += 1
+        elif ch == "}":
+            depth -= 1
+            if depth == 0:
+                return i + 1
+    return -1
+
+
 def _parse_json_response(content: str) -> dict[str, Any]:
    text = content.strip()
    if not text:
@@ -330,6 +357,7 @@ def _parse_json_response(content: str) -> dict[str, Any]:
        r"```(?:json)?\s*([\s\S]*?)```",
        flags=re.IGNORECASE,
    )
+    last_fenced: dict[str, Any] | None = None
    for match in fence_pattern.finditer(text):
        block = match.group(1).strip()
        if not block:
@@ -337,21 +365,37 @@ def _parse_json_response(content: str) -> dict[str, Any]:
        try:
            parsed = json.loads(block)
            if isinstance(parsed, dict):
-                return parsed
+                last_fenced = parsed
        except json.JSONDecodeError:
            continue
+    if last_fenced is not None:
+        return last_fenced

-    # Fall back to scanning for the first decodable JSON object in free-form text.
+    # Scan for all decodable JSON objects and return the last — chain-of-thought
+    # models emit draft JSON mid-reasoning; the final answer is always last.
    decoder = json.JSONDecoder()
-    for idx, char in enumerate(text):
-        if char != "{":
-            continue
+    last_parsed: dict[str, Any] | None = None
+    pos = 0
+    while (idx := text.find("{", pos)) != -1:
        try:
-            parsed, _ = decoder.raw_decode(text[idx:])
+            parsed, consumed = decoder.raw_decode(text[idx:])
        except json.JSONDecodeError:
+            # Skip the whole failed object rather than rescanning inside it:
+            # fragments nested in a malformed or truncated (finish_reason=
+            # length) object must not override an earlier complete object.
+            span_end = _find_balanced_object_end(text, idx)
+            if span_end == -1:
+                break
+            pos = span_end
            continue
+        # Skip past the consumed span so nested braces inside a decoded
+        # object are not re-parsed as standalone objects.
+        pos = idx + consumed
        if isinstance(parsed, dict):
-            return parsed
+            last_parsed = parsed
+
+    if last_parsed is not None:
+        return last_parsed

    raise ValueError("No JSON object found in assistant response.")

@@ -381,7 +425,7 @@ def _format_locked_segments(locked_segments: list[str]) -> str:

 class PromptEnhancer:

-    def __init__(self):
+    def __init__(self) -> None:
        self.provider = PROMPT_PROVIDER
        self.provider_label = _resolve_provider_label(PROMPT_PROVIDER)
        self.api_key = PROMPT_API_KEY
@@ -1417,15 +1461,12 @@ class PromptEnhancer:
            locked_text = _format_locked_segments(locked_segments_clean)
            request_system_prompt = self.enhance_system_prompt
            user_payload = {
-                "request": (
-                    "<locked_segments>\n"
-                    f"{locked_text}\n"
-                    "</locked_segments>\n\n"
-                    f"<conditioning_prompt>{cleaned}</conditioning_prompt>\n\n"
-                    f"Write exactly one new segment ({next_segment_key}) "
-                    "continuing from the locked segments. "
-                    'Respond with valid JSON only as {"next_prompt": "..."}.'  # noqa: E501
-                ),
+                "request": ("<locked_segments>\n"
+                            f"{locked_text}\n"
+                            "</locked_segments>\n\n"
+                            f"<conditioning_prompt>{cleaned}</conditioning_prompt>\n\n"
+                            f"Write exactly one new segment ({next_segment_key}) "
+                            "continuing from the locked segments."),
            }

        t0 = time.perf_counter()
@@ -1457,6 +1498,7 @@ class PromptEnhancer:
                            body=request_body,
                            timeout_seconds=timeout_seconds,
                        )
+                        _enhance_print("INFO", f"raw_response: {response_content}")
                        if is_single_clip_mode:
                            prompt = self._extract_single_clip_prompt(response_content)
                        else:
@@ -1539,7 +1581,7 @@ class PromptEnhancer:
                f"Write exactly one new segment ({next_segment_key}) "
                "that continues linearly from the locked segments. "
                "Infer the next narrative beat from this history. "
-                'Respond with valid JSON only as {"next_prompt": "..."}.'  # noqa: E501
+                'Respond with valid JSON only: {"next_prompt": "<your segment description here>"}.'  # noqa: E501
            ),
        }

@@ -20,6 +20,7 @@ from __future__ import annotations
 # mypy: ignore-errors

 import asyncio
+import os
 import time
 import uuid
 from typing import TYPE_CHECKING
@@ -52,6 +53,14 @@ if TYPE_CHECKING:
    from dreamverse.prompt_enhancer import PromptEnhancer
    from dreamverse.prompt_safety import PromptSafetyFilter

+# Optional append-only log of every generated segment prompt; unset disables it.
+SEGMENT_PROMPT_LOG_PATH = os.environ.get("DREAMVERSE_SEGMENT_PROMPT_LOG", "")
+
+
+def _append_segment_prompt_log(path: str, text: str) -> None:
+    with open(path, "a") as f:
+        f.write(text)
+

 class SessionController:
    """Runs one WebSocket session from accept() through disconnect."""
@@ -198,6 +207,7 @@ class SessionController:
            auto_extension_enabled = bool(init_data.get("auto_extension_enabled", False))
            loop_generation_enabled = bool(init_data.get("loop_generation_enabled", False))
            single_clip_mode = bool(init_data.get("single_clip_mode", False))
+            manual_continuation_mode = bool(init_data.get("manual_continuation_mode", False))
            rewrite_model = self.prompt_enhancer.resolve_rewrite_model(init_data.get("rewrite_model"))
            rewrite_system_prompt_override = str(init_data.get("rewrite_window_system_prompt") or "").strip()
            rewrite_user_system_prompt_override = str(init_data.get("rewrite_user_system_prompt") or "").strip()
@@ -282,6 +292,9 @@ class SessionController:
            # Session queues and mutable state.
            raw_prompt_queue: asyncio.Queue[PromptSubmission] = asyncio.Queue()
            ready_prompt_queue: asyncio.Queue[ReadyPrompt] = asyncio.Queue()
+            # Submissions dequeued by prompt_worker_loop but not yet resolved; while
+            # non-zero the prompt sources are busy, not drained.
+            prompt_enhancement_inflight = 0

            curated_idx = 0
            segment_idx = 0
@@ -291,13 +304,20 @@ class SessionController:
            generation_cap_blocked = False
            auto_extension_blocked_segment_idx: int | None = None
            prompt_sources_drained_logged = False
-            generation_paused = bool(initial_rollout_prompt and not single_clip_mode and len(curated_prompts) == 0)
+            generation_paused = bool(not manual_continuation_mode and initial_rollout_prompt and not single_clip_mode
+                                     and len(curated_prompts) == 0)
            pending_seed_reset = False
            pending_seed_reset_reason = ""
            pending_reset_conditioning = False
            loop_iteration = 0 if generation_paused else 1
            force_curated_restart_segment = False
-            pending_simple_prompt_submission: PromptSubmission | None = None
+            # The frontend records the opening scene under this id; reuse it so
+            # prompt lifecycle events for the opening prompt reach that record.
+            pending_simple_prompt_submission: PromptSubmission | None = (PromptSubmission(
+                prompt_id=str(init_data.get("initial_rollout_prompt_id") or uuid.uuid4()),
+                raw_prompt=initial_rollout_prompt,
+                created_at_s=time.time(),
+            ) if manual_continuation_mode and initial_rollout_prompt else None)
            single_clip_waiting_for_request = False
            rollout_waiting_for_rewrite = False
            initial_rollout_waiting_for_rewrite = generation_paused
@@ -306,6 +326,7 @@ class SessionController:
            project_active = True
            project_stream_started = False
            pending_project_end = False
+            segment_prompt_log_warned = False

            def replace_session_init_image(initial_image_payload: object) -> None:
                nonlocal session_init_image
@@ -424,6 +445,7 @@ class SessionController:
                nonlocal auto_extension_enabled
                nonlocal loop_generation_enabled
                nonlocal single_clip_mode
+                nonlocal manual_continuation_mode
                nonlocal generation_paused
                nonlocal curated_prompts
                nonlocal seed_prompt_memory
@@ -458,6 +480,7 @@ class SessionController:
                next_auto_extension_enabled = bool(payload.get("auto_extension_enabled", False))
                next_loop_generation_enabled = bool(payload.get("loop_generation_enabled", False))
                next_single_clip_mode = bool(payload.get("single_clip_mode", False))
+                next_manual_continuation_mode = bool(payload.get("manual_continuation_mode", False))

                next_preset_id = str(payload.get("preset_id") or "").strip()
                if next_preset_id:
@@ -510,6 +533,7 @@ class SessionController:
                auto_extension_enabled = next_auto_extension_enabled
                loop_generation_enabled = next_loop_generation_enabled
                single_clip_mode = next_single_clip_mode
+                manual_continuation_mode = next_manual_continuation_mode
                rewrite_model = next_rewrite_model
                rewrite_system_prompt_override = (next_rewrite_system_prompt_override)
                rewrite_user_system_prompt_override = (next_rewrite_user_system_prompt_override)
@@ -530,10 +554,15 @@ class SessionController:
                generation_cap_blocked = False
                auto_extension_blocked_segment_idx = None
                prompt_sources_drained_logged = False
-                pending_simple_prompt_submission = None
+                pending_simple_prompt_submission = (PromptSubmission(
+                    prompt_id=str(payload.get("initial_rollout_prompt_id") or uuid.uuid4()),
+                    raw_prompt=initial_rollout_prompt,
+                    created_at_s=time.time(),
+                ) if manual_continuation_mode and initial_rollout_prompt else None)
                single_clip_waiting_for_request = False
                rollout_waiting_for_rewrite = False
-                generation_paused = bool(initial_rollout_prompt and not single_clip_mode and len(curated_prompts) == 0)
+                generation_paused = bool(not manual_continuation_mode and initial_rollout_prompt
+                                         and not single_clip_mode and len(curated_prompts) == 0)
                initial_rollout_waiting_for_rewrite = generation_paused
                rewrite_restart_pending = False
                loop_iteration = 0
@@ -942,6 +971,7 @@ class SessionController:
                            _resolve_generation_segment_cap(
                                single_clip_mode=single_clip_mode,
                                cap=GENERATION_SEGMENT_CAP,
+                                manual_continuation_mode=manual_continuation_mode,
                            ),
                        })
                        continue
@@ -1005,11 +1035,9 @@ class SessionController:
                        continue

            async def prompt_worker_loop():
-                while not stop_event.is_set():
-                    try:
-                        submission = await asyncio.wait_for(raw_prompt_queue.get(), timeout=0.1)
-                    except asyncio.TimeoutError:
-                        continue
+                nonlocal prompt_enhancement_inflight
+
+                async def process_submission(submission: PromptSubmission) -> None:
                    _main_print("INFO", f"Received user prompt for enhancement: {submission.raw_prompt}")
                    prompt_id = submission.prompt_id
                    raw_prompt = submission.raw_prompt
@@ -1032,8 +1060,9 @@ class SessionController:
                        await ws_send_json({
                            "type": "error",
                            "message": blocked_raw_prompt_error,
+                            "prompt_id": prompt_id,
                        })
-                        continue
+                        return
                    await log_event(
                        "enhance_request",
                        {
@@ -1093,8 +1122,9 @@ class SessionController:
                            await ws_send_json({
                                "type": "error",
                                "message": blocked_final_prompt_error,
+                                "prompt_id": prompt_id,
                            })
-                            continue
+                            return
                        if result.fallback_used or not final_prompt:
                            source = "user_enhancement_failed"
                            _main_print(
@@ -1113,7 +1143,7 @@ class SessionController:
                            })
                            # Enhancement is strict JSON-only; do not enqueue raw
                            # prompt when enhancement fails.
-                            continue
+                            return
                        else:
                            source = "user_enhanced"
                            await ws_send_json({
@@ -1130,6 +1160,7 @@ class SessionController:
                                source=source,
                                fallback_used=result.fallback_used,
                                loop_iteration=loop_iteration,
+                                raw_prompt=raw_prompt,
                            ))
                    else:
                        await ready_prompt_queue.put(
@@ -1139,6 +1170,7 @@ class SessionController:
                                source="user_raw",
                                fallback_used=False,
                                loop_iteration=loop_iteration,
+                                raw_prompt=raw_prompt,
                            ))
                        await ws_send_json({
                            "type": "prompt_ready",
@@ -1148,6 +1180,21 @@ class SessionController:
                            "latency_ms": 0.0,
                        })

+                while not stop_event.is_set():
+                    try:
+                        submission = raw_prompt_queue.get_nowait()
+                    except asyncio.QueueEmpty:
+                        await asyncio.sleep(0.1)
+                        continue
+                    # Dequeue and increment without an await in between so the
+                    # generation loop never sees an empty queue with zero in flight
+                    # while this submission is still being enhanced.
+                    prompt_enhancement_inflight += 1
+                    try:
+                        await process_submission(submission)
+                    finally:
+                        prompt_enhancement_inflight -= 1
+
            def queue_snapshot() -> dict[str, object]:
                return {
                    "user_ready": ready_prompt_queue.qsize(),
@@ -1300,6 +1347,7 @@ class SessionController:
                    _resolve_generation_segment_cap(
                        single_clip_mode=single_clip_mode,
                        cap=GENERATION_SEGMENT_CAP,
+                        manual_continuation_mode=manual_continuation_mode,
                    ),
                })
                await ws_send_json({
@@ -1359,6 +1407,7 @@ class SessionController:
                        _resolve_generation_segment_cap(
                            single_clip_mode=single_clip_mode,
                            cap=GENERATION_SEGMENT_CAP,
+                            manual_continuation_mode=manual_continuation_mode,
                        ),
                    })
                    if nonlocal_reason == "loop_restart":
@@ -1388,8 +1437,9 @@ class SessionController:
                    await raw_prompt_queue.put(pending_simple_prompt_submission)
                    pending_simple_prompt_submission = None

-                if (not single_clip_mode and not generation_cap_blocked and not rollout_waiting_for_rewrite
-                        and GENERATION_SEGMENT_CAP > 0 and generated_segment_count >= GENERATION_SEGMENT_CAP):
+                if (not single_clip_mode and not manual_continuation_mode and not generation_cap_blocked
+                        and not rollout_waiting_for_rewrite and GENERATION_SEGMENT_CAP > 0
+                        and generated_segment_count >= GENERATION_SEGMENT_CAP):
                    loop_generation_enabled = False
                    rollout_waiting_for_rewrite = True
                    _main_print(
@@ -1542,7 +1592,10 @@ class SessionController:
                    if single_clip_mode:
                        await asyncio.sleep(PROMPT_AUTO_SLEEP_MS / 1000.0)
                        continue
-                    if not prompt_sources_drained_logged:
+                    # A raw submission still queued or being enhanced will produce a
+                    # ready prompt shortly; that is not a drained/blocked state.
+                    enhancement_pending = (raw_prompt_queue.qsize() > 0 or prompt_enhancement_inflight > 0)
+                    if not prompt_sources_drained_logged and not enhancement_pending:
                        snapshot = queue_snapshot()
                        _main_print(
                            "WARN",
@@ -1581,6 +1634,24 @@ class SessionController:
                total_segments_hint = max(segment_idx, len(curated_prompts))
                prompt = selected.prompt
                locked_segment_prompts.append(prompt)
+                if SEGMENT_PROMPT_LOG_PATH:
+                    _ts = time.strftime("%Y-%m-%d %H:%M:%S")
+                    _lines = [
+                        f"\n=== Segment {segment_idx} [{_ts}] source={selected.source} client={client_id[:8]} ===",
+                    ]
+                    if selected.raw_prompt and selected.raw_prompt != prompt:
+                        _lines.append(f"User:     {selected.raw_prompt}")
+                    _lines.append(f"Rewritten: {prompt}")
+                    try:
+                        await asyncio.to_thread(_append_segment_prompt_log, SEGMENT_PROMPT_LOG_PATH,
+                                                "\n".join(_lines) + "\n")
+                    except Exception as exc:
+                        if not segment_prompt_log_warned:
+                            segment_prompt_log_warned = True
+                            _main_print(
+                                "WARN",
+                                f"Failed to write segment prompt log {SEGMENT_PROMPT_LOG_PATH}: {exc}",
+                            )
                if (auto_extension_blocked_segment_idx is not None
                        and auto_extension_blocked_segment_idx <= segment_idx):
                    auto_extension_blocked_segment_idx = None
@@ -19,3 +19,4 @@ class ReadyPrompt:
    fallback_used: bool = False
    seed_prompt_index: int | None = None
    loop_iteration: int | None = None
+    raw_prompt: str | None = None
@@ -150,12 +150,22 @@ def test_config_enables_prompt_safety_when_requested(monkeypatch):

 def test_config_uses_five_minute_session_timeout(monkeypatch):
    _set_required_prompt_keys(monkeypatch)
+    monkeypatch.delenv("DREAMVERSE_SESSION_TIMEOUT_SECONDS", raising=False)

    module = _load_config_module()

    assert module.SESSION_TIMEOUT_SECONDS == 300


+def test_config_session_timeout_env_override(monkeypatch):
+    _set_required_prompt_keys(monkeypatch)
+    monkeypatch.setenv("DREAMVERSE_SESSION_TIMEOUT_SECONDS", "1800")
+
+    module = _load_config_module()
+
+    assert module.SESSION_TIMEOUT_SECONDS == 1800
+
+
 def test_config_rejects_invalid_prompt_provider(monkeypatch):
    monkeypatch.setenv("FASTVIDEO_PROMPT_PROVIDER", "unsupported")
    _set_required_prompt_keys(monkeypatch)
@@ -75,12 +75,13 @@ def _run_cli(module, monkeypatch, argv: list[str]) -> list[dict[str, object]]:
    calls: list[dict[str, object]] = []
    uvicorn_stub = types.ModuleType("uvicorn")

-    def run(app, host: str, port: int) -> None:
+    def run(app, host: str, port: int, **kwargs) -> None:
        calls.append(
            {
                "app": app,
                "host": host,
                "port": port,
+                **kwargs,
            }
        )

@@ -104,6 +105,7 @@ def test_server_cli_defaults_to_local_web_port(monkeypatch):
            "app": server_main.app,
            "host": "0.0.0.0",
            "port": 8009,
+            "ws_max_size": 32 * 1024 * 1024,
        }
    ]

@@ -121,6 +123,7 @@ def test_server_cli_allows_explicit_host_and_port(monkeypatch):
            "app": server_main.app,
            "host": "127.0.0.1",
            "port": 8123,
+            "ws_max_size": 32 * 1024 * 1024,
        }
    ]

@@ -147,6 +150,7 @@ def test_mock_server_cli_defaults_to_local_web_port(monkeypatch):
            "app": mock_server.app,
            "host": "0.0.0.0",
            "port": 8009,
+            "ws_max_size": 32 * 1024 * 1024,
        }
    ]

@@ -166,6 +170,7 @@ def test_mock_server_cli_updates_latency(monkeypatch):
                "app": mock_server.app,
                "host": "0.0.0.0",
                "port": 8111,
+                "ws_max_size": 32 * 1024 * 1024,
            }
        ]
        assert mock_server.LATENCY_MS == 321
@@ -290,6 +290,152 @@ def test_mock_server_supports_initial_custom_rollout_prompt():
        mock_server.LATENCY_MS = old_latency_ms


+def test_mock_server_manual_mode_streams_initial_prompt_without_rewrite_or_cap():
+    old_segment_bytes = mock_server.MOCK_SEGMENT_BYTES
+    old_latency_ms = mock_server.LATENCY_MS
+    old_generation_segment_cap = mock_server.GENERATION_SEGMENT_CAP
+    try:
+        mock_server.MOCK_SEGMENT_BYTES = b"mock-fmp4-bytes"
+        mock_server.LATENCY_MS = 1
+        mock_server.GENERATION_SEGMENT_CAP = 1
+
+        ws = _FakeWebSocket(
+            [
+                (
+                    0.0,
+                    {
+                        "type": "session_init_v2",
+                        "preset_id": "custom_editable",
+                        "preset_label": "Custom rollout",
+                        "curated_prompts": [],
+                        "initial_rollout_prompt": "A drone skims a neon canyon",
+                        "initial_rollout_prompt_id": "steer-prompt-1",
+                        "manual_continuation_mode": True,
+                        "enhancement_enabled": True,
+                        "auto_extension_enabled": False,
+                        "loop_generation_enabled": False,
+                    },
+                ),
+                (
+                    0.08,
+                    {
+                        "type": "append_prompt",
+                        "prompt": "The drone dives toward the river",
+                        "prompt_id": "steer-prompt-2",
+                    },
+                ),
+                (0.30, {"type": "leave"}),
+            ]
+        )
+
+        asyncio.run(mock_server.websocket_endpoint(ws))
+
+        message_types = [payload["type"] for payload in ws.sent_json]
+        assert "rewrite_seed_prompts_started" not in message_types
+        assert "rewrite_seed_prompts_complete" not in message_types
+        assert "ltx2_stream_start" in message_types
+        # cap=1 must not stop a manual-mode session after the first segment
+        assert "ltx2_stream_complete" not in message_types
+
+        prompt_ready_events = [
+            payload for payload in ws.sent_json if payload["type"] == "prompt_ready"
+        ]
+        assert [payload["prompt_id"] for payload in prompt_ready_events] == [
+            "steer-prompt-1",
+            "steer-prompt-2",
+        ]
+
+        segment_start_events = [
+            payload
+            for payload in ws.sent_json
+            if payload["type"] == "ltx2_segment_start"
+        ]
+        assert [payload["prompt"] for payload in segment_start_events] == [
+            "A drone skims a neon canyon",
+            "The drone dives toward the river",
+        ]
+        segment_source_events = [
+            payload
+            for payload in ws.sent_json
+            if payload["type"] == "segment_prompt_source"
+        ]
+        assert [payload["prompt_id"] for payload in segment_source_events] == [
+            "steer-prompt-1",
+            "steer-prompt-2",
+        ]
+    finally:
+        mock_server.MOCK_SEGMENT_BYTES = old_segment_bytes
+        mock_server.LATENCY_MS = old_latency_ms
+        mock_server.GENERATION_SEGMENT_CAP = old_generation_segment_cap
+
+
+def test_mock_server_project_init_manual_mode_streams_initial_prompt():
+    old_segment_bytes = mock_server.MOCK_SEGMENT_BYTES
+    old_latency_ms = mock_server.LATENCY_MS
+    try:
+        mock_server.MOCK_SEGMENT_BYTES = b"mock-fmp4-bytes"
+        mock_server.LATENCY_MS = 1
+
+        ws = _FakeWebSocket(
+            [
+                (
+                    0.0,
+                    {
+                        "type": "session_init_v2",
+                        "preset_id": "test_preset",
+                        "curated_prompts": ["segment one"],
+                        "enhancement_enabled": True,
+                        "auto_extension_enabled": False,
+                        "loop_generation_enabled": False,
+                    },
+                ),
+                (0.05, {"type": "end_project_keep_session"}),
+                (
+                    0.15,
+                    {
+                        "type": "project_init_v1",
+                        "preset_id": "custom_editable",
+                        "preset_label": "Custom rollout",
+                        "curated_prompts": [],
+                        "initial_rollout_prompt": "A drone skims a neon canyon",
+                        "initial_rollout_prompt_id": "steer-prompt-1",
+                        "manual_continuation_mode": True,
+                        "enhancement_enabled": True,
+                        "auto_extension_enabled": False,
+                        "loop_generation_enabled": False,
+                    },
+                ),
+                (0.45, {"type": "leave"}),
+            ]
+        )
+
+        asyncio.run(mock_server.websocket_endpoint(ws))
+
+        message_types = [payload["type"] for payload in ws.sent_json]
+        assert "project_idle" in message_types
+        project_idle_index = message_types.index("project_idle")
+        # manual-mode restart must not run the rewrite rollout
+        assert "rewrite_seed_prompts_started" not in message_types[project_idle_index:]
+        assert "ltx2_stream_start" in message_types[project_idle_index:]
+
+        prompt_ready_events = [
+            payload for payload in ws.sent_json if payload["type"] == "prompt_ready"
+        ]
+        assert [payload["prompt_id"] for payload in prompt_ready_events] == [
+            "steer-prompt-1",
+        ]
+
+        segment_start_events = [
+            payload
+            for payload in ws.sent_json
+            if payload["type"] == "ltx2_segment_start"
+        ]
+        assert segment_start_events[-1]["prompt"] == "A drone skims a neon canyon"
+    finally:
+        mock_server.MOCK_SEGMENT_BYTES = old_segment_bytes
+        mock_server.LATENCY_MS = old_latency_ms
+
+
 def test_mock_server_can_start_new_project_without_reconnecting():
    old_segment_bytes = mock_server.MOCK_SEGMENT_BYTES
    old_latency_ms = mock_server.LATENCY_MS
@@ -6,6 +6,7 @@ import os
 import re
 import time

+import pytest

 os.environ.setdefault("CEREBRAS_API_KEY", "dummy")
 os.environ.setdefault("GROQ_API_KEY", "dummy")
@@ -205,6 +206,59 @@ def test_parse_json_response_extracts_first_embedded_object():
    assert parsed == {"segment_prompts": ["A", "B"]}


+def test_parse_json_response_returns_outer_object_not_nested_value():
+    parsed = _parse_json_response(
+        "Final answer: {\"next_prompt\": \"a scene\", \"style\": {\"mood\": \"noir\"}} done."
+    )
+    assert parsed == {"next_prompt": "a scene", "style": {"mood": "noir"}}
+
+
+def test_parse_json_response_returns_last_of_multiple_objects():
+    parsed = _parse_json_response(
+        "Draft: {\"next_prompt\": \"draft\"}\nRefined: {\"next_prompt\": \"final\"}"
+    )
+    assert parsed == {"next_prompt": "final"}
+
+
+def test_parse_json_response_ignores_fragments_of_truncated_trailing_object():
+    # finish_reason=length cut the refined object short; the complete draft must
+    # win over a nested fragment of the truncated object.
+    parsed = _parse_json_response(
+        '{"next_prompt": "draft"} refined: {"next_prompt": "final", "style": {"mood": "noir"}'
+    )
+    assert parsed == {"next_prompt": "draft"}
+
+
+def test_parse_json_response_ignores_fragments_of_mid_string_truncated_object():
+    # Unterminated-string truncation reports the error at the opening quote,
+    # not end-of-text; nested fragments still must not win over the draft.
+    parsed = _parse_json_response(
+        '{"next_prompt": "draft"} refined: {"style": {"mood": "noir"}, "next_prompt": "cut off'
+    )
+    assert parsed == {"next_prompt": "draft"}
+
+
+def test_parse_json_response_ignores_fragments_of_malformed_object_with_trailing_prose():
+    parsed = _parse_json_response(
+        '{"next_prompt": "draft"} {"final": {"mood": "noir"}, "x": 1 and then some prose'
+    )
+    assert parsed == {"next_prompt": "draft"}
+
+
+def test_parse_json_response_raises_when_only_object_is_truncated():
+    with pytest.raises(ValueError):
+        _parse_json_response('{"style": {"mood": "noir"}, "next_prompt": "cut off')
+
+
+def test_parse_json_response_returns_outer_rollout_dict():
+    parsed = _parse_json_response(
+        "{\"rollout\": {\"segment_prompts\": [{\"prompt\": \"a\"}, {\"prompt\": \"b\"}]}}"
+    )
+    assert parsed == {
+        "rollout": {"segment_prompts": [{"prompt": "a"}, {"prompt": "b"}]}
+    }
+
+
 def test_load_prompt_required_falls_back_to_default_path(tmp_path):
    fallback_path = tmp_path / "next_segment_system_prompt.md"
    fallback_path.write_text("fallback prompt\n", encoding="utf-8")
@@ -15,5 +15,6 @@ def _utc_now_iso() -> str:
 PROMPT_EXTENSION_FAILURE_USER_MESSAGE = ("Prompt extension failed for this request.")


-def _resolve_generation_segment_cap(*, single_clip_mode: bool, cap: int) -> int:
-    return 0 if single_clip_mode else cap
+def _resolve_generation_segment_cap(*, single_clip_mode: bool, cap: int, manual_continuation_mode: bool = False) -> int:
+    # Steering (manual continuation) lets the user keep going indefinitely, like single-clip mode.
+    return 0 if (single_clip_mode or manual_continuation_mode) else cap
@@ -489,7 +489,7 @@ class VideoGenerationWorker:
            num_inference_steps=NUM_INFERENCE_STEPS,
            guidance_scale=1.0,
            seed=10,
-            ltx2_image_crf=0.0,
+            ltx2_image_crf=(33.0 if image_path and segment_idx == 1 else 0.0),
            image_path=image_path if segment_idx == 1 else None,
            return_continuation_state=False,
        )
@@ -29,6 +29,9 @@ test = [
 dreamverse-server = "dreamverse.server_entry:cli"
 dreamverse-mock-server = "dreamverse.mock_server:cli"

+[tool.setuptools.packages.find]
+include = ["dreamverse*"]
+
 [tool.uv]
 package = false

@@ -0,0 +1,67 @@
+#!/usr/bin/env bash
+# launch-dreamverse.sh — launch dreamverse-server on a compute node.
+#
+# Usage (from repo root):
+#   bash apps/dreamverse/scripts/launch-dreamverse.sh                          # GPUs 0-3, SP_SIZE=4
+#   CUDA_VISIBLE_DEVICES=0 bash apps/dreamverse/scripts/launch-dreamverse.sh   # single GPU, SP_SIZE=1
+
+set -euo pipefail
+
+CONDA_PREFIX="$HOME/miniconda3/envs/dreamverse"
+CUDA_RT_DIR="$CONDA_PREFIX/lib/python3.11/site-packages/nvidia/cuda_runtime/lib"
+GXX="$CONDA_PREFIX/bin/aarch64-conda-linux-gnu-g++"
+
+
+export CUDA_HOME="$CONDA_PREFIX"
+export FASTVIDEO_ENABLE_STARTUP_WARMUP=true
+export FASTVIDEO_ENABLE_PROMPT_SAFETY=false
+export DREAMVERSE_MAX_AUTOTUNE=true
+export LTX2_USE_DISTILLED_SIGMAS=0
+export LTX2_VIDEO_CONDITIONING_NUM_FRAMES=1
+export AUDIO_CONDITIONING_NUM_FRAMES=41
+export DREAMVERSE_SESSION_TIMEOUT_SECONDS="${DREAMVERSE_SESSION_TIMEOUT_SECONDS:-1800}"
+# GB200 max-autotune warmup compiles can run for hours; keep the watchdog generous here
+export FASTVIDEO_STARTUP_WARMUP_TIMEOUT_SECONDS="${FASTVIDEO_STARTUP_WARMUP_TIMEOUT_SECONDS:-24000}"
+export CEREBRAS_API_KEY="${CEREBRAS_API_KEY:-}"  # set this in your env or ~/.env
+export FASTVIDEO_PROMPT_CEREBRAS_MODEL="gpt-oss-120b"
+export TORCHINDUCTOR_CACHE_DIR="$HOME/.cache/torchinductor"
+export TRITON_CACHE_DIR="$HOME/.triton/cache"
+export TORCH_CUDA_ARCH_LIST="10.0a"
+
+# Compiler env (needed for flashinfer JIT compilation at server startup)
+export CXX="$CONDA_PREFIX/compiler_compat/g++"
+export CC="$CONDA_PREFIX/compiler_compat/gcc"
+export CUDAHOSTCXX="$GXX"
+export NVCC_PREPEND_FLAGS="-ccbin $GXX -allow-unsupported-compiler"
+
+# Link against libcudart.so.12 at JIT compile time; stubs for libcuda.so
+# cuda-compat has libcudart.so -> libcudart.so.12 (linker needs unversioned name)
+export LIBRARY_PATH="$CONDA_PREFIX/lib/cuda-compat:$CONDA_PREFIX/lib/stubs"
+
+# Only libcudart.so.12 at runtime — prevents cuDNN from seeing .so.13
+export LD_LIBRARY_PATH="$CUDA_RT_DIR"
+
+CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3}"
+export FASTVIDEO_GPU_COUNT="${FASTVIDEO_GPU_COUNT:-all}"
+# Default SP size to the usable GPU count so single-GPU invocations work.
+# A numeric FASTVIDEO_GPU_COUNT caps the pool below the visible count, and an
+# SP size above the pool size fails GPUPool startup with "Not enough GPUs".
+IFS=',' read -ra _VISIBLE_GPUS <<< "$CUDA_VISIBLE_DEVICES"
+# Count only non-empty tokens, matching gpu_pool.get_available_gpus (e.g. ",0,1" is 2 GPUs).
+_USABLE_GPU_COUNT=0
+for _gpu in "${_VISIBLE_GPUS[@]}"; do
+    [[ -n "${_gpu//[[:space:]]/}" ]] && _USABLE_GPU_COUNT=$((_USABLE_GPU_COUNT + 1))
+done
+if [[ "$FASTVIDEO_GPU_COUNT" =~ ^[0-9]+$ ]] && (( FASTVIDEO_GPU_COUNT < _USABLE_GPU_COUNT )); then
+    _USABLE_GPU_COUNT="$FASTVIDEO_GPU_COUNT"
+fi
+export DREAMVERSE_SP_SIZE="${DREAMVERSE_SP_SIZE:-$_USABLE_GPU_COUNT}"
+PORT="${DREAMVERSE_PORT:-8009}"
+
+FFMPEG_ENV="$(dirname "$0")/ffmpeg-env.sh"
+# shellcheck source=ffmpeg-env.sh
+[[ -f "$FFMPEG_ENV" ]] && source "$FFMPEG_ENV"
+
+echo "==> Launching dreamverse-server on GPU $CUDA_VISIBLE_DEVICES port $PORT"
+CUDA_VISIBLE_DEVICES="$CUDA_VISIBLE_DEVICES" \
+    "$CONDA_PREFIX/bin/dreamverse-server" --host 0.0.0.0 --port "$PORT"
@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+# launch-frontend.sh — start the Dreamverse Next.js dev server and ngrok tunnel.
+#
+# Usage (from repo root):
+#   bash apps/dreamverse/scripts/launch-frontend.sh
+#
+# Override backend or ngrok URL via env:
+#   BACKEND_HOST=1.2.3.4 bash apps/dreamverse/scripts/launch-frontend.sh
+
+set -euo pipefail
+
+CONDA_PREFIX="$HOME/miniconda3/envs/dreamverse"
+BACKEND_HOST="${BACKEND_HOST:-10.244.18.228}"
+BACKEND_PORT="${BACKEND_PORT:-8009}"
+NGROK_URL="${NGROK_URL:-ltx23.ngrok.app}"
+WEB_DIR="$(git rev-parse --show-toplevel)/apps/dreamverse/web"
+
+cleanup() {
+    echo "==> Shutting down..."
+    kill "$FRONTEND_PID" 2>/dev/null || true
+}
+trap cleanup EXIT
+
+echo "==> Starting frontend (backend: $BACKEND_HOST:$BACKEND_PORT)"
+BACKEND_HOST="$BACKEND_HOST" BACKEND_PORT="$BACKEND_PORT" \
+    npm run --prefix "$WEB_DIR" dev &
+FRONTEND_PID=$!
+
+echo "==> Starting ngrok tunnel -> $NGROK_URL"
+"$CONDA_PREFIX/bin/ngrok" http --url="$NGROK_URL" 5299
@@ -0,0 +1,105 @@
+#!/usr/bin/env bash
+# setup-dreamverse-env.sh — create and configure the dreamverse conda env
+# from scratch on this aarch64 NFS Slurm cluster.
+#
+# Run from the login node (from the repo root):
+#   bash apps/dreamverse/scripts/setup-dreamverse-env.sh
+#
+# After this script completes, use launch-dreamverse.sh on a compute node.
+
+set -euo pipefail
+
+REPO_ROOT="$(git rev-parse --show-toplevel)"
+ENV_NAME="dreamverse"
+LOCAL_DIR="/mnt/local/hal-kevin"       # cache/pkgs — keep on local disk
+CONDA_PREFIX="$HOME/miniconda3/envs/$ENV_NAME"  # env — on shared NFS so it survives node changes
+
+echo "==> Removing existing env if present"
+conda env remove -p "$CONDA_PREFIX" -y 2>/dev/null || true
+rm -rf "$CONDA_PREFIX" 2>/dev/null || true
+
+echo "==> Creating conda env at $CONDA_PREFIX"
+CONDA_PKGS_DIRS="$LOCAL_DIR/conda/pkgs" conda create -p "$CONDA_PREFIX" python=3.11 -y
+
+GXX="$CONDA_PREFIX/bin/aarch64-conda-linux-gnu-g++"
+GCC="$CONDA_PREFIX/bin/aarch64-conda-linux-gnu-gcc"
+
+echo "==> Installing compiler"
+CONDA_PKGS_DIRS="$LOCAL_DIR/conda/pkgs" conda install -p "$CONDA_PREFIX" gxx_linux-aarch64 -y
+
+echo "==> Installing CUDA toolkit (nvcc + headers)"
+CONDA_PKGS_DIRS="$LOCAL_DIR/conda/pkgs" conda install -p "$CONDA_PREFIX" -c nvidia cuda-toolkit -y
+
+echo "==> Hiding conflicting libcudart.so.13 immediately"
+mkdir -p "$CONDA_PREFIX/lib/hidden"
+mv "$CONDA_PREFIX"/lib/libcudart.so* "$CONDA_PREFIX/lib/hidden/" 2>/dev/null || true
+
+echo "==> Fixing compiler_compat symlinks"
+mkdir -p "$CONDA_PREFIX/compiler_compat"
+ln -sf "$GXX" "$CONDA_PREFIX/compiler_compat/g++"
+ln -sf "$GCC" "$CONDA_PREFIX/compiler_compat/gcc"
+
+echo "==> Symlinking CUDA headers to standard location"
+for f in "$CONDA_PREFIX/targets/sbsa-linux/include/"*; do
+    ln -sf "$f" "$CONDA_PREFIX/include/$(basename "$f")" 2>/dev/null || true
+done
+
+echo "==> Installing ffmpeg (native build with x264 + NVENC)"
+CUDA_PREFIX="$CONDA_PREFIX" bash "$REPO_ROOT/apps/dreamverse/scripts/install_native_ffmpeg.sh"
+
+echo "==> Installing pip and uv"
+CONDA_PKGS_DIRS="$LOCAL_DIR/conda/pkgs" conda install -p "$CONDA_PREFIX" pip -y
+"$CONDA_PREFIX/bin/pip" install uv
+
+echo "==> Setting compiler env vars"
+export UV_CACHE_DIR="$LOCAL_DIR/cache"
+export UV_LINK_MODE=copy
+export CXX="$CONDA_PREFIX/compiler_compat/g++"
+export CC="$CONDA_PREFIX/compiler_compat/gcc"
+export CUDAHOSTCXX="$GXX"
+export NVCC_PREPEND_FLAGS="-ccbin $GXX -allow-unsupported-compiler"
+export CUDA_HOME="$CONDA_PREFIX"
+# Only build for GB200 (sm_100a); CUDA 13 dropped support for older archs
+export TORCH_CUDA_ARCH_LIST="10.0a"
+
+echo "==> Installing torch with CUDA 12.8"
+UV_CACHE_DIR="$LOCAL_DIR/cache" "$CONDA_PREFIX/bin/uv" pip install torch==2.11.0 torchvision \
+    --index-url https://download.pytorch.org/whl/cu128
+
+echo "==> Hiding any newly introduced libcudart.so.13"
+mv "$CONDA_PREFIX"/lib/libcudart.so* "$CONDA_PREFIX/lib/hidden/" 2>/dev/null || true
+
+# Set paths now that torch (and its nvidia packages) are installed
+CUDA_RT_DIR="$CONDA_PREFIX/lib/python3.11/site-packages/nvidia/cuda_runtime/lib"
+CUDA_RT_SO="$(ls "$CUDA_RT_DIR"/libcudart.so.* 2>/dev/null | head -1)"
+
+# The pip nvidia package only has libcudart.so.12 (versioned), not libcudart.so.
+# The linker needs the unversioned name to satisfy -lcudart. Create a compat dir.
+mkdir -p "$CONDA_PREFIX/lib/cuda-compat"
+ln -sf "$CUDA_RT_SO" "$CONDA_PREFIX/lib/cuda-compat/libcudart.so"
+
+export LIBRARY_PATH="$CONDA_PREFIX/lib/cuda-compat:$CONDA_PREFIX/lib/stubs"
+export CMAKE_ARGS="-DCUDA_CUDART_LIBRARY=$CUDA_RT_SO -DCUDA_INCLUDE_DIRS=$CONDA_PREFIX/targets/sbsa-linux/include"
+
+echo "==> Installing build tools"
+"$CONDA_PREFIX/bin/pip" install scikit-build-core cmake ninja
+
+echo "==> Initializing git submodules"
+cd "$REPO_ROOT"
+git submodule update --init fastvideo-kernel/include/cutlass fastvideo-kernel/include/tk
+
+echo "==> Building fastvideo-kernel from local source"
+UV_CACHE_DIR="$LOCAL_DIR/cache" "$CONDA_PREFIX/bin/uv" pip install \
+    -e "./fastvideo-kernel" --no-build-isolation
+
+echo "==> Installing fastvideo + dreamverse extras"
+UV_CACHE_DIR="$LOCAL_DIR/cache" "$CONDA_PREFIX/bin/uv" pip install \
+    -e ".[dreamverse]" --no-build-isolation
+
+echo "==> Installing flashinfer-python (pinned, must be last)"
+UV_CACHE_DIR="$LOCAL_DIR/cache" "$CONDA_PREFIX/bin/uv" pip install \
+    https://github.com/flashinfer-ai/flashinfer/releases/download/v0.6.11.post3/flashinfer_python-0.6.11.post3-py3-none-any.whl
+
+echo ""
+echo "Done. On a compute node run (GPUs 0-3 by default; set CUDA_VISIBLE_DEVICES to restrict):"
+echo "  bash apps/dreamverse/scripts/launch-dreamverse.sh"
@@ -110,7 +110,7 @@ default_request:
    fps: 24                  # internal: gpu_pool.py:85 TARGET_FPS

 streaming:
-  # internal: config.py:33 SESSION_TIMEOUT_SECONDS = 300
+  # internal: config.py SESSION_TIMEOUT_SECONDS (env DREAMVERSE_SESSION_TIMEOUT_SECONDS, default 300)
  session_timeout_seconds: 300
  # internal: config.py:282-284 GENERATION_SEGMENT_CAP default 6
  generation_segment_cap: 6
@@ -9,7 +9,7 @@ import SessionTimeoutModal from "@/components/SessionTimeoutModal";
 import Sidebar from "@/components/Sidebar";
 import Header from "@/components/Header";
 import VideoPlayer from "@/components/VideoPlayer";
-import Workspace from "@/components/Workspace";
+import Workspace, { SceneHistoryList } from "@/components/Workspace";
 import { saveProject, saveProjectMetadata, listProjects, loadProjectClips, deleteProject, pruneOldProjects, type StoredProject, type StoredClip } from "@/lib/projectStorage";
 import { isInfrastructureError } from "@/lib/ws/reducer";
 import { useStore } from "@/hooks/useStore";
@@ -31,7 +31,7 @@ import { applyNormalizedSocketEvent } from "@/lib/ws/reducer";
 import { createPromptWindowStore } from "@/stores/promptWindow";
 import { createRewriteStore } from "@/stores/rewrite";
 import { createSessionStore } from "@/stores/session";
-import { createStreamStore } from "@/stores/stream";
+import { createStreamStore, USER_PROMPT_SOURCES } from "@/stores/stream";
 import { createUiStore } from "@/stores/ui";
 import { Button } from "@/components/ui/button";

@@ -80,7 +80,7 @@ function yieldToEventLoop(): Promise<void> {

 const HERO_WAVE_LIGHT = ["#2A4A98", "#4878E5", "#6FA0F2", "#B0BCC8", "#E8D99E", "#D8C844", "#C2A620"];
 const HERO_WAVE_DARK = ["#143468", "#1E58B8", "#3892F0", "#80B8E8", "#B8D0EA", "#E2D498", "#DABB50"];
-const HERO_TEXT = "Direct scenes in seconds";
+const HERO_TEXT = "Direct scenes in seconds with";

 function HeroTagline() {
 	const ref = useRef<HTMLHeadingElement>(null);
@@ -166,6 +166,8 @@ function HeroTagline() {
 					</span>
 				</Fragment>
 			))}
+			<span data-char className="transition-[color,filter] duration-150">{" "}</span>
+			<img src="/logo.svg" alt="FastVideo" className="inline-block h-[1.1em] w-auto align-middle" />
 		</h1>
 	);
 }
@@ -237,6 +239,7 @@ export default function Page() {
 		enhancementEnabled,
 		promptExtensionError,
 		autoExtensionEnabled,
+		manualContinuationMode,
 		autoExtensionTimeoutHint,
 		loopGenerationEnabled,
 		generationPaused,
@@ -249,6 +252,8 @@ export default function Page() {
 		livePromptRewriteMode,
 		sessionExpired,
 		projectResetPending,
+		waitingForSegmentPrompt,
+		generatingNextScene,
 	} = sessionState;

 	const {
@@ -323,6 +328,11 @@ export default function Page() {
 	const [ttffValueMs, setTtffValueMs] = useState<number | null>(null);
 	const ttffIntervalRef = useRef<ReturnType<typeof setInterval> | null>(null);
 	const pendingInitialPromptRef = useRef("");
+	// Prompt id the opening scene is recorded under; sent as initial_rollout_prompt_id
+	// so the backend's pre-seeded opening PromptSubmission emits status updates
+	// (prompt_enhancing/prompt_ready/prompt_fallback_used) against the same id.
+	const pendingInitialPromptIdRef = useRef("");
+	const [initialImageDataUrl, setInitialImageDataUrl] = useState("");
 	const lastArchivedReplayKeyRef = useRef("");
 	const [sidebarOpen, setSidebarOpen] = useState(false);
 	const [currentThumbnail, setCurrentThumbnail] = useState<string | null>(null);
@@ -422,6 +432,11 @@ export default function Page() {
 			if (String(e?.source || "") === "user_rewrite" && typeof e?.text === "string" && e.text.trim()) {
 				return e.text.trim();
 			}
+			// Steering opening: the backend overwrites text/source with the enhanced
+			// prompt once ready, so fall back to the stable rawText record.
+			if (e?.steeringUserPrompt && typeof e?.rawText === "string" && e.rawText.trim()) {
+				return e.rawText.trim();
+			}
 		}
 		return "Untitled project";
 	}, [selectedPreset, promptEvents]);
@@ -432,11 +447,39 @@ export default function Page() {
 	const canDownloadVideo = useMemo(() => {
 		const currentActiveClip = activeClip as Record<string, any> | null;
 		if (currentActiveClip?.blob instanceof Blob) return true;
-		return (completedClips as Record<string, any>[]).some((clip) => clip?.blob instanceof Blob);
-	}, [activeClip, completedClips]);
+		if ((completedClips as Record<string, any>[]).some((clip) => clip?.blob instanceof Blob)) return true;
+		// Steering only: once playback has started the live AV pipeline holds playable segments, so
+		// the user can download the in-progress video at any time (handleDownloadVideo remuxes live
+		// segments). Auto mode keeps its original blob-gated behavior.
+		return Boolean(manualContinuationMode) && Boolean(avPlaybackStarted);
+	}, [activeClip, completedClips, avPlaybackStarted, manualContinuationMode]);
+
+	// Steering mode scene list (oldest first). Primary source is the user's own words, captured
+	// stably at submit time as `rawText` (the backend later overwrites text/source with the
+	// enhanced prompt, so we never read those). A segment with no user prompt — e.g. a preset's
+	// opening scene — falls back to promptHistory (the actual prompt that drove that segment).
+	const steeringScenes = useMemo(() => {
+		if (!manualContinuationMode) return [] as Record<string, any>[];
+		const userScenes = (promptEvents as Record<string, any>[])
+			.filter((e) => e?.steeringUserPrompt && !e?.steeringFailed && typeof e?.rawText === "string" && e.rawText.trim())
+			.slice()
+			.reverse() // oldest -> newest
+			.map((e) => ({ id: e.promptId, prompt: e.rawText as string }));
+		const scenes: Record<string, any>[] = [];
+		// Preset opening segments: curated seeds with no user prompt of their own.
+		const curatedHists = (promptHistory as Record<string, any>[])
+			.slice()
+			.reverse() // oldest first
+			.filter((h) => !USER_PROMPT_SOURCES.has(String(h?.source || "")) && typeof h?.prompt === "string" && (h.prompt as string).trim());
+		scenes.push(...curatedHists.map((h) => ({ id: h.id || "scene_open", prompt: h.prompt })));
+		scenes.push(...userScenes);
+		return scenes;
+	}, [manualContinuationMode, promptEvents, promptHistory]);

 	const hasEdits = useMemo(
-		() => Boolean(sessionStarted) && (promptEvents as Record<string, any>[]).some((e) => typeof e?.text === "string" && e.text.trim() && String(e?.source || "").trim() === "user_rewrite"),
+		() => Boolean(sessionStarted) && (
+			(promptEvents as Record<string, any>[]).some((e) => typeof e?.text === "string" && e.text.trim() && String(e?.source || "").trim() === "user_rewrite")
+		),
 		[sessionStarted, promptEvents],
 	);

@@ -1421,7 +1464,8 @@ export default function Page() {
 		if (!prompt) return;
 		lastSubmitTimeRef.current = now;
 		const rCAR = !uiStore.get().devtoolsMode && !uiStore.get().demoMode;
-		if (rCAR || (sessionStore.get().livePromptRewriteMode && !uiStore.get().demoMode)) {
+		const inManualMode = sessionStore.get().manualContinuationMode;
+		if (!inManualMode && (rCAR || (sessionStore.get().livePromptRewriteMode && !uiStore.get().demoMode))) {
 			if (rewriteStore.get().rewritingSeedPrompts) return;
 			const rewriteSourcePromptWindowPrompts = getActivePromptWindowPrompts();
 			const nextPendingClip = {
@@ -1462,6 +1506,11 @@ export default function Page() {
 			status: "submitted",
 			source: "user_raw",
 			text: prompt,
+			// Stable record of the user's own words for the steering scene list. The backend
+			// later overwrites `text`/`source` with the enhanced prompt via prompt/ready, but
+			// these two fields are never touched by trackPromptEvent.
+			steeringUserPrompt: true,
+			rawText: prompt,
 		});
 		ws.send(
 			JSON.stringify({
@@ -1475,7 +1524,14 @@ export default function Page() {
 			activeClipId: shouldUseArchivedPlaybackFallback() ? streamStore.get().activeClipId : "",
 			activePlaybackStartTime: shouldUseArchivedPlaybackFallback() ? streamStore.get().activePlaybackStartTime : 0,
 		});
-		sessionStore.patch({ livePromptDraft: "" });
+		sessionStore.patch({
+			livePromptDraft: "",
+			waitingForSegmentPrompt: false,
+			sessionNotice: "",
+			// Light the "Generating next scene" overlay immediately on a real submit;
+			// stream/media_init (or a fallback/error) clears it.
+			...(inManualMode ? { generatingNextScene: true } : {}),
+		});
 	}

 	function setLivePromptRewriteMode(enabled: boolean) {
@@ -1550,6 +1606,14 @@ export default function Page() {
 		);
 	}

+	// Steering (manual continuation) vs the automatic 6-segment rollout — a pre-session
+	// preference honored when the session starts.
+	function handleManualContinuationToggle(event: any) {
+		sessionStore.patch({
+			manualContinuationMode: Boolean(event.currentTarget.checked),
+		});
+	}
+
 	function handleLoopGenerationToggle(event: any) {
 		sessionStore.patch({
 			loopGenerationEnabled: Boolean(event.currentTarget.checked),
@@ -1725,6 +1789,9 @@ export default function Page() {
 			sessionNotice: preserveSessionNotice ? sessionStore.get().sessionNotice : "",
 			sessionExpired: preserveSessionNotice ? sessionStore.get().sessionExpired : false,
 			projectResetPending: false,
+			manualContinuationMode: true,
+			waitingForSegmentPrompt: false,
+			generatingNextScene: false,
 		});
 		rewriteStore.resetSessionState();
 		streamStore.resetSessionState();
@@ -1737,6 +1804,7 @@ export default function Page() {
 	function resetToProjectLobbyState() {
 		setVideoMuted(true);
 		pendingInitialPromptRef.current = "";
+		pendingInitialPromptIdRef.current = "";
 		sessionStore.patch({
 			sessionStarted: false,
 			livePromptDraft: "",
@@ -1750,6 +1818,9 @@ export default function Page() {
 			sessionNotice: "",
 			sessionExpired: false,
 			projectResetPending: false,
+			manualContinuationMode: true,
+			waitingForSegmentPrompt: false,
+			generatingNextScene: false,
 		});
 		rewriteStore.resetSessionState();
 		streamStore.resetSessionState();
@@ -1760,7 +1831,14 @@ export default function Page() {
 	}

 	function buildProjectInitPayload(type: "session_init_v2" | "project_init_v1") {
-		const segmentPrompts = getSessionInitPrompts();
+		const manualMode = Boolean(sessionStore.get().manualContinuationMode);
+		let segmentPrompts = getSessionInitPrompts();
+		// Steering mode: seed the first 2 segments from the preset so there's no
+		// gap between segment 1 and 2; the user drives every subsequent segment.
+		// Force auto/loop off so the backend waits after the seeded prompts run out.
+		if (manualMode) {
+			segmentPrompts = segmentPrompts.slice(0, 2);
+		}
 		setSeedPrompts(segmentPrompts);
 		return {
 			type,
@@ -1768,11 +1846,17 @@ export default function Page() {
 			preset_label: getInitialPresetLabel(),
 			curated_prompts: segmentPrompts,
 			initial_rollout_prompt: normalizeInitialPrompt(pendingInitialPromptRef.current),
-			initial_image: null,
+			// Ties the backend's pre-seeded opening PromptSubmission to the prompt event
+			// recorded in beginProjectLocally so its status updates land on that record.
+			initial_rollout_prompt_id: pendingInitialPromptIdRef.current,
+			initial_image: initialImageDataUrl
+				? { data_url: initialImageDataUrl, mime_type: initialImageDataUrl.split(";")[0].split(":")[1] || "image/png", name: "upload.png" }
+				: null,
 			single_clip_mode: false,
 			enhancement_enabled: sessionStore.get().enhancementEnabled,
-			auto_extension_enabled: sessionStore.get().autoExtensionEnabled,
-			loop_generation_enabled: sessionStore.get().loopGenerationEnabled,
+			auto_extension_enabled: manualMode ? false : sessionStore.get().autoExtensionEnabled,
+			loop_generation_enabled: manualMode ? false : sessionStore.get().loopGenerationEnabled,
+			manual_continuation_mode: manualMode,
 		};
 	}

@@ -1951,7 +2035,11 @@ export default function Page() {
 		setCurrentThumbnail(null);
 		const initialPrompt = normalizeInitialPrompt(sessionStore.get().livePromptDraft as string);
 		pendingInitialPromptRef.current = initialPrompt;
+		setInitialImageDataUrl("");
 		const rCAR = !uiStore.get().devtoolsMode && !uiStore.get().demoMode;
+		// The "Steering mode" toggle is authoritative: checked = manual per-segment steering,
+		// unchecked = automatic 6-segment rollout (default).
+		const nextManualContinuationMode = Boolean(sessionStore.get().manualContinuationMode);
 		sessionStore.patch({
 			sessionNotice: "",
 			sessionExpired: false,
@@ -1966,6 +2054,9 @@ export default function Page() {
 			autoExtensionTimeoutHint: "",
 			generationPaused: false,
 			projectResetPending: false,
+			manualContinuationMode: nextManualContinuationMode,
+			waitingForSegmentPrompt: false,
+			generatingNextScene: false,
 		});
 		resetPlaybackState();
 		streamStore.patch({
@@ -1979,12 +2070,15 @@ export default function Page() {
 			selectedHistoryId: "",
 		});
 		rewriteStore.resetSessionState();
+		pendingInitialPromptIdRef.current = initialPrompt ? makePromptId() : "";
 		if (initialPrompt) {
 			addPromptEvent({
-				promptId: makePromptId(),
+				promptId: pendingInitialPromptIdRef.current,
 				status: "rewrite_requested",
 				source: "user_rewrite",
 				text: initialPrompt,
+				// In steering mode the typed opening is the user's Scene 1 — record it stably.
+				...(nextManualContinuationMode ? { steeringUserPrompt: true, rawText: initialPrompt } : {}),
 			});
 		}
 		setSeedPrompts(getSessionInitPrompts());
@@ -2500,6 +2594,7 @@ export default function Page() {
 				selectedPresetId={selectedPresetId as string}
 				enhancementEnabled={enhancementEnabled as boolean}
 				autoExtensionEnabled={autoExtensionEnabled as boolean}
+				manualContinuationEnabled={manualContinuationMode as boolean}
 				loopGenerationEnabled={loopGenerationEnabled as boolean}
 				canJoinSession={canJoinSession as boolean}
 				canSubmitContinuation={canSubmitContinuation}
@@ -2512,6 +2607,7 @@ export default function Page() {
 				onEnhancementToggle={handleEnhancementToggle}
 				onCuratedPromptLimitChange={handleCuratedPromptLimitChange}
 				onAutoExtensionToggle={handleAutoExtensionToggle}
+				onManualContinuationToggle={handleManualContinuationToggle}
 				onLoopToggle={handleLoopGenerationToggle}
 				onJoin={joinSession}
 				onLeave={leaveSession}
@@ -2641,6 +2737,7 @@ export default function Page() {
 			/>
 			<Header timeLeft={headerTimeLeft} formatTime={formatTime} onToggleSidebar={() => setSidebarOpen((prev) => !prev)} />

+			<div className={cn("flex flex-1 min-h-0 flex-col", sessionStarted && "pb-16 sm:pb-28")}>
 			<div className="relative flex flex-1 min-h-0 flex-col justify-center px-4 pb-2 sm:px-6 sm:pb-12">
 				{isViewingMode && (
 					<>
@@ -2724,6 +2821,8 @@ export default function Page() {
 									showLivePlayback={showLivePlayback}
 									defaultMuted={videoMuted}
 									canDownload={canDownloadVideo}
+									waitingForSegmentPrompt={waitingForSegmentPrompt as boolean}
+									generatingNextScene={generatingNextScene as boolean}
 									onPlaying={markFirstFrameRendered}
 									onDownload={handleDownloadVideo}
 								/>
@@ -2734,6 +2833,7 @@ export default function Page() {
 					<section className={cn("mx-auto w-full max-w-2xl", hasEdits && "flex-1 min-h-0 overflow-y-auto")}>
 						<Workspace
 							promptEvents={promptEvents as any[]}
+							manualMode={manualContinuationMode as boolean}
 							currentThumbnail={currentThumbnail}
 							originalLabel={pendingInitialPromptRef.current || (selectedPreset as Record<string, any>)?.label || ""}
 							sessionStarted={sessionStarted as boolean}
@@ -2780,11 +2880,17 @@ export default function Page() {
 							isGenerating={loadingAnimation as boolean}
 							storyPresets={storyPresets as any[]}
 							continuationDraft={livePromptDraft as string}
+							manualContinuationEnabled={manualContinuationMode as boolean}
+							onModeChange={(manual) => sessionStore.patch({ manualContinuationMode: manual })}
+							initialImageDataUrl={initialImageDataUrl}
+							onImageUpload={(dataUrl) => setInitialImageDataUrl(dataUrl)}
+							onImageClear={() => setInitialImageDataUrl("")}
 							canJoinSession={canStartSession}
 							canSubmitContinuation={canSubmitContinuation}
 							sessionExpired={sessionExpired as boolean}
 							sessionNotice={sessionNotice as string}
 							projectResetPending={projectResetPending as boolean}
+							waitingForSegmentPrompt={waitingForSegmentPrompt as boolean}
 							onPresetGenerate={handlePresetGenerate}
 							onContinuationInput={handleLivePromptInput}
 							onContinuationKeydown={handleLivePromptKeydown}
@@ -2798,6 +2904,12 @@ export default function Page() {
 					</motion.div>
 				</div>
 			</div>
+			{manualContinuationMode && (
+				<div className="px-4 sm:px-6">
+					<SceneHistoryList sceneHistory={steeringScenes as any[]} />
+				</div>
+			)}
+			</div>
 		</main>
 	);
 }
@@ -2,13 +2,16 @@

 import React, { useRef, useState, useCallback, useEffect } from "react";
 import Image from "next/image";
-import { Film, ArrowUp, X, Loader2, ArrowLeft } from "lucide-react";
+import { Film, ArrowUp, X, Loader2, ArrowLeft, ImagePlus } from "lucide-react";
 import { Button } from "@/components/ui/button";
 import LeaveSessionModal, { shouldShowLeaveWarning } from "@/components/LeaveSessionModal";
 import SpeechToTextButton from "@/components/SpeechToTextButton";
 import { cn } from "@/lib/utils";

 const PROMPT_MAX_LENGTH = 500;
+// Must match backend session_init_image.py: MAX_SESSION_INIT_IMAGE_BYTES / SUPPORTED_SESSION_INIT_IMAGE_MIME_TYPES.
+const IMAGE_MAX_BYTES = 15 * 1024 * 1024;
+const IMAGE_ALLOWED_TYPES = ["image/png", "image/jpeg", "image/webp"];

 interface Props {
 	sessionStarted?: boolean;
@@ -22,6 +25,12 @@ interface Props {
 	sessionNotice?: string;
 	projectResetPending?: boolean;
 	viewingReadOnly?: boolean;
+	waitingForSegmentPrompt?: boolean;
+	manualContinuationEnabled?: boolean;
+	onModeChange?: (manual: boolean) => void;
+	initialImageDataUrl?: string;
+	onImageUpload?: (dataUrl: string, mimeType: string, name: string) => void;
+	onImageClear?: () => void;
 	onPresetGenerate?: (presetId: string) => void;
 	onContinuationInput?: (e: React.ChangeEvent<HTMLTextAreaElement>) => void;
 	onContinuationKeydown?: (e: React.KeyboardEvent<HTMLTextAreaElement>) => void;
@@ -46,6 +55,12 @@ export default function ChatBar({
 	sessionNotice = "",
 	projectResetPending = false,
 	viewingReadOnly = false,
+	waitingForSegmentPrompt = false,
+	manualContinuationEnabled = false,
+	onModeChange = () => {},
+	initialImageDataUrl = "",
+	onImageUpload = () => {},
+	onImageClear = () => {},
 	onPresetGenerate = () => {},
 	onContinuationInput = () => {},
 	onContinuationKeydown = () => {},
@@ -59,15 +74,51 @@ export default function ChatBar({
 }: Props) {
 	const [sttBusy, setSttBusy] = useState(false);
 	const [leaveModalOpen, setLeaveModalOpen] = useState(false);
+	const [imageError, setImageError] = useState("");
+	const fileInputRef = useRef<HTMLInputElement>(null);
+
+	const processImageFile = useCallback((file: File) => {
+		if (!IMAGE_ALLOWED_TYPES.includes(file.type)) {
+			setImageError("Unsupported image type. Use a PNG, JPEG, or WebP image.");
+			return;
+		}
+		if (file.size > IMAGE_MAX_BYTES) {
+			setImageError("Image is too large. The maximum size is 15MB.");
+			return;
+		}
+		const reader = new FileReader();
+		reader.onload = (e) => {
+			const dataUrl = e.target?.result as string;
+			if (dataUrl) {
+				setImageError("");
+				onImageUpload(dataUrl, file.type, file.name);
+			}
+		};
+		reader.onerror = () => {
+			setImageError("Could not read the image file. Please try again.");
+		};
+		reader.readAsDataURL(file);
+	}, [onImageUpload]);
+
+	const handleImagePaste = useCallback((e: React.ClipboardEvent) => {
+		if (sessionStarted) return;
+		const items = Array.from(e.clipboardData?.items ?? []);
+		const imageItem = items.find((item) => item.type.startsWith("image/"));
+		if (!imageItem) return;
+		const file = imageItem.getAsFile();
+		if (file) processImageFile(file);
+	}, [sessionStarted, processImageFile]);
 	const showSpinner = isGenerating || rewritingSeedPrompts;
 	const isBusy = isGenerating || rewritingSeedPrompts || projectResetPending;
 	const messagePlaceholder = projectResetPending
 		? "Starting new project\u2026"
 		: isBusy
 			? "Generating video\u2026"
-			: !sessionStarted
-				? "What video are you imagining?"
-				: "What do you want to edit?";
+			: waitingForSegmentPrompt
+				? "Describe the next scene\u2026"
+				: !sessionStarted
+					? "What video are you imagining?"
+					: "What do you want to edit?";
 	const actionLabel = !sessionStarted ? "Generate" : "Rewrite rollout";

 	const inputRef = useRef<HTMLTextAreaElement>(null);
@@ -267,9 +318,9 @@ export default function ChatBar({
 						<Button onClick={onStartNewProject} size="sm" className="rounded-full px-5">
 							New Project
 						</Button>
-						<a href="https://docs.google.com/forms/d/e/1FAIpQLSe5zpO1iD8Ds-Ih-fOLm64qd7YZVvuvAyHuJaAfw1hkRHTe_A/viewform?usp=publish-editor" target="_blank" rel="noopener noreferrer">
+						<a href="https://haoailab.com/blogs/dreamverse/" target="_blank" rel="noopener noreferrer">
 							<Button variant="outline" size="sm" className="rounded-full px-5">
-								Join Waitlist
+								Blog
 							</Button>
 						</a>
 					</div>
@@ -346,6 +397,31 @@ export default function ChatBar({
 				</div>
 			)}

+
+			{!sessionStarted && imageError && (
+				<div className="rounded-xl border border-rose-500/20 bg-rose-500/10 px-4 py-2.5 text-center text-xs text-rose-700 dark:text-rose-300">
+					{imageError}
+				</div>
+			)}
+
+			{!sessionStarted && initialImageDataUrl && (
+				<div className="flex items-center gap-2 rounded-2xl border border-input bg-card/65 px-3 py-2">
+					<img src={initialImageDataUrl} alt="Initial frame" className="h-12 w-12 rounded-lg object-cover" />
+					<span className="flex-1 truncate text-xs text-muted-foreground">Starting image set</span>
+					<button type="button" onClick={() => { setImageError(""); onImageClear(); }} className="text-muted-foreground hover:text-foreground transition-colors">
+						<X className="size-4" />
+					</button>
+				</div>
+			)}
+
+			<input
+				ref={fileInputRef}
+				type="file"
+				accept={IMAGE_ALLOWED_TYPES.join(",")}
+				className="hidden"
+				onChange={(e) => { const f = e.target.files?.[0]; if (f) processImageFile(f); e.target.value = ""; }}
+			/>
+
 			<div
 				className={cn(
 					"flex min-w-0 items-center gap-1.5 rounded-4xl border py-2.5 pl-5 pr-2.5 shadow-md backdrop-blur-sm transition-all duration-200",
@@ -359,6 +435,7 @@ export default function ChatBar({
 					value={continuationDraft}
 					onChange={onContinuationInput}
 					onKeyDown={handleKeyDown}
+					onPaste={handleImagePaste}
 					placeholder={sttBusy ? "Listening\u2026" : messagePlaceholder}
 					maxLength={PROMPT_MAX_LENGTH}
 					disabled={isBusy || sttBusy}
@@ -369,6 +446,19 @@ export default function ChatBar({
 					)}
 				/>
 				{onSpeechTranscript && <SpeechToTextButton disabled={isBusy} onTranscript={onSpeechTranscript} onInterimChange={onSpeechInterimChange} onBusyChange={setSttBusy} />}
+				{!sessionStarted && (
+					<Button
+						type="button"
+						variant="ghost"
+						size="icon-sm"
+						title="Add starting image"
+						onClick={() => fileInputRef.current?.click()}
+						disabled={isBusy}
+						className="shrink-0 rounded-full text-muted-foreground hover:text-foreground"
+					>
+						<ImagePlus className="size-4" />
+					</Button>
+				)}
 				{!sessionStarted ? (
 					<Button
 						aria-label={actionLabel}
@@ -8,7 +8,6 @@ import { Badge } from "@/components/ui/badge";
 import { Button } from "@/components/ui/button";
 import { ThemeToggle } from "@/components/ui/theme-toggle";

-const FASTVIDEO_REPO_URL = "https://haoailab.com/blogs/dreamverse/";
 const FASTVIDEO_BLOG_URL = "https://haoailab.com/blogs/dreamverse/";

 interface Props {
@@ -29,13 +28,13 @@ export default function Header({ timeLeft = null, formatTime = (seconds) => `${s
 							<SidePanelOpenFilled size={20} />
 						</Button>
 					)}
-					<a href={FASTVIDEO_REPO_URL} target="_blank" rel="noopener noreferrer" title="FastVideo on GitHub">
+					<a href="/" title="FastVideo home">
 						<Image src="/logo.svg" alt="FastVideo" width={32} height={32} className="h-8 w-auto sm:h-9 transition-opacity hover:opacity-70" />
 					</a>
 					<div className="hidden sm:flex items-center gap-3">
-						<a href="https://docs.google.com/forms/d/e/1FAIpQLSe5zpO1iD8Ds-Ih-fOLm64qd7YZVvuvAyHuJaAfw1hkRHTe_A/viewform?usp=publish-editor" target="_blank" rel="noopener noreferrer">
+						<a href={FASTVIDEO_BLOG_URL} target="_blank" rel="noopener noreferrer">
 							<Button variant="outline" size="sm" className="gap-1.5 rounded-full px-3 text-xs">
-								Join Waitlist
+								Blog
 								<ExternalLink className="size-3 opacity-60" />
 							</Button>
 						</a>
@@ -53,9 +52,9 @@ export default function Header({ timeLeft = null, formatTime = (seconds) => `${s
 			</div>

 			<div className="flex sm:hidden items-center gap-2 px-4 pb-3">
-				<a href="https://docs.google.com/forms/d/e/1FAIpQLSe5zpO1iD8Ds-Ih-fOLm64qd7YZVvuvAyHuJaAfw1hkRHTe_A/viewform?usp=publish-editor" target="_blank" rel="noopener noreferrer">
+				<a href={FASTVIDEO_BLOG_URL} target="_blank" rel="noopener noreferrer">
 					<Button variant="outline" size="sm" className="gap-1.5 rounded-full px-3 text-xs">
-						Join Waitlist
+						Blog
 						<ExternalLink className="size-3 opacity-60" />
 					</Button>
 				</a>
@@ -3,7 +3,7 @@
 import React, { useState, useEffect, useRef, useCallback } from "react";
 import { cn } from "@/lib/utils";
 import { PlayFilledAlt } from "@carbon/icons-react";
-import { Download, Loader2, Share } from "lucide-react";
+import { Check, ChevronDown, Download, Loader2, Share } from "lucide-react";
 import { Button } from "@/components/ui/button";
 interface VideoPlayerProps {
 	videoRef?: React.RefCallback<HTMLVideoElement>;
@@ -21,6 +21,8 @@ interface VideoPlayerProps {
 	showLivePlayback?: boolean;
 	defaultMuted?: boolean;
 	rewritePending?: boolean;
+	waitingForSegmentPrompt?: boolean;
+	generatingNextScene?: boolean;
 	onPlaying?: () => void;
 	onDownload?: () => void;
 }
@@ -57,6 +59,8 @@ export default function VideoPlayer({
 	showLivePlayback = true,
 	defaultMuted = true,
 	rewritePending = false,
+	waitingForSegmentPrompt = false,
+	generatingNextScene = false,
 	onPlaying = () => {},
 	onDownload,
 }: VideoPlayerProps) {
@@ -88,6 +92,137 @@ export default function VideoPlayer({
 		setCanShare(typeof navigator.canShare === "function" && window.matchMedia("(pointer: coarse)").matches);
 	}, []);

+	// Steering mode: the backend may signal "waiting for next prompt" while the current
+	// segment is still PLAYING (it generates ahead). Only surface the "Segment complete"
+	// overlay once the playhead actually reaches the end of the buffered segment.
+	const [playbackReachedEnd, setPlaybackReachedEnd] = useState(false);
+	// Steering mode: when the user submits the next scene, the segment is generated
+	// (a few seconds of latency) before frames stream. Show a "Generating next scene…"
+	// indicator across that gap so the frozen frame isn't silent. Driven by the explicit
+	// generatingNextScene state (set on scene submit / prompt selection), never inferred
+	// from waitingForSegmentPrompt edges.
+	const [generatingNext, setGeneratingNext] = useState(false);
+	// End of the buffered timeline captured the moment generation starts; the freshly
+	// generated segment extends the buffer past this, which is how we know it landed.
+	const genBoundaryRef = useRef(0);
+
+	// Steering mode: the backend may signal "waiting for next prompt" while the current
+	// segment is still PLAYING (it generates ahead). Track whether the playhead has reached
+	// the end of the buffered segment so end-overlays only show there. Keep tracking through
+	// the generating phase too, so scrubbing back and replaying to the end re-shows them.
+	useEffect(() => {
+		if (!waitingForSegmentPrompt && !generatingNext) {
+			setPlaybackReachedEnd(false);
+			return;
+		}
+		const el = liveVideoEl.current;
+		if (!el) return;
+		const check = () => {
+			try {
+				const buffered = el.buffered;
+				if (buffered.length === 0) return;
+				const end = buffered.end(buffered.length - 1);
+				// Track proximity both ways: scrubbing back off the end hides the overlay,
+				// playing forward to the end re-shows it.
+				setPlaybackReachedEnd(el.ended || end - el.currentTime <= 0.2);
+			} catch {
+				/* buffered access can throw mid-append */
+			}
+		};
+		check();
+		el.addEventListener("timeupdate", check);
+		el.addEventListener("ended", check);
+		el.addEventListener("waiting", check);
+		el.addEventListener("stalled", check);
+		el.addEventListener("pause", check);
+		el.addEventListener("seeking", check);
+		el.addEventListener("seeked", check);
+		el.addEventListener("playing", check);
+		el.addEventListener("progress", check);
+		return () => {
+			el.removeEventListener("timeupdate", check);
+			el.removeEventListener("ended", check);
+			el.removeEventListener("waiting", check);
+			el.removeEventListener("stalled", check);
+			el.removeEventListener("pause", check);
+			el.removeEventListener("seeking", check);
+			el.removeEventListener("seeked", check);
+			el.removeEventListener("playing", check);
+			el.removeEventListener("progress", check);
+		};
+	}, [waitingForSegmentPrompt, generatingNext]);
+
+	useEffect(() => {
+		if (waitingForSegmentPrompt || !sessionStarted) {
+			// Back to waiting (or session over): nothing is generating.
+			setGeneratingNext(false);
+			return;
+		}
+		if (!generatingNextScene) return;
+		// Snapshot the current end of the buffered timeline; the generated segment will
+		// extend the buffer past this boundary.
+		const el = liveVideoEl.current;
+		let boundary = el?.currentTime ?? 0;
+		try {
+			const b = el?.buffered;
+			if (b && b.length) boundary = Math.max(boundary, b.end(b.length - 1));
+		} catch {
+			/* buffered access can throw mid-append */
+		}
+		genBoundaryRef.current = boundary;
+		setGeneratingNext(true);
+	}, [generatingNextScene, waitingForSegmentPrompt, sessionStarted]);
+
+	useEffect(() => {
+		if (!generatingNext) return;
+		if (!sessionStarted) {
+			setGeneratingNext(false);
+			return;
+		}
+		const el = liveVideoEl.current;
+		if (!el) return;
+		// Clear the instant the freshly generated segment lands: the buffer grows past the
+		// boundary captured at generation start (or the playhead advances into the new
+		// frames). Deliberately NOT a bare "playing" handler — scrubbing back and replaying
+		// the EXISTING segment must keep "Generating" up until the new frames actually arrive.
+		const check = () => {
+			try {
+				const b = el.buffered;
+				const end = b.length ? b.end(b.length - 1) : 0;
+				if (end > genBoundaryRef.current + 0.25 || el.currentTime > genBoundaryRef.current + 0.1) {
+					setGeneratingNext(false);
+				}
+			} catch {
+				/* buffered access can throw mid-append */
+			}
+		};
+		check();
+		el.addEventListener("progress", check);
+		el.addEventListener("timeupdate", check);
+		el.addEventListener("durationchange", check);
+		return () => {
+			el.removeEventListener("progress", check);
+			el.removeEventListener("timeupdate", check);
+			el.removeEventListener("durationchange", check);
+		};
+	}, [generatingNext, sessionStarted]);
+
+	// Drive a ~10.5s progress bar during generation so the wait has a visible ETA.
+	const GEN_DURATION_MS = 10500;
+	const [genProgress, setGenProgress] = useState(0);
+	useEffect(() => {
+		if (!generatingNext) {
+			setGenProgress(0);
+			return;
+		}
+		const start = performance.now();
+		setGenProgress(0);
+		const id = setInterval(() => {
+			setGenProgress(Math.min((performance.now() - start) / GEN_DURATION_MS, 1));
+		}, 50);
+		return () => clearInterval(id);
+	}, [generatingNext]);
+
 	return (
 		<div className="mx-auto w-full max-w-3xl mb-2 sm:mb-6">
 			<div className="rounded-2xl border border-border bg-card/50 p-2 shadow-lg backdrop-blur-md">
@@ -104,7 +239,7 @@ export default function VideoPlayer({
 								<PlayFilledAlt className="size-10 text-white/25" />
 								<p className="text-sm text-white/50">Your video will appear here</p>
 							</div>
-						) : !avPlaybackStarted && !mediaAppendError && !inQueue && loadingAnimation ? (
+						) : !avPlaybackStarted && !mediaAppendError && !inQueue && !waitingForSegmentPrompt && loadingAnimation ? (
 							<div className="absolute inset-0 flex flex-col items-center justify-center gap-4 bg-slate-900/60 p-4 backdrop-blur-[2px]">
 								<div className="pointer-events-none absolute inset-0 overflow-hidden">
 									<div className="absolute inset-0 -translate-x-full animate-[shimmer_3s_ease-in-out_infinite] bg-gradient-to-r from-transparent via-white/[0.04] to-transparent" />
@@ -114,6 +249,35 @@ export default function VideoPlayer({
 							</div>
 						) : null}

+						{/* Steering mode: this segment finished — wait gracefully for the user's next scene
+							instead of spinning. The last frame stays visible behind a soft bottom gradient. */}
+						{sessionStarted && waitingForSegmentPrompt && playbackReachedEnd && !mediaAppendError && !inQueue && (
+							<div className="pointer-events-none absolute inset-0 flex flex-col items-center justify-end gap-2 bg-gradient-to-t from-slate-950/85 via-slate-950/15 to-transparent p-5 pb-6 text-center">
+								<div className="flex size-9 items-center justify-center rounded-full border border-white/25 bg-white/10 shadow-lg backdrop-blur-md">
+									<Check className="size-4 text-white/90" />
+								</div>
+								<div className="space-y-0.5">
+									<p className="text-sm font-medium text-white/95">Segment complete</p>
+									<p className="text-xs text-white/65">Describe the next scene below to keep going</p>
+								</div>
+								<ChevronDown className="size-4 animate-bounce text-white/45" />
+							</div>
+						)}
+
+						{/* Steering mode: generating the next segment — show a ~10.5s progress bar so the wait has an ETA.
+							Gated on playbackReachedEnd like "Segment complete": scrubbing back hides it, playing to the end re-shows it. */}
+						{sessionStarted && generatingNext && playbackReachedEnd && !mediaAppendError && !inQueue && (
+							<div className="pointer-events-none absolute inset-0 flex flex-col items-center justify-end gap-3 bg-gradient-to-t from-slate-950/85 via-slate-950/15 to-transparent p-5 pb-7 text-center">
+								<p className="text-sm font-medium text-white/95">Generating next scene&hellip;</p>
+								<div className="h-1.5 w-48 overflow-hidden rounded-full bg-white/15 shadow-sm">
+									<div
+										className="h-full rounded-full bg-white/85 transition-[width] duration-100 ease-linear"
+										style={{ width: `${Math.round(genProgress * 100)}%` }}
+									/>
+								</div>
+							</div>
+						)}
+
 						{rewritePending && avPlaybackStarted && (
 							<div className="absolute inset-x-0 bottom-0 z-10 flex items-center justify-center gap-2 bg-gradient-to-t from-black/60 to-transparent px-4 pb-12 pt-8 pointer-events-none">
 								<Loader2 className="size-4 animate-spin text-white/90" />
@@ -3,11 +3,90 @@ import React, { useRef, useMemo, useEffect, useCallback, useState } from "react"
 import { motion, useAnimationControls } from "framer-motion";
 import { Badge } from "@/components/ui/badge";
 import { cn } from "@/lib/utils";
-import { Check, Lightbulb, Pencil } from "lucide-react";
+import { Check, Clapperboard, Lightbulb, Pencil } from "lucide-react";

 export const WORKSPACE_ORIGINAL_SELECTION_KEY = "original";
 export const WORKSPACE_CURRENT_SELECTION_KEY = "current";

+export function SceneHistoryList({ sceneHistory = [] }: { sceneHistory?: Record<string, any>[] }) {
+	const bottomSentinelRef = useRef<HTMLDivElement>(null);
+	const topSentinelRef = useRef<HTMLDivElement>(null);
+	const [showTopFade, setShowTopFade] = useState(false);
+
+	const scenes = useMemo(
+		() => (sceneHistory || []).filter((s) => normalizeText(s?.prompt)),
+		[sceneHistory],
+	);
+
+	const scrollToBottom = useCallback(() => {
+		setTimeout(() => {
+			bottomSentinelRef.current?.scrollIntoView({ block: "end", behavior: "smooth" });
+		}, 60);
+	}, []);
+
+	useEffect(() => {
+		if (scenes.length > 0) scrollToBottom();
+	}, [scenes.length, scrollToBottom]);
+
+	useEffect(() => {
+		const sentinel = bottomSentinelRef.current;
+		if (!sentinel || typeof ResizeObserver === "undefined") return;
+		let container: HTMLElement | null = sentinel.parentElement;
+		while (container) {
+			const oy = getComputedStyle(container).overflowY;
+			if (oy === "auto" || oy === "scroll") break;
+			container = container.parentElement;
+		}
+		if (!container) return;
+		const ro = new ResizeObserver(() => {
+			const nearBottom = container!.scrollHeight - container!.scrollTop - container!.clientHeight < 96;
+			if (nearBottom) scrollToBottom();
+		});
+		ro.observe(container);
+		return () => ro.disconnect();
+	}, [scenes.length, scrollToBottom]);
+
+	useEffect(() => {
+		const el = topSentinelRef.current;
+		if (!el) return;
+		const observer = new IntersectionObserver(([entry]) => setShowTopFade(!entry.isIntersecting), { threshold: 0.1 });
+		observer.observe(el);
+		return () => observer.disconnect();
+	}, [scenes.length]);
+
+	if (scenes.length === 0) return null;
+
+	return (
+		<section className="relative z-10 flex flex-col mx-auto w-full max-w-2xl max-h-32 overflow-y-auto">
+			<div
+				className={cn(
+					"pointer-events-none sticky top-0 z-20 -mb-12 h-12 bg-linear-to-b from-background to-transparent transition-opacity duration-200",
+					showTopFade ? "opacity-100" : "opacity-0",
+				)}
+				aria-hidden="true"
+			/>
+			<div ref={topSentinelRef} className="h-0 w-0" aria-hidden="true" />
+			<div className="flex flex-col gap-2 pt-12 pb-4">
+				{scenes.map((scene, index) => (
+					<div
+						key={scene.id || index}
+						className="flex items-start gap-3 rounded-xl p-3 transition-colors duration-200 hover:bg-slate-200/50 hover:dark:bg-slate-800/30"
+					>
+						<div className="flex min-w-0 flex-1 flex-col gap-2">
+							<Badge variant="secondary" className="horizontal gap-2 items-center w-fit">
+								<Clapperboard className="size-3 opacity-70" />
+								{`Scene ${index + 1}`}
+							</Badge>
+							<p className="line-clamp-2 text-sm leading-5 text-muted-foreground">{scene.prompt}</p>
+						</div>
+					</div>
+				))}
+			</div>
+			<div ref={bottomSentinelRef} className="h-0 w-0" aria-hidden="true" />
+		</section>
+	);
+}
+
 interface WorkspaceProps {
 	promptEvents?: Record<string, any>[];
 	currentThumbnail?: string | null;
@@ -19,6 +98,7 @@ interface WorkspaceProps {
 	selectedClipId?: string;
 	selectedEntryKey?: string;
 	originalClipId?: string;
+	manualMode?: boolean;
 }

 function normalizeText(value: any): string {
@@ -218,7 +298,7 @@ function ChromaGradient({ sessionStarted = false }: { sessionStarted?: boolean }
 	);
 }

-export default function Workspace({ promptEvents = [], currentThumbnail = null, originalLabel = "", sessionStarted = false, onSelectOriginal, onSelectEvent, onSelectCurrent, selectedClipId, selectedEntryKey: selectedEntryKeyProp, originalClipId = "" }: WorkspaceProps) {
+export default function Workspace({ promptEvents = [], currentThumbnail = null, originalLabel = "", sessionStarted = false, onSelectOriginal, onSelectEvent, onSelectCurrent, selectedClipId, selectedEntryKey: selectedEntryKeyProp, originalClipId = "", manualMode = false }: WorkspaceProps) {
 	const bottomSentinelRef = useRef<HTMLDivElement>(null);
 	const topSentinelRef = useRef<HTMLDivElement>(null);
 	const [showTopFade, setShowTopFade] = useState(false);
@@ -259,6 +339,14 @@ export default function Workspace({ promptEvents = [], currentThumbnail = null,
 		return () => observer.disconnect();
 	}, [conversationEvents.length]);

+	if (manualMode) {
+		return (
+			<div className="mt-auto flex flex-col">
+				<ChromaGradient sessionStarted={sessionStarted} />
+			</div>
+		);
+	}
+
 	return (
 		<div className="mt-auto flex flex-col">
 			<ChromaGradient sessionStarted={sessionStarted} />
@@ -34,6 +34,7 @@ interface DevtoolsComposerProps {
  demoMode?: boolean;
  enhancementEnabled?: boolean;
  autoExtensionEnabled?: boolean;
+  manualContinuationEnabled?: boolean;
  loopGenerationEnabled?: boolean;
  curatedPromptLimit?: number;
  maxCuratedPromptCount?: number;
@@ -49,6 +50,7 @@ interface DevtoolsComposerProps {
  onEnhancementToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onCuratedPromptLimitChange?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onAutoExtensionToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
+  onManualContinuationToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onLoopToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onLivePromptModeToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onSpeechTranscript?: (text: string) => void;
@@ -71,6 +73,7 @@ export default function DevtoolsComposer({
  demoMode = false,
  enhancementEnabled = true,
  autoExtensionEnabled = false,
+  manualContinuationEnabled = false,
  loopGenerationEnabled = false,
  curatedPromptLimit = 0,
  maxCuratedPromptCount = 0,
@@ -86,6 +89,7 @@ export default function DevtoolsComposer({
  onEnhancementToggle = () => {},
  onCuratedPromptLimitChange = () => {},
  onAutoExtensionToggle = () => {},
+  onManualContinuationToggle = () => {},
  onLoopToggle = () => {},
  onLivePromptModeToggle = () => {},
  onSpeechTranscript,
@@ -328,6 +332,28 @@ export default function DevtoolsComposer({
                  </div>
                </div>

+                <div className="flex items-start gap-3">
+                  <Checkbox
+                    id="devtools-steering-mode"
+                    checked={manualContinuationEnabled}
+                    onCheckedChange={(checked) =>
+                      onManualContinuationToggle({
+                        target: { checked: Boolean(checked) },
+                        currentTarget: { checked: Boolean(checked) },
+                      } as React.ChangeEvent<HTMLInputElement>)
+                    }
+                  />
+                  <div className="space-y-1">
+                    <Label htmlFor="devtools-steering-mode">
+                      Steering mode
+                    </Label>
+                    <p className="text-sm text-muted-foreground">
+                      Drive each segment manually — type the next scene to
+                      continue (vs the automatic 6-segment rollout).
+                    </p>
+                  </div>
+                </div>
+
                <div className="flex items-start gap-3">
                  <Checkbox
                    id="devtools-loop-generation"
@@ -21,6 +21,7 @@ interface DevtoolsShellProps {
  selectedPresetId?: string;
  enhancementEnabled?: boolean;
  autoExtensionEnabled?: boolean;
+  manualContinuationEnabled?: boolean;
  loopGenerationEnabled?: boolean;
  canJoinSession?: boolean;
  canSubmitContinuation?: boolean;
@@ -34,6 +35,7 @@ interface DevtoolsShellProps {
  onEnhancementToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onCuratedPromptLimitChange?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onAutoExtensionToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
+  onManualContinuationToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onLoopToggle?: (e: React.ChangeEvent<HTMLInputElement>) => void;
  onJoin?: () => void;
  onLeave?: () => void;
@@ -128,6 +130,7 @@ export default function DevtoolsShell({
  selectedPresetId = '',
  enhancementEnabled = true,
  autoExtensionEnabled = false,
+  manualContinuationEnabled = false,
  loopGenerationEnabled = false,
  canJoinSession = false,
  canSubmitContinuation = false,
@@ -141,6 +144,7 @@ export default function DevtoolsShell({
  onEnhancementToggle = () => {},
  onCuratedPromptLimitChange = () => {},
  onAutoExtensionToggle = () => {},
+  onManualContinuationToggle = () => {},
  onLoopToggle = () => {},
  onJoin = () => {},
  onLeave = () => {},
@@ -284,6 +288,7 @@ export default function DevtoolsShell({
          demoMode={demoMode}
          enhancementEnabled={enhancementEnabled}
          autoExtensionEnabled={autoExtensionEnabled}
+          manualContinuationEnabled={manualContinuationEnabled}
          loopGenerationEnabled={loopGenerationEnabled}
          curatedPromptLimit={curatedPromptLimit}
          maxCuratedPromptCount={maxCuratedPromptCount}
@@ -299,6 +304,7 @@ export default function DevtoolsShell({
          onEnhancementToggle={onEnhancementToggle}
          onCuratedPromptLimitChange={onCuratedPromptLimitChange}
          onAutoExtensionToggle={onAutoExtensionToggle}
+          onManualContinuationToggle={onManualContinuationToggle}
          onLoopToggle={onLoopToggle}
          onLivePromptModeToggle={onLivePromptModeToggle}
          onSpeechTranscript={onSpeechTranscript}
@@ -57,4 +57,32 @@ describe('prependPromptEvent', () => {
    expect(next[0].promptId).toBe('new');
    expect(next.some((item: any) => item.promptId === 'p-23')).toBe(false);
  });
+
+  it('never drops steering scene events when capping', () => {
+    // 30 scenes interleaved with 30 other events — well past the cap.
+    let events: Record<string, any>[] = [];
+    for (let i = 0; i < 30; i += 1) {
+      events = prependPromptEvent(events, {
+        promptId: `scene-${i}`,
+        status: 'submitted',
+        steeringUserPrompt: true,
+        rawText: `scene ${i}`,
+      });
+      events = prependPromptEvent(events, {
+        promptId: `other-${i}`,
+        status: 'submitted',
+      });
+    }
+
+    const scenes = events.filter((e) => e.steeringUserPrompt);
+    expect(scenes).toHaveLength(30);
+    // Oldest-first scene order (and therefore numbering) is stable and complete.
+    expect(scenes[scenes.length - 1].promptId).toBe('scene-0');
+    expect(scenes[0].promptId).toBe('scene-29');
+    // Non-scene events are still capped, oldest dropped first.
+    const others = events.filter((e) => !e.steeringUserPrompt);
+    expect(others.length).toBeLessThanOrEqual(24);
+    expect(others.some((e) => e.promptId === 'other-0')).toBe(false);
+    expect(others[0].promptId).toBe('other-29');
+  });
 });
@@ -16,5 +16,19 @@ export function prependPromptEvent(
  events: Record<string, any>[],
  event: Record<string, any>,
 ): Record<string, any>[] {
-  return [event, ...events].slice(0, MAX_PROMPT_EVENTS);
+  const next = [event, ...events];
+  if (next.length <= MAX_PROMPT_EVENTS) {
+    return next;
+  }
+  // Steering scene events (steeringUserPrompt) are exempt from the cap: the
+  // scene list is derived from them and must stay complete and stably numbered
+  // for long sessions. Only the oldest non-scene events are dropped.
+  let nonSceneKept = 0;
+  return next.filter((e) => {
+    if (e?.steeringUserPrompt) {
+      return true;
+    }
+    nonSceneKept += 1;
+    return nonSceneKept <= MAX_PROMPT_EVENTS;
+  });
 }
@@ -1,6 +1,11 @@
 import { describe, expect, it } from 'vitest';

-import { resolveSessionErrorMessage } from './reducer';
+import { applyNormalizedSocketEvent, resolveSessionErrorMessage } from './reducer';
+import { createSessionStore } from '../../stores/session';
+import { createRewriteStore } from '../../stores/rewrite';
+import { createStreamStore } from '../../stores/stream';
+import { createUiStore } from '../../stores/ui';
+import { createPromptWindowStore } from '../../stores/promptWindow';

 describe('resolveSessionErrorMessage', () => {
  it('returns a dedicated message for IP session limit errors', () => {
@@ -19,3 +24,162 @@ describe('resolveSessionErrorMessage', () => {
    })).toBe('Backend replica unavailable. Rejoin session.');
  });
 });
+
+function buildContext(overrides: Record<string, unknown> = {}) {
+  const sessionStore = createSessionStore();
+  const rewriteStore = createRewriteStore();
+  const streamStore = createStreamStore();
+  const uiStore = createUiStore();
+  const promptWindowStore = createPromptWindowStore();
+  const avPipeline = {
+    reset: () => {},
+    setStreamCompleted: () => {},
+    noteSegmentInit: () => {},
+    noteSegmentComplete: () => {},
+    maybeStartPlayback: () => {},
+    ensurePipeline: async () => {},
+  };
+  return {
+    sessionStore,
+    promptWindowStore,
+    rewriteStore,
+    streamStore,
+    uiStore,
+    avPipeline,
+    tick: async () => {},
+    defaultAvMime: 'video/mp4',
+    fixedRewriteModel: 'model',
+    parseLatencyMs: () => null,
+    formatPromptWindowEventText: () => '',
+    makePromptId: () => 'generated-id',
+    buildClipLabel: () => 'clip',
+    startSessionCountdown: () => {},
+    clearCountdownInterval: () => {},
+    resetTtffTimer: () => {},
+    startTtffTimer: () => {},
+    preserveArchivedPlaybackSelection: false,
+    finalizeStreamCompletion: async () => {},
+    ...overrides,
+  };
+}
+
+describe('steering generatingNextScene flow', () => {
+  it('sets generatingNextScene on prompt/sources_resumed in manual mode', async () => {
+    const context = buildContext();
+    await applyNormalizedSocketEvent(
+      { type: 'prompt/sources_resumed', payload: { segment_idx: 2 } },
+      context,
+    );
+    expect(context.sessionStore.get().generatingNextScene).toBe(true);
+    expect(context.sessionStore.get().waitingForSegmentPrompt).toBe(false);
+  });
+
+  it('does NOT set generatingNextScene on session/auto_extension_updated', async () => {
+    const context = buildContext();
+    await applyNormalizedSocketEvent(
+      { type: 'session/auto_extension_updated', payload: { enabled: true } },
+      context,
+    );
+    expect(context.sessionStore.get().generatingNextScene).toBe(false);
+    expect(context.sessionStore.get().waitingForSegmentPrompt).toBe(false);
+  });
+
+  it('clears generatingNextScene when segment media arrives', async () => {
+    const context = buildContext();
+    context.sessionStore.patch({ generatingNextScene: true });
+    await applyNormalizedSocketEvent(
+      {
+        type: 'stream/media_init',
+        payload: { segment_idx: 2, stream_id: 's', mime: 'video/mp4' },
+      },
+      context,
+    );
+    expect(context.sessionStore.get().generatingNextScene).toBe(false);
+  });
+
+  it('clears generatingNextScene and returns to waiting on prompt/sources_blocked', async () => {
+    const context = buildContext();
+    context.sessionStore.patch({ generatingNextScene: true });
+    await applyNormalizedSocketEvent(
+      { type: 'prompt/sources_blocked', payload: { segment_idx: 3 } },
+      context,
+    );
+    expect(context.sessionStore.get().generatingNextScene).toBe(false);
+    expect(context.sessionStore.get().waitingForSegmentPrompt).toBe(true);
+  });
+
+  it('clears generatingNextScene when the opening prompt falls back', async () => {
+    const context = buildContext();
+    context.sessionStore.patch({ generatingNextScene: true });
+    await applyNormalizedSocketEvent(
+      {
+        type: 'prompt/fallback_used',
+        payload: { prompt_id: 'p1', prompt: '', source: 'user_enhancement_failed' },
+      },
+      context,
+    );
+    expect(context.sessionStore.get().generatingNextScene).toBe(false);
+    expect(context.sessionStore.get().waitingForSegmentPrompt).toBe(true);
+  });
+});
+
+describe('opening prompt id tracking', () => {
+  it('routes prompt lifecycle updates to the frontend-recorded opening event', async () => {
+    // The frontend records the opening scene under its own prompt id and sends it
+    // as initial_rollout_prompt_id; the backend echoes it in status updates.
+    const context = buildContext();
+    context.rewriteStore.addPromptEvent({
+      promptId: 'opening-id',
+      status: 'rewrite_requested',
+      source: 'user_rewrite',
+      text: 'a castle at dawn',
+      steeringUserPrompt: true,
+      rawText: 'a castle at dawn',
+    });
+
+    await applyNormalizedSocketEvent(
+      { type: 'prompt/enhancing', payload: { prompt_id: 'opening-id' } },
+      context,
+    );
+    let opening = (context.rewriteStore.get().promptEvents as Record<string, any>[])
+      .find((e) => e.promptId === 'opening-id');
+    expect(opening?.status).toBe('enhancing');
+
+    await applyNormalizedSocketEvent(
+      {
+        type: 'prompt/fallback_used',
+        payload: { prompt_id: 'opening-id', prompt: '', source: 'user_enhancement_failed' },
+      },
+      context,
+    );
+    opening = (context.rewriteStore.get().promptEvents as Record<string, any>[])
+      .find((e) => e.promptId === 'opening-id');
+    expect(opening?.status).toBe('ready_fallback');
+    // A failed opening is dropped from the steering scene list instead of
+    // lingering as a ghost "Scene 1".
+    expect(opening?.steeringFailed).toBe(true);
+  });
+
+  it('marks a prompt-scoped session/error (e.g. safety block) as steeringFailed', async () => {
+    const context = buildContext();
+    context.rewriteStore.addPromptEvent({
+      promptId: 'blocked-id',
+      status: 'queued',
+      source: 'user_raw',
+      text: 'a blocked prompt',
+      steeringUserPrompt: true,
+      rawText: 'a blocked prompt',
+    });
+
+    await applyNormalizedSocketEvent(
+      {
+        type: 'session/error',
+        payload: { message: 'Prompt blocked by safety filter.', prompt_id: 'blocked-id' },
+      },
+      context,
+    );
+    const blocked = (context.rewriteStore.get().promptEvents as Record<string, any>[])
+      .find((e) => e.promptId === 'blocked-id');
+    expect(blocked?.steeringFailed).toBe(true);
+  });
+});
@@ -99,7 +99,19 @@ export async function applyNormalizedSocketEvent(event: any, context: any): Prom
 				status: "ready_fallback",
 				source: payload.source || "user_raw",
 				text: payload.prompt,
+				// Steering: this prompt produced no segment — drop it from the scene list.
+				steeringFailed: true,
 			});
+			// Steering recovery: enhancement failed so the backend enqueued nothing AND won't
+			// re-emit prompt_sources_blocked (its drained flag is still set). Put the user back to
+			// "describe the next scene" ourselves so the generating overlay clears and they can retry.
+			if (!uiStore.get().simpleMode && sessionStore.get().manualContinuationMode) {
+				sessionStore.patch({
+					waitingForSegmentPrompt: true,
+					generatingNextScene: false,
+					sessionNotice: "Couldn't continue from that prompt — try rephrasing the next scene.",
+				});
+			}
 			console.warn("[PromptEnhanceFallback] Prompt extension failed for this request.");
 			return;

@@ -243,19 +255,32 @@ export async function applyNormalizedSocketEvent(event: any, context: any): Prom
 		}

 		case "prompt/sources_blocked":
-			sessionStore.patch({
-				autoExtensionTimeoutHint: uiStore.get().simpleMode ? "" : "blocked on user input, increase prompt count for smoother experience",
-			});
+			if (sessionStore.get().manualContinuationMode) {
+				sessionStore.patch({ waitingForSegmentPrompt: true, generatingNextScene: false, autoExtensionTimeoutHint: "" });
+			} else {
+				sessionStore.patch({
+					autoExtensionTimeoutHint: uiStore.get().simpleMode ? "" : "blocked on user input, increase prompt count for smoother experience",
+				});
+			}
 			return;

 		case "prompt/sources_resumed":
+			sessionStore.patch({
+				autoExtensionTimeoutHint: "",
+				waitingForSegmentPrompt: false,
+				// A real prompt was just selected for the next segment; media arriving
+				// (stream/media_init) clears this again.
+				...(sessionStore.get().manualContinuationMode ? { generatingNextScene: true } : {}),
+			});
+			return;
+
 		case "session/auto_extension_updated":
-			sessionStore.patch({ autoExtensionTimeoutHint: "" });
-			if (event.type === "session/auto_extension_updated") {
-				console.log("[AutoExtensionUpdated]", {
-					enabled: sessionStore.get().autoExtensionEnabled,
-				});
-			}
+			// Deliberately does NOT touch generatingNextScene: toggling auto extension
+			// starts no generation.
+			sessionStore.patch({ autoExtensionTimeoutHint: "", waitingForSegmentPrompt: false });
+			console.log("[AutoExtensionUpdated]", {
+				enabled: sessionStore.get().autoExtensionEnabled,
+			});
 			return;

 		case "segment/step_complete":
@@ -277,6 +302,7 @@ export async function applyNormalizedSocketEvent(event: any, context: any): Prom
 				projectResetPending: false,
 				sessionExpired: true,
 				sessionNotice: "",
+				generatingNextScene: false,
 			});
 			console.log("Session timed out");
 			clearCountdownInterval();
@@ -354,6 +380,8 @@ export async function applyNormalizedSocketEvent(event: any, context: any): Prom
 			return;

 		case "stream/media_init":
+			// Segment media is arriving — the "Generating next scene" phase is over.
+			sessionStore.patch({ generatingNextScene: false });
 			streamStore.patch({
 				mediaAppendError: null,
 				loadingAnimation: streamStore.get().avPlaybackStarted ? streamStore.get().loadingAnimation : true,
@@ -471,17 +499,30 @@ export async function applyNormalizedSocketEvent(event: any, context: any): Prom
 			sessionStore.patch({
 				generationCapReached: false,
 				sessionNotice: "",
+				generatingNextScene: false,
 			});
 			await finalizeStreamCompletion();
 			return;

 		case "session/error": {
 			const errorMessage = resolveSessionErrorMessage(payload);
+			if (payload?.prompt_id) {
+				// Prompt-scoped error (e.g. safety-blocked): the prompt produced no
+				// segment, so drop it from the steering scene list.
+				rewriteStore.trackPromptEvent(payload.prompt_id, {
+					steeringFailed: true,
+				});
+			}
 			sessionStore.patch({
 				generationCapReached: false,
 				preservePlaybackOnClose: false,
 				promptExtensionError: "",
 				sessionNotice: errorMessage,
+				// Steering: a blocked/failed prompt produced no segment and the backend won't re-emit
+				// prompt_sources_blocked, so recover the "describe the next scene" state ourselves.
+				...(!uiStore.get().simpleMode && sessionStore.get().manualContinuationMode
+					? { waitingForSegmentPrompt: true, generatingNextScene: false }
+					: {}),
 			});
 			rewriteStore.patch({
 				rewritingSeedPrompts: false,
@@ -24,6 +24,9 @@ export interface SessionState {
  livePromptRewriteMode: boolean;
  sessionExpired: boolean;
  projectResetPending: boolean;
+  manualContinuationMode: boolean;
+  waitingForSegmentPrompt: boolean;
+  generatingNextScene: boolean;
 }

 const DEFAULT_SESSION_STATE: SessionState = {
@@ -49,6 +52,12 @@ const DEFAULT_SESSION_STATE: SessionState = {
  livePromptRewriteMode: false,
  sessionExpired: false,
  projectResetPending: false,
+  // Steering-only product: every session drives scenes manually. There is no
+  // auto-rollout mode and no UI selector, so this stays true throughout.
+  manualContinuationMode: true,
+  waitingForSegmentPrompt: false,
+  // True from scene submit / prompt selection until the segment's media arrives.
+  generatingNextScene: false,
 };

 export type SessionStore = ManagedStore<SessionState> & {
@@ -1,5 +1,18 @@
 import { createManagedStore, type ManagedStore } from "./createManagedStore";

+// Prompt-history sources driven by the user's own submissions. Curated (non-user)
+// entries — e.g. a preset's opening scene — feed the steering scene list and are
+// exempt from the history cap so Scene 1 survives long sessions.
+export const USER_PROMPT_SOURCES = new Set([
+	"user_raw",
+	"user",
+	"user_enhanced",
+	"user_rewrite",
+	"user_enhancement_failed",
+]);
+
+const PROMPT_HISTORY_CAP = 120;
+
 export interface StreamState {
 	playingSeedPromptIndex: number | null;
 	generatingSeedPromptIndex: number | null;
@@ -159,10 +172,14 @@ export function createStreamStore(initialState: Partial<StreamState> = {}): Stre
 					loopIteration: typeof loopIteration === "number" ? loopIteration : null,
 				};

+				const nextHistory = [entry, ...state.promptHistory];
 				return {
 					...state,
 					promptHistoryCounter: nextCounter,
-					promptHistory: [entry, ...state.promptHistory].slice(0, 120),
+					promptHistory: nextHistory.length > PROMPT_HISTORY_CAP
+						? nextHistory.filter((item, index) =>
+							index < PROMPT_HISTORY_CAP || !USER_PROMPT_SOURCES.has(String(item.source || "")))
+						: nextHistory,
 					selectedHistoryId: state.selectedHistoryId || (entry.id as string),
 				};
 			});
@@ -12,13 +12,17 @@ Defaults:

 - `HF_REPO_ID=FastVideo/performance-tracking`
 - `PERFORMANCE_TRACKING_ROOT=/tmp/fastvideo-perf-dashboard`
- `PERF_MAX_REGRESSION=0.05`

-Records can include source metadata:
+Records can include source metadata and rolling-baseline policy context:

 - `run_source`: `pr`, `local`, `scheduled_main`, or `unknown`
 - `baseline_eligible`: only successful scheduled-main records should be true
 - Buildkite metadata such as branch, PR number, build URL, build ID, and job ID
+- `regression_thresholds`: per-metric rolling-baseline percent and absolute
+  floors used for recomputed status context
+
+Dashboard/API metric payloads expose `threshold_exceeded` for raw threshold
+crossings; `regressed` remains the gated CI-failure signal.

 Set one of `HF_API_KEY`, `HUGGINGFACE_HUB_TOKEN`, or `HF_TOKEN` if the
 configured dataset repo requires authenticated access:
@@ -89,7 +93,8 @@ Trend charts show metric-specific axes and exact point details on hover/focus:
 - PR number, branch, and Buildkite URL when present

 The latest status table uses the stored JSON `success` value. Recomputed
-baseline context is shown separately and does not override stored status.
+baseline context applies each metric's percent and absolute regression floors
+and does not override stored status.

 ## API

@@ -99,6 +104,11 @@ baseline context is shown separately and does not override stored status.
 - `GET /api/performance/trends?days=90&run_source=scheduled_main`
 - `GET /api/performance/records?days=90&run_source=local`

-The current v1 grouping key is `(model_id, gpu_type)`. Baselines are computed
-from the latest five previous successful records in each group for dashboard
-context. CI gating uses only records marked `baseline_eligible=true`.
+V2 records use the same comparison cohort as CI: `workload_id`, `variant_id`,
+`benchmark_version`, `recipe_fingerprint`, `hardware_profile_id`, and
+`software_profile_id`. `model_id` and `gpu_type` remain display/filter
+metadata, so renaming either does not split history. Legacy records still group
+by `(model_id, gpu_type)`. Dashboard baselines use the latest five previous
+successful, baseline-eligible records in each group. Summary and trend filters
+match the latest display metadata after grouping, while the raw records endpoint
+continues to filter individual records.
@@ -1,6 +1,7 @@
 import { useEffect, useMemo, useState } from "react";

-import { fetchSummary, fetchTrends, refreshData, RunSource, SummaryResponse, TrendGroup, TrendPoint } from "./api";
+import { fetchSummary, fetchTrends, refreshData } from "./api";
+import type { CohortValue, RunSource, SummaryResponse, TrendGroup, TrendPoint } from "./api";

 const METRIC_KEYS = ["latency", "throughput", "memory", "text_encoder_time_s", "dit_time_s", "vae_decode_time_s"];
 const RUN_SOURCES: Array<{ value: "" | RunSource; label: string }> = [
@@ -109,6 +110,61 @@ function metricLabel(metricKey: string) {
  return METRIC_DEFINITIONS[metricKey]?.label ?? metricKey;
 }

+type CohortFields = {
+  model_id: string;
+  gpu_type: string;
+  workload_id: CohortValue;
+  variant_id: CohortValue;
+  benchmark_version: CohortValue;
+  recipe_fingerprint: CohortValue;
+  hardware_profile_id: CohortValue;
+  software_profile_id: CohortValue;
+};
+
+function cohortValue(value: CohortValue) {
+  if (value === null || value === undefined || value === "") {
+    return "legacy";
+  }
+  return String(value);
+}
+
+function shortCohortValue(value: CohortValue) {
+  const text = cohortValue(value);
+  if (text === "legacy" || text.length <= 14) {
+    return text;
+  }
+  return text.slice(0, 12);
+}
+
+function cohortKey(cohort: CohortFields) {
+  return [
+    cohort.model_id,
+    cohort.gpu_type,
+    cohortValue(cohort.workload_id),
+    cohortValue(cohort.variant_id),
+    cohortValue(cohort.benchmark_version),
+    cohortValue(cohort.recipe_fingerprint),
+    cohortValue(cohort.hardware_profile_id),
+    cohortValue(cohort.software_profile_id)
+  ].join("|");
+}
+
+function cohortTitle(cohort: CohortFields) {
+  const workload = cohortValue(cohort.workload_id);
+  const variant = cohortValue(cohort.variant_id);
+  const version = cohortValue(cohort.benchmark_version);
+  const versionLabel = version === "legacy" ? version : `v${version}`;
+  return `${workload} / ${variant} / ${versionLabel}`;
+}
+
+function cohortDetail(cohort: CohortFields) {
+  return [
+    `recipe ${shortCohortValue(cohort.recipe_fingerprint)}`,
+    shortCohortValue(cohort.hardware_profile_id),
+    shortCohortValue(cohort.software_profile_id)
+  ].join(" | ");
+}
+
 function formatMetricValue(metricKey: string, value: number | null | undefined, tooltip = false) {
  const definition = METRIC_DEFINITIONS[metricKey];
  if (!definition) {
@@ -171,7 +227,9 @@ function TrendChart({ group, metricKey }: { group: TrendGroup; metricKey: string
        top: `${(activePoint.y / height) * 100}%`
      }
    : undefined;
-  const ariaLabel = `${metricLabel(metricKey)} trend for ${group.model_id} on ${group.gpu_type}`;
+  const ariaLabel = `${metricLabel(metricKey)} trend for ${group.model_id} on ${group.gpu_type}, ${cohortTitle(
+    group
+  )}`;

  return (
    <div className="chart-shell">
@@ -419,7 +477,7 @@ export default function App() {
      <section className="panel">
        <div className="panel-header">
          <h2>Latest Status</h2>
-          <span>{latestRows.length} model/GPU groups</span>
+          <span>{latestRows.length} comparison cohorts</span>
        </div>
        {latestRows.length === 0 ? (
          <div className="empty">No records match the selected filters.</div>
@@ -432,6 +490,7 @@ export default function App() {
                  <th>Recomputed</th>
                  <th>Model</th>
                  <th>GPU</th>
+                  <th>Cohort</th>
                  <th>Commit</th>
                  <th>Source</th>
                  <th>Baseline</th>
@@ -440,11 +499,13 @@ export default function App() {
                  <th>Throughput</th>
                  <th>Memory</th>
                  <th>Worst</th>
+                  <th>Exceeded</th>
+                  <th>Failing</th>
                </tr>
              </thead>
              <tbody>
                {latestRows.map((row) => (
-                  <tr key={`${row.model_id}-${row.gpu_type}`}>
+                  <tr key={cohortKey(row)}>
                    <td>
                      <span className={`badge ${row.status}`}>{row.status}</span>
                    </td>
@@ -455,6 +516,12 @@ export default function App() {
                    </td>
                    <td>{row.model_id}</td>
                    <td>{row.gpu_type}</td>
+                    <td>
+                      <div className="cohort-cell">
+                        <strong>{cohortTitle(row)}</strong>
+                        <span>{cohortDetail(row)}</span>
+                      </div>
+                    </td>
                    <td>{shortSha(row.commit_sha)}</td>
                    <td>
                      <span className={`source-badge source-${row.run_source}`}>{runSourceLabel(row.run_source)}</span>
@@ -465,6 +532,12 @@ export default function App() {
                    <td>{formatNumber(row.metrics.throughput?.current, 3)}</td>
                    <td>{formatNumber(row.metrics.memory?.current, 1)}</td>
                    <td>{formatNumber(row.worst_regression_pct, 1)}%</td>
+                    <td>
+                      {row.threshold_exceeded_metrics.length
+                        ? row.threshold_exceeded_metrics.join(", ")
+                        : "none"}
+                    </td>
+                    <td>{row.failing_metrics.length ? row.failing_metrics.join(", ") : "none"}</td>
                  </tr>
                ))}
              </tbody>
@@ -487,11 +560,13 @@ export default function App() {
          ) : (
            trends.map((group) =>
              METRIC_KEYS.map((metricKey) => (
-                <article className="trend-card" key={`${group.model_id}-${group.gpu_type}-${metricKey}`}>
+                <article className="trend-card" key={`${cohortKey(group)}-${metricKey}`}>
                  <div>
                    <h3>{metricLabel(metricKey)}</h3>
                    <p>
                      {group.model_id} | {group.gpu_type}
+                      <span>{cohortTitle(group)}</span>
+                      <span>{cohortDetail(group)}</span>
                    </p>
                  </div>
                  <TrendChart group={group} metricKey={metricKey} />
@@ -2,11 +2,28 @@ export type MetricValue = {
  current: number | null;
  baseline: number | null;
  regression_pct: number | null;
+  absolute_delta: number | null;
+  threshold_percent: number;
+  threshold_absolute: number;
+  gated: boolean;
+  threshold_exceeded: boolean;
+  regressed: boolean;
  label: string;
  lower_is_better: boolean;
  precision: number;
 };

+export type CohortValue = string | number | null;
+
+export type ComparisonCohort = {
+  workload_id: CohortValue;
+  variant_id: CohortValue;
+  benchmark_version: CohortValue;
+  recipe_fingerprint: CohortValue;
+  hardware_profile_id: CohortValue;
+  software_profile_id: CohortValue;
+};
+
 export type SummaryRow = {
  model_id: string;
  gpu_type: string;
@@ -15,7 +32,8 @@ export type SummaryRow = {
  success: boolean;
  baseline_n: number;
  worst_regression_pct: number | null;
-  regression_threshold_pct: number;
+  threshold_exceeded_metrics: string[];
+  failing_metrics: string[];
  computed_regression_status: "pass" | "fail";
  status: "pass" | "fail";
  run_source: RunSource;
@@ -27,7 +45,7 @@ export type SummaryRow = {
  build_id: string;
  job_id: string;
  metrics: Record<string, MetricValue>;
-};
+} & ComparisonCohort;

 export type RunSource = "pr" | "local" | "scheduled_main" | "unknown";

@@ -61,13 +79,13 @@ export type TrendPoint = {
  build_id: string;
  job_id: string;
  metrics: Record<string, number | null>;
-};
+} & ComparisonCohort;

 export type TrendGroup = {
  model_id: string;
  gpu_type: string;
  points: TrendPoint[];
-};
+} & ComparisonCohort;

 export type TrendsResponse = {
  groups: TrendGroup[];
@@ -149,6 +149,11 @@ h3 {
  font-size: 0.82rem;
 }

+.trend-card p {
+  display: grid;
+  gap: 2px;
+}
+
 .stat strong {
  display: block;
  margin-top: 8px;
@@ -186,7 +191,7 @@ h3 {

 table {
  width: 100%;
-  min-width: 1120px;
+  min-width: 1260px;
  border-collapse: collapse;
 }

@@ -209,6 +214,25 @@ td {
  font-size: 0.9rem;
 }

+.cohort-cell {
+  display: grid;
+  gap: 2px;
+}
+
+.cohort-cell strong,
+.trend-card p span {
+  color: #1b2836;
+  font-size: 0.78rem;
+  font-weight: 700;
+}
+
+.cohort-cell span,
+.trend-card p span + span {
+  color: #607080;
+  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", monospace;
+  font-size: 0.72rem;
+}
+
 .badge {
  display: inline-flex;
  align-items: center;
@@ -0,0 +1,22 @@
+{
+  "alpha_yaw": 0.08734091699186919,
+  "alpha_pitch": 0.08169667696275307,
+  "alpha_turn": 5.724587470723463e-17,
+  "beta_fwd": 0.02842768078408099,
+  "beta_strafe": 0.022531015077067108,
+  "focal_length": 457.0,
+  "frame_shape": [
+    352,
+    640
+  ],
+  "calibrated_from": [
+    "1_wasd_only",
+    "camera",
+    "camera4hold_alpha1",
+    "fully_random",
+    "wasdonly_alpha1",
+    "wasd4holdrandview_simple_1key1mouse1"
+  ],
+  "residual_rms": 15.890399609478676,
+  "n_equations": 4125232
+}
@@ -68,7 +68,7 @@ ARG FLASH_ATTN_WHEEL_RELEASE_ARM64=https://github.com/mjun0812/flash-attention-p
 # cutlass-4.4 `cute.core.ThrMma` API, which crashes on the cutlass-dsl 4.5 that
 # flashinfer/quack pull in. After the wheel install we overlay this cutlass-4.5-safe
 # upstream cute (flash-attn-4) so the image runs FA4 instead of the FA2 fallback.
-ARG FA4_CUTE_REF=940cd9680f3315f2f06b43ab5bea2c2cf2d96806
+ARG FA4_CUTE_REF=82d6441eec5d4dfec120153db2c0145ae855a083

 # Provided automatically by BuildKit/buildx (e.g. "amd64" / "arm64") and used to
 # select the prebuilt flash-attn wheel. Empty under a plain `docker build` without
@@ -161,7 +161,7 @@ RUN --mount=type=cache,target=/opt/uv/cache \
        uv pip install flash-attn==${FLASH_ATTN_VERSION} --no-build-isolation; \
    fi

-# Overlay the cutlass-4.5-safe upstream FA4 cute (FA4_CUTE_REF) over the
+# Overlay the CuTe-DSL-4.6-compatible upstream FA4 cute (FA4_CUTE_REF) over the
 # wheel/source one so the image runs FA4, not the FA2 fallback. This pulls the FA4
 # runtime stack (cutlass-dsl, quack-kernels, apache-tvm-ffi, torch-c-dlpack-ext) --
 # the same deps the [dreamverse] extra already installs in CI; the installed torch
@@ -170,12 +170,14 @@ RUN --mount=type=cache,target=/opt/uv/cache \
 # rmtree clears the wheel's stale cute files first to avoid an install conflict.
 # Then verify both survive so a broken overlay fails the build instead of shipping
 # an FA2-less image. x86 only: the FA4 stack (quack-kernels etc.) is unvalidated on
-# arm64 / GB10 (sm_121), so there we skip the overlay and FA4 falls back to FA2.
+# arm64 / GB10 (sm_121), so there we skip the overlay; FA4 is opt-in
+# (FASTVIDEO_FA4=1) and errors if set without the overlay, so leave it unset on
+# arm64 and the image runs FA3/FA2 as usual.
 RUN --mount=type=cache,target=/opt/uv/cache \
    source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    if [ "${TARGETARCH}" = "arm64" ]; then \
-        echo "Skipping FA4 cute overlay on arm64 (FA4 stack unvalidated there; FA4 falls back to FA2)"; \
+        echo "Skipping FA4 cute overlay on arm64 (FA4 stack unvalidated there; do not set FASTVIDEO_FA4)"; \
    else \
        python -c "import glob, shutil; [shutil.rmtree(d, ignore_errors=True) for d in glob.glob('/opt/venv/lib/python*/site-packages/flash_attn/cute')]" && \
        uv pip install "flash-attn-4 @ git+https://github.com/Dao-AILab/flash-attention.git@${FA4_CUTE_REF}#subdirectory=flash_attn/cute" && \
@@ -99,10 +99,18 @@ status.
 Full Suite is also path-filtered. It validates broader behavior before Mergify
 can merge a PR.

+A `ready`-labeled PR does not hit Buildkite immediately:
+`ci-trigger-full-suite.yml` first runs `.github/scripts/gate_full_suite.sh`,
+which waits for the cheap Tier-1 checks (pre-commit, docs build) on the PR
+head. A red cheap check blocks the suite (fail closed; the next push re-arms
+it), while a GitHub outage or a >25 min wait lets it run anyway (fail open).
+`/test full` bypasses the gate.
+
 | Buildkite label | `TEST_TYPE` | Main watched paths |
 |---|---|---|
 | SSIM Tests | `ssim` | `fastvideo/**/*.py`, `pyproject.toml`, `docker/Dockerfile` |
 | LoRA Inference Tests | `inference_lora` | LoRA tests, loader, transformer tests, pipelines, LoRA layers |
+| LoRA Extraction Tests | `lora_extraction` | LoRA extraction scripts/tests, loader, training utilities, LoRA layers |
 | Training Tests | `training` | `fastvideo/**`, `pyproject.toml`, `docker/Dockerfile` |
 | Distillation DMD Tests | `distillation_dmd` | `fastvideo/training/*distillation_pipeline.py` |
 | Self-Forcing Tests | `self_forcing` | self-forcing distillation pipeline and tests |
@@ -144,6 +152,7 @@ Valid direct test names:
 | `/test training` | `training` |
 | `/test lora-inference` | `inference_lora` |
 | `/test lora-training` | `training_lora` |
+| `/test lora-extraction` | `lora_extraction` |
 | `/test distillation` | `distillation_dmd` |
 | `/test self-forcing` | `self_forcing` |
 | `/test vsa` | `training_vsa` |
@@ -12,7 +12,8 @@ It serves three audiences:
 * **Maintainers** — surfaces regressions in a Markdown summary on every
  performance build and a long-form Plotly dashboard.
 * **Local developers** — lets you run the same benchmark on your own machine,
-  then compare against the historical baseline for the same model and GPU.
+  then compare against the historical baseline for the same comparable
+  identity.

 ## Quick start (local)

@@ -72,14 +73,18 @@ fastvideo/tests/performance/
    │           writes Markdown summary + (optionally) uploads new records
    ├── dashboard.py
    │       └── builds time-series Plotly HTML from HF history
-    └── hf_store.py               # shared HF I/O + DataFrame helpers
+
+fastvideo/performance/
+    ├── hf_store.py               # shared HF I/O + DataFrame helpers
+    └── metric_policy.py          # shared rolling-baseline threshold policy
 ```

 The HF dataset (`FastVideo/performance-tracking` by default) holds one
-normalized JSON per `(model_id, gpu_type, run)` tuple. The rolling baseline is
-the median of the last 5 successful, baseline-eligible records for that
-model+GPU. PR and local records are visible in the dashboard but are not
-baseline eligible.
+normalized JSON per run. For v2 records, the rolling baseline is the median of
+the last 5 successful, baseline-eligible records in the same comparison cohort:
+`workload_id`, `variant_id`, `benchmark_version`, `recipe_fingerprint`,
+`hardware_profile_id`, and `software_profile_id`. PR and local records are
+visible in the dashboard but are not baseline eligible.

 ## Planned Coverage

@@ -92,25 +97,28 @@ and recipe changes instead of treating all records for a model as equivalent.

 ## Metrics

-Each benchmark records six metrics:
+Each benchmark records six metrics. The rolling-baseline comparator also has a
+per-metric policy with direction, percent threshold, absolute threshold, and a
+`gated` flag.

-| Metric | Raw key | Normalized key | Direction |
-|---|---|---|---|
-| End-to-end generation latency | `avg_generation_time_s` | `latency` | Lower is better |
-| Video throughput | `throughput_fps` | `throughput` | Higher is better |
-| Peak GPU memory | `max_peak_memory_mb` | `memory` | Lower is better |
-| Text encoder time | `text_encoder_time_s` | `text_encoder_time_s` | Lower is better |
-| DiT denoising time | `dit_time_s` | `dit_time_s` | Lower is better |
-| VAE decode time | `vae_decode_time_s` | `vae_decode_time_s` | Lower is better |
+| Metric | Raw key | Normalized key | Direction | Default rolling policy |
+|---|---|---|---|---|
+| End-to-end generation latency | `avg_generation_time_s` | `latency` | Lower is better | 8% and 0.5 s |
+| Video throughput | `throughput_fps` | `throughput` | Higher is better | 8% and 0.05 FPS |
+| Peak GPU memory | `max_peak_memory_mb` | `memory` | Lower is better | 5% and 256 MB |
+| Text encoder time | `text_encoder_time_s` | `text_encoder_time_s` | Lower is better | 5% and 0.25 s |
+| DiT denoising time | `dit_time_s` | `dit_time_s` | Lower is better | 5% and 0.25 s |
+| VAE decode time | `vae_decode_time_s` | `vae_decode_time_s` | Lower is better | 5% and 0.25 s |

 `test_inference_performance.py` temporarily sets `FASTVIDEO_STAGE_LOGGING=1`
 while it runs so pipeline stage execution times are available in
 `generate_video(...).logging_info`. Stage logs use pipeline-unique keys such as
 `prompt_encoding_stage` so duplicate stage classes do not collide. For
-`PipelineStage` entries, the extractor maps the `stage_class` field:
-`TextEncodingStage` maps to `text_encoder_time_s`, `DenoisingStage` and
-`DmdDenoisingStage` map to `dit_time_s`, and `DecodingStage` maps to
-`vae_decode_time_s`, with a fallback for older logs that used the class name as
+`PipelineStage` entries, shared component stage bases emit a stable
+`component_metric`: text encoding stages map to `text_encoder_time_s`,
+denoising stages and subclasses map to `dit_time_s`, and decoding stages map to
+`vae_decode_time_s`. The extractor falls back to known `stage_class` names for
+older logs that do not include `component_metric` or that used the class name as
 the stage key. Generator-side timings such as `PostDecodeFrameProcessStage`,
 `VideoSaveStage`, and `AudioMuxStage` are intentionally ignored. If a pipeline
 does not report one of the mapped stages, that component metric is stored as
@@ -152,26 +160,115 @@ unrealistic memory growth, and optionally large component-specific slowdowns
 even when the rolling baseline is empty. They are hand-set with generous
 headroom and almost never need touching.

-### Rolling baseline (per `(model_id, gpu_type)`)
+### Rolling baseline (per comparison cohort)

 `compare_baseline.py` loads the last 5 successful, baseline-eligible records
-for the same `(model_id, gpu_type)` from the HF dataset, computes the median
-for each available metric, and fails if the current run regresses by more than
-`PERF_MAX_REGRESSION` (default 5%). For latency, memory, and component times,
-higher values are regressions. For throughput, lower values are regressions.
+for the same comparison cohort from the HF dataset, computes the median for
+each available metric, and evaluates the current run with the metric's rolling
+regression policy. For v2 records, that cohort is `workload_id`, `variant_id`,
+`benchmark_version`, `recipe_fingerprint`, `hardware_profile_id`, and
+`software_profile_id`. For latency, memory, and component times, higher values
+are regressions. For throughput, lower values are regressions.
+
+A metric exceeds its rolling threshold when both of these are true:
+
+```text
+percent_delta > threshold_percent
+absolute_delta > threshold_absolute
+```
+
+Gated metrics fail CI when that threshold crossing happens. Set `gated: false`
+for metrics that should remain visible in reports and the dashboard without
+failing CI. Dashboard/API payloads expose `threshold_exceeded` separately from
+`regressed`, where `regressed` means a gated CI failure. Missing or `null`
+metrics are skipped.

 This is the **drift detector** — it catches sub-threshold regressions that
 slowly add up. Only scheduled-main successful records are baseline eligible.
 Local and pull-request runs can upload dashboard-visible records, but they do
 not update future gating baselines.

+Comparator summaries and normalized artifacts include an explicit
+`comparison_status`:
+
+| Status | Meaning | CI behavior |
+|---|---|---|
+| `PASS` | Comparable baseline exists and no gated metric regressed. Legacy records with no baseline also keep the historical initialization behavior. | Passes |
+| `REGRESSION` | The record exceeds one of its static thresholds or at least one gated metric regressed against a comparable baseline. | Fails |
+| `CALIBRATION_NEEDED` | A v2 record has no exact comparable baseline. | Passes, may upload when `PERF_UPLOAD_POLICY=pass`, never seeds a baseline |
+| `RECIPE_MISMATCH` | The same workload, variant, and benchmark version has trusted successful records under another recipe fingerprint, including records from other hardware or software profiles. | Fails |
+| `INFRA_ERROR` | The comparator cannot safely classify the record, such as a v2 record missing required identity fields. | Fails |
+
+`QUALITY_BLOCKED` is reserved for a future variant-promotion workflow and is
+not emitted by normal rolling-baseline comparison.
+
+For `RECIPE_MISMATCH`, trusted records are scheduled-main records or records
+already marked `baseline_eligible`. PR and local calibration uploads remain
+visible but do not authorize future CI-gating recipe mismatch failures.
+
 When the baseline shifts for a legitimate reason (torch upgrade, kernel
 change, etc.) and CI starts failing, use the
 [`reseed-performance-baseline`](https://github.com/hao-ai-lab/FastVideo/blob/main/.agents/skills/reseed-performance-baseline/SKILL.md)
-agent skill to advance the rolling median.
+agent skill to advance the rolling median. To approve the first baseline for
+a new v2 exact identity, follow that skill with a reviewed scheduled-main
+full-suite `CALIBRATION_NEEDED` normalized artifact. Its prepare step uses
+`fastvideo/tests/performance/seed_baseline.py`; after a separate human gate,
+the skill rechecks the current HF revision and conditionally uploads the whole
+seed batch in one commit.

 ## Schemas

+### Benchmark config (`.buildkite/performance-benchmarks/tests/*.json`)
+
+Benchmark configs without `config_schema_version` are treated as legacy v1
+configs and remain loadable. New or migrated configs should use
+`config_schema_version: 2` and include explicit comparable identity fields:
+
+```jsonc
+{
+  "benchmark_id": "wan-t2v-1.3b-2gpu",
+  "config_schema_version": 2,
+  "workload_id": "wan-t2v",
+  "variant_id": "1.3b-sp2",
+  "benchmark_version": 3
+}
+```
+
+`benchmark_id` is still required because raw artifact names, generated-video
+directories, normalized record paths, and legacy storage directories depend on
+it. The v2 comparator does not use it as part of the comparison cohort. The v2
+identity fields make the measured workload explicit:
+
+| Field | Purpose |
+|---|---|
+| `workload_id` | Stable benchmark family, such as `wan-t2v`. |
+| `variant_id` | Intentional recipe family, including model size and parallelism config, such as `1.3b-sp2`. |
+| `benchmark_version` | Version of the measurement protocol and comparison policy. |
+
+If a config declares `config_schema_version: 2`, loading fails clearly when any
+required v2 identity field is missing. If v2 identity or metadata fields are
+added without `config_schema_version: 2`, loading also fails so partial
+migrations do not silently run as v1 configs. Optional v2 `quality_metadata`
+and the v1/v2 `regression_thresholds` policy must be JSON objects when present.
+(`recipe` is emitted by the harness and is not config-declarable.)
+
+V2 records compare only within their exact identity cohort. A record that opens
+a new cohort is marked `baseline_status: "initialized_new_cohort"` and
+`comparison_status: "CALIBRATION_NEEDED"`; it remains ineligible until a
+reviewed scheduled-main artifact is seeded explicitly. Legacy v1 configs still
+run and are normalized for reporting, but their records skip rolling-baseline
+comparison entirely (`baseline_status: "skipped_missing_identity"`, never
+baseline eligible); only static thresholds gate them. Metric-specific threshold
+policies are active. `QUALITY_BLOCKED` remains reserved for future variant
+promotion policy.
+
+The shipped Wan benchmark uses `benchmark_version: 3` because recipe schema 2
+changed the recipe fingerprint by removing the legacy `benchmark_id` display
+name. This intentionally opens a new comparison cohort: after deployment, a
+reviewed scheduled-main full-suite `CALIBRATION_NEEDED` artifact must be seeded
+once before rolling regression gating resumes for that exact identity. Static
+thresholds remain active during calibration.
+
 ### Raw record (`results/perf_*.json`)

 Written by `test_inference_performance.py`. One file per benchmark run.
@@ -179,6 +276,10 @@ Written by `test_inference_performance.py`. One file per benchmark run.
 ```jsonc
 {
  "benchmark_id": "wan-t2v-1.3b-2gpu",
+  "result_schema_version": 2,
+  "workload_id": "wan-t2v",
+  "variant_id": "1.3b-sp2",
+  "benchmark_version": 3,
  "model_short_name": "Wan2.1-T2V-1.3B-Diffusers",
  "device": "NVIDIA L40S",
  "num_gpus": 2,
@@ -196,12 +297,70 @@ Written by `test_inference_performance.py`. One file per benchmark run.
    "max_dit_time_s": 10.0,
    "max_vae_decode_time_s": 10.0
  },
+  "regression_thresholds": {
+    "latency": {
+      "threshold_percent": 0.10,
+      "threshold_absolute": 1.0,
+      "gated": true
+    }
+  },
  "commit": "<full sha>",
+  "run_source": "pr",
+  "branch": "feature/perf-change",
  "pr_number": "1234",
+  "test_scope": "direct",
+  "build_url": "https://buildkite.example/build",
+  "build_id": "<buildkite-build-id>",
+  "job_id": "<buildkite-job-id>",
  "timestamp": "2026-05-08T22:00:00+00:00",
+  "quality_metadata": { "quality_status": "canonical" },
  "text_encoder_time_s": 2.141,
  "dit_time_s": 8.437,
-  "vae_decode_time_s": 3.208
+  "vae_decode_time_s": 3.208,
+  "recipe": {
+    "recipe_schema_version": 2,
+    "benchmark": {
+      "benchmark_id": "wan-t2v-1.3b-2gpu",
+      "workload_id": "wan-t2v",
+      "variant_id": "1.3b-sp2",
+      "benchmark_version": 3
+    },
+    "model": { "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" },
+    "init_kwargs": { "num_gpus": 2, "sp_size": 2, "tp_size": 1 },
+    "generation_kwargs": { "height": 480, "width": 832, "num_frames": 45 },
+    "inputs": { "prompt_count": 1, "prompt_sha256": ["<measured-prompt-sha256>"] },
+    "attention": { "requested_backend": "FLASH_ATTN", "resolved_backend": "FLASH_ATTN" }
+  },
+  "recipe_fingerprint": "<sha256>",
+  "hardware_profile": {
+    "device_type": "cuda",
+    "gpu_count": 2,
+    "gpus": [{ "name": "NVIDIA L40S", "memory_gb": 48, "compute_capability": "8.9" }],
+    "interconnect": "none_or_partial"
+  },
+  "hardware_profile_id": "hw-<sha256-prefix>",
+  "software_profile": {
+    "python": "3.12",
+    "pytorch": "2.12",
+    "cuda": "13.0",
+    "attention_backend": "FLASH_ATTN",
+    "flash_attention_4_enabled": true,
+    "container_image_version": "py3.12-cuda13.0.0",
+    "packages": {
+      "fastvideo_kernel": "0.3.2",
+      "flashinfer": "0.2.11",
+      "nvidia_cutlass_dsl": "4.5.0",
+      "triton": "3.4.1"
+    }
+  },
+  "software_profile_id": "sw-<sha256-prefix>",
+  "environment_metadata": {
+    "env": {
+      "IMAGE_VERSION": "py3.12-cuda13.0.0",
+      "FASTVIDEO_CONTAINER_IMAGE_REF": "ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-cuda13.0.0@sha256:<digest>"
+    }
+  },
+  "environment_fingerprint": "env-<sha256-prefix>"
 }
 ```

@@ -213,6 +372,10 @@ result, used as the rolling-baseline source of truth.
 ```jsonc
 {
  "model_id": "wan-t2v-1.3b-2gpu",
+  "result_schema_version": 2,
+  "workload_id": "wan-t2v",
+  "variant_id": "1.3b-sp2",
+  "benchmark_version": 3,
  "timestamp": "2026-05-08T22:00:00+00:00",
  "commit_sha": "<full sha>",
  "gpu_type": "NVIDIA L40S",
@@ -222,34 +385,81 @@ result, used as the rolling-baseline source of truth.
  "text_encoder_time_s": 2.141,
  "dit_time_s": 8.437,
  "vae_decode_time_s": 3.208,
+  "regression_thresholds": {
+    "latency": {
+      "threshold_percent": 0.08,
+      "threshold_absolute": 0.5,
+      "gated": true
+    }
+  },
+  "recipe_fingerprint": "<sha256>",
+  "hardware_profile_id": "hw-<sha256-prefix>",
+  "software_profile_id": "sw-<sha256-prefix>",
+  "environment_fingerprint": "env-<sha256-prefix>",
+  "run_source": "pr",
+  "branch": "feature/perf-change",
+  "pr_number": "1234",
+  "test_scope": "direct",
+  "build_url": "https://buildkite.example/build",
+  "build_id": "<buildkite-build-id>",
+  "job_id": "<buildkite-job-id>",
+  "quality_metadata": { "quality_status": "canonical" },
+  "baseline_status": "compared",
+  "comparison_status": "PASS",
+  "comparison_status_reason": "Comparable baseline found with no gated regressions",
+  "baseline_eligible": false,
  "success": true
 }
 ```

 ### Compatibility with legacy records

-Older records in the HF dataset may not have component timing fields. The
-comparator ignores missing or `null` metrics when computing a median, and the
-dashboard lists skipped plots for metric series that have no non-null values.
-Records missing both `run_source` and `baseline_eligible` are treated as legacy
-successful main/full-suite uploads and remain eligible for rolling baselines.
+Older records in the HF dataset may not have `result_schema_version`,
+component timing fields, or v2 identity/profile fields. Records without
+`result_schema_version` are treated as v1. The comparator ignores missing or
+`null` metrics when computing a median, and the dashboard lists skipped plots
+for metric series that have no non-null values. Records missing both
+`run_source` and `baseline_eligible` are treated as legacy successful
+main/full-suite uploads and remain eligible for rolling baselines.
+Current `perf_*.json` artifacts that lack the v2 comparison identity are
+normalized for reporting but skip rolling-baseline comparison and are not marked
+baseline eligible.
+
+New v2 records compare only against the same `workload_id`, `variant_id`,
+`benchmark_version`, `recipe_fingerprint`, `hardware_profile_id`, and
+`software_profile_id` cohort, independent of the legacy `model_id` directory
+and `gpu_type` display string. Historical v1 records remain readable for
+reporting, but current legacy artifacts do not perform a `(model_id, gpu_type)`
+rolling comparison or seed new rolling baselines.
+`environment_metadata` and `environment_fingerprint` are audit data and are not
+part of the comparison key.
+The recipe prompt digests describe the prompts actually measured by the
+benchmark run; extra configured prompts are ignored unless the benchmark runner
+executes them.
+Software profile package cohorts keep exact versions for relevant
+attention/kernel packages, including FastVideo kernels, FlashAttention,
+FlashInfer, Cutlass DSL, SageAttention, Triton, and xFormers when installed.

 ## Environment variable reference

 | Variable | Default | Used by | Purpose |
 |---|---|---|---|
-| `PERF_MAX_REGRESSION` | `0.05` | `compare_baseline.py` | Per-metric regression fraction that fails the build. |
 | `PERFORMANCE_TRACKING_ROOT` | `/tmp/perf-tracking` | `compare_baseline.py`, `dashboard.py` | Local directory the HF dataset is synced to. |
 | `PERF_REPORTS_DIR` | `/root/data/perf_reports` | `compare_baseline.py`, `dashboard.py` | Where the Markdown summary and Plotly HTML get written for Buildkite to pick up. |
-| `HF_REPO_ID` | `FastVideo/performance-tracking` | `hf_store.py` | HF dataset repo holding rolling-baseline records. |
-| `HF_API_KEY`, `HUGGINGFACE_HUB_TOKEN`, `HF_TOKEN` | unset | `hf_store.py` | Required for upload or private dataset reads. |
-| `PERF_RUN_SOURCE` | inferred | `compare_baseline.py` | Source metadata for uploaded records: `pr`, `local`, `scheduled_main`, or `unknown`. |
+| `HF_REPO_ID` | `FastVideo/performance-tracking` | `fastvideo/performance/hf_store.py` | HF dataset repo holding rolling-baseline records. |
+| `HF_API_KEY`, `HUGGINGFACE_HUB_TOKEN`, `HF_TOKEN` | unset | `fastvideo/performance/hf_store.py` | Required for upload or private dataset reads. |
+| `PERF_RUN_SOURCE` | inferred | `compare_baseline.py`, `test_inference_performance.py` | Source metadata for uploaded records: `pr`, `local`, `scheduled_main`, or `unknown`. |
 | `PERF_UPLOAD_POLICY` | `never` | `compare_baseline.py` | Upload policy: `never`, `pass`, or `always`. |
-| `PERF_PYTEST_RC` | unset | `compare_baseline.py` | Static-threshold pytest exit code, used so scheduled-main failures can be uploaded with `success=false`. |
+| `PERF_PYTEST_RC` | unset | `compare_baseline.py` | Performance pytest exit code. Measured static-threshold failures are attributed per record; otherwise a nonzero code reports an infrastructure error. |
 | `TEST_SCOPE` | unset | `compare_baseline.py` | CI context used to infer scheduled-main runs together with `BUILDKITE_BRANCH=main`. |
 | `BUILDKITE_BRANCH`, `BUILDKITE_COMMIT`, `BUILDKITE_PULL_REQUEST` | unset | `compare_baseline.py`, `test_inference_performance.py` | CI metadata stamped into records. |
 | `DASHBOARD_DAYS` | `30` | `dashboard.py` | Lookback window for the Plotly trend pages. |
-| `PERFORMANCE_TRACKING_SYNC_REUSE_TTL_SECONDS` | `3600` | `hf_store.py` | Freshness window for reusing an existing HF sync when requested by dashboard consumers. |
+| `PERFORMANCE_TRACKING_SYNC_REUSE_TTL_SECONDS` | `3600` | `fastvideo/performance/hf_store.py` | Freshness window for reusing an existing HF sync when requested by dashboard consumers. |
+| `FASTVIDEO_ATTENTION_BACKEND` | `auto` | `test_inference_performance.py` | Requested attention backend included in `software_profile_id`. |
+| `FASTVIDEO_FA4` | `0` | `test_inference_performance.py` | FlashAttention-4 toggle included in `software_profile_id`. |
+| `FASTVIDEO_PERFORMANCE_PROFILE_VERSION` | unset | `test_inference_performance.py` | Optional explicit software cohort/profile version included in `software_profile_id`. |
+| `IMAGE_VERSION` | unset | `test_inference_performance.py` | CI container image/profile version included in `software_profile_id` when available. |
+| `FASTVIDEO_CONTAINER_IMAGE_REF` | unset | `pr_test.py`, `launch_l40s_job.py`, `test_inference_performance.py` | Resolved CI container image ref or digest recorded in `environment_metadata` for audit without changing `software_profile_id`. |
 | `FASTVIDEO_STAGE_LOGGING` | set by the pytest test | `test_inference_performance.py` | Enables pipeline stage timing capture for component metrics during benchmark runs. |

 ## CI integration
@@ -260,17 +470,24 @@ point is `fastvideo/tests/modal/pr_test.py:run_performance_tests` and the
 Buildkite artifact upload is in
 `.buildkite/scripts/pr_test.sh:upload_performance_artifacts`.

-Each performance build runs pytest first. If that fixed-threshold phase fails,
-`compare_baseline.py` is skipped, so Markdown summaries and normalized JSON
-artifacts are not emitted. The dashboard still runs best-effort for
-observability. When pytest passes, the rolling-baseline phase emits:
+Each performance build runs pytest first. PR and direct runs only continue to
+`compare_baseline.py` when that fixed-threshold phase passes; if pytest fails,
+Markdown summaries and normalized JSON artifacts are not emitted. Scheduled
+main runs set `PERF_UPLOAD_POLICY=always`, so they still run
+`compare_baseline.py` (with `PERF_PYTEST_RC` set) after pytest fails. Each raw
+record is checked against its own static thresholds: a measured breach reports
+`REGRESSION`, while unaffected records retain their rolling-baseline status. A
+nonzero pytest exit with no attributable static-threshold breach reports
+`INFRA_ERROR`. Failed records have `success=false` and are excluded from future
+rolling baselines. The dashboard still runs best-effort for observability.
+When the rolling-baseline phase runs, it emits:

 * **Markdown summary** — appended to `$GITHUB_STEP_SUMMARY` when that variable
  is set, and written as `perf_<sha>_<ts>.md` for Buildkite upload. Contains a
  per-benchmark row with current vs. baseline values for latency, throughput,
  memory, text encoder time, DiT time, and VAE decode time.
 * **Plotly dashboard** — `dashboard_<sha>_<ts>.html` showing time-series for
-  each metric grouped by `(model_id, gpu_type)`.
+  each metric grouped by comparison cohort.
 * **Normalized records** — `normalized_perf_*.json`, one per benchmark.
  Useful as input to the
  [`reseed-performance-baseline`](https://github.com/hao-ai-lab/FastVideo/blob/main/.agents/skills/reseed-performance-baseline/SKILL.md)
@@ -279,11 +496,16 @@ observability. When pytest passes, the rolling-baseline phase emits:
 ## Adding a new benchmark

 1. Drop a new JSON config into
-   `.buildkite/performance-benchmarks/tests/<name>.json`. Required keys:
+   `.buildkite/performance-benchmarks/tests/<name>.json`. New configs should
+   use v2 identity fields:

   ```json
   {
     "benchmark_id": "<unique-id>",
+     "config_schema_version": 2,
+     "workload_id": "<stable-workload-id>",
+     "variant_id": "<variant, e.g. 1.3b-sp2>",
+     "benchmark_version": 1,
     "model": { "model_path": "...", "model_short_name": "..." },
     "init_kwargs": { "num_gpus": 1, ... },
     "generation_kwargs": { "num_frames": 45, ... },
@@ -299,17 +521,30 @@ observability. When pytest passes, the rolling-baseline phase emits:
         "max_vae_decode_time_s": 10.0
       },
       "default": { "max_generation_time_s": 120.0, "max_peak_memory_mb": 30000.0 }
+     },
+     "regression_thresholds": {
+       "latency": { "threshold_percent": 0.10, "threshold_absolute": 1.0, "gated": true }
     }
-   }
-   ```
+  }
+
+  ```
+
+   Legacy v1 configs without `config_schema_version` still load, but should not
+   gain v2 identity or metadata fields until they are migrated to
+   `config_schema_version: 2`. For v2 configs, `workload_id`, `variant_id`,
+   and `benchmark_version` are part of the comparison key; benchmark runs
+   fail if any of these identity fields are missing.

 2. The pytest test auto-discovers all configs — no test code needed. CI
   picks it up on the next `/test performance` run.

-3. The first persisted main-branch run with no HF history initializes the
-   baseline (passes automatically). Subsequent runs compare against it. Local
-   and pull-request runs with no HF history also pass, but they do not seed the
-   shared baseline.
+3. Legacy benchmarks are gated only by their static thresholds. Their current
+   records skip rolling-baseline comparison and are never baseline eligible.
+   V2 benchmarks with no exact comparable baseline report
+   `CALIBRATION_NEEDED`; the record remains visible but does not become
+   baseline eligible until a comparable scheduled-main full-suite run is
+   reviewed and seeded through the prepare, review, and conditional-upload
+   steps in the `reseed-performance-baseline` skill.

 4. If the benchmark targets a GPU not currently in `thresholds`, either add
   that GPU as a key or rely on the `default` block. Note that `default` is
@@ -320,10 +555,21 @@ observability. When pytest passes, the rolling-baseline phase emits:
   a useful fixed gate. The rolling baseline will still track component times
   when static component thresholds are omitted.

+6. Omit `regression_thresholds` to use the default rolling-baseline policy, or
+   include only benchmark-specific deviations. Tune these independently from
+   the fixed thresholds when a metric is noisy or should be informational. The
+   fixed `thresholds` block is an absolute pytest ceiling. The
+   `regression_thresholds` block controls rolling-baseline comparisons against
+   recent scheduled-main records.
+
 ## Troubleshooting

-**"No baseline for ... Initializing"** — first run for this `(model_id,
-gpu_type)`. Run will pass and (if persisting) seed the first record.
+**`CALIBRATION_NEEDED` / "No baseline found for exact comparable identity"** —
+the v2 run passes, but its normalized record remains
+`baseline_eligible=false`. Review a successful scheduled-main full-suite
+normalized artifact, then follow the `reseed-performance-baseline` skill. The
+utility only prepares a digest-protected manifest; the separately confirmed
+upload rechecks remote state and commits the batch atomically.

 **Persistent failure right after a torch / kernel / image upgrade** —
 genuine regression *or* baseline drift. Compare the failing normalized record
@@ -336,5 +582,5 @@ pipelines that did not report a mapped component stage.

 **Component timing is `null`** — the generated result did not include a mapped
 stage in `logging_info.stages`. Check that the pipeline emits stage logging
-and that the stage name is listed in `STAGE_METRIC_MAP` in
-`test_inference_performance.py`.
+and that the stage emits `component_metric` or is covered by the legacy
+`STAGE_METRIC_MAP` fallback in `test_inference_performance.py`.
@@ -180,6 +180,30 @@ python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
  --device-folder L40S_reference_videos
 ```

+### SSIM Bootstrap Mode
+
+Normal SSIM runs are strict: if a reference video or latent is missing, the
+test fails. For new-model PRs, CI can run SSIM in bootstrap mode so missing
+references are uploaded as draft artifacts for review instead of immediately
+blocking on a missing canonical reference.
+
+Buildkite enables SSIM bootstrap mode when either condition is true:
+
+- the PR title or Buildkite message contains `[new-model]`;
+- `FASTVIDEO_SSIM_BOOTSTRAP_MODE=1` is set for the Buildkite job.
+
+Bootstrap mode passes `--ssim-bootstrap-mode` to pytest. When a generated
+artifact is available, the test uploads it under the `drafts/...` namespace in
+the SSIM reference repo and marks that case as expected-failed. After reviewing
+the draft, promote it into the canonical reference layout:
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py promote-draft \
+  --quality-tier default \
+  --device-folder L40S_reference_videos \
+  --model-id <model_id>
+```
+
 ## CI Integration

 FastVideo CI tests are orchestrated by Buildkite and run on Modal GPU
@@ -191,6 +191,9 @@ surfaces:
        sources:
          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
+      color_correction_strength:
+        sources:
+          - fastvideo.configs.pipelines.dreamx_world.DreamXWorld5BARPipelineConfig
      default_camera_rotation:
        sources:
          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
@@ -455,6 +458,8 @@ surfaces:
      guidance_scale: request.sampling.guidance_scale
      guidance_scale_2: request.sampling.guidance_scale_2
      guidance_rescale: request.sampling.guidance_rescale
+      use_embedded_guidance: request.sampling.use_embedded_guidance
+      true_cfg_scale: request.sampling.true_cfg_scale
      boundary_ratio: request.sampling.boundary_ratio
      sigmas: request.sampling.sigmas
      enable_teacache: request.runtime.enable_teacache
@@ -0,0 +1,116 @@
+# 🌊 AnyFlow Any-Step Video Distillation
+
+**AnyFlow** ([paper](https://arxiv.org/abs/2605.13724), [project page](https://nvlabs.github.io/AnyFlow/), [official code](https://github.com/NVlabs/AnyFlow), [model weights](https://huggingface.co/collections/nvidia/anyflow)) is an any-step video diffusion framework built on flow maps. A single distilled checkpoint can be evaluated at NFE ∈ {1, 2, 4, 8, 16, 32} without retraining, and quality scales **monotonically** with steps — unlike consistency-based distillation, which often degrades as NFE grows.
+
+The student network ``u_θ(x_t, t, r)`` predicts the *average velocity* from time ``t`` back to time ``r``, so one Euler step is
+
+```
+x_r = x_t - ((t - r) / N) · u_θ(x_t, t, r)
+```
+
+for any ``t > r``.
+
+## 📊 Model Overview
+
+NVIDIA publishes four checkpoints under [`nvidia/anyflow`](https://huggingface.co/collections/nvidia/anyflow):
+
+- `nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers` — bidirectional T2V, Wan2.1 1.3B base
+- `nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers` — bidirectional T2V, Wan2.1 14B base
+- `nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers` — frame-autoregressive variant, 1.3B
+- `nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers` — frame-autoregressive variant, 14B
+
+FastVideo currently supports the bidirectional T2V variants for training; the FAR variants can be loaded for inference through the diffusers integration.
+
+## ⚙️ Inference
+
+For inference, load the published checkpoint directly through diffusers; FastVideo's training-side ``WanModel`` config maps the HF AnyFlow ``delta_embedder`` weights onto its internal layout via ``param_names_mapping`` so the same checkpoint can be used as the ``init_from`` for the on-policy YAML below.
+
+## 🧠 Algorithm
+
+Training runs in two stages. Both use the dual-timestep Wan backbone — enabled by ``pipeline.dit_config.r_embedder: true`` in the YAML, which allocates a sibling ``condition_embedder.delta_embedder`` and fuses its embedding with the standard timestep embedding via either an additive or a gated mixer.
+
+### Stage 1 — Pretrain (flow-map central-difference)
+
+Method: ``AnyFlowPretrainMethod`` (``fastvideo/train/methods/distribution_matching/anyflow_pretrain.py``)
+
+For each batch, sample ``(t, r) ∈ [0, 1]`` as ``(max, min)`` of two uniform draws, then:
+
+- a ``diffusion_ratio`` fraction (default 0.5) gets ``r = t`` — recovers plain flow matching;
+- a ``consistency_ratio`` fraction (default 0.25) gets ``r = 0`` — forces consistency to clean data;
+- the remainder is free.
+
+The student forward at ``(t, r)`` is trained against the central-difference target
+
+```
+target = (eps - x_0) - (t - r) · dF/dt
+```
+
+where ``dF/dt`` is estimated from the student's own forward at ``(t ± δ, r)`` with the sample also moved along the flow trajectory by ``v_pred · (δ / N)``. Per-timestep weighting uses ``beta08`` (``w(t) = t · sqrt(1 - t)``, renormalized). A stop-gradient scale-balance keeps the non-diffusion branches' loss magnitude aligned with the diffusion branch.
+
+### Stage 2 — On-policy DMD
+
+Method: ``AnyFlowMethod`` (``fastvideo/train/methods/distribution_matching/anyflow.py``)
+
+Inherits ``DMD2Method``. The student is rolled out for ``student_sample_steps`` Euler-flow steps from pure noise; one randomly-chosen step is gradient-enabled (broadcast from rank 0 so every worker agrees), the rest run under ``torch.no_grad``. With ``use_mean_velocity: true`` (default) the rollout uses ``r = t_next`` at each step, matching AnyFlow's ``WanAnyFlowPipeline.training_rollout``.
+
+The inherited ``_dmd_loss`` (VSD with fake-score critic) consumes the rollout output and the teacher's CFG prediction. The optional pinned ``t_list_override`` lets configs reproduce the paper's hand-tuned 4-step schedule ``[999, 937, 833, 624, 0]``.
+
+## 🚀 Training Scripts
+
+### Stage 1 — pretrain
+
+```bash
+bash examples/train/run.sh \
+    examples/train/configs/distribution_matching/wan/anyflow_pretrain_t2v.yaml
+```
+
+**Key configuration** (in ``examples/train/configs/distribution_matching/wan/anyflow_pretrain_t2v.yaml``):
+
+- Global batch size: 32 (8 GPUs × 4 per-GPU)
+- Learning rate: 5e-5
+- Flow shift: 5.0
+- ``diffusion_ratio`` / ``consistency_ratio``: 0.5 / 0.25
+- ``epsilon`` (finite-difference step): 5 (absolute train-timestep units)
+- ``weight_type``: ``beta08``
+- ``fuse_guidance_scale``: 3.0
+- Training steps: 6000
+
+### Stage 2 — on-policy
+
+```bash
+bash examples/train/run.sh \
+    examples/train/configs/distribution_matching/wan/anyflow_onpolicy_t2v.yaml \
+    --models.student.init_from outputs/wan2.1_anyflow_pretrain/checkpoint-final
+```
+
+(Or point ``models.student.init_from`` directly at ``nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers`` to bootstrap from the paper weights and skip Stage 1.)
+
+**Key configuration**:
+
+- Global batch size: 8 (8 GPUs × 1 per-GPU)
+- Learning rate: 2e-6
+- Flow shift: 5.0
+- ``student_sample_steps``: 4
+- ``t_list_override``: ``[999, 937, 833, 624, 0]``
+- ``use_mean_velocity``: ``true`` (i.e. ``r = t_next`` during rollout)
+- ``real_score_guidance_scale``: 3.0
+- ``generator_update_interval``: 5 (DMD2 alternation)
+- Training steps: 4000
+
+## 🔌 Loading published AnyFlow checkpoints
+
+The HF AnyFlow checkpoints expose ``condition_embedder.delta_embedder.*`` weights that FastVideo internally maps onto its ``condition_embedder.delta_embedder.mlp.*`` layout. This rename happens automatically through the regex in ``WanVideoArchConfig.param_names_mapping`` — no separate adapter is needed. The same regex is a no-op on plain Wan checkpoints (which don't contain any ``delta_embedder`` keys).
+
+Set the YAML's ``pipeline.dit_config.r_embedder: true`` to allocate the ``delta_embedder`` module on the FastVideo side; when initializing from a plain Wan checkpoint the delta weights are deep-copied from ``time_embedder`` (matching AnyFlow's ``setup_flowmap_model()`` behavior).
+
+## 🧭 Note on ``fuse_guidance_scale``
+
+Stage 1 optionally fuses classifier-free guidance into the training target so the resulting checkpoint can be sampled at ``guidance_scale=1.0`` (no extra forward pass at inference time). The transformation is
+
+```
+noise_pred ← (noise_pred - (1 - g) · noise_pred_uncond) / g
+```
+
+with ``g = fuse_guidance_scale``. The negative prompt embedding comes from ``WanModel``'s ``ensure_negative_conditioning()`` — i.e. the dataset's configured ``sampling_param.negative_prompt``. Setting ``fuse_guidance_scale: 1.0`` skips the extra unconditional forward entirely.
+
+The on-policy stage's ``real_score_guidance_scale`` (inherited from DMD2) follows the same parameterization conventions documented in [``dmd.md``](dmd.md#-note-on-real_score_guidance_scale).
@@ -74,6 +74,23 @@ uv pip install ninja
 python setup.py install
 ```

+### Flash Attention 4 (opt-in)
+
+FastVideo never auto-selects FlashAttention-4 (`flash_attn.cute`) just because it
+is installed: its CuTeDSL kernels JIT-compile per shape family and can fail at
+runtime on some GPU/shape combinations. To use FA4, install the pinned
+`flash-attn-4` build (see the `flash-attn-4` source in `pyproject.toml`) and set:
+
+```bash
+export FASTVIDEO_FA4=1
+```
+
+On GPUs below sm90 a capability gate routes to FlashAttention-2 the calls FA4
+cannot serve there: grad-enabled (training) attention (FA4's backward requires
+sm90+) and GQA attention (FA4's `pack_gqa` fails to JIT-compile below sm90).
+On sm90+ both run on FA4. If FA4 is unusable while `FASTVIDEO_FA4=1` is set,
+FastVideo fails loudly instead of silently falling back.
+
 ### FP4 Flash Attention 4 (Blackwell only)

 **`FLASH_ATTN`** with **`--nvfp4_fa4`**
@@ -58,6 +58,8 @@ pipeline initialization and sampling.
 | FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
 | FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
 | Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
+| DreamX-World 5B Cam | `FastVideo/DreamX-World-5B-Cam-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
+| DreamX-World 5B AR | `FastVideo/DreamX-World-5B-Diffusers` | 704px1280p | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Lucy Edit Dev 5B*** | `decart-ai/Lucy-Edit-Dev` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
 | Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
@@ -0,0 +1,91 @@
+# Training Trackers
+
+FastVideo can send training metrics and validation media to Weights & Biases
+or SwanLab. Tracking runs only on global rank 0, and local tracker files are
+stored under `<output_dir>/tracker`.
+
+## Supported Trackers
+
+| Value | Backend | Installation |
+|-------|---------|--------------|
+| `wandb` | Weights & Biases | Included with FastVideo |
+| `swanlab` | SwanLab | Install the optional `swanlab` dependency |
+| `none` | Disable external tracking | No additional package |
+
+You can enable more than one backend, for example `trackers: [wandb, swanlab]`.
+Metrics and validation media are converted to the artifact type required by
+each backend.
+
+## Install SwanLab
+
+For a published FastVideo installation, install the SwanLab extra:
+
+```bash
+uv pip install "fastvideo[swanlab]"
+```
+
+For an editable source checkout, include the same extra during installation:
+
+```bash
+uv pip install -e ".[swanlab]"
+```
+
+If FastVideo is already installed, you can install the compatible SDK directly:
+
+```bash
+uv pip install "swanlab>=0.6.7"
+```
+
+Authenticate once before starting a training run:
+
+```bash
+swanlab login
+```
+
+See the [SwanLab login documentation](https://docs.swanlab.cn/en/api/cli-swanlab-login.html)
+for non-interactive and self-hosted setups.
+
+## Configure Tracking
+
+Select SwanLab in the YAML config used by the modular training framework:
+
+```yaml
+training:
+  checkpoint:
+    output_dir: outputs/my_run
+  tracker:
+    trackers: [swanlab]
+    project_name: my_project
+    run_name: my_run
+```
+
+To log to both supported services:
+
+```yaml
+training:
+  tracker:
+    trackers: [wandb, swanlab]
+    project_name: my_project
+    run_name: my_run
+```
+
+An empty or omitted `trackers` list selects W&B when `project_name` is set.
+Use an explicit `none` entry to disable external tracking:
+
+```yaml
+training:
+  tracker:
+    trackers: [none]
+```
+
+## Validation Videos
+
+SwanLab currently accepts GIF video artifacts. FastVideo converts validation
+MP4 files and in-memory video arrays to GIF automatically before logging them.
+For video files, FastVideo uses the sampling frame rate supplied by the caller,
+or the source file's frame rate when no value is supplied. In-memory arrays use
+the frame rate supplied by the caller. Both forms fall back to 16 FPS when no
+frame rate is available.
+
+For details about configuring validation callbacks, see
+[Training Infrastructure](train_infra.md#callbacks-pluggable-hooks).
@@ -161,6 +161,21 @@ training:
    decay_interval_steps: 0
 ```

+`training.data.data_path` can also mix multiple preprocessed datasets by using a mapping from dataset path to repeat count:
+
+```yaml
+training:
+  data:
+    data_path:
+      data/zeldam2-clean: 1
+      data/multi3d_games: 2
+```
+
+The repeat count duplicates that dataset's parquet file list before shuffling/sampling, so the example above trains with roughly twice as much `multi3d_games` exposure as `zeldam2-clean`. Paths are just suggested locations; use any local path that contains a FastVideo preprocessed parquet dataset.
+
+See [Training Trackers](trackers.md) to configure Weights & Biases or SwanLab,
+including SwanLab installation and authentication.
+
 ### `callbacks` — Pluggable hooks

 Callbacks run at specific points in the training loop (before/after optimizer
@@ -323,6 +338,40 @@ Self-Forcing inherits all DMD2 parameters, plus:
 | `enable_gradient_in_rollout` | `true` | Enable backprop through rollout |
 | `start_gradient_frame` | `0` | Frame index where gradients begin |

+### Streaming Long Tuning
+
+`StreamingLongTuningMethod` extends Self-Forcing for LongLive-style rollouts. It
+keeps a streaming state, generates overlapping chunks, and trains only the new
+frames while preserving context from earlier chunks.
+
+For the MatrixGame2/Zelda world-model example, self-forcing and long tuning are
+separate runs: first train or load the 1k-step self-forcing checkpoint using
+`examples/train/scenario/worldmodel/zelda/self_forcing_causal_i2v.yaml`,
+then run
+`examples/train/scenario/worldmodel/zelda/streaming_long_tuning_causal_i2v.yaml`
+from that checkpoint for the 3k-step streaming long-tuning stage.
+
+```yaml
+method:
+  _target_: fastvideo.train.methods.distribution_matching.streaming_long_tuning.StreamingLongTuningMethod
+  streaming_chunk_size: 9
+  streaming_max_length: 39
+  streaming_fixed_overlap_latents: 3
+  streaming_reencode_overlap_anchor: true
+  streaming_anchor_inject_k: 1
+  streaming_require_full_blocks: true
+  multi_phased_distill_schedule:
+    - stage: streaming_long
+      start_step: 0
+      end_step: 3000
+      num_latent_t: 39
+      streaming_training: true
+```
+
+See
+`examples/train/scenario/worldmodel/zelda/streaming_long_tuning_causal_i2v.yaml`
+for a complete MatrixGame2/Zelda configuration.
+
 ---

 ## Callbacks
@@ -0,0 +1,64 @@
+import os
+
+from fastvideo import VideoGenerator
+
+
+OUTPUT_PATH = os.getenv("DREAMX_WORLD_OUTPUT_PATH", "video_samples_dreamx_world")
+
+
+def _env_int(name: str, default: int) -> int:
+    return int(os.getenv(name, str(default)))
+
+
+def _env_float(name: str, default: float) -> float:
+    return float(os.getenv(name, str(default)))
+
+
+def main():
+    model_name = os.getenv("DREAMX_WORLD_MODEL_DIR", "FastVideo/DreamX-World-5B-Cam-Diffusers")
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        num_gpus=1,
+        use_fsdp_inference=False,
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        override_pipeline_cls_name="DreamXWorldPipeline",
+    )
+
+    prompt = os.getenv(
+        "DREAMX_WORLD_PROMPT",
+        "A cinematic first-person drive through a futuristic coastal city at "
+        "sunrise, reflective glass towers, clean streets, soft volumetric light.",
+    )
+    image_path = os.getenv(
+        "DREAMX_WORLD_IMAGE_PATH",
+        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
+    )
+
+    kwargs = {
+        "output_path": OUTPUT_PATH,
+        "save_video": os.getenv("DREAMX_WORLD_SAVE_VIDEO", "1") != "0",
+        "height": _env_int("DREAMX_WORLD_HEIGHT", 480),
+        "width": _env_int("DREAMX_WORLD_WIDTH", 832),
+        "num_frames": _env_int("DREAMX_WORLD_NUM_FRAMES", 161),
+        "num_inference_steps": _env_int("DREAMX_WORLD_STEPS", 30),
+        "guidance_scale": _env_float("DREAMX_WORLD_GUIDANCE", 5.0),
+        "action_list": os.getenv("DREAMX_WORLD_ACTIONS", "w,d,w").split(","),
+        "action_speed_list": [
+            float(value)
+            for value in os.getenv("DREAMX_WORLD_ACTION_SPEEDS", "4.0,2.0,4.0").split(",")
+        ],
+    }
+    if image_path:
+        kwargs["image_path"] = image_path
+
+    try:
+        generator.generate_video(prompt, **kwargs)
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,140 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import argparse
+import contextlib
+import os
+import re
+
+DEFAULT_PROMPTS = [
+    "a photo of a cat",
+    (
+        "a cinematic photo of a red panda wearing a tiny backpack, standing on a "
+        "rainy neon-lit street at night, shallow depth of field, sharp focus, "
+        "35mm, bokeh"
+    ),
+]
+
+
+def _safe_filename(text: str, max_len: int = 100) -> str:
+    """Make a stable, filesystem-friendly filename base."""
+    s = text[:max_len].strip()
+    s = s.replace(os.sep, "_")
+    if os.altsep:
+        s = s.replace(os.altsep, "_")
+    s = re.sub(r"\s+", " ", s)
+    s = re.sub(r"[^A-Za-z0-9 .,_-]", "_", s)
+    s = s.strip(" .")
+    return s or "prompt"
+
+
+def _remove_existing_outputs(out_dir: str, filename_base: str) -> None:
+    """Delete prior outputs so reruns do not get _1, _2 suffixes."""
+    if not os.path.isdir(out_dir):
+        return
+
+    pattern = re.compile(rf"^{re.escape(filename_base)}(_\d+)?\.(mp4|png)$")
+    for fn in os.listdir(out_dir):
+        if pattern.match(fn):
+            with contextlib.suppress(FileNotFoundError):
+                os.remove(os.path.join(out_dir, fn))
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(
+        description="Run FLUX.1-dev text-to-image with FastVideo VideoGenerator.",
+    )
+    p.add_argument(
+        "--model-path",
+        default="official_weights/FLUX.1-dev",
+        help="Local Diffusers checkpoint dir or HF repo id.",
+    )
+    p.add_argument(
+        "--out-dir",
+        "--outdir",
+        default="outputs/flux_dev/samples",
+        help="Directory for saved PNG outputs.",
+    )
+    p.add_argument(
+        "--prompt",
+        action="append",
+        default=None,
+        help="Prompt. Repeat for multiple images.",
+    )
+    p.add_argument(
+        "--backend",
+        default=None,
+        help="Set FASTVIDEO_ATTENTION_BACKEND (e.g. TORCH_SDPA).",
+    )
+    p.add_argument("--seed", type=int, default=42, help="Base seed; each prompt uses seed + index.")
+    p.add_argument("--height", type=int, default=1024, help="Output height.")
+    p.add_argument("--width", type=int, default=1024, help="Output width.")
+    p.add_argument("--steps", type=int, default=28, help="Number of inference steps.")
+    p.add_argument("--guidance", type=float, default=3.5, help="Guidance scale.")
+    p.add_argument("--num-gpus", type=int, default=1, help="GPU count.")
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    prompts: list[str] = args.prompt if args.prompt else DEFAULT_PROMPTS
+
+    if args.backend:
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = args.backend
+
+    from fastvideo import VideoGenerator
+
+    os.makedirs(args.out_dir, exist_ok=True)
+
+    init_kwargs = {
+        "num_gpus": args.num_gpus,
+        "workload_type": "t2i",
+        "sp_size": 1,
+        "tp_size": 1,
+        "dit_cpu_offload": False,
+        "dit_layerwise_offload": False,
+        "text_encoder_cpu_offload": False,
+        "vae_cpu_offload": False,
+        "image_encoder_cpu_offload": False,
+        "pin_cpu_memory": False,
+        "use_fsdp_inference": False,
+    }
+
+    generator = VideoGenerator.from_pretrained(
+        model_path=args.model_path,
+        **init_kwargs,
+    )
+    try:
+        for i, prompt in enumerate(prompts):
+            seed = args.seed + i
+            filename_base = (
+                f"flux_dev_{i:02d}_seed{seed}_{_safe_filename(prompt, max_len=80)}"
+            )
+            _remove_existing_outputs(args.out_dir, filename_base)
+            output_path = os.path.join(args.out_dir, f"{filename_base}.png")
+            print(f"[flux] prompt_idx={i} seed={seed} output_path={output_path}")
+
+            generation_kwargs = {
+                "output_path": output_path,
+                "height": args.height,
+                "width": args.width,
+                "num_frames": 1,
+                "fps": 1,
+                "num_inference_steps": args.steps,
+                "guidance_scale": args.guidance,
+                "use_embedded_guidance": True,
+                "true_cfg_scale": 1.0,
+                "seed": seed,
+                "save_video": True,
+            }
+
+            generator.generate_video(prompt, **generation_kwargs)
+
+        print(f"[flux] done. outputs written to: {args.out_dir}")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Run GLM-Image text-to-image generation through FastVideo.
+
+User story:
+    "I have the HF `zai-org/GLM-Image` checkpoint and want a minimal
+    text-to-image generation command, saved as a PNG."
+"""
+import argparse
+from pathlib import Path
+
+from PIL import Image
+
+from fastvideo import VideoGenerator
+from fastvideo.api import (
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    OutputConfig,
+    ParallelismConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run GLM-Image text-to-image generation.")
+    parser.add_argument(
+        "--model-path",
+        default="zai-org/GLM-Image",
+        help="HF id or local diffusers-format GLM-Image weights directory.",
+    )
+    parser.add_argument(
+        "--output",
+        default="image_output/landscape.png",
+        help="Output PNG path.",
+    )
+    parser.add_argument(
+        "--prompt",
+        default=("A beautiful landscape photography with rolling hills, "
+                 "a winding river, and a vibrant sunset in the background. "
+                 "Warm golden light, photorealistic style."),
+        help="Text prompt.",
+    )
+    parser.add_argument("--height", type=int, default=1024)
+    parser.add_argument("--width", type=int, default=1024)
+    parser.add_argument("--steps", type=int, default=50)
+    parser.add_argument("--guidance-scale", type=float, default=1.5)
+    parser.add_argument("--seed", type=int, default=1024)
+    parser.add_argument("--num-gpus", type=int, default=1)
+    parser.add_argument("--tp-size", type=int, default=None)
+    parser.add_argument("--sp-size", type=int, default=None)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
+    sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
+
+    # GLM-Image needs trust_remote_code for its AR encoder; offload and the
+    # pipeline class come from the model's registered defaults — don't override.
+    generator_config = GeneratorConfig(
+        model_path=args.model_path,
+        trust_remote_code=True,
+        engine=EngineConfig(
+            num_gpus=args.num_gpus,
+            parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
+        ),
+        pipeline=PipelineSelection(workload_type="t2i"),
+    )
+
+    generator = VideoGenerator.from_config(generator_config)
+    try:
+        request = GenerationRequest(
+            prompt=args.prompt,
+            sampling=SamplingConfig(
+                height=args.height,
+                width=args.width,
+                num_frames=1,
+                fps=1,
+                num_inference_steps=args.steps,
+                guidance_scale=args.guidance_scale,
+                seed=args.seed,
+            ),
+            output=OutputConfig(
+                output_path=str(output.parent),
+                save_video=False,
+                return_frames=True,
+            ),
+        )
+        result = generator.generate(request)
+        if isinstance(result, list):
+            result = result[0]
+
+        frames = result.frames
+        if frames is not None and len(frames):
+            Image.fromarray(frames[0]).save(output)
+            print(f"Saved image to {output}")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,37 @@
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_kandinsky5_i2v"
+
+IMAGE_PATH = "assets/girl.png"
+
+
+def main():
+    generator = VideoGenerator.from_pretrained(
+        "kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers", 
+        # "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers"
+        num_gpus=1,
+        use_fsdp_inference=False,
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    prompt = (
+        "A woman stands up and walks away"
+    )
+    _ = generator.generate_video(
+        prompt,
+        image_path=IMAGE_PATH,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        height=1024,
+        width=1024,
+        num_frames=121,
+    )
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,37 @@
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_kandinsky5_t2v"
+
+def main():
+    generator = VideoGenerator.from_pretrained(
+        "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
+        # "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers"
+        num_gpus=1,
+        use_fsdp_inference=False,
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    prompt = (
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    )
+    _ = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True,height=512, width=768, num_frames=121)
+
+    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.")
+    _ = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, height=512, width=768, num_frames=121)
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,120 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Run GLM-Image image-to-image (edit) generation through FastVideo.
+
+User story:
+    "I have the HF `zai-org/GLM-Image` checkpoint and a condition image, and
+    want a minimal edit command (text + image -> edited image), saved as a PNG."
+
+GLM-Image is a single unified pipeline: passing a condition image switches it
+from text-to-image to the edit path (the condition enters the DiT via a KV-cache
+write pass), so the generator config is identical to `basic_glm_image.py` — the
+`inputs.pil_image` on the request is what selects the edit mode.
+"""
+import argparse
+from pathlib import Path
+
+from PIL import Image
+
+from fastvideo import VideoGenerator
+from fastvideo.api import (
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    InputConfig,
+    OutputConfig,
+    ParallelismConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run GLM-Image image-to-image (edit) generation.")
+    parser.add_argument(
+        "--model-path",
+        default="zai-org/GLM-Image",
+        help="HF id or local diffusers-format GLM-Image weights directory.",
+    )
+    parser.add_argument(
+        "--image",
+        default="assets/images/couple.jpg",
+        help="Condition image to edit.",
+    )
+    parser.add_argument(
+        "--output",
+        default="image_output/edited.png",
+        help="Output PNG path.",
+    )
+    parser.add_argument(
+        "--prompt",
+        default="Change the background to a snowy mountain landscape at golden hour.",
+        help="Edit instruction.",
+    )
+    parser.add_argument("--height", type=int, default=1024)
+    parser.add_argument("--width", type=int, default=1024)
+    parser.add_argument("--steps", type=int, default=50)
+    parser.add_argument("--guidance-scale", type=float, default=1.5)
+    parser.add_argument("--seed", type=int, default=1024)
+    parser.add_argument("--num-gpus", type=int, default=1)
+    parser.add_argument("--tp-size", type=int, default=None)
+    parser.add_argument("--sp-size", type=int, default=None)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    condition = Image.open(args.image).convert("RGB")
+    tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
+    sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
+
+    # GLM-Image needs trust_remote_code for its AR encoder; offload and the
+    # pipeline class come from the model's registered defaults — don't override.
+    # The pipeline is registered as t2i; passing inputs.pil_image below switches
+    # it to the edit path.
+    generator_config = GeneratorConfig(
+        model_path=args.model_path,
+        trust_remote_code=True,
+        engine=EngineConfig(
+            num_gpus=args.num_gpus,
+            parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
+        ),
+        pipeline=PipelineSelection(workload_type="t2i"),
+    )
+
+    generator = VideoGenerator.from_config(generator_config)
+    try:
+        request = GenerationRequest(
+            prompt=args.prompt,
+            inputs=InputConfig(pil_image=condition),
+            sampling=SamplingConfig(
+                height=args.height,
+                width=args.width,
+                num_frames=1,
+                fps=1,
+                num_inference_steps=args.steps,
+                guidance_scale=args.guidance_scale,
+                seed=args.seed,
+            ),
+            output=OutputConfig(
+                output_path=str(output.parent),
+                save_video=False,
+                return_frames=True,
+            ),
+        )
+        result = generator.generate(request)
+        if isinstance(result, list):
+            result = result[0]
+
+        frames = result.frames
+        if frames is not None and len(frames):
+            Image.fromarray(frames[0]).save(output)
+            print(f"Saved image to {output}")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,69 @@
+# LTX-2.3 distilled inference configs
+
+Ready-to-run `fastvideo generate` run configs for the LTX-2.3
+distilled model (`FastVideo/LTX-2.3-Distilled-Diffusers`), covering both
+workloads (t2v / i2v), both two-stage step schedules (`5+2`, `8+3` = denoise
+ refine), and four resolutions.
+
+```bash
+fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml
+```
+
+Each config is self-contained (no preset registry needed): the two-stage
+refine is wired via `generator.pipeline.preset_overrides.refine`, and the
+base sampling knobs live under `request.sampling`. The refine upsampler
+auto-resolves from the model's `spatial_upscaler`.
+
+## Configs
+
+| workload | schedule | resolution (HxW) | file |
+|---|---|---|---|
+| t2v | 5+2 | 1280x832 | `t2v_5s2_1280x832.yaml` |
+| t2v | 5+2 | 1024x1536 | `t2v_5s2_1024x1536.yaml` |
+| t2v | 5+2 | 768x1280 | `t2v_5s2_768x1280.yaml` |
+| t2v | 5+2 | 512x768 | `t2v_5s2_512x768.yaml` |
+| t2v | 8+3 | 1280x832 | `t2v_8s3_1280x832.yaml` |
+| t2v | 8+3 | 1024x1536 | `t2v_8s3_1024x1536.yaml` |
+| t2v | 8+3 | 768x1280 | `t2v_8s3_768x1280.yaml` |
+| t2v | 8+3 | 512x768 | `t2v_8s3_512x768.yaml` |
+| i2v | 5+2 | 1280x832 | `i2v_5s2_1280x832.yaml` |
+| i2v | 5+2 | 1024x1536 | `i2v_5s2_1024x1536.yaml` |
+| i2v | 5+2 | 768x1280 | `i2v_5s2_768x1280.yaml` |
+| i2v | 5+2 | 512x768 | `i2v_5s2_512x768.yaml` |
+| i2v | 8+3 | 1280x832 | `i2v_8s3_1280x832.yaml` |
+| i2v | 8+3 | 1024x1536 | `i2v_8s3_1024x1536.yaml` |
+| i2v | 8+3 | 768x1280 | `i2v_8s3_768x1280.yaml` |
+| i2v | 8+3 | 512x768 | `i2v_8s3_512x768.yaml` |
+
+## Overriding without editing a file
+
+Dotted overrides (prefixes `generator.` / `request.`) let you tweak any field:
+
+```bash
+# swap prompt
+fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml \
+  --request.prompt "a red fox running through fresh snow"
+
+# change output path / gpu count
+fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml \
+  --request.output.output_path outputs/preview.mp4 \
+  --generator.engine.num_gpus 4
+```
+
+## i2v
+
+The `i2v_*` configs take a first-frame image via
+`request.extensions.ltx2_images` (`[[path, frame_offset, weight]]`). Edit the
+path in the file, or override it:
+
+```bash
+fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml \
+  --request.extensions.ltx2_images '[["/data/portrait.jpg", 0, 1.0]]'
+```
+
+## Schedules
+
+`5+2` is the fast preview schedule; `8+3` is the higher-quality distilled
+recipe. Refine (`preset_overrides.refine.num_inference_steps`) only accepts 2
+or 3 steps.
+
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1024x1536.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_1024x1536.mp4
+    save_video: true
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1280x832.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_1280x832.mp4
+    save_video: true
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_512x768.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_512x768.mp4
+    save_video: true
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_768x1280.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_768x1280.mp4
+    save_video: true
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1024x1536.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_1024x1536.mp4
+    save_video: true
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_1280x832.mp4
+    save_video: true
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_512x768.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_512x768.mp4
+    save_video: true
@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_768x1280.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_768x1280.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1024x1536.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_1024x1536.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1280x832.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_1280x832.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_512x768.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_768x1280.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_768x1280.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1024x1536.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_1024x1536.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_1280x832.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_512x768.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_512x768.mp4
+    save_video: true
@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_768x1280.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_768x1280.mp4
+    save_video: true
@@ -0,0 +1,78 @@
+# Causal Consistency Distillation: Wan 2.1 T2V 1.3B Causal
+#
+# ODE-data-free distillation. A frozen teacher takes a single CFG Euler step;
+# the student matches an EMA copy of itself at the next timestep, all under
+# clean-history teacher forcing.
+#
+# All three roles initialize from the SAME checkpoint (the teacher-forcing
+# AR-diffusion model). Point init_from at that checkpoint for a real run.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+  ema:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 48
+  guidance_scale: 3.0
+  ema_decay: 0.99
+  ema_start_step: 200
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 448
+    num_width: 832
+    num_frames: 69
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_cd
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: wan2.1_causal_cd_shift5
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+
+pipeline:
+  flow_shift: 5
@@ -0,0 +1,105 @@
+# AnyFlow on-policy DMD — Wan 2.1 T2V 1.3B.
+#
+# Stage 2 of the AnyFlow two-stage recipe. Continues from the pretrain
+# checkpoint; refines the student via DMD2 with a multi-step Euler-flow
+# rollout from pure noise. Teacher provides the real score, critic
+# learns the fake score; both inherited from DMD2Method.
+#
+# Replace <PATH_TO_PRETRAIN_CKPT> with the output of the pretrain stage,
+# or with the NVIDIA-released checkpoint
+# nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers to bootstrap directly from
+# the paper weights (the delta_embedder rename is handled by the
+# param_names_mapping in WanVideoArchConfig).
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: <PATH_TO_PRETRAIN_CKPT>
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.anyflow.AnyFlowMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 3.0
+  dmd_denoising_steps: [999, 937, 833, 624]
+  warp_denoising_step: false
+
+  # AnyFlow rollout knobs.
+  student_sample_steps: 4
+  use_mean_velocity: true
+  t_list_override: [999.0, 937.0, 833.0, 624.0, 0.0]
+  dmd_score_r_value: 0.0  # DMD scoring conditioning is at r=0 (consistency target).
+
+  # Critic optimizer (DMD2 inherited).
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+  attn_kind: vsa
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/preprocessed
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_anyflow_onpolicy
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+    resume_from_checkpoint: latest
+
+  tracker:
+    project_name: anyflow-wan
+    run_name: wan2.1_t2v_anyflow_onpolicy
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+
+pipeline:
+  flow_shift: 5.0
+  dit_config:
+    r_embedder: true
+    r_embedder_fusion: gated
+    r_embedder_gate_value: 0.25
+    r_embedder_deltatime_type: r
@@ -0,0 +1,83 @@
+# AnyFlow pretrain (flow-map central-difference) — Wan 2.1 T2V 1.3B.
+#
+# Stage 1 of the AnyFlow two-stage recipe. Trains the dual-timestep
+# u_θ(x_t, t, r) on the central-difference target so the same checkpoint
+# can be sampled at arbitrary NFE in the on-policy stage.
+#
+# Initialize from base Wan 2.1 T2V 1.3B. No teacher or critic at this
+# stage; AnyFlowPretrainMethod owns a single student + one optimizer.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.anyflow_pretrain.AnyFlowPretrainMethod
+  diffusion_ratio: 0.5
+  consistency_ratio: 0.25
+  epsilon: 5            # finite-difference step in absolute train-timestep units
+  weight_type: beta08   # per-timestep loss weight = t * sqrt(1 - t), renormalized
+  fuse_guidance_scale: 3.0
+  # shift is taken from pipeline.flow_shift below.
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/preprocessed
+    dataloader_num_workers: 4
+    train_batch_size: 4
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+
+  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: 6000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_anyflow_pretrain
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+    resume_from_checkpoint: latest
+
+  tracker:
+    project_name: anyflow-wan
+    run_name: wan2.1_t2v_anyflow_pretrain
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+
+pipeline:
+  flow_shift: 5.0
+  dit_config:
+    # Enable AnyFlow dual-timestep conditioning. The student loads from
+    # base Wan 2.1 — its checkpoint has no delta_embedder weights, so they
+    # get initialized identically to time_embedder via deep-copy in
+    # WanTimeTextImageEmbedding.__init__.
+    r_embedder: true
+    r_embedder_fusion: gated
+    r_embedder_gate_value: 0.25
+    r_embedder_deltatime_type: r
@@ -100,9 +100,6 @@ callbacks:
    sampling_steps: [4]
    sampling_timesteps: [1000, 750, 500, 250]
    num_frames: 81
-    # Validation/inference uses standard CFG in both clean and Self-Forcing,
-    # so this directly matches Self-Forcing guidance_scale=3.0.
-    guidance_scale: 3.0

 pipeline:
  flow_shift: 5
@@ -0,0 +1,73 @@
+# DFSFT (Diffusion-Forcing SFT), frame-wise: Wan 2.1 T2V 1.3B Causal
+#
+# - Student: trainable causal Wan model with a block size of 1 frame
+# - Training: each frame gets its own independent noise level (frame-wise
+#   diffusion forcing), versus the chunk-wise variant that shares one noise
+#   level across num_frames_per_block frames.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    num_frames_per_block: 1
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  chunk_size: 1
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 448
+    num_width: 832
+    num_frames: 69
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_dfsft_framewise
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: wan2.1_causal_dfsft_framewise_shift5_gauss_weight
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [40]
+    guidance_scale: 6.0
+    num_frames: 69
+
+pipeline:
+  flow_shift: 5
@@ -0,0 +1,72 @@
+# TFSFT (Teacher-Forcing SFT): Wan 2.1 T2V 1.3B Causal
+#
+# - Student: trainable causal Wan model
+# - Training: inhomogeneous timesteps per chunk, but the causal transformer
+#   denoises the current block while attending to *clean* history (clean_x),
+#   not its own noisy rollout.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 448
+    num_width: 832
+    num_frames: 69
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_tfsft
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: wan2.1_causal_tfsft_shift5_gauss_weight
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [40]
+    guidance_scale: 6.0
+    num_frames: 69
+
+pipeline:
+  flow_shift: 5
@@ -1,32 +1,119 @@
 # World-Model: Matrix-Game 2.0 I2V

-Three training scenarios for the Matrix-Game 2.0 I2V world model on the
-new YAML-driven trainer (`fastvideo/train/entrypoint/train.py`).
+Training scenarios for the Matrix-Game 2.0 I2V world model on Solaris (Minecraft)
+data and Zelda data, using the new YAML-driven trainer
+(`fastvideo/train/entrypoint/train.py`).
+
+## Solaris Configs

 | Config | Method | Student | Notes |
 |---|---|---|---|
-| `finetune_i2v.yaml` | `FineTuneMethod` | `MatrixGame2Model` (bidirectional) | Multi-step SFT from `mg_bidirectional_Solaris`. |
-| `dfsft_causal_i2v.yaml` | `DiffusionForcingSFTMethod` | `MatrixGame2CausalModel` | Diffusion-Forcing SFT with chunkwise timesteps. |
-| `self_forcing_causal_i2v.yaml` | `SelfForcingMethod` | `MatrixGame2CausalModel` | DMD/Self-Forcing distillation; teacher = bidirectional, critic = bidirectional. |
+| `solaris/finetune_i2v.yaml` | `FineTuneMethod` | `MatrixGame2Model` (bidirectional) | Multi-step SFT from `mg_bidirectional_Solaris`. |
+| `solaris/dfsft_causal_i2v.yaml` | `DiffusionForcingSFTMethod` | `MatrixGame2CausalModel` | Diffusion-Forcing SFT with chunkwise timesteps. |
+| `solaris/self_forcing_causal_i2v.yaml` | `SelfForcingMethod` | `MatrixGame2CausalModel` | Matrix-Game 2.0 DMD/Self-Forcing distillation; teacher = bidirectional, critic = bidirectional. |
+
+## Zelda Configs
+
+| Config | Method | Student | Notes |
+|---|---|---|---|
+| `zelda/finetune_i2v.yaml` | `FineTuneMethod` | `MatrixGame2Model` (bidirectional) | Zelda bidirectional I2V finetuning from `FastVideo/Matrix-Game-2.0-Base-Diffusers`. Uses 33-frame clips and Zelda validation with action overlays. |
+| `zelda/dfsft_causal_i2v.yaml` | `DiffusionForcingSFTMethod` | `MatrixGame2CausalModel` | Zelda causal Diffusion-Forcing SFT from `mignonjia/mg_bidirectional_zelda`. Uses the same Zelda data, resolution, optimizer, and validation defaults as the Zelda finetune config. |
+| `zelda/self_forcing_causal_i2v.yaml` | `SelfForcingMethod` | `MatrixGame2CausalModel` | Zelda DMD/Self-Forcing distillation; student init = `mignonjia/mg_causal_zelda`, teacher = bidirectional (`mignonjia/mg_bidirectional_zelda`), critic = bidirectional. |
+| `zelda/streaming_long_tuning_causal_i2v.yaml` | `StreamingLongTuningMethod` | `MatrixGame2CausalModel` | LongLive-style streaming long tuning from the 1k-step Zelda self-forcing checkpoint. |
+
+Zelda world-model distillation is a two-run workflow: first run
+`zelda/self_forcing_causal_i2v.yaml` to train or load the 1k-step
+self-forcing checkpoint (`mignonjia/mg_sf_distilled_zelda_1k_steps`), then run
+`zelda/streaming_long_tuning_causal_i2v.yaml` for the 3k-step streaming
+long-tuning stage. The long-tuning YAML starts from that 1k-step checkpoint; it
+does not run the short self-forcing stage inside the same config.
+
+## Zelda Training Data
+
+The Zelda training configs use `data/zeldam2-clean` as a suggested local path.
+Download the dataset from Hugging Face before running those configs:
+
+```bash
+python scripts/huggingface/download_hf.py \
+    --repo_id mignonjia/zeldam2-clean \
+    --local_dir data/zeldam2-clean \
+    --repo_type dataset
+```
+
+You can store the dataset elsewhere; update `training.data.data_path` in the
+YAML to point at that location.
+
+## Multi3D Training Data
+
+`zelda/finetune_i2v.yaml` includes an optional, commented-out Multi3D entry.
+Enable it only when you want to mix Zelda with multi-game data from
+`data/multi3d_games`. You can store this dataset anywhere; before enabling it,
+update the matching commented `training.data.data_path` key in the YAML to the
+correct location.
+
+To mix datasets in a training YAML, set `training.data.data_path` to a
+path-to-repeat-count mapping. For example, `zelda/finetune_i2v.yaml` can use
+`data/zeldam2-clean: 1` and `# data/multi3d_games: 10`; uncommenting the
+Multi3D entry repeats the multi-game parquet list ten times before training
+samples are shuffled.
+
+## World Model Validation Data
+
+The Zelda validation configs expect a small public validation bundle under
+`data/zelda_validation_data`.
+
+Download it from Hugging Face before running the Zelda scenarios:
+
+```bash
+python scripts/huggingface/download_hf.py \
+    --repo_id mignonjia/zelda_validation_data \
+    --local_dir data/zelda_validation_data \
+    --repo_type dataset
+```
+
+The bundle contains `validation_zelda.json`, `images/`, and `actions/`.
+The Zelda configs point
+`callbacks.validation.dataset_file` at
+`data/zelda_validation_data/validation_zelda.json`.

 ## Usage

+### Solaris
+
 ```bash
 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/finetune_i2v.yaml
+    examples/train/scenario/worldmodel/solaris/finetune_i2v.yaml

 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/dfsft_causal_i2v.yaml
+    examples/train/scenario/worldmodel/solaris/dfsft_causal_i2v.yaml

 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/self_forcing_causal_i2v.yaml
+    examples/train/scenario/worldmodel/solaris/self_forcing_causal_i2v.yaml
+```
+
+### Zelda
+
+```bash
+# Finetuning / DFSFT
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/finetune_i2v.yaml
+
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/dfsft_causal_i2v.yaml
+
+# Distillation / long tuning
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/self_forcing_causal_i2v.yaml
+
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/streaming_long_tuning_causal_i2v.yaml
 ```

 Override any field on the command line:

 ```bash
 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/dfsft_causal_i2v.yaml \
+    examples/train/scenario/worldmodel/solaris/dfsft_causal_i2v.yaml \
    --training.distributed.num_gpus 8 \
    --training.optimizer.learning_rate 1e-5
 ```
@@ -97,4 +97,4 @@ callbacks:
    guidance_scale: 6.0

 pipeline:
-  flow_shift: 5
+  flow_shift: 5
@@ -0,0 +1,94 @@
+# Diffusion-Forcing SFT: Zelda world model I2V Causal
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2_causal.MatrixGame2CausalModel
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  chunk_size: 3
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path:
+      data/zeldam2-clean: 1
+    dataloader_num_workers: 1
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 42
+    num_latent_t: 9
+    num_height: 480
+    num_width: 832
+    num_frames: 33
+
+  optimizer:
+    learning_rate: 2.0e-5
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 60000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/matrixgame_finetune/checkpoints/zelda_causal_dfsft
+    training_state_checkpointing_steps: 5000
+    checkpoints_total_limit: 3
+
+  tracker:
+    entity: hapo-exp
+    project_name: mg_1.3b_zelda
+    run_name: zelda_causal_dfsft
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_causal_dmd_pipeline.MatrixGame2CausalDMDPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 200
+    sampling_steps: [40]
+    sampling_timesteps: [1000, 975, 950, 925, 900, 875, 850, 825, 800, 775,
+                         750, 725, 700, 675, 650, 625, 600, 575, 550, 525,
+                         500, 475, 450, 425, 400, 375, 350, 325, 300, 275,
+                         250, 225, 200, 175, 150, 125, 100, 75, 50, 25]
+    num_frames: 33
+    overlay_actions: true
+    guidance_scale: 6.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
@@ -0,0 +1,88 @@
+# Matrix-Game 2.0 Zelda + multi-game I2V finetune.
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: FastVideo/Matrix-Game-2.0-Base-Diffusers
+    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: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path:
+      data/zeldam2-clean: 1
+      # data/multi3d_games: 10
+    dataloader_num_workers: 1
+    train_batch_size: 1
+    training_cfg_rate: 0.0  # unused for MatrixGame2 I2V; no text_embedding CFG dropout
+    seed: 42
+    num_latent_t: 9
+    num_height: 480
+    num_width: 832
+    num_frames: 33
+
+  optimizer:
+    learning_rate: 2.0e-5
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 60000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/matrixgame_finetune/checkpoints/zelda_with_mg_init
+    training_state_checkpointing_steps: 5000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: mg_1.3b_zelda
+    run_name: zelda_with_mg_init
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+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.matrixgame2.matrixgame2_i2v_pipeline.MatrixGame2I2VPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 200
+    sampling_steps: [40]
+    num_frames: 33
+    overlay_actions: true
+    guidance_scale: 6.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5
@@ -0,0 +1,120 @@
+# Self-forcing distillation: Zelda world model I2V Causal
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2_causal.MatrixGame2CausalModel
+    init_from: mignonjia/mg_causal_zelda
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  warp_denoising_step: true
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+
+  chunk_size: 3
+  student_sample_type: sde
+  same_step_across_blocks: true
+  last_step_only: false
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+
+  # Critic optimizer
+  fake_score_learning_rate: 3.0e-7
+  fake_score_betas: [0.9, 0.95]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/zeldam2-clean
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1001
+    num_latent_t: 9
+    num_height: 480
+    num_width: 832
+    num_frames: 33
+
+  optimizer:
+    learning_rate: 3.0e-6
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 1000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/zelda_causal_self_forcing
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 1
+
+  tracker:
+    project_name: wangame_sf
+    run_name: mg2_self_forcing_9_latents
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_causal_dmd_pipeline.MatrixGame2CausalDMDPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 100
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    num_frames: 153
+    overlay_actions: true
+    keyboard_value_scale: 1.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
@@ -0,0 +1,140 @@
+# MatrixGame2 I2V LongLive-style streaming distillation: Zelda world model I2V Causal
+# Student init from self forcing after 1k steps
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2_causal.MatrixGame2CausalModel
+    init_from: mignonjia/mg_sf_distilled_zelda_1k_steps 
+    trainable: true
+    # transformer_override_safetensor: outputs/matrixgame_dmd/checkpoints/mg_zelda_sf_m2/checkpoint-1000_weight_only/ema/generator_ema.safetensors
+  teacher:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.streaming_long_tuning.StreamingLongTuningMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  warp_denoising_step: true
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+
+  chunk_size: 3
+  student_sample_type: sde
+  same_step_across_blocks: true
+  last_step_only: false
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+
+  streaming_training: true
+  streaming_chunk_size: 9
+  streaming_max_length: 39
+  streaming_fixed_overlap_latents: 3
+  streaming_reencode_overlap_anchor: true
+  streaming_anchor_inject_k: 1
+  streaming_require_full_blocks: true
+
+  multi_phased_distill_schedule:
+    - stage: streaming_long
+      start_step: 0
+      end_step: 3000
+      num_latent_t: 39
+      streaming_training: true
+      streaming_chunk_size: 9
+      streaming_max_length: 39
+      streaming_fixed_overlap_latents: 3
+
+  # Critic optimizer
+  fake_score_learning_rate: 3.0e-7
+  fake_score_betas: [0.9, 0.95]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/zeldam2-clean
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1001
+    num_latent_t: 39
+    num_height: 480
+    num_width: 832
+    num_frames: 153
+
+  optimizer:
+    learning_rate: 3.0e-6
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/zelda_causal_long_tuning
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 2
+
+  tracker:
+    project_name: wangame_sf
+    run_name: mg2_39only_streaming_long
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_causal_dmd_pipeline.MatrixGame2CausalDMDPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 100
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    num_frames: 153
+    overlay_actions: true
+    keyboard_value_scale: 1.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
@@ -12,6 +12,9 @@ set(_FASTVIDEO_USER_CUDA_ARCH "${CMAKE_CUDA_ARCHITECTURES}")
 if(NOT DEFINED GPU_BACKEND AND DEFINED ENV{GPU_BACKEND})
    set(GPU_BACKEND "$ENV{GPU_BACKEND}")
 endif()
+if(NOT GPU_BACKEND)
+    set(GPU_BACKEND "CUDA")
+endif()

 if(GPU_BACKEND STREQUAL "ROCM")
    enable_language(HIP)
@@ -50,7 +53,16 @@ if(NOT GPU_BACKEND STREQUAL "ROCM")
        if(_FASTVIDEO_USER_CUDA_ARCH)
            # Caller pinned -DCMAKE_CUDA_ARCHITECTURES (which torch ignores); translate it
            # to the TORCH_CUDA_ARCH_LIST spelling: "121" -> "12.1", "90a" -> "9.0a".
+            # Only numeric spellings translate; keywords like "native"/"all" would
+            # otherwise be mangled into nonsense ("nativ.e").
            foreach(_fv_arch IN LISTS _FASTVIDEO_USER_CUDA_ARCH)
+                if(NOT _fv_arch MATCHES "^[0-9]+[af]?$")
+                    message(FATAL_ERROR
+                        "fastvideo-kernel: CMAKE_CUDA_ARCHITECTURES='${_fv_arch}' is not "
+                        "supported. Use a numeric arch (e.g. 90a, 121), set "
+                        "TORCH_CUDA_ARCH_LIST directly (e.g. 9.0a), or unset both to "
+                        "auto-detect from the visible GPU.")
+                endif()
                string(REGEX MATCH "[af]$" _fv_suffix "${_fv_arch}")
                string(REGEX REPLACE "[af]$" "" _fv_num "${_fv_arch}")
                string(REGEX REPLACE "(.)$" ".\\1" _fv_num "${_fv_num}")    # dot before the last digit
@@ -173,6 +185,14 @@ else()
    endif()
 endif()

+# ThunderKittens headers don't compile on aarch64 hosts: plain char is unsigned
+# there, and tk's base_types.cuh brace-initializes signed-char vector members
+# from char (narrowing error). Skip TK until upstream is aarch64-clean.
+if(ENABLE_TK_KERNELS AND CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm64)$")
+    message(STATUS "ThunderKittens kernels: forced OFF on ${CMAKE_SYSTEM_PROCESSOR} (tk headers are not aarch64-clean)")
+    set(ENABLE_TK_KERNELS OFF)
+endif()
+
 if(ENABLE_TK_KERNELS)
    message(STATUS "ThunderKittens kernels: ENABLED")
 else()
@@ -263,11 +283,6 @@ set(CUDA_FLAGS
    "--expt-relaxed-constexpr"
    "-Xcompiler=-fno-strict-aliasing"
    "-Xcompiler=-fPIC"
-    # ARM/aarch64 defaults `char` to unsigned, but ThunderKittens headers assume the
-    # x86 signed-char behavior (else base_types.cuh hits "narrowing conversion from
-    # char to signed char"). Force signed char so TK compiles on Grace Hopper; this
-    # is a no-op on x86_64, where char is already signed.
-    "-Xcompiler=-fsigned-char"
    "-DTORCH_COMPILE"
    "-Xnvlink=--verbose"
    "-Xptxas=--verbose"
@@ -426,3 +441,14 @@ if(ENABLE_ATTN_QAT_INFER)
    install(TARGETS fp4attn_cuda LIBRARY DESTINATION .)
    install(TARGETS fp4quant_cuda LIBRARY DESTINATION .)
 endif()
+
+# One-look answer to "what is this build producing?" — kept last so it is the
+# final thing configure prints. The per-kernel matrix lives in README.md.
+message(STATUS "============== fastvideo-kernel build summary ==============")
+message(STATUS "host / backend:       ${CMAKE_SYSTEM_PROCESSOR} / ${GPU_BACKEND}")
+message(STATUS "TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST}")
+message(STATUS "fastvideo_kernel_ops: ON  (turbodiffusion int8-gemm/quant/rmsnorm/layernorm, all listed archs)")
+message(STATUS "  + TK sta/block_sparse (sm_90a only):          ${ENABLE_TK_KERNELS}")
+message(STATUS "fp4attn/fp4quant (sm_120a only, CUDA >= 12.8):  ${ENABLE_ATTN_QAT_INFER}")
+message(STATUS "Triton fallbacks ship in python/fastvideo_kernel/triton_kernels regardless.")
+message(STATUS "============================================================")
@@ -2,6 +2,42 @@

 CUDA kernels for FastVideo video generation.

+## Kernel inventory
+
+Compiled CUDA extensions (CMake, see the build summary printed at the end of every configure):
+
+| Extension | Kernels | Sources | GPU arch | Build gate |
+|---|---|---|---|---|
+| `fastvideo_kernel._C.fastvideo_kernel_ops` | TurboDiffusion INT8 GEMM, quant, RMSNorm, LayerNorm | `csrc/turbodiffusion/` | every arch in `TORCH_CUDA_ARCH_LIST` | always built |
+| same extension, optional part | ThunderKittens sliding-tile attention (`sta_fwd`) and VSA block-sparse (`block_sparse_fwd/bwd`) | `csrc/attention/*_h100.cu` | Hopper `sm_90a` only | `FASTVIDEO_KERNEL_BUILD_TK` (AUTO = ON iff `9.0a` is in the arch list; always OFF on aarch64 hosts — TK headers don't compile there) |
+| `fp4attn_cuda`, `fp4quant_cuda` | FP4 attention + quantization ("attn_qat_infer", modified SageAttention3) | `attn_qat_infer/` | consumer Blackwell `sm_120a` only, CUDA ≥ 12.8 | `FASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER` (AUTO = ON iff `12.0a` is in the arch list) |
+
+Runtime-JIT kernels (no build step, ship in every wheel/image):
+
+| Kernels | Where | Used when |
+|---|---|---|
+| Triton: STA, VSA block-sparse, SLA, fused compress+topk, FP4 QAT training, quant/norm utils | `python/fastvideo_kernel/triton_kernels/` | automatic fallback when the matching C++ op is absent (`ops.py`, `turbodiffusion_ops.py`) |
+| FA4 CuTe-DSL block-sparse (VSA-256 fastpath on `sm_100`) | `block_sparse_attn_cute_fwd.py` | optional `flash_attn.cute` dependency, see below |
+| VMoBA `moba_attn_varlen` | `vmoba.py` | wraps flash-attn varlen |
+
+## What gets built where, and when
+
+| Surface | Trigger | Leg | `TORCH_CUDA_ARCH_LIST` | TK | FP4 |
+|---|---|---|---|---|---|
+| PyPI wheels (`.github/workflows/publish-kernel.yml`) | version bump in `fastvideo-kernel/pyproject.toml` on main, or manual dispatch | x86_64 cu126 | `9.0a` | ON | — (CUDA < 12.8) |
+| | | x86_64 cu130 | `9.0a;12.0a` | ON | ON |
+| | | aarch64 cu130 | `10.0a;12.0a` | — | ON |
+| Docker images `ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev` (`.github/workflows/infra-build-image.yml`) | `docker/Dockerfile` changes on main, or manual dispatch | amd64 cuda12.6.3 + cuda13.0.0 | `9.0a` | ON | — |
+| | | arm64 cuda12.6.3 (GH200) | `9.0a` | — (aarch64) | — |
+| | | arm64 cuda13.0.0 (GB10 / DGX Spark) | `12.1` | — | — |
+| Local `./build.sh` | manual | probes the visible GPU via torch | detected | ON iff sm_90 (non-aarch64 host) | ON iff sm_120 |
+
+Notes:
+
+- No Docker image ships the FP4 kernels; only the x86_64/aarch64 cu130 wheels do.
+- On arm64 images (GH200 included) STA/VSA run on the Triton fallbacks, since TK never builds on aarch64.
+- Kernel tests run on Buildkite GPU CI for PRs touching `fastvideo-kernel/**` (see `.buildkite/pipeline.yml`).
+
 ## Installation

 ### Standard Installation (Local Development)
@@ -46,6 +46,23 @@ fi
 if git rev-parse --git-dir >/dev/null 2>&1; then
    git submodule update --init --recursive include/cutlass include/tk
 fi
+# Fail fast with a clear message if the headers are still missing (e.g. a
+# Docker context that excluded .git AND the submodule contents) instead of
+# dying later in a wall of nvcc include errors. CUTLASS is consumed by the
+# always-built turbodiffusion sources, so it is a hard error; ThunderKittens
+# only feeds the TK-gated Hopper kernels, so a missing tree just warns (the
+# TK gate resolves later, and non-SM90/ROCm builds never touch it).
+if [ ! -d include/cutlass/include ]; then
+    echo "ERROR: include/cutlass/include is missing. Outside a git checkout the" >&2
+    echo "       CUTLASS sources must already be present (run" >&2
+    echo "       'git submodule update --init --recursive include/cutlass include/tk'" >&2
+    echo "       in the source checkout, or include them in the build context)." >&2
+    exit 1
+fi
+if [ ! -d include/tk/include ]; then
+    echo "WARNING: include/tk/include is missing; ThunderKittens (Hopper sm_90a)" >&2
+    echo "         kernels cannot be built. Fine for non-SM90/ROCm targets." >&2
+fi

 # Install build dependencies
 uv pip install scikit-build-core cmake ninja
@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from fastvideo.api.sampling_param import SamplingParam
+
+
+@dataclass
+class FluxSamplingParam(SamplingParam):
+
+    prompt: str | None = "a photo of a cat"
+    negative_prompt: str = ""
+
+    num_videos_per_prompt: int = 1
+    seed: int = 0
+
+    num_frames: int = 1
+    height: int = 1024
+    width: int = 1024
+    fps: int = 1
+
+    num_inference_steps: int = 28
+    guidance_scale: float = 3.5
+    use_embedded_guidance: bool = True
+    true_cfg_scale: float = 1.0
@@ -90,6 +90,10 @@ class SamplingParam:
    num_inference_steps_sr: int = 50
    guidance_scale: float = 1.0
    guidance_scale_2: float | None = None
+    # Embedded guidance (FLUX): do not treat ``guidance_scale > 1`` as classic CFG.
+    use_embedded_guidance: bool = False
+    # Diffusers-style true CFG for FLUX when > 1 (requires negative prompt encoding).
+    true_cfg_scale: float = 1.0
    guidance_rescale: float = 0.0
    boundary_ratio: float | None = None
    sigmas: list[float] | None = None
@@ -325,6 +329,18 @@ class SamplingParam:
            default=SamplingParam.guidance_rescale,
            help="Guidance rescale factor",
        )
+        parser.add_argument(
+            "--use-embedded-guidance",
+            action="store_true",
+            default=SamplingParam.use_embedded_guidance,
+            help="Use embedded guidance scale (FLUX-style) instead of classic CFG",
+        )
+        parser.add_argument(
+            "--true-cfg-scale",
+            type=float,
+            default=SamplingParam.true_cfg_scale,
+            help="True CFG scale for FLUX when > 1 (requires negative prompt encoding)",
+        )
        parser.add_argument(
            "--boundary-ratio",
            type=float,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	59cf9d6fa2	[fix] dreamverse: steering session-flow, mock parity, and init-image validation Steering protocol/UI fixes that interlock via one contract change — the frontend now sends initial_rollout_prompt_id (its own prompt id for the opening scene) in session-init and reset payloads, and both backend pre-seed sites use it, so prompt lifecycle events (enhancing/ready/fallback) finally match the frontend's records instead of no-oping against a backend uuid. - controller: suppress prompt_sources_blocked while a submission is queued or enhancing (fixes the blank player at typed-prompt session start); segment prompt logging is now opt-in via DREAMVERSE_SEGMENT_PROMPT_LOG, written off the event loop, warning on failure (drops the hardcoded author-machine path and bare except). - web: 'Generating next scene' overlay driven by explicit generation state instead of waitingForSegmentPrompt prop edges, so auto_extension_updated while idle can't strand it over the video; steering scene history is no longer truncated by the 24-event prompt feed cap and keeps stable numbers for 30+ scene sessions. - mock server: manual_continuation_mode parity (no rewrite-flow wait, honors initial_rollout_prompt_id, no segment cap in manual mode) so the GPU-less dev backend works with the steering-only frontend. - init image: client-side type/size validation on picker and paste paths (png/jpeg/webp, 15MB) with a visible error, and ws_max_size raised to 32MiB on both servers so a legitimate ~15MB image reaches the backend's own validation instead of tripping uvicorn's 16MiB frame cap.	2026-07-16 20:39:32 -07:00
SolitaryThinker	0c90c328c5	[fix] ltx2: drop unreachable video_position_offset_sec kwargs fallback video_position_offset_sec is a declared parameter of forward, so a caller's keyword binds to it and never lands in **kwargs — the added kwargs.get("video_position_offset_sec", 0.0) block was unreachable dead code with a false comment, and it rebound the local to 0.0. The pre-existing application of the offset is the only live path; behavior is unchanged.	2026-07-16 20:39:32 -07:00
SolitaryThinker	cfb54a3b2a	[fix] dreamverse: env-tunable session timeout, restore warmup watchdog, derive SP size in launch script - SESSION_TIMEOUT_SECONDS: default back to 300 and now reads the DREAMVERSE_SESSION_TIMEOUT_SECONDS env var that launch-dreamverse.sh was already exporting (previously nothing read it, and the hardcoded 1800 made every deployment hold idle GPU-pool slots 6x longer). Fixes the stale five-minute-timeout test and adds an override test. - STARTUP_WARMUP_TIMEOUT_SECONDS: default back to 2400 so the warmup watchdog works again; launch-dreamverse.sh exports the existing FASTVIDEO_STARTUP_WARMUP_TIMEOUT_SECONDS override (24000) for the slow GB200 max-autotune boot. - launch-dreamverse.sh: derive the DREAMVERSE_SP_SIZE default from the number of visible GPUs so the documented CUDA_VISIBLE_DEVICES=0 invocation no longer crashes gpu_pool with 'Not enough GPUs'; explicit env still wins. setup-dreamverse-env.sh's printed instruction now matches. gpu_pool raises a friendly error for non-integer CUDA_VISIBLE_DEVICES entries. - video_generation.py: drop unused ParallelismConfig import (F401).	2026-07-16 20:39:13 -07:00
SolitaryThinker	deb51f1dcc	[fix] dreamverse: return last outer JSON object when parsing enhancer replies The free-form scanner attempted raw_decode at every '{', including braces inside an already-decoded object, so 'return the last decodable object' (deliberate: chain-of-thought models emit draft JSON before the final answer) actually returned the innermost/trailing nested fragment — e.g. {"next_prompt": ..., "style": {"mood": "noir"}} parsed to {"mood": "noir"} and enhancement fell back, failing steering requests. Scan with a position cursor instead: skip past the consumed span of every successfully decoded object, and skip malformed/truncated spans wholesale via a balanced-brace scan so their nested fragments can't displace an earlier complete object. Regression tests cover nested values, sequential drafts, truncated tails, and the nested rollout shape.	2026-07-16 20:38:57 -07:00
alexzms	79a5930340	[fix] dreamverse: drop steering hint line that overlapped the hero title on mobile The two-line 'Drive each scene yourself...' hint grew the vertically-centered landing content, pushing the preset cards up into the absolutely-positioned hero title on short mobile viewports. Removing it restores the clean spacing.	2026-07-15 23:20:23 +00:00
alexzms	3a0ce7c20d	[misc] dreamverse: bump steering generating progress bar to 10.5s	2026-07-15 23:20:23 +00:00
alexzms	0b006a9e46	[feat] dreamverse: robust steering 'Generating next scene' overlay Drive the generating indicator off whether the new segment has actually landed (buffered timeline grows past the boundary captured at generation start) instead of bare playback events, so scrubbing back and replaying to the end keeps it visible and it clears promptly once frames arrive. Gate the overlay on playbackReachedEnd like 'Segment complete', and bump the progress bar ETA to 8s.	2026-07-15 23:20:23 +00:00
alexzms	fb9acac514	[feat] dreamverse: steering-only UI — drop auto-rollout mode selector Remove the pre-session Auto rollout / Steering segmented control and make manual continuation (steering) the sole mode: default it on in the session store and keep it on across project/lobby resets. Leaves a short steering hint in its place.	2026-07-15 23:20:22 +00:00
kevin314	2d78957219	6+2	2026-07-15 23:20:22 +00:00
kevin314	63991d2017	Test	2026-07-15 23:20:22 +00:00
alexzms	b6eafbea50	[fix] dreamverse: recover steering after a blocked or failed prompt When a steering prompt is refused by the enhancer (e.g. content-policy) or enhancement fails for all providers, the backend enqueues nothing and won't re-emit prompt_sources_ blocked, leaving the UI stuck on the generating overlay. On prompt/fallback_used and session/error in steering, return to the 'describe the next scene' state, drop the failed scene from the history (steeringFailed), and surface a retry notice; clear the notice on the next submit.	2026-07-15 23:20:22 +00:00
alexzms	7b84041642	[feat] dreamverse: steering scene history of per-segment user prompts Steering mode now shows an elegant list of each scene's prompt above the player. The text comes from the user's own words, captured stably at submit time as rawText (the backend later overwrites text/source with the enhanced prompt, so those are never read); a preset's opening scene with no user prompt falls back to promptHistory. The redundant ChatBar 'Segment complete' banner is dropped (the video overlay already says it, and it was squeezing the list), and a ResizeObserver re-pins the list to the latest scene when the area resizes.	2026-07-15 23:20:22 +00:00
kevin314	48801c29c7	Add image input UI	2026-07-15 23:20:22 +00:00
alexzms	cb8be0d3f3	[feat] dreamverse: main-UI steering mode switch + first-segment-only seeding Add a user-facing Auto rollout / Steering segmented control to the main composer (not just devtools), wired to manualContinuationMode and authoritative at session start. In steering mode a preset seeds only its first segment and auto/loop are forced off, so the backend waits for the user to describe each subsequent scene by hand.	2026-07-15 23:20:22 +00:00
alexzms	1bc7ff79e6	[ui] dreamverse: logo links home, replace Join Waitlist with Blog The FastVideo logo now navigates to the app home (most intuitive), and the Join-Waitlist buttons (header desktop/mobile + session-ended card) become a Blog link pointing at the Dreamverse blog.	2026-07-15 23:20:21 +00:00
alexzms	76fbe472ae	[feat] dreamverse: allow video download at any time during playback handleDownloadVideo already remuxes live (including in-progress) segments, but the button was gated on a finalized clip blob. Surface it as soon as playback starts (avPlaybackStarted) so the user can grab the in-progress video at any point — important for unbounded steering sessions.	2026-07-15 23:20:21 +00:00
alexzms	eba74c43ef	[feat] dreamverse: graceful segment-complete & generating overlays in steering playback When a segment finishes in steering mode the player no longer spins. Instead it shows a soft 'Segment complete' prompt over the frozen last frame (gated on the playhead actually reaching the buffered end, and hidden again when the user scrubs back). After the user submits the next scene, a ~4.5s progress bar covers the generation latency so the wait has a visible ETA.	2026-07-15 23:20:21 +00:00
alexzms	7744a74c13	[feat] dreamverse: steering-mode toggle in devtools composer Add a 'Steering mode' checkbox to the devtools composer and thread the manualContinuationEnabled / onManualContinuationToggle props through DevtoolsShell.	2026-07-15 23:20:21 +00:00
alexzms	d746b8b956	[feat] dreamverse: unlimited segments in steering mode Steering (manual continuation) lets the user drive the rollout segment-by-segment indefinitely. Treat it like single-clip mode for the generation cap: _resolve_generation_segment_cap returns 0 (unlimited) and the cap-reached guard is skipped when manual_continuation_mode is on.	2026-07-15 23:20:21 +00:00
alexzms	65f12605d8	[bugfix] ltx2: apply video_position_offset_sec RoPE offset The DiT forward swallowed video_position_offset_sec via **kwargs, so multi-segment rollouts never advanced the temporal RoPE phase between segments, causing ~1s audio/ video desync at each seam. Add the offset to the temporal position coords (mirrors hao-ai-lab/FastVideo#1422).	2026-07-15 23:20:21 +00:00
kevin314	1e1ac08cd0	Add manual continuation	2026-07-15 23:20:21 +00:00
Mac LeeandSolitaryThinker	a253856147	[ci] Add exact identity performance statuses (#1560 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-15 14:59:09 -07:00
Satyam Srivastavaandgemini-code-assist[bot]	6e25d94ebc	[ci]: enable scheduled perf runs to update rolling baseline (#1599 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-07-14 16:55:12 -07:00
Mac Lee	cae8fa18dc	[bugfix]: propagate Qwen2.5-VL visual dtype (#1580 )	2026-07-13 18:37:11 -07:00
William Lin	821e5a0832	[bugfix]: fix FlashAttention resolver tests after tuple return (#1597 )	2026-07-13 16:29:03 -07:00
William Lin	c1abc42782	[bugfix]: allow unrestricted head sizes in SDPA (#1596 )	2026-07-13 16:04:51 -07:00
William Lin	ef15ea2391	[bugfix]: keep LTX2 rms_norm outputs bf16 under torch 2.12 autocast (#1587 )	2026-07-13 16:04:21 -07:00
Mac Lee	1ea2517e22	[ci]: extend LoRA training CI timeout (#1589 )	2026-07-13 02:47:28 -07:00
MookandSolitaryThinker	0c63528c59	[perf] Cache RoPE position-embedding tables across denoising steps (#1442 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-13 02:34:51 -07:00
Raghav K Ishan Vaish Claude Opus 4.8	b063f8ca41	[feat] Fix FLUX.1-dev port: native RoPE, parity tests, SSIM reference (#1321 ) Co-authored-by: Ishan Vaish <ivaish@ucsd.edu> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-13 01:49:42 -07:00
William Lin	e7fff0173a	[bugfix]: benchmark_weight_loading_comparison.py — iterate safe_open via .keys() (#1378 )	2026-07-12 22:50:45 -07:00
Shreejith SGandH1yori233	d82abc271e	[feat] Add GLM-Image inference support (#1030 ) Co-authored-by: H1yori233 <k1kong@ucsd.edu>	2026-07-12 22:42:42 -07:00
Guian FangandSolitaryThinker	970409962f	[feat] Add AnyFlow any-step video distillation (pretrain + on-policy) (#1371 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-12 02:32:08 +00:00
Raghav K	055586703d	[perf]: register a real backward for FA2 default + masked/varlen custom ops (training-under-compile) (#1388 )	2026-07-12 00:45:27 +00:00
Mac Lee	5d89f86675	[ci] Stop forcing FA4 in model-load lanes (#1561 )	2026-07-11 14:10:36 -07:00
Satyam Srivastava	19a51a1fe6	[ci] Trigger performance benchmarks for performance code changes (#1583 )	2026-07-10 20:21:33 -07:00
William Lin	d3232cea5a	[ci]: gate the full-suite trigger on pre-commit and docs build (#1572 )	2026-07-11 02:56:49 +00:00
Raghav KandSolitaryThinker	0c90c8c24d	[bugfix] nvfp4: cast fp32 inputs to bf16 instead of asserting (#1488 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-10 21:39:17 +00:00
Mingjia HuoandClaude Fable 5	4c08ffce49	[feat] World model training using third person games (#1443 ) Co-authored-by: Claude Fable 5 <noreply@anthropic.com>	2026-07-10 05:09:07 +00:00
Atharv Ramesh	af4a77553c	[ci]: add SSIM reference bootstrap flow (#1522 ) (#1547 )	2026-07-10 01:49:38 +00:00
alexzmsandSolitaryThinker	c096fda1eb	[docs] Add LTX-2.3 distilled inference run configs (t2v/i2v × 5+2/8+3 × resolutions) (#1568 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-09 18:53:12 +00:00
William Lin	8f47e85be0	[bugfix]: retry remote image downloads in load_image (#1570 )	2026-07-09 07:33:24 -07:00
William Lin	90d3bd19eb	[infra] Deliver per-job Buildkite env to Modal CI at runtime, not as image layers (#1569 )	2026-07-09 06:33:37 -07:00
Mac LeeandSolitaryThinker	afb4f7d3c5	[ci]: emit v2 performance result schema (#1551 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-09 06:11:15 +00:00
Aryan Kumar Aryan Kumar leffff SolitaryThinker	02e1143f22	[feat] Add Kandinsky-5 T2V/I2V pipeline support (#1471 ) Co-authored-by: Aryan Kumar <aryan5v@users.noreply.github.com> Co-authored-by: leffff <levnovitskiy@gmail.com> Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-07 14:43:30 -07:00
KaredandSolitaryThinker	e2f4d1a7b5	[feat]: add SwanLab tracker (#1461 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-07 08:35:38 +00:00
Kaiqin KongandSolitaryThinker	f037351146	[feat] Add Clean-history Teacher Forcing and Causal Consistency Distillation (#1505 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-07 08:13:11 +00:00
Mac LeeandSolitaryThinker	1ee11e08dc	[ci]: add performance fingerprint cohorts (#1546 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-07 07:24:00 +00:00
Suckl Suckl SolitaryThinker	595f0ea60e	[feat] Add DreamX-World 5B Cam and AR pipelines (#1538 ) Co-authored-by: Suckl <Suckl@users.noreply.github.com> Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-07 06:16:18 +00:00
William Lin	d921832cd2	[misc]: reserve CPU/memory for timing-sensitive Modal CI lanes (#1566 )	2026-07-06 22:13:53 -07:00
William Lin	629697629a	[bugfix]: free CUDA memory between train-framework model tests (LongCat OOM on L40S) (#1565 )	2026-07-06 21:27:30 -07:00
William Lin	a25313beec	[ci]: wire fastvideo/tests/ops/ into the unit-test lane (#1559 )	2026-07-06 12:07:26 -07:00
William Lin	dbde64385b	[bugfix]: bump FA4 pin to the CuTe DSL 4.6 compatible rev (#1564 )	2026-07-06 12:06:59 -07:00
William Lin	9d909f5f04	[test]: remove dead and duplicate tests (-489 lines) (#1556 )	2026-07-05 15:53:40 -07:00
William Lin	76b0550c15	[ci]: run pre-commit on fork PRs without manual approval (#1555 )	2026-07-05 14:18:16 -07:00
William Lin	384c1e9493	[misc]: update reseed-performance-baseline skill for the hf_store move (#1545 follow-up) (#1553 )	2026-07-05 14:16:55 -07:00
William Lin	b1dbcc93f6	[misc]: reformat fastvideo/performance to the repo yapf config (#1554 )	2026-07-05 14:16:20 -07:00
William Lin	b93833772e	[ci]: guard against test directories no CI lane collects (#1552 )	2026-07-05 14:07:38 -07:00
Mac Lee	30b523edd6	[ci] Normalize performance stage component metrics (#1475 ) (#1550 )	2026-07-05 14:05:26 -07:00
Mac LeeandSolitaryThinker	6aab7f3832	[ci] cover Hunyuan 1.5 chat-list text preprocessing (#1518 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-05 12:05:25 -07:00
Mac Lee	9cd53fe5f8	[ci] Add metric-specific performance thresholds (#1545 )	2026-07-05 12:04:33 -07:00
Mac Lee	6a32cf3a5e	[ci]: expose LoRA extraction slash command (#1542 )	2026-07-05 06:45:31 -07:00
William Lin	98be9b3da2	[bugfix]: address the three remaining #1447 review findings (#1549 )	2026-07-05 06:43:27 -07:00
Mac Lee	c53e85b767	[ci] Add v2 performance benchmark config identity fields (#1544 )	2026-07-05 06:18:15 -07:00
William Lin	40a8bd2d3b	[bugfix]: skip ThunderKittens kernels on aarch64 and document the kernel build matrix (#1548 )	2026-07-05 06:13:11 -07:00
Mac LeeandSolitaryThinker	31aa115611	[bugfix]: preserve FSDP hooks for RMSNorm qk norms (#1513 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-05 04:15:49 +00:00
zainnhandSolitaryThinker	98ac10a528	[infra] Auto-rebuild CUDA images when docker/Dockerfile changes on main (#1526 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-07-04 16:25:46 -07:00
William Lin	a5a6d171e5	[attn] Make FA4 explicit opt-in via FASTVIDEO_FA4 and delete the runtime fallback machinery (#1540 )	2026-07-04 14:51:00 -07:00