[bugfix]: keep text-encoder CPU offload on by default

Revert the default flips from 40c8b7c1 (OffloadConfig.text_encoder and
FastVideoArgs.text_encoder_cpu_offload False -> True). Flipping the
global default changes the baseline VRAM footprint for every model
family, not just Kandinsky, and breaks the unit lane:
fastvideo/tests/api/test_parser.py::test_load_run_config_supports_yaml_roundtrip
still golden-checks text_encoder offload as True.

Nothing in this PR needs the flip: the Kandinsky examples pass
text_encoder_cpu_offload explicitly, and the offload-aware
TextEncodingStage (kept) makes offload work for Kandinsky's CLIP
either way. Offload stays opt-out rather than opt-in.

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

@@ -69,7 +69,7 @@ approval, then upload reviewed accepted baseline records.
 | `model_id` | Yes | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. |
 | `gpu_type` | Yes | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. Baselines are GPU-specific. |
 | `source_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:
@@ -148,8 +148,7 @@ 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 +182,7 @@ 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.
 
@@ -192,7 +191,7 @@ 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`:
@@ -200,8 +199,8 @@ Then back up only 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
 )
@@ -235,7 +234,7 @@ first baseline seed. Continue, but report that baseline history was empty.
 Load the last 5 successful records for the target:
 
 ```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",
@@ -372,7 +371,7 @@ prepared records plus backup on disk.
 Use the shared storage helper so the path and repo type match CI:
 
 ```python
-from hf_store import upload_record
+from fastvideo.performance.hf_store import upload_record
 
 upload_record("<local_record_path>", record, strict=True)
 ```
@@ -460,7 +459,7 @@ 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,
+- `fastvideo/performance/hf_store.py` — HF sync, record loading,
   `sanitize()`, and `upload_record()`.
 - `fastvideo/tests/performance/test_inference_performance.py` — source result
   JSON schema.

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

@@ -1,5 +1,9 @@
 {
   "benchmark_id": "wan-t2v-1.3b-2gpu",
+  "config_schema_version": 2,
+  "workload_id": "wan-t2v-1.3b",
+  "variant_id": "canonical",
+  "benchmark_version": 1,
   "description": "Wan2.1 T2V 1.3B inference performance",
   "model": {
     "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",

.buildkite/pipeline.yml

@@ -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"

.github/workflows/ci-precommit.yml

@@ -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"

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

@@ -125,7 +125,7 @@ jobs:
           set -euo pipefail
           TEST_NAME=$(echo "$COMMENT" | grep -oP '(?<=/test\s)\S+' | head -1 || true)
 
-          VALID="encoder vae transformer kernel unit 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 +136,7 @@ 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

.github/workflows/infra-build-image.yml

@@ -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:

apps/dreamverse/docker/Dockerfile

@@ -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

apps/performance_dashboard/README.md

@@ -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
 

apps/performance_dashboard/frontend/src/App.tsx

@@ -440,6 +440,8 @@ export default function App() {
                   <th>Throughput</th>
                   <th>Memory</th>
                   <th>Worst</th>
+                  <th>Exceeded</th>
+                  <th>Failing</th>
                 </tr>
               </thead>
               <tbody>
@@ -465,6 +467,12 @@ 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>

apps/performance_dashboard/frontend/src/api.ts

@@ -2,6 +2,12 @@ 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;
@@ -15,7 +21,8 @@ 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;

docker/Dockerfile

@@ -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" && \

docs/contributing/ci_architecture.md

@@ -103,6 +103,7 @@ can merge a PR.
 |---|---|---|
 | 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 +145,7 @@ 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` |

docs/contributing/performance_benchmarks.md

@@ -72,7 +72,10 @@ 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
@@ -92,25 +95,28 @@ 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
@@ -156,9 +162,22 @@ headroom and almost never need touching.
 
 `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 each available metric, and evaluates the current run with the metric's
+rolling regression policy. 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.
@@ -172,6 +191,45 @@ agent skill to advance the rolling median.
 
 ## 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-1.3b",
+  "variant_id": "canonical",
+  "benchmark_version": 1
+}
+```
+
+`benchmark_id` is still required in this phase because raw artifact names,
+generated-video directories, normalized record paths, and the current rolling
+baseline comparator still depend on it. The v2 identity fields are config
+metadata that make the measured workload explicit:
+
+| Field | Purpose |
+|---|---|
+| `workload_id` | Stable benchmark family, such as `wan-t2v-1.3b`. |
+| `variant_id` | Intentional recipe family, such as `canonical`. |
+| `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 metadata fields
+reserved for follow-up work, such as `recipe`, `metric_threshold_policy`, and
+`quality_metadata`, must be JSON objects when present.
+
+Recipe fingerprinting, hardware/software profile IDs, exact-identity
+comparison, metric-specific threshold policy behavior, promoted baselines, and
+dashboard regrouping are separate follow-up changes. Until those land, rolling
+baseline comparison remains keyed by `(model_id, gpu_type)`.
+
 ### Raw record (`results/perf_*.json`)
 
 Written by `test_inference_performance.py`. One file per benchmark run.
@@ -179,6 +237,10 @@ Written by `test_inference_performance.py`. One file per benchmark run.
 ```jsonc
 {
   "benchmark_id": "wan-t2v-1.3b-2gpu",
+  "config_schema_version": 2,
+  "workload_id": "wan-t2v-1.3b",
+  "variant_id": "canonical",
+  "benchmark_version": 1,
   "model_short_name": "Wan2.1-T2V-1.3B-Diffusers",
   "device": "NVIDIA L40S",
   "num_gpus": 2,
@@ -196,6 +258,13 @@ 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>",
   "pr_number": "1234",
   "timestamp": "2026-05-08T22:00:00+00:00",
@@ -222,6 +291,13 @@ 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
+    }
+  },
   "success": true
 }
 ```
@@ -238,18 +314,17 @@ successful main/full-suite uploads and remain eligible for rolling baselines.
 
 | 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. |
+| `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` | 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`. |
 | `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_STAGE_LOGGING` | set by the pytest test | `test_inference_performance.py` | Enables pipeline stage timing capture for component metrics during benchmark runs. |
 
 ## CI integration
@@ -261,9 +336,11 @@ 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:
+PR/direct runs skip `compare_baseline.py` because they only upload passing
+records. Scheduled-main runs still execute `compare_baseline.py` with
+`PERF_PYTEST_RC` set so the failed canonical attempt is visible in normalized
+JSON and dashboard history. The dashboard 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
@@ -279,11 +356,16 @@ 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": "canonical",
+     "benchmark_version": 1,
      "model": { "model_path": "...", "model_short_name": "..." },
      "init_kwargs": { "num_gpus": 1, ... },
      "generation_kwargs": { "num_frames": 45, ... },
@@ -299,9 +381,17 @@ 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`.
 
 2. The pytest test auto-discovers all configs — no test code needed. CI
    picks it up on the next `/test performance` run.
@@ -320,6 +410,13 @@ 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,
@@ -336,5 +433,5 @@ 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`.

docs/inference/optimizations.md

@@ -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`**

examples/inference/basic/basic_kandinsky5_i2v.py

@@ -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()

examples/inference/basic/basic_kandinsky5_t2v.py

@@ -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()

fastvideo-kernel/CMakeLists.txt

@@ -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 "============================================================")

fastvideo-kernel/README.md

@@ -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)

fastvideo-kernel/build.sh

@@ -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

fastvideo/attention/backends/flash_attn.py

@@ -1,15 +1,39 @@
 # SPDX-License-Identifier: Apache-2.0
 
+import importlib.util
 import os
+
 import torch
 import torch.nn.functional as F
 from dataclasses import dataclass
 
-try:
-    from fastvideo.attention.utils.flash_attn_cute import flash_attn_func
+from fastvideo import envs
+from fastvideo.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+    AttentionMetadataBuilder,
+)
+from fastvideo.logger import init_logger
 
+logger = init_logger(__name__)
+
+# FA4 (flash_attn.cute) is explicit opt-in via FASTVIDEO_FA4=1, mirroring the
+# kernel package's FASTVIDEO_VSA_CUTEDSL: its CuTeDSL kernels JIT-compile per
+# shape family and can fail at runtime on some arch/shape combinations, so it
+# is never auto-selected just because it is installed. Below sm90 a capability
+# gate in flash_attn_cute routes to FA2 the calls FA4 cannot serve there:
+# grad-enabled (its backward asserts sm90+) and GQA (pack_gqa fails CuTeDSL
+# JIT, observed on sm_89).
+if envs.FASTVIDEO_FA4:
+    try:
+        from fastvideo.attention.utils.flash_attn_cute import flash_attn_func
+    except ImportError as e:
+        raise RuntimeError(f"FASTVIDEO_FA4=1 but flash_attn.cute (FA4) is not usable ({e}); "
+                           "fix the FA4 install (see the flash-attn-4 pin in pyproject.toml) "
+                           "or unset FASTVIDEO_FA4.") from e
     fa_version = "4"
-except ImportError:
+else:
     try:
         from flash_attn_interface import flash_attn_func as flash_attn_3_func
 
@@ -21,6 +45,12 @@ except ImportError:
         from flash_attn import flash_attn_func as flash_attn_2_func
         flash_attn_func = flash_attn_2_func
         fa_version = "2"
+    try:
+        if importlib.util.find_spec("flash_attn.cute") is not None:
+            logger.info("flash_attn.cute (FA4) is installed but not enabled; "
+                        "set FASTVIDEO_FA4=1 to use it for inference.")
+    except ImportError:
+        pass
 
 # torch.compile traceability: the FA4/cute path (fa_version=="4") is
 # already a registered torch.library custom op, so dynamo treats it as a
@@ -87,9 +117,10 @@ if fa_version in ("2", "3"):
             return _fa_default(q, k, v, softmax_scale=softmax_scale, causal=causal)
         return torch.ops.fastvideo._flash_attn_default_forward(q, k, v, softmax_scale, causal)
 elif fa_version == "4":
-    # FA4 path: `flash_attn_func` is already a torch.library custom op
-    # (registered in `fastvideo.attention.utils.flash_attn_cute`), so a
-    # passthrough is enough — no extra registration needed.
+    # FA4 path: `flash_attn_func` (from `flash_attn_cute`) goes through a
+    # registered torch.library custom op (with an FA4 backward on sm90+;
+    # grad-enabled and GQA calls below sm90 route to FA2), so a passthrough
+    # is enough — no extra registration needed.
     def flash_attn_func_compilable(q, k, v, softmax_scale=None, causal=False):
         return flash_attn_func(q, k, v, softmax_scale=softmax_scale, causal=causal)
 else:
@@ -99,17 +130,6 @@ else:
     raise RuntimeError(f"Unsupported FlashAttention version: {fa_version!r} — expected "
                        f"'2', '3', or '4' from the import probe above.")
 
-from fastvideo.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionMetadata,
-    AttentionMetadataBuilder,
-)
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-_WARNED_NON_FA_DTYPE = False
 logger.info("Using FlashAttention-%s backend", fa_version)
 
 # FP4 FA4 support: quantize Q/K to NVFP4 E2M1 for block-scaled MMA on Blackwell.
@@ -271,12 +291,8 @@ class FlashAttentionImpl(AttentionImpl):
         # SP). Cast through bf16 and restore, matching TORCH_SDPA's tolerance.
         orig_dtype = query.dtype
         if orig_dtype not in (torch.float16, torch.bfloat16):
-            global _WARNED_NON_FA_DTYPE
-            if not _WARNED_NON_FA_DTYPE:
-                _WARNED_NON_FA_DTYPE = True
-                logger.warning(
-                    "FLASH_ATTN received %s inputs; casting to bfloat16 for the "
-                    "kernel and restoring on output.", orig_dtype)
+            logger.warning_once(f"FLASH_ATTN received {orig_dtype} inputs; casting to "
+                                f"bfloat16 for the kernel and restoring on output.")
             query = query.to(torch.bfloat16)
             key = key.to(torch.bfloat16)
             value = value.to(torch.bfloat16)

fastvideo/attention/backends/nabla.py

@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: Apache-2.0
+"""NABLA block-sparse flex-attention backend (Kandinsky5 "nabla" checkpoints).
+
+The block mask is data-dependent: nablaT_v2 mean-pools 64-token blocks of the
+fractal-ordered sequence, thresholds the softmaxed block map, and ORs it with a
+precomputed spatio-temporal-window (STA) mask carried on the attention
+metadata. The mask spans the full sequence, so this backend does not support
+sequence parallelism — use it via LocalAttention only.
+"""
+
+import math
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+
+try:
+    from torch.nn.attention.flex_attention import BlockMask, flex_attention
+    flex_attention = torch.compile(flex_attention, dynamic=False, mode="max-autotune-no-cudagraphs")
+    CAN_USE_FLEX_ATTN = True
+except ImportError:
+    CAN_USE_FLEX_ATTN = False
+
+from fastvideo.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+    AttentionMetadataBuilder,
+)
+
+
+def nablaT_v2(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    sta: torch.Tensor,
+    thr: float = 0.9,
+) -> "BlockMask":
+    q = q.transpose(1, 2).contiguous()
+    k = k.transpose(1, 2).contiguous()
+
+    # Map estimation
+    B, h, S, D = q.shape
+    s1 = S // 64
+    qa = q.reshape(B, h, s1, 64, D).mean(-2)
+    ka = k.reshape(B, h, s1, 64, D).mean(-2).transpose(-2, -1)
+    map = qa @ ka
+
+    map = torch.softmax(map / math.sqrt(D), dim=-1)
+    # Map binarization
+    vals, inds = map.sort(-1)
+    cvals = vals.cumsum_(-1)
+    mask = (cvals >= 1 - thr).int()
+    mask = mask.gather(-1, inds.argsort(-1))
+
+    mask = torch.logical_or(mask, sta)
+
+    # BlockMask creation
+    kv_nb = mask.sum(-1).to(torch.int32)
+    kv_inds = mask.argsort(dim=-1, descending=True).to(torch.int32)
+    return BlockMask.from_kv_blocks(torch.zeros_like(kv_nb), kv_inds, kv_nb, kv_inds, BLOCK_SIZE=64, mask_mod=None)
+
+
+class NablaAttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "NABLA_ATTN"
+
+    @staticmethod
+    def get_impl_cls() -> type["NablaAttentionImpl"]:
+        return NablaAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["NablaAttentionMetadata"]:
+        return NablaAttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["NablaAttentionMetadataBuilder"]:
+        return NablaAttentionMetadataBuilder
+
+
+@dataclass
+class NablaAttentionMetadata(AttentionMetadata):
+    # Block-level STA window mask [1, 1, S/64, S/64], precomputed once per run.
+    sta_mask: torch.Tensor = None  # type: ignore[assignment]
+    # Cumulative-probability threshold for block-map binarization.
+    P: float = 0.9
+    visual_shape: tuple[int, int, int] = (0, 0, 0)
+
+
+class NablaAttentionMetadataBuilder(AttentionMetadataBuilder):
+
+    def __init__(self) -> None:
+        pass
+
+    def prepare(self) -> None:
+        pass
+
+    def build(
+        self,
+        current_timestep: int,
+        sta_mask: torch.Tensor,
+        P: float,
+        visual_shape: tuple[int, int, int],
+        **kwargs: Any,
+    ) -> NablaAttentionMetadata:
+        return NablaAttentionMetadata(
+            current_timestep=current_timestep,
+            sta_mask=sta_mask,
+            P=P,
+            visual_shape=visual_shape,
+        )
+
+
+class NablaAttentionImpl(AttentionImpl):
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        softmax_scale: float,
+        causal: bool = False,
+        num_kv_heads: int | None = None,
+        prefix: str = "",
+        **extra_impl_args,
+    ) -> None:
+        if not CAN_USE_FLEX_ATTN:
+            raise RuntimeError("NABLA attention requires torch.nn.attention.flex_attention, "
+                               "which is unavailable in this PyTorch build.")
+        if causal:
+            raise ValueError("NABLA attention does not support causal masking.")
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: NablaAttentionMetadata,
+    ) -> torch.Tensor:
+        # q/k/v: [B, S, heads, head_dim], fractal-ordered by the model; S % 64 == 0.
+        block_mask = nablaT_v2(query, key, attn_metadata.sta_mask, thr=attn_metadata.P)
+        return flex_attention(
+            query=query.transpose(1, 2),
+            key=key.transpose(1, 2),
+            value=value.transpose(1, 2),
+            block_mask=block_mask,
+        ).transpose(1, 2)

fastvideo/attention/layer.py

@@ -252,6 +252,7 @@ class LocalAttention(nn.Module):
                  causal: bool = False,
                  supported_attention_backends: tuple[AttentionBackendEnum, ...]
                  | None = None,
+                 default_backend: AttentionBackendEnum | None = None,
                  **extra_impl_args) -> None:
         super().__init__()
         if softmax_scale is None:
@@ -262,7 +263,10 @@ class LocalAttention(nn.Module):
             num_kv_heads = num_heads
 
         dtype = get_compute_dtype()
-        attn_backend = get_attn_backend(head_size, dtype, supported_attention_backends=supported_attention_backends)
+        attn_backend = get_attn_backend(head_size,
+                                        dtype,
+                                        supported_attention_backends=supported_attention_backends,
+                                        default_backend=default_backend)
         impl_cls = attn_backend.get_impl_cls()
         self.attn_impl = impl_cls(num_heads=num_heads,
                                   head_size=head_size,

fastvideo/attention/selector.py

@@ -84,8 +84,9 @@ def get_attn_backend(
     dtype: torch.dtype,
     supported_attention_backends: tuple[AttentionBackendEnum, ...]
     | None = None,
+    default_backend: AttentionBackendEnum | None = None,
 ) -> type[AttentionBackend]:
-    return _cached_get_attn_backend(head_size, dtype, supported_attention_backends)
+    return _cached_get_attn_backend(head_size, dtype, supported_attention_backends, default_backend)
 
 
 @cache
@@ -94,6 +95,7 @@ def _cached_get_attn_backend(
     dtype: torch.dtype,
     supported_attention_backends: tuple[AttentionBackendEnum, ...]
     | None = None,
+    default_backend: AttentionBackendEnum | None = None,
 ) -> type[AttentionBackend]:
     # Check whether a particular choice of backend was
     # previously forced.
@@ -112,6 +114,12 @@ def _cached_get_attn_backend(
         if backend_by_env_var is not None:
             selected_backend = backend_name_to_enum(backend_by_env_var)
 
+    # Layer-level default (e.g. a checkpoint that requires a specific sparse
+    # backend). Lower precedence than the global force and the env var, so
+    # users can still override it.
+    if selected_backend is None and default_backend is not None:
+        selected_backend = default_backend
+
     # get device-specific attn_backend
     from fastvideo.platforms import current_platform
 

fastvideo/attention/utils/flash_attn_cute.py

@@ -4,10 +4,9 @@ import functools
 from collections.abc import Callable
 
 import torch
-from flash_attn import flash_attn_func as _flash_attn_2_func
-from flash_attn import flash_attn_varlen_func as _flash_attn_2_varlen_func
 
 from fastvideo.logger import init_logger
+from fastvideo.platforms import current_platform
 
 logger = init_logger(__name__)
 
@@ -16,7 +15,8 @@ if torch.cuda.is_available():
         from flash_attn.cute.interface import _flash_attn_bwd, _flash_attn_fwd
     except ImportError:
         # flash_attn.cute (FA4) is simply not installed -- expected on builds
-        # without it; callers fall back to FA3/FA2 quietly.
+        # without it; callers handle the ImportError (the FASTVIDEO_FA4 gate in
+        # flash_attn.py raises, the FP4 probe treats FA4 as unavailable).
         raise
     except Exception as e:
         # flash_attn.cute IS installed but failed to import -- almost always an
@@ -24,23 +24,57 @@ if torch.cuda.is_available():
         # 'cutlass.cute.core' has no attribute 'ThrMma'" (an AttributeError, not
         # ImportError). This is fixable by pinning a compatible
         # nvidia-cutlass-dsl, so warn loudly, then re-raise as ImportError so
-        # callers fall back to FA3/FA2 instead of crashing worker init.
+        # callers can handle it uniformly.
         logger.warning(
-            "flash_attn.cute (FA4) is installed but failed to import (%r); "
-            "falling back to FA3/FA2. This is usually an nvidia-cutlass-dsl "
-            "version mismatch -- pin a compatible nvidia-cutlass-dsl to "
-            "restore FA4.", e)
+            "flash_attn.cute (FA4) is installed but failed to import (%r). "
+            "This is usually an nvidia-cutlass-dsl version mismatch -- pin a "
+            "compatible nvidia-cutlass-dsl to restore FA4.", e)
         raise ImportError(f"flash_attn.cute (FA4) import failed: {e!r}") from e
 else:
     # This error will be caught in flash_attn.py or flash_attn_no_pad.py
     raise ImportError("flash_attn.cute is only available on CUDA devices; this error must be handled internally")
 
+try:
+    # FA2 serves the calls FA4 cute cannot on pre-sm90 GPUs (backward, GQA).
+    # Optional so FA4-only installs can still import this module.
+    from flash_attn import flash_attn_func as _flash_attn_2_func
+    from flash_attn import flash_attn_varlen_func as _flash_attn_2_varlen_func
+except ImportError:
+    _flash_attn_2_func = None
+    _flash_attn_2_varlen_func = None
+
 
 def _check_dropout(dropout_p: float) -> None:
     if dropout_p != 0.0:
         raise NotImplementedError(f"flash_attn.cute does not support dropout (got dropout_p={dropout_p})")
 
 
+@functools.cache
+def _sm90_or_newer() -> bool:
+    return current_platform.has_device_capability(90)
+
+
+def _use_fa2(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> bool:
+    if _sm90_or_newer():
+        return False
+    # Pre-sm90 FA4 cute limitations, both served by FA2 (deterministic
+    # capability gate, not a runtime fallback):
+    #   * the backward asserts sm90+ (L40S/sm_89 dies on its arch check);
+    #   * GQA fails CuTeDSL JIT in pack_gqa ("ValueError: Operation creation
+    #     failed", observed on sm_89 with HunyuanGameCraft/LTX2).
+    if q.shape[-2] != k.shape[-2]:
+        return True
+    return torch.is_grad_enabled() and any(t.requires_grad for t in (q, k, v))
+
+
+def _fa2_or_raise(fa2_func: Callable | None) -> Callable:
+    if fa2_func is None:
+        raise RuntimeError("this attention call cannot run on FA4 cute below sm90 (its backward and "
+                           "GQA support require sm90+) and flash-attn 2, which serves it there, is "
+                           "not installed.")
+    return fa2_func
+
+
 @torch.library.custom_op(
     "fastvideo::_flash_attn_cute_forward",
     mutates_args=(),
@@ -243,70 +277,6 @@ torch.library.register_autograd(
 )
 
 
-# FA4's CuTeDSL kernels JIT-compile per shape family, and some configurations
-# fail MLIR op creation at runtime even though the import succeeded (observed:
-# GQA models on sm_89 dying in pack_gqa with "ValueError: Operation creation
-# failed"). Degrade to FA2 once, process-wide, instead of crashing inference.
-class _FA4Policy:
-    """Per-call gate for the FA4 cute fast path, with FA2 as the fallback.
-
-    FA4 is skipped when:
-      * a previous call failed at runtime -- CuTeDSL JIT compilation is
-        shape-dependent, so the first failure disables FA4 for the rest of
-        the process instead of retrying a broken JIT on every call; or
-      * the call needs autograd -- FA4's backward asserts sm90+ (L40S/sm_89
-        dies on its arch check) and is unvalidated for training in this repo
-        (its lse is not even allocated through our inference-shaped custom
-        op), so training keeps the pre-FA4 behavior: FA2 on every device.
-    """
-
-    def __init__(self) -> None:
-        self.broken = False
-
-    def use_fa4(self, *tensors: torch.Tensor) -> bool:
-        if self.broken:
-            return False
-        return not (torch.is_grad_enabled() and any(t.requires_grad for t in tensors))
-
-    def mark_broken(self, error: Exception) -> None:
-        if not self.broken:
-            self.broken = True
-            logger.warning(
-                "flash_attn.cute (FA4) failed at runtime (%r); falling back "
-                "to FA2 for the rest of this process.", error)
-
-
-_FA4 = _FA4Policy()
-
-
-def _with_fa2_fallback(fa2_func: Callable) -> Callable:
-    """Pair an FA4 cute wrapper with its FA2 twin of the same signature.
-
-    The decorated body runs only when ``_FA4`` allows it; otherwise (or after
-    the first FA4 runtime failure) the call is served by ``fa2_func``.
-    ``NotImplementedError`` is a contract error (e.g. dropout), not a JIT
-    failure, so it propagates without disabling FA4.
-    """
-
-    def decorator(fa4_func: Callable) -> Callable:
-
-        @functools.wraps(fa4_func)
-        def wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, *args, **kwargs) -> torch.Tensor:
-            if _FA4.use_fa4(q, k, v):
-                try:
-                    return fa4_func(q, k, v, *args, **kwargs)
-                except NotImplementedError:
-                    raise
-                except Exception as e:  # CuTeDSL compile errors surface as ValueError
-                    _FA4.mark_broken(e)
-            return fa2_func(q, k, v, *args, **kwargs)
-
-        return wrapper
-
-    return decorator
-
-
-@_with_fa2_fallback(_flash_attn_2_func)
 def flash_attn_func(
     q: torch.Tensor,
     k: torch.Tensor,
@@ -317,6 +287,16 @@ def flash_attn_func(
     deterministic: bool = False,
 ) -> torch.Tensor:
     """Only returns the output, not the lse."""
+    if _use_fa2(q, k, v):
+        return _fa2_or_raise(_flash_attn_2_func)(
+            q,
+            k,
+            v,
+            dropout_p=dropout_p,
+            softmax_scale=softmax_scale,
+            causal=causal,
+            deterministic=deterministic,
+        )
     _check_dropout(dropout_p)
     out, _ = torch.ops.fastvideo._flash_attn_cute_forward(q, k, v, softmax_scale, causal, deterministic)
     return out
@@ -392,7 +372,6 @@ def flash_attn_fp4_func(
     return torch.ops.fastvideo._flash_attn_cute_fp4_forward(q, k, v, sfq, sfk, softmax_scale, causal)
 
 
-@_with_fa2_fallback(_flash_attn_2_varlen_func)
 def flash_attn_varlen_func(
     q: torch.Tensor,
     k: torch.Tensor,
@@ -407,6 +386,20 @@ def flash_attn_varlen_func(
     deterministic: bool = False,
 ) -> torch.Tensor:
     """Only returns the output, not the lse."""
+    if _use_fa2(q, k, v):
+        return _fa2_or_raise(_flash_attn_2_varlen_func)(
+            q,
+            k,
+            v,
+            cu_seqlens_q,
+            cu_seqlens_k,
+            max_seqlen_q,
+            max_seqlen_k,
+            dropout_p=dropout_p,
+            softmax_scale=softmax_scale,
+            causal=causal,
+            deterministic=deterministic,
+        )
     _check_dropout(dropout_p)
     out, _ = torch.ops.fastvideo._flash_attn_cute_varlen_forward(
         q,

fastvideo/attention/utils/flash_attn_no_pad.py

@@ -21,24 +21,35 @@ from einops import rearrange
 from flash_attn import flash_attn_varlen_qkvpacked_func
 from flash_attn.bert_padding import pad_input, unpad_input
 
+from fastvideo import envs
+
 
 def _resolve_flash_attn_varlen_func() -> Any:
-    try:
-        from fastvideo.attention.utils.flash_attn_cute import (
-            flash_attn_varlen_func as flash_attn_varlen_func_cute, )
+    if envs.FASTVIDEO_FA4:
+        # FA4 cute is explicit opt-in (see fastvideo/attention/backends/
+        # flash_attn.py); with FASTVIDEO_FA4=1 an unimportable FA4 build must
+        # fail loudly here rather than fall through to FA3/FA2. RuntimeError,
+        # not ImportError: importers like bsa_attn.py treat ImportError as
+        # "flash-attn not installed" and silently degrade to reference kernels.
+        try:
+            from fastvideo.attention.utils.flash_attn_cute import (
+                flash_attn_varlen_func as flash_attn_varlen_func_cute, )
+        except ImportError as e:
+            raise RuntimeError(f"FASTVIDEO_FA4=1 but flash_attn.cute (FA4) is not usable ({e}); "
+                               "fix the FA4 install (see the flash-attn-4 pin in pyproject.toml) "
+                               "or unset FASTVIDEO_FA4.") from e
 
         return flash_attn_varlen_func_cute
+    try:
+        from flash_attn_interface import (
+            flash_attn_varlen_func as flash_attn_varlen_func_interface, )
+
+        return flash_attn_varlen_func_interface
     except ImportError:
-        try:
-            from flash_attn_interface import (
-                flash_attn_varlen_func as flash_attn_varlen_func_interface, )
+        from flash_attn import (
+            flash_attn_varlen_func as flash_attn_varlen_func_flash, )
 
-            return flash_attn_varlen_func_interface
-        except ImportError:
-            from flash_attn import (
-                flash_attn_varlen_func as flash_attn_varlen_func_flash, )
-
-            return flash_attn_varlen_func_flash
+        return flash_attn_varlen_func_flash
 
 
 flash_attn_varlen_func_impl = _resolve_flash_attn_varlen_func()

fastvideo/configs/models/dits/kandinsky5.py

@@ -2,14 +2,24 @@
 from dataclasses import dataclass, field
 
 from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+from fastvideo.platforms import AttentionBackendEnum
+
+
+def _is_kandinsky5_transformer_block(n: str, m) -> bool:
+    return ("text_transformer_blocks" in n or "visual_transformer_blocks" in n) and n.split(".")[-1].isdigit()
 
 
 @dataclass
 class Kandinsky5ArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [
-        lambda n, m:
-        ("text_transformer_blocks" in n or "visual_transformer_blocks" in n) and n.split(".")[-1].isdigit()
-    ])
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [_is_kandinsky5_transformer_block])
+
+    # NABLA block-sparse attention for attention_type="nabla" checkpoints, plus
+    # the dense backends every DiT supports.
+    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
+        AttentionBackendEnum.NABLA_ATTN,
+        AttentionBackendEnum.FLASH_ATTN,
+        AttentionBackendEnum.TORCH_SDPA,
+    )
 
     # Native FastVideo implementation uses the same parameter names as diffusers
     # except FFN internals: Diffusers FFN uses `in_layer/out_layer`, while

fastvideo/configs/models/encoders/base.py

@@ -43,11 +43,16 @@ class TextEncoderArchConfig(EncoderArchConfig):
     require_processor: bool = False
 
     def __post_init__(self) -> None:
-        self.tokenizer_kwargs = {
+        # update_model_arch re-runs __post_init__ after pipeline configs may
+        # have customized tokenizer_kwargs (e.g. kandinsky5/gen3c/longcat set
+        # "padding"); rebuilding the dict here would silently wipe those
+        # customizations, so only fill in defaults for keys not already set.
+        defaults = {
             "truncation": True,
             "max_length": self.text_len,
             "return_tensors": "pt",
         }
+        self.tokenizer_kwargs = defaults | self.tokenizer_kwargs
 
 
 @dataclass

fastvideo/configs/pipelines/__init__.py

@@ -5,6 +5,7 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.kandinsky5 import Kandinsky5I2VConfig, Kandinsky5T2VConfig
 from fastvideo.configs.pipelines.matrixgame2 import MatrixGame2I2V480PConfig
 from fastvideo.configs.pipelines.matrixgame3 import MatrixGame3I2V720PConfig
 from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
@@ -16,5 +17,6 @@ __all__ = [
     "HunyuanConfig", "FastHunyuanConfig", "HunyuanGameCraftPipelineConfig", "PipelineConfig", "Hunyuan15T2V480PConfig",
     "Hunyuan15T2V720PConfig", "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig", "WanI2V720PConfig",
     "SelfForcingWanT2V480PConfig", "LucyEditDevConfig", "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig",
-    "HYWorldConfig", "MatrixGame2I2V480PConfig", "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
+    "HYWorldConfig", "Kandinsky5T2VConfig", "Kandinsky5I2VConfig", "MatrixGame2I2V480PConfig",
+    "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/kandinsky5.py

@@ -0,0 +1,116 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits import Kandinsky5VideoConfig
+from fastvideo.configs.models.encoders import BaseEncoderOutput, CLIPTextConfig
+from fastvideo.configs.models.encoders.reason1 import Reason1Config
+from fastvideo.configs.models.vaes import HunyuanVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
+
+# Byte-exact copy of the upstream Kandinsky5/diffusers template, including the
+# "promt"/"scren" typos: the checkpoints were trained with this exact system  # codespell:ignore promt,scren
+# prompt, and ENCODE_START_IDX below is the tokenized length of everything
+# before the user prompt. Fixing the typos shifts user content to index 127
+# and mis-conditions every generation.
+KANDINSKY5_PROMPT_TEMPLATE = "\n".join([
+    "<|im_start|>system\nYou are a promt engineer. Describe the video in detail.",  # codespell:ignore promt
+    "Describe how the camera moves or shakes, describe the zoom and view angle, whether it follows the objects.",
+    "Describe the location of the video, main characters or objects and their action.",
+    "Describe the dynamism of the video and presented actions.",
+    "Name the visual style of the video: whether it is a professional footage, user generated content, some kind of animation, video game or scren content.",  # codespell:ignore scren
+    "Describe the visual effects, postprocessing and transitions if they are presented in the video.",
+    "Pay attention to the order of key actions shown in the scene.<|im_end|>",
+    "<|im_start|>user\n{}<|im_end|>",
+])
+KANDINSKY5_PROMPT_TEMPLATE_ENCODE_START_IDX = 129
+
+
+def kandinsky5_qwen_preprocess_text(prompt: str) -> str:
+    if not prompt.strip():
+        prompt = "."
+    return KANDINSKY5_PROMPT_TEMPLATE.format(prompt)
+
+
+def kandinsky5_qwen_postprocess_text(outputs: BaseEncoderOutput,
+                                     mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    if outputs.hidden_states is None:
+        raise RuntimeError("Kandinsky5 Qwen prompt embeddings require hidden_states.")
+    hidden_states = outputs.hidden_states[-1]
+    prompt_embeds = hidden_states[:, KANDINSKY5_PROMPT_TEMPLATE_ENCODE_START_IDX:]
+    mask = mask[:, KANDINSKY5_PROMPT_TEMPLATE_ENCODE_START_IDX:]
+    if prompt_embeds.shape[1] == 0:
+        prompt_embeds = hidden_states[:, -1:]
+        mask = torch.ones((mask.shape[0], 1), dtype=mask.dtype, device=mask.device)
+    return prompt_embeds, mask
+
+
+def kandinsky5_clip_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
+    if outputs.pooler_output is None:
+        raise RuntimeError("Kandinsky5 CLIP pooled output is required.")
+    return outputs.pooler_output
+
+
+@dataclass
+class Kandinsky5T2VConfig(PipelineConfig):
+    """Kandinsky-5.0 Lite text-to-video pipeline configuration."""
+
+    dit_config: DiTConfig = field(default_factory=Kandinsky5VideoConfig)
+    vae_config: VAEConfig = field(default_factory=HunyuanVAEConfig)
+
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (Reason1Config(), CLIPTextConfig()))
+    preprocess_text_funcs: tuple[Callable[[str], Any], ...] = field(
+        default_factory=lambda: (kandinsky5_qwen_preprocess_text, preprocess_text))
+    postprocess_text_funcs: tuple[Callable[..., Any], ...] = field(
+        default_factory=lambda: (kandinsky5_qwen_postprocess_text, kandinsky5_clip_postprocess_text))
+
+    dit_precision: str = "bf16"
+    vae_precision: str = "bf16"
+    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", "bf16"))
+    text_encoder_max_lengths: tuple[int, ...] = field(
+        default_factory=lambda: (KANDINSKY5_PROMPT_TEMPLATE_ENCODE_START_IDX + 512, 77))
+
+    flow_shift: float | None = 5.0
+    vae_tiling: bool = True
+
+    def __post_init__(self) -> None:
+        if len(self.text_encoder_configs) != 2:
+            raise ValueError(f"Kandinsky5 pipeline requires exactly 2 text encoders (qwen and clip), "
+                             f"but got {len(self.text_encoder_configs)} encoder(s).")
+
+        self.vae_config.load_encoder = False
+        self.vae_config.load_decoder = True
+
+        qwen_cfg = self.text_encoder_configs[0]
+        qwen_cfg.arch_config.output_hidden_states = True
+        qwen_cfg.arch_config.tokenizer_kwargs.update({
+            "padding": True,
+            "truncation": True,
+            "return_tensors": "pt",
+        })
+
+        clip_cfg = self.text_encoder_configs[1]
+        clip_cfg.arch_config.tokenizer_kwargs.update({
+            "padding": "max_length",
+            "max_length": 77,
+            "truncation": True,
+            "add_special_tokens": True,
+            "return_tensors": "pt",
+        })
+
+
+@dataclass
+class Kandinsky5I2VConfig(Kandinsky5T2VConfig):
+    """Kandinsky-5.0 image-to-video pipeline configuration."""
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        # I2V needs the VAE encoder to encode the conditioning image.
+        self.vae_config.load_encoder = True

fastvideo/envs.py

@@ -20,6 +20,7 @@ if TYPE_CHECKING:
     FASTVIDEO_LOGGING_CONFIG_PATH: str | None = None
     FASTVIDEO_TRACE_FUNCTION: int = 0
     FASTVIDEO_ATTENTION_BACKEND: str | None = None
+    FASTVIDEO_FA4: bool = False
     FASTVIDEO_WORKER_MULTIPROC_METHOD: str = "spawn"
     FASTVIDEO_TARGET_DEVICE: str = "cuda"
     MAX_JOBS: str | None = None
@@ -207,9 +208,18 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # - "VIDEO_SPARSE_ATTN": use Video Sparse Attention
     # - "SAGE_ATTN": use Sage Attention
     # - "SAGE_ATTN_THREE": use Sage Attention 3
+    # FLASH_ATTN uses FlashAttention-3/2; to run FlashAttention-4 set
+    # FASTVIDEO_FA4=1 as well (see below).
     "FASTVIDEO_ATTENTION_BACKEND":
     lambda: os.getenv("FASTVIDEO_ATTENTION_BACKEND", None),
 
+    # If set (=1), the FLASH_ATTN backend uses FlashAttention-4
+    # (flash_attn.cute). FA4 is opt-in and never auto-selected just because it
+    # is installed. Below sm90, grad-enabled and GQA calls are routed to FA2
+    # (FA4's backward asserts sm90+ and its pack_gqa fails to JIT there).
+    "FASTVIDEO_FA4":
+    lambda: os.getenv("FASTVIDEO_FA4", "0") != "0",
+
     # Use dedicated multiprocess context for workers.
     "FASTVIDEO_WORKER_MULTIPROC_METHOD":
     lambda: os.getenv("FASTVIDEO_WORKER_MULTIPROC_METHOD", "spawn"),

fastvideo/models/dits/causal_wanvideo.py

@@ -323,9 +323,9 @@ class CausalWanTransformerBlock(nn.Module):
         value, _ = self.to_v(norm_hidden_states)
 
         if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
         if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)
 
         query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
         key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))

fastvideo/models/dits/kandinsky5.py

@@ -10,13 +10,21 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from fastvideo.attention import LocalAttention
+from fastvideo.attention.backends.nabla import CAN_USE_FLEX_ATTN, flex_attention, nablaT_v2
 from fastvideo.configs.models.dits import Kandinsky5VideoConfig
 from fastvideo.layers.layernorm import LayerNormScaleShift
 from fastvideo.layers.linear import ReplicatedLinear
 from fastvideo.layers.mlp import MLP
+from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
 from fastvideo.platforms import AttentionBackendEnum
 
+logger = init_logger(__name__)
+
+if not CAN_USE_FLEX_ATTN:
+    logger.warning("torch.nn.attention.flex_attention is unavailable in this PyTorch build; "
+                   "Kandinsky5 NABLA sparse attention (Pro checkpoints) cannot be used.")
+
 FRACTAL_PIXEL_SIZE = 8
 _ARCH_CONFIG_DEFAULTS = Kandinsky5VideoConfig().arch_config
 
@@ -263,10 +271,9 @@ class Kandinsky5Modulation(nn.Module):
 
 
 def _apply_rotary(x: torch.Tensor, rope: torch.Tensor) -> torch.Tensor:
-    orig_dtype = x.dtype
     x_ = x.reshape(*x.shape[:-1], -1, 1, 2).to(torch.float32)
     x_out = (rope * x_).sum(dim=-1)
-    return x_out.reshape(*x.shape).to(orig_dtype)
+    return x_out.reshape(*x.shape).to(x.dtype)
 
 
 class Kandinsky5Attention(nn.Module):
@@ -277,6 +284,7 @@ class Kandinsky5Attention(nn.Module):
         head_dim: int,
         supported_attention_backends: tuple[AttentionBackendEnum, ...] | None,
         prefix: str = "",
+        use_nabla: bool = False,
     ):
         super().__init__()
         assert num_channels % head_dim == 0
@@ -306,6 +314,17 @@ class Kandinsky5Attention(nn.Module):
             causal=False,
             supported_attention_backends=supported_attention_backends,
         )
+        # NABLA checkpoints get a second attention layer whose backend defaults
+        # to NABLA_ATTN; FASTVIDEO_ATTENTION_BACKEND still overrides it.
+        self.nabla_attention = None
+        if use_nabla:
+            self.nabla_attention = LocalAttention(
+                num_heads=self.num_heads,
+                head_size=head_dim,
+                causal=False,
+                supported_attention_backends=supported_attention_backends,
+                default_backend=AttentionBackendEnum.NABLA_ATTN,
+            )
 
     def forward(
         self,
@@ -339,27 +358,54 @@ class Kandinsky5Attention(nn.Module):
             key = _apply_rotary(key, rotary_emb).type_as(key)
 
         if sparse_params is not None:
-            raise NotImplementedError(
-                "Sparse attention is not yet supported for Kandinsky5 in FastVideo."
-            )
+            if self.nabla_attention is None:
+                raise RuntimeError("sparse_params passed to an attention layer built without use_nabla; "
+                                   "this checkpoint/config combination is inconsistent.")
+            try:
+                # Backend impl reads sta_mask/P from the forward-context
+                # attention metadata built by the denoising stage.
+                hidden_states = self.nabla_attention(query, key, value)
+            except AssertionError as exc:
+                # Standalone parity tests call the model without a pipeline
+                # forward context; run the NABLA kernel directly.
+                if "Forward context is not set" not in str(exc):
+                    raise
+                attn_mask = nablaT_v2(query, key, sparse_params["sta_mask"], thr=sparse_params["P"])
+                hidden_states = flex_attention(
+                    query=query.transpose(1, 2),
+                    key=key.transpose(1, 2),
+                    value=value.transpose(1, 2),
+                    block_mask=attn_mask,
+                ).transpose(1, 2)
+        else:
+            try:
+                hidden_states = self.local_attention(query, key, value)
 
-        try:
-            hidden_states = self.local_attention(query, key, value)
-        except AssertionError as exc:
-            # LocalAttention requires pipeline forward context. Standalone
-            # parity tests call the model directly, so fallback to Torch SDPA.
-            if "Forward context is not set" not in str(exc):
-                raise
-            query = query.transpose(1, 2)
-            key = key.transpose(1, 2)
-            value = value.transpose(1, 2)
-            hidden_states = F.scaled_dot_product_attention(query,
-                                                           key,
-                                                           value,
-                                                           attn_mask=None,
-                                                           is_causal=False)
-            hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = hidden_states.flatten(2)
+            except AssertionError as exc:
+                # LocalAttention requires pipeline forward context. Standalone
+                # parity tests call the model directly, so fallback to Torch SDPA.
+                if "Forward context is not set" not in str(exc):
+                    raise
+
+                query_shape = query.shape[:-2]
+                key_shape = key.shape[:-2]
+                query = query.reshape(query_shape[0], -1, self.num_heads,
+                                      query.shape[-1]).transpose(1, 2)
+                key = key.reshape(key_shape[0], -1, self.num_heads,
+                                  key.shape[-1]).transpose(1, 2)
+                value = value.reshape(key_shape[0], -1, self.num_heads,
+                                      value.shape[-1]).transpose(1, 2)
+                hidden_states = F.scaled_dot_product_attention(
+                    query,
+                    key,
+                    value,
+                    attn_mask=None,
+                    is_causal=False,
+                )
+                hidden_states = hidden_states.transpose(1, 2).reshape(
+                    *query_shape, self.num_heads, -1)
+                
+        hidden_states = hidden_states.flatten(-2, -1)
 
         hidden_states, _ = self.out_layer(hidden_states)
         return hidden_states
@@ -476,7 +522,8 @@ class Kandinsky5TransformerDecoderBlock(nn.Module):
                  head_dim: int,
                  supported_attention_backends: tuple[AttentionBackendEnum, ...]
                  | None = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_nabla: bool = False):
         super().__init__()
         self.visual_modulation = Kandinsky5Modulation(time_dim, model_dim, 9)
 
@@ -491,7 +538,8 @@ class Kandinsky5TransformerDecoderBlock(nn.Module):
             model_dim,
             head_dim,
             supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.self_attention")
+            prefix=f"{prefix}.self_attention",
+            use_nabla=use_nabla)
 
         self.cross_attention_norm = LayerNormScaleShift(
             model_dim,
@@ -624,7 +672,8 @@ class Kandinsky5Transformer3DModel(BaseDiT):
                                               arch.ff_dim,
                                               head_dim,
                                               self._supported_attention_backends,
-                                              prefix=f"{config.prefix}.visual_transformer_blocks.{i}")
+                                              prefix=f"{config.prefix}.visual_transformer_blocks.{i}",
+                                              use_nabla=arch.attention_type == "nabla")
             for i in range(arch.num_visual_blocks)
         ])
 
@@ -694,6 +743,7 @@ class Kandinsky5Transformer3DModel(BaseDiT):
                                                   scale_factor)
         to_fractal = sparse_params[
             "to_fractal"] if sparse_params is not None else False
+        
         visual_embed, visual_rope = fractal_flatten(visual_embed, visual_rope,
                                                     visual_shape,
                                                     block_mask=to_fractal)
@@ -724,6 +774,7 @@ class Kandinsky5Transformer3DModel(BaseDiT):
 
         if return_dict:
             return Kandinsky5TransformerOutput(sample=x)
+        
         return x
 
     def materialize_non_persistent_buffers(self, device: torch.device,

fastvideo/models/dits/matrixgame2/causal_model.py

@@ -537,9 +537,9 @@ class CausalMatrixGame2TransformerBlock(nn.Module):
         value, _ = self.to_v(norm_hidden_states)
 
         if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
         if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)
 
         query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
         key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))

fastvideo/models/encoders/qwen2_5_vl_custom.py

@@ -467,6 +467,18 @@ class Qwen2_5_VisionTransformerPretrainedModel(nn.Module):
         return hidden_states
 
 
+def _compute_default_rope_parameters(config, device=None, seq_len=None, **kwargs):
+    # transformers>=5 removes the "default" entry from ROPE_INIT_FUNCTIONS and
+    # moves rope_theta inside rope_parameters; replicate the 4.x default init.
+    rope_params = getattr(config, "rope_parameters", None) or getattr(config, "rope_scaling", None) or {}
+    base = rope_params.get("rope_theta", getattr(config, "rope_theta", 10000.0))
+    head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+    partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
+    dim = int(head_dim * partial_rotary_factor)
+    inv_freq = 1.0 / (base**(torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, 1.0
+
+
 class Qwen2_5_VLRotaryEmbedding(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig, device=None):
         super().__init__()
@@ -479,7 +491,14 @@ class Qwen2_5_VLRotaryEmbedding(nn.Module):
         self.original_max_seq_len = config.max_position_embeddings
 
         self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        if self.rope_type in ROPE_INIT_FUNCTIONS:
+            self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        elif self.rope_type == "default":
+            # transformers>=5 drops the "default" entry from ROPE_INIT_FUNCTIONS.
+            self.rope_init_fn = _compute_default_rope_parameters
+        else:
+            raise KeyError(f"Unsupported rope_type '{self.rope_type}'; available: "
+                           f"{['default', *ROPE_INIT_FUNCTIONS]}")
 
         inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
         self.register_buffer("inv_freq", inv_freq, persistent=False)
@@ -948,6 +967,16 @@ QWEN2_5_VL_ATTENTION_CLASSES = {
 # If FlashAttention2 is not available, transparently fall back to SDPA.
 if not is_flash_attn_2_available():
     QWEN2_5_VL_ATTENTION_CLASSES["flash_attention_2"] = Qwen2_5_VLSdpaAttention
+else:
+    # transformers>=5 only resolves the flash-attn functions when the model
+    # preloads them via its attention interface; this module bypasses
+    # PreTrainedModel, so _flash_attention_forward(implementation=None) raises
+    # unless we preload here.
+    try:
+        from transformers.modeling_flash_attention_utils import lazy_import_flash_attention
+        lazy_import_flash_attention("flash_attention_2")
+    except (ImportError, ValueError):
+        QWEN2_5_VL_ATTENTION_CLASSES["flash_attention_2"] = Qwen2_5_VLSdpaAttention
     
 class Qwen2_5_VLDecoderLayer(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig, layer_idx: int):
@@ -1035,7 +1064,7 @@ class Qwen2_5_VLModel(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig):
         super().__init__()
         self.config = config
-        self.padding_idx = config.pad_token_id
+        self.padding_idx = getattr(config, "pad_token_id", None)
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
@@ -1408,6 +1437,18 @@ class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput):
     rope_deltas: Optional[torch.LongTensor] = None
 
 
+def _flatten_text_config(config):
+    # transformers>=5 stops forwarding text-model attributes (hidden_size,
+    # vocab_size, rope_scaling, ...) from the composite Qwen2_5_VLConfig to
+    # config.text_config; this module reads them from the top level.
+    text_config = getattr(config, "text_config", None)
+    if text_config is not None:
+        for key, value in text_config.to_dict().items():
+            if not hasattr(config, key):
+                setattr(config, key, value)
+    return config
+
+
 class Qwen2_5_VLForConditionalGenerationSimple(nn.Module):
     _tied_weights_keys = ["lm_head.weight"]
     config_class = Qwen2_5_VLConfig
@@ -1415,6 +1456,7 @@ class Qwen2_5_VLForConditionalGenerationSimple(nn.Module):
 
     def __init__(self, config):
         super().__init__()
+        config = _flatten_text_config(config)
         self.config = config
         self.visual = Qwen2_5_VisionTransformerPretrainedModel(config.vision_config)
 

fastvideo/models/loader/component_loader.py

@@ -320,6 +320,19 @@ class TextEncoderLoader(ComponentLoader):
                 f"text encoder index {idx} out of range for text_encoder_configs (len={len(encoder_configs)}), model_path={model_path}"
             )
         encoder_config = encoder_configs[idx]
+        if (
+            model_config.get("architectures") == ["CLIPModel"]
+            and isinstance(model_config.get("text_config"), dict)
+        ):
+            valid_arch_fields = {
+                f.name for f in dataclasses.fields(encoder_config.arch_config)
+            }
+            model_config = {
+                key: value
+                for key, value in deepcopy(model_config["text_config"]).items()
+                if key in valid_arch_fields
+            }
+            model_config["architectures"] = ["CLIPTextModel"]
         encoder_config.update_model_arch(model_config)
         if idx < 0 or idx >= len(encoder_precisions):
             raise IndexError(
@@ -404,7 +417,9 @@ class TextEncoderLoader(ComponentLoader):
             else:
                 loaded_weights: set[str] = model.load_weights(
                     self._get_all_weights(
-                        model, model_path, to_cpu=use_cpu_offload
+                        model,
+                        model_path,
+                        to_cpu=fastvideo_args.text_encoder_cpu_offload,
                     )
                 )  # type: ignore
 

fastvideo/performance/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Performance benchmark and dashboard utilities."""

fastvideo/performance/hf_store.py

@@ -57,9 +57,7 @@ def is_baseline_eligible_record(record: dict[str, Any]) -> bool:
     """
     if record.get("baseline_eligible") is True:
         return True
-    if "baseline_eligible" not in record and "run_source" not in record:
-        return True
-    return False
+    return "baseline_eligible" not in record and "run_source" not in record
 
 
 def resolve_hf_token() -> str | None:
@@ -312,8 +310,12 @@ def load_records_for_model(
 # ---------------------------------------------------------------------------
 
 _NUMERIC_COLS = (
-    "latency", "throughput", "memory",
-    "text_encoder_time_s", "dit_time_s", "vae_decode_time_s",
+    "latency",
+    "throughput",
+    "memory",
+    "text_encoder_time_s",
+    "dit_time_s",
+    "vae_decode_time_s",
 )
 
 

fastvideo/performance/metric_policy.py

@@ -0,0 +1,116 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Metric policy for rolling performance baseline comparisons."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import Any
+
+
+@dataclass(frozen=True)
+class MetricPolicy:
+    key: str
+    label: str
+    precision: int
+    lower_is_better: bool
+    threshold_percent: float
+    threshold_absolute: float
+    gated: bool = True
+
+
+@dataclass(frozen=True)
+class MetricDelta:
+    absolute: float
+    percent: float
+    threshold_exceeded: bool
+    regressed: bool
+
+
+DEFAULT_METRIC_POLICIES: tuple[MetricPolicy, ...] = (
+    MetricPolicy("latency", "Latency", 3, True, 0.08, 0.5),
+    MetricPolicy("throughput", "Throughput", 3, False, 0.08, 0.05),
+    MetricPolicy("memory", "Memory", 1, True, 0.05, 256.0),
+    MetricPolicy("text_encoder_time_s", "Text Enc", 3, True, 0.05, 0.25),
+    MetricPolicy("dit_time_s", "DiT", 3, True, 0.05, 0.25),
+    MetricPolicy("vae_decode_time_s", "VAE Decode", 3, True, 0.05, 0.25),
+)
+
+
+def _optional_float(value: Any) -> float | None:
+    if value is None or isinstance(value, bool):
+        return None
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _optional_bool(value: Any) -> bool | None:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        normalized = value.strip().lower()
+        if normalized in {"1", "true", "yes", "on"}:
+            return True
+        if normalized in {"0", "false", "no", "off"}:
+            return False
+    return None
+
+
+def resolve_metric_policies(threshold_overrides: Mapping[str, Any] | None, ) -> tuple[MetricPolicy, ...]:
+    """Return default metric policies with optional per-metric overrides."""
+
+    if not isinstance(threshold_overrides, Mapping):
+        threshold_overrides = {}
+    policies: list[MetricPolicy] = []
+    for base_policy in DEFAULT_METRIC_POLICIES:
+        raw_override = threshold_overrides.get(base_policy.key, {})
+        if not isinstance(raw_override, Mapping):
+            raw_override = {}
+
+        threshold_percent = _optional_float(raw_override.get("threshold_percent"))
+        threshold_absolute = _optional_float(raw_override.get("threshold_absolute"))
+        gated = _optional_bool(raw_override.get("gated"))
+
+        policies.append(
+            MetricPolicy(
+                key=base_policy.key,
+                label=base_policy.label,
+                precision=base_policy.precision,
+                lower_is_better=base_policy.lower_is_better,
+                threshold_percent=(base_policy.threshold_percent if threshold_percent is None else threshold_percent),
+                threshold_absolute=(base_policy.threshold_absolute
+                                    if threshold_absolute is None else threshold_absolute),
+                gated=base_policy.gated if gated is None else gated,
+            ))
+    return tuple(policies)
+
+
+def serialize_metric_thresholds(policies: tuple[MetricPolicy, ...], ) -> dict[str, dict[str, float | bool]]:
+    return {
+        policy.key: {
+            "threshold_percent": policy.threshold_percent,
+            "threshold_absolute": policy.threshold_absolute,
+            "gated": policy.gated,
+        }
+        for policy in policies
+    }
+
+
+def regression_delta(
+    policy: MetricPolicy,
+    current: float,
+    baseline: float,
+) -> MetricDelta | None:
+    if baseline <= 0:
+        return None
+    absolute_delta = current - baseline if policy.lower_is_better else baseline - current
+    percent_delta = absolute_delta / baseline
+    threshold_exceeded = (percent_delta > policy.threshold_percent and absolute_delta > policy.threshold_absolute)
+    return MetricDelta(
+        absolute=absolute_delta,
+        percent=percent_delta,
+        threshold_exceeded=threshold_exceeded,
+        regressed=policy.gated and threshold_exceeded,
+    )

fastvideo/performance_dashboard/api.py

@@ -13,7 +13,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 
-from fastvideo.tests.performance import hf_store
+from fastvideo.performance import hf_store
 
 from .service import build_latest_summary, build_trends, filter_records
 
@@ -150,7 +150,6 @@ def create_app(store: PerformanceDataStore | None = None) -> FastAPI:
         filtered = filter_records(loaded, model_id=model_id, gpu_type=gpu_type)
         rows = build_latest_summary(
             filtered,
-            max_regression=float(os.environ.get("PERF_MAX_REGRESSION", "0.05")),
             run_source=run_source,
         )
         return {

fastvideo/performance_dashboard/metrics.py

@@ -1,26 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 """Metric definitions shared by the performance dashboard backend."""
 
-from __future__ import annotations
+from fastvideo.performance.metric_policy import DEFAULT_METRIC_POLICIES
 
-from dataclasses import dataclass
-
-
-@dataclass(frozen=True)
-class MetricDefinition:
-    key: str
-    label: str
-    precision: int
-    lower_is_better: bool
-
-
-METRICS: tuple[MetricDefinition, ...] = (
-    MetricDefinition("latency", "Latency", 3, True),
-    MetricDefinition("throughput", "Throughput", 3, False),
-    MetricDefinition("memory", "Memory", 1, True),
-    MetricDefinition("text_encoder_time_s", "Text Encoder", 3, True),
-    MetricDefinition("dit_time_s", "DiT", 3, True),
-    MetricDefinition("vae_decode_time_s", "VAE Decode", 3, True),
-)
+METRICS = DEFAULT_METRIC_POLICIES
 
 METRIC_BY_KEY = {metric.key: metric for metric in METRICS}

fastvideo/performance_dashboard/service.py

@@ -13,9 +13,8 @@ from collections import defaultdict
 from datetime import datetime, timezone
 from typing import Any
 
-from fastvideo.tests.performance.hf_store import is_baseline_eligible_record, safe_float
-
-from .metrics import METRICS
+from fastvideo.performance.hf_store import is_baseline_eligible_record, safe_float
+from fastvideo.performance.metric_policy import regression_delta, resolve_metric_policies
 
 Record = dict[str, Any]
 
@@ -95,19 +94,9 @@ def baseline_value(records: list[Record], metric_key: str) -> float | None:
     return float(statistics.median(values))
 
 
-def regression_percent(metric_key: str, current: float | None, baseline: float | None) -> float | None:
-    if current is None or baseline is None or baseline <= 0:
-        return None
-    metric = next(metric for metric in METRICS if metric.key == metric_key)
-    if metric.lower_is_better:
-        return (current - baseline) / baseline * 100.0
-    return (baseline - current) / baseline * 100.0
-
-
 def build_latest_summary(records: list[Record],
                          *,
                          baseline_window: int = 5,
-                         max_regression: float = 0.05,
                          run_source: str | None = None) -> list[Record]:
     rows: list[Record] = []
     for (model_id, gpu_type), group in group_by_model_gpu(records).items():
@@ -123,52 +112,58 @@ def build_latest_summary(records: list[Record],
             if record is not latest and record.get("success", True) and is_baseline_eligible_record(record)
         ]
         baseline_records = baseline_pool[-baseline_window:]
+        metric_policies = resolve_metric_policies(latest.get("regression_thresholds"))
 
         metrics: dict[str, Record] = {}
         regressions: list[float] = []
-        for metric in METRICS:
-            current = safe_float(latest.get(metric.key))
-            baseline = baseline_value(baseline_records, metric.key)
-            regression = regression_percent(metric.key, current, baseline)
-            metrics[metric.key] = {
+        failing_metrics: list[str] = []
+        threshold_exceeded_metrics: list[str] = []
+        for policy in metric_policies:
+            current = safe_float(latest.get(policy.key))
+            baseline = baseline_value(baseline_records, policy.key)
+            delta = None
+            if current is not None and baseline is not None:
+                delta = regression_delta(policy, current, baseline)
+            regression = None if delta is None else delta.percent * 100.0
+            metrics[policy.key] = {
                 "current": current,
                 "baseline": baseline,
                 "regression_pct": regression,
-                "label": metric.label,
-                "lower_is_better": metric.lower_is_better,
-                "precision": metric.precision,
+                "absolute_delta": None if delta is None else delta.absolute,
+                "threshold_percent": policy.threshold_percent * 100.0,
+                "threshold_absolute": policy.threshold_absolute,
+                "gated": policy.gated,
+                "threshold_exceeded": False if delta is None else delta.threshold_exceeded,
+                "regressed": False if delta is None else delta.regressed,
+                "label": policy.label,
+                "lower_is_better": policy.lower_is_better,
+                "precision": policy.precision,
             }
             if regression is not None:
                 regressions.append(regression)
+            if delta is not None and delta.threshold_exceeded:
+                threshold_exceeded_metrics.append(policy.key)
+            if delta is not None and delta.regressed:
+                failing_metrics.append(policy.key)
 
         worst_regression = max(regressions) if regressions else None
         success = bool(latest.get("success", True))
         status = "pass" if success else "fail"
 
         rows.append({
-            "model_id":
-            model_id,
-            "gpu_type":
-            gpu_type,
-            "timestamp":
-            latest.get("timestamp"),
-            "commit_sha":
-            latest.get("commit_sha"),
+            "model_id": model_id,
+            "gpu_type": gpu_type,
+            "timestamp": latest.get("timestamp"),
+            "commit_sha": latest.get("commit_sha"),
             **record_metadata(latest),
-            "success":
-            success,
-            "baseline_n":
-            len(baseline_records),
-            "worst_regression_pct":
-            worst_regression,
-            "regression_threshold_pct":
-            max_regression * 100.0,
-            "computed_regression_status":
-            "fail" if worst_regression is not None and worst_regression > max_regression * 100.0 else "pass",
-            "status":
-            status,
-            "metrics":
-            metrics,
+            "success": success,
+            "baseline_n": len(baseline_records),
+            "worst_regression_pct": worst_regression,
+            "threshold_exceeded_metrics": threshold_exceeded_metrics,
+            "failing_metrics": failing_metrics,
+            "computed_regression_status": "fail" if failing_metrics else "pass",
+            "status": status,
+            "metrics": metrics,
         })
 
     return sorted(rows, key=lambda row: (row["status"] != "fail", row["model_id"], row["gpu_type"]))
@@ -179,14 +174,15 @@ def build_trends(records: list[Record]) -> list[Record]:
     for (model_id, gpu_type), group in group_by_model_gpu(records).items():
         points = []
         for record in group:
+            metric_policies = resolve_metric_policies(record.get("regression_thresholds"))
             point = {
                 "timestamp": record.get("timestamp"),
                 "commit_sha": record.get("commit_sha"),
                 **record_metadata(record),
                 "success": bool(record.get("success", True)),
                 "metrics": {
-                    metric.key: safe_float(record.get(metric.key))
-                    for metric in METRICS
+                    policy.key: safe_float(record.get(policy.key))
+                    for policy in metric_policies
                 },
             }
             points.append(point)

fastvideo/pipelines/basic/kandinsky5/__init__.py

@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0

fastvideo/pipelines/basic/kandinsky5/kandinsky5_i2v_pipeline.py

@@ -0,0 +1,89 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages.input_validation import InputValidationStage
+from fastvideo.pipelines.stages.kandinsky5 import (
+    Kandinsky5DecodingStage,
+    Kandinsky5DenoisingStage,
+    Kandinsky5ImageEncodingStage,
+    Kandinsky5LatentPreparationStage,
+    Kandinsky5NormalizationStage,
+)
+from fastvideo.pipelines.stages.text_encoding import TextEncodingStage
+from fastvideo.pipelines.stages.timestep_preparation import TimestepPreparationStage
+
+
+class Kandinsky5I2VPipeline(ComposedPipelineBase):
+    """Kandinsky-5.0 image-to-video pipeline."""
+
+    _required_config_modules = [
+        "scheduler",
+        "text_encoder",
+        "text_encoder_2",
+        "tokenizer",
+        "tokenizer_2",
+        "transformer",
+        "vae",
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
+        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
+
+        self.add_stage(
+            stage_name="text_encoding_stage",
+            stage=TextEncodingStage(
+                text_encoders=[
+                    self.get_module("text_encoder"),
+                    self.get_module("text_encoder_2"),
+                ],
+                tokenizers=[
+                    self.get_module("tokenizer"),
+                    self.get_module("tokenizer_2"),
+                ],
+            ),
+        )
+
+        self.add_stage(
+            stage_name="timestep_preparation_stage",
+            stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")),
+        )
+
+        self.add_stage(
+            stage_name="latent_preparation_stage",
+            stage=Kandinsky5LatentPreparationStage(
+                scheduler=self.get_module("scheduler"),
+                transformer=self.get_module("transformer"),
+            ),
+        )
+
+        # Runs AFTER latent preparation so the initial noise is the seeded
+        # generator's first draw (official kandinsky-5 RNG order); this stage
+        # then samples the image latent and places it into the latents.
+        self.add_stage(
+            stage_name="image_encoding_stage",
+            stage=Kandinsky5ImageEncodingStage(vae=self.get_module("vae")),
+        )
+
+        self.add_stage(
+            stage_name="denoising_stage",
+            stage=Kandinsky5DenoisingStage(
+                transformer=self.get_module("transformer"),
+                scheduler=self.get_module("scheduler"),
+            ),
+        )
+
+        self.add_stage(
+            stage_name="normalization_stage",
+            stage=Kandinsky5NormalizationStage(),
+        )
+
+        self.add_stage(
+            stage_name="decoding_stage",
+            stage=Kandinsky5DecodingStage(vae=self.get_module("vae"), pipeline=self),
+        )
+
+
+EntryClass = Kandinsky5I2VPipeline

fastvideo/pipelines/basic/kandinsky5/kandinsky5_pipeline.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages.input_validation import InputValidationStage
+from fastvideo.pipelines.stages.kandinsky5 import (
+    Kandinsky5DecodingStage,
+    Kandinsky5DenoisingStage,
+    Kandinsky5LatentPreparationStage,
+)
+from fastvideo.pipelines.stages.text_encoding import TextEncodingStage
+from fastvideo.pipelines.stages.timestep_preparation import TimestepPreparationStage
+
+
+class Kandinsky5T2VPipeline(ComposedPipelineBase):
+    """Kandinsky-5.0 Lite text-to-video pipeline."""
+
+    _required_config_modules = [
+        "scheduler",
+        "text_encoder",
+        "text_encoder_2",
+        "tokenizer",
+        "tokenizer_2",
+        "transformer",
+        "vae",
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
+        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
+
+        self.add_stage(
+            stage_name="text_encoding_stage",
+            stage=TextEncodingStage(
+                text_encoders=[
+                    self.get_module("text_encoder"),
+                    self.get_module("text_encoder_2"),
+                ],
+                tokenizers=[
+                    self.get_module("tokenizer"),
+                    self.get_module("tokenizer_2"),
+                ],
+            ),
+        )
+
+        self.add_stage(
+            stage_name="timestep_preparation_stage",
+            stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")),
+        )
+
+        self.add_stage(
+            stage_name="latent_preparation_stage",
+            stage=Kandinsky5LatentPreparationStage(
+                scheduler=self.get_module("scheduler"),
+                transformer=self.get_module("transformer"),
+            ),
+        )
+
+        self.add_stage(
+            stage_name="denoising_stage",
+            stage=Kandinsky5DenoisingStage(
+                transformer=self.get_module("transformer"),
+                scheduler=self.get_module("scheduler"),
+            ),
+        )
+
+        self.add_stage(
+            stage_name="decoding_stage",
+            stage=Kandinsky5DecodingStage(vae=self.get_module("vae"), pipeline=self),
+        )
+
+
+EntryClass = Kandinsky5T2VPipeline

fastvideo/pipelines/basic/kandinsky5/presets.py

@@ -0,0 +1,165 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Kandinsky-5 model family pipeline presets."""
+
+from fastvideo.api.presets import InferencePreset, PresetStageSpec
+
+_NEGATIVE_PROMPT = ("Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, "
+                    "deformed, walking backwards")
+
+_DENOISE_STAGE = PresetStageSpec(
+    name="denoise",
+    kind="denoising",
+    description="Main Kandinsky-5 denoising pass",
+    allowed_overrides=frozenset({
+        "num_inference_steps",
+        "guidance_scale",
+    }),
+)
+
+KANDINSKY5_T2V_LITE_5S = InferencePreset(
+    name="kandinsky5_t2v_lite_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Lite T2V 5s",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 5.0,
+        "num_inference_steps": 50,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_T2V_LITE_DISTILLED_5S = InferencePreset(
+    name="kandinsky5_t2v_lite_distilled_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Lite T2V Distilled 5s",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 16,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_T2V_PRO_5S = InferencePreset(
+    name="kandinsky5_t2v_pro_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro T2V 5s",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 5.0,
+        "num_inference_steps": 50,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_T2V_PRO_DISTILLED_5S = InferencePreset(
+    name="kandinsky5_t2v_pro_distilled_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro T2V Distilled 5s",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 16,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_I2V_LITE_5S = InferencePreset(
+    name="kandinsky5_i2v_lite_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Lite I2V 5s",
+    workload_type="i2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 5.0,
+        "num_inference_steps": 50,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_I2V_PRO_5S = InferencePreset(
+    name="kandinsky5_i2v_pro_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro I2V 5s",
+    workload_type="i2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 5.0,
+        "num_inference_steps": 50,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_I2V_LITE_DISTILLED_5S = InferencePreset(
+    name="kandinsky5_i2v_lite_distilled_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Lite I2V Distilled 5s",
+    workload_type="i2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 16,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_I2V_PRO_DISTILLED_5S = InferencePreset(
+    name="kandinsky5_i2v_pro_distilled_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro I2V Distilled 5s",
+    workload_type="i2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 16,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+ALL_PRESETS = (KANDINSKY5_T2V_LITE_5S, KANDINSKY5_T2V_LITE_DISTILLED_5S, KANDINSKY5_T2V_PRO_5S,
+               KANDINSKY5_T2V_PRO_DISTILLED_5S, KANDINSKY5_I2V_LITE_5S, KANDINSKY5_I2V_LITE_DISTILLED_5S,
+               KANDINSKY5_I2V_PRO_5S, KANDINSKY5_I2V_PRO_DISTILLED_5S)

fastvideo/pipelines/stages/__init__.py

@@ -33,6 +33,8 @@ from fastvideo.pipelines.basic.ltx2.stages import (  # noqa: F401
 from fastvideo.pipelines.stages.matrixgame2_denoising import MatrixGame2CausalDenoisingStage
 from fastvideo.pipelines.stages.matrixgame3_denoising import MatrixGame3DenoisingStage
 from fastvideo.pipelines.stages.hyworld_denoising import HYWorldDenoisingStage
+from fastvideo.pipelines.stages.kandinsky5 import (Kandinsky5DecodingStage, Kandinsky5DenoisingStage,
+                                                   Kandinsky5LatentPreparationStage)
 from fastvideo.pipelines.stages.gamecraft_denoising import GameCraftDenoisingStage
 from fastvideo.pipelines.stages.gen3c_stages import (Gen3CCFGPolicyStage, Gen3CConditioningStage, Gen3CDenoisingStage,
                                                      Gen3CLatentPreparationStage)
@@ -65,6 +67,9 @@ __all__ = [
     "MatrixGame2CausalDenoisingStage",
     "MatrixGame3DenoisingStage",
     "HYWorldDenoisingStage",
+    "Kandinsky5DecodingStage",
+    "Kandinsky5DenoisingStage",
+    "Kandinsky5LatentPreparationStage",
     "GameCraftDenoisingStage",
     "Gen3CCFGPolicyStage",
     "Gen3CConditioningStage",

fastvideo/pipelines/stages/base.py

@@ -34,6 +34,7 @@ class PipelineStage(ABC):
     composed with other stages to create a complete pipeline. Each stage is responsible
     for a specific part of the process, such as prompt encoding, latent preparation, etc.
     """
+    performance_component_metric: str | None = None
 
     def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
         """
@@ -155,6 +156,9 @@ class PipelineStage(ABC):
                 logger.info("[%s] Execution completed in %s ms", stage_name, execution_time * 1000)
                 batch.logging_info.add_stage_execution_time(stage_key, execution_time)
                 batch.logging_info.add_stage_metric(stage_key, "stage_class", stage_class_name)
+                component_metric = self.performance_component_metric
+                if component_metric is not None:
+                    batch.logging_info.add_stage_metric(stage_key, "component_metric", component_metric)
             except Exception as e:
                 torch.cuda.synchronize()
                 execution_time = time.perf_counter() - start_time

fastvideo/pipelines/stages/decoding.py

@@ -28,6 +28,7 @@ class DecodingStage(PipelineStage):
     This stage handles the decoding of latent representations into the final
     output format (e.g., pixel values).
     """
+    performance_component_metric = "vae_decode_time_s"
 
     def __init__(self, vae, pipeline=None) -> None:
         self.vae: ParallelTiledVAE = vae

fastvideo/pipelines/stages/denoising.py

@@ -51,6 +51,7 @@ class DenoisingStage(PipelineStage):
     This stage handles the iterative denoising process that transforms
     the initial noise into the final output.
     """
+    performance_component_metric = "dit_time_s"
 
     def __init__(self, transformer, scheduler, pipeline=None, transformer_2=None, vae=None) -> None:
         super().__init__()
@@ -1187,6 +1188,7 @@ class Cosmos25V2WDenoisingStage(Cosmos25DenoisingStage):
 
 class Cosmos25AutoDenoisingStage(PipelineStage):
     """Route Cosmos 2.5 denoising to T2W vs V2W/I2W."""
+    performance_component_metric = "dit_time_s"
 
     def __init__(self, transformer, scheduler) -> None:
         super().__init__()

fastvideo/pipelines/stages/kandinsky5.py

@@ -0,0 +1,534 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import contextlib
+from typing import Any
+
+import PIL
+import torch
+from diffusers.utils.torch_utils import randn_tensor
+from tqdm.auto import tqdm
+
+from fastvideo.attention.backends.nabla import NablaAttentionMetadataBuilder
+from fastvideo.distributed import get_local_torch_device
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.forward_context import set_forward_context
+from fastvideo.logger import init_logger
+from fastvideo.models.loader.component_loader import TransformerLoader, VAELoader
+from fastvideo.models.vaes.common import ParallelTiledVAE
+from fastvideo.models.vision_utils import normalize, numpy_to_pt, pil_to_numpy, resize
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.pipelines.stages.base import PipelineStage
+from fastvideo.pipelines.stages.decoding import DecodingStage
+from fastvideo.pipelines.stages.encoding import EncodingStage
+from fastvideo.pipelines.stages.validators import StageValidators as V
+from fastvideo.pipelines.stages.validators import VerificationResult
+from fastvideo.utils import PRECISION_TO_TYPE
+
+logger = init_logger(__name__)
+
+
+class Kandinsky5LatentPreparationStage(PipelineStage):
+
+    def __init__(self, scheduler, transformer) -> None:
+        super().__init__()
+        self.scheduler = scheduler
+        self.transformer = transformer
+
+    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        if batch.height is None or batch.width is None:
+            raise ValueError("height and width must be provided for Kandinsky5.")
+        height = int(batch.height)
+        width = int(batch.width)
+        num_frames = int(batch.num_frames)
+
+        temporal_ratio = fastvideo_args.pipeline_config.vae_config.arch_config.temporal_compression_ratio
+        spatial_ratio = fastvideo_args.pipeline_config.vae_config.arch_config.spatial_compression_ratio
+        patch_size = fastvideo_args.pipeline_config.dit_config.arch_config.patch_size
+
+        if num_frames % temporal_ratio != 1:
+            num_frames = num_frames // temporal_ratio * temporal_ratio + 1
+            batch.num_frames = num_frames
+
+        required_divisor = spatial_ratio * patch_size[1]
+        if height % required_divisor != 0 or width % required_divisor != 0:
+            raise ValueError(f"Kandinsky5 height/width must be divisible by {required_divisor}; "
+                             f"got height={height}, width={width}.")
+
+        # NABLA sparse attention (Pro checkpoints) reshapes the post-patch grid
+        # into 8x8 blocks; validate here instead of crashing mid-denoise after
+        # all the encoding work is done.
+        arch_cfg = getattr(self.transformer, "config", None) or fastvideo_args.pipeline_config.dit_config.arch_config
+        if getattr(arch_cfg, "attention_type", "regular") == "nabla":
+            nabla_divisor = required_divisor * 8
+            if height % nabla_divisor != 0 or width % nabla_divisor != 0:
+                raise ValueError(f"Kandinsky5 NABLA checkpoints require height/width divisible by {nabla_divisor}; "
+                                 f"got height={height}, width={width}.")
+
+        if isinstance(batch.prompt, list):
+            batch_size = len(batch.prompt)
+        elif batch.prompt is not None:
+            batch_size = 1
+        else:
+            batch_size = batch.prompt_embeds[0].shape[0]
+        batch_size *= batch.num_videos_per_prompt
+
+        dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.dit_precision]
+        device = get_local_torch_device()
+        num_latent_frames = (num_frames - 1) // temporal_ratio + 1
+        num_channels = getattr(
+            self.transformer,
+            "in_visual_dim",
+            fastvideo_args.pipeline_config.dit_config.arch_config.in_visual_dim,
+        )
+        shape = (
+            batch_size,
+            num_latent_frames,
+            height // spatial_ratio,
+            width // spatial_ratio,
+            num_channels,
+        )
+
+        if isinstance(batch.generator, list) and len(batch.generator) != batch_size:
+            raise ValueError(f"generator list length {len(batch.generator)} does not match batch size {batch_size}.")
+
+        if batch.latents is None:
+            latents = randn_tensor(shape, generator=batch.generator, device=device, dtype=dtype)
+            if hasattr(self.scheduler, "init_noise_sigma"):
+                latents = latents * self.scheduler.init_noise_sigma
+        else:
+            latents = batch.latents.to(device=device, dtype=dtype)
+
+        visual_cond = getattr(self.transformer, "visual_cond", False)
+        if visual_cond and latents.shape[-1] == num_channels:
+            cond = torch.zeros_like(latents)
+            cond_mask = torch.zeros(
+                (*latents.shape[:-1], 1),
+                device=latents.device,
+                dtype=latents.dtype,
+            )
+            latents = torch.cat([latents, cond, cond_mask], dim=-1)
+
+        # I2V image conditioning is placed by Kandinsky5ImageEncodingStage,
+        # which runs AFTER this stage so the initial noise is the generator's
+        # first draw (matching the official kandinskylab/kandinsky-5 order).
+        batch.latents = latents
+        batch.raw_latent_shape = (
+            batch_size,
+            num_channels,
+            num_latent_frames,
+            height // spatial_ratio,
+            width // spatial_ratio,
+        )
+        return batch
+
+    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("num_frames", batch.num_frames, V.positive_int)
+        result.add_check("height", batch.height, V.positive_int)
+        result.add_check("width", batch.width, V.positive_int)
+        result.add_check("latents", batch.latents, V.none_or_tensor)
+        return result
+
+    def verify_output(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("latents", batch.latents, [V.is_tensor, V.with_dims(5)])
+        return result
+
+
+class Kandinsky5DenoisingStage(PipelineStage):
+
+    def __init__(self, transformer, scheduler) -> None:
+        super().__init__()
+        self.transformer = transformer
+        self.scheduler = scheduler
+
+    @staticmethod
+    def _scale_factor(height: int, width: int) -> tuple[float, float, float]:
+        if 480 <= height <= 854 and 480 <= width <= 854:
+            return (1.0, 2.0, 2.0)
+        return (1.0, 3.16, 3.16)
+
+    @staticmethod
+    def _text_rope_pos(mask: torch.Tensor, device: torch.device) -> torch.Tensor:
+        seq_len = int(mask.sum(1).max().item())
+        return torch.arange(seq_len, device=device)
+
+    @staticmethod
+    def fast_sta_nabla(
+        T: int,
+        H: int,
+        W: int,
+        wT: int = 3,
+        wH: int = 3,
+        wW: int = 3,
+        device: torch.device | str = "cuda",
+    ) -> torch.Tensor:
+        """
+        Create a sparse temporal attention (STA) mask for efficient video generation.
+
+        This method generates a mask that limits attention to nearby frames and spatial positions, reducing
+        computational complexity for video generation.
+
+        Args:
+            T (int): Number of temporal frames
+            H (int): Height in latent space
+            W (int): Width in latent space
+            wT (int): Temporal attention window size
+            wH (int): Height attention window size
+            wW (int): Width attention window size
+            device (str): Device to create tensor on
+
+        Returns:
+            torch.Tensor: Sparse attention mask of shape (T*H*W, T*H*W)
+        """
+        max_extent = int(torch.tensor([T, H, W], device=device).amax().item())
+        r = torch.arange(0, max_extent, 1, dtype=torch.int16, device=device)
+        mat = (r.unsqueeze(1) - r.unsqueeze(0)).abs()
+        sta_t, sta_h, sta_w = (
+            mat[:T, :T].flatten(),
+            mat[:H, :H].flatten(),
+            mat[:W, :W].flatten(),
+        )
+        sta_t = sta_t <= wT // 2
+        sta_h = sta_h <= wH // 2
+        sta_w = sta_w <= wW // 2
+        sta_hw = (sta_h.unsqueeze(1) * sta_w.unsqueeze(0)).reshape(H, H, W, W).transpose(1, 2).flatten()
+        sta = (sta_t.unsqueeze(1) * sta_hw.unsqueeze(0)).reshape(T, T, H * W, H * W).transpose(1, 2)
+        return sta.reshape(T * H * W, T * H * W)
+
+    def get_sparse_params(self, sample: torch.Tensor, device: torch.device) -> dict[str, Any] | None:
+        """
+        Generate sparse attention parameters for the transformer based on sample dimensions.
+
+        This method computes the sparse attention configuration needed for efficient video processing in the
+        transformer model.
+
+        Args:
+            sample (torch.Tensor): Input sample tensor
+            device (torch.device): Device to place tensors on
+
+        Returns:
+            Dict: Dictionary containing sparse attention parameters
+        """
+        assert self.transformer.config.patch_size[0] == 1
+        _, T, H, W, _ = sample.shape
+        T, H, W = (
+            T // self.transformer.config.patch_size[0],
+            H // self.transformer.config.patch_size[1],
+            W // self.transformer.config.patch_size[2],
+        )
+        if self.transformer.config.attention_type == "nabla":
+            sta_mask = self.fast_sta_nabla(
+                T,
+                H // 8,
+                W // 8,
+                self.transformer.config.attention_wT,
+                self.transformer.config.attention_wH,
+                self.transformer.config.attention_wW,
+                device=device,
+            )
+
+            sparse_params = {
+                "sta_mask": sta_mask.unsqueeze_(0).unsqueeze_(0),
+                "attention_type": self.transformer.config.attention_type,
+                "to_fractal": True,
+                "P": self.transformer.config.attention_P,
+                "wT": self.transformer.config.attention_wT,
+                "wW": self.transformer.config.attention_wW,
+                "wH": self.transformer.config.attention_wH,
+                "add_sta": self.transformer.config.attention_add_sta,
+                "visual_shape": (T, H, W),
+                "method": self.transformer.config.attention_method,
+            }
+        else:
+            sparse_params = None
+
+        return sparse_params
+
+    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        if batch.timesteps is None:
+            raise ValueError("timesteps must be prepared before Kandinsky5 denoising.")
+        if batch.latents is None:
+            raise ValueError("latents must be prepared before Kandinsky5 denoising.")
+        if not fastvideo_args.model_loaded["transformer"]:
+            loader = TransformerLoader()
+            self.transformer = loader.load(fastvideo_args.model_paths["transformer"], fastvideo_args)
+            fastvideo_args.model_loaded["transformer"] = True
+
+        device = get_local_torch_device()
+        target_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.dit_precision]
+        autocast_enabled = target_dtype != torch.float32 and not fastvideo_args.disable_autocast
+        latents = batch.latents
+        num_channels = getattr(
+            self.transformer,
+            "in_visual_dim",
+            fastvideo_args.pipeline_config.dit_config.arch_config.in_visual_dim,
+        )
+
+        prompt_embeds = batch.prompt_embeds[0].to(device=device, dtype=target_dtype)
+        pooled = batch.prompt_embeds[1].to(device=device, dtype=target_dtype)
+        if batch.prompt_attention_mask is None or not batch.prompt_attention_mask:
+            raise ValueError("Kandinsky5 requires Qwen prompt attention masks.")
+        text_rope_pos = self._text_rope_pos(batch.prompt_attention_mask[0].to(device), device)
+
+        neg_prompt_embeds = None
+        neg_pooled = None
+        negative_text_rope_pos = None
+        if batch.do_classifier_free_guidance and batch.negative_prompt_embeds:
+            neg_prompt_embeds = batch.negative_prompt_embeds[0].to(device=device, dtype=target_dtype)
+            neg_pooled = batch.negative_prompt_embeds[1].to(device=device, dtype=target_dtype)
+            if batch.negative_attention_mask is None or not batch.negative_attention_mask:
+                raise ValueError("Kandinsky5 requires Qwen negative attention masks for CFG.")
+            negative_text_rope_pos = self._text_rope_pos(batch.negative_attention_mask[0].to(device), device)
+
+        height = int(batch.height)
+        width = int(batch.width)
+        temporal_ratio = fastvideo_args.pipeline_config.vae_config.arch_config.temporal_compression_ratio
+        spatial_ratio = fastvideo_args.pipeline_config.vae_config.arch_config.spatial_compression_ratio
+        num_latent_frames = (int(batch.num_frames) - 1) // temporal_ratio + 1
+        visual_rope_pos = [
+            torch.arange(num_latent_frames, device=device),
+            torch.arange(height // spatial_ratio // 2, device=device),
+            torch.arange(width // spatial_ratio // 2, device=device),
+        ]
+        scale_factor = self._scale_factor(height, width)
+
+        sparse_params = self.get_sparse_params(latents, device)
+
+        # I2V keeps the first (conditioning) frame fixed during denoising.
+        # Key off the actual image conditioning, not transformer.visual_cond:
+        # official T2V checkpoints also ship visual_cond=True, and skipping
+        # frame 0 for them leaves it as undenoised noise.
+        cond_frames = 1 if batch.image_latent is not None else 0
+
+        trajectory_timesteps: list[torch.Tensor] = []
+        trajectory_latents: list[torch.Tensor] = []
+
+        with tqdm(total=batch.num_inference_steps, desc="Kandinsky5 Denoising") as progress_bar:
+            for i, timestep in enumerate(batch.timesteps):
+                if hasattr(self, "interrupt") and self.interrupt:
+                    continue
+
+                t_expand = timestep.unsqueeze(0).repeat(latents.shape[0]).to(device=device, dtype=target_dtype)
+                attn_metadata = None
+                if sparse_params is not None:
+                    attn_metadata = NablaAttentionMetadataBuilder().build(
+                        current_timestep=i,
+                        sta_mask=sparse_params["sta_mask"],
+                        P=sparse_params["P"],
+                        visual_shape=sparse_params["visual_shape"],
+                    )
+                autocast_ctx = (torch.autocast(device_type="cuda", dtype=target_dtype, enabled=autocast_enabled)
+                                if device.type == "cuda" else contextlib.nullcontext())
+                with set_forward_context(current_timestep=i, attn_metadata=attn_metadata,
+                                         forward_batch=batch), autocast_ctx:
+                    pred_velocity = self.transformer(
+                        hidden_states=latents.to(dtype=target_dtype),
+                        encoder_hidden_states=prompt_embeds,
+                        pooled_projections=pooled,
+                        timestep=t_expand,
+                        visual_rope_pos=visual_rope_pos,
+                        text_rope_pos=text_rope_pos,
+                        scale_factor=scale_factor,
+                        sparse_params=sparse_params,
+                        return_dict=True,
+                    ).sample
+
+                    if neg_prompt_embeds is not None and neg_pooled is not None:
+                        uncond_pred_velocity = self.transformer(
+                            hidden_states=latents.to(dtype=target_dtype),
+                            encoder_hidden_states=neg_prompt_embeds,
+                            pooled_projections=neg_pooled,
+                            timestep=t_expand,
+                            visual_rope_pos=visual_rope_pos,
+                            text_rope_pos=negative_text_rope_pos,
+                            scale_factor=scale_factor,
+                            sparse_params=sparse_params,
+                            return_dict=True,
+                        ).sample
+                        pred_velocity = uncond_pred_velocity + batch.guidance_scale * (pred_velocity -
+                                                                                       uncond_pred_velocity)
+
+                latents[:, cond_frames:, :, :, :num_channels] = self.scheduler.step(
+                    pred_velocity[:, cond_frames:],
+                    timestep,
+                    latents[:, cond_frames:, :, :, :num_channels],
+                    return_dict=False,
+                )[0]
+
+                if batch.return_trajectory_latents:
+                    trajectory_timesteps.append(timestep)
+                    # latents is mutated in place, so snapshot a channels-first copy.
+                    trajectory_latents.append(latents[..., :num_channels].permute(0, 4, 1, 2, 3).cpu())
+
+                if i == len(batch.timesteps) - 1 or (i + 1) % self.scheduler.order == 0:
+                    progress_bar.update()
+
+        if trajectory_latents:
+            batch.trajectory_latents = torch.stack(trajectory_latents, dim=1)
+            batch.trajectory_timesteps = torch.stack(trajectory_timesteps, dim=0).cpu()
+
+        batch.latents = latents[:, :, :, :, :num_channels]
+        return batch
+
+    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("latents", batch.latents, [V.is_tensor, V.with_dims(5)])
+        result.add_check("prompt_embeds", batch.prompt_embeds, V.list_not_empty)
+        return result
+
+
+class Kandinsky5DecodingStage(DecodingStage):
+
+    def __init__(self, vae: ParallelTiledVAE, pipeline=None) -> None:
+        super().__init__(vae=vae, pipeline=pipeline)
+
+    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        if batch.latents is None:
+            raise ValueError("latents must be available before Kandinsky5 decoding.")
+        # Kandinsky5 latents are channels-last [B, T, H, W, C]; the base stage
+        # (and the trajectory latents recorded by the denoising stage) work
+        # channels-first.
+        batch.latents = batch.latents.permute(0, 4, 1, 2, 3).contiguous()
+        return super().forward(batch, fastvideo_args)
+
+
+class Kandinsky5ImageEncodingStage(EncodingStage):
+    """Encode the conditioning image into a VAE latent for I2V."""
+
+    def __init__(self, vae: ParallelTiledVAE, pipeline=None) -> None:
+        super().__init__(vae=vae)
+
+    @staticmethod
+    def _preprocess(image, height: int, width: int) -> torch.Tensor:
+        if isinstance(image, PIL.Image.Image):
+            image = resize(image, height, width)
+            image = numpy_to_pt(pil_to_numpy(image))  # always lands in [0, 1]
+            return normalize(image)  # [0, 1] -> [-1, 1]
+        # Tensor input: no reliable way to tell [0, 1] from an already
+        # normalized [-1, 1] tensor whose values happen to be non-negative,
+        # so mirror diffusers' heuristic and say what we assumed.
+        if image.min() >= 0:
+            logger.warning("Kandinsky5 conditioning image tensor has no negative values; "
+                           "assuming range [0, 1] and normalizing to [-1, 1]. "
+                           "Pass a [-1, 1] tensor with negative values to skip normalization.")
+            image = normalize(image)  # [0, 1] -> [-1, 1]
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+        if image.shape[-2:] != (height, width):
+            image = torch.nn.functional.interpolate(image.float(),
+                                                    size=(height, width),
+                                                    mode="bilinear",
+                                                    antialias=True)
+        return image
+
+    @torch.no_grad()
+    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        if batch.pil_image is None:
+            raise ValueError("Kandinsky5 I2V requires an input image.")
+
+        if not fastvideo_args.model_loaded["vae"]:
+            vae = getattr(self, "vae", None)
+            if vae is None:
+                loader = VAELoader()
+                vae = loader.load(fastvideo_args.model_paths["vae"], fastvideo_args)
+                self.vae = vae
+            fastvideo_args.model_loaded["vae"] = True
+
+        device = get_local_torch_device()
+        vae = self.vae.to(device)
+        self.vae = vae
+        vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
+        vae_autocast_enabled = vae_dtype != torch.float32 and not fastvideo_args.disable_autocast
+
+        # [B, C, H, W] -> [B, C, 1, H, W]
+        image = self._preprocess(batch.pil_image, int(batch.height), int(batch.width))
+        image = image.to(device=device, dtype=torch.float32).unsqueeze(2)
+
+        # Encode the single conditioning frame without tiling (matches diffusers).
+        # The untested causal-VAE spatial_tiled_encode path corrupts the latent.
+        prev_use_tiling = vae.use_tiling
+        vae.use_tiling = False
+        try:
+            with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled):
+                if not vae_autocast_enabled:
+                    image = image.to(vae_dtype)
+                # Sample with the batch generator (diffusers parity); mode()
+                # would make seed-for-seed reproduction of the reference
+                # pipeline impossible.
+                generator = batch.generator
+                if isinstance(generator, list) and len(generator) != image.shape[0]:
+                    generator = generator[0]
+                image_latent = vae.encode(image).sample(generator=generator)
+        finally:
+            vae.use_tiling = prev_use_tiling
+
+        image_latent = image_latent * vae.scaling_factor
+        # [B, C, 1, H, W] -> [B, 1, H, W, C] to match channels-last latents
+        batch.image_latent = image_latent.permute(0, 2, 3, 4, 1).contiguous()
+
+        # Place the conditioning latent into the prepared latents: frame 0 of
+        # the main channels, the visual_cond channel block, and the mask.
+        # NOTE: the official kandinsky-5 repo leaves the visual_cond block
+        # zeros (generation_utils.py generate()), while the diffusers port
+        # copies the image latent into it. A same-seed A/B on
+        # Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers showed the Diffusers
+        # export requires the copy: zeroing the block produces smeared faces
+        # mid-video. Keep the diffusers semantics for Diffusers-format
+        # checkpoints.
+        latents = batch.latents
+        image_latent = batch.image_latent.to(device=latents.device, dtype=latents.dtype)
+        num_channels = image_latent.shape[-1]
+        latents[:, 0:1, :, :, :num_channels] = image_latent
+        if latents.shape[-1] > num_channels:
+            latents[:, 0:1, :, :, num_channels:2 * num_channels] = image_latent
+            latents[:, 0:1, :, :, 2 * num_channels:] = 1.0
+        batch.latents = latents
+
+        if fastvideo_args.vae_cpu_offload:
+            vae.to("cpu")
+        return batch
+
+    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("pil_image", batch.pil_image, V.not_none)
+        result.add_check("height", batch.height, V.positive_int)
+        result.add_check("width", batch.width, V.positive_int)
+        # This stage runs after latent preparation and writes into its output.
+        result.add_check("latents", batch.latents, [V.is_tensor, V.with_dims(5)])
+        return result
+
+    def verify_output(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
+        result = VerificationResult()
+        result.add_check("image_latent", batch.image_latent, [V.is_tensor, V.with_dims(5)])
+        return result
+
+
+class Kandinsky5NormalizationStage(PipelineStage):
+    """Normalize the first latent frames to reduce I2V conditioning artifacts."""
+
+    COND_FRAMES = 4
+    REFERENCE_FRAMES = 5
+
+    @staticmethod
+    def _adaptive_mean_std(source: torch.Tensor, reference: torch.Tensor) -> torch.Tensor:
+        source_mean = source.mean(dim=(1, 2, 3, 4), keepdim=True)
+        source_std = source.std(dim=(1, 2, 3, 4), keepdim=True)
+        # Magic constants limit how far the first frames may drift.
+        ref_mean = torch.clamp(reference.mean(dim=(1, 2, 3, 4), keepdim=True), source_mean - 0.05, source_mean + 0.1)
+        ref_std = torch.clamp(reference.std(dim=(1, 2, 3, 4), keepdim=True), source_std - 0.1, source_std + 0.25)
+        normalized = (source - source_mean) / source_std
+        return normalized * ref_std + ref_mean
+
+    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        latents = batch.latents
+        n = self.COND_FRAMES
+        if latents is None or latents.shape[1] <= n:
+            return batch
+
+        reference = latents[:, n:n + min(self.REFERENCE_FRAMES, latents.shape[1] - 1)]
+        latents[:, :n] = self._adaptive_mean_std(latents[:, :n].clone(), reference)
+        batch.latents = latents
+        return batch

fastvideo/pipelines/stages/text_encoding.py

@@ -8,6 +8,8 @@ This module contains implementations of prompt encoding stages for diffusion pip
 import torch
 from typing import Any
 
+from torch.distributed.tensor import DTensor
+
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.forward_context import set_forward_context
@@ -24,6 +26,7 @@ class TextEncodingStage(PipelineStage):
     This stage handles the encoding of text prompts into the embedding space
     expected by the diffusion model.
     """
+    performance_component_metric = "text_encoder_time_s"
 
     def __init__(self, text_encoders, tokenizers) -> None:
         """
@@ -202,6 +205,21 @@ class TextEncodingStage(PipelineStage):
             encoder_config = encoder_cfgs[i]
             preprocess_func = preprocess_funcs[i]
             postprocess_func = postprocess_funcs[i]
+            # cpu_offload semantics: params rest on CPU between calls but the
+            # forward computes on GPU. FSDP2-wrapped encoders (CPUOffloadPolicy;
+            # DTensor params) stream themselves per-layer — leave inputs on the
+            # param device and let FSDP's root pre-forward move them. A plain
+            # module parked on CPU by text_encoder_cpu_offload is swapped to the
+            # target device for the forward and back afterwards, mirroring the
+            # image-encoder/VAE offload pattern.
+            first_param = next(text_encoder.parameters(), None)
+            encoder_device = first_param.device if first_param is not None else torch.device(target_device)
+            moved_for_forward = False
+            if (first_param is not None and not isinstance(first_param, DTensor)
+                    and encoder_device.type != torch.device(target_device).type):
+                text_encoder = text_encoder.to(target_device)
+                encoder_device = torch.device(target_device)
+                moved_for_forward = True
 
             tok_kwargs = dict(encoder_config.tokenizer_kwargs)
             if max_length is not None:
@@ -246,7 +264,7 @@ class TextEncodingStage(PipelineStage):
                     # pre-format prompts into message lists upstream and rely on
                     # the inner tokenizer + full tokenizer_kwargs (which include
                     # add_generation_prompt). Preserve that original path exactly.
-                    text_inputs = tok.apply_chat_template(processed_texts, **tok_kwargs).to(target_device)
+                    text_inputs = tok.apply_chat_template(processed_texts, **tok_kwargs).to(encoder_device)
                 else:
                     # Two-step approach matching Diffusers: format with chat
                     # template first, then tokenize the resulting strings.
@@ -260,9 +278,9 @@ class TextEncodingStage(PipelineStage):
                             enable_thinking=False,
                         )
                         formatted_texts.append(formatted)
-                    text_inputs = tokenizer(formatted_texts, **tok_kwargs).to(target_device)
+                    text_inputs = tokenizer(formatted_texts, **tok_kwargs).to(encoder_device)
             else:
-                text_inputs = tok(processed_texts, **tok_kwargs).to(target_device)
+                text_inputs = tok(processed_texts, **tok_kwargs).to(encoder_device)
 
             input_ids = text_inputs["input_ids"]
             attention_mask = text_inputs["attention_mask"]
@@ -282,15 +300,18 @@ class TextEncodingStage(PipelineStage):
             except Exception:
                 prompt_embeds, attention_mask = postprocess_func(outputs, attention_mask)
             if is_ltx2 and getattr(outputs, "hidden_states", None):
-                audio_embed = outputs.hidden_states[0]
+                audio_embed = outputs.hidden_states[0].to(device=target_device)
                 if dtype is not None:
                     audio_embed = audio_embed.to(dtype=dtype)
                 audio_embeds_list.append(audio_embed)
+            prompt_embeds = prompt_embeds.to(device=target_device)
             if dtype is not None:
                 prompt_embeds = prompt_embeds.to(dtype=dtype)
             embeds_list.append(prompt_embeds)
             if return_attention_mask:
-                attn_masks_list.append(attention_mask)
+                attn_masks_list.append(attention_mask.to(device=target_device))
+            if moved_for_forward and fastvideo_args.text_encoder_cpu_offload:
+                text_encoder.to("cpu")
         self._last_audio_embeds = audio_embeds_list if is_ltx2 else None
         return self.return_embeds(embeds_list, attn_masks_list, return_type, return_attention_mask, indices)
 
@@ -350,6 +371,7 @@ class Cosmos25TextEncodingStage(PipelineStage):
     Cosmos 2.5 uses Reason1 (Qwen2.5-VL) and relies on the encoder's
     `compute_text_embeddings_online()`.
     """
+    performance_component_metric = "text_encoder_time_s"
 
     def __init__(self, text_encoder) -> None:
         super().__init__()

fastvideo/platforms/cuda.py

@@ -157,6 +157,14 @@ class CudaPlatformBase(Platform):
                 "ATTN_QAT_TRAIN selected but fastvideo_kernel.triton_kernels.attn_qat_train is not built. "
                 "Silent fallback would produce a non-QAT training run; refusing to proceed. "
                 "Install the training kernel or pick a different FASTVIDEO_ATTENTION_BACKEND.")
+        elif selected_backend == AttentionBackendEnum.NABLA_ATTN:
+            from fastvideo.attention.backends.nabla import CAN_USE_FLEX_ATTN
+            if CAN_USE_FLEX_ATTN:
+                logger.info("Using NABLA block-sparse flex-attention backend.")
+                return "fastvideo.attention.backends.nabla.NablaAttentionBackend"
+            raise ImportError("NABLA_ATTN selected but torch.nn.attention.flex_attention is unavailable in this "
+                              "PyTorch build. Silent fallback to dense attention would be orders of magnitude "
+                              "slower and diverge from the reference; upgrade PyTorch or pick a different backend.")
         elif selected_backend == AttentionBackendEnum.VIDEO_SPARSE_ATTN:
             try:
                 from fastvideo_kernel import video_sparse_attn  # noqa: F401

fastvideo/platforms/interface.py

@@ -22,6 +22,7 @@ class AttentionBackendEnum(enum.Enum):
     VMOBA_ATTN = enum.auto()
     SLA_ATTN = enum.auto()
     SAGE_SLA_ATTN = enum.auto()
+    NABLA_ATTN = enum.auto()
     NO_ATTENTION = enum.auto()
 
 

fastvideo/registry.py

@@ -27,6 +27,7 @@ from fastvideo.configs.pipelines.hunyuan15 import (Hunyuan15T2V480PConfig, Hunyu
                                                    Hunyuan15T2V720PConfig, Hunyuan15I2V720PConfig,
                                                    Hunyuan15SR1080PConfig)
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.kandinsky5 import Kandinsky5I2VConfig, Kandinsky5T2VConfig
 from fastvideo.configs.pipelines.lingbotworld import LingBotWorldI2V480PConfig
 from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
 from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
@@ -117,6 +118,10 @@ class ConfigInfo:
     workload_types: tuple[WorkloadType, ...]
     model_family: str | None = None
     default_preset: str | None = None
+    # When set, overrides the model_index `_class_name` for pipeline resolution.
+    # Lets a model family map to a specific pipeline class by path/detector
+    # (e.g. a T2V and I2V checkpoint that share a `_class_name`).
+    pipeline_cls_name: str | None = None
 
 
 # The central registry mapping a model name to its configuration information
@@ -137,6 +142,7 @@ def register_configs(
     model_detectors: list[Callable[[str], bool]] | None = None,
     model_family: str | None = None,
     default_preset: str | None = None,
+    pipeline_cls_name: str | None = None,
 ) -> None:
     """Register config classes for a model family.
 
@@ -151,6 +157,7 @@ def register_configs(
         workload_types=workload_types,
         model_family=model_family,
         default_preset=default_preset,
+        pipeline_cls_name=pipeline_cls_name,
     )
 
     if hf_model_paths:
@@ -490,18 +497,174 @@ def _register_configs() -> None:
         default_preset="lingbotworld_i2v",
     )
 
+    def _kandinsky5_detector(require: tuple[str, ...] = (), exclude: tuple[str, ...] = ()) -> Callable[[str], bool]:
+
+        def detect(path: str) -> bool:
+            path_lower = path.lower()
+            if "kandinsky5" not in path_lower and "kandinsky-5" not in path_lower:
+                return False
+            return (all(token in path_lower for token in require) and not any(token in path_lower for token in exclude))
+
+        return detect
+
+    # t2v/i2v exclude each other so a checkpoint stored under a directory
+    # containing the other token (e.g. ~/i2v_experiments/kandinsky5-t2v-ft)
+    # falls through to the model_index _class_name fallback detectors below
+    # instead of being misrouted.
+    _is_kandinsky5_t2v = _kandinsky5_detector(require=("t2v", ), exclude=("i2v", ))
+    _is_kandinsky5_i2v = _kandinsky5_detector(require=("i2v", ), exclude=("t2v", ))
+    _is_kandinsky5_t2v_lite = _kandinsky5_detector(require=("t2v", "lite"), exclude=("i2v", "distilled"))
+    _is_kandinsky5_t2v_pro = _kandinsky5_detector(require=("t2v", "pro"), exclude=("i2v", "distilled"))
+    _is_kandinsky5_t2v_lite_distilled = _kandinsky5_detector(require=("t2v", "lite", "distilled"), exclude=("i2v", ))
+    _is_kandinsky5_t2v_pro_distilled = _kandinsky5_detector(require=("t2v", "pro", "distilled"), exclude=("i2v", ))
+    _is_kandinsky5_i2v_lite = _kandinsky5_detector(require=("i2v", "lite"), exclude=("t2v", "distilled"))
+    _is_kandinsky5_i2v_pro = _kandinsky5_detector(require=("i2v", "pro"), exclude=("t2v", "distilled"))
+    _is_kandinsky5_i2v_lite_distilled = _kandinsky5_detector(require=("i2v", "lite", "distilled"), exclude=("t2v", ))
+    _is_kandinsky5_i2v_pro_distilled = _kandinsky5_detector(require=("i2v", "pro", "distilled"), exclude=("t2v", ))
+
     # Kandinsky5 Lite T2V
     register_configs(
         sampling_param_cls=None,
-        pipeline_config_cls=PipelineConfig,
+        pipeline_config_cls=Kandinsky5T2VConfig,
         workload_types=(WorkloadType.T2V, ),
         hf_model_paths=[
             "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
         ],
         model_detectors=[
-            lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5")),
+            _is_kandinsky5_t2v_lite,
         ],
         model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_lite_5s",
+        pipeline_cls_name="Kandinsky5T2VPipeline",
+    )
+
+    # Kandinsky5 Pro T2V
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(WorkloadType.T2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers",
+        ],
+        model_detectors=[
+            _is_kandinsky5_t2v_pro,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_pro_5s",
+    )
+
+    # Kandinsky5 Lite T2V Distilled
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(WorkloadType.T2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers",
+        ],
+        model_detectors=[
+            _is_kandinsky5_t2v_lite_distilled,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_lite_distilled_5s",
+    )
+
+    # Kandinsky5 Pro T2V Distilled
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(WorkloadType.T2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers",
+        ],
+        model_detectors=[
+            _is_kandinsky5_t2v_pro_distilled,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_pro_distilled_5s",
+    )
+
+    # Kandinsky5 Lite I2V
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(WorkloadType.I2V, ),
+        hf_model_paths=["kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers"],
+        model_detectors=[
+            _is_kandinsky5_i2v_lite,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_lite_5s",
+        pipeline_cls_name="Kandinsky5I2VPipeline",
+    )
+
+    # Kandinsky5 Pro I2V
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(WorkloadType.I2V, ),
+        hf_model_paths=["kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"],
+        model_detectors=[
+            _is_kandinsky5_i2v_pro,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_pro_5s",
+    )
+
+    # Kandinsky5 Pro I2V Distilled
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(WorkloadType.I2V, ),
+        hf_model_paths=["kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers"],
+        model_detectors=[
+            _is_kandinsky5_i2v_pro_distilled,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_pro_distilled_5s",
+    )
+
+    # Kandinsky5 Lite I2V Distilled (no official hub repo yet; local
+    # conversions get distilled sampling defaults instead of the sft ones the
+    # fallback would apply).
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(),
+        model_detectors=[
+            _is_kandinsky5_i2v_lite_distilled,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_lite_distilled_5s",
+        pipeline_cls_name="Kandinsky5I2VPipeline",
+    )
+
+    # Kandinsky5 fallbacks — registered AFTER the variant detectors so those
+    # win first-match. Catch checkpoints the variant detectors cannot resolve:
+    # token-less local paths matched via the model_index _class_name
+    # ("kandinsky5t2vpipeline" carries no lite/pro marker), variant combos
+    # without a dedicated entry (e.g. I2V Lite distilled), and t2v+i2v
+    # ambiguous paths resolved by the checkpoint's _class_name.
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(),
+        model_detectors=[
+            _is_kandinsky5_t2v,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_lite_5s",
+        pipeline_cls_name="Kandinsky5T2VPipeline",
+    )
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(),
+        model_detectors=[
+            _is_kandinsky5_i2v,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_lite_5s",
+        pipeline_cls_name="Kandinsky5I2VPipeline",
     )
 
     # LongCat (T2V, I2V, VC use same config; workload varies by path)
@@ -550,7 +713,7 @@ def _register_configs() -> None:
             "FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers",
             "FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers",
             "FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers",
-            # Legacy HF paths (kept for backward compat — pre-rename names):
+            # Legacy HF paths (kept for backward compat - pre-rename names):
             "FastVideo/Matrix-Game-2.0-Base-Diffusers",
             "FastVideo/Matrix-Game-2.0-GTA-Diffusers",
             "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers",
@@ -902,7 +1065,10 @@ def get_model_info(
     assert config_info is not None, "config_info must be resolved"
 
     if override_pipeline_cls_name:
-        pipeline_name = override_pipeline_cls_name
+        # Explicit override: skip config resolution entirely so checkpoints
+        # without a diffusers model_index.json keep working (and no download
+        # is triggered just to log the replaced name).
+        pipeline_name: str | None = override_pipeline_cls_name
         logger.info("Using override pipeline class name %s", pipeline_name)
     else:
         if os.path.exists(model_path):
@@ -911,6 +1077,12 @@ def get_model_info(
             config = maybe_download_model_index(model_path)
 
         pipeline_name = config.get("_class_name")
+        if config_info.pipeline_cls_name is not None:
+            # The resolved (path/detector-based) config pins the pipeline class,
+            # e.g. an I2V checkpoint whose `_class_name` would otherwise resolve
+            # to the T2V pipeline.
+            logger.info("Pinning pipeline class name from %s to %s", pipeline_name, config_info.pipeline_cls_name)
+            pipeline_name = config_info.pipeline_cls_name
 
     if pipeline_name is None:
         raise ValueError("Model config does not contain a _class_name attribute. "
@@ -961,6 +1133,8 @@ def _register_presets() -> None:
         ALL_PRESETS as HUNYUAN15_PRESETS, )
     from fastvideo.pipelines.basic.hyworld.presets import (
         ALL_PRESETS as HYWORLD_PRESETS, )
+    from fastvideo.pipelines.basic.kandinsky5.presets import (
+        ALL_PRESETS as KANDINSKY5_PRESETS, )
     from fastvideo.pipelines.basic.lingbotworld.presets import (
         ALL_PRESETS as LINGBOTWORLD_PRESETS, )
     from fastvideo.pipelines.basic.longcat.presets import (
@@ -990,6 +1164,7 @@ def _register_presets() -> None:
         HUNYUAN_PRESETS,
         HUNYUAN15_PRESETS,
         HYWORLD_PRESETS,
+        KANDINSKY5_PRESETS,
         LINGBOTWORLD_PRESETS,
         LONGCAT_PRESETS,
         LTX2_PRESETS,

fastvideo/tests/attention/test_flash_attn_no_pad_resolver.py

@@ -3,15 +3,16 @@
 
 Landed in PR #1225 slice 5 (Attn-QAT 5/12). The resolver centralises the
 varlen-flash-attn import-fallback logic that several backends
-(``bsa_attn.py``, ``video_sparse_attn.py``) used to duplicate. The fallback
+(``bsa_attn.py``, ``video_sparse_attn.py``) used to duplicate. The resolution
 order is:
 
-    1. ``fastvideo.attention.utils.flash_attn_cute``
+    1. ``fastvideo.attention.utils.flash_attn_cute`` -- only when
+       ``FASTVIDEO_FA4=1`` (explicit opt-in), and then it must import or the
+       resolver raises RuntimeError instead of falling through
     2. ``flash_attn_interface``
     3. ``flash_attn``
 
-These tests verify that the resolver picks the highest-priority impl
-available and falls through cleanly on ``ImportError``. CPU-only, no
+These tests verify the opt-in gate and the FA3/FA2 fallthrough. CPU-only, no
 flash-attn install required.
 """
 
@@ -35,8 +36,36 @@ def _reload_resolver_module():
     return importlib.import_module("fastvideo.attention.utils.flash_attn_no_pad")
 
 
-def test_resolver_falls_back_when_cute_unavailable(monkeypatch) -> None:
-    """When ``flash_attn_cute`` is unimportable, resolver tries the next impl."""
+def test_resolver_skips_cute_without_opt_in(monkeypatch) -> None:
+    """Without ``FASTVIDEO_FA4=1`` the resolver must not even attempt the cute
+    import."""
+    monkeypatch.delenv("FASTVIDEO_FA4", raising=False)
+    attempted: list[str] = []
+    real_import = builtins.__import__
+
+    def spying_import(name, globals=None, locals=None, fromlist=(), level=0):
+        attempted.append(name)
+        return real_import(name, globals, locals, fromlist, level)
+
+    monkeypatch.setattr(builtins, "__import__", spying_import)
+
+    mod = _reload_resolver_module()
+    resolved = mod._resolve_flash_attn_varlen_func()
+    assert resolved is not None
+    assert resolved.__name__ == "flash_attn_varlen_func"
+    assert "fastvideo.attention.utils.flash_attn_cute" not in attempted
+
+
+def test_resolver_raises_when_opted_in_but_cute_unavailable(monkeypatch) -> None:
+    """With ``FASTVIDEO_FA4=1`` an unimportable cute build fails loudly instead
+    of silently falling through to FA3/FA2.
+
+    The resolver runs at module import time, so the reload itself must raise.
+    It raises RuntimeError (not ImportError) so importers that treat
+    ImportError as "flash-attn not installed" (``bsa_attn.py``) cannot swallow
+    the opted-in failure.
+    """
+    monkeypatch.setenv("FASTVIDEO_FA4", "1")
     real_import = builtins.__import__
 
     def patched_import(name, globals=None, locals=None, fromlist=(), level=0):
@@ -46,21 +75,17 @@ def test_resolver_falls_back_when_cute_unavailable(monkeypatch) -> None:
 
     monkeypatch.setattr(builtins, "__import__", patched_import)
 
-    mod = _reload_resolver_module()
-    resolved = mod._resolve_flash_attn_varlen_func()
-    assert resolved is not None
-    assert resolved.__name__ == "flash_attn_varlen_func"
+    with pytest.raises(RuntimeError, match="cute disabled for test"):
+        _reload_resolver_module()
 
 
-def test_resolver_returns_flash_attn_when_cute_and_interface_unavailable(monkeypatch) -> None:
+def test_resolver_returns_flash_attn_when_interface_unavailable(monkeypatch) -> None:
     """The terminal fallback is the plain ``flash_attn`` import."""
+    monkeypatch.delenv("FASTVIDEO_FA4", raising=False)
     real_import = builtins.__import__
 
     def patched_import(name, globals=None, locals=None, fromlist=(), level=0):
-        if name in {
-                "fastvideo.attention.utils.flash_attn_cute",
-                "flash_attn_interface",
-        }:
+        if name == "flash_attn_interface":
             raise ImportError(f"{name} disabled for test")
         return real_import(name, globals, locals, fromlist, level)
 

fastvideo/tests/contract/test_ci_test_collection.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Guard: every test directory must be collected by some CI lane or be on
+the explicit allowlist below.
+
+Three separate incidents on 2026-07-05 found test files that no CI lane
+ever collects (fastvideo/tests/stages/, tests/local_tests/ additions in
+PR #1509, and this sweep found seven dark directories in total): the tests
+pass review, merge, and then silently never run. This test makes going
+dark an explicit, reviewed decision instead of an accident: adding a new
+test directory fails CI until it is either wired into a lane or
+allowlisted here with a reason.
+
+Pure text analysis — no fastvideo imports, no GPU, no torch.
+"""
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+TESTS_ROOT = REPO_ROOT / "fastvideo" / "tests"
+
+# Files whose text constitutes "a CI lane references this directory".
+CI_SOURCES = [
+    TESTS_ROOT / "modal" / "pr_test.py",
+    TESTS_ROOT / "modal" / "ssim_test.py",
+    *sorted((REPO_ROOT / ".buildkite").rglob("*.yml")),
+    *sorted((REPO_ROOT / ".buildkite").rglob("*.sh")),
+]
+
+# Directories that intentionally have no CI lane today. Every entry needs a
+# reason; remove the entry when the directory gets wired into a lane.
+# State as found on 2026-07-05 — these SHOULD shrink over time, not grow.
+ALLOWLIST = {
+    "attention": "no lane yet — GPU attention-backend tests, run manually",
+    "audio": "no lane yet — audio encoder tests, run manually",
+    "distributed": "no lane yet — multi-GPU torchrun tests, run manually",
+    "hooks": "no lane yet — run manually",
+    "layers": "no lane yet — torchrun FSDP dispatch tests, run manually",
+    "nightly": "by design: nightly cadence, not per-PR",
+    "ops": "no lane yet — GPU op tests, run manually",
+    "modal": "CI infrastructure itself, not a test suite",
+}
+
+
+def _dirs_with_tests() -> list[str]:
+    dirs = []
+    for child in sorted(TESTS_ROOT.iterdir()):
+        if child.is_dir() and any(child.rglob("test_*.py")):
+            dirs.append(child.name)
+    return dirs
+
+
+def _ci_text() -> str:
+    return "\n".join(
+        src.read_text(errors="replace") for src in CI_SOURCES if src.exists())
+
+
+def test_every_test_directory_is_collected_or_allowlisted():
+    ci_text = _ci_text()
+    dark = [
+        name for name in _dirs_with_tests()
+        if f"tests/{name}" not in ci_text and name not in ALLOWLIST
+    ]
+    assert not dark, (
+        f"Test directories not referenced by any CI lane and not "
+        f"allowlisted: {dark}. Wire them into a lane in "
+        f"fastvideo/tests/modal/pr_test.py (or a Buildkite step), or add an "
+        f"allowlist entry with a reason in {__file__}.")
+
+
+def test_local_tests_stays_out_of_ci():
+    # tests/local_tests/ (repo root) is developer-local by design (author
+    # decision, 2026-07-05): parity scaffolds and machine-specific checks
+    # that must never gate CI. Fail if any CI source starts collecting it.
+    assert "tests/local_tests" not in _ci_text(), (
+        "tests/local_tests/ is local-only by design; remove the CI "
+        "reference or move the tests into a fastvideo/tests/ lane.")
+
+
+def test_allowlist_entries_are_still_real_directories():
+    # A stale allowlist hides regressions; entries must track reality.
+    missing = [
+        name for name in ALLOWLIST
+        if name != "modal" and not (TESTS_ROOT / name).is_dir()
+    ]
+    assert not missing, (
+        f"Allowlisted directories no longer exist — remove them: {missing}")

fastvideo/tests/layers/test_rmsnorm_forward_dispatch.py

@@ -0,0 +1,203 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import subprocess
+from pathlib import Path
+from typing import Any
+
+import pytest
+import torch
+import torch.distributed as dist
+from torch.distributed import init_device_mesh
+from torch.distributed.fsdp import CPUOffloadPolicy, fully_shard
+from torch.distributed.tensor import DTensor
+
+from fastvideo.layers.layernorm import RMSNorm
+
+WORLD_SIZE = 2
+HIDDEN_SIZE = 8
+SEED = 1379
+REPO_ROOT = Path(__file__).resolve().parents[3]
+
+
+def _run_torchrun(script_path: Path, mode: str, output_path: Path) -> None:
+    # --standalone binds the rendezvous port atomically, avoiding the
+    # free-port-probe race a hand-picked --master_port would have.
+    cmd = [
+        "torchrun",
+        "--standalone",
+        "--nproc_per_node",
+        str(WORLD_SIZE),
+        str(script_path),
+        "--rmsnorm-fsdp-worker",
+        "--mode",
+        mode,
+        "--output",
+        str(output_path),
+    ]
+    env = os.environ.copy()
+    env.setdefault("TORCHDYNAMO_DISABLE", "1")
+    try:
+        process = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            env=env,
+            timeout=120,
+        )
+    except subprocess.TimeoutExpired as error:
+        raise RuntimeError(
+            f"{mode} worker timed out after 120 seconds\n"
+            f"STDOUT:\n{error.stdout}\n"
+            f"STDERR:\n{error.stderr}"
+        ) from error
+    if process.returncode != 0:
+        raise RuntimeError(
+            f"{mode} worker failed with code {process.returncode}\n"
+            f"STDOUT:\n{process.stdout}\n"
+            f"STDERR:\n{process.stderr}"
+        )
+
+
+def _summarize_tensor(tensor: torch.Tensor | Any) -> dict[str, Any]:
+    return {
+        "type": type(tensor).__name__,
+        "is_dtensor": isinstance(tensor, DTensor),
+        "shape": list(tensor.shape) if hasattr(tensor, "shape") else None,
+        "device": str(tensor.device) if hasattr(tensor, "device") else None,
+        "dtype": str(tensor.dtype) if hasattr(tensor, "dtype") else None,
+    }
+
+
+def _run_worker(mode: str, output_path: Path) -> None:
+    if mode not in {
+        "module_no_offload",
+        "direct_no_offload",
+        "module_cpu_offload",
+        "direct_cpu_offload",
+    }:
+        raise ValueError(f"Unsupported mode: {mode}")
+
+    dist.init_process_group("nccl")
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    device = torch.device(f"cuda:{local_rank}")
+    torch.cuda.set_device(device)
+    torch.manual_seed(SEED + rank)
+
+    try:
+        mesh = init_device_mesh("cuda", (world_size,))
+        norm = RMSNorm(HIDDEN_SIZE, eps=1e-6, has_weight=True).to(device)
+        with torch.no_grad():
+            norm.weight.fill_(1.0)
+
+        fsdp_kwargs: dict[str, Any] = {"mesh": mesh}
+        if mode.endswith("cpu_offload"):
+            fsdp_kwargs["offload_policy"] = CPUOffloadPolicy(pin_memory=False)
+        # fully_shard is applied to the bare RMSNorm to make the hook bypass
+        # observable. Production sharding (fsdp_load.shard_model) only wraps
+        # whole transformer blocks, whose pre-forward all-gather localizes norm
+        # weights before the qk-norm call sites run, so this pins the dispatch
+        # invariant rather than reproducing a production topology.
+        fully_shard(norm, **fsdp_kwargs)
+
+        x = torch.randn(2, 3, HIDDEN_SIZE, device=device, dtype=torch.bfloat16)
+        call_kind = "direct" if mode.startswith("direct") else "module"
+
+        try:
+            if call_kind == "direct":
+                output = norm.forward_native(x)
+            else:
+                output = norm(x)
+            torch.cuda.synchronize(device)
+            result = {
+                "rank": rank,
+                "ok": True,
+                "mode": mode,
+                "weight": _summarize_tensor(norm.weight),
+                "output": _summarize_tensor(output),
+            }
+        except Exception as exc:
+            result = {
+                "rank": rank,
+                "ok": False,
+                "mode": mode,
+                "error_type": type(exc).__name__,
+                "error": str(exc),
+                "weight": _summarize_tensor(norm.weight),
+            }
+
+        gathered = [None for _ in range(world_size)] if rank == 0 else None
+        dist.gather_object(result, object_gather_list=gathered, dst=0)
+        if rank == 0:
+            output_path.write_text(json.dumps(gathered, indent=2), encoding="utf-8")
+        dist.barrier()
+    finally:
+        dist.destroy_process_group()
+
+
+@pytest.mark.parametrize(
+    ("mode", "expect_ok"),
+    [
+        ("module_no_offload", True),
+        ("direct_no_offload", False),
+        ("module_cpu_offload", True),
+        ("direct_cpu_offload", False),
+    ],
+)
+def test_rmsnorm_forward_native_bypasses_fsdp_hooks(mode: str, expect_ok: bool, tmp_path: Path) -> None:
+    if not torch.cuda.is_available():
+        pytest.skip("This test requires CUDA.")
+    if torch.cuda.device_count() < WORLD_SIZE:
+        pytest.skip(f"This test requires at least {WORLD_SIZE} CUDA devices.")
+
+    output_path = tmp_path / f"{mode}.json"
+    _run_torchrun(Path(__file__).resolve(), mode, output_path)
+    results = json.loads(output_path.read_text(encoding="utf-8"))
+    print(f"\n{mode} results:\n{json.dumps(results, indent=2)}")
+
+    if expect_ok:
+        failures = [result for result in results if not result["ok"]]
+        assert not failures, json.dumps(results, indent=2)
+        return
+
+    successes = [result for result in results if result["ok"]]
+    assert not successes, json.dumps(results, indent=2)
+    error_text = "\n".join(result.get("error", "") for result in results)
+    # Pin the specific bypassed-hook failure: "got mixed torch.Tensor and
+    # DTensor" ("Tensor" alone is a substring of "DTensor", so it adds nothing).
+    assert "mixed" in error_text and "DTensor" in error_text, json.dumps(results, indent=2)
+
+
+def test_no_direct_forward_native_calls_in_models() -> None:
+    """Direct .forward_native(...) calls bypass nn.Module.__call__ and FSDP
+    hooks (issue #1379); model code must use module dispatch instead."""
+    models_dir = REPO_ROOT / "fastvideo" / "models"
+    offenders = [
+        str(path.relative_to(REPO_ROOT))
+        for path in sorted(models_dir.rglob("*.py"))
+        if ".forward_native(" in path.read_text(encoding="utf-8")
+    ]
+    assert not offenders, f"Replace .forward_native(...) with module dispatch in: {offenders}"
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--rmsnorm-fsdp-worker", action="store_true")
+    parser.add_argument("--mode", type=str, default=None)
+    parser.add_argument("--output", type=str, default=None)
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = _parse_args()
+    if not args.rmsnorm_fsdp_worker:
+        raise SystemExit("This module is intended to be run by pytest.")
+    if args.mode is None or args.output is None:
+        raise SystemExit("--mode and --output are required in worker mode.")
+    _run_worker(mode=args.mode, output_path=Path(args.output))

fastvideo/tests/modal/launch_l40s_job.py

@@ -32,7 +32,8 @@ import modal
 
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 try:
-    from modal_image_utils import resolve_image_ref  # noqa: E402
+    from modal_image_utils import (  # noqa: E402
+        resolve_image_ref, resolve_uv_torch_backend)
 except ModuleNotFoundError:
     # Remote Modal containers re-import this module but mount only the
     # entrypoint file; the digest resolution already happened at local
@@ -40,6 +41,9 @@ except ModuleNotFoundError:
     def resolve_image_ref(image_ref: str) -> str:
         return image_ref
 
+    def resolve_uv_torch_backend(image_tag: str) -> str | None:
+        return os.environ.get("UV_TORCH_BACKEND")
+
 app = modal.App("fastvideo-gpu-job")
 
 REPO_DIR = "/FastVideo"
@@ -72,12 +76,7 @@ local_secrets = modal.Secret.from_dict({
 # Mutable tags inherit the registry image's baked backend, including custom
 # FASTVIDEO_MODAL_IMAGE overrides. Explicit CUDA tags also work with older
 # images that predate the baked setting, and a caller override always wins.
-uv_torch_backend_override = os.environ.get("UV_TORCH_BACKEND")
-if not uv_torch_backend_override:
-    if "cuda13" in IMAGE_TAG.lower():
-        uv_torch_backend_override = "cu130"
-    elif "cuda12.6" in IMAGE_TAG.lower():
-        uv_torch_backend_override = "cu126"
+uv_torch_backend_override = resolve_uv_torch_backend(IMAGE_TAG)
 
 image = (
     modal.Image.from_registry(IMAGE_REF, add_python="3.12")
@@ -98,6 +97,9 @@ image = (
         "TOKENIZERS_PARALLELISM": "false",
         **({"UV_TORCH_BACKEND": uv_torch_backend_override} if uv_torch_backend_override else {}),
         "FASTVIDEO_ATTENTION_BACKEND": os.environ.get("FASTVIDEO_ATTENTION_BACKEND", "FLASH_ATTN"),
+        # FA4 is opt-in (FASTVIDEO_FA4); keep CI parity with the seeded
+        # references. Caller override wins.
+        "FASTVIDEO_FA4": os.environ.get("FASTVIDEO_FA4", "1"),
     })
 )
 

fastvideo/tests/modal/modal_image_utils.py

@@ -5,6 +5,7 @@ through the ``fastvideo`` package, so they keep working on thin CI hosts that
 have ``modal`` but not torch.
 """
 import json
+import os
 import urllib.request
 
 _REGISTRY = "ghcr.io"
@@ -55,3 +56,23 @@ def resolve_image_ref(image_ref: str) -> str:
         print(f"WARNING: could not resolve {image_ref} to a digest ({error}); "
               "Modal may reuse a stale cached image for this tag.")
     return image_ref
+
+
+def resolve_uv_torch_backend(image_tag: str) -> str | None:
+    """UV_TORCH_BACKEND for a launcher image tag.
+
+    A caller-set UV_TORCH_BACKEND always wins. Otherwise sniff the CUDA
+    version from an explicit image tag (cuda13 -> cu130, cuda12.6 -> cu126)
+    so uv resolves torch against the image's toolkit. Mutable tags (e.g.
+    py3.12-latest) return None and inherit the registry image's baked
+    backend, which keeps a latest-tag CUDA transition safe.
+    """
+    override = os.environ.get("UV_TORCH_BACKEND")
+    if override:
+        return override
+    tag = image_tag.lower()
+    if "cuda13" in tag:
+        return "cu130"
+    if "cuda12.6" in tag:
+        return "cu126"
+    return None

fastvideo/tests/modal/pr_test.py

@@ -5,7 +5,8 @@ import modal
 
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 try:
-    from modal_image_utils import resolve_image_ref  # noqa: E402
+    from modal_image_utils import (  # noqa: E402
+        resolve_image_ref, resolve_uv_torch_backend)
 except ModuleNotFoundError:
     # Remote Modal containers re-import this module but mount only the
     # entrypoint file; the digest resolution already happened at local
@@ -13,6 +14,9 @@ except ModuleNotFoundError:
     def resolve_image_ref(image_ref: str) -> str:
         return image_ref
 
+    def resolve_uv_torch_backend(image_tag: str) -> str | None:
+        return os.environ.get("UV_TORCH_BACKEND")
+
 app = modal.App()
 
 model_vol = modal.Volume.from_name("hf-model-weights")
@@ -24,12 +28,7 @@ print(f"Using image: {image_ref}")
 # Mutable tags inherit the registry image's baked backend, keeping a latest-tag
 # transition safe. Explicit CUDA tags also work with older images that predate
 # the baked setting, and a caller override always wins.
-uv_torch_backend_override = os.environ.get("UV_TORCH_BACKEND")
-if not uv_torch_backend_override:
-    if "cuda13" in image_tag.lower():
-        uv_torch_backend_override = "cu130"
-    elif "cuda12.6" in image_tag.lower():
-        uv_torch_backend_override = "cu126"
+uv_torch_backend_override = resolve_uv_torch_backend(image_tag)
 
 image = (modal.Image.from_registry(
     image_ref, add_python="3.12"
@@ -61,6 +60,10 @@ image = (modal.Image.from_registry(
     **({
         "UV_TORCH_BACKEND": uv_torch_backend_override
     } if uv_torch_backend_override else {}),
+    # FA4 is opt-in (FASTVIDEO_FA4); CI lanes keep it enabled to match the
+    # SSIM/perf baselines. Caller override wins.
+    "FASTVIDEO_FA4":
+    os.environ.get("FASTVIDEO_FA4", "1"),
     "HF_REPO_ID":
     "FastVideo/performance-tracking",
 }))
@@ -103,12 +106,30 @@ def run_test_command(test_command: str,
     if pr_number:
         print(f"PR number: {pr_number}")
 
-    # For PRs (including forks), use GitHub's PR refs to get the correct commit
+    # The blob-less clone (--filter=blob:none) defers all file-content
+    # downloads to the checkout, so BOTH paths below perform a large lazy blob
+    # fetch from GitHub. Retry transient GitHub/HTTP2 disconnects on each;
+    # otherwise Modal shards can fail before pytest starts.
+    def with_retries(inner_command: str) -> str:
+        return f"""
+        for attempt in 1 2 3; do
+            {inner_command} &&
+            break
+
+            status=$?
+            if [ "$attempt" -eq 3 ]; then
+                exit "$status"
+            fi
+            sleep $((attempt * 5))
+        done"""
+
+    # For PRs (including forks), use GitHub's PR refs to get the correct commit.
     if pr_number and pr_number != "false":
-        checkout_command = f"git fetch --prune origin refs/pull/{pr_number}/head && git checkout FETCH_HEAD"
+        checkout_command = with_retries(f"git fetch --prune --no-tags --depth=1 origin refs/pull/{pr_number}/head &&"
+                                        "\n            git checkout --detach FETCH_HEAD")
         print(f"Using PR ref for checkout: {checkout_command}")
     else:
-        checkout_command = f"git checkout {git_commit}"
+        checkout_command = with_retries(f"git checkout {git_commit}")
         print(f"Using direct commit checkout: {checkout_command}")
 
     build_kernel_command = """
@@ -122,7 +143,7 @@ def run_test_command(test_command: str,
     command = f"""
     source $HOME/.local/bin/env &&
     source /opt/venv/bin/activate &&
-    git clone {git_repo} /FastVideo &&
+    git clone --filter=blob:none --no-checkout {git_repo} /FastVideo &&
     cd /FastVideo &&
     {checkout_command} &&
     git submodule update --init --recursive &&
@@ -273,7 +294,7 @@ def run_self_forcing_tests():
 @app.function(gpu="L40S:1", image=image, timeout=900)
 def run_unit_test():
     run_test(
-        "pytest ./fastvideo/tests/api/ ./fastvideo/tests/contract/ ./fastvideo/tests/dataset/ ./fastvideo/tests/workflow/ ./fastvideo/tests/entrypoints/ ./fastvideo/tests/train/ --ignore=./fastvideo/tests/entrypoints/test_openai_api_integration.py --ignore=./fastvideo/tests/train/models --ignore=./fastvideo/tests/train/methods -vs"
+        "pytest ./fastvideo/tests/api/ ./fastvideo/tests/contract/ ./fastvideo/tests/dataset/ ./fastvideo/tests/workflow/ ./fastvideo/tests/entrypoints/ ./fastvideo/tests/train/ ./fastvideo/tests/stages/ --ignore=./fastvideo/tests/entrypoints/test_openai_api_integration.py --ignore=./fastvideo/tests/train/models --ignore=./fastvideo/tests/train/methods -vs"
     )
 
 

fastvideo/tests/modal/ssim_test.py

@@ -13,7 +13,8 @@ import modal
 
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 try:
-    from modal_image_utils import resolve_image_ref  # noqa: E402
+    from modal_image_utils import (  # noqa: E402
+        resolve_image_ref, resolve_uv_torch_backend)
 except ModuleNotFoundError:
     # Remote Modal containers re-import this module but mount only the
     # entrypoint file; the digest resolution already happened at local
@@ -21,6 +22,9 @@ except ModuleNotFoundError:
     def resolve_image_ref(image_ref: str) -> str:
         return image_ref
 
+    def resolve_uv_torch_backend(image_tag: str) -> str | None:
+        return os.environ.get("UV_TORCH_BACKEND")
+
 app = modal.App()
 
 model_vol = modal.Volume.from_name("hf-model-weights")
@@ -32,12 +36,7 @@ print(f"Using image: {image_ref}")
 # Mutable tags inherit the registry image's baked backend, keeping a latest-tag
 # transition safe. Explicit CUDA tags also work with older images that predate
 # the baked setting, and a caller override always wins.
-uv_torch_backend_override = os.environ.get("UV_TORCH_BACKEND")
-if not uv_torch_backend_override:
-    if "cuda13" in image_tag.lower():
-        uv_torch_backend_override = "cu130"
-    elif "cuda12.6" in image_tag.lower():
-        uv_torch_backend_override = "cu126"
+uv_torch_backend_override = resolve_uv_torch_backend(image_tag)
 
 image = (
     modal.Image.from_registry(image_ref, add_python="3.12")
@@ -64,6 +63,9 @@ image = (
             "BUILDKITE_PULL_REQUEST": os.environ.get("BUILDKITE_PULL_REQUEST", ""),
             "IMAGE_VERSION": image_version,
             **({"UV_TORCH_BACKEND": uv_torch_backend_override} if uv_torch_backend_override else {}),
+            # FA4 is opt-in (FASTVIDEO_FA4); the SSIM references were seeded
+            # with FA4 inference, so keep it enabled in CI. Caller override wins.
+            "FASTVIDEO_FA4": os.environ.get("FASTVIDEO_FA4", "1"),
         }
     )
 )

fastvideo/tests/performance/compare_baseline.py

@@ -8,8 +8,8 @@ This script:
    baseline-eligible successful records (filtered by gpu_type),
 4) writes normalized records back to the HF dataset repo according to
    PERF_UPLOAD_POLICY,
-5) exits non-zero if any metric regresses by more than PERF_MAX_REGRESSION
-   (default 5%).
+5) exits non-zero if any gated metric exceeds both its percent and absolute
+   regression floors.
 """
 
 import glob
@@ -21,21 +21,36 @@ from datetime import datetime, timezone
 from typing import Any
 
 try:
-    from .hf_store import (
+    from fastvideo.performance.hf_store import (
         load_records_for_model,
         safe_float,
         sanitize,
         sync_from_hf,
         upload_record,
     )
+    from fastvideo.performance.metric_policy import (
+        MetricPolicy,
+        regression_delta,
+        resolve_metric_policies,
+        serialize_metric_thresholds,
+    )
 except ImportError:
-    from hf_store import (
+    repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+    if repo_root not in sys.path:
+        sys.path.insert(0, repo_root)
+    from fastvideo.performance.hf_store import (
         load_records_for_model,
         safe_float,
         sanitize,
         sync_from_hf,
         upload_record,
     )
+    from fastvideo.performance.metric_policy import (
+        MetricPolicy,
+        regression_delta,
+        resolve_metric_policies,
+        serialize_metric_thresholds,
+    )
 
 RESULTS_DIR = os.path.join(
     os.path.dirname(os.path.abspath(__file__)),
@@ -46,25 +61,9 @@ TRACKING_ROOT = os.environ.get(
     "/tmp/perf-tracking",
 )
 PERF_REPORTS_DIR = os.environ.get("PERF_REPORTS_DIR", "/root/data/perf_reports")
-MAX_REGRESSION = float(os.environ.get("PERF_MAX_REGRESSION", "0.05"))
 UPLOAD_POLICY = os.environ.get("PERF_UPLOAD_POLICY", "never").strip().lower()
 VALID_UPLOAD_POLICIES = {"never", "pass", "always"}
 VALID_RUN_SOURCES = {"pr", "local", "scheduled_main", "unknown"}
-METRICS = (
-    ("latency", "Latency", 3),
-    ("throughput", "Throughput", 3),
-    ("memory", "Memory", 1),
-    ("text_encoder_time_s", "Text Enc", 3),
-    ("dit_time_s", "DiT", 3),
-    ("vae_decode_time_s", "VAE Decode", 3),
-)
-LOWER_IS_BETTER_METRICS = {
-    "latency",
-    "memory",
-    "text_encoder_time_s",
-    "dit_time_s",
-    "vae_decode_time_s",
-}
 
 
 def _should_persist_tracking() -> bool:
@@ -168,6 +167,7 @@ def normalize_performance_result(result: dict[str, Any]) -> dict[str, Any]:
     text_encoder_time = safe_float(result.get("text_encoder_time_s"))
     dit_time = safe_float(result.get("dit_time_s"))
     vae_decode_time = safe_float(result.get("vae_decode_time_s"))
+    metric_policies = resolve_metric_policies(result.get("regression_thresholds"))
 
     return {
         "model_id": model_id,
@@ -180,6 +180,7 @@ def normalize_performance_result(result: dict[str, Any]) -> dict[str, Any]:
         "text_encoder_time_s": text_encoder_time,
         "dit_time_s": dit_time,
         "vae_decode_time_s": vae_decode_time,
+        "regression_thresholds": serialize_metric_thresholds(metric_policies),
         "success": True,
         **_record_metadata(_detect_run_source(), result),
     }
@@ -229,55 +230,41 @@ def _baseline_metric(records: list[dict[str, Any]], key: str) -> float | None:
     return statistics.median(values)
 
 
+def _metric_policy_summary(policy: MetricPolicy) -> str:
+    gated = "gated" if policy.gated else "info"
+    return (
+        f"{gated}, >{policy.threshold_percent * 100:.1f}% "
+        f"and >{policy.threshold_absolute:.{policy.precision}f}"
+    )
+
+
 def _check_regressions(
     current: dict[str, Any],
     baseline_records: list[dict[str, Any]],
-    max_regression: float,
+    metric_policies: tuple[MetricPolicy, ...],
 ) -> list[str]:
     failures: list[str] = []
 
-    for metric, _label, _precision in METRICS:
-        if metric not in LOWER_IS_BETTER_METRICS:
+    for policy in metric_policies:
+        baseline = _baseline_metric(baseline_records, policy.key)
+        curr = safe_float(current.get(policy.key))
+        if baseline is None or curr is None:
             continue
-        baseline = _baseline_metric(baseline_records, metric)
-        curr = safe_float(current.get(metric))
-        if baseline is None or curr is None or baseline <= 0:
+        delta = regression_delta(policy, curr, baseline)
+        if delta is None or not delta.regressed:
             continue
-        regression = (curr - baseline) / baseline
-        if regression > max_regression:
-            failures.append(f"{current['model_id']} {metric} regressed by "
-                            f"{regression * 100:.1f}% "
-                            f"(current={curr:.3f}, baseline_median={baseline:.3f})")
-
-    baseline_tp = _baseline_metric(baseline_records, "throughput")
-    curr_tp = safe_float(current.get("throughput"))
-    if baseline_tp is not None and curr_tp is not None and baseline_tp > 0:
-        regression = (baseline_tp - curr_tp) / baseline_tp
-        if regression > max_regression:
-            failures.append(f"{current['model_id']} throughput regressed by "
-                            f"{regression * 100:.1f}% "
-                            f"(current={curr_tp:.3f}, baseline_median={baseline_tp:.3f})")
+        failures.append(
+            f"{current['model_id']} {policy.key} regressed by "
+            f"{delta.percent * 100:.1f}% and "
+            f"{delta.absolute:.{policy.precision}f} "
+            f"(current={curr:.{policy.precision}f}, "
+            f"baseline_median={baseline:.{policy.precision}f}, "
+            f"threshold={_metric_policy_summary(policy)})"
+        )
 
     return failures
 
 
-def _metric_delta_percent(
-    metric: str,
-    current: dict[str, Any],
-    baseline_records: list[dict[str, Any]],
-) -> float | None:
-    curr = safe_float(current.get(metric))
-    baseline = _baseline_metric(baseline_records, metric)
-    if curr is None or baseline is None or baseline <= 0:
-        return None
-
-    if metric in LOWER_IS_BETTER_METRICS:
-        return (curr - baseline) / baseline * 100.0
-    if metric == "throughput":
-        return (baseline - curr) / baseline * 100.0
-    return None
-
-
 def _compact_value(value: float | None, precision: int = 3) -> str:
     if value is None:
         return "n/a"
@@ -287,23 +274,42 @@ def _compact_value(value: float | None, precision: int = 3) -> str:
 def _build_summary_row(
     record: dict[str, Any],
     baseline_records: list[dict[str, Any]],
+    metric_policies: tuple[MetricPolicy, ...],
     has_failed: bool,
 ) -> dict[str, Any]:
     """Format a single benchmark result as a row for the Markdown table."""
 
-    metric_values: dict[str, dict[str, float | None]] = {}
+    metric_values: dict[str, dict[str, Any]] = {}
     regressions: list[float] = []
-    for metric, _label, _precision in METRICS:
-        curr = safe_float(record.get(metric))
-        baseline = _baseline_metric(baseline_records, metric)
-        regression = _metric_delta_percent(metric, record, baseline_records)
-        metric_values[metric] = {
+    failing_metrics: list[str] = []
+    threshold_exceeded_metrics: list[str] = []
+    for policy in metric_policies:
+        curr = safe_float(record.get(policy.key))
+        baseline = _baseline_metric(baseline_records, policy.key)
+        delta = (
+            regression_delta(policy, curr, baseline)
+            if curr is not None and baseline is not None
+            else None
+        )
+        regression = None if delta is None else delta.percent * 100.0
+        absolute_delta = None if delta is None else delta.absolute
+        metric_values[policy.key] = {
             "curr": curr,
             "base": baseline,
             "regression_pct": regression,
+            "absolute_delta": absolute_delta,
+            "threshold_percent": policy.threshold_percent * 100.0,
+            "threshold_absolute": policy.threshold_absolute,
+            "gated": policy.gated,
+            "threshold_exceeded": False if delta is None else delta.threshold_exceeded,
+            "regressed": False if delta is None else delta.regressed,
         }
         if regression is not None:
             regressions.append(regression)
+        if delta is not None and delta.threshold_exceeded:
+            threshold_exceeded_metrics.append(policy.key)
+        if delta is not None and delta.regressed:
+            failing_metrics.append(policy.key)
 
     worst_regression_pct = max(regressions) if regressions else None
 
@@ -313,40 +319,50 @@ def _build_summary_row(
         "baseline_n": len(baseline_records),
         "metrics": metric_values,
         "worst_regression_pct": worst_regression_pct,
+        "threshold_exceeded_metrics": threshold_exceeded_metrics,
+        "failing_metrics": failing_metrics,
         "failed": has_failed,
     }
 
 
 def _build_markdown_summary(
     summary_rows: list[dict[str, Any]],
-    max_regression: float,
+    metric_policies: tuple[MetricPolicy, ...],
 ) -> str:
     lines = [
         "## Performance Baseline Comparison",
         "",
-        f"Threshold: regressions greater than {max_regression * 100:.1f}% fail",
+        "Threshold: gated metrics fail only when both percent and absolute "
+        "regression floors are exceeded.",
         "",
         ("| Model | GPU | Baseline N | Latency (curr/base) | "
          "Throughput (curr/base) | Memory (curr/base) | "
          "Text Enc (curr/base) | DiT (curr/base) | "
-         "VAE Decode (curr/base) | Worst Regression | Status |"),
-        "|---|---|---:|---|---|---|---|---|---|---:|---|",
+         "VAE Decode (curr/base) | Worst Regression | Exceeded Metrics | "
+         "Failing Metrics | Status |"),
+        "|---|---|---:|---|---|---|---|---|---|---:|---|---|---|",
     ]
 
     for row in summary_rows:
         metric_cells = []
-        for metric, _label, precision in METRICS:
-            values = row["metrics"][metric]
-            metric_cells.append(f"{_compact_value(values['curr'], precision)} / "
-                                f"{_compact_value(values['base'], precision)}")
+        for policy in metric_policies:
+            values = row["metrics"][policy.key]
+            metric_cells.append(f"{_compact_value(values['curr'], policy.precision)} / "
+                                f"{_compact_value(values['base'], policy.precision)}")
 
         worst_reg = ("n/a" if row["worst_regression_pct"] is None else f"{row['worst_regression_pct']:.1f}%")
+        exceeded_metrics = (
+            ", ".join(row["threshold_exceeded_metrics"])
+            if row["threshold_exceeded_metrics"]
+            else "none"
+        )
+        failing_metrics = ", ".join(row["failing_metrics"]) if row["failing_metrics"] else "none"
         status = "FAIL" if row["failed"] else "PASS"
 
         lines.append(f"| {row['model_id']} | {row['gpu_type']} | "
                      f"{row['baseline_n']} | "
                      f"{' | '.join(metric_cells)} | "
-                     f"{worst_reg} | {status} |")
+                     f"{worst_reg} | {exceeded_metrics} | {failing_metrics} | {status} |")
 
     return "\n".join(lines) + "\n"
 
@@ -400,6 +416,7 @@ def main() -> int:
 
     for raw in current_results:
         record = _normalize_record(raw)
+        metric_policies = resolve_metric_policies(record.get("regression_thresholds"))
 
         baseline_records = load_records_for_model(
             TRACKING_ROOT,
@@ -416,7 +433,7 @@ def main() -> int:
             failures: list[str] = []
             record["success"] = True
         else:
-            failures = _check_regressions(record, baseline_records, MAX_REGRESSION)
+            failures = _check_regressions(record, baseline_records, metric_policies)
         if static_threshold_failed:
             failures.append(f"{record['model_id']} fixed-threshold phase failed "
                             f"(PERF_PYTEST_RC={os.environ.get('PERF_PYTEST_RC')})")
@@ -434,10 +451,10 @@ def main() -> int:
             print("Tracking upload skipped for "
                   f"{record['model_id']} ({record['run_source']}, success={record['success']})")
 
-        summary_rows.append(_build_summary_row(record, baseline_records, bool(failures)))
+        summary_rows.append(_build_summary_row(record, baseline_records, metric_policies, bool(failures)))
 
     commit_sha = os.environ.get("BUILDKITE_COMMIT", "unknown")[:7]
-    markdown = _build_markdown_summary(summary_rows, MAX_REGRESSION)
+    markdown = _build_markdown_summary(summary_rows, resolve_metric_policies(None))
     _emit_markdown_summary(markdown, commit_sha)
 
     if all_failures:

fastvideo/tests/performance/dashboard.py

@@ -1,12 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 import os
+import sys
 from html import escape
 from datetime import datetime
 
 import plotly.express as px
 import pandas as pd
 
-from hf_store import sync_from_hf, load_as_dataframe
+try:
+    from fastvideo.performance.hf_store import load_as_dataframe, sync_from_hf
+except ImportError:
+    repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+    if repo_root not in sys.path:
+        sys.path.insert(0, repo_root)
+    from fastvideo.performance.hf_store import load_as_dataframe, sync_from_hf
 
 TRACKING_ROOT = os.environ.get(
     "PERFORMANCE_TRACKING_ROOT",

fastvideo/tests/performance/test_benchmark_config_validation.py

@@ -0,0 +1,116 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from fastvideo.tests.performance.test_inference_performance import (
+    _benchmark_display_id,
+    _config_identity_metadata,
+    _is_v2_config,
+    _validate_benchmark_config,
+)
+
+
+def _v2_config():
+    return {
+        "benchmark_id": "wan-t2v-1.3b-2gpu",
+        "config_schema_version": 2,
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
+    }
+
+
+def test_v1_benchmark_config_without_schema_version_validates():
+    cfg = {
+        "benchmark_id": "legacy-benchmark",
+    }
+
+    _validate_benchmark_config(cfg, "legacy.json")
+
+    assert _is_v2_config(cfg) is False
+    assert _config_identity_metadata(cfg) == {}
+    assert _benchmark_display_id(cfg) == "legacy-benchmark"
+
+
+def test_v2_benchmark_config_identity_validates_and_is_preserved():
+    cfg = _v2_config()
+    cfg["quality_metadata"] = {"some": "data"}
+
+    _validate_benchmark_config(cfg, "wan.json")
+
+    assert _is_v2_config(cfg) is True
+    assert _config_identity_metadata(cfg) == {
+        "config_schema_version": 2,
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
+        "quality_metadata": {"some": "data"},
+    }
+
+
+def test_v2_benchmark_config_missing_identity_fields_fails_clearly():
+    cfg = _v2_config()
+    del cfg["variant_id"]
+    del cfg["benchmark_version"]
+
+    expected = "wan.json: missing required v2 identity fields: variant_id, benchmark_version"
+    with pytest.raises(ValueError, match=expected):
+        _validate_benchmark_config(cfg, "wan.json")
+
+
+@pytest.mark.parametrize(
+    ("field", "value"),
+    [
+        ("workload_id", {}),
+        ("workload_id", ""),
+        ("workload_id", "   "),
+        ("variant_id", []),
+        ("variant_id", ""),
+        ("variant_id", "   "),
+    ],
+)
+def test_v2_benchmark_config_rejects_invalid_string_identity_values(field, value):
+    cfg = _v2_config()
+    cfg[field] = value
+
+    expected = f"wan.json: v2 identity field {field!r} must be a non-empty string"
+    with pytest.raises(ValueError, match=expected):
+        _validate_benchmark_config(cfg, "wan.json")
+
+
+@pytest.mark.parametrize("value", [None, "1", 1.5, True])
+def test_v2_benchmark_config_rejects_invalid_benchmark_version_values(value):
+    cfg = _v2_config()
+    cfg["benchmark_version"] = value
+
+    expected = "wan.json: v2 identity field 'benchmark_version' must be an integer"
+    with pytest.raises(ValueError, match=expected):
+        _validate_benchmark_config(cfg, "wan.json")
+
+
+def test_partial_v2_identity_requires_schema_version():
+    cfg = {
+        "benchmark_id": "wan-t2v-1.3b-2gpu",
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
+    }
+
+    expected = "wan.json: v2 benchmark identity fields require config_schema_version=2"
+    with pytest.raises(ValueError, match=expected):
+        _validate_benchmark_config(cfg, "wan.json")
+
+
+def test_optional_v2_metadata_fields_must_be_objects():
+    cfg = {
+        "benchmark_id": "wan-t2v-1.3b-2gpu",
+        "config_schema_version": 2,
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
+        "quality_metadata": ["not", "an", "object"],
+    }
+
+    expected = "wan.json: optional v2 metadata field 'quality_metadata' must be an object"
+    with pytest.raises(ValueError, match=expected):
+        _validate_benchmark_config(cfg, "wan.json")

fastvideo/tests/performance/test_compare_baseline_policy.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from fastvideo.tests.performance import compare_baseline
+from fastvideo.performance.metric_policy import resolve_metric_policies
 
 
 def _raw_result():
@@ -72,9 +73,157 @@ def test_normalized_record_includes_source_metadata(monkeypatch):
     assert record["job_id"] == "job-1"
 
 
+def test_normalized_record_includes_effective_regression_thresholds(monkeypatch):
+    monkeypatch.setenv("PERF_RUN_SOURCE", "pr")
+    raw = _raw_result()
+    raw["regression_thresholds"] = {
+        "latency": {
+            "threshold_percent": 0.09,
+            "threshold_absolute": 0.75,
+            "gated": True,
+        },
+        "throughput": {
+            "gated": False,
+        },
+    }
+
+    record = compare_baseline.normalize_performance_result(raw)
+
+    assert record["regression_thresholds"]["latency"] == {
+        "threshold_percent": 0.09,
+        "threshold_absolute": 0.75,
+        "gated": True,
+    }
+    assert record["regression_thresholds"]["throughput"]["gated"] is False
+
+
+def test_invalid_regression_threshold_container_uses_defaults():
+    policies = resolve_metric_policies(["not", "a", "mapping"])
+
+    latency = next(policy for policy in policies if policy.key == "latency")
+    assert latency.threshold_percent == 0.08
+    assert latency.threshold_absolute == 0.5
+    assert latency.gated is True
+
+
+def test_boolean_regression_threshold_values_are_ignored():
+    policies = resolve_metric_policies({
+        "latency": {
+            "threshold_percent": True,
+            "threshold_absolute": False,
+            "gated": "false",
+        }
+    })
+
+    latency = next(policy for policy in policies if policy.key == "latency")
+    assert latency.threshold_percent == 0.08
+    assert latency.threshold_absolute == 0.5
+    assert latency.gated is False
+
+
 def test_baseline_eligibility_only_for_successful_scheduled_main():
     assert compare_baseline._is_baseline_eligible("scheduled_main", True) is True
     assert compare_baseline._is_baseline_eligible("scheduled_main", False) is False
     assert compare_baseline._is_baseline_eligible("pr", True) is False
     assert compare_baseline._is_baseline_eligible("local", True) is False
 
+
+def test_latency_regression_requires_percent_and_absolute_floors():
+    baseline = [{"latency": 10.0}]
+    current = {"model_id": "wan", "latency": 10.6}
+
+    percent_only = resolve_metric_policies({
+        "latency": {
+            "threshold_percent": 0.05,
+            "threshold_absolute": 0.75,
+        }
+    })
+    absolute_only = resolve_metric_policies({
+        "latency": {
+            "threshold_percent": 0.10,
+            "threshold_absolute": 0.5,
+        }
+    })
+    both = resolve_metric_policies({
+        "latency": {
+            "threshold_percent": 0.05,
+            "threshold_absolute": 0.5,
+        }
+    })
+
+    assert compare_baseline._check_regressions(current, baseline, percent_only) == []
+    assert compare_baseline._check_regressions(current, baseline, absolute_only) == []
+
+    failures = compare_baseline._check_regressions(current, baseline, both)
+    assert len(failures) == 1
+    assert "latency regressed by 6.0% and 0.600" in failures[0]
+
+
+def test_throughput_regression_uses_higher_is_better_direction():
+    baseline = [{"throughput": 10.0}]
+    current = {"model_id": "wan", "throughput": 9.0}
+    policies = resolve_metric_policies({
+        "throughput": {
+            "threshold_percent": 0.05,
+            "threshold_absolute": 0.5,
+        }
+    })
+
+    failures = compare_baseline._check_regressions(current, baseline, policies)
+
+    assert len(failures) == 1
+    assert "throughput regressed by 10.0% and 1.000" in failures[0]
+
+
+def test_memory_regression_uses_metric_specific_absolute_floor():
+    baseline = [{"memory": 10000.0}]
+    current = {"model_id": "wan", "memory": 10600.0}
+    policies = resolve_metric_policies({
+        "memory": {
+            "threshold_percent": 0.05,
+            "threshold_absolute": 256.0,
+        }
+    })
+
+    failures = compare_baseline._check_regressions(current, baseline, policies)
+
+    assert len(failures) == 1
+    assert "memory regressed by 6.0% and 600.0" in failures[0]
+
+
+def test_component_metric_can_gate_independently():
+    baseline = [{"dit_time_s": 8.0}]
+    current = {"model_id": "wan", "dit_time_s": 8.6}
+    policies = resolve_metric_policies({
+        "dit_time_s": {
+            "threshold_percent": 0.05,
+            "threshold_absolute": 0.25,
+        }
+    })
+
+    failures = compare_baseline._check_regressions(current, baseline, policies)
+
+    assert len(failures) == 1
+    assert "dit_time_s regressed by 7.5% and 0.600" in failures[0]
+
+
+def test_informational_metric_remains_visible_without_failing():
+    baseline = [{"throughput": 10.0}]
+    current = {"model_id": "wan", "gpu_type": "NVIDIA L40S", "throughput": 8.0}
+    policies = resolve_metric_policies({
+        "throughput": {
+            "threshold_percent": 0.01,
+            "threshold_absolute": 0.01,
+            "gated": False,
+        }
+    })
+
+    row = compare_baseline._build_summary_row(current, baseline, policies, False)
+
+    assert compare_baseline._check_regressions(current, baseline, policies) == []
+    assert row["metrics"]["throughput"]["regression_pct"] == 20.0
+    assert row["metrics"]["throughput"]["gated"] is False
+    assert row["metrics"]["throughput"]["threshold_exceeded"] is True
+    assert row["metrics"]["throughput"]["regressed"] is False
+    assert row["threshold_exceeded_metrics"] == ["throughput"]
+    assert row["failing_metrics"] == []

fastvideo/tests/performance/test_dashboard_api.py

@@ -68,6 +68,8 @@ def test_summary_endpoint_returns_latest_group_status():
     assert body["count"] == 1
     assert body["status_counts"] == {"pass": 1, "fail": 0}
     assert body["rows"][0]["metrics"]["latency"]["baseline"] == 10.0
+    assert body["rows"][0]["metrics"]["latency"]["threshold_exceeded"] is True
+    assert body["rows"][0]["threshold_exceeded_metrics"] == ["latency", "throughput"]
     assert body["rows"][0]["computed_regression_status"] == "fail"
 
 

fastvideo/tests/performance/test_dashboard_service.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
+from fastvideo.performance import hf_store
 from fastvideo.performance_dashboard.service import build_latest_summary, build_trends, filter_records
-from fastvideo.tests.performance import hf_store
 
 
 def _record(ts, commit, latency, throughput, success=True, **metadata):
@@ -28,16 +28,23 @@ def test_build_latest_summary_uses_previous_successful_records_for_baseline():
         _record("2026-01-03T00:00:00+00:00", "c" * 40, 11.0, 9.0),
     ]
 
-    rows = build_latest_summary(records, max_regression=0.05)
+    rows = build_latest_summary(records)
 
     assert len(rows) == 1
     row = rows[0]
     assert row["baseline_n"] == 1
     assert row["metrics"]["latency"]["baseline"] == 10.0
     assert row["metrics"]["latency"]["regression_pct"] == 10.0
+    assert row["metrics"]["latency"]["absolute_delta"] == 1.0
+    assert row["metrics"]["latency"]["threshold_percent"] == 8.0
+    assert row["metrics"]["latency"]["threshold_absolute"] == 0.5
+    assert row["metrics"]["latency"]["threshold_exceeded"] is True
+    assert row["metrics"]["latency"]["regressed"] is True
     assert row["metrics"]["throughput"]["regression_pct"] == 10.0
     assert row["status"] == "pass"
     assert row["computed_regression_status"] == "fail"
+    assert row["threshold_exceeded_metrics"] == ["latency", "throughput"]
+    assert row["failing_metrics"] == ["latency", "throughput"]
 
 
 def test_build_latest_summary_status_uses_latest_record_success_field():
@@ -73,7 +80,7 @@ def test_build_latest_summary_run_source_filter_keeps_canonical_baseline():
         ),
     ]
 
-    rows = build_latest_summary(records, max_regression=0.05, run_source="pr")
+    rows = build_latest_summary(records, run_source="pr")
 
     assert len(rows) == 1
     assert rows[0]["run_source"] == "pr"
@@ -83,6 +90,60 @@ def test_build_latest_summary_run_source_filter_keeps_canonical_baseline():
     assert rows[0]["computed_regression_status"] == "fail"
 
 
+def test_build_latest_summary_requires_absolute_floor_for_computed_regression():
+    records = [
+        _record("2026-01-01T00:00:00+00:00", "a" * 40, 10.0, 10.0),
+        _record(
+            "2026-01-02T00:00:00+00:00",
+            "b" * 40,
+            10.6,
+            10.0,
+            regression_thresholds={
+                "latency": {
+                    "threshold_percent": 0.05,
+                    "threshold_absolute": 0.75,
+                    "gated": True,
+                }
+            },
+        ),
+    ]
+
+    rows = build_latest_summary(records)
+
+    assert round(rows[0]["metrics"]["latency"]["regression_pct"], 1) == 6.0
+    assert round(rows[0]["metrics"]["latency"]["absolute_delta"], 3) == 0.6
+    assert rows[0]["metrics"]["latency"]["threshold_exceeded"] is False
+    assert rows[0]["metrics"]["latency"]["regressed"] is False
+    assert rows[0]["computed_regression_status"] == "pass"
+
+
+def test_build_latest_summary_separates_informational_threshold_crossing():
+    records = [
+        _record("2026-01-01T00:00:00+00:00", "a" * 40, 10.0, 10.0),
+        _record(
+            "2026-01-02T00:00:00+00:00",
+            "b" * 40,
+            10.6,
+            10.0,
+            regression_thresholds={
+                "latency": {
+                    "threshold_percent": 0.05,
+                    "threshold_absolute": 0.5,
+                    "gated": False,
+                }
+            },
+        ),
+    ]
+
+    rows = build_latest_summary(records)
+
+    assert rows[0]["metrics"]["latency"]["threshold_exceeded"] is True
+    assert rows[0]["metrics"]["latency"]["regressed"] is False
+    assert rows[0]["threshold_exceeded_metrics"] == ["latency"]
+    assert rows[0]["failing_metrics"] == []
+    assert rows[0]["computed_regression_status"] == "pass"
+
+
 def test_filter_records_and_trends_preserve_metric_points():
     records = [
         _record("2026-01-01T00:00:00+00:00", "a" * 40, 10.0, 10.0),

fastvideo/tests/performance/test_inference_performance.py

@@ -28,6 +28,17 @@ STAGE_METRIC_MAP: dict[str, str] = {
     "DmdDenoisingStage": "dit_time_s",
     "DecodingStage": "vae_decode_time_s",
 }
+V2_CONFIG_SCHEMA_VERSION = 2
+V2_REQUIRED_IDENTITY_FIELDS = (
+    "workload_id",
+    "variant_id",
+    "benchmark_version",
+)
+V2_OPTIONAL_METADATA_FIELDS = (
+    "recipe",
+    "metric_threshold_policy",
+    "quality_metadata",
+)
 
 # -- Config discovery -------------------------------------------------------
 
@@ -42,6 +53,71 @@ _BENCHMARKS_DIR = os.path.join(
 )
 
 
+def _has_v2_fields(cfg):
+    v2_fields = V2_REQUIRED_IDENTITY_FIELDS + V2_OPTIONAL_METADATA_FIELDS
+    return any(field in cfg for field in v2_fields)
+
+
+def _is_v2_config(cfg):
+    return cfg.get("config_schema_version") == V2_CONFIG_SCHEMA_VERSION
+
+
+def _validate_non_empty_string(value, field, path):
+    if not isinstance(value, str) or not value.strip():
+        raise ValueError(f"{path}: v2 identity field {field!r} must be a non-empty string")
+
+
+def _validate_integer(value, field, path):
+    if isinstance(value, bool) or not isinstance(value, int):
+        raise ValueError(f"{path}: v2 identity field {field!r} must be an integer")
+
+
+def _validate_benchmark_config(cfg, path="<memory>"):
+    missing_common = [field for field in ("benchmark_id",) if field not in cfg]
+    if missing_common:
+        raise ValueError(f"{path}: missing required benchmark config fields: {', '.join(missing_common)}")
+
+    schema_version = cfg.get("config_schema_version")
+    if schema_version is None:
+        if _has_v2_fields(cfg):
+            raise ValueError(f"{path}: v2 benchmark identity fields require config_schema_version=2")
+        return
+
+    if schema_version != V2_CONFIG_SCHEMA_VERSION:
+        raise ValueError(f"{path}: unsupported benchmark config_schema_version={schema_version!r}")
+
+    missing_v2 = [field for field in V2_REQUIRED_IDENTITY_FIELDS if field not in cfg]
+    if missing_v2:
+        raise ValueError(f"{path}: missing required v2 identity fields: {', '.join(missing_v2)}")
+
+    _validate_non_empty_string(cfg["workload_id"], "workload_id", path)
+    _validate_non_empty_string(cfg["variant_id"], "variant_id", path)
+    _validate_integer(cfg["benchmark_version"], "benchmark_version", path)
+
+    for field in V2_OPTIONAL_METADATA_FIELDS:
+        if field in cfg and not isinstance(cfg[field], Mapping):
+            raise ValueError(f"{path}: optional v2 metadata field {field!r} must be an object")
+
+
+def _config_identity_metadata(cfg):
+    if not _is_v2_config(cfg):
+        return {}
+    metadata = {
+        "config_schema_version": cfg["config_schema_version"],
+        "workload_id": cfg["workload_id"],
+        "variant_id": cfg["variant_id"],
+        "benchmark_version": cfg["benchmark_version"],
+    }
+    for field in V2_OPTIONAL_METADATA_FIELDS:
+        if field in cfg:
+            metadata[field] = cfg[field]
+    return metadata
+
+
+def _benchmark_display_id(cfg):
+    return cfg["benchmark_id"]
+
+
 def _discover_benchmarks():
     """Glob benchmark JSON configs and return list of (id, config) tuples."""
     pattern = os.path.join(_BENCHMARKS_DIR, "*.json")
@@ -49,6 +125,7 @@ def _discover_benchmarks():
     for path in sorted(glob.glob(pattern)):
         with open(path) as f:
             cfg = json.load(f)
+        _validate_benchmark_config(cfg, path)
         configs.append(cfg)
     return configs
 
@@ -102,7 +179,11 @@ def _extract_component_times(result: dict) -> dict[str, float | None]:
             logger.debug("Skipping malformed stage '%s' data: %r", stage_name, stage_data)
             continue
         stage_class = stage_data.get("stage_class", stage_name)
-        metric_key = STAGE_METRIC_MAP.get(stage_class)
+        component_metric = stage_data.get("component_metric")
+        if isinstance(component_metric, str) and component_metric in component_times:
+            metric_key = component_metric
+        else:
+            metric_key = STAGE_METRIC_MAP.get(stage_class)
         if metric_key is None:
             logger.debug("Unmapped stage '%s' class '%s' (%.3fs)",
                          stage_name,
@@ -219,6 +300,7 @@ def _run_benchmark(cfg):
 
     results = {
         "benchmark_id": cfg["benchmark_id"],
+        **_config_identity_metadata(cfg),
         "model_short_name": model_info.get("model_short_name", ""),
         "device": device_name,
         "num_gpus": init_kwargs.get("num_gpus", 1),
@@ -231,6 +313,7 @@ def _run_benchmark(cfg):
         "max_peak_memory_mb": round(max_peak_memory, 1),
         "individual_peak_memories_mb": [round(m, 1) for m in peak_memories],
         "thresholds": thresholds,
+        "regression_thresholds": cfg.get("regression_thresholds", {}),
         "commit": os.environ.get("BUILDKITE_COMMIT", ""),
         "pr_number": os.environ.get("BUILDKITE_PULL_REQUEST", ""),
         "timestamp": datetime.now(timezone.utc).isoformat(),
@@ -275,7 +358,7 @@ def _run_benchmark(cfg):
 @pytest.mark.parametrize(
     "cfg",
     _BENCHMARK_CONFIGS,
-    ids=[c["benchmark_id"] for c in _BENCHMARK_CONFIGS],
+    ids=[_benchmark_display_id(c) for c in _BENCHMARK_CONFIGS],
 )
 def test_inference_performance(cfg):
     """Measure generation latency, peak GPU memory, and component-level timings

fastvideo/tests/performance/test_inference_performance_component_times.py

@@ -1,13 +1,20 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from fastvideo.pipelines.pipeline_batch_info import PipelineLoggingInfo
+from fastvideo.pipelines.stages.denoising import Cosmos25AutoDenoisingStage, DenoisingStage
+from fastvideo.pipelines.stages.text_encoding import Cosmos25TextEncodingStage
 from fastvideo.tests.performance.test_inference_performance import _extract_component_times
 
 
+class SubclassStyleDenoisingStage(DenoisingStage):
+    pass
+
+
 def test_extract_component_times_handles_pipeline_logging_info_object():
     logging_info = PipelineLoggingInfo()
     logging_info.add_stage_execution_time("prompt_encoding_stage", 1.25)
     logging_info.add_stage_metric("prompt_encoding_stage", "stage_class", "TextEncodingStage")
+    logging_info.add_stage_metric("prompt_encoding_stage", "component_metric", "text_encoder_time_s")
 
     assert _extract_component_times({"logging_info": logging_info}) == {
         "text_encoder_time_s": 1.25,
@@ -45,6 +52,35 @@ def test_extract_component_times_uses_stage_class_for_pipeline_stage_keys():
     }
 
 
+def test_denoising_stage_subclasses_inherit_component_metric():
+    assert SubclassStyleDenoisingStage.performance_component_metric == "dit_time_s"
+
+
+def test_cosmos25_direct_pipeline_stages_define_component_metrics():
+    assert Cosmos25TextEncodingStage.performance_component_metric == "text_encoder_time_s"
+    assert Cosmos25AutoDenoisingStage.performance_component_metric == "dit_time_s"
+
+
+def test_extract_component_times_uses_component_metric_for_stage_subclasses():
+    result = {
+        "logging_info": {
+            "stages": {
+                "denoising_stage": {
+                    "execution_time": 4.2,
+                    "stage_class": "CosmosDenoisingStage",
+                    "component_metric": "dit_time_s",
+                },
+            },
+        },
+    }
+
+    assert _extract_component_times(result) == {
+        "text_encoder_time_s": None,
+        "dit_time_s": 4.2,
+        "vae_decode_time_s": None,
+    }
+
+
 def test_extract_component_times_keeps_legacy_class_name_keys():
     # Backward-compatibility check for logs produced before pipeline-unique
     # stage keys carried a separate stage_class field.

fastvideo/tests/stages/test_text_encoding.py

@@ -21,6 +21,26 @@ class FakeTokenizer:
             "attention_mask": torch.ones(B, seq_len, dtype=torch.long),
         })
 
+
+class FakeChatTokenizer:
+    def __init__(self):
+        self.last_messages = None
+        self.last_kwargs = None
+
+    def apply_chat_template(self, messages, **kwargs):
+        self.last_messages = messages
+        self.last_kwargs = kwargs
+        assert isinstance(messages[0], list)
+        assert messages[0][0]["role"] == "system"
+        assert messages[0][1]["role"] == "user"
+        B = len(messages)
+        seq_len = int(kwargs.get("max_length", 4))
+        return TensorDict({
+            "input_ids": torch.arange(B * seq_len).view(B, seq_len),
+            "attention_mask": torch.ones(B, seq_len, dtype=torch.long),
+        })
+
+
 class FakeTextEncoder(torch.nn.Module):
     def __init__(self, hidden_size=8):
         super().__init__()
@@ -38,6 +58,14 @@ class FakeTextEncoder(torch.nn.Module):
 def id_preprocess(x: str) -> str:
     return x
 
+
+def chat_list_preprocess(x: str):
+    return [
+        {"role": "system", "content": "Describe the video."},
+        {"role": "user", "content": x if x else " "},
+    ]
+
+
 def take_mean_postprocess(outputs: BaseEncoderOutput) -> torch.Tensor:
     # [B, T, H] -> [B, H]
     return outputs.last_hidden_state.mean(dim=1)
@@ -156,3 +184,32 @@ def test_encode_text_does_not_force_hidden_states_for_ltx2_prefix():
     stage.encode_text("a", fastvideo_args, encoder_index=[0])
 
     assert stage.text_encoders[0].last_output_hidden_states is False
+
+
+def test_chat_list_preprocess_output_is_not_stripped():
+    fastvideo_args, hidden = make_args(num_encoders=1, text_len=5, hidden_size=8)
+    encoder_config = fastvideo_args.pipeline_config.text_encoder_configs[0]
+    encoder_config.is_chat_model = True
+    encoder_config.treat_empty_as_dot = True
+    fastvideo_args.pipeline_config.preprocess_text_funcs = (chat_list_preprocess, )
+
+    tokenizer = FakeChatTokenizer()
+    stage = TextEncodingStage(
+        text_encoders=[FakeTextEncoder(hidden_size=hidden)],
+        tokenizers=[tokenizer],
+    )
+
+    embeds, masks = stage.encode_text(
+        "a robotic arm welding a metal structure",
+        fastvideo_args,
+        encoder_index=[0],
+        return_attention_mask=True,
+    )
+
+    assert embeds[0].shape == (1, hidden)
+    assert masks[0].shape == (1, 5)
+    assert tokenizer.last_messages == [[
+        {"role": "system", "content": "Describe the video."},
+        {"role": "user", "content": "a robotic arm welding a metal structure"},
+    ]]
+    assert tokenizer.last_kwargs["return_tensors"] == "pt"

fastvideo/tests/train/methods/test_wan_causal_dfsft.py

@@ -127,4 +127,12 @@ def test_wan_causal_dfsft_single_train_step(
 
     # 5a-ii: device-keyed grad-norm regression on top of the same harness.
     # Skips when the current GPU has no seeded reference.
-    check_grad_norm_regression("test_wan_causal_dfsft", model.transformer)
+    # rtol above the harness default: the causal model compiles flex_attention
+    # with max-autotune (required for Wan 1.3B's head config), and the
+    # timing-based kernel selection is bimodal across L40S containers —
+    # observed 3.2562 vs 3.5860 (10.13% apart) with identical code, straddling
+    # the default 10%. 12% covers both winners; real wiring breakage (dead
+    # grads, scale bugs) still lands far outside it.
+    check_grad_norm_regression("test_wan_causal_dfsft",
+                               model.transformer,
+                               rtol=0.12)