[bugfix]: address verify-pass findings on the 1546 fix branch

- recipe_fingerprint also excludes attention.resolved_backend (a silent runtime fallback must fail the gate in its cohort, not open a fresh ungated one); requested_backend stays in the hash; regression test extended to both runtime-resolved values. - Legacy v1 configs no longer crash after the GPU measurement: identity fields are emitted only for config_schema_version=2 configs, and the comparator falls back to the legacy (model_id, gpu_type) cohort when a record carries no identity fields (partial identity still raises). - 'recipe' removed from V2_OPTIONAL_METADATA_FIELDS: the generated recipe owns the key now; declaring it in a config fails validation loudly instead of being silently overwritten. - Docs: stale 'until those land' paragraph reconciled with what this branch actually lands; baseline_status documented.
[docs]: align example records with the merged identity taxonomy
2026-07-05 12:09:46 -07:00 · 2026-07-05 12:09:46 -07:00 · 2026-07-05 12:09:46 -07:00 · 2026-07-05 12:09:13 -07:00 · 2026-07-05 12:08:43 -07:00 · 2026-07-05 12:08:43 -07:00
151 changed files with 956 additions and 10927 deletions
@@ -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: `0.05` (5%). |
+| `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%). |
 | `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:
@@ -148,7 +148,8 @@ 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 `0.05`. Print a table with per-source values, batch median, and
+Default `max_intra_batch_regression` to `PERF_MAX_REGRESSION` when set,
+otherwise `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
@@ -182,7 +183,7 @@ present, that run is not a valid source for baseline reseeding.

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

-Use `fastvideo/performance/hf_store.py` helpers directly. Do **not** use
+Use `fastvideo/tests/performance/hf_store.py` helpers directly. Do **not** use
 `compare_baseline.py` as a sync shortcut; on full main runs it can persist
 records, while this step must only fetch and back up existing history.

@@ -191,7 +192,7 @@ 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}"
-python -c 'from fastvideo.performance.hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
+PYTHONPATH=fastvideo/tests/performance python -c 'from hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
 ```

 Then back up only the sanitized model directory under `/tmp`:
@@ -199,8 +200,8 @@ 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=$(python - <<'PY'
-from fastvideo.performance.hf_store import sanitize
+MODEL_SAFE=$(PYTHONPATH=fastvideo/tests/performance python - <<'PY'
+from hf_store import sanitize
 print(sanitize("<model_id>"))
 PY
 )
@@ -234,7 +235,7 @@ first baseline seed. Continue, but report that baseline history was empty.
 Load the last 5 successful records for the target:

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

 records = load_records_for_model(
    "/tmp/perf-tracking",
@@ -371,7 +372,7 @@ prepared records plus backup on disk.
 Use the shared storage helper so the path and repo type match CI:

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

 upload_record("<local_record_path>", record, strict=True)
 ```
@@ -459,7 +460,7 @@ 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/performance/hf_store.py` — HF sync, record loading,
+- `fastvideo/tests/performance/hf_store.py` — HF sync, record loading,
  `sanitize()`, and `upload_record()`.
 - `fastvideo/tests/performance/test_inference_performance.py` — source result
  JSON schema.
@@ -1,9 +1,9 @@
 {
  "benchmark_id": "wan-t2v-1.3b-2gpu",
  "config_schema_version": 2,
-  "workload_id": "wan-t2v",
-  "variant_id": "1.3b-sp2",
-  "benchmark_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",
@@ -1,11 +1,7 @@
 name: pre-commit

 on:
-  # 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:
+  pull_request:
    branches: [main]
  workflow_call:
    inputs:
@@ -19,25 +15,12 @@ permissions:

 jobs:
  pre-commit:
-    if: github.event.pull_request.draft != true
+    if: github.event_name == 'workflow_call' || github.event.pull_request.draft != true
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
      with:
        ref: ${{ inputs.ref || '' }}
-    # For PR events, lint the PR head — but keep the hook definitions from
-    # the base branch so an untrusted PR cannot alter what gets executed.
-    - name: Save trusted hook config
-      if: github.event_name == 'pull_request_target'
-      run: cp .pre-commit-config.yaml "$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"
@@ -68,7 +68,7 @@ ARG FLASH_ATTN_WHEEL_RELEASE_ARM64=https://github.com/mjun0812/flash-attention-p
 # cutlass-4.4 `cute.core.ThrMma` API, which crashes on the cutlass-dsl 4.5 that
 # flashinfer/quack pull in. After the wheel install we overlay this cutlass-4.5-safe
 # upstream cute (flash-attn-4) so the image runs FA4 instead of the FA2 fallback.
-ARG FA4_CUTE_REF=82d6441eec5d4dfec120153db2c0145ae855a083
+ARG FA4_CUTE_REF=940cd9680f3315f2f06b43ab5bea2c2cf2d96806

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

-# Overlay the CuTe-DSL-4.6-compatible upstream FA4 cute (FA4_CUTE_REF) over the
+# Overlay the cutlass-4.5-safe upstream FA4 cute (FA4_CUTE_REF) over the
 # wheel/source one so the image runs FA4, not the FA2 fallback. This pulls the FA4
 # runtime stack (cutlass-dsl, quack-kernels, apache-tvm-ffi, torch-c-dlpack-ext) --
 # the same deps the [dreamverse] extra already installs in CI; the installed torch
@@ -79,11 +79,10 @@ fastvideo/performance/
 ```

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

 ## Planned Coverage

@@ -113,11 +112,10 @@ per-metric policy with direction, percent threshold, absolute threshold, and a
 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, 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
+`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
 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
@@ -159,16 +157,13 @@ unrealistic memory growth, and optionally large component-specific slowdowns
 even when the rolling baseline is empty. They are hand-set with generous
 headroom and almost never need touching.

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

 `compare_baseline.py` loads the last 5 successful, baseline-eligible records
-for the same comparison cohort from the HF dataset, computes the median for
-each available metric, and evaluates the current run with the metric's
-rolling regression policy. For v2 records, that cohort is `model_id`,
-`gpu_type`, `workload_id`, `variant_id`, `benchmark_version`,
-`recipe_fingerprint`, `hardware_profile_id`, and `software_profile_id`. For
-latency, memory, and component times, higher values are regressions. For
-throughput, lower values are regressions.
+for the same `(model_id, gpu_type)` from the HF dataset, computes the median
+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:

@@ -205,9 +200,9 @@ configs and remain loadable. New or migrated configs should use
 {
  "benchmark_id": "wan-t2v-1.3b-2gpu",
  "config_schema_version": 2,
-  "workload_id": "wan-t2v",
-  "variant_id": "1.3b-sp2",
-  "benchmark_version": 2
+  "workload_id": "wan-t2v-1.3b",
+  "variant_id": "canonical",
+  "benchmark_version": 1
 }
 ```

@@ -218,8 +213,8 @@ metadata that make the measured workload explicit:

 | Field | Purpose |
 |---|---|
-| `workload_id` | Stable benchmark family, such as `wan-t2v`. |
-| `variant_id` | Intentional recipe family, including model size and parallelism config, such as `1.3b-sp2`. |
+| `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
@@ -234,10 +229,8 @@ Recipe fingerprinting, hardware/software profile IDs, exact-identity
 comparison, and dashboard cohort grouping land with this change: v2 records
 compare only within their identity cohort, and a record that opens a NEW
 cohort is marked `baseline_status: "initialized_new_cohort"` (regression
-gating starts once that cohort accumulates history). Legacy v1 configs still
-run and are normalized for reporting, but their records skip rolling-baseline
-comparison entirely (`baseline_status: "skipped_missing_identity"`, never
-baseline eligible); only static thresholds gate them. Metric-specific
+gating starts once that cohort accumulates history). Legacy v1 configs keep
+the `(model_id, gpu_type)` rolling-baseline comparison. Metric-specific
 threshold policies and promoted baselines remain separate follow-ups.

 ### Raw record (`results/perf_*.json`)
@@ -247,10 +240,10 @@ Written by `test_inference_performance.py`. One file per benchmark run.
 ```jsonc
 {
  "benchmark_id": "wan-t2v-1.3b-2gpu",
-  "result_schema_version": 2,
-  "workload_id": "wan-t2v",
-  "variant_id": "1.3b-sp2",
-  "benchmark_version": 2,
+  "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,
@@ -276,15 +269,8 @@ Written by `test_inference_performance.py`. One file per benchmark run.
    }
  },
  "commit": "<full sha>",
-  "run_source": "pr",
-  "branch": "feature/perf-change",
  "pr_number": "1234",
-  "test_scope": "direct",
-  "build_url": "https://buildkite.example/build",
-  "build_id": "<buildkite-build-id>",
-  "job_id": "<buildkite-job-id>",
  "timestamp": "2026-05-08T22:00:00+00:00",
-  "quality_metadata": { "quality_status": "canonical" },
  "text_encoder_time_s": 2.141,
  "dit_time_s": 8.437,
  "vae_decode_time_s": 3.208,
@@ -292,9 +278,9 @@ Written by `test_inference_performance.py`. One file per benchmark run.
    "recipe_schema_version": 1,
    "benchmark": {
      "benchmark_id": "wan-t2v-1.3b-2gpu",
-      "workload_id": "wan-t2v",
-      "variant_id": "1.3b-sp2",
-      "benchmark_version": 2
+      "workload_id": "wan-t2v-1.3b",
+      "variant_id": "canonical",
+      "benchmark_version": 1
    },
    "model": { "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" },
    "init_kwargs": { "num_gpus": 2, "sp_size": 2, "tp_size": 1 },
@@ -335,10 +321,9 @@ result, used as the rolling-baseline source of truth.
 ```jsonc
 {
  "model_id": "wan-t2v-1.3b-2gpu",
-  "result_schema_version": 2,
-  "workload_id": "wan-t2v",
-  "variant_id": "1.3b-sp2",
-  "benchmark_version": 2,
+  "workload_id": "wan-t2v-1.3b",
+  "variant_id": "canonical",
+  "benchmark_version": 1,
  "timestamp": "2026-05-08T22:00:00+00:00",
  "commit_sha": "<full sha>",
  "gpu_type": "NVIDIA L40S",
@@ -359,30 +344,17 @@ result, used as the rolling-baseline source of truth.
  "hardware_profile_id": "hw-<sha256-prefix>",
  "software_profile_id": "sw-<sha256-prefix>",
  "environment_fingerprint": "env-<sha256-prefix>",
-  "run_source": "pr",
-  "branch": "feature/perf-change",
-  "pr_number": "1234",
-  "test_scope": "direct",
-  "build_url": "https://buildkite.example/build",
-  "build_id": "<buildkite-build-id>",
-  "job_id": "<buildkite-job-id>",
-  "quality_metadata": { "quality_status": "canonical" },
  "success": true
 }
 ```

 ### Compatibility with legacy records

-Older records in the HF dataset may not have `result_schema_version`,
-component timing fields, or v2 identity/profile fields. Records without
-`result_schema_version` are treated as v1. The comparator ignores missing or
-`null` metrics when computing a median, and the dashboard lists skipped plots
-for metric series that have no non-null values. Records missing both
-`run_source` and `baseline_eligible` are treated as legacy successful
-main/full-suite uploads and remain eligible for rolling baselines.
-Current `perf_*.json` artifacts that lack the v2 comparison identity are
-normalized for reporting but skip rolling-baseline comparison and are not marked
-baseline eligible.
+Older records in the HF dataset may not have component timing fields. The
+comparator ignores missing or `null` metrics when computing a median, and the
+dashboard lists skipped plots for metric series that have no non-null values.
+Records missing both `run_source` and `baseline_eligible` are treated as legacy
+successful main/full-suite uploads and remain eligible for rolling baselines.

 New records compare only against the same `model_id`, `gpu_type`,
 `workload_id`, `variant_id`, `benchmark_version`, `recipe_fingerprint`,
@@ -404,7 +376,7 @@ FlashInfer, Cutlass DSL, SageAttention, Triton, and xFormers when installed.
 | `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` | `fastvideo/performance/hf_store.py` | HF dataset repo holding rolling-baseline records. |
 | `HF_API_KEY`, `HUGGINGFACE_HUB_TOKEN`, `HF_TOKEN` | unset | `fastvideo/performance/hf_store.py` | Required for upload or private dataset reads. |
-| `PERF_RUN_SOURCE` | inferred | `compare_baseline.py`, `test_inference_performance.py` | Source metadata for uploaded records: `pr`, `local`, `scheduled_main`, or `unknown`. |
+| `PERF_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`. |
@@ -421,14 +393,11 @@ point is `fastvideo/tests/modal/pr_test.py:run_performance_tests` and the
 Buildkite artifact upload is in
 `.buildkite/scripts/pr_test.sh:upload_performance_artifacts`.

-Each performance build runs pytest first. PR and direct runs only continue to
-`compare_baseline.py` when that fixed-threshold phase passes; if pytest fails,
-Markdown summaries and normalized JSON artifacts are not emitted. Scheduled
-main runs set `PERF_UPLOAD_POLICY=always`, so they still run
-`compare_baseline.py` (with `PERF_PYTEST_RC` set) after a fixed-threshold
-failure. Those failed scheduled main runs emit summaries and normalized
-records, upload records with `success=false`, and are excluded from future
-rolling baselines. The dashboard still runs best-effort for observability.
+Each performance build runs pytest first. If that fixed-threshold phase fails,
+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
@@ -436,7 +405,7 @@ When the rolling-baseline phase runs, it emits:
  per-benchmark row with current vs. baseline values for latency, throughput,
  memory, text encoder time, DiT time, and VAE decode time.
 * **Plotly dashboard** — `dashboard_<sha>_<ts>.html` showing time-series for
-  each metric grouped by comparison cohort.
+  each metric grouped by `(model_id, gpu_type)`.
 * **Normalized records** — `normalized_perf_*.json`, one per benchmark.
  Useful as input to the
  [`reseed-performance-baseline`](https://github.com/hao-ai-lab/FastVideo/blob/main/.agents/skills/reseed-performance-baseline/SKILL.md)
@@ -453,7 +422,7 @@ When the rolling-baseline phase runs, it emits:
     "benchmark_id": "<unique-id>",
     "config_schema_version": 2,
     "workload_id": "<stable-workload-id>",
-     "variant_id": "<variant, e.g. 1.3b-sp2>",
+     "variant_id": "canonical",
     "benchmark_version": 1,
     "model": { "model_path": "...", "model_short_name": "..." },
     "init_kwargs": { "num_gpus": 1, ... },
@@ -510,8 +479,8 @@ When the rolling-baseline phase runs, it emits:

 ## Troubleshooting

-**"No baseline for ... Initializing"** — first run for this comparison cohort.
-Run will pass and (if persisting) seed the first record.
+**"No baseline for ... Initializing"** — first run for this `(model_id,
+gpu_type)`. Run will pass and (if persisting) seed the first record.

 **Persistent failure right after a torch / kernel / image upgrade** —
 genuine regression *or* baseline drift. Compare the failing normalized record
@@ -524,5 +493,5 @@ 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 emits `component_metric` or is covered by the legacy
-`STAGE_METRIC_MAP` fallback in `test_inference_performance.py`.
+and that the stage name is listed in `STAGE_METRIC_MAP` in
+`test_inference_performance.py`.
@@ -191,9 +191,6 @@ surfaces:
        sources:
          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
          - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      color_correction_strength:
-        sources:
-          - fastvideo.configs.pipelines.dreamx_world.DreamXWorld5BARPipelineConfig
      default_camera_rotation:
        sources:
          - fastvideo.configs.pipelines.gen3c.Gen3CConfig
@@ -58,8 +58,6 @@ pipeline initialization and sampling.
 | FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
 | FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
 | Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
-| DreamX-World 5B Cam | `FastVideo/DreamX-World-5B-Cam-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| DreamX-World 5B AR | `FastVideo/DreamX-World-5B-Diffusers` | 704px1280p | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Lucy Edit Dev 5B*** | `decart-ai/Lucy-Edit-Dev` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
 | Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
@@ -1,91 +0,0 @@
-# Training Trackers
-
-FastVideo can send training metrics and validation media to Weights & Biases
-or SwanLab. Tracking runs only on global rank 0, and local tracker files are
-stored under `<output_dir>/tracker`.
-
-## Supported Trackers
-
-| Value | Backend | Installation |
-|-------|---------|--------------|
-| `wandb` | Weights & Biases | Included with FastVideo |
-| `swanlab` | SwanLab | Install the optional `swanlab` dependency |
-| `none` | Disable external tracking | No additional package |
-
-You can enable more than one backend, for example `trackers: [wandb, swanlab]`.
-Metrics and validation media are converted to the artifact type required by
-each backend.
-
-## Install SwanLab
-
-For a published FastVideo installation, install the SwanLab extra:
-
-```bash
-uv pip install "fastvideo[swanlab]"
-```
-
-For an editable source checkout, include the same extra during installation:
-
-```bash
-uv pip install -e ".[swanlab]"
-```
-
-If FastVideo is already installed, you can install the compatible SDK directly:
-
-```bash
-uv pip install "swanlab>=0.6.7"
-```
-
-Authenticate once before starting a training run:
-
-```bash
-swanlab login
-```
-
-See the [SwanLab login documentation](https://docs.swanlab.cn/en/api/cli-swanlab-login.html)
-for non-interactive and self-hosted setups.
-
-## Configure Tracking
-
-Select SwanLab in the YAML config used by the modular training framework:
-
-```yaml
-training:
-  checkpoint:
-    output_dir: outputs/my_run
-  tracker:
-    trackers: [swanlab]
-    project_name: my_project
-    run_name: my_run
-```
-
-To log to both supported services:
-
-```yaml
-training:
-  tracker:
-    trackers: [wandb, swanlab]
-    project_name: my_project
-    run_name: my_run
-```
-
-An empty or omitted `trackers` list selects W&B when `project_name` is set.
-Use an explicit `none` entry to disable external tracking:
-
-```yaml
-training:
-  tracker:
-    trackers: [none]
-```
-
-## Validation Videos
-
-SwanLab currently accepts GIF video artifacts. FastVideo converts validation
-MP4 files and in-memory video arrays to GIF automatically before logging them.
-For video files, FastVideo uses the sampling frame rate supplied by the caller,
-or the source file's frame rate when no value is supplied. In-memory arrays use
-the frame rate supplied by the caller. Both forms fall back to 16 FPS when no
-frame rate is available.
-
-For details about configuring validation callbacks, see
-[Training Infrastructure](train_infra.md#callbacks-pluggable-hooks).
@@ -161,9 +161,6 @@ training:
    decay_interval_steps: 0
 ```

-See [Training Trackers](trackers.md) to configure Weights & Biases or SwanLab,
-including SwanLab installation and authentication.
-
 ### `callbacks` — Pluggable hooks

 Callbacks run at specific points in the training loop (before/after optimizer
@@ -1,64 +0,0 @@
-import os
-
-from fastvideo import VideoGenerator
-
-
-OUTPUT_PATH = os.getenv("DREAMX_WORLD_OUTPUT_PATH", "video_samples_dreamx_world")
-
-
-def _env_int(name: str, default: int) -> int:
-    return int(os.getenv(name, str(default)))
-
-
-def _env_float(name: str, default: float) -> float:
-    return float(os.getenv(name, str(default)))
-
-
-def main():
-    model_name = os.getenv("DREAMX_WORLD_MODEL_DIR", "FastVideo/DreamX-World-5B-Cam-Diffusers")
-    generator = VideoGenerator.from_pretrained(
-        model_name,
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        override_pipeline_cls_name="DreamXWorldPipeline",
-    )
-
-    prompt = os.getenv(
-        "DREAMX_WORLD_PROMPT",
-        "A cinematic first-person drive through a futuristic coastal city at "
-        "sunrise, reflective glass towers, clean streets, soft volumetric light.",
-    )
-    image_path = os.getenv(
-        "DREAMX_WORLD_IMAGE_PATH",
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
-    )
-
-    kwargs = {
-        "output_path": OUTPUT_PATH,
-        "save_video": os.getenv("DREAMX_WORLD_SAVE_VIDEO", "1") != "0",
-        "height": _env_int("DREAMX_WORLD_HEIGHT", 480),
-        "width": _env_int("DREAMX_WORLD_WIDTH", 832),
-        "num_frames": _env_int("DREAMX_WORLD_NUM_FRAMES", 161),
-        "num_inference_steps": _env_int("DREAMX_WORLD_STEPS", 30),
-        "guidance_scale": _env_float("DREAMX_WORLD_GUIDANCE", 5.0),
-        "action_list": os.getenv("DREAMX_WORLD_ACTIONS", "w,d,w").split(","),
-        "action_speed_list": [
-            float(value)
-            for value in os.getenv("DREAMX_WORLD_ACTION_SPEEDS", "4.0,2.0,4.0").split(",")
-        ],
-    }
-    if image_path:
-        kwargs["image_path"] = image_path
-
-    try:
-        generator.generate_video(prompt, **kwargs)
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,37 +0,0 @@
-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()
@@ -1,37 +0,0 @@
-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()
@@ -1,69 +0,0 @@
-# LTX-2.3 distilled inference configs
-
-Ready-to-run `fastvideo generate` run configs for the LTX-2.3
-distilled model (`FastVideo/LTX-2.3-Distilled-Diffusers`), covering both
-workloads (t2v / i2v), both two-stage step schedules (`5+2`, `8+3` = denoise
-+ refine), and four resolutions.
-
-```bash
-fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml
-```
-
-Each config is self-contained (no preset registry needed): the two-stage
-refine is wired via `generator.pipeline.preset_overrides.refine`, and the
-base sampling knobs live under `request.sampling`. The refine upsampler
-auto-resolves from the model's `spatial_upscaler`.
-
-## Configs
-
-| workload | schedule | resolution (HxW) | file |
-|---|---|---|---|
-| t2v | 5+2 | 1280x832 | `t2v_5s2_1280x832.yaml` |
-| t2v | 5+2 | 1024x1536 | `t2v_5s2_1024x1536.yaml` |
-| t2v | 5+2 | 768x1280 | `t2v_5s2_768x1280.yaml` |
-| t2v | 5+2 | 512x768 | `t2v_5s2_512x768.yaml` |
-| t2v | 8+3 | 1280x832 | `t2v_8s3_1280x832.yaml` |
-| t2v | 8+3 | 1024x1536 | `t2v_8s3_1024x1536.yaml` |
-| t2v | 8+3 | 768x1280 | `t2v_8s3_768x1280.yaml` |
-| t2v | 8+3 | 512x768 | `t2v_8s3_512x768.yaml` |
-| i2v | 5+2 | 1280x832 | `i2v_5s2_1280x832.yaml` |
-| i2v | 5+2 | 1024x1536 | `i2v_5s2_1024x1536.yaml` |
-| i2v | 5+2 | 768x1280 | `i2v_5s2_768x1280.yaml` |
-| i2v | 5+2 | 512x768 | `i2v_5s2_512x768.yaml` |
-| i2v | 8+3 | 1280x832 | `i2v_8s3_1280x832.yaml` |
-| i2v | 8+3 | 1024x1536 | `i2v_8s3_1024x1536.yaml` |
-| i2v | 8+3 | 768x1280 | `i2v_8s3_768x1280.yaml` |
-| i2v | 8+3 | 512x768 | `i2v_8s3_512x768.yaml` |
-
-## Overriding without editing a file
-
-Dotted overrides (prefixes `generator.` / `request.`) let you tweak any field:
-
-```bash
-# swap prompt
-fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml \
-  --request.prompt "a red fox running through fresh snow"
-
-# change output path / gpu count
-fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml \
-  --request.output.output_path outputs/preview.mp4 \
-  --generator.engine.num_gpus 4
-```
-
-## i2v
-
-The `i2v_*` configs take a first-frame image via
-`request.extensions.ltx2_images` (`[[path, frame_offset, weight]]`). Edit the
-path in the file, or override it:
-
-```bash
-fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml \
-  --request.extensions.ltx2_images '[["/data/portrait.jpg", 0, 1.0]]'
-```
-
-## Schedules
-
-`5+2` is the fast preview schedule; `8+3` is the higher-quality distilled
-recipe. Refine (`preset_overrides.refine.num_inference_steps`) only accepts 2
-or 3 steps.
-
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 5+2 two-stage at 1024x1536.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1024x1536.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 1024
-    width: 1536
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_5s2_1024x1536.mp4
-    save_video: true
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 5+2 two-stage at 1280x832.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1280x832.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 1280
-    width: 832
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_5s2_1280x832.mp4
-    save_video: true
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 5+2 two-stage at 512x768.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_512x768.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 512
-    width: 768
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_5s2_512x768.mp4
-    save_video: true
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 5+2 two-stage at 768x1280.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_768x1280.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 768
-    width: 1280
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_5s2_768x1280.mp4
-    save_video: true
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 8+3 two-stage at 1024x1536.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1024x1536.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 1024
-    width: 1536
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_8s3_1024x1536.mp4
-    save_video: true
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 8+3 two-stage at 1280x832.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 1280
-    width: 832
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_8s3_1280x832.mp4
-    save_video: true
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 8+3 two-stage at 512x768.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_512x768.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 512
-    width: 768
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_8s3_512x768.mp4
-    save_video: true
@@ -1,38 +0,0 @@
-# LTX-2.3 distilled i2v — 8+3 two-stage at 768x1280.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_768x1280.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: i2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
-  sampling:
-    height: 768
-    width: 1280
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  # i2v conditioning — replace the path with your own first-frame image.
-  extensions:
-    ltx2_images:
-      - ["/path/to/your/first_frame.jpg", 0, 1.0]
-    ltx2_image_crf: 0.0
-  output:
-    output_path: outputs/ltx2_3_i2v_8s3_768x1280.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 5+2 two-stage at 1024x1536.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1024x1536.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 1024
-    width: 1536
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  output:
-    output_path: outputs/ltx2_3_t2v_5s2_1024x1536.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 5+2 two-stage at 1280x832.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1280x832.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 1280
-    width: 832
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  output:
-    output_path: outputs/ltx2_3_t2v_5s2_1280x832.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 5+2 two-stage at 512x768.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 512
-    width: 768
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  output:
-    output_path: outputs/ltx2_3_t2v_5s2_512x768.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 5+2 two-stage at 768x1280.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_768x1280.yaml
-#
-# Stage 1 denoises for 5 steps at half resolution; the latents are then
-# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 2
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 768
-    width: 1280
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 5
-  output:
-    output_path: outputs/ltx2_3_t2v_5s2_768x1280.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 8+3 two-stage at 1024x1536.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1024x1536.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 1024
-    width: 1536
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  output:
-    output_path: outputs/ltx2_3_t2v_8s3_1024x1536.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 8+3 two-stage at 1280x832.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 1280
-    width: 832
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  output:
-    output_path: outputs/ltx2_3_t2v_8s3_1280x832.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 8+3 two-stage at 512x768.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_512x768.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 512
-    width: 768
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  output:
-    output_path: outputs/ltx2_3_t2v_8s3_512x768.mp4
-    save_video: true
@@ -1,33 +0,0 @@
-# LTX-2.3 distilled t2v — 8+3 two-stage at 768x1280.
-#
-# Run:
-#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_768x1280.yaml
-#
-# Stage 1 denoises for 8 steps at half resolution; the latents are then
-# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
-# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
-generator:
-  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
-  engine:
-    num_gpus: 1
-  pipeline:
-    workload_type: t2v
-    preset_overrides:
-      refine:
-        enabled: true
-        num_inference_steps: 3
-        guidance_scale: 1.0
-        add_noise: true
-request:
-  prompt: >-
-    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
-  sampling:
-    height: 768
-    width: 1280
-    num_frames: 121
-    fps: 24
-    guidance_scale: 1.0
-    num_inference_steps: 8
-  output:
-    output_path: outputs/ltx2_3_t2v_8s3_768x1280.mp4
-    save_video: true
@@ -1,78 +0,0 @@
-# Causal Consistency Distillation: Wan 2.1 T2V 1.3B Causal
-#
-# ODE-data-free distillation. A frozen teacher takes a single CFG Euler step;
-# the student matches an EMA copy of itself at the next timestep, all under
-# clean-history teacher forcing.
-#
-# All three roles initialize from the SAME checkpoint (the teacher-forcing
-# AR-diffusion model). Point init_from at that checkpoint for a real run.
-
-models:
-  student:
-    _target_: fastvideo.train.models.wan.WanCausalModel
-    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-    trainable: true
-  teacher:
-    _target_: fastvideo.train.models.wan.WanCausalModel
-    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-    trainable: false
-  ema:
-    _target_: fastvideo.train.models.wan.WanCausalModel
-    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-    trainable: false
-
-method:
-  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
-  discrete_cd_N: 48
-  guidance_scale: 3.0
-  ema_decay: 0.99
-  ema_start_step: 200
-
-training:
-  distributed:
-    num_gpus: 8
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 8
-
-  data:
-    data_path: data/Wan-Syn_77x448x832_600k
-    dataloader_num_workers: 4
-    train_batch_size: 1
-    training_cfg_rate: 0.0
-    seed: 1000
-    num_latent_t: 18
-    num_height: 448
-    num_width: 832
-    num_frames: 69
-
-  optimizer:
-    learning_rate: 2.0e-6
-    betas: [0.9, 0.999]
-    weight_decay: 0.01
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    max_train_steps: 3000
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/wan2.1_causal_cd
-    training_state_checkpointing_steps: 1000
-    checkpoints_total_limit: 3
-
-  tracker:
-    project_name: distillation_wan_r
-    run_name: wan2.1_causal_cd_shift5
-
-  model:
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    max_grad_norm: 1.0
-
-pipeline:
-  flow_shift: 5
@@ -1,73 +0,0 @@
-# DFSFT (Diffusion-Forcing SFT), frame-wise: Wan 2.1 T2V 1.3B Causal
-#
-# - Student: trainable causal Wan model with a block size of 1 frame
-# - Training: each frame gets its own independent noise level (frame-wise
-#   diffusion forcing), versus the chunk-wise variant that shares one noise
-#   level across num_frames_per_block frames.
-
-models:
-  student:
-    _target_: fastvideo.train.models.wan.WanCausalModel
-    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-    trainable: true
-    num_frames_per_block: 1
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
-  chunk_size: 1
-
-training:
-  distributed:
-    num_gpus: 8
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 8
-
-  data:
-    data_path: data/Wan-Syn_77x448x832_600k
-    dataloader_num_workers: 4
-    train_batch_size: 1
-    training_cfg_rate: 0.0
-    seed: 1000
-    num_latent_t: 18
-    num_height: 448
-    num_width: 832
-    num_frames: 69
-
-  optimizer:
-    learning_rate: 2.0e-6
-    betas: [0.9, 0.999]
-    weight_decay: 0.01
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    max_train_steps: 4000
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/wan2.1_causal_dfsft_framewise
-    training_state_checkpointing_steps: 1000
-    checkpoints_total_limit: 3
-
-  tracker:
-    project_name: distillation_wan_r
-    run_name: wan2.1_causal_dfsft_framewise_shift5_gauss_weight
-
-  model:
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    max_grad_norm: 1.0
-  validation:
-    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
-    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
-    every_steps: 50
-    sampling_steps: [40]
-    guidance_scale: 6.0
-    num_frames: 69
-
-pipeline:
-  flow_shift: 5
@@ -1,72 +0,0 @@
-# TFSFT (Teacher-Forcing SFT): Wan 2.1 T2V 1.3B Causal
-#
-# - Student: trainable causal Wan model
-# - Training: inhomogeneous timesteps per chunk, but the causal transformer
-#   denoises the current block while attending to *clean* history (clean_x),
-#   not its own noisy rollout.
-
-models:
-  student:
-    _target_: fastvideo.train.models.wan.WanCausalModel
-    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-    trainable: true
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
-  chunk_size: 3
-
-training:
-  distributed:
-    num_gpus: 8
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 8
-
-  data:
-    data_path: data/Wan-Syn_77x448x832_600k
-    dataloader_num_workers: 4
-    train_batch_size: 1
-    training_cfg_rate: 0.0
-    seed: 1000
-    num_latent_t: 18
-    num_height: 448
-    num_width: 832
-    num_frames: 69
-
-  optimizer:
-    learning_rate: 2.0e-6
-    betas: [0.9, 0.999]
-    weight_decay: 0.01
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    max_train_steps: 4000
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/wan2.1_causal_tfsft
-    training_state_checkpointing_steps: 1000
-    checkpoints_total_limit: 3
-
-  tracker:
-    project_name: distillation_wan_r
-    run_name: wan2.1_causal_tfsft_shift5_gauss_weight
-
-  model:
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    max_grad_norm: 1.0
-  validation:
-    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
-    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
-    every_steps: 50
-    sampling_steps: [40]
-    guidance_scale: 6.0
-    num_frames: 69
-
-pipeline:
-  flow_shift: 5
@@ -338,11 +338,7 @@ class FlashAttentionImpl(AttentionImpl):
                )

            qkv = torch.stack([query, key, value], dim=2)
-            key_padding_mask = _key_padding_mask_from_attn_mask(attn_mask, attn_mask.shape[-1]).to(device=query.device)
-            if key_padding_mask.shape[-1] > qkv.shape[1]:
-                raise ValueError("Invalid key padding mask length for FLASH_ATTN: "
-                                 f"expected at most {qkv.shape[1]}, got {key_padding_mask.shape[-1]}")
-            attn_mask_padded = F.pad(key_padding_mask, (qkv.shape[1] - key_padding_mask.shape[-1], 0), value=True)
+            attn_mask_padded = F.pad(attn_mask, (qkv.shape[1] - attn_mask.shape[1], 0), value=True)
            output = flash_attn_no_pad(qkv, attn_mask_padded, causal=False, dropout_p=0, softmax_scale=None)
        elif self.nvfp4_fa4:
            output = self._forward_nvfp4(query, key, value)
@@ -1,147 +0,0 @@
-# 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)
@@ -2,7 +2,6 @@

 import torch
 from dataclasses import dataclass
-from torch.nn import functional as F
 from fastvideo.attention.backends.abstract import (  # FlashAttentionMetadata,
    AttentionBackend, AttentionImpl, AttentionMetadata, AttentionMetadataBuilder)
 from fastvideo.logger import init_logger
@@ -20,7 +19,7 @@ class SDPABackend(AttentionBackend):

    @staticmethod
    def get_name() -> str:
-        return "TORCH_SDPA"
+        return "SDPA"

    @staticmethod
    def get_impl_cls() -> type["SDPAImpl"]:
@@ -50,51 +49,9 @@ class SDPAMetadataBuilder(AttentionMetadataBuilder):
            current_timestep: int,
            attn_mask: torch.Tensor,
    ) -> SDPAMetadata:
-        # Store the mask exactly as passed. The metadata is cross-backend:
-        # call sites (HYWorld, HunyuanVideo15) build SDPAMetadata while the
-        # layer's selector may pick FLASH_ATTN, and the shared convention for
-        # padding masks is the tokenizer-style 2D [batch, key_len]. Any
-        # reshaping for torch.sdpa happens inside the SDPA impl
-        # (_normalize_attn_mask_for_sdpa).
        return SDPAMetadata(current_timestep=current_timestep, attn_mask=attn_mask)


-def _normalize_attn_mask_for_sdpa(
-    attn_mask: torch.Tensor | None,
-    query: torch.Tensor,
-    key: torch.Tensor,
-) -> torch.Tensor | None:
-    if attn_mask is None:
-        return None
-
-    attn_mask = attn_mask.to(device=query.device)
-    # F.scaled_dot_product_attention only accepts bool or float masks;
-    # tokenizers commonly produce int64 0/1 padding masks.
-    if attn_mask.dtype != torch.bool and not attn_mask.dtype.is_floating_point:
-        attn_mask = attn_mask != 0
-
-    key_len = key.shape[-2]
-    if attn_mask.shape[-1] > key_len:
-        raise ValueError("Invalid attention mask length for SDPA: "
-                         f"expected at most {key_len}, got {attn_mask.shape[-1]}")
-    if attn_mask.shape[-1] < key_len:
-        # Front-pad as "attend": double-stream layouts (HYWorld) prepend
-        # non-text tokens the tokenizer mask does not cover.
-        valid_value = True if attn_mask.dtype == torch.bool else 0.0
-        attn_mask = F.pad(attn_mask, (key_len - attn_mask.shape[-1], 0), value=valid_value)
-
-    if attn_mask.dim() == 2:
-        # In-tree producers pass 2D [batch, key_len] padding masks; lift to a
-        # broadcastable [batch, 1, 1, key_len] here so torch.sdpa does not
-        # reinterpret 2D as its documented [query_len, key_len] broadcast.
-        return attn_mask[:, None, None, :]
-    if attn_mask.dim() == 3:
-        return attn_mask[:, None, :, :]
-    if attn_mask.dim() == 4:
-        return attn_mask
-    raise ValueError(f"Unsupported attention mask shape for SDPA: {attn_mask.shape}")
-
-
 class SDPAImpl(AttentionImpl):

    def __init__(
@@ -125,7 +82,6 @@ class SDPAImpl(AttentionImpl):

        attn_mask = attn_metadata.attn_mask if (attn_metadata is not None
                                                and hasattr(attn_metadata, "attn_mask")) else None
-        attn_mask = _normalize_attn_mask_for_sdpa(attn_mask, query, key)
        attn_kwargs = {
            "attn_mask": attn_mask,
            "dropout_p": self.dropout,
@@ -252,7 +252,6 @@ 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:
@@ -263,10 +262,7 @@ 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,
-                                        default_backend=default_backend)
+        attn_backend = get_attn_backend(head_size, dtype, supported_attention_backends=supported_attention_backends)
        impl_cls = attn_backend.get_impl_cls()
        self.attn_impl = impl_cls(num_heads=num_heads,
                                  head_size=head_size,
@@ -84,9 +84,8 @@ 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, default_backend)
+    return _cached_get_attn_backend(head_size, dtype, supported_attention_backends)


@cache
@@ -95,7 +94,6 @@ 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.
@@ -114,12 +112,6 @@ 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

@@ -1,6 +1,5 @@
 from fastvideo.configs.models.dits.cosmos import CosmosVideoConfig
 from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
-from fastvideo.configs.models.dits.dreamx_world import DreamXWorldARConfig, DreamXWorldConfig
 from fastvideo.configs.models.dits.flux_2 import Flux2Config
 from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftConfig
 from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
@@ -14,7 +13,7 @@ from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 from fastvideo.configs.models.dits.kandinsky5 import Kandinsky5VideoConfig

 __all__ = [
-    "HunyuanVideoConfig", "HunyuanVideo15Config", "HunyuanGameCraftConfig", "WanVideoConfig", "DreamXWorldConfig",
-    "DreamXWorldARConfig", "CosmosVideoConfig", "Cosmos25VideoConfig", "LongCatVideoConfig", "LTX2VideoConfig",
-    "HYWorldConfig", "Kandinsky5VideoConfig", "MagiHumanVideoConfig", "StableAudioConfig", "Flux2Config"
+    "HunyuanVideoConfig", "HunyuanVideo15Config", "HunyuanGameCraftConfig", "WanVideoConfig", "CosmosVideoConfig",
+    "Cosmos25VideoConfig", "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig", "Kandinsky5VideoConfig",
+    "MagiHumanVideoConfig", "StableAudioConfig", "Flux2Config"
 ]
@@ -1,71 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-from fastvideo.configs.models.dits.wanvideo import WanVideoArchConfig
-
-
-@dataclass
-class DreamXWorldArchConfig(WanVideoArchConfig):
-    """DreamX-World DiT config with camera PRoPE control fields."""
-
-    add_control_adapter: bool = True
-    cam_method: str | None = "prope"
-    attn_compress: int = 1
-    cam_self_attn_layers: tuple[int, ...] | None = None
-
-
-@dataclass
-class DreamXWorldConfig(DiTConfig):
-    arch_config: DiTArchConfig = field(default_factory=DreamXWorldArchConfig)
-
-    prefix: str = "Wan"
-
-
-@dataclass
-class DreamXWorldARArchConfig(DreamXWorldArchConfig):
-    """DreamX-World-5B autoregressive causal DiT config."""
-
-    model_type: str = "ti2v"
-    patch_size: tuple[int, int, int] = (1, 2, 2)
-    text_len: int = 512
-    text_dim: int = 4096
-    freq_dim: int = 256
-    attn_compress: int = 4
-    cam_self_attn_layers: tuple[int, ...] | None = tuple(range(30))
-    local_attn_size: int = 12
-    sink_size: int = 3
-    num_frames_per_block: int = 3
-    rope_cache_policy: str = "block_relativistic"
-    # The official AR checkpoint (AMAP-ML/DreamX-World ``model.safetensors``)
-    # already uses FastVideo's native key names and the converter copies the
-    # tensors verbatim, so every rule is an identity. The rules enumerate the
-    # full state-dict surface of ``DreamXWorldARTransformer3DModel`` (norm1 /
-    # norm2 / head.norm are affine-free and have no parameters).
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            r"^patch_embedding\.(.*)$": r"patch_embedding.\1",
-            r"^text_embedding\.([02])\.(.*)$": r"text_embedding.\1.\2",
-            r"^time_embedding\.([02])\.(.*)$": r"time_embedding.\1.\2",
-            r"^time_projection\.1\.(.*)$": r"time_projection.1.\1",
-            r"^blocks\.(\d+)\.self_attn\.(q|k|v|o)\.(.*)$": r"blocks.\1.self_attn.\2.\3",
-            r"^blocks\.(\d+)\.self_attn\.norm_(q|k)\.weight$": r"blocks.\1.self_attn.norm_\2.weight",
-            r"^blocks\.(\d+)\.cross_attn\.(q|k|v|o)\.(.*)$": r"blocks.\1.cross_attn.\2.\3",
-            r"^blocks\.(\d+)\.cross_attn\.norm_(q|k)\.weight$": r"blocks.\1.cross_attn.norm_\2.weight",
-            r"^blocks\.(\d+)\.cam_self_attn\.(q_proj|k_proj|v_proj|out_proj)\.(.*)$": r"blocks.\1.cam_self_attn.\2.\3",
-            r"^blocks\.(\d+)\.cam_self_attn\.norm_(q|k)\.weight$": r"blocks.\1.cam_self_attn.norm_\2.weight",
-            r"^blocks\.(\d+)\.norm3\.(.*)$": r"blocks.\1.norm3.\2",
-            r"^blocks\.(\d+)\.ffn\.([02])\.(.*)$": r"blocks.\1.ffn.\2.\3",
-            r"^blocks\.(\d+)\.modulation$": r"blocks.\1.modulation",
-            r"^head\.head\.(.*)$": r"head.head.\1",
-            r"^head\.modulation$": r"head.modulation",
-        })
-    reverse_param_names_mapping: dict = field(default_factory=dict)
-    lora_param_names_mapping: dict = field(default_factory=dict)
-
-
-@dataclass
-class DreamXWorldARConfig(DreamXWorldConfig):
-    arch_config: DiTArchConfig = field(default_factory=DreamXWorldARArchConfig)
-
-    prefix: str = "Wan"
@@ -2,24 +2,14 @@
 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: [_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,
-    )
+    _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()
+    ])

    # Native FastVideo implementation uses the same parameter names as diffusers
    # except FFN internals: Diffusers FFN uses `in_layer/out_layer`, while
@@ -43,16 +43,11 @@ class TextEncoderArchConfig(EncoderArchConfig):
    require_processor: bool = False

    def __post_init__(self) -> None:
-        # 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 = {
+        self.tokenizer_kwargs = {
            "truncation": True,
            "max_length": self.text_len,
            "return_tensors": "pt",
        }
-        self.tokenizer_kwargs = defaults | self.tokenizer_kwargs


@dataclass
@@ -1,12 +1,10 @@
 from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BARPipelineConfig, DreamXWorld5BCamPipelineConfig
 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
@@ -18,6 +16,5 @@ __all__ = [
    "HunyuanConfig", "FastHunyuanConfig", "HunyuanGameCraftPipelineConfig", "PipelineConfig", "Hunyuan15T2V480PConfig",
    "Hunyuan15T2V720PConfig", "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig", "WanI2V720PConfig",
    "SelfForcingWanT2V480PConfig", "LucyEditDevConfig", "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig",
-    "DreamXWorld5BCamPipelineConfig", "DreamXWorld5BARPipelineConfig", "HYWorldConfig", "Kandinsky5T2VConfig",
-    "Kandinsky5I2VConfig", "MatrixGame2I2V480PConfig", "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
+    "HYWorldConfig", "MatrixGame2I2V480PConfig", "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
 ]
@@ -1,127 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World-5B-Cam FastVideo model configuration helpers."""
-
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits.dreamx_world import (DreamXWorldARArchConfig, DreamXWorldARConfig,
-                                                        DreamXWorldArchConfig, DreamXWorldConfig)
-from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
-from fastvideo.configs.models.encoders.t5 import T5ArchConfig
-from fastvideo.configs.models.vaes import WanVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.configs.pipelines.wan import LucyEditDevConfig, t5_postprocess_text
-
-
-def make_dreamx_world_5b_cam_dit_config() -> DreamXWorldConfig:
-    """Return the DreamX-World DiT config matching DreamX-World-5B-Cam."""
-    return DreamXWorldConfig(arch_config=DreamXWorldArchConfig(
-        num_attention_heads=24,
-        attention_head_dim=128,
-        in_channels=48,
-        out_channels=48,
-        ffn_dim=14336,
-        num_layers=30,
-        cross_attn_norm=True,
-        qk_norm="rms_norm_across_heads",
-        add_control_adapter=True,
-        cam_method="prope",
-        attn_compress=1,
-        cam_self_attn_layers=None,
-    ))
-
-
-def make_dreamx_world_5b_ar_dit_config() -> DreamXWorldARConfig:
-    """Return the DreamX-World-5B autoregressive causal DiT config."""
-    return DreamXWorldARConfig(arch_config=DreamXWorldARArchConfig(
-        model_type="ti2v",
-        num_attention_heads=24,
-        attention_head_dim=128,
-        in_channels=48,
-        out_channels=48,
-        ffn_dim=14336,
-        num_layers=30,
-        cross_attn_norm=True,
-        qk_norm=True,
-        add_control_adapter=True,
-        cam_method="prope",
-        attn_compress=4,
-        cam_self_attn_layers=tuple(range(30)),
-        local_attn_size=12,
-        sink_size=3,
-        num_frames_per_block=3,
-    ))
-
-
-def make_dreamx_world_5b_cam_vae_config() -> WanVAEConfig:
-    """Return the Wan2.2 48-channel VAE config used by DreamX-World-5B-Cam."""
-    return LucyEditDevConfig().vae_config
-
-
-def make_dreamx_world_5b_cam_text_encoder_config() -> T5Config:
-    """Return the UMT5-XXL text encoder config used by DreamX-World-5B-Cam."""
-    return T5Config(
-        arch_config=T5ArchConfig(
-            vocab_size=256384,
-            d_model=4096,
-            d_kv=64,
-            d_ff=10240,
-            num_layers=24,
-            num_decoder_layers=None,
-            num_heads=64,
-            relative_attention_num_buckets=32,
-            dropout_rate=0.0,
-            text_len=512,
-            feed_forward_proj="gelu",
-            is_encoder_decoder=False,
-        ),
-        prefix="umt5",
-    )
-
-
-@dataclass
-class DreamXWorld5BCamPipelineConfig(PipelineConfig):
-    """Pipeline config for the first-scope DreamX-World-5B-Cam mode."""
-
-    dit_config: DiTConfig = field(default_factory=make_dreamx_world_5b_cam_dit_config)
-    vae_config: VAEConfig = field(default_factory=make_dreamx_world_5b_cam_vae_config)
-    text_encoder_configs: tuple[EncoderConfig,
-                                ...] = field(default_factory=lambda: (make_dreamx_world_5b_cam_text_encoder_config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (t5_postprocess_text, ))
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", ))
-    flow_shift: float | None = 3.0
-    ti2v_task: bool = True
-    expand_timesteps: bool = True
-    vae_tiling: bool = False
-    vae_sp: bool = False
-    vae_precision: str = "fp32"
-    vae_decode_precision: str | None = "bf16"
-    dit_precision: str = "bf16"
-
-    def __post_init__(self) -> None:
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-        self.dit_config.expand_timesteps = self.expand_timesteps
-
-
-@dataclass
-class DreamXWorld5BARPipelineConfig(DreamXWorld5BCamPipelineConfig):
-    """Pipeline config for DreamX-World-5B autoregressive forcing."""
-
-    dit_config: DiTConfig = field(default_factory=make_dreamx_world_5b_ar_dit_config)
-    flow_shift: float | None = 5.0
-    ti2v_task: bool = True
-    is_causal: bool = True
-    dmd_denoising_steps: tuple[int, ...] = (1000, 750, 500, 250)
-    warp_denoising_step: bool = True
-    context_noise: float = 0.1
-    num_frames_per_block: int = 3
-    color_correction_strength: float = 1.0
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.dit_config.expand_timesteps = True
@@ -1,122 +0,0 @@
-# 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).")
-        if len(self.text_encoder_precisions) != 2:
-            raise ValueError("Kandinsky5 pipeline requires exactly 2 text encoder precisions, "
-                             f"but got {len(self.text_encoder_precisions)}.")
-        if len(self.text_encoder_max_lengths) != 2:
-            raise ValueError("Kandinsky5 pipeline requires exactly 2 text encoder max lengths, "
-                             f"but got {len(self.text_encoder_max_lengths)}.")
-
-        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
@@ -23,7 +23,6 @@ If you only need to use the distributed environment without model parallelism,
 you can skip the model parallel initialization and destruction steps.
 """
 import contextlib
-import gc
 import os
 import pickle
 import weakref
@@ -1007,13 +1006,6 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
    if shutdown_ray:
        import ray  # Lazy import Ray
        ray.shutdown()
-    # Actually free GPU memory, as the name promises. FSDP-wrapped modules sit
-    # in reference cycles (module <-> FSDP state <-> hooks), so dropping the
-    # last user reference does not free their parameters until a gc pass runs.
-    # Without this, back-to-back model-load tests in one pytest session OOM.
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()


 def get_same_node_ranks(pg: ProcessGroup | StatelessProcessGroup, source_rank: int = 0) -> list[int]:
@@ -437,7 +437,6 @@ class CausalWanTransformer3DModel(BaseDiT):

        # Causal-specific
        self.block_mask = None
-        self.teacher_forcing_block_mask = None
        self.num_frame_per_block = config.arch_config.num_frames_per_block
        assert self.num_frame_per_block <= 3
        self.independent_first_frame = False
@@ -501,70 +500,6 @@ class CausalWanTransformer3DModel(BaseDiT):

        return block_mask

-    @staticmethod
-    def _prepare_teacher_forcing_mask(
-        device: torch.device | str, num_frames: int = 21,
-        frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1
-    ) -> BlockMask:
-        """Attention mask for the teacher-forcing ``[clean | noisy]`` sequence.
-
-        A noisy token attends to its own block plus the clean context of all
-        strictly previous blocks; clean tokens are block-wise causal.
-        """
-        if local_attn_size != -1:
-            raise NotImplementedError(
-                f"Teacher forcing ignores local_attn_size={local_attn_size}: "
-                "unlike the block-wise causal mask, this mask always attends "
-                "to the full clean context. Windowed teacher forcing is not "
-                "implemented; use local_attn_size=-1 for teacher-forcing "
-                "training.")
-        total_length = num_frames * frame_seqlen * 2
-        padded_length = math.ceil(total_length / 128) * 128 - total_length
-
-        clean_ends = num_frames * frame_seqlen
-        context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
-        noise_context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
-        noise_context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
-        noise_noise_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
-        noise_noise_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
-
-        attention_block_size = frame_seqlen * num_frame_per_block
-        frame_indices = torch.arange(
-            start=0, end=num_frames * frame_seqlen,
-            step=attention_block_size, device=device, dtype=torch.long
-        )
-        for start in frame_indices:
-            context_ends[start:start + attention_block_size] = start + attention_block_size
-
-        noisy_image_start_list = torch.arange(
-            num_frames * frame_seqlen, total_length,
-            step=attention_block_size, device=device, dtype=torch.long
-        )
-        noisy_image_end_list = noisy_image_start_list + attention_block_size
-        for block_index, (start, end) in enumerate(zip(noisy_image_start_list, noisy_image_end_list)):
-            noise_noise_starts[start:end] = start
-            noise_noise_ends[start:end] = end
-            noise_context_ends[start:end] = block_index * attention_block_size
-
-        def attention_mask(b, h, q_idx, kv_idx):
-            clean_mask = (q_idx < clean_ends) & (kv_idx < context_ends[q_idx])
-            c1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx])
-            c2 = (kv_idx < noise_context_ends[q_idx]) & (kv_idx >= noise_context_starts[q_idx])
-            noise_mask = (q_idx >= clean_ends) & (c1 | c2)
-            eye_mask = q_idx == kv_idx
-            return eye_mask | clean_mask | noise_mask
-
-        block_mask = create_block_mask(
-            attention_mask, B=None, H=None,
-            Q_LEN=total_length + padded_length, KV_LEN=total_length + padded_length,
-            _compile=False, device=device)
-
-        if not dist.is_initialized() or dist.get_rank() == 0:
-            print(f" cache a teacher-forcing mask with block size of {num_frame_per_block} frames")
-            print(block_mask)
-
-        return block_mask
-
    def _forward_inference(
                self,
                hidden_states: torch.Tensor,
@@ -693,12 +628,9 @@ class CausalWanTransformer3DModel(BaseDiT):
                encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
                | None = None,
                start_frame: int = 0,
-                clean_x: torch.Tensor | None = None,
-                aug_t: torch.Tensor | None = None,
                **kwargs) -> torch.Tensor:

        orig_dtype = hidden_states.dtype
-        teacher_forcing = clean_x is not None
        if not isinstance(encoder_hidden_states, torch.Tensor):
            encoder_hidden_states = encoder_hidden_states[0]
        if isinstance(encoder_hidden_states_image,
@@ -731,26 +663,15 @@ class CausalWanTransformer3DModel(BaseDiT):
        freqs_cis = (freqs_cos,
                     freqs_sin) if freqs_cos is not None else None

-        if teacher_forcing:
-            if self.teacher_forcing_block_mask is None:
-                self.teacher_forcing_block_mask = self._prepare_teacher_forcing_mask(
-                    device=hidden_states.device,
-                    num_frames=num_frames,
-                    frame_seqlen=post_patch_height * post_patch_width,
-                    num_frame_per_block=self.num_frame_per_block,
-                    local_attn_size=self.local_attn_size,
-                )
-            block_mask = self.teacher_forcing_block_mask
-        else:
-            if self.block_mask is None:
-                self.block_mask = self._prepare_blockwise_causal_attn_mask(
-                    device=hidden_states.device,
-                    num_frames=num_frames,
-                    frame_seqlen=post_patch_height * post_patch_width,
-                    num_frame_per_block=self.num_frame_per_block,
-                    local_attn_size=self.local_attn_size
-                )
-            block_mask = self.block_mask
+        # Construct blockwise causal attn mask
+        if self.block_mask is None:
+            self.block_mask = self._prepare_blockwise_causal_attn_mask(
+                device=hidden_states.device,
+                num_frames=num_frames,
+                frame_seqlen=post_patch_height * post_patch_width,
+                num_frame_per_block=self.num_frame_per_block,
+                local_attn_size=self.local_attn_size
+            )

        hidden_states = self.patch_embedding(hidden_states)
        grid_sizes = torch.stack(
@@ -758,7 +679,6 @@ class CausalWanTransformer3DModel(BaseDiT):
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states.new_zeros(1, self.text_len - encoder_hidden_states.size(1), encoder_hidden_states.size(2))], dim=1)
-        encoder_hidden_states_text = encoder_hidden_states

        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
                        timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
@@ -774,35 +694,18 @@ class CausalWanTransformer3DModel(BaseDiT):

        assert encoder_hidden_states.dtype == orig_dtype

-        if teacher_forcing:
-            # Tile RoPE/modulation so clean frame i and noisy frame i share a position.
-            clean_tokens = self.patch_embedding(clean_x).flatten(2).transpose(1, 2)
-            hidden_states = torch.cat([clean_tokens, hidden_states], dim=1)
-            if aug_t is None:
-                aug_t = torch.zeros_like(timestep)
-            _, timestep_proj_clean, _, _ = self.condition_embedder(
-                aug_t.flatten(), encoder_hidden_states_text, None)
-            timestep_proj_clean = timestep_proj_clean.unflatten(
-                1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
-            timestep_proj = torch.cat([timestep_proj_clean, timestep_proj], dim=1)
-            freqs_cis = (torch.cat([freqs_cos, freqs_cos], dim=0),
-                         torch.cat([freqs_sin, freqs_sin], dim=0))
-
        # 4. Transformer blocks
        if torch.is_grad_enabled() and self.gradient_checkpointing:
            for block in self.blocks:
                hidden_states = self._gradient_checkpointing_func(
                    block, hidden_states, encoder_hidden_states,
                    timestep_proj, freqs_cis,
-                    block_mask=block_mask)
+                    block_mask=self.block_mask)
        else:
            for block in self.blocks:
                hidden_states = block(hidden_states, encoder_hidden_states,
                                        timestep_proj, freqs_cis,
-                                        block_mask=block_mask)
-
-        if teacher_forcing:
-            hidden_states = hidden_states[:, hidden_states.shape[1] // 2:]
+                                        block_mask=self.block_mask)

        # 5. Output norm, projection & unpatchify
        temb = temb.unflatten(dim=0, sizes=timestep.shape).unsqueeze(2)
@@ -1,511 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import math
-from typing import Any
-
-import torch
-import torch.nn as nn
-
-from fastvideo.configs.models.dits.dreamx_world import DreamXWorldConfig
-from fastvideo.distributed.communication_op import (
-    sequence_model_parallel_all_gather_with_unpad,
-    sequence_model_parallel_shard)
-from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.layers.layernorm import RMSNorm
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-from fastvideo.models.dits.wanvideo import (LayerNormScaleShift,
-                                            PatchEmbed,
-                                            WanTimeTextImageEmbedding,
-                                            WanTransformer3DModel,
-                                            WanTransformerBlock)
-from fastvideo.platforms import AttentionBackendEnum, current_platform
-from fastvideo.attention import LocalAttention
-from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.layers.quantization import QuantizationConfig
-from fastvideo.models.dits.base import BaseDiT
-
-
-def _dreamx_invert_se3(transforms: torch.Tensor) -> torch.Tensor:
-    assert transforms.shape[-2:] == (4, 4)
-    rot_inv = transforms[..., :3, :3].transpose(-1, -2)
-    out = torch.zeros_like(transforms)
-    out[..., :3, :3] = rot_inv
-    out[..., :3, 3] = -torch.einsum("...ij,...j->...i", rot_inv,
-                                    transforms[..., :3, 3])
-    out[..., 3, 3] = 1.0
-    return out.to(dtype=transforms.dtype)
-
-
-def _dreamx_lift_k(intrinsics: torch.Tensor) -> torch.Tensor:
-    assert intrinsics.shape[-2:] == (3, 3)
-    out = torch.zeros(intrinsics.shape[:-2] + (4, 4),
-                      device=intrinsics.device,
-                      dtype=intrinsics.dtype)
-    out[..., :3, :3] = intrinsics
-    out[..., 3, 3] = 1.0
-    return out
-
-
-def _dreamx_invert_k(intrinsics: torch.Tensor) -> torch.Tensor:
-    assert intrinsics.shape[-2:] == (3, 3)
-    out = torch.zeros_like(intrinsics)
-    out[..., 0, 0] = 1.0 / intrinsics[..., 0, 0]
-    out[..., 1, 1] = 1.0 / intrinsics[..., 1, 1]
-    out[..., 0, 2] = -intrinsics[..., 0, 2] / intrinsics[..., 0, 0]
-    out[..., 1, 2] = -intrinsics[..., 1, 2] / intrinsics[..., 1, 1]
-    out[..., 2, 2] = 1.0
-    return out.to(dtype=intrinsics.dtype)
-
-
-def _dreamx_apply_tiled_projmat(feats: torch.Tensor,
-                                matrix: torch.Tensor) -> torch.Tensor:
-    batch, num_heads, seq_len, feat_dim = feats.shape
-    proj_dim = matrix.shape[-1]
-    assert feat_dim % proj_dim == 0
-
-    if matrix.shape[1] == seq_len:
-        feats = feats.view(batch, num_heads, seq_len, feat_dim // proj_dim,
-                           proj_dim)
-        out = torch.einsum("btij,bntpj->bntpi", matrix, feats)
-        return out.reshape(batch, num_heads, seq_len, feat_dim)
-
-    cameras = matrix.shape[1]
-    assert seq_len > cameras and seq_len % cameras == 0
-    feats = feats.reshape(batch, num_heads, cameras, -1,
-                          feat_dim // proj_dim, proj_dim)
-    out = torch.einsum("bcij,bncpkj->bncpki", matrix, feats)
-    return out.reshape(batch, num_heads, seq_len, feat_dim)
-
-
-def _dreamx_prope_qkv(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
-                      viewmats: torch.Tensor, intrinsics: torch.Tensor):
-    batch, num_heads, seq_len, head_dim = q.shape
-    cameras = viewmats.shape[1]
-    assert q.shape == k.shape == v.shape
-    assert viewmats.shape == (batch, cameras, 4, 4)
-    assert intrinsics.shape == (batch, cameras, 3, 3)
-    assert head_dim % 4 == 0
-
-    intrinsics_norm = torch.zeros_like(intrinsics)
-    intrinsics_norm[..., 0, 0] = intrinsics[..., 0, 0]
-    intrinsics_norm[..., 1, 1] = intrinsics[..., 1, 1]
-    intrinsics_norm[..., 2, 2] = 1.0
-
-    proj = torch.einsum("...ij,...jk->...ik",
-                        _dreamx_lift_k(intrinsics_norm), viewmats)
-    proj_t = proj.transpose(-1, -2).to(dtype=viewmats.dtype)
-    proj_inv = torch.einsum(
-        "...ij,...jk->...ik",
-        _dreamx_invert_se3(viewmats),
-        _dreamx_lift_k(_dreamx_invert_k(intrinsics_norm)),
-    ).to(dtype=viewmats.dtype)
-
-    q = _dreamx_apply_tiled_projmat(q, proj_t)
-    k = _dreamx_apply_tiled_projmat(k, proj_inv)
-    v = _dreamx_apply_tiled_projmat(v, proj_inv)
-    return q, k, v, proj
-
-
-class DreamXPropeSelfAttention(nn.Module):
-    """DreamX-World parallel PRoPE camera self-attention branch."""
-
-    def __init__(self,
-                 dim: int,
-                 attn_dim: int,
-                 num_heads: int,
-                 qk_norm: str | bool = True,
-                 eps: float = 1e-6,
-                 quant_config: QuantizationConfig | None = None,
-                 prefix: str = ""):
-        super().__init__()
-        assert attn_dim % num_heads == 0
-        self.attn_dim = attn_dim
-        self.num_heads = num_heads
-        self.head_dim = attn_dim // num_heads
-        self.qk_norm = qk_norm
-
-        self.q_proj = ReplicatedLinear(dim,
-                                       attn_dim,
-                                       quant_config=quant_config,
-                                       prefix=f"{prefix}.q_proj")
-        self.k_proj = ReplicatedLinear(dim,
-                                       attn_dim,
-                                       quant_config=quant_config,
-                                       prefix=f"{prefix}.k_proj")
-        self.v_proj = ReplicatedLinear(dim,
-                                       attn_dim,
-                                       quant_config=quant_config,
-                                       prefix=f"{prefix}.v_proj")
-        self.out_proj = ReplicatedLinear(attn_dim,
-                                         dim,
-                                         quant_config=quant_config,
-                                         prefix=f"{prefix}.out_proj")
-
-        if qk_norm == "rms_norm":
-            self.norm_q = RMSNorm(self.head_dim, eps=eps)
-            self.norm_k = RMSNorm(self.head_dim, eps=eps)
-        elif qk_norm in (True, "rms_norm_across_heads"):
-            self.norm_q = RMSNorm(attn_dim, eps=eps)
-            self.norm_k = RMSNorm(attn_dim, eps=eps)
-        elif qk_norm is False:
-            self.norm_q = nn.Identity()
-            self.norm_k = nn.Identity()
-        else:
-            raise ValueError(f"Unsupported qk_norm for DreamX PRoPE: {qk_norm}")
-
-        nn.init.zeros_(self.out_proj.weight)
-        if self.out_proj.bias is not None:
-            nn.init.zeros_(self.out_proj.bias)
-
-        self.attn = LocalAttention(
-            num_heads=num_heads,
-            head_size=self.head_dim,
-            dropout_rate=0,
-            softmax_scale=None,
-            causal=False,
-            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
-                                          AttentionBackendEnum.TORCH_SDPA))
-
-    def forward(self, hidden_states: torch.Tensor,
-                y_camera: dict[str, torch.Tensor]) -> torch.Tensor:
-        if get_sp_world_size() > 1:
-            # The transformer shards the sequence before the block loop and
-            # this branch uses LocalAttention (no all-to-all): under
-            # sequence parallelism each rank would attend only within its
-            # own shard — silently wrong output. Fail loudly until this
-            # path is ported to DistributedAttention and validated.
-            raise NotImplementedError(
-                "DreamXPropeSelfAttention does not support sequence "
-                "parallelism yet (LocalAttention on a sharded sequence "
-                "corrupts output). Run with sp_size=1.")
-        batch_size, seq_len, _ = hidden_states.shape
-
-        query, _ = self.q_proj(hidden_states)
-        key, _ = self.k_proj(hidden_states)
-        value, _ = self.v_proj(hidden_states)
-
-        if self.qk_norm == "rms_norm":
-            query = query.view(batch_size, seq_len, self.num_heads,
-                               self.head_dim)
-            key = key.view(batch_size, seq_len, self.num_heads, self.head_dim)
-            query = self.norm_q(query)
-            key = self.norm_k(key)
-        else:
-            query = self.norm_q(query).view(batch_size, seq_len,
-                                            self.num_heads, self.head_dim)
-            key = self.norm_k(key).view(batch_size, seq_len, self.num_heads,
-                                        self.head_dim)
-
-        value = value.view(batch_size, seq_len, self.num_heads, self.head_dim)
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        query, key, value, output_projection = _dreamx_prope_qkv(
-            query,
-            key,
-            value,
-            viewmats=y_camera["viewmats"],
-            intrinsics=y_camera["K"],
-        )
-
-        out = self.attn(query.transpose(1, 2), key.transpose(1, 2),
-                        value.transpose(1, 2))
-        out = _dreamx_apply_tiled_projmat(out.transpose(1, 2),
-                                          output_projection).transpose(1, 2)
-        out = out.flatten(2)
-        out, _ = self.out_proj(out)
-        return out
-
-
-class DreamXWorldTransformerBlock(WanTransformerBlock):
-
-    def __init__(self,
-                 dim: int,
-                 ffn_dim: int,
-                 num_heads: int,
-                 qk_norm: str = "rms_norm_across_heads",
-                 cross_attn_norm: bool = False,
-                 eps: float = 1e-6,
-                 added_kv_proj_dim: int | None = None,
-                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
-                 | None = None,
-                 quant_config: QuantizationConfig | None = None,
-                 prefix: str = "",
-                 add_control_adapter: bool = True,
-                 cam_method: str | None = "prope",
-                 attn_compress: int = 1,
-                 cam_self_attn_layers: tuple[int, ...] | None = None,
-                 layer_idx: int | None = None):
-        super().__init__(dim, ffn_dim, num_heads, qk_norm, cross_attn_norm,
-                         eps, added_kv_proj_dim,
-                         supported_attention_backends, quant_config, prefix)
-        self.cam_self_attn = None
-        add_cam_attn = add_control_adapter and cam_method == "prope"
-        if add_cam_attn and cam_self_attn_layers is not None:
-            add_cam_attn = layer_idx in cam_self_attn_layers
-        if add_cam_attn:
-            if num_heads % attn_compress != 0 or dim % attn_compress != 0:
-                raise ValueError("DreamX attn_compress must divide dim and num_heads")
-            self.cam_self_attn = DreamXPropeSelfAttention(
-                dim,
-                dim // attn_compress,
-                num_heads // attn_compress,
-                qk_norm=qk_norm,
-                eps=eps,
-                quant_config=quant_config,
-                prefix=f"{prefix}.cam_self_attn")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        freqs_cis: tuple[torch.Tensor, torch.Tensor],
-        original_seq_len: int,
-        y_camera: dict[str, torch.Tensor] | None = None,
-    ) -> torch.Tensor:
-        if hidden_states.dim() == 4:
-            hidden_states = hidden_states.squeeze(1)
-        orig_dtype = hidden_states.dtype
-
-        if temb.dim() == 4:
-            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
-                self.scale_shift_table.unsqueeze(0) + temb.float()).chunk(
-                    6, dim=2)
-            shift_msa = shift_msa.squeeze(2)
-            scale_msa = scale_msa.squeeze(2)
-            gate_msa = gate_msa.squeeze(2)
-            c_shift_msa = c_shift_msa.squeeze(2)
-            c_scale_msa = c_scale_msa.squeeze(2)
-            c_gate_msa = c_gate_msa.squeeze(2)
-        else:
-            e = self.scale_shift_table + temb.float()
-            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
-                6, dim=1)
-        assert shift_msa.dtype == torch.float32
-
-        norm_hidden_states = (self.norm1(hidden_states.float()) *
-                              (1 + scale_msa) + shift_msa).to(orig_dtype)
-        query, _ = self.to_q(norm_hidden_states)
-        key, _ = self.to_k(norm_hidden_states)
-        value, _ = self.to_v(norm_hidden_states)
-
-        if self.norm_q is not None:
-            query = self.norm_q(query)
-        if self.norm_k is not None:
-            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))
-        value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
-
-        attn_output, _ = self.attn1(
-            query,
-            key,
-            value,
-            original_seq_len,
-            freqs_cis=freqs_cis,
-        )
-        attn_output = attn_output.flatten(2)
-        attn_output, _ = self.to_out(attn_output)
-        attn_output = attn_output.squeeze(1)
-        if self.cam_self_attn is not None and y_camera is not None:
-            attn_output = attn_output + self.cam_self_attn(
-                norm_hidden_states, y_camera)
-
-        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
-        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
-            hidden_states, attn_output, gate_msa, null_shift, null_scale)
-        norm_hidden_states, hidden_states = norm_hidden_states.to(
-            orig_dtype), hidden_states.to(orig_dtype)
-
-        attn_output = self.attn2(norm_hidden_states,
-                                 context=encoder_hidden_states,
-                                 context_lens=None)
-        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
-            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
-        norm_hidden_states, hidden_states = norm_hidden_states.to(
-            orig_dtype), hidden_states.to(orig_dtype)
-
-        ff_output = self.ffn(norm_hidden_states)
-        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
-        hidden_states = hidden_states.to(orig_dtype)
-
-        return hidden_states
-
-
-class DreamXWorldTransformer3DModel(WanTransformer3DModel):
-    _fsdp_shard_conditions = DreamXWorldConfig()._fsdp_shard_conditions
-    _compile_conditions = DreamXWorldConfig()._compile_conditions
-    _supported_attention_backends = DreamXWorldConfig(
-    )._supported_attention_backends
-    param_names_mapping = DreamXWorldConfig().param_names_mapping
-    reverse_param_names_mapping = DreamXWorldConfig().reverse_param_names_mapping
-    lora_param_names_mapping = DreamXWorldConfig().lora_param_names_mapping
-
-    def __init__(self, config: DreamXWorldConfig, hf_config: dict[str,
-                                                                  Any]) -> None:
-        BaseDiT.__init__(self, config=config, hf_config=hf_config)
-        self.quant_config = config.quant_config
-
-        inner_dim = config.num_attention_heads * config.attention_head_dim
-        self.hidden_size = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.in_channels = config.in_channels
-        self.out_channels = config.out_channels
-        self.num_channels_latents = config.num_channels_latents
-        self.patch_size = config.patch_size
-        self.text_len = config.text_len
-
-        assert config.num_attention_heads % get_sp_world_size() == 0, f"The number of attention heads ({config.num_attention_heads}) must be divisible by the sequence parallel size ({get_sp_world_size()})"
-
-        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
-                                          embed_dim=inner_dim,
-                                          patch_size=config.patch_size,
-                                          flatten=False)
-        self.condition_embedder = WanTimeTextImageEmbedding(
-            dim=inner_dim,
-            time_freq_dim=config.freq_dim,
-            text_embed_dim=config.text_dim,
-            image_embed_dim=config.image_dim,
-        )
-        self.blocks = nn.ModuleList([
-            DreamXWorldTransformerBlock(
-                inner_dim,
-                config.ffn_dim,
-                config.num_attention_heads,
-                config.qk_norm,
-                config.cross_attn_norm,
-                config.eps,
-                config.added_kv_proj_dim,
-                self._supported_attention_backends,
-                quant_config=config.quant_config,
-                prefix=f"{config.prefix}.blocks.{i}",
-                add_control_adapter=config.add_control_adapter,
-                cam_method=config.cam_method,
-                attn_compress=config.attn_compress,
-                cam_self_attn_layers=config.cam_self_attn_layers,
-                layer_idx=i)
-            for i in range(config.num_layers)
-        ])
-        self.norm_out = LayerNormScaleShift(inner_dim,
-                                            norm_type="layer",
-                                            eps=config.eps,
-                                            elementwise_affine=False,
-                                            dtype=torch.float32,
-                                            compute_dtype=torch.float32)
-        self.proj_out = nn.Linear(
-            inner_dim, config.out_channels * math.prod(config.patch_size))
-        self.scale_shift_table = nn.Parameter(
-            torch.randn(1, 2, inner_dim) / inner_dim**0.5)
-        self.gradient_checkpointing = False
-        self.__post_init__()
-
-    def forward(self,
-                hidden_states: torch.Tensor,
-                encoder_hidden_states: torch.Tensor | list[torch.Tensor],
-                timestep: torch.LongTensor,
-                encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
-                | None = None,
-                guidance=None,
-                y_camera: dict[str, torch.Tensor] | None = None,
-                **kwargs) -> torch.Tensor:
-        orig_dtype = hidden_states.dtype
-        if encoder_hidden_states is not None and not isinstance(
-                encoder_hidden_states, torch.Tensor):
-            encoder_hidden_states = encoder_hidden_states[0]
-        if isinstance(encoder_hidden_states_image,
-                      list) and len(encoder_hidden_states_image) > 0:
-            encoder_hidden_states_image = encoder_hidden_states_image[0]
-        else:
-            encoder_hidden_states_image = None
-
-        batch_size, _, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.patch_size
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        d = self.hidden_size // self.num_attention_heads
-        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
-        freqs_cos, freqs_sin = get_rotary_pos_embed(
-            (post_patch_num_frames, post_patch_height, post_patch_width),
-            self.hidden_size,
-            self.num_attention_heads,
-            rope_dim_list,
-            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
-            rope_theta=10000)
-        freqs_cis = (freqs_cos.to(hidden_states.device).float(),
-                     freqs_sin.to(hidden_states.device).float())
-
-        hidden_states = self.patch_embedding(hidden_states)
-        hidden_states = hidden_states.flatten(2).transpose(1, 2)
-        hidden_states, original_seq_len = sequence_model_parallel_shard(
-            hidden_states, dim=1)
-
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            timestep = timestep.flatten()
-        else:
-            ts_seq_len = None
-
-        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-            timestep,
-            encoder_hidden_states,
-            encoder_hidden_states_image,
-            timestep_seq_len=ts_seq_len)
-        if ts_seq_len is not None:
-            timestep_proj = timestep_proj.unflatten(2, (6, -1))
-        else:
-            timestep_proj = timestep_proj.unflatten(1, (6, -1))
-
-        if encoder_hidden_states_image is not None:
-            if encoder_hidden_states is not None:
-                encoder_hidden_states = torch.concat(
-                    [encoder_hidden_states_image, encoder_hidden_states], dim=1)
-            else:
-                encoder_hidden_states = encoder_hidden_states_image
-
-        if current_platform.is_mps() or current_platform.is_npu():
-            encoder_hidden_states = encoder_hidden_states.to(orig_dtype)
-
-        assert encoder_hidden_states.dtype == orig_dtype
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block in self.blocks:
-                hidden_states = self._gradient_checkpointing_func(
-                    block, hidden_states, encoder_hidden_states,
-                    timestep_proj, freqs_cis, original_seq_len, y_camera)
-        else:
-            for block in self.blocks:
-                hidden_states = block(hidden_states, encoder_hidden_states,
-                                      timestep_proj, freqs_cis, original_seq_len,
-                                      y_camera=y_camera)
-
-        if temb.dim() == 3:
-            shift, scale = (self.scale_shift_table.unsqueeze(0) +
-                            temb.unsqueeze(2)).chunk(2, dim=2)
-            shift = shift.squeeze(2)
-            scale = scale.squeeze(2)
-        else:
-            shift, scale = (self.scale_shift_table +
-                            temb.unsqueeze(1)).chunk(2, dim=1)
-
-        hidden_states = self.norm_out(hidden_states, shift, scale)
-        hidden_states = sequence_model_parallel_all_gather_with_unpad(
-            hidden_states, original_seq_len, dim=1)
-        hidden_states = self.proj_out(hidden_states)
-
-        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
-                                              post_patch_height,
-                                              post_patch_width, p_t, p_h, p_w,
-                                              -1)
-        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
-        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
-
-        return output
-
-
-EntryClass = DreamXWorldTransformer3DModel
@@ -1,920 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World autoregressive causal DiT.
-
-Adapted from DreamX-World's Apache-2.0
-``wan/modules/causal_camera_model_2_2_prope_infinity.py``. The implementation is
-kept native to FastVideo: no production import from DreamX, Diffusers, or
-Transformers is required.
-"""
-
-from typing import Any
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from fastvideo.configs.models.dits.dreamx_world import DreamXWorldARConfig
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.models.dits.base import BaseDiT
-from fastvideo.models.dits.dreamx_world import (_dreamx_apply_tiled_projmat,
-                                               _dreamx_prope_qkv)
-
-
-def attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
-    # Deliberately raw SDPA rather than fastvideo.attention.LocalAttention:
-    # (1) LocalAttention dispatches through the attention-backend registry, so
-    #     FLASH_ATTN could be selected and its kernel is not bit-identical to
-    #     torch SDPA — the AR KV-cache rollout must stay numerically frozen;
-    # (2) LocalAttention requires an active ForwardContext, which direct
-    #     transformer invocations (parity tests) do not set;
-    # (3) the sibling causal model keeps raw SDPA in the same KV-cache window
-    #     path (matrixgame2/causal_model.py).
-    # Sequence-parallel gap: this model never shards the sequence; run with
-    # sp_size=1 (see fastvideo/layers/AGENTS.md on documenting raw SDPA).
-    q_bhld = q.transpose(1, 2)
-    k_bhld = k.transpose(1, 2)
-    v_bhld = v.transpose(1, 2)
-    out = F.scaled_dot_product_attention(q_bhld, k_bhld, v_bhld, dropout_p=0.0)
-    return out.transpose(1, 2)
-
-
-def prope_qkv(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
-              viewmats: torch.Tensor, Ks: torch.Tensor):
-    q, k, v, output_projection = _dreamx_prope_qkv(q, k, v, viewmats, Ks)
-
-    def apply_fn_o(x: torch.Tensor) -> torch.Tensor:
-        return _dreamx_apply_tiled_projmat(x, output_projection)
-
-    return q, k, v, apply_fn_o
-
-
-def sinusoidal_embedding_1d(dim, position):
-    assert dim % 2 == 0
-    half = dim // 2
-    position = position.type(torch.float64)
-    sinusoid = torch.outer(
-        position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
-    return torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
-
-
-def rope_params(max_seq_len, dim, theta=10000):
-    assert dim % 2 == 0
-    freqs = torch.outer(
-        torch.arange(max_seq_len),
-        1.0 / torch.pow(theta,
-                        torch.arange(0, dim, 2).to(torch.float64).div(dim)))
-    return torch.polar(torch.ones_like(freqs), freqs)
-
-
-class WanRMSNorm(nn.Module):
-    """Kept private instead of fastvideo.layers.layernorm.RMSNorm.
-
-    The official DreamX-World ``model_2_2.py`` computes the RMS statistics in
-    the *input* dtype — the upstream code has the fp32 upcast explicitly
-    commented out (``# return self._norm(x.float())...``). FastVideo's RMSNorm
-    always normalizes in fp32, which is not bit-identical under bf16, so the
-    verbatim implementation stays.
-    """
-
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.dim = dim
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        return self._norm(x).type_as(x) * self.weight
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
-
-
-class WanLayerNorm(nn.LayerNorm):
-    """Kept private instead of fastvideo.layers.layernorm.FP32LayerNorm.
-
-    The official DreamX-World ``model_2_2.py`` normalizes in the *input* dtype
-    (no ``x.float()`` upcast, unlike Wan2.1). FP32LayerNorm casts input and
-    affine params to fp32, which is not bit-identical under bf16, so the
-    verbatim implementation stays.
-    """
-
-    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
-        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
-
-    def forward(self, x):
-        return super().forward(x).type_as(x)
-
-
-class WanCrossAttention(nn.Module):
-    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6):
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.q = ReplicatedLinear(dim, dim)
-        self.k = ReplicatedLinear(dim, dim)
-        self.v = ReplicatedLinear(dim, dim)
-        self.o = ReplicatedLinear(dim, dim)
-        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-
-    def _kv(self, context, b, n, d):
-        k, _ = self.k(context)
-        k = self.norm_k(k).view(b, -1, n, d)
-        v, _ = self.v(context)
-        v = v.view(b, -1, n, d)
-        return k, v
-
-    def forward(self, x, context, context_lens, crossattn_cache=None):
-        b, n, d = x.size(0), self.num_heads, self.head_dim
-        q, _ = self.q(x)
-        q = self.norm_q(q).view(b, -1, n, d)
-
-        if crossattn_cache is not None:
-            if not crossattn_cache["is_init"]:
-                crossattn_cache["is_init"] = True
-                k, v = self._kv(context, b, n, d)
-                crossattn_cache["k"] = k
-                crossattn_cache["v"] = v
-            else:
-                k = crossattn_cache["k"]
-                v = crossattn_cache["v"]
-        else:
-            k, v = self._kv(context, b, n, d)
-
-        x = attention(q, k, v)
-        x = x.flatten(2)
-        out, _ = self.o(x)
-        return out
-
-
-def block_relativistic_rope(x, grid_sizes, freqs, start_frame=0, relative_frame_indices=None):
-    """
-    Apply Block-Relativistic RoPE to input tensor.
-    Adapted from Infinity-RoPE (https://arxiv.org/abs/2511.20649).
-
-    Args:
-        x: Input tensor [B, L, num_heads, head_dim]
-        grid_sizes: Tensor [B, 3] containing (F, H, W)
-        freqs: RoPE frequencies
-        start_frame: Starting frame index for sequential RoPE
-        relative_frame_indices: Optional tensor [F] specifying explicit frame indices
-                               for Block-Relativistic RoPE. Overrides start_frame if provided.
-    """
-    n, c = x.size(2), x.size(3) // 2
-    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
-
-    output = []
-    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
-        seq_len = f * h * w
-        x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(
-            seq_len, n, -1, 2))
-
-        if relative_frame_indices is not None:
-            frame_indices = relative_frame_indices.long()
-            freqs_temporal = freqs[0][frame_indices].view(f, 1, 1, -1).expand(f, h, w, -1)
-        else:
-            freqs_temporal = freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1)
-
-        freqs_i = torch.cat([
-            freqs_temporal,
-            freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
-            freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
-        ], dim=-1).reshape(seq_len, 1, -1)
-
-        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
-        x_i = torch.cat([x_i, x[i, seq_len:]])
-        output.append(x_i)
-
-    return torch.stack(output).type_as(x)
-
-
-class CausalWanSelfAttention(nn.Module):
-    """Self-attention with KV cache and Block-Relativistic RoPE for causal inference."""
-
-    def __init__(self, dim, num_heads, local_attn_size=6, sink_size=1,
-                 qk_norm=True, eps=1e-6):
-        assert dim % num_heads == 0
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.local_attn_size = local_attn_size
-        self.sink_size = sink_size
-        self.qk_norm = qk_norm
-        self.eps = eps
-        self.max_attention_size = 39600 if local_attn_size == -1 else local_attn_size * 880
-
-        self.q = ReplicatedLinear(dim, dim)
-        self.k = ReplicatedLinear(dim, dim)
-        self.v = ReplicatedLinear(dim, dim)
-        self.o = ReplicatedLinear(dim, dim)
-        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-
-    def forward(self, x, seq_lens, grid_sizes, freqs, kv_cache,
-                current_start=0, cache_start=None, sink_recache_after_switch=False):
-        """
-        Args:
-            x: Shape [B, L, C]
-            seq_lens: Shape [B]
-            grid_sizes: Shape [B, 3] containing (F, H, W)
-            freqs: RoPE frequencies [1024, head_dim / 2]
-            kv_cache: Dict with 'k', 'v', 'global_end_index', 'local_end_index'
-            current_start: Current position in the global token sequence
-        """
-        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
-        if cache_start is None:
-            cache_start = current_start
-
-        q, _ = self.q(x)
-        q = self.norm_q(q).view(b, s, n, d)
-        k, _ = self.k(x)
-        k = self.norm_k(k).view(b, s, n, d)
-        v, _ = self.v(x)
-        v = v.view(b, s, n, d)
-
-        frame_seqlen = math.prod(grid_sizes[0][1:]).item()
-        num_new_frames = grid_sizes[0][0].item()
-        current_end = current_start + q.shape[1]
-        sink_tokens = self.sink_size * frame_seqlen
-        kv_cache_size = kv_cache["k"].shape[1]
-        num_new_tokens = q.shape[1]
-
-        cache_update_info = None
-        is_recompute = current_end <= kv_cache["global_end_index"].item() and current_start > 0
-
-        if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
-                num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
-            # === ROLLING MODE: cache full, evict oldest non-sink tokens ===
-            num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
-            num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens
-            local_end_index = kv_cache["local_end_index"].item() + current_end - \
-                kv_cache["global_end_index"].item() - num_evicted_tokens
-            local_start_index = local_end_index - num_new_tokens
-
-            temp_k = kv_cache["k"].detach().clone()
-            temp_v = kv_cache["v"].detach().clone()
-            temp_k[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                temp_k[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-            temp_v[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                temp_v[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-
-            write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-            roped_offset = max(0, write_start_index - local_start_index)
-            write_len = max(0, local_end_index - write_start_index)
-            if write_len > 0:
-                temp_k[:, write_start_index:local_end_index] = k[:, roped_offset:roped_offset + write_len]
-                temp_v[:, write_start_index:local_end_index] = v[:, roped_offset:roped_offset + write_len]
-
-            # Block-Relativistic RoPE: query uses window-relative indices
-            query_relative_indices = torch.arange(
-                self.local_attn_size - num_new_frames, self.local_attn_size, device=q.device)
-            roped_query = block_relativistic_rope(
-                q, grid_sizes, freqs, relative_frame_indices=query_relative_indices).type_as(v)
-
-            # Block-Relativistic RoPE: cached K uses position-in-window indices
-            num_cache_frames = local_end_index // frame_seqlen
-            cache_relative_indices = torch.arange(0, num_cache_frames, device=k.device)
-            cache_grid_sizes = grid_sizes.clone()
-            cache_grid_sizes[0, 0] = num_cache_frames
-            roped_temp_k = block_relativistic_rope(
-                temp_k[:, :local_end_index].view(b, num_cache_frames, frame_seqlen, n, d).flatten(1, 2),
-                cache_grid_sizes, freqs, relative_frame_indices=cache_relative_indices).type_as(v)
-
-            cache_update_info = {
-                "action": "roll_and_insert",
-                "sink_tokens": sink_tokens,
-                "num_rolled_tokens": num_rolled_tokens,
-                "num_evicted_tokens": num_evicted_tokens,
-                "local_start_index": local_start_index,
-                "local_end_index": local_end_index,
-                "write_start_index": write_start_index,
-                "write_end_index": local_end_index,
-                "new_k": k[:, roped_offset:roped_offset + write_len],
-                "new_v": v[:, roped_offset:roped_offset + write_len],
-                "current_end": current_end,
-                "is_recompute": is_recompute
-            }
-        else:
-            # === DIRECT INSERT MODE: cache not yet full ===
-            local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
-            local_start_index = local_end_index - num_new_tokens
-
-            temp_k = kv_cache["k"].detach().clone()
-            temp_v = kv_cache["v"].detach().clone()
-
-            write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-            if sink_recache_after_switch:
-                write_start_index = local_start_index
-            roped_offset = max(0, write_start_index - local_start_index)
-            write_len = max(0, local_end_index - write_start_index)
-            if write_len > 0:
-                temp_k[:, write_start_index:local_end_index] = k[:, roped_offset:roped_offset + write_len]
-                temp_v[:, write_start_index:local_end_index] = v[:, roped_offset:roped_offset + write_len]
-
-            # RoPE with relative indices (growing sequentially before cache fills)
-            current_frame_in_window = local_start_index // frame_seqlen
-            query_relative_indices = torch.arange(
-                current_frame_in_window, current_frame_in_window + num_new_frames, device=q.device)
-            roped_query = block_relativistic_rope(
-                q, grid_sizes, freqs, relative_frame_indices=query_relative_indices).type_as(v)
-
-            num_cache_frames = local_end_index // frame_seqlen
-            cache_relative_indices = torch.arange(0, num_cache_frames, device=k.device)
-            cache_grid_sizes = grid_sizes.clone()
-            cache_grid_sizes[0, 0] = num_cache_frames
-            roped_temp_k = block_relativistic_rope(
-                temp_k[:, :local_end_index].view(b, num_cache_frames, frame_seqlen, n, d).flatten(1, 2),
-                cache_grid_sizes, freqs, relative_frame_indices=cache_relative_indices).type_as(v)
-
-            cache_update_info = {
-                "action": "direct_insert",
-                "local_start_index": local_start_index,
-                "local_end_index": local_end_index,
-                "write_start_index": write_start_index,
-                "write_end_index": local_end_index,
-                "new_k": k[:, roped_offset:roped_offset + write_len],
-                "new_v": v[:, roped_offset:roped_offset + write_len],
-                "current_end": current_end,
-                "is_recompute": is_recompute
-            }
-
-        # Attention: sink tokens + local window
-        if sink_tokens > 0:
-            local_budget = self.max_attention_size - sink_tokens
-            k_sink = roped_temp_k[:, :sink_tokens]
-            v_sink = temp_v[:, :sink_tokens]
-            if local_budget > 0:
-                local_start_for_window = max(sink_tokens, local_end_index - local_budget)
-                k_local = roped_temp_k[:, local_start_for_window:local_end_index]
-                v_local = temp_v[:, local_start_for_window:local_end_index]
-                k_cat = torch.cat([k_sink, k_local], dim=1)
-                v_cat = torch.cat([v_sink, v_local], dim=1)
-            else:
-                k_cat = k_sink
-                v_cat = v_sink
-            x = attention(roped_query, k_cat, v_cat)
-        else:
-            window_start = max(0, local_end_index - self.max_attention_size)
-            x = attention(
-                roped_query,
-                roped_temp_k[:, window_start:local_end_index],
-                temp_v[:, window_start:local_end_index])
-
-        x = x.flatten(2)
-        x, _ = self.o(x)
-        return x, (current_end, local_end_index, cache_update_info)
-
-
-class CausalPropeSelfAttention(nn.Module):
-    """PRoPE self-attention with optional KV cache for camera-controlled inference."""
-
-    def __init__(self, dim, attn_dim, num_heads, window_size=(-1, -1),
-                 local_attn_size=-1, sink_size=0, qk_norm=True, eps=1e-6):
-        assert dim % num_heads == 0
-        assert attn_dim % num_heads == 0
-        super().__init__()
-        self.dim = dim
-        self.attn_dim = attn_dim
-        self.num_heads = num_heads
-        self.head_dim = attn_dim // num_heads
-        self.local_attn_size = local_attn_size
-        self.sink_size = sink_size
-        self.qk_norm = qk_norm
-        self.eps = eps
-        self.window_size = window_size
-        self.max_attention_size = 39600 if local_attn_size == -1 else local_attn_size * 880
-
-        self.q_proj = ReplicatedLinear(dim, attn_dim)
-        self.k_proj = ReplicatedLinear(dim, attn_dim)
-        self.v_proj = ReplicatedLinear(dim, attn_dim)
-        self.out_proj = ReplicatedLinear(attn_dim, dim)
-
-        self.norm_q = WanRMSNorm(attn_dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(attn_dim, eps=eps) if qk_norm else nn.Identity()
-
-        nn.init.zeros_(self.out_proj.weight)
-        nn.init.zeros_(self.out_proj.bias)
-
-    def forward(self, x, cam_viewmats, cam_K, seq_lens, grid_sizes, freqs,
-                kv_cache=None, current_start=0, cache_start=None,
-                sink_recache_after_switch=False, cache_update_policy="commit_detached"):
-        """
-        Args:
-            x: Shape [B, L, C]
-            cam_viewmats: Camera view matrices
-            cam_K: Camera intrinsics
-            kv_cache: Optional KV cache dict. When None, runs full attention over current chunk.
-        """
-        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
-        if cache_start is None:
-            cache_start = current_start
-
-        q, _ = self.q_proj(x)
-        q = self.norm_q(q).view(b, s, n, d)
-        k, _ = self.k_proj(x)
-        k = self.norm_k(k).view(b, s, n, d)
-        v, _ = self.v_proj(x)
-        v = v.view(b, s, n, d)
-
-        # Apply PRoPE (Positional Rotary Position Embedding from camera parameters)
-        q_t, k_t, v_t, apply_fn_o = prope_qkv(
-            q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2),
-            viewmats=cam_viewmats, Ks=cam_K)
-        proped_q = q_t.transpose(1, 2)
-        proped_k = k_t.transpose(1, 2)
-        proped_v = v_t.transpose(1, 2)
-
-        if kv_cache is None:
-            # No cache: full attention over current chunk
-            x_out = attention(proped_q, proped_k, proped_v)
-        else:
-            # KV cache mode with rolling cache support
-            frame_seqlen = math.prod(grid_sizes[0][1:]).item()
-            num_new_tokens = s
-            current_end = current_start + num_new_tokens
-            sink_tokens = self.sink_size * frame_seqlen
-            kv_cache_size = kv_cache["k"].shape[1]
-            is_recompute = (current_end <= kv_cache["global_end_index"].item()) and (current_start > 0)
-
-            if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
-                    num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
-                # === ROLLING MODE ===
-                num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
-                num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens
-                local_end_index = kv_cache["local_end_index"].item() + current_end - \
-                    kv_cache["global_end_index"].item() - num_evicted_tokens
-                local_start_index = local_end_index - num_new_tokens
-
-                if cache_update_policy != "none":
-                    with torch.no_grad():
-                        kv_cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            kv_cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                        kv_cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            kv_cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-
-                    write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-                    roped_offset = max(0, write_start_index - local_start_index)
-                    write_len = max(0, local_end_index - write_start_index)
-                    if write_len > 0:
-                        with torch.no_grad():
-                            kv_cache["k"][:, write_start_index:local_end_index] = proped_k[:, roped_offset:roped_offset + write_len].detach()
-                            kv_cache["v"][:, write_start_index:local_end_index] = proped_v[:, roped_offset:roped_offset + write_len].detach()
-            else:
-                # === DIRECT INSERT MODE ===
-                local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
-                local_start_index = local_end_index - num_new_tokens
-
-                if cache_update_policy != "none":
-                    write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-                    if sink_recache_after_switch:
-                        write_start_index = local_start_index
-                    roped_offset = max(0, write_start_index - local_start_index)
-                    write_len = max(0, local_end_index - write_start_index)
-                    if write_len > 0:
-                        with torch.no_grad():
-                            kv_cache["k"][:, write_start_index:local_end_index] = proped_k[:, roped_offset:roped_offset + write_len].detach()
-                            kv_cache["v"][:, write_start_index:local_end_index] = proped_v[:, roped_offset:roped_offset + write_len].detach()
-
-            # Attention: sink tokens + local window
-            if sink_tokens > 0:
-                local_budget = self.max_attention_size - sink_tokens
-                k_sink = kv_cache["k"][:, :sink_tokens].detach()
-                v_sink = kv_cache["v"][:, :sink_tokens].detach()
-                if local_budget > 0:
-                    local_start_for_window = max(sink_tokens, local_end_index - local_budget)
-                    k_local = kv_cache["k"][:, local_start_for_window:local_end_index].detach()
-                    v_local = kv_cache["v"][:, local_start_for_window:local_end_index].detach()
-                    k_cat = torch.cat([k_sink, k_local], dim=1)
-                    v_cat = torch.cat([v_sink, v_local], dim=1)
-                else:
-                    k_cat = k_sink
-                    v_cat = v_sink
-                x_out = attention(proped_q, k_cat, v_cat)
-            else:
-                window_start = max(0, local_end_index - self.max_attention_size)
-                x_out = attention(
-                    proped_q,
-                    kv_cache["k"][:, window_start:local_end_index].detach(),
-                    kv_cache["v"][:, window_start:local_end_index].detach())
-
-            if not is_recompute and cache_update_policy != "none":
-                kv_cache["global_end_index"].fill_(current_end)
-                kv_cache["local_end_index"].fill_(local_end_index)
-
-        # Apply inverse PRoPE
-        x = apply_fn_o(x_out.transpose(1, 2)).transpose(1, 2)
-        x = x.flatten(2)
-        x, _ = self.out_proj(x)
-        return x
-
-
-class CausalWanAttentionBlock(nn.Module):
-
-    def __init__(self, dim, ffn_dim, num_heads, local_attn_size=-1, sink_size=0,
-                 qk_norm=True, cross_attn_norm=False, eps=1e-6, **kwargs):
-        super().__init__()
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.num_heads = num_heads
-        self.local_attn_size = local_attn_size
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        self.add_control_adapter = kwargs.get('add_control_adapter', False)
-        self.cam_method = kwargs.get('cam_method')
-        self.attn_compress = kwargs.get('attn_compress', 1)
-        self.layer_idx = kwargs.get('layer_idx')
-        cam_self_attn_layers = kwargs.get('cam_self_attn_layers')
-
-        # layers
-        self.norm1 = WanLayerNorm(dim, eps)
-        self.self_attn = CausalWanSelfAttention(
-            dim, num_heads, local_attn_size, sink_size, qk_norm, eps)
-        self.norm3 = WanLayerNorm(
-            dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
-        self.cross_attn = WanCrossAttention(dim, num_heads, (-1, -1), qk_norm, eps)
-        self.norm2 = WanLayerNorm(dim, eps)
-        # nn.Linear (not ReplicatedLinear) on purpose: the official checkpoint
-        # stores these as positional Sequential keys (ffn.0 / ffn.2) that the
-        # copy-only converter and the strict-load tests require verbatim, and
-        # ReplicatedLinear's (out, bias) tuple return cannot compose inside
-        # nn.Sequential without renaming the state-dict surface.
-        self.ffn = nn.Sequential(
-            nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
-            nn.Linear(ffn_dim, dim))
-
-        # PRoPE self-attention branch for camera control
-        add_cam_attn = self.add_control_adapter and self.cam_method == 'prope'
-        if add_cam_attn and cam_self_attn_layers is not None:
-            add_cam_attn = self.layer_idx in cam_self_attn_layers
-        if add_cam_attn:
-            self.cam_self_attn = CausalPropeSelfAttention(
-                dim, dim // self.attn_compress, num_heads,
-                local_attn_size=local_attn_size, sink_size=sink_size,
-                qk_norm=qk_norm, eps=eps)
-
-        # modulation
-        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
-
-    def forward(self, x, e, seq_lens, grid_sizes, freqs, context, context_lens,
-                kv_cache, crossattn_cache=None, current_start=0, cache_start=None,
-                cam_viewmats=None, cam_K=None, sink_recache_after_switch=False,
-                cache_update_policy="commit_detached"):
-        num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
-        e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2)
-
-        # self-attention
-        attn_input = (self.norm1(x).unflatten(
-            dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0]).flatten(1, 2)
-        y, cache_update_info = self.self_attn(
-            attn_input, seq_lens, grid_sizes, freqs, kv_cache,
-            current_start, cache_start, sink_recache_after_switch)
-
-        # PRoPE camera attention (parallel branch)
-        if hasattr(self, 'cam_self_attn') and cam_viewmats is not None and cam_K is not None:
-            prope_kv_cache = None
-            if kv_cache is not None and "prope_k" in kv_cache:
-                prope_kv_cache = {
-                    "k": kv_cache["prope_k"],
-                    "v": kv_cache["prope_v"],
-                    "global_end_index": kv_cache["prope_global_end_index"],
-                    "local_end_index": kv_cache["prope_local_end_index"],
-                }
-            y = y + self.cam_self_attn(
-                attn_input, cam_viewmats, cam_K, seq_lens, grid_sizes, freqs,
-                kv_cache=prope_kv_cache, current_start=current_start,
-                cache_start=cache_start, cache_update_policy=cache_update_policy)
-
-        x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[2]).flatten(1, 2)
-
-        # cross-attention & FFN
-        x = x + self.cross_attn(self.norm3(x), context, context_lens,
-                                crossattn_cache=crossattn_cache)
-        y = self.ffn(
-            (self.norm2(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen))
-             * (1 + e[4]) + e[3]).flatten(1, 2))
-        x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[5]).flatten(1, 2)
-
-        return x, cache_update_info
-
-
-class CausalHead(nn.Module):
-
-    def __init__(self, dim, out_dim, patch_size, eps=1e-6):
-        super().__init__()
-        self.dim = dim
-        self.out_dim = out_dim
-        self.patch_size = patch_size
-        self.eps = eps
-
-        out_dim = math.prod(patch_size) * out_dim
-        self.norm = WanLayerNorm(dim, eps)
-        self.head = ReplicatedLinear(dim, out_dim)
-        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
-
-    def forward(self, x, e):
-        num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
-        e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2)
-        x, _ = self.head(
-            self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen))
-            * (1 + e[1]) + e[0])
-        return x
-
-
-class DreamXWorldARTransformer3DModel(BaseDiT):
-    """DreamX-World-5B autoregressive causal transformer."""
-
-    _fsdp_shard_conditions = DreamXWorldARConfig()._fsdp_shard_conditions
-    _compile_conditions = DreamXWorldARConfig()._compile_conditions
-    _supported_attention_backends = DreamXWorldARConfig()._supported_attention_backends
-    param_names_mapping = DreamXWorldARConfig().param_names_mapping
-    reverse_param_names_mapping = DreamXWorldARConfig().reverse_param_names_mapping
-    lora_param_names_mapping = DreamXWorldARConfig().lora_param_names_mapping
-    _no_split_modules = ["CausalWanAttentionBlock"]
-
-    def __init__(self, config: DreamXWorldARConfig, hf_config: dict[str, Any]) -> None:
-        super().__init__(config=config, hf_config=hf_config)
-
-        model_type = config.model_type
-        patch_size = config.patch_size
-        text_len = config.text_len
-        in_dim = config.in_channels
-        dim = config.hidden_size
-        ffn_dim = config.ffn_dim
-        freq_dim = config.freq_dim
-        text_dim = config.text_dim
-        out_dim = config.out_channels
-        num_heads = config.num_attention_heads
-        num_layers = config.num_layers
-        local_attn_size = config.local_attn_size
-        sink_size = config.sink_size
-        qk_norm = bool(config.qk_norm)
-        cross_attn_norm = config.cross_attn_norm
-        eps = config.eps
-        add_control_adapter = config.add_control_adapter
-        cam_method = config.cam_method
-        attn_compress = config.attn_compress
-        cam_self_attn_layers = config.cam_self_attn_layers
-
-        assert model_type in ['t2v', 'i2v', 'ti2v']
-        self.model_type = model_type
-        self.patch_size = patch_size
-        self.text_len = text_len
-        self.in_dim = in_dim
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.freq_dim = freq_dim
-        self.text_dim = text_dim
-        self.out_dim = out_dim
-        self.num_heads = num_heads
-        self.num_layers = num_layers
-        self.local_attn_size = local_attn_size
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        # embeddings — nn.Linear inside nn.Sequential on purpose: the official
-        # checkpoint keys are positional (text_embedding.0/.2, time_embedding.0/.2,
-        # time_projection.1) and must load verbatim (see ffn comment above).
-        self.patch_embedding = nn.Conv3d(
-            in_dim, dim, kernel_size=patch_size, stride=patch_size)
-        self.text_embedding = nn.Sequential(
-            nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'),
-            nn.Linear(dim, dim))
-        self.time_embedding = nn.Sequential(
-            nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
-        self.time_projection = nn.Sequential(
-            nn.SiLU(), nn.Linear(dim, dim * 6))
-
-        # transformer blocks
-        self.blocks = nn.ModuleList([
-            CausalWanAttentionBlock(
-                dim, ffn_dim, num_heads, local_attn_size, sink_size,
-                qk_norm, cross_attn_norm, eps,
-                add_control_adapter=add_control_adapter,
-                cam_method=cam_method,
-                attn_compress=attn_compress,
-                layer_idx=layer_idx,
-                cam_self_attn_layers=cam_self_attn_layers)
-            for layer_idx in range(num_layers)
-        ])
-        for layer_idx, block in enumerate(self.blocks):
-            block.self_attn.layer_idx = layer_idx
-            block.self_attn.num_layers = self.num_layers
-
-        # head
-        self.head = CausalHead(dim, out_dim, patch_size, eps)
-
-        # RoPE frequencies
-        assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
-        d = dim // num_heads
-        self.freqs = torch.cat([
-            rope_params(1024, d - 4 * (d // 6)),
-            rope_params(1024, 2 * (d // 6)),
-            rope_params(1024, 2 * (d // 6))
-        ], dim=1)
-
-        self.num_attention_heads = num_heads
-        self.attention_head_dim = dim // num_heads
-        self.hidden_size = dim
-        self.in_channels = in_dim
-        self.out_channels = out_dim
-        self.num_channels_latents = out_dim
-        self.init_weights()
-        self.num_frame_per_block = config.arch_config.num_frames_per_block
-        self.__post_init__()
-
-    def forward(self, x=None, t=None, context=None, seq_len=None, y=None, y_camera=None,
-                kv_cache=None, crossattn_cache=None, current_start=0,
-                cache_start=0, cache_update_policy="commit_detached",
-                hidden_states=None, encoder_hidden_states=None, timestep=None, **kwargs):
-        """
-        Causal inference with KV caching.
-        See Algorithm 2 of CausVid (https://arxiv.org/abs/2412.07772).
-
-        Args:
-            x: List of input video tensors [C_in, F, H, W]
-            t: Timestep tensor [B, L]
-            context: List of text embeddings [L, C]
-            seq_len: Maximum sequence length for positional encoding
-            y: Optional conditional video inputs (I2V mode)
-            y_camera: Camera parameters dict {'viewmats': ..., 'K': ...}
-            kv_cache: List of KV cache dicts per transformer block
-            crossattn_cache: List of cross-attention cache dicts
-            current_start: Current position in global token sequence
-            cache_start: Cache start position
-            cache_update_policy: Cache update strategy ('commit_detached' or 'none')
-
-        Returns:
-            Stacked output tensors [B, C_out, F, H/8, W/8]
-        """
-        if x is None and hidden_states is not None:
-            x = [sample for sample in hidden_states]
-        if t is None and timestep is not None:
-            t = timestep
-        if context is None and encoder_hidden_states is not None:
-            if isinstance(encoder_hidden_states, torch.Tensor):
-                context = [sample for sample in encoder_hidden_states]
-            else:
-                context = encoder_hidden_states
-        if seq_len is None:
-            if torch.is_tensor(t):
-                seq_len = int(t.shape[1]) if t.dim() > 1 else int(t.numel())
-            elif x is not None:
-                sample = x[0]
-                seq_len = (sample.shape[1] // self.patch_size[0]) * (sample.shape[2] // self.patch_size[1]) * (sample.shape[3] // self.patch_size[2])
-        if x is None or t is None or context is None or seq_len is None:
-            raise ValueError("DreamXWorldARTransformer3DModel requires x/t/context/seq_len or FastVideo aliases")
-
-        device = self.patch_embedding.weight.device
-        if self.freqs.is_meta or self.freqs.device != device:
-            d = self.dim // self.num_heads
-            self.freqs = torch.cat([
-                rope_params(1024, d - 4 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-            ], dim=1).to(device)
-
-        if y is not None:
-            x = [torch.cat([u, v], dim=0) for u, v in zip(x, y, strict=True)]
-
-        # patch embedding
-        x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
-        grid_sizes = torch.stack(
-            [torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
-        x = [u.flatten(2).transpose(1, 2) for u in x]
-        seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
-        assert seq_lens.max() <= seq_len
-        x = torch.cat(x)
-
-        # time embedding
-        e = self.time_embedding(
-            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x))
-        e0 = self.time_projection(e).unflatten(
-            1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
-
-        # text embedding
-        context_lens = None
-        context = self.text_embedding(
-            torch.stack([
-                torch.cat(
-                    [u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
-                for u in context
-            ]))
-
-        # camera parameters
-        if y_camera is not None and isinstance(y_camera, dict):
-            cam_viewmats = y_camera['viewmats']
-            cam_K = y_camera['K']
-        else:
-            cam_viewmats = None
-            cam_K = None
-
-        block_kwargs = dict(
-            e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.freqs,
-            context=context, context_lens=context_lens,
-            cam_viewmats=cam_viewmats, cam_K=cam_K,
-            cache_update_policy=cache_update_policy,
-        )
-
-        cache_update_infos = []
-        for block_index, block in enumerate(self.blocks):
-            block_kwargs.update({
-                "kv_cache": kv_cache[block_index] if kv_cache is not None else None,
-                "crossattn_cache": crossattn_cache[block_index] if crossattn_cache is not None else None,
-                "current_start": current_start,
-                "cache_start": cache_start,
-            })
-            x, block_cache_update_info = block(x, **block_kwargs)
-            if kv_cache is not None:
-                cache_update_infos.append((block_index, block_cache_update_info))
-
-        # Apply deferred cache updates
-        if kv_cache is not None and cache_update_infos and cache_update_policy != "none":
-            self._apply_cache_updates(kv_cache, cache_update_infos)
-
-        # head & unpatchify
-        x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))
-        x = self.unpatchify(x, grid_sizes)
-        return torch.stack(x)
-
-    def _apply_cache_updates(self, kv_cache, cache_update_infos):
-        """Apply deferred cache updates collected from all transformer blocks.
-
-        For Block-Relativistic RoPE, this stores un-roped K values in the cache.
-        RoPE is applied dynamically during attention based on each token's current
-        relative position in the sliding window.
-        """
-        with torch.no_grad():
-            for block_index, (current_end, local_end_index, update_info) in cache_update_infos:
-                if update_info is not None:
-                    cache = kv_cache[block_index]
-
-                    if update_info["action"] == "roll_and_insert":
-                        sink_tokens = update_info["sink_tokens"]
-                        num_rolled_tokens = update_info["num_rolled_tokens"]
-                        num_evicted_tokens = update_info["num_evicted_tokens"]
-                        write_start_index = update_info.get("write_start_index", update_info["local_start_index"])
-                        write_end_index = update_info.get("write_end_index", update_info["local_end_index"])
-                        new_k = update_info["new_k"].detach()
-                        new_v = update_info["new_v"].detach()
-
-                        cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                        cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-
-                        if write_end_index > write_start_index and new_k.shape[1] == (write_end_index - write_start_index):
-                            cache["k"][:, write_start_index:write_end_index] = new_k
-                            cache["v"][:, write_start_index:write_end_index] = new_v
-
-                    elif update_info["action"] == "direct_insert":
-                        write_start_index = update_info.get("write_start_index", update_info["local_start_index"])
-                        write_end_index = update_info.get("write_end_index", update_info["local_end_index"])
-                        new_k = update_info["new_k"].detach()
-                        new_v = update_info["new_v"].detach()
-
-                        if write_end_index > write_start_index and new_k.shape[1] == (write_end_index - write_start_index):
-                            cache["k"][:, write_start_index:write_end_index] = new_k
-                            cache["v"][:, write_start_index:write_end_index] = new_v
-
-                is_recompute = False if update_info is None else update_info.get("is_recompute", False)
-                if not is_recompute:
-                    kv_cache[block_index]["global_end_index"].fill_(current_end)
-                    kv_cache[block_index]["local_end_index"].fill_(local_end_index)
-
-    def unpatchify(self, x, grid_sizes):
-        """Reconstruct video tensors from patch embeddings."""
-        c = self.out_dim
-        out = []
-        for u, v in zip(x, grid_sizes.tolist(), strict=True):
-            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
-            u = torch.einsum('fhwpqrc->cfphqwr', u)
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size, strict=True)])
-            out.append(u)
-        return out
-
-    def init_weights(self):
-        """Initialize model parameters using Xavier initialization."""
-        for m in self.modules():
-            if isinstance(m, (nn.Linear, ReplicatedLinear)):
-                nn.init.xavier_uniform_(m.weight)
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-
-        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
-        for m in self.text_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=.02)
-        for m in self.time_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=.02)
-
-        nn.init.zeros_(self.head.head.weight)
-
-
-EntryClass = DreamXWorldARTransformer3DModel
@@ -699,8 +699,8 @@ class IndividualTokenRefinerBlock(nn.Module):
        if mask is None:
            attn_output = self.attn(q, k, v) 
        else:
-            from fastvideo.attention.backends.sdpa import SDPAMetadataBuilder
-            attn_metadata = SDPAMetadataBuilder().build(
+            from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+            attn_metadata = FlashAttnMetadataBuilder().build(
                current_timestep=0,
                attn_mask=mask,
            )
@@ -173,11 +173,8 @@ class HYWorldDoubleStreamBlock(MMDoubleStreamBlock):
        img_v_prope = img_v_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
        # end hyworld

-        # The metadata only carries the text padding mask; the executing
-        # attention kernel is still whatever the layer's selector picked
-        # (flash-attn when installed), not necessarily torch SDPA.
-        from fastvideo.attention.backends.sdpa import SDPAMetadataBuilder
-        attn_metadata = SDPAMetadataBuilder().build(
+        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+        attn_metadata = FlashAttnMetadataBuilder().build(
            current_timestep=0,
            attn_mask=encoder_attention_mask,
        )
@@ -195,9 +192,14 @@ class HYWorldDoubleStreamBlock(MMDoubleStreamBlock):
            )
        
        # begin hyworld
-        # attention with prope (same text mask, so reuse the metadata)
+        # attention with prope
+        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+        attn_metadata_prope = FlashAttnMetadataBuilder().build(
+            current_timestep=0,
+            attn_mask=encoder_attention_mask,
+        )       
        # NOTE: Do NOT pass freqs_cis to prope attention - HY-WorldPlay does not apply RoPE to prope
-        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
+        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata_prope):
            img_attn_prope, _ = self.attn(
                img_q_prope,
                img_k_prope,
@@ -10,21 +10,13 @@ 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

@@ -271,9 +263,10 @@ 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(x.dtype)
+    return x_out.reshape(*x.shape).to(orig_dtype)


 class Kandinsky5Attention(nn.Module):
@@ -284,7 +277,6 @@ 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
@@ -314,17 +306,6 @@ 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,
@@ -358,54 +339,27 @@ class Kandinsky5Attention(nn.Module):
            key = _apply_rotary(key, rotary_emb).type_as(key)

        if sparse_params is not None:
-            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)
+            raise NotImplementedError(
+                "Sparse attention is not yet supported for Kandinsky5 in FastVideo."
+            )

-            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)
+        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)

        hidden_states, _ = self.out_layer(hidden_states)
        return hidden_states
@@ -522,8 +476,7 @@ class Kandinsky5TransformerDecoderBlock(nn.Module):
                 head_dim: int,
                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
                 | None = None,
-                 prefix: str = "",
-                 use_nabla: bool = False):
+                 prefix: str = ""):
        super().__init__()
        self.visual_modulation = Kandinsky5Modulation(time_dim, model_dim, 9)

@@ -538,8 +491,7 @@ class Kandinsky5TransformerDecoderBlock(nn.Module):
            model_dim,
            head_dim,
            supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.self_attention",
-            use_nabla=use_nabla)
+            prefix=f"{prefix}.self_attention")

        self.cross_attention_norm = LayerNormScaleShift(
            model_dim,
@@ -672,8 +624,7 @@ class Kandinsky5Transformer3DModel(BaseDiT):
                                              arch.ff_dim,
                                              head_dim,
                                              self._supported_attention_backends,
-                                              prefix=f"{config.prefix}.visual_transformer_blocks.{i}",
-                                              use_nabla=arch.attention_type == "nabla")
+                                              prefix=f"{config.prefix}.visual_transformer_blocks.{i}")
            for i in range(arch.num_visual_blocks)
        ])

@@ -743,7 +694,6 @@ 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)
@@ -774,7 +724,6 @@ class Kandinsky5Transformer3DModel(BaseDiT):

        if return_dict:
            return Kandinsky5TransformerOutput(sample=x)
-        
        return x

    def materialize_non_persistent_buffers(self, device: torch.device,
@@ -467,18 +467,6 @@ 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__()
@@ -491,14 +479,7 @@ class Qwen2_5_VLRotaryEmbedding(nn.Module):
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
-        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]}")
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
@@ -967,16 +948,6 @@ 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):
@@ -1064,7 +1035,7 @@ class Qwen2_5_VLModel(nn.Module):
    def __init__(self, config: Qwen2_5_VLConfig):
        super().__init__()
        self.config = config
-        self.padding_idx = getattr(config, "pad_token_id", None)
+        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
@@ -1437,18 +1408,6 @@ 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
@@ -1456,7 +1415,6 @@ 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)

@@ -320,19 +320,6 @@ 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(
@@ -417,9 +404,7 @@ class TextEncoderLoader(ComponentLoader):
            else:
                loaded_weights: set[str] = model.load_weights(
                    self._get_all_weights(
-                        model,
-                        model_path,
-                        to_cpu=fastvideo_args.text_encoder_cpu_offload,
+                        model, model_path, to_cpu=use_cpu_offload
                    )
                )  # type: ignore

@@ -11,7 +11,7 @@ import tempfile
 from abc import ABC, abstractmethod
 from collections.abc import Callable, Set
 from dataclasses import dataclass, field
-from functools import cache, lru_cache
+from functools import lru_cache
 from typing import NoReturn, TypeVar, cast

 import cloudpickle
@@ -32,8 +32,6 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
    "HYWorldTransformer3DModel":
    ("dits", "hyworld", "HYWorldTransformer3DModel"),
    "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
-    "DreamXWorldTransformer3DModel": ("dits", "dreamx_world", "DreamXWorldTransformer3DModel"),
-    "DreamXWorldARTransformer3DModel": ("dits", "dreamx_world_ar", "DreamXWorldARTransformer3DModel"),
    "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
    "CosmosTransformer3DModel": ("dits", "cosmos", "CosmosTransformer3DModel"),
    "Cosmos25Transformer3DModel": ("dits", "cosmos2_5", "Cosmos25Transformer3DModel"),
@@ -50,8 +48,6 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
 _IMAGE_TO_VIDEO_DIT_MODELS = {
    # "HunyuanVideoTransformer3DModel": ("dits", "hunyuanvideo", "HunyuanVideoDiT"),
    "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
-    "DreamXWorldTransformer3DModel": ("dits", "dreamx_world", "DreamXWorldTransformer3DModel"),
-    "DreamXWorldARTransformer3DModel": ("dits", "dreamx_world_ar", "DreamXWorldARTransformer3DModel"),
    "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
    "MatrixGame2WanModel": ("dits", "matrixgame2", "MatrixGame2WanModel"),
    "CausalMatrixGame2WanModel": ("dits", "matrixgame2", "CausalMatrixGame2WanModel"),
@@ -145,7 +141,7 @@ _LEGACY_FAST_VIDEO_MODELS = {
 MODELS_PATH = os.path.dirname(__file__)


-@cache
+@lru_cache(maxsize=None)
 def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:
    discovered_models: dict[str, tuple[str, str, str]] = {}
    for root, dirs, files in os.walk(MODELS_PATH):
@@ -160,7 +156,7 @@ def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:

            filepath = os.path.join(root, filename)
            try:
-                with open(filepath, encoding="utf-8") as f:
+                with open(filepath, "r", encoding="utf-8") as f:
                    source = f.read()
                tree = ast.parse(source, filename=filename)

@@ -1,9 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0

-import io
 import os
 import tempfile
-import time
 from collections.abc import Callable
 from typing import Any
 from urllib.parse import unquote, urlparse
@@ -18,10 +16,6 @@ import torch.nn.functional as F
 import torchvision.transforms.functional as TF
 from packaging import version

-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
 if version.parse(version.parse(
        PIL.__version__).base_version) >= version.parse("9.1.0"):
    PIL_INTERPOLATION = {
@@ -95,39 +89,6 @@ def normalize(images: np.ndarray | torch.Tensor) -> np.ndarray | torch.Tensor:
    return 2.0 * images - 1.0


-def _fetch_image_bytes(url: str,
-                       attempts: int = 3,
-                       timeout: float = 30.0) -> bytes:
-    """Download ``url`` fully, retrying transient network errors.
-
-    Reads the whole body via ``response.content`` instead of handing PIL a
-    ``.raw`` stream: a connection dropped mid-body (e.g. an HF-CDN
-    ``IncompleteRead``) then fails here, where it can be retried, instead of
-    surfacing as a truncated image inside PIL.
-    """
-    for attempt in range(attempts):
-        try:
-            response = requests.get(url, timeout=timeout)
-            response.raise_for_status()
-            return response.content
-        except requests.RequestException as e:
-            status = getattr(getattr(e, "response", None), "status_code",
-                             None)
-            # Permanent client errors (4xx except 408/429) won't heal on
-            # retry — fail fast.
-            if (status is not None and 400 <= status < 500
-                    and status not in (408, 429)):
-                raise
-            if attempt == attempts - 1:
-                raise
-            backoff = 2**attempt
-            logger.warning(
-                "Failed to download image %s (attempt %d/%d): %s. "
-                "Retrying in %ds.", url, attempt + 1, attempts, e, backoff)
-            time.sleep(backoff)
-    raise AssertionError("unreachable")
-
-
 # adapted from diffusers.utils import load_image
 def load_image(
    image: str | PIL.Image.Image,
@@ -149,7 +110,7 @@ def load_image(
    """
    if isinstance(image, str):
        if image.startswith("http://") or image.startswith("https://"):
-            image = PIL.Image.open(io.BytesIO(_fetch_image_bytes(image)))
+            image = PIL.Image.open(requests.get(image, stream=True).raw)
        elif os.path.isfile(image):
            image = PIL.Image.open(image)
        else:
@@ -337,12 +337,8 @@ 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",
 )


@@ -36,7 +36,6 @@ DEFAULT_METRIC_POLICIES: tuple[MetricPolicy, ...] = (
    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
@@ -58,7 +57,9 @@ def _optional_bool(value: Any) -> bool | None:
    return None


-def resolve_metric_policies(threshold_overrides: Mapping[str, Any] | None, ) -> tuple[MetricPolicy, ...]:
+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):
@@ -79,15 +80,25 @@ def resolve_metric_policies(threshold_overrides: Mapping[str, Any] | None, ) ->
                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),
+                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]]:
+def serialize_metric_thresholds(
+    policies: tuple[MetricPolicy, ...],
+) -> dict[str, dict[str, float | bool]]:
    return {
        policy.key: {
            "threshold_percent": policy.threshold_percent,
@@ -107,7 +118,10 @@ def regression_delta(
        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)
+    threshold_exceeded = (
+        percent_delta > policy.threshold_percent
+        and absolute_delta > policy.threshold_absolute
+    )
    return MetricDelta(
        absolute=absolute_delta,
        percent=percent_delta,
@@ -86,32 +86,20 @@ def record_metadata(record: Record) -> Record:
    }


-def _cohort_metadata_value(value: Any) -> Any:
-    if value is None:
-        return ""
-    if isinstance(value, str) and not value.strip():
-        return ""
-    return value
-
-
-def _cohort_key_value(value: Any) -> str:
-    return str(_cohort_metadata_value(value))
-
-
 def record_comparison_metadata(record: Record) -> Record:
-    return {key: _cohort_metadata_value(record.get(key)) for key in COMPARISON_COHORT_KEYS}
+    return {key: record.get(key) or "" for key in COMPARISON_COHORT_KEYS}


 def comparison_cohort_key(record: Record) -> CohortKey:
    return (
        str(record.get("model_id") or "unknown"),
        str(record.get("gpu_type") or "unknown"),
-        _cohort_key_value(record.get("workload_id")),
-        _cohort_key_value(record.get("variant_id")),
-        _cohort_key_value(record.get("benchmark_version")),
-        _cohort_key_value(record.get("recipe_fingerprint")),
-        _cohort_key_value(record.get("hardware_profile_id")),
-        _cohort_key_value(record.get("software_profile_id")),
+        str(record.get("workload_id") or ""),
+        str(record.get("variant_id") or ""),
+        str(record.get("benchmark_version") or ""),
+        str(record.get("recipe_fingerprint") or ""),
+        str(record.get("hardware_profile_id") or ""),
+        str(record.get("software_profile_id") or ""),
    )


@@ -158,10 +146,9 @@ def build_latest_summary(records: list[Record],
            continue

        latest = latest_candidates[-1]
-        latest_index = next(index for index, record in enumerate(group) if record is latest)
        baseline_pool = [
-            record for record in group[:latest_index]
-            if record.get("success", True) and is_baseline_eligible_record(record)
+            record for record in group
+            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"))
@@ -1,28 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo.configs.pipelines.dreamx_world import (
-    DreamXWorld5BARPipelineConfig,
-    DreamXWorld5BCamPipelineConfig,
-    make_dreamx_world_5b_ar_dit_config,
-    make_dreamx_world_5b_cam_dit_config,
-    make_dreamx_world_5b_cam_text_encoder_config,
-    make_dreamx_world_5b_cam_vae_config,
-)
-from fastvideo.pipelines.basic.dreamx_world.dreamx_world_ar_pipeline import DreamXWorldARPipeline
-from fastvideo.pipelines.basic.dreamx_world.dreamx_world_pipeline import DreamXWorldPipeline
-from fastvideo.pipelines.basic.dreamx_world.stages import (
-    DREAMX_Y_CAMERA_KEY,
-    DreamXWorldCameraConditioningStage,
-)
-
-__all__ = [
-    "DREAMX_Y_CAMERA_KEY",
-    "DreamXWorld5BARPipelineConfig",
-    "DreamXWorld5BCamPipelineConfig",
-    "DreamXWorldCameraConditioningStage",
-    "DreamXWorldARPipeline",
-    "DreamXWorldPipeline",
-    "make_dreamx_world_5b_ar_dit_config",
-    "make_dreamx_world_5b_cam_dit_config",
-    "make_dreamx_world_5b_cam_text_encoder_config",
-    "make_dreamx_world_5b_cam_vae_config",
-]
@@ -1,219 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World autoregressive causal denoising stage."""
-
-from __future__ import annotations
-
-from typing import Any
-
-import torch
-from tqdm.auto import tqdm
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.basic.dreamx_world.stages import DREAMX_Y_CAMERA_KEY
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.denoising import DenoisingStage
-
-
-class DreamXWorldARCausalDenoisingStage(DenoisingStage):
-    """Official DreamX AR-forcing denoising loop with KV cache."""
-
-    _AR_NOISE_SEED_OFFSET = 1_000_003
-
-    def __init__(self, transformer, scheduler, pipeline=None, vae=None) -> None:
-        super().__init__(transformer=transformer, scheduler=scheduler, pipeline=pipeline, vae=vae)
-        self.num_transformer_blocks = len(self.transformer.blocks)
-        self.num_frame_per_block = int(getattr(self.transformer, "num_frame_per_block", 3))
-        self.local_attn_size = int(getattr(self.transformer, "local_attn_size", 12))
-
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        assert batch.latents is not None, "latents must be prepared before DreamX AR denoising"
-        assert batch.prompt_embeds, "prompt embeds must be prepared before DreamX AR denoising"
-        latents = batch.latents
-        device = latents.device
-        target_dtype = torch.bfloat16
-        autocast_enabled = device.type == "cuda" and not fastvideo_args.disable_autocast
-
-        frame_seq_length = (latents.shape[-2] // self.transformer.patch_size[1]) * (latents.shape[-1] //
-                                                                                    self.transformer.patch_size[2])
-        timesteps = torch.tensor(
-            tuple(getattr(fastvideo_args.pipeline_config, "dmd_denoising_steps", (1000, 750, 500, 250))),
-            dtype=torch.long,
-        ).cpu()
-        if getattr(fastvideo_args.pipeline_config, "warp_denoising_step", True):
-            self.scheduler.set_timesteps(1000)
-            scheduler_timesteps = torch.cat((self.scheduler.timesteps.cpu(), torch.tensor([0], dtype=torch.float32)))
-            timesteps = scheduler_timesteps[1000 - timesteps]
-        timesteps = timesteps.to(device)
-
-        if latents.shape[2] % self.num_frame_per_block != 0:
-            raise ValueError("DreamX AR latent frames must be divisible by num_frame_per_block")
-
-        y_camera = batch.extra.get(DREAMX_Y_CAMERA_KEY, batch.extra.get("y_camera"))
-        if isinstance(y_camera, dict):
-            y_camera = {
-                k: v.to(device=device, dtype=target_dtype) if torch.is_tensor(v) else v
-                for k, v in y_camera.items()
-            }
-
-        if batch.image_latent is not None and batch.image_latent.shape[1] == latents.shape[1]:
-            latents[:, :, :batch.image_latent.shape[2]] = batch.image_latent.to(device=device, dtype=latents.dtype)
-
-        kv_cache = self._initialize_kv_cache(latents.shape[0], target_dtype, device, frame_seq_length)
-        crossattn_cache = self._initialize_crossattn_cache(latents.shape[0], target_dtype, device)
-        prompt = batch.prompt_embeds[0]
-        if torch.is_tensor(prompt):
-            prompt = prompt.to(device=device, dtype=target_dtype)
-            context = [sample for sample in prompt]
-        else:
-            context = prompt
-
-        num_blocks = latents.shape[2] // self.num_frame_per_block
-        start = 0
-        first_frame_mask = torch.ones_like(latents)
-        first_frame_mask[:, :, 0] = 0
-        base_generator = batch.generator[0] if isinstance(batch.generator, list) else batch.generator
-        noise_generator = self._make_noise_generator(base_generator, device)
-
-        with tqdm(total=num_blocks * len(timesteps), desc="DreamX AR denoising", leave=False) as progress:
-            for _ in range(num_blocks):
-                current_num_frames = self.num_frame_per_block
-                block_latents = latents[:, :, start:start + current_num_frames]
-                noisy_input = block_latents.clone()
-                mask_block = first_frame_mask[:, :, start:start + current_num_frames]
-                camera_block = self._slice_camera(y_camera, start, current_num_frames)
-
-                for idx, current_timestep in enumerate(timesteps):
-                    timestep = torch.full(
-                        (latents.shape[0], current_num_frames * frame_seq_length),
-                        int(current_timestep.item()),
-                        device=device,
-                        dtype=torch.long,
-                    )
-                    if start == 0:
-                        timestep[:, :frame_seq_length] = 0
-                    with torch.autocast(device_type="cuda", dtype=target_dtype, enabled=autocast_enabled):
-                        denoised = self.transformer(
-                            hidden_states=block_latents.to(target_dtype),
-                            encoder_hidden_states=torch.stack(context).to(target_dtype),
-                            timestep=timestep,
-                            y_camera=camera_block,
-                            kv_cache=kv_cache,
-                            crossattn_cache=crossattn_cache,
-                            current_start=start * frame_seq_length,
-                        )
-                    denoised = denoised.to(latents.dtype)
-                    if idx < len(timesteps) - 1:
-                        next_timestep = torch.full((latents.shape[0], current_num_frames),
-                                                   int(timesteps[idx + 1].item()),
-                                                   device=device,
-                                                   dtype=torch.long)
-                        noise_kwargs = {"device": device, "dtype": denoised.dtype}
-                        if noise_generator is not None:
-                            noise_kwargs["generator"] = noise_generator
-                        noise = torch.randn(denoised.permute(0, 2, 1, 3, 4).shape, **noise_kwargs)
-                        block_btchw = self.scheduler.add_noise(
-                            denoised.permute(0, 2, 1, 3, 4).flatten(0, 1),
-                            noise.flatten(0, 1),
-                            next_timestep.flatten(),
-                        ).unflatten(0, (latents.shape[0], current_num_frames))
-                        block_latents = block_btchw.permute(0, 2, 1, 3, 4)
-                        block_latents = block_latents * mask_block + noisy_input * (1 - mask_block)
-                    else:
-                        block_latents = denoised * mask_block + noisy_input * (1 - mask_block)
-                    progress.update()
-
-                latents[:, :, start:start + current_num_frames] = block_latents
-                self._update_context_cache(block_latents, context, camera_block, kv_cache, crossattn_cache, start,
-                                           frame_seq_length, target_dtype, autocast_enabled,
-                                           float(getattr(fastvideo_args.pipeline_config, "context_noise", 0.1)))
-                start += current_num_frames
-
-        batch.latents = latents
-        return batch
-
-    def _make_noise_generator(self, generator: torch.Generator | None, device: torch.device) -> torch.Generator | None:
-        if generator is None:
-            return None
-        if getattr(generator, "device", None) == device:
-            return generator
-        seed = int(generator.initial_seed()) + self._AR_NOISE_SEED_OFFSET
-        return torch.Generator(device=device).manual_seed(seed)
-
-    @staticmethod
-    def _context_noise_timestep(context_noise: float) -> int:
-        if 0.0 < context_noise <= 1.0:
-            return int(context_noise * 1000)
-        return int(context_noise)
-
-    def _slice_camera(self, y_camera: Any, start: int, num_frames: int):
-        if not isinstance(y_camera, dict):
-            return y_camera
-        return {
-            "viewmats": y_camera["viewmats"][:, start:start + num_frames],
-            "K": y_camera["K"][:, start:start + num_frames],
-        }
-
-    def _initialize_kv_cache(self, batch_size: int, dtype: torch.dtype, device: torch.device,
-                             frame_seq_length: int) -> list[dict[str, Any]]:
-        size = self.local_attn_size * frame_seq_length if self.local_attn_size != -1 else 18480
-        heads = self.transformer.num_attention_heads
-        head_dim = self.transformer.attention_head_dim
-        cam_self_attn = next(
-            (getattr(block, "cam_self_attn", None)
-             for block in self.transformer.blocks if getattr(block, "cam_self_attn", None) is not None),
-            None,
-        )
-
-        caches = []
-        for _ in range(self.num_transformer_blocks):
-            cache = {
-                "k": torch.zeros(batch_size, size, heads, head_dim, dtype=dtype, device=device),
-                "v": torch.zeros(batch_size, size, heads, head_dim, dtype=dtype, device=device),
-                "global_end_index": torch.tensor([0], dtype=torch.long, device=device),
-                "local_end_index": torch.tensor([0], dtype=torch.long, device=device),
-            }
-            if cam_self_attn is not None:
-                cam_heads = int(cam_self_attn.num_heads)
-                cam_head_dim = int(cam_self_attn.head_dim)
-                cache.update({
-                    "prope_k":
-                    torch.zeros(batch_size, size, cam_heads, cam_head_dim, dtype=dtype, device=device),
-                    "prope_v":
-                    torch.zeros(batch_size, size, cam_heads, cam_head_dim, dtype=dtype, device=device),
-                    "prope_global_end_index":
-                    torch.tensor([0], dtype=torch.long, device=device),
-                    "prope_local_end_index":
-                    torch.tensor([0], dtype=torch.long, device=device),
-                })
-            caches.append(cache)
-        return caches
-
-    def _initialize_crossattn_cache(self, batch_size: int, dtype: torch.dtype, device: torch.device):
-        heads = self.transformer.num_attention_heads
-        head_dim = self.transformer.attention_head_dim
-        return [{
-            "k": torch.zeros(batch_size, 512, heads, head_dim, dtype=dtype, device=device),
-            "v": torch.zeros(batch_size, 512, heads, head_dim, dtype=dtype, device=device),
-            "is_init": False,
-        } for _ in range(self.num_transformer_blocks)]
-
-    def _update_context_cache(self, block_latents: torch.Tensor, context: Any, camera_block: Any,
-                              kv_cache: list[dict[str, Any]], crossattn_cache: list[dict[str, Any]], start: int,
-                              frame_seq_length: int, target_dtype: torch.dtype, autocast_enabled: bool,
-                              context_noise: float) -> None:
-        timestep = torch.full(
-            (block_latents.shape[0], block_latents.shape[2] * frame_seq_length),
-            self._context_noise_timestep(context_noise),
-            device=block_latents.device,
-            dtype=torch.long,
-        )
-        with torch.autocast(device_type="cuda", dtype=target_dtype, enabled=autocast_enabled):
-            self.transformer(
-                hidden_states=block_latents.to(target_dtype),
-                encoder_hidden_states=torch.stack(context).to(target_dtype),
-                timestep=timestep,
-                y_camera=camera_block,
-                kv_cache=kv_cache,
-                crossattn_cache=crossattn_cache,
-                current_start=start * frame_seq_length,
-            )
@@ -1,228 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import math
-from dataclasses import dataclass
-
-import numpy as np
-import torch
-from scipy.interpolate import interp1d
-from scipy.spatial.transform import Rotation, Slerp
-
-_ACTION_TO_MOTION = {
-    "w": "forward",
-    "a": "left",
-    "d": "right",
-    "s": "backward",
-    "j": "left_rot",
-    "l": "right_rot",
-    "i": "up_rot",
-    "k": "down_rot",
-}
-_TRANSLATION_BASE_UNIT = 1.0
-_ROTATION_BASE_UNIT = 10.0
-_INTRINSIC_ROW = [0.8, 0.5, 0.5, 0.5]
-
-
-@dataclass
-class DreamXCamera:
-    fx: float
-    fy: float
-    cx: float
-    cy: float
-    w2c_mat: np.ndarray
-
-    @property
-    def c2w_mat(self) -> np.ndarray:
-        return np.linalg.inv(self.w2c_mat)
-
-    @classmethod
-    def from_pose_row(cls, row: list[float]) -> DreamXCamera:
-        w2c_mat = np.eye(4, dtype=np.float64)
-        w2c_mat[:3, :] = np.asarray(row[7:], dtype=np.float64).reshape(3, 4)
-        return cls(
-            fx=float(row[1]),
-            fy=float(row[2]),
-            cx=float(row[3]),
-            cy=float(row[4]),
-            w2c_mat=w2c_mat,
-        )
-
-
-def _translation_step(motion_type: str, current_pose: dict[str, np.ndarray], value: float, duration: int) -> np.ndarray:
-    if motion_type in ("forward", "backward"):
-        yaw = np.radians(current_pose["rotation"][1])
-        pitch = np.radians(current_pose["rotation"][0])
-        forward = np.array([-math.sin(yaw) * math.cos(pitch), math.sin(pitch), math.cos(yaw) * math.cos(pitch)])
-        direction = 1 if motion_type == "forward" else -1
-        return forward * value * direction / duration
-    if motion_type in ("left", "right"):
-        yaw = np.radians(current_pose["rotation"][1])
-        right = np.array([math.cos(yaw), 0.0, math.sin(yaw)])
-        direction = -1 if motion_type == "left" else 1
-        return right * value * direction / duration
-    return np.zeros(3)
-
-
-def _rotation_step(motion_type: str, value: float, duration: int) -> np.ndarray:
-    if not motion_type.endswith("rot"):
-        return np.zeros(3)
-    axis = motion_type.split("_")[0]
-    rotation = np.zeros(3)
-    if axis == "left":
-        rotation[1] = value
-    elif axis == "right":
-        rotation[1] = -value
-    elif axis == "up":
-        rotation[0] = -value
-    elif axis == "down":
-        rotation[0] = value
-    return rotation / duration
-
-
-def _euler_to_quaternion(angles: np.ndarray) -> list[float]:
-    pitch, yaw, roll = np.radians(angles)
-    cy = math.cos(yaw * 0.5)
-    sy = math.sin(yaw * 0.5)
-    cp = math.cos(pitch * 0.5)
-    sp = math.sin(pitch * 0.5)
-    cr = math.cos(roll * 0.5)
-    sr = math.sin(roll * 0.5)
-    return [
-        cy * cp * cr + sy * sp * sr,
-        cy * sp * cr + sy * cp * sr,
-        sy * cp * cr - cy * sp * sr,
-        cy * cp * sr - sy * sp * cr,
-    ]
-
-
-def _quaternion_to_rotation_matrix(quaternion: list[float]) -> np.ndarray:
-    qw, qx, qy, qz = quaternion
-    return np.array([
-        [1 - 2 * (qy**2 + qz**2), 2 * (qx * qy - qw * qz), 2 * (qx * qz + qw * qy)],
-        [2 * (qx * qy + qw * qz), 1 - 2 * (qx**2 + qz**2), 2 * (qy * qz - qw * qx)],
-        [2 * (qx * qz - qw * qy), 2 * (qy * qz + qw * qx), 1 - 2 * (qx**2 + qy**2)],
-    ])
-
-
-def _pose_rows_from_actions(action_seq: list[str], action_speed_list: list[float], duration: int) -> list[list[float]]:
-    if len(action_seq) != len(action_speed_list):
-        raise ValueError("action_seq and action_speed_list must have the same length")
-
-    positions: list[np.ndarray] = []
-    rotations: list[np.ndarray] = []
-    current_pose = {
-        "position": np.array([0.0, 0.0, 0.0]),
-        "rotation": np.array([0.0, 0.0, 0.0]),
-    }
-
-    for action_id, speed in zip(action_seq, action_speed_list, strict=True):
-        motion_types = [_ACTION_TO_MOTION[key] for key in list(action_id)]
-        translation_step = np.zeros(3)
-        rotation_step = np.zeros(3)
-        for motion_type in motion_types:
-            translation_step += _translation_step(motion_type, current_pose,
-                                                  float(speed) * _TRANSLATION_BASE_UNIT, duration)
-            rotation_step += _rotation_step(motion_type, float(speed) * _ROTATION_BASE_UNIT, duration)
-
-        segment_positions = []
-        segment_rotations = []
-        for index in range(1, duration + 1):
-            segment_positions.append(current_pose["position"] + translation_step * index)
-            segment_rotations.append(current_pose["rotation"] + rotation_step * index)
-        current_pose["position"] = segment_positions[-1].copy()
-        current_pose["rotation"] = segment_rotations[-1].copy()
-        positions.extend(segment_positions)
-        rotations.extend(segment_rotations)
-
-    rows: list[list[float]] = [[0.0] + _INTRINSIC_ROW + [0.0, 0.0] +
-                               [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]]
-    for index, (position, rotation) in enumerate(zip(positions, rotations, strict=False)):
-        rotation_matrix = _quaternion_to_rotation_matrix(_euler_to_quaternion(rotation))
-        translation = -rotation_matrix @ position
-        extrinsic = np.hstack([rotation_matrix, translation.reshape(3, 1)])
-        rows.append([float(index)] + _INTRINSIC_ROW + [0.0, 0.0] + extrinsic.flatten().tolist())
-    return rows
-
-
-def _interpolate_camera_poses(
-    cameras: list[DreamXCamera],
-    src_indices: np.ndarray,
-    tgt_indices: np.ndarray,
-) -> list[DreamXCamera]:
-    if len(cameras) <= 1:
-        return [cameras[0]] * len(tgt_indices) if cameras else []
-    src_rot_mat = np.array([camera.w2c_mat[:3, :3] for camera in cameras])
-    src_trans_vec = np.array([camera.w2c_mat[:3, 3] for camera in cameras])
-
-    dets = np.linalg.det(src_rot_mat)
-    flip_handedness = dets.size > 0 and np.median(dets) < 0.0
-    if flip_handedness:
-        flip_mat = np.diag([1.0, 1.0, -1.0]).astype(src_rot_mat.dtype)
-        src_rot_mat = src_rot_mat @ flip_mat
-
-    trans = interp1d(src_indices, src_trans_vec, axis=0, kind="linear", bounds_error=False,
-                     fill_value="extrapolate")(tgt_indices)
-    quats = Rotation.from_matrix(src_rot_mat).as_quat().copy()
-    for index in range(1, len(quats)):
-        if np.dot(quats[index], quats[index - 1]) < 0:
-            quats[index] = -quats[index]
-    rot = Slerp(src_indices, Rotation.from_quat(quats))(tgt_indices).as_matrix()
-    if flip_handedness:
-        rot = rot @ flip_mat
-
-    ref = cameras[0]
-    result = []
-    for index in range(len(tgt_indices)):
-        w2c_mat = np.eye(4, dtype=np.float64)
-        w2c_mat[:3, :] = np.hstack([rot[index], trans[index].reshape(3, 1)])
-        result.append(DreamXCamera(ref.fx, ref.fy, ref.cx, ref.cy, w2c_mat))
-    return result
-
-
-def _relative_c2w_poses(cameras: list[DreamXCamera]) -> np.ndarray:
-    abs_w2cs = [camera.w2c_mat for camera in cameras]
-    abs_c2ws = [camera.c2w_mat for camera in cameras]
-    target_cam_c2w = np.eye(4, dtype=np.float64)
-    abs2rel = target_cam_c2w @ abs_w2cs[0]
-    poses = [target_cam_c2w] + [abs2rel @ c2w for c2w in abs_c2ws[1:]]
-    return np.asarray(poses, dtype=np.float32)
-
-
-def _invert_se3(transforms: torch.Tensor) -> torch.Tensor:
-    rotation_inv = transforms[..., :3, :3].transpose(-1, -2)
-    output = torch.zeros_like(transforms)
-    output[..., :3, :3] = rotation_inv
-    output[..., :3, 3] = -torch.einsum("...ij,...j->...i", rotation_inv, transforms[..., :3, 3])
-    output[..., 3, 3] = 1.0
-    return output
-
-
-def build_dreamx_camera_condition(
-    action_seq: list[str],
-    action_speed_list: list[float],
-    *,
-    num_frames: int,
-    height: int,
-    width: int,
-    dtype: torch.dtype = torch.float32,
-    device: torch.device | str = "cpu",
-) -> dict[str, torch.Tensor]:
-    del height, width  # DreamX-World-5B-Cam uses fixed normalized intrinsics.
-    duration = math.ceil(num_frames / len(action_seq))
-    rows = _pose_rows_from_actions(action_seq, action_speed_list, duration)[:num_frames]
-    cameras = [DreamXCamera.from_pose_row(row) for row in rows]
-
-    latent_frame_count = 1 + (len(cameras) - 1) // 4
-    src_indices = np.arange(len(cameras), dtype=np.float64)
-    tgt_indices = np.linspace(0, len(cameras) - 1, latent_frame_count)
-    cameras = _interpolate_camera_poses(cameras, src_indices, tgt_indices)
-
-    c2ws = torch.as_tensor(_relative_c2w_poses(cameras), dtype=dtype, device=device)
-    viewmats = _invert_se3(c2ws)
-
-    intrinsics = torch.zeros((latent_frame_count, 3, 3), dtype=dtype, device=device)
-    intrinsics[:, 0, 0] = 969.6969696969696 / (960.0 * 2)
-    intrinsics[:, 1, 1] = 969.6969696969696 / (540.0 * 2)
-    intrinsics[:, 2, 2] = 1.0
-    return {"viewmats": viewmats, "K": intrinsics}
@@ -1,20 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Compatibility exports for DreamX-World pipeline configs."""
-
-from fastvideo.configs.pipelines.dreamx_world import (
-    DreamXWorld5BARPipelineConfig,
-    DreamXWorld5BCamPipelineConfig,
-    make_dreamx_world_5b_ar_dit_config,
-    make_dreamx_world_5b_cam_dit_config,
-    make_dreamx_world_5b_cam_text_encoder_config,
-    make_dreamx_world_5b_cam_vae_config,
-)
-
-__all__ = [
-    "DreamXWorld5BARPipelineConfig",
-    "DreamXWorld5BCamPipelineConfig",
-    "make_dreamx_world_5b_ar_dit_config",
-    "make_dreamx_world_5b_cam_dit_config",
-    "make_dreamx_world_5b_cam_text_encoder_config",
-    "make_dreamx_world_5b_cam_vae_config",
-]
@@ -1,67 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World-5B autoregressive pipeline entrypoint."""
-
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BARPipelineConfig
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.basic.dreamx_world.ar_denoising import DreamXWorldARCausalDenoisingStage
-from fastvideo.pipelines.basic.dreamx_world.stages import (
-    DreamXWorldCameraConditioningStage,
-    DreamXWorldImageVAEEncodingStage,
-)
-from fastvideo.pipelines.stages import (
-    ConditioningStage,
-    DecodingStage,
-    InputValidationStage,
-    LatentPreparationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-
-logger = init_logger(__name__)
-
-
-class DreamXWorldARPipeline(LoRAPipeline, ComposedPipelineBase):
-    """DreamX-World-5B autoregressive causal camera pipeline."""
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-    pipeline_config_cls = DreamXWorld5BARPipelineConfig
-    sampling_params_cls = SamplingParam
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(shift=fastvideo_args.pipeline_config.flow_shift)
-        self.modules["scheduler"].set_timesteps(1000)
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-        self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer", None),
-                       ))
-        self.add_stage(stage_name="image_latent_preparation_stage",
-                       stage=DreamXWorldImageVAEEncodingStage(vae=self.get_module("vae")))
-        self.add_stage(stage_name="dreamx_camera_conditioning_stage", stage=DreamXWorldCameraConditioningStage())
-        self.add_stage(stage_name="denoising_stage",
-                       stage=DreamXWorldARCausalDenoisingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler"),
-                           vae=self.get_module("vae"),
-                           pipeline=self,
-                       ))
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-        logger.info("DreamXWorldARPipeline initialized with autoregressive causal denoising")
-
-
-EntryClass = DreamXWorldARPipeline
@@ -1,78 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World video pipeline entrypoint."""
-
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BCamPipelineConfig
-from fastvideo.pipelines.basic.dreamx_world.stages import DreamXWorldCameraConditioningStage
-from fastvideo.pipelines.stages import (
-    ConditioningStage,
-    DecodingStage,
-    DenoisingStage,
-    InputValidationStage,
-    LatentPreparationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-
-logger = init_logger(__name__)
-
-
-class DreamXWorldPipeline(LoRAPipeline, ComposedPipelineBase):
-    """DreamX-World-5B-Cam pipeline with native FastVideo camera conditioning."""
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-    pipeline_config_cls = DreamXWorld5BCamPipelineConfig
-    sampling_params_cls = SamplingParam
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(shift=fastvideo_args.pipeline_config.flow_shift)
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-
-        self.add_stage(
-            stage_name="prompt_encoding_stage",
-            stage=TextEncodingStage(
-                text_encoders=[self.get_module("text_encoder")],
-                tokenizers=[self.get_module("tokenizer")],
-            ),
-        )
-
-        self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())
-
-        self.add_stage(
-            stage_name="timestep_preparation_stage",
-            stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")),
-        )
-
-        self.add_stage(
-            stage_name="latent_preparation_stage",
-            stage=LatentPreparationStage(
-                scheduler=self.get_module("scheduler"),
-                transformer=self.get_module("transformer", None),
-            ),
-        )
-
-        self.add_stage(stage_name="dreamx_camera_conditioning_stage", stage=DreamXWorldCameraConditioningStage())
-
-        self.add_stage(
-            stage_name="denoising_stage",
-            stage=DenoisingStage(
-                transformer=self.get_module("transformer"),
-                transformer_2=self.get_module("transformer_2", None),
-                scheduler=self.get_module("scheduler"),
-                vae=self.get_module("vae"),
-                pipeline=self,
-            ),
-        )
-
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-        logger.info("DreamXWorldPipeline initialized with native camera conditioning")
-
-
-EntryClass = DreamXWorldPipeline
@@ -1,58 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World model family pipeline presets."""
-
-from fastvideo.api.presets import InferencePreset, PresetStageSpec
-
-_NEGATIVE_PROMPT_CN = ("色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，"
-                       "静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，"
-                       "多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，"
-                       "形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，"
-                       "背景人很多，倒着走")
-
-_DENOISE_STAGE = PresetStageSpec(
-    name="denoise",
-    kind="denoising",
-    description="DreamX-World camera-conditioned denoising pass",
-    allowed_overrides=frozenset({
-        "num_inference_steps",
-        "guidance_scale",
-    }),
-)
-
-DREAMX_WORLD_5B_CAM = InferencePreset(
-    name="dreamx_world_5b_cam",
-    version=1,
-    model_family="dreamx_world",
-    description="DreamX-World 5B camera-control video generation",
-    workload_type="i2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 480,
-        "width": 832,
-        "num_frames": 161,
-        "fps": 16,
-        "guidance_scale": 5.0,
-        "num_inference_steps": 30,
-        "negative_prompt": _NEGATIVE_PROMPT_CN,
-    },
-)
-
-DREAMX_WORLD_5B_AR = InferencePreset(
-    name="dreamx_world_5b_ar",
-    version=1,
-    model_family="dreamx_world",
-    description="DreamX-World 5B autoregressive camera-control generation",
-    workload_type="i2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 704,
-        "width": 1280,
-        "num_frames": 1005,
-        "fps": 16,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 4,
-        "negative_prompt": _NEGATIVE_PROMPT_CN,
-    },
-)
-
-ALL_PRESETS = (DREAMX_WORLD_5B_CAM, DREAMX_WORLD_5B_AR)
@@ -1,156 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World pipeline stages."""
-
-from __future__ import annotations
-
-import torch
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.pipelines.stages.validators import VerificationResult
-
-from fastvideo.pipelines.basic.dreamx_world.camera_conditioning import (
-    build_dreamx_camera_condition, )
-
-DREAMX_Y_CAMERA_KEY = "dreamx_y_camera"
-
-logger = init_logger(__name__)
-
-
-class DreamXWorldCameraConditioningStage(PipelineStage):
-    """Build PRoPE camera conditioning for DreamX-World-5B-Cam."""
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        del fastvideo_args
-        if DREAMX_Y_CAMERA_KEY in batch.extra:
-            return batch
-
-        action_seq = batch.extra.get("dreamx_action_seq", batch.action_list)
-        action_speed_list = batch.extra.get("dreamx_action_speed_list", batch.action_speed_list)
-        if action_seq is None:
-            action_seq = ["w"]
-        if action_speed_list is None:
-            action_speed_list = [4]
-
-        if isinstance(action_seq, str):
-            action_seq = [action_seq]
-        if isinstance(action_speed_list, int | float):
-            action_speed_list = [action_speed_list]
-        if len(action_speed_list) == 1 and len(action_seq) > 1:
-            action_speed_list = list(action_speed_list) * len(action_seq)
-        action_speed_list = [float(speed) for speed in action_speed_list]
-
-        height = int(batch.height) if batch.height is not None else 704
-        width = int(batch.width) if batch.width is not None else 1280
-        num_frames = int(batch.num_frames)
-        dtype = batch.latents.dtype if torch.is_tensor(batch.latents) else torch.float32
-        device = batch.latents.device if torch.is_tensor(batch.latents) else "cpu"
-
-        y_camera = build_dreamx_camera_condition(
-            list(action_seq),
-            action_speed_list,
-            num_frames=num_frames,
-            height=height,
-            width=width,
-            dtype=dtype,
-            device=device,
-        )
-        batch.extra[DREAMX_Y_CAMERA_KEY] = {key: value.unsqueeze(0) for key, value in y_camera.items()}
-        return batch
-
-    def verify_output(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> VerificationResult:
-        del fastvideo_args
-        result = VerificationResult()
-        y_camera = batch.extra.get(DREAMX_Y_CAMERA_KEY)
-        result.add_check("dreamx_y_camera", y_camera, lambda value: isinstance(value, dict))
-        if isinstance(y_camera, dict):
-            result.add_check("dreamx_y_camera.viewmats", y_camera.get("viewmats"), torch.is_tensor)
-            result.add_check("dreamx_y_camera.K", y_camera.get("K"), torch.is_tensor)
-        return result
-
-
-class DreamXWorldImageVAEEncodingStage(PipelineStage):
-    """Encode the conditioning image into the first-frame latent.
-
-    Official AR-forcing flow (AMAP-ML/DreamX-World inference_ar_forcing.py):
-    the input image is resized, normalized to [-1, 1], VAE-encoded
-    deterministically, and written into frame 0 of the noise — the causal
-    denoiser then treats frame 0 as clean context. This stage produces
-    ``batch.image_latent`` ([B, C, 1, H_lat, W_lat]); the injection into
-    the latents happens in DreamXWorldARCausalDenoisingStage.
-    """
-
-    def __init__(self, vae) -> None:
-        super().__init__()
-        self.vae = vae
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        if batch.pil_image is None:
-            # No conditioning image: the causal denoiser falls back to
-            # running from pure noise (frame 0 uninitialized). Warn loudly —
-            # this pipeline is registered I2V and the official flow always
-            # forces from a frame.
-            logger.warning("DreamXWorldARPipeline called without an input image; "
-                           "first-frame context will be noise (T2V-style). Pass an "
-                           "image for the official AR-forcing behavior.")
-            return batch
-
-        from fastvideo.platforms import get_local_torch_device
-        from fastvideo.utils import PRECISION_TO_TYPE
-
-        device = get_local_torch_device()
-        image = batch.pil_image
-        if not isinstance(image, torch.Tensor):
-            import numpy as np
-            import PIL.Image
-            assert isinstance(image, PIL.Image.Image)
-            width = batch.width if isinstance(batch.width, int) else batch.width[0]
-            height = batch.height if isinstance(batch.height, int) else batch.height[0]
-            image = image.convert("RGB").resize((width, height), PIL.Image.Resampling.LANCZOS)
-            arr = torch.from_numpy(np.asarray(image)).float().permute(2, 0, 1) / 255.0
-            image = (arr - 0.5) / 0.5  # official Normalize([0.5], [0.5])
-            image = image.unsqueeze(0)  # [1, C, H, W]
-        if image.dim() == 4:
-            image = image.unsqueeze(2)  # [B, C, 1, H, W]
-        elif image.dim() == 5:
-            image = image[:, :, :1]
-        image = image.to(device=device, dtype=torch.float32)
-
-        vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-        vae_autocast_enabled = (vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
-        self.vae = self.vae.to(device)
-        with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled):
-            if not vae_autocast_enabled:
-                image = image.to(vae_dtype)
-            encoder_output = self.vae.encode(image)
-
-        # Official encode_to_latent is deterministic ((mean - mu) / sigma per
-        # channel); the posterior mean + shift/scale is the FastVideo
-        # equivalent of that normalization.
-        latent = encoder_output.mean
-        if getattr(self.vae, "shift_factor", None) is not None:
-            shift = self.vae.shift_factor
-            latent = latent - (shift.to(latent.device, latent.dtype) if isinstance(shift, torch.Tensor) else shift)
-        scale = self.vae.scaling_factor
-        latent = latent * (scale.to(latent.device, latent.dtype) if isinstance(scale, torch.Tensor) else scale)
-
-        batch.image_latent = latent
-        return batch
-
-    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        result = VerificationResult()
-        return result
@@ -1 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
@@ -1,89 +0,0 @@
-# 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
@@ -1,74 +0,0 @@
-# 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
@@ -1,165 +0,0 @@
-# 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)
@@ -19,39 +19,6 @@ from fastvideo.pipelines.stages import (DecodingStage, InputValidationStage, LTX
 logger = init_logger(__name__)


-def _resolve_refine_path(model_path: str, value: str | None) -> str | None:
-    """Resolve a refine component path, preferring paths inside the local
-    model snapshot when the value is relative."""
-    if value is None:
-        return None
-    if os.path.isabs(value):
-        return value
-    candidate = os.path.join(model_path, value)
-    if os.path.exists(candidate):
-        return candidate
-    return value
-
-
-def _resolve_refine_upsampler_path(model_path: str, model_index: dict[str, Any]) -> str | None:
-    """Resolve the refine upsampler directory for a local model snapshot.
-
-    model_index.json keys ("fastvideo_refine_upsampler_path", then
-    "spatial_upsampler") are the documented override and win when present.
-    Otherwise probe the snapshot root: published LTX-2 distilled checkpoints
-    bundle the upsampler weights on disk (spelled "spatial_upscaler")
-    without declaring them in model_index.json.
-    """
-    path = _resolve_refine_path(model_path, model_index.get("fastvideo_refine_upsampler_path"))
-    if path is None and "spatial_upsampler" in model_index:
-        path = _resolve_refine_path(model_path, "spatial_upsampler")
-    if path is None:
-        for dirname in ("spatial_upscaler", "spatial_upsampler"):
-            candidate = os.path.join(model_path, dirname)
-            if os.path.isdir(candidate):
-                return candidate
-    return path
-
-
 class LTX2Pipeline(LoRAPipeline):

    _required_config_modules = [
@@ -222,16 +189,28 @@ class LTX2Pipeline(LoRAPipeline):
        # Apply optional FastVideo-specific refine defaults embedded in
        # model_index.json. These are bundled with distilled checkpoints
        # so the pipeline can self-configure without explicit user kwargs.
+        def _resolve_refine_path(value: str | None) -> str | None:
+            if value is None:
+                return None
+            if os.path.isabs(value):
+                return value
+            candidate = os.path.join(self.model_path, value)
+            if os.path.exists(candidate):
+                return candidate
+            return value
+
        if (model_index.get("fastvideo_refine_enabled") is True and fastvideo_args.refine_enabled is None):
            fastvideo_args.ltx2_refine_enabled = True
        if (fastvideo_args.refine_upsampler_path is None and fastvideo_args.ltx2_refine_upsampler_path is None):
-            fastvideo_args.ltx2_refine_upsampler_path = (_resolve_refine_upsampler_path(self.model_path, model_index))
+            fastvideo_args.ltx2_refine_upsampler_path = _resolve_refine_path(
+                model_index.get("fastvideo_refine_upsampler_path"))
+            if (fastvideo_args.ltx2_refine_upsampler_path is None and "spatial_upsampler" in model_index):
+                fastvideo_args.ltx2_refine_upsampler_path = (_resolve_refine_path("spatial_upsampler"))
        if (fastvideo_args.refine_transformer_path is None and fastvideo_args.ltx2_refine_transformer_path is None):
            fastvideo_args.ltx2_refine_transformer_path = (_resolve_refine_path(
-                self.model_path, model_index.get("fastvideo_refine_transformer_path")))
+                model_index.get("fastvideo_refine_transformer_path")))
        if (fastvideo_args.refine_lora_path is None and fastvideo_args.ltx2_refine_lora_path is None):
-            fastvideo_args.ltx2_refine_lora_path = _resolve_refine_path(self.model_path,
-                                                                        model_index.get("fastvideo_refine_lora_path"))
+            fastvideo_args.ltx2_refine_lora_path = _resolve_refine_path(model_index.get("fastvideo_refine_lora_path"))
        if (fastvideo_args.refine_num_inference_steps is None
                and fastvideo_args.ltx2_refine_num_inference_steps == FastVideoArgs.ltx2_refine_num_inference_steps
                and model_index.get("fastvideo_refine_num_inference_steps") is not None):
@@ -247,11 +226,10 @@ class LTX2Pipeline(LoRAPipeline):
        if (fastvideo_args.refine_add_noise is None and model_index.get("fastvideo_refine_add_noise") is not None):
            fastvideo_args.ltx2_refine_add_noise = bool(model_index["fastvideo_refine_add_noise"])
        if (fastvideo_args.refine_noise_path is None and fastvideo_args.ltx2_refine_noise_path is None):
-            fastvideo_args.ltx2_refine_noise_path = _resolve_refine_path(self.model_path,
-                                                                         model_index.get("fastvideo_refine_noise_path"))
+            fastvideo_args.ltx2_refine_noise_path = _resolve_refine_path(model_index.get("fastvideo_refine_noise_path"))
        if (fastvideo_args.refine_audio_noise_path is None and fastvideo_args.ltx2_refine_audio_noise_path is None):
            fastvideo_args.ltx2_refine_audio_noise_path = (_resolve_refine_path(
-                self.model_path, model_index.get("fastvideo_refine_audio_noise_path")))
+                model_index.get("fastvideo_refine_audio_noise_path")))

        model_index.pop("_class_name")
        model_index.pop("_diffusers_version")
@@ -33,8 +33,6 @@ 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)
@@ -67,9 +65,6 @@ __all__ = [
    "MatrixGame2CausalDenoisingStage",
    "MatrixGame3DenoisingStage",
    "HYWorldDenoisingStage",
-    "Kandinsky5DecodingStage",
-    "Kandinsky5DenoisingStage",
-    "Kandinsky5LatentPreparationStage",
    "GameCraftDenoisingStage",
    "Gen3CCFGPolicyStage",
    "Gen3CConditioningStage",
@@ -34,7 +34,6 @@ 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:
        """
@@ -156,9 +155,6 @@ 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
@@ -28,7 +28,6 @@ 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
@@ -51,7 +51,6 @@ 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__()
@@ -191,19 +190,6 @@ class DenoisingStage(PipelineStage):
            },
        )

-        dreamx_y_camera = batch.extra.get("dreamx_y_camera", batch.extra.get("y_camera"))
-        if isinstance(dreamx_y_camera, dict):
-            dreamx_y_camera = {
-                key: value.to(device=local_device, dtype=target_dtype) if torch.is_tensor(value) else value
-                for key, value in dreamx_y_camera.items()
-            }
-        dreamx_camera_kwargs = self.prepare_extra_func_kwargs(
-            self.transformer.forward,
-            {
-                "y_camera": dreamx_y_camera,
-            },
-        )
-
        for key in ("flux2_txt_ids", "flux2_img_ids"):
            value = batch.extra.get(key)
            if torch.is_tensor(value):
@@ -255,11 +241,7 @@ class DenoisingStage(PipelineStage):
            # the image latent instead of appending along the channel dim
            assert batch.image_latent is None, "TI2V task should not have image latents"
            assert self.vae is not None, "VAE is not provided for TI2V task"
-            vae_device = next(self.vae.parameters()).device
-            self.vae = self.vae.to(local_device)
            z = self.vae.encode(batch.pil_image).mean.float()
-            if getattr(fastvideo_args, "vae_cpu_offload", False):
-                self.vae = self.vae.to(vae_device)
            if (hasattr(self.vae, "shift_factor") and self.vae.shift_factor is not None):
                if isinstance(self.vae.shift_factor, torch.Tensor):
                    z -= self.vae.shift_factor.to(z.device, z.dtype)
@@ -512,7 +494,6 @@ class DenoisingStage(PipelineStage):
                            **pos_cond_kwargs,
                            **action_kwargs,
                            **camera_kwargs,
-                            **dreamx_camera_kwargs,
                            **timesteps_r_kwarg,
                            **flux2_id_kwargs,
                        )
@@ -555,7 +536,6 @@ class DenoisingStage(PipelineStage):
                                    **neg_cond_kwargs,
                                    **action_kwargs,
                                    **camera_kwargs,
-                                    **dreamx_camera_kwargs,
                                    **timesteps_r_kwarg,
                                    **flux2_id_kwargs,
                                )
@@ -1207,7 +1187,6 @@ 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__()
@@ -1,545 +0,0 @@
-# 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_h = spatial_ratio * patch_size[1]
-        required_divisor_w = spatial_ratio * patch_size[2]
-        if height % required_divisor_h != 0 or width % required_divisor_w != 0:
-            raise ValueError(f"Kandinsky5 height must be divisible by {required_divisor_h} and width by "
-                             f"{required_divisor_w}; "
-                             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_h = required_divisor_h * 8
-            nabla_divisor_w = required_divisor_w * 8
-            if height % nabla_divisor_h != 0 or width % nabla_divisor_w != 0:
-                raise ValueError(f"Kandinsky5 NABLA checkpoints require height divisible by {nabla_divisor_h} and "
-                                 f"width divisible by {nabla_divisor_w}; "
-                                 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}.")
-
-        visual_cond = getattr(self.transformer, "visual_cond", False)
-
-        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:
-            valid_shapes = [shape]
-            if visual_cond:
-                valid_shapes.append((*shape[:-1], num_channels * 2 + 1))
-            if tuple(batch.latents.shape) not in valid_shapes:
-                raise ValueError(f"Provided latents shape {list(batch.latents.shape)} does not match expected "
-                                 f"Kandinsky5 latent shape(s): {[list(s) for s in valid_shapes]}.")
-            latents = batch.latents.to(device=device, dtype=dtype)
-
-        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:
-                    break
-
-                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.min_list_length(2))
-        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
@@ -8,8 +8,6 @@ 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
@@ -26,7 +24,6 @@ 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:
        """
@@ -205,21 +202,6 @@ 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:
@@ -264,7 +246,7 @@ 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(encoder_device)
+                    text_inputs = tok.apply_chat_template(processed_texts, **tok_kwargs).to(target_device)
                else:
                    # Two-step approach matching Diffusers: format with chat
                    # template first, then tokenize the resulting strings.
@@ -278,9 +260,9 @@ class TextEncodingStage(PipelineStage):
                            enable_thinking=False,
                        )
                        formatted_texts.append(formatted)
-                    text_inputs = tokenizer(formatted_texts, **tok_kwargs).to(encoder_device)
+                    text_inputs = tokenizer(formatted_texts, **tok_kwargs).to(target_device)
            else:
-                text_inputs = tok(processed_texts, **tok_kwargs).to(encoder_device)
+                text_inputs = tok(processed_texts, **tok_kwargs).to(target_device)

            input_ids = text_inputs["input_ids"]
            attention_mask = text_inputs["attention_mask"]
@@ -300,18 +282,15 @@ 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].to(device=target_device)
+                audio_embed = outputs.hidden_states[0]
                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.to(device=target_device))
-            if moved_for_forward and fastvideo_args.text_encoder_cpu_offload:
-                text_encoder.to("cpu")
+                attn_masks_list.append(attention_mask)
        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)

@@ -371,7 +350,6 @@ 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__()
@@ -219,16 +219,6 @@ class StageValidators:

        return validator

-    @staticmethod
-    def min_list_length(min_length: int) -> Callable[[Any], bool]:
-        """Return a validator that checks if a list has at least min_length items."""
-
-        def validator(value: Any) -> bool:
-            return StageValidators.list_min_length(value, min_length)
-
-        validator.__name__ = f"list_min_length_{min_length}"
-        return validator
-
    @staticmethod
    def min_dims(min_dims: int) -> Callable[[Any], bool]:
        """Return a validator that checks if tensor has at least min_dims dimensions and no NaN values."""
@@ -157,14 +157,6 @@ 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
@@ -22,7 +22,6 @@ 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()


@@ -20,7 +20,6 @@ from fastvideo.configs.pipelines.cosmos2_5 import (
    Cosmos25Config,
    Cosmos25_14BConfig,
 )
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BARPipelineConfig, DreamXWorld5BCamPipelineConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.gen3c import Gen3CConfig
@@ -28,7 +27,6 @@ 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
@@ -119,10 +117,6 @@ 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
@@ -143,7 +137,6 @@ 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.

@@ -158,7 +151,6 @@ 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:
@@ -498,174 +490,18 @@ 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=Kandinsky5T2VConfig,
+        pipeline_config_cls=PipelineConfig,
        workload_types=(WorkloadType.T2V, ),
        hf_model_paths=[
            "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
        ],
        model_detectors=[
-            _is_kandinsky5_t2v_lite,
+            lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5")),
        ],
        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)
@@ -714,7 +550,7 @@ 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",
@@ -937,40 +773,6 @@ def _register_configs() -> None:
        model_family="wan",
        default_preset="wan_2_2_ti2v_5b",
    )
-
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=DreamXWorld5BCamPipelineConfig,
-        workload_types=(WorkloadType.I2V, ),
-        hf_model_paths=[
-            "FastVideo/DreamX-World-5B-Cam-Diffusers",
-        ],
-        model_detectors=[
-            # Mutually exclusive with the AR detector below: Cam requires an
-            # explicit "cam" marker so hyphenated AR local paths (e.g.
-            # /ckpts/dreamx-world-5b-converted) don't first-match here —
-            # detector resolution is first-match in registration order.
-            lambda path:
-            ("dreamx-world" in path.lower() and "cam" in path.lower()) or "dreamxworldpipeline" in path.lower()
-        ],
-        model_family="dreamx_world",
-        default_preset="dreamx_world_5b_cam",
-    )
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=DreamXWorld5BARPipelineConfig,
-        workload_types=(WorkloadType.I2V, ),
-        hf_model_paths=[
-            "FastVideo/DreamX-World-5B-Diffusers",
-        ],
-        model_detectors=[
-            lambda path:
-            ("dreamx-world-5b" in path.lower() and "cam" not in path.lower()) or "dreamxworldarpipeline" in path.lower(
-            )
-        ],
-        model_family="dreamx_world",
-        default_preset="dreamx_world_5b_ar",
-    )
    register_configs(
        sampling_param_cls=None,
        pipeline_config_cls=FastWan2_2_TI2V_5B_Config,
@@ -1100,10 +902,7 @@ def get_model_info(
    assert config_info is not None, "config_info must be resolved"

    if 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
+        pipeline_name = override_pipeline_cls_name
        logger.info("Using override pipeline class name %s", pipeline_name)
    else:
        if os.path.exists(model_path):
@@ -1112,12 +911,6 @@ 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. "
@@ -1158,8 +951,6 @@ def _register_presets() -> None:
    from fastvideo.api.presets import register_preset
    from fastvideo.pipelines.basic.cosmos.presets import (
        ALL_PRESETS as COSMOS_PRESETS, )
-    from fastvideo.pipelines.basic.dreamx_world.presets import (
-        ALL_PRESETS as DREAMX_WORLD_PRESETS, )
    from fastvideo.pipelines.basic.gamecraft.presets import (
        ALL_PRESETS as GAMECRAFT_PRESETS, )
    from fastvideo.pipelines.basic.gen3c.presets import (
@@ -1170,8 +961,6 @@ 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 (
@@ -1195,14 +984,12 @@ def _register_presets() -> None:

    all_preset_groups = (
        COSMOS_PRESETS,
-        DREAMX_WORLD_PRESETS,
        FLUX2_PRESETS,
        GAMECRAFT_PRESETS,
        GEN3C_PRESETS,
        HUNYUAN_PRESETS,
        HUNYUAN15_PRESETS,
        HYWORLD_PRESETS,
-        KANDINSKY5_PRESETS,
        LINGBOTWORLD_PRESETS,
        LONGCAT_PRESETS,
        LTX2_PRESETS,
@@ -17,6 +17,46 @@ class TestLegacyTorchCompileKwargsTranslation:
    first-class :class:`CompileConfig` fields and anything unknown falls
    into ``extras``."""

+    def test_all_typed_keys_promoted(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {
+                "enable_torch_compile": True,
+                "torch_compile_kwargs": {
+                    "backend": "inductor",
+                    "fullgraph": True,
+                    "mode": "max-autotune-no-cudagraphs",
+                    "dynamic": False,
+                },
+            },
+        )
+        compile_config = config.engine.compile
+        assert compile_config.enabled is True
+        assert compile_config.backend == "inductor"
+        assert compile_config.fullgraph is True
+        assert compile_config.mode == "max-autotune-no-cudagraphs"
+        assert compile_config.dynamic is False
+        assert compile_config.extras == {}
+
+    def test_unknown_keys_land_in_extras(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {
+                "enable_torch_compile": True,
+                "torch_compile_kwargs": {
+                    "backend": "inductor",
+                    "options": {"triton.cudagraphs": False},
+                    "disable": False,
+                },
+            },
+        )
+        compile_config = config.engine.compile
+        assert compile_config.backend == "inductor"
+        assert compile_config.extras == {
+            "options": {"triton.cudagraphs": False},
+            "disable": False,
+        }
+
    def test_empty_kwargs_produces_empty_extras(self) -> None:
        config = legacy_from_pretrained_to_config(
            "/models/ltx2",
@@ -77,6 +117,13 @@ class TestLegacyLtx2VaeTilingTranslation:
    ``generator.pipeline.vae_tiling``; reverse direction emits the
    legacy name back to FastVideoArgs."""

+    def test_forward_routes_to_pipeline_vae_tiling(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {"ltx2_vae_tiling": False},
+        )
+        assert config.pipeline.vae_tiling is False
+
    def test_true_round_trips(self) -> None:
        config = legacy_from_pretrained_to_config(
            "/models/ltx2",
@@ -1,43 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""CPU-only regression test for the SDPA metadata mask contract.
-
-SDPAMetadata is cross-backend: HYWorld and HunyuanVideo15 build it while the
-layer's attention selector may pick FLASH_ATTN, and the shared convention
-for padding masks is the tokenizer-style 2D [batch, key_len]. The builder
-must store 2D masks unchanged; only the torch-sdpa impl lifts them to 4D
-internally.
-
-Regression guard for the CI break where the builder lifted 2D -> 4D and the
-FLASH_ATTN equal-length branch padded ``attn_mask.shape[1]`` (== 1 for 4D)
-into a garbage-length mask (CUDA device-side assert, SIGABRT).
-"""
-
-from __future__ import annotations
-
-import torch
-
-from fastvideo.attention.backends.sdpa import (SDPAMetadataBuilder, _normalize_attn_mask_for_sdpa)
-
-
-def test_builder_keeps_2d_padding_mask_2d() -> None:
-    """A tokenizer-style 2D int mask must be stored as-is (cross-backend contract)."""
-    mask = torch.tensor([[1, 1, 0], [1, 0, 0]], dtype=torch.int64)
-    md = SDPAMetadataBuilder().build(current_timestep=0, attn_mask=mask)
-    assert md.attn_mask is not None
-    assert md.attn_mask.dim() == 2
-    assert md.attn_mask.shape == (2, 3)
-    assert torch.equal(md.attn_mask, mask)
-
-
-def test_normalize_lifts_2d_int_mask_to_4d_bool_for_torch_sdpa() -> None:
-    """The torch-sdpa consumer coerces int -> bool and lifts 2D -> [B,1,1,K]."""
-    batch, q_len, k_len, heads, head_dim = 2, 4, 3, 1, 8
-    query = torch.zeros(batch, heads, q_len, head_dim)
-    key = torch.zeros(batch, heads, k_len, head_dim)
-    mask = torch.tensor([[1, 1, 0], [1, 0, 0]], dtype=torch.int64)
-
-    out = _normalize_attn_mask_for_sdpa(mask, query, key)
-    assert out is not None
-    assert out.dtype == torch.bool
-    assert out.shape == (batch, 1, 1, k_len)
-    assert torch.equal(out[:, 0, 0, :], mask != 0)
@@ -1,84 +0,0 @@
-# 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",
-    "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}")
@@ -1,58 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Guard the Modal FA4 defaults that keep CI lanes on their intended backend.
-
-Pure text/AST analysis: no fastvideo imports, no torch, no Modal client.
-"""
-
-from __future__ import annotations
-
-import ast
-from pathlib import Path
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-MODAL_ROOT = REPO_ROOT / "fastvideo" / "tests" / "modal"
-PR_TEST = MODAL_ROOT / "pr_test.py"
-LAUNCH_L40S_JOB = MODAL_ROOT / "launch_l40s_job.py"
-SSIM_TEST = MODAL_ROOT / "ssim_test.py"
-
-
-def _function_strings(path: Path, function_name: str) -> str:
-    source = path.read_text(encoding="utf-8")
-    tree = ast.parse(source)
-    for node in tree.body:
-        if isinstance(node, ast.FunctionDef) and node.name == function_name:
-            return "\n".join(
-                child.value
-                for child in ast.walk(node)
-                if isinstance(child, ast.Constant)
-                and isinstance(child.value, str)
-            )
-    raise AssertionError(f"{function_name} not found in {path}")
-
-
-def test_generic_l40s_launcher_defaults_fa4_off():
-    source = LAUNCH_L40S_JOB.read_text(encoding="utf-8")
-    assert '"FASTVIDEO_FA4": os.environ.get("FASTVIDEO_FA4", "0")' in source
-
-
-def test_ssim_launcher_keeps_fa4_enabled_by_default():
-    source = SSIM_TEST.read_text(encoding="utf-8")
-    assert '"FASTVIDEO_FA4": os.environ.get("FASTVIDEO_FA4", "1")' in source
-
-
-def test_pr_model_load_and_training_lanes_disable_fa4():
-    lanes = {
-        "run_transformer_tests": "pytest ./fastvideo/tests/transformers -vs",
-        "run_training_tests": "pytest ./fastvideo/tests/training/Vanilla -srP",
-        "run_training_lora_tests": "pytest ./fastvideo/tests/training/lora/test_lora_training.py -srP",
-        "run_training_tests_VSA": "pytest ./fastvideo/tests/training/VSA -srP",
-        "run_distill_dmd_tests": "pytest ./fastvideo/tests/training/distill/test_distill_dmd.py -vs",
-        "run_self_forcing_tests": "pytest ./fastvideo/tests/training/self-forcing/test_self_forcing.py -vs",
-        "run_train_framework_tests": "pytest ./fastvideo/tests/train/models ./fastvideo/tests/train/methods -vs",
-        "seed_grad_norm_references": "pytest ./fastvideo/tests/train/methods -vs -rs",
-    }
-
-    for function_name, pytest_command in lanes.items():
-        function_strings = _function_strings(PR_TEST, function_name)
-        assert "FASTVIDEO_FA4=0" in function_strings
-        assert pytest_command in function_strings
@@ -1,80 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""CPU-only tests for load_image URL retry behavior.
-
-A HF-CDN IncompleteRead mid-body previously killed SSIM runs (build 4202,
-test_wan_i2v) because load_image handed PIL a partial ``.raw`` stream with no
-retry. These tests mock requests.get: a transient failure must be retried and
-a full-body success must yield a usable PIL image.
-"""
-import io
-from unittest import mock
-
-import PIL.Image
-import pytest
-import requests
-
-from fastvideo.models import vision_utils
-
-URL = "https://example.com/image.png"
-
-
-def _png_bytes() -> bytes:
-    buf = io.BytesIO()
-    PIL.Image.new("RGB", (4, 4), (255, 0, 0)).save(buf, format="PNG")
-    return buf.getvalue()
-
-
-def _good_response() -> mock.Mock:
-    response = mock.Mock()
-    response.content = _png_bytes()
-    response.raise_for_status = mock.Mock()
-    return response
-
-
-def test_load_image_retries_transient_error_then_succeeds(monkeypatch):
-    monkeypatch.setattr(vision_utils.time, "sleep", lambda _s: None)
-    get = mock.Mock(side_effect=[
-        # The shape that broke build 4202: connection dropped mid-body.
-        requests.exceptions.ChunkedEncodingError(
-            "IncompleteRead(1024 bytes read, 2048 more expected)"),
-        _good_response(),
-    ])
-    monkeypatch.setattr(vision_utils.requests, "get", get)
-
-    image = vision_utils.load_image(URL)
-
-    assert get.call_count == 2
-    assert get.call_args.kwargs["timeout"] == 30.0
-    assert isinstance(image, PIL.Image.Image)
-    assert image.size == (4, 4)
-    assert image.mode == "RGB"
-
-
-def test_load_image_raises_after_exhausting_retries(monkeypatch):
-    sleeps: list[float] = []
-    monkeypatch.setattr(vision_utils.time, "sleep", sleeps.append)
-    get = mock.Mock(
-        side_effect=requests.exceptions.ConnectionError("connection reset"))
-    monkeypatch.setattr(vision_utils.requests, "get", get)
-
-    with pytest.raises(requests.exceptions.ConnectionError):
-        vision_utils.load_image(URL)
-
-    assert get.call_count == 3
-    assert sleeps == [1, 2]
-
-
-def test_load_image_does_not_retry_on_client_error(monkeypatch):
-    sleeps: list[float] = []
-    monkeypatch.setattr(vision_utils.time, "sleep", sleeps.append)
-    response = mock.Mock(status_code=404)
-    error = requests.HTTPError("404 Client Error", response=response)
-    response.raise_for_status.side_effect = error
-    get = mock.Mock(return_value=response)
-    monkeypatch.setattr(vision_utils.requests, "get", get)
-
-    with pytest.raises(requests.HTTPError):
-        vision_utils.load_image("https://example.com/missing.png")
-
-    assert get.call_count == 1
-    assert sleeps == []
@@ -12,7 +12,6 @@ from fastvideo.configs.pipelines import CosmosConfig, PipelineConfig, WanT2V480P
 from fastvideo.forward_context import set_forward_context
 from fastvideo.logger import init_logger
 from fastvideo.models.loader.component_loader import TextEncoderLoader
-from fastvideo.tests.utils import skip_if_gated_repo_inaccessible
 from fastvideo.utils import maybe_download_model, PRECISION_TO_TYPE
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.configs.models.encoders import T5Config, T5LargeConfig
@@ -37,11 +36,9 @@ def t5_model_paths_and_config():
@pytest.fixture
 def t5_large_model_paths_and_config():
    base_model_path = "nvidia/Cosmos-Predict2-2B-Video2World"
-    local_dir = os.path.join('data', base_model_path)
-    skip_if_gated_repo_inaccessible(base_model_path,
-                                    local_path=local_dir,
-                                    test_name="Cosmos T5-large encoder test")
-    model_path = maybe_download_model(base_model_path, local_dir=local_dir)
+    model_path = maybe_download_model(base_model_path,
+                                      local_dir=os.path.join(
+                                          'data', base_model_path))
    text_encoder_path = os.path.join(model_path, "text_encoder")
    tokenizer_path = os.path.join(model_path, "tokenizer")
    return text_encoder_path, tokenizer_path, CosmosConfig()
@@ -97,10 +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). Generic ad hoc jobs should follow the
-        # product default unless a caller opts in through the local env or
-        # --env-vars.
-        "FASTVIDEO_FA4": os.environ.get("FASTVIDEO_FA4", "0"),
+        # FA4 is opt-in (FASTVIDEO_FA4); keep CI parity with the seeded
+        # references. Caller override wins.
+        "FASTVIDEO_FA4": os.environ.get("FASTVIDEO_FA4", "1"),
    })
 )

@@ -30,15 +30,6 @@ print(f"Using image: {image_ref}")
 # the baked setting, and a caller override always wins.
 uv_torch_backend_override = resolve_uv_torch_backend(image_tag)

-# INVARIANT: this image definition must be byte-identical for every CI job at
-# a given base image digest -- one build, shared cache across all concurrent
-# lanes. Never put a per-job/per-commit value (BUILDKITE_*, TEST_SCOPE,
-# env-derived overrides) into the image via .env()/run_commands: it becomes an
-# image layer, so whenever the base digest changes every concurrent job
-# rebuilds its own image variant (~15-20 min each), blowing the Buildkite job
-# budget. `image_ref` is the only env-derived input allowed here, because it
-# *selects* the base digest. Per-job values arrive at runtime via
-# `ci_env_secret` below.
 image = (modal.Image.from_registry(
    image_ref, add_python="3.12"
 ).run_commands("rm -rf /FastVideo").apt_install(
@@ -46,45 +37,41 @@ image = (modal.Image.from_registry(
 ).run_commands(
    "curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable"
 ).run_commands("echo 'source ~/.cargo/env' >> ~/.bashrc").env({
-    "PATH": "/root/.cargo/bin:$PATH",
-    "HF_REPO_ID": "FastVideo/performance-tracking",
+    "PATH":
+    "/root/.cargo/bin:$PATH",
+    "BUILDKITE_REPO":
+    os.environ.get("BUILDKITE_REPO", ""),
+    "BUILDKITE_COMMIT":
+    os.environ.get("BUILDKITE_COMMIT", ""),
+    "BUILDKITE_PULL_REQUEST":
+    os.environ.get("BUILDKITE_PULL_REQUEST", ""),
+    "BUILDKITE_BRANCH":
+    os.environ.get("BUILDKITE_BRANCH", ""),
+    "BUILDKITE_BUILD_URL":
+    os.environ.get("BUILDKITE_BUILD_URL", ""),
+    "BUILDKITE_BUILD_ID":
+    os.environ.get("BUILDKITE_BUILD_ID", ""),
+    "BUILDKITE_JOB_ID":
+    os.environ.get("BUILDKITE_JOB_ID", ""),
+    "TEST_SCOPE":
+    os.environ.get("TEST_SCOPE", ""),
+    "IMAGE_VERSION":
+    image_version,
+    **({
+        "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",
 }))

 dreamverse_image = (image.run_commands(
    "curl -fsSL https://deb.nodesource.com/setup_22.x | bash -"
 ).apt_install("nodejs").run_commands("node --version && npm --version"))

-# Per-job/per-invocation values are injected into the container environment at
-# RUNTIME via this secret (attached to every function below), so the image
-# stays identical across jobs. Consumers (run_test_command's checkout,
-# fastvideo/tests/performance/{compare_baseline,identity}.py, the FA4
-# resolver, `uv pip install`) all read os.environ at runtime, so nothing else
-# changes.
-ci_env_secret = modal.Secret.from_dict({
-    "BUILDKITE_REPO": os.environ.get("BUILDKITE_REPO", ""),
-    "BUILDKITE_COMMIT": os.environ.get("BUILDKITE_COMMIT", ""),
-    "BUILDKITE_PULL_REQUEST": os.environ.get("BUILDKITE_PULL_REQUEST", ""),
-    "BUILDKITE_BRANCH": os.environ.get("BUILDKITE_BRANCH", ""),
-    "BUILDKITE_BUILD_URL": os.environ.get("BUILDKITE_BUILD_URL", ""),
-    "BUILDKITE_BUILD_ID": os.environ.get("BUILDKITE_BUILD_ID", ""),
-    "BUILDKITE_JOB_ID": os.environ.get("BUILDKITE_JOB_ID", ""),
-    "TEST_SCOPE": os.environ.get("TEST_SCOPE", ""),
-    "IMAGE_VERSION": image_version,
-    **({
-        "UV_TORCH_BACKEND": uv_torch_backend_override
-    } if uv_torch_backend_override else {}),
-    # FA4 is opt-in (FASTVIDEO_FA4). Keep the default enabled for
-    # inference/perf parity; model-load and training lanes that do not exercise
-    # FA4 explicitly set FASTVIDEO_FA4=0 in their command strings below.
-    # Caller override wins.
-    "FASTVIDEO_FA4": os.environ.get("FASTVIDEO_FA4", "1"),
-})
-
-hf_secret = modal.Secret.from_dict(
-    {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
-wandb_secret = modal.Secret.from_dict(
-    {"WANDB_API_KEY": os.environ.get("WANDB_API_KEY", "")})
-

 def run_test(pytest_command: str):
    """Helper function to run a test suite with custom pytest command"""
@@ -160,7 +147,10 @@ def run_test_command(test_command: str,
@app.function(gpu="H100:1",
              image=image,
              timeout=1200,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_encoder_tests():
    run_test(
@@ -171,7 +161,10 @@ def run_encoder_tests():
@app.function(gpu="L40S:1",
              image=image,
              timeout=1200,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_vae_tests():
    run_test(
@@ -182,109 +175,113 @@ def run_vae_tests():
@app.function(gpu="L40S:1",
              image=image,
              timeout=900,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_transformer_tests():
    run_test(
-        "export HF_HOME='/root/data/.cache' && hf auth login --token $HF_API_KEY && "
-        "FASTVIDEO_FA4=0 pytest ./fastvideo/tests/transformers -vs"
+        "export HF_HOME='/root/data/.cache' && hf auth login --token $HF_API_KEY && pytest ./fastvideo/tests/transformers -vs"
    )


@app.function(gpu="L40S:4",
-              cpu=8.0,
-              memory=32768,
              image=image,
              timeout=900,
-              secrets=[wandb_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"WANDB_API_KEY": os.environ.get("WANDB_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_training_tests():
    run_test(
-        "export HF_HOME='/root/data/.cache' && wandb login $WANDB_API_KEY && "
-        "FASTVIDEO_FA4=0 pytest ./fastvideo/tests/training/Vanilla -srP"
+        "export HF_HOME='/root/data/.cache' && wandb login $WANDB_API_KEY && pytest ./fastvideo/tests/training/Vanilla -srP"
    )


@app.function(gpu="L40S:2",
-              cpu=8.0,
-              memory=32768,
              image=image,
              timeout=900,
-              secrets=[wandb_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"WANDB_API_KEY": os.environ.get("WANDB_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_training_lora_tests():
    run_test(
-        "export HF_HOME='/root/data/.cache' && wandb login $WANDB_API_KEY && "
-        "FASTVIDEO_FA4=0 pytest ./fastvideo/tests/training/lora/test_lora_training.py -srP"
+        "export HF_HOME='/root/data/.cache' && wandb login $WANDB_API_KEY && pytest ./fastvideo/tests/training/lora/test_lora_training.py -srP"
    )


@app.function(gpu="H100:2",
              image=image,
              timeout=900,
-              secrets=[wandb_secret, ci_env_secret])
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"WANDB_API_KEY": os.environ.get("WANDB_API_KEY", "")})
+              ])
 def run_training_tests_VSA():
    run_test(
-        "wandb login $WANDB_API_KEY && FASTVIDEO_FA4=0 pytest ./fastvideo/tests/training/VSA -srP"
+        "wandb login $WANDB_API_KEY && pytest ./fastvideo/tests/training/VSA -srP"
    )


-@app.function(gpu="H100:1", image=image, timeout=900, secrets=[ci_env_secret])
+@app.function(gpu="H100:1", image=image, timeout=900)
 def run_kernel_tests():
    run_test("pytest fastvideo-kernel/tests/ -vs")


-# @app.function(gpu="H100:1", image=image, timeout=900, secrets=[ci_env_secret])
+# @app.function(gpu="H100:1", image=image, timeout=900)
 # def run_precision_tests_VSA():
 #     # VSA correctness is covered by the same file now
 #     run_test("pytest fastvideo-kernel/tests/test_correctness.py")

-# @app.function(gpu="L40S:1", image=image, timeout=900, secrets=[ci_env_secret])
+# @app.function(gpu="L40S:1", image=image, timeout=900)
 # def run_precision_tests_vmoba():
 #     run_test("pytest fastvideo-kernel/tests/test_vmoba_correctness.py")


-@app.function(gpu="L40S:1", image=image, timeout=900, secrets=[ci_env_secret])
+@app.function(gpu="L40S:1", image=image, timeout=900)
 def run_inference_tests_vmoba():
    run_test('python fastvideo/tests/inference/vmoba/test_vmoba_inference.py')


-@app.function(gpu="L40S:1", image=image, timeout=1200, secrets=[ci_env_secret])
+@app.function(gpu="L40S:1", image=image, timeout=1200)
 def run_inference_lora_tests():
    run_test(
        "pytest ./fastvideo/tests/inference/lora/test_lora_inference_similarity.py -vs"
    )


-@app.function(gpu="L40S:2", image=image, timeout=900, secrets=[ci_env_secret])
+@app.function(gpu="L40S:2", image=image, timeout=900)
 def run_distill_dmd_tests():
    run_test(
-        "FASTVIDEO_FA4=0 pytest ./fastvideo/tests/training/distill/test_distill_dmd.py -vs")
+        "pytest ./fastvideo/tests/training/distill/test_distill_dmd.py -vs")


@app.function(gpu="L40S:2",
              image=image,
              timeout=900,
-              secrets=[wandb_secret, ci_env_secret])
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"WANDB_API_KEY": os.environ.get("WANDB_API_KEY", "")})
+              ])
 def run_self_forcing_tests():
    run_test(
-        "wandb login $WANDB_API_KEY && "
-        "FASTVIDEO_FA4=0 pytest ./fastvideo/tests/training/self-forcing/test_self_forcing.py -vs"
+        "wandb login $WANDB_API_KEY && pytest ./fastvideo/tests/training/self-forcing/test_self_forcing.py -vs"
    )


-@app.function(gpu="L40S:1", image=image, timeout=900, secrets=[ci_env_secret])
+@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/ ./fastvideo/tests/stages/ ./fastvideo/tests/ops/ ./fastvideo/tests/training/test_trackers.py ./fastvideo/tests/attention/test_sdpa_metadata_mask_contract.py --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"
    )


 # TODO: David: GPU only used to resolve import time requirement (not needed for this test). Maybe make those imports lazy?
-@app.function(gpu="L40S:1",
-              image=dreamverse_image,
-              timeout=1800,
-              secrets=[ci_env_secret])
+@app.function(gpu="L40S:1", image=dreamverse_image, timeout=1800)
 def run_dreamverse_app_tests():
    run_test_command(
        install_command="",
@@ -321,23 +318,26 @@ def run_dreamverse_app_tests():


@app.function(gpu="L40S:1",
-              cpu=8.0,
-              memory=32768,
              image=image,
              timeout=1800,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_train_framework_tests():
    run_test(
-        "export HF_HOME='/root/data/.cache' && hf auth login --token $HF_API_KEY && "
-        "FASTVIDEO_FA4=0 pytest ./fastvideo/tests/train/models ./fastvideo/tests/train/methods -vs"
+        "export HF_HOME='/root/data/.cache' && hf auth login --token $HF_API_KEY && pytest ./fastvideo/tests/train/models ./fastvideo/tests/train/methods -vs"
    )


@app.function(gpu="L40S:1",
              image=image,
              timeout=1800,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def seed_grad_norm_references():
    """Record the per-method grad-norm reference for the **CI GPU (L40S only)**.
@@ -356,15 +356,17 @@ def seed_grad_norm_references():
    the local command and the ``_DEVICE_MAPPINGS`` table.
    """
    run_test(
-        "export HF_HOME='/root/data/.cache' && hf auth login --token $HF_API_KEY && "
-        "FASTVIDEO_FA4=0 FASTVIDEO_GRADNORM_UPDATE=1 pytest ./fastvideo/tests/train/methods -vs -rs"
+        "export HF_HOME='/root/data/.cache' && hf auth login --token $HF_API_KEY && FASTVIDEO_GRADNORM_UPDATE=1 pytest ./fastvideo/tests/train/methods -vs -rs"
    )


@app.function(gpu="L40S:1",
              image=image,
              timeout=3600,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_eval_tests():
    # Eval metric regression: drives the high-level fastvideo.eval API on a
@@ -387,7 +389,10 @@ def run_eval_tests():
@app.function(gpu="L40S:1",
              image=image,
              timeout=3600,
-              secrets=[hf_secret, ci_env_secret])
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ])
 def run_lora_extraction_tests():
    run_test(
        "hf auth login --token $HF_API_KEY && pytest ./fastvideo/tests/lora_extraction/test_lora_extraction.py"
@@ -395,11 +400,12 @@ def run_lora_extraction_tests():


@app.function(gpu="L40S:2",
-              cpu=8.0,
-              memory=32768,
              image=image,
              timeout=1800,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_performance_tests():
    # PR/direct records are uploaded only on pass; scheduled main uploads pass
@@ -421,8 +427,6 @@ def run_performance_tests():
        "export PERF_RUN_SOURCE='unknown'; "
        "export PERF_UPLOAD_POLICY='never'; "
        "fi; "
-        "(nvidia-smi --query-gpu=index,timestamp,clocks.sm,clocks.max.sm,power.draw,power.limit,temperature.gpu "
-        "--format=csv -l 10 > /tmp/gpu_telemetry.csv 2>/dev/null &); "
        "pytest ./fastvideo/tests/performance -vs; "
        "PYTEST_RC=$?; "
        "PERF_RC=0; "
@@ -431,8 +435,6 @@ def run_performance_tests():
        "PERF_RC=$?; "
        "fi; "
        "python ./fastvideo/tests/performance/dashboard.py || true; "
-        "echo '--- GPU telemetry (clocks.sm vs clocks.max.sm reveals capped hosts) ---'; "
-        "cat /tmp/gpu_telemetry.csv || true; "
        "FINAL_RC=$PYTEST_RC; "
        "if [ $FINAL_RC -eq 0 ]; then FINAL_RC=$PERF_RC; fi; "
        "exit $FINAL_RC")
@@ -441,7 +443,10 @@ def run_performance_tests():
@app.function(gpu="L40S:1",
              image=image,
              timeout=1800,
-              secrets=[hf_secret, ci_env_secret],
+              secrets=[
+                  modal.Secret.from_dict(
+                      {"HF_API_KEY": os.environ.get("HF_API_KEY", "")})
+              ],
              volumes={"/root/data": model_vol})
 def run_api_server_tests():
    run_test(
@@ -57,12 +57,10 @@ image = (
    .run_commands("echo 'source ~/.cargo/env' >> ~/.bashrc")
    .env(
        {
-            # INVARIANT: keep this image identical for every CI job at a given
-            # base digest -- per-job/per-commit values (BUILDKITE_*) must never
-            # be baked here as image layers or every job rebuilds its own image
-            # variant. repo/commit/PR already reach the container as
-            # run_ssim_partition() arguments.
            "PATH": "/root/.cargo/bin:$PATH",
+            "BUILDKITE_REPO": os.environ.get("BUILDKITE_REPO", ""),
+            "BUILDKITE_COMMIT": os.environ.get("BUILDKITE_COMMIT", ""),
+            "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
@@ -34,9 +34,6 @@ class TestAbsMaxFP8LinearMethod(unittest.TestCase):
            method._convert_scale(scale)

    def test_create_weights_rejects_invalid_dtype(self):
-        # float64 is outside the supported set (bf16/fp16/fp32). The test
-        # originally passed float32, which create_weights has accepted since
-        # PR #981 — it never ran in CI, so the mismatch went unnoticed.
        method = AbsMaxFP8LinearMethod()
        layer = nn.Module()
        with self.assertRaisesRegex(AssertionError, "only supports"):
@@ -46,7 +43,7 @@ class TestAbsMaxFP8LinearMethod(unittest.TestCase):
                output_partition_sizes=[3],
                input_size=2,
                output_size=3,
-                params_dtype=torch.float64,
+                params_dtype=torch.float32,
            )

    def test_absmax_fp8_parameter_weight_loader(self):
@@ -66,7 +66,6 @@ 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"}
 IDENTITY_KEYS = (
-    "result_schema_version",
    "workload_id",
    "variant_id",
    "benchmark_version",
@@ -78,8 +77,6 @@ IDENTITY_KEYS = (
    "software_profile_id",
    "environment_metadata",
    "environment_fingerprint",
-    "quality_metadata",
-    "variant_metadata",
 )
 COMPARISON_IDENTITY_KEYS = (
    "workload_id",
@@ -146,22 +143,18 @@ def _result_failed_static_thresholds() -> bool:


 def _record_metadata(run_source: str, result: dict[str, Any]) -> dict[str, Any]:
-    raw_run_source = str(result.get("run_source") or "").strip().lower()
-    if raw_run_source in VALID_RUN_SOURCES:
-        run_source = raw_run_source
-
    pr_number = result.get("pr_number") or os.environ.get("BUILDKITE_PULL_REQUEST", "")
    if not _truthy_pr_number(str(pr_number)):
        pr_number = ""
    return {
        "run_source": run_source,
        "baseline_eligible": False,
-        "branch": result.get("branch") or os.environ.get("BUILDKITE_BRANCH", ""),
+        "branch": os.environ.get("BUILDKITE_BRANCH", ""),
        "pr_number": pr_number,
-        "test_scope": result.get("test_scope") or os.environ.get("TEST_SCOPE", ""),
-        "build_url": result.get("build_url") or os.environ.get("BUILDKITE_BUILD_URL", ""),
-        "build_id": result.get("build_id") or os.environ.get("BUILDKITE_BUILD_ID", ""),
-        "job_id": result.get("job_id") or os.environ.get("BUILDKITE_JOB_ID", ""),
+        "test_scope": os.environ.get("TEST_SCOPE", ""),
+        "build_url": os.environ.get("BUILDKITE_BUILD_URL", ""),
+        "build_id": os.environ.get("BUILDKITE_BUILD_ID", ""),
+        "job_id": os.environ.get("BUILDKITE_JOB_ID", ""),
    }


@@ -185,6 +178,11 @@ def _comparison_identity_filters(record: dict[str, Any]) -> dict[str, str]:
        key for key in COMPARISON_IDENTITY_KEYS
        if key not in record or record[key] is None or (isinstance(record[key], str) and not record[key].strip())
    ]
+    if len(missing) == len(COMPARISON_IDENTITY_KEYS):
+        # Legacy v1 record: no identity fields at all. Keep the pre-v2
+        # (model_id, gpu_type) rolling-baseline comparison instead of
+        # crashing — v1 configs are documented as loadable.
+        return {}
    if missing:
        raise ValueError("Performance record missing required comparison identity fields: " + ", ".join(missing))
    return {
@@ -193,18 +191,6 @@ def _comparison_identity_filters(record: dict[str, Any]) -> dict[str, str]:
    }


-def _comparison_identity_filters_or_none(record: dict[str, Any]) -> dict[str, str] | None:
-    try:
-        return _comparison_identity_filters(record)
-    except ValueError as exc:
-        print("Skipping rolling baseline comparison for "
-              f"{record.get('model_id', 'unknown')} on "
-              f"{record.get('gpu_type', 'unknown')}: {exc}. "
-              "The normalized artifact will still be written, but this record "
-              "will not be baseline eligible.")
-        return None
-
-
 def _format_identity_filters(filters: dict[str, str]) -> str:
    if not filters:
        return ""
@@ -495,53 +481,43 @@ def main() -> int:
    for raw in current_results:
        record = _normalize_record(raw)
        metric_policies = resolve_metric_policies(record.get("regression_thresholds"))
-        identity_filters = _comparison_identity_filters_or_none(record)

-        baseline_records: list[dict[str, Any]] = []
-        if identity_filters is None:
-            # Records without the full v2 identity block skip rolling-baseline
-            # comparison entirely: only the static thresholds gate them and
-            # they never become baseline eligible.
-            record["baseline_status"] = "skipped_missing_identity"
+        baseline_records = load_records_for_model(
+            TRACKING_ROOT,
+            record["model_id"],
+            record["gpu_type"],
+            **_comparison_identity_filters(record),
+            last_n=5,
+            successful_only=True,
+            baseline_eligible_only=True,
+        )
+
+        if not baseline_records:
+            # A brand-new cohort has NO regression gating until history
+            # accumulates — make that loud and machine-readable instead of
+            # an indistinguishable pass, so a cohort shift (intended or
+            # accidental, e.g. an identity-field change) never silently
+            # blinds the comparison.
+            record["baseline_status"] = "initialized_new_cohort"
+            print("=" * 72)
+            print(f"WARNING: NO BASELINE — initializing a NEW cohort for "
+                  f"{record['model_id']} on {record['gpu_type']}"
+                  f"{_format_identity_filters(_comparison_identity_filters(record))}")
+            print("Regression gating is INACTIVE for this cohort until "
+                  "baseline history accumulates. If this cohort shift is "
+                  "unexpected, check the identity fields above.")
+            print("=" * 72)
            failures: list[str] = []
+            record["success"] = True
        else:
-            baseline_records = load_records_for_model(
-                TRACKING_ROOT,
-                record["model_id"],
-                record["gpu_type"],
-                **identity_filters,
-                last_n=5,
-                successful_only=True,
-                baseline_eligible_only=True,
-            )
-
-            if not baseline_records:
-                # A brand-new cohort has NO regression gating until history
-                # accumulates — make that loud and machine-readable instead of
-                # an indistinguishable pass, so a cohort shift (intended or
-                # accidental, e.g. an identity-field change) never silently
-                # blinds the comparison.
-                record["baseline_status"] = "initialized_new_cohort"
-                print("=" * 72)
-                print(f"WARNING: NO BASELINE — initializing a NEW cohort for "
-                      f"{record['model_id']} on {record['gpu_type']}"
-                      f"{_format_identity_filters(identity_filters)}")
-                print("Regression gating is INACTIVE for this cohort until "
-                      "baseline history accumulates. If this cohort shift is "
-                      "unexpected, check the identity fields above.")
-                print("=" * 72)
-                failures = []
-            else:
-                record["baseline_status"] = "compared"
-                failures = _check_regressions(record, baseline_records, metric_policies)
+            record["baseline_status"] = "compared"
+            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')})")

        record["success"] = not failures
-        record["baseline_eligible"] = (
-            identity_filters is not None and _is_baseline_eligible(record["run_source"], record["success"])
-        )
+        record["baseline_eligible"] = _is_baseline_eligible(record["run_source"], record["success"])
        all_failures.extend(failures)

        _write_normalized_artifact(record)
@@ -14,9 +14,9 @@ def _v2_config():
    return {
        "benchmark_id": "wan-t2v-1.3b-2gpu",
        "config_schema_version": 2,
-        "workload_id": "wan-t2v",
-        "variant_id": "1.3b-sp2",
-        "benchmark_version": 2,
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
    }


@@ -41,9 +41,9 @@ def test_v2_benchmark_config_identity_validates_and_is_preserved():
    assert _is_v2_config(cfg) is True
    assert _config_identity_metadata(cfg) == {
        "config_schema_version": 2,
-        "workload_id": "wan-t2v",
-        "variant_id": "1.3b-sp2",
-        "benchmark_version": 2,
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
        "quality_metadata": {"some": "data"},
    }

@@ -91,9 +91,9 @@ def test_v2_benchmark_config_rejects_invalid_benchmark_version_values(value):
 def test_partial_v2_identity_requires_schema_version():
    cfg = {
        "benchmark_id": "wan-t2v-1.3b-2gpu",
-        "workload_id": "wan-t2v",
-        "variant_id": "1.3b-sp2",
-        "benchmark_version": 2,
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
    }

    expected = "wan.json: v2 benchmark identity fields require config_schema_version=2"
@@ -105,9 +105,9 @@ 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",
-        "variant_id": "1.3b-sp2",
-        "benchmark_version": 2,
+        "workload_id": "wan-t2v-1.3b",
+        "variant_id": "canonical",
+        "benchmark_version": 1,
        "quality_metadata": ["not", "an", "object"],
    }

@@ -1,7 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0

-import json
-
 import pytest

 from fastvideo.tests.performance import compare_baseline
@@ -127,7 +125,6 @@ def test_normalized_record_preserves_identity_metadata(monkeypatch):
    monkeypatch.setenv("PERF_RUN_SOURCE", "pr")
    raw = _raw_result()
    raw.update({
-        "result_schema_version": 2,
        "workload_id": "wan-t2v",
        "variant_id": "1.3b-sp2",
        "benchmark_version": 2,
@@ -149,14 +146,10 @@ def test_normalized_record_preserves_identity_metadata(monkeypatch):
            },
        },
        "environment_fingerprint": "env-1",
-        "quality_metadata": {
-            "quality_status": "canonical",
-        },
    })

    record = compare_baseline.normalize_performance_result(raw)

-    assert record["result_schema_version"] == 2
    assert record["workload_id"] == "wan-t2v"
    assert record["variant_id"] == "1.3b-sp2"
    assert record["benchmark_version"] == 2
@@ -168,87 +161,6 @@ def test_normalized_record_preserves_identity_metadata(monkeypatch):
    assert record["software_profile_id"] == "sw-1"
    assert record["environment_metadata"] == {"env": {"IMAGE_VERSION": "latest"}}
    assert record["environment_fingerprint"] == "env-1"
-    assert record["quality_metadata"] == {"quality_status": "canonical"}
-
-
-def test_normalized_record_prefers_raw_v2_provenance(monkeypatch):
-    monkeypatch.setenv("PERF_RUN_SOURCE", "pr")
-    monkeypatch.setenv("BUILDKITE_BRANCH", "feature/from-env")
-    monkeypatch.setenv("TEST_SCOPE", "direct")
-    monkeypatch.setenv("BUILDKITE_BUILD_URL", "https://buildkite.example/env")
-    monkeypatch.setenv("BUILDKITE_BUILD_ID", "env-build")
-    monkeypatch.setenv("BUILDKITE_JOB_ID", "env-job")
-    raw = _raw_result()
-    raw.update({
-        "result_schema_version": 2,
-        "run_source": "scheduled_main",
-        "branch": "main",
-        "test_scope": "full",
-        "build_url": "https://buildkite.example/raw",
-        "build_id": "raw-build",
-        "job_id": "raw-job",
-        "pr_number": "false",
-    })
-
-    record = compare_baseline.normalize_performance_result(raw)
-
-    assert record["result_schema_version"] == 2
-    assert record["run_source"] == "scheduled_main"
-    assert record["branch"] == "main"
-    assert record["test_scope"] == "full"
-    assert record["build_url"] == "https://buildkite.example/raw"
-    assert record["build_id"] == "raw-build"
-    assert record["job_id"] == "raw-job"
-    assert record["pr_number"] == ""
-
-
-def test_v1_normalized_record_has_no_result_schema_version(monkeypatch):
-    monkeypatch.setenv("PERF_RUN_SOURCE", "pr")
-
-    record = compare_baseline.normalize_performance_result(_raw_result())
-
-    assert "result_schema_version" not in record
-
-
-def test_main_writes_v1_current_artifact_without_comparison_identity(monkeypatch, tmp_path, capsys):
-    results_dir = tmp_path / "results"
-    reports_dir = tmp_path / "reports"
-    tracking_root = tmp_path / "tracking"
-    results_dir.mkdir()
-    (results_dir / "perf_legacy.json").write_text(json.dumps(_raw_result()), encoding="utf-8")
-    uploaded_records = []
-
-    monkeypatch.setenv("PERF_RUN_SOURCE", "scheduled_main")
-    monkeypatch.delenv("PERF_PYTEST_RC", raising=False)
-    monkeypatch.setattr(compare_baseline, "RESULTS_DIR", str(results_dir))
-    monkeypatch.setattr(compare_baseline, "PERF_REPORTS_DIR", str(reports_dir))
-    monkeypatch.setattr(compare_baseline, "TRACKING_ROOT", str(tracking_root))
-    monkeypatch.setattr(compare_baseline, "UPLOAD_POLICY", "always")
-    monkeypatch.setattr(compare_baseline, "sync_from_hf", lambda local_dir, strict=False: local_dir)
-
-    def fail_load_records_for_model(*_args, **_kwargs):
-        raise AssertionError("legacy current records should skip v2 baseline lookup")
-
-    def fake_upload_record(_path, record, *, strict=False):
-        uploaded_records.append(record.copy())
-
-    monkeypatch.setattr(compare_baseline, "load_records_for_model", fail_load_records_for_model)
-    monkeypatch.setattr(compare_baseline, "upload_record", fake_upload_record)
-
-    assert compare_baseline.main() == 0
-
-    output = capsys.readouterr().out
-    normalized_files = list((reports_dir / "results").glob("normalized_perf_*.json"))
-    assert "Skipping rolling baseline comparison" in output
-    assert "workload_id" in output
-    assert len(normalized_files) == 1
-
-    normalized = json.loads(normalized_files[0].read_text(encoding="utf-8"))
-    assert "result_schema_version" not in normalized
-    assert normalized["success"] is True
-    assert normalized["baseline_eligible"] is False
-    assert len(uploaded_records) == 1
-    assert uploaded_records[0]["baseline_eligible"] is False


 def test_normalized_record_reads_identity_labels_from_recipe(monkeypatch):
@@ -154,8 +154,8 @@ def test_dashboard_endpoints_filter_and_return_run_source_metadata():
    assert summary["count"] == 1
    assert summary["rows"][0]["run_source"] == "pr"
    assert summary["rows"][0]["pr_number"] == "123"
-    assert summary["rows"][0]["baseline_n"] == 0
-    assert summary["rows"][0]["metrics"]["latency"]["baseline"] is None
+    assert summary["rows"][0]["baseline_n"] == 1
+    assert summary["rows"][0]["metrics"]["latency"]["baseline"] == 11.0
    assert summary["filters"]["run_source"] == "pr"
    assert trends["count"] == 1
    assert trends["groups"][0]["points"][0]["run_source"] == "pr"
@@ -142,47 +142,6 @@ def test_build_latest_summary_separates_informational_threshold_crossing():
    assert rows[0]["threshold_exceeded_metrics"] == ["latency"]
    assert rows[0]["failing_metrics"] == []
    assert rows[0]["computed_regression_status"] == "pass"
-
-
-def test_build_latest_summary_run_source_filter_excludes_future_baselines():
-    records = [
-        _record(
-            "2026-01-01T00:00:00+00:00",
-            "a" * 40,
-            10.0,
-            10.0,
-            run_source="scheduled_main",
-            baseline_eligible=True,
-        ),
-        _record(
-            "2026-01-02T00:00:00+00:00",
-            "b" * 40,
-            11.0,
-            9.0,
-            run_source="pr",
-            baseline_eligible=False,
-            pr_number="123",
-        ),
-        _record(
-            "2026-01-03T00:00:00+00:00",
-            "c" * 40,
-            30.0,
-            3.0,
-            run_source="scheduled_main",
-            baseline_eligible=True,
-        ),
-    ]
-
-    rows = build_latest_summary(records, run_source="pr")
-
-    assert len(rows) == 1
-    assert rows[0]["run_source"] == "pr"
-    assert rows[0]["baseline_n"] == 1
-    assert rows[0]["metrics"]["latency"]["baseline"] == 10.0
-    assert rows[0]["metrics"]["throughput"]["baseline"] == 10.0
-
-
-
 def test_build_latest_summary_keeps_identity_cohorts_separate():
    records = [
        _record(
@@ -227,7 +186,7 @@ def test_build_latest_summary_keeps_identity_cohorts_separate():
        ),
    ]

-    rows = build_latest_summary(records)
+    rows = build_latest_summary(records, max_regression=0.05)
    recipe_b_row = next(row for row in rows if row["recipe_fingerprint"] == "recipe-b")

    assert len(rows) == 2
@@ -282,7 +241,7 @@ def test_build_latest_summary_keeps_variant_versions_separate():
        ),
    ]

-    rows = build_latest_summary(records)
+    rows = build_latest_summary(records, max_regression=0.05)
    version_2_row = next(row for row in rows if row["benchmark_version"] == 2)

    assert len(rows) == 2
@@ -290,58 +249,6 @@ def test_build_latest_summary_keeps_variant_versions_separate():
    assert version_2_row["metrics"]["latency"]["baseline"] == 20.0


-def test_dashboard_identity_preserves_zero_benchmark_version():
-    records = [
-        _record(
-            "2026-01-01T00:00:00+00:00",
-            "a" * 40,
-            10.0,
-            10.0,
-            workload_id="wan-t2v",
-            variant_id="1.3b-sp2",
-            benchmark_version=0,
-            recipe_fingerprint="recipe-a",
-            hardware_profile_id="hw-l40s",
-            software_profile_id="sw-cu130",
-            run_source="scheduled_main",
-            baseline_eligible=True,
-        ),
-        _record(
-            "2026-01-02T00:00:00+00:00",
-            "b" * 40,
-            11.0,
-            9.0,
-            workload_id="wan-t2v",
-            variant_id="1.3b-sp2",
-            benchmark_version=0,
-            recipe_fingerprint="recipe-a",
-            hardware_profile_id="hw-l40s",
-            software_profile_id="sw-cu130",
-        ),
-        _record(
-            "2026-01-03T00:00:00+00:00",
-            "c" * 40,
-            20.0,
-            5.0,
-        ),
-    ]
-
-    rows = build_latest_summary(records)
-    version_zero_row = next(row for row in rows if row["benchmark_version"] == 0)
-    legacy_row = next(row for row in rows if row["benchmark_version"] == "")
-    trends = build_trends(records)
-    version_zero_trend = next(trend for trend in trends if trend["benchmark_version"] == 0)
-    legacy_trend = next(trend for trend in trends if trend["benchmark_version"] == "")
-
-    assert len(rows) == 2
-    assert version_zero_row["baseline_n"] == 1
-    assert version_zero_row["metrics"]["latency"]["baseline"] == 10.0
-    assert legacy_row["baseline_n"] == 0
-    assert len(trends) == 2
-    assert version_zero_trend["points"][0]["benchmark_version"] == 0
-    assert legacy_trend["points"][0]["benchmark_version"] == ""
-
-
 def test_filter_records_and_trends_preserve_metric_points():
    records = [
        _record("2026-01-01T00:00:00+00:00", "a" * 40, 10.0, 10.0),
@@ -55,9 +55,6 @@ V2_OPTIONAL_METADATA_FIELDS = (
    "metric_threshold_policy",
    "quality_metadata",
 )
-RESULT_SCHEMA_VERSION = 2
-VALID_RUN_SOURCES = {"pr", "local", "scheduled_main", "unknown"}
-OPTIONAL_RESULT_METADATA_FIELDS = ("quality_metadata", "variant_metadata")

 # -- Config discovery -------------------------------------------------------

@@ -198,11 +195,7 @@ 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)
-        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)
+        metric_key = STAGE_METRIC_MAP.get(stage_class)
        if metric_key is None:
            logger.debug("Unmapped stage '%s' class '%s' (%.3fs)",
                         stage_name,
@@ -234,17 +227,6 @@ def _run_generation(generator, prompt, generation_kwargs):
    component_times = _extract_component_times(result)
    return elapsed, peak_memory_mb, component_times

-
-def _validate_run_counts(run_config: Mapping[str, Any], benchmark_id: str) -> tuple[int, int]:
-    num_warmup = run_config.get("num_warmup_runs", 1)
-    num_measure = run_config.get("num_measurement_runs", 3)
-    if isinstance(num_warmup, bool) or not isinstance(num_warmup, int) or num_warmup < 0:
-        raise ValueError(f"{benchmark_id}: run_config.num_warmup_runs must be a non-negative integer")
-    if isinstance(num_measure, bool) or not isinstance(num_measure, int) or num_measure < 1:
-        raise ValueError(f"{benchmark_id}: run_config.num_measurement_runs must be a positive integer")
-    return num_warmup, num_measure
-
-
 def _write_results(results):
    """Write JSON results to the results directory."""
    script_dir = os.path.dirname(os.path.abspath(__file__))
@@ -365,101 +347,6 @@ def _build_identity_fields(cfg, init_kwargs, prompt, runtime_identity):
    }


-def _truthy_pr_number(value: str | None) -> bool:
-    return bool(value and value not in {"false", "0", "None", "none"})
-
-
-def _detect_run_source() -> str:
-    explicit = os.environ.get("PERF_RUN_SOURCE", "").strip().lower()
-    if explicit in VALID_RUN_SOURCES:
-        return explicit
-    if _truthy_pr_number(os.environ.get("BUILDKITE_PULL_REQUEST")):
-        return "pr"
-    if os.environ.get("BUILDKITE_BRANCH") == "main" and os.environ.get("TEST_SCOPE") == "full":
-        return "scheduled_main"
-    if not os.environ.get("BUILDKITE_COMMIT"):
-        return "local"
-    return "unknown"
-
-
-def _ci_provenance_fields() -> dict[str, str]:
-    pr_number = os.environ.get("BUILDKITE_PULL_REQUEST", "")
-    if not _truthy_pr_number(pr_number):
-        pr_number = ""
-    return {
-        "run_source": _detect_run_source(),
-        "branch": os.environ.get("BUILDKITE_BRANCH", ""),
-        "pr_number": pr_number,
-        "test_scope": os.environ.get("TEST_SCOPE", ""),
-        "build_url": os.environ.get("BUILDKITE_BUILD_URL", ""),
-        "build_id": os.environ.get("BUILDKITE_BUILD_ID", ""),
-        "job_id": os.environ.get("BUILDKITE_JOB_ID", ""),
-    }
-
-
-def _configured_result_metadata(cfg: Mapping[str, Any]) -> dict[str, Any]:
-    return {
-        field: cfg[field]
-        for field in OPTIONAL_RESULT_METADATA_FIELDS
-        if field in cfg and cfg[field] is not None
-    }
-
-
-def _build_result_record(
-    *,
-    cfg: Mapping[str, Any],
-    model_info: Mapping[str, Any],
-    init_kwargs: Mapping[str, Any],
-    gen_kwargs: Mapping[str, Any],
-    num_warmup: int,
-    num_measure: int,
-    thresholds: Mapping[str, Any],
-    times: list[float],
-    peak_memories: list[float],
-    all_component_times: list[dict],
-    prompt: str,
-    runtime_identity: Mapping[str, Any],
-    device_name: str,
-    timestamp: str | None = None,
-) -> dict[str, Any]:
-    if not times or not peak_memories:
-        raise ValueError("Cannot build a performance result record without measurement runs")
-    avg_time = sum(times) / len(times)
-    max_peak_memory = max(peak_memories)
-    num_frames = gen_kwargs.get("num_frames")
-    throughput_fps = (1.0 / avg_time) if avg_time > 0 else None
-    if isinstance(num_frames, (int, float)) and avg_time > 0:
-        throughput_fps = num_frames / avg_time
-
-    return {
-        "benchmark_id": cfg["benchmark_id"],
-        "result_schema_version": RESULT_SCHEMA_VERSION,
-        "model_short_name": model_info.get("model_short_name", ""),
-        "device": device_name,
-        "num_gpus": init_kwargs.get("num_gpus", 1),
-        "num_warmup_runs": num_warmup,
-        "num_measurement_runs": num_measure,
-        "avg_generation_time_s": round(avg_time, 3),
-        "individual_times_s": [round(t, 3) for t in times],
-        "throughput_fps": round(throughput_fps, 3)
-        if throughput_fps is not None else None,
-        "max_peak_memory_mb": round(max_peak_memory, 1),
-        "individual_peak_memories_mb": [round(m, 1) for m in peak_memories],
-        "thresholds": dict(thresholds),
-        "regression_thresholds": cfg.get("regression_thresholds", {}),
-        "commit": os.environ.get("BUILDKITE_COMMIT", ""),
-        **_ci_provenance_fields(),
-        "timestamp": timestamp or datetime.now(timezone.utc).isoformat(),
-        **_configured_result_metadata(cfg),
-        "text_encoder_time_s": _avg_component(all_component_times,
-                                              "text_encoder_time_s"),
-        "dit_time_s": _avg_component(all_component_times, "dit_time_s"),
-        "vae_decode_time_s": _avg_component(all_component_times,
-                                            "vae_decode_time_s"),
-        **_build_identity_fields(cfg, init_kwargs, prompt, runtime_identity),
-    }
-
-
 # -- Test -------------------------------------------------------------------

 def _run_benchmark(cfg):
@@ -475,7 +362,8 @@ def _run_benchmark(cfg):
    prompts = cfg.get("test_prompts", ["A cinematic video."])
    prompt = prompts[0]

-    num_warmup, num_measure = _validate_run_counts(run_config, cfg["benchmark_id"])
+    num_warmup = run_config.get("num_warmup_runs", 1)
+    num_measure = run_config.get("num_measurement_runs", 3)
    thresholds = _get_thresholds(cfg)

    # Remap JSON keys to VideoGenerator kwargs
@@ -522,21 +410,37 @@ def _run_benchmark(cfg):
    avg_time = sum(times) / len(times)
    max_peak_memory = max(peak_memories)
    device_name = torch.cuda.get_device_name()
-    results = _build_result_record(
-        cfg=cfg,
-        model_info=model_info,
-        init_kwargs=init_kwargs,
-        gen_kwargs=gen_kwargs,
-        num_warmup=num_warmup,
-        num_measure=num_measure,
-        thresholds=thresholds,
-        times=times,
-        peak_memories=peak_memories,
-        all_component_times=all_component_times,
-        prompt=prompt,
-        runtime_identity=runtime_identity,
-        device_name=device_name,
-    )
+    num_frames = gen_kwargs.get("num_frames")
+    throughput_fps = (1.0 / avg_time) if avg_time > 0 else None
+    if isinstance(num_frames, (int, float)) and avg_time > 0:
+        throughput_fps = num_frames / avg_time
+
+    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),
+        "num_warmup_runs": num_warmup,
+        "num_measurement_runs": num_measure,
+        "avg_generation_time_s": round(avg_time, 3),
+        "individual_times_s": [round(t, 3) for t in times],
+        "throughput_fps": round(throughput_fps, 3)
+        if throughput_fps is not None else None,
+        "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(),
+        "text_encoder_time_s": _avg_component(all_component_times,
+                                              "text_encoder_time_s"),
+        "dit_time_s": _avg_component(all_component_times, "dit_time_s"),
+        "vae_decode_time_s": _avg_component(all_component_times,
+                                            "vae_decode_time_s"),
+        **_build_identity_fields(cfg, init_kwargs, prompt, runtime_identity),
+    }

    logger.info(
        "Performance results: avg_time=%.2fs, "
@@ -1,20 +1,13 @@
 # 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,
@@ -52,35 +45,6 @@ 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.
@@ -1,176 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import pytest
-
-from fastvideo.tests.performance import test_inference_performance as perf_test
-
-
-def _benchmark_config():
-    return {
-        "benchmark_id": "wan-t2v-1.3b-2gpu",
-        "workload_id": "wan-t2v",
-        "variant_id": "1.3b-sp2",
-        "benchmark_version": 2,
-        "quality_metadata": {
-            "quality_status": "canonical",
-        },
-        "model": {
-            "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-            "model_short_name": "Wan2.1-T2V-1.3B",
-        },
-        "generation_kwargs": {
-            "num_frames": 45,
-        },
-    }
-
-
-def _identity_fields():
-    return {
-        "workload_id": "wan-t2v",
-        "variant_id": "1.3b-sp2",
-        "benchmark_version": 2,
-        "recipe": {
-            "recipe_schema_version": 1,
-            "benchmark": {
-                "benchmark_id": "wan-t2v-1.3b-2gpu",
-                "workload_id": "wan-t2v",
-                "variant_id": "1.3b-sp2",
-                "benchmark_version": 2,
-            },
-        },
-        "recipe_fingerprint": "recipe-1",
-        "hardware_profile": {
-            "device_type": "cuda",
-            "gpu_count": 2,
-        },
-        "hardware_profile_id": "hw-l40s",
-        "software_profile": {
-            "python": "3.12",
-            "pytorch": "2.12",
-            "cuda": "13.0",
-        },
-        "software_profile_id": "sw-cu130",
-        "environment_metadata": {
-            "env": {
-                "IMAGE_VERSION": "py3.12-cuda13.0.0",
-            },
-        },
-        "environment_fingerprint": "env-ci",
-    }
-
-
-def test_build_result_record_emits_v2_wan_shape(monkeypatch):
-    monkeypatch.setenv("PERF_RUN_SOURCE", "scheduled_main")
-    monkeypatch.setenv("BUILDKITE_COMMIT", "a" * 40)
-    monkeypatch.setenv("BUILDKITE_PULL_REQUEST", "false")
-    monkeypatch.setenv("BUILDKITE_BRANCH", "main")
-    monkeypatch.setenv("TEST_SCOPE", "full")
-    monkeypatch.setenv("BUILDKITE_BUILD_URL", "https://buildkite.example/build")
-    monkeypatch.setenv("BUILDKITE_BUILD_ID", "build-1")
-    monkeypatch.setenv("BUILDKITE_JOB_ID", "job-1")
-    monkeypatch.setattr(perf_test, "_build_identity_fields", lambda *_args: _identity_fields())
-
-    record = perf_test._build_result_record(
-        cfg=_benchmark_config(),
-        model_info={"model_short_name": "Wan2.1-T2V-1.3B"},
-        init_kwargs={"num_gpus": 2},
-        gen_kwargs={"num_frames": 45},
-        num_warmup=2,
-        num_measure=3,
-        thresholds={
-            "max_generation_time_s": 34.0,
-            "max_peak_memory_mb": 11000.0,
-        },
-        times=[30.0, 31.0, 32.0],
-        peak_memories=[10000.0, 10100.0, 10050.0],
-        all_component_times=[
-            {
-                "text_encoder_time_s": 1.0,
-                "dit_time_s": 8.0,
-                "vae_decode_time_s": 3.0,
-            },
-            {
-                "text_encoder_time_s": 1.2,
-                "dit_time_s": 8.2,
-                "vae_decode_time_s": 3.2,
-            },
-            {
-                "text_encoder_time_s": None,
-                "dit_time_s": 8.4,
-                "vae_decode_time_s": 3.4,
-            },
-        ],
-        prompt="A cinematic video.",
-        runtime_identity={
-            "resolved_attention_backend": "FLASH_ATTN",
-        },
-        device_name="NVIDIA L40S",
-        timestamp="2026-07-05T00:00:00+00:00",
-    )
-
-    assert record["result_schema_version"] == perf_test.RESULT_SCHEMA_VERSION
-    assert record["benchmark_id"] == "wan-t2v-1.3b-2gpu"
-    assert record["workload_id"] == "wan-t2v"
-    assert record["variant_id"] == "1.3b-sp2"
-    assert record["benchmark_version"] == 2
-    assert record["recipe_fingerprint"] == "recipe-1"
-    assert record["hardware_profile_id"] == "hw-l40s"
-    assert record["software_profile_id"] == "sw-cu130"
-    assert record["environment_fingerprint"] == "env-ci"
-    assert record["environment_metadata"]["env"]["IMAGE_VERSION"] == "py3.12-cuda13.0.0"
-    assert record["quality_metadata"] == {"quality_status": "canonical"}
-    assert record["run_source"] == "scheduled_main"
-    assert record["branch"] == "main"
-    assert record["test_scope"] == "full"
-    assert record["build_url"] == "https://buildkite.example/build"
-    assert record["build_id"] == "build-1"
-    assert record["job_id"] == "job-1"
-    assert record["pr_number"] == ""
-    assert record["commit"] == "a" * 40
-    assert record["avg_generation_time_s"] == 31.0
-    assert record["throughput_fps"] == 1.452
-    assert record["max_peak_memory_mb"] == 10100.0
-    assert record["text_encoder_time_s"] == 1.1
-    assert record["dit_time_s"] == 8.2
-    assert record["vae_decode_time_s"] == 3.2
-
-
-def test_validate_run_counts_rejects_zero_measurement_runs():
-    with pytest.raises(ValueError, match="num_measurement_runs"):
-        perf_test._validate_run_counts({
-            "num_warmup_runs": 1,
-            "num_measurement_runs": 0,
-        }, "wan-t2v-1.3b-2gpu")
-
-
-def test_validate_run_counts_rejects_negative_warmup_runs():
-    with pytest.raises(ValueError, match="num_warmup_runs"):
-        perf_test._validate_run_counts({
-            "num_warmup_runs": -1,
-            "num_measurement_runs": 3,
-        }, "wan-t2v-1.3b-2gpu")
-
-
-def test_validate_run_counts_returns_defaults():
-    assert perf_test._validate_run_counts({}, "wan-t2v-1.3b-2gpu") == (1, 3)
-
-
-def test_build_result_record_rejects_empty_measurements(monkeypatch):
-    monkeypatch.setattr(perf_test, "_build_identity_fields", lambda *_args: _identity_fields())
-
-    with pytest.raises(ValueError, match="measurement runs"):
-        perf_test._build_result_record(
-            cfg=_benchmark_config(),
-            model_info={},
-            init_kwargs={},
-            gen_kwargs={},
-            num_warmup=0,
-            num_measure=0,
-            thresholds={},
-            times=[],
-            peak_memories=[],
-            all_component_times=[],
-            prompt="A cinematic video.",
-            runtime_identity={},
-            device_name="NVIDIA L40S",
-        )
@@ -3,9 +3,9 @@
 from __future__ import annotations

 import os
-from collections.abc import Iterator
 from contextlib import contextmanager
 from logging import Logger
+from typing import Iterator

 from fastvideo import VideoGenerator
 from fastvideo.tests.ssim.reference_utils import (
@@ -67,12 +67,6 @@ def _find_reference_video(reference_folder: str, prompt: str) -> str:
    raise FileNotFoundError("Reference video missing")


-def _remove_stale_generated_video(output_dir: str, output_video_name: str) -> None:
-    stale_path = os.path.join(output_dir, output_video_name)
-    if os.path.exists(stale_path):
-        os.remove(stale_path)
-
-
 def _assert_similarity(
    *,
    logger: Logger,
@@ -220,7 +214,6 @@ def run_text_to_video_similarity_test(
        )
        output_video_name = f"{prompt[:100].strip()}.mp4"
        os.makedirs(output_dir, exist_ok=True)
-        _remove_stale_generated_video(output_dir, output_video_name)

        params_map = select_ssim_params(
            default_params_map,
@@ -296,7 +289,6 @@ def run_image_to_video_similarity_test(
        )
        output_video_name = f"{prompt[:100].strip()}.mp4"
        os.makedirs(output_dir, exist_ok=True)
-        _remove_stale_generated_video(output_dir, output_video_name)

        params_map = select_ssim_params(
            default_params_map,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	af4a8d093f	[bugfix]: address verify-pass findings on the 1546 fix branch - recipe_fingerprint also excludes attention.resolved_backend (a silent runtime fallback must fail the gate in its cohort, not open a fresh ungated one); requested_backend stays in the hash; regression test extended to both runtime-resolved values. - Legacy v1 configs no longer crash after the GPU measurement: identity fields are emitted only for config_schema_version=2 configs, and the comparator falls back to the legacy (model_id, gpu_type) cohort when a record carries no identity fields (partial identity still raises). - 'recipe' removed from V2_OPTIONAL_METADATA_FIELDS: the generated recipe owns the key now; declaring it in a config fails validation loudly instead of being silently overwritten. - Docs: stale 'until those land' paragraph reconciled with what this branch actually lands; baseline_status documented.	2026-07-05 12:09:46 -07:00
SolitaryThinker	0f1f6caea1	[docs]: align example records with the merged identity taxonomy	2026-07-05 12:09:46 -07:00
SolitaryThinker	acc8128878	[bugfix]: pin recipe_fingerprint to declared inputs and make cohort initialization loud Rebase of #1546 onto main resolved the identity taxonomy in favor of the merged #1544 values (workload_id=wan-t2v-1.3b, variant_id=canonical, benchmark_version=1, config_schema_version=2). On top: - recipe_fingerprint() no longer hashes the runtime-resolved HF snapshot revision (kept in the recipe as audit metadata) — upstream repo commits with unchanged weights no longer churn the cohort and reset baselines. - The no-baseline branch now emits a loud warning block and a machine-readable baseline_status=initialized_new_cohort field instead of an indistinguishable success, so cohort shifts never silently disable regression gating. - Regression test: resolved revision must not change the fingerprint.	2026-07-05 12:09:46 -07:00
Mac Lee	066d4118c0	[ci]: align remaining performance cohort consumers	2026-07-05 12:09:13 -07:00
Mac Lee	716ce80a6f	[ci]: complete performance cohort identity	2026-07-05 12:08:43 -07:00
Mac Lee	1746d866bc	[ci]: tighten performance cohort identity	2026-07-05 12:08:43 -07:00
Mac Lee	91e211b192	[ci]: add performance fingerprint cohorts	2026-07-05 12:08:43 -07:00