[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.

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

@@ -69,7 +69,7 @@ approval, then upload reviewed accepted baseline records.
 | `model_id` | Yes | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. |
 | `gpu_type` | Yes | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. Baselines are GPU-specific. |
 | `source_results` | Yes | One or more local paths or Buildkite artifact URLs for accepted shifted performance JSONs. Prefer normalized `normalized_perf_*.json` artifacts emitted by `compare_baseline.py`. Accept `source_result` as an alias only for a single JSON. |
-| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `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.

.github/workflows/ci-precommit.yml

@@ -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"

apps/performance_dashboard/frontend/src/App.tsx

@@ -1,6 +1,7 @@
 import { useEffect, useMemo, useState } from "react";
 
-import { fetchSummary, fetchTrends, refreshData, RunSource, SummaryResponse, TrendGroup, TrendPoint } from "./api";
+import { fetchSummary, fetchTrends, refreshData } from "./api";
+import type { CohortValue, RunSource, SummaryResponse, TrendGroup, TrendPoint } from "./api";
 
 const METRIC_KEYS = ["latency", "throughput", "memory", "text_encoder_time_s", "dit_time_s", "vae_decode_time_s"];
 const RUN_SOURCES: Array<{ value: "" | RunSource; label: string }> = [
@@ -109,6 +110,61 @@ function metricLabel(metricKey: string) {
   return METRIC_DEFINITIONS[metricKey]?.label ?? metricKey;
 }
 
+type CohortFields = {
+  model_id: string;
+  gpu_type: string;
+  workload_id: CohortValue;
+  variant_id: CohortValue;
+  benchmark_version: CohortValue;
+  recipe_fingerprint: CohortValue;
+  hardware_profile_id: CohortValue;
+  software_profile_id: CohortValue;
+};
+
+function cohortValue(value: CohortValue) {
+  if (value === null || value === undefined || value === "") {
+    return "legacy";
+  }
+  return String(value);
+}
+
+function shortCohortValue(value: CohortValue) {
+  const text = cohortValue(value);
+  if (text === "legacy" || text.length <= 14) {
+    return text;
+  }
+  return text.slice(0, 12);
+}
+
+function cohortKey(cohort: CohortFields) {
+  return [
+    cohort.model_id,
+    cohort.gpu_type,
+    cohortValue(cohort.workload_id),
+    cohortValue(cohort.variant_id),
+    cohortValue(cohort.benchmark_version),
+    cohortValue(cohort.recipe_fingerprint),
+    cohortValue(cohort.hardware_profile_id),
+    cohortValue(cohort.software_profile_id)
+  ].join("|");
+}
+
+function cohortTitle(cohort: CohortFields) {
+  const workload = cohortValue(cohort.workload_id);
+  const variant = cohortValue(cohort.variant_id);
+  const version = cohortValue(cohort.benchmark_version);
+  const versionLabel = version === "legacy" ? version : `v${version}`;
+  return `${workload} / ${variant} / ${versionLabel}`;
+}
+
+function cohortDetail(cohort: CohortFields) {
+  return [
+    `recipe ${shortCohortValue(cohort.recipe_fingerprint)}`,
+    shortCohortValue(cohort.hardware_profile_id),
+    shortCohortValue(cohort.software_profile_id)
+  ].join(" | ");
+}
+
 function formatMetricValue(metricKey: string, value: number | null | undefined, tooltip = false) {
   const definition = METRIC_DEFINITIONS[metricKey];
   if (!definition) {
@@ -171,7 +227,9 @@ function TrendChart({ group, metricKey }: { group: TrendGroup; metricKey: string
         top: `${(activePoint.y / height) * 100}%`
       }
     : undefined;
-  const ariaLabel = `${metricLabel(metricKey)} trend for ${group.model_id} on ${group.gpu_type}`;
+  const ariaLabel = `${metricLabel(metricKey)} trend for ${group.model_id} on ${group.gpu_type}, ${cohortTitle(
+    group
+  )}`;
 
   return (
     <div className="chart-shell">
@@ -419,7 +477,7 @@ export default function App() {
       <section className="panel">
         <div className="panel-header">
           <h2>Latest Status</h2>
-          <span>{latestRows.length} model/GPU groups</span>
+          <span>{latestRows.length} comparison cohorts</span>
         </div>
         {latestRows.length === 0 ? (
           <div className="empty">No records match the selected filters.</div>
@@ -432,6 +490,7 @@ export default function App() {
                   <th>Recomputed</th>
                   <th>Model</th>
                   <th>GPU</th>
+                  <th>Cohort</th>
                   <th>Commit</th>
                   <th>Source</th>
                   <th>Baseline</th>
@@ -446,7 +505,7 @@ export default function App() {
               </thead>
               <tbody>
                 {latestRows.map((row) => (
-                  <tr key={`${row.model_id}-${row.gpu_type}`}>
+                  <tr key={cohortKey(row)}>
                     <td>
                       <span className={`badge ${row.status}`}>{row.status}</span>
                     </td>
@@ -457,6 +516,12 @@ export default function App() {
                     </td>
                     <td>{row.model_id}</td>
                     <td>{row.gpu_type}</td>
+                    <td>
+                      <div className="cohort-cell">
+                        <strong>{cohortTitle(row)}</strong>
+                        <span>{cohortDetail(row)}</span>
+                      </div>
+                    </td>
                     <td>{shortSha(row.commit_sha)}</td>
                     <td>
                       <span className={`source-badge source-${row.run_source}`}>{runSourceLabel(row.run_source)}</span>
@@ -495,11 +560,13 @@ export default function App() {
           ) : (
             trends.map((group) =>
               METRIC_KEYS.map((metricKey) => (
-                <article className="trend-card" key={`${group.model_id}-${group.gpu_type}-${metricKey}`}>
+                <article className="trend-card" key={`${cohortKey(group)}-${metricKey}`}>
                   <div>
                     <h3>{metricLabel(metricKey)}</h3>
                     <p>
                       {group.model_id} | {group.gpu_type}
+                      <span>{cohortTitle(group)}</span>
+                      <span>{cohortDetail(group)}</span>
                     </p>
                   </div>
                   <TrendChart group={group} metricKey={metricKey} />

apps/performance_dashboard/frontend/src/api.ts

@@ -13,6 +13,17 @@ export type MetricValue = {
   precision: number;
 };
 
+export type CohortValue = string | number | null;
+
+export type ComparisonCohort = {
+  workload_id: CohortValue;
+  variant_id: CohortValue;
+  benchmark_version: CohortValue;
+  recipe_fingerprint: CohortValue;
+  hardware_profile_id: CohortValue;
+  software_profile_id: CohortValue;
+};
+
 export type SummaryRow = {
   model_id: string;
   gpu_type: string;
@@ -34,7 +45,7 @@ export type SummaryRow = {
   build_id: string;
   job_id: string;
   metrics: Record<string, MetricValue>;
-};
+} & ComparisonCohort;
 
 export type RunSource = "pr" | "local" | "scheduled_main" | "unknown";
 
@@ -68,13 +79,13 @@ export type TrendPoint = {
   build_id: string;
   job_id: string;
   metrics: Record<string, number | null>;
-};
+} & ComparisonCohort;
 
 export type TrendGroup = {
   model_id: string;
   gpu_type: string;
   points: TrendPoint[];
-};
+} & ComparisonCohort;
 
 export type TrendsResponse = {
   groups: TrendGroup[];

apps/performance_dashboard/frontend/src/styles.css

@@ -149,6 +149,11 @@ h3 {
   font-size: 0.82rem;
 }
 
+.trend-card p {
+  display: grid;
+  gap: 2px;
+}
+
 .stat strong {
   display: block;
   margin-top: 8px;
@@ -186,7 +191,7 @@ h3 {
 
 table {
   width: 100%;
-  min-width: 1120px;
+  min-width: 1260px;
   border-collapse: collapse;
 }
 
@@ -209,6 +214,25 @@ td {
   font-size: 0.9rem;
 }
 
+.cohort-cell {
+  display: grid;
+  gap: 2px;
+}
+
+.cohort-cell strong,
+.trend-card p span {
+  color: #1b2836;
+  font-size: 0.78rem;
+  font-weight: 700;
+}
+
+.cohort-cell span,
+.trend-card p span + span {
+  color: #607080;
+  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", monospace;
+  font-size: 0.72rem;
+}
+
 .badge {
   display: inline-flex;
   align-items: center;

docs/contributing/performance_benchmarks.md

@@ -112,11 +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
@@ -222,13 +221,17 @@ If a config declares `config_schema_version: 2`, loading fails clearly when any
 required v2 identity field is missing. If v2 identity or metadata fields are
 added without `config_schema_version: 2`, loading also fails so partial
 migrations do not silently run as v1 configs. Optional v2 metadata fields
-reserved for follow-up work, such as `recipe`, `metric_threshold_policy`, and
-`quality_metadata`, must be JSON objects when present.
+reserved for follow-up work, such as `metric_threshold_policy` and
+`quality_metadata`, must be JSON objects when present. (`recipe` is emitted
+by the harness and is not config-declarable.)
 
 Recipe fingerprinting, hardware/software profile IDs, exact-identity
-comparison, metric-specific threshold policy behavior, promoted baselines, and
-dashboard regrouping are separate follow-up changes. Until those land, rolling
-baseline comparison remains keyed by `(model_id, gpu_type)`.
+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 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`)
 
@@ -270,7 +273,43 @@ Written by `test_inference_performance.py`. One file per benchmark run.
   "timestamp": "2026-05-08T22:00:00+00:00",
   "text_encoder_time_s": 2.141,
   "dit_time_s": 8.437,
-  "vae_decode_time_s": 3.208
+  "vae_decode_time_s": 3.208,
+  "recipe": {
+    "recipe_schema_version": 1,
+    "benchmark": {
+      "benchmark_id": "wan-t2v-1.3b-2gpu",
+      "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 },
+    "generation_kwargs": { "height": 480, "width": 832, "num_frames": 45 },
+    "inputs": { "prompt_count": 1, "prompt_sha256": ["<measured-prompt-sha256>"] },
+    "attention": { "requested_backend": "FLASH_ATTN", "resolved_backend": "FLASH_ATTN" }
+  },
+  "recipe_fingerprint": "<sha256>",
+  "hardware_profile": {
+    "device_type": "cuda",
+    "gpu_count": 2,
+    "gpus": [{ "name": "NVIDIA L40S", "memory_gb": 48, "compute_capability": "8.9" }],
+    "interconnect": "none_or_partial"
+  },
+  "hardware_profile_id": "hw-<sha256-prefix>",
+  "software_profile": {
+    "python": "3.12",
+    "pytorch": "2.12",
+    "cuda": "13.0",
+    "packages": {
+      "fastvideo_kernel": "0.3.2",
+      "flashinfer": "0.2.11",
+      "nvidia_cutlass_dsl": "4.5.0",
+      "triton": "3.4.1"
+    }
+  },
+  "software_profile_id": "sw-<sha256-prefix>",
+  "environment_metadata": { "env": { "IMAGE_VERSION": "py3.12-cuda13.0.0" } },
+  "environment_fingerprint": "env-<sha256-prefix>"
 }
 ```
 
@@ -282,6 +321,9 @@ result, used as the rolling-baseline source of truth.
 ```jsonc
 {
   "model_id": "wan-t2v-1.3b-2gpu",
+  "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",
@@ -298,6 +340,10 @@ result, used as the rolling-baseline source of truth.
       "gated": true
     }
   },
+  "recipe_fingerprint": "<sha256>",
+  "hardware_profile_id": "hw-<sha256-prefix>",
+  "software_profile_id": "sw-<sha256-prefix>",
+  "environment_fingerprint": "env-<sha256-prefix>",
   "success": true
 }
 ```
@@ -310,6 +356,18 @@ 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`,
+`hardware_profile_id`, and `software_profile_id` cohort.
+`environment_metadata` and `environment_fingerprint` are audit data and are not
+part of the comparison key.
+The recipe prompt digests describe the prompts actually measured by the
+benchmark run; extra configured prompts are ignored unless the benchmark runner
+executes them.
+Software profile package cohorts keep exact versions for relevant
+attention/kernel packages, including FastVideo kernels, FlashAttention,
+FlashInfer, Cutlass DSL, SageAttention, Triton, and xFormers when installed.
+
 ## Environment variable reference
 
 | Variable | Default | Used by | Purpose |
@@ -391,7 +449,9 @@ When the rolling-baseline phase runs, it emits:
 
    Legacy v1 configs without `config_schema_version` still load, but should not
    gain v2 identity or metadata fields until they are migrated to
-   `config_schema_version: 2`.
+   `config_schema_version: 2`. For v2 configs, `workload_id`, `variant_id`,
+   and `benchmark_version` are part of the comparison key; benchmark runs
+   fail if any of these identity fields are missing.
 
 2. The pytest test auto-discovers all configs — no test code needed. CI
    picks it up on the next `/test performance` run.
@@ -433,5 +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`.

examples/inference/basic/basic_kandinsky5_i2v.py

@@ -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()

examples/inference/basic/basic_kandinsky5_t2v.py

@@ -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()

fastvideo/attention/backends/nabla.py

@@ -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)

fastvideo/attention/backends/sdpa.py

@@ -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"]:
@@ -50,50 +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, whose impl requires the
-        # tokenizer-style 2D [batch, key_len] padding mask. 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__(
@@ -124,7 +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,

fastvideo/attention/layer.py

@@ -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,

fastvideo/attention/selector.py

@@ -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
 

fastvideo/configs/models/dits/kandinsky5.py

@@ -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

fastvideo/configs/models/encoders/base.py

@@ -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

fastvideo/configs/pipelines/__init__.py

@@ -5,7 +5,6 @@ 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
@@ -17,6 +16,5 @@ __all__ = [
     "HunyuanConfig", "FastHunyuanConfig", "HunyuanGameCraftPipelineConfig", "PipelineConfig", "Hunyuan15T2V480PConfig",
     "Hunyuan15T2V720PConfig", "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig", "WanI2V720PConfig",
     "SelfForcingWanT2V480PConfig", "LucyEditDevConfig", "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig",
-    "HYWorldConfig", "Kandinsky5T2VConfig", "Kandinsky5I2VConfig", "MatrixGame2I2V480PConfig",
-    "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
+    "HYWorldConfig", "MatrixGame2I2V480PConfig", "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/kandinsky5.py

@@ -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

fastvideo/models/dits/hunyuanvideo15.py

@@ -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,
             )

fastvideo/models/dits/hyworld/hyworld.py

@@ -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,

fastvideo/models/dits/kandinsky5.py

@@ -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,

fastvideo/models/encoders/qwen2_5_vl_custom.py

@@ -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)
 

fastvideo/models/loader/component_loader.py

@@ -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
 

fastvideo/performance/hf_store.py

@@ -268,16 +268,31 @@ def load_records_for_model(
     model_id: str,
     gpu_type: str | None = None,
     *,
+    workload_id: str | None = None,
+    variant_id: str | None = None,
+    benchmark_version: str | None = None,
+    recipe_fingerprint: str | None = None,
+    hardware_profile_id: str | None = None,
+    software_profile_id: str | None = None,
     last_n: int | None = None,
     successful_only: bool = True,
     baseline_eligible_only: bool = False,
 ) -> list[dict[str, Any]]:
-    """Return records for a specific *model_id*, optionally filtered by GPU.
+    """Return records for a specific *model_id*, optionally filtered by cohort.
 
     Args:
         local_dir: Root directory previously populated by :func:`sync_from_hf`.
         model_id: Matches the ``model_id`` field inside each JSON record.
         gpu_type: When set, only records whose ``gpu_type`` matches are returned.
+        workload_id: When set, only records from the same workload are returned.
+        variant_id: When set, only records from the same workload variant are returned.
+        benchmark_version: When set, only records from the same benchmark version are returned.
+        recipe_fingerprint: When set, only records from the same benchmark
+            recipe are returned.
+        hardware_profile_id: When set, only records from the same hardware
+            cohort are returned.
+        software_profile_id: When set, only records from the same software
+            cohort are returned.
         last_n: When set, return only the most recent *n* records (after all
             other filters). Useful for sliding-window baseline calculations.
         successful_only: Passed through to :func:`load_records`.
@@ -299,6 +314,18 @@ def load_records_for_model(
     if gpu_type is not None:
         records = [r for r in records if r.get("gpu_type") == gpu_type]
 
+    identity_filters = {
+        "workload_id": workload_id,
+        "variant_id": variant_id,
+        "benchmark_version": benchmark_version,
+        "recipe_fingerprint": recipe_fingerprint,
+        "hardware_profile_id": hardware_profile_id,
+        "software_profile_id": software_profile_id,
+    }
+    for key, expected in identity_filters.items():
+        if expected is not None:
+            records = [r for r in records if str(r.get(key)) == str(expected)]
+
     if last_n is not None:
         records = records[-last_n:]
 
@@ -310,12 +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",
 )
 
 

fastvideo/performance/metric_policy.py

@@ -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,

fastvideo/performance_dashboard/service.py

@@ -17,6 +17,15 @@ from fastvideo.performance.hf_store import is_baseline_eligible_record, safe_flo
 from fastvideo.performance.metric_policy import regression_delta, resolve_metric_policies
 
 Record = dict[str, Any]
+CohortKey = tuple[str, str, str, str, str, str, str, str]
+COMPARISON_COHORT_KEYS = (
+    "workload_id",
+    "variant_id",
+    "benchmark_version",
+    "recipe_fingerprint",
+    "hardware_profile_id",
+    "software_profile_id",
+)
 
 
 def parse_timestamp(value: Any) -> datetime | None:
@@ -77,15 +86,44 @@ def record_metadata(record: Record) -> Record:
     }
 
 
-def group_by_model_gpu(records: list[Record]) -> dict[tuple[str, str], list[Record]]:
-    groups: dict[tuple[str, str], list[Record]] = defaultdict(list)
+def record_comparison_metadata(record: Record) -> Record:
+    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"),
+        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 ""),
+    )
+
+
+def group_by_comparison_cohort(records: list[Record]) -> dict[CohortKey, list[Record]]:
+    groups: dict[CohortKey, list[Record]] = defaultdict(list)
     for record in records:
-        model_id = str(record.get("model_id") or "unknown")
-        gpu_type = str(record.get("gpu_type") or "unknown")
-        groups[(model_id, gpu_type)].append(record)
+        groups[comparison_cohort_key(record)].append(record)
     return {key: sorted(value, key=record_sort_key) for key, value in groups.items()}
 
 
+def comparison_sort_key(record: Record) -> CohortKey:
+    return comparison_cohort_key(record)
+
+
+def latest_row_sort_key(row: Record) -> tuple[Any, ...]:
+    return (row["status"] != "fail", *comparison_sort_key(row))
+
+
+def group_identity(key: CohortKey) -> tuple[str, str]:
+    model_id = key[0]
+    gpu_type = key[1]
+    return model_id, gpu_type
+
+
 def baseline_value(records: list[Record], metric_key: str) -> float | None:
     values = [safe_float(record.get(metric_key)) for record in records]
     values = [value for value in values if value is not None]
@@ -99,7 +137,8 @@ def build_latest_summary(records: list[Record],
                          baseline_window: int = 5,
                          run_source: str | None = None) -> list[Record]:
     rows: list[Record] = []
-    for (model_id, gpu_type), group in group_by_model_gpu(records).items():
+    for key, group in group_by_comparison_cohort(records).items():
+        model_id, gpu_type = group_identity(key)
         latest_candidates = group
         if run_source:
             latest_candidates = [record for record in group if record_run_source(record) == run_source]
@@ -156,6 +195,7 @@ def build_latest_summary(records: list[Record],
             "timestamp": latest.get("timestamp"),
             "commit_sha": latest.get("commit_sha"),
             **record_metadata(latest),
+            **record_comparison_metadata(latest),
             "success": success,
             "baseline_n": len(baseline_records),
             "worst_regression_pct": worst_regression,
@@ -166,12 +206,13 @@ def build_latest_summary(records: list[Record],
             "metrics": metrics,
         })
 
-    return sorted(rows, key=lambda row: (row["status"] != "fail", row["model_id"], row["gpu_type"]))
+    return sorted(rows, key=latest_row_sort_key)
 
 
 def build_trends(records: list[Record]) -> list[Record]:
     trends: list[Record] = []
-    for (model_id, gpu_type), group in group_by_model_gpu(records).items():
+    for key, group in group_by_comparison_cohort(records).items():
+        model_id, gpu_type = group_identity(key)
         points = []
         for record in group:
             metric_policies = resolve_metric_policies(record.get("regression_thresholds"))
@@ -179,6 +220,7 @@ def build_trends(records: list[Record]) -> list[Record]:
                 "timestamp": record.get("timestamp"),
                 "commit_sha": record.get("commit_sha"),
                 **record_metadata(record),
+                **record_comparison_metadata(record),
                 "success": bool(record.get("success", True)),
                 "metrics": {
                     policy.key: safe_float(record.get(policy.key))
@@ -189,6 +231,7 @@ def build_trends(records: list[Record]) -> list[Record]:
         trends.append({
             "model_id": model_id,
             "gpu_type": gpu_type,
+            **record_comparison_metadata(group[-1]),
             "points": points,
         })
-    return sorted(trends, key=lambda trend: (trend["model_id"], trend["gpu_type"]))
+    return sorted(trends, key=comparison_sort_key)

fastvideo/pipelines/basic/kandinsky5/__init__.py

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

fastvideo/pipelines/basic/kandinsky5/kandinsky5_i2v_pipeline.py

@@ -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

fastvideo/pipelines/basic/kandinsky5/kandinsky5_pipeline.py

@@ -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

fastvideo/pipelines/basic/kandinsky5/presets.py

@@ -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)

fastvideo/pipelines/stages/__init__.py

@@ -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",

fastvideo/pipelines/stages/base.py

@@ -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

fastvideo/pipelines/stages/decoding.py

@@ -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

fastvideo/pipelines/stages/denoising.py

@@ -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__()
@@ -1188,7 +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__()

fastvideo/pipelines/stages/kandinsky5.py

@@ -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

fastvideo/pipelines/stages/text_encoding.py

@@ -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__()

fastvideo/pipelines/stages/validators.py

@@ -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."""

fastvideo/platforms/cuda.py

@@ -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

fastvideo/platforms/interface.py

@@ -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()
 
 

fastvideo/registry.py

@@ -27,7 +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
@@ -118,10 +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
@@ -142,7 +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.
 
@@ -157,7 +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:
@@ -497,174 +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)
@@ -713,7 +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",
@@ -1065,10 +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):
@@ -1077,12 +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. "
@@ -1133,8 +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 (
@@ -1164,7 +990,6 @@ def _register_presets() -> None:
         HUNYUAN_PRESETS,
         HUNYUAN15_PRESETS,
         HYWORLD_PRESETS,
-        KANDINSKY5_PRESETS,
         LINGBOTWORLD_PRESETS,
         LONGCAT_PRESETS,
         LTX2_PRESETS,

fastvideo/tests/attention/test_sdpa_metadata_mask_contract.py

@@ -1,42 +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, whose impl requires the
-tokenizer-style 2D [batch, key_len] padding mask (flash_attn.py pads
-``attn_mask.shape[1]`` assuming 2D). The builder must therefore 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 consumer produced a garbage-length pad (CUDA device-side assert).
-"""
-
-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)

fastvideo/tests/contract/test_ci_test_collection.py

@@ -1,85 +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",
-    "ops": "no lane yet — GPU op tests, run manually",
-    "modal": "CI infrastructure itself, not a test suite",
-}
-
-
-def _dirs_with_tests() -> list[str]:
-    dirs = []
-    for child in sorted(TESTS_ROOT.iterdir()):
-        if child.is_dir() and any(child.rglob("test_*.py")):
-            dirs.append(child.name)
-    return dirs
-
-
-def _ci_text() -> str:
-    return "\n".join(
-        src.read_text(errors="replace") for src in CI_SOURCES if src.exists())
-
-
-def test_every_test_directory_is_collected_or_allowlisted():
-    ci_text = _ci_text()
-    dark = [
-        name for name in _dirs_with_tests()
-        if f"tests/{name}" not in ci_text and name not in ALLOWLIST
-    ]
-    assert not dark, (
-        f"Test directories not referenced by any CI lane and not "
-        f"allowlisted: {dark}. Wire them into a lane in "
-        f"fastvideo/tests/modal/pr_test.py (or a Buildkite step), or add an "
-        f"allowlist entry with a reason in {__file__}.")
-
-
-def test_local_tests_stays_out_of_ci():
-    # tests/local_tests/ (repo root) is developer-local by design (author
-    # decision, 2026-07-05): parity scaffolds and machine-specific checks
-    # that must never gate CI. Fail if any CI source starts collecting it.
-    assert "tests/local_tests" not in _ci_text(), (
-        "tests/local_tests/ is local-only by design; remove the CI "
-        "reference or move the tests into a fastvideo/tests/ lane.")
-
-
-def test_allowlist_entries_are_still_real_directories():
-    # A stale allowlist hides regressions; entries must track reality.
-    missing = [
-        name for name in ALLOWLIST
-        if name != "modal" and not (TESTS_ROOT / name).is_dir()
-    ]
-    assert not missing, (
-        f"Allowlisted directories no longer exist — remove them: {missing}")

fastvideo/tests/encoders/test_t5_encoder.py

@@ -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()

fastvideo/tests/modal/pr_test.py

@@ -276,7 +276,7 @@ def run_self_forcing_tests():
 @app.function(gpu="L40S:1", image=image, timeout=900)
 def run_unit_test():
     run_test(
-        "pytest ./fastvideo/tests/api/ ./fastvideo/tests/contract/ ./fastvideo/tests/dataset/ ./fastvideo/tests/workflow/ ./fastvideo/tests/entrypoints/ ./fastvideo/tests/train/ ./fastvideo/tests/stages/ ./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"
     )
 
 

fastvideo/tests/performance/compare_baseline.py

@@ -5,7 +5,8 @@ This script:
 1) reads current benchmark results from fastvideo/tests/performance/results,
 2) syncs the canonical baseline from the configured HF dataset repo,
 3) compares each current record against the median of up to 5 prior
-   baseline-eligible successful records (filtered by gpu_type),
+   baseline-eligible successful records in the same workload/variant/version,
+   GPU, recipe, hardware, and software cohort,
 4) writes normalized records back to the HF dataset repo according to
    PERF_UPLOAD_POLICY,
 5) exits non-zero if any gated metric exceeds both its percent and absolute
@@ -64,6 +65,27 @@ PERF_REPORTS_DIR = os.environ.get("PERF_REPORTS_DIR", "/root/data/perf_reports")
 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 = (
+    "workload_id",
+    "variant_id",
+    "benchmark_version",
+    "recipe",
+    "recipe_fingerprint",
+    "hardware_profile",
+    "hardware_profile_id",
+    "software_profile",
+    "software_profile_id",
+    "environment_metadata",
+    "environment_fingerprint",
+)
+COMPARISON_IDENTITY_KEYS = (
+    "workload_id",
+    "variant_id",
+    "benchmark_version",
+    "recipe_fingerprint",
+    "hardware_profile_id",
+    "software_profile_id",
+)
 
 
 def _should_persist_tracking() -> bool:
@@ -136,6 +158,46 @@ def _record_metadata(run_source: str, result: dict[str, Any]) -> dict[str, Any]:
     }
 
 
+def _identity_metadata(result: dict[str, Any]) -> dict[str, Any]:
+    metadata = {
+        key: result[key]
+        for key in IDENTITY_KEYS
+        if key in result and result[key] is not None
+    }
+    recipe = result.get("recipe")
+    benchmark = recipe.get("benchmark") if isinstance(recipe, dict) else None
+    if isinstance(benchmark, dict):
+        for key in ("workload_id", "variant_id", "benchmark_version"):
+            if key not in metadata and benchmark.get(key) is not None:
+                metadata[key] = benchmark[key]
+    return metadata
+
+
+def _comparison_identity_filters(record: dict[str, Any]) -> dict[str, str]:
+    missing = [
+        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 {
+        key: str(record[key])
+        for key in COMPARISON_IDENTITY_KEYS
+    }
+
+
+def _format_identity_filters(filters: dict[str, str]) -> str:
+    if not filters:
+        return ""
+    compact = ", ".join(f"{key}={value}" for key, value in filters.items())
+    return f" ({compact})"
+
+
 def _load_current_results() -> list[dict[str, Any]]:
     pattern = os.path.join(RESULTS_DIR, "perf_*.json")
     records: list[dict[str, Any]] = []
@@ -169,7 +231,7 @@ def normalize_performance_result(result: dict[str, Any]) -> dict[str, Any]:
     vae_decode_time = safe_float(result.get("vae_decode_time_s"))
     metric_policies = resolve_metric_policies(result.get("regression_thresholds"))
 
-    return {
+    record = {
         "model_id": model_id,
         "timestamp": timestamp,
         "commit_sha": commit_sha,
@@ -184,6 +246,8 @@ def normalize_performance_result(result: dict[str, Any]) -> dict[str, Any]:
         "success": True,
         **_record_metadata(_detect_run_source(), result),
     }
+    record.update(_identity_metadata(result))
+    return record
 
 
 def _normalize_record(result: dict[str, Any]) -> dict[str, Any]:
@@ -422,17 +486,31 @@ def main() -> int:
             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:
-            print(f"No baseline for {record['model_id']} on "
-                  f"{record['gpu_type']}. Initializing...")
+            # 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:
+            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 "

fastvideo/tests/performance/dashboard.py

@@ -29,15 +29,76 @@ METRICS = (
     "dit_time_s",
     "vae_decode_time_s",
 )
+COMPARISON_COHORT_KEYS = (
+    "workload_id",
+    "variant_id",
+    "benchmark_version",
+    "recipe_fingerprint",
+    "hardware_profile_id",
+    "software_profile_id",
+)
+GROUP_KEYS = ("model_id", "gpu_type", *COMPARISON_COHORT_KEYS)
+
+
+def _cohort_value(value: object) -> str:
+    if value is None:
+        return ""
+    try:
+        if pd.isna(value):
+            return ""
+    except (TypeError, ValueError):
+        pass
+    return str(value)
+
+
+def _display_value(value: object) -> str:
+    return _cohort_value(value) or "legacy"
+
+
+def _short_value(value: object) -> str:
+    text = _display_value(value)
+    if text == "legacy" or len(text) <= 14:
+        return text
+    return text[:12]
+
+
+def _cohort_title(group_key: tuple[object, ...]) -> str:
+    workload = _display_value(group_key[2])
+    variant = _display_value(group_key[3])
+    version = _display_value(group_key[4])
+    version_label = version if version == "legacy" else f"v{version}"
+    return f"{workload} / {variant} / {version_label}"
+
+
+def _cohort_detail(group_key: tuple[object, ...]) -> str:
+    return " | ".join((
+        f"recipe {_short_value(group_key[5])}",
+        _short_value(group_key[6]),
+        _short_value(group_key[7]),
+    ))
+
+
+def _group_record(group_key: tuple[object, ...]) -> dict[str, str]:
+    return {
+        key: _cohort_value(value)
+        for key, value in zip(GROUP_KEYS, group_key)
+    }
+
+
+def _dashboard_frame(df: pd.DataFrame) -> pd.DataFrame:
+    dashboard_df = df.copy()
+    for key in GROUP_KEYS:
+        if key not in dashboard_df.columns:
+            dashboard_df[key] = ""
+        dashboard_df[key] = dashboard_df[key].map(_cohort_value)
+    return dashboard_df
 
 # -----------------------------
 # 1. Grouping
 # -----------------------------
 def group_data(df: pd.DataFrame):
-    # Group only by model+GPU so each group produces a time-series line.
-    # config_id (commit SHA) is carried as a column for hover/color use.
-    keys = ["model_id", "gpu_type"]
-    return df.groupby(keys, dropna=False)
+    # Group by the same comparison cohort used by baseline gating.
+    return _dashboard_frame(df).groupby(list(GROUP_KEYS), dropna=False)
 
 # -----------------------------
 # 2. Plot builder
@@ -46,15 +107,20 @@ def build_plots(df: pd.DataFrame) -> tuple[list, list[dict[str, object]]]:
     figs = []
     skipped_metrics: list[dict[str, object]] = []
 
-    for (model_id, gpu_type), g in group_data(df):
+    for group_key, g in group_data(df):
+        model_id, gpu_type = group_key[:2]
+        group_record = _group_record(group_key)
+        cohort_title = _cohort_title(group_key)
+        cohort_detail = _cohort_detail(group_key)
         g = g.sort_values("timestamp")
 
         # One chart per metric so the y-axes aren't on wildly different scales
         for metric in METRICS:
             if metric not in g.columns:
                 skipped_metrics.append({
-                    "model_id": model_id,
-                    "gpu_type": gpu_type,
+                    **group_record,
+                    "cohort": cohort_title,
+                    "cohort_detail": cohort_detail,
                     "metric": metric,
                     "reason": "column missing from loaded records",
                     "records": len(g),
@@ -65,8 +131,9 @@ def build_plots(df: pd.DataFrame) -> tuple[list, list[dict[str, object]]]:
             non_null = int(g[metric].notna().sum())
             if non_null == 0:
                 skipped_metrics.append({
-                    "model_id": model_id,
-                    "gpu_type": gpu_type,
+                    **group_record,
+                    "cohort": cohort_title,
+                    "cohort_detail": cohort_detail,
                     "metric": metric,
                     "reason": "no non-null values in loaded records",
                     "records": len(g),
@@ -79,8 +146,8 @@ def build_plots(df: pd.DataFrame) -> tuple[list, list[dict[str, object]]]:
                 x="timestamp",
                 y=metric,
                 markers=True,
-                hover_data=["config_id", "commit_sha"],
-                title=f"{model_id} | {gpu_type} | {metric}",
+                hover_data=["config_id", "commit_sha", *COMPARISON_COHORT_KEYS],
+                title=f"{model_id} | {gpu_type} | {cohort_title} | {cohort_detail} | {metric}",
                 labels={"timestamp": "Time", metric: metric},
             )
             figs.append(fig)
@@ -95,7 +162,7 @@ def render_skipped_metrics(skipped_metrics: list[dict[str, object]]) -> str:
     rows = [
         "<h3>Skipped Metric Plots</h3>",
         "<table>",
-        ("<thead><tr><th>Model</th><th>GPU</th><th>Metric</th>"
+        ("<thead><tr><th>Model</th><th>GPU</th><th>Cohort</th><th>Metric</th>"
          "<th>Records</th><th>Non-null</th><th>Reason</th></tr></thead>"),
         "<tbody>",
     ]
@@ -104,6 +171,7 @@ def render_skipped_metrics(skipped_metrics: list[dict[str, object]]) -> str:
             "<tr>"
             f"<td>{escape(str(item['model_id']))}</td>"
             f"<td>{escape(str(item['gpu_type']))}</td>"
+            f"<td>{escape(str(item['cohort']))}<br><code>{escape(str(item['cohort_detail']))}</code></td>"
             f"<td>{escape(str(item['metric']))}</td>"
             f"<td>{item['records']}</td>"
             f"<td>{item['non_null']}</td>"
@@ -165,6 +233,7 @@ def main() -> None:
         print("Skipped metric plots:")
         for item in skipped_metrics:
             print(f"  - {item['model_id']} | {item['gpu_type']} | "
+                  f"{item['cohort']} | {item['cohort_detail']} | "
                   f"{item['metric']}: {item['reason']} "
                   f"({item['non_null']}/{item['records']} non-null)")
     print(f"Generated {len(figs)} metric plot(s)")

fastvideo/tests/performance/identity.py

@@ -0,0 +1,435 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Comparable identity helpers for performance benchmark records."""
+
+from __future__ import annotations
+
+import dataclasses
+import enum
+import hashlib
+import importlib.metadata
+import json
+import os
+import platform
+import re
+from collections.abc import Mapping, Sequence
+from pathlib import Path
+from typing import Any
+
+import torch
+
+RECIPE_SCHEMA_VERSION = 1
+PROFILE_ID_LENGTH = 16
+_PATH_EXCLUDED_GENERATION_KEYS = {"output_path", "output_video_name"}
+_PROMPT_KEYS = {"prompt", "negative_prompt", "neg_prompt"}
+REQUIRED_BENCHMARK_IDENTITY_KEYS = (
+    "benchmark_id",
+    "workload_id",
+    "variant_id",
+    "benchmark_version",
+)
+_PROFILE_ENV_VARS = (
+    "CUDA_VISIBLE_DEVICES",
+    "FASTVIDEO_ATTENTION_BACKEND",
+    "IMAGE_VERSION",
+    "UV_TORCH_BACKEND",
+)
+_PACKAGE_DISTRIBUTIONS = {
+    "fastvideo_kernel": ("fastvideo-kernel", "fastvideo_kernel"),
+    "flash_attn": ("flash-attn", "flash_attn"),
+    "flash_attn_4": ("flash-attn-4",),
+    "flash_attention_fp4": ("flash-attention-fp4",),
+    "flashinfer": ("flashinfer-python", "flashinfer"),
+    "nvidia_cutlass_dsl": ("nvidia-cutlass-dsl", "cutlass-dsl"),
+    "sageattention": ("sageattention",),
+    "sageattn3": ("sageattn3",),
+    "triton": ("triton",),
+    "xformers": ("xformers",),
+}
+
+
+def canonical_json(payload: Any) -> str:
+    """Return deterministic, compact JSON for a recipe/profile payload."""
+
+    return json.dumps(
+        _canonicalize(payload),
+        sort_keys=True,
+        separators=(",", ":"),
+        ensure_ascii=True,
+    )
+
+
+def sha256_hexdigest(payload: str | bytes) -> str:
+    if isinstance(payload, str):
+        payload = payload.encode("utf-8")
+    return hashlib.sha256(payload).hexdigest()
+
+
+def recipe_fingerprint(recipe: Mapping[str, Any]) -> str:
+    # Runtime-resolved values are audit metadata, not identity: hashing the
+    # resolved HF snapshot revision would churn the cohort (and via the
+    # no-baseline init path, silently reset the rolling baseline) on ANY
+    # upstream repo commit, including model-card edits with unchanged
+    # weights. The fingerprint is pinned to declared inputs only; the
+    # resolved values stay in the stored recipe for auditability.
+    pruned = {
+        key: (dict(value) if isinstance(value, Mapping) else value)
+        for key, value in recipe.items()
+    }
+    model = pruned.get("model")
+    if isinstance(model, dict):
+        model.pop("resolved_revision", None)
+    attention = pruned.get("attention")
+    if isinstance(attention, dict):
+        # Same reasoning as resolved_revision: with requested_backend="auto",
+        # a silent runtime fallback (e.g. a broken flash-attn wheel) must NOT
+        # open a fresh ungated cohort and seed degraded numbers as the new
+        # baseline — it should fail the regression gate in the old cohort.
+        # Intentional backend changes are declared via requested_backend,
+        # which stays in the hash.
+        attention.pop("resolved_backend", None)
+    return sha256_hexdigest(canonical_json(pruned))
+
+
+def profile_id(prefix: str, profile: Mapping[str, Any]) -> str:
+    return f"{prefix}-{sha256_hexdigest(canonical_json(profile))[:PROFILE_ID_LENGTH]}"
+
+
+def hardware_profile_id(profile: Mapping[str, Any]) -> str:
+    return profile_id("hw", profile)
+
+
+def software_profile_id(profile: Mapping[str, Any]) -> str:
+    return profile_id("sw", profile)
+
+
+def environment_fingerprint(metadata: Mapping[str, Any]) -> str:
+    return profile_id("env", metadata)
+
+
+def benchmark_identity_from_config(cfg: Mapping[str, Any]) -> dict[str, Any]:
+    missing = [
+        key for key in REQUIRED_BENCHMARK_IDENTITY_KEYS
+        if _none_if_empty(cfg.get(key)) is None
+    ]
+    if missing:
+        raise ValueError("Benchmark config missing required identity fields: " + ", ".join(missing))
+    return {
+        key: cfg[key]
+        for key in REQUIRED_BENCHMARK_IDENTITY_KEYS
+    }
+
+
+def build_recipe_from_benchmark_config(
+    cfg: Mapping[str, Any],
+    *,
+    attention_backend: str | None = None,
+    resolved_attention_backend: Any | None = None,
+    resolved_model_revision: Any | None = None,
+    measured_prompts: Sequence[Any] | None = None,
+) -> dict[str, Any]:
+    """Build a deterministic recipe document from a benchmark config.
+
+    The recipe captures fields that describe what benchmark workload was run. It
+    intentionally excludes timings, timestamps, commit metadata, and output paths.
+    """
+
+    model = dict(cfg.get("model") or {})
+    init_kwargs = dict(cfg.get("init_kwargs") or {})
+    generation_kwargs = dict(cfg.get("generation_kwargs") or {})
+    benchmark_identity = benchmark_identity_from_config(cfg)
+    prompts = list(measured_prompts) if measured_prompts is not None else list(
+        cfg.get("test_prompts") or ["A cinematic video."])
+    if attention_backend is None:
+        attention_backend = os.environ.get("FASTVIDEO_ATTENTION_BACKEND")
+
+    negative_prompt = generation_kwargs.pop("negative_prompt", generation_kwargs.pop("neg_prompt", None))
+    generation_recipe = {
+        key: value
+        for key, value in generation_kwargs.items()
+        if key not in _PATH_EXCLUDED_GENERATION_KEYS and key not in _PROMPT_KEYS
+    }
+
+    return {
+        "recipe_schema_version": RECIPE_SCHEMA_VERSION,
+        "benchmark": benchmark_identity,
+        "model": {
+            "model_path": normalize_model_path(model.get("model_path")),
+            "revision": _none_if_empty(model.get("revision") or cfg.get("revision")),
+            "resolved_revision": _none_if_empty(resolved_model_revision),
+        },
+        "init_kwargs": init_kwargs,
+        "generation_kwargs": generation_recipe,
+        "inputs": {
+            "prompt_count": len(prompts),
+            "prompt_sha256": [_optional_digest(prompt) for prompt in prompts],
+            "negative_prompt_sha256": _optional_digest(negative_prompt),
+        },
+        "attention": {
+            "requested_backend": _none_if_empty(attention_backend) or "auto",
+            "resolved_backend": _none_if_empty(resolved_attention_backend),
+        },
+    }
+
+
+def normalize_model_path(value: Any) -> str | None:
+    if value is None:
+        return None
+    text = os.fspath(value) if isinstance(value, os.PathLike) else str(value)
+    text = text.strip()
+    if not text:
+        return None
+    if _looks_like_local_path(text):
+        return Path(text).expanduser().as_posix()
+    return re.sub(r"/+", "/", text)
+
+
+def resolved_revision_from_model_path(value: Any) -> str | None:
+    normalized = normalize_model_path(value)
+    if normalized is None:
+        return None
+    parts = Path(normalized).parts
+    for index, part in enumerate(parts[:-1]):
+        if part == "snapshots":
+            revision = parts[index + 1]
+            if re.fullmatch(r"[0-9a-f]{40}", revision):
+                return revision
+    return None
+
+
+def hardware_profile(
+    *,
+    num_gpus: int | None = None,
+    gpu_devices: Sequence[Mapping[str, Any]] | None = None,
+    device_type: str | None = None,
+    interconnect: str | None = None,
+) -> dict[str, Any]:
+    """Return normalized hardware cohort metadata.
+
+    ``gpu_devices`` is an injection point for tests and non-CUDA callers.
+    """
+
+    if gpu_devices is not None:
+        devices = [_normalize_gpu_device(device) for device in gpu_devices]
+        return {
+            "device_type": device_type or "cuda",
+            "gpu_count": num_gpus if num_gpus is not None else len(devices),
+            "gpus": devices,
+            "interconnect": interconnect or "unknown",
+        }
+
+    if not torch.cuda.is_available():
+        return {
+            "device_type": device_type or "cpu",
+            "gpu_count": 0,
+            "gpus": [],
+            "interconnect": interconnect or "none",
+        }
+
+    gpu_count = int(num_gpus if num_gpus is not None else torch.cuda.device_count())
+    devices = []
+    cuda_device_count = torch.cuda.device_count()
+    for device_id in range(gpu_count):
+        if device_id >= cuda_device_count:
+            devices.append(_normalize_gpu_device({}))
+            continue
+        props = torch.cuda.get_device_properties(device_id)
+        capability = torch.cuda.get_device_capability(device_id)
+        devices.append(
+            _normalize_gpu_device({
+                "name": props.name,
+                "memory_bytes": props.total_memory,
+                "compute_capability": f"{capability[0]}.{capability[1]}",
+            }))
+
+    return {
+        "device_type": device_type or "cuda",
+        "gpu_count": gpu_count,
+        "gpus": devices,
+        "interconnect": interconnect or _detect_cuda_interconnect(gpu_count),
+    }
+
+
+def software_profile(
+    *,
+    python_version: str | None = None,
+    torch_version: str | None = None,
+    cuda_version: str | None = None,
+    package_versions: Mapping[str, str | None] | None = None,
+) -> dict[str, Any]:
+    """Return normalized software cohort metadata.
+
+    Python, PyTorch, and CUDA use major/minor cohorts. Attention and kernel
+    packages keep exact versions because patch releases can change performance.
+    """
+
+    versions = dict(package_versions) if package_versions is not None else _installed_package_versions()
+    return {
+        "python": _major_minor(python_version or platform.python_version()),
+        "pytorch": _major_minor(torch_version or torch.__version__),
+        "cuda": _major_minor(cuda_version or torch.version.cuda),
+        "packages": {
+            name: str(version)
+            for name, version in sorted(versions.items())
+            if version
+        },
+    }
+
+
+def environment_metadata(
+    *,
+    env: Mapping[str, str] | None = None,
+    package_versions: Mapping[str, str | None] | None = None,
+    hardware: Mapping[str, Any] | None = None,
+    software: Mapping[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Return audit metadata kept separate from comparable identity keys."""
+
+    source_env = env if env is not None else os.environ
+    full_package_versions = dict(package_versions) if package_versions is not None else _installed_package_versions()
+    return {
+        "python": {
+            "version": platform.python_version(),
+            "implementation": platform.python_implementation(),
+        },
+        "platform": {
+            "system": platform.system(),
+            "machine": platform.machine(),
+            "release": platform.release(),
+        },
+        "torch": {
+            "version": torch.__version__,
+            "cuda": torch.version.cuda,
+            "cudnn": torch.backends.cudnn.version(),
+        },
+        "env": {
+            key: source_env.get(key)
+            for key in _PROFILE_ENV_VARS
+            if source_env.get(key) is not None
+        },
+        "packages": {
+            key: value
+            for key, value in sorted(full_package_versions.items())
+            if value
+        },
+        "hardware_profile": dict(hardware) if hardware is not None else hardware_profile(),
+        "software_profile": dict(software) if software is not None else software_profile(
+            package_versions=full_package_versions),
+    }
+
+
+def _canonicalize(value: Any) -> Any:
+    if dataclasses.is_dataclass(value) and not isinstance(value, type):
+        return _canonicalize(dataclasses.asdict(value))
+    if isinstance(value, Mapping):
+        return {
+            str(key): _canonicalize(value[key])
+            for key in sorted(value, key=lambda item: str(item))
+        }
+    if isinstance(value, (set, frozenset)):
+        return [_canonicalize(item) for item in sorted(value, key=lambda item: str(item))]
+    if isinstance(value, tuple):
+        return [_canonicalize(item) for item in value]
+    if isinstance(value, list):
+        return [_canonicalize(item) for item in value]
+    if isinstance(value, enum.Enum):
+        return value.name
+    if isinstance(value, Path):
+        return value.expanduser().as_posix()
+    if isinstance(value, os.PathLike):
+        return os.fspath(value)
+    if isinstance(value, type):
+        return f"{value.__module__}.{value.__qualname__}"
+    if isinstance(value, torch.dtype):
+        return str(value).removeprefix("torch.")
+    return value
+
+
+def _none_if_empty(value: Any) -> Any | None:
+    if value is None:
+        return None
+    if isinstance(value, str) and not value.strip():
+        return None
+    return value
+
+
+def _optional_digest(value: Any) -> str | None:
+    if value is None:
+        return None
+    return sha256_hexdigest(str(value))
+
+
+def _looks_like_local_path(value: str) -> bool:
+    if value.startswith(("/", "./", "../", "~")):
+        return True
+    looks_like_hf_repo = re.match(r"^[A-Za-z0-9_.-]+/[A-Za-z0-9_.-]+$", value)
+    has_separator = "/" in value or "\\" in value or os.sep in value
+    return has_separator and looks_like_hf_repo is None
+
+
+def _normalize_gpu_device(device: Mapping[str, Any]) -> dict[str, Any]:
+    memory_gb = device.get("memory_gb")
+    if memory_gb is None and device.get("memory_bytes") is not None:
+        memory_gb = round(float(device["memory_bytes"]) / (1024**3))
+    return {
+        "name": _none_if_empty(device.get("name")) or "unknown",
+        "memory_gb": memory_gb,
+        "compute_capability": _none_if_empty(device.get("compute_capability")),
+    }
+
+
+def _detect_cuda_interconnect(gpu_count: int) -> str:
+    if gpu_count <= 1:
+        return "single_gpu"
+    try:
+        from fastvideo.platforms import current_platform
+
+        if current_platform.is_cuda() and current_platform.is_full_nvlink(list(range(gpu_count))):
+            return "full_nvlink"
+        return "none_or_partial"
+    except Exception:
+        return "unknown"
+
+
+def _installed_package_versions() -> dict[str, str | None]:
+    return {
+        name: _distribution_version(*distribution_names)
+        for name, distribution_names in _PACKAGE_DISTRIBUTIONS.items()
+    }
+
+
+def _distribution_version(*names: str) -> str | None:
+    for name in names:
+        try:
+            return importlib.metadata.version(name)
+        except importlib.metadata.PackageNotFoundError:
+            continue
+    return None
+
+
+def _major_minor(version: str | None) -> str | None:
+    if not version:
+        return None
+    match = re.search(r"(\d+)\.(\d+)", version)
+    if match is None:
+        return version
+    return f"{match.group(1)}.{match.group(2)}"
+
+
+__all__ = [
+    "RECIPE_SCHEMA_VERSION",
+    "REQUIRED_BENCHMARK_IDENTITY_KEYS",
+    "benchmark_identity_from_config",
+    "canonical_json",
+    "environment_fingerprint",
+    "environment_metadata",
+    "hardware_profile",
+    "hardware_profile_id",
+    "normalize_model_path",
+    "profile_id",
+    "recipe_fingerprint",
+    "resolved_revision_from_model_path",
+    "build_recipe_from_benchmark_config",
+    "sha256_hexdigest",
+    "software_profile",
+    "software_profile_id",
+]

fastvideo/tests/performance/test_compare_baseline_policy.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
+import pytest
+
 from fastvideo.tests.performance import compare_baseline
 from fastvideo.performance.metric_policy import resolve_metric_policies
 
@@ -119,6 +121,110 @@ def test_boolean_regression_threshold_values_are_ignored():
     assert latency.threshold_percent == 0.08
     assert latency.threshold_absolute == 0.5
     assert latency.gated is False
+def test_normalized_record_preserves_identity_metadata(monkeypatch):
+    monkeypatch.setenv("PERF_RUN_SOURCE", "pr")
+    raw = _raw_result()
+    raw.update({
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 2,
+        "recipe": {
+            "recipe_schema_version": 1,
+        },
+        "recipe_fingerprint": "recipe-1",
+        "hardware_profile": {
+            "gpu_count": 1,
+        },
+        "hardware_profile_id": "hw-1",
+        "software_profile": {
+            "python": "3.12",
+        },
+        "software_profile_id": "sw-1",
+        "environment_metadata": {
+            "env": {
+                "IMAGE_VERSION": "latest",
+            },
+        },
+        "environment_fingerprint": "env-1",
+    })
+
+    record = compare_baseline.normalize_performance_result(raw)
+
+    assert record["workload_id"] == "wan-t2v"
+    assert record["variant_id"] == "1.3b-sp2"
+    assert record["benchmark_version"] == 2
+    assert record["recipe"] == {"recipe_schema_version": 1}
+    assert record["recipe_fingerprint"] == "recipe-1"
+    assert record["hardware_profile"] == {"gpu_count": 1}
+    assert record["hardware_profile_id"] == "hw-1"
+    assert record["software_profile"] == {"python": "3.12"}
+    assert record["software_profile_id"] == "sw-1"
+    assert record["environment_metadata"] == {"env": {"IMAGE_VERSION": "latest"}}
+    assert record["environment_fingerprint"] == "env-1"
+
+
+def test_normalized_record_reads_identity_labels_from_recipe(monkeypatch):
+    monkeypatch.setenv("PERF_RUN_SOURCE", "pr")
+    raw = _raw_result()
+    raw["recipe"] = {
+        "benchmark": {
+            "workload_id": "wan-t2v",
+            "variant_id": "1.3b-sp2",
+            "benchmark_version": 2,
+        },
+    }
+
+    record = compare_baseline.normalize_performance_result(raw)
+
+    assert record["workload_id"] == "wan-t2v"
+    assert record["variant_id"] == "1.3b-sp2"
+    assert record["benchmark_version"] == 2
+
+
+def test_comparison_identity_filters_use_full_issue_key():
+    record = {
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 2,
+        "recipe_fingerprint": "recipe-1",
+        "hardware_profile_id": "hw-1",
+        "software_profile_id": "sw-1",
+    }
+
+    assert compare_baseline._comparison_identity_filters(record) == {
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": "2",
+        "recipe_fingerprint": "recipe-1",
+        "hardware_profile_id": "hw-1",
+        "software_profile_id": "sw-1",
+    }
+
+
+def test_comparison_identity_filters_require_full_issue_key():
+    record = {
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 0,
+        "recipe_fingerprint": "recipe-1",
+        "hardware_profile_id": "hw-1",
+    }
+
+    with pytest.raises(ValueError, match="software_profile_id"):
+        compare_baseline._comparison_identity_filters(record)
+
+
+def test_comparison_identity_filters_keep_zero_version():
+    record = {
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 0,
+        "recipe_fingerprint": "recipe-1",
+        "hardware_profile_id": "hw-1",
+        "software_profile_id": "sw-1",
+    }
+
+    assert compare_baseline._comparison_identity_filters(record)["benchmark_version"] == "0"
 
 
 def test_baseline_eligibility_only_for_successful_scheduled_main():

fastvideo/tests/performance/test_dashboard_api.py

@@ -56,8 +56,34 @@ def _record(model_id, gpu_type, ts, commit, latency, throughput, success=True, *
 
 def test_summary_endpoint_returns_latest_group_status():
     app = create_app(FakeStore([
-        _record("wan", "NVIDIA L40S", "2026-01-01T00:00:00+00:00", "a" * 40, 10.0, 10.0),
-        _record("wan", "NVIDIA L40S", "2026-01-02T00:00:00+00:00", "b" * 40, 11.0, 9.0),
+        _record(
+            "wan",
+            "NVIDIA L40S",
+            "2026-01-01T00:00:00+00:00",
+            "a" * 40,
+            10.0,
+            10.0,
+            workload_id="wan-t2v",
+            variant_id="1.3b-sp2",
+            benchmark_version=2,
+            recipe_fingerprint="recipe-a",
+            hardware_profile_id="hw-l40s",
+            software_profile_id="sw-cu130",
+        ),
+        _record(
+            "wan",
+            "NVIDIA L40S",
+            "2026-01-02T00:00:00+00:00",
+            "b" * 40,
+            11.0,
+            9.0,
+            workload_id="wan-t2v",
+            variant_id="1.3b-sp2",
+            benchmark_version=2,
+            recipe_fingerprint="recipe-a",
+            hardware_profile_id="hw-l40s",
+            software_profile_id="sw-cu130",
+        ),
     ]))
     client = TestClient(app)
 
@@ -71,6 +97,10 @@ def test_summary_endpoint_returns_latest_group_status():
     assert body["rows"][0]["metrics"]["latency"]["threshold_exceeded"] is True
     assert body["rows"][0]["threshold_exceeded_metrics"] == ["latency", "throughput"]
     assert body["rows"][0]["computed_regression_status"] == "fail"
+    assert body["rows"][0]["workload_id"] == "wan-t2v"
+    assert body["rows"][0]["variant_id"] == "1.3b-sp2"
+    assert body["rows"][0]["benchmark_version"] == 2
+    assert body["rows"][0]["recipe_fingerprint"] == "recipe-a"
 
 
 def test_summary_status_is_independent_of_days_window():

fastvideo/tests/performance/test_dashboard_plotly.py

@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pandas as pd
+
+from fastvideo.tests.performance import dashboard
+
+
+def _record(recipe_fingerprint="recipe-a", software_profile_id="sw-a", **overrides):
+    record = {
+        "model_id": "wan-t2v-1.3b-2gpu",
+        "gpu_type": "NVIDIA L40S",
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 2,
+        "recipe_fingerprint": recipe_fingerprint,
+        "hardware_profile_id": "hw-l40s",
+        "software_profile_id": software_profile_id,
+        "timestamp": "2026-01-01T00:00:00+00:00",
+        "commit_sha": "a" * 40,
+        "config_id": "aaaaaaa",
+        "latency": 10.0,
+        "throughput": 4.5,
+        "memory": 10000.0,
+        "text_encoder_time_s": 2.0,
+        "dit_time_s": 8.0,
+        "vae_decode_time_s": 3.0,
+    }
+    record.update(overrides)
+    return record
+
+
+def test_group_data_uses_full_comparison_cohort():
+    df = pd.DataFrame([
+        _record(recipe_fingerprint="recipe-a", software_profile_id="sw-a"),
+        _record(recipe_fingerprint="recipe-b", software_profile_id="sw-b"),
+    ])
+
+    groups = list(dashboard.group_data(df))
+
+    assert len(groups) == 2
+    assert {key[5] for key, _group in groups} == {"recipe-a", "recipe-b"}
+    assert {key[7] for key, _group in groups} == {"sw-a", "sw-b"}
+
+
+def test_group_data_fills_legacy_cohort_columns():
+    df = pd.DataFrame([{
+        "model_id": "legacy-wan",
+        "gpu_type": "NVIDIA L40S",
+        "timestamp": "2026-01-01T00:00:00+00:00",
+        "commit_sha": "a" * 40,
+        "config_id": "aaaaaaa",
+        "latency": 10.0,
+    }])
+
+    groups = list(dashboard.group_data(df))
+
+    assert len(groups) == 1
+    assert groups[0][0][2:] == ("", "", "", "", "", "")
+
+
+def test_build_plots_labels_distinct_cohorts():
+    df = pd.DataFrame([
+        _record(recipe_fingerprint="recipe-a", software_profile_id="sw-a"),
+        _record(recipe_fingerprint="recipe-b", software_profile_id="sw-b"),
+    ])
+
+    figs, skipped_metrics = dashboard.build_plots(df)
+    titles = [fig.layout.title.text for fig in figs]
+
+    assert len(figs) == len(dashboard.METRICS) * 2
+    assert skipped_metrics == []
+    assert any("recipe recipe-a | hw-l40s | sw-a" in title for title in titles)
+    assert any("recipe recipe-b | hw-l40s | sw-b" in title for title in titles)
+
+
+def test_skipped_metric_table_includes_cohort_identity():
+    df = pd.DataFrame([{
+        "model_id": "wan-t2v-1.3b-2gpu",
+        "gpu_type": "NVIDIA L40S",
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 2,
+        "recipe_fingerprint": "recipe-a",
+        "hardware_profile_id": "hw-l40s",
+        "software_profile_id": "sw-a",
+        "timestamp": "2026-01-01T00:00:00+00:00",
+        "commit_sha": "a" * 40,
+        "config_id": "aaaaaaa",
+        "latency": 10.0,
+    }])
+
+    _figs, skipped_metrics = dashboard.build_plots(df)
+    html = dashboard.render_skipped_metrics(skipped_metrics[:1])
+
+    assert skipped_metrics[0]["cohort"] == "wan-t2v / 1.3b-sp2 / v2"
+    assert skipped_metrics[0]["cohort_detail"] == "recipe recipe-a | hw-l40s | sw-a"
+    assert "wan-t2v / 1.3b-sp2 / v2" in html
+    assert "recipe recipe-a | hw-l40s | sw-a" in html

fastvideo/tests/performance/test_dashboard_service.py

@@ -142,6 +142,111 @@ 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_keeps_identity_cohorts_separate():
+    records = [
+        _record(
+            "2026-01-01T00:00:00+00:00",
+            "a" * 40,
+            10.0,
+            10.0,
+            recipe_fingerprint="recipe-a",
+            workload_id="wan-t2v",
+            variant_id="1.3b-sp2",
+            benchmark_version=2,
+            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,
+            20.0,
+            5.0,
+            recipe_fingerprint="recipe-b",
+            workload_id="wan-t2v",
+            variant_id="1.3b-sp2",
+            benchmark_version=2,
+            hardware_profile_id="hw-l40s",
+            software_profile_id="sw-cu130",
+            run_source="scheduled_main",
+            baseline_eligible=True,
+        ),
+        _record(
+            "2026-01-03T00:00:00+00:00",
+            "c" * 40,
+            22.0,
+            4.5,
+            recipe_fingerprint="recipe-b",
+            workload_id="wan-t2v",
+            variant_id="1.3b-sp2",
+            benchmark_version=2,
+            hardware_profile_id="hw-l40s",
+            software_profile_id="sw-cu130",
+        ),
+    ]
+
+    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
+    assert recipe_b_row["baseline_n"] == 1
+    assert recipe_b_row["metrics"]["latency"]["baseline"] == 20.0
+    assert recipe_b_row["workload_id"] == "wan-t2v"
+    assert recipe_b_row["variant_id"] == "1.3b-sp2"
+    assert recipe_b_row["benchmark_version"] == 2
+
+
+def test_build_latest_summary_keeps_variant_versions_separate():
+    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=1,
+            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,
+            20.0,
+            5.0,
+            workload_id="wan-t2v",
+            variant_id="1.3b-sp2",
+            benchmark_version=2,
+            recipe_fingerprint="recipe-a",
+            hardware_profile_id="hw-l40s",
+            software_profile_id="sw-cu130",
+            run_source="scheduled_main",
+            baseline_eligible=True,
+        ),
+        _record(
+            "2026-01-03T00:00:00+00:00",
+            "c" * 40,
+            22.0,
+            4.5,
+            workload_id="wan-t2v",
+            variant_id="1.3b-sp2",
+            benchmark_version=2,
+            recipe_fingerprint="recipe-a",
+            hardware_profile_id="hw-l40s",
+            software_profile_id="sw-cu130",
+        ),
+    ]
+
+    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
+    assert version_2_row["baseline_n"] == 1
+    assert version_2_row["metrics"]["latency"]["baseline"] == 20.0
 
 
 def test_filter_records_and_trends_preserve_metric_points():
@@ -218,3 +323,78 @@ def test_load_records_can_filter_baseline_eligible_records(tmp_path):
         "2026-01-02T00:00:00+00:00",
         "2026-01-03T00:00:00+00:00",
     }
+
+
+def test_load_records_for_model_filters_identity_cohort(tmp_path):
+    model_dir = tmp_path / "wan"
+    model_dir.mkdir()
+    (model_dir / "matching.json").write_text(
+        """
+        {
+          "model_id": "wan",
+          "gpu_type": "NVIDIA L40S",
+          "timestamp": "2026-01-01T00:00:00+00:00",
+          "success": true,
+          "baseline_eligible": true,
+          "workload_id": "wan-t2v",
+          "variant_id": "1.3b-sp2",
+          "benchmark_version": 2,
+          "recipe_fingerprint": "recipe-a",
+          "hardware_profile_id": "hw-l40s",
+          "software_profile_id": "sw-cu130"
+        }
+        """,
+        encoding="utf-8",
+    )
+    (model_dir / "other_recipe.json").write_text(
+        """
+        {
+          "model_id": "wan",
+          "gpu_type": "NVIDIA L40S",
+          "timestamp": "2026-01-02T00:00:00+00:00",
+          "success": true,
+          "baseline_eligible": true,
+          "workload_id": "wan-t2v",
+          "variant_id": "1.3b-sp2",
+          "benchmark_version": 2,
+          "recipe_fingerprint": "recipe-b",
+          "hardware_profile_id": "hw-l40s",
+          "software_profile_id": "sw-cu130"
+        }
+        """,
+        encoding="utf-8",
+    )
+    (model_dir / "other_version.json").write_text(
+        """
+        {
+          "model_id": "wan",
+          "gpu_type": "NVIDIA L40S",
+          "timestamp": "2026-01-03T00:00:00+00:00",
+          "success": true,
+          "baseline_eligible": true,
+          "workload_id": "wan-t2v",
+          "variant_id": "1.3b-sp2",
+          "benchmark_version": 3,
+          "recipe_fingerprint": "recipe-a",
+          "hardware_profile_id": "hw-l40s",
+          "software_profile_id": "sw-cu130"
+        }
+        """,
+        encoding="utf-8",
+    )
+
+    records = hf_store.load_records_for_model(
+        str(tmp_path),
+        "wan",
+        "NVIDIA L40S",
+        workload_id="wan-t2v",
+        variant_id="1.3b-sp2",
+        benchmark_version="2",
+        recipe_fingerprint="recipe-a",
+        hardware_profile_id="hw-l40s",
+        software_profile_id="sw-cu130",
+        baseline_eligible_only=True,
+    )
+
+    assert len(records) == 1
+    assert records[0]["recipe_fingerprint"] == "recipe-a"

fastvideo/tests/performance/test_identity.py

@@ -0,0 +1,422 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from copy import deepcopy
+
+import pytest
+import torch
+
+from fastvideo.tests.performance import identity as identity_module
+from fastvideo.tests.performance.identity import (
+    build_recipe_from_benchmark_config,
+    canonical_json,
+    environment_fingerprint,
+    environment_metadata,
+    hardware_profile,
+    hardware_profile_id,
+    recipe_fingerprint,
+    resolved_revision_from_model_path,
+    software_profile,
+    software_profile_id,
+)
+
+
+def _benchmark_config():
+    return {
+        "benchmark_id": "wan-t2v-1.3b-2gpu",
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 2,
+        "model": {
+            "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+            "model_short_name": "Wan2.1-T2V-1.3B",
+        },
+        "init_kwargs": {
+            "num_gpus": 2,
+            "sp_size": 2,
+            "tp_size": 1,
+            "vae_sp": True,
+            "vae_tiling": True,
+            "text_encoder_precisions": ["fp32"],
+        },
+        "generation_kwargs": {
+            "height": 480,
+            "width": 832,
+            "num_frames": 45,
+            "num_inference_steps": 4,
+            "guidance_scale": 3,
+            "embedded_cfg_scale": 6,
+            "seed": 1024,
+            "fps": 24,
+            "neg_prompt": "low quality",
+        },
+        "test_prompts": ["A cinematic video."],
+    }
+
+
+def _fingerprint(cfg, attention_backend="FLASH_ATTN"):
+    recipe = build_recipe_from_benchmark_config(cfg, attention_backend=attention_backend)
+    return recipe_fingerprint(recipe)
+
+
+def test_canonical_json_is_stable_for_mapping_order():
+    left = {"b": [2, 1], "a": {"z": "last", "m": "middle"}}
+    right = {"a": {"m": "middle", "z": "last"}, "b": [2, 1]}
+
+    assert canonical_json(left) == canonical_json(right)
+
+
+def test_canonical_json_handles_sets_deterministically():
+    assert canonical_json({"values": {"b", "a"}}) == '{"values":["a","b"]}'
+    assert canonical_json({"values": frozenset(("b", "a"))}) == '{"values":["a","b"]}'
+
+
+def test_same_config_produces_same_recipe_fingerprint():
+    cfg = _benchmark_config()
+
+    assert _fingerprint(cfg) == _fingerprint(deepcopy(cfg))
+
+
+def test_recipe_includes_first_class_benchmark_identity():
+    recipe = build_recipe_from_benchmark_config(_benchmark_config())
+
+    assert recipe["benchmark"] == {
+        "benchmark_id": "wan-t2v-1.3b-2gpu",
+        "workload_id": "wan-t2v",
+        "variant_id": "1.3b-sp2",
+        "benchmark_version": 2,
+    }
+
+
+def test_recipe_requires_first_class_benchmark_identity():
+    cfg = _benchmark_config()
+    del cfg["workload_id"]
+
+    with pytest.raises(ValueError, match="workload_id"):
+        build_recipe_from_benchmark_config(cfg)
+
+
+def test_semantically_equivalent_sequence_forms_match():
+    cfg = _benchmark_config()
+    equivalent = deepcopy(cfg)
+    equivalent["init_kwargs"]["text_encoder_precisions"] = tuple(
+        equivalent["init_kwargs"]["text_encoder_precisions"])
+
+    assert _fingerprint(cfg) == _fingerprint(equivalent)
+
+
+def test_output_path_does_not_change_recipe_fingerprint():
+    cfg = _benchmark_config()
+    with_output_path = deepcopy(cfg)
+    with_output_path["generation_kwargs"]["output_path"] = "/tmp/generated"
+
+    assert _fingerprint(cfg) == _fingerprint(with_output_path)
+
+
+def test_runtime_resolved_values_do_not_change_recipe_fingerprint():
+    # Runtime-resolved values are audit metadata: an upstream repo commit
+    # (even a README-only edit) or a silent attention-backend fallback must
+    # not churn the cohort — that would open a fresh ungated cohort and let
+    # degraded numbers seed a new baseline. Declared inputs (including
+    # requested_backend) stay in the hash.
+    cfg = _benchmark_config()
+    recipe = build_recipe_from_benchmark_config(
+        cfg, attention_backend="FLASH_ATTN",
+        resolved_attention_backend="FLASH_ATTN",
+        resolved_model_revision="aaaa1111")
+    changed = build_recipe_from_benchmark_config(
+        cfg, attention_backend="FLASH_ATTN",
+        resolved_attention_backend="TORCH_SDPA",
+        resolved_model_revision="bbbb2222")
+
+    assert recipe["model"]["resolved_revision"] == "aaaa1111"  # kept for audit
+    assert recipe["attention"]["resolved_backend"] == "FLASH_ATTN"  # kept for audit
+    assert recipe_fingerprint(recipe) == recipe_fingerprint(changed)
+
+
+def test_measured_prompt_override_ignores_unused_configured_prompts():
+    cfg = _benchmark_config()
+    changed = deepcopy(cfg)
+    changed["test_prompts"] = [
+        "Unused prompt that should not affect this measured workload.",
+        cfg["test_prompts"][0],
+    ]
+
+    recipe = build_recipe_from_benchmark_config(cfg, measured_prompts=[cfg["test_prompts"][0]])
+    changed_recipe = build_recipe_from_benchmark_config(changed, measured_prompts=[cfg["test_prompts"][0]])
+
+    assert recipe_fingerprint(recipe) == recipe_fingerprint(changed_recipe)
+
+
+def test_num_inference_steps_changes_recipe_fingerprint():
+    cfg = _benchmark_config()
+    changed = deepcopy(cfg)
+    changed["generation_kwargs"]["num_inference_steps"] = 8
+
+    assert _fingerprint(cfg) != _fingerprint(changed)
+
+
+def test_benchmark_version_changes_recipe_fingerprint():
+    cfg = _benchmark_config()
+    changed = deepcopy(cfg)
+    changed["benchmark_version"] = 3
+
+    assert _fingerprint(cfg) != _fingerprint(changed)
+
+
+def test_attention_backend_changes_recipe_fingerprint():
+    cfg = _benchmark_config()
+
+    assert _fingerprint(cfg, attention_backend="FLASH_ATTN") != _fingerprint(
+        cfg, attention_backend="TORCH_SDPA")
+
+
+def test_precision_changes_recipe_fingerprint():
+    cfg = _benchmark_config()
+    changed = deepcopy(cfg)
+    changed["init_kwargs"]["text_encoder_precisions"] = ["bf16"]
+
+    assert _fingerprint(cfg) != _fingerprint(changed)
+
+
+def test_dimensions_and_seed_change_recipe_fingerprint():
+    cfg = _benchmark_config()
+    different_dimensions = deepcopy(cfg)
+    different_dimensions["generation_kwargs"]["width"] = 1024
+    different_seed = deepcopy(cfg)
+    different_seed["generation_kwargs"]["seed"] = 2048
+
+    assert _fingerprint(cfg) != _fingerprint(different_dimensions)
+    assert _fingerprint(cfg) != _fingerprint(different_seed)
+
+
+def test_distributed_layout_changes_recipe_fingerprint():
+    cfg = _benchmark_config()
+    changed = deepcopy(cfg)
+    changed["init_kwargs"]["sp_size"] = 1
+    changed["init_kwargs"]["tp_size"] = 2
+
+    assert _fingerprint(cfg) != _fingerprint(changed)
+
+
+def test_software_profile_uses_exact_attention_kernel_package_versions_for_id():
+    profile_a = software_profile(
+        python_version="3.12.4",
+        torch_version="2.12.0+cu130",
+        cuda_version="13.0",
+        package_versions={
+            "triton": "3.4.1",
+            "fastvideo_kernel": "0.3.2",
+            "flash_attn_4": "4.0.0.dev0",
+            "flashinfer": "0.2.11",
+            "nvidia_cutlass_dsl": "4.5.0",
+        },
+    )
+    profile_b = software_profile(
+        python_version="3.12.5",
+        torch_version="2.12.1+cu130",
+        cuda_version="13.0",
+        package_versions={
+            "triton": "3.4.9",
+            "fastvideo_kernel": "0.3.7",
+            "flash_attn_4": "4.0.0.dev1",
+            "flashinfer": "0.2.12",
+            "nvidia_cutlass_dsl": "4.5.1",
+        },
+    )
+
+    assert profile_a == {
+        "python": "3.12",
+        "pytorch": "2.12",
+        "cuda": "13.0",
+        "packages": {
+            "fastvideo_kernel": "0.3.2",
+            "flash_attn_4": "4.0.0.dev0",
+            "flashinfer": "0.2.11",
+            "nvidia_cutlass_dsl": "4.5.0",
+            "triton": "3.4.1",
+        },
+    }
+    assert profile_b["python"] == "3.12"
+    assert profile_b["pytorch"] == "2.12"
+    assert profile_b["cuda"] == "13.0"
+    assert software_profile_id(profile_a) != software_profile_id(profile_b)
+
+
+def test_installed_package_versions_checks_attention_kernel_distributions(monkeypatch):
+    versions = {
+        "fastvideo-kernel": "0.3.2",
+        "flash-attn": "2.8.1",
+        "flash-attn-4": "4.0.0.dev0",
+        "flash-attention-fp4": "0.1.0",
+        "flashinfer-python": "0.2.11",
+        "nvidia-cutlass-dsl": "4.5.0",
+    }
+
+    def fake_version(name):
+        if name not in versions:
+            raise identity_module.importlib.metadata.PackageNotFoundError(name)
+        return versions[name]
+
+    monkeypatch.setattr(identity_module.importlib.metadata, "version", fake_version)
+
+    installed = identity_module._installed_package_versions()
+
+    assert installed["fastvideo_kernel"] == "0.3.2"
+    assert installed["flash_attn"] == "2.8.1"
+    assert installed["flash_attn_4"] == "4.0.0.dev0"
+    assert installed["flash_attention_fp4"] == "0.1.0"
+    assert installed["flashinfer"] == "0.2.11"
+    assert installed["nvidia_cutlass_dsl"] == "4.5.0"
+
+
+def test_hardware_profile_id_uses_normalized_gpu_cohort():
+    profile = hardware_profile(
+        num_gpus=2,
+        gpu_devices=[
+            {
+                "name": "NVIDIA L40S",
+                "memory_bytes": 48 * 1024**3,
+                "compute_capability": "8.9",
+            },
+            {
+                "name": "NVIDIA L40S",
+                "memory_gb": 48,
+                "compute_capability": "8.9",
+            },
+        ],
+        interconnect="none_or_partial",
+    )
+
+    assert profile == {
+        "device_type": "cuda",
+        "gpu_count": 2,
+        "gpus": [
+            {
+                "name": "NVIDIA L40S",
+                "memory_gb": 48,
+                "compute_capability": "8.9",
+            },
+            {
+                "name": "NVIDIA L40S",
+                "memory_gb": 48,
+                "compute_capability": "8.9",
+            },
+        ],
+        "interconnect": "none_or_partial",
+    }
+    assert hardware_profile_id(profile).startswith("hw-")
+
+
+def test_hardware_profile_pads_missing_requested_cuda_devices(monkeypatch):
+    class Props:
+        name = "NVIDIA L40S"
+        total_memory = 48 * 1024**3
+
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(torch.cuda, "device_count", lambda: 1)
+    monkeypatch.setattr(torch.cuda, "get_device_properties", lambda device_id: Props())
+    monkeypatch.setattr(torch.cuda, "get_device_capability", lambda device_id: (8, 9))
+
+    profile = hardware_profile(num_gpus=2, interconnect="unknown")
+
+    assert profile["gpu_count"] == 2
+    assert profile["gpus"] == [
+        {
+            "name": "NVIDIA L40S",
+            "memory_gb": 48,
+            "compute_capability": "8.9",
+        },
+        {
+            "name": "unknown",
+            "memory_gb": None,
+            "compute_capability": None,
+        },
+    ]
+
+
+def test_local_path_detection_checks_both_path_separators(monkeypatch):
+    monkeypatch.setattr(identity_module.os, "sep", "\\")
+
+    assert identity_module._looks_like_local_path("models/local/checkpoint") is True
+    assert identity_module._looks_like_local_path(r"C:\models\checkpoint") is True
+    assert identity_module._looks_like_local_path("Wan-AI/Wan2.1-T2V-1.3B-Diffusers") is False
+
+
+def test_environment_metadata_is_separate_audit_fingerprint():
+    cfg = _benchmark_config()
+    recipe_hash = _fingerprint(cfg)
+    audit_a = environment_metadata(
+        env={"IMAGE_VERSION": "py3.12-cuda13.0.0"},
+        package_versions={"triton": "3.4.1"},
+        hardware={"device_type": "cuda", "gpu_count": 2},
+        software={"python": "3.12", "pytorch": "2.12", "cuda": "13.0"},
+    )
+    audit_b = environment_metadata(
+        env={"IMAGE_VERSION": "py3.12-cuda13.0.1"},
+        package_versions={"triton": "3.4.1"},
+        hardware={"device_type": "cuda", "gpu_count": 2},
+        software={"python": "3.12", "pytorch": "2.12", "cuda": "13.0"},
+    )
+
+    assert recipe_hash == _fingerprint(cfg)
+    assert environment_fingerprint(audit_a) != environment_fingerprint(audit_b)
+
+
+def test_resolved_revision_from_hf_snapshot_path():
+    revision = "a" * 40
+
+    assert resolved_revision_from_model_path(
+        f"/root/.cache/huggingface/hub/models--org--repo/snapshots/{revision}") == revision
+    assert resolved_revision_from_model_path("/models/local-repo") is None
+
+
+def test_runtime_identity_from_generator_summarizes_worker_records():
+    from fastvideo.tests.performance.test_inference_performance import _runtime_identity_from_generator
+
+    revision = "b" * 40
+
+    class FakeExecutor:
+        def collective_rpc(self, _method):
+            return [
+                {
+                    "resolved_attention_backends": ["FLASH_ATTN"],
+                    "resolved_model_path": f"/root/.cache/huggingface/hub/models--org--repo/snapshots/{revision}",
+                },
+                {
+                    "resolved_attention_backends": ["FLASH_ATTN"],
+                    "resolved_model_path": f"/root/.cache/huggingface/hub/models--org--repo/snapshots/{revision}",
+                },
+            ]
+
+    class FakeGenerator:
+        executor = FakeExecutor()
+
+    assert _runtime_identity_from_generator(FakeGenerator()) == {
+        "resolved_attention_backend": "FLASH_ATTN",
+        "resolved_model_revision": revision,
+    }
+
+
+def test_collect_worker_identity_handles_torch_module_pipeline():
+    from fastvideo.tests.performance.test_inference_performance import _collect_worker_identity
+
+    class Backend:
+        name = "FLASH_ATTN"
+
+    class Leaf(torch.nn.Module):
+        backend = Backend()
+
+    class Pipeline(torch.nn.Module):
+        model_path = "/models/local"
+
+        def __init__(self):
+            super().__init__()
+            self.leaf = Leaf()
+
+    class Worker:
+        pipeline = Pipeline()
+
+    assert _collect_worker_identity(Worker()) == {
+        "resolved_attention_backends": ["FLASH_ATTN"],
+        "resolved_model_path": "/models/local",
+    }

fastvideo/tests/performance/test_inference_performance.py

@@ -12,12 +12,25 @@ import os
 import time
 from collections.abc import Mapping
 from datetime import datetime, timezone
+from typing import Any
 
 import torch
 import pytest
 
 from fastvideo import VideoGenerator
 from fastvideo.logger import init_logger
+from fastvideo.tests.performance.identity import (
+    benchmark_identity_from_config,
+    build_recipe_from_benchmark_config,
+    environment_fingerprint,
+    environment_metadata,
+    hardware_profile,
+    hardware_profile_id,
+    recipe_fingerprint,
+    resolved_revision_from_model_path,
+    software_profile,
+    software_profile_id,
+)
 from fastvideo.worker.multiproc_executor import MultiprocExecutor
 
 logger = init_logger(__name__)
@@ -34,8 +47,11 @@ V2_REQUIRED_IDENTITY_FIELDS = (
     "variant_id",
     "benchmark_version",
 )
+# "recipe" is no longer config-declarable: the fingerprint follow-up landed,
+# and the generated recipe document owns that key in emitted records. A config
+# declaring it now fails validation loudly instead of being silently
+# overwritten by the generated one.
 V2_OPTIONAL_METADATA_FIELDS = (
-    "recipe",
     "metric_threshold_policy",
     "quality_metadata",
 )
@@ -179,11 +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,
@@ -231,6 +243,110 @@ def _write_results(results):
     logger.info("Performance results written to %s", filepath)
 
 
+def _backend_name(value) -> str:
+    if hasattr(value, "name"):
+        return str(value.name)
+    return str(value)
+
+
+def _collect_worker_identity(worker) -> dict[str, Any]:
+    pipeline = getattr(worker, "pipeline", None)
+    model_path = getattr(pipeline, "model_path", None)
+    backends: set[str] = set()
+
+    modules = getattr(pipeline, "modules", None)
+    if isinstance(modules, Mapping):
+        module_iter = modules.values()
+    elif isinstance(pipeline, torch.nn.Module):
+        module_iter = (pipeline,)
+    else:
+        module_iter = ()
+
+    for module in module_iter:
+        if isinstance(module, torch.nn.Module):
+            for submodule in module.modules():
+                backend = getattr(submodule, "backend", None)
+                if backend is not None:
+                    backends.add(_backend_name(backend))
+        else:
+            backend = getattr(module, "backend", None)
+            if backend is not None:
+                backends.add(_backend_name(backend))
+
+    return {
+        "resolved_attention_backends": sorted(backends),
+        "resolved_model_path": model_path,
+    }
+
+
+def _single_or_list(values: set[str]) -> str | list[str] | None:
+    ordered = sorted(values)
+    if not ordered:
+        return None
+    if len(ordered) == 1:
+        return ordered[0]
+    return ordered
+
+
+def _runtime_identity_from_generator(generator) -> dict[str, Any]:
+    worker_records = generator.executor.collective_rpc(_collect_worker_identity)
+    resolved_backends: set[str] = set()
+    resolved_revisions: set[str] = set()
+
+    for record in worker_records:
+        resolved_backends.update(record.get("resolved_attention_backends") or [])
+        revision = resolved_revision_from_model_path(record.get("resolved_model_path"))
+        if revision is not None:
+            resolved_revisions.add(revision)
+
+    return {
+        "resolved_attention_backend": _single_or_list(resolved_backends),
+        "resolved_model_revision": _single_or_list(resolved_revisions),
+    }
+
+
+def _benchmark_identity_fields(cfg):
+    identity = benchmark_identity_from_config(cfg)
+    return {
+        key: identity[key]
+        for key in ("workload_id", "variant_id", "benchmark_version")
+    }
+
+
+def _build_identity_fields(cfg, init_kwargs, prompt, runtime_identity):
+    # Legacy v1 configs (no config_schema_version) stay on the legacy
+    # (model_id, gpu_type) cohort: they lack the required identity fields,
+    # and building a recipe for them would raise AFTER the GPU measurement
+    # completed. The validator already enforces that v2 configs carry the
+    # identity fields, so v2 records always get the full identity block.
+    if cfg.get("config_schema_version") is None:
+        return {}
+    recipe = build_recipe_from_benchmark_config(
+        cfg,
+        resolved_attention_backend=runtime_identity.get("resolved_attention_backend"),
+        resolved_model_revision=runtime_identity.get("resolved_model_revision"),
+        measured_prompts=[prompt],
+    )
+    num_gpus = init_kwargs.get("num_gpus", cfg.get("run_config", {}).get("required_gpus", 1))
+    hw_profile = hardware_profile(num_gpus=num_gpus)
+    sw_profile = software_profile()
+    env_metadata = environment_metadata(
+        hardware=hw_profile,
+        software=sw_profile,
+    )
+    return {
+        "recipe": recipe,
+        "recipe_fingerprint": recipe_fingerprint(recipe),
+        "hardware_profile": hw_profile,
+        "hardware_profile_id": hardware_profile_id(hw_profile),
+        "software_profile": sw_profile,
+        "software_profile_id": software_profile_id(sw_profile),
+        "environment_metadata": env_metadata,
+        "environment_fingerprint": environment_fingerprint(env_metadata),
+        **_benchmark_identity_fields(cfg),
+    }
+
+
 # -- Test -------------------------------------------------------------------
 
 def _run_benchmark(cfg):
@@ -268,6 +384,7 @@ def _run_benchmark(cfg):
             model_path=model_info["model_path"],
             **init_kwargs,
         )
+        runtime_identity = _runtime_identity_from_generator(generator)
 
         for i in range(num_warmup):
             logger.info("Warmup run %d/%d", i + 1, num_warmup)
@@ -322,6 +439,7 @@ def _run_benchmark(cfg):
         "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(

fastvideo/tests/performance/test_inference_performance_component_times.py

@@ -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.

fastvideo/tests/ssim/test_kandinsky5_similarity.py

@@ -1,109 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""SSIM regression test for Kandinsky-5.0 Lite text-to-video.
-
-Runs a reduced-size generation (256x384, 21 frames) with a fixed seed and
-compares against a device-specific reference video via MS-SSIM. The preset
-default is 512x768 x 121 frames; the reduced size keeps CI cheap while the
-patch (1, 2, 2) and VAE temporal-4 / spatial-8 constraints stay satisfied
-(dims divisible by 16, num_frames % 4 == 1).
-"""
-import os
-
-import pytest
-
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.basic.kandinsky5.presets import KANDINSKY5_T2V_LITE_5S
-from fastvideo.tests.ssim.inference_similarity_utils import (
-    resolve_inference_device_reference_folder,
-    run_text_to_video_similarity_test,
-)
-
-logger = init_logger(__name__)
-
-REQUIRED_GPUS = 1
-
-device_reference_folder = resolve_inference_device_reference_folder(logger)
-
-_PRESET_DEFAULTS = KANDINSKY5_T2V_LITE_5S.defaults
-
-KANDINSKY5_T2V_PARAMS = {
-    "num_gpus": 1,
-    "model_path": "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
-    "height": 256,
-    "width": 384,
-    "num_frames": 21,
-    "num_inference_steps": _PRESET_DEFAULTS["num_inference_steps"],
-    "guidance_scale": _PRESET_DEFAULTS["guidance_scale"],
-    "seed": 1024,
-    "sp_size": 1,
-    "tp_size": 1,
-    "fps": _PRESET_DEFAULTS["fps"],
-    "neg_prompt": _PRESET_DEFAULTS["negative_prompt"],
-}
-
-KANDINSKY5_T2V_FULL_QUALITY_PARAMS = {
-    **KANDINSKY5_T2V_PARAMS,
-    "height": _PRESET_DEFAULTS["height"],
-    "width": _PRESET_DEFAULTS["width"],
-    # num_frames stays at the inherited reduced 21 even at full quality
-    # (preset default: 121).
-}
-
-KANDINSKY5_T2V_MODEL_TO_PARAMS = {
-    "Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers": KANDINSKY5_T2V_PARAMS,
-}
-FULL_QUALITY_KANDINSKY5_T2V_MODEL_TO_PARAMS = {
-    "Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers": KANDINSKY5_T2V_FULL_QUALITY_PARAMS,
-}
-
-KANDINSKY5_T2V_TEST_PROMPTS = [
-    "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.",
-]
-
-
-@pytest.mark.parametrize("prompt", KANDINSKY5_T2V_TEST_PROMPTS)
-@pytest.mark.parametrize("attention_backend_name", ["FLASH_ATTN"])
-@pytest.mark.parametrize("model_id", list(KANDINSKY5_T2V_MODEL_TO_PARAMS.keys()))
-def test_kandinsky5_t2v_inference_similarity(
-    prompt: str,
-    attention_backend_name: str,
-    model_id: str,
-) -> None:
-    # The SSIM lane image bakes FASTVIDEO_FA4=1, but on L40S (sm89) the CLIP
-    # encoder's dense MHA inference call routes into the FA4 CuTeDSL JIT,
-    # which fails to compile there (nvvm.fmax signature mismatch vs the
-    # image's nvidia_cutlass_dsl). Pin FA4 off so reference seeding and CI
-    # runs both use the FA2 path with identical numerics. Scoped to this test
-    # and restored afterwards: the other SSIM references are seeded with FA4
-    # on, so a module-level pin would corrupt every test collected in the
-    # same pytest process.
-    saved_fa4 = os.environ.get("FASTVIDEO_FA4")
-    os.environ["FASTVIDEO_FA4"] = "0"
-    try:
-        run_text_to_video_similarity_test(
-            logger=logger,
-            script_dir=os.path.dirname(os.path.abspath(__file__)),
-            device_reference_folder=device_reference_folder,
-            prompt=prompt,
-            attention_backend_name=attention_backend_name,
-            model_id=model_id,
-            default_params_map=KANDINSKY5_T2V_MODEL_TO_PARAMS,
-            full_quality_params_map=FULL_QUALITY_KANDINSKY5_T2V_MODEL_TO_PARAMS,
-            min_acceptable_ssim=0.93,
-            # Match examples/inference/basic/basic_kandinsky5_t2v.py: FSDP
-            # inference is not used for Kandinsky-5, and the Qwen2.5-VL text
-            # encoder stays on CPU between encodes.
-            init_kwargs_override={
-                "use_fsdp_inference": False,
-                "text_encoder_cpu_offload": True,
-                "pin_cpu_memory": True,
-            },
-        )
-    finally:
-        if saved_fa4 is None:
-            os.environ.pop("FASTVIDEO_FA4", None)
-        else:
-            os.environ["FASTVIDEO_FA4"] = saved_fa4

fastvideo/tests/transformers/test_cosmos.py

@@ -11,7 +11,6 @@ from fastvideo.forward_context import set_forward_context
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
 from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.tests.utils import skip_if_gated_repo_inaccessible
 from fastvideo.utils import maybe_download_model
 from fastvideo.configs.models.dits import CosmosVideoConfig
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
@@ -26,12 +25,18 @@ BASE_MODEL_PATH = "nvidia/Cosmos-Predict2-2B-Video2World"
 
 
 def _resolve_model_path() -> str:
-    local_dir = os.path.join("data", BASE_MODEL_PATH)
-    skip_if_gated_repo_inaccessible(BASE_MODEL_PATH,
-                                    local_path=local_dir,
-                                    test_name="Cosmos transformer test",
-                                    allow_module_level=True)
-    return maybe_download_model(BASE_MODEL_PATH, local_dir=local_dir)
+    try:
+        return maybe_download_model(
+            BASE_MODEL_PATH,
+            local_dir=os.path.join("data", BASE_MODEL_PATH),
+        )
+    except ValueError as exc:
+        pytest.skip(
+            "Skipping Cosmos transformer test because the configured "
+            "HuggingFace token cannot access the gated Cosmos weights: "
+            f"{exc}",
+            allow_module_level=True,
+        )
 
 
 MODEL_PATH = _resolve_model_path()

fastvideo/tests/transformers/test_cosmos2_5.py

@@ -19,7 +19,6 @@ if os.path.exists(COSMOS_PREDICT2_5_PATH) and COSMOS_PREDICT2_5_PATH not in sys.
 
 from fastvideo.forward_context import set_forward_context
 from fastvideo.logger import init_logger
-from fastvideo.tests.utils import skip_if_gated_repo_inaccessible
 from fastvideo.utils import maybe_download_model
 # Use Cosmos 2.5 specific config
 from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
@@ -44,14 +43,7 @@ CHECKPOINT_SUBDIR = "base/post-trained"
 CHECKPOINT_FILENAME = "81edfebe-bd6a-4039-8c1d-737df1a790bf_ema_bf16.pt"
 
 
-def _resolve_model_path() -> str:
-    skip_if_gated_repo_inaccessible(BASE_MODEL_PATH,
-                                    test_name="Cosmos 2.5 transformer test",
-                                    allow_module_level=True)
-    return maybe_download_model(BASE_MODEL_PATH, local_dir=None)
-
-
-MODEL_PATH = _resolve_model_path()
+MODEL_PATH = maybe_download_model(BASE_MODEL_PATH, local_dir=None)
 TRANSFORMER_PATH = os.path.join(MODEL_PATH, CHECKPOINT_SUBDIR, "transformer")
 if not os.path.exists(TRANSFORMER_PATH):
     # Try without subdirectory
@@ -600,3 +592,4 @@ if __name__ == "__main__":
     # Run tests directly
     test_cosmos25_transformer()
     test_cosmos25_transformer_video()
+

fastvideo/tests/transformers/test_hyworld.py

@@ -15,7 +15,6 @@ from fastvideo.forward_context import set_forward_context
 from fastvideo.logger import init_logger
 from fastvideo.models.loader.component_loader import TransformerLoader
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.platforms import AttentionBackendEnum
 from fastvideo.utils import maybe_download_model
 
 logger = init_logger(__name__)
@@ -25,25 +24,13 @@ os.environ["MASTER_PORT"] = "29503"
 
 MODEL_PATH = maybe_download_model("FastVideo/HY-WorldPlay-Bidirectional-Diffusers")
 TRANSFORMER_PATH = os.path.join(MODEL_PATH, "transformer")
-REFERENCE_LATENTS = {
-    AttentionBackendEnum.FLASH_ATTN: -197132.85557549074,
-    AttentionBackendEnum.TORCH_SDPA: -211007.95158730447,
-}
+REFERENCE_LATENT = -197132.85557549074  # Pre-computed reference value
 
 
 @pytest.mark.usefixtures("distributed_setup")
 def test_hyworld_transformer():
     transformer_path = TRANSFORMER_PATH
 
-    # An env-forced backend is knowable before the multi-GB download and GPU
-    # load; the authoritative check on the layer's resolved backend still runs
-    # after construction below.
-    forced_backend_name = os.environ.get("FASTVIDEO_ATTENTION_BACKEND")
-    if forced_backend_name:
-        forced_backend = getattr(AttentionBackendEnum, forced_backend_name, None)
-        if forced_backend is not None and forced_backend not in REFERENCE_LATENTS:
-            pytest.skip(f"HYWorld transformer test has no reference latent for {forced_backend_name}.")
-
     sp_rank = get_sp_parallel_rank()
     sp_world_size = get_sp_world_size()
 
@@ -64,9 +51,6 @@ def test_hyworld_transformer():
     loader = TransformerLoader()
     model = loader.load(transformer_path, args).to(device, dtype=precision)
     model.eval()
-    attention_backend = model.double_blocks[0].attn.backend
-    if attention_backend not in REFERENCE_LATENTS:
-        pytest.skip(f"HYWorld transformer test has no reference latent for {attention_backend}.")
 
     total_params = sum(p.numel() for p in model.parameters())
     weight_sum = sum(p.to(torch.float64).sum().item() for p in model.parameters())
@@ -163,10 +147,9 @@ def test_hyworld_transformer():
 
     latent = output.double().sum().item()
 
-    reference_latent = REFERENCE_LATENTS[attention_backend]
-    diff = abs(reference_latent - latent)
-    relative_diff = diff / abs(reference_latent)
-    logger.info(f"Reference latent: {reference_latent}, Current latent: {latent}")
+    diff = abs(REFERENCE_LATENT - latent)
+    relative_diff = diff / abs(REFERENCE_LATENT)
+    logger.info(f"Reference latent: {REFERENCE_LATENT}, Current latent: {latent}")
     logger.info(f"Absolute diff: {diff}, Relative diff: {relative_diff * 100:.4f}%")
 
     # Allow 0.5% relative difference

fastvideo/tests/utils.py

@@ -11,41 +11,6 @@ from pytorch_msssim import ms_ssim, ssim
 logger = init_logger(__name__)
 
 
-def skip_if_gated_repo_inaccessible(repo_id: str,
-                                    *,
-                                    local_path: str | None = None,
-                                    test_name: str = "test",
-                                    allow_module_level: bool = False) -> None:
-    """Skip a test when ``repo_id`` is gated/private and the token lacks access.
-
-    Local weights win: when ``local_path`` already exists the check is skipped
-    entirely, so cached machines keep running offline. Transient hub failures
-    (offline, DNS, 429/5xx) do NOT skip either — the subsequent download will
-    serve from cache or fail loudly, preserving the CI failure signal. Only a
-    positively-identified authorization problem produces a skip.
-    """
-    import pytest
-
-    if local_path is not None and os.path.exists(local_path):
-        return
-
-    from huggingface_hub import HfApi
-    from huggingface_hub.errors import GatedRepoError, RepositoryNotFoundError
-
-    try:
-        HfApi().auth_check(repo_id)
-    except (GatedRepoError, RepositoryNotFoundError) as exc:
-        pytest.skip(
-            f"Skipping {test_name}: the configured HuggingFace token cannot "
-            f"access the gated repo {repo_id}: {exc}",
-            allow_module_level=allow_module_level,
-        )
-    except Exception as exc:  # noqa: BLE001 - hub unreachable, proxies, 5xx
-        logger.warning(
-            "Gated-repo access probe for %s failed (%s); proceeding — the "
-            "download will serve from cache or fail loudly.", repo_id, exc)
-
-
 def _read_video_frames(path: str) -> torch.Tensor:
     """Read video frames as a ``(T, C, H, W)`` uint8 tensor.