[bugfix]: harden FastWan attention routing

The capability validation added for #1254 raised whenever the resolved
backend was incompatible and any explicit selection was active, even when
the resolution had already substituted a fallback for the pinned backend.
That killed legitimate loads: matrixgame2's CLIP vision encoder has
head_size=80, so a pinned FLASH_ATTN is replaced by SDPA at the platform
level, and the validator then blamed the FLASH_ATTN pin for SDPA's
declared head-size limits, aborting the whole model.

Hard-fail only when the explicit selection was actually honored and that
very backend rejects the layer (a real user error); a resolution that
fell back to a different backend degrades to the one-per-cached-resolution
warning, matching auto-selection.

.buildkite/pipeline.yml

@@ -436,7 +436,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile"
               config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                command: "timeout 25m .buildkite/scripts/pr_test.sh"
                 label: ":test_tube: LoRA Training Tests"
                 env:
                   - TEST_TYPE=training_lora
@@ -481,6 +481,7 @@ steps:
                 - "fastvideo/layers/**"
                 - "fastvideo/worker/**"
                 - "fastvideo/entrypoints/**"
+                - "fastvideo/performance/**"
                 - "fastvideo/tests/performance/**"
                 - ".buildkite/performance-benchmarks/**"
                 - "pyproject.toml"

.buildkite/scripts/pr_test.sh

@@ -76,10 +76,27 @@ EFFECTIVE_PR=${BUILDKITE_PULL_REQUEST:-false}
 if [ "$EFFECTIVE_PR" = "false" ] && [ -n "${PR_NUMBER:-}" ]; then
     EFFECTIVE_PR=$PR_NUMBER
 fi
-MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR BUILDKITE_BRANCH=${BUILDKITE_BRANCH:-} TEST_SCOPE=${TEST_SCOPE:-} BUILDKITE_BUILD_URL=${BUILDKITE_BUILD_URL:-} BUILDKITE_BUILD_ID=${BUILDKITE_BUILD_ID:-} BUILDKITE_JOB_ID=${BUILDKITE_JOB_ID:-} IMAGE_VERSION=$IMAGE_VERSION"
+MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR BUILDKITE_BRANCH=${BUILDKITE_BRANCH:-} BUILDKITE_SOURCE=${BUILDKITE_SOURCE:-} TEST_SCOPE=${TEST_SCOPE:-} BUILDKITE_BUILD_URL=${BUILDKITE_BUILD_URL:-} BUILDKITE_BUILD_ID=${BUILDKITE_BUILD_ID:-} BUILDKITE_JOB_ID=${BUILDKITE_JOB_ID:-} IMAGE_VERSION=$IMAGE_VERSION"
 
 POST_RUN_HOOK=""
 
+is_truthy() {
+    case "${1:-}" in
+        1|true|TRUE|yes|YES|on|ON) return 0 ;;
+        *) return 1 ;;
+    esac
+}
+
+ssim_bootstrap_args() {
+    local title="${PR_TITLE:-}"
+    local message="${BUILDKITE_MESSAGE:-}"
+    if is_truthy "${FASTVIDEO_SSIM_BOOTSTRAP_MODE:-}" \
+        || [[ "$title" == *"[new-model]"* ]] \
+        || [[ "$message" == *"[new-model]"* ]]; then
+        printf ' --bootstrap-mode'
+    fi
+}
+
 upload_performance_artifacts() {
     SHORT_SHA=${BUILDKITE_COMMIT:0:7}
     LOCAL_DIR="downloaded_reports"
@@ -172,7 +189,12 @@ case "$TEST_TYPE" in
         ;;
     "ssim")
         log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_SSIM_TEST_FILE::run_ssim_tests"
+        SSIM_BOOTSTRAP_ARGS=$(ssim_bootstrap_args)
+        if [ -n "$SSIM_BOOTSTRAP_ARGS" ]; then
+            log "SSIM bootstrap mode enabled for new-model reference draft generation"
+        fi
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run "
+        MODAL_COMMAND+="$MODAL_SSIM_TEST_FILE::run_ssim_tests$SSIM_BOOTSTRAP_ARGS"
         ;;
     "training")
         log "Running training tests..."

.github/scripts/gate_full_suite.sh

@@ -0,0 +1,133 @@
+#!/usr/bin/env bash
+# Gate the expensive Buildkite full suite on the cheap GitHub checks.
+#
+# Polls the workflow runs for the PR head commit and only exits 0 once the
+# watched cheap workflows (pre-commit, docs build) have succeeded, so the
+# 'ready' label cannot burn ~20 GPU lanes on a head that a cheap check has
+# already doomed.
+#
+# Semantics:
+#   - watched run completed with a bad conclusion  -> exit 1 (fail CLOSED:
+#     no full suite; the next push re-arms via the 'synchronize' trigger)
+#   - watched run cancelled                         -> still pending: the docs
+#     workflow's repo-global 'pages' concurrency group cancels runs superseded
+#     by unrelated pushes, so 'cancelled' is not a verdict on this PR
+#   - watched runs pending                          -> poll until done
+#   - docs run absent                               -> not applicable after a
+#     short grace period ('Deploy Documentation' is path-filtered on PRs)
+#   - pre-commit run absent                         -> keep polling: pre-commit
+#     is never path-filtered, so its absence is always anomalous
+#   - 'ready' label removed while waiting           -> exit 1 (fail CLOSED:
+#     un-labeling is a deliberate maintainer action)
+#   - GitHub API unreachable or timeout             -> exit 0 (fail OPEN,
+#     loud warning: never brick CI on a GitHub outage)
+#
+# Required env: PR_SHA (PR head commit), PR_NUMBER, GITHUB_REPOSITORY, GH_TOKEN.
+set -euo pipefail
+
+: "${PR_SHA:?PR_SHA (PR head commit) is required}"
+: "${PR_NUMBER:?PR_NUMBER (pull request number) is required}"
+: "${GITHUB_REPOSITORY:?GITHUB_REPOSITORY is required}"
+
+# Workflow-level `name:` values that must be green before the full suite
+# may start. "Deploy Documentation" is path-filtered on PRs, so its run may
+# legitimately never exist; pre-commit always runs, so it must appear.
+WATCHED_NAMES='["pre-commit", "Deploy Documentation"]'
+WATCHED_REGEX='^(pre-commit|Deploy Documentation)$'
+POLL_SECS="${POLL_SECS:-20}"
+GRACE_SECS="${GRACE_SECS:-60}"
+MAX_WAIT_SECS="${MAX_WAIT_SECS:-1500}"
+
+# Bound each API call so a hung connection hits the 3-strike fail-open path
+# instead of pinning the loop until the job timeout (which would fail closed
+# on exactly the GitHub-outage case this script is meant to survive).
+if command -v timeout >/dev/null 2>&1; then
+  gh_api() { timeout 30 gh api "$@"; }
+else
+  gh_api() { gh api "$@"; }  # macOS dev boxes; CI always has coreutils timeout
+fi
+
+# The workflow checked the label before starting the gate, but the wait can
+# last ~25 min: re-check once before any exit 0 and fail closed if 'ready'
+# was removed in the meantime. An API error here proceeds (the label was
+# present when the gate started; never brick CI on an outage).
+recheck_ready_label() {
+  local pr_json
+  if pr_json=$(gh_api "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}" 2>/dev/null); then
+    if ! jq -e '[.labels[]?.name] | index("ready")' <<<"$pr_json" >/dev/null 2>&1; then
+      echo "::error::PR #${PR_NUMBER} no longer has the 'ready' label —" \
+        "NOT triggering the Buildkite full suite. Re-add the label to re-arm."
+      exit 1
+    fi
+  else
+    echo "::warning::Could not re-check the 'ready' label on PR #${PR_NUMBER}; proceeding (it was present when the gate started)."
+  fi
+}
+
+start=$(date +%s)
+api_fails=0
+missing=""
+
+while true; do
+  elapsed=$(( $(date +%s) - start ))
+
+  if runs_json=$(gh_api "repos/${GITHUB_REPOSITORY}/actions/runs?head_sha=${PR_SHA}&per_page=100" 2>/dev/null) \
+     && state=$(jq --arg re "$WATCHED_REGEX" '
+          [.workflow_runs[]? | select(.name // "" | test($re))]
+          | group_by(.name) | map(max_by(.id))
+          | map({name, status, conclusion})' <<<"$runs_json" 2>/dev/null); then
+    api_fails=0
+    echo "t+${elapsed}s watched checks: $(jq -c . <<<"$state")"
+
+    failed=$(jq -r '[.[] | select(.status == "completed"
+          and (.conclusion | IN("success", "skipped", "neutral", "cancelled") | not))]
+        | map(.name) | join(", ")' <<<"$state")
+    if [ -n "$failed" ]; then
+      echo "::error::Cheap check(s) failed on ${PR_SHA}: ${failed}." \
+        "NOT triggering the Buildkite full suite. Push a fix (the 'ready'" \
+        "label re-arms on every push), or re-run the failed check and then" \
+        "re-run this workflow."
+      exit 1
+    fi
+
+    # 'cancelled' counts as pending: wait for a re-run to reach a real verdict
+    # (bounded by MAX_WAIT, then the fail-open below).
+    pending=$(jq '[.[] | select(.status != "completed" or .conclusion == "cancelled")] | length' <<<"$state")
+    missing=$(jq -r --argjson watched "$WATCHED_NAMES" '($watched - map(.name)) | join(", ")' <<<"$state")
+    if [ "$pending" -eq 0 ]; then
+      if [ -z "$missing" ]; then
+        recheck_ready_label
+        echo "All watched cheap checks are green — full suite may proceed."
+        exit 0
+      fi
+      case "$missing" in
+        *pre-commit*)
+          echo "pre-commit run not found for ${PR_SHA} yet; waiting (pre-commit is never path-filtered, so its absence is anomalous)."
+          ;;
+        *)
+          if [ "$elapsed" -ge "$GRACE_SECS" ]; then
+            recheck_ready_label
+            echo "::warning::Watched run(s) never appeared for ${PR_SHA}: ${missing} (path-filtered, likely not applicable). Proceeding on the checks that did run."
+            exit 0
+          fi
+          echo "Waiting up to ${GRACE_SECS}s grace for path-filtered run(s) to appear: ${missing}."
+          ;;
+      esac
+    fi
+  else
+    api_fails=$(( api_fails + 1 ))
+    echo "::warning::GitHub API error querying workflow runs for ${PR_SHA} (attempt ${api_fails}/3)."
+    if [ "$api_fails" -ge 3 ]; then
+      recheck_ready_label
+      echo "::warning::FAILING OPEN: cannot query GitHub check status — triggering the full suite WITHOUT the cheap-check gate."
+      exit 0
+    fi
+  fi
+
+  if [ "$elapsed" -ge "$MAX_WAIT_SECS" ]; then
+    recheck_ready_label
+    echo "::warning::FAILING OPEN: watched checks still pending after $(( MAX_WAIT_SECS / 60 )) min${missing:+ (never appeared: ${missing})} — triggering the full suite anyway."
+    exit 0
+  fi
+  sleep "$POLL_SECS"
+done

.github/scripts/test_gate_full_suite.sh

@@ -0,0 +1,122 @@
+#!/usr/bin/env bash
+# Self-test for gate_full_suite.sh using a mocked `gh`. No network, runs on
+# any dev box: bash .github/scripts/test_gate_full_suite.sh
+set -u
+here=$(cd "$(dirname "$0")" && pwd)
+tmp=$(mktemp -d)
+trap 'rm -rf "$tmp"' EXIT
+
+# Mock gh. Asserts the exact endpoint (including head_sha) it is called
+# with — an endpoint typo in the gate script fails the test rather than
+# silently serving canned data. On the runs endpoint it serves
+# $MOCK_DIR/response_<call#>.json, sticking on the highest existing file,
+# and exits 1 if none exist (simulates a GitHub API outage). On the pulls
+# endpoint it serves $MOCK_DIR/pr.json, defaulting to a 'ready'-labeled PR.
+cat > "$tmp/gh" <<'EOF'
+#!/usr/bin/env bash
+if [ "${1:-}" != "api" ]; then
+  echo "unexpected gh invocation: $*" >> "$MOCK_DIR/endpoint_error"
+  exit 2
+fi
+case "${2:-}" in
+  "repos/o/r/actions/runs?head_sha=deadbeef&per_page=100")
+    n=$(( $(cat "$MOCK_DIR/count" 2>/dev/null || echo 0) + 1 ))
+    echo "$n" > "$MOCK_DIR/count"
+    while [ "$n" -gt 0 ]; do
+      if [ -f "$MOCK_DIR/response_$n.json" ]; then
+        cat "$MOCK_DIR/response_$n.json"
+        exit 0
+      fi
+      n=$(( n - 1 ))
+    done
+    echo "api outage" >&2
+    exit 1
+    ;;
+  "repos/o/r/pulls/42")
+    if [ -f "$MOCK_DIR/pr.json" ]; then
+      cat "$MOCK_DIR/pr.json"
+    else
+      echo '{"labels": [{"name": "ready"}]}'
+    fi
+    ;;
+  *)
+    echo "unexpected gh endpoint: $2" >> "$MOCK_DIR/endpoint_error"
+    exit 2
+    ;;
+esac
+EOF
+chmod +x "$tmp/gh"
+
+PC_OK='{"name": "pre-commit", "id": 1, "status": "completed", "conclusion": "success"}'
+PC_BAD='{"name": "pre-commit", "id": 1, "status": "completed", "conclusion": "failure"}'
+PC_PENDING='{"name": "pre-commit", "id": 1, "status": "in_progress", "conclusion": null}'
+DOCS_OK='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "success"}'
+DOCS_BAD='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "failure"}'
+DOCS_CANCELLED='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "cancelled"}'
+OTHER='{"name": "Trigger Full Suite", "id": 3, "status": "in_progress", "conclusion": null}'
+NULL_NAME='{"name": null, "id": 4, "status": "completed", "conclusion": "failure"}'
+PC_OK_RERUN='{"name": "pre-commit", "id": 5, "status": "completed", "conclusion": "success"}'
+
+fails=0
+want_log=""  # optional: expect() also greps out.log for this regex, then resets
+pr_json=""   # optional: served for the pulls (label re-check) endpoint, then resets
+raw_body=""  # optional: serve responses verbatim instead of wrapping in workflow_runs
+expect() { # <name> <expected-exit> <response json>...
+  local name=$1 want=$2 dir i=1
+  shift 2
+  dir=$(mktemp -d "$tmp/test_XXXXXX")
+  for body in "$@"; do
+    if [ -n "$raw_body" ]; then
+      printf '%s' "$body" > "$dir/response_$i.json"
+    else
+      printf '{"workflow_runs": [%s]}' "$body" > "$dir/response_$i.json"
+    fi
+    i=$(( i + 1 ))
+  done
+  [ -n "$pr_json" ] && printf '%s' "$pr_json" > "$dir/pr.json"
+  ( export PATH="$tmp:$PATH" MOCK_DIR="$dir" PR_SHA=deadbeef PR_NUMBER=42 \
+      GITHUB_REPOSITORY=o/r POLL_SECS=0 GRACE_SECS=1 MAX_WAIT_SECS=3
+    bash "$here/gate_full_suite.sh" > "$dir/out.log" 2>&1 )
+  local rc=$?
+  if [ "$rc" -ne "$want" ]; then
+    echo "FAIL: $name (exit $rc, want $want)"
+    cat "$dir/out.log"
+    fails=1
+  elif [ -f "$dir/endpoint_error" ]; then
+    echo "FAIL: $name (mock gh got an unexpected call)"
+    cat "$dir/endpoint_error"
+    fails=1
+  elif [ -n "$want_log" ] && ! grep -Eq "$want_log" "$dir/out.log"; then
+    echo "FAIL: $name (log does not match: $want_log)"
+    cat "$dir/out.log"
+    fails=1
+  else
+    echo "ok: $name"
+  fi
+  want_log="" pr_json="" raw_body=""
+}
+
+expect "both green -> proceed" 0 "$PC_OK, $DOCS_OK, $OTHER, $NULL_NAME"
+expect "docs build failed -> blocked" 1 "$PC_OK, $DOCS_BAD"
+expect "pre-commit failed -> blocked" 1 "$PC_BAD"
+expect "pending then green -> proceed" 0 "$PC_PENDING" "$PC_OK, $DOCS_OK"
+want_log="never appeared.*Deploy Documentation"
+expect "docs run absent (path-filtered) -> proceed after grace" 0 "$PC_OK"
+expect "API outage -> fail open" 0
+want_log="FAILING OPEN"
+expect "pending past MAX_WAIT -> fail open" 0 "$PC_PENDING"
+want_log="FAILING OPEN"
+expect "unrelated runs only -> no grace, fail open at MAX_WAIT" 0 "$OTHER"
+expect "cancelled docs then green -> proceed" 0 \
+  "$PC_OK, $DOCS_CANCELLED" "$PC_OK, $DOCS_OK"
+want_log="FAILING OPEN"
+expect "cancelled docs forever -> fail open at MAX_WAIT" 0 "$PC_OK, $DOCS_CANCELLED"
+want_log="FAILING OPEN"
+expect "pre-commit absent -> no grace, fail open at MAX_WAIT" 0 "$DOCS_OK"
+expect "duplicate run names -> latest wins" 0 "$PC_BAD, $PC_OK_RERUN, $DOCS_OK"
+raw_body=1
+expect "garbage response body -> fail open" 0 "this is not json"
+pr_json='{"labels": [{"name": "other"}]}'
+expect "ready label removed mid-gate -> blocked" 1 "$PC_OK, $DOCS_OK"
+
+exit "$fails"

.github/workflows/ci-precommit.yml

@@ -47,3 +47,6 @@ jobs:
     - uses: pre-commit/action@v3.0.1
       with:
         extra_args: --all-files --hook-stage manual
+    # After pre-commit so a self-test failure cannot mask lint failures.
+    - name: Full-suite gate self-test
+      run: bash .github/scripts/test_gate_full_suite.sh

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

@@ -52,6 +52,7 @@ jobs:
             core.setOutput('pr_sha', pr.head.sha);
             core.setOutput('pr_branch', pr.head.ref);
             core.setOutput('pr_number', String(prNumber));
+            core.setOutput('pr_title', pr.title);
 
       - name: Trigger Full Suite
         if: steps.perm.outputs.has_write == 'true'
@@ -60,6 +61,7 @@ jobs:
           PR_SHA: ${{ steps.label.outputs.pr_sha }}
           PR_BRANCH: ${{ steps.label.outputs.pr_branch }}
           PR_NUMBER: ${{ steps.label.outputs.pr_number }}
+          PR_TITLE: ${{ steps.label.outputs.pr_title }}
           BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
           BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
         run: |
@@ -71,6 +73,7 @@ jobs:
               --arg commit "$PR_SHA" \
               --arg branch "$PR_BRANCH" \
               --arg message "Full Suite for PR #${PR_NUMBER} (via /merge)" \
+              --arg pr_title "$PR_TITLE" \
               --argjson pr_id "$PR_NUMBER" \
               '{
                 commit: $commit,
@@ -80,11 +83,12 @@ jobs:
                 pull_request_id: $pr_id,
                 pull_request_base_branch: "main",
                 env: {
-                  TEST_SCOPE: "full",
-                  FULL_SUITE: "true",
-                  PR_NUMBER: ($pr_id | tostring)
-                }
-              }')"
+                    TEST_SCOPE: "full",
+                    FULL_SUITE: "true",
+                    PR_NUMBER: ($pr_id | tostring),
+                    PR_TITLE: $pr_title
+                  }
+                }')"
 
   parse-command:
     if: >-
@@ -241,6 +245,7 @@ jobs:
           TEST_SCOPE: ${{ needs.parse-command.outputs.test_scope }}
           FULL_SUITE: ${{ needs.parse-command.outputs.full_suite }}
           TEST_TYPE: ${{ needs.parse-command.outputs.test_type }}
+          PR_TITLE: ${{ github.event.issue.title }}
           BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
           BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
         run: |
@@ -257,6 +262,7 @@ jobs:
               --arg full_suite "$FULL_SUITE" \
               --arg test_type "$TEST_TYPE" \
               --arg pr_number "$PR_NUMBER" \
+              --arg pr_title "$PR_TITLE" \
               '{
                 commit: $commit,
                 branch: $branch,
@@ -266,8 +272,9 @@ jobs:
                 pull_request_base_branch: "main",
                 env: {
                   TEST_SCOPE: $test_scope,
-                  FULL_SUITE: $full_suite,
-                  TEST_TYPE: $test_type,
-                  PR_NUMBER: $pr_number
-                }
-              }')"
+                    FULL_SUITE: $full_suite,
+                    TEST_TYPE: $test_type,
+                    PR_NUMBER: $pr_number,
+                    PR_TITLE: $pr_title
+                  }
+                }')"

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

@@ -7,6 +7,7 @@ on:
 permissions:
   contents: read
   pull-requests: read
+  actions: read
 
 concurrency:
   group: full-suite-${{ github.event.pull_request.number }}
@@ -18,6 +19,8 @@ jobs:
       (github.event.action == 'labeled' && github.event.label.name == 'ready')
       || github.event.action == 'synchronize'
     runs-on: ubuntu-latest
+    # Gate below may wait for cheap checks (up to MAX_WAIT_SECS = 25 min).
+    timeout-minutes: 35
     steps:
       - name: Check ready label
         id: check
@@ -49,6 +52,20 @@ jobs:
               "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds/${build_num}/cancel"
           done
 
+      # Checks out the BASE branch (default for pull_request_target), so PR
+      # authors cannot tamper with the gate script.
+      - name: Checkout gate script
+        if: steps.check.outputs.has_ready == 'true'
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Wait for pre-commit and docs build
+        if: steps.check.outputs.has_ready == 'true'
+        env:
+          GH_TOKEN: ${{ github.token }}
+          PR_SHA: ${{ github.event.pull_request.head.sha }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+        run: bash .github/scripts/gate_full_suite.sh
+
       - name: Trigger Buildkite Full Suite
         if: steps.check.outputs.has_ready == 'true'
         env:
@@ -56,6 +73,7 @@ jobs:
           PR_SHA: ${{ github.event.pull_request.head.sha }}
           PR_BRANCH: ${{ github.event.pull_request.head.ref }}
           PR_NUMBER: ${{ github.event.pull_request.number }}
+          PR_TITLE: ${{ github.event.pull_request.title }}
           BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
           BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
         run: |
@@ -67,6 +85,7 @@ jobs:
               --arg commit "$PR_SHA" \
               --arg branch "$PR_BRANCH" \
               --arg message "Full Suite for PR #${PR_NUMBER}" \
+              --arg pr_title "$PR_TITLE" \
               --argjson pr_id "$PR_NUMBER" \
               '{
                 commit: $commit,
@@ -78,6 +97,7 @@ jobs:
                 env: {
                   TEST_SCOPE: "full",
                   FULL_SUITE: "true",
-                  PR_NUMBER: ($pr_id | tostring)
+                  PR_NUMBER: ($pr_id | tostring),
+                  PR_TITLE: $pr_title
                 }
               }')"

.gitignore

@@ -72,8 +72,7 @@ docs/distillation/examples/
 # Python pickle files
 *.pkl
 
-# Reference videos
-!fastvideo/tests/ssim/reference_videos/**/*.mp4
+# Reference videos (negations must come after the catch-all on line below)
 
 # Static images
 !docs/assets/images/**/*.png
@@ -127,6 +126,8 @@ apps/dreamverse/web/.env.production.local
 .sisyphus/
 openspec/
 fastvideo/tests/ssim/reference_videos/**
+!fastvideo/tests/ssim/reference_videos/**/*.mp4
+!fastvideo/tests/ssim/reference_videos/**/*.png
 
 # Editor logs and local Python version pins (accidentally committed)
 *.nvimlog

assets/eval/worldmodel_synthetic_flow_calibration.json

@@ -0,0 +1,22 @@
+{
+  "alpha_yaw": 0.08734091699186919,
+  "alpha_pitch": 0.08169667696275307,
+  "alpha_turn": 5.724587470723463e-17,
+  "beta_fwd": 0.02842768078408099,
+  "beta_strafe": 0.022531015077067108,
+  "focal_length": 457.0,
+  "frame_shape": [
+    352,
+    640
+  ],
+  "calibrated_from": [
+    "1_wasd_only",
+    "camera",
+    "camera4hold_alpha1",
+    "fully_random",
+    "wasdonly_alpha1",
+    "wasd4holdrandview_simple_1key1mouse1"
+  ],
+  "residual_rms": 15.890399609478676,
+  "n_equations": 4125232
+}

docs/contributing/ci_architecture.md

@@ -99,6 +99,13 @@ status.
 Full Suite is also path-filtered. It validates broader behavior before Mergify
 can merge a PR.
 
+A `ready`-labeled PR does not hit Buildkite immediately:
+`ci-trigger-full-suite.yml` first runs `.github/scripts/gate_full_suite.sh`,
+which waits for the cheap Tier-1 checks (pre-commit, docs build) on the PR
+head. A red cheap check blocks the suite (fail closed; the next push re-arms
+it), while a GitHub outage or a >25 min wait lets it run anyway (fail open).
+`/test full` bypasses the gate.
+
 | Buildkite label | `TEST_TYPE` | Main watched paths |
 |---|---|---|
 | SSIM Tests | `ssim` | `fastvideo/**/*.py`, `pyproject.toml`, `docker/Dockerfile` |

docs/contributing/performance_benchmarks.md

@@ -79,10 +79,11 @@ fastvideo/performance/
 ```
 
 The HF dataset (`FastVideo/performance-tracking` by default) holds one
-normalized JSON per `(model_id, gpu_type, run)` tuple. The rolling baseline is
-the median of the last 5 successful, baseline-eligible records for that
-model+GPU. PR and local records are visible in the dashboard but are not
-baseline eligible.
+normalized JSON per run. For v2 records, the rolling baseline is the median of
+the last 5 successful, baseline-eligible records in the same comparison cohort:
+`model_id`, `gpu_type`, `workload_id`, `variant_id`, `benchmark_version`,
+`recipe_fingerprint`, `hardware_profile_id`, and `software_profile_id`. PR and
+local records are visible in the dashboard but are not baseline eligible.
 
 ## Planned Coverage
 
@@ -158,13 +159,16 @@ unrealistic memory growth, and optionally large component-specific slowdowns
 even when the rolling baseline is empty. They are hand-set with generous
 headroom and almost never need touching.
 
-### Rolling baseline (per `(model_id, gpu_type)`)
+### Rolling baseline (per comparison cohort)
 
 `compare_baseline.py` loads the last 5 successful, baseline-eligible records
-for the same `(model_id, gpu_type)` from the HF dataset, computes the median
-for each available metric, and evaluates the current run with the metric's
-rolling regression policy. For latency, memory, and component times, higher
-values are regressions. For throughput, lower values are regressions.
+for the same comparison cohort from the HF dataset, computes the median for
+each available metric, and evaluates the current run with the metric's
+rolling regression policy. For v2 records, that cohort is `model_id`,
+`gpu_type`, `workload_id`, `variant_id`, `benchmark_version`,
+`recipe_fingerprint`, `hardware_profile_id`, and `software_profile_id`. For
+latency, memory, and component times, higher values are regressions. For
+throughput, lower values are regressions.
 
 A metric exceeds its rolling threshold when both of these are true:
 
@@ -230,8 +234,10 @@ Recipe fingerprinting, hardware/software profile IDs, exact-identity
 comparison, and dashboard cohort grouping land with this change: v2 records
 compare only within their identity cohort, and a record that opens a NEW
 cohort is marked `baseline_status: "initialized_new_cohort"` (regression
-gating starts once that cohort accumulates history). Legacy v1 configs keep
-the `(model_id, gpu_type)` rolling-baseline comparison. Metric-specific
+gating starts once that cohort accumulates history). Legacy v1 configs still
+run and are normalized for reporting, but their records skip rolling-baseline
+comparison entirely (`baseline_status: "skipped_missing_identity"`, never
+baseline eligible); only static thresholds gate them. Metric-specific
 threshold policies and promoted baselines remain separate follow-ups.
 
 ### Raw record (`results/perf_*.json`)
@@ -241,7 +247,7 @@ Written by `test_inference_performance.py`. One file per benchmark run.
 ```jsonc
 {
   "benchmark_id": "wan-t2v-1.3b-2gpu",
-  "config_schema_version": 2,
+  "result_schema_version": 2,
   "workload_id": "wan-t2v",
   "variant_id": "1.3b-sp2",
   "benchmark_version": 2,
@@ -270,8 +276,15 @@ Written by `test_inference_performance.py`. One file per benchmark run.
     }
   },
   "commit": "<full sha>",
+  "run_source": "pr",
+  "branch": "feature/perf-change",
   "pr_number": "1234",
+  "test_scope": "direct",
+  "build_url": "https://buildkite.example/build",
+  "build_id": "<buildkite-build-id>",
+  "job_id": "<buildkite-job-id>",
   "timestamp": "2026-05-08T22:00:00+00:00",
+  "quality_metadata": { "quality_status": "canonical" },
   "text_encoder_time_s": 2.141,
   "dit_time_s": 8.437,
   "vae_decode_time_s": 3.208,
@@ -322,6 +335,7 @@ result, used as the rolling-baseline source of truth.
 ```jsonc
 {
   "model_id": "wan-t2v-1.3b-2gpu",
+  "result_schema_version": 2,
   "workload_id": "wan-t2v",
   "variant_id": "1.3b-sp2",
   "benchmark_version": 2,
@@ -345,17 +359,30 @@ result, used as the rolling-baseline source of truth.
   "hardware_profile_id": "hw-<sha256-prefix>",
   "software_profile_id": "sw-<sha256-prefix>",
   "environment_fingerprint": "env-<sha256-prefix>",
+  "run_source": "pr",
+  "branch": "feature/perf-change",
+  "pr_number": "1234",
+  "test_scope": "direct",
+  "build_url": "https://buildkite.example/build",
+  "build_id": "<buildkite-build-id>",
+  "job_id": "<buildkite-job-id>",
+  "quality_metadata": { "quality_status": "canonical" },
   "success": true
 }
 ```
 
 ### Compatibility with legacy records
 
-Older records in the HF dataset may not have component timing fields. The
-comparator ignores missing or `null` metrics when computing a median, and the
-dashboard lists skipped plots for metric series that have no non-null values.
-Records missing both `run_source` and `baseline_eligible` are treated as legacy
-successful main/full-suite uploads and remain eligible for rolling baselines.
+Older records in the HF dataset may not have `result_schema_version`,
+component timing fields, or v2 identity/profile fields. Records without
+`result_schema_version` are treated as v1. The comparator ignores missing or
+`null` metrics when computing a median, and the dashboard lists skipped plots
+for metric series that have no non-null values. Records missing both
+`run_source` and `baseline_eligible` are treated as legacy successful
+main/full-suite uploads and remain eligible for rolling baselines.
+Current `perf_*.json` artifacts that lack the v2 comparison identity are
+normalized for reporting but skip rolling-baseline comparison and are not marked
+baseline eligible.
 
 New records compare only against the same `model_id`, `gpu_type`,
 `workload_id`, `variant_id`, `benchmark_version`, `recipe_fingerprint`,
@@ -377,7 +404,7 @@ FlashInfer, Cutlass DSL, SageAttention, Triton, and xFormers when installed.
 | `PERF_REPORTS_DIR` | `/root/data/perf_reports` | `compare_baseline.py`, `dashboard.py` | Where the Markdown summary and Plotly HTML get written for Buildkite to pick up. |
 | `HF_REPO_ID` | `FastVideo/performance-tracking` | `fastvideo/performance/hf_store.py` | HF dataset repo holding rolling-baseline records. |
 | `HF_API_KEY`, `HUGGINGFACE_HUB_TOKEN`, `HF_TOKEN` | unset | `fastvideo/performance/hf_store.py` | Required for upload or private dataset reads. |
-| `PERF_RUN_SOURCE` | inferred | `compare_baseline.py` | Source metadata for uploaded records: `pr`, `local`, `scheduled_main`, or `unknown`. |
+| `PERF_RUN_SOURCE` | inferred | `compare_baseline.py`, `test_inference_performance.py` | Source metadata for uploaded records: `pr`, `local`, `scheduled_main`, or `unknown`. |
 | `PERF_UPLOAD_POLICY` | `never` | `compare_baseline.py` | Upload policy: `never`, `pass`, or `always`. |
 | `PERF_PYTEST_RC` | unset | `compare_baseline.py` | Static-threshold pytest exit code, used so scheduled-main failures can be uploaded with `success=false`. |
 | `TEST_SCOPE` | unset | `compare_baseline.py` | CI context used to infer scheduled-main runs together with `BUILDKITE_BRANCH=main`. |
@@ -394,11 +421,14 @@ point is `fastvideo/tests/modal/pr_test.py:run_performance_tests` and the
 Buildkite artifact upload is in
 `.buildkite/scripts/pr_test.sh:upload_performance_artifacts`.
 
-Each performance build runs pytest first. If that fixed-threshold phase fails,
-PR/direct runs skip `compare_baseline.py` because they only upload passing
-records. Scheduled-main runs still execute `compare_baseline.py` with
-`PERF_PYTEST_RC` set so the failed canonical attempt is visible in normalized
-JSON and dashboard history. The dashboard runs best-effort for observability.
+Each performance build runs pytest first. PR and direct runs only continue to
+`compare_baseline.py` when that fixed-threshold phase passes; if pytest fails,
+Markdown summaries and normalized JSON artifacts are not emitted. Scheduled
+main runs set `PERF_UPLOAD_POLICY=always`, so they still run
+`compare_baseline.py` (with `PERF_PYTEST_RC` set) after a fixed-threshold
+failure. Those failed scheduled main runs emit summaries and normalized
+records, upload records with `success=false`, and are excluded from future
+rolling baselines. The dashboard still runs best-effort for observability.
 When the rolling-baseline phase runs, it emits:
 
 * **Markdown summary** — appended to `$GITHUB_STEP_SUMMARY` when that variable
@@ -406,7 +436,7 @@ When the rolling-baseline phase runs, it emits:
   per-benchmark row with current vs. baseline values for latency, throughput,
   memory, text encoder time, DiT time, and VAE decode time.
 * **Plotly dashboard** — `dashboard_<sha>_<ts>.html` showing time-series for
-  each metric grouped by `(model_id, gpu_type)`.
+  each metric grouped by comparison cohort.
 * **Normalized records** — `normalized_perf_*.json`, one per benchmark.
   Useful as input to the
   [`reseed-performance-baseline`](https://github.com/hao-ai-lab/FastVideo/blob/main/.agents/skills/reseed-performance-baseline/SKILL.md)
@@ -480,8 +510,8 @@ When the rolling-baseline phase runs, it emits:
 
 ## Troubleshooting
 
-**"No baseline for ... Initializing"** — first run for this `(model_id,
-gpu_type)`. Run will pass and (if persisting) seed the first record.
+**"No baseline for ... Initializing"** — first run for this comparison cohort.
+Run will pass and (if persisting) seed the first record.
 
 **Persistent failure right after a torch / kernel / image upgrade** —
 genuine regression *or* baseline drift. Compare the failing normalized record

docs/contributing/testing.md

@@ -180,6 +180,30 @@ python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
   --device-folder L40S_reference_videos
 ```
 
+### SSIM Bootstrap Mode
+
+Normal SSIM runs are strict: if a reference video or latent is missing, the
+test fails. For new-model PRs, CI can run SSIM in bootstrap mode so missing
+references are uploaded as draft artifacts for review instead of immediately
+blocking on a missing canonical reference.
+
+Buildkite enables SSIM bootstrap mode when either condition is true:
+
+- the PR title or Buildkite message contains `[new-model]`;
+- `FASTVIDEO_SSIM_BOOTSTRAP_MODE=1` is set for the Buildkite job.
+
+Bootstrap mode passes `--ssim-bootstrap-mode` to pytest. When a generated
+artifact is available, the test uploads it under the `drafts/...` namespace in
+the SSIM reference repo and marks that case as expected-failed. After reviewing
+the draft, promote it into the canonical reference layout:
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py promote-draft \
+  --quality-tier default \
+  --device-folder L40S_reference_videos \
+  --model-id <model_id>
+```
+
 ## CI Integration
 
 FastVideo CI tests are orchestrated by Buildkite and run on Modal GPU

docs/design/inference_schema_parity_inventory.yaml

@@ -458,6 +458,8 @@ surfaces:
       guidance_scale: request.sampling.guidance_scale
       guidance_scale_2: request.sampling.guidance_scale_2
       guidance_rescale: request.sampling.guidance_rescale
+      use_embedded_guidance: request.sampling.use_embedded_guidance
+      true_cfg_scale: request.sampling.true_cfg_scale
       boundary_ratio: request.sampling.boundary_ratio
       sigmas: request.sampling.sigmas
       enable_teacache: request.runtime.enable_teacache

docs/distillation/anyflow.md

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

docs/inference/support_matrix.md

@@ -56,7 +56,7 @@ pipeline initialization and sampling.
 | Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn (Legacy Branch) | Sage Attn | VSA | BSA |
 |------------|---------------------|-------------|----------|-------------------|-----------|-----|-----|
 | FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
-| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
+| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ❌ | ⭕ |
 | Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
 | DreamX-World 5B Cam | `FastVideo/DreamX-World-5B-Cam-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | DreamX-World 5B AR | `FastVideo/DreamX-World-5B-Diffusers` | 704px1280p | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |

docs/training/train_infra.md

@@ -161,6 +161,18 @@ training:
     decay_interval_steps: 0
 ```
 
+`training.data.data_path` can also mix multiple preprocessed datasets by using a mapping from dataset path to repeat count:
+
+```yaml
+training:
+  data:
+    data_path:
+      data/zeldam2-clean: 1
+      data/multi3d_games: 2
+```
+
+The repeat count duplicates that dataset's parquet file list before shuffling/sampling, so the example above trains with roughly twice as much `multi3d_games` exposure as `zeldam2-clean`. Paths are just suggested locations; use any local path that contains a FastVideo preprocessed parquet dataset.
+
 See [Training Trackers](trackers.md) to configure Weights & Biases or SwanLab,
 including SwanLab installation and authentication.
 
@@ -326,6 +338,40 @@ Self-Forcing inherits all DMD2 parameters, plus:
 | `enable_gradient_in_rollout` | `true` | Enable backprop through rollout |
 | `start_gradient_frame` | `0` | Frame index where gradients begin |
 
+### Streaming Long Tuning
+
+`StreamingLongTuningMethod` extends Self-Forcing for LongLive-style rollouts. It
+keeps a streaming state, generates overlapping chunks, and trains only the new
+frames while preserving context from earlier chunks.
+
+For the MatrixGame2/Zelda world-model example, self-forcing and long tuning are
+separate runs: first train or load the 1k-step self-forcing checkpoint using
+`examples/train/scenario/worldmodel/zelda/self_forcing_causal_i2v.yaml`,
+then run
+`examples/train/scenario/worldmodel/zelda/streaming_long_tuning_causal_i2v.yaml`
+from that checkpoint for the 3k-step streaming long-tuning stage.
+
+```yaml
+method:
+  _target_: fastvideo.train.methods.distribution_matching.streaming_long_tuning.StreamingLongTuningMethod
+  streaming_chunk_size: 9
+  streaming_max_length: 39
+  streaming_fixed_overlap_latents: 3
+  streaming_reencode_overlap_anchor: true
+  streaming_anchor_inject_k: 1
+  streaming_require_full_blocks: true
+  multi_phased_distill_schedule:
+    - stage: streaming_long
+      start_step: 0
+      end_step: 3000
+      num_latent_t: 39
+      streaming_training: true
+```
+
+See
+`examples/train/scenario/worldmodel/zelda/streaming_long_tuning_causal_i2v.yaml`
+for a complete MatrixGame2/Zelda configuration.
+
 ---
 
 ## Callbacks

examples/inference/basic/basic_flux_dev.py

@@ -0,0 +1,140 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import argparse
+import contextlib
+import os
+import re
+
+DEFAULT_PROMPTS = [
+    "a photo of a cat",
+    (
+        "a cinematic photo of a red panda wearing a tiny backpack, standing on a "
+        "rainy neon-lit street at night, shallow depth of field, sharp focus, "
+        "35mm, bokeh"
+    ),
+]
+
+
+def _safe_filename(text: str, max_len: int = 100) -> str:
+    """Make a stable, filesystem-friendly filename base."""
+    s = text[:max_len].strip()
+    s = s.replace(os.sep, "_")
+    if os.altsep:
+        s = s.replace(os.altsep, "_")
+    s = re.sub(r"\s+", " ", s)
+    s = re.sub(r"[^A-Za-z0-9 .,_-]", "_", s)
+    s = s.strip(" .")
+    return s or "prompt"
+
+
+def _remove_existing_outputs(out_dir: str, filename_base: str) -> None:
+    """Delete prior outputs so reruns do not get _1, _2 suffixes."""
+    if not os.path.isdir(out_dir):
+        return
+
+    pattern = re.compile(rf"^{re.escape(filename_base)}(_\d+)?\.(mp4|png)$")
+    for fn in os.listdir(out_dir):
+        if pattern.match(fn):
+            with contextlib.suppress(FileNotFoundError):
+                os.remove(os.path.join(out_dir, fn))
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(
+        description="Run FLUX.1-dev text-to-image with FastVideo VideoGenerator.",
+    )
+    p.add_argument(
+        "--model-path",
+        default="official_weights/FLUX.1-dev",
+        help="Local Diffusers checkpoint dir or HF repo id.",
+    )
+    p.add_argument(
+        "--out-dir",
+        "--outdir",
+        default="outputs/flux_dev/samples",
+        help="Directory for saved PNG outputs.",
+    )
+    p.add_argument(
+        "--prompt",
+        action="append",
+        default=None,
+        help="Prompt. Repeat for multiple images.",
+    )
+    p.add_argument(
+        "--backend",
+        default=None,
+        help="Set FASTVIDEO_ATTENTION_BACKEND (e.g. TORCH_SDPA).",
+    )
+    p.add_argument("--seed", type=int, default=42, help="Base seed; each prompt uses seed + index.")
+    p.add_argument("--height", type=int, default=1024, help="Output height.")
+    p.add_argument("--width", type=int, default=1024, help="Output width.")
+    p.add_argument("--steps", type=int, default=28, help="Number of inference steps.")
+    p.add_argument("--guidance", type=float, default=3.5, help="Guidance scale.")
+    p.add_argument("--num-gpus", type=int, default=1, help="GPU count.")
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    prompts: list[str] = args.prompt if args.prompt else DEFAULT_PROMPTS
+
+    if args.backend:
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = args.backend
+
+    from fastvideo import VideoGenerator
+
+    os.makedirs(args.out_dir, exist_ok=True)
+
+    init_kwargs = {
+        "num_gpus": args.num_gpus,
+        "workload_type": "t2i",
+        "sp_size": 1,
+        "tp_size": 1,
+        "dit_cpu_offload": False,
+        "dit_layerwise_offload": False,
+        "text_encoder_cpu_offload": False,
+        "vae_cpu_offload": False,
+        "image_encoder_cpu_offload": False,
+        "pin_cpu_memory": False,
+        "use_fsdp_inference": False,
+    }
+
+    generator = VideoGenerator.from_pretrained(
+        model_path=args.model_path,
+        **init_kwargs,
+    )
+    try:
+        for i, prompt in enumerate(prompts):
+            seed = args.seed + i
+            filename_base = (
+                f"flux_dev_{i:02d}_seed{seed}_{_safe_filename(prompt, max_len=80)}"
+            )
+            _remove_existing_outputs(args.out_dir, filename_base)
+            output_path = os.path.join(args.out_dir, f"{filename_base}.png")
+            print(f"[flux] prompt_idx={i} seed={seed} output_path={output_path}")
+
+            generation_kwargs = {
+                "output_path": output_path,
+                "height": args.height,
+                "width": args.width,
+                "num_frames": 1,
+                "fps": 1,
+                "num_inference_steps": args.steps,
+                "guidance_scale": args.guidance,
+                "use_embedded_guidance": True,
+                "true_cfg_scale": 1.0,
+                "seed": seed,
+                "save_video": True,
+            }
+
+            generator.generate_video(prompt, **generation_kwargs)
+
+        print(f"[flux] done. outputs written to: {args.out_dir}")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_glm_image.py

@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Run GLM-Image text-to-image generation through FastVideo.
+
+User story:
+    "I have the HF `zai-org/GLM-Image` checkpoint and want a minimal
+    text-to-image generation command, saved as a PNG."
+"""
+import argparse
+from pathlib import Path
+
+from PIL import Image
+
+from fastvideo import VideoGenerator
+from fastvideo.api import (
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    OutputConfig,
+    ParallelismConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run GLM-Image text-to-image generation.")
+    parser.add_argument(
+        "--model-path",
+        default="zai-org/GLM-Image",
+        help="HF id or local diffusers-format GLM-Image weights directory.",
+    )
+    parser.add_argument(
+        "--output",
+        default="image_output/landscape.png",
+        help="Output PNG path.",
+    )
+    parser.add_argument(
+        "--prompt",
+        default=("A beautiful landscape photography with rolling hills, "
+                 "a winding river, and a vibrant sunset in the background. "
+                 "Warm golden light, photorealistic style."),
+        help="Text prompt.",
+    )
+    parser.add_argument("--height", type=int, default=1024)
+    parser.add_argument("--width", type=int, default=1024)
+    parser.add_argument("--steps", type=int, default=50)
+    parser.add_argument("--guidance-scale", type=float, default=1.5)
+    parser.add_argument("--seed", type=int, default=1024)
+    parser.add_argument("--num-gpus", type=int, default=1)
+    parser.add_argument("--tp-size", type=int, default=None)
+    parser.add_argument("--sp-size", type=int, default=None)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
+    sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
+
+    # GLM-Image needs trust_remote_code for its AR encoder; offload and the
+    # pipeline class come from the model's registered defaults — don't override.
+    generator_config = GeneratorConfig(
+        model_path=args.model_path,
+        trust_remote_code=True,
+        engine=EngineConfig(
+            num_gpus=args.num_gpus,
+            parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
+        ),
+        pipeline=PipelineSelection(workload_type="t2i"),
+    )
+
+    generator = VideoGenerator.from_config(generator_config)
+    try:
+        request = GenerationRequest(
+            prompt=args.prompt,
+            sampling=SamplingConfig(
+                height=args.height,
+                width=args.width,
+                num_frames=1,
+                fps=1,
+                num_inference_steps=args.steps,
+                guidance_scale=args.guidance_scale,
+                seed=args.seed,
+            ),
+            output=OutputConfig(
+                output_path=str(output.parent),
+                save_video=False,
+                return_frames=True,
+            ),
+        )
+        result = generator.generate(request)
+        if isinstance(result, list):
+            result = result[0]
+
+        frames = result.frames
+        if frames is not None and len(frames):
+            Image.fromarray(frames[0]).save(output)
+            print(f"Saved image to {output}")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_kandinsky5_i2v.py

@@ -0,0 +1,37 @@
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_kandinsky5_i2v"
+
+IMAGE_PATH = "assets/girl.png"
+
+
+def main():
+    generator = VideoGenerator.from_pretrained(
+        "kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers", 
+        # "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers"
+        num_gpus=1,
+        use_fsdp_inference=False,
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    prompt = (
+        "A woman stands up and walks away"
+    )
+    _ = generator.generate_video(
+        prompt,
+        image_path=IMAGE_PATH,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        height=1024,
+        width=1024,
+        num_frames=121,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_kandinsky5_t2v.py

@@ -0,0 +1,37 @@
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_kandinsky5_t2v"
+
+def main():
+    generator = VideoGenerator.from_pretrained(
+        "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
+        # "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers"
+        num_gpus=1,
+        use_fsdp_inference=False,
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    prompt = (
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    )
+    _ = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True,height=512, width=768, num_frames=121)
+
+    prompt2 = (
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic.")
+    _ = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, height=512, width=768, num_frames=121)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/edit_glm_image.py

@@ -0,0 +1,120 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Run GLM-Image image-to-image (edit) generation through FastVideo.
+
+User story:
+    "I have the HF `zai-org/GLM-Image` checkpoint and a condition image, and
+    want a minimal edit command (text + image -> edited image), saved as a PNG."
+
+GLM-Image is a single unified pipeline: passing a condition image switches it
+from text-to-image to the edit path (the condition enters the DiT via a KV-cache
+write pass), so the generator config is identical to `basic_glm_image.py` — the
+`inputs.pil_image` on the request is what selects the edit mode.
+"""
+import argparse
+from pathlib import Path
+
+from PIL import Image
+
+from fastvideo import VideoGenerator
+from fastvideo.api import (
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    InputConfig,
+    OutputConfig,
+    ParallelismConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run GLM-Image image-to-image (edit) generation.")
+    parser.add_argument(
+        "--model-path",
+        default="zai-org/GLM-Image",
+        help="HF id or local diffusers-format GLM-Image weights directory.",
+    )
+    parser.add_argument(
+        "--image",
+        default="assets/images/couple.jpg",
+        help="Condition image to edit.",
+    )
+    parser.add_argument(
+        "--output",
+        default="image_output/edited.png",
+        help="Output PNG path.",
+    )
+    parser.add_argument(
+        "--prompt",
+        default="Change the background to a snowy mountain landscape at golden hour.",
+        help="Edit instruction.",
+    )
+    parser.add_argument("--height", type=int, default=1024)
+    parser.add_argument("--width", type=int, default=1024)
+    parser.add_argument("--steps", type=int, default=50)
+    parser.add_argument("--guidance-scale", type=float, default=1.5)
+    parser.add_argument("--seed", type=int, default=1024)
+    parser.add_argument("--num-gpus", type=int, default=1)
+    parser.add_argument("--tp-size", type=int, default=None)
+    parser.add_argument("--sp-size", type=int, default=None)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    condition = Image.open(args.image).convert("RGB")
+    tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
+    sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
+
+    # GLM-Image needs trust_remote_code for its AR encoder; offload and the
+    # pipeline class come from the model's registered defaults — don't override.
+    # The pipeline is registered as t2i; passing inputs.pil_image below switches
+    # it to the edit path.
+    generator_config = GeneratorConfig(
+        model_path=args.model_path,
+        trust_remote_code=True,
+        engine=EngineConfig(
+            num_gpus=args.num_gpus,
+            parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
+        ),
+        pipeline=PipelineSelection(workload_type="t2i"),
+    )
+
+    generator = VideoGenerator.from_config(generator_config)
+    try:
+        request = GenerationRequest(
+            prompt=args.prompt,
+            inputs=InputConfig(pil_image=condition),
+            sampling=SamplingConfig(
+                height=args.height,
+                width=args.width,
+                num_frames=1,
+                fps=1,
+                num_inference_steps=args.steps,
+                guidance_scale=args.guidance_scale,
+                seed=args.seed,
+            ),
+            output=OutputConfig(
+                output_path=str(output.parent),
+                save_video=False,
+                return_frames=True,
+            ),
+        )
+        result = generator.generate(request)
+        if isinstance(result, list):
+            result = result[0]
+
+        frames = result.frames
+        if frames is not None and len(frames):
+            Image.fromarray(frames[0]).save(output)
+            print(f"Saved image to {output}")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/gradio/serving/ray_serve_backend.py

@@ -127,11 +127,12 @@ def save_image_from_base64(image_data: str, output_dir: str) -> Optional[str]:
 
 
 def setup_model_environment(model_path: str) -> None:
-    # if "fullattn" in model_path.lower():
-    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
-    # else:
-    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
-    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+    from fastvideo.platforms import AttentionBackendEnum
+    from fastvideo.registry import get_pipeline_config_cls_from_name
+
+    pipeline_config = get_pipeline_config_cls_from_name(model_path)()
+    attention_backend = pipeline_config.dit_config.required_attention_backend or AttentionBackendEnum.FLASH_ATTN
+    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = attention_backend.name
     os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
 
 

examples/inference/ltx2_3/README.md

@@ -0,0 +1,69 @@
+# LTX-2.3 distilled inference configs
+
+Ready-to-run `fastvideo generate` run configs for the LTX-2.3
+distilled model (`FastVideo/LTX-2.3-Distilled-Diffusers`), covering both
+workloads (t2v / i2v), both two-stage step schedules (`5+2`, `8+3` = denoise
++ refine), and four resolutions.
+
+```bash
+fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml
+```
+
+Each config is self-contained (no preset registry needed): the two-stage
+refine is wired via `generator.pipeline.preset_overrides.refine`, and the
+base sampling knobs live under `request.sampling`. The refine upsampler
+auto-resolves from the model's `spatial_upscaler`.
+
+## Configs
+
+| workload | schedule | resolution (HxW) | file |
+|---|---|---|---|
+| t2v | 5+2 | 1280x832 | `t2v_5s2_1280x832.yaml` |
+| t2v | 5+2 | 1024x1536 | `t2v_5s2_1024x1536.yaml` |
+| t2v | 5+2 | 768x1280 | `t2v_5s2_768x1280.yaml` |
+| t2v | 5+2 | 512x768 | `t2v_5s2_512x768.yaml` |
+| t2v | 8+3 | 1280x832 | `t2v_8s3_1280x832.yaml` |
+| t2v | 8+3 | 1024x1536 | `t2v_8s3_1024x1536.yaml` |
+| t2v | 8+3 | 768x1280 | `t2v_8s3_768x1280.yaml` |
+| t2v | 8+3 | 512x768 | `t2v_8s3_512x768.yaml` |
+| i2v | 5+2 | 1280x832 | `i2v_5s2_1280x832.yaml` |
+| i2v | 5+2 | 1024x1536 | `i2v_5s2_1024x1536.yaml` |
+| i2v | 5+2 | 768x1280 | `i2v_5s2_768x1280.yaml` |
+| i2v | 5+2 | 512x768 | `i2v_5s2_512x768.yaml` |
+| i2v | 8+3 | 1280x832 | `i2v_8s3_1280x832.yaml` |
+| i2v | 8+3 | 1024x1536 | `i2v_8s3_1024x1536.yaml` |
+| i2v | 8+3 | 768x1280 | `i2v_8s3_768x1280.yaml` |
+| i2v | 8+3 | 512x768 | `i2v_8s3_512x768.yaml` |
+
+## Overriding without editing a file
+
+Dotted overrides (prefixes `generator.` / `request.`) let you tweak any field:
+
+```bash
+# swap prompt
+fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml \
+  --request.prompt "a red fox running through fresh snow"
+
+# change output path / gpu count
+fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml \
+  --request.output.output_path outputs/preview.mp4 \
+  --generator.engine.num_gpus 4
+```
+
+## i2v
+
+The `i2v_*` configs take a first-frame image via
+`request.extensions.ltx2_images` (`[[path, frame_offset, weight]]`). Edit the
+path in the file, or override it:
+
+```bash
+fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml \
+  --request.extensions.ltx2_images '[["/data/portrait.jpg", 0, 1.0]]'
+```
+
+## Schedules
+
+`5+2` is the fast preview schedule; `8+3` is the higher-quality distilled
+recipe. Refine (`preset_overrides.refine.num_inference_steps`) only accepts 2
+or 3 steps.
+

examples/inference/ltx2_3/i2v_5s2_1024x1536.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1024x1536.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_1024x1536.mp4
+    save_video: true

examples/inference/ltx2_3/i2v_5s2_1280x832.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_1280x832.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_1280x832.mp4
+    save_video: true

examples/inference/ltx2_3/i2v_5s2_512x768.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_512x768.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_512x768.mp4
+    save_video: true

examples/inference/ltx2_3/i2v_5s2_768x1280.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 5+2 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_5s2_768x1280.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_5s2_768x1280.mp4
+    save_video: true

examples/inference/ltx2_3/i2v_8s3_1024x1536.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1024x1536.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_1024x1536.mp4
+    save_video: true

examples/inference/ltx2_3/i2v_8s3_1280x832.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_1280x832.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_1280x832.mp4
+    save_video: true

examples/inference/ltx2_3/i2v_8s3_512x768.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_512x768.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_512x768.mp4
+    save_video: true

examples/inference/ltx2_3/i2v_8s3_768x1280.yaml

@@ -0,0 +1,38 @@
+# LTX-2.3 distilled i2v — 8+3 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/i2v_8s3_768x1280.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: i2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    The subject slowly turns toward the camera with a soft, natural expression, hair and clothing swaying gently, shallow depth of field, subtle cinematic motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  # i2v conditioning — replace the path with your own first-frame image.
+  extensions:
+    ltx2_images:
+      - ["/path/to/your/first_frame.jpg", 0, 1.0]
+    ltx2_image_crf: 0.0
+  output:
+    output_path: outputs/ltx2_3_i2v_8s3_768x1280.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_5s2_1024x1536.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1024x1536.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_1024x1536.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_5s2_1280x832.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_1280x832.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_1280x832.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_5s2_512x768.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_512x768.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_512x768.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_5s2_768x1280.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 5+2 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_5s2_768x1280.yaml
+#
+# Stage 1 denoises for 5 steps at half resolution; the latents are then
+# spatially upsampled and refined for 2 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 2
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 5
+  output:
+    output_path: outputs/ltx2_3_t2v_5s2_768x1280.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_8s3_1024x1536.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 1024x1536.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1024x1536.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1024
+    width: 1536
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_1024x1536.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_8s3_1280x832.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 1280x832.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_1280x832.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 1280
+    width: 832
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_1280x832.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_8s3_512x768.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 512x768.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_512x768.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 512
+    width: 768
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_512x768.mp4
+    save_video: true

examples/inference/ltx2_3/t2v_8s3_768x1280.yaml

@@ -0,0 +1,33 @@
+# LTX-2.3 distilled t2v — 8+3 two-stage at 768x1280.
+#
+# Run:
+#   fastvideo generate --config examples/inference/ltx2_3/t2v_8s3_768x1280.yaml
+#
+# Stage 1 denoises for 8 steps at half resolution; the latents are then
+# spatially upsampled and refined for 3 steps (refine only supports 2 or 3).
+# The refine upsampler auto-resolves from the model's `spatial_upscaler`.
+generator:
+  model_path: FastVideo/LTX-2.3-Distilled-Diffusers
+  engine:
+    num_gpus: 1
+  pipeline:
+    workload_type: t2v
+    preset_overrides:
+      refine:
+        enabled: true
+        num_inference_steps: 3
+        guidance_scale: 1.0
+        add_noise: true
+request:
+  prompt: >-
+    A cinematic drone shot flying over dramatic coastal cliffs at sunrise, golden light spilling across the water, gentle waves breaking on the rocks below, ultra-detailed, smooth camera motion.
+  sampling:
+    height: 768
+    width: 1280
+    num_frames: 121
+    fps: 24
+    guidance_scale: 1.0
+    num_inference_steps: 8
+  output:
+    output_path: outputs/ltx2_3_t2v_8s3_768x1280.mp4
+    save_video: true

examples/train/configs/distribution_matching/wan/anyflow_onpolicy_t2v.yaml

@@ -0,0 +1,105 @@
+# AnyFlow on-policy DMD — Wan 2.1 T2V 1.3B.
+#
+# Stage 2 of the AnyFlow two-stage recipe. Continues from the pretrain
+# checkpoint; refines the student via DMD2 with a multi-step Euler-flow
+# rollout from pure noise. Teacher provides the real score, critic
+# learns the fake score; both inherited from DMD2Method.
+#
+# Replace <PATH_TO_PRETRAIN_CKPT> with the output of the pretrain stage,
+# or with the NVIDIA-released checkpoint
+# nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers to bootstrap directly from
+# the paper weights (the delta_embedder rename is handled by the
+# param_names_mapping in WanVideoArchConfig).
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: <PATH_TO_PRETRAIN_CKPT>
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.anyflow.AnyFlowMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 3.0
+  dmd_denoising_steps: [999, 937, 833, 624]
+  warp_denoising_step: false
+
+  # AnyFlow rollout knobs.
+  student_sample_steps: 4
+  use_mean_velocity: true
+  t_list_override: [999.0, 937.0, 833.0, 624.0, 0.0]
+  dmd_score_r_value: 0.0  # DMD scoring conditioning is at r=0 (consistency target).
+
+  # Critic optimizer (DMD2 inherited).
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+  attn_kind: vsa
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/preprocessed
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_anyflow_onpolicy
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+    resume_from_checkpoint: latest
+
+  tracker:
+    project_name: anyflow-wan
+    run_name: wan2.1_t2v_anyflow_onpolicy
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+
+pipeline:
+  flow_shift: 5.0
+  dit_config:
+    r_embedder: true
+    r_embedder_fusion: gated
+    r_embedder_gate_value: 0.25
+    r_embedder_deltatime_type: r

examples/train/configs/distribution_matching/wan/anyflow_pretrain_t2v.yaml

@@ -0,0 +1,83 @@
+# AnyFlow pretrain (flow-map central-difference) — Wan 2.1 T2V 1.3B.
+#
+# Stage 1 of the AnyFlow two-stage recipe. Trains the dual-timestep
+# u_θ(x_t, t, r) on the central-difference target so the same checkpoint
+# can be sampled at arbitrary NFE in the on-policy stage.
+#
+# Initialize from base Wan 2.1 T2V 1.3B. No teacher or critic at this
+# stage; AnyFlowPretrainMethod owns a single student + one optimizer.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.anyflow_pretrain.AnyFlowPretrainMethod
+  diffusion_ratio: 0.5
+  consistency_ratio: 0.25
+  epsilon: 5            # finite-difference step in absolute train-timestep units
+  weight_type: beta08   # per-timestep loss weight = t * sqrt(1 - t), renormalized
+  fuse_guidance_scale: 3.0
+  # shift is taken from pipeline.flow_shift below.
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/preprocessed
+    dataloader_num_workers: 4
+    train_batch_size: 4
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+
+  optimizer:
+    learning_rate: 5.0e-5
+    betas: [0.9, 0.999]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 6000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_anyflow_pretrain
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+    resume_from_checkpoint: latest
+
+  tracker:
+    project_name: anyflow-wan
+    run_name: wan2.1_t2v_anyflow_pretrain
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+
+pipeline:
+  flow_shift: 5.0
+  dit_config:
+    # Enable AnyFlow dual-timestep conditioning. The student loads from
+    # base Wan 2.1 — its checkpoint has no delta_embedder weights, so they
+    # get initialized identically to time_embedder via deep-copy in
+    # WanTimeTextImageEmbedding.__init__.
+    r_embedder: true
+    r_embedder_fusion: gated
+    r_embedder_gate_value: 0.25
+    r_embedder_deltatime_type: r

examples/train/configs/distribution_matching/wan/self_forcing_causal_t2v.yaml

@@ -100,9 +100,6 @@ callbacks:
     sampling_steps: [4]
     sampling_timesteps: [1000, 750, 500, 250]
     num_frames: 81
-    # Validation/inference uses standard CFG in both clean and Self-Forcing,
-    # so this directly matches Self-Forcing guidance_scale=3.0.
-    guidance_scale: 3.0
 
 pipeline:
   flow_shift: 5

examples/train/configs/fine_tuning/wan/fast_ti2v_fullattn_lora.yaml

@@ -1,6 +1,8 @@
-# FastWan 2.2 TI2V 5B FullAttn LoRA finetune with VSA.
+# FastWan 2.2 TI2V 5B FullAttn LoRA finetune with dense attention.
 #
 # Notes:
+# - The FullAttn checkpoint has no VSA gate-compression weights. Train its
+#   dense attention projections without enabling VIDEO_SPARSE_ATTN.
 # - This model still uses T2V-style preprocessed parquet data in the new
 #   training stack.
 # - Validation/inference should use WanPipeline rather than the I2V pipeline;
@@ -21,7 +23,6 @@ models:
         - to_k
         - to_v
         - to_out
-        - to_gate_compress
 
 method:
   _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
@@ -59,21 +60,18 @@ training:
     gradient_accumulation_steps: 5
 
   checkpoint:
-    output_dir: outputs/fast_wan_ti2v_fullattn_lora_vsa
+    output_dir: outputs/fast_wan_ti2v_fullattn_lora
     training_state_checkpointing_steps: 5
     checkpoints_total_limit: 2
     resume_from_checkpoint: null
 
   tracker:
     project_name: fastvideo
-    run_name: fast_wan_ti2v_fullattn_lora_vsa
+    run_name: fast_wan_ti2v_fullattn_lora
 
   model:
     enable_gradient_checkpointing_type: full
 
-  vsa:
-    sparsity: 0.8
-
 callbacks:
   grad_clip:
     _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback

examples/train/scenario/worldmodel/README.md

@@ -1,32 +1,119 @@
 # World-Model: Matrix-Game 2.0 I2V
 
-Three training scenarios for the Matrix-Game 2.0 I2V world model on the
-new YAML-driven trainer (`fastvideo/train/entrypoint/train.py`).
+Training scenarios for the Matrix-Game 2.0 I2V world model on Solaris (Minecraft)
+data and Zelda data, using the new YAML-driven trainer
+(`fastvideo/train/entrypoint/train.py`).
+
+## Solaris Configs
 
 | Config | Method | Student | Notes |
 |---|---|---|---|
-| `finetune_i2v.yaml` | `FineTuneMethod` | `MatrixGame2Model` (bidirectional) | Multi-step SFT from `mg_bidirectional_Solaris`. |
-| `dfsft_causal_i2v.yaml` | `DiffusionForcingSFTMethod` | `MatrixGame2CausalModel` | Diffusion-Forcing SFT with chunkwise timesteps. |
-| `self_forcing_causal_i2v.yaml` | `SelfForcingMethod` | `MatrixGame2CausalModel` | DMD/Self-Forcing distillation; teacher = bidirectional, critic = bidirectional. |
+| `solaris/finetune_i2v.yaml` | `FineTuneMethod` | `MatrixGame2Model` (bidirectional) | Multi-step SFT from `mg_bidirectional_Solaris`. |
+| `solaris/dfsft_causal_i2v.yaml` | `DiffusionForcingSFTMethod` | `MatrixGame2CausalModel` | Diffusion-Forcing SFT with chunkwise timesteps. |
+| `solaris/self_forcing_causal_i2v.yaml` | `SelfForcingMethod` | `MatrixGame2CausalModel` | Matrix-Game 2.0 DMD/Self-Forcing distillation; teacher = bidirectional, critic = bidirectional. |
+
+## Zelda Configs
+
+| Config | Method | Student | Notes |
+|---|---|---|---|
+| `zelda/finetune_i2v.yaml` | `FineTuneMethod` | `MatrixGame2Model` (bidirectional) | Zelda bidirectional I2V finetuning from `FastVideo/Matrix-Game-2.0-Base-Diffusers`. Uses 33-frame clips and Zelda validation with action overlays. |
+| `zelda/dfsft_causal_i2v.yaml` | `DiffusionForcingSFTMethod` | `MatrixGame2CausalModel` | Zelda causal Diffusion-Forcing SFT from `mignonjia/mg_bidirectional_zelda`. Uses the same Zelda data, resolution, optimizer, and validation defaults as the Zelda finetune config. |
+| `zelda/self_forcing_causal_i2v.yaml` | `SelfForcingMethod` | `MatrixGame2CausalModel` | Zelda DMD/Self-Forcing distillation; student init = `mignonjia/mg_causal_zelda`, teacher = bidirectional (`mignonjia/mg_bidirectional_zelda`), critic = bidirectional. |
+| `zelda/streaming_long_tuning_causal_i2v.yaml` | `StreamingLongTuningMethod` | `MatrixGame2CausalModel` | LongLive-style streaming long tuning from the 1k-step Zelda self-forcing checkpoint. |
+
+Zelda world-model distillation is a two-run workflow: first run
+`zelda/self_forcing_causal_i2v.yaml` to train or load the 1k-step
+self-forcing checkpoint (`mignonjia/mg_sf_distilled_zelda_1k_steps`), then run
+`zelda/streaming_long_tuning_causal_i2v.yaml` for the 3k-step streaming
+long-tuning stage. The long-tuning YAML starts from that 1k-step checkpoint; it
+does not run the short self-forcing stage inside the same config.
+
+## Zelda Training Data
+
+The Zelda training configs use `data/zeldam2-clean` as a suggested local path.
+Download the dataset from Hugging Face before running those configs:
+
+```bash
+python scripts/huggingface/download_hf.py \
+    --repo_id mignonjia/zeldam2-clean \
+    --local_dir data/zeldam2-clean \
+    --repo_type dataset
+```
+
+You can store the dataset elsewhere; update `training.data.data_path` in the
+YAML to point at that location.
+
+## Multi3D Training Data
+
+`zelda/finetune_i2v.yaml` includes an optional, commented-out Multi3D entry.
+Enable it only when you want to mix Zelda with multi-game data from
+`data/multi3d_games`. You can store this dataset anywhere; before enabling it,
+update the matching commented `training.data.data_path` key in the YAML to the
+correct location.
+
+To mix datasets in a training YAML, set `training.data.data_path` to a
+path-to-repeat-count mapping. For example, `zelda/finetune_i2v.yaml` can use
+`data/zeldam2-clean: 1` and `# data/multi3d_games: 10`; uncommenting the
+Multi3D entry repeats the multi-game parquet list ten times before training
+samples are shuffled.
+
+## World Model Validation Data
+
+The Zelda validation configs expect a small public validation bundle under
+`data/zelda_validation_data`.
+
+Download it from Hugging Face before running the Zelda scenarios:
+
+```bash
+python scripts/huggingface/download_hf.py \
+    --repo_id mignonjia/zelda_validation_data \
+    --local_dir data/zelda_validation_data \
+    --repo_type dataset
+```
+
+The bundle contains `validation_zelda.json`, `images/`, and `actions/`.
+The Zelda configs point
+`callbacks.validation.dataset_file` at
+`data/zelda_validation_data/validation_zelda.json`.
 
 ## Usage
 
+### Solaris
+
 ```bash
 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/finetune_i2v.yaml
+    examples/train/scenario/worldmodel/solaris/finetune_i2v.yaml
 
 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/dfsft_causal_i2v.yaml
+    examples/train/scenario/worldmodel/solaris/dfsft_causal_i2v.yaml
 
 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/self_forcing_causal_i2v.yaml
+    examples/train/scenario/worldmodel/solaris/self_forcing_causal_i2v.yaml
+```
+
+### Zelda
+
+```bash
+# Finetuning / DFSFT
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/finetune_i2v.yaml
+
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/dfsft_causal_i2v.yaml
+
+# Distillation / long tuning
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/self_forcing_causal_i2v.yaml
+
+bash examples/train/run.sh \
+    examples/train/scenario/worldmodel/zelda/streaming_long_tuning_causal_i2v.yaml
 ```
 
 Override any field on the command line:
 
 ```bash
 bash examples/train/run.sh \
-    examples/train/scenario/worldmodel/dfsft_causal_i2v.yaml \
+    examples/train/scenario/worldmodel/solaris/dfsft_causal_i2v.yaml \
     --training.distributed.num_gpus 8 \
     --training.optimizer.learning_rate 1e-5
 ```

examples/train/scenario/worldmodel/solaris/self_forcing_causal_i2v.yaml

@@ -97,4 +97,4 @@ callbacks:
     guidance_scale: 6.0
 
 pipeline:
-  flow_shift: 5
+  flow_shift: 5

examples/train/scenario/worldmodel/zelda/dfsft_causal_i2v.yaml

@@ -0,0 +1,94 @@
+# Diffusion-Forcing SFT: Zelda world model I2V Causal
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2_causal.MatrixGame2CausalModel
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  chunk_size: 3
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path:
+      data/zeldam2-clean: 1
+    dataloader_num_workers: 1
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 42
+    num_latent_t: 9
+    num_height: 480
+    num_width: 832
+    num_frames: 33
+
+  optimizer:
+    learning_rate: 2.0e-5
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 60000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/matrixgame_finetune/checkpoints/zelda_causal_dfsft
+    training_state_checkpointing_steps: 5000
+    checkpoints_total_limit: 3
+
+  tracker:
+    entity: hapo-exp
+    project_name: mg_1.3b_zelda
+    run_name: zelda_causal_dfsft
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_causal_dmd_pipeline.MatrixGame2CausalDMDPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 200
+    sampling_steps: [40]
+    sampling_timesteps: [1000, 975, 950, 925, 900, 875, 850, 825, 800, 775,
+                         750, 725, 700, 675, 650, 625, 600, 575, 550, 525,
+                         500, 475, 450, 425, 400, 375, 350, 325, 300, 275,
+                         250, 225, 200, 175, 150, 125, 100, 75, 50, 25]
+    num_frames: 33
+    overlay_actions: true
+    guidance_scale: 6.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1

examples/train/scenario/worldmodel/zelda/finetune_i2v.yaml

@@ -0,0 +1,88 @@
+# Matrix-Game 2.0 Zelda + multi-game I2V finetune.
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: FastVideo/Matrix-Game-2.0-Base-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path:
+      data/zeldam2-clean: 1
+      # data/multi3d_games: 10
+    dataloader_num_workers: 1
+    train_batch_size: 1
+    training_cfg_rate: 0.0  # unused for MatrixGame2 I2V; no text_embedding CFG dropout
+    seed: 42
+    num_latent_t: 9
+    num_height: 480
+    num_width: 832
+    num_frames: 33
+
+  optimizer:
+    learning_rate: 2.0e-5
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 60000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/matrixgame_finetune/checkpoints/zelda_with_mg_init
+    training_state_checkpointing_steps: 5000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: mg_1.3b_zelda
+    run_name: zelda_with_mg_init
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_i2v_pipeline.MatrixGame2I2VPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 200
+    sampling_steps: [40]
+    num_frames: 33
+    overlay_actions: true
+    guidance_scale: 6.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5

examples/train/scenario/worldmodel/zelda/self_forcing_causal_i2v.yaml

@@ -0,0 +1,120 @@
+# Self-forcing distillation: Zelda world model I2V Causal
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2_causal.MatrixGame2CausalModel
+    init_from: mignonjia/mg_causal_zelda
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  warp_denoising_step: true
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+
+  chunk_size: 3
+  student_sample_type: sde
+  same_step_across_blocks: true
+  last_step_only: false
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+
+  # Critic optimizer
+  fake_score_learning_rate: 3.0e-7
+  fake_score_betas: [0.9, 0.95]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/zeldam2-clean
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1001
+    num_latent_t: 9
+    num_height: 480
+    num_width: 832
+    num_frames: 33
+
+  optimizer:
+    learning_rate: 3.0e-6
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 1000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/zelda_causal_self_forcing
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 1
+
+  tracker:
+    project_name: wangame_sf
+    run_name: mg2_self_forcing_9_latents
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_causal_dmd_pipeline.MatrixGame2CausalDMDPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 100
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    num_frames: 153
+    overlay_actions: true
+    keyboard_value_scale: 1.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1

examples/train/scenario/worldmodel/zelda/streaming_long_tuning_causal_i2v.yaml

@@ -0,0 +1,140 @@
+# MatrixGame2 I2V LongLive-style streaming distillation: Zelda world model I2V Causal
+# Student init from self forcing after 1k steps
+
+models:
+  student:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2_causal.MatrixGame2CausalModel
+    init_from: mignonjia/mg_sf_distilled_zelda_1k_steps 
+    trainable: true
+    # transformer_override_safetensor: outputs/matrixgame_dmd/checkpoints/mg_zelda_sf_m2/checkpoint-1000_weight_only/ema/generator_ema.safetensors
+  teacher:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.matrixgame2.matrixgame2.MatrixGame2Model
+    init_from: mignonjia/mg_bidirectional_zelda
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.streaming_long_tuning.StreamingLongTuningMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  warp_denoising_step: true
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+
+  chunk_size: 3
+  student_sample_type: sde
+  same_step_across_blocks: true
+  last_step_only: false
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+
+  streaming_training: true
+  streaming_chunk_size: 9
+  streaming_max_length: 39
+  streaming_fixed_overlap_latents: 3
+  streaming_reencode_overlap_anchor: true
+  streaming_anchor_inject_k: 1
+  streaming_require_full_blocks: true
+
+  multi_phased_distill_schedule:
+    - stage: streaming_long
+      start_step: 0
+      end_step: 3000
+      num_latent_t: 39
+      streaming_training: true
+      streaming_chunk_size: 9
+      streaming_max_length: 39
+      streaming_fixed_overlap_latents: 3
+
+  # Critic optimizer
+  fake_score_learning_rate: 3.0e-7
+  fake_score_betas: [0.9, 0.95]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/zeldam2-clean
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1001
+    num_latent_t: 39
+    num_height: 480
+    num_width: 832
+    num_frames: 153
+
+  optimizer:
+    learning_rate: 3.0e-6
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/zelda_causal_long_tuning
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 2
+
+  tracker:
+    project_name: wangame_sf
+    run_name: mg2_39only_streaming_long
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  dit_precision: fp32
+
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_causal_dmd_pipeline.MatrixGame2CausalDMDPipeline
+    dataset_file: data/zelda_validation_data/validation_zelda.json
+    every_steps: 100
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    num_frames: 153
+    overlay_actions: true
+    keyboard_value_scale: 1.0
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - vbench.temporal_flickering
+        - vbench.motion_smoothness
+        - vbench.subject_consistency
+        - vbench.background_consistency
+        - vbench.dynamic_degree
+        - optical_flow.synthetic_optical_flow
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1

fastvideo/api/flux.py

@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from fastvideo.api.sampling_param import SamplingParam
+
+
+@dataclass
+class FluxSamplingParam(SamplingParam):
+
+    prompt: str | None = "a photo of a cat"
+    negative_prompt: str = ""
+
+    num_videos_per_prompt: int = 1
+    seed: int = 0
+
+    num_frames: int = 1
+    height: int = 1024
+    width: int = 1024
+    fps: int = 1
+
+    num_inference_steps: int = 28
+    guidance_scale: float = 3.5
+    use_embedded_guidance: bool = True
+    true_cfg_scale: float = 1.0

fastvideo/api/sampling_param.py

@@ -90,6 +90,10 @@ class SamplingParam:
     num_inference_steps_sr: int = 50
     guidance_scale: float = 1.0
     guidance_scale_2: float | None = None
+    # Embedded guidance (FLUX): do not treat ``guidance_scale > 1`` as classic CFG.
+    use_embedded_guidance: bool = False
+    # Diffusers-style true CFG for FLUX when > 1 (requires negative prompt encoding).
+    true_cfg_scale: float = 1.0
     guidance_rescale: float = 0.0
     boundary_ratio: float | None = None
     sigmas: list[float] | None = None
@@ -325,6 +329,18 @@ class SamplingParam:
             default=SamplingParam.guidance_rescale,
             help="Guidance rescale factor",
         )
+        parser.add_argument(
+            "--use-embedded-guidance",
+            action="store_true",
+            default=SamplingParam.use_embedded_guidance,
+            help="Use embedded guidance scale (FLUX-style) instead of classic CFG",
+        )
+        parser.add_argument(
+            "--true-cfg-scale",
+            type=float,
+            default=SamplingParam.true_cfg_scale,
+            help="True CFG scale for FLUX when > 1 (requires negative prompt encoding)",
+        )
         parser.add_argument(
             "--boundary-ratio",
             type=float,

fastvideo/api/schema.py

@@ -150,6 +150,7 @@ class SamplingConfig:
     guidance_scale_2: float | None = None
     guidance_rescale: float = 0.0
     true_cfg_scale: float | None = None
+    use_embedded_guidance: bool | None = None
     boundary_ratio: float | None = None
     sigmas: list[float] | None = None
 

fastvideo/attention/backends/abstract.py

@@ -47,6 +47,78 @@ class AttentionBackend(ABC):
     def get_builder_cls() -> type["AttentionMetadataBuilder"]:
         raise NotImplementedError
 
+    # ------------------------------------------------------------------
+    # Capability self-description
+    #
+    # These let the selector validate a requested backend against a
+    # layer's needs and emit a clear reason on mismatch, instead of
+    # failing later with an opaque kernel error (see #1254). They are
+    # additive: the defaults describe the least-restrictive behavior, so
+    # a backend that does not override them keeps today's behavior.
+    # Several backends already declare ``get_supported_head_sizes``; this
+    # formalizes that hook on the base class and adds the other
+    # capability axes. Only override with values verifiable from the
+    # backend's implementation.
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def get_supported_head_sizes() -> list[int] | None:
+        """Attention head sizes this backend supports.
+
+        Return None for no restriction.
+        """
+        return None
+
+    @classmethod
+    def get_supported_dtypes(cls) -> tuple[torch.dtype, ...]:
+        """Floating-point dtypes this backend supports."""
+        return (torch.float16, torch.bfloat16)
+
+    @classmethod
+    def supports_attention_mask(cls) -> bool:
+        """Whether this backend can consume an explicit attention mask/bias.
+
+        Dense backends generally can; tiled/sparse video backends generally
+        cannot, since they impose their own sparsity pattern.
+        """
+        return True
+
+    @classmethod
+    def supports_varlen(cls) -> bool:
+        """Whether this backend supports single-launch variable-length
+        sequence packing (``cu_seqlens``), as in ``flash_attn_varlen_func``.
+        """
+        return False
+
+    @classmethod
+    def validate_compatibility(
+        cls,
+        head_size: int,
+        dtype: torch.dtype,
+        *,
+        needs_attention_mask: bool = False,
+        needs_varlen: bool = False,
+    ) -> str | None:
+        """Return None if this backend is compatible with the given
+        requirements, else a human-readable reason for the mismatch.
+
+        The selector surfaces this reason, so an unsupported request is
+        never silently dropped.
+        """
+        supported_sizes = cls.get_supported_head_sizes()
+        if supported_sizes is not None and head_size not in supported_sizes:
+            return (f"{cls.get_name()} does not support head_size="
+                    f"{head_size} (supported: {supported_sizes})")
+        if dtype not in cls.get_supported_dtypes():
+            return f"{cls.get_name()} does not support dtype={dtype}"
+        if needs_attention_mask and not cls.supports_attention_mask():
+            return (f"{cls.get_name()} cannot consume the attention mask this "
+                    "layer requires")
+        if needs_varlen and not cls.supports_varlen():
+            return (f"{cls.get_name()} does not support variable-length "
+                    "sequence packing (varlen)")
+        return None
+
 
 @dataclass
 class AttentionMetadata:

fastvideo/attention/backends/flash_attn.py

@@ -1,13 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
-import importlib.util
 import os
-
 import torch
 import torch.nn.functional as F
 from dataclasses import dataclass
 
-from fastvideo import envs
+from fastvideo.attention.utils.flash_attn_default import (
+    fa_version,
+    flash_attn_func_compilable,
+)
+
 from fastvideo.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
@@ -17,119 +19,6 @@ from fastvideo.attention.backends.abstract import (
 from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
-
-# FA4 (flash_attn.cute) is explicit opt-in via FASTVIDEO_FA4=1, mirroring the
-# kernel package's FASTVIDEO_VSA_CUTEDSL: its CuTeDSL kernels JIT-compile per
-# shape family and can fail at runtime on some arch/shape combinations, so it
-# is never auto-selected just because it is installed. Below sm90 a capability
-# gate in flash_attn_cute routes to FA2 the calls FA4 cannot serve there:
-# grad-enabled (its backward asserts sm90+) and GQA (pack_gqa fails CuTeDSL
-# JIT, observed on sm_89).
-if envs.FASTVIDEO_FA4:
-    try:
-        from fastvideo.attention.utils.flash_attn_cute import flash_attn_func
-    except ImportError as e:
-        raise RuntimeError(f"FASTVIDEO_FA4=1 but flash_attn.cute (FA4) is not usable ({e}); "
-                           "fix the FA4 install (see the flash-attn-4 pin in pyproject.toml) "
-                           "or unset FASTVIDEO_FA4.") from e
-    fa_version = "4"
-else:
-    try:
-        from flash_attn_interface import flash_attn_func as flash_attn_3_func
-
-        # flash_attn 3 no longer have a different API, see following commit:
-        # https://github.com/Dao-AILab/flash-attention/commit/ed209409acedbb2379f870bbd03abce31a7a51b7
-        flash_attn_func = flash_attn_3_func
-        fa_version = "3"
-    except ImportError:
-        from flash_attn import flash_attn_func as flash_attn_2_func
-        flash_attn_func = flash_attn_2_func
-        fa_version = "2"
-    try:
-        if importlib.util.find_spec("flash_attn.cute") is not None:
-            logger.info("flash_attn.cute (FA4) is installed but not enabled; "
-                        "set FASTVIDEO_FA4=1 to use it for inference.")
-    except ImportError:
-        pass
-
-# torch.compile traceability: the FA4/cute path (fa_version=="4") is
-# already a registered torch.library custom op, so dynamo treats it as a
-# graph node. The external FA2/FA3 `flash_attn_func` is NOT — dynamo
-# breaks the graph at the call site (observed: wanvideo.py self-attn,
-# once per layer every step), which fragments the compiled region and
-# blocks CUDA-graph capture. Wrap the FA2/FA3 default call in a custom
-# op (mirrors the FP4 `flash_attn_cute` template) so it becomes an
-# opaque-but-traceable node. The kernel still runs eager inside the op
-# (correct — flash-attn must run eager); only dynamo's treatment of the
-# boundary changes, so numerics are unchanged (SSIM-gate to confirm).
-if fa_version in ("2", "3"):
-    _fa_default = flash_attn_func
-
-    # Scope: this op covers exactly the q/k/v + softmax_scale + causal
-    # call shape used by FlashAttentionImpl.forward's default branch
-    # (see `flash_attn_func_compilable(...)` call site below). The
-    # masked/no-pad and varlen / cross-attn paths use different
-    # entry points (`flash_attn_no_pad`, `flash_attn_varlen_*`) which
-    # are intentionally out of scope for this PR — wrapping them is a
-    # natural follow-up. The wrapper's signature is the contract: any
-    # extra kwarg (dropout_p, window_size, alibi_slopes, deterministic,
-    # return_attn_probs, ...) raises TypeError at the call site, so
-    # silent loss of kwargs is not a failure mode.
-    @torch.library.custom_op(
-        "fastvideo::_flash_attn_default_forward",
-        mutates_args=(),
-        device_types="cuda",
-    )
-    def _flash_attn_default_forward(
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        softmax_scale: float | None,
-        causal: bool,
-    ) -> torch.Tensor:
-        return _fa_default(q, k, v, softmax_scale=softmax_scale, causal=causal)
-
-    @torch.library.register_fake("fastvideo::_flash_attn_default_forward")
-    def _flash_attn_default_forward_fake(
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        softmax_scale: float | None,
-        causal: bool,
-    ) -> torch.Tensor:
-        del softmax_scale, causal
-        # FA2/FA3 default path: [batch, seqlen_q, nheads, head_dim_v],
-        # same dtype/device as q (head dim taken from v).
-        return q.new_empty(q.shape[0], q.shape[1], q.shape[2], v.shape[-1])
-
-    def flash_attn_func_compilable(q, k, v, softmax_scale=None, causal=False):
-        # Autograd carve-out. The custom op above registers a forward + fake
-        # kernel but NO backward (register_autograd), so it is opaque to
-        # autograd. Inference runs under no_grad / inference_mode and routes
-        # through the traceable custom op — that is the torch.compile win, and
-        # the only path this PR claims. Training backprops through attention,
-        # so route grad-enabled calls to the original FA2/FA3 `flash_attn_func`
-        # (itself an autograd.Function, so backward is correct) at the cost of a
-        # dynamo graph break on the training path — i.e. pre-PR behavior, no
-        # regression. Full autograd parity for the custom op (mirroring the FP4
-        # cute template) is a tracked follow-up.
-        if torch.is_grad_enabled() and (q.requires_grad or k.requires_grad or v.requires_grad):
-            return _fa_default(q, k, v, softmax_scale=softmax_scale, causal=causal)
-        return torch.ops.fastvideo._flash_attn_default_forward(q, k, v, softmax_scale, causal)
-elif fa_version == "4":
-    # FA4 path: `flash_attn_func` (from `flash_attn_cute`) goes through a
-    # registered torch.library custom op (with an FA4 backward on sm90+;
-    # grad-enabled and GQA calls below sm90 route to FA2), so a passthrough
-    # is enough — no extra registration needed.
-    def flash_attn_func_compilable(q, k, v, softmax_scale=None, causal=False):
-        return flash_attn_func(q, k, v, softmax_scale=softmax_scale, causal=causal)
-else:
-    # Defensive: the probe above only ever sets fa_version to "2", "3",
-    # or "4"; an unexpected value means an import/probe regression and
-    # we want a loud error at import, not a silent NameError later.
-    raise RuntimeError(f"Unsupported FlashAttention version: {fa_version!r} — expected "
-                       f"'2', '3', or '4' from the import probe above.")
-
 logger.info("Using FlashAttention-%s backend", fa_version)
 
 # FP4 FA4 support: quantize Q/K to NVFP4 E2M1 for block-scaled MMA on Blackwell.
@@ -309,9 +198,17 @@ class FlashAttentionImpl(AttentionImpl):
         attn_metadata: FlashAttnMetadata,
     ):
         if (attn_metadata is not None and hasattr(attn_metadata, "attn_mask") and attn_metadata.attn_mask is not None):
+            # Route through the *_compilable wrappers so dynamo sees one
+            # traceable node for each masked entry point (the unpad/pad
+            # bookkeeping runs eager inside the custom op). On FA2 these
+            # wrappers go through ops with full register_autograd, so
+            # training also backprops through the op (no graph break on
+            # the training path); on FA3/FA4 they carve out to the
+            # autograd.Function for grad-enabled calls — see
+            # fastvideo/attention/utils/flash_attn_no_pad.py.
             from fastvideo.attention.utils.flash_attn_no_pad import (
-                flash_attn_no_pad,
-                flash_attn_varlen_qk_no_pad,
+                flash_attn_no_pad_compilable as flash_attn_no_pad,
+                flash_attn_varlen_qk_no_pad_compilable as flash_attn_varlen_qk_no_pad,
             )
 
             attn_mask = attn_metadata.attn_mask
@@ -338,7 +235,11 @@ class FlashAttentionImpl(AttentionImpl):
                 )
 
             qkv = torch.stack([query, key, value], dim=2)
-            attn_mask_padded = F.pad(attn_mask, (qkv.shape[1] - attn_mask.shape[1], 0), value=True)
+            key_padding_mask = _key_padding_mask_from_attn_mask(attn_mask, attn_mask.shape[-1]).to(device=query.device)
+            if key_padding_mask.shape[-1] > qkv.shape[1]:
+                raise ValueError("Invalid key padding mask length for FLASH_ATTN: "
+                                 f"expected at most {qkv.shape[1]}, got {key_padding_mask.shape[-1]}")
+            attn_mask_padded = F.pad(key_padding_mask, (qkv.shape[1] - key_padding_mask.shape[-1], 0), value=True)
             output = flash_attn_no_pad(qkv, attn_mask_padded, causal=False, dropout_p=0, softmax_scale=None)
         elif self.nvfp4_fa4:
             output = self._forward_nvfp4(query, key, value)

fastvideo/attention/backends/nabla.py

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

fastvideo/attention/backends/sdpa.py

@@ -2,6 +2,7 @@
 
 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
@@ -14,12 +15,23 @@ class SDPABackend(AttentionBackend):
     accept_output_buffer: bool = True
 
     @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        return [32, 64, 96, 128, 160, 192, 224, 256]
+    def get_supported_head_sizes() -> list[int] | None:
+        # torch.nn.functional.scaled_dot_product_attention is head-size
+        # agnostic: the math backend handles any size and the fused kernels
+        # fall back internally when they cannot. The previous list
+        # ([32, 64, ..., 256]) was copied from FlashAttentionBackend in the
+        # v1 refactor (#270) and wrongly excluded sizes such as 80 (e.g.
+        # CLIP vision encoders). None means "no restriction".
+        return None
+
+    @classmethod
+    def get_supported_dtypes(cls) -> tuple[torch.dtype, ...]:
+        # torch.nn.functional.scaled_dot_product_attention also runs in fp32.
+        return (torch.float16, torch.bfloat16, torch.float32)
 
     @staticmethod
     def get_name() -> str:
-        return "SDPA"
+        return "TORCH_SDPA"
 
     @staticmethod
     def get_impl_cls() -> type["SDPAImpl"]:
@@ -49,9 +61,51 @@ class SDPAMetadataBuilder(AttentionMetadataBuilder):
             current_timestep: int,
             attn_mask: torch.Tensor,
     ) -> SDPAMetadata:
+        # Store the mask exactly as passed. The metadata is cross-backend:
+        # call sites (HYWorld, HunyuanVideo15) build SDPAMetadata while the
+        # layer's selector may pick FLASH_ATTN, and the shared convention for
+        # padding masks is the tokenizer-style 2D [batch, key_len]. Any
+        # reshaping for torch.sdpa happens inside the SDPA impl
+        # (_normalize_attn_mask_for_sdpa).
         return SDPAMetadata(current_timestep=current_timestep, attn_mask=attn_mask)
 
 
+def _normalize_attn_mask_for_sdpa(
+    attn_mask: torch.Tensor | None,
+    query: torch.Tensor,
+    key: torch.Tensor,
+) -> torch.Tensor | None:
+    if attn_mask is None:
+        return None
+
+    attn_mask = attn_mask.to(device=query.device)
+    # F.scaled_dot_product_attention only accepts bool or float masks;
+    # tokenizers commonly produce int64 0/1 padding masks.
+    if attn_mask.dtype != torch.bool and not attn_mask.dtype.is_floating_point:
+        attn_mask = attn_mask != 0
+
+    key_len = key.shape[-2]
+    if attn_mask.shape[-1] > key_len:
+        raise ValueError("Invalid attention mask length for SDPA: "
+                         f"expected at most {key_len}, got {attn_mask.shape[-1]}")
+    if attn_mask.shape[-1] < key_len:
+        # Front-pad as "attend": double-stream layouts (HYWorld) prepend
+        # non-text tokens the tokenizer mask does not cover.
+        valid_value = True if attn_mask.dtype == torch.bool else 0.0
+        attn_mask = F.pad(attn_mask, (key_len - attn_mask.shape[-1], 0), value=valid_value)
+
+    if attn_mask.dim() == 2:
+        # In-tree producers pass 2D [batch, key_len] padding masks; lift to a
+        # broadcastable [batch, 1, 1, key_len] here so torch.sdpa does not
+        # reinterpret 2D as its documented [query_len, key_len] broadcast.
+        return attn_mask[:, None, None, :]
+    if attn_mask.dim() == 3:
+        return attn_mask[:, None, :, :]
+    if attn_mask.dim() == 4:
+        return attn_mask
+    raise ValueError(f"Unsupported attention mask shape for SDPA: {attn_mask.shape}")
+
+
 class SDPAImpl(AttentionImpl):
 
     def __init__(
@@ -82,6 +136,7 @@ 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/backends/video_sparse_attn.py

@@ -119,6 +119,12 @@ class VideoSparseAttentionBackend(AttentionBackend):
     def get_supported_head_sizes() -> list[int]:
         return [64, 128]
 
+    @classmethod
+    def supports_attention_mask(cls) -> bool:
+        # VSA imposes its own tiled sparsity pattern; it does not consume a
+        # general dense attention mask.
+        return False
+
     @staticmethod
     def get_name() -> str:
         return "VIDEO_SPARSE_ATTN"

fastvideo/attention/backends/vmoba.py

@@ -18,6 +18,12 @@ class VMOBAAttentionBackend(AttentionBackend):
 
     accept_output_buffer: bool = True
 
+    @classmethod
+    def supports_attention_mask(cls) -> bool:
+        # VMoBA selects blocks via its own MoBA routing; it does not consume
+        # a general dense attention mask.
+        return False
+
     @staticmethod
     def get_name() -> str:
         return "VMOBA_ATTN"

fastvideo/attention/layer.py

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

fastvideo/attention/selector.py

@@ -69,6 +69,7 @@ def global_force_attn_backend(attn_backend: AttentionBackendEnum | None) -> None
     '''
     global forced_attn_backend
     forced_attn_backend = attn_backend
+    _cached_get_attn_backend.cache_clear()
 
 
 def get_global_forced_attn_backend() -> AttentionBackendEnum | None:
@@ -79,38 +80,93 @@ def get_global_forced_attn_backend() -> AttentionBackendEnum | None:
     return forced_attn_backend
 
 
+def get_selected_attn_backend() -> AttentionBackendEnum | None:
+    '''
+    The attention backend selected before any per-layer filtering.
+
+    A global force (see `global_force_attn_backend`) takes precedence over the
+    `FASTVIDEO_ATTENTION_BACKEND` env var; returns None when neither is set
+    (automatic selection). This is the single source of the force-over-env
+    precedence rule, shared by `_cached_get_attn_backend` and
+    `check_attn_backend_requirement`.
+    '''
+    forced_backend = get_global_forced_attn_backend()
+    if forced_backend is not None:
+        return forced_backend
+    backend_by_env_var = envs.FASTVIDEO_ATTENTION_BACKEND
+    return backend_name_to_enum(backend_by_env_var) if backend_by_env_var is not None else None
+
+
+def check_attn_backend_requirement(
+    required_backend: AttentionBackendEnum | None,
+    *,
+    model_name: str = "This model",
+) -> AttentionBackendEnum | None:
+    '''
+    Resolve the selected attention backend, enforcing a model's hard requirement.
+
+    Some model families are only numerically correct with a specific backend
+    (e.g. FastWan is sparse-distilled with VSA and produces wrong outputs
+    otherwise). Rather than silently forcing the backend -- which would only
+    reach some construction sites and leave others (e.g. the denoising stage)
+    resolving a different backend -- we require the user to select it explicitly
+    and fail loudly otherwise, mirroring how the platform layer hard-fails on
+    missing explicitly-requested backends.
+
+    Arguments:
+
+    * required_backend: backend the model requires, or None for no requirement
+    * model_name: name used in the error message
+
+    Returns:
+
+    * the selected attention backend (`get_selected_attn_backend`), or None if
+      unset and unrequired
+
+    Raises:
+
+    * ValueError if `required_backend` is set but is not the selected backend
+    '''
+    selected_backend = get_selected_attn_backend()
+    if required_backend is not None and selected_backend != required_backend:
+        selected_name = selected_backend.name if selected_backend is not None else "unset"
+        raise ValueError(f"{model_name} requires the {required_backend.name} attention backend, but the effective "
+                         f"FASTVIDEO_ATTENTION_BACKEND is {selected_name}. This checkpoint is only correct with "
+                         f"{required_backend.name}; set FASTVIDEO_ATTENTION_BACKEND={required_backend.name} before "
+                         f"loading the model.")
+    return selected_backend
+
+
 def get_attn_backend(
     head_size: int,
     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)
+    # Resolve the global force / environment override before entering the
+    # cached function so backend changes participate in the cache key. This
+    # also validates the environment on every lookup, including cache hits.
+    selected_backend = get_selected_attn_backend()
+    return _cached_get_attn_backend(head_size, dtype, supported_attention_backends, selected_backend, default_backend)
 
 
 @cache
 def _cached_get_attn_backend(
     head_size: int,
     dtype: torch.dtype,
-    supported_attention_backends: tuple[AttentionBackendEnum, ...]
-    | None = None,
+    supported_attention_backends: tuple[AttentionBackendEnum, ...] | None,
+    selected_backend: AttentionBackendEnum | None,
+    default_backend: AttentionBackendEnum | None = None,
 ) -> type[AttentionBackend]:
-    # Check whether a particular choice of backend was
-    # previously forced.
-    #
-    # THIS SELECTION OVERRIDES THE FASTVIDEO_ATTENTION_BACKEND
-    # ENVIRONMENT VARIABLE.
     if not supported_attention_backends:
         raise ValueError("supported_attention_backends is empty")
-    selected_backend = None
-    backend_by_global_setting: AttentionBackendEnum | None = (get_global_forced_attn_backend())
-    if backend_by_global_setting is not None:
-        selected_backend = backend_by_global_setting
-    else:
-        # Check the environment variable and override if specified
-        backend_by_env_var: str | None = envs.FASTVIDEO_ATTENTION_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
@@ -127,7 +183,26 @@ def _cached_get_attn_backend(
     attention_cls = current_platform.get_attn_backend_cls(selected_backend, head_size, dtype)
     if not attention_cls:
         raise ValueError(f"Invalid attention backend for {current_platform.device_name}")
-    return cast(type[AttentionBackend], resolve_obj_by_qualname(attention_cls))
+    backend = cast(type[AttentionBackend], resolve_obj_by_qualname(attention_cls))
+
+    # Validate the resolved backend against its self-described capabilities
+    # (see AttentionBackend.validate_compatibility, #1254). An explicit
+    # selection hard-fails only when it was actually honored: resolution can
+    # substitute a fallback for the selected backend (e.g. a FLASH_ATTN pin on
+    # a CLIP layer whose head size only SDPA serves -- see
+    # CudaPlatformBase.get_attn_backend_cls -- or a pin outside the layer's
+    # supported set above). Such a layer never participated in the pin, so its
+    # fallback degrades to the auto-selection warning -- emitted once per
+    # resolution, since this function is cached.
+    incompatibility = backend.validate_compatibility(head_size, dtype)
+    if incompatibility is not None:
+        if selected_backend is not None and backend.get_name() == selected_backend.name:
+            raise ValueError(f"Attention backend {selected_backend.name} was explicitly selected but is incompatible "
+                             f"with this layer: {incompatibility}. Select a compatible backend or unset "
+                             "FASTVIDEO_ATTENTION_BACKEND.")
+        logger.warning("Resolved attention backend %s may be incompatible with this layer: %s", backend.get_name(),
+                       incompatibility)
+    return backend
 
 
 @contextmanager

fastvideo/attention/utils/flash_attn_default.py

@@ -0,0 +1,251 @@
+# SPDX-License-Identifier: Apache-2.0
+"""torch.compile-traceable wrapper for the FA2/FA3/FA4 default attention path.
+
+The FA4/cute path (`fa_version == "4"`) is already a registered
+`torch.library.custom_op` in `fastvideo.attention.utils.flash_attn_cute`, so
+dynamo treats it as a graph node. The external FA2/FA3 ``flash_attn_func`` is
+NOT — dynamo breaks the graph at the call site (observed: wanvideo.py
+self-attn, once per layer every step), which fragments the compiled region
+and blocks CUDA-graph capture. Wrap the FA2/FA3 default call in a custom op
+(mirrors the FP4 `flash_attn_cute` template) so it becomes an
+opaque-but-traceable node. The kernel still runs eager inside the op
+(correct — flash-attn must run eager); only dynamo's treatment of the
+boundary changes, so numerics are unchanged (SSIM-gated).
+
+Autograd: FA2 has full ``register_autograd`` parity — the custom op's
+backward calls flash_attn's ``_flash_attn_backward`` directly, so training
+backprops *through* the op (no graph break on the training path either).
+FA3 currently keeps the no-backward + carve-out pattern from PR #1373
+because FA3's private backward signature wants validation on a real Hopper
+box (gated on Kuan-Hao's Modal FA3 setup PR). Once that lands the FA3 path
+can mirror FA2.
+
+Lives in `attention/utils/` (sibling of `flash_attn_cute.py` and
+`flash_attn_no_pad.py`) so it can be imported by any backend that wants the
+traceable FA default call without pulling in backend dispatch logic. The
+backend (`attention/backends/flash_attn.py`) just imports
+`flash_attn_func_compilable` and `fa_version` from here.
+"""
+
+import importlib.util
+
+import torch
+
+from fastvideo import envs
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
+
+# Pick the same backend the rest of FastVideo picked for `flash_attn_func`
+# (FA4/cute → FA3 → FA2). Mirror the precedence used in
+# `attention/utils/flash_attn_no_pad.py` so the two probes always agree.
+#
+# FA4 (flash_attn.cute) is explicit opt-in via FASTVIDEO_FA4=1: its CuTeDSL
+# kernels JIT-compile per shape family and can fail at runtime on some
+# arch/shape combinations, so it is never auto-selected just because it is
+# installed.
+if envs.FASTVIDEO_FA4:
+    try:
+        from fastvideo.attention.utils.flash_attn_cute import flash_attn_func
+    except ImportError as e:
+        raise RuntimeError(f"FASTVIDEO_FA4=1 but flash_attn.cute (FA4) is not usable ({e}); "
+                           "fix the FA4 install (see the flash-attn-4 pin in pyproject.toml) "
+                           "or unset FASTVIDEO_FA4.") from e
+    fa_version = "4"
+else:
+    try:
+        from flash_attn_interface import flash_attn_func as flash_attn_3_func
+
+        # flash_attn 3 no longer has a different API, see following commit:
+        # https://github.com/Dao-AILab/flash-attention/commit/ed209409acedbb2379f870bbd03abce31a7a51b7
+        flash_attn_func = flash_attn_3_func
+        fa_version = "3"
+    except ImportError:
+        from flash_attn import flash_attn_func as flash_attn_2_func
+        flash_attn_func = flash_attn_2_func
+        fa_version = "2"
+    try:
+        if importlib.util.find_spec("flash_attn.cute") is not None:
+            logger.info("flash_attn.cute (FA4) is installed but not enabled; "
+                        "set FASTVIDEO_FA4=1 to use it for inference.")
+    except ImportError:
+        pass
+
+if fa_version == "2":
+    # Scope: this op covers exactly the q/k/v + softmax_scale + causal call
+    # shape used by FlashAttentionImpl.forward's default branch (see
+    # `flash_attn_func_compilable(...)` call site in
+    # `attention/backends/flash_attn.py`). The masked/no-pad and varlen /
+    # cross-attn paths use different entry points
+    # (`flash_attn_no_pad`, `flash_attn_varlen_*`) which live in
+    # `attention/utils/flash_attn_no_pad.py`. The wrapper's signature is the
+    # contract: any extra kwarg (dropout_p, window_size, alibi_slopes,
+    # deterministic, return_attn_probs, ...) raises TypeError at the call
+    # site, so silent loss of kwargs is not a failure mode.
+    from flash_attn.flash_attn_interface import _flash_attn_backward as _fa2_backward
+    _fa_default = flash_attn_func
+
+    @torch.library.custom_op(
+        "fastvideo::_flash_attn_default_forward",
+        mutates_args=(),
+        device_types="cuda",
+    )
+    def _flash_attn_default_forward(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        softmax_scale: float | None,
+        causal: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # `return_attn_probs=True` asks FA2 to also return softmax_lse +
+        # S_dmask. We need softmax_lse to feed the backward; S_dmask is the
+        # dropout mask (always None here since dropout_p is fixed at 0).
+        out, softmax_lse, _ = _fa_default(q, k, v, softmax_scale=softmax_scale, causal=causal, return_attn_probs=True)
+        return out, softmax_lse
+
+    @torch.library.register_fake("fastvideo::_flash_attn_default_forward")
+    def _flash_attn_default_forward_fake(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        softmax_scale: float | None,
+        causal: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        del softmax_scale, causal
+        # FA2 default path: out = [batch, seqlen_q, nheads, head_dim_v],
+        # softmax_lse = [batch, nheads, seqlen_q], fp32 regardless of q dtype.
+        b, sq, hq = q.shape[0], q.shape[1], q.shape[2]
+        out = q.new_empty(b, sq, hq, v.shape[-1])
+        lse = q.new_empty(b, hq, sq, dtype=torch.float32)
+        return out, lse
+
+    def _flash_attn_default_setup_context(ctx, inputs, output):
+        q, k, v, softmax_scale, causal = inputs
+        out, lse = output
+        ctx.save_for_backward(q, k, v, out, lse)
+        # `lse` is an auxiliary output we save to feed FA2's backward; nobody
+        # should differentiate through it. Mark it non-differentiable so
+        # autograd errors loudly if a caller wires it into a loss, rather
+        # than silently producing zero/None grads through the `del grad_lse`
+        # in our backward.
+        ctx.mark_non_differentiable(lse)
+        # FA2's *forward* substitutes `1 / sqrt(head_dim)` for `softmax_scale=None`
+        # internally; FA2's *backward* (`_flash_attn_backward`) demands a concrete
+        # float in its C++ schema and rejects None at the binding boundary. Resolve
+        # the default here so the value saved on ctx (and passed to backward) is
+        # always a real float — matches what FA2's own autograd.Function does.
+        if softmax_scale is None:
+            softmax_scale = q.shape[-1]**-0.5
+        ctx.softmax_scale = softmax_scale
+        ctx.causal = causal
+
+    def _flash_attn_default_backward(ctx, grad_out, grad_lse):
+        # We only differentiate `out`; softmax_lse is saved-for-backward, not
+        # a real differentiable output. (Mirrors the FP4 cute template.)
+        del grad_lse
+        q, k, v, out, lse = ctx.saved_tensors
+        dq = torch.empty_like(q)
+        dk = torch.empty_like(k)
+        dv = torch.empty_like(v)
+        # FA2's `_flash_attn_backward` writes into dq/dk/dv in place. The
+        # extra kwargs (window_size_*, softcap, alibi_slopes, deterministic,
+        # rng_state) are pinned to the same defaults the forward wrapper
+        # uses — flash-attn==2.8.1 (the version FastVideo pins) requires
+        # all of them explicitly. `rng_state=None` is correct for our
+        # `dropout_p=0` configuration.
+        _fa2_backward(
+            grad_out,
+            q,
+            k,
+            v,
+            out,
+            lse,
+            dq,
+            dk,
+            dv,
+            dropout_p=0.0,
+            softmax_scale=ctx.softmax_scale,
+            causal=ctx.causal,
+            window_size_left=-1,
+            window_size_right=-1,
+            softcap=0.0,
+            alibi_slopes=None,
+            deterministic=False,
+            rng_state=None,
+        )
+        return dq, dk, dv, None, None
+
+    torch.library.register_autograd(
+        "fastvideo::_flash_attn_default_forward",
+        _flash_attn_default_backward,
+        setup_context=_flash_attn_default_setup_context,
+    )
+
+    def flash_attn_func_compilable(q, k, v, softmax_scale=None, causal=False):
+        # Backward is registered: autograd flows through the op (training
+        # path is also traceable; no carve-out needed). Public API matches
+        # `flash_attn_func` — returns just `out`; we drop the saved-for-
+        # backward `lse` here so callers see the original single-tensor
+        # contract.
+        out, _ = torch.ops.fastvideo._flash_attn_default_forward(q, k, v, softmax_scale, causal)
+        return out
+elif fa_version == "3":
+    # FA3 path: same forward+fake custom op as the original PR #1373, with
+    # the autograd carve-out kept. The full backward (mirroring the FA2 leg
+    # above) wants a Hopper box for grad-check validation, which we don't
+    # have until Kuan-Hao's Modal FA3 setup PR lands. Until then this keeps
+    # inference traceable + training correct (via the original
+    # autograd.Function path + a pre-PR-style graph break on training).
+    _fa_default = flash_attn_func
+
+    @torch.library.custom_op(
+        "fastvideo::_flash_attn_default_forward",
+        mutates_args=(),
+        device_types="cuda",
+    )
+    def _flash_attn_default_forward(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        softmax_scale: float | None,
+        causal: bool,
+    ) -> torch.Tensor:
+        return _fa_default(q, k, v, softmax_scale=softmax_scale, causal=causal)
+
+    @torch.library.register_fake("fastvideo::_flash_attn_default_forward")
+    def _flash_attn_default_forward_fake(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        softmax_scale: float | None,
+        causal: bool,
+    ) -> torch.Tensor:
+        del softmax_scale, causal
+        return q.new_empty(q.shape[0], q.shape[1], q.shape[2], v.shape[-1])
+
+    def flash_attn_func_compilable(q, k, v, softmax_scale=None, causal=False):
+        # Autograd carve-out. The custom op above registers a forward + fake
+        # kernel but NO backward (register_autograd), so it is opaque to
+        # autograd. Inference runs under no_grad / inference_mode and routes
+        # through the traceable custom op — that is the torch.compile win, and
+        # the only path this PR claims. Training backprops through attention,
+        # so route grad-enabled calls to the original FA2/FA3 `flash_attn_func`
+        # (itself an autograd.Function, so backward is correct) at the cost of a
+        # dynamo graph break on the training path — i.e. pre-PR behavior, no
+        # regression. Full autograd parity for the custom op (mirroring the FP4
+        # cute template) is a tracked follow-up.
+        if torch.is_grad_enabled() and (q.requires_grad or k.requires_grad or v.requires_grad):
+            return _fa_default(q, k, v, softmax_scale=softmax_scale, causal=causal)
+        return torch.ops.fastvideo._flash_attn_default_forward(q, k, v, softmax_scale, causal)
+elif fa_version == "4":
+    # FA4 path: `flash_attn_func` is already a torch.library custom op
+    # (registered in `fastvideo.attention.utils.flash_attn_cute`), so a
+    # passthrough is enough — no extra registration needed.
+    def flash_attn_func_compilable(q, k, v, softmax_scale=None, causal=False):
+        return flash_attn_func(q, k, v, softmax_scale=softmax_scale, causal=causal)
+else:
+    # Defensive: the probe above only ever sets fa_version to "2", "3",
+    # or "4"; an unexpected value means an import/probe regression and
+    # we want a loud error at import, not a silent NameError later.
+    raise RuntimeError(f"Unsupported FlashAttention version: {fa_version!r} — expected "
+                       f"'2', '3', or '4' from the import probe above.")

fastvideo/attention/utils/flash_attn_no_pad.py

@@ -24,7 +24,7 @@ from flash_attn.bert_padding import pad_input, unpad_input
 from fastvideo import envs
 
 
-def _resolve_flash_attn_varlen_func() -> Any:
+def _resolve_flash_attn_varlen_func() -> tuple[Any, str]:
     if envs.FASTVIDEO_FA4:
         # FA4 cute is explicit opt-in (see fastvideo/attention/backends/
         # flash_attn.py); with FASTVIDEO_FA4=1 an unimportable FA4 build must
@@ -39,20 +39,28 @@ def _resolve_flash_attn_varlen_func() -> Any:
                                "fix the FA4 install (see the flash-attn-4 pin in pyproject.toml) "
                                "or unset FASTVIDEO_FA4.") from e
 
-        return flash_attn_varlen_func_cute
+        return flash_attn_varlen_func_cute, "4"
     try:
         from flash_attn_interface import (
             flash_attn_varlen_func as flash_attn_varlen_func_interface, )
 
-        return flash_attn_varlen_func_interface
+        return flash_attn_varlen_func_interface, "3"
     except ImportError:
         from flash_attn import (
             flash_attn_varlen_func as flash_attn_varlen_func_flash, )
 
-        return flash_attn_varlen_func_flash
+        return flash_attn_varlen_func_flash, "2"
 
 
-flash_attn_varlen_func_impl = _resolve_flash_attn_varlen_func()
+flash_attn_varlen_func_impl, _FA_VARLEN_VERSION = _resolve_flash_attn_varlen_func()
+
+# FA2-only: the private varlen backward we register against the custom ops
+# below. FA3 / FA4 have different private signatures and validation paths
+# (Hopper / Blackwell boxes) — those legs keep the autograd carve-out
+# pattern from PR #1373 until their setup PRs land.
+if _FA_VARLEN_VERSION == "2":
+    from flash_attn.flash_attn_interface import (
+        _flash_attn_varlen_backward as _fa2_varlen_backward, )
 
 
 def flash_attn_no_pad(
@@ -191,3 +199,473 @@ def flash_attn_varlen_qk_no_pad(
         h=nheads,
     )
     return output
+
+
+# ---------------------------------------------------------------------------
+# torch.compile traceability + register_autograd parity for the masked /
+# varlen attention paths.
+#
+# Wraps the two entry points `FlashAttentionImpl.forward` calls
+# (`flash_attn_no_pad`, `flash_attn_varlen_qk_no_pad`) as
+# `torch.library.custom_op`s so dynamo sees one traceable node — the
+# internal unpad / pad bookkeeping (data-dependent `nnz` shapes) runs
+# eager inside the op, and the op's outputs are the statically-shaped
+# padded tensors. This mirrors the FA2 default-path wrapper in
+# `fastvideo/attention/backends/flash_attn.py`.
+#
+# Autograd: on FA2 we register a real backward (`register_autograd`)
+# that calls FA2's `_flash_attn_varlen_backward` on the unpadded form
+# — re-unpadding the saved padded tensors using the saved mask. The
+# `softmax_lse` from the varlen forward is naturally unpadded
+# (`[nheads, total_q]`); we pad it to `[batch, nheads, seqlen]` on
+# the way out (statically shaped) and re-unpad in backward. So
+# training backprops *through* the op (no graph break on the training
+# path either).
+#
+# FA3 / FA4 keep the autograd carve-out pattern from PR #1373: the
+# custom op has forward + fake only, and `*_compilable` falls back to
+# the original autograd.Function for grad-enabled calls. Those legs
+# are gated on Hopper-class / Blackwell-class boxes for backward
+# validation and ship as separate follow-ups.
+
+if _FA_VARLEN_VERSION == "2":
+    # ---------- masked self-attention: flash_attn_no_pad (FA2) ----------
+
+    @torch.library.custom_op(
+        "fastvideo::_flash_attn_no_pad_forward",
+        mutates_args=(),
+        device_types="cuda",
+    )
+    def _flash_attn_no_pad_forward(
+        qkv: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        b, s, _three, h, d = qkv.shape
+        x = rearrange(qkv, "b s three h d -> b s (three h d)")
+        x_unpad, indices, cu_seqlens, max_s, _ = unpad_input(x, key_padding_mask)
+        x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=h)
+        out_unpad, lse_unpad, _ = flash_attn_varlen_qkvpacked_func(x_unpad,
+                                                                   cu_seqlens,
+                                                                   max_s,
+                                                                   dropout_p,
+                                                                   softmax_scale=softmax_scale,
+                                                                   causal=causal,
+                                                                   deterministic=deterministic,
+                                                                   return_attn_probs=True)
+        # Pad out: [nnz, h, d] -> [b, s, h, d]
+        out_padded = rearrange(pad_input(rearrange(out_unpad, "nnz h d -> nnz (h d)"), indices, b, s),
+                               "b s (h d) -> b s h d",
+                               h=h)
+        # Pad lse: FA2 varlen returns [nheads, total_q]. Transpose to [total_q,
+        # nheads], pad to [b, s, nheads], permute to [b, nheads, s] — statically
+        # shaped so register_fake matches.
+        lse_padded = pad_input(lse_unpad.t().contiguous(), indices, b, s).permute(0, 2, 1).contiguous()
+        return out_padded, lse_padded
+
+    @torch.library.register_fake("fastvideo::_flash_attn_no_pad_forward")
+    def _flash_attn_no_pad_forward_fake(
+        qkv: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        del key_padding_mask, causal, dropout_p, softmax_scale, deterministic
+        b, s, _three, h, d = qkv.shape
+        out = qkv.new_empty(b, s, h, d)
+        lse = qkv.new_empty(b, h, s, dtype=torch.float32)
+        return out, lse
+
+    def _flash_attn_no_pad_setup_context(ctx, inputs, output):
+        qkv, key_padding_mask, causal, dropout_p, softmax_scale, deterministic = inputs
+        out, lse = output
+        ctx.save_for_backward(qkv, out, lse, key_padding_mask)
+        # Auxiliary output, not differentiable — see default-path note.
+        ctx.mark_non_differentiable(lse)
+        # FA2's varlen backward requires a concrete float for softmax_scale.
+        if softmax_scale is None:
+            softmax_scale = qkv.shape[-1]**-0.5  # head_dim from qkv's last dim
+        ctx.softmax_scale = softmax_scale
+        ctx.causal = causal
+        ctx.dropout_p = dropout_p
+        ctx.deterministic = deterministic
+
+    def _flash_attn_no_pad_backward(ctx, grad_out, grad_lse):
+        # lse is saved-for-backward, not differentiated.
+        del grad_lse
+        qkv, out_padded, lse_padded, key_padding_mask = ctx.saved_tensors
+        b, s, _three, h, d = qkv.shape
+
+        # One `unpad_input` call (on qkv) gives us indices + cu_seqlens + max_s;
+        # reuse those for out / dout / lse below via direct indexing instead
+        # of redundant `unpad_input` calls (each of which would re-run
+        # `nonzero` + `cumsum` + a `.max().item()` GPU→CPU sync).
+        x = rearrange(qkv, "b s three h d -> b s (three h d)")
+        x_unpad, indices, cu_seqlens, max_s, _ = unpad_input(x, key_padding_mask)
+        x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=h)
+        q_unpad, k_unpad, v_unpad = (t.contiguous() for t in x_unpad.unbind(dim=1))
+
+        # Direct-index variants reuse `indices` (computed above).
+        out_unpad = out_padded.flatten(0, 1)[indices].view(-1, h, d).contiguous()
+        dout_unpad = grad_out.flatten(0, 1)[indices].view(-1, h, d).contiguous()
+        # lse_padded [b, h, s] -> [b, s, h] -> [nnz, h] -> [h, nnz].
+        lse_unpad = lse_padded.permute(0, 2, 1).contiguous().flatten(0, 1)[indices].t().contiguous()
+
+        dq_unpad = torch.empty_like(q_unpad)
+        dk_unpad = torch.empty_like(k_unpad)
+        dv_unpad = torch.empty_like(v_unpad)
+        _fa2_varlen_backward(
+            dout_unpad,
+            q_unpad,
+            k_unpad,
+            v_unpad,
+            out_unpad,
+            lse_unpad,
+            dq_unpad,
+            dk_unpad,
+            dv_unpad,
+            cu_seqlens_q=cu_seqlens,
+            cu_seqlens_k=cu_seqlens,
+            max_seqlen_q=max_s,
+            max_seqlen_k=max_s,
+            dropout_p=ctx.dropout_p,
+            softmax_scale=ctx.softmax_scale,
+            causal=ctx.causal,
+            window_size_left=-1,
+            window_size_right=-1,
+            softcap=0.0,
+            alibi_slopes=None,
+            deterministic=ctx.deterministic,
+            rng_state=None,
+        )
+
+        # Re-pad each grad and stack into dqkv.
+        def _repad(dt_unpad: torch.Tensor) -> torch.Tensor:
+            padded = pad_input(rearrange(dt_unpad, "nnz h d -> nnz (h d)"), indices, b, s)
+            return rearrange(padded, "b s (h d) -> b s h d", h=h)
+
+        dqkv = torch.stack([_repad(dq_unpad), _repad(dk_unpad), _repad(dv_unpad)], dim=2)
+        # 6 inputs total: qkv, key_padding_mask, causal, dropout_p, softmax_scale, deterministic.
+        return dqkv, None, None, None, None, None
+
+    torch.library.register_autograd(
+        "fastvideo::_flash_attn_no_pad_forward",
+        _flash_attn_no_pad_backward,
+        setup_context=_flash_attn_no_pad_setup_context,
+    )
+
+    # ---------- cross-attention: flash_attn_varlen_qk_no_pad (FA2) ----------
+
+    @torch.library.custom_op(
+        "fastvideo::_flash_attn_varlen_qk_no_pad_forward",
+        mutates_args=(),
+        device_types="cuda",
+    )
+    def _flash_attn_varlen_qk_no_pad_forward(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        query_padding_mask: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        b, sq, h, d = query.shape
+        q_unpad, q_indices, cu_seqlens_q, max_seqlen_q, _ = unpad_input(rearrange(query, "b s h d -> b s (h d)"),
+                                                                        query_padding_mask)
+        k_unpad, _, cu_seqlens_k, max_seqlen_k, _ = unpad_input(rearrange(key, "b s h d -> b s (h d)"),
+                                                                key_padding_mask)
+        v_unpad, _, _, _, _ = unpad_input(rearrange(value, "b s h d -> b s (h d)"), key_padding_mask)
+        q_unpad = rearrange(q_unpad, "nnz (h d) -> nnz h d", h=h)
+        k_unpad = rearrange(k_unpad, "nnz (h d) -> nnz h d", h=h)
+        v_unpad = rearrange(v_unpad, "nnz (h d) -> nnz h d", h=h)
+        out_unpad, lse_unpad, _ = flash_attn_varlen_func_impl(q_unpad,
+                                                              k_unpad,
+                                                              v_unpad,
+                                                              cu_seqlens_q,
+                                                              cu_seqlens_k,
+                                                              max_seqlen_q,
+                                                              max_seqlen_k,
+                                                              dropout_p=dropout_p,
+                                                              softmax_scale=softmax_scale,
+                                                              causal=causal,
+                                                              deterministic=deterministic,
+                                                              return_attn_probs=True)
+        # Pad out: [nnz_q, h, d] -> [b, sq, h, d]
+        out_padded = rearrange(pad_input(rearrange(out_unpad, "nnz h d -> nnz (h d)"), q_indices, b, sq),
+                               "b s (h d) -> b s h d",
+                               h=h)
+        # Pad lse: [h, nnz_q] -> [b, h, sq]
+        lse_padded = pad_input(lse_unpad.t().contiguous(), q_indices, b, sq).permute(0, 2, 1).contiguous()
+        return out_padded, lse_padded
+
+    @torch.library.register_fake("fastvideo::_flash_attn_varlen_qk_no_pad_forward")
+    def _flash_attn_varlen_qk_no_pad_forward_fake(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        query_padding_mask: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        del key, query_padding_mask, key_padding_mask
+        del causal, dropout_p, softmax_scale, deterministic
+        b, sq, h, _ = query.shape
+        # `out`'s head_dim comes from value (d_v), matching the real forward's
+        # out_padded ([b, sq, h, d_v]); it can differ from query's d_q.
+        out = query.new_empty(b, sq, h, value.shape[-1])
+        lse = query.new_empty(b, h, sq, dtype=torch.float32)
+        return out, lse
+
+    def _flash_attn_varlen_qk_no_pad_setup_context(ctx, inputs, output):
+        (query, key, value, query_padding_mask, key_padding_mask, causal, dropout_p, softmax_scale,
+         deterministic) = inputs
+        out, lse = output
+        ctx.save_for_backward(query, key, value, out, lse, query_padding_mask, key_padding_mask)
+        # Auxiliary output, not differentiable — see default-path note.
+        ctx.mark_non_differentiable(lse)
+        if softmax_scale is None:
+            softmax_scale = query.shape[-1]**-0.5
+        ctx.softmax_scale = softmax_scale
+        ctx.causal = causal
+        ctx.dropout_p = dropout_p
+        ctx.deterministic = deterministic
+
+    def _flash_attn_varlen_qk_no_pad_backward(ctx, grad_out, grad_lse):
+        del grad_lse
+        (query, key, value, out_padded, lse_padded, query_padding_mask, key_padding_mask) = ctx.saved_tensors
+        b, sq, h, d = query.shape
+        sk = key.shape[1]
+
+        # One `unpad_input` call per distinct mask; reuse the returned
+        # indices via direct indexing for everything else that shares
+        # the same mask (v with k_mask; out/dout/lse with q_mask; the
+        # final repad of dk/dv also reuses k_indices). Avoids ~4
+        # redundant `unpad_input` calls + their GPU→CPU `.max().item()`
+        # syncs.
+        q_unpad, q_indices, cu_seqlens_q, max_seqlen_q, _ = unpad_input(rearrange(query, "b s h d -> b s (h d)"),
+                                                                        query_padding_mask)
+        k_unpad, k_indices, cu_seqlens_k, max_seqlen_k, _ = unpad_input(rearrange(key, "b s h d -> b s (h d)"),
+                                                                        key_padding_mask)
+        q_unpad = rearrange(q_unpad, "nnz (h d) -> nnz h d", h=h).contiguous()
+        k_unpad = rearrange(k_unpad, "nnz (h d) -> nnz h d", h=h).contiguous()
+        v_unpad = value.flatten(0, 1)[k_indices].view(-1, h, d).contiguous()
+
+        # out / dout / lse follow q's shape, so index with q_indices.
+        out_unpad = out_padded.flatten(0, 1)[q_indices].view(-1, h, d).contiguous()
+        dout_unpad = grad_out.flatten(0, 1)[q_indices].view(-1, h, d).contiguous()
+        lse_unpad = lse_padded.permute(0, 2, 1).contiguous().flatten(0, 1)[q_indices].t().contiguous()
+
+        dq_unpad = torch.empty_like(q_unpad)
+        dk_unpad = torch.empty_like(k_unpad)
+        dv_unpad = torch.empty_like(v_unpad)
+        _fa2_varlen_backward(
+            dout_unpad,
+            q_unpad,
+            k_unpad,
+            v_unpad,
+            out_unpad,
+            lse_unpad,
+            dq_unpad,
+            dk_unpad,
+            dv_unpad,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_q,
+            max_seqlen_k=max_seqlen_k,
+            dropout_p=ctx.dropout_p,
+            softmax_scale=ctx.softmax_scale,
+            causal=ctx.causal,
+            window_size_left=-1,
+            window_size_right=-1,
+            softcap=0.0,
+            alibi_slopes=None,
+            deterministic=ctx.deterministic,
+            rng_state=None,
+        )
+
+        # k_indices is already available from the unpad_input above —
+        # no need to recompute it for the dk/dv repad.
+        def _repad(dt_unpad: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int) -> torch.Tensor:
+            padded = pad_input(rearrange(dt_unpad, "nnz h d -> nnz (h d)"), indices, batch, seqlen)
+            return rearrange(padded, "b s (h d) -> b s h d", h=h)
+
+        dq_padded = _repad(dq_unpad, q_indices, b, sq)
+        dk_padded = _repad(dk_unpad, k_indices, b, sk)
+        dv_padded = _repad(dv_unpad, k_indices, b, sk)
+        # 9 inputs total: query, key, value, q_mask, k_mask, causal, dropout_p,
+        # softmax_scale, deterministic.
+        return dq_padded, dk_padded, dv_padded, None, None, None, None, None, None
+
+    torch.library.register_autograd(
+        "fastvideo::_flash_attn_varlen_qk_no_pad_forward",
+        _flash_attn_varlen_qk_no_pad_backward,
+        setup_context=_flash_attn_varlen_qk_no_pad_setup_context,
+    )
+
+    # ---------- public dispatchers (FA2: autograd flows through the op) -----
+
+
+    def flash_attn_no_pad_compilable(qkv,
+                                     key_padding_mask,
+                                     causal=False,
+                                     dropout_p=0.0,
+                                     softmax_scale=None,
+                                     deterministic=False):
+        """dynamo-traceable wrapper around ``flash_attn_no_pad`` (registered op,
+        full register_autograd on FA2 — both inference and training go through
+        the op, no graph break on either)."""
+        out, _ = torch.ops.fastvideo._flash_attn_no_pad_forward(qkv, key_padding_mask, causal, dropout_p, softmax_scale,
+                                                                deterministic)
+        return out
+
+    def flash_attn_varlen_qk_no_pad_compilable(query,
+                                               key,
+                                               value,
+                                               query_padding_mask,
+                                               key_padding_mask,
+                                               causal=False,
+                                               dropout_p=0.0,
+                                               softmax_scale=None,
+                                               deterministic=False):
+        """dynamo-traceable wrapper around ``flash_attn_varlen_qk_no_pad`` (registered
+        op, full register_autograd on FA2)."""
+        out, _ = torch.ops.fastvideo._flash_attn_varlen_qk_no_pad_forward(query, key, value, query_padding_mask,
+                                                                          key_padding_mask, causal, dropout_p,
+                                                                          softmax_scale, deterministic)
+        return out
+
+else:
+    # ---------- FA3 / FA4: carve-out (forward+fake only, no real backward) ---
+    # Same pattern as the parked varlen-extension and the FA3 default leg in
+    # `fastvideo/attention/backends/flash_attn.py`. Real backward for these
+    # versions is a follow-up gated on Hopper / Blackwell box validation.
+
+    @torch.library.custom_op(
+        "fastvideo::_flash_attn_no_pad_forward",
+        mutates_args=(),
+        device_types="cuda",
+    )
+    def _flash_attn_no_pad_forward(
+        qkv: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> torch.Tensor:
+        return flash_attn_no_pad(  # type: ignore[no-untyped-call]
+            qkv,
+            key_padding_mask,
+            causal=causal,
+            dropout_p=dropout_p,
+            softmax_scale=softmax_scale,
+            deterministic=deterministic)
+
+    @torch.library.register_fake("fastvideo::_flash_attn_no_pad_forward")
+    def _flash_attn_no_pad_forward_fake(
+        qkv: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> torch.Tensor:
+        del key_padding_mask, causal, dropout_p, softmax_scale, deterministic
+        b, s, _three, h, d = qkv.shape
+        return qkv.new_empty(b, s, h, d)
+
+    @torch.library.custom_op(
+        "fastvideo::_flash_attn_varlen_qk_no_pad_forward",
+        mutates_args=(),
+        device_types="cuda",
+    )
+    def _flash_attn_varlen_qk_no_pad_forward(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        query_padding_mask: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> torch.Tensor:
+        return flash_attn_varlen_qk_no_pad(  # type: ignore[no-untyped-call]
+            query,
+            key,
+            value,
+            query_padding_mask=query_padding_mask,
+            key_padding_mask=key_padding_mask,
+            causal=causal,
+            dropout_p=dropout_p,
+            softmax_scale=softmax_scale,
+            deterministic=deterministic)
+
+    @torch.library.register_fake("fastvideo::_flash_attn_varlen_qk_no_pad_forward")
+    def _flash_attn_varlen_qk_no_pad_forward_fake(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        query_padding_mask: torch.Tensor,
+        key_padding_mask: torch.Tensor,
+        causal: bool,
+        dropout_p: float,
+        softmax_scale: float | None,
+        deterministic: bool,
+    ) -> torch.Tensor:
+        del key, query_padding_mask, key_padding_mask
+        del causal, dropout_p, softmax_scale, deterministic
+        b, sq, h, _ = query.shape
+        # `out`'s head_dim comes from value (d_v), matching the real forward's
+        # output ([b, sq, h, d_v]); it can differ from query's d_q.
+        return query.new_empty(b, sq, h, value.shape[-1])
+
+    def flash_attn_no_pad_compilable(qkv,
+                                     key_padding_mask,
+                                     causal=False,
+                                     dropout_p=0.0,
+                                     softmax_scale=None,
+                                     deterministic=False):
+        if torch.is_grad_enabled() and qkv.requires_grad:
+            return flash_attn_no_pad(qkv,
+                                     key_padding_mask,
+                                     causal=causal,
+                                     dropout_p=dropout_p,
+                                     softmax_scale=softmax_scale,
+                                     deterministic=deterministic)
+        return torch.ops.fastvideo._flash_attn_no_pad_forward(qkv, key_padding_mask, causal, dropout_p, softmax_scale,
+                                                              deterministic)
+
+    def flash_attn_varlen_qk_no_pad_compilable(query,
+                                               key,
+                                               value,
+                                               query_padding_mask,
+                                               key_padding_mask,
+                                               causal=False,
+                                               dropout_p=0.0,
+                                               softmax_scale=None,
+                                               deterministic=False):
+        if torch.is_grad_enabled() and (query.requires_grad or key.requires_grad or value.requires_grad):
+            return flash_attn_varlen_qk_no_pad(query,
+                                               key,
+                                               value,
+                                               query_padding_mask=query_padding_mask,
+                                               key_padding_mask=key_padding_mask,
+                                               causal=causal,
+                                               dropout_p=dropout_p,
+                                               softmax_scale=softmax_scale,
+                                               deterministic=deterministic)
+        return torch.ops.fastvideo._flash_attn_varlen_qk_no_pad_forward(query, key, value, query_padding_mask,
+                                                                        key_padding_mask, causal, dropout_p,
+                                                                        softmax_scale, deterministic)

fastvideo/configs/models/dits/__init__.py

@@ -1,7 +1,9 @@
 from fastvideo.configs.models.dits.cosmos import CosmosVideoConfig
 from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
 from fastvideo.configs.models.dits.dreamx_world import DreamXWorldARConfig, DreamXWorldConfig
+from fastvideo.configs.models.dits.flux import FluxDiTConfig
 from fastvideo.configs.models.dits.flux_2 import Flux2Config
+from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
 from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftConfig
 from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
@@ -15,6 +17,7 @@ from fastvideo.configs.models.dits.kandinsky5 import Kandinsky5VideoConfig
 
 __all__ = [
     "HunyuanVideoConfig", "HunyuanVideo15Config", "HunyuanGameCraftConfig", "WanVideoConfig", "DreamXWorldConfig",
-    "DreamXWorldARConfig", "CosmosVideoConfig", "Cosmos25VideoConfig", "LongCatVideoConfig", "LTX2VideoConfig",
-    "HYWorldConfig", "Kandinsky5VideoConfig", "MagiHumanVideoConfig", "StableAudioConfig", "Flux2Config"
+    "DreamXWorldARConfig", "CosmosVideoConfig", "Cosmos25VideoConfig", "FluxDiTConfig", "Flux2Config",
+    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig", "Kandinsky5VideoConfig", "MagiHumanVideoConfig",
+    "StableAudioConfig", "GlmImageDiTConfig"
 ]

fastvideo/configs/models/dits/base.py

@@ -48,6 +48,58 @@ class DiTConfig(ModelConfig):
     # FastVideoDiT-specific parameters
     prefix: str = ""
     quant_config: QuantizationConfig | None = None
+    # Some model families are only numerically correct with a specific attention
+    # backend (e.g. FastWan is sparse-distilled with VSA). When set, the model
+    # fails loudly if the selected FASTVIDEO_ATTENTION_BACKEND does not match it.
+    # None = no requirement (the common case).
+    required_attention_backend: AttentionBackendEnum | None = None
+    # A checkpoint can support the model's ordinary attention implementation
+    # while still being incompatible with a particular backend-specific block
+    # layout. Keep those checkpoint constraints separate from the architecture's
+    # general _supported_attention_backends list.
+    incompatible_attention_backends: tuple[AttentionBackendEnum, ...] = ()
+
+    @staticmethod
+    def _parse_attention_backend(backend: AttentionBackendEnum | str, field_name: str) -> AttentionBackendEnum:
+        if isinstance(backend, AttentionBackendEnum):
+            return backend
+        if isinstance(backend, str):
+            try:
+                return AttentionBackendEnum[backend]
+            except KeyError as exc:
+                valid_backends = ", ".join(AttentionBackendEnum.__members__)
+                raise ValueError(
+                    f"Unknown attention backend {backend!r} in {field_name}; expected one of: {valid_backends}"
+                ) from exc
+        raise TypeError(f"{field_name} must contain AttentionBackendEnum values or names, got {type(backend).__name__}")
+
+    def update_model_config(self, source_model_dict: dict[str, Any]) -> None:
+        source_model_dict = source_model_dict.copy()
+        required_backend = source_model_dict.get("required_attention_backend")
+        if isinstance(required_backend, str):
+            source_model_dict["required_attention_backend"] = self._parse_attention_backend(
+                required_backend, "required_attention_backend")
+
+        incompatible_backends = source_model_dict.get("incompatible_attention_backends")
+        if incompatible_backends is not None:
+            if not isinstance(incompatible_backends, list | tuple):
+                raise TypeError("incompatible_attention_backends must be a list or tuple")
+            source_model_dict["incompatible_attention_backends"] = tuple(
+                self._parse_attention_backend(backend, "incompatible_attention_backends")
+                for backend in incompatible_backends)
+
+        super().update_model_config(source_model_dict)
+
+    def validate_attention_backend_compatibility(
+        self,
+        selected_backend: AttentionBackendEnum | None,
+        *,
+        model_name: str = "This model",
+    ) -> None:
+        if selected_backend in self.incompatible_attention_backends:
+            assert selected_backend is not None
+            raise ValueError(f"{model_name} is incompatible with the {selected_backend.name} attention backend. "
+                             "Select a compatible attention backend before loading the model.")
 
     @staticmethod
     def add_cli_args(parser: Any, prefix: str = "dit-config") -> Any:

fastvideo/configs/models/dits/flux.py

@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+@dataclass
+class FluxTransformer2DArchConfig(DiTArchConfig):
+
+    patch_size: int = 1
+    in_channels: int = 64
+    out_channels: int | None = None
+    num_layers: int = 19
+    num_single_layers: int = 38
+    attention_head_dim: int = 128
+    num_attention_heads: int = 24
+    joint_attention_dim: int = 4096
+    pooled_projection_dim: int = 768
+    guidance_embeds: bool = True
+    axes_dims_rope: tuple[int, int, int] = (16, 56, 56)
+
+
+@dataclass
+class FluxDiTConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=FluxTransformer2DArchConfig)
+    prefix: str = "flux"

fastvideo/configs/models/dits/glm_image.py

@@ -0,0 +1,61 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_blocks(n: str, m) -> bool:
+    return "transformer_blocks" in n and str.isdigit(n.split(".")[-1])
+
+
+@dataclass
+class GlmImageDiTArchConfig(DiTArchConfig):
+
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
+
+    hidden_size: int = 4096
+    num_attention_heads: int = 32
+    attention_head_dim: int = 128
+    in_channels: int = 16
+    out_channels: int = 16
+    num_layers: int = 30
+
+    text_embed_dim: int = 1472
+    time_embed_dim: int = 512
+    condition_dim: int = 256
+
+    prior_vq_quantizer_codebook_size: int = 16384
+
+    patch_size: int = 2
+
+    max_height: int = 2048
+    max_width: int = 2048
+
+    qk_norm: str = "layer_norm"
+    eps: float = 1e-5
+
+    exclude_lora_layers: list[str] = field(
+        default_factory=lambda: ["image_projector", "glyph_projector", "prior_token_embedding"])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^glyph_projector\.net\.0\.proj\.(.*)$": r"glyph_projector.fc_in.\1",
+            r"^glyph_projector\.net\.2\.(.*)$": r"glyph_projector.fc_out.\1",
+            r"^prior_projector\.net\.0\.proj\.(.*)$": r"prior_projector.fc_in.\1",
+            r"^prior_projector\.net\.2\.(.*)$": r"prior_projector.fc_out.\1",
+            r"^transformer_blocks\.(\d+)\.ff\.net\.0\.proj\.(.*)$": r"transformer_blocks.\1.ff.fc_in.\2",
+            r"^transformer_blocks\.(\d+)\.ff\.net\.2\.(.*)$": r"transformer_blocks.\1.ff.fc_out.\2",
+        })
+
+    reverse_param_names_mapping: dict = field(default_factory=dict)
+    lora_param_names_mapping: dict = field(default_factory=dict)
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.num_channels_latents = self.out_channels
+
+
+@dataclass
+class GlmImageDiTConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=GlmImageDiTArchConfig)
+    prefix: str = "GlmImage"

fastvideo/configs/models/dits/kandinsky5.py

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

fastvideo/configs/models/dits/wanvideo.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import Literal
 
 from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
 
@@ -19,6 +20,11 @@ class WanVideoArchConfig(DiTArchConfig):
             r"^condition_embedder\.text_embedder\.linear_2\.(.*)$": r"condition_embedder.text_embedder.fc_out.\1",
             r"^condition_embedder\.time_embedder\.linear_1\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_in.\1",
             r"^condition_embedder\.time_embedder\.linear_2\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_out.\1",
+            # AnyFlow dual-timestep checkpoints expose delta_embedder weights with the
+            # same internal layout as time_embedder. The regex is harmless on plain
+            # Wan checkpoints (no delta_embedder keys to match).
+            r"^condition_embedder\.delta_embedder\.linear_1\.(.*)$": r"condition_embedder.delta_embedder.mlp.fc_in.\1",
+            r"^condition_embedder\.delta_embedder\.linear_2\.(.*)$": r"condition_embedder.delta_embedder.mlp.fc_out.\1",
             r"^condition_embedder\.time_proj\.(.*)$": r"condition_embedder.time_modulation.linear.\1",
             r"^condition_embedder\.image_embedder\.ff\.net\.0\.proj\.(.*)$":
             r"condition_embedder.image_embedder.ff.fc_in.\1",
@@ -86,6 +92,14 @@ class WanVideoArchConfig(DiTArchConfig):
     # "relativistic" keeps long rollouts in-distribution; a no-op unless sink_size > 0 and local_attn_size > 0.
     rope_cache_policy: str = "absolute"
 
+    # AnyFlow dual-timestep conditioning. Defaults preserve bit-identity with
+    # the legacy single-timestep forward (no delta_embedder allocated, no
+    # extra computation on the embedder forward path).
+    r_embedder: bool = False
+    r_embedder_fusion: Literal["additive", "gated"] = "additive"
+    r_embedder_gate_value: float = 0.25
+    r_embedder_deltatime_type: Literal["r", "t-r"] = "r"
+
     def __post_init__(self):
         super().__post_init__()
         self.out_channels = self.out_channels or self.in_channels

fastvideo/configs/models/encoders/base.py

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

fastvideo/configs/models/vaes/__init__.py

@@ -2,6 +2,7 @@ from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
 from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
 from fastvideo.configs.models.vaes.gamecraftvae import GameCraftVAEConfig
 from fastvideo.configs.models.vaes.gen3cvae import Gen3CVAEConfig
+from fastvideo.configs.models.vaes.glm_image import GlmImageVAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
 from fastvideo.configs.models.vaes.ltx2vae import LTX2VAEConfig
@@ -21,4 +22,5 @@ __all__ = [
     "OobleckVAEArchConfig",
     "OobleckVAEConfig",
     "Flux2VAEConfig",
+    "GlmImageVAEConfig",
 ]

fastvideo/configs/models/vaes/glm_image.py

@@ -0,0 +1,95 @@
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.vaes.autoencoder_kl import (AutoencoderKLArchConfig, AutoencoderKLVAEConfig)
+
+_GLM_IMAGE_LATENTS_MEAN: tuple[float, ...] = (
+    -0.2080078125,
+    1.875,
+    -0.470703125,
+    -1.265625,
+    -1.421875,
+    0.77734375,
+    -0.3671875,
+    -0.9453125,
+    0.318359375,
+    0.7734375,
+    -0.1884765625,
+    -0.022216796875,
+    -0.220703125,
+    -1.59375,
+    -0.81640625,
+    -0.255859375,
+)
+_GLM_IMAGE_LATENTS_STD: tuple[float, ...] = (
+    3.0625,
+    2.203125,
+    2.265625,
+    4.84375,
+    2.5,
+    3.9375,
+    2.203125,
+    3.03125,
+    2.1875,
+    2.046875,
+    2.71875,
+    2.390625,
+    2.390625,
+    2.453125,
+    2.25,
+    2.15625,
+)
+
+
+@dataclass
+class GlmImageVAEArchConfig(AutoencoderKLArchConfig):
+    act_fn: str = "silu"
+    block_out_channels: tuple[int, ...] = (128, 512, 1024, 1024)
+    down_block_types: tuple[str, ...] = (
+        "DownEncoderBlock2D",
+        "DownEncoderBlock2D",
+        "DownEncoderBlock2D",
+        "DownEncoderBlock2D",
+    )
+    up_block_types: tuple[str, ...] = (
+        "UpDecoderBlock2D",
+        "UpDecoderBlock2D",
+        "UpDecoderBlock2D",
+        "UpDecoderBlock2D",
+    )
+    force_upcast: bool = True
+    in_channels: int = 3
+    out_channels: int = 3
+    latent_channels: int = 16
+    latents_mean: tuple[float, ...] = _GLM_IMAGE_LATENTS_MEAN
+    latents_std: tuple[float, ...] = _GLM_IMAGE_LATENTS_STD
+    layers_per_block: int = 3
+    mid_block_add_attention: bool = False
+    norm_num_groups: int = 32
+    sample_size: int = 1024
+    scaling_factor: float = 0.18215
+    shift_factor: float | None = None
+    use_quant_conv: bool = False
+    use_post_quant_conv: bool = False
+
+    temporal_compression_ratio: int = 1
+    spatial_compression_ratio: int = 8
+
+
+@dataclass
+class GlmImageVAEConfig(AutoencoderKLVAEConfig):
+    arch_config: GlmImageVAEArchConfig = field(default_factory=GlmImageVAEArchConfig)
+
+    use_tiling: bool = True
+    use_temporal_tiling: bool = False
+    use_parallel_tiling: bool = False
+
+    tile_sample_min_height: int = 512
+    tile_sample_min_width: int = 512
+    tile_sample_stride_height: int = 384
+    tile_sample_stride_width: int = 384
+
+    load_encoder: bool = True
+    load_decoder: bool = True

fastvideo/configs/pipelines/__init__.py

@@ -6,6 +6,7 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.kandinsky5 import Kandinsky5I2VConfig, Kandinsky5T2VConfig
 from fastvideo.configs.pipelines.matrixgame2 import MatrixGame2I2V480PConfig
 from fastvideo.configs.pipelines.matrixgame3 import MatrixGame3I2V720PConfig
 from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
@@ -17,6 +18,6 @@ __all__ = [
     "HunyuanConfig", "FastHunyuanConfig", "HunyuanGameCraftPipelineConfig", "PipelineConfig", "Hunyuan15T2V480PConfig",
     "Hunyuan15T2V720PConfig", "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig", "WanI2V720PConfig",
     "SelfForcingWanT2V480PConfig", "LucyEditDevConfig", "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig",
-    "DreamXWorld5BCamPipelineConfig", "DreamXWorld5BARPipelineConfig", "HYWorldConfig", "MatrixGame2I2V480PConfig",
-    "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
+    "DreamXWorld5BCamPipelineConfig", "DreamXWorld5BARPipelineConfig", "HYWorldConfig", "Kandinsky5T2VConfig",
+    "Kandinsky5I2VConfig", "MatrixGame2I2V480PConfig", "MatrixGame3I2V720PConfig", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/base.py

@@ -284,6 +284,12 @@ class PipelineConfig:
                 model_dict = asdict(value)
                 # Model Arch Config should be hidden away from the users
                 model_dict.pop("arch_config")
+                if isinstance(value, DiTConfig):
+                    if value.required_attention_backend is not None:
+                        model_dict["required_attention_backend"] = value.required_attention_backend.name
+                    model_dict["incompatible_attention_backends"] = [
+                        backend.name for backend in value.incompatible_attention_backends
+                    ]
                 output_dict[key] = model_dict
             elif isinstance(value, tuple) and all(isinstance(v, ModelConfig) for v in value):
                 model_dicts = []

fastvideo/configs/pipelines/flux.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models import EncoderConfig
+from fastvideo.configs.models.dits.flux import FluxDiTConfig
+from fastvideo.configs.models.encoders import (
+    BaseEncoderOutput,
+    CLIPTextConfig,
+    T5LargeConfig,
+)
+from fastvideo.configs.models.vaes.autoencoder_kl import AutoencoderKLVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
+
+
+def _flux_clip_pooled_postprocess(outputs: BaseEncoderOutput) -> torch.Tensor:
+    """CLIP branch for FLUX: Diffusers uses pooled prompt embeddings only."""
+    if outputs.pooler_output is None:
+        raise RuntimeError(
+            "FLUX CLIP conditioning requires pooler_output. Ensure the CLIP text encoder returns pooled features.")
+    return outputs.pooler_output
+
+
+def _flux_t5_sequence_postprocess(outputs: BaseEncoderOutput) -> torch.Tensor:
+    if outputs.last_hidden_state is None:
+        raise RuntimeError("FLUX T5 conditioning requires last_hidden_state.")
+    return outputs.last_hidden_state
+
+
+@dataclass
+class FluxPipelineConfig(PipelineConfig):
+    """Pipeline layout for Diffusers FLUX.1-dev (CLIP + T5 + packed DiT + FlowMatch)."""
+
+    scheduler_arch: str = "FlowMatchEulerDiscreteScheduler"
+    transformer_arch: str = "FluxTransformer2DModel"
+    vae_arch: str = "AutoencoderKL"
+    text_encoder_archs: tuple[str, ...] = ("CLIPTextModel", "T5EncoderModel")
+    tokenizer_archs: tuple[str, ...] = ("CLIPTokenizer", "T5TokenizerFast")
+
+    dit_config: FluxDiTConfig = field(default_factory=FluxDiTConfig)
+    vae_config: AutoencoderKLVAEConfig = field(default_factory=AutoencoderKLVAEConfig)
+
+    embedded_cfg_scale: float = 3.5
+    flow_shift: float | None = None
+
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (CLIPTextConfig(), T5LargeConfig()))
+    preprocess_text_funcs: tuple[Callable[[str], str],
+                                 ...] = field(default_factory=lambda: (preprocess_text, preprocess_text))
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor], ...] = field(
+        default_factory=lambda: (_flux_clip_pooled_postprocess, _flux_t5_sequence_postprocess))
+
+    dit_precision: str = "bf16"
+    vae_precision: str = "fp32"
+    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp32", "bf16"))
+
+    def __post_init__(self) -> None:
+        te_cfgs = list(self.text_encoder_configs)
+        if len(te_cfgs) >= 1:
+            te_cfgs[0].tokenizer_kwargs.setdefault("padding", "max_length")
+            te_cfgs[0].tokenizer_kwargs.setdefault("max_length", 77)
+            te_cfgs[0].tokenizer_kwargs.setdefault("truncation", True)
+            te_cfgs[0].tokenizer_kwargs.setdefault("return_tensors", "pt")
+        if len(te_cfgs) >= 2:
+            cap = 512
+            te_cfgs[1].tokenizer_kwargs["max_length"] = min(int(te_cfgs[1].tokenizer_kwargs.get("max_length", cap)),
+                                                            cap)
+            te_cfgs[1].tokenizer_kwargs.setdefault("padding", "max_length")
+            te_cfgs[1].tokenizer_kwargs.setdefault("truncation", True)
+            te_cfgs[1].tokenizer_kwargs.setdefault("return_tensors", "pt")

fastvideo/configs/pipelines/glm_image.py

@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+from collections.abc import Callable
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
+from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
+from fastvideo.configs.models.vaes.glm_image import GlmImageVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
+
+
+def glm_image_t5_postprocess(outputs: BaseEncoderOutput) -> torch.Tensor:
+    mask: torch.Tensor = outputs.attention_mask
+    hidden_state: torch.Tensor = outputs.last_hidden_state
+    seq_lens = mask.gt(0).sum(dim=1).long()
+
+    assert torch.isnan(hidden_state).sum() == 0, "T5 hidden states contain NaN"
+
+    max_len = 512
+    prompt_embeds = [u[:min(v, max_len)] for u, v in zip(hidden_state, seq_lens, strict=True)]
+    prompt_embeds_tensor: torch.Tensor = torch.stack(
+        [torch.cat([u, u.new_zeros(max_len - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0)
+
+    return prompt_embeds_tensor
+
+
+@dataclass
+class GlmImageConfig(PipelineConfig):
+
+    dit_config: DiTConfig = field(default_factory=GlmImageDiTConfig)
+    dit_precision: str = "bf16"
+
+    vae_config: VAEConfig = field(default_factory=GlmImageVAEConfig)
+    vae_precision: str = "fp32"
+    vae_tiling: bool = True
+    vae_sp: bool = False
+
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (T5Config(), ))
+    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp32", ))
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
+                                  ...] = field(default_factory=lambda: (glm_image_t5_postprocess, ))
+
+    flow_shift: float | None = 1.0
+    embedded_cfg_scale: float = 7.5

fastvideo/configs/pipelines/kandinsky5.py

@@ -0,0 +1,122 @@
+# 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/configs/pipelines/wan.py

@@ -12,6 +12,9 @@ from fastvideo.configs.models.encoders import (BaseEncoderOutput, CLIPVisionConf
 from fastvideo.configs.models.vaes import WanVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEArchConfig
 from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo.platforms import AttentionBackendEnum
+
+FASTWAN_REQUIRED_ATTENTION_BACKEND = AttentionBackendEnum.VIDEO_SPARSE_ATTN
 
 
 def t5_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
@@ -64,7 +67,7 @@ class WanT2V480PConfig(PipelineConfig):
 
     # WanConfig-specific added parameters
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         self.vae_config.load_encoder = False
         self.vae_config.load_decoder = True
 
@@ -123,6 +126,10 @@ class FastWan2_1_T2V_480P_Config(WanT2V480PConfig):
     flow_shift: float | None = 8.0
     dmd_denoising_steps: list[int] | None = field(default_factory=lambda: [1000, 757, 522])
 
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        self.dit_config.required_attention_backend = FASTWAN_REQUIRED_ATTENTION_BACKEND
+
 
 @dataclass
 class Wan2_2_TI2V_5B_Config(WanT2V480PConfig):
@@ -277,6 +284,27 @@ class FastWan2_2_TI2V_5B_Config(Wan2_2_TI2V_5B_Config):
     flow_shift: float | None = 5.0
     dmd_denoising_steps: list[int] | None = field(default_factory=lambda: [1000, 757, 522])
 
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        self.dit_config.required_attention_backend = FASTWAN_REQUIRED_ATTENTION_BACKEND
+
+
+@dataclass
+class FastWan2_2_TI2V_5B_FullAttn_Config(Wan2_2_TI2V_5B_Config):
+    """FullAttn (dense-attention) DMD variant of FastWan 2.2 TI2V 5B.
+
+    Shares FastWan's DMD denoising schedule but uses the published dense
+    checkpoint layout, which has no VSA gate-compression weights.
+    """
+
+    flow_shift: float | None = 5.0
+    dmd_denoising_steps: list[int] | None = field(default_factory=lambda: [1000, 757, 522])
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        self.dit_config.required_attention_backend = None
+        self.dit_config.incompatible_attention_backends = (FASTWAN_REQUIRED_ATTENTION_BACKEND, )
+
 
 @dataclass
 class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):

fastvideo/dataset/parquet_dataset_map_style.py

@@ -2,6 +2,7 @@
 import os
 import pickle
 import random
+from collections.abc import Sequence
 from typing import Any
 
 import pyarrow as pa
@@ -94,8 +95,56 @@ class DP_SP_BatchSampler(Sampler[list[int]]):
         return len(self.sp_group_local_indices) // self.batch_size
 
 
-def get_parquet_files_and_length(path: str):
-    dataset_root = os.path.realpath(os.path.expanduser(path))
+def _parse_data_path_specs(path: str | Sequence[str] | dict[str, int]) -> list[tuple[str, int]]:
+    """Parse one or more dataset roots with old-framework repeat counts."""
+    if isinstance(path, dict):
+        return [
+            (str(root), int(repeat))
+            for root, repeat in path.items()
+            if int(repeat) > 0
+        ]
+    if isinstance(path, Sequence) and not isinstance(path, str):
+        return [(str(root), 1) for root in path]
+
+    specs: list[tuple[str, int]] = []
+    for part in str(path).split(","):
+        part = part.strip()
+        if not part:
+            continue
+        if ":" in part:
+            dir_path, count_str = part.rsplit(":", 1)
+            count = int(count_str)
+        else:
+            dir_path, count = part, 1
+        if count > 0:
+            specs.append((dir_path.strip(), count))
+    return specs
+
+
+def get_parquet_files_and_length(path: str | Sequence[str] | dict[str, int]):
+    specs = _parse_data_path_specs(path)
+    if len(specs) != 1 or specs[0][1] != 1:
+        all_file_names: list[str] = []
+        all_lengths: list[int] = []
+        for root, repeat in specs:
+            file_names, lengths = get_parquet_files_and_length(str(root))
+            for _ in range(repeat):
+                all_file_names.extend(file_names)
+                all_lengths.extend(lengths)
+        if not all_file_names:
+            raise FileNotFoundError(
+                "No parquet files found under dataset paths: "
+                f"{path}. "
+                "Please verify these paths point to preprocessed parquet data."
+            )
+        file_lengths = sorted(
+            zip(all_file_names, all_lengths, strict=True),
+            key=lambda x: x[0],
+        )
+        file_names_sorted, lengths_sorted = zip(*file_lengths, strict=True)
+        return file_names_sorted, lengths_sorted
+
+    dataset_root = os.path.realpath(os.path.expanduser(specs[0][0]))
     # Check if cached info exists
     cache_dir = os.path.join(dataset_root, "map_style_cache")
     cache_file = os.path.join(cache_dir, "file_info.pkl")
@@ -268,7 +317,7 @@ class LatentsParquetMapStyleDataset(Dataset):
 
     def __init__(
         self,
-        path: str,
+        path: str | Sequence[str] | dict[str, int],
         batch_size: int,
         parquet_schema: pa.Schema,
         cfg_rate: float = 0.0,
@@ -358,7 +407,6 @@ class LatentsParquetMapStyleDataset(Dataset):
     def __len__(self):
         return sum(self.lengths)
 
-
 # ────────────────────────────────────────────────────────────────────────────
 # 3.  Loader helper – everything else stays just like your original trainer
 # ────────────────────────────────────────────────────────────────────────────

fastvideo/envs.py

@@ -68,6 +68,24 @@ def maybe_convert_int(value: str | None) -> int | None:
     return int(value)
 
 
+def get_attention_backend() -> str | None:
+    """Read FASTVIDEO_ATTENTION_BACKEND, failing loudly on an unknown value.
+
+    Returns None when unset. A typo'd / invalid backend name raises immediately
+    with the list of valid backends, rather than silently falling back to
+    auto-selection.
+    """
+    backend = os.getenv("FASTVIDEO_ATTENTION_BACKEND", None)
+    if backend is None:
+        return None
+    # Imported lazily to avoid a circular import at module load time.
+    from fastvideo.platforms.interface import AttentionBackendEnum
+    if backend not in AttentionBackendEnum.__members__:
+        valid = ", ".join(AttentionBackendEnum.__members__)
+        raise ValueError(f"Invalid FASTVIDEO_ATTENTION_BACKEND={backend!r}. Valid backends are: {valid}")
+    return backend
+
+
 # The begin-* and end* here are used by the documentation generator
 # to extract the used env vars.
 
@@ -211,7 +229,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # FLASH_ATTN uses FlashAttention-3/2; to run FlashAttention-4 set
     # FASTVIDEO_FA4=1 as well (see below).
     "FASTVIDEO_ATTENTION_BACKEND":
-    lambda: os.getenv("FASTVIDEO_ATTENTION_BACKEND", None),
+    get_attention_backend,
 
     # If set (=1), the FLASH_ATTN backend uses FlashAttention-4
     # (flash_attn.cute). FA4 is opt-in and never auto-selected just because it

fastvideo/eval/README.md

@@ -122,6 +122,44 @@ to run a subset of the Evaluator's registered metrics on this batch
 (useful for scoring different corpora with different metric subsets in
 successive calls without burning model loads).
 
+### Training-time validation metrics
+
+The modular trainer can score validation videos during training through
+`callbacks.validation.metrics`. Each distributed rank that writes local
+validation videos also runs its local metrics on `cuda:<local_rank>`;
+rank 0 only merges scalar summaries and logs artifacts.
+
+```yaml
+callbacks:
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.matrixgame2.matrixgame2_causal_dmd_pipeline.MatrixGame2CausalDMDPipeline
+    dataset_file: examples/distill/MatrixGame2.0/validation.json
+    sampling_steps: [4]
+    metrics:
+      enabled: true
+      names:
+        - vbench.imaging_quality
+        - vbench.aesthetic_quality
+        - optical_flow.synthetic_optical_flow
+        - common.fvd
+      skip_missing_deps: true
+      strict: false
+      unload_after_validation: true
+
+      # `synthetic_optical_flow` also needs actions in the validation
+      # manifest (`action_path`) plus a repo-local calibration file.
+      calibration_path: assets/eval/worldmodel_synthetic_flow_calibration.json
+```
+
+Metric summaries are written under
+`<output_dir>/eval/step_<step>/inference_steps_<n>_rank_<rank>.json` and
+scalar means are logged with the `metrics/validation/...` prefix.
+Install `fastvideo[eval]` for optical-flow dependencies such as
+`ptlflow`; VBench metrics use the pinned submodule under
+`fastvideo/third_party/eval/vbench`. FVD uses `ref_video` entries from
+the validation manifest when present, or the standard
+`FASTVIDEO_FVD_REF_FEATURES` / eval-cache reference feature path.
+
 ### CLI
 
 ```bash
@@ -350,5 +388,3 @@ sweep several baselines into a table, see
 
 - **MIND** metrics. Depend on a separate `vipe` upstream submodule.
 - **VBench-2.0**. Sibling vbench2 package; needs its own port.
-- **Training-time eval callback** (`EvalCallback`) and the
-  `RolloutEvaluator` helper.

fastvideo/eval/metrics/optical_flow/synthetic_optical_flow/_thirdperson.py

@@ -63,7 +63,7 @@ class ThirdPersonCalibration:
     beta_fwd: float
     beta_strafe: float
     focal_length: float
-    r_z: float
+    r_z: float = 0.0
     r_y: float = 0.0
     init_pitch: float = 0.0
     notes: str = ""

fastvideo/layers/quantization/nvfp4_config.py

@@ -261,6 +261,22 @@ def _mm_fp4(
     )
 
 
+def _coerce_fp4_input_dtype(x: torch.Tensor) -> torch.Tensor:
+    """Coerce an activation to a dtype the FP4 linear accepts.
+
+    The pre-attention norm can emit fp32 (e.g. in eager mode, without the
+    torch.compile fusion that keeps it bf16). The FP4 linear emits bf16
+    regardless (see _mm_fp4 out dtype), so cast fp32 -> bf16 rather than
+    failing, matching the sibling fastvideo/layers/fp4linear.py. Non-floating
+    inputs (e.g. int/bool) are a genuine error and are rejected fast.
+    """
+    if not x.is_floating_point():
+        raise TypeError(f"fp4 linear expects floating-point inputs, got {x.dtype}")
+    if x.dtype not in (torch.bfloat16, torch.float16):
+        x = x.to(torch.bfloat16)
+    return x
+
+
 class NVFP4QuantizeMethod(QuantizeMethodBase):
 
     def __init__(self, layer_prefix: str = ""):
@@ -285,8 +301,7 @@ class NVFP4QuantizeMethod(QuantizeMethodBase):
 
     def quantize_input(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         SfLayout, _, _ = _require_flashinfer()
-        assert x.dtype == torch.bfloat16 or x.dtype == torch.float16, (
-            f"only allow bf16/fp16 inputs to fp4 linear, got {x.dtype}")
+        x = _coerce_fp4_input_dtype(x)
         x_2d = x.view(-1, x.shape[-1])
         x_fp4, x_scale = _nvfp4_quantize(
             x_2d,
@@ -332,8 +347,7 @@ class NVFP4QuantizeMethod(QuantizeMethodBase):
             if x_scale.dim() > 2:
                 x_scale = x_scale.view(-1, x_scale.shape[-1])
         else:
-            assert x.dtype == torch.bfloat16 or x.dtype == torch.float16, (
-                f"only allow bf16/fp16 inputs to fp4 linear, got {x.dtype}")
+            x = _coerce_fp4_input_dtype(x)
             x = x.view(-1, x.shape[-1])
             x_global_sf = self.x_global_sf
             x_fp4, x_scale = _nvfp4_quantize(

fastvideo/layers/rotary_embedding.py

@@ -295,6 +295,7 @@ def get_1d_rotary_pos_embed(
     interpolation_factor: float = 1.0,
     dtype: torch.dtype = torch.float32,
     use_real: bool = True,
+    freqs_dtype: torch.dtype | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
     Precompute the frequency tensor for complex exponential (cis) with given dimensions.
@@ -319,12 +320,16 @@ def get_1d_rotary_pos_embed(
     if isinstance(pos, int):
         pos = torch.arange(pos).float()
 
+    # freqs_dtype is an alias for dtype (Diffusers-compatible calling convention).
+    if freqs_dtype is not None:
+        dtype = freqs_dtype
+
     # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
     # has some connection to NTK literature
     if theta_rescale_factor != 1.0:
         theta *= theta_rescale_factor**(dim / (dim - 2))
 
-    freqs = 1.0 / (theta**(torch.arange(0, dim, 2)[:(dim // 2)].to(dtype) / dim))  # [D/2]
+    freqs = 1.0 / (theta**(torch.arange(0, dim, 2, device=pos.device)[:(dim // 2)].to(dtype) / dim))  # [D/2]
     freqs = torch.outer(pos * interpolation_factor, freqs)  # [S, D/2]
     freqs_cos = freqs.cos()  # [S, D/2]
     freqs_sin = freqs.sin()  # [S, D/2]
@@ -445,6 +450,21 @@ def get_nd_rotary_pos_embed(
     return cos, sin
 
 
+_ROTARY_POS_EMBED_CACHE: dict[tuple, tuple[torch.Tensor, torch.Tensor]] = {}
+# Bound the table cache so long-running servers / causal models (which vary
+# start_frame per frame) cannot grow it without limit; entries are large float64
+# tensors. Least-recently-used eviction keeps the active resolution(s) hot while
+# capping memory.
+_ROTARY_POS_EMBED_CACHE_MAXSIZE = 16
+
+
+def _hashable(value: Any) -> Any:
+    """Return a hashable view of a scalar or sequence for use in a cache key."""
+    if isinstance(value, list | tuple):
+        return tuple(value)
+    return value
+
+
 def get_rotary_pos_embed(
     rope_sizes,
     hidden_size,
@@ -495,6 +515,31 @@ def get_rotary_pos_embed(
         sp_rank = 0
         sp_world_size = 1
 
+    # Memoize on every output-affecting argument; the table is constant across
+    # denoising steps, so this avoids recomputing the float64 cos/sin tables.
+    cache_key = (
+        _hashable(rope_sizes),
+        tuple(rope_dim_list),
+        rope_theta,
+        _hashable(theta_rescale_factor),
+        _hashable(interpolation_factor),
+        shard_dim,
+        sp_rank,
+        sp_world_size,
+        dtype,
+        start_frame,
+        use_real,
+    )
+    cached = _ROTARY_POS_EMBED_CACHE.get(cache_key)
+    if cached is not None:
+        # Move to most-recently-used position so the active table is not evicted
+        # when several resolutions / buckets share the process (LRU recency).
+        # Pop with a default: a concurrent eviction between the get() above and
+        # here would otherwise raise KeyError on the hit path.
+        if _ROTARY_POS_EMBED_CACHE.pop(cache_key, None) is not None:
+            _ROTARY_POS_EMBED_CACHE[cache_key] = cached
+        return cached
+
     freqs_cos, freqs_sin = get_nd_rotary_pos_embed(
         rope_dim_list,
         rope_sizes,
@@ -508,6 +553,14 @@ def get_rotary_pos_embed(
         start_frame=start_frame,
         use_real=use_real,
     )
+    # The returned tensors are shared cache entries: callers must never mutate
+    # them in place. Note .to(device) is an identity alias when the tensor is
+    # already on the target device (e.g. CPU runs), so it does NOT guarantee a
+    # copy — treat the tables as read-only and copy before any in-place op.
+    # Reached only on a miss, so evict the least-recently-used entry at capacity.
+    if len(_ROTARY_POS_EMBED_CACHE) >= _ROTARY_POS_EMBED_CACHE_MAXSIZE:
+        _ROTARY_POS_EMBED_CACHE.pop(next(iter(_ROTARY_POS_EMBED_CACHE)))
+    _ROTARY_POS_EMBED_CACHE[cache_key] = (freqs_cos, freqs_sin)
     return freqs_cos, freqs_sin
 
 

fastvideo/models/dits/causal_wanvideo.py

@@ -665,7 +665,8 @@ class CausalWanTransformer3DModel(BaseDiT):
             else:
                 causal_kwargs = {
                     "kv_cache": kv_cache[block_index],
-                    "crossattn_cache": crossattn_cache[block_index],
+                    "crossattn_cache": (crossattn_cache[block_index]
+                                        if crossattn_cache is not None else None),
                     "current_start": current_start,
                     "cache_start": cache_start,
                     "block_mask": self.block_mask,

fastvideo/models/dits/flux.py

@@ -0,0 +1,578 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from contextlib import nullcontext
+from dataclasses import dataclass
+import math
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from fastvideo.layers.rotary_embedding import apply_rotary_emb, get_1d_rotary_pos_embed
+
+from fastvideo.attention import DistributedAttention
+from fastvideo.configs.models import DiTConfig
+from fastvideo.forward_context import get_forward_context, set_forward_context
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.visual_embedding import Timesteps
+from fastvideo.models.dits.base import BaseDiT
+from fastvideo.models.dits.sd3 import (
+    CombinedTimestepTextProjEmbeddings,
+    SD3AdaLayerNormContinuous,
+    SD3AdaLayerNormZero,
+    SD3FeedForward,
+    SD3TextProjection,
+    SD3TimestepEmbedding,
+)
+from fastvideo.platforms import AttentionBackendEnum
+
+
+@dataclass
+class FluxTransformer2DModelOutput:
+    sample: torch.Tensor
+
+
+class FluxPosEmbed(nn.Module):
+    """1D RoPE axes concatenated per Diffusers `FluxPosEmbed`."""
+
+    def __init__(self, theta: int, axes_dim: list[int]) -> None:
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        n_axes = ids.shape[-1]
+        cos_out: list[torch.Tensor] = []
+        sin_out: list[torch.Tensor] = []
+        pos = ids.float()
+        is_mps = ids.device.type == "mps"
+        is_npu = ids.device.type == "npu"
+        freqs_dtype = torch.float32 if (is_mps or is_npu) else torch.float64
+        for i in range(n_axes):
+            cos, sin = get_1d_rotary_pos_embed(
+                self.axes_dim[i],
+                pos[:, i],
+                theta=self.theta,
+                use_real=True,
+                freqs_dtype=freqs_dtype,
+            )
+            cos_out.append(cos)
+            sin_out.append(sin)
+        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
+        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
+        return freqs_cos, freqs_sin
+
+
+class FluxCombinedTimestepGuidanceTextProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim: int, pooled_projection_dim: int) -> None:
+        super().__init__()
+        self.time_proj = Timesteps(
+            num_channels=256,
+            flip_sin_to_cos=True,
+            downscale_freq_shift=0,
+        )
+        self.timestep_embedder = SD3TimestepEmbedding(
+            in_channels=256,
+            time_embed_dim=embedding_dim,
+            act_fn="silu",
+        )
+        self.guidance_embedder = SD3TimestepEmbedding(
+            in_channels=256,
+            time_embed_dim=embedding_dim,
+            act_fn="silu",
+        )
+        self.text_embedder = SD3TextProjection(
+            pooled_projection_dim,
+            embedding_dim,
+            act_fn="silu",
+        )
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        guidance: torch.Tensor,
+        pooled_projection: torch.Tensor,
+    ) -> torch.Tensor:
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=pooled_projection.dtype))
+        guidance_proj = self.time_proj(guidance)
+        guidance_emb = self.guidance_embedder(guidance_proj.to(dtype=pooled_projection.dtype))
+        time_guidance_emb = timesteps_emb + guidance_emb
+        pooled_projections = self.text_embedder(pooled_projection)
+        return time_guidance_emb + pooled_projections
+
+
+class FluxAdaLayerNormZeroSingle(nn.Module):
+    def __init__(self, embedding_dim: int, bias: bool = True) -> None:
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = ReplicatedLinear(embedding_dim, 3 * embedding_dim, bias=bias)
+        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        emb, _ = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa
+
+
+class FluxJointAttention(nn.Module):
+    """Joint attention: text tokens precede image tokens (Diffusers order)."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+    ) -> None:
+        super().__init__()
+        self.heads = num_attention_heads
+        self.head_dim = attention_head_dim
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm_q = nn.RMSNorm(attention_head_dim, eps=1e-6)
+        self.norm_k = nn.RMSNorm(attention_head_dim, eps=1e-6)
+        self.norm_added_q = nn.RMSNorm(attention_head_dim, eps=1e-6)
+        self.norm_added_k = nn.RMSNorm(attention_head_dim, eps=1e-6)
+
+        self.to_q = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.to_k = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.to_v = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.add_q_proj = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.add_k_proj = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.add_v_proj = ReplicatedLinear(dim, self.inner_dim, bias=True)
+
+        self.to_out = nn.ModuleList(
+            [
+                ReplicatedLinear(self.inner_dim, dim, bias=True),
+                nn.Dropout(0.0),
+            ]
+        )
+        self.to_add_out = ReplicatedLinear(self.inner_dim, dim, bias=True)
+
+        self.attn = DistributedAttention(
+            num_heads=num_attention_heads,
+            head_size=attention_head_dim,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        image_rotary_emb: tuple[torch.Tensor, torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size = hidden_states.shape[0]
+        text_seq_len = encoder_hidden_states.shape[1]
+        img_seq_len = hidden_states.shape[1]
+
+        q, _ = self.to_q(hidden_states)
+        k, _ = self.to_k(hidden_states)
+        v, _ = self.to_v(hidden_states)
+        q = q.view(batch_size, img_seq_len, self.heads, self.head_dim)
+        k = k.view(batch_size, img_seq_len, self.heads, self.head_dim)
+        v = v.view(batch_size, img_seq_len, self.heads, self.head_dim)
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+
+        enc_q, _ = self.add_q_proj(encoder_hidden_states)
+        enc_k, _ = self.add_k_proj(encoder_hidden_states)
+        enc_v, _ = self.add_v_proj(encoder_hidden_states)
+        enc_q = enc_q.view(batch_size, text_seq_len, self.heads, self.head_dim)
+        enc_k = enc_k.view(batch_size, text_seq_len, self.heads, self.head_dim)
+        enc_v = enc_v.view(batch_size, text_seq_len, self.heads, self.head_dim)
+        enc_q = self.norm_added_q(enc_q)
+        enc_k = self.norm_added_k(enc_k)
+
+        q = torch.cat([enc_q, q], dim=1)
+        k = torch.cat([enc_k, k], dim=1)
+        v = torch.cat([enc_v, v], dim=1)
+
+        q = apply_rotary_emb(q, image_rotary_emb, sequence_dim=1)
+        k = apply_rotary_emb(k, image_rotary_emb, sequence_dim=1)
+
+        joint_out, _ = self.attn(q, k, v)
+        joint_out = joint_out.reshape(batch_size, text_seq_len + img_seq_len, self.inner_dim)
+
+        enc_out = joint_out[:, :text_seq_len]
+        img_out = joint_out[:, text_seq_len:]
+
+        img_out, _ = self.to_out[0](img_out)
+        img_out = self.to_out[1](img_out)
+        enc_out, _ = self.to_add_out(enc_out)
+        return img_out, enc_out
+
+
+class FluxSingleStreamAttention(nn.Module):
+    """Self-attention on concatenated text+image sequence (single blocks)."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+    ) -> None:
+        super().__init__()
+        self.heads = num_attention_heads
+        self.head_dim = attention_head_dim
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm_q = nn.RMSNorm(attention_head_dim, eps=1e-6)
+        self.norm_k = nn.RMSNorm(attention_head_dim, eps=1e-6)
+        self.to_q = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.to_k = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.to_v = ReplicatedLinear(dim, self.inner_dim, bias=True)
+        self.attn = DistributedAttention(
+            num_heads=num_attention_heads,
+            head_size=attention_head_dim,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        image_rotary_emb: tuple[torch.Tensor, torch.Tensor],
+    ) -> torch.Tensor:
+        batch_size, seq_len, _ = hidden_states.shape
+        q, _ = self.to_q(hidden_states)
+        k, _ = self.to_k(hidden_states)
+        v, _ = self.to_v(hidden_states)
+        q = q.view(batch_size, seq_len, self.heads, self.head_dim)
+        k = k.view(batch_size, seq_len, self.heads, self.head_dim)
+        v = v.view(batch_size, seq_len, self.heads, self.head_dim)
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+        q = apply_rotary_emb(q, image_rotary_emb, sequence_dim=1)
+        k = apply_rotary_emb(k, image_rotary_emb, sequence_dim=1)
+        out, _ = self.attn(q, k, v)
+        return out.reshape(batch_size, seq_len, self.inner_dim)
+
+
+class FluxTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+    ) -> None:
+        super().__init__()
+        self.norm1 = SD3AdaLayerNormZero(dim)
+        self.norm1_context = SD3AdaLayerNormZero(dim)
+        self.attn = FluxJointAttention(
+            dim=dim,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=attention_head_dim,
+            supported_attention_backends=supported_attention_backends,
+        )
+        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = SD3FeedForward(
+            dim=dim,
+            dim_out=dim,
+            activation_fn="gelu-approximate",
+        )
+        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_context = SD3FeedForward(
+            dim=dim,
+            dim_out=dim,
+            activation_fn="gelu-approximate",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: tuple[torch.Tensor, torch.Tensor],
+        joint_attention_kwargs: dict[str, Any] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        del joint_attention_kwargs
+        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
+        (norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp) = self.norm1_context(
+            encoder_hidden_states, emb=temb
+        )
+
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = hidden_states + attn_output
+
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        ff_output = self.ff(norm_hidden_states)
+        hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
+
+        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
+        encoder_hidden_states = encoder_hidden_states + context_attn_output
+
+        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+        context_ff_output = self.ff_context(norm_encoder_hidden_states)
+        encoder_hidden_states = encoder_hidden_states + (c_gate_mlp.unsqueeze(1) * context_ff_output)
+        if encoder_hidden_states.dtype == torch.float16:
+            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
+
+        return encoder_hidden_states, hidden_states
+
+
+class FluxSingleTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_ratio: float = 4.0,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+    ) -> None:
+        super().__init__()
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.norm = FluxAdaLayerNormZeroSingle(dim)
+        self.proj_mlp = ReplicatedLinear(dim, mlp_hidden_dim, bias=True)
+        self.act_mlp = nn.GELU(approximate="tanh")
+        self.proj_out = ReplicatedLinear(dim + mlp_hidden_dim, dim, bias=True)
+        self.attn = FluxSingleStreamAttention(
+            dim=dim,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=attention_head_dim,
+            supported_attention_backends=supported_attention_backends,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: tuple[torch.Tensor, torch.Tensor],
+        joint_attention_kwargs: dict[str, Any] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        del joint_attention_kwargs
+        text_seq_len = encoder_hidden_states.shape[1]
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+        residual = hidden_states
+        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states)[0])
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        gate = gate.unsqueeze(1)
+        hidden_states = gate * self.proj_out(hidden_states)[0]
+        hidden_states = residual + hidden_states
+        if hidden_states.dtype == torch.float16:
+            hidden_states = hidden_states.clip(-65504, 65504)
+        encoder_hidden_states = hidden_states[:, :text_seq_len]
+        hidden_states = hidden_states[:, text_seq_len:]
+        return encoder_hidden_states, hidden_states
+
+
+class FluxTransformer2DModel(BaseDiT):
+    """FastVideo FLUX transformer; load Diffusers FLUX safetensors 1:1."""
+
+    _fsdp_shard_conditions = [
+        lambda n, m: (n.startswith("transformer_blocks.") or n.startswith("single_transformer_blocks."))
+        and n.split(".")[-1].isdigit(),
+    ]
+    _compile_conditions = _fsdp_shard_conditions
+    # HF weight names already match this module layout (cf. SGLang regex maps).
+    param_names_mapping: dict[str, Any] = {}
+    reverse_param_names_mapping: dict[str, Any] = {}
+    lora_param_names_mapping: dict[str, Any] = {}
+    _supported_attention_backends = (
+        AttentionBackendEnum.FLASH_ATTN,
+        AttentionBackendEnum.TORCH_SDPA,
+    )
+
+    def __init__(self, config: DiTConfig, hf_config: dict[str, Any], **kwargs) -> None:
+        del kwargs
+        super().__init__(config=config, hf_config=hf_config)
+        self.fastvideo_config = config
+        self.hf_config = hf_config
+        arch = config.arch_config
+
+        out_ch = arch.out_channels
+        self.out_channels = out_ch if out_ch is not None else arch.in_channels
+        self.inner_dim = arch.num_attention_heads * arch.attention_head_dim
+        self.hidden_size = self.inner_dim
+        self.num_attention_heads = arch.num_attention_heads
+        self.num_channels_latents = arch.in_channels
+
+        axes_list = list(arch.axes_dims_rope)
+        self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_list)
+        if arch.guidance_embeds:
+            self.time_text_embed = FluxCombinedTimestepGuidanceTextProjEmbeddings(
+                embedding_dim=self.inner_dim,
+                pooled_projection_dim=arch.pooled_projection_dim,
+            )
+        else:
+            self.time_text_embed = CombinedTimestepTextProjEmbeddings(
+                embedding_dim=self.inner_dim,
+                pooled_projection_dim=arch.pooled_projection_dim,
+            )
+        self.context_embedder = ReplicatedLinear(arch.joint_attention_dim, self.inner_dim)
+        self.x_embedder = ReplicatedLinear(arch.in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=arch.num_attention_heads,
+                    attention_head_dim=arch.attention_head_dim,
+                    supported_attention_backends=self._supported_attention_backends,
+                )
+                for _ in range(arch.num_layers)
+            ]
+        )
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=arch.num_attention_heads,
+                    attention_head_dim=arch.attention_head_dim,
+                    supported_attention_backends=self._supported_attention_backends,
+                )
+                for _ in range(arch.num_single_layers)
+            ]
+        )
+
+        self.norm_out = SD3AdaLayerNormContinuous(
+            self.inner_dim,
+            self.inner_dim,
+            elementwise_affine=False,
+            eps=1e-6,
+            bias=True,
+            norm_type="layer_norm",
+        )
+        self.proj_out = ReplicatedLinear(
+            self.inner_dim,
+            arch.patch_size * arch.patch_size * self.out_channels,
+            bias=True,
+        )
+        self.gradient_checkpointing = False
+        self.__post_init__()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None = None,
+        pooled_projections: torch.Tensor | None = None,
+        timestep: torch.LongTensor | torch.Tensor | None = None,
+        img_ids: torch.Tensor | None = None,
+        txt_ids: torch.Tensor | None = None,
+        guidance: torch.Tensor | None = None,
+        joint_attention_kwargs: dict[str, Any] | None = None,
+        return_dict: bool = True,
+        controlnet_block_samples: Any | None = None,
+        controlnet_single_block_samples: Any | None = None,
+        controlnet_blocks_repeat: bool = False,
+        **kwargs: Any,
+    ) -> FluxTransformer2DModelOutput | tuple[torch.Tensor, ...]:
+        del kwargs
+        if encoder_hidden_states is None:
+            raise ValueError("encoder_hidden_states must be provided")
+        if pooled_projections is None:
+            raise ValueError("pooled_projections must be provided")
+        if timestep is None:
+            raise ValueError("timestep must be provided")
+        if img_ids is None or txt_ids is None:
+            raise ValueError("img_ids and txt_ids must be provided")
+
+        arch = self.fastvideo_config.arch_config
+        if arch.guidance_embeds and guidance is None:
+            raise ValueError("guidance must be provided when guidance_embeds=True")
+
+        if timestep.dim() == 0:
+            timestep = timestep[None]
+        if timestep.dim() > 1:
+            timestep = timestep.reshape(-1)
+        if timestep.shape[0] == 1 and hidden_states.shape[0] > 1:
+            timestep = timestep.expand(hidden_states.shape[0])
+
+        try:
+            get_forward_context()
+            forward_context = nullcontext()
+        except AssertionError:
+            if timestep.numel() == 0:
+                ts0 = 0
+            elif torch.is_floating_point(timestep):
+                ts0 = int(round(timestep[0].item() * 1000))
+            else:
+                ts0 = int(timestep[0].item())
+            forward_context = set_forward_context(current_timestep=ts0, attn_metadata=None)
+
+        with forward_context:
+            hidden_states, _ = self.x_embedder(hidden_states)
+
+            ts = timestep.to(hidden_states.dtype) * 1000
+            g = None if guidance is None else guidance.to(hidden_states.dtype) * 1000
+
+            if arch.guidance_embeds:
+                assert g is not None
+                temb = self.time_text_embed(ts, g, pooled_projections)
+            else:
+                temb = self.time_text_embed(timestep=ts, pooled_projection=pooled_projections)
+
+            encoder_hidden_states, _ = self.context_embedder(encoder_hidden_states)
+
+            if txt_ids.ndim == 3:
+                txt_ids = txt_ids[0]
+            if img_ids.ndim == 3:
+                img_ids = img_ids[0]
+
+            ids = torch.cat((txt_ids, img_ids), dim=0)
+            image_rotary_emb = self.pos_embed(ids)
+
+            jkwargs = joint_attention_kwargs or {}
+
+            for idx, block in enumerate(self.transformer_blocks):
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=jkwargs,
+                )
+                if controlnet_block_samples:
+                    interval = len(self.transformer_blocks) / len(controlnet_block_samples)
+                    interval = int(math.ceil(interval))
+                    if controlnet_blocks_repeat:
+                        hidden_states = hidden_states + controlnet_block_samples[idx % len(controlnet_block_samples)]
+                    else:
+                        hidden_states = hidden_states + controlnet_block_samples[idx // interval]
+
+            for idx, block in enumerate(self.single_transformer_blocks):
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=jkwargs,
+                )
+                if controlnet_single_block_samples:
+                    interval = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                    interval = int(math.ceil(interval))
+                    hidden_states = hidden_states + controlnet_single_block_samples[idx // interval]
+
+            hidden_states = self.norm_out(hidden_states, temb)
+            output, _ = self.proj_out(hidden_states)
+
+            if not return_dict:
+                return (output,)
+            return FluxTransformer2DModelOutput(sample=output)
+
+
+EntryClass = FluxTransformer2DModel

fastvideo/models/dits/glm_image.py

@@ -0,0 +1,776 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from fastvideo.layers.mlp import MLP
+
+from fastvideo.attention import LocalAttention
+from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
+from fastvideo.layers.layernorm import ScaleResidualLayerNormScaleShift
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.rotary_embedding import _apply_rotary_emb
+from fastvideo.layers.visual_embedding import Timesteps
+from fastvideo.models.dits.base import BaseDiT
+from fastvideo.platforms import AttentionBackendEnum
+
+
+class GlmImageLayerKVCache:
+
+    def __init__(self):
+        self.k_cache = None
+        self.v_cache = None
+        self.mode: Optional[str] = None
+
+    def store(self, k: torch.Tensor, v: torch.Tensor):
+        # Append along seq (dim=1).
+        if self.k_cache is None:
+            self.k_cache = k
+            self.v_cache = v
+        else:
+            self.k_cache = torch.cat([self.k_cache, k], dim=1)
+            self.v_cache = torch.cat([self.v_cache, v], dim=1)
+
+    def get(self):
+        return self.k_cache, self.v_cache
+
+    def clear(self):
+        self.k_cache = None
+        self.v_cache = None
+        self.mode = None
+
+
+class GlmImageKVCache:
+
+    def __init__(self, num_layers: int):
+        self.num_layers = num_layers
+        self.caches = [GlmImageLayerKVCache() for _ in range(num_layers)]
+
+    def __getitem__(self, layer_idx: int) -> GlmImageLayerKVCache:
+        return self.caches[layer_idx]
+
+    def set_mode(self, mode: Optional[str]):
+        if mode is not None and mode not in ["write", "read", "skip"]:
+            raise ValueError(
+                f"Invalid mode: {mode}, must be one of 'write', 'read', 'skip'"
+            )
+        for cache in self.caches:
+            cache.mode = mode
+
+    def clear(self):
+        for cache in self.caches:
+            cache.clear()
+
+
+# =============================================================================
+# Timestep and Text Projection
+# =============================================================================
+class GlmImageTimestepEmbedding(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+    ):
+        super().__init__()
+        if out_dim is None:
+            out_dim = time_embed_dim
+        self.linear_1 = ReplicatedLinear(in_channels, time_embed_dim, bias=True)
+        if act_fn == "silu":
+            self.act = nn.SiLU()
+        elif act_fn == "gelu":
+            self.act = nn.GELU(approximate="tanh")
+        else:
+            self.act = nn.SiLU()
+        self.linear_2 = ReplicatedLinear(time_embed_dim, out_dim, bias=True)
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        sample, _ = self.linear_1(sample)
+        sample = self.act(sample)
+        sample, _ = self.linear_2(sample)
+        return sample
+
+
+class GlmImageTextProjection(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_size: int,
+        out_features: int = None,
+        act_fn: str = "silu",
+    ):
+        super().__init__()
+        if out_features is None:
+            out_features = hidden_size
+        self.linear_1 = ReplicatedLinear(in_features, hidden_size, bias=True)
+        if act_fn == "silu":
+            self.act_1 = nn.SiLU()
+        elif act_fn == "gelu_tanh":
+            self.act_1 = nn.GELU(approximate="tanh")
+        else:
+            self.act_1 = nn.SiLU()
+        self.linear_2 = ReplicatedLinear(hidden_size, out_features, bias=True)
+
+    def forward(self, caption: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states, _ = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class GlmImageCombinedTimestepSizeEmbeddings(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        condition_dim: int,
+        pooled_projection_dim: int,
+        timesteps_dim: int = 256,
+    ):
+        super().__init__()
+
+        self.time_proj = Timesteps(
+            num_channels=timesteps_dim, flip_sin_to_cos=True, downscale_freq_shift=0
+        )
+        self.condition_proj = Timesteps(
+            num_channels=condition_dim, flip_sin_to_cos=True, downscale_freq_shift=0
+        )
+        self.timestep_embedder = GlmImageTimestepEmbedding(
+            in_channels=timesteps_dim, time_embed_dim=embedding_dim
+        )
+        self.condition_embedder = GlmImageTextProjection(
+            pooled_projection_dim, embedding_dim, act_fn="silu"
+        )
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        target_size: torch.Tensor,
+        crop_coords: torch.Tensor,
+        hidden_dtype: torch.dtype,
+    ) -> torch.Tensor:
+        timesteps_proj = self.time_proj(timestep)
+
+        crop_coords_proj = self.condition_proj(crop_coords.flatten()).view(
+            crop_coords.size(0), -1
+        )
+        target_size_proj = self.condition_proj(target_size.flatten()).view(
+            target_size.size(0), -1
+        )
+
+        condition_proj = torch.cat([crop_coords_proj, target_size_proj], dim=1)
+
+        timesteps_emb = self.timestep_embedder(
+            timesteps_proj.to(dtype=hidden_dtype)
+        )
+        condition_emb = self.condition_embedder(
+            condition_proj.to(dtype=hidden_dtype)
+        )
+
+        conditioning = timesteps_emb + condition_emb
+        return conditioning
+
+
+# =============================================================================
+# Image Projector
+# =============================================================================
+class GlmImageImageProjector(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int = 16,
+        hidden_size: int = 2560,
+        patch_size: int = 2,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.proj = nn.Linear(in_channels * patch_size**2, hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, channel, height, width = hidden_states.shape
+        post_patch_height = height // self.patch_size
+        post_patch_width = width // self.patch_size
+
+        hidden_states = hidden_states.reshape(
+            batch_size,
+            channel,
+            post_patch_height,
+            self.patch_size,
+            post_patch_width,
+            self.patch_size,
+        )
+        hidden_states = (
+            hidden_states.permute(0, 2, 4, 1, 3, 5).flatten(3, 5).flatten(1, 2)
+        )
+        hidden_states = self.proj(hidden_states)
+
+        return hidden_states
+
+
+# =============================================================================
+# AdaLayerNorm
+# =============================================================================
+class GlmImageAdaLayerNormZero(nn.Module):
+
+    def __init__(self, embedding_dim: int, dim: int) -> None:
+        super().__init__()
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
+        self.norm_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
+        self.linear = ReplicatedLinear(embedding_dim, 12 * dim, bias=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+    ) -> Tuple[torch.Tensor, ...]:
+        dtype = hidden_states.dtype
+        norm_hidden_states = self.norm(hidden_states).to(dtype=dtype)
+        norm_encoder_hidden_states = self.norm_context(encoder_hidden_states).to(
+            dtype=dtype
+        )
+
+        emb, _ = self.linear(temb)
+        (
+            shift_msa,
+            c_shift_msa,
+            scale_msa,
+            c_scale_msa,
+            gate_msa,
+            c_gate_msa,
+            shift_mlp,
+            c_shift_mlp,
+            scale_mlp,
+            c_scale_mlp,
+            gate_mlp,
+            c_gate_mlp,
+        ) = emb.chunk(12, dim=1)
+
+        hidden_states = norm_hidden_states * (
+            1 + scale_msa.unsqueeze(1)
+        ) + shift_msa.unsqueeze(1)
+        encoder_hidden_states = norm_encoder_hidden_states * (
+            1 + c_scale_msa.unsqueeze(1)
+        ) + c_shift_msa.unsqueeze(1)
+
+        return (
+            hidden_states,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            encoder_hidden_states,
+            c_gate_msa,
+            c_shift_mlp,
+            c_scale_mlp,
+            c_gate_mlp,
+        )
+
+
+# =============================================================================
+# Attention
+# =============================================================================
+class GlmImageAttention(nn.Module):
+
+    def __init__(
+        self,
+        query_dim: int,
+        heads: int,
+        dim_head: int,
+        out_dim: int,
+        bias: bool = True,
+        qk_norm: str = "layer_norm",
+        elementwise_affine: bool = False,
+        eps: float = 1e-5,
+        supported_attention_backends: set[AttentionBackendEnum] | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.heads = out_dim // dim_head if out_dim is not None else heads
+        self.num_kv_heads = self.heads
+        self.dim_head = dim_head
+        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
+        self.inner_kv_dim = self.inner_dim
+        self.out_dim = out_dim if out_dim is not None else query_dim
+
+        self.to_q = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
+        self.to_k = ReplicatedLinear(query_dim, self.inner_kv_dim, bias=bias)
+        self.to_v = ReplicatedLinear(query_dim, self.inner_kv_dim, bias=bias)
+
+        self.to_out = nn.ModuleList(
+            [ReplicatedLinear(self.inner_dim, self.out_dim, bias=True)]
+        )
+
+        if qk_norm is None:
+            self.norm_q = None
+            self.norm_k = None
+        elif qk_norm == "layer_norm":
+            self.norm_q = nn.LayerNorm(
+                dim_head, eps=eps, elementwise_affine=elementwise_affine
+            )
+            self.norm_k = nn.LayerNorm(
+                dim_head, eps=eps, elementwise_affine=elementwise_affine
+            )
+        else:
+            raise ValueError(f"unknown qk_norm: {qk_norm}")
+
+        self.attn = LocalAttention(
+            num_heads=self.heads,
+            head_size=dim_head,
+            num_kv_heads=self.heads,
+            softmax_scale=None,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        kv_cache: Optional[GlmImageLayerKVCache] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        dtype = encoder_hidden_states.dtype
+
+        batch_size, text_seq_length, embed_dim = encoder_hidden_states.shape
+        batch_size, image_seq_length, embed_dim = hidden_states.shape
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        # 1. QKV projections
+        query, _ = self.to_q(hidden_states)
+        key, _ = self.to_k(hidden_states)
+        value, _ = self.to_v(hidden_states)
+
+        query = query.unflatten(2, (self.heads, -1))
+        key = key.unflatten(2, (self.heads, -1))
+        value = value.unflatten(2, (self.heads, -1))
+
+        # 2. QK normalization
+        if self.norm_q is not None:
+            query = self.norm_q(query).to(dtype=dtype)
+        if self.norm_k is not None:
+            key = self.norm_k(key).to(dtype=dtype)
+
+        # 3. Rotational positional embeddings applied to latent stream
+        if image_rotary_emb is not None:
+            cos, sin = image_rotary_emb
+
+            query[:, text_seq_length:, :, :] = _apply_rotary_emb(
+                query[:, text_seq_length:, :, :], cos, sin, is_neox_style=True
+            )
+            key[:, text_seq_length:, :, :] = _apply_rotary_emb(
+                key[:, text_seq_length:, :, :], cos, sin, is_neox_style=True
+            )
+
+        # 4. KV Cache handling
+        if kv_cache is not None:
+            if kv_cache.mode == "write":
+                kv_cache.store(key, value)
+            elif kv_cache.mode == "read":
+                # Prepend cached condition k/v along seq (dim=1).
+                k_cache, v_cache = kv_cache.get()
+                key = torch.cat([k_cache, key], dim=1) if k_cache is not None else key
+                value = (
+                    torch.cat([v_cache, value], dim=1) if v_cache is not None else value
+                )
+            elif kv_cache.mode == "skip":
+                pass
+
+        hidden_states = self.attn(query, key, value)
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # 6. Output projection
+        hidden_states, _ = self.to_out[0](hidden_states)
+
+        encoder_hidden_states, hidden_states = hidden_states.split(
+            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
+        )
+        return hidden_states, encoder_hidden_states
+
+
+# =============================================================================
+# Transformer Block
+# =============================================================================
+class GlmImageTransformerBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int = 2560,
+        num_attention_heads: int = 64,
+        attention_head_dim: int = 40,
+        time_embed_dim: int = 512,
+        supported_attention_backends: set[AttentionBackendEnum] | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        # 1. Attention
+        self.norm1 = GlmImageAdaLayerNormZero(time_embed_dim, dim)
+
+        self.attn1 = GlmImageAttention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            out_dim=dim,
+            bias=True,
+            qk_norm="layer_norm",
+            elementwise_affine=False,
+            eps=1e-5,
+            supported_attention_backends=supported_attention_backends,
+            prefix=f"{prefix}.attn1",
+        )
+
+        # 2. Feedforward with fused ScaleResidualLayerNorm
+        self.norm2 = ScaleResidualLayerNormScaleShift(
+            dim, norm_type="layer", eps=1e-5, elementwise_affine=False
+        )
+        self.norm2_context = ScaleResidualLayerNormScaleShift(
+            dim, norm_type="layer", eps=1e-5, elementwise_affine=False
+        )
+        self.ff = MLP(input_dim=dim, mlp_hidden_dim=dim * 4, output_dim=dim, act_type="gelu_pytorch_tanh")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[
+            Union[
+                Tuple[torch.Tensor, torch.Tensor],
+                List[Tuple[torch.Tensor, torch.Tensor]],
+            ]
+        ] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        kv_cache: Optional[GlmImageLayerKVCache] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 1. Timestep conditioning
+        (
+            norm_hidden_states,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            norm_encoder_hidden_states,
+            c_gate_msa,
+            c_shift_mlp,
+            c_scale_mlp,
+            c_gate_mlp,
+        ) = self.norm1(hidden_states, encoder_hidden_states, temb)
+
+        # 2. Attention
+        if attention_kwargs is None:
+            attention_kwargs = {}
+
+        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            kv_cache=kv_cache,
+            **attention_kwargs,
+        )
+
+        # 3. Feedforward (fused residual + norm + scale/shift)
+        norm_hidden_states, hidden_states = self.norm2(
+            hidden_states,
+            attn_hidden_states,
+            gate_msa.unsqueeze(1),
+            shift_mlp.unsqueeze(1),
+            scale_mlp.unsqueeze(1),
+        )
+        norm_encoder_hidden_states, encoder_hidden_states = self.norm2_context(
+            encoder_hidden_states,
+            attn_encoder_hidden_states,
+            c_gate_msa.unsqueeze(1),
+            c_shift_mlp.unsqueeze(1),
+            c_scale_mlp.unsqueeze(1),
+        )
+
+        ff_output = self.ff(norm_hidden_states)
+        ff_output_context = self.ff(norm_encoder_hidden_states)
+        hidden_states = hidden_states + ff_output * gate_mlp.unsqueeze(1)
+        encoder_hidden_states = (
+            encoder_hidden_states + ff_output_context * c_gate_mlp.unsqueeze(1)
+        )
+
+        return hidden_states, encoder_hidden_states
+
+
+# =============================================================================
+# Rotary Positional Embedding
+# =============================================================================
+class GlmImageRotaryPosEmbed(nn.Module):
+
+    def __init__(self, dim: int, patch_size: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.patch_size = patch_size
+        self.theta = theta
+        self._cache_key: tuple | None = None
+        self._cache_value: tuple[torch.Tensor, torch.Tensor] | None = None
+
+    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        _, _, raw_h, raw_w = hidden_states.shape
+        height = raw_h // self.patch_size
+        width = raw_w // self.patch_size
+        device = hidden_states.device
+
+        cache_key = (height, width, device.type,
+                     device.index if device.index is not None else -1)
+        if self._cache_key == cache_key and self._cache_value is not None:
+            return self._cache_value
+
+        dim_h, dim_w = self.dim // 2, self.dim // 2
+        h_inv_freq = 1.0 / (
+            self.theta
+            ** (
+                torch.arange(0, dim_h, 2, dtype=torch.float32, device=device)[
+                    : (dim_h // 2)
+                ]
+                / dim_h
+            )
+        )
+        w_inv_freq = 1.0 / (
+            self.theta
+            ** (
+                torch.arange(0, dim_w, 2, dtype=torch.float32, device=device)[
+                    : (dim_w // 2)
+                ]
+                / dim_w
+            )
+        )
+        h_seq = torch.arange(height, device=device)
+        w_seq = torch.arange(width, device=device)
+        freqs_h = torch.outer(h_seq, h_inv_freq).unsqueeze(1).expand(height, width, -1)
+        freqs_w = torch.outer(w_seq, w_inv_freq).unsqueeze(0).expand(height, width, -1)
+
+        freqs = torch.cat([freqs_h, freqs_w], dim=-1).reshape(height * width, -1)
+        result = (freqs.cos(), freqs.sin())
+        self._cache_key = cache_key
+        self._cache_value = result
+        return result
+
+
+# =============================================================================
+# Final AdaLayerNorm
+# =============================================================================
+class GlmImageAdaLayerNormContinuous(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        conditioning_embedding_dim: int,
+        elementwise_affine: bool = True,
+        eps: float = 1e-5,
+        bias: bool = True,
+        norm_type: str = "layer_norm",
+    ):
+        super().__init__()
+        self.linear = nn.Linear(
+            conditioning_embedding_dim, embedding_dim * 2, bias=bias
+        )
+        if norm_type == "layer_norm":
+            self.norm = nn.LayerNorm(embedding_dim, eps, elementwise_affine, bias)
+        elif norm_type == "rms_norm":
+            self.norm = nn.RMSNorm(embedding_dim, eps, elementwise_affine)
+        else:
+            raise ValueError(f"unknown norm_type {norm_type}")
+
+    def forward(
+        self, x: torch.Tensor, conditioning_embedding: torch.Tensor
+    ) -> torch.Tensor:
+        emb = self.linear(conditioning_embedding.to(x.dtype))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
+# =============================================================================
+# Main Model
+# =============================================================================
+class GlmImageTransformer2DModel(BaseDiT):
+
+    _fsdp_shard_conditions = GlmImageDiTConfig().arch_config._fsdp_shard_conditions
+    _compile_conditions = GlmImageDiTConfig().arch_config._compile_conditions
+    _supported_attention_backends = (
+        AttentionBackendEnum.FLASH_ATTN,
+        AttentionBackendEnum.TORCH_SDPA,
+    )
+
+    param_names_mapping = GlmImageDiTConfig().arch_config.param_names_mapping
+    reverse_param_names_mapping = {}
+    lora_param_names_mapping = {}
+
+    def __init__(
+        self,
+        config: GlmImageDiTConfig,
+        hf_config: dict[str, Any],
+    ):
+        super().__init__(config=config, hf_config=hf_config)
+
+        arch_config = config.arch_config
+
+        self.in_channels = arch_config.in_channels
+        self.out_channels = arch_config.out_channels
+        self.patch_size = arch_config.patch_size
+        self.num_layers = arch_config.num_layers
+        self.attention_head_dim = arch_config.attention_head_dim
+        self.num_attention_heads = arch_config.num_attention_heads
+        self.text_embed_dim = arch_config.text_embed_dim
+        self.time_embed_dim = arch_config.time_embed_dim
+
+        # GlmImage uses 2 additional SDXL-like conditions - target_size, crop_coords
+        # Each of these are sincos embeddings of shape 2 * condition_dim
+        pooled_projection_dim = 2 * 2 * arch_config.condition_dim
+        inner_dim = arch_config.num_attention_heads * arch_config.attention_head_dim
+
+        self.hidden_size = inner_dim
+        self.num_channels_latents = arch_config.out_channels
+
+        # 1. RoPE
+        self.rotary_emb = GlmImageRotaryPosEmbed(
+            arch_config.attention_head_dim, arch_config.patch_size, theta=10000.0
+        )
+
+        # 2. Patch & Text-timestep embedding
+        self.image_projector = GlmImageImageProjector(
+            arch_config.in_channels, inner_dim, arch_config.patch_size
+        )
+        self.glyph_projector = MLP(
+            input_dim=arch_config.text_embed_dim,
+            mlp_hidden_dim=inner_dim,
+            output_dim=inner_dim,
+            act_type="gelu",
+        )
+        self.prior_token_embedding = nn.Embedding(
+            arch_config.prior_vq_quantizer_codebook_size, inner_dim
+        )
+        self.prior_projector = MLP(
+            input_dim=inner_dim,
+            mlp_hidden_dim=inner_dim,
+            output_dim=inner_dim,
+            act_type="silu",
+        )
+
+        self.time_condition_embed = GlmImageCombinedTimestepSizeEmbeddings(
+            embedding_dim=arch_config.time_embed_dim,
+            condition_dim=arch_config.condition_dim,
+            pooled_projection_dim=pooled_projection_dim,
+            timesteps_dim=arch_config.time_embed_dim,
+        )
+
+        # 3. Transformer blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                GlmImageTransformerBlock(
+                    inner_dim,
+                    arch_config.num_attention_heads,
+                    arch_config.attention_head_dim,
+                    arch_config.time_embed_dim,
+                    supported_attention_backends=self._supported_attention_backends,
+                    prefix=f"transformer_blocks.{i}",
+                )
+                for i in range(arch_config.num_layers)
+            ]
+        )
+
+        # 4. Output projection
+        self.norm_out = GlmImageAdaLayerNormContinuous(
+            inner_dim, arch_config.time_embed_dim, elementwise_affine=False
+        )
+        self.proj_out = nn.Linear(
+            inner_dim,
+            arch_config.patch_size * arch_config.patch_size * arch_config.out_channels,
+            bias=True,
+        )
+
+        self.gradient_checkpointing = False
+
+        self.__post_init__()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        prior_token_id: torch.Tensor,
+        prior_token_drop: torch.Tensor,
+        timestep: torch.LongTensor,
+        target_size: torch.Tensor,
+        crop_coords: torch.Tensor,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        kv_caches: Optional[GlmImageKVCache] = None,
+        kv_caches_mode: Optional[str] = None,
+        freqs_cis: Optional[
+            Union[
+                Tuple[torch.Tensor, torch.Tensor],
+                List[Tuple[torch.Tensor, torch.Tensor]],
+            ]
+        ] = None,
+        guidance: torch.Tensor = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        if kv_caches is not None:
+            kv_caches.set_mode(kv_caches_mode)
+
+        batch_size, num_channels, height, width = hidden_states.shape
+
+        if isinstance(encoder_hidden_states, list):
+            encoder_hidden_states = encoder_hidden_states[0]
+
+        # 1. RoPE
+        image_rotary_emb = freqs_cis
+        if image_rotary_emb is None:
+            image_rotary_emb = self.rotary_emb(hidden_states)
+
+        # 2. Patch & Timestep embeddings
+        p = self.patch_size
+        post_patch_height = height // p
+        post_patch_width = width // p
+
+        hidden_states = self.image_projector(hidden_states)
+        encoder_hidden_states = self.glyph_projector(encoder_hidden_states)
+        prior_embedding = self.prior_token_embedding(prior_token_id)
+        # Zero dropped priors by multiply: boolean indexing + .any() syncs each step.
+        keep = (~prior_token_drop).to(device=prior_embedding.device, dtype=prior_embedding.dtype)
+        while keep.dim() < prior_embedding.dim():
+            keep = keep.unsqueeze(-1)
+        prior_embedding = prior_embedding * keep
+        prior_hidden_states = self.prior_projector(prior_embedding)
+        hidden_states = hidden_states + prior_hidden_states
+
+        temb = self.time_condition_embed(
+            timestep, target_size, crop_coords, hidden_states.dtype
+        )
+        temb = F.silu(temb)
+
+        # 3. Transformer blocks
+        for idx, block in enumerate(self.transformer_blocks):
+            hidden_states, encoder_hidden_states = block(
+                hidden_states,
+                encoder_hidden_states,
+                temb,
+                image_rotary_emb,
+                attention_kwargs,
+                kv_cache=kv_caches[idx] if kv_caches is not None else None,
+            )
+
+        # 4. Output norm & projection
+        hidden_states = self.norm_out(hidden_states, temb)
+        hidden_states = self.proj_out(hidden_states)
+
+        # 5. Unpatchify
+        hidden_states = hidden_states.reshape(
+            batch_size, post_patch_height, post_patch_width, -1, p, p
+        )
+        output = hidden_states.permute(0, 3, 1, 4, 2, 5).flatten(4, 5).flatten(2, 3)
+
+        return output.float()
+
+
+EntryClass = GlmImageTransformer2DModel

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.flash_attn import FlashAttnMetadataBuilder
-            attn_metadata = FlashAttnMetadataBuilder().build(
+            from fastvideo.attention.backends.sdpa import SDPAMetadataBuilder
+            attn_metadata = SDPAMetadataBuilder().build(
                 current_timestep=0,
                 attn_mask=mask,
             )

fastvideo/models/dits/hyworld/hyworld.py

@@ -173,8 +173,11 @@ class HYWorldDoubleStreamBlock(MMDoubleStreamBlock):
         img_v_prope = img_v_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
         # end hyworld
 
-        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
-        attn_metadata = FlashAttnMetadataBuilder().build(
+        # 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(
             current_timestep=0,
             attn_mask=encoder_attention_mask,
         )
@@ -192,14 +195,9 @@ class HYWorldDoubleStreamBlock(MMDoubleStreamBlock):
             )
         
         # begin hyworld
-        # attention with prope
-        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
-        attn_metadata_prope = FlashAttnMetadataBuilder().build(
-            current_timestep=0,
-            attn_mask=encoder_attention_mask,
-        )       
+        # attention with prope (same text mask, so reuse the metadata)
         # 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_prope):
+        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
             img_attn_prope, _ = self.attn(
                 img_q_prope,
                 img_k_prope,

fastvideo/models/dits/kandinsky5.py

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

fastvideo/models/dits/ltx2.py

@@ -72,8 +72,13 @@ def _rms_norm_dispatch(
     """Use QuACK RMSNorm only for LTX-2 refine stage, else Torch RMSNorm."""
     if _is_ltx2_refine_stage():
         return _quack_rmsnorm(x, weight=weight, eps=eps)
+    # Official LTX-2 runs the DiT natively in bf16 with no autocast, so its
+    # rms_norm outputs stay bf16 on every torch version. torch 2.12 added
+    # rms_norm to autocast's float32 cast policy, which under our
+    # autocast(bfloat16) forward returns float32 instead. Restore the input
+    # dtype to match the official numerics (and torch <= 2.11 behavior).
     return torch.nn.functional.rms_norm(
-        x, (x.shape[-1], ), weight=weight, eps=eps)
+        x, (x.shape[-1], ), weight=weight, eps=eps).to(x.dtype)
 
 
 class StageAwareRMSNorm(nn.RMSNorm):

fastvideo/models/dits/matrixgame2/action_module.py

@@ -16,6 +16,8 @@ from fastvideo.layers.rotary_embedding import (
 )
 from fastvideo.platforms import AttentionBackendEnum
 
+from .utils import retain_kv_with_sink
+
 
 DISABLE_COMPILE = False
 flex_attention = torch.compile(
@@ -100,6 +102,7 @@ def _update_kv_cache_and_attend(
     start_frame: int,
     num_frame_per_block: int,
     local_attn_size: int,
+    sink_size: int,
     *,
     use_k_for_num_tokens: bool = False,
     store_first_only: bool = False,
@@ -133,7 +136,6 @@ def _update_kv_cache_and_attend(
         k.shape[1] if use_k_for_num_tokens else q.shape[1]
     ) == num_frame_per_block
 
-    sink_size = 0
     max_attention_size = local_attn_size
     sink_tokens = sink_size * 1
     kv_cache_size = kv_cache["k"].shape[1]
@@ -206,16 +208,22 @@ def _update_kv_cache_and_attend(
 
     # Store new k, v in cache
     if store_first_only:
-        kv_cache["k"][:, local_start_index:local_end_index] = k[:1]
-        kv_cache["v"][:, local_start_index:local_end_index] = v[:1]
+        kv_cache["k"][:, local_start_index:local_end_index] = k[
+            : kv_cache["k"].shape[0]
+        ]
+        kv_cache["v"][:, local_start_index:local_end_index] = v[
+            : kv_cache["v"].shape[0]
+        ]
     else:
         kv_cache["k"][:, local_start_index:local_end_index] = k
         kv_cache["v"][:, local_start_index:local_end_index] = v
 
-    # Retrieve from cache and perform attention
-    cache_start = max(0, local_end_index - max_attention_size)
-    cached_k = kv_cache["k"][:, cache_start:local_end_index]
-    cached_v = kv_cache["v"][:, cache_start:local_end_index]
+    cached_k, cached_v = retain_kv_with_sink(
+        kv_cache["k"][:, :local_end_index],
+        kv_cache["v"][:, :local_end_index],
+        min(local_end_index, max_attention_size),
+        sink_tokens,
+    )
 
     if repeat_factor is not None:
         cached_k = cached_k.repeat(repeat_factor, 1, 1, 1)
@@ -261,6 +269,7 @@ class ActionModule(nn.Module):
         enable_mouse=True,
         enable_keyboard=True,
         local_attn_size=6,
+        sink_size=0,
         blocks: list | None = None,
     ):
         super().__init__()
@@ -274,6 +283,7 @@ class ActionModule(nn.Module):
         )
         blocks = blocks if blocks is not None else []
         self.local_attn_size = local_attn_size
+        self.sink_size = sink_size
         self.enable_mouse = enable_mouse
         self.enable_keyboard = enable_keyboard
 
@@ -565,6 +575,7 @@ class ActionModule(nn.Module):
                     start_frame,
                     num_frame_per_block,
                     self.local_attn_size,
+                    self.sink_size,
                     use_k_for_num_tokens=False,
                 )
         else:
@@ -689,6 +700,7 @@ class ActionModule(nn.Module):
                         start_frame,
                         num_frame_per_block,
                         self.local_attn_size,
+                        self.sink_size,
                         use_k_for_num_tokens=True,
                         store_first_only=True,
                         repeat_factor=S,
@@ -719,6 +731,7 @@ class ActionModule(nn.Module):
                         start_frame,
                         num_frame_per_block,
                         self.local_attn_size,
+                        self.sink_size,
                         use_k_for_num_tokens=True,
                     )
             else:

fastvideo/models/dits/matrixgame2/causal_model.py

@@ -46,6 +46,7 @@ from fastvideo.platforms import AttentionBackendEnum, current_platform
 
 from .action_module import ActionModule
 from .model import MatrixGame2CrossAttention
+from .utils import retain_kv_with_sink
 
 logger = init_logger(__name__)
 
@@ -345,9 +346,12 @@ class CausalMatrixGame2SelfAttention(nn.Module):
                 kv_cache["k"][:, local_start_index:local_end_index] = stored_key
                 kv_cache["v"][:, local_start_index:local_end_index] = v
 
-            kv_start = max(0, local_end_index - max_attention_size)
-            k_for_attn = kv_cache["k"][:, kv_start:local_end_index]
-            v_for_attn = kv_cache["v"][:, kv_start:local_end_index]
+            k_for_attn, v_for_attn = retain_kv_with_sink(
+                kv_cache["k"][:, :local_end_index],
+                kv_cache["v"][:, :local_end_index],
+                min(local_end_index, max_attention_size),
+                sink_tokens,
+            )
 
             if relativistic:
                 window_len, query_lo, _ = relativistic_window_offsets(
@@ -418,6 +422,7 @@ class CausalMatrixGame2TransformerBlock(nn.Module):
         self.hidden_dim = dim
         self.num_attention_heads = num_heads
         self.local_attn_size = local_attn_size
+        self.sink_size = sink_size
         dim_head = dim // num_heads
         if qk_norm == "rms_norm":
             self.norm_q = RMSNorm(dim_head, eps=eps)
@@ -474,6 +479,7 @@ class CausalMatrixGame2TransformerBlock(nn.Module):
                 ),
                 patch_size=action_config["patch_size"],
                 local_attn_size=local_attn_size,
+                sink_size=sink_size,
                 qk_norm=action_config["qk_norm"],
                 qkv_bias=action_config["qkv_bias"],
                 vae_time_compression_ratio=action_config[
@@ -647,6 +653,8 @@ class CausalMatrixGame2WanModel(BaseDiT):
         self.num_channels_latents = config.num_channels_latents
         self.patch_size = config.patch_size
 
+        # Long tuning controls the causal window from YAML; consume it here
+        # like Wan so train wrappers do not need runtime patching.
         arch_cfg = getattr(config, "arch_config", None)
         self.local_attn_size = (
             getattr(
@@ -662,6 +670,12 @@ class CausalMatrixGame2WanModel(BaseDiT):
             if arch_cfg
             else getattr(config, "sink_size", 0)
         )
+        if self.sink_size < 0:
+            raise ValueError("sink_size must be non-negative")
+        if self.local_attn_size != -1 and self.sink_size >= self.local_attn_size:
+            raise ValueError(
+                "sink_size must be smaller than local_attn_size for "
+                "MatrixGame2 causal attention")
         self.rope_cache_policy = (
             getattr(arch_cfg, "rope_cache_policy",
                     getattr(config, "rope_cache_policy", "absolute"))
@@ -749,6 +763,52 @@ class CausalMatrixGame2WanModel(BaseDiT):
 
         self.__post_init__()
 
+    def set_causal_attention_window(
+        self,
+        *,
+        local_attn_size: int | None = None,
+        sink_size: int | None = None,
+    ) -> None:
+        local_attn_size = (
+            self.local_attn_size if local_attn_size is None else int(local_attn_size)
+        )
+        sink_size = self.sink_size if sink_size is None else int(sink_size)
+        if sink_size < 0:
+            raise ValueError("sink_size must be non-negative")
+        if local_attn_size != -1 and sink_size >= local_attn_size:
+            raise ValueError(
+                "sink_size must be smaller than local_attn_size for "
+                "MatrixGame2 causal attention")
+
+        self.local_attn_size = local_attn_size
+        self.sink_size = sink_size
+        self.block_mask = None
+        self.block_mask_keyboard = None
+        self.block_mask_mouse = None
+
+        for block in self.blocks:
+            if hasattr(block, "local_attn_size"):
+                block.local_attn_size = local_attn_size
+            if hasattr(block, "sink_size"):
+                block.sink_size = sink_size
+
+            attn1 = getattr(block, "attn1", None)
+            if attn1 is not None:
+                if hasattr(attn1, "local_attn_size"):
+                    attn1.local_attn_size = local_attn_size
+                if hasattr(attn1, "sink_size"):
+                    attn1.sink_size = sink_size
+
+            action_model = getattr(block, "action_model", None)
+            if action_model is not None:
+                if hasattr(action_model, "local_attn_size"):
+                    action_model.local_attn_size = local_attn_size
+                if hasattr(action_model, "sink_size"):
+                    action_model.sink_size = sink_size
+
+    def set_sink_size(self, sink_size: int) -> None:
+        self.set_causal_attention_window(sink_size=sink_size)
+
     @staticmethod
     def _prepare_blockwise_causal_attn_mask(
         device: torch.device | str,
@@ -756,6 +816,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
         frame_seqlen: int = 880,
         num_frame_per_block: int = 1,
         local_attn_size: int = -1,
+        sink_size: int = 0,
     ) -> BlockMask:
         total_length = num_frames * frame_seqlen
         padded_length = math.ceil(total_length / 128) * 128 - total_length
@@ -782,7 +843,10 @@ class CausalMatrixGame2WanModel(BaseDiT):
             else:
                 return (
                     (kv_idx < ends[q_idx])
-                    & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))
+                    & (
+                        (kv_idx < sink_size * frame_seqlen)
+                        | (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))
+                    )
                 ) | (q_idx == kv_idx)
 
         block_mask = create_block_mask(
@@ -796,8 +860,9 @@ class CausalMatrixGame2WanModel(BaseDiT):
         )
 
         if not dist.is_initialized() or dist.get_rank() == 0:
-            print(
-                f" cache a block wise causal mask with block size of {num_frame_per_block} frames"
+            logger.info(
+                "cache a block wise causal mask with block size of %s frames",
+                num_frame_per_block,
             )
 
         return block_mask
@@ -809,6 +874,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
         frame_seqlen: int = 880,
         num_frame_per_block: int = 1,
         local_attn_size: int = -1,
+        sink_size: int = 0,
     ) -> BlockMask:
         total_length2 = num_frames * frame_seqlen
         padded_length2 = math.ceil(total_length2 / 32) * 32 - total_length2
@@ -834,7 +900,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
             else:
                 return (
                     (kv_idx < ends2[q_idx])
-                    & (kv_idx >= (ends2[q_idx] - local_attn_size))
+                    & ((kv_idx < sink_size) | (kv_idx >= (ends2[q_idx] - local_attn_size)))
                 ) | (q_idx == kv_idx)
 
         block_mask2 = create_block_mask(
@@ -848,8 +914,9 @@ class CausalMatrixGame2WanModel(BaseDiT):
         )
 
         if not dist.is_initialized() or dist.get_rank() == 0:
-            print(
-                f" cache a block wise causal mask for keyboard with block size of {num_frame_per_block} frames"
+            logger.info(
+                "cache a block wise causal mask for keyboard with block size of %s frames",
+                num_frame_per_block,
             )
 
         return block_mask2
@@ -861,6 +928,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
         frame_seqlen: int = 1,
         num_frame_per_block: int = 1,
         local_attn_size: int = -1,
+        sink_size: int = 0,
     ) -> BlockMask:
         total_length2 = num_frames * frame_seqlen
         padded_length2 = math.ceil(total_length2 / 32) * 32 - total_length2
@@ -886,7 +954,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
             else:
                 return (
                     (kv_idx < ends2[q_idx])
-                    & (kv_idx >= (ends2[q_idx] - local_attn_size))
+                    & ((kv_idx < sink_size) | (kv_idx >= (ends2[q_idx] - local_attn_size)))
                 ) | (q_idx == kv_idx)
 
         block_mask2 = create_block_mask(
@@ -900,8 +968,9 @@ class CausalMatrixGame2WanModel(BaseDiT):
         )
 
         if not dist.is_initialized() or dist.get_rank() == 0:
-            print(
-                f" cache a block wise causal mask for action with block size of {num_frame_per_block} frames"
+            logger.info(
+                "cache a block wise causal mask for action with block size of %s frames",
+                num_frame_per_block,
             )
 
         return block_mask2
@@ -1063,6 +1132,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
             frame_seqlen=post_patch_height * post_patch_width,
             num_frame_per_block=self.num_frame_per_block,
             local_attn_size=self.local_attn_size,
+            sink_size=self.sink_size,
         )
         if self.use_rope_keyboard:
             block_mask_keyboard = self._prepare_blockwise_causal_attn_mask_action(
@@ -1071,6 +1141,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
                 frame_seqlen=1,
                 num_frame_per_block=self.num_frame_per_block,
                 local_attn_size=self.local_attn_size,
+                sink_size=self.sink_size,
             )
         else:
             block_mask_keyboard = self._prepare_blockwise_causal_attn_mask_keyboard(
@@ -1079,6 +1150,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
                 frame_seqlen=post_patch_height * post_patch_width,
                 num_frame_per_block=self.num_frame_per_block,
                 local_attn_size=self.local_attn_size,
+                sink_size=self.sink_size,
             )
         block_mask_mouse = self._prepare_blockwise_causal_attn_mask_action(
             device=hidden_states.device,
@@ -1086,6 +1158,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
             frame_seqlen=1,
             num_frame_per_block=self.num_frame_per_block,
             local_attn_size=self.local_attn_size,
+            sink_size=self.sink_size,
         )
         if kv_cache is None:
             kv_cache = [None] * len(self.blocks)
@@ -1239,6 +1312,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
                 frame_seqlen=post_patch_height * post_patch_width,
                 num_frame_per_block=self.num_frame_per_block,
                 local_attn_size=self.local_attn_size,
+                sink_size=self.sink_size,
             )
         if self.block_mask_keyboard is None:
             if self.use_rope_keyboard:
@@ -1248,6 +1322,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
                     frame_seqlen=1,
                     num_frame_per_block=self.num_frame_per_block,
                     local_attn_size=self.local_attn_size,
+                    sink_size=self.sink_size,
                 )
             else:
                 self.block_mask_keyboard = self._prepare_blockwise_causal_attn_mask_keyboard(
@@ -1256,6 +1331,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
                     frame_seqlen=post_patch_height * post_patch_width,
                     num_frame_per_block=self.num_frame_per_block,
                     local_attn_size=self.local_attn_size,
+                    sink_size=self.sink_size,
                 )
         if self.block_mask_mouse is None:
             self.block_mask_mouse = self._prepare_blockwise_causal_attn_mask_action(
@@ -1264,6 +1340,7 @@ class CausalMatrixGame2WanModel(BaseDiT):
                 frame_seqlen=1,
                 num_frame_per_block=self.num_frame_per_block,
                 local_attn_size=self.local_attn_size,
+                sink_size=self.sink_size,
             )
 
         hidden_states = self.patch_embedding(hidden_states)

fastvideo/models/dits/matrixgame2/model.py

@@ -452,7 +452,7 @@ class MatrixGame2WanModel(BaseDiT):
             and len(encoder_hidden_states_image) > 0
         ):
             encoder_hidden_states_image = encoder_hidden_states_image[0]
-        else:
+        elif not isinstance(encoder_hidden_states_image, torch.Tensor):
             encoder_hidden_states_image = None
 
         batch_size, num_channels, num_frames, height, width = (

fastvideo/models/dits/matrixgame2/utils.py

@@ -4,12 +4,18 @@ import asyncio
 import os
 import random
 
+import numpy as np
 import torch
 from PIL import Image
 
 from fastvideo.distributed.parallel_state import get_local_torch_device
 from fastvideo.utils import logger
 
+try:
+    import cv2
+except ImportError:
+    cv2 = None
+
 
 CAM_VALUE = 0.1
 CAMERA_MAP = {
@@ -34,6 +40,256 @@ KEYBOARD_MAP_7 = {  # templerun_distilled_model: still/w/s/left/right/a/d
     "d": [0, 0, 0, 0, 0, 0, 1],  # d
 }
 KEYBOARD_MAP = KEYBOARD_MAP_4  # Default for backward compatibility
+SOLARIS_MOVEMENT_KEY_INDICES = {
+    "W": 11,
+    "S": 12,
+    "A": 13,
+    "D": 14,
+}
+
+
+def retain_kv_with_sink(
+    k: torch.Tensor,
+    v: torch.Tensor,
+    target_len: int,
+    sink_size: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if k.shape[1] <= target_len or sink_size <= 0:
+        return k[:, -target_len:], v[:, -target_len:]
+
+    sink_len = min(int(sink_size), target_len, k.shape[1])
+    tail_len = target_len - sink_len
+    sink_k = k[:, :sink_len]
+    sink_v = v[:, :sink_len]
+    if tail_len <= 0:
+        return sink_k, sink_v
+
+    tail_k = k[:, sink_len:][:, -tail_len:]
+    tail_v = v[:, sink_len:][:, -tail_len:]
+    return torch.cat([sink_k, tail_k], dim=1), torch.cat([sink_v, tail_v], dim=1)
+
+
+def _require_cv2() -> bool:
+    if cv2 is None:
+        logger.warning(
+            "OpenCV is not available; skipping MatrixGame2 validation overlay."
+        )
+        return False
+    return True
+
+
+def _matrixgame2_overlay_keys_from_keyboard(
+    keyboard_frame: np.ndarray | torch.Tensor,
+) -> dict[str, bool]:
+    vec = np.asarray(keyboard_frame, dtype=np.float32).reshape(-1)
+    dim = vec.shape[0]
+
+    if dim >= 23:
+        return {
+            key: bool(vec[idx] > 0.5)
+            for key, idx in SOLARIS_MOVEMENT_KEY_INDICES.items()
+        }
+    if dim == 7:
+        return {
+            "W": bool(vec[1] > 0.5),
+            "S": bool(vec[2] > 0.5),
+            "A": bool(vec[5] > 0.5),
+            "D": bool(vec[6] > 0.5),
+        }
+    if dim >= 6:
+        return {
+            "W": bool(vec[0] > 0.5),
+            "S": bool(vec[1] > 0.5),
+            "A": bool(vec[2] > 0.5),
+            "D": bool(vec[3] > 0.5),
+        }
+    if dim == 4:
+        return {
+            "W": bool(vec[0] > 0.5),
+            "S": bool(vec[1] > 0.5),
+            "A": bool(vec[2] > 0.5),
+            "D": bool(vec[3] > 0.5),
+        }
+    if dim == 2:
+        return {
+            "W": bool(vec[0] > 0.5),
+            "S": bool(vec[1] > 0.5),
+            "A": False,
+            "D": False,
+        }
+    return {"W": False, "S": False, "A": False, "D": False}
+
+
+def draw_rounded_rectangle(
+    image: np.ndarray,
+    top_left: tuple[int, int],
+    bottom_right: tuple[int, int],
+    color: tuple[int, int, int],
+    radius: int = 10,
+    alpha: float = 0.5,
+) -> None:
+    if not _require_cv2():
+        return
+
+    overlay = image.copy()
+    x1, y1 = top_left
+    x2, y2 = bottom_right
+
+    cv2.rectangle(overlay, (x1 + radius, y1), (x2 - radius, y2), color, -1)
+    cv2.rectangle(overlay, (x1, y1 + radius), (x2, y2 - radius), color, -1)
+    cv2.ellipse(overlay, (x1 + radius, y1 + radius), (radius, radius), 180, 0, 90, color, -1)
+    cv2.ellipse(overlay, (x2 - radius, y1 + radius), (radius, radius), 270, 0, 90, color, -1)
+    cv2.ellipse(overlay, (x1 + radius, y2 - radius), (radius, radius), 90, 0, 90, color, -1)
+    cv2.ellipse(overlay, (x2 - radius, y2 - radius), (radius, radius), 0, 0, 90, color, -1)
+    cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
+
+
+def draw_keys_on_frame(
+    frame: np.ndarray,
+    keys: dict[str, bool],
+    key_size: tuple[int, int] = (30, 30),
+    top_margin: int = 15,
+) -> None:
+    if not _require_cv2():
+        return
+
+    left_margin = 15
+    gap = 3
+    key_positions = {
+        "W": (left_margin + key_size[0] + gap, top_margin),
+        "A": (left_margin, top_margin + key_size[1] + gap),
+        "S": (left_margin + key_size[0] + gap, top_margin + key_size[1] + gap),
+        "D": (
+            left_margin + (key_size[0] + gap) * 2,
+            top_margin + key_size[1] + gap,
+        ),
+    }
+
+    for key, (x, y) in key_positions.items():
+        pressed = keys.get(key, False)
+        color = (0, 255, 0) if pressed else (200, 200, 200)
+        alpha = 0.8 if pressed else 0.5
+        draw_rounded_rectangle(
+            frame,
+            (x, y),
+            (x + key_size[0], y + key_size[1]),
+            color,
+            radius=5,
+            alpha=alpha,
+        )
+        text_size = cv2.getTextSize(
+            key, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1
+        )[0]
+        text_x = x + (key_size[0] - text_size[0]) // 2
+        text_y = y + (key_size[1] + text_size[1]) // 2
+        cv2.putText(
+            frame,
+            key,
+            (text_x, text_y),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (0, 0, 0),
+            1,
+        )
+
+
+def draw_mouse_on_frame(
+    frame: np.ndarray,
+    yaw: float,
+    pitch: float,
+    top_margin: int = 15,
+) -> None:
+    if not _require_cv2():
+        return
+
+    _, width, _ = frame.shape
+    right_margin = 15
+    crosshair_radius = 25
+    crosshair_x = width - right_margin - crosshair_radius
+    crosshair_y = top_margin + crosshair_radius
+
+    dx = int(yaw * crosshair_radius * 8)
+    dy = int(-pitch * crosshair_radius * 8)
+    max_arrow = crosshair_radius - 5
+    dx = max(-max_arrow, min(max_arrow, dx))
+    dy = max(-max_arrow, min(max_arrow, dy))
+
+    cv2.circle(frame, (crosshair_x, crosshair_y), crosshair_radius, (50, 50, 50), -1)
+    cv2.circle(frame, (crosshair_x, crosshair_y), crosshair_radius, (200, 200, 200), 1)
+    cv2.line(
+        frame,
+        (crosshair_x - crosshair_radius + 5, crosshair_y),
+        (crosshair_x + crosshair_radius - 5, crosshair_y),
+        (100, 100, 100),
+        1,
+    )
+    cv2.line(
+        frame,
+        (crosshair_x, crosshair_y - crosshair_radius + 5),
+        (crosshair_x, crosshair_y + crosshair_radius - 5),
+        (100, 100, 100),
+        1,
+    )
+
+    if abs(dx) > 1 or abs(dy) > 1:
+        cv2.arrowedLine(
+            frame,
+            (crosshair_x, crosshair_y),
+            (crosshair_x + dx, crosshair_y + dy),
+            (0, 255, 0),
+            2,
+            tipLength=0.3,
+        )
+
+
+def overlay_validation_actions_on_frames(
+    frames: list[np.ndarray],
+    keyboard_cond: np.ndarray | torch.Tensor | None = None,
+    mouse_cond: np.ndarray | torch.Tensor | None = None,
+) -> list[np.ndarray]:
+    if (keyboard_cond is None and mouse_cond is None) or not _require_cv2():
+        return frames
+
+    if keyboard_cond is not None and torch.is_tensor(keyboard_cond):
+        keyboard_cond = keyboard_cond.detach().cpu().float().numpy()
+    if mouse_cond is not None and torch.is_tensor(mouse_cond):
+        mouse_cond = mouse_cond.detach().cpu().float().numpy()
+
+    if keyboard_cond is not None:
+        keyboard_cond = np.asarray(keyboard_cond, dtype=np.float32)
+        if keyboard_cond.ndim == 3 and keyboard_cond.shape[0] == 1:
+            keyboard_cond = keyboard_cond[0]
+    if mouse_cond is not None:
+        mouse_cond = np.asarray(mouse_cond, dtype=np.float32)
+        if mouse_cond.ndim == 3 and mouse_cond.shape[0] == 1:
+            mouse_cond = mouse_cond[0]
+
+    processed_frames: list[np.ndarray] = []
+    for frame_idx, frame in enumerate(frames):
+        frame = np.ascontiguousarray(frame.copy())
+        if keyboard_cond is not None and frame_idx < len(keyboard_cond):
+            draw_keys_on_frame(
+                frame,
+                _matrixgame2_overlay_keys_from_keyboard(keyboard_cond[frame_idx]),
+            )
+        if mouse_cond is not None and frame_idx < len(mouse_cond):
+            # MG action convention: mouse[0]=pitch, mouse[1]=yaw.
+            pitch = float(mouse_cond[frame_idx, 0])
+            yaw = float(mouse_cond[frame_idx, 1])
+            draw_mouse_on_frame(frame, yaw=yaw, pitch=pitch)
+        processed_frames.append(frame)
+
+    return processed_frames
+
+
+def scale_keyboard_condition(
+    keyboard_condition: torch.Tensor,
+    scale: float,
+) -> torch.Tensor:
+    if float(scale) == 1.0:
+        return keyboard_condition
+    return keyboard_condition * float(scale)
+
 
 def expand_action_to_frames(action: dict, num_frames: int) -> tuple[torch.Tensor, torch.Tensor]:
     result = {}

fastvideo/models/dits/wanvideo.py

@@ -1,14 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
+import copy
 import math
 from typing import Any
 
 import torch
 import torch.nn as nn
 
-import fastvideo.envs as envs
 from fastvideo.attention import (DistributedAttention, DistributedAttention_VSA,
                                  LocalAttention)
+from fastvideo.attention.selector import check_attn_backend_requirement
 from fastvideo.configs.models.dits import WanVideoConfig
 from fastvideo.distributed.communication_op import (
     sequence_model_parallel_all_gather_with_unpad,
@@ -59,6 +60,11 @@ class WanTimeTextImageEmbedding(nn.Module):
         time_freq_dim: int,
         text_embed_dim: int,
         image_embed_dim: int | None = None,
+        *,
+        r_embedder: bool = False,
+        r_embedder_fusion: str = "additive",
+        r_embedder_gate_value: float = 0.25,
+        r_embedder_deltatime_type: str = "r",
     ):
         super().__init__()
 
@@ -77,14 +83,57 @@ class WanTimeTextImageEmbedding(nn.Module):
         if image_embed_dim is not None:
             self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
 
+        # AnyFlow dual-timestep support. When r_embedder is False the forward
+        # path bypasses delta_embedder entirely and the output is byte-identical
+        # to the legacy single-timestep implementation.
+        self._r_embedder_enabled = bool(r_embedder)
+        self._r_embedder_fusion = r_embedder_fusion
+        self._r_embedder_deltatime_type = r_embedder_deltatime_type
+        if self._r_embedder_enabled:
+            if r_embedder_fusion not in ("additive", "gated"):
+                raise ValueError(
+                    "r_embedder_fusion must be one of {additive, gated}, "
+                    f"got {r_embedder_fusion!r}")
+            if r_embedder_deltatime_type not in ("r", "t-r"):
+                raise ValueError(
+                    "r_embedder_deltatime_type must be one of {r, t-r}, "
+                    f"got {r_embedder_deltatime_type!r}")
+            # Deep-copy preserves identical initialization with time_embedder,
+            # matching AnyFlow reference setup_flowmap_model() behavior.
+            self.delta_embedder = copy.deepcopy(self.time_embedder)
+            # Non-persistent buffer — gate is a hyperparameter, not learned.
+            self.register_buffer(
+                "_r_embedder_gate",
+                torch.tensor(float(r_embedder_gate_value)),
+                persistent=False,
+            )
+        else:
+            self.delta_embedder = None
+
     def forward(
         self,
         timestep: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
         encoder_hidden_states_image: torch.Tensor | None = None,
         timestep_seq_len: int | None = None,
+        r_timestep: torch.Tensor | None = None,
     ):
         temb = self.time_embedder(timestep, timestep_seq_len)
+
+        if self._r_embedder_enabled and r_timestep is not None:
+            assert self.delta_embedder is not None
+            if self._r_embedder_deltatime_type == "r":
+                delta_input = r_timestep
+            else:
+                delta_input = timestep - r_timestep
+            delta_emb = self.delta_embedder(delta_input, timestep_seq_len)
+            gate = self._r_embedder_gate
+            if self._r_embedder_fusion == "gated":
+                temb = (1.0 - gate) * temb + gate * delta_emb
+            else:
+                # Additive (additional channel, no convex blend).
+                temb = temb + gate * delta_emb
+
         timestep_proj = self.time_modulation(temb)
 
         if self.text_embedder is not None:
@@ -558,6 +607,21 @@ class WanTransformerBlock_VSA(nn.Module):
         return hidden_states
 
 
+def _select_wan_transformer_block(
+    config: WanVideoConfig,
+    *,
+    model_name: str,
+) -> type[WanTransformerBlock] | type[WanTransformerBlock_VSA]:
+    # Resolve checkpoint-level requirements before choosing a block layout.
+    # Sparse FastWan checkpoints require VSA's extra gate-compression weights;
+    # dense checkpoints explicitly reject that layout.
+    attn_backend = check_attn_backend_requirement(config.required_attention_backend, model_name=model_name)
+    config.validate_attention_backend_compatibility(attn_backend, model_name=model_name)
+    if attn_backend == AttentionBackendEnum.VIDEO_SPARSE_ATTN:
+        return WanTransformerBlock_VSA
+    return WanTransformerBlock
+
+
 class WanTransformer3DModel(BaseDiT):
     _fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
     _compile_conditions = WanVideoConfig()._compile_conditions
@@ -570,6 +634,7 @@ class WanTransformer3DModel(BaseDiT):
     def __init__(self, config: WanVideoConfig, hf_config: dict[str,
                                                                Any]) -> None:
         super().__init__(config=config, hf_config=hf_config)
+        transformer_block = _select_wan_transformer_block(config, model_name=type(self).__name__)
         self.quant_config = config.quant_config
 
         inner_dim = config.num_attention_heads * config.attention_head_dim
@@ -595,11 +660,13 @@ class WanTransformer3DModel(BaseDiT):
             time_freq_dim=config.freq_dim,
             text_embed_dim=config.text_dim,
             image_embed_dim=config.image_dim,
+            r_embedder=config.r_embedder,
+            r_embedder_fusion=config.r_embedder_fusion,
+            r_embedder_gate_value=config.r_embedder_gate_value,
+            r_embedder_deltatime_type=config.r_embedder_deltatime_type,
         )
 
         # 3. Transformer blocks
-        attn_backend = envs.FASTVIDEO_ATTENTION_BACKEND
-        transformer_block = WanTransformerBlock_VSA if attn_backend == "VIDEO_SPARSE_ATTN" else WanTransformerBlock
         self.blocks = nn.ModuleList([
             transformer_block(inner_dim,
                               config.ffn_dim,
@@ -636,6 +703,7 @@ class WanTransformer3DModel(BaseDiT):
                 encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
                 | None = None,
                 guidance=None,
+                r_timestep: torch.Tensor | None = None,
                 **kwargs) -> torch.Tensor:
         orig_dtype = hidden_states.dtype
         if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
@@ -684,8 +752,17 @@ class WanTransformer3DModel(BaseDiT):
         else:
             ts_seq_len = None
 
+        # AnyFlow dual-timestep — match timestep's flattening so embedder
+        # sees aligned shapes.
+        if r_timestep is not None and r_timestep.dim() == 2:
+            r_timestep = r_timestep.flatten()
+
         temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-            timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len)
+            timestep,
+            encoder_hidden_states,
+            encoder_hidden_states_image,
+            timestep_seq_len=ts_seq_len,
+            r_timestep=r_timestep)
         if ts_seq_len is not None:
             # batch_size, seq_len, 6, inner_dim
             timestep_proj = timestep_proj.unflatten(2, (6, -1))

fastvideo/models/encoders/glm_image_ar_loader.py

@@ -0,0 +1,64 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Sole HF-import boundary for GLM-Image's AR encoder (lazy-wrapper exception E001)."""
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class GlmImageARLoader(nn.Module):
+
+    def __init__(self, model_path: str, processor_path: str | None = None,
+                 *, torch_dtype: torch.dtype = torch.bfloat16,
+                 trust_remote_code: bool = True) -> None:
+        super().__init__()
+        from transformers import (AutoProcessor,
+                                  GlmImageForConditionalGeneration)
+        logger.info("Loading GLM-Image AR encoder from %s", model_path)
+        self._model = GlmImageForConditionalGeneration.from_pretrained(
+            model_path,
+            torch_dtype=torch_dtype,
+            trust_remote_code=trust_remote_code,
+        )
+        if processor_path is not None:
+            logger.info("Loading GLM-Image processor from %s", processor_path)
+            self.processor = AutoProcessor.from_pretrained(
+                processor_path, trust_remote_code=trust_remote_code)
+        else:
+            self.processor = None
+
+    @torch.no_grad()
+    def generate(self, *args: Any, **kwargs: Any) -> torch.Tensor:
+        return self._model.generate(*args, **kwargs)
+
+    @torch.no_grad()
+    def get_image_features(self, pixel_values: torch.Tensor,
+                           image_grid_thw: torch.Tensor) -> Any:
+        return self._model.get_image_features(pixel_values, image_grid_thw)
+
+    @torch.no_grad()
+    def get_image_tokens(self, image_embeds: torch.Tensor,
+                         image_grid_thw: torch.Tensor) -> torch.Tensor:
+        return self._model.get_image_tokens(image_embeds, image_grid_thw)
+
+    @property
+    def config(self):  # type: ignore[no-untyped-def]
+        return self._model.config
+
+    @property
+    def generation_config(self):  # type: ignore[no-untyped-def]
+        return self._model.generation_config
+
+    def to(self, *args, **kwargs):  # type: ignore[override]
+        self._model = self._model.to(*args, **kwargs)
+        return super().to(*args, **kwargs)
+
+    def eval(self):  # type: ignore[override]
+        self._model = self._model.eval()
+        return super().eval()

fastvideo/models/encoders/qwen2_5_vl_custom.py

@@ -75,6 +75,25 @@ if not is_flash_attn_2_available():
 
 logger = logging.get_logger(__name__)
 
+
+def _resolve_torch_dtype(dtype, default: torch.dtype = torch.float32) -> torch.dtype:
+    if isinstance(dtype, torch.dtype):
+        return dtype
+    if isinstance(dtype, str):
+        normalized = dtype.strip().removeprefix("torch.").lower()
+        return {
+            "bfloat16": torch.bfloat16,
+            "bf16": torch.bfloat16,
+            "float16": torch.float16,
+            "fp16": torch.float16,
+            "half": torch.float16,
+            "float32": torch.float32,
+            "fp32": torch.float32,
+            "float": torch.float32,
+        }.get(normalized, default)
+    return default
+
+
 class Qwen2_5_VLMLP(nn.Module):
     def __init__(self, config, bias: bool = False):
         super().__init__()
@@ -304,10 +323,13 @@ class Qwen2_5_VisionTransformerPretrainedModel(nn.Module):
     config_class = Qwen2_5_VLVisionConfig
     _no_split_modules = ["Qwen2_5_VLVisionBlock"]
 
-    def __init__(self, config) -> None:
+    def __init__(self, config, parent_torch_dtype=None) -> None:
         super().__init__()
 
-        self.dtype = torch.bfloat16 if config.torch_dtype == "bfloat16" else torch.float32
+        config_torch_dtype = getattr(config, "torch_dtype", None)
+        self.dtype = _resolve_torch_dtype(
+            config_torch_dtype if config_torch_dtype is not None else parent_torch_dtype
+        )
 
         self.spatial_merge_size = config.spatial_merge_size
         self.patch_size = config.patch_size
@@ -467,6 +489,18 @@ class Qwen2_5_VisionTransformerPretrainedModel(nn.Module):
         return hidden_states
 
 
+def _compute_default_rope_parameters(config, device=None, seq_len=None, **kwargs):
+    # transformers>=5 removes the "default" entry from ROPE_INIT_FUNCTIONS and
+    # moves rope_theta inside rope_parameters; replicate the 4.x default init.
+    rope_params = getattr(config, "rope_parameters", None) or getattr(config, "rope_scaling", None) or {}
+    base = rope_params.get("rope_theta", getattr(config, "rope_theta", 10000.0))
+    head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+    partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
+    dim = int(head_dim * partial_rotary_factor)
+    inv_freq = 1.0 / (base**(torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, 1.0
+
+
 class Qwen2_5_VLRotaryEmbedding(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig, device=None):
         super().__init__()
@@ -479,7 +513,14 @@ class Qwen2_5_VLRotaryEmbedding(nn.Module):
         self.original_max_seq_len = config.max_position_embeddings
 
         self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        if self.rope_type in ROPE_INIT_FUNCTIONS:
+            self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        elif self.rope_type == "default":
+            # transformers>=5 drops the "default" entry from ROPE_INIT_FUNCTIONS.
+            self.rope_init_fn = _compute_default_rope_parameters
+        else:
+            raise KeyError(f"Unsupported rope_type '{self.rope_type}'; available: "
+                           f"{['default', *ROPE_INIT_FUNCTIONS]}")
 
         inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
         self.register_buffer("inv_freq", inv_freq, persistent=False)
@@ -948,6 +989,16 @@ QWEN2_5_VL_ATTENTION_CLASSES = {
 # If FlashAttention2 is not available, transparently fall back to SDPA.
 if not is_flash_attn_2_available():
     QWEN2_5_VL_ATTENTION_CLASSES["flash_attention_2"] = Qwen2_5_VLSdpaAttention
+else:
+    # transformers>=5 only resolves the flash-attn functions when the model
+    # preloads them via its attention interface; this module bypasses
+    # PreTrainedModel, so _flash_attention_forward(implementation=None) raises
+    # unless we preload here.
+    try:
+        from transformers.modeling_flash_attention_utils import lazy_import_flash_attention
+        lazy_import_flash_attention("flash_attention_2")
+    except (ImportError, ValueError):
+        QWEN2_5_VL_ATTENTION_CLASSES["flash_attention_2"] = Qwen2_5_VLSdpaAttention
     
 class Qwen2_5_VLDecoderLayer(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig, layer_idx: int):
@@ -1035,7 +1086,7 @@ class Qwen2_5_VLModel(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig):
         super().__init__()
         self.config = config
-        self.padding_idx = config.pad_token_id
+        self.padding_idx = getattr(config, "pad_token_id", None)
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
@@ -1408,6 +1459,18 @@ class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput):
     rope_deltas: Optional[torch.LongTensor] = None
 
 
+def _flatten_text_config(config):
+    # transformers>=5 stops forwarding text-model attributes (hidden_size,
+    # vocab_size, rope_scaling, ...) from the composite Qwen2_5_VLConfig to
+    # config.text_config; this module reads them from the top level.
+    text_config = getattr(config, "text_config", None)
+    if text_config is not None:
+        for key, value in text_config.to_dict().items():
+            if not hasattr(config, key):
+                setattr(config, key, value)
+    return config
+
+
 class Qwen2_5_VLForConditionalGenerationSimple(nn.Module):
     _tied_weights_keys = ["lm_head.weight"]
     config_class = Qwen2_5_VLConfig
@@ -1415,8 +1478,12 @@ 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)
+        self.visual = Qwen2_5_VisionTransformerPretrainedModel(
+            config.vision_config,
+            parent_torch_dtype=getattr(config, "torch_dtype", None),
+        )
 
         self.model = Qwen2_5_VLModel(config)
         self.vocab_size = config.vocab_size

fastvideo/models/loader/benchmarks/benchmark_weight_loading_comparison.py

@@ -42,7 +42,7 @@ def load_independent(files: list[str], device: str):
     """Before-PR behavior: every rank reads every tensor from disk to GPU."""
     for st_file in files:
         with safe_open(st_file, framework="pt", device=device) as f:
-            for name in f:
+            for name in f.keys():  # noqa: SIM118
                 param = f.get_tensor(name)
                 yield name, param
 
@@ -54,7 +54,7 @@ def load_broadcast(files: list[str], device: str, node_group,
     handles = []
     for st_file in files:
         with safe_open(st_file, framework="pt", device=device) as f:
-            for name in f:
+            for name in f.keys():  # noqa: SIM118
                 if local_rank == 0:
                     param = f.get_tensor(name)
                 else: