[refactor]: group model-family settings under pipeline.model

pipeline.model is a tagged union: a mapping with exactly one key, which
names the model family (ltx2, minimax_h3, longcat, or generic) and selects
that family's options class. The former sibling sections pipeline.ltx2,
pipeline.minimax_h3, and pipeline.longcat, and the DiT/VAE architecture
override dicts pipeline.dit and pipeline.vae, live inside the family block.
The parser implements tagged unions as a general mechanism (a dataclass
with a TAGS table); the family is readable as pipeline.model.family.

Resolution validates the tag against the family that the registry chose
for model_path (validate_model_family raises and names the registry class
and the right block) and fills the family's empty block when the input has
none (fill_model_family), so readers never see None. Family-only fills and
derivations (the MiniMax-H3 parallel-VAE environment variables, the LTX-2
tile-size derivation and refine preset copy, the LongCat BSA mirrors) run
only for their family.

Against the 3f6893a0 goldens every PipelineConfig is identical and no
other field differs beyond the allowed categories; family-only settings
are no longer decided on models of another family. LingBot-Video reads
its refiner switch from pipeline.preset_overrides.refine.enabled and loads
the refiner by default when the checkpoint has one, as at 3f6893a0.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CbSuTKA9oX4YsECdcBPAsX

.agents/skills/add-model-09-pipeline/templates/pipeline_parity_test.py

@@ -80,26 +80,36 @@ def _run_fastvideo_pipeline(model_path: Path, params: dict[str, Any]) -> Any:
 
     generator = VideoGenerator.from_pretrained(
         str(model_path),
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=False,
+        {
+            "engine": {
+                "num_gpus": 1,
+                "use_fsdp_inference": False,
+                "offload": {
+                    "dit": False,
+                    "vae": False,
+                    "text_encoder": False,
+                },
+            },
+        },
     )
     try:
-        return generator.generate_video(
-            prompt=params["prompt"],
-            negative_prompt=params.get("negative_prompt"),
-            output_path=f"outputs_{_MODEL_FAMILY}/pipeline_parity",
-            save_video=False,
-            height=params.get("height"),
-            width=params.get("width"),
-            num_frames=params.get("num_frames"),
-            fps=params.get("fps"),
-            num_inference_steps=params["num_inference_steps"],
-            guidance_scale=params.get("guidance_scale"),
-            seed=params["seed"],
-        )
+        return generator.generate({
+            "prompt": params["prompt"],
+            "negative_prompt": params.get("negative_prompt"),
+            "sampling": {
+                "height": params.get("height"),
+                "width": params.get("width"),
+                "num_frames": params.get("num_frames"),
+                "fps": params.get("fps"),
+                "num_inference_steps": params["num_inference_steps"],
+                "guidance_scale": params.get("guidance_scale"),
+                "seed": params["seed"],
+            },
+            "output": {
+                "output_path": f"outputs_{_MODEL_FAMILY}/pipeline_parity",
+                "save_video": False,
+            },
+        })
     finally:
         generator.shutdown()
 

.agents/skills/ci-runner/SKILL.md

@@ -0,0 +1,99 @@
+---
+name: ci-runner
+description: Work on FastVideo's Slurm-only, change-aware GPU CI lanes, static Buildkite graph, trusted ci-runner policy, lane scripts, and GB200 validation.
+---
+
+# Slinky Slurm CI lanes
+
+FastVideo's `ci-runner` Buildkite queue is the control plane for all active
+GPU CI. A private host-owned dispatcher leases GPUs from the Slinky Slurm tray
+and runs the immutable PR SHA inside an isolated Enroot container. Buildkite
+pipeline upload and Slurm submission occur on the login plane; every test
+payload executes on Slurm compute.
+
+The files under `fastvideo/tests/modal/` and `.buildkite/scripts/pr_test.sh`
+are dormant rollback code. Never add an active Buildkite or slash-command
+route to them. `pr_test.sh` must continue to reject Buildkite invocations.
+
+The private operator bundle is deliberately outside this repository because
+it contains site paths and credentials. See
+`docs/contributing/ci_architecture.md`; this skill covers the repository half
+and the coordination contract with that bundle.
+
+## Invariants
+
+- `.buildkite/pipeline.yml` contains exactly one static step for every active
+  GPU lane. Each step pins a unique key and label, a 90-minute timeout, the
+  trusted `/opt/fastvideo-ci-runner/run-ci` command (`run-unit` is the one
+  compatibility wrapper), step-level internal `TEST_TYPE`, and
+  `queue: "ci-runner"`.
+- Active CI contains no `pr_test.sh` command, Modal invocation, default queue,
+  Buildkite plugin, `soft_fail`, or job-controlled artifact glob.
+- The six Fastcheck lanes use `:microscope:` labels. Full-Suite-only lanes use
+  `:test_tube:` or `:bar_chart:` so direct reruns update the right aggregate.
+- SSIM and vanilla training request all four GPUs. Keep both in the
+  `fastvideo/slinky/whole-tray` Buildkite concurrency group with a limit of one
+  so the second job does not consume an agent or command timeout while waiting
+  for the same tray.
+- `/test full` schedules all twenty lanes. `/merge`, `ready`, and new pushes to
+  ready PRs use the trusted base-branch planner in
+  `.github/scripts/plan_merge_ci.py`: automatic Fastcheck remains the universal
+  six-lane baseline, and the merge build adds only path-relevant integration
+  lanes. Unknown source/build paths fail closed to all fourteen additive lanes.
+  The trusted uploader still normalizes and validates the complete static graph
+  before Buildkite evaluates its plan conditions.
+- Focused merge builds may pass allowlisted golden-gate and SSIM test basenames.
+  The private host validates the lane plan and basenames before staging them,
+  and the in-container scripts validate them again. Direct `/test ssim`,
+  explicit `/test full`, and the weekly main-branch schedule run the complete
+  SSIM matrix.
+- The trusted uploader serves exactly three entry pipelines:
+  `pr-fastcheck` for automatic PR builds, `ci` for slash-command/ready-label
+  API builds, and `fastvideo-performance-lane` for the weekly schedule. Keep
+  incoming GitHub webhook processing disabled on `ci` so it cannot duplicate
+  `pr-fastcheck` on every PR update.
+- Test payloads live in `.buildkite/scripts/unit_test.sh` or executable
+  `.buildkite/scripts/lanes/<lane>.sh`. Backend policy (GPU count, extras,
+  secrets, kernel build, artifacts) stays in the agent-owned lane table.
+- Tests must preserve an inherited `MASTER_PORT`. Packed containers share the
+  tray network namespace, so the private runner assigns a distinct port range
+  per GPU lease and the SSIM scheduler assigns task offsets within its range.
+- The ARM64 runner image includes the pinned FA4 CuTe overlay validated on
+  GB200. Keep SSIM at `FASTVIDEO_FA4=1` because its references were seeded with
+  FA4; keep lanes with FA2 baselines at `FASTVIDEO_FA4=0`. A runner image change
+  must revalidate both the FA4 import and an actual GB200 forward kernel.
+- `fastvideo/tests/ssim/ci_runner.py` is the active four-GPU SSIM scheduler.
+  New SSIM files are discovered through `REQUIRED_GPUS` and
+  `*_MODEL_TO_PARAMS`; do not wire them through the dormant Modal scheduler.
+- The host policy fail-closes unknown tuples. A repository-side lane change is
+  inert until the operator updates the private lane table and uploader policy
+  in the same rollout.
+
+## Adding or changing a lane
+
+1. Read the closest `AGENTS.md` and the domain-specific testing guide.
+2. Add or update the executable lane payload under `.buildkite/scripts/`.
+   Keep it deterministic and free of host-specific paths or credential fetches.
+3. Add the static pipeline step and canonical `/test <name>` mapping. Keep the
+   `<name>-ci` alias only when compatibility requires it.
+4. Add its source/test path ownership to `.github/scripts/plan_merge_ci.py`.
+   Prefer the narrowest correctness-preserving lane set; leave unknown paths
+   fail-closed. Extend `fastvideo/tests/contract/test_ci_test_collection.py`,
+   `test_merge_ci_plan.py`, and focused CPU-only scheduler/policy tests.
+5. Coordinate the private lane row: GPU count (1-4), wall time, script, scope
+   pairs, step key, command, HF cache/token, tracking mode, extras, attention
+   backend policy, kernel policy, and artifact relay. Active training lanes
+   keep W&B offline and do not stage a W&B credential.
+6. Update the trusted pipeline-uploader schema. A mismatch must reject the
+   pipeline rather than silently skip a lane.
+7. Run `pre-commit run --files <changed paths>`, the planner's representative
+   diff matrix, contract tests, private driver tests, and a real GB200 canary.
+   Multi-GPU, hardware-reference, training, performance, and SSIM changes need
+   their own target-hardware evidence.
+
+## Rollback
+
+Rollback the Slurm routing/configuration change or pause the `ci-runner` queue.
+Do not silently reactivate Modal. A manual Modal experiment requires the
+explicit local opt-in documented in `ci_architecture.md`; returning it to
+production CI needs a separate reviewed decision.

.agents/skills/decompose-pipeline-pr/SKILL.md

@@ -84,7 +84,9 @@ fastvideo/configs/models/dits/__init__.py
 fastvideo/configs/models/encoders/__init__.py
 fastvideo/configs/models/vaes/__init__.py
 fastvideo/envs.py
-fastvideo/fastvideo_args.py
+fastvideo/api/schema.py
+fastvideo/api/resolution.py
+fastvideo/api/inference_resolution.py
 fastvideo/distributed/**
 fastvideo/layers/**
 fastvideo/attention/**

.agents/skills/env-var-conventions/SKILL.md

@@ -0,0 +1,80 @@
+---
+name: env-var-conventions
+description: Add, read, rename, or remove an environment variable in FastVideo, or change the environment-variable policy. Use before touching fastvideo/envs.py, os.environ, os.getenv, or monkeypatch.setenv in fastvideo/, and when fastvideo/tests/contract/test_env_policy.py fails.
+---
+
+# Environment Variable Conventions
+
+## Purpose
+
+FastVideo registers its environment variables as typed fields in
+`fastvideo/envs.py`. The policy that governs them is
+`docs/contributing/env_vars.md`, and the contract test
+`fastvideo/tests/contract/test_env_policy.py` enforces the policy in the unit
+CI lane. This skill routes an environment-variable change through that policy.
+The policy doc is the single source of the rules; read it instead of relying
+on a summary here.
+
+## Prerequisites
+
+- Read `docs/contributing/env_vars.md` in full.
+- Decide whether the setting belongs in an environment variable or an argument
+  (rule 5 in the policy doc). Settings that users change per deployment are
+  arguments; add them as typed config fields in `fastvideo/api/schema.py`
+  instead.
+
+## Inputs
+
+| Parameter  | Required | Description                                                    |
+| ---------- | -------- | -------------------------------------------------------------- |
+| `change`   | Yes      | Add, read, rename, or remove a variable, or change the policy. |
+| `variable` | Yes      | The variable name, with the `FASTVIDEO_` prefix.               |
+
+## Steps
+
+1. **Declare or edit the variable in `fastvideo/envs.py`.**
+   - Pick the field type and category that the policy doc lists.
+   - Write a description that states what the variable does and its units.
+   - To rename, keep the old name in `deprecated_names`. To remove, add the
+     name to `DEPRECATED_VARIABLES`. Update the uses in `examples/`,
+     `scripts/`, `docs/`, `apps/`, and the tests.
+2. **Read the variable with `envs.NAME.get()` inside a function.**
+   - In tests, change the value with `envs.NAME.override(value)`, and a variable
+     outside the registry with `envs.override_external(name, value)`; the
+     `env_overrides` fixture keeps either until the end of the test.
+   - Name a variable that only tests read `FASTVIDEO_TEST_*`.
+   - Do not call `os.environ`, `os.getenv`, or `monkeypatch.setenv` for a
+     FastVideo variable.
+   - To set a variable that another tool reads, call `envs.set_external`,
+     `envs.setdefault_external`, or `envs.unset_external`.
+3. **Regenerate the table in the policy doc.**
+   - Run `python fastvideo/tests/contract/test_env_policy.py`.
+4. **Run the contract test.**
+   - Run `pytest fastvideo/tests/contract/test_env_policy.py`.
+   - When the test reports a fixed known violation, delete or lower its entry
+     in `KNOWN_VIOLATIONS`. Never add an entry to `KNOWN_VIOLATIONS`.
+5. **When the policy itself changes, update the policy doc and the contract
+   test in the same pull request.**
+   - The rules in `docs/contributing/env_vars.md`, the checks and allowlist in
+     `fastvideo/tests/contract/test_env_policy.py`, and this skill must agree.
+
+## Outputs
+
+- A registry entry in `fastvideo/envs.py` and call sites that use
+  `envs.NAME.get()`.
+- A regenerated table in `docs/contributing/env_vars.md`.
+- A passing `fastvideo/tests/contract/test_env_policy.py`.
+
+## Example Usage
+
+```
+Add a FASTVIDEO_DEBUG_MY_STAGE switch that logs MyStage inputs.
+```
+
+## References
+
+- `docs/contributing/env_vars.md`: the policy, the field types, and the
+  violation kinds that the contract test reports.
+- `fastvideo/envs.py`: the registry.
+- `fastvideo/tests/contract/test_env_policy.py`: the contract test,
+  `EXTERNAL_ALLOWLIST`, and `KNOWN_VIOLATIONS`.

.agents/skills/reseed-ssim-references/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: reseed-ssim-references
-description: Re-seed HF reference videos for a single existing SSIM test on Modal L40S. Always backs up current refs locally first, regenerates on Modal, pauses for the user to eyeball before-vs-after quality, then overwrites the targeted `<model_id>` subtree on `FastVideo/ssim-reference-videos` with `--force`. Use when an intentional code change (model port fix, attention backend swap, kernel upgrade, hyperparameter change) has invalidated existing refs and they need to be regenerated. Pairs with `seed-ssim-references`, which is for first-time seeding only.
+description: Re-seed HF reference videos for a single existing SSIM test on Modal L40S. Always backs up current refs locally first, regenerates on Modal, pauses for the user to eyeball before-vs-after quality, then overwrites the targeted model subtree on `FastVideo/ssim-reference-videos` with `--force`. Use when an intentional code change (model port fix, attention backend swap, kernel upgrade, hyperparameter change) has invalidated existing refs and they need to be regenerated. Pairs with `seed-ssim-references`, which is for first-time seeding only.
 ---
 
 # Re-seed SSIM Reference Videos
@@ -13,7 +13,7 @@ on HF — the old refs are overwritten — so the skill always:
 
 1. Confirms intent with a one-liner the user has to type.
 2. Downloads the existing refs as a local, timestamped backup.
-3. Regenerates on Modal L40S (same code path that CI uses).
+3. Regenerates through the manual legacy Modal L40S maintenance path.
 4. Pauses for a side-by-side eyeball of backup vs new mp4s.
 5. Uploads with `--force`, scoped to the single `--model-id`.
 6. Reminds the user to keep the backup until the PR lands.
@@ -51,12 +51,13 @@ harder to recover from than failing closed.
 
 Hardcoded:
 
-- Modal GPU: **L40S** (matches CI; re-seeding from another SKU produces refs
-  that L40S CI cannot match).
+- Modal GPU: **L40S**. This is a manual reference-maintenance target, not the
+  active Slurm CI compute path; changing the SKU also changes the historical
+  `L40S_reference_videos` contract.
 - Quality tier: **`default`**. `full_quality` is a separate, deliberate
   operation.
 - HF repo: `FastVideo/ssim-reference-videos` (override via
-  `FASTVIDEO_SSIM_REFERENCE_HF_REPO`).
+  `FASTVIDEO_TEST_SSIM_REFERENCE_HF_REPO`).
 - Device folder: `L40S_reference_videos`.
 
 ## Prerequisites

.agents/skills/seed-ssim-references/SKILL.md

@@ -35,7 +35,8 @@ The skill is run **manually**, once per new test. Before invoking it, the user
 has already sanity-tested the new test locally — it launches `VideoGenerator`
 and writes an artefact without crashing (the missing-reference assertion at
 the end is expected). The skill does not re-test locally; it goes straight
-to Modal L40S (which is what CI uses).
+to the manual legacy Modal L40S reference-maintenance target. Active CI runs
+on the Slinky Slurm cluster and only consumes the resulting references.
 
 ## When to use
 
@@ -61,7 +62,8 @@ Prompt the user for it if they didn't supply it.
 
 Everything else is fixed:
 
-- Modal runner GPU: **L40S** (hardcoded in `fastvideo/tests/modal/ssim_test.py`).
+- Modal maintenance GPU: **L40S** (hardcoded in
+  `fastvideo/tests/modal/ssim_test.py`; this is not the active CI compute path).
 - Device folder: `L40S_reference_videos`.
 - Quality tier: `default` (the tier CI runs). The `full_quality` tier is not
   seeded by this skill.

.buildkite/scripts/lanes/api_server.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the OpenAI-compatible API lane.
+set -euo pipefail
+
+exec pytest ./fastvideo/tests/entrypoints/test_openai_api_integration.py -vs

.buildkite/scripts/lanes/distillation_dmd.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the distillation-DMD lane.
+set -euo pipefail
+
+exec pytest ./fastvideo/tests/training/distill/test_distill_dmd.py -vs

.buildkite/scripts/lanes/dreamverse.sh

@@ -0,0 +1,87 @@
+#!/usr/bin/env bash
+# DreamVerse needs a GPU for import-time device resolution, but it does not
+# build or exercise fastvideo-kernel. A checksummed Node archive is installed
+# in the disposable Slurm container because the shared CI image is
+# Python/CUDA focused.
+set -euo pipefail
+
+node_version=v22.23.2
+case $(uname -m) in
+  aarch64 | arm64)
+    node_arch=arm64
+    node_archive_sha256=013b59cfd2819703a6f4a14ab891fc46fc2a4e3f5bcd92de3fb4929b43e35b30
+    ;;
+  x86_64 | amd64)
+    node_arch=x64
+    node_archive_sha256=b294a556e639d64338823920e5866c21c02741742d2e1529ee1a225c1ec9252a
+    ;;
+  *)
+    echo "Unsupported architecture for DreamVerse Node runtime: $(uname -m)" >&2
+    exit 2
+    ;;
+esac
+node_archive="node-${node_version}-linux-${node_arch}.tar.gz"
+node_runtime_root=$(mktemp -d -t fastvideo-node.XXXXXX)
+node_archive_path="${node_runtime_root}/${node_archive}"
+node_install_dir="${node_runtime_root}/${node_archive%.tar.gz}"
+curl --proto '=https' --tlsv1.2 --retry 5 --retry-all-errors \
+  --location --fail --silent --show-error \
+  "https://nodejs.org/dist/${node_version}/${node_archive}" \
+  --output "$node_archive_path"
+printf '%s  %s\n' "$node_archive_sha256" "$node_archive_path" | sha256sum --check --status
+tar -xzf "$node_archive_path" -C "$node_runtime_root"
+export PATH="${node_install_dir}/bin:${PATH}"
+node --version
+npm --version
+
+export PYTHONPATH="$(pwd)/apps/dreamverse${PYTHONPATH:+:$PYTHONPATH}"
+pytest apps/dreamverse/dreamverse/tests -q
+
+cd apps/dreamverse/web
+npm ci
+npm run typecheck
+npm test
+machine_arch=$(uname -m)
+if [[ $machine_arch =~ ^(aarch64|arm64)$ ]]; then
+  npx playwright install --with-deps chromium firefox
+else
+  npx playwright install --with-deps chromium webkit firefox
+fi
+
+master_port=${MASTER_PORT:-7959}
+BACKEND_PORT=${BACKEND_PORT:-$((master_port + 50))}
+python -m uvicorn dreamverse.mock_server:app --host 127.0.0.1 --port "$BACKEND_PORT" &
+mock_server_pid=$!
+cleanup() {
+  kill "$mock_server_pid" 2>/dev/null || true
+  wait "$mock_server_pid" 2>/dev/null || true
+}
+trap cleanup EXIT INT TERM
+
+for _ in {1..30}; do
+  curl -fsS "http://127.0.0.1:$BACKEND_PORT/healthz" && break
+  sleep 1
+done
+curl -fsS "http://127.0.0.1:$BACKEND_PORT/healthz"
+
+if [[ $machine_arch =~ ^(aarch64|arm64)$ ]]; then
+  # Playwright WebKit traps before opening a page on Linux ARM64, and its
+  # bundled Chromium lacks the H.264/AAC codecs used by the fMP4 assertions.
+  # Firefox covers every flow, including streaming. Chromium and its mobile
+  # profile still cover all codec-independent UI behavior on GB200.
+  BACKEND_HOST=127.0.0.1 BACKEND_PORT="$BACKEND_PORT" CI=1 \
+    npm run e2e -- --project=firefox
+  BACKEND_HOST=127.0.0.1 BACKEND_PORT="$BACKEND_PORT" CI=1 \
+    npm run e2e -- \
+      --project=chromium \
+      --project=mobile-chromium \
+      --grep-invert='streams, plays, and surfaces a downloadable clip|starts a new project and switches back to the prior session|saved projects persist across a page reload'
+else
+  BACKEND_HOST=127.0.0.1 BACKEND_PORT="$BACKEND_PORT" CI=1 \
+    npm run e2e -- \
+      --project=chromium \
+      --project=webkit \
+      --project=firefox \
+      --project=mobile-safari \
+      --project=mobile-chromium
+fi

.buildkite/scripts/lanes/encoder.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the encoder lane.
+set -euo pipefail
+
+exec pytest ./fastvideo/tests/encoders -vs

.buildkite/scripts/lanes/eval.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the evaluation lane.
+set -euo pipefail
+
+exec pytest ./fastvideo/tests/eval -vs

.buildkite/scripts/lanes/golden_gate.sh

@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the golden-gate lane. Environment (HF_HOME
+# and authentication) is the runner's responsibility.
+set -euo pipefail
+
+golden_root=./fastvideo/tests/golden_gate
+selected=${FASTVIDEO_GOLDEN_TEST_FILES-}
+if [ -z "$selected" ]; then
+  if [ "${TEST_SCOPE:-}" = merge ]; then
+    echo "Missing FASTVIDEO_GOLDEN_TEST_FILES for merge scope" >&2
+    exit 2
+  fi
+  selected=all
+fi
+if [ "$selected" = all ]; then
+  exec pytest "$golden_root" -xvs
+fi
+
+[[ $selected =~ ^test_[a-z0-9_]+\.py(,test_[a-z0-9_]+\.py)*$ ]] || {
+  echo "Invalid FASTVIDEO_GOLDEN_TEST_FILES selection" >&2
+  exit 2
+}
+
+IFS=, read -r -a golden_files <<< "$selected"
+golden_paths=()
+for golden_file in "${golden_files[@]}"; do
+  golden_path="$golden_root/$golden_file"
+  [ -f "$golden_path" ] || {
+    echo "Selected golden test does not exist: $golden_file" >&2
+    exit 2
+  }
+  golden_paths+=("$golden_path")
+done
+
+exec pytest "${golden_paths[@]}" -xvs

.buildkite/scripts/lanes/inference_lora.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the LoRA-inference lane.
+set -euo pipefail
+
+exec pytest ./fastvideo/tests/inference/lora/test_lora_inference_similarity.py -vs

.buildkite/scripts/lanes/inference_vmoba.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the VMoBA-inference lane.
+set -euo pipefail
+
+exec python fastvideo/tests/inference/vmoba/test_vmoba_inference.py

.buildkite/scripts/lanes/kernel_tests.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the custom-kernel lane.
+set -euo pipefail
+
+exec pytest fastvideo-kernel/tests/ -vs

.buildkite/scripts/lanes/lora_extraction.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the LoRA-extraction lane.
+set -euo pipefail
+
+exec pytest ./fastvideo/tests/lora_extraction/test_lora_extraction.py -vs

.buildkite/scripts/lanes/performance.sh

@@ -0,0 +1,52 @@
+#!/usr/bin/env bash
+# Canonical Slurm performance lane. Reports are written outside the checkout
+# so the trusted host driver can upload them after untrusted code exits.
+set -uo pipefail
+
+export PERFORMANCE_TRACKING_ROOT=/tmp/perf-tracking
+export PERF_REPORTS_DIR=/workspace/artifacts/performance
+mkdir -p "$PERF_REPORTS_DIR"
+
+if [[ ${BUILDKITE_PULL_REQUEST:-false} =~ ^[1-9][0-9]*$ ]]; then
+  export PERF_RUN_SOURCE=pr
+  export PERF_UPLOAD_POLICY=pass
+elif [ "${BUILDKITE_BRANCH:-}" = main ] \
+  && { [ "${BUILDKITE_SOURCE:-}" = schedule ] || [ "${TEST_SCOPE:-}" = full ]; }; then
+  export PERF_RUN_SOURCE=scheduled_main
+  export PERF_UPLOAD_POLICY=always
+elif [ "${TEST_SCOPE:-}" = direct ]; then
+  export PERF_RUN_SOURCE=unknown
+  export PERF_UPLOAD_POLICY=pass
+else
+  export PERF_RUN_SOURCE=unknown
+  export PERF_UPLOAD_POLICY=never
+fi
+
+nvidia-smi \
+  --query-gpu=index,timestamp,clocks.sm,clocks.max.sm,power.draw,power.limit,temperature.gpu \
+  --format=csv -l 10 > "$PERF_REPORTS_DIR/gpu_telemetry.csv" 2>/dev/null &
+telemetry_pid=$!
+cleanup() {
+  kill "$telemetry_pid" 2>/dev/null || true
+  wait "$telemetry_pid" 2>/dev/null || true
+}
+trap cleanup EXIT INT TERM
+
+pytest ./fastvideo/tests/performance -vs
+pytest_rc=$?
+compare_rc=0
+if [ "$pytest_rc" -eq 0 ] || [ "$PERF_UPLOAD_POLICY" = always ]; then
+  PERF_PYTEST_RC=$pytest_rc python ./fastvideo/tests/performance/compare_baseline.py
+  compare_rc=$?
+fi
+python ./fastvideo/tests/performance/dashboard.py || true
+cp -f fastvideo/tests/performance/results/*.json "$PERF_REPORTS_DIR/" 2>/dev/null || true
+
+echo "--- GPU telemetry (clocks.sm vs clocks.max.sm reveals capped hosts) ---"
+cat "$PERF_REPORTS_DIR/gpu_telemetry.csv" || true
+
+final_rc=$pytest_rc
+if [ "$final_rc" -eq 0 ]; then
+  final_rc=$compare_rc
+fi
+exit "$final_rc"

.buildkite/scripts/lanes/self_forcing.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the self-forcing lane.
+set -euo pipefail
+
+export WANDB_MODE=offline
+exec pytest ./fastvideo/tests/training/self-forcing/test_self_forcing.py -vs

.buildkite/scripts/lanes/ssim.sh

@@ -0,0 +1,40 @@
+#!/usr/bin/env bash
+# Canonical four-GPU SSIM lane for the Slinky Slurm worker.
+set -euo pipefail
+
+args=()
+if [ "${FASTVIDEO_SSIM_BOOTSTRAP_MODE:-0}" = 1 ]; then
+  args+=(--bootstrap-mode)
+fi
+selected=${FASTVIDEO_SSIM_TEST_FILES-}
+if [ -z "$selected" ]; then
+  if [ "${TEST_SCOPE:-}" = merge ]; then
+    echo "Missing FASTVIDEO_SSIM_TEST_FILES for merge scope" >&2
+    exit 2
+  fi
+  selected=all
+fi
+if [ "$selected" != all ]; then
+  [[ $selected =~ ^test_[a-z0-9_]+\.py(,test_[a-z0-9_]+\.py)*$ ]] || {
+    echo "Invalid FASTVIDEO_SSIM_TEST_FILES selection" >&2
+    exit 2
+  }
+  IFS=, read -r -a ssim_files <<< "$selected"
+  for ssim_file in "${ssim_files[@]}"; do
+    args+=(--test-file "$ssim_file")
+  done
+fi
+
+# MoGe's utils3d dependency builds glcontext from source on ARM64. The current
+# runner image predates the baked-in X11 headers below, so keep this guarded
+# bootstrap until every deployed image digest contains libx11-dev.
+if [ ! -f /usr/include/X11/Xlib.h ]; then
+  apt-get -o Acquire::Retries=5 update
+  apt-get -o Acquire::Retries=5 install -y --no-install-recommends libx11-dev
+  rm -rf /var/lib/apt/lists/*
+fi
+
+uv pip install git+https://github.com/microsoft/MoGe.git
+uv pip install k_diffusion einops_exts alias_free_torch torchsde
+
+exec python fastvideo/tests/ssim/ci_runner.py "${args[@]}"

.buildkite/scripts/lanes/train_framework.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the modular training-framework lane.
+set -euo pipefail
+
+exec pytest ./fastvideo/tests/train/models ./fastvideo/tests/train/methods -vs

.buildkite/scripts/lanes/training.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the legacy vanilla-training lane.
+set -euo pipefail
+
+export WANDB_MODE=offline
+exec pytest ./fastvideo/tests/training/Vanilla -srP

.buildkite/scripts/lanes/training_lora.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the legacy LoRA-training lane.
+set -euo pipefail
+
+export WANDB_MODE=offline
+exec pytest ./fastvideo/tests/training/lora/test_lora_training.py -srP

.buildkite/scripts/lanes/training_vsa.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the legacy VSA-training lane.
+set -euo pipefail
+
+export WANDB_MODE=offline
+exec pytest ./fastvideo/tests/training/VSA -srP

.buildkite/scripts/lanes/transformer.sh

@@ -0,0 +1,9 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the transformer lane.
+set -euo pipefail
+
+# The existing block reference records an absent FASTVIDEO_FA4 (FA2). Keep
+# that reference identity; the component lane also selects FA2 explicitly.
+env -u FASTVIDEO_FA4 pytest ./fastvideo/tests/golden_gate/test_wan_t2v.py -xvs
+pytest ./fastvideo/tests/golden_gate/test_wan_causal.py -xvs
+exec pytest ./fastvideo/tests/transformers -vs

.buildkite/scripts/lanes/vae.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+# Canonical Slurm CI selection for the VAE lane.
+set -euo pipefail
+
+pytest ./fastvideo/tests/golden_gate/test_wan_vae.py -xvs
+exec pytest ./fastvideo/tests/vaes -vs

.buildkite/scripts/pr_test.sh

@@ -1,6 +1,19 @@
 #!/bin/bash
 set -uo pipefail
 
+# DORMANT ROLLBACK ONLY. Active CI is Slurm-only and pipeline.yml never calls
+# this launcher. Refuse every Buildkite invocation even if a stale step or
+# operator typo reaches this file; local rollback experiments require an
+# explicit opt-in.
+if [ -n "${BUILDKITE:-}" ]; then
+  echo "Legacy Modal CI is disabled; use the Slinky Slurm runner." >&2
+  exit 2
+fi
+if [ "${FASTVIDEO_ENABLE_LEGACY_MODAL_CI:-0}" != 1 ]; then
+  echo "Legacy Modal CI is dormant. Set FASTVIDEO_ENABLE_LEGACY_MODAL_CI=1 only for a manual rollback test." >&2
+  exit 2
+fi
+
 log() {
   echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
 }

.buildkite/scripts/unit_test.sh

@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+exec pytest \
+  ./fastvideo/tests/api/ \
+  ./fastvideo/tests/contract/ \
+  ./fastvideo/tests/dataset/ \
+  ./fastvideo/tests/workflow/ \
+  ./fastvideo/tests/entrypoints/ \
+  ./fastvideo/tests/loader/ \
+  ./fastvideo/tests/pipelines/ \
+  ./fastvideo/tests/platforms/ \
+  ./fastvideo/tests/train/ \
+  ./fastvideo/tests/stages/ \
+  ./fastvideo/tests/ops/ \
+  ./fastvideo/tests/worker/ \
+  ./fastvideo/tests/training/test_trackers.py \
+  ./fastvideo/tests/attention/test_sdpa_metadata_mask_contract.py \
+  ./fastvideo/tests/attention/test_vsa_h3_tile_grad_safety.py \
+  ./fastvideo/tests/modal/test_kernel_build_cache.py \
+  ./fastvideo/tests/modal/test_pr_test.py \
+  ./fastvideo/tests/modal/test_ssim_test.py \
+  --ignore=./fastvideo/tests/entrypoints/test_openai_api_integration.py \
+  --ignore=./fastvideo/tests/train/models \
+  --ignore=./fastvideo/tests/train/methods \
+  -vs

.github/PULL_REQUEST_TEMPLATE.md

@@ -8,10 +8,10 @@ PR TITLE: Must start with a type tag, e.g.:
 MERGE WORKFLOW:
   1. Ensure pre-commit passes and you have at least 1 approval
   2. Comment /merge (or add the "ready" label) to enter the Merge Queue
-  3. Full Test Suite runs automatically on a staging branch → auto-merge on success
+  3. A path-aware merge gate runs only relevant integration tests → auto-merge on success
 
 ON-DEMAND TESTING (write access required):
-  /test full       — Full Test Suite        /test ssim        — SSIM regression
+  /test full       — Explicit all-lane run  /test ssim        — Full SSIM regression
   /test training   — Training pipeline      /test encoder     — Encoder tests
   /test transformer — Transformer tests     /test vae         — VAE tests
   /test kernel     — CUDA kernel tests      /test unit        — Unit tests

.github/scripts/gate_full_suite.sh

@@ -1,14 +1,14 @@
 #!/usr/bin/env bash
-# Gate the expensive Buildkite full suite on the cheap GitHub checks.
+# Gate the path-aware Buildkite merge plan 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.
+# 'ready' label cannot burn path-selected 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)
+#     no merge gate; 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
@@ -29,7 +29,7 @@ set -euo pipefail
 : "${PR_NUMBER:?PR_NUMBER (pull request number) is required}"
 : "${GITHUB_REPOSITORY:?GITHUB_REPOSITORY is required}"
 
-# Workflow-level `name:` values that must be green before the full suite
+# Workflow-level `name:` values that must be green before the merge gate
 # 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"]'
@@ -56,7 +56,7 @@ recheck_ready_label() {
   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 #${PR_NUMBER} no longer has the 'ready' label —" \
-        "NOT triggering the Buildkite full suite. Re-add the label to re-arm."
+        "NOT triggering the Buildkite merge gate. Re-add the label to re-arm."
       exit 1
     fi
   else
@@ -84,7 +84,7 @@ while true; do
         | map(.name) | join(", ")' <<<"$state")
     if [ -n "$failed" ]; then
       echo "::error::Cheap check(s) failed on ${PR_SHA}: ${failed}." \
-        "NOT triggering the Buildkite full suite. Push a fix (the 'ready'" \
+        "NOT triggering the Buildkite merge gate. Push a fix (the 'ready'" \
         "label re-arms on every push), or re-run the failed check and then" \
         "re-run this workflow."
       exit 1
@@ -97,7 +97,7 @@ while true; do
     if [ "$pending" -eq 0 ]; then
       if [ -z "$missing" ]; then
         recheck_ready_label
-        echo "All watched cheap checks are green — full suite may proceed."
+        echo "All watched cheap checks are green — merge gate may proceed."
         exit 0
       fi
       case "$missing" in
@@ -119,14 +119,14 @@ while true; do
     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."
+      echo "::warning::FAILING OPEN: cannot query GitHub check status — triggering the merge gate 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."
+    echo "::warning::FAILING OPEN: watched checks still pending after $(( MAX_WAIT_SECS / 60 )) min${missing:+ (never appeared: ${missing})} — triggering the merge gate anyway."
     exit 0
   fi
   sleep "$POLL_SECS"

.github/scripts/plan_merge_ci.py

@@ -0,0 +1,581 @@
+#!/usr/bin/env python3
+"""Select the additive GPU integration lanes needed by a PR diff.
+
+Fastcheck is the universal six-lane baseline and is intentionally not repeated
+here.  This planner selects only the more expensive merge-gate lanes.  Unknown
+source/build paths fail closed to the complete integration set, while explicit
+documentation and repository-metadata paths require no additional GPU work.
+"""
+
+from __future__ import annotations
+
+import argparse
+import fnmatch
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import TextIO
+
+MERGE_LANES = (
+    "golden-gate",
+    "ssim",
+    "lora-inference",
+    "lora-extraction",
+    "training",
+    "distillation",
+    "self-forcing",
+    "lora-training",
+    "training-vsa",
+    "inference-vmoba",
+    "performance",
+    "api-server",
+    "train-framework",
+    "eval",
+)
+
+LANE_SCRIPT_TO_KEY = {
+    "api_server.sh": "api-server",
+    "distillation_dmd.sh": "distillation",
+    "eval.sh": "eval",
+    "golden_gate.sh": "golden-gate",
+    "inference_lora.sh": "lora-inference",
+    "inference_vmoba.sh": "inference-vmoba",
+    "lora_extraction.sh": "lora-extraction",
+    "performance.sh": "performance",
+    "self_forcing.sh": "self-forcing",
+    "ssim.sh": "ssim",
+    "train_framework.sh": "train-framework",
+    "training.sh": "training",
+    "training_lora.sh": "lora-training",
+    "training_vsa.sh": "training-vsa",
+}
+
+FASTCHECK_LANE_SCRIPTS = {
+    "dreamverse.sh",
+    "encoder.sh",
+    "kernel_tests.sh",
+    "transformer.sh",
+    "vae.sh",
+}
+
+LEGACY_TRAINING_LANES = (
+    "training",
+    "distillation",
+    "self-forcing",
+    "lora-training",
+    "training-vsa",
+)
+
+ALL_TRAINING_LANES = (*LEGACY_TRAINING_LANES, "train-framework")
+
+SSIM_SMOKE_TESTS = (
+    "test_flux_t2i_similarity.py",
+    "test_wan_t2v_similarity.py",
+)
+
+SAFE_PATTERNS = (
+    "*.md",
+    "*.rst",
+    ".agents/**",
+    ".claude/**",
+    ".codex/**",
+    ".github/ISSUE_TEMPLATE/**",
+    ".github/PULL_REQUEST_TEMPLATE.md",
+    ".github/dependabot.yml",
+    ".github/mergify.yml",
+    ".github/scripts/**",
+    ".github/workflows/**",
+    ".buildkite/scripts/pre_commit.sh",
+    ".git-blame-ignore-revs",
+    ".gitattributes",
+    ".gitignore",
+    ".pre-commit-config.yaml",
+    "AGENTS.md",
+    "CITATION.cff",
+    "CODE_OF_CONDUCT.md",
+    "CONTRIBUTING.md",
+    "LICENSE",
+    "NOTICE",
+    "__init__.py",
+    "collect_env.py",
+    "SECURITY.md",
+    "assets/**",
+    "comfyui/**",
+    "docs/**",
+    "examples/**",
+    "mkdocs.yml",
+    "requirements-mkdocs.in",
+    "requirements-mkdocs.txt",
+    "scripts/**",
+    "tests/__init__.py",
+    "tests/local_tests/**",
+)
+
+ALL_IMPACT_PATTERNS = (
+    ".buildkite/pipeline.yml",
+    "docker/**",
+    "pyproject.toml",
+    "requirements*.txt",
+    "setup.cfg",
+    "setup.py",
+    "uv.lock",
+)
+
+
+@dataclass(frozen=True)
+class FamilyCoverage:
+    pattern: re.Pattern[str]
+    golden_tests: tuple[str, ...]
+    ssim_tests: tuple[str, ...]
+
+
+FAMILY_COVERAGE = (
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])dreamx(_world)?([/_.-]|$)"),
+        ("test_dreamx.py", ),
+        ("test_dreamx_world_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])flux[_-]?2([/_.-]|$)"),
+        ("test_flux2_klein.py", ),
+        ("test_flux2_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])flux(?![_-]?2)([/_.-]|$)"),
+        ("test_flux.py", ),
+        ("test_flux_t2i_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])(hunyuan)?gamecraft([/_.-]|$)"),
+        ("test_gamecraft.py", ),
+        ("test_gamecraft_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])gen3c([/_.-]|$)"),
+        ("test_gen3c.py", ),
+        ("test_gen3c_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])glm[_-]?image([/_.-]|$)"),
+        ("test_glm_image.py", ),
+        ("test_glm_image_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])kandinsky[_-]?5([/_.-]|$)"),
+        ("test_kandinsky5.py", ),
+        ("test_kandinsky5_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])lingbot([a-z0-9_-]*)([/_.-]|$)"),
+        ("test_lingbot.py", ),
+        ("test_lingbot_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])longcat([/_.-]|$)"),
+        ("test_longcat.py", ),
+        ("test_longcat_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])ltx[_-]?2([/_.-]|$)"),
+        ("test_ltx2.py", ),
+        ("test_ltx2_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])matrixgame[_-]?2([/_.-]|$)"),
+        ("test_matrixgame.py", ),
+        ("test_matrixgame2_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])matrixgame[_-]?3([/_.-]|$)"),
+        ("test_matrixgame.py", ),
+        ("test_matrixgame3_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])minimax[_-]?h3([/_.-]|$)"),
+        ("test_minimax_h3_t2v.py", ),
+        ("test_minimax_h3_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])sd[_-]?3([._-]?5)?([/_.-]|$)"),
+        ("test_sd35.py", ),
+        ("test_sd35_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])stable[_-]?audio([/_.-]|$)"),
+        ("test_stable_audio.py", ),
+        ("test_stable_audio_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])turbo(diffusion)?([/_.-]|$)"),
+        (),
+        ("test_turbodiffusion_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])wan(video|vae)?([/_.-]|$)"),
+        ("test_wan_t2v.py", "test_wan_vae.py", "test_wan_causal.py", "test_wan_denoising.py"),
+        (
+            "test_causal_similarity.py",
+            "test_wan_i2v_similarity.py",
+            "test_wan_t2v_similarity.py",
+        ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])z[_-]?image([/_.-]|$)"),
+        ("test_zimage.py", ),
+        ("test_zimage_similarity.py", ),
+    ),
+)
+
+
+@dataclass
+class MergePlan:
+    lanes: set[str] = field(default_factory=set)
+    golden_tests: set[str] = field(default_factory=set)
+    ssim_tests: set[str] = field(default_factory=set)
+    golden_all: bool = False
+    ssim_all: bool = False
+    reasons: list[str] = field(default_factory=list)
+
+    def add_lanes(self, *lanes: str, reason: str) -> None:
+        unknown = set(lanes) - set(MERGE_LANES)
+        if unknown:
+            raise ValueError(f"Unknown merge lanes: {sorted(unknown)}")
+        self.lanes.update(lanes)
+        self.reasons.append(reason)
+
+    def add_golden(self, tests: tuple[str, ...], reason: str) -> None:
+        self.add_lanes("golden-gate", reason=reason)
+        self.golden_tests.update(tests)
+
+    def add_ssim(self, tests: tuple[str, ...], reason: str) -> None:
+        self.add_lanes("ssim", reason=reason)
+        self.ssim_tests.update(tests)
+
+    def require_all(self, reason: str) -> None:
+        self.lanes.update(MERGE_LANES)
+        self.golden_all = True
+        self.ssim_all = True
+        self.reasons.append(reason)
+
+    def ordered_lanes(self) -> tuple[str, ...]:
+        return tuple(lane for lane in MERGE_LANES if lane in self.lanes)
+
+    def encoded_lanes(self) -> str:
+        lanes = self.ordered_lanes()
+        return "," + ",".join(lanes or ("none", )) + ","
+
+    def encoded_golden_tests(self) -> str:
+        if "golden-gate" not in self.lanes:
+            return "none"
+        if self.golden_all or not self.golden_tests:
+            return "all"
+        return ",".join(sorted(self.golden_tests))
+
+    def encoded_ssim_tests(self) -> str:
+        if "ssim" not in self.lanes:
+            return "none"
+        if self.ssim_all or not self.ssim_tests:
+            return "all"
+        return ",".join(sorted(self.ssim_tests))
+
+
+def _matches_any(path: str, patterns: tuple[str, ...]) -> bool:
+    return any(fnmatch.fnmatchcase(path, pattern) for pattern in patterns)
+
+
+def _family_coverage(path: str) -> tuple[set[str], set[str]]:
+    normalized = path.lower()
+    golden: set[str] = set()
+    ssim: set[str] = set()
+    for family in FAMILY_COVERAGE:
+        if family.pattern.search(normalized):
+            golden.update(family.golden_tests)
+            ssim.update(family.ssim_tests)
+    # Select the component actually touched, including compatibility paths.
+    # Family configs/pipeline wiring can affect all four Wan gates.
+    if re.search(r"(^|[/_.-])wan(video|vae)?([/_.-]|$)", normalized):
+        if (normalized.endswith(("/wan/vae.py", "/wan/vae_config.py", "/vaes/wanvae.py"))
+                or normalized.endswith("/wan/stages/conditioning.py")):
+            golden = {"test_wan_vae.py"}
+        elif normalized.endswith(("/wan/causal_transformer.py", "/dits/causal_wanvideo.py",
+                                  "/wan/stages/causal_denoising.py")):
+            golden = {"test_wan_causal.py"}
+        elif (normalized == "fastvideo/models/dits/wanvideo.py"
+              or normalized.endswith(("/wan/transformer.py", "/wan/stages/denoising.py", "/wan/stages/dmd.py"))):
+            golden = {"test_wan_t2v.py", "test_wan_denoising.py"}
+    return golden, ssim
+
+
+def _select_output_coverage(plan: MergePlan, path: str) -> None:
+    golden, ssim = _family_coverage(path)
+    if golden:
+        plan.add_golden(tuple(sorted(golden)), reason=f"model-family golden coverage: {path}")
+    else:
+        plan.golden_all = True
+        plan.add_lanes("golden-gate", reason=f"shared output golden coverage: {path}")
+    if ssim:
+        plan.add_ssim(tuple(sorted(ssim)), reason=f"model-family SSIM coverage: {path}")
+    else:
+        plan.add_ssim(SSIM_SMOKE_TESTS, reason=f"shared output SSIM smoke coverage: {path}")
+
+
+def classify_paths(paths: list[str]) -> MergePlan:
+    plan = MergePlan()
+    normalized_paths: list[str] = []
+    for raw_path in paths:
+        path = raw_path.strip()
+        while path.startswith("./"):
+            path = path[2:]
+        if path:
+            normalized_paths.append(path)
+    normalized_paths = sorted(set(normalized_paths))
+    if not normalized_paths:
+        plan.require_all("changed-file list was empty; failing closed")
+        return plan
+
+    for path in normalized_paths:
+        if path == "__FASTVIDEO_CI_PLAN_ALL__":
+            plan.require_all("changed-file API failed; failing closed")
+            continue
+
+        if path in {"requirements-mkdocs.in", "requirements-mkdocs.txt"}:
+            plan.reasons.append(f"documentation dependencies need no GPU integration: {path}")
+            continue
+
+        if _matches_any(path, ALL_IMPACT_PATTERNS):
+            plan.require_all(f"cross-cutting build/runtime surface: {path}")
+            continue
+
+        lane_script_prefix = ".buildkite/scripts/lanes/"
+        if path.startswith(lane_script_prefix):
+            script_name = Path(path).name
+            lane = LANE_SCRIPT_TO_KEY.get(script_name)
+            if lane is None:
+                if script_name in FASTCHECK_LANE_SCRIPTS:
+                    plan.reasons.append(f"covered by automatic Fastcheck lane: {path}")
+                else:
+                    plan.require_all(f"unknown lane script: {path}")
+            elif lane == "golden-gate":
+                plan.golden_all = True
+                plan.add_lanes(lane, reason=f"golden lane implementation: {path}")
+            elif lane == "ssim":
+                plan.ssim_all = True
+                plan.add_lanes(lane, reason=f"SSIM lane implementation: {path}")
+            else:
+                plan.add_lanes(lane, reason=f"lane implementation: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/golden_gate/"):
+            name = Path(path).name
+            if name.startswith("test_") and name.endswith(".py"):
+                plan.add_golden((name, ), reason=f"changed golden test: {path}")
+            elif name in {"AGENTS.md", "README.md"}:
+                plan.reasons.append(f"golden documentation only: {path}")
+            else:
+                plan.golden_all = True
+                plan.add_lanes("golden-gate", reason=f"shared golden harness/reference: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/ssim/"):
+            name = Path(path).name
+            if name.startswith("test_") and name.endswith(".py"):
+                plan.add_ssim((name, ), reason=f"changed SSIM test: {path}")
+            elif path.endswith((".py", ".json", ".pt", ".png", ".mp4")):
+                plan.ssim_all = True
+                plan.add_lanes("ssim", reason=f"shared SSIM harness/reference: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/performance/") or path.startswith(".buildkite/performance-benchmarks/"):
+            plan.add_lanes("performance", reason=f"performance coverage: {path}")
+            continue
+        if path.startswith(("fastvideo/performance/", "fastvideo/performance_dashboard/",
+                            "apps/performance_dashboard/")):
+            plan.add_lanes("performance", reason=f"performance implementation: {path}")
+            continue
+        if path.startswith("fastvideo/benchmarks/"):
+            if "/mlx_" in path or Path(path).name.startswith("mlx_"):
+                plan.reasons.append(f"covered by the path-filtered macOS MLX workflow: {path}")
+            else:
+                plan.add_lanes("performance", reason=f"benchmark implementation: {path}")
+            continue
+        if path.startswith("fastvideo/tests/eval/") or path.startswith("fastvideo/eval/"):
+            plan.add_lanes("eval", reason=f"evaluation coverage: {path}")
+            continue
+        if path.startswith("fastvideo/third_party/eval/"):
+            plan.add_lanes("eval", reason=f"vendored evaluation implementation: {path}")
+            continue
+        if path.startswith("fastvideo/tests/lora_extraction/") or path.startswith("scripts/lora_extraction/"):
+            plan.add_lanes("lora-extraction", reason=f"LoRA extraction coverage: {path}")
+            continue
+        if path.startswith("fastvideo/tests/inference/lora/"):
+            plan.add_lanes("lora-inference", reason=f"LoRA inference coverage: {path}")
+            continue
+        if path.startswith("fastvideo/tests/inference/vmoba/"):
+            plan.add_lanes("inference-vmoba", reason=f"VMoBA inference coverage: {path}")
+            continue
+        if path.startswith(("fastvideo/dataset/", "fastvideo/workflow/", "fastvideo/pipelines/preprocess/",
+                            "fastvideo/pipelines/training/")):
+            plan.add_lanes(*ALL_TRAINING_LANES, reason=f"shared data/training input surface: {path}")
+            continue
+        if path.startswith("fastvideo/tests/train/") or path.startswith("fastvideo/train/"):
+            plan.add_lanes("train-framework", reason=f"modular training coverage: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/training/"):
+            lowered = path.lower()
+            if "/vanilla/" in lowered:
+                plan.add_lanes("training", reason=f"vanilla training coverage: {path}")
+            elif "/distill/" in lowered:
+                plan.add_lanes("distillation", reason=f"distillation coverage: {path}")
+            elif "/self-forcing/" in lowered:
+                plan.add_lanes("self-forcing", reason=f"self-forcing coverage: {path}")
+            elif "/lora/" in lowered:
+                plan.add_lanes("lora-training", reason=f"LoRA training coverage: {path}")
+            elif "/vsa/" in lowered:
+                plan.add_lanes("training-vsa", reason=f"VSA training coverage: {path}")
+            else:
+                plan.add_lanes(*LEGACY_TRAINING_LANES, reason=f"shared legacy training coverage: {path}")
+            continue
+
+        if path.startswith("fastvideo/training/"):
+            lowered = path.lower()
+            if "self_forcing" in lowered:
+                plan.add_lanes("self-forcing", reason=f"self-forcing implementation: {path}")
+            elif "distill" in lowered:
+                plan.add_lanes("distillation", reason=f"distillation implementation: {path}")
+            elif "lora" in lowered:
+                plan.add_lanes("lora-training", reason=f"LoRA training implementation: {path}")
+            else:
+                plan.add_lanes(*LEGACY_TRAINING_LANES, reason=f"shared legacy training implementation: {path}")
+            continue
+
+        lowered = path.lower()
+        if "vmoba" in lowered and path.startswith(("fastvideo/", ".buildkite/")):
+            plan.add_lanes("inference-vmoba", reason=f"VMoBA implementation: {path}")
+            plan.add_golden(("test_wan_t2v.py", ), reason=f"VMoBA end-to-end coverage: {path}")
+            continue
+        if "lora" in lowered and path.startswith("fastvideo/"):
+            plan.add_lanes(
+                "lora-inference",
+                "lora-extraction",
+                "lora-training",
+                reason=f"shared LoRA implementation: {path}",
+            )
+            _select_output_coverage(plan, path)
+            continue
+
+        if path.startswith("fastvideo/entrypoints/") or path.startswith("fastvideo/api/"):
+            plan.add_lanes("api-server", reason=f"API/entrypoint integration: {path}")
+            if "openai" not in lowered and "/cli/" not in lowered:
+                _select_output_coverage(plan, path)
+            continue
+        if path.startswith("fastvideo/worker/"):
+            plan.add_lanes("api-server", reason=f"worker/API integration: {path}")
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith("fastvideo/distributed/"):
+            plan.add_lanes(
+                "training",
+                "train-framework",
+                reason=f"distributed runtime integration: {path}",
+            )
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith(("fastvideo/hooks/", "fastvideo/platforms/", "fastvideo/third_party/")):
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith(("fastvideo/models/", "fastvideo/pipelines/", "fastvideo/configs/",
+                            "fastvideo/layers/", "fastvideo/attention/")):
+            _select_output_coverage(plan, path)
+            continue
+        if path in {
+                "fastvideo/forward_context.py",
+                "fastvideo/image_processor.py",
+                "fastvideo/registry.py",
+                "fastvideo/utils.py",
+        }:
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith("fastvideo/mlx_runtime/"):
+            plan.reasons.append(f"covered by the path-filtered macOS MLX workflow: {path}")
+            continue
+        if path.startswith("fastvideo/logging_utils/") or path in {
+                "fastvideo/__init__.py",
+                "fastvideo/envs.py",
+                "fastvideo/logger.py",
+                "fastvideo/profiler.py",
+                "fastvideo/version.py",
+        }:
+            plan.reasons.append(f"covered by automatic Fastcheck: {path}")
+            continue
+        if path.startswith(("fastvideo-kernel/", "csrc/")):
+            plan.add_golden(("test_wan_t2v.py", ), reason=f"kernel integration smoke: {path}")
+            plan.add_ssim(("test_wan_t2v_similarity.py", ), reason=f"kernel numerical smoke: {path}")
+            continue
+
+        if path.startswith("apps/dreamverse/"):
+            # DreamVerse is already one of the six automatic Fastcheck lanes.
+            plan.reasons.append(f"covered by automatic DreamVerse Fastcheck: {path}")
+            continue
+        if path.startswith("fastvideo/tests/"):
+            # The automatic unit/component Fastcheck lanes own the remaining
+            # package tests. Domain-specific expensive test roots were handled
+            # above.
+            plan.reasons.append(f"covered by automatic Fastcheck: {path}")
+            continue
+        if path in {".buildkite/scripts/unit_test.sh", ".buildkite/scripts/pr_test.sh"}:
+            plan.reasons.append(f"covered by automatic unit Fastcheck: {path}")
+            continue
+        if _matches_any(path, SAFE_PATTERNS):
+            plan.reasons.append(f"no additional GPU integration needed: {path}")
+            continue
+
+        plan.require_all(f"unclassified path; failing closed: {path}")
+
+    return plan
+
+
+def _write_github_output(output: TextIO, plan: MergePlan) -> None:
+    output.write(f"merge_test_plan={plan.encoded_lanes()}\n")
+    output.write(f"merge_golden_tests={plan.encoded_golden_tests()}\n")
+    output.write(f"merge_ssim_tests={plan.encoded_ssim_tests()}\n")
+    output.write(f"merge_plan_label={','.join(plan.ordered_lanes()) or 'none'}\n")
+
+
+def _write_summary(output: TextIO, plan: MergePlan) -> None:
+    output.write("## Change-aware merge test plan\n\n")
+    output.write("| Selection | Value |\n|---|---|\n")
+    output.write(f"| Additional Slurm lanes | `{','.join(plan.ordered_lanes()) or 'none'}` |\n")
+    output.write(f"| Golden tests | `{plan.encoded_golden_tests()}` |\n")
+    output.write(f"| SSIM tests | `{plan.encoded_ssim_tests()}` |\n\n")
+    output.write("Fastcheck remains the universal six-lane baseline.\n")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--paths-file", type=Path, required=True)
+    parser.add_argument("--github-output", type=Path)
+    parser.add_argument("--summary-file", type=Path)
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    paths = args.paths_file.read_text(encoding="utf-8").splitlines()
+    plan = classify_paths(paths)
+    print(f"MERGE_TEST_PLAN={plan.encoded_lanes()}")
+    print(f"MERGE_GOLDEN_TESTS={plan.encoded_golden_tests()}")
+    print(f"MERGE_SSIM_TESTS={plan.encoded_ssim_tests()}")
+    for reason in plan.reasons:
+        print(f"- {reason}")
+    if args.github_output:
+        with args.github_output.open("a", encoding="utf-8") as output:
+            _write_github_output(output, plan)
+    if args.summary_file:
+        with args.summary_file.open("a", encoding="utf-8") as output:
+            _write_summary(output, plan)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

.github/scripts/test_gate_full_suite.sh

@@ -53,7 +53,7 @@ PC_PENDING='{"name": "pre-commit", "id": 1, "status": "in_progress", "conclusion
 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}'
+OTHER='{"name": "Trigger Merge Gate", "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"}'
 

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

@@ -190,6 +190,7 @@ jobs:
         if: ${{ !inputs.push_by_digest }}
         run: |
           echo "✅ Python ${{ inputs.python_version }} image successfully built and pushed to ${{ steps.image.outputs.name }}:${{ inputs.tag_suffix }}-sha-${GITHUB_SHA::7}"
+          echo "Digest: ${{ steps.build-push.outputs.digest }}"
           echo "To run tests with this image, manually trigger the 'Run Tests' workflow."
 
       - name: Digest success message

.github/workflows/ci-aggregate-status.yml

@@ -26,29 +26,48 @@ jobs:
               per_page: 100,
             });
 
-            const bkStatuses = data.statuses.filter(
-              s => s.context.startsWith('buildkite/ci/')
-            );
-
-            const FASTCHECK_PREFIX = 'buildkite/ci/microscope-';
+            // Buildkite derives the GitHub context prefix from the label emoji.
+            // Keep hard Full Suite lanes in test-tube/bar-chart namespaces and
+            // Fastcheck lanes in microscope so targeted reruns cannot clear the
+            // wrong aggregate status. Automatic PR jobs use pr-fastcheck while
+            // slash-command and Full Suite jobs use ci; normalize the suffix
+            // and keep the newest status for each logical lane.
+            const FASTCHECK_PREFIXES = [
+              'buildkite/pr-fastcheck/microscope-',
+              'buildkite/ci/microscope-',
+            ];
             const FULL_SUITE_PREFIXES = [
               'buildkite/ci/test-tube-',
               'buildkite/ci/bar-chart-',
             ];
 
-            const fastcheck = bkStatuses.filter(
-              s => s.context.startsWith(FASTCHECK_PREFIX)
-            );
-            const fullSuite = bkStatuses.filter(
-              s => FULL_SUITE_PREFIXES.some(p => s.context.startsWith(p))
-            );
+            function newestByLane(prefixes) {
+              const statuses = new Map();
+              for (const status of data.statuses) {
+                const prefix = prefixes.find(p => status.context.startsWith(p));
+                if (!prefix) continue;
+                const lane = status.context.slice(prefix.length);
+                const previous = statuses.get(lane);
+                if (!previous || Date.parse(status.updated_at) > Date.parse(previous.updated_at)) {
+                  statuses.set(lane, status);
+                }
+              }
+              return statuses;
+            }
 
-            if (
-              fastcheck.length > 0
-              && fastcheck.every(s => s.state === 'success')
-            ) {
+            const fastcheck = newestByLane(FASTCHECK_PREFIXES);
+            const fullSuiteOnly = newestByLane(FULL_SUITE_PREFIXES);
+            const fastcheckPassed =
+              fastcheck.size === 6
+              && [...fastcheck.values()].every(s => s.state === 'success');
+            const fullSuitePassed =
+              fastcheckPassed
+              && fullSuiteOnly.size === 14
+              && [...fullSuiteOnly.values()].every(s => s.state === 'success');
+
+            if (fastcheckPassed) {
               core.info(
-                `All ${fastcheck.length} fastcheck tests passed — updating fastcheck-passed`
+                `All ${fastcheck.size} fastcheck tests passed — updating fastcheck-passed`
               );
               await github.rest.repos.createCommitStatus({
                 owner: context.repo.owner,
@@ -56,17 +75,13 @@ jobs:
                 sha,
                 state: 'success',
                 context: 'fastcheck-passed',
-                description:
-                  `All ${fastcheck.length} fastcheck tests passed`,
+                description: `All ${fastcheck.size} fastcheck tests passed`,
               });
             }
 
-            if (
-              fullSuite.length > 0
-              && fullSuite.every(s => s.state === 'success')
-            ) {
+            if (fullSuitePassed) {
               core.info(
-                `All ${fullSuite.length} full suite tests passed — updating full-suite-passed`
+                'All 20 full suite tests passed — updating full-suite-passed'
               );
               await github.rest.repos.createCommitStatus({
                 owner: context.repo.owner,
@@ -74,7 +89,6 @@ jobs:
                 sha,
                 state: 'success',
                 context: 'full-suite-passed',
-                description:
-                  `All ${fullSuite.length} full suite tests passed`,
+                description: 'All 20 full suite tests passed',
               });
             }

.github/workflows/ci-macos-mlx.yml

@@ -0,0 +1,169 @@
+name: macOS MLX Smoke
+
+on:
+  pull_request:
+    branches: [main]
+    paths:
+      - ".github/workflows/ci-macos-mlx.yml"
+      - "fastvideo/mlx_runtime/**"
+      - "fastvideo/tests/mlx/**"
+      - "fastvideo/tests/platforms/test_mps_vsa_error.py"
+      - "fastvideo/tests/platforms/test_cpu_sdpa.py"
+      - "fastvideo/platforms/cpu.py"
+      - "fastvideo/platforms/mps.py"
+      - "fastvideo/platforms/__init__.py"
+      - "fastvideo/__init__.py"
+      - "examples/inference/basic/mlx_*.py"
+      - "fastvideo/benchmarks/mlx_*.py"
+      - "pyproject.toml"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+concurrency:
+  group: macos-mlx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  mlx-smoke:
+    if: github.event_name == 'workflow_dispatch' || github.event.pull_request.draft != true
+    runs-on: macos-15
+    timeout-minutes: 25
+    env:
+      FASTVIDEO_ATTENTION_BACKEND: TORCH_SDPA
+      TOKENIZERS_PARALLELISM: "false"
+      MASTER_ADDR: localhost
+      MASTER_PORT: "29513"
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+
+      - uses: astral-sh/setup-uv@v3
+
+      - name: Install lightweight MLX smoke dependencies
+        run: |
+          uv pip install --system \
+            --index-url https://download.pytorch.org/whl/cpu \
+            torch==2.11.0 torchvision torchaudio
+          uv pip install --system \
+            pytest numpy scipy pillow imageio einops cloudpickle filelock \
+            PyYAML diffusers huggingface_hub remote-pdb safetensors loguru mlx \
+            "ftfy>=6.3.1" "opencv-python>=4.10.0.84" psutil "transformers>=5.0.0"
+
+      - name: Show Apple runtime
+        run: |
+          python - <<'PY'
+          import platform
+          import mlx.core as mx
+          import torch
+
+          print("machine:", platform.machine())
+          print("processor:", platform.processor())
+          print("mlx default device:", mx.default_device())
+          memory_size = mx.metal.device_info().get("memory_size") if mx.metal.is_available() else "metal unavailable"
+          print("mlx memory_size:", memory_size)
+          print("torch:", torch.__version__)
+          print("torch mps available:", torch.backends.mps.is_available())
+          PY
+
+      - name: Run MLX smoke tests
+        run: |
+          python -m pytest \
+            fastvideo/tests/mlx/test_dmd_sampling.py \
+            fastvideo/tests/mlx/test_memory_limits.py \
+            fastvideo/tests/mlx/test_quant_capability.py \
+            fastvideo/tests/mlx/test_mlx_dit_parity.py \
+            fastvideo/tests/mlx/test_mlx_compile_parity.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint_compat.py \
+            fastvideo/tests/mlx/test_mlx_affine_dq_gemm.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_parity.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa_regressions.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_mode.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_fastwan_benchmark.py \
+            fastvideo/tests/mlx/test_taehv_decode.py \
+            fastvideo/tests/mlx/test_frame_upsample.py \
+            fastvideo/tests/mlx/test_mlx_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_refine.py \
+            fastvideo/tests/mlx/test_mlx_prompt_enhance.py \
+            fastvideo/tests/mlx/test_mlx_prompt_to_video_decode.py \
+            fastvideo/tests/mlx/test_mlx_wan22_prompt_cache_fingerprint.py \
+            fastvideo/tests/mlx/test_wan22_sample.py \
+            fastvideo/tests/mlx/test_windowed_attention.py \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_download_unavailable_has_specific_error \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_backend_regression_is_not_skip_eligible \
+            fastvideo/tests/platforms/test_mps_vsa_error.py \
+            fastvideo/tests/platforms/test_cpu_sdpa.py \
+            -v -s -o faulthandler_timeout=120
+
+  # Same tests on MLX's CPU backend. Hosted macOS runners are scarce and
+  # slower to schedule; this Linux job gives fast PR signal on the identical
+  # graph (the parity tests were designed to be backend-agnostic), while the
+  # macOS job above stays the source of truth for Metal behavior.
+  mlx-smoke-linux-cpu:
+    if: github.event_name == 'workflow_dispatch' || github.event.pull_request.draft != true
+    runs-on: ubuntu-latest
+    timeout-minutes: 20
+    env:
+      FASTVIDEO_ATTENTION_BACKEND: TORCH_SDPA
+      TOKENIZERS_PARALLELISM: "false"
+      MASTER_ADDR: localhost
+      MASTER_PORT: "29513"
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+
+      - uses: astral-sh/setup-uv@v3
+
+      - name: Install lightweight MLX smoke dependencies (CPU backend)
+        run: |
+          uv pip install --system \
+            --index-url https://download.pytorch.org/whl/cpu \
+            torch==2.11.0 torchvision torchaudio
+          uv pip install --system \
+            pytest numpy scipy pillow imageio einops cloudpickle filelock \
+            PyYAML diffusers huggingface_hub remote-pdb safetensors loguru "mlx[cpu]" \
+            "ftfy>=6.3.1" "opencv-python>=4.10.0.84" psutil "transformers>=5.0.0"
+
+      - name: Run MLX smoke tests (CPU backend)
+        run: |
+          python -m pytest \
+            fastvideo/tests/mlx/test_dmd_sampling.py \
+            fastvideo/tests/mlx/test_memory_limits.py \
+            fastvideo/tests/mlx/test_quant_capability.py \
+            fastvideo/tests/mlx/test_mlx_dit_parity.py \
+            fastvideo/tests/mlx/test_mlx_compile_parity.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint_compat.py \
+            fastvideo/tests/mlx/test_mlx_affine_dq_gemm.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_parity.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa_regressions.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_mode.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_fastwan_benchmark.py \
+            fastvideo/tests/mlx/test_taehv_decode.py \
+            fastvideo/tests/mlx/test_frame_upsample.py \
+            fastvideo/tests/mlx/test_mlx_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_refine.py \
+            fastvideo/tests/mlx/test_mlx_prompt_enhance.py \
+            fastvideo/tests/mlx/test_mlx_prompt_to_video_decode.py \
+            fastvideo/tests/mlx/test_mlx_wan22_prompt_cache_fingerprint.py \
+            fastvideo/tests/mlx/test_wan22_sample.py \
+            fastvideo/tests/mlx/test_windowed_attention.py \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_download_unavailable_has_specific_error \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_backend_regression_is_not_skip_eligible \
+            fastvideo/tests/platforms/test_mps_vsa_error.py \
+            fastvideo/tests/platforms/test_cpu_sdpa.py \
+            -v -s -o faulthandler_timeout=120

.github/workflows/ci-scheduled-ssim.yml

@@ -0,0 +1,44 @@
+name: Scheduled Full SSIM
+
+on:
+  schedule:
+    - cron: "0 5 * * 0"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  trigger:
+    if: github.repository == 'hao-ai-lab/FastVideo'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Trigger weekly full SSIM on Slinky Slurm
+        env:
+          BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
+          SOURCE_SHA: ${{ github.sha }}
+          SOURCE_BRANCH: ${{ github.event.repository.default_branch }}
+          BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
+          BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
+        run: |
+          set -euo pipefail
+          curl -sS --fail-with-body -X POST \
+            "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
+            -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
+            -H "Content-Type: application/json" \
+            --data-raw "$(jq -n \
+              --arg commit "$SOURCE_SHA" \
+              --arg branch "$SOURCE_BRANCH" \
+              '{
+                commit: $commit,
+                branch: $branch,
+                message: "Weekly full SSIM on Slinky Slurm",
+                ignore_pipeline_branch_filters: true,
+                env: {
+                  TEST_SCOPE: "scheduled",
+                  FULL_SUITE: "false",
+                  TEST_TYPE: "ssim",
+                  PR_NUMBER: "false",
+                  PR_TITLE: "Scheduled full SSIM"
+                }
+              }')"

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

@@ -33,7 +33,6 @@ jobs:
             core.setOutput('has_write', String(hasWrite));
 
       - name: Add ready label and react
-        id: label
         if: steps.perm.outputs.has_write == 'true'
         uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
         with:
@@ -48,47 +47,6 @@ jobs:
               comment_id: context.payload.comment.id,
               content: 'rocket',
             });
-            const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-            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'
-        env:
-          BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
-          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: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
-        run: |
-          curl -sS --fail-with-body -X POST \
-            "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
-            -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
-            -H "Content-Type: application/json" \
-            --data-raw "$(jq -n \
-              --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,
-                branch: $branch,
-                message: $message,
-                ignore_pipeline_branch_filters: true,
-                pull_request_id: $pr_id,
-                pull_request_base_branch: "main",
-                env: {
-                    TEST_SCOPE: "full",
-                    FULL_SUITE: "true",
-                    PR_NUMBER: ($pr_id | tostring),
-                    PR_TITLE: $pr_title
-                  }
-                }')"
 
   parse-command:
     if: >-
@@ -129,7 +87,7 @@ jobs:
           set -euo pipefail
           TEST_NAME=$(echo "$COMMENT" | grep -oP '(?<=/test\s)\S+' | head -1 || true)
 
-          VALID="encoder vae transformer kernel unit dreamverse ssim golden-gate training lora-inference lora-training lora-extraction distillation self-forcing vsa vmoba performance api train-framework eval full fastcheck pre-commit"
+          VALID="encoder vae transformer kernel unit dreamverse ssim golden-gate training lora-inference lora-training lora-extraction distillation self-forcing vsa vmoba performance api train-framework eval unit-ci kernel-ci dreamverse-ci ssim-ci golden-gate-ci encoder-ci vae-ci transformer-ci lora-inference-ci lora-training-ci lora-extraction-ci training-ci distillation-ci self-forcing-ci vsa-ci vmoba-ci performance-ci api-ci train-framework-ci eval-ci full fastcheck pre-commit"
           if [ -z "$TEST_NAME" ] || ! echo "$VALID" | grep -qw "$TEST_NAME"; then
             echo "Unknown test: '$TEST_NAME'. Valid: $VALID"
             exit 1
@@ -137,7 +95,17 @@ jobs:
 
           declare -A MAP=(
             [encoder]=encoder [vae]=vae [transformer]=transformer
-            [kernel]=kernel_tests [unit]=unit_test [dreamverse]=dreamverse_app
+            [kernel]=kernel_tests [unit]=unit_test [unit-ci]=unit_test_ci
+            [kernel-ci]=kernel_tests_ci [dreamverse-ci]=dreamverse_app_ci
+            [ssim-ci]=ssim_ci [vmoba-ci]=inference_vmoba_ci
+            [golden-gate-ci]=golden_gate_ci [training-ci]=training_ci
+            [encoder-ci]=encoder_ci [vae-ci]=vae_ci [transformer-ci]=transformer_ci
+            [lora-inference-ci]=inference_lora_ci [lora-training-ci]=training_lora_ci
+            [lora-extraction-ci]=lora_extraction_ci [distillation-ci]=distillation_dmd_ci
+            [self-forcing-ci]=self_forcing_ci [vsa-ci]=training_vsa_ci
+            [performance-ci]=performance_ci [api-ci]=api_server_ci
+            [train-framework-ci]=train_framework_ci [eval-ci]=eval_ci
+            [dreamverse]=dreamverse_app
             [ssim]=ssim [golden-gate]=golden_gate [training]=training
             [lora-inference]=inference_lora [lora-training]=training_lora
             [lora-extraction]=lora_extraction

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

@@ -1,4 +1,4 @@
-name: Trigger Full Suite
+name: Trigger Merge Gate
 
 on:
   pull_request_target:
@@ -10,7 +10,7 @@ permissions:
   actions: read
 
 concurrency:
-  group: full-suite-${{ github.event.pull_request.number }}
+  group: merge-gate-${{ github.event.pull_request.number }}
   cancel-in-progress: false
 
 jobs:
@@ -34,29 +34,72 @@ jobs:
             });
             const hasReady = pr.labels.some(l => l.name === 'ready');
             core.setOutput('has_ready', String(hasReady));
-            if (!hasReady) core.info('No ready label — skipping Full Suite trigger.');
+            core.setOutput('changed_files', String(pr.changed_files));
+            if (!hasReady) core.info('No ready label — skipping merge-gate trigger.');
 
       - name: Cancel previous Buildkite builds
         if: steps.check.outputs.has_ready == 'true'
         env:
           BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
           PR_BRANCH: ${{ github.event.pull_request.head.ref }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
         run: |
-          # Find running builds for this branch with TEST_SCOPE=full and cancel them
-          builds=$(curl -sS -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
-            "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds?branch=${PR_BRANCH}&state=running,scheduled" \
-            | jq -r '.[] | select(try (.env.TEST_SCOPE == "full") catch false) | .number')
+          # Match both branch and PR number: forks can reuse the same branch name.
+          builds=$(curl -sS --get -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
+            --data-urlencode "branch=$PR_BRANCH" \
+            --data-urlencode "state=running,scheduled" \
+            "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds" \
+            | jq -r --arg pr_number "$PR_NUMBER" \
+              '.[] | select((.env.TEST_SCOPE? == "merge") and (.env.PR_NUMBER? == $pr_number)) | .number')
           for build_num in $builds; do
             echo "Cancelling Buildkite build #$build_num"
             curl -sS -X PUT -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
               "https://api.buildkite.com/v2/organizations/${{ 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
+      # Check out the immutable BASE SHA: pull_request_target must never run a
+      # planner or gate script from the untrusted PR head.
+      - name: Checkout trusted merge planner
         if: steps.check.outputs.has_ready == 'true'
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+          ref: ${{ github.event.pull_request.base.sha }}
+          persist-credentials: false
+
+      - name: Collect changed paths
+        if: steps.check.outputs.has_ready == 'true'
+        env:
+          GH_TOKEN: ${{ github.token }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          EXPECTED_CHANGED_FILES: ${{ steps.check.outputs.changed_files }}
+        run: |
+          set -euo pipefail
+          changed_json="$RUNNER_TEMP/merge-changed-files.json"
+          changed_paths="$RUNNER_TEMP/merge-changed-paths.txt"
+          if gh api --paginate --slurp \
+              "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}/files?per_page=100" \
+              > "$changed_json"; then
+            observed=$(jq '[.[][] | .filename] | unique | length' "$changed_json")
+            if [ "$observed" = "$EXPECTED_CHANGED_FILES" ]; then
+              jq -r '.[][] | .filename, (.previous_filename // empty)' "$changed_json" \
+                | sort -u > "$changed_paths"
+            else
+              echo "::warning::Changed-file API returned $observed of $EXPECTED_CHANGED_FILES paths; selecting all merge lanes."
+              echo '__FASTVIDEO_CI_PLAN_ALL__' > "$changed_paths"
+            fi
+          else
+            echo "::warning::Changed-file API failed; selecting all merge lanes."
+            echo '__FASTVIDEO_CI_PLAN_ALL__' > "$changed_paths"
+          fi
+
+      - name: Select minimal merge tests
+        id: plan
+        if: steps.check.outputs.has_ready == 'true'
+        run: |
+          python3 .github/scripts/plan_merge_ci.py \
+            --paths-file "$RUNNER_TEMP/merge-changed-paths.txt" \
+            --github-output "$GITHUB_OUTPUT" \
+            --summary-file "$GITHUB_STEP_SUMMARY"
 
       - name: Wait for pre-commit and docs build
         if: steps.check.outputs.has_ready == 'true'
@@ -66,7 +109,7 @@ jobs:
           PR_NUMBER: ${{ github.event.pull_request.number }}
         run: bash .github/scripts/gate_full_suite.sh
 
-      - name: Trigger Buildkite Full Suite
+      - name: Trigger Buildkite merge gate
         if: steps.check.outputs.has_ready == 'true'
         env:
           BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
@@ -76,6 +119,10 @@ jobs:
           PR_TITLE: ${{ github.event.pull_request.title }}
           BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
           BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
+          MERGE_TEST_PLAN: ${{ steps.plan.outputs.merge_test_plan }}
+          MERGE_GOLDEN_TESTS: ${{ steps.plan.outputs.merge_golden_tests }}
+          MERGE_SSIM_TESTS: ${{ steps.plan.outputs.merge_ssim_tests }}
+          MERGE_PLAN_LABEL: ${{ steps.plan.outputs.merge_plan_label }}
         run: |
           curl -sS --fail-with-body -X POST \
             "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
@@ -84,8 +131,11 @@ jobs:
             --data-raw "$(jq -n \
               --arg commit "$PR_SHA" \
               --arg branch "$PR_BRANCH" \
-              --arg message "Full Suite for PR #${PR_NUMBER}" \
+              --arg message "Merge gate [${MERGE_PLAN_LABEL}] for PR #${PR_NUMBER}" \
               --arg pr_title "$PR_TITLE" \
+              --arg merge_test_plan "$MERGE_TEST_PLAN" \
+              --arg merge_golden_tests "$MERGE_GOLDEN_TESTS" \
+              --arg merge_ssim_tests "$MERGE_SSIM_TESTS" \
               --argjson pr_id "$PR_NUMBER" \
               '{
                 commit: $commit,
@@ -95,8 +145,11 @@ jobs:
                 pull_request_id: $pr_id,
                 pull_request_base_branch: "main",
                 env: {
-                  TEST_SCOPE: "full",
+                  TEST_SCOPE: "merge",
                   FULL_SUITE: "true",
+                  MERGE_TEST_PLAN: $merge_test_plan,
+                  MERGE_GOLDEN_TESTS: $merge_golden_tests,
+                  MERGE_SSIM_TESTS: $merge_ssim_tests,
                   PR_NUMBER: ($pr_id | tostring),
                   PR_TITLE: $pr_title
                 }

.github/workflows/community-welcome.yml

@@ -38,17 +38,17 @@ jobs:
 
             **How our CI works:**
 
-            PRs run a two-tier CI system:
+            PRs run a three-tier CI system:
             1. **Pre-commit** — formatting (yapf), linting (ruff), type checking (mypy). Runs immediately on every PR.
-            2. **Fastcheck** — core GPU tests (encoders, VAEs, transformers, kernels, unit tests). Runs automatically via Buildkite on relevant file changes (~10-15 min).
-            3. **Full Suite** — integration tests, training pipelines, SSIM regression. Runs only when a reviewer adds the `ready` label.
+            2. **Fastcheck** — six core GPU lanes run automatically via Buildkite (~10-15 min).
+            3. **Merge gate** — a reviewer adds `ready`; changed paths select only the relevant integration, training, golden, or SSIM coverage.
 
             **Before your PR is reviewed:**
             - [ ] `pre-commit run --all-files` passes locally
             - [ ] You've added or updated tests for your changes
             - [ ] The PR description explains what and why
 
-            If pre-commit fails, a bot comment will explain how to fix it. Fastcheck and Full Suite results appear in the Checks section below.
+            If pre-commit fails, a bot comment will explain how to fix it. Fastcheck and merge-gate results appear in the Checks section below.
 
             **Useful links:**
             - [Contributing Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)

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

@@ -13,6 +13,11 @@ on:
         required: false
         default: false
         type: boolean
+      build_ci_runner_image:
+        description: 'Build the ARM64 CUDA 13 CI runner image (sm_100)'
+        required: false
+        default: false
+        type: boolean
   # Auto-rebuild the CUDA images when a repository-controlled image input
   # changes on main. This includes the trusted SM89 kernel artifact's source,
   # metadata/key helper, ABI dependency metadata, and build orchestration.
@@ -198,6 +203,28 @@ jobs:
           docker buildx imagetools create "${TAG_ARGS[@]}" "${IMAGE_REFS[@]}"
           docker buildx imagetools inspect "${TAGS[0]}"
 
+  # The CI runner is ARM64 like DGX Spark, but targets sm_100 rather than sm_121.
+  # Publish a single-architecture variant so the self-hosted CI runner can reuse
+  # the exact prebuilt kernel instead of compiling it in every job.
+  build-ci-runner-image:
+    if: ${{ (github.event_name == 'push' && github.repository == 'hao-ai-lab/FastVideo') || github.event.inputs.build_ci_runner_image == 'true' }}
+    uses: ./.github/workflows/_template-build-image.yml
+    with:
+      python_version: '3.12'
+      dockerfile_path: docker/Dockerfile
+      tag_suffix: py3.12-cuda13.0.0-sm100
+      runner: ubuntu-24.04-arm
+      architecture: arm64
+      build_args: |
+        PYTHON_VERSION=3.12
+        CUDA_VERSION=13.0.0
+        UV_TORCH_BACKEND=cu130
+        TORCH_CUDA_ARCH_LIST=10.0
+        CMAKE_BUILD_PARALLEL_LEVEL=1
+        FLASH_ATTN_WHEEL_TAG=cu130torch2.12
+        FLASH_ATTN_WHEEL_RELEASE_ARM64=https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.22
+    secrets: inherit
+
   # Dreamverse matrix: {backend, UI} x {12.6.3, 13.0.0}, Python 3.12. Torch backend
   # matches the base CUDA (cu126 / cu130). Keep these images amd64-only until the
   # required FA4 dependency stack is available and validated on arm64.

.github/workflows/infra-docs.yml

@@ -6,6 +6,7 @@ on:
     paths:
       - 'docs/**'
       - 'examples/**'
+      - 'scripts/inference/**'
       - 'mkdocs.yml'
       - 'requirements-mkdocs.in'
       - 'requirements-mkdocs.txt'
@@ -16,6 +17,7 @@ on:
     paths:
       - 'docs/**'
       - 'examples/**'
+      - 'scripts/inference/**'
       - 'mkdocs.yml'
       - 'requirements-mkdocs.in'
       - 'requirements-mkdocs.txt'

.github/workflows/publish-kernel.yml

@@ -62,8 +62,9 @@ jobs:
               cuda-version: '13.0.0'
               torch-cuda-short: 'cu130'
           platform:
-            # x86_64 builds the full cu126 + cu130 set (cu130 ships the consumer
-            # Blackwell sm_120a FP4 kernels).
+            # x86_64 builds the full cu126 + cu130 set. cu130 ships the
+            # data-center Blackwell sm_100a/sm_103a VSA and consumer sm_120a FP4
+            # kernels.
             - os: ubuntu-22.04
               arch: x86_64
               wheel-plat: manylinux_2_35_x86_64
@@ -124,7 +125,7 @@ jobs:
       - name: Install dependencies (GCC, Clang, CUDA Paths, Git)
         run: |
           sudo apt update
-          sudo apt install -y git patchelf gcc-11 g++-11 clang-11
+          sudo apt install -y git gcc-11 g++-11 clang-11
           sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
 
           # Allow Git to Access Safe Directory
@@ -168,17 +169,18 @@ jobs:
           #     covers sm_120a; turbodiffusion covers sm_100a+sm_120a. The sm_100 FP4
           #     forward is the FA4 CuTe DSL path in the fastvideo package (PR #1221),
           #     JIT-compiled at runtime — not built into this wheel.
-          #   * x86_64 cu130 = Hopper TK + consumer Blackwell sm_120a FP4.
+          #   * x86_64 cu130 = Hopper TK + data-center Blackwell sm_100a/sm_103a VSA
+          #     + consumer Blackwell sm_120a FP4.
           #   * x86_64 cu126 = Hopper TK only (older drivers; CUDA < 12.8 has no FP4).
           # The per-arch split in CMakeLists pins the FP4 targets to sm_120a and builds
           # the main extension for the full arch list. CMAKE_BUILD_PARALLEL_LEVEL caps
           # Ninja so heavy CUTLASS/TK template TUs don't OOM the 16 GB runner (exit 143).
           if [ "${{ matrix.platform.arch }}" = "aarch64" ]; then
-            export TORCH_CUDA_ARCH_LIST="10.0a;12.0a"
+            export TORCH_CUDA_ARCH_LIST="10.0a;10.3a;12.0a"
             export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=OFF -DFASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER=ON"
             export CMAKE_BUILD_PARALLEL_LEVEL=1
           elif [ "${{ matrix.torch-cuda.torch-cuda-short }}" = "cu130" ]; then
-            export TORCH_CUDA_ARCH_LIST="9.0a;12.0a"
+            export TORCH_CUDA_ARCH_LIST="9.0a;10.0a;10.3a;12.0a"
             export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DFASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
             # A single FP4 TU (attn_qat_infer) can use ~8-12 GB on its own, so serialize.
             export CMAKE_BUILD_PARALLEL_LEVEL=1
@@ -194,7 +196,11 @@ jobs:
           python -m build --wheel --outdir dist
 
           # Fix the wheel to be manylinux compliant
-          uv pip install --system auditwheel
+          # Ubuntu 22.04 ships patchelf 0.14.3, while current auditwheel
+          # requires at least 0.14.5. Use the stable PyPI binary on both
+          # x86_64 and aarch64 release runners.
+          uv pip install --system auditwheel patchelf==0.17.2.4
+          patchelf --version
           # Point auditwheel at torch libs, but do not vendor them into the wheel.
           TORCH_LIB_DIR=$(python - <<'PY'
           import os
@@ -211,7 +217,8 @@ jobs:
             --exclude libtorch.so \
             --exclude libc10.so \
             --exclude libc10_cuda.so \
-            --exclude libtorch_python.so
+            --exclude libtorch_python.so \
+            --exclude libnccl.so.2
           # Move fixed wheels back to dist for upload consistency
           rm dist/*.whl
           mv fixed_dist/*.whl dist/

.gitignore

@@ -23,6 +23,7 @@ Miniconda3-latest-Linux-x86_64.sh
 *validation/
 data/
 outputs/
+outputs_audio/
 outputs_video
 checkpoints/
 sbatch.sh
@@ -54,6 +55,8 @@ eggs/
 
 # MkDocs documentation
 site/
+docs/assets/cookbook-serving.json
+examples/serving/clients/node_modules/
 docs/getting_started/examples/
 docs/examples/
 docs/inference/examples/
@@ -132,6 +135,9 @@ fastvideo/tests/ssim/reference_videos/**
 !fastvideo/tests/ssim/reference_videos/**/*.mp4
 !fastvideo/tests/ssim/reference_videos/**/*.png
 
+# Local H3 MLX kernel / exactness benches (JSON, logs, frames, videos)
+.kernel_bench/
+
 # Editor logs and local Python version pins (accidentally committed)
 *.nvimlog
 .nvimlog

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ exclude: |
     tests/.*|
     scripts/.*|
     fastvideo/dataset/.*|
-    fastvideo/models/.*|
+    fastvideo/models/(?!wan/(config|vae_config|pipeline_config|definition|__init__)\.py$).*|
     ^apps/dreamverse/web/.*|
     examples/.*|
     \.agents/.*|

AGENTS.md

@@ -66,14 +66,18 @@ Local guidance lives next to the code. Read the in-scope file before editing:
 | `fastvideo/AGENTS.md` | Core package map, public API, registry-driven model dispatch |
 | `fastvideo/configs/AGENTS.md` | Arch + pipeline config dataclasses, `param_names_mapping` |
 | `fastvideo/models/AGENTS.md` | DiT / VAE / encoder / scheduler / loader layout (pre-commit excluded) |
+| `fastvideo/models/wan/AGENTS.md` | Wan family-local transformers, VAE, configs, and the SP sharding invariant |
 | `fastvideo/layers/AGENTS.md` | Tensor-parallel linear/attention layer rules for ports |
 | `fastvideo/attention/AGENTS.md` | Backend registry + env-var override |
 | `fastvideo/pipelines/AGENTS.md` | Stage ABC, `basic/<model>/`, `preprocess/`, presets |
+| `fastvideo/pipelines/basic/wan/AGENTS.md` | Wan sampling stages, first-frame conditioning, DMD/causal boundaries |
+| `fastvideo/pipelines/basic/magi_human/AGENTS.md` | MagiHuman umbrella repo, lazy-loaded components, packing invariants |
 | `fastvideo/training/AGENTS.md` | Legacy monolithic pipelines (frozen for existing models) |
 | `fastvideo/train/AGENTS.md` | New modular trainer (methods × models × callbacks, YAML) |
 | `fastvideo/tests/AGENTS.md` | Test taxonomy, conftest, pre-commit-excluded path |
 | `fastvideo/tests/ssim/AGENTS.md` | GPU SSIM regression authoring + reference video sync |
 | `scripts/checkpoint_conversion/AGENTS.md` | Adding a converter for a new HF/official checkpoint |
+| `apps/dreamverse/AGENTS.md` | DreamVerse app structure and conventions |
 
 ## Critical: Two Training Stacks Coexist
 

README.md

@@ -3,12 +3,16 @@
 </div>
 
 <p align="center">
-     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://github.com/hao-ai-lab/FastVideo/discussions/1097" target="_blank"> <b> WeChat </b> </a> |
+     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://haoailab.com/FastVideo/cookbook/"><b>Cookbook</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://github.com/hao-ai-lab/FastVideo/discussions/1097" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
 **FastVideo is a unified post-training and real-time inference framework for accelerated video generation.**
 
 ## NEWS
+- `2026/09/15`: Release [FastH3 8-Step V2](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2), an eight-forward data-free DMD2 checkpoint distilled from MiniMax-H3 with 80% Video Sparse Attention. Run it with `examples/inference/basic/basic_fasth3_8step.py` or the [FastH3 8-Step V2 recipe](https://haoailab.com/FastVideo/cookbook/minimax-h3/).
+- `2026/09/01`: FastH3 now runs locally on Apple Silicon through MLX and on NVIDIA DGX Spark through CUDA 13, including two-Spark inference. Follow the [FastH3 recipes](https://haoailab.com/FastVideo/cookbook/minimax-h3/) and read the [Blog](https://haoailab.com/blogs/fasth3-local/).
+- `2026/08/27`: [FastH3 Preview v1](https://haoailab.com/blogs/fasth3-preview/) is an open-weight 4-step sparse-distilled MiniMax-H3 model for synchronized video-and-audio generation, developed in collaboration with [Nuva Lab](https://nuvalab.ai/) and the [NVIDIA FastGen team](https://github.com/NVlabs/FastGen). Download the recommended [VSA / Data-Free weights](https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree), or see the [full FastH3 collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3).
+- `2026/08/19`: FastVideo now supports MLX on Apple Silicon with [FastMetal-QAD](https://huggingface.co/collections/FastVideo/fastmetal), a family of 1.3B, 5B, and 14B models optimized for Mac. Follow the [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/) and read the [Blog](https://haoailab.com/blogs/fastmetal/).
 - `2026/06/23`: Release FastWan-QAD: 5s of Video generated in 1.8s E2E. See the [FastWan-QAD models](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B), [Attn-QAT training guide](https://haoailab.com/FastVideo/training/attn_qat/), and [blog](https://haoailab.com/blogs/fastwan-qad/).
 - `2026/03/17`: Release demo: Into the Dreamverse: Vibe Directing in FastVideo, check out the [Blog](https://haoailab.com/blogs/dreamverse/).
 - `2026/03/13`: Release demo: Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU, check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/).
@@ -60,7 +64,12 @@ UV_TORCH_BACKEND=cu126 uv pip install fastvideo
 ```
 
 Use `UV_TORCH_BACKEND=cu130` on CUDA 13. Apple silicon users should follow the
-[MPS installation guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mps/).
+[MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/).
+
+> **On an Apple Silicon Mac?** Install with `uv pip install -e '.[mlx]'` from
+> a clone, then pick a recipe in the
+> [cookbook](https://haoailab.com/FastVideo/cookbook/). See the
+> [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/).
 
 Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.
 
@@ -78,7 +87,7 @@ Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virt
    https://hao-ai-lab.github.io/FastVideo/getting_started/installation/):
      - NVIDIA GPU, x86_64                         -> docs/getting_started/installation/gpu.md
      - NVIDIA DGX Spark / GB10, aarch64, CUDA 13  -> docs/getting_started/installation/spark.md
-     - Apple Silicon, macOS                       -> docs/getting_started/installation/mps.md
+     - Apple Silicon, macOS                       -> docs/getting_started/installation/mlx.md
 3. Use uv for every step. If a command fails, debug it and tell me what you changed.
 4. Verify the result:
      python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())"
@@ -126,18 +135,20 @@ def main():
     # Create a video generator with a pre-trained model
     generator = VideoGenerator.from_pretrained(
         "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
-        num_gpus=1,  # Adjust based on your hardware
+        {"engine": {"num_gpus": 1}},  # Adjust based on your hardware
     )
 
     # Define a prompt for your video
     prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."
 
     # Generate the video
-    video = generator.generate_video(
-        prompt,
-        output_path="my_videos/",  # Controls where videos are saved
-        save_video=True
-    )
+    video = generator.generate({
+        "prompt": prompt,
+        "output": {
+            "output_path": "my_videos/",  # Controls where videos are saved
+            "save_video": True,
+        },
+    })
 
 if __name__ == '__main__':
     main()

apps/dreamverse/README.md

@@ -97,13 +97,33 @@ dreamverse-server --port 8009
 dreamverse-mock-server --port 8009
 ```
 
+### Run Dreamverse with FastH3
+
+Select the VSA data-free FastH3 Preview profile when you start the backend:
+
+```bash
+DREAMVERSE_MODEL_ID=fast-h3 dreamverse-server --port 8009
+```
+
+The `fast-h3` profile uses four visible GPUs by default. It loads the `MiniMaxAI/MiniMax-H3` base checkpoint and the
+`vsa-datafree/adapter_model.safetensors` adapter from
+`FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA`. Each request generates a 124-frame, 768×1344 video with
+synchronized audio and five sigma-grid points. Dreamverse uses the last frame of each segment as first-frame
+conditioning for the following segment.
+
+Set `CUDA_VISIBLE_DEVICES` when you need to choose the four physical GPUs:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 DREAMVERSE_MODEL_ID=fast-h3 dreamverse-server --port 8009
+```
+
 > **Expect a slow first boot.** With `torch.compile` and startup warmup enabled
 > (the default), the backend compiles the segment 1 and segment 2 inference
 > paths before it reports ready — this can take **tens of minutes on a cold
 > cache**, regardless of how you deploy (local, server, Docker, or Modal).
 > `/healthz` responds as soon as the process is up; `/readyz` stays `503` until
-> warmup finishes. For a faster, uncompiled startup while testing, set
-> `FASTVIDEO_ENABLE_STARTUP_WARMUP=0` before starting the backend.
+> warmup finishes. To defer compilation until the first generated request while
+> testing, set `FASTVIDEO_ENABLE_STARTUP_WARMUP=0` before starting the backend.
 
 ## Frontend Setup
 
@@ -219,6 +239,7 @@ selection, and mock-server behavior:
 pytest apps/dreamverse/dreamverse/tests/test_config.py \
   apps/dreamverse/dreamverse/tests/test_entrypoints.py \
   apps/dreamverse/dreamverse/tests/test_gpu_pool.py \
+  apps/dreamverse/dreamverse/tests/test_minimax_h3_generation.py \
   apps/dreamverse/dreamverse/tests/test_mock_server.py -q
 ```
 

apps/dreamverse/arch.md

@@ -42,7 +42,7 @@ Near-term OSS note:
 - `apps/dreamverse/dreamverse/main.py`: websocket endpoint, request handling,
   session state machine, rewrite orchestration, REST routes, and stream relay
 - `apps/dreamverse/dreamverse/gpu_pool.py`: GPU worker processes, warmup, model
-  loading, and `generate_video()` calls through FastVideo
+  loading, and `generate()` calls through FastVideo
 - `apps/dreamverse/dreamverse/prompt_enhancer.py`: prompt enhancement, rollout
   rewrite execution, provider selection, and timeout/fallback behavior
 - `apps/dreamverse/dreamverse/rewrite_prompt_payload.py`: canonical rewrite request payload
@@ -139,7 +139,18 @@ session.
 - startup warmup
 - user join/leave commands
 - `USER_STEP` execution for each segment
-- continuation state between segments
+- generation-command routing and stream-result delivery
+
+Model generation has a separate ownership boundary inside each GPU process:
+
+- `apps/dreamverse/dreamverse/generation_worker.py` selects the backend that the active model profile declares and owns
+  the backend lifecycle.
+- `apps/dreamverse/dreamverse/ltx2_generation.py` owns LTX-2 generator configuration, video and audio continuation, and
+  runtime LoRA application.
+- `apps/dreamverse/dreamverse/minimax_h3_generation.py` owns the VSA data-free FastH3 adapter, FastH3 generator and
+  request configuration, and last-frame continuation through MiniMax H3 first-frame conditioning.
+- `apps/dreamverse/dreamverse/generation_contracts.py` defines the decoded media and stream-trimming result that both
+  model backends return to `apps/dreamverse/dreamverse/gpu_pool.py`.
 
 `apps/dreamverse/dreamverse/prompt_enhancer.py` manages:
 

apps/dreamverse/dreamverse/benchmarks/benchmark_pipeline.py

@@ -1,6 +1,6 @@
 """Benchmark the LTX-2 generation pipeline driven by the dreamverse Python SDK path.
 
-Mirrors how ``apps/dreamverse/dreamverse/video_generation.py`` constructs
+Mirrors how ``apps/dreamverse/dreamverse/ltx2_generation.py`` constructs
 ``GeneratorConfig`` and calls ``VideoGenerator.generate()``, then
 captures per-stage timings via the ``FASTVIDEO_STAGE_LOGGING=1`` log
 hooks (same mechanism as ``FastVideo-internal/examples/inference/basic/
@@ -52,7 +52,8 @@ import torch  # noqa: E402
 
 from fastvideo import VideoGenerator  # noqa: E402
 from fastvideo.api import (  # noqa: E402
-    ComponentConfig, CompileConfig, EngineConfig, GeneratorConfig, OffloadConfig, PipelineSelection, QuantizationConfig,
+    ComponentConfig, CompileConfig, EngineConfig, GenerationResult, GeneratorConfig, OffloadConfig, PipelineSelection,
+    QuantizationConfig,
 )
 
 DEFAULT_PROMPT = ("A cinematic drone shot over coastal cliffs at sunrise, golden "
@@ -128,9 +129,9 @@ def _build_generator_config(model_path: str, enable_compile: bool, num_gpus: int
     )
 
 
-def _extract_stage_times(result: dict) -> OrderedDict[str, float]:
+def _extract_stage_times(result: GenerationResult) -> OrderedDict[str, float]:
     out: OrderedDict[str, float] = OrderedDict()
-    info = result.get("logging_info") if isinstance(result, dict) else None
+    info = result.logging_info if isinstance(result, GenerationResult) else None
     if info is None:
         return out
     stages = getattr(info, "stages", None)
@@ -162,19 +163,25 @@ def _do_one_run(generator: VideoGenerator, prompt: str, *, height: int, width: i
     _reset_peak_gpu()
     t0 = time.perf_counter()
     try:
-        result = generator.generate_video(
-            prompt=prompt,
-            negative_prompt="",
-            save_video=False,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            fps=24,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=1.0,
-            seed=seed,
-            ltx2_image_crf=0.0,
-        )
+        result = generator.generate({
+            "prompt": prompt,
+            "negative_prompt": "",
+            "sampling": {
+                "height": height,
+                "width": width,
+                "num_frames": num_frames,
+                "fps": 24,
+                "num_inference_steps": num_inference_steps,
+                "guidance_scale": 1.0,
+                "seed": seed,
+            },
+            "output": {
+                "save_video": False
+            },
+            "extensions": {
+                "ltx2_image_crf": 0.0
+            },
+        })
         if torch.cuda.is_available():
             torch.cuda.synchronize()
     except Exception as exc:

apps/dreamverse/dreamverse/config.py

@@ -1,5 +1,6 @@
 import os
 from pathlib import Path
+from typing import cast
 
 _REPO_ROOT = Path(__file__).resolve().parents[1]
 _SERVER_ROOT = Path(__file__).resolve().parent
@@ -55,16 +56,34 @@ FRONTEND_STATIC_DIR_CANDIDATES = _resolve_frontend_static_dir_candidates()
 MODEL_REGISTRY = {
     "fast-ltx2": {
         "name": "FastLTX2",
+        "generation_backend": "ltx2",
+        "default_sp_size": 1,
         "model_path": "FastVideo/LTX2-Distilled-Diffusers",
         "config_model_path": "FastVideo/LTX2-Distilled-Diffusers",
         "lora_repo": "FastVideo/LTX2-OmniNFT-LoRA",
     },
     "fast-ltx23": {
         "name": "FastLTX23",
+        "generation_backend": "ltx2",
+        "default_sp_size": 1,
         "model_path": "FastVideo/LTX-2.3-Distilled-Diffusers",
         "config_model_path": "FastVideo/LTX-2.3-Distilled-Diffusers",
         "lora_repo": "FastVideo/LTX-2.3-OmniNFT-LoRA",
     },
+    "fast-h3": {
+        "name": "FastH3",
+        "generation_backend": "minimax_h3",
+        "default_sp_size": 4,
+        "model_path": "MiniMaxAI/MiniMax-H3",
+        "adapter_repo": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+        "adapter_filename": "vsa-datafree/adapter_model.safetensors",
+        "attention_backend": "VIDEO_SPARSE_ATTN_H3",
+        "height": 768,
+        "width": 1344,
+        "num_frames": 124,
+        "num_inference_steps": 5,
+        "seed": 1000,
+    },
 }
 
 DEFAULT_MODEL_ID = "fast-ltx2"
@@ -171,7 +190,7 @@ def _optional_env(*names: str) -> str | None:
 DEVTOOLS_ENABLED = _env_bool("FASTVIDEO_ENABLE_DEVTOOLS", False)
 PROMPT_SAFETY_ENABLED = _env_bool("FASTVIDEO_ENABLE_PROMPT_SAFETY", False)
 DREAMVERSE_MAX_AUTOTUNE = _env_bool("DREAMVERSE_MAX_AUTOTUNE", True)
-DREAMVERSE_SP_SIZE = max(1, _env_int("DREAMVERSE_SP_SIZE", 1))
+DREAMVERSE_SP_SIZE = max(1, _env_int("DREAMVERSE_SP_SIZE", cast(int, MODEL_CONFIG["default_sp_size"])))
 
 DREAMVERSE_MODEL_PATH = (os.getenv("DREAMVERSE_MODEL_PATH", "").strip() or None)
 if DREAMVERSE_MODEL_PATH:
@@ -213,7 +232,7 @@ def _resolve_lora_spec(spec: str) -> str | None:
     if not spec:
         return None
     if spec.lower() == "omninft":
-        return MODEL_CONFIG.get("lora_repo")
+        return cast(str | None, MODEL_CONFIG.get("lora_repo"))
     if spec.lower() in AVAILABLE_LORAS:
         return AVAILABLE_LORAS[spec.lower()]["repo"]
     return spec

apps/dreamverse/dreamverse/generation_contracts.py

@@ -0,0 +1,46 @@
+"""Shared contract between DreamVerse generation backends and GPU workers."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Protocol
+
+
+@dataclass
+class StepResult:
+    """Decoded media and stream-trimming metadata for one DreamVerse segment."""
+
+    frames: list
+    audio: Any
+    audio_sample_rate: int | None
+    timings: dict[str, float]
+    head_trim_frames: int
+    head_trim_audio_frames: int
+
+
+class GenerationBackend(Protocol):
+    """Model-owned generation operations used by one GPU worker process."""
+
+    def initialize(self, model_config: dict | None = None) -> None:
+        ...
+
+    def shutdown(self) -> None:
+        ...
+
+    def clear_conditioning(self) -> None:
+        ...
+
+    def generate_step(
+        self,
+        prompt: str,
+        segment_idx: int,
+        image_path: str | None,
+        reset_conditioning: bool,
+    ) -> StepResult:
+        ...
+
+    def warmup(self, prompt: str) -> dict[str, float]:
+        ...
+
+    def apply_lora_stack(self, stack: list[tuple[str, float]]) -> tuple[str | None, str | None]:
+        ...

apps/dreamverse/dreamverse/generation_worker.py

@@ -0,0 +1,96 @@
+"""Select and own one model-specific generation backend per GPU process."""
+
+from __future__ import annotations
+
+from dreamverse.config import MODEL_CONFIG
+from dreamverse.generation_contracts import GenerationBackend, StepResult
+
+
+def _create_generation_backend(backend_name: str, gpu_id: int) -> GenerationBackend:
+    """Construct the backend that owns the selected model family's behavior."""
+    if backend_name == "ltx2":
+        from dreamverse.ltx2_generation import LTX2GenerationBackend
+
+        return LTX2GenerationBackend(gpu_id)
+    if backend_name == "minimax_h3":
+        from dreamverse.minimax_h3_generation import MiniMaxH3GenerationBackend
+
+        return MiniMaxH3GenerationBackend(gpu_id)
+    raise ValueError(f"Unsupported DreamVerse generation backend: {backend_name!r}")
+
+
+class VideoGenerationWorker:
+    """Delegate GPU lifecycle and generation calls to the active model backend."""
+
+    def __init__(self, gpu_id: int):
+        self.gpu_id = gpu_id
+        self.model_config: dict = dict(MODEL_CONFIG)
+        self.backend_name: str | None = None
+        self.backend: GenerationBackend | None = None
+
+    def initialize(self, model_config: dict | None = None) -> None:
+        """Load the requested model through its generation backend.
+
+        Model selection belongs here so the GPU process and streaming layers
+        use one stable media contract without importing model-specific code.
+        """
+        requested_model_config = dict(model_config) if model_config is not None else dict(self.model_config)
+        backend_name = requested_model_config.get("generation_backend")
+        if not isinstance(backend_name, str) or not backend_name:
+            raise ValueError("DreamVerse model configuration requires `generation_backend`.")
+
+        candidate_backend = self.backend
+        if candidate_backend is None or self.backend_name != backend_name:
+            if candidate_backend is not None:
+                candidate_backend.shutdown()
+            candidate_backend = _create_generation_backend(backend_name, self.gpu_id)
+
+        try:
+            candidate_backend.initialize(requested_model_config)
+        except Exception:
+            try:
+                candidate_backend.shutdown()
+            except Exception as shutdown_error:
+                print(f"[GPU {self.gpu_id}] Backend cleanup after initialization failure: {shutdown_error}")
+            self.backend = None
+            self.backend_name = None
+            raise
+
+        self.model_config = requested_model_config
+        self.backend = candidate_backend
+        self.backend_name = backend_name
+
+    def _require_backend(self) -> GenerationBackend:
+        """Return the initialized backend or fail before processing a command."""
+        if self.backend is None:
+            raise RuntimeError("Generation backend is not initialized.")
+        return self.backend
+
+    def shutdown(self) -> None:
+        """Release model resources owned by the selected backend."""
+        if self.backend is not None:
+            self.backend.shutdown()
+
+    def clear_conditioning(self) -> None:
+        self._require_backend().clear_conditioning()
+
+    def generate_step(
+        self,
+        prompt: str,
+        segment_idx: int,
+        image_path: str | None,
+        reset_conditioning: bool,
+    ) -> StepResult:
+        """Generate one segment through the selected model backend."""
+        return self._require_backend().generate_step(
+            prompt,
+            segment_idx,
+            image_path,
+            reset_conditioning,
+        )
+
+    def warmup(self, prompt: str) -> dict[str, float]:
+        return self._require_backend().warmup(prompt)
+
+    def apply_lora_stack(self, stack: list[tuple[str, float]]) -> tuple[str | None, str | None]:
+        return self._require_backend().apply_lora_stack(stack)

apps/dreamverse/dreamverse/gpu_pool.py

@@ -12,7 +12,7 @@ from enum import Enum
 from multiprocessing import Process, Queue
 
 from dreamverse.config import (
-    DEFAULT_MODEL_ID,
+    ACTIVE_MODEL_ID,
     DREAMVERSE_SP_SIZE,
     MODEL_REGISTRY,
     STARTUP_WARMUP_ENABLED,
@@ -54,7 +54,7 @@ from dreamverse.worker_ipc import (
 def _parse_requested_gpu_limit() -> int | None:
     raw_value = os.getenv("FASTVIDEO_GPU_COUNT", "").strip().lower()
     if not raw_value:
-        return 1
+        return DREAMVERSE_SP_SIZE
     if raw_value == "all":
         return None
     try:
@@ -164,12 +164,12 @@ def gpu_worker_process(
     os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
 
-    from dreamverse.video_generation import VideoGenerationWorker
+    from dreamverse.generation_worker import VideoGenerationWorker
 
     worker = VideoGenerationWorker(gpu_id)
 
     def event_loop(first_cmd: Command = None):
-        """Blocking event loop for LTX2; dispatches user commands."""
+        """Block on generation commands after the model is initialized."""
         print(f"[GPU {gpu_id}] Entering event loop")
 
         def handle_command(cmd: Command):
@@ -435,7 +435,7 @@ class GPUSlot:
         self._response_reader_task: asyncio.Task | None = None
         self._active: bool = False
         self._reader_lock: asyncio.Lock | None = None
-        self.current_model_id: str = DEFAULT_MODEL_ID
+        self.current_model_id: str | None = ACTIVE_MODEL_ID
         self.shared_stream_buffer = None
         self.shared_stream_buffer_size = SHARED_STREAM_BUFFER_BYTES
 
@@ -690,7 +690,7 @@ class GPUSlot:
     async def join_user(self, user_id: str, model_id: str = None) -> JoinAck:
         """Add a user to this GPU."""
         if model_id is None:
-            model_id = DEFAULT_MODEL_ID
+            model_id = ACTIVE_MODEL_ID
 
         # Reload model if a different one is requested
         if model_id != self.current_model_id and model_id in MODEL_REGISTRY:
@@ -705,16 +705,23 @@ class GPUSlot:
             self.connected_users.clear()
 
             model_config = MODEL_REGISTRY[model_id]
-            reload_response = await self._send_command(Command(CommandType.RELOAD_MODEL,
-                                                               payload=ReloadModelPayload(model_config=model_config),
-                                                               user_id="__reload__"),
-                                                       timeout=600.0)
+            try:
+                reload_response = await self._send_command(Command(
+                    CommandType.RELOAD_MODEL,
+                    payload=ReloadModelPayload(model_config=model_config),
+                    user_id="__reload__"),
+                                                           timeout=600.0)
+            except Exception:
+                self.current_model_id = None
+                raise
             match reload_response:
                 case ReloadAck():
                     pass
                 case WorkerError(message=msg):
+                    self.current_model_id = None
                     raise RuntimeError(f"Model reload failed: {msg}")
                 case _:
+                    self.current_model_id = None
                     raise RuntimeError(f"Unexpected reload response: "
                                        f"{type(reload_response).__name__}")
 

apps/dreamverse/dreamverse/ltx2_generation.py

@@ -1,9 +1,9 @@
-"""LTX2 model lifecycle and continuation conditioning.
+"""LTX-2 model lifecycle and continuation conditioning.
 
 Runs inside a GPU worker subprocess.  Owns the model, the audio
 encoder, and the per-session continuation state carried across
 segments.  Callers must set ``os.environ["CUDA_VISIBLE_DEVICES"]``
-before constructing ``VideoGenerationWorker`` — all ``fastvideo.*``
+before constructing ``LTX2GenerationBackend`` — all ``fastvideo.*``
 imports are deferred to method bodies so nothing touches CUDA at
 module import time.
 """
@@ -14,9 +14,7 @@ import gc
 import os
 import re
 import time
-from dataclasses import dataclass
-from typing import Any
-
+from typing import TYPE_CHECKING
 import numpy as np
 import torch
 
@@ -35,6 +33,10 @@ from dreamverse.config import (
     DREAMVERSE_LORA_STACK,
     _resolve_lora_spec,
 )
+from dreamverse.generation_contracts import StepResult
+
+if TYPE_CHECKING:
+    from fastvideo.api import GenerationResult
 
 # Multi-frame decoded continuation defaults from
 # examples/inference/basic/basic_ltx2_distilled_video_continuation.py.
@@ -80,22 +82,6 @@ def _reset_lora_registry(worker) -> dict:
     return {"status": "lora_registry_reset"}
 
 
-@dataclass
-class StepResult:
-    """Output of one generation step.
-
-    ``head_trim_frames`` / ``head_trim_audio_frames`` are derived here
-    so downstream AV streaming never needs to import conditioning
-    constants.
-    """
-    frames: list
-    audio: Any
-    audio_sample_rate: int | None
-    timings: dict
-    head_trim_frames: int
-    head_trim_audio_frames: int
-
-
 class ContinuationState:
     """Per-session video + audio conditioning carried across segments."""
 
@@ -113,8 +99,8 @@ class ContinuationState:
         self.video_images = None
         self.audio_latents = None
 
-    def apply_video(self, request_kwargs: dict, segment_idx: int) -> None:
-        """Seed next-segment kwargs with the cached tail frames."""
+    def apply_video(self, request: dict, segment_idx: int) -> None:
+        """Seed the next-segment request with the cached tail frames."""
         if segment_idx <= 1 or not self.video_images:
             return
         from PIL import Image
@@ -128,21 +114,21 @@ class ContinuationState:
                 arr = np.clip(arr, 0, 255).astype(np.uint8)
                 noisy.append(Image.fromarray(arr))
             cond_images = noisy
-        request_kwargs["ltx2_video_conditions"] = [(
+        request["extensions"]["ltx2_video_conditions"] = [(
             cond_images,
             LTX2_VIDEO_CONDITIONING_FRAME_IDX,
             LTX2_VIDEO_CONDITIONING_STRENGTH,
         )]
-        request_kwargs["ltx2_images"] = None
-        request_kwargs["image_path"] = None
+        request["extensions"]["ltx2_images"] = None
+        request["inputs"]["image_path"] = None
 
     def apply_audio(
         self,
-        request_kwargs: dict,
+        request: dict,
         segment_idx: int,
         audio_lps: float,
     ) -> None:
-        """Seed next-segment kwargs with clean audio latents + denoise mask.
+        """Seed the next-segment request with clean audio latents + denoise mask.
 
         When audio conditioning is longer than video, extend audio
         generation and shift video RoPE forward so the audio prefix
@@ -159,9 +145,9 @@ class ContinuationState:
         audio_extra = max(0, AUDIO_CONDITIONING_NUM_FRAMES - LTX2_VIDEO_CONDITIONING_NUM_FRAMES)
         if audio_extra > 0:
             audio_num_frames = NUM_FRAMES + audio_extra
-            request_kwargs["audio_num_frames"] = (audio_num_frames)
+            request["extensions"]["audio_num_frames"] = (audio_num_frames)
             prefix_sec = float(audio_extra) / 24.0
-            request_kwargs["video_position_offset_sec"] = prefix_sec
+            request["extensions"]["video_position_offset_sec"] = prefix_sec
 
         new_duration = float(NUM_FRAMES + audio_extra) / 24.0
         total_T = max(
@@ -179,8 +165,8 @@ class ContinuationState:
         mask = torch.ones((B, 1, total_T, 1), dtype=torch.float32)
         mask[:, :, :audio_cond_T, :] = (1.0 - AUDIO_CONDITIONING_STRENGTH)
 
-        request_kwargs["ltx2_audio_clean_latent"] = clean
-        request_kwargs["ltx2_audio_denoise_mask"] = mask
+        request["extensions"]["ltx2_audio_clean_latent"] = clean
+        request["extensions"]["ltx2_audio_denoise_mask"] = mask
 
     def save_video(self, frames: list) -> None:
         """Snapshot trailing N frames as PIL images for next-segment conditioning."""
@@ -202,7 +188,7 @@ class ContinuationState:
         self.audio_latents = latents.detach().clone().cpu()
 
 
-class VideoGenerationWorker:
+class LTX2GenerationBackend:
     """Single-GPU LTX2 generator with continuation state.
 
     Caller must set ``os.environ["CUDA_VISIBLE_DEVICES"]`` before
@@ -324,7 +310,7 @@ class VideoGenerationWorker:
             ),
         )
 
-        self.generator = VideoGenerator.from_pretrained(config=generator_config)
+        self.generator = VideoGenerator.from_config(generator_config)
         print(f"[GPU {self.gpu_id}] After model load: {self._gpu_mem()}")
 
         lora_stack = DREAMVERSE_LORA_STACK or ([(DREAMVERSE_LORA_PATH,
@@ -421,7 +407,7 @@ class VideoGenerationWorker:
             return
 
         loader = ComponentLoader.for_module_type("audio_encoder", "diffusers")
-        enc = loader.load(audio_vae_path, self.generator.fastvideo_args)
+        enc = loader.load(audio_vae_path, self.generator.resolved_config)
         target = getattr(enc, "model", enc)
 
         proc = AudioProcessor(
@@ -478,51 +464,59 @@ class VideoGenerationWorker:
 
         prompt = self._inject_style_trigger(prompt)
 
-        request_kwargs = dict(
-            prompt=prompt,
-            negative_prompt="",
-            save_video=False,
-            height=FRAME_HEIGHT,
-            width=FRAME_WIDTH,
-            num_frames=NUM_FRAMES,
-            fps=24,
-            num_inference_steps=NUM_INFERENCE_STEPS,
-            guidance_scale=1.0,
-            seed=10,
-            ltx2_image_crf=0.0,
-            image_path=image_path if segment_idx == 1 else None,
-            return_continuation_state=False,
-        )
+        request = {
+            "prompt": prompt,
+            "negative_prompt": "",
+            "inputs": {
+                "image_path": image_path if segment_idx == 1 else None
+            },
+            "sampling": {
+                "height": FRAME_HEIGHT,
+                "width": FRAME_WIDTH,
+                "num_frames": NUM_FRAMES,
+                "fps": 24,
+                "num_inference_steps": NUM_INFERENCE_STEPS,
+                "guidance_scale": 1.0,
+                "seed": 10,
+            },
+            "output": {
+                "save_video": False
+            },
+            "extensions": {
+                "ltx2_image_crf": 0.0,
+                "return_continuation_state": False,
+            },
+        }
 
         if reset_conditioning:
             self.continuation.clear()
 
         audio_lps = (DEFAULT_LTX2_AUDIO_SAMPLE_RATE / DEFAULT_LTX2_AUDIO_HOP_LENGTH / DEFAULT_LTX2_AUDIO_DOWNSAMPLE)
 
-        # Phase 1: seed kwargs with prior-segment conditioning.
-        self.continuation.apply_video(request_kwargs, segment_idx)
-        self.continuation.apply_audio(request_kwargs, segment_idx, audio_lps)
+        # Phase 1: seed the request with prior-segment conditioning.
+        self.continuation.apply_video(request, segment_idx)
+        self.continuation.apply_audio(request, segment_idx, audio_lps)
 
         # Phase 2: generate.
         t0 = time.perf_counter()
-        result = self.generator.generate_video(**request_kwargs)
+        result = self.generator.generate(request)
         torch.cuda.synchronize()
         timings["generation_ms"] = (time.perf_counter() - t0) * 1000
 
-        if not isinstance(result, dict):
-            raise RuntimeError("Expected dictionary output from generate_video.")
-        frames = result.get("frames")
+        if isinstance(result, list):
+            raise RuntimeError("Expected a single GenerationResult from generate.")
+        frames = result.frames
         if not isinstance(frames, list) or len(frames) == 0:
             raise RuntimeError("Generation did not return frames.")
-        audio = result.get("audio")
-        audio_sample_rate = result.get("audio_sample_rate")
+        audio = result.audio
+        audio_sample_rate = result.audio_sample_rate
         if audio is not None and audio_sample_rate is None:
             # LTX2 audio decoding stage uses 24kHz output by default.
             audio_sample_rate = 24000
             print(f"[GPU {self.gpu_id}] audio_sample_rate missing from result; "
                   f"defaulting to {audio_sample_rate}Hz")
 
-        timings["generation_time_ms"] = result.get("generation_time", 0.0) * 1000
+        timings["generation_time_ms"] = (result.generation_time or 0.0) * 1000
 
         # Phase 3: snapshot continuation state for the next segment.
         t_save_start = time.perf_counter()
@@ -560,7 +554,7 @@ class VideoGenerationWorker:
         self,
         audio: object,
         audio_sample_rate: int | None,
-        result: dict,
+        result: "GenerationResult",
         segment_idx: int,
     ) -> torch.Tensor | None:
         """Pick which tensor to cache for next-segment audio conditioning."""
@@ -575,7 +569,7 @@ class VideoGenerationWorker:
                       f"for segment {segment_idx + 1}")
                 return re_encoded
             return None
-        audio_latents = result.get("ltx2_audio_latents")
+        audio_latents = result.extra.get("ltx2_audio_latents")
         if audio_latents is not None:
             print(f"[GPU {self.gpu_id}] Cached audio latents "
                   f"shape={tuple(audio_latents.shape)} "

apps/dreamverse/dreamverse/minimax_h3_generation.py

@@ -0,0 +1,294 @@
+"""FastH3 model lifecycle and first-frame continuation for DreamVerse."""
+
+from __future__ import annotations
+
+import gc
+import os
+import time
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+import torch
+
+from dreamverse.config import DREAMVERSE_SP_SIZE
+from dreamverse.generation_contracts import StepResult
+
+if TYPE_CHECKING:
+    from PIL.Image import Image
+
+
+def _required_config_str(model_config: dict, field_name: str) -> str:
+    """Read one required non-empty string from a DreamVerse model profile."""
+    value = model_config.get(field_name)
+    if not isinstance(value, str) or not value.strip():
+        raise ValueError(f"FastH3 model configuration requires `{field_name}`.")
+    return value.strip()
+
+
+class MiniMaxH3GenerationBackend:
+    """Run the VSA data-free FastH3 adapter and retain one continuation frame."""
+
+    def __init__(self, gpu_id: int):
+        self.gpu_id = gpu_id
+        self.generator: Any | None = None
+        self.model_config: dict = {}
+        self.continuation_image: Image | None = None
+
+    def _gpu_mem(self) -> str:
+        allocated_gib = torch.cuda.memory_allocated() / 1024**3
+        reserved_gib = torch.cuda.memory_reserved() / 1024**3
+        return f"alloc={allocated_gib:.2f}GiB, reserved={reserved_gib:.2f}GiB"
+
+    @staticmethod
+    def _configure_environment(attention_backend: str) -> None:
+        """Apply the fixed boot-time switches from the FastH3 reference recipe."""
+        os.environ.update({
+            "FASTVIDEO_ATTENTION_BACKEND": attention_backend,
+            "FASTVIDEO_FA4": "1",
+            "FASTVIDEO_MINIMAX_H3_FUSIONS": "all",
+            "FASTVIDEO_VSA_SM100A": "0",
+        })
+        os.environ.pop("FASTVIDEO_INFERENCE_TORCH_COMPILE", None)
+
+    def initialize(self, model_config: dict | None = None) -> None:
+        """Download the fixed Preview adapter and load the FastH3 generator.
+
+        The model profile owns the base checkpoint, adapter file, attention
+        backend, and generation geometry. The backend translates that profile
+        into FastVideo's typed generator configuration.
+        """
+        if model_config is not None:
+            self.model_config = dict(model_config)
+        if not self.model_config:
+            raise ValueError("FastH3 initialization requires a model configuration.")
+
+        if self.generator is not None:
+            self.generator.shutdown()
+            self.generator = None
+            gc.collect()
+            torch.cuda.empty_cache()
+
+        self.clear_conditioning()
+        model_path = _required_config_str(self.model_config, "model_path")
+        adapter_repo = _required_config_str(self.model_config, "adapter_repo")
+        adapter_filename = _required_config_str(self.model_config, "adapter_filename")
+        attention_backend = _required_config_str(self.model_config, "attention_backend")
+        self._configure_environment(attention_backend)
+
+        from huggingface_hub import hf_hub_download
+
+        from fastvideo import VideoGenerator
+        from fastvideo.api import (
+            AttentionConfig,
+            CompileConfig,
+            ComponentConfig,
+            EngineConfig,
+            GeneratorConfig,
+            MiniMaxH3Options,
+            OffloadConfig,
+            ParallelismConfig,
+            PipelineSelection,
+        )
+
+        adapter_path = hf_hub_download(repo_id=adapter_repo, filename=adapter_filename)
+        use_vsa = attention_backend == "VIDEO_SPARSE_ATTN_H3"
+        generator_config = GeneratorConfig(
+            model_path=model_path,
+            pipeline=PipelineSelection(
+                components=ComponentConfig(lora_path=adapter_path, lora_strength=1.0),
+                model=MiniMaxH3Options(vae_parallel_decode=True, vae_parallel_decode_strategy="gather"),
+            ),
+            engine=EngineConfig(
+                num_gpus=DREAMVERSE_SP_SIZE,
+                parallelism=ParallelismConfig(tp_size=1, sp_size=DREAMVERSE_SP_SIZE),
+                offload=OffloadConfig(
+                    dit=False,
+                    dit_layerwise=False,
+                    text_encoder=True,
+                    image_encoder=True,
+                    vae=True,
+                    pin_cpu_memory=True,
+                ),
+                compile=CompileConfig(enabled=False, vae_enabled=True, regional=attention_backend == "FLASH_ATTN"),
+                attention=AttentionConfig(
+                    backend=attention_backend,
+                    vsa_sparsity=0.9 if use_vsa else None,
+                    vsa_tile_size=64 if use_vsa else None,
+                ),
+                use_fsdp_inference=False,
+            ),
+        )
+
+        print(f"[GPU {self.gpu_id}] Loading FastH3 model: {model_path}")
+        print(f"[GPU {self.gpu_id}] FastH3 adapter: {adapter_repo}/{adapter_filename}")
+        print(f"[GPU {self.gpu_id}] Before model load: {self._gpu_mem()}")
+        self.generator = VideoGenerator.from_config(generator_config)
+        print(f"[GPU {self.gpu_id}] FastH3 loaded: {self._gpu_mem()} (warmup pending)")
+
+    def shutdown(self) -> None:
+        """Release the FastVideo generator and cached continuation image."""
+        self.clear_conditioning()
+        if self.generator is not None:
+            self.generator.shutdown()
+            self.generator = None
+
+    def clear_conditioning(self) -> None:
+        """Release the first-frame image retained for the next segment."""
+        if self.continuation_image is not None:
+            self.continuation_image.close()
+            self.continuation_image = None
+
+    @staticmethod
+    def _load_rgb_image(image_path: str) -> Image:
+        """Load an image into an independent RGB buffer with no open file handle."""
+        from PIL import Image
+
+        with Image.open(image_path) as image:
+            return image.convert("RGB").copy()
+
+    def _select_conditioning_image(
+        self,
+        segment_idx: int,
+        image_path: str | None,
+        reset_conditioning: bool,
+    ) -> tuple[Image | None, bool]:
+        """Select the initial upload or retained last frame for one segment."""
+        if reset_conditioning:
+            self.clear_conditioning()
+        if segment_idx > 1 and self.continuation_image is not None:
+            return self.continuation_image.copy(), True
+        if segment_idx > 1 and not reset_conditioning:
+            raise RuntimeError(f"FastH3 segment {segment_idx} requires a retained continuation frame.")
+        if segment_idx == 1 and image_path:
+            return self._load_rgb_image(image_path), False
+        return None, False
+
+    def _build_request(self, prompt: str, conditioning_image: Image | None):
+        """Build the typed FastVideo request owned by the FastH3 profile."""
+        from fastvideo.api import GenerationRequest, InputConfig, OutputConfig, SamplingConfig
+
+        return GenerationRequest(
+            prompt=prompt,
+            negative_prompt="",
+            inputs=InputConfig(pil_image=conditioning_image),
+            sampling=SamplingConfig(
+                height=int(self.model_config["height"]),
+                width=int(self.model_config["width"]),
+                num_frames=int(self.model_config["num_frames"]),
+                fps=24,
+                num_inference_steps=int(self.model_config["num_inference_steps"]),
+                guidance_scale=1.0,
+                batch_cfg=False,
+                seed=int(self.model_config["seed"]),
+            ),
+            output=OutputConfig(save_video=False, return_frames=True),
+        )
+
+    def _save_continuation_frame(self, frames: list) -> None:
+        """Retain the last decoded frame as first-frame conditioning."""
+        from PIL import Image
+
+        self.clear_conditioning()
+        self.continuation_image = Image.fromarray(np.ascontiguousarray(frames[-1])).convert("RGB")
+
+    def generate_step(
+        self,
+        prompt: str,
+        segment_idx: int,
+        image_path: str | None,
+        reset_conditioning: bool,
+    ) -> StepResult:
+        """Generate one synchronized FastH3 segment and retain its last frame.
+
+        Later segments use MiniMax H3's first-frame-to-video path. The first
+        conditioned frame and its matching audio duration are trimmed before
+        streaming so adjacent segments do not duplicate media.
+        """
+        if self.generator is None:
+            raise RuntimeError("FastH3 generator is not initialized.")
+        conditioning_image, uses_continuation = self._select_conditioning_image(
+            segment_idx,
+            image_path,
+            reset_conditioning,
+        )
+        request = self._build_request(prompt, conditioning_image)
+        started = time.perf_counter()
+        try:
+            result = self.generator.generate(request)
+        finally:
+            if conditioning_image is not None:
+                conditioning_image.close()
+        torch.cuda.synchronize()
+        generation_ms = (time.perf_counter() - started) * 1000.0
+
+        if isinstance(result, list):
+            raise RuntimeError("FastH3 returned multiple results for one DreamVerse segment.")
+        frames = result.frames
+        if not isinstance(frames, list) or not frames:
+            raise RuntimeError("FastH3 generation did not return decoded frames.")
+        audio = result.audio
+        audio_sample_rate = result.audio_sample_rate
+        if audio is not None and audio_sample_rate is None:
+            raise RuntimeError("FastH3 returned audio without an audio sample rate.")
+
+        save_started = time.perf_counter()
+        self._save_continuation_frame(frames)
+        save_conditioning_ms = (time.perf_counter() - save_started) * 1000.0
+        timings = {
+            "generation_ms": generation_ms,
+            "generation_time_ms": float(result.generation_time or 0.0) * 1000.0,
+            "save_conditioning_ms": save_conditioning_ms,
+            "e2e_latency_ms": (time.perf_counter() - started) * 1000.0,
+        }
+        trim_frames = 1 if uses_continuation else 0
+        print(f"[GPU {self.gpu_id}] FastH3 segment {segment_idx}: "
+              f"{len(frames)} frames, gen={generation_ms:.0f}ms, "
+              f"save_conditioning={save_conditioning_ms:.0f}ms, "
+              f"e2e={timings['e2e_latency_ms']:.0f}ms")
+        return StepResult(
+            frames=frames,
+            audio=audio,
+            audio_sample_rate=audio_sample_rate,
+            timings=timings,
+            head_trim_frames=trim_frames,
+            head_trim_audio_frames=trim_frames,
+        )
+
+    def warmup(self, prompt: str) -> dict[str, float]:
+        """Compile the FastH3 text and first-frame paths before readiness."""
+        warmup_prompt = (prompt or "").strip()
+        if not warmup_prompt:
+            raise RuntimeError("Startup warmup prompt must be non-empty.")
+        print(f"[GPU {self.gpu_id}] FastH3 startup warmup starting "
+              "(synthetic segments: text-to-video, first-frame-to-video)")
+        started = time.perf_counter()
+        text_result = self.generate_step(
+            warmup_prompt,
+            segment_idx=1,
+            image_path=None,
+            reset_conditioning=True,
+        )
+        first_frame_result = self.generate_step(
+            warmup_prompt,
+            segment_idx=2,
+            image_path=None,
+            reset_conditioning=False,
+        )
+        total_ms = (time.perf_counter() - started) * 1000.0
+        self.clear_conditioning()
+        text_ms = float(text_result.timings.get("e2e_latency_ms", 0.0))
+        first_frame_ms = float(first_frame_result.timings.get("e2e_latency_ms", 0.0))
+        print(f"[GPU {self.gpu_id}] FastH3 startup warmup complete: "
+              f"text_to_video={text_ms:.0f}ms, "
+              f"first_frame_to_video={first_frame_ms:.0f}ms, "
+              f"total={total_ms:.0f}ms")
+        return {
+            "warmup_text_to_video_ms": text_ms,
+            "warmup_first_frame_to_video_ms": first_frame_ms,
+            "warmup_total_ms": total_ms,
+        }
+
+    def apply_lora_stack(self, stack: list[tuple[str, float]]) -> tuple[str | None, str | None]:
+        """Reject runtime LoRA mutation because FastH3 uses one startup adapter."""
+        del stack
+        raise RuntimeError("FastH3 uses its fixed startup adapter and does not support runtime LoRA changes.")

apps/dreamverse/dreamverse/session/controller.py

@@ -30,7 +30,7 @@ from dreamverse.session_init_image import cleanup_session_init_image, persist_se
 from dreamverse.worker_ipc import MediaChunk, MediaComplete, MediaInit
 
 from dreamverse.config import (
-    DEFAULT_MODEL_ID,
+    ACTIVE_MODEL_ID,
     GENERATION_SEGMENT_CAP,
     PROMPT_AUTO_SLEEP_MS,
     PROMPT_AUTO_TIMEOUT_MS,
@@ -264,7 +264,7 @@ class SessionController:
             timeout_task = asyncio.create_task(session_timeout())
 
             # Join the engine on this GPU.
-            await slot.join_user(client_id, model_id=DEFAULT_MODEL_ID)
+            await slot.join_user(client_id, model_id=ACTIVE_MODEL_ID)
 
             # Notify client they're connected to a GPU.
             await ws_send_json({

apps/dreamverse/dreamverse/tests/test_config.py

@@ -2,13 +2,14 @@ from __future__ import annotations
 
 import importlib.util
 from pathlib import Path
+from types import ModuleType
 
 import pytest
 
 SERVER_DIR = Path(__file__).resolve().parents[1]
 
 
-def _load_config_module():
+def _load_config_module() -> ModuleType:
     spec = importlib.util.spec_from_file_location(
         "server_config_test_module",
         SERVER_DIR / "config.py",
@@ -20,7 +21,7 @@ def _load_config_module():
     return module
 
 
-def _set_required_prompt_keys(monkeypatch):
+def _set_required_prompt_keys(monkeypatch: pytest.MonkeyPatch) -> None:
     monkeypatch.setenv("CEREBRAS_API_KEY", "cerebras-key")
     monkeypatch.setenv("GROQ_API_KEY", "groq-key")
 
@@ -150,3 +151,38 @@ def test_config_rejects_invalid_prompt_provider(monkeypatch):
 
     with pytest.raises(RuntimeError, match="Invalid FASTVIDEO_PROMPT_PROVIDER"):
         _load_config_module()
+
+
+def test_config_registers_vsa_datafree_fasth3_profile(monkeypatch):
+    """The FastH3 registry entry owns the complete fixed Preview recipe."""
+    _set_required_prompt_keys(monkeypatch)
+
+    module = _load_config_module()
+
+    assert module.MODEL_REGISTRY["fast-h3"] == {
+        "name": "FastH3",
+        "generation_backend": "minimax_h3",
+        "default_sp_size": 4,
+        "model_path": "MiniMaxAI/MiniMax-H3",
+        "adapter_repo": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+        "adapter_filename": "vsa-datafree/adapter_model.safetensors",
+        "attention_backend": "VIDEO_SPARSE_ATTN_H3",
+        "height": 768,
+        "width": 1344,
+        "num_frames": 124,
+        "num_inference_steps": 5,
+        "seed": 1000,
+    }
+
+
+def test_config_uses_fasth3_sequence_parallel_default(monkeypatch):
+    """Selecting FastH3 defaults DreamVerse to its four-GPU topology."""
+    _set_required_prompt_keys(monkeypatch)
+    monkeypatch.setenv("DREAMVERSE_MODEL_ID", "fast-h3")
+    monkeypatch.delenv("DREAMVERSE_SP_SIZE", raising=False)
+
+    module = _load_config_module()
+
+    assert module.ACTIVE_MODEL_ID == "fast-h3"
+    assert module.MODEL_CONFIG["generation_backend"] == "minimax_h3"
+    assert module.DREAMVERSE_SP_SIZE == 4

apps/dreamverse/dreamverse/tests/test_generation_worker.py

@@ -0,0 +1,9 @@
+from dreamverse.generation_worker import _create_generation_backend
+from dreamverse.ltx2_generation import LTX2GenerationBackend
+
+
+def test_create_generation_backend_ltx2_module_import():
+    backend = _create_generation_backend("ltx2", gpu_id=3)
+
+    assert isinstance(backend, LTX2GenerationBackend)
+    assert backend.gpu_id == 3

apps/dreamverse/dreamverse/tests/test_gpu_pool.py

@@ -63,6 +63,14 @@ def test_get_available_gpus_defaults_to_first_visible_device(monkeypatch):
     assert gpu_pool.get_available_gpus() == [3]
 
 
+def test_get_available_gpus_defaults_to_active_model_sequence_parallel_size(monkeypatch):
+    monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "0,1,2,3,4")
+    monkeypatch.delenv("FASTVIDEO_GPU_COUNT", raising=False)
+    monkeypatch.setattr(gpu_pool, "DREAMVERSE_SP_SIZE", 4)
+
+    assert gpu_pool.get_available_gpus() == [0, 1, 2, 3]
+
+
 def test_get_available_gpus_rejects_invalid_gpu_count(monkeypatch):
     monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
     monkeypatch.setenv("FASTVIDEO_GPU_COUNT", "zero")
@@ -71,6 +79,23 @@ def test_get_available_gpus_rejects_invalid_gpu_count(monkeypatch):
         gpu_pool.get_available_gpus()
 
 
+def test_join_user_failed_reload_marks_model_uninitialized(monkeypatch):
+    """A failed model reload forces the next join to reload a model."""
+    slot = gpu_pool.GPUSlot(gpu_id=0, cuda_device="0")
+    slot.current_model_id = "fast-ltx2"
+
+    async def fake_send_command(command, timeout):
+        del command, timeout
+        return gpu_pool.WorkerError(user_id="__reload__", message="load failed")
+
+    monkeypatch.setattr(slot, "_send_command", fake_send_command)
+
+    with pytest.raises(RuntimeError, match="Model reload failed"):
+        asyncio.run(slot.join_user("client-id", model_id="fast-h3"))
+
+    assert slot.current_model_id is None
+
+
 def test_send_command_raises_on_worker_death():
     """A worker that consumes a command and exits without replying must
     surface as RuntimeError via sentinel detection, not after the long
@@ -92,9 +117,9 @@ def test_send_command_raises_on_worker_death():
     ready = resp_q.get(timeout=30.0)
     assert ready == "READY"
 
-    async def runner():
+    async def runner() -> None:
         slot = gpu_pool.GPUSlot(gpu_id=0, cuda_device="0")
-        slot.process = proc
+        slot.process = proc  # type: ignore[assignment]
         slot.command_queue = cmd_q
         slot.response_queue = resp_q
 

apps/dreamverse/dreamverse/tests/test_import_contract.py

@@ -13,15 +13,14 @@ FORBIDDEN_PREFIXES = (
     "fastvideo.models",
     "fastvideo.layers",
     "fastvideo.worker",
-    "fastvideo.fastvideo_args",
 )
 ALLOWED_INTERNAL_IMPORTS = {
     (
-        "video_generation.py",
+        "ltx2_generation.py",
         "fastvideo.models.audio.ltx2_audio_processing",
     ),
     (
-        "video_generation.py",
+        "ltx2_generation.py",
         "fastvideo.models.loader.component_loader",
     ),
 }

apps/dreamverse/dreamverse/tests/test_minimax_h3_generation.py

@@ -0,0 +1,246 @@
+from __future__ import annotations
+
+import os
+from types import SimpleNamespace
+from typing import Any
+
+import numpy as np
+import pytest
+
+import dreamverse.generation_worker as generation_worker
+from dreamverse.minimax_h3_generation import MiniMaxH3GenerationBackend
+
+
+FASTH3_MODEL_CONFIG = {
+    "name": "FastH3",
+    "generation_backend": "minimax_h3",
+    "default_sp_size": 4,
+    "model_path": "MiniMaxAI/MiniMax-H3",
+    "adapter_repo": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+    "adapter_filename": "vsa-datafree/adapter_model.safetensors",
+    "attention_backend": "VIDEO_SPARSE_ATTN_H3",
+    "height": 768,
+    "width": 1344,
+    "num_frames": 124,
+    "num_inference_steps": 5,
+    "seed": 1000,
+}
+
+
+class _RecordingGenerator:
+    """Record typed requests and return small synchronized media fixtures."""
+
+    def __init__(self) -> None:
+        self.requests: list[Any] = []
+        self.conditioning_pixels: list[np.ndarray | None] = []
+
+    def generate(self, request):
+        """Capture the request and return two tiny video frames with audio."""
+        self.requests.append(request)
+        conditioning_image = request.inputs.pil_image
+        self.conditioning_pixels.append(
+            None if conditioning_image is None else np.asarray(conditioning_image).copy())
+        frames = [
+            np.full((2, 3, 3), 10, dtype=np.uint8),
+            np.full((2, 3, 3), 20, dtype=np.uint8),
+        ]
+        return SimpleNamespace(
+            frames=frames,
+            audio=np.zeros((2, 16), dtype=np.float32),
+            audio_sample_rate=44100,
+            generation_time=0.25,
+        )
+
+
+def test_initialize_builds_vsa_datafree_fasth3_generator(monkeypatch):
+    """Initialization translates the DreamVerse profile into typed FastVideo config."""
+    from fastvideo import VideoGenerator
+
+    captured = {}
+    fake_generator = SimpleNamespace(shutdown=lambda: None)
+
+    def fake_from_config(config):
+        captured["config"] = config
+        return fake_generator
+
+    def fake_download(**kwargs):
+        captured["download"] = kwargs
+        return f"/models/{kwargs['filename']}"
+
+    monkeypatch.setattr("huggingface_hub.hf_hub_download", fake_download)
+    monkeypatch.setattr(VideoGenerator, "from_config", fake_from_config)
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.DREAMVERSE_SP_SIZE", 4)
+    monkeypatch.setenv("FASTVIDEO_ATTENTION_BACKEND", "test-attention")
+    monkeypatch.setenv("FASTVIDEO_FA4", "0")
+    monkeypatch.setenv("FASTVIDEO_MINIMAX_H3_FUSIONS", "0")
+    monkeypatch.setenv("FASTVIDEO_VSA_SM100A", "1")
+    monkeypatch.setenv("FASTVIDEO_INFERENCE_TORCH_COMPILE", "1")
+
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    monkeypatch.setattr(backend, "_gpu_mem", lambda: "alloc=0.00GiB, reserved=0.00GiB")
+    backend.initialize(FASTH3_MODEL_CONFIG)
+
+    config = captured["config"]
+    assert captured["download"] == {
+        "repo_id": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+        "filename": "vsa-datafree/adapter_model.safetensors",
+    }
+    assert config.model_path == "MiniMaxAI/MiniMax-H3"
+    assert config.pipeline.components.lora_path.endswith("vsa-datafree/adapter_model.safetensors")
+    assert config.pipeline.components.lora_strength == 1.0
+    assert config.pipeline.experimental == {}
+    assert config.engine.attention.backend == "VIDEO_SPARSE_ATTN_H3"
+    assert config.engine.attention.vsa_sparsity == 0.9
+    assert config.engine.attention.vsa_tile_size == 64
+    assert config.engine.compile.regional is False
+    assert config.pipeline.model.vae_parallel_decode is True
+    assert config.pipeline.model.vae_parallel_decode_strategy == "gather"
+    assert config.engine.num_gpus == 4
+    assert config.engine.parallelism.tp_size == 1
+    assert config.engine.parallelism.sp_size == 4
+    assert config.engine.offload.dit is False
+    assert config.engine.offload.dit_layerwise is False
+    assert config.engine.offload.text_encoder is True
+    assert config.engine.offload.vae is True
+    assert config.engine.compile.vae_enabled is True
+    assert config.engine.use_fsdp_inference is False
+    assert os.environ["FASTVIDEO_ATTENTION_BACKEND"] == "VIDEO_SPARSE_ATTN_H3"
+    assert os.environ["FASTVIDEO_FA4"] == "1"
+    assert os.environ["FASTVIDEO_MINIMAX_H3_FUSIONS"] == "all"
+    assert os.environ["FASTVIDEO_VSA_SM100A"] == "0"
+    assert "FASTVIDEO_INFERENCE_TORCH_COMPILE" not in os.environ
+
+
+def test_initialize_selects_declared_generation_backend(monkeypatch):
+    """The GPU worker constructs the backend that the active model profile declares."""
+    from unittest.mock import Mock
+
+    selected_backend = Mock()
+    monkeypatch.setattr(
+        generation_worker,
+        "_create_generation_backend",
+        lambda backend_name, gpu_id: selected_backend,
+    )
+    worker = generation_worker.VideoGenerationWorker(gpu_id=3)
+
+    worker.initialize(FASTH3_MODEL_CONFIG)
+
+    assert worker.backend_name == "minimax_h3"
+    assert worker.backend is selected_backend
+    selected_backend.initialize.assert_called_once_with(FASTH3_MODEL_CONFIG)
+
+
+def test_initialize_failure_clears_backend_ownership(monkeypatch):
+    """A failed family change leaves the GPU worker explicitly uninitialized."""
+    ltx_backend = SimpleNamespace(initialize=lambda config: None, shutdown=lambda: None)
+
+    def fail_initialize(config):
+        del config
+        raise RuntimeError("load failed")
+
+    fasth3_backend = SimpleNamespace(
+        initialize=fail_initialize,
+        shutdown=lambda: None,
+    )
+    backends = {
+        "ltx2": ltx_backend,
+        "minimax_h3": fasth3_backend,
+    }
+    monkeypatch.setattr(
+        generation_worker,
+        "_create_generation_backend",
+        lambda backend_name, gpu_id: backends[backend_name],
+    )
+    worker = generation_worker.VideoGenerationWorker(gpu_id=3)
+    worker.initialize({"generation_backend": "ltx2"})
+
+    with pytest.raises(RuntimeError, match="load failed"):
+        worker.initialize(FASTH3_MODEL_CONFIG)
+
+    assert worker.backend is None
+    assert worker.backend_name is None
+    assert worker.model_config == {"generation_backend": "ltx2"}
+
+
+def test_generate_step_uses_last_frame_for_continuation(monkeypatch):
+    """A later segment receives the prior segment's last decoded frame."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.torch.cuda.synchronize", lambda: None)
+
+    first_result = backend.generate_step(
+        "first prompt",
+        segment_idx=1,
+        image_path=None,
+        reset_conditioning=True,
+    )
+    second_result = backend.generate_step(
+        "second prompt",
+        segment_idx=2,
+        image_path=None,
+        reset_conditioning=False,
+    )
+
+    first_request = backend.generator.requests[0]
+    assert first_request.inputs.pil_image is None
+    assert first_request.negative_prompt == ""
+    assert first_request.sampling.height == 768
+    assert first_request.sampling.width == 1344
+    assert first_request.sampling.num_frames == 124
+    assert first_request.sampling.num_inference_steps == 5
+    assert first_request.sampling.fps == 24
+    assert first_request.sampling.guidance_scale == 1.0
+    assert first_request.sampling.batch_cfg is False
+    assert first_request.sampling.seed == 1000
+    assert first_request.output.save_video is False
+    assert first_request.output.return_frames is True
+    assert backend.generator.conditioning_pixels[1].tolist() == np.full((2, 3, 3), 20).tolist()
+    assert first_result.head_trim_frames == 0
+    assert first_result.head_trim_audio_frames == 0
+    assert second_result.head_trim_frames == 1
+    assert second_result.head_trim_audio_frames == 1
+    assert second_result.audio_sample_rate == 44100
+
+
+def test_generate_step_reset_uses_text_to_video_path(monkeypatch):
+    """Resetting continuation produces an unconditioned text-to-video request."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.torch.cuda.synchronize", lambda: None)
+
+    backend.generate_step("first prompt", 1, None, True)
+    reset_result = backend.generate_step("reset prompt", 2, None, True)
+
+    assert backend.generator.requests[-1].inputs.pil_image is None
+    assert reset_result.head_trim_frames == 0
+    assert reset_result.head_trim_audio_frames == 0
+
+
+def test_generate_step_missing_continuation_frame(monkeypatch):
+    """A later segment fails when no reset or retained frame defines its input."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+
+    with pytest.raises(RuntimeError, match="requires a retained continuation frame"):
+        backend.generate_step("later prompt", 2, None, False)
+
+    assert backend.generator.requests == []
+
+
+def test_warmup_exercises_text_and_first_frame_paths(monkeypatch):
+    """Warmup covers both request shapes used by a DreamVerse session."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.torch.cuda.synchronize", lambda: None)
+
+    timings = backend.warmup("warmup prompt")
+
+    assert backend.generator.conditioning_pixels[0] is None
+    assert backend.generator.conditioning_pixels[1] is not None
+    assert backend.continuation_image is None
+    assert "warmup_text_to_video_ms" in timings
+    assert "warmup_first_frame_to_video_ms" in timings

apps/dreamverse/dreamverse/tests/test_prompt_enhancer.py

@@ -331,11 +331,11 @@ def test_rewrite_prompt_sequence_accepts_numbered_prose_output():
     ]
 
 
-def test_enhance_prompt_prefers_cerebras_before_groq_fallback():
+def test_enhance_prompt_uses_groq_when_it_returns_first():
     enhancer = _build_staged_enhancer(
         cerebras_payload=_chat_payload_with_content('{"prompt":"Cerebras prompt"}'),
         groq_payload=_chat_payload_with_content('{"prompt":"Groq prompt"}'),
-        cerebras_delay_s=0.01,
+        cerebras_delay_s=0.08,
         groq_delay_s=0.01,
     )
 
@@ -346,12 +346,12 @@ def test_enhance_prompt_prefers_cerebras_before_groq_fallback():
 
     assert result.fallback_used is False
     assert result.error is None
-    assert result.provider == "cerebras"
+    assert result.provider == "groq"
     assert result.model == "gpt-test"
-    assert result.prompt == "Cerebras prompt"
+    assert result.prompt == "Groq prompt"
     assert enhancer.get_provider_success_counts() == {
-        "cerebras": 1,
-        "groq": 0,
+        "cerebras": 0,
+        "groq": 1,
     }
 
 

apps/dreamverse/gpu-pool.drawio

@@ -70,7 +70,7 @@
                 <mxCell id="dispatcher" value="command dispatcher&#xa;&#xa;gpu_worker_process() branches on&#xa;CommandType; asserts payload type&#xa;&#xa;INIT / WARMUP / RELOAD_MODEL&#xa;USER_JOIN / USER_STEP / USER_LEAVE&#xa;SHUTDOWN" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffe6cc;strokeColor=#d79b00;fontSize=11;align=left;spacingLeft=10;spacingTop=8;fontStyle=1;" parent="1" vertex="1">
                     <mxGeometry x="120" y="1120" width="240" height="120" as="geometry"/>
                 </mxCell>
-                <mxCell id="do_step" value="VideoGenerationWorker.generate_step()&#xa;video_generation.py:380&#xa;&#xa;reads + updates ContinuationState,&#xa;calls generator" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e1d5e7;strokeColor=#9673a6;fontSize=11;align=left;spacingLeft=10;spacingTop=8;fontStyle=1;" parent="1" vertex="1">
+                <mxCell id="do_step" value="VideoGenerationWorker.generate_step()&#xa;ltx2_generation.py:380&#xa;&#xa;reads + updates ContinuationState,&#xa;calls generator" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e1d5e7;strokeColor=#9673a6;fontSize=11;align=left;spacingLeft=10;spacingTop=8;fontStyle=1;" parent="1" vertex="1">
                     <mxGeometry x="460" y="1120" width="240" height="120" as="geometry"/>
                 </mxCell>
                 <mxCell id="stream_av" value="stream_fmp4()&#xa;av_streaming.py:121&#xa;&#xa;trims overlap, pipes to ffmpeg,&#xa;publishes StreamInit / StreamChunk /&#xa;StreamComplete via callback" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#b1d8d7;strokeColor=#23445d;fontSize=11;align=left;spacingLeft=10;spacingTop=8;fontStyle=1;" parent="1" vertex="1">
@@ -79,13 +79,13 @@
                 <mxCell id="Ot8BU52QTIb4EhyRSe7I-2" value="" style="edgeStyle=none;html=1;" parent="1" source="generator" target="Ot8BU52QTIb4EhyRSe7I-1" edge="1">
                     <mxGeometry relative="1" as="geometry"/>
                 </mxCell>
-                <mxCell id="generator" value="VideoGenerator  (fastvideo)&#xa;&#xa;LTX2 DiT + refine upsampler&#xa;FP4 quant, torch.compile&#xa;&#xa;owned by VideoGenerationWorker&#xa;video_generation.py:211" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e1d5e7;strokeColor=#9673a6;fontSize=11;" parent="1" vertex="1">
+                <mxCell id="generator" value="VideoGenerator  (fastvideo)&#xa;&#xa;LTX2 DiT + refine upsampler&#xa;FP4 quant, torch.compile&#xa;&#xa;owned by VideoGenerationWorker&#xa;ltx2_generation.py:211" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e1d5e7;strokeColor=#9673a6;fontSize=11;" parent="1" vertex="1">
                     <mxGeometry x="460" y="1300" width="240" height="100" as="geometry"/>
                 </mxCell>
                 <mxCell id="ffmpeg" value="ffmpeg subprocess&#xa;&#xa;libx264 / *_nvenc&#xa;fragmented mp4" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffffff;strokeColor=#d79b00;fontSize=11;" parent="1" vertex="1">
                     <mxGeometry x="800" y="1300" width="260" height="100" as="geometry"/>
                 </mxCell>
-                <mxCell id="caches" value="ContinuationState&#xa;video_generation.py:89&#xa;&#xa;• video_images: list[PIL.Image]&#xa;• audio_latents: torch.Tensor (CPU)&#xa;&#xa;carried across segments" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e1d5e7;strokeColor=#9673a6;fontSize=11;align=left;spacingLeft=10;spacingTop=8;" parent="1" vertex="1">
+                <mxCell id="caches" value="ContinuationState&#xa;ltx2_generation.py:89&#xa;&#xa;• video_images: list[PIL.Image]&#xa;• audio_latents: torch.Tensor (CPU)&#xa;&#xa;carried across segments" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#e1d5e7;strokeColor=#9673a6;fontSize=11;align=left;spacingLeft=10;spacingTop=8;" parent="1" vertex="1">
                     <mxGeometry x="120" y="1300" width="240" height="100" as="geometry"/>
                 </mxCell>
                 <mxCell id="e_cp" value="acquire" style="edgeStyle=orthogonalEdgeStyle;rounded=0;html=1;strokeColor=#6c8ebf;endArrow=classic;fontSize=11;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" parent="1" source="client" target="pool" edge="1">
@@ -207,7 +207,7 @@
                         </Array>
                     </mxGeometry>
                 </mxCell>
-                <mxCell id="e_dsg" value="generator.generate_video()" style="edgeStyle=orthogonalEdgeStyle;rounded=0;html=1;strokeColor=#9673a6;endArrow=classic;fontSize=10;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" parent="1" source="do_step" target="generator" edge="1">
+                <mxCell id="e_dsg" value="generator.generate()" style="edgeStyle=orthogonalEdgeStyle;rounded=0;html=1;strokeColor=#9673a6;endArrow=classic;fontSize=10;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" parent="1" source="do_step" target="generator" edge="1">
                     <mxGeometry relative="1" as="geometry"/>
                 </mxCell>
                 <mxCell id="e_dscache" value="read / write" style="edgeStyle=orthogonalEdgeStyle;rounded=0;html=1;strokeColor=#d6b656;endArrow=classic;startArrow=classic;fontSize=10;exitX=0;exitY=0.8;exitDx=0;exitDy=0;entryX=1;entryY=0.2;entryDx=0;entryDy=0;" parent="1" source="do_step" target="caches" edge="1">
@@ -250,7 +250,7 @@
                         <mxPoint x="690" y="880"/>
                     </Array>
                 </mxCell>
-                <mxCell id="legend" value="Legend&#xa;&#xa;■ blue  client / external&#xa;■ green  main-process pool/slot&#xa;    (methods — italic label)&#xa;■ yellow  containers (routing state)&#xa;■ red  IPC primitives (mp.Queue, mp.RawArray)&#xa;&#xa;Worker subprocess modules:&#xa;■ orange  gpu_pool.py (dispatcher)&#xa;■ lavender  video_generation.py&#xa;■ teal  av_streaming.py&#xa;■ gray  worker_ipc.py (shared types)&#xa;&#xa;Flow:&#xa;  client → pool → slot&#xa;  → _send_command(_tagged) → command_queue&#xa;  → dispatcher → generate_step()&#xa;  → stream_fmp4() → ffmpeg&#xa;  → shared_buf + response_queue&#xa;  → _response_reader → futures / stream_queues&#xa;  → client awaits (via main.py AV loop)" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#f5f5f5;strokeColor=#999999;fontSize=11;align=left;spacingLeft=10;spacingTop=8;" parent="1" vertex="1">
+                <mxCell id="legend" value="Legend&#xa;&#xa;■ blue  client / external&#xa;■ green  main-process pool/slot&#xa;    (methods — italic label)&#xa;■ yellow  containers (routing state)&#xa;■ red  IPC primitives (mp.Queue, mp.RawArray)&#xa;&#xa;Worker subprocess modules:&#xa;■ orange  gpu_pool.py (dispatcher)&#xa;■ lavender  ltx2_generation.py&#xa;■ teal  av_streaming.py&#xa;■ gray  worker_ipc.py (shared types)&#xa;&#xa;Flow:&#xa;  client → pool → slot&#xa;  → _send_command(_tagged) → command_queue&#xa;  → dispatcher → generate_step()&#xa;  → stream_fmp4() → ffmpeg&#xa;  → shared_buf + response_queue&#xa;  → _response_reader → futures / stream_queues&#xa;  → client awaits (via main.py AV loop)" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#f5f5f5;strokeColor=#999999;fontSize=11;align=left;spacingLeft=10;spacingTop=8;" parent="1" vertex="1">
                     <mxGeometry x="39" y="-200" width="270" height="380" as="geometry"/>
                 </mxCell>
                 <mxCell id="Ot8BU52QTIb4EhyRSe7I-1" value="FastVideo video_generator" style="whiteSpace=wrap;html=1;fontSize=11;fillColor=#e1d5e7;strokeColor=#9673a6;rounded=1;" parent="1" vertex="1">
@@ -389,10 +389,10 @@
                 <mxCell id="cw2" value="from fastvideo.entrypoints.video_generator import VideoGenerator&#xa;from fastvideo.models.dits.ltx2 import DEFAULT_LTX2_AUDIO_*&#xa;&#xa;** Dreamverse reaches into fastvideo internals here **" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffe0b2;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
                     <mxGeometry x="675" y="695" width="550" height="60" as="geometry"/>
                 </mxCell>
-                <mxCell id="cw3" value="on Command(INIT):&#xa;   VideoGenerationWorker.initialize()    (video_generation.py:247)&#xa;      maybe_download_model(model_id)&#xa;      VideoGenerator.from_pretrained(path, FP4Config, PipelineConfig)&#xa;      load audio VAE, resolve refine upsampler&#xa;      resp_q.put(InitAck(success=True))" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffffff;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
+                <mxCell id="cw3" value="on Command(INIT):&#xa;   VideoGenerationWorker.initialize()    (ltx2_generation.py:247)&#xa;      maybe_download_model(model_id)&#xa;      VideoGenerator.from_pretrained(path, FP4Config, PipelineConfig)&#xa;      load audio VAE, resolve refine upsampler&#xa;      resp_q.put(InitAck(success=True))" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffffff;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
                     <mxGeometry x="675" y="765" width="550" height="95" as="geometry"/>
                 </mxCell>
-                <mxCell id="cw4" value="on Command(WARMUP) with WarmupPayload:&#xa;   VideoGenerationWorker.warmup(payload.prompt)    (video_generation.py:518)&#xa;      two synthetic segments prime caches + torch.compile&#xa;   resp_q.put(WarmupComplete(timings=...))" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffffff;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
+                <mxCell id="cw4" value="on Command(WARMUP) with WarmupPayload:&#xa;   VideoGenerationWorker.warmup(payload.prompt)    (ltx2_generation.py:518)&#xa;      two synthetic segments prime caches + torch.compile&#xa;   resp_q.put(WarmupComplete(timings=...))" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffffff;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
                     <mxGeometry x="675" y="870" width="550" height="55" as="geometry"/>
                 </mxCell>
                 <mxCell id="cw5" value="enter main worker loop → waits for JOIN_USER / USER_STEP / LEAVE" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#c8e6c9;strokeColor=#388e3c;fontSize=11;fontStyle=1;fontFamily=monospace;" parent="1" vertex="1">
@@ -534,7 +534,7 @@
                         <mxPoint x="1040" y="1610" as="targetPoint"/>
                     </mxGeometry>
                 </mxCell>
-                <mxCell id="dm11a" value="10a. worker runs:&#xa;VideoGenerationWorker.generate_step()&#xa;    (video_generation.py:380)&#xa;  → generator.generate_video()&#xa;  → updates ContinuationState&#xa;then stream_fmp4() (av_streaming.py:121)&#xa;  → ffmpeg (rawvideo+wav → fmp4)" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffe0b2;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
+                <mxCell id="dm11a" value="10a. worker runs:&#xa;VideoGenerationWorker.generate_step()&#xa;    (ltx2_generation.py:380)&#xa;  → generator.generate()&#xa;  → updates ContinuationState&#xa;then stream_fmp4() (av_streaming.py:121)&#xa;  → ffmpeg (rawvideo+wav → fmp4)" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffe0b2;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
                     <mxGeometry x="955" y="1640" width="180" height="70" as="geometry"/>
                 </mxCell>
                 <mxCell id="dm11" value="10b. resp_q.put(MediaInit / MediaChunk / MediaComplete / StepComplete)" style="endArrow=classic;html=1;strokeColor=#b85450;fontSize=10;labelBackgroundColor=#ffffff;" parent="1" edge="1">

apps/dreamverse/serve_configs/streaming_demo.yaml

@@ -64,8 +64,8 @@ generator:
     # internal: pipeline_config.dit_config.quant_config = FP4Config()
     # set in gpu_pool.py:280 (via the legacy in-place mutation). The
     # public typed surface resolves "NVFP4" to NVFP4Config() and pins
-    # it on dit_config in FastVideoArgs.__post_init__. Comment this
-    # block out on hosts without flashinfer / NVFP4 hardware.
+    # it on dit_config when resolution materializes the PipelineConfig.
+    # Comment this block out on hosts without flashinfer / NVFP4 hardware.
     quantization:
       transformer_quant: NVFP4
 

apps/dreamverse/web/e2e/preset-prompt-generation.spec.ts

@@ -67,7 +67,7 @@ test.describe('preset prompt generation', () => {
     // on a B200 plus encode/transfer time. The "Continuation flipped
     // to Generating + Leave button rendered" pair above is the proof
     // the integration works: FE → /readyz → /curated-presets → WS
-    // /ws → BE → GPU pool → VideoGenerator.generate_video, all green.
+    // /ws → BE → GPU pool → VideoGenerator.generate, all green.
     const video = page.locator('video').first();
     await expect(video).toHaveCount(1);
   });

apps/fastvideo_studio/database.py

@@ -9,6 +9,7 @@ from __future__ import annotations
 
 import contextlib
 import logging
+import json
 import sqlite3
 import threading
 from pathlib import Path
@@ -46,8 +47,13 @@ DEFAULT_SETTINGS: dict[str, Any] = {
 
 
 def _sqlite_row_get(row: sqlite3.Row, key: str, default: Any) -> Any:
-    """Like dict.get for sqlite3.Row (Row has no .get on Python 3.10)."""
-    return row[key] if key in row else default  # noqa: SIM401
+    """Like dict.get for sqlite3.Row (Row has no .get on Python 3.10).
+
+    NOTE: `key in row` tests Row *values*, not column names, so the membership
+    check has to go through .keys() -- otherwise every lookup falls back to the
+    default and jobs restored from the database lose their stored fields.
+    """
+    return row[key] if key in row.keys() else default  # noqa: SIM401, SIM118
 
 
 def _get_db_path(data_dir: Path) -> Path:
@@ -83,6 +89,9 @@ def _migrate_db(conn: sqlite3.Connection) -> None:
     _add_column_if_missing(conn, "jobs", "fps", "INTEGER", "24")
     _add_column_if_missing(conn, "jobs", "workload_type", "TEXT", "'t2v'")
     _add_column_if_missing(conn, "jobs", "image_path", "TEXT", "''")
+    _add_column_if_missing(conn, "jobs", "name", "TEXT", "''")
+    _add_column_if_missing(conn, "jobs", "last_image_path", "TEXT", "''")
+    _add_column_if_missing(conn, "jobs", "references_json", "TEXT", "''")
     _add_column_if_missing(conn, "jobs", "job_type", "TEXT", "'inference'")
     _add_column_if_missing(conn, "jobs", "data_path", "TEXT", "''")
     _add_column_if_missing(conn, "jobs", "max_train_steps", "INTEGER", "1000")
@@ -242,7 +251,8 @@ class Database:
         self._execute(
             """
             INSERT INTO jobs (
-                id, model_id, prompt, workload_type, image_path, job_type, status,
+                id, model_id, name, prompt, workload_type, image_path,
+                last_image_path, references_json, job_type, status,
                 created_at, started_at, finished_at, error, output_path, log_file_path,
                 num_inference_steps, num_frames, height, width, guidance_scale,
                 guidance_rescale, fps, seed, num_gpus, dit_cpu_offload,
@@ -254,14 +264,17 @@ class Database:
                 dmd_use_vsa, dmd_vsa_sparsity, dmd_denoising_steps,
                 real_score_guidance_scale,
                 generator_update_interval, real_score_model_path, fake_score_model_path
-            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             """,
             (
                 job["id"],
                 job["model_id"],
+                job.get("name", ""),
                 job["prompt"],
                 job.get("workload_type", "t2v"),
                 job.get("image_path", ""),
+                job.get("last_image_path", ""),
+                json.dumps(job.get("references") or []),
                 job.get("job_type", "inference"),
                 job["status"],
                 job["created_at"],
@@ -540,9 +553,12 @@ def _row_to_job(row: sqlite3.Row) -> dict[str, Any]:
     result = {
         "id": row["id"],
         "model_id": row["model_id"],
+        "name": _sqlite_row_get(row, "name", "") or "",
         "prompt": row["prompt"],
         "workload_type": _sqlite_row_get(row, "workload_type", "t2v"),
         "image_path": _sqlite_row_get(row, "image_path", "") or "",
+        "last_image_path": _sqlite_row_get(row, "last_image_path", "") or "",
+        "references": _sqlite_row_get(row, "references_json", "") or "",
         "job_type": _sqlite_row_get(row, "job_type", "inference"),
         "status": row["status"],
         "created_at": row["created_at"],

apps/fastvideo_studio/e2e/create-job-interaction.spec.ts

@@ -0,0 +1,63 @@
+import { expect, test } from '@playwright/test';
+
+import { skipWithoutMock } from './helpers';
+
+test.describe('create job interactions', () => {
+  skipWithoutMock();
+
+  for (const jobType of ['inference', 'finetuning', 'distillation']) {
+    test(`${jobType} remains interactive after repeated dialog dismissals`, async ({ page }) => {
+      await page.goto(`/${jobType}`);
+      const trigger = page.getByRole('button', { name: 'Create Job', exact: true });
+      const dialog = page.getByRole('dialog');
+
+      // Exercise both dismissal paths and reopen without reloading the page.
+      for (const closeWithEscape of [false, true]) {
+        await trigger.click();
+        await page.getByRole('menuitem').first().click();
+        await expect(dialog).toBeVisible();
+        if (closeWithEscape) {
+          await page.keyboard.press('Escape');
+        } else {
+          await dialog.getByRole('button', { name: 'Close', exact: true }).click();
+        }
+        await expect(dialog).toBeHidden();
+        await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+        await expect(trigger).toBeFocused();
+      }
+
+      await page.getByRole('link', { name: 'Datasets', exact: true }).click();
+      await expect(page).toHaveURL(/\/datasets$/);
+    });
+  }
+
+  test('preserves keyboard menu dismissal and dialog focus trapping', async ({ page }) => {
+    await page.goto('/inference');
+    const trigger = page.getByRole('button', { name: 'Create Job', exact: true });
+    await trigger.focus();
+    await page.keyboard.press('Enter');
+    const firstItem = page.getByRole('menuitem').first();
+    await expect(firstItem).toBeFocused();
+    await page.keyboard.press('Escape');
+    await expect(page.getByRole('menu')).toBeHidden();
+    await expect(trigger).toBeFocused();
+    await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+
+    await page.keyboard.press('Enter');
+    await expect(firstItem).toBeFocused();
+    await page.keyboard.press('Enter');
+    const dialog = page.getByRole('dialog');
+    await expect(dialog).toBeVisible();
+    await expect(dialog.getByLabel('Name (optional)')).toBeFocused();
+
+    // Shift+Tab from the first field wraps to Close, then Tab wraps back.
+    await page.keyboard.press('Shift+Tab');
+    await expect(dialog.getByRole('button', { name: 'Close', exact: true })).toBeFocused();
+    await page.keyboard.press('Tab');
+    await expect(dialog.getByLabel('Name (optional)')).toBeFocused();
+    await page.keyboard.press('Escape');
+    await expect(dialog).toBeHidden();
+    await expect(trigger).toBeFocused();
+    await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+  });
+});

apps/fastvideo_studio/e2e/create-job.spec.ts

@@ -1,6 +1,6 @@
 import { expect, test } from '@playwright/test';
 
-import { skipWithoutMock } from './helpers';
+import { API_BASE, skipWithoutMock } from './helpers';
 
 /**
  * Create-job flow: open the Create Job modal on /inference, fill the prompt
@@ -10,7 +10,8 @@ import { skipWithoutMock } from './helpers';
 test.describe('create inference job', () => {
   skipWithoutMock();
 
-  test('creates a T2V job and shows it in the queue', async ({ page }) => {
+  test('creates a T2V job and starts it without refreshing', async ({ page, request }) => {
+    await request.put(`${API_BASE}/settings`, { data: { autoStartJob: false } });
     await page.goto('/inference');
 
     // The trigger opens a real menu on click, so this path works for touch,
@@ -38,5 +39,20 @@ test.describe('create inference job', () => {
     // Modal closes and the queue refreshes with the newly created job.
     await expect(dialog).toBeHidden();
     await expect(page.getByText(prompt)).toBeVisible();
+    await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+
+    const card = page.getByRole('article').filter({ hasText: prompt });
+    await expect(card.getByText('pending', { exact: true })).toBeVisible();
+    const started = page.waitForResponse((response) =>
+      response.url().startsWith(`${API_BASE}/jobs/`) &&
+      response.url().endsWith('/start') &&
+      response.request().method() === 'POST',
+    );
+    await card.getByRole('button', { name: 'Start', exact: true }).click();
+    expect((await started).ok()).toBe(true);
+    await expect(card.getByText('running', { exact: true })).toBeVisible();
+
+    await page.getByRole('link', { name: 'Datasets', exact: true }).click();
+    await expect(page).toHaveURL(/\/datasets$/);
   });
 });

apps/fastvideo_studio/job_runner.py

@@ -10,7 +10,9 @@ from __future__ import annotations
 import atexit
 import collections
 import contextlib
+import copy
 import enum
+import json
 import logging
 import logging.handlers
 import multiprocessing as mp
@@ -123,10 +125,13 @@ class LogBufferHandler(logging.Handler):
 class Job:
     id: str
     model_id: str
-    prompt: str
+    name: str = ""
+    prompt: str = ""
     workload_type: str = "t2v"
     job_type: str = "inference"
     image_path: str = ""
+    last_image_path: str = ""
+    references: list[dict[str, Any]] = field(default_factory=list)
     status: JobStatus = JobStatus.PENDING
     created_at: float = field(default_factory=time.time)
     started_at: float | None = None
@@ -145,6 +150,7 @@ class Job:
     negative_prompt: str = ""
     num_gpus: int = 1
     dit_cpu_offload: bool = False
+    dit_layerwise_offload: bool = False
     text_encoder_cpu_offload: bool = False
     vae_cpu_offload: bool = False
     image_encoder_cpu_offload: bool = False
@@ -180,10 +186,13 @@ class Job:
         return {
             "id": self.id,
             "model_id": self.model_id,
+            "name": self.name,
             "prompt": self.prompt,
             "workload_type": self.workload_type,
             "job_type": self.job_type,
             "image_path": self.image_path,
+            "last_image_path": self.last_image_path,
+            "references": self.references,
             "status": self.status.value,
             "created_at": self.created_at,
             "started_at": self.started_at,
@@ -202,6 +211,7 @@ class Job:
             "negative_prompt": self.negative_prompt,
             "num_gpus": self.num_gpus,
             "dit_cpu_offload": self.dit_cpu_offload,
+            "dit_layerwise_offload": self.dit_layerwise_offload,
             "text_encoder_cpu_offload": self.text_encoder_cpu_offload,
             "vae_cpu_offload": self.vae_cpu_offload,
             "image_encoder_cpu_offload": self.image_encoder_cpu_offload,
@@ -232,6 +242,75 @@ class Job:
         }
 
 
+MINIMAX_H3_REF2VA_PIPELINE = "MiniMaxH3Ref2VAModularPipeline"
+
+
+def _build_h3_references(raw: list[dict[str, Any]]) -> list[Any]:
+    """Turn the API's reference dicts into MiniMaxH3Reference objects.
+
+    Imported lazily so the API server starts without pulling in fastvideo.
+    """
+    from fastvideo.pipelines.basic.minimax_h3 import MiniMaxH3Reference
+
+    built = []
+    for i, ref in enumerate(raw):
+        source = (ref or {}).get("source")
+        if not source:
+            raise ValueError(f"reference {i} has no source")
+        if not os.path.isfile(source):
+            raise ValueError(f"reference {i} source not found: {source}")
+        kwargs: dict[str, Any] = {
+            "source": source,
+            "media_type": (ref.get("media_type") or "image"),
+        }
+        for opt in ("soundtrack", "fps", "sample_rate"):
+            if ref.get(opt) not in (None, ""):
+                kwargs[opt] = ref[opt]
+        built.append(MiniMaxH3Reference(**kwargs))
+    return built
+
+
+JOB_LOG_FILENAME = "out.log"
+
+
+def _job_log_path(output_dir: str, job_id: str) -> str:
+    """Each job's log lives beside its outputs: <output_dir>/<job_id>/out.log."""
+    return os.path.join(output_dir, job_id, JOB_LOG_FILENAME)
+
+
+def _decode_references(value: Any) -> list[dict[str, Any]]:
+    """Reference lists round-trip through the DB as JSON text."""
+    if not value:
+        return []
+    if isinstance(value, list):
+        return list(value)
+    try:
+        decoded = json.loads(value)
+    except (TypeError, ValueError):
+        logger.warning("Could not decode stored references: %r", value)
+        return []
+    return list(decoded) if isinstance(decoded, list) else []
+
+
+def _generator_is_alive(generator: Any) -> bool:
+    """True if the generator's worker processes are all still running.
+
+    A cached VideoGenerator holds a MultiprocExecutor whose workers are separate
+    processes; nothing notices when they exit. Probing `proc.is_alive()` is what
+    the executor itself uses during shutdown. Anything unexpected in the object
+    graph is treated as alive so a probe failure can never wedge the cache.
+    """
+    executor = getattr(generator, "executor", None)
+    workers = getattr(executor, "workers", None)
+    if not workers:
+        return True
+    try:
+        return all(w.proc.is_alive() for w in workers)
+    except Exception:
+        logger.debug("Worker liveness probe failed", exc_info=True)
+        return True
+
+
 class JobRunner:
     """Manages video generation jobs, their execution, and generator caching."""
 
@@ -276,7 +355,7 @@ class JobRunner:
         """Populate job's log buffer from its log file if it exists."""
         path = job.log_file_path
         if not path:
-            path = os.path.join(self.log_dir, f"{job.id}.log")
+            path = _job_log_path(self.output_dir, job.id)
         if not os.path.isfile(path):
             return
         try:
@@ -314,10 +393,13 @@ class JobRunner:
                 job = Job(
                     id=row["id"],
                     model_id=row["model_id"],
+                    name=row.get("name", "") or "",
                     prompt=row["prompt"],
                     workload_type=row.get("workload_type", "t2v"),
                     job_type=row.get("job_type", "inference"),
                     image_path=row.get("image_path", "") or "",
+                    last_image_path=row.get("last_image_path", "") or "",
+                    references=_decode_references(row.get("references")),
                     data_path=row.get("data_path", "") or "",
                     max_train_steps=row.get("max_train_steps", 1000),
                     train_batch_size=row.get("train_batch_size", 1),
@@ -350,6 +432,7 @@ class JobRunner:
                     negative_prompt=row.get("negative_prompt", "") or "",
                     num_gpus=row.get("num_gpus", 1),
                     dit_cpu_offload=row.get("dit_cpu_offload", False),
+                    dit_layerwise_offload=row.get("dit_layerwise_offload", False),
                     text_encoder_cpu_offload=row.get("text_encoder_cpu_offload", False),
                     vae_cpu_offload=row.get("vae_cpu_offload", False),
                     image_encoder_cpu_offload=row.get("image_encoder_cpu_offload", False),
@@ -394,9 +477,12 @@ class JobRunner:
         job_id: str,
         model_id: str,
         prompt: str,
+        name: str = "",
         workload_type: str = "t2v",
         job_type: str = "inference",
         image_path: str = "",
+        last_image_path: str = "",
+        references: list[dict[str, Any]] | None = None,
         data_path: str = "",
         max_train_steps: int = 1000,
         train_batch_size: int = 1,
@@ -422,6 +508,7 @@ class JobRunner:
         num_gpus: int = 1,
         negative_prompt: str = "",
         dit_cpu_offload: bool = False,
+        dit_layerwise_offload: bool = False,
         text_encoder_cpu_offload: bool = False,
         vae_cpu_offload: bool = False,
         image_encoder_cpu_offload: bool = False,
@@ -435,10 +522,13 @@ class JobRunner:
         job = Job(
             id=job_id,
             model_id=model_id,
+            name=(name or "").strip(),
             prompt=prompt.strip(),
             workload_type=workload_type or "t2v",
             job_type=job_type or "inference",
             image_path=image_path or "",
+            last_image_path=last_image_path or "",
+            references=list(references or []),
             data_path=data_path or "",
             max_train_steps=max_train_steps,
             train_batch_size=train_batch_size,
@@ -464,6 +554,7 @@ class JobRunner:
             negative_prompt=negative_prompt or "",
             num_gpus=num_gpus,
             dit_cpu_offload=dit_cpu_offload,
+            dit_layerwise_offload=dit_layerwise_offload,
             text_encoder_cpu_offload=text_encoder_cpu_offload,
             vae_cpu_offload=vae_cpu_offload,
             image_encoder_cpu_offload=image_encoder_cpu_offload,
@@ -521,6 +612,84 @@ class JobRunner:
         logger.info("Deleted job %s", job.id)
         return True
 
+    CONFIG_FIELDS: tuple[str, ...] = (
+        "model_id",
+        "name",
+        "prompt",
+        "workload_type",
+        "job_type",
+        "image_path",
+        "last_image_path",
+        "references",
+        "negative_prompt",
+        "num_inference_steps",
+        "num_frames",
+        "height",
+        "width",
+        "guidance_scale",
+        "guidance_rescale",
+        "fps",
+        "seed",
+        "num_gpus",
+        "dit_cpu_offload",
+        "dit_layerwise_offload",
+        "text_encoder_cpu_offload",
+        "vae_cpu_offload",
+        "image_encoder_cpu_offload",
+        "use_fsdp_inference",
+        "enable_torch_compile",
+        "vsa_sparsity",
+        "tp_size",
+        "sp_size",
+        "data_path",
+        "max_train_steps",
+        "train_batch_size",
+        "learning_rate",
+        "num_latent_t",
+        "validation_dataset_file",
+        "lora_rank",
+        "dmd_use_vsa",
+        "dmd_vsa_sparsity",
+        "dmd_denoising_steps",
+        "real_score_guidance_scale",
+        "generator_update_interval",
+        "real_score_model_path",
+        "fake_score_model_path",
+    )
+
+    def duplicate_job(self, job_id: str, new_job_id: str) -> Job:
+        """Create a new pending job with an existing job's configuration.
+
+        Runtime state (status, timings, logs, outputs) is not carried over.
+        """
+        with self._jobs_lock:
+            source = self._jobs.get(job_id)
+        if source is None:
+            raise ValueError(f"Job {job_id} not found")
+        config = {f: copy.deepcopy(getattr(source, f)) for f in self.CONFIG_FIELDS}
+        return self.create_job(job_id=new_job_id, **config)
+
+    #: Editable exactly when startable: the same set start_job() accepts.
+    EDITABLE_STATUSES = (JobStatus.PENDING, JobStatus.FAILED, JobStatus.STOPPED)
+
+    def update_job_config(self, job_id: str, updates: dict[str, Any]) -> Job:
+        """Edit the configuration of a job that has not produced a result."""
+        with self._jobs_lock:
+            job = self._jobs.get(job_id)
+        if job is None:
+            raise ValueError(f"Job {job_id} not found")
+        if job.status not in self.EDITABLE_STATUSES:
+            allowed = ", ".join(s.value for s in self.EDITABLE_STATUSES)
+            raise ValueError(f"Job is {job.status.value}; only {allowed} jobs can be edited. "
+                             "Duplicate it instead.")
+        unknown = set(updates) - set(self.CONFIG_FIELDS)
+        if unknown:
+            raise ValueError(f"Not editable: {', '.join(sorted(unknown))}")
+        for field_name, value in updates.items():
+            setattr(job, field_name, value)
+        self._save_job(job)
+        return job
+
     def start_job(self, job_id: str) -> Job:
         """Start (or restart) a pending / stopped / failed job.
         
@@ -623,6 +792,8 @@ class JobRunner:
         workload_type: str,
         num_gpus: int,
         dit_cpu_offload: bool = False,
+        dit_layerwise_offload: bool = False,
+        override_pipeline_cls_name: str | None = None,
         text_encoder_cpu_offload: bool = False,
         vae_cpu_offload: bool = False,
         image_encoder_cpu_offload: bool = False,
@@ -638,6 +809,10 @@ class JobRunner:
             workload_type,
             num_gpus,
             dit_cpu_offload,
+            dit_layerwise_offload,
+            # Ref2VA loads different DiT weights (transformer_ref), so the
+            # override must key the cache or a t2v/i2v generator gets reused.
+            override_pipeline_cls_name,
             text_encoder_cpu_offload,
             vae_cpu_offload,
             image_encoder_cpu_offload,
@@ -650,8 +825,21 @@ class JobRunner:
 
         # Generators are cached by model_id and configuration parameters
         with self._generators_lock:
-            if cache_key in self._generators:
-                return self._generators[cache_key]
+            cached = self._generators.get(cache_key)
+            if cached is not None:
+                if _generator_is_alive(cached):
+                    return cached
+                # Workers can exit while a generator sits idle in the cache;
+                # reusing it fails every later job with the same config.
+                logger.warning(
+                    "Cached generator for %s has dead workers; reloading.",
+                    model_id,
+                )
+                self._generators.pop(cache_key, None)
+                try:
+                    cached.shutdown()
+                except Exception:
+                    logger.debug("Shutdown of the dead generator failed", exc_info=True)
 
         # Import lazily so starting the server is fast even without a GPU.
         from fastvideo import VideoGenerator
@@ -674,18 +862,40 @@ class JobRunner:
             sp_size,
         )
 
-        gen = VideoGenerator.from_pretrained(
-            model_id,
-            workload_type=workload_type,
-            dit_cpu_offload=dit_cpu_offload,
-            text_encoder_cpu_offload=text_encoder_cpu_offload,
-            vae_cpu_offload=vae_cpu_offload,
-            image_encoder_cpu_offload=image_encoder_cpu_offload,
-            use_fsdp_inference=use_fsdp_inference,
-            enable_torch_compile=enable_torch_compile,
-            VSA_sparsity=vsa_sparsity,
-            tp_size=tp_size,
-            sp_size=sp_size,
+        gen = VideoGenerator.from_config(
+            {
+                "model_path": model_id,
+                "engine": {
+                    "num_gpus": num_gpus,
+                    "parallelism": {
+                        "tp_size": tp_size,
+                        "sp_size": sp_size,
+                    },
+                    "offload": {
+                        "dit": dit_cpu_offload,
+                        "dit_layerwise": dit_layerwise_offload,
+                        "text_encoder": text_encoder_cpu_offload,
+                        "image_encoder": image_encoder_cpu_offload,
+                        "vae": vae_cpu_offload,
+                    },
+                    "compile": {
+                        "enabled": enable_torch_compile
+                    },
+                    "attention": {
+                        "vsa_sparsity": vsa_sparsity
+                    },
+                    "use_fsdp_inference": use_fsdp_inference,
+                },
+                "pipeline": {
+                    "workload_type":
+                    workload_type,
+                    **({
+                        "components": {
+                            "override_pipeline_cls_name": override_pipeline_cls_name
+                        }
+                    } if override_pipeline_cls_name else {}),
+                },
+            },
             log_queue=log_queue,
         )
 
@@ -706,10 +916,9 @@ class JobRunner:
     def _run_training_job(self, job: Job):
         """Run a finetuning, distillation, or LoRA job via subprocess."""
         buf = job._log_buf
-        os.makedirs(self.log_dir, exist_ok=True)
-        job.log_file_path = os.path.join(self.log_dir, f"{job.id}.log")
         job_output_dir = os.path.join(self.output_dir, job.id)
         os.makedirs(job_output_dir, exist_ok=True)
+        job.log_file_path = _job_log_path(self.output_dir, job.id)
 
         if not job.data_path or not os.path.isdir(job.data_path):
             job.status = JobStatus.FAILED
@@ -827,8 +1036,8 @@ class JobRunner:
 
     def _run_inference_job(self, job: Job):
         buf = job._log_buf
-        os.makedirs(self.log_dir, exist_ok=True)
-        job.log_file_path = os.path.join(self.log_dir, f"{job.id}.log")
+        os.makedirs(os.path.join(self.output_dir, job.id), exist_ok=True)
+        job.log_file_path = _job_log_path(self.output_dir, job.id)
 
         # Add file handler to persist logs
         file_handler = logging.FileHandler(job.log_file_path, mode='w', encoding='utf-8')
@@ -875,77 +1084,69 @@ class JobRunner:
             buf.phase = "loading model"
             logger.info("Loading model...")
 
-            # Run generator creation in a background thread so we
-            # can poll _stop_event while the (potentially slow)
-            # model download / load is in progress.
-            _gen_result: list[Any] = []
-            _gen_error: list[BaseException] = []
+            # The generator MUST be created on this thread: building it spawns
+            # the executor's worker processes, and they are torn down if the
+            # creating thread exits. Running it in a helper thread (to poll
+            # _stop_event during load) made every collective_rpc fail with
+            # ConnectionResetError.
+            if job._stop_event.is_set():
+                job.status = JobStatus.STOPPED
+                job.finished_at = time.time()
+                self._save_job(job)
+                logger.warning("Job %s stopped before model loading", job.id)
+                buf.phase = "stopped"
+                return
 
-            def _load_generator() -> None:
-                try:
-                    gen = self._get_or_create_generator(
-                        job.model_id,
-                        job.workload_type,
-                        job.num_gpus,
-                        dit_cpu_offload=job.dit_cpu_offload,
-                        text_encoder_cpu_offload=(job.text_encoder_cpu_offload),
-                        vae_cpu_offload=job.vae_cpu_offload,
-                        image_encoder_cpu_offload=(job.image_encoder_cpu_offload),
-                        use_fsdp_inference=job.use_fsdp_inference,
-                        enable_torch_compile=(job.enable_torch_compile),
-                        vsa_sparsity=job.vsa_sparsity,
-                        tp_size=job.tp_size,
-                        sp_size=job.sp_size,
-                        log_queue=log_queue,
-                    )
-                    _gen_result.append(gen)
-                except BaseException as exc:
-                    _gen_error.append(exc)
-
-            loader = threading.Thread(
-                target=_load_generator,
-                daemon=True,
+            generator = self._get_or_create_generator(
+                job.model_id,
+                job.workload_type,
+                job.num_gpus,
+                dit_cpu_offload=job.dit_cpu_offload,
+                dit_layerwise_offload=job.dit_layerwise_offload,
+                override_pipeline_cls_name=(MINIMAX_H3_REF2VA_PIPELINE if job.references else None),
+                text_encoder_cpu_offload=(job.text_encoder_cpu_offload),
+                vae_cpu_offload=job.vae_cpu_offload,
+                image_encoder_cpu_offload=(job.image_encoder_cpu_offload),
+                use_fsdp_inference=job.use_fsdp_inference,
+                enable_torch_compile=(job.enable_torch_compile),
+                vsa_sparsity=job.vsa_sparsity,
+                tp_size=job.tp_size,
+                sp_size=job.sp_size,
+                log_queue=log_queue,
             )
-            loader.start()
-
-            while loader.is_alive():
-                if job._stop_event.is_set():
-                    job.status = JobStatus.STOPPED
-                    job.finished_at = time.time()
-                    self._save_job(job)
-                    logger.warning(
-                        "Job %s stopped during model loading",
-                        job.id,
-                    )
-                    buf.phase = "stopped"
-                    return
-                loader.join(timeout=0.5)
-
-            if _gen_error:
-                raise _gen_error[0]
-
-            generator = _gen_result[0]
             buf.phase = "generating"
             logger.info("Starting generation for job %s (model=%s)", job.id, job.model_id)
 
-            gen_kwargs: dict[str, Any] = {
+            # Without a name FastVideo derives the filename from the prompt.
+            safe_name = re.sub(r'[\\/:*?"<>|]+', "", job.name).strip().strip(".")
+            output_target = (os.path.join(job_output_dir, f"{safe_name[:80]}.mp4") if safe_name else job_output_dir)
+            request: dict[str, Any] = {
                 "prompt": job.prompt,
-                "output_path": job_output_dir,
-                "save_video": True,
-                "num_inference_steps": job.num_inference_steps,
-                "num_frames": job.num_frames,
-                "height": job.height,
-                "width": job.width,
-                "guidance_scale": job.guidance_scale,
-                "guidance_rescale": job.guidance_rescale,
-                "fps": job.fps,
-                "seed": job.seed,
                 "negative_prompt": job.negative_prompt or "",
-                "log_queue": log_queue,
+                "sampling": {
+                    "num_inference_steps": job.num_inference_steps,
+                    "num_frames": job.num_frames,
+                    "height": job.height,
+                    "width": job.width,
+                    "guidance_scale": job.guidance_scale,
+                    "guidance_rescale": job.guidance_rescale,
+                    "fps": job.fps,
+                    "seed": job.seed,
+                },
+                "output": {
+                    "output_path": output_target,
+                    "save_video": True,
+                },
             }
             if job.image_path:
-                gen_kwargs["image_path"] = job.image_path
-            generator.generate_video(**gen_kwargs)
+                request.setdefault("inputs", {})["image_path"] = job.image_path
+            if job.references:
+                request.setdefault("inputs", {})["references"] = _build_h3_references(job.references)
+            if job.last_image_path:
+                # _prepare_fl2va requires a PIL image, not a path.
+                from PIL import Image as _PILImage
+                request.setdefault("inputs", {})["last_image"] = _PILImage.open(job.last_image_path)
+            generator.generate(request, log_queue=log_queue)
 
             buf.phase = "saving"
             logger.info("Generation completed, searching for output file...")
@@ -977,7 +1178,7 @@ class JobRunner:
 
         except Exception as exception:
             error_msg = str(exception)
-            logger.error("Critical error in job thread: %s", error_msg)
+            logger.exception("Critical error in job thread: %s", error_msg)
             job.status = JobStatus.FAILED
             job.error = f"Critical error ({type(exception).__name__}): {error_msg}"
             job.finished_at = time.time()

apps/fastvideo_studio/models/create_job_request.py

@@ -1,15 +1,20 @@
 # SPDX-License-Identifier: Apache-2.0
 """Request model for creating a job."""
 
+from typing import Any
+
 from pydantic import BaseModel
 
 
 class CreateJobRequest(BaseModel):
     model_id: str
+    name: str = ""
     prompt: str
     workload_type: str = "t2v"
     job_type: str = "inference"
     image_path: str = ""
+    last_image_path: str = ""
+    references: list[dict[str, Any]] | None = None
     data_path: str = ""
     max_train_steps: int = 1000
     train_batch_size: int = 1
@@ -28,6 +33,7 @@ class CreateJobRequest(BaseModel):
     seed: int = 1024
     num_gpus: int = 1
     dit_cpu_offload: bool = False
+    dit_layerwise_offload: bool = False
     text_encoder_cpu_offload: bool = False
     vae_cpu_offload: bool = False
     image_encoder_cpu_offload: bool = False

apps/fastvideo_studio/package.json

@@ -17,20 +17,11 @@
 		"start:all": "concurrently --kill-others-on-fail \"npm:start:api\" \"npm:start:web\""
 	},
 	"dependencies": {
-		"@radix-ui/react-dialog": "^1.1.0",
-		"@radix-ui/react-dropdown-menu": "^2.1.24",
-		"@radix-ui/react-label": "^2.1.8",
-		"@radix-ui/react-scroll-area": "^1.2.10",
-		"@radix-ui/react-select": "^2.2.6",
-		"@radix-ui/react-separator": "^1.1.8",
-		"@radix-ui/react-slider": "^1.2.0",
-		"@radix-ui/react-slot": "^1.2.4",
-		"@radix-ui/react-switch": "^1.1.0",
-		"@radix-ui/react-tabs": "^1.1.0",
 		"class-variance-authority": "^0.7.1",
 		"clsx": "^2.1.1",
 		"lucide-react": "^0.577.0",
 		"next": "15.5.18",
+		"radix-ui": "^1.6.7",
 		"react": "^19.1.0",
 		"react-dom": "^19.1.0",
 		"sonner": "^2.0.7",

apps/fastvideo_studio/server.py

@@ -18,6 +18,7 @@ import argparse
 import contextlib
 import logging
 import os
+import re
 import shutil
 import signal
 import time
@@ -116,7 +117,30 @@ def list_models(workload_type: str | None = None) -> list[dict[str, Any]]:
     return _available_models
 
 
+def _safe_upload_name(filename: str | None, ext: str) -> str:
+    """A filesystem-safe version of the client's filename, keeping it readable.
+
+    Uploads live under a per-file uuid directory, so the basename does not have
+    to be unique -- only safe. Keeping the original name means the path stays
+    self-describing wherever it travels: the database, job logs, and payloads
+    copied back out to the API.
+    """
+    stem = os.path.basename(filename or "").rsplit(".", 1)[0]
+    stem = re.sub(r"[^A-Za-z0-9._-]+", "_", stem).strip("._-")
+    return f"{stem[:80] or 'upload'}{ext}"
+
+
+def _upload_destination(ext: str, filename: str | None) -> str:
+    """<upload_dir>/<uuid4>/<safe original name><ext>"""
+    directory = os.path.join(upload_dir, uuid.uuid4().hex)
+    os.makedirs(directory, exist_ok=True)
+    return os.path.join(directory, _safe_upload_name(filename, ext))
+
+
 ALLOWED_IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+ALLOWED_VIDEO_EXTENSIONS = {".mp4", ".mov", ".mkv", ".webm", ".avi"}
+ALLOWED_AUDIO_EXTENSIONS = {".wav", ".mp3", ".flac", ".m4a", ".ogg"}
+ALLOWED_MEDIA_EXTENSIONS = (ALLOWED_IMAGE_EXTENSIONS | ALLOWED_VIDEO_EXTENSIONS | ALLOWED_AUDIO_EXTENSIONS)
 
 
 @app.post("/api/upload-image")
@@ -136,8 +160,7 @@ async def upload_image(file: Annotated[UploadFile, File()], ) -> dict[str, str]:
                     f"{', '.join(ALLOWED_IMAGE_EXTENSIONS)}"),
         )
     os.makedirs(upload_dir, exist_ok=True)
-    unique_name = f"{uuid.uuid4().hex}{ext}"
-    dest_path = os.path.join(upload_dir, unique_name)
+    dest_path = _upload_destination(ext, file.filename)
     try:
         contents = await file.read()
         with open(dest_path, "wb") as f:
@@ -150,6 +173,47 @@ async def upload_image(file: Annotated[UploadFile, File()], ) -> dict[str, str]:
     return {"path": os.path.abspath(dest_path)}
 
 
+@app.post("/api/upload-media")
+async def upload_media(file: Annotated[UploadFile, File()], ) -> dict[str, str]:
+    """Upload an image, video or audio file for Ref2VA references.
+
+    Returns the absolute path plus the media_type MiniMax-H3 expects, so the
+    caller does not have to re-derive it from the extension.
+    """
+    global upload_dir  # noqa: PLW0603
+    if not upload_dir:
+        raise HTTPException(
+            status_code=503,
+            detail="Upload directory not configured",
+        )
+    ext = Path(file.filename or "").suffix.lower()
+    if ext not in ALLOWED_MEDIA_EXTENSIONS:
+        raise HTTPException(
+            status_code=400,
+            detail=(f"Invalid file type. Allowed: "
+                    f"{', '.join(sorted(ALLOWED_MEDIA_EXTENSIONS))}"),
+        )
+    if ext in ALLOWED_VIDEO_EXTENSIONS:
+        media_type = "video"
+    elif ext in ALLOWED_AUDIO_EXTENSIONS:
+        media_type = "audio"
+    else:
+        media_type = "image"
+
+    os.makedirs(upload_dir, exist_ok=True)
+    dest_path = _upload_destination(ext, file.filename)
+    try:
+        contents = await file.read()
+        with open(dest_path, "wb") as f:
+            f.write(contents)
+    except OSError as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to save upload: {e}",
+        ) from e
+    return {"path": os.path.abspath(dest_path), "media_type": media_type}
+
+
 ALLOWED_VIDEO_EXTENSIONS = {".mp4", ".webm", ".avi", ".mov", ".mkv"}
 
 
@@ -282,10 +346,13 @@ def create_job(req: CreateJobRequest) -> dict[str, Any]:
     job = job_runner.create_job(
         job_id=str(uuid.uuid4()),
         model_id=req.model_id,
+        name=req.name or "",
         prompt=req.prompt,
         workload_type=req.workload_type or "t2v",
         job_type=job_type,
         image_path=req.image_path or "",
+        last_image_path=req.last_image_path or "",
+        references=req.references or [],
         data_path=data_path,
         max_train_steps=req.max_train_steps,
         train_batch_size=req.train_batch_size,
@@ -304,6 +371,7 @@ def create_job(req: CreateJobRequest) -> dict[str, Any]:
         seed=req.seed,
         num_gpus=req.num_gpus,
         dit_cpu_offload=req.dit_cpu_offload,
+        dit_layerwise_offload=req.dit_layerwise_offload,
         text_encoder_cpu_offload=req.text_encoder_cpu_offload,
         vae_cpu_offload=req.vae_cpu_offload,
         image_encoder_cpu_offload=req.image_encoder_cpu_offload,
@@ -336,6 +404,28 @@ def create_job(req: CreateJobRequest) -> dict[str, Any]:
     return job.to_dict()
 
 
+@app.post("/api/jobs/{job_id}/duplicate", status_code=201)
+def duplicate_job(job_id: str) -> dict[str, Any]:
+    """Create a new pending job with the same configuration as an existing one."""
+    try:
+        job = job_runner.duplicate_job(job_id, str(uuid.uuid4()))
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e)) from e
+    return job.to_dict()
+
+
+@app.patch("/api/jobs/{job_id}")
+def update_job(job_id: str, updates: dict[str, Any]) -> dict[str, Any]:
+    """Edit a pending job's configuration. Started jobs cannot be edited."""
+    try:
+        job = job_runner.update_job_config(job_id, updates)
+    except ValueError as e:
+        detail = str(e)
+        status = 404 if "not found" in detail else 400
+        raise HTTPException(status_code=status, detail=detail) from e
+    return job.to_dict()
+
+
 @app.post("/api/jobs/{job_id}/start")
 def start_job(job_id: str) -> dict[str, Any]:
     """Start (or restart) a pending / stopped / failed job."""

apps/fastvideo_studio/src/components/jobs/CreateJobButton.test.tsx

@@ -1,24 +1,26 @@
-import { render, screen } from '@testing-library/react';
+import { render, screen, waitFor, within } from '@testing-library/react';
 import userEvent from '@testing-library/user-event';
-import { describe, expect, it, vi } from 'vitest';
+import { beforeEach, describe, expect, it, vi } from 'vitest';
 
 import CreateJobButton from './CreateJobButton';
+import { getDatasets, getModels } from '@/lib/api';
 
-vi.mock('./CreateJobModal', () => ({
-  default: ({
-    isOpen,
-    workloadType,
-  }: {
-    isOpen: boolean;
-    workloadType: string;
-  }) =>
-    isOpen ? (
-      <div role="dialog" data-workload-type={workloadType}>
-        Create job form
-      </div>
-    ) : null,
+vi.mock('@/lib/api', () => ({
+  createJob: vi.fn(),
+  getModels: vi.fn(),
+  getDatasets: vi.fn(),
+  uploadImage: vi.fn(),
+  getSettings: vi.fn(),
+  updateSettings: vi.fn(),
 }));
 
+beforeEach(() => {
+  vi.mocked(getModels).mockResolvedValue([
+    { id: 'wan/t2v-1.3b', label: 'Wan T2V' },
+  ]);
+  vi.mocked(getDatasets).mockResolvedValue([]);
+});
+
 describe('CreateJobButton', () => {
   it('opens the workload menu on click and selects an item', async () => {
     const user = userEvent.setup();
@@ -27,10 +29,9 @@ describe('CreateJobButton', () => {
     await user.click(screen.getByRole('button', { name: 'Create Job' }));
     await user.click(screen.getByRole('menuitem', { name: /I2V/i }));
 
-    expect(screen.getByRole('dialog')).toHaveAttribute(
-      'data-workload-type',
-      'i2v',
-    );
+    expect(
+      screen.getByRole('dialog', { name: 'New Inference Job (I2V)' }),
+    ).toBeInTheDocument();
   });
 
   it('opens and operates the workload menu from the keyboard', async () => {
@@ -45,9 +46,42 @@ describe('CreateJobButton', () => {
     expect(firstItem).toHaveFocus();
     await user.keyboard('{Enter}');
 
-    expect(screen.getByRole('dialog')).toHaveAttribute(
-      'data-workload-type',
-      't2v',
+    expect(
+      screen.getByRole('dialog', { name: 'New Inference Job (T2V)' }),
+    ).toBeInTheDocument();
+    await user.keyboard('{Escape}');
+    await waitFor(() =>
+      expect(screen.queryByRole('dialog')).not.toBeInTheDocument(),
     );
+    await waitFor(() =>
+      expect(document.body.style.pointerEvents).not.toBe('none'),
+    );
+    expect(trigger).toHaveFocus();
   });
+
+  it.each(['inference', 'finetuning', 'distillation'] as const)(
+    'restores page interaction after closing the real %s dialog',
+    async (jobType) => {
+      const user = userEvent.setup();
+      render(<CreateJobButton jobType={jobType} />);
+      const trigger = screen.getByRole('button', { name: 'Create Job' });
+
+      // Keep the real Dialog mounted: mocking it hides conflicting Radix layers.
+      for (let attempt = 0; attempt < 2; attempt++) {
+        await user.click(trigger);
+        await user.click(screen.getAllByRole('menuitem')[0]);
+        const dialog = screen.getByRole('dialog');
+        await user.click(
+          within(dialog).getByRole('button', { name: 'Close' }),
+        );
+        await waitFor(() =>
+          expect(screen.queryByRole('dialog')).not.toBeInTheDocument(),
+        );
+        await waitFor(() =>
+          expect(document.body.style.pointerEvents).not.toBe('none'),
+        );
+        expect(trigger).toHaveFocus();
+      }
+    },
+  );
 });

apps/fastvideo_studio/src/components/jobs/CreateJobButton.tsx

@@ -2,7 +2,7 @@
 
 import * as React from 'react';
 import { ChevronDown } from 'lucide-react';
-import * as DropdownMenu from '@radix-ui/react-dropdown-menu';
+import { DropdownMenu } from 'radix-ui';
 
 import CreateJobModal from '@/components/jobs/CreateJobModal';
 import { Button } from '@/components/ui/button';
@@ -16,6 +16,7 @@ interface CreateJobButtonProps {
 
 export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
   const options = WORKLOAD_OPTIONS[jobType] ?? [];
+  const triggerRef = React.useRef<HTMLButtonElement>(null);
 
   const [modalOpen, setModalOpen] = React.useState(false);
   const [workloadType, setWorkloadType] = React.useState(
@@ -36,7 +37,7 @@ export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
     <>
       <DropdownMenu.Root>
         <DropdownMenu.Trigger asChild>
-          <Button type="button" className="gap-1.5">
+          <Button ref={triggerRef} type="button" className="gap-1.5">
             Create Job
             <ChevronDown className="size-3.5 opacity-85" aria-hidden />
           </Button>
@@ -66,6 +67,11 @@ export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
       <CreateJobModal
         isOpen={modalOpen}
         onClose={() => setModalOpen(false)}
+        onCloseAutoFocus={(event) => {
+          // This dialog opens from a menu item, so it has no DialogTrigger.
+          event.preventDefault();
+          triggerRef.current?.focus();
+        }}
         onSuccess={handleSuccess}
         jobType={jobType}
         workloadType={workloadType}

apps/fastvideo_studio/src/components/jobs/CreateJobModal.tsx

@@ -22,30 +22,60 @@ import { useStore } from '@/hooks/useStore';
 import { defaultOptionsStore } from '@/stores/defaultOptions';
 import {
   createJob,
+  updateJob,
   getDatasets,
   getModels,
   uploadImage,
+  uploadMedia,
   type CreateJobRequest,
   type Model,
 } from '@/lib/api';
 import { getDefaultModelForWorkload } from '@/lib/defaultOptions';
 import { WORKLOAD_OPTIONS } from '@/lib/jobConfig';
 import type { JobType } from '@/lib/types';
+import {
+  H3_MAX_REFERENCES,
+  labelReferences,
+  referencePromptSeed,
+  validateReferences,
+  type H3Reference,
+} from '@/lib/h3References';
+import {
+  EMPTY_H3_PROMPT_FIELDS,
+  H3_PROMPT_SECTIONS,
+  H3_SECTION_HINTS,
+  H3_SECTION_LABELS,
+  isEmptyPromptFields,
+  parseH3Prompt,
+  serializeH3Prompt,
+  type H3PromptFields,
+} from '@/lib/h3Prompt';
+import { jobToFormFields, type JobLike } from '@/lib/jobToFields';
 
 export interface CreateJobModalProps {
   isOpen: boolean;
   onClose: () => void;
+  onCloseAutoFocus?: React.ComponentProps<
+    typeof DialogContent
+  >['onCloseAutoFocus'];
   onSuccess: () => void;
   jobType: JobType;
   workloadType: string;
+  /** When set, the modal edits this pending job instead of creating a new one. */
+  editingJob?: JobLike | null;
+  /** Show the configuration without allowing changes (started/finished jobs). */
+  readOnly?: boolean;
 }
 
 export default function CreateJobModal({
   isOpen,
   onClose,
+  onCloseAutoFocus,
   onSuccess,
   jobType,
   workloadType,
+  editingJob,
+  readOnly = false,
 }: CreateJobModalProps) {
   const { options } = useStore(defaultOptionsStore);
 
@@ -55,8 +85,22 @@ export default function CreateJobModal({
 
   const [models, setModels] = React.useState<Model[]>([]);
   const [modelId, setModelId] = React.useState('');
+  const [name, setName] = React.useState('');
   const [prompt, setPrompt] = React.useState('');
   const [imagePath, setImagePath] = React.useState('');
+  const [lastImagePath, setLastImagePath] = React.useState('');
+  const [references, setReferences] = React.useState<H3Reference[]>([]);
+  const [isUploadingReference, setIsUploadingReference] = React.useState(false);
+  const [referenceError, setReferenceError] = React.useState<string | null>(null);
+  const [promptFields, setPromptFields] = React.useState<H3PromptFields>(
+    EMPTY_H3_PROMPT_FIELDS,
+  );
+  const [useGuidedPrompt, setUseGuidedPrompt] = React.useState(true);
+  const [lastImageFileName, setLastImageFileName] = React.useState('');
+  const [isUploadingLastImage, setIsUploadingLastImage] = React.useState(false);
+  const [lastImageUploadError, setLastImageUploadError] = React.useState<
+    string | null
+  >(null);
   const [imageFileName, setImageFileName] = React.useState('');
   const [isUploadingImage, setIsUploadingImage] = React.useState(false);
   const [negativePrompt, setNegativePrompt] = React.useState('');
@@ -70,12 +114,51 @@ export default function CreateJobModal({
   const [seed, setSeed] = React.useState(1024);
   const [numGpus, setNumGpus] = React.useState(1);
   const [ditCpuOffload, setDitCpuOffload] = React.useState(false);
+  const [ditLayerwiseOffload, setDitLayerwiseOffload] = React.useState(false);
   const [textEncoderCpuOffload, setTextEncoderCpuOffload] =
     React.useState(false);
   const [vaeCpuOffload, setVaeCpuOffload] = React.useState(false);
   const [imageEncoderCpuOffload, setImageEncoderCpuOffload] =
     React.useState(false);
   const [useFsdpInference, setUseFsdpInference] = React.useState(false);
+
+  // H3 is the only registered model with an end frame or references.
+  const supportsLastImage = modelId.toLowerCase().includes('minimax-h3');
+  const usingReferences = supportsLastImage && references.length > 0;
+
+  // JobCard re-renders on every job-list poll, so `editingJob` is a fresh
+  // object each time. Effects must depend on these, never on the object.
+  const editingJobId = editingJob?.id ?? null;
+  const editingJobModelId = editingJob?.model_id ?? null;
+
+  // Layerwise offload and FSDP compete for the DiT weights and the device offload
+  // policy (resolve_device_offload_conflicts in fastvideo/api/device_policy.py)
+  // silently picks a winner; resolve it visibly here.
+  // dit_cpu_offload is deliberately not interlocked -- it is a modifier, not a
+  // competing strategy.
+  const handleDitLayerwiseOffloadChange = React.useCallback((next: boolean) => {
+    setDitLayerwiseOffload(next);
+    if (next) {
+      setUseFsdpInference(false);
+    }
+  }, []);
+
+  const handleUseFsdpInferenceChange = React.useCallback((next: boolean) => {
+    setUseFsdpInference(next);
+    if (next) {
+      setDitLayerwiseOffload(false);
+    }
+  }, []);
+
+  const handleNumGpusChange = React.useCallback((next: number) => {
+    setNumGpus(next);
+    if (next > 1) {
+      // Dropping back to one GPU leaves FSDP alone: single-GPU FSDP is a
+      // valid way to reach its CPU offload (docs/inference/offloading.md).
+      setUseFsdpInference(true);
+      setDitLayerwiseOffload(false);
+    }
+  }, []);
   const [enableTorchCompile, setEnableTorchCompile] = React.useState(false);
   const [vsaSparsity, setVsaSparsity] = React.useState(0);
   const [tpSize, setTpSize] = React.useState(-1);
@@ -126,6 +209,47 @@ export default function CreateJobModal({
     const justOpened = isOpen && !justOpenedRef.current;
     justOpenedRef.current = isOpen;
     if (!justOpened) return;
+    if (editingJob) {
+      // Must not fall through to the defaults below: a partially-seeded
+      // form silently edits values the user never saw.
+      const f = jobToFormFields(editingJob);
+      setModelId(f.modelId);
+      setName(f.name);
+      setPrompt(f.prompt);
+      setNegativePrompt(f.negativePrompt);
+      setImagePath(f.imagePath);
+      setImageFileName(f.imagePath.split('/').pop() ?? '');
+      setLastImagePath(f.lastImagePath);
+      setLastImageFileName(f.lastImagePath.split('/').pop() ?? '');
+      setReferences(f.references);
+      setPromptFields(f.promptFields ?? EMPTY_H3_PROMPT_FIELDS);
+      setUseGuidedPrompt(f.promptFields !== null);
+      setNumInferenceSteps(f.numInferenceSteps);
+      setNumFrames(f.numFrames);
+      setHeight(f.height);
+      setWidth(f.width);
+      setGuidanceScale(f.guidanceScale);
+      setGuidanceRescale(f.guidanceRescale);
+      setFps(f.fps);
+      setSeed(f.seed);
+      setNumGpus(f.numGpus);
+      setDitCpuOffload(f.ditCpuOffload);
+      setDitLayerwiseOffload(f.ditLayerwiseOffload);
+      setTextEncoderCpuOffload(f.textEncoderCpuOffload);
+      setVaeCpuOffload(f.vaeCpuOffload);
+      setImageEncoderCpuOffload(f.imageEncoderCpuOffload);
+      setUseFsdpInference(f.useFsdpInference);
+      setEnableTorchCompile(f.enableTorchCompile);
+      setVsaSparsity(f.vsaSparsity);
+      setTpSize(f.tpSize);
+      setSpSize(f.spSize);
+      setReferenceError(null);
+      setModelLoadError(null);
+      setImageUploadError(null);
+      setLastImageUploadError(null);
+      setSubmitError(null);
+      return;
+    }
     const opts = options;
     setNumInferenceSteps(opts.numInferenceSteps);
     setNumFrames(workloadType === 't2i' ? 1 : opts.numFrames);
@@ -137,6 +261,7 @@ export default function CreateJobModal({
     setSeed(opts.seed);
     setNumGpus(opts.numGpus);
     setDitCpuOffload(opts.ditCpuOffload);
+    setDitLayerwiseOffload(opts.ditLayerwiseOffload ?? false);
     setTextEncoderCpuOffload(opts.textEncoderCpuOffload);
     setVaeCpuOffload(opts.vaeCpuOffload);
     setImageEncoderCpuOffload(opts.imageEncoderCpuOffload);
@@ -151,8 +276,16 @@ export default function CreateJobModal({
         inferenceWorkload as 't2v' | 'i2v' | 't2i',
       ),
     );
+    setName('');
     setImagePath('');
     setImageFileName('');
+    setLastImagePath('');
+    setLastImageFileName('');
+    setLastImageUploadError(null);
+    setReferences([]);
+    setReferenceError(null);
+    setPromptFields(EMPTY_H3_PROMPT_FIELDS);
+    setUseGuidedPrompt(true);
     setSelectedDatasetId('');
     setSelectedValidationDatasetId('');
     setModelLoadError(null);
@@ -168,7 +301,7 @@ export default function CreateJobModal({
       setRealScoreModelPath('');
       setFakeScoreModelPath('');
     }
-  }, [isOpen, workloadType, inferenceWorkload, options]);
+  }, [isOpen, workloadType, inferenceWorkload, options, editingJobId]);
 
   // Load the models available for this workload.
   React.useEffect(() => {
@@ -188,7 +321,16 @@ export default function CreateJobModal({
           opts,
           inferenceWorkload as 't2v' | 'i2v' | 't2i',
         );
-        const chosen = ids.includes(defaultId) ? defaultId : (list[0]?.id ?? '');
+        // When editing, the job's own model wins over the workload default --
+        // this resolves after the seeding effect, so choosing a default here
+        // would silently swap the model out from under the user.
+        const editedId = editingJobModelId;
+        const chosen =
+          editedId && ids.includes(editedId)
+            ? editedId
+            : ids.includes(defaultId)
+              ? defaultId
+              : (list[0]?.id ?? '');
         setModelId(chosen);
         if (workloadType === 'dmd_t2v') {
           setRealScoreModelPath(chosen);
@@ -210,7 +352,7 @@ export default function CreateJobModal({
     return () => {
       stale = true;
     };
-  }, [isOpen, inferenceWorkload, workloadType]);
+  }, [isOpen, inferenceWorkload, workloadType, editingJobModelId]);
 
   // Training jobs need a dataset; load the ready datasets when relevant.
   React.useEffect(() => {
@@ -262,6 +404,106 @@ export default function CreateJobModal({
     }
   }
 
+  async function handleLastImageChange(
+    e: React.ChangeEvent<HTMLInputElement>,
+  ) {
+    const file = e.target.files?.[0];
+    if (!file) {
+      setLastImagePath('');
+      setLastImageFileName('');
+      setLastImageUploadError(null);
+      return;
+    }
+    setIsUploadingLastImage(true);
+    setLastImageFileName(file.name);
+    setLastImageUploadError(null);
+    try {
+      const { path } = await uploadImage(file);
+      setLastImagePath(path);
+    } catch (error) {
+      console.error('Failed to upload end image:', error);
+      setLastImagePath('');
+      setLastImageFileName('');
+      setLastImageUploadError(
+        error instanceof Error
+          ? `${error.message}. Choose the image again to retry.`
+          : 'The image could not be uploaded. Choose it again to retry.',
+      );
+    } finally {
+      setIsUploadingLastImage(false);
+    }
+  }
+
+  async function handleAddReference(
+    e: React.ChangeEvent<HTMLInputElement>,
+  ) {
+    const file = e.target.files?.[0];
+    e.target.value = '';               // allow re-picking the same file
+    if (!file) return;
+    setIsUploadingReference(true);
+    setReferenceError(null);
+    try {
+      const { path, media_type } = await uploadMedia(file);
+      const next: H3Reference[] = [
+        ...references,
+        {
+          id: `${Date.now()}-${file.name}`,
+          source: path,
+          media_type,
+          fileName: file.name,
+        },
+      ];
+      setReferences(next);
+      setReferenceError(validateReferences(next));
+    } catch (error) {
+      console.error('Failed to upload reference:', error);
+      setReferenceError(
+        error instanceof Error ? error.message : 'The file could not be uploaded.',
+      );
+    } finally {
+      setIsUploadingReference(false);
+    }
+  }
+
+  function removeReference(id: string) {
+    const next = references.filter((r) => r.id !== id);
+    setReferences(next);
+    setReferenceError(validateReferences(next));
+  }
+
+  function seedPromptFields() {
+    setPromptFields({
+      ...EMPTY_H3_PROMPT_FIELDS,
+      ...referencePromptSeed(references),
+    });
+    setUseGuidedPrompt(true);
+  }
+
+  function setPromptField(section: string, value: string) {
+    setPromptFields((prev) => ({ ...prev, [section]: value }));
+  }
+
+  // Switching between the guided fields and the raw editor keeps whatever was
+  // typed: serialize on the way out, parse back on the way in.
+  function toggleGuidedPrompt() {
+    if (useGuidedPrompt) {
+      if (!isEmptyPromptFields(promptFields)) {
+        setPrompt(serializeH3Prompt(promptFields));
+      }
+      setUseGuidedPrompt(false);
+    } else {
+      const parsed = parseH3Prompt(prompt);
+      if (parsed) setPromptFields(parsed);
+      setUseGuidedPrompt(true);
+    }
+  }
+
+  function clearLastImage() {
+    setLastImagePath('');
+    setLastImageFileName('');
+    setLastImageUploadError(null);
+  }
+
   function clearImage() {
     setImagePath('');
     setImageFileName('');
@@ -271,7 +513,16 @@ export default function CreateJobModal({
 
   async function handleSubmit(e: React.FormEvent<HTMLFormElement>) {
     e.preventDefault();
-    if (isInference && workloadType === 'i2v' && !imagePath) return;
+    if (isInference && workloadType === 'i2v' && !imagePath && !usingReferences)
+      return;
+    if (usingReferences && validateReferences(references)) return;
+    if (
+      usingReferences &&
+      useGuidedPrompt &&
+      isEmptyPromptFields(promptFields) &&
+      !prompt.trim()
+    )
+      return;
     // Send the dataset id; the backend resolves it to the on-disk media dir.
     const effectiveDataPath = selectedDatasetId ?? '';
     if (!isInference && !selectedDatasetId) return;
@@ -285,14 +536,35 @@ export default function CreateJobModal({
     try {
       const payload: CreateJobRequest = {
         model_id: modelId,
-        prompt,
+        name: name.trim(),
+        prompt:
+          usingReferences && useGuidedPrompt && !isEmptyPromptFields(promptFields)
+            ? serializeH3Prompt(promptFields)
+            : prompt,
         workload_type: workloadType,
         job_type: effectiveJobType,
         ...(isInference
           ? {
-              ...(workloadType === 'i2v' && imagePath
+              // Ref2VA and the FL2VA keyframes are mutually exclusive:
+              // _prepare_ref2va rejects image_path/last_image_path outright
+              // when references are present.
+              ...(workloadType === 'i2v' && !usingReferences && imagePath
                 ? { image_path: imagePath }
                 : {}),
+              ...(workloadType === 'i2v' &&
+              supportsLastImage &&
+              !usingReferences &&
+              lastImagePath
+                ? { last_image_path: lastImagePath }
+                : {}),
+              ...(workloadType === 'i2v' && supportsLastImage && references.length
+                ? {
+                    references: references.map((r) => ({
+                      source: r.source,
+                      media_type: r.media_type,
+                    })),
+                  }
+                : {}),
               negative_prompt: negativePrompt,
               num_inference_steps: numInferenceSteps,
               num_frames: numFrames,
@@ -304,6 +576,7 @@ export default function CreateJobModal({
               seed,
               num_gpus: numGpus,
               dit_cpu_offload: ditCpuOffload,
+              dit_layerwise_offload: ditLayerwiseOffload,
               text_encoder_cpu_offload: textEncoderCpuOffload,
               vae_cpu_offload: vaeCpuOffload,
               image_encoder_cpu_offload: imageEncoderCpuOffload,
@@ -334,7 +607,14 @@ export default function CreateJobModal({
                 : {}),
             }),
       };
-      await createJob(payload);
+      if (editingJob) {
+        await updateJob(
+          editingJob.id,
+          payload as unknown as Record<string, unknown>,
+        );
+      } else {
+        await createJob(payload);
+      }
       onSuccess();
       onClose();
     } catch (err) {
@@ -356,9 +636,9 @@ export default function CreateJobModal({
 
   const workloadLabel =
     WORKLOAD_OPTIONS[jobType]?.find((o) => o.type === workloadType)?.label ?? '';
-  const title = `New ${jobType.charAt(0).toUpperCase() + jobType.slice(1)} Job${
-    workloadLabel ? ` (${workloadLabel})` : ''
-  }`;
+  const title = `${readOnly ? 'View' : editingJob ? 'Edit' : 'New'} ${
+    jobType.charAt(0).toUpperCase() + jobType.slice(1)
+  } Job${workloadLabel ? ` (${workloadLabel})` : ''}`;
 
   return (
     <Dialog
@@ -369,6 +649,7 @@ export default function CreateJobModal({
     >
       <DialogContent
         className="max-h-[90vh] w-[90vw] max-w-[850px] overflow-y-auto"
+        onCloseAutoFocus={onCloseAutoFocus}
         onEscapeKeyDown={(e) => {
           if (isSubmitting) e.preventDefault();
         }}
@@ -385,6 +666,23 @@ export default function CreateJobModal({
           autoComplete="off"
           className="flex flex-col gap-3.5"
         >
+          {/* disabled cascades to every control inside; display:contents
+              keeps the parent's flex layout. */}
+          <fieldset
+            disabled={readOnly}
+            style={{ display: 'contents' }}
+            className="contents"
+          >
+          <FieldRow htmlFor="modal-name" label="Name (optional)">
+            <Input
+              id="modal-name"
+              value={name}
+              onChange={(e) => setName(e.target.value)}
+              placeholder="Shown on the job card and used for the output filename"
+              disabled={isSubmitting}
+            />
+          </FieldRow>
+
           <FieldRow htmlFor="modal-modelId" label="Model">
             <NativeSelect
               id="modal-modelId"
@@ -429,12 +727,12 @@ export default function CreateJobModal({
                 type="file"
                 accept=".png,.jpg,.jpeg,.webp,.bmp"
                 onChange={handleImageChange}
-                disabled={isSubmitting || isUploadingImage}
+                disabled={isSubmitting || isUploadingImage || usingReferences}
                 aria-describedby={
                   imageUploadError ? 'modal-image-error' : undefined
                 }
                 aria-invalid={imageUploadError ? true : undefined}
-                required
+                required={!usingReferences}
                 className="h-auto py-2 file:mr-3 file:cursor-pointer file:rounded-md file:border-0 file:bg-secondary file:px-2 file:py-1 file:text-sm file:text-secondary-foreground"
               />
               {imageFileName && (
@@ -462,24 +760,169 @@ export default function CreateJobModal({
             </FieldRow>
           )}
 
-          <FieldRow
-            htmlFor="modal-prompt"
-            label={isInference ? 'Prompt' : 'Description'}
-          >
-            <Textarea
-              id="modal-prompt"
-              value={prompt}
-              onChange={(e) => setPrompt(e.target.value)}
-              rows={isInference ? 3 : 2}
-              placeholder={
-                isInference
-                  ? 'A curious raccoon peers through a vibrant field of yellow sunflowers…'
-                  : 'Brief description of this training job…'
-              }
-              required
-              disabled={isSubmitting}
-            />
-          </FieldRow>
+          {isInference && workloadType === 'i2v' && supportsLastImage && (
+            <FieldRow htmlFor="modal-last-image" label="End Frame (optional)">
+              <Input
+                id="modal-last-image"
+                type="file"
+                accept=".png,.jpg,.jpeg,.webp,.bmp"
+                onChange={handleLastImageChange}
+                disabled={isSubmitting || isUploadingLastImage}
+                aria-describedby={
+                  lastImageUploadError ? 'modal-last-image-error' : undefined
+                }
+                aria-invalid={lastImageUploadError ? true : undefined}
+                className="h-auto py-2 file:mr-3 file:cursor-pointer file:rounded-md file:border-0 file:bg-secondary file:px-2 file:py-1 file:text-sm file:text-secondary-foreground"
+              />
+              {lastImageFileName && (
+                <span className="mt-0.5 text-xs text-muted-foreground">
+                  {isUploadingLastImage ? 'Uploading…' : lastImageFileName} ·{' '}
+                  <button
+                    type="button"
+                    onClick={clearLastImage}
+                    disabled={isSubmitting || isUploadingLastImage}
+                    className="text-accent-blue underline-offset-2 hover:underline disabled:cursor-not-allowed disabled:opacity-50"
+                  >
+                    Clear
+                  </button>
+                </span>
+              )}
+              {lastImageUploadError && (
+                <p
+                  id="modal-last-image-error"
+                  role="alert"
+                  className="text-sm text-destructive"
+                >
+                  {lastImageUploadError}
+                </p>
+              )}
+            </FieldRow>
+          )}
+
+          {isInference && workloadType === 'i2v' && supportsLastImage && (
+            <FieldRow htmlFor="modal-reference" label="References (Ref2VA)">
+              <Input
+                id="modal-reference"
+                type="file"
+                accept=".png,.jpg,.jpeg,.webp,.bmp,.mp4,.mov,.mkv,.webm,.avi,.wav,.mp3,.flac,.m4a,.ogg"
+                onChange={handleAddReference}
+                disabled={
+                  isSubmitting ||
+                  isUploadingReference ||
+                  references.length >= H3_MAX_REFERENCES
+                }
+                className="h-auto py-2 file:mr-3 file:cursor-pointer file:rounded-md file:border-0 file:bg-secondary file:px-2 file:py-1 file:text-sm file:text-secondary-foreground"
+              />
+              {isUploadingReference && (
+                <span className="mt-0.5 text-xs text-muted-foreground">
+                  Uploading…
+                </span>
+              )}
+              {references.length > 0 && (
+                <ul className="mt-1 flex list-none flex-col gap-1 p-0">
+                  {references.map((reference, index) => (
+                    <li
+                      key={reference.id}
+                      className="flex items-center gap-2 text-xs text-muted-foreground"
+                    >
+                      <code className="font-mono text-accent-blue">
+                        {labelReferences(references)[index]}
+                      </code>
+                      <span className="truncate">{reference.fileName}</span>
+                      <button
+                        type="button"
+                        onClick={() => removeReference(reference.id)}
+                        disabled={isSubmitting}
+                        className="ml-auto text-accent-blue underline-offset-2 hover:underline disabled:cursor-not-allowed disabled:opacity-50"
+                      >
+                        Remove
+                      </button>
+                    </li>
+                  ))}
+                </ul>
+              )}
+              {references.length > 0 && (
+                <button
+                  type="button"
+                  onClick={seedPromptFields}
+                  disabled={isSubmitting}
+                  className="mt-1 self-start text-xs text-accent-blue underline-offset-2 hover:underline disabled:cursor-not-allowed disabled:opacity-50"
+                >
+                  Fill prompt sections from references
+                </button>
+              )}
+              {referenceError && (
+                <p role="alert" className="mt-0.5 text-xs text-destructive">
+                  {referenceError}
+                </p>
+              )}
+              <span className="mt-0.5 text-xs text-muted-foreground">
+                Ref2VA replaces the keyframes: up to 9 images, 3 videos, 3 audio
+                (12 total). Audio needs at least one image or video.
+              </span>
+            </FieldRow>
+          )}
+
+          {usingReferences && useGuidedPrompt ? (
+            /* Six-section format from the model's reference prompt guide. */
+            <>
+              {H3_PROMPT_SECTIONS.map((section) => (
+                <FieldRow
+                  key={section}
+                  htmlFor={`modal-prompt-${section}`}
+                  label={H3_SECTION_LABELS[section]}
+                >
+                  <Textarea
+                    id={`modal-prompt-${section}`}
+                    value={promptFields[section]}
+                    onChange={(e) => setPromptField(section, e.target.value)}
+                    rows={section === 'detailed_description' ? 5 : 2}
+                    placeholder={H3_SECTION_HINTS[section]}
+                    disabled={isSubmitting}
+                  />
+                </FieldRow>
+              ))}
+              <button
+                type="button"
+                onClick={toggleGuidedPrompt}
+                disabled={isSubmitting}
+                className="self-start text-xs text-accent-blue underline-offset-2 hover:underline disabled:cursor-not-allowed disabled:opacity-50"
+              >
+                Edit as raw prompt
+              </button>
+            </>
+          ) : (
+            <>
+              <FieldRow
+                htmlFor="modal-prompt"
+                label={isInference ? 'Prompt' : 'Description'}
+              >
+                <Textarea
+                  id="modal-prompt"
+                  value={prompt}
+                  onChange={(e) => setPrompt(e.target.value)}
+                  rows={isInference ? 3 : 2}
+                  placeholder={
+                    isInference
+                      ? 'A curious raccoon peers through a vibrant field of yellow sunflowers…'
+                      : 'Brief description of this training job…'
+                  }
+                  required={!(usingReferences && useGuidedPrompt)}
+                  disabled={isSubmitting}
+                />
+              </FieldRow>
+              {usingReferences && (
+                <button
+                  type="button"
+                  onClick={toggleGuidedPrompt}
+                  disabled={isSubmitting}
+                  className="self-start text-xs text-accent-blue underline-offset-2 hover:underline disabled:cursor-not-allowed disabled:opacity-50"
+                >
+                  Edit as prompt sections
+                </button>
+              )}
+            </>
+          )}
 
           {isInference && (
             <FieldRow htmlFor="modal-negative-prompt" label="Negative Prompt">
@@ -849,6 +1292,13 @@ export default function CreateJobModal({
                   onChange={setDitCpuOffload}
                   disabled={isSubmitting}
                 />
+                <ToggleRow
+                  id="modal-dit-layerwise-offload"
+                  label="DiT Layerwise Offload"
+                  checked={ditLayerwiseOffload}
+                  onChange={handleDitLayerwiseOffloadChange}
+                  disabled={isSubmitting}
+                />
                 <ToggleRow
                   id="modal-text-encoder-cpu-offload"
                   label="Text Encoder CPU Offload"
@@ -860,7 +1310,7 @@ export default function CreateJobModal({
                   id="modal-use-fsdp-inference"
                   label="Use FSDP Inference"
                   checked={useFsdpInference}
-                  onChange={setUseFsdpInference}
+                  onChange={handleUseFsdpInferenceChange}
                   disabled={isSubmitting}
                 />
                 <ToggleRow
@@ -891,7 +1341,7 @@ export default function CreateJobModal({
                   max={8}
                   step={1}
                   value={numGpus}
-                  onChange={setNumGpus}
+                  onChange={handleNumGpusChange}
                   disabled={isSubmitting}
                 />
                 <NumberRow
@@ -906,6 +1356,8 @@ export default function CreateJobModal({
             </details>
           )}
 
+          </fieldset>
+
           <div className="flex flex-col items-start gap-2">
             {submitError && (
               <p role="alert" className="text-sm text-destructive">
@@ -914,14 +1366,22 @@ export default function CreateJobModal({
             )}
             <Button
               type="submit"
+              hidden={readOnly}
               disabled={
+                readOnly ||
                 isSubmitting ||
                 isUploadingImage ||
                 !!modelLoadError ||
                 !!datasetLoadError
               }
             >
-              {isSubmitting ? 'Creating…' : 'Create Job'}
+              {isSubmitting
+                ? editingJob
+                  ? 'Saving…'
+                  : 'Creating…'
+                : editingJob
+                  ? 'Save Changes'
+                  : 'Create Job'}
             </Button>
           </div>
         </form>

apps/fastvideo_studio/src/components/jobs/JobCard.tsx

@@ -3,11 +3,13 @@
 import * as React from 'react';
 import { Timer } from 'lucide-react';
 
+import CreateJobModal from '@/components/jobs/CreateJobModal';
 import { Badge, type BadgeProps } from '@/components/ui/badge';
 import { Button } from '@/components/ui/button';
 import { useStore } from '@/hooks/useStore';
 import {
   deleteJob,
+  duplicateJob,
   downloadJobVideo,
   startJob,
   stopJob,
@@ -62,6 +64,8 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
   const isSelected = activeJobId === job.id;
 
   const [isLoading, setIsLoading] = React.useState(false);
+  const [isEditing, setIsEditing] = React.useState(false);
+  const [isViewing, setIsViewing] = React.useState(false);
   const [currentTime, setCurrentTime] = React.useState(() => Date.now());
 
   const elapsedTime = computeElapsed(job, currentTime);
@@ -103,6 +107,21 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
     }
   }
 
+  async function handleDuplicate(e: React.MouseEvent) {
+    e.preventDefault();
+    e.stopPropagation();
+    if (isLoading) return;
+    setIsLoading(true);
+    try {
+      await duplicateJob(job.id);
+      onJobUpdated?.();
+    } catch (err) {
+      alert(err instanceof Error ? err.message : 'Failed to duplicate job');
+    } finally {
+      setIsLoading(false);
+    }
+  }
+
   async function handleDelete(e: React.MouseEvent) {
     e.preventDefault();
     e.stopPropagation();
@@ -156,16 +175,23 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
       >
         <span className="flex flex-wrap items-center justify-between gap-2">
           <span className="text-[0.95rem] font-semibold text-foreground">
-            {job.model_id}
+            {job.name?.trim() || job.model_id}
           </span>
           <Badge variant={BADGE_VARIANTS[job.status] ?? 'secondary'}>
             {job.status}
           </Badge>
         </span>
         <span className="max-w-full overflow-hidden text-ellipsis whitespace-nowrap text-sm text-muted-foreground">
-          {job.prompt}
+          {job.name?.trim() ? `${job.model_id} · ${job.prompt}` : job.prompt}
         </span>
         <span className="flex flex-wrap items-center gap-4 text-xs text-muted-foreground">
+          {/* Short job id; logs and output dirs are keyed on the full UUID. */}
+          <span
+            className="font-mono text-muted-foreground/80"
+            title={job.id}
+          >
+            {job.id.slice(0, 8)}
+          </span>
           {job.job_type === 'inference' ? (
             <>
               <span>{job.num_frames} frames</span>
@@ -226,6 +252,51 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
               Download Video
             </Button>
           )}
+        {!(
+          job.status === 'pending' ||
+          job.status === 'failed' ||
+          job.status === 'stopped'
+        ) && (
+          <Button
+            size="sm"
+            variant="outline"
+            onClick={(e) => {
+              e.preventDefault();
+              e.stopPropagation();
+              setIsViewing(true);
+            }}
+            disabled={isLoading}
+            title="View this job's configuration"
+          >
+            View
+          </Button>
+        )}
+        {(job.status === 'pending' ||
+          job.status === 'failed' ||
+          job.status === 'stopped') && (
+          <Button
+            size="sm"
+            variant="outline"
+            onClick={(e) => {
+              e.preventDefault();
+              e.stopPropagation();
+              setIsEditing(true);
+            }}
+            disabled={isLoading}
+            title="Edit this job's configuration"
+          >
+            Edit
+          </Button>
+        )}
+        <Button
+          size="sm"
+          variant="outline"
+          onClick={handleDuplicate}
+          disabled={isLoading}
+          title="Create a new pending job with this configuration"
+        >
+          Duplicate
+        </Button>
         <Button
           size="sm"
           variant="destructive"
@@ -235,6 +306,30 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
           Delete
         </Button>
       </div>
+      {isViewing && (
+        <CreateJobModal
+          isOpen
+          readOnly
+          editingJob={job}
+          jobType={(job.job_type ?? 'inference') as never}
+          workloadType={job.workload_type ?? 't2v'}
+          onClose={() => setIsViewing(false)}
+          onSuccess={() => setIsViewing(false)}
+        />
+      )}
+      {isEditing && (
+        <CreateJobModal
+          isOpen
+          editingJob={job}
+          jobType={(job.job_type ?? 'inference') as never}
+          workloadType={job.workload_type ?? 't2v'}
+          onClose={() => setIsEditing(false)}
+          onSuccess={() => {
+            setIsEditing(false);
+            onJobUpdated?.();
+          }}
+        />
+      )}
     </article>
   );
 }

apps/fastvideo_studio/src/components/jobs/JobDetailsSidebar.tsx

@@ -153,9 +153,16 @@ export default function JobDetailsSidebar({
       }}
     >
       <div className="flex items-center justify-between border-b border-border px-5 py-4">
-        <h2 className="m-0 text-base font-semibold text-foreground">
-          Job Details
-        </h2>
+        <div className="min-w-0">
+          <h2 className="m-0 text-base font-semibold text-foreground">
+            Job Details
+          </h2>
+          {/* Full job id: keys ~/h3_studio_logs/<id>.log, the output directory
+              and every API route, so make it selectable for copy/paste. */}
+          <code className="mt-0.5 block select-all truncate font-mono text-xs text-muted-foreground">
+            {job.id}
+          </code>
+        </div>
         <div className="flex items-center gap-2">
           <Button
             type="button"

apps/fastvideo_studio/src/components/ui/accessibility.test.tsx

@@ -1,5 +1,7 @@
+import * as React from 'react';
 import { render, screen } from '@testing-library/react';
-import { describe, expect, it } from 'vitest';
+import userEvent from '@testing-library/user-event';
+import { describe, expect, it, vi } from 'vitest';
 
 import { Button } from './button';
 import { Input } from './input';
@@ -8,6 +10,24 @@ import { Slider } from './slider';
 import { Switch } from './switch';
 
 describe('shared control accessibility', () => {
+  it('forwards refs and click handlers to the asChild button', async () => {
+    const user = userEvent.setup();
+    const ref = React.createRef<HTMLButtonElement>();
+    const onClick = vi.fn();
+    render(
+      <Button asChild ref={ref} onClick={onClick}>
+        <button type="button">Slotted action</button>
+      </Button>,
+    );
+
+    const button = screen.getByRole('button', { name: 'Slotted action' });
+    expect(screen.getAllByRole('button')).toHaveLength(1);
+    expect(ref.current).toBe(button);
+    await user.click(button);
+    expect(onClick).toHaveBeenCalledTimes(1);
+    expect(button).toHaveFocus();
+  });
+
   it('keeps button, input, and select targets at least 44px tall', () => {
     render(
       <>

apps/fastvideo_studio/src/components/ui/button.tsx

@@ -1,7 +1,7 @@
 "use client";
 
 import * as React from "react";
-import { Slot } from "@radix-ui/react-slot";
+import { Slot } from "radix-ui";
 import { cva, type VariantProps } from "class-variance-authority";
 
 import { cn } from "@/lib/utils";
@@ -37,7 +37,7 @@ export interface ButtonProps extends React.ButtonHTMLAttributes<HTMLButtonElemen
 }
 
 const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(({ className, variant, size, asChild = false, ...props }, ref) => {
-	const Comp = asChild ? Slot : "button";
+	const Comp = asChild ? Slot.Root : "button";
 	return <Comp className={cn(buttonVariants({ variant, size, className }))} ref={ref} {...props} />;
 });
 Button.displayName = "Button";

apps/fastvideo_studio/src/components/ui/dialog.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as DialogPrimitive from '@radix-ui/react-dialog';
+import { Dialog as DialogPrimitive } from 'radix-ui';
 import { X } from 'lucide-react';
 
 import { cn } from '@/lib/utils';

apps/fastvideo_studio/src/components/ui/label.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as LabelPrimitive from '@radix-ui/react-label';
+import { Label as LabelPrimitive } from 'radix-ui';
 import { cva, type VariantProps } from 'class-variance-authority';
 
 import { cn } from '@/lib/utils';

apps/fastvideo_studio/src/components/ui/scroll-area.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as ScrollAreaPrimitive from '@radix-ui/react-scroll-area';
+import { ScrollArea as ScrollAreaPrimitive } from 'radix-ui';
 
 import { cn } from '@/lib/utils';
 

apps/fastvideo_studio/src/components/ui/select.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as SelectPrimitive from '@radix-ui/react-select';
+import { Select as SelectPrimitive } from 'radix-ui';
 import { Check, ChevronDown, ChevronUp } from 'lucide-react';
 
 import { cn } from '@/lib/utils';

apps/fastvideo_studio/src/components/ui/separator.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as SeparatorPrimitive from '@radix-ui/react-separator';
+import { Separator as SeparatorPrimitive } from 'radix-ui';
 
 import { cn } from '@/lib/utils';
 

apps/fastvideo_studio/src/components/ui/slider.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as SliderPrimitive from '@radix-ui/react-slider';
+import { Slider as SliderPrimitive } from 'radix-ui';
 
 import { cn } from '@/lib/utils';
 

apps/fastvideo_studio/src/components/ui/switch.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as SwitchPrimitives from '@radix-ui/react-switch';
+import { Switch as SwitchPrimitives } from 'radix-ui';
 
 import { cn } from '@/lib/utils';
 

apps/fastvideo_studio/src/components/ui/tabs.tsx

@@ -1,7 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import * as TabsPrimitive from '@radix-ui/react-tabs';
+import { Tabs as TabsPrimitive } from 'radix-ui';
 
 import { cn } from '@/lib/utils';
 

apps/fastvideo_studio/src/lib/api.ts

@@ -56,6 +56,8 @@ export function getJobVideoUrl(jobId: string): string {
 
 export interface CreateJobRequest {
 	model_id: string;
+	/** Optional label; the card and output filename fall back to the prompt. */
+	name?: string;
 	prompt: string;
 	workload_type?: string;
 	job_type?: JobType;
@@ -152,6 +154,65 @@ export async function updateSettings(
 	return response.json();
 }
 
+export type MediaType = "image" | "video" | "audio";
+
+/**
+ * Upload an image, video or audio file for a MiniMax-H3 Ref2VA reference.
+ * The server derives media_type from the extension and returns it, so callers
+ * do not have to duplicate that mapping.
+ */
+/** Create a new pending job with the same configuration as an existing one. */
+export async function duplicateJob(jobId: string): Promise<{ id: string }> {
+	const baseApiUrl = getApiBaseUrl();
+	const response = await fetch(`${baseApiUrl}/jobs/${jobId}/duplicate`, {
+		method: "POST",
+	});
+	if (!response.ok) {
+		const err = await response
+			.json()
+			.catch(() => ({ detail: "Duplicate failed" }));
+		throw new Error(err.detail || "Duplicate failed");
+	}
+	return response.json();
+}
+
+/** Edit a pending job's configuration. Started jobs are rejected by the API. */
+export async function updateJob(
+	jobId: string,
+	updates: Record<string, unknown>,
+): Promise<unknown> {
+	const baseApiUrl = getApiBaseUrl();
+	const response = await fetch(`${baseApiUrl}/jobs/${jobId}`, {
+		method: "PATCH",
+		headers: { "Content-Type": "application/json" },
+		body: JSON.stringify(updates),
+	});
+	if (!response.ok) {
+		const err = await response.json().catch(() => ({ detail: "Update failed" }));
+		throw new Error(err.detail || "Update failed");
+	}
+	return response.json();
+}
+
+export async function uploadMedia(
+	file: File,
+): Promise<{ path: string; media_type: MediaType }> {
+	const baseApiUrl = getApiBaseUrl();
+	const formData = new FormData();
+	formData.append("file", file);
+	const response = await fetch(`${baseApiUrl}/upload-media`, {
+		method: "POST",
+		body: formData,
+	});
+	if (!response.ok) {
+		const err = await response
+			.json()
+			.catch(() => ({ detail: "Upload failed" }));
+		throw new Error(err.detail || "Upload failed");
+	}
+	return response.json();
+}
+
 export async function uploadImage(file: File): Promise<{ path: string }> {
 	const baseApiUrl = getApiBaseUrl();
 	const formData = new FormData();

apps/fastvideo_studio/src/lib/defaultOptions.ts

@@ -16,6 +16,7 @@ export interface DefaultOptions {
 	seed: number;
 	numGpus: number;
 	ditCpuOffload: boolean;
+	ditLayerwiseOffload: boolean;
 	textEncoderCpuOffload: boolean;
 	vaeCpuOffload: boolean;
 	imageEncoderCpuOffload: boolean;
@@ -44,6 +45,7 @@ export const DEFAULT_OPTIONS: DefaultOptions = {
 	seed: 1024,
 	numGpus: 1,
 	ditCpuOffload: false,
+	ditLayerwiseOffload: false,
 	textEncoderCpuOffload: false,
 	vaeCpuOffload: false,
 	imageEncoderCpuOffload: false,

apps/fastvideo_studio/src/lib/h3Prompt.test.ts

@@ -0,0 +1,65 @@
+import { describe, expect, it } from "vitest";
+import {
+	EMPTY_H3_PROMPT_FIELDS,
+	H3_PROMPT_SECTIONS,
+	isEmptyPromptFields,
+	parseH3Prompt,
+	serializeH3Prompt,
+	type H3PromptFields,
+} from "@/lib/h3Prompt";
+
+const filled: H3PromptFields = {
+	subject_definitions: "<Subject 1> is the dog in <Picture 1>.",
+	summary: "[reference generation] The target video shows <Subject 1>.",
+	retention_analysis: "<Subject 1> (appears in [Shot 1]): fully_preserved - fur retained.",
+	detailed_description: "[Shot 1] A medium shot establishes <Subject 1>.",
+	overall_soundscape: "Room tone throughout.",
+	non_diegetic_music: "N/A",
+};
+
+describe("serializeH3Prompt", () => {
+	it("emits sections in order, flush left, blank-line separated", () => {
+		const out = serializeH3Prompt(filled);
+		expect(out).toBe(
+			[
+				"subject_definitions:\n<Subject 1> is the dog in <Picture 1>.",
+				"summary:\n[reference generation] The target video shows <Subject 1>.",
+				"retention_analysis:\n<Subject 1> (appears in [Shot 1]): fully_preserved - fur retained.",
+				"detailed_description:\n[Shot 1] A medium shot establishes <Subject 1>.",
+				"overall_soundscape:\nRoom tone throughout.",
+				"non_diegetic_music:\nN/A",
+			].join("\n\n"),
+		);
+		// content is never indented
+		expect(out).not.toMatch(/\n {2}\S/);
+	});
+
+	it("fills blank sections with N/A rather than dropping them", () => {
+		const out = serializeH3Prompt({ ...EMPTY_H3_PROMPT_FIELDS, summary: "x" });
+		for (const s of H3_PROMPT_SECTIONS) expect(out).toContain(`${s}:`);
+		expect(out).toContain("non_diegetic_music:\nN/A");
+	});
+});
+
+describe("parseH3Prompt", () => {
+	it("round-trips a serialized prompt", () => {
+		expect(parseH3Prompt(serializeH3Prompt(filled))).toEqual(filled);
+	});
+
+	it("keeps multi-line section bodies", () => {
+		const p = parseH3Prompt("summary:\nline one\nline two\n\ndetailed_description:\nd");
+		expect(p?.summary).toBe("line one\nline two");
+		expect(p?.detailed_description).toBe("d");
+	});
+
+	it("returns null for a plain prompt", () => {
+		expect(parseH3Prompt("a toy car drives into a plush dog")).toBeNull();
+	});
+});
+
+describe("isEmptyPromptFields", () => {
+	it("detects blank vs filled", () => {
+		expect(isEmptyPromptFields(EMPTY_H3_PROMPT_FIELDS)).toBe(true);
+		expect(isEmptyPromptFields(filled)).toBe(false);
+	});
+});

apps/fastvideo_studio/src/lib/h3Prompt.ts

@@ -0,0 +1,94 @@
+/**
+ * MiniMax-H3 full-reference prompt sections. Serialization follows the worked
+ * example in the model's VIDEO_PROMPT_WRITING_GUIDE_ref_en.md: `section_name:`
+ * on its own line, content flush left, one blank line between sections.
+ */
+
+export const H3_PROMPT_SECTIONS = [
+  'subject_definitions',
+  'summary',
+  'retention_analysis',
+  'detailed_description',
+  'overall_soundscape',
+  'non_diegetic_music',
+] as const;
+
+export type H3PromptSection = (typeof H3_PROMPT_SECTIONS)[number];
+
+export type H3PromptFields = Record<H3PromptSection, string>;
+
+export const EMPTY_H3_PROMPT_FIELDS: H3PromptFields = {
+  subject_definitions: '',
+  summary: '',
+  retention_analysis: '',
+  detailed_description: '',
+  overall_soundscape: '',
+  non_diegetic_music: '',
+};
+
+export const H3_SECTION_LABELS: Record<H3PromptSection, string> = {
+  subject_definitions: 'Subject definitions',
+  summary: 'Summary',
+  retention_analysis: 'Retention analysis',
+  detailed_description: 'Detailed description',
+  overall_soundscape: 'Overall soundscape',
+  non_diegetic_music: 'Non-diegetic music',
+};
+
+/** Per-section guidance, condensed from the guide's rules for each section. */
+export const H3_SECTION_HINTS: Record<H3PromptSection, string> = {
+  subject_definitions:
+    'One line per <Subject N>. A subject may draw on several references, e.g. "<Subject 2> is the dog in <Picture 2>, <Picture 3>, and <Picture 4>."',
+  summary:
+    'One paragraph, starting with a [task type] prefix: reference generation, keyframe completion, video editing, video continuation, audio reuse, audio reference. Combine with " + ".',
+  retention_analysis:
+    'Per subject: "<Subject 1> (appears in [Shot 1], [Shot 2]): fully_preserved - what is retained."',
+  detailed_description:
+    'Playback order, using [Shot N] markers, timestamps, (S1) speaker tags and <d>[English] dialogue</d>.',
+  overall_soundscape: 'Ambience and physical sounds. N/A if none.',
+  non_diegetic_music:
+    'Background music audible only to the audience. N/A if none.',
+};
+
+/** True when every section is blank. */
+export function isEmptyPromptFields(fields: H3PromptFields): boolean {
+  return H3_PROMPT_SECTIONS.every((s) => !fields[s].trim());
+}
+
+/**
+ * Join the sections into the prompt string the model is given. Blank sections
+ * become "N/A" rather than being dropped, matching the guide's example.
+ */
+export function serializeH3Prompt(fields: H3PromptFields): string {
+  return H3_PROMPT_SECTIONS.map((section) => {
+    const body = fields[section].trim() || 'N/A';
+    return `${section}:\n${body}`;
+  }).join('\n\n');
+}
+
+/**
+ * Split a serialized prompt back into sections, so switching between the
+ * guided fields and the raw editor does not lose work. Returns null when the
+ * text is not in section format (a plain prompt, say).
+ */
+export function parseH3Prompt(text: string): H3PromptFields | null {
+  const fields = { ...EMPTY_H3_PROMPT_FIELDS };
+  const headings = new Set<string>(H3_PROMPT_SECTIONS);
+  let current: H3PromptSection | null = null;
+  let found = false;
+
+  for (const line of text.split('\n')) {
+    const heading = line.trim().replace(/:$/, '');
+    if (line.trim().endsWith(':') && headings.has(heading)) {
+      current = heading as H3PromptSection;
+      found = true;
+      continue;
+    }
+    if (current) fields[current] += (fields[current] ? '\n' : '') + line;
+  }
+  if (!found) return null;
+  for (const section of H3_PROMPT_SECTIONS) {
+    fields[section] = fields[section].trim();
+  }
+  return fields;
+}

apps/fastvideo_studio/src/lib/h3References.test.ts

@@ -0,0 +1,70 @@
+import { describe, expect, it } from "vitest";
+import {
+	labelReferences,
+	referencePromptSeed,
+	validateReferences,
+	type H3Reference,
+} from "@/lib/h3References";
+
+const ref = (media_type: H3Reference["media_type"], n: number): H3Reference => ({
+	id: `${media_type}-${n}`,
+	source: `/tmp/${media_type}${n}`,
+	media_type,
+	fileName: `${media_type}${n}`,
+});
+
+describe("labelReferences", () => {
+	it("numbers each media type independently, in list order", () => {
+		expect(
+			labelReferences([ref("image", 1), ref("video", 1), ref("image", 2)]),
+		).toEqual(["<Picture 1>", "<Video 1>", "<Picture 2>"]);
+	});
+});
+
+describe("validateReferences", () => {
+	it("accepts an empty list and a normal mix", () => {
+		expect(validateReferences([])).toBeNull();
+		expect(validateReferences([ref("image", 1), ref("audio", 1)])).toBeNull();
+	});
+
+	it("rejects audio-only lists", () => {
+		expect(validateReferences([ref("audio", 1)])).toMatch(/paired/);
+	});
+
+	it("enforces the per-type caps", () => {
+		const videos = [1, 2, 3, 4].map((n) => ref("video", n));
+		expect(validateReferences(videos)).toMatch(/At most 3 video/);
+	});
+
+	it("enforces the overall cap", () => {
+		const many = Array.from({ length: 13 }, (_, i) => ref("image", i));
+		expect(validateReferences(many)).toMatch(/At most 12/);
+	});
+});
+
+describe("referencePromptSeed", () => {
+	it("cites the real labels and never indents content", () => {
+		const seed = referencePromptSeed([ref("image", 1), ref("video", 1)]);
+		expect(seed.subject_definitions).toContain("<Picture 1>");
+		expect(seed.subject_definitions).toContain("<Video 1>");
+		for (const value of Object.values(seed)) {
+			expect(value).not.toMatch(/^ {2}\S/m);
+		}
+	});
+
+	it("uses the guide's retention_analysis form", () => {
+		const seed = referencePromptSeed([ref("image", 1)]);
+		expect(seed.retention_analysis).toMatch(/fully_preserved - /);
+	});
+
+	it("starts the summary with a bracketed task type", () => {
+		const seed = referencePromptSeed([ref("image", 1)]);
+		expect(seed.summary).toMatch(/^\[[a-z +]+\]/);
+	});
+
+	it("describes audio references separately", () => {
+		const seed = referencePromptSeed([ref("image", 1), ref("audio", 1)]);
+		expect(seed.subject_definitions).toContain("<Audio 1>");
+		expect(seed.retention_analysis).toContain("<Audio 1>: reference -");
+	});
+});

apps/fastvideo_studio/src/lib/h3References.ts

@@ -0,0 +1,90 @@
+/**
+ * MiniMax-H3 Ref2VA reference helpers. Labels mirror the per-type counters in
+ * `build_ref2va_presentation`, so what the user sees is what the model is shown.
+ */
+import type { MediaType } from "@/lib/api";
+
+export interface H3Reference {
+	/** stable key for React lists */
+	id: string;
+	source: string;
+	media_type: MediaType;
+	fileName: string;
+}
+
+/** Per-media-type caps enforced by validate_references (reference.py). */
+export const H3_REFERENCE_LIMITS: Record<MediaType, number> = {
+	image: 9,
+	video: 3,
+	audio: 3,
+};
+export const H3_MAX_REFERENCES = 12;
+
+const LABEL_FOR: Record<MediaType, string> = {
+	image: "Picture",
+	video: "Video",
+	audio: "Audio",
+};
+
+/** Label each reference the way the pipeline will, e.g. "<Picture 2>". */
+export function labelReferences(refs: H3Reference[]): string[] {
+	const counts: Record<MediaType, number> = { image: 0, video: 0, audio: 0 };
+	return refs.map((ref) => {
+		counts[ref.media_type] += 1;
+		return `<${LABEL_FOR[ref.media_type]} ${counts[ref.media_type]}>`;
+	});
+}
+
+/** Human-readable reason the list is invalid, or null when it is acceptable. */
+export function validateReferences(refs: H3Reference[]): string | null {
+	if (refs.length === 0) return null;
+	if (refs.length > H3_MAX_REFERENCES) {
+		return `At most ${H3_MAX_REFERENCES} references (have ${refs.length}).`;
+	}
+	const counts: Record<MediaType, number> = { image: 0, video: 0, audio: 0 };
+	for (const ref of refs) counts[ref.media_type] += 1;
+	for (const type of Object.keys(counts) as MediaType[]) {
+		if (counts[type] > H3_REFERENCE_LIMITS[type]) {
+			return `At most ${H3_REFERENCE_LIMITS[type]} ${type} references (have ${counts[type]}).`;
+		}
+	}
+	if (counts.audio === refs.length) {
+		return "Audio references must be paired with at least one image or video.";
+	}
+	return null;
+}
+
+/** Seed the guided prompt fields from the current reference list. */
+export function referencePromptSeed(
+	refs: H3Reference[],
+): Record<string, string> {
+	const labels = labelReferences(refs);
+	const visual = labels.filter((l) => !l.startsWith("<Audio"));
+	const audio = labels.filter((l) => l.startsWith("<Audio"));
+
+	const subjects = visual.map(
+		(label, i) =>
+			`<Subject ${i + 1}> is the subject from ${label}; describe appearance and distinguishing features.`,
+	);
+	for (const label of audio) {
+		subjects.push(`${label} is the audio reference; describe what it provides.`);
+	}
+
+	const retention = visual.map(
+		(_, i) =>
+			`<Subject ${i + 1}> (appears in [Shot 1]): fully_preserved - what is retained.`,
+	);
+	for (const label of audio) {
+		retention.push(`${label}: reference - how it guides the audio.`);
+	}
+
+	return {
+		subject_definitions: subjects.join("\n"),
+		summary: "[reference generation] Describe the target video and each reference's role.",
+		retention_analysis: retention.join("\n"),
+		detailed_description:
+			"[Shot 1] Describe composition, subjects, environment, lighting, action and camera movement, saying where each reference takes effect.",
+		overall_soundscape: "",
+		non_diegetic_music: "",
+	};
+}

apps/fastvideo_studio/src/lib/jobToFields.test.ts

@@ -0,0 +1,74 @@
+import { describe, expect, it } from "vitest";
+import { jobToFormFields, referenceFileName, type JobLike } from "@/lib/jobToFields";
+
+const job: JobLike = {
+	id: "abc",
+	model_id: "MiniMaxAI/MiniMax-H3",
+	prompt: "subject_definitions:\n<Subject 1> is a dog.\n\nsummary:\n[reference generation] x",
+	workload_type: "i2v",
+	references: [
+		{ source: "/uploads/9f/wukong_source.mp4", media_type: "video" },
+		{ source: "/uploads/2a/MonkeyKing_0.jpg", media_type: "image" },
+	],
+	num_frames: 141,
+	height: 768,
+	width: 1344,
+	guidance_scale: 1.0,
+	guidance_rescale: 0.1,
+	seed: 0,
+	num_gpus: 4,
+	use_fsdp_inference: true,
+};
+
+describe("jobToFormFields", () => {
+	it("carries the settings that differ from form defaults", () => {
+		const f = jobToFormFields(job);
+		expect(f.numFrames).toBe(141);
+		expect(f.height).toBe(768);
+		expect(f.width).toBe(1344);
+		expect(f.guidanceScale).toBe(1.0);
+		expect(f.guidanceRescale).toBe(0.1);
+		expect(f.seed).toBe(0);          // 0 must survive, not fall back to 1024
+		expect(f.numGpus).toBe(4);
+		expect(f.useFsdpInference).toBe(true);
+	});
+
+	it("does not let falsy-but-valid values fall through to defaults", () => {
+		const f = jobToFormFields({ ...job, seed: 0, vsa_sparsity: 0, guidance_rescale: 0 });
+		expect(f.seed).toBe(0);
+		expect(f.vsaSparsity).toBe(0);
+		expect(f.guidanceRescale).toBe(0);
+	});
+
+	it("rebuilds the reference list with readable names", () => {
+		const f = jobToFormFields(job);
+		expect(f.references).toHaveLength(2);
+		expect(f.references[0].fileName).toBe("wukong_source.mp4");
+		expect(f.references[1].media_type).toBe("image");
+		expect(new Set(f.references.map((r) => r.id)).size).toBe(2);
+	});
+
+	it("splits a six-section prompt back into fields", () => {
+		const f = jobToFormFields(job);
+		expect(f.promptFields?.subject_definitions).toBe("<Subject 1> is a dog.");
+		expect(f.promptFields?.summary).toBe("[reference generation] x");
+	});
+
+	it("returns null promptFields for a plain prompt", () => {
+		const f = jobToFormFields({ ...job, prompt: "a toy car" });
+		expect(f.promptFields).toBeNull();
+		expect(f.prompt).toBe("a toy car");
+	});
+
+	it("handles a job with no references", () => {
+		const f = jobToFormFields({ ...job, references: null });
+		expect(f.references).toEqual([]);
+	});
+});
+
+describe("referenceFileName", () => {
+	it("takes the basename", () => {
+		expect(referenceFileName("/a/b/c.mp4")).toBe("c.mp4");
+		expect(referenceFileName("c.mp4")).toBe("c.mp4");
+	});
+});

apps/fastvideo_studio/src/lib/jobToFields.ts

@@ -0,0 +1,119 @@
+/**
+ * Map a persisted job back onto the create-job form's fields. Every value the
+ * form seeds from defaults must be covered here, or edit mode silently shows
+ * defaults for whatever is missing.
+ */
+import type { H3Reference } from "@/lib/h3References";
+import { parseH3Prompt, type H3PromptFields } from "@/lib/h3Prompt";
+
+export interface JobLike {
+	id: string;
+	model_id: string;
+	name?: string;
+	prompt: string;
+	workload_type?: string;
+	job_type?: string;
+	image_path?: string;
+	last_image_path?: string;
+	references?: { source: string; media_type: string }[] | null;
+	negative_prompt?: string;
+	num_inference_steps?: number;
+	num_frames?: number;
+	height?: number;
+	width?: number;
+	guidance_scale?: number;
+	guidance_rescale?: number;
+	fps?: number;
+	seed?: number;
+	num_gpus?: number;
+	dit_cpu_offload?: boolean;
+	dit_layerwise_offload?: boolean;
+	text_encoder_cpu_offload?: boolean;
+	vae_cpu_offload?: boolean;
+	image_encoder_cpu_offload?: boolean;
+	use_fsdp_inference?: boolean;
+	enable_torch_compile?: boolean;
+	vsa_sparsity?: number;
+	tp_size?: number;
+	sp_size?: number;
+}
+
+export interface JobFormFields {
+	modelId: string;
+	name: string;
+	workloadType: string;
+	jobType: string;
+	prompt: string;
+	negativePrompt: string;
+	imagePath: string;
+	lastImagePath: string;
+	references: H3Reference[];
+	promptFields: H3PromptFields | null;
+	numInferenceSteps: number;
+	numFrames: number;
+	height: number;
+	width: number;
+	guidanceScale: number;
+	guidanceRescale: number;
+	fps: number;
+	seed: number;
+	numGpus: number;
+	ditCpuOffload: boolean;
+	ditLayerwiseOffload: boolean;
+	textEncoderCpuOffload: boolean;
+	vaeCpuOffload: boolean;
+	imageEncoderCpuOffload: boolean;
+	useFsdpInference: boolean;
+	enableTorchCompile: boolean;
+	vsaSparsity: number;
+	tpSize: number;
+	spSize: number;
+}
+
+/** Uploads keep their original basename, so this is the display name. */
+export function referenceFileName(source: string): string {
+	return source.split("/").filter(Boolean).pop() ?? source;
+}
+
+export function jobToFormFields(job: JobLike): JobFormFields {
+	const refs: H3Reference[] = (job.references ?? []).map((r, i) => ({
+		id: `${job.id}-${i}`,
+		source: r.source,
+		media_type: r.media_type as H3Reference["media_type"],
+		fileName: referenceFileName(r.source),
+	}));
+
+	return {
+		modelId: job.model_id,
+		name: job.name ?? "",
+		workloadType: job.workload_type ?? "t2v",
+		jobType: job.job_type ?? "inference",
+		prompt: job.prompt ?? "",
+		negativePrompt: job.negative_prompt ?? "",
+		imagePath: job.image_path ?? "",
+		lastImagePath: job.last_image_path ?? "",
+		references: refs,
+		// null when the prompt is not in six-section form; the caller then keeps
+		// the raw editor rather than silently dropping content into fields.
+		promptFields: parseH3Prompt(job.prompt ?? ""),
+		numInferenceSteps: job.num_inference_steps ?? 50,
+		numFrames: job.num_frames ?? 81,
+		height: job.height ?? 480,
+		width: job.width ?? 832,
+		guidanceScale: job.guidance_scale ?? 5.0,
+		guidanceRescale: job.guidance_rescale ?? 0.0,
+		fps: job.fps ?? 24,
+		seed: job.seed ?? 1024,
+		numGpus: job.num_gpus ?? 1,
+		ditCpuOffload: job.dit_cpu_offload ?? false,
+		ditLayerwiseOffload: job.dit_layerwise_offload ?? false,
+		textEncoderCpuOffload: job.text_encoder_cpu_offload ?? false,
+		vaeCpuOffload: job.vae_cpu_offload ?? false,
+		imageEncoderCpuOffload: job.image_encoder_cpu_offload ?? false,
+		useFsdpInference: job.use_fsdp_inference ?? false,
+		enableTorchCompile: job.enable_torch_compile ?? false,
+		vsaSparsity: job.vsa_sparsity ?? 0,
+		tpSize: job.tp_size ?? -1,
+		spSize: job.sp_size ?? -1,
+	};
+}

apps/fastvideo_studio/src/lib/types.ts

@@ -5,6 +5,7 @@ export type JobType = "inference" | "finetuning" | "distillation";
 export interface Job {
 	id: string;
 	model_id: string;
+	name?: string;
 	prompt: string;
 	job_type?: JobType;
 	workload_type?: string;

apps/fastvideo_studio/tests/test_duplicate_and_edit.py

@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Duplicating a job's config, and editing one that has not started."""
+import uuid
+
+import pytest
+
+from fastvideo_studio.database import Database
+from fastvideo_studio.job_runner import JobRunner, JobStatus
+
+
+@pytest.fixture
+def runner(tmp_path):
+    return JobRunner(
+        output_dir=str(tmp_path / "out"),
+        log_dir=str(tmp_path / "logs"),
+        database=Database(tmp_path / "t.db"),
+    )
+
+
+def _make(runner, **over):
+    kwargs = dict(
+        job_id=str(uuid.uuid4()),
+        model_id="MiniMaxAI/MiniMax-H3",
+        prompt="p",
+        workload_type="i2v",
+        num_frames=141,
+        guidance_scale=1.0,
+        num_gpus=4,
+        references=[{"source": "/x/clip.mp4", "media_type": "video"}],
+    )
+    kwargs.update(over)
+    return runner.create_job(**kwargs)
+
+
+def test_duplicate_copies_config_but_not_runtime_state(runner):
+    src = _make(runner)
+    src.status = JobStatus.COMPLETED
+    src.output_path = "/x/out.mp4"
+
+    dup = runner.duplicate_job(src.id, str(uuid.uuid4()))
+
+    assert dup.id != src.id
+    assert dup.status is JobStatus.PENDING
+    assert dup.output_path is None
+    for field in ("model_id", "prompt", "workload_type", "num_frames",
+                  "guidance_scale", "num_gpus", "references"):
+        assert getattr(dup, field) == getattr(src, field)
+
+
+def test_duplicate_deep_copies_references(runner):
+    src = _make(runner)
+    dup = runner.duplicate_job(src.id, str(uuid.uuid4()))
+    dup.references[0]["source"] = "/changed"
+    assert src.references[0]["source"] == "/x/clip.mp4"
+
+
+def test_duplicate_unknown_job(runner):
+    with pytest.raises(ValueError, match="not found"):
+        runner.duplicate_job("nope", str(uuid.uuid4()))
+
+
+def test_edit_pending_job(runner):
+    job = _make(runner)
+    updated = runner.update_job_config(job.id, {"num_frames": 192, "seed": 7})
+    assert updated.num_frames == 192
+    assert updated.seed == 7
+
+
+@pytest.mark.parametrize("status", [JobStatus.FAILED, JobStatus.STOPPED])
+def test_edit_allows_restartable_jobs(runner, status):
+    """Editable exactly when startable: neither has produced an output."""
+    job = _make(runner)
+    job.status = status
+    assert runner.update_job_config(job.id, {"seed": 7}).seed == 7
+
+
+@pytest.mark.parametrize("status", [JobStatus.COMPLETED, JobStatus.RUNNING])
+def test_edit_rejects_jobs_with_or_producing_a_result(runner, status):
+    job = _make(runner)
+    job.status = status
+    with pytest.raises(ValueError, match="can be edited"):
+        runner.update_job_config(job.id, {"seed": 7})
+
+
+def test_edit_rejects_unknown_field(runner):
+    job = _make(runner)
+    with pytest.raises(ValueError, match="Not editable"):
+        runner.update_job_config(job.id, {"status": "completed"})
+
+
+def test_name_is_carried_by_duplicate(runner):
+    src = _make(runner, name="wukong swap v2")
+    dup = runner.duplicate_job(src.id, str(uuid.uuid4()))
+    assert dup.name == "wukong swap v2"
+
+
+def test_name_is_editable(runner):
+    job = _make(runner, name="a")
+    assert runner.update_job_config(job.id, {"name": "b"}).name == "b"

apps/fastvideo_studio/tests/test_upload_naming.py

@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Uploaded files keep a readable basename under a unique directory."""
+from __future__ import annotations
+
+import pytest
+
+from fastvideo_studio.server import _safe_upload_name
+
+
+@pytest.mark.parametrize(
+    ("filename", "ext", "expected"),
+    [
+        ("wukong_source.mp4", ".mp4", "wukong_source.mp4"),
+        ("MonkeyKing_0.jpg", ".jpg", "MonkeyKing_0.jpg"),
+        ("my clip (final).mp4", ".mp4", "my_clip_final.mp4"),
+        ("../../etc/passwd.png", ".png", "passwd.png"),
+        ("/abs/path/frame.png", ".png", "frame.png"),
+        ("émoji✨.png", ".png", "moji.png"),
+        ("", ".png", "upload.png"),
+        (None, ".png", "upload.png"),
+        ("...", ".png", "upload.png"),
+    ],
+)
+def test_safe_upload_name(filename, ext, expected):
+    assert _safe_upload_name(filename, ext) == expected
+
+
+def test_long_names_are_capped():
+    out = _safe_upload_name("x" * 300 + ".png", ".png")
+    assert out == "x" * 80 + ".png"
+
+
+def test_no_path_separators_survive():
+    for bad in ("a/b.png", "a\\b.png", "../x.png"):
+        assert "/" not in _safe_upload_name(bad, ".png")
+        assert "\\" not in _safe_upload_name(bad, ".png")