[ci] Cherrypick perf regression skills to component time commit

[ci] Add performance baseline reseed skill and failed-run artifacts

.agents/STATUS.md

@@ -0,0 +1,94 @@
+# Agent Infrastructure — Status Dashboard
+
+Developer-maintained overview of all agent components and their maturity.
+Use this to understand what exists, how complete it is, and how much to trust it.
+
+_Last synced: 2026-03-02_
+
+> To resync this dashboard, use the workflow: `.agents/workflows/sync-dashboard.md`
+
+---
+
+## Summary
+
+| Category | Total | ✅ Ready | 🟡 Draft | 🔴 Stub | Trust |
+|----------|-------|---------|---------|---------|-------|
+| Skills | 8 | 0 | 8 | 0 | Low — newly created, untested |
+| Workflows (SOPs) | 4 | 0 | 4 | 0 | Low — newly created, untested |
+| Memory files | 4 | 1 | 3 | 0 | Medium — codebase_map is solid |
+| Lessons | 0 | — | — | — | N/A — empty |
+| Exploration logs | 0 | — | — | — | N/A — empty |
+
+---
+
+## Skills (`.agents/skills/`)
+
+| Skill | File | Status | Trust | Tested | Notes |
+|-------|------|--------|-------|--------|-------|
+| Launch Experiment | `launch-experiment.md` | 🟡 Draft | Low | ❌ | Needs dry-run validation |
+| Monitor Experiment | `monitor-experiment.md` | 🟡 Draft | Low | ❌ | Requires W&B API access to test |
+| Summarize Run | `summarize-run.md` | 🟡 Draft | Low | ❌ | Pattern from existing test infra |
+| Log Experiment | `log-experiment.md` | 🟡 Draft | Low | ❌ | Journal formatting only |
+| Evaluate Video Quality | `evaluate-video-quality.md` | 🟡 Draft | Low | ❌ | SSIM section most mature |
+| Index Related Work | `index-related-work.md` | 🟡 Draft | Low | ❌ | Schema defined, no entries yet |
+| Search Related Work | `search-related-work.md` | 🟡 Draft | Low | ❌ | Depends on indexed entries |
+| Skill Template | `SKILL_TEMPLATE.md` | ✅ Ready | High | ✅ | Meta-template, stable |
+
+### Trust Level Definitions
+- **High**: Tested in production, validated against real experiments
+- **Medium**: Logic is sound, partially tested or based on existing patterns
+- **Low**: Newly written, not yet validated
+- **None**: Placeholder only
+
+---
+
+## Workflows / SOPs (`.agents/workflows/`)
+
+| Workflow | File | Status | Trust | Tested | Notes |
+|----------|------|--------|-------|--------|-------|
+| Experiment Lifecycle | `experiment-lifecycle.md` | 🟡 Draft | Low | ❌ | End-to-end flow, untested |
+| Evaluation Development | `evaluation-development.md` | 🟡 Draft | Low | ❌ | Metric dev process |
+| Experiment Journaling | `experiment-journaling.md` | 🟡 Draft | Low | ❌ | Journaling cadence |
+| Lesson Capture | `lesson-capture.md` | 🟡 Draft | Low | ❌ | Post-experiment reflection |
+| Sync Dashboard | `sync-dashboard.md` | 🟡 Draft | Low | ❌ | This dashboard's updater |
+
+---
+
+## Memory (`.agents/memory/`)
+
+| File | Status | Trust | Notes |
+|------|--------|-------|-------|
+| `codebase_map.md` | ✅ Ready | High | Synthesized from full repo research |
+| `experiment_journal.md` | 🟡 Draft | Medium | Schema defined, no entries yet |
+| `evaluation_registry.md` | 🟡 Draft | Medium | SSIM/loss metrics documented |
+| `related_work/README.md` | 🟡 Draft | Medium | Schema defined, no entries yet |
+
+---
+
+## Lessons (`.agents/lessons/`)
+
+| File | Category | Severity | Notes |
+|------|----------|----------|-------|
+
+_No lessons captured yet._
+
+---
+
+## Exploration Logs (`.agents/exploration/`)
+
+| File | Status | Topic | Notes |
+|------|--------|-------|-------|
+
+_No exploration logs yet._
+
+---
+
+## What to Do Next
+
+1. **Validate skills**: Run a minimal training experiment using the
+   `experiment-lifecycle` SOP to test `launch-experiment` → `monitor-experiment`
+   → `summarize-run` end-to-end.
+2. **Index first related work**: Use `index-related-work` to add at least one
+   paper (e.g., the Self-Forcing paper used in the codebase).
+3. **Capture first lesson**: After the validation run, capture any findings.
+4. **Promote to Ready**: As each skill/SOP is tested, update its status here.

.agents/exploration/README.md

@@ -0,0 +1,46 @@
+# Exploration Logs
+
+This directory holds draft procedures and investigation notes for tasks that
+don't yet have a standardized skill or SOP. Each exploration should follow this
+template.
+
+## When to Create an Exploration Log
+
+- You are working on a task with no existing skill or workflow.
+- You are experimenting with a new metric, training technique, or tool.
+- You want to document findings before they are promoted to a standard.
+
+## File Naming
+
+`<topic-slug>.md` — e.g., `fvd-metric-investigation.md`
+
+## Template
+
+```markdown
+# Exploration Log: <Topic>
+
+## Status: draft | under_review | promoted | abandoned
+
+## Context
+<Why this exploration is needed — link to experiment or task if applicable.>
+
+## Progress
+- [ ] Step 1: ...
+- [ ] Step 2: ...
+
+## Findings
+<What you have learned so far.>
+
+## Mistakes / Dead Ends
+<What didn't work and why — these become lessons.>
+
+## Proposed Standardization
+<If this works, describe the skill/SOP/workflow to create.>
+```
+
+## Lifecycle
+
+1. **Create** during exploration mode.
+2. **Update** as you make progress.
+3. **Promote**: If findings are solid, create a skill in `.agents/skills/` or an SOP in `.agents/workflows/`.
+4. **Archive mistakes**: Move failures into `.agents/lessons/`.

.agents/lessons/2026-05-07_conversion-cast-bf16-suffix-allowlist.md

@@ -1,73 +0,0 @@
----
-date: 2026-05-07
-experiment: PR #1280 (daVinci-MagiHuman port), distill DiT parity bring-up
-category: porting
-severity: important
----
-
-# Conversion `--cast-bf16` Needs an FP32-Keep Suffix Allowlist
-
-## What Happened
-
-`scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py --cast-bf16`
-produced a converted distill DiT checkpoint that loaded cleanly, ran end-to-
-end, and emitted reasonable output — but `test_magi_human_distill_parity`
-showed `diff_mean=0.114` against the upstream reference. The base DiT was
-bit-exact with the same conversion script. Only the distill variant
-regressed.
-
-The error was small enough that visual quality looked normal, but large
-enough to fail bit-exact parity. The MagiHuman base + distill DiTs share
-most of their architecture, so a difference that affected only distill was
-counterintuitive.
-
-## Root Cause
-
-`--cast-bf16` was downcasting **all** fp32 tensors to bf16 indiscriminately.
-The base checkpoint and the FastVideo `final_linear` / adapter modules
-require eight specific tensors to remain in fp32:
-
-- LayerNorm `gamma` / `beta` weights for the final residual exit
-- Adapter projection biases
-- A handful of scale parameters in the output projection chain
-
-These tensors participate in chains where bf16 precision causes accumulation
-error large enough to drift the parity check. The base DiT happened to not
-hit those specific chains in the path the test exercised (different
-attention mask shape, different audio interleave); the distill variant did.
-
-## Fix / Workaround
-
-Added `_FP32_KEEP_SUFFIXES` allowlist to
-`convert_magi_human_to_diffusers.py` (commit `829f70d3`) and gated `--cast-
-bf16` on it. Tensors whose state-dict key ends with any allowlisted suffix
-keep their original fp32 dtype regardless of the flag.
-
-Distill DiT parity went from `diff_mean=0.114` (silently wrong) to bit-exact
-in one commit.
-
-## Prevention
-
-1. **Treat `--cast-bf16` as opinionated, not blanket.** Any conversion
-   script that supports a global dtype downcast flag MUST own an explicit
-   allowlist of fp32-keep tensors, documented at the top of the file.
-
-2. **Check two things** for any converter that ships a `--cast-bf16`-style
-   flag:
-   - Run the parity test for **every** variant of the model (base, distill,
-     SR, etc.), not just the headline variant. Different variants exercise
-     different code paths.
-   - Diff the converted checkpoint's dtype map against the upstream
-     reference and assert the allowlist covers every fp32 tensor in the
-     reference.
-
-3. **For MagiHuman specifically**: if you add or rename DiT modules that
-   touch `final_linear`, the adapter, or any LayerNorm in the residual exit
-   path, **check that any fp32-required tensors are covered by
-   `_FP32_KEEP_SUFFIXES`** in the conversion script and re-run
-   `test_magi_human_distill_parity` (it's the canary).
-
-4. The lesson generalizes beyond MagiHuman: any DiT that uses bf16 mixed
-   precision but keeps specific tensors in fp32 (a common pattern with
-   flash-attn-style backends) needs this allowlist for any conversion that
-   downcasts.

.agents/lessons/2026-05-07_dit-dtype-boundary-with-flash-attn.md

@@ -1,77 +0,0 @@
----
-date: 2026-05-07
-experiment: PR #1280 (daVinci-MagiHuman port), DiT parity bring-up
-category: porting
-severity: important
----
-
-# DiT Dtype Boundary Alignment with Flash-Attn-Style Backends
-
-## What Happened
-
-DiT bit-exact parity for daVinci-MagiHuman against the upstream reference
-sat at `diff_max=0.5` after the architecture port was complete and weight
-loading was correct. The error grew with depth (later layers diverged more
-than earlier ones), suggesting an accumulating numerical drift rather than
-a structural mismatch. None of the obvious culprits (RoPE, GQA expansion,
-attention mask handling) accounted for the pattern.
-
-## Root Cause
-
-Four cumulative dtype-boundary mismatches, each individually small but
-together pushing parity from `diff_max=0.5` to bit-exact (`diff_max=0.0`):
-
-1. **SDPA inputs were not cast to bf16.** Upstream's `flash_attn_with_cp`
-   internally casts Q/K/V to bf16 at `dit_module.py:508` before the kernel.
-   FastVideo was passing fp32 tensors through, getting numerically different
-   intermediates even though the kernel accepts both.
-
-2. **Post-attention output was kept in bf16 across the per-head gating
-   multiply.** Upstream upcasts to fp32 before the gating, FastVideo did the
-   gate in bf16 then upcast.
-
-3. **A residual-stream cast at the block boundary.** FastVideo had a
-   `.to(bf16)` then `.to(fp32)` at the start of each block. Upstream keeps
-   the residual stream **continuously in fp32** across all 40 layers; only
-   the inputs to specific kernels are temporarily downcast.
-
-4. **Parity test scheduler used a double-shift.** A separate per-block fix
-   (Wave 11 production migration) — single-shift schedule is what upstream
-   uses; the parity test was double-shifting.
-
-## Fix / Workaround
-
-Four cumulative changes in `fastvideo/models/dits/magi_human.py` (commit
-`3a4816cb`), each with a comment at the call site explaining the upstream
-parity rationale:
-
-- Cast SDPA inputs to bf16 right before the attention call.
-- Upcast attention output to fp32 before the per-head gate multiply.
-- Drop the residual-stream `.to(bf16)`/`.to(fp32)` wrapper at the block
-  boundary; let the residual stay fp32 throughout.
-- Single-shift schedule in the parity test fixture (matches upstream Wave 11).
-
-## Prevention
-
-1. **For any DiT port with a flash-attn-style backend**, treat the dtype of
-   the residual stream as a load-bearing invariant, not a performance knob.
-   Document it in the model's per-pipeline AGENTS.md. MagiHuman's invariant:
-   *residual stream stays fp32 across all blocks; only kernel inputs are
-   temporarily bf16*.
-
-2. **Use layer-by-layer activation hooks** when DiT parity is close-but-not-
-   bit-exact and the gap grows with depth. The
-   `fastvideo/hooks/activation_trace.py` infra exists exactly for this case
-   (see `docs/contributing/activation_trace.md`). In MagiHuman's case it would have localized the
-   first divergence point in one pass.
-
-3. **DiT ports must account for:**
-   - SDPA input dtype must match the upstream kernel's internal cast.
-   - Post-attention upcast happens **before** any per-head gate, not after.
-   - Residual stream dtype across block boundaries is a parity invariant.
-   These rules apply to any DiT port whose upstream uses a flash-attn-style
-   backend (`flash_attn_with_cp`, `flex_flash_attn_func`, etc.).
-
-4. **Add an "intermediate-layer parity" test** for new DiT ports — comparing
-   activations at layer 5, 10, 20, 30 — not just the final output. A growing-
-   with-depth pattern is otherwise indistinguishable from "almost right".

.agents/lessons/2026-05-07_silent-channel-major-packing-bugs.md

@@ -1,68 +0,0 @@
----
-date: 2026-05-07
-experiment: PR #1280 (daVinci-MagiHuman port), Wave 14
-category: porting
-severity: critical
----
-
-# Silent Channel-Major Token-Packing Bugs
-
-## What Happened
-
-While porting daVinci-MagiHuman (`fastvideo/pipelines/basic/magi_human/`),
-the pipeline-parity test passed bit-exactly but the E2E user-visible output
-was **pure static noise**. Latent tensors compared identically against the
-upstream reference at every checkpointed boundary, yet decoded videos showed
-no recognizable content. The discrepancy reproduced on every variant
-(base / distill / SR-540p / SR-1080p) with the same noise profile.
-
-## Root Cause
-
-Video tokens were being packed **spatial-major** instead of **channel-major**:
-
-```python
-# What we had (spatial-major, WRONG)
-einops.rearrange(x, "b c (T pT) (H pH) (W pW) -> b (T H W) (pT pH pW C)", ...)
-
-# What upstream's UnfoldNd produces (channel-major, CORRECT)
-einops.rearrange(x, "b c (T pT) (H pH) (W pW) -> b (T H W) (C pT pH pW)", ...)
-```
-
-A single-character einops reorder. The pipeline-parity test used FastVideo's
-own packer on **both** sides of the comparison, so the bug was invisible there
-— both sides agreed on the wrong layout. The DiT consumed those tokens
-without complaint because the channel dimension only matters at decode time,
-when the VAE's first conv expects channel-major input. By that point the test
-boundary was already passed.
-
-The bug was load-bearing for any token-packed format that downstream feeds
-into a `UnfoldNd`-shaped consumer. Wave 14 of the port took multiple bug-hunt
-iterations and an Oracle consultation to localize.
-
-## Fix / Workaround
-
-Single-character einops change in `stages/latent_preparation.py:_img2tokens`
-(commit `6d190693` of the original PR). After the fix, all four variants
-produced expected E2E output and the pipeline-parity tests still passed
-because both sides of the parity check are now correct.
-
-## Prevention
-
-1. **Never use the FastVideo-side packer on both sides of a parity test.**
-   At least one parity boundary must compare against an upstream tensor
-   produced by the upstream packer. For MagiHuman this means a separate
-   `_img2tokens` parity test that feeds upstream `UnfoldNd` output as the
-   reference, not FastVideo's reformatted equivalent.
-
-2. **Add an E2E hash check** alongside latent-parity. The mp4 SHA was the
-   first signal that something was wrong; if it had been part of the standard
-   parity battery, the bug would have surfaced in Wave 1, not Wave 14. See
-   `fastvideo/tests/ssim/test_magi_human_similarity.py` for the CI version.
-
-3. **For any new model port that involves explicit tensor reshaping into
-   tokens**, document the expected packing order (`(C pT pH pW)` vs
-   `(pT pH pW C)`) at the call site and assert the layout matches the
-   downstream consumer's expectation.
-
-4. DiT parity for any model that does video token packing should include an
-   E2E hash check, not just latent bit-exactness.

.agents/lessons/2026-05-22_dreamverse-ci-streaming-imports-need-gpu.md

@@ -1,41 +0,0 @@
----
-date: 2026-05-22
-experiment: PR #1386 DreamVerse app CI backend tests
-category: infrastructure
-severity: important
----
-
-# DreamVerse App CI Streaming Imports Need GPU
-
-## What Happened
-
-DreamVerse app CI backend pytest collection imports FastVideo streaming surfaces.
-When those tests run in a CPU-only Modal environment, collection can fail before
-any app assertions run with Triton reporting:
-
-```text
-RuntimeError: 0 active drivers
-```
-
-## Root Cause
-
-Some streaming import paths can import `fastvideo_kernel` at module import time.
-Triton then probes for an active GPU driver during pytest collection. A CPU-only
-Modal container has no active driver, so the failure appears as an import-time
-collection error rather than a DreamVerse app behavior failure.
-
-## Fix / Workaround
-
-For PR #1386, use a surgical CI fix: allocate a GPU to
-`run_dreamverse_app_tests`. Do not refactor core streaming/kernel imports just to
-unstick this app CI path.
-
-Keep `build_kernel=False` for this job. The DreamVerse app backend test imports
-streaming surfaces but does not need to rebuild or exercise custom kernels.
-
-## Prevention
-
-When adding or modifying DreamVerse app CI jobs that import FastVideo streaming
-modules, make the GPU requirement explicit if the import graph may touch
-`fastvideo_kernel`. Prefer small CI resource fixes for app test collection issues
-unless the product code genuinely requires lazy import cleanup.

.agents/lessons/2026-09-29_streaming-ipc-semaphore-enoent.md

@@ -1,66 +0,0 @@
----
-date: 2026-09-29
-experiment: Wan-VACE multi-GPU inference on shared Slurm nodes
-category: infrastructure
-severity: important
----
-
-# Spawn workers fail with ENOENT in `SemLock._rebuild`
-
-## What Happened
-
-On Slurm nodes, standard `VideoGenerator` runs with the `mp` executor sometimes
-failed during worker startup. A spawn child raised `FileNotFoundError` from
-`multiprocessing.synchronize.SemLock._rebuild` while unpickling its arguments,
-even though the parent still held its Queue objects. Failures showed up on
-several nodes and looked intermittent.
-
-## Root Cause
-
-The cluster's Slurm epilog `80-epilog-cleanup-shm-tmp.sh` runs
-`find /dev/shm -maxdepth 2 -user "$SLURM_JOB_USER" -delete` at the end of
-**every** job. When one of a user's jobs ends, the epilog deletes that user's
-`/dev/shm` files on the node, including those that the user's *other* running
-jobs are still using. Python's POSIX named semaphores (`/dev/shm/sem.mp-*`) are
-among them. If the deletion lands between queue creation and a spawn child
-unpickling it, the child fails with ENOENT.
-
-Evidence:
-
-- **Timing.** Every recorded deletion burst during a probe job matched, to the
-  second, the end of another job by the same user on the same node
-  (`sacct -u $USER -N <node>` end times).
-- **Controlled reproduction.**
-  - Job A held a spawn `Lock` semaphore and a marker file in `/dev/shm`.
-  - Job B, on the same node, started and then ended.
-  - Job B's start deleted nothing.
-  - Within 1 s of job B's end, both of job A's objects were gone, and job A's
-    finalizer then hit the same ENOENT.
-- **Still unexplained.** One historical failure has no same-user job
-  end in its window, so another deleter cannot be ruled out for it. logind
-  `RemoveIPC` was the earlier hypothesis; it is not needed to explain the other
-  cases.
-
-## Fix / Workaround
-
-- **FastVideo hardening.** Standard inference no longer creates the two
-  streaming queues it never uses. `FastVideoArgs.enable_streaming_ipc_queues`
-  defaults to `False`, and `StreamingVideoGenerator` sets it to `True`. This
-  removes the exposure for standard inference only.
-- **Still exposed.** Streaming queues, NCCL shared-memory segments, and
-  DataLoader shared memory on the same node can still be deleted by the epilog.
-- **Cluster fix (administrators).** Clean `/dev/shm` only when the user has no
-  other job on the node, or give each job a private `/dev/shm` with
-  `JobContainerType=job_container/tmpfs`.
-- **User workaround.** Don't co-locate your own jobs on one node
-  (`--exclusive=user`), or avoid ending short jobs next to long-running ones.
-
-## Prevention
-
-- Before blaming an application for `/dev/shm` ENOENT on Slurm, read the
-  node-local prolog/epilog hooks (`/cm/local/apps/slurm/var/{prologs,epilogs}`).
-  Then correlate the failure window with `sacct -u $USER -N <node>` job end
-  times.
-- Do not pass IPC primitives to spawn workers unless the worker needs them.
-- `fastvideo/tests/worker/test_multiproc_executor.py` checks that standard
-  workers survive semaphore removal during spawn.

.agents/memory/codebase-map/README.md

@@ -0,0 +1,129 @@
+# FastVideo-WorldModel — Codebase Map
+
+High-level structural index for agent orientation. Updated 2026-03-08.
+
+## Repository Layout
+
+```
+FastVideo-WorldModel/
+├── fastvideo/                 # Core Python package
+│   ├── models/                # Model implementations
+│   │   ├── dits/              #   DiT transformers (wanvideo, ltx2, ...)
+│   │   ├── vaes/              #   VAE models
+│   │   ├── encoders/          #   Text/image encoders (T5, CLIP)
+│   │   ├── schedulers/        #   Noise schedulers
+│   │   ├── upsamplers/        #   Super-resolution models
+│   │   ├── audio/             #   Audio models
+│   │   └── loader/            #   Component loaders for HF repos
+│   ├── configs/               # Configuration system
+│   │   ├── models/            #   Arch configs + param_names_mapping
+│   │   ├── pipelines/         #   Pipeline wiring
+│   │   └── sample/            #   Default sampling parameters
+│   ├── pipelines/             # End-to-end pipelines
+│   │   ├── basic/             #   Per-model pipelines (wan/, ltx2/, ...)
+│   │   └── stages/            #   Reusable pipeline stages
+│   ├── train/                 # Refactored training framework (YAML-driven, preferred)
+│   │   ├── trainer.py         #   Main training loop coordinator
+│   │   ├── entrypoint/        #   Training entrypoint (train.py) + checkpoint conversion
+│   │   ├── methods/           #   Training algorithms (FineTune, DFSFT, DMD2, SelfForcing)
+│   │   │   ├── base.py        #     TrainingMethod ABC
+│   │   │   ├── fine_tuning/   #     FineTuneMethod, DiffusionForcingSFTMethod
+│   │   │   └── distribution_matching/  # DMD2Method, SelfForcingMethod
+│   │   ├── models/            #   Per-role model wrappers (ModelBase, CausalModelBase)
+│   │   │   └── wan/           #     WanModel, WanCausalModel
+│   │   ├── callbacks/         #   Composable hooks (grad_clip, ema, validation)
+│   │   └── utils/             #   Config, builder, checkpoint, optimizer, tracking
+│   ├── training/              # Legacy training infrastructure (being phased out)
+│   │   ├── trackers.py        #   W&B tracker (BaseTracker → WandbTracker)
+│   │   ├── training_utils.py  #   Checkpointing, grad clipping, state dicts
+│   │   ├── training_pipeline.py        # Base training pipeline
+│   │   ├── wan_training_pipeline.py    # Wan T2V training
+│   │   ├── wan_i2v_training_pipeline.py # Wan I2V training
+│   │   ├── distillation_pipeline.py    # Distillation base
+│   │   ├── wan_distillation_pipeline.py # Wan distillation
+│   │   ├── self_forcing_distillation_pipeline.py # Self-forcing distill
+│   │   ├── ltx2_training_pipeline.py   # LTX-2 training
+│   │   └── matrixgame_training_pipeline.py # MatrixGame training
+│   ├── attention/             # Attention backends
+│   ├── distributed/           # Sequence/tensor parallel utilities
+│   ├── layers/                # Tensor-parallel layers
+│   ├── tests/                 # Package-level tests
+│   │   ├── training/          #   Training regression tests (W&B summary comparison)
+│   │   ├── ssim/              #   SSIM visual regression tests
+│   │   ├── encoders/          #   Encoder parity tests
+│   │   └── modal/             #   Modal CI test runner
+│   └── registry.py            # Unified config registry
+├── fastvideo-kernel/          # CUDA/custom kernels (separate build: ./build.sh)
+├── scripts/                   # Utility scripts
+│   ├── distill/               #   Distillation launch scripts
+│   ├── inference/             #   Inference scripts
+│   ├── checkpoint_conversion/ #   Weight conversion tools
+│   ├── finetune/              #   Finetune scripts
+│   └── preprocess/            #   Data preprocessing
+├── examples/                  # Ready-to-run examples
+│   ├── training/              #   Training examples (finetune/, consistency_finetune/)
+│   ├── distill/               #   Distillation examples
+│   ├── inference/             #   Inference examples
+│   └── dataset/               #   Dataset examples
+├── docs/                      # MkDocs documentation source
+│   ├── design/overview.md     #   Architecture overview
+│   ├── training/              #   Training guides
+│   └── contributing/          #   Contributor guides + coding_agents.md
+├── tests/                     # Top-level tests (local_tests/)
+├── AGENTS.md                  # Agent coding guidelines
+└── .agents/                    # Agent infrastructure (you are here)
+```
+
+## Key Training Entrypoints
+
+### New framework (`fastvideo/train/`) — preferred
+
+| Method | Config Example | Launch Pattern |
+|--------|---------------|----------------|
+| FineTune (Wan) | `examples/train/finetune_wan2.1_t2v_1.3B_vsa_*.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
+| DFSFT (Wan causal) | `examples/train/dfsft_wan_causal_t2v_1.3B.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
+| DMD2 distillation | `examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
+| Self-Forcing | `examples/train/self_forcing_wan_causal_t2v_1.3B.yaml` | `torchrun -m fastvideo.train.entrypoint.train --config <yaml>` |
+
+### Legacy pipelines (`fastvideo/training/`) — being phased out
+
+| Pipeline | Entrypoint | Launch Pattern |
+|----------|-----------|----------------|
+| Wan T2V finetune | `fastvideo/training/wan_training_pipeline.py` | `torchrun --nproc_per_node N` |
+| Wan I2V finetune | `fastvideo/training/wan_i2v_training_pipeline.py` | `torchrun --nproc_per_node N` |
+| Wan distillation (DMD) | `fastvideo/training/wan_distillation_pipeline.py` | `torchrun --nproc_per_node N` |
+| Self-forcing distill | `fastvideo/training/wan_self_forcing_distillation_pipeline.py` | `torchrun --nproc_per_node N` |
+| LTX-2 finetune | `fastvideo/training/ltx2_training_pipeline.py` | `torchrun --nproc_per_node N` |
+| MatrixGame | `fastvideo/training/matrixgame_training_pipeline.py` | `torchrun --nproc_per_node N` |
+
+## W&B Integration
+
+- **Tracker classes**: `fastvideo/training/trackers.py`
+  - `WandbTracker` — logs metrics, videos, timing
+  - `SequentialTracker` — fan-out to multiple trackers
+  - `DummyTracker` — no-op for offline/test
+- **Run summary location**: `<output_dir>/tracker/wandb/latest-run/files/wandb-summary.json`
+- **Reference summaries**: `fastvideo/tests/training/*/` (e.g., `a40_reference_wandb_summary.json`)
+- **Environment**: `WANDB_API_KEY`, `WANDB_BASE_URL`, `WANDB_MODE`
+
+## Critical Environment Variables
+
+| Variable | Purpose |
+|----------|---------|
+| `WANDB_API_KEY` | W&B authentication |
+| `WANDB_MODE` | `online` / `offline` |
+| `FASTVIDEO_ATTENTION_BACKEND` | `FLASH_ATTN` / `TORCH_SDPA` |
+| `TOKENIZERS_PARALLELISM` | Set `false` to avoid fork warnings |
+| `HF_HOME` | HuggingFace cache directory |
+
+## Build & Test Commands
+
+```bash
+uv pip install -e ".[dev]"                        # Editable install
+pre-commit run --all-files                        # Lint/format/spell
+pytest tests/                                     # Top-level tests
+pytest fastvideo/tests/ -v                        # Package tests
+pytest fastvideo/tests/training/Vanilla -srP      # Training loss regression
+pytest fastvideo/tests/ssim/ -vs                  # SSIM visual regression
+cd fastvideo-kernel && ./build.sh                 # Build kernels
+```

.agents/memory/evaluation-registry/README.md

@@ -0,0 +1,327 @@
+# Evaluation Metrics Registry
+
+Living catalog of all evaluation metrics for FastVideo-WorldModel video quality
+assessment. Each metric includes a detailed explanation, implementation status,
+usage instructions, and interpretation guide.
+
+_Last updated: 2026-03-02_
+
+---
+
+## Metric Summary
+
+| Metric | Category | Status | Location | Trust |
+|--------|----------|--------|----------|-------|
+| **FVD** | Distribution | ✅ Implemented | `benchmarks/fvd/` | High |
+| **SSIM** | Reference | ✅ Implemented | `fastvideo/tests/ssim/` | High |
+| **LPIPS** | Perceptual | ✅ Implemented | `scripts/lora_extraction/` | Medium |
+| **Loss trajectory** | Training signal | ✅ Implemented | W&B `train_loss` | Medium |
+| **Grad norm stability** | Training signal | ✅ Implemented | W&B `grad_norm` | Medium |
+| **GameWorld Score** | Multi-dim benchmark | 🟡 External | Matrix-Game repo | Low |
+| **Human preference** | Gold standard | 🔴 Manual | N/A | Highest |
+
+---
+
+## Implemented Metrics
+
+### FVD — Fréchet Video Distance
+
+**Category**: Distribution-level quality metric
+**Status**: ✅ Fully implemented in `benchmarks/fvd/`
+**Trust**: High — standard protocol, I3D feature extractor
+
+#### What It Measures
+FVD measures the distance between the **distribution** of generated videos and
+a distribution of real/reference videos. It works by:
+1. Extracting spatiotemporal features from both real and generated video sets
+   using a pretrained **I3D** (Inflated 3D ConvNet) model.
+2. Modeling each set of features as a multivariate Gaussian (mean + covariance).
+3. Computing the **Fréchet distance** between the two Gaussians.
+
+Lower FVD = generated videos are more statistically similar to real videos.
+
+#### Why It Matters
+- FVD is the **de facto standard** for benchmarking video generation models.
+- It captures both **visual quality** (are individual frames realistic?) and
+  **temporal coherence** (do frames flow naturally?).
+- Matrix-Game 2.0, Open-Sora, and most video generation papers report FVD.
+
+#### Limitations
+- Requires a **large sample set** (standard protocol uses 2048 videos) to
+  produce stable statistics. Small sample sizes yield noisy results.
+- Measures **distributional similarity**, not per-video quality. A model could
+  have low FVD by generating a diverse set of "roughly okay" videos.
+- The I3D model was trained on Kinetics-400 (human actions). It may be less
+  sensitive to domain-specific artifacts in non-human-action videos (e.g.,
+  driving, game environments).
+- Does not directly measure text-video alignment or action controllability.
+
+#### How to Use
+
+```python
+# Programmatic
+from benchmarks.fvd import compute_fvd_with_config, FVDConfig
+
+config = FVDConfig.fvd2048_16f()  # Standard: 2048 videos, 16 frames
+results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
+print(f"FVD: {results['fvd']:.2f}")
+```
+
+```bash
+# CLI
+python -m benchmarks.fvd.cli \
+    --real-path data/real/ \
+    --gen-path outputs/gen/ \
+    --protocol fvd2048_16f
+```
+
+**Preset protocols**:
+| Protocol | Videos | Frames | Use Case |
+|----------|--------|--------|----------|
+| `fvd2048_16f` | 2048 | 16 | Standard benchmark (papers) |
+| `fvd2048_128f` | 2048 | 128 | Long video evaluation |
+| `quick_test` | 100 | 16 | Fast dev iteration |
+
+**Feature extractors**: `i3d` (default, standard), `clip`, `videomae`
+
+#### Interpretation
+| FVD Range | Interpretation |
+|-----------|---------------|
+| < 100 | Excellent — near-real quality |
+| 100–300 | Good — competitive with SOTA |
+| 300–600 | Fair — noticeable gap from real |
+| > 600 | Poor — significant quality issues |
+
+> FVD values are dataset-dependent. Always compare against baselines evaluated
+> on the same real video distribution.
+
+---
+
+### SSIM — Structural Similarity Index
+
+**Category**: Per-frame reference comparison
+**Status**: ✅ Implemented in `fastvideo/tests/ssim/`
+**Trust**: High — used in CI regression tests
+
+#### What It Measures
+SSIM compares two images (or video frames) based on three components:
+1. **Luminance**: brightness similarity
+2. **Contrast**: dynamic range similarity
+3. **Structure**: spatial pattern similarity
+
+The final score is a value in [0, 1] where 1.0 = identical.
+
+#### Why It Matters
+- Used as a **regression guard** in CI: ensures model updates don't degrade
+  visual output below a threshold.
+- More perceptually meaningful than raw pixel MSE.
+- Fast to compute — suitable for automated testing.
+
+#### Limitations
+- Requires a **pixel-aligned reference** video. Cannot compare videos with
+  different seeds, prompts, or angles.
+- Operates **per-frame** — does not capture temporal coherence.
+- Insensitive to some perceptual artifacts (color shifts, high-frequency noise).
+
+#### How to Use
+
+```bash
+pytest fastvideo/tests/ssim/ -vs
+```
+
+#### Interpretation
+| SSIM Range | Quality |
+|------------|---------|
+| > 0.90 | Excellent — very close to reference |
+| 0.80–0.90 | Good — acceptable for most uses |
+| 0.70–0.80 | Fair — noticeable differences |
+| < 0.70 | Poor — significant divergence |
+
+---
+
+### LPIPS — Learned Perceptual Image Patch Similarity
+
+**Category**: Per-frame perceptual distance
+**Status**: ✅ Implemented in `scripts/lora_extraction/lora_inference_comparison.py`
+**Trust**: Medium — available but only used for LoRA comparison currently
+
+#### What It Measures
+LPIPS uses a pretrained neural network (AlexNet by default) to extract
+deep features from two images and computes the distance between them in
+feature space. Unlike SSIM, LPIPS correlates much more strongly with
+**human perceptual judgments**.
+
+Lower LPIPS = more perceptually similar.
+
+#### Why It Matters
+- Best available automated proxy for **human visual judgments** at the frame
+  level.
+- Captures semantic and structural differences that SSIM misses (e.g., texture
+  changes, minor recoloring).
+- Used for validating LoRA merge quality.
+
+#### Limitations
+- Per-frame metric — no temporal awareness.
+- Requires reference video (paired comparison only).
+- Slightly slower than SSIM due to neural network forward pass.
+
+#### How to Use
+
+```bash
+python scripts/lora_extraction/lora_inference_comparison.py \
+  --base merged_model \
+  --ft path/to/finetuned \
+  --adapter NONE \
+  --output-dir results \
+  --prompt "A cat" \
+  --compute-lpips
+```
+
+#### Interpretation
+| LPIPS Range | Quality |
+|-------------|---------|
+| < 0.10 | Excellent — nearly indistinguishable |
+| 0.10–0.20 | Good — minor perceptual differences |
+| 0.20–0.40 | Fair — noticeable differences |
+| > 0.40 | Poor — clearly different |
+
+---
+
+### Loss Trajectory
+
+**Category**: Training signal proxy
+**Status**: ✅ Active (from W&B `train_loss`)
+**Trust**: Medium — proxy, not direct quality measure
+
+#### What It Measures
+Tracks the training loss over time. A healthy training run shows:
+- **Decreasing loss** over the first hundreds of steps.
+- **Stable gradient norms** (no wild spikes).
+- **Consistent step times** (no infrastructure issues).
+
+#### Why It Matters
+- Cheapest evaluation signal — available in real-time from W&B.
+- Critical for the **30-minute quality check** workflow.
+- At later training stages (when loss becomes meaningful), trajectory shape
+  can predict final model quality.
+
+#### Context: How This Evolves
+The team's experience shows evaluation signals change during a project:
+- **Early stage**: Loss may be flat or meaningless → focus on SSIM & visual
+  inspection instead.
+- **Mid stage**: Loss starts decreasing → trajectory shape becomes useful.
+- **Late stage**: Loss is meaningful → can compare trajectories across runs.
+
+This dynamic is a key insight from the team's workflow: don't over-rely on
+loss early; don't ignore it late.
+
+---
+
+### Grad Norm Stability
+
+**Category**: Training health diagnostic
+**Status**: ✅ Active (from W&B `grad_norm`)
+**Trust**: Medium — diagnostic, not quality metric
+
+#### What It Measures
+The magnitude of gradients during training. Stable grad norms indicate
+healthy optimization. Spikes or NaN values indicate training instability.
+
+#### Alert Thresholds
+| Condition | Meaning |
+|-----------|---------|
+| Stable ~0.3–0.5 | Normal training |
+| Single spike > 3× average | Possible bad batch, monitor |
+| NaN or Inf | 🔴 Training has diverged — stop run |
+| Increasing trend | Learning rate may be too high |
+
+---
+
+## External Benchmarks
+
+### GameWorld Score Benchmark (Matrix-Game)
+
+**Category**: Multi-dimensional evaluation framework for interactive world models
+**Status**: 🟡 External — not implemented in-repo
+**Source**: [Matrix-Game 1.0 benchmark](https://github.com/SkyworkAI/Matrix-Game), used in [Matrix-Game 2.0 paper](https://arxiv.org/abs/2508.13009)
+
+#### What It Measures
+A comprehensive benchmark examining **four critical capabilities**:
+
+| Dimension | What It Evaluates | Example Signals |
+|-----------|-------------------|-----------------|
+| **Visual quality** | Frame-level realism, absence of artifacts | Color fidelity, sharpness, coherence |
+| **Temporal quality** | Smoothness across frames, motion consistency | Jitter, flickering, temporal aliasing |
+| **Action controllability** | Response to input actions (keyboard/mouse) | Action delay, correctness, smoothness |
+| **Physical rule understanding** | Adherence to physics (gravity, collision) | Object persistence, plausible motion |
+
+#### Context from Matrix-Game 2.0
+- Evaluation uses **597-frame composite action sequences** over 32 Minecraft
+  scenes and 16 wild scenes.
+- Action controllability assessment is **Minecraft-specific** — cannot be
+  directly applied to wild/general scenes.
+- The paper notes that models that "collapse" to static frames can
+  paradoxically score higher on consistency metrics — beware of this confound.
+
+#### Relevance to FastVideo
+- Matrix-Game 2.0 is built on SkyReels-V2/Wan2.1 architecture — **same model
+  family as FastVideo**.
+- Their distillation uses DMD-based Self-Forcing — **same technique** as our
+  `self_forcing_distillation_pipeline.py`.
+- GameWorld Score dimensions are a useful framework for thinking about world
+  model quality even outside gaming contexts.
+
+---
+
+## Human Preference Evaluation
+
+**Category**: Gold-standard quality assessment
+**Status**: 🔴 Manual process — no automated implementation
+**Priority**: **Highest** — this is the most important evaluation signal
+**Trust**: Highest — but expensive
+
+### What It Measures
+Human evaluators compare generated videos and rate them on dimensions like:
+- Overall quality and realism
+- Temporal coherence and smoothness
+- Prompt adherence / action correctness
+- Absence of artifacts
+
+#### Why It's the Most Important Metric
+All automated metrics are **proxies** for human judgment. They can be gamed
+or may miss artifacts that humans easily notice. Human preference is the
+ultimate ground truth for video generation quality.
+
+#### Cost & Practicality
+| Approach | Cost | Scale | When to Use |
+|----------|------|-------|-------------|
+| Internal team review | Low | ~10–50 videos | Every major checkpoint |
+| Crowdsource (MTurk, Scale) | Medium | 100+ videos | Pre-release validation |
+| A/B preference test | Medium | Pairs | Comparing two model versions |
+
+#### Recommended Protocol
+1. Sample 10–20 videos from the model at a checkpoint.
+2. Include diverse prompts (easy + hard, short + long).
+3. Have 2–3 evaluators score each video 1–5 on: quality, coherence, fidelity.
+4. Record scores in the experiment journal.
+
+---
+
+## Metrics NOT Used
+
+| Metric | Reason |
+|--------|--------|
+| ~~CLIP-Score~~ | Not used by the team. Measures text-image alignment using CLIP embeddings, but not well-suited for video temporal quality. |
+| Inception Score (IS) | Less informative than FVD for video; primarily an image metric. |
+| PSNR | Pixel-level metric; less perceptually meaningful than SSIM/LPIPS. |
+
+---
+
+## Adding a New Metric
+
+Follow the SOP: `.agents/workflows/evaluation-development.md`
+
+1. Prototype in `.agents/exploration/`
+2. Validate on known-good and known-bad samples
+3. Add to this registry
+4. Update the `evaluate-video-quality` skill

.agents/memory/experiment-journal/README.md

@@ -0,0 +1,21 @@
+# Experiment Journal
+
+Living log of all experiments. Each entry captures what was tried, the result,
+and any insights. Newest entries go at the top.
+
+_No experiments logged yet. Use the `log-experiment` skill to add entries._
+
+<!-- TEMPLATE — copy and fill for each new experiment:
+
+## [YYYY-MM-DD] Experiment: <name>
+- **Hypothesis**: <what you expected to learn>
+- **Config**: model=..., lr=..., sp_size=..., gpus=..., script=...
+- **W&B run**: <run_id or URL>
+- **Duration**: <total wall time>
+- **Key metrics**: loss=..., step_time=..., grad_norm=...
+- **Checkpoint**: <path>
+- **Insight**: <what was learned>
+- **Status**: running | completed | failed | abandoned
+- **Related lessons**: `.agents/lessons/<filename>.md`
+
+-->

.agents/memory/index.jsonl

@@ -0,0 +1,4 @@
+{"name": "codebase-map", "description": "High-level structural index of the FastVideo-WorldModel repository", "path": "codebase-map/README.md", "status": "ready", "trust": "high"}
+{"name": "evaluation-registry", "description": "Catalog of all evaluation metrics with detailed explanations, implementation status, and usage guides", "path": "evaluation-registry/README.md", "status": "draft", "trust": "medium"}
+{"name": "experiment-journal", "description": "Living log of all experiments with hypotheses, configs, metrics, and insights", "path": "experiment-journal/README.md", "status": "draft", "trust": "medium"}
+{"name": "related-work", "description": "Index of related papers, repos, and blog posts with structured comparisons to FastVideo", "path": "related-work/README.md", "status": "draft", "trust": "low"}

.agents/memory/related-work/README.md

@@ -0,0 +1,34 @@
+# Related Work Index
+
+Each file in this directory is a structured summary of a related paper, repo,
+or blog post relevant to FastVideo-WorldModel training.
+
+## File Format
+
+Each file is named `<slug>.md` and follows this structure:
+
+```markdown
+---
+title: <paper/repo title>
+source: <URL or citation>
+type: paper | repo | blog
+date_indexed: <ISO-8601>
+tags: [world-model, distillation, evaluation, reward-shaping, ...]
+---
+
+## Summary
+<1-2 paragraph summary of the work.>
+
+## Key Differences from FastVideo
+- <Bullet points comparing their approach to ours.>
+
+## Actionable Insights
+- <What we could adopt or adapt.>
+```
+
+## How to Add New Entries
+
+Use the `index-related-work` skill, or manually create a file following the
+template above.
+
+_No related work indexed yet._

.agents/onboarding/README.md

@@ -0,0 +1,76 @@
+# Agent Onboarding — FastVideo-WorldModel
+
+Welcome, agent. This is the **master onboarding** guide. Follow the steps below,
+then check if a **domain-specific onboarding** exists for your task.
+
+## Domain-Specific Onboarding
+
+If your task falls into one of these areas, read the specialized guide **after**
+completing the general steps below:
+
+| Domain | Guide | When to Use |
+|--------|-------|-------------|
+| **WorldModel Training** | `worldmodel-training/README.md` | Training, finetuning, distillation, experiment management |
+
+---
+
+## Step 1: Understand the Codebase
+
+Read these files to build your context:
+
+| Priority | File | What you learn |
+|----------|------|----------------|
+| 1 | `AGENTS.md` | Coding guidelines, build/test commands, PR conventions |
+| 2 | `docs/design/overview.md` | Architecture: models, pipelines, configs, registry |
+| 3 | `fastvideo/train/` | Refactored training framework (YAML-driven, modular methods/models/callbacks) |
+| 4 | `docs/training/overview.md` | Training data flow and preprocessing |
+| 5 | `docs/training/finetune.md` | Training arguments, parallelism, LoRA, validation |
+| 6 | `docs/contributing/coding_agents.md` | How to add model pipelines with agent assistance |
+
+## Step 2: Discover Available Resources
+
+Read these two index files to see what skills and memory modules exist:
+
+- **`.agents/skills/index.jsonl`** — catalog of all agent skills (name + description)
+- **`.agents/memory/index.jsonl`** — catalog of all memory modules (name + description)
+
+Each entry has a `path` field pointing to the full content. Only load the
+full README.md for modules relevant to your current task.
+
+## Step 3: Check for Existing Skills & SOPs
+
+Before writing new code or procedures:
+
+1. **Skills**: Read `.agents/skills/index.jsonl` — find a matching skill by description.
+2. **Workflows/SOPs**: Browse `.agents/workflows/` — step-by-step procedures for common tasks.
+3. **Lessons**: Browse `.agents/lessons/` — known pitfalls and their fixes.
+
+If a skill or SOP exists for your task, **use it**. If not, you are in **exploration mode** — see Step 4.
+
+## Step 4: Exploration Mode
+
+If no existing skill/SOP covers your task:
+
+1. Document your progress in `.agents/exploration/<topic>.md` using the template in `.agents/exploration/README.md`.
+2. At the end of your session, reflect:
+   - **What worked** → propose a new skill or SOP in the exploration log.
+   - **What failed** → create a lesson in `.agents/lessons/`.
+3. Flag the exploration log for human review.
+
+## Quick Reference
+
+```
+.agents/
+├── ONBOARDING.md          ← you are here
+├── STATUS.md              ← dashboard: completeness & trust of all components
+├── skills/                ← reusable agent skills
+├── workflows/             ← SOPs and procedures
+├── memory/                ← persistent context (folder per topic + index.jsonl)
+│   ├── index.jsonl
+│   ├── codebase-map/
+│   ├── experiment-journal/
+│   ├── evaluation-registry/
+│   └── related-work/
+├── lessons/               ← mistakes and fixes
+└── exploration/           ← draft procedures
+```

.agents/onboarding/worldmodel-training/README.md

@@ -0,0 +1,302 @@
+# WorldModel Training — Agent Onboarding
+
+Specialized onboarding for agents working on FastVideo-WorldModel training,
+distillation, and evaluation. Read the master onboarding (`.agents/onboarding/README.md`)
+first, then come here.
+
+---
+
+## Domain Context
+
+FastVideo-WorldModel trains **interactive world models** — video generation systems
+that respond to user actions (keyboard/mouse) in real-time. The architecture is
+based on **Wan2.1** (SkyReels-V2) DiT models with causal attention for
+auto-regressive streaming generation.
+
+**Key techniques you will work with:**
+- Full finetuning and LoRA on Wan / LTX-2 / MatrixGame models
+- DMD-based distillation (few-step generation)
+- Self-Forcing distillation (causal streaming)
+- Diffusion-Forcing SFT (DFSFT) for causal models
+- VSA (Variable Sparsity Acceleration) for efficient training
+
+---
+
+## Training Code: Two Generations
+
+### New modular framework: `fastvideo/train/` (preferred)
+
+The refactored training code uses a **YAML-only config-driven** architecture
+with composable methods, per-role models, and a callback system. All new
+training work should use this framework.
+
+### Legacy pipelines: `fastvideo/training/` (deprecated)
+
+The old monolithic pipeline classes (`WanTrainingPipeline`,
+`DistillationPipeline`, etc.) still exist but are being phased out. The new
+framework imports select utilities from `fastvideo/training/` for backward
+compatibility (EMA, gradient clipping, checkpoint wrappers).
+
+---
+
+## Essential Reading (Training-Specific)
+
+Read these **in order** before touching any training code:
+
+| # | File | What You Learn |
+|---|------|----------------|
+| 1 | `docs/training/overview.md` | Training data flow: raw video → text embeddings + video latents → training |
+| 2 | `docs/training/finetune.md` | Training arguments, parallelism (SP/TP), LoRA, validation settings |
+| 3 | `docs/training/data_preprocess.md` | How to preprocess datasets into the expected format |
+| 4 | `docs/design/overview.md` | Architecture: models, pipelines, configs, registry |
+
+---
+
+## New Training Framework (`fastvideo/train/`)
+
+### Architecture Overview
+
+```
+fastvideo/train/
+├── __init__.py                    → exports Trainer
+├── trainer.py                     → main training loop coordinator
+├── entrypoint/
+│   ├── train.py                   → YAML-only training entrypoint
+│   └── dcp_to_diffusers.py        → checkpoint conversion utility
+├── methods/                       → training algorithms (TrainingMethod ABC)
+│   ├── base.py                    → TrainingMethod base class
+│   ├── fine_tuning/
+│   │   ├── finetune.py            → FineTuneMethod (supervised finetuning)
+│   │   └── dfsft.py               → DiffusionForcingSFTMethod (causal)
+│   ├── distribution_matching/
+│   │   ├── dmd2.py                → DMD2Method (distribution matching distill)
+│   │   └── self_forcing.py        → SelfForcingMethod (causal streaming)
+│   ├── knowledge_distillation/    → (stub, not yet implemented)
+│   └── consistency_model/         → (stub, not yet implemented)
+├── models/                        → per-role model instances
+│   ├── base.py                    → ModelBase & CausalModelBase (ABC)
+│   └── wan/
+│       ├── wan.py                 → WanModel (non-causal)
+│       └── wan_causal.py          → WanCausalModel (causal streaming)
+├── callbacks/                     → training hooks & monitoring
+│   ├── callback.py                → Callback base class + CallbackDict
+│   ├── grad_clip.py               → GradNormClipCallback
+│   ├── ema.py                     → EMACallback (shadow weights)
+│   └── validation.py              → ValidationCallback (sampling + eval)
+└── utils/                         → configuration, building, checkpointing
+    ├── builder.py                 → build_from_config() (config → runtime)
+    ├── checkpoint.py              → CheckpointManager (DCP-based)
+    ├── config.py                  → load_run_config() (YAML → RunConfig)
+    ├── training_config.py         → TypedConfig dataclasses
+    ├── optimizer.py               → build_optimizer_and_scheduler()
+    ├── instantiate.py             → resolve_target() + instantiate()
+    ├── tracking.py                → build_tracker() (W&B, etc.)
+    ├── dataloader.py              → dataloader utilities
+    ├── module_state.py            → apply_trainable()
+    └── moduleloader.py            → load_module_from_path()
+```
+
+### Key Concepts
+
+**TrainingMethod** (`methods/base.py`): Abstract base class for all training
+algorithms. Owns role models (student, teacher, critic), manages checkpoint
+state, and defines the training step interface.
+
+**ModelBase** (`models/base.py`): Per-role model wrapper. Each role (student,
+teacher, critic) gets its own `ModelBase` instance owning a `transformer` and
+`noise_scheduler`. `CausalModelBase` extends this for streaming models.
+
+**Callback system** (`callbacks/`): Composable hooks for gradient clipping,
+EMA, validation, etc. Configured via YAML, dispatched by `CallbackDict`.
+
+**Config system** (`utils/config.py`, `utils/training_config.py`): YAML files
+are parsed into typed `RunConfig` dataclass trees. Models and methods use
+`_target_` fields for instantiation (similar to Hydra).
+
+### Training Flow
+
+```
+run_training_from_config(config_path)
+  → load_run_config()           # YAML → RunConfig
+  → init_distributed()          # TP/SP setup
+  → build_from_config()         # instantiate models, method, dataloader
+  → Trainer.run()               # main loop:
+      ├─ callbacks.on_train_start()
+      ├─ checkpoint_manager.maybe_resume()
+      ├─ for step in range(max_steps):
+      │    ├─ method.single_train_step(batch)
+      │    ├─ method.backward()
+      │    ├─ callbacks.on_before_optimizer_step()
+      │    ├─ method.optimizers_schedulers_step()
+      │    ├─ tracker.log(metrics, step)
+      │    ├─ callbacks.on_training_step_end()
+      │    └─ checkpoint_manager.maybe_save(step)
+      ├─ callbacks.on_train_end()
+      └─ checkpoint_manager.save_final()
+```
+
+### Training Methods
+
+| Method | Class | Use Case |
+|--------|-------|----------|
+| **FineTune** | `FineTuneMethod` | Single-role supervised finetuning |
+| **DFSFT** | `DiffusionForcingSFTMethod` | Diffusion-forcing SFT with inhomogeneous timesteps |
+| **DMD2** | `DMD2Method` | Multi-role distribution matching distillation (student + teacher + critic) |
+| **Self-Forcing** | `SelfForcingMethod` | Extends DMD2 for causal student rollouts |
+
+### Launching Training (New Framework)
+
+Training is launched via `torchrun` with a single YAML config:
+
+```bash
+torchrun --nproc_per_node <N_GPUS> \
+  -m fastvideo.train.entrypoint.train \
+  --config examples/train/<config>.yaml
+```
+
+### Example YAML Configs
+
+| Config | Method | Description |
+|--------|--------|-------------|
+| `examples/train/finetune_wan2.1_t2v_1.3B_vsa_phase3.4_0.9sparsity.yaml` | FineTune | Wan 1.3B finetuning with VSA sparsity |
+| `examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml` | DMD2 | Wan 1.3B distillation (student + teacher + critic) |
+| `examples/train/dfsft_wan_causal_t2v_1.3B.yaml` | DFSFT | Causal Wan 1.3B diffusion-forcing SFT |
+| `examples/train/self_forcing_wan_causal_t2v_1.3B.yaml` | Self-Forcing | Causal streaming distillation |
+
+### Checkpointing (New Framework)
+
+**CheckpointManager** (`utils/checkpoint.py`) saves via `torch.distributed.checkpoint`:
+
+```
+output_dir/
+└─ checkpoint-{step}/
+   ├─ dcp/                    # DCP state dict
+   ├─ config.json             # resolved training config
+   └─ .fastvideo_metadata.json
+```
+
+Checkpoint state includes: role model weights, per-role optimizers/schedulers,
+CUDA RNG state, and callback state (e.g., EMA shadow weights).
+
+### Config Structure
+
+A YAML config defines the full training pipeline:
+
+```yaml
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    model_path: ...
+    trainable: true
+  teacher:  # optional, for distillation
+    _target_: fastvideo.train.models.wan.WanModel
+    model_path: ...
+    trainable: false
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.FineTuneMethod
+  # method-specific params...
+
+training:
+  distributed: { num_gpus: 8, tp_size: 1, sp_size: 8 }
+  data: { data_path: ..., batch_size: 1 }
+  optimizer: { lr: 1e-5, lr_scheduler: constant_with_warmup }
+  loop: { max_train_steps: 1000 }
+  checkpoint: { output_dir: ./outputs }
+  tracker: { trackers: [wandb], project_name: ... }
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.GradNormClipCallback
+    max_grad_norm: 1.0
+  validation:
+    _target_: fastvideo.train.callbacks.ValidationCallback
+    validation_steps: 100
+```
+
+---
+
+## Legacy Training Pipelines (`fastvideo/training/`)
+
+> **Note:** Use the new `fastvideo/train/` framework for new work. This section
+> is retained for reference on existing pipelines not yet migrated.
+
+| Pipeline | Entrypoint | Use Case |
+|----------|-----------|----------|
+| Wan T2V finetune | `fastvideo/training/wan_training_pipeline.py` | Standard text-to-video finetune / LoRA |
+| Wan I2V finetune | `fastvideo/training/wan_i2v_training_pipeline.py` | Image-to-video (first frame conditioned) |
+| MatrixGame finetune | `fastvideo/training/matrixgame_training_pipeline.py` | Action-conditioned world model |
+| LTX-2 finetune | `fastvideo/training/ltx2_training_pipeline.py` | LTX-2 architecture finetuning |
+| Wan DMD distillation | `fastvideo/training/wan_distillation_pipeline.py` | Few-step distillation via DMD |
+| Self-Forcing distill | `fastvideo/training/wan_self_forcing_distillation_pipeline.py` | Causal streaming distillation |
+
+---
+
+## Key Infrastructure
+
+### W&B Integration
+- **Tracker**: `fastvideo/training/trackers.py` — `WandbTracker` class
+- **New framework tracker**: `fastvideo/train/utils/tracking.py` — `build_tracker()`
+- **Env vars**: `WANDB_API_KEY`, `WANDB_BASE_URL`, `WANDB_MODE`
+
+### Parallelism
+- **SP** (Sequence Parallel): splits video frames across GPUs — `sp_size: N`
+- **TP** (Tensor Parallel): splits model layers across GPUs — `tp_size: N`
+- Typical configs: SP=2–8, TP=1–2
+
+---
+
+## Evaluation (for training runs)
+
+Read `.agents/memory/evaluation-registry/README.md` for the full metric catalog.
+
+**Quick summary for training agents:**
+| Metric | When to Use | Trust |
+|--------|-------------|-------|
+| **Loss trajectory** | Every run, real-time from W&B | Medium |
+| **SSIM** | When comparing against reference outputs | High |
+| **FVD** | For benchmarking model quality (`benchmarks/fvd/`) | High |
+| **LPIPS** | LoRA merge validation | Medium |
+| **Human preference** | Major checkpoints | Highest |
+
+---
+
+## Common Workflows
+
+| Task | Skill / SOP |
+|------|-------------|
+| Launch a training run | `.agents/skills/launch-experiment/SKILL.md` |
+| Monitor a running experiment | `.agents/skills/monitor-experiment/SKILL.md` |
+| Summarize final results | `.agents/skills/summarize-run/SKILL.md` |
+| Full experiment lifecycle | `.agents/workflows/experiment-lifecycle.md` |
+| Capture lessons from failures | `.agents/workflows/lesson-capture.md` |
+
+---
+
+## World Model–Specific Concepts
+
+### Action Injection (MatrixGame)
+The MatrixGame pipeline adds **action modules** to each DiT block, enabling
+frame-level mouse/keyboard input conditioning. The action sequence is injected
+per-frame alongside the latent video tokens.
+
+### Causal Architecture
+For streaming generation, the model uses **causal attention** (each frame only
+attends to previous frames). This enables auto-regressive chunk-by-chunk
+generation — critical for real-time interactive world models.
+
+### Self-Forcing Distillation
+A **data-free** distillation method where the student model is trained to
+generate coherent video sequences by being forced to use its own previous
+outputs (rather than ground-truth) as context. This produces models robust to
+their own error accumulation during long auto-regressive generation.
+
+### DMD Distillation (Distribution Matching Distillation)
+Reduces inference steps from ~50 to 3–4 by training a student model to match
+the output distribution of the teacher model. Uses a critic network to estimate
+distribution divergence.
+
+### Diffusion-Forcing SFT (DFSFT)
+Supervised finetuning with **inhomogeneous timesteps** across chunks — each
+chunk in a causal sequence can have a different noise level, training the model
+to handle mixed-fidelity contexts.

.agents/skills/SKILL_TEMPLATE.md

@@ -50,6 +50,8 @@ Each skill lives in its own directory under `.agents/skills/`:
 └── assets/           # Optional: templates, resources
 ```
 
-Skill discovery is directory-based; no hand-maintained registry entry is
-required. Run `.agents/scripts/sync-skills.sh` if a local Claude Code checkout
-needs refreshed `.claude/skills/` symlinks.
+After creating a new skill, add an entry to `.agents/skills/index.jsonl`:
+
+```json
+{"name": "<skill-name>", "description": "<description>", "path": "<skill-name>/SKILL.md", "status": "draft", "trust": "low"}
+```

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

@@ -1,99 +0,0 @@
----
-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/dreamverse-deploy/SKILL.md

@@ -1,196 +0,0 @@
----
-name: dreamverse-deploy
-description: Use when redeploying the migrated Dreamverse app backend and frontend on a chosen local GPU; tears down existing ports, launches services, and waits for readiness checks.
----
-
-# dreamverse-deploy — redeploy migrated Dreamverse on a chosen GPU
-
-**Scope:** project (lives in this repo at `.agents/skills/dreamverse-deploy/`)
-
-**When to use:** you want to (re)launch the migrated `apps/dreamverse/` backend
-and frontend on this dev node, pinned to a specific physical GPU. Tears down
-any existing deploy on the same ports first, then boots fresh and waits for
-both `/readyz` and the FE root to return 200.
-
-## Prerequisites
-
-- Working tree containing `apps/dreamverse/`
-- `dreamverse-server` installed from this checkout; if missing, run
-  `uv pip install -e ".[dreamverse]"`
-- Local conda env at `~/miniconda3/envs/fv-main/` with `flashinfer-python`,
-  `cerebras-cloud-sdk`, `openai` installed (override the default path with
-  `DREAMVERSE_PYTHON=/path/to/python`)
-- `~/.env` exporting `CEREBRAS_API_KEY`, `GROQ_API_KEY`, etc.
-- npm available in `$PATH` (or set `NPM=/path/to/npm`)
-- `gcc-13` + `g++-13` at `/usr/bin/` (workaround for nvcc gcc-15 rejection)
-- **Recommended:** native ffmpeg at `$HOME/opt/ffmpeg-native/bin/ffmpeg`, built
-  via `bash apps/dreamverse/scripts/install_native_ffmpeg.sh`. The deploy
-  detects that binary directly and exports it for the backend. The installer's
-  generated `apps/dreamverse/scripts/ffmpeg-env.sh` is for manual launches.
-  When the binary is missing, the deploy falls back to system ffmpeg with a
-  warning. Set
-  `DREAMVERSE_REQUIRE_NATIVE_FFMPEG=true` to make the missing binary a hard
-  failure.
-
-If any required prereq is missing, the script fails fast with a clear message.
-
-## Usage
-
-```bash
-# Deploy on GPU 4 with the current web port. The legacy helper default remains
-# 5274, so pass 5299 explicitly. Torch compile and warmup are both off.
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh 4 8009 5299
-
-# Deploy on GPU 6 with custom ports
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh 6 8089 5275
-
-# Deploy on GPU 0 with warmup enabled
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --warmup 0 8009 5299
-
-# Deploy with torch.compile enabled (max-autotune; first segment ~3-4min,
-# subsequent segments save ~3s — only worth it for benchmarking)
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --torch-compile 4 8009 5299
-
-# Deploy with both warmup AND torch.compile enabled
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --warmup --torch-compile 4 8009 5299
-
-# Flags can appear before, between, or after positional args
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh 4 8089 5275 --warmup
-```
-
-### Arguments
-
-| Position | Name | Default | Notes |
-|---|---|---|---|
-| 1 | `GPU` | (required) | Physical GPU index, e.g. `4` |
-| 2 | `BACKEND_PORT` | `8009` | TCP port for the FastAPI server |
-| 3 | `FRONTEND_PORT` | `5274` | TCP port for the Next.js dev server |
-
-### Flags
-
-| Flag | Default | Notes |
-|---|---|---|
-| `--warmup` / `--no-warmup` | off | Run GPU warmup at boot (~minutes). Overrides `DREAMVERSE_WARMUP` |
-| `--torch-compile` / `--no-torch-compile` | off | Enable max-autotune `torch.compile`. First segment ~3-4min when on, ~45s when off. Overrides `DREAMVERSE_TORCH_COMPILE` |
-| `--nvenc` / `--no-nvenc` | off | Use `h264_nvenc` hardware encoder instead of `libx264` software. Eliminates ~1100ms/segment of CPU encoding cost (raises realtime ratio from ~0.78x → ≥1.0x, eliminating inter-segment buffer-drain stutter). Requires native ffmpeg built with `--enable-nvenc` (the install script's default since the NVENC update). Hard-fails up-front if the binary is missing or lacks NVENC. Overrides `DREAMVERSE_NVENC` |
-| `-h` / `--help` | — | Show usage |
-
-Flags can appear in any position relative to the positional args. Explicit flag values always win over env-var defaults.
-
-### Environment variables (used when no flag is given)
-
-| Var | Default | Purpose |
-|---|---|---|
-| `DREAMVERSE_WARMUP` | `false` | Same as `--warmup`/`--no-warmup`. Flag takes precedence |
-| `DREAMVERSE_TORCH_COMPILE` | `false` | Same as `--torch-compile`/`--no-torch-compile`. Flag takes precedence |
-| `DREAMVERSE_NVENC` | `false` | Same as `--nvenc`/`--no-nvenc`. Flag takes precedence |
-| `DREAMVERSE_PYTHON` | `~/miniconda3/envs/fv-main/bin/python` | Conda environment used for the flashinfer prerequisite probe; `dreamverse-server` itself is resolved from `PATH` |
-| `DREAMVERSE_REPO_ROOT` | git rev-parse | Repo root override |
-| `DREAMVERSE_LOG_DIR` | `/tmp/opencode/dreamverse-deploy` | Directory for the per-GPU backend and per-port frontend logs |
-| `DREAMVERSE_REQUIRE_NATIVE_FFMPEG` | `false` | If `true`, fail when `$HOME/opt/ffmpeg-native/bin/ffmpeg` is absent |
-
-## What it does
-
-1. Validates prereqs.
-2. Kills any process on the target backend/frontend ports + waits for the
-   target GPU to release memory (allows up to 30s for cleanup).
-3. Sources `~/.env`.
-4. Exports the env recipe required for boot:
-   - `CUDA_VISIBLE_DEVICES=<gpu>`
-   - `FASTVIDEO_ENABLE_DEVTOOLS=1`
-   - `FASTVIDEO_ENABLE_STARTUP_WARMUP=<DREAMVERSE_WARMUP>`
-   - `FASTVIDEO_GPU_COUNT=1`
-   - `ENABLE_TORCH_COMPILE=<0|1 derived from DREAMVERSE_TORCH_COMPILE>`
-   - `CC=/usr/bin/gcc-13 CXX=/usr/bin/g++-13 CUDAHOSTCXX=/usr/bin/g++-13`
-   - `NVCC_PREPEND_FLAGS="-ccbin /usr/bin/gcc-13 -allow-unsupported-compiler"`
-   - `FASTVIDEO_FFMPEG_BIN=$HOME/opt/ffmpeg-native/bin/ffmpeg` +
-     `FASTVIDEO_VIDEO_CODEC=<libx264|h264_nvenc>` (when the native binary exists)
-5. Launches the installed `dreamverse-server` console command in a detached
-   `setsid` session and captures its PID.
-6. Polls `/readyz` until 200. The budget is 5 minutes by default, 8 minutes
-   with one startup optimization enabled, and 15 minutes with both warmup and
-   `torch.compile` enabled.
-7. Launches the devtools frontend through npm in a detached session and
-   captures its PID.
-8. Polls FE `/` until 200 (max 60s).
-9. Prints URLs, PIDs, and log paths.
-
-## What it does NOT do
-
-- Does not modify `~/.env` or the FastVideo `.venv`.
-- Does not push code or commit anything.
-- Does not run Playwright. Use the e2e wrapper separately:
-  ```bash
-  cd apps/dreamverse/web
-  PLAYWRIGHT_SKIP_WEBSERVER=1 BACKEND_HOST=127.0.0.1 BACKEND_PORT=8009 \
-    PLAYWRIGHT_BASE_URL=http://127.0.0.1:5299 \
-    NEXT_PUBLIC_INCLUDE_DEVTOOLS=1 \
-    npm exec -- playwright test
-  ```
-  The standard suite runs by default; the long-running two-segment
-  audio-continuation spec is gated behind
-  `PLAYWRIGHT_LONG_RUNNING=1` (see below).
-
-## Long-running e2e (paired with `--warmup --torch-compile`)
-
-[`apps/dreamverse/web/e2e/long-running-segments.spec.ts`](../../../apps/dreamverse/web/e2e/long-running-segments.spec.ts)
-drives a real two-segment session through the FE, captures every WS
-frame, and asserts segments 1 AND 2 both reach `media_segment_complete`
-with at least one binary fMP4 chunk per segment. It guards against the
-BrokenPipe regression previously caused by dropped LTX-2 audio continuation
-kwargs.
-Skipped by default. Enable with:
-
-```bash
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh \
-    --warmup --torch-compile 4 8009 5299
-
-cd apps/dreamverse/web
-PLAYWRIGHT_SKIP_WEBSERVER=1 \
-  BACKEND_HOST=127.0.0.1 \
-  BACKEND_PORT=8009 \
-  PLAYWRIGHT_BASE_URL=http://127.0.0.1:5299 \
-  NEXT_PUBLIC_INCLUDE_DEVTOOLS=1 \
-  PLAYWRIGHT_LONG_RUNNING=1 \
-  npm exec -- playwright test e2e/long-running-segments.spec.ts
-```
-
-Expected runtime: ~7-9 minutes on a B200 (torch.compile max-autotune
-warm-up dominates the cold start; per-test timeout is 900s). The spec
-hard-fails on any WS `error`/`step_error` frame so the BrokenPipe
-regression surfaces with the actual ffmpeg/audio diagnostics rather
-than an opaque "test timed out".
-
-## Teardown
-
-Stop both services without redeploying:
-
-```bash
-# Stop services on default ports (port-pattern based)
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --stop
-
-# Stop AND nuke any process holding GPU N
-./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --stop 4
-```
-
-The redeploy path (`<GPU>` mode) automatically nukes any process holding the
-target GPU before launching — including orphan `multiproc_executor` worker
-subprocesses left over from a parent backend that was killed without grace.
-This was the failure mode of an earlier naive port-only kill: parent dies,
-children survive, GPU stays full, next deploy OOMs.
-
-## Notes
-
-- The installed `dreamverse-server` console command enters
-  `apps/dreamverse/dreamverse/server_entry.py`, which loads the current
-  Dreamverse runtime from `apps/dreamverse/dreamverse/`.
-- The B200 / sm_100a NVCC flags are mandatory on this dev node because the
-  conda toolchain ships gcc-15, which nvcc rejects. The script requires the
-  configured gcc-13 and g++-13 binaries during preflight.
-
-## Deployment boundary
-
-This skill is for a local checkout on a directly attached GPU. For a container
-image, use `apps/dreamverse/docker/README.md`. For Modal, follow
-`apps/dreamverse/scripts/modal/README.md`; do not adapt this process-killing
-workflow to a remote deployment.

.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh

@@ -1,460 +0,0 @@
-#!/usr/bin/env bash
-# See ../SKILL.md for full usage.
-
-set -euo pipefail
-
-is_pid_alive() {
-  kill -0 "$1" 2>/dev/null
-}
-
-terminate_pid() {
-  local pid="$1"
-  local label="${2:-pid=${pid}}"
-
-  [[ -n "${pid}" ]] && [[ "${pid}" != "$$" ]] || return 0
-  is_pid_alive "${pid}" || return 0
-
-  kill "${pid}" 2>/dev/null || true
-  for _ in $(seq 1 10); do
-    is_pid_alive "${pid}" || return 0
-    sleep 0.5
-  done
-
-  if is_pid_alive "${pid}"; then
-    kill -9 "${pid}" 2>/dev/null && echo "        force-killed ${label}" || true
-  fi
-}
-
-terminate_pattern() {
-  local pattern="$1"
-  local pid
-
-  if ! command -v pgrep >/dev/null 2>&1; then
-    pkill -TERM -f "${pattern}" 2>/dev/null || true
-    sleep 2
-    pkill -KILL -f "${pattern}" 2>/dev/null || true
-    return 0
-  fi
-
-  for pid in $(pgrep -f -- "${pattern}" 2>/dev/null || true); do
-    terminate_pid "${pid}" "pattern='${pattern}' pid=${pid}"
-  done
-}
-
-list_port_pids() {
-  local port="$1"
-
-  if command -v lsof >/dev/null 2>&1; then
-    lsof -t -iTCP:"${port}" -sTCP:LISTEN 2>/dev/null || true
-    return 0
-  fi
-
-  ss -tlnp 2>/dev/null | awk -v port=":${port}" '
-    $0 ~ port {
-      while (match($0, /pid=[0-9]+/)) {
-        print substr($0, RSTART + 4, RLENGTH - 4)
-        $0 = substr($0, RSTART + RLENGTH)
-      }
-    }
-  ' || true
-}
-
-if [[ "${1:-}" == "--stop" ]]; then
-  for pat in 'apps/dreamverse/dreamverse/main.py' 'dreamverse-server --host 0.0.0.0 --port' 'next dev --port' 'next-server (v'; do
-    terminate_pattern "${pat}"
-  done
-  if [[ -n "${2:-}" ]] && [[ "${2}" =~ ^[0-9]+$ ]]; then
-    gpu_uuid="$(nvidia-smi --query-gpu=index,uuid --format=csv,noheader 2>/dev/null | awk -F', ' -v g="${2}" '$1==g {print $2}')"
-    if [[ -n "${gpu_uuid}" ]]; then
-      for pid in $(nvidia-smi --query-compute-apps=pid,gpu_uuid --format=csv,noheader 2>/dev/null \
-                    | awk -F', ' -v u="${gpu_uuid}" '$2==u {print $1}'); do
-        terminate_pid "${pid}" "GPU${2} pid=${pid}"
-      done
-    fi
-  fi
-  sleep 2
-  echo "stopped: ports may take a few seconds to free"
-  exit 0
-fi
-
-# ---------------------------------------------------------------------------
-# Args
-# ---------------------------------------------------------------------------
-
-usage() {
-  cat <<USAGE
-Usage: $(basename "$0") [FLAGS] <GPU> [BACKEND_PORT] [FRONTEND_PORT]
-       $(basename "$0") --stop [GPU]
-
-Positional:
-  GPU            Physical GPU index (required), e.g. 4
-  BACKEND_PORT   default 8009
-  FRONTEND_PORT  default 5274
-
-Flags (override env vars when both set):
-  --warmup / --no-warmup            run GPU warmup at boot (default off)
-  --torch-compile / --no-torch-compile
-                                     enable max-autotune torch.compile
-                                     (default off — first segment ~3-4min
-                                     when on, ~45s when off)
-  --nvenc / --no-nvenc              use h264_nvenc hardware encoder (default
-                                     off — uses libx264 software encoder).
-                                     Requires native ffmpeg built with NVENC.
-  -h, --help                        show this help
-
-Env overrides:
-  DREAMVERSE_WARMUP                 'true'|'false' (default false)
-  DREAMVERSE_TORCH_COMPILE          'true'|'false' (default false)
-  DREAMVERSE_NVENC                  'true'|'false' (default false)
-  DREAMVERSE_REPO_ROOT              default: \$(git rev-parse --show-toplevel)
-  DREAMVERSE_LOG_DIR                default: /tmp/opencode/dreamverse-deploy
-  DREAMVERSE_REQUIRE_NATIVE_FFMPEG  'true'|'false' (default false)
-USAGE
-}
-
-WARMUP_OVERRIDE=""
-TORCH_COMPILE_OVERRIDE=""
-NVENC_OVERRIDE=""
-POSITIONAL=()
-while [[ $# -gt 0 ]]; do
-  case "$1" in
-    -h|--help)              usage; exit 0 ;;
-    --warmup)               WARMUP_OVERRIDE=true; shift ;;
-    --no-warmup)            WARMUP_OVERRIDE=false; shift ;;
-    --torch-compile)        TORCH_COMPILE_OVERRIDE=true; shift ;;
-    --no-torch-compile)     TORCH_COMPILE_OVERRIDE=false; shift ;;
-    --nvenc)                NVENC_OVERRIDE=true; shift ;;
-    --no-nvenc)             NVENC_OVERRIDE=false; shift ;;
-    --)                     shift; while [[ $# -gt 0 ]]; do POSITIONAL+=("$1"); shift; done ;;
-    -*)                     echo "error: unknown flag '$1'" >&2; usage >&2; exit 2 ;;
-    *)                      POSITIONAL+=("$1"); shift ;;
-  esac
-done
-set -- "${POSITIONAL[@]+"${POSITIONAL[@]}"}"
-
-if [[ $# -lt 1 ]]; then
-  usage >&2
-  exit 2
-fi
-
-GPU="${1}"
-BACKEND_PORT="${2:-8009}"
-FRONTEND_PORT="${3:-5274}"
-
-if ! [[ "${GPU}" =~ ^[0-9]+$ ]]; then
-  echo "error: GPU must be a non-negative integer (got '${GPU}')" >&2
-  exit 2
-fi
-
-WARMUP="${WARMUP_OVERRIDE:-${DREAMVERSE_WARMUP:-false}}"
-case "${WARMUP}" in
-  true|false) ;;
-  *) echo "error: warmup must be 'true' or 'false' (got '${WARMUP}')" >&2; exit 2 ;;
-esac
-
-TORCH_COMPILE="${TORCH_COMPILE_OVERRIDE:-${DREAMVERSE_TORCH_COMPILE:-false}}"
-case "${TORCH_COMPILE}" in
-  true|false) ;;
-  *) echo "error: torch-compile must be 'true' or 'false' (got '${TORCH_COMPILE}')" >&2; exit 2 ;;
-esac
-TORCH_COMPILE_FLAG=$([[ "${TORCH_COMPILE}" == "true" ]] && echo 1 || echo 0)
-
-NVENC="${NVENC_OVERRIDE:-${DREAMVERSE_NVENC:-false}}"
-case "${NVENC}" in
-  true|false) ;;
-  *) echo "error: nvenc must be 'true' or 'false' (got '${NVENC}')" >&2; exit 2 ;;
-esac
-
-REPO_ROOT="${DREAMVERSE_REPO_ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}"
-LOG_DIR="${DREAMVERSE_LOG_DIR:-/tmp/opencode/dreamverse-deploy}"
-
-# ---------------------------------------------------------------------------
-# Prereq checks
-# ---------------------------------------------------------------------------
-
-bail() { echo "error: $*" >&2; exit 3; }
-
-[[ -d "${REPO_ROOT}/apps/dreamverse" ]] \
-  || bail "REPO_ROOT '${REPO_ROOT}' does not contain apps/dreamverse/. Are you on a migration branch?"
-DREAMVERSE_SERVER="$(command -v dreamverse-server 2>/dev/null || true)"
-[[ -n "${DREAMVERSE_SERVER}" ]] && [[ -x "${DREAMVERSE_SERVER}" ]] \
-  || bail "dreamverse-server not executable or not in PATH (run: uv pip install -e \".[dreamverse]\")"
-
-CONDA_ENV_PYTHON="${DREAMVERSE_PYTHON:-${HOME}/miniconda3/envs/fv-main/bin/python}"
-[[ -x "${CONDA_ENV_PYTHON}" ]] \
-  || bail "conda env python missing at ${CONDA_ENV_PYTHON} (set DREAMVERSE_PYTHON to override)"
-"${CONDA_ENV_PYTHON}" -c 'import flashinfer' 2>/dev/null \
-  || bail "flashinfer-python not installed in ${CONDA_ENV_PYTHON} (run: ${CONDA_ENV_PYTHON} -m pip install flashinfer-python --no-build-isolation)"
-
-NPM="${NPM:-npm}"
-NPM_REQUESTED="${NPM}"
-NPM="$(command -v "${NPM}" 2>/dev/null || true)"
-[[ -n "${NPM}" ]] && [[ -x "${NPM}" ]] || bail "npm not executable or not in PATH: ${NPM_REQUESTED} (set NPM to override)"
-
-GCC13="$(command -v "${GCC13:-gcc-13}" 2>/dev/null || true)"
-GPP13="$(command -v "${GPP13:-g++-13}" 2>/dev/null || true)"
-[[ -n "${GCC13}" ]] && command -v "${GCC13}" >/dev/null 2>&1 \
-  || bail "gcc-13 not found or not executable (needed for nvcc workaround). Set GCC13 or install gcc-13 in PATH"
-[[ -n "${GPP13}" ]] && command -v "${GPP13}" >/dev/null 2>&1 \
-  || bail "g++-13 not found or not executable (needed for nvcc workaround). Set GPP13 or install g++-13 in PATH"
-
-[[ -f "${HOME}/.env" ]] || echo "warn: ${HOME}/.env missing — provider API keys may be unset" >&2
-
-NATIVE_FFMPEG_BIN="${HOME}/opt/ffmpeg-native/bin/ffmpeg"
-if [[ "${NVENC}" == "true" ]]; then
-  NATIVE_VIDEO_CODEC=h264_nvenc
-else
-  NATIVE_VIDEO_CODEC=libx264
-fi
-REQUIRE_NATIVE_FFMPEG="${DREAMVERSE_REQUIRE_NATIVE_FFMPEG:-false}"
-case "${REQUIRE_NATIVE_FFMPEG}" in
-  true|false) ;;
-  *) bail "DREAMVERSE_REQUIRE_NATIVE_FFMPEG must be 'true' or 'false' (got '${REQUIRE_NATIVE_FFMPEG}')" ;;
-esac
-if [[ -x "${NATIVE_FFMPEG_BIN}" ]]; then
-  if [[ "${NVENC}" == "true" ]]; then
-    encoder_list="$("${NATIVE_FFMPEG_BIN}" -hide_banner -encoders 2>/dev/null || true)"
-    if [[ "${encoder_list}" != *h264_nvenc* ]]; then
-      bail "--nvenc requested but ${NATIVE_FFMPEG_BIN} was not built with NVENC. Rebuild: bash apps/dreamverse/scripts/install_native_ffmpeg.sh (with ENABLE_NVENC=1, the default)"
-    fi
-    if ! "${NATIVE_FFMPEG_BIN}" -hide_banner -loglevel error -y \
-            -f lavfi -i 'color=red:size=64x64:rate=24:duration=0.2' \
-            -c:v h264_nvenc -f null - >/dev/null 2>&1; then
-      bail "--nvenc requested but the GPU on this host has no NVENC silicon (probe failed: 'OpenEncodeSessionEx unsupported device'). Datacenter Blackwell (B200) and some H100 SKUs ship without NVENC; --nvenc only works on hosts with NVENC-capable GPUs (RTX 50-series, T4, A10, etc.)."
-    fi
-  fi
-  echo "        native ffmpeg: ${NATIVE_FFMPEG_BIN} (codec=${NATIVE_VIDEO_CODEC})"
-elif [[ "${REQUIRE_NATIVE_FFMPEG}" == "true" ]] || [[ "${NVENC}" == "true" ]]; then
-  bail "${NATIVE_FFMPEG_BIN} missing (required by --nvenc or DREAMVERSE_REQUIRE_NATIVE_FFMPEG=true). Run: bash apps/dreamverse/scripts/install_native_ffmpeg.sh"
-else
-  echo "warn: ${NATIVE_FFMPEG_BIN} missing — backend will fall back to system ffmpeg (\$(command -v ffmpeg))." >&2
-  echo "      Build native ffmpeg with: bash apps/dreamverse/scripts/install_native_ffmpeg.sh" >&2
-fi
-echo "        python:        ${CONDA_ENV_PYTHON}"
-
-mkdir -p "${LOG_DIR}"
-
-# ---------------------------------------------------------------------------
-# Teardown anything on target ports
-# ---------------------------------------------------------------------------
-
-echo "[1/8] killing any existing deploy on ports ${BACKEND_PORT}/${FRONTEND_PORT} and GPU ${GPU}..."
-
-kill_port_pid() {
-  local port="$1"
-  local pid
-
-  for pid in $(list_port_pids "${port}"); do
-    terminate_pid "${pid}" "port=${port} pid=${pid}"
-  done
-}
-
-for pat in "dreamverse-server --host 0.0.0.0 --port ${BACKEND_PORT}" "next dev --port ${FRONTEND_PORT}" "NEXT_PUBLIC_INCLUDE_DEVTOOLS=1 next dev --port ${FRONTEND_PORT}"; do
-  terminate_pattern "${pat}"
-done
-kill_port_pid "${BACKEND_PORT}"
-kill_port_pid "${FRONTEND_PORT}"
-
-gpu_uuid="$(nvidia-smi --query-gpu=index,uuid --format=csv,noheader 2>/dev/null | awk -F', ' -v g="${GPU}" '$1==g {print $2}')"
-if [[ -n "${gpu_uuid}" ]]; then
-  for pid in $(nvidia-smi --query-compute-apps=pid,gpu_uuid --format=csv,noheader 2>/dev/null \
-                | awk -F', ' -v u="${gpu_uuid}" '$2==u {print $1}'); do
-    if [[ -n "${pid}" ]] && [[ "${pid}" != "$$" ]]; then
-      cmd="$(ps -p "${pid}" -o comm= 2>/dev/null || true)"
-      terminate_pid "${pid}" "GPU${GPU} pid=${pid} (${cmd:-?})"
-    fi
-  done
-fi
-
-for i in $(seq 1 30); do
-  free_be=true
-  free_fe=true
-  ss -tln 2>/dev/null | grep -qE ":${BACKEND_PORT}\b" && free_be=false
-  ss -tln 2>/dev/null | grep -qE ":${FRONTEND_PORT}\b" && free_fe=false
-  gpu_mem="$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits 2>/dev/null | sed -n "$((GPU + 1))p" || echo 99999)"
-  if "${free_be}" && "${free_fe}" && [[ "${gpu_mem}" -lt 1000 ]]; then
-    break
-  fi
-  sleep 1
-done
-
-gpu_mem="$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits 2>/dev/null | sed -n "$((GPU + 1))p" || echo 0)"
-echo "        ports cleared; GPU${GPU} at ${gpu_mem} MiB"
-
-# ---------------------------------------------------------------------------
-# Launch backend
-# ---------------------------------------------------------------------------
-
-echo "[2/8] launching backend on GPU ${GPU} port ${BACKEND_PORT} (warmup=${WARMUP} torch_compile=${TORCH_COMPILE} nvenc=${NVENC})..."
-
-backend_log="${LOG_DIR}/backend-gpu${GPU}.log"
-: > "${backend_log}"
-
-setsid bash -c "
-  set -a
-  if [[ -f \"${HOME}/.env\" ]]; then
-    source \"${HOME}/.env\"
-  fi
-  set +a
-  if [[ -x \"${NATIVE_FFMPEG_BIN}\" ]]; then
-    export FASTVIDEO_FFMPEG_BIN=\"${NATIVE_FFMPEG_BIN}\"
-    export FASTVIDEO_VIDEO_CODEC=\"${NATIVE_VIDEO_CODEC}\"
-  fi
-  export DREAMVERSE_PYTHON=\"${CONDA_ENV_PYTHON}\"
-  export CUDA_VISIBLE_DEVICES=${GPU}
-  export FASTVIDEO_ENABLE_DEVTOOLS=1
-  export FASTVIDEO_ENABLE_STARTUP_WARMUP=${WARMUP}
-  export FASTVIDEO_GPU_COUNT=1
-  export ENABLE_TORCH_COMPILE=${TORCH_COMPILE_FLAG}
-  export CC=${GCC13}
-  export CXX=${GPP13}
-  export CUDAHOSTCXX=${GPP13}
-  export NVCC_PREPEND_FLAGS=\"-ccbin ${GCC13} -allow-unsupported-compiler\"
-  cd \"${REPO_ROOT}\"
-  exec \"${DREAMVERSE_SERVER}\" --host 0.0.0.0 --port ${BACKEND_PORT}
-" > "${backend_log}" 2>&1 < /dev/null &
-disown
-
-# Wait briefly, then resolve actual python PID (the inner process, not the
-# wrapper bash).
-sleep 4
-backend_pid="$(pgrep -f "dreamverse-server --host 0.0.0.0 --port ${BACKEND_PORT}" | head -1 || true)"
-
-if [[ -z "${backend_pid}" ]]; then
-  echo "error: backend failed to spawn. Last 30 lines of log:" >&2
-  tail -30 "${backend_log}" >&2
-  exit 4
-fi
-
-echo "        backend pid=${backend_pid} log=${backend_log}"
-
-# Poll /readyz. Deadline scales with warmup + torch.compile flags
-# because warmup runs two synthetic segments before /readyz=200, and
-# torch.compile max-autotune adds ~3-4min cold start to the first
-# segment. Empirical worst case (warmup=true, torch_compile=true):
-# ~7 min on B200; we budget 15 min for safety.
-if [[ "${WARMUP}" == "true" ]] && [[ "${TORCH_COMPILE}" == "true" ]]; then
-  READYZ_BUDGET_SECONDS=900
-elif [[ "${WARMUP}" == "true" ]] || [[ "${TORCH_COMPILE}" == "true" ]]; then
-  READYZ_BUDGET_SECONDS=480
-else
-  READYZ_BUDGET_SECONDS=300
-fi
-READYZ_POLL_INTERVAL=6
-READYZ_MAX_ITERS=$(( READYZ_BUDGET_SECONDS / READYZ_POLL_INTERVAL ))
-
-echo "[3/8] polling http://127.0.0.1:${BACKEND_PORT}/readyz (budget=${READYZ_BUDGET_SECONDS}s) ..."
-ready=0
-for i in $(seq 1 ${READYZ_MAX_ITERS}); do
-  code="$(curl -s -o /dev/null -w '%{http_code}' --max-time 2 "http://127.0.0.1:${BACKEND_PORT}/readyz" 2>/dev/null || echo 000)"
-  if [[ "${code}" == "200" ]]; then
-    ready=1
-    break
-  fi
-  if ! kill -0 "${backend_pid}" 2>/dev/null; then
-    echo "error: backend pid ${backend_pid} died. Last 50 lines:" >&2
-    tail -50 "${backend_log}" >&2
-    exit 5
-  fi
-  sleep ${READYZ_POLL_INTERVAL}
-done
-
-if [[ "${ready}" != "1" ]]; then
-  echo "error: backend did not become /readyz=200 within ${READYZ_BUDGET_SECONDS}s. Last 50 lines:" >&2
-  tail -50 "${backend_log}" >&2
-  exit 5
-fi
-
-echo "[4/8] backend /readyz OK"
-
-# ---------------------------------------------------------------------------
-# Launch frontend
-# ---------------------------------------------------------------------------
-
-echo "[5/8] launching frontend on port ${FRONTEND_PORT}..."
-
-frontend_log="${LOG_DIR}/frontend-port${FRONTEND_PORT}.log"
-: > "${frontend_log}"
-
-# Resolve dev script: dev:devtools forces port 5274 + devtools env. If the
-# requested port differs, run `next dev --port` directly with devtools env.
-fe_cmd="run dev:devtools"
-if [[ "${FRONTEND_PORT}" != "5274" ]]; then
-  fe_cmd="exec -- next dev --port ${FRONTEND_PORT}"
-fi
-
-setsid bash -c "
-  cd \"${REPO_ROOT}/apps/dreamverse/web\"
-  export NEXT_PUBLIC_INCLUDE_DEVTOOLS=1
-  export BACKEND_URL=http://127.0.0.1:${BACKEND_PORT}
-  export BACKEND_HOST=127.0.0.1
-  export BACKEND_PORT=${BACKEND_PORT}
-  exec '${NPM}' ${fe_cmd}
-" > "${frontend_log}" 2>&1 < /dev/null &
-disown
-
-sleep 4
-frontend_pid="$(pgrep -f "next dev --port ${FRONTEND_PORT}" | head -1 || true)"
-if [[ -z "${frontend_pid}" ]]; then
-  echo "error: frontend failed to spawn. Last 30 lines:" >&2
-  tail -30 "${frontend_log}" >&2
-  exit 6
-fi
-
-echo "        frontend pid=${frontend_pid} log=${frontend_log}"
-
-# Poll FE root
-echo "[6/8] polling http://127.0.0.1:${FRONTEND_PORT}/ ..."
-fe_ready=0
-for i in $(seq 1 30); do
-  code="$(curl -s -o /dev/null -w '%{http_code}' --max-time 2 "http://127.0.0.1:${FRONTEND_PORT}/" 2>/dev/null || echo 000)"
-  if [[ "${code}" == "200" ]]; then
-    fe_ready=1
-    break
-  fi
-  if ! kill -0 "${frontend_pid}" 2>/dev/null; then
-    echo "error: frontend pid ${frontend_pid} died. Last 30 lines:" >&2
-    tail -30 "${frontend_log}" >&2
-    exit 7
-  fi
-  sleep 2
-done
-
-if [[ "${fe_ready}" != "1" ]]; then
-  echo "error: frontend did not respond 200 within 60s. Last 30 lines:" >&2
-  tail -30 "${frontend_log}" >&2
-  exit 7
-fi
-
-echo "[7/8] frontend / OK"
-
-# ---------------------------------------------------------------------------
-# Print summary
-# ---------------------------------------------------------------------------
-
-cwd="$(readlink "/proc/${backend_pid}/cwd" 2>/dev/null || echo unknown)"
-gpu_mem_now="$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits 2>/dev/null | sed -n "$((GPU + 1))p" || echo 0)"
-ffmpeg_in_use="$(tr '\0' '\n' < "/proc/${backend_pid}/environ" 2>/dev/null | sed -n 's/^FASTVIDEO_FFMPEG_BIN=//p' | head -1)"
-[[ -z "${ffmpeg_in_use}" ]] && ffmpeg_in_use="$(command -v ffmpeg 2>/dev/null || echo '<not found>') (system fallback)"
-
-cat <<SUMMARY
-[8/8] redeploy OK
-
-  Frontend  : http://localhost:${FRONTEND_PORT}    (PID ${frontend_pid})
-  Backend   : http://localhost:${BACKEND_PORT}     (PID ${backend_pid})
-              cwd=${cwd}
-              gpu=${GPU} mem=${gpu_mem_now} MiB
-              ffmpeg=${ffmpeg_in_use}
-
-  Logs      : ${backend_log}
-              ${frontend_log}
-
-  Stop      : ./.agents/skills/dreamverse-deploy/scripts/dreamverse-deploy.sh --stop
-
-  E2E       : cd apps/dreamverse/web && \\
-                PLAYWRIGHT_SKIP_WEBSERVER=1 \\
-                BACKEND_URL=http://127.0.0.1:${BACKEND_PORT} \\
-                PLAYWRIGHT_BASE_URL=http://127.0.0.1:${FRONTEND_PORT} \\
-                NEXT_PUBLIC_INCLUDE_DEVTOOLS=1 \\
-                npm exec -- playwright test
-SUMMARY

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

@@ -1,79 +0,0 @@
----
-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 through `fastvideo/fastvideo_args.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/evaluate-video-quality/SKILL.md

@@ -0,0 +1,128 @@
+---
+name: evaluate-video-quality
+description: Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)
+---
+
+# Evaluate Video Quality
+
+## Purpose
+Assess the quality of videos generated by a training run. Combines multiple
+signals to give a holistic quality assessment. This skill is **evolving** —
+new metrics will be added as they are developed.
+
+## Prerequisites
+- Generated videos available locally or via W&B artifacts.
+- For SSIM: reference videos from official implementations.
+- For caption consistency: LLM access (optional, stub for now).
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `video_paths` | Yes | List of paths to generated videos |
+| `reference_paths` | No | Paths to reference videos (for SSIM) |
+| `prompts` | No | Prompts used to generate videos (for caption check) |
+| `loss_summary` | No | Path to W&B summary JSON (for loss trajectory) |
+| `metrics` | No | Which metrics to run (default: all available) |
+
+## Available Metrics
+
+Check `.agents/memory/evaluation-registry/README.md` for the current catalog.
+
+### SSIM (Active)
+
+Leverages the existing infrastructure in `fastvideo/tests/ssim/`.
+
+```bash
+pytest fastvideo/tests/ssim/ -vs --video-path <generated> --reference-path <reference>
+```
+
+Or use the SSIM utility directly:
+
+```python
+from fastvideo.tests.ssim.ssim_utils import compute_ssim
+score = compute_ssim(generated_video, reference_video)
+# score > 0.85 is typically "acceptable"
+```
+
+**Interpretation**:
+| SSIM Range | Quality |
+|------------|---------|
+| > 0.90 | Excellent — very close to reference |
+| 0.80–0.90 | Good — acceptable for most uses |
+| 0.70–0.80 | Fair — noticeable differences |
+| < 0.70 | Poor — significant quality issues |
+
+### Loss Trajectory (Active)
+
+Analyze the loss curve shape from W&B summary:
+
+```python
+import json
+with open(loss_summary_path) as f:
+    summary = json.load(f)
+
+final_loss = summary["train_loss"]
+runtime = summary["_runtime"]
+steps = summary["_step"]
+```
+
+**Early-stage heuristics** (first 500 steps):
+- Loss should be decreasing (even slightly).
+- Grad norm should be stable (no wild oscillations).
+- If loss is flat or increasing, flag for review.
+
+### Caption Consistency (Draft — Not Yet Calibrated)
+
+Use an LLM to evaluate whether the video content matches the input prompt.
+
+```
+Prompt: "A golden retriever playing in the snow"
+Video: <path>
+
+Score the video on:
+1. Object presence (is there a golden retriever?)
+2. Action accuracy (is it playing?)
+3. Environment match (is there snow?)
+4. Overall coherence (does it look natural?)
+
+Each 1-5, total /20.
+```
+
+> ⚠️ This metric is in **draft** status. Results should not be treated as
+> ground truth until calibrated against human judgments.
+
+## Steps
+
+1. **Identify available metrics** — Check `.agents/memory/evaluation-registry/README.md`.
+2. **Run each metric** — Collect scores.
+3. **Aggregate** — Produce a combined quality report.
+4. **Log** — Update the experiment journal with quality results.
+
+## Outputs
+
+```markdown
+## Video Quality Report: <experiment_name>
+
+| Metric | Score | Threshold | Status |
+|--------|-------|-----------|--------|
+| SSIM (avg) | 0.87 | > 0.80 | ✅ Pass |
+| Loss trajectory | decreasing | decreasing | ✅ Pass |
+| Caption consistency | 16/20 | > 14/20 | ✅ Pass |
+
+### Per-Video Scores
+| Video | SSIM | Caption |
+|-------|------|---------|
+| video_001.mp4 | 0.89 | 17/20 |
+| video_002.mp4 | 0.85 | 15/20 |
+```
+
+## References
+- `fastvideo/tests/ssim/` — SSIM test infrastructure
+- `fastvideo/tests/training/Vanilla/test_training_loss.py` — loss comparison
+- `.agents/memory/evaluation-registry/README.md` — metric catalog
+
+## Changelog
+| Date | Change |
+|------|--------|
+| 2026-03-02 | Initial version with SSIM, loss trajectory, caption consistency stub |

.agents/skills/index-related-work/SKILL.md

@@ -0,0 +1,94 @@
+---
+name: index-related-work
+description: Ingest a paper or repository into the related work index
+---
+
+# Index Related Work
+
+## Purpose
+Create a structured summary of a related paper, repository, or blog post and
+add it to `.agents/memory/related-work/` for future reference. This builds the
+agent's knowledge base for making informed decisions about training, evaluation,
+and architecture choices.
+
+## Prerequisites
+- Access to the paper/repo (URL, PDF, or local clone).
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `source` | Yes | URL, citation, or local path |
+| `type` | Yes | `paper`, `repo`, or `blog` |
+| `tags` | No | List of tags (default: inferred from content) |
+
+## Steps
+
+### 1. Extract key information
+
+For **papers**: Read abstract, method section, experimental setup, and results.
+For **repos**: Read README, key source files, and training scripts.
+For **blogs**: Read the full post.
+
+Focus on:
+- What problem does it solve?
+- What architecture/technique is used?
+- How does it relate to FastVideo's approach?
+
+### 2. Create the index entry
+
+Write to `.agents/memory/related-work/<slug>.md`:
+
+```markdown
+---
+title: <title>
+source: <URL or citation>
+type: paper | repo | blog
+date_indexed: <ISO-8601>
+tags: [world-model, distillation, evaluation, ...]
+---
+
+## Summary
+<1-2 paragraph summary.>
+
+## Key Differences from FastVideo
+- <comparison points>
+
+## Actionable Insights
+- <what we could adopt or adapt>
+```
+
+### 3. Update the catalog
+
+If `.agents/memory/related-work/_catalog.md` exists, append the new entry.
+If not, create it:
+
+```markdown
+# Related Work Catalog
+
+| Slug | Title | Type | Tags | Date |
+|------|-------|------|------|------|
+| <slug> | <title> | <type> | <tags> | <date> |
+```
+
+## Outputs
+- New file in `.agents/memory/related-work/<slug>.md`.
+- Updated catalog.
+
+## Example Usage
+
+```
+Index the Self-Forcing paper:
+
+  source: https://arxiv.org/abs/2406.xxxxx
+  type: paper
+  tags: [world-model, self-forcing, distillation]
+```
+
+## References
+- `.agents/memory/related-work/README.md` — schema documentation
+
+## Changelog
+| Date | Change |
+|------|--------|
+| 2026-03-02 | Initial version |

.agents/skills/index.jsonl

@@ -0,0 +1,10 @@
+{"name": "launch-experiment", "description": "Generate and execute a training launch command for FastVideo models", "path": "launch-experiment/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "monitor-experiment", "description": "Poll a running W&B training run for progress and emit structured alerts", "path": "monitor-experiment/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "summarize-run", "description": "Extract a W&B run summary into a structured experiment report", "path": "summarize-run/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "log-experiment", "description": "Append or update an experiment entry in the experiment journal", "path": "log-experiment/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "evaluate-video-quality", "description": "Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)", "path": "evaluate-video-quality/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "index-related-work", "description": "Ingest a paper or repository into the related work index", "path": "index-related-work/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "search-related-work", "description": "Query the related work index for relevant papers, repos, or comparisons", "path": "search-related-work/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "seed-ssim-references", "description": "Run a new or updated fastvideo/tests/ssim/ test on Modal, pull generated videos, and upload them to FastVideo/ssim-reference-videos so the test has a regression baseline", "path": "seed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "reseed-ssim-references", "description": "Re-seed (overwrite) HF reference videos for an existing fastvideo/tests/ssim/ test and a single model id on Modal L40S. Always backs up current refs first, regenerates on Modal, pauses for the user to eyeball before-vs-after, then uploads with --force scoped to --model-id. Sister skill to seed-ssim-references; use when intentional code change has invalidated existing refs", "path": "reseed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "reseed-performance-baseline", "description": "Re-seed the HF performance-tracking baseline for an intentional runtime, dependency, or environment-caused benchmark shift. Use when performance CI fails because metrics such as latency, throughput, component time, or peak memory changed for an accepted reason and the rolling median baseline must be advanced by replicating one reviewed shifted source result into three success=true records, or five records when explicitly requested", "path": "reseed-performance-baseline/SKILL.md", "status": "draft", "trust": "low"}

.agents/skills/launch-experiment/SKILL.md

@@ -0,0 +1,127 @@
+---
+name: launch-experiment
+description: Generate and execute a training launch command for FastVideo models
+---
+
+# Launch Experiment
+
+## Purpose
+Construct a fully-specified `torchrun` training command for a FastVideo model
+given a target pipeline, dataset, and hyperparameter overrides. This skill
+automates the boilerplate of setting environment variables, picking the right
+entrypoint, and applying defaults from the closest example script.
+
+## Prerequisites
+- The repo is cloned and `fastvideo` is installed (`uv pip install -e ".[dev]"`).
+- Dataset is preprocessed (see `docs/training/data_preprocess.md`).
+- `WANDB_API_KEY` is set in the environment (or `WANDB_MODE=offline` for local).
+- GPU resources are available (multi-GPU requires NCCL).
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `pipeline` | Yes | Training pipeline type: `finetune`, `distill-dmd`, `self-forcing`, `lora`, `consistency` |
+| `model` | Yes | Model family: `wan-t2v-1.3B`, `wan-i2v-14B`, `ltx2`, `matrixgame` |
+| `data_path` | Yes | Path to preprocessed dataset (parquet) |
+| `num_gpus` | Yes | Number of GPUs |
+| `overrides` | No | Dict of hyperparameter overrides (any CLI arg) |
+| `output_dir` | No | Output directory (default: `outputs/<model>_<pipeline>`) |
+| `run_name` | No | W&B run name (default: auto-generated) |
+
+## Steps
+
+### 1. Identify the training entrypoint
+
+| Pipeline | Entrypoint |
+|----------|-----------|
+| `finetune` (Wan T2V) | `fastvideo/training/wan_training_pipeline.py` |
+| `finetune` (Wan I2V) | `fastvideo/training/wan_i2v_training_pipeline.py` |
+| `finetune` (LTX-2) | `fastvideo/training/ltx2_training_pipeline.py` |
+| `finetune` (MatrixGame) | `fastvideo/training/matrixgame_training_pipeline.py` |
+| `distill-dmd` | `fastvideo/training/wan_distillation_pipeline.py` |
+| `self-forcing` | `fastvideo/training/wan_self_forcing_distillation_pipeline.py` |
+
+### 2. Resolve default hyperparameters
+
+Find the closest example script in `examples/training/` for the model:
+
+| Model | Example Script Directory |
+|-------|-------------------------|
+| `wan-t2v-1.3B` | `examples/training/finetune/wan_t2v_1.3B/crush_smol/` |
+| `wan-i2v-14B` | `examples/training/finetune/wan_i2v_14B_480p/crush_smol/` |
+| `ltx2` | `examples/training/finetune/ltx2/` |
+| `matrixgame` | `examples/training/finetune/MatrixGame2.0/` |
+| `distill-dmd` | `scripts/distill/v1_distill_dmd_wan.sh` |
+
+Read the script to extract default values for:
+- `--learning_rate`, `--train_batch_size`, `--sp_size`, `--tp_size`
+- `--num_latent_t`, `--num_height`, `--num_width`, `--num_frames`
+- `--gradient_accumulation_steps`, `--max_train_steps`
+- `--mixed_precision`, `--weight_decay`, `--max_grad_norm`
+- `--validation_steps`, `--validation_sampling_steps`
+
+### 3. Set environment variables
+
+```bash
+export WANDB_API_KEY="${WANDB_API_KEY}"
+export WANDB_BASE_URL="https://api.wandb.ai"
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+export TOKENIZERS_PARALLELISM=false
+export TRITON_CACHE_DIR=/tmp/triton_cache
+```
+
+### 4. Construct the torchrun command
+
+```bash
+torchrun --nnodes 1 --nproc_per_node <num_gpus> \
+    <entrypoint> \
+    --pretrained_model_name_or_path <model_hf_id> \
+    --data_path "<data_path>" \
+    --output_dir "<output_dir>" \
+    --wandb_run_name "<run_name>" \
+    --tracker_project_name "<project_name>" \
+    --log_validation \
+    <...all hyperparameters...>
+```
+
+### 5. Log to experiment journal
+
+After launching, append an entry to `.agents/memory/experiment-journal/README.md`:
+
+```markdown
+## [YYYY-MM-DD] Experiment: <run_name>
+- **Hypothesis**: <user-provided or auto-generated>
+- **Config**: model=<model>, lr=<lr>, sp_size=<sp>, gpus=<n>, script=<entrypoint>
+- **W&B run**: <pending — will be updated by monitor skill>
+- **Status**: running
+```
+
+## Outputs
+- A ready-to-execute shell command.
+- An experiment journal entry.
+
+## Example Usage
+
+```
+Launch a Wan T2V 1.3B finetune on 4 GPUs with lr=5e-5 and max_train_steps=1000:
+
+  pipeline: finetune
+  model: wan-t2v-1.3B
+  data_path: data/crush_smol_preprocessed/
+  num_gpus: 4
+  overrides:
+    learning_rate: 5e-5
+    max_train_steps: 1000
+```
+
+## References
+- `examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh`
+- `scripts/distill/v1_distill_dmd_wan.sh`
+- `docs/training/finetune.md` (training arguments table)
+- `fastvideo/training/trackers.py` (tracker initialization)
+
+## Changelog
+| Date | Change |
+|------|--------|
+| 2026-03-02 | Initial version |

.agents/skills/log-experiment/SKILL.md

@@ -0,0 +1,87 @@
+---
+name: log-experiment
+description: Append or update an experiment entry in the experiment journal
+---
+
+# Log Experiment
+
+## Purpose
+Create or update an entry in `.agents/memory/experiment-journal/README.md` to maintain
+a living record of all experiments and their outcomes.
+
+## Prerequisites
+- `.agents/memory/experiment-journal/README.md` exists.
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `name` | Yes | Experiment name / identifier |
+| `hypothesis` | No | What you expected to learn |
+| `config` | Yes | Key config: model, lr, sp_size, gpus, script |
+| `wandb_run` | No | W&B run ID or URL |
+| `duration` | No | Total wall time |
+| `metrics` | No | Key metrics dict (loss, step_time, grad_norm) |
+| `checkpoint` | No | Path to checkpoint |
+| `insight` | No | What was learned |
+| `status` | Yes | `running`, `completed`, `failed`, `abandoned` |
+| `lessons` | No | Paths to related lesson files |
+
+## Steps
+
+### 1. Check for existing entry
+
+Search `.agents/memory/experiment-journal/README.md` for an entry with the same name.
+If found, update it instead of creating a duplicate.
+
+### 2. Format the entry
+
+```markdown
+## [YYYY-MM-DD] Experiment: <name>
+- **Hypothesis**: <hypothesis or "N/A">
+- **Config**: model=<model>, lr=<lr>, sp_size=<sp>, gpus=<n>, script=<script>
+- **W&B run**: <wandb_run or "pending">
+- **Duration**: <duration or "in progress">
+- **Key metrics**: loss=<loss>, step_time=<step_time>, grad_norm=<grad_norm>
+- **Checkpoint**: <checkpoint or "N/A">
+- **Insight**: <insight or "pending">
+- **Status**: <status>
+- **Related lessons**: <lessons or "none">
+```
+
+### 3. Insert at the top of the journal
+
+New entries go at the top of the file (after the header), so the most recent
+experiments are always visible first.
+
+### 4. Warn on duplicates
+
+If a similar experiment name exists with `status: completed`, warn that this
+may be a repeat. If it's `status: running`, assume this is an update.
+
+## Outputs
+- Updated `.agents/memory/experiment-journal/README.md`.
+
+## Example Usage
+
+```
+Log a completed experiment:
+
+  name: wan-t2v-finetune-lr5e5-sp4
+  config: model=wan-t2v-1.3B, lr=5e-5, sp_size=4, gpus=4
+  wandb_run: fastvideo/training/run_abc123
+  duration: 2h 15m
+  metrics: {loss: 0.065, step_time: 2.3, grad_norm: 0.35}
+  checkpoint: outputs/wan_finetune/checkpoint-1000
+  insight: LR 5e-5 converges 30% faster than 1e-5 with no quality loss
+  status: completed
+```
+
+## References
+- `.agents/memory/experiment-journal/README.md` — journal file
+- `.agents/workflows/experiment-lifecycle.md` — when to log
+
+## Changelog
+| Date | Change |
+|------|--------|
+| 2026-03-02 | Initial version |

.agents/skills/monitor-experiment/SKILL.md

@@ -0,0 +1,134 @@
+---
+name: monitor-experiment
+description: Poll a running W&B training run for progress and emit structured alerts
+---
+
+# Monitor Experiment
+
+## Purpose
+Continuously (or on-demand) check a running experiment's W&B metrics and emit
+alerts for anomalies. Supports the "30-minute quality check" paradigm: after
+the first 30 minutes of a long training run, produce a checkpoint quality
+report before committing more resources.
+
+## Prerequisites
+- `WANDB_API_KEY` is set in the environment.
+- The experiment is actively logging to W&B (not in `WANDB_MODE=offline`).
+- For offline mode: read from local `wandb-summary.json` instead.
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `run_id` | Yes* | W&B run ID (e.g., `entity/project/run_id`) |
+| `output_dir` | Yes* | Local output directory (for offline mode fallback) |
+| `poll_interval` | No | Seconds between polls (default: 60) |
+| `alert_on` | No | List of alert conditions to enable (default: all) |
+
+\* One of `run_id` or `output_dir` is required.
+
+## Steps
+
+### 1. Connect to the run
+
+**Online mode** (preferred):
+
+```python
+import wandb
+api = wandb.Api()
+run = api.run("<run_id>")
+```
+
+**Offline fallback**:
+
+```python
+import json
+summary_path = f"{output_dir}/tracker/wandb/latest-run/files/wandb-summary.json"
+with open(summary_path) as f:
+    summary = json.load(f)
+```
+
+### 2. Track key metrics
+
+| Metric | W&B Key | Description |
+|--------|---------|-------------|
+| Training loss | `train_loss` | Primary training loss |
+| Gradient norm | `grad_norm` | Gradient magnitude |
+| Step time | `step_time` | Wall-clock seconds per step |
+| Learning rate | `learning_rate` | Current LR |
+| Avg step time | `avg_step_time` | Running average step time |
+| Validation videos | `validation_videos_*` | Generated validation samples |
+
+### 3. Evaluate alert conditions
+
+| Alert | Condition | Severity |
+|-------|-----------|----------|
+| **Loss spike** | `current_loss > 3 × rolling_avg_loss` | 🔴 Critical |
+| **NaN/Inf gradient** | `grad_norm` is NaN or Inf | 🔴 Critical |
+| **Step time regression** | `step_time > 2 × baseline_step_time` | 🟡 Warning |
+| **No progress** | No new W&B logs for > 10 minutes | 🟡 Warning |
+| **Loss plateau** | Loss change < 1% over last 100 steps | 🟢 Info |
+
+### 4. Emit structured status
+
+Output format (agent-consumable):
+
+```json
+{
+  "run_id": "...",
+  "step": 500,
+  "metrics": {
+    "train_loss": 0.078,
+    "grad_norm": 0.41,
+    "step_time": 2.5,
+    "learning_rate": 1e-6
+  },
+  "alerts": [
+    {"type": "loss_spike", "severity": "critical", "message": "Loss jumped to 0.45 (avg: 0.08)"}
+  ],
+  "status": "running"
+}
+```
+
+### 5. 30-Minute Quality Check
+
+After the first 30 minutes of wall-clock time:
+1. Summarize the loss curve shape (decreasing? at what rate?).
+2. Check if validation videos have been generated.
+3. Report step count, loss at start vs. current, and estimated time to completion.
+4. Produce a go/no-go recommendation.
+
+```markdown
+## 30-Minute Check: <run_name>
+- **Steps completed**: 150
+- **Loss**: 0.12 → 0.08 (↓ 33%)
+- **Grad norm**: stable at ~0.4
+- **Step time**: 2.5s/step (consistent)
+- **Validation videos**: 5 generated at step 100
+- **Recommendation**: ✅ Continue — loss is decreasing normally
+```
+
+## Outputs
+- Structured JSON status updates.
+- Alert messages for anomalous conditions.
+- 30-minute checkpoint quality report.
+
+## Example Usage
+
+```
+Monitor W&B run "fastvideo/Wan_distillation/abc123":
+
+  run_id: fastvideo/Wan_distillation/abc123
+  poll_interval: 120
+  alert_on: [loss_spike, nan_gradient, step_time_regression]
+```
+
+## References
+- `fastvideo/training/trackers.py` — `WandbTracker` implementation
+- `fastvideo/tests/training/Vanilla/test_training_loss.py` — how summaries are compared
+- `fastvideo/tests/training/Vanilla/a40_reference_wandb_summary.json` — reference summary format
+
+## Changelog
+| Date | Change |
+|------|--------|
+| 2026-03-02 | Initial version |

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

@@ -1,41 +1,29 @@
 ---
 name: reseed-performance-baseline
-description: Re-seed the HF performance-tracking baseline for an intentional runtime, dependency, environment-caused benchmark shift, or reviewed v2 calibration using one or more reviewed normalized performance JSONs. Use when performance CI fails because metrics such as latency, throughput, component time, or peak memory changed for an accepted reason and the rolling median baseline in FastVideo/performance-tracking must be advanced, or when a new v2 exact comparable identity needs its first approved baseline. The workflow backs up existing history under /tmp, validates all source JSONs for the same legacy (model_id, gpu_type) target or the same v2 exact identity, rejects internally inconsistent source batches, uploads one success=true baseline record per accepted source JSON, and offers to clean local temp state after a successful upload.
+description: Re-seed the HF performance-tracking baseline for an intentional runtime, dependency, or environment-caused benchmark shift. Use when performance CI fails because metrics such as latency, throughput, component time, or peak memory changed for an accepted reason and the rolling median baseline in FastVideo/performance-tracking must be advanced by replicating one reviewed shifted source result into three success=true records, or five records when explicitly requested.
 ---
 
 # Re-seed Performance Baseline
 
 ## Purpose
 
-Replace or advance the rolling performance baseline in the HF dataset
-`FastVideo/performance-tracking`. Legacy targets are scoped by
-`(model_id, gpu_type)`. V2 targets are scoped by exact comparable identity:
-`workload_id`, `variant_id`, `benchmark_version`, `hardware_profile_id`,
-`software_profile_id`, and `recipe_fingerprint`.
+Replace or advance the rolling performance baseline for a single
+`(model_id, gpu_type)` pair in the HF dataset
+`FastVideo/performance-tracking`.
 
-Performance comparison uses the median of up to the last 5 successful,
-baseline-eligible records for the same target. Failed or calibration-only
-records are useful audit history, but they do not move the future baseline
-because `compare_baseline.py` loads records with `successful_only=True` and
-`baseline_eligible_only=True`.
+Performance comparison uses the median of up to the last 5 successful records
+for the same model and GPU. Failed records are useful audit history, but they
+do not move the future baseline because `compare_baseline.py` loads records
+with `successful_only=True`.
 
-This skill now reseeds from a reviewed batch of one or more source performance
-JSONs. It uploads one new `success=true` record per accepted source JSON; it
-does not blindly replicate one measurement into 3 or 5 records. The effective
-reseed size is therefore dynamic and equals the number of provided, validated,
-internally consistent source JSONs.
+For a 5-record median, one shifted record is not enough to move the median if
+the other four records are from the old runtime. This skill therefore creates
+3 reviewed `success=true` records from one accepted shifted source result by
+default. If the user explicitly asks for a full reset, create 5 records.
 
-For baseline shifts with existing history, if the operator provides fewer than
-3 records, call out that the last-5 rolling median may not move immediately. If
-the operator provides 3 consistent shifted records, the rolling median usually
-moves immediately. If the operator provides 5 consistent shifted records, the
-last-5 window is effectively reset to the new runtime profile. For the first
-approved v2 baseline of a new exact identity, one reviewed calibration seed is
-enough for the next comparable run to leave `CALIBRATION_NEEDED`.
-
-These records are intentional operator-approved baseline resets, not ordinary
-independent main-branch persistence. Mark them clearly with provenance fields
-so the HF history remains auditable.
+These replicated records are an intentional operator-approved baseline reset,
+not independent measurements. Mark them clearly with provenance fields so the
+HF history remains auditable.
 
 Use this skill when a performance test fails for an intentional and reviewed
 reason, such as a torch/runtime/container upgrade that legitimately increases
@@ -49,11 +37,10 @@ approval, then upload reviewed accepted baseline records.
   allowed regression threshold, and maintainers agree the shift is caused by
   an intentional runtime, dependency, hardware image, or benchmark environment
   change rather than a FastVideo logic regression.
-- One or more shifted source result JSONs have been reviewed and accepted, and
-  the operator wants to use those exact reviewed results to advance the rolling
-  baseline.
-- The source batch is internally consistent: no provided source JSON regresses
-  against the batch median by more than the configured tolerance.
+- One shifted source result has been reviewed and accepted, and the operator
+  wants to replicate it into 3 successful records so the rolling median moves
+  immediately. Use 5 records only when the user explicitly asks to fully reset
+  the last-5 window.
 
 ## When not to use
 
@@ -64,46 +51,34 @@ approval, then upload reviewed accepted baseline records.
   separate gate from the rolling HF baseline and may need a code review change.
 - There is no clear source run, commit, and rationale. Baseline history is a
   production signal; do not edit it without provenance.
-- The provided source JSONs disagree materially with each other. Rerun or
-  investigate instead of uploading a noisy reseed batch.
 
 ## Inputs
 
 | Parameter | Required | Description |
 |-----------|----------|-------------|
-| `model_id` | Legacy required; v2 inferred | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. For v2 records, use the `model_id` from each source artifact only as the upload directory; comparison is by exact identity. |
-| `gpu_type` | Legacy required; v2 inferred | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. V2 hardware matching uses `hardware_profile_id`; preserve `gpu_type` as display metadata. |
-| `source_results` | Yes | One or more local paths or Buildkite artifact URLs for accepted shifted performance JSONs. Prefer normalized `normalized_perf_*.json` artifacts emitted by `compare_baseline.py`. Accept `source_result` as an alias only for a single JSON. |
-| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `0.05` (5%). |
+| `model_id` | Yes | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. |
+| `gpu_type` | Yes | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. Baselines are GPU-specific. |
+| `source_result` | Yes | Path or Buildkite artifact URL for one accepted shifted performance JSON. Prefer the normalized `normalized_perf_*.json` artifact emitted by `compare_baseline.py`. |
+| `replica_count` | No | Number of success records to create from `source_result`. Default: `3`. Only use `5` if the user explicitly asks for a full reset. |
 | `intent_rationale` | Yes | One-line explanation for why the baseline shift is legitimate. This is written into provenance and should be reused in the PR. |
 
 Hardcoded defaults:
 
 - HF repo: `FastVideo/performance-tracking` (`HF_REPO_ID` override is
   supported by the code, but use the default unless the user explicitly asks).
-- Local sync root: `/tmp/perf-tracking` (`PERFORMANCE_TRACKING_ROOT` override
-  is supported).
-- Prepared-record staging root: `/tmp/performance_reseed_prepared`
-  (`PERFORMANCE_RESEED_STAGING_ROOT` override is supported). Keep it separate
-  and non-nested from the sync root.
-- Backup root: `/tmp/performance_reseed_backup`.
-- Download scratch root for source artifact URLs: `/tmp/performance_reseed_source`.
-- Baseline window: last 5 `success=true`, `baseline_eligible=true` records
-  for the same legacy `(model_id, gpu_type)` target or the same v2 exact
-  comparable identity.
-- Reseed count: dynamic. Upload exactly one accepted seed record per validated
-  source JSON.
+- Local sync root: `/tmp/perf-tracking` or a timestamped local backup under
+  `performance_reseed_backup/`.
+- Baseline window: last 5 `success=true` records for the same
+  `(model_id, gpu_type)`.
+- Default reseed count: 3 replicated `success=true` records from one reviewed
+  source result. Explicit full-reset count: 5.
 
 ## Steps
 
-### 1. Validate the target and source results
+### 1. Validate the target and source result
 
-Normalize `source_results` to a list. If the user passes a single
-`source_result`, treat it as a one-element `source_results` list and report
-that a single record may not move the last-5 median immediately.
-
-If any source result is a Buildkite artifact URL, download it first into a
-local scratch directory under `/tmp/performance_reseed_source/` and use the
+If `source_result` is a Buildkite artifact URL, download it first into a
+local scratch directory such as `performance_reseed_source/` and use that
 downloaded JSON path for the rest of the workflow. If the agent cannot access
 the artifact because Buildkite authentication is missing, ask the user to
 download the artifact manually and provide the local path.
@@ -114,8 +89,8 @@ Prefer the normalized Buildkite artifact emitted by `compare_baseline.py`:
 perf_reports/results/normalized_perf_*.json
 ```
 
-That file is already in the HF tracking schema. Load each normalized JSON
-directly:
+That file is already in the HF tracking schema. Load it directly and confirm
+it has the expected baseline fields:
 
 ```python
 import json
@@ -124,86 +99,73 @@ with open(source_result, encoding="utf-8") as f:
     record = json.load(f)
 ```
 
-Classify the source batch before continuing:
+If only the older raw `fastvideo/tests/performance/results/perf_*.json`
+artifact is available, normalize it with `compare_baseline.py`'s shared helper
+before continuing. Run this from the repository root with
+`PYTHONPATH=fastvideo/tests/performance` so the script-local `hf_store` import
+resolves the same way it does in CI:
 
-- **Legacy source records** have no v2 exact identity fields. Stop if any
-  normalized record's `model_id` or `gpu_type` does not match the requested
-  `model_id` and `gpu_type`.
-- **V2 source records** have exact identity fields. Stop unless every source
-  record has all six comparable identity fields and they are identical across
-  the batch: `workload_id`, `variant_id`, `benchmark_version`,
-  `hardware_profile_id`, `software_profile_id`, and `recipe_fingerprint`.
-  Do not fall back to legacy `(model_id, gpu_type)` matching for v2 records.
+```python
+import json
+from compare_baseline import normalize_performance_result
 
-The source records may have `success: false` when they came from failed
-rolling baseline comparisons. That is expected; only the reviewed reseed
-records become new `success: true` baseline records after explicit approval.
-For a first v2 baseline seed, the source records must instead be successful
-scheduled-main full-suite `CALIBRATION_NEEDED` normalized artifacts. Reject PR,
-local, direct-run, non-main-branch, or non-full-suite calibration artifacts as
-seed sources.
+with open(source_result, encoding="utf-8") as f:
+    record = normalize_performance_result(json.load(f))
+```
 
-Sort validated source records by their original `timestamp` ascending before
-preparing the seed records. If a source timestamp is missing or unparsable,
-preserve input order for those records and print a warning. This makes the
-fresh reseed timestamps deterministic and makes it clear which records enter
-the last-5 window when more than 5 source JSONs are provided.
+The raw-to-normalized helper maps:
+
+- `model_id` comes from `benchmark_id`.
+- `gpu_type` comes from `device`.
+- `memory` comes from `max_peak_memory_mb`.
+- `latency` comes from `avg_generation_time_s`.
+- `throughput` comes from `throughput_fps`.
+- component timings come from the raw `text_encoder_time_s`, `dit_time_s`,
+  and `vae_decode_time_s` fields when present. If an older raw artifact lacks
+  those keys, they normalize to `None`; that source can still reseed latency,
+  throughput, and memory, but it cannot move component-time baselines.
+
+Stop if the normalized record's `model_id` or `gpu_type` does not match the
+requested `model_id` and `gpu_type`.
+
+The source record may have `success: false` when it came from a failed rolling
+baseline comparison. That is expected; only the reviewed reseed replicas become
+new `success: true` baseline records after explicit approval.
+
+Set `replica_count` to `3` by default. Set it to `5` only when the user
+explicitly asks to upload the same shifted source result 5 times for a full
+last-5 reset. Reject other counts unless the user gives a concrete reason.
 
 Check that `HF_API_KEY` is exported. The sync path may be public, but the
 upload path requires write access.
 
-### 1a. Check source batch consistency
-
-Before syncing or preparing uploads, reject source batches that are internally
-inconsistent. Use the same metric direction as `compare_baseline.py`:
-
-- Lower is better: `latency`, `memory`, `text_encoder_time_s`, `dit_time_s`,
-  `vae_decode_time_s`.
-- Higher is better: `throughput`.
-
-For each metric with at least two non-null source values:
-
-1. Compute the source batch median.
-2. For lower-is-better metrics, compute `(source_value - batch_median) / batch_median`.
-3. For `throughput`, compute `(batch_median - source_value) / batch_median`.
-4. Stop if any source record regresses against the batch median by more than
-   `max_intra_batch_regression`.
-
-Default `max_intra_batch_regression` to `0.05`. Print a table with per-source values, batch median, and
-worst intra-batch regression.
-
-This check prevents uploading a mixed batch where one JSON is materially
-slower or faster than the others. If the batch fails this check, ask the user
-to provide a cleaner batch or explicitly investigate the variance. Do not
-silently drop outliers unless the user gives a concrete reviewed reason and a
-new source list.
-
-### 1b. How to obtain source results from CI
+### 1a. How to obtain `source_result` from CI
 
 The performance CI exports normalized source results for failed rolling
-baseline comparisons when `compare_baseline.py` ran. The preferred artifacts
-come from:
+baseline comparisons when `compare_baseline.py` ran. The preferred artifact
+comes from:
 
 ```text
 perf_reports/results/normalized_perf_*.json
 ```
 
-The normal operator flow is:
+and is uploaded by Buildkite with the performance reports. The normal operator
+flow is:
 
-1. Open the failed Buildkite performance job or several reruns of the same
-   benchmark after the accepted environment shift.
-2. Download the `normalized_perf_*.json` artifacts for the target benchmark.
-3. Pass all reviewed local paths or artifact URLs as `source_results`.
+1. Open the failed Buildkite performance job.
+2. Download the `normalized_perf_*.json` artifact for the failed benchmark.
+3. Pass the local path or artifact URL as `source_result`.
 
-Do not scrape the Markdown performance summary to reconstruct JSON. The
-normalized JSON artifacts are the only supported source of truth for reseed
-metrics and provenance. Raw `fastvideo/tests/performance/results/perf_*.json`
-artifacts are not accepted by this skill. If no normalized JSON artifact is
-present, that run is not a valid source for baseline reseeding.
+Do not scrape the Markdown performance summary to reconstruct the JSON. The
+normalized JSON artifact is the source of truth for reseed metrics and
+provenance. If only a raw `fastvideo/tests/performance/results/perf_*.json`
+artifact is present, normalize it with `normalize_performance_result()` before
+continuing. If no JSON artifact is present, the benchmark likely failed before
+writing results, so that run is not a valid source for baseline reseeding.
 
-### 2. Sync and back up existing HF records under /tmp
+### 2. Sync and back up existing HF records
 
-Use `fastvideo/performance/hf_store.py` helpers directly. Do **not** use
+Use `fastvideo/tests/performance/hf_store.py` helpers directly. Do **not** use
 `compare_baseline.py` as a sync shortcut; on full main runs it can persist
 records, while this step must only fetch and back up existing history.
 
@@ -212,45 +174,32 @@ The sync command pattern is:
 ```bash
 export PERFORMANCE_TRACKING_ROOT="${PERFORMANCE_TRACKING_ROOT:-/tmp/perf-tracking}"
 export HF_REPO_ID="${HF_REPO_ID:-FastVideo/performance-tracking}"
-python -c 'from fastvideo.performance.hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
+PYTHONPATH=fastvideo/tests/performance python -c 'from hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
 ```
 
-For legacy records, back up the sanitized model directory under `/tmp`:
+Then back up only the sanitized model directory:
 
 ```bash
 SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
 TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
 MODEL_SAFE=$(python - <<'PY'
-from fastvideo.performance.hf_store import sanitize
+from fastvideo.tests.performance.hf_store import sanitize
 print(sanitize("<model_id>"))
 PY
 )
-BACKUP_DIR="/tmp/performance_reseed_backup/${TIMESTAMP}_${SHORT_COMMIT}_${MODEL_SAFE}"
+BACKUP_DIR="performance_reseed_backup/${TIMESTAMP}_${SHORT_COMMIT}_${MODEL_SAFE}"
 mkdir -p "$BACKUP_DIR"
 cp -R "${PERFORMANCE_TRACKING_ROOT}/${MODEL_SAFE}" "$BACKUP_DIR/" 2>/dev/null || true
 ```
 
-For v2 records, back up the full local tracking root after sync. Exact identity
-lookup scans across model directories, so a benchmark rename may have relevant
-history outside the current source artifact's `model_id` directory:
-
-```bash
-BACKUP_DIR="/tmp/performance_reseed_backup/${TIMESTAMP}_${SHORT_COMMIT}_v2_exact_identity"
-mkdir -p "$BACKUP_DIR"
-cp -R "${PERFORMANCE_TRACKING_ROOT}" "$BACKUP_DIR/tracking-root"
-```
-
 Write provenance next to the backup:
 
 ```bash
 cat > "$BACKUP_DIR/PROVENANCE.txt" <<EOF
 model_id: <model_id>
 gpu_type: <gpu_type>
-source_results:
-  - <source_result_1>
-  - <source_result_2>
-reseed_record_count: <len(source_results)>
-max_intra_batch_regression: <threshold>
+source_result: <source_result>
+replica_count: <3_or_5>
 head_commit: $(git rev-parse HEAD)
 timestamp_utc: $(date -u +%FT%TZ)
 reason: <intent_rationale>
@@ -262,12 +211,10 @@ first baseline seed. Continue, but report that baseline history was empty.
 
 ### 3. Compute old baseline and candidate shift
 
-Load the last 5 successful baseline records for the target.
-
-For legacy targets:
+Load the last 5 successful records for the target:
 
 ```python
-from fastvideo.performance.hf_store import load_records_for_model
+from fastvideo.tests.performance.hf_store import load_records_for_model
 
 records = load_records_for_model(
     "/tmp/perf-tracking",
@@ -275,33 +222,11 @@ records = load_records_for_model(
     "<gpu_type>",
     last_n=5,
     successful_only=True,
-    baseline_eligible_only=True,
 )
 ```
 
-For v2 exact-identity targets:
-
-```python
-from fastvideo.performance.hf_store import load_records_for_identity
-
-records = load_records_for_identity(
-    "/tmp/perf-tracking",
-    {
-        "workload_id": "<workload_id>",
-        "variant_id": "<variant_id>",
-        "benchmark_version": "<benchmark_version>",
-        "hardware_profile_id": "<hardware_profile_id>",
-        "software_profile_id": "<software_profile_id>",
-        "recipe_fingerprint": "<recipe_fingerprint>",
-    },
-    last_n=5,
-    successful_only=True,
-    baseline_eligible_only=True,
-)
-```
-
-Print a small table showing old medians, source batch medians, candidate
-medians after appending the proposed seed records, and source batch spread for:
+Print a small table showing the source result metrics, the replicated
+candidate median, and the old medians for:
 
 - `latency`
 - `throughput`
@@ -312,29 +237,28 @@ medians after appending the proposed seed records, and source batch spread for:
 
 Also print how many successful old records exist. Make clear:
 
-- 1 seed record usually does not move an existing last-5 median by itself, but
-  it is enough to establish the first v2 baseline for a new exact identity.
-- 3 consistent seed records usually move the last-5 median immediately.
-- 5 consistent seed records effectively reset the last-5 window.
-- The records are intentional approved baseline resets and must be labeled
-  that way.
+- 1 shifted record only seeds audit history and usually does not move the
+  median.
+- 3 replicated shifted records in a 5-record window move the median
+  immediately.
+- 5 replicated shifted records fully reset the rolling window to the source
+  result's runtime profile.
+- Replicated records are not independent measurements; they are an intentional
+  approved baseline reset and must be labeled that way.
 
 ### 4. Confirm intent
 
 Require an explicit confirmation phrase before preparing the upload:
 
-> About to RE-SEED performance baseline for `<target description>`.
+> About to RE-SEED performance baseline for `<model_id>` on `<gpu_type>`.
 > This will upload `<N>` new `success=true` records to
-> `FastVideo/performance-tracking/<sanitize(model_id)>/` or the source
-> artifact's v2 model directory, one per accepted source JSON.
+> `FastVideo/performance-tracking/<sanitize(model_id)>/`.
 >
 > Reason: `<intent_rationale>`
-> Source results: `<source_results>`
-> Reseed record count: `<N>`
-> Max intra-batch regression: `<threshold>`
-> Note: these records come from a reviewed source batch and are intended to
-> move the rolling median to the accepted runtime profile. They are not
-> ordinary main-branch persistence.
+> Source result: `<source_result>`
+> Replica count: `<replica_count>`
+> Note: these records replicate one reviewed measurement to force the rolling
+> median to the accepted runtime profile.
 > HEAD: `<git rev-parse --short=12 HEAD>`
 > Backup: `<BACKUP_DIR>`
 >
@@ -344,63 +268,11 @@ Do not continue unless the user types exactly `confirm performance reseed`.
 
 ### 5. Create the accepted seed records
 
-Create one seed record from each normalized source result.
+Create `replica_count` normalized records from the single source result. Use
+an explicit allowlist; do not copy the raw result JSON wholesale.
 
-For first v2 baseline seeds, use the scoped utility. It validates exact
-identity, requires successful scheduled-main full-suite `CALIBRATION_NEEDED`
-source artifacts, preserves the normalized v2 identity and metadata fields,
-and writes seed records with `success=true`, `baseline_eligible=true`, and
-`comparison_status=PASS`:
-
-```bash
-python fastvideo/tests/performance/seed_baseline.py \
-  --source-result <normalized_perf_1.json> \
-  --source-result <normalized_perf_2.json> \
-  --intent-rationale "<intent_rationale>" \
-  --max-intra-batch-regression 0.05 \
-  --tracking-root "${PERFORMANCE_TRACKING_ROOT}" \
-  --staging-root "${PERFORMANCE_RESEED_STAGING_ROOT:-/tmp/performance_reseed_prepared}"
-```
-
-The utility is prepare-only and intentionally has no upload option. Upload the
-scoped records only after the separate confirmation in step 6.
-
-The utility validates against an isolated fresh HF snapshot and leaves
-`PERFORMANCE_TRACKING_ROOT` untouched; that argument only proves the staging
-root is separate from the operator's tracking mirror. Before writing, it stops
-if the exact identity already has a successful baseline-eligible record or if
-the workload/variant/version already trusts another recipe. It atomically
-reserves the exact identity and writes a digest-protected upload manifest bound
-to the current HF endpoint, repository id, and repository type. Keep the
-prepared records, manifest, source files, and reservation unchanged until the
-operation is uploaded or explicitly cleaned up.
-
-If the prepared seed records look correct, upload only those scoped records in
-step 7. Do not rerun the utility with a different source list after approval.
-
-For legacy reseeds or accepted v2 baseline shifts from regression artifacts,
-create one seed record from each normalized source result. Do not copy the
-source JSON wholesale.
-
-Infer the baseline field allowlist from all existing HF records for the target
-after syncing, including both `success=true` and `success=false` records. For
-legacy targets the target is `(model_id, gpu_type)`. For v2 baseline-shift
-reseeds the target is the exact comparable identity. Use the union of
-non-provenance keys present in those target records, preserving only fields
-that also exist in the normalized source record or are explicitly set by the
-reseed workflow. Always include `model_id`, `timestamp`, `success`,
-`baseline_eligible`, and `comparison_status` because the upload path and
-baseline loader depend on them. Always set `timestamp` to a fresh reseed
-timestamp, `success` to `true`, `baseline_eligible` to `true`, and
-`comparison_status` to `PASS`. Do not include unrelated source-only fields
-that are absent from existing HF records.
-
-Exclude existing provenance or operator metadata from the inferred baseline
-field allowlist. At minimum, exclude keys prefixed with `baseline_reseed` and
-any fields known to be local-only audit metadata.
-
-If there are no previous HF records for the target, fall back to this default
-baseline field list:
+Each record must include only these baseline fields plus the reseed provenance
+fields below:
 
 - `model_id`
 - `timestamp`
@@ -412,25 +284,27 @@ baseline field list:
 - `text_encoder_time_s`
 - `dit_time_s`
 - `vae_decode_time_s`
-- `success`
-- `baseline_eligible`
-- `comparison_status`
+- `success: true`
 
-For v2 baseline-shift reseeds with no previous HF records for the exact
-identity, also preserve:
+For normalized `normalized_perf_*.json` sources, these fields already exist.
+For older raw `perf_*.json` sources, map the raw fields exactly as
+`normalize_performance_result()` in `compare_baseline.py` does:
 
-- `workload_id`
-- `variant_id`
-- `benchmark_version`
-- `recipe_fingerprint`
-- `hardware_profile_id`
-- `software_profile_id`
-- `recipe`
-- `hardware_profile`
-- `software_profile`
-- `software_comparison_profile`
+| Normalized field | Raw source field |
+|------------------|------------------|
+| `model_id` | `benchmark_id` |
+| `gpu_type` | `device` |
+| `latency` | `avg_generation_time_s` |
+| `throughput` | `throughput_fps` |
+| `memory` | `max_peak_memory_mb` |
+| `text_encoder_time_s` | `text_encoder_time_s` |
+| `dit_time_s` | `dit_time_s` |
+| `vae_decode_time_s` | `vae_decode_time_s` |
+| `commit_sha` | `commit` |
 
-Do not upload extra fields from the source artifact.
+Do not upload raw-only fields such as `model_short_name`, `num_gpus`,
+`num_warmup_runs`, `num_measurement_runs`, `individual_times_s`,
+`individual_peak_memories_mb`, `thresholds`, or `pr_number`.
 
 Optional provenance fields are allowed and useful:
 
@@ -438,30 +312,13 @@ Optional provenance fields are allowed and useful:
 - `baseline_reseed_reason`
 - `baseline_reseed_source_result`
 - `baseline_reseed_source_timestamp`
-- `baseline_reseed_source_success`
+- `baseline_reseed_replicated_source: true`
 - `baseline_reseed_batch_size`
 - `baseline_reseed_batch_index`
 - `baseline_reseed_operator`
-- `baseline_reseed_max_intra_batch_regression`
 
-The v2 calibration seed utility writes analogous first-seed provenance:
-
-- `baseline_seed: true`
-- `baseline_seed_reason`
-- `baseline_seed_source_result`
-- `baseline_seed_source_status`
-- `baseline_seed_source_timestamp`
-- `baseline_seed_source_success`
-- `baseline_seed_source_run_source`
-- `baseline_seed_source_branch`
-- `baseline_seed_source_test_scope`
-- `baseline_seed_source_pr_number`
-- `baseline_seed_batch_size`
-- `baseline_seed_batch_index`
-- `baseline_seed_operator`
-
-Use a fresh reseed timestamp for each seed record, not the original source
-result timestamp. This is required because
+Use a fresh reseed timestamp for each replicated record, not the original
+source result timestamp. This is required because
 `load_records_for_model(..., last_n=5)` keeps the last records after loading
 the model directory; stale filenames/timestamps may not enter the last-5
 window and therefore may not move the median. Preserve the original source
@@ -470,10 +327,11 @@ timestamp in `baseline_reseed_source_timestamp`.
 Use the existing filename convention from `_write_tracking_record()`:
 `<sanitize(timestamp)>_<sanitize(commit_sha)>.json` under the sanitized model
 directory, but include a deterministic suffix such as `_reseed_01`,
-`_reseed_02`, and so on before `.json` so multiple records from the same
-batch do not overwrite each other.
+`_reseed_02`, and `_reseed_03` before `.json` so the replicated files do not
+overwrite each other. For a 5-record full reset, continue through
+`_reseed_05`.
 
-If a source record already exists on HF with `success=false`, do not edit it
+If the source record already exists on HF with `success=false`, do not edit it
 in place unless the user explicitly asked for an audit-preserving correction.
 Prefer uploading new accepted seed records so failed history remains visible.
 
@@ -481,13 +339,10 @@ Prefer uploading new accepted seed records so failed history remains visible.
 
 Print:
 
-- Backup directory path under `/tmp`.
-- Prepared local record paths under `PERFORMANCE_RESEED_STAGING_ROOT`.
-- Prepared upload-manifest path under the identity reservation.
+- Backup directory path.
 - HF paths that will receive the new records.
 - Old rolling medians.
-- Source batch medians, source batch spread, reseed count, and candidate
-  medians.
+- Source metrics, replica count, and candidate median.
 - Rationale.
 
 Ask the user to reply exactly `upload`. Anything else aborts and leaves the
@@ -495,125 +350,57 @@ prepared records plus backup on disk.
 
 ### 7. Upload only the scoped records
 
-For a first v2 calibration seed, use the manifest uploader after the user
-replies exactly `upload`:
+Use the shared storage helper so the path and repo type match CI:
 
-```bash
-python -c 'from fastvideo.tests.performance.seed_baseline import upload_prepared_seed_manifest; print(upload_prepared_seed_manifest("<prepared_manifest>"))'
+```python
+from fastvideo.tests.performance.hf_store import upload_record
+
+upload_record("<local_record_path>", record, strict=True)
 ```
 
-The uploader verifies the source and prepared-record digests, pins and scans
-the current HF revision, rechecks exact-identity and recipe-cohort conflicts,
-and writes the entire batch in one commit whose `parent_commit` must still be
-current. A concurrent Hub update makes the commit fail. Do not retry
-automatically: preserve staging, refresh/review remote state, and request a new
-explicit `upload` after the conflict is understood. Each record goes to:
+Run it once per prepared record. Each upload goes to:
 
 ```text
 FastVideo/performance-tracking/<sanitize(model_id)>/<record_filename>.json
 ```
 
-Never call `upload_record()` once per first-seed record: that can partially
-land the batch and has no compare-and-swap guard.
+Never bulk upload the whole tracking root. Never modify another model's
+directory in the same operation.
 
-For a legacy reseed or an accepted v2 baseline shift, the first-seed manifest
-validator does not apply because an eligible baseline already exists. Upload
-only the individually reviewed records prepared in step 5 with the shared
-`upload_record(local_path, record, strict=True)` helper. Stop on the first
-failure and report exactly which records reached HF; do not silently rerun or
-replicate the remainder.
-
-Never bulk upload the tracking or staging root, and never modify another
-model's directory in the same operation.
-
-### 8. Report outcome and offer cleanup
+### 8. Report outcome
 
 Report:
 
 - Uploaded HF paths.
-- Backup directory under `/tmp`.
-- Local tracking root, usually `/tmp/perf-tracking`.
+- Backup directory.
 - Old baseline window count and medians.
-- Source batch medians, source batch spread, reseed count, and candidate
-  medians.
-- Expected effect based on reseed count.
+- Source metrics, replica count, and candidate median.
+- Expected effect: 3 replicated shifted records move the 5-record median; 5
+  replicated shifted records fully reset the window to the accepted source
+  result.
 - Any separate threshold changes still needed in
   `.buildkite/performance-benchmarks/tests/*.json`.
 
 Include the `intent_rationale` in the PR or follow-up comment so reviewers can
 distinguish an accepted baseline shift from a hidden regression.
 
-After the upload is verified, ask whether the user wants to clear temporary
-local state. Explain what each directory is for:
-
-- `PERFORMANCE_TRACKING_ROOT`, usually `/tmp/perf-tracking`: read-only local
-  synced mirror used for operator review and reporting. First-v2 preparation
-  independently proves remote state from a fresh temporary HF snapshot.
-- `PERFORMANCE_RESEED_STAGING_ROOT`, usually
-  `/tmp/performance_reseed_prepared`: prepared local seed records used for the
-  scoped upload, plus the identity reservation and digest manifest. Keeping
-  this separate prevents aborted preparations from appearing in later
-  baseline reads.
-- `/tmp/performance_reseed_backup/<...>`: local backup of the target model's
-  pre-reseed HF history plus `PROVENANCE.txt`, kept so a bad reseed can be
-  audited or corrected.
-- `/tmp/performance_reseed_source/<...>` when used: downloaded source JSON
-  artifacts from Buildkite URLs.
-
-Ask:
-
-> Reseed succeeded. Do you want me to delete the local temp tracking mirror,
-> this reseed's prepared staging records, source downloads, and reseed backup
-> under `/tmp`? These files are local safety/audit artifacts only; HF already
-> has the uploaded records.
->
-> Reply `cleanup reseed temp` to delete them, anything else to keep them.
-
-Do not delete anything unless the user replies exactly
-`cleanup reseed temp`. If cleanup is requested, remove only the specific
-directories and prepared record paths created for this reseed. Do not remove
-the shared staging root when it contains other records. Remove this operation's
-identity reservation only with its prepared records and manifest, and never
-remove unrelated `/tmp` contents.
-
 ## Failure modes and handling
 
 - **`HF_API_KEY` unset.** Stop before upload. Do not create an untracked
   process that appears to have reseeded but never reached HF.
 - **Source result does not match target.** Stop. The wrong benchmark or GPU
   would poison a separate baseline.
-- **Source batch is internally inconsistent.** Stop if any source regresses
-  against the source batch median by more than `max_intra_batch_regression`.
-  Ask for cleaner sources or a reviewed explanation before continuing.
-- **Too few source records to move the median.** Continue only after making
-  clear that one or two records may not immediately move an existing last-5
-  median. This warning does not block a first v2 calibration seed for an exact
-  identity with no eligible baseline yet.
-- **The source results are noisy or suspicious.** Stop. Reseeding amplifies
-  those measurements into the baseline, so they must be reviewed first.
+- **`replica_count` is 5 but the user did not explicitly ask for a full
+  reset.** Stop and use the default count of 3.
+- **The source result is noisy or suspicious.** Stop. Replicating one result
+  amplifies that measurement into the baseline, so it must be reviewed first.
 - **HF sync fails.** Stop for destructive reseeds. A stale or empty sync can
   make the old baseline look missing.
-- **The exact v2 identity already has an eligible baseline.** Stop. The
-  `CALIBRATION_NEEDED` artifact is stale; use the reviewed baseline-shift path
-  instead of the first-seed utility.
-- **The workload/variant/version trusts another recipe.** Stop. The source is
-  stale relative to the current recipe cohort and must not bypass
-  `RECIPE_MISMATCH` by creating a second trusted recipe.
-- **The staging root already has a prepared seed for the exact identity.**
-  Stop and reuse, upload, or explicitly clean that preparation. Do not prepare
-  another copy of the same measurement.
-- **The conditional Hub commit loses its parent race.** Stop without retrying.
-  Keep the preparation, refresh and review the new remote state, then request
-  a new explicit `upload` only if the seed is still valid.
 - **Candidate still violates fixed thresholds.** Report that this skill only
   handles the rolling HF baseline; update benchmark JSON thresholds in code
   review if maintainers accept the new absolute limit.
 - **The user aborts at either confirmation.** Leave the backup and prepared
   records on disk. Nothing should be uploaded.
-- **The user declines cleanup.** Keep `/tmp/perf-tracking`, the prepared seed
-  records under `/tmp/performance_reseed_prepared`, the source download
-  directory if any, and `/tmp/performance_reseed_backup/<...>` in place for
-  audit/debugging.
 - **A bad seed was uploaded.** Use the backup and HF history to identify the
   uploaded file, then remove or supersede it with an explicitly reviewed
   corrective record. Do not silently rewrite unrelated history.
@@ -624,9 +411,8 @@ remove unrelated `/tmp` contents.
   intentional baseline replacement.
 - `fastvideo/tests/performance/compare_baseline.py` — normalization, rolling
   median comparison, and persistence rules.
-- `fastvideo/performance/hf_store.py` — HF sync and record loading helpers.
-- `fastvideo/tests/performance/seed_baseline.py` — first-seed preparation,
-  staging reservation, manifest validation, and conditional batch upload.
+- `fastvideo/tests/performance/hf_store.py` — HF sync, record loading,
+  `sanitize()`, and `upload_record()`.
 - `fastvideo/tests/performance/test_inference_performance.py` — source result
   JSON schema.
 - `.buildkite/performance-benchmarks/tests/*.json` — fixed absolute benchmark
@@ -637,6 +423,4 @@ remove unrelated `/tmp` contents.
 | Date | Change |
 |------|--------|
 | 2026-05-03 | Initial version. Sister workflow to `reseed-ssim-references`, scoped to one performance `(model_id, gpu_type)` baseline seed with backup, confirmation, provenance, and `success=true` upload. |
-| 2026-05-03 | Previous policy: replicate one approved shifted source result into 3 success records by default, or 5 only when explicitly requested. Add provenance marker for replicated-source reseeds. Superseded by the 2026-05-08 dynamic multi-source policy. |
-| 2026-05-08 | Replace fixed 3/5 replication with dynamic multi-source reseeding: upload one seed record per reviewed source JSON, validate intra-batch consistency, move backup/source scratch under `/tmp`, and ask whether to clean temp state after successful upload. |
-| 2026-07-13 | Keep first-v2-seed preparation outside the canonical mirror, reserve staging identities atomically, reject stale or replayed calibration seeds, and upload reviewed manifests with a single parent-guarded Hub commit. |
+| 2026-05-03 | Current policy: replicate one approved shifted source result into 3 success records by default, or 5 only when explicitly requested. Add provenance marker for replicated-source reseeds. |

.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 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_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.
 ---
 
 # 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 through the manual legacy Modal L40S maintenance path.
+3. Regenerates on Modal L40S (same code path that CI uses).
 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.
@@ -51,13 +51,12 @@ harder to recover from than failing closed.
 
 Hardcoded:
 
-- 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.
+- Modal GPU: **L40S** (matches CI; re-seeding from another SKU produces refs
+  that L40S CI cannot match).
 - Quality tier: **`default`**. `full_quality` is a separate, deliberate
   operation.
 - HF repo: `FastVideo/ssim-reference-videos` (override via
-  `FASTVIDEO_TEST_SSIM_REFERENCE_HF_REPO`).
+  `FASTVIDEO_SSIM_REFERENCE_HF_REPO`).
 - Device folder: `L40S_reference_videos`.
 
 ## Prerequisites

.agents/skills/search-related-work/SKILL.md

@@ -0,0 +1,82 @@
+---
+name: search-related-work
+description: Query the related work index for relevant papers, repos, or comparisons
+---
+
+# Search Related Work
+
+## Purpose
+Search through `.agents/memory/related-work/` to find indexed papers, repos,
+or blog posts relevant to a query. Use this when you need to understand how
+other work compares to FastVideo's approach, or when looking for techniques
+to adopt.
+
+## Prerequisites
+- The related work index has entries (`.agents/memory/related-work/*.md`).
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `query` | Yes | Natural language query |
+| `tags` | No | Filter by tags (e.g., `[distillation, evaluation]`) |
+| `type` | No | Filter by type (`paper`, `repo`, `blog`) |
+
+## Steps
+
+### 1. Search the index
+
+Use grep-based search through `.agents/memory/related-work/`:
+
+```bash
+# Search by content
+grep -rl "<query>" .agents/memory/related-work/
+
+# Search by tags (in frontmatter)
+grep -l "tags:.*<tag>" .agents/memory/related-work/*.md
+```
+
+### 2. Rank results
+
+For each matching file:
+1. Read the file.
+2. Score relevance to the query based on:
+   - Title match
+   - Tag match
+   - Content match (summary, differences, insights)
+3. Return top results.
+
+### 3. Format output
+
+```markdown
+## Related Work Search: "<query>"
+
+### 1. <Title> (relevance: high)
+- **Source**: <URL>
+- **Tags**: <tags>
+- **Key insight**: <most relevant excerpt>
+- **File**: `.agents/memory/related-work/<slug>.md`
+
+### 2. <Title> (relevance: medium)
+...
+```
+
+## Outputs
+- Ranked list of relevant related work entries with excerpts.
+
+## Example Usage
+
+```
+Search for work related to video quality evaluation metrics:
+
+  query: "video generation quality evaluation metrics"
+  tags: [evaluation]
+```
+
+## References
+- `.agents/memory/related-work/README.md` — index schema
+
+## Changelog
+| Date | Change |
+|------|--------|
+| 2026-03-02 | Initial version |

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

@@ -35,8 +35,7 @@ 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 the manual legacy Modal L40S reference-maintenance target. Active CI runs
-on the Slinky Slurm cluster and only consumes the resulting references.
+to Modal L40S (which is what CI uses).
 
 ## When to use
 
@@ -62,8 +61,7 @@ Prompt the user for it if they didn't supply it.
 
 Everything else is fixed:
 
-- Modal maintenance GPU: **L40S** (hardcoded in
-  `fastvideo/tests/modal/ssim_test.py`; this is not the active CI compute path).
+- Modal runner GPU: **L40S** (hardcoded in `fastvideo/tests/modal/ssim_test.py`).
 - Device folder: `L40S_reference_videos`.
 - Quality tier: `default` (the tier CI runs). The `full_quality` tier is not
   seeded by this skill.
@@ -105,8 +103,8 @@ Detect artefact type by inspecting the file's imports / helper call:
 - **pixel** (`.mp4`) — file imports
   `run_text_to_video_similarity_test` / `run_image_to_video_similarity_test`
   from `fastvideo.tests.ssim.inference_similarity_utils`, OR uses the
-  legacy custom-inline helper pattern (see `test_gen3c`). Default to pixel
-  when both heuristics fail.
+  legacy custom-inline helper pattern (see `test_gamecraft`,
+  `test_longcat`, etc.). Default to pixel when both heuristics fail.
 
 Record `ARTEFACT_TYPE ∈ {pixel, latent}` for use in step 4. Steps 2, 3, 5,
 and 6 are artefact-type-agnostic — `_iter_reference_files`,
@@ -152,12 +150,10 @@ modal run fastvideo/tests/modal/ssim_test.py \
 Env prefix rationale (parity with CI; see `.buildkite/pipeline.yml:1-3` and
 `.buildkite/scripts/pr_test.sh:62-83`):
 - `IMAGE_VERSION=py3.12-latest`: pins the Modal image tag to the same one CI
-  uses. The published `py3.12-latest` and `latest` tags point at Python 3.12 /
-  CUDA 12.6.3 / cu126; `py3.12-cuda12.6.3-latest` is the explicit alias for the
-  same image. CUDA 13 / cu130 is available under the explicit
-  `py3.12-cuda13.0.0-latest` tag. This tag policy comes from
-  `infra-build-image.yml`; the unparameterized `docker/Dockerfile` build itself
-  still defaults to CUDA 13 / cu130.
+  uses. Without this, `ssim_test.py:17` falls back to `latest`, which on
+  GHCR is built from `Dockerfile.python3.10` — different Python, torch, and
+  flash-attn wheel than CI's `py3.12-latest` (`infra-build-image.yml:51-67`,
+  `_template-build-image.yml:65-101`).
 - `BUILDKITE_REPO`/`BUILDKITE_COMMIT`/`BUILDKITE_PULL_REQUEST`: mirror what
   Buildkite exports. `ssim_test.py:38-46` bakes these into the image's
   `.env(...)` block; mismatched values can perturb in-container code paths

.agents/skills/summarize-run/SKILL.md

@@ -0,0 +1,137 @@
+---
+name: summarize-run
+description: Extract a W&B run summary into a structured experiment report
+---
+
+# Summarize Run
+
+## Purpose
+After a training run completes (or at any checkpoint), extract key metrics from
+the W&B run summary and produce a structured markdown report. Supports both
+online (W&B API) and offline (local `wandb-summary.json`) modes.
+
+## Prerequisites
+- Run has completed or reached a checkpoint with a saved summary.
+- For online: `WANDB_API_KEY` set in environment.
+- For offline: access to `<output_dir>/tracker/wandb/latest-run/files/wandb-summary.json`.
+
+## Inputs
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `run_id` | Yes* | W&B run ID for online access |
+| `output_dir` | Yes* | Local output dir for offline access |
+| `reference_run` | No | Path to reference `wandb-summary.json` for comparison |
+| `experiment_name` | No | Name for the journal entry (default: from W&B) |
+
+\* One of `run_id` or `output_dir` is required.
+
+## Steps
+
+### 1. Load run summary
+
+**Online**:
+
+```python
+import wandb
+api = wandb.Api()
+run = api.run("<run_id>")
+summary = dict(run.summary)
+config = dict(run.config)
+```
+
+**Offline** (existing codebase pattern from `fastvideo/tests/training/`):
+
+```python
+import json
+summary_path = f"{output_dir}/tracker/wandb/latest-run/files/wandb-summary.json"
+with open(summary_path) as f:
+    summary = json.load(f)
+```
+
+### 2. Extract key fields
+
+| Field | Source | Description |
+|-------|--------|-------------|
+| `train_loss` | `summary["train_loss"]` | Final training loss |
+| `avg_step_time` | `summary["avg_step_time"]` | Average seconds per step |
+| `step_time` | `summary["step_time"]` | Last step time |
+| `grad_norm` | `summary["grad_norm"]` | Final gradient norm |
+| `learning_rate` | `summary["learning_rate"]` | Final LR |
+| `_step` | `summary["_step"]` | Total steps completed |
+| `_runtime` | `summary["_runtime"]` | Total wall-clock seconds |
+| `validation_videos_*` | `summary[key]` | Validation video artifacts |
+
+### 3. Compare against reference (optional)
+
+Follow the pattern in `fastvideo/tests/training/Vanilla/test_training_loss.py`:
+
+```python
+# Fields to compare
+compare_fields = ["train_loss", "grad_norm", "avg_step_time"]
+tolerance = 0.05  # 5% relative tolerance
+
+for field in compare_fields:
+    ref_val = reference_summary[field]
+    cur_val = summary[field]
+    diff_pct = abs(cur_val - ref_val) / abs(ref_val) * 100
+    status = "✅" if diff_pct < tolerance * 100 else "⚠️"
+    print(f"{status} {field}: {cur_val:.4f} (ref: {ref_val:.4f}, diff: {diff_pct:.1f}%)")
+```
+
+### 4. Generate report
+
+```markdown
+# Run Summary: <experiment_name>
+
+| Metric | Value | Reference | Diff |
+|--------|-------|-----------|------|
+| Train Loss | 0.0788 | 0.0800 | -1.5% ✅ |
+| Avg Step Time | 2.81s | 2.80s | +0.4% ✅ |
+| Grad Norm | 0.408 | 0.410 | -0.5% ✅ |
+| Total Steps | 500 | — | — |
+| Wall Time | 23m 30s | — | — |
+
+## Configuration
+- Model: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+- Learning Rate: 1e-6
+- Batch Size: 1
+- GPUs: 8 × (SP=1, TP=1)
+- Mixed Precision: bf16
+
+## Validation Videos
+<list of validation video paths if available>
+
+## Notes
+<any observations or anomalies>
+```
+
+### 5. Update experiment journal
+
+Append or update the experiment's entry in `.agents/memory/experiment-journal/README.md`
+with the final metrics and status.
+
+## Outputs
+- Structured markdown report.
+- Updated experiment journal entry.
+
+## Example Usage
+
+```
+Summarize the run in output directory "outputs/wan_finetune":
+
+  output_dir: outputs/wan_finetune
+  reference_run: fastvideo/tests/training/Vanilla/a40_reference_wandb_summary.json
+  experiment_name: wan-t2v-finetune-lr1e6
+```
+
+## References
+- `fastvideo/tests/training/Vanilla/test_training_loss.py` — reference comparison pattern
+- `fastvideo/tests/training/Vanilla/a40_reference_wandb_summary.json` — example summary
+- `fastvideo/tests/training/lora/test_lora_training.py` — LoRA summary comparison
+- `fastvideo/training/trackers.py` — tracker summary generation
+
+## Changelog
+| Date | Change |
+|------|--------|
+| 2026-03-02 | Initial version |

.agents/workflows/evaluation-development.md

@@ -0,0 +1,54 @@
+---
+description: How to develop, validate, and register a new evaluation metric
+---
+
+# Evaluation Development SOP
+
+Standard procedure for adding new video quality evaluation metrics to the
+FastVideo agent toolkit.
+
+## When to Use
+
+- You need a metric that doesn't exist in `.agents/memory/evaluation-registry/README.md`.
+- An existing metric needs significant changes to its methodology.
+- You're exploring a new evaluation approach.
+
+## Steps
+
+### 1. Research
+
+- Search `.agents/memory/related-work/` for existing evaluation approaches.
+- Check the `evaluation_registry.md` for current metrics and their limitations.
+- Review literature: FVD, CLIP-Score, human preference, etc.
+
+### 2. Prototype
+
+- Write a standalone script in `.agents/exploration/<metric-name>.md`.
+- Keep it simple: one script, minimal dependencies.
+- Test on a few known-good and known-bad video samples.
+
+### 3. Validate
+
+- **Known-good test**: Metric should score high on reference-quality videos.
+- **Known-bad test**: Metric should score low on degraded/unrelated videos.
+- **Sensitivity test**: Small quality differences should produce meaningful
+  score differences.
+- Document thresholds and their justification.
+
+### 4. Register
+
+Update `.agents/memory/evaluation-registry/README.md`:
+- Add the metric with status `Active`.
+- Document location, thresholds, and trust level.
+
+### 5. Integrate
+
+Update `.agents/skills/evaluate-video-quality.md`:
+- Add the new metric as a section.
+- Include code examples and interpretation guide.
+
+### 6. Document
+
+- Move the exploration log content into the skill.
+- Clean up the exploration file or mark it as `promoted`.
+- If anything went wrong during development, create a lesson.

.agents/workflows/experiment-journaling.md

@@ -0,0 +1,47 @@
+---
+description: When and how to log experiments in the experiment journal
+---
+
+# Experiment Journaling SOP
+
+Ensures every experiment is properly recorded with context and outcomes.
+
+## When to Log
+
+**Always.** Every experiment — even quick tests — should be journaled.
+
+## Steps
+
+### 1. Before Launch — Create Draft Entry
+
+Use the `log-experiment` skill with `status: running`:
+- Include hypothesis and config.
+- Leave metrics, duration, and insight blank.
+
+### 2. After 30-Minute Check — Update with Initial Metrics
+
+Update the entry with:
+- Current loss and its trajectory direction.
+- Step time.
+- Number of validation videos generated.
+- Preliminary go/no-go assessment.
+
+### 3. On Completion — Fill Final Entry
+
+Update the entry with `status: completed`:
+- Final loss, grad norm, avg step time.
+- Total duration and steps.
+- Checkpoint path.
+- Key insight.
+
+### 4. On Failure — Document Failure Mode
+
+Update the entry with `status: failed`:
+- What went wrong (OOM, NaN, crash, etc.).
+- At what step the failure occurred.
+- Create a lesson in `.agents/lessons/` for non-trivial failures.
+
+### 5. Cross-Reference
+
+- Link related lessons: `**Related lessons**: .agents/lessons/<filename>.md`
+- Link related experiments: if this is a follow-up, reference the prior entry.

.agents/workflows/experiment-lifecycle.md

@@ -0,0 +1,87 @@
+---
+description: End-to-end experiment lifecycle from hypothesis to lessons learned
+---
+
+# Experiment Lifecycle SOP
+
+Standard operating procedure for running ML training experiments on
+FastVideo-WorldModel. Every experiment should follow this flow.
+
+## Overview
+
+```
+Plan → Launch → Monitor → Summarize → Journal → Reflect
+```
+
+## Steps
+
+### 1. Plan the Experiment
+
+Before launching:
+- [ ] Define a clear **hypothesis** (what you expect to learn).
+- [ ] Select the **model** and **pipeline** type (finetune, distill, lora, etc.).
+- [ ] Prepare the **dataset** (preprocessed into parquet format).
+- [ ] Review existing experiments in `.agents/memory/experiment-journal/README.md` for related work.
+- [ ] Check `.agents/lessons/` for known pitfalls with this configuration.
+- [ ] Document the plan in the experiment journal as a draft entry.
+
+### 2. Launch the Experiment
+
+Use the `launch-experiment` skill:
+- Provide: pipeline, model, data_path, num_gpus, and any hyperparameter overrides.
+- The skill generates the `torchrun` command and creates a journal entry.
+- Verify the command looks correct before executing.
+
+Reference: `.agents/skills/launch-experiment.md`
+
+### 3. Monitor the Experiment
+
+Use the `monitor-experiment` skill:
+- Provide the W&B run ID (or output_dir for offline).
+- Monitor alerts: loss spikes, NaN gradients, step time regressions.
+- At the **30-minute mark**: perform the quality check.
+  - Is loss decreasing?
+  - Are validation videos reasonable?
+  - Is step time consistent?
+- **Decision point**: Continue or abort based on the 30-min check.
+
+Reference: `.agents/skills/monitor-experiment.md`
+
+### 4. Summarize the Run
+
+After completion (or at any checkpoint), use the `summarize-run` skill:
+- Extract final metrics from W&B summary.
+- Compare against reference runs if available.
+- Generate a structured report.
+
+Reference: `.agents/skills/summarize-run.md`
+
+### 5. Update the Experiment Journal
+
+Use the `log-experiment` skill to update the journal entry:
+- Fill in final metrics, duration, checkpoint paths.
+- Record the key insight learned.
+- Set status to `completed`, `failed`, or `abandoned`.
+
+Reference: `.agents/skills/log-experiment.md`
+
+### 6. Reflect and Capture Lessons
+
+After every experiment:
+- **What went right?** → Note in the journal insight field.
+- **What went wrong?** → Create a lesson in `.agents/lessons/`:
+  - Use the template in `.agents/lessons/README.md`.
+  - Cross-reference the experiment journal entry.
+- **What was surprising?** → Consider creating an exploration log if this
+  warrants further investigation.
+
+Reference: `.agents/workflows/lesson-capture.md`
+
+## Validation Criteria
+
+This SOP is validated when an agent can:
+1. Follow steps 1–6 end-to-end for a minimal training run
+   (e.g., `examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh`
+   with `--max_train_steps 5`).
+2. Produce a complete experiment journal entry.
+3. Generate a run summary report.

.agents/workflows/lesson-capture.md

@@ -0,0 +1,71 @@
+---
+description: Post-experiment reflection to capture lessons learned
+---
+
+# Lesson Capture SOP
+
+Systematic procedure for turning experiment outcomes into persistent knowledge.
+
+## When to Use
+
+After **every** completed or failed experiment. Even successful experiments
+can yield lessons (e.g., "LR 5e-5 works better than 1e-5 for LoRA").
+
+## Steps
+
+### 1. Review the Experiment
+
+Read the experiment journal entry. Ask:
+- Did anything go wrong?
+- Was anything surprising?
+- Did anything take longer than expected?
+- Was a workaround needed?
+
+### 2. Decide: Lesson or Not?
+
+| Situation | Action |
+|-----------|--------|
+| Something broke | Create a lesson (category: `infrastructure` or `data`) |
+| Hyperparameter choice mattered | Create a lesson (category: `hyperparameter`) |
+| Porting issue found | Create a lesson (category: `porting`) |
+| Evaluation metric was misleading | Create a lesson (category: `evaluation`) |
+| Everything went smoothly | No lesson needed, but note in the journal insight |
+
+### 3. Create the Lesson File
+
+In `.agents/lessons/`, create `<YYYY-MM-DD>_<short-slug>.md`:
+
+```markdown
+---
+date: <ISO-8601>
+experiment: <journal entry reference>
+category: hyperparameter | data | infrastructure | evaluation | porting
+severity: critical | important | minor
+---
+
+# <Short Descriptive Title>
+
+## What Happened
+<description>
+
+## Root Cause
+<analysis>
+
+## Fix / Workaround
+<resolution>
+
+## Prevention
+<how to avoid in future>
+```
+
+### 4. Cross-Reference
+
+- Update the experiment journal entry with a link to the lesson file.
+- If a similar lesson already exists, add a reference or update it.
+
+### 5. Periodic Pattern Review
+
+Every ~10 lessons, scan for patterns:
+- Multiple lessons in the same category → consider a new skill or SOP.
+- Repeated mistakes → strengthen the relevant SOP with a checklist item.
+- Infrastructure issues → propose a codebase fix.

.agents/workflows/sync-dashboard.md

@@ -0,0 +1,67 @@
+---
+description: Synchronize the STATUS.md dashboard by scanning .agents/ directories
+---
+
+# Sync Dashboard
+
+Updates `.agents/STATUS.md` by scanning the skills, workflows, memory, lessons,
+and exploration directories to reflect what actually exists on disk.
+
+## When to Use
+
+- After adding, removing, or renaming any file in `.agents/`.
+- Periodically (e.g., at end of each conversation session).
+- When the dashboard feels out of date.
+
+## Steps
+
+### 1. Scan directories
+
+List all files in each directory:
+
+```bash
+echo "=== Skills ==="
+ls -1 .agents/skills/*.md 2>/dev/null | grep -v SKILL_TEMPLATE
+
+echo "=== Workflows ==="
+ls -1 .agents/workflows/*.md 2>/dev/null
+
+echo "=== Memory ==="
+ls -1 .agents/memory/*.md 2>/dev/null
+ls -1 .agents/memory/related-work/*.md 2>/dev/null | grep -v README
+
+echo "=== Lessons ==="
+ls -1 .agents/lessons/*.md 2>/dev/null | grep -v README
+
+echo "=== Exploration ==="
+ls -1 .agents/exploration/*.md 2>/dev/null | grep -v README
+```
+
+### 2. Compare with STATUS.md
+
+For each file found:
+- If it's in STATUS.md → leave it (preserve status/trust/tested fields).
+- If it's NOT in STATUS.md → add it with status `🔴 Stub`, trust `None`, tested `❌`.
+
+For each entry in STATUS.md:
+- If the file no longer exists → mark it as `❌ Removed` or delete the row.
+
+### 3. Update counts
+
+Recalculate the summary table at the top:
+- Count files per category.
+- Count by status (Ready, Draft, Stub).
+
+### 4. Update timestamp
+
+Set `_Last synced: <current date>_` at the top of STATUS.md.
+
+### 5. Review
+
+Read through the updated STATUS.md for accuracy. Flag anything that looks wrong.
+
+## Notes
+
+- Do NOT change trust levels during sync — those are set manually after testing.
+- Do NOT change status during sync — status changes require actual validation.
+- This workflow only handles structural sync (file existence), not content review.

.buildkite/performance-benchmarks/tests/wan-t2v-1.3b-gb10-1gpu.json

@@ -1,51 +0,0 @@
-{
-  "benchmark_id": "wan-t2v-1.3b-1gpu-gb10",
-  "config_schema_version": 2,
-  "workload_id": "wan-t2v",
-  "variant_id": "1.3b-sp1",
-  "benchmark_version": 3,
-  "description": "Wan2.1 T2V 1.3B single-GPU inference performance on NVIDIA DGX Spark (GB10). Single-GPU variant of wan-t2v-1.3b (same workload_id for dashboard comparability). Gated to the GB10 via run_config.gpu_types so it does not run on the shared H100/L40S lanes.",
-  "model": {
-    "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    "model_short_name": "Wan2.1-T2V-1.3B"
-  },
-  "init_kwargs": {
-    "num_gpus": 1,
-    "flow_shift": 7.0,
-    "sp_size": 1,
-    "tp_size": 1,
-    "vae_sp": false,
-    "vae_tiling": true,
-    "text_encoder_precisions": ["fp32"]
-  },
-  "generation_kwargs": {
-    "height": 480,
-    "width": 832,
-    "num_frames": 45,
-    "num_inference_steps": 4,
-    "guidance_scale": 3,
-    "embedded_cfg_scale": 6,
-    "seed": 1024,
-    "fps": 24,
-    "neg_prompt": "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
-  },
-  "test_prompts": [
-    "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting."
-  ],
-  "run_config": {
-    "num_warmup_runs": 2,
-    "num_measurement_runs": 5,
-    "required_gpus": 1,
-    "gpu_types": ["GB10"]
-  },
-  "thresholds": {
-    "GB10": {
-      "max_generation_time_s": 55.0,
-      "max_peak_memory_mb": 12000.0
-    },
-    "default": {
-      "max_generation_time_s": 120.0,
-      "max_peak_memory_mb": 40000.0
-    }
-  }
-}

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

@@ -1,9 +1,5 @@
 {
   "benchmark_id": "wan-t2v-1.3b-2gpu",
-  "config_schema_version": 2,
-  "workload_id": "wan-t2v",
-  "variant_id": "1.3b-sp2",
-  "benchmark_version": 3,
   "description": "Wan2.1 T2V 1.3B inference performance",
   "model": {
     "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
@@ -47,7 +43,10 @@
     },
     "default": {
       "max_generation_time_s": 120.0,
-      "max_peak_memory_mb": 30000.0
+      "max_peak_memory_mb": 30000.0,
+      "max_text_encoder_time_s": 5.0,
+      "max_dit_time_s": 10.0,
+      "max_vae_decode_time_s": 10.0
     }
   }
 }

.buildkite/pipeline.yml

@@ -1,9 +1,6 @@
 env:
   IMAGE_VERSION: "py3.12-latest"
   BUILDKITE_CLEAN_CHECKOUT: true
-  # Slurm workers clone the immutable commit and initialize submodules inside
-  # their isolated container. The Buildkite login-plane checkout is a no-op.
-  BUILDKITE_GIT_SUBMODULES: false
 
 notify:
   - github_commit_status:
@@ -11,474 +8,431 @@ notify:
     if: build.env("TEST_SCOPE") == "fastcheck" || build.env("TEST_SCOPE") == null
   - github_commit_status:
       context: "full-suite-passed"
-    if: build.env("TEST_SCOPE") == "full" || build.env("TEST_SCOPE") == "merge"
+    if: build.env("TEST_SCOPE") == "full"
   - github_commit_status:
       context: "direct-test-completed"
     if: build.env("TEST_SCOPE") == "direct"
-  - github_commit_status:
-      context: "scheduled-ssim-passed"
-    if: build.env("TEST_SCOPE") == "scheduled"
 
-# This is the complete active GPU CI surface. Every command is a trusted host
-# dispatcher, and every test payload executes inside the Slinky Slurm tray.
-# fastvideo/tests/modal remains available only for an explicit manual rollback;
-# no active pipeline or slash-command route invokes it.
-# Buildkite hands jobs to free agents in the order they appear here. Golden-gate comes first
-# because every later merge lane waits for it; the fastcheck lanes follow from longest to
-# shortest measured runtime, so the longest lane never starts last and stretches the build.
 steps:
-  - label: ":test_tube: Golden-Gate Tests"
-    key: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,golden-gate,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "golden_gate" || build.env("TEST_TYPE") == "golden_gate_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "golden_gate_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
+  # ============================================================
+  # Direct test: triggered by /test <name> slash command.
+  # Labels match fastcheck/full-suite counterparts so the GitHub
+  # check status overwrites the original failed check.
+  # Only ONE step executes per build (gated by TEST_TYPE).
+  # ============================================================
 
-  - label: ":microscope: Unit Tests"
-    key: "unit"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,unit,/) ||
-      build.env("TEST_SCOPE") == "fastcheck" ||
-      build.env("TEST_SCOPE") == null ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "unit_test" || build.env("TEST_TYPE") == "unit_test_ci"))
-    command: "/opt/fastvideo-ci-runner/run-unit"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "unit_test_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
+  # --- Fastcheck-scope direct tests ---
+    - label: ":microscope: Encoder Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "encoder"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":microscope: VAE Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "vae"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":microscope: Transformer Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "transformer"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":microscope: Kernel Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "kernel_tests"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":microscope: Unit Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "unit_test"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
 
-  - label: ":microscope: Kernel Tests"
-    key: "kernel-tests"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,kernel-tests,/) ||
-      build.env("TEST_SCOPE") == "fastcheck" ||
-      build.env("TEST_SCOPE") == null ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "kernel_tests" || build.env("TEST_TYPE") == "kernel_tests_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "kernel_tests_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
+  # --- Full-suite-scope direct tests ---
+    - label: ":bar_chart: SSIM Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "ssim"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+          - exit_status: 1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: LoRA Inference Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "inference_lora"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: Training Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: Distillation DMD Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "distillation_dmd"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: Self-Forcing Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "self_forcing"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: LoRA Training Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training_lora"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+          - exit_status: 1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: Training Tests VSA"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training_vsa"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+          - exit_status: 1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: Inference Tests VMoBA"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "inference_vmoba"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: Performance Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "performance"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
+    - label: ":test_tube: API Server Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "api_server"
+      command: "timeout 90m .buildkite/scripts/pr_test.sh"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      agents:
+        queue: "default"
 
-  - label: ":microscope: DreamVerse App Tests"
-    key: "dreamverse"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,dreamverse,/) ||
-      build.env("TEST_SCOPE") == "fastcheck" ||
-      build.env("TEST_SCOPE") == null ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "dreamverse_app" || build.env("TEST_TYPE") == "dreamverse_app_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "dreamverse_app_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
+  # ============================================================
+  # Fastcheck: Runs on every PR (~10-15 min parallel)
+  # Core component validation: encoders, VAEs, transformers,
+  # CUDA kernels, and unit tests.
+  # ============================================================
+    - label: "Trigger Fastcheck"
+      if: build.env("TEST_SCOPE") == "fastcheck" || build.env("TEST_SCOPE") == null
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      plugins:
+        - monorepo-diff#v1.4.0:
+            diff: 'git fetch origin "${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}" && git diff --name-only "origin/${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}...HEAD"'
+            watch:
+            - path:
+                - "fastvideo/models/encoders/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/encoders/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 20m .buildkite/scripts/pr_test.sh"
+                label: ":microscope: Encoder Tests"
+                env:
+                  - TEST_TYPE=encoder
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/models/vaes/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/vaes/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 20m .buildkite/scripts/pr_test.sh"
+                label: ":microscope: VAE Tests"
+                env:
+                  - TEST_TYPE=vae
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/models/dits/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/transformers/**"
+                - "fastvideo/layers/**"
+                - "fastvideo/attention/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":microscope: Transformer Tests"
+                env:
+                  - TEST_TYPE=transformer
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo-kernel/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":microscope: Kernel Tests"
+                env:
+                  - TEST_TYPE=kernel_tests
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/**"
+                - ".buildkite/**"
+                - ".github/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":microscope: Unit Tests"
+                env:
+                  - TEST_TYPE=unit_test
+                agents:
+                  queue: "default"
 
-  - label: ":microscope: Encoder Tests"
-    key: "encoder"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,encoder,/) ||
-      build.env("TEST_SCOPE") == "fastcheck" ||
-      build.env("TEST_SCOPE") == null ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "encoder" || build.env("TEST_TYPE") == "encoder_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "encoder_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":microscope: VAE Tests"
-    key: "vae"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,vae,/) ||
-      build.env("TEST_SCOPE") == "fastcheck" ||
-      build.env("TEST_SCOPE") == null ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "vae" || build.env("TEST_TYPE") == "vae_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "vae_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":microscope: Transformer Tests"
-    key: "transformer"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,transformer,/) ||
-      build.env("TEST_SCOPE") == "fastcheck" ||
-      build.env("TEST_SCOPE") == null ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "transformer" || build.env("TEST_TYPE") == "transformer_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "transformer_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":bar_chart: SSIM Tests"
-    key: "ssim"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      build.env("TEST_SCOPE") == "scheduled" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,ssim,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "ssim" || build.env("TEST_TYPE") == "ssim_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    concurrency: 1
-    concurrency_group: "fastvideo/slinky/whole-tray"
-    env:
-      TEST_TYPE: "ssim_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-        - exit_status: 1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: LoRA Inference Tests"
-    key: "lora-inference"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,lora-inference,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "inference_lora" || build.env("TEST_TYPE") == "inference_lora_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "inference_lora_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: LoRA Extraction Tests"
-    key: "lora-extraction"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,lora-extraction,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "lora_extraction" || build.env("TEST_TYPE") == "lora_extraction_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "lora_extraction_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Training Tests"
-    key: "training"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,training,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "training" || build.env("TEST_TYPE") == "training_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    concurrency: 1
-    concurrency_group: "fastvideo/slinky/whole-tray"
-    env:
-      TEST_TYPE: "training_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Distillation DMD Tests"
-    key: "distillation"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,distillation,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "distillation_dmd" || build.env("TEST_TYPE") == "distillation_dmd_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "distillation_dmd_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Self-Forcing Tests"
-    key: "self-forcing"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,self-forcing,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "self_forcing" || build.env("TEST_TYPE") == "self_forcing_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "self_forcing_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: LoRA Training Tests"
-    key: "lora-training"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,lora-training,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "training_lora" || build.env("TEST_TYPE") == "training_lora_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "training_lora_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-        - exit_status: 1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Training Tests VSA"
-    key: "training-vsa"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,training-vsa,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "training_vsa" || build.env("TEST_TYPE") == "training_vsa_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "training_vsa_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-        - exit_status: 1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Inference Tests VMoBA"
-    key: "inference-vmoba"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,inference-vmoba,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "inference_vmoba" || build.env("TEST_TYPE") == "inference_vmoba_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "inference_vmoba_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Performance Tests"
-    key: "performance"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,performance,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "performance" || build.env("TEST_TYPE") == "performance_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "performance_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: API Server Tests"
-    key: "api-server"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,api-server,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "api_server" || build.env("TEST_TYPE") == "api_server_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "api_server_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Train Framework Tests"
-    key: "train-framework"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,train-framework,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "train_framework" || build.env("TEST_TYPE") == "train_framework_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "train_framework_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
-
-  - label: ":test_tube: Eval Metrics Tests"
-    key: "eval"
-    depends_on: "golden-gate"
-    if: |
-      build.env("TEST_SCOPE") == "full" ||
-      (build.env("TEST_SCOPE") == "merge" &&
-       build.env("MERGE_TEST_PLAN") =~ /,eval,/) ||
-      (build.env("TEST_SCOPE") == "direct" &&
-       (build.env("TEST_TYPE") == "eval" || build.env("TEST_TYPE") == "eval_ci"))
-    command: "/opt/fastvideo-ci-runner/run-ci"
-    timeout_in_minutes: 90
-    env:
-      TEST_TYPE: "eval_ci"
-    retry:
-      automatic:
-        - exit_status: 128
-          limit: 3
-        - exit_status: -1
-          limit: 2
-    agents:
-      queue: "ci-runner"
+  # ============================================================
+  # Full Suite: Runs when TEST_SCOPE=full
+  # Triggered by adding the 'ready' label (via ci-trigger-full-suite.yml)
+  # or on-demand via /test full slash command.
+  # Includes integration tests, SSIM regression, training pipelines,
+  # and performance benchmarks.
+  # ============================================================
+    - label: "Trigger Full Suite"
+      if: build.env("TEST_SCOPE") == "full"
+      retry:
+        automatic:
+          - exit_status: 128
+            limit: 3
+          - exit_status: -1
+            limit: 2
+      plugins:
+        - monorepo-diff#v1.4.0:
+            diff: 'git fetch origin "${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}" && git diff --name-only "origin/${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}...HEAD"'
+            watch:
+            - path:
+                - "fastvideo/**/*.py"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 90m .buildkite/scripts/pr_test.sh"
+                label: ":bar_chart: SSIM Tests"
+                env:
+                  - TEST_TYPE=ssim
+                retry:
+                  automatic:
+                    - exit_status: 1
+                      limit: 2
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/tests/lora/**"
+                - "fastvideo/models/loader/**"
+                - "fastvideo/tests/transformers/**"
+                - "fastvideo/pipelines/**"
+                - "fastvideo/layers/lora/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 20m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: LoRA Inference Tests"
+                env:
+                  - TEST_TYPE=inference_lora
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: Training Tests"
+                env:
+                  - TEST_TYPE=training
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/training/*distillation_pipeline.py"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: Distillation DMD Tests"
+                env:
+                  - TEST_TYPE=distillation_dmd
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/training/*self_forcing_distillation_pipeline.py"
+                - "fastvideo/tests/training/self-forcing/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: Self-Forcing Tests"
+                env:
+                  - TEST_TYPE=self_forcing
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: LoRA Training Tests"
+                env:
+                  - TEST_TYPE=training_lora
+                retry:
+                  automatic:
+                    - exit_status: 1
+                      limit: 2
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/**"
+                - "fastvideo-kernel/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: Training Tests VSA"
+                env:
+                  - TEST_TYPE=training_vsa
+                retry:
+                  automatic:
+                    - exit_status: 1
+                      limit: 2
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo-kernel/**"
+                - "fastvideo/attention/backends/vmoba.py"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: Inference Tests VMoBA"
+                env:
+                  - TEST_TYPE=inference_vmoba
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/models/dits/**"
+                - "fastvideo/pipelines/**"
+                - "fastvideo/attention/**"
+                - "fastvideo/layers/**"
+                - "fastvideo/worker/**"
+                - "fastvideo/entrypoints/**"
+                - "fastvideo/tests/performance/**"
+                - ".buildkite/performance-benchmarks/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: Performance Tests"
+                env:
+                  - TEST_TYPE=performance
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/entrypoints/openai/**"
+                - "fastvideo/entrypoints/cli/serve.py"
+                - "fastvideo/tests/entrypoints/test_openai_api_integration.py"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                label: ":test_tube: API Server Tests"
+                env:
+                  - TEST_TYPE=api_server
+                agents:
+                  queue: "default"

.buildkite/scripts/lanes/api_server.sh

@@ -1,5 +0,0 @@
-#!/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

@@ -1,5 +0,0 @@
-#!/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

@@ -1,87 +0,0 @@
-#!/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

@@ -1,5 +0,0 @@
-#!/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

@@ -1,5 +0,0 @@
-#!/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

@@ -1,35 +0,0 @@
-#!/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

@@ -1,5 +0,0 @@
-#!/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

@@ -1,5 +0,0 @@
-#!/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

@@ -1,5 +0,0 @@
-#!/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

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

.buildkite/scripts/lanes/performance.sh

@@ -1,64 +0,0 @@
-#!/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/test_inference_performance.py -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
-# The trusted host relays only .md/.html/.json/.csv from PERF_REPORTS_DIR, so
-# mirror each captured worker log with an allowlisted extension.
-for worker_log in fastvideo/tests/performance/results/worker_logs/*.log; do
-  [ -f "$worker_log" ] || continue
-  base=$(basename "${worker_log%.log}")
-  # WorkerLogCapture keeps a .log.1 backup after rollover, and read_log_tail
-  # includes it; mirror that retained history too so the artifact is complete.
-  if [ -f "$worker_log.1" ]; then
-    cp -f "$worker_log.1" "$PERF_REPORTS_DIR/${base}.1.md" 2>/dev/null || true
-  fi
-  cp -f "$worker_log" "$PERF_REPORTS_DIR/${base}.md" 2>/dev/null || true
-done
-
-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

@@ -1,6 +0,0 @@
-#!/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

@@ -1,40 +0,0 @@
-#!/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

@@ -1,5 +0,0 @@
-#!/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

@@ -1,6 +0,0 @@
-#!/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

@@ -1,6 +0,0 @@
-#!/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

@@ -1,6 +0,0 @@
-#!/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

@@ -1,9 +0,0 @@
-#!/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

@@ -1,6 +0,0 @@
-#!/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

@@ -1,19 +1,6 @@
 #!/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')] $1"
 }
@@ -89,27 +76,10 @@ EFFECTIVE_PR=${BUILDKITE_PULL_REQUEST:-false}
 if [ "$EFFECTIVE_PR" = "false" ] && [ -n "${PR_NUMBER:-}" ]; then
     EFFECTIVE_PR=$PR_NUMBER
 fi
-MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR BUILDKITE_BRANCH=${BUILDKITE_BRANCH:-} BUILDKITE_SOURCE=${BUILDKITE_SOURCE:-} TEST_SCOPE=${TEST_SCOPE:-} BUILDKITE_BUILD_URL=${BUILDKITE_BUILD_URL:-} BUILDKITE_BUILD_ID=${BUILDKITE_BUILD_ID:-} BUILDKITE_JOB_ID=${BUILDKITE_JOB_ID:-} IMAGE_VERSION=$IMAGE_VERSION"
+MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$EFFECTIVE_PR BUILDKITE_BRANCH=${BUILDKITE_BRANCH:-} TEST_SCOPE=${TEST_SCOPE:-} IMAGE_VERSION=$IMAGE_VERSION"
 
 POST_RUN_HOOK=""
 
-is_truthy() {
-    case "${1:-}" in
-        1|true|TRUE|yes|YES|on|ON) return 0 ;;
-        *) return 1 ;;
-    esac
-}
-
-ssim_bootstrap_args() {
-    local title="${PR_TITLE:-}"
-    local message="${BUILDKITE_MESSAGE:-}"
-    if is_truthy "${FASTVIDEO_SSIM_BOOTSTRAP_MODE:-}" \
-        || [[ "$title" == *"[new-model]"* ]] \
-        || [[ "$message" == *"[new-model]"* ]]; then
-        printf ' --bootstrap-mode'
-    fi
-}
-
 upload_performance_artifacts() {
     SHORT_SHA=${BUILDKITE_COMMIT:0:7}
     LOCAL_DIR="downloaded_reports"
@@ -200,18 +170,9 @@ case "$TEST_TYPE" in
         log "Running transformer tests..."
         MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
         ;;
-    "golden_gate")
-        log "Running golden-gate tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_golden_gate_tests"
-        ;;
     "ssim")
         log "Running SSIM tests..."
-        SSIM_BOOTSTRAP_ARGS=$(ssim_bootstrap_args)
-        if [ -n "$SSIM_BOOTSTRAP_ARGS" ]; then
-            log "SSIM bootstrap mode enabled for new-model reference draft generation"
-        fi
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run "
-        MODAL_COMMAND+="$MODAL_SSIM_TEST_FILE::run_ssim_tests$SSIM_BOOTSTRAP_ARGS"
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_SSIM_TEST_FILE::run_ssim_tests"
         ;;
     "training")
         log "Running training tests..."
@@ -250,18 +211,6 @@ case "$TEST_TYPE" in
         log "Running unit tests..."
         MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
         ;;
-    "dreamverse_app")
-        log "Running DreamVerse app tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_dreamverse_app_tests"
-        ;;
-    "train_framework")
-        log "Running fastvideo.train framework tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_train_framework_tests"
-        ;;
-    "eval")
-        log "Running eval metric tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_eval_tests"
-        ;;
     "lora_extraction")
         log "Running LoRA extraction tests..."
         MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_lora_extraction_tests"

.buildkite/scripts/unit_test.sh

@@ -1,42 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-# Collect the whole attention directory so new files cannot land uncovered.
-# Its FA2/FA3 regression files skip when FA4 is selected (the Modal image
-# enables FA4 by default), so pin FA4 off for the directory to be real
-# coverage on every runner rather than a nominal collection.
-export FASTVIDEO_FA4=0
-
-# The livestream app's tests are CPU-only; its single gpu-marked module is
-# deselected, and DreamVerse's GPU tests have their own lane.
-exec pytest \
-  ./apps/infinite_livestream/infinite_livestream/tests \
-  ./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/schedulers/ \
-  ./fastvideo/tests/train/ \
-  ./fastvideo/tests/stages/ \
-  ./fastvideo/tests/ops/ \
-  ./fastvideo/tests/worker/ \
-  ./fastvideo/tests/training/test_runner.py \
-  ./fastvideo/tests/training/test_trackers.py \
-  ./fastvideo/tests/training/test_ltx2_rope_fps.py \
-  ./fastvideo/tests/inference/test_basic_fasth3_omniref_pdd.py \
-  ./fastvideo/tests/inference/test_inference_regional_compile.py \
-  ./fastvideo/tests/attention/ \
-  ./fastvideo/tests/layers/test_pdd_linear.py \
-  ./fastvideo/tests/layers/test_triton_fused_norm.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 \
-  -m "not gpu" \
-  -vs

.dockerignore

@@ -1,31 +0,0 @@
-# Build-context excludes: keep the context small and the `COPY . .` layer cache
-# stable. Docker uploads everything here to the daemon and bakes it into a layer;
-# without this, the 6.5 GB host .venv alone is shipped + cached on every build.
-#
-# IMPORTANT: do NOT ignore .git — fastvideo-kernel's build runs
-# `git submodule update --init --recursive`, which needs the repo metadata.
-
-# Virtualenvs — the image builds its own /opt/venv
-.venv/
-venv/
-env/
-
-# Python caches & build/test/lint artifacts
-**/__pycache__/
-*.py[cod]
-*.egg-info/
-.eggs/
-.pytest_cache/
-.mypy_cache/
-.ruff_cache/
-.cache/
-
-# Local run outputs / logs (not needed in the image)
-outputs/
-wandb/
-*.log
-
-# Editor / OS cruft
-.DS_Store
-.idea/
-.vscode/

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,16 +2,15 @@
 PR TITLE: Must start with a type tag, e.g.:
   [feat] Add new model       [bugfix] Fix VAE tiling      [refactor] Restructure pipeline
   [perf] Optimize kernel     [ci] Update tests             [docs] Add guide
-  [misc] Cleanup configs     [new-model] Port Flux2        [infra] Add trace hooks
-  [skill] Add agent skill
+  [misc] Cleanup configs     [new-model] Port Flux2
 
 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. A path-aware merge gate runs only relevant integration tests → auto-merge on success
+  3. Full Test Suite runs automatically on a staging branch → auto-merge on success
 
 ON-DEMAND TESTING (write access required):
-  /test full       — Explicit all-lane run  /test ssim        — Full SSIM regression
+  /test full       — Full Test Suite        /test ssim        — 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
@@ -61,10 +60,3 @@ Fixes #
 **For model/pipeline changes, also check:**
 - [ ] I verified SSIM regression tests pass
 - [ ] I updated the support matrix if adding a new model
-
-**For new models or serving runtimes:**
-
-- [ ] I added or updated the native serving YAML and [cookbook deployment](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/contributing/cookbook_configuration.md),
-      or explained why this is not applicable.
-- [ ] I documented the GPU type/count and minimum VRAM for the serving setup with supporting measurements,
-      or marked the minimum as unknown and any VRAM estimate as an estimate.

.github/mergify.yml

@@ -3,7 +3,7 @@ merge_protections:
     if:
       - base = main
     success_conditions:
-      - "title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model|skill|skills|infra)\\]"
+      - "title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model)\\]"
       - "#approved-reviews-by>=1"
       - check-success~=pre-commit
       - check-success=fastcheck-passed
@@ -79,22 +79,6 @@ pull_request_rules:
       label:
         add: ["type: new-model"]
 
-  - name: "label type: infra"
-    conditions:
-      - "title~=(?i)^\\[infra\\]"
-      - -closed
-    actions:
-      label:
-        add: ["type: infra"]
-
-  - name: "label type: skill"
-    conditions:
-      - "title~=(?i)^\\[skills?\\]"
-      - -closed
-    actions:
-      label:
-        add: ["type: skill"]
-
   # ============================================================
   # Scope labels (from changed files)
   # ============================================================
@@ -186,13 +170,13 @@ pull_request_rules:
       label:
         add: ["scope: docs"]
 
-  - name: "label scope: studio"
+  - name: "label scope: ui"
     conditions:
-      - files~=^apps/fastvideo_studio/
+      - files~=^ui/
       - -closed
     actions:
       label:
-        add: ["scope: studio"]
+        add: ["scope: ui"]
 
   - name: "label scope: model"
     conditions:
@@ -205,6 +189,40 @@ pull_request_rules:
       label:
         add: ["scope: model"]
 
+  # ============================================================
+  # Pre-commit failure help comment
+  # ============================================================
+
+  - name: comment on pre-commit failure
+    conditions:
+      - check-failure~=pre-commit
+      - -closed
+    actions:
+      comment:
+        message: |
+          ## Pre-commit checks failed
+
+          Hi @{{author}}, the pre-commit checks have failed. To fix them locally:
+
+          ```bash
+          # Install pre-commit if you haven't already
+          uv pip install pre-commit
+          pre-commit install
+
+          # Run all checks and auto-fix what's possible
+          pre-commit run --all-files
+          ```
+
+          Common fixes:
+          - **yapf**: `yapf -i <file>` (formatting)
+          - **ruff**: `ruff check --fix <file>` (linting)
+          - **codespell**: `codespell --write-changes <file>` (spelling)
+
+          After fixing, commit and push the changes. The checks will re-run automatically.
+
+          For future commits, `pre-commit` will run automatically on changed files before each commit.
+
+
   # ============================================================
   # Merge conflict detection
   # ============================================================
@@ -217,6 +235,16 @@ pull_request_rules:
     actions:
       label:
         add: [needs-rebase]
+      comment:
+        message: |
+          This PR has merge conflicts with the base branch. Please rebase:
+
+          ```bash
+          git fetch origin main
+          git rebase origin/main
+          # Resolve any conflicts, then:
+          git push --force-with-lease
+          ```
 
   - name: remove conflict label when resolved
     conditions:
@@ -228,13 +256,13 @@ pull_request_rules:
         remove: [needs-rebase]
 
   # ============================================================
-  # Auto-merge
+  # Auto-merge and auto-rebase
   # ============================================================
 
   - name: auto-merge when ready and all checks pass
     conditions:
       - label=ready
-      - "title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model|skill|skills|infra)\\]"
+      - "title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model)\\]"
       - "#approved-reviews-by>=1"
       - check-success~=pre-commit
       - check-success=fastcheck-passed
@@ -246,5 +274,43 @@ pull_request_rules:
       merge:
         method: squash
 
+  - name: auto-update when ready
+    conditions:
+      - label=ready
+      - "#approved-reviews-by>=1"
+      - -conflict
+      - -closed
+      - -draft
+    actions:
+      update: {}
+
+  # ============================================================
+  # PR title format help
+  # ============================================================
+
+  - name: comment on invalid PR title format
+    conditions:
+      - -closed
+      - -draft
+      - "-title~=(?i)^\\[(feat|feature|bugfix|fix|refactor|perf|ci|doc|docs|misc|chore|kernel|new.?model)\\]"
+    actions:
+      comment:
+        message: |
+          ## ⚠️ PR title format required
+
+          Your PR title must start with a type tag in brackets. Examples:
+          - `[feat] Add new model support`
+          - `[bugfix] Fix VAE tiling corruption`
+          - `[refactor] Restructure training pipeline`
+          - `[perf] Optimize attention kernel`
+          - `[ci] Update test infrastructure`
+          - `[docs] Add inference guide`
+          - `[misc] Clean up configs`
+          - `[new-model] Port Flux2 to FastVideo`
+
+          Valid tags: `feat`, `feature`, `bugfix`, `fix`, `refactor`, `perf`, `ci`, `doc`, `docs`, `misc`, `chore`, `kernel`, `new-model`
+
+          Please update your PR title and the merge protection check will pass automatically.
+
 merge_protections_settings:
   reporting_method: check-runs

.github/scripts/gate_full_suite.sh

@@ -1,133 +0,0 @@
-#!/usr/bin/env bash
-# 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 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 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
-#   - watched runs pending                          -> poll until done
-#   - docs run absent                               -> not applicable after a
-#     short grace period ('Deploy Documentation' is path-filtered on PRs)
-#   - pre-commit run absent                         -> keep polling: pre-commit
-#     is never path-filtered, so its absence is always anomalous
-#   - 'ready' label removed while waiting           -> exit 1 (fail CLOSED:
-#     un-labeling is a deliberate maintainer action)
-#   - GitHub API unreachable or timeout             -> exit 0 (fail OPEN,
-#     loud warning: never brick CI on a GitHub outage)
-#
-# Required env: PR_SHA (PR head commit), PR_NUMBER, GITHUB_REPOSITORY, GH_TOKEN.
-set -euo pipefail
-
-: "${PR_SHA:?PR_SHA (PR head commit) is required}"
-: "${PR_NUMBER:?PR_NUMBER (pull request number) is required}"
-: "${GITHUB_REPOSITORY:?GITHUB_REPOSITORY is required}"
-
-# Workflow-level `name:` values that must be green before the merge gate
-# may start. "Deploy Documentation" is path-filtered on PRs, so its run may
-# legitimately never exist; pre-commit always runs, so it must appear.
-WATCHED_NAMES='["pre-commit", "Deploy Documentation"]'
-WATCHED_REGEX='^(pre-commit|Deploy Documentation)$'
-POLL_SECS="${POLL_SECS:-20}"
-GRACE_SECS="${GRACE_SECS:-60}"
-MAX_WAIT_SECS="${MAX_WAIT_SECS:-1500}"
-
-# Bound each API call so a hung connection hits the 3-strike fail-open path
-# instead of pinning the loop until the job timeout (which would fail closed
-# on exactly the GitHub-outage case this script is meant to survive).
-if command -v timeout >/dev/null 2>&1; then
-  gh_api() { timeout 30 gh api "$@"; }
-else
-  gh_api() { gh api "$@"; }  # macOS dev boxes; CI always has coreutils timeout
-fi
-
-# The workflow checked the label before starting the gate, but the wait can
-# last ~25 min: re-check once before any exit 0 and fail closed if 'ready'
-# was removed in the meantime. An API error here proceeds (the label was
-# present when the gate started; never brick CI on an outage).
-recheck_ready_label() {
-  local pr_json
-  if pr_json=$(gh_api "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}" 2>/dev/null); then
-    if ! jq -e '[.labels[]?.name] | index("ready")' <<<"$pr_json" >/dev/null 2>&1; then
-      echo "::error::PR #${PR_NUMBER} no longer has the 'ready' label —" \
-        "NOT triggering the Buildkite merge gate. Re-add the label to re-arm."
-      exit 1
-    fi
-  else
-    echo "::warning::Could not re-check the 'ready' label on PR #${PR_NUMBER}; proceeding (it was present when the gate started)."
-  fi
-}
-
-start=$(date +%s)
-api_fails=0
-missing=""
-
-while true; do
-  elapsed=$(( $(date +%s) - start ))
-
-  if runs_json=$(gh_api "repos/${GITHUB_REPOSITORY}/actions/runs?head_sha=${PR_SHA}&per_page=100" 2>/dev/null) \
-     && state=$(jq --arg re "$WATCHED_REGEX" '
-          [.workflow_runs[]? | select(.name // "" | test($re))]
-          | group_by(.name) | map(max_by(.id))
-          | map({name, status, conclusion})' <<<"$runs_json" 2>/dev/null); then
-    api_fails=0
-    echo "t+${elapsed}s watched checks: $(jq -c . <<<"$state")"
-
-    failed=$(jq -r '[.[] | select(.status == "completed"
-          and (.conclusion | IN("success", "skipped", "neutral", "cancelled") | not))]
-        | map(.name) | join(", ")' <<<"$state")
-    if [ -n "$failed" ]; then
-      echo "::error::Cheap check(s) failed on ${PR_SHA}: ${failed}." \
-        "NOT triggering the Buildkite merge gate. Push a fix (the 'ready'" \
-        "label re-arms on every push), or re-run the failed check and then" \
-        "re-run this workflow."
-      exit 1
-    fi
-
-    # 'cancelled' counts as pending: wait for a re-run to reach a real verdict
-    # (bounded by MAX_WAIT, then the fail-open below).
-    pending=$(jq '[.[] | select(.status != "completed" or .conclusion == "cancelled")] | length' <<<"$state")
-    missing=$(jq -r --argjson watched "$WATCHED_NAMES" '($watched - map(.name)) | join(", ")' <<<"$state")
-    if [ "$pending" -eq 0 ]; then
-      if [ -z "$missing" ]; then
-        recheck_ready_label
-        echo "All watched cheap checks are green — merge gate may proceed."
-        exit 0
-      fi
-      case "$missing" in
-        *pre-commit*)
-          echo "pre-commit run not found for ${PR_SHA} yet; waiting (pre-commit is never path-filtered, so its absence is anomalous)."
-          ;;
-        *)
-          if [ "$elapsed" -ge "$GRACE_SECS" ]; then
-            recheck_ready_label
-            echo "::warning::Watched run(s) never appeared for ${PR_SHA}: ${missing} (path-filtered, likely not applicable). Proceeding on the checks that did run."
-            exit 0
-          fi
-          echo "Waiting up to ${GRACE_SECS}s grace for path-filtered run(s) to appear: ${missing}."
-          ;;
-      esac
-    fi
-  else
-    api_fails=$(( api_fails + 1 ))
-    echo "::warning::GitHub API error querying workflow runs for ${PR_SHA} (attempt ${api_fails}/3)."
-    if [ "$api_fails" -ge 3 ]; then
-      recheck_ready_label
-      echo "::warning::FAILING OPEN: cannot query GitHub check status — triggering the 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: ${missing})} — triggering the merge gate anyway."
-    exit 0
-  fi
-  sleep "$POLL_SECS"
-done

.github/scripts/plan_merge_ci.py

@@ -1,578 +0,0 @@
-#!/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 collections.abc import Iterable
-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"(^|[/_.-])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"(^|[/_.-])gen3c([/_.-]|$)"),
-        ("test_gen3c.py", ),
-        ("test_gen3c_similarity.py", ),
-    ),
-    FamilyCoverage(
-        re.compile(r"(^|[/_.-])hunyuan(video)?15([a-z0-9_-]*)([/_.-]|$)"),
-        (),
-        ("test_hunyuan15_i2v_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"(^|[/_.-])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"(^|[/_.-])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",
-            "test_wan_ti2v_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 _normalize_path(raw_path: str) -> str:
-    path = raw_path.strip()
-    while path.startswith("./"):
-        path = path[2:]
-    return path
-
-
-def classify_paths(paths: list[str], removed_paths: Iterable[str] = ()) -> MergePlan:
-    """Plan merge lanes for ``paths``.
-
-    ``removed_paths`` lists changed paths that no longer exist at the PR head
-    (deleted files and rename sources); removed golden/SSIM tests are not run.
-    """
-    plan = MergePlan()
-    normalized_paths = sorted({path for path in map(_normalize_path, paths) if path})
-    removed = {path for path in map(_normalize_path, removed_paths) if path}
-    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"):
-                if path in removed:
-                    plan.reasons.append(f"removed golden test has nothing to run: {path}")
-                else:
-                    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"):
-                if path in removed:
-                    plan.reasons.append(f"removed SSIM test has nothing to run: {path}")
-                else:
-                    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/fastvideo_args.py",
-                "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("apps/infinite_livestream/"):
-            # The app's CPU-only tests run in the automatic unit Fastcheck lane.
-            plan.reasons.append(f"covered by automatic unit 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("--removed-paths-file", type=Path)
-    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()
-    removed_paths = (args.removed_paths_file.read_text(encoding="utf-8").splitlines()
-                     if args.removed_paths_file else [])
-    plan = classify_paths(paths, removed_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

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

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

@@ -12,42 +12,10 @@ on:
       tag_suffix:
         required: true
         type: string
-      image_name:
-        required: false
-        type: string
-        default: fastvideo-dev
-      build_args:
-        required: false
-        type: string
-        default: ''
-      include_latest_tags:
-        required: false
-        type: boolean
-        default: true
-      mark_as_latest:
-        required: false
-        type: boolean
-        default: false
-      runner:
-        required: false
-        type: string
-        default: ubuntu-latest
-      architecture:
-        required: false
-        type: string
-        default: amd64
-      push_by_digest:
-        required: false
-        type: boolean
-        default: false
-      digest_artifact_name:
-        required: false
-        type: string
-        default: ''
 
 jobs:
   build-and-push:
-    runs-on: ${{ inputs.runner }}
+    runs-on: ubuntu-latest
     permissions:
       contents: read
       packages: write
@@ -55,10 +23,6 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-        # The Docker context intentionally includes .git so the kernel build can
-        # initialize its pinned submodules. Do not copy the checkout token with it.
-        with:
-          persist-credentials: false
       
       - name: Free up disk space
         run: |
@@ -97,26 +61,17 @@ jobs:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Normalize image reference
-        id: image
-        env:
-          IMAGE: ghcr.io/${{ github.repository }}/${{ inputs.image_name }}
-        run: echo "name=${IMAGE,,}" >> "${GITHUB_OUTPUT}"
       
       - name: Prepare tags
         id: prepare-tags
         run: |
           SHORT_SHA=$(echo ${{ github.sha }} | cut -c1-7)
           
-          TAGS="type=raw,value=${{ inputs.tag_suffix }}-sha-${SHORT_SHA}"
-
-          if [[ "${{ inputs.include_latest_tags }}" == "true" ]]; then
-            TAGS="type=raw,value=${{ inputs.tag_suffix }}-latest\n${TAGS}"
-          fi
-
-          # Tag the designated default variant as the global `latest` image
-          if [[ "${{ inputs.include_latest_tags }}" == "true" && "${{ inputs.mark_as_latest }}" == "true" ]]; then
+          TAGS="type=raw,value=${{ inputs.tag_suffix }}-latest"
+          TAGS="${TAGS}\ntype=raw,value=${{ inputs.tag_suffix }}-sha-${SHORT_SHA}"
+          
+          # Set Python 3.10 as the default image
+          if [[ "${{ inputs.python_version }}" == "3.10" ]]; then
             TAGS="${TAGS}\ntype=raw,value=latest"
           fi
           
@@ -130,72 +85,22 @@ jobs:
         id: meta
         uses: docker/metadata-action@v5
         with:
-          images: ${{ steps.image.outputs.name }}
+          images: ghcr.io/${{ github.repository }}/fastvideo-dev
           tags: ${{ steps.prepare-tags.outputs.tags }}
       
       - name: Build and push Docker image
-        if: ${{ !inputs.push_by_digest }}
         id: build-push
         uses: docker/build-push-action@v6
         with:
           context: .
           file: ${{ inputs.dockerfile_path }}
-          platforms: linux/${{ inputs.architecture }}
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
-          build-args: ${{ inputs.build_args }}
-          cache-from: type=gha,scope=${{ inputs.image_name }}-${{ inputs.tag_suffix }}-${{ inputs.architecture }}
-          cache-to: type=gha,mode=max,scope=${{ inputs.image_name }}-${{ inputs.tag_suffix }}-${{ inputs.architecture }}
-
-      # Multi-architecture callers publish immutable manifests by digest here,
-      # then create the shared user-facing tags in a single downstream job.
-      # This prevents architecture jobs from racing to replace the same tag.
-      - name: Build and push Docker image by digest
-        if: ${{ inputs.push_by_digest }}
-        id: build-push-digest
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          file: ${{ inputs.dockerfile_path }}
-          platforms: linux/${{ inputs.architecture }}
-          labels: ${{ steps.meta.outputs.labels }}
-          build-args: ${{ inputs.build_args }}
-          outputs: type=image,name=${{ steps.image.outputs.name }},push-by-digest=true,name-canonical=true,push=true
-          cache-from: type=gha,scope=${{ inputs.image_name }}-${{ inputs.tag_suffix }}-${{ inputs.architecture }}
-          cache-to: type=gha,mode=max,scope=${{ inputs.image_name }}-${{ inputs.tag_suffix }}-${{ inputs.architecture }}
-
-      - name: Export digest
-        if: ${{ inputs.push_by_digest }}
-        env:
-          DIGEST: ${{ steps.build-push-digest.outputs.digest }}
-        run: |
-          if [[ -z "${{ inputs.digest_artifact_name }}" ]]; then
-            echo "digest_artifact_name is required when push_by_digest is true" >&2
-            exit 1
-          fi
-          mkdir -p /tmp/digests
-          touch "/tmp/digests/${DIGEST#sha256:}"
-
-      - name: Upload digest
-        if: ${{ inputs.push_by_digest }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ inputs.digest_artifact_name }}
-          path: /tmp/digests/*
-          if-no-files-found: error
-          retention-days: 1
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
       
       - name: Success message
-        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
-        if: ${{ inputs.push_by_digest }}
-        env:
-          DIGEST: ${{ steps.build-push-digest.outputs.digest }}
-        run: |
-          echo "✅ Python ${{ inputs.python_version }} linux/${{ inputs.architecture }} image pushed as ${DIGEST}"
+          echo "✅ Python ${{ inputs.python_version }} image successfully built and pushed to ghcr.io/${{ github.repository }}/fastvideo-dev:${{ inputs.tag_suffix }}-latest"
+          echo "To run tests with this image, manually trigger the 'Run Tests' workflow." 

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

@@ -26,54 +26,29 @@ jobs:
               per_page: 100,
             });
 
-            // 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 bkStatuses = data.statuses.filter(
+              s => s.context.startsWith('buildkite/ci/')
+            );
+
+            const FASTCHECK_PREFIX = 'buildkite/ci/microscope-';
             const FULL_SUITE_PREFIXES = [
               'buildkite/ci/test-tube-',
               'buildkite/ci/bar-chart-',
             ];
 
-            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;
-            }
-
-            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');
-
-            // Direct reruns may repair a failed suite, never create a gate for
-            // a suite that did not run.
-            const failedAggregate = context => data.statuses.some(
-              s => s.context === context && s.state === 'failure'
+            const fastcheck = bkStatuses.filter(
+              s => s.context.startsWith(FASTCHECK_PREFIX)
+            );
+            const fullSuite = bkStatuses.filter(
+              s => FULL_SUITE_PREFIXES.some(p => s.context.startsWith(p))
             );
 
-            if (failedAggregate('fastcheck-passed') && fastcheckPassed) {
+            if (
+              fastcheck.length > 0
+              && fastcheck.every(s => s.state === 'success')
+            ) {
               core.info(
-                `All ${fastcheck.size} fastcheck tests passed — updating fastcheck-passed`
+                `All ${fastcheck.length} fastcheck tests passed — updating fastcheck-passed`
               );
               await github.rest.repos.createCommitStatus({
                 owner: context.repo.owner,
@@ -81,13 +56,17 @@ jobs:
                 sha,
                 state: 'success',
                 context: 'fastcheck-passed',
-                description: `All ${fastcheck.size} fastcheck tests passed`,
+                description:
+                  `All ${fastcheck.length} fastcheck tests passed`,
               });
             }
 
-            if (failedAggregate('full-suite-passed') && fullSuitePassed) {
+            if (
+              fullSuite.length > 0
+              && fullSuite.every(s => s.state === 'success')
+            ) {
               core.info(
-                'All 20 full suite tests passed — updating full-suite-passed'
+                `All ${fullSuite.length} full suite tests passed — updating full-suite-passed`
               );
               await github.rest.repos.createCommitStatus({
                 owner: context.repo.owner,
@@ -95,6 +74,7 @@ jobs:
                 sha,
                 state: 'success',
                 context: 'full-suite-passed',
-                description: 'All 20 full suite tests passed',
+                description:
+                  `All ${fullSuite.length} full suite tests passed`,
               });
             }

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

@@ -1,170 +0,0 @@
-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: "127.0.0.1"
-      MASTER_PORT: "29513"
-      GLOO_SOCKET_IFNAME: lo0
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - 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==2.12.0 torchvision torchaudio
-          uv pip install --system \
-            pytest pytest-timeout 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())
-          device_info = mx.metal.device_info() if mx.metal.is_available() else "metal unavailable"
-          print("mlx device_info:", device_info)
-          print("torch:", torch.__version__)
-          print("torch mps available:", torch.backends.mps.is_available())
-          PY
-
-      - name: Run MLX smoke tests
-        run: |
-          python -m pytest \
-            fastvideo/mlx_runtime/tests/ \
-            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 --timeout=120 -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"
-
-      - 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==2.12.0 torchvision torchaudio
-          uv pip install --system \
-            pytest pytest-timeout 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/mlx_runtime/tests/ \
-            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 --timeout=120 -o faulthandler_timeout=120

.github/workflows/ci-precommit.yml

@@ -1,11 +1,7 @@
 name: pre-commit
 
 on:
-  # pull_request_target instead of pull_request: the workflow definition and
-  # the hook config are always taken from the BASE branch, so fork /
-  # first-time-contributor PRs run immediately without a maintainer clicking
-  # "Approve and run". The PR head is checked out as data only.
-  pull_request_target:
+  pull_request:
     branches: [main]
   workflow_call:
     inputs:
@@ -19,36 +15,12 @@ permissions:
 
 jobs:
   pre-commit:
-    if: github.event.pull_request.draft != true
+    if: github.event_name == 'workflow_call' || github.event.pull_request.draft != true
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
       with:
         ref: ${{ inputs.ref || '' }}
-    # For PR events, lint the PR head — but keep the hook definitions from
-    # the base branch so an untrusted PR cannot alter what gets executed.
-    # The gate scripts are saved too: the self-test step below executes them,
-    # so it must run the base-branch copies, not the PR head's.
-    - name: Save trusted hook config and gate scripts
-      if: github.event_name == 'pull_request_target'
-      run: |
-        cp .pre-commit-config.yaml "$RUNNER_TEMP/trusted-pre-commit-config.yaml"
-        cp -a .github/scripts "$RUNNER_TEMP/trusted-scripts"
-        echo "GATE_SCRIPTS_DIR=$RUNNER_TEMP/trusted-scripts" >> "$GITHUB_ENV"
-    # allow-unsafe-pr-checkout acknowledges checkout's pull_request_target
-    # guard: the head is data for the trusted hooks to lint; nothing from it
-    # is executed (config and gate scripts are pinned to the base branch
-    # above) and credentials are not persisted. SHA-pinned to v4.4.0 because
-    # actionlint's action schema does not know the new input yet.
-    - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 # v4.4.0
-      if: github.event_name == 'pull_request_target'
-      with:
-        ref: ${{ github.event.pull_request.head.sha }}
-        persist-credentials: false
-        allow-unsafe-pr-checkout: true
-    - name: Restore trusted hook config
-      if: github.event_name == 'pull_request_target'
-      run: cp "$RUNNER_TEMP/trusted-pre-commit-config.yaml" .pre-commit-config.yaml
     - uses: actions/setup-python@v5
       with:
         python-version: "3.12"
@@ -58,8 +30,3 @@ jobs:
     - uses: pre-commit/action@v3.0.1
       with:
         extra_args: --all-files --hook-stage manual
-    # After pre-commit so a self-test failure cannot mask lint failures.
-    # GATE_SCRIPTS_DIR points at the base-branch copy on fork PRs (set above);
-    # push / workflow_call runs use the checked-out tree directly.
-    - name: Full-suite gate self-test
-      run: bash "${GATE_SCRIPTS_DIR:-.github/scripts}/test_gate_full_suite.sh"

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

@@ -1,44 +0,0 @@
-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

@@ -32,7 +32,8 @@ jobs:
             }
             core.setOutput('has_write', String(hasWrite));
 
-      - name: Add ready label
+      - name: Add ready label and react
+        id: label
         if: steps.perm.outputs.has_write == 'true'
         uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
         with:
@@ -40,33 +41,50 @@ jobs:
             const owner = context.repo.owner;
             const repo = context.repo.repo;
             const prNumber = context.payload.issue.number;
+            try { await github.rest.issues.removeLabel({ owner, repo, issue_number: prNumber, name: 'ready' }); } catch {}
             await github.rest.issues.addLabels({ owner, repo, issue_number: prNumber, labels: ['ready'] });
-
-      - name: React to comment
-        if: steps.perm.outputs.has_write == 'true'
-        continue-on-error: true
-        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
-        with:
-          script: |
             await github.rest.reactions.createForIssueComment({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
+              owner, repo,
               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));
 
-  trigger-merge-gate:
-    needs: handle-merge
-    if: needs.handle-merge.result == 'success'
-    permissions:
-      actions: read
-      contents: read
-      pull-requests: read
-    uses: ./.github/workflows/ci-trigger-full-suite.yml
-    with:
-      pr_number: ${{ github.event.issue.number }}
-    secrets:
-      BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
+      - 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 }}
+          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 #${PR_NUMBER} (via /merge)" \
+              --argjson pr_id "$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)
+                }
+              }')"
 
   parse-command:
     if: >-
@@ -107,7 +125,7 @@ jobs:
           set -euo pipefail
           TEST_NAME=$(echo "$COMMENT" | grep -oP '(?<=/test\s)\S+' | head -1 || true)
 
-          VALID="encoder vae transformer kernel unit dreamverse ssim 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"
+          VALID="encoder vae transformer kernel unit ssim training lora-inference lora-training distillation self-forcing vsa vmoba performance api full fastcheck pre-commit"
           if [ -z "$TEST_NAME" ] || ! echo "$VALID" | grep -qw "$TEST_NAME"; then
             echo "Unknown test: '$TEST_NAME'. Valid: $VALID"
             exit 1
@@ -115,24 +133,12 @@ jobs:
 
           declare -A MAP=(
             [encoder]=encoder [vae]=vae [transformer]=transformer
-            [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
+            [kernel]=kernel_tests [unit]=unit_test
+            [ssim]=ssim [training]=training
             [lora-inference]=inference_lora [lora-training]=training_lora
-            [lora-extraction]=lora_extraction
             [distillation]=distillation_dmd [self-forcing]=self_forcing
             [vsa]=training_vsa [vmoba]=inference_vmoba
             [performance]=performance [api]=api_server
-            [train-framework]=train_framework [eval]=eval
           )
 
           if [ "$TEST_NAME" = "full" ]; then
@@ -233,7 +239,6 @@ jobs:
           TEST_SCOPE: ${{ needs.parse-command.outputs.test_scope }}
           FULL_SUITE: ${{ needs.parse-command.outputs.full_suite }}
           TEST_TYPE: ${{ needs.parse-command.outputs.test_type }}
-          PR_TITLE: ${{ github.event.issue.title }}
           BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
           BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
         run: |
@@ -250,7 +255,6 @@ jobs:
               --arg full_suite "$FULL_SUITE" \
               --arg test_type "$TEST_TYPE" \
               --arg pr_number "$PR_NUMBER" \
-              --arg pr_title "$PR_TITLE" \
               '{
                 commit: $commit,
                 branch: $branch,
@@ -260,9 +264,8 @@ jobs:
                 pull_request_base_branch: "main",
                 env: {
                   TEST_SCOPE: $test_scope,
-                    FULL_SUITE: $full_suite,
-                    TEST_TYPE: $test_type,
-                    PR_NUMBER: $pr_number,
-                    PR_TITLE: $pr_title
-                  }
-                }')"
+                  FULL_SUITE: $full_suite,
+                  TEST_TYPE: $test_type,
+                  PR_NUMBER: $pr_number
+                }
+              }')"

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

@@ -1,239 +1,63 @@
-name: Trigger Merge Gate
+name: Trigger Full Suite
 
 on:
   pull_request_target:
     types: [labeled, synchronize]
-  workflow_call:
-    inputs:
-      pr_number:
-        description: Pull request number to enter into the merge gate
-        required: true
-        type: number
-    secrets:
-      BUILDKITE_API_TOKEN:
-        required: true
 
 permissions:
   contents: read
   pull-requests: read
-  actions: read
+
+concurrency:
+  group: full-suite-${{ github.event.pull_request.number }}
+  cancel-in-progress: false
 
 jobs:
   trigger:
     if: >-
-      inputs.pr_number > 0
-      || (github.event.action == 'labeled' && github.event.label.name == 'ready')
+      (github.event.action == 'labeled' && github.event.label.name == 'ready')
       || github.event.action == 'synchronize'
     runs-on: ubuntu-latest
-    # Job-level concurrency: only this guarded job acquires the group, so an
-    # unrelated `labeled` event (which skips the job) cannot cancel an in-flight
-    # gate and then skip its replacement. The newest real trigger (`ready`,
-    # push, or `/merge`) supersedes the in-flight run, whose Buildkite build the
-    # cancel step below replaces.
-    concurrency:
-      group: merge-gate-${{ inputs.pr_number || github.event.pull_request.number }}
-      cancel-in-progress: true
-    # Gate below may wait for cheap checks (up to MAX_WAIT_SECS = 25 min).
-    timeout-minutes: 35
     steps:
       - name: Check ready label
         id: check
         uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
-        env:
-          CALLED_PR_NUMBER: ${{ inputs.pr_number }}
         with:
           script: |
-            const eventPrNumber = context.payload.pull_request?.number;
-            const calledPrNumber = Number(process.env.CALLED_PR_NUMBER);
-            const prNumber = eventPrNumber ?? calledPrNumber;
-            if (!Number.isSafeInteger(prNumber) || prNumber <= 0) {
-              core.setFailed(`Invalid pull request number: ${process.env.CALLED_PR_NUMBER}`);
-              return;
-            }
             const { data: pr } = await github.rest.pulls.get({
               owner: context.repo.owner,
               repo: context.repo.repo,
-              pull_number: prNumber,
+              pull_number: context.payload.pull_request.number,
             });
-            if (pr.state !== 'open') {
-              core.setFailed(`PR #${prNumber} is not open.`);
-              return;
-            }
-            if (pr.base.repo.full_name !== context.payload.repository.full_name
-                || pr.base.ref !== context.payload.repository.default_branch) {
-              core.setFailed(`PR #${prNumber} does not target this repository's default branch.`);
-              return;
-            }
             const hasReady = pr.labels.some(l => l.name === 'ready');
             core.setOutput('has_ready', String(hasReady));
-            core.setOutput('changed_files', String(pr.changed_files));
-            core.setOutput('pr_number', String(pr.number));
-            core.setOutput('head_sha', pr.head.sha);
-            core.setOutput('head_ref', pr.head.ref);
-            core.setOutput('base_sha', pr.base.sha);
-            core.setOutput('title', pr.title);
-            if (!hasReady) core.info('No ready label — skipping merge-gate trigger.');
+            if (!hasReady) core.info('No ready label — skipping Full Suite trigger.');
 
       - name: Cancel previous Buildkite builds
-        # Cancelling stale builds only saves agent time. If it cannot run, the
-        # merge gate must still be triggered by the steps below, so a failure
-        # here is reported and stepped over rather than ending the job.
-        continue-on-error: true
-        timeout-minutes: 3
         if: steps.check.outputs.has_ready == 'true'
         env:
-          BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
-          BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
           BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
-          PR_BRANCH: ${{ steps.check.outputs.head_ref }}
-          PR_NUMBER: ${{ steps.check.outputs.pr_number }}
+          PR_BRANCH: ${{ github.event.pull_request.head.ref }}
         run: |
-          set -euo pipefail
-          response_file=$(mktemp)
-          builds_file=$(mktemp)
-          trap 'rm -f "$response_file" "$builds_file"' EXIT
-
-          if [[ ! "$PR_NUMBER" =~ ^[1-9][0-9]*$ ]]; then
-            echo "::warning::Invalid pull request number; stale Buildkite builds may continue."
-            exit 1
-          fi
-
-          if ! curl -sS --fail-with-body --connect-timeout 5 --max-time 20 --get \
-            -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
-            --data-urlencode "branch=$PR_BRANCH" \
-            --data-urlencode "state[]=running" \
-            --data-urlencode "state[]=scheduled" \
-            --data-urlencode "state[]=failing" \
-            --data-urlencode "exclude_jobs=true" \
-            --data-urlencode "exclude_pipeline=true" \
-            --output "$response_file" \
-            "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds"; then
-            echo "::warning::Could not list Buildkite builds; stale merge-gate builds may continue."
-            exit 1
-          fi
-
-          if ! jq -e '
-            if type != "array" then false
-            else all(.[];
-              if type != "object" then false
-              else
-                (.number | if type == "number" then . > 0 and floor == . else false end)
-                and (
-                  (.env? | if . == null then {} else . end) as $env
-                  | if ($env | type) != "object" then false
-                    else
-                      ($env.TEST_SCOPE? | . == null or type == "string")
-                      and ($env.PR_NUMBER? | . == null or type == "string")
-                    end
-                )
-              end
-            )
-            end
-          ' "$response_file" >/dev/null 2>&1; then
-            # Do not print the response body: it is remote data and may contain
-            # multiline values that would be interpreted as workflow commands.
-            echo "::warning::Buildkite returned an invalid build list; stale merge-gate builds may continue."
-            exit 1
-          fi
-
-          # Match both branch and PR number: forks can reuse the same branch name.
-          if ! jq -r --arg pr_number "$PR_NUMBER" '
-            .[]
-            | select((.env.TEST_SCOPE? == "merge") and (.env.PR_NUMBER? == $pr_number))
-            | .number
-          ' "$response_file" > "$builds_file"; then
-            echo "::warning::Could not select stale Buildkite builds; stale merge-gate builds may continue."
-            exit 1
-          fi
-
-          cancellation_failed=0
-          while IFS= read -r build_num; do
+          # Find running builds for this branch with TEST_SCOPE=full and cancel them
+          builds=$(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')
+          for build_num in $builds; do
             echo "Cancelling Buildkite build #$build_num"
-            if ! curl -sS --fail-with-body --connect-timeout 5 --max-time 20 -o /dev/null -X PUT \
-              -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
-              "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds/${build_num}/cancel"; then
-              echo "::warning::Could not cancel Buildkite build #$build_num; trying remaining builds."
-              cancellation_failed=1
-            fi
-          done < "$builds_file"
+            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
 
-          if (( cancellation_failed != 0 )); then
-            exit 1
-          fi
-
-      # Check out the immutable BASE SHA: neither pull_request_target nor the
-      # privileged slash-command call may run code 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: ${{ steps.check.outputs.base_sha }}
-          persist-credentials: false
-
-      - name: Collect changed paths
-        if: steps.check.outputs.has_ready == 'true'
-        env:
-          GH_TOKEN: ${{ github.token }}
-          PR_NUMBER: ${{ steps.check.outputs.pr_number }}
-          EXPECTED_CHANGED_FILES: ${{ steps.check.outputs.changed_files }}
-        run: |
-          set -euo pipefail
-          changed_json="$RUNNER_TEMP/merge-changed-files.json"
-          changed_paths="$RUNNER_TEMP/merge-changed-paths.txt"
-          removed_paths="$RUNNER_TEMP/merge-removed-paths.txt"
-          : > "$removed_paths"
-          if gh api --paginate --slurp \
-              "repos/${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"
-              # Paths absent from the PR head, so the planner never selects a deleted test.
-              jq -r '.[][] | if .status == "removed" then .filename
-                  elif .status == "renamed" then (.previous_filename // empty) else empty end' \
-                "$changed_json" | sort -u > "$removed_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" \
-            --removed-paths-file "$RUNNER_TEMP/merge-removed-paths.txt" \
-            --github-output "$GITHUB_OUTPUT" \
-            --summary-file "$GITHUB_STEP_SUMMARY"
-
-      - name: Wait for pre-commit and docs build
-        if: steps.check.outputs.has_ready == 'true'
-        env:
-          GH_TOKEN: ${{ github.token }}
-          PR_SHA: ${{ steps.check.outputs.head_sha }}
-          PR_NUMBER: ${{ steps.check.outputs.pr_number }}
-        run: bash .github/scripts/gate_full_suite.sh
-
-      - name: Trigger Buildkite merge gate
+      - name: Trigger Buildkite Full Suite
         if: steps.check.outputs.has_ready == 'true'
         env:
           BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
-          PR_SHA: ${{ steps.check.outputs.head_sha }}
-          PR_BRANCH: ${{ steps.check.outputs.head_ref }}
-          PR_NUMBER: ${{ steps.check.outputs.pr_number }}
-          PR_TITLE: ${{ steps.check.outputs.title }}
+          PR_SHA: ${{ github.event.pull_request.head.sha }}
+          PR_BRANCH: ${{ github.event.pull_request.head.ref }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
           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/${BK_PIPELINE}/builds" \
@@ -242,11 +66,7 @@ jobs:
             --data-raw "$(jq -n \
               --arg commit "$PR_SHA" \
               --arg branch "$PR_BRANCH" \
-              --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" \
+              --arg message "Full Suite for PR #${PR_NUMBER}" \
               --argjson pr_id "$PR_NUMBER" \
               '{
                 commit: $commit,
@@ -256,12 +76,8 @@ jobs:
                 pull_request_id: $pr_id,
                 pull_request_base_branch: "main",
                 env: {
-                  TEST_SCOPE: "merge",
+                  TEST_SCOPE: "full",
                   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: $pr_title
+                  PR_NUMBER: ($pr_id | tostring)
                 }
               }')"

.github/workflows/community-stale.yml

@@ -28,7 +28,24 @@ jobs:
           days-before-issue-stale: 90
           days-before-issue-close: 30
           stale-issue-label: 'stale'
+          stale-issue-message: >
+            This issue has been automatically marked as stale because it has not
+            had any activity within 90 days. It will be automatically closed if
+            no further activity occurs within 30 days. Leave a comment if you
+            feel this issue should remain open. Thank you!
+          close-issue-message: >
+            This issue has been automatically closed due to inactivity. Please
+            feel free to reopen if you feel it is still relevant. Thank you!
 
           days-before-pr-stale: 60
           days-before-pr-close: 14
           stale-pr-label: 'stale'
+          stale-pr-message: >
+            This pull request has been automatically marked as stale because it
+            has not had any activity within 60 days. It will be automatically
+            closed if no further activity occurs within 14 days. Leave a comment
+            if you feel this pull request should remain open. Thank you!
+          close-pr-message: >
+            This pull request has been automatically closed due to inactivity.
+            Please feel free to reopen if you intend to continue working on it.
+            Thank you!

.github/workflows/community-welcome.yml

@@ -0,0 +1,56 @@
+name: Welcome First-Time Contributors
+
+on:
+  issues:
+    types: [opened]
+  pull_request_target:
+    types: [opened]
+
+permissions:
+  issues: write
+  pull-requests: write
+
+jobs:
+  welcome:
+    if: github.repository == 'hao-ai-lab/FastVideo'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/first-interaction@34f15e814fe48ac9312ccf29db4e74fa767cbab7 # v1.3.0
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          issue-message: |
+            Welcome to FastVideo! Thanks for opening your first issue.
+
+            To help us investigate, please include:
+            - **FastVideo version**: `pip show fastvideo`
+            - **GPU**: `nvidia-smi` output (GPU model, driver, CUDA version)
+            - **Python version**: `python --version`
+            - **OS**: e.g., Ubuntu 22.04
+
+            If this is a bug, a minimal reproduction script helps us fix it faster.
+
+            Useful links:
+            - [Documentation](https://hao-ai-lab.github.io/FastVideo)
+            - [Contributing Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
+            - [Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)
+          pr-message: |
+            Welcome to FastVideo! Thanks for your first pull request.
+
+            **How our CI works:**
+
+            PRs run a two-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.
+
+            **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.
+
+            **Useful links:**
+            - [Contributing Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
+            - [Development Roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899)
+            - [Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)

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

@@ -3,255 +3,65 @@ name: Build and Push Docker Images
 on:
   workflow_dispatch:
     inputs:
-      build_cuda_matrix:
-        description: 'Build multi-arch CUDA images (12.6.3 default; 13.0.0 explicitly tagged)'
-        required: false
-        default: true
-        type: boolean
-      build_dreamverse_matrix:
-        description: 'Build the amd64 Dreamverse matrix (backend + UI x CUDA 12.6.3/13.0.0)'
+      python_3_10:
+        description: 'Build Python 3.10 image'
         required: false
         default: false
         type: boolean
-      build_ci_runner_image:
-        description: 'Build the ARM64 CUDA 13 CI runner image (sm_100)'
+      python_3_11:
+        description: 'Build Python 3.11 image'
+        required: false
+        default: false
+        type: boolean
+      python_3_12:
+        description: 'Build Python 3.12 image'
+        required: false
+        default: false
+        type: boolean
+      python_3_12_cuda_12_9:
+        description: 'Build Python 3.12 image Cuda 12.9'
         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.
-  # Dreamverse (apps/dreamverse/docker/Dockerfile) and the ROCm Dockerfile stay
-  # manual-dispatch only.
-  push:
-    branches: [main]
-    paths:
-      - '.dockerignore'
-      - '.github/workflows/_template-build-image.yml'
-      - '.github/workflows/infra-build-image.yml'
-      - '.gitmodules'
-      - 'docker/Dockerfile'
-      - 'docker/uv-excludes'
-      - 'fastvideo-kernel/**'
-      - 'fastvideo/tests/modal/kernel_build_cache.py'
-      - 'pyproject.toml'
 
 
 permissions:
   contents: read
   packages: write
 
-# One static group, no cancellation: every run of this workflow writes the same
-# mutable registry tags (latest, py3.12-latest, ...), so runs must serialize —
-# concurrent push/dispatch runs would race on those tags, and cancelling a run
-# mid-publish can strand the cu126/cu130 tag families at different commits. An
-# in-flight superseded build wastes its runner time, but its tags are then
-# overwritten by the newer queued run. GitHub keeps a single pending run per
-# group: the newest queued run replaces any older queued one.
-concurrency:
-  group: infra-build-image
-  cancel-in-progress: false
-
 jobs:
-  # CUDA matrix: Python 3.12 x {12.6.3, 13.0.0} x {amd64, arm64}. Each architecture
-  # builds natively and pushes only by digest; publish-cuda-manifests is the sole
-  # owner of the shared tags. CUDA 12.6 arm64 targets Hopper (sm_90a / GH200 class),
-  # because CUDA 12.6 cannot compile sm_121. CUDA 13 arm64 targets GB10 (sm_121).
-  # 12.6.3/cu126 owns the default `py3.12`/`latest` tags and keeps its versioned
-  # aliases; 13.0.0/cu130 is published under explicit versioned tags. Flash-attn
-  # 2.8.3 comes from the architecture-specific prebuilt releases.
-  build-cuda-images:
-    # Runs on a manual dispatch when build_cuda_matrix is set, or automatically
-    # on an in-scope main push (inputs are null on push). The
-    # repository guard keeps fork syncs from auto-building; manual dispatch
-    # still works in forks.
-    if: ${{ (github.event_name == 'push' && github.repository == 'hao-ai-lab/FastVideo') || github.event.inputs.build_cuda_matrix == 'true' }}
-    strategy:
-      fail-fast: false
-      matrix:
-        cuda:
-          - version: '12.6.3'
-            suffix: '-cuda12.6.3'
-            torch_backend: 'cu126'
-            fa_tag: 'cu126torch2.12'
-            cmake_build_parallel_level: '4'
-            torch_cuda_arch_list:
-              amd64: '9.0a'
-              arm64: '9.0a'
-          - version: '13.0.0'
-            suffix: '-cuda13.0.0'
-            torch_backend: 'cu130'
-            fa_tag: 'cu130torch2.12'
-            cmake_build_parallel_level: '1'
-            torch_cuda_arch_list:
-              amd64: '9.0a'
-              arm64: '12.1'
-        architecture:
-          - name: amd64
-            runner: ubuntu-latest
-          - name: arm64
-            runner: ubuntu-24.04-arm
+  build-python-3-10:
+    if: ${{ github.event.inputs.python_3_10 == 'true' }}
+    uses: ./.github/workflows/_template-build-image.yml
+    with:
+      python_version: '3.10'
+      dockerfile_path: docker/Dockerfile.python3.10
+      tag_suffix: py3.10
+    secrets: inherit
+
+  build-python-3-11:
+    if: ${{ github.event.inputs.python_3_11 == 'true' }}
+    uses: ./.github/workflows/_template-build-image.yml
+    with:
+      python_version: '3.11'
+      dockerfile_path: docker/Dockerfile.python3.11
+      tag_suffix: py3.11
+    secrets: inherit
+
+  build-python-3-12:
+    if: ${{ github.event.inputs.python_3_12 == 'true' }}
     uses: ./.github/workflows/_template-build-image.yml
     with:
       python_version: '3.12'
-      dockerfile_path: docker/Dockerfile
-      tag_suffix: py3.12${{ matrix.cuda.suffix }}
-      runner: ${{ matrix.architecture.runner }}
-      architecture: ${{ matrix.architecture.name }}
-      push_by_digest: true
-      digest_artifact_name: fastvideo-dev-cuda${{ matrix.cuda.version }}-${{ matrix.architecture.name }}
-      build_args: |
-        PYTHON_VERSION=3.12
-        CUDA_VERSION=${{ matrix.cuda.version }}
-        UV_TORCH_BACKEND=${{ matrix.cuda.torch_backend }}
-        TORCH_CUDA_ARCH_LIST=${{ matrix.cuda.torch_cuda_arch_list[matrix.architecture.name] }}
-        CMAKE_BUILD_PARALLEL_LEVEL=${{ matrix.cuda.cmake_build_parallel_level }}
-        FLASH_ATTN_WHEEL_TAG=${{ matrix.cuda.fa_tag }}
-        FLASH_ATTN_WHEEL_RELEASE=https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.17
-        FLASH_ATTN_WHEEL_RELEASE_ARM64=https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.22
+      dockerfile_path: docker/Dockerfile.python3.12
+      tag_suffix: py3.12
     secrets: inherit
 
-  publish-cuda-manifests:
-    # !cancelled(): publish lanes whose digests exist even if a sibling build
-    # leg failed (the digest-count check fails incomplete lanes); it also
-    # bypasses skipped-needs propagation, hence the explicit skipped check.
-    if: ${{ !cancelled() && needs.build-cuda-images.result != 'skipped' }}
-    needs: build-cuda-images
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    strategy:
-      fail-fast: false
-      matrix:
-        cuda:
-          - version: '12.6.3'
-            suffix: '-cuda12.6.3'
-            is_default: true
-          - version: '13.0.0'
-            suffix: '-cuda13.0.0'
-            is_default: false
-    steps:
-      - name: Download architecture digests
-        uses: actions/download-artifact@v4
-        with:
-          path: /tmp/digests
-          pattern: fastvideo-dev-cuda${{ matrix.cuda.version }}-*
-          merge-multiple: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to GitHub Container Registry
-        uses: docker/login-action@v3
-        with:
-          registry: ghcr.io
-          username: ${{ github.repository_owner }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Normalize image reference
-        id: image
-        env:
-          IMAGE: ghcr.io/${{ github.repository }}/fastvideo-dev
-        run: echo "name=${IMAGE,,}" >> "${GITHUB_OUTPUT}"
-
-      - name: Prepare tags
-        id: prepare-tags
-        env:
-          VERSIONED_TAG_PREFIX: py3.12${{ matrix.cuda.suffix }}
-          PUBLISH_DEFAULT_TAGS: ${{ matrix.cuda.is_default }}
-        run: |
-          SHORT_SHA="${GITHUB_SHA::7}"
-          TAGS="type=raw,value=${VERSIONED_TAG_PREFIX}-latest\ntype=raw,value=${VERSIONED_TAG_PREFIX}-sha-${SHORT_SHA}"
-          if [[ "${PUBLISH_DEFAULT_TAGS}" == "true" ]]; then
-            TAGS="type=raw,value=latest\ntype=raw,value=py3.12-latest\ntype=raw,value=py3.12-sha-${SHORT_SHA}\n${TAGS}"
-          fi
-          {
-            echo "tags<<EOF"
-            echo -e "${TAGS}"
-            echo "EOF"
-          } >> "${GITHUB_OUTPUT}"
-
-      - name: Extract metadata for Docker
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ steps.image.outputs.name }}
-          tags: ${{ steps.prepare-tags.outputs.tags }}
-
-      - name: Create multi-architecture manifests
-        env:
-          IMAGE: ${{ steps.image.outputs.name }}
-        run: |
-          mapfile -t DIGESTS < <(find /tmp/digests -maxdepth 1 -type f -printf '%f\n' | sort)
-          if [[ "${#DIGESTS[@]}" -ne 2 ]]; then
-            echo "Expected exactly two architecture digests, found ${#DIGESTS[@]}" >&2
-            exit 1
-          fi
-
-          mapfile -t TAGS < <(jq -r '.tags[]' <<< "${DOCKER_METADATA_OUTPUT_JSON}")
-          TAG_ARGS=()
-          for TAG in "${TAGS[@]}"; do
-            TAG_ARGS+=(--tag "${TAG}")
-          done
-
-          IMAGE_REFS=()
-          for DIGEST in "${DIGESTS[@]}"; do
-            IMAGE_REFS+=("${IMAGE}@sha256:${DIGEST}")
-          done
-
-          docker buildx imagetools create "${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' }}
+  build-python-3-12-cuda-12-9:
+    if: ${{ github.event.inputs.python_3_12_cuda_12_9 == '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.
-  build-dreamverse:
-    if: ${{ github.event.inputs.build_dreamverse_matrix == 'true' }}
-    strategy:
-      fail-fast: false
-      matrix:
-        cuda:
-          - version: '12.6.3'
-            torch_backend: 'cu126'
-          - version: '13.0.0'
-            torch_backend: 'cu130'
-        variant:
-          - name: backend
-            ui: '0'
-          - name: ui
-            ui: '1'
-    uses: ./.github/workflows/_template-build-image.yml
-    with:
-      python_version: '3.12'
-      dockerfile_path: apps/dreamverse/docker/Dockerfile
-      tag_suffix: dreamverse-${{ matrix.variant.name }}-cuda${{ matrix.cuda.version }}
-      image_name: dreamverse
-      build_args: |
-        CUDA_VERSION=${{ matrix.cuda.version }}
-        UV_TORCH_BACKEND=${{ matrix.cuda.torch_backend }}
-        BUILD_DREAMVERSE_UI=${{ matrix.variant.ui }}
-      include_latest_tags: false
+      dockerfile_path: docker/Dockerfile.python3.12.cuda12.9.1
+      tag_suffix: py3.12-cuda12.9.1
     secrets: inherit

.github/workflows/infra-docs.yml

@@ -5,23 +5,15 @@ on:
     branches: [ main ]
     paths:
       - 'docs/**'
-      - 'examples/**'
-      - 'scripts/inference/**'
       - 'mkdocs.yml'
-      - 'requirements-mkdocs.in'
       - 'requirements-mkdocs.txt'
-      - 'scripts/check_docs_links.py'
       - '.github/workflows/infra-docs.yml'
   pull_request:
     branches: [ main ]
     paths:
       - 'docs/**'
-      - 'examples/**'
-      - 'scripts/inference/**'
       - 'mkdocs.yml'
-      - 'requirements-mkdocs.in'
       - 'requirements-mkdocs.txt'
-      - 'scripts/check_docs_links.py'
       - '.github/workflows/infra-docs.yml'
 
 permissions:
@@ -39,8 +31,6 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -56,27 +46,15 @@ jobs:
       - name: Setup Pages
         uses: actions/configure-pages@v4
 
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '22'
-
-      - name: Install cookbook dependencies
-        run: npm ci --prefix docs
-
-      - name: Validate and generate cookbook catalogs
-        run: |
-          npm run check:cookbook --prefix docs
-          npm run test:cookbook --prefix docs
-          python docs/tests/test_cookbook_assets.py
-          npm run build:catalog --prefix docs
-
-      - name: Build documentation
-        run: mkdocs build
+      - name: Generate docs examples
+        run: python docs/generate_examples.py
 
       - name: Check docs links
         run: python scripts/check_docs_links.py
 
+      - name: Build documentation
+        run: mkdocs build
+
       - name: Upload artifact
         uses: actions/upload-pages-artifact@v3
         with:

.github/workflows/publish-kernel.yml

@@ -47,40 +47,29 @@ jobs:
     name: Build Wheel
     needs: check-version-change
     if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
-    runs-on: ${{ matrix.platform.os }}
+    runs-on: ${{ matrix.os }}
 
     strategy:
       fail-fast: false
       matrix:
-          python-version: ['3.12']
+          os: [ubuntu-22.04]
+          python-version: ['3.10', '3.11', '3.12']
           torch-cuda:
-            # torch 2.12 dropped cu128; cu130 (CUDA 13) is the default wheel, cu126 covers older drivers.
-            - torch-version: '2.12.0'
-              cuda-version: '12.6.3'
-              torch-cuda-short: 'cu126'
-            - torch-version: '2.12.0'
-              cuda-version: '13.0.0'
-              torch-cuda-short: 'cu130'
-          platform:
-            # 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
-          # aarch64 is Blackwell (GB200 sm_100a/sm_103a + sm_120a + DGX Spark sm_121a), not
-          # Hopper, and Blackwell needs CUDA >= 12.8 — so only the cu130 leg applies.
-          # Added via include so x86 keeps cu126 + cu130 while aarch64 stays cu130-only.
-          include:
-            - python-version: '3.12'
-              torch-cuda:
-                torch-version: '2.12.0'
-                cuda-version: '13.0.0'
-                torch-cuda-short: 'cu130'
-              platform:
-                os: ubuntu-22.04-arm
-                arch: aarch64
-                wheel-plat: manylinux_2_35_aarch64
+            # - torch-version: '2.5.1'
+            #   cuda-version: '12.4.1'
+            #   torch-cuda-short: 'cu124'
+            # - torch-version: '2.6.0'
+            #   cuda-version: '12.6.3'
+            #   torch-cuda-short: 'cu126'
+            # - torch-version: '2.7.1'
+            #   cuda-version: '12.8.0'
+            #   torch-cuda-short: 'cu128'
+            # - torch-version: '2.9.1'
+            #   cuda-version: '12.8.0'
+            #   torch-cuda-short: 'cu128'
+            - torch-version: '2.10.0'
+              cuda-version: '12.8.0'
+              torch-cuda-short: 'cu128'
 
     steps:
       - name: Free up disk space
@@ -115,7 +104,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install CUDA ${{ matrix.torch-cuda.cuda-version }}
-        uses: Jimver/cuda-toolkit@v0.2.35
+        uses: Jimver/cuda-toolkit@v0.2.21
         id: cuda-toolkit
         with:
           cuda: ${{ matrix.torch-cuda.cuda-version }}
@@ -125,7 +114,7 @@ jobs:
       - name: Install dependencies (GCC, Clang, CUDA Paths, Git)
         run: |
           sudo apt update
-          sudo apt install -y git gcc-11 g++-11 clang-11
+          sudo apt install -y git patchelf 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
@@ -163,45 +152,15 @@ jobs:
 
           cd fastvideo-kernel
           git submodule update --init --recursive  # Ensure ThunderKittens submodule is initialized
-          # Release builds run on GPU-less runners, so set kernels + arch explicitly:
-          #   * aarch64 = Blackwell (GB200 sm_100a/sm_103a + sm_120a + DGX Spark sm_121a), NOT
-          #     Hopper, so TK (sm_90a wgmma) is OFF. The C++ FP4 (attn_qat_infer)
-          #     covers sm_120a+sm_121a; turbodiffusion covers every listed arch. 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 + 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 requested
-          # sm_120a/sm_121a 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 [ "${{ matrix.platform.arch }}" = "aarch64" ]; then
-            export TORCH_CUDA_ARCH_LIST="10.0a;10.3a;12.0a;12.1a"
-            export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=OFF -DFASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER=ON"
-            export CMAKE_BUILD_PARALLEL_LEVEL=1
-          elif [ "${{ matrix.torch-cuda.torch-cuda-short }}" = "cu130" ]; then
-            export TORCH_CUDA_ARCH_LIST="9.0a;10.0a;10.3a;12.0a"
-            export CMAKE_ARGS="${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
-          else
-            export TORCH_CUDA_ARCH_LIST="9.0a"
-            export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
-            # Hopper-only: the two TK TUs are the heavy ones (single-arch) and fit side
-            # by side; -j4 overlaps the light TUs without OOMing (~12-14 GB peak).
-            export CMAKE_BUILD_PARALLEL_LEVEL=4
-          fi
+          # Release builds are produced on GPU-less runners, so force-enable TK and target Hopper.
+          export TORCH_CUDA_ARCH_LIST="9.0a"
+          export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
 
           # Build standard wheel (no local version suffix) for PyPI
           python -m build --wheel --outdir dist
 
           # Fix the wheel to be manylinux compliant
-          # 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
+          uv pip install --system auditwheel
           # Point auditwheel at torch libs, but do not vendor them into the wheel.
           TORCH_LIB_DIR=$(python - <<'PY'
           import os
@@ -211,25 +170,24 @@ jobs:
           PY
           )
           export LD_LIBRARY_PATH="${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}"
-          # Target manylinux_2_35 (Ubuntu 22.04 native), per-arch plat tag.
-          auditwheel repair dist/*.whl --plat ${{ matrix.platform.wheel-plat }} -w fixed_dist \
+          # Target manylinux_2_35 (Ubuntu 22.04 native)
+          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist \
             --exclude libtorch_cuda.so \
             --exclude libtorch_cpu.so \
             --exclude libtorch.so \
             --exclude libc10.so \
             --exclude libc10_cuda.so \
-            --exclude libtorch_python.so \
-            --exclude libnccl.so.2
+            --exclude libtorch_python.so
           # Move fixed wheels back to dist for upload consistency
           rm dist/*.whl
           mv fixed_dist/*.whl dist/
 
       - name: Upload wheel artifact
-        # Upload every matrix leg as a distinct artifact (for inspection / GitHub releases).
-        # The publish job below selects which CUDA build is pushed to PyPI.
+        # Only upload if it's the "main" CUDA version we want on PyPI
+        # We upload all to artifacts for inspection/GH releases, but give them distinct artifact names
         uses: actions/upload-artifact@v4
         with:
-          name: fastvideo_kernel-py${{ matrix.python-version }}-${{ matrix.torch-cuda.torch-cuda-short }}-${{ matrix.platform.arch }}-torch${{ matrix.torch-cuda.torch-version }}
+          name: fastvideo_kernel-py${{ matrix.python-version }}-${{ matrix.torch-cuda.torch-cuda-short }}-torch${{ matrix.torch-cuda.torch-version }}
           path: fastvideo-kernel/dist/*.whl
           retention-days: 90
 
@@ -246,20 +204,13 @@ jobs:
 
       - uses: actions/setup-python@v5
         with:
-          python-version: '3.12'
+          python-version: '3.10'
 
       - name: Download PyPI wheels
-        # Publish the cu130 (CUDA 13) wheels to PyPI for both architectures:
-        #   x86_64  — Hopper sm_90a TK + consumer Blackwell sm_120a FP4
-        #   aarch64 — Blackwell: turbodiffusion (sm_100a/sm_103a/sm_120a/sm_121a)
-        #             + C++ FP4 (sm_120a/sm_121a);
-        #             no TK (Hopper). sm_100 FP4 forward is the FA4 CuTe DSL path in the
-        #             fastvideo package (#1221), shipped/JIT separately.
-        # The x86_64 cu126 wheel stays available as a build artifact / GitHub-release asset.
         uses: actions/download-artifact@v4
         with:
           path: fastvideo-kernel/dist/
-          pattern: 'fastvideo_kernel-py*-cu130-*'
+          pattern: 'fastvideo_kernel-py*'
           merge-multiple: true
 
       - name: Install uv

.gitignore

@@ -6,7 +6,6 @@ results/
 wandb/
 *.ipynb
 *.jpg
-!examples/datasets/lingbotworld2/image.jpg
 *.safetensors
 *.mp4
 *.png
@@ -23,7 +22,6 @@ Miniconda3-latest-Linux-x86_64.sh
 *validation/
 data/
 outputs/
-outputs_audio/
 outputs_video
 checkpoints/
 sbatch.sh
@@ -36,8 +34,6 @@ env
 *.log
 weights/
 logs/
-official_weights/
-converted_weights/
 
 # SSIM test outputs
 fastvideo/tests/ssim/generated_videos/
@@ -54,12 +50,7 @@ eggs/
 
 # MkDocs documentation
 site/
-docs/assets/cookbook-serving.json
-docs/assets/cookbook-config/
-examples/serving/clients/node_modules/
-docs/node_modules/
 docs/getting_started/examples/
-docs/examples/
 docs/inference/examples/
 docs/training/examples/
 docs/distillation/examples/
@@ -78,8 +69,8 @@ docs/distillation/examples/
 # Python pickle files
 *.pkl
 
-# Reference videos (negations must come after the catch-all on line below)
-!fastvideo/tests/nightly/reference_video_*.mp4
+# Reference videos
+!fastvideo/tests/ssim/reference_videos/**/*.mp4
 
 # Static images
 !docs/assets/images/**/*.png
@@ -94,57 +85,10 @@ docs/distillation/examples/
 dmd_t2v_output/
 preprocess_output_text/
 
-# SvelteKit / Node artifacts under apps/fastvideo_studio/: see apps/fastvideo_studio/.gitignore
-
-# Next.js / Node artifacts under apps/dreamverse/web/
-apps/dreamverse/web/node_modules/
-apps/dreamverse/web/.next/
-apps/dreamverse/web/out/
-apps/dreamverse/web/coverage/
-apps/dreamverse/web/test-results/
-apps/dreamverse/web/playwright-report/
-apps/dreamverse/web/.env.local
-apps/dreamverse/web/.env.development.local
-apps/dreamverse/web/.env.test.local
-
-# Generated by apps/dreamverse/scripts/install_native_ffmpeg.sh — host-specific
-apps/dreamverse/scripts/ffmpeg-env.sh
-apps/dreamverse/web/.env.production.local
-
-# Unignore migrated Dreamverse product assets — root .gitignore globally
-# ignores *.png/*.jpg/*.mp4/*.gif, but apps/dreamverse/web/public/ MUST
-# be tracked (logo, icons, k2.png, etc.).
-!apps/dreamverse/web/public/**/*.png
-!apps/dreamverse/web/public/**/*.jpg
-!apps/dreamverse/web/public/**/*.jpeg
-!apps/dreamverse/web/public/**/*.mp4
-!apps/dreamverse/web/public/**/*.gif
-!apps/dreamverse/web/prompts/**/*.png
-!apps/dreamverse/web/prompts/**/*.jpg
-!apps/dreamverse/web/prompts/**/*.jpeg
-!apps/dreamverse/web/prompts/**/*.mp4
-!apps/dreamverse/web/prompts/**/*.gif
-!apps/dreamverse/gpu-pool.svg
-!apps/dreamverse/gpu-pool.drawio
+# Next.js / Node artifacts under ui/: see ui/.gitignore
 
 .claude/
 .codex/
-.agents/tmp/
 .sisyphus/
 openspec/
 fastvideo/tests/ssim/reference_videos/**
-!fastvideo/tests/ssim/reference_videos/**/*.mp4
-!fastvideo/tests/ssim/reference_videos/**/*.png
-fastvideo/tests/ssim/.reference_videos_download.lock
-
-# Local H3 MLX kernel / exactness benches (JSON, logs, frames, videos)
-.kernel_bench/
-
-# Editor logs and local Python version pins (accidentally committed)
-*.nvimlog
-.nvimlog
-.python-version
-/LTX-2-Reference/
-/DFDReference/
-scripts/benchmarks/minimax_h3_pro6000/headline_results/
-fastvideo/tests/ssim/.reference_videos_download.lock

.gitmodules

@@ -4,9 +4,3 @@
 [submodule "fastvideo-kernel/include/cutlass"]
 	path = fastvideo-kernel/include/cutlass
 	url = https://github.com/NVIDIA/cutlass.git
-[submodule "fastvideo/third_party/eval/vbench"]
-	path = fastvideo/third_party/eval/vbench
-	url = https://github.com/Vchitect/VBench.git
-[submodule "fastvideo/third_party/eval/vqeval"]
-	path = fastvideo/third_party/eval/vqeval
-	url = https://github.com/JiusiServe/LongVideoSparseAttention.git

.nvimlog

@@ -0,0 +1 @@
+WRN 2026-03-26T13:46:33.469 ?.19646    server_start:193: Failed to start server: operation not permitted: /var/folders/z_/h_6myyk14d1b7z87z3vy4mjh0000gn/T/nvim.dsynkd/iSe0el/nvim.19646.0

.pre-commit-config.yaml

@@ -6,11 +6,10 @@ exclude: |
     fastvideo/third_party/.*|
     fastvideo-kernel/.*|
     assets/.*|
-    (?<!docs/)tests/.*|
+    tests/.*|
     scripts/.*|
     fastvideo/dataset/.*|
-    fastvideo/models/(?!wan/(config|vae_config|pipeline_config|definition|__init__)\.py$).*|
-    ^apps/dreamverse/web/.*|
+    fastvideo/models/.*|
     examples/.*|
     \.agents/.*|
     .github/workflows/publish-fastvideo.yml|
@@ -22,7 +21,6 @@ repos:
   hooks:
   - id: yapf
     args: [--in-place, --verbose]
-    language_version: python3.12
     additional_dependencies: [toml] # TODO: Remove when yapf is upgraded
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.11.12
@@ -65,14 +63,6 @@ repos:
     language: system
     always_run: true
     pass_filenames: false
-  - id: cookbook-javascript
-    name: Cookbook JavaScript lint and format
-    entry: npm run check:cookbook --prefix docs
-    language: system
-    # CI's trusted manual stage must not execute PR-authored npm scripts.
-    stages: [pre-commit]
-    files: ^docs/(js/(build-)?cookbook-.*\.mjs|tests/.*\.test\.mjs|eslint\.config\.mjs|prettier\.config\.mjs|package(-lock)?\.json)$
-    pass_filenames: false
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion

.python-version

@@ -0,0 +1 @@
+3.12

AGENTS.md

@@ -11,7 +11,7 @@
 - Static assets: `assets/` (including `assets/images/`, `assets/videos/`, and `assets/prompts/`) and `comfyui/assets/`.
 
 ## Build, Test, and Development Commands
-- `UV_TORCH_BACKEND=cu126 uv pip install -e ".[dev]"`: editable CUDA 12 install with lint/test extras (`cu130` on CUDA 13).
+- `uv pip install -e ".[dev]"`: editable install with lint/test extras.
 - `pre-commit install --hook-type pre-commit --hook-type commit-msg`: enable local hooks.
 - `pre-commit run --all-files`: run formatter/lint/type/spelling checks.
 - `pytest tests/`: run top-level test suite.
@@ -44,18 +44,17 @@
 
 ## Agent Infrastructure
 
-This repository is agent-friendly. Before doing any work:
+This repository is agent-friendly. Before doing any work, read:
 
-1. Read the nearest in-scope `AGENTS.md` for every directory you may edit.
-2. Read the relevant user-facing design or contributor guide.
-3. Check `.agents/skills/*/SKILL.md` for a task-specific workflow.
-4. Search `.agents/lessons/` for relevant pitfalls.
+1. `.agents/onboarding/README.md` — full onboarding guide with step-by-step instructions.
+2. `.agents/memory/codebase-map/README.md` — structural index of the entire repository.
+3. `.agents/skills/` — available agent skills (check if one exists before writing code).
+4. `.agents/workflows/` — SOPs for common procedures (experiment lifecycle, evaluation, etc.).
+5. `.agents/lessons/` — known pitfalls and their documented fixes.
 
-Architecture, commands, and operational guidance belong beside the code or
-under `docs/`. Do not maintain static codebase maps, experiment journals,
-branch-state snapshots, or duplicate user-facing documentation under
-`.agents/`. Put a reusable procedure directly in a skill or contributor guide
-and capture durable failures in `.agents/lessons/`.
+If you are exploring a new procedure that has no existing SOP, document your
+progress in `.agents/exploration/` and flag it for review at the end of your
+session.
 
 ## Per-Directory AGENTS.md
 
@@ -66,18 +65,14 @@ 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

@@ -3,21 +3,14 @@
 </div>
 
 <p align="center">
-     | <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> |
+     | <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> |
 </p>
 
 **FastVideo is a unified post-training and real-time inference framework for accelerated video generation.**
 
 ## NEWS
-- `2026/10/06`: FastH3 V2 now runs on a single consumer machine: NVIDIA RTX 5090, RTX 4090 and RTX PRO 6000 GPUs, DGX Spark and Apple Silicon. We also release [FastH3 Trim](https://huggingface.co/FastVideo/FastVideo-FastH3-Trim-8-Step-NVFP4), an experimental pruned model that is 4.2× smaller than base H3 and runs in as little as 8 GB of GPU memory. Get the [models](https://huggingface.co/collections/FastVideo/fastvideo-fasth3) and read the [Blog](https://haoailab.com/blogs/fasth3-rtx/).
-- `2026/10/06`: FastVideo now supports [Kandinsky 6](https://x.com/kandinskylab_ai/status/2107374635218055345) from Kandinsky Lab: text- and image-to-video with synchronized audio (base and 10-step distilled pi-Flow checkpoints) plus video super-resolution up to 4x. See the [Kandinsky 6 recipes](https://haoailab.com/FastVideo/cookbook/kandinsky6/).
-- `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/).
+- `2026/03/17`: Release Live demo: [Into the Dreamverse: Vibe Directing in FastVideo](https://dreamverse.fastvideo.org/), check out the [Blog](https://haoailab.com/blogs/dreamverse/).
+- `2026/03/13`: Release Live demo: [Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU](https://1080p.fastvideo.org/), check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/).
 - `2025/11/19`: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py).
 - `2025/08/04`: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
 
@@ -39,7 +32,7 @@ FastVideo has the following features:
   - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup
   - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
   - Causal distillation through Self-Forcing
-  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for the supported training workflows, and the [support matrix](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for supported models.
+  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
 - State-of-the-art performance optimizations for inference
   - Sequence Parallelism for distributed inference
   - Multiple state-of-the-art attention backends
@@ -49,8 +42,6 @@ FastVideo has the following features:
   - Support H100, A100, 4090
   - Support Linux, Windows, MacOS
   - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for full list of supported models, hardware assumptions, and optimization compatibility.
-- Realtime video generation & editing
-  - [Dreamverse](apps/dreamverse/README.md): stream and "vibe direct" video in realtime ([live demo](https://dreamverse.fastvideo.org/)), deployable on local GPU, a self-hosted B200 server, Docker, or serverless Modal
 
 ## Getting Started
 
@@ -61,42 +52,12 @@ We recommend using [uv](https://docs.astral.sh/uv/) to create a clean environmen
 uv venv --python 3.12 --seed
 source .venv/bin/activate
 
-# Install FastVideo on NVIDIA CUDA 12
-UV_TORCH_BACKEND=cu126 uv pip install fastvideo
+# Install FastVideo
+uv pip install fastvideo
 ```
 
-Use `UV_TORCH_BACKEND=cu130` on CUDA 13. Apple silicon users should follow the
-[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.
 
-> **On an NVIDIA DGX Spark (GB10 / ARM64 + CUDA 13)?** There's no prebuilt ARM wheel for the FastVideo CUDA kernel, so it's an editable from-source install (`UV_TORCH_BACKEND=cu130 uv pip install -e .`, which compiles that kernel for you) rather than `UV_TORCH_BACKEND=cu130 uv pip install fastvideo`. A compatible prebuilt ARM64 FlashAttention wheel is available separately. Follow the [DGX Spark install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/spark/).
-
-### Install with an AI coding agent
-
-FastVideo is a monorepo with rich agent guidance (see [`AGENTS.md`](AGENTS.md)). If you use Claude Code, Cursor, or another coding agent, paste the prompt below — it detects your platform and follows the matching guide:
-
-```text
-Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virtual environment.
-
-1. Detect the platform: run `uname -m`, `nvidia-smi`, and `nvcc --version`.
-2. Read and follow the matching install guide exactly (in this repo, or at
-   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/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())"
-     fastvideo --help
-5. Report which platform you detected and any deviations you had to make.
-```
-
 ## Sparse Distillation
 
 For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
@@ -108,19 +69,6 @@ See below for recipes and datasets:
 | [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P)       | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k)     |
 | [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers)   | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) |
 
-## Dreamverse — Realtime Video Generation & Editing
-
-[Dreamverse](apps/dreamverse/README.md) is FastVideo's realtime video generation
-and editing platform — "vibe directing" a video as it streams. It lives in the
-monorepo under [`apps/dreamverse/`](apps/dreamverse/) and ships its own backend
-(`dreamverse-server`) plus a web UI.
-
-Try the [live demo](https://dreamverse.fastvideo.org/), read the
-[blog](https://haoailab.com/blogs/dreamverse/), or run it yourself. Dreamverse
-deploys on a local GPU, a self-hosted B200 server over SSH, Docker, or
-serverless [Modal](apps/dreamverse/scripts/modal/README.md) — see the
-[Dreamverse README](apps/dreamverse/README.md).
-
 ## Inference
 
 ### Generating Your First Video
@@ -154,8 +102,6 @@ if __name__ == '__main__':
     main()
 ```
 
-`num_gpus=1` runs the worker in-process (weights load once, no extra Python process). On Colab/Kaggle-style machines with ~16GB host RAM, keep `num_gpus=1`; free-tier system memory does not grow with extra T4s, so `num_gpus>1` is likely to OOM.
-
 Run the script with:
 
 ```bash

apps/dreamverse/AGENTS.md

@@ -1,215 +0,0 @@
-# Dreamverse Agent Notes
-
-## Repo-local workflows
-
-- Use `.agents/skills/dreamverse-deploy/` to redeploy or stop the local
-  backend/frontend stack on a chosen physical GPU.
-- Use `apps/dreamverse/scripts/modal/README.md` for Modal deployments and
-  `apps/dreamverse/docker/README.md` for image builds. The local deploy skill
-  intentionally does not manage remote deployments.
-
-## Repo layout
-
-Current paths:
-
-- `apps/dreamverse/web/`: Next.js frontend, client-side stores, websocket
-  event reduction, prompt-window editing, devtools UI.
-- `apps/dreamverse/dreamverse/`: current Python FastAPI runtime, websocket
-  protocol, prompt enhancement, prompt rewrite orchestration, GPU worker
-  lifecycle.
-- `apps/dreamverse/dreamverse/tests/`: backend unit and integration-oriented
-  tests.
-- `apps/dreamverse/dreamverse/benchmarks/`: prompt-provider latency/token
-  benchmarking scripts.
-
-Planned paths during the OSS reorg:
-
-- `controller/`: local control plane for provider credentials, compute
-  lifecycle, and proxying.
-- `runtime/`: eventual rename of `apps/dreamverse/dreamverse/` once the
-  controller/runtime split is stable.
-- `providers/`: provider adapters for local, Runpod, and Modal.
-
-Important rule:
-
-- Until the split lands, treat `apps/dreamverse/dreamverse/` as the
-  authoritative runtime and keep provider orchestration out of
-  `apps/dreamverse/web`.
-
-## System split
-
-Dreamverse is moving toward a three-part local-first architecture.
-
-- Frontend owns local UI state, drafts, inspection tools, and user-triggered
-  actions.
-- Controller will own local-only credentials, compute provisioning, runtime
-  lifecycle, and HTTP/websocket proxying.
-- Runtime owns generation sessions, prompt rewrite, prompt safety, and
-  websocket semantics.
-
-The browser should only talk to the local Dreamverse process, never directly to
-Modal or Runpod.
-
-## Current runtime responsibilities
-
-The runtime in `apps/dreamverse/dreamverse/` is responsible for:
-
-- websocket session lifecycle on `/ws`
-- queueing, GPU assignment, worker startup, and stream chunk emission
-- seed prompt memory and the active prompt window used for generation
-- prompt enhancement and prompt rewrite execution
-- prompt safety checks
-- persistence and reload of prompt system prompt files
-- curated preset append/read routes in devtools mode
-- health and readiness endpoints
-
-Relevant files:
-
-- `apps/dreamverse/dreamverse/main.py`: websocket protocol, session state
-  machine, REST routes
-- `apps/dreamverse/dreamverse/gpu_pool.py`: FastVideo-backed generation
-  workers
-- `apps/dreamverse/dreamverse/prompt_enhancer.py`: provider clients, prompt
-  enhancement, rewrite execution
-- `apps/dreamverse/dreamverse/rewrite_prompt_payload.py`: canonical rewrite
-  request body format
-- `apps/dreamverse/dreamverse/config.py`: prompt file paths, provider
-  configuration, runtime flags
-
-## Frontend responsibilities
-
-The frontend in `apps/dreamverse/web/` is responsible for:
-
-- collecting user input and deciding whether to send raw prompts or rewrite
-  requests
-- maintaining client-side stores for session, prompt-window, stream, rewrite,
-  and UI state
-- rendering prompt history, playback state, devtools controls, and rewrite
-  inspection
-- building the prompt-window snapshot sent with rewrite requests
-- reducing websocket events into UI state
-- showing compute status and controller-driven errors once the controller lands
-
-Relevant files:
-
-- `apps/dreamverse/web/src/app/page.tsx`: main orchestration, websocket
-  connect/send paths
-- `apps/dreamverse/web/src/lib/ws/reducer.ts`: applies normalized websocket
-  events to stores
-- `apps/dreamverse/web/src/stores/promptWindow.ts`: prompt window and
-  preset/editor state
-- `apps/dreamverse/web/src/stores/rewrite.ts`: rewrite activity timeline and
-  flags
-- `apps/dreamverse/web/src/lib/prompts/promptWindowSnapshot.ts`: rewrite snapshot
-  normalization and padding
-
-## Planned controller responsibilities
-
-The future local controller should own:
-
-- local-only provider credential loading and storage
-- provider selection
-- runtime provisioning, reuse, shutdown, and health checks
-- proxying frontend HTTP and websocket traffic to the active runtime
-- surfacing provisioning, ready, failed, and idle states to the frontend
-- durable local settings that should survive ephemeral remote runtimes
-
-The controller should not own:
-
-- prompt rewrite logic
-- seed prompt memory
-- generation queue semantics
-- websocket event schemas
-
-## Prompt rewrite contract
-
-Prompt rewrite is a shared flow with a strict ownership split.
-
-Frontend responsibilities:
-
-- decide when a user action should trigger `rewrite_seed_prompts` instead of
-  `append_prompt`
-- send `rewrite_instruction` and a snapshot of the current prompt window
-- pad the rewrite snapshot to the runtime-expected segment count using
-  `buildRewritePromptWindowSnapshotFromPrompts(...)`
-- show rewrite activity and raw LLM output in local inspection UI
-
-Runtime responsibilities:
-
-- validate and normalize `prompt_window_prompts`
-- choose the rewrite system prompt and provider/model/temperature
-- build the canonical LLM request body in
-  `apps/dreamverse/dreamverse/rewrite_prompt_payload.py`
-- run the rewrite through `PromptEnhancer.rewrite_prompt_sequence(...)`
-- apply safety filtering to rewritten prompts
-- replace the authoritative seed prompt memory when rewrite succeeds
-- emit `seed_prompts_updated` and `rewrite_seed_prompts_complete`
-
-Controller responsibilities:
-
-- proxy the request and response
-- surface runtime availability and provider lifecycle failures
-
-Important rule:
-
-- The frontend may suggest the prompt window to rewrite, but the runtime owns
-  the actual rewritten rollout and the authoritative prompt window after
-  acceptance.
-
-## Rewrite modes
-
-There are two runtime rewrite modes:
-
-- edit existing rollout: when `prompt_window_prompts` is non-empty, rewrite the
-  current rollout while preserving segment count and ordering
-- new rollout: when the prompt window is empty but there is a
-  `rewrite_instruction`, generate a fresh rollout
-
-The frontend should not emulate runtime rewrite behavior locally. It should
-prepare the snapshot, send it, and display the result.
-
-## Prompt window ownership
-
-- Frontend owns editable drafts, selected preset UI, and prompt-window
-  inspection state.
-- Runtime owns the active seed prompt memory used for actual generation.
-- After any runtime event with reason `rewrite`, the frontend must replace its
-  prompt window from the server payload instead of keeping a locally-derived
-  version.
-
-## Devtools and persistence ownership
-
-Prompt config editing is runtime-owned persistence with frontend-owned forms
-today.
-
-- Frontend loads and edits drafts through `/prompt-system-config`.
-- Runtime reads and writes prompt files and reloads runtime prompt config.
-
-Curated presets follow the same pattern:
-
-- frontend submits append requests and may update local UI optimistically from
-  the response
-- runtime persists the JSON file and resolves overlay vs fallback file paths
-
-During the controller reorg, avoid moving durable user settings into ephemeral
-remote runtimes. Controller-owned local persistence is preferred for anything
-that must survive provider restarts.
-
-## Editing guidance
-
-- Do not move rewrite logic into the frontend or controller.
-- Do not make the frontend the source of truth for the generated prompt window
-  after rewrite.
-- If you change websocket message types or payload fields in
-  `apps/dreamverse/dreamverse/main.py`, update the reducer in
-  `apps/dreamverse/web/src/lib/ws/reducer.ts` in the same change.
-- If you change rewrite request shape, update both
-  `apps/dreamverse/web/src/lib/prompts/promptWindowSnapshot.ts` and
-  `apps/dreamverse/dreamverse/rewrite_prompt_payload.py`.
-- If you add controller-managed status or error payloads, keep them separate
-  from runtime websocket events unless there is a strong reason to merge them.
-- If you change prompt file paths or devtools persistence, update
-  `apps/dreamverse/dreamverse/`, the frontend devtools UI, and any
-  controller-owned local persistence logic together.
-- Keep provider adapters focused on runtime lifecycle and reachability, not on
-  prompt or session semantics.

apps/dreamverse/README.md

@@ -1,349 +0,0 @@
-# Dreamverse
-
-Dreamverse is the FastVideo realtime video generation & editing platform. It lives in this monorepo under `apps/dreamverse/`.
-
-**Deploy on:** [local GPU](#quick-start-local-gpu) · [self-hosted B200 (SSH)](#server-b200-deployment-ssh) · [Docker](docker/README.md) · [Modal](scripts/modal/README.md)
-
-## Install Dreamverse
-
-You can install Dreamverse using one of the methods below.
-
-### Method 1: With uv pip
-
-```bash
-pip install --upgrade pip
-pip install uv
-uv venv .venv --python 3.12
-source .venv/bin/activate
-UV_TORCH_BACKEND=cu126 uv pip install "fastvideo[dreamverse]"
-```
-
-### Method 2: From source
-
-```bash
-git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo
-
-pip install --upgrade pip
-pip install uv
-uv venv .venv --python 3.12
-source .venv/bin/activate
-UV_TORCH_BACKEND=cu126 uv pip install -e ".[dreamverse]"
-```
-
-Use `UV_TORCH_BACKEND=cu130` instead on CUDA 13.
-
-### Method 3: Using Docker
-
-```bash
-git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo
-
-apps/dreamverse/docker/docker_build.sh
-```
-
-See `apps/dreamverse/docker/README.md` for Docker build and run option details.
-
-## Optional: Building FFmpeg For Better Performance
-
-For full streaming performance in a non-Docker install, build a custom FFmpeg
-binary from a FastVideo source checkout. The command below is repo-relative,
-so run it from the repository root:
-
-```bash
-bash apps/dreamverse/scripts/install_native_ffmpeg.sh
-```
-
-The installer supports Linux `x86_64` and `aarch64`. It prefers conda-forge
-triplet compilers when those commands are on `PATH`, otherwise it falls back to
-system `gcc`/`g++` (plain venv). On `x86_64`, x264's hand-tuned SIMD also
-requires `nasm`; install via whichever path fits your host:
-
-```bash
-sudo apt install nasm                       # Debian/Ubuntu
-conda install -c conda-forge nasm           # inside an active conda env
-```
-
-No sudo and no conda? Build `nasm` from source (~30s, installs into `$HOME`):
-
-```bash
-(
-  mkdir -p "$HOME/src" "$HOME/opt" && cd "$HOME/src"
-  curl -fsSL -O https://www.nasm.us/pub/nasm/releasebuilds/2.16.03/nasm-2.16.03.tar.gz
-  tar -xf nasm-2.16.03.tar.gz && cd nasm-2.16.03
-  ./configure --prefix="$HOME/opt/nasm" && make -j"$(nproc)" && make install
-)
-export PATH="$HOME/opt/nasm/bin:$PATH"      # add to ~/.bashrc to persist
-```
-
-The installer writes to `~/opt/ffmpeg-native/` and emits
-`apps/dreamverse/scripts/ffmpeg-env.sh`. Source it before starting the backend
-so Dreamverse uses the custom FFmpeg binary:
-
-```bash
-source apps/dreamverse/scripts/ffmpeg-env.sh
-dreamverse-server
-```
-
-Docker images already run this FFmpeg build during image creation and source the
-generated environment file at container startup.
-
-## Launch Dreamverse
-
-Start the backend with the installed Dreamverse commands:
-
-```bash
-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. To defer compilation until the first generated request while
-> testing, set `FASTVIDEO_ENABLE_STARTUP_WARMUP=0` before starting the backend.
-
-## Frontend Setup
-
-Install the web dependencies once from the FastVideo checkout:
-
-```bash
-cd apps/dreamverse/web
-npm ci
-```
-
-The frontend package uses `package-lock.json`; use npm for installs and scripts.
-
-## Quick Start: Local GPU
-
-### Start Backend
-
-Export the API keys used for prompt rewrite and prompt enhancement:
-
-```bash
-export CEREBRAS_API_KEY=...
-export GROQ_API_KEY=...
-```
-
-If you built the optional native FFmpeg binary above, source its environment
-file in the same shell before starting the backend:
-
-```bash
-source apps/dreamverse/scripts/ffmpeg-env.sh
-dreamverse-server --host 0.0.0.0 --port 8009
-```
-
-The Dreamverse backend defaults to `0.0.0.0:8009` and starts one GPU worker on
-the first visible GPU by default.
-
-### Cosmos Predict2.5 DFD continuation (experimental)
-
-Dreamverse can combine two converted Cosmos Predict2.5 2B packages: the
-distilled Text2World student creates an unconditioned first segment, then the
-Data-Forcing Distillation (DFD) Video2World student conditions each later
-segment on the prior terminal frame. Point the runtime at both local converted
-packages:
-
-```bash
-export DREAMVERSE_MODEL_ID=cosmos25-dfd
-export DREAMVERSE_MODEL_PATH=/path/to/Cosmos-Predict2.5-2B-Distilled-TrigFlow-FastVideo
-export DREAMVERSE_COSMOS25_DFD_MODEL_PATH=/path/to/Cosmos-Predict2.5-2B-DFD-FastVideo
-export ENABLE_TORCH_COMPILE=0
-dreamverse-server --host 0.0.0.0 --port 8009
-```
-
-The backend loads and warms both model roles before reporting ready. Both use
-BF16, Torch SDPA, 704x1280 output, 24 FPS, and four steps. Bootstrap segments
-contain 77 frames. DFD segments contain 81 decoded frames, but Dreamverse drops
-the repeated conditioning frame before streaming, leaving 80 new frames. An
-initial user image selects DFD immediately without treating that first frame as
-a cross-segment overlap.
-
-The profile uses a 30-minute session lease because sequential generation on
-GB10-class hardware can exceed Dreamverse's five-minute default while the GPU
-is still making progress. Deployments can override the lease with
-`FASTVIDEO_SESSION_TIMEOUT_SECONDS`.
-
-Cosmos does not produce audio, so the backend supplies duration-matched silent
-24 kHz audio for the existing browser streaming contract and trims 1,000 audio
-samples with each repeated DFD boundary frame. Runtime LoRA changes are not
-supported. Full segments take roughly 145 seconds on GB10, so this profile is a
-continuation-quality integration rather than a real-time configuration.
-
-### Check Readiness
-
-In another shell, verify that the backend process is alive:
-
-```bash
-curl http://localhost:8009/healthz
-```
-
-Then wait for GPU workers and startup warmup to finish:
-
-```bash
-curl http://localhost:8009/readyz
-```
-
-You can also run the same readiness path with:
-
-```bash
-BACKEND_HOST=localhost BACKEND_PORT=8009 apps/dreamverse/scripts/smoke_local.sh
-```
-
-If a backend is already running and you only want the script to probe it:
-
-```bash
-DREAMVERSE_SMOKE_START_BACKEND=0 apps/dreamverse/scripts/smoke_local.sh
-```
-
-### Start Frontend
-
-Start the frontend:
-
-```bash
-cd apps/dreamverse/web
-BACKEND_HOST=localhost BACKEND_PORT=8009 npm run dev
-```
-
-Open `http://localhost:5299`.
-
-## Server B200 deployment (SSH)
-
-Deploying on a remote GPU host (for example a B200 box) is a local install run
-over SSH, plus a few server-specific concerns. Two paths:
-
-### Option A: Native (source install)
-
-SSH in, then follow [Install → From source](#method-2-from-source) and
-(recommended) [Building FFmpeg](#optional-building-ffmpeg-for-better-performance),
-then start the backend as in [Quick Start: Local GPU](#quick-start-local-gpu).
-For a remote host, a few things differ from localhost:
-
-- Bind all interfaces: `dreamverse-server --host 0.0.0.0 --port 8009`.
-- Point the frontend/client at the host: `BACKEND_HOST=<b200-host> BACKEND_PORT=8009 npm run dev`.
-- Keep the backend alive across SSH sessions (`tmux` / `systemd` / `nohup`).
-- Expose / firewall port `8009`, or front it with a reverse proxy + auth.
-
-### Option B: Docker (on the server)
-
-SSH in, then follow [Install → Using Docker](#method-3-using-docker) and the run
-steps in [`docker/README.md`](docker/README.md):
-
-```bash
-CEREBRAS_API_KEY="<key>" GROQ_API_KEY="<key>" apps/dreamverse/docker/docker_run.sh
-```
-
-## Quick Start: Mock Backend (For UI development)
-
-The mock server emulates the Dreamverse backend protocol and streams a
-synthetic FFmpeg-generated fMP4 clip, so the frontend can run without a GPU.
-
-```bash
-dreamverse-mock-server --latency 200 --port 8009
-```
-
-## Tests
-
-Run the focused backend tests that validate local startup wiring, config, GPU
-selection, and mock-server behavior:
-
-```bash
-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
-```
-
-Run the broader Dreamverse backend suite:
-
-```bash
-pytest apps/dreamverse/dreamverse/tests -q
-```
-
-Run the frontend tests:
-
-```bash
-cd apps/dreamverse/web
-npm test
-```
-
-Run the frontend e2e tests:
-
-```bash
-cd apps/dreamverse/web
-npm run e2e
-```
-
-## Troubleshooting
-
-`dreamverse-server` exits with an install hint
-
-- install the Dreamverse extra with `UV_TORCH_BACKEND=cu126 uv pip install -e ".[dreamverse]"` from a
-  source checkout, or `UV_TORCH_BACKEND=cu126 uv pip install "fastvideo[dreamverse]"` from PyPI
-  (`cu130` on CUDA 13).
-
-Prompt-provider environment variable errors
-
-- set `CEREBRAS_API_KEY`
-- set `GROQ_API_KEY`
-- direct `dreamverse-server` launches do not source `~/.env`; export the keys
-  in the shell or use the bundled launch scripts, which source `~/.env`.
-
-`/readyz` stays at `503`
-
-- wait for model loading and startup warmup to finish
-- confirm a compatible CUDA GPU is visible to the process
-- check backend logs for worker startup or warmup failures
-- for a startup/debug pass without warmup, set
-  `FASTVIDEO_ENABLE_STARTUP_WARMUP=0` before starting the backend
-
-Only one GPU is used
-
-- this is the default local behavior
-- set `FASTVIDEO_GPU_COUNT=<N>` to start N GPU worker subprocesses inside one
-  backend instance
-- set `FASTVIDEO_GPU_COUNT=all` to start one worker for every visible GPU
-- use `CUDA_VISIBLE_DEVICES` first if you need to pin the visible GPU set
-
-Frontend cannot connect to backend
-
-- confirm the backend is running on `8009`; if not, point the frontend at it
-  with `BACKEND_HOST=<host> BACKEND_PORT=<port> npm run dev`
-- confirm `http://localhost:8009/healthz` responds before starting the frontend
-- confirm `http://localhost:8009/readyz` returns `200` before clicking Generate
-- use `apps/dreamverse/scripts/smoke_local.sh` for a repeatable local startup
-  check
-
-Mock backend fails during startup
-
-- install FFmpeg or set `FASTVIDEO_FFMPEG_BIN` to an FFmpeg binary
-- for non-mock local GPU streaming performance, use the native FFmpeg installer
-  described above
-
-## Notes
-
-Dreamverse owns its backend app under `apps/dreamverse/dreamverse/`. It expects
-`dreamverse-server`, not `fastvideo serve`.

apps/dreamverse/arch.md

@@ -1,365 +0,0 @@
-# Dreamverse Architecture
-
-## Overview
-
-Dreamverse currently has two main runtime pieces:
-
-- `apps/dreamverse/web/`: Next.js frontend
-- `apps/dreamverse/dreamverse/`: Python FastAPI runtime
-
-Today, the browser talks directly to the Dreamverse runtime over HTTP and a
-single websocket on `/ws`. The frontend owns UI state and interaction flow. The
-server owns generation state, prompt rewrite, prompt safety, websocket session
-semantics, and GPU-backed execution.
-
-Near-term OSS note:
-
-- `apps/dreamverse/dreamverse/` is the current runtime implementation.
-- A future `controller/` layer is planned for local-only compute management and
-  provider orchestration, but it does not exist yet.
-
-## Repo Map
-
-### Frontend
-
-- `apps/dreamverse/web/src/app/page.tsx`: main client orchestration,
-  websocket connect, init payloads, send paths, and top-level app behavior
-- `apps/dreamverse/web/src/lib/ws/reducer.ts`: reduces normalized websocket
-  events into client stores
-- `apps/dreamverse/web/src/stores/session.ts`: connection, mode, and top-level
-  session UI
-- `apps/dreamverse/web/src/stores/promptWindow.ts`: editable prompt window and
-  seed prompt UI state
-- `apps/dreamverse/web/src/stores/rewrite.ts`: rewrite activity timeline and
-  inspection state
-- `apps/dreamverse/web/src/stores/stream.ts`: playback and stream-related
-  client state
-- `apps/dreamverse/web/src/lib/prompts/promptWindowSnapshot.ts`: prompt-window snapshot
-  building for rewrite requests
-
-### Server
-
-- `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
-- `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
-  building
-- `apps/dreamverse/dreamverse/config.py`: runtime flags, prompt file paths, provider settings, and
-  warmup config
-- `apps/dreamverse/dreamverse/session_init_image.py`: validates and persists uploaded initial
-  images for segment 1
-
-## Current Split Of Responsibility
-
-### Frontend owns
-
-- local UI state and client-side stores
-- prompt drafts and prompt window editing
-- websocket connection management
-- deciding which user action to send:
-  - `session_init_v2`
-  - `project_init_v1`
-  - `append_prompt`
-  - `rewrite_seed_prompts`
-  - `simple_generate`
-- showing rewrite progress, stream status, prompt history, and devtools views
-
-### Server owns
-
-- websocket session lifecycle and protocol
-- GPU assignment and worker lifecycle
-- the authoritative seed prompt memory used for generation
-- prompt rewrite execution and prompt safety
-- actual generation queue semantics
-- stream chunk emission and segment lifecycle events
-- prompt config and preset persistence routes
-- health and readiness endpoints
-
-Important rule:
-
-- The frontend may propose prompt-window state for rewrite, but the server is
-  the source of truth for the rewritten rollout and the active prompt memory
-  used for generation.
-
-## End-To-End Flow
-
-1. The frontend opens `/ws`.
-2. The frontend sends `session_init_v2` with the initial prompt-window state,
-   preset metadata, and current toggles.
-3. The server validates init data, persists an optional initial image, acquires
-   a GPU slot, and emits session status such as `gpu_assigned`.
-4. The server starts or resumes project generation and emits events like
-   `ltx2_stream_start`, `ltx2_segment_start`, media init/chunks, and completion
-   events.
-5. The frontend reduces those websocket events into its stores and updates the
-   UI.
-6. User actions such as appending prompts, rewriting seed prompts, or starting
-   a single custom clip go back to the server over the same websocket.
-
-## Frontend Architecture
-
-The frontend is store-driven.
-
-- `page.tsx` wires together websocket setup, send helpers, reducer
-  application, and top-level interaction flows.
-- Store modules separate concerns like session state, rewrite state, prompt
-  window state, and stream state.
-- The websocket reducer is responsible for turning normalized runtime events
-  into store updates. If the server event schema changes, the reducer must
-  change with it.
-
-The frontend is intentionally not responsible for:
-
-- generating rewritten prompts locally
-- deciding final prompt safety outcomes
-- reconstructing server session state from scratch
-- inventing its own generation semantics independent of the runtime
-
-## Server Architecture
-
-The current runtime is a FastAPI app with a single long-lived websocket per
-session.
-
-`apps/dreamverse/dreamverse/main.py` manages:
-
-- websocket connect/init
-- prompt queues
-- project and segment state
-- prompt enhancement/rewrite triggers
-- stream relay from GPU workers to the browser
-- session logging and REST endpoints
-
-`apps/dreamverse/dreamverse/gpu_pool.py` manages:
-
-- model loading through FastVideo
-- one or more worker processes
-- startup warmup
-- user join/leave commands
-- `USER_STEP` execution for each segment
-- 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:
-
-- prompt enhancement for user-submitted prompts
-- rollout rewrite requests for the prompt window
-- provider selection and fallback across configured prompt providers
-- response normalization and safety-aware failure handling
-
-## FastAPI Surface
-
-The server is a single FastAPI application created in
-`apps/dreamverse/dreamverse/main.py`.
-
-Current built-in FastAPI docs are enabled:
-
-- `/docs`: Swagger UI
-- `/redoc`: ReDoc
-- `/openapi.json`: OpenAPI schema
-
-The runtime also mounts the frontend static build at `/` when one of the
-configured frontend static directories exists. It does not expose the backend
-Python package as static content.
-
-## HTTP API
-
-The current HTTP API is small. Most realtime behavior still goes through the
-websocket.
-
-### Core health and status routes
-
-- `GET /healthz`
-  - process liveness probe
-  - returns a small payload with `status`, `service`, and timestamp
-- `GET /readyz`
-  - readiness probe
-  - returns `503` until prompt services are initialized and at least one GPU
-    worker is ready
-  - returns readiness and GPU pool summary fields such as ready workers, total
-    GPUs, warmup counts, and queue size
-- `GET /status`
-  - returns the current GPU pool status payload from `gpu_pool`
-- `GET /internal/monitor/sessions`
-  - internal monitoring payload for session dashboards
-  - includes pending session count, max available sessions, prompt provider
-    success counts, and timestamp
-
-### Prompt config routes
-
-- `GET /prompt-system-config`
-  - returns the editable prompt-system configuration currently loaded by
-    `PromptEnhancer`
-- `POST /prompt-system-config`
-  - saves prompt-system configuration to disk and reloads prompt config in the
-    runtime
-  - current editable fields include:
-    - next-segment system prompt
-    - auto-extension system prompt
-    - rewrite-window system prompt
-    - rewrite-user system prompt
-    - rewrite model
-    - rewrite temperature
-
-### Devtools-only preset routes
-
-These exist only when `DEVTOOLS_ENABLED` is true in
-`apps/dreamverse/dreamverse/config.py`.
-
-- `GET /curated-presets`
-  - returns merged curated presets, applying the local overlay file on top of
-    the fallback file when both exist
-- `POST /curated-presets/append`
-  - appends a new curated preset to the overlay presets file
-  - validates non-empty label, normalized id, and at least two non-empty
-    segment prompts
-
-## Websocket API
-
-`WS /ws` is the main runtime API.
-
-The websocket owns:
-
-- session init
-- project init and reset
-- prompt append
-- prompt rewrite
-- generation toggles
-- segment lifecycle events
-- media stream delivery
-- runtime error delivery
-
-The websocket is the authoritative API for realtime Dreamverse behavior. The
-HTTP routes mainly support health checks, devtools persistence, and monitoring.
-
-## Prompt Rewrite Architecture
-
-Prompt rewrite is a shared flow with strict ownership boundaries.
-
-### Frontend responsibilities
-
-- collect the rewrite instruction
-- build the prompt-window snapshot from current client state
-- send `rewrite_seed_prompts`
-- show rewrite progress, raw output, fallback state, and resulting prompt list
-
-### Server responsibilities
-
-- validate and normalize the prompt-window payload
-- choose rewrite model, system prompt, timeout, and temperature
-- build the canonical prompt payload in
-  `apps/dreamverse/dreamverse/rewrite_prompt_payload.py`
-- execute rewrite through `PromptEnhancer`
-- apply safety filtering to rewritten prompts
-- replace the authoritative seed prompt memory when rewrite succeeds
-- emit `seed_prompts_updated` and `rewrite_seed_prompts_complete`
-
-Important rule:
-
-- The frontend owns editable drafts.
-- The server owns the accepted rollout.
-
-After a successful rewrite, the frontend should replace its prompt-window view
-from the server payload instead of preserving a locally-derived version.
-
-## Prompt Modes
-
-There are three related prompt paths in the current system:
-
-### Initial rollout
-
-- The frontend sends seed prompts during `session_init_v2`.
-- The server uses those prompts as the initial seed prompt memory.
-- If the rollout starts from an empty prompt window plus an initial rewrite
-  instruction, the server can pause generation until rewrite completes.
-
-### Live append
-
-- The frontend sends `append_prompt`.
-- The server may enhance that prompt, safety-check it, enqueue it, and use it
-  as the next generated segment.
-
-### Rewrite
-
-- The frontend sends `rewrite_seed_prompts`.
-- The server rewrites the entire seed prompt window or generates a new rollout,
-  depending on the payload and current state.
-
-## Initial Image And Segment Handling
-
-The frontend currently sends `initial_image` as part of session init or
-`simple_generate`.
-
-The server:
-
-- validates and persists the image
-- uses it only for segment 1 when present
-- keeps continuation state for later segments in the GPU worker
-
-This means the runtime, not the frontend, decides how segment 1 image
-conditioning and later continuation conditioning are applied.
-
-## Websocket Contract
-
-The websocket is the main integration surface between UI and runtime.
-
-Typical incoming messages from the frontend:
-
-- `session_init_v2`
-- `project_init_v1`
-- `append_prompt`
-- `rewrite_seed_prompts`
-- `simple_generate`
-- `set_enhancement`
-- `set_auto_extension`
-- `set_loop_generation`
-
-Typical outgoing messages from the server:
-
-- `gpu_assigned`
-- `ltx2_stream_start`
-- `ltx2_segment_start`
-- `segment_prompt_source`
-- `prompt_received`
-- `prompt_ready`
-- `prompt_enhancing`
-- `seed_prompts_updated`
-- `rewrite_seed_prompts_complete`
-- `media_init`
-- `media_segment_complete`
-- `project_idle`
-- `error`
-
-Binary websocket frames carry media chunks for playback.
-
-## Current And Planned Architecture
-
-Current architecture:
-
-- browser -> `apps/dreamverse/web`
-- `apps/dreamverse/web` -> `apps/dreamverse/dreamverse/main.py`
-- `apps/dreamverse/dreamverse/main.py` ->
-  `apps/dreamverse/dreamverse/gpu_pool.py`
-- `gpu_pool.py` -> FastVideo runtime
-
-Planned architecture:
-
-- browser -> `apps/dreamverse/web`
-- `apps/dreamverse/web` -> local `controller/`
-- `controller/` -> local or remote Dreamverse runtime
-- runtime -> FastVideo runtime
-
-That future controller split should not move prompt rewrite, session state, or
-generation semantics out of the runtime.

apps/dreamverse/design.md

@@ -1,600 +0,0 @@
-# Dreamverse OSS Design
-
-## Overview
-
-Dreamverse should ship as a local-first open source application.
-
-- The browser talks only to a local Dreamverse control plane on the user's
-  machine.
-- Provider credentials stay local to that machine.
-- Dreamverse may provision compute on the user's behalf, but Dreamverse does
-  not host that control path as a service.
-
-This keeps the UX simple without turning Dreamverse into a credential-holding
-hosted platform.
-
-## Goals
-
-- Support three compute modes behind one product surface:
-  - local GPU
-  - managed remote GPU via Runpod
-  - managed remote GPU via Modal
-- Keep prompt rewrite, websocket session state, and generation behavior
-  consistent across providers.
-- Keep provider API keys out of browser state and out of any hosted service.
-- Make the existing runtime reusable as the common serving contract.
-- Minimize provider-specific code and isolate it behind a narrow interface.
-
-## Non-goals
-
-- Do not make the frontend call provider APIs directly.
-- Do not unify providers at the level of SSH, VM, serverless, or pod
-  semantics.
-- Do not move prompt rewrite logic into the frontend or controller.
-- Do not require remote compute for the basic product path.
-
-## Current State
-
-Today the repo contains two major pieces:
-
-- `apps/dreamverse/web/`: Next.js frontend
-- `apps/dreamverse/dreamverse/`: FastAPI runtime that owns websocket state,
-  prompt rewrite, prompt safety, and GPU-backed generation
-
-The current runtime already exposes useful health and streaming surfaces such
-as `/healthz`, `/readyz`, `/status`, and `/ws`.
-
-## Target Architecture
-
-The target open source structure should be:
-
-```text
-Dreamverse/
-├── apps/dreamverse/
-│   ├── web/          # browser UI
-│   ├── dreamverse/   # current FastAPI websocket/generation runtime
-│   ├── controller/   # local control plane and provider lifecycle
-│   ├── providers/    # provider adapters
-│   ├── tests/
-│   │   ├── contract/
-│   │   ├── controller/
-│   │   └── smoke/
-│   └── design.md
-└── ...
-```
-
-Near-term note:
-
-- `apps/dreamverse/dreamverse/` is the current runtime implementation.
-- We can keep the code there initially and rename it to `runtime/` only after
-  the controller lands.
-
-## Trust Model
-
-Dreamverse is local-only for control and secrets.
-
-- The user launches Dreamverse on their own machine.
-- Provider API keys are entered into the local app or local CLI.
-- The controller uses those credentials to provision or connect to compute.
-- The browser never talks to Modal or Runpod directly.
-- Dreamverse-hosted infrastructure is not involved.
-
-This is the key reason the provider-based path is acceptable for OSS.
-
-## Responsibility Split
-
-### `apps/dreamverse/web`
-
-The frontend should own:
-
-- UI state, drafts, and local interaction state
-- websocket event reduction into client stores
-- selection of compute mode and display of cost/health/status
-- local forms for provider configuration
-- sending prompt requests and rewrite requests to the local controller
-
-The frontend should not own:
-
-- provider credentials after submission
-- provider API calls
-- runtime lifecycle
-- authoritative prompt window after rewrite
-- prompt safety or generation policy
-
-### `controller`
-
-The local controller should own:
-
-- provider credential loading and local-only storage
-- compute mode selection
-- provisioning, reuse, shutdown, and health monitoring of runtimes
-- reverse proxying HTTP and websocket traffic from the frontend to the active
-  runtime
-- user-visible status such as provisioning, ready, failed, and idle shutdown
-- local persistence for user settings that must survive ephemeral runtimes
-
-The controller should not own:
-
-- prompt rewrite logic
-- seed prompt memory semantics
-- generation queue behavior
-- provider-specific UI state
-
-### `runtime`
-
-The runtime should remain the authoritative owner of:
-
-- `/ws` session state
-- prompt rewrite execution
-- prompt safety
-- seed prompt memory and prompt-window state used for generation
-- generation orchestration and GPU worker lifecycle
-- websocket event schemas
-
-This preserves the current model and avoids splitting state across layers.
-
-## Runtime Contract
-
-Provider abstraction should happen around a stable Dreamverse runtime contract,
-not around infrastructure details.
-
-Minimum runtime surface:
-
-- `GET /healthz`
-- `GET /readyz`
-- `GET /status`
-- `GET/POST /prompt-system-config` if devtools persists config through the
-  runtime
-- curated preset routes if those remain runtime-backed
-- `WS /ws`
-
-Important rule:
-
-- The controller only needs to know how to reach a healthy runtime.
-- The runtime remains provider-agnostic.
-
-## Provider Abstraction
-
-Use a narrow provider interface:
-
-```python
-class ComputeProvider(Protocol):
-    async def ensure_runtime(self, spec: RuntimeSpec) -> RuntimeHandle: ...
-    async def wait_until_ready(self, handle: RuntimeHandle) -> None: ...
-    async def stop_runtime(self, handle: RuntimeHandle) -> None: ...
-```
-
-`RuntimeHandle` should include:
-
-- `provider`
-- `runtime_id`
-- `base_url`
-- `ws_url`
-- runtime auth headers or tokens if needed
-- lifecycle metadata
-- cost or hardware metadata for UI display
-
-The controller should work only with `RuntimeHandle`, never with raw SSH hosts
-or provider-specific payloads after resolution.
-
-## Provider Notes
-
-### Local
-
-Local mode should be the reference implementation.
-
-- Start the runtime as a local subprocess or connect to an already-running
-  local runtime URL.
-- Reuse the same runtime contract as remote providers.
-- Make this the first supported path and the main smoke-test target.
-
-### Runpod
-
-Runpod should be treated as pod lifecycle plus runtime reachability.
-
-- Prefer prepared images or templates that auto-start the Dreamverse runtime.
-- Prefer exposed HTTP/TCP ports for steady-state traffic.
-- Use SSH only for bootstrap fallback, diagnostics, or repair.
-- Avoid a design where the controller shells into the pod for every action.
-
-### Modal
-
-Modal should be treated as deployment-based runtime hosting.
-
-- Wrap the Dreamverse runtime in a thin Modal entrypoint if needed.
-- Reuse the same runtime behavior behind that wrapper.
-- Do not model Modal as a machine that Dreamverse logs into.
-- Do not force the websocket runtime into a per-request serverless handler
-  shape.
-
-## Config and Persistence
-
-Remote compute may be ephemeral, so mutable user configuration should not live
-only inside remote runtimes.
-
-Keep durable state local to the user's machine unless there is a strong reason
-otherwise:
-
-- provider selection
-- provider credentials or credential references
-- default hardware preferences
-- editable prompt presets
-- prompt system prompt overrides
-- idle shutdown policy
-
-Runtime-local state should be treated as disposable unless explicitly synced.
-
-## Prompt Rewrite Ownership
-
-Prompt rewrite remains runtime-owned even after the controller is added.
-
-Frontend responsibilities:
-
-- collect the rewrite instruction
-- build the prompt-window snapshot
-- display rewrite activity and results
-
-Runtime responsibilities:
-
-- validate and normalize the prompt window
-- choose the rewrite system prompt and model settings
-- execute rewrite
-- apply safety filtering
-- replace authoritative seed prompt memory
-- emit the canonical completion events
-
-Controller responsibilities:
-
-- proxy the request and response
-- surface runtime availability and failure state
-
-This boundary should not move.
-
-## Recommended Rollout
-
-1. Finish the path reorg so docs and code agree on `apps/dreamverse/web`.
-2. Introduce `controller/` as a local-only API/proxy process.
-3. Keep `apps/dreamverse/dreamverse/` as the runtime and adapt it behind the
-   controller.
-4. Add `local` provider first.
-5. Add "bring your own runtime URL" as an escape hatch.
-6. Add automated Runpod provisioning.
-7. Add Modal deployment support.
-8. Rename `apps/dreamverse/dreamverse/` to `runtime/` once the split is stable.
-
-## Implementation Plan
-
-The implementation should start with the smallest milestone that gives users a
-working local GPU setup without forcing the controller/provider architecture
-into the first patch series.
-
-### Milestone 0: Make local GPU the official baseline
-
-Goal:
-
-- A user with a working `fastvideo` install can run the Dreamverse backend on a
-  local GPU and connect to it from `apps/dreamverse/web`.
-
-Non-goals for this milestone:
-
-- no controller process yet
-- no provider abstraction yet
-- no Runpod or Modal support yet
-- no secret-management UI yet
-
-Reasoning:
-
-- `apps/dreamverse/dreamverse/` already is the real local GPU runtime.
-- `apps/dreamverse/web` already knows how to talk to a backend over `/ws` and
-  REST rewrites.
-- The shortest path is to make the existing local path explicit, reliable, and
-  tested before adding another layer.
-
-### Milestone 0 work items
-
-#### 0.1 Fix repo path assumptions after the frontend move
-
-Current issue:
-
-- Some paths still assume `prod-ui/`, but the frontend now lives at
-  `apps/dreamverse/web/`.
-
-Required changes:
-
-- update prompt/preset path resolution in
-  `apps/dreamverse/dreamverse/config.py`
-- update docs that still mention `prod-ui`
-- audit any frontend build settings that assume the old repo root
-
-This is prerequisite cleanup. Local GPU mode should not depend on stale
-monorepo paths.
-
-#### 0.2 Make local runtime startup the primary supported entrypoint
-
-Required outcome:
-
-- one documented backend command
-- one documented frontend command
-- one clear env contract for local development
-
-Expected shape:
-
-```bash
-uv pip install -e ".[dreamverse]"
-dreamverse-server --host 0.0.0.0 --port 8009
-
-cd apps/dreamverse/web
-npm ci
-BACKEND_HOST=localhost BACKEND_PORT=8009 npm run dev
-```
-
-Optional but useful:
-
-- add a small root helper script or Make target for local startup
-- add a `dreamverse-doctor` or lightweight startup check later
-
-#### 0.3 Define the minimum local runtime contract
-
-For Milestone 0, the frontend should rely only on the current runtime surface:
-
-- `/ws`
-- `/status`
-- `/healthz`
-- `/readyz`
-- existing prompt/devtools routes
-
-Do not add a second local API layer yet unless the current runtime surface is
-proven insufficient.
-
-#### 0.4 Make failure states explicit in the UI
-
-Local GPU mode fails in a few predictable ways:
-
-- backend not reachable
-- backend reachable but not ready
-- `fastvideo` or model runtime missing
-- no compatible GPU available
-
-Minimum implementation:
-
-- show a clear connection error when `/ws` or `/status` fails
-- surface readiness failures in a human-readable way
-- avoid silent retry loops that hide backend startup failures
-
-This is a small UI pass, not a controller project.
-
-#### 0.5 Add a minimal local smoke test path
-
-At this milestone, local GPU support is "done" only if there is a repeatable
-test path for the local runtime contract.
-
-Minimum test additions:
-
-- backend tests for `/healthz`, `/readyz`, and `/status`
-- a frontend integration test that assumes a reachable backend URL and verifies
-  connection lifecycle behavior
-- one local smoke script that starts the backend and verifies readiness before
-  the frontend is launched
-
-### Milestone 1: Introduce a thin local controller
-
-Goal:
-
-- Preserve the same local GPU behavior, but place a stable local control-plane
-  API in front of the runtime.
-
-This should happen only after Milestone 0 is stable.
-
-Scope:
-
-- add `controller/`
-- proxy `/ws` and the needed REST routes to `apps/dreamverse/dreamverse/`
-- expose controller-owned status for "backend starting", "runtime ready", and
-  "runtime failed"
-- optionally spawn the local runtime as a subprocess
-
-Non-goal:
-
-- do not add remote provider logic yet
-
-Reasoning:
-
-- the controller earns its complexity only once it stabilizes the local
-  contract that future providers will share
-
-### Milestone 2: Provider abstraction on top of the controller
-
-Goal:
-
-- Keep the same frontend contract while allowing the controller to resolve a
-  runtime via `local`, then later `runpod` and `modal`.
-
-At this point:
-
-- define `ComputeProvider`
-- implement `providers/local.py`
-- move local-runtime subprocess management behind the provider interface
-
-The first provider should be `local`, because it is cheapest to debug and
-matches the runtime most closely.
-
-## Minimal Code Change Order
-
-If we want the shortest path to a working local GPU milestone, the change order
-should be:
-
-1. Fix `apps/dreamverse/dreamverse/config.py` and any remaining path
-   assumptions from `prod-ui` to `apps/dreamverse/web`.
-2. Update `README.md` to document the real local GPU startup flow.
-3. Confirm `apps/dreamverse/web` connects cleanly to the local wrapper-backed
-   backend.
-4. Improve frontend error handling for backend-not-ready and backend-missing
-   cases.
-5. Add a local smoke test and keep existing backend/frontend tests green.
-6. Only then introduce `controller/`.
-
-## Test Plan for the Local GPU Milestone
-
-### Backend
-
-Keep the current Python test suite as the base:
-
-- `apps/dreamverse/dreamverse/tests/test_health_endpoints.py`
-- `apps/dreamverse/dreamverse/tests/test_mock_server.py`
-- `apps/dreamverse/dreamverse/tests/test_prompt_enhancer.py`
-- `apps/dreamverse/dreamverse/tests/test_rewrite_prompt_payload.py`
-- related config and logging tests
-
-Add or tighten tests for:
-
-- path resolution in `apps/dreamverse/dreamverse/config.py`
-- readiness behavior when GPU pool initialization fails
-- startup error messaging when `fastvideo` is unavailable
-
-### Frontend
-
-Keep the current Vitest suite as the base:
-
-- websocket reducer tests
-- prompt-window snapshot tests
-- integration tests under `apps/dreamverse/web/src/app/`
-
-Add or tighten tests for:
-
-- connection failure UX when backend is down
-- readiness failure UX when backend returns non-ready status
-- backend routing configuration through `BACKEND_HOST` and `BACKEND_PORT`
-
-### Manual smoke path
-
-The first manual smoke checklist should be:
-
-1. start `dreamverse-server`
-2. confirm `GET /healthz` returns 200
-3. confirm `GET /readyz` returns 200 after warmup
-4. start `apps/dreamverse/web`
-5. confirm the UI opens and the websocket connects
-6. submit a prompt and verify the first generation starts
-
-This checklist should be written down in the README once the milestone is
-implemented.
-
-## Test Strategy
-
-The test suite should preserve one rule: provider changes must not be able to
-break prompt rewrite, websocket semantics, or runtime behavior silently.
-
-### 1. Runtime unit and integration tests
-
-Keep and expand the current `pytest` coverage in
-`apps/dreamverse/dreamverse/tests/test_*.py`.
-
-Focus areas:
-
-- config loading
-- prompt rewrite payload normalization
-- prompt enhancement and prompt safety
-- health/readiness endpoints
-- websocket session behavior
-- session logging
-- mock runtime behavior
-
-These tests should remain provider-agnostic.
-
-### 2. Controller unit tests
-
-Add a Python test suite for the controller state machine.
-
-Key cases:
-
-- provider selection and validation
-- credential loading from local config or env
-- runtime lifecycle transitions:
-  - idle
-  - provisioning
-  - ready
-  - failed
-  - stopping
-- idle timeout and cleanup behavior
-- retry and backoff behavior
-- HTTP and websocket proxy routing
-
-These tests should use fake providers and fake runtimes by default.
-
-### 3. Provider contract tests
-
-Each provider should pass the same contract tests.
-
-Examples:
-
-- `ensure_runtime()` returns a usable `RuntimeHandle`
-- `wait_until_ready()` surfaces timeout vs readiness correctly
-- `stop_runtime()` is safe to call twice
-- provider errors are mapped into stable controller error types
-
-Use recorded fixtures or fakes wherever possible to avoid spend in CI.
-
-### 4. Web contract tests
-
-The frontend already has useful Vitest coverage under
-`apps/dreamverse/web/src`.
-Preserve that and expand around the controller split.
-
-Priority areas:
-
-- websocket event reduction
-- prompt-window snapshot construction
-- rewrite request shaping
-- compute-status UI
-- failure and reconnect UX
-
-The frontend should mock the local controller API, not provider APIs.
-
-### 5. Cross-layer protocol tests
-
-Add contract fixtures that validate shared payloads across layers.
-
-Important fixtures:
-
-- websocket event payloads
-- rewrite request payloads
-- runtime status payloads
-- controller status payloads
-
-These can be simple JSON fixtures validated by both Python and TypeScript
-tests. They will catch drift earlier than end-to-end tests.
-
-### 6. Smoke tests
-
-Add a small number of high-signal smoke tests:
-
-- local provider + mock runtime
-- local provider + real runtime when GPU is available
-- controller startup + frontend health path
-
-These should be cheap enough for routine local use.
-
-### 7. Provider-backed manual or nightly tests
-
-Real Modal and Runpod tests should be opt-in.
-
-- Do not run them in default CI.
-- Gate them behind explicit credentials and flags.
-- Keep them focused on provisioning and reachability, not full product
-  regression.
-
-This avoids flaky and expensive CI while still validating real provider flows.
-
-## Testing Recommendations for the Next Step
-
-The next practical additions should be:
-
-1. A controller test suite in Python using fake providers.
-2. Shared contract fixtures for websocket and rewrite payloads.
-3. A smoke test that starts the local controller against the existing mock
-   runtime.
-
-I would not add Playwright yet. The current web stack already has Vitest and
-integration-style component tests, which are cheaper and better aligned with
-the immediate reorg. Add browser automation only after the controller path is
-stable.

apps/dreamverse/docker/Dockerfile

@@ -1,103 +0,0 @@
-# syntax=docker/dockerfile:1.7
-# CUDA base. CUDA_VERSION/UBUNTU_VERSION feed the default tag (matches the
-# unified docker/Dockerfile); override BUILD_BASE_IMAGE wholesale for a mirror.
-ARG CUDA_VERSION=13.0.0
-ARG UBUNTU_VERSION=22.04
-ARG BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu${UBUNTU_VERSION}
-FROM ${BUILD_BASE_IMAGE}
-
-ARG BUILD_FASTVIDEO_KERNEL_FROM_SOURCE=0
-ARG BUILD_DREAMVERSE_UI=0
-
-ENV DEBIAN_FRONTEND=noninteractive \
-    PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy \
-    UV_CACHE_DIR=/opt/uv/cache
-
-# pyproject no longer pins a PyTorch index; GPU-less build host -> pin explicitly
-# (auto would fall back to CPU). Matches the base CUDA: cu130 (13.0) / cu126 (12.6).
-ARG UV_TORCH_BACKEND=cu130
-ENV UV_TORCH_BACKEND=${UV_TORCH_BACKEND}
-
-SHELL ["/bin/bash", "-c"]
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        gcc-11 g++-11 clang-11 \
-        make cmake ninja-build pkg-config nasm \
-        git curl wget ca-certificates \
-        libssl-dev zlib1g-dev \
-    && rm -rf /var/lib/apt/lists/* \
-    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 \
-        --slave /usr/bin/g++ g++ /usr/bin/g++-11
-
-# Version-agnostic /usr/local/cuda symlink so this works for any CUDA_VERSION.
-ENV CUDA_HOME=/usr/local/cuda
-ENV PATH=/root/.local/bin:/opt/venv/bin:${CUDA_HOME}/bin:${PATH}
-ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
-ENV VIRTUAL_ENV=/opt/venv
-
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-
-RUN uv venv --python 3.12 --seed /opt/venv \
-    && echo 'source /opt/venv/bin/activate' >> /root/.bashrc
-
-WORKDIR /opt/FastVideo
-
-COPY . /opt/FastVideo
-
-RUN --mount=type=cache,target=/opt/uv/cache \
-    source /opt/venv/bin/activate \
-    && uv pip install "/opt/FastVideo[dreamverse]"
-
-# Standard docker build does not expose GPUs, while fastvideo-kernel/build.sh
-# detects the CUDA architecture with torch at build time. The FastVideo package
-# install above brings in the pinned fastvideo-kernel package; rebuild from the
-# copied source only on hosts configured for build-time GPU access.
-RUN --mount=type=cache,target=/opt/uv/cache \
-    if [[ "${BUILD_FASTVIDEO_KERNEL_FROM_SOURCE}" == "1" ]]; then \
-        source /opt/venv/bin/activate \
-        && cd /opt/FastVideo/fastvideo-kernel \
-        && ./build.sh; \
-    else \
-        echo "Skipping source fastvideo-kernel build; using installed fastvideo-kernel package."; \
-    fi
-
-RUN if [[ "${BUILD_DREAMVERSE_UI}" == "1" ]]; then \
-        curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \
-        && apt-get install -y --no-install-recommends nodejs \
-        && rm -rf /var/lib/apt/lists/* \
-        && cd /opt/FastVideo/apps/dreamverse/web \
-        && npm ci --ignore-scripts \
-        && NEXT_OUTPUT_EXPORT=1 npm run build \
-        && rm -rf node_modules .next; \
-    else \
-        echo "Skipping Dreamverse UI build; Building backend-only image."; \
-    fi
-
-# The monorepo ffmpeg installer force-selects conda compiler triplets for
-# local dev shells. Inside this image we explicitly opt into the system
-# gcc/g++ toolchain.
-RUN source /opt/venv/bin/activate \
-    && INSTALL_PREFIX=/opt/ffmpeg-native \
-       SOURCE_DIR=/tmp/ffmpeg-native-src \
-       FFMPEG_NATIVE_CC=/usr/bin/gcc \
-       FFMPEG_NATIVE_CXX=/usr/bin/g++ \
-       bash /opt/FastVideo/apps/dreamverse/scripts/install_native_ffmpeg.sh
-
-# FASTVIDEO_FA4: FA4 (flash_attn.cute) is opt-in; this image installs it via
-# the dreamverse extra and is validated with it, so enable it here.
-ENV FASTVIDEO_DREAMVERSE_HOME=/var/lib/dreamverse \
-    STREAM_MODE=av_fmp4 \
-    FASTVIDEO_ENABLE_PROMPT_SAFETY=0 \
-    FASTVIDEO_FA4=1 \
-    HF_HOME=/root/.cache/huggingface
-
-RUN mkdir -p /var/lib/dreamverse
-
-EXPOSE 8009
-
-HEALTHCHECK --interval=30s --timeout=5s --start-period=2700s --retries=3 \
-  CMD curl -fsS http://127.0.0.1:8009/healthz || exit 1
-
-ENTRYPOINT ["/opt/FastVideo/apps/dreamverse/docker/docker_entrypoint.sh"]
-CMD ["dreamverse-server", "--host", "0.0.0.0", "--port", "8009"]

apps/dreamverse/docker/Dockerfile.dockerignore

@@ -1,46 +0,0 @@
-.git/
-**/.git/
-.venv/
-**/.venv/
-.*dreamverse*/
-**/__pycache__/
-**/*.pyc
-**/*.pyo
-**/*.egg-info/
-.pytest_cache/
-**/.pytest_cache/
-.mypy_cache/
-.ruff_cache/
-.cache/
-
-apps/dreamverse/web/node_modules/
-apps/dreamverse/web/.next/
-apps/dreamverse/web/out/
-apps/dreamverse/web/dist/
-apps/dreamverse/web/test-results/
-apps/dreamverse/web/playwright-report/
-
-apps/dreamverse/outputs/
-apps/dreamverse/dreamverse/outputs/
-apps/dreamverse/dreamverse/prompts.local/
-apps/dreamverse/logs/
-outputs/
-outputs_video/
-quality_check_outputs/
-data/
-logs/
-slurm-logs/
-wandb/
-
-.env
-.env.*
-**/prompts.local/
-.codex/
-.agents/
-.opencode/
-.agent_tmp/
-.vscode/
-.idea/
-*.log
-*.tmp
-*.pdf

apps/dreamverse/docker/README.md

@@ -1,97 +0,0 @@
-# Dreamverse Docker Image
-
-This folder contains the Docker image for Dreamverse inside the FastVideo
-monorepo. Build commands use the FastVideo repository root as the Docker
-context, so run the helper scripts from this folder or from any path in the
-checkout.
-
-## Build
-
-```bash
-apps/dreamverse/docker/docker_build.sh
-```
-
-The image defaults to `dreamverse:dev`. Override it with:
-
-```bash
-DREAMVERSE_IMAGE=dreamverse:local apps/dreamverse/docker/docker_build.sh
-```
-
-Backend-only remains the default image. To include the static Dreamverse UI
-served by the backend, set `BUILD_DREAMVERSE_UI=1` and choose a specific image
-tag:
-
-```bash
-BUILD_DREAMVERSE_UI=1 DREAMVERSE_IMAGE=<image-tag> apps/dreamverse/docker/docker_build.sh
-```
-
-Prefer SHA-specific tags for deployable images; avoid `latest`.
-
-The Dockerfile defaults to CUDA 13.0.0 with the cu130 PyTorch backend. CI also
-builds a CUDA 12.6.3 / cu126 image. Select that local build explicitly with:
-
-```bash
-CUDA_VERSION=12.6.3 apps/dreamverse/docker/docker_build.sh
-```
-
-The helper derives `UV_TORCH_BACKEND=cu126` for CUDA 12.x and `cu130` for CUDA
-13.x. Set `UV_TORCH_BACKEND` explicitly for a custom CUDA version. The legacy
-complete-image-tag override remains supported and selects the same matching
-backend:
-
-```bash
-CUDA_TAG=12.6.3-cudnn-devel-ubuntu22.04 apps/dreamverse/docker/docker_build.sh
-```
-
-Do not set `CUDA_TAG` and `CUDA_VERSION` together. The image installs FastVideo
-from this checkout with the `dreamverse` extra, including FA4 flash-attention
-and FlashInfer for NVFP4 quantization, and builds native FFmpeg.
-
-FastVideo's pinned `fastvideo-kernel==0.3.2` package is installed by default.
-To rebuild `fastvideo-kernel` from this checkout during the image build, set:
-
-```bash
-BUILD_FASTVIDEO_KERNEL_FROM_SOURCE=1 apps/dreamverse/docker/docker_build.sh
-```
-
-That source build detects the GPU architecture with torch during `docker
-build`. On hosts where Docker does not expose GPUs during build, leave the
-default package install path enabled.
-
-## Run
-
-```bash
-CEREBRAS_API_KEY="<your-key>" \
-GROQ_API_KEY="<your-key>" \
-apps/dreamverse/docker/docker_run.sh
-```
-
-The container serves Dreamverse on host port `8009` by default and mounts:
-
-```text
-$HOME/.cache/huggingface -> /root/.cache/huggingface
-apps/dreamverse/outputs -> /var/lib/dreamverse/outputs
-```
-
-Override the host port and output directory with `BACKEND_PORT` and
-`DREAMVERSE_OUTPUTS_DIR`.
-
-To pin the container to a specific host GPU, pass Docker's GPU request syntax:
-
-```bash
-DREAMVERSE_DOCKER_GPUS=device=4 FASTVIDEO_GPU_COUNT=1 \
-  CEREBRAS_API_KEY="<your-key>" \
-  GROQ_API_KEY="<your-key>" \
-  apps/dreamverse/docker/docker_run.sh
-```
-
-## Smoke
-
-```bash
-CEREBRAS_API_KEY=placeholder \
-GROQ_API_KEY=placeholder \
-apps/dreamverse/docker/docker_smoke.sh
-```
-
-The smoke script starts the container, polls `/healthz`, then polls `/readyz`.
-It removes the container on exit unless `DREAMVERSE_KEEP_CONTAINER=1` is set.

apps/dreamverse/docker/docker_build.sh

@@ -1,88 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(cd -- "${SCRIPT_DIR}/../../.." && pwd)"
-IMAGE="${DREAMVERSE_IMAGE:-dreamverse:dev}"
-ROOT_DOCKERIGNORE="${REPO_ROOT}/.dockerignore"
-DOCKERFILE_DOCKERIGNORE="${SCRIPT_DIR}/Dockerfile.dockerignore"
-CREATED_ROOT_DOCKERIGNORE_SYMLINK=0
-
-cleanup_root_dockerignore_symlink() {
-  if [[ "${CREATED_ROOT_DOCKERIGNORE_SYMLINK}" == "1" && -L "${ROOT_DOCKERIGNORE}" ]] && \
-    [[ "$(readlink "${ROOT_DOCKERIGNORE}")" == "${DOCKERFILE_DOCKERIGNORE}" ]]; then
-    rm -- "${ROOT_DOCKERIGNORE}"
-  fi
-}
-trap cleanup_root_dockerignore_symlink EXIT INT TERM
-
-if [[ -z "${DOCKER_BUILDKIT:-}" ]] && docker buildx version >/dev/null 2>&1; then
-  export DOCKER_BUILDKIT=1
-fi
-
-build_args=()
-cuda_version="${CUDA_VERSION:-}"
-torch_backend="${UV_TORCH_BACKEND:-}"
-
-# CUDA_TAG was the Dockerfile's original override and contains the complete
-# nvidia/cuda tag (for example, 12.6.3-cudnn-devel-ubuntu22.04). Keep accepting
-# it while translating it to the parameterized Dockerfile inputs.
-if [[ -n "${CUDA_TAG:-}" ]]; then
-  if [[ -n "${CUDA_VERSION:-}" ]]; then
-    printf 'CUDA_TAG and CUDA_VERSION cannot both be set. Use CUDA_VERSION for new builds.\n' >&2
-    exit 2
-  fi
-
-  cuda_version="${CUDA_TAG%%-*}"
-  if [[ ! "${cuda_version}" =~ ^[0-9]+\.[0-9]+(\.[0-9]+)?$ ]]; then
-    printf 'Cannot infer CUDA_VERSION from CUDA_TAG=%s. Use CUDA_VERSION and UV_TORCH_BACKEND instead.\n' \
-      "${CUDA_TAG}" >&2
-    exit 2
-  fi
-  build_args+=(--build-arg "BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_TAG}")
-fi
-
-if [[ -n "${cuda_version}" ]]; then
-  build_args+=(--build-arg "CUDA_VERSION=${cuda_version}")
-fi
-
-if [[ -z "${torch_backend}" && -n "${cuda_version}" ]]; then
-  case "${cuda_version}" in
-    12.*) torch_backend=cu126 ;;
-    13.*) torch_backend=cu130 ;;
-    *)
-      printf 'No default UV_TORCH_BACKEND for CUDA_VERSION=%s. Set UV_TORCH_BACKEND explicitly.\n' \
-        "${cuda_version}" >&2
-      exit 2
-      ;;
-  esac
-fi
-
-if [[ -n "${torch_backend}" ]]; then
-  build_args+=(--build-arg "UV_TORCH_BACKEND=${torch_backend}")
-fi
-
-[[ -n "${BUILD_FASTVIDEO_KERNEL_FROM_SOURCE:-}" ]] && \
-  build_args+=(--build-arg "BUILD_FASTVIDEO_KERNEL_FROM_SOURCE=${BUILD_FASTVIDEO_KERNEL_FROM_SOURCE}")
-build_args+=(--build-arg "BUILD_DREAMVERSE_UI=${BUILD_DREAMVERSE_UI:-0}")
-
-if [[ "${DOCKER_BUILDKIT:-}" == "1" ]]; then
-  if [[ -L "${ROOT_DOCKERIGNORE}" ]] && \
-    [[ "$(readlink "${ROOT_DOCKERIGNORE}")" == "${DOCKERFILE_DOCKERIGNORE}" ]]; then
-    rm -- "${ROOT_DOCKERIGNORE}"
-    printf 'Removed stale temporary root .dockerignore symlink: %s\n' "${ROOT_DOCKERIGNORE}"
-  fi
-elif [[ -e "${ROOT_DOCKERIGNORE}" || -L "${ROOT_DOCKERIGNORE}" ]]; then
-  printf 'Using existing root .dockerignore: %s\n' "${ROOT_DOCKERIGNORE}"
-else
-  ln -s -- "${DOCKERFILE_DOCKERIGNORE}" "${ROOT_DOCKERIGNORE}"
-  CREATED_ROOT_DOCKERIGNORE_SYMLINK=1
-  printf 'Created temporary root .dockerignore symlink for legacy Docker builder: %s -> %s\n' \
-    "${ROOT_DOCKERIGNORE}" "${DOCKERFILE_DOCKERIGNORE}"
-fi
-
-docker build \
-  -f "${SCRIPT_DIR}/Dockerfile" \
-  -t "${IMAGE}" \
-  "${build_args[@]}" \
-  "${REPO_ROOT}"

apps/dreamverse/docker/docker_entrypoint.sh

@@ -1,13 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-source /opt/venv/bin/activate
-
-if [[ -f /opt/FastVideo/apps/dreamverse/scripts/ffmpeg-env.sh ]]; then
-  source /opt/FastVideo/apps/dreamverse/scripts/ffmpeg-env.sh
-fi
-
-: "${CEREBRAS_API_KEY:?CEREBRAS_API_KEY must be set (pass with -e CEREBRAS_API_KEY=...)}"
-: "${GROQ_API_KEY:?GROQ_API_KEY must be set (pass with -e GROQ_API_KEY=...)}"
-
-exec "$@"

apps/dreamverse/docker/docker_run.sh

@@ -1,30 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-: "${CEREBRAS_API_KEY:?CEREBRAS_API_KEY not set on host}"
-: "${GROQ_API_KEY:?GROQ_API_KEY not set on host}"
-
-SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
-DREAMVERSE_ROOT="$(cd -- "${SCRIPT_DIR}/.." && pwd)"
-
-IMAGE="${DREAMVERSE_IMAGE:-dreamverse:dev}"
-PORT="${BACKEND_PORT:-8009}"
-HF_CACHE="${HF_HOME:-$HOME/.cache/huggingface}"
-OUTPUTS_DIR="${DREAMVERSE_OUTPUTS_DIR:-${DREAMVERSE_ROOT}/outputs}"
-GPU_REQUEST="${DREAMVERSE_DOCKER_GPUS:-all}"
-
-mkdir -p "${HF_CACHE}" "${OUTPUTS_DIR}"
-
-env_args=(
-  -e "CEREBRAS_API_KEY=${CEREBRAS_API_KEY}"
-  -e "GROQ_API_KEY=${GROQ_API_KEY}"
-)
-[[ -n "${ENABLE_TORCH_COMPILE:-}" ]] && env_args+=(-e "ENABLE_TORCH_COMPILE=${ENABLE_TORCH_COMPILE}")
-[[ -n "${FASTVIDEO_GPU_COUNT:-}" ]] && env_args+=(-e "FASTVIDEO_GPU_COUNT=${FASTVIDEO_GPU_COUNT}")
-
-exec docker run --rm --gpus "${GPU_REQUEST}" --init \
-  -p "${PORT}:8009" \
-  "${env_args[@]}" \
-  -v "${HF_CACHE}:/root/.cache/huggingface" \
-  -v "${OUTPUTS_DIR}:/var/lib/dreamverse/outputs" \
-  "${IMAGE}"

apps/dreamverse/docker/docker_smoke.sh

@@ -1,75 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-: "${CEREBRAS_API_KEY:?CEREBRAS_API_KEY not set on host}"
-: "${GROQ_API_KEY:?GROQ_API_KEY not set on host}"
-
-SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
-DREAMVERSE_ROOT="$(cd -- "${SCRIPT_DIR}/.." && pwd)"
-
-IMAGE="${DREAMVERSE_IMAGE:-dreamverse:dev}"
-PORT="${BACKEND_PORT:-8009}"
-HF_CACHE="${HF_HOME:-$HOME/.cache/huggingface}"
-OUTPUTS_DIR="${DREAMVERSE_OUTPUTS_DIR:-${DREAMVERSE_ROOT}/outputs}"
-NAME="${DREAMVERSE_NAME:-dreamverse}"
-TIMEOUT_SECONDS="${DREAMVERSE_SMOKE_TIMEOUT_SECONDS:-1200}"
-POLL_SECONDS="${DREAMVERSE_SMOKE_POLL_SECONDS:-5}"
-GPU_REQUEST="${DREAMVERSE_DOCKER_GPUS:-all}"
-
-mkdir -p "${HF_CACHE}" "${OUTPUTS_DIR}"
-
-docker rm -f "${NAME}" >/dev/null 2>&1 || true
-
-env_args=(
-  -e "CEREBRAS_API_KEY=${CEREBRAS_API_KEY}"
-  -e "GROQ_API_KEY=${GROQ_API_KEY}"
-  -e "ENABLE_TORCH_COMPILE=${ENABLE_TORCH_COMPILE:-0}"
-)
-[[ -n "${FASTVIDEO_GPU_COUNT:-}" ]] && env_args+=(-e "FASTVIDEO_GPU_COUNT=${FASTVIDEO_GPU_COUNT}")
-
-container_id="$(
-  docker run -d --rm --gpus "${GPU_REQUEST}" --init \
-    -p "${PORT}:8009" \
-    "${env_args[@]}" \
-    -v "${HF_CACHE}:/root/.cache/huggingface" \
-    -v "${OUTPUTS_DIR}:/var/lib/dreamverse/outputs" \
-    --name "${NAME}" \
-    "${IMAGE}"
-)"
-
-cleanup() {
-  if [[ "${DREAMVERSE_KEEP_CONTAINER:-0}" != "1" ]]; then
-    docker rm -f "${NAME}" >/dev/null 2>&1 || true
-  fi
-}
-trap cleanup EXIT
-
-wait_for_endpoint() {
-  local path="$1"
-  local label="$2"
-  local deadline=$((SECONDS + TIMEOUT_SECONDS))
-  local url="http://127.0.0.1:${PORT}${path}"
-
-  echo "Waiting for ${label} at ${url}"
-  while (( SECONDS < deadline )); do
-    if curl -fsS "${url}" >/dev/null 2>&1; then
-      echo "${label} ok"
-      return 0
-    fi
-    if ! docker ps --format '{{.Names}}' | grep -qx "${NAME}"; then
-      echo "Container exited before ${label} became healthy." >&2
-      docker logs "${container_id}" >&2 || true
-      return 1
-    fi
-    sleep "${POLL_SECONDS}"
-  done
-
-  echo "Timed out waiting for ${label}." >&2
-  docker logs "${container_id}" >&2 || true
-  return 1
-}
-
-wait_for_endpoint "/healthz" "healthz"
-wait_for_endpoint "/readyz" "readyz"
-
-echo "Dreamverse Docker smoke passed for ${IMAGE} on host port ${PORT}."

apps/dreamverse/dreamverse/_deps.py

@@ -1,15 +0,0 @@
-from __future__ import annotations
-
-DREAMVERSE_RUNTIME_DEPS_MESSAGE = (
-    "Dreamverse runtime deps missing — install with pip install 'fastvideo[dreamverse]'.")
-
-
-def require_dreamverse_runtime_deps() -> None:
-    try:
-        import cerebras.cloud.sdk  # noqa: F401
-        import openai  # noqa: F401
-    except ModuleNotFoundError as exc:
-        missing_root = (exc.name or "").split(".", 1)[0]
-        if missing_root in {"cerebras", "openai"}:
-            raise SystemExit(DREAMVERSE_RUNTIME_DEPS_MESSAGE) from exc
-        raise

apps/dreamverse/dreamverse/assets.py

@@ -1,184 +0,0 @@
-"""Bounded, runtime-local media library shared by the HTTP and generation APIs."""
-
-from __future__ import annotations
-
-import json
-import math
-import os
-import re
-import shutil
-import subprocess
-import tempfile
-import threading
-import uuid
-from dataclasses import dataclass
-from pathlib import Path
-
-from PIL import Image, UnidentifiedImageError
-
-IMAGE_LIMIT = 15 * 1024 * 1024
-MEDIA_LIMIT = 100 * 1024 * 1024
-STORE_LIMIT = 2 * 1024 * 1024 * 1024
-ASSET_LIMIT = 100
-MAX_MEDIA_SECONDS = 30
-MIME_TYPES = {
-    "image/png": ("image", ".png"),
-    "image/jpeg": ("image", ".jpg"),
-    "image/webp": ("image", ".webp"),
-    "video/mp4": ("video", ".mp4"),
-    "video/quicktime": ("video", ".mov"),
-    "video/webm": ("video", ".webm"),
-    "audio/mpeg": ("audio", ".mp3"),
-    "audio/mp4": ("audio", ".m4a"),
-    "audio/x-m4a": ("audio", ".m4a"),
-    "audio/wav": ("audio", ".wav"),
-    "audio/x-wav": ("audio", ".wav"),
-    "audio/flac": ("audio", ".flac"),
-    "audio/x-flac": ("audio", ".flac"),
-    "audio/ogg": ("audio", ".ogg"),
-    "audio/webm": ("audio", ".webm"),
-}
-
-
-@dataclass(frozen=True)
-class StoredAsset:
-    asset_id: str
-    kind: str
-    path: str
-    name: str
-    mime_type: str
-    size: int
-
-    def public(self) -> dict:
-        return {
-            "asset_id": self.asset_id,
-            "kind": self.kind,
-            "name": self.name,
-            "mime_type": self.mime_type,
-            "size": self.size,
-            "url": f"/assets/{self.asset_id}",
-        }
-
-
-def validate_media(path: Path, mime_type: str) -> None:
-    """Inspect content, not filenames; refuse playlists and non-media uploads."""
-    kind = MIME_TYPES[mime_type][0]
-    if kind == "image":
-        try:
-            with Image.open(path) as img:
-                expected = {"image/png": "PNG", "image/jpeg": "JPEG", "image/webp": "WEBP"}[mime_type]
-                if img.format != expected:
-                    raise ValueError("The image content does not match its file type.")
-                if img.width * img.height > 16_777_216:
-                    raise ValueError("Images must contain at most 16 megapixels.")
-                if getattr(img, "is_animated", False):
-                    raise ValueError("Use a still image or upload the animation as a video.")
-                img.verify()
-        except (UnidentifiedImageError, OSError, Image.DecompressionBombError) as exc:
-            raise ValueError("The image could not be decoded. Use PNG, JPEG, or WebP.") from exc
-        return
-
-    probe = shutil.which(os.getenv("FASTVIDEO_FFPROBE_BIN", "ffprobe"))
-    if not probe:
-        raise ValueError("This runtime needs ffprobe installed to accept video and audio assets.")
-    try:
-        result = subprocess.run(
-            [
-                probe, "-v", "error", "-protocol_whitelist", "file,pipe", "-format_whitelist",
-                "mov,matroska,webm,mp3,wav,flac,ogg", "-show_format", "-show_streams", "-of", "json",
-                str(path)
-            ],
-            check=True,
-            capture_output=True,
-            timeout=15,
-        )
-        info = json.loads(result.stdout)
-        formats = set(info.get("format", {}).get("format_name", "").split(","))
-        if not formats.intersection({"mov", "mp4", "matroska", "webm", "mp3", "wav", "flac", "ogg"}):
-            raise ValueError("Upload a media file, not a playlist or external reference.")
-        streams = [stream for stream in info.get("streams", []) if stream.get("codec_type") == kind]
-        if not streams:
-            raise ValueError(f"The file contains no {kind} stream.")
-        for stream in info.get("streams", []):
-            if stream.get("codec_type") == "audio" and int(stream.get("channels", 0)) not in (1, 2):
-                raise ValueError("H3 references require mono or stereo audio, including video soundtracks.")
-        duration = float(info.get("format", {}).get("duration", "nan"))
-        if not math.isfinite(duration) or not 0 < duration <= MAX_MEDIA_SECONDS:
-            raise ValueError(f"Reference video and audio must be between 0 and {MAX_MEDIA_SECONDS} seconds long.")
-        for stream in streams:
-            if kind == "video" and int(stream.get("width", 0)) * int(stream.get("height", 0)) > 8_294_400:
-                raise ValueError("Reference videos must be 4K or smaller.")
-    except (subprocess.SubprocessError, json.JSONDecodeError, OSError) as exc:
-        raise ValueError("The media file could not be decoded. Check its format and try again.") from exc
-
-
-class AssetStore:
-    """Assets live until deletion or runtime exit; pinned generation inputs cannot be deleted."""
-
-    def __init__(self) -> None:
-        self._directory: tempfile.TemporaryDirectory | None = None
-        self._assets: dict[str, StoredAsset] = {}
-        self._pins: dict[str, int] = {}
-        self._lock = threading.RLock()
-
-    def staging_path(self, mime_type: str) -> Path:
-        with self._lock:
-            if mime_type not in MIME_TYPES:
-                raise ValueError("Unsupported media type. Use PNG/JPEG/WebP, MP4/WebM/MOV, or WAV/MP3/M4A/FLAC/OGG.")
-            if len(self._assets) >= ASSET_LIMIT or sum(item.size for item in self._assets.values()) >= STORE_LIMIT:
-                raise ValueError("The runtime asset library is full. Remove unused assets before uploading more.")
-            if self._directory is None:
-                self._directory = tempfile.TemporaryDirectory(prefix="dreamverse-assets-")
-            return Path(self._directory.name) / f"{uuid.uuid4().hex}{MIME_TYPES[mime_type][1]}"
-
-    def add(self, path: Path, name: str, mime_type: str) -> StoredAsset:
-        validate_media(path, mime_type)
-        size = path.stat().st_size
-        if size == 0 or size > (IMAGE_LIMIT if MIME_TYPES[mime_type][0] == "image" else MEDIA_LIMIT):
-            raise ValueError("The asset is empty or exceeds its upload size limit.")
-        with self._lock:
-            if len(self._assets) >= ASSET_LIMIT or size + sum(item.size
-                                                              for item in self._assets.values()) > STORE_LIMIT:
-                raise ValueError("The runtime asset library is full. Remove unused assets before uploading more.")
-            if self._directory is None or path.parent != Path(self._directory.name):
-                raise ValueError("The asset must be uploaded to this runtime.")
-            display_name = re.sub(r"[\x00-\x1f\x7f/\\]", "_", name).strip()[:200] or "Untitled asset"
-            asset = StoredAsset(path.stem, MIME_TYPES[mime_type][0], str(path), display_name, mime_type, size)
-            self._assets[asset.asset_id] = asset
-            return asset
-
-    def get(self, asset_id: str) -> StoredAsset:
-        with self._lock:
-            if not isinstance(asset_id, str) or not re.fullmatch(r"[a-f0-9]{32}", asset_id):
-                raise ValueError("Invalid asset ID. Upload or select an asset from the library.")
-            asset = self._assets.get(asset_id)
-            if asset is None or not Path(asset.path).is_file():
-                raise ValueError("An asset is no longer available. Upload it again and reselect it.")
-            return asset
-
-    def pin(self, asset_ids: list[str]) -> None:
-        with self._lock:
-            for asset_id in asset_ids:
-                self.get(asset_id)
-            for asset_id in asset_ids:
-                self._pins[asset_id] = self._pins.get(asset_id, 0) + 1
-
-    def release(self, asset_ids: list[str]) -> None:
-        with self._lock:
-            for asset_id in asset_ids:
-                count = self._pins.get(asset_id, 0)
-                if count > 1:
-                    self._pins[asset_id] = count - 1
-                else:
-                    self._pins.pop(asset_id, None)
-
-    def delete(self, asset_id: str) -> None:
-        with self._lock:
-            asset = self.get(asset_id)
-            if self._pins.get(asset_id, 0):
-                raise ValueError("This asset is in use by a generation session. End the session before deleting it.")
-            Path(asset.path).unlink(missing_ok=True)
-            del self._assets[asset_id]
-
-
-asset_store = AssetStore()

apps/dreamverse/dreamverse/av_streaming.py

@@ -1,445 +0,0 @@
-# pyright: reportMissingTypeArgument=false, reportArgumentType=false, reportOptionalSubscript=false, reportOptionalMemberAccess=false, reportConstantRedefinition=false, reportCallIssue=false
-# ruff: noqa: UP007, SIM108, SIM105
-# mypy: ignore-errors
-"""ffmpeg fMP4 muxing with chunk-level event emission.
-
-Self-contained: spawns ffmpeg as a subprocess, pipes raw frames into
-its stdin, reads fragmented-MP4 chunks from stdout, and publishes each
-chunk as a ``StreamEvent`` via the caller-supplied ``publish``
-callback.  Knows nothing about multiprocessing queues, the GPU pool,
-or individual users — the caller decides what "publish" means.
-"""
-import fcntl
-import os
-import shutil
-import subprocess
-import tempfile
-import threading
-import time
-import uuid
-import wave
-from dataclasses import dataclass
-from typing import Union
-from collections.abc import Callable
-
-import numpy as np
-import torch
-
-FFMPEG_BIN = shutil.which(os.getenv("FASTVIDEO_FFMPEG_BIN", "ffmpeg"))
-AV_MEDIA_MIME = os.getenv(
-    "STREAM_MIME_TYPE",
-    'video/mp4; codecs="avc1.42E01E,mp4a.40.2"',
-)
-AV_CHUNK_SIZE_BYTES = 1048576
-TARGET_FPS = 24
-AV_FRAGMENT_DURATION_US = int(os.getenv("FASTVIDEO_FRAG_US", "250000"))
-X264_GOP_FRAMES = int(os.getenv("FASTVIDEO_X264_GOP", "12"))
-X264_PROFILE = os.getenv("FASTVIDEO_X264_PROFILE", "baseline").strip().lower()
-if X264_PROFILE not in {"baseline", "main", "high", "main10", "high10"}:
-    print(f"[WARN] Unsupported FASTVIDEO_X264_PROFILE={X264_PROFILE}; using baseline")
-    X264_PROFILE = "baseline"
-USE_SHARED_STREAM_BUFFER = (os.getenv("FASTVIDEO_USE_SHARED_STREAM_BUFFER", "1").strip().lower()
-                            not in {"0", "false", "no"})
-SHARED_STREAM_BUFFER_BYTES = int(os.getenv("FASTVIDEO_SHARED_STREAM_BUFFER_BYTES", str(256 * 1024 * 1024)))
-
-
-@dataclass
-class StreamInit:
-    """First event emitted — tells the consumer the stream is starting."""
-    stream_id: str
-    mime: str
-    uses_shared_buffer: bool
-
-
-@dataclass
-class StreamChunk:
-    """One fMP4 chunk.  Either ``chunk`` (raw bytes) or
-    ``chunk_offset``+``chunk_length`` (read from the shared buffer)
-    will be populated, never both."""
-    stream_id: str
-    chunk: bytes | None = None
-    chunk_offset: int | None = None
-    chunk_length: int | None = None
-    uses_shared_buffer: bool = False
-
-
-@dataclass
-class StreamComplete:
-    """Final event emitted — muxing finished successfully."""
-    stream_id: str
-    chunks: int
-
-
-StreamEvent = Union[StreamInit, StreamChunk, StreamComplete]
-
-
-def generate_stream_id(segment_idx: int) -> str:
-    """Convenience: build a stream id of the form ``seg007-abcd1234``."""
-    return f"seg{segment_idx:03d}-{uuid.uuid4().hex[:8]}"
-
-
-def _normalize_audio_tensor(audio: object) -> tuple[np.ndarray, int] | None:
-    """Convert audio tensor/array into int16 ndarray [samples, channels]."""
-    if audio is None:
-        return None
-
-    if torch.is_tensor(audio):
-        audio_np = audio.detach().cpu().float().numpy()
-    else:
-        audio_np = np.asarray(audio, dtype=np.float32)
-
-    if audio_np.ndim == 1:
-        audio_np = audio_np[:, None]
-    elif audio_np.ndim == 2:
-        if audio_np.shape[0] <= 8 and audio_np.shape[1] > audio_np.shape[0]:
-            audio_np = audio_np.T
-    else:
-        return None
-
-    audio_np = np.clip(audio_np, -1.0, 1.0)
-    audio_int16 = (audio_np * 32767.0).astype(np.int16)
-    num_channels = audio_int16.shape[1]
-    return audio_int16, num_channels
-
-
-def _write_audio_wav(
-    audio_int16: np.ndarray,
-    num_channels: int,
-    sample_rate: int,
-) -> str:
-    """Write normalized int16 audio to a temporary WAV file."""
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
-        wav_path = f.name
-    try:
-        with wave.open(wav_path, "wb") as wav_file:
-            wav_file.setnchannels(num_channels)
-            wav_file.setsampwidth(2)
-            wav_file.setframerate(sample_rate)
-            wav_file.writeframes(audio_int16.tobytes())
-    except Exception:
-        try:
-            os.unlink(wav_path)
-        except FileNotFoundError:
-            pass
-        raise
-    return wav_path
-
-
-def stream_fmp4(
-    *,
-    frames: list[np.ndarray],
-    audio: object,
-    audio_sample_rate: int | None,
-    stream_id: str,
-    timings: dict,
-    head_trim_frames: int = 0,
-    head_trim_audio_frames: int | None = None,
-    shared_buffer=None,
-    shared_buffer_bytes: int = 0,
-    publish: Callable[[StreamEvent], None],
-    log_prefix: str = "",
-) -> tuple[bool, str | None]:
-    """Encode frames+audio with ffmpeg, publish each fMP4 chunk as an event.
-
-    Args:
-        frames: RGB24 video frames as HxWx3 uint8 arrays.
-        audio: 1D/2D tensor or ndarray, float values in [-1, 1].
-        audio_sample_rate: sample rate of ``audio``.
-        stream_id: caller-supplied identifier carried on every event.
-        timings: dict mutated in place with ffmpeg/stream timing metrics.
-        head_trim_frames: video frames to drop from the start
-            (conditioning overlap).
-        head_trim_audio_frames: video-frame-equivalent audio to drop.
-            Defaults to ``head_trim_frames``.
-        shared_buffer: optional ``mp.RawArray``-compatible object; when
-            provided, chunks are written into it and emitted by offset
-            rather than by bytes (avoids IPC copies).
-        shared_buffer_bytes: size of ``shared_buffer`` in bytes.
-        publish: callback invoked once per stream event.
-        log_prefix: prepended to warning prints (e.g. ``"[GPU 0]"``).
-
-    Returns:
-        ``(True, None)`` on success, ``(False, error_message)`` on
-        failure.  On mid-stream failure, a ``StreamInit`` may have
-        already been published — the caller is responsible for
-        handling that.
-    """
-    if not frames:
-        return False, "no frames returned"
-    if audio is None:
-        return False, "audio is None"
-    if audio_sample_rate is None:
-        return False, "audio_sample_rate is None"
-    if FFMPEG_BIN is None:
-        return False, "ffmpeg not found"
-
-    if head_trim_audio_frames is None:
-        head_trim_audio_frames = head_trim_frames
-
-    normalized_audio = _normalize_audio_tensor(audio)
-    if normalized_audio is None:
-        shape_hint = getattr(audio, "shape", None)
-        return False, f"unsupported audio shape={shape_hint}"
-    audio_int16, num_channels = normalized_audio
-
-    if head_trim_frames < 0:
-        return False, (f"head_trim_frames must be >= 0, "
-                       f"got {head_trim_frames}")
-    if head_trim_frames >= len(frames):
-        return False, (f"head_trim_frames={head_trim_frames} removes "
-                       f"all {len(frames)} frames in segment")
-
-    out_frames = (frames[head_trim_frames:] if head_trim_frames > 0 else frames)
-    sample_rate = int(audio_sample_rate)
-    if head_trim_audio_frames > 0:
-        trim_start_samples = int(round((head_trim_audio_frames / float(TARGET_FPS)) * sample_rate))
-        if trim_start_samples >= audio_int16.shape[0]:
-            return False, ("audio too short after overlap trim: "
-                           f"trim_start_samples={trim_start_samples}"
-                           f", audio_samples={audio_int16.shape[0]}")
-        keep_samples = int(round((len(out_frames) / float(TARGET_FPS)) * sample_rate))
-        trim_end_samples = min(
-            audio_int16.shape[0],
-            trim_start_samples + keep_samples,
-        )
-        if trim_end_samples <= trim_start_samples:
-            return False, ("invalid audio trim range: "
-                           f"start={trim_start_samples}, "
-                           f"end={trim_end_samples}")
-        audio_int16 = audio_int16[trim_start_samples:trim_end_samples]
-
-    height = int(out_frames[0].shape[0])
-    width = int(out_frames[0].shape[1])
-    codec = os.getenv("FASTVIDEO_VIDEO_CODEC", "libx264")
-    t_wav_start = time.perf_counter()
-    wav_path = _write_audio_wav(audio_int16, num_channels, sample_rate)
-    wav_write_ms = (time.perf_counter() - t_wav_start) * 1000
-
-    cmd = [
-        FFMPEG_BIN,
-        "-hide_banner",
-        "-loglevel",
-        "error",
-        "-y",
-        "-f",
-        "rawvideo",
-        "-pix_fmt",
-        "rgb24",
-        "-s:v",
-        f"{width}x{height}",
-        "-r",
-        str(TARGET_FPS),
-        "-i",
-        "pipe:0",
-        "-i",
-        wav_path,
-        "-c:v",
-        codec,
-    ]
-    if codec.endswith("_nvenc"):
-        cmd += [
-            "-preset",
-            os.getenv("FASTVIDEO_NVENC_PRESET", "p1"),
-            "-tune",
-            os.getenv("FASTVIDEO_NVENC_TUNE", "ull"),
-            "-rc",
-            os.getenv("FASTVIDEO_NVENC_RC", "constqp"),
-            "-qp",
-            os.getenv("FASTVIDEO_NVENC_QP", "28"),
-            "-bf",
-            os.getenv("FASTVIDEO_NVENC_BF", "0"),
-        ]
-    else:
-        cmd += [
-            "-preset",
-            os.getenv("FASTVIDEO_X264_PRESET", "ultrafast"),
-            "-tune",
-            "zerolatency",
-            "-profile:v",
-            X264_PROFILE,
-            # Emit frequent keyframes so fragments are independently playable.
-            "-g",
-            str(X264_GOP_FRAMES),
-            "-keyint_min",
-            str(X264_GOP_FRAMES),
-            "-x264-params",
-            "scenecut=0",
-        ]
-    cmd += [
-        "-c:a",
-        "aac",
-        "-pix_fmt",
-        os.getenv("FASTVIDEO_OUTPUT_PIX_FMT", "yuv420p"),
-        "-shortest",
-        "-movflags",
-        "+frag_keyframe+empty_moov+default_base_moof",
-        "-frag_duration",
-        str(AV_FRAGMENT_DURATION_US),
-        "-flush_packets",
-        "1",
-        "-muxdelay",
-        "0",
-        "-muxpreload",
-        "0",
-        "-f",
-        "mp4",
-        "pipe:1",
-    ]
-
-    proc: subprocess.Popen | None = None
-    stderr_chunks: list[bytes] = []
-    writer_error: list[Exception | None] = [None]
-    t_stream_start = time.perf_counter()
-    use_shared_buffer = (USE_SHARED_STREAM_BUFFER and shared_buffer is not None and shared_buffer_bytes > 0)
-    shared_write_offset = 0
-    shared_buffer_fallback = False
-    shared_np = (np.frombuffer(
-        shared_buffer,
-        dtype=np.uint8,
-        count=shared_buffer_bytes,
-    ) if use_shared_buffer else None)
-    chunk_intervals_ms: list[float] = []
-    chunk_publish_ms: list[float] = []
-    chunk_read_ms: list[float] = []
-
-    try:
-        t_proc_spawn_start = time.perf_counter()
-        proc = subprocess.Popen(
-            cmd,
-            stdin=subprocess.PIPE,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
-            bufsize=0,
-        )
-        assert proc.stdin is not None
-        assert proc.stdout is not None
-        assert proc.stderr is not None
-        if hasattr(fcntl, "F_SETPIPE_SZ"):
-            fcntl.fcntl(proc.stdin.fileno(), fcntl.F_SETPIPE_SZ, 1048576)
-            fcntl.fcntl(proc.stdout.fileno(), fcntl.F_SETPIPE_SZ, 1048576)
-        ffmpeg_spawn_ms = (time.perf_counter() - t_proc_spawn_start) * 1000
-
-        def _write_frames():
-            try:
-                for frame in out_frames:
-                    proc.stdin.write(np.ascontiguousarray(frame).tobytes())
-                proc.stdin.close()
-            except Exception as exc:
-                writer_error[0] = exc
-                try:
-                    proc.stdin.close()
-                except Exception:
-                    pass
-
-        def _read_stderr():
-            try:
-                while True:
-                    data = proc.stderr.read(4096)
-                    if not data:
-                        break
-                    stderr_chunks.append(data)
-            except Exception:
-                pass
-
-        writer_thread = threading.Thread(target=_write_frames, daemon=True)
-        stderr_thread = threading.Thread(target=_read_stderr, daemon=True)
-        writer_thread.start()
-        stderr_thread.start()
-
-        publish(StreamInit(
-            stream_id=stream_id,
-            mime=AV_MEDIA_MIME,
-            uses_shared_buffer=use_shared_buffer,
-        ))
-
-        chunk_count = 0
-        total_bytes = 0
-        first_chunk_ms: float | None = None
-        last_chunk_emit_t = time.perf_counter()
-        while True:
-            t_read_start = time.perf_counter()
-            chunk = proc.stdout.read(AV_CHUNK_SIZE_BYTES)
-            t_read_end = time.perf_counter()
-            if not chunk:
-                break
-            chunk_read_ms.append((t_read_end - t_read_start) * 1000)
-            chunk_count += 1
-            total_bytes += len(chunk)
-            if first_chunk_ms is None:
-                first_chunk_ms = (t_read_end - t_stream_start) * 1000
-            chunk_intervals_ms.append((t_read_end - last_chunk_emit_t) * 1000)
-            t_publish_start = time.perf_counter()
-            if use_shared_buffer and not shared_buffer_fallback:
-                chunk_len = len(chunk)
-                write_end = shared_write_offset + chunk_len
-                if write_end <= shared_buffer_bytes:
-                    shared_np[shared_write_offset:write_end] = np.frombuffer(chunk, dtype=np.uint8)
-                    publish(
-                        StreamChunk(
-                            stream_id=stream_id,
-                            chunk_offset=shared_write_offset,
-                            chunk_length=chunk_len,
-                            uses_shared_buffer=True,
-                        ))
-                    shared_write_offset = write_end
-                    chunk_publish_ms.append((time.perf_counter() - t_publish_start) * 1000)
-                    last_chunk_emit_t = time.perf_counter()
-                    continue
-                shared_buffer_fallback = True
-                print(f"{log_prefix} Shared stream buffer exhausted at "
-                      f"{shared_write_offset / (1024 * 1024):.1f}MB; "
-                      "falling back to queue chunk bytes")
-            publish(StreamChunk(
-                stream_id=stream_id,
-                chunk=chunk,
-            ))
-            chunk_publish_ms.append((time.perf_counter() - t_publish_start) * 1000)
-            last_chunk_emit_t = time.perf_counter()
-
-        writer_thread.join(timeout=5.0)
-        rc = proc.wait()
-        stderr_thread.join(timeout=1.0)
-
-        if rc != 0:
-            stderr_tail = b"".join(stderr_chunks).decode(errors="ignore")[-1200:]
-            return False, f"ffmpeg av_fmp4 stream failed (rc={rc}): {stderr_tail}"
-        if writer_error[0] is not None:
-            return False, f"ffmpeg frame writer failed: {writer_error[0]}"
-
-        timings["av_encode_stream_ms"] = (time.perf_counter() - t_stream_start) * 1000
-        timings["av_stream_bytes"] = total_bytes
-        timings["av_trim_head_frames"] = head_trim_frames
-        timings["av_trim_head_audio_frames"] = head_trim_audio_frames
-        timings["av_frames_encoded"] = len(out_frames)
-        timings["av_shared_buffer_used"] = (bool(use_shared_buffer and not shared_buffer_fallback))
-        timings["av_wav_write_ms"] = wav_write_ms
-        timings["av_ffmpeg_spawn_ms"] = ffmpeg_spawn_ms
-        timings["av_first_chunk_ms"] = first_chunk_ms or 0.0
-        if chunk_intervals_ms:
-            timings["av_chunk_interval_ms_min"] = min(chunk_intervals_ms)
-            timings["av_chunk_interval_ms_median"] = float(np.median(chunk_intervals_ms))
-            timings["av_chunk_interval_ms_p95"] = (float(np.percentile(chunk_intervals_ms, 95)))
-            timings["av_chunk_interval_ms_max"] = max(chunk_intervals_ms)
-        if chunk_publish_ms:
-            timings["av_chunk_publish_ms_median"] = float(np.median(chunk_publish_ms))
-            timings["av_chunk_publish_ms_p95"] = float(np.percentile(chunk_publish_ms, 95))
-        if chunk_read_ms:
-            timings["av_chunk_read_ms_median"] = float(np.median(chunk_read_ms))
-            timings["av_chunk_read_ms_p95"] = float(np.percentile(chunk_read_ms, 95))
-        publish(StreamComplete(
-            stream_id=stream_id,
-            chunks=chunk_count,
-        ))
-        return True, None
-    except Exception as exc:
-        return False, str(exc)
-    finally:
-        if proc is not None and proc.poll() is None:
-            try:
-                proc.kill()
-            except Exception:
-                pass
-        try:
-            os.remove(wav_path)
-        except OSError:
-            pass

apps/dreamverse/dreamverse/benchmarks/benchmark_av_streaming.py

@@ -1,288 +0,0 @@
-"""Benchmark the AV streaming hot-path used by dreamverse-server.
-
-Measures wall-time, encoded byte volume, and chunk count for
-``av_streaming.stream_fmp4`` over synthetic frames + audio at
-production resolution. Sweeps codecs (default: ``libx264`` and
-``h264_nvenc`` if the active ffmpeg supports it) and ffmpeg presets so
-the deploy can pick a configuration that achieves a >=1.0 realtime
-ratio (5.04s of generated video produced in <=5.04s wall-time).
-
-Usage::
-
-    python -m dreamverse.benchmarks.benchmark_av_streaming
-    python -m dreamverse.benchmarks.benchmark_av_streaming \\
-        --frames 121 --width 1920 --height 1088 --runs 3 \\
-        --codecs libx264 h264_nvenc --x264-preset ultrafast \\
-        --nvenc-preset p1
-    FASTVIDEO_FFMPEG_BIN=$HOME/opt/ffmpeg-native/bin/ffmpeg \\
-        python -m dreamverse.benchmarks.benchmark_av_streaming
-
-Skips ``h264_nvenc`` automatically if the binary lacks the encoder. This is
-the regression guard for software-encoding overhead that can drain the
-inter-segment playback buffer.
-"""
-from __future__ import annotations
-
-import argparse
-import os
-import shutil
-import statistics
-import subprocess
-import sys
-import time
-from collections.abc import Iterable
-from dataclasses import dataclass
-
-import numpy as np
-import torch
-
-_HERE = os.path.dirname(os.path.abspath(__file__))
-_SERVER_ROOT = os.path.dirname(_HERE)
-if _SERVER_ROOT not in sys.path:
-    sys.path.insert(0, _SERVER_ROOT)
-
-from dreamverse.av_streaming import stream_fmp4  # noqa: E402
-
-
-@dataclass
-class BenchResult:
-    codec: str
-    preset: str
-    runs: int
-    wall_ms_min: float
-    wall_ms_median: float
-    wall_ms_p95: float
-    wall_ms_max: float
-    bytes_median: int
-    chunks_median: float
-    realtime_ratio_median: float
-    error: str | None = None
-
-
-def _make_synthetic_frames(num: int, width: int, height: int, seed: int) -> list[np.ndarray]:
-    rng = np.random.default_rng(seed)
-    base = rng.integers(0, 255, size=(height, width, 3), dtype=np.uint8)
-    out: list[np.ndarray] = []
-    for i in range(num):
-        f = base.copy()
-        f[:, :, 0] = (f[:, :, 0].astype(np.int32) + i * 2) % 256
-        out.append(f)
-    return out
-
-
-def _make_synthetic_audio(num_frames: int, fps: int, sample_rate: int, seed: int) -> torch.Tensor:
-    duration_s = num_frames / fps
-    samples = int(round(duration_s * sample_rate))
-    rng = np.random.default_rng(seed + 1)
-    audio = (rng.uniform(-0.1, 0.1, (2, samples))).astype(np.float32)
-    return torch.from_numpy(audio)
-
-
-def _ffmpeg_supports(codec: str, ffmpeg_bin: str) -> bool:
-    try:
-        out = subprocess.run(
-            [ffmpeg_bin, "-hide_banner", "-encoders"],
-            capture_output=True,
-            text=True,
-            check=False,
-            timeout=10,
-        ).stdout
-    except Exception:
-        return False
-    needle = f" {codec} "
-    return any(needle in line for line in out.splitlines())
-
-
-def _run_one(frames: list[np.ndarray], audio: torch.Tensor, sample_rate: int, codec: str,
-             preset: str) -> tuple[float, int, int, str | None]:
-    timings: dict = {}
-    chunks: list = []
-
-    def _publish(event):
-        chunks.append(event)
-
-    prev_codec = os.environ.get("FASTVIDEO_VIDEO_CODEC")
-    prev_preset = os.environ.get("FASTVIDEO_X264_PRESET")
-    prev_nvenc_preset = os.environ.get("FASTVIDEO_NVENC_PRESET")
-    os.environ["FASTVIDEO_VIDEO_CODEC"] = codec
-    if codec.endswith("_nvenc"):
-        os.environ["FASTVIDEO_NVENC_PRESET"] = preset
-    else:
-        os.environ["FASTVIDEO_X264_PRESET"] = preset
-
-    t0 = time.perf_counter()
-    try:
-        ok, err = stream_fmp4(
-            frames=frames,
-            audio=audio,
-            audio_sample_rate=sample_rate,
-            stream_id="bench",
-            timings=timings,
-            head_trim_frames=0,
-            head_trim_audio_frames=0,
-            shared_buffer=None,
-            shared_buffer_bytes=0,
-            publish=_publish,
-            log_prefix="[bench]",
-        )
-    finally:
-        if prev_codec is None:
-            os.environ.pop("FASTVIDEO_VIDEO_CODEC", None)
-        else:
-            os.environ["FASTVIDEO_VIDEO_CODEC"] = prev_codec
-        if prev_preset is None:
-            os.environ.pop("FASTVIDEO_X264_PRESET", None)
-        else:
-            os.environ["FASTVIDEO_X264_PRESET"] = prev_preset
-        if prev_nvenc_preset is None:
-            os.environ.pop("FASTVIDEO_NVENC_PRESET", None)
-        else:
-            os.environ["FASTVIDEO_NVENC_PRESET"] = prev_nvenc_preset
-    wall_ms = (time.perf_counter() - t0) * 1000.0
-    if not ok:
-        return wall_ms, 0, 0, err or "stream_fmp4 returned False"
-    total_bytes = int(timings.get("av_stream_bytes", 0))
-    return wall_ms, total_bytes, len(chunks), None
-
-
-def benchmark(codecs: Iterable[str], runs: int, frames_n: int, width: int, height: int, fps: int, sample_rate: int,
-              x264_preset: str, nvenc_preset: str, seed: int, ffmpeg_bin: str) -> list[BenchResult]:
-    print(f"[bench] ffmpeg_bin={ffmpeg_bin}")
-    print(f"[bench] frames={frames_n} {width}x{height} fps={fps} "
-          f"audio_sr={sample_rate} runs/codec={runs}")
-    frames = _make_synthetic_frames(frames_n, width, height, seed)
-    audio = _make_synthetic_audio(frames_n, fps, sample_rate, seed)
-    playable_s = frames_n / fps
-    print(f"[bench] playable={playable_s:.3f}s "
-          f"(realtime_ratio = playable / wall_time; >= 1.0 means no "
-          f"buffer drain)")
-
-    results: list[BenchResult] = []
-    for codec in codecs:
-        preset = nvenc_preset if codec.endswith("_nvenc") else x264_preset
-        if not _ffmpeg_supports(codec, ffmpeg_bin):
-            results.append(
-                BenchResult(codec=codec,
-                            preset=preset,
-                            runs=0,
-                            wall_ms_min=0,
-                            wall_ms_median=0,
-                            wall_ms_p95=0,
-                            wall_ms_max=0,
-                            bytes_median=0,
-                            chunks_median=0,
-                            realtime_ratio_median=0,
-                            error=f"{codec} not in ffmpeg"))
-            continue
-        walls: list[float] = []
-        sizes: list[int] = []
-        chunkcounts: list[int] = []
-        last_err: str | None = None
-        for run in range(runs):
-            wall_ms, total_bytes, chunk_count, err = _run_one(frames, audio, sample_rate, codec, preset)
-            print(f"[bench] codec={codec:12s} preset={preset:9s} "
-                  f"run={run + 1}/{runs}  wall={wall_ms:7.1f}ms  "
-                  f"bytes={total_bytes:>9d}  chunks={chunk_count:>3d}  "
-                  f"realtime={playable_s / (wall_ms / 1000.0):5.2f}x"
-                  f"{'  ERR=' + err if err else ''}")
-            if err is not None:
-                last_err = err
-                continue
-            walls.append(wall_ms)
-            sizes.append(total_bytes)
-            chunkcounts.append(chunk_count)
-        if not walls:
-            results.append(
-                BenchResult(codec=codec,
-                            preset=preset,
-                            runs=0,
-                            wall_ms_min=0,
-                            wall_ms_median=0,
-                            wall_ms_p95=0,
-                            wall_ms_max=0,
-                            bytes_median=0,
-                            chunks_median=0,
-                            realtime_ratio_median=0,
-                            error=last_err or "all runs failed"))
-            continue
-        walls_sorted = sorted(walls)
-        p95_idx = max(0, int(round(0.95 * (len(walls_sorted) - 1))))
-        wall_med = statistics.median(walls)
-        results.append(
-            BenchResult(
-                codec=codec,
-                preset=preset,
-                runs=len(walls),
-                wall_ms_min=min(walls),
-                wall_ms_median=wall_med,
-                wall_ms_p95=walls_sorted[p95_idx],
-                wall_ms_max=max(walls),
-                bytes_median=int(statistics.median(sizes)),
-                chunks_median=statistics.median(chunkcounts),
-                realtime_ratio_median=playable_s / (wall_med / 1000.0),
-            ))
-    return results
-
-
-def _print_summary(results: list[BenchResult]) -> None:
-    print()
-    print("=== summary ===")
-    header = (f"{'codec':14s} {'preset':10s} {'runs':>4s} "
-              f"{'wall_med_ms':>11s} {'wall_p95_ms':>11s} "
-              f"{'bytes_med':>10s} {'realtime':>8s}  notes")
-    print(header)
-    print("-" * len(header))
-    for r in results:
-        if r.error is not None:
-            print(f"{r.codec:14s} {r.preset:10s} {r.runs:>4d} "
-                  f"{'-':>11s} {'-':>11s} {'-':>10s} {'-':>8s}  "
-                  f"ERR: {r.error}")
-            continue
-        print(f"{r.codec:14s} {r.preset:10s} {r.runs:>4d} "
-              f"{r.wall_ms_median:>11.1f} {r.wall_ms_p95:>11.1f} "
-              f"{r.bytes_median:>10d} {r.realtime_ratio_median:>7.2f}x  "
-              f"{'OK' if r.realtime_ratio_median >= 1.0 else 'BUFFER DRAINS'}")
-
-
-def main() -> int:
-    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    p.add_argument("--frames", type=int, default=121, help="num frames (default: 121, matches NUM_FRAMES)")
-    p.add_argument("--width", type=int, default=1920)
-    p.add_argument("--height", type=int, default=1088)
-    p.add_argument("--fps", type=int, default=24)
-    p.add_argument("--sample-rate", type=int, default=24000)
-    p.add_argument("--runs", type=int, default=3, help="runs per codec (default: 3 — 1 warmup + 2 timed in median)")
-    p.add_argument("--codecs",
-                   nargs="+",
-                   default=["libx264", "h264_nvenc"],
-                   help="codecs to benchmark; missing ones are skipped")
-    p.add_argument("--x264-preset", default="ultrafast")
-    p.add_argument("--nvenc-preset", default="p1")
-    p.add_argument("--seed", type=int, default=0)
-    args = p.parse_args()
-
-    ffmpeg_bin = shutil.which(os.getenv("FASTVIDEO_FFMPEG_BIN", "ffmpeg"))
-    if ffmpeg_bin is None:
-        print("ffmpeg not found", file=sys.stderr)
-        return 2
-
-    results = benchmark(
-        codecs=args.codecs,
-        runs=args.runs,
-        frames_n=args.frames,
-        width=args.width,
-        height=args.height,
-        fps=args.fps,
-        sample_rate=args.sample_rate,
-        x264_preset=args.x264_preset,
-        nvenc_preset=args.nvenc_preset,
-        seed=args.seed,
-        ffmpeg_bin=ffmpeg_bin,
-    )
-    _print_summary(results)
-    any_below = any(r.error is None and r.realtime_ratio_median < 1.0 for r in results)
-    return 1 if any_below else 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())