mc dfsft

fix tf scheduler
add logger
2026-04-01 03:53:20 +00:00 · 2026-03-17 18:19:27 -07:00 · 2026-03-16 17:27:59 -07:00 · 2026-03-16 15:49:09 -07:00 · 2026-03-15 20:55:53 -07:00 · 2026-03-15 16:59:34 -07:00
269 changed files with 25716 additions and 763933 deletions
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+# 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.
@@ -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/`.
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+# Lessons Learned Database
+
+This directory stores documented mistakes, unexpected behaviors, and their fixes.
+Each lesson is a permanent record that helps agents and humans avoid repeating
+past errors.
+
+## When to Create a Lesson
+
+- An experiment failed for a non-obvious reason.
+- A configuration or hyperparameter choice led to wasted compute.
+- A porting, data, or infrastructure issue was discovered and resolved.
+- A workaround was needed for a known framework/library bug.
+
+## File Naming
+
+`<YYYY-MM-DD>_<short-slug>.md` — e.g., `2026-03-02_lr-too-high-for-lora.md`
+
+## Template
+
+```markdown
+---
+date: <ISO-8601>
+experiment: <reference to experiment_journal.md entry, if applicable>
+category: hyperparameter | data | infrastructure | evaluation | porting | other
+severity: critical | important | minor
+---
+
+# <Short Descriptive Title>
+
+## What Happened
+<Description of the problem and its symptoms.>
+
+## Root Cause
+<Analysis of why it happened.>
+
+## Fix / Workaround
+<What resolved the issue.>
+
+## Prevention
+<How to avoid this in the future — updated skills, SOPs, or checks.>
+```
+
+## Usage
+
+- Before starting a task, **search this directory** for relevant lessons.
+- After completing or failing a task, **check if a new lesson should be created**.
+- Periodically review lessons for **patterns** — recurring themes may warrant
+  a new skill, SOP, or codebase fix.
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+# 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
+```
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+# 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
@@ -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`
+
+-->
@@ -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"}
@@ -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._
@@ -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
+```
@@ -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.
@@ -0,0 +1,57 @@
+---
+name: <skill-name>
+description: <one-line description — Codex uses this for implicit invocation matching>
+---
+
+# <Skill Name>
+
+## Purpose
+<Why this skill exists and when to use it.>
+
+## Prerequisites
+- <What must be true before using this skill>
+
+## Inputs
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `param1` | Yes | ... |
+
+## Steps
+
+1. **Step 1 title**
+   - Detail...
+
+2. **Step 2 title**
+   - Detail...
+
+## Outputs
+- <What this skill produces>
+
+## Example Usage
+
+```
+<Example invocation or prompt snippet>
+```
+
+## References
+- <Links to relevant files in the codebase>
+
+---
+
+## Folder Structure
+
+Each skill lives in its own directory under `.agents/skills/`:
+
+```
+.agents/skills/<skill-name>/
+├── SKILL.md          # Required: instructions + metadata (this file)
+├── scripts/          # Optional: executable helper scripts
+├── references/       # Optional: documentation, papers
+└── assets/           # Optional: templates, resources
+```
+
+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"}
+```
@@ -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 |
@@ -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 |
@@ -0,0 +1,7 @@
+{"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"}
@@ -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 |
@@ -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 |
@@ -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 |
@@ -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 |
@@ -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 |
@@ -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.
@@ -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.
@@ -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.
@@ -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.
@@ -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.
@@ -0,0 +1,46 @@
+{
+  "benchmark_id": "wan-t2v-1.3b-2gpu",
+  "description": "Wan2.1 T2V 1.3B inference performance",
+  "model": {
+    "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    "model_short_name": "Wan2.1-T2V-1.3B"
+  },
+  "init_kwargs": {
+    "num_gpus": 2,
+    "flow_shift": 7.0,
+    "sp_size": 2,
+    "tp_size": 1,
+    "vae_sp": true,
+    "vae_tiling": true,
+    "text_encoder_precisions": ["fp32"]
+  },
+  "generation_kwargs": {
+    "height": 480,
+    "width": 832,
+    "num_frames": 45,
+    "num_inference_steps": 4,
+    "guidance_scale": 3,
+    "embedded_cfg_scale": 6,
+    "seed": 1024,
+    "fps": 24,
+    "neg_prompt": "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": 1,
+    "num_measurement_runs": 3,
+    "required_gpus": 2
+  },
+  "thresholds": {
+    "L40S": {
+      "max_generation_time_s": 34.0,
+      "max_peak_memory_mb": 11000.0
+    },
+    "default": {
+      "max_generation_time_s": 120.0,
+      "max_peak_memory_mb": 30000.0
+    }
+  }
+}
@@ -139,18 +139,6 @@ steps:
                  - TEST_TYPE=training_vsa
                agents:
                  queue: "default"
-            - path:
-                - "fastvideo/**"
-                - "fastvideo-kernel/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Inference Tests STA"
-                env:
-                  - TEST_TYPE=inference_sta
-                agents:
-                  queue: "default"
            - path:
                - "fastvideo-kernel/**"
                - "pyproject.toml"
@@ -183,6 +171,37 @@ steps:
                  - TEST_TYPE=unit_test
                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: "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: "API Server Tests"
+                env:
+                  - TEST_TYPE=api_server
+                agents:
+                  queue: "default"
            # - path:
            #     - "scripts/lora_extraction/**"
            #     - "pyproject.toml"
@@ -51,6 +51,7 @@ else
 fi

 MODAL_TEST_FILE="fastvideo/tests/modal/pr_test.py"
+MODAL_SSIM_TEST_FILE="fastvideo/tests/modal/ssim_test.py"

 if [ -z "${TEST_TYPE:-}" ]; then
    log "Error: TEST_TYPE environment variable is not set"
@@ -75,7 +76,7 @@ case "$TEST_TYPE" in
        ;;
    "ssim")
        log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_SSIM_TEST_FILE::run_ssim_tests"
        ;;
    "training")
        log "Running training tests..."
@@ -89,10 +90,6 @@ case "$TEST_TYPE" in
        log "Running training VSA tests..."
        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests_VSA"
        ;;
-    "inference_sta")
-        log "Running inference STA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_STA"
-        ;;
    "kernel_tests")
        log "Running kernel tests..."
        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_kernel_tests"
@@ -122,6 +119,14 @@ case "$TEST_TYPE" in
        log "Running LoRA extraction tests..."
        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_lora_extraction_tests"
        ;;
+    "performance")
+        log "Running performance tests..."
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_performance_tests"
+        ;;
+    "api_server")
+        log "Running API server integration tests..."
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_api_server_tests"
+        ;;
    *)
        log "Error: Unknown test type: $TEST_TYPE"
        exit 1
@@ -0,0 +1,43 @@
+## Purpose
+
+<!-- What does this PR do? Link the related issue if applicable. -->
+
+Fixes #
+
+## Changes
+
+<!-- Describe your changes concisely. What approach did you take? -->
+
+-
+
+## Test Plan
+
+<!-- How did you verify your changes? Paste exact commands and output. -->
+
+```bash
+# Commands you ran
+```
+
+## Test Results
+
+<!-- Paste test output, before/after comparisons, or SSIM scores for model changes. -->
+
+<details>
+<summary>Test output</summary>
+
+```
+# Paste output here
+```
+
+</details>
+
+## Checklist
+
+- [ ] I ran `pre-commit run --all-files` and fixed all issues
+- [ ] I added or updated tests for my changes
+- [ ] I updated documentation if needed
+- [ ] I considered GPU memory impact of my changes
+
+**For model/pipeline changes, also check:**
+- [ ] I verified SSIM regression tests pass
+- [ ] I updated the support matrix if adding a new model
@@ -47,16 +47,6 @@ on:
        required: false
        default: false
        type: boolean
-      run_inference_test_STA:
-        description: "Run inference-test-STA"
-        required: false
-        default: false
-        type: boolean
-      run_precision_test_STA:
-        description: "Run precision-test-STA"
-        required: false
-        default: false
-        type: boolean
      run_precision_test_VSA:
        description: "Run precision-test-VSA"
        required: false
@@ -90,8 +80,6 @@ jobs:
      transformer-test: ${{ steps.filter.outputs.transformer-test }}
      training-test: ${{ steps.filter.outputs.training-test }}
      training-test-VSA: ${{ steps.filter.outputs.training-test-VSA }}
-      inference-test-STA: ${{ steps.filter.outputs.inference-test-STA }}
-      precision-test-STA: ${{ steps.filter.outputs.precision-test-STA }}
      precision-test-VSA: ${{ steps.filter.outputs.precision-test-VSA }}
      unit-test: ${{ steps.filter.outputs.unit-test }}
    steps:
@@ -106,12 +94,6 @@ jobs:
              - 'docker/Dockerfile.python3.10'
              - 'docker/Dockerfile.python3.11'
              - 'docker/Dockerfile.python3.12'
-            sta-kernel-paths: &sta-kernel-paths
-              - 'csrc/attn/sliding_tile_attn/**'
-              - 'csrc/attn/sliding_tile_attn/tk/**'
-              - 'csrc/attn/sliding_tile_attn/setup.py'
-              - 'csrc/attn/sliding_tile_attn/config_sta.py'
-              - 'csrc/attn/sliding_tile_attn/st_attn.cpp'
            vsa-kernel-paths: &vsa-kernel-paths
              - 'csrc/attn/video_sparse_attn/**'
              - 'csrc/attn/video_sparse_attn/tk/**'
@@ -148,13 +130,6 @@ jobs:
              - 'fastvideo/**'
              - *common-paths
              - *vsa-kernel-paths
-            inference-test-STA:
-              - 'fastvideo/**'
-              - *common-paths
-              - *sta-kernel-paths
-            precision-test-STA:
-              - *common-paths
-              - *sta-kernel-paths
            precision-test-VSA:
              - *common-paths
              - *vsa-kernel-paths
@@ -282,44 +257,6 @@ jobs:
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}

-  inference-test-STA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.inference-test-STA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_inference_test_STA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "inference-test-STA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 2
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/tests/inference/STA -srP"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  precision-test-STA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.precision-test-STA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_precision_test_STA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "precision-test-STA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 1
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && python csrc/attn/tests/test_sta.py"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
  precision-test-VSA:
    needs: change-filter
    if: >-
@@ -378,7 +315,7 @@ jobs:

  runpod-cleanup:
    # Add other jobs to this list as you create them
-    needs: [encoder-test, vae-test, transformer-test, ssim-test, training-test, training-test-VSA, inference-test-STA, precision-test-STA, precision-test-VSA] 
+    needs: [encoder-test, vae-test, transformer-test, ssim-test, training-test, training-test-VSA, precision-test-VSA] 
    if: ${{ always() && ((github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || github.event_name == 'workflow_dispatch') }}
    runs-on: ubuntu-latest
    steps:
@@ -395,7 +332,7 @@ jobs:

      - name: Cleanup all RunPod instances
        env:
-          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12", "training-test", "training-test-VSA", "inference-test-STA", "precision-test-STA", "precision-test-VSA"]'
+          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12", "training-test", "training-test-VSA", "precision-test-VSA"]'
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
        run: python .github/scripts/runpod_cleanup.py
@@ -1,249 +0,0 @@
-name: Publish Sliding Tile Attention Kernel to PyPI on Version Change
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "csrc/attn/sliding_tile_attn/setup.py"
-  workflow_dispatch:
-
-jobs:
-  check-version-change:
-    runs-on: ubuntu-latest
-    outputs:
-      version-changed: ${{ steps.check-version.outputs.changed }}
-      new-version: ${{ steps.check-version.outputs.new-version }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 2
-
-      - name: Check if version changed
-        id: check-version
-        run: |
-          cd csrc/attn/sliding_tile_attn
-          # Get current commit's version
-          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.py)
-          echo "New version: $NEW_VERSION"
-          
-          # Get previous version from git history
-          OLD_VERSION=$(git show HEAD~1:./setup.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
-          echo "Old version: $OLD_VERSION"
-          
-          if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
-            echo "Version changed from $OLD_VERSION to $NEW_VERSION"
-            echo "changed=true" >> $GITHUB_OUTPUT
-            echo "new-version=$NEW_VERSION" >> $GITHUB_OUTPUT
-          else
-            echo "Version did not change"
-            echo "changed=false" >> $GITHUB_OUTPUT
-          fi
-
-  build_wheels:
-    name: Build Wheel
-    needs: check-version-change
-    if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
-    runs-on: ${{ matrix.os }}
-
-    strategy:
-      fail-fast: false
-      matrix:
-          # Using ubuntu-20.04 instead of 22.04 for more compatibility (glibc). Ideally we'd use the
-          # manylinux docker image, but I haven't figured out how to install CUDA on manylinux.
-          os: [ubuntu-22.04]
-          python-version: ['3.10', '3.11', '3.12', '3.13']
-          torch-version: ['2.5.1', '2.6.0']
-          cuda-version: ['12.4.1', '12.5.1', '12.6.3']
-
-    steps:
-      - name: Free up disk space
-        run: |
-          echo "Initial disk space:"
-          df -h
-          
-          # Remove large directories
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /usr/local/share/boost
-          sudo rm -rf /usr/share/swift
-          sudo rm -rf /usr/local/lib/node_modules
-          sudo rm -rf /usr/local/share/powershell
-          sudo rm -rf /usr/share/rust
-          sudo rm -rf /usr/local/.ghcup
-          
-          # Remove cached files
-          sudo rm -rf /var/lib/apt/lists/*
-          sudo rm -rf /var/cache/apt/archives/*
-          
-          echo "Disk space after cleanup:"
-          df -h
-
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install CUDA ${{ matrix.cuda-version }}
-        uses: Jimver/cuda-toolkit@v0.2.21
-        id: cuda-toolkit
-        with:
-          cuda: ${{ matrix.cuda-version }}
-          linux-local-args: '["--toolkit"]'
-          method: 'network'
-
-      - name: Install dependencies (GCC, Clang, CUDA Paths, Git)
-        run: |
-          sudo apt update
-          sudo apt install -y git patchelf gcc-11 g++-11 clang-11
-          sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
-
-          # Allow Git to Access Safe Directory
-          git config --global --add safe.directory /__w/FastVideo/FastVideo
-
-          # Set CUDA environment variables
-          export CUDA_HOME=/usr/local/cuda-${{ matrix.cuda-version }}
-          export PATH=${CUDA_HOME}/bin:${PATH}
-          export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
-          
-          # Verify installation
-          gcc --version
-          g++ --version
-          clang-11 --version
-          nvcc --version
-
-      - name: Install PyTorch ${{ matrix.torch-version }}+cu${{ matrix.cuda-version }}
-        run: |
-          pip install --upgrade pip
-          # With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
-          # AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
-          pip install typing-extensions==4.12.2
-          # We want to figure out the CUDA version to download pytorch
-          # e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
-          # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
-          export TORCH_CUDA_VERSION=124
-          pip install --no-cache-dir torch==${{ matrix.torch-version }} --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
-          nvcc --version
-          python --version
-          python -c "import torch; print('PyTorch:', torch.__version__)"
-          python -c "import torch; print('CUDA:', torch.version.cuda)"
-          python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
-
-      - name: Build wheel
-        run: |
-          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
-          
-          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
-          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
-          # However this still fails so I'm using a newer version of setuptools
-          pip install setuptools
-          pip install ninja packaging wheel
-
-          cd csrc/attn/sliding_tile_attn  # Move into the correct folder
-          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup.py bdist_wheel --dist-dir=dist
-
-      - name: Rename wheel file
-        run: |
-          cd csrc/attn/sliding_tile_attn
-
-          CUDA_SHORT_VERSION=$(echo ${{ matrix.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
-          TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-version }} | cut -d. -f1,2)
-          # Get the correct version format
-          tmpname=cu${CUDA_SHORT_VERSION}torch${TORCH_SHORT_VERSION}
-          wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2")
-          # Rename with version information
-          ls dist/*whl |xargs -I {} mv {} dist/${wheel_name}
-          echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
-
-      - name: Upload wheel artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.wheel_name }}
-          path: csrc/attn/sliding_tile_attn/dist/*.whl
-          retention-days: 90
-
-  publish_package:
-    name: Publish package
-    needs: [build_wheels, check-version-change]
-    if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
-    runs-on: ubuntu-22.04
-    permissions:
-      id-token: write  # Needed for OIDC Trusted Publishing
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-      
-      - name: Install CUDA 12.4.1
-        uses: Jimver/cuda-toolkit@v0.2.21
-        id: cuda-toolkit
-        with:
-          cuda: 12.4.1
-          linux-local-args: '["--toolkit"]'
-          method: 'network'
-          sub-packages: '["nvcc"]'
-
-      - name: Install dependencies (GCC, Clang, CUDA Paths, Git)
-        run: |
-          sudo apt update
-          sudo apt install -y git patchelf gcc-11 g++-11 clang-11
-          sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
-
-          # Allow Git to Access Safe Directory
-          git config --global --add safe.directory /__w/FastVideo/FastVideo
-
-          # Set CUDA environment variables
-          export CUDA_HOME=/usr/local/cuda-12.4.1
-          export PATH=${CUDA_HOME}/bin:${PATH}
-          export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
-          
-          # Verify installation
-          gcc --version
-          g++ --version
-          clang-11 --version
-          nvcc --version
-
-      - name: Install PyTorch 2.5.1+cu12.4.1
-        run: |
-          pip install --upgrade pip
-          # With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
-          # AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
-          pip install typing-extensions==4.12.2
-          # We want to figure out the CUDA version to download pytorch
-          # e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
-          # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
-          export TORCH_CUDA_VERSION=124
-          pip install --no-cache-dir torch==2.5.1 --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
-          nvcc --version
-          python --version
-          python -c "import torch; print('PyTorch:', torch.__version__)"
-          python -c "import torch; print('CUDA:', torch.version.cuda)"
-          python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
-
-      - name: Build source distribution
-        run: |
-          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
-          
-          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
-          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
-          # However this still fails so I'm using a newer version of setuptools
-          pip install setuptools
-          pip install ninja packaging wheel
-
-          cd csrc/attn/sliding_tile_attn  # Move into the correct folder
-          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup.py sdist --dist-dir=dist
-
-      - name: Publish release distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          packages-dir: csrc/attn/sliding_tile_attn/dist/
@@ -83,3 +83,7 @@ docs/distillation/examples/

 dmd_t2v_output/
 preprocess_output_text/
+
+.claude/
+.codex/
+openspec/
@@ -60,7 +60,7 @@ repos:
  hooks:
  - id: mypy
    args: [--python-version, '3.10', --follow-imports, "skip", "--disable-error-code", "union-attr", "--disable-error-code", "override" ]
-    additional_dependencies: [types-cachetools, types-setuptools, types-PyYAML, types-requests]
+    additional_dependencies: [types-aiofiles, types-cachetools, types-setuptools, types-PyYAML, types-requests]
 - repo: local
  hooks:
  - id: check-filenames
@@ -40,3 +40,17 @@
  - test evidence (`pytest`/SSIM outputs or rationale if skipped),
  - linked issue/PR context,
  - screenshots or sample outputs for UI/demo/docs changes.
+
+## Agent Infrastructure
+
+This repository is agent-friendly. Before doing any work, read:
+
+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.
+
+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.
@@ -44,15 +44,15 @@ FastVideo has the following features:

 ## Getting Started

-We recommend using an environment manager such as `Conda` to create a clean environment:
+We recommend using [uv](https://docs.astral.sh/uv/) to create a clean environment. If you previously used Conda, switching to uv generally gives faster and more stable installs.

 ```bash
-# Create and activate a new conda environment
-conda create -n fastvideo python=3.12
-conda activate fastvideo
+# Create and activate a new uv environment
+uv venv --python 3.12 --seed
+source .venv/bin/activate

 # Install FastVideo
-pip install fastvideo
+uv pip install fastvideo
 ```

 Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.
@@ -93,7 +93,6 @@ def main():
    # Generate the video
    video = generator.generate_video(
        prompt,
-        return_frames=True,  # Also return frames from this call (defaults to False)
        output_path="my_videos/",  # Controls where videos are saved
        save_video=True
    )
@@ -9,11 +9,12 @@ import datetime
 import locale
 import os
 import re
+import shutil
 import subprocess
 import sys
-# Unlike the rest of the PyTorch this file must be python2 compliant.
-# This script outputs relevant system environment info
-# Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
+# This script outputs relevant system environment info.
+# Run it with: python collect_env.py
+# Requires Python 3.10+ (matches fastvideo); uses shutil.which (Python 3.3+).
 from collections import namedtuple

 from fastvideo.envs import environment_variables
@@ -495,8 +496,7 @@ def get_pip_packages(run_lambda, patterns=None):

        if pip_available:
            cmd = [sys.executable, '-mpip', 'list', '--format=freeze']
-        elif os.environ.get("UV") is not None:
-            print("uv is set")
+        elif shutil.which("uv") is not None:
            cmd = ["uv", "pip", "list", "--format=freeze"]
        else:
            raise RuntimeError(
@@ -443,7 +443,6 @@
        1025,
        "fixed",
        24,
-        -99999,
        -99999
      ],
      "auto_widget_states": {
@@ -491,11 +490,6 @@
          "isAuto": true,
          "value": -99999,
          "cachedValue": "X://insert/path/here.mp4"
-        },
-        "enable_teacache": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
        }
      }
    },
@@ -642,4 +636,4 @@
    "VHS_KeepIntermediate": true
  },
  "version": 0.4
-}
+}
@@ -353,8 +353,7 @@
        1024,
        "fixed",
        24,
-        "X://insert/path/here.mp4",
-        true
+        "X://insert/path/here.mp4"
      ],
      "auto_widget_states": {
        "height": {
@@ -401,11 +400,6 @@
          "isAuto": true,
          "value": "X://insert/path/here.mp4",
          "cachedValue": "X://insert/path/here.mp4"
-        },
-        "enable_teacache": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
        }
      }
    },
@@ -694,4 +688,4 @@
    "VHS_KeepIntermediate": true
  },
  "version": 0.4
-}
+}
@@ -31,9 +31,6 @@ class InferenceArgs:
                "image_path": ("STRING", {
                    "default": "X://insert/path/here.mp4"
                }),
-                "enable_teacache": ([True, False], {
-                    "default": False
-                }),
            }
        }

@@ -57,7 +54,6 @@ class InferenceArgs:
        seed,
        fps,
        image_path,
-        enable_teacache,
    ):
        raw_args = {
            "height": height,
@@ -69,7 +65,6 @@ class InferenceArgs:
            "seed": seed,
            "fps": fps,
            "image_path": image_path,
-            "enable_teacache": enable_teacache,
        }

        # Filter out keys where value is -99999, handling different types properly
@@ -552,7 +552,7 @@ app.registerExtension({
                ]
                const floatWidgetNames = ["embedded_cfg_scale", "guidance_scale"]
                const comboWidgetNames = ["vae_tiling", "vae_precision", "vae_sp", "text_encoder_precision", "precision",
-                    "load_encoder", "load_decoder", "use_tiling", "use_temporal_tiling", "use_parallel_tiling", "dit_cpu_offload", "enable_teacache"
+                    "load_encoder", "load_decoder", "use_tiling", "use_temporal_tiling", "use_parallel_tiling", "dit_cpu_offload"
                ]
                const stringWidgetNames = ["prefix", "quant_config", "lora_config", "image_path"]

@@ -54,7 +54,8 @@ To use this:
 1. **Set Context**: In your pipeline or generation loop, use the `set_forward_context` context manager.
 2. **Access Context**: Inside your attention backend, use `get_forward_context()`.

-See [`docs/attention/sta/index.md`](../sta/index.md) (Sliding Tile Attention) for an example of how complex configuration (window sizes) is passed this way.
+See [`docs/attention/sta/index.md`](../sta/index.md) for a legacy STA example
+of passing complex configuration (window sizes) through `ForwardContext`.

 ## 3. Adding Compiled Kernels (C++/CUDA)

@@ -5,13 +5,16 @@ FastVideo provides highly optimized custom attention kernels to accelerate video
 ## Supported Kernels

 * **[Video Sparse Attention (VSA)](vsa/index.md)**: Sparse attention mechanism selecting top-k blocks.
-* **[Sliding Tile Attention (STA)](sta/index.md)**: Optimized attention for window-based video generation.
+* **[Sliding Tile Attention (STA)](sta/index.md)**: STA kernel support is kept in
+  `fastvideo-kernel`; full FastVideo STA pipeline workflow is archived in
+  `sta_do_not_delete`.
 * **Backend development guide**: See the developer guide at
  [Attention Backend Development](../contributing/attention_backend.md).

 ## General Build Instructions

-These instructions apply to building the `fastvideo-kernel` package from source, which includes both STA and VSA kernels.
+These instructions apply to building the `fastvideo-kernel` package from
+source, which includes both STA and VSA kernels.

 ### Prerequisites

@@ -1,27 +1,84 @@
 # Sliding Tile Attention (STA)

-Optimized attention for window-based video generation (e.g., HunyuanVideo).
+STA inference integration is archived from `main`.

-## Installation
+The full STA pipeline code (including mask search and STA inference wiring in
+`fastvideo/`) is preserved in:

-STA is included in the `fastvideo-kernel` package. See the [main Attention page](../index.md) for build instructions.
+- https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete

-## Usage
+In this branch, STA kernels in `fastvideo-kernel` are still kept.

-```python
-from fastvideo_kernel import sliding_tile_attention
+## Why STA is not in `main`

-# q, k, v: [batch_size, num_heads, seq_length, head_dim]
-# window_size: List of (t, h, w) tiles. Tile size is (6, 8, 8).
-# text_length: Number of text tokens (0-256)
+We do not keep STA pipeline integration in `main` because we believe Video
+Sparse Attention (VSA) is strictly better than STA for the actively maintained
+FastVideo inference path.

-out = sliding_tile_attention(
-    q, k, v, 
-    window_size=[(3, 3, 3)],  # Example window
-    text_length=256
-)
+## What to checkout for STA workflows
+
+To run the full STA workflow, switch to the archived branch:
+
+```bash
+git fetch origin
+git checkout sta_do_not_delete
 ```

+## Mask Search (archive branch)
+
+The reference script is:
+
+- `examples/inference/sta_mask_search/inference_wan_sta.sh`
+
+It runs two stages:
+
+1. `STA_searching` (full search), output at
+   `inference_results/sta/mask_search_full`
+2. `STA_tuning` (sparse tuning), output at
+   `inference_results/sta/mask_search_sparse`
+
+Run:
+
+```bash
+export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
+export FASTVIDEO_ATTENTION_CONFIG=assets/mask_strategy_wan.json
+
+bash examples/inference/sta_mask_search/inference_wan_sta.sh
+```
+
+## STA Inference (archive branch)
+
+With a selected mask strategy, run inference with:
+
+```bash
+export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
+export FASTVIDEO_ATTENTION_CONFIG=assets/mask_strategy_wan.json
+
+fastvideo generate \
+  --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers \
+  --num-gpus 2 \
+  --tp-size 2 \
+  --sp-size 2 \
+  --height 768 \
+  --width 1280 \
+  --num-frames 69 \
+  --num-inference-steps 50 \
+  --prompt "A cinematic wildlife shot of a lion walking in golden grasslands." \
+  --output-path outputs_video/STA/
+```
+
+Python usage on the archive branch can also set `STA_mode` in
+`VideoGenerator.from_pretrained(...)`:
+
+- `STA_searching`
+- `STA_tuning`
+- `STA_inference`
+
+## Kernel-level API (current branch)
+
+STA kernels remain available from `fastvideo-kernel`. See
+[Attention overview](../index.md) for build instructions.
+
 ## Citation

 If you use Sliding Tile Attention in your research, please cite:
@@ -14,24 +14,11 @@ improving performance, or fixing a bug.

 For a full install checklist, see `docs/getting_started/installation/gpu.md`.

-## Local development (Conda + editable install)
+## Local development (UV + editable install)

-Install Miniconda:
+If you previously used Conda for local setup, switch to uv for a faster and more stable development environment.

-```bash
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
-bash Miniconda3-latest-Linux-x86_64.sh
-source ~/.bashrc
-```
-
-Create and activate a Conda environment:
-
-```bash
-conda create -n fastvideo python=3.12 -y
-conda activate fastvideo
-```
-
-Install `uv` (optional, but recommended):
+Install `uv`:

 ```bash
 curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -39,6 +26,20 @@ curl -LsSf https://astral.sh/uv/install.sh | sh
 wget -qO- https://astral.sh/uv/install.sh | sh
 ```

+Create and activate a uv environment (recommended):
+
+```bash
+uv venv --python 3.12 --seed
+source .venv/bin/activate
+```
+
+Conda alternative (supported):
+
+```bash
+conda create -n fastvideo python=3.12 -y
+conda activate fastvideo
+```
+
 Clone the repo:

 ```bash
@@ -51,7 +52,7 @@ Install FastVideo in editable mode and set up hooks:
 uv pip install -e .[dev]

 # Optional: FlashAttention (builds native kernels)
-uv pip install flash-attn --no-build-isolation
+uv pip install flash-attn --no-build-isolation -v

 # Linting, formatting, static typing
 pre-commit install --hook-type pre-commit --hook-type commit-msg
@@ -8,7 +8,7 @@ This guide explains how to add and run tests in FastVideo. The testing suite is
 * **Component Tests**: Located in `fastvideo/tests/encoders`, `fastvideo/tests/transformers`, and `fastvideo/tests/vaes`. These verify the loading and basic functionality of model components.
 * **SSIM Tests**: Located in `fastvideo/tests/ssim`. These are regression tests that compare generated videos against reference videos using the Structural Similarity Index Measure (SSIM) to detect quality degradation.
 * **Training Tests**: Located in `fastvideo/tests/training`. These validate training loops, loss calculations, and specific training techniques like LoRA, Distillation, and VSA.
-* **Inference Tests**: Located in `fastvideo/tests/inference`. These test specialized inference pipelines and optimizations (e.g., STA, V-MoBA).
+* **Inference Tests**: Located in `fastvideo/tests/inference`. These test specialized inference pipelines and optimizations (e.g., VSA, V-MoBA).

 For now, we will focus on **SSIM Tests**.

@@ -0,0 +1,369 @@
+# Training Architecture
+
+!!! warning "Work in Progress"
+    This training architecture (`fastvideo/train/`) is under active development
+    and is replacing the older `fastvideo/training/` module. APIs, config
+    formats, and supported methods may change. See the
+    [Current Status](#current-status) section for what is implemented so far.
+
+FastVideo's training framework (`fastvideo/train/`) is built around a
+**pluggable, YAML-driven architecture** that cleanly separates **models**,
+**training methods**, and **infrastructure** into independent, composable
+layers. A single YAML config file is all that is needed to train any supported
+model with any supported algorithm — no code changes required to mix and match.
+
+---
+
+## Motivation
+
+Training video diffusion models involves a tangle of concerns: model loading,
+noise scheduling, distillation algorithms, distributed strategies,
+checkpointing, and validation. Existing training scripts tend to hard-wire
+these together, making it painful to:
+
+1. **Try a new distillation algorithm** on an existing model (requires forking
+   the training loop).
+2. **Add a new model** to an existing algorithm (requires re-implementing
+   boilerplate).
+3. **Switch distributed strategies** (FSDP, TP, SP) without touching algorithm
+   code.
+4. **Resume, checkpoint, and validate** uniformly across all combinations.
+
+The training framework solves this by making each axis of variation an
+independent plugin.
+
+---
+
+## Architecture Overview
+
+```
+YAML Config
+    |
+    v
+------------------+     +---------------------+     +------------------+
+|   Models Layer   |     |   Methods Layer     |     | Infrastructure   |
+|  (per-role)      |     |   (algorithm)       |     |   Layer          |
+|                  |     |                     |     |                  |
+| - ModelBase      |<----|  - TrainingMethod   |---->| - Trainer        |
+| - CausalModelBase|     |  - single_train_step|     | - Callbacks      |
+|                  |     |  - backward         |     | - Checkpoint     |
+| Roles:           |     |  - optimizers       |     | - Tracker (W&B)  |
+|  student         |     |                     |     | - Dataloader     |
+|  teacher         |     | Algorithms:         |     |                  |
+|  critic          |     |  DMD2, SelfForcing, |     | Distributed:     |
+|                  |     |  SFT, DFSFT         |     |  HSDP, TP, SP    |
+------------------+     +---------------------+     +------------------+
+```
+
+### Three Layers
+
+| Layer | Responsibility | Extension point |
+|-------|---------------|-----------------|
+| **Models** (`fastvideo/train/models/`) | Load transformer + scheduler, define `predict_noise`, `predict_x0`, `add_noise`, `backward`. Each training role (student/teacher/critic) is an independent instance. | Subclass `ModelBase` (or `CausalModelBase` for streaming). |
+| **Methods** (`fastvideo/train/methods/`) | Implement the training algorithm: own role models, define `single_train_step` + `backward`, manage optimizers/schedulers. | Subclass `TrainingMethod`. |
+| **Infrastructure** (`fastvideo/train/trainer.py`, `utils/`, `callbacks/`) | Training loop, gradient accumulation, distributed setup, checkpointing (DCP), W&B tracking, validation, EMA, grad clipping. | Add callbacks; everything else is shared. |
+
+---
+
+## YAML-Driven Configuration
+
+Everything is configured declaratively. The `_target_` field selects the Python
+class to instantiate:
+
+```yaml
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method
+  rollout_mode: simulate
+  dmd_denoising_steps: [1000, 850, 700, 550, 350, 275, 200, 125]
+  generator_update_interval: 5
+  real_score_guidance_scale: 3.5
+  # ...
+
+training:
+  distributed: { num_gpus: 8, sp_size: 1, tp_size: 1 }
+  data: { data_path: ..., num_latent_t: 20, num_frames: 77 }
+  optimizer: { learning_rate: 2.0e-6, betas: [0.0, 0.999] }
+  loop: { max_train_steps: 4000 }
+  checkpoint: { output_dir: outputs/my_run }
+
+callbacks:
+  grad_clip: { max_grad_norm: 1.0 }
+  validation: { pipeline_target: ..., every_steps: 100 }
+```
+
+To switch from DMD2 to SFT, change the `method._target_` and remove the
+teacher/critic — no code changes needed.
+
+---
+
+## Model Abstraction
+
+### `ModelBase` — Standard (Bidirectional) Models
+
+Every role gets its own `ModelBase` instance owning a `transformer` and
+`noise_scheduler`. The base class defines:
+
+- **`prepare_batch()`** — Convert raw dataloader output into forward-ready
+  `TrainingBatch`.
+- **`add_noise()`** — Apply forward-process noise at a given timestep.
+- **`predict_noise()` / `predict_x0()`** — Run the transformer and return
+  predictions.
+- **`backward()`** — Backward pass that restores forward context (attention
+  metadata, timesteps).
+- **`init_preprocessors()`** — Lazy-load VAE, build dataloader (called only on
+  the student).
+
+### `CausalModelBase` — Streaming / Causal Models
+
+Extends `ModelBase` with streaming inference primitives for causal video
+generation:
+
+```python
+class CausalModelBase(ModelBase):
+    def clear_caches(self, *, cache_tag: str = "pos") -> None: ...
+    def predict_noise_streaming(
+        self, ..., cache_tag, store_kv, cur_start_frame
+    ) -> Tensor | None: ...
+    def predict_x0_streaming(
+        self, ..., cache_tag, store_kv, cur_start_frame
+    ) -> Tensor | None: ...
+```
+
+KV caches are **internal** to the model instance, keyed by `cache_tag`. The
+method controls when to store (`store_kv=True`) vs. read-only
+(`store_kv=False`), enabling block-by-block causal rollout during training.
+
+---
+
+## Training Methods
+
+### DMD2 (Distribution Matching Distillation)
+
+**Roles:** student (trainable) + teacher (frozen) + critic (trainable)
+
+The student learns to generate clean video in few steps by matching the
+teacher's score function, with a critic network providing a learned fake-score
+baseline.
+
+- **Rollout modes:**
+  - `simulate` — Student starts from pure noise and iteratively denoises
+    through the full step schedule.
+  - `data_latent` — Student denoises from a single randomly-noised data
+    sample.
+- **Losses:** Generator loss (DMD gradient) + critic flow-matching loss, with
+  alternating updates (`generator_update_interval`).
+
+### Self-Forcing (Causal DMD)
+
+**Roles:** student (causal, trainable) + teacher (frozen) + critic (trainable)
+
+Extends DMD2 for **streaming/causal video generation**. The key idea: during
+training, the student processes video in temporal chunks, using its own
+previously-denoised outputs as context for future chunks — simulating online
+autoregressive rollout.
+
+- Video is split into blocks of `chunk_size` latent frames.
+- Each block is denoised through the student's step schedule; a random
+  early-exit step is sampled per block.
+- After denoising a block, its output is fed back (with optional
+  `context_noise`) as KV cache context for subsequent blocks via
+  `predict_noise_streaming(store_kv=True)`.
+- Supports SDE and ODE sampling during rollout.
+- Selective gradient control: `enable_gradient_in_rollout`,
+  `start_gradient_frame`.
+
+### Supervised Fine-Tuning (SFT)
+
+**Roles:** student only
+
+Standard flow-matching loss between predicted and ground-truth noise/x0.
+
+### Diffusion-Forcing SFT (DFSFT)
+
+**Roles:** student only
+
+SFT with **inhomogeneous (per-chunk) timesteps** — each temporal chunk in a
+video gets a different noise level. This trains the model to handle mixed-noise
+inputs, which is a prerequisite for causal/streaming inference where earlier
+frames are cleaner than later ones.
+
+---
+
+## Training Loop
+
+The `Trainer` runs a standard loop with pluggable method and callbacks:
+
+```
+for step in range(start_step, max_steps):
+    for accum_iter in range(grad_accum_steps):
+        batch <- dataloader
+        loss_map, outputs, metrics <- method.single_train_step(batch, step)
+        method.backward(loss_map, outputs)
+
+    callbacks.on_before_optimizer_step()   # grad clipping
+    method.optimizers_schedulers_step()
+    method.optimizers_zero_grad()
+    callbacks.on_training_step_end()       # logging
+    checkpoint_manager.maybe_save(step)
+    callbacks.on_validation_begin()        # periodic inference
+```
+
+### Callbacks
+
+- **GradNormClipCallback** — Per-module gradient norm logging + global
+  clipping.
+- **ValidationCallback** — Periodic inference sampling with configurable
+  pipeline, sampling steps, and guidance scale.
+- **EMACallback** — Exponential moving average of student weights.
+
+### Checkpointing
+
+- DCP (Distributed Checkpoint) format, compatible with FSDP/HSDP.
+- Saves: model weights, optimizer states, scheduler states, RNG states (per
+  role).
+- Full resume support: auto-restores step counter and all RNG states.
+
+---
+
+## Getting Started
+
+```bash
+# Install
+uv pip install -e .[dev]
+
+# Run DMD2 distillation on Wan 2.1
+torchrun --nproc_per_node=8 -m fastvideo.train.entrypoint.train \
+    --config examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml
+
+# Run SFT fine-tuning
+torchrun --nproc_per_node=8 -m fastvideo.train.entrypoint.train \
+    --config examples/train/finetune_wan2.1_t2v_1.3B_vsa_phase3.4_0.9sparsity.yaml
+```
+
+Example configs are in `examples/train/`.
+
+---
+
+## File Structure
+
+```
+fastvideo/train/
+  trainer.py                  # Training loop
+  models/
+    base.py                   # ModelBase, CausalModelBase ABCs
+    wan/wan.py                # Wan 2.1 T2V model plugin
+    wangame/wangame.py        # WanGame 2.1 I2V model plugin
+    wangame/wangame_causal.py # WanGame causal (streaming) plugin
+  methods/
+    base.py                   # TrainingMethod ABC
+    distribution_matching/
+      dmd2.py                 # DMD2 distillation
+      self_forcing.py         # Self-Forcing (causal DMD)
+    fine_tuning/
+      finetune.py             # Supervised fine-tuning
+      dfsft.py                # Diffusion-forcing SFT
+  callbacks/
+    grad_clip.py              # Gradient clipping + norm logging
+    validation.py             # Periodic inference validation
+    ema.py                    # EMA weight averaging
+  entrypoint/
+    train.py                  # CLI entrypoint (torchrun)
+  utils/
+    config.py                 # YAML parser -> RunConfig
+    builder.py                # build_from_config: model/method instantiation
+    training_config.py        # TrainingConfig dataclass
+    dataloader.py             # Dataset/dataloader construction
+    optimizer.py              # Optimizer/scheduler construction
+    checkpoint.py             # DCP save/resume
+    tracking.py               # W&B tracker
+```
+
+---
+
+## Current Status
+
+| Component | Status |
+|-----------|--------|
+| Core framework (trainer, config, callbacks) | Implemented and tested |
+| `WanModel` (Wan 2.1 T2V) | Implemented and tested |
+| `WanGameModel` (WanGame 2.1 I2V) | Implemented and tested |
+| `WanGameCausalModel` (streaming) | Implemented and tested |
+| `WanCausalModel` (Wan T2V causal) | In progress |
+| DMD2 method | Implemented and tested |
+| Self-Forcing method | Implemented and tested |
+| SFT method | Implemented and tested |
+| DFSFT method | Implemented and tested |
+| DCP checkpointing + resume | Implemented and tested |
+| EMA callback | Implemented |
+| Validation callback | Implemented and tested |
+| Causal DMD inference pipeline | Implemented |
+
+---
+
+## Open Questions
+
+We welcome community feedback on the following topics:
+
+### Model Plugin API
+
+The current `ModelBase` interface requires implementing 6 methods. Is this the
+right granularity?
+
+- Should `prepare_batch` be split into separate concerns (noise sampling,
+  timestep sampling, attention metadata)?
+- Should `backward` be lifted out of the model and into the method/trainer?
+
+### Causal Streaming Interface
+
+`CausalModelBase` adds `predict_noise_streaming` / `predict_x0_streaming` with
+cache management. Alternatives considered:
+
+- **(a) Current:** Cache is internal to the model, keyed by `cache_tag`.
+  Simple but couples cache lifecycle to model.
+- **(b) External cache:** Method owns the cache dict, passes it into predict
+  calls. More explicit but verbose.
+- **(c) Context manager:** `with model.streaming_context(tag) as ctx: ...` —
+  cleaner lifecycle but harder to compose.
+
+### Method Composition
+
+Currently, methods are monolithic classes. Should we support composing methods
+(e.g., DFSFT pre-training followed by Self-Forcing distillation) within a
+single config? Or is sequential training with checkpoint handoff sufficient?
+
+### New Models and Methods
+
+What models and training methods should we prioritize next?
+
+- **Models:** HunyuanVideo, CogVideoX, other Wan variants?
+- **Methods:** Consistency models, progressive distillation, reward-based
+  fine-tuning?
+
+### Distributed Strategy
+
+Currently supports HSDP (hybrid sharded data parallel) + TP + SP. Are there
+scenarios where the current distributed setup is insufficient? Should we add
+pipeline parallelism for very large models?
+
+---
+
+## References
+
+- [Self-Forcing paper](https://arxiv.org/abs/2406.05477) — Chen et al., 2024.
+- [DMD2 paper](https://arxiv.org/abs/2405.14867) — Yin et al., 2024.
+- [Diffusion Forcing paper](https://arxiv.org/abs/2407.01392) — Chen et al.,
+  2024.
@@ -8,17 +8,9 @@ FastVideo supports the following hardware platforms:

 ## Quick Installation

-### Using pip
+### Using uv (recommended)

-```bash
-# Create and activate a new conda environment
-conda create -n fastvideo python=3.12
-conda activate fastvideo
-
-pip install fastvideo
-```
-
-### Using uv
+Use uv as the default environment manager for faster and more stable installs.

 ```bash
 # Create and activate a new uv environment
@@ -28,21 +20,32 @@ source .venv/bin/activate
 uv pip install fastvideo
 ```

+### Using Conda (alternative)
+
+```bash
+# Create and activate a new conda environment
+conda create -n fastvideo python=3.12 -y
+conda activate fastvideo
+
+pip install fastvideo
+```
+
 ### From source

 ```bash
 git clone https://github.com/hao-ai-lab/FastVideo.git
 cd FastVideo
-pip install -e .
-
-# or if you are using uv
 uv pip install -e .
+
+# optional: install flash-attn
+uv pip install flash-attn --no-build-isolation -v
 ```

-Also optionally install flash-attn:
+Alternative with Conda environment:

 ```bash
-pip install flash-attn --no-build-isolation
+pip install -e .
+pip install flash-attn --no-build-isolation -v
 ```

 ## Hardware Requirements
@@ -12,8 +12,20 @@ Instructions to install FastVideo for NVIDIA CUDA GPUs.
 ## Set up using Python
 ### Create a new Python environment

-#### Conda
-You can create a new python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html)
+#### uv
+Recommended default: use [uv](https://docs.astral.sh/uv/) for faster and more stable environment setup.
+
+Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, create a new environment using:
+
+```console
+# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools`.
+uv venv --python 3.12 --seed
+source .venv/bin/activate
+```
+
+#### Conda (alternative)
+You can also create a Python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).
+
 ##### 1. Install Miniconda (if not already installed)

 ```bash
@@ -25,34 +37,35 @@ source ~/.bashrc
 ##### 2. Create and activate a Conda environment for FastVideo

 ```bash
-# (Recommended) Create a new conda environment.
+# Create and activate a Conda environment
 conda create -n fastvideo python=3.12 -y
 conda activate fastvideo
 ```

-#### uv
-
-Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
-
-```console
-# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment.
-uv venv --python 3.12 --seed
-source .venv/bin/activate
-```
-
 ### Installation

-```bash
-pip install fastvideo
+#### With uv (recommended)

-# or if you are using uv
+```bash
 uv pip install fastvideo
 ```

-Also optionally install flash-attn:
+Also optionally install FlashAttention:

 ```bash
-pip install flash-attn --no-build-isolation
+uv pip install flash-attn --no-build-isolation -v
+```
+
+#### With Conda environment (alternative)
+
+```bash
+pip install fastvideo
+```
+
+Also optionally install FlashAttention:
+
+```bash
+pip install flash-attn --no-build-isolation -v
 ```

 ### Installation from Source
@@ -68,18 +81,27 @@ git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
 Basic installation:

 ```bash
-pip install -e .
-
-# or if you are using uv
 uv pip install -e .
 ```

+Alternative with Conda environment:
+
+```bash
+pip install -e .
+```
+
 ### Optional Dependencies

 #### Flash Attention

 ```bash
-pip install flash-attn --no-build-isolation
+uv pip install flash-attn --no-build-isolation -v
+```
+
+Alternative with Conda environment:
+
+```bash
+pip install flash-attn --no-build-isolation -v
 ```

 ## Set up using Docker
@@ -11,9 +11,20 @@ Instructions to install FastVideo for Apple Silicon.

 ### Create a new Python environment

-#### Conda
+#### uv
+Recommended default: use [uv](https://docs.astral.sh/uv/) for faster and more stable environment setup.

-You can create a new python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html)
+Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, create a new environment using:
+
+```console
+# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools`.
+uv venv --python 3.12 --seed
+source .venv/bin/activate
+```
+
+#### Conda (alternative)
+
+You can also create a Python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).

 ##### 1. Install Miniconda (if not already installed)

@@ -26,21 +37,10 @@ source ~/.zshrc
 ##### 2. Create and activate a Conda environment for FastVideo

 ```bash
-# (Recommended) Create a new conda environment.
 conda create -n fastvideo python=3.12.4 -y
 conda activate fastvideo
 ```

-#### uv
-
-Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
-
-```console
-# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment.
-uv venv --python 3.12 --seed
-source .venv/bin/activate
-```
-
 ### Dependencies

 ```
@@ -49,11 +49,16 @@ brew install ffmpeg

 ### Installation

+#### With uv (recommended)
+
+```bash
+uv pip install fastvideo
+```
+
+#### With Conda environment (alternative)
+
 ```bash
 pip install fastvideo
-
-# or if you are using uv
-uv pip install fastvideo
 ```

 ### Installation from Source
@@ -69,12 +74,15 @@ git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
 Basic installation:

 ```bash
-pip install -e .
-
-# or if you are using uv
 uv pip install -e .
 ```

+Alternative with Conda environment:
+
+```bash
+pip install -e .
+```
+
 ## Development Environment Setup

 If you're planning to contribute to FastVideo please see the following page:
@@ -7,18 +7,18 @@ Get up and running with FastVideo in minutes!
 First, install FastVideo:

 ```bash
-# Create and activate a new conda environment
-conda create -n fastvideo python=3.12
-conda activate fastvideo
+# If you previously used Conda, use uv instead for a faster, more stable setup
+uv venv --python 3.12 --seed
+source .venv/bin/activate

 # Install FastVideo
-pip install fastvideo
+uv pip install fastvideo
 ```

 Also optionally install flash-attn:

 ```bash
-pip install flash-attn --no-build-isolation
+uv pip install flash-attn --no-build-isolation -v
 ```

 ## Basic Usage
@@ -41,7 +41,6 @@ def main():
    # Generate the video
    video = generator.generate_video(
        prompt,
-        return_frames=True,  # Also return frames from this call (defaults to False)
        output_path="my_videos/",  # Controls where videos are saved
        save_video=True
    )
@@ -26,8 +26,7 @@ FastVideo is an inference and post-training framework for diffusion models. It f
 FastVideo has the following features:

 - State-of-the-art performance optimizations for inference
-  - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
-  - [TeaCache](https://arxiv.org/pdf/2411.19108)
+  - [Sliding Tile Attention](attention/sta/index.md)
  - [Sage Attention](https://arxiv.org/abs/2410.02367)
 - E2E post-training support
  - Data preprocessing pipeline for video data
@@ -45,7 +44,7 @@ Use the navigation menu on the left to explore different sections:
 - **Inference**: Learn how to use FastVideo for video generation
 - **Training**: Data preprocessing and fine-tuning workflows
 - **Distillation**: Post-training optimization techniques
- **Sliding Tile Attention**: Advanced attention mechanisms
+- **Sliding Tile Attention**: Legacy workflow docs and kernel notes
 - **Video Sparse Attention**: Efficient attention for video models
 - **Design**: Framework architecture and design principles
 - **Developer Guide**: Contributing and development setup
@@ -123,7 +123,7 @@ self.attn = DistributedAttention(
    softmax_scale=None,
    causal=False,
    supported_attention_backends=(
-        AttentionBackendEnum.SLIDING_TILE_ATTN,
+        AttentionBackendEnum.VIDEO_SPARSE_ATTN,
        AttentionBackendEnum.FLASH_ATTN,
        AttentionBackendEnum.TORCH_SDPA,
    )
@@ -0,0 +1,460 @@
+# Inference Architecture
+
+This section documents the FastVideo inference pipeline: how models are
+discovered, configs resolved, components loaded, and stages composed to
+generate video. Training-specific code paths (FSDP, gradient checkpointing)
+are out of scope.
+
+## Registries
+
+FastVideo uses three registries that work together to resolve a
+user-provided `model_path` into a runnable pipeline.
+
+### Model Registry (`fastvideo/models/registry.py`)
+
+Maps HuggingFace architecture class names (e.g. `"WanTransformer3DModel"`)
+to FastVideo model classes. Two discovery mechanisms:
+
+1. **Hardcoded dicts** — `_TEXT_TO_VIDEO_DIT_MODELS`, `_VAE_MODELS`,
+   `_SCHEDULERS`, `_TEXT_ENCODER_MODELS`, `_IMAGE_ENCODER_MODELS`,
+   `_UPSAMPLERS`, `_AUDIO_MODELS`. Each entry is
+   `{hf_class_name: (component_name, module_name, class_name)}`.
+2. **AST-based discovery** — `_discover_and_register_models()` walks
+   `fastvideo/models/` and parses each `.py` file's AST looking for an
+   `EntryClass` variable assignment. Discovered models take priority over
+   hardcoded entries. For example,
+   `fastvideo/models/dits/wanvideo.py` exports
+   `EntryClass = WanTransformer3DModel`.
+
+Both feed into a unified `_FAST_VIDEO_MODELS` dict, which populates the
+singleton `ModelRegistry` — an instance of `_ModelRegistry`. Components are
+wrapped in `_LazyRegisteredModel` for deferred import.
+
+**Key API:** `ModelRegistry.resolve_model_cls(architectures)` iterates
+candidate architecture strings and returns the first matching
+`(model_cls, arch)` tuple. Called by `TransformerLoader`, `VAELoader`, etc.
+
+### Config Registry (`fastvideo/registry.py`)
+
+Maps model paths and names to `(PipelineConfig, SamplingParam)` class
+pairs. Registration happens at module load via `_register_configs()`, which
+calls `register_configs()` for each model family:
+
+```python
+register_configs(
+    sampling_param_cls=WanT2V_1_3B_SamplingParam,
+    pipeline_config_cls=WanT2V480PConfig,
+    hf_model_paths=["Wan-AI/Wan2.1-T2V-1.3B-Diffusers"],
+    model_detectors=[lambda path: "wanpipeline" in path.lower()],
+)
+```
+
+Each call populates three data structures:
+- `_CONFIG_REGISTRY: dict[str, ConfigInfo]` — auto-incrementing ID to
+  `ConfigInfo(sampling_param_cls, pipeline_config_cls)`.
+- `_MODEL_HF_PATH_TO_NAME: dict[str, str]` — HF path to registry ID.
+- `_MODEL_NAME_DETECTORS: list[tuple[str, Callable]]` — lambda detectors.
+
+**Resolution priority** (`_get_config_info()`):
+1. Exact HF path match in `_MODEL_HF_PATH_TO_NAME`.
+2. Partial match on short model name (last path segment, case-insensitive).
+3. Detector-based match — runs each detector against the lowercased path
+   and the `_class_name` from `model_index.json`.
+4. `RuntimeError` if no match.
+
+**Top-level resolver:** `get_model_info(model_path, pipeline_type,
+workload_type)` combines config resolution with pipeline resolution to
+return a `ModelInfo(pipeline_cls, sampling_param_cls, pipeline_config_cls)`.
+
+### Pipeline Registry (`fastvideo/pipelines/pipeline_registry.py`)
+
+Discovers pipeline classes by scanning Python packages under
+`fastvideo/pipelines/{basic,preprocess,training}/`.
+
+`import_pipeline_classes()` iterates architecture subdirectories
+(e.g. `wan/`, `hunyuan/`), imports each module, and collects those
+exporting an `EntryClass` attribute. Supports single class or list.
+Returns `{pipeline_type_str: {pipeline_class_name: pipeline_cls}}`.
+
+`_PipelineRegistry.resolve_pipeline_cls(pipeline_name, pipeline_type,
+workload_type)` looks up the pipeline class by the `_class_name` field
+from `model_index.json`.
+
+## Config Mechanism
+
+### Config Hierarchy
+
+```
+PipelineConfig                    (fastvideo/configs/pipelines/base.py)
+├── WanT2V480PConfig              (fastvideo/configs/pipelines/wan.py)
+│   ├── WanT2V720PConfig
+│   └── WanI2V480PConfig
+├── HunyuanConfig                 (fastvideo/configs/pipelines/hunyuan.py)
+├── LTX2T2VConfig                 (fastvideo/configs/pipelines/ltx2.py)
+├── CosmosConfig                  (fastvideo/configs/pipelines/cosmos.py)
+└── ... (15+ model families)
+```
+
+`PipelineConfig` holds:
+- Video generation params: `embedded_cfg_scale`, `flow_shift`,
+  `disable_autocast`, `is_causal`.
+- Nested model configs: `dit_config: DiTConfig`, `vae_config: VAEConfig`,
+  `text_encoder_configs: tuple[EncoderConfig, ...]`.
+- Precision settings: `dit_precision`, `vae_precision`,
+  `text_encoder_precisions`.
+
+Model-specific subclasses override defaults. For example,
+`WanT2V480PConfig` sets `flow_shift=3.0` and uses `WanVideoConfig` as
+its DiT config.
+
+### ModelConfig / ArchConfig (`fastvideo/configs/models/base.py`)
+
+`ModelConfig` wraps an `ArchConfig` using `__getattr__` proxy — attribute
+access falls through to `arch_config` transparently. `ArchConfig` holds
+architecture fields from `config.json` (hidden_size, num_attention_heads,
+etc.) and is immutable after initialization. `update_model_arch()` writes
+to `ArchConfig`; `update_model_config()` writes to `ModelConfig` fields.
+
+Concrete hierarchy: `DiTConfig` → `DiTArchConfig`, `VAEConfig` →
+`VAEArchConfig`, `EncoderConfig` → `EncoderArchConfig`.
+
+### Config Construction
+
+- `PipelineConfig.from_pretrained(model_path)` — resolves config class
+  via `get_pipeline_config_cls_from_name()`, instantiates with defaults.
+- `PipelineConfig.from_kwargs(kwargs)` — resolves class, optionally loads
+  JSON via `load_from_json()`, then applies CLI overrides via
+  `update_config_from_dict()`.
+- `dump_to_json()` / `load_from_json()` — JSON persistence. Callable
+  fields and `arch_config` are excluded from dumps.
+
+### SamplingParam (`fastvideo/configs/sample/`)
+
+Generation parameters separate from pipeline config. Each model family
+provides defaults:
+
+```python
+@dataclass
+class WanT2V_1_3B_SamplingParam(SamplingParam):
+    height: int = 480
+    width: int = 832
+    num_frames: int = 81
+    guidance_scale: float = 3.0
+    num_inference_steps: int = 50
+```
+
+## Component Loading
+
+### ComponentLoader (`fastvideo/models/loader/component_loader.py`)
+
+Abstract base with a `load(model_path, fastvideo_args)` method.
+`ComponentLoader.for_module_type(module_type, library)` is a factory
+that dispatches to specialized loaders via a `module_loaders` dict:
+
+| Module type | Loader class | Library |
+|---|---|---|
+| `scheduler` | `SchedulerLoader` | diffusers |
+| `transformer`, `transformer_2`, `transformer_3` | `TransformerLoader` | diffusers |
+| `vae` | `VAELoader` | diffusers |
+| `text_encoder`, `text_encoder_2`, `text_encoder_3` | `TextEncoderLoader` | transformers |
+| `tokenizer`, `tokenizer_2`, `tokenizer_3` | `TokenizerLoader` | transformers |
+| `image_encoder` | `ImageEncoderLoader` | transformers |
+| `image_processor`, `feature_extractor` | `ImageProcessorLoader` | transformers |
+| `audio_vae`, `audio_decoder` | `AudioDecoderLoader` | diffusers |
+| `vocoder` | `VocoderLoader` | diffusers |
+| `upsampler`, `upsampler_2` | `UpsamplerLoader` | diffusers |
+
+`TransformerLoader` reads `config.json` from the component directory,
+resolves the class via `ModelRegistry.resolve_model_cls()`, instantiates
+the model, and loads safetensors weights. CPU offload and layerwise
+offload are applied based on `FastVideoArgs`.
+
+Unknown module types fall back to `GenericComponentLoader`.
+
+### model_index.json
+
+Diffusers-format JSON at the model root. Keys are module names; values
+are `[library, class_name]` tuples:
+
+```json
+{
+    "_class_name": "WanPipeline",
+    "_diffusers_version": "0.24.0",
+    "transformer": ["diffusers", "WanTransformer3DModel"],
+    "vae": ["diffusers", "AutoencoderKLWan"],
+    "text_encoder": ["transformers", "UMT5EncoderModel"],
+    "tokenizer": ["transformers", "AutoTokenizer"],
+    "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"]
+}
+```
+
+`ComposedPipelineBase.load_modules()` reads this file via
+`_load_config()`, strips metadata keys (`_class_name`,
+`_diffusers_version`, `_name_or_path`), detects MoE pipelines via
+`boundary_ratio`, then loads each module listed in the pipeline's
+`required_config_modules`. Modules not in `required_config_modules` are
+skipped. `_extra_config_module_map` allows aliasing (e.g. mapping
+`"transformer_2"` to an alternate directory name).
+
+`PipelineComponentLoader.load_module()` orchestrates per-component
+loading by calling `ComponentLoader.for_module_type()` then `.load()`.
+
+## Stage Design
+
+### PipelineStage (`fastvideo/pipelines/stages/base.py`)
+
+Abstract base class using the Template Method pattern:
+
+- `__call__(batch, fastvideo_args)` — orchestrates verification, timing,
+  and error handling. Not overridden by subclasses.
+- `forward(batch, fastvideo_args) -> ForwardBatch` — abstract, contains
+  the stage logic.
+- `verify_input()` / `verify_output()` — optional hooks returning
+  `VerificationResult`. Default: no checks.
+
+When `fastvideo_args.enable_stage_verification` is `True`, `__call__`
+runs input verification before `forward()` and output verification after.
+When `envs.FASTVIDEO_STAGE_LOGGING` is set, execution time is measured
+with `torch.cuda.synchronize()` and logged.
+
+### ForwardBatch (`fastvideo/pipelines/pipeline_batch_info.py`)
+
+Dataclass carrying all pipeline state between stages. Key field groups:
+
+- **Inputs**: `prompt`, `negative_prompt`, `image_path`, `pil_image`,
+  `video_path`.
+- **Embeddings**: `prompt_embeds: list[Tensor]`,
+  `negative_prompt_embeds`, `prompt_attention_mask`, `image_embeds`.
+- **Latents**: `latents`, `image_latent`, `noise_pred`,
+  `lq_latents`.
+- **Dimensions**: `height`, `width`, `num_frames`, `height_latents`,
+  `width_latents`.
+- **Scheduler**: `timesteps`, `num_inference_steps`, `guidance_scale`,
+  `sigmas`.
+- **Task-specific**: `mouse_cond`/`keyboard_cond` (MatrixGame), `pose`
+  (HYWorld), `camera_states` (GameCraft), `c2ws_plucker_emb`
+  (LingBotWorld).
+- **Output**: `output: Tensor | None`.
+- **Logging**: `logging_info: PipelineLoggingInfo`.
+
+`__post_init__` enables CFG when `guidance_scale > 1.0` or LTX2 text
+CFG scales differ from 1.0.
+
+### Stage Catalog
+
+Standard stages (typical execution order):
+
+| Stage | File | Purpose |
+|---|---|---|
+| `InputValidationStage` | `stages/input_validation.py` | Validates input dimensions and types |
+| `TextEncodingStage` | `stages/text_encoding.py` | Encodes prompts via text encoders |
+| `ImageEncodingStage` | `stages/image_encoding.py` | Encodes input images (I2V pipelines) |
+| `ConditioningStage` | `stages/conditioning.py` | Prepares conditioning embeddings |
+| `TimestepPreparationStage` | `stages/timestep_preparation.py` | Sets up scheduler timesteps |
+| `LatentPreparationStage` | `stages/latent_preparation.py` | Initializes noise latents |
+| `DenoisingStage` | `stages/denoising.py` | Main diffusion denoising loop |
+| `DecodingStage` | `stages/decoding.py` | Decodes latents to video via VAE |
+
+Specialized variants: `CausalDenoisingStage`, `LTX2DenoisingStage`,
+`LongCatDenoisingStage`, `GameCraftDenoisingStage`,
+`HYWorldDenoisingStage`, `MatrixGameDenoisingStage`,
+`SRDenoisingStage`, `LTX2AudioDecodingStage`, `SD35ConditioningStage`,
+`LTX2TextEncodingStage`, `LTX2LatentPreparationStage`.
+
+### Verification System (`fastvideo/pipelines/stages/validators.py`)
+
+`StageValidators` (aliased as `V`) provides static validators:
+`not_none`, `positive_int`, `is_tensor`, `tensor_with_dims`,
+`positive_int_divisible(divisor)`, etc.
+
+`VerificationResult` collects check results:
+
+```python
+result = VerificationResult()
+result.add_check("height", batch.height, V.positive_int_divisible(8))
+result.add_check("width", batch.width, V.positive_int_divisible(8))
+```
+
+`is_valid()` returns whether all checks passed. `get_failure_summary()`
+provides detailed error messages. Failed verification raises
+`StageVerificationError`.
+
+## Pipeline Architecture
+
+### ComposedPipelineBase (`fastvideo/pipelines/composed_pipeline_base.py`)
+
+Abstract base for all inference pipelines. Lifecycle:
+
+1. **`__init__(model_path, fastvideo_args)`** — initializes distributed
+   environment via `maybe_init_distributed_environment_and_model_parallel
+   (tp_size, sp_size)`, then calls `load_modules()` to populate
+   `self.modules`.
+2. **`post_init()`** — calls `initialize_pipeline()` (model-specific
+   setup), `create_pipeline_stages()` (abstract — subclasses wire stages),
+   optionally applies `torch.compile` to transformers, and calls
+   `warmup_sequence_parallel_communication()`.
+3. **`forward(batch, fastvideo_args)`** — iterates `self.stages` calling
+   each stage in order. Decorated with `@torch.no_grad()`.
+
+Key class attributes:
+- `_required_config_modules: list[str]` — module names to load from
+  `model_index.json`.
+- `_extra_config_module_map: dict[str, str]` — aliases for module dirs.
+- `is_video_pipeline: bool` — whether this produces video output.
+
+Key methods:
+- `add_stage(name, stage)` — appends to `_stages` list and
+  `_stage_name_mapping` dict, also sets attribute on `self`.
+- `get_module(name, default)` — retrieves a loaded module.
+- `from_pretrained(model_path, **kwargs)` — class method constructing
+  `FastVideoArgs` and calling `cls(...)` then `post_init()`.
+
+### LoRAPipeline (`fastvideo/pipelines/lora_pipeline.py`)
+
+Extends `ComposedPipelineBase` with LoRA adapter support. Sits in the
+MRO between the concrete pipeline and `ComposedPipelineBase`:
+
+```python
+class WanPipeline(LoRAPipeline, ComposedPipelineBase):
+    ...
+```
+
+Key functionality:
+- `convert_to_lora_layers()` — scans transformer blocks, replaces target
+  linear layers (default: q/k/v/o projections) with LoRA equivalents via
+  `get_lora_layer()`.
+- `set_lora_adapter(path)` — loads safetensors containing `lora_A`,
+  `lora_B`, `lora_alpha` and maps weights to internal layers.
+- `merge_lora_weights()` / `unmerge_lora_weights()` — activates or
+  deactivates LoRA in the forward pass.
+- `LoRAModelLayers` — groups LoRA layers by transformer block for
+  efficient layerwise offload.
+
+### Distributed Inference
+
+`maybe_init_distributed_environment_and_model_parallel(tp_size, sp_size)`
+in `fastvideo/distributed/` initializes `torch.distributed` and creates
+tensor-parallel (TP) and sequence-parallel (SP) process groups.
+
+Key APIs: `get_tp_rank()`, `get_tp_world_size()`, `get_sp_rank()`,
+`get_sp_world_size()`, `get_world_rank()`, `get_world_size()`.
+
+`warmup_sequence_parallel_communication()` pre-warms NCCL communicators
+to avoid slow first forward passes.
+
+Usage: `torchrun --nproc-per-node=N -m fastvideo.entrypoints.cli.main
+generate --model-path ... --tp-size N --sp-size M`.
+
+### torch.compile Integration
+
+When `fastvideo_args.enable_torch_compile` is `True`,
+`_maybe_compile_pipeline_module()` checks for a `_compile_conditions`
+attribute on the module. If present, only matching submodules are
+compiled. Otherwise, the entire module is compiled. FSDP-wrapped
+modules are skipped.
+
+### Entry Points
+
+**Python API** (`fastvideo/entrypoints/video_generator.py`):
+
+```python
+generator = VideoGenerator.from_pretrained(
+    model_path="Wan-AI/Wan2.1-T2V-14B-Diffusers",
+    num_gpus=1, tp_size=1, sp_size=1,
+)
+result = generator.generate_video(
+    prompt="A cat dancing",
+    height=720, width=1280, num_frames=81,
+)
+```
+
+**CLI** (`fastvideo/entrypoints/cli/`):
+
+```bash
+fastvideo generate \
+    --model-path "Wan-AI/Wan2.1-T2V-14B-Diffusers" \
+    --prompt "A cat dancing" \
+    --num-gpus 1
+```
+
+**FastVideoArgs** (`fastvideo/fastvideo_args.py`): Central args dataclass.
+Key fields: `model_path`, `mode` (`ExecutionMode`), `workload_type`
+(`WorkloadType`), `pipeline_config` (`PipelineConfig`), `num_gpus`,
+`tp_size`, `sp_size`, `lora_path`, `dit_cpu_offload`,
+`dit_layerwise_offload`, `enable_torch_compile`,
+`enable_stage_verification`.
+
+Constructed via `FastVideoArgs.from_kwargs(**kwargs)` which resolves the
+`PipelineConfig` from the registry, applies JSON config if provided, and
+merges CLI overrides.
+
+## End-to-End Inference Flow
+
+```
+User: VideoGenerator.from_pretrained(model_path, **kwargs)
+  │
+  ├─ FastVideoArgs.from_kwargs() → PipelineConfig resolved via registry
+  ├─ get_model_info() → ModelInfo(pipeline_cls, sampling_param_cls, ...)
+  │   ├─ model_index.json read → _class_name extracted
+  │   ├─ pipeline_registry resolves pipeline_cls from _class_name
+  │   └─ config_registry resolves config classes from model_path
+  │
+  ├─ pipeline_cls.__init__(model_path, fastvideo_args)
+  │   ├─ maybe_init_distributed(tp_size, sp_size)
+  │   └─ load_modules() → reads model_index.json, loads each component
+  │       ├─ ComponentLoader.for_module_type() → specialized loader
+  │       └─ loader.load() → model class resolved, weights loaded
+  │
+  └─ pipeline.post_init()
+      ├─ initialize_pipeline() → model-specific setup
+      ├─ create_pipeline_stages() → stages wired with modules
+      ├─ torch.compile (if enabled)
+      └─ warmup_sequence_parallel_communication()
+
+User: generator.generate_video(prompt, ...)
+  │
+  ├─ ForwardBatch constructed from SamplingParam + user args
+  └─ pipeline.forward(batch, fastvideo_args)
+      ├─ InputValidationStage → validates dims
+      ├─ TextEncodingStage → prompt → embeddings
+      ├─ ConditioningStage → prepares conditioning
+      ├─ TimestepPreparationStage → scheduler timesteps
+      ├─ LatentPreparationStage → random noise
+      ├─ DenoisingStage → iterative denoising loop
+      └─ DecodingStage → latents → video frames
+```
+
+## Adding a New Model Family — Checklist
+
+1. **Pipeline config** — Create a `PipelineConfig` subclass in
+   `fastvideo/configs/pipelines/<model>.py`. Set DiT/VAE/encoder configs,
+   flow_shift, precision defaults.
+
+2. **Sampling param** — Create a `SamplingParam` subclass in
+   `fastvideo/configs/sample/<model>.py`. Set default height, width,
+   num_frames, guidance_scale, num_inference_steps.
+
+3. **Register configs** — In `fastvideo/registry.py`, add a
+   `register_configs()` call inside `_register_configs()` with
+   `hf_model_paths` and/or `model_detectors`.
+
+4. **Pipeline class** — Create a subclass of `ComposedPipelineBase` (or
+   `LoRAPipeline` + `ComposedPipelineBase`) in
+   `fastvideo/pipelines/basic/<model>/<model>_pipeline.py`.
+   - Set `_required_config_modules` listing needed components.
+   - Implement `create_pipeline_stages()` wiring stages via `add_stage()`.
+   - Optionally override `initialize_pipeline()` for custom setup.
+   - Export `EntryClass = YourPipeline` at module level.
+
+5. **Model classes** (if custom) — Add DiT/VAE implementations in
+   `fastvideo/models/dits/` or `fastvideo/models/vaes/` with
+   `EntryClass = YourModel`. The AST discovery will register them
+   automatically.
+
+6. **Custom stages** (if needed) — Subclass `PipelineStage` in
+   `fastvideo/pipelines/stages/`, implement `forward()`, optionally
+   implement `verify_input()`/`verify_output()`.
+
+7. **Verify** — Run `fastvideo generate --model-path <path> --prompt
+   "test" --num-inference-steps 2` to confirm the pipeline loads and
+   generates output.
@@ -61,12 +61,11 @@ def main():
        prompt, 
        sampling_param=sampling_param, 
        output_path="my_videos/",  # Controls where videos are saved
-        return_frames=True,  # Also return frames from this call (defaults to False)
        save_video=True
    )

-    # If return_frames=True, video contains the generated frames as a NumPy array
-    print(f"Generated {len(video)} frames")
+    # If return_frames=True, frames are available in video["frames"]
+    print(f"Generated {len(video['frames'])} frames")

 if __name__ == '__main__':
    main()
@@ -76,6 +75,8 @@ if __name__ == '__main__':

 The CLI supports `--config` with JSON or YAML. Command-line arguments override
 config file values.
+By default, `fastvideo generate` uses `return_frames=false` unless you set
+`--return-frames` (or `return_frames: true` in config).

 ```bash
 fastvideo generate --config config.yaml
@@ -11,15 +11,15 @@ This page contains step-by-step instructions to get you quickly started with vid

 ## Installation

-We recommend using an environment manager such as `Conda` to create a clean environment:
+If you previously used Conda, we recommend using [uv](https://docs.astral.sh/uv/) instead for a faster and more stable environment setup:

 ```bash
-# Create and activate a new conda environment
-conda create -n fastvideo python=3.12
-conda activate fastvideo
+# Create and activate a new uv environment
+uv venv --python 3.12 --seed
+source .venv/bin/activate

 # Install FastVideo
-pip install fastvideo
+uv pip install fastvideo
 ```

 For advanced installation options, see the [Installation Guide](../getting_started/installation.md).
@@ -44,7 +44,6 @@ def main():
    # Generate the video
    video = generator.generate_video(
        prompt,
-        return_frames=True,  # Also return frames from this call (defaults to False)
        output_path="my_videos/",  # Controls where videos are saved
        save_video=True
    )
@@ -8,26 +8,24 @@ This page describes the various options for speeding up generation times in Fast
 - Optimized Attention Backends

  - [Flash Attention](#flash-attention)
-  - [Sliding Tile Attention](#sliding-tile-attention)
+  - [Sliding Tile Attention (Archived)](#sliding-tile-attention-archived)
  - [Sage Attention](#sage-attention)
  - [Sage Attention 3](#sage-attention-3)

- Caching Techniques
-  - [TeaCache](#teacache)
-
 ## Attention Backends

 ### Available Backends

 - Torch SDPA: `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA`
 - Flash Attention 2 and 3: `FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN`
- Sliding Tile Attention: `FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN`
 - Video Sparse Attention: `FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN`
 - Sage Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN`
 - Sage Attention 3: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN_THREE`
 - Video MoBA Attention: `FASTVIDEO_ATTENTION_BACKEND=VMOBA_ATTN`
 - Sparse Linear Attention: `FASTVIDEO_ATTENTION_BACKEND=SLA_ATTN`
 - SageSLA Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_SLA_ATTN`
+- Sliding Tile Attention (archived branch only):
+  `FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN`

 ### Configuring Backends

@@ -38,7 +36,7 @@ There are two ways to configure the attention backend in FastVideo.
 In python, set the `FASTVIDEO_ATTENTION_BACKEND` environment variable before instantiating `VideoGenerator` like this:

 ```python
-os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLIDING_TILE_ATTN"
+os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
 ```

 #### 2. In CLI
@@ -69,12 +67,20 @@ pip install ninja
 python setup.py install
 ```

-### Sliding Tile Attention
+### Sliding Tile Attention (Archived)

 **`SLIDING_TILE_ATTN`**

-Sliding Tile Attention is provided by `fastvideo-kernel`.
-See [STA docs](../attention/sta/index.md) for installation details.
+The full STA integration in `fastvideo/` is archived from `main` and preserved
+at:
+
+- https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
+
+We keep STA off `main` because we believe VSA is strictly better than STA for
+the actively maintained FastVideo path.
+
+Kernel code in `fastvideo-kernel` is still retained. For mask search and STA
+inference workflow, see [STA docs](../attention/sta/index.md).

 ### Video Sparse Attention

@@ -117,63 +123,24 @@ These backends are model-specific and require the corresponding kernels and
 dependencies. Use the support matrix and model examples to confirm compatibility
 before enabling them.

-## Teacache
-
-TeaCache is an optimization technique supported in FastVideo that can significantly speed up video generation by skipping redundant calculations across diffusion steps. This guide explains how to enable and configure TeaCache for optimal performance in FastVideo.
-
-### What is TeaCache?
-
-See the official [TeaCache](https://github.com/ali-vilab/TeaCache) repo and their paper for more details.
-
-### How to Enable TeaCache
-
-Enabling TeaCache is straightforward - simply add the `enable_teacache=True` parameter to your `generate_video()` call:
-
-```python
-# ... previous code
-generator.generate_video(
-    prompt="Your prompt here",
-    sampling_param=params,
-    enable_teacache=True
-)
-# more code ...
-```
-
-### Complete Example
-
-At the bottom is a complete example of using TeaCache for faster video generation. You can run it using the following command:
-
-```bash
-python examples/inference/optimizations/teacache_example.py
-```
-
-### Advanced Configuration
-
-While TeaCache works well with default settings, you can fine-tune its behavior by adjusting the threshold value:
-
-1. Lower threshold values (e.g., 0.1) will result in more skipped calculations and faster generation with slightly more potential for quality degradation
-2. Higher threshold values (e.g., 0.15-0.23) will skip fewer calculations but maintain quality closer to the original
-
-Note that the optimal threshold depends on your specific model and content.
-
 ## Benchmarking different optimizations

-To benchmark the performance improvement, try generating the same video with and without TeaCache enabled and compare the generation times:
+To benchmark backend performance, generate the same prompt with the same seed and compare end-to-end generation times:

 ```python
-# Without TeaCache
-start_time = time.perf_counter()
-generator.generate_video(prompt="Your prompt", enable_teacache=False)
-standard_time = time.perf_counter() - start_time
+import os
+import time

-# With TeaCache
-start_time = time.perf_counter()
-generator.generate_video(prompt="Your prompt", enable_teacache=True)
-teacache_time = time.perf_counter() - start_time
-
-print(f"Standard generation: {standard_time:.2f} seconds")
-print(f"TeaCache generation: {teacache_time:.2f} seconds")
-print(f"Speedup: {standard_time/teacache_time:.2f}x")
+for backend in ["TORCH_SDPA", "FLASH_ATTN", "SAGE_ATTN"]:
+    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = backend
+    generator = VideoGenerator.from_pretrained("your-model-id")
+    start_time = time.perf_counter()
+    generator.generate_video(
+        prompt="Your prompt",
+        seed=1024,
+    )
+    elapsed = time.perf_counter() - start_time
+    print(f"{backend}: {elapsed:.2f}s")
 ```

-Note: If you want to benchmark different attention backends, you'll need to reinstantiate `VideoGenerator`.
+Note: reinstantiate `VideoGenerator` after changing `FASTVIDEO_ATTENTION_BACKEND`.
@@ -6,6 +6,13 @@ For the canonical, code-level list of model IDs recognized by
 `VideoGenerator.from_pretrained(...)`, see the registrations in
 `fastvideo/registry.py` (`register_configs(...)` entries).

+!!! note
+    The full STA integration in `fastvideo/` is archived from `main` and kept
+    in `sta_do_not_delete`:
+    https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
+    We do this because we believe VSA is strictly better than STA for the
+    actively maintained `main` inference path.
+
 The symbols used have the following meanings:

 - ✅ = Full compatibility
@@ -46,7 +53,7 @@ pipeline initialization and sampling.
  }
 </style>

-| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | VSA | BSA |
+| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn (Legacy Branch) | Sage Attn | VSA | BSA |
 |------------|---------------------|-------------|----------|-------------------|-----------|-----|-----|
 | FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
 | FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
@@ -69,6 +76,9 @@ pipeline initialization and sampling.

 **Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.

+`Sliding Tile Attn (Legacy Branch)` entries refer to the archived
+`sta_do_not_delete` branch workflow, not active `main` inference wiring.
+
 ## Canonical Supported IDs

 The authoritative source for model-ID recognition is
@@ -78,7 +88,9 @@ resolve default pipeline and sampling configuration for it.
 ## Special requirements

 ### Sliding Tile Attention
- Currently only Hopper GPUs (H100s) are supported.
+- Full STA pipeline usage is on the archived branch:
+  https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
+- STA currently requires Hopper GPUs (H100s).

 ### TurboWan2.1 (TurboDiffusion)
 - Uses TurboDiffusionPipeline with RCM scheduler for 1-4 step generation
@@ -0,0 +1,595 @@
+# Training Infrastructure
+
+FastVideo's training infrastructure (`fastvideo/train/`) is a YAML-driven
+framework for training and distilling video diffusion models. A single config
+file controls everything — models, algorithms, distributed strategy,
+checkpointing, and validation — with no code changes needed to mix and match.
+
+!!! note "Relationship to legacy training"
+    This system replaces the older script-based training in `fastvideo/training/`.
+    The legacy scripts still work for basic fine-tuning, but new development
+    should use the config-driven system documented here.
+
+---
+
+## Quick Start
+
+### Launch with the helper script
+
+```bash
+bash examples/train/run.sh examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml
+```
+
+The script auto-detects available GPUs and sets up `torchrun`. Override with
+environment variables:
+
+```bash
+NUM_GPUS=4 NNODES=2 NODE_RANK=0 \
+    MASTER_ADDR=10.0.0.1 MASTER_PORT=29501 \
+    bash examples/train/run.sh my_config.yaml
+```
+
+### Launch directly with torchrun
+
+```bash
+torchrun --nproc_per_node=8 \
+    fastvideo/train/entrypoint/train.py \
+    --config examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml
+```
+
+### CLI flags
+
+| Flag | Description |
+|------|-------------|
+| `--config` | Path to YAML config file (required) |
+| `--resume-from-checkpoint` | Path to a DCP checkpoint directory to resume from |
+| `--override-output-dir` | Override `training.checkpoint.output_dir` |
+| `--dry-run` | Validate config and exit without training |
+
+---
+
+## Config Format
+
+Every run is defined by a single YAML file with five top-level sections.
+See `examples/train/example.yaml` for a fully-commented reference.
+
+### `models` — Role-based model instances
+
+Each entry defines a model role. The `_target_` field specifies the Python class
+to instantiate:
+
+```yaml
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+```
+
+Common model parameters:
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `_target_` | *(required)* | Python class path for the model |
+| `init_from` | *(required)* | HuggingFace repo ID or local checkpoint path |
+| `trainable` | `true` | Whether the model's parameters require gradients |
+| `disable_custom_init_weights` | `false` | Skip custom weight initialization (use for teacher/critic) |
+| `flow_shift` | `3.0` | Timestep shifting factor |
+| `enable_gradient_checkpointing_type` | `null` | Gradient checkpointing (`"full"` or `null`) |
+
+Which roles are needed depends on the training method:
+
+| Method | Required roles |
+|--------|---------------|
+| Fine-tune (SFT) | `student` |
+| Diffusion-Forcing SFT | `student` |
+| DMD2 | `student`, `teacher`, `critic` |
+| Self-Forcing | `student` (causal), `teacher`, `critic` |
+
+### `method` — Training algorithm
+
+Selects and configures the training algorithm:
+
+```yaml
+method:
+  _target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method
+  rollout_mode: simulate
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  generator_update_interval: 5
+```
+
+To switch algorithms, change `_target_` and adjust the method-specific keys.
+See [Training Methods](#training-methods) for details on each algorithm.
+
+### `training` — Typed infrastructure config
+
+This section maps to typed dataclasses with defaults and validation:
+
+```yaml
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1            # sequence parallelism
+    tp_size: 1            # tensor parallelism
+    hsdp_replicate_dim: 1 # HSDP replication dimension
+    hsdp_shard_dim: 8     # HSDP sharding dimension
+
+  data:
+    data_path: data/my_dataset
+    train_batch_size: 1
+    dataloader_num_workers: 4
+    training_cfg_rate: 0.1  # classifier-free guidance dropout rate
+    seed: 1000
+    num_latent_t: 20
+    num_height: 448
+    num_width: 832
+    num_frames: 77
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant  # constant, linear, cosine, polynomial
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/my_run
+    training_state_checkpointing_steps: 1000  # 0 = disabled
+    checkpoints_total_limit: 3                # 0 = keep all
+
+  tracker:
+    project_name: my_project
+    run_name: my_run
+
+  model:
+    weighting_scheme: uniform   # uniform, logit_normal, mode
+    precondition_outputs: false
+    enable_gradient_checkpointing_type: full
+
+  vsa:
+    sparsity: 0.0         # 0.0 = disabled
+    decay_rate: 0.0
+    decay_interval_steps: 0
+```
+
+### `callbacks` — Pluggable hooks
+
+Callbacks run at specific points in the training loop (before/after optimizer
+steps, at validation time, etc.):
+
+```yaml
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+
+  ema:
+    _target_: fastvideo.train.callbacks.ema.EMACallback
+    decay: 0.9999
+    start_iter: 0
+
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_pipeline.WanPipeline
+    dataset_file: path/to/validation.json
+    every_steps: 100
+    sampling_steps: [4]
+    guidance_scale: 5.0
+```
+
+See [Callbacks](#callbacks) for details on each callback.
+
+### `pipeline` — Inference pipeline overrides
+
+Optional overrides for the inference pipeline used during validation:
+
+```yaml
+pipeline:
+  flow_shift: 8
+```
+
+---
+
+## Training Methods
+
+### Supervised Fine-Tuning (SFT)
+
+Standard flow-matching loss. The simplest method — train the student to predict
+noise (or clean x0) from noised data samples.
+
+```yaml
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
+  attn_kind: dense   # "dense" or "vsa"
+```
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `attn_kind` | `"dense"` | Attention mode: `"dense"` (standard) or `"vsa"` (sparse) |
+
+### Diffusion-Forcing SFT (DFSFT)
+
+SFT with **per-chunk inhomogeneous timesteps** — each temporal chunk of the
+video gets a different noise level. This is a prerequisite for training causal /
+streaming models that must handle mixed-noise inputs.
+
+```yaml
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  chunk_size: 3
+  min_timestep_ratio: 0.0
+  max_timestep_ratio: 1.0
+  attn_kind: dense
+```
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `chunk_size` | `3` | Latent frames per temporal chunk |
+| `min_timestep_ratio` | `0.0` | Lower bound of timestep sampling range |
+| `max_timestep_ratio` | `1.0` | Upper bound of timestep sampling range |
+| `attn_kind` | `"dense"` | `"dense"` or `"vsa"` |
+
+### DMD2 (Distribution Matching Distillation)
+
+Distill a many-step teacher into a few-step student. The student learns to match
+the teacher's score function, guided by a trainable critic network.
+
+```yaml
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method
+  rollout_mode: simulate
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.5
+
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+```
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `rollout_mode` | *(required)* | `"simulate"` (pure noise) or `"data_latent"` (from data) |
+| `dmd_denoising_steps` | *(required)* | Timestep schedule for student rollout |
+| `generator_update_interval` | `1` | Update student every N critic steps |
+| `real_score_guidance_scale` | `1.0` | CFG scale for teacher predictions |
+| `fake_score_learning_rate` | *(required)* | Critic optimizer learning rate |
+| `fake_score_betas` | *(required)* | Critic optimizer Adam betas |
+| `fake_score_lr_scheduler` | *(required)* | Critic LR scheduler type |
+
+### Self-Forcing (Causal DMD)
+
+Extends DMD2 for **streaming / causal video generation**. The student processes
+video in temporal chunks, feeding its own denoised outputs as context for future
+chunks — simulating autoregressive rollout during training.
+
+Requires a causal model class (e.g., `WanCausalModel`) for the student:
+
+```yaml
+models:
+  student:
+    _target_: fastvideo.train.models.wan.wan_causal.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  student_sample_type: sde
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+```
+
+Self-Forcing inherits all DMD2 parameters, plus:
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `student_sample_type` | `"sde"` | `"sde"` or `"ode"` for intermediate steps |
+| `same_step_across_blocks` | `false` | Use same exit timestep for all blocks |
+| `last_step_only` | `false` | Always exit at the final denoising step |
+| `context_noise` | `0.0` | Noise added to context frames (0 = clean) |
+| `enable_gradient_in_rollout` | `true` | Enable backprop through rollout |
+| `start_gradient_frame` | `0` | Frame index where gradients begin |
+
+---
+
+## Callbacks
+
+Callbacks are pluggable hooks that run at specific points in the training loop.
+Configure them under the `callbacks` section.
+
+### GradNormClipCallback
+
+Clips gradient norms before the optimizer step. Optionally logs per-module
+gradient norms to the tracker.
+
+```yaml
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0      # 0.0 = disabled
+    log_grad_norms: false
+```
+
+### EMACallback
+
+Maintains an exponential moving average of the student's weights. The EMA
+weights are automatically swapped in during validation.
+
+```yaml
+callbacks:
+  ema:
+    _target_: fastvideo.train.callbacks.ema.EMACallback
+    decay: 0.9999
+    start_iter: 0   # delay EMA updates until this iteration
+```
+
+The EMA callback owns its own state and checkpoints independently — EMA weights
+are saved and restored automatically on resume.
+
+### ValidationCallback
+
+Runs inference with the trained model at regular intervals, saving generated
+videos and logging them to the tracker (W&B).
+
+```yaml
+callbacks:
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_pipeline.WanPipeline
+    dataset_file: path/to/validation.json
+    every_steps: 100
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]  # explicit timestep list
+    guidance_scale: 5.0
+    rollout_mode: parallel  # "parallel" or "streaming"
+```
+
+The validation dataset is a JSON file containing a list of prompt strings.
+If EMA is enabled, validation automatically uses the EMA weights.
+
+---
+
+## Checkpointing and Resume
+
+### Checkpoint format
+
+Checkpoints use PyTorch Distributed Checkpoint (DCP) format, compatible with
+FSDP/HSDP sharding. Each checkpoint saves:
+
+- Model weights (all roles)
+- Optimizer states (all roles)
+- LR scheduler states
+- RNG states (for exact reproducibility)
+- EMA shadow weights (if enabled)
+- Training step counter
+
+Checkpoints are saved to `<output_dir>/checkpoint-<step>/`.
+
+### Saving checkpoints
+
+```yaml
+training:
+  checkpoint:
+    output_dir: outputs/my_run
+    training_state_checkpointing_steps: 1000  # save every N steps (0 = off)
+    checkpoints_total_limit: 3                # rolling window (0 = keep all)
+```
+
+### Resuming training
+
+Use `--resume-from-checkpoint` to resume from a specific checkpoint:
+
+```bash
+# Via the helper script
+bash examples/train/run.sh my_config.yaml --resume outputs/my_run/checkpoint-2000
+
+# Via torchrun directly
+torchrun --nproc_per_node=8 \
+    fastvideo/train/entrypoint/train.py \
+    --config my_config.yaml \
+    --resume-from-checkpoint outputs/my_run/checkpoint-2000
+```
+
+Or set it in the YAML:
+
+```yaml
+training:
+  checkpoint:
+    resume_from_checkpoint: outputs/my_run/checkpoint-2000
+```
+
+### Reproducibility
+
+The training entrypoint enables deterministic mode automatically:
+
+- `torch.backends.cudnn.benchmark = False`
+- `torch.backends.cudnn.deterministic = True`
+- `torch.use_deterministic_algorithms(True)`
+
+A shared CUDA RNG generator is seeded from `training.data.seed` and threaded
+through all random operations (noise sampling, timestep sampling, etc.).
+Ranks within the same sequence-parallel group share a seed, ensuring identical
+noise across SP shards.
+
+---
+
+## Distributed Training
+
+The framework supports HSDP (Hybrid Sharded Data Parallel), Tensor Parallelism
+(TP), and Sequence Parallelism (SP):
+
+```yaml
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1            # sequence parallelism group size
+    tp_size: 1            # tensor parallelism group size
+    hsdp_replicate_dim: 1 # number of HSDP replicas
+    hsdp_shard_dim: 8     # number of HSDP shards
+```
+
+**HSDP** shards model parameters across `hsdp_shard_dim` GPUs and replicates
+across `hsdp_replicate_dim` groups. The product
+`hsdp_replicate_dim * hsdp_shard_dim` should equal `num_gpus`.
+
+**Sequence parallelism** splits the sequence (video frames) across `sp_size`
+GPUs within each data-parallel group. Useful for long videos that don't fit on a
+single GPU.
+
+---
+
+## VSA (Variable Sparse Attention)
+
+VSA progressively increases attention sparsity during training, reducing compute
+while maintaining quality:
+
+```yaml
+training:
+  vsa:
+    sparsity: 0.9             # target sparsity level
+    decay_rate: 0.03          # sparsity increment per decay interval
+    decay_interval_steps: 1   # steps between sparsity increases
+```
+
+The effective sparsity at step `t` is
+`min(sparsity, decay_rate * (t // decay_interval_steps))`.
+
+---
+
+## Extending the Framework
+
+### Adding a new model
+
+1. Create a new module under `fastvideo/train/models/` (e.g.,
+   `fastvideo/train/models/mymodel/mymodel.py`).
+2. Subclass `ModelBase` (or `CausalModelBase` for streaming models).
+3. Implement the required methods:
+   - `prepare_batch()` — convert raw dataloader output to `TrainingBatch`
+   - `add_noise()` — forward-process noise addition
+   - `predict_noise()` — run the transformer forward pass
+   - `backward()` — backward pass with forward context restoration
+4. Reference it in your YAML config:
+
+```yaml
+models:
+  student:
+    _target_: fastvideo.train.models.mymodel.mymodel.MyModel
+    init_from: my-org/my-model
+    trainable: true
+```
+
+### Adding a new training method
+
+1. Create a new module under `fastvideo/train/methods/`.
+2. Subclass `TrainingMethod`.
+3. Implement the required methods:
+   - `single_train_step()` — one forward pass returning losses, outputs, metrics
+   - `get_optimizers()` — return optimizer list
+   - `get_lr_schedulers()` — return scheduler list
+4. Reference it in your config:
+
+```yaml
+method:
+  _target_: fastvideo.train.methods.my_method.MyMethod
+  my_param: 42
+```
+
+Method-specific parameters are accessible via `self.method_config` (a plain
+dict).
+
+### Adding a new callback
+
+1. Create a new module under `fastvideo/train/callbacks/`.
+2. Subclass `Callback`.
+3. Override the hooks you need: `on_train_start`, `on_training_step_end`,
+   `on_before_optimizer_step`, etc.
+4. Optionally implement `state_dict()` / `load_state_dict()` for checkpoint
+   persistence.
+5. Add it to your config:
+
+```yaml
+callbacks:
+  my_callback:
+    _target_: fastvideo.train.callbacks.my_callback.MyCallback
+    my_param: 42
+```
+
+---
+
+## File Structure
+
+```
+fastvideo/train/
+  entrypoint/
+    train.py                  # CLI entrypoint (torchrun)
+  trainer.py                  # Training loop orchestrator
+  models/
+    base.py                   # ModelBase, CausalModelBase ABCs
+    wan/
+      wan.py                  # Wan 2.1 T2V model
+      wan_causal.py           # Wan causal (streaming) model
+  methods/
+    base.py                   # TrainingMethod ABC
+    distribution_matching/
+      dmd2.py                 # DMD2 distillation
+      self_forcing.py         # Self-Forcing (causal DMD)
+    fine_tuning/
+      finetune.py             # Supervised fine-tuning
+      dfsft.py                # Diffusion-forcing SFT
+  callbacks/
+    callback.py               # Callback ABC and CallbackDict
+    grad_clip.py              # Gradient clipping + norm logging
+    ema.py                    # EMA weight averaging
+    validation.py             # Periodic inference validation
+  utils/
+    config.py                 # YAML parser -> RunConfig
+    training_config.py        # Typed config dataclasses
+    builder.py                # Model/method instantiation
+    optimizer.py              # Optimizer/scheduler construction
+    checkpoint.py             # DCP save/resume
+    dataloader.py             # Dataset/dataloader construction
+    tracking.py               # W&B tracker
+```
+
+---
+
+## Related Docs
+
+- [Training Architecture](../design/training_architecture.md) — design
+  rationale, model/method abstractions, and open questions.
+- [Training Overview](overview.md) — data requirements and preprocessing.
+- [Data Preprocessing](data_preprocess.md) — how to prepare datasets.
+- [Config Reference](../../examples/train/configs/example.yaml) — fully-commented
+  YAML config with all fields and defaults.
@@ -49,7 +49,9 @@ combinations.
 If forcing a backend fails, verify optional dependencies are installed:

 - `FLASH_ATTN`: `flash-attn`
- `SLIDING_TILE_ATTN` and `VIDEO_SPARSE_ATTN`: `fastvideo-kernel`
+- `VIDEO_SPARSE_ATTN`: `fastvideo-kernel`
+- `SLIDING_TILE_ATTN`: STA legacy workflow in
+  `sta_do_not_delete` + `fastvideo-kernel`
 - `SAGE_ATTN` / `SAGE_ATTN_THREE`: SageAttention packages

 As a fallback, use:
@@ -11,7 +11,7 @@ def main():
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
+        num_gpus=2,
        use_fsdp_inference=False, # set to True if GPU is out of memory 
        dit_cpu_offload=True, # DiT need to be offloaded for MoE
        vae_cpu_offload=False,
@@ -168,7 +168,7 @@ def prepare_sampling_params(video_request: VideoGenerationRequest, default_param
    params.height = video_request.height
    params.width = video_request.width
    params.save_video = False
-    params.return_frames = False
+    params.return_frames = True
    
    return params

@@ -229,7 +229,7 @@ class BaseModelDeployment:
            sampling_param=params,
            image_path=image_path,
            save_video=False,
-            return_frames=False,
+            return_frames=True,
        )
        inference_time = time.time() - inference_start_time

@@ -3,7 +3,3 @@
 ```bash
 python examples/inference/optimizations/attention_example.py
 ```
-
-```bash
-python examples/inference/optimizations/teacache_example.py
-```
@@ -1,47 +0,0 @@
-import time
-
-from fastvideo import VideoGenerator, SamplingParam
-
-
-def main():
-    start_time = time.perf_counter()
-
-    gen = VideoGenerator.from_pretrained(
-        model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        num_gpus=1,
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
-    )
-    load_time = time.perf_counter() - start_time
-    print(f"Model loading time: {load_time:.2f} seconds")
-
-    gen_start_time = time.perf_counter()
-
-    params = SamplingParam.from_pretrained(
-        model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    )
-    # this controls the threshold for the tea cache
-    params.teacache_params.teacache_thresh = 0.08
-    gen.generate_video(
-        prompt=
-        "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.",
-        sampling_param=params,
-        height=480,
-        width=832,
-        num_frames=61,  # 85 ,77 
-        num_inference_steps=50,
-        enable_teacache=True,
-        seed=1024,
-        output_path="example_outputs/")
-    
-    generation_time = time.perf_counter() - gen_start_time
-    print(f"Video generation time: {generation_time:.2f} seconds")
-
-    total_time = time.perf_counter() - start_time
-    print(f"Total execution time: {total_time:.2f} seconds")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,5 +1,44 @@
-# STA Mask Search Examples
+# STA Mask Search (Archived Workflow)
+
+The full STA integration is preserved in:
+
+- https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
+
+Switch to the branch before running mask search:

 ```bash
+git fetch origin
+git checkout sta_do_not_delete
+```
+
+Run mask search + tuning:
+
+```bash
+export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
+export FASTVIDEO_ATTENTION_CONFIG=assets/mask_strategy_wan.json
+
 bash examples/inference/sta_mask_search/inference_wan_sta.sh
-```
+```
+
+This script runs:
+
+- `STA_searching` and writes to `inference_results/sta/mask_search_full`
+- `STA_tuning` and writes to `inference_results/sta/mask_search_sparse`
+
+Example STA inference (same archived branch):
+
+```bash
+export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
+export FASTVIDEO_ATTENTION_CONFIG=assets/mask_strategy_wan.json
+
+fastvideo generate \
+  --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers \
+  --num-gpus 2 \
+  --tp-size 2 \
+  --sp-size 2 \
+  --height 768 \
+  --width 1280 \
+  --num-frames 69 \
+  --num-inference-steps 50 \
+  --prompt "A cinematic wildlife shot of a lion walking in golden grasslands."
+```
@@ -1,39 +0,0 @@
-#!/bin/bash
-
-export FASTVIDEO_ATTENTION_CONFIG=assets/mask_strategy_wan.json
-export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
-export MODEL_BASE=Wan-AI/Wan2.1-T2V-14B-Diffusers
-
-base_port=29503
-num_gpu=1
-gpu_ids=$(seq 0 $((num_gpu-1)))
-skip_time_steps=12
-
-output_path="inference_results/sta/mask_search_full"
-STA_mode="STA_searching"
-for i in $gpu_ids; do
-    port=$((base_port+i))
-    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_${i}.txt \
-        --output_path $output_path \
-        --STA_mode $STA_mode &
-    sleep 1
-done
-wait
-echo "STA searching completed"
-
-output_path="inference_results/sta/mask_search_sparse"
-STA_mode="STA_tuning"
-for i in $gpu_ids; do
-    port=$((base_port+i))
-    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_${i}.txt \
-        --output_path $output_path \
-        --STA_mode $STA_mode \
-        --skip_time_steps $skip_time_steps &
-    sleep 1
-done
-wait
-echo "STA tuning completed"
-
-echo "All jobs completed"
@@ -1,63 +0,0 @@
-import os
-import argparse
-from fastvideo import VideoGenerator, SamplingParam
-
-def main(args):
-    os.makedirs(args.output_path, exist_ok=True)
-    # Create a video generator with a pre-trained model
-    generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-14B-Diffusers",
-        num_gpus=args.num_gpus,  # Adjust based on your hardware
-        STA_mode=args.STA_mode,
-        skip_time_steps=args.skip_time_steps
-    )
-
-    # Prompts for your video
-    prompt = args.prompt
-    prompt_path = args.prompt_path
-    negative_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"
-
-    if prompt_path is not None:
-        with open(prompt_path, "r") as f:
-            prompts = f.readlines()
-    else:
-        prompts = [prompt]
-
-    params = SamplingParam(
-        height=args.height,
-        width=args.width,
-        num_frames=args.num_frames,
-        num_inference_steps=args.num_inference_steps,
-        fps=args.fps,
-        guidance_scale=args.guidance_scale,
-        seed=args.seed,
-        return_frames=True,  # Also return frames from this call (defaults to False)
-        output_path=args.output_path,  # Controls where videos are saved
-        save_video=True,
-        negative_prompt=negative_prompt
-    )
-
-    # Generate the video
-    for prompt in prompts:
-        video = generator.generate_video(
-            prompt,
-            sampling_param=params,
-        )
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--prompt", type=str, default="A man is dancing.")
-    parser.add_argument("--prompt_path", type=str, default=None)
-    parser.add_argument("--height", type=int, default=768)
-    parser.add_argument("--width", type=int, default=1280)
-    parser.add_argument("--num_frames", type=int, default=69)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--fps", type=int, default=16)
-    parser.add_argument("--guidance_scale", type=float, default=5.0)
-    parser.add_argument("--seed", type=int, default=12345)
-    parser.add_argument("--output_path", type=str, default="my_videos/")
-    parser.add_argument("--num_gpus", type=int, default=1)
-    parser.add_argument("--STA_mode", type=str, default="STA_searching")
-    parser.add_argument("--skip_time_steps", type=int, default=12)
-    args = parser.parse_args()
-    main(args)
@@ -0,0 +1,70 @@
+# DFSFT (Diffusion-Forcing SFT): Wan 2.1 T2V 1.3B Causal
+#
+# - Student: trainable causal Wan model
+# - Training: inhomogeneous timesteps per chunk (diffusion forcing)
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  chunk_size: 3
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 20
+    num_height: 448
+    num_width: 832
+    num_frames: 77
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_dfsft
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: wan2.1_causal_dfsft
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_64.json
+    every_steps: 50
+    sampling_steps: [40]
+    guidance_scale: 6.0
+    num_frames: 69
+
+pipeline:
+  flow_shift: 8
@@ -0,0 +1,95 @@
+# V3 config: WanGame causal Diffusion-Forcing SFT (DFSFT).
+#
+# Uses _target_-based instantiation — each model role is an independent
+# class instance; the method class is resolved directly from the YAML.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wangame.WanGameCausalModel
+    init_from: /mnt/weka/home/hao.zhang/kaiqin/wg_models/WanGame-2.1-0306-61500steps
+    trainable: true
+    # transformer_override_safetensor: /mnt/weka/home/hao.zhang/mhuo/FastVideo-hyw/outputs/wangame_dfsft_causal_v3/checkpoint-best-step-36500/transformer/model.safetensors
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  attn_kind: dense
+  # use_ema: true
+  chunk_size: 3
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: >-
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0204_2130/preprocessed:1,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0204_1600/preprocessed:0,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0205_1330/data/0_static_plus_w_only/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0205_1330/data/1_wasd_only/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0206_1200/data/wasdonly_alpha1/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0206_1200/data/camera/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/camera4hold_alpha1/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/wasd4holdrandview_simple_1key1mouse1/preprocessed:3
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 352
+    num_width: 640
+    num_frames: 69
+    apply_bot_died_filter: true
+
+  optimizer:
+    learning_rate: 1e-4
+    betas: [0.9, 0.95]
+    weight_decay: 1e-5
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 60000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wangame_dfsft_causal_v3
+    # resume_from_checkpoint: /mnt/weka/home/hao.zhang/mhuo/FastVideo-hyw/outputs/wangame_dfsft_causal_v3/checkpoint-best-step-36500
+    training_state_checkpointing_steps: 1000000
+    weight_only_checkpointing_steps: 1000000
+    checkpoints_total_limit: 0
+    best_checkpoint_start_step: 1000000
+    best_checkpoint_top_k: 0
+
+  tracker:
+    project_name: distillation_wangame_r
+    run_name: wangame_dfsft_causal_v3
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+  # ema:
+  #   _target_: fastvideo.train.callbacks.ema.EMACallback
+  #   beta: 0.9999
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback
+    pipeline_target: fastvideo.pipelines.basic.wan.wangame_causal_dmd_pipeline.WangameCausalOdeDMDPipeline
+    dataset_file: examples/training/finetune/WanGame2.1_1.3b_i2v/validation_8.json
+    every_steps: 500
+    sampling_steps: [40]
+    scheduler_target: fastvideo.models.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler
+    guidance_scale: 1.0
+    num_frames: 69
+    evaluate_ptlflow: false
+
+pipeline:
+  flow_shift: 3
@@ -0,0 +1,94 @@
+# DMD2 distillation: Wan 2.1 T2V 1.3B (teacher 50-step -> student 4-step).
+#
+# - Teacher: frozen pretrained Wan 2.1 T2V 1.3B
+# - Student: trainable, initialized from the same pretrained weights
+# - Critic:  trainable, initialized from the same pretrained weights
+# - Validation: 4-step SDE sampling
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.5
+  dmd_denoising_steps: [1000, 750, 500, 250]
+
+  # Critic optimizer (required — no fallback to training.optimizer)
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 20
+    num_height: 448
+    num_width: 832
+    num_frames: 77
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_dmd2_4steps
+    training_state_checkpointing_steps: 20
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan
+    run_name: wan2.1_dmd2_4steps
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_dmd_pipeline.WanDMDPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    guidance_scale: 6.0
+  ema:                                                                                                                     
+    _target_: fastvideo.train.callbacks.ema.EMACallback                                                                    
+    decay: 0.98                                                                                                          
+    start_iter: 0        # delay EMA updates until this iteration 
+
+pipeline:
+  flow_shift: 8
@@ -0,0 +1,194 @@
+# ==============================================================================
+# Full configuration reference for fastvideo.train
+#
+# Legend:
+#   [TYPED]    — parsed into a typed dataclass; fields are validated with
+#                defaults. Unknown keys are silently ignored.
+#   [FREE]     — free-form dict passed as-is to the target class / method.
+#                Keys depend on the _target_ class constructor / method_config.
+#   [RESOLVED] — parsed by PipelineConfig.from_kwargs(); auto-populated from
+#                the model's config files. Only scalar overrides are useful.
+# ==============================================================================
+
+# ------------------------------------------------------------------------------
+# models: [FREE]
+#
+# Each role is instantiated via _target_(*, training_config=..., **kwargs).
+# Keys here are constructor kwargs of the _target_ class (e.g. WanModel).
+# You can define any role name (student, teacher, critic, etc.).
+# ------------------------------------------------------------------------------
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel    # required
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers     # required: HF repo or local path
+    trainable: true                                   # default: true
+    disable_custom_init_weights: false                # default: false
+    flow_shift: 3.0                                   # default: 3.0
+    enable_gradient_checkpointing_type: null           # default: null (falls back to training.model)
+
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+
+# ------------------------------------------------------------------------------
+# method: [FREE]
+#
+# Instantiated via _target_(*, cfg=RunConfig, role_models=...).
+# All keys besides _target_ are available in self.method_config (a plain dict).
+# Keys depend entirely on the method class.
+# ------------------------------------------------------------------------------
+method:
+  _target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method  # required
+
+  # --- DMD2-specific keys (read from self.method_config) ---
+  rollout_mode: simulate              # required: "simulate" or "data_latent"
+  generator_update_interval: 5        # default: 1
+  dmd_denoising_steps: [1000, 750, 500, 250]  # SDE timestep schedule
+
+  # Critic optimizer (all required — no fallback)
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+  # CFG conditioning policy (optional)
+  # cfg_uncond:
+  #   on_missing: error               # "error" or "ignore"
+  #   text: keep                      # "keep", "zero", "drop", "negative_prompt"
+  #   image: keep                     # "keep", "zero", "drop"
+  #   action: keep                    # "keep", "zero", "drop"
+
+  # --- FineTuneMethod keys (if using finetune instead) ---
+  # _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
+
+# ------------------------------------------------------------------------------
+# training: [TYPED] -> TrainingConfig
+#
+# Every field below has a typed default. Unknown keys are ignored.
+# ------------------------------------------------------------------------------
+training:
+
+  # --- training.distributed [TYPED] -> DistributedConfig ---
+  distributed:
+    num_gpus: 8                       # default: 1
+    tp_size: 1                        # default: 1
+    sp_size: 1                        # default: 1 (defaults to num_gpus in loader)
+    hsdp_replicate_dim: 1             # default: 1
+    hsdp_shard_dim: 8                 # default: -1 (defaults to num_gpus in loader)
+    pin_cpu_memory: false             # default: false
+
+  # --- training.data [TYPED] -> DataConfig ---
+  data:
+    data_path: data/my_dataset        # default: ""
+    train_batch_size: 1               # default: 1
+    dataloader_num_workers: 4         # default: 0
+    training_cfg_rate: 0.1            # default: 0.0
+    seed: 1000                        # default: 0
+    num_height: 448                   # default: 0
+    num_width: 832                    # default: 0
+    num_latent_t: 20                  # default: 0
+    num_frames: 77                    # default: 0
+
+  # --- training.optimizer [TYPED] -> OptimizerConfig ---
+  # Note: only for the student optimizer. Critic optimizer is in method config.
+  optimizer:
+    learning_rate: 2.0e-6             # default: 0.0
+    betas: [0.9, 0.999]              # default: [0.9, 0.999]
+    weight_decay: 0.01                # default: 0.0
+    lr_scheduler: constant            # default: "constant"
+    lr_warmup_steps: 0                # default: 0
+    lr_num_cycles: 0                  # default: 0
+    lr_power: 0.0                     # default: 0.0
+    min_lr_ratio: 0.5                 # default: 0.5
+
+  # --- training.loop [TYPED] -> TrainingLoopConfig ---
+  loop:
+    max_train_steps: 10000            # default: 0
+    gradient_accumulation_steps: 1    # default: 1
+
+  # --- training.checkpoint [TYPED] -> CheckpointConfig ---
+  checkpoint:
+    output_dir: outputs/my_run        # default: ""
+    resume_from_checkpoint: ""        # default: "" (or use --resume-from-checkpoint CLI)
+    training_state_checkpointing_steps: 1000  # default: 0 (disabled)
+    checkpoints_total_limit: 3        # default: 0 (keep all)
+
+  # --- training.tracker [TYPED] -> TrackerConfig ---
+  tracker:
+    trackers: []                      # default: [] (auto-adds "wandb" if project_name is set)
+    project_name: my_project          # default: "fastvideo"
+    run_name: my_run                  # default: ""
+
+  # --- training.vsa [TYPED] -> VSAConfig ---
+  vsa:
+    sparsity: 0.0                     # default: 0.0 (0.0 = disabled)
+
+  # --- training.model [TYPED] -> ModelTrainingConfig ---
+  model:
+    weighting_scheme: uniform         # default: "uniform"
+    logit_mean: 0.0                   # default: 0.0
+    logit_std: 1.0                    # default: 1.0
+    mode_scale: 1.0                   # default: 1.0
+    precondition_outputs: false       # default: false
+    moba_config: {}                   # default: {}
+    enable_gradient_checkpointing_type: full  # default: null ("full" or null)
+
+  # --- training top-level [TYPED] ---
+  dit_precision: fp32                 # default: "fp32" (master weight precision)
+  # model_path: ...                   # default: "" (auto-derived from models.student.init_from)
+
+# ------------------------------------------------------------------------------
+# callbacks: [FREE]
+#
+# Each callback is instantiated via _target_(*, **kwargs).
+# The callback name (e.g. "grad_clip") is arbitrary — only _target_ matters.
+# training_config is injected automatically (not from YAML).
+# ------------------------------------------------------------------------------
+callbacks:
+
+  # --- GradNormClipCallback ---
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback  # optional if using default registry
+    max_grad_norm: 1.0                # default: 0.0 (0.0 = disabled)
+    log_grad_norms: false             # default: false
+
+  # --- EMACallback ---
+  # ema:
+  #   _target_: fastvideo.train.callbacks.ema.EMACallback
+  #   decay: 0.9999                   # default: 0.9999
+  #   start_iter: 0                   # default: 0
+
+  # --- ValidationCallback ---
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback  # optional if using default registry
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_pipeline.WanPipeline  # required
+    dataset_file: path/to/validation.json     # required
+    every_steps: 100                  # default: 100
+    sampling_steps: [4]               # default: [40]
+    guidance_scale: 5.0               # default: null (uses model default)
+    num_frames: null                  # default: null (derived from training.data)
+    output_dir: null                  # default: null (falls back to training.checkpoint.output_dir)
+    sampling_timesteps: null          # default: null (explicit timestep list for streaming)
+
+# ------------------------------------------------------------------------------
+# pipeline: [RESOLVED] -> PipelineConfig
+#
+# Parsed by PipelineConfig.from_kwargs(). Most fields are auto-populated from
+# the model's config files (vae_config, dit_config, text_encoder_configs, etc.).
+# Only scalar overrides are typically needed here.
+# ------------------------------------------------------------------------------
+pipeline:
+  flow_shift: 3                       # default: null (model-specific)
+  # flow_shift_sr: null               # default: null (super-resolution shift)
+  # embedded_cfg_scale: 6.0           # default: 6.0
+  # is_causal: false                  # default: false
+  # vae_tiling: true                  # default: true
+  # vae_sp: true                      # default: true
+  # disable_autocast: false           # default: false
@@ -0,0 +1,74 @@
+# V3 config: Wan 2.1 T2V 1.3B finetune with VSA (phase 3.4, 0.9 sparsity).
+#
+# Uses _target_-based instantiation — each model role is an independent
+# class instance; the method class is resolved directly from the YAML.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 8
+    hsdp_shard_dim: 1
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 20
+    num_height: 448
+    num_width: 832
+    num_frames: 77
+
+  optimizer:
+    learning_rate: 1.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/phase3.4_wan2.1_finetune_vsa_0.9_v3
+    training_state_checkpointing_steps: 100
+    checkpoints_total_limit: 3
+    resume_from_checkpoint: latest
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: phase3.4_wan_finetune_vsa_0.9_v3
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  vsa:
+    sparsity: 0.9
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_pipeline.WanPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [50]
+    guidance_scale: 5.0
+
+pipeline:
+  flow_shift: 3
@@ -0,0 +1,98 @@
+# Self-Forcing distillation: Wan 2.1 T2V 1.3B Causal
+#
+# - Teacher: frozen pretrained Wan 2.1 T2V 1.3B
+# - Student: trainable causal Wan model
+# - Critic:  trainable, initialized from pretrained weights
+# - Training: streaming rollout with SDE sampling
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.5
+  dmd_denoising_steps: [1000, 750, 500, 250]
+
+  chunk_size: 3
+  student_sample_type: sde
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+
+  # Critic optimizer
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 20
+    num_height: 448
+    num_width: 832
+    num_frames: 77
+
+  optimizer:
+    learning_rate: 1e-5
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_self_forcing
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: self-forcing-wan
+    run_name: wan2.1_causal_self_forcing
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    num_frames: 69
+    guidance_scale: 6.0
+
+pipeline:
+  flow_shift: 5
@@ -0,0 +1,105 @@
+# Self-Forcing distillation: Wan 2.1 T2V 1.3B Causal with VSA (0.9 sparsity)
+#
+# - Teacher: frozen pretrained Wan 2.1 T2V 14B
+# - Student: trainable causal Wan model with VSA sparse attention
+# - Critic:  trainable, initialized from pretrained weights
+# - Training: streaming rollout with SDE sampling + VSA 0.9 sparsity
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.5
+  dmd_denoising_steps: [1000, 750, 500, 250]
+
+  chunk_size: 3
+  student_sample_type: sde
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+
+  # Critic optimizer
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+  attn_kind: vsa
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 20
+    num_height: 448
+    num_width: 832
+    num_frames: 77
+
+  optimizer:
+    learning_rate: 1e-5
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_self_forcing_vsa_0.9
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: self-forcing-wan
+    run_name: wan2.1_causal_self_forcing_vsa_0.9
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+  vsa:
+    sparsity: 0.9
+    decay_rate: 0.03
+    decay_interval_steps: 1
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    num_frames: 69
+    guidance_scale: 6.0
+
+pipeline:
+  flow_shift: 5
@@ -0,0 +1,102 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wangame.WanGameCausalModel
+    init_from: /mnt/weka/home/hao.zhang/kaiqin/wg_models/SFWanGame-2.1-0308-10000steps
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wangame.WanGameModel
+    init_from: /mnt/weka/home/hao.zhang/kaiqin/wg_models/WanGame-2.1-0306-61500steps
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wangame.WanGameModel
+    init_from: /mnt/weka/home/hao.zhang/kaiqin/wg_models/WanGame-2.1-0306-61500steps
+    trainable: true
+    disable_custom_init_weights: true
+
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 1.0
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  warp_denoising_step: true
+
+  chunk_size: 3
+  student_sample_type: sde
+  context_noise: 0.0
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+
+  # Critic optimizer
+  fake_score_learning_rate: 8.0e-6
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+
+  data:
+    data_path: >-
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0204_2130/preprocessed:1,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0204_1600/preprocessed:0,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0205_1330/data/0_static_plus_w_only/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0205_1330/data/1_wasd_only/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0206_1200/data/wasdonly_alpha1/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0206_1200/data/camera/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/camera4hold_alpha1/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/wasd4holdrandview_simple_1key1mouse1/preprocessed:3
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 352
+    num_width: 640
+    num_frames: 69
+    apply_bot_died_filter: true
+
+  optimizer:
+    learning_rate: 1e-5
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 6
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wangame_1.3b_self_forcing
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 5
+
+  tracker:
+    project_name: wangame_sf
+    run_name: wangame_1.3b_self_forcing
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wangame_causal_dmd_pipeline.WangameCausalSdeDMDPipeline
+    scheduler_target: fastvideo.models.schedulers.scheduling_self_forcing_flow_match.SelfForcingFlowMatchScheduler
+    dataset_file: examples/training/finetune/WanGame2.1_1.3b_i2v/validation_4.json
+    every_steps: 5
+    sampling_steps: [4]
+    sampling_timesteps: [1000, 750, 500, 250]
+    num_frames: 69
+    guidance_scale: 1.0
+    evaluate_ptlflow: false
+
+pipeline:
+  flow_shift: 3
@@ -0,0 +1,94 @@
+# V3 config: WanGame causal Diffusion-Forcing SFT (DFSFT).
+#
+# Uses _target_-based instantiation — each model role is an independent
+# class instance; the method class is resolved directly from the YAML.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wangame.WanGameCausalModel
+    init_from: /mnt/weka/home/hao.zhang/kaiqin/wg_models/WanGame-2.1-0306-61500steps
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  attn_kind: dense
+  # use_ema: true
+  chunk_size: 3
+  min_timestep_ratio: 0.02
+  max_timestep_ratio: 0.98
+
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+
+  data:
+    data_path: >-
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0204_2130/preprocessed:1,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0204_1600/preprocessed:0,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0205_1330/data/0_static_plus_w_only/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0205_1330/data/1_wasd_only/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0206_1200/data/wasdonly_alpha1/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0206_1200/data/camera/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/camera4hold_alpha1/preprocessed:3,
+      /mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/wasd4holdrandview_simple_1key1mouse1/preprocessed:3
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 352
+    num_width: 640
+    num_frames: 69
+    apply_bot_died_filter: true
+
+  optimizer:
+    learning_rate: 1e-5
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 10000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wangame_tfsft_causal
+    # resume_from_checkpoint: /mnt/weka/home/hao.zhang/mhuo/FastVideo-hyw/outputs/wangame_dfsft_causal_v3/checkpoint-best-step-36500
+    training_state_checkpointing_steps: 5000
+    weight_only_checkpointing_steps: 5000
+    checkpoints_total_limit: 10
+    best_checkpoint_start_step: 2000
+    best_checkpoint_top_k: 5
+
+  tracker:
+    project_name: distillation_wangame_r
+    run_name: wangame_tfsft_causal
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
+    max_grad_norm: 1.0
+  # ema:
+  #   _target_: fastvideo.train.callbacks.ema.EMACallback
+  #   beta: 0.9999
+  validation:
+    _target_: fastvideo.train.callbacks.validation.ValidationCallback
+    pipeline_target: fastvideo.pipelines.basic.wan.wangame_causal_dmd_pipeline.WangameCausalOdeDMDPipeline
+    dataset_file: examples/training/finetune/WanGame2.1_1.3b_i2v/validation_4.json
+    every_steps: 500
+    sampling_steps: [40]
+    scheduler_target: fastvideo.models.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler
+    guidance_scale: 1.0
+    num_frames: 69
+    evaluate_ptlflow: false
+
+pipeline:
+  flow_shift: 3
@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+# Launch training from a YAML config.
+#
+# Usage:
+#   bash examples/train/run.sh <config.yaml> [--dotted.key value ...]
+#
+# Examples:
+#   bash examples/train/run.sh examples/train/finetune_wan2.1_t2v_1.3B_vsa_phase3.4_0.9sparsity.yaml
+#   bash examples/train/run.sh examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml --dry-run
+#   bash examples/train/run.sh examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml \
+#       --training.distributed.num_gpus 4 \
+#       --training.optimizer.learning_rate 1e-5
+#   bash examples/train/run.sh examples/train/distill_wan2.1_t2v_1.3B_dmd2.yaml \
+#       --training.checkpoint.resume_from_checkpoint outputs/my_run/checkpoint-1000
+#
+# Logs are written to logs/<config_name>_<timestamp>.log (and also printed to stdout).
+
+set -euo pipefail
+
+CONFIG="${1:?Usage: $0 <config.yaml> [extra flags...]}"
+shift
+
+# ── GPU / node settings ──────────────────────────────────────────
+NUM_GPUS="${NUM_GPUS:-$(nvidia-smi -L 2>/dev/null | wc -l)}"
+NUM_GPUS="${NUM_GPUS:-8}"
+NNODES="${NNODES:-1}"
+NODE_RANK="${NODE_RANK:-0}"
+MASTER_ADDR="${MASTER_ADDR:-127.0.0.1}"
+MASTER_PORT="${MASTER_PORT:-29501}"
+export TOKENIZERS_PARALLELISM=false
+# ── W&B ──────────────────────────────────────────────────────────
+export WANDB_API_KEY="${WANDB_API_KEY:-7ff8b6e8356924f7a6dd51a0342dd1a422ea9352}"
+export WANDB_MODE="${WANDB_MODE:-online}"
+
+
+# ── Log file ─────────────────────────────────────────────────────
+CONFIG_NAME="$(basename "${CONFIG}" .yaml)"
+TIMESTAMP="$(date +%Y%m%d_%H%M%S)"
+LOG_DIR="${LOG_DIR:-examples/train}"
+mkdir -p "${LOG_DIR}"
+LOG_FILE="${LOG_DIR}/${CONFIG_NAME}_${TIMESTAMP}.log"
+
+set +u
+source ~/conda/miniconda/bin/activate
+conda activate mhuo-fv
+set -u
+export PYTHONPATH="/mnt/weka/home/hao.zhang/mhuo/FastVideo-refactor:${PYTHONPATH:-}"
+
+echo "=== Train Training ==="
+echo "Config:      ${CONFIG}"
+echo "Num GPUs:    ${NUM_GPUS}"
+echo "Num Nodes:   ${NNODES}"
+echo "Node Rank:   ${NODE_RANK}"
+echo "Master:      ${MASTER_ADDR}:${MASTER_PORT}"
+echo "Extra args:  $*"
+echo "Log file:    ${LOG_FILE}"
+echo "=============================="
+
+python -m torch.distributed.run \
+    --nnodes "${NNODES}" \
+    --node_rank "${NODE_RANK}" \
+    --nproc_per_node "${NUM_GPUS}" \
+    --master_addr "${MASTER_ADDR}" \
+    --master_port "${MASTER_PORT}" \
+    fastvideo/train/entrypoint/train.py \
+    --config "${CONFIG}" \
+    "$@" \
+    2>&1 | tee "${LOG_FILE}"
@@ -0,0 +1,80 @@
+#!/bin/bash
+#SBATCH --job-name=wg-sf
+#SBATCH --partition=main
+#SBATCH --nodes=4
+#SBATCH --ntasks=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=examples/train/slurm_%j.out
+#SBATCH --error=examples/train/slurm_%j.err
+#SBATCH --exclusive
+
+set -euo pipefail
+
+CONFIG="${1:?Usage: sbatch examples/train/run.slurm <config.yaml> [extra flags...]}"
+shift
+EXTRA_ARGS=("$@")
+set --
+
+cd /mnt/weka/home/hao.zhang/mhuo/FastVideo-refactor
+
+get_num_gpus() {
+    if [[ -n "${NUM_GPUS:-}" ]]; then
+        echo "${NUM_GPUS}"
+        return
+    fi
+    if [[ -n "${SLURM_GPUS_ON_NODE:-}" ]]; then
+        echo "${SLURM_GPUS_ON_NODE%%(*}"
+        return
+    fi
+    if [[ -n "${SLURM_GPUS_PER_NODE:-}" ]]; then
+        echo "${SLURM_GPUS_PER_NODE%%(*}"
+        return
+    fi
+    if command -v nvidia-smi >/dev/null 2>&1; then
+        nvidia-smi -L 2>/dev/null | wc -l | tr -d " "
+    else
+        echo 8
+    fi
+}
+
+export NNODES="${NNODES:-${SLURM_JOB_NUM_NODES:-1}}"
+export NUM_GPUS="$(get_num_gpus)"
+export MASTER_PORT="${MASTER_PORT:-29501}"
+if [[ -z "${MASTER_ADDR:-}" ]]; then
+    nodes=( $(scontrol show hostnames "${SLURM_JOB_NODELIST}") )
+    export MASTER_ADDR="${nodes[0]}"
+fi
+
+export NCCL_P2P_DISABLE="${NCCL_P2P_DISABLE:-1}"
+export TORCH_NCCL_ENABLE_MONITORING="${TORCH_NCCL_ENABLE_MONITORING:-0}"
+export NCCL_DEBUG_SUBSYS="${NCCL_DEBUG_SUBSYS:-INIT,NET}"
+export TOKENIZERS_PARALLELISM=false
+export WANDB_API_KEY="${WANDB_API_KEY:-7ff8b6e8356924f7a6dd51a0342dd1a422ea9352}"
+export WANDB_MODE="${WANDB_MODE:-online}"
+
+set +u
+source ~/conda/miniconda/bin/activate
+conda activate mhuo-fv
+set -u
+export PYTHONPATH="/mnt/weka/home/hao.zhang/mhuo/FastVideo-refactor:${PYTHONPATH:-}"
+
+echo "=== Distillation Training (Slurm) ==="
+echo "Config:      ${CONFIG}"
+echo "Num GPUs:    ${NUM_GPUS}"
+echo "Num Nodes:   ${NNODES}"
+echo "Master:      ${MASTER_ADDR}:${MASTER_PORT}"
+echo "Extra args:  ${EXTRA_ARGS[*]:-}"
+echo "====================================="
+
+srun torchrun \
+    --nnodes "${NNODES}" \
+    --nproc_per_node "${NUM_GPUS}" \
+    --rdzv_backend c10d \
+    --rdzv_endpoint "${MASTER_ADDR}:${MASTER_PORT}" \
+    --node_rank "${SLURM_PROCID}" \
+    fastvideo/train/entrypoint/train.py \
+    --config "${CONFIG}" \
+    "${EXTRA_ARGS[@]}"
@@ -0,0 +1,114 @@
+#!/usr/bin/env bash
+# Submit a multi-node Slurm training job.
+#
+# Usage:
+#   bash examples/train/run_slurm.sh <config.yaml> <num_nodes> [--dotted.key value ...]
+#
+# Examples:
+#   bash examples/train/run_slurm.sh examples/train/configs/example.yaml 2
+#   bash examples/train/run_slurm.sh examples/train/configs/distill_wan2.1_t2v_1.3B_dmd2.yaml 4 \
+#       --training.optimizer.learning_rate 1e-5
+#   bash examples/train/run_slurm.sh examples/train/configs/example.yaml 8 \
+#       --training.checkpoint.resume_from_checkpoint outputs/my_run/checkpoint-1000
+#
+# Environment variables (override defaults):
+#   PARTITION       Slurm partition           (default: main)
+#   NUM_GPUS        GPUs per node             (default: 8)
+#   CPUS_PER_TASK   CPUs per task             (default: 128)
+#   MEM             Memory per node           (default: 1440G)
+#   JOB_NAME        Slurm job name            (default: derived from config)
+#   OUTPUT_DIR      Directory for slurm logs  (default: slurm_logs)
+#   MASTER_PORT     Rendezvous port           (default: 29500)
+#   EXCLUDE         Nodes to exclude          (default: "")
+#   WANDB_API_KEY   W&B API key               (default: "")
+#   WANDB_MODE      W&B mode                  (default: online)
+
+set -euo pipefail
+
+CONFIG="${1:?Usage: $0 <config.yaml> <num_nodes> [extra flags...]}"
+NUM_NODES="${2:?Usage: $0 <config.yaml> <num_nodes> [extra flags...]}"
+shift 2
+
+# ── Defaults ──────────────────────────────────────────────────────
+PARTITION="${PARTITION:-main}"
+NUM_GPUS="${NUM_GPUS:-8}"
+CPUS_PER_TASK="${CPUS_PER_TASK:-128}"
+MEM="${MEM:-1440G}"
+MASTER_PORT="${MASTER_PORT:-29500}"
+EXCLUDE="${EXCLUDE:-}"
+WANDB_API_KEY="${WANDB_API_KEY:-}"
+WANDB_MODE="${WANDB_MODE:-online}"
+
+TOTAL_GPUS=$(( NUM_NODES * NUM_GPUS ))
+
+CONFIG_NAME="$(basename "${CONFIG}" .yaml)"
+JOB_NAME="${JOB_NAME:-${CONFIG_NAME}}"
+OUTPUT_DIR="${OUTPUT_DIR:-logs/slurm}"
+mkdir -p "${OUTPUT_DIR}"
+
+# ── Build sbatch args ─────────────────────────────────────────────
+SBATCH_ARGS=(
+    --job-name="${JOB_NAME}"
+    --partition="${PARTITION}"
+    --nodes="${NUM_NODES}"
+    --ntasks="${NUM_NODES}"
+    --ntasks-per-node=1
+    --gres="gpu:${NUM_GPUS}"
+    --cpus-per-task="${CPUS_PER_TASK}"
+    --mem="${MEM}"
+    --output="${OUTPUT_DIR}/${JOB_NAME}_%j.out"
+    --error="${OUTPUT_DIR}/${JOB_NAME}_%j.err"
+    --exclusive
+)
+
+if [[ -n "${EXCLUDE}" ]]; then
+    SBATCH_ARGS+=(--exclude="${EXCLUDE}")
+fi
+
+# ── Collect extra overrides for the training script ───────────────
+EXTRA_ARGS=("$@")
+
+echo "=== Slurm Training Submission ==="
+echo "Config:      ${CONFIG}"
+echo "Nodes:       ${NUM_NODES}"
+echo "GPUs/node:   ${NUM_GPUS}"
+echo "Total GPUs:  ${TOTAL_GPUS}"
+echo "Partition:   ${PARTITION}"
+echo "Job name:    ${JOB_NAME}"
+echo "Extra args:  ${EXTRA_ARGS[*]:-}"
+echo "================================="
+
+# ── Submit ────────────────────────────────────────────────────────
+sbatch "${SBATCH_ARGS[@]}" <<EOF
+#!/bin/bash
+set -e -x
+
+# ── Environment ───────────────────────────────────────────────────
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export TRITON_CACHE_DIR=/tmp/triton_cache_\${SLURM_PROCID}
+export TOKENIZERS_PARALLELISM=false
+export WANDB_API_KEY="${WANDB_API_KEY}"
+export WANDB_MODE="${WANDB_MODE}"
+
+# ── Rendezvous ────────────────────────────────────────────────────
+export MASTER_PORT=${MASTER_PORT}
+nodes=( \$(scontrol show hostnames \$SLURM_JOB_NODELIST) )
+export MASTER_ADDR=\${nodes[0]}
+export NODE_RANK=\$SLURM_PROCID
+
+echo "MASTER_ADDR: \$MASTER_ADDR"
+echo "NODE_RANK:   \$NODE_RANK"
+
+# ── Launch ────────────────────────────────────────────────────────
+srun torchrun \\
+    --nnodes \$SLURM_JOB_NUM_NODES \\
+    --nproc_per_node ${NUM_GPUS} \\
+    --node_rank \$SLURM_PROCID \\
+    --rdzv_backend=c10d \\
+    --rdzv_endpoint="\$MASTER_ADDR:\$MASTER_PORT" \\
+    fastvideo/train/entrypoint/train.py \\
+    --config ${CONFIG} \\
+    --training.distributed.num_gpus ${TOTAL_GPUS} \\
+    ${EXTRA_ARGS[*]:-}
+EOF
@@ -2,35 +2,15 @@
  "data": [
    {
      "caption": "In the video, a woman is elegantly showcasing her earrings, bringing attention to their intricate design with a gentle touch of her fingers. She is bathed in ambient purple and pink lighting, which casts a soft glow on her delicate features and enhances the vivid tones of her lipstick and eye makeup. Her hair is styled to frame her face smoothly, emphasizing the contours of her jawline and cheekbones. The background features a blurred neon light, adding an artistic and modern touch to the overall aesthetic.",
-      "video_path": "Fashion/mixkit-face-of-an-elegant-and-captivating-woman-41914_clip_1.mp4",
-      "num_inference_steps":  3,
-      "height": 448,
-      "width": 832,
-      "num_frames": 61
    },
    {
      "caption": "In the video, a lone rider guides a majestic horse across an expansive, open field as the sun sets in the background. The rider, dressed in a classic blue shirt and wide-brimmed hat, sits confidently in the saddle, silhouetted against the warm glow of the evening sky. The horse moves gracefully, its mane and tail flowing with each step, creating a sense of harmony between horse and rider. Surrounding the pair, towering trees form a natural border, their leaves gently rustling in the breeze. The shadows lengthen on the ground, accentuating the serene and timeless feel of the scene. The distant hills and wooden fences frame the horizon, adding depth to the tranquil landscape. A few horses graze peacefully in the background, blending into the pastoral setting. The overall ambiance evokes a sense of calmness and quietude, capturing a perfect moment in the golden light of dusk.",
-      "video_path": "Man/mixkit-a-rancher-riding-a-horse-at-sunset-1143_clip_1.mp4",
-      "num_inference_steps":  3,
-      "height": 448,
-      "width": 832,
-      "num_frames": 61
    },
    {
      "caption": "In a dimly lit, eerie setting, a mysterious pink bottle labeled \"Authentic 100% organic POISON\" sits prominently in the foreground, casting a menacing aura. The bottle is accentuated by green fog, which swirls lightly around it, enhancing its sinister allure. Behind it, a shadowy golden bottle adorned with a spider emblem subtly emerges, adding an extra layer of mystery to the scene. Dim candles provide faint, flickering light, which complements the dark atmosphere, making the setting ideal for an illusion of hidden dangers.",
-      "video_path": "smoke/mixkit-poison-in-halloween-ritual-33879_clip_1.mp4",
-      "num_inference_steps":  3,
-      "height": 448,
-      "width": 832,
-      "num_frames": 61
    },
    {
      "caption": "The video opens with a tranquil scene in the heart of a dense forest, emphasizing two large, textured tree trunks in the foreground framing the view. Sunlight filters through the canopy above, casting intricate patterns of light and shadow on the trees and the ground. Between the tree trunks, a clear view of a calm, muddy river unfolds, its surface shimmering under the gentle sunlight. The riverbank is decorated with a variety of small bushes and vibrant foliage, subtly transitioning into the deep greens of tall, leafy plants. In the background, the dense forest looms, filled with dark, towering trees, their branches intertwining to form an intricate canopy. The scene is bathed in the soft glow of the sun, creating a serene and picturesque setting. Occasional sunbeams pierce through the foliage, adding a magical aura to the landscape. The vibrant reds and oranges of the smaller plants add contrast, bringing warmth to the earthy tones of the scenery. Overall, this harmonious blend of natural elements creates a peaceful and idyllic forest setting.",
-      "video_path": "forest/mixkit-view-of-a-river-between-two-old-trees-560_clip_1.mp4",
-      "num_inference_steps":  3,
-      "height": 448,
-      "width": 832,
-      "num_frames": 61
    }
  ]
 }
@@ -0,0 +1,44 @@
+{
+    "data": [
+        {
+            "caption": "00 Val-00: W",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000002.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/W.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "01 Val-01: S",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000003.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/S.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "02 Val-02: A",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000004.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/A.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "03 Val-03: D",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000005.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/D.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        }
+    ]
+}
@@ -0,0 +1,84 @@
+{
+    "data": [
+        {
+            "caption": "00 Val-00: W",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000002.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/W.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "01 Val-01: S",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000003.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/S.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "02 Val-02: A",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000004.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/A.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "03 Val-03: D",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000005.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/D.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "04 Val-04: u",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000000.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/u.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "05 Val-05: d",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000001.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/d.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "06 Val-06: l",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000006.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/l.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "07 Val-07: r",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000007.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/r.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        }
+    ]
+}
@@ -0,0 +1,324 @@
+{
+    "data": [
+        {
+            "caption": "00 Val-00: W",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000002.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/W.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "01 Val-01: S",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000003.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/S.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "02 Val-02: A",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000004.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/A.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "03 Val-03: D",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000005.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/D.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "04 Val-04: u",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000000.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/u.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "05 Val-05: d",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000001.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/d.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "06 Val-06: l",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000006.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/l.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "07 Val-07: r",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000007.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/r.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "08 Val-00: key rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000002.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_1_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "09 Val-01: key rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000003.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_1_action_rand_2.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "10 Val-02: camera rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000004.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/camera_1_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "11 Val-03: camera rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000005.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/camera_1_action_rand_2.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "12 Val-00: key+camera excl rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000002.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_excl_1_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "13 Val-01: key+camera excl rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000003.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_excl_1_action_rand_2.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "14 Val-02: key+camera rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000004.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_1_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "15 Val-03: key+camera rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000005.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_1_action_rand_2.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "16 Val-04: (simultaneous) key rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000000.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_2_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "17 Val-05: (simultaneous) camera rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000001.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/camera_2_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "18 Val-06: (simultaneous) key+camera excl rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000006.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_excl_2_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "19 Val-07: (simultaneous) key+camera rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000007.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_2_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "20 Val-08: W+A",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/humanplay/000005.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/WA.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "21 Val-09: S+u",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/humanplay/000013.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/S_u.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "22 Val-08: Still",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/humanplay/000005.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/still.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "23 Val-09: Still",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/humanplay/000013.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/still.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "24 Val-06: key+camera excl rand Frame 4",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000006.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_excl_1_action_rand_1_f4.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "25 Val-07: key+camera excl rand Frame 4",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/mc_wasd_10/validate/000007.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_excl_1_action_rand_2_f4.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "26 Train-00",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/wasd4holdrandview_simple_1key1mouse1/first_frame/000500.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/wasd4holdrandview_simple_1key1mouse1/videos/000500_action.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "27 Train-01",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/wasd4holdrandview_simple_1key1mouse1/first_frame/001000.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/traindata_0208_2000/data/wasd4holdrandview_simple_1key1mouse1/videos/001000_action.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "28 Doom-00: W",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/doom/000000.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/W.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "29 Doom-01: key rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/doom/000001.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_1_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "30 Doom-02: camera rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/doom/000002.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/camera_1_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        },
+        {
+            "caption": "31 Doom-03: key+camera excl rand",
+            "image_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/doom/000003.jpg",
+            "action_path": "/mnt/weka/home/hao.zhang/mhuo/FastVideo/examples/training/finetune/WanGame2.1_1.3b_i2v/actions/key_camera_excl_1_action_rand_1.npy",
+            "video_path": null,
+            "num_inference_steps": 40,
+            "height": 352,
+            "width": 640,
+            "num_frames": 77
+        }
+    ]
+}
@@ -6,6 +6,7 @@ DATASET_PATH="data/crush-smol/"
 OUTPUT_DIR="data/crush-smol_processed_t2v/"

 torchrun --nproc_per_node=$GPU_NUM \
+    --master_port=29513 \
    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
    --model_path $MODEL_PATH \
    --mode preprocess \
@@ -7,6 +7,13 @@ CUDA kernels for FastVideo video generation.
 ### Standard Installation (Local Development)
 This will automatically detect your GPU architecture. If an NVIDIA Hopper (H100/sm_90a) GPU is detected, ThunderKittens kernels will be enabled. Otherwise, they will be skipped, and the package will use Triton fallbacks at runtime.

+Before installation, set CUDA toolchain paths:
+
+```bash
+export CUDA_HOME=/usr/local/cuda
+export CUDACXX=$CUDA_HOME/bin/nvcc
+```
+
 ```bash
 git submodule update --init --recursive
 cd fastvideo-kernel
@@ -62,6 +69,8 @@ This package also includes kernels from [TurboDiffusion](https://github.com/thu-
  - Any CUDA GPU for Triton-based fallbacks.
 - **Build**:
  - CUDA Toolkit 12.3+
+  - `CUDA_HOME` must be set (for example, `/usr/local/cuda`)
+  - `CUDACXX` must be set (for example, `$CUDA_HOME/bin/nvcc`)
  - C++20 compatible compiler (GCC 10+, Clang 11+)

 ## Acknowledgement
@@ -3,8 +3,10 @@ set -ex

 # Simple build script wrapping uv/pip
 # Usage:
-#   ./build.sh                # local dev build (auto-detect / skip TK kernels when not available)
-#   ./build.sh --release      # force-enable Hopper/TK kernels for release builds (no GPU required)
+#   ./build.sh  # local build (torch-based arch detection, TK only on SM90)
+# Environment overrides (if set, they win over auto-detection):
+#   TORCH_CUDA_ARCH_LIST
+#   CMAKE_ARGS (for FASTVIDEO_KERNEL_BUILD_TK / CMAKE_CUDA_ARCHITECTURES / GPU_BACKEND)

 echo "Building fastvideo-kernel..."

@@ -12,7 +14,7 @@ echo "Building fastvideo-kernel..."
 git submodule update --init --recursive

 # Install build dependencies
-pip install scikit-build-core cmake ninja
+uv pip install scikit-build-core cmake ninja

 RELEASE=0
 GPU_BACKEND=CUDA
@@ -24,15 +26,68 @@ for arg in "$@"; do
    esac
 done

-# Force-enable ThunderKittens kernels and compile for Hopper.
-export TORCH_CUDA_ARCH_LIST="9.0a"
-export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
+has_cmake_arg() {
+    local key="$1"
+    [[ "${CMAKE_ARGS:-}" =~ (^|[[:space:]])-D${key}(=|$) ]]
+}

-export CMAKE_ARGS="${CMAKE_ARGS:-} -DGPU_BACKEND=${GPU_BACKEND}"
+detect_with_torch() {
+    uv run --active --no-project python -c "import torch
+if not torch.cuda.is_available():
+    raise RuntimeError('torch.cuda.is_available() is false')
+mj, mn = torch.cuda.get_device_capability(0)
+print(f'{mj}.{mn}')"
+}
+
+if [ "${GPU_BACKEND}" = "CUDA" ]; then
+    detected_cc="$(detect_with_torch)" || {
+        echo "ERROR: torch-based CUDA arch detection failed in uv environment." >&2
+        echo "       Ensure torch is installed and CUDA is available in the uv-selected Python." >&2
+        exit 1
+    }
+
+    cc_major="${detected_cc%%.*}"
+    cc_minor="${detected_cc##*.}"
+    cmake_arch="${cc_major}${cc_minor}"
+    echo "Detected compute capability via torch: ${detected_cc} (sm_${cmake_arch})"
+
+    # Respect explicit overrides.
+    if [ -z "${TORCH_CUDA_ARCH_LIST:-}" ]; then
+        if [ "${cc_major}" = "9" ] && [ "${cc_minor}" = "0" ]; then
+            export TORCH_CUDA_ARCH_LIST="9.0a"
+        else
+            export TORCH_CUDA_ARCH_LIST="${cc_major}.${cc_minor}"
+        fi
+    fi
+
+    # ThunderKittens build targeting:
+    # - SM90: compile Hopper/TK kernels with 90a.
+    # - Others (e.g., SM100): compile non-TK path with detected arch.
+    if ! has_cmake_arg "CMAKE_CUDA_ARCHITECTURES"; then
+        if [ "${cc_major}" = "9" ] && [ "${cc_minor}" = "0" ]; then
+            CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=90a"
+        else
+            CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${cmake_arch}"
+        fi
+    fi
+
+    if ! has_cmake_arg "FASTVIDEO_KERNEL_BUILD_TK"; then
+        if [ "${cc_major}" = "9" ] && [ "${cc_minor}" = "0" ]; then
+            CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON"
+        else
+            CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=OFF"
+        fi
+    fi
+fi
+
+if ! has_cmake_arg "GPU_BACKEND"; then
+    CMAKE_ARGS="${CMAKE_ARGS:-} -DGPU_BACKEND=${GPU_BACKEND}"
+fi
+export CMAKE_ARGS

 echo "TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST:-<unset>}"
 echo "CMAKE_ARGS: ${CMAKE_ARGS:-<unset>}"
 echo "GPU_BACKEND: ${GPU_BACKEND:-<unset>}"
 # Build and install
 # Use -v for verbose output
-pip install . -v --no-build-isolation
+uv pip install . -v --no-build-isolation
@@ -1,20 +1,15 @@
 import math
 import torch
 from .block_sparse_attn import block_sparse_attn
-from .triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
 from .triton_kernels.st_attn_triton import sliding_tile_attention_triton
-from .triton_kernels.index import map_to_index

 # Try to load the C++ extension
 try:
    from fastvideo_kernel._C import fastvideo_kernel_ops
    sta_fwd = getattr(fastvideo_kernel_ops, "sta_fwd", None)
-    block_sparse_fwd = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
-    block_sparse_bwd = getattr(fastvideo_kernel_ops, "block_sparse_bwd", None)
 except ImportError:
    sta_fwd = None
-    block_sparse_fwd = None
-    block_sparse_bwd = None
+

 def sliding_tile_attention(
    q: torch.Tensor,
@@ -135,14 +130,8 @@ def video_sparse_attn(
    mask = torch.zeros_like(scores,
                            dtype=torch.bool).scatter_(-1, topk_idx, True)

-    idx, num = map_to_index(mask)
-    
-    if block_sparse_fwd is not None:
-        # Use autograd-enabled wrapper so backward works (and still uses SM90 kernel when available)
-        out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
-    else:
-        # Triton-only forward (kept for environments without the wrapper deps)
-        out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num, variable_block_sizes)
+    # out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
+    out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]

    if compress_attn_weight is not None:
        return out_c * compress_attn_weight + out_s
@@ -1,405 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import json
-import os
-from collections import defaultdict
-from typing import Any
-
-import numpy as np
-
-from fastvideo.utils import dict_to_3d_list
-
-
-def configure_sta(mode: str = 'STA_searching',
-                  layer_num: int = 40,
-                  time_step_num: int = 50,
-                  head_num: int = 40,
-                  **kwargs) -> list[list[list[Any]]]:
-    """
-    Configure Sliding Tile Attention (STA) parameters based on the specified mode.
-    
-    Parameters:
-    ----------
-    mode : str
-        The STA mode to use. Options are:
-        - 'STA_searching': Generate a set of mask candidates for initial search
-        - 'STA_tuning': Select best mask strategy based on previously saved results
-        - 'STA_inference': Load and use a previously tuned mask strategy
-    layer_num: int, number of layers
-    time_step_num: int, number of timesteps
-    head_num: int, number of heads
-    
-    **kwargs : dict
-        Mode-specific parameters:
-        
-        For 'STA_searching':
-        - mask_candidates: list of str, optional, mask candidates to use
-        - mask_selected: list of int, optional, indices of selected masks
-        
-        For 'STA_tuning':
-        - mask_search_files_path: str, required, path to mask search results
-        - mask_candidates: list of str, optional, mask candidates to use
-        - mask_selected: list of int, optional, indices of selected masks
-        - skip_time_steps: int, optional, number of time steps to use full attention (default 12)
-        - save_dir: str, optional, directory to save mask strategy (default "mask_candidates")
-        
-        For 'STA_inference':
-        - load_path: str, optional, path to load mask strategy (default "mask_candidates/mask_strategy.json")
-    """
-    valid_modes = [
-        'STA_searching', 'STA_tuning', 'STA_inference', 'STA_tuning_cfg'
-    ]
-    if mode not in valid_modes:
-        raise ValueError(f"Mode must be one of {valid_modes}, got {mode}")
-
-    if mode == 'STA_searching':
-        # Get parameters with defaults
-        mask_candidates: list[str] | None = kwargs.get('mask_candidates')
-        if mask_candidates is None:
-            raise ValueError(
-                "mask_candidates is required for STA_searching mode")
-        mask_selected: list[int] = kwargs.get('mask_selected',
-                                              list(range(len(mask_candidates))))
-
-        # Parse selected masks
-        selected_masks: list[list[int]] = []
-        for index in mask_selected:
-            mask = mask_candidates[index]
-            masks_list = [int(x) for x in mask.split(',')]
-            selected_masks.append(masks_list)
-
-        # Create 3D mask structure with fixed dimensions (t=50, l=60)
-        masks_3d: list[list[list[list[int]]]] = []
-        for i in range(time_step_num):  # Fixed t dimension = 50
-            row = []
-            for j in range(layer_num):  # Fixed l dimension = 60
-                row.append(selected_masks)  # Add all masks at each position
-            masks_3d.append(row)
-
-        return masks_3d
-
-    elif mode == 'STA_tuning':
-        # Get required parameters
-        mask_search_files_path: str | None = kwargs.get(
-            'mask_search_files_path')
-        if not mask_search_files_path:
-            raise ValueError(
-                "mask_search_files_path is required for STA_tuning mode")
-
-        # Get optional parameters with defaults
-        mask_candidates_tuning: list[str] | None = kwargs.get('mask_candidates')
-        if mask_candidates_tuning is None:
-            raise ValueError("mask_candidates is required for STA_tuning mode")
-        mask_selected_tuning: list[int] = kwargs.get(
-            'mask_selected', list(range(len(mask_candidates_tuning))))
-        skip_time_steps_tuning: int | None = kwargs.get('skip_time_steps')
-        save_dir_tuning: str | None = kwargs.get('save_dir', "mask_candidates")
-
-        # Parse selected masks
-        selected_masks_tuning: list[list[int]] = []
-        for index in mask_selected_tuning:
-            mask = mask_candidates_tuning[index]
-            masks_list = [int(x) for x in mask.split(',')]
-            selected_masks_tuning.append(masks_list)
-
-        # Read JSON results
-        results = read_specific_json_files(mask_search_files_path)
-        averaged_results = average_head_losses(results, selected_masks_tuning)
-
-        # Add full attention mask for specific cases
-        full_attention_mask_tuning: list[int] | None = kwargs.get(
-            'full_attention_mask')
-        if full_attention_mask_tuning is not None:
-            selected_masks_tuning.append(full_attention_mask_tuning)
-
-        # Select best mask strategy
-        timesteps_tuning: int = kwargs.get('timesteps', time_step_num)
-        if skip_time_steps_tuning is None:
-            skip_time_steps_tuning = 12
-        mask_strategy, sparsity, strategy_counts = select_best_mask_strategy(
-            averaged_results, selected_masks_tuning, skip_time_steps_tuning,
-            timesteps_tuning, head_num)
-
-        # Save mask strategy
-        if save_dir_tuning is not None:
-            os.makedirs(save_dir_tuning, exist_ok=True)
-            file_path = os.path.join(
-                save_dir_tuning,
-                f'mask_strategy_s{skip_time_steps_tuning}.json')
-            with open(file_path, 'w') as f:
-                json.dump(mask_strategy, f, indent=4)
-            print(f"Successfully saved mask_strategy to {file_path}")
-
-        # Print sparsity and strategy counts for information
-        print(f"Overall sparsity: {sparsity:.4f}")
-        print("\nStrategy usage counts:")
-        total_heads = time_step_num * layer_num * head_num  # Fixed dimensions
-        for strategy, count in strategy_counts.items():
-            print(
-                f"Strategy {strategy}: {count} heads ({count/total_heads*100:.2f}%)"
-            )
-
-        # Convert dictionary to 3D list with fixed dimensions
-        mask_strategy_3d = dict_to_3d_list(mask_strategy,
-                                           t_max=time_step_num,
-                                           l_max=layer_num,
-                                           h_max=head_num)
-
-        return mask_strategy_3d
-    elif mode == 'STA_tuning_cfg':
-        # Get required parameters for both positive and negative paths
-        mask_search_files_path_pos: str | None = kwargs.get(
-            'mask_search_files_path_pos')
-        mask_search_files_path_neg: str | None = kwargs.get(
-            'mask_search_files_path_neg')
-        save_dir_cfg: str | None = kwargs.get('save_dir')
-
-        if not mask_search_files_path_pos or not mask_search_files_path_neg or not save_dir_cfg:
-            raise ValueError(
-                "mask_search_files_path_pos, mask_search_files_path_neg, and save_dir are required for STA_tuning_cfg mode"
-            )
-
-        # Get optional parameters with defaults
-        mask_candidates_cfg: list[str] | None = kwargs.get('mask_candidates')
-        if mask_candidates_cfg is None:
-            raise ValueError(
-                "mask_candidates is required for STA_tuning_cfg mode")
-        mask_selected_cfg: list[int] = kwargs.get(
-            'mask_selected', list(range(len(mask_candidates_cfg))))
-        skip_time_steps_cfg: int | None = kwargs.get('skip_time_steps')
-
-        # Parse selected masks
-        selected_masks_cfg: list[list[int]] = []
-        for index in mask_selected_cfg:
-            mask = mask_candidates_cfg[index]
-            masks_list = [int(x) for x in mask.split(',')]
-            selected_masks_cfg.append(masks_list)
-
-        # Read JSON results for both positive and negative paths
-        pos_results = read_specific_json_files(mask_search_files_path_pos)
-        neg_results = read_specific_json_files(mask_search_files_path_neg)
-        # Combine positive and negative results into one list
-        combined_results = pos_results + neg_results
-
-        # Average the combined results
-        averaged_results = average_head_losses(combined_results,
-                                               selected_masks_cfg)
-
-        # Add full attention mask for specific cases
-        full_attention_mask_cfg: list[int] | None = kwargs.get(
-            'full_attention_mask')
-        if full_attention_mask_cfg is not None:
-            selected_masks_cfg.append(full_attention_mask_cfg)
-
-        timesteps_cfg: int = kwargs.get('timesteps', time_step_num)
-        if skip_time_steps_cfg is None:
-            skip_time_steps_cfg = 12
-        # Select best mask strategy using combined results
-        mask_strategy, sparsity, strategy_counts = select_best_mask_strategy(
-            averaged_results, selected_masks_cfg, skip_time_steps_cfg,
-            timesteps_cfg, head_num)
-
-        # Save mask strategy
-        os.makedirs(save_dir_cfg, exist_ok=True)
-        file_path = os.path.join(save_dir_cfg,
-                                 f'mask_strategy_s{skip_time_steps_cfg}.json')
-        with open(file_path, 'w') as f:
-            json.dump(mask_strategy, f, indent=4)
-        print(f"Successfully saved mask_strategy to {file_path}")
-
-        # Print sparsity and strategy counts for information
-        print(f"Overall sparsity: {sparsity:.4f}")
-        print("\nStrategy usage counts:")
-        total_heads = time_step_num * layer_num * head_num  # Fixed dimensions
-        for strategy, count in strategy_counts.items():
-            print(
-                f"Strategy {strategy}: {count} heads ({count/total_heads*100:.2f}%)"
-            )
-
-        # Convert dictionary to 3D list with fixed dimensions
-        mask_strategy_3d = dict_to_3d_list(mask_strategy,
-                                           t_max=time_step_num,
-                                           l_max=layer_num,
-                                           h_max=head_num)
-
-        return mask_strategy_3d
-
-    else:  # STA_inference
-        # Get parameters with defaults
-        load_path: str | None = kwargs.get(
-            'load_path', "mask_candidates/mask_strategy.json")
-        if load_path is None:
-            raise ValueError("load_path is required for STA_inference mode")
-
-        # Load previously saved mask strategy
-        with open(load_path) as f:
-            mask_strategy = json.load(f)
-
-        # Convert dictionary to 3D list with fixed dimensions
-        mask_strategy_3d = dict_to_3d_list(mask_strategy,
-                                           t_max=time_step_num,
-                                           l_max=layer_num,
-                                           h_max=head_num)
-
-        return mask_strategy_3d
-
-
-# Helper functions
-
-
-def read_specific_json_files(folder_path: str) -> list[dict[str, Any]]:
-    """Read and parse JSON files containing mask search results."""
-    json_contents: list[dict[str, Any]] = []
-
-    # List files only in the current directory (no walk)
-    files = os.listdir(folder_path)
-    # Filter files
-    matching_files = [f for f in files if 'mask' in f and f.endswith('.json')]
-    print(f"Found {len(matching_files)} matching files: {matching_files}")
-
-    for file_name in matching_files:
-        file_path = os.path.join(folder_path, file_name)
-        with open(file_path) as file:
-            data = json.load(file)
-            json_contents.append(data)
-
-    return json_contents
-
-
-def average_head_losses(
-        results: list[dict[str, Any]],
-        selected_masks: list[list[int]]) -> dict[str, dict[str, np.ndarray]]:
-    """Average losses across all prompts for each mask strategy."""
-    # Initialize a dictionary to store the averaged results
-    averaged_losses: dict[str, dict[str, np.ndarray]] = {}
-    loss_type = 'L2_loss'
-    # Get all loss types (e.g., 'L2_loss')
-    averaged_losses[loss_type] = {}
-
-    for mask in selected_masks:
-        mask_str = str(mask)
-        data_shape = np.array(results[0][loss_type][mask_str]).shape
-        accumulated_data = np.zeros(data_shape)
-
-        # Sum across all prompts
-        for prompt_result in results:
-            accumulated_data += np.array(prompt_result[loss_type][mask_str])
-
-        # Average by dividing by number of prompts
-        averaged_data = accumulated_data / len(results)
-        averaged_losses[loss_type][mask_str] = averaged_data
-
-    return averaged_losses
-
-
-def select_best_mask_strategy(
-        averaged_results: dict[str, dict[str, np.ndarray]],
-        selected_masks: list[list[int]],
-        skip_time_steps: int = 12,
-        timesteps: int = 50,
-        head_num: int = 40
-) -> tuple[dict[str, list[int]], float, dict[str, int]]:
-    """Select the best mask strategy for each head based on loss minimization."""
-    best_mask_strategy: dict[str, list[int]] = {}
-    loss_type = 'L2_loss'
-    # Get the shape of time steps and layers
-    layers = len(averaged_results[loss_type][str(selected_masks[0])][0])
-
-    # Counter for sparsity calculation
-    total_tokens = 0  # total number of masked tokens
-    total_length = 0  # total sequence length
-
-    strategy_counts: dict[str, int] = {
-        str(strategy): 0
-        for strategy in selected_masks
-    }
-    full_attn_strategy = selected_masks[-1]  # Last strategy is full attention
-    print(f"Strategy {full_attn_strategy}, skip first {skip_time_steps} steps ")
-
-    for t in range(timesteps):
-        for layer_idx in range(layers):
-            for h in range(head_num):
-                if t < skip_time_steps:  # First steps use full attention
-                    strategy = full_attn_strategy
-                else:
-                    # Get losses for this head across all strategies
-                    head_losses = []
-                    for strategy in selected_masks[:
-                                                   -1]:  # Exclude full attention
-                        head_losses.append(averaged_results[loss_type][str(
-                            strategy)][t][layer_idx][h])
-
-                    # Find which strategy gives minimum loss
-                    best_strategy_idx = np.argmin(head_losses)
-                    strategy = selected_masks[best_strategy_idx]
-
-                best_mask_strategy[f'{t}_{layer_idx}_{h}'] = strategy
-
-                # Calculate sparsity
-                nums = strategy  # strategy is already a list of numbers
-                total_tokens += nums[0] * nums[1] * nums[
-                    2]  # masked tokens for chosen strategy
-                total_length += full_attn_strategy[0] * full_attn_strategy[
-                    1] * full_attn_strategy[2]
-
-                # Count strategy usage
-                strategy_counts[str(strategy)] += 1
-
-    overall_sparsity = 1 - total_tokens / total_length
-
-    return best_mask_strategy, overall_sparsity, strategy_counts
-
-
-def save_mask_search_results(
-        mask_search_final_result: list[dict[str, list[float]]],
-        prompt: str,
-        mask_strategies: list[str],
-        output_dir: str = 'output/mask_search_result/') -> str | None:
-    if not mask_search_final_result:
-        print("No mask search results to save")
-        return None
-
-    # Create result dictionary with defaultdict for nested lists
-    mask_search_dict: dict[str, dict[str, list[list[float]]]] = {
-        "L2_loss": defaultdict(list),
-        "L1_loss": defaultdict(list)
-    }
-
-    mask_selected = list(range(len(mask_strategies)))
-    selected_masks: list[list[int]] = []
-    for index in mask_selected:
-        mask = mask_strategies[index]
-        masks_list = [int(x) for x in mask.split(',')]
-        selected_masks.append(masks_list)
-
-    # Process each mask strategy
-    for i, mask_strategy in enumerate(selected_masks):
-        mask_strategy_str = str(mask_strategy)
-        # Process L2 loss
-        step_results: list[list[float]] = []
-        for step_data in mask_search_final_result:
-            if isinstance(step_data, dict) and "L2_loss" in step_data:
-                layer_losses = [float(loss) for loss in step_data["L2_loss"]]
-                step_results.append(layer_losses)
-        mask_search_dict["L2_loss"][mask_strategy_str] = step_results
-
-        step_results = []
-        for step_data in mask_search_final_result:
-            if isinstance(step_data, dict) and "L1_loss" in step_data:
-                layer_losses = [float(loss) for loss in step_data["L1_loss"]]
-                step_results.append(layer_losses)
-        mask_search_dict["L1_loss"][mask_strategy_str] = step_results
-
-    # Create the output directory if it doesn't exist
-    os.makedirs(output_dir, exist_ok=True)
-
-    # Create a filename based on the first 20 characters of the prompt
-    filename = prompt[:50].replace(" ", "_")
-    filepath = os.path.join(output_dir, f'mask_search_{filename}.json')
-
-    # Save the results to a JSON file
-    with open(filepath, 'w') as f:
-        json.dump(mask_search_dict, f, indent=4)
-
-    print(f"Successfully saved mask research results to {filepath}")
-
-    return filepath
@@ -4,26 +4,36 @@ import torch
 import torch.nn.functional as F
 from flash_attn import flash_attn_func as flash_attn_2_func
 from dataclasses import dataclass
+
 try:
-    from flash_attn_interface import flash_attn_func as flash_attn_3_func
+    from fastvideo.attention.utils.flash_attn_cute import flash_attn_func

-    # flash_attn 3 no longer have a different API, see following commit:
-    # https://github.com/Dao-AILab/flash-attention/commit/ed209409acedbb2379f870bbd03abce31a7a51b7
-    flash_attn_func = flash_attn_3_func
+    fa_version = "4"
 except ImportError:
-    flash_attn_func = flash_attn_2_func
+    try:
+        from flash_attn_interface import flash_attn_func as flash_attn_3_func

-from fastvideo.attention.backends.abstract import (AttentionBackend,
-                                                   AttentionImpl,
-                                                   AttentionMetadata,
-                                                   AttentionMetadataBuilder)
+        # flash_attn 3 no longer have a different API, see following commit:
+        # https://github.com/Dao-AILab/flash-attention/commit/ed209409acedbb2379f870bbd03abce31a7a51b7
+        flash_attn_func = flash_attn_3_func
+        fa_version = "3"
+    except ImportError:
+        flash_attn_func = flash_attn_2_func
+        fa_version = "2"
+
+from fastvideo.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+    AttentionMetadataBuilder,
+)
 from fastvideo.logger import init_logger

 logger = init_logger(__name__)
+logger.info("Using FlashAttention-%s backend", fa_version)


 class FlashAttentionBackend(AttentionBackend):
-
    accept_output_buffer: bool = True

    @staticmethod
@@ -117,11 +127,13 @@ class FlashAttentionImpl(AttentionImpl):
                    f"expected {key_len}, got {key_padding_mask.shape[-1]}")
            return key_padding_mask

-        if attn_metadata is not None and hasattr(
-                attn_metadata,
-                "attn_mask") and attn_metadata.attn_mask is not None:
+        if (attn_metadata is not None and hasattr(attn_metadata, "attn_mask")
+                and attn_metadata.attn_mask is not None):
            from fastvideo.attention.utils.flash_attn_no_pad import (
-                flash_attn_no_pad, flash_attn_varlen_qk_no_pad)
+                flash_attn_no_pad,
+                flash_attn_varlen_qk_no_pad,
+            )
+
            attn_mask = attn_metadata.attn_mask

            # flash_attn_no_pad packs q/k/v as one tensor and assumes equal q/k
@@ -160,5 +172,6 @@ class FlashAttentionImpl(AttentionImpl):
                key,
                value,
                softmax_scale=self.softmax_scale,
-                causal=self.causal)
+                causal=self.causal,
+            )
        return output
@@ -1,263 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import json
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-from einops import rearrange
-from fastvideo_kernel import sliding_tile_attention
-
-import fastvideo.envs as envs
-from fastvideo.attention.backends.abstract import (AttentionBackend,
-                                                   AttentionImpl,
-                                                   AttentionMetadata,
-                                                   AttentionMetadataBuilder)
-from fastvideo.distributed import get_sp_group
-from fastvideo.forward_context import ForwardContext, get_forward_context
-from fastvideo.logger import init_logger
-from fastvideo.utils import dict_to_3d_list
-
-logger = init_logger(__name__)
-
-
-class RangeDict(dict):
-
-    def __getitem__(self, item: int) -> str:
-        for key in self.keys():
-            if isinstance(key, tuple):
-                low, high = key
-                if low <= item <= high:
-                    return str(super().__getitem__(key))
-            elif key == item:
-                return str(super().__getitem__(key))
-        raise KeyError(f"seq_len {item} not supported for STA")
-
-
-class SlidingTileAttentionBackend(AttentionBackend):
-
-    accept_output_buffer: bool = True
-
-    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        # TODO(will-refactor): check this
-        return [32, 64, 96, 128, 160, 192, 224, 256]
-
-    @staticmethod
-    def get_name() -> str:
-        return "SLIDING_TILE_ATTN"
-
-    @staticmethod
-    def get_impl_cls() -> type["SlidingTileAttentionImpl"]:
-        return SlidingTileAttentionImpl
-
-    @staticmethod
-    def get_metadata_cls() -> type["SlidingTileAttentionMetadata"]:
-        return SlidingTileAttentionMetadata
-
-    @staticmethod
-    def get_builder_cls() -> type["SlidingTileAttentionMetadataBuilder"]:
-        return SlidingTileAttentionMetadataBuilder
-
-
-@dataclass
-class SlidingTileAttentionMetadata(AttentionMetadata):
-    current_timestep: int
-    STA_param: list[list[
-        Any]]  # each timestep with one metadata, shape [num_layers, num_heads]
-
-
-class SlidingTileAttentionMetadataBuilder(AttentionMetadataBuilder):
-
-    def __init__(self):
-        pass
-
-    def prepare(self):
-        pass
-
-    def build(  # type: ignore
-        self,
-        STA_param: list[list[Any]],
-        current_timestep: int,
-        **kwargs: dict[str, Any],
-    ) -> SlidingTileAttentionMetadata:
-        param = STA_param
-        if param is None:
-            return SlidingTileAttentionMetadata(
-                current_timestep=current_timestep, STA_param=[])
-        return SlidingTileAttentionMetadata(current_timestep=current_timestep,
-                                            STA_param=param[current_timestep])
-
-
-class SlidingTileAttentionImpl(AttentionImpl):
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        causal: bool,
-        softmax_scale: float,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        **extra_impl_args,
-    ) -> None:
-        # TODO(will-refactor): for now this is the mask strategy, but maybe we should
-        # have a more general config for STA?
-        config_file = envs.FASTVIDEO_ATTENTION_CONFIG
-        if config_file is None:
-            raise ValueError("FASTVIDEO_ATTENTION_CONFIG is not set")
-
-        # TODO(kevin): get mask strategy for different STA modes
-        with open(config_file) as f:
-            mask_strategy = json.load(f)
-        self.mask_strategy = dict_to_3d_list(mask_strategy)
-
-        self.prefix = prefix
-        sp_group = get_sp_group()
-        self.sp_size = sp_group.world_size
-        # STA config
-        self.STA_base_tile_size = [6, 8, 8]
-        self.dit_seq_shape_mapping = RangeDict({
-            (115200, 115456): '30x48x80',
-            82944: '36x48x48',
-            69120: '18x48x80',
-        })
-        self.full_window_mapping = {
-            '30x48x80': [5, 6, 10],
-            '36x48x48': [6, 6, 6],
-            '18x48x80': [3, 6, 10]
-        }
-
-    def tile(self, x: torch.Tensor) -> torch.Tensor:
-        return rearrange(
-            x,
-            "b (n_t ts_t n_h ts_h n_w ts_w) h d -> b (n_t n_h n_w ts_t ts_h ts_w) h d",
-            n_t=self.full_window_size[0],
-            n_h=self.full_window_size[1],
-            n_w=self.full_window_size[2],
-            ts_t=self.STA_base_tile_size[0],
-            ts_h=self.STA_base_tile_size[1],
-            ts_w=self.STA_base_tile_size[2])
-
-    def untile(self, x: torch.Tensor) -> torch.Tensor:
-        x = rearrange(
-            x,
-            "b (n_t n_h n_w ts_t ts_h ts_w) h d -> b (n_t ts_t n_h ts_h n_w ts_w) h d",
-            n_t=self.full_window_size[0],
-            n_h=self.full_window_size[1],
-            n_w=self.full_window_size[2],
-            ts_t=self.STA_base_tile_size[0],
-            ts_h=self.STA_base_tile_size[1],
-            ts_w=self.STA_base_tile_size[2])
-        return x
-
-    def preprocess_qkv(
-        self,
-        qkv: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        img_sequence_length = qkv.shape[1]
-        self.dit_seq_shape_str = self.dit_seq_shape_mapping[img_sequence_length]
-        self.full_window_size = self.full_window_mapping[self.dit_seq_shape_str]
-        self.dit_seq_shape_int = list(
-            map(int, self.dit_seq_shape_str.split('x')))
-        self.img_seq_length = self.dit_seq_shape_int[
-            0] * self.dit_seq_shape_int[1] * self.dit_seq_shape_int[2]
-        return self.tile(qkv)
-
-    def postprocess_output(
-        self,
-        output: torch.Tensor,
-        attn_metadata: SlidingTileAttentionMetadata,
-    ) -> torch.Tensor:
-        return self.untile(output)
-
-    def forward(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        attn_metadata: SlidingTileAttentionMetadata,
-    ) -> torch.Tensor:
-        if self.mask_strategy is None:
-            raise ValueError(
-                "mask_strategy cannot be None for SlidingTileAttention")
-        if self.mask_strategy[0] is None:
-            raise ValueError(
-                "mask_strategy[0] cannot be None for SlidingTileAttention")
-
-        timestep = attn_metadata.current_timestep
-        forward_context: ForwardContext = get_forward_context()
-        forward_batch = forward_context.forward_batch
-        if forward_batch is None:
-            raise ValueError("forward_batch cannot be None")
-        # pattern:'.double_blocks.0.attn.impl' or '.single_blocks.0.attn.impl'
-        layer_idx = int(self.prefix.split('.')[-3])
-        if attn_metadata.STA_param is None or len(
-                attn_metadata.STA_param) <= layer_idx:
-            raise ValueError("Invalid STA_param")
-        STA_param = attn_metadata.STA_param[layer_idx]
-
-        text_length = q.shape[1] - self.img_seq_length
-        has_text = text_length > 0
-
-        query = q.transpose(1, 2).contiguous()
-        key = k.transpose(1, 2).contiguous()
-        value = v.transpose(1, 2).contiguous()
-
-        head_num = query.size(1)
-        sp_group = get_sp_group()
-        current_rank = sp_group.rank_in_group
-        start_head = current_rank * head_num
-
-        # searching or tuning mode
-        if len(STA_param) < head_num * sp_group.world_size:
-            sparse_attn_hidden_states_all = []
-            full_mask_window = STA_param[-1]
-            for window_size in STA_param[:-1]:
-                sparse_hidden_states = sliding_tile_attention(
-                    query, key, value, [window_size] * head_num, text_length,
-                    has_text, self.dit_seq_shape_str).transpose(1, 2)
-                sparse_attn_hidden_states_all.append(sparse_hidden_states)
-
-            hidden_states = sliding_tile_attention(
-                query, key, value, [full_mask_window] * head_num, text_length,
-                has_text, self.dit_seq_shape_str).transpose(1, 2)
-
-            attn_L2_loss = []
-            attn_L1_loss = []
-            # average loss across all heads
-            for sparse_attn_hidden_states in sparse_attn_hidden_states_all:
-                # L2 loss
-                attn_L2_loss_ = torch.mean((sparse_attn_hidden_states.float() -
-                                            hidden_states.float())**2,
-                                           dim=[0, 1, 3]).cpu().numpy()
-                attn_L2_loss_ = [round(float(x), 6) for x in attn_L2_loss_]
-                attn_L2_loss.append(attn_L2_loss_)
-                # L1 loss
-                attn_L1_loss_ = torch.mean(
-                    torch.abs(sparse_attn_hidden_states.float() -
-                              hidden_states.float()),
-                    dim=[0, 1, 3]).cpu().numpy()
-                attn_L1_loss_ = [round(float(x), 6) for x in attn_L1_loss_]
-                attn_L1_loss.append(attn_L1_loss_)
-
-            layer_loss_save = {"L2_loss": attn_L2_loss, "L1_loss": attn_L1_loss}
-
-            if forward_batch.is_cfg_negative:
-                if forward_batch.mask_search_final_result_neg is not None:
-                    forward_batch.mask_search_final_result_neg[timestep].append(
-                        layer_loss_save)
-            else:
-                if forward_batch.mask_search_final_result_pos is not None:
-                    forward_batch.mask_search_final_result_pos[timestep].append(
-                        layer_loss_save)
-        else:
-            windows = [
-                STA_param[head_idx + start_head] for head_idx in range(head_num)
-            ]
-
-            hidden_states = sliding_tile_attention(
-                query, key, value, windows, text_length, has_text,
-                self.dit_seq_shape_str).transpose(1, 2)
-
-        return hidden_states
@@ -66,11 +66,11 @@ class DistributedAttention(nn.Module):
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
+        original_seq_len: int | None = None,
        replicated_q: torch.Tensor | None = None,
        replicated_k: torch.Tensor | None = None,
        replicated_v: torch.Tensor | None = None,
        freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
-        attention_mask: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        """Forward pass for distributed attention.
        
@@ -78,10 +78,10 @@ class DistributedAttention(nn.Module):
            q (torch.Tensor): Query tensor [batch_size, seq_len, num_heads, head_dim]
            k (torch.Tensor): Key tensor [batch_size, seq_len, num_heads, head_dim]
            v (torch.Tensor): Value tensor [batch_size, seq_len, num_heads, head_dim]
+            original_seq_len (int): Original (unpadded) full sequence length
            replicated_q (Optional[torch.Tensor]): Replicated query tensor, typically for text tokens
            replicated_k (Optional[torch.Tensor]): Replicated key tensor
            replicated_v (Optional[torch.Tensor]): Replicated value tensor
-            attention_mask (Optional[torch.Tensor]): Attention mask [batch_size, seq_len]
            
        Returns:
            Tuple[torch.Tensor, Optional[torch.Tensor]]: A tuple containing:
@@ -91,7 +91,7 @@ class DistributedAttention(nn.Module):
        # Check input shapes
        assert q.dim() == 4 and k.dim() == 4 and v.dim(
        ) == 4, "Expected 4D tensors"
-        batch_size, seq_len, num_heads, head_dim = q.shape
+        batch_size, _, num_heads, _ = q.shape
        local_rank = get_sp_parallel_rank()
        world_size = get_sp_world_size()

@@ -107,15 +107,11 @@ class DistributedAttention(nn.Module):
                                                    scatter_dim=2,
                                                    gather_dim=1)

-        # After all-to-all, each rank has the full sequence but only a subset of heads
-        # The attention mask should now apply to the full sequence length
-        # Since mask is [batch, full_seq_len], it's already in the correct format
-
-        # LOAY TODO, instead of slicing repeatedly maintain an original qkv and rewrite into that
-        valid_seq_len = None
-        if attention_mask is not None:
-            valid_seq_len = (attention_mask[0] == 1).sum().item()
-            qkv = qkv[:, :valid_seq_len, :, :]
+        # After all-to-all, each rank has the full sequence but only a subset of heads.
+        # Trim away SP padding for attention compute, then pad back before returning.
+        original_seq_len = original_seq_len or qkv.shape[1]
+        pad_seq_len = qkv.shape[1] - original_seq_len
+        qkv = qkv[:, :original_seq_len, :, :]

        if freqs_cis is not None:
            cos, sin = freqs_cis
@@ -145,7 +141,7 @@ class DistributedAttention(nn.Module):
        # Redistribute back if using sequence parallelism
        replicated_output = None
        if replicated_q is not None:
-            split_idx = seq_len * world_size if valid_seq_len is None else valid_seq_len
+            split_idx = original_seq_len
            replicated_output = output[:, split_idx:]
            output = output[:, :split_idx]
            # TODO: make this asynchronous
@@ -154,9 +150,7 @@ class DistributedAttention(nn.Module):
        # Apply backend-specific postprocess_output
        output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)

-        if attention_mask is not None:
-            pad_len = (attention_mask[0] == 0).sum().item()
-            output = torch.nn.functional.pad(output, (0, 0, 0, 0, 0, pad_len))
+        output = torch.nn.functional.pad(output, (0, 0, 0, 0, 0, pad_seq_len))

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -175,12 +169,12 @@ class DistributedAttention_VSA(DistributedAttention):
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
+        original_seq_len: int,
        replicated_q: torch.Tensor | None = None,
        replicated_k: torch.Tensor | None = None,
        replicated_v: torch.Tensor | None = None,
        gate_compress: torch.Tensor | None = None,
        freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
-        attention_mask: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        """Forward pass for distributed attention.
        
@@ -188,11 +182,11 @@ class DistributedAttention_VSA(DistributedAttention):
            q (torch.Tensor): Query tensor [batch_size, seq_len, num_heads, head_dim]
            k (torch.Tensor): Key tensor [batch_size, seq_len, num_heads, head_dim]
            v (torch.Tensor): Value tensor [batch_size, seq_len, num_heads, head_dim]
+            original_seq_len (int): Original (unpadded) full sequence length
            gate_compress (torch.Tensor): Gate compress tensor [batch_size, seq_len, num_heads, head_dim]
            replicated_q (Optional[torch.Tensor]): Replicated query tensor, typically for text tokens
            replicated_k (Optional[torch.Tensor]): Replicated key tensor
            replicated_v (Optional[torch.Tensor]): Replicated value tensor
-            attention_mask (Optional[torch.Tensor]): Attention mask [batch_size, seq_len]
            
        Returns:
            Tuple[torch.Tensor, Optional[torch.Tensor]]: A tuple containing:
@@ -221,11 +215,8 @@ class DistributedAttention_VSA(DistributedAttention):
                                                     gather_dim=1)

        # After all-to-all, each rank has the full sequence but only a subset of heads
-        # The attention mask should now apply to the full sequence length
-
-        if attention_mask is not None:
-            valid_seq_len = (attention_mask[0] == 1).sum().item()
-            qkvg = qkvg[:, :valid_seq_len, :, :]
+        pad_seq_len = qkvg.shape[1] - original_seq_len
+        qkvg = qkvg[:, :original_seq_len, :, :]

        if freqs_cis is not None:
            cos, sin = freqs_cis
@@ -246,9 +237,7 @@ class DistributedAttention_VSA(DistributedAttention):
        # Apply backend-specific postprocess_output
        output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)

-        if attention_mask is not None:
-            pad_len = (attention_mask[0] == 0).sum().item()
-            output = torch.nn.functional.pad(output, (0, 0, 0, 0, 0, pad_len))
+        output = torch.nn.functional.pad(output, (0, 0, 0, 0, 0, pad_seq_len))

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -0,0 +1,264 @@
+from __future__ import annotations
+
+import torch
+if torch.cuda.is_available():
+    from flash_attn.cute.interface import _flash_attn_bwd, _flash_attn_fwd
+else:
+    # This error will be caught in flash_attn.py or flash_attn_no_pad.py
+    raise ImportError(
+        "flash_attn.cute is only available on CUDA devices; this error must be handled internally"
+    )
+
+
+def _check_dropout(dropout_p: float) -> None:
+    if dropout_p != 0.0:
+        raise NotImplementedError(
+            f"flash_attn.cute does not support dropout (got dropout_p={dropout_p})"
+        )
+
+
+@torch.library.custom_op(
+    "fastvideo::_flash_attn_cute_forward",
+    mutates_args=(),
+    device_types="cuda",
+)
+def _flash_attn_cute_forward(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    softmax_scale: float | None,
+    causal: bool,
+    deterministic: bool,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    out, lse = _flash_attn_fwd(
+        q,
+        k,
+        v,
+        softmax_scale=softmax_scale,
+        causal=causal,
+        window_size_left=None,
+        window_size_right=None,
+        softcap=0.0,
+        num_splits=1,
+        pack_gqa=None,
+    )
+    return out, lse
+
+
+@torch.library.register_fake("fastvideo::_flash_attn_cute_forward")
+def _flash_attn_cute_forward_fake(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    softmax_scale: float | None,
+    causal: bool,
+    deterministic: bool,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    del k, softmax_scale, causal, deterministic
+    batch, seqlen_q, nheads = q.shape[:3]
+    out = q.new_empty(batch, seqlen_q, nheads, v.shape[-1])
+    lse = q.new_empty(batch, nheads, seqlen_q, dtype=torch.float32)
+    return out, lse
+
+
+def _flash_attn_cute_setup_context(ctx: torch.autograd.function.FunctionCtx,
+                                   inputs, output) -> None:
+    q, k, v, softmax_scale, causal, deterministic = inputs
+    out, lse = output
+    ctx.save_for_backward(q, k, v, out, lse)
+    ctx.softmax_scale = softmax_scale
+    ctx.causal = causal
+    ctx.deterministic = deterministic
+
+
+def _flash_attn_cute_backward(
+    ctx: torch.autograd.function.FunctionCtx,
+    grad_out: torch.Tensor,
+    grad_lse: torch.Tensor | None,
+):
+    del grad_lse
+    q, k, v, out, lse = ctx.saved_tensors
+    dq, dk, dv = _flash_attn_bwd(
+        q,
+        k,
+        v,
+        out,
+        grad_out,
+        lse,
+        softmax_scale=ctx.softmax_scale,
+        causal=ctx.causal,
+        softcap=0.0,
+        window_size_left=None,
+        window_size_right=None,
+        deterministic=ctx.deterministic,
+    )
+    return dq, dk, dv, None, None, None
+
+
+torch.library.register_autograd(
+    "fastvideo::_flash_attn_cute_forward",
+    _flash_attn_cute_backward,
+    setup_context=_flash_attn_cute_setup_context,
+)
+
+
+@torch.library.custom_op(
+    "fastvideo::_flash_attn_cute_varlen_forward",
+    mutates_args=(),
+    device_types="cuda",
+)
+def _flash_attn_cute_varlen_forward(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens_q: torch.Tensor,
+    cu_seqlens_k: torch.Tensor,
+    max_seqlen_q: int,
+    max_seqlen_k: int,
+    softmax_scale: float | None,
+    causal: bool,
+    deterministic: bool,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    out, lse = _flash_attn_fwd(
+        q,
+        k,
+        v,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        max_seqlen_q=max_seqlen_q,
+        max_seqlen_k=max_seqlen_k,
+        softmax_scale=softmax_scale,
+        causal=causal,
+        window_size_left=None,
+        window_size_right=None,
+        softcap=0.0,
+        num_splits=1,
+        pack_gqa=None,
+    )
+    return out, lse
+
+
+@torch.library.register_fake("fastvideo::_flash_attn_cute_varlen_forward")
+def _flash_attn_cute_varlen_forward_fake(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens_q: torch.Tensor,
+    cu_seqlens_k: torch.Tensor,
+    max_seqlen_q: int,
+    max_seqlen_k: int,
+    softmax_scale: float | None,
+    causal: bool,
+    deterministic: bool,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    del k, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, softmax_scale
+    del causal
+    del deterministic
+    total_q, nheads = q.shape[:2]
+    out = q.new_empty(total_q, nheads, v.shape[-1])
+    lse = q.new_empty(nheads, total_q, dtype=torch.float32)
+    return out, lse
+
+
+def _flash_attn_cute_varlen_setup_context(
+        ctx: torch.autograd.function.FunctionCtx, inputs, output) -> None:
+    (
+        q,
+        k,
+        v,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlen_q,
+        max_seqlen_k,
+        softmax_scale,
+        causal,
+        deterministic,
+    ) = inputs
+    out, lse = output
+    ctx.save_for_backward(q, k, v, out, lse, cu_seqlens_q, cu_seqlens_k)
+    ctx.max_seqlen_q = max_seqlen_q
+    ctx.max_seqlen_k = max_seqlen_k
+    ctx.softmax_scale = softmax_scale
+    ctx.causal = causal
+    ctx.deterministic = deterministic
+
+
+def _flash_attn_cute_varlen_backward(
+    ctx: torch.autograd.function.FunctionCtx,
+    grad_out: torch.Tensor,
+    grad_lse: torch.Tensor | None,
+):
+    del grad_lse
+    q, k, v, out, lse, cu_seqlens_q, cu_seqlens_k = ctx.saved_tensors
+    dq, dk, dv = _flash_attn_bwd(
+        q,
+        k,
+        v,
+        out,
+        grad_out,
+        lse,
+        softmax_scale=ctx.softmax_scale,
+        causal=ctx.causal,
+        softcap=0.0,
+        window_size_left=None,
+        window_size_right=None,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        max_seqlen_q=ctx.max_seqlen_q,
+        max_seqlen_k=ctx.max_seqlen_k,
+        deterministic=ctx.deterministic,
+    )
+    return dq, dk, dv, None, None, None, None, None, None, None
+
+
+torch.library.register_autograd(
+    "fastvideo::_flash_attn_cute_varlen_forward",
+    _flash_attn_cute_varlen_backward,
+    setup_context=_flash_attn_cute_varlen_setup_context,
+)
+
+
+def flash_attn_func(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    dropout_p: float = 0.0,
+    softmax_scale: float | None = None,
+    causal: bool = False,
+    deterministic: bool = False,
+) -> torch.Tensor:
+    """Only returns the output, not the lse."""
+    _check_dropout(dropout_p)
+    out, _ = torch.ops.fastvideo._flash_attn_cute_forward(
+        q, k, v, softmax_scale, causal, deterministic)
+    return out
+
+
+def flash_attn_varlen_func(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens_q: torch.Tensor,
+    cu_seqlens_k: torch.Tensor,
+    max_seqlen_q: int,
+    max_seqlen_k: int,
+    dropout_p: float = 0.0,
+    softmax_scale: float | None = None,
+    causal: bool = False,
+    deterministic: bool = False,
+) -> torch.Tensor:
+    """Only returns the output, not the lse."""
+    _check_dropout(dropout_p)
+    out, _ = torch.ops.fastvideo._flash_attn_cute_varlen_forward(
+        q,
+        k,
+        v,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlen_q,
+        max_seqlen_k,
+        softmax_scale,
+        causal,
+        deterministic,
+    )
+    return out
@@ -15,16 +15,29 @@
 # See the License for the specific language governing permissions and limitations under the License.

 from einops import rearrange
+from flash_attn.bert_padding import pad_input, unpad_input
+from flash_attn import flash_attn_varlen_qkvpacked_func
+
+try:
+    from fastvideo.attention.utils.flash_attn_cute import (
+        flash_attn_varlen_func as flash_attn_varlen_func_impl, )
+except ImportError:
+    try:
+        from flash_attn_interface import (
+            flash_attn_varlen_func as flash_attn_varlen_func_impl, )
+    except ImportError:
+        from flash_attn import (
+            flash_attn_varlen_func as flash_attn_varlen_func_impl, )


-def flash_attn_no_pad(qkv,
-                      key_padding_mask,
-                      causal=False,
-                      dropout_p=0.0,
-                      softmax_scale=None,
-                      deterministic=False):
-    from flash_attn import flash_attn_varlen_qkvpacked_func
-    from flash_attn.bert_padding import pad_input, unpad_input
+def flash_attn_no_pad(
+    qkv,
+    key_padding_mask,
+    causal=False,
+    dropout_p=0.0,
+    softmax_scale=None,
+    deterministic=False,
+):
    batch_size = qkv.shape[0]
    seqlen = qkv.shape[1]
    nheads = qkv.shape[-2]
@@ -46,22 +59,28 @@ def flash_attn_no_pad(qkv,
        deterministic=deterministic,
    )
    output = rearrange(
-        pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices,
-                  batch_size, seqlen),
+        pad_input(
+            rearrange(output_unpad, "nnz h d -> nnz (h d)"),
+            indices,
+            batch_size,
+            seqlen,
+        ),
        "b s (h d) -> b s h d",
        h=nheads,
    )
    return output


-def flash_attn_no_pad_v3(qkv,
-                         key_padding_mask,
-                         causal=False,
-                         dropout_p=0.0,
-                         softmax_scale=None,
-                         deterministic=False):
-    from flash_attn.bert_padding import pad_input, unpad_input
-    from flash_attn_interface import flash_attn_varlen_func as flash_attn_varlen_func_v3
+def flash_attn_no_pad_v3(
+    qkv,
+    key_padding_mask,
+    causal=False,
+    dropout_p=0.0,
+    softmax_scale=None,
+    deterministic=False,
+):
+    from flash_attn_interface import (
+        flash_attn_varlen_func as flash_attn_varlen_func_v3, )

    if flash_attn_varlen_func_v3 is None:
        raise ImportError("FlashAttention V3 backend not available")
@@ -80,22 +99,29 @@ def flash_attn_no_pad_v3(qkv,
    key_unpad = rearrange(key_unpad, "nnz (h d) -> nnz h d", h=nheads)
    value_unpad = rearrange(value_unpad, "nnz (h d) -> nnz h d", h=nheads)

-    output_unpad = flash_attn_varlen_func_v3(query_unpad,
-                                             key_unpad,
-                                             value_unpad,
-                                             cu_seqlens_q,
-                                             cu_seqlens_k,
-                                             max_seqlen_q,
-                                             max_seqlen_q,
-                                             softmax_scale=softmax_scale,
-                                             causal=causal,
-                                             deterministic=deterministic)
+    output_unpad = flash_attn_varlen_func_v3(
+        query_unpad,
+        key_unpad,
+        value_unpad,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlen_q,
+        max_seqlen_q,
+        softmax_scale=softmax_scale,
+        causal=causal,
+        deterministic=deterministic,
+    )

-    output = rearrange(pad_input(
-        rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size,
-        seqlen),
-                       "b s (h d) -> b s h d",
-                       h=nheads)
+    output = rearrange(
+        pad_input(
+            rearrange(output_unpad, "nnz h d -> nnz (h d)"),
+            indices,
+            batch_size,
+            seqlen,
+        ),
+        "b s (h d) -> b s h d",
+        h=nheads,
+    )
    return output


@@ -110,16 +136,6 @@ def flash_attn_varlen_qk_no_pad(
    softmax_scale=None,
    deterministic=False,
 ):
-    from flash_attn.bert_padding import pad_input, unpad_input
-
-    try:
-        from flash_attn_interface import flash_attn_varlen_func as flash_attn_varlen_func_impl
-    except ImportError:
-        from flash_attn import flash_attn_varlen_func as flash_attn_varlen_func_impl
-
-    if flash_attn_varlen_func_impl is None:
-        raise ImportError("FlashAttention varlen backend not available")
-
    batch_size, q_seqlen, nheads, _ = query.shape

    query_unpad, q_indices, cu_seqlens_q, max_seqlen_q, _ = unpad_input(
@@ -148,8 +164,12 @@ def flash_attn_varlen_qk_no_pad(
    )

    output = rearrange(
-        pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), q_indices,
-                  batch_size, q_seqlen),
+        pad_input(
+            rearrange(output_unpad, "nnz h d -> nnz (h d)"),
+            q_indices,
+            batch_size,
+            q_seqlen,
+        ),
        "b s (h d) -> b s h d",
        h=nheads,
    )
@@ -43,6 +43,5 @@
      "require_post_norm": null
    }
  ],
-  "mask_strategy_file_path": null,
  "enable_torch_compile": false
-}
+}
@@ -24,7 +24,6 @@ class ModelConfig:
    arch_config: ArchConfig = field(default_factory=ArchConfig)

    # FastVideo-specific parameters here
-    # i.e. STA, quantization, teacache

    def __getattr__(self, name):
        # Only called if 'name' is not found in ModelConfig directly
@@ -7,9 +7,11 @@ from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
 from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
 from fastvideo.configs.models.dits.hyworld import HYWorldConfig
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig

 __all__ = [
    "HunyuanVideoConfig", "HunyuanVideo15Config", "HunyuanGameCraftConfig",
    "WanVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
+    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig",
+    "WanGameVideoConfig"
 ]
@@ -15,11 +15,10 @@ class DiTArchConfig(ArchConfig):
    reverse_param_names_mapping: dict = field(default_factory=dict)
    lora_param_names_mapping: dict = field(default_factory=dict)
    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
-        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
-        AttentionBackendEnum.VIDEO_SPARSE_ATTN, AttentionBackendEnum.VMOBA_ATTN,
-        AttentionBackendEnum.SAGE_ATTN_THREE, AttentionBackendEnum.SLA_ATTN,
-        AttentionBackendEnum.SAGE_SLA_ATTN)
+        AttentionBackendEnum.SAGE_ATTN, AttentionBackendEnum.FLASH_ATTN,
+        AttentionBackendEnum.TORCH_SDPA, AttentionBackendEnum.VIDEO_SPARSE_ATTN,
+        AttentionBackendEnum.VMOBA_ATTN, AttentionBackendEnum.SAGE_ATTN_THREE,
+        AttentionBackendEnum.SLA_ATTN, AttentionBackendEnum.SAGE_SLA_ATTN)

    hidden_size: int = 0
    num_attention_heads: int = 0
@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.wanvideo import (
+    WanVideoArchConfig,
+    WanVideoConfig,
+)
+
+
+@dataclass
+class WanGameVideoArchConfig(WanVideoArchConfig):
+    """Wangame keeps WanVideo architecture defaults and checkpoint mappings."""
+
+
+@dataclass
+class WanGameVideoConfig(WanVideoConfig):
+    arch_config: WanGameVideoArchConfig = field(
+        default_factory=WanGameVideoArchConfig
+    )
+    prefix: str = "WanGame"
@@ -78,7 +78,6 @@ class Qwen2_5_VLArchConfig(TextEncoderArchConfig):
            "add_generation_prompt": True,
            "tokenize": True,
            "return_dict": True,
-            "padding": "max_length",
            "max_length": 1000 + 108,
            "truncation": True,
            "return_tensors": "pt",
@@ -63,7 +63,6 @@ class T5ArchConfig(TextEncoderArchConfig):
            self.dense_act_fn = "gelu_new"

        self.tokenizer_kwargs = {
-            "padding": "max_length",
            "truncation": True,
            "max_length": self.text_len,
            "add_special_tokens": True,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
mignonjia	8fa6ba6178	mc dfsft	2026-04-01 03:53:20 +00:00
H1yori233	2e5fef787b	fix tf scheduler	2026-03-17 18:19:27 -07:00
H1yori233	43d87816bd	add logger	2026-03-16 17:27:59 -07:00
H1yori233	2ace7dc6f4	update df scheduler	2026-03-16 15:49:09 -07:00
H1yori233	0ca75db738	fix train / val step mismatch	2026-03-15 20:55:53 -07:00
H1yori233	375ffd3fd5	make visualization in 1 panel	2026-03-15 16:59:34 -07:00
H1yori233	2615ba4291	upload more validation to wandb	2026-03-15 16:43:59 -07:00
RandNMR73	474dd71f28	config	2026-03-15 22:10:50 +00:00
RandNMR73	98ad2d2db6	wangame	2026-03-15 22:01:33 +00:00
alexzms	d92858659d	[feat] Self-Forcing methods in refactored training infra (#1164 )	2026-03-09 20:20:59 -07:00
Zhang Peiyuan alexzms Peiyuan Zhang	1a383f3f66	[refactor] train v1 clean up Co-authored-by: alexzms <3036648523@qq.com> Co-authored-by: Peiyuan Zhang <a1286225768@slurm-h200-204-215.slurm-compute.tenant-slurm.svc.cluster.local>	2026-03-09 18:58:56 -07:00
alexzmsandPeiyuan Zhang	bc27a032c5	[feat] Refactor training framework into fastvideo/train (#1159 ) Co-authored-by: Peiyuan Zhang <a1286225768@gmail.com>	2026-03-09 15:16:42 -07:00
alexzms	2b13e117f0	[Feat] Add causal Wan pipeline with multi-step denoising (#1161 )	2026-03-08 13:32:04 -07:00
Junda Chen	99c166c381	feat: Building agent friendly repo (#1151 )	2026-03-07 17:46:29 -08:00
XOR-op	95066245db	[misc] FlashAttention 4 support (#1114 )	2026-03-07 16:53:43 -08:00
Jinzhe Panandgemini-code-assist[bot]	6dcaac768b	[CI] PR template (#1157 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-03-07 11:51:28 -08:00
Ajay Anubolu	02c1c49b75	[CI] Add inference performance regression tests (#1140 )	2026-03-07 08:26:54 +08:00
Zhang Peiyuan	cd1b7cf139	[Refactor] SP Mask --> original seq len; HunyuanVideo 1.5 does not need mask (#1142 )	2026-03-04 11:44:47 +08:00
Ajay Anubolu	e63b7d8ac4	[Feat] Added OpenAI-compatible API server and benchmark script (#1109 )	2026-03-02 17:12:32 -05:00
Jinzhe Pan	5190c1bb1e	[Doc] add doc for inference architecture (#1147 )	2026-03-02 13:44:27 -08:00
Darren	2cb3bba658	[bugfix]: fix a bug where collect_env was not running properly... (#1145 )	2026-03-02 10:57:29 -08:00
Jinzhe Pan	f9e1c46c3c	[CI][Feat] launch 2 instance to run ssim (#1137 )	2026-03-01 01:49:29 -08:00
Peiyuan Zhang	e1eda47589	remove temporal frame adjustment	2026-02-27 20:47:16 +00:00
Zhang Peiyuan	d902967208	Py/fix sp (#1138 )	2026-02-27 12:14:44 +08:00
Zhang Peiyuan	fea556269b	[Misc] Fix memory leakage in VideoGenerator (#1132 )	2026-02-26 19:51:32 -08:00
William Lin	69dd3c68f6	[bugfix] fix matrix game kv indexing and CI (#1135 )	2026-02-26 01:24:42 -08:00
Jinzhe Pan	5433f6e80b	[fix] preprocessing issue (#1134 )	2026-02-25 21:49:52 -08:00
Junda (David) Su	e315657066	[docs] [kernel] Migrate to uv (#1127 )	2026-02-25 14:13:50 -08:00
Zhang Peiyuan	f8d9a0c57f	[misc] fix hunyuan (#1125 )	2026-02-25 08:26:29 +08:00
Jinzhe PanandWill Lin	fa6d276925	[Feat] Improved CI (#1119 ) Co-authored-by: Will Lin <wlsaidhi@gmail.com>	2026-02-24 12:53:02 -08:00
Zhang PeiyuanandWill Lin	fc80d95d7e	[Misc] Remove STA (#1124 ) Co-authored-by: Will Lin <wlsaidhi@gmail.com>	2026-02-23 15:14:42 -08:00
Shao DuanandSolitaryThinker	37cab18780	[bugfix] Added ltx2 guidance missing modulation term (#1100 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2026-02-23 14:22:31 -08:00
Zhang Peiyuan	128d0b7fc5	[Misc] Remove Teacache (#1121 )	2026-02-22 16:53:07 -08:00
Matthew Noto	8092f02e6d	small refactor in post-processing to improve efficiency (#1123 )	2026-02-22 16:45:44 -08:00