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

pipeline.model is a tagged union: a mapping with exactly one key, which names the model family (ltx2, minimax_h3, longcat, or generic) and selects that family's options class. The former sibling sections pipeline.ltx2, pipeline.minimax_h3, and pipeline.longcat, and the DiT/VAE architecture override dicts pipeline.dit and pipeline.vae, live inside the family block. The parser implements tagged unions as a general mechanism (a dataclass with a TAGS table); the family is readable as pipeline.model.family. Resolution validates the tag against the family that the registry chose for model_path (validate_model_family raises and names the registry class and the right block) and fills the family's empty block when the input has none (fill_model_family), so readers never see None. Family-only fills and derivations (the MiniMax-H3 parallel-VAE environment variables, the LTX-2 tile-size derivation and refine preset copy, the LongCat BSA mirrors) run only for their family. Against the 3f6893a0 goldens every PipelineConfig is identical and no other field differs beyond the allowed categories; family-only settings are no longer decided on models of another family. LingBot-Video reads its refiner switch from pipeline.preset_overrides.refine.enabled and loads the refiner by default when the checkpoint has one, as at 3f6893a0. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CbSuTKA9oX4YsECdcBPAsX
[refactor]: decide every config value before the config freezes, and drop the from_pretrained keywords
2026-10-05 06:04:17 +00:00 · 2026-10-04 07:14:31 +00:00 · 2026-10-04 04:45:02 +00:00 · 2026-10-03 06:46:52 +00:00 · 2026-10-03 06:35:19 +00:00 · 2026-10-03 06:28:44 +00:00
1026 changed files with 108491 additions and 19072 deletions
@@ -80,26 +80,36 @@ def _run_fastvideo_pipeline(model_path: Path, params: dict[str, Any]) -> Any:

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

@@ -84,7 +84,9 @@ fastvideo/configs/models/dits/__init__.py
 fastvideo/configs/models/encoders/__init__.py
 fastvideo/configs/models/vaes/__init__.py
 fastvideo/envs.py
-fastvideo/fastvideo_args.py
+fastvideo/api/schema.py
+fastvideo/api/resolution.py
+fastvideo/api/inference_resolution.py
 fastvideo/distributed/**
 fastvideo/layers/**
 fastvideo/attention/**
@@ -0,0 +1,80 @@
+---
+name: env-var-conventions
+description: Add, read, rename, or remove an environment variable in FastVideo, or change the environment-variable policy. Use before touching fastvideo/envs.py, os.environ, os.getenv, or monkeypatch.setenv in fastvideo/, and when fastvideo/tests/contract/test_env_policy.py fails.
+---
+
+# Environment Variable Conventions
+
+## Purpose
+
+FastVideo registers its environment variables as typed fields in
+`fastvideo/envs.py`. The policy that governs them is
+`docs/contributing/env_vars.md`, and the contract test
+`fastvideo/tests/contract/test_env_policy.py` enforces the policy in the unit
+CI lane. This skill routes an environment-variable change through that policy.
+The policy doc is the single source of the rules; read it instead of relying
+on a summary here.
+
+## Prerequisites
+
+- Read `docs/contributing/env_vars.md` in full.
+- Decide whether the setting belongs in an environment variable or an argument
+  (rule 5 in the policy doc). Settings that users change per deployment are
+  arguments; add them as typed config fields in `fastvideo/api/schema.py`
+  instead.
+
+## Inputs
+
+| Parameter  | Required | Description                                                    |
+| ---------- | -------- | -------------------------------------------------------------- |
+| `change`   | Yes      | Add, read, rename, or remove a variable, or change the policy. |
+| `variable` | Yes      | The variable name, with the `FASTVIDEO_` prefix.               |
+
+## Steps
+
+1. **Declare or edit the variable in `fastvideo/envs.py`.**
+   - Pick the field type and category that the policy doc lists.
+   - Write a description that states what the variable does and its units.
+   - To rename, keep the old name in `deprecated_names`. To remove, add the
+     name to `DEPRECATED_VARIABLES`. Update the uses in `examples/`,
+     `scripts/`, `docs/`, `apps/`, and the tests.
+2. **Read the variable with `envs.NAME.get()` inside a function.**
+   - In tests, change the value with `envs.NAME.override(value)`, and a variable
+     outside the registry with `envs.override_external(name, value)`; the
+     `env_overrides` fixture keeps either until the end of the test.
+   - Name a variable that only tests read `FASTVIDEO_TEST_*`.
+   - Do not call `os.environ`, `os.getenv`, or `monkeypatch.setenv` for a
+     FastVideo variable.
+   - To set a variable that another tool reads, call `envs.set_external`,
+     `envs.setdefault_external`, or `envs.unset_external`.
+3. **Regenerate the table in the policy doc.**
+   - Run `python fastvideo/tests/contract/test_env_policy.py`.
+4. **Run the contract test.**
+   - Run `pytest fastvideo/tests/contract/test_env_policy.py`.
+   - When the test reports a fixed known violation, delete or lower its entry
+     in `KNOWN_VIOLATIONS`. Never add an entry to `KNOWN_VIOLATIONS`.
+5. **When the policy itself changes, update the policy doc and the contract
+   test in the same pull request.**
+   - The rules in `docs/contributing/env_vars.md`, the checks and allowlist in
+     `fastvideo/tests/contract/test_env_policy.py`, and this skill must agree.
+
+## Outputs
+
+- A registry entry in `fastvideo/envs.py` and call sites that use
+  `envs.NAME.get()`.
+- A regenerated table in `docs/contributing/env_vars.md`.
+- A passing `fastvideo/tests/contract/test_env_policy.py`.
+
+## Example Usage
+
+```
+Add a FASTVIDEO_DEBUG_MY_STAGE switch that logs MyStage inputs.
+```
+
+## References
+
+- `docs/contributing/env_vars.md`: the policy, the field types, and the
+  violation kinds that the contract test reports.
+- `fastvideo/envs.py`: the registry.
+- `fastvideo/tests/contract/test_env_policy.py`: the contract test,
+  `EXTERNAL_ALLOWLIST`, and `KNOWN_VIOLATIONS`.
@@ -57,7 +57,7 @@ Hardcoded:
 - Quality tier: **`default`**. `full_quality` is a separate, deliberate
  operation.
 - HF repo: `FastVideo/ssim-reference-videos` (override via
-  `FASTVIDEO_SSIM_REFERENCE_HF_REPO`).
+  `FASTVIDEO_TEST_SSIM_REFERENCE_HF_REPO`).
 - Device folder: `L40S_reference_videos`.

 ## Prerequisites
@@ -16,6 +16,7 @@ exec pytest \
  ./fastvideo/tests/worker/ \
  ./fastvideo/tests/training/test_trackers.py \
  ./fastvideo/tests/attention/test_sdpa_metadata_mask_contract.py \
+  ./fastvideo/tests/attention/test_vsa_h3_tile_grad_safety.py \
  ./fastvideo/tests/modal/test_kernel_build_cache.py \
  ./fastvideo/tests/modal/test_pr_test.py \
  ./fastvideo/tests/modal/test_ssim_test.py \
@@ -489,7 +489,6 @@ def classify_paths(paths: list[str]) -> MergePlan:
            _select_output_coverage(plan, path)
            continue
        if path in {
-                "fastvideo/fastvideo_args.py",
                "fastvideo/forward_context.py",
                "fastvideo/image_processor.py",
                "fastvideo/registry.py",
@@ -92,6 +92,7 @@ jobs:
            fastvideo/tests/mlx/test_frame_upsample.py \
            fastvideo/tests/mlx/test_mlx_fast_spatial.py \
            fastvideo/tests/mlx/test_mlx_refine.py \
+            fastvideo/tests/mlx/test_mlx_prompt_enhance.py \
            fastvideo/tests/mlx/test_mlx_prompt_to_video_decode.py \
            fastvideo/tests/mlx/test_mlx_wan22_prompt_cache_fingerprint.py \
            fastvideo/tests/mlx/test_wan22_sample.py \
@@ -156,6 +157,7 @@ jobs:
            fastvideo/tests/mlx/test_frame_upsample.py \
            fastvideo/tests/mlx/test_mlx_fast_spatial.py \
            fastvideo/tests/mlx/test_mlx_refine.py \
+            fastvideo/tests/mlx/test_mlx_prompt_enhance.py \
            fastvideo/tests/mlx/test_mlx_prompt_to_video_decode.py \
            fastvideo/tests/mlx/test_mlx_wan22_prompt_cache_fingerprint.py \
            fastvideo/tests/mlx/test_wan22_sample.py \
@@ -176,11 +176,11 @@ jobs:
          # the main extension for the full arch list. CMAKE_BUILD_PARALLEL_LEVEL caps
          # Ninja so heavy CUTLASS/TK template TUs don't OOM the 16 GB runner (exit 143).
          if [ "${{ matrix.platform.arch }}" = "aarch64" ]; then
-            export TORCH_CUDA_ARCH_LIST="10.0a;12.0a"
+            export TORCH_CUDA_ARCH_LIST="10.0a;10.3a;12.0a"
            export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=OFF -DFASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER=ON"
            export CMAKE_BUILD_PARALLEL_LEVEL=1
          elif [ "${{ matrix.torch-cuda.torch-cuda-short }}" = "cu130" ]; then
-            export TORCH_CUDA_ARCH_LIST="9.0a;10.0a;12.0a"
+            export TORCH_CUDA_ARCH_LIST="9.0a;10.0a;10.3a;12.0a"
            export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DFASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
            # A single FP4 TU (attn_qat_infer) can use ~8-12 GB on its own, so serialize.
            export CMAKE_BUILD_PARALLEL_LEVEL=1
@@ -66,14 +66,18 @@ Local guidance lives next to the code. Read the in-scope file before editing:
 | `fastvideo/AGENTS.md` | Core package map, public API, registry-driven model dispatch |
 | `fastvideo/configs/AGENTS.md` | Arch + pipeline config dataclasses, `param_names_mapping` |
 | `fastvideo/models/AGENTS.md` | DiT / VAE / encoder / scheduler / loader layout (pre-commit excluded) |
+| `fastvideo/models/wan/AGENTS.md` | Wan family-local transformers, VAE, configs, and the SP sharding invariant |
 | `fastvideo/layers/AGENTS.md` | Tensor-parallel linear/attention layer rules for ports |
 | `fastvideo/attention/AGENTS.md` | Backend registry + env-var override |
 | `fastvideo/pipelines/AGENTS.md` | Stage ABC, `basic/<model>/`, `preprocess/`, presets |
+| `fastvideo/pipelines/basic/wan/AGENTS.md` | Wan sampling stages, first-frame conditioning, DMD/causal boundaries |
+| `fastvideo/pipelines/basic/magi_human/AGENTS.md` | MagiHuman umbrella repo, lazy-loaded components, packing invariants |
 | `fastvideo/training/AGENTS.md` | Legacy monolithic pipelines (frozen for existing models) |
 | `fastvideo/train/AGENTS.md` | New modular trainer (methods × models × callbacks, YAML) |
 | `fastvideo/tests/AGENTS.md` | Test taxonomy, conftest, pre-commit-excluded path |
 | `fastvideo/tests/ssim/AGENTS.md` | GPU SSIM regression authoring + reference video sync |
 | `scripts/checkpoint_conversion/AGENTS.md` | Adding a converter for a new HF/official checkpoint |
+| `apps/dreamverse/AGENTS.md` | DreamVerse app structure and conventions |

 ## Critical: Two Training Stacks Coexist

@@ -9,9 +9,10 @@
 **FastVideo is a unified post-training and real-time inference framework for accelerated video generation.**

 ## NEWS
+- `2026/09/15`: Release [FastH3 8-Step V2](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2), an eight-forward data-free DMD2 checkpoint distilled from MiniMax-H3 with 80% Video Sparse Attention. Run it with `examples/inference/basic/basic_fasth3_8step.py` or the [FastH3 8-Step V2 recipe](https://haoailab.com/FastVideo/cookbook/minimax-h3/).
 - `2026/09/01`: FastH3 now runs locally on Apple Silicon through MLX and on NVIDIA DGX Spark through CUDA 13, including two-Spark inference. Follow the [FastH3 recipes](https://haoailab.com/FastVideo/cookbook/minimax-h3/) and read the [Blog](https://haoailab.com/blogs/fasth3-local/).
 - `2026/08/27`: [FastH3 Preview v1](https://haoailab.com/blogs/fasth3-preview/) is an open-weight 4-step sparse-distilled MiniMax-H3 model for synchronized video-and-audio generation, developed in collaboration with [Nuva Lab](https://nuvalab.ai/) and the [NVIDIA FastGen team](https://github.com/NVlabs/FastGen). Download the recommended [VSA / Data-Free weights](https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree), or see the [full FastH3 collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3).
- `2026/08/19`: FastVideo now supports MLX on Apple Silicon with [FastMetal-QAD](https://huggingface.co/collections/FastVideo/fastmetal), a family of 1.3B, 5B, and 14B models optimized for Mac—follow the [Apple Silicon guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mps/) and read the [Blog](https://haoailab.com/blogs/fastmetal/).
+- `2026/08/19`: FastVideo now supports MLX on Apple Silicon with [FastMetal-QAD](https://huggingface.co/collections/FastVideo/fastmetal), a family of 1.3B, 5B, and 14B models optimized for Mac. Follow the [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/) and read the [Blog](https://haoailab.com/blogs/fastmetal/).
 - `2026/06/23`: Release FastWan-QAD: 5s of Video generated in 1.8s E2E. See the [FastWan-QAD models](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B), [Attn-QAT training guide](https://haoailab.com/FastVideo/training/attn_qat/), and [blog](https://haoailab.com/blogs/fastwan-qad/).
 - `2026/03/17`: Release demo: Into the Dreamverse: Vibe Directing in FastVideo, check out the [Blog](https://haoailab.com/blogs/dreamverse/).
 - `2026/03/13`: Release demo: Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU, check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/).
@@ -63,13 +64,12 @@ UV_TORCH_BACKEND=cu126 uv pip install fastvideo
 ```

 Use `UV_TORCH_BACKEND=cu130` on CUDA 13. Apple silicon users should follow the
-[MPS installation guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mps/).
+[MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/).

-> **On an Apple Silicon Mac?** FastVideo runs FastMetal-QAD through an MLX
-> runtime. Install with `uv pip install -e '.[mlx]'`, download
-> [`FastVideo/FastMetal-1.3B-QAD`](https://huggingface.co/FastVideo/FastMetal-1.3B-QAD),
-> and follow the
-> [Apple Silicon guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mps/).
+> **On an Apple Silicon Mac?** Install with `uv pip install -e '.[mlx]'` from
+> a clone, then pick a recipe in the
+> [cookbook](https://haoailab.com/FastVideo/cookbook/). See the
+> [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/).

 Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.

@@ -87,7 +87,7 @@ Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virt
   https://hao-ai-lab.github.io/FastVideo/getting_started/installation/):
     - NVIDIA GPU, x86_64                         -> docs/getting_started/installation/gpu.md
     - NVIDIA DGX Spark / GB10, aarch64, CUDA 13  -> docs/getting_started/installation/spark.md
-     - Apple Silicon, macOS                       -> docs/getting_started/installation/mps.md
+     - Apple Silicon, macOS                       -> docs/getting_started/installation/mlx.md
 3. Use uv for every step. If a command fails, debug it and tell me what you changed.
 4. Verify the result:
     python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())"
@@ -135,18 +135,20 @@ def main():
    # Create a video generator with a pre-trained model
    generator = VideoGenerator.from_pretrained(
        "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
-        num_gpus=1,  # Adjust based on your hardware
+        {"engine": {"num_gpus": 1}},  # Adjust based on your hardware
    )

    # Define a prompt for your video
    prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."

    # Generate the video
-    video = generator.generate_video(
-        prompt,
-        output_path="my_videos/",  # Controls where videos are saved
-        save_video=True
-    )
+    video = generator.generate({
+        "prompt": prompt,
+        "output": {
+            "output_path": "my_videos/",  # Controls where videos are saved
+            "save_video": True,
+        },
+    })

 if __name__ == '__main__':
    main()
@@ -42,7 +42,7 @@ Near-term OSS note:
 - `apps/dreamverse/dreamverse/main.py`: websocket endpoint, request handling,
  session state machine, rewrite orchestration, REST routes, and stream relay
 - `apps/dreamverse/dreamverse/gpu_pool.py`: GPU worker processes, warmup, model
-  loading, and `generate_video()` calls through FastVideo
+  loading, and `generate()` calls through FastVideo
 - `apps/dreamverse/dreamverse/prompt_enhancer.py`: prompt enhancement, rollout
  rewrite execution, provider selection, and timeout/fallback behavior
 - `apps/dreamverse/dreamverse/rewrite_prompt_payload.py`: canonical rewrite request payload
@@ -52,7 +52,8 @@ import torch  # noqa: E402

 from fastvideo import VideoGenerator  # noqa: E402
 from fastvideo.api import (  # noqa: E402
-    ComponentConfig, CompileConfig, EngineConfig, GeneratorConfig, OffloadConfig, PipelineSelection, QuantizationConfig,
+    ComponentConfig, CompileConfig, EngineConfig, GenerationResult, GeneratorConfig, OffloadConfig, PipelineSelection,
+    QuantizationConfig,
 )

 DEFAULT_PROMPT = ("A cinematic drone shot over coastal cliffs at sunrise, golden "
@@ -128,9 +129,9 @@ def _build_generator_config(model_path: str, enable_compile: bool, num_gpus: int
    )


-def _extract_stage_times(result: dict) -> OrderedDict[str, float]:
+def _extract_stage_times(result: GenerationResult) -> OrderedDict[str, float]:
    out: OrderedDict[str, float] = OrderedDict()
-    info = result.get("logging_info") if isinstance(result, dict) else None
+    info = result.logging_info if isinstance(result, GenerationResult) else None
    if info is None:
        return out
    stages = getattr(info, "stages", None)
@@ -162,19 +163,25 @@ def _do_one_run(generator: VideoGenerator, prompt: str, *, height: int, width: i
    _reset_peak_gpu()
    t0 = time.perf_counter()
    try:
-        result = generator.generate_video(
-            prompt=prompt,
-            negative_prompt="",
-            save_video=False,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            fps=24,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=1.0,
-            seed=seed,
-            ltx2_image_crf=0.0,
-        )
+        result = generator.generate({
+            "prompt": prompt,
+            "negative_prompt": "",
+            "sampling": {
+                "height": height,
+                "width": width,
+                "num_frames": num_frames,
+                "fps": 24,
+                "num_inference_steps": num_inference_steps,
+                "guidance_scale": 1.0,
+                "seed": seed,
+            },
+            "output": {
+                "save_video": False
+            },
+            "extensions": {
+                "ltx2_image_crf": 0.0
+            },
+        })
        if torch.cuda.is_available():
            torch.cuda.synchronize()
    except Exception as exc:
@@ -14,6 +14,7 @@ import gc
 import os
 import re
 import time
+from typing import TYPE_CHECKING
 import numpy as np
 import torch

@@ -34,6 +35,9 @@ from dreamverse.config import (
 )
 from dreamverse.generation_contracts import StepResult

+if TYPE_CHECKING:
+    from fastvideo.api import GenerationResult
+
 # Multi-frame decoded continuation defaults from
 # examples/inference/basic/basic_ltx2_distilled_video_continuation.py.
 # Overridable via environment variables.
@@ -95,8 +99,8 @@ class ContinuationState:
        self.video_images = None
        self.audio_latents = None

-    def apply_video(self, request_kwargs: dict, segment_idx: int) -> None:
-        """Seed next-segment kwargs with the cached tail frames."""
+    def apply_video(self, request: dict, segment_idx: int) -> None:
+        """Seed the next-segment request with the cached tail frames."""
        if segment_idx <= 1 or not self.video_images:
            return
        from PIL import Image
@@ -110,21 +114,21 @@ class ContinuationState:
                arr = np.clip(arr, 0, 255).astype(np.uint8)
                noisy.append(Image.fromarray(arr))
            cond_images = noisy
-        request_kwargs["ltx2_video_conditions"] = [(
+        request["extensions"]["ltx2_video_conditions"] = [(
            cond_images,
            LTX2_VIDEO_CONDITIONING_FRAME_IDX,
            LTX2_VIDEO_CONDITIONING_STRENGTH,
        )]
-        request_kwargs["ltx2_images"] = None
-        request_kwargs["image_path"] = None
+        request["extensions"]["ltx2_images"] = None
+        request["inputs"]["image_path"] = None

    def apply_audio(
        self,
-        request_kwargs: dict,
+        request: dict,
        segment_idx: int,
        audio_lps: float,
    ) -> None:
-        """Seed next-segment kwargs with clean audio latents + denoise mask.
+        """Seed the next-segment request with clean audio latents + denoise mask.

        When audio conditioning is longer than video, extend audio
        generation and shift video RoPE forward so the audio prefix
@@ -141,9 +145,9 @@ class ContinuationState:
        audio_extra = max(0, AUDIO_CONDITIONING_NUM_FRAMES - LTX2_VIDEO_CONDITIONING_NUM_FRAMES)
        if audio_extra > 0:
            audio_num_frames = NUM_FRAMES + audio_extra
-            request_kwargs["audio_num_frames"] = (audio_num_frames)
+            request["extensions"]["audio_num_frames"] = (audio_num_frames)
            prefix_sec = float(audio_extra) / 24.0
-            request_kwargs["video_position_offset_sec"] = prefix_sec
+            request["extensions"]["video_position_offset_sec"] = prefix_sec

        new_duration = float(NUM_FRAMES + audio_extra) / 24.0
        total_T = max(
@@ -161,8 +165,8 @@ class ContinuationState:
        mask = torch.ones((B, 1, total_T, 1), dtype=torch.float32)
        mask[:, :, :audio_cond_T, :] = (1.0 - AUDIO_CONDITIONING_STRENGTH)

-        request_kwargs["ltx2_audio_clean_latent"] = clean
-        request_kwargs["ltx2_audio_denoise_mask"] = mask
+        request["extensions"]["ltx2_audio_clean_latent"] = clean
+        request["extensions"]["ltx2_audio_denoise_mask"] = mask

    def save_video(self, frames: list) -> None:
        """Snapshot trailing N frames as PIL images for next-segment conditioning."""
@@ -306,7 +310,7 @@ class LTX2GenerationBackend:
            ),
        )

-        self.generator = VideoGenerator.from_pretrained(config=generator_config)
+        self.generator = VideoGenerator.from_config(generator_config)
        print(f"[GPU {self.gpu_id}] After model load: {self._gpu_mem()}")

        lora_stack = DREAMVERSE_LORA_STACK or ([(DREAMVERSE_LORA_PATH,
@@ -403,7 +407,7 @@ class LTX2GenerationBackend:
            return

        loader = ComponentLoader.for_module_type("audio_encoder", "diffusers")
-        enc = loader.load(audio_vae_path, self.generator.fastvideo_args)
+        enc = loader.load(audio_vae_path, self.generator.resolved_config)
        target = getattr(enc, "model", enc)

        proc = AudioProcessor(
@@ -460,51 +464,59 @@ class LTX2GenerationBackend:

        prompt = self._inject_style_trigger(prompt)

-        request_kwargs = dict(
-            prompt=prompt,
-            negative_prompt="",
-            save_video=False,
-            height=FRAME_HEIGHT,
-            width=FRAME_WIDTH,
-            num_frames=NUM_FRAMES,
-            fps=24,
-            num_inference_steps=NUM_INFERENCE_STEPS,
-            guidance_scale=1.0,
-            seed=10,
-            ltx2_image_crf=0.0,
-            image_path=image_path if segment_idx == 1 else None,
-            return_continuation_state=False,
-        )
+        request = {
+            "prompt": prompt,
+            "negative_prompt": "",
+            "inputs": {
+                "image_path": image_path if segment_idx == 1 else None
+            },
+            "sampling": {
+                "height": FRAME_HEIGHT,
+                "width": FRAME_WIDTH,
+                "num_frames": NUM_FRAMES,
+                "fps": 24,
+                "num_inference_steps": NUM_INFERENCE_STEPS,
+                "guidance_scale": 1.0,
+                "seed": 10,
+            },
+            "output": {
+                "save_video": False
+            },
+            "extensions": {
+                "ltx2_image_crf": 0.0,
+                "return_continuation_state": False,
+            },
+        }

        if reset_conditioning:
            self.continuation.clear()

        audio_lps = (DEFAULT_LTX2_AUDIO_SAMPLE_RATE / DEFAULT_LTX2_AUDIO_HOP_LENGTH / DEFAULT_LTX2_AUDIO_DOWNSAMPLE)

-        # Phase 1: seed kwargs with prior-segment conditioning.
-        self.continuation.apply_video(request_kwargs, segment_idx)
-        self.continuation.apply_audio(request_kwargs, segment_idx, audio_lps)
+        # Phase 1: seed the request with prior-segment conditioning.
+        self.continuation.apply_video(request, segment_idx)
+        self.continuation.apply_audio(request, segment_idx, audio_lps)

        # Phase 2: generate.
        t0 = time.perf_counter()
-        result = self.generator.generate_video(**request_kwargs)
+        result = self.generator.generate(request)
        torch.cuda.synchronize()
        timings["generation_ms"] = (time.perf_counter() - t0) * 1000

-        if not isinstance(result, dict):
-            raise RuntimeError("Expected dictionary output from generate_video.")
-        frames = result.get("frames")
+        if isinstance(result, list):
+            raise RuntimeError("Expected a single GenerationResult from generate.")
+        frames = result.frames
        if not isinstance(frames, list) or len(frames) == 0:
            raise RuntimeError("Generation did not return frames.")
-        audio = result.get("audio")
-        audio_sample_rate = result.get("audio_sample_rate")
+        audio = result.audio
+        audio_sample_rate = result.audio_sample_rate
        if audio is not None and audio_sample_rate is None:
            # LTX2 audio decoding stage uses 24kHz output by default.
            audio_sample_rate = 24000
            print(f"[GPU {self.gpu_id}] audio_sample_rate missing from result; "
                  f"defaulting to {audio_sample_rate}Hz")

-        timings["generation_time_ms"] = result.get("generation_time", 0.0) * 1000
+        timings["generation_time_ms"] = (result.generation_time or 0.0) * 1000

        # Phase 3: snapshot continuation state for the next segment.
        t_save_start = time.perf_counter()
@@ -542,7 +554,7 @@ class LTX2GenerationBackend:
        self,
        audio: object,
        audio_sample_rate: int | None,
-        result: dict,
+        result: "GenerationResult",
        segment_idx: int,
    ) -> torch.Tensor | None:
        """Pick which tensor to cache for next-segment audio conditioning."""
@@ -557,7 +569,7 @@ class LTX2GenerationBackend:
                      f"for segment {segment_idx + 1}")
                return re_encoded
            return None
-        audio_latents = result.get("ltx2_audio_latents")
+        audio_latents = result.extra.get("ltx2_audio_latents")
        if audio_latents is not None:
            print(f"[GPU {self.gpu_id}] Cached audio latents "
                  f"shape={tuple(audio_latents.shape)} "
@@ -79,32 +79,24 @@ class MiniMaxH3GenerationBackend:

        from fastvideo import VideoGenerator
        from fastvideo.api import (
+            AttentionConfig,
            CompileConfig,
            ComponentConfig,
            EngineConfig,
            GeneratorConfig,
+            MiniMaxH3Options,
            OffloadConfig,
            ParallelismConfig,
            PipelineSelection,
        )

        adapter_path = hf_hub_download(repo_id=adapter_repo, filename=adapter_filename)
-        experimental = {
-            "attention_backend": attention_backend,
-            "inference_torch_compile": attention_backend == "FLASH_ATTN",
-            "vae_parallel_decode": True,
-            "vae_parallel_decode_strategy": "gather",
-        }
-        if attention_backend == "VIDEO_SPARSE_ATTN_H3":
-            experimental.update({
-                "VSA_sparsity": 0.9,
-                "VSA_tile_size": 64,
-            })
+        use_vsa = attention_backend == "VIDEO_SPARSE_ATTN_H3"
        generator_config = GeneratorConfig(
            model_path=model_path,
            pipeline=PipelineSelection(
                components=ComponentConfig(lora_path=adapter_path, lora_strength=1.0),
-                experimental=experimental,
+                model=MiniMaxH3Options(vae_parallel_decode=True, vae_parallel_decode_strategy="gather"),
            ),
            engine=EngineConfig(
                num_gpus=DREAMVERSE_SP_SIZE,
@@ -117,7 +109,12 @@ class MiniMaxH3GenerationBackend:
                    vae=True,
                    pin_cpu_memory=True,
                ),
-                compile=CompileConfig(enabled=False, vae_enabled=True),
+                compile=CompileConfig(enabled=False, vae_enabled=True, regional=attention_backend == "FLASH_ATTN"),
+                attention=AttentionConfig(
+                    backend=attention_backend,
+                    vsa_sparsity=0.9 if use_vsa else None,
+                    vsa_tile_size=64 if use_vsa else None,
+                ),
                use_fsdp_inference=False,
            ),
        )
@@ -13,7 +13,6 @@ FORBIDDEN_PREFIXES = (
    "fastvideo.models",
    "fastvideo.layers",
    "fastvideo.worker",
-    "fastvideo.fastvideo_args",
 )
 ALLOWED_INTERNAL_IMPORTS = {
    (
@@ -88,14 +88,13 @@ def test_initialize_builds_vsa_datafree_fasth3_generator(monkeypatch):
    assert config.model_path == "MiniMaxAI/MiniMax-H3"
    assert config.pipeline.components.lora_path.endswith("vsa-datafree/adapter_model.safetensors")
    assert config.pipeline.components.lora_strength == 1.0
-    assert config.pipeline.experimental == {
-        "attention_backend": "VIDEO_SPARSE_ATTN_H3",
-        "inference_torch_compile": False,
-        "vae_parallel_decode": True,
-        "vae_parallel_decode_strategy": "gather",
-        "VSA_sparsity": 0.9,
-        "VSA_tile_size": 64,
-    }
+    assert config.pipeline.experimental == {}
+    assert config.engine.attention.backend == "VIDEO_SPARSE_ATTN_H3"
+    assert config.engine.attention.vsa_sparsity == 0.9
+    assert config.engine.attention.vsa_tile_size == 64
+    assert config.engine.compile.regional is False
+    assert config.pipeline.model.vae_parallel_decode is True
+    assert config.pipeline.model.vae_parallel_decode_strategy == "gather"
    assert config.engine.num_gpus == 4
    assert config.engine.parallelism.tp_size == 1
    assert config.engine.parallelism.sp_size == 4
@@ -331,11 +331,11 @@ def test_rewrite_prompt_sequence_accepts_numbered_prose_output():
    ]


-def test_enhance_prompt_prefers_cerebras_before_groq_fallback():
+def test_enhance_prompt_uses_groq_when_it_returns_first():
    enhancer = _build_staged_enhancer(
        cerebras_payload=_chat_payload_with_content('{"prompt":"Cerebras prompt"}'),
        groq_payload=_chat_payload_with_content('{"prompt":"Groq prompt"}'),
-        cerebras_delay_s=0.01,
+        cerebras_delay_s=0.08,
        groq_delay_s=0.01,
    )

@@ -346,12 +346,12 @@ def test_enhance_prompt_prefers_cerebras_before_groq_fallback():

    assert result.fallback_used is False
    assert result.error is None
-    assert result.provider == "cerebras"
+    assert result.provider == "groq"
    assert result.model == "gpt-test"
-    assert result.prompt == "Cerebras prompt"
+    assert result.prompt == "Groq prompt"
    assert enhancer.get_provider_success_counts() == {
-        "cerebras": 1,
-        "groq": 0,
+        "cerebras": 0,
+        "groq": 1,
    }


@@ -207,7 +207,7 @@
                        </Array>
                    </mxGeometry>
                </mxCell>
-                <mxCell id="e_dsg" value="generator.generate_video()" style="edgeStyle=orthogonalEdgeStyle;rounded=0;html=1;strokeColor=#9673a6;endArrow=classic;fontSize=10;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" parent="1" source="do_step" target="generator" edge="1">
+                <mxCell id="e_dsg" value="generator.generate()" style="edgeStyle=orthogonalEdgeStyle;rounded=0;html=1;strokeColor=#9673a6;endArrow=classic;fontSize=10;exitX=0.5;exitY=1;exitDx=0;exitDy=0;entryX=0.5;entryY=0;entryDx=0;entryDy=0;" parent="1" source="do_step" target="generator" edge="1">
                    <mxGeometry relative="1" as="geometry"/>
                </mxCell>
                <mxCell id="e_dscache" value="read / write" style="edgeStyle=orthogonalEdgeStyle;rounded=0;html=1;strokeColor=#d6b656;endArrow=classic;startArrow=classic;fontSize=10;exitX=0;exitY=0.8;exitDx=0;exitDy=0;entryX=1;entryY=0.2;entryDx=0;entryDy=0;" parent="1" source="do_step" target="caches" edge="1">
@@ -534,7 +534,7 @@
                        <mxPoint x="1040" y="1610" as="targetPoint"/>
                    </mxGeometry>
                </mxCell>
-                <mxCell id="dm11a" value="10a. worker runs:&#xa;VideoGenerationWorker.generate_step()&#xa;    (ltx2_generation.py:380)&#xa;  → generator.generate_video()&#xa;  → updates ContinuationState&#xa;then stream_fmp4() (av_streaming.py:121)&#xa;  → ffmpeg (rawvideo+wav → fmp4)" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffe0b2;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
+                <mxCell id="dm11a" value="10a. worker runs:&#xa;VideoGenerationWorker.generate_step()&#xa;    (ltx2_generation.py:380)&#xa;  → generator.generate()&#xa;  → updates ContinuationState&#xa;then stream_fmp4() (av_streaming.py:121)&#xa;  → ffmpeg (rawvideo+wav → fmp4)" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffe0b2;strokeColor=#d79b00;fontSize=10;align=left;spacingLeft=8;fontFamily=monospace;" parent="1" vertex="1">
                    <mxGeometry x="955" y="1640" width="180" height="70" as="geometry"/>
                </mxCell>
                <mxCell id="dm11" value="10b. resp_q.put(MediaInit / MediaChunk / MediaComplete / StepComplete)" style="endArrow=classic;html=1;strokeColor=#b85450;fontSize=10;labelBackgroundColor=#ffffff;" parent="1" edge="1">
@@ -64,8 +64,8 @@ generator:
    # internal: pipeline_config.dit_config.quant_config = FP4Config()
    # set in gpu_pool.py:280 (via the legacy in-place mutation). The
    # public typed surface resolves "NVFP4" to NVFP4Config() and pins
-    # it on dit_config in FastVideoArgs.__post_init__. Comment this
-    # block out on hosts without flashinfer / NVFP4 hardware.
+    # it on dit_config when resolution materializes the PipelineConfig.
+    # Comment this block out on hosts without flashinfer / NVFP4 hardware.
    quantization:
      transformer_quant: NVFP4

@@ -67,7 +67,7 @@ test.describe('preset prompt generation', () => {
    // on a B200 plus encode/transfer time. The "Continuation flipped
    // to Generating + Leave button rendered" pair above is the proof
    // the integration works: FE → /readyz → /curated-presets → WS
-    // /ws → BE → GPU pool → VideoGenerator.generate_video, all green.
+    // /ws → BE → GPU pool → VideoGenerator.generate, all green.
    const video = page.locator('video').first();
    await expect(video).toHaveCount(1);
  });
@@ -0,0 +1,63 @@
+import { expect, test } from '@playwright/test';
+
+import { skipWithoutMock } from './helpers';
+
+test.describe('create job interactions', () => {
+  skipWithoutMock();
+
+  for (const jobType of ['inference', 'finetuning', 'distillation']) {
+    test(`${jobType} remains interactive after repeated dialog dismissals`, async ({ page }) => {
+      await page.goto(`/${jobType}`);
+      const trigger = page.getByRole('button', { name: 'Create Job', exact: true });
+      const dialog = page.getByRole('dialog');
+
+      // Exercise both dismissal paths and reopen without reloading the page.
+      for (const closeWithEscape of [false, true]) {
+        await trigger.click();
+        await page.getByRole('menuitem').first().click();
+        await expect(dialog).toBeVisible();
+        if (closeWithEscape) {
+          await page.keyboard.press('Escape');
+        } else {
+          await dialog.getByRole('button', { name: 'Close', exact: true }).click();
+        }
+        await expect(dialog).toBeHidden();
+        await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+        await expect(trigger).toBeFocused();
+      }
+
+      await page.getByRole('link', { name: 'Datasets', exact: true }).click();
+      await expect(page).toHaveURL(/\/datasets$/);
+    });
+  }
+
+  test('preserves keyboard menu dismissal and dialog focus trapping', async ({ page }) => {
+    await page.goto('/inference');
+    const trigger = page.getByRole('button', { name: 'Create Job', exact: true });
+    await trigger.focus();
+    await page.keyboard.press('Enter');
+    const firstItem = page.getByRole('menuitem').first();
+    await expect(firstItem).toBeFocused();
+    await page.keyboard.press('Escape');
+    await expect(page.getByRole('menu')).toBeHidden();
+    await expect(trigger).toBeFocused();
+    await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+
+    await page.keyboard.press('Enter');
+    await expect(firstItem).toBeFocused();
+    await page.keyboard.press('Enter');
+    const dialog = page.getByRole('dialog');
+    await expect(dialog).toBeVisible();
+    await expect(dialog.getByLabel('Name (optional)')).toBeFocused();
+
+    // Shift+Tab from the first field wraps to Close, then Tab wraps back.
+    await page.keyboard.press('Shift+Tab');
+    await expect(dialog.getByRole('button', { name: 'Close', exact: true })).toBeFocused();
+    await page.keyboard.press('Tab');
+    await expect(dialog.getByLabel('Name (optional)')).toBeFocused();
+    await page.keyboard.press('Escape');
+    await expect(dialog).toBeHidden();
+    await expect(trigger).toBeFocused();
+    await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+  });
+});
@@ -1,6 +1,6 @@
 import { expect, test } from '@playwright/test';

-import { skipWithoutMock } from './helpers';
+import { API_BASE, skipWithoutMock } from './helpers';

 /**
 * Create-job flow: open the Create Job modal on /inference, fill the prompt
@@ -10,7 +10,8 @@ import { skipWithoutMock } from './helpers';
 test.describe('create inference job', () => {
  skipWithoutMock();

-  test('creates a T2V job and shows it in the queue', async ({ page }) => {
+  test('creates a T2V job and starts it without refreshing', async ({ page, request }) => {
+    await request.put(`${API_BASE}/settings`, { data: { autoStartJob: false } });
    await page.goto('/inference');

    // The trigger opens a real menu on click, so this path works for touch,
@@ -38,5 +39,20 @@ test.describe('create inference job', () => {
    // Modal closes and the queue refreshes with the newly created job.
    await expect(dialog).toBeHidden();
    await expect(page.getByText(prompt)).toBeVisible();
+    await expect(page.locator('body')).toHaveCSS('pointer-events', 'auto');
+
+    const card = page.getByRole('article').filter({ hasText: prompt });
+    await expect(card.getByText('pending', { exact: true })).toBeVisible();
+    const started = page.waitForResponse((response) =>
+      response.url().startsWith(`${API_BASE}/jobs/`) &&
+      response.url().endsWith('/start') &&
+      response.request().method() === 'POST',
+    );
+    await card.getByRole('button', { name: 'Start', exact: true }).click();
+    expect((await started).ok()).toBe(true);
+    await expect(card.getByText('running', { exact: true })).toBeVisible();
+
+    await page.getByRole('link', { name: 'Datasets', exact: true }).click();
+    await expect(page).toHaveURL(/\/datasets$/);
  });
 });
@@ -862,23 +862,40 @@ class JobRunner:
            sp_size,
        )

-        gen = VideoGenerator.from_pretrained(
-            model_id,
-            workload_type=workload_type,
-            num_gpus=num_gpus,
-            dit_layerwise_offload=dit_layerwise_offload,
-            **({
-                "override_pipeline_cls_name": override_pipeline_cls_name
-            } if override_pipeline_cls_name else {}),
-            dit_cpu_offload=dit_cpu_offload,
-            text_encoder_cpu_offload=text_encoder_cpu_offload,
-            vae_cpu_offload=vae_cpu_offload,
-            image_encoder_cpu_offload=image_encoder_cpu_offload,
-            use_fsdp_inference=use_fsdp_inference,
-            enable_torch_compile=enable_torch_compile,
-            VSA_sparsity=vsa_sparsity,
-            tp_size=tp_size,
-            sp_size=sp_size,
+        gen = VideoGenerator.from_config(
+            {
+                "model_path": model_id,
+                "engine": {
+                    "num_gpus": num_gpus,
+                    "parallelism": {
+                        "tp_size": tp_size,
+                        "sp_size": sp_size,
+                    },
+                    "offload": {
+                        "dit": dit_cpu_offload,
+                        "dit_layerwise": dit_layerwise_offload,
+                        "text_encoder": text_encoder_cpu_offload,
+                        "image_encoder": image_encoder_cpu_offload,
+                        "vae": vae_cpu_offload,
+                    },
+                    "compile": {
+                        "enabled": enable_torch_compile
+                    },
+                    "attention": {
+                        "vsa_sparsity": vsa_sparsity
+                    },
+                    "use_fsdp_inference": use_fsdp_inference,
+                },
+                "pipeline": {
+                    "workload_type":
+                    workload_type,
+                    **({
+                        "components": {
+                            "override_pipeline_cls_name": override_pipeline_cls_name
+                        }
+                    } if override_pipeline_cls_name else {}),
+                },
+            },
            log_queue=log_queue,
        )

@@ -1103,30 +1120,33 @@ class JobRunner:
            # Without a name FastVideo derives the filename from the prompt.
            safe_name = re.sub(r'[\\/:*?"<>|]+', "", job.name).strip().strip(".")
            output_target = (os.path.join(job_output_dir, f"{safe_name[:80]}.mp4") if safe_name else job_output_dir)
-            gen_kwargs: dict[str, Any] = {
+            request: dict[str, Any] = {
                "prompt": job.prompt,
-                "output_path": output_target,
-                "save_video": True,
-                "num_inference_steps": job.num_inference_steps,
-                "num_frames": job.num_frames,
-                "height": job.height,
-                "width": job.width,
-                "guidance_scale": job.guidance_scale,
-                "guidance_rescale": job.guidance_rescale,
-                "fps": job.fps,
-                "seed": job.seed,
                "negative_prompt": job.negative_prompt or "",
-                "log_queue": log_queue,
+                "sampling": {
+                    "num_inference_steps": job.num_inference_steps,
+                    "num_frames": job.num_frames,
+                    "height": job.height,
+                    "width": job.width,
+                    "guidance_scale": job.guidance_scale,
+                    "guidance_rescale": job.guidance_rescale,
+                    "fps": job.fps,
+                    "seed": job.seed,
+                },
+                "output": {
+                    "output_path": output_target,
+                    "save_video": True,
+                },
            }
            if job.image_path:
-                gen_kwargs["image_path"] = job.image_path
+                request.setdefault("inputs", {})["image_path"] = job.image_path
            if job.references:
-                gen_kwargs["references"] = _build_h3_references(job.references)
+                request.setdefault("inputs", {})["references"] = _build_h3_references(job.references)
            if job.last_image_path:
                # _prepare_fl2va requires a PIL image, not a path.
                from PIL import Image as _PILImage
-                gen_kwargs["last_image"] = _PILImage.open(job.last_image_path)
-            generator.generate_video(**gen_kwargs)
+                request.setdefault("inputs", {})["last_image"] = _PILImage.open(job.last_image_path)
+            generator.generate(request, log_queue=log_queue)

            buf.phase = "saving"
            logger.info("Generation completed, searching for output file...")
@@ -17,20 +17,11 @@
 		"start:all": "concurrently --kill-others-on-fail \"npm:start:api\" \"npm:start:web\""
 	},
 	"dependencies": {
-		"@radix-ui/react-dialog": "^1.1.0",
-		"@radix-ui/react-dropdown-menu": "^2.1.24",
-		"@radix-ui/react-label": "^2.1.8",
-		"@radix-ui/react-scroll-area": "^1.2.10",
-		"@radix-ui/react-select": "^2.2.6",
-		"@radix-ui/react-separator": "^1.1.8",
-		"@radix-ui/react-slider": "^1.2.0",
-		"@radix-ui/react-slot": "^1.2.4",
-		"@radix-ui/react-switch": "^1.1.0",
-		"@radix-ui/react-tabs": "^1.1.0",
 		"class-variance-authority": "^0.7.1",
 		"clsx": "^2.1.1",
 		"lucide-react": "^0.577.0",
 		"next": "15.5.18",
+		"radix-ui": "^1.6.7",
 		"react": "^19.1.0",
 		"react-dom": "^19.1.0",
 		"sonner": "^2.0.7",
@@ -1,24 +1,26 @@
-import { render, screen } from '@testing-library/react';
+import { render, screen, waitFor, within } from '@testing-library/react';
 import userEvent from '@testing-library/user-event';
-import { describe, expect, it, vi } from 'vitest';
+import { beforeEach, describe, expect, it, vi } from 'vitest';

 import CreateJobButton from './CreateJobButton';
+import { getDatasets, getModels } from '@/lib/api';

-vi.mock('./CreateJobModal', () => ({
-  default: ({
-    isOpen,
-    workloadType,
-  }: {
-    isOpen: boolean;
-    workloadType: string;
-  }) =>
-    isOpen ? (
-      <div role="dialog" data-workload-type={workloadType}>
-        Create job form
-      </div>
-    ) : null,
+vi.mock('@/lib/api', () => ({
+  createJob: vi.fn(),
+  getModels: vi.fn(),
+  getDatasets: vi.fn(),
+  uploadImage: vi.fn(),
+  getSettings: vi.fn(),
+  updateSettings: vi.fn(),
 }));

+beforeEach(() => {
+  vi.mocked(getModels).mockResolvedValue([
+    { id: 'wan/t2v-1.3b', label: 'Wan T2V' },
+  ]);
+  vi.mocked(getDatasets).mockResolvedValue([]);
+});
+
 describe('CreateJobButton', () => {
  it('opens the workload menu on click and selects an item', async () => {
    const user = userEvent.setup();
@@ -27,10 +29,9 @@ describe('CreateJobButton', () => {
    await user.click(screen.getByRole('button', { name: 'Create Job' }));
    await user.click(screen.getByRole('menuitem', { name: /I2V/i }));

-    expect(screen.getByRole('dialog')).toHaveAttribute(
-      'data-workload-type',
-      'i2v',
-    );
+    expect(
+      screen.getByRole('dialog', { name: 'New Inference Job (I2V)' }),
+    ).toBeInTheDocument();
  });

  it('opens and operates the workload menu from the keyboard', async () => {
@@ -45,9 +46,42 @@ describe('CreateJobButton', () => {
    expect(firstItem).toHaveFocus();
    await user.keyboard('{Enter}');

-    expect(screen.getByRole('dialog')).toHaveAttribute(
-      'data-workload-type',
-      't2v',
+    expect(
+      screen.getByRole('dialog', { name: 'New Inference Job (T2V)' }),
+    ).toBeInTheDocument();
+    await user.keyboard('{Escape}');
+    await waitFor(() =>
+      expect(screen.queryByRole('dialog')).not.toBeInTheDocument(),
    );
+    await waitFor(() =>
+      expect(document.body.style.pointerEvents).not.toBe('none'),
+    );
+    expect(trigger).toHaveFocus();
  });
+
+  it.each(['inference', 'finetuning', 'distillation'] as const)(
+    'restores page interaction after closing the real %s dialog',
+    async (jobType) => {
+      const user = userEvent.setup();
+      render(<CreateJobButton jobType={jobType} />);
+      const trigger = screen.getByRole('button', { name: 'Create Job' });
+
+      // Keep the real Dialog mounted: mocking it hides conflicting Radix layers.
+      for (let attempt = 0; attempt < 2; attempt++) {
+        await user.click(trigger);
+        await user.click(screen.getAllByRole('menuitem')[0]);
+        const dialog = screen.getByRole('dialog');
+        await user.click(
+          within(dialog).getByRole('button', { name: 'Close' }),
+        );
+        await waitFor(() =>
+          expect(screen.queryByRole('dialog')).not.toBeInTheDocument(),
+        );
+        await waitFor(() =>
+          expect(document.body.style.pointerEvents).not.toBe('none'),
+        );
+        expect(trigger).toHaveFocus();
+      }
+    },
+  );
 });
@@ -2,7 +2,7 @@

 import * as React from 'react';
 import { ChevronDown } from 'lucide-react';
-import * as DropdownMenu from '@radix-ui/react-dropdown-menu';
+import { DropdownMenu } from 'radix-ui';

 import CreateJobModal from '@/components/jobs/CreateJobModal';
 import { Button } from '@/components/ui/button';
@@ -16,6 +16,7 @@ interface CreateJobButtonProps {

 export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
  const options = WORKLOAD_OPTIONS[jobType] ?? [];
+  const triggerRef = React.useRef<HTMLButtonElement>(null);

  const [modalOpen, setModalOpen] = React.useState(false);
  const [workloadType, setWorkloadType] = React.useState(
@@ -36,7 +37,7 @@ export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
    <>
      <DropdownMenu.Root>
        <DropdownMenu.Trigger asChild>
-          <Button type="button" className="gap-1.5">
+          <Button ref={triggerRef} type="button" className="gap-1.5">
            Create Job
            <ChevronDown className="size-3.5 opacity-85" aria-hidden />
          </Button>
@@ -66,6 +67,11 @@ export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
      <CreateJobModal
        isOpen={modalOpen}
        onClose={() => setModalOpen(false)}
+        onCloseAutoFocus={(event) => {
+          // This dialog opens from a menu item, so it has no DialogTrigger.
+          event.preventDefault();
+          triggerRef.current?.focus();
+        }}
        onSuccess={handleSuccess}
        jobType={jobType}
        workloadType={workloadType}
@@ -55,6 +55,9 @@ import { jobToFormFields, type JobLike } from '@/lib/jobToFields';
 export interface CreateJobModalProps {
  isOpen: boolean;
  onClose: () => void;
+  onCloseAutoFocus?: React.ComponentProps<
+    typeof DialogContent
+  >['onCloseAutoFocus'];
  onSuccess: () => void;
  jobType: JobType;
  workloadType: string;
@@ -67,6 +70,7 @@ export interface CreateJobModalProps {
 export default function CreateJobModal({
  isOpen,
  onClose,
+  onCloseAutoFocus,
  onSuccess,
  jobType,
  workloadType,
@@ -127,8 +131,9 @@ export default function CreateJobModal({
  const editingJobId = editingJob?.id ?? null;
  const editingJobModelId = editingJob?.model_id ?? null;

-  // Layerwise offload and FSDP compete for the DiT weights and FastVideoArgs
-  // silently picks a winner (fastvideo_args.py:859); resolve it visibly here.
+  // Layerwise offload and FSDP compete for the DiT weights and the device offload
+  // policy (resolve_device_offload_conflicts in fastvideo/api/device_policy.py)
+  // silently picks a winner; resolve it visibly here.
  // dit_cpu_offload is deliberately not interlocked -- it is a modifier, not a
  // competing strategy.
  const handleDitLayerwiseOffloadChange = React.useCallback((next: boolean) => {
@@ -644,6 +649,7 @@ export default function CreateJobModal({
    >
      <DialogContent
        className="max-h-[90vh] w-[90vw] max-w-[850px] overflow-y-auto"
+        onCloseAutoFocus={onCloseAutoFocus}
        onEscapeKeyDown={(e) => {
          if (isSubmitting) e.preventDefault();
        }}
@@ -1,5 +1,7 @@
+import * as React from 'react';
 import { render, screen } from '@testing-library/react';
-import { describe, expect, it } from 'vitest';
+import userEvent from '@testing-library/user-event';
+import { describe, expect, it, vi } from 'vitest';

 import { Button } from './button';
 import { Input } from './input';
@@ -8,6 +10,24 @@ import { Slider } from './slider';
 import { Switch } from './switch';

 describe('shared control accessibility', () => {
+  it('forwards refs and click handlers to the asChild button', async () => {
+    const user = userEvent.setup();
+    const ref = React.createRef<HTMLButtonElement>();
+    const onClick = vi.fn();
+    render(
+      <Button asChild ref={ref} onClick={onClick}>
+        <button type="button">Slotted action</button>
+      </Button>,
+    );
+
+    const button = screen.getByRole('button', { name: 'Slotted action' });
+    expect(screen.getAllByRole('button')).toHaveLength(1);
+    expect(ref.current).toBe(button);
+    await user.click(button);
+    expect(onClick).toHaveBeenCalledTimes(1);
+    expect(button).toHaveFocus();
+  });
+
  it('keeps button, input, and select targets at least 44px tall', () => {
    render(
      <>
@@ -1,7 +1,7 @@
 "use client";

 import * as React from "react";
-import { Slot } from "@radix-ui/react-slot";
+import { Slot } from "radix-ui";
 import { cva, type VariantProps } from "class-variance-authority";

 import { cn } from "@/lib/utils";
@@ -37,7 +37,7 @@ export interface ButtonProps extends React.ButtonHTMLAttributes<HTMLButtonElemen
 }

 const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(({ className, variant, size, asChild = false, ...props }, ref) => {
-	const Comp = asChild ? Slot : "button";
+	const Comp = asChild ? Slot.Root : "button";
 	return <Comp className={cn(buttonVariants({ variant, size, className }))} ref={ref} {...props} />;
 });
 Button.displayName = "Button";
@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as DialogPrimitive from '@radix-ui/react-dialog';
+import { Dialog as DialogPrimitive } from 'radix-ui';
 import { X } from 'lucide-react';

 import { cn } from '@/lib/utils';
@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as LabelPrimitive from '@radix-ui/react-label';
+import { Label as LabelPrimitive } from 'radix-ui';
 import { cva, type VariantProps } from 'class-variance-authority';

 import { cn } from '@/lib/utils';
@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as ScrollAreaPrimitive from '@radix-ui/react-scroll-area';
+import { ScrollArea as ScrollAreaPrimitive } from 'radix-ui';

 import { cn } from '@/lib/utils';

@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as SelectPrimitive from '@radix-ui/react-select';
+import { Select as SelectPrimitive } from 'radix-ui';
 import { Check, ChevronDown, ChevronUp } from 'lucide-react';

 import { cn } from '@/lib/utils';
@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as SeparatorPrimitive from '@radix-ui/react-separator';
+import { Separator as SeparatorPrimitive } from 'radix-ui';

 import { cn } from '@/lib/utils';

@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as SliderPrimitive from '@radix-ui/react-slider';
+import { Slider as SliderPrimitive } from 'radix-ui';

 import { cn } from '@/lib/utils';

@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as SwitchPrimitives from '@radix-ui/react-switch';
+import { Switch as SwitchPrimitives } from 'radix-ui';

 import { cn } from '@/lib/utils';

@@ -1,7 +1,7 @@
 'use client';

 import * as React from 'react';
-import * as TabsPrimitive from '@radix-ui/react-tabs';
+import { Tabs as TabsPrimitive } from 'radix-ui';

 import { cn } from '@/lib/utils';

@@ -15,6 +15,9 @@ from fastvideo import VideoGenerator as FastVideoGenerator

 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))

+# InferenceArgs keys that are SamplingConfig fields of a GenerationRequest.
+_SAMPLING_INFERENCE_ARGS = ("height", "width", "num_frames", "num_inference_steps", "guidance_scale", "seed", "fps")
+

 # Custom exception for interruption
 class GenerationInterruptedException(Exception):
@@ -154,7 +157,17 @@ class VideoGenerator:
        """Thread function to run the generation"""
        try:
            if self.generator is not None:
-                self.generator.generate_video(prompt=prompt, output_path=output_path, **inference_args)
+                # Place each InferenceArgs value in the GenerationRequest section that owns it.
+                request: dict[str, Any] = {"prompt": prompt, "output": {"output_path": output_path}}
+                for key, value in inference_args.items():
+                    if key == "image_path":
+                        section = "inputs"
+                    elif key in _SAMPLING_INFERENCE_ARGS:
+                        section = "sampling"
+                    else:
+                        section = "extensions"
+                    request.setdefault(section, {})[key] = value
+                self.generator.generate(request)
                self._generation_result = os.path.join(output_path, f"{prompt[:100]}.mp4")
            else:
                raise RuntimeError("Generator is not initialized")
@@ -253,9 +266,24 @@ class VideoGenerator:
        if self.generator is None:
            print('generation_args', generation_args)
            print('pipeline_config', pipeline_config)
-            self.generator = FastVideoGenerator.from_pretrained(model_path=model_path,
-                                                                **generation_args,
-                                                                pipeline_config=pipeline_config)
+            # Place each generation argument at its GeneratorConfig engine path.
+            engine_config: dict[str, Any] = {}
+            if "num_gpus" in generation_args:
+                engine_config["num_gpus"] = generation_args["num_gpus"]
+            for parallelism_key in ("tp_size", "sp_size"):
+                if parallelism_key in generation_args:
+                    engine_config.setdefault("parallelism", {})[parallelism_key] = generation_args[parallelism_key]
+            if "dit_cpu_offload" in generation_args:
+                engine_config["offload"] = {"dit": generation_args["dit_cpu_offload"]}
+            self.generator = FastVideoGenerator.from_config({
+                "model_path": model_path,
+                "engine": engine_config,
+                "pipeline": {
+                    "experimental": {
+                        "pipeline_config": pipeline_config
+                    }
+                },
+            })

        print('inference_args', inference_args)

@@ -1,5 +1,5 @@
 {
-  "version": 8,
+  "version": 11,
  "recipes": [
    {
      "id": "fastwan21-t2v",
@@ -445,13 +445,13 @@
    {
      "id": "fasth3-preview-cuda",
      "group": "fasth3-preview",
-      "group_label": "FastH3 Preview",
+      "group_label": "FastH3 V1",
      "group_task": "4-step text to video + audio",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Few-step text to video (with audio)",
-      "label": "FastH3 Preview on CUDA",
-      "summary": "Run the DMD2-distilled FastH3 Preview with four DiT forwards, trained H3 sparse attention, compiled decode, and synchronized audio.",
+      "label": "FastH3 V1 on CUDA",
+      "summary": "Run FastH3 V1 with four DiT forwards, trained H3 sparse attention, compiled decode, and synchronized audio.",
      "model": "FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2",
      "source": "examples/inference/basic/basic_fasth3.py",
      "serving": {
@@ -484,13 +484,13 @@
        "prepare": "hf download FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2 --local-dir ./FastH3-Preview-v0.2\npython scripts/checkpoint_conversion/convert_minimax_h3_mlx.py --model-root ./FastH3-Preview-v0.2/transformer --out ./FastH3-MLX --formats \"int6\""
      },
      "group": "fasth3-preview",
-      "group_label": "FastH3 Preview",
+      "group_label": "FastH3 V1",
      "group_task": "4-step text to video + audio",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Few-step text to video (with audio)",
-      "label": "FastH3 Preview on MLX",
-      "summary": "Run FastH3 Preview on Apple Silicon with a locally converted INT6 DiT, streamed Qwen3-VL conditioning, and native MLX video and audio VAEs.",
+      "label": "FastH3 V1 on MLX",
+      "summary": "Run FastH3 V1 on Apple Silicon with a locally converted INT6 DiT, streamed Qwen3-VL conditioning, and native MLX video and audio VAEs.",
      "model": "FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2",
      "source": "examples/inference/basic/mlx_fasth3.py",
      "command": "hf download FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2 --local-dir ./FastH3-Preview-v0.2\npython scripts/checkpoint_conversion/convert_minimax_h3_mlx.py --model-root ./FastH3-Preview-v0.2/transformer --out ./FastH3-MLX --formats \"int6\"\npython examples/inference/basic/mlx_fasth3.py --model-root ./FastH3-Preview-v0.2 --mlx-checkpoint ./FastH3-MLX/int6 --prompt \"(S1) A presenter says <d>[English] FastVideo runs FastH3.</d>\" --height 480 --width 832 --num-frames 124 --seed 2026 --output-path ./outputs/fasth3_int6.mp4",
@@ -516,13 +516,13 @@
    {
      "id": "fasth3-preview-spark",
      "group": "fasth3-preview",
-      "group_label": "FastH3 Preview",
+      "group_label": "FastH3 V1",
      "group_task": "4-step text to video + audio",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Few-step text to video (with audio)",
-      "label": "FastH3 Preview on one DGX Spark",
-      "summary": "Run FastH3 Preview on one GB10 with Triton VSA, FA4 off, and lazy module load. Height, width, frames, and steps in the YAML are examples.",
+      "label": "FastH3 V1 on one DGX Spark",
+      "summary": "Run FastH3 V1 on one GB10 with Triton VSA, FA4 off, and lazy module load. Height, width, frames, and steps in the YAML are examples.",
      "model": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree",
      "source": "examples/inference/basic/basic_fasth3_spark.yaml",
      "serving": {
@@ -544,19 +544,19 @@
      "limitations": [
        "Install from the DGX Spark guide, not the generic CUDA extra. GB10 has no FA4 / sm_100a VSA kernel; keep FASTVIDEO_FA4=0 and FASTVIDEO_VSA_SM100A=0.",
        "Legal num_frames values are 17n+5, capped at 345 (15 s). Native 16:9 sizes include 832x480 and 1344x768.",
-        "Lazy module load reloads Qwen3-VL and the DiT between phases of each request. Do not pass --no-lazy-module-load on this box.",
+        "Lazy module load reloads Qwen3-VL and the DiT between phases of each request. Do not set engine.offload.lazy_module_load to false on this box.",
        "A 345-frame request on one Spark can OOM. Prefer 124 or 243 frames, TAEH3 decode, or two Sparks over QSFP."
      ]
    },
    {
      "id": "fasth3-spark-pair",
      "group": "fasth3-preview",
-      "group_label": "FastH3 Preview",
+      "group_label": "FastH3 V1",
      "group_task": "4-step text to video + audio",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Few-step text to video (with audio)",
-      "label": "FastH3 Preview on two DGX Sparks",
+      "label": "FastH3 V1 on two DGX Sparks",
      "summary": "Run one FastH3 clip across two GB10s with Ray sequence parallel over QSFP RoCE. Sequential load and lazy module load stay on because SP replicates the DiT on each node.",
      "model": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree",
      "source": "examples/inference/basic/basic_fasth3_spark_pair.yaml",
@@ -578,6 +578,83 @@
        "Height, width, frames, and steps in the YAML are examples. Edit them or pass CLI flags. See docs/getting_started/installation/spark_pair.md."
      ]
    },
+    {
+      "id": "fasth3-8step-v2-cuda",
+      "group": "fasth3-8step-v2",
+      "group_label": "FastH3 V2",
+      "group_task": "8-step text to video + audio",
+      "family": "minimax_h3",
+      "stage": "inference",
+      "task": "Few-step text to video (with audio)",
+      "label": "FastH3 V2 on CUDA",
+      "summary": "Run FastH3 V2, the eight-forward checkpoint (video/audio shifts 10/3, VSA 0.8, 64-token tiles) with the trained DMD ladder loaded from the checkpoint's fastvideo_inference.json.",
+      "model": "FastVideo/FastVideo-FastH3-8-Step-V2",
+      "source": "examples/inference/basic/basic_fasth3_8step.py",
+      "serving": {
+        "source": "examples/serving/openai_fasth3_8step.yaml",
+        "install": "UV_TORCH_BACKEND=cu130 uv pip install -e \".[fasth3]\""
+      },
+      "command": "UV_TORCH_BACKEND=cu130 uv pip install -e \".[fasth3]\"\npython examples/inference/basic/basic_fasth3_8step.py --prompt \"(S1) A presenter says <d>[English] FastVideo runs FastH3.</d>\" --profile strict --no-inference-torch-compile --no-compile-vae",
+      "gpu_types": ["NVIDIA"],
+      "hardware": {
+        "platform": "cuda",
+        "gpu_count": 4,
+        "accelerator": "NVIDIA GB200",
+        "evidence": "validated",
+        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1852"
+      },
+      "evidence": "Verified",
+      "expected_artifact": "Warmup and measured MP4 files under outputs/fasth3_8step/",
+      "modes": ["T2VA", "8-step FastH3"],
+      "knobs": [
+        {"key": "num_gpus", "label": "GPUs", "hint": "Sequence-parallel degree", "flag": "--num-gpus", "options": [1, 2, 4, 8], "default": 4},
+        {"key": "video_decode_backend", "label": "VAE decode", "hint": "Fidelity vs. speed", "flag": "--video-decode-backend", "options": [{"value": "h3-vae", "label": "Full H3 VAE"}, {"value": "taeh3", "label": "TAEH3 preview"}], "default": "h3-vae"}
+      ],
+      "limitations": [
+        "Nine sigma-grid points (eight transformer forwards) are fixed by the checkpoint's trained ladder; the example rejects any other --steps.",
+        "Validated with the eager strict route (--profile strict --no-inference-torch-compile --no-compile-vae). The compiled all profile has not been measured for this checkpoint.",
+        "T2AV only; no FL2VA/Ref2VA distillation and no matching LoRA. V2 uses eight forwards rather than V1's four."
+      ]
+    },
+    {
+      "id": "fasth3-8step-v2-mlx",
+      "serving": {
+        "source": "examples/serving/mlx_fasth3_8step.yaml",
+        "install": "uv pip install -e \".[mlx]\"",
+        "prepare": "hf download FastVideo/FastVideo-FastH3-8-Step-V2 --local-dir ./FastH3-8-Step-V2\npython scripts/checkpoint_conversion/convert_minimax_h3_mlx.py --model-root ./FastH3-8-Step-V2/transformer --out ./FastH3-8-Step-V2-MLX --formats \"int8\" --include-vsa"
+      },
+      "group": "fasth3-8step-v2",
+      "group_label": "FastH3 V2",
+      "group_task": "8-step text to video + audio",
+      "family": "minimax_h3",
+      "stage": "inference",
+      "task": "Few-step text to video (with audio)",
+      "label": "FastH3 V2 on MLX",
+      "summary": "Run FastH3 V2 on Apple Silicon with a locally converted INT8 DiT, the checkpoint's DMD contract ladder, and trained VSA (sparsity 0.8, 64-token tiles).",
+      "model": "FastVideo/FastVideo-FastH3-8-Step-V2",
+      "source": "examples/inference/basic/mlx_fasth3_8step.py",
+      "command": "hf download FastVideo/FastVideo-FastH3-8-Step-V2 --local-dir ./FastH3-8-Step-V2\npython scripts/checkpoint_conversion/convert_minimax_h3_mlx.py --model-root ./FastH3-8-Step-V2/transformer --out ./FastH3-8-Step-V2-MLX --formats \"int8\" --include-vsa\npython examples/inference/basic/mlx_fasth3_8step.py --model-root ./FastH3-8-Step-V2 --mlx-checkpoint ./FastH3-8-Step-V2-MLX/int8 --prompt \"(S1) A presenter says <d>[English] FastVideo runs FastH3.</d>\" --height 480 --width 832 --num-frames 124 --seed 2026 --output-path ./outputs/fasth3_8step_int8.mp4",
+      "gpu_types": ["Apple Silicon"],
+      "hardware": {
+        "platform": "mlx",
+        "accelerator": "Apple M4 Max",
+        "system_memory": "36 GB unified memory",
+        "peak_memory": "27.39 GiB peak MLX memory during denoising",
+        "evidence": "validated",
+        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1863"
+      },
+      "evidence": "Verified",
+      "expected_artifact": "MP4 with H.264 video and stereo AAC audio at outputs/fasth3_8step_int8.mp4",
+      "modes": ["T2VA", "8-step FastH3", "trained VSA"],
+      "knobs": [
+        {"key": "video_decode_backend", "label": "VAE decode", "hint": "Fidelity vs. speed", "flag": "--video-decode-backend", "options": [{"value": "h3-vae", "label": "Full H3 VAE"}, {"value": "taeh3", "label": "TAEH3 preview"}], "default": "h3-vae"}
+      ],
+      "limitations": [
+        "Convert with --include-vsa. mlx_fasth3_8step.py turns VSA on (sparsity 0.8, tile 64). A dense export fails at configure_vsa.",
+        "--steps 8 or 9 both run the eight trained forwards. Reuse the preview VAE, audio VAE, text encoder, and tokenizer if those directories already exist.",
+        "The MLX path supports T2VA only. FL2VA, Ref2VA, and two-pass refinement are not wired."
+      ]
+    },
    {
      "id": "minimax-h3-fl2va",
      "family": "minimax_h3",
@@ -136,7 +136,7 @@
    if (platform === "mps") {
      return {
        id: "mps",
-        label: "Apple Silicon · MPS",
+        label: "Apple Silicon · PyTorch MPS",
        hint: recipe.hardware?.minimum_memory || recipe.hardware?.system_memory || "Memory not recorded",
      };
    }
@@ -519,10 +519,10 @@
          option.setAttribute("aria-pressed", String(selected));
        });
        servingAvailability.textContent = profile
-          ? "The playground and API clients share one server process. Both workflows can run on your own machine."
+          ? "The playground and the OpenAI Python client share one server process. Both workflows can run on your own machine."
          : servingLoadFailed
            ? "Server examples could not be loaded. Open the H3 server guide below, or use Python directly."
-            : "This recipe uses Python directly. For the playground and API clients, choose FastH3 Preview with CUDA, MLX, or one Spark.";
+            : "This recipe uses Python directly. FastH3 V1 and FastH3 V2 can also run a local server for the playground and the OpenAI Python client.";
        servingPanel.hidden = !useServer;
        commandBlock.hidden = useServer;
        root.querySelector("[data-cookbook-python-note]").hidden = useServer;
@@ -537,7 +537,7 @@
            ? "Start once, then change prompts in the playground or your app. On a DGX Spark, lazy module load still reloads Qwen3-VL and the DiT between phases of each request, so later prompts are not a free hot cache."
            : "Start once, then change prompts in the playground or your app. CUDA requests reuse the loaded model. The Python SDK can also reuse a generator within one process.";
        servingPanel.querySelector("[data-cookbook-install-guide]").href = isMLX
-          ? "../../getting_started/installation/mps/#run-fasth3-preview"
+          ? "../../getting_started/installation/mlx/"
          : isSpark
            ? "../../getting_started/installation/spark/"
            : "../../getting_started/installation/gpu/";
@@ -574,7 +574,7 @@
      });

      description.textContent = useServer
-        ? `FastH3 Preview generates video with audio. This server profile uses the checked-in ${runtime.label} configuration.`
+        ? `${recipe.group_label || recipe.label} generates video with audio. Start the local server, then use the playground or the OpenAI Python client. This profile uses the checked-in ${runtime.label} configuration.`
        : recipe.summary;
      label.textContent = useServer ? `${recipe.group_label || recipe.label} · Server` : recipe.label;
      model.textContent = recipe.model;
@@ -18,8 +18,8 @@ the CUDA `fastvideo-kernel` package:
 - **Dense-only checkpoints** (the default converter) drop the 50 gate
  matrices and keep fused SDPA. They remain valid for dense inference.
 - **VSA-capable checkpoints** retain those gates, quantize them on the same
-  affine grid, and record `vsa.capable` in `mlx_h3_dit.json`. Runtime VSA is
-  still off until you pass `--vsa`.
+  affine grid, and record `vsa.capable` in `mlx_h3_dit.json`. Preview leaves
+  runtime VSA off until you pass `--vsa`. `mlx_fasth3_8step.py` turns it on.
 - **Tile sizes** 64 `(4, 4, 4)` and 256 `(4, 8, 8)`. Prefix keys can be
  `exempt` or `compete`. `--vsa-dense-first-n-steps` and `--vsa-dense-layers`
  force dense SDPA on the selected steps or blocks.
@@ -32,9 +32,12 @@ the CUDA `fastvideo-kernel` package:
  but does not yet match reference video. `--vsa-impl reference` is the same
  as `auto`.

-See the [Apple Silicon guide](../../getting_started/installation/mps.md) for
-conversion and `mlx_fasth3.py` flags. Do not enable VSA on a dense-only
-checkpoint; reconvert with `--include-vsa` first.
+See the [MLX install guide](../../getting_started/installation/mlx.md)
+and the [MiniMax H3 cookbook](../../cookbook/minimax-h3.md) for conversion
+and `mlx_fasth3.py` / `mlx_fasth3_8step.py` flags. Do not enable
+VSA on a dense-only checkpoint; reconvert with `--include-vsa` first. V1
+VSA is opt-in. FastH3 V2 converts with `--include-vsa` and turns VSA on
+by default.

 H3 uses fused MLX RMSNorm by default, including dense inference. This can
 change BF16 rounding relative to the older explicit normalization path.
@@ -30,7 +30,7 @@ FastVideo and the reference model first produce different numbers?"
 | General logging | `init_logger(__name__)` |
 | Per-stage timing | `FASTVIDEO_STAGE_LOGGING` |
 | Profiling kernel timings | `FASTVIDEO_TORCH_PROFILER_DIR` (see [Profiling](profiling.md)) |
-| Function-call tracing | `FASTVIDEO_TRACE_FUNCTION` (heavy) |
+| Function-call tracing | `fastvideo.logger.enable_trace_function_call()` (heavy) |

 ## Quickstart

@@ -57,19 +57,15 @@ Minimal usage example (based on `examples/inference/basic/basic.py`):

 ```python
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam

 model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # or official_weights/<model_name>/
-generator = VideoGenerator.from_pretrained(model_id, num_gpus=1)
+generator = VideoGenerator.from_pretrained(model_id, {"engine": {"num_gpus": 1}})

-sampling = SamplingParam.from_pretrained(model_id)
-sampling.num_frames = 45
-video = generator.generate_video(
-    "A vibrant city street at sunset.",
-    sampling_param=sampling,
-    output_path="video_samples",
-    save_video=True,
-)
+video = generator.generate({
+    "prompt": "A vibrant city street at sunset.",
+    "sampling": {"num_frames": 45},
+    "output": {"output_path": "video_samples", "save_video": True},
+})
 ```

 ## Some questions to ask yourself before starting
@@ -0,0 +1,294 @@
+# Environment Variables
+
+FastVideo reads environment variables for expert switches, debugging, profiling, and the settings that launchers such
+as `torchrun` provide. This page is the policy for those variables. The contract test
+`fastvideo/tests/contract/test_env_policy.py` enforces the policy in the unit CI lane, and the coding-agent skill
+`.agents/skills/env-var-conventions/SKILL.md` points here. When the policy changes, update this page and the contract
+test in the same pull request.
+
+## Rules
+
+1. **Register every FastVideo variable in `fastvideo/envs.py`.** Each entry declares a type, a default, a category,
+   and a description. Variables that other tools own (CUDA, NCCL, PyTorch, launchers) are not registered; code reads
+   them directly with `os.environ.get("NAME")`, and the name must be in the external-variable allowlist
+   (`EXTERNAL_ALLOWLIST` in the contract test). When FastVideo sets such a variable for the other tool, it calls
+   `envs.set_external`, `envs.setdefault_external`, or `envs.unset_external`, and the name must be in
+   `EXTERNAL_WRITE_ALLOWLIST`. Variables that FastVideo's CI and CI tooling define (for example `TEST_SCOPE` and
+   `PERF_RUN_SOURCE`) keep their names, and test code under `fastvideo/tests/` reads them directly; they are listed
+   in `CI_ONLY_VARIABLES` in the contract test, together with the file that sets each one.
+2. **Read with `envs.NAME.get()`, write with `envs.NAME.set()`, and change a value in tests with
+   `envs.NAME.override()`.** Each type has one parsing rule. A value that the rule rejects raises
+   `fastvideo.envs.EnvVarError` instead of falling back to the default.
+3. **Name FastVideo variables with the `FASTVIDEO_` prefix.** The second word states the purpose where one applies:
+   `ENABLE_`, `DISABLE_`, `USE_`, `FORCE_`, `DEBUG_`, `TEST_`. Variables that only tests read use
+   `FASTVIDEO_TEST_`, for example `FASTVIDEO_TEST_SD35_MODEL_DIR`, and the category `test`.
+4. **Keep a renamed variable as a deprecated alias until the next minor release.** Setting the old name logs a
+   warning. Delete a variable that no code reads, and list it in `DEPRECATED_VARIABLES` so that setting it logs a
+   warning.
+5. **Give each setting one source: an argument or an environment variable.** Settings that users change per
+   deployment are arguments (CLI or YAML). Expert switches, emergency off switches, and debugging and test switches
+   are environment variables.
+6. **Read variables inside functions.** `envs.NAME.get()` runs when the function runs, so a changed value takes
+   effect without re-importing a module. Module level, class bodies, decorators, and default argument values run at
+   import time.
+7. **Do not write the environment to pass values between parts of FastVideo.** Pass an argument instead. Tests use
+   `envs.NAME.override()`, and `envs.override_external()` for variables outside the registry; both restore the
+   previous value.
+
+## Field types
+
+| Class        | Value type       | Parsing rule                                                                        |
+| ------------ | ---------------- | ----------------------------------------------------------------------------------- |
+| `EnvBool`    | `bool`           | `1`, `true`, `yes`, `on` are true; `0`, `false`, `no`, `off`, and `""` are false.   |
+|              |                  | Case-insensitive; surrounding whitespace is ignored.                                |
+| `EnvInt`     | `int`            | `int(value)`                                                                        |
+| `EnvFloat`   | `float`          | `float(value)`                                                                      |
+| `EnvStr`     | `str` or `None`  | The raw string. A `None` default means that the variable has no default.            |
+| `EnvPath`    | `str` or `None`  | The raw string with a leading `~` expanded.                                         |
+| `EnvChoice`  | `str`            | Stripped and lower-cased, then checked against the declared `choices`.              |
+
+A default can be a zero-argument function; `get()` calls it on each read while the variable is unset. The path roots
+use this to follow `XDG_CONFIG_HOME` and `XDG_CACHE_HOME`.
+
+Using a field without a method, as in `if envs.FASTVIDEO_FA4:`, raises `TypeError`.
+
+## Add a variable
+
+1. Declare the variable in the matching section of `fastvideo/envs.py`:
+
+    ```python
+    FASTVIDEO_DEBUG_MY_STAGE = EnvBool(False, category="debug", doc="Log the inputs of MyStage.")
+    ```
+
+    The category is one of the values in `envs.CATEGORIES`.
+
+2. Read the variable inside a function:
+
+    ```python
+    import fastvideo.envs as envs
+
+    def forward(self, batch):
+        if envs.FASTVIDEO_DEBUG_MY_STAGE.get():
+            logger.info("MyStage inputs: %s", batch.keys())
+    ```
+
+3. Regenerate the table at the end of this page:
+
+    ```bash
+    python fastvideo/tests/contract/test_env_policy.py
+    ```
+
+4. Run the contract test:
+
+    ```bash
+    pytest fastvideo/tests/contract/test_env_policy.py
+    ```
+
+In a test, change the value with `override`, which restores the previous value on exit:
+
+```python
+with envs.FASTVIDEO_DEBUG_MY_STAGE.override(True):
+    run_stage()
+```
+
+For a variable outside the registry, such as `MASTER_PORT` or `TEST_SCOPE`, use `envs.override_external`. To keep
+an override until the end of a test, enter it through the `env_overrides` fixture from `fastvideo/tests/conftest.py`,
+which restores every value at teardown:
+
+```python
+def test_my_stage(env_overrides):
+    env_overrides.enter_context(envs.FASTVIDEO_DEBUG_MY_STAGE.override(True))
+    env_overrides.enter_context(envs.override_external("MASTER_PORT", "29512"))
+    run_stage()
+```
+
+In test code under `fastvideo/tests/`, `override_external` accepts any name that code may read directly
+(`EXTERNAL_ALLOWLIST`, `CI_ONLY_VARIABLES`) or that is in `EXTERNAL_WRITE_ALLOWLIST`. Library code may write only
+the names in `EXTERNAL_WRITE_ALLOWLIST`.
+
+## Rename or remove a variable
+
+To rename a variable, declare it under the new name and list the old name in `deprecated_names`:
+
+```python
+FASTVIDEO_LTX2_USE_DISTILLED_SIGMAS = EnvBool(True,
+                                              category="sampling",
+                                              doc="...",
+                                              deprecated_names=("LTX2_USE_DISTILLED_SIGMAS", ))
+```
+
+`get()` reads an old name only when the new name is unset, and logs a warning once. Update the uses of the old name
+in `examples/`, `scripts/`, `docs/`, `apps/`, and the tests in the same pull request. Delete the old name in the next
+minor release.
+
+To remove a variable that no code reads, delete its entry and add the name to `DEPRECATED_VARIABLES` in
+`fastvideo/envs.py` with a reason. The config resolution step `warn_deprecated_environment_variables` in
+`fastvideo/api/inference_resolution.py` calls `envs.warn_deprecated_variables()`, which logs a warning for each listed
+variable that is set. Delete the entry in the next minor release.
+
+## What the contract test checks
+
+The test parses every Python file under `fastvideo/`, including `fastvideo/tests/`, with Python's `ast` module. It
+skips `fastvideo/third_party/`, which is copied from upstream projects, and the registry `fastvideo/envs.py`. It does
+not check `apps/`, `examples/`, `scripts/`, `fastvideo-kernel/`, or `docs/`.
+
+It reports each violation as `<path>: <kind> <name>`:
+
+| Kind               | Code that triggers it                                        | Fix                                          |
+| ------------------ | ------------------------------------------------------------ | -------------------------------------------- |
+| `read`             | `os.getenv`, `os.environ.get`, `os.environ[...]`, or         | Register the variable and call               |
+|                    | `"NAME" in os.environ` with a name outside the allowlist, or | `envs.NAME.get()`. For a variable that       |
+|                    | with a name built at runtime (`<dynamic>`)                   | another tool owns, add it to                 |
+|                    |                                                              | `EXTERNAL_ALLOWLIST` with a reason.          |
+| `write`            | `os.environ[...] = ...`, `setdefault`, `pop`, `del`,         | Pass an argument instead. In tests, use      |
+|                    | `os.putenv`, `os.unsetenv`, `monkeypatch.setenv`/`delenv`,   | `envs.NAME.override()`, or                   |
+|                    | or an `envs.*_external` call with a name that the            | `envs.override_external()` for a variable    |
+|                    | helper does not accept                                       | outside the registry. For a variable that    |
+|                    |                                                              | another tool reads, call an                  |
+|                    |                                                              | `envs.*_external` helper and add the name to |
+|                    |                                                              | `EXTERNAL_WRITE_ALLOWLIST` with a reason.    |
+| `whole-environ`    | `os.environ.copy()`, `dict(os.environ)`, iteration,          | Read the specific variables that the code    |
+|                    | `mock.patch.dict(os.environ, ...)`, `os.environ.update`      | needs.                                       |
+| `bare-field`       | A registry field used without calling one of its methods,    | Call `envs.NAME.get()`.                      |
+|                    | as in `envs.NAME == "auto"` or `getter = envs.NAME.get`      |                                              |
+| `import-time-read` | `envs.NAME.get()` outside a function                         | Move the read into the function that uses    |
+|                    |                                                              | the value.                                   |
+| `prefix`           | A registry entry without the `FASTVIDEO_` prefix             | Rename the variable and keep the old name in |
+|                    |                                                              | `deprecated_names`.                          |
+| `unread`           | A registry entry that no code reads with `get()` or          | Delete the variable and add it to            |
+|                    | `is_set()`                                                   | `DEPRECATED_VARIABLES`.                      |
+
+The test recognizes `os` imported under another name, `from os import environ, getenv`, and a name held in a
+module-level string constant. Code that reaches the environment through `importlib` or `getattr(os, "environ")` is
+left to code review.
+
+The test also checks that every registry entry has a category from `envs.CATEGORIES` and a description, and that the
+table at the end of this page matches the registry.
+
+**Known violations.** `KNOWN_VIOLATIONS` in the contract test lists the violations that existed when the policy was
+introduced. The list only shrinks. A violation that is not in the list fails the test. A listed violation that no
+longer exists also fails the test, so the fixing pull request deletes its entry.
+
+## Registered variables
+
+<!-- BEGIN GENERATED ENV TABLE: python fastvideo/tests/contract/test_env_policy.py -->
+| Variable                                                   | Type                           | Default                                               | Category    | Description                                                                                                                                                                                                                                              |
+| ---------------------------------------------------------- | ------------------------------ | ----------------------------------------------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `FASTVIDEO_CONFIG_ROOT`                                    | path                           | computed                                              | path        | Root directory for FastVideo configuration files, at runtime and at installation. Defaults to ~/.config/fastvideo, or $XDG_CONFIG_HOME/fastvideo when XDG_CONFIG_HOME is set.                                                                            |
+| `FASTVIDEO_CACHE_ROOT`                                     | path                           | computed                                              | path        | Root directory for FastVideo cache files. Defaults to ~/.cache/fastvideo, or $XDG_CACHE_HOME/fastvideo when XDG_CACHE_HOME is set.                                                                                                                       |
+| `FASTVIDEO_REASON1_WEIGHTS_PATH`                           | str                            | unset                                                 | path        | Local path or Hugging Face id of Reason1 weights to load instead of the checkpoint's own.                                                                                                                                                                |
+| `FASTVIDEO_HOST_IP`                                        | str                            | `""`                                                  | distributed | IP address of this node when the node has several network interfaces. Set it on each node for multi-node inference.                                                                                                                                      |
+| `FASTVIDEO_LOOPBACK_IP`                                    | str                            | `""`                                                  | distributed | Loopback IP address to use instead of the detected one.                                                                                                                                                                                                  |
+| `FASTVIDEO_RAY_PER_WORKER_GPUS`                            | float                          | `1.0`                                                 | distributed | GPUs per Ray worker. A fraction lets Ray schedule several actors on one GPU, so other actors can share the GPUs with FastVideo.                                                                                                                          |
+| `FASTVIDEO_NCCL_SO_PATH`                                   | str                            | unset                                                 | distributed | Path to the NCCL library file. Needed because the nccl&gt;=2.19 that PyTorch ships has a bug (https://github.com/NVIDIA/nccl/issues/1234).                                                                                                               |
+| `FASTVIDEO_HCCL_SO_PATH`                                   | str                            | unset                                                 | distributed | Path to the HCCL library file on Ascend NPUs. Deprecated names: `HCCL_SO_PATH`.                                                                                                                                                                          |
+| `FASTVIDEO_WORKER_MULTIPROC_METHOD`                        | one of spawn, fork, forkserver | `spawn`                                               | distributed | Multiprocessing start method for worker processes.                                                                                                                                                                                                       |
+| `FASTVIDEO_ULYSSES_A2A`                                    | one of off, auto               | `off`                                                 | distributed | Sequence-parallel all-to-all backend. off uses the NCCL path in DistributedAutograd.AllToAll4D. auto uses the fused NVLink kernel when the group is a load-store accessible mesh of 2, 4, 6, or 8 ranks in eager execution, and the NCCL path otherwise. |
+| `FASTVIDEO_CONFIGURE_LOGGING`                              | bool                           | `1`                                                   | logging     | Configure logging at import. When true, FastVideo uses its default logging configuration or the file in FASTVIDEO_LOGGING_CONFIG_PATH.                                                                                                                   |
+| `FASTVIDEO_LOGGING_CONFIG_PATH`                            | str                            | unset                                                 | logging     | Path to a JSON logging configuration file.                                                                                                                                                                                                               |
+| `FASTVIDEO_LOGGING_LEVEL`                                  | str                            | `INFO`                                                | logging     | Default logging level.                                                                                                                                                                                                                                   |
+| `FASTVIDEO_LOGGING_PREFIX`                                 | str                            | `""`                                                  | logging     | Prefix prepended to every log message.                                                                                                                                                                                                                   |
+| `FASTVIDEO_STAGE_LOGGING`                                  | bool                           | `0`                                                   | logging     | Log the time that each pipeline stage takes.                                                                                                                                                                                                             |
+| `FASTVIDEO_ATTENTION_BACKEND`                              | str                            | unset                                                 | attention   | Attention backend, as an AttentionBackendEnum name such as TORCH_SDPA, FLASH_ATTN, VIDEO_SPARSE_ATTN, SAGE_ATTN, or SAGE_ATTN_THREE. Config resolution uses it when engine.attention.backend is unset. An unsupported name raises an error.              |
+| `FASTVIDEO_FA4`                                            | bool                           | `0`                                                   | attention   | The FLASH_ATTN backend uses FlashAttention-4 (flash_attn.cute) instead of FA3 or FA2.                                                                                                                                                                    |
+| `FASTVIDEO_MINIMAX_H3_FA4_PACKED_VARLEN`                   | bool                           | `0`                                                   | attention   | MiniMax-H3 dense DiT self-attention uses the FlashAttention-4 packed-varlen entry point. This changes the floating-point reduction order, so it is an inference-only opt-in.                                                                             |
+| `FASTVIDEO_VSA_SM100A`                                     | bool                           | `0`                                                   | attention   | VIDEO_SPARSE_ATTN_H3 sends no-grad tile-64 forwards to the data-center Blackwell (sm_100a) kernel. fastvideo-kernel reads the same variable with the same rule.                                                                                          |
+| `FASTVIDEO_NVFP4_FA4`                                      | bool                           | `0`                                                   | attention   | FlashAttention-4 quantizes Q and K to NVFP4. An explicit nvfp4_fa4 attention implementation argument takes precedence.                                                                                                                                   |
+| `FASTVIDEO_DISABLE_ATTENTION_COMPILE`                      | bool                           | `1`                                                   | attention   | Keep attention forward out of torch.compile graphs (torch.compiler.disable). Set it to 0 to let attention constructed under that setting be traced. Setting it explicitly to true also blocks regional compile.                                          |
+| `FASTVIDEO_MLX_WINDOW`                                     | int                            | `0`                                                   | attention   | MLX FastWan windowed attention size in tokens. 0 uses full attention.                                                                                                                                                                                    |
+| `FASTVIDEO_MLX_WINDOW_SINK`                                | int                            | `0`                                                   | attention   | Number of sink tokens that MLX windowed attention always attends to.                                                                                                                                                                                     |
+| `FASTVIDEO_INFERENCE_TORCH_COMPILE`                        | bool                           | `0`                                                   | performance | Compile each DiT transformer block with fullgraph torch.compile at inference. Same as engine.compile.regional=True.                                                                                                                                      |
+| `FASTVIDEO_VAE_PARALLEL_DECODE`                            | bool                           | `0`                                                   | performance | MiniMax-H3 VAE decode splits its temporal chunks across the sequence-parallel ranks instead of running serially on the output rank. Same as pipeline.model.minimax_h3.vae_parallel_decode=True.                                                          |
+| `FASTVIDEO_VAE_PARALLEL_ENCODE`                            | bool                           | `0`                                                   | performance | MiniMax-H3 reference-video VAE encode splits its temporal chunks across the sequence-parallel ranks. Same as pipeline.model.minimax_h3.vae_parallel_encode=True.                                                                                         |
+| `FASTVIDEO_VAE_PARALLEL_DECODE_STRATEGY`                   | str                            | unset                                                 | performance | Collective that moves chunks in parallel VAE decode: gather (used when unset) or all_gather.                                                                                                                                                             |
+| `FASTVIDEO_MINIMAX_H3_FUSIONS`                             | str                            | `""`                                                  | performance | MiniMax-H3 inference-only Triton fusions: all, 1, or a comma-separated subset of modulate,qknorm_rope,swiglu. Empty, 0, or none keeps the eager implementation.                                                                                          |
+| `FASTVIDEO_FSDP2_AUTOWRAP`                                 | bool                           | `0`                                                   | performance | FSDP2 shards modules by parameter count instead of the model's shard conditions. Not supported by self-forcing distillation.                                                                                                                             |
+| `FASTVIDEO_FSDP2_MIN_PARAMS`                               | int                            | `10000000`                                            | performance | Minimum parameter count of a module that FASTVIDEO_FSDP2_AUTOWRAP shards.                                                                                                                                                                                |
+| `FASTVIDEO_MLX_COMPILE`                                    | bool                           | `0`                                                   | performance | Compile the MLX DiT forward with mx.compile.                                                                                                                                                                                                             |
+| `FASTVIDEO_MLX_FAST_NORM`                                  | bool                           | `0`                                                   | performance | Use MLX fast normalization kernels.                                                                                                                                                                                                                      |
+| `FASTVIDEO_MLX_DQ_GEMM`                                    | str                            | `1`                                                   | performance | MLX dequantized GEMM for affine-quantized weights: 0 turns it off, 1 uses the measured minimum row count, and an integer sets the minimum row count.                                                                                                     |
+| `FASTVIDEO_LTX2_VAE_CHANNELS_LAST_3D`                      | bool                           | `1`                                                   | performance | LTX-2 VAE uses the channels_last_3d memory format.                                                                                                                                                                                                       |
+| `FASTVIDEO_LTX2_DISABLE_AUDIO_AUTOCAST`                    | bool                           | `1`                                                   | performance | LTX-2 audio decoding runs without CUDA autocast. Deprecated names: `LTX2_DISABLE_AUDIO_AUTOCAST`.                                                                                                                                                        |
+| `FASTVIDEO_FLUX2_DISABLE_BF16_REDUCED_PRECISION_REDUCTION` | bool                           | `0`                                                   | performance | Flux denoising disables reduced-precision reductions in bf16 matmuls, which tightens accumulation for the 4-step Klein model.                                                                                                                            |
+| `FASTVIDEO_FFMPEG_BIN`                                     | str                            | `ffmpeg`                                              | output      | ffmpeg executable used to save video with audio.                                                                                                                                                                                                         |
+| `FASTVIDEO_VIDEO_CODEC`                                    | str                            | `libx264`                                             | output      | ffmpeg video codec for saved videos.                                                                                                                                                                                                                     |
+| `FASTVIDEO_NVENC_PRESET`                                   | str                            | `p1`                                                  | output      | NVENC preset when the codec is an \*_nvenc codec.                                                                                                                                                                                                        |
+| `FASTVIDEO_NVENC_TUNE`                                     | str                            | `ull`                                                 | output      | NVENC tune option.                                                                                                                                                                                                                                       |
+| `FASTVIDEO_NVENC_RC`                                       | str                            | `constqp`                                             | output      | NVENC rate-control mode.                                                                                                                                                                                                                                 |
+| `FASTVIDEO_NVENC_QP`                                       | str                            | `28`                                                  | output      | NVENC quantization parameter.                                                                                                                                                                                                                            |
+| `FASTVIDEO_NVENC_BF`                                       | str                            | `0`                                                   | output      | NVENC number of B-frames.                                                                                                                                                                                                                                |
+| `FASTVIDEO_X264_PRESET`                                    | str                            | `ultrafast`                                           | output      | x264 preset for non-NVENC codecs.                                                                                                                                                                                                                        |
+| `FASTVIDEO_OUTPUT_PIX_FMT`                                 | str                            | `yuv420p`                                             | output      | ffmpeg pixel format for saved videos.                                                                                                                                                                                                                    |
+| `FASTVIDEO_NVTX_PROFILE`                                   | bool                           | `0`                                                   | profiling   | Emit NVTX ranges for external profilers such as Nsight Systems.                                                                                                                                                                                          |
+| `FASTVIDEO_TORCH_PROFILER_DIR`                             | path                           | unset                                                 | profiling   | Enables the torch profiler and sets the directory for its traces. Must be an absolute path.                                                                                                                                                              |
+| `FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES`                   | bool                           | `0`                                                   | profiling   | Torch profiler records shapes.                                                                                                                                                                                                                           |
+| `FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY`             | bool                           | `0`                                                   | profiling   | Torch profiler profiles memory.                                                                                                                                                                                                                          |
+| `FASTVIDEO_TORCH_PROFILER_WITH_STACK`                      | bool                           | `0`                                                   | profiling   | Torch profiler captures stacks. Costs about 1.5x runtime and 1.4x trace size.                                                                                                                                                                            |
+| `FASTVIDEO_TORCH_PROFILER_WITH_FLOPS`                      | bool                           | `0`                                                   | profiling   | Torch profiler profiles FLOPs.                                                                                                                                                                                                                           |
+| `FASTVIDEO_TORCH_PROFILE_REGIONS`                          | str                            | `""`                                                  | profiling   | Comma-separated profiler regions to record. The torch profiler requires at least one region.                                                                                                                                                             |
+| `FASTVIDEO_TRACE_ACTIVATIONS`                              | bool                           | `0`                                                   | debug       | Enable activation trace hooks.                                                                                                                                                                                                                           |
+| `FASTVIDEO_TRACE_LAYERS`                                   | str                            | `""`                                                  | debug       | Regex filter for traced module names. Empty means all.                                                                                                                                                                                                   |
+| `FASTVIDEO_TRACE_STATS`                                    | str                            | `abs_mean,sum`                                        | debug       | Comma-separated activation statistics dumped for each output tensor.                                                                                                                                                                                     |
+| `FASTVIDEO_TRACE_OUTPUT`                                   | str                            | `/tmp/fv_trace_<pid>.jsonl`                           | debug       | JSONL path for activation traces. The literal &lt;pid&gt; is replaced at runtime.                                                                                                                                                                        |
+| `FASTVIDEO_TRACE_STEPS`                                    | str                            | `""`                                                  | debug       | Comma-separated denoising step indices. Empty means all.                                                                                                                                                                                                 |
+| `FASTVIDEO_H3_VSA_PROBE`                                   | str                            | unset                                                 | debug       | Output directory for the VSA-H3 attention-mass probe, which writes one .pt file per step, layer, and rank. Keeps the model out of regional compile.                                                                                                      |
+| `FASTVIDEO_LTX2_GEMMA_LOG`                                 | str                            | `""`                                                  | debug       | Log file for LTX-2 Gemma text-encoder hidden states, used by parity tests. Deprecated names: `LTX2_FASTVIDEO_GEMMA_LOG`.                                                                                                                                 |
+| `FASTVIDEO_COSMOS25_LOG_KNOBS`                             | bool                           | `0`                                                   | debug       | Log the Cosmos 2.5 latent-preparation conditioning inputs.                                                                                                                                                                                               |
+| `FASTVIDEO_CFG_GATE_STEP`                                  | float                          | `1.0`                                                 | sampling    | CFG gating fraction in [0, 1]. Steps before len(timesteps) \* X run the conditional and unconditional forwards; later steps reuse the cached difference. 1.0 disables gating.                                                                            |
+| `FASTVIDEO_LTX2_USE_DISTILLED_SIGMAS`                      | bool                           | `1`                                                   | sampling    | LTX-2 uses the distilled sigma schedule when pipeline.model.ltx2.use_distilled_sigmas is also true. Deprecated names: `LTX2_USE_DISTILLED_SIGMAS`.                                                                                                       |
+| `FASTVIDEO_EVAL_CACHE`                                     | path                           | computed                                              | eval        | Cache directory for evaluation models and datasets. Defaults to $FASTVIDEO_CACHE_ROOT/eval.                                                                                                                                                              |
+| `FASTVIDEO_PHYSICS_IQ_BUCKET_URL`                          | str                            | `https://storage.googleapis.com/physics-iq-benchmark` | eval        | Base URL of the Physics-IQ benchmark bucket.                                                                                                                                                                                                             |
+| `FASTVIDEO_VBENCH_FULL_INFO_JSON`                          | str                            | unset                                                 | eval        | Path to VBench_full_info.json, used instead of the vendored copy. Deprecated names: `VBENCH_FULL_INFO_JSON`.                                                                                                                                             |
+| `FASTVIDEO_FVD_REF_FEATURES`                               | str                            | unset                                                 | eval        | Cached reference-feature file for the FVD metric.                                                                                                                                                                                                        |
+| `FASTVIDEO_FAD_REF_FEATURES`                               | str                            | unset                                                 | eval        | Cached reference-feature file for the audio Frechet distance metric.                                                                                                                                                                                     |
+| `FASTVIDEO_TEST_LTX2_OVERFIT_DATA_DIR`                     | str                            | `data/cats`                                           | test        | Raw data directory for preprocess_ltx2_overfit.py. Deprecated names: `LTX2_OVERFIT_DATA_DIR`.                                                                                                                                                            |
+| `FASTVIDEO_TEST_LTX2_OVERFIT_CAPTION_JSON`                 | str                            | `videos2caption_1_sample.json`                        | test        | Caption file, relative to the raw data directory. Deprecated names: `LTX2_OVERFIT_CAPTION_JSON`.                                                                                                                                                         |
+| `FASTVIDEO_TEST_LTX2_OVERFIT_VIDEO_SUBDIR`                 | str                            | `video`                                               | test        | Video subdirectory, relative to the raw data directory. Deprecated names: `LTX2_OVERFIT_VIDEO_SUBDIR`.                                                                                                                                                   |
+| `FASTVIDEO_TEST_LTX2_OVERFIT_OUTPUT_DIR`                   | str                            | `data/ltx2_overfit_preprocessed`                      | test        | Output directory for preprocess_ltx2_overfit.py. Deprecated names: `LTX2_OVERFIT_OUTPUT_DIR`.                                                                                                                                                            |
+| `FASTVIDEO_TEST_LTX2_OVERFIT_MODEL`                        | str                            | `FastVideo/LTX2-Distilled-Diffusers`                  | test        | Model repository whose encoders preprocess_ltx2_overfit.py uses. Deprecated names: `LTX2_OVERFIT_MODEL`.                                                                                                                                                 |
+| `FASTVIDEO_TEST_LTX2_OVERFIT_NUM_COPIES`                   | int                            | `4`                                                   | test        | Number of copies of the overfit sample in the parquet file. Deprecated names: `LTX2_OVERFIT_NUM_COPIES`.                                                                                                                                                 |
+| `FASTVIDEO_TEST_KANDINSKY5_OVERFIT_DATA_DIR`               | str                            | `data/kandinsky5_overfit`                             | test        | Raw data directory for preprocess_kandinsky5_overfit.py. Deprecated names: `KANDINSKY5_OVERFIT_DATA_DIR`.                                                                                                                                                |
+| `FASTVIDEO_TEST_KANDINSKY5_OVERFIT_OUTPUT_DIR`             | str                            | `data/kandinsky5_overfit_preprocessed`                | test        | Output directory for preprocess_kandinsky5_overfit.py. Deprecated names: `KANDINSKY5_OVERFIT_OUTPUT_DIR`.                                                                                                                                                |
+| `FASTVIDEO_TEST_SSIM_REFERENCE_HF_REPO`                    | str                            | `FastVideo/ssim-reference-videos`                     | test        | Hugging Face repository that holds the SSIM reference videos. Deprecated names: `FASTVIDEO_SSIM_REFERENCE_HF_REPO`.                                                                                                                                      |
+| `FASTVIDEO_TEST_SSIM_REFERENCE_HF_REPO_TYPE`               | str                            | `dataset`                                             | test        | Repository type of FASTVIDEO_TEST_SSIM_REFERENCE_HF_REPO. Deprecated names: `FASTVIDEO_SSIM_REFERENCE_HF_REPO_TYPE`.                                                                                                                                     |
+| `FASTVIDEO_TEST_SSIM_SKIP_REFERENCE_DOWNLOAD`              | bool                           | `0`                                                   | test        | SSIM tests use local reference videos without downloading. Deprecated names: `FASTVIDEO_SSIM_SKIP_REFERENCE_DOWNLOAD`.                                                                                                                                   |
+| `FASTVIDEO_TEST_SSIM_FULL_QUALITY`                         | bool                           | `0`                                                   | test        | SSIM tests use the full-quality sampling configurations. Deprecated names: `FASTVIDEO_SSIM_FULL_QUALITY`.                                                                                                                                                |
+| `FASTVIDEO_TEST_NIGHTLY`                                   | bool                           | `0`                                                   | test        | Run the nightly end-to-end overfit tests. Deprecated names: `FASTVIDEO_NIGHTLY`.                                                                                                                                                                         |
+| `FASTVIDEO_TEST_ULYSSES_FAULT_RANK`                        | str                            | unset                                                 | test        | Rank that fails in the Ulysses fault-injection test. The test sets it for its worker processes. Deprecated names: `FASTVIDEO_ULYSSES_FAULT_RANK`.                                                                                                        |
+| `FASTVIDEO_TEST_ULYSSES_FAULT_STAGE`                       | str                            | unset                                                 | test        | Stage that fails in the Ulysses fault-injection test. The test sets it for its worker processes. Deprecated names: `FASTVIDEO_ULYSSES_FAULT_STAGE`.                                                                                                      |
+| `FASTVIDEO_TEST_GOLDEN_GATE_DIR`                           | str                            | unset                                                 | test        | Local directory of golden-gate reference tensors. Deprecated names: `FASTVIDEO_GOLDEN_GATE_DIR`.                                                                                                                                                         |
+| `FASTVIDEO_TEST_WAN22_5B_ALLOW_LOW_MEMORY`                 | bool                           | `0`                                                   | test        | Run the MLX Wan2.2 5B real-weights parity test on hosts with little memory. Deprecated names: `FASTVIDEO_WAN22_5B_ALLOW_LOW_MEMORY`.                                                                                                                     |
+| `FASTVIDEO_TEST_WAN22_5B_ROOT`                             | str                            | unset                                                 | test        | Local Wan2.2 5B checkpoint for the MLX real-weights parity test. Deprecated names: `FASTVIDEO_WAN22_5B_ROOT`.                                                                                                                                            |
+| `FASTVIDEO_TEST_GRADNORM_UPDATE`                           | bool                           | `0`                                                   | test        | Gradient-norm regression tests update their references. Deprecated names: `FASTVIDEO_GRADNORM_UPDATE`.                                                                                                                                                   |
+| `FASTVIDEO_TEST_DREAMX_WORLD_SSIM_MODEL_PATH`              | str                            | `FastVideo/DreamX-World-5B-Cam-Diffusers`             | test        | Model for the DreamX-World camera SSIM test. Deprecated names: `DREAMX_WORLD_SSIM_MODEL_PATH`.                                                                                                                                                           |
+| `FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH`           | str                            | `FastVideo/DreamX-World-5B-Diffusers`                 | test        | Model for the DreamX-World autoregressive SSIM test. Deprecated names: `DREAMX_WORLD_AR_SSIM_MODEL_PATH`.                                                                                                                                                |
+| `FASTVIDEO_TEST_FLUX_T2I_MODEL_DIR`                        | str                            | `black-forest-labs/FLUX.1-dev`                        | test        | Model for the Flux text-to-image SSIM test. Deprecated names: `FLUX_T2I_MODEL_DIR`.                                                                                                                                                                      |
+| `FASTVIDEO_TEST_FLUX_TRANSFORMER_PATH`                     | str                            | unset                                                 | test        | Local Flux transformer for the Flux transformer test. Deprecated names: `FLUX_TRANSFORMER_PATH`.                                                                                                                                                         |
+| `FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH`                      | str                            | `FastVideo/HunyuanGameCraft-Diffusers`                | test        | Model for the HunyuanGameCraft SSIM test. Deprecated names: `GAMECRAFT_MODEL_PATH`.                                                                                                                                                                      |
+| `FASTVIDEO_TEST_GEN3C_MODEL_PATH`                          | str                            | `FastVideo/GEN3C-Cosmos-7B-Diffusers`                 | test        | Model for the GEN3C SSIM test. Deprecated names: `GEN3C_MODEL_PATH`.                                                                                                                                                                                     |
+| `FASTVIDEO_TEST_GEN3C_IMAGE_PATH`                          | str                            | unset                                                 | test        | Input image for the GEN3C SSIM test. Deprecated names: `GEN3C_TEST_IMAGE_PATH`.                                                                                                                                                                          |
+| `FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR`               | str                            | unset                                                 | test        | Local official GLM-Image weights for the GLM-Image SSIM test. Deprecated names: `GLM_IMAGE_LOCAL_WEIGHTS_DIR`.                                                                                                                                           |
+| `FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR`                       | str                            | unset                                                 | test        | Model for the GLM-Image SSIM test. Deprecated names: `GLM_IMAGE_MODEL_DIR`.                                                                                                                                                                              |
+| `FASTVIDEO_TEST_KANDINSKY5_E2E_NUM_GPUS`                   | int                            | `1`                                                   | test        | GPUs for the Kandinsky5 nightly end-to-end overfit test. Deprecated names: `KANDINSKY5_E2E_NUM_GPUS`.                                                                                                                                                    |
+| `FASTVIDEO_TEST_KANDINSKY5_E2E_WRITE_REFERENCE`            | bool                           | `0`                                                   | test        | The Kandinsky5 nightly end-to-end test writes a missing reference video. Deprecated names: `KANDINSKY5_E2E_WRITE_REFERENCE`.                                                                                                                             |
+| `FASTVIDEO_TEST_LONGCAT_MODEL_ROOT`                        | str                            | unset                                                 | test        | Local LongCat-Video checkpoint for the golden-gate test. Deprecated names: `LONGCAT_MODEL_ROOT`.                                                                                                                                                         |
+| `FASTVIDEO_TEST_MINIMAX_H3_GATE_GOLDEN_DIR`                | str                            | unset                                                 | test        | Local directory of MiniMax-H3 golden-gate tensors. Deprecated names: `MINIMAX_H3_GATE_GOLDEN_DIR`.                                                                                                                                                       |
+| `FASTVIDEO_TEST_MINIMAX_H3_GATE_LAYER`                     | int                            | `0`                                                   | test        | Transformer layer that the MiniMax-H3 golden-gate test checks. Deprecated names: `MINIMAX_H3_GATE_LAYER`.                                                                                                                                                |
+| `FASTVIDEO_TEST_MINIMAX_H3_MODEL_ROOT`                     | str                            | unset                                                 | test        | Local MiniMax-H3 checkpoint for the golden-gate test. Deprecated names: `MINIMAX_H3_MODEL_ROOT`.                                                                                                                                                         |
+| `FASTVIDEO_TEST_SD35_MODEL_DIR`                            | str                            | `stabilityai/stable-diffusion-3.5-medium`             | test        | Model for the Stable Diffusion 3.5 SSIM test. Deprecated names: `SD35_MODEL_DIR`.                                                                                                                                                                        |
+| `FASTVIDEO_TEST_TAEH3_REFERENCE_DIR`                       | str                            | unset                                                 | test        | Upstream taehv checkout for the MLX TAEH3 parity test. Deprecated names: `TAEH3_REFERENCE_DIR`.                                                                                                                                                          |
+| `FASTVIDEO_TEST_ZIMAGE_MODEL_DIR`                          | str                            | `Tongyi-MAI/Z-Image-Turbo`                            | test        | Model for the Z-Image SSIM test. Deprecated names: `ZIMAGE_MODEL_DIR`.                                                                                                                                                                                   |
+| `FASTVIDEO_TEST_ZIMAGE_MODEL_REVISION`                     | str                            | `f332072aa78be7aecdf3ee76d5c247082da564a6`            | test        | Hugging Face revision of the Z-Image model for its SSIM test. Deprecated names: `ZIMAGE_MODEL_REVISION`.                                                                                                                                                 |
+
+Variables that FastVideo no longer reads; setting one logs a warning:
+
+| Deprecated variable                       | Reason           |
+| ----------------------------------------- | ---------------- |
+| `FASTVIDEO_TARGET_DEVICE`                 | no code reads it |
+| `FASTVIDEO_USE_PRECOMPILED`               | no code reads it |
+| `FASTVIDEO_RINGBUFFER_WARNING_INTERVAL`   | no code reads it |
+| `FASTVIDEO_ENGINE_ITERATION_TIMEOUT_S`    | no code reads it |
+| `FASTVIDEO_SERVER_DEV_MODE`               | no code reads it |
+| `FASTVIDEO_TEST_DYNAMO_FULLGRAPH_CAPTURE` | no code reads it |
+| `FASTVIDEO_TRACE_FUNCTION`                | no code reads it |
+<!-- END GENERATED ENV TABLE -->
@@ -112,7 +112,7 @@ per-metric policy with direction, percent threshold, absolute threshold, and a

 `test_inference_performance.py` temporarily sets `FASTVIDEO_STAGE_LOGGING=1`
 while it runs so pipeline stage execution times are available in
-`generate_video(...).logging_info`. Stage logs use pipeline-unique keys such as
+`generate(...).logging_info`. Stage logs use pipeline-unique keys such as
 `prompt_encoding_stage` so duplicate stage classes do not collide. For
 `PipelineStage` entries, shared component stage bases emit a stable
 `component_metric`: text encoding stages map to `text_encoder_time_s`,
@@ -5,7 +5,7 @@ hide:

 # Cosmos recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="cosmos" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="cosmos" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # FLUX recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="flux" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="flux" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # GLM-Image recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="glm_image" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="glm_image" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # Hunyuan recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="hunyuan" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="hunyuan" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -41,7 +41,7 @@ hide:
        <span class="cookbook-family-tile__footer">
          <span class="cookbook-family-tile__footer-top">
            <span><strong>MiniMax H3</strong><small>Video + stereo audio</small></span>
-            <span class="cookbook-count">8 recipes</span>
+            <span class="cookbook-count">9 recipes</span>
          </span>
          <ul class="cookbook-mode-row">
            <li>T2VA</li>
@@ -5,7 +5,7 @@ hide:

 # Kandinsky 5 recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="kandinsky5" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="kandinsky5" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # LongCat recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="longcat" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="longcat" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # LTX recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="ltx2" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="ltx2" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # Matrix Game recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="matrixgame" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="matrixgame" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,13 @@ hide:

 # MiniMax H3 recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="minimax_h3" data-default-recipe="fasth3-preview-cuda" data-recipes="../../assets/cookbook-recipes.json?v=8">
+FastH3 is two distilled MiniMax-H3 checkpoints. **V1** is the four-step
+launch. Some Hub repo names still say Preview. That name is historical. V1 is
+a full model, not a demo. **V2** is the eight-step checkpoint. More forwards
+is why V2 is the higher-quality FastH3. The V2 schedule contract is in
+[FastH3 distilled checkpoint schedules](../inference/fasth3-distilled.md).
+
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="minimax_h3" data-default-recipe="fasth3-preview-cuda" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -16,7 +22,7 @@ hide:
        <p class="cookbook-eyebrow">Primary focus · Inference</p>
        <h2>MiniMax H3 recipes</h2>
        <p>Generate video and audio with H3. Run a server on CUDA, one DGX Spark, or Apple Silicon MLX to iterate on prompts, or call the pipeline directly from Python.</p>
-        <span class="cookbook-count" data-cookbook-count>8 maintained recipes</span>
+        <span class="cookbook-count" data-cookbook-count>9 maintained recipes</span>
      </div>
    </div>
    <div class="cookbook-lifecycle" aria-label="Lifecycle stages">
@@ -41,9 +47,9 @@ hide:
    <h2 id="h3-modes-heading">Supported modes</h2>
    <p>
      CUDA covers T2VA, FL2VA, and Ref2VA on the full checkpoint, plus FastH3
-      Preview and FastH3 LoRA. FastH3 Preview also has a DGX Spark runtime with
-      a 1-Spark or 2-Spark device row. MLX is T2VA only. Temporal <code>--fast</code>,
-      spatial <code>--fast-spatial</code>, and opt-in VSA are flags on the same
+      V1 and FastH3 V2. FastH3 V1 also has a DGX Spark runtime with
+      a 1-Spark or 2-Spark device row. MLX is T2VA only: V1 and V2.
+      Temporal <code>--fast</code>, spatial <code>--fast-spatial</code>, and opt-in VSA are flags on the same
      MLX script, not extra recipes.
    </p>
    <div class="cookbook-modes__table-wrap">
@@ -58,8 +64,8 @@ hide:
        <tbody>
          <tr>
            <td>T2VA</td>
-            <td>Full H3, FastH3 Preview, FastH3 LoRA</td>
-            <td>FastH3 Preview after a local DiT conversion</td>
+            <td>Full H3, FastH3 V1, FastH3 LoRA, FastH3 V2</td>
+            <td>FastH3 V1 or FastH3 V2 after a local DiT conversion</td>
          </tr>
          <tr>
            <td>FL2VA</td>
@@ -93,7 +99,7 @@ hide:
          </tr>
          <tr>
            <td>DGX Spark</td>
-            <td>FastH3 Preview on one GB10, or two Sparks with Ray sequence parallel (<code>sp_size=2</code>) over QSFP RoCE. Select NVIDIA DGX Spark, then 1 Spark or 2 Sparks.</td>
+            <td>FastH3 V1 on one GB10, or two Sparks with Ray sequence parallel (<code>sp_size=2</code>) over QSFP RoCE. Select NVIDIA DGX Spark, then 1 Spark or 2 Sparks.</td>
            <td>Not wired</td>
          </tr>
        </tbody>
@@ -251,7 +257,7 @@ cd FastVideo</code></pre>
      <pre><code>UV_TORCH_BACKEND=cu130 uv pip install -e ".[fasth3]"</code></pre>
      <p class="cookbook-eyebrow">Apple Silicon</p>
      <pre><code>uv pip install -e ".[mlx]"</code></pre>
-      <p>Follow the <a href="../../getting_started/installation/mps/#run-fasth3-preview">Apple Silicon guide</a> for the download, conversion, and storage requirements.</p>
+      <p>Follow the <a href="../../getting_started/installation/mlx/">MLX install guide</a> for the extra, <code>ffmpeg</code>, and a clone. Then pick FastH3 V1 or V2 in the builder above.</p>
      <p class="cookbook-eyebrow">NVIDIA DGX Spark</p>
      <pre><code>UV_TORCH_BACKEND=cu130 uv pip install -e .</code></pre>
      <p>Follow the <a href="../../getting_started/installation/spark/">DGX Spark install guide</a> for ARM64 CUDA 13. One Spark is a local process. Two Sparks need Ray on the QSFP link:</p>
@@ -266,10 +272,10 @@ cd FastVideo</code></pre>
      <ul>
        <li>The full CUDA H3 examples request four GPUs by default. Their sources do not claim a GPU model or memory minimum.</li>
        <li>The FastH3 CUDA performance profile was measured on four GB200 GPUs. Use its strict profile when exact operation order matters more than the measured performance configuration.</li>
-        <li>The MLX source runtime supports T2VA, optional temporal <code>--fast</code>, optional spatial <code>--fast-spatial</code>, and opt-in VSA on <code>--include-vsa</code> checkpoints. FL2VA, Ref2VA, and two-pass refinement are not wired.</li>
+        <li>The MLX source runtime supports T2VA, optional temporal <code>--fast</code>, optional spatial <code>--fast-spatial</code>, and opt-in VSA on <code>--include-vsa</code> checkpoints. FastH3 V2 MLX converts with <code>--include-vsa</code> and runs eight forwards. FL2VA, Ref2VA, and two-pass refinement are not wired.</li>
        <li>GPU count and VAE decode backend are configurable in the builder above for FastH3 CUDA recipes. Only the value shown by default has a recorded run; other supported values are unmeasured here.</li>
-        <li>DGX Spark is a runtime on FastH3 Preview, not a separate family card. Select NVIDIA DGX Spark, then 1 Spark or 2 Sparks. The CUDA GPU-count knob does not apply to Spark.</li>
-        <li>GB10 has no FA4 / sm_100a VSA kernel. Keep <code>FASTVIDEO_FA4=0</code> and <code>FASTVIDEO_VSA_SM100A=0</code>. Legal <code>num_frames</code> values are <code>17n+5</code>, capped at 345 (15 s). A 345-frame request on one Spark can OOM.</li>
+        <li>DGX Spark is a runtime on FastH3 V1, not a separate family card. Select NVIDIA DGX Spark, then 1 Spark or 2 Sparks. The CUDA GPU-count knob does not apply to Spark.</li>
+        <li>GB10 has no FA4 / sm_100a VSA kernel. Keep <code>FASTVIDEO_FA4=0</code> and <code>FASTVIDEO_VSA_SM100A=0</code>. Legal <code>num_frames</code> values are <code>17n+5</code>, capped at 362 (15.08 s). A 345-frame request on one Spark can OOM.</li>
        <li>Gated or missing checkpoints: run <code>huggingface-cli login</code> and confirm you accepted the model's license on Hugging Face.</li>
      </ul>
  </div>
@@ -5,7 +5,7 @@ hide:

 # MMAudio recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="mmaudio" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="mmaudio" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -8,8 +8,9 @@ JavaScript client examples use that interface, but requests go to FastVideo.
 You do not need an OpenAI account or cloud key.

 The [H3 recipe selector](minimax-h3.md) provides the same workflow with runtime
-selection. This guide covers FastH3 Preview text-to-video/audio. Other H3
-recipes keep their direct Python commands.
+selection. This guide covers FastH3 V1 (four forwards) and FastH3 V2
+(eight forwards). Start a server, then iterate in the playground or with the
+OpenAI Python client. Other H3 recipes keep their direct Python commands.

 CUDA requests reuse one loaded `VideoGenerator`. The Python SDK can do the same
 when you reuse the generator across `generate()` calls. MLX keeps one
@@ -36,6 +37,14 @@ advertises it as `fasth3`. It configures four CUDA GPUs but does not record
 a GPU model or VRAM requirement. This is a source-backed server profile, not
 the measured GB200 Python performance profile. Compilation is disabled.

+For FastH3 V2, use the same install and the 8-step config (nine sigma
+points, eight DiT forwards, VSA sparsity 0.8):
+
+```bash
+UV_TORCH_BACKEND=cu130 uv pip install -e ".[fasth3]"
+fastvideo serve --config examples/serving/openai_fasth3_8step.yaml --server.host 127.0.0.1
+```
+
 Keep the server running. In another terminal, check readiness:

 ```bash
@@ -58,7 +67,7 @@ FASTVIDEO_VSA_SM100A=0 FASTVIDEO_FA4=0 FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_
 The configuration loads `FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree`
 on one GB10 and advertises it as `fasth3`. Lazy module load still reloads
 Qwen3-VL and the DiT between phases of each request. Legal `num_frames` values
-are `17n+5`, capped at 345 (15 s); a 345-frame request on one Spark can OOM.
+are `17n+5`, capped at 362 (15.08 s); a 345-frame request on one Spark can OOM.
 There is no cookbook server for two Sparks; use the generate YAML after
 [pairing two Sparks](../getting_started/installation/spark_pair.md).

@@ -72,7 +81,7 @@ After model loading completes, the response is `{"status":"ok"}`.

 ### Apple Silicon MLX

-Complete the [Apple Silicon installation](../getting_started/installation/mps.md#run-fasth3-preview),
+Complete the [MLX install](../getting_started/installation/mlx.md),
 including `ffmpeg`. From your FastVideo clone, install the MLX extra:

 ```bash
@@ -111,6 +120,22 @@ The MLX server has no recorded device or unified-memory requirement. The
 direct Python recipe's M4 Max measurements are not a server benchmark or a
 minimum-memory claim.

+For FastH3 V2, convert that checkpoint's transformer with `--include-vsa`
+and start the 8-step config. Do not overwrite a V1 export. The
+[MiniMax H3 cookbook](minimax-h3.md) has the same download and convert
+commands as the Python recipe.
+
+```bash
+hf download FastVideo/FastVideo-FastH3-8-Step-V2 --local-dir ./FastH3-8-Step-V2
+python scripts/checkpoint_conversion/convert_minimax_h3_mlx.py --model-root ./FastH3-8-Step-V2/transformer --out ./FastH3-8-Step-V2-MLX --formats "int8" --include-vsa
+python -m fastvideo.entrypoints.openai.mlx_server --config examples/serving/mlx_fasth3_8step.yaml
+```
+
+That adapter passes `num_steps=8` when the HTTP field is `num_inference_steps=9`,
+and it enables the trained VSA recipe (sparsity 0.8, 64-token tiles). Reuse the
+preview VAE, audio VAE, text encoder, and tokenizer if those directories already
+exist; edit the YAML paths if your files live elsewhere.
+
 ## Open the playground

 Open [the local H3 playground](http://127.0.0.1:8000/playground/) after startup.
@@ -136,9 +161,11 @@ or manage a GPU server for you.
 ## Generate with cURL or an SDK

 These examples use the server's resolution, frame count, and sampling defaults.
-Do not copy Sora-specific durations or resolutions onto H3. Both server configs
-use 124 frames, 24 fps, and the five-point distilled sigma schedule with four
-DiT forwards. CUDA and one Spark use 1344 × 768; MLX uses 832 × 480.
+Do not copy Sora-specific durations or resolutions onto H3. V1 configs use
+124 frames, 24 fps, and the five-point distilled sigma schedule with four DiT
+forwards. V2 configs use nine sigma points and eight DiT forwards. CUDA
+and one Spark use 1344 × 768; MLX uses 832 × 480. The OpenAI Python client is
+the same for every FastH3 server that advertises `fasth3`.

 Each client submits a job, checks for completion or failure, and downloads an
 MP4 named after the job ID. Polling stops after 30 minutes; a timeout does not
@@ -5,7 +5,7 @@ hide:

 # Stable Audio recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="stable_audio" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="stable_audio" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # Stable Diffusion recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="sd35" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="sd35" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # TurboDiffusion recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="turbodiffusion" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="turbodiffusion" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -5,7 +5,7 @@ hide:

 # Wan recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="wan" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="wan" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -186,7 +186,7 @@ cd FastVideo</code></pre>
      <ul>
        <li>Out of memory on the A14B recipes: the checked-in sources already enable CPU offload; see <a href="../../inference/configuration/">Configuration</a> for the offload surface before reducing resolution or frames.</li>
        <li>The FastWan2.1 recipe requires <code>VIDEO_SPARSE_ATTN</code>; confirm the environment variable in the command was set in the same shell.</li>
-        <li>FastMetal MLX: install with <code>uv pip install -e ".[mlx]"</code>, then follow the <a href="../../getting_started/installation/mps/">Apple Silicon guide</a>. CUDA FastWan-QAD checkpoints are refused on the MLX runtime.</li>
+        <li>FastMetal MLX: install with the <a href="../../getting_started/installation/mlx/">MLX install guide</a>, then pick a FastMetal recipe in the builder. CUDA FastWan-QAD checkpoints are refused on the MLX runtime.</li>
        <li>FastMetal 5B uses <code>mlx_wan22_generate.py</code>. 1.3B and 14B use <code>mlx_wan_prompt_to_video.py</code>.</li>
        <li>Gated or missing checkpoints: run <code>huggingface-cli login</code> and confirm you accepted the model's license on Hugging Face.</li>
      </ul>
@@ -5,7 +5,7 @@ hide:

 # Z-Image recipes

-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="zimage" data-recipes="../../assets/cookbook-recipes.json?v=8">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="zimage" data-recipes="../../assets/cookbook-recipes.json?v=11">
  <header class="cookbook-family-header">
    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
    <div class="cookbook-family-header__body">
@@ -2,136 +2,158 @@ status_definitions:
  kept: "Public field remains on a public adapter surface with the same meaning."
  moved: "Public field remains supported but normalizes into a different nested path."
  preset_owned: "Public field remains supported only through a model/preset-specific surface."
-  compatibility_only: "Legacy public field remains adapter-only during migration and is not part of the canonical typed schema."
+  compatibility_only: "Public field that an adapter or an open mapping accepts outside the typed fields of the canonical schema."
  private_only: "Field should only be handled by private adapters and is not a public FastVideo compatibility promise."
-  internal_only: "Field is runtime/config plumbing and should not be part of the new public typed inference API."
+  internal_only: "Field is runtime/config plumbing that model code, config resolution, or the runtime fills; it is not a public input."
+  unsupported: "Typed config field that no runtime code reads; resolution rejects a value."

 surfaces:
-  fastvideo_args:
+  generator_config:
+    kept:
+      - model_path
+      - mode
+      - revision
+      - trust_remote_code
+      - engine.num_gpus
+      - engine.execution_backend
+      - engine.parallelism.tp_size
+      - engine.parallelism.sp_size
+      - engine.parallelism.hsdp_replicate_dim
+      - engine.parallelism.hsdp_shard_dim
+      - engine.parallelism.dist_timeout
+      - engine.offload.dit
+      - engine.offload.dit_layerwise
+      - engine.offload.text_encoder
+      - engine.offload.image_encoder
+      - engine.offload.vae
+      - engine.offload.pin_cpu_memory
+      - engine.compile.enabled
+      - engine.compile.backend
+      - engine.compile.fullgraph
+      - engine.compile.mode
+      - engine.compile.dynamic
+      - engine.compile.extras
+      - engine.enable_stage_verification
+      - engine.use_fsdp_inference
+      - engine.disable_autocast
+      - engine.attention.nvfp4_fa4
+      - pipeline.components.lora_path
+      - pipeline.components.lora_strength
+      - pipeline.output_type
+      - engine.parallelism.master_port
+      - engine.offload.lazy_module_load
+      - engine.compile.text_encoder_enabled
+      - engine.compile.vae_enabled
+      - engine.compile.audio_vae_enabled
+      - engine.compile.regional
+      - engine.compile.dit_kwargs
+      - engine.compile.text_encoder_kwargs
+      - engine.compile.vae_kwargs
+      - engine.compile.audio_vae_kwargs
+      - engine.attention.backend
+      - engine.attention.vsa_sparsity
+      - engine.attention.vsa_tile_size
+      - engine.attention.moba_config_path
+      - engine.precision.dit
+      - engine.precision.vae
+      - engine.precision.vae_decode
+      - engine.precision.image_encoder
+      - engine.precision.text_encoders
+      - engine.quantization.text_encoder_quant
+      - engine.quantization.transformer_quant
+      - pipeline.workload_type
+      - pipeline.components.config_root
+      - pipeline.components.pipeline_config_path
+      - pipeline.components.text_encoder_weights
+      - pipeline.components.transformer_weights
+      - pipeline.components.transformer_2_weights
+      - pipeline.components.upsampler_weights
+      - pipeline.components.lora_nickname
+      - pipeline.components.lora_target_modules
+      - pipeline.components.override_pipeline_cls_name
+      - pipeline.components.override_transformer_cls_name
+      - pipeline.vae_tiling
+      - pipeline.vae_sp
+      - pipeline.flow_shift
+      - pipeline.embedded_cfg_scale
+      - pipeline.dmd_denoising_steps
+      - pipeline.boundary_ratio
+      - pipeline.model.generic.dit
+      - pipeline.model.generic.vae
+      - pipeline.model.ltx2.dit
+      - pipeline.model.ltx2.vae
+      - pipeline.model.minimax_h3.dit
+      - pipeline.model.minimax_h3.vae
+      - pipeline.model.longcat.dit
+      - pipeline.model.longcat.vae
+    unsupported:
+      - pipeline.preset
+      - pipeline.preset_version
+      - pipeline.components.vae_weights
    moved:
-      model_path: generator.model_path
-      workload_type: generator.pipeline.workload_type
-      distributed_executor_backend: generator.engine.execution_backend
-      trust_remote_code: generator.trust_remote_code
-      revision: generator.revision
-      num_gpus: generator.engine.num_gpus
-      tp_size: generator.engine.parallelism.tp_size
-      sp_size: generator.engine.parallelism.sp_size
-      hsdp_replicate_dim: generator.engine.parallelism.hsdp_replicate_dim
-      hsdp_shard_dim: generator.engine.parallelism.hsdp_shard_dim
-      dist_timeout: generator.engine.parallelism.dist_timeout
-      lora_path: generator.pipeline.components.lora_path
-      lora_nickname: generator.pipeline.components.lora_nickname
-      lora_strength: generator.pipeline.components.lora_strength
-      dit_cpu_offload: generator.engine.offload.dit
-      use_fsdp_inference: generator.engine.use_fsdp_inference
-      dit_layerwise_offload: generator.engine.offload.dit_layerwise
-      text_encoder_cpu_offload: generator.engine.offload.text_encoder
-      image_encoder_cpu_offload: generator.engine.offload.image_encoder
-      vae_cpu_offload: generator.engine.offload.vae
-      pin_cpu_memory: generator.engine.offload.pin_cpu_memory
-      lazy_module_load: generator.engine.offload.lazy_module_load
-      enable_torch_compile: generator.engine.compile.enabled
-      enable_torch_compile_text_encoder: generator.engine.compile.text_encoder_enabled
-      enable_torch_compile_vae: generator.engine.compile.vae_enabled
-      enable_torch_compile_audio_vae: generator.engine.compile.audio_vae_enabled
-      torch_compile_kwargs: generator.engine.compile.backend,fullgraph,mode,dynamic,extras
-      torch_compile_kwargs_dit: generator.engine.compile.dit_kwargs
-      torch_compile_kwargs_text_encoder: generator.engine.compile.text_encoder_kwargs
-      torch_compile_kwargs_vae: generator.engine.compile.vae_kwargs
-      torch_compile_kwargs_audio_vae: generator.engine.compile.audio_vae_kwargs
-      transformer_quant: generator.engine.quantization.transformer_quant
-      disable_autocast: generator.engine.disable_autocast
-      enable_stage_verification: generator.engine.enable_stage_verification
-      prompt_txt: request.inputs.prompt_path
-      override_text_encoder_safetensors: generator.pipeline.components.text_encoder_weights
-      override_text_encoder_quant: generator.engine.quantization.text_encoder_quant
-      transformer_quant: generator.engine.quantization.transformer_quant
-      override_transformer_cls_name: generator.pipeline.components.override_transformer_cls_name
-      init_weights_from_safetensors: generator.pipeline.components.transformer_weights
-      init_weights_from_safetensors_2: generator.pipeline.components.transformer_2_weights
-      override_pipeline_cls_name: generator.pipeline.components.override_pipeline_cls_name
-      boundary_ratio: request.sampling.boundary_ratio
-      ltx2_vae_tiling: generator.pipeline.vae_tiling
-      refine_enabled: generator.pipeline.preset_overrides.refine.enabled
-      refine_upsampler_path: generator.pipeline.components.upsampler_weights
-      refine_lora_path: generator.pipeline.components.lora_path
-      refine_num_inference_steps: request.stage_overrides.refine.num_inference_steps
-      refine_guidance_scale: request.stage_overrides.refine.guidance_scale
-      refine_add_noise: generator.pipeline.preset_overrides.refine.add_noise
-      ltx2_refine_enabled: generator.pipeline.preset_overrides.refine.enabled
-      ltx2_refine_upsampler_path: generator.pipeline.components.upsampler_weights
-      ltx2_refine_lora_path: generator.pipeline.components.lora_path
-      ltx2_refine_num_inference_steps: request.stage_overrides.refine.num_inference_steps
-      ltx2_refine_guidance_scale: request.stage_overrides.refine.guidance_scale
-      ltx2_refine_add_noise: generator.pipeline.preset_overrides.refine.add_noise
+      pipeline.preset_overrides:
+        target: generator.pipeline.model.ltx2.refine
+        note: "Only the refine mapping applies: resolution copies pipeline.preset_overrides.refine into the pipeline.model.ltx2.refine fields of the same names when the model is LTX-2. Other keys have no effect."
    preset_owned:
-      ltx2_vae_spatial_tile_size_in_pixels: generator.pipeline.preset_overrides.ltx2.vae.spatial_tile_size_in_pixels
-      ltx2_vae_spatial_tile_overlap_in_pixels: generator.pipeline.preset_overrides.ltx2.vae.spatial_tile_overlap_in_pixels
-      ltx2_vae_temporal_tile_size_in_frames: generator.pipeline.preset_overrides.ltx2.vae.temporal_tile_size_in_frames
-      ltx2_vae_temporal_tile_overlap_in_frames: generator.pipeline.preset_overrides.ltx2.vae.temporal_tile_overlap_in_frames
-      ltx2_initial_latent_path: request.extensions.ltx2.initial_latent_path
-      ltx2_audio_latent_path: request.extensions.ltx2.audio_latent_path
+      - pipeline.model.ltx2.vae_spatial_tile_size_in_pixels
+      - pipeline.model.ltx2.vae_spatial_tile_overlap_in_pixels
+      - pipeline.model.ltx2.vae_temporal_tile_size_in_frames
+      - pipeline.model.ltx2.vae_temporal_tile_overlap_in_frames
+      - pipeline.model.ltx2.initial_latent_path
+      - pipeline.model.ltx2.audio_latent_path
+      - pipeline.model.ltx2.legacy_native_noise_order
+      - pipeline.model.ltx2.use_distilled_sigmas
+      - pipeline.model.ltx2.refine.enabled
+      - pipeline.model.ltx2.refine.num_inference_steps
+      - pipeline.model.ltx2.refine.guidance_scale
+      - pipeline.model.ltx2.refine.add_noise
+      - pipeline.model.ltx2.refine.image_crf
+      - pipeline.model.ltx2.refine.video_position_offset_sec
+      - pipeline.model.ltx2.refine.transformer_path
+      - pipeline.model.ltx2.refine.lora_path
+      - pipeline.model.ltx2.refine.noise_path
+      - pipeline.model.ltx2.refine.audio_noise_path
+      - pipeline.model.minimax_h3.sequential_load
+      - pipeline.model.minimax_h3.video_decode_backend
+      - pipeline.model.minimax_h3.taeh3_checkpoint
+      - pipeline.model.minimax_h3.taeh3_chunk_size
+      - pipeline.model.minimax_h3.vae_parallel_decode
+      - pipeline.model.minimax_h3.vae_parallel_encode
+      - pipeline.model.minimax_h3.vae_parallel_decode_strategy
+      - pipeline.model.longcat.enable_bsa
+      - pipeline.model.longcat.bsa_sparsity
+      - pipeline.model.longcat.bsa_cdf_threshold
+      - pipeline.model.longcat.bsa_chunk_q
+      - pipeline.model.longcat.bsa_chunk_k
    compatibility_only:
-      mode: "Legacy multi-mode FastVideoArgs switch; typed inference config should not expose execution mode."
-      inference_mode: "Legacy boolean mirror of mode; kept only through adapters while FastVideoArgs remains."
-      lora_target_modules: "Legacy LoRA configuration surface pending dedicated component API."
-      output_type: "Legacy output formatting surface pending GenerationResult cleanup."
-      VSA_sparsity: "Model-specific inference optimization not yet represented in the typed public schema."
-      VSA_tile_size: "VSA-H3 tile geometry request; model-specific optimization not yet represented in the typed public schema."
-      inference_torch_compile: "Regional inference compile opt-in currently carried through PipelineSelection.experimental rather than CompileConfig."
-      vae_parallel_decode: "MiniMax-H3 sequence-parallel VAE decode opt-in; model-specific optimization not yet represented in the typed public schema."
-      h3_sequential_load: "MiniMax-H3 sequential text-encoder then DiT/VAE load; model-specific optimization not yet represented in the typed public schema."
-      video_decode_backend: "MiniMax-H3 video decoder selection (full VAE vs TAEH3 preview); model-specific optimization not yet represented in the typed public schema."
-      taeh3_checkpoint: "Optional local TAEH3 safetensors path; model-specific optimization not yet represented in the typed public schema."
-      taeh3_chunk_size: "TAEH3 temporal chunk length; model-specific optimization not yet represented in the typed public schema."
-      vae_parallel_encode: "MiniMax-H3 sequence-parallel reference VAE encode opt-in; model-specific optimization not yet represented in the typed public schema."
-      vae_parallel_decode_strategy: "Chunk-transport collective for vae_parallel_decode; model-specific optimization not yet represented in the typed public schema."
-      attention_backend: "Process-wide default attention-backend request applied per component at load time; kernel-selection knob not yet represented in the typed public schema."
-      moba_config_path: "Model-specific MoBA optimization surface not yet represented in the typed public schema."
-      master_port: "Executor/bootstrap compatibility field; not part of the canonical inference schema."
-      refine_transformer_path: "Generic stage-2 refine transformer override; no typed equivalent yet."
-      refine_noise_path: "Generic stage-2 refine noise override; no typed equivalent yet."
-      refine_audio_noise_path: "Generic stage-2 refine audio noise override; no typed equivalent yet."
-      ltx2_refine_transformer_path: "LTX-2 refine transformer carrier; no typed equivalent yet."
-      ltx2_refine_noise_path: "LTX-2 refine noise carrier; no typed equivalent yet."
-      ltx2_refine_audio_noise_path: "LTX-2 refine audio noise carrier; no typed equivalent yet."
-      ltx2_legacy_native_noise_order: "LTX-2 SSIM compatibility knob preserving legacy native latent noise ordering."
-      ltx2_use_distilled_sigmas: "LTX-2 compatibility knob gating use of distilled sigma schedule."
-    private_only:
-      ray_placement_group: "Ray deployment-only field."
-      ray_runtime_env: "Ray deployment-only field."
+      pipeline.experimental: "Open mapping for settings without a typed path: the pipeline_config source (a JSON path, a mapping, or a PipelineConfig), keys that runtime code reads by name (for example ray_runtime_env), and PipelineConfig attribute overrides for model-only fields (for example flow_shift_sr). Resolution rejects a key whose PipelineConfig attribute has a typed path."
    internal_only:
-      pipeline_config: "Legacy internal carrier object."
-      preprocess_config: "Legacy preprocess carrier object."
-      moba_config: "Derived runtime config loaded from moba_config_path."
-      model_paths: "Runtime bookkeeping."
-      model_loaded: "Runtime bookkeeping."
+      engine.attention.moba_config: "V-MoBA attention settings that resolution loads from engine.attention.moba_config_path."

  pipeline_config_base:
    moved:
+      model_path: generator.model_path
      pipeline_config_path: generator.pipeline.components.pipeline_config_path
+      embedded_cfg_scale: generator.pipeline.embedded_cfg_scale
+      flow_shift: generator.pipeline.flow_shift
+      disable_autocast: generator.engine.disable_autocast
+      vae_tiling: generator.pipeline.vae_tiling
+      vae_sp: generator.pipeline.vae_sp
+      dmd_denoising_steps: generator.pipeline.dmd_denoising_steps
+      boundary_ratio: generator.pipeline.boundary_ratio
+      dit_precision: generator.engine.precision.dit
+      vae_precision: generator.engine.precision.vae
+      vae_decode_precision: generator.engine.precision.vae_decode
+      image_encoder_precision: generator.engine.precision.image_encoder
+      text_encoder_precisions: generator.engine.precision.text_encoders
    preset_owned:
-      embedded_cfg_scale: generator.pipeline.preset_overrides.embedded_cfg_scale
-      flow_shift: generator.pipeline.preset_overrides.flow_shift
-      flow_shift_sr: generator.pipeline.preset_overrides.flow_shift_sr
-      is_causal: generator.pipeline.preset_overrides.is_causal
-      vae_tiling: generator.pipeline.preset_overrides.vae_tiling
-      vae_sp: generator.pipeline.preset_overrides.vae_sp
-      dmd_denoising_steps: generator.pipeline.preset_overrides.dmd_denoising_steps
-      ti2v_task: generator.pipeline.preset_overrides.ti2v_task
-      lucy_edit_task: generator.pipeline.preset_overrides.lucy_edit_task
-      boundary_ratio: generator.pipeline.preset_overrides.boundary_ratio
+      flow_shift_sr: generator.pipeline.experimental.flow_shift_sr
+      is_causal: generator.pipeline.experimental.is_causal
+      ti2v_task: generator.pipeline.experimental.ti2v_task
+      lucy_edit_task: generator.pipeline.experimental.lucy_edit_task
    compatibility_only:
-      model_path: "Redundant with generator.model_path."
-      disable_autocast: "Duplicated by generator.engine.disable_autocast during migration."
-      dit_precision: "Precision override pending dedicated typed component precision design."
      upsampler_precision: "Precision override pending dedicated typed component precision design."
-      vae_precision: "Precision override pending dedicated typed component precision design."
-      vae_decode_precision: "Decode-only precision override pending dedicated typed component precision design."
-      image_encoder_precision: "Precision override pending dedicated typed component precision design."
      image_encoder_precisions: "Precision overrides pending dedicated typed component precision design."
-      text_encoder_precisions: "Precision override pending dedicated typed component precision design."
    internal_only:
      dit_config: "Legacy internal component config object."
      upsampler_config: "Legacy internal component config object."
@@ -144,6 +166,22 @@ surfaces:
      scheduler_step_in_fp32: "Runtime scheduler precision toggle; not part of the public typed inference API."

  pipeline_config_extensions:
+    moved:
+      enable_bsa:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+        target: generator.pipeline.model.longcat.enable_bsa
+      bsa_sparsity:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+        target: generator.pipeline.model.longcat.bsa_sparsity
+      bsa_cdf_threshold:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+        target: generator.pipeline.model.longcat.bsa_cdf_threshold
+      bsa_chunk_q:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+        target: generator.pipeline.model.longcat.bsa_chunk_q
+      bsa_chunk_k:
+        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
+        target: generator.pipeline.model.longcat.bsa_chunk_k
    preset_owned:
      flux2_text_encoder_type:
        sources:
@@ -324,18 +362,8 @@ surfaces:
          - fastvideo.configs.pipelines.wan.WanI2V720PConfig
          - fastvideo.configs.pipelines.wan.WanT2V480PConfig
          - fastvideo.configs.pipelines.wan.WanT2V720PConfig
-      bsa_cdf_threshold:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_chunk_k:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_chunk_q:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
      bsa_params:
        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_sparsity:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      enable_bsa:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
      enable_kv_cache:
        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
      enhance_hf:
@@ -520,6 +548,7 @@ surfaces:
      cfg_truncation: request.sampling.cfg_truncation
      guidance_rescale: request.sampling.guidance_rescale
      use_embedded_guidance: request.sampling.use_embedded_guidance
+      embedded_cfg_scale: request.sampling.embedded_cfg_scale
      true_cfg_scale: request.sampling.true_cfg_scale
      boundary_ratio: request.sampling.boundary_ratio
      sigmas: request.sampling.sigmas
@@ -28,19 +28,15 @@ Minimal usage (from `examples/inference/basic/basic.py`):

 ```python
 from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam

 model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # or official_weights/<model_name>/
-generator = VideoGenerator.from_pretrained(model_id, num_gpus=1)
+generator = VideoGenerator.from_pretrained(model_id, {"engine": {"num_gpus": 1}})

-sampling = SamplingParam.from_pretrained(model_id)
-sampling.num_frames = 45
-video = generator.generate_video(
-    "A vibrant city street at sunset.",
-    sampling_param=sampling,
-    output_path="video_samples",
-    save_video=True,
-)
+video = generator.generate({
+    "prompt": "A vibrant city street at sunset.",
+    "sampling": {"num_frames": 45},
+    "output": {"output_path": "video_samples", "save_video": True},
+})
 ```

 ## Configuration system
@@ -74,8 +70,11 @@ not override checkpoint manifests, user pipeline overrides, or component
 precision settings. HF IDs, local checkpoints, and old config imports retain
 their existing resolution behavior, including first-match detector ordering.

-`FastVideoArgs` (in `fastvideo/fastvideo_args.py`) provides runtime settings and
-is passed into pipeline construction and stages.
+`ResolvedGeneratorConfig` (in `fastvideo/api/resolution.py`) provides runtime
+settings and is passed into pipeline construction and stages as `resolved_config`.
+`resolve_inference_config` (in `fastvideo/api/inference_resolution.py`) builds it
+from the typed config in `fastvideo/api/schema.py` and attaches the model's
+frozen `PipelineConfig` as `resolved_config.pipeline_config`.

 ## Weights and Diffusers format

@@ -32,7 +32,7 @@ from fastvideo.api import (

 | Surface | Availability | Notes |
 | --- | --- | --- |
-| `VideoGenerator.from_pretrained(model_path, **typed_kwargs)` | Today | `typed_kwargs` is a stable subset from `GeneratorConfig` — no flat legacy LTX-2 kwargs (guaranteed after PR 6) |
+| `VideoGenerator.from_pretrained(model_path, config)` | Today | `config` is a nested `GeneratorConfig` mapping without `model_path`; no flat keywords |
 | `VideoGenerator.generate(request: GenerationRequest) -> GenerationResult` | Today | Aggregated; Dynamo wraps in `asyncio.to_thread` under `asyncio.Lock` |
 | `VideoGenerator.generate_async(request) -> AsyncGenerator[VideoEvent, None]` | **PR 7.10** | Canonical execution substrate; sync wrapper reroutes through this |
 | `VideoGenerator.default_health_check_request() -> GenerationRequest` | **PR 7.10** | 256x256 / 8 frames / 1 step; lets Dynamo build its health payload without knowing any FastVideo internals |
@@ -225,7 +225,7 @@ async def init_video_generation(runtime, config, shutdown_endpoints):
    from fastvideo.api import config_to_dict

    server_args, dynamo_args = config.server_args, config.dynamo_args
-    generator = VideoGenerator.from_pretrained(**config.fastvideo_kwargs())
+    generator = VideoGenerator.from_config(build_generator_config(server_args))

    dump_config(dynamo_args.dump_config_to, config)

@@ -262,7 +262,8 @@ this adapter can build the config purely from the public typed schema:
 def build_generator_config(args) -> "GeneratorConfig":
    from fastvideo.api import (
        CompileConfig, ComponentConfig, EngineConfig, GeneratorConfig,
-        OffloadConfig, ParallelismConfig, PipelineSelection,
+        LTX2Options, LTX2RefineOptions, OffloadConfig, ParallelismConfig,
+        PipelineSelection,
    )
    return GeneratorConfig(
        model_path=args.model_path,
@@ -275,10 +276,8 @@ def build_generator_config(args) -> "GeneratorConfig":
        pipeline=PipelineSelection(
            workload_type=args.workload or "t2v",
            preset=args.preset,  # e.g. "ltx2_two_stage"
-            components=ComponentConfig(
-                upsampler_weights=args.refine_upsampler,
-                lora_path=args.refine_lora,
-            ),
+            components=ComponentConfig(upsampler_weights=args.refine_upsampler),
+            model=LTX2Options(refine=LTX2RefineOptions(lora_path=args.refine_lora)),
        ),
    )
 ```
@@ -310,8 +309,11 @@ re-chase FastVideo drift:
 2. `ContinuationState.payload` is JSON-serializable or references
   opaque blob ids. Dynamo can round-trip it through RPC without
   special-casing torch tensors.
-3. `VideoGenerator.from_pretrained` accepts a typed `GeneratorConfig`;
-   legacy flat kwargs are compatibility-only and deprecate in PR 13.
+3. `VideoGenerator.from_pretrained(model_path, config)` takes a typed
+   `GeneratorConfig` or its nested mapping; any flat keyword raises
+   `TypeError` that points to the nested config.
+   `VideoGenerator.from_config(...)` takes the same settings with
+   `model_path` inside.
 4. `generate_async` (PR 7.10+) emits events in order
   `Progress* → Partial* → Final`; the final event always has exactly
   one occurrence per request.
@@ -329,7 +331,6 @@ at FastVideo's CI — before the Dynamo-side integration even knows.
 * Anything under `fastvideo.pipelines.*` directly (pipelines are
  internal; presets identify them by name on
  `PipelineSelection.preset`).
-* `fastvideo.fastvideo_args.FastVideoArgs` (legacy compat type).
 * `fastvideo.api.compat.*` private helpers
  (`_validate_continuation_state` etc.) — the public boundary is
  `VideoGenerator` + `fastvideo.api`.
@@ -25,11 +25,13 @@ requests. HTTP handling and job polling remain asynchronous.
 | `GET` | `/v1/videos/{id}/content` | Download a completed MP4 |
 | `DELETE` | `/v1/videos/{id}` | Delete a job and its completed artifact |
 | `POST` | `/v1/images` | Generate an image |
+| `POST` | `/v1/images/generations` | OpenAI-compatible alias for image generation |
 | `POST` | `/v1/images/edits` | Generate an image from image references |
 | `GET` | `/v1/images/{id}/content` | Download a generated image |
 | `GET` | `/health` | Liveness probe |

-`POST /v1/videos/generations` remains an alias for older FastVideo clients.
+`POST /v1/videos/generations` remains an alias for older FastVideo clients, and
+`POST /v1/images/generations` is the OpenAI Python client's image generation path.
 The OpenAI Python and JavaScript clients can create, retrieve, list, download,
 and delete video jobs. Use the [H3 server cookbook](../../cookbook/openai-api.md)
 for pinned client versions and executable examples. Download variants other
@@ -7,7 +7,7 @@ FastVideo supports the following hardware platforms:
 - **NVIDIA DGX Spark / GB10 (ARM64 + CUDA 13)** — [install](installation/spark.md),
  [performance](installation/spark_performance.md),
  [pair two Sparks](installation/spark_pair.md)
- [Apple silicon](installation/mps.md)
+- [Apple silicon (MLX)](installation/mlx.md)

 ## Quick Installation

@@ -15,7 +15,7 @@ FastVideo supports the following hardware platforms:

 Use uv as the default environment manager for faster and more stable installs.
 The commands below target NVIDIA CUDA 12; use `UV_TORCH_BACKEND=cu130` on
-CUDA 13. Apple silicon users should follow the [MPS guide](installation/mps.md).
+CUDA 13. Apple silicon users should follow the [MLX install guide](installation/mlx.md).

 ```bash
 # Create and activate a new uv environment
@@ -0,0 +1,65 @@
+# Install FastVideo with MLX
+
+Install FastVideo on a Mac, then generate from the cookbook. Local video uses
+the native MLX runtime, not CUDA, and not the old PyTorch MPS demo at
+`examples/inference/basic/basic_mps.py`.
+
+## Requirements
+
+- macOS 14 or newer
+- Python 3.12
+- `ffmpeg` (`brew install ffmpeg`)
+
+## Install
+
+Cookbook commands run from a clone.
+
+```bash
+git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
+uv venv --python 3.12 --seed
+source .venv/bin/activate
+brew install ffmpeg
+uv pip install -e ".[mlx]"
+```
+
+Conda is optional. After you activate a Conda env, still install with
+`uv pip` as above.
+
+`uv pip install "fastvideo[mlx]"` from PyPI installs the extra only. It does
+not ship the example scripts the cookbook copies.
+
+## Generate a video
+
+Open the cookbook. Select Apple Silicon as the runtime. Each recipe has a
+Python command. FastH3 also has a server path for the playground and the
+OpenAI Python client.
+
+- [Wan recipes](../../cookbook/wan.md) for FastMetal 1.3B, 5B, and 14B
+- [MiniMax H3 recipes](../../cookbook/minimax-h3.md) for FastH3 V1 and FastH3 V2
+- [H3 server guide](../../cookbook/openai-api.md) for the playground, cURL, and SDKs
+
+FastH3 is two distilled MiniMax-H3 checkpoints. V1 is the four-step launch.
+Some Hub repo names still say Preview. That name is historical. V1 is a full
+model, not a demo. V2 is the eight-step checkpoint. More forwards is why V2
+is the higher-quality FastH3.
+
+Recorded shapes and evidence live in the
+[support matrix](../../inference/support_matrix.md#apple-silicon-native-runtime).
+
+## Hardware
+
+- FastMetal 1.3B and 5B: 16 GB unified memory and up
+- FastMetal 14B: 36 GB unified memory and up
+- FastH3 V1 and V2: validated on an M4 Max with 36 GB unified memory
+
+## Troubleshooting
+
+- **`basic_mps.py` is the wrong path.** That script is PyTorch MPS. Use an
+  Apple Silicon recipe in the cookbook.
+- **Muxing fails.** Install `ffmpeg` with Homebrew.
+- **A cookbook command cannot find a script.** Run it from the FastVideo
+  clone after `uv pip install -e ".[mlx]"`.
+
+If that does not match what you see, open an issue on the
+[GitHub repository](https://github.com/hao-ai-lab/FastVideo) or ask in the
+[Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ).
@@ -1,260 +0,0 @@
-# MPS (Apple Silicon)
-
-Install FastVideo on Apple Silicon and run FastMetal-QAD or FastH3 Preview.
-
-Apple Silicon uses the MLX runtime. FastMetal-QAD ships ready-to-run MLX
-checkpoints; FastH3 Preview currently requires a local MLX DiT conversion.
-See the [FastMetal-QAD blog](https://haoailab.com/blogs/fastmetal/) and the
-[FastMetal collection](https://huggingface.co/collections/FastVideo/fastmetal).
-
-## Requirements
-
- **OS: macOS 14 or newer**
- **Python: 3.12.4**
-
-## Set up using Python
-
-### Create a new Python environment
-
-#### 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
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh
-bash Miniconda3-latest-MacOSX-arm64.sh
-source ~/.zshrc
-```
-
-##### 2. Create and activate a Conda environment for FastVideo
-
-```bash
-conda create -n fastvideo python=3.12.4 -y
-conda activate fastvideo
-```
-
-### Dependencies
-
-```
-brew install ffmpeg
-```
-
-### Installation
-
-FastMetal's native Apple Silicon runtime requires the `mlx` extra.
-
-#### With uv (recommended)
-
-```bash
-uv pip install "fastvideo[mlx]"
-```
-
-#### With Conda environment (alternative)
-
-`uv` works inside an active conda env too, so prefer `uv pip` for the actual install:
-
-```bash
-uv pip install "fastvideo[mlx]"
-```
-
-### Installation from Source
-
-#### 1. Clone the FastVideo repository
-
-```bash
-git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
-```
-
-#### 2. Install FastVideo
-
-Basic installation:
-
-```bash
-uv pip install -e ".[mlx]"
-```
-
-Alternative with Conda environment:
-
-```bash
-uv pip install -e ".[mlx]"
-```
-
-## Run FastMetal-QAD
-
-Each release is self-contained. Download one checkpoint and point both
-`--model-root` and `--mlx-checkpoint` at it (the example also auto-detects
-`mlx_dit.json` under `--model-root`).
-
-| Checkpoint | Script | Mac tier |
-| --- | --- | --- |
-| [`FastVideo/FastMetal-1.3B-QAD`](https://huggingface.co/FastVideo/FastMetal-1.3B-QAD) | `mlx_wan_prompt_to_video.py` | 16 GB+ |
-| [`FastVideo/FastMetal-5B-QAD`](https://huggingface.co/FastVideo/FastMetal-5B-QAD) | `mlx_wan22_generate.py` | 16 GB+ |
-| [`FastVideo/FastMetal-14B-QAD`](https://huggingface.co/FastVideo/FastMetal-14B-QAD) | `mlx_wan_prompt_to_video.py` | 36 GB+ |
-
-```bash
-hf download FastVideo/FastMetal-1.3B-QAD --local-dir ./FastMetal-1.3B-QAD
-
-python examples/inference/basic/mlx_wan_prompt_to_video.py \
-  --model-root ./FastMetal-1.3B-QAD \
-  --mlx-checkpoint ./FastMetal-1.3B-QAD \
-  --height 480 --width 832 --num-frames 81 \
-  --prompt "A bird's-eye view of a misty forest valley at dawn."
-```
-
-14B uses the same script. Point both flags at `./FastMetal-14B-QAD`. That repo also ships an EMA variant: keep `--model-root` at the repo root and set `--mlx-checkpoint ./FastMetal-14B-QAD/ema`.
-
-Wan2.2 5B uses a different latent layout, so it has its own entrypoint:
-
-```bash
-hf download FastVideo/FastMetal-5B-QAD --local-dir ./FastMetal-5B-QAD
-
-python examples/inference/basic/mlx_wan22_generate.py \
-  --mlx-checkpoint ./FastMetal-5B-QAD \
-  --text-encoder-root ./FastMetal-5B-QAD \
-  --vae-root ./FastMetal-5B-QAD/vae \
-  --height 704 --width 1280 --num-frames 81 \
-  --prompt "A cinematic portrait with soft neon lighting and smooth camera motion."
-```
-
-CUDA FastWan-QAD (`FastVideo/FastWan-QAD-1.3B`, `FastVideo/FastWan-QAD-FP8-1.3B`) is a separate NVIDIA release. The MLX examples look for FastMetal packed weights (`mlx_dit.json`).
-
-`basic_mps.py` is a generic PyTorch MPS demo. For local video on Mac, use the FastMetal commands above.
-
-## Run FastH3 Preview
-
-FastH3 Preview uses the existing MLX runtime for text-to-video-with-audio
-(T2VA). The runtime streams the Qwen3-VL text conditioner, loads one
-heavyweight component at a time, denoises synchronized video and audio
-latents with a converted INT8, INT6, or INT4 DiT, and decodes both modalities
-with native MLX VAEs.
-
-Download the FastH3 snapshot, then convert one or more DiT formats:
-
-```bash
-hf download FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2 \
-  --local-dir ./FastH3-Preview-v0.2
-
-python scripts/checkpoint_conversion/convert_minimax_h3_mlx.py \
-  --model-root ./FastH3-Preview-v0.2/transformer \
-  --out ./FastH3-MLX \
-  --formats "int6"
-```
-
-Dense conversion drops the trained VSA gate projections. To keep them (INT6
-weight-only, same affine grid as the other linear matrices) write a **new**
-directory:
-
-```bash
-python scripts/checkpoint_conversion/convert_minimax_h3_mlx.py \
-  --model-root ./FastH3-Preview-v0.2/transformer \
-  --out ./FastH3-MLX-vsa \
-  --formats "int6" \
-  --include-vsa
-```
-
-Do not overwrite an existing dense export such as `./FastH3-MLX/int6`.
-
-Run the baseline path:
-
-```bash
-python examples/inference/basic/mlx_fasth3.py \
-  --model-root ./FastH3-Preview-v0.2 \
-  --mlx-checkpoint ./FastH3-MLX/int6 \
-  --prompt "(S1) A presenter says <d>[English] Fast H3 is amazing.</d>" \
-  --height 480 --width 832 --num-frames 124 --seed 2026 \
-  --output-path ./outputs/fasth3_int6.mp4
-```
-
-Add `--fast` for temporal fast mode. It denoises a shorter video sequence,
-uses MLX RIFE to restore the requested frame count, and keeps the audio
-sequence at full duration:
-
-```bash
-python examples/inference/basic/mlx_fasth3.py \
-  --model-root ./FastH3-Preview-v0.2 \
-  --mlx-checkpoint ./FastH3-MLX/int6 \
-  --prompt "(S1) A presenter says <d>[English] Fast H3 is even faster.</d>" \
-  --height 720 --width 1280 --num-frames 124 --seed 2027 \
-  --fast \
-  --output-path ./outputs/fasth3_int6_fast_720p.mp4
-```
-
-Add `--fast-spatial` for spatial fast mode, `--fast`'s spatial twin. It
-denoises and decodes on the smallest 32px-aligned canvas covering the
-requested size divided by `--fast-spatial-scale` (a 480x832 request runs on a
-256x416 canvas), then resamples the decoded frames up to the requested size
-in pixel space. It composes with `--fast`. This is a speed/quality trade-off
-and stays off by default: the output carries the reduced canvas's detail
-budget, so it reads softer than a native-resolution render, with the unsharp
-pass countering some but not all of the difference:
-
-```bash
-python examples/inference/basic/mlx_fasth3.py \
-  --model-root ./FastH3-Preview-v0.2 \
-  --mlx-checkpoint ./FastH3-MLX/int6 \
-  --prompt "(S1) A presenter says <d>[English] Fast H3 is fastest.</d>" \
-  --height 480 --width 832 --num-frames 124 --seed 2028 \
-  --fast --fast-spatial \
-  --output-path ./outputs/fasth3_int6_fast_spatial.mp4
-```
-
-VSA is off by default. A dense-only checkpoint (no `--include-vsa`) keeps the
-existing fused-SDPA path. After converting with `--include-vsa`, enable the
-sparse path explicitly:
-
-```bash
-python examples/inference/basic/mlx_fasth3.py \
-  --model-root ./FastH3-Preview-v0.2 \
-  --mlx-checkpoint ./FastH3-MLX-vsa/int6 \
-  --vsa --vsa-sparsity 0.9 --vsa-tile-size 64 --vsa-prefix-mode exempt \
-  --prompt "(S1) A presenter says <d>[English] Fast H3 is amazing.</d>" \
-  --height 720 --width 1280 --num-frames 124 --seed 2026 \
-  --output-path ./outputs/fasth3_int6_vsa_720p.mp4
-```
-
-`--vsa-impl auto` uses the chunked gather+SDPA **reference** path.
-`--vsa-impl simd` is an opt-in SIMD-group kernel (tile 64, head dim 128) that
-falls back to reference on unsupported shapes. It is not the default.
-`--vsa-impl reference` is the same as `auto`.
-
-!!! note "Current MLX scope"
-    This source runtime supports T2VA, temporal `--fast`, spatial
-    `--fast-spatial`, and opt-in VSA. FL2VA, Ref2VA, two-pass refinement, and
-    `VideoGenerator` registry dispatch are not wired yet. INT8/INT6/INT4
-    quantization is **weight-only**; VSA attention Q/K/V stay BF16. Old dense
-    MLX checkpoints remain valid for dense inference and raise a reconvert
-    error if `--vsa` is set. The checkpoint uses the MiniMax H3
-    Community License; review the model card before use or redistribution.
-
-## Development Environment Setup
-
-If you're planning to contribute to FastVideo please see the following page:
-[Contributor Guide](../../contributing/overview.md)
-
-## Hardware Requirements
-
- **1.3B / 5B:** 16 GB unified memory and up (M1 and later)
- **14B:** 36 GB unified memory and up
- **FastH3 Preview:** validated on an M4 Max with 36 GB unified memory; use one
-  converted DiT format at a time and leave substantial free disk space for the
-  source snapshot plus the converted checkpoint
- Fanless 13-inch MacBook Air can run 1.3B and 5B at the same resolutions
-
-## Troubleshooting
-
-If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).
-
-You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.
@@ -147,7 +147,7 @@ that can't move here.
 Two Sparks with QSFP cables: [Pair two NVIDIA DGX Sparks](spark_pair.md) for
 one FastH3 clip across both GPUs (`sp_size=2` over Ray). Copy-paste commands
 for one or two Sparks also live on the
-[MiniMax H3 cookbook](../../cookbook/minimax-h3.md): pick FastH3 Preview,
+[MiniMax H3 cookbook](../../cookbook/minimax-h3.md): pick FastH3 V1,
 then NVIDIA DGX Spark, then 1 Spark or 2 Sparks.

 ## Development Environment Setup
@@ -7,7 +7,7 @@ by the QSFP ConnectX-7 cables can run **one clip faster** and can hold a
 **longer clip** (up to the FastH3 15 s cap).

 Copy-paste commands for both counts are on the
-[MiniMax H3 cookbook](../../cookbook/minimax-h3.md): FastH3 Preview → NVIDIA DGX
+[MiniMax H3 cookbook](../../cookbook/minimax-h3.md): FastH3 V1 → NVIDIA DGX
 Spark → 1 Spark or 2 Sparks.

 This is FastVideo sequence parallel (`sp_size=2`) over Ray, not a third-party
@@ -17,7 +17,7 @@ xDiT vendor. Do not install xDiT for this path.

 | Goal | How | Use two Sparks? |
 |---|---|---|
-| Two independent videos at once | One process per box, `num_gpus=1` | Throughput only. Each clip still takes the 1-GPU time for that size. |
+| Two independent videos at once | One process per box, `engine.num_gpus: 1` | Throughput only. Each clip still takes the 1-GPU time for that size. |
 | One clip, faster | Ray + `sp_size=2` + parallel VAE | **Yes.** One 768×1344×124 recipe was 292 s vs 374 s on one GB10. |
 | One clip, longer | Same, more frames | **Yes.** 345 frames (~14.4 s at 24 fps) finished in 587 s at 768×1344. |

@@ -35,7 +35,7 @@ over ~21 GB/s RoCE.
  QSFP; do not download 100+ GB twice over Wi-Fi.
 - Ray in the FastVideo venv (`uv pip install ray` if it is not already there).

-Each Spark has **one** GPU. `num_gpus=2` therefore means two nodes, which is
+Each Spark has **one** GPU. `engine.num_gpus: 2` therefore means two nodes, which is
 why the executor must be Ray (`mp` only works inside one process tree).

 ## 1. Put IPv4 on the QSFP NICs
@@ -109,7 +109,7 @@ Run the driver on the **head**, same venv, same QSFP IP.
 `basic_fasth3.py` defaults target a four-GPU GB200 profile: 768×1344, `sm100a`
 VSA, FA4, four GPUs. On Sparks you must override the kernel flags. Height,
 width, frames, steps, seed, and prompt are yours. Change them. Legal
-`num_frames` values are `17n+5`, capped at 345.
+`num_frames` values are `17n+5`, capped at 362.

 GB10 has no FA4 / sm_100a VSA kernel, so `--vsa-kernel triton --no-fa4` stays
 required on this box. `--execution-backend ray` is optional when `RAY_ADDRESS`
@@ -152,8 +152,8 @@ Stop the cluster when you are done: `ray stop` on both nodes.
 ## FastH3 frame counts

 H3 is 24 fps. Legal `num_frames` values are `17n+5`. The pipeline rejects
-clips longer than **15 s**. The longest legal length is **345 frames**
-(14.375 s). 360 frames aligns to 362 and fails the duration check.
+clips longer than **15 s**. The longest legal length is **362 frames**
+(15.083 s). 360 frames aligns to 362 and is accepted.

 ## Measured on two GB10s (2026-08-31)

@@ -179,7 +179,7 @@ VAE, same 4-step schedule:
 | Two Sparks, SP=2 | 2 | 124 | **215.2 s** | 72.4 s |

 Those medians used `--height` / `--width` / `--num-frames` as CLI flags. Swap
-them. Native 480p on this model is 480×832, 124 frames. The 15 s cap is 345
+them. Native 480p on this model is 480×832, 124 frames. The 15 s cap is 362
 frames.

 The first VAE decode still pays `torch.compile`. Later `generate()` calls in
@@ -197,8 +197,8 @@ sm_100a VSA kernel is not on this chip, so denoise is slower than a GB200
 | NCCL hangs or uses Wi-Fi | `source spark_pair_env.sh`. Confirm `NCCL_SOCKET_IFNAME` is the QSFP NIC. |
 | Gloo `connectFullMesh` / `remote=[127.0.0.1]` | Two 1-GPU nodes must not use loopback as the Gloo store. Source `spark_pair_env.sh` so `GLOO_SOCKET_IFNAME` is the QSFP NIC on **each** box. FastVideo no longer copies that NIC name from the driver onto workers. |
 | Second `generate()` crashes `NoneType.parameters` | Sequential load used to drop the text encoder without reloading it. This branch reloads Qwen for later requests so `--warmup --repeats N` works. |
-| OOM / `earlyoom` prefers Python | Lazy module load must stay on (do not pass `--no-lazy-module-load`). Peak GPU during 345-frame denoise is ~90 GiB/node. |
-| `num_gpus=2` on one Spark | Each Spark has one GPU. Use Ray across two nodes, or `num_gpus=1` on one box. |
+| OOM / `earlyoom` prefers Python | Lazy module load must stay on (do not pass `--no-lazy-module-load` to `basic_fasth3.py` or set `engine.offload.lazy_module_load: false`). Peak GPU during 345-frame denoise is ~90 GiB/node. |
+| `engine.num_gpus: 2` on one Spark | Each Spark has one GPU. Use Ray across two nodes, or `engine.num_gpus: 1` on one box. |

 ## What we are not claiming

@@ -62,10 +62,12 @@ nothing to set. If you run a model that still defaults to an fp32 decode, set th
 decode-only override yourself:

 ```python
-from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo import VideoGenerator

-pipeline_config = PipelineConfig.from_pretrained(model_id)
-pipeline_config.vae_decode_precision = "bf16"   # decode-only; leaves encode precision alone
+generator = VideoGenerator.from_config({
+    "model_path": model_id,
+    "engine": {"precision": {"vae_decode": "bf16"}},  # decode-only; leaves encode precision alone
+})
 ```

 Decode is output-only, so lowering its precision is safe. (Encode seeds the
@@ -166,8 +168,9 @@ is power-cycled. To avoid it:
  encoder is still resident, the process is a typical `earlyoom` kill (Python is
  preferred). On unified memory, `lazy_module_load` auto-enables and owns that
  split (encoder, then DiT, then VAE; DiT can drop before decode). Sequential
-  load is the H3-only fallback when lazy is off; do not pass
-  `--no-lazy-module-load` here. Geometry scalars come from checkpoint
+  load is the H3-only fallback when lazy is off; do not set
+  `engine.offload.lazy_module_load` to false here (`--no-lazy-module-load` in
+  `basic_fasth3.py` and `basic_minimax_h3_t2v.py`). Geometry scalars come from checkpoint
  `config.json`, not live weights. See [Offloading](../../inference/offloading.md).
 - **FastH3 TAEH3** (`--video-decode-backend taeh3`) is an opt-in preview decoder.
  T2VA never materializes the 9.7 GiB video VAE (DiT still loads after Qwen via
@@ -181,6 +184,9 @@ is power-cycled. To avoid it:
  and frame counts are valid. Weights stay replicated, so lazy module load
  (auto on GB10) is still required on each box. Bring-up and knobs:
  [Pair two NVIDIA DGX Sparks](spark_pair.md).
+- A worker's SIGTERM log and traceback show where it was interrupted, not why it
+  was selected; confirm the cause in the `earlyoom` service or system logs. A
+  later SIGKILL or kernel OOM kill cannot be caught and reported by Python.

 ## Gotchas specific to the GB10

@@ -198,7 +204,7 @@ A few things that surprise people on this box (beyond the memory notes above):
  build recent enough to include its `transformers`-compatibility handling before
  running it.
 - **MiniMax H3 worker init can look healthy and still die on the first generate**
-  if deferred loading is off (`--no-lazy-module-load` and sequential also off)
+  if deferred loading is off (`engine.offload.lazy_module_load: false` and sequential also off)
  and encoder, VAE, and DiT load together. On GB10 the log should show
  `lazy_module_load owns deferral` (or, if lazy is off, sequential
  `Released MiniMax-H3 text encoder after conditioning` before
@@ -210,7 +210,7 @@ from fastvideo.pipelines.stages import (
    InputValidationStage, CLIPTextEncodingStage, TimestepPreparationStage,
    LatentPreparationStage, DenoisingStage, DecodingStage
 )
-from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.api.resolution import ResolvedGeneratorConfig
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 import torch

@@ -226,11 +226,11 @@ class MyCustomPipeline(ComposedPipelineBase):
    def required_config_modules(self) -> List[str]:
        return self._required_config_modules
        
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
+    def initialize_pipeline(self, resolved_config: ResolvedGeneratorConfig):
        """Initialize pipeline-specific components."""
        pass
        
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+    def create_pipeline_stages(self, resolved_config: ResolvedGeneratorConfig):
        """Set up pipeline stages with proper dependency injection."""
        self.add_stage(
            stage_name="input_validation_stage",
@@ -294,7 +294,7 @@ class MyCustomStage(PipelineStage):
        self.custom_module = custom_module
        self.other_param = other_param
        
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+    def forward(self, batch: ForwardBatch, resolved_config: ResolvedGeneratorConfig) -> ForwardBatch:
        # Access input data
        input_data = batch.some_attribute
        
@@ -103,6 +103,10 @@ PipelineConfig                    (fastvideo/configs/pipelines/base.py)
 - Precision settings: `dit_precision`, `vae_precision`,
  `text_encoder_precisions`.

+These generation and precision attributes hold the model defaults. Resolution
+copies them into the typed fields of the resolved config (`pipeline.flow_shift`,
+`engine.precision.dit`, ...), and runtime code reads the typed fields.
+
 Model-specific subclasses override defaults. For example,
 `WanT2V480PConfig` sets `flow_shift=3.0` and uses `WanVideoConfig` as
 its DiT config.
@@ -127,9 +131,11 @@ Concrete hierarchy: `DiTConfig` → `DiTArchConfig`, `VAEConfig` →

 - `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()`.
+- `PipelineConfig.from_source(model_path, source)` — resolves the registry
+  class of `model_path`, then updates it from `source`: a JSON path loaded
+  via `load_from_json()`, a mapping of field values applied via
+  `update_pipeline_config()`, or a `PipelineConfig` that replaces the
+  registry instance.
 - `dump_to_json()` / `load_from_json()` — JSON persistence. Callable
  fields and `arch_config` are excluded from dumps.

@@ -147,7 +153,7 @@ sp = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")

 ### ComponentLoader (`fastvideo/models/loader/component_loader.py`)

-Abstract base with a `load(model_path, fastvideo_args)` method.
+Abstract base with a `load(model_path, resolved_config)` method.
 `ComponentLoader.for_module_type(module_type, library)` is a factory
 that dispatches to specialized loaders via a `module_loaders` dict:

@@ -167,7 +173,7 @@ that dispatches to specialized loaders via a `module_loaders` dict:
 `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`.
+offload are applied based on `resolved_config.engine.offload`.

 Unknown module types fall back to `GenericComponentLoader`.

@@ -205,14 +211,14 @@ loading by calling `ComponentLoader.for_module_type()` then `.load()`.

 Abstract base class using the Template Method pattern:

- `__call__(batch, fastvideo_args)` — orchestrates verification, timing,
+- `__call__(batch, resolved_config)` — orchestrates verification, timing,
  and error handling. Not overridden by subclasses.
- `forward(batch, fastvideo_args) -> ForwardBatch` — abstract, contains
+- `forward(batch, resolved_config) -> 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__`
+When `resolved_config.engine.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.
@@ -295,7 +301,7 @@ provides detailed error messages. Failed verification raises

 Abstract base for all inference pipelines. Lifecycle:

-1. **`__init__(model_path, fastvideo_args)`** — initializes distributed
+1. **`__init__(model_path, resolved_config)`** — initializes distributed
   environment via `maybe_init_distributed_environment_and_model_parallel
   (tp_size, sp_size)`, then calls `load_modules()` to populate
   `self.modules`.
@@ -303,7 +309,7 @@ Abstract base for all inference pipelines. Lifecycle:
   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
+3. **`forward(batch, resolved_config)`** — iterates `self.stages` calling
   each stage in order. Decorated with `@torch.no_grad()`.

 Key class attributes:
@@ -316,8 +322,9 @@ 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()`.
+- `from_pretrained(model_path, *, resolved_config)` — class method that
+  builds the pipeline from a resolved config (from
+  `resolve_inference_config({...})`) by calling `cls(...)` then `post_init()`.

 ### LoRAPipeline (`fastvideo/pipelines/lora_pipeline.py`)

@@ -352,12 +359,12 @@ Key APIs: `get_tp_rank()`, `get_tp_world_size()`, `get_sp_rank()`,
 `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`.
+Usage: `fastvideo generate --config run.yaml
+--generator.engine.parallelism.tp_size N --generator.engine.parallelism.sp_size M`.

 ### torch.compile Integration

-When `fastvideo_args.enable_torch_compile` is `True`,
+When `resolved_config.engine.compile.enabled` 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
@@ -369,47 +376,53 @@ modules are skipped.

 ```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,
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+    {"engine": {"num_gpus": 1, "parallelism": {"tp_size": 1, "sp_size": 1}}},
 )
+result = generator.generate({
+    "prompt": "A cat dancing",
+    "sampling": {"height": 720, "width": 1280, "num_frames": 81},
+})
 ```

 **CLI** (`fastvideo/entrypoints/cli/`):

 ```bash
+# run.yaml holds `generator: {model_path: Wan-AI/Wan2.1-T2V-14B-Diffusers}`.
 fastvideo generate \
-    --model-path "Wan-AI/Wan2.1-T2V-14B-Diffusers" \
-    --prompt "A cat dancing" \
-    --num-gpus 1
+    --config run.yaml \
+    --request.prompt "A cat dancing" \
+    --generator.engine.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`.
+**ResolvedGeneratorConfig** (`fastvideo/api/resolution.py`): The frozen
+runtime config that the executor, workers, pipelines, stages, and loaders
+read. Key paths: `model_path`, `mode` (`ExecutionMode`),
+`pipeline.workload_type` (`WorkloadType`), `engine.num_gpus`,
+`engine.parallelism.tp_size`, `engine.parallelism.sp_size`,
+`pipeline.components.lora_path`, `engine.offload.dit`,
+`engine.offload.dit_layerwise`, `engine.compile.enabled`,
+`engine.enable_stage_verification`, and `pipeline_config` (the frozen
+`PipelineConfig`).

-Constructed via `FastVideoArgs.from_kwargs(**kwargs)` which resolves the
-`PipelineConfig` from the registry, applies JSON config if provided, and
-merges CLI overrides.
+Built by `resolve_inference_config(config)`
+(`fastvideo/api/inference_resolution.py`), which runs the named resolution
+steps (environment variables, model defaults, derived values, validation) in
+order, records each decision, and then builds the `PipelineConfig` from the
+registry, applies a JSON config if provided, and freezes it.

 ## End-to-End Inference Flow

 ```
-User: VideoGenerator.from_pretrained(model_path, **kwargs)
+User: VideoGenerator.from_pretrained(model_path, config)
  │
-  ├─ FastVideoArgs.from_kwargs() → PipelineConfig resolved via registry
+  ├─ resolve_inference_config() → 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)
+  ├─ pipeline_cls.__init__(model_path, resolved_config)
  │   ├─ maybe_init_distributed(tp_size, sp_size)
  │   └─ load_modules() → reads model_index.json, loads each component
  │       ├─ ComponentLoader.for_module_type() → specialized loader
@@ -421,10 +434,10 @@ User: VideoGenerator.from_pretrained(model_path, **kwargs)
      ├─ torch.compile (if enabled)
      └─ warmup_sequence_parallel_communication()

-User: generator.generate_video(prompt, ...)
+User: generator.generate(request)
  │
  ├─ ForwardBatch constructed from SamplingParam + user args
-  └─ pipeline.forward(batch, fastvideo_args)
+  └─ pipeline.forward(batch, resolved_config)
      ├─ InputValidationStage → validates dims
      ├─ TextEncodingStage → prompt → embeddings
      ├─ ConditioningStage → prepares conditioning
@@ -8,7 +8,7 @@ FastVideo automatically distributes the generation process when multiple GPUs ar
 # Will use 4 GPUs in parallel for faster generation
 generator = VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    num_gpus=4,
+    {"engine": {"num_gpus": 4}},
 )
 ```

@@ -30,58 +30,53 @@ bring-up: [Pair two NVIDIA DGX Sparks](../getting_started/installation/spark_pai

 ## Customizing Generation

- `PipelineConfig`: Initialization time parameters
- `SamplingParam`: Generation time parameters
-
-You can customize generation behavior using `PipelineConfig` and
-`SamplingParam`:
+`VideoGenerator.from_pretrained(model_path, config)` takes the startup
+settings as a nested mapping at their typed config paths, such as
+`{"engine": {"num_gpus": 2, "offload": {"dit": False}}}`; it is
+`VideoGenerator.from_config` with `model_path` added to the mapping. Pass
+generation settings to `VideoGenerator.generate` as a request:

 ```python
-from fastvideo import VideoGenerator, SamplingParam, PipelineConfig
+from fastvideo import VideoGenerator

 def main():
    model_name = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-    config = PipelineConfig.from_pretrained(model_name)
-    config.vae_precision = "fp16"

    # Create the generator
-    generator = VideoGenerator.from_pretrained(
-        model_name,
-        num_gpus=1,
-        dit_layerwise_offload=True,  # FastVideoArgs option
-        pipeline_config=config
-    )
-
-    # Create and customize sampling parameters
-    sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
-    
-    # How many frames to generate
-    sampling_param.num_frames = 45
-    
-    # Video resolution (width, height)
-    sampling_param.width = 1024
-    sampling_param.height = 576
-    
-    # How many steps we denoise the video (higher = better quality, slower generation)
-    sampling_param.num_inference_steps = 30
-    
-    # How strongly the video conforms to the prompt (higher = more faithful to prompt)
-    sampling_param.guidance_scale = 7.5
-    
-    # Random seed for reproducibility
-    sampling_param.seed = 42  # Optional, leave unset for random results
+    generator = VideoGenerator.from_config({
+        "model_path": model_name,
+        "engine": {
+            "num_gpus": 1,
+            "offload": {"dit_layerwise": True},
+            "precision": {"vae": "fp16"},
+        },
+    })

    # Generate video with custom parameters
    prompt = "A beautiful sunset over a calm ocean, with gentle waves."
-    video = generator.generate_video(
-        prompt, 
-        sampling_param=sampling_param, 
-        output_path="my_videos/",  # Controls where videos are saved
-        save_video=True
-    )
+    video = generator.generate({
+        "prompt": prompt,
+        "sampling": {
+            # How many frames to generate
+            "num_frames": 45,
+            # Video resolution (width, height)
+            "width": 1024,
+            "height": 576,
+            # How many steps we denoise the video (higher = better quality, slower generation)
+            "num_inference_steps": 30,
+            # How strongly the video conforms to the prompt (higher = more faithful to prompt)
+            "guidance_scale": 7.5,
+            # Random seed for reproducibility
+            "seed": 42,  # Optional, leave unset for random results
+        },
+        "output": {
+            "output_path": "my_videos/",  # Controls where videos are saved
+            "save_video": True,
+        },
+    })

-    # If return_frames=True, frames are available in video["frames"]
-    print(f"Generated {len(video['frames'])} frames")
+    # If return_frames=True, frames are available in video.frames
+    print(f"Generated {len(video.frames)} frames")

 if __name__ == '__main__':
    main()
@@ -125,6 +120,26 @@ Override individual values from the CLI with dotted paths:
 fastvideo generate --config config.yaml --request.sampling.seed 42
 ```

+## Where a Value Came From
+
+FastVideo resolves the generator config once at startup and records the source of every value: the input config
+(`input`; `explicit` tells whether you wrote the value or it is the schema default), a `FASTVIDEO_*` environment
+variable, the model's defaults, or a derived value. A worker's device policy and values read from checkpoint files
+are recorded too.
+
+```python
+generator = VideoGenerator.from_config(config)
+generator.resolved_config.provenance("engine.parallelism.sp_size")
+# PathProvenance(path='engine.parallelism.sp_size', value=2, source='derive_parallel_sizes', ...)
+
+result = generator.generate(request)
+result.resolved_request.provenance("sampling.num_frames")
+# PathProvenance(..., value=81, source='fill_sampling_defaults[preset wan_t2v_1_3b]', explicit=False)
+```
+
+`resolved_config.provenance_table()` lists every path. Every value is decided before resolution ends, including the
+device offload policy and the checkpoint defaults; after that, `resolved_config` is read-only.
+
 ## Performance Optimization

 For configuring optimizations, please see our [optimizations guide](optimizations.md)
@@ -0,0 +1,90 @@
+# FastH3 distilled checkpoint schedules
+
+Base MiniMax-H3 still uses the scheduler shifts in its checkpoint (video 12,
+audio 3), BF16 text encoding, and the existing uniform schedule. The default
+`basic_fasth3.py` example still targets the four-forward preview. Selecting a
+shift-10 eight-forward checkpoint is an explicit choice of model and recipe;
+it does not change either default or enable NVFP4.
+
+## Eight-forward T2AV recipe
+
+The public checkpoint is
+[`FastVideo/FastVideo-FastH3-8-Step-V2`](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2)
+(MiniMax H3 Community License), trained with video/audio shifts 10/3, VSA
+sparsity 0.8, 64-token tiles, and the DMD rungs
+`[999, 874, 749, 624, 500, 375, 250, 125]`. `basic_fasth3_8step.py` pins that
+checkpoint and recipe as defaults; it shares the preview example's CLI, so every
+other flag works unchanged:
+
+```bash
+python examples/inference/basic/basic_fasth3_8step.py \
+  --prompt 'A slow cinematic drone shot glides over a coastal town; gulls call over the harbor.' \
+  --num-gpus 4 --vsa-kernel sm100a \
+  --profile strict --no-inference-torch-compile --no-compile-vae \
+  --height 768 --width 1344 --num-frames 124 \
+  --output outputs/fasth3-8step
+```
+
+Pass `--model-path` to use a local snapshot of the full export (not just its
+`transformer` subdirectory). `--steps` is the number of sigma-grid points,
+including the terminal zero; nine points run exactly eight transformer
+forwards, and the script rejects any other value because the checkpoint's
+ladder has eight rungs. The rungs are unshifted noise levels on the 1000-step training
+clock; each scheduler applies its own shift once, and the transformer receives
+H3 clean-time values (`1 - sigma`). A uniform nine-point grid is not a substitute
+for those rungs.
+
+Compilation and H3 fusions are disabled above to establish an eager reference;
+they can be evaluated separately. On hardware without the sm100a extension,
+use `--vsa-kernel triton`; compare outputs and performance before adopting that
+backend. This recipe is T2AV-only, not a distilled `transformer_ref` model.
+
+## Export metadata and validation
+
+The export's `fastvideo_inference.json` supplies the trained ladder. The
+schedule fields of `fasth3-inference-contract-v1` are:
+
+```json
+{
+  "schema_version": "fasth3-inference-contract-v1",
+  "dmd_denoising_steps": [999, 874, 749, 624, 500, 375, 250, 125],
+  "num_inference_steps": 9,
+  "transformer_forwards": 8,
+  "video_scheduler_shift": 10.0,
+  "audio_scheduler_shift": 3.0
+}
+```
+
+The loader keeps this file when downloading the selected H3 components from
+Hugging Face. It checks that the two declared shifts agree with
+`scheduler/scheduler_config.json` and `audio_scheduler/scheduler_config.json`.
+Missing/invalid rungs, inconsistent counts, or an explicit conflicting ladder
+are errors. The denoiser rejects a request with the wrong number of grid points.
+The metadata does not silently change request dimensions, step count, attention
+backend, sparsity, precision, or offload/compile settings: set those explicitly
+as above.
+
+For exports without this sidecar, an explicit ladder is supported via
+`MiniMaxH3PipelineConfig.dmd_denoising_steps`, or through the typed API's
+`PipelineSelection(dmd_denoising_steps=[...])`. The shifts
+still come from the checkpoint scheduler configs. Keep generic `flow_shift`
+unset: H3 has separate video and audio shifts, not one shared shift.
+
+This documents execution support for the published checkpoint. It is not a
+quality claim: compare video/audio output against base MiniMax-H3 on your own
+prompts before adopting it.
+
+## Apple Silicon
+
+`mlx_fasth3.py` stays on FastH3 V1 and its uniform AdaLN cache.
+`mlx_fasth3_8step.py` is the eight-forward MLX recipe for FastH3 V2. It
+reads the same `fastvideo_inference.json` rungs and shifts, and it expects an
+MLX DiT whose AdaLN cache was converted from that contract. Reuse the V1
+snapshot's VAE, audio VAE, text encoder, and tokenizer; only the DiT and the
+sidecar change. Rank-reduced AdaLN checkpoints are unchanged and are not
+produced by the MLX converter.
+
+Install is in the
+[MLX install guide](../getting_started/installation/mlx.md). Generation and
+serving are in the [MiniMax H3 cookbook](../cookbook/minimax-h3.md) and the
+[H3 server guide](../cookbook/openai-api.md).
@@ -5,10 +5,10 @@ This page contains step-by-step instructions to get you quickly started with vid
 ## Requirements

 - **OS**: Linux (tested on Ubuntu 22.04+), or macOS on Apple silicon via the
-  [MPS installation guide](../getting_started/installation/mps.md)
+  [MLX install guide](../getting_started/installation/mlx.md)
 - **Python**: 3.10-3.12
 - **CUDA**: 12.6 or 13.0 (NVIDIA GPUs)
- **GPU**: At least one NVIDIA GPU, or an Apple silicon chip with MPS
+- **GPU**: At least one NVIDIA GPU, or an Apple silicon chip with the MLX runtime

 ## Installation

@@ -38,18 +38,20 @@ def main():
    # Create a video generator with a pre-trained model
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        num_gpus=1,  # Adjust based on your hardware
+        {"engine": {"num_gpus": 1}},  # Adjust based on your hardware
    )

    # Define a prompt for your video
    prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."

    # Generate the video
-    video = generator.generate_video(
-        prompt,
-        output_path="my_videos/",  # Controls where videos are saved
-        save_video=True
-    )
+    video = generator.generate({
+        "prompt": prompt,
+        "output": {
+            "output_path": "my_videos/",  # Controls where videos are saved
+            "save_video": True,
+        },
+    })

 if __name__ == '__main__':
    main()
@@ -75,23 +77,24 @@ Please see the [support matrix](support_matrix.md) for the list of supported mod
 You can generate a video starting from an initial image:

 ```python
-from fastvideo import VideoGenerator, SamplingParam
+from fastvideo import VideoGenerator

 def main():
    # Create the generator
    model_name = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-    generator = VideoGenerator.from_pretrained(model_name, num_gpus=1)
-
-    # Set up parameters with an initial image
-    sampling_param = SamplingParam.from_pretrained(model_name)
-    sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
-    sampling_param.num_frames = 107
+    generator = VideoGenerator.from_pretrained(model_name, {"engine": {"num_gpus": 1}})

    # Generate video based on the image
    prompt = "A photograph coming to life with gentle movement"
-    generator.generate_video(prompt, sampling_param=sampling_param,
-                             output_path="my_videos/",
-                             save_video=True)
+    generator.generate({
+        "prompt": prompt,
+        # Set up parameters with an initial image
+        "inputs": {
+            "image_path": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg",
+        },
+        "sampling": {"num_frames": 107},
+        "output": {"output_path": "my_videos/", "save_video": True},
+    })

 if __name__ == '__main__':
    main()
@@ -106,12 +109,12 @@ Common issues and their solutions:
 If you encounter CUDA out of memory errors:

 - Reduce `num_frames` or video resolution
- Enable FastVideo offloading options such as `dit_layerwise_offload=True`
-  (single GPU) or `use_fsdp_inference=True` (multi-GPU)
+- Enable FastVideo offloading options such as `engine.offload.dit_layerwise: true`
+  (single GPU) or `engine.use_fsdp_inference: true` (multi-GPU)
 - Try a smaller model or use distilled versions
- Use `num_gpus` > 1 if multiple GPUs are available
- Try enabling FSDP inference with `use_fsdp_inference=True` (may slow down generation)
- Try enabling DiT layerwise offload with `dit_layerwise_offload=True` (now only a few models support this, but may introduce less overhead than FSDP)
+- Use `engine.num_gpus` > 1 if multiple GPUs are available
+- Try enabling FSDP inference with `engine.use_fsdp_inference: true` (may slow down generation)
+- Try enabling DiT layerwise offload with `engine.offload.dit_layerwise: true` (now only a few models support this, but may introduce less overhead than FSDP)

 ### Slow Generation

@@ -10,7 +10,9 @@ that tradeoff. It is not a lossless acceleration of the full VAE.

 ## Generate a video

-Use your existing MLX FastH3 environment and converted checkpoint:
+Use your existing MLX FastH3 environment and converted checkpoint. The same
+`--video-decode-backend taeh3` flag works on `mlx_fasth3.py` (V1) and
+`mlx_fasth3_8step.py` (V2):

 ```bash
 python examples/inference/basic/mlx_fasth3.py \
@@ -68,7 +70,7 @@ Run the numerical tests against a local TAEHV checkout containing the released
 weights:

 ```bash
-TAEH3_REFERENCE_DIR=/path/to/taehv \
+FASTVIDEO_TEST_TAEH3_REFERENCE_DIR=/path/to/taehv \
  python -m pytest fastvideo/tests/mlx/test_mlx_taeh3.py -q
 ```

@@ -4,15 +4,17 @@ This page describes how to use offloading techniques for inference to reduce GPU

 ## Default Behavior

-```python
-dit_cpu_offload: bool = True
-use_fsdp_inference: bool = False
-dit_layerwise_offload: bool = True
-text_encoder_cpu_offload: bool = True
-image_encoder_cpu_offload: bool = True
-vae_cpu_offload: bool = True
-pin_cpu_memory: bool = True
-lazy_module_load: bool | None = None
+```yaml
+engine:
+  use_fsdp_inference: false
+  offload:
+    dit: true               # dit_cpu_offload
+    dit_layerwise: true     # dit_layerwise_offload
+    text_encoder: true      # text_encoder_cpu_offload
+    image_encoder: true     # image_encoder_cpu_offload
+    vae: true               # vae_cpu_offload
+    pin_cpu_memory: true
+    lazy_module_load: null  # auto
 ```

 On unified-memory accelerators such as NVIDIA GB10 and Apple silicon, FastVideo
@@ -35,8 +37,8 @@ channels, DiT patch size) so those stages do not materialize weights just to
 read two integers. The MLX FastH3 runtime always uses this phase order. When
 host offload is off, DiT safetensors are read onto the accelerator instead of
 CPU-then-copy. Both flags default to auto (`None`) and turn on for
-unified-memory devices such as GB10; lazy then disables sequential. Pass
-`--no-lazy-module-load` to keep every component resident (sequential may still
+unified-memory devices such as GB10; lazy then disables sequential. Set
+`engine.offload.lazy_module_load: false` to keep every component resident (sequential may still
 auto-arm). Two-node Spark
 jobs still need this split: sequence parallel replicates the DiT on each GB10
 (~66 GiB of weights plus activations). See
@@ -45,7 +47,10 @@ jobs still need this split: sequence parallel replicates the DiT on each GB10
 ## Behavior Explanation

 !!! note
-    For CLI usage, replace underscores (`_`) with hyphens (`-`).
+    `VideoGenerator.from_pretrained` accepts the option names below as keywords, except `lazy_module_load` and
+    `h3_sequential_load`. In a YAML config or a dotted override, each option is a typed field:
+    `engine.use_fsdp_inference`, `engine.offload.<field>` as listed in the defaults above, and
+    `pipeline.model.minimax_h3.sequential_load` for `h3_sequential_load`.

 ### `use_fsdp_inference`

@@ -104,8 +109,8 @@ because the encoder has been released.
 Leave the default on Spark / DGX Spark when `lazy_module_load` is off. When
 both would arm (the GB10 auto case), lazy owns deferral and sequential stands
 down so VAE `torch.compile` can attach to the lazy proxy. Force
-`--h3-sequential-load` only when you need the split on a discrete GPU without
-lazy load. Use `--no-h3-sequential-load` when you need more than one prompt per
+`pipeline.model.minimax_h3.sequential_load: true` only when you need the split on a discrete GPU without
+lazy load. Set `pipeline.model.minimax_h3.sequential_load: false` when you need more than one prompt per
 worker and have enough memory to keep the encoder.

 ### `text_encoder_cpu_offload`
@@ -160,7 +165,7 @@ options above cannot help with because they act after loading. It is
 particularly relevant on unified-memory devices, where host and device draw on
 the same pool and moving weights to the host frees nothing. FastVideo
 auto-enables it there (`lazy_module_load=None`). Leave it off when the model
-already fits, or pass `--no-lazy-module-load` to keep components resident for
+already fits, or set `engine.offload.lazy_module_load: false` to keep components resident for
 later `generate()` calls.

 This option applies to inference only. Training keeps every component resident
@@ -203,19 +208,25 @@ from fastvideo import VideoGenerator

 generator = VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    num_gpus=1,
-    # Recommended for single GPU
-    dit_layerwise_offload=True,
-    # Enable if OOM happens
-    vae_cpu_offload=True,
-    image_encoder_cpu_offload=True,
-    text_encoder_cpu_offload=True,
-    # Speeds up CPU-GPU transfer
-    pin_cpu_memory=True,
+    {
+        "engine": {
+            "num_gpus": 1,
+            "offload": {
+                # Recommended for single GPU
+                "dit_layerwise": True,
+                # Enable if OOM happens
+                "vae": True,
+                "image_encoder": True,
+                "text_encoder": True,
+                # Speeds up CPU-GPU transfer
+                "pin_cpu_memory": True,
+            },
+        },
+    },
 )

 prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers."
-video = generator.generate_video(prompt, output_path="output/", save_video=True)
+video = generator.generate({"prompt": prompt, "output": {"output_path": "output/", "save_video": True}})
 ```

 ### Multi-GPU with FSDP
@@ -225,18 +236,24 @@ from fastvideo import VideoGenerator

 generator = VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    num_gpus=2,
-    # Recommended for multi-GPU
-    use_fsdp_inference=True,
-    dit_layerwise_offload=False,
-    dit_cpu_offload=False,
-    # Enable if OOM happens
-    vae_cpu_offload=True,
-    image_encoder_cpu_offload=True,
-    text_encoder_cpu_offload=True,
-    pin_cpu_memory=True,
+    {
+        "engine": {
+            "num_gpus": 2,
+            # Recommended for multi-GPU
+            "use_fsdp_inference": True,
+            "offload": {
+                "dit_layerwise": False,
+                "dit": False,
+                # Enable if OOM happens
+                "vae": True,
+                "image_encoder": True,
+                "text_encoder": True,
+                "pin_cpu_memory": True,
+            },
+        },
+    },
 )

 prompt = "A majestic lion strides across the golden savanna."
-video = generator.generate_video(prompt, output_path="output/", save_video=True)
+video = generator.generate({"prompt": prompt, "output": {"output_path": "output/", "save_video": True}})
 ```
@@ -166,22 +166,26 @@ Enable FP4 attention via the `--nvfp4_fa4` flag:
 python examples/inference/optimizations/fp4_attn_wan2_1_1_3b.py --nvfp4_fa4
 ```

-Or in Python via the `nvfp4_fa4` kwarg (sets env vars automatically):
+Or in Python via the `engine.attention.nvfp4_fa4` field (resolution sets the env vars):

 ```python
 from fastvideo import VideoGenerator
 gen = VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    nvfp4_fa4=True,
-    num_gpus=1,
-    use_fsdp_inference=False,  # FSDP is incompatible with FP4 pointer path
+    {
+        "engine": {
+            "attention": {"nvfp4_fa4": True},
+            "num_gpus": 1,
+            "use_fsdp_inference": False,  # FSDP is incompatible with FP4 pointer path
+        },
+    },
 )
-gen.generate_video(prompt="A raccoon in sunflowers", save_video=True)
+gen.generate(request={"prompt": "A raccoon in sunflowers", "output": {"save_video": True}})
 ```

 #### Known Limitations

- `use_fsdp_inference=True` is incompatible with the FP4 path (FSDP shards invalidate tensor pointers)
+- `engine.use_fsdp_inference: true` is incompatible with the FP4 path (FSDP shards invalidate tensor pointers)
 - Per-call cosine similarity vs BF16: ~0.99 (slight quantization error accumulates over denoising steps)
 - Only supports `headdim >= 128`

@@ -205,15 +209,15 @@ import os
 os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "ATTN_QAT_INFER"

 from fastvideo import VideoGenerator
-from fastvideo.layers.quantization import get_quantization_config
-gen = VideoGenerator.from_pretrained(
-    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    num_gpus=1,
-    # Wan-2.1 uses the nvfp4_qat config (NVFP4 is LTX2-specific). Pass an
-    # instance — the bare string is not resolved on the from_pretrained path.
-    transformer_quant=get_quantization_config("nvfp4_qat")(),
-    use_fsdp_inference=False,     # FSDP shards invalidate the FP4 tensor pointers
-)
+gen = VideoGenerator.from_config({
+    "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    "engine": {
+        "num_gpus": 1,
+        "use_fsdp_inference": False,     # FSDP shards invalidate the FP4 tensor pointers
+        # Wan-2.1 uses the nvfp4_qat config (NVFP4 is LTX2-specific).
+        "quantization": {"transformer_quant": "nvfp4_qat"},
+    },
+})
 gen.generate(request={"prompt": "A raccoon in sunflowers", "output": {"save_video": True}})
 ```

@@ -294,21 +298,41 @@ automatically.
 ### Requirements

 - **GPU**: sm89+ (H100, L40S, RTX 4090, or newer) for hardware FP8 compute
+- **ROCm**: CDNA4 (MI350X / MI355X, gfx950) runs the FP8 `_scaled_mm` path through
+  hipBLASLt (OCP e4m3fn). MI300X (gfx942) only exposes the `fnuz` FP8 formats and
+  takes the bf16 dequant fallback like a pre-sm89 GPU.
 - No additional packages required beyond the base FastVideo install

 ### Usage

+```python
+from fastvideo import VideoGenerator
+
+gen = VideoGenerator.from_config({
+    "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    "engine": {"quantization": {"transformer_quant": "FP8"}},  # per-tensor (default)
+})
+gen.generate(request={"prompt": "A raccoon in sunflowers", "output": {"save_video": True}})
+```
+
+`engine.quantization.transformer_quant` takes a quantization registry name and builds that config with its default
+arguments. To pass constructor arguments, such as per-channel granularity, set the config instance on the DiT config
+through `pipeline.model.generic.dit` instead:
+
 ```python
 from fastvideo import VideoGenerator
 from fastvideo.layers.quantization import get_quantization_config

-gen = VideoGenerator.from_pretrained(
-    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    # Pass an instance — the bare string is not resolved on the from_pretrained path.
-    transformer_quant=get_quantization_config("FP8")(),          # per-tensor (default)
-    # transformer_quant=get_quantization_config("FP8")(granularity="channel"),  # slower, higher accuracy
-)
-gen.generate(request={"prompt": "A raccoon in sunflowers", "output": {"save_video": True}})
+gen = VideoGenerator.from_config({
+    "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    "pipeline": {
+        "model": {
+            "generic": {
+                "dit": {"quant_config": get_quantization_config("FP8")(granularity="channel")},  # slower, higher accuracy
+            },
+        },
+    },
+})
 ```

 Or run the example script:
@@ -338,7 +362,7 @@ end-to-end speedup. It is **off by default** and enabled per-run.
 ```python
 generator = VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    enable_torch_compile=True,
+    {"engine": {"compile": {"enabled": True}}},
 )
 ```

@@ -373,12 +397,14 @@ device is unsupported. Legacy VSA, MiniMax-H3 tile-256 VSA, and the explicit
 eager with one warning instead of failing mid-denoise.

 ```python
-generator = VideoGenerator.from_pretrained(
-    "MiniMaxAI/MiniMax-H3",
-    inference_torch_compile=True,  # or FASTVIDEO_INFERENCE_TORCH_COMPILE=1
-)
+generator = VideoGenerator.from_config({
+    "model_path": "MiniMaxAI/MiniMax-H3",
+    "engine": {"compile": {"regional": True}},  # or FASTVIDEO_INFERENCE_TORCH_COMPILE=1
+})
 ```

+In a YAML config, set `generator.engine.compile.regional: true`.
+
 Do not combine it with `torch_compile_kwargs['mode']` (the loader injects
 inductor options, and torch.compile forbids mode+options); it is
 independent of `enable_torch_compile`, and when both are set the regional
@@ -387,7 +413,7 @@ compile wins for the DiT.
 ### What to expect from generic compile

 The Wan result below measures the existing generic
-`enable_torch_compile=True` path. It is useful evidence that compile can help,
+`engine.compile.enabled: true` path. It is useful evidence that compile can help,
 but it is **not** a benchmark or numerical gate for the stricter regional
 fullgraph path above.

@@ -430,12 +456,12 @@ not asserted by any standing SSIM regression here — the SSIM tests in
 run with `enable_torch_compile` disabled. If you depend on compile
 output staying close to eager (or your previous compiled run), run an
 MS-SSIM gate on *your* config, especially when combining
-`enable_torch_compile=True` with other numerics-affecting flags
+`engine.compile.enabled: true` with other numerics-affecting flags
 (quantized attention backends, FP4, layerwise offload edge cases).

 ### Known interactions

- **Layerwise CPU offload** (`dit_layerwise_offload=True`, the default):
+- **Layerwise CPU offload** (`engine.offload.dit_layerwise: true`, the default):
  the offload hook previously caused an implicit graph break once per
  transformer layer, fragmenting the compiled region. Addressed in
  hao-ai-lab/FastVideo#1365 — keep that fix to get a clean compiled
@@ -450,16 +476,17 @@ MS-SSIM gate on *your* config, especially when combining
  grad-enabled path remain outside it. Use the default inductor mode shown
  above unless your exact configuration has its own gate.

-Extra `torch.compile` options are passed through `torch_compile_kwargs`
-(a dict), accepted by `VideoGenerator.from_pretrained(...)` and by the
-CLI as a JSON string via `--torch-compile-kwargs`. Example (currently
+Extra `torch.compile` options live at `engine.compile.backend`,
+`fullgraph`, `mode`, and `dynamic`; any other `torch.compile` kwargs go in
+`engine.compile.extras`. Set them in the nested config, in a config file,
+or as a CLI dotted override (for example
+`--generator.engine.compile.mode reduce-overhead`). Example (currently
 **not** recommended — see the CUDA-graphs caveat above):

 ```python
 VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    enable_torch_compile=True,
-    torch_compile_kwargs={"mode": "reduce-overhead"},  # may error today
+    {"engine": {"compile": {"enabled": True, "mode": "reduce-overhead"}}},  # may error today
 )
 ```

@@ -472,7 +499,7 @@ config; **discard the first generation** (graph build):
 import time
 from fastvideo import VideoGenerator

-gen = VideoGenerator.from_pretrained("your-model-id", enable_torch_compile=True)
+gen = VideoGenerator.from_pretrained("your-model-id", {"engine": {"compile": {"enabled": True}}})
 req = {"prompt": "Your prompt", "sampling": {"seed": 1024},
       "output": {"save_video": False}}
 gen.generate(req)                                            # warmup: graph build, discard
@@ -496,10 +523,10 @@ 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,
-    )
+    generator.generate({
+        "prompt": "Your prompt",
+        "sampling": {"seed": 1024},
+    })
    elapsed = time.perf_counter() - start_time
    print(f"{backend}: {elapsed:.2f}s")
 ```
@@ -185,13 +185,14 @@ optimizations: absence means **untested**, not incompatible.
 | MLX FastMetal T2V 1.3B | [`FastVideo/FastMetal-1.3B-QAD`](https://huggingface.co/FastVideo/FastMetal-1.3B-QAD) | 480x832, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode | Apple M4 Max, 16 GB+ unified memory | Released |
 | MLX FastMetal T2V 5B | [`FastVideo/FastMetal-5B-QAD`](https://huggingface.co/FastVideo/FastMetal-5B-QAD) | 480p / 720p, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode; optional `--fast`, `--fast-spatial`, `--refine`. The checked-in example is T2V. CUDA Wan2.2 TI2V 5B is the image-capable path. | Apple M4 Max, 16 GB+ unified memory | Released |
 | MLX FastMetal T2V 14B | [`FastVideo/FastMetal-14B-QAD`](https://huggingface.co/FastVideo/FastMetal-14B-QAD) | 480p / 720p, 81 frames, 3-step DMD, INT8 DiT + TAEHV decode | Apple M4 Max, 36 GB+ unified memory | Released |
-| MLX FastH3 Preview T2VA | [`FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2`](https://huggingface.co/FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2) + locally converted DiT | 480p / 720p, 124 frames, 4-step DMD2, INT8/INT6/INT4 **weight-only** DiT, native video + audio VAE; optional temporal RIFE fast mode; optional spatial fast mode; optional VSA (tile 64/256, exempt/compete) on `--include-vsa` checkpoints | Apple M4 Max, 36 GB unified memory | Source runtime; T2VA only |
+| MLX FastH3 V1 T2VA | [`FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2`](https://huggingface.co/FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2) + locally converted DiT | 480p / 720p, 124 frames, 4-step DMD2, INT8/INT6/INT4 **weight-only** DiT, native video + audio VAE; optional temporal RIFE fast mode; optional spatial fast mode; optional VSA (tile 64/256, exempt/compete) on `--include-vsa` checkpoints | Apple M4 Max, 36 GB unified memory | Source runtime; T2VA only |
+| MLX FastH3 V2 T2VA | [`FastVideo/FastVideo-FastH3-8-Step-V2`](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2) + locally converted DiT | 480p / 720p, 124 frames, 8-step DMD2, INT8 **weight-only** DiT with `--include-vsa`, trained VSA 0.8 / tile 64, native video + audio VAE; AdaLN cache from `fastvideo_inference.json` | Apple M4 Max, 36 GB unified memory | Source runtime; T2VA only |

 Apple Silicon uses the native MLX runtime. FastMetal-QAD is the packaged Wan
-release, while FastH3 Preview currently uses a source checkout and local DiT
-conversion. CUDA FastWan-QAD (`FastVideo/FastWan-QAD-1.3B`,
+release. FastH3 V1 and FastH3 V2 use a source checkout and local DiT conversion.
+CUDA FastWan-QAD (`FastVideo/FastWan-QAD-1.3B`,
 `FastVideo/FastWan-QAD-FP8-1.3B`) is the NVIDIA release. See the
-[Apple Silicon guide](../getting_started/installation/mps.md) and the
+[MLX install guide](../getting_started/installation/mlx.md) and the
 [FastMetal-QAD blog](https://haoailab.com/blogs/fastmetal/).

 **Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
@@ -223,8 +224,9 @@ Per the installation guides:
  the [DGX Spark install guide](../getting_started/installation/spark.md).
  Two Sparks over QSFP use Ray sequence parallel; see
  [Pair two NVIDIA DGX Sparks](../getting_started/installation/spark_pair.md).
- **Apple silicon** — macOS 14 or newer; FastMetal-QAD via the MLX runtime. See the
-  [Apple Silicon guide](../getting_started/installation/mps.md). The older
+- **Apple silicon** — macOS 14 or newer; MLX runtime for FastMetal-QAD, FastH3
+  V1, and FastH3 V2. See the
+  [MLX install guide](../getting_started/installation/mlx.md). The older
  [`basic_mps.py`](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_mps.py)
  demo is PyTorch MPS only.

@@ -19,46 +19,56 @@ bash examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_

 ## Preprocessing Pipeline

-The new preprocessing pipeline supports multiple dataset formats and video loaders:
+The preprocessing pipeline supports multiple dataset formats and video loaders. It reads a `PreprocessRunConfig`
+YAML file (`fastvideo/api/training_schema.py`): the model and workload type at the top level, and the preprocessing
+settings in the `preprocess` section (the fields of `PreprocessConfig` in `fastvideo/configs/configs.py`):
+
+```yaml
+# preprocess_t2v.yaml
+model_path: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+pipeline:
+  workload_type: t2v
+preprocess:
+  video_loader_type: torchvision
+  dataset_type: merged
+  preprocess_video_batch_size: 2
+  dataloader_num_workers: 0
+  max_height: 480
+  max_width: 832
+  num_frames: 77
+  train_fps: 16
+  samples_per_file: 8
+  flush_frequency: 8
+  video_length_tolerance_range: 5
+```
+
+Pass the file with `--config`. Each dotted override after it sets one field, for example the values that come from
+shell variables:

 ```bash
 GPU_NUM=2
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATASET_PATH="data/crush-smol/"
 OUTPUT_DIR="data/crush-smol_processed_t2v/"

 torchrun --nproc_per_node=$GPU_NUM \
    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
-    --model_path $MODEL_PATH \
-    --mode preprocess \
-    --workload_type t2v \
-    --preprocess.video_loader_type torchvision \
-    --preprocess.dataset_type merged \
-    --preprocess.dataset_path $DATASET_PATH \
-    --preprocess.dataset_output_dir $OUTPUT_DIR \
-    --preprocess.preprocess_video_batch_size 2 \
-    --preprocess.dataloader_num_workers 0 \
-    --preprocess.max_height 480 \
-    --preprocess.max_width 832 \
-    --preprocess.num_frames 77 \
-    --preprocess.train_fps 16 \
-    --preprocess.samples_per_file 8 \
-    --preprocess.flush_frequency 8 \
-    --preprocess.video_length_tolerance_range 5
+    --config preprocess_t2v.yaml \
+    --preprocess.dataset_path "$DATASET_PATH" \
+    --preprocess.dataset_output_dir "$OUTPUT_DIR"
 ```

 ### Key Parameters

-| Parameter | Description |
-|-----------|-------------|
-| `--workload_type` | Task type: `t2v` (text-to-video) or `i2v` (image-to-video) |
-| `--preprocess.dataset_type` | Input format: `hf` (HuggingFace) or `merged` (local folder) |
-| `--preprocess.dataset_path` | Path to dataset (HF repo ID or local folder) |
-| `--preprocess.dataset_output_dir` | Output directory for Parquet files |
-| `--preprocess.video_loader_type` | Video decoder: `torchcodec` or `torchvision` |
-| `--preprocess.max_height` / `max_width` | Target resolution for videos |
-| `--preprocess.num_frames` | Number of frames to extract per video |
-| `--preprocess.train_fps` | Target FPS for frame extraction |
+| Parameter                             | Description                                                 |
+| ------------------------------------- | ----------------------------------------------------------- |
+| `pipeline.workload_type`              | Task type: `t2v` (text-to-video) or `i2v` (image-to-video)  |
+| `preprocess.dataset_type`             | Input format: `hf` (HuggingFace) or `merged` (local folder) |
+| `preprocess.dataset_path`             | Path to dataset (HF repo ID or local folder)                |
+| `preprocess.dataset_output_dir`       | Output directory for Parquet files                          |
+| `preprocess.video_loader_type`        | Video decoder: `torchcodec` or `torchvision`                |
+| `preprocess.max_height` / `max_width` | Target resolution for videos                                |
+| `preprocess.num_frames`               | Number of frames to extract per video                       |
+| `preprocess.train_fps`                | Target FPS for frame extraction                             |

 ## Dataset Formats

@@ -4,48 +4,58 @@ This guide covers finetuning video diffusion models with FastVideo, including fu

 ## Training Arguments

-FastVideo training scripts use several argument groups:
+Each training launcher passes a `TrainingRunConfig` YAML file to its entry point with `--config` (the schema is in
+`fastvideo/api/training_schema.py`). A dotted override after `--config` sets one field, for example
+`--training.optimizer.learning_rate 1e-5` or `--engine.num_gpus "$NUM_GPUS"`:
+
+```bash
+torchrun --nnodes 1 --nproc_per_node 4 \
+    fastvideo/training/wan_training_pipeline.py \
+    --config finetune_t2v.yaml \
+    --engine.num_gpus 4
+```
+
+The settings are grouped as follows:

 ### Training Arguments

-| Argument | Description |
-|----------|-------------|
-| `--max_train_steps` | Total training steps |
-| `--train_batch_size` | Batch size per GPU |
-| `--gradient_accumulation_steps` | Steps to accumulate before optimizer update |
-| `--num_latent_t` | Temporal latent dimension (reduce to save memory) |
-| `--num_height` / `--num_width` | Video resolution |
-| `--num_frames` | Number of frames per video |
-| `--output_dir` | Directory for checkpoints |
+| Config path                                            | Description                                       |
+| ------------------------------------------------------ | ------------------------------------------------- |
+| `training.loop.max_train_steps`                        | Total training steps                              |
+| `training.data.train_batch_size`                       | Batch size per GPU                                |
+| `training.loop.gradient_accumulation_steps`            | Steps to accumulate before optimizer update       |
+| `training.data.num_latent_t`                           | Temporal latent dimension (reduce to save memory) |
+| `training.data.num_height` / `training.data.num_width` | Video resolution                                  |
+| `training.data.num_frames`                             | Number of frames per video                        |
+| `training.checkpoint.output_dir`                       | Directory for checkpoints                         |

 ### Parallelism Arguments

-| Argument | Description |
-|----------|-------------|
-| `--num_gpus` | Total number of GPUs |
-| `--sp_size` | Sequence parallel size (increase to reduce memory per GPU) |
-| `--tp_size` | Tensor parallel size |
-| `--hsdp_replicate_dim` | HSDP replication dimension |
-| `--hsdp_shard_dim` | HSDP sharding dimension |
+| Config path                             | Description                                                |
+| --------------------------------------- | ---------------------------------------------------------- |
+| `engine.num_gpus`                       | Total number of GPUs                                       |
+| `engine.parallelism.sp_size`            | Sequence parallel size (increase to reduce memory per GPU) |
+| `engine.parallelism.tp_size`            | Tensor parallel size                                       |
+| `engine.parallelism.hsdp_replicate_dim` | HSDP replication dimension                                 |
+| `engine.parallelism.hsdp_shard_dim`     | HSDP sharding dimension                                    |

 ### Optimizer Arguments

-| Argument | Description |
-|----------|-------------|
-| `--learning_rate` | Base learning rate |
-| `--mixed_precision` | Precision mode (`bf16` recommended) |
-| `--weight_decay` | Weight decay for regularization |
-| `--max_grad_norm` | Gradient clipping threshold |
+| Config path                        | Description                     |
+| ---------------------------------- | ------------------------------- |
+| `training.optimizer.learning_rate` | Base learning rate              |
+| `training.optimizer.weight_decay`  | Weight decay for regularization |
+| `training.optimizer.max_grad_norm` | Gradient clipping threshold     |

 ### Validation Arguments

-| Argument | Description |
-|----------|-------------|
-| `--log_validation` | Enable validation logging |
-| `--validation_dataset_file` | JSON file with validation prompts |
-| `--validation_steps` | Run validation every N steps |
-| `--validation_sampling_steps` | Inference steps for validation |
-| `--validation_guidance_scale` | CFG scale for validation |
+| Config path                          | Description                                                 |
+| ------------------------------------ | ----------------------------------------------------------- |
+| `training.validation.enabled`        | Enable validation logging                                   |
+| `training.validation.dataset_file`   | JSON file with validation prompts                           |
+| `training.validation.every_steps`    | Run validation every N steps                                |
+| `training.validation.sampling_steps` | Inference steps for validation (a list, for example `[50]`) |
+| `training.validation.guidance_scale` | CFG scale for validation                                    |

 ## Full Finetuning

@@ -59,8 +69,8 @@ bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh
 **Typical settings:**

 - Learning rate: `1e-5` to `5e-5`
- Gradient checkpointing: `--enable_gradient_checkpointing_type "full"`
- Memory scaling: Increase `--sp_size` or reduce `--num_latent_t` to fit in memory
+- Gradient checkpointing: `training.model.enable_gradient_checkpointing_type: full`
+- Memory scaling: Increase `engine.parallelism.sp_size` or reduce `training.data.num_latent_t` to fit in memory

 ## Attention Quantization-Aware Training

@@ -80,10 +90,10 @@ LoRA (Low-Rank Adaptation) trains lightweight adapters while keeping the base mo

 ### LoRA-Specific Arguments

-| Argument | Description |
-|----------|-------------|
-| `--lora_training True` | Enable LoRA mode |
-| `--lora_rank` | Rank of LoRA adapters (16, 32, 64, 128) |
+| Config path                   | Description                             |
+| ----------------------------- | --------------------------------------- |
+| `training.lora.enabled: true` | Enable LoRA mode                        |
+| `training.lora.rank`          | Rank of LoRA adapters (16, 32, 64, 128) |

 ### Learning Rate for LoRA

@@ -103,7 +113,7 @@ bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v_lora.sh

 Key differences from full finetune:

- Add `--lora_training True --lora_rank 32`
+- Set `training.lora.enabled: true` and `training.lora.rank: 32`
 - Use higher learning rate (10–20× full finetune)
 - Can run on fewer GPUs (even single GPU)
 - Outputs adapter weights instead of full model
@@ -186,4 +196,5 @@ Each example includes:
 - `preprocess_*.sh` — run preprocessing
 - `finetune_*.sh` — full finetune launcher
 - `finetune_*_lora.sh` — LoRA finetune launcher
+- a YAML file next to each launcher — the `TrainingRunConfig` that the launcher passes with `--config`
 - `validation.json` — validation prompts
@@ -16,111 +16,17 @@ DATA_DIR="data/matrixgame2"
 VALIDATION_DATASET_FILE="examples/distill/MatrixGame2.0/validation.json"
 NUM_GPUS=1

-# Training arguments
-training_args=(
-  --tracker_project_name "matrixgame2_sf"
-  --output_dir "checkpoints/matrixgame2_sf_${RUN_NAME}"
-  --wandb_run_name "${RUN_NAME}_test"
-  --max_train_steps 5
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 352
-  --num_width 640
-  --enable_gradient_checkpointing_type "full"
-  --simulate_generator_forward
-  --num_frames 81
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  # --enable_gradient_masking
-  # --gradient_mask_last_n_frames 21
-  # --init_weights_from_safetensors "path/to/generator_ema.safetensors"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --log_visualization
-  --visualization-steps 100
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 3e-6
-  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 400
-  --training_state_checkpointing_steps 400
-  --weight_decay 0
-  --betas "0.9,0.95"
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --flow_shift 5
-  --seed 1000
-  --use_ema True
-  --ema_decay 0.99
-  --ema_start_step 200
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,750,500,250'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --dfake_gen_update_ratio 5
-  --real_score_guidance_scale 3.0
-  --fake_score_learning_rate 3e-7
-  --fake_score_betas "0.9,0.95"
-  --warp_denoising_step
-)
-
-self_forcing_args=(
-  --independent_first_frame False  # Whether to treat first frame independently
-  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
-  --last_step_only False  # Whether to only use the last denoising step
-  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
-)
-
 torchrun \
  --nnodes 1 \
  --nproc_per_node $NUM_GPUS \
    fastvideo/training/matrixgame2_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"
+    --config examples/distill/MatrixGame2.0/distill_dmd.yaml \
+    --model_path "$GENERATOR_MODEL_PATH" \
+    --engine.num_gpus "$NUM_GPUS" \
+    --engine.parallelism.hsdp_shard_dim "$NUM_GPUS" \
+    --training.distillation.real_score_model_path "$REAL_SCORE_MODEL_PATH" \
+    --training.distillation.fake_score_model_path "$FAKE_SCORE_MODEL_PATH" \
+    --training.data.data_path "$DATA_DIR" \
+    --training.checkpoint.output_dir "checkpoints/matrixgame2_sf_${RUN_NAME}" \
+    --training.tracker.run_name "${RUN_NAME}_test" \
+    --training.validation.dataset_file "$VALIDATION_DATASET_FILE"
@@ -63,93 +63,6 @@ FAKE_SCORE_MODEL_PATH="FastVideo/Matrix-Game-2.0-Base-Diffusers"
 DATA_DIR="data/matrixgame2"
 VALIDATION_DATASET_FILE="examples/distill/MatrixGame2.0/validation.json"

-training_args=(
-  --tracker_project_name "matrixgame2_sf"
-  --output_dir "checkpoints/matrixgame2_sf_${RUN_NAME}"
-  --wandb_run_name "${RUN_NAME}_test"
-  --max_train_steps 1200
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
-  --num_latent_t 21
-  --num_height 352
-  --num_width 640
-  --simulate_generator_forward
-  --num_frames 81
-  --num_frame_per_block 3
-  # --init_weights_from_safetensors "path/to/generator_ema.safetensors"
-)
-
-parallel_args=(
-  --num_gpus "${TOTAL_GPUS}"
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim "${TOTAL_GPUS}"
-)
-
-model_args=(
-  --model_path "${GENERATOR_MODEL_PATH}"
-  --pretrained_model_name_or_path "${GENERATOR_MODEL_PATH}"
-  --real_score_model_path "${REAL_SCORE_MODEL_PATH}"
-  --fake_score_model_path "${FAKE_SCORE_MODEL_PATH}"
-)
-
-dataset_args=(
-  --data_path "${DATA_DIR}"
-  --dataloader_num_workers 4
-)
-
-validation_args=(
-  --log_validation
-  --log_visualization
-  --visualization-steps 100
-  --validation_dataset_file "${VALIDATION_DATASET_FILE}"
-  --validation_steps 100
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0"
-)
-
-optimizer_args=(
-  --learning_rate 3e-6
-  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 400
-  --training_state_checkpointing_steps 400
-  --weight_decay 0
-  --betas "0.9,0.95"
-  --max_grad_norm 1.0
-)
-
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --flow_shift 5
-  --seed 1000
-  --use_ema True
-  --ema_decay 0.99
-  --ema_start_step 200
-)
-
-dmd_args=(
-  --dmd_denoising_steps "1000,750,500,250"
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --dfake_gen_update_ratio 5
-  --real_score_guidance_scale 3.0
-  --fake_score_learning_rate 3e-7
-  --fake_score_betas "0.9,0.95"
-  --warp_denoising_step
-)
-
-self_forcing_args=(
-  --independent_first_frame False
-  --same_step_across_blocks True
-  --last_step_only False
-  --context_noise 0
-)
-
 srun python -m torch.distributed.run \
  --nnodes "${SLURM_JOB_NUM_NODES}" \
  --nproc_per_node "${GPUS_PER_NODE}" \
@@ -157,12 +70,13 @@ srun python -m torch.distributed.run \
  --rdzv_backend=c10d \
  --rdzv_endpoint="${MASTER_ADDR}:${MASTER_PORT}" \
  fastvideo/training/matrixgame2_self_forcing_distillation_pipeline.py \
-  "${parallel_args[@]}" \
-  "${model_args[@]}" \
-  "${dataset_args[@]}" \
-  "${training_args[@]}" \
-  "${optimizer_args[@]}" \
-  "${validation_args[@]}" \
-  "${miscellaneous_args[@]}" \
-  "${dmd_args[@]}" \
-  "${self_forcing_args[@]}"
+  --config examples/distill/MatrixGame2.0/distill_dmd_slurm.yaml \
+  --model_path "${GENERATOR_MODEL_PATH}" \
+  --engine.num_gpus "${TOTAL_GPUS}" \
+  --engine.parallelism.hsdp_shard_dim "${TOTAL_GPUS}" \
+  --training.distillation.real_score_model_path "${REAL_SCORE_MODEL_PATH}" \
+  --training.distillation.fake_score_model_path "${FAKE_SCORE_MODEL_PATH}" \
+  --training.data.data_path "${DATA_DIR}" \
+  --training.checkpoint.output_dir "checkpoints/matrixgame2_sf_${RUN_NAME}" \
+  --training.tracker.run_name "${RUN_NAME}_test" \
+  --training.validation.dataset_file "${VALIDATION_DATASET_FILE}"
@@ -0,0 +1,68 @@
+# Read by distill_dmd.sh, which passes model_path, engine.num_gpus, engine.parallelism.hsdp_shard_dim,
+# training.distillation.real_score_model_path, training.distillation.fake_score_model_path, training.data.data_path,
+# training.checkpoint.output_dir, training.tracker.run_name, and training.validation.dataset_file on the command line.
+engine:
+  parallelism:
+    tp_size: 1
+    sp_size: 1
+    hsdp_replicate_dim: 1
+  precision:
+    dit: fp32
+pipeline:
+  # components:
+  #   transformer_weights: path/to/generator_ema.safetensors
+  flow_shift: 5.0
+  dmd_denoising_steps: [1000, 750, 500, 250]
+training:
+  data:
+    dataloader_num_workers: 4
+    num_height: 352
+    num_width: 640
+    num_frames: 81
+    train_batch_size: 1
+    num_latent_t: 21
+    training_cfg_rate: 0.0
+    seed: 1000
+    train_sp_batch_size: 1
+  optimizer:
+    learning_rate: 3.0e-06
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    max_grad_norm: 1.0
+  loop:
+    max_train_steps: 5
+    gradient_accumulation_steps: 1
+  checkpoint:
+    training_state_checkpointing_steps: 400
+    weight_only_checkpointing_steps: 400
+    checkpoints_total_limit: 3
+  tracker:
+    project_name: matrixgame2_sf
+  validation:
+    enabled: true
+    sampling_steps: [4]
+    guidance_scale: 6.0
+    every_steps: 100
+    log_visualization: true
+    visualization_steps: 100
+  distillation:
+    min_timestep_ratio: 0.02
+    max_timestep_ratio: 0.98
+    real_score_guidance_scale: 3.0
+    fake_score_learning_rate: 3.0e-07
+    fake_score_betas: [0.9, 0.95]
+    simulate_generator_forward: true
+    warp_denoising_step: true
+  ema:
+    enabled: true
+    decay: 0.99
+    start_step: 200
+  self_forcing:
+    dfake_gen_update_ratio: 5
+    num_frame_per_block: 3  # Frame generation block size for self-forcing
+    independent_first_frame: false  # Whether to treat first frame independently
+    same_step_across_blocks: true  # Whether to use same denoising step across all blocks
+    last_step_only: false  # Whether to only use the last denoising step
+    context_noise: 0  # Amount of noise to add during context caching (0 = no noise)
+  model:
+    enable_gradient_checkpointing_type: full
@@ -0,0 +1,66 @@
+# Read by distill_dmd.slurm, which passes model_path, engine.num_gpus, engine.parallelism.hsdp_shard_dim,
+# training.distillation.real_score_model_path, training.distillation.fake_score_model_path, training.data.data_path,
+# training.checkpoint.output_dir, training.tracker.run_name, and training.validation.dataset_file on the command line.
+engine:
+  parallelism:
+    tp_size: 1
+    sp_size: 1
+    hsdp_replicate_dim: 1
+  precision:
+    dit: fp32
+pipeline:
+  # components:
+  #   transformer_weights: path/to/generator_ema.safetensors
+  flow_shift: 5.0
+  dmd_denoising_steps: [1000, 750, 500, 250]
+training:
+  data:
+    dataloader_num_workers: 4
+    num_height: 352
+    num_width: 640
+    num_frames: 81
+    train_batch_size: 1
+    num_latent_t: 21
+    training_cfg_rate: 0.0
+    seed: 1000
+    train_sp_batch_size: 1
+  optimizer:
+    learning_rate: 3.0e-06
+    betas: [0.9, 0.95]
+    weight_decay: 0.0
+    max_grad_norm: 1.0
+  loop:
+    max_train_steps: 1200
+    gradient_accumulation_steps: 2
+  checkpoint:
+    training_state_checkpointing_steps: 400
+    weight_only_checkpointing_steps: 400
+    checkpoints_total_limit: 3
+  tracker:
+    project_name: matrixgame2_sf
+  validation:
+    enabled: true
+    sampling_steps: [4]
+    guidance_scale: 6.0
+    every_steps: 100
+    log_visualization: true
+    visualization_steps: 100
+  distillation:
+    min_timestep_ratio: 0.02
+    max_timestep_ratio: 0.98
+    real_score_guidance_scale: 3.0
+    fake_score_learning_rate: 3.0e-07
+    fake_score_betas: [0.9, 0.95]
+    simulate_generator_forward: true
+    warp_denoising_step: true
+  ema:
+    enabled: true
+    decay: 0.99
+    start_step: 200
+  self_forcing:
+    dfake_gen_update_ratio: 5
+    num_frame_per_block: 3
+    independent_first_frame: false
+    same_step_across_blocks: true
+    last_step_only: false
+    context_noise: 0
@@ -36,105 +36,16 @@ VALIDATION_DATASET_FILE=your_validation_data_dir
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

-training_args=(
-  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  
-  --output_dir your_output_dir
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --enable_gradient_checkpointing_type "full"
-  --log_visualization
-  --simulate_generator_forward
-  --num_frames 81
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  --enable_gradient_masking
-  --gradient_mask_last_n_frames 21
-)
-
-parallel_args=(
-  --num_gpus $NUM_GPUS # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --betas '0.0,0.999'
-  --max_grad_norm 1.0
-)
-
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --flow_shift 5
-  --seed 1000
-  --use_ema True
-  --ema_decay 0.99
-  --ema_start_step 100
-  --init_weights_from_safetensors your_ode_init_weights_path
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,750,500,250'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --dfake_gen_update_ratio 5
-  --real_score_guidance_scale 3.0
-  --fake_score_learning_rate 8e-6
-  --fake_score_betas '0.0,0.999'
-  --warp_denoising_step
-)
-
-self_forcing_args=(
-  --independent_first_frame False  # Whether to treat first frame independently
-  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
-  --last_step_only False  # Whether to only use the last denoising step
-  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
-)
-
 torchrun \
 --nnodes 1 \
 --master_port $MASTER_PORT \
 --nproc_per_node $NUM_GPUS \
    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"
+    --config examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.yaml \
+    --model_path "$GENERATOR_MODEL_PATH" \
+    --engine.num_gpus "$NUM_GPUS" \
+    --engine.parallelism.hsdp_shard_dim "$NUM_GPUS" \
+    --training.distillation.real_score_model_path "$REAL_SCORE_MODEL_PATH" \
+    --training.distillation.fake_score_model_path "$FAKE_SCORE_MODEL_PATH" \
+    --training.data.data_path "$DATA_DIR" \
+    --training.validation.dataset_file "$VALIDATION_DATASET_FILE"
@@ -0,0 +1,68 @@
+# Read by distill_dmd_t2v_1.3B.sh, which passes model_path, engine.num_gpus, engine.parallelism.hsdp_shard_dim,
+# training.distillation.real_score_model_path, training.distillation.fake_score_model_path, training.data.data_path, and
+# training.validation.dataset_file on the command line.
+engine:
+  parallelism:
+    tp_size: 1
+    sp_size: 1
+    hsdp_replicate_dim: 1  # 64
+  precision:
+    dit: fp32
+pipeline:
+  components:
+    transformer_weights: your_ode_init_weights_path
+  flow_shift: 5.0
+  dmd_denoising_steps: [1000, 750, 500, 250]
+training:
+  data:
+    dataloader_num_workers: 4
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+    train_batch_size: 1
+    num_latent_t: 21
+    training_cfg_rate: 0.0
+    seed: 1000
+    train_sp_batch_size: 1
+  optimizer:
+    learning_rate: 1.0e-05
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    max_grad_norm: 1.0
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+  checkpoint:
+    output_dir: your_output_dir
+    training_state_checkpointing_steps: 500
+    weight_only_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+  tracker:
+    project_name: SFwan_t2v_distill_self_forcing_dmd
+  validation:
+    enabled: true
+    sampling_steps: [4]
+    guidance_scale: 6.0  # not used for dmd inference
+    every_steps: 50
+    log_visualization: true
+  distillation:
+    min_timestep_ratio: 0.02
+    max_timestep_ratio: 0.98
+    real_score_guidance_scale: 3.0
+    fake_score_learning_rate: 8.0e-06
+    fake_score_betas: [0.0, 0.999]
+    simulate_generator_forward: true
+    warp_denoising_step: true
+  ema:
+    enabled: true
+    decay: 0.99
+    start_step: 100
+  self_forcing:
+    dfake_gen_update_ratio: 5
+    num_frame_per_block: 3  # Frame generation block size for self-forcing
+    independent_first_frame: false  # Whether to treat first frame independently
+    same_step_across_blocks: true  # Whether to use same denoising step across all blocks
+    last_step_only: false  # Whether to only use the last denoising step
+    context_noise: 0  # Amount of noise to add during context caching (0 = no noise)
+  model:
+    enable_gradient_checkpointing_type: full
@@ -8,17 +8,7 @@ OUTPUT_DIR="data/crush-smol_processed_t2v/"

 torchrun --nproc_per_node=$GPU_NUM \
    fastvideo/pipelines/preprocess/v1_preprocess.py \
+    --config examples/distill/SFWan2.1-T2V/preprocess_data.yaml \
    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --seed 42 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 81 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --train_fps 16 \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 
+    --preprocess.data_merge_path $DATA_MERGE_PATH \
+    --preprocess.dataset_output_dir=$OUTPUT_DIR
@@ -0,0 +1,19 @@
+# Read by preprocess_data.sh, which passes model_path, preprocess.data_merge_path, and preprocess.dataset_output_dir.
+# Offload settings of the per-task preprocessing pipelines (v1_preprocess.py).
+engine:
+  offload:
+    dit_layerwise: true
+    image_encoder: true
+    pin_cpu_memory: true
+preprocess:
+  preprocess_video_batch_size: 8
+  seed: 42
+  max_height: 480
+  max_width: 832
+  num_frames: 81
+  dataloader_num_workers: 0
+  train_fps: 16
+  samples_per_file: 8
+  flush_frequency: 8
+  video_length_tolerance_range: 5
+  preprocess_task: t2v
@@ -49,96 +49,6 @@ VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

-training_args=(
-  --tracker_project_name SFwan2.2_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
-  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan2.2_t2v_finetune"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 448  # Updated to match Wan2.2 config
-  --num_width 832   # Updated to match Wan2.2 config
-  --enable_gradient_checkpointing_type "full"
-  --simulate_generator_forward
-  # --log_visualization
-  --num_frames 81
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  --enable_gradient_masking
-  --gradient_mask_last_n_frames 21
-  # --init_weights_from_safetensors /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/high/3k/
-  # --init_weights_from_safetensors_2 /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/low/3k/
-)
-
-parallel_args=(
-  --num_gpus 32 # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim 32
-)
-
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --betas '0.0,0.999'
-  --max_grad_norm 1.0
-)
-
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --flow_shift 5
-  --seed 1000
-  --use_ema True
-  --ema_decay 0.99
-  --ema_start_step 100
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,750,500,250'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --dfake_gen_update_ratio 5
-  --real_score_guidance_scale 3.0
-  --fake_score_learning_rate 8e-6
-  --fake_score_betas '0.0,0.999'
-  --warp_denoising_step
-)
-
-self_forcing_args=(
-  --independent_first_frame False  # Whether to treat first frame independently
-  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
-  --last_step_only False  # Whether to only use the last denoising step
-  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
-)
-
 srun torchrun \
 --nnodes $SLURM_JOB_NUM_NODES \
 --nproc_per_node $NUM_GPUS \
@@ -146,12 +56,9 @@ srun torchrun \
 --rdzv_backend=c10d \
 --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"
+    --config examples/distill/SFWan2.2-A14B/distill_dmd.yaml \
+    --model_path "$GENERATOR_MODEL_PATH" \
+    --training.distillation.real_score_model_path "$REAL_SCORE_MODEL_PATH" \
+    --training.distillation.fake_score_model_path "$FAKE_SCORE_MODEL_PATH" \
+    --training.data.data_path "$DATA_DIR" \
+    --training.validation.dataset_file "$VALIDATION_DATASET_FILE"
@@ -0,0 +1,72 @@
+# Read by distill_dmd.sh, which passes model_path, training.distillation.real_score_model_path,
+# training.distillation.fake_score_model_path, training.data.data_path, and training.validation.dataset_file on the
+# command line.
+engine:
+  num_gpus: 32  # 64
+  parallelism:
+    tp_size: 1
+    sp_size: 1
+    hsdp_replicate_dim: 1  # 64
+    hsdp_shard_dim: 32
+  precision:
+    dit: fp32
+pipeline:
+  components:
+    # transformer_weights: /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/high/3k/
+    # transformer_2_weights: /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/low/3k/
+    override_transformer_cls_name: CausalWanTransformer3DModel
+  flow_shift: 5.0
+  dmd_denoising_steps: [1000, 750, 500, 250]
+training:
+  data:
+    dataloader_num_workers: 4
+    num_height: 448  # Updated to match Wan2.2 config
+    num_width: 832  # Updated to match Wan2.2 config
+    num_frames: 81
+    train_batch_size: 1
+    num_latent_t: 21
+    training_cfg_rate: 0.0
+    seed: 1000
+    train_sp_batch_size: 1
+  optimizer:
+    learning_rate: 1.0e-05
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    max_grad_norm: 1.0
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+  checkpoint:
+    output_dir: /mnt/sharefs/users/hao.zhang/SFwan2.2_t2v_finetune
+    training_state_checkpointing_steps: 500
+    weight_only_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+  tracker:
+    project_name: SFwan2.2_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
+  validation:
+    enabled: true
+    sampling_steps: [4]
+    guidance_scale: 6.0  # not used for dmd inference
+    every_steps: 20
+    # log_visualization: true
+  distillation:
+    min_timestep_ratio: 0.02
+    max_timestep_ratio: 0.98
+    real_score_guidance_scale: 3.0
+    fake_score_learning_rate: 8.0e-06
+    fake_score_betas: [0.0, 0.999]
+    simulate_generator_forward: true
+    warp_denoising_step: true
+  ema:
+    enabled: true
+    decay: 0.99
+    start_step: 100
+  self_forcing:
+    dfake_gen_update_ratio: 5
+    num_frame_per_block: 3  # Frame generation block size for self-forcing
+    independent_first_frame: false  # Whether to treat first frame independently
+    same_step_across_blocks: true  # Whether to use same denoising step across all blocks
+    last_step_only: false  # Whether to only use the last denoising step
+    context_noise: 0  # Amount of noise to add during context caching (0 = no noise)
+  model:
+    enable_gradient_checkpointing_type: full
@@ -47,84 +47,6 @@ OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

-# Training arguments
-training_args=(
-  --tracker_project_name wan_t2v_distill_dmd_VSA
-  --output_dir $OUTPUT_DIR
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 81
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 64
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 200
-  --validation_sampling_steps "3"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 2e-6
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-)
-
-# DMD arguments
-dmd_args=(
-  --dmd_denoising_steps '1000,757,522'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --generator_update_interval 5
-  --real_score_guidance_scale 3.5
-  --VSA_sparsity 0.8
-)
-
 srun torchrun \
 --nnodes $SLURM_JOB_NUM_NODES \
 --nproc_per_node $NUM_GPUS \
@@ -132,11 +54,10 @@ srun torchrun \
 --rdzv_backend=c10d \
 --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
    fastvideo/training/wan_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    --config examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/distill_dmd_VSA_t2v_1.3B.yaml \
+    --model_path "$MODEL_PATH" \
+    --training.distillation.real_score_model_path "$REAL_SCORE_MODEL_PATH" \
+    --training.distillation.fake_score_model_path "$FAKE_SCORE_MODEL_PATH" \
+    --training.data.data_path "$DATA_DIR" \
+    --training.checkpoint.output_dir "$OUTPUT_DIR" \
+    --training.validation.dataset_file "$VALIDATION_DATASET_FILE"
@@ -0,0 +1,55 @@
+# Read by distill_dmd_VSA_t2v_1.3B.slurm, which passes model_path, training.distillation.real_score_model_path,
+# training.distillation.fake_score_model_path, training.data.data_path, training.checkpoint.output_dir, and
+# training.validation.dataset_file on the command line.
+engine:
+  num_gpus: 64
+  parallelism:
+    tp_size: 1
+    sp_size: 1
+    hsdp_replicate_dim: 64
+    hsdp_shard_dim: 1
+  attention:
+    vsa_sparsity: 0.8
+  precision:
+    dit: fp32
+pipeline:
+  flow_shift: 8.0
+  dmd_denoising_steps: [1000, 757, 522]
+training:
+  data:
+    dataloader_num_workers: 4
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+    train_batch_size: 1
+    num_latent_t: 21
+    training_cfg_rate: 0.0
+    seed: 1000
+    train_sp_batch_size: 1
+  optimizer:
+    learning_rate: 2.0e-06
+    weight_decay: 0.01
+    max_grad_norm: 1.0
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+  checkpoint:
+    training_state_checkpointing_steps: 500
+    weight_only_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+  tracker:
+    project_name: wan_t2v_distill_dmd_VSA
+  validation:
+    enabled: true
+    sampling_steps: [3]
+    guidance_scale: 6.0  # not used for dmd inference
+    every_steps: 200
+  distillation:
+    generator_update_interval: 5
+    min_timestep_ratio: 0.02
+    max_timestep_ratio: 0.98
+    real_score_guidance_scale: 3.5
+  ema:
+    start_step: 0
+  model:
+    enable_gradient_checkpointing_type: full
@@ -47,84 +47,6 @@ OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

-# Training arguments
-training_args=(
-  --tracker_project_name wan_t2v_distill_dmd_VSA
-  --output_dir "$OUTPUT_DIR"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 81
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus 64
-  --sp_size 4
-  --tp_size 1
-  --hsdp_replicate_dim 8
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 200
-  --validation_sampling_steps "3"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 2e-6
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 3
-  --seed 1000
-)
-
-# DMD arguments
-dmd_args=(
-  --dmd_denoising_steps '1000,757,522'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --generator_update_interval 5
-  --real_score_guidance_scale 3.5
-  --VSA_sparsity 0.9
-)
-
 srun torchrun \
 --nnodes $SLURM_JOB_NUM_NODES \
 --nproc_per_node $NUM_GPUS \
@@ -132,11 +54,10 @@ srun torchrun \
 --rdzv_backend=c10d \
 --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
    fastvideo/training/wan_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    --config examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/distill_dmd_VSA_t2v_14B.yaml \
+    --model_path "$MODEL_PATH" \
+    --training.distillation.real_score_model_path "$REAL_SCORE_MODEL_PATH" \
+    --training.distillation.fake_score_model_path "$FAKE_SCORE_MODEL_PATH" \
+    --training.data.data_path "$DATA_DIR" \
+    --training.checkpoint.output_dir "$OUTPUT_DIR" \
+    --training.validation.dataset_file "$VALIDATION_DATASET_FILE"
@@ -0,0 +1,55 @@
+# Read by distill_dmd_VSA_t2v_14B.slurm, which passes model_path, training.distillation.real_score_model_path,
+# training.distillation.fake_score_model_path, training.data.data_path, training.checkpoint.output_dir, and
+# training.validation.dataset_file on the command line.
+engine:
+  num_gpus: 64
+  parallelism:
+    tp_size: 1
+    sp_size: 4
+    hsdp_replicate_dim: 8
+    hsdp_shard_dim: 8
+  attention:
+    vsa_sparsity: 0.9
+  precision:
+    dit: fp32
+pipeline:
+  flow_shift: 3.0
+  dmd_denoising_steps: [1000, 757, 522]
+training:
+  data:
+    dataloader_num_workers: 4
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+    train_batch_size: 1
+    num_latent_t: 21
+    training_cfg_rate: 0.0
+    seed: 1000
+    train_sp_batch_size: 1
+  optimizer:
+    learning_rate: 2.0e-06
+    weight_decay: 0.01
+    max_grad_norm: 1.0
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+  checkpoint:
+    training_state_checkpointing_steps: 500
+    weight_only_checkpointing_steps: 500
+    checkpoints_total_limit: 3
+  tracker:
+    project_name: wan_t2v_distill_dmd_VSA
+  validation:
+    enabled: true
+    sampling_steps: [3]
+    guidance_scale: 6.0  # not used for dmd inference
+    every_steps: 200
+  distillation:
+    generator_update_interval: 5
+    min_timestep_ratio: 0.02
+    max_timestep_ratio: 0.98
+    real_score_guidance_scale: 3.5
+  ema:
+    start_step: 0
+  model:
+    enable_gradient_checkpointing_type: full
@@ -47,83 +47,6 @@ OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

-# Training arguments
-training_args=(
-  --tracker_project_name wan_t2v_distill_dmd
-  --output_dir "$OUTPUT_DIR"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 81
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 64
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 200
-  --validation_sampling_steps "3"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 2e-6
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-)
-
-# DMD arguments
-dmd_args=(
-  --dmd_denoising_steps '1000,757,522'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --generator_update_interval 5
-  --real_score_guidance_scale 3.5
-)
-
 srun torchrun \
 --nnodes $SLURM_JOB_NUM_NODES \
 --nproc_per_node $NUM_GPUS \
@@ -131,11 +54,10 @@ srun torchrun \
 --rdzv_backend=c10d \
 --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
    fastvideo/training/wan_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    --config examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/distill_dmd_t2v_1.3B.yaml \
+    --model_path "$MODEL_PATH" \
+    --training.distillation.real_score_model_path "$REAL_SCORE_MODEL_PATH" \
+    --training.distillation.fake_score_model_path "$FAKE_SCORE_MODEL_PATH" \
+    --training.data.data_path "$DATA_DIR" \
+    --training.checkpoint.output_dir "$OUTPUT_DIR" \
+    --training.validation.dataset_file "$VALIDATION_DATASET_FILE"
--- a/Show More
+++ b/Show More