update

Post-first-run corrections to the seed-ssim-references skill:
- modal volume get needs --force when the local parent directory
  already exists, otherwise it errors with [Errno 21] Is a directory.
- The downloaded tree has an extra generated_videos/ level (from the
  volume layout in _sync_generated_videos_to_volume), so copy-local's
  --generated-dir must include it.

.agents/scripts/sync-skills.sh

@@ -0,0 +1,96 @@
+#!/usr/bin/env bash
+# Sync .agents/skills/ into .claude/skills/ via per-skill symlinks.
+#
+# Why: Claude Code only scans .claude/skills/ and ~/.claude/skills/ for
+# user-invocable skills (no skillsPath config exists — see
+# https://code.claude.com/docs/en/skills.md). This repo's skills live
+# in .agents/skills/ so they travel with the repo and stay under git.
+# Run this once after cloning (or after adding/removing a skill) to
+# expose them to Claude Code without maintaining a parallel tree.
+#
+# Usage:
+#   .agents/scripts/sync-skills.sh
+#
+# Idempotent and safe to re-run. Prunes stale symlinks whose source
+# has been removed from .agents/skills/. Leaves hand-written
+# .claude/skills/<name>/ directories untouched (only symlinks are
+# managed).
+
+set -euo pipefail
+
+REPO_ROOT="$(git -C "$(dirname "$0")" rev-parse --show-toplevel)"
+SRC_DIR="$REPO_ROOT/.agents/skills"
+DST_DIR="$REPO_ROOT/.claude/skills"
+
+if [[ ! -d "$SRC_DIR" ]]; then
+    echo "Error: $SRC_DIR does not exist." >&2
+    exit 1
+fi
+
+mkdir -p "$DST_DIR"
+
+linked=0
+unchanged=0
+skipped=0
+pruned=0
+
+link_skill() {
+    local name="$1"
+    local src="$SRC_DIR/$name"
+    local dst="$DST_DIR/$name"
+    # Relative target keeps symlinks portable across clones.
+    local rel="../../.agents/skills/$name"
+
+    if [[ -L "$dst" ]]; then
+        if [[ "$(readlink "$dst")" == "$rel" ]]; then
+            unchanged=$((unchanged + 1))
+            return
+        fi
+        rm "$dst"
+    elif [[ -e "$dst" ]]; then
+        echo "Skipped (not a symlink): .claude/skills/$name" >&2
+        skipped=$((skipped + 1))
+        return
+    fi
+
+    ln -s "$rel" "$dst"
+    echo "Linked: .claude/skills/$name -> $rel"
+    linked=$((linked + 1))
+}
+
+prune_stale() {
+    local link="$1"
+    local target
+    target="$(readlink "$link")"
+    case "$target" in
+        ../../.agents/skills/*) ;;
+        *) return ;;
+    esac
+    local name="${target##*/}"
+    if [[ ! -d "$SRC_DIR/$name" ]]; then
+        rm "$link"
+        echo "Pruned stale: .claude/skills/$(basename "$link")"
+        pruned=$((pruned + 1))
+    fi
+}
+
+for src in "$SRC_DIR"/*/; do
+    [[ -d "$src" ]] || continue
+    name="$(basename "$src")"
+    # Only treat directories that actually contain a SKILL.md as skills.
+    [[ -f "$src/SKILL.md" ]] || continue
+    link_skill "$name"
+done
+
+shopt -s nullglob
+for link in "$DST_DIR"/*; do
+    [[ -L "$link" ]] || continue
+    prune_stale "$link"
+done
+shopt -u nullglob
+
+printf "\nSummary: %d linked, %d unchanged, %d pruned" "$linked" "$unchanged" "$pruned"
+if [[ "$skipped" -gt 0 ]]; then
+    printf ", %d skipped (non-symlink collision)" "$skipped"
+fi
+printf "\n"

.agents/skills/index.jsonl

@@ -5,3 +5,4 @@
 {"name": "evaluate-video-quality", "description": "Evaluate generated video quality using available metrics (SSIM, loss trajectory, caption consistency)", "path": "evaluate-video-quality/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "index-related-work", "description": "Ingest a paper or repository into the related work index", "path": "index-related-work/SKILL.md", "status": "draft", "trust": "low"}
 {"name": "search-related-work", "description": "Query the related work index for relevant papers, repos, or comparisons", "path": "search-related-work/SKILL.md", "status": "draft", "trust": "low"}
+{"name": "seed-ssim-references", "description": "Run a new or updated fastvideo/tests/ssim/ test on Modal, pull generated videos, and upload them to FastVideo/ssim-reference-videos so the test has a regression baseline", "path": "seed-ssim-references/SKILL.md", "status": "draft", "trust": "low"}

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

@@ -0,0 +1,250 @@
+---
+name: seed-ssim-references
+description: Seed HF reference videos for a single newly-added SSIM test. Runs the test on Modal L40S, downloads the generated mp4s via `modal volume get`, pauses for the user to eyeball quality, then uploads only that test's files to `FastVideo/ssim-reference-videos`. Use when a new `fastvideo/tests/ssim/test_*_similarity.py` has just been added and has no references on HF yet.
+---
+
+# Seed SSIM Reference Videos
+
+## Purpose
+
+A brand-new SSIM test in `fastvideo/tests/ssim/` fails forever until its
+reference videos exist on the HF dataset (`FastVideo/ssim-reference-videos`).
+This skill:
+
+1. Runs the test on Modal's L40S pool to generate the videos.
+2. Downloads them to the local repo via `modal volume get`.
+3. Pauses so the user can eyeball the mp4s and confirm quality.
+4. Uploads only the new test's files to HF, with a guard that refuses to
+   overwrite anything already present.
+
+The skill is run **manually**, once per new test. Before invoking it, the user
+has already sanity-tested the new test locally — it launches `VideoGenerator`
+and writes an mp4 without crashing. The skill does not re-test locally; it
+goes straight to Modal L40S (which is what CI uses).
+
+## When to use
+
+- A new `test_*_similarity.py` file has been added in `fastvideo/tests/ssim/`
+  and the HF dataset has no `reference_videos/default/L40S_reference_videos/<model_id>/`
+  subtree for it yet.
+
+## When not to use
+
+- Regular CI runs — once refs exist, `pytest fastvideo/tests/ssim/` downloads
+  them automatically.
+- Re-seeding an existing test. That requires `--force` on the upload step, and
+  is out of scope here; treat as a separate, deliberate operation.
+
+## Inputs
+
+The skill has **one required input**: the path to the new SSIM test file.
+Prompt the user for it if they didn't supply it.
+
+| Parameter | Required | Description |
+|-----------|----------|-------------|
+| `test_file` | Yes | e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`. The skill's first action is to ask for this if missing. |
+
+Everything else is fixed:
+
+- Modal runner GPU: **L40S** (hardcoded in `fastvideo/tests/modal/ssim_test.py`).
+- Device folder: `L40S_reference_videos`.
+- Quality tier: `default` (the tier CI runs). The `full_quality` tier is not
+  seeded by this skill.
+- HF repo: `FastVideo/ssim-reference-videos` (dataset).
+- Multi-model test files: all model ids in `*_MODEL_TO_PARAMS` are seeded
+  together; the Modal run produces one mp4 per (model, prompt, backend) and
+  the upload scopes by `--model-id`, looping if there is more than one.
+
+## Prerequisites
+
+The user has confirmed:
+
+- `modal` CLI authenticated.
+- `HF_API_KEY` (or `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`) exported with write
+  access to `FastVideo/ssim-reference-videos`.
+- The test file runs locally end-to-end (generates an mp4; SSIM assertion
+  failure due to missing reference is expected and fine).
+
+Fail fast if the token env var is missing.
+
+## Steps
+
+### 1. Ask for the test file
+
+If the user didn't name one, ask: *"Which SSIM test file do you want to seed
+references for? (e.g. `fastvideo/tests/ssim/test_ltx2_similarity.py`)"*.
+
+Validate:
+
+- Path exists and matches `fastvideo/tests/ssim/test_*_similarity.py`.
+- File defines a `*_MODEL_TO_PARAMS` dict — grep it to extract the set of
+  model ids. Those ids drive step 5.
+
+If either check fails, stop and tell the user what's wrong.
+
+### 2. Run the test on Modal L40S
+
+Pick a subdir name so repeated runs don't collide:
+
+```bash
+SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
+TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
+SUBDIR="${TIMESTAMP}_${SHORT_COMMIT}"
+```
+
+Then launch the Modal run:
+
+```bash
+modal run fastvideo/tests/modal/ssim_test.py \
+    --git-repo="$(git config --get remote.origin.url)" \
+    --git-commit="$(git rev-parse HEAD)" \
+    --hf-api-key="$HF_API_KEY" \
+    --test-files="<test_file>" \
+    --sync-generated-to-volume \
+    --generated-volume-subdir="$SUBDIR" \
+    --skip-reference-download \
+    --no-fail-fast
+```
+
+Flag rationale:
+- `--skip-reference-download`: no refs exist yet, so conftest must not try to
+  pull them.
+- `--no-fail-fast`: lets the test finish generation before `_assert_similarity`
+  raises `FileNotFoundError: Reference video folder does not exist`. The
+  expected failure is what we want — the mp4 has already been written.
+- `--sync-generated-to-volume` + `--generated-volume-subdir`: copies the
+  generated mp4s to the `hf-model-weights` Modal volume under
+  `ssim_generated_videos/default/<SUBDIR>/generated_videos/` so we can pull
+  them locally.
+
+The Modal run will end with a nonzero exit (expected) and print a
+`modal volume get hf-model-weights ssim_generated_videos/default/<SUBDIR>/generated_videos ./generated_videos_modal/default`
+command. Capture that `<SUBDIR>` — you need it for step 3.
+
+### 3. Download generated videos locally
+
+```bash
+modal volume get --force hf-model-weights \
+    ssim_generated_videos/default/"$SUBDIR"/generated_videos \
+    ./generated_videos_modal/default
+```
+
+`--force` is required when the parent `./generated_videos_modal/default`
+already exists; without it, `modal volume get` errors with `[Errno 21] Is a
+directory`. Safe to pass on the first run too.
+
+After this, the mp4s live at
+`./generated_videos_modal/default/generated_videos/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
+The extra `generated_videos/` level comes from the volume layout in
+`_sync_generated_videos_to_volume` (`ssim_test.py`) — the command copies
+`<repo>/fastvideo/tests/ssim/generated_videos/<tier>` to
+`ssim_generated_videos/<tier>/<SUBDIR>/generated_videos/`, and `modal volume
+get` preserves that trailing `generated_videos/` segment.
+
+### 4. PAUSE — user reviews quality
+
+Print the list of downloaded mp4s and their paths, then stop. Tell the user:
+
+> "Generated videos downloaded to `./generated_videos_modal/default/generated_videos/L40S_reference_videos/`. Please open them and confirm the quality looks correct. Reply **`upload`** to continue, or anything else to abort."
+
+Do not proceed until the user explicitly says `upload`. If they abort, leave
+everything on disk so they can inspect further — no cleanup.
+
+### 5. Copy into the local reference layout
+
+Scoped copy — only the new test's mp4s. Loop over each `<model_id>` extracted
+in step 1:
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py copy-local \
+    --quality-tier default \
+    --device-folder L40S_reference_videos \
+    --generated-dir ./generated_videos_modal/default/generated_videos/L40S_reference_videos
+```
+
+(The `--generated-dir` points at the device-folder root inside the
+downloaded tree; `copy-local` walks all `<model>/<backend>/*.mp4`
+underneath it. Since the Modal run was scoped to a single test file via
+`--test-files`, only that test's model(s) are present — so the copy is
+implicitly per-test.)
+
+Result: `fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/<model_id>/<backend>/<prompt>.mp4`.
+
+### 6. Upload to HF — scoped per model_id, with overwrite guard
+
+For each `<model_id>`:
+
+```bash
+python fastvideo/tests/ssim/reference_videos_cli.py upload \
+    --quality-tier default \
+    --device-folder L40S_reference_videos \
+    --model-id "<model_id>"
+```
+
+The upload command:
+
+- Uploads **only** `reference_videos/default/L40S_reference_videos/<model_id>/`.
+- **Refuses** if any file already exists at that path on HF (this is the
+  guard — seeding a new test should never clobber existing refs). To override,
+  the user must re-run with `--force`. If the guard fires, stop and report
+  exactly which files exist; do not silently `--force`.
+
+Reads the HF token from `HF_API_KEY` / `HUGGINGFACE_HUB_TOKEN` / `HF_TOKEN`.
+
+### 7. Report success
+
+List what was uploaded (paths in repo) and remind the user to push any
+related code changes. Do **not** auto-verify by re-running Modal — the user
+can run `pytest fastvideo/tests/ssim/<test_file>` later to confirm end-to-end;
+it will auto-download the refs they just uploaded.
+
+## Failure modes and how to handle them
+
+- **`HF_API_KEY` unset.** Stop before step 2. The Modal run needs it (passed
+  via `--hf-api-key`), and step 6 needs it for upload.
+- **Modal run fails before generation.** No mp4s on the volume — nothing to
+  download. Fix the test locally (`pytest fastvideo/tests/ssim/<test_file>`)
+  and retry from step 2.
+- **`./generated_videos_modal/default/L40S_reference_videos/` missing after
+  `modal volume get`.** The run didn't produce videos (most likely the test
+  crashed before writing, or `REQUIRED_GPUS` exceeded the partition capacity
+  — see Modal logs).
+- **Upload guard fires (files already exist).** The test name / model id
+  collides with something already on HF. Verify the user actually wants to
+  replace existing refs; if so, re-run the upload with `--force`. If not,
+  rename the model id in `*_MODEL_TO_PARAMS` and re-seed.
+- **Quality looks wrong in step 4.** Abort. The mp4s stay on disk for
+  inspection. The fix is usually in the test's params (resolution, steps,
+  seed) — edit the test, then re-run the skill.
+
+## Design notes (for future skill maintainers)
+
+- The skill deliberately runs on Modal, **not** locally, because the CI
+  runner is L40S. Seeding from a different GPU SKU produces refs that CI's
+  L40S runs can't match (SSIM drifts across SKUs).
+- The skill is default-tier only. `full_quality` refs are seeded by a
+  separate, deliberate operation — they double runtime and aren't what CI
+  gates on.
+- The overwrite guard in `reference_videos_cli.py upload` is default-on
+  specifically because this skill exists. Re-seeding is a distinct operation
+  that requires explicit `--force`.
+
+## References
+
+- `fastvideo/tests/modal/ssim_test.py` — Modal orchestrator; see
+  `--sync-generated-to-volume`, `--generated-volume-subdir`,
+  `--skip-reference-download`, `--no-fail-fast`.
+- `fastvideo/tests/ssim/reference_videos_cli.py` — `copy-local`, `upload`
+  (with `--model-id`, `--force`), `download`, `ensure` subcommands.
+- `fastvideo/tests/ssim/README.md` — reference layout, HF repo conventions.
+- `fastvideo/tests/ssim/inference_similarity_utils.py` —
+  `run_text_to_video_similarity_test` + `_build_init_kwargs`: what each test
+  config passes to `VideoGenerator.from_pretrained`.
+
+## Changelog
+
+| Date | Change |
+|------|--------|
+| 2026-04-17 | Initial version (Modal sync-to-volume flow). |
+| 2026-04-21 | Rewrite: single-test scope, explicit user-review pause, per-`model_id` upload, HF overwrite guard. Dropped `scripts/seed_ssim.sh`. |
+| 2026-04-21 | Post-first-run fixes: `modal volume get` needs `--force` when parent exists; download tree has an extra `generated_videos/` level so `--generated-dir` must reflect it. |

docs/design/inference_schema_parity_inventory.yaml

@@ -29,7 +29,7 @@ surfaces:
       vae_cpu_offload: generator.engine.offload.vae
       pin_cpu_memory: generator.engine.offload.pin_cpu_memory
       enable_torch_compile: generator.engine.compile.enabled
-      torch_compile_kwargs: generator.engine.compile.kwargs
+      torch_compile_kwargs: generator.engine.compile.backend,fullgraph,mode,dynamic,extras
       disable_autocast: generator.engine.disable_autocast
       enable_stage_verification: generator.engine.enable_stage_verification
       prompt_txt: request.inputs.prompt_path
@@ -40,8 +40,8 @@ surfaces:
       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
     preset_owned:
-      ltx2_vae_tiling: generator.pipeline.preset_overrides.ltx2.vae_tiling
       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

fastvideo-kernel/build.sh

@@ -61,7 +61,16 @@ has_cmake_arg() {
 }
 
 detect_with_torch() {
-    uv run --active --no-project python -c "import torch
+    # Prefer the active venv's python directly over `uv run --active --no-project`,
+    # which on some uv versions provisions its own interpreter and misses packages
+    # installed into VIRTUAL_ENV.
+    local py
+    if [[ -n "${VIRTUAL_ENV:-}" && -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        py="${VIRTUAL_ENV}/bin/python"
+    else
+        py="$(command -v python3 || command -v python)"
+    fi
+    "${py}" -c "import torch
 if not torch.cuda.is_available():
     raise RuntimeError('torch.cuda.is_available() is false')
 mj, mn = torch.cuda.get_device_capability(0)

fastvideo/api/compat.py

@@ -16,6 +16,7 @@ from fastvideo.api.request_metadata import (
     reset_tracking_roots,
 )
 from fastvideo.api.schema import (
+    CompileConfig,
     GenerationRequest,
     GeneratorConfig,
     InputConfig,
@@ -25,6 +26,7 @@ from fastvideo.api.schema import (
 )
 from fastvideo.api.sampling_param import SamplingParam
 from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.basic.ltx2.stage_overrides import REFINE_FLAT_KEYS
 from fastvideo.utils import shallow_asdict
 
 _INPUT_FIELD_NAMES = {field.name for field in fields(InputConfig)}
@@ -38,6 +40,7 @@ _LEGACY_REQUEST_ALIASES = {
 _REQUEST_PIPELINE_OVERRIDE_FIELDS = frozenset({
     "embedded_cfg_scale",
 })
+_COMPILE_TYPED_KEYS = ("backend", "fullgraph", "mode", "dynamic")
 
 
 def normalize_generator_config(config: GeneratorConfig | Mapping[str, Any], ) -> GeneratorConfig:
@@ -80,6 +83,8 @@ def legacy_from_pretrained_to_config(
     components: dict[str, Any] = {}
     quantization: dict[str, Any] = {}
     experimental: dict[str, Any] = {}
+    preset_overrides: dict[str, Any] = {}
+    preset_refine: dict[str, Any] = {}
 
     for key, value in kwargs.items():
         if key == "revision":
@@ -107,7 +112,30 @@ def legacy_from_pretrained_to_config(
         elif key == "enable_torch_compile":
             compile_config["enabled"] = value
         elif key == "torch_compile_kwargs":
-            compile_config["kwargs"] = deepcopy(value)
+            remaining: dict[str, Any] = (dict(value) if isinstance(value, Mapping) else {})
+            for first_class in _COMPILE_TYPED_KEYS:
+                if first_class in remaining:
+                    compile_config[first_class] = remaining.pop(first_class)
+            if remaining:
+                compile_config["extras"] = remaining
+        elif key == "ltx2_vae_tiling":
+            pipeline["vae_tiling"] = value
+        elif key == "config_model_path":
+            components["config_root"] = value
+        elif key == "ltx2_refine_enabled":
+            preset_refine["enabled"] = value
+        elif key == "ltx2_refine_upsampler_path":
+            # Empty string means "no upsampler"; keep typed None.
+            components["upsampler_weights"] = value or None
+        elif key == "ltx2_refine_lora_path":
+            # Empty string means "no refine LoRA"; keep typed None.
+            components["lora_path"] = value or None
+        elif key == "ltx2_refine_add_noise":
+            preset_refine["add_noise"] = value
+        elif key == "ltx2_refine_num_inference_steps":
+            preset_refine["num_inference_steps"] = value
+        elif key == "ltx2_refine_guidance_scale":
+            preset_refine["guidance_scale"] = value
         elif key in {"enable_stage_verification", "use_fsdp_inference", "disable_autocast"}:
             engine[key] = value
         elif key == "override_text_encoder_quant":
@@ -147,6 +175,10 @@ def legacy_from_pretrained_to_config(
 
     if components:
         pipeline["components"] = components
+    if preset_refine:
+        preset_overrides["refine"] = preset_refine
+    if preset_overrides:
+        pipeline["preset_overrides"] = preset_overrides
     if experimental:
         pipeline["experimental"] = experimental
     if pipeline:
@@ -162,12 +194,8 @@ def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, An
         unsupported.append("pipeline.preset")
     if normalized.pipeline.preset_version is not None:
         unsupported.append("pipeline.preset_version")
-    if normalized.pipeline.components.config_root is not None:
-        unsupported.append("pipeline.components.config_root")
     if normalized.pipeline.components.vae_weights is not None:
         unsupported.append("pipeline.components.vae_weights")
-    if normalized.pipeline.components.upsampler_weights is not None:
-        unsupported.append("pipeline.components.upsampler_weights")
     if unsupported:
         joined = ", ".join(unsupported)
         raise NotImplementedError(f"VideoGenerator compatibility adapter does not support {joined} yet")
@@ -191,13 +219,15 @@ def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, An
         "vae_cpu_offload": engine.offload.vae,
         "pin_cpu_memory": engine.offload.pin_cpu_memory,
         "enable_torch_compile": engine.compile.enabled,
-        "torch_compile_kwargs": deepcopy(engine.compile.kwargs),
+        "torch_compile_kwargs": _compile_config_to_torch_kwargs(engine.compile),
         "enable_stage_verification": engine.enable_stage_verification,
         "use_fsdp_inference": engine.use_fsdp_inference,
         "disable_autocast": engine.disable_autocast,
     }
     if normalized.pipeline.workload_type is not None:
         kwargs["workload_type"] = normalized.pipeline.workload_type
+    if normalized.pipeline.vae_tiling is not None:
+        kwargs["ltx2_vae_tiling"] = normalized.pipeline.vae_tiling
 
     quantization = engine.quantization
     if quantization is not None and quantization.text_encoder_quant is not None:
@@ -220,8 +250,18 @@ def generator_config_to_fastvideo_args(config: GeneratorConfig | Mapping[str, An
         kwargs["init_weights_from_safetensors"] = components.transformer_weights
     if components.transformer_2_weights is not None:
         kwargs["init_weights_from_safetensors_2"] = components.transformer_2_weights
+    if components.config_root is not None:
+        kwargs["config_model_path"] = components.config_root
+    if components.upsampler_weights is not None:
+        kwargs["ltx2_refine_upsampler_path"] = components.upsampler_weights
 
-    kwargs.update(deepcopy(normalized.pipeline.preset_overrides))
+    preset_overrides = deepcopy(normalized.pipeline.preset_overrides)
+    refine = preset_overrides.pop("refine", None)
+    if isinstance(refine, Mapping):
+        for key in REFINE_FLAT_KEYS:
+            if key in refine:
+                kwargs[f"ltx2_refine_{key}"] = refine[key]
+    kwargs.update(preset_overrides)
     kwargs.update(deepcopy(normalized.pipeline.experimental))
     return FastVideoArgs.from_kwargs(**kwargs)
 
@@ -316,6 +356,20 @@ def _looks_like_run_or_serve_config(raw: Mapping[str, Any]) -> bool:
     return isinstance(raw.get("generator"), Mapping)
 
 
+def _compile_config_to_torch_kwargs(compile_config: CompileConfig, ) -> dict[str, Any]:
+    """Flatten typed ``CompileConfig`` back to the legacy
+    ``torch_compile_kwargs`` dict, emitting only explicitly-set typed
+    fields and merging ``extras`` on top."""
+    out: dict[str, Any] = {}
+    for key in _COMPILE_TYPED_KEYS:
+        value = getattr(compile_config, key)
+        if value is not None:
+            out[key] = value
+    if compile_config.extras:
+        out.update(deepcopy(compile_config.extras))
+    return out
+
+
 def _sampling_param_to_request_raw(sampling_param: SamplingParam | None, ) -> dict[str, Any]:
     if sampling_param is None:
         return {}

fastvideo/api/schema.py

@@ -33,8 +33,19 @@ class OffloadConfig:
 
 @dataclass
 class CompileConfig:
+    """Typed ``torch.compile`` configuration.
+
+    ``backend``/``fullgraph``/``mode``/``dynamic`` are the four most
+    common ``torch.compile`` knobs. ``extras`` holds any remaining
+    ``torch.compile`` kwargs (e.g. ``options``, ``disable``).
+    """
+
     enabled: bool = False
-    kwargs: dict[str, Any] = field(default_factory=dict)
+    backend: str | None = None
+    fullgraph: bool | None = None
+    mode: str | None = None
+    dynamic: bool | None = None
+    extras: dict[str, Any] = field(default_factory=dict)
 
 
 @dataclass
@@ -76,6 +87,8 @@ class PipelineSelection:
     preset: str | None = None
     preset_version: int | None = None
     components: ComponentConfig = field(default_factory=ComponentConfig)
+    vae_tiling: bool | None = None
+    """Tile-based VAE decode. ``None`` keeps the model's default."""
     preset_overrides: dict[str, Any] = field(default_factory=dict)
     experimental: dict[str, Any] = field(default_factory=dict)
 

fastvideo/configs/pipelines/__init__.py

@@ -5,7 +5,7 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
-from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
+from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
 from fastvideo.registry import get_pipeline_config_cls_from_name
 from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig, WanI2V480PConfig, WanI2V720PConfig,
                                              WanT2V480PConfig, WanT2V720PConfig)

fastvideo/pipelines/basic/ltx2/presets.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 """LTX2 model family pipeline presets."""
 from fastvideo.api.presets import InferencePreset, PresetStageSpec
+from fastvideo.pipelines.basic.ltx2.stage_overrides import (
+    REFINE_STAGE_OVERRIDE_FIELDS, )
 
 _LTX2_NEGATIVE_PROMPT = ("blurry, out of focus, overexposed, underexposed, low contrast, "
                          "washed out colors, excessive noise, grainy texture, poor lighting, "
@@ -30,6 +32,13 @@ _DENOISE_STAGE = PresetStageSpec(
     }),
 )
 
+_REFINE_STAGE = PresetStageSpec(
+    name="refine",
+    kind="refinement",
+    description="Latent-upsample + second-pass refine",
+    allowed_overrides=REFINE_STAGE_OVERRIDE_FIELDS,
+)
+
 LTX2_BASE = InferencePreset(
     name="ltx2_base",
     version=1,
@@ -77,4 +86,29 @@ LTX2_DISTILLED = InferencePreset(
     },
 )
 
-ALL_PRESETS = (LTX2_BASE, LTX2_DISTILLED)
+LTX2_TWO_STAGE = InferencePreset(
+    name="ltx2_two_stage",
+    version=1,
+    model_family="ltx2",
+    description="LTX-2 distilled with 2x spatial refine (stage 1 half-res + stage 2 upsample+denoise)",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, _REFINE_STAGE),
+    defaults={
+        "seed": 10,
+        "height": 1024,
+        "width": 1536,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 8,
+        "negative_prompt": "",
+    },
+    stage_defaults={
+        "refine": {
+            "num_inference_steps": 2,
+            "guidance_scale": 1.0,
+        },
+    },
+)
+
+ALL_PRESETS = (LTX2_BASE, LTX2_DISTILLED, LTX2_TWO_STAGE)

fastvideo/pipelines/basic/ltx2/stage_overrides.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Typed override surfaces for the LTX-2 two-stage refine flow.
+
+* ``preset_overrides.refine`` — init-time knobs (see
+  :class:`LTX2RefinePresetOverride`).
+* ``stage_overrides.refine`` — per-request knobs (see
+  :class:`LTX2RefineStageOverride`).
+
+Asset paths live on :class:`~fastvideo.api.schema.ComponentConfig`
+(``upsampler_weights`` and ``lora_path``).
+"""
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, fields
+from typing import Any
+
+
+@dataclass
+class LTX2RefinePresetOverride:
+    """Init-time refine wiring under ``preset_overrides.refine``."""
+
+    enabled: bool | None = None
+    add_noise: bool | None = None
+
+
+@dataclass
+class LTX2RefineStageOverride:
+    """Per-request refine tuning under ``stage_overrides.refine``."""
+
+    num_inference_steps: int | None = None
+    guidance_scale: float | None = None
+    image_crf: int | None = None
+    video_position_offset_sec: float | None = None
+
+
+def refine_override_to_dict(override: LTX2RefinePresetOverride | LTX2RefineStageOverride, ) -> dict[str, Any]:
+    """Serialise a refine override, dropping ``None`` entries so only
+    user-set fields reach ``preset_overrides.refine`` or
+    ``stage_overrides.refine``."""
+    return {k: v for k, v in asdict(override).items() if v is not None}
+
+
+REFINE_PRESET_OVERRIDE_FIELDS: frozenset[str] = frozenset(f.name for f in fields(LTX2RefinePresetOverride))
+REFINE_STAGE_OVERRIDE_FIELDS: frozenset[str] = frozenset(f.name for f in fields(LTX2RefineStageOverride))
+REFINE_FLAT_KEYS: frozenset[str] = (REFINE_PRESET_OVERRIDE_FIELDS | REFINE_STAGE_OVERRIDE_FIELDS)
+
+__all__ = [
+    "LTX2RefinePresetOverride",
+    "LTX2RefineStageOverride",
+    "REFINE_FLAT_KEYS",
+    "REFINE_PRESET_OVERRIDE_FIELDS",
+    "REFINE_STAGE_OVERRIDE_FIELDS",
+    "refine_override_to_dict",
+]

fastvideo/pipelines/basic/ltx2/stages/__init__.py

@@ -0,0 +1,17 @@
+# SPDX-License-Identifier: Apache-2.0
+"""LTX-2 family pipeline stages."""
+from fastvideo.pipelines.basic.ltx2.stages.ltx2_audio_decoding import (
+    LTX2AudioDecodingStage, )
+from fastvideo.pipelines.basic.ltx2.stages.ltx2_denoising import (
+    LTX2DenoisingStage, )
+from fastvideo.pipelines.basic.ltx2.stages.ltx2_latent_preparation import (
+    LTX2LatentPreparationStage, )
+from fastvideo.pipelines.basic.ltx2.stages.ltx2_text_encoding import (
+    LTX2TextEncodingStage, )
+
+__all__ = [
+    "LTX2AudioDecodingStage",
+    "LTX2DenoisingStage",
+    "LTX2LatentPreparationStage",
+    "LTX2TextEncodingStage",
+]

fastvideo/pipelines/stages/__init__.py

@@ -25,10 +25,12 @@ from fastvideo.pipelines.stages.latent_preparation import (Cosmos25LatentPrepara
                                                            Cosmos25AutoLatentPreparationStage,
                                                            Cosmos25T2WLatentPreparationStage,
                                                            Cosmos25V2WLatentPreparationStage, LatentPreparationStage)
-from fastvideo.pipelines.stages.ltx2_audio_decoding import LTX2AudioDecodingStage
-from fastvideo.pipelines.stages.ltx2_denoising import LTX2DenoisingStage
-from fastvideo.pipelines.stages.ltx2_latent_preparation import (LTX2LatentPreparationStage)
-from fastvideo.pipelines.stages.ltx2_text_encoding import LTX2TextEncodingStage
+from fastvideo.pipelines.basic.ltx2.stages import (
+    LTX2AudioDecodingStage,
+    LTX2DenoisingStage,
+    LTX2LatentPreparationStage,
+    LTX2TextEncodingStage,
+)
 from fastvideo.pipelines.stages.matrixgame_denoising import (MatrixGameCausalDenoisingStage)
 from fastvideo.pipelines.stages.hyworld_denoising import HYWorldDenoisingStage
 from fastvideo.pipelines.stages.gamecraft_denoising import GameCraftDenoisingStage

fastvideo/registry.py

@@ -26,7 +26,7 @@ from fastvideo.configs.pipelines.hunyuan15 import (Hunyuan15T2V480PConfig, Hunyu
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
 from fastvideo.configs.pipelines.lingbotworld import LingBotWorldI2V480PConfig
 from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
-from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
+from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
 from fastvideo.configs.pipelines.turbodiffusion import (
     TurboDiffusionI2V_A14B_Config,
     TurboDiffusionT2V_14B_Config,

fastvideo/tests/api/test_compat_translation.py

@@ -0,0 +1,181 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Tests for ``fastvideo.api.compat`` translation helpers covering the
+typed CompileConfig + PipelineSelection.vae_tiling surfaces promoted in
+PR 6.
+"""
+from __future__ import annotations
+
+from fastvideo.api.compat import (
+    generator_config_to_fastvideo_args,
+    legacy_from_pretrained_to_config,
+)
+from fastvideo.api.schema import CompileConfig, GeneratorConfig
+
+
+class TestLegacyTorchCompileKwargsTranslation:
+    """Legacy ``torch_compile_kwargs={...}`` gets split across the four
+    first-class :class:`CompileConfig` fields and anything unknown falls
+    into ``extras``."""
+
+    def test_all_typed_keys_promoted(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {
+                "enable_torch_compile": True,
+                "torch_compile_kwargs": {
+                    "backend": "inductor",
+                    "fullgraph": True,
+                    "mode": "max-autotune-no-cudagraphs",
+                    "dynamic": False,
+                },
+            },
+        )
+        compile_config = config.engine.compile
+        assert compile_config.enabled is True
+        assert compile_config.backend == "inductor"
+        assert compile_config.fullgraph is True
+        assert compile_config.mode == "max-autotune-no-cudagraphs"
+        assert compile_config.dynamic is False
+        assert compile_config.extras == {}
+
+    def test_unknown_keys_land_in_extras(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {
+                "enable_torch_compile": True,
+                "torch_compile_kwargs": {
+                    "backend": "inductor",
+                    "options": {"triton.cudagraphs": False},
+                    "disable": False,
+                },
+            },
+        )
+        compile_config = config.engine.compile
+        assert compile_config.backend == "inductor"
+        assert compile_config.extras == {
+            "options": {"triton.cudagraphs": False},
+            "disable": False,
+        }
+
+    def test_empty_kwargs_produces_empty_extras(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {"torch_compile_kwargs": {}},
+        )
+        compile_config = config.engine.compile
+        assert compile_config.extras == {}
+        assert compile_config.backend is None
+
+
+class TestCompileConfigRoundTrip:
+    """typed CompileConfig -> FastVideoArgs.torch_compile_kwargs
+    reconstruction drops ``None`` typed fields and merges ``extras``."""
+
+    def test_only_typed_fields_emitted(self, monkeypatch) -> None:
+        _stub_fastvideo_args_from_kwargs(monkeypatch)
+        config = GeneratorConfig(
+            model_path="/models/ltx2",
+            engine=_engine_with_compile(
+                CompileConfig(enabled=True, backend="inductor", fullgraph=True)),
+        )
+        args = generator_config_to_fastvideo_args(config)
+        assert args.kwargs["enable_torch_compile"] is True
+        assert args.kwargs["torch_compile_kwargs"] == {
+            "backend": "inductor",
+            "fullgraph": True,
+        }
+
+    def test_extras_merged_into_torch_compile_kwargs(self, monkeypatch) -> None:
+        _stub_fastvideo_args_from_kwargs(monkeypatch)
+        config = GeneratorConfig(
+            model_path="/models/ltx2",
+            engine=_engine_with_compile(
+                CompileConfig(
+                    enabled=True,
+                    mode="reduce-overhead",
+                    extras={"options": {"triton.cudagraphs": False}},
+                )),
+        )
+        args = generator_config_to_fastvideo_args(config)
+        assert args.kwargs["torch_compile_kwargs"] == {
+            "mode": "reduce-overhead",
+            "options": {"triton.cudagraphs": False},
+        }
+
+    def test_none_fields_suppressed(self, monkeypatch) -> None:
+        _stub_fastvideo_args_from_kwargs(monkeypatch)
+        config = GeneratorConfig(
+            model_path="/models/ltx2",
+            engine=_engine_with_compile(CompileConfig()),
+        )
+        args = generator_config_to_fastvideo_args(config)
+        assert args.kwargs["torch_compile_kwargs"] == {}
+
+
+class TestLegacyLtx2VaeTilingTranslation:
+    """``ltx2_vae_tiling`` flat kwarg promotes to
+    ``generator.pipeline.vae_tiling``; reverse direction emits the
+    legacy name back to FastVideoArgs."""
+
+    def test_forward_routes_to_pipeline_vae_tiling(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {"ltx2_vae_tiling": False},
+        )
+        assert config.pipeline.vae_tiling is False
+
+    def test_true_round_trips(self) -> None:
+        config = legacy_from_pretrained_to_config(
+            "/models/ltx2",
+            {"ltx2_vae_tiling": True},
+        )
+        assert config.pipeline.vae_tiling is True
+
+    def test_unset_stays_none(self) -> None:
+        config = legacy_from_pretrained_to_config("/models/ltx2", {})
+        assert config.pipeline.vae_tiling is None
+
+    def test_reverse_emits_legacy_name(self, monkeypatch) -> None:
+        _stub_fastvideo_args_from_kwargs(monkeypatch)
+        config = GeneratorConfig(
+            model_path="/models/ltx2",
+            engine=_engine_with_compile(CompileConfig()),
+        )
+        config.pipeline.vae_tiling = False
+        args = generator_config_to_fastvideo_args(config)
+        assert args.kwargs["ltx2_vae_tiling"] is False
+
+    def test_reverse_unset_skips_key(self, monkeypatch) -> None:
+        _stub_fastvideo_args_from_kwargs(monkeypatch)
+        config = GeneratorConfig(
+            model_path="/models/ltx2",
+            engine=_engine_with_compile(CompileConfig()),
+        )
+        args = generator_config_to_fastvideo_args(config)
+        assert "ltx2_vae_tiling" not in args.kwargs
+
+
+# -------------------------------------------------------------------
+# Helpers
+# -------------------------------------------------------------------
+
+
+def _engine_with_compile(compile_config):
+    """Build an ``EngineConfig`` that carries the supplied compile block."""
+    from fastvideo.api.schema import EngineConfig
+    engine = EngineConfig()
+    engine.compile = compile_config
+    return engine
+
+
+def _stub_fastvideo_args_from_kwargs(monkeypatch):
+    """Swap ``FastVideoArgs.from_kwargs`` for a capture-only stub so
+    translation tests don't need to construct a valid FastVideoArgs."""
+    from fastvideo import fastvideo_args as fva
+
+    class _Captured:
+
+        def __init__(self, **kw):
+            self.kwargs = kw
+
+    monkeypatch.setattr(fva.FastVideoArgs, "from_kwargs", _Captured)

fastvideo/tests/api/test_ltx2_gpu_pool_translation.py

@@ -0,0 +1,269 @@
+# SPDX-License-Identifier: Apache-2.0
+"""gpu_pool-style flat-kwarg integration tests.
+
+Mirrors the ``load_kwargs`` dict that the FastVideo-internal
+``ui/ltx2-streaming/server/gpu_pool.py`` passes to
+``VideoGenerator.from_pretrained(**load_kwargs)`` and asserts that the
+public typed ``GeneratorConfig`` surface can represent it end-to-end,
+with no fields silently falling through to ``pipeline.experimental``.
+"""
+from __future__ import annotations
+
+from copy import deepcopy
+
+import pytest
+
+from fastvideo.api.compat import (
+    generator_config_to_fastvideo_args,
+    legacy_from_pretrained_to_config,
+)
+
+
+# Mirrors FastVideo-internal/ui/ltx2-streaming/server/gpu_pool.py
+# :lines 233-260 (load_kwargs constructed for VideoGenerator.from_pretrained).
+# Skipped here because they are opaque Python objects that legitimately
+# belong in experimental:
+#   - pipeline_config=<PipelineConfig instance>
+#   - enable_torch_compile_text_encoder (not in public FastVideoArgs)
+GPU_POOL_LOAD_KWARGS = {
+    "config_model_path": "/models/ltx2-distilled/config",
+    "num_gpus": 1,
+    "dit_layerwise_offload": False,
+    "use_fsdp_inference": False,
+    "dit_cpu_offload": False,
+    "vae_cpu_offload": False,
+    "text_encoder_cpu_offload": False,
+    "pin_cpu_memory": True,
+    "ltx2_vae_tiling": False,
+    "ltx2_refine_enabled": True,
+    "ltx2_refine_upsampler_path": "/models/ltx2-distilled/spatial_upsampler",
+    "ltx2_refine_lora_path": "",
+    "ltx2_refine_num_inference_steps": 2,
+    "ltx2_refine_guidance_scale": 1.0,
+    "ltx2_refine_add_noise": True,
+    "enable_torch_compile": True,
+    "torch_compile_kwargs": {
+        "backend": "inductor",
+        "fullgraph": True,
+        "mode": "max-autotune-no-cudagraphs",
+        "dynamic": False,
+    },
+}
+
+
+class TestGpuPoolForwardTranslation:
+    """gpu_pool flat kwargs -> typed GeneratorConfig."""
+
+    @pytest.fixture(scope="class")
+    def config(self):
+        return legacy_from_pretrained_to_config(
+            "FastVideo/LTX2-Distilled-Diffusers",
+            GPU_POOL_LOAD_KWARGS,
+        )
+
+    def test_model_path_set(self, config) -> None:
+        assert config.model_path == "FastVideo/LTX2-Distilled-Diffusers"
+
+    def test_engine_basics(self, config) -> None:
+        assert config.engine.num_gpus == 1
+        assert config.engine.use_fsdp_inference is False
+
+    def test_offload_config(self, config) -> None:
+        assert config.engine.offload.dit is False
+        assert config.engine.offload.dit_layerwise is False
+        assert config.engine.offload.vae is False
+        assert config.engine.offload.text_encoder is False
+        assert config.engine.offload.pin_cpu_memory is True
+
+    def test_compile_config_typed_fields_extracted(self, config) -> None:
+        compile_config = config.engine.compile
+        assert compile_config.enabled is True
+        assert compile_config.backend == "inductor"
+        assert compile_config.fullgraph is True
+        assert compile_config.mode == "max-autotune-no-cudagraphs"
+        assert compile_config.dynamic is False
+        assert compile_config.extras == {}
+
+    def test_vae_tiling_routed_to_pipeline(self, config) -> None:
+        assert config.pipeline.vae_tiling is False
+
+    def test_config_model_path_routed_to_components(self, config) -> None:
+        assert config.pipeline.components.config_root == "/models/ltx2-distilled/config"
+
+    def test_refine_upsampler_routed_to_components(self, config) -> None:
+        assert config.pipeline.components.upsampler_weights == (
+            "/models/ltx2-distilled/spatial_upsampler")
+
+    def test_empty_refine_lora_becomes_none(self, config) -> None:
+        # gpu_pool passes "" to keep refine LoRA disabled; typed schema
+        # treats that as "no LoRA" rather than an empty-string path.
+        assert config.pipeline.components.lora_path is None
+
+    def test_refine_preset_overrides(self, config) -> None:
+        refine = config.pipeline.preset_overrides.get("refine", {})
+        assert refine == {
+            "enabled": True,
+            "num_inference_steps": 2,
+            "guidance_scale": 1.0,
+            "add_noise": True,
+        }
+
+    def test_no_experimental_leakage(self, config) -> None:
+        """Every gpu_pool kwarg should have a typed home — nothing should
+        silently fall through to ``pipeline.experimental``."""
+        assert config.pipeline.experimental == {}
+
+
+class TestGpuPoolReverseTranslation:
+    """typed GeneratorConfig -> FastVideoArgs kwargs reproduces the
+    original gpu_pool flat-kwarg shape, so callers can wire a public
+    ``gpu_pool`` through ``generator_config_to_fastvideo_args`` without
+    the runtime noticing."""
+
+    @pytest.fixture
+    def args_kwargs(self, monkeypatch):
+        from fastvideo import fastvideo_args as fva
+
+        captured: dict[str, object] = {}
+
+        def _capture(**kw):
+            captured.update(kw)
+            return _Captured(**kw)
+
+        class _Captured:
+
+            def __init__(self, **kw):
+                self.kwargs = kw
+
+        monkeypatch.setattr(fva.FastVideoArgs, "from_kwargs", _capture)
+
+        config = legacy_from_pretrained_to_config(
+            "FastVideo/LTX2-Distilled-Diffusers",
+            GPU_POOL_LOAD_KWARGS,
+        )
+        generator_config_to_fastvideo_args(config)
+        return captured
+
+    def test_ltx2_refine_flags_reemitted(self, args_kwargs) -> None:
+        assert args_kwargs["ltx2_refine_enabled"] is True
+        assert args_kwargs["ltx2_refine_add_noise"] is True
+        assert args_kwargs["ltx2_refine_num_inference_steps"] == 2
+        assert args_kwargs["ltx2_refine_guidance_scale"] == 1.0
+
+    def test_refine_upsampler_path_reemitted(self, args_kwargs) -> None:
+        assert args_kwargs["ltx2_refine_upsampler_path"] == (
+            "/models/ltx2-distilled/spatial_upsampler")
+
+    def test_config_model_path_reemitted(self, args_kwargs) -> None:
+        assert args_kwargs["config_model_path"] == "/models/ltx2-distilled/config"
+
+    def test_torch_compile_kwargs_reassembled(self, args_kwargs) -> None:
+        assert args_kwargs["torch_compile_kwargs"] == {
+            "backend": "inductor",
+            "fullgraph": True,
+            "mode": "max-autotune-no-cudagraphs",
+            "dynamic": False,
+        }
+
+    def test_vae_tiling_reemitted_with_legacy_name(self, args_kwargs) -> None:
+        assert args_kwargs["ltx2_vae_tiling"] is False
+
+    def test_no_stray_refine_dict(self, args_kwargs) -> None:
+        """preset_overrides.refine must flatten to ltx2_refine_* kwargs
+        rather than landing as a nested ``refine`` kwarg that
+        FastVideoArgs doesn't understand."""
+        assert "refine" not in args_kwargs
+
+
+class TestRefineFlattenCoversAllTypedFields:
+    """Every field on LTX2Refine{Preset,Stage}Override must survive the
+    round-trip through preset_overrides.refine back to ltx2_refine_*
+    kwargs. Guards against the hardcoded-key-tuple regression where
+    image_crf / video_position_offset_sec silently dropped."""
+
+    def test_all_fields_reemitted(self, monkeypatch) -> None:
+        from fastvideo import fastvideo_args as fva
+        from fastvideo.api.compat import (
+            generator_config_to_fastvideo_args,
+        )
+        from fastvideo.api.schema import GeneratorConfig, PipelineSelection
+        from fastvideo.pipelines.basic.ltx2.stage_overrides import (
+            REFINE_FLAT_KEYS, )
+
+        captured: dict[str, object] = {}
+
+        class _Captured:
+
+            def __init__(self, **kw):
+                self.kwargs = kw
+
+        def _capture(**kw):
+            captured.update(kw)
+            return _Captured(**kw)
+
+        monkeypatch.setattr(fva.FastVideoArgs, "from_kwargs", _capture)
+
+        refine_payload = {
+            # Preset-override fields.
+            "enabled": True,
+            "add_noise": False,
+            # Stage-override fields.
+            "num_inference_steps": 3,
+            "guidance_scale": 1.5,
+            "image_crf": 18,
+            "video_position_offset_sec": 2.5,
+        }
+        assert set(refine_payload) == REFINE_FLAT_KEYS, (
+            "payload must cover every typed field to exercise the flatten loop")
+
+        config = GeneratorConfig(
+            model_path="/models/ltx2",
+            pipeline=PipelineSelection(preset_overrides={"refine": refine_payload}),
+        )
+        generator_config_to_fastvideo_args(config)
+
+        for key, value in refine_payload.items():
+            assert captured[f"ltx2_refine_{key}"] == value
+
+
+class TestCompileExtrasPreserved:
+    """Additional torch.compile kwargs beyond the four typed fields
+    round-trip through ``CompileConfig.extras``."""
+
+    def test_extras_preserved(self, monkeypatch) -> None:
+        from fastvideo import fastvideo_args as fva
+
+        captured: dict[str, object] = {}
+
+        def _capture(**kw):
+            captured.update(kw)
+
+            class _Captured:
+
+                def __init__(self, **kw):
+                    self.kwargs = kw
+
+            return _Captured(**kw)
+
+        monkeypatch.setattr(fva.FastVideoArgs, "from_kwargs", _capture)
+
+        kwargs = deepcopy(GPU_POOL_LOAD_KWARGS)
+        kwargs["torch_compile_kwargs"] = {
+            "backend": "inductor",
+            "options": {"triton.cudagraphs": False},
+            "disable": False,
+        }
+        config = legacy_from_pretrained_to_config(
+            "FastVideo/LTX2-Distilled-Diffusers", kwargs)
+        assert config.engine.compile.backend == "inductor"
+        assert config.engine.compile.extras == {
+            "options": {"triton.cudagraphs": False},
+            "disable": False,
+        }
+
+        generator_config_to_fastvideo_args(config)
+        assert captured["torch_compile_kwargs"] == {
+            "backend": "inductor",
+            "options": {"triton.cudagraphs": False},
+            "disable": False,
+        }

fastvideo/tests/api/test_ltx2_stage_overrides.py

@@ -0,0 +1,121 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Tests for typed LTX-2 stage override dataclasses."""
+from __future__ import annotations
+
+import pytest
+
+from fastvideo.api.errors import ConfigValidationError
+from fastvideo.api.presets import get_preset, validate_stage_overrides
+from fastvideo.pipelines.basic.ltx2.stage_overrides import (
+    LTX2RefinePresetOverride,
+    LTX2RefineStageOverride,
+    REFINE_PRESET_OVERRIDE_FIELDS,
+    REFINE_STAGE_OVERRIDE_FIELDS,
+    refine_override_to_dict,
+)
+
+
+class TestRefineStageOverrideDataclass:
+
+    def test_all_fields_default_to_none(self) -> None:
+        override = LTX2RefineStageOverride()
+        assert override.num_inference_steps is None
+        assert override.guidance_scale is None
+        assert override.image_crf is None
+        assert override.video_position_offset_sec is None
+
+    def test_explicit_construction(self) -> None:
+        override = LTX2RefineStageOverride(
+            num_inference_steps=2,
+            guidance_scale=1.0,
+            image_crf=18,
+            video_position_offset_sec=2.5,
+        )
+        assert override.num_inference_steps == 2
+        assert override.guidance_scale == 1.0
+        assert override.image_crf == 18
+        assert override.video_position_offset_sec == 2.5
+
+    def test_to_dict_drops_none(self) -> None:
+        override = LTX2RefineStageOverride(num_inference_steps=3)
+        assert refine_override_to_dict(override) == {
+            "num_inference_steps": 3,
+        }
+
+    def test_to_dict_with_all_fields(self) -> None:
+        override = LTX2RefineStageOverride(
+            num_inference_steps=2,
+            guidance_scale=1.0,
+            image_crf=18,
+            video_position_offset_sec=0.0,
+        )
+        assert refine_override_to_dict(override) == {
+            "num_inference_steps": 2,
+            "guidance_scale": 1.0,
+            "image_crf": 18,
+            "video_position_offset_sec": 0.0,
+        }
+
+    def test_fields_accessor_matches_dataclass(self) -> None:
+        assert REFINE_STAGE_OVERRIDE_FIELDS == frozenset({
+            "num_inference_steps",
+            "guidance_scale",
+            "image_crf",
+            "video_position_offset_sec",
+        })
+
+
+class TestRefinePresetOverrideDataclass:
+
+    def test_all_fields_default_to_none(self) -> None:
+        override = LTX2RefinePresetOverride()
+        assert override.enabled is None
+        assert override.add_noise is None
+
+    def test_to_dict_drops_none(self) -> None:
+        override = LTX2RefinePresetOverride(enabled=True)
+        assert refine_override_to_dict(override) == {
+            "enabled": True,
+        }
+
+    def test_to_dict_with_all_fields(self) -> None:
+        override = LTX2RefinePresetOverride(enabled=True, add_noise=False)
+        assert refine_override_to_dict(override) == {
+            "enabled": True,
+            "add_noise": False,
+        }
+
+    def test_fields_accessor_matches_dataclass(self) -> None:
+        assert REFINE_PRESET_OVERRIDE_FIELDS == frozenset({
+            "enabled",
+            "add_noise",
+        })
+
+
+class TestStageOverridesMirrorPresetSchema:
+    """The ltx2_two_stage preset's refine stage schema must list
+    exactly the :class:`LTX2RefineStageOverride` field names."""
+
+    def test_allowed_overrides_mirror_dataclass(self) -> None:
+        import fastvideo.registry  # noqa: F401
+        preset = get_preset("ltx2_two_stage", "ltx2")
+        refine_schema = next(
+            s for s in preset.stage_schemas if s.name == "refine")
+        assert refine_schema.allowed_overrides == REFINE_STAGE_OVERRIDE_FIELDS
+
+    def test_roundtrip_through_validate_stage_overrides(self) -> None:
+        import fastvideo.registry  # noqa: F401
+        preset = get_preset("ltx2_two_stage", "ltx2")
+        override = LTX2RefineStageOverride(
+            num_inference_steps=3,
+            guidance_scale=1.0,
+        )
+        validate_stage_overrides(
+            preset, {"refine": refine_override_to_dict(override)})
+
+    def test_unknown_field_rejected(self) -> None:
+        import fastvideo.registry  # noqa: F401
+        preset = get_preset("ltx2_two_stage", "ltx2")
+        with pytest.raises(ConfigValidationError):
+            validate_stage_overrides(
+                preset, {"refine": {"unknown_key": 1}})

fastvideo/tests/api/test_parser.py

@@ -111,7 +111,14 @@ def test_load_run_config_supports_yaml_roundtrip(tmp_path) -> None:
                     "vae": True,
                     "pin_cpu_memory": True,
                 },
-                "compile": {"enabled": False, "kwargs": {}},
+                "compile": {
+                    "enabled": False,
+                    "backend": None,
+                    "fullgraph": None,
+                    "mode": None,
+                    "dynamic": None,
+                    "extras": {},
+                },
                 "enable_stage_verification": True,
                 "use_fsdp_inference": False,
                 "disable_autocast": False,
@@ -133,6 +140,7 @@ def test_load_run_config_supports_yaml_roundtrip(tmp_path) -> None:
                     "override_pipeline_cls_name": None,
                     "override_transformer_cls_name": None,
                 },
+                "vae_tiling": None,
                 "preset_overrides": {},
                 "experimental": {},
             },

fastvideo/tests/api/test_presets.py

@@ -334,7 +334,7 @@ class TestLtx2Presets:
         import fastvideo.registry  # noqa: F401
         presets = get_presets_for_family("ltx2")
         names = {p.name for p in presets}
-        assert names == {"ltx2_base", "ltx2_distilled"}
+        assert names == {"ltx2_base", "ltx2_distilled", "ltx2_two_stage"}
 
     def test_ltx2_base_lookup(self) -> None:
         import fastvideo.registry  # noqa: F401
@@ -349,6 +349,43 @@ class TestLtx2Presets:
         assert p.defaults["num_inference_steps"] == 8
         assert p.defaults["guidance_scale"] == 1.0
 
+    def test_ltx2_two_stage_is_two_stage(self) -> None:
+        import fastvideo.registry  # noqa: F401
+        p = get_preset("ltx2_two_stage", "ltx2")
+        assert len(p.stage_schemas) == 2
+        assert p.stage_schemas[0].name == "denoise"
+        assert p.stage_schemas[1].name == "refine"
+        assert p.stage_schemas[1].kind == "refinement"
+
+    def test_ltx2_two_stage_stage_defaults(self) -> None:
+        import fastvideo.registry  # noqa: F401
+        p = get_preset("ltx2_two_stage", "ltx2")
+        refine = p.stage_defaults["refine"]
+        # stage-2 refine only supports 2 or 3 denoising steps; preset
+        # defaults to 2 (matches gpu_pool.py load_kwargs).
+        assert refine["num_inference_steps"] == 2
+        assert refine["guidance_scale"] == 1.0
+
+    def test_ltx2_two_stage_refine_overrides_valid(self) -> None:
+        import fastvideo.registry  # noqa: F401
+        p = get_preset("ltx2_two_stage", "ltx2")
+        validate_stage_overrides(
+            p, {"refine": {"num_inference_steps": 3}})
+        validate_stage_overrides(
+            p, {"refine": {"guidance_scale": 1.0}})
+        validate_stage_overrides(
+            p, {"refine": {"image_crf": 18}})
+        validate_stage_overrides(
+            p, {"refine": {"video_position_offset_sec": 2.5}})
+
+    def test_ltx2_two_stage_rejects_unknown_refine_override(self) -> None:
+        import fastvideo.registry  # noqa: F401
+        from fastvideo.api.errors import ConfigValidationError
+        p = get_preset("ltx2_two_stage", "ltx2")
+        with pytest.raises(ConfigValidationError):
+            validate_stage_overrides(
+                p, {"refine": {"bogus_field": 1}})
+
 
 # -------------------------------------------------------------------
 # Hunyuan preset integration

fastvideo/tests/api/test_schema_parity_inventory.py

@@ -46,24 +46,48 @@ def _flatten_status_section(section: dict, valid_statuses: set[str]) -> set[str]
     return names
 
 
-def _get_extra_dataclass_fields(package_name: str, base_cls: type) -> set[str]:
-    package = importlib.import_module(package_name)
+def _get_extra_dataclass_fields(
+    package_names: str | tuple[str, ...],
+    base_cls: type,
+) -> set[str]:
+    """Collect dataclass fields declared on ``base_cls`` subclasses found
+    under any of the given package roots.
+
+    Accepts either a single package name (string) or a tuple of package
+    roots — the latter supports the PR 6 colocation where each model
+    family's ``PipelineConfig`` subclass moves from
+    ``fastvideo.configs.pipelines.<family>`` to
+    ``fastvideo.pipelines.basic.<family>.pipeline_configs``.
+    """
+    if isinstance(package_names, str):
+        package_names = (package_names, )
+
     base_fields = {f.name for f in dataclasses.fields(base_cls)}
     extras: set[str] = set()
-    if not hasattr(package, "__path__"):
-        return extras
-    for _, modname, _ in pkgutil.iter_modules(package.__path__):
-        if modname == "__pycache__":
+
+    for package_name in package_names:
+        package = importlib.import_module(package_name)
+        if not hasattr(package, "__path__"):
             continue
-        module = importlib.import_module(f"{package_name}.{modname}")
-        for obj in vars(module).values():
-            if (
-                isinstance(obj, type)
-                and dataclasses.is_dataclass(obj)
-                and issubclass(obj, base_cls)
-                and obj is not base_cls
-            ):
-                extras.update(f.name for f in dataclasses.fields(obj) if f.name not in base_fields)
+        for _, modname, is_pkg in pkgutil.walk_packages(
+                package.__path__, prefix=f"{package_name}."):
+            # Flat ``configs.pipelines.<family>`` modules carry the config
+            # directly; colocated ``basic.<family>.pipeline_configs``
+            # submodules do too. Everything else under ``basic`` is heavy
+            # model code we don't need to import for a schema check.
+            basename = modname.rsplit(".", 1)[-1]
+            is_flat = modname.startswith("fastvideo.configs.pipelines.")
+            if not is_flat and basename != "pipeline_configs":
+                continue
+            module = importlib.import_module(modname)
+            for obj in vars(module).values():
+                if (isinstance(obj, type)
+                        and dataclasses.is_dataclass(obj)
+                        and issubclass(obj, base_cls)
+                        and obj is not base_cls):
+                    extras.update(
+                        f.name for f in dataclasses.fields(obj)
+                        if f.name not in base_fields)
     return extras
 
 
@@ -177,7 +201,10 @@ def test_pipeline_config_base_fields_are_classified() -> None:
 
 def test_pipeline_config_extension_fields_are_classified() -> None:
     inventory = _load_inventory()
-    expected = _get_extra_dataclass_fields("fastvideo.configs.pipelines", PipelineConfig)
+    expected = _get_extra_dataclass_fields(
+        ("fastvideo.configs.pipelines", "fastvideo.pipelines.basic"),
+        PipelineConfig,
+    )
     actual = _flatten_status_section(
         inventory["surfaces"]["pipeline_config_extensions"],
         set(inventory["status_definitions"]),

fastvideo/tests/modal/ssim_test.py

@@ -480,10 +480,10 @@ def _prepare_ssim_workspace(
     {checkout_command}
     rm -rf fastvideo/tests/ssim/reference_videos
     git_retry git submodule update --init --recursive
+    uv pip install -e .[test]
     cd fastvideo-kernel
     ./build.sh
     cd ..
-    uv pip install -e .[test]
     uv pip install git+https://github.com/microsoft/MoGe.git
     export HF_HOME='/root/data/.cache'
     hf auth login --token "$HF_API_KEY"

fastvideo/tests/ssim/inference_similarity_utils.py

@@ -23,6 +23,7 @@ DEVICE_MAPPINGS = (
     ("L40S", "L40S"),
     ("H100", "H100"),
     ("H200", "H200"),
+    ("B200", "B200"),
 )
 
 

fastvideo/tests/ssim/reference_videos_cli.py

@@ -251,6 +251,8 @@ def upload_reference_videos(
     reference_dirs_by_tier: Sequence[tuple[str, Path]],
     token: str,
     private: bool,
+    model_id: str | None = None,
+    force: bool = False,
 ) -> None:
     HfApi, _ = _load_hf_sdk()
     api = HfApi(token=token)
@@ -261,15 +263,44 @@ def upload_reference_videos(
         exist_ok=True,
     )
 
+    try:
+        existing_repo_files = set(
+            api.list_repo_files(repo_id=repo_id, repo_type=repo_type))
+    except Exception:
+        # Fresh repo or list failure — treat as empty so upload can proceed.
+        existing_repo_files = set()
+
     for quality_tier, reference_dir in reference_dirs_by_tier:
         if not reference_dir.exists():
             raise FileNotFoundError(f"Reference directory not found: {reference_dir}")
-        path_in_repo = f"{REFERENCE_VIDEOS_DIRNAME}/{quality_tier}/{reference_dir.name}"
-        print(f"Uploading {reference_dir.name} ({quality_tier}) to {repo_id} ...")
+        base_in_repo = f"{REFERENCE_VIDEOS_DIRNAME}/{quality_tier}/{reference_dir.name}"
+        if model_id:
+            folder_path = reference_dir / model_id
+            if not folder_path.exists():
+                raise FileNotFoundError(
+                    f"Model subfolder not found for upload: {folder_path}")
+            path_in_repo = f"{base_in_repo}/{model_id}"
+        else:
+            folder_path = reference_dir
+            path_in_repo = base_in_repo
+
+        conflicts = sorted(
+            f for f in existing_repo_files
+            if f.startswith(f"{path_in_repo}/") or f == path_in_repo)
+        if conflicts and not force:
+            preview = "\n".join(f"  - {c}" for c in conflicts[:10])
+            more = f"\n  ... and {len(conflicts) - 10} more" if len(conflicts) > 10 else ""
+            raise RuntimeError(
+                f"Refusing to overwrite existing HF files under {path_in_repo} "
+                f"({len(conflicts)} file(s) already present):\n{preview}{more}\n"
+                f"Re-run with --force to overwrite.")
+
+        target_desc = f"{reference_dir.name}/{model_id}" if model_id else reference_dir.name
+        print(f"Uploading {target_desc} ({quality_tier}) to {repo_id}/{path_in_repo} ...")
         api.upload_folder(
             repo_id=repo_id,
             repo_type=repo_type,
-            folder_path=str(reference_dir),
+            folder_path=str(folder_path),
             path_in_repo=path_in_repo,
             token=token,
         )
@@ -500,6 +531,22 @@ def _build_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="Create/use a private repo instead of public.",
     )
+    upload_parser.add_argument(
+        "--model-id",
+        default=None,
+        help=(
+            "Restrict upload to a single model subfolder "
+            "(reference_videos/<tier>/<device>/<model_id>). "
+            "Use when seeding references for a single new test."),
+    )
+    upload_parser.add_argument(
+        "--force",
+        action="store_true",
+        help=(
+            "Allow overwriting files that already exist at the target path on "
+            "Hugging Face. Off by default so seeding a new test cannot "
+            "clobber existing references."),
+    )
 
     ensure_parser = subparsers.add_parser(
         "ensure",
@@ -607,6 +654,8 @@ def main(argv: Sequence[str] | None = None) -> int:
             reference_dirs_by_tier=reference_dirs_by_tier,
             token=token,
             private=args.private,
+            model_id=args.model_id,
+            force=args.force,
         )
         print("Upload complete.")
         return 0

fastvideo/tests/ssim/test_ltx2_similarity.py

@@ -0,0 +1,93 @@
+# SPDX-License-Identifier: Apache-2.0
+"""SSIM-based similarity test for LTX-2 distilled text-to-video.
+
+Parameters derived from examples/inference/basic/basic_ltx2_distilled.py,
+with resolution + num_inference_steps reduced to keep GPU CI runtime
+bounded. Full-quality variant (via ``--ssim-full-quality``) falls back
+to the ``ltx2_distilled`` preset defaults.
+"""
+import os
+
+import pytest
+
+from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.logger import init_logger
+from fastvideo.tests.ssim.inference_similarity_utils import (
+    resolve_inference_device_reference_folder,
+    run_text_to_video_similarity_test,
+)
+
+logger = init_logger(__name__)
+
+REQUIRED_GPUS = 2
+
+device_reference_folder = resolve_inference_device_reference_folder(logger)
+
+LTX2_DISTILLED_PARAMS = {
+    "num_gpus": 2,
+    "model_path": "FastVideo/LTX2-Distilled-Diffusers",
+    "height": 512,
+    "width": 768,
+    "num_frames": 45,
+    "num_inference_steps": 4,
+    "guidance_scale": 1.0,
+    "seed": 10,
+    "sp_size": 2,
+    "tp_size": 1,
+    "fps": 24,
+    "ltx2_vae_tiling": True,
+}
+_LTX2_DISTILLED_FULL_QUALITY_DEFAULTS = SamplingParam.from_pretrained(
+    LTX2_DISTILLED_PARAMS["model_path"])
+LTX2_DISTILLED_FULL_QUALITY_PARAMS = {
+    "num_gpus": LTX2_DISTILLED_PARAMS["num_gpus"],
+    "model_path": LTX2_DISTILLED_PARAMS["model_path"],
+    "height": _LTX2_DISTILLED_FULL_QUALITY_DEFAULTS.height,
+    "width": _LTX2_DISTILLED_FULL_QUALITY_DEFAULTS.width,
+    "num_frames": _LTX2_DISTILLED_FULL_QUALITY_DEFAULTS.num_frames,
+    "num_inference_steps":
+        _LTX2_DISTILLED_FULL_QUALITY_DEFAULTS.num_inference_steps,
+    "guidance_scale": _LTX2_DISTILLED_FULL_QUALITY_DEFAULTS.guidance_scale,
+    "seed": _LTX2_DISTILLED_FULL_QUALITY_DEFAULTS.seed,
+    "sp_size": LTX2_DISTILLED_PARAMS["sp_size"],
+    "tp_size": LTX2_DISTILLED_PARAMS["tp_size"],
+    "fps": _LTX2_DISTILLED_FULL_QUALITY_DEFAULTS.fps,
+    "ltx2_vae_tiling": LTX2_DISTILLED_PARAMS["ltx2_vae_tiling"],
+}
+
+LTX2_DISTILLED_MODEL_TO_PARAMS = {
+    "LTX2-Distilled-Diffusers": LTX2_DISTILLED_PARAMS,
+}
+FULL_QUALITY_LTX2_DISTILLED_MODEL_TO_PARAMS = {
+    "LTX2-Distilled-Diffusers": LTX2_DISTILLED_FULL_QUALITY_PARAMS,
+}
+
+LTX2_DISTILLED_TEST_PROMPTS = [
+    "A warm sunny backyard. The camera starts in a tight cinematic "
+    "close-up of a woman and a man in their 30s, facing each other with "
+    "serious expressions. The camera slowly pans right, revealing a "
+    "grandfather in the garden wearing enormous butterfly wings, waving "
+    "his arms in the air like he's trying to take off. The tone is "
+    "deadpan, absurd, and quietly tragic.",
+]
+
+
+@pytest.mark.parametrize("prompt", LTX2_DISTILLED_TEST_PROMPTS)
+@pytest.mark.parametrize("attention_backend_name", ["FLASH_ATTN"])
+@pytest.mark.parametrize("model_id", list(LTX2_DISTILLED_MODEL_TO_PARAMS.keys()))
+def test_ltx2_distilled_inference_similarity(
+    prompt: str,
+    attention_backend_name: str,
+    model_id: str,
+) -> None:
+    run_text_to_video_similarity_test(
+        logger=logger,
+        script_dir=os.path.dirname(os.path.abspath(__file__)),
+        device_reference_folder=device_reference_folder,
+        prompt=prompt,
+        attention_backend_name=attention_backend_name,
+        model_id=model_id,
+        default_params_map=LTX2_DISTILLED_MODEL_TO_PARAMS,
+        full_quality_params_map=FULL_QUALITY_LTX2_DISTILLED_MODEL_TO_PARAMS,
+        min_acceptable_ssim=0.98,
+    )

tests/local_tests/test_ltx2_registry.py

@@ -12,7 +12,7 @@ from fastvideo.registry import (
     get_pipeline_config_cls_from_name,
     get_sampling_param_cls_for_name,
 )
-from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
+from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
 
 
 @pytest.mark.parametrize(