Add jobs timers

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Aryan Kumar <aryan5v@users.noreply.github.com>

.agents/skills/add-model-01-prep/scripts/clone_reference_repo.py

@@ -10,9 +10,7 @@ from pathlib import Path
 
 
 def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Clone a reference repo for FastVideo parity tests."
-    )
+    parser = argparse.ArgumentParser(description="Clone a reference repo for FastVideo parity tests.")
     parser.add_argument("repo_url", help="Official reference repository URL")
     parser.add_argument("target_dir", help="Directory to clone into")
     parser.add_argument("--branch", help="Branch or tag to clone")
@@ -62,9 +60,7 @@ def gitignore_entry_for(target: Path) -> str:
     try:
         relative = resolved.relative_to(root)
     except ValueError as exc:
-        raise ValueError(
-            "--update-gitignore requires target_dir to be under the current directory"
-        ) from exc
+        raise ValueError("--update-gitignore requires target_dir to be under the current directory") from exc
 
     text = relative.as_posix().rstrip("/")
     return "/" + text + "/"

.agents/skills/add-model-01-prep/scripts/download_hf_weights.py

@@ -8,14 +8,12 @@ import os
 import sys
 from pathlib import Path
 
-
 HF_TOKEN_ENV_KEYS = ("HF_TOKEN", "HUGGINGFACE_HUB_TOKEN", "HF_API_KEY")
 
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
-        description="Download a HF model snapshot or selected files into a local directory."
-    )
+        description="Download a HF model snapshot or selected files into a local directory.")
     parser.add_argument("repo_id", help="HF repo id, for example Org/Model")
     parser.add_argument("local_dir", help="Destination directory")
     parser.add_argument("--repo-type", default="model", help="HF repo type (default: model)")

.agents/skills/add-model-01-prep/scripts/inspect_hf_layout.py

@@ -10,7 +10,6 @@ import sys
 from pathlib import Path
 from typing import Any
 
-
 HF_TOKEN_ENV_KEYS = ("HF_TOKEN", "HUGGINGFACE_HUB_TOKEN", "HF_API_KEY")
 RAW_WEIGHT_SUFFIXES = (".safetensors", ".pt", ".pth", ".ckpt", ".bin")
 KNOWN_COMPONENTS = {
@@ -34,8 +33,7 @@ KNOWN_COMPONENTS = {
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
-        description="Classify a HF repo or local directory as Diffusers, raw, custom, or unknown."
-    )
+        description="Classify a HF repo or local directory as Diffusers, raw, custom, or unknown.")
     parser.add_argument("source", help="HF repo id or local weights directory")
     parser.add_argument("--repo-type", default="model", help="HF repo type (default: model)")
     parser.add_argument("--revision", help="HF revision to inspect")
@@ -94,14 +92,12 @@ def load_remote_files(
 ) -> list[str]:
     from huggingface_hub import list_repo_files
 
-    return sorted(
-        list_repo_files(
-            repo_id,
-            repo_type=repo_type,
-            revision=revision,
-            token=token,
-        )
-    )
+    return sorted(list_repo_files(
+        repo_id,
+        repo_type=repo_type,
+        revision=revision,
+        token=token,
+    ))
 
 
 def load_remote_model_index(
@@ -215,24 +211,24 @@ def build_result(args: argparse.Namespace) -> dict[str, Any]:
         "components_seen": components,
         "file_count": len(files),
         "file_scan_truncated": truncated,
-        "files_sample": files[: args.sample_limit],
+        "files_sample": files[:args.sample_limit],
     }
 
 
 def print_human(result: dict[str, Any]) -> None:
     for key in (
-        "source",
-        "source_kind",
-        "repo_type",
-        "revision",
-        "token_env",
-        "source_layout",
-        "needs_conversion",
-        "model_index_class",
-        "model_index_diffusers_version",
-        "model_index_error",
-        "file_count",
-        "file_scan_truncated",
+            "source",
+            "source_kind",
+            "repo_type",
+            "revision",
+            "token_env",
+            "source_layout",
+            "needs_conversion",
+            "model_index_class",
+            "model_index_diffusers_version",
+            "model_index_error",
+            "file_count",
+            "file_scan_truncated",
     ):
         value = result.get(key)
         if value is not None:

.agents/skills/add-model-02-parity/templates/component_parity_test.py

@@ -18,7 +18,6 @@ import pytest
 import torch
 from torch.testing import assert_close
 
-
 os.environ.setdefault("MASTER_ADDR", "localhost")
 os.environ.setdefault("MASTER_PORT", "29519")
 os.environ.setdefault("DISABLE_SP", "1")
@@ -35,15 +34,10 @@ FASTVIDEO_CONFIG_CLASS = "<FastVideoConfig>"  # TODO.
 FASTVIDEO_MODEL_MODULE = "fastvideo.models.<bucket>.<module>"  # TODO.
 FASTVIDEO_MODEL_CLASS = "<FastVideoModel>"  # TODO.
 
-OFFICIAL_REF_DIR = Path(
-    os.getenv("<FAMILY_UPPER>_OFFICIAL_REF_DIR", REPO_ROOT / "<ReferenceDir>")
-)
-LOCAL_WEIGHTS_DIR = Path(
-    os.getenv("<FAMILY_UPPER>_LOCAL_WEIGHTS_DIR", REPO_ROOT / "official_weights" / FAMILY)
-)
-CONVERTED_WEIGHTS_DIR = Path(
-    os.getenv("<FAMILY_UPPER>_CONVERTED_WEIGHTS_DIR", REPO_ROOT / "converted_weights" / FAMILY)
-)
+OFFICIAL_REF_DIR = Path(os.getenv("<FAMILY_UPPER>_OFFICIAL_REF_DIR", REPO_ROOT / "<ReferenceDir>"))
+LOCAL_WEIGHTS_DIR = Path(os.getenv("<FAMILY_UPPER>_LOCAL_WEIGHTS_DIR", REPO_ROOT / "official_weights" / FAMILY))
+CONVERTED_WEIGHTS_DIR = Path(os.getenv("<FAMILY_UPPER>_CONVERTED_WEIGHTS_DIR",
+                                       REPO_ROOT / "converted_weights" / FAMILY))
 
 
 def _resolve_hf_token() -> str | None:
@@ -99,18 +93,14 @@ def _load_official_model(device: torch.device, dtype: torch.dtype) -> torch.nn.M
     model = OfficialClass()  # TODO: pass official config kwargs.
     state_dict = {}  # TODO: load official state dict from LOCAL_WEIGHTS_DIR.
     missing, unexpected = model.load_state_dict(state_dict, strict=True)
-    assert not missing and not unexpected, (
-        f"official load mismatch missing={missing[:5]} unexpected={unexpected[:5]}"
-    )
+    assert not missing and not unexpected, (f"official load mismatch missing={missing[:5]} unexpected={unexpected[:5]}")
     return model.to(device=device, dtype=dtype).eval()
 
 
 def _load_fastvideo_model(device: torch.device, dtype: torch.dtype) -> torch.nn.Module:
     """Load the FastVideo component with the same tensor content."""
     if not CONVERTED_WEIGHTS_DIR.exists() and not LOCAL_WEIGHTS_DIR.exists():
-        pytest.skip(
-            f"No FastVideo loadable weights: {CONVERTED_WEIGHTS_DIR} or {LOCAL_WEIGHTS_DIR}"
-        )
+        pytest.skip(f"No FastVideo loadable weights: {CONVERTED_WEIGHTS_DIR} or {LOCAL_WEIGHTS_DIR}")
 
     # TODO: replace with the bucket-specific FastVideo config/class/loader.
     # DiT examples:
@@ -127,8 +117,7 @@ def _load_fastvideo_model(device: torch.device, dtype: torch.dtype) -> torch.nn.
     state_dict = {}  # TODO: load converted or directly mapped state dict.
     missing, unexpected = model.load_state_dict(state_dict, strict=True)
     assert not missing and not unexpected, (
-        f"FastVideo load mismatch missing={missing[:5]} unexpected={unexpected[:5]}"
-    )
+        f"FastVideo load mismatch missing={missing[:5]} unexpected={unexpected[:5]}")
     return model.to(device=device, dtype=dtype).eval()
 
 
@@ -187,11 +176,9 @@ def test_component_parity():
 
     assert official_out.shape == fastvideo_out.shape
     diff = (official_out - fastvideo_out).abs()
-    print(
-        f"official abs_mean={official_out.abs().mean().item():.6f} "
-        f"fastvideo abs_mean={fastvideo_out.abs().mean().item():.6f} "
-        f"diff_max={diff.max().item():.6f} diff_mean={diff.mean().item():.6f}"
-    )
+    print(f"official abs_mean={official_out.abs().mean().item():.6f} "
+          f"fastvideo abs_mean={fastvideo_out.abs().mean().item():.6f} "
+          f"diff_max={diff.max().item():.6f} diff_mean={diff.mean().item():.6f}")
 
     # TODO: pick tolerance by scope:
     # - single block / same kernel: 1e-4

.agents/skills/add-model-07-conversion/templates/family_to_diffusers.py

@@ -27,7 +27,6 @@ try:
 except ImportError:  # pragma: no cover - optional local conversion dependency
     snapshot_download = None
 
-
 # TODO: fill with authoritative component prefixes for monolithic checkpoints.
 # Example: {"model.model.": "transformer", "pretransform.model.": "vae"}
 COMPONENT_PREFIXES: dict[str, str] = {}
@@ -47,10 +46,7 @@ SKIP_PATTERNS: tuple[str, ...] = ()
 
 
 def _hf_token() -> str | None:
-    return (
-        os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN")
-        or os.environ.get("HF_API_KEY")
-    )
+    return (os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") or os.environ.get("HF_API_KEY"))
 
 
 def resolve_src(src: str, revision: str | None) -> Path:
@@ -95,11 +91,10 @@ def apply_mapping(key: str) -> str | None:
     return key
 
 
-def split_monolithic(
-    state: dict[str, torch.Tensor],
-) -> dict[str, OrderedDict[str, torch.Tensor]]:
+def split_monolithic(state: dict[str, torch.Tensor], ) -> dict[str, OrderedDict[str, torch.Tensor]]:
     components: dict[str, OrderedDict[str, torch.Tensor]] = {
-        name: OrderedDict() for name in set(COMPONENT_PREFIXES.values())
+        name: OrderedDict()
+        for name in set(COMPONENT_PREFIXES.values())
     }
     intentionally_skipped: list[str] = []
     unowned: list[str] = []
@@ -117,10 +112,8 @@ def split_monolithic(
             unowned.append(key)
     if unowned:
         sample = ", ".join(unowned[:10])
-        raise ValueError(
-            f"Unowned monolithic keys: {len(unowned)}. "
-            f"Add COMPONENT_PREFIXES or SKIP_PATTERNS entries. Sample: {sample}"
-        )
+        raise ValueError(f"Unowned monolithic keys: {len(unowned)}. "
+                         f"Add COMPONENT_PREFIXES or SKIP_PATTERNS entries. Sample: {sample}")
     if intentionally_skipped:
         print(f"Intentionally skipped {len(intentionally_skipped)} keys")
     return {name: weights for name, weights in components.items() if weights}
@@ -143,8 +136,12 @@ def build_component_configs(_src_dir: Path) -> dict[str, dict[str, Any]]:
     # TODO: emit config content accepted by FastVideo loaders. Most components use
     # config.json; schedulers use scheduler_config.json.
     return {
-        "transformer": {"_class_name": "<FastVideoTransformerClass>"},
-        "vae": {"_class_name": "<FastVideoVAEClass>"},
+        "transformer": {
+            "_class_name": "<FastVideoTransformerClass>"
+        },
+        "vae": {
+            "_class_name": "<FastVideoVAEClass>"
+        },
     }
 
 
@@ -177,19 +174,13 @@ def build_model_index(
     }
     if revision:
         index["_fastvideo_converted_revision"] = revision
-    return {
-        key: value
-        for key, value in index.items()
-        if key.startswith("_") or key in available_components
-    }
+    return {key: value for key, value in index.items() if key.startswith("_") or key in available_components}
 
 
 def validate_component_configs(configs: dict[str, dict[str, Any]]) -> None:
     # TODO: instantiate each FastVideo config and call update_model_arch(...) or
     # update_model_config(...) with this JSON so unknown emitted keys fail here.
-    placeholder_configs = [
-        name for name, config in configs.items() if "<" in json.dumps(config)
-    ]
+    placeholder_configs = [name for name, config in configs.items() if "<" in json.dumps(config)]
     if placeholder_configs:
         raise ValueError(f"Replace config placeholders for: {placeholder_configs}")
 
@@ -201,9 +192,7 @@ def verify_conversion(
     del dst_dir, components
     # TODO: load each emitted stateful component through its production loader and
     # assert strict load, or document exact allowed missing/unexpected keys.
-    raise NotImplementedError(
-        "Implement production config validation and strict-load checks"
-    )
+    raise NotImplementedError("Implement production config validation and strict-load checks")
 
 
 def write_component(
@@ -216,9 +205,7 @@ def write_component(
     if component_dir.exists() and any(component_dir.iterdir()):
         shutil.rmtree(component_dir)
     component_dir.mkdir(parents=True, exist_ok=True)
-    save_file(
-        dict(state), str(component_dir / "diffusion_pytorch_model.safetensors")
-    )
+    save_file(dict(state), str(component_dir / "diffusion_pytorch_model.safetensors"))
     if config is not None:
         config_path = component_dir / config_filename(name)
         with config_path.open("w", encoding="utf-8") as f:
@@ -261,9 +248,7 @@ def convert(
 
     if layout in {"monolithic", "raw_official"}:
         # TODO: replace model.safetensors with the official monolithic file name.
-        components = split_monolithic(
-            load_checkpoint(default_monolithic_checkpoint(src_path))
-        )
+        components = split_monolithic(load_checkpoint(default_monolithic_checkpoint(src_path)))
     elif layout in {"separate_components", "mixed"}:
         if not src_path.is_dir():
             raise ValueError(f"{layout} layout requires a source directory: {src_path}")
@@ -271,9 +256,7 @@ def convert(
     else:
         raise ValueError(f"Unsupported template layout: {layout}")
 
-    copied = (
-        copy_passthrough(src_path, dst_dir) if src_path.is_dir() else []
-    )
+    copied = (copy_passthrough(src_path, dst_dir) if src_path.is_dir() else [])
     configs = build_component_configs(src_path if src_path.is_dir() else src_path.parent)
     validate_component_configs(configs)
     for name, state in components.items():
@@ -289,9 +272,7 @@ def convert(
 
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--src", required=True, help="HF repo id, local dir, or checkpoint path"
-    )
+    parser.add_argument("--src", required=True, help="HF repo id, local dir, or checkpoint path")
     parser.add_argument("--revision", help="HF branch, tag, or commit for repo sources")
     parser.add_argument(
         "--dst",

.agents/skills/add-model-08-trace/templates/block_trace_debug.py

@@ -24,8 +24,8 @@ from typing import Any
 
 import torch
 
-FAMILY: str = "<family>"       # e.g. "magi_human", "ltx2", "wan"
-COMPONENT: str = "<component>" # e.g. "dit", "vae", "encoder"
+FAMILY: str = "<family>"  # e.g. "magi_human", "ltx2", "wan"
+COMPONENT: str = "<component>"  # e.g. "dit", "vae", "encoder"
 DRILL_LAYER_ENV: str = "<FAMILY>_DEBUG_DRILL_LAYER"
 HYPOTHESIS_ENV: str = "<FAMILY>_DEBUG_PATCH_<HYPOTHESIS>"
 REL_THRESHOLD: float = 0.005  # 0.5% abs_mean drift flags a block as divergent
@@ -94,6 +94,7 @@ def _attach_block_hooks(
     handles: list[Any] = []
 
     def _hook(name: str):
+
         def fn(_module, _inputs, outputs):
             t = outputs[0] if isinstance(outputs, tuple) else outputs
             if not torch.is_tensor(t):
@@ -101,6 +102,7 @@ def _attach_block_hooks(
             log.append({"side": label, **_stat(name, t)})
             if tensors is not None:
                 tensors[name] = t.detach().float().cpu()
+
         return fn
 
     def _pre_hook(name: str):
@@ -114,6 +116,7 @@ def _attach_block_hooks(
             log.append({"side": label, **_stat(key, t)})
             if tensors is not None:
                 tensors[key] = t.detach().float().cpu()
+
         return fn
 
     # TODO: adapt attribute paths to your model. Remove adapter block if absent.
@@ -131,43 +134,21 @@ def _attach_block_hooks(
             # magi-human uses: attention, mlp.pre_norm, mlp.up_gate_proj,
             # mlp.down_proj (pre+post), mlp, attn_post_norm, mlp_post_norm.
             if hasattr(layer, "attention"):
-                handles.append(
-                    layer.attention.register_forward_hook(_hook(f"{tag}.attention"))
-                )
+                handles.append(layer.attention.register_forward_hook(_hook(f"{tag}.attention")))
             if hasattr(layer, "mlp"):
                 mlp = layer.mlp
                 if hasattr(mlp, "pre_norm"):
-                    handles.append(
-                        mlp.pre_norm.register_forward_hook(_hook(f"{tag}.mlp.pre_norm"))
-                    )
+                    handles.append(mlp.pre_norm.register_forward_hook(_hook(f"{tag}.mlp.pre_norm")))
                 if hasattr(mlp, "up_gate_proj"):
-                    handles.append(
-                        mlp.up_gate_proj.register_forward_hook(
-                            _hook(f"{tag}.mlp.up_gate_proj")
-                        )
-                    )
+                    handles.append(mlp.up_gate_proj.register_forward_hook(_hook(f"{tag}.mlp.up_gate_proj")))
                 if hasattr(mlp, "down_proj"):
-                    handles.append(
-                        mlp.down_proj.register_forward_pre_hook(
-                            _pre_hook(f"{tag}.mlp.down_proj")
-                        )
-                    )
-                    handles.append(
-                        mlp.down_proj.register_forward_hook(_hook(f"{tag}.mlp.down_proj"))
-                    )
+                    handles.append(mlp.down_proj.register_forward_pre_hook(_pre_hook(f"{tag}.mlp.down_proj")))
+                    handles.append(mlp.down_proj.register_forward_hook(_hook(f"{tag}.mlp.down_proj")))
                 handles.append(mlp.register_forward_hook(_hook(f"{tag}.mlp")))
             if hasattr(layer, "attn_post_norm"):
-                handles.append(
-                    layer.attn_post_norm.register_forward_hook(
-                        _hook(f"{tag}.attn_post_norm")
-                    )
-                )
+                handles.append(layer.attn_post_norm.register_forward_hook(_hook(f"{tag}.attn_post_norm")))
             if hasattr(layer, "mlp_post_norm"):
-                handles.append(
-                    layer.mlp_post_norm.register_forward_hook(
-                        _hook(f"{tag}.mlp_post_norm")
-                    )
-                )
+                handles.append(layer.mlp_post_norm.register_forward_hook(_hook(f"{tag}.mlp_post_norm")))
     return handles
 
 
@@ -193,11 +174,9 @@ def _write_log(entries: list[dict], path: Path) -> None:
     path.parent.mkdir(parents=True, exist_ok=True)
     with open(path, "w") as f:
         for e in entries:
-            f.write(
-                f"{e['name']} {e['shape']} "
-                f"{e['abs_mean']:.8f} {e['sum']:.4f} "
-                f"{e['min']:.6f} {e['max']:.6f}\n"
-            )
+            f.write(f"{e['name']} {e['shape']} "
+                    f"{e['abs_mean']:.8f} {e['sum']:.4f} "
+                    f"{e['min']:.6f} {e['max']:.6f}\n")
 
 
 def _sort_key(name: str, drill_layer: int) -> tuple:
@@ -205,9 +184,14 @@ def _sort_key(name: str, drill_layer: int) -> tuple:
         return (0, "")
     if name.startswith(f"L{drill_layer:02d}."):
         sub_order = {
-            "attention": 0, "attn_post_norm": 1, "mlp.pre_norm": 2,
-            "mlp.up_gate_proj": 3, "mlp.down_proj<in>": 4,
-            "mlp.down_proj": 5, "mlp": 6, "mlp_post_norm": 7,
+            "attention": 0,
+            "attn_post_norm": 1,
+            "mlp.pre_norm": 2,
+            "mlp.up_gate_proj": 3,
+            "mlp.down_proj<in>": 4,
+            "mlp.down_proj": 5,
+            "mlp": 6,
+            "mlp_post_norm": 7,
         }.get(name.split(".", 1)[1], 9)
         return (1, f"block[{drill_layer:02d}]", sub_order)
     if name.startswith("block["):
@@ -216,10 +200,8 @@ def _sort_key(name: str, drill_layer: int) -> tuple:
 
 
 def _print_table(by_name: dict[str, dict], drill_layer: int) -> int | None:
-    hdr = (
-        f"{'name':<18} {'up_shape':<22} {'up_absmean':>12} {'fv_absmean':>12} "
-        f"{'absmean_diff':>14} {'rel%':>8} {'up_sum':>14} {'fv_sum':>14} {'sum_diff':>12}"
-    )
+    hdr = (f"{'name':<18} {'up_shape':<22} {'up_absmean':>12} {'fv_absmean':>12} "
+           f"{'absmean_diff':>14} {'rel%':>8} {'up_sum':>14} {'fv_sum':>14} {'sum_diff':>12}")
     print(f"\n{hdr}\n{'-' * len(hdr)}")
     first_div: int | None = None
     for name in sorted(by_name.keys(), key=lambda n: _sort_key(n, drill_layer)):
@@ -235,11 +217,9 @@ def _print_table(by_name: dict[str, dict], drill_layer: int) -> int | None:
             flag = " <<< DIVERGE"
             if first_div is None:
                 first_div = int(name[len("block["):-1])
-        print(
-            f"{name:<18} {str(up['shape']):<22} {up['abs_mean']:>12.6f} "
-            f"{fv['abs_mean']:>12.6f} {am_diff:>14.6f} {am_rel * 100:>7.3f}% "
-            f"{up['sum']:>14.4f} {fv['sum']:>14.4f} {sum_diff:>12.4f}{flag}"
-        )
+        print(f"{name:<18} {str(up['shape']):<22} {up['abs_mean']:>12.6f} "
+              f"{fv['abs_mean']:>12.6f} {am_diff:>14.6f} {am_rel * 100:>7.3f}% "
+              f"{up['sum']:>14.4f} {fv['sum']:>14.4f} {sum_diff:>12.4f}{flag}")
     return first_div
 
 
@@ -255,10 +235,8 @@ def _print_elementwise(up_t: dict[str, torch.Tensor], fv_t: dict[str, torch.Tens
             continue
         diff = (a - b).abs()
         rel = (diff.mean().item() / max(a.abs().mean().item(), 1e-9)) * 100
-        print(
-            f"{name:<30} {str(tuple(a.shape)):<22} "
-            f"{diff.max().item():>12.6f} {diff.mean().item():>12.6f} {rel:>9.4f}%"
-        )
+        print(f"{name:<30} {str(tuple(a.shape)):<22} "
+              f"{diff.max().item():>12.6f} {diff.mean().item():>12.6f} {rel:>9.4f}%")
 
 
 def main() -> None:

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

@@ -43,12 +43,10 @@ def _add_official_to_path() -> Path:
 
 def _log_tensor_stats(label: str, tensor: torch.Tensor) -> None:
     value = tensor.detach().float()
-    print(
-        f"[{_MODEL_FAMILY} PIPELINE] {label}: shape={tuple(tensor.shape)} "
-        f"dtype={tensor.dtype} device={tensor.device} "
-        f"min={value.min().item():.6f} max={value.max().item():.6f} "
-        f"mean={value.mean().item():.6f} std={value.std().item():.6f}"
-    )
+    print(f"[{_MODEL_FAMILY} PIPELINE] {label}: shape={tuple(tensor.shape)} "
+          f"dtype={tensor.dtype} device={tensor.device} "
+          f"min={value.min().item():.6f} max={value.max().item():.6f} "
+          f"mean={value.mean().item():.6f} std={value.std().item():.6f}")
 
 
 def _extract_tensor(output: Any, key: str) -> torch.Tensor:
@@ -73,10 +71,8 @@ def _run_official_pipeline(
     device: torch.device,
 ) -> Any:
     del official_path, params, device
-    pytest.skip(
-        "TODO: import the official pipeline/factory, load official weights, "
-        "run with params, and return the comparison target."
-    )
+    pytest.skip("TODO: import the official pipeline/factory, load official weights, "
+                "run with params, and return the comparison target.")
 
 
 def _run_fastvideo_pipeline(model_path: Path, params: dict[str, Any]) -> Any:
@@ -146,8 +142,6 @@ def test_todo_model_family_pipeline_official_parity() -> None:
     assert official_tensor.shape == fastvideo_tensor.shape
 
     diff = (official_tensor - fastvideo_tensor).abs()
-    print(
-        f"diff max={diff.max().item():.6f} "
-        f"mean={diff.mean().item():.6f} median={diff.median().item():.6f}"
-    )
+    print(f"diff max={diff.max().item():.6f} "
+          f"mean={diff.mean().item():.6f} median={diff.median().item():.6f}")
     assert_close(fastvideo_tensor, official_tensor, atol=1e-2, rtol=1e-2)

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

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

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

@@ -1,6 +1,6 @@
 ---
 name: reseed-ssim-references
-description: Re-seed HF reference videos for a single existing SSIM test on Modal L40S. Always backs up current refs locally first, regenerates on Modal, pauses for the user to eyeball before-vs-after quality, then overwrites the targeted `<model_id>` subtree on `FastVideo/ssim-reference-videos` with `--force`. Use when an intentional code change (model port fix, attention backend swap, kernel upgrade, hyperparameter change) has invalidated existing refs and they need to be regenerated. Pairs with `seed-ssim-references`, which is for first-time seeding only.
+description: Re-seed HF reference videos for a single existing SSIM test on Modal L40S. Always backs up current refs locally first, regenerates on Modal, pauses for the user to eyeball before-vs-after quality, then overwrites the targeted model subtree on `FastVideo/ssim-reference-videos` with `--force`. Use when an intentional code change (model port fix, attention backend swap, kernel upgrade, hyperparameter change) has invalidated existing refs and they need to be regenerated. Pairs with `seed-ssim-references`, which is for first-time seeding only.
 ---
 
 # Re-seed SSIM Reference Videos
@@ -13,7 +13,7 @@ on HF — the old refs are overwritten — so the skill always:
 
 1. Confirms intent with a one-liner the user has to type.
 2. Downloads the existing refs as a local, timestamped backup.
-3. Regenerates on Modal L40S (same code path that CI uses).
+3. Regenerates through the manual legacy Modal L40S maintenance path.
 4. Pauses for a side-by-side eyeball of backup vs new mp4s.
 5. Uploads with `--force`, scoped to the single `--model-id`.
 6. Reminds the user to keep the backup until the PR lands.
@@ -51,8 +51,9 @@ harder to recover from than failing closed.
 
 Hardcoded:
 
-- Modal GPU: **L40S** (matches CI; re-seeding from another SKU produces refs
-  that L40S CI cannot match).
+- Modal GPU: **L40S**. This is a manual reference-maintenance target, not the
+  active Slurm CI compute path; changing the SKU also changes the historical
+  `L40S_reference_videos` contract.
 - Quality tier: **`default`**. `full_quality` is a separate, deliberate
   operation.
 - HF repo: `FastVideo/ssim-reference-videos` (override via

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

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

.buildkite/pipeline.yml

@@ -1,7 +1,8 @@
 env:
   IMAGE_VERSION: "py3.12-latest"
   BUILDKITE_CLEAN_CHECKOUT: true
-  # Buildkite only launches Modal; remote jobs initialize their own submodules.
+  # Slurm workers clone the immutable commit and initialize submodules inside
+  # their isolated container. The Buildkite login-plane checkout is a no-op.
   BUILDKITE_GIT_SUBMODULES: false
 
 notify:
@@ -10,528 +11,471 @@ notify:
     if: build.env("TEST_SCOPE") == "fastcheck" || build.env("TEST_SCOPE") == null
   - github_commit_status:
       context: "full-suite-passed"
-    if: build.env("TEST_SCOPE") == "full"
+    if: build.env("TEST_SCOPE") == "full" || build.env("TEST_SCOPE") == "merge"
   - github_commit_status:
       context: "direct-test-completed"
     if: build.env("TEST_SCOPE") == "direct"
+  - github_commit_status:
+      context: "scheduled-ssim-passed"
+    if: build.env("TEST_SCOPE") == "scheduled"
 
+# This is the complete active GPU CI surface. Every command is a trusted host
+# dispatcher, and every test payload executes inside the Slinky Slurm tray.
+# fastvideo/tests/modal remains available only for an explicit manual rollback;
+# no active pipeline or slash-command route invokes it.
 steps:
-  # ============================================================
-  # Direct test: triggered by /test <name> slash command.
-  # Labels match fastcheck/full-suite counterparts so the GitHub
-  # check status overwrites the original failed check.
-  # Only ONE step executes per build (gated by TEST_TYPE).
-  # ============================================================
+  - label: ":microscope: Encoder Tests"
+    key: "encoder"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,encoder,/) ||
+      build.env("TEST_SCOPE") == "fastcheck" ||
+      build.env("TEST_SCOPE") == null ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "encoder" || build.env("TEST_TYPE") == "encoder_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "encoder_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
 
-  # --- Fastcheck-scope direct tests ---
-    - label: ":microscope: Encoder Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "encoder"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: VAE Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "vae"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: Transformer Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "transformer"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: Kernel Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "kernel_tests"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: Unit Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "unit_test"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":microscope: DreamVerse App Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "dreamverse_app"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
+  - label: ":microscope: VAE Tests"
+    key: "vae"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,vae,/) ||
+      build.env("TEST_SCOPE") == "fastcheck" ||
+      build.env("TEST_SCOPE") == null ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "vae" || build.env("TEST_TYPE") == "vae_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "vae_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
 
-  # --- Full-suite-scope direct tests ---
-    - label: ":bar_chart: SSIM Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "ssim"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-          - exit_status: 1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: LoRA Inference Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "inference_lora"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: LoRA Extraction Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "lora_extraction"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Training Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Distillation DMD Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "distillation_dmd"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Self-Forcing Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "self_forcing"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: LoRA Training Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training_lora"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-          - exit_status: 1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Training Tests VSA"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "training_vsa"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-          - exit_status: 1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Inference Tests VMoBA"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "inference_vmoba"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Performance Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "performance"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: API Server Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "api_server"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Train Framework Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "train_framework"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
-    - label: ":test_tube: Eval Metrics Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "eval"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
+  - label: ":microscope: Transformer Tests"
+    key: "transformer"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,transformer,/) ||
+      build.env("TEST_SCOPE") == "fastcheck" ||
+      build.env("TEST_SCOPE") == null ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "transformer" || build.env("TEST_TYPE") == "transformer_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "transformer_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
 
-  # ============================================================
-  # Fastcheck: Runs on every PR (~10-15 min parallel)
-  # Core component validation: encoders, VAEs, transformers,
-  # CUDA kernels, and unit tests.
-  # ============================================================
-    - label: "Trigger Fastcheck"
-      if: build.env("TEST_SCOPE") == "fastcheck" || build.env("TEST_SCOPE") == null
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      plugins:
-        - monorepo-diff#v1.4.0:
-            diff: 'git fetch origin "${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}" && git diff --name-only "origin/${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}...HEAD"'
-            watch:
-            - path:
-                - "fastvideo/models/encoders/**"
-                - "fastvideo/models/loader/**"
-                - "fastvideo/tests/encoders/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 20m .buildkite/scripts/pr_test.sh"
-                label: ":microscope: Encoder Tests"
-                env:
-                  - TEST_TYPE=encoder
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/models/vaes/**"
-                - "fastvideo/models/loader/**"
-                - "fastvideo/tests/vaes/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 20m .buildkite/scripts/pr_test.sh"
-                label: ":microscope: VAE Tests"
-                env:
-                  - TEST_TYPE=vae
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/models/dits/**"
-                - "fastvideo/models/loader/**"
-                - "fastvideo/tests/transformers/**"
-                - "fastvideo/layers/**"
-                - "fastvideo/attention/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: ":microscope: Transformer Tests"
-                env:
-                  - TEST_TYPE=transformer
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo-kernel/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: ":microscope: Kernel Tests"
-                env:
-                  - TEST_TYPE=kernel_tests
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/**"
-                - ".buildkite/**"
-                - ".github/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: ":microscope: Unit Tests"
-                env:
-                  - TEST_TYPE=unit_test
-                agents:
-                  queue: "default"
-            - path:
-                - "apps/dreamverse/**"
-                - "pyproject.toml"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: ":microscope: DreamVerse App Tests"
-                env:
-                  - TEST_TYPE=dreamverse_app
-                agents:
-                  queue: "default"
+  - label: ":microscope: Kernel Tests"
+    key: "kernel-tests"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,kernel-tests,/) ||
+      build.env("TEST_SCOPE") == "fastcheck" ||
+      build.env("TEST_SCOPE") == null ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "kernel_tests" || build.env("TEST_TYPE") == "kernel_tests_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "kernel_tests_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
 
-  # ============================================================
-  # Full Suite: Runs when TEST_SCOPE=full
-  # Triggered by adding the 'ready' label (via ci-trigger-full-suite.yml)
-  # or on-demand via /test full slash command.
-  # Includes integration tests, SSIM regression, training pipelines,
-  # and performance benchmarks.
-  # ============================================================
-    - label: "Trigger Full Suite"
-      if: build.env("TEST_SCOPE") == "full"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      plugins:
-        - monorepo-diff#v1.4.0:
-            diff: 'git fetch origin "${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}" && git diff --name-only "origin/${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}...HEAD"'
-            watch:
-            - path:
-                - "fastvideo/**/*.py"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 90m .buildkite/scripts/pr_test.sh"
-                label: ":bar_chart: SSIM Tests"
-                env:
-                  - TEST_TYPE=ssim
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/tests/lora/**"
-                - "fastvideo/models/loader/**"
-                - "fastvideo/tests/transformers/**"
-                - "fastvideo/pipelines/**"
-                - "fastvideo/layers/lora/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 20m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: LoRA Inference Tests"
-                env:
-                  - TEST_TYPE=inference_lora
-                agents:
-                  queue: "default"
-            - path:
-                - "scripts/lora_extraction/**"
-                - "fastvideo/tests/lora_extraction/**"
-                - "fastvideo/models/loader/**"
-                - "fastvideo/training/training_utils.py"
-                - "fastvideo/layers/lora/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 90m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: LoRA Extraction Tests"
-                env:
-                  - TEST_TYPE=lora_extraction
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Training Tests"
-                env:
-                  - TEST_TYPE=training
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/training/*distillation_pipeline.py"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Distillation DMD Tests"
-                env:
-                  - TEST_TYPE=distillation_dmd
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/training/*self_forcing_distillation_pipeline.py"
-                - "fastvideo/tests/training/self-forcing/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Self-Forcing Tests"
-                env:
-                  - TEST_TYPE=self_forcing
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 25m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: LoRA Training Tests"
-                env:
-                  - TEST_TYPE=training_lora
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/**"
-                - "fastvideo-kernel/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Training Tests VSA"
-                env:
-                  - TEST_TYPE=training_vsa
-                retry:
-                  automatic:
-                    - exit_status: 1
-                      limit: 2
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo-kernel/**"
-                - "fastvideo/attention/backends/vmoba.py"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Inference Tests VMoBA"
-                env:
-                  - TEST_TYPE=inference_vmoba
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/models/dits/**"
-                - "fastvideo/pipelines/**"
-                - "fastvideo/attention/**"
-                - "fastvideo/layers/**"
-                - "fastvideo/worker/**"
-                - "fastvideo/entrypoints/**"
-                - "fastvideo/performance/**"
-                - "fastvideo/tests/performance/**"
-                - ".buildkite/performance-benchmarks/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Performance Tests"
-                env:
-                  - TEST_TYPE=performance
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/entrypoints/openai/**"
-                - "fastvideo/entrypoints/cli/serve.py"
-                - "fastvideo/tests/entrypoints/test_openai_api_integration.py"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: API Server Tests"
-                env:
-                  - TEST_TYPE=api_server
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/train/**"
-                - "fastvideo/tests/train/models/**"
-                - "fastvideo/tests/train/fixtures/**"
-                - "fastvideo/models/dits/**"
-                - "fastvideo/models/loader/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Train Framework Tests"
-                env:
-                  - TEST_TYPE=train_framework
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/eval/**"
-                - "fastvideo/tests/eval/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile"
-              config:
-                command: "timeout 90m .buildkite/scripts/pr_test.sh"
-                label: ":test_tube: Eval Metrics Tests"
-                env:
-                  - TEST_TYPE=eval
-                agents:
-                  queue: "default"
+  - label: ":microscope: Unit Tests"
+    key: "unit"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,unit,/) ||
+      build.env("TEST_SCOPE") == "fastcheck" ||
+      build.env("TEST_SCOPE") == null ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "unit_test" || build.env("TEST_TYPE") == "unit_test_ci"))
+    command: "/opt/fastvideo-ci-runner/run-unit"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "unit_test_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":microscope: DreamVerse App Tests"
+    key: "dreamverse"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,dreamverse,/) ||
+      build.env("TEST_SCOPE") == "fastcheck" ||
+      build.env("TEST_SCOPE") == null ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "dreamverse_app" || build.env("TEST_TYPE") == "dreamverse_app_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "dreamverse_app_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Golden-Gate Tests"
+    key: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,golden-gate,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "golden_gate" || build.env("TEST_TYPE") == "golden_gate_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "golden_gate_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":bar_chart: SSIM Tests"
+    key: "ssim"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      build.env("TEST_SCOPE") == "scheduled" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,ssim,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "ssim" || build.env("TEST_TYPE") == "ssim_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    concurrency: 1
+    concurrency_group: "fastvideo/slinky/whole-tray"
+    env:
+      TEST_TYPE: "ssim_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+        - exit_status: 1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: LoRA Inference Tests"
+    key: "lora-inference"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,lora-inference,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "inference_lora" || build.env("TEST_TYPE") == "inference_lora_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "inference_lora_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: LoRA Extraction Tests"
+    key: "lora-extraction"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,lora-extraction,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "lora_extraction" || build.env("TEST_TYPE") == "lora_extraction_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "lora_extraction_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Training Tests"
+    key: "training"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,training,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "training" || build.env("TEST_TYPE") == "training_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    concurrency: 1
+    concurrency_group: "fastvideo/slinky/whole-tray"
+    env:
+      TEST_TYPE: "training_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Distillation DMD Tests"
+    key: "distillation"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,distillation,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "distillation_dmd" || build.env("TEST_TYPE") == "distillation_dmd_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "distillation_dmd_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Self-Forcing Tests"
+    key: "self-forcing"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,self-forcing,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "self_forcing" || build.env("TEST_TYPE") == "self_forcing_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "self_forcing_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: LoRA Training Tests"
+    key: "lora-training"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,lora-training,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "training_lora" || build.env("TEST_TYPE") == "training_lora_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "training_lora_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+        - exit_status: 1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Training Tests VSA"
+    key: "training-vsa"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,training-vsa,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "training_vsa" || build.env("TEST_TYPE") == "training_vsa_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "training_vsa_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+        - exit_status: 1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Inference Tests VMoBA"
+    key: "inference-vmoba"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,inference-vmoba,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "inference_vmoba" || build.env("TEST_TYPE") == "inference_vmoba_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "inference_vmoba_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Performance Tests"
+    key: "performance"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,performance,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "performance" || build.env("TEST_TYPE") == "performance_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "performance_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: API Server Tests"
+    key: "api-server"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,api-server,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "api_server" || build.env("TEST_TYPE") == "api_server_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "api_server_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Train Framework Tests"
+    key: "train-framework"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,train-framework,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "train_framework" || build.env("TEST_TYPE") == "train_framework_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "train_framework_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"
+
+  - label: ":test_tube: Eval Metrics Tests"
+    key: "eval"
+    depends_on: "golden-gate"
+    if: |
+      build.env("TEST_SCOPE") == "full" ||
+      (build.env("TEST_SCOPE") == "merge" &&
+       build.env("MERGE_TEST_PLAN") =~ /,eval,/) ||
+      (build.env("TEST_SCOPE") == "direct" &&
+       (build.env("TEST_TYPE") == "eval" || build.env("TEST_TYPE") == "eval_ci"))
+    command: "/opt/fastvideo-ci-runner/run-ci"
+    timeout_in_minutes: 90
+    env:
+      TEST_TYPE: "eval_ci"
+    retry:
+      automatic:
+        - exit_status: 128
+          limit: 3
+        - exit_status: -1
+          limit: 2
+    agents:
+      queue: "ci-runner"

.buildkite/scripts/lanes/api_server.sh

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

.buildkite/scripts/lanes/distillation_dmd.sh

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

.buildkite/scripts/lanes/dreamverse.sh

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

.buildkite/scripts/lanes/encoder.sh

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

.buildkite/scripts/lanes/eval.sh

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

.buildkite/scripts/lanes/golden_gate.sh

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

.buildkite/scripts/lanes/inference_lora.sh

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

.buildkite/scripts/lanes/inference_vmoba.sh

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

.buildkite/scripts/lanes/kernel_tests.sh

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

.buildkite/scripts/lanes/lora_extraction.sh

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

.buildkite/scripts/lanes/performance.sh

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

.buildkite/scripts/lanes/self_forcing.sh

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

.buildkite/scripts/lanes/ssim.sh

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

.buildkite/scripts/lanes/train_framework.sh

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

.buildkite/scripts/lanes/training.sh

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

.buildkite/scripts/lanes/training_lora.sh

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

.buildkite/scripts/lanes/training_vsa.sh

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

.buildkite/scripts/lanes/transformer.sh

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

.buildkite/scripts/lanes/vae.sh

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

.buildkite/scripts/pr_test.sh

@@ -1,6 +1,19 @@
 #!/bin/bash
 set -uo pipefail
 
+# DORMANT ROLLBACK ONLY. Active CI is Slurm-only and pipeline.yml never calls
+# this launcher. Refuse every Buildkite invocation even if a stale step or
+# operator typo reaches this file; local rollback experiments require an
+# explicit opt-in.
+if [ -n "${BUILDKITE:-}" ]; then
+  echo "Legacy Modal CI is disabled; use the Slinky Slurm runner." >&2
+  exit 2
+fi
+if [ "${FASTVIDEO_ENABLE_LEGACY_MODAL_CI:-0}" != 1 ]; then
+  echo "Legacy Modal CI is dormant. Set FASTVIDEO_ENABLE_LEGACY_MODAL_CI=1 only for a manual rollback test." >&2
+  exit 2
+fi
+
 log() {
   echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
 }
@@ -187,6 +200,10 @@ case "$TEST_TYPE" in
         log "Running transformer tests..."
         MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
         ;;
+    "golden_gate")
+        log "Running golden-gate tests..."
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_golden_gate_tests"
+        ;;
     "ssim")
         log "Running SSIM tests..."
         SSIM_BOOTSTRAP_ARGS=$(ssim_bootstrap_args)

.buildkite/scripts/unit_test.sh

@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+exec pytest \
+  ./fastvideo/tests/api/ \
+  ./fastvideo/tests/contract/ \
+  ./fastvideo/tests/dataset/ \
+  ./fastvideo/tests/workflow/ \
+  ./fastvideo/tests/entrypoints/ \
+  ./fastvideo/tests/loader/ \
+  ./fastvideo/tests/pipelines/ \
+  ./fastvideo/tests/platforms/ \
+  ./fastvideo/tests/train/ \
+  ./fastvideo/tests/stages/ \
+  ./fastvideo/tests/ops/ \
+  ./fastvideo/tests/worker/ \
+  ./fastvideo/tests/training/test_trackers.py \
+  ./fastvideo/tests/attention/test_sdpa_metadata_mask_contract.py \
+  ./fastvideo/tests/modal/test_kernel_build_cache.py \
+  ./fastvideo/tests/modal/test_pr_test.py \
+  ./fastvideo/tests/modal/test_ssim_test.py \
+  --ignore=./fastvideo/tests/entrypoints/test_openai_api_integration.py \
+  --ignore=./fastvideo/tests/train/models \
+  --ignore=./fastvideo/tests/train/methods \
+  -vs

.github/PULL_REQUEST_TEMPLATE.md

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

.github/scripts/gate_full_suite.sh

@@ -1,14 +1,14 @@
 #!/usr/bin/env bash
-# Gate the expensive Buildkite full suite on the cheap GitHub checks.
+# Gate the path-aware Buildkite merge plan on the cheap GitHub checks.
 #
 # Polls the workflow runs for the PR head commit and only exits 0 once the
 # watched cheap workflows (pre-commit, docs build) have succeeded, so the
-# 'ready' label cannot burn ~20 GPU lanes on a head that a cheap check has
-# already doomed.
+# 'ready' label cannot burn path-selected GPU lanes on a head that a cheap
+# check has already doomed.
 #
 # Semantics:
 #   - watched run completed with a bad conclusion  -> exit 1 (fail CLOSED:
-#     no full suite; the next push re-arms via the 'synchronize' trigger)
+#     no merge gate; the next push re-arms via the 'synchronize' trigger)
 #   - watched run cancelled                         -> still pending: the docs
 #     workflow's repo-global 'pages' concurrency group cancels runs superseded
 #     by unrelated pushes, so 'cancelled' is not a verdict on this PR
@@ -29,7 +29,7 @@ set -euo pipefail
 : "${PR_NUMBER:?PR_NUMBER (pull request number) is required}"
 : "${GITHUB_REPOSITORY:?GITHUB_REPOSITORY is required}"
 
-# Workflow-level `name:` values that must be green before the full suite
+# Workflow-level `name:` values that must be green before the merge gate
 # may start. "Deploy Documentation" is path-filtered on PRs, so its run may
 # legitimately never exist; pre-commit always runs, so it must appear.
 WATCHED_NAMES='["pre-commit", "Deploy Documentation"]'
@@ -56,7 +56,7 @@ recheck_ready_label() {
   if pr_json=$(gh_api "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}" 2>/dev/null); then
     if ! jq -e '[.labels[]?.name] | index("ready")' <<<"$pr_json" >/dev/null 2>&1; then
       echo "::error::PR #${PR_NUMBER} no longer has the 'ready' label —" \
-        "NOT triggering the Buildkite full suite. Re-add the label to re-arm."
+        "NOT triggering the Buildkite merge gate. Re-add the label to re-arm."
       exit 1
     fi
   else
@@ -84,7 +84,7 @@ while true; do
         | map(.name) | join(", ")' <<<"$state")
     if [ -n "$failed" ]; then
       echo "::error::Cheap check(s) failed on ${PR_SHA}: ${failed}." \
-        "NOT triggering the Buildkite full suite. Push a fix (the 'ready'" \
+        "NOT triggering the Buildkite merge gate. Push a fix (the 'ready'" \
         "label re-arms on every push), or re-run the failed check and then" \
         "re-run this workflow."
       exit 1
@@ -97,7 +97,7 @@ while true; do
     if [ "$pending" -eq 0 ]; then
       if [ -z "$missing" ]; then
         recheck_ready_label
-        echo "All watched cheap checks are green — full suite may proceed."
+        echo "All watched cheap checks are green — merge gate may proceed."
         exit 0
       fi
       case "$missing" in
@@ -119,14 +119,14 @@ while true; do
     echo "::warning::GitHub API error querying workflow runs for ${PR_SHA} (attempt ${api_fails}/3)."
     if [ "$api_fails" -ge 3 ]; then
       recheck_ready_label
-      echo "::warning::FAILING OPEN: cannot query GitHub check status — triggering the full suite WITHOUT the cheap-check gate."
+      echo "::warning::FAILING OPEN: cannot query GitHub check status — triggering the merge gate WITHOUT the cheap-check gate."
       exit 0
     fi
   fi
 
   if [ "$elapsed" -ge "$MAX_WAIT_SECS" ]; then
     recheck_ready_label
-    echo "::warning::FAILING OPEN: watched checks still pending after $(( MAX_WAIT_SECS / 60 )) min${missing:+ (never appeared: ${missing})} — triggering the full suite anyway."
+    echo "::warning::FAILING OPEN: watched checks still pending after $(( MAX_WAIT_SECS / 60 )) min${missing:+ (never appeared: ${missing})} — triggering the merge gate anyway."
     exit 0
   fi
   sleep "$POLL_SECS"

.github/scripts/plan_merge_ci.py

@@ -0,0 +1,582 @@
+#!/usr/bin/env python3
+"""Select the additive GPU integration lanes needed by a PR diff.
+
+Fastcheck is the universal six-lane baseline and is intentionally not repeated
+here.  This planner selects only the more expensive merge-gate lanes.  Unknown
+source/build paths fail closed to the complete integration set, while explicit
+documentation and repository-metadata paths require no additional GPU work.
+"""
+
+from __future__ import annotations
+
+import argparse
+import fnmatch
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import TextIO
+
+MERGE_LANES = (
+    "golden-gate",
+    "ssim",
+    "lora-inference",
+    "lora-extraction",
+    "training",
+    "distillation",
+    "self-forcing",
+    "lora-training",
+    "training-vsa",
+    "inference-vmoba",
+    "performance",
+    "api-server",
+    "train-framework",
+    "eval",
+)
+
+LANE_SCRIPT_TO_KEY = {
+    "api_server.sh": "api-server",
+    "distillation_dmd.sh": "distillation",
+    "eval.sh": "eval",
+    "golden_gate.sh": "golden-gate",
+    "inference_lora.sh": "lora-inference",
+    "inference_vmoba.sh": "inference-vmoba",
+    "lora_extraction.sh": "lora-extraction",
+    "performance.sh": "performance",
+    "self_forcing.sh": "self-forcing",
+    "ssim.sh": "ssim",
+    "train_framework.sh": "train-framework",
+    "training.sh": "training",
+    "training_lora.sh": "lora-training",
+    "training_vsa.sh": "training-vsa",
+}
+
+FASTCHECK_LANE_SCRIPTS = {
+    "dreamverse.sh",
+    "encoder.sh",
+    "kernel_tests.sh",
+    "transformer.sh",
+    "vae.sh",
+}
+
+LEGACY_TRAINING_LANES = (
+    "training",
+    "distillation",
+    "self-forcing",
+    "lora-training",
+    "training-vsa",
+)
+
+ALL_TRAINING_LANES = (*LEGACY_TRAINING_LANES, "train-framework")
+
+SSIM_SMOKE_TESTS = (
+    "test_flux_t2i_similarity.py",
+    "test_wan_t2v_similarity.py",
+)
+
+SAFE_PATTERNS = (
+    "*.md",
+    "*.rst",
+    ".agents/**",
+    ".claude/**",
+    ".codex/**",
+    ".github/ISSUE_TEMPLATE/**",
+    ".github/PULL_REQUEST_TEMPLATE.md",
+    ".github/dependabot.yml",
+    ".github/mergify.yml",
+    ".github/scripts/**",
+    ".github/workflows/**",
+    ".buildkite/scripts/pre_commit.sh",
+    ".git-blame-ignore-revs",
+    ".gitattributes",
+    ".gitignore",
+    ".pre-commit-config.yaml",
+    "AGENTS.md",
+    "CITATION.cff",
+    "CODE_OF_CONDUCT.md",
+    "CONTRIBUTING.md",
+    "LICENSE",
+    "NOTICE",
+    "__init__.py",
+    "collect_env.py",
+    "SECURITY.md",
+    "assets/**",
+    "comfyui/**",
+    "docs/**",
+    "examples/**",
+    "mkdocs.yml",
+    "requirements-mkdocs.in",
+    "requirements-mkdocs.txt",
+    "scripts/**",
+    "tests/__init__.py",
+    "tests/local_tests/**",
+)
+
+ALL_IMPACT_PATTERNS = (
+    ".buildkite/pipeline.yml",
+    "docker/**",
+    "pyproject.toml",
+    "requirements*.txt",
+    "setup.cfg",
+    "setup.py",
+    "uv.lock",
+)
+
+
+@dataclass(frozen=True)
+class FamilyCoverage:
+    pattern: re.Pattern[str]
+    golden_tests: tuple[str, ...]
+    ssim_tests: tuple[str, ...]
+
+
+FAMILY_COVERAGE = (
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])dreamx(_world)?([/_.-]|$)"),
+        ("test_dreamx.py", ),
+        ("test_dreamx_world_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])flux[_-]?2([/_.-]|$)"),
+        ("test_flux2_klein.py", ),
+        ("test_flux2_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])flux(?![_-]?2)([/_.-]|$)"),
+        ("test_flux.py", ),
+        ("test_flux_t2i_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])(hunyuan)?gamecraft([/_.-]|$)"),
+        ("test_gamecraft.py", ),
+        ("test_gamecraft_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])gen3c([/_.-]|$)"),
+        ("test_gen3c.py", ),
+        ("test_gen3c_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])glm[_-]?image([/_.-]|$)"),
+        ("test_glm_image.py", ),
+        ("test_glm_image_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])kandinsky[_-]?5([/_.-]|$)"),
+        ("test_kandinsky5.py", ),
+        ("test_kandinsky5_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])lingbot([a-z0-9_-]*)([/_.-]|$)"),
+        ("test_lingbot.py", ),
+        ("test_lingbot_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])longcat([/_.-]|$)"),
+        ("test_longcat.py", ),
+        ("test_longcat_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])ltx[_-]?2([/_.-]|$)"),
+        ("test_ltx2.py", ),
+        ("test_ltx2_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])matrixgame[_-]?2([/_.-]|$)"),
+        ("test_matrixgame.py", ),
+        ("test_matrixgame2_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])matrixgame[_-]?3([/_.-]|$)"),
+        ("test_matrixgame.py", ),
+        ("test_matrixgame3_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])minimax[_-]?h3([/_.-]|$)"),
+        ("test_minimax_h3_t2v.py", ),
+        ("test_minimax_h3_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])sd[_-]?3([._-]?5)?([/_.-]|$)"),
+        ("test_sd35.py", ),
+        ("test_sd35_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])stable[_-]?audio([/_.-]|$)"),
+        ("test_stable_audio.py", ),
+        ("test_stable_audio_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])turbo(diffusion)?([/_.-]|$)"),
+        (),
+        ("test_turbodiffusion_similarity.py", ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])wan(video|vae)?([/_.-]|$)"),
+        ("test_wan_t2v.py", "test_wan_vae.py", "test_wan_causal.py", "test_wan_denoising.py"),
+        (
+            "test_causal_similarity.py",
+            "test_wan_i2v_similarity.py",
+            "test_wan_t2v_similarity.py",
+        ),
+    ),
+    FamilyCoverage(
+        re.compile(r"(^|[/_.-])z[_-]?image([/_.-]|$)"),
+        ("test_zimage.py", ),
+        ("test_zimage_similarity.py", ),
+    ),
+)
+
+
+@dataclass
+class MergePlan:
+    lanes: set[str] = field(default_factory=set)
+    golden_tests: set[str] = field(default_factory=set)
+    ssim_tests: set[str] = field(default_factory=set)
+    golden_all: bool = False
+    ssim_all: bool = False
+    reasons: list[str] = field(default_factory=list)
+
+    def add_lanes(self, *lanes: str, reason: str) -> None:
+        unknown = set(lanes) - set(MERGE_LANES)
+        if unknown:
+            raise ValueError(f"Unknown merge lanes: {sorted(unknown)}")
+        self.lanes.update(lanes)
+        self.reasons.append(reason)
+
+    def add_golden(self, tests: tuple[str, ...], reason: str) -> None:
+        self.add_lanes("golden-gate", reason=reason)
+        self.golden_tests.update(tests)
+
+    def add_ssim(self, tests: tuple[str, ...], reason: str) -> None:
+        self.add_lanes("ssim", reason=reason)
+        self.ssim_tests.update(tests)
+
+    def require_all(self, reason: str) -> None:
+        self.lanes.update(MERGE_LANES)
+        self.golden_all = True
+        self.ssim_all = True
+        self.reasons.append(reason)
+
+    def ordered_lanes(self) -> tuple[str, ...]:
+        return tuple(lane for lane in MERGE_LANES if lane in self.lanes)
+
+    def encoded_lanes(self) -> str:
+        lanes = self.ordered_lanes()
+        return "," + ",".join(lanes or ("none", )) + ","
+
+    def encoded_golden_tests(self) -> str:
+        if "golden-gate" not in self.lanes:
+            return "none"
+        if self.golden_all or not self.golden_tests:
+            return "all"
+        return ",".join(sorted(self.golden_tests))
+
+    def encoded_ssim_tests(self) -> str:
+        if "ssim" not in self.lanes:
+            return "none"
+        if self.ssim_all or not self.ssim_tests:
+            return "all"
+        return ",".join(sorted(self.ssim_tests))
+
+
+def _matches_any(path: str, patterns: tuple[str, ...]) -> bool:
+    return any(fnmatch.fnmatchcase(path, pattern) for pattern in patterns)
+
+
+def _family_coverage(path: str) -> tuple[set[str], set[str]]:
+    normalized = path.lower()
+    golden: set[str] = set()
+    ssim: set[str] = set()
+    for family in FAMILY_COVERAGE:
+        if family.pattern.search(normalized):
+            golden.update(family.golden_tests)
+            ssim.update(family.ssim_tests)
+    # Select the component actually touched, including compatibility paths.
+    # Family configs/pipeline wiring can affect all four Wan gates.
+    if re.search(r"(^|[/_.-])wan(video|vae)?([/_.-]|$)", normalized):
+        if (normalized.endswith(("/wan/vae.py", "/wan/vae_config.py", "/vaes/wanvae.py"))
+                or normalized.endswith("/wan/stages/conditioning.py")):
+            golden = {"test_wan_vae.py"}
+        elif normalized.endswith(("/wan/causal_transformer.py", "/dits/causal_wanvideo.py",
+                                  "/wan/stages/causal_denoising.py")):
+            golden = {"test_wan_causal.py"}
+        elif (normalized == "fastvideo/models/dits/wanvideo.py"
+              or normalized.endswith(("/wan/transformer.py", "/wan/stages/denoising.py", "/wan/stages/dmd.py"))):
+            golden = {"test_wan_t2v.py", "test_wan_denoising.py"}
+    return golden, ssim
+
+
+def _select_output_coverage(plan: MergePlan, path: str) -> None:
+    golden, ssim = _family_coverage(path)
+    if golden:
+        plan.add_golden(tuple(sorted(golden)), reason=f"model-family golden coverage: {path}")
+    else:
+        plan.golden_all = True
+        plan.add_lanes("golden-gate", reason=f"shared output golden coverage: {path}")
+    if ssim:
+        plan.add_ssim(tuple(sorted(ssim)), reason=f"model-family SSIM coverage: {path}")
+    else:
+        plan.add_ssim(SSIM_SMOKE_TESTS, reason=f"shared output SSIM smoke coverage: {path}")
+
+
+def classify_paths(paths: list[str]) -> MergePlan:
+    plan = MergePlan()
+    normalized_paths: list[str] = []
+    for raw_path in paths:
+        path = raw_path.strip()
+        while path.startswith("./"):
+            path = path[2:]
+        if path:
+            normalized_paths.append(path)
+    normalized_paths = sorted(set(normalized_paths))
+    if not normalized_paths:
+        plan.require_all("changed-file list was empty; failing closed")
+        return plan
+
+    for path in normalized_paths:
+        if path == "__FASTVIDEO_CI_PLAN_ALL__":
+            plan.require_all("changed-file API failed; failing closed")
+            continue
+
+        if path in {"requirements-mkdocs.in", "requirements-mkdocs.txt"}:
+            plan.reasons.append(f"documentation dependencies need no GPU integration: {path}")
+            continue
+
+        if _matches_any(path, ALL_IMPACT_PATTERNS):
+            plan.require_all(f"cross-cutting build/runtime surface: {path}")
+            continue
+
+        lane_script_prefix = ".buildkite/scripts/lanes/"
+        if path.startswith(lane_script_prefix):
+            script_name = Path(path).name
+            lane = LANE_SCRIPT_TO_KEY.get(script_name)
+            if lane is None:
+                if script_name in FASTCHECK_LANE_SCRIPTS:
+                    plan.reasons.append(f"covered by automatic Fastcheck lane: {path}")
+                else:
+                    plan.require_all(f"unknown lane script: {path}")
+            elif lane == "golden-gate":
+                plan.golden_all = True
+                plan.add_lanes(lane, reason=f"golden lane implementation: {path}")
+            elif lane == "ssim":
+                plan.ssim_all = True
+                plan.add_lanes(lane, reason=f"SSIM lane implementation: {path}")
+            else:
+                plan.add_lanes(lane, reason=f"lane implementation: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/golden_gate/"):
+            name = Path(path).name
+            if name.startswith("test_") and name.endswith(".py"):
+                plan.add_golden((name, ), reason=f"changed golden test: {path}")
+            elif name in {"AGENTS.md", "README.md"}:
+                plan.reasons.append(f"golden documentation only: {path}")
+            else:
+                plan.golden_all = True
+                plan.add_lanes("golden-gate", reason=f"shared golden harness/reference: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/ssim/"):
+            name = Path(path).name
+            if name.startswith("test_") and name.endswith(".py"):
+                plan.add_ssim((name, ), reason=f"changed SSIM test: {path}")
+            elif path.endswith((".py", ".json", ".pt", ".png", ".mp4")):
+                plan.ssim_all = True
+                plan.add_lanes("ssim", reason=f"shared SSIM harness/reference: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/performance/") or path.startswith(".buildkite/performance-benchmarks/"):
+            plan.add_lanes("performance", reason=f"performance coverage: {path}")
+            continue
+        if path.startswith(("fastvideo/performance/", "fastvideo/performance_dashboard/",
+                            "apps/performance_dashboard/")):
+            plan.add_lanes("performance", reason=f"performance implementation: {path}")
+            continue
+        if path.startswith("fastvideo/benchmarks/"):
+            if "/mlx_" in path or Path(path).name.startswith("mlx_"):
+                plan.reasons.append(f"covered by the path-filtered macOS MLX workflow: {path}")
+            else:
+                plan.add_lanes("performance", reason=f"benchmark implementation: {path}")
+            continue
+        if path.startswith("fastvideo/tests/eval/") or path.startswith("fastvideo/eval/"):
+            plan.add_lanes("eval", reason=f"evaluation coverage: {path}")
+            continue
+        if path.startswith("fastvideo/third_party/eval/"):
+            plan.add_lanes("eval", reason=f"vendored evaluation implementation: {path}")
+            continue
+        if path.startswith("fastvideo/tests/lora_extraction/") or path.startswith("scripts/lora_extraction/"):
+            plan.add_lanes("lora-extraction", reason=f"LoRA extraction coverage: {path}")
+            continue
+        if path.startswith("fastvideo/tests/inference/lora/"):
+            plan.add_lanes("lora-inference", reason=f"LoRA inference coverage: {path}")
+            continue
+        if path.startswith("fastvideo/tests/inference/vmoba/"):
+            plan.add_lanes("inference-vmoba", reason=f"VMoBA inference coverage: {path}")
+            continue
+        if path.startswith(("fastvideo/dataset/", "fastvideo/workflow/", "fastvideo/pipelines/preprocess/",
+                            "fastvideo/pipelines/training/")):
+            plan.add_lanes(*ALL_TRAINING_LANES, reason=f"shared data/training input surface: {path}")
+            continue
+        if path.startswith("fastvideo/tests/train/") or path.startswith("fastvideo/train/"):
+            plan.add_lanes("train-framework", reason=f"modular training coverage: {path}")
+            continue
+
+        if path.startswith("fastvideo/tests/training/"):
+            lowered = path.lower()
+            if "/vanilla/" in lowered:
+                plan.add_lanes("training", reason=f"vanilla training coverage: {path}")
+            elif "/distill/" in lowered:
+                plan.add_lanes("distillation", reason=f"distillation coverage: {path}")
+            elif "/self-forcing/" in lowered:
+                plan.add_lanes("self-forcing", reason=f"self-forcing coverage: {path}")
+            elif "/lora/" in lowered:
+                plan.add_lanes("lora-training", reason=f"LoRA training coverage: {path}")
+            elif "/vsa/" in lowered:
+                plan.add_lanes("training-vsa", reason=f"VSA training coverage: {path}")
+            else:
+                plan.add_lanes(*LEGACY_TRAINING_LANES, reason=f"shared legacy training coverage: {path}")
+            continue
+
+        if path.startswith("fastvideo/training/"):
+            lowered = path.lower()
+            if "self_forcing" in lowered:
+                plan.add_lanes("self-forcing", reason=f"self-forcing implementation: {path}")
+            elif "distill" in lowered:
+                plan.add_lanes("distillation", reason=f"distillation implementation: {path}")
+            elif "lora" in lowered:
+                plan.add_lanes("lora-training", reason=f"LoRA training implementation: {path}")
+            else:
+                plan.add_lanes(*LEGACY_TRAINING_LANES, reason=f"shared legacy training implementation: {path}")
+            continue
+
+        lowered = path.lower()
+        if "vmoba" in lowered and path.startswith(("fastvideo/", ".buildkite/")):
+            plan.add_lanes("inference-vmoba", reason=f"VMoBA implementation: {path}")
+            plan.add_golden(("test_wan_t2v.py", ), reason=f"VMoBA end-to-end coverage: {path}")
+            continue
+        if "lora" in lowered and path.startswith("fastvideo/"):
+            plan.add_lanes(
+                "lora-inference",
+                "lora-extraction",
+                "lora-training",
+                reason=f"shared LoRA implementation: {path}",
+            )
+            _select_output_coverage(plan, path)
+            continue
+
+        if path.startswith("fastvideo/entrypoints/") or path.startswith("fastvideo/api/"):
+            plan.add_lanes("api-server", reason=f"API/entrypoint integration: {path}")
+            if "openai" not in lowered and "/cli/" not in lowered:
+                _select_output_coverage(plan, path)
+            continue
+        if path.startswith("fastvideo/worker/"):
+            plan.add_lanes("api-server", reason=f"worker/API integration: {path}")
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith("fastvideo/distributed/"):
+            plan.add_lanes(
+                "training",
+                "train-framework",
+                reason=f"distributed runtime integration: {path}",
+            )
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith(("fastvideo/hooks/", "fastvideo/platforms/", "fastvideo/third_party/")):
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith(("fastvideo/models/", "fastvideo/pipelines/", "fastvideo/configs/",
+                            "fastvideo/layers/", "fastvideo/attention/")):
+            _select_output_coverage(plan, path)
+            continue
+        if path in {
+                "fastvideo/fastvideo_args.py",
+                "fastvideo/forward_context.py",
+                "fastvideo/image_processor.py",
+                "fastvideo/registry.py",
+                "fastvideo/utils.py",
+        }:
+            _select_output_coverage(plan, path)
+            continue
+        if path.startswith("fastvideo/mlx_runtime/"):
+            plan.reasons.append(f"covered by the path-filtered macOS MLX workflow: {path}")
+            continue
+        if path.startswith("fastvideo/logging_utils/") or path in {
+                "fastvideo/__init__.py",
+                "fastvideo/envs.py",
+                "fastvideo/logger.py",
+                "fastvideo/profiler.py",
+                "fastvideo/version.py",
+        }:
+            plan.reasons.append(f"covered by automatic Fastcheck: {path}")
+            continue
+        if path.startswith(("fastvideo-kernel/", "csrc/")):
+            plan.add_golden(("test_wan_t2v.py", ), reason=f"kernel integration smoke: {path}")
+            plan.add_ssim(("test_wan_t2v_similarity.py", ), reason=f"kernel numerical smoke: {path}")
+            continue
+
+        if path.startswith("apps/dreamverse/"):
+            # DreamVerse is already one of the six automatic Fastcheck lanes.
+            plan.reasons.append(f"covered by automatic DreamVerse Fastcheck: {path}")
+            continue
+        if path.startswith("fastvideo/tests/"):
+            # The automatic unit/component Fastcheck lanes own the remaining
+            # package tests. Domain-specific expensive test roots were handled
+            # above.
+            plan.reasons.append(f"covered by automatic Fastcheck: {path}")
+            continue
+        if path in {".buildkite/scripts/unit_test.sh", ".buildkite/scripts/pr_test.sh"}:
+            plan.reasons.append(f"covered by automatic unit Fastcheck: {path}")
+            continue
+        if _matches_any(path, SAFE_PATTERNS):
+            plan.reasons.append(f"no additional GPU integration needed: {path}")
+            continue
+
+        plan.require_all(f"unclassified path; failing closed: {path}")
+
+    return plan
+
+
+def _write_github_output(output: TextIO, plan: MergePlan) -> None:
+    output.write(f"merge_test_plan={plan.encoded_lanes()}\n")
+    output.write(f"merge_golden_tests={plan.encoded_golden_tests()}\n")
+    output.write(f"merge_ssim_tests={plan.encoded_ssim_tests()}\n")
+    output.write(f"merge_plan_label={','.join(plan.ordered_lanes()) or 'none'}\n")
+
+
+def _write_summary(output: TextIO, plan: MergePlan) -> None:
+    output.write("## Change-aware merge test plan\n\n")
+    output.write("| Selection | Value |\n|---|---|\n")
+    output.write(f"| Additional Slurm lanes | `{','.join(plan.ordered_lanes()) or 'none'}` |\n")
+    output.write(f"| Golden tests | `{plan.encoded_golden_tests()}` |\n")
+    output.write(f"| SSIM tests | `{plan.encoded_ssim_tests()}` |\n\n")
+    output.write("Fastcheck remains the universal six-lane baseline.\n")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--paths-file", type=Path, required=True)
+    parser.add_argument("--github-output", type=Path)
+    parser.add_argument("--summary-file", type=Path)
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    paths = args.paths_file.read_text(encoding="utf-8").splitlines()
+    plan = classify_paths(paths)
+    print(f"MERGE_TEST_PLAN={plan.encoded_lanes()}")
+    print(f"MERGE_GOLDEN_TESTS={plan.encoded_golden_tests()}")
+    print(f"MERGE_SSIM_TESTS={plan.encoded_ssim_tests()}")
+    for reason in plan.reasons:
+        print(f"- {reason}")
+    if args.github_output:
+        with args.github_output.open("a", encoding="utf-8") as output:
+            _write_github_output(output, plan)
+    if args.summary_file:
+        with args.summary_file.open("a", encoding="utf-8") as output:
+            _write_summary(output, plan)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

.github/scripts/test_gate_full_suite.sh

@@ -53,7 +53,7 @@ PC_PENDING='{"name": "pre-commit", "id": 1, "status": "in_progress", "conclusion
 DOCS_OK='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "success"}'
 DOCS_BAD='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "failure"}'
 DOCS_CANCELLED='{"name": "Deploy Documentation", "id": 2, "status": "completed", "conclusion": "cancelled"}'
-OTHER='{"name": "Trigger Full Suite", "id": 3, "status": "in_progress", "conclusion": null}'
+OTHER='{"name": "Trigger Merge Gate", "id": 3, "status": "in_progress", "conclusion": null}'
 NULL_NAME='{"name": null, "id": 4, "status": "completed", "conclusion": "failure"}'
 PC_OK_RERUN='{"name": "pre-commit", "id": 5, "status": "completed", "conclusion": "success"}'
 

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

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

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

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

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

@@ -0,0 +1,167 @@
+name: macOS MLX Smoke
+
+on:
+  pull_request:
+    branches: [main]
+    paths:
+      - ".github/workflows/ci-macos-mlx.yml"
+      - "fastvideo/mlx_runtime/**"
+      - "fastvideo/tests/mlx/**"
+      - "fastvideo/tests/platforms/test_mps_vsa_error.py"
+      - "fastvideo/tests/platforms/test_cpu_sdpa.py"
+      - "fastvideo/platforms/cpu.py"
+      - "fastvideo/platforms/mps.py"
+      - "fastvideo/platforms/__init__.py"
+      - "fastvideo/__init__.py"
+      - "examples/inference/basic/mlx_*.py"
+      - "fastvideo/benchmarks/mlx_*.py"
+      - "pyproject.toml"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+concurrency:
+  group: macos-mlx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  mlx-smoke:
+    if: github.event_name == 'workflow_dispatch' || github.event.pull_request.draft != true
+    runs-on: macos-15
+    timeout-minutes: 25
+    env:
+      FASTVIDEO_ATTENTION_BACKEND: TORCH_SDPA
+      TOKENIZERS_PARALLELISM: "false"
+      MASTER_ADDR: localhost
+      MASTER_PORT: "29513"
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+
+      - uses: astral-sh/setup-uv@v3
+
+      - name: Install lightweight MLX smoke dependencies
+        run: |
+          uv pip install --system \
+            --index-url https://download.pytorch.org/whl/cpu \
+            torch==2.11.0 torchvision torchaudio
+          uv pip install --system \
+            pytest numpy scipy pillow imageio einops cloudpickle filelock \
+            PyYAML diffusers huggingface_hub remote-pdb safetensors loguru mlx \
+            "ftfy>=6.3.1" "opencv-python>=4.10.0.84" psutil "transformers>=5.0.0"
+
+      - name: Show Apple runtime
+        run: |
+          python - <<'PY'
+          import platform
+          import mlx.core as mx
+          import torch
+
+          print("machine:", platform.machine())
+          print("processor:", platform.processor())
+          print("mlx default device:", mx.default_device())
+          memory_size = mx.metal.device_info().get("memory_size") if mx.metal.is_available() else "metal unavailable"
+          print("mlx memory_size:", memory_size)
+          print("torch:", torch.__version__)
+          print("torch mps available:", torch.backends.mps.is_available())
+          PY
+
+      - name: Run MLX smoke tests
+        run: |
+          python -m pytest \
+            fastvideo/tests/mlx/test_dmd_sampling.py \
+            fastvideo/tests/mlx/test_memory_limits.py \
+            fastvideo/tests/mlx/test_quant_capability.py \
+            fastvideo/tests/mlx/test_mlx_dit_parity.py \
+            fastvideo/tests/mlx/test_mlx_compile_parity.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint_compat.py \
+            fastvideo/tests/mlx/test_mlx_affine_dq_gemm.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_parity.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa_regressions.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_mode.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_fastwan_benchmark.py \
+            fastvideo/tests/mlx/test_taehv_decode.py \
+            fastvideo/tests/mlx/test_frame_upsample.py \
+            fastvideo/tests/mlx/test_mlx_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_refine.py \
+            fastvideo/tests/mlx/test_mlx_prompt_to_video_decode.py \
+            fastvideo/tests/mlx/test_mlx_wan22_prompt_cache_fingerprint.py \
+            fastvideo/tests/mlx/test_wan22_sample.py \
+            fastvideo/tests/mlx/test_windowed_attention.py \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_download_unavailable_has_specific_error \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_backend_regression_is_not_skip_eligible \
+            fastvideo/tests/platforms/test_mps_vsa_error.py \
+            fastvideo/tests/platforms/test_cpu_sdpa.py \
+            -v -s -o faulthandler_timeout=120
+
+  # Same tests on MLX's CPU backend. Hosted macOS runners are scarce and
+  # slower to schedule; this Linux job gives fast PR signal on the identical
+  # graph (the parity tests were designed to be backend-agnostic), while the
+  # macOS job above stays the source of truth for Metal behavior.
+  mlx-smoke-linux-cpu:
+    if: github.event_name == 'workflow_dispatch' || github.event.pull_request.draft != true
+    runs-on: ubuntu-latest
+    timeout-minutes: 20
+    env:
+      FASTVIDEO_ATTENTION_BACKEND: TORCH_SDPA
+      TOKENIZERS_PARALLELISM: "false"
+      MASTER_ADDR: localhost
+      MASTER_PORT: "29513"
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+
+      - uses: astral-sh/setup-uv@v3
+
+      - name: Install lightweight MLX smoke dependencies (CPU backend)
+        run: |
+          uv pip install --system \
+            --index-url https://download.pytorch.org/whl/cpu \
+            torch==2.11.0 torchvision torchaudio
+          uv pip install --system \
+            pytest numpy scipy pillow imageio einops cloudpickle filelock \
+            PyYAML diffusers huggingface_hub remote-pdb safetensors loguru "mlx[cpu]" \
+            "ftfy>=6.3.1" "opencv-python>=4.10.0.84" psutil "transformers>=5.0.0"
+
+      - name: Run MLX smoke tests (CPU backend)
+        run: |
+          python -m pytest \
+            fastvideo/tests/mlx/test_dmd_sampling.py \
+            fastvideo/tests/mlx/test_memory_limits.py \
+            fastvideo/tests/mlx/test_quant_capability.py \
+            fastvideo/tests/mlx/test_mlx_dit_parity.py \
+            fastvideo/tests/mlx/test_mlx_compile_parity.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint.py \
+            fastvideo/tests/mlx/test_mlx_checkpoint_compat.py \
+            fastvideo/tests/mlx/test_mlx_affine_dq_gemm.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_parity.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_vsa_regressions.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_mode.py \
+            fastvideo/tests/mlx/test_mlx_minimax_h3_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_fastwan_benchmark.py \
+            fastvideo/tests/mlx/test_taehv_decode.py \
+            fastvideo/tests/mlx/test_frame_upsample.py \
+            fastvideo/tests/mlx/test_mlx_fast_spatial.py \
+            fastvideo/tests/mlx/test_mlx_refine.py \
+            fastvideo/tests/mlx/test_mlx_prompt_to_video_decode.py \
+            fastvideo/tests/mlx/test_mlx_wan22_prompt_cache_fingerprint.py \
+            fastvideo/tests/mlx/test_wan22_sample.py \
+            fastvideo/tests/mlx/test_windowed_attention.py \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_download_unavailable_has_specific_error \
+            fastvideo/tests/mlx/test_mlx_rife_interpolation.py::test_rife_backend_regression_is_not_skip_eligible \
+            fastvideo/tests/platforms/test_mps_vsa_error.py \
+            fastvideo/tests/platforms/test_cpu_sdpa.py \
+            -v -s -o faulthandler_timeout=120

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

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

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

@@ -33,7 +33,6 @@ jobs:
             core.setOutput('has_write', String(hasWrite));
 
       - name: Add ready label and react
-        id: label
         if: steps.perm.outputs.has_write == 'true'
         uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
         with:
@@ -48,47 +47,6 @@ jobs:
               comment_id: context.payload.comment.id,
               content: 'rocket',
             });
-            const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-            core.setOutput('pr_sha', pr.head.sha);
-            core.setOutput('pr_branch', pr.head.ref);
-            core.setOutput('pr_number', String(prNumber));
-            core.setOutput('pr_title', pr.title);
-
-      - name: Trigger Full Suite
-        if: steps.perm.outputs.has_write == 'true'
-        env:
-          BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
-          PR_SHA: ${{ steps.label.outputs.pr_sha }}
-          PR_BRANCH: ${{ steps.label.outputs.pr_branch }}
-          PR_NUMBER: ${{ steps.label.outputs.pr_number }}
-          PR_TITLE: ${{ steps.label.outputs.pr_title }}
-          BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
-          BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
-        run: |
-          curl -sS --fail-with-body -X POST \
-            "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
-            -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
-            -H "Content-Type: application/json" \
-            --data-raw "$(jq -n \
-              --arg commit "$PR_SHA" \
-              --arg branch "$PR_BRANCH" \
-              --arg message "Full Suite for PR #${PR_NUMBER} (via /merge)" \
-              --arg pr_title "$PR_TITLE" \
-              --argjson pr_id "$PR_NUMBER" \
-              '{
-                commit: $commit,
-                branch: $branch,
-                message: $message,
-                ignore_pipeline_branch_filters: true,
-                pull_request_id: $pr_id,
-                pull_request_base_branch: "main",
-                env: {
-                    TEST_SCOPE: "full",
-                    FULL_SUITE: "true",
-                    PR_NUMBER: ($pr_id | tostring),
-                    PR_TITLE: $pr_title
-                  }
-                }')"
 
   parse-command:
     if: >-
@@ -129,7 +87,7 @@ jobs:
           set -euo pipefail
           TEST_NAME=$(echo "$COMMENT" | grep -oP '(?<=/test\s)\S+' | head -1 || true)
 
-          VALID="encoder vae transformer kernel unit dreamverse ssim training lora-inference lora-training lora-extraction distillation self-forcing vsa vmoba performance api train-framework eval full fastcheck pre-commit"
+          VALID="encoder vae transformer kernel unit dreamverse ssim golden-gate training lora-inference lora-training lora-extraction distillation self-forcing vsa vmoba performance api train-framework eval unit-ci kernel-ci dreamverse-ci ssim-ci golden-gate-ci encoder-ci vae-ci transformer-ci lora-inference-ci lora-training-ci lora-extraction-ci training-ci distillation-ci self-forcing-ci vsa-ci vmoba-ci performance-ci api-ci train-framework-ci eval-ci full fastcheck pre-commit"
           if [ -z "$TEST_NAME" ] || ! echo "$VALID" | grep -qw "$TEST_NAME"; then
             echo "Unknown test: '$TEST_NAME'. Valid: $VALID"
             exit 1
@@ -137,8 +95,18 @@ jobs:
 
           declare -A MAP=(
             [encoder]=encoder [vae]=vae [transformer]=transformer
-            [kernel]=kernel_tests [unit]=unit_test [dreamverse]=dreamverse_app
-            [ssim]=ssim [training]=training
+            [kernel]=kernel_tests [unit]=unit_test [unit-ci]=unit_test_ci
+            [kernel-ci]=kernel_tests_ci [dreamverse-ci]=dreamverse_app_ci
+            [ssim-ci]=ssim_ci [vmoba-ci]=inference_vmoba_ci
+            [golden-gate-ci]=golden_gate_ci [training-ci]=training_ci
+            [encoder-ci]=encoder_ci [vae-ci]=vae_ci [transformer-ci]=transformer_ci
+            [lora-inference-ci]=inference_lora_ci [lora-training-ci]=training_lora_ci
+            [lora-extraction-ci]=lora_extraction_ci [distillation-ci]=distillation_dmd_ci
+            [self-forcing-ci]=self_forcing_ci [vsa-ci]=training_vsa_ci
+            [performance-ci]=performance_ci [api-ci]=api_server_ci
+            [train-framework-ci]=train_framework_ci [eval-ci]=eval_ci
+            [dreamverse]=dreamverse_app
+            [ssim]=ssim [golden-gate]=golden_gate [training]=training
             [lora-inference]=inference_lora [lora-training]=training_lora
             [lora-extraction]=lora_extraction
             [distillation]=distillation_dmd [self-forcing]=self_forcing

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

@@ -1,4 +1,4 @@
-name: Trigger Full Suite
+name: Trigger Merge Gate
 
 on:
   pull_request_target:
@@ -10,7 +10,7 @@ permissions:
   actions: read
 
 concurrency:
-  group: full-suite-${{ github.event.pull_request.number }}
+  group: merge-gate-${{ github.event.pull_request.number }}
   cancel-in-progress: false
 
 jobs:
@@ -34,29 +34,72 @@ jobs:
             });
             const hasReady = pr.labels.some(l => l.name === 'ready');
             core.setOutput('has_ready', String(hasReady));
-            if (!hasReady) core.info('No ready label — skipping Full Suite trigger.');
+            core.setOutput('changed_files', String(pr.changed_files));
+            if (!hasReady) core.info('No ready label — skipping merge-gate trigger.');
 
       - name: Cancel previous Buildkite builds
         if: steps.check.outputs.has_ready == 'true'
         env:
           BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
           PR_BRANCH: ${{ github.event.pull_request.head.ref }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
         run: |
-          # Find running builds for this branch with TEST_SCOPE=full and cancel them
-          builds=$(curl -sS -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
-            "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds?branch=${PR_BRANCH}&state=running,scheduled" \
-            | jq -r '.[] | select(try (.env.TEST_SCOPE == "full") catch false) | .number')
+          # Match both branch and PR number: forks can reuse the same branch name.
+          builds=$(curl -sS --get -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
+            --data-urlencode "branch=$PR_BRANCH" \
+            --data-urlencode "state=running,scheduled" \
+            "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds" \
+            | jq -r --arg pr_number "$PR_NUMBER" \
+              '.[] | select((.env.TEST_SCOPE? == "merge") and (.env.PR_NUMBER? == $pr_number)) | .number')
           for build_num in $builds; do
             echo "Cancelling Buildkite build #$build_num"
             curl -sS -X PUT -H "Authorization: Bearer $BUILDKITE_API_TOKEN" \
               "https://api.buildkite.com/v2/organizations/${{ vars.BUILDKITE_ORG_SLUG }}/pipelines/${{ vars.BUILDKITE_PIPELINE_SLUG }}/builds/${build_num}/cancel"
           done
 
-      # Checks out the BASE branch (default for pull_request_target), so PR
-      # authors cannot tamper with the gate script.
-      - name: Checkout gate script
+      # Check out the immutable BASE SHA: pull_request_target must never run a
+      # planner or gate script from the untrusted PR head.
+      - name: Checkout trusted merge planner
         if: steps.check.outputs.has_ready == 'true'
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+          ref: ${{ github.event.pull_request.base.sha }}
+          persist-credentials: false
+
+      - name: Collect changed paths
+        if: steps.check.outputs.has_ready == 'true'
+        env:
+          GH_TOKEN: ${{ github.token }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          EXPECTED_CHANGED_FILES: ${{ steps.check.outputs.changed_files }}
+        run: |
+          set -euo pipefail
+          changed_json="$RUNNER_TEMP/merge-changed-files.json"
+          changed_paths="$RUNNER_TEMP/merge-changed-paths.txt"
+          if gh api --paginate --slurp \
+              "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}/files?per_page=100" \
+              > "$changed_json"; then
+            observed=$(jq '[.[][] | .filename] | unique | length' "$changed_json")
+            if [ "$observed" = "$EXPECTED_CHANGED_FILES" ]; then
+              jq -r '.[][] | .filename, (.previous_filename // empty)' "$changed_json" \
+                | sort -u > "$changed_paths"
+            else
+              echo "::warning::Changed-file API returned $observed of $EXPECTED_CHANGED_FILES paths; selecting all merge lanes."
+              echo '__FASTVIDEO_CI_PLAN_ALL__' > "$changed_paths"
+            fi
+          else
+            echo "::warning::Changed-file API failed; selecting all merge lanes."
+            echo '__FASTVIDEO_CI_PLAN_ALL__' > "$changed_paths"
+          fi
+
+      - name: Select minimal merge tests
+        id: plan
+        if: steps.check.outputs.has_ready == 'true'
+        run: |
+          python3 .github/scripts/plan_merge_ci.py \
+            --paths-file "$RUNNER_TEMP/merge-changed-paths.txt" \
+            --github-output "$GITHUB_OUTPUT" \
+            --summary-file "$GITHUB_STEP_SUMMARY"
 
       - name: Wait for pre-commit and docs build
         if: steps.check.outputs.has_ready == 'true'
@@ -66,7 +109,7 @@ jobs:
           PR_NUMBER: ${{ github.event.pull_request.number }}
         run: bash .github/scripts/gate_full_suite.sh
 
-      - name: Trigger Buildkite Full Suite
+      - name: Trigger Buildkite merge gate
         if: steps.check.outputs.has_ready == 'true'
         env:
           BUILDKITE_API_TOKEN: ${{ secrets.BUILDKITE_API_TOKEN }}
@@ -76,6 +119,10 @@ jobs:
           PR_TITLE: ${{ github.event.pull_request.title }}
           BK_ORG: ${{ vars.BUILDKITE_ORG_SLUG }}
           BK_PIPELINE: ${{ vars.BUILDKITE_PIPELINE_SLUG }}
+          MERGE_TEST_PLAN: ${{ steps.plan.outputs.merge_test_plan }}
+          MERGE_GOLDEN_TESTS: ${{ steps.plan.outputs.merge_golden_tests }}
+          MERGE_SSIM_TESTS: ${{ steps.plan.outputs.merge_ssim_tests }}
+          MERGE_PLAN_LABEL: ${{ steps.plan.outputs.merge_plan_label }}
         run: |
           curl -sS --fail-with-body -X POST \
             "https://api.buildkite.com/v2/organizations/${BK_ORG}/pipelines/${BK_PIPELINE}/builds" \
@@ -84,8 +131,11 @@ jobs:
             --data-raw "$(jq -n \
               --arg commit "$PR_SHA" \
               --arg branch "$PR_BRANCH" \
-              --arg message "Full Suite for PR #${PR_NUMBER}" \
+              --arg message "Merge gate [${MERGE_PLAN_LABEL}] for PR #${PR_NUMBER}" \
               --arg pr_title "$PR_TITLE" \
+              --arg merge_test_plan "$MERGE_TEST_PLAN" \
+              --arg merge_golden_tests "$MERGE_GOLDEN_TESTS" \
+              --arg merge_ssim_tests "$MERGE_SSIM_TESTS" \
               --argjson pr_id "$PR_NUMBER" \
               '{
                 commit: $commit,
@@ -95,8 +145,11 @@ jobs:
                 pull_request_id: $pr_id,
                 pull_request_base_branch: "main",
                 env: {
-                  TEST_SCOPE: "full",
+                  TEST_SCOPE: "merge",
                   FULL_SUITE: "true",
+                  MERGE_TEST_PLAN: $merge_test_plan,
+                  MERGE_GOLDEN_TESTS: $merge_golden_tests,
+                  MERGE_SSIM_TESTS: $merge_ssim_tests,
                   PR_NUMBER: ($pr_id | tostring),
                   PR_TITLE: $pr_title
                 }

.github/workflows/community-welcome.yml

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

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

@@ -13,16 +13,28 @@ on:
         required: false
         default: false
         type: boolean
-  # Auto-rebuild the CUDA images when their Dockerfile changes on main. The CUDA
-  # matrix is the only lane that builds from docker/Dockerfile, so a path-scoped
-  # push trigger is a sufficient change detector on its own -- no separate
-  # detect-changes/paths-filter job is needed now that there is a single
-  # in-scope Dockerfile. Dreamverse (apps/dreamverse/docker/Dockerfile) and the
-  # rocm Dockerfile stay manual-dispatch only.
+      build_ci_runner_image:
+        description: 'Build the ARM64 CUDA 13 CI runner image (sm_100)'
+        required: false
+        default: false
+        type: boolean
+  # Auto-rebuild the CUDA images when a repository-controlled image input
+  # changes on main. This includes the trusted SM89 kernel artifact's source,
+  # metadata/key helper, ABI dependency metadata, and build orchestration.
+  # Dreamverse (apps/dreamverse/docker/Dockerfile) and the ROCm Dockerfile stay
+  # manual-dispatch only.
   push:
     branches: [main]
     paths:
+      - '.dockerignore'
+      - '.github/workflows/_template-build-image.yml'
+      - '.github/workflows/infra-build-image.yml'
+      - '.gitmodules'
       - 'docker/Dockerfile'
+      - 'docker/uv-excludes'
+      - 'fastvideo-kernel/**'
+      - 'fastvideo/tests/modal/kernel_build_cache.py'
+      - 'pyproject.toml'
 
 
 permissions:
@@ -50,7 +62,7 @@ jobs:
   # 2.8.3 comes from the architecture-specific prebuilt releases.
   build-cuda-images:
     # Runs on a manual dispatch when build_cuda_matrix is set, or automatically
-    # on a push that changed docker/Dockerfile (inputs are null on push). The
+    # on an in-scope main push (inputs are null on push). The
     # repository guard keeps fork syncs from auto-building; manual dispatch
     # still works in forks.
     if: ${{ (github.event_name == 'push' && github.repository == 'hao-ai-lab/FastVideo') || github.event.inputs.build_cuda_matrix == 'true' }}
@@ -191,6 +203,28 @@ jobs:
           docker buildx imagetools create "${TAG_ARGS[@]}" "${IMAGE_REFS[@]}"
           docker buildx imagetools inspect "${TAGS[0]}"
 
+  # The CI runner is ARM64 like DGX Spark, but targets sm_100 rather than sm_121.
+  # Publish a single-architecture variant so the self-hosted CI runner can reuse
+  # the exact prebuilt kernel instead of compiling it in every job.
+  build-ci-runner-image:
+    if: ${{ (github.event_name == 'push' && github.repository == 'hao-ai-lab/FastVideo') || github.event.inputs.build_ci_runner_image == 'true' }}
+    uses: ./.github/workflows/_template-build-image.yml
+    with:
+      python_version: '3.12'
+      dockerfile_path: docker/Dockerfile
+      tag_suffix: py3.12-cuda13.0.0-sm100
+      runner: ubuntu-24.04-arm
+      architecture: arm64
+      build_args: |
+        PYTHON_VERSION=3.12
+        CUDA_VERSION=13.0.0
+        UV_TORCH_BACKEND=cu130
+        TORCH_CUDA_ARCH_LIST=10.0
+        CMAKE_BUILD_PARALLEL_LEVEL=1
+        FLASH_ATTN_WHEEL_TAG=cu130torch2.12
+        FLASH_ATTN_WHEEL_RELEASE_ARM64=https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.9.22
+    secrets: inherit
+
   # Dreamverse matrix: {backend, UI} x {12.6.3, 13.0.0}, Python 3.12. Torch backend
   # matches the base CUDA (cu126 / cu130). Keep these images amd64-only until the
   # required FA4 dependency stack is available and validated on arm64.

.github/workflows/infra-docs.yml

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

.github/workflows/publish-kernel.yml

@@ -62,8 +62,9 @@ jobs:
               cuda-version: '13.0.0'
               torch-cuda-short: 'cu130'
           platform:
-            # x86_64 builds the full cu126 + cu130 set (cu130 ships the consumer
-            # Blackwell sm_120a FP4 kernels).
+            # x86_64 builds the full cu126 + cu130 set. cu130 ships the
+            # data-center Blackwell sm_100a/sm_103a VSA and consumer sm_120a FP4
+            # kernels.
             - os: ubuntu-22.04
               arch: x86_64
               wheel-plat: manylinux_2_35_x86_64
@@ -124,7 +125,7 @@ jobs:
       - name: Install dependencies (GCC, Clang, CUDA Paths, Git)
         run: |
           sudo apt update
-          sudo apt install -y git patchelf gcc-11 g++-11 clang-11
+          sudo apt install -y git gcc-11 g++-11 clang-11
           sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
 
           # Allow Git to Access Safe Directory
@@ -168,7 +169,8 @@ jobs:
           #     covers sm_120a; turbodiffusion covers sm_100a+sm_120a. The sm_100 FP4
           #     forward is the FA4 CuTe DSL path in the fastvideo package (PR #1221),
           #     JIT-compiled at runtime — not built into this wheel.
-          #   * x86_64 cu130 = Hopper TK + consumer Blackwell sm_120a FP4.
+          #   * x86_64 cu130 = Hopper TK + data-center Blackwell sm_100a/sm_103a VSA
+          #     + consumer Blackwell sm_120a FP4.
           #   * x86_64 cu126 = Hopper TK only (older drivers; CUDA < 12.8 has no FP4).
           # The per-arch split in CMakeLists pins the FP4 targets to sm_120a and builds
           # the main extension for the full arch list. CMAKE_BUILD_PARALLEL_LEVEL caps
@@ -178,7 +180,7 @@ jobs:
             export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=OFF -DFASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER=ON"
             export CMAKE_BUILD_PARALLEL_LEVEL=1
           elif [ "${{ matrix.torch-cuda.torch-cuda-short }}" = "cu130" ]; then
-            export TORCH_CUDA_ARCH_LIST="9.0a;12.0a"
+            export TORCH_CUDA_ARCH_LIST="9.0a;10.0a;12.0a"
             export CMAKE_ARGS="${CMAKE_ARGS:-} -DFASTVIDEO_KERNEL_BUILD_TK=ON -DFASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER=ON -DCMAKE_CUDA_ARCHITECTURES=90a"
             # A single FP4 TU (attn_qat_infer) can use ~8-12 GB on its own, so serialize.
             export CMAKE_BUILD_PARALLEL_LEVEL=1
@@ -194,7 +196,11 @@ jobs:
           python -m build --wheel --outdir dist
 
           # Fix the wheel to be manylinux compliant
-          uv pip install --system auditwheel
+          # Ubuntu 22.04 ships patchelf 0.14.3, while current auditwheel
+          # requires at least 0.14.5. Use the stable PyPI binary on both
+          # x86_64 and aarch64 release runners.
+          uv pip install --system auditwheel patchelf==0.17.2.4
+          patchelf --version
           # Point auditwheel at torch libs, but do not vendor them into the wheel.
           TORCH_LIB_DIR=$(python - <<'PY'
           import os
@@ -211,7 +217,8 @@ jobs:
             --exclude libtorch.so \
             --exclude libc10.so \
             --exclude libc10_cuda.so \
-            --exclude libtorch_python.so
+            --exclude libtorch_python.so \
+            --exclude libnccl.so.2
           # Move fixed wheels back to dist for upload consistency
           rm dist/*.whl
           mv fixed_dist/*.whl dist/

.gitignore

@@ -6,7 +6,7 @@ results/
 wandb/
 *.ipynb
 *.jpg
-!examples/dataset/lingbotworld2/image.jpg
+!examples/datasets/lingbotworld2/image.jpg
 *.safetensors
 *.mp4
 *.png
@@ -23,6 +23,7 @@ Miniconda3-latest-Linux-x86_64.sh
 *validation/
 data/
 outputs/
+outputs_audio/
 outputs_video
 checkpoints/
 sbatch.sh
@@ -54,6 +55,8 @@ eggs/
 
 # MkDocs documentation
 site/
+docs/assets/cookbook-serving.json
+examples/serving/clients/node_modules/
 docs/getting_started/examples/
 docs/examples/
 docs/inference/examples/
@@ -75,6 +78,7 @@ docs/distillation/examples/
 *.pkl
 
 # Reference videos (negations must come after the catch-all on line below)
+!fastvideo/tests/nightly/reference_video_*.mp4
 
 # Static images
 !docs/assets/images/**/*.png
@@ -131,6 +135,9 @@ fastvideo/tests/ssim/reference_videos/**
 !fastvideo/tests/ssim/reference_videos/**/*.mp4
 !fastvideo/tests/ssim/reference_videos/**/*.png
 
+# Local H3 MLX kernel / exactness benches (JSON, logs, frames, videos)
+.kernel_bench/
+
 # Editor logs and local Python version pins (accidentally committed)
 *.nvimlog
 .nvimlog

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ exclude: |
     tests/.*|
     scripts/.*|
     fastvideo/dataset/.*|
-    fastvideo/models/.*|
+    fastvideo/models/(?!wan/(config|vae_config|pipeline_config|definition|__init__)\.py$).*|
     ^apps/dreamverse/web/.*|
     examples/.*|
     \.agents/.*|
@@ -22,6 +22,7 @@ repos:
   hooks:
   - id: yapf
     args: [--in-place, --verbose]
+    language_version: python3.12
     additional_dependencies: [toml] # TODO: Remove when yapf is upgraded
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.11.12

README.md

@@ -3,12 +3,15 @@
 </div>
 
 <p align="center">
-     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://github.com/hao-ai-lab/FastVideo/discussions/1097" target="_blank"> <b> WeChat </b> </a> |
+     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://haoailab.com/FastVideo/cookbook/"><b>Cookbook</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://github.com/hao-ai-lab/FastVideo/discussions/1097" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
 **FastVideo is a unified post-training and real-time inference framework for accelerated video generation.**
 
 ## NEWS
+- `2026/09/01`: 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/).
 - `2026/06/23`: 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/).
@@ -33,7 +36,7 @@ FastVideo has the following features:
   - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup
   - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
   - Causal distillation through Self-Forcing
-  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
+  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for the supported training workflows, and the [support matrix](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for supported models.
 - State-of-the-art performance optimizations for inference
   - Sequence Parallelism for distributed inference
   - Multiple state-of-the-art attention backends
@@ -62,6 +65,12 @@ 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/).
 
+> **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/).
+
 Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.
 
 > **On an NVIDIA DGX Spark (GB10 / ARM64 + CUDA 13)?** There's no prebuilt ARM wheel for the FastVideo CUDA kernel, so it's an editable from-source install (`UV_TORCH_BACKEND=cu130 uv pip install -e .`, which compiles that kernel for you) rather than `UV_TORCH_BACKEND=cu130 uv pip install fastvideo`. A compatible prebuilt ARM64 FlashAttention wheel is available separately. Follow the [DGX Spark install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/spark/).

apps/dreamverse/README.md

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

apps/dreamverse/arch.md

@@ -139,7 +139,18 @@ session.
 - startup warmup
 - user join/leave commands
 - `USER_STEP` execution for each segment
-- continuation state between segments
+- generation-command routing and stream-result delivery
+
+Model generation has a separate ownership boundary inside each GPU process:
+
+- `apps/dreamverse/dreamverse/generation_worker.py` selects the backend that the active model profile declares and owns
+  the backend lifecycle.
+- `apps/dreamverse/dreamverse/ltx2_generation.py` owns LTX-2 generator configuration, video and audio continuation, and
+  runtime LoRA application.
+- `apps/dreamverse/dreamverse/minimax_h3_generation.py` owns the VSA data-free FastH3 adapter, FastH3 generator and
+  request configuration, and last-frame continuation through MiniMax H3 first-frame conditioning.
+- `apps/dreamverse/dreamverse/generation_contracts.py` defines the decoded media and stream-trimming result that both
+  model backends return to `apps/dreamverse/dreamverse/gpu_pool.py`.
 
 `apps/dreamverse/dreamverse/prompt_enhancer.py` manages:
 

apps/dreamverse/dreamverse/benchmarks/benchmark_pipeline.py

@@ -1,6 +1,6 @@
 """Benchmark the LTX-2 generation pipeline driven by the dreamverse Python SDK path.
 
-Mirrors how ``apps/dreamverse/dreamverse/video_generation.py`` constructs
+Mirrors how ``apps/dreamverse/dreamverse/ltx2_generation.py`` constructs
 ``GeneratorConfig`` and calls ``VideoGenerator.generate()``, then
 captures per-stage timings via the ``FASTVIDEO_STAGE_LOGGING=1`` log
 hooks (same mechanism as ``FastVideo-internal/examples/inference/basic/

apps/dreamverse/dreamverse/config.py

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

apps/dreamverse/dreamverse/generation_contracts.py

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

apps/dreamverse/dreamverse/generation_worker.py

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

apps/dreamverse/dreamverse/gpu_pool.py

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

apps/dreamverse/dreamverse/ltx2_generation.py

@@ -1,9 +1,9 @@
-"""LTX2 model lifecycle and continuation conditioning.
+"""LTX-2 model lifecycle and continuation conditioning.
 
 Runs inside a GPU worker subprocess.  Owns the model, the audio
 encoder, and the per-session continuation state carried across
 segments.  Callers must set ``os.environ["CUDA_VISIBLE_DEVICES"]``
-before constructing ``VideoGenerationWorker`` — all ``fastvideo.*``
+before constructing ``LTX2GenerationBackend`` — all ``fastvideo.*``
 imports are deferred to method bodies so nothing touches CUDA at
 module import time.
 """
@@ -14,9 +14,6 @@ import gc
 import os
 import re
 import time
-from dataclasses import dataclass
-from typing import Any
-
 import numpy as np
 import torch
 
@@ -35,6 +32,7 @@ from dreamverse.config import (
     DREAMVERSE_LORA_STACK,
     _resolve_lora_spec,
 )
+from dreamverse.generation_contracts import StepResult
 
 # Multi-frame decoded continuation defaults from
 # examples/inference/basic/basic_ltx2_distilled_video_continuation.py.
@@ -80,22 +78,6 @@ def _reset_lora_registry(worker) -> dict:
     return {"status": "lora_registry_reset"}
 
 
-@dataclass
-class StepResult:
-    """Output of one generation step.
-
-    ``head_trim_frames`` / ``head_trim_audio_frames`` are derived here
-    so downstream AV streaming never needs to import conditioning
-    constants.
-    """
-    frames: list
-    audio: Any
-    audio_sample_rate: int | None
-    timings: dict
-    head_trim_frames: int
-    head_trim_audio_frames: int
-
-
 class ContinuationState:
     """Per-session video + audio conditioning carried across segments."""
 
@@ -202,7 +184,7 @@ class ContinuationState:
         self.audio_latents = latents.detach().clone().cpu()
 
 
-class VideoGenerationWorker:
+class LTX2GenerationBackend:
     """Single-GPU LTX2 generator with continuation state.
 
     Caller must set ``os.environ["CUDA_VISIBLE_DEVICES"]`` before

apps/dreamverse/dreamverse/minimax_h3_generation.py

@@ -0,0 +1,297 @@
+"""FastH3 model lifecycle and first-frame continuation for DreamVerse."""
+
+from __future__ import annotations
+
+import gc
+import os
+import time
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+import torch
+
+from dreamverse.config import DREAMVERSE_SP_SIZE
+from dreamverse.generation_contracts import StepResult
+
+if TYPE_CHECKING:
+    from PIL.Image import Image
+
+
+def _required_config_str(model_config: dict, field_name: str) -> str:
+    """Read one required non-empty string from a DreamVerse model profile."""
+    value = model_config.get(field_name)
+    if not isinstance(value, str) or not value.strip():
+        raise ValueError(f"FastH3 model configuration requires `{field_name}`.")
+    return value.strip()
+
+
+class MiniMaxH3GenerationBackend:
+    """Run the VSA data-free FastH3 adapter and retain one continuation frame."""
+
+    def __init__(self, gpu_id: int):
+        self.gpu_id = gpu_id
+        self.generator: Any | None = None
+        self.model_config: dict = {}
+        self.continuation_image: Image | None = None
+
+    def _gpu_mem(self) -> str:
+        allocated_gib = torch.cuda.memory_allocated() / 1024**3
+        reserved_gib = torch.cuda.memory_reserved() / 1024**3
+        return f"alloc={allocated_gib:.2f}GiB, reserved={reserved_gib:.2f}GiB"
+
+    @staticmethod
+    def _configure_environment(attention_backend: str) -> None:
+        """Apply the fixed boot-time switches from the FastH3 reference recipe."""
+        os.environ.update({
+            "FASTVIDEO_ATTENTION_BACKEND": attention_backend,
+            "FASTVIDEO_FA4": "1",
+            "FASTVIDEO_MINIMAX_H3_FUSIONS": "all",
+            "FASTVIDEO_VSA_SM100A": "0",
+        })
+        os.environ.pop("FASTVIDEO_INFERENCE_TORCH_COMPILE", None)
+
+    def initialize(self, model_config: dict | None = None) -> None:
+        """Download the fixed Preview adapter and load the FastH3 generator.
+
+        The model profile owns the base checkpoint, adapter file, attention
+        backend, and generation geometry. The backend translates that profile
+        into FastVideo's typed generator configuration.
+        """
+        if model_config is not None:
+            self.model_config = dict(model_config)
+        if not self.model_config:
+            raise ValueError("FastH3 initialization requires a model configuration.")
+
+        if self.generator is not None:
+            self.generator.shutdown()
+            self.generator = None
+            gc.collect()
+            torch.cuda.empty_cache()
+
+        self.clear_conditioning()
+        model_path = _required_config_str(self.model_config, "model_path")
+        adapter_repo = _required_config_str(self.model_config, "adapter_repo")
+        adapter_filename = _required_config_str(self.model_config, "adapter_filename")
+        attention_backend = _required_config_str(self.model_config, "attention_backend")
+        self._configure_environment(attention_backend)
+
+        from huggingface_hub import hf_hub_download
+
+        from fastvideo import VideoGenerator
+        from fastvideo.api import (
+            CompileConfig,
+            ComponentConfig,
+            EngineConfig,
+            GeneratorConfig,
+            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,
+            })
+        generator_config = GeneratorConfig(
+            model_path=model_path,
+            pipeline=PipelineSelection(
+                components=ComponentConfig(lora_path=adapter_path, lora_strength=1.0),
+                experimental=experimental,
+            ),
+            engine=EngineConfig(
+                num_gpus=DREAMVERSE_SP_SIZE,
+                parallelism=ParallelismConfig(tp_size=1, sp_size=DREAMVERSE_SP_SIZE),
+                offload=OffloadConfig(
+                    dit=False,
+                    dit_layerwise=False,
+                    text_encoder=True,
+                    image_encoder=True,
+                    vae=True,
+                    pin_cpu_memory=True,
+                ),
+                compile=CompileConfig(enabled=False, vae_enabled=True),
+                use_fsdp_inference=False,
+            ),
+        )
+
+        print(f"[GPU {self.gpu_id}] Loading FastH3 model: {model_path}")
+        print(f"[GPU {self.gpu_id}] FastH3 adapter: {adapter_repo}/{adapter_filename}")
+        print(f"[GPU {self.gpu_id}] Before model load: {self._gpu_mem()}")
+        self.generator = VideoGenerator.from_config(generator_config)
+        print(f"[GPU {self.gpu_id}] FastH3 loaded: {self._gpu_mem()} (warmup pending)")
+
+    def shutdown(self) -> None:
+        """Release the FastVideo generator and cached continuation image."""
+        self.clear_conditioning()
+        if self.generator is not None:
+            self.generator.shutdown()
+            self.generator = None
+
+    def clear_conditioning(self) -> None:
+        """Release the first-frame image retained for the next segment."""
+        if self.continuation_image is not None:
+            self.continuation_image.close()
+            self.continuation_image = None
+
+    @staticmethod
+    def _load_rgb_image(image_path: str) -> Image:
+        """Load an image into an independent RGB buffer with no open file handle."""
+        from PIL import Image
+
+        with Image.open(image_path) as image:
+            return image.convert("RGB").copy()
+
+    def _select_conditioning_image(
+        self,
+        segment_idx: int,
+        image_path: str | None,
+        reset_conditioning: bool,
+    ) -> tuple[Image | None, bool]:
+        """Select the initial upload or retained last frame for one segment."""
+        if reset_conditioning:
+            self.clear_conditioning()
+        if segment_idx > 1 and self.continuation_image is not None:
+            return self.continuation_image.copy(), True
+        if segment_idx > 1 and not reset_conditioning:
+            raise RuntimeError(f"FastH3 segment {segment_idx} requires a retained continuation frame.")
+        if segment_idx == 1 and image_path:
+            return self._load_rgb_image(image_path), False
+        return None, False
+
+    def _build_request(self, prompt: str, conditioning_image: Image | None):
+        """Build the typed FastVideo request owned by the FastH3 profile."""
+        from fastvideo.api import GenerationRequest, InputConfig, OutputConfig, SamplingConfig
+
+        return GenerationRequest(
+            prompt=prompt,
+            negative_prompt="",
+            inputs=InputConfig(pil_image=conditioning_image),
+            sampling=SamplingConfig(
+                height=int(self.model_config["height"]),
+                width=int(self.model_config["width"]),
+                num_frames=int(self.model_config["num_frames"]),
+                fps=24,
+                num_inference_steps=int(self.model_config["num_inference_steps"]),
+                guidance_scale=1.0,
+                batch_cfg=False,
+                seed=int(self.model_config["seed"]),
+            ),
+            output=OutputConfig(save_video=False, return_frames=True),
+        )
+
+    def _save_continuation_frame(self, frames: list) -> None:
+        """Retain the last decoded frame as first-frame conditioning."""
+        from PIL import Image
+
+        self.clear_conditioning()
+        self.continuation_image = Image.fromarray(np.ascontiguousarray(frames[-1])).convert("RGB")
+
+    def generate_step(
+        self,
+        prompt: str,
+        segment_idx: int,
+        image_path: str | None,
+        reset_conditioning: bool,
+    ) -> StepResult:
+        """Generate one synchronized FastH3 segment and retain its last frame.
+
+        Later segments use MiniMax H3's first-frame-to-video path. The first
+        conditioned frame and its matching audio duration are trimmed before
+        streaming so adjacent segments do not duplicate media.
+        """
+        if self.generator is None:
+            raise RuntimeError("FastH3 generator is not initialized.")
+        conditioning_image, uses_continuation = self._select_conditioning_image(
+            segment_idx,
+            image_path,
+            reset_conditioning,
+        )
+        request = self._build_request(prompt, conditioning_image)
+        started = time.perf_counter()
+        try:
+            result = self.generator.generate(request)
+        finally:
+            if conditioning_image is not None:
+                conditioning_image.close()
+        torch.cuda.synchronize()
+        generation_ms = (time.perf_counter() - started) * 1000.0
+
+        if isinstance(result, list):
+            raise RuntimeError("FastH3 returned multiple results for one DreamVerse segment.")
+        frames = result.frames
+        if not isinstance(frames, list) or not frames:
+            raise RuntimeError("FastH3 generation did not return decoded frames.")
+        audio = result.audio
+        audio_sample_rate = result.audio_sample_rate
+        if audio is not None and audio_sample_rate is None:
+            raise RuntimeError("FastH3 returned audio without an audio sample rate.")
+
+        save_started = time.perf_counter()
+        self._save_continuation_frame(frames)
+        save_conditioning_ms = (time.perf_counter() - save_started) * 1000.0
+        timings = {
+            "generation_ms": generation_ms,
+            "generation_time_ms": float(result.generation_time or 0.0) * 1000.0,
+            "save_conditioning_ms": save_conditioning_ms,
+            "e2e_latency_ms": (time.perf_counter() - started) * 1000.0,
+        }
+        trim_frames = 1 if uses_continuation else 0
+        print(f"[GPU {self.gpu_id}] FastH3 segment {segment_idx}: "
+              f"{len(frames)} frames, gen={generation_ms:.0f}ms, "
+              f"save_conditioning={save_conditioning_ms:.0f}ms, "
+              f"e2e={timings['e2e_latency_ms']:.0f}ms")
+        return StepResult(
+            frames=frames,
+            audio=audio,
+            audio_sample_rate=audio_sample_rate,
+            timings=timings,
+            head_trim_frames=trim_frames,
+            head_trim_audio_frames=trim_frames,
+        )
+
+    def warmup(self, prompt: str) -> dict[str, float]:
+        """Compile the FastH3 text and first-frame paths before readiness."""
+        warmup_prompt = (prompt or "").strip()
+        if not warmup_prompt:
+            raise RuntimeError("Startup warmup prompt must be non-empty.")
+        print(f"[GPU {self.gpu_id}] FastH3 startup warmup starting "
+              "(synthetic segments: text-to-video, first-frame-to-video)")
+        started = time.perf_counter()
+        text_result = self.generate_step(
+            warmup_prompt,
+            segment_idx=1,
+            image_path=None,
+            reset_conditioning=True,
+        )
+        first_frame_result = self.generate_step(
+            warmup_prompt,
+            segment_idx=2,
+            image_path=None,
+            reset_conditioning=False,
+        )
+        total_ms = (time.perf_counter() - started) * 1000.0
+        self.clear_conditioning()
+        text_ms = float(text_result.timings.get("e2e_latency_ms", 0.0))
+        first_frame_ms = float(first_frame_result.timings.get("e2e_latency_ms", 0.0))
+        print(f"[GPU {self.gpu_id}] FastH3 startup warmup complete: "
+              f"text_to_video={text_ms:.0f}ms, "
+              f"first_frame_to_video={first_frame_ms:.0f}ms, "
+              f"total={total_ms:.0f}ms")
+        return {
+            "warmup_text_to_video_ms": text_ms,
+            "warmup_first_frame_to_video_ms": first_frame_ms,
+            "warmup_total_ms": total_ms,
+        }
+
+    def apply_lora_stack(self, stack: list[tuple[str, float]]) -> tuple[str | None, str | None]:
+        """Reject runtime LoRA mutation because FastH3 uses one startup adapter."""
+        del stack
+        raise RuntimeError("FastH3 uses its fixed startup adapter and does not support runtime LoRA changes.")

apps/dreamverse/dreamverse/session/controller.py

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

apps/dreamverse/dreamverse/tests/conftest.py

@@ -3,7 +3,6 @@ from __future__ import annotations
 import sys
 from pathlib import Path
 
-
 TESTS_DIR = Path(__file__).resolve().parent
 DREAMVERSE_PACKAGE_DIR = TESTS_DIR.parent
 DREAMVERSE_APP_DIR = DREAMVERSE_PACKAGE_DIR.parent

apps/dreamverse/dreamverse/tests/test_config.py

@@ -2,14 +2,14 @@ from __future__ import annotations
 
 import importlib.util
 from pathlib import Path
+from types import ModuleType
 
 import pytest
 
-
 SERVER_DIR = Path(__file__).resolve().parents[1]
 
 
-def _load_config_module():
+def _load_config_module() -> ModuleType:
     spec = importlib.util.spec_from_file_location(
         "server_config_test_module",
         SERVER_DIR / "config.py",
@@ -21,7 +21,7 @@ def _load_config_module():
     return module
 
 
-def _set_required_prompt_keys(monkeypatch):
+def _set_required_prompt_keys(monkeypatch: pytest.MonkeyPatch) -> None:
     monkeypatch.setenv("CEREBRAS_API_KEY", "cerebras-key")
     monkeypatch.setenv("GROQ_API_KEY", "groq-key")
 
@@ -53,9 +53,7 @@ def test_config_defaults_to_cerebras_with_parallel_groq_fallback_stage(monkeypat
     module = _load_config_module()
 
     assert module.PROMPT_PROVIDER == "cerebras"
-    assert module.PROMPT_PROVIDER_RUNTIME_STAGES == (
-        ("cerebras", "groq"),
-    )
+    assert module.PROMPT_PROVIDER_RUNTIME_STAGES == (("cerebras", "groq"), )
     assert module.PROMPT_PROVIDER_PRIORITY == (
         "cerebras",
         "groq",
@@ -86,9 +84,7 @@ def test_config_ignores_legacy_groq_primary_override(monkeypatch):
     module = _load_config_module()
 
     assert module.PROMPT_PROVIDER == "cerebras"
-    assert module.PROMPT_PROVIDER_RUNTIME_STAGES == (
-        ("cerebras", "groq"),
-    )
+    assert module.PROMPT_PROVIDER_RUNTIME_STAGES == (("cerebras", "groq"), )
     assert module.PROMPT_PROVIDER_PRIORITY == (
         "cerebras",
         "groq",
@@ -106,24 +102,17 @@ def test_config_uses_local_overlay_paths_when_devtools_enabled(monkeypatch, tmp_
 
     assert module.DEVTOOLS_ENABLED is True
     assert module.FRONTEND_ROOT.as_posix().endswith("apps/dreamverse/web")
-    assert module.PROMPT_ENHANCE_SYSTEM_PROMPT_PATH.endswith(
-        "dreamverse/prompts.local/next_segment_system_prompt.md"
-    )
+    assert module.PROMPT_ENHANCE_SYSTEM_PROMPT_PATH.endswith("dreamverse/prompts.local/next_segment_system_prompt.md")
     assert module.PROMPT_ENHANCE_SYSTEM_PROMPT_FALLBACK_PATH.endswith(
-        "dreamverse/prompts/next_segment_system_prompt.md"
-    )
+        "dreamverse/prompts/next_segment_system_prompt.md")
     assert module.PROMPT_REWRITE_USER_SYSTEM_PROMPT_PATH.endswith(
-        "dreamverse/prompts.local/rewrite_user_system_prompt.md"
-    )
+        "dreamverse/prompts.local/rewrite_user_system_prompt.md")
     assert module.PROMPT_REWRITE_USER_SYSTEM_PROMPT_FALLBACK_PATH.endswith(
-        "dreamverse/prompts/rewrite_user_system_prompt.md"
-    )
+        "dreamverse/prompts/rewrite_user_system_prompt.md")
     assert module.CURATED_PRESETS_FILE_PATH.endswith(
-        "apps/dreamverse/web/prompts.local/selected_ltx2_continuation_story_presets.json"
-    )
+        "apps/dreamverse/web/prompts.local/selected_ltx2_continuation_story_presets.json")
     assert module.CURATED_PRESETS_FALLBACK_FILE_PATH.endswith(
-        "apps/dreamverse/web/prompts/selected_ltx2_continuation_story_presets.json"
-    )
+        "apps/dreamverse/web/prompts/selected_ltx2_continuation_story_presets.json")
     assert module.FRONTEND_STATIC_DIR_CANDIDATES[:2] == (
         str(module.FRONTEND_ROOT / "out"),
         str(module.FRONTEND_ROOT / "dist"),
@@ -162,3 +151,38 @@ def test_config_rejects_invalid_prompt_provider(monkeypatch):
 
     with pytest.raises(RuntimeError, match="Invalid FASTVIDEO_PROMPT_PROVIDER"):
         _load_config_module()
+
+
+def test_config_registers_vsa_datafree_fasth3_profile(monkeypatch):
+    """The FastH3 registry entry owns the complete fixed Preview recipe."""
+    _set_required_prompt_keys(monkeypatch)
+
+    module = _load_config_module()
+
+    assert module.MODEL_REGISTRY["fast-h3"] == {
+        "name": "FastH3",
+        "generation_backend": "minimax_h3",
+        "default_sp_size": 4,
+        "model_path": "MiniMaxAI/MiniMax-H3",
+        "adapter_repo": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+        "adapter_filename": "vsa-datafree/adapter_model.safetensors",
+        "attention_backend": "VIDEO_SPARSE_ATTN_H3",
+        "height": 768,
+        "width": 1344,
+        "num_frames": 124,
+        "num_inference_steps": 5,
+        "seed": 1000,
+    }
+
+
+def test_config_uses_fasth3_sequence_parallel_default(monkeypatch):
+    """Selecting FastH3 defaults DreamVerse to its four-GPU topology."""
+    _set_required_prompt_keys(monkeypatch)
+    monkeypatch.setenv("DREAMVERSE_MODEL_ID", "fast-h3")
+    monkeypatch.delenv("DREAMVERSE_SP_SIZE", raising=False)
+
+    module = _load_config_module()
+
+    assert module.ACTIVE_MODEL_ID == "fast-h3"
+    assert module.MODEL_CONFIG["generation_backend"] == "minimax_h3"
+    assert module.DREAMVERSE_SP_SIZE == 4

apps/dreamverse/dreamverse/tests/test_entrypoints.py

@@ -9,6 +9,7 @@ from fastapi.testclient import TestClient
 import fastvideo.entrypoints.streaming as streaming_entrypoints
 import pytest
 
+
 def _install_stack03_import_stubs(monkeypatch):
     """Keep entrypoint tests focused while later-stack runtime modules are absent."""
     if not hasattr(streaming_entrypoints, "build_health_router"):
@@ -17,6 +18,7 @@ def _install_stack03_import_stubs(monkeypatch):
     gpu_pool_stub = types.ModuleType("dreamverse.gpu_pool")
 
     class GPUPool:
+
         def __init__(self, _gpu_ids):
             pass
 
@@ -49,6 +51,7 @@ def _install_stack03_import_stubs(monkeypatch):
     controller_stub = types.ModuleType("dreamverse.session.controller")
 
     class SessionController:
+
         def __init__(self, **_kwargs):
             pass
 
@@ -76,13 +79,11 @@ def _run_cli(module, monkeypatch, argv: list[str]) -> list[dict[str, object]]:
     uvicorn_stub = types.ModuleType("uvicorn")
 
     def run(app, host: str, port: int) -> None:
-        calls.append(
-            {
-                "app": app,
-                "host": host,
-                "port": port,
-            }
-        )
+        calls.append({
+            "app": app,
+            "host": host,
+            "port": port,
+        })
 
     uvicorn_stub.run = run
     monkeypatch.setitem(sys.modules, "uvicorn", uvicorn_stub)
@@ -99,13 +100,11 @@ def test_server_cli_defaults_to_local_web_port(monkeypatch):
     server_main = _import_server_main(monkeypatch)
     calls = _run_cli(server_main, monkeypatch, ["dreamverse-server"])
 
-    assert calls == [
-        {
-            "app": server_main.app,
-            "host": "0.0.0.0",
-            "port": 8009,
-        }
-    ]
+    assert calls == [{
+        "app": server_main.app,
+        "host": "0.0.0.0",
+        "port": 8009,
+    }]
 
 
 def test_server_cli_allows_explicit_host_and_port(monkeypatch):
@@ -116,13 +115,11 @@ def test_server_cli_allows_explicit_host_and_port(monkeypatch):
         ["dreamverse-server", "--host", "127.0.0.1", "--port", "8123"],
     )
 
-    assert calls == [
-        {
-            "app": server_main.app,
-            "host": "127.0.0.1",
-            "port": 8123,
-        }
-    ]
+    assert calls == [{
+        "app": server_main.app,
+        "host": "127.0.0.1",
+        "port": 8123,
+    }]
 
 
 def test_server_does_not_expose_backend_source_as_static_assets(monkeypatch):
@@ -142,13 +139,11 @@ def test_mock_server_cli_defaults_to_local_web_port(monkeypatch):
         ["dreamverse-mock-server"],
     )
 
-    assert calls == [
-        {
-            "app": mock_server.app,
-            "host": "0.0.0.0",
-            "port": 8009,
-        }
-    ]
+    assert calls == [{
+        "app": mock_server.app,
+        "host": "0.0.0.0",
+        "port": 8009,
+    }]
 
 
 def test_mock_server_cli_updates_latency(monkeypatch):
@@ -161,13 +156,11 @@ def test_mock_server_cli_updates_latency(monkeypatch):
             ["dreamverse-mock-server", "--latency", "321", "--port", "8111"],
         )
 
-        assert calls == [
-            {
-                "app": mock_server.app,
-                "host": "0.0.0.0",
-                "port": 8111,
-            }
-        ]
+        assert calls == [{
+            "app": mock_server.app,
+            "host": "0.0.0.0",
+            "port": 8111,
+        }]
         assert mock_server.LATENCY_MS == 321
     finally:
         mock_server.LATENCY_MS = old_latency_ms

apps/dreamverse/dreamverse/tests/test_generation_worker.py

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

apps/dreamverse/dreamverse/tests/test_gpu_pool.py

@@ -7,7 +7,6 @@ from types import SimpleNamespace
 
 import pytest
 
-
 import dreamverse.gpu_pool as gpu_pool
 
 
@@ -64,6 +63,14 @@ def test_get_available_gpus_defaults_to_first_visible_device(monkeypatch):
     assert gpu_pool.get_available_gpus() == [3]
 
 
+def test_get_available_gpus_defaults_to_active_model_sequence_parallel_size(monkeypatch):
+    monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "0,1,2,3,4")
+    monkeypatch.delenv("FASTVIDEO_GPU_COUNT", raising=False)
+    monkeypatch.setattr(gpu_pool, "DREAMVERSE_SP_SIZE", 4)
+
+    assert gpu_pool.get_available_gpus() == [0, 1, 2, 3]
+
+
 def test_get_available_gpus_rejects_invalid_gpu_count(monkeypatch):
     monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
     monkeypatch.setenv("FASTVIDEO_GPU_COUNT", "zero")
@@ -72,6 +79,23 @@ def test_get_available_gpus_rejects_invalid_gpu_count(monkeypatch):
         gpu_pool.get_available_gpus()
 
 
+def test_join_user_failed_reload_marks_model_uninitialized(monkeypatch):
+    """A failed model reload forces the next join to reload a model."""
+    slot = gpu_pool.GPUSlot(gpu_id=0, cuda_device="0")
+    slot.current_model_id = "fast-ltx2"
+
+    async def fake_send_command(command, timeout):
+        del command, timeout
+        return gpu_pool.WorkerError(user_id="__reload__", message="load failed")
+
+    monkeypatch.setattr(slot, "_send_command", fake_send_command)
+
+    with pytest.raises(RuntimeError, match="Model reload failed"):
+        asyncio.run(slot.join_user("client-id", model_id="fast-h3"))
+
+    assert slot.current_model_id is None
+
+
 def test_send_command_raises_on_worker_death():
     """A worker that consumes a command and exits without replying must
     surface as RuntimeError via sentinel detection, not after the long
@@ -85,9 +109,7 @@ def test_send_command_raises_on_worker_death():
     cmd_q = ctx.Queue()
     resp_q = ctx.Queue()
 
-    proc = ctx.Process(
-        target=_child_consume_and_exit, args=(cmd_q, resp_q)
-    )
+    proc = ctx.Process(target=_child_consume_and_exit, args=(cmd_q, resp_q))
     proc.start()
 
     # Wait for the spawn child to fully boot.  Allow generous time —
@@ -95,9 +117,9 @@ def test_send_command_raises_on_worker_death():
     ready = resp_q.get(timeout=30.0)
     assert ready == "READY"
 
-    async def runner():
+    async def runner() -> None:
         slot = gpu_pool.GPUSlot(gpu_id=0, cuda_device="0")
-        slot.process = proc
+        slot.process = proc  # type: ignore[assignment]
         slot.command_queue = cmd_q
         slot.response_queue = resp_q
 

apps/dreamverse/dreamverse/tests/test_import_contract.py

@@ -7,7 +7,7 @@ ALLOWED_PREFIXES = (
     "fastvideo.entrypoints.video_generator",
     "fastvideo.configs",
 )
-ALLOWED_EXACT = ("fastvideo",)
+ALLOWED_EXACT = ("fastvideo", )
 FORBIDDEN_PREFIXES = (
     "fastvideo.pipelines",
     "fastvideo.models",
@@ -17,11 +17,11 @@ FORBIDDEN_PREFIXES = (
 )
 ALLOWED_INTERNAL_IMPORTS = {
     (
-        "video_generation.py",
+        "ltx2_generation.py",
         "fastvideo.models.audio.ltx2_audio_processing",
     ),
     (
-        "video_generation.py",
+        "ltx2_generation.py",
         "fastvideo.models.loader.component_loader",
     ),
 }
@@ -38,19 +38,13 @@ def test_dreamverse_server_imports_only_public_fastvideo_surfaces() -> None:
         except SyntaxError as task_exc:
             raise AssertionError(f"Failed to parse {path}") from task_exc
         for node in ast.walk(tree):
-            names = (
-                [a.name for a in node.names] if isinstance(node, ast.Import)
-                else [node.module] if isinstance(node, ast.ImportFrom) and node.module
-                else []
-            )
+            names = ([a.name for a in node.names] if isinstance(node, ast.Import) else
+                     [node.module] if isinstance(node, ast.ImportFrom) and node.module else [])
             for name in names:
                 if not name:
                     continue
                 rel_path = str(path.relative_to(root))
-                if (
-                    name.startswith(FORBIDDEN_PREFIXES)
-                    and (rel_path, name) not in ALLOWED_INTERNAL_IMPORTS
-                ):
+                if (name.startswith(FORBIDDEN_PREFIXES) and (rel_path, name) not in ALLOWED_INTERNAL_IMPORTS):
                     bad.append((str(path.relative_to(root)), getattr(node, "lineno", 0), name))
 
     assert bad == [], f"Forbidden internal imports: {bad}"

apps/dreamverse/dreamverse/tests/test_minimax_h3_generation.py

@@ -0,0 +1,247 @@
+from __future__ import annotations
+
+import os
+from types import SimpleNamespace
+from typing import Any
+
+import numpy as np
+import pytest
+
+import dreamverse.generation_worker as generation_worker
+from dreamverse.minimax_h3_generation import MiniMaxH3GenerationBackend
+
+
+FASTH3_MODEL_CONFIG = {
+    "name": "FastH3",
+    "generation_backend": "minimax_h3",
+    "default_sp_size": 4,
+    "model_path": "MiniMaxAI/MiniMax-H3",
+    "adapter_repo": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+    "adapter_filename": "vsa-datafree/adapter_model.safetensors",
+    "attention_backend": "VIDEO_SPARSE_ATTN_H3",
+    "height": 768,
+    "width": 1344,
+    "num_frames": 124,
+    "num_inference_steps": 5,
+    "seed": 1000,
+}
+
+
+class _RecordingGenerator:
+    """Record typed requests and return small synchronized media fixtures."""
+
+    def __init__(self) -> None:
+        self.requests: list[Any] = []
+        self.conditioning_pixels: list[np.ndarray | None] = []
+
+    def generate(self, request):
+        """Capture the request and return two tiny video frames with audio."""
+        self.requests.append(request)
+        conditioning_image = request.inputs.pil_image
+        self.conditioning_pixels.append(
+            None if conditioning_image is None else np.asarray(conditioning_image).copy())
+        frames = [
+            np.full((2, 3, 3), 10, dtype=np.uint8),
+            np.full((2, 3, 3), 20, dtype=np.uint8),
+        ]
+        return SimpleNamespace(
+            frames=frames,
+            audio=np.zeros((2, 16), dtype=np.float32),
+            audio_sample_rate=44100,
+            generation_time=0.25,
+        )
+
+
+def test_initialize_builds_vsa_datafree_fasth3_generator(monkeypatch):
+    """Initialization translates the DreamVerse profile into typed FastVideo config."""
+    from fastvideo import VideoGenerator
+
+    captured = {}
+    fake_generator = SimpleNamespace(shutdown=lambda: None)
+
+    def fake_from_config(config):
+        captured["config"] = config
+        return fake_generator
+
+    def fake_download(**kwargs):
+        captured["download"] = kwargs
+        return f"/models/{kwargs['filename']}"
+
+    monkeypatch.setattr("huggingface_hub.hf_hub_download", fake_download)
+    monkeypatch.setattr(VideoGenerator, "from_config", fake_from_config)
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.DREAMVERSE_SP_SIZE", 4)
+    monkeypatch.setenv("FASTVIDEO_ATTENTION_BACKEND", "test-attention")
+    monkeypatch.setenv("FASTVIDEO_FA4", "0")
+    monkeypatch.setenv("FASTVIDEO_MINIMAX_H3_FUSIONS", "0")
+    monkeypatch.setenv("FASTVIDEO_VSA_SM100A", "1")
+    monkeypatch.setenv("FASTVIDEO_INFERENCE_TORCH_COMPILE", "1")
+
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    monkeypatch.setattr(backend, "_gpu_mem", lambda: "alloc=0.00GiB, reserved=0.00GiB")
+    backend.initialize(FASTH3_MODEL_CONFIG)
+
+    config = captured["config"]
+    assert captured["download"] == {
+        "repo_id": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+        "filename": "vsa-datafree/adapter_model.safetensors",
+    }
+    assert config.model_path == "MiniMaxAI/MiniMax-H3"
+    assert config.pipeline.components.lora_path.endswith("vsa-datafree/adapter_model.safetensors")
+    assert config.pipeline.components.lora_strength == 1.0
+    assert config.pipeline.experimental == {
+        "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.engine.num_gpus == 4
+    assert config.engine.parallelism.tp_size == 1
+    assert config.engine.parallelism.sp_size == 4
+    assert config.engine.offload.dit is False
+    assert config.engine.offload.dit_layerwise is False
+    assert config.engine.offload.text_encoder is True
+    assert config.engine.offload.vae is True
+    assert config.engine.compile.vae_enabled is True
+    assert config.engine.use_fsdp_inference is False
+    assert os.environ["FASTVIDEO_ATTENTION_BACKEND"] == "VIDEO_SPARSE_ATTN_H3"
+    assert os.environ["FASTVIDEO_FA4"] == "1"
+    assert os.environ["FASTVIDEO_MINIMAX_H3_FUSIONS"] == "all"
+    assert os.environ["FASTVIDEO_VSA_SM100A"] == "0"
+    assert "FASTVIDEO_INFERENCE_TORCH_COMPILE" not in os.environ
+
+
+def test_initialize_selects_declared_generation_backend(monkeypatch):
+    """The GPU worker constructs the backend that the active model profile declares."""
+    from unittest.mock import Mock
+
+    selected_backend = Mock()
+    monkeypatch.setattr(
+        generation_worker,
+        "_create_generation_backend",
+        lambda backend_name, gpu_id: selected_backend,
+    )
+    worker = generation_worker.VideoGenerationWorker(gpu_id=3)
+
+    worker.initialize(FASTH3_MODEL_CONFIG)
+
+    assert worker.backend_name == "minimax_h3"
+    assert worker.backend is selected_backend
+    selected_backend.initialize.assert_called_once_with(FASTH3_MODEL_CONFIG)
+
+
+def test_initialize_failure_clears_backend_ownership(monkeypatch):
+    """A failed family change leaves the GPU worker explicitly uninitialized."""
+    ltx_backend = SimpleNamespace(initialize=lambda config: None, shutdown=lambda: None)
+
+    def fail_initialize(config):
+        del config
+        raise RuntimeError("load failed")
+
+    fasth3_backend = SimpleNamespace(
+        initialize=fail_initialize,
+        shutdown=lambda: None,
+    )
+    backends = {
+        "ltx2": ltx_backend,
+        "minimax_h3": fasth3_backend,
+    }
+    monkeypatch.setattr(
+        generation_worker,
+        "_create_generation_backend",
+        lambda backend_name, gpu_id: backends[backend_name],
+    )
+    worker = generation_worker.VideoGenerationWorker(gpu_id=3)
+    worker.initialize({"generation_backend": "ltx2"})
+
+    with pytest.raises(RuntimeError, match="load failed"):
+        worker.initialize(FASTH3_MODEL_CONFIG)
+
+    assert worker.backend is None
+    assert worker.backend_name is None
+    assert worker.model_config == {"generation_backend": "ltx2"}
+
+
+def test_generate_step_uses_last_frame_for_continuation(monkeypatch):
+    """A later segment receives the prior segment's last decoded frame."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.torch.cuda.synchronize", lambda: None)
+
+    first_result = backend.generate_step(
+        "first prompt",
+        segment_idx=1,
+        image_path=None,
+        reset_conditioning=True,
+    )
+    second_result = backend.generate_step(
+        "second prompt",
+        segment_idx=2,
+        image_path=None,
+        reset_conditioning=False,
+    )
+
+    first_request = backend.generator.requests[0]
+    assert first_request.inputs.pil_image is None
+    assert first_request.negative_prompt == ""
+    assert first_request.sampling.height == 768
+    assert first_request.sampling.width == 1344
+    assert first_request.sampling.num_frames == 124
+    assert first_request.sampling.num_inference_steps == 5
+    assert first_request.sampling.fps == 24
+    assert first_request.sampling.guidance_scale == 1.0
+    assert first_request.sampling.batch_cfg is False
+    assert first_request.sampling.seed == 1000
+    assert first_request.output.save_video is False
+    assert first_request.output.return_frames is True
+    assert backend.generator.conditioning_pixels[1].tolist() == np.full((2, 3, 3), 20).tolist()
+    assert first_result.head_trim_frames == 0
+    assert first_result.head_trim_audio_frames == 0
+    assert second_result.head_trim_frames == 1
+    assert second_result.head_trim_audio_frames == 1
+    assert second_result.audio_sample_rate == 44100
+
+
+def test_generate_step_reset_uses_text_to_video_path(monkeypatch):
+    """Resetting continuation produces an unconditioned text-to-video request."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.torch.cuda.synchronize", lambda: None)
+
+    backend.generate_step("first prompt", 1, None, True)
+    reset_result = backend.generate_step("reset prompt", 2, None, True)
+
+    assert backend.generator.requests[-1].inputs.pil_image is None
+    assert reset_result.head_trim_frames == 0
+    assert reset_result.head_trim_audio_frames == 0
+
+
+def test_generate_step_missing_continuation_frame(monkeypatch):
+    """A later segment fails when no reset or retained frame defines its input."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+
+    with pytest.raises(RuntimeError, match="requires a retained continuation frame"):
+        backend.generate_step("later prompt", 2, None, False)
+
+    assert backend.generator.requests == []
+
+
+def test_warmup_exercises_text_and_first_frame_paths(monkeypatch):
+    """Warmup covers both request shapes used by a DreamVerse session."""
+    backend = MiniMaxH3GenerationBackend(gpu_id=0)
+    backend.model_config = dict(FASTH3_MODEL_CONFIG)
+    backend.generator = _RecordingGenerator()
+    monkeypatch.setattr("dreamverse.minimax_h3_generation.torch.cuda.synchronize", lambda: None)
+
+    timings = backend.warmup("warmup prompt")
+
+    assert backend.generator.conditioning_pixels[0] is None
+    assert backend.generator.conditioning_pixels[1] is not None
+    assert backend.continuation_image is None
+    assert "warmup_text_to_video_ms" in timings
+    assert "warmup_first_frame_to_video_ms" in timings

apps/dreamverse/dreamverse/tests/test_mock_server.py

@@ -6,7 +6,6 @@ import os
 
 from fastapi import WebSocketDisconnect
 
-
 os.environ.setdefault("CEREBRAS_API_KEY", "dummy")
 os.environ.setdefault("GROQ_API_KEY", "dummy")
 
@@ -14,6 +13,7 @@ import dreamverse.mock_server as mock_server
 
 
 class _FakeWebSocket:
+
     def __init__(self, messages: list[tuple[float, dict[str, object]]]):
         self._messages = messages
         self._index = 0
@@ -49,34 +49,34 @@ def test_mock_server_matches_current_single5s_protocol():
         mock_server.MOCK_SEGMENT_BYTES = b"mock-fmp4-bytes"
         mock_server.LATENCY_MS = 1
 
-        ws = _FakeWebSocket(
-            [
-                (
-                    0.0,
-                    {
-                        "type": "session_init_v2",
-                        "preset_id": "simple_prompt_1",
-                        "curated_prompts": ["selected prompt"],
-                        "single_clip_mode": True,
-                        "enhancement_enabled": False,
-                        "auto_extension_enabled": False,
-                        "loop_generation_enabled": False,
-                    },
-                ),
-                (
-                    0.01,
-                    {
-                        "type": "simple_generate",
-                        "preset_id": "simple_custom_prompt",
-                        "prompt_id": "simple_custom_prompt",
-                        "prompt": "custom prompt",
-                        "enhancement_enabled": True,
-                        "initial_image": None,
-                    },
-                ),
-                (0.20, {"type": "leave"}),
-            ]
-        )
+        ws = _FakeWebSocket([
+            (
+                0.0,
+                {
+                    "type": "session_init_v2",
+                    "preset_id": "simple_prompt_1",
+                    "curated_prompts": ["selected prompt"],
+                    "single_clip_mode": True,
+                    "enhancement_enabled": False,
+                    "auto_extension_enabled": False,
+                    "loop_generation_enabled": False,
+                },
+            ),
+            (
+                0.01,
+                {
+                    "type": "simple_generate",
+                    "preset_id": "simple_custom_prompt",
+                    "prompt_id": "simple_custom_prompt",
+                    "prompt": "custom prompt",
+                    "enhancement_enabled": True,
+                    "initial_image": None,
+                },
+            ),
+            (0.20, {
+                "type": "leave"
+            }),
+        ])
 
         asyncio.run(mock_server.websocket_endpoint(ws))
 
@@ -92,24 +92,14 @@ def test_mock_server_matches_current_single5s_protocol():
         assert message_types.count("ltx2_stream_complete") == 2
         assert "prompt_sources_blocked" not in message_types
 
-        segment_start_events = [
-            payload
-            for payload in ws.sent_json
-            if payload["type"] == "ltx2_segment_start"
-        ]
-        gpu_assigned_event = next(
-            payload for payload in ws.sent_json if payload["type"] == "gpu_assigned"
-        )
+        segment_start_events = [payload for payload in ws.sent_json if payload["type"] == "ltx2_segment_start"]
+        gpu_assigned_event = next(payload for payload in ws.sent_json if payload["type"] == "gpu_assigned")
         assert gpu_assigned_event["session_timeout"] == mock_server.SESSION_TIMEOUT_SECONDS
         assert [payload["segment_idx"] for payload in segment_start_events] == [1, 1]
         assert segment_start_events[0]["prompt"] == "selected prompt"
         assert segment_start_events[1]["prompt"] == "custom prompt"
 
-        step_complete_events = [
-            payload
-            for payload in ws.sent_json
-            if payload["type"] == "step_complete"
-        ]
+        step_complete_events = [payload for payload in ws.sent_json if payload["type"] == "step_complete"]
         assert len(step_complete_events) == 2
         assert step_complete_events[0]["latency_ms"] == {
             "total": 121.0,
@@ -134,29 +124,29 @@ def test_mock_server_regular_cap_waits_for_rewrite_rollout():
         mock_server.LATENCY_MS = 1
         mock_server.GENERATION_SEGMENT_CAP = 1
 
-        ws = _FakeWebSocket(
-            [
-                (
-                    0.0,
-                    {
-                        "type": "session_init_v2",
-                        "preset_id": "test_preset",
-                        "curated_prompts": ["segment one"],
-                        "enhancement_enabled": True,
-                        "auto_extension_enabled": False,
-                        "loop_generation_enabled": False,
-                    },
-                ),
-                (
-                    0.02,
-                    {
-                        "type": "rewrite_seed_prompts",
-                        "rewrite_instruction": "start a new rollout",
-                    },
-                ),
-                (0.20, {"type": "leave"}),
-            ]
-        )
+        ws = _FakeWebSocket([
+            (
+                0.0,
+                {
+                    "type": "session_init_v2",
+                    "preset_id": "test_preset",
+                    "curated_prompts": ["segment one"],
+                    "enhancement_enabled": True,
+                    "auto_extension_enabled": False,
+                    "loop_generation_enabled": False,
+                },
+            ),
+            (
+                0.02,
+                {
+                    "type": "rewrite_seed_prompts",
+                    "rewrite_instruction": "start a new rollout",
+                },
+            ),
+            (0.20, {
+                "type": "leave"
+            }),
+        ])
 
         asyncio.run(mock_server.websocket_endpoint(ws))
 
@@ -166,11 +156,7 @@ def test_mock_server_regular_cap_waits_for_rewrite_rollout():
         assert "generation_cap_reached" not in message_types
         assert "prompt_sources_blocked" not in message_types
 
-        segment_start_events = [
-            payload
-            for payload in ws.sent_json
-            if payload["type"] == "ltx2_segment_start"
-        ]
+        segment_start_events = [payload for payload in ws.sent_json if payload["type"] == "ltx2_segment_start"]
         assert [payload["segment_idx"] for payload in segment_start_events] == [1, 1]
         assert segment_start_events[0]["prompt"] == "segment one"
         assert segment_start_events[1]["prompt"] == "segment one [start a new rollout]"
@@ -187,54 +173,40 @@ def test_mock_server_rewrite_during_active_segment_restarts_from_first_rewritten
         mock_server.MOCK_SEGMENT_BYTES = b"mock-fmp4-bytes"
         mock_server.LATENCY_MS = 100
 
-        ws = _FakeWebSocket(
-            [
-                (
-                    0.0,
-                    {
-                        "type": "session_init_v2",
-                        "preset_id": "test_preset",
-                        "curated_prompts": ["segment one", "segment two"],
-                        "enhancement_enabled": True,
-                        "auto_extension_enabled": False,
-                        "loop_generation_enabled": False,
-                    },
-                ),
-                (
-                    0.02,
-                    {
-                        "type": "rewrite_seed_prompts",
-                        "rewrite_instruction": "restart from rewrite",
-                    },
-                ),
-                (0.40, {"type": "leave"}),
-            ]
-        )
+        ws = _FakeWebSocket([
+            (
+                0.0,
+                {
+                    "type": "session_init_v2",
+                    "preset_id": "test_preset",
+                    "curated_prompts": ["segment one", "segment two"],
+                    "enhancement_enabled": True,
+                    "auto_extension_enabled": False,
+                    "loop_generation_enabled": False,
+                },
+            ),
+            (
+                0.02,
+                {
+                    "type": "rewrite_seed_prompts",
+                    "rewrite_instruction": "restart from rewrite",
+                },
+            ),
+            (0.40, {
+                "type": "leave"
+            }),
+        ])
 
         asyncio.run(mock_server.websocket_endpoint(ws))
 
-        segment_start_events = [
-            payload
-            for payload in ws.sent_json
-            if payload["type"] == "ltx2_segment_start"
-        ]
+        segment_start_events = [payload for payload in ws.sent_json if payload["type"] == "ltx2_segment_start"]
         assert [payload["prompt"] for payload in segment_start_events[:2]] == [
             "segment one",
             "segment one [restart from rewrite]",
         ]
-        assert all(
-            payload["prompt"] != "segment two"
-            for payload in segment_start_events[1:]
-        )
-        reset_events = [
-            payload
-            for payload in ws.sent_json
-            if payload.get("type") == "seed_prompts_reset_applied"
-        ]
-        assert any(
-            payload.get("reason") == "rewrite_during_generation"
-            for payload in reset_events
-        )
+        assert all(payload["prompt"] != "segment two" for payload in segment_start_events[1:])
+        reset_events = [payload for payload in ws.sent_json if payload.get("type") == "seed_prompts_reset_applied"]
+        assert any(payload.get("reason") == "rewrite_during_generation" for payload in reset_events)
     finally:
         mock_server.MOCK_SEGMENT_BYTES = old_segment_bytes
         mock_server.LATENCY_MS = old_latency_ms
@@ -247,24 +219,24 @@ def test_mock_server_supports_initial_custom_rollout_prompt():
         mock_server.MOCK_SEGMENT_BYTES = b"mock-fmp4-bytes"
         mock_server.LATENCY_MS = 1
 
-        ws = _FakeWebSocket(
-            [
-                (
-                    0.0,
-                    {
-                        "type": "session_init_v2",
-                        "preset_id": "custom_editable",
-                        "preset_label": "Custom rollout",
-                        "curated_prompts": [],
-                        "initial_rollout_prompt": "A moonbase corridor thriller with flooding",
-                        "enhancement_enabled": True,
-                        "auto_extension_enabled": False,
-                        "loop_generation_enabled": False,
-                    },
-                ),
-                (0.20, {"type": "leave"}),
-            ]
-        )
+        ws = _FakeWebSocket([
+            (
+                0.0,
+                {
+                    "type": "session_init_v2",
+                    "preset_id": "custom_editable",
+                    "preset_label": "Custom rollout",
+                    "curated_prompts": [],
+                    "initial_rollout_prompt": "A moonbase corridor thriller with flooding",
+                    "enhancement_enabled": True,
+                    "auto_extension_enabled": False,
+                    "loop_generation_enabled": False,
+                },
+            ),
+            (0.20, {
+                "type": "leave"
+            }),
+        ])
 
         asyncio.run(mock_server.websocket_endpoint(ws))
 
@@ -276,15 +248,9 @@ def test_mock_server_supports_initial_custom_rollout_prompt():
         assert "ltx2_stream_start" in message_types
         assert "prompt_sources_blocked" not in message_types
 
-        segment_start_events = [
-            payload
-            for payload in ws.sent_json
-            if payload["type"] == "ltx2_segment_start"
-        ]
+        segment_start_events = [payload for payload in ws.sent_json if payload["type"] == "ltx2_segment_start"]
         assert segment_start_events
-        assert segment_start_events[0]["prompt"] == (
-            "A moonbase corridor thriller with flooding [segment 1]"
-        )
+        assert segment_start_events[0]["prompt"] == ("A moonbase corridor thriller with flooding [segment 1]")
     finally:
         mock_server.MOCK_SEGMENT_BYTES = old_segment_bytes
         mock_server.LATENCY_MS = old_latency_ms
@@ -297,35 +263,37 @@ def test_mock_server_can_start_new_project_without_reconnecting():
         mock_server.MOCK_SEGMENT_BYTES = b"mock-fmp4-bytes"
         mock_server.LATENCY_MS = 40
 
-        ws = _FakeWebSocket(
-            [
-                (
-                    0.0,
-                    {
-                        "type": "session_init_v2",
-                        "preset_id": "test_preset",
-                        "curated_prompts": ["segment one"],
-                        "enhancement_enabled": True,
-                        "auto_extension_enabled": False,
-                        "loop_generation_enabled": False,
-                    },
-                ),
-                (0.02, {"type": "end_project_keep_session"}),
-                (
-                    0.20,
-                    {
-                        "type": "project_init_v1",
-                        "preset_id": "test_preset_2",
-                        "preset_label": "Test Preset 2",
-                        "curated_prompts": ["segment two"],
-                        "enhancement_enabled": True,
-                        "auto_extension_enabled": False,
-                        "loop_generation_enabled": False,
-                    },
-                ),
-                (0.40, {"type": "leave"}),
-            ]
-        )
+        ws = _FakeWebSocket([
+            (
+                0.0,
+                {
+                    "type": "session_init_v2",
+                    "preset_id": "test_preset",
+                    "curated_prompts": ["segment one"],
+                    "enhancement_enabled": True,
+                    "auto_extension_enabled": False,
+                    "loop_generation_enabled": False,
+                },
+            ),
+            (0.02, {
+                "type": "end_project_keep_session"
+            }),
+            (
+                0.20,
+                {
+                    "type": "project_init_v1",
+                    "preset_id": "test_preset_2",
+                    "preset_label": "Test Preset 2",
+                    "curated_prompts": ["segment two"],
+                    "enhancement_enabled": True,
+                    "auto_extension_enabled": False,
+                    "loop_generation_enabled": False,
+                },
+            ),
+            (0.40, {
+                "type": "leave"
+            }),
+        ])
 
         asyncio.run(mock_server.websocket_endpoint(ws))
 
@@ -336,16 +304,11 @@ def test_mock_server_can_start_new_project_without_reconnecting():
 
         project_idle_index = message_types.index("project_idle")
         stream_start_indexes = [
-            index for index, message_type in enumerate(message_types)
-            if message_type == "ltx2_stream_start"
+            index for index, message_type in enumerate(message_types) if message_type == "ltx2_stream_start"
         ]
         assert stream_start_indexes[0] < project_idle_index < stream_start_indexes[1]
 
-        segment_start_events = [
-            payload
-            for payload in ws.sent_json
-            if payload["type"] == "ltx2_segment_start"
-        ]
+        segment_start_events = [payload for payload in ws.sent_json if payload["type"] == "ltx2_segment_start"]
         assert [payload["prompt"] for payload in segment_start_events[:2]] == [
             "segment one",
             "segment two",

apps/dreamverse/dreamverse/tests/test_prompt_enhancer.py

@@ -6,7 +6,6 @@ import os
 import re
 import time
 
-
 os.environ.setdefault("CEREBRAS_API_KEY", "dummy")
 os.environ.setdefault("GROQ_API_KEY", "dummy")
 
@@ -22,6 +21,7 @@ from dreamverse.prompt_enhancer import (
 
 
 class _FakeResponse:
+
     def __init__(self, payload: dict):
         self._payload = payload
 
@@ -30,6 +30,7 @@ class _FakeResponse:
 
 
 class _FakeSyncCompletions:
+
     def __init__(self, payload: dict):
         self._payload = payload
 
@@ -38,6 +39,7 @@ class _FakeSyncCompletions:
 
 
 class _FakeSyncClient:
+
     def __init__(self, payload: dict):
         self.chat = type(
             "_FakeChat",
@@ -47,6 +49,7 @@ class _FakeSyncClient:
 
 
 class _DelayedSyncCompletions:
+
     def __init__(self, payload: dict, delay_s: float = 0.0, exc: Exception | None = None):
         self._payload = payload
         self._delay_s = delay_s
@@ -61,29 +64,26 @@ class _DelayedSyncCompletions:
 
 
 class _DelayedSyncClient:
+
     def __init__(self, payload: dict, delay_s: float = 0.0, exc: Exception | None = None):
         self.chat = type(
             "_FakeChat",
             (),
-            {
-                "completions": _DelayedSyncCompletions(
-                    payload,
-                    delay_s=delay_s,
-                    exc=exc,
-                )
-            },
+            {"completions": _DelayedSyncCompletions(
+                payload,
+                delay_s=delay_s,
+                exc=exc,
+            )},
         )()
 
 
 def _chat_payload_with_content(content: str) -> dict:
     return {
-        "choices": [
-            {
-                "message": {
-                    "content": content,
-                }
+        "choices": [{
+            "message": {
+                "content": content,
             }
-        ]
+        }]
     }
 
 
@@ -172,6 +172,7 @@ def _build_staged_enhancer(
 
 
 class _FakeOpenAIClient:
+
     def __init__(self, **kwargs):
         self.kwargs = kwargs
         self.chat = type(
@@ -182,6 +183,7 @@ class _FakeOpenAIClient:
 
 
 class _FakeCerebrasClient:
+
     def __init__(self, **kwargs):
         self.kwargs = kwargs
         self.chat = type(
@@ -192,16 +194,12 @@ class _FakeCerebrasClient:
 
 
 def test_parse_json_response_accepts_fenced_json_with_prose():
-    parsed = _parse_json_response(
-        "Here is the rewrite:\n```json\n{\"segment_prompts\":[\"A\",\"B\"]}\n```\nThanks."
-    )
+    parsed = _parse_json_response("Here is the rewrite:\n```json\n{\"segment_prompts\":[\"A\",\"B\"]}\n```\nThanks.")
     assert parsed == {"segment_prompts": ["A", "B"]}
 
 
 def test_parse_json_response_extracts_first_embedded_object():
-    parsed = _parse_json_response(
-        "Model output:\n{\"segment_prompts\":[\"A\",\"B\"]}\n(complete)"
-    )
+    parsed = _parse_json_response("Model output:\n{\"segment_prompts\":[\"A\",\"B\"]}\n(complete)")
     assert parsed == {"segment_prompts": ["A", "B"]}
 
 
@@ -268,16 +266,12 @@ def test_build_client_supports_groq_provider(monkeypatch):
 
 def test_rewrite_prompt_sequence_accepts_segment_prompts_output():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     result = asyncio.run(
         enhancer.rewrite_prompt_sequence(
             ["prompt one", "prompt two"],
             rewrite_instruction="make it cinematic",
-        )
-    )
+        ))
     assert result.fallback_used is False
     assert result.error is None
     assert result.rollout_id == "preset_a"
@@ -286,15 +280,12 @@ def test_rewrite_prompt_sequence_accepts_segment_prompts_output():
 
 
 def test_rewrite_prompt_sequence_accepts_legacy_rewritten_prompts_output():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content('{"rewritten_prompts":["A","B"]}')
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content('{"rewritten_prompts":["A","B"]}'))
     result = asyncio.run(
         enhancer.rewrite_prompt_sequence(
             ["prompt one", "prompt two"],
             rewrite_instruction="make it cinematic",
-        )
-    )
+        ))
     assert result.fallback_used is False
     assert result.error is None
     assert result.rollout_id == "current_rollout"
@@ -303,19 +294,14 @@ def test_rewrite_prompt_sequence_accepts_legacy_rewritten_prompts_output():
 
 
 def test_rewrite_prompt_sequence_accepts_segment_dicts_without_top_level_rollout_metadata():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"segments":[{"prompt":"A"},{"text":"B"}]}'
-        )
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content('{"segments":[{"prompt":"A"},{"text":"B"}]}'))
     result = asyncio.run(
         enhancer.rewrite_prompt_sequence(
             ["prompt one", "prompt two"],
             preset_id="preset_a",
             preset_label="Preset A",
             rewrite_instruction="make it cinematic",
-        )
-    )
+        ))
     assert result.fallback_used is False
     assert result.error is None
     assert result.rollout_id == "preset_a"
@@ -329,14 +315,12 @@ def test_rewrite_prompt_sequence_accepts_numbered_prose_output():
             "The user is asking for a cinematic rewrite.\n\n"
             "1. A dog bounds across the moon's dusty surface, kicking up silver regolith as it chases a rabbit beneath the black sky.\n"
             "2. The rabbit darts around a crater rim while the dog lunges after it, Earth glowing blue in the distance.\n"
-        )
-    )
+        ))
     result = asyncio.run(
         enhancer.rewrite_prompt_sequence(
             ["prompt one", "prompt two"],
             rewrite_instruction="make it cinematic",
-        )
-    )
+        ))
     assert result.fallback_used is False
     assert result.error is None
     assert result.rollout_id == "current_rollout"
@@ -355,12 +339,10 @@ def test_enhance_prompt_prefers_cerebras_before_groq_fallback():
         groq_delay_s=0.01,
     )
 
-    result = asyncio.run(
-        enhancer.enhance_prompt(
-            "A rainy alley at night",
-            mode="single_clip",
-        )
-    )
+    result = asyncio.run(enhancer.enhance_prompt(
+        "A rainy alley at night",
+        mode="single_clip",
+    ))
 
     assert result.fallback_used is False
     assert result.error is None
@@ -381,12 +363,10 @@ def test_enhance_prompt_uses_groq_when_cerebras_fails():
         groq_delay_s=0.01,
     )
 
-    result = asyncio.run(
-        enhancer.enhance_prompt(
-            "A rainy alley at night",
-            mode="single_clip",
-        )
-    )
+    result = asyncio.run(enhancer.enhance_prompt(
+        "A rainy alley at night",
+        mode="single_clip",
+    ))
 
     assert result.fallback_used is False
     assert result.error is None
@@ -408,13 +388,11 @@ def test_enhance_prompt_can_use_groq_when_cerebras_times_out():
     enhancer.http_timeout_ms = 50
     enhancer.default_timeout_ms = 50
 
-    result = asyncio.run(
-        enhancer.enhance_prompt(
-            "A rainy alley at night",
-            mode="single_clip",
-            timeout_ms=50,
-        )
-    )
+    result = asyncio.run(enhancer.enhance_prompt(
+        "A rainy alley at night",
+        mode="single_clip",
+        timeout_ms=50,
+    ))
 
     assert result.fallback_used is False
     assert result.error is None
@@ -434,12 +412,10 @@ def test_enhance_prompt_can_use_cerebras_when_it_returns_first():
         groq_delay_s=0.08,
     )
 
-    result = asyncio.run(
-        enhancer.enhance_prompt(
-            "A rainy alley at night",
-            mode="single_clip",
-        )
-    )
+    result = asyncio.run(enhancer.enhance_prompt(
+        "A rainy alley at night",
+        mode="single_clip",
+    ))
 
     assert result.fallback_used is False
     assert result.error is None
@@ -453,15 +429,12 @@ def test_enhance_prompt_can_use_cerebras_when_it_returns_first():
 
 
 def test_rewrite_prompt_sequence_keeps_raw_output_on_parse_error():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content("I cannot comply with JSON right now.")
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content("I cannot comply with JSON right now."))
     result = asyncio.run(
         enhancer.rewrite_prompt_sequence(
             ["prompt one", "prompt two"],
             rewrite_instruction="make it cinematic",
-        )
-    )
+        ))
     assert result.fallback_used is True
     assert "No JSON object found in assistant response." in (result.error or "")
     assert result.raw_response_text == "I cannot comply with JSON right now."
@@ -473,9 +446,7 @@ def test_rewrite_prompt_sequence_keeps_raw_output_on_parse_error():
 def test_rewrite_prompt_sequence_uses_current_rollout_payload_shape():
     enhancer = _build_test_enhancer(
         _chat_payload_with_content(
-            '{"id":"rewritten_rollout","label":"Rewritten Rollout","segment_prompts":["A","B"]}'
-        )
-    )
+            '{"id":"rewritten_rollout","label":"Rewritten Rollout","segment_prompts":["A","B"]}'))
     captured = {
         "body": None,
         "timeout_seconds": None,
@@ -486,8 +457,7 @@ def test_rewrite_prompt_sequence_uses_current_rollout_payload_shape():
         captured["timeout_seconds"] = timeout_seconds
         return (
             _chat_payload_with_content(
-                '{"id":"rewritten_rollout","label":"Rewritten Rollout","segment_prompts":["A","B"]}'
-            ),
+                '{"id":"rewritten_rollout","label":"Rewritten Rollout","segment_prompts":["A","B"]}'),
             '{"id":"rewritten_rollout","label":"Rewritten Rollout","segment_prompts":["A","B"]}',
         )
 
@@ -502,8 +472,7 @@ def test_rewrite_prompt_sequence_uses_current_rollout_payload_shape():
             rewrite_model="gpt-test",
             rewrite_temperature=0.2,
             timeout_ms=800,
-        )
-    )
+        ))
 
     assert result.fallback_used is False
     assert captured["body"]["messages"][0] == {
@@ -512,12 +481,12 @@ def test_rewrite_prompt_sequence_uses_current_rollout_payload_shape():
     }
     assert captured["body"]["messages"][1]["role"] == "user"
     assert prompt_enhancer_module.json.loads(captured["body"]["messages"][1]["content"]) == {
-        "mode": "edit_existing_rollout",
-        "request": (
-            "Rewrite all segment prompts with improved continuity and cinematic detail. "
-            "Keep count and ordering identical."
-        ),
-        "user_instruction": "make it cinematic",
+        "mode":
+        "edit_existing_rollout",
+        "request": ("Rewrite all segment prompts with improved continuity and cinematic detail. "
+                    "Keep count and ordering identical."),
+        "user_instruction":
+        "make it cinematic",
         "current_rollout": {
             "id": "preset_a",
             "label": "Preset A",
@@ -528,11 +497,8 @@ def test_rewrite_prompt_sequence_uses_current_rollout_payload_shape():
 
 def test_rewrite_prompt_sequence_supports_new_rollout_mode():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"custom_editable","label":"Custom rollout","segment_prompts":['
-            '"A","B","C","D","E","F"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"custom_editable","label":"Custom rollout","segment_prompts":['
+                                   '"A","B","C","D","E","F"]}'))
     captured = {
         "body": None,
     }
@@ -541,10 +507,8 @@ def test_rewrite_prompt_sequence_supports_new_rollout_mode():
         del timeout_seconds
         captured["body"] = body
         return (
-            _chat_payload_with_content(
-                '{"id":"custom_editable","label":"Custom rollout","segment_prompts":['
-                '"A","B","C","D","E","F"]}'
-            ),
+            _chat_payload_with_content('{"id":"custom_editable","label":"Custom rollout","segment_prompts":['
+                                       '"A","B","C","D","E","F"]}'),
             '{"id":"custom_editable","label":"Custom rollout","segment_prompts":['
             '"A","B","C","D","E","F"]}',
         )
@@ -560,30 +524,29 @@ def test_rewrite_prompt_sequence_supports_new_rollout_mode():
             rewrite_model="gpt-test",
             rewrite_temperature=0.2,
             timeout_ms=800,
-        )
-    )
+        ))
 
     assert result.fallback_used is False
     assert result.prompts == ["A", "B", "C", "D", "E", "F"]
     assert prompt_enhancer_module.json.loads(captured["body"]["messages"][1]["content"]) == {
-        "mode": "new_rollout",
-        "request": (
-            "Rewrite all segment prompts with improved continuity and cinematic detail. "
-            "Keep count and ordering identical."
-        ),
-        "user_instruction": "A moonbase corridor thriller with flooding and red alarms",
-        "desired_segment_count": 6,
-        "rollout_id_hint": "custom_editable",
-        "rollout_label_hint": "Custom rollout",
+        "mode":
+        "new_rollout",
+        "request": ("Rewrite all segment prompts with improved continuity and cinematic detail. "
+                    "Keep count and ordering identical."),
+        "user_instruction":
+        "A moonbase corridor thriller with flooding and red alarms",
+        "desired_segment_count":
+        6,
+        "rollout_id_hint":
+        "custom_editable",
+        "rollout_label_hint":
+        "Custom rollout",
     }
 
 
 def test_rewrite_prompt_sequence_uses_session_override_system_prompt():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     enhancer.rewrite_all_system_prompt = "shared system prompt"
     captured = {
         "body": None,
@@ -593,9 +556,7 @@ def test_rewrite_prompt_sequence_uses_session_override_system_prompt():
         del timeout_seconds
         captured["body"] = body
         return (
-            _chat_payload_with_content(
-                '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-            ),
+            _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'),
             '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}',
         )
 
@@ -609,8 +570,7 @@ def test_rewrite_prompt_sequence_uses_session_override_system_prompt():
             rewrite_instruction="make it cinematic",
             rewrite_model="gpt-test",
             system_prompt_override="session specific system prompt",
-        )
-    )
+        ))
 
     assert result.fallback_used is False
     assert captured["body"]["messages"][0] == {
@@ -621,10 +581,7 @@ def test_rewrite_prompt_sequence_uses_session_override_system_prompt():
 
 def test_resolve_rewrite_new_rollout_system_prompt_uses_dedicated_prompt():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     enhancer.rewrite_all_system_prompt = "shared rewrite system prompt"
     enhancer.rewrite_user_system_prompt = "new rollout rewrite system prompt"
 
@@ -635,24 +592,17 @@ def test_resolve_rewrite_new_rollout_system_prompt_uses_dedicated_prompt():
 
 def test_resolve_rewrite_new_rollout_system_prompt_prefers_override():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     enhancer.rewrite_all_system_prompt = "shared rewrite system prompt"
     enhancer.rewrite_user_system_prompt = "new rollout rewrite system prompt"
 
-    resolved = enhancer.resolve_rewrite_new_rollout_system_prompt(
-        "session specific system prompt"
-    )
+    resolved = enhancer.resolve_rewrite_new_rollout_system_prompt("session specific system prompt")
 
     assert resolved == "session specific system prompt"
 
 
 def test_generate_auto_prompt_uses_selected_model():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content('{"next_prompt":"Auto next"}')
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content('{"next_prompt":"Auto next"}'))
     enhancer.auto_system_prompt = "auto system prompt"
     enhancer.rewrite_model_options = ["gpt-test", "gpt-alt"]
     enhancer.rewrite_default_model = "gpt-test"
@@ -680,8 +630,7 @@ def test_generate_auto_prompt_uses_selected_model():
             next_segment_idx=2,
             model="gpt-alt",
             timeout_ms=800,
-        )
-    )
+        ))
     assert result.fallback_used is False
     assert result.error is None
     assert result.prompt == "Auto next"
@@ -690,9 +639,7 @@ def test_generate_auto_prompt_uses_selected_model():
 
 
 def test_enhance_prompt_uses_selected_model():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content('{"next_prompt":"Enhanced next"}')
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content('{"next_prompt":"Enhanced next"}'))
     enhancer.enhance_system_prompt = "enhance system prompt"
     enhancer.auto_system_prompt = "auto system prompt"
     enhancer.rewrite_model_options = ["gpt-test", "gpt-alt"]
@@ -722,8 +669,7 @@ def test_enhance_prompt_uses_selected_model():
             next_segment_idx=2,
             model="gpt-alt",
             timeout_ms=800,
-        )
-    )
+        ))
     assert result.fallback_used is False
     assert result.error is None
     assert result.prompt == "Enhanced next"
@@ -732,9 +678,7 @@ def test_enhance_prompt_uses_selected_model():
 
 
 def test_enhance_prompt_single_clip_uses_auto_extension_prompt_and_prompt_field():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content('{"prompt":"Extended single clip"}')
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content('{"prompt":"Extended single clip"}'))
     enhancer.enhance_system_prompt = "enhance system prompt"
     enhancer.auto_system_prompt = "auto system prompt"
     enhancer.rewrite_model_options = ["gpt-test", "gpt-alt"]
@@ -764,14 +708,12 @@ def test_enhance_prompt_single_clip_uses_auto_extension_prompt_and_prompt_field(
 
     enhancer._request_content = _fake_request_content  # type: ignore[attr-defined]
 
-    result = asyncio.run(
-        enhancer.enhance_prompt(
-            "short 5s idea",
-            mode="single_clip",
-            model="gpt-alt",
-            timeout_ms=800,
-        )
-    )
+    result = asyncio.run(enhancer.enhance_prompt(
+        "short 5s idea",
+        mode="single_clip",
+        model="gpt-alt",
+        timeout_ms=800,
+    ))
     assert result.fallback_used is False
     assert result.error is None
     assert result.prompt == "Extended single clip"
@@ -784,17 +726,15 @@ def test_enhance_prompt_single_clip_uses_auto_extension_prompt_and_prompt_field(
             "single 5-second LTX-2.3 video clip. Respond with "
             'valid JSON only as {"prompt": "..."}.'  # noqa: E501
         ),
-        "user_prompt": "short 5s idea",
+        "user_prompt":
+        "short 5s idea",
     }
 
 
 def test_enhance_prompt_single_clip_rejects_plain_text_response():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            "Medium shot of a woman by a rainy cafe window as she lifts her "
-            "phone, exhales softly, and the camera makes a slow push in."
-        )
-    )
+        _chat_payload_with_content("Medium shot of a woman by a rainy cafe window as she lifts her "
+                                   "phone, exhales softly, and the camera makes a slow push in."))
     enhancer.auto_system_prompt = "auto system prompt"
 
     result = asyncio.run(
@@ -803,17 +743,14 @@ def test_enhance_prompt_single_clip_rejects_plain_text_response():
             mode="single_clip",
             model="gpt-test",
             timeout_ms=800,
-        )
-    )
+        ))
     assert result.fallback_used is True
     assert "No JSON object found in assistant response." in result.error
     assert result.prompt == ""
 
 
 def test_enhance_prompt_single_clip_rejects_segment_prompts_json():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content('{"segment_prompts":["A","B"]}')
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content('{"segment_prompts":["A","B"]}'))
     enhancer.auto_system_prompt = "auto system prompt"
     enhancer.rewrite_model_options = ["gpt-test"]
     enhancer.rewrite_default_model = "gpt-test"
@@ -824,17 +761,14 @@ def test_enhance_prompt_single_clip_rejects_segment_prompts_json():
             mode="single_clip",
             model="gpt-test",
             timeout_ms=800,
-        )
-    )
+        ))
     assert result.fallback_used is True
     assert result.prompt == ""
     assert "Missing prompt string." in (result.error or "")
 
 
 def test_enhance_prompt_requires_json_and_does_not_fallback_to_raw_text():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content("A cinematic continuation with slow dolly movement.")
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content("A cinematic continuation with slow dolly movement."))
     enhancer.enhance_system_prompt = "enhance system prompt"
     enhancer.rewrite_model_options = ["gpt-test"]
     enhancer.rewrite_default_model = "gpt-test"
@@ -846,17 +780,14 @@ def test_enhance_prompt_requires_json_and_does_not_fallback_to_raw_text():
             next_segment_idx=2,
             model="gpt-test",
             timeout_ms=800,
-        )
-    )
+        ))
     assert result.fallback_used is True
     assert result.prompt == ""
     assert "No JSON object found in assistant response." in (result.error or "")
 
 
 def test_generate_auto_prompt_requires_json_and_does_not_fallback_to_raw_text():
-    enhancer = _build_test_enhancer(
-        _chat_payload_with_content("A calm, grounded continuation with subtle motion.")
-    )
+    enhancer = _build_test_enhancer(_chat_payload_with_content("A calm, grounded continuation with subtle motion."))
     enhancer.auto_system_prompt = "auto system prompt"
     enhancer.rewrite_model_options = ["gpt-test"]
     enhancer.rewrite_default_model = "gpt-test"
@@ -867,34 +798,30 @@ def test_generate_auto_prompt_requires_json_and_does_not_fallback_to_raw_text():
             next_segment_idx=2,
             model="gpt-test",
             timeout_ms=800,
-        )
-    )
+        ))
     assert result.fallback_used is True
     assert result.prompt == ""
     assert "No JSON object found in assistant response." in (result.error or "")
 
 
 def test_rewrite_prompt_sequence_includes_raw_json_when_content_empty():
-    enhancer = _build_test_enhancer(
-        {
-            "choices": [
-                {
-                    "finish_reason": "length",
-                    "message": {
-                        "content": [],
-                        "refusal": None,
-                    },
-                }
-            ],
-            "usage": {"completion_tokens": 0},
-        }
-    )
+    enhancer = _build_test_enhancer({
+        "choices": [{
+            "finish_reason": "length",
+            "message": {
+                "content": [],
+                "refusal": None,
+            },
+        }],
+        "usage": {
+            "completion_tokens": 0
+        },
+    })
     result = asyncio.run(
         enhancer.rewrite_prompt_sequence(
             ["prompt one", "prompt two"],
             rewrite_instruction="make it cinematic",
-        )
-    )
+        ))
     assert result.fallback_used is True
     assert "No rewrite segment prompts found in assistant response." in (result.error or "")
     assert isinstance(result.raw_response_text, str)
@@ -903,10 +830,7 @@ def test_rewrite_prompt_sequence_includes_raw_json_when_content_empty():
 
 def test_get_rewrite_model_config_returns_fixed_defaults():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     enhancer.rewrite_default_model = "gpt-oss-120b"
     enhancer.rewrite_model_options = ["gpt-oss-120b"]
 
@@ -918,10 +842,7 @@ def test_get_rewrite_model_config_returns_fixed_defaults():
 
 def test_get_prompt_config_includes_auto_extension_prompt():
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     enhancer.enhance_system_prompt_path = "/tmp/next.md"
     enhancer.auto_system_prompt_path = "/tmp/auto.md"
     enhancer.rewrite_all_system_prompt_path = "/tmp/rewrite.md"
@@ -948,19 +869,14 @@ def test_get_prompt_config_includes_auto_extension_prompt():
 
 def test_get_prompt_config_reports_loaded_fallback_prompt_path(tmp_path):
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     rewrite_fallback_path = tmp_path / "rewrite_window_system_prompt.md"
     rewrite_fallback_path.write_text("rewrite prompt\n", encoding="utf-8")
     next_path = tmp_path / "next.md"
     next_path.write_text("next prompt\n", encoding="utf-8")
     auto_path = tmp_path / "auto.md"
     auto_path.write_text("auto prompt\n", encoding="utf-8")
-    enhancer.rewrite_all_system_prompt_path = str(
-        tmp_path / "prompts.local" / "rewrite_window_system_prompt.md"
-    )
+    enhancer.rewrite_all_system_prompt_path = str(tmp_path / "prompts.local" / "rewrite_window_system_prompt.md")
     enhancer.rewrite_all_system_prompt_fallback_path = str(rewrite_fallback_path)
     enhancer.enhance_system_prompt_path = str(next_path)
     enhancer.auto_system_prompt_path = str(auto_path)
@@ -973,14 +889,9 @@ def test_get_prompt_config_reports_loaded_fallback_prompt_path(tmp_path):
     assert config["rewrite_window_system_prompt_path"] == str(rewrite_fallback_path)
 
 
-def test_reload_system_prompts_falls_back_to_rewrite_window_when_user_prompt_empty(
-    tmp_path,
-):
+def test_reload_system_prompts_falls_back_to_rewrite_window_when_user_prompt_empty(tmp_path, ):
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     next_path = tmp_path / "next.md"
     auto_path = tmp_path / "auto.md"
     rewrite_path = tmp_path / "rewrite_window_system_prompt.md"
@@ -1007,10 +918,7 @@ def test_reload_system_prompts_falls_back_to_rewrite_window_when_user_prompt_emp
 
 def test_save_prompt_config_updates_auto_extension_prompt(tmp_path):
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     next_path = tmp_path / "next.md"
     auto_path = tmp_path / "auto.md"
     rewrite_path = tmp_path / "rewrite.md"
@@ -1021,9 +929,7 @@ def test_save_prompt_config_updates_auto_extension_prompt(tmp_path):
     enhancer.auto_system_prompt_path = str(auto_path)
     enhancer.rewrite_all_system_prompt_path = str(rewrite_path)
 
-    config = enhancer.save_prompt_config(
-        auto_extension_system_prompt="auto updated",
-    )
+    config = enhancer.save_prompt_config(auto_extension_system_prompt="auto updated", )
 
     assert auto_path.read_text(encoding="utf-8").strip() == "auto updated"
     assert config["auto_extension_system_prompt"] == "auto updated"
@@ -1031,10 +937,7 @@ def test_save_prompt_config_updates_auto_extension_prompt(tmp_path):
 
 def test_save_prompt_config_updates_rewrite_user_prompt(tmp_path):
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     next_path = tmp_path / "next.md"
     auto_path = tmp_path / "auto.md"
     rewrite_path = tmp_path / "rewrite.md"
@@ -1052,9 +955,7 @@ def test_save_prompt_config_updates_rewrite_user_prompt(tmp_path):
     enhancer.rewrite_all_system_prompt_fallback_path = None
     enhancer.rewrite_user_system_prompt_fallback_path = None
 
-    config = enhancer.save_prompt_config(
-        rewrite_user_system_prompt="rewrite user updated",
-    )
+    config = enhancer.save_prompt_config(rewrite_user_system_prompt="rewrite user updated", )
 
     assert rewrite_user_path.read_text(encoding="utf-8").strip() == "rewrite user updated"
     assert config["rewrite_user_system_prompt"] == "rewrite user updated"
@@ -1062,10 +963,7 @@ def test_save_prompt_config_updates_rewrite_user_prompt(tmp_path):
 
 def test_save_prompt_config_updates_rewrite_model(tmp_path):
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     next_path = tmp_path / "next.md"
     auto_path = tmp_path / "auto.md"
     rewrite_path = tmp_path / "rewrite.md"
@@ -1081,9 +979,7 @@ def test_save_prompt_config_updates_rewrite_model(tmp_path):
     enhancer.rewrite_default_model = "gpt-test"
     enhancer.rewrite_model_options = ["gpt-test", "gpt-alt"]
 
-    config = enhancer.save_prompt_config(
-        rewrite_model="gpt-alt",
-    )
+    config = enhancer.save_prompt_config(rewrite_model="gpt-alt", )
 
     assert enhancer.rewrite_default_model == "gpt-alt"
     assert config["rewrite_model"] == "gpt-alt"
@@ -1092,10 +988,7 @@ def test_save_prompt_config_updates_rewrite_model(tmp_path):
 
 def test_save_prompt_config_updates_rewrite_temperature(tmp_path):
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     next_path = tmp_path / "next.md"
     auto_path = tmp_path / "auto.md"
     rewrite_path = tmp_path / "rewrite.md"
@@ -1109,9 +1002,7 @@ def test_save_prompt_config_updates_rewrite_temperature(tmp_path):
     enhancer.auto_system_prompt_fallback_path = None
     enhancer.rewrite_all_system_prompt_fallback_path = None
 
-    config = enhancer.save_prompt_config(
-        rewrite_temperature=1.3,
-    )
+    config = enhancer.save_prompt_config(rewrite_temperature=1.3, )
 
     assert enhancer.rewrite_default_temperature == 1.3
     assert config["rewrite_temperature"] == 1.3
@@ -1119,10 +1010,7 @@ def test_save_prompt_config_updates_rewrite_temperature(tmp_path):
 
 def test_save_prompt_config_creates_versioned_backup_for_existing_prompt(tmp_path):
     enhancer = _build_test_enhancer(
-        _chat_payload_with_content(
-            '{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'
-        )
-    )
+        _chat_payload_with_content('{"id":"preset_a","label":"Preset A","segment_prompts":["A","B"]}'))
     next_path = tmp_path / "next.md"
     auto_path = tmp_path / "auto.md"
     rewrite_path = tmp_path / "rewrite_window_system_prompt.md"
@@ -1136,13 +1024,9 @@ def test_save_prompt_config_creates_versioned_backup_for_existing_prompt(tmp_pat
     enhancer.auto_system_prompt_fallback_path = None
     enhancer.rewrite_all_system_prompt_fallback_path = None
 
-    enhancer.save_prompt_config(
-        rewrite_window_system_prompt="rewrite updated",
-    )
+    enhancer.save_prompt_config(rewrite_window_system_prompt="rewrite updated", )
 
-    backup_paths = sorted(
-        tmp_path.glob("rewrite_window_system_prompt.*.bak.md")
-    )
+    backup_paths = sorted(tmp_path.glob("rewrite_window_system_prompt.*.bak.md"))
 
     assert rewrite_path.read_text(encoding="utf-8").strip() == "rewrite updated"
     assert len(backup_paths) == 1

apps/dreamverse/dreamverse/tests/test_realtime_stress.py

@@ -27,13 +27,8 @@ try:
 except ModuleNotFoundError:
     websockets = None  # type: ignore[assignment]
 
-
-DEFAULT_PRESET_FILE = (
-    Path(__file__).resolve().parents[2]
-    / "web"
-    / "prompts"
-    / "selected_ltx2_continuation_story_presets.json"
-)
+DEFAULT_PRESET_FILE = (Path(__file__).resolve().parents[2] / "web" / "prompts" /
+                       "selected_ltx2_continuation_story_presets.json")
 
 
 def utc_now_iso() -> str:
@@ -65,10 +60,7 @@ def safe_percentile(values: list[float], percentile: float) -> float | None:
     if lower == upper:
         return sorted_values[lower]
     fraction = rank - lower
-    return (
-        sorted_values[lower]
-        + (sorted_values[upper] - sorted_values[lower]) * fraction
-    )
+    return (sorted_values[lower] + (sorted_values[upper] - sorted_values[lower]) * fraction)
 
 
 def summarize_series(values: list[float]) -> dict[str, float | int | None]:
@@ -145,24 +137,16 @@ def load_curated_prompts(
     selected_id = str(selected.get("id", "")).strip() or "unknown_preset"
     raw_prompts = selected.get("segment_prompts", [])
     if not isinstance(raw_prompts, list):
-        raise ValueError(
-            f"Preset {selected_id} has invalid segment_prompts (must be list)."
-        )
+        raise ValueError(f"Preset {selected_id} has invalid segment_prompts (must be list).")
 
-    prompts = [
-        str(prompt).strip()
-        for prompt in raw_prompts
-        if isinstance(prompt, str) and str(prompt).strip()
-    ]
+    prompts = [str(prompt).strip() for prompt in raw_prompts if isinstance(prompt, str) and str(prompt).strip()]
     if not prompts:
         raise ValueError(f"Preset {selected_id} has no non-empty prompts.")
 
     limited = prompts[:curated_limit]
     if not limited:
-        raise ValueError(
-            f"curated_limit={curated_limit} produced no prompts for preset "
-            f"{selected_id}."
-        )
+        raise ValueError(f"curated_limit={curated_limit} produced no prompts for preset "
+                         f"{selected_id}.")
     return selected_id, limited, len(prompts)
 
 
@@ -224,11 +208,11 @@ async def run_single_session(
 
     try:
         async with websockets.connect(
-            url,
-            max_size=None,
-            ping_interval=None,
-            open_timeout=connect_timeout_s,
-            close_timeout=2.0,
+                url,
+                max_size=None,
+                ping_interval=None,
+                open_timeout=connect_timeout_s,
+                close_timeout=2.0,
         ) as ws:
             connect_finish_monotonic = time.monotonic()
             session_data["connect_finish_ts_utc"] = utc_now_iso()
@@ -249,9 +233,7 @@ async def run_single_session(
                 timeout_remaining = session_timeout_s - elapsed_s
                 if timeout_remaining <= 0:
                     session_data["status"] = "timeout"
-                    session_data["error"] = (
-                        f"Session timed out after {session_timeout_s:.1f}s."
-                    )
+                    session_data["error"] = (f"Session timed out after {session_timeout_s:.1f}s.")
                     break
 
                 recv_start_epoch = time.time()
@@ -265,9 +247,7 @@ async def run_single_session(
                     )
                 except asyncio.TimeoutError:
                     session_data["status"] = "timeout"
-                    session_data["error"] = (
-                        "Timed out waiting for websocket message."
-                    )
+                    session_data["error"] = ("Timed out waiting for websocket message.")
                     break
                 except Exception as exc:
                     session_data["status"] = "failed"
@@ -288,20 +268,16 @@ async def run_single_session(
 
                     chunk_gap_ms: float | None = None
                     if last_chunk_finish_monotonic is not None:
-                        chunk_gap_ms = (
-                            recv_finish_monotonic - last_chunk_finish_monotonic
-                        ) * 1000.0
+                        chunk_gap_ms = (recv_finish_monotonic - last_chunk_finish_monotonic) * 1000.0
 
-                    session_data["chunks"].append(
-                        {
-                            "segment_idx": current_segment_idx,
-                            "chunk_idx": session_data["total_chunks"],
-                            "size_bytes": len(message),
-                            "chunk_start_ts_utc": recv_start_iso,
-                            "chunk_finish_ts_utc": recv_finish_iso,
-                            "chunk_gap_ms": chunk_gap_ms,
-                        }
-                    )
+                    session_data["chunks"].append({
+                        "segment_idx": current_segment_idx,
+                        "chunk_idx": session_data["total_chunks"],
+                        "size_bytes": len(message),
+                        "chunk_start_ts_utc": recv_start_iso,
+                        "chunk_finish_ts_utc": recv_finish_iso,
+                        "chunk_gap_ms": chunk_gap_ms,
+                    })
                     last_chunk_finish_monotonic = recv_finish_monotonic
                     last_chunk_finish_epoch = recv_finish_epoch
                     session_data["last_chunk_finish_ts_utc"] = recv_finish_iso
@@ -321,9 +297,7 @@ async def run_single_session(
                 if msg_type == "gpu_assigned":
                     session_data["gpu_assigned_ts_utc"] = recv_finish_iso
                     if connect_finish_monotonic is not None:
-                        session_data["queue_wait_ms"] = (
-                            recv_finish_monotonic - connect_finish_monotonic
-                        ) * 1000.0
+                        session_data["queue_wait_ms"] = (recv_finish_monotonic - connect_finish_monotonic) * 1000.0
                 elif msg_type == "ltx2_stream_start":
                     if initial_total_segments is None:
                         parsed_total = parse_int(data.get("total_segments"))
@@ -338,20 +312,13 @@ async def run_single_session(
                     session_data["media_segments_completed"] += 1
                     if first_media_segment_complete_epoch is None:
                         first_media_segment_complete_epoch = recv_finish_epoch
-                        session_data[
-                            "first_media_segment_complete_ts_utc"
-                        ] = recv_finish_iso
+                        session_data["first_media_segment_complete_ts_utc"] = recv_finish_iso
                 elif msg_type == "ltx2_segment_complete":
                     session_data["segments_completed"] += 1
                     seg_idx = parse_int(data.get("segment_idx"))
-                    if (
-                        initial_total_segments is not None
-                        and seg_idx is not None
-                        and seg_idx >= initial_total_segments
-                    ):
-                        session_data[
-                            "target_segment_complete_ts_utc"
-                        ] = recv_finish_iso
+                    if (initial_total_segments is not None and seg_idx is not None
+                            and seg_idx >= initial_total_segments):
+                        session_data["target_segment_complete_ts_utc"] = recv_finish_iso
                         await asyncio.sleep(post_complete_wait_s)
                         session_data["leave_sent_ts_utc"] = utc_now_iso()
                         try:
@@ -362,15 +329,11 @@ async def run_single_session(
                         break
                 elif msg_type == "session_timeout":
                     session_data["status"] = "timeout"
-                    session_data["error"] = str(
-                        data.get("message") or "Backend session timeout"
-                    )
+                    session_data["error"] = str(data.get("message") or "Backend session timeout")
                     break
                 elif msg_type == "error":
                     session_data["status"] = "failed"
-                    session_data["error"] = str(
-                        data.get("message") or "Backend error message"
-                    )
+                    session_data["error"] = str(data.get("message") or "Backend error message")
                     break
 
             if session_data["status"] == "failed" and session_data["error"] is None:
@@ -379,29 +342,18 @@ async def run_single_session(
         session_data["status"] = "failed"
         session_data["error"] = f"WebSocket connect/run failed: {exc}"
 
-    if (
-        first_chunk_finish_epoch is not None
-        and last_chunk_finish_epoch is not None
-        and session_data["total_chunk_bytes"] > 0
-    ):
+    if (first_chunk_finish_epoch is not None and last_chunk_finish_epoch is not None
+            and session_data["total_chunk_bytes"] > 0):
         duration_s = last_chunk_finish_epoch - first_chunk_finish_epoch
         if duration_s > 0:
-            session_data["session_goodput_mbps"] = (
-                session_data["total_chunk_bytes"] * 8.0 / duration_s / 1_000_000.0
-            )
+            session_data["session_goodput_mbps"] = (session_data["total_chunk_bytes"] * 8.0 / duration_s / 1_000_000.0)
 
-    if (
-        first_chunk_finish_epoch is not None
-        and first_media_segment_complete_epoch is not None
-    ):
-        session_data["first_chunk_before_first_media_complete"] = (
-            first_chunk_finish_epoch < first_media_segment_complete_epoch
-        )
+    if (first_chunk_finish_epoch is not None and first_media_segment_complete_epoch is not None):
+        session_data["first_chunk_before_first_media_complete"] = (first_chunk_finish_epoch
+                                                                   < first_media_segment_complete_epoch)
 
     session_data["close_ts_utc"] = utc_now_iso()
-    session_data["duration_ms"] = (
-        time.monotonic() - session_start_monotonic
-    ) * 1000.0
+    session_data["duration_ms"] = (time.monotonic() - session_start_monotonic) * 1000.0
     return session_data
 
 
@@ -412,14 +364,11 @@ async def run_worker_sessions(
     config: dict[str, Any],
 ) -> list[dict[str, Any]]:
     tasks = [
-        asyncio.create_task(
-            run_single_session(
-                worker_id=worker_id,
-                worker_session_idx=idx,
-                config=config,
-            )
-        )
-        for idx in range(session_count)
+        asyncio.create_task(run_single_session(
+            worker_id=worker_id,
+            worker_session_idx=idx,
+            config=config,
+        )) for idx in range(session_count)
     ]
     if not tasks:
         return []
@@ -437,29 +386,23 @@ def worker_entry(
     try:
         ready_queue.put({"worker_id": worker_id, "status": "ready"})
         start_event.wait()
-        sessions = asyncio.run(
-            run_worker_sessions(
-                worker_id=worker_id,
-                session_count=session_count,
-                config=config,
-            )
-        )
-        result_queue.put(
-            {
-                "worker_id": worker_id,
-                "status": "ok",
-                "sessions": sessions,
-            }
-        )
+        sessions = asyncio.run(run_worker_sessions(
+            worker_id=worker_id,
+            session_count=session_count,
+            config=config,
+        ))
+        result_queue.put({
+            "worker_id": worker_id,
+            "status": "ok",
+            "sessions": sessions,
+        })
     except Exception as exc:
-        result_queue.put(
-            {
-                "worker_id": worker_id,
-                "status": "error",
-                "error": str(exc),
-                "traceback": traceback.format_exc(),
-            }
-        )
+        result_queue.put({
+            "worker_id": worker_id,
+            "status": "error",
+            "error": str(exc),
+            "traceback": traceback.format_exc(),
+        })
 
 
 def build_summary(
@@ -517,33 +460,22 @@ def build_summary(
     if len(all_chunk_finish_epochs) >= 2 and total_chunk_bytes > 0:
         duration_s = max(all_chunk_finish_epochs) - min(all_chunk_finish_epochs)
         if duration_s > 0:
-            global_goodput_mbps = (
-                total_chunk_bytes * 8.0 / duration_s / 1_000_000.0
-            )
+            global_goodput_mbps = (total_chunk_bytes * 8.0 / duration_s / 1_000_000.0)
 
-    bucket_throughputs_mbps = [
-        (bytes_count * 8.0) / 1_000_000.0
-        for _, bytes_count in sorted(bucket_bytes.items())
-    ]
+    bucket_throughputs_mbps = [(bytes_count * 8.0) / 1_000_000.0 for _, bytes_count in sorted(bucket_bytes.items())]
     bucket_stats = summarize_series(bucket_throughputs_mbps)
 
-    chunk_gap_threshold_breaches = [
-        value for value in chunk_gaps if value >= chunk_gap_threshold_ms
-    ]
+    chunk_gap_threshold_breaches = [value for value in chunk_gaps if value >= chunk_gap_threshold_ms]
     non_success = len(sessions) - status_counts.get("success", 0)
 
     fail_reasons: list[str] = []
     if non_success > 0:
-        fail_reasons.append(
-            f"{non_success} session(s) did not complete successfully."
-        )
+        fail_reasons.append(f"{non_success} session(s) did not complete successfully.")
     if not chunk_gaps:
         fail_reasons.append("No chunk gap data collected.")
     if chunk_gap_threshold_breaches:
-        fail_reasons.append(
-            f"{len(chunk_gap_threshold_breaches)} chunk gap(s) were >= "
-            f"{chunk_gap_threshold_ms:.0f}ms."
-        )
+        fail_reasons.append(f"{len(chunk_gap_threshold_breaches)} chunk gap(s) were >= "
+                            f"{chunk_gap_threshold_ms:.0f}ms.")
 
     passed = len(fail_reasons) == 0
     progressive_ratio = None
@@ -554,20 +486,18 @@ def build_summary(
         "passed": passed,
         "fail_reasons": fail_reasons,
         "sessions": {
-            "total": len(sessions),
-            "success": status_counts.get("success", 0),
-            "failed": status_counts.get("failed", 0),
-            "timeout": status_counts.get("timeout", 0),
-            "protocol_error": status_counts.get("protocol_error", 0),
-            "other": (
-                len(sessions)
-                - (
-                    status_counts.get("success", 0)
-                    + status_counts.get("failed", 0)
-                    + status_counts.get("timeout", 0)
-                    + status_counts.get("protocol_error", 0)
-                )
-            ),
+            "total":
+            len(sessions),
+            "success":
+            status_counts.get("success", 0),
+            "failed":
+            status_counts.get("failed", 0),
+            "timeout":
+            status_counts.get("timeout", 0),
+            "protocol_error":
+            status_counts.get("protocol_error", 0),
+            "other": (len(sessions) - (status_counts.get("success", 0) + status_counts.get("failed", 0) +
+                                       status_counts.get("timeout", 0) + status_counts.get("protocol_error", 0))),
         },
         "chunk_gap_ms": {
             **chunk_gap_stats,
@@ -606,51 +536,39 @@ def print_summary(
     bucket_bw = bandwidth["bucketed_1s"]
 
     print("=== LTX2 Realtime Stress Test Summary ===")
-    print(
-        "Run: "
-        f"url={run_info['url']} clients={run_info['clients']} "
-        f"processes={run_info['processes']} "
-        f"preset={run_info['preset_id']} "
-        f"curated_limit={run_info['curated_limit']}"
-    )
-    print(
-        "Sessions: "
-        f"total={sessions['total']} success={sessions['success']} "
-        f"failed={sessions['failed']} timeout={sessions['timeout']} "
-        f"protocol_error={sessions['protocol_error']}"
-    )
-    print(
-        "Chunk gap ms: "
-        f"min={format_num(chunk_gap['min'])} "
-        f"p50={format_num(chunk_gap['p50'])} "
-        f"p95={format_num(chunk_gap['p95'])} "
-        f"p99={format_num(chunk_gap['p99'])} "
-        f"max={format_num(chunk_gap['max'])} "
-        f"threshold={format_num(chunk_gap['threshold_ms'])} "
-        f"breaches={chunk_gap['breach_count']}"
-    )
-    print(
-        "Queue wait ms: "
-        f"min={format_num(queue_wait['min'])} "
-        f"p50={format_num(queue_wait['p50'])} "
-        f"p95={format_num(queue_wait['p95'])} "
-        f"max={format_num(queue_wait['max'])}"
-    )
+    print("Run: "
+          f"url={run_info['url']} clients={run_info['clients']} "
+          f"processes={run_info['processes']} "
+          f"preset={run_info['preset_id']} "
+          f"curated_limit={run_info['curated_limit']}")
+    print("Sessions: "
+          f"total={sessions['total']} success={sessions['success']} "
+          f"failed={sessions['failed']} timeout={sessions['timeout']} "
+          f"protocol_error={sessions['protocol_error']}")
+    print("Chunk gap ms: "
+          f"min={format_num(chunk_gap['min'])} "
+          f"p50={format_num(chunk_gap['p50'])} "
+          f"p95={format_num(chunk_gap['p95'])} "
+          f"p99={format_num(chunk_gap['p99'])} "
+          f"max={format_num(chunk_gap['max'])} "
+          f"threshold={format_num(chunk_gap['threshold_ms'])} "
+          f"breaches={chunk_gap['breach_count']}")
+    print("Queue wait ms: "
+          f"min={format_num(queue_wait['min'])} "
+          f"p50={format_num(queue_wait['p50'])} "
+          f"p95={format_num(queue_wait['p95'])} "
+          f"max={format_num(queue_wait['max'])}")
     ratio = progressive["ratio"]
     ratio_text = "n/a" if ratio is None else f"{ratio * 100:.2f}%"
-    print(
-        "Progressive streaming: "
-        f"{progressive['success_sessions']}/"
-        f"{progressive['eligible_sessions']} ({ratio_text})"
-    )
-    print(
-        "Bandwidth Mbps: "
-        f"per_session_avg={format_num(per_session_bw['avg'])} "
-        f"per_session_p95={format_num(per_session_bw['p95'])} "
-        f"global={format_num(bandwidth['global_goodput_mbps'])} "
-        f"bucket_avg={format_num(bucket_bw['avg_mbps'])} "
-        f"bucket_peak={format_num(bucket_bw['peak_mbps'])}"
-    )
+    print("Progressive streaming: "
+          f"{progressive['success_sessions']}/"
+          f"{progressive['eligible_sessions']} ({ratio_text})")
+    print("Bandwidth Mbps: "
+          f"per_session_avg={format_num(per_session_bw['avg'])} "
+          f"per_session_p95={format_num(per_session_bw['p95'])} "
+          f"global={format_num(bandwidth['global_goodput_mbps'])} "
+          f"bucket_avg={format_num(bucket_bw['avg_mbps'])} "
+          f"bucket_peak={format_num(bucket_bw['peak_mbps'])}")
     print(f"VERDICT: {'PASS' if summary['passed'] else 'FAIL'}")
     if summary["fail_reasons"]:
         print("Fail reasons:")
@@ -670,10 +588,8 @@ def distribute_sessions(total_clients: int, process_count: int) -> list[int]:
 
 def run_stress(args: argparse.Namespace) -> tuple[dict[str, Any], int]:
     if websockets is None:
-        raise RuntimeError(
-            "Missing dependency: websockets. Install it before running this "
-            "stress test."
-        )
+        raise RuntimeError("Missing dependency: websockets. Install it before running this "
+                           "stress test.")
 
     preset_file = Path(args.preset_file).expanduser().resolve()
     selected_preset_id, curated_prompts, total_prompt_count = load_curated_prompts(
@@ -735,13 +651,8 @@ def run_stress(args: argparse.Namespace) -> tuple[dict[str, Any], int]:
 
         start_event.set()
 
-        result_deadline = (
-            time.monotonic()
-            + args.connect_timeout_s
-            + args.session_timeout_s
-            + args.post_complete_wait_s
-            + 180.0
-        )
+        result_deadline = (time.monotonic() + args.connect_timeout_s + args.session_timeout_s +
+                           args.post_complete_wait_s + 180.0)
         worker_results: list[dict[str, Any]] = []
         while len(worker_results) < len(processes):
             timeout_s = max(0.1, result_deadline - time.monotonic())
@@ -765,24 +676,20 @@ def run_stress(args: argparse.Namespace) -> tuple[dict[str, Any], int]:
             if result.get("status") == "ok":
                 sessions.extend(result.get("sessions", []))
             else:
-                worker_errors.append(
-                    {
-                        "worker_id": result.get("worker_id"),
-                        "error": result.get("error"),
-                        "traceback": result.get("traceback"),
-                    }
-                )
+                worker_errors.append({
+                    "worker_id": result.get("worker_id"),
+                    "error": result.get("error"),
+                    "traceback": result.get("traceback"),
+                })
 
         received_workers = {result.get("worker_id") for result in worker_results}
         expected_workers = set(range(len(processes)))
         missing_workers = sorted(expected_workers - received_workers)
         for worker_id in missing_workers:
-            worker_errors.append(
-                {
-                    "worker_id": worker_id,
-                    "error": "No worker result received.",
-                }
-            )
+            worker_errors.append({
+                "worker_id": worker_id,
+                "error": "No worker result received.",
+            })
 
         run_end_epoch = time.time()
         run_end_iso = iso_from_epoch(run_end_epoch)
@@ -795,9 +702,8 @@ def run_stress(args: argparse.Namespace) -> tuple[dict[str, Any], int]:
 
         if worker_errors:
             summary["passed"] = False
-            summary["fail_reasons"] = list(summary["fail_reasons"]) + [
-                f"{len(worker_errors)} worker error(s) occurred."
-            ]
+            summary["fail_reasons"] = list(
+                summary["fail_reasons"]) + [f"{len(worker_errors)} worker error(s) occurred."]
 
         output_payload = {
             "run_info": {
@@ -833,9 +739,7 @@ def run_stress(args: argparse.Namespace) -> tuple[dict[str, Any], int]:
 
 
 def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Multiprocess realtime stress test for LTX2 streaming.",
-    )
+    parser = argparse.ArgumentParser(description="Multiprocess realtime stress test for LTX2 streaming.", )
     parser.add_argument(
         "-u",
         "--url",

apps/dreamverse/dreamverse/tests/test_session_init_image.py

@@ -47,13 +47,11 @@ def test_persist_session_init_image_returns_none_when_missing_data():
 
 def test_persist_session_init_image_rejects_unsupported_mime():
     with pytest.raises(ValueError, match="PNG, JPEG, or WebP"):
-        persist_session_init_image(
-            {
-                "name": "frame.gif",
-                "mime_type": "image/gif",
-                "data_url": "data:image/gif;base64,R0lGODlhAQABAAAAACw=",
-            }
-        )
+        persist_session_init_image({
+            "name": "frame.gif",
+            "mime_type": "image/gif",
+            "data_url": "data:image/gif;base64,R0lGODlhAQABAAAAACw=",
+        })
 
 
 def test_persist_session_init_image_rejects_large_payload(monkeypatch):
@@ -66,10 +64,8 @@ def test_persist_session_init_image_rejects_large_payload(monkeypatch):
     monkeypatch.setattr(base64, "b64decode", fake_b64decode)
 
     with pytest.raises(ValueError, match="15 MB or smaller"):
-        persist_session_init_image(
-            {
-                "name": "frame.png",
-                "mime_type": "image/png",
-                "data_url": data_url,
-            }
-        )
+        persist_session_init_image({
+            "name": "frame.png",
+            "mime_type": "image/png",
+            "data_url": data_url,
+        })

apps/dreamverse/gpu-pool.drawio

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

apps/dreamverse/scripts/modal/modal_app.py

@@ -8,12 +8,10 @@ import modal
 
 IMAGE = os.environ.get("DREAMVERSE_IMAGE")
 if not IMAGE:
-    raise RuntimeError(
-        "DREAMVERSE_IMAGE is required. Set it to a published SHA-specific Dreamverse image, "
-        "for example a dreamverse-backend-cuda13.0.0-sha-* tag or a "
-        "dreamverse-ui-cuda13.0.0-sha-* tag if serving the static UI. "
-        "CUDA 12 / cu126 images use the corresponding cuda12.6.3 tag."
-    )
+    raise RuntimeError("DREAMVERSE_IMAGE is required. Set it to a published SHA-specific Dreamverse image, "
+                       "for example a dreamverse-backend-cuda13.0.0-sha-* tag or a "
+                       "dreamverse-ui-cuda13.0.0-sha-* tag if serving the static UI. "
+                       "CUDA 12 / cu126 images use the corresponding cuda12.6.3 tag.")
 
 # ``@modal.web_server`` invokes ``serve()`` directly and bypasses the image
 # ENTRYPOINT (``docker/docker_entrypoint.sh``).  That entrypoint normally
@@ -65,14 +63,10 @@ def serve():
     # ``or ""`` collapses ``None`` (unset) into an empty string, ``.strip()``
     # collapses whitespace-only values (e.g. ``"   "``) — both should be
     # treated as missing.
-    missing = [
-        k for k in _REQUIRED_SECRET_KEYS
-        if not (os.environ.get(k) or "").strip()
-    ]
+    missing = [k for k in _REQUIRED_SECRET_KEYS if not (os.environ.get(k) or "").strip()]
     if missing:
-        raise RuntimeError(
-            "dreamverse-api-keys secret is missing required entries: "
-            f"{', '.join(missing)}. Add them with `modal secret create "
-            "dreamverse-api-keys ... --force` and redeploy "
-            "(see apps/dreamverse/scripts/modal/README.md).")
+        raise RuntimeError("dreamverse-api-keys secret is missing required entries: "
+                           f"{', '.join(missing)}. Add them with `modal secret create "
+                           "dreamverse-api-keys ... --force` and redeploy "
+                           "(see apps/dreamverse/scripts/modal/README.md).")
     subprocess.Popen(["dreamverse-server", "--host", "0.0.0.0", "--port", "8009"])

apps/fastvideo_studio/README.md

@@ -16,6 +16,9 @@ The UI currently supports:
 ```
 apps/fastvideo_studio/
 ├── server.py / job_runner.py / database.py   # FastAPI backend + job lifecycle
+├── job_queue.py                              # Queue scheduling rules + last-frame references (GPU-free)
+├── merge.py                                  # Join a scene's clips into one video (ffmpeg stream copy, GPU-free)
+├── trim.py                                   # Cut a clip to an in/out range, non-destructively (GPU-free)
 ├── mock_server.py                            # In-memory API mock for e2e tests
 ├── models/                                   # Pydantic request models (shared with the mock)
 ├── training_config.py                        # Studio workloads → fastvideo/train YAML configs
@@ -88,6 +91,93 @@ Running it this way allows you to pass command line parameters.
 The API server defaults to port 8189. To configure this, you can edit `.env.local` file.
 Refer to `.env.example` for reference.
 
+### Job queue
+
+`POST /api/jobs/queue` puts jobs in a queue; the server runs them oldest-first,
+`--max-concurrent-jobs` at a time (default 1). Start on a job while that many
+are already running is refused; queue it instead. Queued jobs survive a server
+restart and carry on.
+
+Inference jobs run on that many long-lived worker threads. The model's GPU
+processes are killed when the thread that started them exits, so a worker that
+never exits is what keeps the model loaded from one job to the next (it loads
+once, not per job). A worker holds one model at a time: a job with a different
+model or settings replaces it, and a failed or stopped job unloads it so the next
+one starts clean. Each extra worker loads its own copy of the model.
+
+A job can start from how another job's video ended, two ways:
+
+* **Last frame** -- a reference whose source is `job-last-frame:<job id>`. A
+  hard, exact opening frame: the prompt is rewritten to say the shot begins
+  from it.
+* **Last clip** -- a reference whose source is `job-last-clip:<job id>`, and
+  `media_type: "video"`. The trailing ~1s of the source job's video instead of
+  a single still, so the model has real motion to continue rather than a pose
+  to guess it from. Costs one of H3's 3 video-reference slots instead of one
+  of its 9 image slots.
+
+Either is extracted when the job runs, so the reference can be added before
+that job has finished, and the queue holds the job until it has. If that job
+fails, is stopped, or is deleted, the waiting job fails with a message saying
+so (and so does anything waiting on *it*); fix the earlier job and queue them
+again. Starting such a job directly while its source is unfinished is refused
+with a 409.
+
+### Merging a scene
+
+**Merge scene** on the Scenes page joins a scene's clips, in the order shown (the
+take chosen for each), into one video under `<output-dir>/merged/`, then plays it
+and offers a download. It is enabled once every clip has finished, and refuses
+otherwise, so a scene missing a clip can't pass for the whole thing.
+
+The clips are copied, not re-encoded (about a second for 15 clips, no quality
+loss). That needs them to share size, frame rate and audio, which clips from one
+scene do; if one differs, the merge says which. Each clip is cut at the end of its
+last video frame, because H3's audio runs a few milliseconds longer and would
+otherwise leave a small gap at every join. It uses `ffmpeg` from `PATH`,
+`$FASTVIDEO_FFMPEG_BIN`, or the copy bundled with `imageio-ffmpeg`.
+
+### Editing a clip's video
+
+**Edit video** on a finished clip in Scenes cuts it to a chosen in/out range and
+adjusts its brightness, contrast and saturation, in place -- other jobs'
+last-frame/last-clip references, and a later merge, all just read the job's
+`output_path`, so nothing else needs to know a clip was edited. Frame-accurate
+(re-encoded, not stream-copied), so the cut isn't limited to the source's
+keyframes. Audio is cut to the same range and carried over too (re-encoded to
+AAC regardless of the source's codec), unless the source has none.
+
+Color grading is per **section**, not the whole clip at once, like a razor/blade
+tool: **Split at current time** cuts the kept range wherever the preview is
+paused, and each resulting section gets its own brightness, contrast and
+saturation sliders, next to a before/after preview of a still frame
+(**Preview** on a section seeks to its start and captures that frame; **Use
+current frame** captures wherever the preview is paused instead). That preview
+is computed entirely in the browser (`src/lib/grading.ts` mirrors `trim.py`'s
+`_apply_grade` pixel math) as the sliders move, so it never waits on the
+server -- only **Apply** does that, to actually re-render the file. **Previous
+frame**/**Next frame** step the preview by exactly one frame (at the clip's
+own fps), for lining up a cut precisely -- the native scrub bar alone isn't
+frame-accurate. **Merge with next** undoes a cut, keeping the earlier of the
+two sections' grade.
+Sections are always contiguous and cover the whole kept range -- there's no
+reordering or gaps, only where to cut and what to grade each side; a plain
+range trim with no cuts is just the one-section case. Audio is one continuous
+track regardless of how many sections there are.
+
+The untouched original is always kept alongside it the first time a clip is
+edited, and **every edit always re-renders from that original** with the full
+set of values shown in the dialog -- range and sections together, in one pass.
+That's what lets adjusting either one leave the other in place instead of each
+one silently discarding the other. The job itself records the range/sections
+of its last edit (`edit_start_seconds`/`edit_end_seconds`/`edit_segments`,
+alongside `output_path`), so reopening the dialog -- even after closing it or
+reloading the page -- shows exactly what produced the current video, not a
+guess: a never-edited clip shows one neutral section covering its full length;
+an edited one shows every section that made it, cuts and start offset
+included. **Restore original** undoes every edit in one step and resets those
+recorded values back to one neutral section too.
+
 ### API Endpoints
 
 | Method   | Path                          | Description                                |
@@ -98,6 +188,15 @@ Refer to `.env.example` for reference.
 | `POST`   | `/api/jobs`                   | Create a new job                           |
 | `POST`   | `/api/jobs/{id}/start`        | Start a pending/stopped/failed job         |
 | `POST`   | `/api/jobs/{id}/stop`         | Request a running job to stop              |
+| `POST`   | `/api/jobs/queue`             | Queue jobs (`{"job_ids": [...]}`), in order |
+| `POST`   | `/api/jobs/{id}/queue`        | Queue one job                              |
+| `POST`   | `/api/jobs/{id}/dequeue`      | Take a queued job back out of the queue    |
+| `POST`   | `/api/jobs/{id}/last-frame`   | Save the job's last frame, for another job to start from |
+| `POST`   | `/api/jobs/{id}/last-clip`    | Save the job's trailing ~1s, for another job to continue from |
+| `POST`   | `/api/scenes/merge`           | Join finished jobs' videos (`{"job_ids": [...], "name": ""}`) in order |
+| `POST`   | `/api/jobs/{id}/trim`         | Cut a job's video to a range and/or grade it section by section (`{"start_seconds": 0, "end_seconds": null, "segments": [{"end_seconds": null, "brightness": 0, "contrast": 1, "saturation": 1}]}`), in place |
+| `POST`   | `/api/jobs/{id}/restore-video`| Undo every edit, back to the original video |
+| `GET`    | `/api/merged/{filename}`      | Stream a merged scene video                |
 | `DELETE` | `/api/jobs/{id}`              | Delete a job                               |
 | `GET`    | `/api/jobs/{id}/video`        | Stream the generated video/image           |
 | `GET`    | `/api/jobs/{id}/logs`         | Get job logs (polling, supports `?after=`) |

apps/fastvideo_studio/database.py

@@ -9,6 +9,7 @@ from __future__ import annotations
 
 import contextlib
 import logging
+import json
 import sqlite3
 import threading
 from pathlib import Path
@@ -46,8 +47,13 @@ DEFAULT_SETTINGS: dict[str, Any] = {
 
 
 def _sqlite_row_get(row: sqlite3.Row, key: str, default: Any) -> Any:
-    """Like dict.get for sqlite3.Row (Row has no .get on Python 3.10)."""
-    return row[key] if key in row else default  # noqa: SIM401
+    """Like dict.get for sqlite3.Row (Row has no .get on Python 3.10).
+
+    NOTE: `key in row` tests Row *values*, not column names, so the membership
+    check has to go through .keys() -- otherwise every lookup falls back to the
+    default and jobs restored from the database lose their stored fields.
+    """
+    return row[key] if key in row.keys() else default  # noqa: SIM401, SIM118
 
 
 def _get_db_path(data_dir: Path) -> Path:
@@ -83,6 +89,9 @@ def _migrate_db(conn: sqlite3.Connection) -> None:
     _add_column_if_missing(conn, "jobs", "fps", "INTEGER", "24")
     _add_column_if_missing(conn, "jobs", "workload_type", "TEXT", "'t2v'")
     _add_column_if_missing(conn, "jobs", "image_path", "TEXT", "''")
+    _add_column_if_missing(conn, "jobs", "name", "TEXT", "''")
+    _add_column_if_missing(conn, "jobs", "last_image_path", "TEXT", "''")
+    _add_column_if_missing(conn, "jobs", "references_json", "TEXT", "''")
     _add_column_if_missing(conn, "jobs", "job_type", "TEXT", "'inference'")
     _add_column_if_missing(conn, "jobs", "data_path", "TEXT", "''")
     _add_column_if_missing(conn, "jobs", "max_train_steps", "INTEGER", "1000")
@@ -101,6 +110,15 @@ def _migrate_db(conn: sqlite3.Connection) -> None:
     _add_column_if_missing(conn, "jobs", "generator_update_interval", "INTEGER", "5")
     _add_column_if_missing(conn, "jobs", "real_score_model_path", "TEXT", "''")
     _add_column_if_missing(conn, "jobs", "fake_score_model_path", "TEXT", "''")
+    _add_column_if_missing(conn, "jobs", "queued_at", "REAL", "NULL")
+    _add_column_if_missing(conn, "jobs", "edit_start_seconds", "REAL", "0.0")
+    _add_column_if_missing(conn, "jobs", "edit_end_seconds", "REAL", "NULL")
+    # Superseded by edit_segments_json (a list of razor-cut sections, each with
+    # its own grade); left in place, unused, rather than risking a DROP COLUMN.
+    _add_column_if_missing(conn, "jobs", "edit_brightness", "REAL", "0.0")
+    _add_column_if_missing(conn, "jobs", "edit_contrast", "REAL", "1.0")
+    _add_column_if_missing(conn, "jobs", "edit_saturation", "REAL", "1.0")
+    _add_column_if_missing(conn, "jobs", "edit_segments_json", "TEXT", "'[]'")
     # Settings table
     _add_column_if_missing(conn, "settings", "vae_cpu_offload", "INTEGER", "0")
     _add_column_if_missing(conn, "settings", "image_encoder_cpu_offload", "INTEGER", "0")
@@ -242,7 +260,8 @@ class Database:
         self._execute(
             """
             INSERT INTO jobs (
-                id, model_id, prompt, workload_type, image_path, job_type, status,
+                id, model_id, name, prompt, workload_type, image_path,
+                last_image_path, references_json, job_type, status,
                 created_at, started_at, finished_at, error, output_path, log_file_path,
                 num_inference_steps, num_frames, height, width, guidance_scale,
                 guidance_rescale, fps, seed, num_gpus, dit_cpu_offload,
@@ -254,14 +273,17 @@ class Database:
                 dmd_use_vsa, dmd_vsa_sparsity, dmd_denoising_steps,
                 real_score_guidance_scale,
                 generator_update_interval, real_score_model_path, fake_score_model_path
-            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             """,
             (
                 job["id"],
                 job["model_id"],
+                job.get("name", ""),
                 job["prompt"],
                 job.get("workload_type", "t2v"),
                 job.get("image_path", ""),
+                job.get("last_image_path", ""),
+                json.dumps(job.get("references") or []),
                 job.get("job_type", "inference"),
                 job["status"],
                 job["created_at"],
@@ -313,6 +335,7 @@ class Database:
             return
         allowed = {
             "status",
+            "queued_at",
             "started_at",
             "finished_at",
             "error",
@@ -332,6 +355,43 @@ class Database:
         self._execute(sql, tuple(vals))
         self._commit()
 
+    #: Job fields stored under a different column name, and how they are encoded.
+    _CONFIG_COLUMNS = {"references": "references_json", "edit_segments": "edit_segments_json"}
+    _JSON_LIST_CONFIG_FIELDS = {"references", "edit_segments"}
+    _BOOL_CONFIG_FIELDS = {
+        "dit_cpu_offload",
+        "text_encoder_cpu_offload",
+        "vae_cpu_offload",
+        "image_encoder_cpu_offload",
+        "use_fsdp_inference",
+        "enable_torch_compile",
+        "dmd_use_vsa",
+    }
+
+    def update_job_config(self, job_id: str, updates: dict[str, Any]) -> None:
+        """Persist edits to a job's configuration (prompt, references, sizes...).
+
+        Fields the table has no column for are skipped, as they are on insert.
+        """
+        columns = _get_table_columns(self._conn(), "jobs")
+        cols = []
+        vals: list[Any] = []
+        for key, value in updates.items():
+            column = self._CONFIG_COLUMNS.get(key, key)
+            if column not in columns or column == "id":
+                continue
+            if key in self._JSON_LIST_CONFIG_FIELDS:
+                value = json.dumps(value or [])
+            elif key in self._BOOL_CONFIG_FIELDS:
+                value = 1 if value else 0
+            cols.append(f"{column} = ?")
+            vals.append(value)
+        if not cols:
+            return
+        vals.append(job_id)
+        self._execute(f"UPDATE jobs SET {', '.join(cols)} WHERE id = ?", tuple(vals))
+        self._commit()
+
     def delete_job(self, job_id: str) -> bool:
         """Delete a job. Returns True if a row was deleted."""
         cur = self._execute("DELETE FROM jobs WHERE id = ?", (job_id, ))
@@ -524,8 +584,31 @@ def _row_to_dataset(row: sqlite3.Row) -> dict[str, Any]:
     }
 
 
-def _row_to_job(row: sqlite3.Row) -> dict[str, Any]:
+class _ColumnRow:
+    """A sqlite3.Row whose ``in`` tests column names.
+
+    sqlite3.Row's own ``in`` tests *values*, so ``"fps" in row`` is always False
+    and a job restored from the database silently lost fps, negative_prompt and
+    the other optional columns.
+    """
+
+    def __init__(self, row: sqlite3.Row):
+        self._row = row
+        self._columns = set(row.keys())
+
+    def __getitem__(self, key: str) -> Any:
+        return self._row[key]
+
+    def __contains__(self, key: object) -> bool:
+        return key in self._columns
+
+    def keys(self) -> list[str]:
+        return self._row.keys()
+
+
+def _row_to_job(sqlite_row: sqlite3.Row) -> dict[str, Any]:
     """Convert a DB row to job dict (snake_case, matching Job.to_dict)."""
+    row = _ColumnRow(sqlite_row)
     int_defaults = {
         "max_train_steps": 1000,
         "train_batch_size": 1,
@@ -540,12 +623,16 @@ def _row_to_job(row: sqlite3.Row) -> dict[str, Any]:
     result = {
         "id": row["id"],
         "model_id": row["model_id"],
+        "name": _sqlite_row_get(row, "name", "") or "",
         "prompt": row["prompt"],
         "workload_type": _sqlite_row_get(row, "workload_type", "t2v"),
         "image_path": _sqlite_row_get(row, "image_path", "") or "",
+        "last_image_path": _sqlite_row_get(row, "last_image_path", "") or "",
+        "references": _sqlite_row_get(row, "references_json", "") or "",
         "job_type": _sqlite_row_get(row, "job_type", "inference"),
         "status": row["status"],
         "created_at": row["created_at"],
+        "queued_at": row["queued_at"] if "queued_at" in row else None,
         "started_at": row["started_at"],
         "finished_at": row["finished_at"],
         "error": row["error"],
@@ -598,6 +685,13 @@ def _row_to_job(row: sqlite3.Row) -> dict[str, Any]:
     for col in ("dmd_denoising_steps", "real_score_model_path", "fake_score_model_path"):
         if col in row:
             result[col] = (row[col] or "") or ""
+    if "edit_start_seconds" in row:
+        start_val: Any = row["edit_start_seconds"]
+        result["edit_start_seconds"] = float(start_val) if start_val is not None else 0.0
+    if "edit_end_seconds" in row:
+        end_val: Any = row["edit_end_seconds"]
+        result["edit_end_seconds"] = float(end_val) if end_val is not None else None
+    result["edit_segments"] = _sqlite_row_get(row, "edit_segments_json", "") or ""
     return result
 
 

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

@@ -13,10 +13,9 @@ test.describe('create inference job', () => {
   test('creates a T2V job and shows it in the queue', async ({ page }) => {
     await page.goto('/inference');
 
-    // The "Create Job" button reveals a workload menu on hover; wait for the
-    // T2V item to become visible before clicking so the CSS hover transition
-    // can't race the click.
-    await page.getByRole('button', { name: /create job/i }).hover();
+    // The trigger opens a real menu on click, so this path works for touch,
+    // mouse, and keyboard users.
+    await page.getByRole('button', { name: /create job/i }).click();
     const t2vItem = page.getByRole('menuitem', { name: /T2V/i });
     await expect(t2vItem).toBeVisible();
     await t2vItem.click();

apps/fastvideo_studio/e2e/gallery.spec.ts

@@ -4,7 +4,7 @@ import { API_BASE, skipWithoutMock } from './helpers';
 
 /**
  * Gallery page: the seeded completed inference job surfaces as a media tile
- * (an <article> wrapping a <video>) captioned with its prompt.
+ * with playback controls or an explicit media-error fallback.
  */
 test.describe('gallery', () => {
   skipWithoutMock();
@@ -30,12 +30,15 @@ test.describe('gallery', () => {
       page.getByRole('heading', { level: 1, name: 'Gallery' }),
     ).toBeVisible();
 
-    // The completed job renders as an <article> containing a <video> tile.
-    const tile = page
-      .locator('article')
-      .filter({ has: page.locator('video') });
-    await expect(tile.first()).toBeVisible();
+    const tile = page.locator('article').filter({ hasText: completed!.prompt });
+    await expect(tile).toBeVisible();
+    await expect(
+      tile.locator('video').or(tile.getByText('Preview unavailable')),
+    ).toBeVisible();
 
-    await expect(page.getByText(completed!.prompt)).toBeVisible();
+    const video = tile.locator('video');
+    if (await video.isVisible()) {
+      await expect(video).toHaveAttribute('controls', '');
+    }
   });
 });

apps/fastvideo_studio/e2e/shell.spec.ts

@@ -42,6 +42,74 @@ test.describe('app shell', () => {
       await expect(
         page.getByRole('heading', { level: 1, name: section.title }),
       ).toBeVisible();
+      await expect(page.getByRole('main')).toHaveCount(1);
     }
   });
+
+  test('keeps navigation and content usable at responsive breakpoints', async ({
+    page,
+  }) => {
+    for (const width of [320, 375, 414, 768]) {
+      await page.setViewportSize({ width, height: 800 });
+      await page.goto('/inference');
+
+      const main = page.getByRole('main');
+      await expect(main).toBeVisible();
+      await expect(
+        page.getByRole('button', { name: /Create Job/i }),
+      ).toBeVisible();
+
+      const initialBox = await main.boundingBox();
+      expect(initialBox?.x).toBe(width < 768 ? 0 : 220);
+      expect(initialBox?.width).toBe(width < 768 ? width : width - 220);
+
+      const navigation = page.getByRole('navigation', {
+        name: 'Primary navigation',
+      });
+      if (width < 768) {
+        await expect(
+          page.getByRole('button', { name: 'Open navigation' }),
+        ).toBeVisible();
+        await page.getByRole('button', { name: 'Open navigation' }).click();
+      }
+      await expect(navigation).toBeVisible();
+      await navigation.getByRole('link', { name: 'Datasets' }).click();
+
+      await expect(page).toHaveURL(/\/datasets$/);
+      expect(
+        await page.evaluate(
+          () => document.documentElement.scrollWidth <= window.innerWidth,
+        ),
+      ).toBe(true);
+    }
+  });
+
+  test('uses full-width detail drawers on mobile', async ({ page }) => {
+    await page.setViewportSize({ width: 320, height: 800 });
+    await page.goto('/inference');
+
+    await page
+      .locator('article button[aria-pressed="false"]')
+      .first()
+      .click();
+    const jobDrawer = page.getByRole('dialog', { name: 'Job details' });
+    await expect(jobDrawer).toBeVisible();
+    expect(await jobDrawer.boundingBox()).toMatchObject({ x: 0, width: 320 });
+    await jobDrawer.getByRole('button', { name: 'Close' }).click();
+
+    await page.goto('/datasets');
+    await page
+      .locator('article button[aria-pressed="false"]')
+      .first()
+      .click();
+
+    const datasetDrawer = page.getByRole('dialog', {
+      name: /dataset details$/,
+    });
+    await expect(datasetDrawer).toBeVisible();
+    expect(await datasetDrawer.boundingBox()).toMatchObject({
+      x: 0,
+      width: 320,
+    });
+  });
 });

apps/fastvideo_studio/frames.py

@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Save the end of a finished clip, so the next clip can continue from it.
+
+Two forms: the final frame alone (a still, for a hard "opens on this exact
+picture" continuation), or a short trailing clip (a few hundred milliseconds of
+video, attached as an H3 video reference so the model has actual motion to
+continue rather than guessing it from one still). Kept apart from ``server.py``
+because it needs nothing from FastVideo -- only PyAV -- and so can be exercised
+without a GPU.
+"""
+
+from __future__ import annotations
+
+import os
+from fractions import Fraction
+from pathlib import Path
+from typing import Any
+
+VIDEO_EXTENSIONS = {".mp4", ".webm", ".avi", ".mov", ".mkv"}
+
+
+class FrameError(Exception):
+    """A request that can't be satisfied, carrying the HTTP status to report."""
+
+    def __init__(self, status_code: int, detail: str):
+        super().__init__(detail)
+        self.status_code = status_code
+        self.detail = detail
+
+
+def save_last_frame(video_path: str, out_path: str) -> None:
+    """Decode ``video_path`` and write its final frame to ``out_path`` as a PNG.
+
+    Clips are a few hundred frames, so decoding through to the end is simpler
+    and more reliable than seeking (which lands on keyframes, not the last frame).
+    """
+    import av
+
+    tmp_path = f"{out_path}.tmp.png"
+    with av.open(video_path) as container:
+        last = None
+        for frame in container.decode(container.streams.video[0]):
+            last = frame
+    if last is None:
+        raise ValueError("The video has no frames.")
+    last.to_image().save(tmp_path)
+    os.replace(tmp_path, out_path)  # atomic, so a reader never sees a half-written file
+
+
+def last_frame_for_job(job: Any, upload_dir: str) -> str:
+    """Path of a PNG of the last frame of ``job``'s video, extracting it if needed.
+
+    Raises :class:`FrameError` with the status the API should return.
+    """
+    status = getattr(job.status, "value", job.status)
+    if status != "completed" or not job.output_path:
+        raise FrameError(404, "No output available for this job")
+    if not os.path.isfile(job.output_path):
+        raise FrameError(404, "Output file not found on disk")
+    if Path(job.output_path).suffix.lower() not in VIDEO_EXTENSIONS:
+        raise FrameError(400, "This job's output is not a video")
+    if not upload_dir:
+        raise FrameError(503, "Upload directory not configured")
+
+    out_dir = os.path.join(upload_dir, "last_frames")
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = os.path.abspath(os.path.join(out_dir, f"last_frame_{job.id}.png"))
+    # Reuse an earlier extraction unless the video has been rewritten since.
+    if not (os.path.isfile(out_path) and os.path.getmtime(out_path) >= os.path.getmtime(job.output_path)):
+        try:
+            save_last_frame(job.output_path, out_path)
+        except Exception as e:
+            raise FrameError(500, f"Could not read the last frame: {e}") from e
+    return out_path
+
+
+DEFAULT_LAST_CLIP_SECONDS = 1.0
+
+
+def save_last_clip(video_path: str, out_path: str, seconds: float = DEFAULT_LAST_CLIP_SECONDS) -> None:
+    """Decode the final ``seconds`` of ``video_path`` and re-encode them to ``out_path``.
+
+    Re-encoded rather than stream-copied: the source's keyframes rarely align
+    with an arbitrary cut ``seconds`` from the end, and a copy can only start on
+    one. Silent -- the trailing video is for motion continuity; voice continuity
+    already comes from each job's own audio reference.
+    """
+    import av
+
+    if seconds <= 0:
+        raise ValueError(f"seconds must be positive, got {seconds}.")
+
+    tmp_path = f"{out_path}.tmp.mp4"
+    with av.open(video_path) as src:
+        video_stream = src.streams.video[0]
+        fps = float(video_stream.average_rate or video_stream.guessed_rate or 24)
+        frames = list(src.decode(video_stream))
+    if not frames:
+        raise ValueError("The video has no frames.")
+    keep = max(1, round(seconds * fps))
+    tail = frames[-keep:]
+
+    with av.open(tmp_path, mode="w") as out:
+        out_stream = out.add_stream("h264", rate=round(fps))
+        out_stream.width, out_stream.height = tail[0].width, tail[0].height
+        out_stream.pix_fmt = "yuv420p"
+        # Each frame needs its own increasing pts at a known time base, or PyAV
+        # stamps every frame identically (pts=0), which corrupts the muxed
+        # container's average-frame-rate metadata -- H3's reference pipeline
+        # trusts that metadata to resample the clip, so a wrong rate there
+        # silently collapses a short clip down to as little as one frame.
+        frame_time_base = Fraction(1, round(fps))
+        for i, frame in enumerate(tail):
+            frame.pts = i
+            frame.time_base = frame_time_base
+            for packet in out_stream.encode(frame):
+                out.mux(packet)
+        for packet in out_stream.encode():
+            out.mux(packet)
+    os.replace(tmp_path, out_path)  # atomic, so a reader never sees a half-written file
+
+
+def last_clip_for_job(job: Any, upload_dir: str, seconds: float = DEFAULT_LAST_CLIP_SECONDS) -> str:
+    """Path of a short video of the end of ``job``'s video, extracting it if needed.
+
+    Raises :class:`FrameError` with the status the API should return.
+    """
+    status = getattr(job.status, "value", job.status)
+    if status != "completed" or not job.output_path:
+        raise FrameError(404, "No output available for this job")
+    if not os.path.isfile(job.output_path):
+        raise FrameError(404, "Output file not found on disk")
+    if Path(job.output_path).suffix.lower() not in VIDEO_EXTENSIONS:
+        raise FrameError(400, "This job's output is not a video")
+    if not upload_dir:
+        raise FrameError(503, "Upload directory not configured")
+
+    out_dir = os.path.join(upload_dir, "last_clips")
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = os.path.abspath(os.path.join(out_dir, f"last_clip_{job.id}.mp4"))
+    if not (os.path.isfile(out_path) and os.path.getmtime(out_path) >= os.path.getmtime(job.output_path)):
+        try:
+            save_last_clip(job.output_path, out_path, seconds)
+        except Exception as e:
+            raise FrameError(500, f"Could not read the end of the clip: {e}") from e
+    return out_path

apps/fastvideo_studio/job_queue.py

@@ -0,0 +1,208 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Scheduling rules for queued jobs, and references to another job's ending.
+
+Kept apart from ``job_runner.py`` because it is pure -- it works on plain
+snapshots and needs neither FastVideo nor a GPU -- so it can be tested anywhere.
+
+A job can start from how another job's video ended -- either its last frame (a
+still, for a hard "opens on this exact picture" continuation) or its last
+second or so (a short video, so the model has actual motion to continue, not
+just a pose to guess motion from). Neither exists until the other job
+finishes, so the reference is *deferred*: its source is ``job-last-frame:<job
+id>`` or ``job-last-clip:<job id>``, resolved to a real file only when the job
+runs. That link is also the queue's dependency graph: a queued job waits until
+every job it takes an ending from has completed.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Iterable, Mapping
+from dataclasses import dataclass, field
+from typing import Any, Protocol
+
+DEFERRED_LAST_FRAME_PREFIX = "job-last-frame:"
+DEFERRED_LAST_CLIP_PREFIX = "job-last-clip:"
+_DEFERRED_PREFIXES = (DEFERRED_LAST_FRAME_PREFIX, DEFERRED_LAST_CLIP_PREFIX)
+
+QUEUED = "queued"
+RUNNING = "running"
+COMPLETED = "completed"
+PENDING = "pending"
+FAILED = "failed"
+STOPPED = "stopped"
+
+
+def deferred_last_frame_source(job_id: str) -> str:
+    return f"{DEFERRED_LAST_FRAME_PREFIX}{job_id}"
+
+
+def deferred_last_clip_source(job_id: str) -> str:
+    return f"{DEFERRED_LAST_CLIP_PREFIX}{job_id}"
+
+
+def dependency_of(reference: Mapping[str, Any] | None) -> str | None:
+    """The job a deferred reference takes its ending from, or None for any other."""
+    source = (reference or {}).get("source")
+    if not isinstance(source, str):
+        return None
+    for prefix in _DEFERRED_PREFIXES:
+        if source.startswith(prefix):
+            return source[len(prefix):] or None
+    return None
+
+
+def dependencies(references: Iterable[Mapping[str, Any]] | None) -> list[str]:
+    """Job ids a job needs finished first, in reference order, without repeats."""
+    found: list[str] = []
+    for ref in references or []:
+        dep = dependency_of(ref)
+        if dep and dep not in found:
+            found.append(dep)
+    return found
+
+
+class JobView(Protocol):
+    """The parts of a job the scheduler looks at."""
+
+    id: str
+    references: Any
+    queued_at: float | None
+
+    @property
+    def status(self) -> Any:  # a JobStatus (str enum) or plain str
+        ...
+
+
+def _status(job: Any) -> str:
+    return str(getattr(job.status, "value", job.status))
+
+
+def circular_dependency(jobs: Mapping[str, JobView], job_id: str) -> list[str] | None:
+    """The chain of job ids if ``job_id`` (transitively) waits on itself, else None."""
+
+    def walk(current: str, path: list[str]) -> list[str] | None:
+        job = jobs.get(current)
+        if job is None:
+            return None
+        for dep in dependencies(job.references):
+            if dep == job_id:
+                return [*path, current, dep]
+            if dep in path or dep == current:
+                continue
+            found = walk(dep, [*path, current])
+            if found:
+                return found
+        return None
+
+    return walk(job_id, [])
+
+
+def _label(job: Any) -> str:
+    name = getattr(job, "name", "") or ""
+    return f"'{name}'" if name else job.id
+
+
+@dataclass
+class Plan:
+    """What to do now: jobs to launch, and jobs that can never run."""
+
+    start: list[str] = field(default_factory=list)
+    fail: dict[str, str] = field(default_factory=dict)  # job id -> reason
+
+
+def plan_dispatch(jobs: Mapping[str, JobView], max_concurrent: int) -> Plan:
+    """Decide which queued jobs launch now and which have become impossible.
+
+    * Queued jobs run oldest-queued first, at most ``max_concurrent`` at a time
+      (counting jobs already running).
+    * A job whose frame source hasn't completed *waits*, and doesn't hold up
+      jobs behind it that don't need it.
+    * A job whose frame source failed, was stopped, or was deleted can never
+      run, so it fails -- and so does anything waiting on *it*.
+    * A job whose frame source is idle (neither queued nor running) keeps
+      waiting: the user may still queue it.
+    """
+    plan = Plan()
+    status = {jid: _status(job) for jid, job in jobs.items()}
+    queued = sorted(
+        (job for job in jobs.values() if status[job.id] == QUEUED),
+        key=lambda job: (job.queued_at or 0.0, job.id),
+    )
+
+    # Failures cascade down a chain, so repeat until nothing new fails.
+    changed = True
+    while changed:
+        changed = False
+        for job in queued:
+            if job.id in plan.fail:
+                continue
+            for dep_id in dependencies(job.references):
+                dep = jobs.get(dep_id)
+                if dep is None:
+                    plan.fail[job.id] = "Waiting on the ending of a job that was deleted."
+                elif dep_id in plan.fail or status[dep_id] in (FAILED, STOPPED):
+                    reason = "failed" if dep_id in plan.fail or status[dep_id] == FAILED else "was stopped"
+                    plan.fail[job.id] = f"Needs the ending of {_label(dep)}, which {reason}."
+                else:
+                    continue
+                status[job.id] = FAILED
+                changed = True
+                break
+
+    slots = max(max_concurrent, 1) - sum(1 for s in status.values() if s == RUNNING)
+    for job in queued:
+        if slots <= 0:
+            break
+        if job.id in plan.fail:
+            continue
+        if all(status.get(dep) == COMPLETED for dep in dependencies(job.references)):
+            plan.start.append(job.id)
+            slots -= 1
+    return plan
+
+
+class UnresolvedReference(ValueError):
+    """A deferred reference can't be turned into a real file."""
+
+
+def resolve_references(
+    references: list[dict[str, Any]] | None,
+    get_job: Callable[[str], Any],
+    last_frame: Callable[[Any], str],
+    last_clip: Callable[[Any], str] | None = None,
+) -> list[dict[str, Any]]:
+    """Copy of ``references`` with each deferred reference made real.
+
+    ``last_frame(job)`` and ``last_clip(job)`` each return the path of an
+    extracted file and may raise (see ``frames.FrameError``); the message is
+    passed on. ``last_clip`` is optional so callers that never attach a
+    trailing-clip reference (e.g. the mock server) don't need to supply one --
+    it is only required if a ``job-last-clip:`` reference is actually present.
+    Other references are returned unchanged, and the stored list is never
+    modified, so a re-run resolves again against the source job's current
+    output.
+    """
+    resolved: list[dict[str, Any]] = []
+    for ref in references or []:
+        source = (ref or {}).get("source")
+        prefix = next((p for p in _DEFERRED_PREFIXES if isinstance(source, str) and source.startswith(p)), None)
+        if prefix is None:
+            resolved.append(ref)
+            continue
+        dep_id = source[len(prefix):] or None
+        extract = last_frame if prefix == DEFERRED_LAST_FRAME_PREFIX else last_clip
+        what = "the last frame of" if prefix == DEFERRED_LAST_FRAME_PREFIX else "the ending of"
+        if extract is None:
+            raise UnresolvedReference("This server can't resolve a trailing-clip reference yet.")
+        dep = get_job(dep_id) if dep_id else None
+        if dep is None:
+            raise UnresolvedReference(f"A reference needs {what} a job that no longer exists.")
+        if _status(dep) != COMPLETED:
+            raise UnresolvedReference(f"A reference needs {what} {_label(dep)}, which hasn't finished "
+                                  f"(it is {_status(dep)}).")
+        try:
+            path = extract(dep)
+        except Exception as exc:  # FrameError carries a readable .detail
+            raise UnresolvedReference(str(getattr(exc, "detail", None) or exc)) from exc
+        resolved.append({**ref, "source": path})
+    return resolved

apps/fastvideo_studio/merge.py

@@ -0,0 +1,223 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Join a scene's clips, in order, into one video.
+
+Kept apart from ``server.py`` because it needs nothing from FastVideo -- only
+PyAV (to read each clip's format) and an ``ffmpeg`` binary -- so it can be
+exercised without a GPU.
+
+Clips from one scene share codec, size and frame rate, so they are joined by
+copying the streams rather than re-encoding: it takes about a second, loses no
+quality, and keeps picture and sound in step. Each clip is cut off at the end of
+its last video frame, because the audio track runs a few milliseconds past it and
+would otherwise open a gap between clips.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+import shutil
+import subprocess
+import tempfile
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+VIDEO_EXTENSIONS = {".mp4", ".mov", ".mkv", ".webm", ".avi"}
+MERGED_DIRNAME = "merged"
+_FILENAME_RE = re.compile(r"^[\w.-]+\.mp4$")
+
+
+class MergeError(Exception):
+    """A merge that can't be done, carrying the HTTP status to report."""
+
+    def __init__(self, status_code: int, detail: str):
+        super().__init__(detail)
+        self.status_code = status_code
+        self.detail = detail
+
+
+def find_ffmpeg() -> str | None:
+    """An ``ffmpeg`` to run: $FASTVIDEO_FFMPEG_BIN, then PATH, then imageio-ffmpeg's bundled one."""
+    configured = os.getenv("FASTVIDEO_FFMPEG_BIN")
+    if configured and (shutil.which(configured) or os.path.isfile(configured)):
+        return shutil.which(configured) or configured
+    on_path = shutil.which("ffmpeg")
+    if on_path:
+        return on_path
+    try:
+        import imageio_ffmpeg
+
+        return imageio_ffmpeg.get_ffmpeg_exe()
+    except Exception:
+        return None
+
+
+@dataclass(frozen=True)
+class ClipInfo:
+    path: str
+    video_codec: str
+    width: int
+    height: int
+    fps: float
+    pix_fmt: str
+    video_seconds: float
+    audio: tuple[str, int, int] | None  # codec, sample rate, channels
+
+    def describe(self) -> str:
+        audio = f"{self.audio[0]} {self.audio[1]} Hz x{self.audio[2]}" if self.audio else "no audio"
+        return f"{self.width}x{self.height} {self.fps:.2f} fps {self.video_codec}/{self.pix_fmt}, {audio}"
+
+    def signature(self) -> tuple[Any, ...]:
+        return (self.video_codec, self.width, self.height, round(self.fps, 3), self.pix_fmt, self.audio)
+
+
+def probe_clip(path: str) -> ClipInfo:
+    """Read what ``check_compatible`` needs from a video file."""
+    import av
+
+    try:
+        with av.open(path) as container:
+            video = next((s for s in container.streams if s.type == "video"), None)
+            if video is None:
+                raise MergeError(409, f"{os.path.basename(path)} has no video stream.")
+            audio = next((s for s in container.streams if s.type == "audio"), None)
+            fps = float(video.average_rate or video.guessed_rate or 0)
+            if video.duration is not None:
+                seconds = float(video.duration * video.time_base)
+            elif container.duration is not None:
+                seconds = container.duration / 1_000_000
+            else:
+                seconds = (video.frames or 0) / fps if fps else 0.0
+            if seconds <= 0:
+                raise MergeError(409, f"{os.path.basename(path)} has no playable video.")
+            return ClipInfo(
+                path=path,
+                video_codec=video.codec_context.name,
+                width=video.codec_context.width,
+                height=video.codec_context.height,
+                fps=fps,
+                pix_fmt=video.codec_context.pix_fmt or "",
+                video_seconds=seconds,
+                audio=((audio.codec_context.name, audio.codec_context.sample_rate, audio.codec_context.channels)
+                       if audio else None),
+            )
+    except MergeError:
+        raise
+    except Exception as exc:  # av.error.* on a corrupt or unreadable file
+        raise MergeError(409, f"Could not read {os.path.basename(path)}: {exc}") from exc
+
+
+def check_compatible(infos: list[ClipInfo], labels: list[str]) -> None:
+    """Refuse clips that can't be joined by copying, naming the first that differs."""
+    first = infos[0]
+    for info, label in zip(infos[1:], labels[1:], strict=True):
+        if info.signature() != first.signature():
+            raise MergeError(
+                409,
+                f"{label} ({info.describe()}) doesn't match {labels[0]} ({first.describe()}). "
+                "Clips need the same size, frame rate and audio to be joined.",
+            )
+
+
+def _concat_list(infos: list[ClipInfo]) -> str:
+    lines = ["ffconcat version 1.0"]
+    for info in infos:
+        escaped = info.path.replace("'", "'\\''")
+        lines += [f"file '{escaped}'", f"outpoint {info.video_seconds:.6f}"]
+    return "\n".join(lines) + "\n"
+
+
+def merge_clips(paths: list[str], out_path: str, labels: list[str] | None = None, ffmpeg: str | None = None) -> float:
+    """Join ``paths`` in order into ``out_path``. Returns the merged length in seconds."""
+    if not paths:
+        raise MergeError(400, "No clips to merge.")
+    labels = labels or [os.path.basename(p) for p in paths]
+    ffmpeg = ffmpeg or find_ffmpeg()
+    if not ffmpeg:
+        raise MergeError(503, "ffmpeg isn't available on the server. Install it or set FASTVIDEO_FFMPEG_BIN.")
+
+    infos = [probe_clip(p) for p in paths]
+    check_compatible(infos, labels)
+
+    os.makedirs(os.path.dirname(out_path) or ".", exist_ok=True)
+    tmp_out = f"{out_path}.tmp.mp4"
+    list_fd, list_path = tempfile.mkstemp(suffix=".ffconcat", text=True)
+    try:
+        with os.fdopen(list_fd, "w", encoding="utf-8") as f:
+            f.write(_concat_list(infos))
+        result = subprocess.run(
+            [ffmpeg, "-y", "-loglevel", "error", "-f", "concat", "-safe", "0", "-i", list_path, "-c", "copy",
+             "-movflags", "+faststart", tmp_out],
+            capture_output=True,
+            text=True,
+            timeout=600,
+            check=False,
+        )
+        if result.returncode != 0:
+            tail = " ".join(result.stderr.strip().splitlines()[-3:])
+            raise MergeError(500, f"ffmpeg failed: {tail or 'unknown error'}")
+        os.replace(tmp_out, out_path)  # atomic, so a reader never sees a half-written file
+    except subprocess.TimeoutExpired as exc:
+        raise MergeError(500, "ffmpeg took too long.") from exc
+    finally:
+        for leftover in (list_path, tmp_out):
+            try:
+                os.remove(leftover)
+            except OSError:
+                pass
+    return sum(i.video_seconds for i in infos)
+
+
+@dataclass(frozen=True)
+class MergeResult:
+    filename: str
+    path: str
+    clips: int
+    seconds: float
+
+
+def _safe_stem(name: str) -> str:
+    return re.sub(r"[^\w.-]+", "-", name).strip("-.")[:60] or "scene"
+
+
+def _label(job: Any) -> str:
+    name = (getattr(job, "name", "") or "").strip()
+    return f"'{name}'" if name else str(job.id)
+
+
+def merge_jobs(jobs: list[Any], out_dir: str, name: str = "") -> MergeResult:
+    """Join the finished videos of ``jobs`` (in the order given) into ``<out_dir>/merged``.
+
+    Every job must be completed with a video on disk; otherwise nothing is made,
+    so a scene missing a clip can't be mistaken for the whole thing.
+    """
+    if not jobs:
+        raise MergeError(400, "No clips to merge.")
+    paths: list[str] = []
+    for job in jobs:
+        status = getattr(job.status, "value", job.status)
+        if status != "completed" or not job.output_path:
+            raise MergeError(409, f"{_label(job)} hasn't finished ({status}), so the scene can't be merged yet.")
+        if not os.path.isfile(job.output_path):
+            raise MergeError(409, f"{_label(job)}'s video is missing from disk.")
+        if Path(job.output_path).suffix.lower() not in VIDEO_EXTENSIONS:
+            raise MergeError(409, f"{_label(job)}'s output isn't a video.")
+        paths.append(job.output_path)
+
+    filename = f"{_safe_stem(name)}-{time.strftime('%Y%m%d-%H%M%S')}.mp4"
+    merged_dir = os.path.join(out_dir, MERGED_DIRNAME)
+    out_path = os.path.join(merged_dir, filename)
+    seconds = merge_clips(paths, out_path, labels=[_label(j) for j in jobs])
+    return MergeResult(filename=filename, path=out_path, clips=len(paths), seconds=seconds)
+
+
+def merged_path(out_dir: str, filename: str) -> str:
+    """Path of a merged video by filename, refusing anything that isn't one."""
+    if not _FILENAME_RE.match(filename):
+        raise MergeError(404, "Merged video not found.")
+    path = os.path.join(out_dir, MERGED_DIRNAME, filename)
+    if not os.path.isfile(path):
+        raise MergeError(404, "Merged video not found.")
+    return path

apps/fastvideo_studio/mock_server.py

@@ -4,8 +4,8 @@ In-memory mock of the FastVideo Studio API (``server.py``).
 
 This mock implements the same ``/api`` routes and response shapes as the real
 FastAPI server but keeps everything in memory and never touches the FastVideo
-library, a GPU, or a database. It is meant to back the Playwright e2e suite so
-the Next.js frontend can be exercised end-to-end without a real backend.
+library or a GPU. It is meant to back the Playwright e2e suite so the Next.js
+frontend can be exercised end-to-end without a real backend.
 
 Job lifecycle is simulated by recording a start timestamp and *computing* the
 status on read: a started job reports ``running`` for a few seconds and then
@@ -13,14 +13,24 @@ flips to ``completed`` with an ``output_path``, so polling the job list / logs
 shows progression. Generated media is a tiny 1s ``testsrc`` MP4 built lazily
 with ffmpeg and cached.
 
+Pass ``--data-dir`` to seed jobs/datasets/settings from a real server's
+persisted SQLite database instead of the built-in fake demo data, so you can
+browse real job history through the mock without a GPU. This is a read-only
+snapshot taken once at startup: nothing created or changed while the mock
+runs is ever written back to that database, so it's safe to point at the
+same ``--data-dir`` a real ``server.py`` is using.
+
 Usage (from the ``apps/`` directory)::
 
     PYTHONPATH=.. python -m fastvideo_studio.mock_server --port 8190
+    PYTHONPATH=.. python -m fastvideo_studio.mock_server --port 8190 --data-dir ../outputs/ui_data
 """
 
 from __future__ import annotations
 
 import contextlib
+import json
+import logging
 import os
 import random
 import shutil
@@ -29,22 +39,36 @@ import tempfile
 import threading
 import time
 import uuid
+from pathlib import Path
+from types import SimpleNamespace
 from typing import Annotated, Any
 
 from fastapi import FastAPI, File, HTTPException, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse, PlainTextResponse
 
-from fastvideo_studio.database import default_settings_dict
-from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, SettingsUpdate, UpdateCaptionRequest,
+from fastvideo_studio.database import Database, default_settings_dict
+from fastvideo_studio.job_queue import circular_dependency, dependencies, plan_dispatch
+from fastvideo_studio.merge import MergeError, merge_jobs, merged_path
+from fastvideo_studio.trim import BRIGHTNESS_RANGE, CONTRAST_RANGE, SATURATION_RANGE
+from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, GradeSegment, MergeSceneRequest,
+                                     QueueJobsRequest, SettingsUpdate, TrimRequest, UpdateCaptionRequest,
                                      model_label)
 
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
+logger = logging.getLogger("fastvideo.studio.mock")
+
 # --- Config -----------------------------------------------------------------
 
 # How long a started job stays "running" before it flips to "completed".
 COMPLETE_AFTER_SECONDS = 3.0
 FFMPEG_BIN = shutil.which(os.getenv("FASTVIDEO_FFMPEG_BIN", "ffmpeg"))
 
+# Marks a job dict as a frozen row loaded from a real --data-dir snapshot
+# (see _load_snapshot_from_db) rather than something this process is
+# simulating. Stripped from API responses by _public_job.
+_FROM_SNAPSHOT_KEY = "_from_db_snapshot"
+
 # A small catalogue of fake models keyed by workload type. Mirrors the real
 # server's {id, label} shape (label derived from the HF-style path).
 _MODELS_BY_WORKLOAD: dict[str, list[str]] = {
@@ -54,6 +78,10 @@ _MODELS_BY_WORKLOAD: dict[str, list[str]] = {
     ],
     "i2v": [
         "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+        # Real registered id (fastvideo/registry.py) -- needed so the frontend's
+        # `modelId.toLowerCase().includes('minimax-h3')` gate (speech-tag
+        # console, references, last-frame upload) actually lights up in the mock.
+        "MiniMaxAI/MiniMax-H3",
     ],
     "t2i": [
         "black-forest-labs/FLUX.1-schnell",
@@ -98,16 +126,76 @@ _mock_media_cache: dict[str, str] = {}
 _mock_media_lock = threading.Lock()
 
 
+def _testsrc_frame(width: int, height: int, index: int):
+    """A cheap, dependency-light stand-in for ffmpeg's `testsrc` pattern:
+    a gradient that shifts over `index` so an encoded clip visibly animates."""
+    import numpy as np
+
+    frame = np.zeros((height, width, 3), dtype=np.uint8)
+    frame[:, :, 0] = np.linspace(0, 255, width, dtype=np.uint8)[None, :]
+    frame[:, :, 1] = np.linspace(0, 255, height, dtype=np.uint8)[:, None]
+    frame[:, :, 2] = (index * 10) % 256
+    return frame
+
+
+def _build_mock_media_with_pyav(kind: str, path: str) -> None:
+    """ffmpeg isn't installed on every dev box; PyAV (a real FastVideo
+    dependency already, used for reference-video decoding) can encode the
+    same tiny clip directly via its bundled libav, no external binary."""
+    from PIL import Image
+
+    if kind == "png":
+        Image.fromarray(_testsrc_frame(320, 240, 0)).save(path)
+        return
+
+    import av as pyav
+
+    # h264 is what every browser's <video> element actually plays; mpeg4
+    # (MPEG-4 Part 2) is a PyAV-always-has-it fallback that no mainstream
+    # browser decodes, so gallery/job previews would just fail to load.
+    try:
+        pyav.codec.Codec("h264", "w")
+        codec_name = "h264"
+    except Exception:
+        logger.warning("PyAV has no h264 encoder available; falling back to mpeg4, which most "
+                        "browsers cannot play in <video>. Install ffmpeg for real preview playback.")
+        codec_name = "mpeg4"
+
+    container = pyav.open(path, mode="w")
+    try:
+        stream = container.add_stream(codec_name, rate=24)
+        stream.width = 320
+        stream.height = 240
+        stream.pix_fmt = "yuv420p"
+        for i in range(24):  # 1s at 24fps, matching the ffmpeg path's clip length
+            frame = pyav.VideoFrame.from_ndarray(_testsrc_frame(320, 240, i), format="rgb24")
+            for packet in stream.encode(frame):
+                container.mux(packet)
+        for packet in stream.encode():
+            container.mux(packet)
+    finally:
+        container.close()
+
+
 def _build_mock_media(kind: str) -> str:
-    """Build (once) a tiny testsrc clip of the given ``kind`` with ffmpeg; cache the path."""
+    """Build (once) a tiny testsrc-like clip of the given ``kind``; cache the path."""
     with _mock_media_lock:
         cached = _mock_media_cache.get(kind)
         if cached and os.path.isfile(cached):
             return cached
-        if not FFMPEG_BIN:
-            raise HTTPException(status_code=500, detail="ffmpeg is required to build mock media")
         fd, path = tempfile.mkstemp(prefix="fvstudio_mock_", suffix=f".{kind}")
         os.close(fd)
+        if not FFMPEG_BIN:
+            try:
+                _build_mock_media_with_pyav(kind, path)
+            except Exception as exc:
+                with contextlib.suppress(OSError):
+                    os.remove(path)
+                raise HTTPException(status_code=500, detail=f"PyAV fallback failed to build mock {kind}: {exc}") from exc
+            if not os.path.isfile(path) or os.path.getsize(path) == 0:
+                raise HTTPException(status_code=500, detail=f"PyAV fallback produced no mock {kind} bytes")
+            _mock_media_cache[kind] = path
+            return path
         if kind == "png":
             command = [
                 FFMPEG_BIN,
@@ -166,33 +254,34 @@ def _build_mock_media(kind: str) -> str:
 
 
 def _new_job_dict(req: CreateJobRequest) -> dict[str, Any]:
-    """Build a job dict (mirrors job_runner.Job.to_dict()) in the pending state."""
+    """Build a job dict (mirrors job_runner.Job.to_dict()) in the pending state.
+
+    Starts from req.model_dump() rather than re-listing CreateJobRequest's
+    fields by hand -- the previous version silently dropped name,
+    last_image_path, references, and every offload/tuning flag because it
+    was never updated as CreateJobRequest grew those fields. Spreading the
+    request means new fields flow through automatically.
+    """
     job_id = str(uuid.uuid4())
     return {
+        **req.model_dump(),
         "id": job_id,
-        "model_id": req.model_id,
-        "prompt": req.prompt,
         "workload_type": req.workload_type or "t2v",
         "job_type": req.job_type or "inference",
         "image_path": req.image_path or "",
+        "negative_prompt": req.negative_prompt or "",
+        "data_path": req.data_path or "",
         "status": "pending",
         "created_at": time.time(),
+        "queued_at": None,
         "started_at": None,
         "finished_at": None,
         "error": None,
         "output_path": None,
         "log_file_path": None,
-        "num_inference_steps": req.num_inference_steps,
-        "num_frames": req.num_frames,
-        "height": req.height,
-        "width": req.width,
-        "guidance_scale": req.guidance_scale,
-        "guidance_rescale": req.guidance_rescale,
-        "fps": req.fps,
-        "seed": req.seed,
-        "negative_prompt": req.negative_prompt or "",
-        "num_gpus": req.num_gpus,
-        "data_path": req.data_path or "",
+        "edit_start_seconds": 0.0,
+        "edit_end_seconds": None,
+        "edit_segments": [],
         "progress": 0.0,
         "progress_msg": "",
         "phase": "pending",
@@ -204,8 +293,13 @@ def _advance_job(job: dict[str, Any]) -> None:
 
     Status is *computed on read* from the recorded start timestamp, so polling
     the job list / logs naturally shows pending -> running -> completed.
+
+    Jobs loaded from a persisted DB snapshot (``_FROM_SNAPSHOT_KEY``) are a
+    frozen historical record, not something this process is simulating — skip
+    them so a real "pending" or "running" row doesn't silently start ticking
+    toward a fake "completed" just because someone polled it.
     """
-    if job["status"] != "running" or not job.get("started_at"):
+    if job.get(_FROM_SNAPSHOT_KEY) or job["status"] != "running" or not job.get("started_at"):
         return
     elapsed = time.time() - job["started_at"]
     if elapsed >= COMPLETE_AFTER_SECONDS:
@@ -219,9 +313,9 @@ def _advance_job(job: dict[str, Any]) -> None:
 
 
 def _public_job(job: dict[str, Any]) -> dict[str, Any]:
-    """Advance + return a copy safe to serialize."""
+    """Advance + return a copy safe to serialize (internal-only keys stripped)."""
     _advance_job(job)
-    return dict(job)
+    return {k: v for k, v in job.items() if not k.startswith("_")}
 
 
 _LOG_TAIL = [
@@ -252,8 +346,8 @@ def _compute_logs(job: dict[str, Any]) -> dict[str, Any]:
     """Return JobLogs-shaped data, growing the visible lines as time passes."""
     seq = _log_sequence(job)
     status = job["status"]
-    if status == "pending":
-        return {"lines": [], "progress": 0.0, "progress_msg": "", "phase": "pending"}
+    if status in ("pending", "queued"):
+        return {"lines": [], "progress": 0.0, "progress_msg": "", "phase": status}
     if status == "completed":
         return {"lines": seq, "progress": 100.0, "progress_msg": "50/50 steps", "phase": "done"}
     if status in ("failed", "stopped"):
@@ -346,6 +440,90 @@ def _seed() -> None:
     }
 
 
+# --- DB snapshot (--data-dir) ------------------------------------------------
+
+
+def _decode_json_list(value: Any) -> list[dict[str, Any]]:
+    """References and edit_segments both round-trip through the DB as JSON text."""
+    if not value:
+        return []
+    if isinstance(value, list):
+        return list(value)
+    try:
+        decoded = json.loads(value)
+    except (TypeError, ValueError):
+        return []
+    return list(decoded) if isinstance(decoded, list) else []
+
+
+def _job_from_snapshot(row: dict[str, Any]) -> dict[str, Any]:
+    """Adapt a persisted job row into the mock's job dict shape.
+
+    Frozen as a read-only historical record (see _FROM_SNAPSHOT_KEY): the mock
+    never actually ran this job, so it doesn't get to simulate progress for
+    one. A row still marked "running" is orphaned -- the real JobRunner
+    reclassifies those as failed on its own restart, so mirror that here.
+    """
+    job = dict(row)
+    status = job["status"]
+    if status == "running":
+        status = "failed"
+        job["error"] = job.get("error") or "Server restarted (job was running)"
+        job["finished_at"] = job.get("finished_at") or time.time()
+    elif status == "queued":
+        # The real server resumes its queue on restart; the mock never runs
+        # snapshot jobs, so leave it as something the user can queue again.
+        status = "pending"
+        job["queued_at"] = None
+    job["status"] = status
+    job["references"] = _decode_json_list(row.get("references"))
+    job["edit_segments"] = _decode_json_list(row.get("edit_segments"))
+    job["progress"] = 100.0 if status == "completed" else 0.0
+    job["progress_msg"] = "50/50 steps" if status == "completed" else ""
+    job["phase"] = "done" if status == "completed" else status
+    job[_FROM_SNAPSHOT_KEY] = True
+    return job
+
+
+def _load_snapshot_from_db(data_dir: Path) -> bool:
+    """Seed jobs/datasets/settings from a real server's persisted SQLite db.
+
+    Read-only: opens the db (Database.__init__ runs the same idempotent
+    schema migrations a real server startup would) and copies rows into the
+    mock's in-memory state; nothing the mock does afterward is written back.
+    Returns False (leaving the caller to fall back to _seed()) if no db file
+    is found at that path, rather than silently creating an empty one.
+    """
+    db_path = data_dir / "fastvideo_ui.db"
+    if not db_path.is_file():
+        logger.warning("--data-dir %s has no fastvideo_ui.db; falling back to fake demo data.", data_dir)
+        return False
+
+    db = Database(db_path)
+    settings = db.get_settings()
+    jobs = [_job_from_snapshot(row) for row in db.get_all_jobs()]
+    datasets = db.get_all_datasets()
+
+    with _state_lock:
+        _settings.update({k: v for k, v in settings.items() if v is not None})
+        _jobs.clear()
+        _jobs.update({j["id"]: j for j in jobs})
+        _datasets.clear()
+        _datasets.update({d["id"]: d for d in datasets})
+        _dataset_files.clear()
+        for d in datasets:
+            captions = db.get_dataset_captions(d["id"])
+            _dataset_files[d["id"]] = {"file_names": list(captions.keys()), "captions": captions}
+
+    logger.info(
+        "Loaded read-only snapshot from %s: %d job(s), %d dataset(s).",
+        db_path,
+        len(jobs),
+        len(datasets),
+    )
+    return True
+
+
 # --- App --------------------------------------------------------------------
 
 app = FastAPI(title="FastVideo Studio Mock API", version="0.1.0")
@@ -357,8 +535,6 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-_seed()
-
 
 @app.get("/api/__mock__")
 def mock_sentinel() -> dict[str, bool]:
@@ -423,6 +599,32 @@ async def upload_image(file: Annotated[UploadFile, File()]) -> dict[str, str]:
     return {"path": f"/mock/uploads/{uuid.uuid4().hex}_{os.path.basename(name)}"}
 
 
+ALLOWED_REFERENCE_VIDEO_EXTENSIONS = {".mp4", ".mov", ".mkv", ".webm", ".avi"}
+ALLOWED_REFERENCE_AUDIO_EXTENSIONS = {".wav", ".mp3", ".flac", ".m4a", ".ogg"}
+ALLOWED_REFERENCE_IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+
+
+@app.post("/api/upload-media")
+async def upload_media(file: Annotated[UploadFile, File()]) -> dict[str, str]:
+    """Ref2VA reference upload (image/video/audio) -- the real server's
+    counterpart to upload_image, which only ever handles images."""
+    name = file.filename or "reference"
+    ext = os.path.splitext(name)[1].lower()
+    allowed = ALLOWED_REFERENCE_IMAGE_EXTENSIONS | ALLOWED_REFERENCE_VIDEO_EXTENSIONS | ALLOWED_REFERENCE_AUDIO_EXTENSIONS
+    if ext not in allowed:
+        raise HTTPException(status_code=400, detail=f"Invalid file type. Allowed: {', '.join(sorted(allowed))}")
+    if ext in ALLOWED_REFERENCE_VIDEO_EXTENSIONS:
+        media_type = "video"
+    elif ext in ALLOWED_REFERENCE_AUDIO_EXTENSIONS:
+        media_type = "audio"
+    else:
+        media_type = "image"
+    return {
+        "path": f"/mock/uploads/{uuid.uuid4().hex}_{os.path.basename(name)}",
+        "media_type": media_type,
+    }
+
+
 @app.post("/api/upload-raw-dataset")
 async def upload_raw_dataset(files: Annotated[list[UploadFile], File()]) -> dict[str, Any]:
     video_exts = {".mp4", ".webm", ".avi", ".mov", ".mkv"}
@@ -446,6 +648,7 @@ def list_jobs(job_type: str | None = None) -> list[dict[str, Any]]:
     # _public_job mutates the shared job dict via _advance_job, so it must run
     # under the lock (matching every other job route) to avoid racing start/stop.
     with _state_lock:
+        _drain_queue()
         jobs = list(_jobs.values())
         if job_type:
             jobs = [j for j in jobs if j.get("job_type") == job_type]
@@ -456,6 +659,7 @@ def list_jobs(job_type: str | None = None) -> list[dict[str, Any]]:
 @app.get("/api/jobs/{job_id}")
 def get_job(job_id: str) -> dict[str, Any]:
     with _state_lock:
+        _drain_queue()
         job = _jobs.get(job_id)
         if job is None:
             raise HTTPException(status_code=404, detail="Job not found")
@@ -466,15 +670,20 @@ def get_job(job_id: str) -> dict[str, Any]:
 def create_job(req: CreateJobRequest) -> dict[str, Any]:
     job = _new_job_dict(req)
     with _state_lock:
+        _drain_queue()
         _jobs[job["id"]] = job
         if _settings.get("autoStartJob"):
             _start(job)
         return _public_job(job)
 
 
-def _start(job: dict[str, Any]) -> None:
+def _start(job: dict[str, Any], at: float | None = None) -> None:
+    # Once the mock is actually driving this job, it's no longer a frozen
+    # historical record -- let _advance_job simulate it like any other.
+    job.pop(_FROM_SNAPSHOT_KEY, None)
     job["status"] = "running"
-    job["started_at"] = time.time()
+    job["queued_at"] = None
+    job["started_at"] = at if at is not None else time.time()
     job["finished_at"] = None
     job["error"] = None
     job["output_path"] = None
@@ -486,17 +695,281 @@ def _start(job: dict[str, Any]) -> None:
     job["phase"] = "starting"
 
 
-@app.post("/api/jobs/{job_id}/start")
-def start_job(job_id: str) -> dict[str, Any]:
+class _QueueView:
+    """The scheduler's view of a mock job dict (it reads attributes, not keys)."""
+
+    def __init__(self, job: dict[str, Any]):
+        self.id = job["id"]
+        self.name = job.get("name", "")
+        self.status = job["status"]
+        self.references = job.get("references")
+        self.queued_at = job.get("queued_at")
+
+
+def _drain_queue() -> None:
+    """Run the queue up to now. Call with ``_state_lock`` held.
+
+    Like everything else in the mock this is computed on read: each pass
+    finishes jobs whose time is up, then starts whatever the queue allows.
+    A job takes over the slot at the moment the previous job *finished* (not at
+    the moment someone happened to poll), so a queue that has been sitting for
+    a minute shows every clip already done rather than advancing one per poll.
+    Same rules as the real runner: one at a time, oldest first, and a clip that
+    takes another's last frame waits for it -- see job_queue.plan_dispatch.
+    """
+    while True:
+        for job in _jobs.values():
+            _advance_job(job)
+        plan = plan_dispatch({jid: _QueueView(j) for jid, j in _jobs.items()}, max_concurrent=1)
+        now = time.time()
+        for job_id, reason in plan.fail.items():
+            job = _jobs[job_id]
+            job["status"] = "failed"
+            job["error"] = reason
+            job["finished_at"] = now
+            job["queued_at"] = None
+            job["phase"] = "failed"
+        if not plan.start:
+            if plan.fail:
+                continue  # a failure can free a slot or fail more jobs
+            return
+        for job_id in plan.start:
+            job = _jobs[job_id]
+            finished = [j["finished_at"] for j in _jobs.values() if j.get("finished_at")]
+            # The slot came free when the last job finished, if that was after the job was queued.
+            slot_free = max([job.get("queued_at") or 0.0, *[f for f in finished if f <= now]])
+            _start(job, at=min(now, slot_free))
+
+
+def _check_dependencies_finished(job: dict[str, Any]) -> None:
+    for dep_id in dependencies(job.get("references")):
+        dep = _jobs.get(dep_id)
+        if dep is None:
+            raise HTTPException(status_code=409,
+                                detail="This job starts from the ending of a job that no longer exists.")
+        if dep["status"] != "completed":
+            label = f"'{dep['name']}'" if dep.get("name") else dep["id"]
+            raise HTTPException(
+                status_code=409,
+                detail=(f"This job starts from the ending of {label}, which hasn't finished "
+                        f"(it is {dep['status']}). Queue it to run after that job."),
+            )
+
+
+# Mirrors JobRunner.EDITABLE_STATUSES: exactly the jobs that can still be started.
+_EDITABLE_STATUSES = ("pending", "failed", "stopped")
+
+
+@app.patch("/api/jobs/{job_id}")
+def update_job(job_id: str, updates: dict[str, Any]) -> dict[str, Any]:
+    """Edit a job's configuration; only jobs that have not produced a result."""
     with _state_lock:
+        _drain_queue()
         job = _jobs.get(job_id)
         if job is None:
             raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
         _advance_job(job)
-        if job["status"] == "running":
-            raise HTTPException(status_code=409, detail="Job is already running")
-        if job["status"] == "completed":
-            raise HTTPException(status_code=409, detail="Job already completed. Delete and re-create to run again.")
+        if job["status"] not in _EDITABLE_STATUSES:
+            raise HTTPException(
+                status_code=400,
+                detail=(f"Job is {job['status']}; only {', '.join(_EDITABLE_STATUSES)} jobs can be edited. "
+                        "Duplicate it instead."),
+            )
+        unknown = set(updates) - set(CreateJobRequest.model_fields)
+        if unknown:
+            raise HTTPException(status_code=400, detail=f"Not editable: {', '.join(sorted(unknown))}")
+        job.update(updates)
+        return _public_job(job)
+
+
+@app.post("/api/jobs/{job_id}/last-frame")
+def extract_last_frame(job_id: str) -> dict[str, str]:
+    """Stand-in for the real route: there is no video to read, so hand back a made-up path."""
+    with _state_lock:
+        _drain_queue()
+        job = _jobs.get(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail="Job not found")
+        _advance_job(job)
+        if job["status"] != "completed" or not job.get("output_path"):
+            raise HTTPException(status_code=404, detail="No output available for this job")
+    return {"path": f"/mock/last_frames/last_frame_{job_id}.png", "media_type": "image"}
+
+
+@app.post("/api/jobs/{job_id}/last-clip")
+def extract_last_clip(job_id: str) -> dict[str, str]:
+    """Stand-in for the real route: there is no video to read, so hand back a made-up path."""
+    with _state_lock:
+        _drain_queue()
+        job = _jobs.get(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail="Job not found")
+        _advance_job(job)
+        if job["status"] != "completed" or not job.get("output_path"):
+            raise HTTPException(status_code=404, detail="No output available for this job")
+    return {"path": f"/mock/last_clips/last_clip_{job_id}.mp4", "media_type": "video"}
+
+
+#: Key holding a trimmed job's frame count before any trim, so it can be restored.
+_ORIGINAL_NUM_FRAMES_KEY = "_original_num_frames"
+
+
+def _trimmable_job(job_id: str) -> dict[str, Any]:
+    """Look up a completed job with an output, or raise. Call with _state_lock held."""
+    _drain_queue()
+    job = _jobs.get(job_id)
+    if job is None:
+        raise HTTPException(status_code=404, detail="Job not found")
+    _advance_job(job)
+    if job["status"] != "completed" or not job.get("output_path"):
+        raise HTTPException(status_code=404, detail="No output available for this job")
+    return job
+
+
+def _check_grade_range(name: str, value: float, bounds: tuple[float, float]) -> None:
+    low, high = bounds
+    if not (low <= value <= high):
+        raise HTTPException(status_code=400, detail=f"{name} must be between {low:g} and {high:g}, got {value:g}.")
+
+
+def _check_segments(segments: list[GradeSegment]) -> None:
+    """Structural checks only (ordering, open-endedness, color bounds) -- the mock has no
+    frames to count, so it doesn't reproduce trim.py's frame-rounding/coverage checks.
+
+    An empty list isn't an error here either, matching trim.py: it defaults to
+    one neutral section covering everything (Pydantic's own default_factory
+    already supplies that when the field is omitted, but an explicit "segments":
+    [] reaches here as a real empty list).
+    """
+    prev_end = 0.0
+    for i, s in enumerate(segments):
+        _check_grade_range("brightness", s.brightness, BRIGHTNESS_RANGE)
+        _check_grade_range("contrast", s.contrast, CONTRAST_RANGE)
+        _check_grade_range("saturation", s.saturation, SATURATION_RANGE)
+        if s.end_seconds is None:
+            if i != len(segments) - 1:
+                raise HTTPException(status_code=400, detail="Only the last section can be open-ended.")
+        else:
+            if s.end_seconds <= prev_end:
+                raise HTTPException(status_code=400, detail="Sections must be in increasing order.")
+            prev_end = s.end_seconds
+
+
+@app.post("/api/jobs/{job_id}/trim")
+def trim_job(job_id: str, req: TrimRequest) -> dict[str, Any]:
+    """Stand-in for the real route: no video to cut or grade, so just resize num_frames to match."""
+    with _state_lock:
+        job = _trimmable_job(job_id)
+        if req.start_seconds < 0:
+            raise HTTPException(status_code=400, detail="The start of the range can't be negative.")
+        _check_segments(req.segments)
+        fps = job.get("fps") or 24
+        original_frames = job.setdefault(_ORIGINAL_NUM_FRAMES_KEY, job["num_frames"])
+        duration = original_frames / fps
+        end = duration if req.end_seconds is None else min(req.end_seconds, duration)
+        if end <= req.start_seconds:
+            raise HTTPException(status_code=400, detail="The end of the range must come after the start.")
+        job["num_frames"] = max(1, round((end - req.start_seconds) * fps))
+        # Recorded so the dialog can reopen showing what's actually applied,
+        # like the real server's edit_* job fields.
+        job["edit_start_seconds"] = req.start_seconds
+        job["edit_end_seconds"] = req.end_seconds
+        job["edit_segments"] = [s.model_dump() for s in req.segments]
+        return _public_job(job)
+
+
+@app.post("/api/jobs/{job_id}/restore-video")
+def restore_job_video(job_id: str) -> dict[str, Any]:
+    """Stand-in for the real route: undo the simulated edit by restoring num_frames."""
+    with _state_lock:
+        job = _trimmable_job(job_id)
+        if _ORIGINAL_NUM_FRAMES_KEY not in job:
+            raise HTTPException(status_code=404, detail="This job has not been edited.")
+        job["num_frames"] = job.pop(_ORIGINAL_NUM_FRAMES_KEY)
+        job["edit_start_seconds"] = 0.0
+        job["edit_end_seconds"] = None
+        job["edit_segments"] = []
+        return _public_job(job)
+
+
+def _check_startable(job: dict[str, Any]) -> None:
+    if job["status"] == "running":
+        raise HTTPException(status_code=409, detail="Job is already running")
+    if job["status"] == "queued":
+        raise HTTPException(status_code=409, detail="Job is already queued")
+    if job["status"] == "completed":
+        raise HTTPException(status_code=409, detail="Job already completed. Delete and re-create to run again.")
+
+
+def _enqueue(job_ids: list[str]) -> list[dict[str, Any]]:
+    """Queue jobs in order, all-or-nothing (mirrors JobRunner.enqueue_jobs). Lock held."""
+    if len(set(job_ids)) != len(job_ids):
+        raise HTTPException(status_code=409, detail="A job was listed more than once.")
+    jobs = []
+    for job_id in job_ids:
+        job = _jobs.get(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
+        _check_startable(job)
+        jobs.append(job)
+    views = {jid: _QueueView(j) for jid, j in _jobs.items()}
+    for job in jobs:
+        loop = circular_dependency(views, job["id"])
+        if loop:
+            raise HTTPException(
+                status_code=409,
+                detail="Jobs would wait on each other's last frames in a loop: " + " -> ".join(loop),
+            )
+    for job in jobs:
+        job.pop(_FROM_SNAPSHOT_KEY, None)
+        job.update(status="queued", queued_at=time.time(), error=None, output_path=None, log_file_path=None,
+                   started_at=None, finished_at=None, progress=0.0, progress_msg="", phase="queued")
+        # Strictly increasing, so jobs queued together keep their order.
+        others = [j["queued_at"] for j in _jobs.values() if j.get("queued_at") and j is not job]
+        job["queued_at"] = max([job["queued_at"], *[o + 1e-6 for o in others]])
+    _drain_queue()
+    return [_public_job(j) for j in jobs]
+
+
+@app.post("/api/jobs/queue")
+def queue_jobs(req: QueueJobsRequest) -> list[dict[str, Any]]:
+    with _state_lock:
+        _drain_queue()
+        return _enqueue(req.job_ids)
+
+
+@app.post("/api/jobs/{job_id}/queue")
+def queue_job(job_id: str) -> dict[str, Any]:
+    with _state_lock:
+        _drain_queue()
+        return _enqueue([job_id])[0]
+
+
+@app.post("/api/jobs/{job_id}/dequeue")
+def dequeue_job(job_id: str) -> dict[str, Any]:
+    with _state_lock:
+        _drain_queue()
+        job = _jobs.get(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
+        if job["status"] != "queued":
+            raise HTTPException(status_code=409, detail=f"Job is not queued (status={job['status']})")
+        job["status"] = "pending"
+        job["queued_at"] = None
+        _drain_queue()
+        return _public_job(job)
+
+
+@app.post("/api/jobs/{job_id}/start")
+def start_job(job_id: str) -> dict[str, Any]:
+    with _state_lock:
+        _drain_queue()
+        job = _jobs.get(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
+        _advance_job(job)
+        _check_startable(job)
+        _check_dependencies_finished(job)
         _start(job)
         return _public_job(job)
 
@@ -504,10 +977,16 @@ def start_job(job_id: str) -> dict[str, Any]:
 @app.post("/api/jobs/{job_id}/stop")
 def stop_job(job_id: str) -> dict[str, Any]:
     with _state_lock:
+        _drain_queue()
         job = _jobs.get(job_id)
         if job is None:
             raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
         _advance_job(job)
+        if job["status"] == "queued":
+            job["status"] = "pending"
+            job["queued_at"] = None
+            _drain_queue()
+            return _public_job(job)
         if job["status"] != "running":
             raise HTTPException(status_code=409, detail=f"Job is not running (status={job['status']})")
         job["status"] = "stopped"
@@ -519,14 +998,17 @@ def stop_job(job_id: str) -> dict[str, Any]:
 @app.delete("/api/jobs/{job_id}")
 def delete_job(job_id: str) -> dict[str, str]:
     with _state_lock:
+        _drain_queue()
         if _jobs.pop(job_id, None) is None:
             raise HTTPException(status_code=404, detail="Job not found")
+        _drain_queue()  # clips waiting on this one's last frame can no longer run
     return {"detail": f"Job {job_id} deleted"}
 
 
 @app.get("/api/jobs/{job_id}/logs")
 def get_job_logs(job_id: str, after: int = 0) -> dict[str, Any]:
     with _state_lock:
+        _drain_queue()
         job = _jobs.get(job_id)
         if job is None:
             raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
@@ -545,6 +1027,7 @@ def get_job_logs(job_id: str, after: int = 0) -> dict[str, Any]:
 @app.get("/api/jobs/{job_id}/video")
 def get_video(job_id: str) -> FileResponse:
     with _state_lock:
+        _drain_queue()
         job = _jobs.get(job_id)
         if job is None:
             raise HTTPException(status_code=404, detail="Job not found")
@@ -552,6 +1035,12 @@ def get_video(job_id: str) -> FileResponse:
         output_path = job.get("output_path")
         if job["status"] != "completed" or not output_path:
             raise HTTPException(status_code=404, detail="No output available for this job")
+    # A job loaded from a --data-dir snapshot may point at a real file this
+    # process never wrote, only reads -- serve it if it's actually there,
+    # otherwise fall back to the same synthetic clip every other job gets.
+    if os.path.isfile(output_path):
+        media_type = "video/mp4" if output_path.endswith(".mp4") else "image/png"
+        return FileResponse(output_path, media_type=media_type, filename=os.path.basename(output_path))
     # Mirror the real server: image workloads (t2i) output a .png served as an
     # image; everything else is a video.
     if output_path.endswith(".png"):
@@ -559,9 +1048,59 @@ def get_video(job_id: str) -> FileResponse:
     return FileResponse(_build_mock_media("mp4"), media_type="video/mp4", filename=f"job_{job_id}.mp4")
 
 
+_MERGED_DIR = tempfile.mkdtemp(prefix="fvstudio_merged_")
+
+
+@app.post("/api/scenes/merge")
+def merge_scene(req: MergeSceneRequest) -> dict[str, Any]:
+    """Real merge (same code as the real server) of whatever videos the jobs have.
+
+    Jobs whose video only exists in the mock get the stand-in clip, so the
+    flow can be tried end to end without generating anything.
+    """
+    with _state_lock:
+        _drain_queue()
+        picked = []
+        for job_id in req.job_ids:
+            job = _jobs.get(job_id)
+            if job is None:
+                raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
+            picked.append(dict(job))
+    all_real = all(j.get("output_path") and os.path.isfile(j["output_path"]) for j in picked)
+    stand_in = None if all_real else _build_mock_media("mp4")
+    jobs = [
+        SimpleNamespace(
+            id=j["id"],
+            name=j.get("name", ""),
+            status=j["status"],
+            output_path=(j["output_path"] if all_real else (stand_in if j["status"] == "completed" else None)),
+        ) for j in picked
+    ]
+    try:
+        result = merge_jobs(jobs, _MERGED_DIR, req.name)
+    except MergeError as e:
+        raise HTTPException(status_code=e.status_code, detail=e.detail) from e
+    return {
+        "filename": result.filename,
+        "url": f"/api/merged/{result.filename}",
+        "clips": result.clips,
+        "seconds": result.seconds,
+    }
+
+
+@app.get("/api/merged/{filename}")
+def get_merged_video(filename: str) -> FileResponse:
+    try:
+        path = merged_path(_MERGED_DIR, filename)
+    except MergeError as e:
+        raise HTTPException(status_code=e.status_code, detail=e.detail) from e
+    return FileResponse(path, media_type="video/mp4", filename=filename)
+
+
 @app.get("/api/jobs/{job_id}/download_log")
 def download_log(job_id: str) -> PlainTextResponse:
     with _state_lock:
+        _drain_queue()
         job = _jobs.get(job_id)
         if job is None:
             raise HTTPException(status_code=404, detail="Job not found")
@@ -651,8 +1190,18 @@ def main() -> None:
     parser.add_argument("--host", default="127.0.0.1", help="Bind address (default: 127.0.0.1)")
     # Default off the real server's 8189 so the mock never shadows a real API.
     parser.add_argument("--port", type=int, default=8190, help="Port number (default: 8190)")
+    parser.add_argument(
+        "--data-dir",
+        default=None,
+        help=("Read-only: seed jobs/datasets/settings from a real server's "
+              "fastvideo_ui.db under this directory (its --data-dir) instead "
+              "of fake demo data. Nothing the mock does is written back."),
+    )
     args = parser.parse_args()
 
+    if args.data_dir is None or not _load_snapshot_from_db(Path(args.data_dir).resolve()):
+        _seed()
+
     uvicorn.run(app, host=args.host, port=args.port, log_level="info")
 
 

apps/fastvideo_studio/models/__init__.py

@@ -6,6 +6,9 @@ from fastvideo_studio.models.create_job_request import CreateJobRequest
 from fastvideo_studio.models.settings_update import SettingsUpdate
 from fastvideo_studio.models.create_dataset_request import CreateDatasetRequest
 from fastvideo_studio.models.update_caption_request import UpdateCaptionRequest
+from fastvideo_studio.models.queue_jobs_request import QueueJobsRequest
+from fastvideo_studio.models.merge_scene_request import MergeSceneRequest
+from fastvideo_studio.models.trim_request import GradeSegment, TrimRequest
 
 
 def model_label(model_path: str) -> str:
@@ -18,5 +21,9 @@ __all__ = [
     "SettingsUpdate",
     "CreateDatasetRequest",
     "UpdateCaptionRequest",
+    "QueueJobsRequest",
+    "MergeSceneRequest",
+    "GradeSegment",
+    "TrimRequest",
     "model_label",
 ]

apps/fastvideo_studio/models/create_job_request.py

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

apps/fastvideo_studio/models/merge_scene_request.py

@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Request model for merging a scene's clips into one video."""
+
+from pydantic import BaseModel
+
+
+class MergeSceneRequest(BaseModel):
+    #: Completed jobs, in the order their videos should play.
+    job_ids: list[str]
+    #: Used to name the merged file.
+    name: str = ""

apps/fastvideo_studio/models/queue_jobs_request.py

@@ -0,0 +1,8 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Request model for queueing jobs."""
+
+from pydantic import BaseModel
+
+
+class QueueJobsRequest(BaseModel):
+    job_ids: list[str]

apps/fastvideo_studio/models/trim_request.py

@@ -0,0 +1,30 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Request model for cutting a job's video and color-grading it section by section."""
+
+from pydantic import BaseModel, Field
+
+
+class GradeSegment(BaseModel):
+    """One razor-cut section of the kept range, and its own color grade.
+
+    ``end_seconds`` is seconds into the *kept* (already start/end-trimmed)
+    range -- the same timeline the preview video plays -- not the original
+    file's. Only the last section in a list may leave it ``None``, meaning
+    "to the end"; sections must be given in increasing order and cover the
+    whole kept range, with no gaps or reordering.
+    """
+    end_seconds: float | None = None
+    #: Added directly to 0-255 pixel values. 0 = unchanged.
+    brightness: float = 0.0
+    #: 1.0 = unchanged, 0 = flat gray, >1 = more contrast.
+    contrast: float = 1.0
+    #: 1.0 = unchanged, 0 = grayscale, >1 = more saturated.
+    saturation: float = 1.0
+
+
+class TrimRequest(BaseModel):
+    start_seconds: float = 0.0
+    #: None keeps to the end of the video.
+    end_seconds: float | None = None
+    #: Defaults to one neutral section covering the whole kept range, i.e. a plain trim with no grading.
+    segments: list[GradeSegment] = Field(default_factory=lambda: [GradeSegment()])

apps/fastvideo_studio/next.config.ts

@@ -4,10 +4,21 @@ import type { NextConfig } from 'next';
 
 const configDir = path.dirname(fileURLToPath(import.meta.url));
 
+// Where /api/* is proxied when the frontend is built with
+// NEXT_PUBLIC_API_BASE_URL=/api, so the web app and API share one origin --
+// e.g. behind a single ngrok tunnel. Read at build time.
+const apiProxyTarget = (process.env.STUDIO_API_PROXY_TARGET || 'http://127.0.0.1:8189').replace(/\/+$/, '');
+
 const nextConfig: NextConfig = {
   // Point tracing at the monorepo root (apps/fastvideo_studio -> apps -> repo
   // root) so Next stops warning about multiple lockfiles in the workspace.
   outputFileTracingRoot: path.join(configDir, '..', '..'),
+  // `next dev` blocks its dev-only assets/HMR for pages opened from a host
+  // other than localhost; allow the ngrok tunnel's domains. No effect in prod.
+  allowedDevOrigins: ['*.ngrok-free.app', '*.ngrok.app', '*.ngrok.io'],
+  async rewrites() {
+    return [{ source: '/api/:path*', destination: `${apiProxyTarget}/api/:path*` }];
+  },
 };
 
 export default nextConfig;

apps/fastvideo_studio/package-lock.json

@@ -9,6 +9,7 @@
 			"version": "0.1.0",
 			"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",
@@ -1936,6 +1937,183 @@
 				}
 			}
 		},
+		"node_modules/@radix-ui/react-dropdown-menu": {
+			"version": "2.1.24",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-dropdown-menu/-/react-dropdown-menu-2.1.24.tgz",
+			"integrity": "sha512-geq8l2rJkxvkXsT9RMgtUE3P8pITFpTsvYpbySi1IH4fZEABD/Gp85myayFgxk0ktljGMJnCbeFkyTusvSvv7g==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/primitive": "1.1.7",
+				"@radix-ui/react-compose-refs": "1.1.5",
+				"@radix-ui/react-context": "1.2.2",
+				"@radix-ui/react-id": "1.1.4",
+				"@radix-ui/react-menu": "2.1.24",
+				"@radix-ui/react-primitive": "2.1.10",
+				"@radix-ui/react-use-controllable-state": "1.2.6"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/primitive": {
+			"version": "1.1.7",
+			"resolved": "https://registry.npmjs.org/@radix-ui/primitive/-/primitive-1.1.7.tgz",
+			"integrity": "sha512-rqWnm76nYT8HoNNqEjpgJ7Pw/DrBj5iBTrmEPo6HTX5+VJyBNOqTdv4g89G63HuR5g0AaENoAcH7Is5fF2kZ8Q==",
+			"license": "MIT"
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-compose-refs": {
+			"version": "1.1.5",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-compose-refs/-/react-compose-refs-1.1.5.tgz",
+			"integrity": "sha512-+48PbAAbq3didjJxa+OaWY2ZwgAKsNiRGyeHKszblZMQ+kcpd9pAaT11cMkGEie0vsOi3QdeTE6d5Fe3Gn61kA==",
+			"license": "MIT",
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-context": {
+			"version": "1.2.2",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-context/-/react-context-1.2.2.tgz",
+			"integrity": "sha512-RHCUGwKHDr0hDGg4X7ma4JG4/+12qxw8rkh5QKdDldlCvtja6nUx1Ef/8HVrJze81lEsgLQlqjzjGNHantgnQA==",
+			"license": "MIT",
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-id": {
+			"version": "1.1.4",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-id/-/react-id-1.1.4.tgz",
+			"integrity": "sha512-TMQp2llA+RYn7JcjnrMnz7wN4pcVttPZnRZo52PLQsoLVKzNlVwUeHmfePgTgRluXFvlD3GD5g5MOVVTJCO0qA==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-primitive": {
+			"version": "2.1.10",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.10.tgz",
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+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-slot": "1.3.3"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-slot": {
+			"version": "1.3.3",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-slot/-/react-slot-1.3.3.tgz",
+			"integrity": "sha512-qx7oqnYbxnK9kYI9m317qmFmEgo6ywqWvbTogdj7cL9p3/yx4M48p7Rnw5z3H890cL/ow/EeWJsuTykeZVXP5Q==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-compose-refs": "1.1.5"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-use-controllable-state": {
+			"version": "1.2.6",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-controllable-state/-/react-use-controllable-state-1.2.6.tgz",
+			"integrity": "sha512-uEQJGT97ZA/TgP/Hydw47lHu+/vQj6z/0jA+WeTbK1o9Rx45GImjpD0tc3W5ad3D6XTSR6e1yEO0FvGq6WQfVQ==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/primitive": "1.1.7",
+				"@radix-ui/react-use-effect-event": "0.0.5",
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-use-effect-event": {
+			"version": "0.0.5",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-effect-event/-/react-use-effect-event-0.0.5.tgz",
+			"integrity": "sha512-7cshFL8HGS/7HEiHH+9kL9HBwp2sa9yX18Knwek6KYWmXwM7pegMgta2AXMQKI+rq3JnfSj9x8wYqFMTdG1Jgg==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-dropdown-menu/node_modules/@radix-ui/react-use-layout-effect": {
+			"version": "1.1.4",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-layout-effect/-/react-use-layout-effect-1.1.4.tgz",
+			"integrity": "sha512-K20DkRkUwDnxEYMBPcg3Y6voLkEy5p5QQmszZgLngKKiC7dzBR/aEuK3w1qlx2JWDUNH6FluahYdgR3BP+QbYw==",
+			"license": "MIT",
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
 		"node_modules/@radix-ui/react-focus-guards": {
 			"version": "1.1.4",
 			"resolved": "https://registry.npmjs.org/@radix-ui/react-focus-guards/-/react-focus-guards-1.1.4.tgz",
@@ -2017,6 +2195,494 @@
 				}
 			}
 		},
+		"node_modules/@radix-ui/react-menu": {
+			"version": "2.1.24",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-menu/-/react-menu-2.1.24.tgz",
+			"integrity": "sha512-uW7RVuU6Lp/ZtfeY4b3kL32zccgEWvPv1+cf17ubYzHa9cL8AHokmk36cG/XEiH/smbQvumnieXX9j/e9RqJWA==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/primitive": "1.1.7",
+				"@radix-ui/react-collection": "1.1.15",
+				"@radix-ui/react-compose-refs": "1.1.5",
+				"@radix-ui/react-context": "1.2.2",
+				"@radix-ui/react-direction": "1.1.4",
+				"@radix-ui/react-dismissable-layer": "1.1.19",
+				"@radix-ui/react-focus-guards": "1.1.6",
+				"@radix-ui/react-focus-scope": "1.1.16",
+				"@radix-ui/react-id": "1.1.4",
+				"@radix-ui/react-popper": "1.3.7",
+				"@radix-ui/react-portal": "1.1.17",
+				"@radix-ui/react-presence": "1.1.10",
+				"@radix-ui/react-primitive": "2.1.10",
+				"@radix-ui/react-roving-focus": "1.1.19",
+				"@radix-ui/react-slot": "1.3.3",
+				"@radix-ui/react-use-callback-ref": "1.1.4",
+				"aria-hidden": "^1.2.4",
+				"react-remove-scroll": "^2.7.2"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
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+			"version": "1.1.7",
+			"resolved": "https://registry.npmjs.org/@radix-ui/primitive/-/primitive-1.1.7.tgz",
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+			"license": "MIT"
+		},
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+			"version": "1.1.15",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-arrow/-/react-arrow-1.1.15.tgz",
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+			"license": "MIT",
+			"dependencies": {
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+				},
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+				}
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+		},
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+			"resolved": "https://registry.npmjs.org/@radix-ui/react-collection/-/react-collection-1.1.15.tgz",
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+			"integrity": "sha512-TMQp2llA+RYn7JcjnrMnz7wN4pcVttPZnRZo52PLQsoLVKzNlVwUeHmfePgTgRluXFvlD3GD5g5MOVVTJCO0qA==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-popper": {
+			"version": "1.3.7",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-popper/-/react-popper-1.3.7.tgz",
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+			"license": "MIT",
+			"dependencies": {
+				"@floating-ui/react-dom": "^2.0.0",
+				"@radix-ui/react-arrow": "1.1.15",
+				"@radix-ui/react-compose-refs": "1.1.5",
+				"@radix-ui/react-context": "1.2.2",
+				"@radix-ui/react-primitive": "2.1.10",
+				"@radix-ui/react-use-callback-ref": "1.1.4",
+				"@radix-ui/react-use-layout-effect": "1.1.4",
+				"@radix-ui/react-use-rect": "1.1.4",
+				"@radix-ui/react-use-size": "1.1.4",
+				"@radix-ui/rect": "1.1.3"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-portal": {
+			"version": "1.1.17",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.1.17.tgz",
+			"integrity": "sha512-vKQLcWypUnwZVvfV7UkGahH2g6ySe8M8R+zYBwPrv5byZ9QAW6cQVvNKo7GgmD+p8aYb6D9JBuvy8/WhOno2wQ==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-primitive": "2.1.10",
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
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+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-presence": {
+			"version": "1.1.10",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-presence/-/react-presence-1.1.10.tgz",
+			"integrity": "sha512-3wyzCQ6+ubRA+D4uv9m95JYLXxmOHp05qjrkjeA7uKHHtjpPggQzc6DAb0URl7j67oR0K2foO4ip27TiX037Bw==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-primitive": {
+			"version": "2.1.10",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.10.tgz",
+			"integrity": "sha512-MucOnzh6hR5mid6VpkbglRAMYMjKLqRnGBbjXkzjK52fuQDd1qbkx78a5P40mkcnVXJdEVxm26E9OPAiUq7nBg==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-slot": "1.3.3"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-roving-focus": {
+			"version": "1.1.19",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-roving-focus/-/react-roving-focus-1.1.19.tgz",
+			"integrity": "sha512-V9jI6hDjT7l3jsCQD9bLNvDLM3tH/gdbOTp7Tefp3hbbgCGQoK7tUvrWiRlcoBHIZ809ElXwNQwVo0B98LuTXQ==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/primitive": "1.1.7",
+				"@radix-ui/react-collection": "1.1.15",
+				"@radix-ui/react-compose-refs": "1.1.5",
+				"@radix-ui/react-context": "1.2.2",
+				"@radix-ui/react-direction": "1.1.4",
+				"@radix-ui/react-id": "1.1.4",
+				"@radix-ui/react-primitive": "2.1.10",
+				"@radix-ui/react-use-callback-ref": "1.1.4",
+				"@radix-ui/react-use-controllable-state": "1.2.6",
+				"@radix-ui/react-use-is-hydrated": "0.1.3",
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"@types/react-dom": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+				"react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-slot": {
+			"version": "1.3.3",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-slot/-/react-slot-1.3.3.tgz",
+			"integrity": "sha512-qx7oqnYbxnK9kYI9m317qmFmEgo6ywqWvbTogdj7cL9p3/yx4M48p7Rnw5z3H890cL/ow/EeWJsuTykeZVXP5Q==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-compose-refs": "1.1.5"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-use-callback-ref": {
+			"version": "1.1.4",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-callback-ref/-/react-use-callback-ref-1.1.4.tgz",
+			"integrity": "sha512-R6OUY2e2fA6Yn6s+VSx5KBV6Nx8LQEhu+cz7LCej18rQ1HLyg9PSC9jP/ZNx0o6FAIK9c0F1kHylzSxKsdlkrQ==",
+			"license": "MIT",
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-use-controllable-state": {
+			"version": "1.2.6",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-controllable-state/-/react-use-controllable-state-1.2.6.tgz",
+			"integrity": "sha512-uEQJGT97ZA/TgP/Hydw47lHu+/vQj6z/0jA+WeTbK1o9Rx45GImjpD0tc3W5ad3D6XTSR6e1yEO0FvGq6WQfVQ==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/primitive": "1.1.7",
+				"@radix-ui/react-use-effect-event": "0.0.5",
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-use-effect-event": {
+			"version": "0.0.5",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-effect-event/-/react-use-effect-event-0.0.5.tgz",
+			"integrity": "sha512-7cshFL8HGS/7HEiHH+9kL9HBwp2sa9yX18Knwek6KYWmXwM7pegMgta2AXMQKI+rq3JnfSj9x8wYqFMTdG1Jgg==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-use-layout-effect": {
+			"version": "1.1.4",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-layout-effect/-/react-use-layout-effect-1.1.4.tgz",
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+			"license": "MIT",
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-use-rect": {
+			"version": "1.1.4",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-rect/-/react-use-rect-1.1.4.tgz",
+			"integrity": "sha512-cSOCh6JlkmfjLyNcLiu2nB4v+nm+dkZ+Q5KHWk/soo4U7ZLiEQFKHK9/YmtBHjfCEaU43IBKQOc4/uJmCaiCTQ==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/rect": "1.1.3"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-use-size": {
+			"version": "1.1.4",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-size/-/react-use-size-1.1.4.tgz",
+			"integrity": "sha512-D3anSY15EJoxrihpsXI6SMrmmonnQtR2ni7arO+Lfdg3O95b9hNXxONk8jA5C8ANdF/h5HMAxejgs8PWJ6rlhw==",
+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/rect": {
+			"version": "1.1.3",
+			"resolved": "https://registry.npmjs.org/@radix-ui/rect/-/rect-1.1.3.tgz",
+			"integrity": "sha512-JtyZR+mqgBibTo8xea3B6ZRmzZiM/YeVBtUkas6zMuXjAlfIFIW2FgqeM9eLyvEaYX66vr6DJMK+4U6LV0KhNw==",
+			"license": "MIT"
+		},
 		"node_modules/@radix-ui/react-popper": {
 			"version": "1.3.2",
 			"resolved": "https://registry.npmjs.org/@radix-ui/react-popper/-/react-popper-1.3.2.tgz",
@@ -2410,6 +3076,21 @@
 				}
 			}
 		},
+		"node_modules/@radix-ui/react-use-is-hydrated": {
+			"version": "0.1.3",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-is-hydrated/-/react-use-is-hydrated-0.1.3.tgz",
+			"integrity": "sha512-umO/aJ+82CpOnhDZUTbILCQf7kU/g0iv+oGs/Q8jw7IkhWBzaEP4sA268PhFAJTFetbwp3ICc6ktpI4TqtxcIw==",
+			"license": "MIT",
+			"peerDependencies": {
+				"@types/react": "*",
+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+			},
+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
 		"node_modules/@radix-ui/react-use-layout-effect": {
 			"version": "1.1.2",
 			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-layout-effect/-/react-use-layout-effect-1.1.2.tgz",

apps/fastvideo_studio/package.json

@@ -18,6 +18,7 @@
 	},
 	"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",

apps/fastvideo_studio/server.py

@@ -18,6 +18,7 @@ import argparse
 import contextlib
 import logging
 import os
+import re
 import shutil
 import signal
 import time
@@ -32,10 +33,12 @@ from fastapi.responses import FileResponse
 
 from fastvideo.registry import (get_registered_model_paths, get_registered_models_with_workloads)
 from fastvideo_studio.database import Database, _get_db_path
+from fastvideo_studio.frames import FrameError, last_clip_for_job, last_frame_for_job
 from fastvideo_studio.gpu import get_gpu_snapshot
 from fastvideo_studio.job_runner import JobRunner, JobStatus
-from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, SettingsUpdate, UpdateCaptionRequest,
-                                     model_label)
+from fastvideo_studio.merge import MergeError, merge_jobs, merged_path
+from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, MergeSceneRequest, QueueJobsRequest,
+                                     SettingsUpdate, TrimRequest, UpdateCaptionRequest, model_label)
 
 logging.basicConfig(
     level=logging.INFO,
@@ -116,7 +119,30 @@ def list_models(workload_type: str | None = None) -> list[dict[str, Any]]:
     return _available_models
 
 
+def _safe_upload_name(filename: str | None, ext: str) -> str:
+    """A filesystem-safe version of the client's filename, keeping it readable.
+
+    Uploads live under a per-file uuid directory, so the basename does not have
+    to be unique -- only safe. Keeping the original name means the path stays
+    self-describing wherever it travels: the database, job logs, and payloads
+    copied back out to the API.
+    """
+    stem = os.path.basename(filename or "").rsplit(".", 1)[0]
+    stem = re.sub(r"[^A-Za-z0-9._-]+", "_", stem).strip("._-")
+    return f"{stem[:80] or 'upload'}{ext}"
+
+
+def _upload_destination(ext: str, filename: str | None) -> str:
+    """<upload_dir>/<uuid4>/<safe original name><ext>"""
+    directory = os.path.join(upload_dir, uuid.uuid4().hex)
+    os.makedirs(directory, exist_ok=True)
+    return os.path.join(directory, _safe_upload_name(filename, ext))
+
+
 ALLOWED_IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+ALLOWED_VIDEO_EXTENSIONS = {".mp4", ".mov", ".mkv", ".webm", ".avi"}
+ALLOWED_AUDIO_EXTENSIONS = {".wav", ".mp3", ".flac", ".m4a", ".ogg"}
+ALLOWED_MEDIA_EXTENSIONS = (ALLOWED_IMAGE_EXTENSIONS | ALLOWED_VIDEO_EXTENSIONS | ALLOWED_AUDIO_EXTENSIONS)
 
 
 @app.post("/api/upload-image")
@@ -136,8 +162,7 @@ async def upload_image(file: Annotated[UploadFile, File()], ) -> dict[str, str]:
                     f"{', '.join(ALLOWED_IMAGE_EXTENSIONS)}"),
         )
     os.makedirs(upload_dir, exist_ok=True)
-    unique_name = f"{uuid.uuid4().hex}{ext}"
-    dest_path = os.path.join(upload_dir, unique_name)
+    dest_path = _upload_destination(ext, file.filename)
     try:
         contents = await file.read()
         with open(dest_path, "wb") as f:
@@ -150,6 +175,47 @@ async def upload_image(file: Annotated[UploadFile, File()], ) -> dict[str, str]:
     return {"path": os.path.abspath(dest_path)}
 
 
+@app.post("/api/upload-media")
+async def upload_media(file: Annotated[UploadFile, File()], ) -> dict[str, str]:
+    """Upload an image, video or audio file for Ref2VA references.
+
+    Returns the absolute path plus the media_type MiniMax-H3 expects, so the
+    caller does not have to re-derive it from the extension.
+    """
+    global upload_dir  # noqa: PLW0603
+    if not upload_dir:
+        raise HTTPException(
+            status_code=503,
+            detail="Upload directory not configured",
+        )
+    ext = Path(file.filename or "").suffix.lower()
+    if ext not in ALLOWED_MEDIA_EXTENSIONS:
+        raise HTTPException(
+            status_code=400,
+            detail=(f"Invalid file type. Allowed: "
+                    f"{', '.join(sorted(ALLOWED_MEDIA_EXTENSIONS))}"),
+        )
+    if ext in ALLOWED_VIDEO_EXTENSIONS:
+        media_type = "video"
+    elif ext in ALLOWED_AUDIO_EXTENSIONS:
+        media_type = "audio"
+    else:
+        media_type = "image"
+
+    os.makedirs(upload_dir, exist_ok=True)
+    dest_path = _upload_destination(ext, file.filename)
+    try:
+        contents = await file.read()
+        with open(dest_path, "wb") as f:
+            f.write(contents)
+    except OSError as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to save upload: {e}",
+        ) from e
+    return {"path": os.path.abspath(dest_path), "media_type": media_type}
+
+
 ALLOWED_VIDEO_EXTENSIONS = {".mp4", ".webm", ".avi", ".mov", ".mkv"}
 
 
@@ -282,10 +348,13 @@ def create_job(req: CreateJobRequest) -> dict[str, Any]:
     job = job_runner.create_job(
         job_id=str(uuid.uuid4()),
         model_id=req.model_id,
+        name=req.name or "",
         prompt=req.prompt,
         workload_type=req.workload_type or "t2v",
         job_type=job_type,
         image_path=req.image_path or "",
+        last_image_path=req.last_image_path or "",
+        references=req.references or [],
         data_path=data_path,
         max_train_steps=req.max_train_steps,
         train_batch_size=req.train_batch_size,
@@ -304,6 +373,7 @@ def create_job(req: CreateJobRequest) -> dict[str, Any]:
         seed=req.seed,
         num_gpus=req.num_gpus,
         dit_cpu_offload=req.dit_cpu_offload,
+        dit_layerwise_offload=req.dit_layerwise_offload,
         text_encoder_cpu_offload=req.text_encoder_cpu_offload,
         vae_cpu_offload=req.vae_cpu_offload,
         image_encoder_cpu_offload=req.image_encoder_cpu_offload,
@@ -336,6 +406,28 @@ def create_job(req: CreateJobRequest) -> dict[str, Any]:
     return job.to_dict()
 
 
+@app.post("/api/jobs/{job_id}/duplicate", status_code=201)
+def duplicate_job(job_id: str) -> dict[str, Any]:
+    """Create a new pending job with the same configuration as an existing one."""
+    try:
+        job = job_runner.duplicate_job(job_id, str(uuid.uuid4()))
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e)) from e
+    return job.to_dict()
+
+
+@app.patch("/api/jobs/{job_id}")
+def update_job(job_id: str, updates: dict[str, Any]) -> dict[str, Any]:
+    """Edit a pending job's configuration. Started jobs cannot be edited."""
+    try:
+        job = job_runner.update_job_config(job_id, updates)
+    except ValueError as e:
+        detail = str(e)
+        status = 404 if "not found" in detail else 400
+        raise HTTPException(status_code=status, detail=detail) from e
+    return job.to_dict()
+
+
 @app.post("/api/jobs/{job_id}/start")
 def start_job(job_id: str) -> dict[str, Any]:
     """Start (or restart) a pending / stopped / failed job."""
@@ -349,6 +441,41 @@ def start_job(job_id: str) -> dict[str, Any]:
         ) from e
 
 
+def _queue_error(e: ValueError) -> HTTPException:
+    return HTTPException(status_code=404 if "not found" in str(e) else 409, detail=str(e))
+
+
+@app.post("/api/jobs/queue")
+def queue_jobs(req: QueueJobsRequest) -> list[dict[str, Any]]:
+    """Queue jobs, in the order given, to run as GPU slots free up.
+
+    A job that starts from another job's last frame waits for that job to
+    complete. All-or-nothing: if any job can't be queued, none are.
+    """
+    try:
+        return [j.to_dict() for j in job_runner.enqueue_jobs(req.job_ids)]
+    except ValueError as e:
+        raise _queue_error(e) from e
+
+
+@app.post("/api/jobs/{job_id}/queue")
+def queue_job(job_id: str) -> dict[str, Any]:
+    """Queue one job to run when a slot is free."""
+    try:
+        return job_runner.enqueue_job(job_id).to_dict()
+    except ValueError as e:
+        raise _queue_error(e) from e
+
+
+@app.post("/api/jobs/{job_id}/dequeue")
+def dequeue_job(job_id: str) -> dict[str, Any]:
+    """Take a queued job out of the queue (back to pending)."""
+    try:
+        return job_runner.dequeue_job(job_id).to_dict()
+    except ValueError as e:
+        raise _queue_error(e) from e
+
+
 @app.post("/api/jobs/{job_id}/stop")
 def stop_job(job_id: str) -> dict[str, Any]:
     """Request a running job to stop.
@@ -575,6 +702,113 @@ def get_video(job_id: str) -> FileResponse:
     return FileResponse(job.output_path, media_type=media_type)
 
 
+@app.post("/api/jobs/{job_id}/last-frame")
+def extract_last_frame(job_id: str) -> dict[str, str]:
+    """Save the last frame of a completed job's video and return its path.
+
+    The path is meant to be attached to another job as an image reference, so
+    the next clip of a scene can start from where this one ended.
+    """
+    job = job_runner.get_job(job_id)
+    if job is None:
+        raise HTTPException(status_code=404, detail="Job not found")
+    try:
+        path = last_frame_for_job(job, upload_dir)
+    except FrameError as e:
+        if e.status_code >= 500:
+            logger.warning("Last-frame extraction failed for job %s: %s", job_id, e.detail)
+        raise HTTPException(status_code=e.status_code, detail=e.detail) from e
+    return {"path": path, "media_type": "image"}
+
+
+@app.post("/api/jobs/{job_id}/last-clip")
+def extract_last_clip(job_id: str) -> dict[str, str]:
+    """Save the trailing second of a completed job's video and return its path.
+
+    The path is meant to be attached to another job as a video reference, so
+    the next clip continues with real motion instead of guessing it from a
+    single still frame.
+    """
+    job = job_runner.get_job(job_id)
+    if job is None:
+        raise HTTPException(status_code=404, detail="Job not found")
+    try:
+        path = last_clip_for_job(job, upload_dir)
+    except FrameError as e:
+        if e.status_code >= 500:
+            logger.warning("Last-clip extraction failed for job %s: %s", job_id, e.detail)
+        raise HTTPException(status_code=e.status_code, detail=e.detail) from e
+    return {"path": path, "media_type": "video"}
+
+
+def _trim_error(e: Exception) -> HTTPException:
+    if isinstance(e, FrameError):
+        return HTTPException(status_code=e.status_code, detail=e.detail)
+    return HTTPException(status_code=404 if "not found" in str(e) else 400, detail=str(e))
+
+
+@app.post("/api/jobs/{job_id}/trim")
+def trim_job(job_id: str, req: TrimRequest) -> dict[str, Any]:
+    """Cut a completed job's video to [start_seconds, end_seconds) and grade it section by section, in place.
+
+    The untouched original is kept, so this can be called again with a
+    different range and/or sections, or undone with POST .../restore-video.
+    """
+    try:
+        segments = [s.model_dump() for s in req.segments]
+        job = job_runner.trim_job(job_id, req.start_seconds, req.end_seconds, segments)
+    except (ValueError, FrameError) as e:
+        raise _trim_error(e) from e
+    return job.to_dict()
+
+
+@app.post("/api/jobs/{job_id}/restore-video")
+def restore_job_video(job_id: str) -> dict[str, Any]:
+    """Undo every trim on a job's video, back to what it originally generated."""
+    try:
+        job = job_runner.restore_job_video(job_id)
+    except (ValueError, FrameError) as e:
+        raise _trim_error(e) from e
+    return job.to_dict()
+
+
+@app.post("/api/scenes/merge")
+def merge_scene(req: MergeSceneRequest) -> dict[str, Any]:
+    """Join the finished clips of a scene, in the order given, into one video.
+
+    The clips are copied, not re-encoded, so this takes about a second.
+    """
+    jobs = []
+    for job_id in req.job_ids:
+        job = job_runner.get_job(job_id)
+        if job is None:
+            raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
+        jobs.append(job)
+    try:
+        result = merge_jobs(jobs, job_runner.output_dir, req.name)
+    except MergeError as e:
+        if e.status_code >= 500:
+            logger.warning("Scene merge failed: %s", e.detail)
+        raise HTTPException(status_code=e.status_code, detail=e.detail) from e
+    logger.info("Merged %d clips into %s", result.clips, result.path)
+    return {
+        "filename": result.filename,
+        "url": f"/api/merged/{result.filename}",
+        "clips": result.clips,
+        "seconds": result.seconds,
+    }
+
+
+@app.get("/api/merged/{filename}")
+def get_merged_video(filename: str) -> FileResponse:
+    """Stream a merged scene video."""
+    try:
+        path = merged_path(job_runner.output_dir, filename)
+    except MergeError as e:
+        raise HTTPException(status_code=e.status_code, detail=e.detail) from e
+    return FileResponse(path, media_type="video/mp4", filename=filename)
+
+
 @app.get("/api/jobs/{job_id}/download_log")
 def get_job_log_file(job_id: str) -> FileResponse:
     """Download the log file for a job."""
@@ -660,6 +894,14 @@ def main() -> None:
         help=("Directory for SQLite database (jobs + settings persistence) "
               f"(default: {default_data_dir})"),
     )
+    parser.add_argument(
+        "--max-concurrent-jobs",
+        type=int,
+        default=1,
+        help=("How many inference jobs may run at once (default: 1). Each gets its own "
+              "long-lived worker that keeps its model loaded between jobs, so raising "
+              "this loads one copy of the model per worker."),
+    )
     parser.add_argument(
         "--verbose",
         action="store_true",
@@ -685,6 +927,8 @@ def main() -> None:
         log_dir=log_dir,
         verbose=args.verbose,
         database=database,
+        max_concurrent_jobs=args.max_concurrent_jobs,
+        upload_dir=upload_dir,
     )
 
     logger.info("Output directory: %s", output_dir)

apps/fastvideo_studio/src/app/datasets/page.test.tsx

@@ -0,0 +1,64 @@
+import { act, fireEvent, render, screen } from '@testing-library/react';
+import { describe, expect, it, vi } from 'vitest';
+
+import { HeaderActionsProvider } from '@/components/shell/HeaderActionsContext';
+import { getDatasets, type Dataset } from '@/lib/api';
+
+import DatasetsPage from './page';
+
+vi.mock('@/lib/api', () => ({
+  getDatasets: vi.fn(),
+}));
+
+vi.mock('@/components/datasets/AddDatasetButton', () => ({
+  default: () => null,
+}));
+
+vi.mock('@/components/datasets/CreateDatasetModal', () => ({
+  default: () => null,
+}));
+
+vi.mock('@/components/datasets/DatasetCard', () => ({
+  default: ({ dataset }: { dataset: Dataset }) => <div>{dataset.name}</div>,
+}));
+
+function renderPage() {
+  return render(
+    <HeaderActionsProvider>
+      <DatasetsPage />
+    </HeaderActionsProvider>,
+  );
+}
+
+describe('DatasetsPage', () => {
+  it('shows loading content before the initial request settles', async () => {
+    let resolveDatasets: (datasets: Dataset[]) => void = () => {};
+    vi.mocked(getDatasets).mockReturnValue(
+      new Promise<Dataset[]>((resolve) => {
+        resolveDatasets = resolve;
+      }),
+    );
+
+    renderPage();
+    expect(screen.getByLabelText('Loading datasets')).toBeInTheDocument();
+    expect(screen.queryByText('No datasets yet.')).not.toBeInTheDocument();
+
+    act(() => resolveDatasets([]));
+    expect(await screen.findByText('No datasets yet.')).toBeInTheDocument();
+  });
+
+  it('shows API failures separately from an empty list and retries', async () => {
+    vi.spyOn(console, 'error').mockImplementation(() => {});
+    vi.mocked(getDatasets).mockRejectedValueOnce(new Error('network down'));
+
+    renderPage();
+    expect(
+      await screen.findByText(/Could not load datasets from the Studio API/),
+    ).toBeInTheDocument();
+    expect(screen.queryByText('No datasets yet.')).not.toBeInTheDocument();
+
+    vi.mocked(getDatasets).mockResolvedValueOnce([]);
+    fireEvent.click(screen.getByRole('button', { name: 'Try Again' }));
+    expect(await screen.findByText('No datasets yet.')).toBeInTheDocument();
+  });
+});

apps/fastvideo_studio/src/app/datasets/page.tsx

@@ -1,12 +1,14 @@
 'use client';
 
 import * as React from 'react';
+import { AlertTriangle } from 'lucide-react';
 
 import AddDatasetButton from '@/components/datasets/AddDatasetButton';
 import CreateDatasetModal from '@/components/datasets/CreateDatasetModal';
 import DatasetCard from '@/components/datasets/DatasetCard';
 import { HeaderActions } from '@/components/shell/HeaderActionsContext';
 import { Card } from '@/components/ui/card';
+import { Button } from '@/components/ui/button';
 import { useStore } from '@/hooks/useStore';
 import { getDatasets } from '@/lib/api';
 import type { Dataset } from '@/lib/api';
@@ -21,18 +23,28 @@ import {
 
 export default function DatasetsPage() {
   const [datasets, setDatasets] = React.useState<Dataset[]>([]);
+  const [isInitialLoading, setIsInitialLoading] = React.useState(true);
   const [error, setError] = React.useState<string | null>(null);
   const { open } = useStore(createDatasetModalStore);
+  const fetchSequence = React.useRef(0);
 
   const fetchDatasets = React.useCallback(async () => {
+    const sequence = ++fetchSequence.current;
     try {
-      setDatasets(await getDatasets());
-      setError(null);
+      const next = await getDatasets();
+      if (sequence === fetchSequence.current) {
+        setDatasets(next);
+        setError(null);
+      }
     } catch (err) {
       console.error('Failed to fetch datasets:', err);
-      // Distinguish an API outage from a genuinely empty list, so the user
-      // isn't told they have no datasets when the server is unreachable.
-      setError(err instanceof Error ? err.message : 'Failed to load datasets');
+      if (sequence === fetchSequence.current) {
+        setError(
+          'Could not load datasets from the Studio API. Check the server and try again.',
+        );
+      }
+    } finally {
+      if (sequence === fetchSequence.current) setIsInitialLoading(false);
     }
   }, []);
 
@@ -50,28 +62,67 @@ export default function DatasetsPage() {
       <HeaderActions>
         <AddDatasetButton />
       </HeaderActions>
-      <main className="mx-auto flex w-full max-w-[850px] flex-col gap-6 px-4 pb-12">
+      <div className="mx-auto flex w-full max-w-[850px] flex-col gap-6 px-4 pb-12">
         <Card className="p-6">
-          <div>
-            {error ? (
-              <p className="py-8 text-center text-destructive">{error}</p>
-            ) : datasets.length === 0 ? (
-              <p className="py-8 text-center text-muted-foreground">
-                No datasets yet.
-              </p>
-            ) : (
-              datasets.map((ds) => (
-                <DatasetCard
-                  key={ds.id}
-                  dataset={ds}
-                  onUpdated={fetchDatasets}
-                  onSelect={() => handleSelectDataset(ds)}
+          <div aria-busy={isInitialLoading}>
+            {isInitialLoading ? (
+              <div
+                aria-label="Loading datasets"
+                className="flex flex-col gap-3 py-2"
+              >
+                {[0, 1, 2].map((item) => (
+                  <div
+                    key={item}
+                    className="h-24 animate-pulse rounded-lg border border-border bg-muted/50"
+                  />
+                ))}
+              </div>
+            ) : error && datasets.length === 0 ? (
+              <div
+                role="alert"
+                className="flex flex-col items-center gap-3 py-8 text-center"
+              >
+                <AlertTriangle
+                  className="size-6 text-destructive"
+                  aria-hidden
                 />
-              ))
+                <p className="max-w-md text-sm text-muted-foreground">
+                  {error}
+                </p>
+                <Button type="button" variant="outline" onClick={fetchDatasets}>
+                  Try Again
+                </Button>
+              </div>
+            ) : (
+              <>
+                {error && (
+                  <p
+                    role="status"
+                    className="mb-3 rounded-lg border border-amber-500/50 bg-amber-500/10 px-3 py-2 text-sm text-foreground"
+                  >
+                    Dataset updates are temporarily unavailable. Showing the
+                    most recent results.
+                  </p>
+                )}
+                {datasets.length === 0 ? (
+                  <p className="py-8 text-center text-muted-foreground">
+                    No datasets yet.
+                  </p>
+                ) : (
+                  datasets.map((ds) => (
+                    <DatasetCard
+                      key={ds.id}
+                      dataset={ds}
+                      onUpdated={fetchDatasets}
+                      onSelect={() => handleSelectDataset(ds)}
+                    />
+                  ))
+                )}
+              </>
             )}
           </div>
         </Card>
-      </main>
+      </div>
       <CreateDatasetModal
         isOpen={open}
         onClose={() => setCreateDatasetModalOpen(false)}

apps/fastvideo_studio/src/app/gallery/page.test.tsx

@@ -1,4 +1,4 @@
-import { render, screen } from '@testing-library/react';
+import { fireEvent, render, screen } from '@testing-library/react';
 import { describe, expect, it, vi } from 'vitest';
 
 import GalleryPage from './page';
@@ -43,6 +43,22 @@ describe('GalleryPage', () => {
     expect(getJobsList).toHaveBeenCalledWith('inference');
   });
 
+  it('provides video controls and a visible fallback when media fails', async () => {
+    vi.mocked(getJobsList).mockResolvedValue([makeJob()]);
+    renderGallery();
+
+    const video = await screen.findByLabelText(
+      'Generated video: a cat surfing a wave',
+    );
+    expect(video).toHaveAttribute('controls');
+
+    fireEvent.error(video);
+    expect(screen.getByText('Preview unavailable')).toBeInTheDocument();
+    expect(
+      screen.getByText('The generated file could not be loaded.'),
+    ).toBeInTheDocument();
+  });
+
   it('shows the empty state when no completed videos exist', async () => {
     vi.mocked(getJobsList).mockResolvedValue([
       makeJob({ status: 'running', output_path: null }),

apps/fastvideo_studio/src/app/gallery/page.tsx

@@ -1,8 +1,9 @@
 'use client';
 
-import { Loader2 } from 'lucide-react';
+import { AlertTriangle, ImageOff, Loader2 } from 'lucide-react';
 import { useEffect, useState } from 'react';
 
+import { Button } from '@/components/ui/button';
 import { Card } from '@/components/ui/card';
 import { getJobVideoUrl, getJobsList } from '@/lib/api';
 import type { Job } from '@/lib/types';
@@ -11,11 +12,61 @@ function isImage(job: Job): boolean {
   return job.output_path?.toLowerCase().endsWith('.png') ?? false;
 }
 
+function GalleryMedia({ job }: { job: Job }) {
+  const [failed, setFailed] = useState(false);
+
+  if (failed) {
+    return (
+      <div
+        role="status"
+        className="flex h-full flex-col items-center justify-center gap-2 px-4 text-center text-muted-foreground"
+      >
+        <ImageOff className="size-7" aria-hidden />
+        <span className="text-sm font-medium">Preview unavailable</span>
+        <span className="text-xs">
+          The generated file could not be loaded.
+        </span>
+      </div>
+    );
+  }
+
+  if (isImage(job)) {
+    return (
+      // eslint-disable-next-line @next/next/no-img-element
+      <img
+        src={getJobVideoUrl(job.id)}
+        alt={job.prompt}
+        className="block h-full w-full object-contain"
+        loading="lazy"
+        onError={() => setFailed(true)}
+      />
+    );
+  }
+
+  return (
+    <video
+      src={getJobVideoUrl(job.id)}
+      aria-label={
+        job.prompt ? `Generated video: ${job.prompt}` : 'Generated video'
+      }
+      className="block h-full w-full object-contain"
+      controls
+      muted
+      loop
+      playsInline
+      preload="metadata"
+      onError={() => setFailed(true)}
+    />
+  );
+}
+
 export default function GalleryPage() {
   const [jobs, setJobs] = useState<Job[]>([]);
   const [isLoading, setIsLoading] = useState(true);
   const [error, setError] = useState<string | null>(null);
 
+  const [reloadKey, setReloadKey] = useState(0);
+
   useEffect(() => {
     let cancelled = false;
     async function load() {
@@ -40,7 +91,13 @@ export default function GalleryPage() {
     return () => {
       cancelled = true;
     };
-  }, []);
+  }, [reloadKey]);
+
+  function retry() {
+    setError(null);
+    setIsLoading(true);
+    setReloadKey((k) => k + 1);
+  }
 
   const galleryJobs = jobs.filter(
     (j) =>
@@ -64,7 +121,16 @@ export default function GalleryPage() {
             <span>Loading gallery…</span>
           </div>
         ) : error ? (
-          <p className="py-8 text-destructive">{error}</p>
+          <div
+            role="alert"
+            className="flex flex-col items-center gap-3 py-8 text-center"
+          >
+            <AlertTriangle className="size-6 text-destructive" aria-hidden />
+            <p className="max-w-md text-sm text-muted-foreground">{error}</p>
+            <Button type="button" variant="outline" onClick={retry}>
+              Try Again
+            </Button>
+          </div>
         ) : galleryJobs.length === 0 ? (
           <p className="py-8 text-center text-muted-foreground">
             No completed videos yet
@@ -77,24 +143,7 @@ export default function GalleryPage() {
                 className="flex flex-col overflow-hidden rounded-lg border border-border bg-background"
               >
                 <div className="relative aspect-video overflow-hidden bg-muted">
-                  {isImage(job) ? (
-                    // eslint-disable-next-line @next/next/no-img-element
-                    <img
-                      src={getJobVideoUrl(job.id)}
-                      alt={job.prompt}
-                      className="block h-full w-full object-contain"
-                      loading="lazy"
-                    />
-                  ) : (
-                    <video
-                      src={getJobVideoUrl(job.id)}
-                      className="block h-full w-full object-contain"
-                      muted
-                      loop
-                      playsInline
-                      preload="metadata"
-                    />
-                  )}
+                  <GalleryMedia job={job} />
                 </div>
                 <p
                   className="line-clamp-3 border-t border-border px-4 py-3 text-sm text-muted-foreground"

apps/fastvideo_studio/src/app/globals.css

@@ -41,7 +41,7 @@
 
 	--border: #e2e8f0;
 	--input: #cbd5e1;
-	--ring: #94a3b8;
+	--ring: #1d4ed8;
 
 	--radius: 0.5rem;
 }
@@ -77,7 +77,7 @@
 
 	--border: #334155;
 	--input: #334155;
-	--ring: #cbd5e1;
+	--ring: #7dd3fc;
 }
 
 @theme inline {
@@ -125,6 +125,7 @@
 html,
 body {
 	min-height: 100%;
+	overflow-x: clip;
 }
 
 html {
@@ -163,6 +164,22 @@ a {
 	color: inherit;
 }
 
+:where(
+	a,
+	button,
+	input,
+	textarea,
+	select,
+	summary,
+	[role="button"],
+	[role="menuitem"],
+	[role="slider"],
+	[tabindex]
+):focus-visible {
+	outline: 3px solid var(--ring) !important;
+	outline-offset: 2px !important;
+}
+
 summary {
 	list-style: none;
 }

apps/fastvideo_studio/src/app/globals.test.ts

@@ -0,0 +1,55 @@
+import { readFileSync } from 'node:fs';
+import { join } from 'node:path';
+import { describe, expect, it } from 'vitest';
+
+const css = readFileSync(join(process.cwd(), 'src/app/globals.css'), 'utf8');
+
+function token(block: string, name: string): string {
+  const match = block.match(new RegExp(`--${name}:\\s*(#[0-9a-fA-F]{6})`));
+  if (!match) throw new Error(`Missing --${name} token`);
+  return match[1];
+}
+
+function luminance(hex: string): number {
+  const channels = hex
+    .slice(1)
+    .match(/.{2}/g)!
+    .map((channel) => parseInt(channel, 16) / 255)
+    .map((channel) =>
+      channel <= 0.04045
+        ? channel / 12.92
+        : ((channel + 0.055) / 1.055) ** 2.4,
+    );
+  return (
+    0.2126 * channels[0] + 0.7152 * channels[1] + 0.0722 * channels[2]
+  );
+}
+
+function contrast(first: string, second: string): number {
+  const firstLuminance = luminance(first);
+  const secondLuminance = luminance(second);
+  return (
+    (Math.max(firstLuminance, secondLuminance) + 0.05) /
+    (Math.min(firstLuminance, secondLuminance) + 0.05)
+  );
+}
+
+describe('global focus styles', () => {
+  it('keeps focus tokens above 3:1 against both page themes', () => {
+    const light = css.match(/:root\s*{([\s\S]*?)\n}/)?.[1] ?? '';
+    const dark = css.match(/\.dark\s*{([\s\S]*?)\n}/)?.[1] ?? '';
+
+    expect(contrast(token(light, 'ring'), token(light, 'background'))).toBeGreaterThanOrEqual(
+      3,
+    );
+    expect(contrast(token(dark, 'ring'), token(dark, 'background'))).toBeGreaterThanOrEqual(
+      3,
+    );
+  });
+
+  it('applies a non-animated three-pixel outline to focus-visible controls', () => {
+    expect(css).toContain('):focus-visible {');
+    expect(css).toContain('outline: 3px solid var(--ring) !important;');
+    expect(css).toContain('outline-offset: 2px !important;');
+  });
+});

apps/fastvideo_studio/src/app/gpus/page.tsx

@@ -4,8 +4,8 @@ import GpuGrid from '@/components/system/GpuGrid';
 
 export default function GpusPage() {
   return (
-    <main className="mx-auto flex w-full max-w-[1100px] flex-col gap-6 px-4 pb-12 pt-6">
+    <div className="mx-auto flex w-full max-w-[1100px] flex-col gap-6 px-4 pb-12 pt-6">
       <GpuGrid />
-    </main>
+    </div>
   );
 }