[docs] Document DGX Spark (GB10) local perf benchmarking + baseline follow-up

Adds a GB10 section to performance_benchmarks.md: the single-benchmark run
command, the local stability-check workflow, reference figures, and the
baseline-seeding follow-up (scheduled-main GB10 runner or a scoped,
maintainer-approved manual calibration that preserves the existing safeguards).

Addresses review feedback on #1679.

.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)

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

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

.buildkite/pipeline.yml

@@ -68,6 +68,17 @@ steps:
             limit: 2
       agents:
         queue: "default"
+    - label: ":vertical_traffic_light: Golden-Gate Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "golden_gate"
+      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"
@@ -404,7 +415,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile"
               config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                command: "timeout 25m .buildkite/scripts/pr_test.sh"
                 label: ":test_tube: Training Tests"
                 env:
                   - TEST_TYPE=training
@@ -415,7 +426,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile"
               config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                command: "timeout 25m .buildkite/scripts/pr_test.sh"
                 label: ":test_tube: Distillation DMD Tests"
                 env:
                   - TEST_TYPE=distillation_dmd

.buildkite/scripts/pr_test.sh

@@ -187,6 +187,10 @@ 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)

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

@@ -129,7 +129,7 @@ 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 full fastcheck pre-commit"
           if [ -z "$TEST_NAME" ] || ! echo "$VALID" | grep -qw "$TEST_NAME"; then
             echo "Unknown test: '$TEST_NAME'. Valid: $VALID"
             exit 1
@@ -138,7 +138,7 @@ jobs:
           declare -A MAP=(
             [encoder]=encoder [vae]=vae [transformer]=transformer
             [kernel]=kernel_tests [unit]=unit_test [dreamverse]=dreamverse_app
-            [ssim]=ssim [training]=training
+            [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/infra-build-image.yml

@@ -13,16 +13,23 @@ 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.
+  # 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 +57,7 @@ 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' }}

.gitignore

@@ -6,7 +6,7 @@ results/
 wandb/
 *.ipynb
 *.jpg
-!examples/dataset/lingbotworld2/image.jpg
+!examples/datasets/lingbotworld2/image.jpg
 *.safetensors
 *.mp4
 *.png
@@ -75,6 +75,7 @@ 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

.pre-commit-config.yaml

@@ -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

@@ -33,7 +33,7 @@ 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

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

@@ -5,7 +5,6 @@ from pathlib import Path
 
 import pytest
 
-
 SERVER_DIR = Path(__file__).resolve().parents[1]
 
 
@@ -53,9 +52,7 @@ 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 +83,7 @@ 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 +101,17 @@ 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"),

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_gpu_pool.py

@@ -7,7 +7,6 @@ from types import SimpleNamespace
 
 import pytest
 
-
 import dreamverse.gpu_pool as gpu_pool
 
 
@@ -85,9 +84,7 @@ 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 —

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",
@@ -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_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/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/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/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
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/primitive": {
+			"version": "1.1.7",
+			"resolved": "https://registry.npmjs.org/@radix-ui/primitive/-/primitive-1.1.7.tgz",
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+			"license": "MIT"
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-arrow": {
+			"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": {
+				"@radix-ui/react-primitive": "2.1.10"
+			},
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+				"@types/react": "*",
+				"@types/react-dom": "*",
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+			},
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+					"optional": true
+				},
+				"@types/react-dom": {
+					"optional": true
+				}
+			}
+		},
+		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-collection": {
+			"version": "1.1.15",
+			"resolved": "https://registry.npmjs.org/@radix-ui/react-collection/-/react-collection-1.1.15.tgz",
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+			"peerDependencies": {
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+			},
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+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
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+				"@types/react": {
+					"optional": true
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+			"resolved": "https://registry.npmjs.org/@radix-ui/react-direction/-/react-direction-1.1.4.tgz",
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+			"peerDependencies": {
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+				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
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+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
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+			"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": {
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+				}
+			}
+		},
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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",
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+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-primitive": "2.1.10",
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
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+					"optional": true
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+				"@types/react-dom": {
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+		},
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+			"license": "MIT",
+			"dependencies": {
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+			},
+			"peerDependencies": {
+				"@types/react": "*",
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+				"@types/react": {
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+				"@types/react-dom": {
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+				}
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+		},
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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"
+			},
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+				"@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",
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+			"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",
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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-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",
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+			"license": "MIT",
+			"dependencies": {
+				"@radix-ui/react-use-layout-effect": "1.1.4"
+			},
+			"peerDependencies": {
+				"@types/react": "*",
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+			"peerDependenciesMeta": {
+				"@types/react": {
+					"optional": true
+				}
+			}
+		},
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+			"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/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>
   );
 }

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

@@ -52,6 +52,20 @@ describe('Settings page', () => {
     expect(updateOption).toHaveBeenCalledWith('numFrames', expect.any(Number));
   });
 
+  it('gives every slider an accessible name', () => {
+    renderPage();
+
+    const sliders = screen.getAllByRole('slider');
+    expect(sliders).toHaveLength(11);
+    for (const slider of sliders) {
+      expect(slider).toHaveAccessibleName();
+    }
+    expect(screen.getByRole('slider', { name: 'Frames' })).toBeInTheDocument();
+    expect(
+      screen.getByRole('slider', { name: 'Guidance Scale' }),
+    ).toBeInTheDocument();
+  });
+
   it('calls resetToDefaults when Reset to Defaults is clicked', () => {
     renderPage();
     fireEvent.click(screen.getByRole('button', { name: 'Reset to Defaults' }));

apps/fastvideo_studio/src/components/datasets/DatasetCard.test.tsx

@@ -25,6 +25,16 @@ beforeEach(() => {
 });
 
 describe('DatasetCard', () => {
+  it('keeps selection and delete buttons as semantic siblings', () => {
+    render(<DatasetCard dataset={dataset} onUpdated={() => {}} />);
+
+    const selectButton = screen.getByRole('button', { pressed: false });
+    const deleteButton = screen.getByRole('button', { name: 'Delete' });
+
+    expect(selectButton).toHaveTextContent('My Dataset');
+    expect(selectButton).not.toContainElement(deleteButton);
+  });
+
   it('renders the name, file count and human-readable size', () => {
     render(<DatasetCard dataset={dataset} onUpdated={() => {}} />);
     expect(screen.getByText('My Dataset')).toBeInTheDocument();
@@ -71,7 +81,7 @@ describe('DatasetCard', () => {
     expect(onSelect).not.toHaveBeenCalled();
   });
 
-  it('selects on keyboard activation of the card body but not of the Delete button', () => {
+  it('keeps the selection and delete actions separate', () => {
     const onSelect = vi.fn();
     render(
       <DatasetCard dataset={dataset} onUpdated={() => {}} onSelect={onSelect} />,
@@ -83,8 +93,12 @@ describe('DatasetCard', () => {
     });
     expect(onSelect).not.toHaveBeenCalled();
 
-    // Activating the card body itself does select.
-    fireEvent.keyDown(screen.getByText('My Dataset'), { key: 'Enter' });
+    // Activating the dedicated selection button selects the dataset.
+    fireEvent.click(
+      screen.getByRole('button', {
+        name: /My Dataset.*3 files.*2.0 KB/,
+      }),
+    );
     expect(onSelect).toHaveBeenCalledTimes(1);
   });
 

apps/fastvideo_studio/src/components/datasets/DatasetCard.tsx

@@ -55,43 +55,33 @@ export default function DatasetCard({
     }
   }
 
-  function handleKeyDown(e: React.KeyboardEvent) {
-    if ((e.target as HTMLElement).closest('button')) return;
-    if (e.key === 'Enter' || e.key === ' ') {
-      e.preventDefault();
-      onSelect();
-    }
-  }
-
   return (
-    <div
+    <article
       className={cn(
-        'mb-3 flex cursor-pointer flex-col gap-[0.6rem] rounded-lg border border-border bg-background px-[1.15rem] py-4',
+        'mb-3 flex items-start gap-3 rounded-lg border border-border bg-background px-[1.15rem] py-4',
         isSelected && 'border-accent-blue bg-accent-blue/5',
       )}
-      onClick={(e) => {
-        if ((e.target as HTMLElement).closest('button')) return;
-        onSelect();
-      }}
-      onKeyDown={handleKeyDown}
-      role="button"
-      tabIndex={0}
     >
-      <div className="flex flex-wrap items-center justify-between gap-2">
+      <button
+        type="button"
+        aria-pressed={isSelected}
+        onClick={onSelect}
+        className="flex min-w-0 flex-1 cursor-pointer flex-col gap-[0.6rem] rounded-md text-left"
+      >
         <span className="text-[0.95rem] font-semibold">{dataset.name}</span>
-        <Button
-          type="button"
-          variant="destructive"
-          size="sm"
-          onClick={handleDelete}
-          disabled={isLoading}
-        >
-          Delete
-        </Button>
-      </div>
-      <div className="text-sm text-muted-foreground">
-        {fileCount} {fileCount === 1 ? 'file' : 'files'} · {sizeLabel}
-      </div>
-    </div>
+        <span className="text-sm text-muted-foreground">
+          {fileCount} {fileCount === 1 ? 'file' : 'files'} · {sizeLabel}
+        </span>
+      </button>
+      <Button
+        type="button"
+        variant="destructive"
+        size="sm"
+        onClick={handleDelete}
+        disabled={isLoading}
+      >
+        Delete
+      </Button>
+    </article>
   );
 }

apps/fastvideo_studio/src/components/datasets/DatasetSidebar.test.tsx

@@ -6,6 +6,9 @@ import * as api from '@/lib/api';
 import type { Dataset } from '@/lib/api';
 
 vi.mock('@/lib/api');
+vi.mock('sonner', () => ({
+  toast: { error: vi.fn() },
+}));
 
 const mockedApi = vi.mocked(api);
 
@@ -27,6 +30,27 @@ beforeEach(() => {
 });
 
 describe('DatasetSidebar', () => {
+  it('fills the mobile viewport without reserving main-content width', async () => {
+    const onWidthChange = vi.fn();
+
+    render(
+      <DatasetSidebar
+        dataset={dataset}
+        isMobile
+        onClose={() => {}}
+        onWidthChange={onWidthChange}
+      />,
+    );
+
+    const drawer = screen.getByRole('dialog', {
+      name: 'My Dataset dataset details',
+    });
+    expect(drawer).toHaveStyle({ width: '100%', maxWidth: 'none' });
+    expect(drawer).toHaveAttribute('aria-modal', 'true');
+    expect(drawer).toHaveFocus();
+    expect(onWidthChange).toHaveBeenCalledWith(0);
+  });
+
   it('lists dataset files after loading', async () => {
     render(<DatasetSidebar dataset={dataset} onClose={() => {}} />);
 
@@ -43,6 +67,16 @@ describe('DatasetSidebar', () => {
     expect(mockedApi.getDatasetMediaUrl).toHaveBeenCalledWith('ds-1', 'b.mp4');
   });
 
+  it('shows a fallback when a dataset preview cannot load', async () => {
+    render(<DatasetSidebar dataset={dataset} onClose={() => {}} />);
+
+    const preview = await screen.findByLabelText('Preview of a.mp4');
+    fireEvent.error(preview);
+
+    expect(screen.getByText('Preview unavailable')).toBeInTheDocument();
+    expect(screen.queryByLabelText('Preview of a.mp4')).not.toBeInTheDocument();
+  });
+
   it('debounces caption save by 500ms', async () => {
     render(<DatasetSidebar dataset={dataset} onClose={() => {}} />);
     const textarea = await screen.findByDisplayValue('cap a');
@@ -99,6 +133,39 @@ describe('DatasetSidebar', () => {
     }
   });
 
+  it('shows a failed save and lets the user retry it', async () => {
+    vi.spyOn(console, 'error').mockImplementation(() => {});
+    mockedApi.updateDatasetCaption
+      .mockRejectedValueOnce(new Error('network down'))
+      .mockResolvedValueOnce(undefined);
+    render(<DatasetSidebar dataset={dataset} onClose={() => {}} />);
+    const textarea = await screen.findByDisplayValue('cap a');
+
+    vi.useFakeTimers();
+    try {
+      fireEvent.change(textarea, { target: { value: 'needs retry' } });
+      await act(async () => {
+        await vi.advanceTimersByTimeAsync(500);
+      });
+
+      expect(screen.getByText(/Not saved/)).toBeInTheDocument();
+      fireEvent.click(screen.getByRole('button', { name: 'Retry' }));
+      await act(async () => {
+        await Promise.resolve();
+      });
+
+      expect(mockedApi.updateDatasetCaption).toHaveBeenCalledTimes(2);
+      expect(mockedApi.updateDatasetCaption).toHaveBeenLastCalledWith(
+        'ds-1',
+        'a.mp4',
+        'needs retry',
+      );
+      expect(screen.getByText('Saved')).toBeInTheDocument();
+    } finally {
+      vi.useRealTimers();
+    }
+  });
+
   it('debounces per file: editing another caption does not cancel a pending save', async () => {
     render(<DatasetSidebar dataset={dataset} onClose={() => {}} />);
     await screen.findByDisplayValue('cap a');

apps/fastvideo_studio/src/components/datasets/DatasetSidebar.tsx

@@ -1,7 +1,8 @@
 'use client';
 
 import * as React from 'react';
-import { X } from 'lucide-react';
+import { ImageOff, X } from 'lucide-react';
+import { toast } from 'sonner';
 
 import DownloadCaptions from '@/components/datasets/DownloadCaptions';
 import { Textarea } from '@/components/ui/textarea';
@@ -13,12 +14,14 @@ import {
   type Dataset,
 } from '@/lib/api';
 import { cn } from '@/lib/utils';
+import { useDrawerFocus } from '@/hooks/useDrawerFocus';
 
 const SIDEBAR_MIN_WIDTH = 320;
 const SIDEBAR_MAX_WIDTH = 900;
 const INITIAL_PAGE_SIZE = 24;
 const PAGE_SIZE = 24;
 const SCROLL_THRESHOLD = 200;
+type CaptionSaveState = 'idle' | 'saving' | 'saved' | 'error';
 
 // Memoized so a caption keystroke re-renders only the edited card, not every
 // visible <video> in the grid (visibleCount grows unbounded with scrolling).
@@ -27,54 +30,101 @@ const DatasetFileCard = React.memo(function DatasetFileCard({
   mediaUrl,
   caption,
   thumbLoaded,
+  saveState,
   onCaptionChange,
+  onCaptionRetry,
   onThumbLoaded,
 }: {
   fileName: string;
   mediaUrl: string;
   caption: string;
   thumbLoaded: boolean;
+  saveState: CaptionSaveState;
   onCaptionChange: (fileName: string, value: string) => void;
+  onCaptionRetry: (fileName: string, value: string) => void;
   onThumbLoaded: (fileName: string) => void;
 }) {
+  const [mediaFailed, setMediaFailed] = React.useState(false);
+
+  React.useEffect(() => {
+    setMediaFailed(false);
+  }, [mediaUrl]);
+
   return (
     <div className="relative flex flex-col overflow-hidden rounded-lg border border-border bg-background">
-      {!thumbLoaded && (
+      {!thumbLoaded && !mediaFailed && (
         <div className="pointer-events-none absolute inset-0 flex items-center justify-center bg-background/70">
           <div className="h-6 w-6 animate-spin rounded-full border-2 border-muted-foreground/40 border-t-accent-blue" />
         </div>
       )}
-      {/* eslint-disable-next-line jsx-a11y/media-has-caption */}
-      <video
-        src={mediaUrl}
-        className="aspect-video w-full bg-border object-cover"
-        muted
-        autoPlay
-        loop
-        playsInline
-        onLoadedData={() => onThumbLoaded(fileName)}
-        onError={() => onThumbLoaded(fileName)}
-      />
+      {mediaFailed ? (
+        <div
+          role="status"
+          className="flex aspect-video w-full flex-col items-center justify-center gap-1 bg-muted px-2 text-center text-muted-foreground"
+        >
+          <ImageOff className="size-5" aria-hidden />
+          <span className="text-xs">Preview unavailable</span>
+        </div>
+      ) : (
+        // eslint-disable-next-line jsx-a11y/media-has-caption
+        <video
+          src={mediaUrl}
+          aria-label={`Preview of ${fileName}`}
+          className="aspect-video w-full bg-border object-cover"
+          muted
+          autoPlay
+          loop
+          playsInline
+          onLoadedData={() => onThumbLoaded(fileName)}
+          onError={() => {
+            setMediaFailed(true);
+            onThumbLoaded(fileName);
+          }}
+        />
+      )}
       <Textarea
+        aria-label={`Caption for ${fileName}`}
         value={caption}
         onChange={(e) => onCaptionChange(fileName, e.target.value)}
         placeholder="Caption"
         rows={2}
         className="min-h-[2.5rem] resize-y rounded-none border-0 bg-transparent p-1.5 text-xs shadow-none focus-visible:border-transparent focus-visible:ring-0"
       />
+      <div
+        aria-live="polite"
+        className="flex min-h-6 items-center px-1.5 pb-1 text-[0.7rem] text-muted-foreground"
+      >
+        {saveState === 'saving' && <span>Saving…</span>}
+        {saveState === 'saved' && <span>Saved</span>}
+        {saveState === 'error' && (
+          <span role="alert" className="text-destructive">
+            Not saved.{' '}
+            <button
+              type="button"
+              onClick={() => onCaptionRetry(fileName, caption)}
+              className="inline-flex min-h-11 items-center font-medium underline underline-offset-2"
+            >
+              Retry
+            </button>
+          </span>
+        )}
+      </div>
     </div>
   );
 });
 
 export default function DatasetSidebar({
   dataset,
+  isMobile = false,
   onClose,
   onWidthChange,
 }: {
   dataset: Dataset;
+  isMobile?: boolean;
   onClose: () => void;
   onWidthChange?: (w: number) => void;
 }) {
+  const drawerRef = useDrawerFocus<HTMLElement>(isMobile);
   const [width, setWidth] = React.useState(400);
   const [isDragging, setIsDragging] = React.useState(false);
   const [fileNames, setFileNames] = React.useState<string[]>([]);
@@ -84,17 +134,21 @@ export default function DatasetSidebar({
   const [thumbLoaded, setThumbLoaded] = React.useState<
     Record<string, boolean>
   >({});
+  const [captionSaveStates, setCaptionSaveStates] = React.useState<
+    Record<string, CaptionSaveState>
+  >({});
 
   // Pending debounced caption saves, keyed per file so editing one caption
   // can't cancel another file's pending save.
   const pendingSaves = React.useRef(
     new Map<string, { timer: ReturnType<typeof setTimeout>; save: () => void }>(),
   );
+  const captionVersions = React.useRef(new Map<string, number>());
   const scrollRef = React.useRef<HTMLDivElement>(null);
 
   React.useEffect(() => {
-    onWidthChange?.(width);
-  }, [width, onWidthChange]);
+    onWidthChange?.(isMobile ? 0 : width);
+  }, [isMobile, width, onWidthChange]);
 
   React.useEffect(() => {
     let cancelled = false;
@@ -106,6 +160,8 @@ export default function DatasetSidebar({
         setCaptions(data.captions);
         setVisibleCount(INITIAL_PAGE_SIZE);
         setThumbLoaded({});
+        setCaptionSaveStates({});
+        captionVersions.current.clear();
       })
       .catch((err) => console.error('Failed to load dataset files:', err))
       .finally(() => {
@@ -138,23 +194,51 @@ export default function DatasetSidebar({
   });
 
   const datasetId = dataset.id;
+  const persistCaption = React.useCallback(
+    (fileName: string, value: string, version: number) => {
+      setCaptionSaveStates((prev) => ({ ...prev, [fileName]: 'saving' }));
+      void updateDatasetCaption(datasetId, fileName, value)
+        .then(() => {
+          if (captionVersions.current.get(fileName) !== version) return;
+          setCaptionSaveStates((prev) => ({ ...prev, [fileName]: 'saved' }));
+        })
+        .catch((error) => {
+          if (captionVersions.current.get(fileName) !== version) return;
+          console.error('Failed to save caption:', error);
+          setCaptionSaveStates((prev) => ({ ...prev, [fileName]: 'error' }));
+          toast.error('Caption was not saved', {
+            description: `${fileName}: check the Studio API, then retry.`,
+          });
+        });
+    },
+    [datasetId],
+  );
+
   const handleCaptionChange = React.useCallback(
     (fileName: string, value: string) => {
       setCaptions((prev) => ({ ...prev, [fileName]: value }));
+      setCaptionSaveStates((prev) => ({ ...prev, [fileName]: 'idle' }));
       const pending = pendingSaves.current.get(fileName);
       if (pending) clearTimeout(pending.timer);
-      const save = () => {
-        updateDatasetCaption(datasetId, fileName, value).catch((err) =>
-          console.error('Failed to save caption:', err),
-        );
-      };
+      const version = (captionVersions.current.get(fileName) ?? 0) + 1;
+      captionVersions.current.set(fileName, version);
+      const save = () => persistCaption(fileName, value, version);
       const timer = setTimeout(() => {
         pendingSaves.current.delete(fileName);
         save();
       }, 500);
       pendingSaves.current.set(fileName, { timer, save });
     },
-    [datasetId],
+    [persistCaption],
+  );
+
+  const handleCaptionRetry = React.useCallback(
+    (fileName: string, value: string) => {
+      const version = (captionVersions.current.get(fileName) ?? 0) + 1;
+      captionVersions.current.set(fileName, version);
+      persistCaption(fileName, value, version);
+    },
+    [persistCaption],
   );
 
   function handleScroll() {
@@ -189,8 +273,16 @@ export default function DatasetSidebar({
 
   return (
     <aside
-      className="fixed bottom-0 right-0 top-[var(--header-height)] z-50 flex max-h-[calc(100vh-var(--header-height))] min-w-[320px] shrink-0 flex-col border-l border-border bg-card"
-      style={{ width, maxWidth: SIDEBAR_MAX_WIDTH }}
+      ref={drawerRef}
+      tabIndex={-1}
+      role="dialog"
+      aria-label={`${dataset.name} dataset details`}
+      aria-modal={isMobile || undefined}
+      className="fixed bottom-0 right-0 top-[var(--header-height)] z-50 flex max-h-[calc(100dvh-var(--header-height))] min-w-0 shrink-0 flex-col border-l border-border bg-card md:min-w-[320px]"
+      style={{
+        width: isMobile ? '100%' : width,
+        maxWidth: isMobile ? 'none' : SIDEBAR_MAX_WIDTH,
+      }}
     >
       <div className="flex shrink-0 items-center justify-between border-b border-border px-5 py-4">
         <h2 className="m-0 min-w-0 truncate text-base font-semibold text-foreground">
@@ -203,7 +295,7 @@ export default function DatasetSidebar({
             onClick={onClose}
             title="Close"
             aria-label="Close"
-            className="flex items-center justify-center rounded-lg p-1.5 text-muted-foreground transition-colors hover:bg-accent hover:text-foreground"
+            className="flex size-11 items-center justify-center rounded-lg text-muted-foreground transition-colors hover:bg-accent hover:text-foreground"
           >
             <X className="h-[18px] w-[18px]" />
           </button>
@@ -231,7 +323,9 @@ export default function DatasetSidebar({
                   mediaUrl={getDatasetMediaUrl(dataset.id, fileName)}
                   caption={captions[fileName] ?? ''}
                   thumbLoaded={!!thumbLoaded[fileName]}
+                  saveState={captionSaveStates[fileName] ?? 'idle'}
                   onCaptionChange={handleCaptionChange}
+                  onCaptionRetry={handleCaptionRetry}
                   onThumbLoaded={markThumbLoaded}
                 />
               ))}
@@ -240,14 +334,14 @@ export default function DatasetSidebar({
         </div>
       </div>
 
-      <div
+      {!isMobile && <div
         role="presentation"
         onMouseDown={onMouseDown}
         className={cn(
           'absolute bottom-0 left-0 top-0 z-[1] w-1.5 cursor-col-resize hover:bg-accent-blue/25',
           isDragging && 'bg-accent-blue/25',
         )}
-      />
+      />}
     </aside>
   );
 }

apps/fastvideo_studio/src/components/datasets/DownloadCaptions.tsx

@@ -6,7 +6,7 @@ import { Button } from '@/components/ui/button';
 import { downloadBlob } from '@/lib/utils';
 
 const MENU_ITEM =
-  'block w-full cursor-pointer px-4 py-2 text-left text-sm font-medium text-foreground transition-colors hover:bg-muted disabled:cursor-not-allowed disabled:opacity-50';
+  'block min-h-11 w-full cursor-pointer px-4 py-2 text-left text-sm font-medium text-foreground transition-colors hover:bg-muted disabled:cursor-not-allowed disabled:opacity-50';
 
 export default function DownloadCaptions({
   fileNames,

apps/fastvideo_studio/src/components/jobs/CreateJobButton.test.tsx

@@ -0,0 +1,53 @@
+import { render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { describe, expect, it, vi } from 'vitest';
+
+import CreateJobButton from './CreateJobButton';
+
+vi.mock('./CreateJobModal', () => ({
+  default: ({
+    isOpen,
+    workloadType,
+  }: {
+    isOpen: boolean;
+    workloadType: string;
+  }) =>
+    isOpen ? (
+      <div role="dialog" data-workload-type={workloadType}>
+        Create job form
+      </div>
+    ) : null,
+}));
+
+describe('CreateJobButton', () => {
+  it('opens the workload menu on click and selects an item', async () => {
+    const user = userEvent.setup();
+    render(<CreateJobButton jobType="inference" />);
+
+    await user.click(screen.getByRole('button', { name: 'Create Job' }));
+    await user.click(screen.getByRole('menuitem', { name: /I2V/i }));
+
+    expect(screen.getByRole('dialog')).toHaveAttribute(
+      'data-workload-type',
+      'i2v',
+    );
+  });
+
+  it('opens and operates the workload menu from the keyboard', async () => {
+    const user = userEvent.setup();
+    render(<CreateJobButton jobType="inference" />);
+
+    const trigger = screen.getByRole('button', { name: 'Create Job' });
+    trigger.focus();
+    await user.keyboard('{Enter}');
+
+    const firstItem = await screen.findByRole('menuitem', { name: /T2V/i });
+    expect(firstItem).toHaveFocus();
+    await user.keyboard('{Enter}');
+
+    expect(screen.getByRole('dialog')).toHaveAttribute(
+      'data-workload-type',
+      't2v',
+    );
+  });
+});

apps/fastvideo_studio/src/components/jobs/CreateJobButton.tsx

@@ -2,6 +2,7 @@
 
 import * as React from 'react';
 import { ChevronDown } from 'lucide-react';
+import * as DropdownMenu from '@radix-ui/react-dropdown-menu';
 
 import CreateJobModal from '@/components/jobs/CreateJobModal';
 import { Button } from '@/components/ui/button';
@@ -33,31 +34,35 @@ export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
 
   return (
     <>
-      <div className="group relative inline-block">
-        <Button type="button" className="gap-1.5">
-          Create Job
-          <ChevronDown className="size-3.5 opacity-85" aria-hidden />
-        </Button>
-        <div
-          role="menu"
-          className="invisible absolute right-0 top-full z-[200] mt-1 min-w-full -translate-y-1 rounded-lg border border-border bg-popover py-1 opacity-0 shadow-lg transition-all duration-150 group-hover:visible group-hover:translate-y-0 group-hover:opacity-100"
-        >
-          {options.map((opt) => (
-            <button
-              key={opt.type}
-              type="button"
-              role="menuitem"
-              onClick={() => openModal(opt.type)}
-              className="block w-full whitespace-nowrap px-4 py-2 text-left text-sm font-medium text-popover-foreground transition-colors hover:bg-secondary"
-            >
-              {opt.label}
-              <span className="mt-0.5 block text-xs font-normal text-muted-foreground">
-                {opt.desc}
-              </span>
-            </button>
-          ))}
-        </div>
-      </div>
+      <DropdownMenu.Root>
+        <DropdownMenu.Trigger asChild>
+          <Button type="button" className="gap-1.5">
+            Create Job
+            <ChevronDown className="size-3.5 opacity-85" aria-hidden />
+          </Button>
+        </DropdownMenu.Trigger>
+        <DropdownMenu.Portal>
+          <DropdownMenu.Content
+            align="end"
+            sideOffset={4}
+            collisionPadding={8}
+            className="z-[200] min-w-48 overflow-hidden rounded-lg border border-border bg-popover py-1 text-popover-foreground shadow-lg"
+          >
+            {options.map((opt) => (
+              <DropdownMenu.Item
+                key={opt.type}
+                onSelect={() => openModal(opt.type)}
+                className="flex min-h-11 cursor-pointer select-none flex-col justify-center px-4 py-2 text-left text-sm font-medium outline-none data-[highlighted]:bg-secondary"
+              >
+                {opt.label}
+                <span className="mt-0.5 block text-xs font-normal text-muted-foreground">
+                  {opt.desc}
+                </span>
+              </DropdownMenu.Item>
+            ))}
+          </DropdownMenu.Content>
+        </DropdownMenu.Portal>
+      </DropdownMenu.Root>
       <CreateJobModal
         isOpen={modalOpen}
         onClose={() => setModalOpen(false)}

apps/fastvideo_studio/src/components/jobs/CreateJobModal.test.tsx

@@ -50,6 +50,57 @@ function renderModal(
 }
 
 describe('CreateJobModal', () => {
+  it('shows a model loading error instead of an empty model list', async () => {
+    vi.spyOn(console, 'error').mockImplementation(() => {});
+    vi.mocked(getModels).mockRejectedValueOnce(new Error('network down'));
+
+    renderModal();
+
+    expect(
+      await screen.findByText(/Models could not be loaded/),
+    ).toBeInTheDocument();
+    expect(screen.getByLabelText('Model')).toHaveAttribute(
+      'aria-invalid',
+      'true',
+    );
+  });
+
+  it('keeps the form open and reports job creation failures', async () => {
+    vi.spyOn(console, 'error').mockImplementation(() => {});
+    vi.mocked(createJob).mockRejectedValueOnce(new Error('API rejected job'));
+    const user = userEvent.setup();
+    const { onClose, onSuccess } = renderModal();
+
+    await screen.findByRole('option', { name: 'Wan T2V (wan/t2v-1.3b)' });
+    await user.type(screen.getByLabelText('Prompt'), 'a careful test prompt');
+    await user.click(screen.getByRole('button', { name: 'Create Job' }));
+
+    expect(
+      await screen.findByText(/API rejected job.*then try again/),
+    ).toBeInTheDocument();
+    expect(onSuccess).not.toHaveBeenCalled();
+    expect(onClose).not.toHaveBeenCalled();
+  });
+
+  it('reports image upload failures next to the file input', async () => {
+    vi.spyOn(console, 'error').mockImplementation(() => {});
+    vi.mocked(uploadImage).mockRejectedValueOnce(new Error('Upload failed'));
+    const user = userEvent.setup();
+    renderModal({ workloadType: 'i2v' });
+
+    await screen.findByRole('option', { name: 'Wan T2V (wan/t2v-1.3b)' });
+    const input = screen.getByLabelText('Image');
+    await user.upload(
+      input,
+      new File(['image'], 'input.png', { type: 'image/png' }),
+    );
+
+    expect(
+      await screen.findByText(/Upload failed.*Choose the image again/),
+    ).toBeInTheDocument();
+    expect(input).toHaveAttribute('aria-invalid', 'true');
+  });
+
   it('renders the form fields for an inference job', async () => {
     renderModal();
 

apps/fastvideo_studio/src/components/jobs/CreateJobModal.tsx

@@ -103,6 +103,17 @@ export default function CreateJobModal({
   const [fakeScoreModelPath, setFakeScoreModelPath] = React.useState('');
   const [isSubmitting, setIsSubmitting] = React.useState(false);
   const [isLoadingModels, setIsLoadingModels] = React.useState(false);
+  const [isLoadingDatasets, setIsLoadingDatasets] = React.useState(false);
+  const [modelLoadError, setModelLoadError] = React.useState<string | null>(
+    null,
+  );
+  const [datasetLoadError, setDatasetLoadError] = React.useState<string | null>(
+    null,
+  );
+  const [imageUploadError, setImageUploadError] = React.useState<string | null>(
+    null,
+  );
+  const [submitError, setSubmitError] = React.useState<string | null>(null);
   const imageInputRef = React.useRef<HTMLInputElement>(null);
 
   // Seed field values from the persisted default options each time the modal
@@ -144,6 +155,10 @@ export default function CreateJobModal({
     setImageFileName('');
     setSelectedDatasetId('');
     setSelectedValidationDatasetId('');
+    setModelLoadError(null);
+    setDatasetLoadError(null);
+    setImageUploadError(null);
+    setSubmitError(null);
     if (workloadType === 'dmd_t2v') {
       setDmdUseVsa(false);
       setDmdVsaSparsity(0.8);
@@ -162,6 +177,7 @@ export default function CreateJobModal({
     // can't overwrite the current workload's model list/selection.
     let stale = false;
     setIsLoadingModels(true);
+    setModelLoadError(null);
     getModels(inferenceWorkload)
       .then((list) => {
         if (stale) return;
@@ -180,7 +196,13 @@ export default function CreateJobModal({
         }
       })
       .catch((e) => {
-        if (!stale) console.error('Failed to load models:', e);
+        if (stale) return;
+        console.error('Failed to load models:', e);
+        setModels([]);
+        setModelId('');
+        setModelLoadError(
+          'Models could not be loaded. Check the Studio API and reopen this form to try again.',
+        );
       })
       .finally(() => {
         if (!stale) setIsLoadingModels(false);
@@ -193,11 +215,22 @@ export default function CreateJobModal({
   // Training jobs need a dataset; load the ready datasets when relevant.
   React.useEffect(() => {
     if (isOpen && !isInference) {
+      setIsLoadingDatasets(true);
+      setDatasetLoadError(null);
       getDatasets()
         .then(setReadyDatasets)
-        .catch(() => setReadyDatasets([]));
+        .catch((error) => {
+          console.error('Failed to load datasets:', error);
+          setReadyDatasets([]);
+          setDatasetLoadError(
+            'Datasets could not be loaded. Check the Studio API and reopen this form to try again.',
+          );
+        })
+        .finally(() => setIsLoadingDatasets(false));
     } else {
       setReadyDatasets([]);
+      setIsLoadingDatasets(false);
+      setDatasetLoadError(null);
     }
   }, [isOpen, isInference]);
 
@@ -206,16 +239,24 @@ export default function CreateJobModal({
     if (!file) {
       setImagePath('');
       setImageFileName('');
+      setImageUploadError(null);
       return;
     }
     setIsUploadingImage(true);
     setImageFileName(file.name);
+    setImageUploadError(null);
     try {
       const { path } = await uploadImage(file);
       setImagePath(path);
-    } catch {
+    } catch (error) {
+      console.error('Failed to upload image:', error);
       setImagePath('');
       setImageFileName('');
+      setImageUploadError(
+        error instanceof Error
+          ? `${error.message}. Choose the image again to retry.`
+          : 'The image could not be uploaded. Choose it again to retry.',
+      );
     } finally {
       setIsUploadingImage(false);
     }
@@ -224,6 +265,7 @@ export default function CreateJobModal({
   function clearImage() {
     setImagePath('');
     setImageFileName('');
+    setImageUploadError(null);
     if (imageInputRef.current) imageInputRef.current.value = '';
   }
 
@@ -239,6 +281,7 @@ export default function CreateJobModal({
       workloadType === 'lora_t2v' ? 'lora' : jobType
     ) as JobType;
     setIsSubmitting(true);
+    setSubmitError(null);
     try {
       const payload: CreateJobRequest = {
         model_id: modelId,
@@ -296,6 +339,11 @@ export default function CreateJobModal({
       onClose();
     } catch (err) {
       console.error('Failed to create job:', err);
+      setSubmitError(
+        err instanceof Error
+          ? `${err.message}. Check the form and Studio API, then try again.`
+          : 'The job could not be created. Check the form and Studio API, then try again.',
+      );
     } finally {
       setIsSubmitting(false);
     }
@@ -343,7 +391,11 @@ export default function CreateJobModal({
               value={modelId}
               onChange={(e) => setModelId(e.target.value)}
               required
-              disabled={isSubmitting || isLoadingModels}
+              aria-describedby={
+                modelLoadError ? 'modal-model-error' : undefined
+              }
+              aria-invalid={modelLoadError ? true : undefined}
+              disabled={isSubmitting || isLoadingModels || !!modelLoadError}
             >
               <option value="" disabled>
                 {isLoadingModels
@@ -358,6 +410,15 @@ export default function CreateJobModal({
                 </option>
               ))}
             </NativeSelect>
+            {modelLoadError && (
+              <p
+                id="modal-model-error"
+                role="alert"
+                className="text-sm text-destructive"
+              >
+                {modelLoadError}
+              </p>
+            )}
           </FieldRow>
 
           {isInference && workloadType === 'i2v' && (
@@ -369,6 +430,10 @@ export default function CreateJobModal({
                 accept=".png,.jpg,.jpeg,.webp,.bmp"
                 onChange={handleImageChange}
                 disabled={isSubmitting || isUploadingImage}
+                aria-describedby={
+                  imageUploadError ? 'modal-image-error' : undefined
+                }
+                aria-invalid={imageUploadError ? true : undefined}
                 required
                 className="h-auto py-2 file:mr-3 file:cursor-pointer file:rounded-md file:border-0 file:bg-secondary file:px-2 file:py-1 file:text-sm file:text-secondary-foreground"
               />
@@ -385,6 +450,15 @@ export default function CreateJobModal({
                   </button>
                 </span>
               )}
+              {imageUploadError && (
+                <p
+                  id="modal-image-error"
+                  role="alert"
+                  className="text-sm text-destructive"
+                >
+                  {imageUploadError}
+                </p>
+              )}
             </FieldRow>
           )}
 
@@ -432,12 +506,22 @@ export default function CreateJobModal({
                     id="modal-dataset"
                     value={selectedDatasetId}
                     onChange={(e) => setSelectedDatasetId(e.target.value)}
-                    disabled={isSubmitting}
+                    aria-describedby={
+                      datasetLoadError ? 'modal-dataset-error' : undefined
+                    }
+                    aria-invalid={datasetLoadError ? true : undefined}
+                    disabled={
+                      isSubmitting || isLoadingDatasets || !!datasetLoadError
+                    }
                   >
                     <option value="" disabled>
-                      {readyDatasets.length === 0
-                        ? 'No datasets (add in Datasets tab)'
-                        : 'Select a dataset…'}
+                      {isLoadingDatasets
+                        ? 'Loading datasets…'
+                        : datasetLoadError
+                          ? 'Datasets unavailable'
+                          : readyDatasets.length === 0
+                            ? 'No datasets (add in Datasets tab)'
+                            : 'Select a dataset…'}
                     </option>
                     {readyDatasets.map((d) => (
                       <option key={d.id} value={d.id}>
@@ -445,6 +529,15 @@ export default function CreateJobModal({
                       </option>
                     ))}
                   </NativeSelect>
+                  {datasetLoadError && (
+                    <p
+                      id="modal-dataset-error"
+                      role="alert"
+                      className="text-sm text-destructive"
+                    >
+                      {datasetLoadError}
+                    </p>
+                  )}
                 </FieldRow>
                 <FieldRow
                   htmlFor="modal-validation-dataset"
@@ -457,7 +550,9 @@ export default function CreateJobModal({
                     onChange={(e) =>
                       setSelectedValidationDatasetId(e.target.value)
                     }
-                    disabled={isSubmitting}
+                    disabled={
+                      isSubmitting || isLoadingDatasets || !!datasetLoadError
+                    }
                   >
                     <option value="">None</option>
                     {readyDatasets.map((d) => (
@@ -811,9 +906,22 @@ export default function CreateJobModal({
             </details>
           )}
 
-          <div>
-            <Button type="submit" disabled={isSubmitting}>
-              {isSubmitting ? 'Creating...' : 'Create Job'}
+          <div className="flex flex-col items-start gap-2">
+            {submitError && (
+              <p role="alert" className="text-sm text-destructive">
+                {submitError}
+              </p>
+            )}
+            <Button
+              type="submit"
+              disabled={
+                isSubmitting ||
+                isUploadingImage ||
+                !!modelLoadError ||
+                !!datasetLoadError
+              }
+            >
+              {isSubmitting ? 'Creating…' : 'Create Job'}
             </Button>
           </div>
         </form>

apps/fastvideo_studio/src/components/jobs/JobCard.test.tsx

@@ -20,6 +20,14 @@ vi.mock('@/lib/api', () => ({
   downloadJobVideo: vi.fn(),
 }));
 
+vi.mock('@/lib/utils', async (importOriginal) => {
+  const actual = await importOriginal<typeof import('@/lib/utils')>();
+  return {
+    ...actual,
+    downloadBlob: vi.fn(),
+  };
+});
+
 const makeJob = (overrides: Partial<Job> = {}): Job =>
   makeBaseJob({
     model_id: 'Wan2.1-T2V',
@@ -46,6 +54,16 @@ beforeEach(() => {
 });
 
 describe('JobCard', () => {
+  it('keeps selection and job action buttons as semantic siblings', () => {
+    render(<JobCard job={makeJob()} />);
+
+    const selectButton = screen.getByRole('button', { pressed: false });
+    const deleteButton = screen.getByRole('button', { name: 'Delete' });
+
+    expect(selectButton).toHaveTextContent('Wan2.1-T2V');
+    expect(selectButton).not.toContainElement(deleteButton);
+  });
+
   it('renders the model, prompt, status and inference meta', () => {
     render(<JobCard job={makeJob()} />);
     expect(screen.getByText('Wan2.1-T2V')).toBeInTheDocument();

apps/fastvideo_studio/src/components/jobs/JobCard.tsx

@@ -119,18 +119,10 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
     }
   }
 
-  function handleSelectJob(e: React.MouseEvent | React.KeyboardEvent) {
-    if ((e.target as HTMLElement).closest('button')) return;
+  function handleSelectJob() {
     setActiveJobId(isSelected ? null : job.id);
   }
 
-  function handleKeyDown(e: React.KeyboardEvent) {
-    if (e.key === 'Enter' || e.key === ' ') {
-      e.preventDefault();
-      handleSelectJob(e);
-    }
-  }
-
   async function handleDownloadVideo(e: React.MouseEvent) {
     e.preventDefault();
     e.stopPropagation();
@@ -148,11 +140,7 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
   }
 
   return (
-    <div
-      role="button"
-      tabIndex={0}
-      onClick={handleSelectJob}
-      onKeyDown={handleKeyDown}
+    <article
       className={cn(
         'mb-3 flex cursor-pointer flex-col gap-2.5 rounded-lg border bg-background p-4 transition-colors last:mb-0',
         isSelected
@@ -160,35 +148,42 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
           : 'border-border hover:border-muted-foreground/40',
       )}
     >
-      <div className="flex flex-wrap items-center justify-between gap-2">
-        <span className="text-[0.95rem] font-semibold text-foreground">
-          {job.model_id}
-        </span>
-        <Badge variant={BADGE_VARIANTS[job.status] ?? 'secondary'}>
-          {job.status}
-        </Badge>
-      </div>
-      <p className="max-w-full overflow-hidden text-ellipsis whitespace-nowrap text-sm text-muted-foreground">
-        {job.prompt}
-      </p>
-      <div className="flex flex-wrap items-center gap-4 text-xs text-muted-foreground">
-        {job.job_type === 'inference' ? (
-          <>
-            <span>{job.num_frames} frames</span>
-            <span>
-              {job.height}×{job.width}
-            </span>
-          </>
-        ) : (
-          <span>{job.workload_type?.replace(/_/g, ' ') ?? job.job_type}</span>
-        )}
-        {elapsedTime && (
-          <span className="inline-flex items-center gap-1">
-            <Timer className="size-3.5" aria-hidden />
-            {elapsedTime}
+      <button
+        type="button"
+        aria-pressed={isSelected}
+        onClick={handleSelectJob}
+        className="flex w-full flex-col gap-2.5 rounded-md text-left"
+      >
+        <span className="flex flex-wrap items-center justify-between gap-2">
+          <span className="text-[0.95rem] font-semibold text-foreground">
+            {job.model_id}
           </span>
-        )}
-      </div>
+          <Badge variant={BADGE_VARIANTS[job.status] ?? 'secondary'}>
+            {job.status}
+          </Badge>
+        </span>
+        <span className="max-w-full overflow-hidden text-ellipsis whitespace-nowrap text-sm text-muted-foreground">
+          {job.prompt}
+        </span>
+        <span className="flex flex-wrap items-center gap-4 text-xs text-muted-foreground">
+          {job.job_type === 'inference' ? (
+            <>
+              <span>{job.num_frames} frames</span>
+              <span>
+                {job.height}×{job.width}
+              </span>
+            </>
+          ) : (
+            <span>{job.workload_type?.replace(/_/g, ' ') ?? job.job_type}</span>
+          )}
+          {elapsedTime && (
+            <span className="inline-flex items-center gap-1">
+              <Timer className="size-3.5" aria-hidden />
+              {elapsedTime}
+            </span>
+          )}
+        </span>
+      </button>
       <div className="flex flex-wrap items-center gap-1.5">
         {job.status === 'running' ? (
           <Button
@@ -240,6 +235,6 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
           Delete
         </Button>
       </div>
-    </div>
+    </article>
   );
 }

apps/fastvideo_studio/src/components/jobs/JobDetailsSidebar.test.tsx

@@ -19,6 +19,32 @@ const makeJob = (overrides: Partial<Job> = {}): Job =>
   });
 
 describe('JobDetailsSidebar', () => {
+  it('fills the mobile viewport without reserving main-content width', async () => {
+    vi.mocked(getJobLogs).mockResolvedValue({
+      lines: [],
+      total: 0,
+      progress: 0,
+      progress_msg: '',
+      phase: '',
+    });
+    const onWidthChange = vi.fn();
+
+    render(
+      <JobDetailsSidebar
+        job={makeJob({ status: 'completed' })}
+        isMobile
+        onClose={vi.fn()}
+        onWidthChange={onWidthChange}
+      />,
+    );
+
+    const drawer = screen.getByRole('dialog', { name: 'Job details' });
+    expect(drawer).toHaveStyle({ width: '100%', maxWidth: 'none' });
+    expect(drawer).toHaveAttribute('aria-modal', 'true');
+    expect(drawer).toHaveFocus();
+    expect(onWidthChange).toHaveBeenCalledWith(0);
+  });
+
   it('renders log lines streamed from the job log poll', async () => {
     vi.mocked(getJobLogs).mockResolvedValue({
       lines: ['boot sequence started', 'loading model weights'],

apps/fastvideo_studio/src/components/jobs/JobDetailsSidebar.tsx

@@ -4,6 +4,7 @@ import * as React from 'react';
 import { X } from 'lucide-react';
 
 import { Button } from '@/components/ui/button';
+import { useDrawerFocus } from '@/hooks/useDrawerFocus';
 import { useResizable } from '@/hooks/useResizable';
 import { downloadJobLog, getJobLogs } from '@/lib/api';
 import type { Job } from '@/lib/types';
@@ -15,13 +16,16 @@ const POLL_INTERVAL_MS = 2000;
 
 export default function JobDetailsSidebar({
   job,
+  isMobile = false,
   onClose,
   onWidthChange,
 }: {
   job: Job;
+  isMobile?: boolean;
   onClose: () => void;
   onWidthChange?: (w: number) => void;
 }) {
+  const drawerRef = useDrawerFocus<HTMLElement>(isMobile);
   const [width, setWidth] = React.useState(360);
   const [isDragging, setIsDragging] = React.useState(false);
   const [isLoading, setIsLoading] = React.useState(false);
@@ -53,8 +57,8 @@ export default function JobDetailsSidebar({
   });
 
   React.useEffect(() => {
-    onWidthChange?.(width);
-  }, [width, onWidthChange]);
+    onWidthChange?.(isMobile ? 0 : width);
+  }, [isMobile, width, onWidthChange]);
 
   // Auto-scroll the console to the bottom whenever new lines land. Runs after
   // commit so scrollHeight reflects the freshly-rendered output.
@@ -137,8 +141,16 @@ export default function JobDetailsSidebar({
 
   return (
     <aside
-      className="fixed bottom-0 right-0 top-[var(--header-height)] z-50 flex max-h-[calc(100vh-var(--header-height))] min-w-[280px] shrink-0 flex-col border-l border-border bg-card"
-      style={{ width, maxWidth: SIDEBAR_MAX_WIDTH }}
+      ref={drawerRef}
+      tabIndex={-1}
+      role="dialog"
+      aria-label="Job details"
+      aria-modal={isMobile || undefined}
+      className="fixed bottom-0 right-0 top-[var(--header-height)] z-50 flex max-h-[calc(100dvh-var(--header-height))] min-w-0 shrink-0 flex-col border-l border-border bg-card md:min-w-[280px]"
+      style={{
+        width: isMobile ? '100%' : width,
+        maxWidth: isMobile ? 'none' : SIDEBAR_MAX_WIDTH,
+      }}
     >
       <div className="flex items-center justify-between border-b border-border px-5 py-4">
         <h2 className="m-0 text-base font-semibold text-foreground">
@@ -195,14 +207,14 @@ export default function JobDetailsSidebar({
         </pre>
       </div>
 
-      <div
+      {!isMobile && <div
         role="presentation"
         onMouseDown={onMouseDown}
         className={cn(
           'absolute bottom-0 left-0 top-0 z-[1] w-1.5 cursor-col-resize hover:bg-accent-blue/25',
           isDragging && 'bg-accent-blue/25',
         )}
-      />
+      />}
     </aside>
   );
 }

apps/fastvideo_studio/src/components/jobs/JobQueue.test.tsx

@@ -1,4 +1,4 @@
-import { act, render, screen, waitFor } from '@testing-library/react';
+import { act, fireEvent, render, screen, waitFor } from '@testing-library/react';
 import { beforeEach, describe, expect, it, vi } from 'vitest';
 
 import JobQueue from '@/components/jobs/JobQueue';
@@ -37,6 +37,46 @@ beforeEach(() => {
 });
 
 describe('JobQueue', () => {
+  it('shows a loading placeholder before the initial request settles', async () => {
+    let resolveJobs: (jobs: Job[]) => void = () => {};
+    vi.mocked(getJobsList).mockReturnValue(
+      new Promise<Job[]>((resolve) => {
+        resolveJobs = resolve;
+      }),
+    );
+
+    render(<JobQueue jobType="inference" />);
+
+    expect(screen.getByLabelText('Loading jobs')).toBeInTheDocument();
+    expect(
+      screen.queryByText('No inference jobs yet. Create one above.'),
+    ).not.toBeInTheDocument();
+
+    act(() => resolveJobs([]));
+    expect(
+      await screen.findByText('No inference jobs yet. Create one above.'),
+    ).toBeInTheDocument();
+  });
+
+  it('shows request failures separately from an empty queue and retries', async () => {
+    vi.spyOn(console, 'error').mockImplementation(() => {});
+    vi.mocked(getJobsList).mockRejectedValueOnce(new Error('network down'));
+    render(<JobQueue jobType="inference" />);
+
+    expect(
+      await screen.findByText(/Could not load jobs from the Studio API/),
+    ).toBeInTheDocument();
+    expect(
+      screen.queryByText('No inference jobs yet. Create one above.'),
+    ).not.toBeInTheDocument();
+
+    vi.mocked(getJobsList).mockResolvedValueOnce([]);
+    fireEvent.click(screen.getByRole('button', { name: 'Try Again' }));
+    expect(
+      await screen.findByText('No inference jobs yet. Create one above.'),
+    ).toBeInTheDocument();
+  });
+
   it('shows an empty placeholder and fetches for the single job type', async () => {
     render(<JobQueue jobType="inference" />);
     expect(

apps/fastvideo_studio/src/components/jobs/JobQueue.tsx

@@ -1,8 +1,10 @@
 'use client';
 
 import * as React from 'react';
+import { AlertTriangle } from 'lucide-react';
 
 import JobCard from '@/components/jobs/JobCard';
+import { Button } from '@/components/ui/button';
 import { useStore } from '@/hooks/useStore';
 import { getJobsList } from '@/lib/api';
 import type { Job, JobType } from '@/lib/types';
@@ -32,6 +34,8 @@ function jobsShallowEqual(a: Job | null, b: Job | null): boolean {
 
 export default function JobQueue({ jobType, jobTypesForList }: JobQueueProps) {
   const [jobs, setJobs] = React.useState<Job[]>([]);
+  const [isInitialLoading, setIsInitialLoading] = React.useState(true);
+  const [error, setError] = React.useState<string | null>(null);
   const { nonce } = useStore(jobsRefreshStore);
   const { activeJobId } = useStore(activeJobStore);
 
@@ -71,11 +75,22 @@ export default function JobQueue({ jobType, jobTypesForList }: JobQueueProps) {
               new Date(a.created_at ?? 0).getTime(),
           );
       }
-      if (seq === fetchSeq.current) setJobs(next);
+      if (seq === fetchSeq.current) {
+        setJobs(next);
+        setError(null);
+      }
     } catch (e) {
       console.error('Failed to fetch jobs:', e);
+      if (seq === fetchSeq.current) {
+        setError(
+          'Could not load jobs from the Studio API. Check the server and try again.',
+        );
+      }
     } finally {
-      if (seq === fetchSeq.current) inFlight.current = false;
+      if (seq === fetchSeq.current) {
+        inFlight.current = false;
+        setIsInitialLoading(false);
+      }
     }
   }, [typesKey]);
 
@@ -122,20 +137,59 @@ export default function JobQueue({ jobType, jobTypesForList }: JobQueueProps) {
   const multiType = typesToFetch.length > 1;
 
   return (
-    <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">
       <section className="p-6">
-        <div>
-          {jobs.length === 0 ? (
+        <div aria-busy={isInitialLoading}>
+          {isInitialLoading ? (
+            <div
+              aria-label="Loading jobs"
+              className="flex flex-col gap-3 py-2"
+            >
+              {[0, 1, 2].map((item) => (
+                <div
+                  key={item}
+                  className="h-32 animate-pulse rounded-lg border border-border bg-muted/50"
+                />
+              ))}
+            </div>
+          ) : error && jobs.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={fetchJobs}>
+                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"
+                >
+                  Job updates are temporarily unavailable. Showing the most
+                  recent results.
+                </p>
+              )}
+              {jobs.length === 0 ? (
             <p className="py-8 text-center text-muted-foreground">
               No {multiType ? 'jobs' : `${jobType} jobs`} yet. Create one above.
             </p>
-          ) : (
-            jobs.map((job) => (
-              <JobCard key={job.id} job={job} onJobUpdated={fetchJobs} />
-            ))
+              ) : (
+                jobs.map((job) => (
+                  <JobCard key={job.id} job={job} onJobUpdated={fetchJobs} />
+                ))
+              )}
+            </>
           )}
         </div>
       </section>
-    </main>
+    </div>
   );
 }

apps/fastvideo_studio/src/components/shell/AppShell.tsx

@@ -9,6 +9,7 @@ import { HeaderActionsProvider } from '@/components/shell/HeaderActionsContext';
 import PrimarySidebar from '@/components/shell/PrimarySidebar';
 import JobDetailsSidebar from '@/components/jobs/JobDetailsSidebar';
 import { Toaster } from '@/components/ui/sonner';
+import { useMediaQuery } from '@/hooks/useMediaQuery';
 import { useStore } from '@/hooks/useStore';
 import {
   activeDatasetStore,
@@ -23,46 +24,80 @@ export function AppShell({ children }: { children: React.ReactNode }) {
   const pathname = usePathname();
   const { activeJob } = useStore(activeJobStore);
   const { activeDataset } = useStore(activeDatasetStore);
+  const isMobile = useMediaQuery('(max-width: 767px)');
 
   const [primaryWidth, setPrimaryWidth] = React.useState(220);
   const [secondaryWidth, setSecondaryWidth] = React.useState(0);
+  const [primaryOpen, setPrimaryOpen] = React.useState(false);
 
   const jobSidebarOpen = JOB_ROUTES.includes(pathname) && activeJob != null;
   const datasetSidebarOpen =
     pathname === '/datasets' && activeDataset != null;
   const secondaryOpen = jobSidebarOpen || datasetSidebarOpen;
+  // Mobile detail drawers claim aria-modal, so everything behind them must
+  // actually be inert — the platform enforces what the ARIA claims.
+  const drawerModal = isMobile && secondaryOpen;
 
   React.useEffect(() => {
     initDefaultOptions();
   }, []);
 
+  React.useEffect(() => {
+    setPrimaryOpen(false);
+  }, [pathname]);
+
   React.useEffect(() => {
     function handleKeyDown(e: KeyboardEvent) {
-      if (e.key === 'Escape' && !document.querySelector('[data-modal]')) {
-        if (activeJobStore.get().activeJob) setActiveJobId(null);
-        if (activeDatasetStore.get().activeDataset) setActiveDatasetId(null);
+      if (e.key !== 'Escape' || document.querySelector('[data-modal]')) return;
+      if (primaryOpen) {
+        setPrimaryOpen(false);
+        return;
       }
+      if (activeJobStore.get().activeJob) setActiveJobId(null);
+      if (activeDatasetStore.get().activeDataset) setActiveDatasetId(null);
     }
     document.addEventListener('keydown', handleKeyDown);
     return () => document.removeEventListener('keydown', handleKeyDown);
-  }, []);
+  }, [primaryOpen]);
 
   return (
     <HeaderActionsProvider>
-      <Header />
+      <div
+        style={{ display: 'contents' }}
+        inert={drawerModal ? true : undefined}
+      >
+        <Header
+          navigationOpen={primaryOpen}
+          onNavigationToggle={() => setPrimaryOpen((open) => !open)}
+        />
+      </div>
       <div
         className="flex overflow-hidden"
         style={{
           marginTop: 'var(--header-height)',
-          height: 'calc(100vh - var(--header-height))',
+          height: 'calc(100dvh - var(--header-height))',
         }}
       >
-        <PrimarySidebar onWidthChange={setPrimaryWidth} />
+        <PrimarySidebar
+          isMobile={isMobile}
+          mobileOpen={primaryOpen}
+          onMobileClose={() => setPrimaryOpen(false)}
+          onWidthChange={setPrimaryWidth}
+        />
+        {primaryOpen && (
+          <button
+            type="button"
+            aria-label="Close navigation"
+            onClick={() => setPrimaryOpen(false)}
+            className="fixed inset-x-0 bottom-0 top-[var(--header-height)] z-40 bg-black/55 md:hidden"
+          />
+        )}
         <main
           className="flex min-w-0 flex-1 flex-col overflow-auto"
+          inert={drawerModal ? true : undefined}
           style={{
-            marginLeft: primaryWidth,
-            marginRight: secondaryOpen ? secondaryWidth : 0,
+            marginLeft: isMobile ? 0 : primaryWidth,
+            marginRight: isMobile || !secondaryOpen ? 0 : secondaryWidth,
           }}
         >
           {children}
@@ -70,6 +105,7 @@ export function AppShell({ children }: { children: React.ReactNode }) {
         {jobSidebarOpen && activeJob && (
           <JobDetailsSidebar
             job={activeJob}
+            isMobile={isMobile}
             onClose={() => setActiveJobId(null)}
             onWidthChange={setSecondaryWidth}
           />
@@ -77,6 +113,7 @@ export function AppShell({ children }: { children: React.ReactNode }) {
         {datasetSidebarOpen && activeDataset && (
           <DatasetSidebar
             dataset={activeDataset}
+            isMobile={isMobile}
             onClose={() => setActiveDatasetId(null)}
             onWidthChange={setSecondaryWidth}
           />

apps/fastvideo_studio/src/components/shell/Header.tsx

@@ -1,8 +1,10 @@
 'use client';
 
+import { Menu, X } from 'lucide-react';
 import { usePathname } from 'next/navigation';
 
 import { useHeaderActions } from '@/components/shell/HeaderActionsContext';
+import { Button } from '@/components/ui/button';
 import { ThemeToggle } from '@/components/ui/theme-toggle';
 
 const TAB_TITLES: Record<string, string> = {
@@ -15,25 +17,47 @@ const TAB_TITLES: Record<string, string> = {
   '/settings': 'Settings',
 };
 
-export default function Header() {
+export default function Header({
+  navigationOpen,
+  onNavigationToggle,
+}: {
+  navigationOpen: boolean;
+  onNavigationToggle: () => void;
+}) {
   const pathname = usePathname();
   const { actions } = useHeaderActions();
   const title = TAB_TITLES[pathname] ?? 'FastVideo';
 
   return (
-    <header className="fixed inset-x-0 top-0 z-[100] flex h-[var(--header-height)] items-center gap-6 border-b border-border bg-background/80 px-6 backdrop-blur">
+    <header className="fixed inset-x-0 top-0 z-[100] flex h-[var(--header-height)] items-center gap-2 border-b border-border bg-background/80 px-2 backdrop-blur sm:px-4 md:gap-6 md:px-6">
+      <Button
+        type="button"
+        variant="outline"
+        size="icon"
+        aria-label={navigationOpen ? 'Close navigation' : 'Open navigation'}
+        aria-controls="primary-navigation"
+        aria-expanded={navigationOpen}
+        onClick={onNavigationToggle}
+        className="shrink-0 md:hidden"
+      >
+        {navigationOpen ? (
+          <X className="size-5" aria-hidden />
+        ) : (
+          <Menu className="size-5" aria-hidden />
+        )}
+      </Button>
       {/* eslint-disable-next-line @next/next/no-img-element */}
       <img
         src="/logo.svg"
         alt="FastVideo Logo"
         width={100}
         height={42}
-        className="block h-[42px] w-[100px]"
+        className="hidden h-[42px] w-[78px] shrink-0 object-contain min-[361px]:block md:w-[100px]"
       />
-      <h1 className="m-0 flex-1 text-xl font-semibold tracking-tight">
+      <h1 className="sr-only m-0 flex-1 text-xl font-semibold tracking-tight md:not-sr-only">
         {title}
       </h1>
-      <div className="flex items-center gap-3">
+      <div className="ml-auto flex min-w-0 items-center gap-2 md:gap-3">
         {actions}
         <ThemeToggle />
       </div>

apps/fastvideo_studio/src/components/shell/PrimarySidebar.tsx

@@ -1,6 +1,7 @@
 'use client';
 
 import * as React from 'react';
+import { X } from 'lucide-react';
 import Link from 'next/link';
 import { usePathname } from 'next/navigation';
 
@@ -19,12 +20,18 @@ const JOB_ROUTES = [
 ] as const;
 
 const TAB_BASE =
-  'block px-5 py-[0.65rem] text-left text-sm text-muted-foreground transition-colors hover:bg-accent/60 hover:text-foreground';
+  'block min-h-11 px-5 py-[0.65rem] text-left text-sm text-muted-foreground transition-colors hover:bg-accent/60 hover:text-foreground';
 const TAB_ACTIVE = 'bg-accent-blue/10 font-medium text-accent-blue';
 
 export default function PrimarySidebar({
+  isMobile,
+  mobileOpen,
+  onMobileClose,
   onWidthChange,
 }: {
+  isMobile: boolean;
+  mobileOpen: boolean;
+  onMobileClose: () => void;
   onWidthChange?: (w: number) => void;
 }) {
   const pathname = usePathname();
@@ -38,8 +45,8 @@ export default function PrimarySidebar({
   const isJobsActive = JOB_ROUTES.some((r) => pathname === r.href);
 
   React.useEffect(() => {
-    onWidthChange?.(layoutWidth);
-  }, [layoutWidth, onWidthChange]);
+    onWidthChange?.(isMobile ? 0 : layoutWidth);
+  }, [isMobile, layoutWidth, onWidthChange]);
 
   React.useEffect(() => {
     if (JOB_ROUTES.some((r) => pathname === r.href)) {
@@ -58,11 +65,37 @@ export default function PrimarySidebar({
 
   return (
     <aside
-      className="fixed bottom-0 left-0 top-[var(--header-height)] z-50 flex max-h-[calc(100vh-var(--header-height))] shrink-0 flex-col border-r border-border bg-card"
-      style={{ width: effectiveWidth }}
+      id="primary-navigation"
+      aria-hidden={isMobile && !mobileOpen}
+      inert={isMobile && !mobileOpen ? true : undefined}
+      className={cn(
+        'fixed bottom-0 left-0 top-[var(--header-height)] z-50 flex max-h-[calc(100dvh-var(--header-height))] shrink-0 flex-col border-r border-border bg-card transition-transform duration-200 md:translate-x-0',
+        mobileOpen ? 'translate-x-0' : '-translate-x-full',
+      )}
+      style={{
+        width: isMobile
+          ? 'min(18rem, calc(100vw - 3rem))'
+          : effectiveWidth,
+      }}
     >
+      {isMobile && (
+        <div className="flex h-14 items-center justify-between border-b border-border px-4">
+          <span className="text-sm font-semibold">Navigation</span>
+          <button
+            type="button"
+            onClick={onMobileClose}
+            aria-label="Close navigation"
+            className="flex size-11 items-center justify-center rounded-lg text-muted-foreground hover:bg-accent hover:text-foreground"
+          >
+            <X className="size-5" aria-hidden />
+          </button>
+        </div>
+      )}
       {!isCollapsed && (
-        <nav className="flex flex-col py-2">
+        <nav
+          aria-label="Primary navigation"
+          className="flex flex-col overflow-y-auto py-2"
+        >
           <div className="flex flex-col">
             <button
               type="button"
@@ -95,6 +128,8 @@ export default function PrimarySidebar({
                   <Link
                     key={route.href}
                     href={route.href}
+                    aria-current={pathname === route.href ? 'page' : undefined}
+                    onClick={onMobileClose}
                     className={cn(
                       TAB_BASE,
                       'px-4 py-2 text-[0.85rem]',
@@ -109,24 +144,32 @@ export default function PrimarySidebar({
           </div>
           <Link
             href="/datasets"
+            aria-current={pathname === '/datasets' ? 'page' : undefined}
+            onClick={onMobileClose}
             className={cn(TAB_BASE, pathname === '/datasets' && TAB_ACTIVE)}
           >
             Datasets
           </Link>
           <Link
             href="/gallery"
+            aria-current={pathname === '/gallery' ? 'page' : undefined}
+            onClick={onMobileClose}
             className={cn(TAB_BASE, pathname === '/gallery' && TAB_ACTIVE)}
           >
             Gallery
           </Link>
           <Link
             href="/gpus"
+            aria-current={pathname === '/gpus' ? 'page' : undefined}
+            onClick={onMobileClose}
             className={cn(TAB_BASE, pathname === '/gpus' && TAB_ACTIVE)}
           >
             GPUs
           </Link>
           <Link
             href="/settings"
+            aria-current={pathname === '/settings' ? 'page' : undefined}
+            onClick={onMobileClose}
             className={cn(TAB_BASE, pathname === '/settings' && TAB_ACTIVE)}
           >
             Settings
@@ -134,7 +177,7 @@ export default function PrimarySidebar({
         </nav>
       )}
 
-      <div
+      {!isMobile && <div
         className={cn(
           'absolute bottom-0 p-2',
           isCollapsed ? '-right-[60px] top-0' : 'right-0',
@@ -145,8 +188,7 @@ export default function PrimarySidebar({
           onClick={() => setIsCollapsed((v) => !v)}
           title={isCollapsed ? 'Expand sidebar' : 'Collapse sidebar'}
           className={cn(
-            'flex items-center justify-center rounded-lg text-muted-foreground transition-colors hover:bg-accent hover:text-foreground',
-            isCollapsed ? 'p-3' : 'p-2',
+            'flex size-11 items-center justify-center rounded-lg text-muted-foreground transition-colors hover:bg-accent hover:text-foreground',
           )}
         >
           <svg
@@ -159,9 +201,9 @@ export default function PrimarySidebar({
             <path d={isCollapsed ? 'M9 18l6-6-6-6' : 'M15 18l-6-6 6-6'} />
           </svg>
         </button>
-      </div>
+      </div>}
 
-      {!isCollapsed && (
+      {!isMobile && !isCollapsed && (
         <div
           role="presentation"
           onMouseDown={onMouseDown}

apps/fastvideo_studio/src/components/system/GpuGrid.test.tsx

@@ -1,4 +1,4 @@
-import { render, screen } from '@testing-library/react';
+import { act, render, screen } from '@testing-library/react';
 import { beforeEach, describe, expect, it, vi } from 'vitest';
 
 import GpuGrid from './GpuGrid';
@@ -73,4 +73,30 @@ describe('GpuGrid', () => {
       await screen.findByText(/Could not reach the API server/),
     ).toBeInTheDocument();
   });
+
+  it('keeps the last snapshot visible and warns when a refresh fails', async () => {
+    vi.useFakeTimers();
+    try {
+      vi.mocked(getGpus)
+        .mockResolvedValueOnce(SNAPSHOT)
+        .mockRejectedValueOnce(new Error('network down'));
+
+      render(<GpuGrid />);
+      await act(async () => {
+        await vi.advanceTimersByTimeAsync(0);
+      });
+      expect(screen.getAllByText('NVIDIA B200')).toHaveLength(2);
+
+      await act(async () => {
+        await vi.advanceTimersByTimeAsync(3000);
+      });
+
+      expect(
+        screen.getByText(/values below may be stale/),
+      ).toBeInTheDocument();
+      expect(screen.getAllByText('NVIDIA B200')).toHaveLength(2);
+    } finally {
+      vi.useRealTimers();
+    }
+  });
 });

apps/fastvideo_studio/src/components/system/GpuGrid.tsx

@@ -1,7 +1,9 @@
 'use client';
 
 import * as React from 'react';
+import { AlertTriangle } from 'lucide-react';
 
+import { Button } from '@/components/ui/button';
 import { Card, CardContent } from '@/components/ui/card';
 import { getGpus, type GpuInfo, type GpuSnapshot } from '@/lib/api';
 import { cn } from '@/lib/utils';
@@ -97,7 +99,8 @@ function GpuCard({ gpu }: { gpu: GpuInfo }) {
 
 export default function GpuGrid() {
   const [snapshot, setSnapshot] = React.useState<GpuSnapshot | null>(null);
-  const [fetchError, setFetchError] = React.useState(false);
+  const [fetchError, setFetchError] = React.useState<string | null>(null);
+  const [retryToken, setRetryToken] = React.useState(0);
 
   React.useEffect(() => {
     let mounted = true;
@@ -110,10 +113,14 @@ export default function GpuGrid() {
         const next = await getGpus();
         if (mounted) {
           setSnapshot(next);
-          setFetchError(false);
+          setFetchError(null);
         }
       } catch {
-        if (mounted) setFetchError(true);
+        if (mounted) {
+          setFetchError(
+            'GPU status could not be refreshed. The values below may be stale.',
+          );
+        }
       } finally {
         inFlight = false;
       }
@@ -125,14 +132,27 @@ export default function GpuGrid() {
       mounted = false;
       clearInterval(interval);
     };
-  }, []);
+  }, [retryToken]);
 
   if (fetchError && !snapshot) {
     return (
-      <p className="py-8 text-center text-muted-foreground">
-        Could not reach the API server. GPU status needs the studio API server
-        running.
-      </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="text-muted-foreground">
+          Could not reach the API server. GPU status needs the Studio API server
+          running.
+        </p>
+        <Button
+          type="button"
+          variant="outline"
+          onClick={() => setRetryToken((token) => token + 1)}
+        >
+          Try Again
+        </Button>
+      </div>
     );
   }
   if (!snapshot) {
@@ -150,9 +170,22 @@ export default function GpuGrid() {
   return (
     <div className="flex flex-col gap-4">
       {fetchError && (
-        <p className="rounded-md border border-amber-500/40 bg-amber-500/10 px-3 py-2 text-sm text-amber-600 dark:text-amber-400">
-          Lost contact with the API server — showing the last known values.
-        </p>
+        <div
+          role="status"
+          aria-live="polite"
+          className="flex flex-wrap items-center gap-3 rounded-lg border border-amber-500/50 bg-amber-500/10 px-3 py-2 text-sm"
+        >
+          <AlertTriangle className="size-4 text-amber-600" aria-hidden />
+          <span className="min-w-0 flex-1">{fetchError}</span>
+          <Button
+            type="button"
+            variant="outline"
+            size="sm"
+            onClick={() => setRetryToken((token) => token + 1)}
+          >
+            Refresh Now
+          </Button>
+        </div>
       )}
       <div className="grid gap-4 [grid-template-columns:repeat(auto-fill,minmax(280px,1fr))]">
         {snapshot.gpus.map((gpu) => (

apps/fastvideo_studio/src/components/ui/accessibility.test.tsx

@@ -0,0 +1,48 @@
+import { render, screen } from '@testing-library/react';
+import { describe, expect, it } from 'vitest';
+
+import { Button } from './button';
+import { Input } from './input';
+import { NativeSelect } from './native-select';
+import { Slider } from './slider';
+import { Switch } from './switch';
+
+describe('shared control accessibility', () => {
+  it('keeps button, input, and select targets at least 44px tall', () => {
+    render(
+      <>
+        <Button size="sm">Small action</Button>
+        <Input aria-label="Text value" />
+        <NativeSelect aria-label="Choice" defaultValue="one">
+          <option value="one">One</option>
+        </NativeSelect>
+      </>,
+    );
+
+    expect(screen.getByRole('button', { name: 'Small action' })).toHaveClass(
+      'h-11',
+    );
+    expect(screen.getByRole('textbox', { name: 'Text value' })).toHaveClass(
+      'h-11',
+    );
+    expect(screen.getByRole('combobox', { name: 'Choice' })).toHaveClass(
+      'h-11',
+    );
+  });
+
+  it('uses 44px switch and slider interaction surfaces', () => {
+    render(
+      <>
+        <Switch aria-label="Enabled" />
+        <Slider aria-label="Amount" defaultValue={[50]} />
+      </>,
+    );
+
+    expect(screen.getByRole('switch', { name: 'Enabled' })).toHaveClass(
+      'size-11',
+    );
+    expect(screen.getByRole('slider', { name: 'Amount' })).toHaveClass(
+      'size-11',
+    );
+  });
+});

apps/fastvideo_studio/src/components/ui/badge.tsx

@@ -29,11 +29,12 @@ const badgeVariants = cva(
 );
 
 export interface BadgeProps
-  extends React.HTMLAttributes<HTMLDivElement>,
+  extends React.HTMLAttributes<HTMLSpanElement>,
     VariantProps<typeof badgeVariants> {}
 
+// A span (phrasing content), so badges stay valid inside buttons and links.
 function Badge({ className, variant, ...props }: BadgeProps) {
-  return <div className={cn(badgeVariants({ variant }), className)} {...props} />;
+  return <span className={cn(badgeVariants({ variant }), className)} {...props} />;
 }
 
 export { Badge, badgeVariants };

apps/fastvideo_studio/src/components/ui/button.tsx

@@ -7,7 +7,7 @@ import { cva, type VariantProps } from "class-variance-authority";
 import { cn } from "@/lib/utils";
 
 const buttonVariants = cva(
-	"inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-xl border !text-sm !font-semibold transition-colors duration-150 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/40 disabled:pointer-events-none disabled:cursor-not-allowed disabled:opacity-50",
+	"inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-xl border !text-sm !font-semibold transition-colors duration-150 disabled:pointer-events-none disabled:cursor-not-allowed disabled:opacity-50",
 	{
 		variants: {
 			variant: {
@@ -18,11 +18,11 @@ const buttonVariants = cva(
 				destructive: "border-rose-500/60 bg-rose-600/90 text-white hover:bg-rose-500",
 			},
 			size: {
-				default: "h-10 px-4 py-2",
-				sm: "h-9 rounded-lg px-3 !text-xs",
-				lg: "h-11 px-5 !text-sm",
-				icon: "size-10",
-				"icon-sm": "size-8",
+				default: "h-11 px-4 py-2",
+				sm: "h-11 rounded-lg px-3 !text-xs",
+				lg: "h-12 px-5 !text-sm",
+				icon: "size-11",
+				"icon-sm": "size-11",
 			},
 		},
 		defaultVariants: {

apps/fastvideo_studio/src/components/ui/dialog.tsx

@@ -42,7 +42,7 @@ const DialogContent = React.forwardRef<
       {...props}
     >
       {children}
-      <DialogPrimitive.Close className="absolute right-4 top-4 rounded-lg p-1 text-muted-foreground opacity-70 transition-opacity hover:bg-secondary hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-sky-400/40 disabled:pointer-events-none">
+      <DialogPrimitive.Close className="absolute right-2 top-2 flex size-11 items-center justify-center rounded-lg text-muted-foreground opacity-70 transition-opacity hover:bg-secondary hover:opacity-100 disabled:pointer-events-none sm:right-4 sm:top-4">
         <X className="h-4 w-4" />
         <span className="sr-only">Close</span>
       </DialogPrimitive.Close>

apps/fastvideo_studio/src/components/ui/input.tsx

@@ -9,7 +9,7 @@ const Input = React.forwardRef<HTMLInputElement, React.ComponentProps<"input">>(
 		<input
 			type={type}
 			className={cn(
-				"flex h-10 w-full rounded-xl border border-input bg-card/60 px-3 py-2 text-sm text-foreground shadow-sm backdrop-blur-md transition-colors placeholder:text-muted-foreground focus-visible:border-sky-400/70 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/25 disabled:cursor-not-allowed disabled:opacity-50",
+				"flex h-11 w-full rounded-xl border border-input bg-card/60 px-3 py-2 text-sm text-foreground shadow-sm backdrop-blur-md transition-colors placeholder:text-muted-foreground focus-visible:border-ring disabled:cursor-not-allowed disabled:opacity-50",
 				className,
 			)}
 			ref={ref}

apps/fastvideo_studio/src/components/ui/native-select.tsx

@@ -11,7 +11,7 @@ const NativeSelect = React.forwardRef<
   <select
     ref={ref}
     className={cn(
-      'flex h-10 w-full appearance-none rounded-xl border border-input bg-card px-3 py-2 text-sm text-foreground shadow-sm transition-colors focus-visible:border-sky-400/70 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/25 disabled:cursor-not-allowed disabled:opacity-50',
+      'flex h-11 w-full appearance-none rounded-xl border border-input bg-card px-3 py-2 text-sm text-foreground shadow-sm transition-colors focus-visible:border-ring disabled:cursor-not-allowed disabled:opacity-50',
       className,
     )}
     {...props}

apps/fastvideo_studio/src/components/ui/select.tsx

@@ -17,7 +17,7 @@ const SelectTrigger = React.forwardRef<
   <SelectPrimitive.Trigger
     ref={ref}
     className={cn(
-      'flex h-10 w-full items-center justify-between gap-2 rounded-xl border border-input bg-card px-3 py-2 text-sm text-foreground shadow-sm outline-none transition-colors placeholder:text-muted-foreground focus:border-sky-400/70 focus:ring-2 focus:ring-sky-400/25 disabled:cursor-not-allowed disabled:opacity-50 [&>span]:line-clamp-1',
+      'flex h-11 w-full items-center justify-between gap-2 rounded-xl border border-input bg-card px-3 py-2 text-sm text-foreground shadow-sm transition-colors placeholder:text-muted-foreground focus-visible:border-ring disabled:cursor-not-allowed disabled:opacity-50 [&>span]:line-clamp-1',
       className,
     )}
     {...props}
@@ -117,7 +117,7 @@ const SelectItem = React.forwardRef<
   <SelectPrimitive.Item
     ref={ref}
     className={cn(
-      'relative flex w-full cursor-default select-none items-center rounded-xl py-2 pl-8 pr-3 text-sm text-foreground outline-none data-[disabled]:pointer-events-none data-[disabled]:opacity-50 data-[highlighted]:bg-accent data-[highlighted]:text-accent-foreground',
+      'relative flex min-h-11 w-full cursor-default select-none items-center rounded-xl py-2 pl-8 pr-3 text-sm text-foreground outline-none data-[disabled]:pointer-events-none data-[disabled]:opacity-50 data-[highlighted]:bg-accent data-[highlighted]:text-accent-foreground',
       className,
     )}
     {...props}

apps/fastvideo_studio/src/components/ui/slider.tsx

@@ -8,21 +8,39 @@ import { cn } from '@/lib/utils';
 const Slider = React.forwardRef<
   React.ElementRef<typeof SliderPrimitive.Root>,
   React.ComponentPropsWithoutRef<typeof SliderPrimitive.Root>
->(({ className, ...props }, ref) => (
-  <SliderPrimitive.Root
-    ref={ref}
-    className={cn(
-      'relative flex w-full touch-none select-none items-center',
+>(
+  (
+    {
       className,
-    )}
-    {...props}
-  >
-    <SliderPrimitive.Track className="relative h-1.5 w-full grow overflow-hidden rounded-full bg-border">
-      <SliderPrimitive.Range className="absolute h-full bg-accent-blue" />
-    </SliderPrimitive.Track>
-    <SliderPrimitive.Thumb className="block h-4 w-4 rounded-full border-2 border-accent-blue bg-accent-blue shadow transition-colors hover:border-accent-blue/80 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/40 disabled:pointer-events-none disabled:opacity-50" />
-  </SliderPrimitive.Root>
-));
+      id,
+      'aria-label': ariaLabel,
+      'aria-labelledby': ariaLabelledBy,
+      'aria-describedby': ariaDescribedBy,
+      ...props
+    },
+    ref,
+  ) => (
+    <SliderPrimitive.Root
+      ref={ref}
+      className={cn(
+        'relative flex h-11 w-full touch-none select-none items-center',
+        className,
+      )}
+      {...props}
+    >
+      <SliderPrimitive.Track className="relative h-1.5 w-full grow overflow-hidden rounded-full bg-border">
+        <SliderPrimitive.Range className="absolute h-full bg-accent-blue" />
+      </SliderPrimitive.Track>
+      <SliderPrimitive.Thumb
+        id={id}
+        aria-label={ariaLabel}
+        aria-labelledby={ariaLabelledBy}
+        aria-describedby={ariaDescribedBy}
+        className="relative block size-11 rounded-full bg-transparent after:absolute after:left-1/2 after:top-1/2 after:size-4 after:-translate-x-1/2 after:-translate-y-1/2 after:rounded-full after:border-2 after:border-accent-blue after:bg-accent-blue after:shadow after:content-[''] hover:after:border-accent-blue/80 disabled:pointer-events-none disabled:opacity-50"
+      />
+    </SliderPrimitive.Root>
+  ),
+);
 Slider.displayName = SliderPrimitive.Root.displayName;
 
 export { Slider };

apps/fastvideo_studio/src/components/ui/switch.tsx

@@ -12,14 +12,14 @@ const Switch = React.forwardRef<
   <SwitchPrimitives.Root
     ref={ref}
     className={cn(
-      'peer inline-flex h-5 w-9 shrink-0 cursor-pointer items-center rounded-full border border-border transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/40 disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:border-accent-blue data-[state=checked]:bg-accent-blue data-[state=unchecked]:bg-background',
+      'peer relative inline-flex size-11 shrink-0 cursor-pointer items-center justify-center rounded-xl bg-transparent transition-colors before:absolute before:h-5 before:w-9 before:rounded-full before:border before:border-border before:bg-background data-[state=checked]:before:border-accent-blue data-[state=checked]:before:bg-accent-blue disabled:cursor-not-allowed disabled:opacity-50',
       className,
     )}
     {...props}
   >
     <SwitchPrimitives.Thumb
       className={cn(
-        'pointer-events-none block h-3.5 w-3.5 rounded-full bg-muted-foreground shadow-lg ring-0 transition-transform data-[state=checked]:translate-x-4 data-[state=checked]:bg-white data-[state=unchecked]:translate-x-0.5',
+        'pointer-events-none absolute left-1.5 top-[15px] z-[1] block h-3.5 w-3.5 rounded-full bg-muted-foreground shadow-lg ring-0 transition-transform data-[state=checked]:translate-x-4 data-[state=checked]:bg-white',
       )}
     />
   </SwitchPrimitives.Root>

apps/fastvideo_studio/src/components/ui/tabs.tsx

@@ -29,7 +29,7 @@ const TabsTrigger = React.forwardRef<
   <TabsPrimitive.Trigger
     ref={ref}
     className={cn(
-      'inline-flex items-center justify-center whitespace-nowrap rounded-md px-3 py-1.5 text-sm font-medium text-muted-foreground transition-colors hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/40 disabled:pointer-events-none disabled:opacity-50 data-[state=active]:bg-card data-[state=active]:text-foreground data-[state=active]:shadow-sm',
+      'inline-flex items-center justify-center whitespace-nowrap rounded-md px-3 py-1.5 text-sm font-medium text-muted-foreground transition-colors hover:text-foreground disabled:pointer-events-none disabled:opacity-50 data-[state=active]:bg-card data-[state=active]:text-foreground data-[state=active]:shadow-sm',
       className,
     )}
     {...props}

apps/fastvideo_studio/src/components/ui/textarea.tsx

@@ -8,7 +8,7 @@ const Textarea = React.forwardRef<HTMLTextAreaElement, React.ComponentProps<"tex
 	return (
 		<textarea
 			className={cn(
-				"flex min-h-24 w-full resize-y rounded-xl border border-input bg-card px-3 py-2 text-sm text-foreground shadow-sm transition-colors placeholder:text-muted-foreground focus-visible:border-sky-400/70 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/25 disabled:cursor-not-allowed disabled:opacity-50",
+				"flex min-h-24 w-full resize-y rounded-xl border border-input bg-card px-3 py-2 text-sm text-foreground shadow-sm transition-colors placeholder:text-muted-foreground focus-visible:border-ring disabled:cursor-not-allowed disabled:opacity-50",
 				className,
 			)}
 			ref={ref}

apps/fastvideo_studio/src/hooks/useDrawerFocus.ts

@@ -0,0 +1,23 @@
+'use client';
+
+import * as React from 'react';
+
+/**
+ * Move focus into a drawer when it opens as a modal (mobile), and hand it
+ * back to the previously focused element on close. Pairs with `inert` on
+ * the background content — together they make `aria-modal` truthful.
+ */
+export function useDrawerFocus<T extends HTMLElement>(active: boolean) {
+  const ref = React.useRef<T | null>(null);
+
+  React.useEffect(() => {
+    if (!active) return;
+    const previous = document.activeElement;
+    ref.current?.focus();
+    return () => {
+      if (previous instanceof HTMLElement) previous.focus();
+    };
+  }, [active]);
+
+  return ref;
+}

apps/fastvideo_studio/src/hooks/useMediaQuery.ts

@@ -0,0 +1,18 @@
+'use client';
+
+import * as React from 'react';
+
+export function useMediaQuery(query: string): boolean {
+  const [matches, setMatches] = React.useState(false);
+
+  React.useEffect(() => {
+    const mediaQuery = window.matchMedia(query);
+    const updateMatch = () => setMatches(mediaQuery.matches);
+
+    updateMatch();
+    mediaQuery.addEventListener('change', updateMatch);
+    return () => mediaQuery.removeEventListener('change', updateMatch);
+  }, [query]);
+
+  return matches;
+}

apps/fastvideo_studio/tests/test_gpu.py

@@ -74,10 +74,8 @@ def test_snapshot_shapes_devices(monkeypatch: pytest.MonkeyPatch) -> None:
     }
 
 
-def test_snapshot_tolerates_missing_sensors(
-        monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setitem(sys.modules, "pynvml",
-                        _make_fake_pynvml(broken_sensors=True))
+def test_snapshot_tolerates_missing_sensors(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setitem(sys.modules, "pynvml", _make_fake_pynvml(broken_sensors=True))
     snap = gpu_mod.get_gpu_snapshot()
     assert snap["available"] is True
     g = snap["gpus"][0]
@@ -86,8 +84,7 @@ def test_snapshot_tolerates_missing_sensors(
     assert g["power_limit_watts"] is None
 
 
-def test_snapshot_reports_nvml_failure(
-        monkeypatch: pytest.MonkeyPatch) -> None:
+def test_snapshot_reports_nvml_failure(monkeypatch: pytest.MonkeyPatch) -> None:
     fake = _make_fake_pynvml()
     fake.nvmlInit = lambda: (_ for _ in ()).throw(_NVMLError("driver gone"))
     monkeypatch.setitem(sys.modules, "pynvml", fake)

apps/fastvideo_studio/tests/test_training_config.py

@@ -130,8 +130,7 @@ def test_dmd_builds_three_role_models_and_method_knobs() -> None:
 
 
 def test_dmd_vsa_maps_to_training_vsa_sparsity() -> None:
-    config = build_training_config(
-        _job("dmd_t2v", dmd_use_vsa=True, dmd_vsa_sparsity=0.9), "out")
+    config = build_training_config(_job("dmd_t2v", dmd_use_vsa=True, dmd_vsa_sparsity=0.9), "out")
     assert config["training"]["vsa"]["sparsity"] == 0.9
 
 
@@ -161,31 +160,27 @@ def test_validation_callback_only_when_file_given() -> None:
     without = build_training_config(_job("full_t2v"), "out")
     assert "validation" not in without["callbacks"]
 
-    with_file = build_training_config(
-        _job("full_t2v", validation_dataset_file="val.json"), "out")
+    with_file = build_training_config(_job("full_t2v", validation_dataset_file="val.json"), "out")
     validation = with_file["callbacks"]["validation"]
     assert validation["dataset_file"] == "val.json"
     assert validation["pipeline_target"].endswith(".WanPipeline")
     assert validation["sampling_steps"] == [50]
 
-    dmd = build_training_config(
-        _job("dmd_t2v", validation_dataset_file="val.json"), "out")
+    dmd = build_training_config(_job("dmd_t2v", validation_dataset_file="val.json"), "out")
     validation = dmd["callbacks"]["validation"]
     assert validation["pipeline_target"].endswith(".WanDMDPipeline")
     assert validation["sampling_steps"] == [3]
     assert validation["sampling_timesteps"] == [1000, 757, 522]
 
     # KD/ODE-init has no sampling-based validation pipeline.
-    ode = build_training_config(
-        _job("ode_init", validation_dataset_file="val.json"), "out")
+    ode = build_training_config(_job("ode_init", validation_dataset_file="val.json"), "out")
     assert "validation" not in ode["callbacks"]
 
 
 def test_ltx2_models_are_rejected() -> None:
     assert is_ltx2_model("Lightricks/LTX-2-19B")
     with pytest.raises(ValueError, match="LTX-2 training is not supported"):
-        build_training_config(
-            _job("full_t2v", model_id="Lightricks/LTX-2-19B"), "out")
+        build_training_config(_job("full_t2v", model_id="Lightricks/LTX-2-19B"), "out")
 
 
 def test_unknown_workload_is_rejected() -> None:
@@ -195,8 +190,7 @@ def test_unknown_workload_is_rejected() -> None:
 
 def test_invalid_denoising_steps_are_rejected() -> None:
     with pytest.raises(ValueError, match="Invalid DMD denoising steps"):
-        build_training_config(
-            _job("dmd_t2v", dmd_denoising_steps="1000,abc"), "out")
+        build_training_config(_job("dmd_t2v", dmd_denoising_steps="1000,abc"), "out")
 
 
 def test_training_env_has_no_backend_override() -> None:
@@ -211,8 +205,7 @@ def test_workloads_match_frontend_job_config() -> None:
     (src/lib/jobConfig.ts) — drift means creatable-but-unrunnable jobs."""
     import re
 
-    job_config = (Path(__file__).resolve().parents[1] / "src" / "lib" /
-                  "jobConfig.ts").read_text(encoding="utf-8")
+    job_config = (Path(__file__).resolve().parents[1] / "src" / "lib" / "jobConfig.ts").read_text(encoding="utf-8")
     all_types = set(re.findall(r'type:\s*"([^"]+)"', job_config))
     inference_types = {"t2v", "i2v", "t2i"}
     assert inference_types <= all_types, "jobConfig.ts parse failed"

docker/Dockerfile

@@ -186,15 +186,40 @@ RUN --mount=type=cache,target=/opt/uv/cache \
 
 COPY . .
 
-# Install FastVideo Unified Kernel exactly once. build.sh initializes only its
-# CUTLASS/ThunderKittens submodules, then compiles for TORCH_CUDA_ARCH_LIST
-# (default Hopper sm_90a) without requiring a live GPU.
+# Build immutable FastVideo kernel wheels for the published image. The requested
+# architecture remains installed for normal image users; amd64 images also carry
+# an SM89 artifact so the predominant L40S Modal lanes can reuse it exactly.
+ARG FASTVIDEO_KERNEL_PREBUILT_DIR=/opt/fastvideo-kernel-prebuilt
 RUN --mount=type=cache,target=/opt/uv/cache \
     source $HOME/.local/bin/env && \
     source /opt/venv/bin/activate && \
+    default_arch="${TORCH_CUDA_ARCH_LIST}" && \
+    default_wheel_dir="${FASTVIDEO_KERNEL_PREBUILT_DIR}/${default_arch}" && \
+    export TORCH_CUDA_ARCH_LIST="${default_arch}" && \
     cd fastvideo-kernel && \
-    CMAKE_BUILD_PARALLEL_LEVEL=${CMAKE_BUILD_PARALLEL_LEVEL} \
-    TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST} ./build.sh
+    CMAKE_ARGS= CMAKE_BUILD_PARALLEL_LEVEL=${CMAKE_BUILD_PARALLEL_LEVEL} \
+    ./build.sh --wheel-dir "${default_wheel_dir}" && \
+    cd /FastVideo && \
+    CMAKE_ARGS= python fastvideo/tests/modal/kernel_build_cache.py write-build-info \
+        --wheel-dir "${default_wheel_dir}" \
+        --output "${default_wheel_dir}/metadata.json" && \
+    if [[ "${TARGETARCH:-amd64}" == "amd64" && "${default_arch}" != "8.9" ]]; then \
+        export TORCH_CUDA_ARCH_LIST=8.9 && \
+        l40s_wheel_dir="${FASTVIDEO_KERNEL_PREBUILT_DIR}/8.9" && \
+        cd /FastVideo/fastvideo-kernel && \
+        CMAKE_ARGS= CMAKE_BUILD_PARALLEL_LEVEL=${CMAKE_BUILD_PARALLEL_LEVEL} \
+        ./build.sh --wheel-dir "${l40s_wheel_dir}" && \
+        cd /FastVideo && \
+        CMAKE_ARGS= python fastvideo/tests/modal/kernel_build_cache.py write-build-info \
+            --wheel-dir "${l40s_wheel_dir}" \
+            --output "${l40s_wheel_dir}/metadata.json"; \
+    fi && \
+    export TORCH_CUDA_ARCH_LIST="${default_arch}" && \
+    default_wheel="$(find "${default_wheel_dir}" -maxdepth 1 -type f \
+        \( -name 'fastvideo_kernel-*.whl' -o -name 'fastvideo-kernel-*.whl' \) \
+        | sort | tail -n 1)" && \
+    uv pip install "${default_wheel}" \
+        --reinstall-package fastvideo-kernel --no-deps
 
 # Install FastVideo itself (editable) now that the source is present, and set up
 # shell configuration. Dependencies and the local kernel are already installed,

docs/contributing/ci_architecture.md

@@ -269,16 +269,26 @@ The docs job:
 ### Docker Images
 
 `.github/workflows/infra-build-image.yml` supports manual `workflow_dispatch`
-runs and automatically rebuilds the CUDA matrix when `docker/Dockerfile`
-changes on `main` in the canonical repository. Manual runs let maintainers
-choose which image families to build. The `fastvideo-dev` matrix builds Python
-3.12 images for CUDA 12.6 and CUDA 13 on native `amd64` and `arm64` runners,
-then publishes one multi-platform manifest per CUDA version. CUDA 12.6 owns the
-`py3.12-latest` and global `latest` tags, as well as the explicit
-`py3.12-cuda12.6.3-latest` alias. CUDA 13 is published under the explicit
-`py3.12-cuda13.0.0-latest` tag. This publication policy does not change the
-unparameterized `docker/Dockerfile` build defaults, which remain CUDA 13 and
-`cu130`.
+runs and automatically rebuilds the CUDA matrix when a repository-controlled
+image input changes on `main` in the canonical repository. Those inputs include
+the CUDA Dockerfile and reusable workflow, dependency metadata, Docker context
+policy, `fastvideo-kernel/**`, and the kernel artifact metadata/key helper.
+Manual runs let maintainers choose which image families to build. The
+`fastvideo-dev` matrix builds Python 3.12 images for CUDA 12.6 and CUDA 13 on
+native `amd64` and `arm64` runners, then publishes one multi-platform manifest
+per CUDA version. CUDA 12.6 owns the `py3.12-latest` and global `latest` tags, as
+well as the explicit `py3.12-cuda12.6.3-latest` alias. CUDA 13 is published under
+the explicit `py3.12-cuda13.0.0-latest` tag. This publication policy does not
+change the unparameterized `docker/Dockerfile` build defaults, which remain CUDA
+13 and `cu130`.
+
+Published amd64 development images keep their configured Hopper kernel wheel
+installed and also carry an immutable SM89 wheel under
+`/opt/fastvideo-kernel-prebuilt`. Modal PR and SSIM jobs select the exact
+source, ABI, and GPU-architecture match from that directory, so L40S jobs reuse
+the trusted image artifact while kernel-changing PRs still build locally. Once
+a kernel or artifact-key change reaches `main`, the image workflow republishes
+the matching trusted artifact before later jobs consume the updated image tag.
 
 The optional Dreamverse matrix builds backend and UI images for CUDA 12.6 and
 CUDA 13 on `amd64`. Dreamverse remains `amd64`-only because its FA4 dependency

docs/contributing/performance_benchmarks.md

@@ -58,6 +58,54 @@ records, see `performance_dashboard/README.md`. The dashboard provides a
 FastAPI API plus a React UI and can be exposed with `ngrok` after building the
 frontend.
 
+## DGX Spark (GB10) local benchmarking
+
+The NVIDIA DGX Spark (GB10) is not available on Modal, so its coverage is
+**local/manual** rather than automated CI. The GB10 benchmark
+`wan-t2v-1.3b-1gpu-gb10` is gated to the GB10 via `run_config.gpu_types`
+(matched as substrings of the CUDA device name), so the shared H100/L40S
+performance lanes discover it and skip it, while a GB10 owner runs it locally.
+
+Run just the GB10 benchmark on a DGX Spark:
+
+```bash
+pytest 'fastvideo/tests/performance/test_inference_performance.py::test_inference_performance[wan-t2v-1.3b-1gpu-gb10]' -vs
+```
+
+To check run-to-run stability (latency, peak memory, throughput), run it a few
+times from a clean results directory, then normalize:
+
+```bash
+rm -f fastvideo/tests/performance/results/perf_*.json
+for i in 1 2 3 4 5; do
+  pytest 'fastvideo/tests/performance/test_inference_performance.py::test_inference_performance[wan-t2v-1.3b-1gpu-gb10]' -vs
+done
+PERF_REPORTS_DIR=/tmp/fastvideo_perf_reports \
+  python fastvideo/tests/performance/compare_baseline.py
+```
+
+`compare_baseline.py` reports `CALIBRATION_NEEDED` until a baseline exists for
+the GB10 identity, and writes one `normalized_perf_*.json` per run. Reference
+figures on a GB10 (torch 2.12.0+cu130, transformers 5.14.0): generation ~39.3 s,
+peak ~8.4 GB, throughput ~1.15 fps, stable to ~0.4% across five runs.
+
+### Seeding the GB10 baseline (follow-up)
+
+Seeding a baseline-eligible record for the GB10 identity is intentionally **not**
+done from a local run: `seed_baseline.py` accepts only `scheduled_main`
+full-suite source artifacts, so ordinary local/manual uploads stay
+`baseline_eligible=false` (dashboard-visible, but they do not move the rolling
+baseline). Establishing the GB10 baseline requires either:
+
+* a scheduled-main performance run on a GB10 CI runner once one is available, or
+* a carefully scoped, maintainer-approved manual calibration path that preserves
+  the existing exact-identity, batch-consistency, provenance, and
+  explicit-approval safeguards — it must **not** make arbitrary local uploads
+  baseline-eligible.
+
+The reviewed five-run GB10 artifacts are the stability evidence for that first
+baseline. Tracked in #1632.
+
 ## Architecture
 
 ```

docs/design/inference_schema_parity_inventory.yaml

@@ -76,6 +76,7 @@ surfaces:
       lora_target_modules: "Legacy LoRA configuration surface pending dedicated component API."
       output_type: "Legacy output formatting surface pending GenerationResult cleanup."
       VSA_sparsity: "Model-specific inference optimization not yet represented in the typed public schema."
+      attention_backend: "Process-wide default attention-backend request applied per component at load time; kernel-selection knob not yet represented in the typed public schema."
       moba_config_path: "Model-specific MoBA optimization surface not yet represented in the typed public schema."
       master_port: "Executor/bootstrap compatibility field; not part of the canonical inference schema."
       refine_transformer_path: "Generic stage-2 refine transformer override; no typed equivalent yet."
@@ -444,7 +445,11 @@ surfaces:
     moved:
       image_path: request.inputs.image_path
       pil_image: request.inputs.pil_image
+      last_image: request.inputs.last_image
+      references: request.inputs.references
       video_path: request.inputs.video_path
+      latents: request.inputs.latents
+      audio_latents: request.inputs.audio_latents
       mouse_cond: request.inputs.mouse_cond
       keyboard_cond: request.inputs.keyboard_cond
       grid_sizes: request.inputs.grid_sizes
@@ -524,7 +529,6 @@ surfaces:
       inpaint_mask: request.extensions.stable_audio.inpaint_mask
     internal_only:
       data_type: "Derived from the request shape and not a public input."
-      latents: "Pre-generated diffusion latents supplied by parity/debug harnesses; not a public input."
 
   sampling_param_extensions: {}
 

docs/getting_started/installation.md

@@ -5,6 +5,7 @@ FastVideo supports the following hardware platforms:
 
 - [NVIDIA CUDA](installation/gpu.md)
 - [NVIDIA DGX Spark / GB10 (ARM64 + CUDA 13)](installation/spark.md)
+  ([performance & tuning](installation/spark_performance.md))
 - [Apple silicon](installation/mps.md)
 
 ## Quick Installation
@@ -54,8 +55,10 @@ UV_TORCH_BACKEND=cu126 uv pip install -e .
 uv pip install flash-attn --no-build-isolation -v
 ```
 
-## Hardware Requirements
+## Requirements
 
+- **Python**: 3.10-3.12 is the tested and recommended range (the commands
+  above pin 3.12)
 - **NVIDIA GPUs**: CUDA 12.6+ with compute capability 7.0+
 - **Apple Silicon**: macOS 14.0+ with M1/M2/M3/M4 chips
 - **CPU**: x86_64 architecture (for CPU-only inference)

docs/getting_started/installation/gpu.md

@@ -134,4 +134,4 @@ If you're planning to contribute to FastVideo please see the following page:
 
 If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).
 
-You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.
+You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.

docs/getting_started/installation/mps.md

@@ -100,4 +100,4 @@ If you're planning to contribute to FastVideo please see the following page:
 
 If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).
 
-You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.
+You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.

docs/getting_started/installation/spark.md

@@ -134,9 +134,16 @@ uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/rele
 
 If you hit other issues, please open an issue on our
 [GitHub repository](https://github.com/hao-ai-lab/FastVideo). You can also join
-our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ)
+our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)
 for additional support.
 
+## Next: performance & tuning
+
+Installed and verified? See [DGX Spark: Performance & Tuning](spark_performance.md)
+for which models are practical on the GB10, what makes them faster, and what
+won't help on this hardware (and why) — so you don't spend a night tuning knobs
+that can't move here.
+
 ## Development Environment Setup
 
 If you're planning to contribute to FastVideo please see the

docs/getting_started/installation/spark_performance.md

@@ -0,0 +1,195 @@
+# DGX Spark (GB10): Performance & Tuning
+
+You have FastVideo [installed on a DGX Spark](spark.md) — this page is what to
+run next. It covers **which models are practical on the GB10, what actually
+makes them faster, and what won't help (and why)**, so you don't burn a night
+tuning knobs that can't move on this hardware.
+
+!!! tip "TL;DR"
+    - **Use distilled few-step models** (e.g. `FastVideo/FastWan2.1-T2V-1.3B-Diffusers`).
+      They run in ~40 s/video. Full-step models are 12–47 min on the GB10.
+    - On few-step models, **VAE decode is the bottleneck**, not attention — it's
+      bandwidth-bound on the Spark's unified memory.
+    - **bf16 VAE decode** is the real, lossless lever (FastVideo already turns it
+      on for Wan). **FlashAttention, linear quantization, and `torch.compile` of
+      the VAE give little or nothing here** — see the table below.
+    - Heavy runs can make the box unreachable — run generations with VAE tiling on
+      and `nice -n 19`. See [Running safely](#running-safely-dont-lock-the-box).
+
+## The hardware reality (this explains everything below)
+
+The GB10 pairs a Blackwell GPU (`sm_121`) with **128 GB of unified LPDDR5X memory
+(~270 GB/s) shared between CPU and GPU**. That bandwidth is roughly **10× below a
+datacenter GPU's HBM**. Two consequences drive every tuning decision:
+
+1. **Memory-bandwidth-bound stages hurt disproportionately.** VAE decode moves a
+   lot of data and becomes the dominant cost on short (few-step) generations.
+2. **Compute-bound stages scale with step count.** Full-step diffusion (50+
+   steps) is denoise-bound and simply takes a long time here.
+
+## Use distilled few-step models
+
+The single biggest lever on the GB10 is **model choice**. A 3-step distilled
+model is ~18× faster than the full-step version of the same architecture:
+
+| Model | Steps | Time / video | Bottleneck |
+|---|---|---|---|
+| FastWan2.1-T2V-1.3B (distilled) | 3 | **~40 s** | VAE decode |
+| Wan2.1-T2V-1.3B (full-step) | 50 | ~12 min | denoise |
+| Cosmos-Predict2.5-2B (full-step) | 51 | ~47 min | denoise |
+| LTX2.3-distilled (+audio) | 8 | ~6 min | mixed |
+
+The bottleneck flips from decode to denoise at around **4 steps**. Below that,
+you're paying mostly for VAE decode; above it, mostly for the denoising loop.
+
+!!! note "Few-step timings are noisy — measure in-process"
+    On a 3-step run, one-time per-process startup (Triton autotune, allocator
+    warmup) dominates and never amortizes, so single-run totals wobble ~±30%.
+    Compare levers **back-to-back in one process or as medians**, never as two
+    separate single runs. The [reproduction script](#reproduce-these-numbers)
+    does this for you.
+
+## bf16 VAE decode — the real lever (already on for Wan)
+
+Because few-step generation is decode-bound, VAE decode precision is where the
+time is. Decoding in **bf16 instead of fp32 is essentially lossless** (MS-SSIM
+~0.9999 vs fp32 on the identical latent) and ~1.14× faster — worth roughly
+5–7% end-to-end on a decode-bound few-step model.
+
+**FastVideo already defaults Wan's decode to bf16** (`vae_decode_precision="bf16"`,
+with encode kept at fp32), so for the recommended Wan/FastWan models there's
+nothing to set. If you run a model that still defaults to an fp32 decode, set the
+decode-only override yourself:
+
+```python
+from fastvideo.configs.pipelines.base import PipelineConfig
+
+pipeline_config = PipelineConfig.from_pretrained(model_id)
+pipeline_config.vae_decode_precision = "bf16"   # decode-only; leaves encode precision alone
+```
+
+Decode is output-only, so lowering its precision is safe. (Encode seeds the
+denoising trajectory for I2V/causal models, so that stays at the pipeline's
+default — don't lower `vae_precision` blindly for those.)
+
+## Memory: one unified 128 GB pool
+
+The GB10 has **no separate VRAM** — CPU and GPU share one 128 GB LPDDR5X pool
+(~118 GB usable). Two practical consequences:
+
+- **`nvidia-smi` reports memory as `[N/A]`** on the GB10, and the system "used"
+  figure conflates CPU + GPU + cache, so it's only a soft upper bound — treat the
+  whole 128 GB as one shared budget. For a per-run figure, use FastVideo's own
+  `peak_memory_mb` (reported on the generation result and by the performance
+  benchmark), which is measured inside the worker that runs the model.
+- **The 128 GB is a *working-set* ceiling, not storage** — the model cache lives
+  on the NVMe (3.7 TB, ample). What has to fit in 128 GB is the weights,
+  activations, and KV cache — and, critically, the **VAE decode buffers**, which
+  is why tiling matters
+  (an untiled high-res decode can spike the pool into swap and lock the box).
+
+The recommended few-step models are comfortable here: their weights are small
+(1.3–2 B) and few-step generation keeps activations modest — a Wan2.1-1.3B
+few-step generation peaks at **~8.4 GB** (measured), a small fraction of the pool.
+The pressure comes from **decode resolution/frames**, not the model — a
+1080p×121-frame untiled decode is what pushes the pool toward its ceiling, which
+is why VAE tiling stays
+on by default.
+
+## What helps vs. what doesn't on the GB10
+
+The honest summary — most "obvious" GPU optimizations don't move the needle on
+this hardware, for reasons specific to it:
+
+| Lever | Effect on the GB10 | Use it? |
+|---|---|---|
+| Distilled few-step model | ~18× vs full-step | ✅ **the primary lever** |
+| bf16 VAE decode | ~1.14×, lossless; ~5–7% e2e on few-step | ✅ default for Wan |
+| VSA (video sparse attention) | works out of the box (Triton kernel auto-selects on `sm_121`) | ✅ automatic |
+| Building FlashAttention | **no speedup** — Torch SDPA already hits an efficient flash kernel on `sm_121`, and FA2 ties it | ❌ not worth building |
+| `torch.compile` of the VAE decode | recompile storm (per-frame varying shapes) → ~1.1× | ❌ dead end |
+| Linear (fp8 / nvfp4) quantization on long-sequence models (e.g. Cosmos) | ~nothing — see below | ❌ wrong lever here |
+| FP4 attention (`ATTN_QAT_INFER`) | works on `sm_121` (runtime allowlist landed in #1647; kernel build is #1598); helps, but needs a QAT-trained checkpoint | ⚠️ opt-in — see below |
+| FP4 linear on short-sequence models (LTX2) | up to −24% denoise at 1080p (#1594) | ⚠️ model/resolution-dependent |
+
+### Why linear quantization is the wrong lever on long-sequence models
+
+Quantizing the linear (GEMM) layers is a natural first instinct, but on a
+long-sequence video model it buys almost nothing on the GB10. A video-DiT denoise
+step is dominated by **O(N²) attention** at these sequence lengths (tens of
+thousands of tokens); the linear layers are a small single-digit fraction of the
+work. Quantizing them faster leaves the attention-bound total essentially
+unchanged — measured at ~1% on Cosmos-2.5, i.e. noise, and full-step CFG models
+also lose quality to per-step quantization error.
+
+The same mechanism **does** help on **short-sequence** models: LTX2's aggressive
+VAE compression gives it short attention sequences, so FP4 linear reaches −24%
+there (#1594). The rule: **on the GB10, the lever that matters is attention
+(sparse or FP4), not the linear layers** — unless the model has short sequences.
+
+### FP4 on the GB10 (opt-in)
+
+Block-scaled FP4 works on `sm_121` under CUDA 13:
+
+- **FP4 attention** (`FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_INFER`, #1598) is
+  numerically correct on the GB10 and ~6% faster end-to-end generation, but it only preserves
+  quality on a **quantization-aware-distilled checkpoint** (e.g.
+  `FastVideo/FastWan-QAD-1.3B`) — stock weights aren't trained to tolerate it.
+- **FP4 linear** helps only where sequences are short (LTX2, above).
+
+The [`qad_fp4_ab.py`](#reproduce-these-numbers) harness reproduces the FP4
+attention A/B on the QAD checkpoint.
+
+## Running safely (don't lock the box)
+
+The GB10 is easy to make **unreachable** — a heavy build or an untiled high-res
+decode starves the ~20 ARM cores and unified memory, `sshd` can't get cycles, and
+you're locked out at *"Connection timed out during banner exchange"* until the box
+is power-cycled. To avoid it:
+
+- **Inference:** keep **VAE tiling on** (the default), use sane resolution/frames,
+  and run under `nice -n 19`:
+
+  ```bash
+  nice -n 19 nohup python your_script.py > run.log 2>&1 &
+  ```
+
+- **Builds** (flash-attn, kernel): `nice -n 19`, `MAX_JOBS=2`, `nohup`. Never a
+  bare foreground high-parallelism build.
+- Leave `*_cpu_offload` at the example defaults — "CPU" offload is the *same*
+  unified RAM on the GB10, so the win is tiling + sane resolution, not offloading.
+
+## Gotchas specific to the GB10
+
+A few things that surprise people on this box (beyond the memory notes above):
+
+- **Don't force `TORCH_SDPA` on a VSA checkpoint** (FastWan, LTX2.3-distilled).
+  The SDPA path builds a model without the gate weights the checkpoint carries and
+  fails to load. Run the model natively — VSA auto-routes to its Triton kernel on
+  `sm_121`.
+- **Few-step timings are noisy run-to-run** (~±30%) — one-time startup dominates a
+  3-step run. Compare in-process / as medians, never two separate single runs (the
+  benchmark script does this).
+- **`nvidia-smi` shows `[N/A]` for memory** — see [Memory](#memory-one-unified-128-gb-pool).
+- **Cosmos-2.5** uses a Qwen2.5-VL text encoder; make sure you're on a FastVideo
+  build recent enough to include its `transformers`-compatibility handling before
+  running it.
+
+## Reproduce these numbers
+
+Two scripts under `examples/inference/optimizations/` reproduce the claims on
+your own GB10:
+
+```bash
+# Headline: few-step generation timing (median) + the bf16-vs-fp32 decode A/B.
+# FASTVIDEO_STAGE_LOGGING=1 also prints the denoise / decode / text split.
+FASTVIDEO_STAGE_LOGGING=1 nice -n 19 \
+    python examples/inference/optimizations/spark_benchmark.py
+
+# FP4 attention quality/speed A/B on the QAD checkpoint (one arm per run).
+QAD_LINEAR=0 FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_INFER nice -n 19 \
+    python examples/inference/optimizations/qad_fp4_ab.py
+```
+
+See also the [Optimizations](../../inference/optimizations.md) reference for the
+full list of attention backends and quantization options.

docs/index.md

@@ -5,7 +5,7 @@
 </div>
 
 <div style="text-align: center;">
-<strong>FastVideo is a unified inference and post-training framework for accelerated video generation.</strong>
+<strong>FastVideo is a unified post-training and real-time inference framework for accelerated video generation.</strong>
 </div>
 
 <div style="text-align: center;">
@@ -25,14 +25,23 @@ FastVideo is an inference and post-training framework for diffusion models. It f
 
 FastVideo has the following features:
 
+- End-to-end post-training support for bidirectional and autoregressive models
+  - Full finetuning and LoRA [finetuning](training/finetune.md) for state-of-the-art open video DiTs
+  - [Data preprocessing pipeline](training/data_preprocess.md) for video, image, and text data
+  - [Distribution Matching Distillation (DMD2)](distillation/dmd.md) stepwise distillation
+  - Sparse attention with [Video Sparse Attention](attention/vsa/index.md)
+  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup
+  - [Attn-QAT training](training/attn_qat.md) for quantization-aware post-training
+  - Causal distillation through Self-Forcing
+  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing
+  - See the [training overview](training/overview.md) for the full training workflow
 - State-of-the-art performance optimizations for inference
-  - [Sliding Tile Attention](attention/sta/index.md)
-  - [Sage Attention](https://arxiv.org/abs/2410.02367)
-- E2E post-training support
-  - Data preprocessing pipeline for video data
-  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 using [Video Sparse Attention](https://arxiv.org/pdf/2505.13389) and [Distribution Matching Distillation](https://tianweiy.github.io/dmd2/)
-  - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs.
-  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.
+  - Sequence parallelism for distributed inference
+  - Multiple state-of-the-art [attention backends](attention/index.md)
+  - User-friendly [CLI](inference/cli.md) and Python API
+  - See the [support matrix](inference/support_matrix.md) for supported models and [optimizations](inference/optimizations.md) for the full list
+- Realtime video generation and editing
+  - [Dreamverse](https://github.com/hao-ai-lab/FastVideo/tree/main/apps/dreamverse): stream and "vibe direct" video in realtime ([live demo](https://dreamverse.fastvideo.org/))
 
 ## Documentation
 

docs/inference/add_pipeline.md

@@ -11,7 +11,7 @@ This guide explains how to implement a custom diffusion pipeline in FastVideo, l
 4. **Register Your Pipeline** - Make it discoverable by the framework
 5. **Configure Your Pipeline** - (Coming soon)
 
-Need help? Join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).
+Need help? Join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ).
 
 ## Step 1: Pipeline Modules
 

docs/inference/inference_quick_start.md

@@ -4,10 +4,11 @@ This page contains step-by-step instructions to get you quickly started with vid
 
 ## Requirements
 
-- **OS**: Linux (Tested on Ubuntu 22.04+)
+- **OS**: Linux (tested on Ubuntu 22.04+), or macOS on Apple silicon via the
+  [MPS installation guide](../getting_started/installation/mps.md)
 - **Python**: 3.10-3.12
-- **CUDA**: 12.6 or 13.0
-- **GPU**: At least one NVIDIA GPU
+- **CUDA**: 12.6 or 13.0 (NVIDIA GPUs)
+- **GPU**: At least one NVIDIA GPU, or an Apple silicon chip with MPS
 
 ## Installation
 
@@ -134,5 +135,4 @@ If the generated video doesn't match your prompt:
 - Learn about [Advanced Inference Configurations](configuration.md)
 - Learn about using [Optimizations](optimizations.md)
 - See [Examples](examples/examples_inference_index.md) for more usage scenarios
-- Join our [Community Discord](https://discord.gg/JA7cksDz86).
-- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).
+- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ).

docs/inference/optimizations.md

@@ -3,6 +3,12 @@
 
 This page describes the various options for speeding up generation times in FastVideo.
 
+!!! note "On a DGX Spark (GB10)?"
+    Several options on this page behave differently on the GB10's unified-memory
+    hardware — some give little or nothing there. See
+    [DGX Spark: Performance & Tuning](../getting_started/installation/spark_performance.md)
+    for what actually helps on that platform and why.
+
 ## Table of Contents
 
 - Optimized Attention Backends
@@ -119,6 +125,22 @@ pip install "nvidia-cutlass-dsl>=4.5.2" apache-tvm-ffi flashinfer-python
 The `--no-deps` flag prevents upgrading torch/torchvision. Use the supported
 PyTorch 2.12.0 and CUDA 13 environment for this kernel.
 
+Branch-to-`nvidia-cutlass-dsl` compatibility (the fork tracks the CuTe DSL API
+surface closely):
+
+| fork branch | cutlass-dsl | notes |
+|---|---|---|
+| `fp4` | `==4.4.2` (+ `nvidia-cutlass-dsl-libs-base==4.4.2`) | validated set on GB200: `quack-kernels==0.4.1`, `flashinfer-python==0.6.8`, `CUTE_DSL_ENABLE_TVM_FFI=1`, `FASTVIDEO_FA4=1` |
+| `fix/cutlass-dsl-4.5` | `>=4.5.2` | carries the `cute.core.ThrMma` -> `cute.ThrMma` fix |
+| any | 4.6-era | unsupported: `cute.make_fragment` was removed at module level; fails at CuTe JIT trace |
+
+`FASTVIDEO_FA4=1` is required alongside the fork: it ships no compiled
+FlashAttention-2, so dense attention paths raise ImportError without the FA4
+opt-in. The same kernel also serves `ATTN_QAT_INFER` on sm_100a/sm_103a
+(datacenter Blackwell) — the selection log's receipt line
+(`ATTN_QAT_INFER resolved: ...`) records the arch, kernel, and quantization
+mode that actually bound.
+
 #### Usage
 
 Enable FP4 attention via the `--nvfp4_fa4` flag:

docs/inference/support_matrix.md

@@ -13,6 +13,97 @@ For the canonical, code-level list of model IDs recognized by
     We do this because we believe VSA is strictly better than STA for the
     actively maintained `main` inference path.
 
+## Registered Model IDs
+
+Every Hugging Face model ID registered in `fastvideo/registry.py` on `main`
+(commit `8d89f30d`), grouped by family. Any ID below can
+be passed to `VideoGenerator.from_pretrained(...)`; FastVideo resolves the
+matching pipeline and sampling defaults. The **Family** column is a
+documentation grouping: it follows each registration's declared `model_family`,
+except `black-forest-labs/FLUX.1-dev`, which declares none and is listed under
+`flux` for readability. The **Workloads** column shows each
+registration's declared `workload_types`; `—` means the entry is registered
+without a UI workload option but is still loadable by ID. The **Example**
+column links a runnable script in `examples/inference/basic/` where one exists.
+
+| Family | HuggingFace Model ID | Workloads | Example |
+|--------|----------------------|-----------|---------|
+| cosmos | `nvidia/Cosmos-Predict2-2B-Video2World` | T2V | — |
+| cosmos25 | `KyleShao/Cosmos-Predict2.5-2B-Diffusers` | T2V | [basic_cosmos2_5_t2w.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_cosmos2_5_t2w.py) |
+| cosmos25 | `nvidia/Cosmos-Predict2.5-14B` | T2V | [basic_cosmos2_5_t2w.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_cosmos2_5_t2w.py) |
+| dreamx_world | `FastVideo/DreamX-World-5B-Cam-Diffusers` | I2V | [basic_dreamx_world.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dreamx_world.py) |
+| dreamx_world | `FastVideo/DreamX-World-5B-Diffusers` | I2V | [basic_dreamx_world.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dreamx_world.py) |
+| flux | `black-forest-labs/FLUX.1-dev` | T2I | [basic_flux_dev.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux_dev.py) |
+| flux2 | `black-forest-labs/FLUX.2-klein-4B`<br>`black-forest-labs/FLUX.2-klein-9B` | T2I | [basic_flux2_klein.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2_klein.py) |
+| flux2 | `black-forest-labs/FLUX.2-dev` | T2I | [basic_flux2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2.py) |
+| gamecraft | `FastVideo/HunyuanGameCraft-Diffusers` | I2V | [basic_gamecraft.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_gamecraft.py) |
+| gen3c | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | T2V | [basic_gen3c.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_gen3c.py) |
+| glm_image | `zai-org/GLM-Image` | T2I | [basic_glm_image.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_glm_image.py) |
+| hunyuan | `hunyuanvideo-community/HunyuanVideo` | T2V | — |
+| hunyuan | `FastVideo/FastHunyuan-diffusers` | T2V | — |
+| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | T2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
+| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled` | I2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
+| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | T2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
+| hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v_distilled` | I2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
+| hunyuan15 | `weizhou03/HunyuanVideo-1.5-Diffusers-1080p`<br>`weizhou03/HunyuanVideo-1.5-Diffusers-1080p-2SR` | — | [basic_hy15_1080p.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15_1080p.py) |
+| hyworld | `FastVideo/HY-WorldPlay-Bidirectional-Diffusers` | — | [basic_hyworld.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hyworld.py) |
+| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
+| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
+| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
+| kandinsky5 | `kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers` | T2V | [basic_kandinsky5_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_t2v.py) |
+| kandinsky5 | `kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers` | I2V | [basic_kandinsky5_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_i2v.py) |
+| kandinsky5 | `kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers` | I2V | [basic_kandinsky5_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_i2v.py) |
+| kandinsky5 | `kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers` | I2V | [basic_kandinsky5_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_kandinsky5_i2v.py) |
+| lingbot_video | `FastVideo/LingBot-Video-MoE-30B-A3B-Diffusers` | T2V | [basic_lingbot_video.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbot_video.py) |
+| lingbot_video | `FastVideo/LingBot-Video-Dense-1.3B-Diffusers` | T2V | [basic_lingbot_video.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbot_video.py) |
+| lingbotworld | `FastVideo/LingBot-World-Base-Cam-Diffusers` | I2V | [basic_lingbotworld_base_cam.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld_base_cam.py) |
+| lingbotworld2 | `robbyant/lingbot-world-v2-14b-causal-fast` | I2V | [basic_lingbotworld2_causal_fast.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld2_causal_fast.py) |
+| longcat | `FastVideo/LongCat-Video-T2V-Diffusers` | T2V | [basic_longcat_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_t2v.py) |
+| longcat | `FastVideo/LongCat-Video-I2V-Diffusers` | I2V | [basic_longcat_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_i2v.py) |
+| longcat | `FastVideo/LongCat-Video-VC-Diffusers` | — | [basic_longcat_vc.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_vc.py) |
+| ltx2 | `FastVideo/LTX2-Distilled-Diffusers`<br>`FastVideo/LTX2.3-Distilled-Diffusers`<br>`FastVideo/LTX-2.3-Distilled-Diffusers` | T2V | [basic_ltx2_distilled.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_distilled.py) |
+| ltx2 | `Lightricks/LTX-2.3`<br>`FastVideo/LTX2.3-base`<br>`FastVideo/LTX2.3-Diffusers` | T2V | [basic_ltx2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2.py) |
+| ltx2 | `Lightricks/LTX-2`<br>`FastVideo/LTX2-base`<br>`FastVideo/LTX2-Diffusers` | T2V | [basic_ltx2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2.py) |
+| matrixgame | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers`<br>`FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers`<br>`FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers`<br>`FastVideo/Matrix-Game-2.0-Base-Diffusers`<br>`FastVideo/Matrix-Game-2.0-GTA-Diffusers`<br>`FastVideo/Matrix-Game-2.0-TempleRun-Diffusers`<br>`mignonjia/mg_longtuning_distilled_zelda`<br>`mignonjia/mg_sf_distilled_zelda_1k_steps`<br>`mignonjia/mg_sf_distilled_zelda`<br>`mignonjia/mg_causal_zelda`<br>`mignonjia/mg_bidirectional_zelda` | I2V | [basic_matrixgame2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame2.py) |
+| matrixgame | `FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers` | I2V | [basic_matrixgame3.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame3.py) |
+| minimax_h3 | `MiniMaxAI/MiniMax-H3` | T2V, I2V | [T2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_t2v.py)<br>[FL2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_fl2va.py)<br>[Ref2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_ref2va.py) |
+| sd35 | `stabilityai/stable-diffusion-3.5-medium` | T2I | [basic_sd35_t2i.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_sd35_t2i.py) |
+| stable_audio | `FastVideo/stable-audio-open-1.0-Diffusers` | T2V | [basic_stable_audio.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_stable_audio.py) |
+| stable_audio | `FastVideo/stable-audio-open-small-Diffusers` | T2V | [basic_stable_audio_small.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_stable_audio_small.py) |
+| turbodiffusion | `loayrashid/TurboWan2.1-T2V-1.3B-Diffusers` | T2V | [basic_turbodiffusion.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion.py) |
+| turbodiffusion | `loayrashid/TurboWan2.1-T2V-14B-Diffusers` | T2V | [basic_turbodiffusion_14b.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion_14b.py) |
+| turbodiffusion | `loayrashid/TurboWan2.2-I2V-A14B-Diffusers` | I2V | [basic_turbodiffusion_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion_i2v.py) |
+| wan | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | T2V | [basic.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic.py) |
+| wan | `Wan-AI/Wan2.1-T2V-14B-Diffusers`<br>`FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers` | T2V | — |
+| wan | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | I2V | — |
+| wan | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | I2V | — |
+| wan | `weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers` | I2V | — |
+| wan | `IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers` | — | [basic_wan2_2_Fun.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2_Fun.py) |
+| wan | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers`<br>`FastVideo/FastWan2.1-T2V-14B-480P-Diffusers` | T2V | [basic_dmd.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dmd.py) |
+| wan | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | T2V, I2V | [basic_wan2_2_ti2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2_ti2v.py) |
+| wan | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers`<br>`FastVideo/FastWan2.2-TI2V-5B-Diffusers` | T2V, I2V | [basic_dmd.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dmd.py) |
+| wan | `decart-ai/Lucy-Edit-Dev`<br>`decart-ai/Lucy-Edit-1.1-Dev` | — | [basic_lucy_edit.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lucy_edit.py) |
+| wan | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | T2V | [basic_wan2_2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2.py) |
+| wan | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | I2V | [basic_wan2_2_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_wan2_2_i2v.py) |
+| wan | `wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers` | T2V | [basic_self_forcing_causal.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal.py) |
+| wan | `rand0nmr/SFWan2.2-T2V-A14B-Diffusers` | T2V | [basic_self_forcing_causal_wan2_2_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_t2v.py) |
+| wan | `FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers` | I2V | [basic_self_forcing_causal_wan2_2_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py) |
+| zimage | `Tongyi-MAI/Z-Image-Turbo` | T2I | [basic_zimage.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_zimage.py) |
+
+**Note (stable_audio)**: the Stable Audio Open pipelines generate audio
+(`StableAudioT2AConfig` / `StableAudioOpenSmallConfig`); they are registered
+under the generic T2V workload option in the registry.
+
+**Note (MiniMax H3)**: T2VA, FL2VA, and Ref2VA all generate video with stereo
+audio. Use the Ref2VA example when passing ordered image, video, or audio
+references.
+
+**Note (Wan-VACE)**: not currently supported — no VACE pipeline or registered
+model ID exists on `main`
+([#1435](https://github.com/hao-ai-lab/FastVideo/issues/1435)). The closest
+supported path is the Wan2.1-Fun control pipeline
+(`IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers`).
+
 The symbols used have the following meanings:
 
 - ✅ = Full compatibility
@@ -25,6 +116,9 @@ The `HuggingFace Model ID` can be passed directly to
 `from_pretrained()`. FastVideo then uses model-specific default settings for
 pipeline initialization and sampling.
 
+Registered models absent from this table have not been validated against these
+optimizations: absence means **untested**, not incompatible.
+
 <style>
   /* Target tables in this section */
   #models-x-optimization + p + table {
@@ -56,7 +150,7 @@ pipeline initialization and sampling.
 | Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn (Legacy Branch) | Sage Attn | VSA | BSA |
 |------------|---------------------|-------------|----------|-------------------|-----------|-----|-----|
 | FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
-| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
+| FastWan2.2 TI2V 5B Full Attn | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
 | Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
 | DreamX-World 5B Cam | `FastVideo/DreamX-World-5B-Cam-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | DreamX-World 5B AR | `FastVideo/DreamX-World-5B-Diffusers` | 704px1280p | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
@@ -65,14 +159,14 @@ pipeline initialization and sampling.
 | Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
 | HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
 | FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
-| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
-| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
-| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
+| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
+| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
+| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
 | Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
 | TurboWan2.1 T2V 1.3B | `loayrashid/TurboWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | TurboWan2.1 T2V 14B | `loayrashid/TurboWan2.1-T2V-14B-Diffusers` | 480P, 720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | TurboWan2.2 I2V A14B | `loayrashid/TurboWan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| LongCat T2V 13.6B | See note** | 480P<br>720P | ❌ | ❌ | ❌ | ⭕ | ✅ |
+| LongCat T2V 13.6B | `FastVideo/LongCat-Video-T2V-Diffusers` | 480P<br>720P | ❌ | ❌ | ❌ | ⭕ | ✅ |
 | Matrix Game 2.0 Base Distilled | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Matrix Game 2.0 GTA Distilled | `FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 | Matrix Game 2.0 TempleRun Distilled | `FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
@@ -98,6 +192,21 @@ resolve default pipeline and sampling configuration for it.
 `FastVideo/GEN3C-Cosmos-7B-Diffusers`) or convert locally with
 `scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py`.
 
+## Hardware and OS
+
+Per the installation guides:
+
+- **NVIDIA GPU (x86_64)** — CUDA 12.6 or 13.0; see the
+  [GPU install guide](../getting_started/installation/gpu.md).
+- **NVIDIA DGX Spark (GB10, aarch64)** — CUDA 13, from-source kernel build; see
+  the [DGX Spark install guide](../getting_started/installation/spark.md).
+- **Apple silicon (MPS)** — macOS 14 or newer; see the
+  [MPS install guide](../getting_started/installation/mps.md) and
+  [`basic_mps.py`](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_mps.py).
+
+Optimization-specific hardware constraints (e.g. STA requiring Hopper) are
+listed under [Special requirements](#special-requirements).
+
 ## Special requirements
 
 ### Sliding Tile Attention

docs/training/attn_qat.md

@@ -49,7 +49,7 @@ configuration reference.
 Download the published preprocessed dataset:
 
 ```bash
-bash examples/training/finetune/wan_t2v_1.3B/mixkit/download_mixkit_data.sh
+bash examples/datasets/mixkit/download_dataset.sh
 ```
 
 ## Stage 1: supervised Attn-QAT fine-tuning

docs/training/finetune.md

@@ -182,7 +182,7 @@ Ready-to-run training scripts are available for multiple models:
 
 Each example includes:
 
-- `download_dataset.sh` — download sample data
+- a README pointing at the matching download script under `examples/datasets/`
 - `preprocess_*.sh` — run preprocessing
 - `finetune_*.sh` — full finetune launcher
 - `finetune_*_lora.sh` — LoRA finetune launcher

docs/training/overview.md

@@ -48,7 +48,7 @@ For the complete two-stage Wan2.1 MixKit quantization-aware workflow, see
 
 Each example includes:
 
-- `download_dataset.sh` — download sample data
+- a README pointing at the matching download script under `examples/datasets/`
 - `preprocess_*.sh` — run preprocessing
 - `finetune_*.sh` — launch training (full finetune or LoRA)
 - `validation.json` — validation prompts for checkpoints

docs/training/train_infra.md

@@ -212,6 +212,29 @@ pipeline:
   flow_shift: 8
 ```
 
+Registered transformer linear-quantization configs can also be selected by
+name. For example, the LTX-2 NVFP4-QAT recipe applies real FP4 forward GEMMs
+with a straight-through-estimator backward to its deployment-targeted
+attention/FFN projections:
+
+```yaml
+pipeline:
+  dit_config:
+    quant_config: nvfp4_qat_train
+```
+
+The LTX-2 recipe in
+`examples/train/configs/overfit_ltx2_t2v_nvfp4_qat.yaml` combines that linear
+configuration with `models.student.attention_backend: ATTN_QAT_TRAIN` for
+video-attention forward/backward. On sm120, its validation callback temporarily
+switches those layers to `ATTN_QAT_INFER`.
+On GB200, set `callbacks.validation.attn_qat_infer: false` to keep validation on
+the train-time QAT backend; the inference kernel is sm120-only.
+
+User-adaptable LTX-2 fine-tuning recipes (full, LoRA, and NVFP4 QAT) live in
+`examples/train/configs/fine_tuning/ltx2/`, alongside the other model
+families under `examples/train/configs/fine_tuning/`.
+
 ---
 
 ## Training Methods

docs/utilities/debugging.md

@@ -77,7 +77,9 @@ If forcing a backend fails, verify optional dependencies are installed:
 - `SAGE_ATTN`: SageAttention package
 - `SAGE_ATTN_THREE`: upstream `sageattn3` package
 - `ATTN_QAT_INFER`: `fastvideo-kernel` checkout/source install that exposes
-  `attn_qat_infer`
+  `attn_qat_infer`, AND a consumer-Blackwell (sm_120/sm_121) GPU -- on any
+  other device the backend reports unavailable (even if a CUDA 13 wheel
+  bundles the extension) and selection falls back to FlashAttention
 - `ATTN_QAT_TRAIN`: `fastvideo-kernel`; its runtime-JIT Triton implementation
   selects an optimized route on SM100, joins the quantized and STE P@V paths on
   SM120, and retains the previous route for unsupported configurations. See

examples/datasets/mixkit/download_dataset.sh

@@ -3,10 +3,11 @@
 #
 # This is the MixKit subset already VAE-encoded (Wan2.1-T2V-1.3B) and text-embedded
 # into Parquet shards, so it can be fed straight to training with no further
-# preprocessing. To build the Parquet from raw videos yourself, see README.md.
+# preprocessing. To build the Parquet from raw videos yourself, see
+# examples/training/finetune/wan_t2v_1.3B/mixkit/README.md.
 #
 # Usage (run from the repo root):
-#   bash examples/training/finetune/wan_t2v_1.3B/mixkit/download_mixkit_data.sh [DATA_ROOT]
+#   bash examples/datasets/mixkit/download_dataset.sh [DATA_ROOT]
 set -euo pipefail
 
 DATA_ROOT=${1:-data/HD-Mixkit-Finetune-Wan}