[ci]: add preprocessing integration coverage

.buildkite/pipeline.yml

@@ -1,8 +1,6 @@
 env:
   IMAGE_VERSION: "py3.12-latest"
   BUILDKITE_CLEAN_CHECKOUT: true
-  # Buildkite only launches Modal; remote jobs initialize their own submodules.
-  BUILDKITE_GIT_SUBMODULES: false
 
 notify:
   - github_commit_status:
@@ -68,8 +66,8 @@ 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"
+    - label: ":microscope: Unit Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "unit_test"
       command: "timeout 90m .buildkite/scripts/pr_test.sh"
       retry:
         automatic:
@@ -79,8 +77,8 @@ steps:
             limit: 2
       agents:
         queue: "default"
-    - label: ":microscope: Unit Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "unit_test"
+    - label: ":microscope: Preprocessing Tests"
+      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "preprocessing"
       command: "timeout 90m .buildkite/scripts/pr_test.sh"
       retry:
         automatic:
@@ -335,6 +333,29 @@ steps:
                   - TEST_TYPE=unit_test
                 agents:
                   queue: "default"
+            - path:
+                - "fastvideo/pipelines/preprocess/**"
+                - "fastvideo/workflow/preprocess/**"
+                - "fastvideo/dataset/dataloader/parquet_io.py"
+                - "fastvideo/dataset/dataloader/record_schema.py"
+                - "fastvideo/dataset/dataloader/schema.py"
+                - "fastvideo/configs/configs.py"
+                - "fastvideo/fastvideo_args.py"
+                - "fastvideo/tests/workflow/test_t2v_preprocessing_e2e.py"
+                - "fastvideo/tests/nightly/reference_video_1_sample_v0.mp4"
+                - "fastvideo/tests/modal/pr_test.py"
+                - ".buildkite/pipeline.yml"
+                - ".buildkite/scripts/pr_test.sh"
+                - ".github/workflows/ci-slash-commands.yml"
+                - "pyproject.toml"
+                - "docker/Dockerfile"
+              config:
+                command: "timeout 20m .buildkite/scripts/pr_test.sh"
+                label: ":microscope: Preprocessing Tests"
+                env:
+                  - TEST_TYPE=preprocessing
+                agents:
+                  queue: "default"
             - path:
                 - "apps/dreamverse/**"
                 - "pyproject.toml"
@@ -415,7 +436,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile"
               config:
-                command: "timeout 25m .buildkite/scripts/pr_test.sh"
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
                 label: ":test_tube: Training Tests"
                 env:
                   - TEST_TYPE=training
@@ -426,7 +447,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile"
               config:
-                command: "timeout 25m .buildkite/scripts/pr_test.sh"
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
                 label: ":test_tube: Distillation DMD Tests"
                 env:
                   - TEST_TYPE=distillation_dmd

.buildkite/scripts/pr_test.sh

@@ -187,10 +187,6 @@ 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)
@@ -237,6 +233,10 @@ case "$TEST_TYPE" in
         log "Running unit tests..."
         MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
         ;;
+    "preprocessing")
+        log "Running preprocessing integration test..."
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_preprocessing_tests"
+        ;;
     "dreamverse_app")
         log "Running DreamVerse app tests..."
         MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_dreamverse_app_tests"

.github/workflows/ci-precommit.yml

@@ -27,25 +27,14 @@ jobs:
         ref: ${{ inputs.ref || '' }}
     # For PR events, lint the PR head — but keep the hook definitions from
     # the base branch so an untrusted PR cannot alter what gets executed.
-    # The gate scripts are saved too: the self-test step below executes them,
-    # so it must run the base-branch copies, not the PR head's.
-    - name: Save trusted hook config and gate scripts
+    - name: Save trusted hook config
       if: github.event_name == 'pull_request_target'
-      run: |
-        cp .pre-commit-config.yaml "$RUNNER_TEMP/trusted-pre-commit-config.yaml"
-        cp -a .github/scripts "$RUNNER_TEMP/trusted-scripts"
-        echo "GATE_SCRIPTS_DIR=$RUNNER_TEMP/trusted-scripts" >> "$GITHUB_ENV"
-    # allow-unsafe-pr-checkout acknowledges checkout's pull_request_target
-    # guard: the head is data for the trusted hooks to lint; nothing from it
-    # is executed (config and gate scripts are pinned to the base branch
-    # above) and credentials are not persisted. SHA-pinned to v4.4.0 because
-    # actionlint's action schema does not know the new input yet.
-    - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 # v4.4.0
+      run: cp .pre-commit-config.yaml "$RUNNER_TEMP/trusted-pre-commit-config.yaml"
+    - uses: actions/checkout@v4
       if: github.event_name == 'pull_request_target'
       with:
         ref: ${{ github.event.pull_request.head.sha }}
         persist-credentials: false
-        allow-unsafe-pr-checkout: true
     - name: Restore trusted hook config
       if: github.event_name == 'pull_request_target'
       run: cp "$RUNNER_TEMP/trusted-pre-commit-config.yaml" .pre-commit-config.yaml
@@ -59,7 +48,5 @@ jobs:
       with:
         extra_args: --all-files --hook-stage manual
     # After pre-commit so a self-test failure cannot mask lint failures.
-    # GATE_SCRIPTS_DIR points at the base-branch copy on fork PRs (set above);
-    # push / workflow_call runs use the checked-out tree directly.
     - name: Full-suite gate self-test
-      run: bash "${GATE_SCRIPTS_DIR:-.github/scripts}/test_gate_full_suite.sh"
+      run: bash .github/scripts/test_gate_full_suite.sh

.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 golden-gate 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 preprocessing dreamverse ssim 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
@@ -137,8 +137,9 @@ jobs:
 
           declare -A MAP=(
             [encoder]=encoder [vae]=vae [transformer]=transformer
-            [kernel]=kernel_tests [unit]=unit_test [dreamverse]=dreamverse_app
-            [ssim]=ssim [golden-gate]=golden_gate [training]=training
+            [kernel]=kernel_tests [unit]=unit_test [preprocessing]=preprocessing
+            [dreamverse]=dreamverse_app
+            [ssim]=ssim [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

@@ -13,23 +13,16 @@ on:
         required: false
         default: false
         type: boolean
-  # Auto-rebuild the CUDA images when a repository-controlled image input
-  # changes on main. This includes the trusted SM89 kernel artifact's source,
-  # metadata/key helper, ABI dependency metadata, and build orchestration.
-  # Dreamverse (apps/dreamverse/docker/Dockerfile) and the ROCm Dockerfile stay
-  # manual-dispatch only.
+  # 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.
   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:
@@ -57,7 +50,7 @@ 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 an in-scope main push (inputs are null on push). The
+    # on a push that changed docker/Dockerfile (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' }}

.github/workflows/infra-docs.yml

@@ -11,7 +11,9 @@ on:
       - 'requirements-mkdocs.txt'
       - 'scripts/check_docs_links.py'
       - '.github/workflows/infra-docs.yml'
-  pull_request:
+  # Run the trusted base-branch workflow so fork PRs can be skipped without
+  # waiting for maintainer approval.
+  pull_request_target:
     branches: [ main ]
     paths:
       - 'docs/**'
@@ -24,21 +26,19 @@ on:
 
 permissions:
   contents: read
-  pages: write
-  id-token: write
-
-concurrency:
-  group: "pages"
-  cancel-in-progress: false
 
 jobs:
   build:
+    # MkDocs executes repository code; only trusted same-repository PRs run it.
+    if: github.event_name == 'push' || github.event.pull_request.head.repo.full_name == github.repository
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
         uses: actions/checkout@v4
         with:
+          ref: ${{ github.event.pull_request.head.sha || github.sha }}
           fetch-depth: 0
+          persist-credentials: false
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -52,6 +52,7 @@ jobs:
         run: uv pip install --system -r requirements-mkdocs.txt
 
       - name: Setup Pages
+        if: github.event_name == 'push'
         uses: actions/configure-pages@v4
 
       - name: Build documentation
@@ -61,17 +62,22 @@ jobs:
         run: python scripts/check_docs_links.py
 
       - name: Upload artifact
+        if: github.event_name == 'push'
         uses: actions/upload-pages-artifact@v3
         with:
           path: ./site
 
   deploy:
+    permissions:
+      pages: write
+      id-token: write
+    concurrency: pages
     environment:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
     runs-on: ubuntu-latest
     needs: build
-    if: github.ref == 'refs/heads/main'
+    if: github.event_name == 'push'
     steps:
       - name: Deploy to GitHub Pages
         id: deployment

.gitignore

@@ -6,7 +6,6 @@ results/
 wandb/
 *.ipynb
 *.jpg
-!examples/datasets/lingbotworld2/image.jpg
 *.safetensors
 *.mp4
 *.png
@@ -35,7 +34,6 @@ env
 *.log
 weights/
 logs/
-/Z-Image/
 official_weights/
 converted_weights/
 
@@ -75,7 +73,6 @@ 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

README.md

@@ -9,7 +9,7 @@
 **FastVideo is a unified post-training and real-time inference framework for accelerated video generation.**
 
 ## NEWS
-- `2026/06/23`: Release FastWan-QAD: 5s of Video generated in 1.8s E2E. See the [FastWan-QAD models](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B), [Attn-QAT training guide](https://haoailab.com/FastVideo/training/attn_qat/), and [blog](https://haoailab.com/blogs/fastwan-qad/).
+- `2026/06/23`: Release FastWan-QAD: 5s of Video generated in 1.8s E2E. [FastWan-QAD models](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B), check out the [Blog](https://haoailab.com/blogs/fastwan-qad/).
 - `2026/03/17`: Release demo: Into the Dreamverse: Vibe Directing in FastVideo, check out the [Blog](https://haoailab.com/blogs/dreamverse/).
 - `2026/03/13`: Release demo: Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU, check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/).
 - `2025/11/19`: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py).
@@ -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 the supported training workflows, and the [support matrix](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for supported models.
+  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
 - State-of-the-art performance optimizations for inference
   - Sequence Parallelism for distributed inference
   - Multiple state-of-the-art attention backends

apps/dreamverse/scripts/launch/launch_backend_dreamverse.sh

@@ -39,17 +39,6 @@ export FASTVIDEO_GENERATION_SEGMENT_CAP="${FASTVIDEO_GENERATION_SEGMENT_CAP:-6}"
 export FASTVIDEO_PROMPT_AUTO_SLEEP_MS="${FASTVIDEO_PROMPT_AUTO_SLEEP_MS:-120}"
 export FASTVIDEO_PROMPT_AUTO_TIMEOUT_MS="${FASTVIDEO_PROMPT_AUTO_TIMEOUT_MS:-1800}"
 
-if [[ "${ENABLE_TORCH_COMPILE}" == "1" ]]; then
-  # Persist Inductor, AOTAutograd, and Triton artifacts across launches.
-  export DREAMVERSE_TORCH_COMPILE_CACHE_ROOT="${DREAMVERSE_TORCH_COMPILE_CACHE_ROOT:-${HOME}/.cache/dreamverse/torch_compile}"
-  export TORCHINDUCTOR_CACHE_DIR="${TORCHINDUCTOR_CACHE_DIR:-${DREAMVERSE_TORCH_COMPILE_CACHE_ROOT}/inductor}"
-  export TRITON_CACHE_DIR="${TRITON_CACHE_DIR:-${DREAMVERSE_TORCH_COMPILE_CACHE_ROOT}/triton}"
-  export TORCHINDUCTOR_FX_GRAPH_CACHE="${TORCHINDUCTOR_FX_GRAPH_CACHE:-1}"
-  export TORCHINDUCTOR_AUTOGRAD_CACHE="${TORCHINDUCTOR_AUTOGRAD_CACHE:-1}"
-  mkdir -p "${TORCHINDUCTOR_CACHE_DIR}" "${TRITON_CACHE_DIR}"
-  echo "[launch-demo] torch.compile cache: ${DREAMVERSE_TORCH_COMPILE_CACHE_ROOT}"
-fi
-
 cd "${DREAMVERSE_ROOT}"
 
 if ! command -v dreamverse-server >/dev/null 2>&1; then

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

@@ -13,9 +13,10 @@ test.describe('create inference job', () => {
   test('creates a T2V job and shows it in the queue', async ({ page }) => {
     await page.goto('/inference');
 
-    // 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();
+    // 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();
     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
- * with playback controls or an explicit media-error fallback.
+ * (an <article> wrapping a <video>) captioned with its prompt.
  */
 test.describe('gallery', () => {
   skipWithoutMock();
@@ -30,15 +30,12 @@ test.describe('gallery', () => {
       page.getByRole('heading', { level: 1, name: 'Gallery' }),
     ).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();
+    // 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 video = tile.locator('video');
-    if (await video.isVisible()) {
-      await expect(video).toHaveAttribute('controls', '');
-    }
+    await expect(page.getByText(completed!.prompt)).toBeVisible();
   });
 });

apps/fastvideo_studio/e2e/generators.spec.ts

@@ -1,39 +0,0 @@
-import { expect, test } from '@playwright/test';
-
-import { skipWithoutMock } from './helpers';
-
-/**
- * Warm-model slot: load a model through the mock (which flips
- * loading -> ready after ~2s, surfaced by the panel's 5s poll), then unload it.
- */
-test.describe('generators', () => {
-  skipWithoutMock();
-
-  test('loads and unloads the resident model', async ({ page }) => {
-    await page.goto('/inference');
-
-    const panel = page.getByRole('region', { name: 'Warm models' });
-    await expect(panel).toBeVisible();
-    await expect(panel.getByText('No model loaded')).toBeVisible();
-
-    await panel
-      .getByLabel('Model to load')
-      .selectOption('Wan-AI/Wan2.1-T2V-1.3B-Diffusers');
-    await panel.getByRole('button', { name: 'Load model' }).click();
-
-    await expect(panel.getByText('Wan2.1 T2V 1.3B Diffusers')).toBeVisible();
-    await expect(panel.getByText('ready')).toBeVisible();
-
-    await panel.getByRole('button', { name: 'Unload' }).click();
-    await expect(panel.getByText('No model loaded')).toBeVisible();
-  });
-
-  test('engine console streams output while open', async ({ page }) => {
-    await page.goto('/inference');
-
-    const engineConsole = page.getByRole('region', { name: 'Engine output' });
-    await engineConsole.getByRole('button', { name: 'Engine output' }).click();
-
-    await expect(engineConsole.getByText(/\[engine\]/).first()).toBeVisible();
-  });
-});

apps/fastvideo_studio/e2e/shell.spec.ts

@@ -42,74 +42,6 @@ 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/gpu.py

@@ -72,90 +72,3 @@ def get_gpu_snapshot() -> dict[str, Any]:
     except Exception as exc:  # NVMLError, driver issues, …
         logger.warning("GPU snapshot failed: %s", exc)
         return {"available": False, "gpus": [], "error": str(exc)}
-
-
-def _remote_gpu_probe() -> dict[str, Any]:
-    """Self-contained per-node NVML probe (runs as a ray task on each node;
-    no fastvideo_studio import — worker environments don't have apps/ on
-    their path, so cloudpickle must carry this by value)."""
-    import contextlib as _ctx
-    import socket as _socket
-    out: dict[str, Any] = {"hostname": _socket.gethostname(), "available": False, "gpus": [], "error": None}
-    try:
-        import pynvml
-        pynvml.nvmlInit()
-        for i in range(pynvml.nvmlDeviceGetCount()):
-            h = pynvml.nvmlDeviceGetHandleByIndex(i)
-            name = pynvml.nvmlDeviceGetName(h)
-            if isinstance(name, bytes):
-                name = name.decode()
-            mem = pynvml.nvmlDeviceGetMemoryInfo(h)
-            util = pynvml.nvmlDeviceGetUtilizationRates(h)
-            temp = power = plimit = None
-            with _ctx.suppress(pynvml.NVMLError):
-                temp = int(pynvml.nvmlDeviceGetTemperature(h, pynvml.NVML_TEMPERATURE_GPU))
-            with _ctx.suppress(pynvml.NVMLError):
-                power = pynvml.nvmlDeviceGetPowerUsage(h) / 1000.0
-                plimit = pynvml.nvmlDeviceGetEnforcedPowerLimit(h) / 1000.0
-            out["gpus"].append({
-                "index": i,
-                "name": name,
-                "utilization": int(util.gpu),
-                "memory_used_mib": int(mem.used / (1024 * 1024)),
-                "memory_total_mib": int(mem.total / (1024 * 1024)),
-                "temperature_c": temp,
-                "power_watts": power,
-                "power_limit_watts": plimit,
-            })
-        out["available"] = True
-    except Exception as exc:  # noqa: BLE001 -- reported per node
-        out["error"] = str(exc)
-    return out
-
-
-def get_cluster_snapshot() -> dict[str, Any]:
-    """Cluster-wide GPU/host telemetry.
-
-    When this process is connected to a ray cluster (a model has been
-    loaded), probe every alive node via per-node ray tasks. Otherwise fall
-    back to this host's NVML snapshot. Never raises.
-    """
-    import socket
-
-    local = get_gpu_snapshot()
-    local_node = {"hostname": socket.gethostname(), "ip": None, "is_this_host": True,
-                  "cpus": None, "ray_gpus": None, **local}
-    out: dict[str, Any] = {"mode": "local", "nodes": [local_node],
-                           "resources": None, "error": None}
-    try:
-        import ray
-        if not ray.is_initialized():
-            out["error"] = "not connected to a ray cluster yet (load a model first); showing the API host only"
-            return out
-        from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy
-        alive = [n for n in ray.nodes() if n.get("Alive")]
-        probe = ray.remote(num_cpus=0)(_remote_gpu_probe)
-        refs = [probe.options(scheduling_strategy=NodeAffinitySchedulingStrategy(
-            node_id=n["NodeID"], soft=True)).remote() for n in alive]
-        snaps = ray.get(refs, timeout=15)
-        nodes = []
-        for n, snap in zip(alive, snaps, strict=True):
-            nodes.append({
-                "ip": n.get("NodeManagerAddress"),
-                "is_this_host": snap.get("hostname") == socket.gethostname(),
-                "cpus": n.get("Resources", {}).get("CPU"),
-                "ray_gpus": n.get("Resources", {}).get("GPU"),
-                **snap,
-            })
-        out["mode"] = "ray"
-        out["nodes"] = nodes
-        out["resources"] = {
-            "gpus_total": ray.cluster_resources().get("GPU", 0.0),
-            "gpus_available": ray.available_resources().get("GPU", 0.0),
-        }
-    except Exception as exc:  # noqa: BLE001 -- degrade to the local view
-        logger.warning("cluster snapshot failed: %s", exc)
-        out["mode"] = "local"
-        out["nodes"] = [local_node]
-        out["error"] = f"cluster probe failed: {exc}"
-    return out

apps/fastvideo_studio/job_runner.py

@@ -41,11 +41,6 @@ _TQDM_FRAC_RE = re.compile(r"\b(\d+)/(\d+)\b")
 
 _MAX_LOG_LINES = 2000  # ring-buffer cap per job
 
-# ray's log relay prefixes worker lines like "(RayWorkerWrapper pid=123, ip=…)"
-# — usually wrapped in ANSI color codes, which must be stripped before matching.
-_RAY_RELAY_RE = re.compile(r"^\(\w+ pid=")
-_ANSI_RE = re.compile(r"\x1b\[[0-9;]*m")
-
 
 class JobStatus(str, enum.Enum):
     PENDING = "pending"
@@ -264,36 +259,13 @@ class JobRunner:
         self._jobs_lock = threading.Lock()
         self._load_jobs()
 
-        # Exactly one generator lives in memory at a time. Loading a new
-        # config always releases the old instance first (shutdown + placement
-        # group teardown); unload deletes it outright.
-        self._generator: Any | None = None
-        self._generator_config: dict[str, Any] | None = None
-        self._generator_state: str = "empty"  # empty | loading | ready | failed
-        self._generator_error: str | None = None
-        self._generator_lock = threading.Lock()  # guards the fields above
-        # Serializes every slot transition (preload, job-triggered replace,
-        # unload). Held for the full duration of a load.
-        self._load_lock = threading.Lock()
-        # All generator creations run on this ONE persistent thread: the mp
-        # executor arms prctl(PR_SET_PDEATHSIG, SIGKILL) in its workers, and
-        # on Linux that fires when the CREATING THREAD exits — a generator
-        # spawned from a short-lived thread loses all its workers (silent
-        # SIGKILL, zombies) the moment that thread finishes.
-        import queue as _queue
-        self._loader_queue: _queue.Queue = _queue.Queue()
-        threading.Thread(target=self._loader_loop, daemon=True,
-                         name="generator-loader").start()
-        # The inference job currently generating, fed by the engine log tee
-        # (ray relays worker output to the driver; tqdm lines land there).
-        self._active_inference_job: Job | None = None
+        # Cache of loaded generators keyed by model config so that we only pay
+        # the model-loading cost once per model configuration.
+        self._generators: dict[tuple, Any] = {}
+        self._generators_lock = threading.Lock()
 
         # Shared Manager for log queues (avoids spawning a new process per job)
         self._mp_manager = get_mp_context().Manager()
-        # One queue for the generator's whole life: mp workers get it at spawn
-        # (creation-time attach). Sending a Manager proxy over the executor's
-        # worker pipes post-hoc (set_log_queue RPC) breaks the pipe.
-        self._worker_log_queue = self._mp_manager.Queue()
         atexit.register(self._shutdown)
 
         # Ensure directories exist
@@ -645,211 +617,6 @@ class JobRunner:
             "phase": job._log_buf.phase,
         }
 
-    @staticmethod
-    def _generator_config_dict(
-        model_id: str,
-        workload_type: str,
-        num_gpus: int,
-        dit_cpu_offload: bool = False,
-        text_encoder_cpu_offload: bool = False,
-        vae_cpu_offload: bool = False,
-        image_encoder_cpu_offload: bool = False,
-        use_fsdp_inference: bool = False,
-        enable_torch_compile: bool = False,
-        vsa_sparsity: float = 0.0,
-        tp_size: int = -1,
-        sp_size: int = -1,
-    ) -> dict[str, Any]:
-        """Canonical engine-config dict; equality here == same generator."""
-        return {
-            "model_id": model_id,
-            "workload_type": workload_type,
-            "num_gpus": num_gpus,
-            "dit_cpu_offload": dit_cpu_offload,
-            "text_encoder_cpu_offload": text_encoder_cpu_offload,
-            "vae_cpu_offload": vae_cpu_offload,
-            "image_encoder_cpu_offload": image_encoder_cpu_offload,
-            "use_fsdp_inference": use_fsdp_inference,
-            "enable_torch_compile": enable_torch_compile,
-            "vsa_sparsity": vsa_sparsity,
-            "tp_size": tp_size,
-            "sp_size": sp_size,
-        }
-
-    def _slot_entry(self) -> dict[str, Any]:
-        return {
-            "state": self._generator_state,
-            "error": self._generator_error,
-            **(self._generator_config or {}),
-        }
-
-    def _running_inference_jobs(self) -> list[str]:
-        with self._jobs_lock:
-            return [j.id for j in self._jobs.values()
-                    if j.status == JobStatus.RUNNING and j.job_type == "inference"]
-
-    def _loader_loop(self) -> None:
-        while True:
-            fn = self._loader_queue.get()
-            try:
-                fn()
-            except BaseException:  # noqa: BLE001 -- surfaced via the caller's box
-                pass
-            finally:
-                self._loader_queue.task_done()
-
-    def _run_on_loader(self, fn: Any) -> Any:
-        """Run ``fn`` on the persistent loader thread and return its result."""
-        box: dict[str, Any] = {}
-        done = threading.Event()
-
-        def wrapped() -> None:
-            try:
-                box["r"] = fn()
-            except BaseException as exc:  # noqa: BLE001 -- re-raised below
-                box["e"] = exc
-            finally:
-                done.set()
-
-        self._loader_queue.put(wrapped)
-        done.wait()
-        if "e" in box:
-            raise box["e"]
-        return box["r"]
-
-    def preload_generator(self, **params: Any) -> dict[str, Any]:
-        """Load a model into memory ahead of time. One load at a time; loading
-        a different config always releases the current instance first."""
-        config = self._generator_config_dict(**params)
-        with self._generator_lock:
-            if self._generator_state == "ready" and self._generator_config == config:
-                return self._slot_entry()
-        if not self._load_lock.acquire(blocking=False):
-            raise RuntimeError("a model load is already in progress")
-        try:
-            if self._running_inference_jobs():
-                raise RuntimeError("cannot swap models while inference jobs are running")
-            with self._generator_lock:
-                self._generator_state = "loading"
-                self._generator_config = config
-                self._generator_error = None
-                entry = self._slot_entry()
-        except BaseException:
-            self._load_lock.release()
-            raise
-
-        def _load() -> None:
-            try:  # the preload owns _load_lock until the load resolves
-                self._run_on_loader(lambda: self._load_into_slot_locked(config))
-            except Exception:  # noqa: BLE001 -- state already set to failed
-                pass
-            finally:
-                self._load_lock.release()
-
-        threading.Thread(target=_load, daemon=True, name="generator-preload").start()
-        return entry
-
-    def _load_into_slot_locked(self, config: dict[str, Any]) -> Any:
-        """Release whatever is resident and load ``config``. Caller MUST hold
-        ``_load_lock``. State is 'loading' on entry or set here."""
-        with self._generator_lock:
-            gen = self._generator
-            self._generator = None
-            self._generator_state = "loading"
-            self._generator_config = config
-            self._generator_error = None
-        if gen is not None:
-            logger.info("Releasing resident generator before loading a new one")
-            gen.shutdown()
-            del gen
-
-        # Import lazily so starting the server is fast even without a GPU.
-        from fastvideo import VideoGenerator
-
-        # Deployment-level knobs (set where the API server is launched):
-        # FASTVIDEO_STUDIO_MODEL_PATHS="id=/local/dir,..." serves a registered
-        # model id from local weights instead of the HF hub;
-        # FASTVIDEO_STUDIO_EXECUTOR_BACKEND=ray runs workers on an existing
-        # Ray cluster (the multi-node path — "mp" spawns local processes only).
-        model_path = config["model_id"]
-        for pair in os.environ.get("FASTVIDEO_STUDIO_MODEL_PATHS", "").split(","):
-            mid, sep, path = pair.partition("=")
-            if sep and mid.strip() == config["model_id"]:
-                model_path = path.strip()
-        executor_kwargs: dict[str, Any] = {}
-        backend = os.environ.get("FASTVIDEO_STUDIO_EXECUTOR_BACKEND")
-        if backend:
-            executor_kwargs["distributed_executor_backend"] = backend
-
-        logger.info("Loading model %s (%s)", config["model_id"],
-                    ", ".join(f"{k}={v}" for k, v in config.items() if k != "model_id"))
-        try:
-            new_gen = VideoGenerator.from_pretrained(
-                model_path,
-                workload_type=config["workload_type"],
-                num_gpus=config["num_gpus"],
-                dit_cpu_offload=config["dit_cpu_offload"],
-                # FastVideoArgs defaults this True, which disables FSDP and
-                # parks a full DiT copy in host RAM per worker — the mp
-                # executor's 4 workers OOM-killed the node silently. The UI's
-                # offload toggles are the studio's contract; layerwise off.
-                dit_layerwise_offload=False,
-                text_encoder_cpu_offload=config["text_encoder_cpu_offload"],
-                vae_cpu_offload=config["vae_cpu_offload"],
-                image_encoder_cpu_offload=config["image_encoder_cpu_offload"],
-                use_fsdp_inference=config["use_fsdp_inference"],
-                enable_torch_compile=config["enable_torch_compile"],
-                VSA_sparsity=config["vsa_sparsity"],
-                tp_size=config["tp_size"],
-                sp_size=config["sp_size"],
-                log_queue=self._worker_log_queue,
-                **executor_kwargs,
-            )
-        except Exception as exc:
-            logger.exception("Model load failed for %s", config["model_id"])
-            with self._generator_lock:
-                self._generator_state = "failed"
-                self._generator_error = str(exc)
-            raise
-        with self._generator_lock:
-            self._generator = new_gen
-            self._generator_config = config
-            self._generator_state = "ready"
-            self._generator_error = None
-        return new_gen
-
-    def list_generators(self) -> list[dict[str, Any]]:
-        """The resident slot, or empty when nothing is loaded."""
-        with self._generator_lock:
-            if self._generator_state == "empty":
-                return []
-            return [self._slot_entry()]
-
-    def unload_generator(self, **_ignored: Any) -> bool:
-        """Shut down and delete the resident generator, freeing GPU memory."""
-        if not self._load_lock.acquire(blocking=False):
-            raise RuntimeError("cannot unload while a model load is in progress")
-        try:
-            running = self._running_inference_jobs()
-            if running:
-                raise RuntimeError(f"cannot unload while inference jobs are running: {running}")
-            with self._generator_lock:
-                gen = self._generator
-                empty = self._generator_state == "empty"
-                self._generator = None
-                self._generator_state = "empty"
-                self._generator_config = None
-                self._generator_error = None
-            if empty:
-                return False
-            if gen is not None:
-                logger.info("Releasing resident generator")
-                gen.shutdown()
-                del gen
-            return True
-        finally:
-            self._load_lock.release()
-
     def _get_or_create_generator(
         self,
         model_id: str,
@@ -866,53 +633,69 @@ class JobRunner:
         sp_size: int = -1,
         log_queue: mp.Queue | None = None,
     ) -> Any:
-        """Return the resident generator if the config matches; otherwise
-        replace the slot. Blocks behind any in-flight load — every slot
-        transition happens under ``_load_lock``, so a job can never observe a
-        half-replaced slot."""
-        del log_queue  # single-slot generators log via the engine tee
-        config = self._generator_config_dict(
-            model_id=model_id,
+        cache_key = (
+            model_id,
+            workload_type,
+            num_gpus,
+            dit_cpu_offload,
+            text_encoder_cpu_offload,
+            vae_cpu_offload,
+            image_encoder_cpu_offload,
+            use_fsdp_inference,
+            enable_torch_compile,
+            vsa_sparsity,
+            tp_size,
+            sp_size,
+        )
+
+        # Generators are cached by model_id and configuration parameters
+        with self._generators_lock:
+            if cache_key in self._generators:
+                return self._generators[cache_key]
+
+        # Import lazily so starting the server is fast even without a GPU.
+        from fastvideo import VideoGenerator
+
+        logger.info(
+            "Loading model %s (workload=%s, num_gpus=%d, offloads: "
+            "dit=%s text_encoder=%s vae=%s image_encoder=%s, fsdp=%s, "
+            "torch_compile=%s, vsa_sparsity=%.2f, tp=%d sp=%d) …",
+            model_id,
+            workload_type,
+            num_gpus,
+            dit_cpu_offload,
+            text_encoder_cpu_offload,
+            vae_cpu_offload,
+            image_encoder_cpu_offload,
+            use_fsdp_inference,
+            enable_torch_compile,
+            vsa_sparsity,
+            tp_size,
+            sp_size,
+        )
+
+        gen = VideoGenerator.from_pretrained(
+            model_id,
             workload_type=workload_type,
-            num_gpus=num_gpus,
             dit_cpu_offload=dit_cpu_offload,
             text_encoder_cpu_offload=text_encoder_cpu_offload,
             vae_cpu_offload=vae_cpu_offload,
             image_encoder_cpu_offload=image_encoder_cpu_offload,
             use_fsdp_inference=use_fsdp_inference,
             enable_torch_compile=enable_torch_compile,
-            vsa_sparsity=vsa_sparsity,
+            VSA_sparsity=vsa_sparsity,
             tp_size=tp_size,
             sp_size=sp_size,
+            log_queue=log_queue,
         )
-        with self._load_lock:  # waits out preloads / other jobs' replaces
-            with self._generator_lock:
-                if (self._generator_state == "ready"
-                        and self._generator_config == config
-                        and self._generator is not None):
-                    return self._generator
-            return self._run_on_loader(lambda: self._load_into_slot_locked(config))
 
-    def feed_engine_line(self, line: str) -> None:
-        """Bridge ray-relayed worker output into the running job's log buffer.
-
-        On the ray backend worker logs cannot cross nodes via the mp queue,
-        but ray already relays them to the driver's stdout — which the engine
-        tee captures. Lines with ray's actor prefix are attributed to the one
-        running inference job, whose buffer parses tqdm into UI progress.
-        Driver-side logging is excluded (it reaches the buffer via the
-        logging handlers already).
-        """
-        job = self._active_inference_job
-        if job is None:
-            return
-        line = _ANSI_RE.sub("", line)
-        if not _RAY_RELAY_RE.match(line):
-            return
-        try:
-            job._log_buf.write(line)
-        except Exception:  # noqa: BLE001 -- never break the tee
-            pass
+        with self._generators_lock:
+            if cache_key not in self._generators:
+                self._generators[cache_key] = gen
+            else:  # Another thread may have created it while we were loading.
+                gen.shutdown()
+                gen = self._generators[cache_key]
+        return gen
 
     def _run_job(self, job: Job):
         if job.job_type == "inference":
@@ -1058,12 +841,10 @@ class JobRunner:
         fastvideo_logger.addHandler(buffer_handler)
         fastvideo_logger.addHandler(file_handler)
 
-        # Worker logs flow through the runner-wide queue the generator was
-        # created with; drain anything stale, then listen for this job.
-        log_queue = self._worker_log_queue
-        with contextlib.suppress(Exception):
-            while True:
-                log_queue.get_nowait()
+        # Queue for worker process logs (fsdp_load, cuda, etc.)
+        # Use Manager().Queue() so it can be shared with spawned workers (spawn
+        # does not inherit memory; mp.Queue only works through inheritance).
+        log_queue = self._mp_manager.Queue()
         queue_listener = logging.handlers.QueueListener(log_queue,
                                                         buffer_handler,
                                                         file_handler,
@@ -1145,7 +926,6 @@ class JobRunner:
 
             generator = _gen_result[0]
             buf.phase = "generating"
-            self._active_inference_job = job  # engine tee feeds tqdm from here
             logger.info("Starting generation for job %s (model=%s)", job.id, job.model_id)
 
             gen_kwargs: dict[str, Any] = {
@@ -1161,6 +941,7 @@ class JobRunner:
                 "fps": job.fps,
                 "seed": job.seed,
                 "negative_prompt": job.negative_prompt or "",
+                "log_queue": log_queue,
             }
             if job.image_path:
                 gen_kwargs["image_path"] = job.image_path
@@ -1204,8 +985,6 @@ class JobRunner:
             buf.phase = "failed"
 
         finally:
-            if self._active_inference_job is job:
-                self._active_inference_job = None
             queue_listener.stop()
             # Remove handlers and close file
             fastvideo_logger.removeHandler(buffer_handler)

apps/fastvideo_studio/mock_server.py

@@ -36,15 +36,13 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse, PlainTextResponse
 
 from fastvideo_studio.database import default_settings_dict
-from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, GeneratorRequest, SettingsUpdate,
-                                     UpdateCaptionRequest, model_label)
+from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, SettingsUpdate, UpdateCaptionRequest,
+                                     model_label)
 
 # --- Config -----------------------------------------------------------------
 
 # How long a started job stays "running" before it flips to "completed".
 COMPLETE_AFTER_SECONDS = 3.0
-# How long a preloading generator stays "loading" before it flips to "ready".
-GENERATOR_READY_AFTER_SECONDS = 2.0
 FFMPEG_BIN = shutil.which(os.getenv("FASTVIDEO_FFMPEG_BIN", "ffmpeg"))
 
 # A small catalogue of fake models keyed by workload type. Mirrors the real
@@ -90,10 +88,6 @@ _DEFAULT_SETTINGS: dict[str, Any] = {
 _state_lock = threading.Lock()
 _settings: dict[str, Any] = dict(_DEFAULT_SETTINGS)
 _jobs: dict[str, dict[str, Any]] = {}
-# The engine's single model slot: None when empty, else the state dict.
-_generator_slot: dict[str, Any] | None = None
-# Fake engine stdout/stderr tail; grows a little on every poll.
-_engine_log_lines: list[str] = ["[engine] FastVideo studio mock engine started"]
 _datasets: dict[str, dict[str, Any]] = {}
 # dataset_id -> {"file_names": [...], "captions": {file_name: caption}}
 _dataset_files: dict[str, dict[str, Any]] = {}
@@ -399,56 +393,6 @@ def list_models(workload_type: str | None = None) -> list[dict[str, Any]]:
     return _models_for(workload_type)
 
 
-# Per-model sampling presets, mirroring the real /api/models/presets shape
-# (keys the model has no recommendation for are simply absent).
-_MODEL_PRESETS: dict[str, dict[str, Any]] = {
-    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": {
-        "height": 480,
-        "width": 832,
-        "num_frames": 81,
-        "fps": 16,
-        "num_inference_steps": 50,
-        "guidance_scale": 3.0,
-        "guidance_rescale": 0.0,
-        "negative_prompt": "Bright tones, overexposed, static, blurred details",
-        "seed": 1024,
-    },
-    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": {
-        "height": 480,
-        "width": 832,
-        "num_frames": 81,
-        "fps": 16,
-        "num_inference_steps": 40,
-        "guidance_scale": 5.0,
-        "seed": 1024,
-    },
-    "black-forest-labs/FLUX.1-schnell": {
-        "height": 1024,
-        "width": 1024,
-        "num_frames": 1,
-        "num_inference_steps": 4,
-        "guidance_scale": 0.0,
-        "seed": 42,
-    },
-}
-_GENERIC_PRESETS: dict[str, Any] = {
-    "height": 720,
-    "width": 1280,
-    "num_frames": 81,
-    "fps": 24,
-    "num_inference_steps": 50,
-    "guidance_scale": 5.0,
-    "guidance_rescale": 0.0,
-    "seed": 1024,
-}
-
-
-@app.get("/api/models/presets")
-def model_presets(model_id: str) -> dict[str, Any]:
-    """Recommended sampling settings; unknown models get generic defaults."""
-    return _MODEL_PRESETS.get(model_id, _GENERIC_PRESETS)
-
-
 # --- GPUs -------------------------------------------------------------------
 
 
@@ -470,49 +414,6 @@ def list_gpus() -> dict[str, Any]:
     return {"available": True, "gpus": gpus, "error": None}
 
 
-@app.get("/api/cluster")
-def cluster_status() -> dict[str, Any]:
-    """Two fake ray nodes x 4 GPUs with per-request jitter, mirroring the
-    real server's /api/cluster shape."""
-    nodes = []
-    for host_idx, (hostname, ip, is_this_host) in enumerate([
-        ("mock-node-0", "10.0.0.10", True),
-        ("mock-node-1", "10.0.0.11", False),
-    ]):
-        gpus = []
-        for index in range(4):
-            base_util = (13 + 29 * index + 41 * host_idx) % 100
-            gpus.append({
-                "index": index,
-                "name": "NVIDIA Mock GPU 80GB",
-                "utilization": max(0, min(100, base_util + random.randint(-5, 5))),
-                "memory_used_mib": 6_144 + 17_408 * index + random.randint(-256, 256),
-                "memory_total_mib": 81_920,
-                "temperature_c": 42 + 6 * index + random.randint(-3, 3),
-                "power_watts": 110.0 + 140.0 * index + random.randint(-20, 20),
-                "power_limit_watts": 700.0,
-            })
-        nodes.append({
-            "hostname": hostname,
-            "ip": ip,
-            "is_this_host": is_this_host,
-            "cpus": 64.0,
-            "ray_gpus": 4.0,
-            "available": True,
-            "error": None,
-            "gpus": gpus,
-        })
-    return {
-        "mode": "ray",
-        "nodes": nodes,
-        "resources": {
-            "gpus_total": 8.0,
-            "gpus_available": 5.0
-        },
-        "error": None,
-    }
-
-
 # --- Uploads ----------------------------------------------------------------
 
 
@@ -669,92 +570,6 @@ def download_log(job_id: str) -> PlainTextResponse:
     return PlainTextResponse("\n".join(lines) + "\n", media_type="text/plain")
 
 
-# --- Generators (warm models) -------------------------------------------------
-
-
-def _advance_generator(entry: dict[str, Any]) -> None:
-    """Flip a loading generator to ready once enough wall-clock time has passed.
-
-    Like job status, generator state is *computed on read*, so polling the
-    generators list naturally shows loading -> ready.
-    """
-    if entry["state"] == "loading" and time.time() - entry["started_at"] >= GENERATOR_READY_AFTER_SECONDS:
-        entry["state"] = "ready"
-
-
-def _running_inference_ids() -> list[str]:
-    return [
-        j["id"] for j in _jobs.values() if j.get("job_type") == "inference" and _public_job(j)["status"] == "running"
-    ]
-
-
-@app.get("/api/generators")
-def list_generators() -> list[dict[str, Any]]:
-    with _state_lock:
-        if _generator_slot is None:
-            return []
-        _advance_generator(_generator_slot)
-        return [dict(_generator_slot)]
-
-
-@app.post("/api/generators/preload", status_code=202)
-def preload_generator(req: GeneratorRequest) -> dict[str, Any]:
-    global _generator_slot
-    valid_ids = {m["id"] for m in _models_for(None)}
-    if req.model_id not in valid_ids:
-        raise HTTPException(
-            status_code=400,
-            detail=f"Unknown model_id '{req.model_id}'. Valid options: {sorted(valid_ids)}",
-        )
-    with _state_lock:
-        if _generator_slot is not None:
-            _advance_generator(_generator_slot)
-            if _generator_slot["state"] == "loading":
-                raise HTTPException(status_code=409, detail="a model load is already in progress")
-            if _generator_slot["state"] == "ready" and all(
-                    _generator_slot.get(k) == v for k, v in req.model_dump().items()):
-                return dict(_generator_slot)
-        running = _running_inference_ids()
-        if running:
-            raise HTTPException(status_code=409,
-                                detail=f"cannot swap models while inference jobs are running: {running}")
-        # Loading a new model always replaces (releases) the resident one.
-        _generator_slot = {"state": "loading", "started_at": time.time(), "error": None, **req.model_dump()}
-        return dict(_generator_slot)
-
-
-@app.post("/api/generators/unload")
-def unload_generator() -> dict[str, Any]:
-    global _generator_slot
-    with _state_lock:
-        if _generator_slot is not None:
-            _advance_generator(_generator_slot)
-            if _generator_slot["state"] == "loading":
-                raise HTTPException(status_code=409, detail="cannot unload while a model load is in progress")
-        if _generator_slot is None:
-            raise HTTPException(status_code=404, detail="No model is loaded")
-        running = _running_inference_ids()
-        if running:
-            raise HTTPException(status_code=409, detail=f"cannot unload while inference jobs are running: {running}")
-        _generator_slot = None
-    return {"unloaded": True}
-
-
-# --- Engine logs --------------------------------------------------------------
-
-
-@app.get("/api/engine/logs")
-def engine_logs(after: int = 0) -> dict[str, Any]:
-    """Growing fake tail of the engine's stdout/stderr: every poll appends a
-    couple of lines so the console visibly streams."""
-    with _state_lock:
-        n = len(_engine_log_lines)
-        _engine_log_lines.append(f"[engine] step {n}: worker heartbeat ok")
-        _engine_log_lines.append(f"[engine] step {n + 1}: gpu mem {random.randint(20, 80)}% used")
-        total = len(_engine_log_lines)
-        return {"lines": _engine_log_lines[max(0, after):], "total": total}
-
-
 # --- Datasets ---------------------------------------------------------------
 
 

apps/fastvideo_studio/models/__init__.py

@@ -3,7 +3,6 @@
 usable by both the real server and the dependency-light mock server."""
 
 from fastvideo_studio.models.create_job_request import CreateJobRequest
-from fastvideo_studio.models.generator_request import GeneratorRequest
 from fastvideo_studio.models.settings_update import SettingsUpdate
 from fastvideo_studio.models.create_dataset_request import CreateDatasetRequest
 from fastvideo_studio.models.update_caption_request import UpdateCaptionRequest
@@ -16,7 +15,6 @@ def model_label(model_path: str) -> str:
 
 __all__ = [
     "CreateJobRequest",
-    "GeneratorRequest",
     "SettingsUpdate",
     "CreateDatasetRequest",
     "UpdateCaptionRequest",

apps/fastvideo_studio/models/generator_request.py

@@ -1,24 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Request model for preloading/unloading a resident generator.
-
-Field names and defaults mirror the engine subset of ``CreateJobRequest`` so
-the UI can send exactly the values it would put on a job — guaranteeing the
-job's generator lookup hits this cache entry.
-"""
-
-from pydantic import BaseModel
-
-
-class GeneratorRequest(BaseModel):
-    model_id: str
-    workload_type: str = "t2v"
-    num_gpus: int = 1
-    dit_cpu_offload: bool = False
-    text_encoder_cpu_offload: bool = False
-    vae_cpu_offload: bool = False
-    image_encoder_cpu_offload: bool = False
-    use_fsdp_inference: bool = False
-    enable_torch_compile: bool = False
-    vsa_sparsity: float = 0.0
-    tp_size: int = -1
-    sp_size: int = -1

apps/fastvideo_studio/package-lock.json

@@ -9,7 +9,6 @@
 			"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",
@@ -1937,183 +1936,6 @@
 				}
 			}
 		},
-		"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",
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-			},
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-			"version": "1.1.4",
-			"resolved": "https://registry.npmjs.org/@radix-ui/react-id/-/react-id-1.1.4.tgz",
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-				"@radix-ui/react-use-effect-event": "0.0.5",
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-			},
-			"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",
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-			"license": "MIT",
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-				"@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",
@@ -2195,494 +2017,6 @@
 				}
 			}
 		},
-		"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",
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-			"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"
-			},
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-					"optional": true
-				}
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-		},
-		"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",
@@ -3076,21 +2410,6 @@
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 			}
 		},
-		"node_modules/@radix-ui/react-use-is-hydrated": {
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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-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

@@ -18,7 +18,6 @@
 	},
 	"dependencies": {
 		"@radix-ui/react-dialog": "^1.1.0",
-		"@radix-ui/react-dropdown-menu": "^2.1.24",
 		"@radix-ui/react-label": "^2.1.8",
 		"@radix-ui/react-scroll-area": "^1.2.10",
 		"@radix-ui/react-select": "^2.2.6",

apps/fastvideo_studio/server.py

@@ -32,10 +32,10 @@ from fastapi.responses import FileResponse
 
 from fastvideo.registry import (get_registered_model_paths, get_registered_models_with_workloads)
 from fastvideo_studio.database import Database, _get_db_path
-from fastvideo_studio.gpu import get_cluster_snapshot, get_gpu_snapshot
+from fastvideo_studio.gpu import get_gpu_snapshot
 from fastvideo_studio.job_runner import JobRunner, JobStatus
-from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, GeneratorRequest, SettingsUpdate,
-                                     UpdateCaptionRequest, model_label)
+from fastvideo_studio.models import (CreateDatasetRequest, CreateJobRequest, SettingsUpdate, UpdateCaptionRequest,
+                                     model_label)
 
 logging.basicConfig(
     level=logging.INFO,
@@ -45,67 +45,6 @@ logger = logging.getLogger("fastvideo.studio.api")
 
 DEFAULT_OUTPUT_DIR = os.path.join(os.path.dirname(__file__), "..", "outputs", "ui_jobs")
 
-
-class _EngineLogBuffer:
-    """Thread-safe ring buffer over the server process's stdout/stderr.
-
-    Because ray relays worker output to the driver (log_to_driver), teeing the
-    server's own streams captures engine output from every rank on every node;
-    under the mp executor, worker logs arrive via the logging handlers which
-    also write to stderr.
-    """
-
-    def __init__(self, maxlen: int = 5000) -> None:
-        import collections
-        import threading
-        self._lines: collections.deque[str] = collections.deque(maxlen=maxlen)
-        self._dropped = 0
-        self._lock = threading.Lock()
-        self._partial = ""
-
-    on_line = None  # optional callable(str), set once at startup
-
-    def write(self, text: str) -> None:
-        with self._lock:
-            buf = self._partial + text
-            *complete, self._partial = buf.split("\n")
-            for line in complete:
-                if len(self._lines) == self._lines.maxlen:
-                    self._dropped += 1
-                self._lines.append(line)
-        if self.on_line is not None:
-            for line in complete:
-                # never break stdout on a bad feed
-                with contextlib.suppress(Exception):
-                    self.on_line(line)
-
-    def get_lines(self, after: int = 0) -> tuple[list[str], int]:
-        with self._lock:
-            total = self._dropped + len(self._lines)
-            start = max(0, after - self._dropped)
-            return list(self._lines)[start:], total
-
-
-class _Tee:
-    """File-like that forwards to the original stream and the ring buffer."""
-
-    def __init__(self, orig: Any, buffer: _EngineLogBuffer) -> None:
-        self._orig = orig
-        self._buffer = buffer
-
-    def write(self, text: str) -> int:
-        self._buffer.write(text)
-        return self._orig.write(text)
-
-    def flush(self) -> None:
-        self._orig.flush()
-
-    def __getattr__(self, name: str) -> Any:
-        return getattr(self._orig, name)
-
-
-engine_log = _EngineLogBuffer()
-
 _available_models: list[dict[str, str]] = [{
     "id": path,
     "label": model_label(path)
@@ -163,13 +102,6 @@ def list_gpus() -> dict[str, Any]:
     return get_gpu_snapshot()
 
 
-@app.get("/api/cluster")
-def cluster_status() -> dict[str, Any]:
-    """Cluster-wide GPU/host telemetry (per-node NVML via ray when connected,
-    the local host otherwise)."""
-    return get_cluster_snapshot()
-
-
 @app.get("/api/models")
 def list_models(workload_type: str | None = None) -> list[dict[str, Any]]:
     """Return the catalogue of available video-generation models.
@@ -184,25 +116,6 @@ def list_models(workload_type: str | None = None) -> list[dict[str, Any]]:
     return _available_models
 
 
-_PRESET_FIELDS = ("height", "width", "num_frames", "fps", "num_inference_steps", "guidance_scale", "guidance_rescale",
-                  "negative_prompt", "seed")
-_preset_cache: dict[str, dict[str, Any]] = {}
-
-
-@app.get("/api/models/presets")
-def model_presets(model_id: str) -> dict[str, Any]:
-    """The model's recommended sampling settings (config-only — never loads
-    weights). The UI populates the job form from these on model selection."""
-    if model_id not in _preset_cache:
-        from fastvideo.api.sampling_param import SamplingParam
-        try:
-            sp = SamplingParam.from_pretrained(model_id)
-        except Exception as exc:  # noqa: BLE001 -- unknown/unresolvable model
-            raise HTTPException(status_code=404, detail=f"No presets for '{model_id}': {exc}") from exc
-        _preset_cache[model_id] = {f: getattr(sp, f) for f in _PRESET_FIELDS if getattr(sp, f, None) is not None}
-    return _preset_cache[model_id]
-
-
 ALLOWED_IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
 
 
@@ -340,58 +253,13 @@ def get_job(job_id: str) -> dict[str, Any]:
     return job.to_dict()
 
 
-@app.get("/api/generators")
-def list_generators() -> list[dict[str, Any]]:
-    """Generators resident in memory plus preloads in flight or failed."""
-    return job_runner.list_generators()
-
-
-@app.post("/api/generators/preload", status_code=202)
-def preload_generator(req: GeneratorRequest) -> dict[str, Any]:
-    """Load a model into memory ahead of time (replacing whatever is
-    resident). One load at a time — 409 while another load is in flight."""
-    valid_ids = {m["id"] for m in _available_models}
-    if req.model_id not in valid_ids and not os.path.isdir(req.model_id):
-        raise HTTPException(
-            status_code=400,
-            detail=(f"Unknown model_id '{req.model_id}'. "
-                    f"Valid options: {sorted(valid_ids)}"),
-        )
-    try:
-        return job_runner.preload_generator(**req.model_dump())
-    except RuntimeError as exc:
-        raise HTTPException(status_code=409, detail=str(exc)) from exc
-
-
-@app.post("/api/generators/unload")
-def unload_generator() -> dict[str, Any]:
-    """Shut down and delete the resident generator, freeing GPU memory."""
-    try:
-        unloaded = job_runner.unload_generator()
-    except RuntimeError as exc:
-        raise HTTPException(status_code=409, detail=str(exc)) from exc
-    if not unloaded:
-        raise HTTPException(status_code=404, detail="No model is loaded")
-    return {"unloaded": True}
-
-
-@app.get("/api/engine/logs")
-def engine_logs(after: int = 0) -> dict[str, Any]:
-    """Incremental tail of the engine's stdout/stderr (driver + relayed
-    worker output). Poll with ?after=<total from the previous response>."""
-    lines, total = engine_log.get_lines(after=after)
-    return {"lines": lines, "total": total}
-
-
 @app.post("/api/jobs", status_code=201)
 def create_job(req: CreateJobRequest) -> dict[str, Any]:
     """Create a new job (does **not** start it automatically)."""
     job_type = req.job_type or "inference"
     if job_type == "inference":
         valid_ids = {m["id"] for m in _available_models}
-        # A local weights directory is as valid as a registered hub id —
-        # the registry resolves the pipeline from its model_index.
-        if req.model_id not in valid_ids and not os.path.isdir(req.model_id):
+        if req.model_id not in valid_ids:
             raise HTTPException(
                 status_code=400,
                 detail=(f"Unknown model_id '{req.model_id}'. "
@@ -754,14 +622,6 @@ def create_local_env(host: str, port: int) -> None:
 
 
 def main() -> None:
-    import sys
-    sys.stdout = _Tee(sys.stdout, engine_log)
-    sys.stderr = _Tee(sys.stderr, engine_log)
-    # handlers created before the tee (module-level basicConfig) hold the
-    # original stream objects — re-point them or their output bypasses the buffer
-    for h in logging.getLogger().handlers:
-        if isinstance(h, logging.StreamHandler) and h.stream in (sys.__stderr__, sys.__stdout__):
-            h.setStream(sys.stderr)  # type: ignore[arg-type]  # duck-typed file-like
     global job_runner, database, upload_dir, verbose, datasets_upload_dir  # noqa: PLW0603
 
     # Set up signal handlers to prevent worker crashes from killing the server
@@ -826,9 +686,6 @@ def main() -> None:
         verbose=args.verbose,
         database=database,
     )
-    # ray relays worker output (incl. denoising tqdm) to the driver's stdout;
-    # feed those lines to the running job so the UI progress bar moves.
-    engine_log.on_line = job_runner.feed_engine_line
 
     logger.info("Output directory: %s", output_dir)
     logger.info("Log directory: %s", log_dir)
@@ -838,10 +695,6 @@ def main() -> None:
         host=args.host,
         port=args.port,
         log_level="info",
-        # The engine-output console tails this process's stdout/stderr; the
-        # frontend polls several endpoints every few seconds, so access-log
-        # lines are pure self-noise there. App/job logging is unaffected.
-        access_log=False,
     )
 
 

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

@@ -1,64 +0,0 @@
-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

@@ -1,14 +1,12 @@
 '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';
@@ -23,28 +21,18 @@ 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 {
-      const next = await getDatasets();
-      if (sequence === fetchSequence.current) {
-        setDatasets(next);
-        setError(null);
-      }
+      setDatasets(await getDatasets());
+      setError(null);
     } catch (err) {
       console.error('Failed to fetch datasets:', err);
-      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);
+      // 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');
     }
   }, []);
 
@@ -62,67 +50,28 @@ export default function DatasetsPage() {
       <HeaderActions>
         <AddDatasetButton />
       </HeaderActions>
-      <div className="mx-auto flex w-full max-w-[850px] flex-col gap-6 px-4 pb-12">
+      <main className="mx-auto flex w-full max-w-[850px] flex-col gap-6 px-4 pb-12">
         <Card className="p-6">
-          <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>
+          <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>
             ) : (
-              <>
-                {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)}
-                    />
-                  ))
-                )}
-              </>
+              datasets.map((ds) => (
+                <DatasetCard
+                  key={ds.id}
+                  dataset={ds}
+                  onUpdated={fetchDatasets}
+                  onSelect={() => handleSelectDataset(ds)}
+                />
+              ))
             )}
           </div>
         </Card>
-      </div>
+      </main>
       <CreateDatasetModal
         isOpen={open}
         onClose={() => setCreateDatasetModalOpen(false)}

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

@@ -1,4 +1,4 @@
-import { fireEvent, render, screen } from '@testing-library/react';
+import { render, screen } from '@testing-library/react';
 import { describe, expect, it, vi } from 'vitest';
 
 import GalleryPage from './page';
@@ -43,22 +43,6 @@ 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

@@ -1,9 +1,8 @@
 'use client';
 
-import { AlertTriangle, ImageOff, Loader2 } from 'lucide-react';
+import { 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';
@@ -12,61 +11,11 @@ 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() {
@@ -91,13 +40,7 @@ export default function GalleryPage() {
     return () => {
       cancelled = true;
     };
-  }, [reloadKey]);
-
-  function retry() {
-    setError(null);
-    setIsLoading(true);
-    setReloadKey((k) => k + 1);
-  }
+  }, []);
 
   const galleryJobs = jobs.filter(
     (j) =>
@@ -121,16 +64,7 @@ export default function GalleryPage() {
             <span>Loading gallery…</span>
           </div>
         ) : error ? (
-          <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>
+          <p className="py-8 text-destructive">{error}</p>
         ) : galleryJobs.length === 0 ? (
           <p className="py-8 text-center text-muted-foreground">
             No completed videos yet
@@ -143,7 +77,24 @@ 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">
-                  <GalleryMedia job={job} />
+                  {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"
+                    />
+                  )}
                 </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: #1d4ed8;
+	--ring: #94a3b8;
 
 	--radius: 0.5rem;
 }
@@ -77,7 +77,7 @@
 
 	--border: #334155;
 	--input: #334155;
-	--ring: #7dd3fc;
+	--ring: #cbd5e1;
 }
 
 @theme inline {
@@ -125,7 +125,6 @@
 html,
 body {
 	min-height: 100%;
-	overflow-x: clip;
 }
 
 html {
@@ -164,22 +163,6 @@ 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

@@ -1,55 +0,0 @@
-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.test.tsx

@@ -1,182 +0,0 @@
-import { render, screen } from '@testing-library/react';
-import { beforeEach, describe, expect, it, vi } from 'vitest';
-
-import GpusPage from './page';
-import { getClusterStatus } from '@/lib/api';
-import type { ClusterSnapshot } from '@/lib/api';
-
-vi.mock('@/lib/api', () => ({
-  getClusterStatus: vi.fn(),
-}));
-
-const RAY_SNAPSHOT: ClusterSnapshot = {
-  mode: 'ray',
-  error: null,
-  resources: { gpus_total: 8, gpus_available: 5 },
-  nodes: [
-    {
-      hostname: 'node-a',
-      ip: '10.0.0.10',
-      is_this_host: true,
-      cpus: 64,
-      ray_gpus: 4,
-      available: true,
-      error: null,
-      gpus: [
-        {
-          index: 0,
-          name: 'NVIDIA B200',
-          utilization: 62,
-          memory_used_mib: 40_960,
-          memory_total_mib: 81_920,
-          temperature_c: 41,
-          power_watts: 312.4,
-          power_limit_watts: 1000,
-        },
-        {
-          index: 1,
-          name: 'NVIDIA B200',
-          utilization: 0,
-          memory_used_mib: 1_024,
-          memory_total_mib: 81_920,
-          temperature_c: null,
-          power_watts: null,
-          power_limit_watts: null,
-        },
-      ],
-    },
-    {
-      hostname: 'node-b',
-      ip: '10.0.0.11',
-      is_this_host: false,
-      cpus: 32,
-      ray_gpus: 2,
-      available: true,
-      error: null,
-      gpus: [
-        {
-          index: 0,
-          name: 'NVIDIA B200',
-          utilization: 90,
-          memory_used_mib: 20_480,
-          memory_total_mib: 81_920,
-          temperature_c: 70,
-          power_watts: 900,
-          power_limit_watts: 1000,
-        },
-      ],
-    },
-  ],
-};
-
-const LOCAL_SNAPSHOT: ClusterSnapshot = {
-  mode: 'local',
-  error:
-    'not connected to a ray cluster yet (load a model first); showing the API host only',
-  resources: null,
-  nodes: [
-    {
-      hostname: 'localhost',
-      ip: null,
-      is_this_host: true,
-      cpus: null,
-      ray_gpus: null,
-      available: true,
-      error: null,
-      gpus: [
-        {
-          index: 0,
-          name: 'NVIDIA RTX 5090',
-          utilization: 12,
-          memory_used_mib: 2_048,
-          memory_total_mib: 32_768,
-          temperature_c: 38,
-          power_watts: 80,
-          power_limit_watts: 575,
-        },
-      ],
-    },
-  ],
-};
-
-beforeEach(() => {
-  vi.mocked(getClusterStatus).mockResolvedValue(RAY_SNAPSHOT);
-});
-
-describe('GpusPage', () => {
-  it('renders the header with mode and GPU totals', async () => {
-    render(<GpusPage />);
-
-    expect(await screen.findByText('ray cluster')).toBeInTheDocument();
-    expect(screen.getByText(/5 \/\s*8 GPUs available/)).toBeInTheDocument();
-  });
-
-  it('renders a section per node with host details and GPU rows', async () => {
-    render(<GpusPage />);
-
-    // Each hostname appears twice: once in the strip, once as a section.
-    expect(await screen.findAllByText('node-a')).toHaveLength(2);
-    expect(screen.getAllByText('node-b')).toHaveLength(2);
-    expect(screen.getByText('10.0.0.10')).toBeInTheDocument();
-    // Only node-a is the API host.
-    expect(screen.getAllByText('API host')).toHaveLength(1);
-    expect(screen.getByText(/64 CPUs · 4 ray GPUs/)).toBeInTheDocument();
-
-    expect(screen.getAllByText('NVIDIA B200')).toHaveLength(3);
-    expect(screen.getByText('GPU 1')).toBeInTheDocument();
-    expect(screen.getByText('62%')).toBeInTheDocument();
-    expect(
-      screen.getByText('40960 / 81920 MiB (40.0 GiB / 80.0 GiB)'),
-    ).toBeInTheDocument();
-    // Optional sensors render only when present.
-    expect(screen.getByText('41°C')).toBeInTheDocument();
-    expect(screen.getByText('312 W / 1000 W')).toBeInTheDocument();
-  });
-
-  it('bars reflect utilization and VRAM values', async () => {
-    render(<GpusPage />);
-    await screen.findAllByText('node-a');
-
-    const utilMeters = screen
-      .getAllByRole('meter', { name: 'Utilization' })
-      .map((m) => m.getAttribute('aria-valuenow'));
-    expect(utilMeters).toEqual(['62', '0', '90']);
-
-    const vramMeters = screen
-      .getAllByRole('meter', { name: 'VRAM' })
-      .map((m) => m.getAttribute('aria-valuenow'));
-    // 40960/81920 = 50%, 1024/81920 ≈ 1%, 20480/81920 = 25%
-    expect(vramMeters).toEqual(['50', '1', '25']);
-  });
-
-  it('renders the compact strip with per-GPU segments', async () => {
-    render(<GpusPage />);
-    await screen.findAllByText('node-a');
-
-    const segments = screen.getAllByRole('img');
-    expect(segments).toHaveLength(3);
-    expect(segments[0]).toHaveAccessibleName(
-      'GPU 0: 62% utilization, 40.0 GiB / 80.0 GiB VRAM',
-    );
-  });
-
-  it('shows the informational banner and local mode', async () => {
-    vi.mocked(getClusterStatus).mockResolvedValue(LOCAL_SNAPSHOT);
-    render(<GpusPage />);
-
-    expect(await screen.findByText('local host only')).toBeInTheDocument();
-    expect(
-      screen.getByText(/not connected to a ray cluster yet/),
-    ).toBeInTheDocument();
-    // No resources in local mode.
-    expect(screen.queryByText(/GPUs available/)).not.toBeInTheDocument();
-  });
-
-  it('explains when the API server is unreachable', async () => {
-    vi.mocked(getClusterStatus).mockRejectedValue(new Error('network down'));
-    render(<GpusPage />);
-    expect(
-      await screen.findByText(/Could not reach the API server/),
-    ).toBeInTheDocument();
-  });
-});

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

@@ -1,258 +1,11 @@
 'use client';
 
-import * as React from 'react';
-import { AlertTriangle, Info } from 'lucide-react';
-
-import ClusterStrip, {
-  clampPercent,
-  formatGib,
-  utilizationColor,
-} from '@/components/cluster/ClusterStrip';
-import { Badge } from '@/components/ui/badge';
-import { Button } from '@/components/ui/button';
-import { Card, CardContent } from '@/components/ui/card';
-import {
-  getClusterStatus,
-  type ClusterGpu,
-  type ClusterNode,
-  type ClusterSnapshot,
-} from '@/lib/api';
-import { cn } from '@/lib/utils';
-
-const POLL_INTERVAL_MS = 5000;
-
-function Meter({
-  label,
-  percent,
-  detail,
-  fillClass,
-}: {
-  label: string;
-  percent: number;
-  detail: string;
-  fillClass: string;
-}) {
-  const clamped = clampPercent(percent);
-  return (
-    <div className="flex flex-col gap-1">
-      <div className="flex items-baseline justify-between gap-2 text-xs">
-        <span className="text-muted-foreground">{label}</span>
-        <span className="font-medium tabular-nums text-foreground">
-          {detail}
-        </span>
-      </div>
-      <div
-        role="meter"
-        aria-label={label}
-        aria-valuenow={Math.round(clamped)}
-        aria-valuemin={0}
-        aria-valuemax={100}
-        className="h-1.5 overflow-hidden rounded-full bg-muted"
-      >
-        <div
-          className={cn(
-            'h-full rounded-full transition-[width] duration-500',
-            fillClass,
-          )}
-          style={{ width: `${clamped}%` }}
-        />
-      </div>
-    </div>
-  );
-}
-
-function GpuRow({ gpu }: { gpu: ClusterGpu }) {
-  const memPercent =
-    gpu.memory_total_mib > 0
-      ? (gpu.memory_used_mib / gpu.memory_total_mib) * 100
-      : 0;
-  return (
-    <div className="grid items-center gap-x-6 gap-y-2 border-t border-border pt-3 first:border-t-0 first:pt-0 md:grid-cols-[minmax(0,1fr)_minmax(0,1.2fr)_minmax(0,1.6fr)_auto]">
-      <div className="flex min-w-0 items-baseline gap-2">
-        <span className="min-w-0 truncate text-sm font-semibold">
-          {gpu.name}
-        </span>
-        <span className="shrink-0 text-xs font-medium uppercase tracking-wider text-muted-foreground">
-          GPU {gpu.index}
-        </span>
-      </div>
-      <Meter
-        label="Utilization"
-        percent={gpu.utilization}
-        detail={`${gpu.utilization}%`}
-        fillClass={utilizationColor(gpu.utilization)}
-      />
-      <Meter
-        label="VRAM"
-        percent={memPercent}
-        detail={`${gpu.memory_used_mib} / ${gpu.memory_total_mib} MiB (${formatGib(gpu.memory_used_mib)} / ${formatGib(gpu.memory_total_mib)})`}
-        fillClass={memPercent >= 90 ? 'bg-rose-500' : 'bg-accent-blue'}
-      />
-      <div className="flex flex-wrap gap-x-4 gap-y-1 text-xs tabular-nums text-muted-foreground md:w-28 md:justify-end">
-        {gpu.temperature_c != null && <span>{gpu.temperature_c}°C</span>}
-        {gpu.power_watts != null && (
-          <span>
-            {Math.round(gpu.power_watts)} W
-            {gpu.power_limit_watts != null &&
-              ` / ${Math.round(gpu.power_limit_watts)} W`}
-          </span>
-        )}
-      </div>
-    </div>
-  );
-}
-
-function NodeSection({ node }: { node: ClusterNode }) {
-  return (
-    <Card>
-      <CardContent className="flex flex-col gap-3 p-5">
-        <div className="flex flex-wrap items-center gap-x-3 gap-y-1">
-          <span className="min-w-0 truncate text-sm font-semibold">
-            {node.hostname}
-          </span>
-          {node.ip && (
-            <span className="text-xs tabular-nums text-muted-foreground">
-              {node.ip}
-            </span>
-          )}
-          {node.is_this_host && <Badge variant="secondary">API host</Badge>}
-          <span className="ml-auto text-xs tabular-nums text-muted-foreground">
-            {node.cpus != null && `${Math.round(node.cpus)} CPUs`}
-            {node.cpus != null && node.ray_gpus != null && ' · '}
-            {node.ray_gpus != null && `${Math.round(node.ray_gpus)} ray GPUs`}
-          </span>
-        </div>
-        {!node.available && (
-          <p className="text-sm text-muted-foreground">
-            GPU telemetry unavailable
-            {node.error ? `: ${node.error}` : '.'}
-          </p>
-        )}
-        {node.gpus.map((gpu) => (
-          <GpuRow key={gpu.index} gpu={gpu} />
-        ))}
-      </CardContent>
-    </Card>
-  );
-}
+import GpuGrid from '@/components/system/GpuGrid';
 
 export default function GpusPage() {
-  const [snapshot, setSnapshot] = React.useState<ClusterSnapshot | null>(null);
-  const [fetchError, setFetchError] = React.useState<string | null>(null);
-  const [retryToken, setRetryToken] = React.useState(0);
-
-  React.useEffect(() => {
-    let mounted = true;
-    let inFlight = false;
-
-    async function poll() {
-      if (inFlight || document.hidden) return;
-      inFlight = true;
-      try {
-        const next = await getClusterStatus();
-        if (mounted) {
-          setSnapshot(next);
-          setFetchError(null);
-        }
-      } catch {
-        if (mounted) {
-          setFetchError(
-            'Cluster status could not be refreshed. The values below may be stale.',
-          );
-        }
-      } finally {
-        inFlight = false;
-      }
-    }
-
-    poll();
-    const interval = setInterval(poll, POLL_INTERVAL_MS);
-    // Refresh immediately when the tab becomes visible again (polls are
-    // skipped while hidden).
-    document.addEventListener('visibilitychange', poll);
-    return () => {
-      mounted = false;
-      clearInterval(interval);
-      document.removeEventListener('visibilitychange', poll);
-    };
-  }, [retryToken]);
-
-  let body: React.ReactNode;
-  if (fetchError && !snapshot) {
-    body = (
-      <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. Cluster status needs the Studio API
-          server running.
-        </p>
-        <Button
-          type="button"
-          variant="outline"
-          onClick={() => setRetryToken((token) => token + 1)}
-        >
-          Try Again
-        </Button>
-      </div>
-    );
-  } else if (!snapshot) {
-    body = <p className="py-8 text-center text-muted-foreground">Loading…</p>;
-  } else {
-    body = (
-      <div className="flex flex-col gap-4">
-        <header className="flex flex-wrap items-center gap-3">
-          <h1 className="text-lg font-semibold">Cluster</h1>
-          <Badge variant="outline">
-            {snapshot.mode === 'ray' ? 'ray cluster' : 'local host only'}
-          </Badge>
-          {snapshot.resources && (
-            <span className="text-sm tabular-nums text-muted-foreground">
-              {Math.round(snapshot.resources.gpus_available)} /{' '}
-              {Math.round(snapshot.resources.gpus_total)} GPUs available
-            </span>
-          )}
-        </header>
-        {snapshot.error && (
-          <div
-            role="status"
-            className="flex flex-wrap items-center gap-3 rounded-lg border border-blue-400/40 bg-blue-500/10 px-3 py-2 text-sm"
-          >
-            <Info className="size-4 shrink-0 text-blue-600" aria-hidden />
-            <span className="min-w-0 flex-1">{snapshot.error}</span>
-          </div>
-        )}
-        {fetchError && (
-          <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>
-        )}
-        <ClusterStrip nodes={snapshot.nodes} />
-        {snapshot.nodes.map((node, i) => (
-          <NodeSection key={`${node.hostname}-${i}`} node={node} />
-        ))}
-      </div>
-    );
-  }
-
   return (
-    <div className="mx-auto flex w-full max-w-[1100px] flex-col gap-6 px-4 pb-12 pt-6">
-      {body}
-    </div>
+    <main className="mx-auto flex w-full max-w-[1100px] flex-col gap-6 px-4 pb-12 pt-6">
+      <GpuGrid />
+    </main>
   );
 }

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

@@ -1,10 +1,8 @@
 'use client';
 
 import CreateJobButton from '@/components/jobs/CreateJobButton';
-import EngineConsole from '@/components/jobs/EngineConsole';
 import { HeaderActions } from '@/components/shell/HeaderActionsContext';
 import JobQueue from '@/components/jobs/JobQueue';
-import WarmModelsPanel from '@/components/jobs/WarmModelsPanel';
 
 export default function InferencePage() {
   return (
@@ -12,8 +10,6 @@ export default function InferencePage() {
       <HeaderActions>
         <CreateJobButton jobType="inference" />
       </HeaderActions>
-      <WarmModelsPanel />
-      <EngineConsole />
       <JobQueue jobType="inference" />
     </>
   );

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

@@ -52,20 +52,6 @@ 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/cluster/ClusterStrip.tsx

@@ -1,93 +0,0 @@
-'use client';
-
-import type { ClusterNode } from '@/lib/api';
-import { cn } from '@/lib/utils';
-
-export function formatGib(mib: number): string {
-  return `${(mib / 1024).toFixed(1)} GiB`;
-}
-
-/** Bar fill class by load: blue when idle, amber under pressure, rose hot. */
-export function utilizationColor(percent: number): string {
-  if (percent >= 85) return 'bg-rose-500';
-  if (percent >= 50) return 'bg-amber-500';
-  return 'bg-accent-blue';
-}
-
-export function clampPercent(percent: number): number {
-  return Math.max(0, Math.min(100, percent));
-}
-
-function GpuSegment({
-  index,
-  utilization,
-  memUsedMib,
-  memTotalMib,
-}: {
-  index: number;
-  utilization: number;
-  memUsedMib: number;
-  memTotalMib: number;
-}) {
-  const memPercent =
-    memTotalMib > 0 ? clampPercent((memUsedMib / memTotalMib) * 100) : 0;
-  const label =
-    `GPU ${index}: ${utilization}% utilization, ` +
-    `${formatGib(memUsedMib)} / ${formatGib(memTotalMib)} VRAM`;
-  return (
-    <div
-      role="img"
-      aria-label={label}
-      title={label}
-      className="flex w-10 shrink-0 flex-col gap-0.5"
-    >
-      <div className="h-1.5 overflow-hidden rounded-full bg-muted">
-        <div
-          className={cn('h-full rounded-full', utilizationColor(utilization))}
-          style={{ width: `${clampPercent(utilization)}%` }}
-        />
-      </div>
-      <div className="h-1.5 overflow-hidden rounded-full bg-muted">
-        <div
-          className={cn(
-            'h-full rounded-full',
-            memPercent >= 90 ? 'bg-rose-500' : 'bg-accent-blue',
-          )}
-          style={{ width: `${memPercent}%` }}
-        />
-      </div>
-    </div>
-  );
-}
-
-/** One compact line per node: hostname + tiny util/VRAM bars per GPU. */
-export default function ClusterStrip({ nodes }: { nodes: ClusterNode[] }) {
-  return (
-    <div className="flex flex-col gap-2">
-      {nodes.map((node, i) => (
-        <div
-          key={`${node.hostname}-${i}`}
-          className="flex items-center gap-3"
-        >
-          <span className="w-40 shrink-0 truncate text-xs font-medium">
-            {node.hostname}
-          </span>
-          <div className="flex min-w-0 flex-wrap items-center gap-1.5">
-            {node.gpus.map((gpu) => (
-              <GpuSegment
-                key={gpu.index}
-                index={gpu.index}
-                utilization={gpu.utilization}
-                memUsedMib={gpu.memory_used_mib}
-                memTotalMib={gpu.memory_total_mib}
-              />
-            ))}
-            {node.gpus.length === 0 && (
-              <span className="text-xs text-muted-foreground">no GPUs</span>
-            )}
-          </div>
-        </div>
-      ))}
-    </div>
-  );
-}

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

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

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

@@ -55,33 +55,43 @@ 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 (
-    <article
+    <div
       className={cn(
-        'mb-3 flex items-start gap-3 rounded-lg border border-border bg-background px-[1.15rem] py-4',
+        'mb-3 flex cursor-pointer flex-col gap-[0.6rem] 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}
     >
-      <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"
-      >
+      <div className="flex flex-wrap items-center justify-between gap-2">
         <span className="text-[0.95rem] font-semibold">{dataset.name}</span>
-        <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>
+        <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>
   );
 }

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

@@ -6,9 +6,6 @@ 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);
 
@@ -30,27 +27,6 @@ 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={() => {}} />);
 
@@ -67,16 +43,6 @@ 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');
@@ -133,39 +99,6 @@ 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

@@ -1,8 +1,7 @@
 'use client';
 
 import * as React from 'react';
-import { ImageOff, X } from 'lucide-react';
-import { toast } from 'sonner';
+import { X } from 'lucide-react';
 
 import DownloadCaptions from '@/components/datasets/DownloadCaptions';
 import { Textarea } from '@/components/ui/textarea';
@@ -14,14 +13,12 @@ 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).
@@ -30,101 +27,54 @@ 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 && !mediaFailed && (
+      {!thumbLoaded && (
         <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>
       )}
-      {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);
-          }}
-        />
-      )}
+      {/* 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)}
+      />
       <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[]>([]);
@@ -134,21 +84,17 @@ 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?.(isMobile ? 0 : width);
-  }, [isMobile, width, onWidthChange]);
+    onWidthChange?.(width);
+  }, [width, onWidthChange]);
 
   React.useEffect(() => {
     let cancelled = false;
@@ -160,8 +106,6 @@ 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(() => {
@@ -194,51 +138,23 @@ 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 version = (captionVersions.current.get(fileName) ?? 0) + 1;
-      captionVersions.current.set(fileName, version);
-      const save = () => persistCaption(fileName, value, version);
+      const save = () => {
+        updateDatasetCaption(datasetId, fileName, value).catch((err) =>
+          console.error('Failed to save caption:', err),
+        );
+      };
       const timer = setTimeout(() => {
         pendingSaves.current.delete(fileName);
         save();
       }, 500);
       pendingSaves.current.set(fileName, { timer, save });
     },
-    [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],
+    [datasetId],
   );
 
   function handleScroll() {
@@ -273,16 +189,8 @@ export default function DatasetSidebar({
 
   return (
     <aside
-      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,
-      }}
+      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 }}
     >
       <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">
@@ -295,7 +203,7 @@ export default function DatasetSidebar({
             onClick={onClose}
             title="Close"
             aria-label="Close"
-            className="flex size-11 items-center justify-center rounded-lg text-muted-foreground transition-colors hover:bg-accent hover:text-foreground"
+            className="flex items-center justify-center rounded-lg p-1.5 text-muted-foreground transition-colors hover:bg-accent hover:text-foreground"
           >
             <X className="h-[18px] w-[18px]" />
           </button>
@@ -323,9 +231,7 @@ 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}
                 />
               ))}
@@ -334,14 +240,14 @@ export default function DatasetSidebar({
         </div>
       </div>
 
-      {!isMobile && <div
+      <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 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';
+  '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';
 
 export default function DownloadCaptions({
   fileNames,

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

@@ -1,53 +0,0 @@
-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

@@ -2,7 +2,6 @@
 
 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';
@@ -34,35 +33,31 @@ export default function CreateJobButton({ jobType }: CreateJobButtonProps) {
 
   return (
     <>
-      <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>
+      <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>
       <CreateJobModal
         isOpen={modalOpen}
         onClose={() => setModalOpen(false)}

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

@@ -4,24 +4,14 @@ import userEvent from '@testing-library/user-event';
 import { beforeEach, describe, expect, it, vi } from 'vitest';
 
 import CreateJobModal from './CreateJobModal';
-import {
-  createJob,
-  getDatasets,
-  getModelPresets,
-  getModels,
-  listGenerators,
-  uploadImage,
-  type GeneratorInfo,
-} from '@/lib/api';
+import { createJob, getDatasets, getModels, uploadImage } from '@/lib/api';
 import { defaultOptionsStore } from '@/stores/defaultOptions';
 import { DEFAULT_OPTIONS } from '@/lib/defaultOptions';
 
 vi.mock('@/lib/api', () => ({
   createJob: vi.fn(),
   getModels: vi.fn(),
-  getModelPresets: vi.fn(),
   getDatasets: vi.fn(),
-  listGenerators: vi.fn(),
   uploadImage: vi.fn(),
   getSettings: vi.fn(),
   updateSettings: vi.fn(),
@@ -32,32 +22,11 @@ const MODELS = [
   { id: 'wan/t2v-14b', label: 'Wan T2V Large', type: 't2v' },
 ];
 
-// A resident engine slot whose engine config differs from the persisted
-// defaults on every field the modal adopts.
-const WARM_SLOT: GeneratorInfo = {
-  state: 'ready',
-  model_id: 'wan/t2v-14b',
-  workload_type: 't2v',
-  num_gpus: 8,
-  dit_cpu_offload: true,
-  text_encoder_cpu_offload: true,
-  vae_cpu_offload: true,
-  image_encoder_cpu_offload: false,
-  use_fsdp_inference: true,
-  enable_torch_compile: true,
-  vsa_sparsity: 0.5,
-  tp_size: 1,
-  sp_size: 8,
-  error: null,
-};
-
 beforeEach(() => {
   // Reset the shared options store to a known baseline for test isolation.
   defaultOptionsStore.set({ options: DEFAULT_OPTIONS });
   vi.mocked(getModels).mockResolvedValue(MODELS);
-  vi.mocked(getModelPresets).mockResolvedValue({});
   vi.mocked(getDatasets).mockResolvedValue([]);
-  vi.mocked(listGenerators).mockResolvedValue([]);
   vi.mocked(uploadImage).mockResolvedValue({ path: '/uploads/x.png' });
   vi.mocked(createJob).mockResolvedValue({ id: 'job-1' } as never);
 });
@@ -81,57 +50,6 @@ 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();
 
@@ -237,173 +155,4 @@ describe('CreateJobModal', () => {
 
     await waitFor(() => expect(onSuccess).toHaveBeenCalledTimes(1));
   });
-
-  it('defaults to the warm resident model and adopts its engine config', async () => {
-    vi.mocked(listGenerators).mockResolvedValue([WARM_SLOT]);
-
-    const user = userEvent.setup();
-    renderModal();
-
-    // Without a warm model the default logic picks the first model
-    // (wan/t2v-1.3b, covered by the seeding test above); the warm slot wins.
-    await waitFor(() =>
-      expect(screen.getByLabelText('Model')).toHaveValue('wan/t2v-14b'),
-    );
-    // The warm selection also triggers its presets fetch.
-    await waitFor(() =>
-      expect(getModelPresets).toHaveBeenCalledWith('wan/t2v-14b'),
-    );
-
-    await user.type(screen.getByLabelText('Prompt'), 'warm run');
-    await user.click(screen.getByRole('button', { name: 'Create Job' }));
-
-    await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
-    // The engine fields mirror the resident slot (not the persisted defaults:
-    // num_gpus 1, all offloads/fsdp/compile false) so the job reuses the warm
-    // instance instead of replacing it.
-    expect(vi.mocked(createJob).mock.calls[0][0]).toMatchObject({
-      model_id: 'wan/t2v-14b',
-      num_gpus: 8,
-      tp_size: 1,
-      sp_size: 8,
-      dit_cpu_offload: true,
-      text_encoder_cpu_offload: true,
-      vae_cpu_offload: true,
-      image_encoder_cpu_offload: false,
-      use_fsdp_inference: true,
-      enable_torch_compile: true,
-      vsa_sparsity: 0.5,
-    });
-  });
-
-  it('restores persisted-default engine fields when switching away from the warm model', async () => {
-    defaultOptionsStore.set({
-      options: { ...DEFAULT_OPTIONS, numGpus: 2, tpSize: 2 },
-    });
-    vi.mocked(listGenerators).mockResolvedValue([WARM_SLOT]);
-
-    const user = userEvent.setup();
-    renderModal();
-
-    await waitFor(() =>
-      expect(screen.getByLabelText('Model')).toHaveValue('wan/t2v-14b'),
-    );
-    await user.selectOptions(screen.getByLabelText('Model'), 'wan/t2v-1.3b');
-    await user.type(screen.getByLabelText('Prompt'), 'cold run');
-    await user.click(screen.getByRole('button', { name: 'Create Job' }));
-
-    await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
-    expect(vi.mocked(createJob).mock.calls[0][0]).toMatchObject({
-      model_id: 'wan/t2v-1.3b',
-      num_gpus: 2,
-      tp_size: 2,
-      use_fsdp_inference: false,
-      enable_torch_compile: false,
-    });
-  });
-
-  it('applies resolution preset chips and the orientation toggle to the payload', async () => {
-    const user = userEvent.setup();
-    renderModal();
-
-    await screen.findByRole('option', { name: 'Wan T2V (wan/t2v-1.3b)' });
-    await user.click(screen.getByText('Options'));
-
-    // 720p chip sets the /32-rounded dims; the orientation toggle swaps them.
-    await user.click(screen.getByRole('button', { name: '720p' }));
-    await user.click(screen.getByRole('button', { name: 'Landscape' }));
-    expect(
-      screen.getByRole('button', { name: 'Portrait' }),
-    ).toBeInTheDocument();
-
-    await user.type(screen.getByLabelText('Prompt'), 'portrait 720p');
-    await user.click(screen.getByRole('button', { name: 'Create Job' }));
-
-    await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
-    expect(vi.mocked(createJob).mock.calls[0][0]).toMatchObject({
-      height: 1280,
-      width: 704,
-    });
-  });
-
-  it('restores the model preset resolution via the Native chip', async () => {
-    vi.mocked(getModelPresets).mockResolvedValue({ height: 720, width: 1280 });
-
-    const user = userEvent.setup();
-    renderModal();
-
-    await screen.findByRole('option', { name: 'Wan T2V (wan/t2v-1.3b)' });
-    // Native is disabled until the selected model's presets have loaded.
-    await waitFor(() =>
-      expect(screen.getByRole('button', { name: 'Native' })).toBeEnabled(),
-    );
-    await user.click(screen.getByText('Options'));
-
-    await user.click(screen.getByRole('button', { name: '1080p' }));
-    await user.click(screen.getByRole('button', { name: 'Native' }));
-
-    await user.type(screen.getByLabelText('Prompt'), 'native dims');
-    await user.click(screen.getByRole('button', { name: 'Create Job' }));
-
-    await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
-    expect(vi.mocked(createJob).mock.calls[0][0]).toMatchObject({
-      height: 720,
-      width: 1280,
-    });
-  });
-
-  it('populates sampling fields from the selected model presets; engine fields stay from defaults', async () => {
-    defaultOptionsStore.set({
-      options: { ...DEFAULT_OPTIONS, numGpus: 4, tpSize: 2, seed: 999 },
-    });
-    vi.mocked(getModelPresets).mockImplementation(async (id) =>
-      id === 'wan/t2v-14b'
-        ? {
-            height: 720,
-            width: 1280,
-            num_frames: 121,
-            fps: 30,
-            num_inference_steps: 40,
-            guidance_scale: 6,
-            guidance_rescale: 0.5,
-            negative_prompt: 'blurry, low quality',
-            seed: 7,
-          }
-        : {},
-    );
-
-    const user = userEvent.setup();
-    renderModal();
-
-    await screen.findByRole('option', { name: 'Wan T2V Large (wan/t2v-14b)' });
-    await user.selectOptions(screen.getByLabelText('Model'), 'wan/t2v-14b');
-    // The negative prompt is the easiest preset-populated field to observe.
-    await waitFor(() =>
-      expect(screen.getByLabelText('Negative Prompt')).toHaveValue(
-        'blurry, low quality',
-      ),
-    );
-
-    await user.type(screen.getByLabelText('Prompt'), 'preset test');
-    await user.click(screen.getByRole('button', { name: 'Create Job' }));
-
-    await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
-    const payload = vi.mocked(createJob).mock.calls[0][0];
-    expect(payload).toMatchObject({
-      model_id: 'wan/t2v-14b',
-      // Sampling fields come from the model presets…
-      height: 720,
-      width: 1280,
-      num_frames: 121,
-      fps: 30,
-      num_inference_steps: 40,
-      guidance_scale: 6,
-      guidance_rescale: 0.5,
-      negative_prompt: 'blurry, low quality',
-      seed: 7,
-      // …while engine fields still come from the persisted defaults.
-      num_gpus: 4,
-      tp_size: 2,
-    });
-  });
 });

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

@@ -23,27 +23,15 @@ import { defaultOptionsStore } from '@/stores/defaultOptions';
 import {
   createJob,
   getDatasets,
-  getModelPresets,
   getModels,
-  listGenerators,
   uploadImage,
   type CreateJobRequest,
-  type GeneratorInfo,
   type Model,
 } from '@/lib/api';
 import { getDefaultModelForWorkload } from '@/lib/defaultOptions';
 import { WORKLOAD_OPTIONS } from '@/lib/jobConfig';
 import type { JobType } from '@/lib/types';
 
-// ponytail: dims pre-rounded to multiples of 32 so every model family accepts
-// them (H3 requires /32) — hence 720p→704 and 1080p→1088. Tooltips show the
-// exact dims; the labels stay 480p/720p/1080p.
-const RESOLUTION_PRESETS = [
-  { label: '480p', height: 480, width: 832 },
-  { label: '720p', height: 704, width: 1280 },
-  { label: '1080p', height: 1088, width: 1920 },
-] as const;
-
 export interface CreateJobModalProps {
   isOpen: boolean;
   onClose: () => void;
@@ -67,9 +55,6 @@ export default function CreateJobModal({
 
   const [models, setModels] = React.useState<Model[]>([]);
   const [modelId, setModelId] = React.useState('');
-  // The engine's resident slot (null when empty/failed), fetched on open so
-  // jobs against the warm model can adopt its engine config.
-  const [warmSlot, setWarmSlot] = React.useState<GeneratorInfo | null>(null);
   const [prompt, setPrompt] = React.useState('');
   const [imagePath, setImagePath] = React.useState('');
   const [imageFileName, setImageFileName] = React.useState('');
@@ -79,12 +64,6 @@ export default function CreateJobModal({
   const [numFrames, setNumFrames] = React.useState(81);
   const [height, setHeight] = React.useState(480);
   const [width, setWidth] = React.useState(832);
-  // The selected model's preset dims, kept so the "Native" chip can restore
-  // them after a resolution chip or manual slider edit.
-  const [nativeDims, setNativeDims] = React.useState<{
-    height: number;
-    width: number;
-  } | null>(null);
   const [guidanceScale, setGuidanceScale] = React.useState(5);
   const [guidanceRescale, setGuidanceRescale] = React.useState(0);
   const [fps, setFps] = React.useState(24);
@@ -124,17 +103,6 @@ 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
@@ -176,10 +144,6 @@ export default function CreateJobModal({
     setImageFileName('');
     setSelectedDatasetId('');
     setSelectedValidationDatasetId('');
-    setModelLoadError(null);
-    setDatasetLoadError(null);
-    setImageUploadError(null);
-    setSubmitError(null);
     if (workloadType === 'dmd_t2v') {
       setDmdUseVsa(false);
       setDmdVsaSparsity(0.8);
@@ -198,35 +162,17 @@ export default function CreateJobModal({
     // can't overwrite the current workload's model list/selection.
     let stale = false;
     setIsLoadingModels(true);
-    setModelLoadError(null);
-    // For inference jobs, prefer the warm resident model (if any) over the
-    // persisted default so new jobs hit the already-loaded engine. The warm
-    // slot is a nice-to-have: if the lookup fails, fall back silently.
-    const warmSlotPromise = isInference
-      ? listGenerators()
-          .then((gens) =>
-            gens[0] && gens[0].state !== 'failed' ? gens[0] : null,
-          )
-          .catch(() => null)
-      : Promise.resolve(null);
-    Promise.all([getModels(inferenceWorkload), warmSlotPromise])
-      .then(([list, slot]) => {
+    getModels(inferenceWorkload)
+      .then((list) => {
         if (stale) return;
         setModels(list);
-        setWarmSlot(slot);
         const ids = list.map((m) => m.id);
         const opts = defaultOptionsStore.get().options;
         const defaultId = getDefaultModelForWorkload(
           opts,
           inferenceWorkload as 't2v' | 'i2v' | 't2i',
         );
-        const warmId = slot?.model_id ?? '';
-        const chosen =
-          warmId && ids.includes(warmId)
-            ? warmId
-            : ids.includes(defaultId)
-              ? defaultId
-              : (list[0]?.id ?? '');
+        const chosen = ids.includes(defaultId) ? defaultId : (list[0]?.id ?? '');
         setModelId(chosen);
         if (workloadType === 'dmd_t2v') {
           setRealScoreModelPath(chosen);
@@ -234,13 +180,7 @@ export default function CreateJobModal({
         }
       })
       .catch((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.',
-        );
+        if (!stale) console.error('Failed to load models:', e);
       })
       .finally(() => {
         if (!stale) setIsLoadingModels(false);
@@ -248,101 +188,16 @@ export default function CreateJobModal({
     return () => {
       stale = true;
     };
-  }, [isOpen, isInference, inferenceWorkload, workloadType]);
-
-  // Whenever the selected model changes (including the initial selection on
-  // open), populate the sampling fields from that model's presets. Missing
-  // keys leave the field as-is; engine fields (GPUs, parallelism, offloads)
-  // come from the warm slot or persisted defaults (effect below). Latest
-  // selection wins: the cleanup marks superseded fetches stale, same as the
-  // model-list effect.
-  React.useEffect(() => {
-    if (!isOpen || !isInference || !modelId) return;
-    let stale = false;
-    setNativeDims(null);
-    getModelPresets(modelId)
-      .then((p) => {
-        if (stale) return;
-        if (p.height !== undefined) setHeight(p.height);
-        if (p.width !== undefined) setWidth(p.width);
-        if (p.height !== undefined && p.width !== undefined)
-          setNativeDims({ height: p.height, width: p.width });
-        if (p.num_frames !== undefined)
-          setNumFrames(workloadType === 't2i' ? 1 : p.num_frames);
-        if (p.fps !== undefined) setFps(p.fps);
-        if (p.num_inference_steps !== undefined)
-          setNumInferenceSteps(p.num_inference_steps);
-        if (p.guidance_scale !== undefined) setGuidanceScale(p.guidance_scale);
-        if (p.guidance_rescale !== undefined)
-          setGuidanceRescale(p.guidance_rescale);
-        if (p.negative_prompt !== undefined)
-          setNegativePrompt(p.negative_prompt);
-        if (p.seed !== undefined) setSeed(p.seed);
-      })
-      .catch((e) => {
-        // Presets are a convenience; on failure keep the current values.
-        console.error('Failed to load model presets:', e);
-      });
-    return () => {
-      stale = true;
-    };
-  }, [isOpen, isInference, modelId, workloadType]);
-
-  // When the selected model IS the warm resident one, adopt the slot's engine
-  // config so the job reuses the loaded instance instead of silently replacing
-  // it (e.g. persisted num_gpus=1 vs a warm 8-GPU slot). Switching away from
-  // the warm model restores the persisted-default engine fields; non-warm to
-  // non-warm switches leave the user's engine edits alone (today's behavior).
-  const wasWarmRef = React.useRef(false);
-  React.useEffect(() => {
-    if (!isOpen || !isInference || !modelId) return;
-    const isWarm = warmSlot?.model_id === modelId;
-    if (isWarm && warmSlot) {
-      setNumGpus(warmSlot.num_gpus);
-      setTpSize(warmSlot.tp_size);
-      setSpSize(warmSlot.sp_size);
-      setDitCpuOffload(warmSlot.dit_cpu_offload);
-      setTextEncoderCpuOffload(warmSlot.text_encoder_cpu_offload);
-      setVaeCpuOffload(warmSlot.vae_cpu_offload);
-      setImageEncoderCpuOffload(warmSlot.image_encoder_cpu_offload);
-      setUseFsdpInference(warmSlot.use_fsdp_inference);
-      setEnableTorchCompile(warmSlot.enable_torch_compile);
-      setVsaSparsity(warmSlot.vsa_sparsity);
-    } else if (wasWarmRef.current) {
-      const opts = defaultOptionsStore.get().options;
-      setNumGpus(opts.numGpus);
-      setTpSize(opts.tpSize);
-      setSpSize(opts.spSize);
-      setDitCpuOffload(opts.ditCpuOffload);
-      setTextEncoderCpuOffload(opts.textEncoderCpuOffload);
-      setVaeCpuOffload(opts.vaeCpuOffload);
-      setImageEncoderCpuOffload(opts.imageEncoderCpuOffload);
-      setUseFsdpInference(opts.useFsdpInference);
-      setEnableTorchCompile(opts.enableTorchCompile);
-      setVsaSparsity(opts.vsaSparsity);
-    }
-    wasWarmRef.current = isWarm;
-  }, [isOpen, isInference, modelId, warmSlot]);
+  }, [isOpen, inferenceWorkload, workloadType]);
 
   // Training jobs need a dataset; load the ready datasets when relevant.
   React.useEffect(() => {
     if (isOpen && !isInference) {
-      setIsLoadingDatasets(true);
-      setDatasetLoadError(null);
       getDatasets()
         .then(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));
+        .catch(() => setReadyDatasets([]));
     } else {
       setReadyDatasets([]);
-      setIsLoadingDatasets(false);
-      setDatasetLoadError(null);
     }
   }, [isOpen, isInference]);
 
@@ -351,24 +206,16 @@ 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 (error) {
-      console.error('Failed to upload image:', error);
+    } catch {
       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);
     }
@@ -377,7 +224,6 @@ export default function CreateJobModal({
   function clearImage() {
     setImagePath('');
     setImageFileName('');
-    setImageUploadError(null);
     if (imageInputRef.current) imageInputRef.current.value = '';
   }
 
@@ -393,7 +239,6 @@ export default function CreateJobModal({
       workloadType === 'lora_t2v' ? 'lora' : jobType
     ) as JobType;
     setIsSubmitting(true);
-    setSubmitError(null);
     try {
       const payload: CreateJobRequest = {
         model_id: modelId,
@@ -451,11 +296,6 @@ 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);
     }
@@ -503,11 +343,7 @@ export default function CreateJobModal({
               value={modelId}
               onChange={(e) => setModelId(e.target.value)}
               required
-              aria-describedby={
-                modelLoadError ? 'modal-model-error' : undefined
-              }
-              aria-invalid={modelLoadError ? true : undefined}
-              disabled={isSubmitting || isLoadingModels || !!modelLoadError}
+              disabled={isSubmitting || isLoadingModels}
             >
               <option value="" disabled>
                 {isLoadingModels
@@ -522,15 +358,6 @@ 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' && (
@@ -542,10 +369,6 @@ 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"
               />
@@ -562,15 +385,6 @@ export default function CreateJobModal({
                   </button>
                 </span>
               )}
-              {imageUploadError && (
-                <p
-                  id="modal-image-error"
-                  role="alert"
-                  className="text-sm text-destructive"
-                >
-                  {imageUploadError}
-                </p>
-              )}
             </FieldRow>
           )}
 
@@ -618,22 +432,12 @@ export default function CreateJobModal({
                     id="modal-dataset"
                     value={selectedDatasetId}
                     onChange={(e) => setSelectedDatasetId(e.target.value)}
-                    aria-describedby={
-                      datasetLoadError ? 'modal-dataset-error' : undefined
-                    }
-                    aria-invalid={datasetLoadError ? true : undefined}
-                    disabled={
-                      isSubmitting || isLoadingDatasets || !!datasetLoadError
-                    }
+                    disabled={isSubmitting}
                   >
                     <option value="" disabled>
-                      {isLoadingDatasets
-                        ? 'Loading datasets…'
-                        : datasetLoadError
-                          ? 'Datasets unavailable'
-                          : readyDatasets.length === 0
-                            ? 'No datasets (add in Datasets tab)'
-                            : 'Select a dataset…'}
+                      {readyDatasets.length === 0
+                        ? 'No datasets (add in Datasets tab)'
+                        : 'Select a dataset…'}
                     </option>
                     {readyDatasets.map((d) => (
                       <option key={d.id} value={d.id}>
@@ -641,15 +445,6 @@ 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"
@@ -662,9 +457,7 @@ export default function CreateJobModal({
                     onChange={(e) =>
                       setSelectedValidationDatasetId(e.target.value)
                     }
-                    disabled={
-                      isSubmitting || isLoadingDatasets || !!datasetLoadError
-                    }
+                    disabled={isSubmitting}
                   >
                     <option value="">None</option>
                     {readyDatasets.map((d) => (
@@ -853,64 +646,6 @@ export default function CreateJobModal({
                     disabled={isSubmitting}
                   />
                 )}
-                <div className="col-span-full flex flex-wrap items-center gap-1.5">
-                  <span className="pl-0.5 text-xs font-normal tracking-wide text-muted-foreground">
-                    Resolution
-                  </span>
-                  {RESOLUTION_PRESETS.map((preset) => (
-                    <Button
-                      key={preset.label}
-                      type="button"
-                      size="sm"
-                      variant="outline"
-                      title={`${preset.height}×${preset.width}`}
-                      onClick={() => {
-                        // Apply in the current orientation so a portrait
-                        // setup stays portrait when switching resolution.
-                        const portrait = height > width;
-                        setHeight(portrait ? preset.width : preset.height);
-                        setWidth(portrait ? preset.height : preset.width);
-                      }}
-                      disabled={isSubmitting}
-                      className="h-6 px-2 text-xs"
-                    >
-                      {preset.label}
-                    </Button>
-                  ))}
-                  <Button
-                    type="button"
-                    size="sm"
-                    variant="outline"
-                    title={
-                      nativeDims
-                        ? `${nativeDims.height}×${nativeDims.width}`
-                        : 'Model preset resolution (unavailable)'
-                    }
-                    onClick={() => {
-                      if (!nativeDims) return;
-                      setHeight(nativeDims.height);
-                      setWidth(nativeDims.width);
-                    }}
-                    disabled={isSubmitting || !nativeDims}
-                    className="h-6 px-2 text-xs"
-                  >
-                    Native
-                  </Button>
-                  <Button
-                    type="button"
-                    size="sm"
-                    variant="outline"
-                    title="Swap height and width"
-                    onClick={() => {
-                      setHeight(width);
-                      setWidth(height);
-                    }}
-                    disabled={isSubmitting}
-                    className="h-6 px-2 text-xs"
-                  >
-                    {height > width ? 'Portrait' : 'Landscape'}
-                  </Button>
-                </div>
                 <SliderRow
                   id="modal-height"
                   label="Height"
@@ -1076,22 +811,9 @@ export default function CreateJobModal({
             </details>
           )}
 
-          <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'}
+          <div>
+            <Button type="submit" disabled={isSubmitting}>
+              {isSubmitting ? 'Creating...' : 'Create Job'}
             </Button>
           </div>
         </form>

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

@@ -1,71 +0,0 @@
-import { act, fireEvent, render, screen } from '@testing-library/react';
-import userEvent from '@testing-library/user-event';
-import { beforeEach, describe, expect, it, vi } from 'vitest';
-
-import EngineConsole from './EngineConsole';
-import { getEngineLogs } from '@/lib/api';
-
-vi.mock('@/lib/api', () => ({
-  getEngineLogs: vi.fn(),
-}));
-
-beforeEach(() => {
-  vi.mocked(getEngineLogs).mockResolvedValue({
-    lines: ['[engine] booted', '[engine] worker heartbeat ok'],
-    total: 2,
-  });
-});
-
-describe('EngineConsole', () => {
-  it('is collapsed by default and does not fetch', () => {
-    render(<EngineConsole />);
-
-    expect(
-      screen.getByRole('button', { name: 'Engine output' }),
-    ).toHaveAttribute('aria-expanded', 'false');
-    expect(getEngineLogs).not.toHaveBeenCalled();
-  });
-
-  it('shows log lines and polls with the cursor while open', async () => {
-    vi.useFakeTimers();
-    try {
-      render(<EngineConsole />);
-      fireEvent.click(screen.getByRole('button', { name: 'Engine output' }));
-
-      // Flush the immediate poll fired on expand.
-      await act(async () => {
-        await vi.advanceTimersByTimeAsync(0);
-      });
-      expect(getEngineLogs).toHaveBeenCalledWith(0);
-      expect(screen.getByText(/\[engine\] booted/)).toBeInTheDocument();
-
-      // The 2s interval polls again, from the previous total.
-      await act(async () => {
-        await vi.advanceTimersByTimeAsync(2000);
-      });
-      expect(getEngineLogs).toHaveBeenLastCalledWith(2);
-
-      // Collapsing stops the polling.
-      fireEvent.click(screen.getByRole('button', { name: 'Engine output' }));
-      const calls = vi.mocked(getEngineLogs).mock.calls.length;
-      await act(async () => {
-        await vi.advanceTimersByTimeAsync(10000);
-      });
-      expect(getEngineLogs).toHaveBeenCalledTimes(calls);
-    } finally {
-      vi.useRealTimers();
-    }
-  });
-
-  it('clear view empties the scrollback locally', async () => {
-    const user = userEvent.setup();
-    render(<EngineConsole />);
-
-    await user.click(screen.getByRole('button', { name: 'Engine output' }));
-    expect(await screen.findByText(/\[engine\] booted/)).toBeInTheDocument();
-
-    await user.click(screen.getByRole('button', { name: 'Clear view' }));
-    expect(screen.queryByText(/\[engine\] booted/)).not.toBeInTheDocument();
-    expect(screen.getByText('Waiting for engine output…')).toBeInTheDocument();
-  });
-});

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

@@ -1,123 +0,0 @@
-'use client';
-
-import * as React from 'react';
-import { ChevronDown, ChevronRight } from 'lucide-react';
-
-import { Button } from '@/components/ui/button';
-import { getEngineLogs } from '@/lib/api';
-
-const POLL_INTERVAL_MS = 2000;
-// Cap the DOM at the last ~500 lines; the server keeps its own ring buffer.
-const MAX_LINES = 500;
-
-// The engine tail includes uvicorn's access log (until a server restart picks
-// up access_log=False); the frontend's own polling would otherwise flood the
-// console with "GET /api/... 200 OK" lines. Keep non-GET and non-2xx lines.
-const ACCESS_LOG_NOISE = /^INFO:\s+[\d.:]+\s+- "(?:GET|HEAD) \S+ HTTP\/[\d.]+" 2\d\d/;
-
-/**
- * Collapsible tail of the engine's stdout/stderr (driver + relayed worker
- * output). Polls only while open; sticks to the bottom unless the user has
- * scrolled up.
- */
-export default function EngineConsole() {
-  const [open, setOpen] = React.useState(false);
-  const [lines, setLines] = React.useState<string[]>([]);
-  // Poll cursor + stick-to-bottom flag live in refs: they must update
-  // synchronously from async polls / scroll events, outside React's cycle.
-  const afterRef = React.useRef(0);
-  const stickRef = React.useRef(true);
-  const consoleRef = React.useRef<HTMLPreElement | null>(null);
-
-  React.useEffect(() => {
-    if (!open) return;
-    let mounted = true;
-    let locked = false;
-
-    async function poll() {
-      if (!mounted || locked) return;
-      locked = true;
-      try {
-        const data = await getEngineLogs(afterRef.current);
-        afterRef.current = data.total;
-        const fresh = data.lines.filter((l) => !ACCESS_LOG_NOISE.test(l));
-        if (mounted && fresh.length > 0) {
-          setLines((prev) => [...prev, ...fresh].slice(-MAX_LINES));
-        }
-      } catch (e) {
-        console.error('Failed to fetch engine logs:', e);
-      } finally {
-        locked = false;
-      }
-    }
-
-    poll();
-    const interval = setInterval(poll, POLL_INTERVAL_MS);
-    return () => {
-      mounted = false;
-      clearInterval(interval);
-    };
-  }, [open]);
-
-  // Follow the tail after new lines land, unless the user scrolled up.
-  React.useEffect(() => {
-    const el = consoleRef.current;
-    if (el && stickRef.current) el.scrollTop = el.scrollHeight;
-  }, [lines]);
-
-  function handleScroll() {
-    const el = consoleRef.current;
-    if (!el) return;
-    stickRef.current = el.scrollHeight - el.scrollTop - el.clientHeight < 40;
-  }
-
-  return (
-    <section
-      aria-label="Engine output"
-      className="mx-auto w-full max-w-[850px] px-10 pt-3"
-    >
-      <div className="rounded-lg border border-border bg-background">
-        <div className="flex items-center gap-2 px-2 py-1.5">
-          <button
-            type="button"
-            onClick={() => setOpen((o) => !o)}
-            aria-expanded={open}
-            className="flex flex-1 items-center gap-2 rounded-md px-2 py-1 text-sm font-semibold text-foreground hover:bg-accent"
-          >
-            {open ? (
-              <ChevronDown className="h-4 w-4" />
-            ) : (
-              <ChevronRight className="h-4 w-4" />
-            )}
-            Engine output
-          </button>
-          {open && (
-            <Button
-              size="sm"
-              variant="ghost"
-              // Resets the local view only; the server buffer is untouched.
-              onClick={() => setLines([])}
-            >
-              Clear view
-            </Button>
-          )}
-        </div>
-        {open && (
-          <pre
-            ref={consoleRef}
-            onScroll={handleScroll}
-            className="m-0 h-64 overflow-auto whitespace-pre-wrap break-words rounded-b-lg border-t border-border bg-zinc-950 p-3 font-mono text-xs leading-normal text-zinc-200"
-          >
-            {lines.length === 0 ? (
-              <span className="italic text-zinc-500">
-                Waiting for engine output…
-              </span>
-            ) : (
-              lines.join('\n')
-            )}
-          </pre>
-        )}
-      </div>
-    </section>
-  );
-}

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

@@ -20,14 +20,6 @@ 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',
@@ -54,16 +46,6 @@ 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

@@ -119,10 +119,18 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
     }
   }
 
-  function handleSelectJob() {
+  function handleSelectJob(e: React.MouseEvent | React.KeyboardEvent) {
+    if ((e.target as HTMLElement).closest('button')) return;
     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();
@@ -140,57 +148,54 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
   }
 
   return (
-    <article
+    <div
+      role="button"
+      tabIndex={0}
+      onClick={handleSelectJob}
+      onKeyDown={handleKeyDown}
       className={cn(
-        'mb-1.5 flex cursor-pointer flex-col gap-1 rounded-lg border bg-background px-3 py-1.5 transition-colors last:mb-0',
+        'mb-3 flex cursor-pointer flex-col gap-2.5 rounded-lg border bg-background p-4 transition-colors last:mb-0',
         isSelected
           ? 'border-accent-blue bg-accent-blue/5'
           : 'border-border hover:border-muted-foreground/40',
       )}
     >
-      <button
-        type="button"
-        aria-pressed={isSelected}
-        onClick={handleSelectJob}
-        className="flex w-full min-w-0 flex-col gap-1 rounded-md text-left"
-      >
-        <span className="flex w-full min-w-0 items-center gap-2">
-        <span className="shrink-0 text-sm font-semibold text-foreground">
+      <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>
-        <span className="ml-auto flex shrink-0 items-center gap-3 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}
+      </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>
-        </span>
-        <span className="w-full whitespace-pre-wrap break-words text-xs text-muted-foreground">
-          {job.prompt}
-        </span>
-      </button>
+          </>
+        ) : (
+          <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>
+        )}
+      </div>
       <div className="flex flex-wrap items-center gap-1.5">
         {job.status === 'running' ? (
           <Button
             size="sm"
             onClick={handleStop}
             disabled={isLoading}
-            className="h-6 px-2 text-xs border-transparent bg-amber-500 text-black shadow-md hover:bg-amber-400"
+            className="border-transparent bg-amber-500 text-black shadow-md hover:bg-amber-400"
           >
             Stop
           </Button>
@@ -199,7 +204,7 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
             size="sm"
             onClick={handleStart}
             disabled={isLoading}
-            className="h-6 px-2 text-xs border-transparent bg-emerald-600 text-white shadow-md hover:bg-emerald-500"
+            className="border-transparent bg-emerald-600 text-white shadow-md hover:bg-emerald-500"
           >
             Restart
           </Button>
@@ -208,7 +213,7 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
             size="sm"
             onClick={handleStart}
             disabled={isLoading}
-            className="h-6 px-2 text-xs border-transparent bg-emerald-600 text-white shadow-md hover:bg-emerald-500"
+            className="border-transparent bg-emerald-600 text-white shadow-md hover:bg-emerald-500"
           >
             Start
           </Button>
@@ -222,7 +227,6 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
               onClick={handleDownloadVideo}
               disabled={isLoading}
               title="Download video"
-              className="h-6 px-2 text-xs"
             >
               Download Video
             </Button>
@@ -232,11 +236,10 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
           variant="destructive"
           onClick={handleDelete}
           disabled={isLoading}
-          className="h-6 px-2 text-xs"
         >
           Delete
         </Button>
       </div>
-    </article>
+    </div>
   );
 }

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

@@ -9,7 +9,6 @@ import { makeJob as makeBaseJob } from '@/test/factories';
 vi.mock('@/lib/api', () => ({
   getJobLogs: vi.fn(),
   downloadJobLog: vi.fn(),
-  getJobVideoUrl: (id: string) => `http://test.local/api/jobs/${id}/video`,
 }));
 
 const makeJob = (overrides: Partial<Job> = {}): Job =>
@@ -20,69 +19,6 @@ 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('plays completed inference output inline; running jobs get no player', async () => {
-    vi.mocked(getJobLogs).mockResolvedValue({
-      lines: [],
-      total: 0,
-      progress: 0,
-      progress_msg: '',
-      phase: '',
-    });
-
-    const { rerender } = render(
-      <JobDetailsSidebar
-        job={makeJob({
-          status: 'completed',
-          output_path: '/outputs/job-1.mp4',
-          prompt: 'a cat surfing a wave',
-        })}
-        onClose={vi.fn()}
-      />,
-    );
-
-    const video = screen.getByLabelText('Generated video: a cat surfing a wave');
-    expect(video.tagName).toBe('VIDEO');
-    expect(video).toHaveAttribute('controls');
-    expect(video).toHaveAttribute(
-      'src',
-      'http://test.local/api/jobs/job-1/video',
-    );
-
-    rerender(
-      <JobDetailsSidebar
-        job={makeJob({ status: 'running', output_path: null })}
-        onClose={vi.fn()}
-      />,
-    );
-    expect(screen.queryByLabelText(/Generated video/)).not.toBeInTheDocument();
-  });
-
   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

@@ -4,9 +4,8 @@ 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, getJobVideoUrl } from '@/lib/api';
+import { downloadJobLog, getJobLogs } from '@/lib/api';
 import type { Job } from '@/lib/types';
 import { cn, downloadBlob } from '@/lib/utils';
 
@@ -16,16 +15,13 @@ 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);
@@ -57,8 +53,8 @@ export default function JobDetailsSidebar({
   });
 
   React.useEffect(() => {
-    onWidthChange?.(isMobile ? 0 : width);
-  }, [isMobile, width, onWidthChange]);
+    onWidthChange?.(width);
+  }, [width, onWidthChange]);
 
   // Auto-scroll the console to the bottom whenever new lines land. Runs after
   // commit so scrollHeight reflects the freshly-rendered output.
@@ -141,16 +137,8 @@ export default function JobDetailsSidebar({
 
   return (
     <aside
-      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,
-      }}
+      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 }}
     >
       <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">
@@ -180,41 +168,6 @@ export default function JobDetailsSidebar({
         </div>
       </div>
 
-      {job.status === 'completed' &&
-        job.output_path &&
-        (job.job_type === 'inference' || !job.job_type) && (
-          <div className="border-b border-border px-5 py-4">
-            <span className="mb-2 block text-xs font-semibold uppercase tracking-wider text-muted-foreground">
-              Output
-            </span>
-            {job.output_path.toLowerCase().endsWith('.png') ? (
-              // eslint-disable-next-line @next/next/no-img-element
-              <img
-                src={getJobVideoUrl(job.id)}
-                alt={
-                  job.prompt
-                    ? `Generated image: ${job.prompt}`
-                    : 'Generated image'
-                }
-                className="block w-full rounded-lg border border-border bg-background object-contain"
-              />
-            ) : (
-              <video
-                src={getJobVideoUrl(job.id)}
-                aria-label={
-                  job.prompt
-                    ? `Generated video: ${job.prompt}`
-                    : 'Generated video'
-                }
-                controls
-                playsInline
-                preload="metadata"
-                className="block max-h-80 w-full rounded-lg border border-border bg-background object-contain"
-              />
-            )}
-          </div>
-        )}
-
       <div className="flex min-h-0 flex-1 flex-col px-5 py-4">
         <div className="mb-2 flex items-center justify-between">
           <span className="text-xs font-semibold uppercase tracking-wider text-muted-foreground">
@@ -242,14 +195,14 @@ export default function JobDetailsSidebar({
         </pre>
       </div>
 
-      {!isMobile && <div
+      <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, fireEvent, render, screen, waitFor } from '@testing-library/react';
+import { act, render, screen, waitFor } from '@testing-library/react';
 import { beforeEach, describe, expect, it, vi } from 'vitest';
 
 import JobQueue from '@/components/jobs/JobQueue';
@@ -37,46 +37,6 @@ 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

@@ -1,10 +1,8 @@
 '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';
@@ -34,8 +32,6 @@ 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);
 
@@ -75,22 +71,11 @@ export default function JobQueue({ jobType, jobTypesForList }: JobQueueProps) {
               new Date(a.created_at ?? 0).getTime(),
           );
       }
-      if (seq === fetchSeq.current) {
-        setJobs(next);
-        setError(null);
-      }
+      if (seq === fetchSeq.current) setJobs(next);
     } 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;
-        setIsInitialLoading(false);
-      }
+      if (seq === fetchSeq.current) inFlight.current = false;
     }
   }, [typesKey]);
 
@@ -137,59 +122,20 @@ export default function JobQueue({ jobType, jobTypesForList }: JobQueueProps) {
   const multiType = typesToFetch.length > 1;
 
   return (
-    <div className="mx-auto flex w-full max-w-[850px] flex-col gap-6 px-4 pb-12">
+    <main className="mx-auto flex w-full max-w-[850px] flex-col gap-6 px-4 pb-12">
       <section className="p-6">
-        <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 ? (
+        <div>
+          {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>
-    </div>
+    </main>
   );
 }

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

@@ -1,188 +0,0 @@
-import { render, screen, waitFor } from '@testing-library/react';
-import userEvent from '@testing-library/user-event';
-import { beforeEach, describe, expect, it, vi } from 'vitest';
-
-import WarmModelsPanel from './WarmModelsPanel';
-import {
-  getModels,
-  listGenerators,
-  preloadGenerator,
-  unloadGenerator,
-  type GeneratorInfo,
-} from '@/lib/api';
-import { DEFAULT_OPTIONS } from '@/lib/defaultOptions';
-import { defaultOptionsStore } from '@/stores/defaultOptions';
-import { toast } from 'sonner';
-
-vi.mock('@/lib/api', () => ({
-  getModels: vi.fn(),
-  listGenerators: vi.fn(),
-  preloadGenerator: vi.fn(),
-  unloadGenerator: vi.fn(),
-  getSettings: vi.fn(),
-  updateSettings: vi.fn(),
-}));
-
-vi.mock('sonner', () => ({
-  toast: { error: vi.fn() },
-}));
-
-const makeGenerator = (
-  overrides: Partial<GeneratorInfo> = {},
-): GeneratorInfo => ({
-  state: 'ready',
-  model_id: 'Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
-  workload_type: 't2v',
-  num_gpus: 1,
-  dit_cpu_offload: false,
-  text_encoder_cpu_offload: false,
-  vae_cpu_offload: false,
-  image_encoder_cpu_offload: false,
-  use_fsdp_inference: false,
-  enable_torch_compile: false,
-  vsa_sparsity: 0,
-  tp_size: -1,
-  sp_size: -1,
-  error: null,
-  ...overrides,
-});
-
-beforeEach(() => {
-  // Reset the shared options store to a known baseline for test isolation.
-  defaultOptionsStore.set({ options: DEFAULT_OPTIONS });
-  vi.mocked(getModels).mockResolvedValue([
-    { id: 'Wan-AI/Wan2.1-T2V-1.3B-Diffusers', label: 'Wan2.1 T2V 1.3B' },
-  ]);
-  vi.mocked(listGenerators).mockResolvedValue([]);
-  vi.mocked(preloadGenerator).mockResolvedValue(
-    makeGenerator({ state: 'loading' }),
-  );
-  vi.mocked(unloadGenerator).mockResolvedValue(undefined);
-});
-
-describe('WarmModelsPanel', () => {
-  it('shows the empty slot when no model is loaded', async () => {
-    render(<WarmModelsPanel />);
-
-    expect(await screen.findByText('No model loaded')).toBeInTheDocument();
-    expect(
-      screen.queryByRole('button', { name: 'Unload' }),
-    ).not.toBeInTheDocument();
-  });
-
-  it('renders the resident slot with its state and config summary', async () => {
-    vi.mocked(listGenerators).mockResolvedValue([
-      makeGenerator({ num_gpus: 8, enable_torch_compile: true }),
-    ]);
-    render(<WarmModelsPanel />);
-
-    // Model label: last path segment with dashes/underscores as spaces.
-    expect(
-      await screen.findByText('Wan2.1 T2V 1.3B Diffusers'),
-    ).toBeInTheDocument();
-    expect(screen.getByText('ready')).toBeInTheDocument();
-    expect(screen.getByText('8 GPU · compile')).toBeInTheDocument();
-    expect(screen.getByRole('button', { name: 'Unload' })).toBeInTheDocument();
-  });
-
-  it('disables loading a new model while a load is in flight', async () => {
-    vi.mocked(listGenerators).mockResolvedValue([
-      makeGenerator({ state: 'loading' }),
-    ]);
-    render(<WarmModelsPanel />);
-
-    expect(await screen.findByText('loading')).toBeInTheDocument();
-    expect(screen.getByRole('button', { name: 'Load model' })).toBeDisabled();
-    expect(
-      screen.queryByRole('button', { name: 'Unload' }),
-    ).not.toBeInTheDocument();
-  });
-
-  it('shows the error on a failed slot and keeps retry enabled', async () => {
-    vi.mocked(listGenerators).mockResolvedValue([
-      makeGenerator({ state: 'failed', error: 'CUDA out of memory' }),
-    ]);
-    render(<WarmModelsPanel />);
-
-    expect(await screen.findByText('failed')).toHaveAttribute(
-      'title',
-      'CUDA out of memory',
-    );
-    await waitFor(() =>
-      expect(screen.getByRole('button', { name: 'Load model' })).toBeEnabled(),
-    );
-  });
-
-  it('labels the load button as a swap when a different model is resident', async () => {
-    vi.mocked(listGenerators).mockResolvedValue([
-      makeGenerator({ model_id: 'FastVideo/FastHunyuan-diffusers' }),
-    ]);
-    render(<WarmModelsPanel />);
-
-    expect(
-      await screen.findByRole('button', { name: 'Load (replaces current)' }),
-    ).toBeInTheDocument();
-  });
-
-  it('loads the selected model with the persisted default options', async () => {
-    defaultOptionsStore.set({
-      options: { ...DEFAULT_OPTIONS, numGpus: 4, enableTorchCompile: true },
-    });
-    const user = userEvent.setup();
-    render(<WarmModelsPanel />);
-
-    const button = await screen.findByRole('button', { name: 'Load model' });
-    await waitFor(() => expect(button).toBeEnabled());
-    await user.click(button);
-
-    await waitFor(() =>
-      expect(preloadGenerator).toHaveBeenCalledWith({
-        model_id: 'Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
-        workload_type: 't2v',
-        num_gpus: 4,
-        dit_cpu_offload: false,
-        text_encoder_cpu_offload: false,
-        vae_cpu_offload: false,
-        image_encoder_cpu_offload: false,
-        use_fsdp_inference: false,
-        enable_torch_compile: true,
-        vsa_sparsity: 0,
-        tp_size: -1,
-        sp_size: -1,
-      }),
-    );
-    // The panel refetches so the new "loading" slot appears promptly.
-    await waitFor(() => expect(listGenerators).toHaveBeenCalledTimes(2));
-  });
-
-  it('surfaces the backend detail when a load is rejected', async () => {
-    vi.mocked(preloadGenerator).mockRejectedValue(
-      new Error('a model load is already in progress'),
-    );
-    const user = userEvent.setup();
-    render(<WarmModelsPanel />);
-
-    const button = await screen.findByRole('button', { name: 'Load model' });
-    await waitFor(() => expect(button).toBeEnabled());
-    await user.click(button);
-
-    await waitFor(() =>
-      expect(toast.error).toHaveBeenCalledWith('Model was not loaded', {
-        description: 'a model load is already in progress',
-      }),
-    );
-  });
-
-  it('unloads the resident model with no payload', async () => {
-    vi.mocked(listGenerators).mockResolvedValue([makeGenerator()]);
-    const user = userEvent.setup();
-    render(<WarmModelsPanel />);
-
-    await user.click(await screen.findByRole('button', { name: 'Unload' }));
-
-    await waitFor(() => expect(unloadGenerator).toHaveBeenCalledTimes(1));
-    expect(vi.mocked(unloadGenerator).mock.calls[0]).toEqual([]);
-    // The slot refreshes after the unload succeeds.
-    await waitFor(() => expect(listGenerators).toHaveBeenCalledTimes(2));
-  });
-});

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

@@ -1,237 +0,0 @@
-'use client';
-
-import * as React from 'react';
-import { toast } from 'sonner';
-
-import { Badge, type BadgeProps } from '@/components/ui/badge';
-import { Button } from '@/components/ui/button';
-import { NativeSelect } from '@/components/ui/native-select';
-import { useStore } from '@/hooks/useStore';
-import {
-  getModels,
-  listGenerators,
-  preloadGenerator,
-  unloadGenerator,
-  type GeneratorInfo,
-  type Model,
-} from '@/lib/api';
-import { getDefaultModelForWorkload } from '@/lib/defaultOptions';
-import { defaultOptionsStore } from '@/stores/defaultOptions';
-
-// Mirrors the backend's model_label(): readable label from an HF-style path.
-function modelLabel(modelId: string): string {
-  return (modelId.split('/').pop() ?? modelId).replace(/[-_]/g, ' ');
-}
-
-// Compact summary of only the non-default engine bits, e.g. "8 GPU · compile".
-function configSummary(gen: GeneratorInfo): string {
-  const parts: string[] = [];
-  if (gen.num_gpus !== 1) parts.push(`${gen.num_gpus} GPU`);
-  if (gen.sp_size !== -1) parts.push(`SP ${gen.sp_size}`);
-  if (gen.tp_size !== -1) parts.push(`TP ${gen.tp_size}`);
-  if (gen.dit_cpu_offload) parts.push('DiT offload');
-  if (gen.text_encoder_cpu_offload) parts.push('TE offload');
-  if (gen.vae_cpu_offload) parts.push('VAE offload');
-  if (gen.image_encoder_cpu_offload) parts.push('image enc offload');
-  if (gen.use_fsdp_inference) parts.push('FSDP');
-  if (gen.enable_torch_compile) parts.push('compile');
-  if (gen.vsa_sparsity > 0) parts.push(`VSA ${gen.vsa_sparsity.toFixed(2)}`);
-  return parts.join(' · ');
-}
-
-const STATE_VARIANTS: Record<GeneratorInfo['state'], BadgeProps['variant']> = {
-  ready: 'success',
-  loading: 'warning',
-  failed: 'destructive',
-};
-
-/**
- * Utility strip for the engine's single model slot: shows the resident model
- * (ready/loading/failed), loads the selected model using the persisted
- * default job options (replacing whatever is resident), and unloads it.
- */
-export default function WarmModelsPanel() {
-  const { options } = useStore(defaultOptionsStore);
-
-  const [slot, setSlot] = React.useState<GeneratorInfo | null>(null);
-  const [models, setModels] = React.useState<Model[]>([]);
-  const [modelId, setModelId] = React.useState('');
-  const [isBusy, setIsBusy] = React.useState(false);
-
-  const fetchSlot = React.useCallback(async () => {
-    try {
-      const list = await listGenerators();
-      setSlot(list[0] ?? null);
-    } catch (e) {
-      console.error('Failed to fetch generators:', e);
-    }
-  }, []);
-
-  React.useEffect(() => {
-    fetchSlot();
-  }, [fetchSlot]);
-
-  // Poll every 5s while a load is in flight; stop otherwise.
-  const isLoading = slot?.state === 'loading';
-  React.useEffect(() => {
-    if (!isLoading) return;
-    const interval = setInterval(fetchSlot, 5000);
-    return () => clearInterval(interval);
-  }, [isLoading, fetchSlot]);
-
-  // Same model catalogue (and default selection) as the create-job modal.
-  React.useEffect(() => {
-    getModels('t2v')
-      .then((list) => {
-        setModels(list);
-        const defaultId = getDefaultModelForWorkload(
-          defaultOptionsStore.get().options,
-          't2v',
-        );
-        setModelId(
-          list.some((m) => m.id === defaultId)
-            ? defaultId
-            : (list[0]?.id ?? ''),
-        );
-      })
-      .catch((e) => console.error('Failed to load models:', e));
-  }, []);
-
-  async function handleLoad() {
-    if (!modelId || isBusy || isLoading) return;
-    setIsBusy(true);
-    try {
-      await preloadGenerator({
-        model_id: modelId,
-        workload_type: 't2v',
-        num_gpus: options.numGpus,
-        dit_cpu_offload: options.ditCpuOffload,
-        text_encoder_cpu_offload: options.textEncoderCpuOffload,
-        vae_cpu_offload: options.vaeCpuOffload,
-        image_encoder_cpu_offload: options.imageEncoderCpuOffload,
-        use_fsdp_inference: options.useFsdpInference,
-        enable_torch_compile: options.enableTorchCompile,
-        vsa_sparsity: options.vsaSparsity,
-        tp_size: options.tpSize,
-        sp_size: options.spSize,
-      });
-      await fetchSlot();
-    } catch (err) {
-      console.error('Failed to load model:', err);
-      toast.error('Model was not loaded', {
-        description:
-          err instanceof Error
-            ? err.message
-            : 'Check the Studio API, then retry.',
-      });
-    } finally {
-      setIsBusy(false);
-    }
-  }
-
-  async function handleUnload() {
-    if (isBusy) return;
-    setIsBusy(true);
-    try {
-      await unloadGenerator();
-      await fetchSlot();
-    } catch (err) {
-      console.error('Failed to unload model:', err);
-      toast.error('Model was not unloaded', {
-        description:
-          err instanceof Error
-            ? err.message
-            : 'Check the Studio API, then retry.',
-      });
-    } finally {
-      setIsBusy(false);
-    }
-  }
-
-  // Loading a model always replaces the resident one — say so on the button.
-  const replaces = slot !== null && !!modelId && slot.model_id !== modelId;
-
-  return (
-    <section
-      aria-label="Warm models"
-      className="mx-auto w-full max-w-[850px] px-10 pt-6"
-    >
-      <div className="flex flex-col gap-3 rounded-lg border border-border bg-background p-4">
-        <div className="flex flex-wrap items-center gap-2">
-          <h2 className="mr-auto text-sm font-semibold text-foreground">
-            Warm Model
-          </h2>
-          <label htmlFor="warm-model-select" className="sr-only">
-            Model to load
-          </label>
-          <NativeSelect
-            id="warm-model-select"
-            value={modelId}
-            onChange={(e) => setModelId(e.target.value)}
-            disabled={isBusy || models.length === 0}
-            className="h-9 w-auto max-w-64 rounded-lg"
-          >
-            <option value="" disabled>
-              {models.length === 0 ? 'Loading models…' : 'Select a model…'}
-            </option>
-            {models.map((model) => (
-              <option key={model.id} value={model.id}>
-                {model.label}
-              </option>
-            ))}
-          </NativeSelect>
-          <Button
-            size="sm"
-            onClick={handleLoad}
-            disabled={isBusy || !modelId || isLoading}
-          >
-            {replaces ? 'Load (replaces current)' : 'Load model'}
-          </Button>
-        </div>
-        <p className="text-xs text-muted-foreground">
-          One model at a time stays resident in GPU memory so jobs skip the
-          load wait; loading a new one replaces it (uses your default job
-          options).
-        </p>
-        <div className="flex min-h-8 flex-wrap items-center gap-2">
-          {slot ? (
-            <>
-              <Badge
-                variant={STATE_VARIANTS[slot.state]}
-                className={
-                  slot.state === 'loading' ? 'animate-pulse' : undefined
-                }
-                title={
-                  slot.state === 'failed' ? (slot.error ?? undefined) : undefined
-                }
-              >
-                {slot.state}
-              </Badge>
-              <span className="text-sm font-medium text-foreground">
-                {modelLabel(slot.model_id)}
-              </span>
-              <span className="text-xs text-muted-foreground">
-                {configSummary(slot)}
-              </span>
-              {slot.state === 'ready' && (
-                <Button
-                  size="sm"
-                  variant="outline"
-                  className="ml-auto"
-                  onClick={handleUnload}
-                  disabled={isBusy}
-                >
-                  Unload
-                </Button>
-              )}
-            </>
-          ) : (
-            <span className="text-sm text-muted-foreground">
-              No model loaded
-            </span>
-          )}
-        </div>
-      </div>
-    </section>
-  );
-}

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

@@ -9,7 +9,6 @@ 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,
@@ -24,80 +23,46 @@ 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]')) return;
-      if (primaryOpen) {
-        setPrimaryOpen(false);
-        return;
+      if (e.key === 'Escape' && !document.querySelector('[data-modal]')) {
+        if (activeJobStore.get().activeJob) setActiveJobId(null);
+        if (activeDatasetStore.get().activeDataset) setActiveDatasetId(null);
       }
-      if (activeJobStore.get().activeJob) setActiveJobId(null);
-      if (activeDatasetStore.get().activeDataset) setActiveDatasetId(null);
     }
     document.addEventListener('keydown', handleKeyDown);
     return () => document.removeEventListener('keydown', handleKeyDown);
-  }, [primaryOpen]);
+  }, []);
 
   return (
     <HeaderActionsProvider>
-      <div
-        style={{ display: 'contents' }}
-        inert={drawerModal ? true : undefined}
-      >
-        <Header
-          navigationOpen={primaryOpen}
-          onNavigationToggle={() => setPrimaryOpen((open) => !open)}
-        />
-      </div>
+      <Header />
       <div
         className="flex overflow-hidden"
         style={{
           marginTop: 'var(--header-height)',
-          height: 'calc(100dvh - var(--header-height))',
+          height: 'calc(100vh - var(--header-height))',
         }}
       >
-        <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"
-          />
-        )}
+        <PrimarySidebar onWidthChange={setPrimaryWidth} />
         <main
           className="flex min-w-0 flex-1 flex-col overflow-auto"
-          inert={drawerModal ? true : undefined}
           style={{
-            marginLeft: isMobile ? 0 : primaryWidth,
-            marginRight: isMobile || !secondaryOpen ? 0 : secondaryWidth,
+            marginLeft: primaryWidth,
+            marginRight: secondaryOpen ? secondaryWidth : 0,
           }}
         >
           {children}
@@ -105,7 +70,6 @@ export function AppShell({ children }: { children: React.ReactNode }) {
         {jobSidebarOpen && activeJob && (
           <JobDetailsSidebar
             job={activeJob}
-            isMobile={isMobile}
             onClose={() => setActiveJobId(null)}
             onWidthChange={setSecondaryWidth}
           />
@@ -113,7 +77,6 @@ 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

@@ -1,63 +1,39 @@
 '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> = {
-  '/inference': 'Studio',
-  '/finetuning': 'Studio',
-  '/distillation': 'Studio',
+  '/inference': 'Jobs',
+  '/finetuning': 'Jobs',
+  '/distillation': 'Jobs',
   '/datasets': 'Datasets',
   '/gallery': 'Gallery',
   '/gpus': 'GPUs',
   '/settings': 'Settings',
 };
 
-export default function Header({
-  navigationOpen,
-  onNavigationToggle,
-}: {
-  navigationOpen: boolean;
-  onNavigationToggle: () => void;
-}) {
+export default function Header() {
   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-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>
+    <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">
       {/* eslint-disable-next-line @next/next/no-img-element */}
       <img
         src="/logo.svg"
         alt="FastVideo Logo"
         width={100}
         height={42}
-        className="hidden h-[42px] w-[78px] shrink-0 object-contain min-[361px]:block md:w-[100px]"
+        className="block h-[42px] w-[100px]"
       />
-      <h1 className="sr-only m-0 flex-1 text-xl font-semibold tracking-tight md:not-sr-only">
+      <h1 className="m-0 flex-1 text-xl font-semibold tracking-tight">
         {title}
       </h1>
-      <div className="ml-auto flex min-w-0 items-center gap-2 md:gap-3">
+      <div className="flex items-center gap-3">
         {actions}
         <ThemeToggle />
       </div>

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

@@ -1,7 +1,6 @@
 'use client';
 
 import * as React from 'react';
-import { X } from 'lucide-react';
 import Link from 'next/link';
 import { usePathname } from 'next/navigation';
 
@@ -20,18 +19,12 @@ const JOB_ROUTES = [
 ] as const;
 
 const TAB_BASE =
-  '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';
+  'block 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();
@@ -45,8 +38,8 @@ export default function PrimarySidebar({
   const isJobsActive = JOB_ROUTES.some((r) => pathname === r.href);
 
   React.useEffect(() => {
-    onWidthChange?.(isMobile ? 0 : layoutWidth);
-  }, [isMobile, layoutWidth, onWidthChange]);
+    onWidthChange?.(layoutWidth);
+  }, [layoutWidth, onWidthChange]);
 
   React.useEffect(() => {
     if (JOB_ROUTES.some((r) => pathname === r.href)) {
@@ -65,37 +58,11 @@ export default function PrimarySidebar({
 
   return (
     <aside
-      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,
-      }}
+      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 }}
     >
-      {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
-          aria-label="Primary navigation"
-          className="flex flex-col overflow-y-auto py-2"
-        >
+        <nav className="flex flex-col py-2">
           <div className="flex flex-col">
             <button
               type="button"
@@ -108,7 +75,7 @@ export default function PrimarySidebar({
                 isJobsActive && TAB_ACTIVE,
               )}
             >
-              <span>Studio</span>
+              <span>Jobs</span>
               <svg
                 viewBox="0 0 24 24"
                 fill="none"
@@ -128,8 +95,6 @@ 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]',
@@ -144,32 +109,24 @@ 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
@@ -177,7 +134,7 @@ export default function PrimarySidebar({
         </nav>
       )}
 
-      {!isMobile && <div
+      <div
         className={cn(
           'absolute bottom-0 p-2',
           isCollapsed ? '-right-[60px] top-0' : 'right-0',
@@ -188,7 +145,8 @@ export default function PrimarySidebar({
           onClick={() => setIsCollapsed((v) => !v)}
           title={isCollapsed ? 'Expand sidebar' : 'Collapse sidebar'}
           className={cn(
-            'flex size-11 items-center justify-center rounded-lg text-muted-foreground transition-colors hover:bg-accent hover:text-foreground',
+            'flex items-center justify-center rounded-lg text-muted-foreground transition-colors hover:bg-accent hover:text-foreground',
+            isCollapsed ? 'p-3' : 'p-2',
           )}
         >
           <svg
@@ -201,9 +159,9 @@ export default function PrimarySidebar({
             <path d={isCollapsed ? 'M9 18l6-6-6-6' : 'M15 18l-6-6 6-6'} />
           </svg>
         </button>
-      </div>}
+      </div>
 
-      {!isMobile && !isCollapsed && (
+      {!isCollapsed && (
         <div
           role="presentation"
           onMouseDown={onMouseDown}

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

@@ -1,4 +1,4 @@
-import { act, render, screen } from '@testing-library/react';
+import { render, screen } from '@testing-library/react';
 import { beforeEach, describe, expect, it, vi } from 'vitest';
 
 import GpuGrid from './GpuGrid';
@@ -73,30 +73,4 @@ 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

@@ -1,9 +1,7 @@
 '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';
@@ -99,8 +97,7 @@ function GpuCard({ gpu }: { gpu: GpuInfo }) {
 
 export default function GpuGrid() {
   const [snapshot, setSnapshot] = React.useState<GpuSnapshot | null>(null);
-  const [fetchError, setFetchError] = React.useState<string | null>(null);
-  const [retryToken, setRetryToken] = React.useState(0);
+  const [fetchError, setFetchError] = React.useState(false);
 
   React.useEffect(() => {
     let mounted = true;
@@ -113,14 +110,10 @@ export default function GpuGrid() {
         const next = await getGpus();
         if (mounted) {
           setSnapshot(next);
-          setFetchError(null);
+          setFetchError(false);
         }
       } catch {
-        if (mounted) {
-          setFetchError(
-            'GPU status could not be refreshed. The values below may be stale.',
-          );
-        }
+        if (mounted) setFetchError(true);
       } finally {
         inFlight = false;
       }
@@ -132,27 +125,14 @@ export default function GpuGrid() {
       mounted = false;
       clearInterval(interval);
     };
-  }, [retryToken]);
+  }, []);
 
   if (fetchError && !snapshot) {
     return (
-      <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>
+      <p className="py-8 text-center text-muted-foreground">
+        Could not reach the API server. GPU status needs the studio API server
+        running.
+      </p>
     );
   }
   if (!snapshot) {
@@ -170,22 +150,9 @@ export default function GpuGrid() {
   return (
     <div className="flex flex-col gap-4">
       {fetchError && (
-        <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>
+        <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 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

@@ -1,48 +0,0 @@
-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

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

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

@@ -42,7 +42,7 @@ const DialogContent = React.forwardRef<
       {...props}
     >
       {children}
-      <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">
+      <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">
         <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-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",
+				"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",
 				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-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',
+      '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',
       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-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',
+      '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',
       className,
     )}
     {...props}
@@ -117,7 +117,7 @@ const SelectItem = React.forwardRef<
   <SelectPrimitive.Item
     ref={ref}
     className={cn(
-      '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',
+      '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',
       className,
     )}
     {...props}

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

@@ -8,39 +8,21 @@ 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,
-      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>
-  ),
-);
+    )}
+    {...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>
+));
 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 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',
+      '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',
       className,
     )}
     {...props}
   >
     <SwitchPrimitives.Thumb
       className={cn(
-        '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',
+        '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',
       )}
     />
   </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 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 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',
       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-ring 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-sky-400/70 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-sky-400/25 disabled:cursor-not-allowed disabled:opacity-50",
 				className,
 			)}
 			ref={ref}

apps/fastvideo_studio/src/hooks/useDrawerFocus.ts

@@ -1,23 +0,0 @@
-'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

@@ -1,18 +0,0 @@
-'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/src/lib/api.ts

@@ -202,33 +202,6 @@ export async function getModels(workloadType?: string): Promise<Model[]> {
 	return response.json();
 }
 
-/**
- * A model's recommended sampling settings. Keys the backend has no value
- * for are absent; the UI leaves those form fields untouched.
- */
-export interface ModelPresets {
-	height?: number;
-	width?: number;
-	num_frames?: number;
-	fps?: number;
-	num_inference_steps?: number;
-	guidance_scale?: number;
-	guidance_rescale?: number;
-	negative_prompt?: string;
-	seed?: number;
-}
-
-export async function getModelPresets(modelId: string): Promise<ModelPresets> {
-	const baseApiUrl = getApiBaseUrl();
-	const response = await fetch(
-		`${baseApiUrl}/models/presets?model_id=${encodeURIComponent(modelId)}`,
-	);
-	if (!response.ok) {
-		throw new Error("Failed to fetch model presets");
-	}
-	return response.json();
-}
-
 export async function getGpus(): Promise<GpuSnapshot> {
 	const baseApiUrl = getApiBaseUrl();
 	const response = await fetch(`${baseApiUrl}/gpus`);
@@ -346,109 +319,6 @@ export async function downloadJobVideo(id: string): Promise<Blob> {
 	return response.blob();
 }
 
-// --- Generators (warm models) ---
-
-/**
- * Engine subset of CreateJobRequest that identifies one resident generator.
- * Mirrors the backend's GeneratorRequest defaults.
- */
-export interface GeneratorRequest {
-	model_id: string;
-	workload_type?: string;
-	num_gpus?: number;
-	dit_cpu_offload?: boolean;
-	text_encoder_cpu_offload?: boolean;
-	vae_cpu_offload?: boolean;
-	image_encoder_cpu_offload?: boolean;
-	use_fsdp_inference?: boolean;
-	enable_torch_compile?: boolean;
-	vsa_sparsity?: number;
-	tp_size?: number;
-	sp_size?: number;
-}
-
-export interface GeneratorInfo {
-	state: "ready" | "loading" | "failed";
-	model_id: string;
-	workload_type: string;
-	num_gpus: number;
-	dit_cpu_offload: boolean;
-	text_encoder_cpu_offload: boolean;
-	vae_cpu_offload: boolean;
-	image_encoder_cpu_offload: boolean;
-	use_fsdp_inference: boolean;
-	enable_torch_compile: boolean;
-	vsa_sparsity: number;
-	tp_size: number;
-	sp_size: number;
-	error: string | null;
-	started_at?: number;
-}
-
-/** The single resident slot: [] when nothing is loaded, else one entry. */
-export async function listGenerators(): Promise<GeneratorInfo[]> {
-	const baseApiUrl = getApiBaseUrl();
-	const response = await fetch(`${baseApiUrl}/generators`);
-	if (!response.ok) {
-		throw new Error("Failed to fetch generators");
-	}
-	return response.json();
-}
-
-export async function preloadGenerator(
-	req: GeneratorRequest,
-): Promise<GeneratorInfo> {
-	const baseApiUrl = getApiBaseUrl();
-	const response = await fetch(`${baseApiUrl}/generators/preload`, {
-		method: "POST",
-		headers: {
-			"Content-Type": "application/json",
-		},
-		body: JSON.stringify(req),
-	});
-	if (!response.ok) {
-		const error = await response
-			.json()
-			.catch(() => ({ detail: "Failed to preload model" }));
-		throw new Error(error.detail || "Failed to preload model");
-	}
-	return response.json();
-}
-
-/** Unload the single resident generator (no body — there is only one slot). */
-export async function unloadGenerator(): Promise<void> {
-	const baseApiUrl = getApiBaseUrl();
-	const response = await fetch(`${baseApiUrl}/generators/unload`, {
-		method: "POST",
-	});
-	if (!response.ok) {
-		const error = await response
-			.json()
-			.catch(() => ({ detail: "Failed to unload model" }));
-		throw new Error(error.detail || "Failed to unload model");
-	}
-}
-
-// --- Engine logs ---
-
-export interface EngineLogs {
-	lines: string[];
-	total: number;
-}
-
-/**
- * Incremental tail of the engine's stdout/stderr. Poll with
- * `after=<total from the previous response>`.
- */
-export async function getEngineLogs(after: number = 0): Promise<EngineLogs> {
-	const baseApiUrl = getApiBaseUrl();
-	const response = await fetch(`${baseApiUrl}/engine/logs?after=${after}`);
-	if (!response.ok) {
-		throw new Error("Failed to fetch engine logs");
-	}
-	return response.json();
-}
-
 // --- Datasets ---
 
 export interface Dataset {
@@ -548,35 +418,3 @@ export function getDatasetMediaUrl(
 	const baseApiUrl = getApiBaseUrl();
 	return `${baseApiUrl}/datasets/${datasetId}/media/${encodeURIComponent(fileName)}`;
 }
-
-// --- Cluster ---
-
-/** Per-GPU telemetry within a cluster node (same shape as GpuInfo). */
-export type ClusterGpu = GpuInfo;
-
-export interface ClusterNode {
-	hostname: string;
-	ip: string | null;
-	is_this_host: boolean;
-	cpus: number | null;
-	ray_gpus: number | null;
-	available: boolean;
-	error: string | null;
-	gpus: ClusterGpu[];
-}
-
-export interface ClusterSnapshot {
-	mode: "ray" | "local";
-	nodes: ClusterNode[];
-	resources: { gpus_total: number; gpus_available: number } | null;
-	error: string | null;
-}
-
-export async function getClusterStatus(): Promise<ClusterSnapshot> {
-	const baseApiUrl = getApiBaseUrl();
-	const response = await fetch(`${baseApiUrl}/cluster`);
-	if (!response.ok) {
-		throw new Error("Failed to fetch cluster status");
-	}
-	return response.json();
-}

apps/fastvideo_studio/tests/test_generator_cache.py

@@ -1,273 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Tests for the single-slot resident generator: preload, list, unload.
-
-Exactly one VideoGenerator lives in memory. One load at a time; loading a new
-config always releases the old instance; unload deletes it. The generator is
-faked at the ``_create_generator_into_slot``/VideoGenerator boundary.
-"""
-
-from __future__ import annotations
-
-import threading
-import time
-from types import SimpleNamespace
-from typing import Any
-
-import pytest
-
-from fastvideo_studio.job_runner import JobRunner, JobStatus
-
-
-class _FakeGenerator:
-    def __init__(self) -> None:
-        self.shutdown_calls = 0
-
-    def shutdown(self) -> None:
-        self.shutdown_calls += 1
-
-
-@pytest.fixture()
-def runner():
-    r = JobRunner.__new__(JobRunner)  # skip __init__: no DB/Manager needed
-    r._jobs = {}
-    r._jobs_lock = threading.Lock()
-    r._generator = None
-    r._generator_config = None
-    r._generator_state = "empty"
-    r._generator_error = None
-    r._generator_lock = threading.Lock()
-    r._load_lock = threading.Lock()
-    r._worker_log_queue = None  # no Manager in unit tests
-    import queue as _queue
-    r._loader_queue = _queue.Queue()
-    threading.Thread(target=r._loader_loop, daemon=True).start()
-    return r
-
-
-def _install_fake_loader(runner, monkeypatch, made: list | None = None,
-                         gate: threading.Event | None = None,
-                         fail: str | None = None):
-    """Replace the VideoGenerator load inside _create_generator_into_slot."""
-    import fastvideo_studio.job_runner as jr
-
-    class _FakeVG:
-        @staticmethod
-        def from_pretrained(model_path, **kwargs):
-            if gate is not None:
-                assert gate.wait(5), "test gate never opened"
-            if fail is not None:
-                raise RuntimeError(fail)
-            gen = _FakeGenerator()
-            if made is not None:
-                made.append((model_path, gen))
-            return gen
-
-    monkeypatch.setitem(__import__("sys").modules, "fastvideo",
-                        SimpleNamespace(VideoGenerator=_FakeVG))
-    return jr
-
-
-def _wait_state(runner, state, timeout=5.0):
-    deadline = time.time() + timeout
-    while time.time() < deadline:
-        with runner._generator_lock:
-            if runner._generator_state == state:
-                return runner._slot_entry()
-        time.sleep(0.02)
-    raise AssertionError(f"never reached state {state}: {runner._generator_state}")
-
-
-def test_preload_then_ready_then_idempotent(runner, monkeypatch):
-    made = []
-    _install_fake_loader(runner, monkeypatch, made=made)
-
-    entry = runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    assert entry["state"] in ("loading", "ready")
-    _wait_state(runner, "ready")
-
-    again = runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    assert again["state"] == "ready"
-    assert len(made) == 1  # same config never reloads
-
-
-def test_only_one_load_at_a_time(runner, monkeypatch):
-    gate = threading.Event()
-    _install_fake_loader(runner, monkeypatch, gate=gate)
-
-    runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    with pytest.raises(RuntimeError, match="already in progress"):
-        runner.preload_generator(model_id="org/b", workload_type="t2v", num_gpus=8)
-    gate.set()
-    _wait_state(runner, "ready")
-
-
-def test_new_config_always_releases_old_instance(runner, monkeypatch):
-    made = []
-    _install_fake_loader(runner, monkeypatch, made=made)
-
-    runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    _wait_state(runner, "ready")
-    first = made[0][1]
-
-    runner.preload_generator(model_id="org/b", workload_type="t2v", num_gpus=8)
-    _wait_state(runner, "ready")
-    assert first.shutdown_calls == 1  # old instance released, not stacked
-    assert [m[0] for m in made] == ["org/a", "org/b"]
-    assert len(runner.list_generators()) == 1
-    assert runner.list_generators()[0]["model_id"] == "org/b"
-
-
-def test_failed_load_reports_error_and_allows_retry(runner, monkeypatch):
-    _install_fake_loader(runner, monkeypatch, fail="no CUDA on this box")
-    runner.preload_generator(model_id="org/broken", workload_type="t2v", num_gpus=1)
-    failed = _wait_state(runner, "failed")
-    assert "no CUDA" in failed["error"]
-
-    # retry after failure must be accepted (this was the reported bug)
-    made = []
-    _install_fake_loader(runner, monkeypatch, made=made)
-    runner.preload_generator(model_id="org/broken", workload_type="t2v", num_gpus=1)
-    _wait_state(runner, "ready")
-    assert len(made) == 1
-
-
-def test_unload_deletes_instance(runner, monkeypatch):
-    made = []
-    _install_fake_loader(runner, monkeypatch, made=made)
-    runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    _wait_state(runner, "ready")
-
-    assert runner.unload_generator() is True
-    assert made[0][1].shutdown_calls == 1
-    assert runner.list_generators() == []
-    assert runner._generator is None
-    assert runner.unload_generator() is False  # nothing resident
-
-
-def test_unload_refuses_while_inference_job_runs(runner, monkeypatch):
-    made = []
-    _install_fake_loader(runner, monkeypatch, made=made)
-    runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    _wait_state(runner, "ready")
-    runner._jobs["j1"] = SimpleNamespace(id="j1", status=JobStatus.RUNNING, job_type="inference")
-
-    with pytest.raises(RuntimeError, match="j1"):
-        runner.unload_generator()
-    assert made[0][1].shutdown_calls == 0
-
-    runner._jobs["j1"].job_type = "finetune"  # training doesn't block
-    assert runner.unload_generator() is True
-
-
-def test_job_waits_for_matching_preload(runner, monkeypatch):
-    gate = threading.Event()
-    made = []
-    _install_fake_loader(runner, monkeypatch, made=made, gate=gate)
-
-    runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    got = []
-    t = threading.Thread(
-        target=lambda: got.append(
-            runner._get_or_create_generator("org/a", "t2v", 8)),
-        daemon=True)
-    t.start()
-    time.sleep(0.2)
-    assert not got  # job blocked on the in-flight load
-    gate.set()
-    t.join(5)
-    assert got and got[0] is made[0][1]
-    assert len(made) == 1  # the job reused the preloaded instance
-
-
-def test_job_with_different_config_replaces_slot(runner, monkeypatch):
-    made = []
-    _install_fake_loader(runner, monkeypatch, made=made)
-    runner.preload_generator(model_id="org/a", workload_type="t2v", num_gpus=8)
-    _wait_state(runner, "ready")
-
-    gen = runner._get_or_create_generator("org/b", "t2v", 4)
-    assert gen is made[1][1]
-    assert made[0][1].shutdown_calls == 1  # old released before new load
-    assert runner.list_generators()[0]["model_id"] == "org/b"
-
-
-def test_job_during_replace_never_sees_half_replaced_slot(runner, monkeypatch):
-    """Regression: user restarted a stale 1-gpu job while an 8-gpu generator
-    was resident; the replace nulled the generator with state still 'ready',
-    and the next (correct) job grabbed None -> AttributeError on
-    generate_video. All transitions now serialize under the load lock."""
-    made = []
-    gate = threading.Event()
-    _install_fake_loader(runner, monkeypatch, made=made)
-    runner.preload_generator(model_id="org/h3", workload_type="t2v", num_gpus=8)
-    _wait_state(runner, "ready")
-
-    _install_fake_loader(runner, monkeypatch, made=made, gate=gate)
-    results: dict[str, Any] = {}
-
-    def job_a():  # stale job: mismatching config triggers a slow replace
-        results["a"] = runner._get_or_create_generator("org/h3", "t2v", 1)
-
-    def job_b():  # correct job arriving mid-replace
-        time.sleep(0.3)
-        results["b"] = runner._get_or_create_generator("org/h3", "t2v", 8)
-
-    ta = threading.Thread(target=job_a, daemon=True)
-    tb = threading.Thread(target=job_b, daemon=True)
-    ta.start(); tb.start()
-    time.sleep(0.6)
-    gate.set()
-    ta.join(10); tb.join(10)
-
-    assert results["a"] is not None and hasattr(results["a"], "shutdown")
-    assert results["b"] is not None and hasattr(results["b"], "shutdown")
-    # b arrived second, so the slot ends at b's 8-gpu config
-    assert runner.list_generators()[0]["num_gpus"] == 8
-
-
-def test_config_dict_matches_request_defaults(runner):
-    """GeneratorRequest defaults and CreateJobRequest defaults must resolve to
-    the same slot config — else preloading never matches the job."""
-    from fastvideo_studio.models import CreateJobRequest, GeneratorRequest
-
-    job = CreateJobRequest(model_id="m", prompt="p").model_dump()
-    pre = GeneratorRequest(model_id="m").model_dump()
-    assert runner._generator_config_dict(**pre) == runner._generator_config_dict(
-        **{k: job[k] for k in pre})
-
-
-def test_engine_log_buffer_incremental_tail():
-    from fastvideo_studio.server import _EngineLogBuffer
-
-    buf = _EngineLogBuffer(maxlen=3)
-    buf.write("a\nb\n")
-    buf.write("c")          # partial line: not visible yet
-    lines, total = buf.get_lines(0)
-    assert lines == ["a", "b"] and total == 2
-    buf.write("!\nd\ne\n")  # completes "c!", then overflows the ring
-    lines, total = buf.get_lines(total)
-    assert lines == ["c!", "d", "e"] and total == 5
-    # reader far behind: dropped lines are skipped, no crash
-    lines, _ = buf.get_lines(0)
-    assert lines == ["c!", "d", "e"]
-
-
-def test_engine_feed_drives_job_progress(runner):
-    from fastvideo_studio.job_runner import Job
-
-    job = Job(id="j-prog", model_id="m", prompt="p")
-    runner._active_inference_job = job
-    # ray wraps relayed lines in ANSI color codes — the bridge must strip them
-    runner.feed_engine_line("\x1b[36m(RayWorkerWrapper pid=123, ip=10.0.0.2)\x1b[0m denoising:  40%|████      | 20/50 [00:30<00:45,  1.5s/it]")
-    assert job._log_buf.progress == 40.0
-    assert job._log_buf.progress_msg == "20/50 steps"
-
-    # driver-side lines (no ray prefix) are NOT double-fed
-    before = job._log_buf.get_lines()[1]
-    runner.feed_engine_line("INFO 08-07 [video_generator.py] driver line")
-    assert job._log_buf.get_lines()[1] == before
-
-    # no active job: no-op
-    runner._active_inference_job = None
-    runner.feed_engine_line("(RayWorkerWrapper pid=123)  90%|████| 45/50")
-    assert job._log_buf.progress == 40.0

docker/Dockerfile

@@ -186,40 +186,15 @@ RUN --mount=type=cache,target=/opt/uv/cache \
 
 COPY . .
 
-# 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
+# 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.
 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="${default_arch}" && \
     cd fastvideo-kernel && \
-    CMAKE_ARGS= CMAKE_BUILD_PARALLEL_LEVEL=${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 "${default_wheel}" \
-        --reinstall-package fastvideo-kernel --no-deps
+    CMAKE_BUILD_PARALLEL_LEVEL=${CMAKE_BUILD_PARALLEL_LEVEL} \
+    TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST} ./build.sh
 
 # Install FastVideo itself (editable) now that the source is present, and set up
 # shell configuration. Dependencies and the local kernel are already installed,

docs/attention/index.md

@@ -8,15 +8,13 @@ FastVideo provides highly optimized custom attention kernels to accelerate video
 * **[Sliding Tile Attention (STA)](sta/index.md)**: STA kernel support is kept in
   `fastvideo-kernel`; full FastVideo STA pipeline workflow is archived in
   `sta_do_not_delete`.
-* **[Attn-QAT Training](../training/attn_qat.md)**: Runtime-JIT Triton forward
-  and backward kernels for role-local quantization-aware training.
 * **Backend development guide**: See the developer guide at
   [Attention Backend Development](../contributing/attention_backend.md).
 
 ## General Build Instructions
 
 These instructions apply to building the `fastvideo-kernel` package from
-source, which includes STA, VSA, and Attn-QAT kernels.
+source, which includes both STA and VSA kernels.
 
 ### Prerequisites
 

docs/contributing/ci_architecture.md

@@ -85,6 +85,7 @@ status.
 | Transformer Tests | `transformer` | `fastvideo/models/dits/**`, `fastvideo/models/loader/**`, `fastvideo/tests/transformers/**`, `fastvideo/layers/**`, `fastvideo/attention/**`, `pyproject.toml`, `docker/Dockerfile` |
 | Kernel Tests | `kernel_tests` | `fastvideo-kernel/**`, `pyproject.toml`, `docker/Dockerfile` |
 | Unit Tests | `unit_test` | `fastvideo/**`, `.buildkite/**`, `.github/**`, `pyproject.toml`, `docker/Dockerfile` |
+| Preprocessing Tests | `preprocessing` | Preprocessing pipelines/workflows, Parquet schema/writer, the integration test, and its CI entrypoints |
 | DreamVerse App Tests | `dreamverse_app` | `apps/dreamverse/**`, `pyproject.toml` |
 
 ### Tier 3: Full Suite
@@ -147,6 +148,7 @@ Valid direct test names:
 | `/test transformer` | `transformer` |
 | `/test kernel` | `kernel_tests` |
 | `/test unit` | `unit_test` |
+| `/test preprocessing` | `preprocessing` |
 | `/test dreamverse` | `dreamverse_app` |
 | `/test ssim` | `ssim` |
 | `/test training` | `training` |
@@ -255,9 +257,10 @@ If you add a new CI test category:
 
 ### Documentation
 
-`.github/workflows/infra-docs.yml` builds documentation for PRs that touch
-`docs/**`, `mkdocs.yml`, `requirements-mkdocs.txt`, or the workflow itself. On
-pushes to `main`, it also deploys the built site to GitHub Pages.
+`.github/workflows/infra-docs.yml` builds documentation for same-repository PRs
+that touch `docs/**`, `mkdocs.yml`, `requirements-mkdocs.txt`, or the workflow
+itself. Fork PRs skip this executable build instead of waiting for maintainer
+approval. On pushes to `main`, it also deploys the built site to GitHub Pages.
 
 The docs job:
 
@@ -269,26 +272,16 @@ The docs job:
 ### Docker Images
 
 `.github/workflows/infra-build-image.yml` supports manual `workflow_dispatch`
-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.
+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`.
 
 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/design/inference_schema_parity_inventory.yaml

@@ -76,7 +76,6 @@ 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."
@@ -334,25 +333,15 @@ surfaces:
       use_distill:
         sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
       scheduler_arch:
-        sources:
-          - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
       text_encoder_archs:
-        sources:
-          - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
       tokenizer_archs:
-        sources:
-          - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
       transformer_arch:
-        sources:
-          - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
       vae_arch:
-        sources:
-          - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
+        sources: [fastvideo.configs.pipelines.sd35.SD35Config]
       expand_timesteps:
         sources:
           - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
@@ -432,8 +421,6 @@ surfaces:
       frame_receptive_field: "MagiHuman internal data-proxy receptive-field setting."
       image_conditioning: "MagiHuman preset variant marker for reference-image conditioning."
       ref_audio_offset: "MagiHuman internal data-proxy audio alignment offset."
-      scheduler_sigma_min: "Z-Image scheduler parity invariant; not part of the public typed inference API."
-      scheduler_use_reference_discrete_timesteps: "Z-Image scheduler parity invariant; not part of the public typed inference API."
       sr_local_attn_layers: "MagiHuman SR internal sparse-attention layer selection."
       text_offset: "MagiHuman internal data-proxy text alignment offset."
       vae_stride: "MagiHuman internal VAE/data-proxy stride setting."
@@ -445,17 +432,12 @@ 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
       pose: request.inputs.pose
       c2ws_plucker_emb: request.inputs.c2ws_plucker_emb
-      action_path: request.inputs.action_path
       refine_from: request.inputs.refine_from
       stage1_video: request.inputs.stage1_video
       prompt: request.prompt
@@ -465,7 +447,6 @@ surfaces:
       output_video_name: request.output.output_video_name
       num_videos_per_prompt: request.sampling.num_videos_per_prompt
       seed: request.sampling.seed
-      max_sequence_length: request.sampling.max_sequence_length
       num_frames: request.sampling.num_frames
       height: request.sampling.height
       width: request.sampling.width
@@ -475,10 +456,7 @@ surfaces:
       num_inference_steps: request.sampling.num_inference_steps
       num_inference_steps_sr: request.sampling.num_inference_steps_sr
       guidance_scale: request.sampling.guidance_scale
-      batch_cfg: request.sampling.batch_cfg
       guidance_scale_2: request.sampling.guidance_scale_2
-      cfg_normalization: request.sampling.cfg_normalization
-      cfg_truncation: request.sampling.cfg_truncation
       guidance_rescale: request.sampling.guidance_rescale
       use_embedded_guidance: request.sampling.use_embedded_guidance
       true_cfg_scale: request.sampling.true_cfg_scale
@@ -529,6 +507,8 @@ 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."
+      max_sequence_length: "Model-specific text-encoder sequence cap; not part of the public typed inference API."
 
   sampling_param_extensions: {}
 

docs/distillation/dmd.md

@@ -2,12 +2,6 @@
 
 We introduce a new finetuning strategy - **Sparse-distill**, which jointly integrates **[DMD](https://arxiv.org/abs/2405.14867)** and **[VSA](https://arxiv.org/abs/2505.13389)** in a single training process. This approach combines the benefits of both distillation to shorten diffusion steps and sparse attention to reduce attention computation, enabling much faster video generation.
 
-!!! tip "Attn-QAT DMD2 workflow"
-    The modular trainer also provides a Wan2.1 MixKit recipe that first
-    fine-tunes with fake-quantized attention, then distills the student to
-    timesteps `[1000, 757, 522]` while teacher and critic remain on Flash
-    Attention. See [Attn-QAT Training](../training/attn_qat.md).
-
 ## 📊 Model Overview
 
 We provide two distilled models:

docs/getting_started/installation.md

@@ -5,7 +5,6 @@ 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
@@ -55,10 +54,8 @@ UV_TORCH_BACKEND=cu126 uv pip install -e .
 uv pip install flash-attn --no-build-isolation -v
 ```
 
-## Requirements
+## Hardware 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-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.
+You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) 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-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.
+You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.

docs/getting_started/installation/spark.md

@@ -134,16 +134,9 @@ 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-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)
+our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ)
 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

@@ -1,195 +0,0 @@
-# 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 post-training and real-time inference framework for accelerated video generation.</strong>
+<strong>FastVideo is a unified inference and post-training framework for accelerated video generation.</strong>
 </div>
 
 <div style="text-align: center;">
@@ -25,23 +25,14 @@ 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
-  - 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/))
+  - [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.
 
 ## 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-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ).
+Need help? Join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).
 
 ## Step 1: Pipeline Modules
 

docs/inference/inference_quick_start.md

@@ -4,11 +4,10 @@ This page contains step-by-step instructions to get you quickly started with vid
 
 ## Requirements
 
-- **OS**: Linux (tested on Ubuntu 22.04+), or macOS on Apple silicon via the
-  [MPS installation guide](../getting_started/installation/mps.md)
+- **OS**: Linux (Tested on Ubuntu 22.04+)
 - **Python**: 3.10-3.12
-- **CUDA**: 12.6 or 13.0 (NVIDIA GPUs)
-- **GPU**: At least one NVIDIA GPU, or an Apple silicon chip with MPS
+- **CUDA**: 12.6 or 13.0
+- **GPU**: At least one NVIDIA GPU
 
 ## Installation
 
@@ -135,4 +134,5 @@ 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 Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ).
+- Join our [Community Discord](https://discord.gg/JA7cksDz86).
+- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).

docs/inference/optimizations.md

@@ -3,12 +3,6 @@
 
 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
@@ -125,22 +119,6 @@ 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

@@ -13,97 +13,6 @@ 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
@@ -116,9 +25,6 @@ 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 {
@@ -150,7 +56,7 @@ optimizations: absence means **untested**, not incompatible.
 | 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 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
@@ -159,14 +65,14 @@ optimizations: absence means **untested**, not incompatible.
 | 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 | `FastVideo/LongCat-Video-T2V-Diffusers` | 480P<br>720P | ❌ | ❌ | ❌ | ⭕ | ✅ |
+| LongCat T2V 13.6B | See note** | 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 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
@@ -192,21 +98,6 @@ 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

@@ -1,147 +0,0 @@
-# Attn-QAT Training
-
-Attn-QAT simulates low-bit attention during training while keeping the rest of
-the training method unchanged. In the modular `fastvideo/train` framework it is
-a per-role model option, not a separate training method: supervised fine-tuning
-and DMD2 still own their losses and optimizer cadence.
-
-This guide covers the QAD Wan2.1-T2V-1.3B MixKit workflow:
-
-1. run a 4,000-step supervised Attn-QAT fine-tune;
-2. export the stage-1 DCP checkpoint to Diffusers format; and
-3. distill the student to three denoising steps with DMD2.
-
-The ready-to-run configs and wrappers are in
-`examples/train/scenario/qad_wan2_1_mixkit/`.
-
-## Role-local attention backends
-
-A DMD2 run owns three independent model roles. Configure the attention backend
-on each role so fake quantization is applied only to the student:
-
-```yaml
-models:
-  student:
-    attention_backend: ATTN_QAT_TRAIN
-  teacher:
-    attention_backend: FLASH_ATTN
-  critic:
-    attention_backend: FLASH_ATTN
-```
-
-The override is active only while that role's transformer is constructed, then
-the previous process-wide backend is restored. This lets student, teacher, and
-critic use different implementations in one process. Invalid role-level names
-fail during configuration instead of silently selecting another backend.
-
-See [Training Infrastructure](train_infra.md) for the complete model-role
-configuration reference.
-
-## Prerequisites
-
-- Install FastVideo and make the `fastvideo-kernel` Python package importable.
-  `ATTN_QAT_TRAIN` intentionally fails instead of falling back to dense
-  attention when its kernel cannot be loaded.
-- Prepare the precomputed MixKit VAE latents and text embeddings.
-- Run the commands below from the repository root. The supplied recipe expects
-  four GPUs by default; set `NUM_GPUS` to override it.
-
-Download the published preprocessed dataset:
-
-```bash
-bash examples/datasets/mixkit/download_dataset.sh
-```
-
-## Stage 1: supervised Attn-QAT fine-tuning
-
-The stage-1 config uses `ATTN_QAT_TRAIN` on the student, sequence parallelism
-across four GPUs, FP32 master weights, and 4,000 optimizer steps:
-
-```bash
-NUM_GPUS=4 \
-  bash examples/train/scenario/qad_wan2_1_mixkit/run_stage1.sh
-```
-
-Pass a dataset directory as the first positional argument when it differs from
-the default:
-
-```bash
-NUM_GPUS=4 \
-  bash examples/train/scenario/qad_wan2_1_mixkit/run_stage1.sh \
-  /path/to/combined_parquet_dataset
-```
-
-The wrapper calls `examples/train/run.sh`; the YAML file remains the source of
-truth for optimizer, validation, checkpointing, and distributed settings.
-
-## Export the stage-1 checkpoint
-
-Modular training checkpoints use Distributed Checkpoint (DCP) format. Export
-the student before using it to initialize stage 2:
-
-```bash
-bash examples/train/scenario/qad_wan2_1_mixkit/export_stage1.sh \
-  checkpoints/wan_t2v_qat_finetune/checkpoint-4000 \
-  checkpoints/wan_t2v_qat_finetune/diffusers
-```
-
-Both arguments are optional; the command above shows their defaults.
-
-## Stage 2: three-step DMD2 distillation
-
-Stage 2 loads the exported student weights, keeps Attn-QAT on the student, and
-uses Flash Attention for the teacher and critic:
-
-```bash
-NUM_GPUS=4 \
-  bash examples/train/scenario/qad_wan2_1_mixkit/run_stage2.sh \
-  data/HD-Mixkit-Finetune-Wan/combined_parquet_dataset \
-  checkpoints/wan_t2v_qat_finetune/diffusers/transformer/model.safetensors
-```
-
-The migrated recipe preserves these behaviors:
-
-| Behavior | Modular configuration |
-|---|---|
-| Student fake-quantized attention | `models.student.attention_backend: ATTN_QAT_TRAIN` |
-| Teacher and critic full-precision attention | Role-local `FLASH_ATTN` |
-| Generator update every five critic steps | `method.generator_update_interval: 5` |
-| Three-step rollout | `method.dmd_denoising_steps: [1000, 757, 522]` |
-| Score timestep range | `method.min_timestep_ratio: 0.02`, `max_timestep_ratio: 0.98` |
-| Legacy guidance `cond + 2(cond - uncond)` | Standard CFG scale `3.0` |
-| Stage handoff | DCP checkpoint to Diffusers export to student override weights |
-
-The timestep ratios apply to randomly sampled teacher and critic score
-timesteps; `dmd_denoising_steps` separately controls the student rollout. See
-[DMD Distillation](../distillation/dmd.md) for general DMD concepts.
-
-## Architecture-specific Triton routing
-
-The training kernel is runtime-JIT-compiled Triton code and selects its route on
-every call. It supports different query and key/value sequence lengths for
-cross-attention; key and value must have the same sequence length.
-
-| Hardware/configuration | Route |
-|---|---|
-| SM100, validated non-causal BF16 QAT configuration with head dimension 128 | Large-tile forward and split 64x64 backward; optimized backward requires a 16-aligned KV length |
-| SM120, including RTX 5090 | Previous forward tiling with joined quantized/STE P@V operations and a shallower backward pipeline for long sequences |
-| Unsupported configurations | Previous Triton implementation |
-
-Warp specialization is disabled automatically on SM100 and SM120 because the
-Triton 3.7 NVWS compiler pass aborts for this kernel on Blackwell. No user
-setting is required.
-
-The available tuning and comparison controls are:
-
-| Environment variable | Default | Effect |
-|---|---|---|
-| `FASTVIDEO_ATTN_QAT_FWD_MODE` | `fast` | Selects `fast`, `balanced`, or `reference` forward tiling on the SM100 optimized route |
-| `FASTVIDEO_ATTN_QAT_FWD_EXACT_M` | `0` | Set to `1` to recompute reference-order softmax statistics and keep `dV` bitwise-compatible on the SM100 optimized route |
-| `FASTVIDEO_ATTN_QAT_SM100_OPTIMIZED` | `1` | Set to `0` to force the previous SM100 forward and backward for comparison |
-| `FASTVIDEO_ATTN_QAT_SM120_JOIN_QAT_PV` | `1` | Set to `0` to compare SM120 against the split P@V path |
-
-The first invocation JIT-compiles the selected configuration; later calls reuse
-the Triton cache. To measure the production shape, run
-`python benchmarks/benchmark_attn_qat_train.py` from `fastvideo-kernel/`.
-
-For import and backend-selection failures, see [Debugging](../utilities/debugging.md).

docs/training/finetune.md

@@ -62,18 +62,6 @@ bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh
 - Gradient checkpointing: `--enable_gradient_checkpointing_type "full"`
 - Memory scaling: Increase `--sp_size` or reduce `--num_latent_t` to fit in memory
 
-## Attention Quantization-Aware Training
-
-Attn-QAT fine-tunes a model while simulating low-bit attention in the forward
-and backward passes. The modular trainer can select the backend per model role,
-so a later DMD2 stage can keep fake quantization on the student while the
-teacher and critic use Flash Attention.
-
-The ready-to-run Wan2.1 MixKit workflow includes supervised fine-tuning,
-checkpoint export, and three-step DMD2 distillation:
-
-**→ [Follow the Attn-QAT training guide](attn_qat.md)**
-
 ## LoRA Finetuning
 
 LoRA (Low-Rank Adaptation) trains lightweight adapters while keeping the base model frozen. This significantly reduces memory usage and training time.
@@ -178,11 +166,10 @@ Ready-to-run training scripts are available for multiple models:
 | Wan2.1 I2V 14B | I2V | `examples/training/finetune/wan_i2v_14B_480p/crush_smol/` |
 | Wan2.1-Fun 1.3B InP | I2V | `examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/` |
 | Wan2.1 VSA | T2V/I2V | `examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/` |
-| Wan2.1 T2V 1.3B Attn-QAT | QAT SFT + DMD2 | `examples/train/scenario/qad_wan2_1_mixkit/` |
 
 Each example includes:
 
-- a README pointing at the matching download script under `examples/datasets/`
+- `download_dataset.sh` — download sample data
 - `preprocess_*.sh` — run preprocessing
 - `finetune_*.sh` — full finetune launcher
 - `finetune_*_lora.sh` — LoRA finetune launcher

docs/training/overview.md

@@ -43,12 +43,9 @@ Ready-to-run examples with preprocessing scripts, training launchers, and valida
 
 **→ [Browse all training examples](examples/examples_training_index.md)**
 
-For the complete two-stage Wan2.1 MixKit quantization-aware workflow, see
-**[Attn-QAT Training](attn_qat.md)**.
-
 Each example includes:
 
-- a README pointing at the matching download script under `examples/datasets/`
+- `download_dataset.sh` — download sample data
 - `preprocess_*.sh` — run preprocessing
 - `finetune_*.sh` — launch training (full finetune or LoRA)
 - `validation.json` — validation prompts for checkpoints
@@ -62,11 +59,9 @@ FastVideo supports several training approaches:
 | **Full finetune** | Adapt entire model to a new domain or style |
 | **LoRA finetune** | Lightweight adaptation with frozen base weights |
 | **VSA finetune** | Finetune with Variable Sparse Attention for efficiency |
-| **Attn-QAT** | Train with fake-quantized attention, optionally followed by DMD2 distillation |
 
 ## Next Steps
 
 1. **Get started**: Pick an example from the [training examples index](examples/examples_training_index.md)
 2. **Prepare data**: Follow [data preprocessing](data_preprocess.md) for your own dataset
-3. **Train with quantized attention**: Follow the [Attn-QAT two-stage recipe](attn_qat.md)
-4. **Run inference**: After training, see [inference examples](../inference/examples/examples_inference_index.md)
+3. **Run inference**: After training, see [inference examples](../inference/examples/examples_inference_index.md)

docs/training/train_infra.md

@@ -81,7 +81,6 @@ Common model parameters:
 | `disable_custom_init_weights` | `false` | Skip custom weight initialization (use for teacher/critic) |
 | `flow_shift` | `3.0` | Timestep shifting factor |
 | `enable_gradient_checkpointing_type` | `null` | Gradient checkpointing (`"full"` or `null`) |
-| `attention_backend` | `null` | Optional role-local backend for Wan models (for example `ATTN_QAT_TRAIN`); overrides the process default only while this role's transformer is built |
 
 Which roles are needed depends on the training method:
 
@@ -212,29 +211,6 @@ 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
@@ -322,8 +298,6 @@ method:
 | `dmd_denoising_steps` | *(required)* | Timestep schedule for student rollout |
 | `generator_update_interval` | `1` | Update student every N critic steps |
 | `real_score_guidance_scale` | `1.0` | CFG scale for teacher predictions |
-| `min_timestep_ratio` | `0.0` | Lower bound for randomly sampled teacher/critic score timesteps |
-| `max_timestep_ratio` | `1.0` | Upper bound for randomly sampled teacher/critic score timesteps |
 | `fake_score_learning_rate` | *(required)* | Critic optimizer learning rate |
 | `fake_score_betas` | *(required)* | Critic optimizer Adam betas |
 | `fake_score_lr_scheduler` | *(required)* | Critic LR scheduler type |

docs/utilities/debugging.md

@@ -77,13 +77,8 @@ 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`, 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
-  [Attn-QAT Training](../training/attn_qat.md) for architecture controls.
+  `attn_qat_infer`
+- `ATTN_QAT_TRAIN`: `fastvideo-kernel` install exposing `fastvideo_kernel`
 
 As a fallback, use:
 

examples/datasets/lingbotworld2/README.md

@@ -1,17 +0,0 @@
-# LingBot World 2 Example Dataset
-
-These files were copied unchanged from the LingBot World 2 repository for the
-FastVideo causal-fast inference example.
-
-- Repository: `https://github.com/Robbyant/lingbot-world-v2.git`
-- Source commit: `94f43115de8d4a4f9f282126528c300a0b232c5f`
-- Source directory: `examples/03`
-
-## Files
-
-- `image.jpg`: source image for image-to-video generation. SHA-256:
-  `6ee3dacfef32cfef504dd698adb8a660cf15f686535c52fed4903fef27c0edd0`
-- `poses.npy`: camera-to-world trajectory matrices. SHA-256:
-  `bd0a23a696e184b0b43e7767eb432bfe644690560fe327fa96961affc941c404`
-- `intrinsics.npy`: camera intrinsic parameters. SHA-256:
-  `821fca6cf957ae8fbb1181307f02479efb1705e04c9e05734cd02fb43462e082`

examples/datasets/wan-syn/download_dataset_720p.sh

@@ -1,3 +0,0 @@
-#!/bin/bash
-# Wan-Syn 720P dataset (77x768x1280, 250k clips).
-python scripts/huggingface/download_hf.py --repo_id "FastVideo/Wan-Syn_77x768x1280_250k" --local_dir "data/Wan-Syn_77x768x1280_250k" --repo_type "dataset"

examples/distill/SFWan2.1-T2V/README.md

@@ -5,7 +5,7 @@ These scripts demonstrate self-forcing distillation (SFwan) for the causal Wan2.
 ## Run the recipe
 1. Download the preprocessed text-video dataset:
    ```bash
-   bash examples/datasets/crush-smol/download_dataset.sh
+   bash examples/distill/SFWan2.1-T2V/download_dataset.sh
    ```
 2. (Optional) Regenerate parquet shards locally:
    ```bash

examples/distill/SFWan2.1-T2V/download_dataset.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"