[misc]: refresh Kandinsky5 e2e oracle calibration comment with current reference numbers

The calibration table/comment cited stats measured against the reference video that shipped in b6ba7003, deleted in that same commit as part of the generator_update_interval fix -- the currently-committed reference (from the corrected run) has slightly different numbers. Recompute and update the comment and both assertion messages; verified the current reference plus its self-calibrating known-bad regression tests (fastvideo/tests/workflow/test_kandinsky5_e2e_media_oracle.py) still pass 4/4 against the new numbers with the same comfortable margin. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
[misc]: add Kandinsky5 QAD e2e golden reference video (recorded bootstrap run)
2026-07-26 02:31:15 -07:00 · 2026-07-26 02:31:15 -07:00 · 2026-07-26 02:31:15 -07:00 · 2026-07-26 02:31:15 -07:00 · 2026-07-26 02:29:52 -07:00 · 2026-07-26 02:29:52 -07:00
241 changed files with 859 additions and 17937 deletions
@@ -68,17 +68,6 @@ steps:
            limit: 2
      agents:
        queue: "default"
-    - label: ":vertical_traffic_light: Golden-Gate Tests"
-      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "golden_gate"
-      command: "timeout 90m .buildkite/scripts/pr_test.sh"
-      retry:
-        automatic:
-          - exit_status: 128
-            limit: 3
-          - exit_status: -1
-            limit: 2
-      agents:
-        queue: "default"
    - label: ":microscope: Unit Tests"
      if: build.env("TEST_SCOPE") == "direct" && build.env("TEST_TYPE") == "unit_test"
      command: "timeout 90m .buildkite/scripts/pr_test.sh"
@@ -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)
@@ -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 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
@@ -138,7 +138,7 @@ jobs:
          declare -A MAP=(
            [encoder]=encoder [vae]=vae [transformer]=transformer
            [kernel]=kernel_tests [unit]=unit_test [dreamverse]=dreamverse_app
-            [ssim]=ssim [golden-gate]=golden_gate [training]=training
+            [ssim]=ssim [training]=training
            [lora-inference]=inference_lora [lora-training]=training_lora
            [lora-extraction]=lora_extraction
            [distillation]=distillation_dmd [self-forcing]=self_forcing
@@ -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' }}
@@ -6,7 +6,6 @@ on:
    paths:
      - 'docs/**'
      - 'examples/**'
-      - 'scripts/inference/**'
      - 'mkdocs.yml'
      - 'requirements-mkdocs.in'
      - 'requirements-mkdocs.txt'
@@ -17,7 +16,6 @@ on:
    paths:
      - 'docs/**'
      - 'examples/**'
-      - 'scripts/inference/**'
      - 'mkdocs.yml'
      - 'requirements-mkdocs.in'
      - 'requirements-mkdocs.txt'
@@ -6,7 +6,7 @@ results/
 wandb/
 *.ipynb
 *.jpg
-!examples/datasets/lingbotworld2/image.jpg
+!examples/dataset/lingbotworld2/image.jpg
 *.safetensors
 *.mp4
 *.png
@@ -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
@@ -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();
@@ -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();
  });
 });
@@ -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,
-    });
-  });
 });
@@ -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": "*",
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@@ -2195,494 +2017,6 @@
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-			"license": "MIT",
-			"dependencies": {
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-				"@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",
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-				"@radix-ui/react-slot": "1.3.3",
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-			"integrity": "sha512-cSOCh6JlkmfjLyNcLiu2nB4v+nm+dkZ+Q5KHWk/soo4U7ZLiEQFKHK9/YmtBHjfCEaU43IBKQOc4/uJmCaiCTQ==",
-			"license": "MIT",
-			"dependencies": {
-				"@radix-ui/rect": "1.1.3"
-			},
-			"peerDependencies": {
-				"@types/react": "*",
-				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
-			},
-			"peerDependenciesMeta": {
-				"@types/react": {
-					"optional": true
-				}
-			}
-		},
-		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/react-use-size": {
-			"version": "1.1.4",
-			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-size/-/react-use-size-1.1.4.tgz",
-			"integrity": "sha512-D3anSY15EJoxrihpsXI6SMrmmonnQtR2ni7arO+Lfdg3O95b9hNXxONk8jA5C8ANdF/h5HMAxejgs8PWJ6rlhw==",
-			"license": "MIT",
-			"dependencies": {
-				"@radix-ui/react-use-layout-effect": "1.1.4"
-			},
-			"peerDependencies": {
-				"@types/react": "*",
-				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
-			},
-			"peerDependenciesMeta": {
-				"@types/react": {
-					"optional": true
-				}
-			}
-		},
-		"node_modules/@radix-ui/react-menu/node_modules/@radix-ui/rect": {
-			"version": "1.1.3",
-			"resolved": "https://registry.npmjs.org/@radix-ui/rect/-/rect-1.1.3.tgz",
-			"integrity": "sha512-JtyZR+mqgBibTo8xea3B6ZRmzZiM/YeVBtUkas6zMuXjAlfIFIW2FgqeM9eLyvEaYX66vr6DJMK+4U6LV0KhNw==",
-			"license": "MIT"
-		},
 		"node_modules/@radix-ui/react-popper": {
 			"version": "1.3.2",
 			"resolved": "https://registry.npmjs.org/@radix-ui/react-popper/-/react-popper-1.3.2.tgz",
@@ -3076,21 +2410,6 @@
 				}
 			}
 		},
-		"node_modules/@radix-ui/react-use-is-hydrated": {
-			"version": "0.1.3",
-			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-is-hydrated/-/react-use-is-hydrated-0.1.3.tgz",
-			"integrity": "sha512-umO/aJ+82CpOnhDZUTbILCQf7kU/g0iv+oGs/Q8jw7IkhWBzaEP4sA268PhFAJTFetbwp3ICc6ktpI4TqtxcIw==",
-			"license": "MIT",
-			"peerDependencies": {
-				"@types/react": "*",
-				"react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
-			},
-			"peerDependenciesMeta": {
-				"@types/react": {
-					"optional": true
-				}
-			}
-		},
 		"node_modules/@radix-ui/react-use-layout-effect": {
 			"version": "1.1.2",
 			"resolved": "https://registry.npmjs.org/@radix-ui/react-use-layout-effect/-/react-use-layout-effect-1.1.2.tgz",
@@ -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",
@@ -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();
-  });
-});
@@ -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)}
@@ -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 }),
@@ -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"
@@ -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;
 }
@@ -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;');
-  });
-});
@@ -4,8 +4,8 @@ import GpuGrid from '@/components/system/GpuGrid';

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

@@ -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>
  );
 }
@@ -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');
@@ -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>
  );
 }
@@ -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,
@@ -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',
-    );
-  });
-});
@@ -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)}
@@ -50,57 +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();

@@ -103,17 +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
@@ -155,10 +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);
@@ -177,7 +162,6 @@ export default function CreateJobModal({
    // can't overwrite the current workload's model list/selection.
    let stale = false;
    setIsLoadingModels(true);
-    setModelLoadError(null);
    getModels(inferenceWorkload)
      .then((list) => {
        if (stale) return;
@@ -196,13 +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);
@@ -215,22 +193,11 @@ export default function CreateJobModal({
  // Training jobs need a dataset; load the ready datasets when relevant.
  React.useEffect(() => {
    if (isOpen && !isInference) {
-      setIsLoadingDatasets(true);
-      setDatasetLoadError(null);
      getDatasets()
        .then(setReadyDatasets)
-        .catch((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]);

@@ -239,24 +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);
    }
@@ -265,7 +224,6 @@ export default function CreateJobModal({
  function clearImage() {
    setImagePath('');
    setImageFileName('');
-    setImageUploadError(null);
    if (imageInputRef.current) imageInputRef.current.value = '';
  }

@@ -281,7 +239,6 @@ export default function CreateJobModal({
      workloadType === 'lora_t2v' ? 'lora' : jobType
    ) as JobType;
    setIsSubmitting(true);
-    setSubmitError(null);
    try {
      const payload: CreateJobRequest = {
        model_id: modelId,
@@ -339,11 +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);
    }
@@ -391,11 +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
@@ -410,15 +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' && (
@@ -430,10 +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"
              />
@@ -450,15 +385,6 @@ export default function CreateJobModal({
                  </button>
                </span>
              )}
-              {imageUploadError && (
-                <p
-                  id="modal-image-error"
-                  role="alert"
-                  className="text-sm text-destructive"
-                >
-                  {imageUploadError}
-                </p>
-              )}
            </FieldRow>
          )}

@@ -506,22 +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}>
@@ -529,15 +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"
@@ -550,9 +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) => (
@@ -906,22 +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>
@@ -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();
@@ -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();
@@ -140,7 +148,11 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
  }

  return (
-    <article
+    <div
+      role="button"
+      tabIndex={0}
+      onClick={handleSelectJob}
+      onKeyDown={handleKeyDown}
      className={cn(
        'mb-3 flex cursor-pointer flex-col gap-2.5 rounded-lg border bg-background p-4 transition-colors last:mb-0',
        isSelected
@@ -148,42 +160,35 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
          : 'border-border hover:border-muted-foreground/40',
      )}
    >
-      <button
-        type="button"
-        aria-pressed={isSelected}
-        onClick={handleSelectJob}
-        className="flex w-full flex-col gap-2.5 rounded-md text-left"
-      >
-        <span className="flex flex-wrap items-center justify-between gap-2">
-          <span className="text-[0.95rem] font-semibold text-foreground">
-            {job.model_id}
-          </span>
-          <Badge variant={BADGE_VARIANTS[job.status] ?? 'secondary'}>
-            {job.status}
-          </Badge>
+      <div className="flex flex-wrap items-center justify-between gap-2">
+        <span className="text-[0.95rem] font-semibold text-foreground">
+          {job.model_id}
        </span>
-        <span className="max-w-full overflow-hidden text-ellipsis whitespace-nowrap text-sm text-muted-foreground">
-          {job.prompt}
-        </span>
-        <span className="flex flex-wrap items-center gap-4 text-xs text-muted-foreground">
-          {job.job_type === 'inference' ? (
-            <>
-              <span>{job.num_frames} frames</span>
-              <span>
-                {job.height}×{job.width}
-              </span>
-            </>
-          ) : (
-            <span>{job.workload_type?.replace(/_/g, ' ') ?? job.job_type}</span>
-          )}
-          {elapsedTime && (
-            <span className="inline-flex items-center gap-1">
-              <Timer className="size-3.5" aria-hidden />
-              {elapsedTime}
+        <Badge variant={BADGE_VARIANTS[job.status] ?? 'secondary'}>
+          {job.status}
+        </Badge>
+      </div>
+      <p className="max-w-full overflow-hidden text-ellipsis whitespace-nowrap text-sm text-muted-foreground">
+        {job.prompt}
+      </p>
+      <div className="flex flex-wrap items-center gap-4 text-xs text-muted-foreground">
+        {job.job_type === 'inference' ? (
+          <>
+            <span>{job.num_frames} frames</span>
+            <span>
+              {job.height}×{job.width}
            </span>
-          )}
-        </span>
-      </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
@@ -235,6 +240,6 @@ export default function JobCard({ job, onJobUpdated }: JobCardProps) {
          Delete
        </Button>
      </div>
-    </article>
+    </div>
  );
 }
@@ -19,32 +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('renders log lines streamed from the job log poll', async () => {
    vi.mocked(getJobLogs).mockResolvedValue({
      lines: ['boot sequence started', 'loading model weights'],
@@ -4,7 +4,6 @@ import * as React from 'react';
 import { X } from 'lucide-react';

 import { Button } from '@/components/ui/button';
-import { useDrawerFocus } from '@/hooks/useDrawerFocus';
 import { useResizable } from '@/hooks/useResizable';
 import { downloadJobLog, getJobLogs } from '@/lib/api';
 import type { Job } from '@/lib/types';
@@ -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">
@@ -207,14 +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>
  );
 }
@@ -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(
@@ -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>
  );
 }
@@ -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}
          />
@@ -1,10 +1,8 @@
 '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> = {
@@ -17,47 +15,25 @@ const TAB_TITLES: Record<string, string> = {
  '/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>
@@ -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"
@@ -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}
@@ -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();
-    }
-  });
 });
@@ -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) => (
@@ -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',
-    );
-  });
-});
@@ -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 };
@@ -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: {
@@ -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>
@@ -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}
@@ -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}
@@ -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}
@@ -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 };
@@ -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>
@@ -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}
@@ -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}
@@ -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;
-}
@@ -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;
-}
@@ -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,
@@ -1,52 +0,0 @@
-{
-  "recipes": [
-    {
-      "id": "fastwan21-t2v",
-      "task": "Text to video",
-      "label": "FastWan2.1 1.3B (distilled + VSA)",
-      "model": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
-      "source": "scripts/inference/inference_wan_VSA_DMD_1_3B.yaml",
-      "command": "FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN fastvideo generate --config scripts/inference/inference_wan_VSA_DMD_1_3B.yaml"
-    },
-    {
-      "id": "wan22-t2v",
-      "task": "Text to video",
-      "label": "Wan2.2 A14B",
-      "model": "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
-      "source": "examples/inference/basic/basic_wan2_2.py",
-      "command": "python examples/inference/basic/basic_wan2_2.py"
-    },
-    {
-      "id": "wan21-i2v",
-      "task": "Image to video",
-      "label": "Wan2.1 14B 480P",
-      "model": "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
-      "source": "scripts/inference/inference_wan_i2v.yaml",
-      "command": "fastvideo generate --config scripts/inference/inference_wan_i2v.yaml"
-    },
-    {
-      "id": "turbowan22-i2v",
-      "task": "Image to video",
-      "label": "TurboWan2.2 A14B",
-      "model": "loayrashid/TurboWan2.2-I2V-A14B-Diffusers",
-      "source": "examples/inference/basic/basic_turbodiffusion_i2v.py",
-      "command": "python examples/inference/basic/basic_turbodiffusion_i2v.py"
-    },
-    {
-      "id": "wan22-ti2v",
-      "task": "Text or image to video",
-      "label": "Wan2.2 TI2V 5B",
-      "model": "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
-      "source": "examples/inference/basic/basic_wan2_2_ti2v.py",
-      "command": "python examples/inference/basic/basic_wan2_2_ti2v.py"
-    },
-    {
-      "id": "matrix-game-2",
-      "task": "Interactive world",
-      "label": "Matrix Game 2.0",
-      "model": "FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers",
-      "source": "examples/inference/basic/basic_matrixgame2.py",
-      "command": "python examples/inference/basic/basic_matrixgame2.py"
-    }
-  ]
-}
@@ -1,60 +0,0 @@
-(() => {
-  let recipesPromise;
-
-  const loadRecipes = (url) => {
-    recipesPromise ||= fetch(url).then((response) => {
-      if (!response.ok) throw new Error(`HTTP ${response.status}`);
-      return response.json();
-    });
-    return recipesPromise;
-  };
-
-  const init = () => {
-    document.querySelectorAll("[data-cookbook]").forEach(async (root) => {
-      if (root.dataset.initialized) return;
-      root.dataset.initialized = "true";
-
-      const select = root.querySelector("[data-cookbook-recipe]");
-      const model = root.querySelector("[data-cookbook-model]");
-      const source = root.querySelector("[data-cookbook-source]");
-      const command = root.querySelector("[data-cookbook-command]");
-      const status = root.querySelector("[data-cookbook-status]");
-
-      try {
-        const { recipes } = await loadRecipes(root.dataset.recipes);
-        const byId = new Map(recipes.map((recipe) => [recipe.id, recipe]));
-        const groups = new Map();
-
-        select.replaceChildren();
-        recipes.forEach((recipe) => {
-          if (!groups.has(recipe.task)) {
-            const group = document.createElement("optgroup");
-            group.label = recipe.task;
-            groups.set(recipe.task, group);
-            select.append(group);
-          }
-          groups.get(recipe.task).append(new Option(recipe.label, recipe.id));
-        });
-
-        const render = () => {
-          const recipe = byId.get(select.value);
-          model.textContent = recipe.model;
-          source.textContent = recipe.source;
-          source.href = `https://github.com/hao-ai-lab/FastVideo/blob/main/${recipe.source}`;
-          command.textContent = recipe.command;
-          status.textContent = `${recipe.label} selected.`;
-        };
-
-        select.addEventListener("change", render);
-        select.disabled = false;
-        render();
-      } catch (error) {
-        status.textContent = "Recipes could not be loaded. Use the examples link below.";
-        console.error("Failed to load FastVideo cookbook recipes", error);
-      }
-    });
-  };
-
-  if (window.document$) window.document$.subscribe(init);
-  else document.addEventListener("DOMContentLoaded", init);
-})();
@@ -42,46 +42,6 @@ img {
    margin: 0 auto;
 }

-.cookbook-picker {
-    padding: 1rem;
-    border: 0.05rem solid var(--md-default-fg-color--lightest);
-    border-radius: 0.2rem;
-}
-
-.cookbook-picker select {
-    width: 100%;
-    padding: 0.6rem;
-    color: var(--md-default-fg-color);
-    background: var(--md-default-bg-color);
-    border: 0.05rem solid var(--md-default-fg-color--lighter);
-    border-radius: 0.2rem;
-}
-
-.cookbook-picker dl {
-    display: grid;
-    grid-template-columns: max-content 1fr;
-    gap: 0.25rem 1rem;
-}
-
-.cookbook-picker dt {
-    font-weight: 700;
-}
-
-.cookbook-picker dd {
-    margin: 0;
-    min-width: 0;
-    overflow-wrap: anywhere;
-}
-
-.cookbook-picker__status {
-    position: absolute;
-    width: 1px;
-    height: 1px;
-    overflow: hidden;
-    clip: rect(0, 0, 0, 0);
-    white-space: nowrap;
-}
-
 .md-typeset .copy-page-button.md-button {
    float: right;
    margin: 0 0 1rem 1rem;
@@ -269,26 +269,16 @@ 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
@@ -1,44 +0,0 @@
-# Inference Cookbook
-
-Choose a complete recipe maintained in the FastVideo repository. Each command
-runs its checked-in source directly, so coupled model, GPU, offload, and
-attention settings do not drift into unsupported combinations.
-
-The commands expect a local clone:
-
-```bash
-git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo
-```
-
-<div class="cookbook-picker" data-cookbook data-recipes="../assets/cookbook-recipes.json">
-  <label for="cookbook-recipe"><strong>Recipe</strong></label>
-  <select id="cookbook-recipe" data-cookbook-recipe disabled>
-    <option>Loading recipes…</option>
-  </select>
-  <dl>
-    <dt>Model</dt>
-    <dd data-cookbook-model>Loading…</dd>
-    <dt>Source</dt>
-    <dd><a data-cookbook-source href="../inference/examples/basic/">Browse maintained examples</a></dd>
-  </dl>
-  <pre><code class="language-bash" data-cookbook-command>Loading…</code></pre>
-  <p class="cookbook-picker__status" role="status" aria-live="polite" data-cookbook-status></p>
-  <noscript>
-    JavaScript is needed for the recipe picker. Browse the
-    <a href="../inference/examples/examples_inference_index/">inference examples</a>
-    instead.
-  </noscript>
-</div>
-
-## Customize a recipe
-
-Start from the checked-in source, then change only the settings your model
-supports:
-
- [Configuration](../inference/configuration.md) covers the Python and CLI
-  config surfaces.
- [Optimizations](../inference/optimizations.md) covers attention backends,
-  compilation, and memory tradeoffs.
- [Support matrix](../inference/support_matrix.md) lists supported models and
-  optimizations.
@@ -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."
@@ -445,11 +444,7 @@ 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
@@ -529,6 +524,7 @@ surfaces:
      inpaint_mask: request.extensions.stable_audio.inpaint_mask
    internal_only:
      data_type: "Derived from the request shape and not a public input."
+      latents: "Pre-generated diffusion latents supplied by parity/debug harnesses; not a public input."

  sampling_param_extensions: {}

@@ -2,7 +2,6 @@
 # adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/docs/source/generate_examples.py

 import itertools
-import json
 import os
 import re
 from dataclasses import dataclass, field
@@ -20,40 +19,6 @@ GENERATED_DOC_PREFIXES = (
    "training/examples/",
    "distillation/examples/",
 )
-COOKBOOK_DATA = ROOT_DIR / "docs/assets/cookbook-recipes.json"
-COOKBOOK_SOURCE_ROOTS = (
-    ROOT_DIR / "examples/inference",
-    ROOT_DIR / "scripts/inference",
-)
-
-
-def validate_cookbook() -> None:
-    """Keep cookbook entries tied to checked-in runnable sources."""
-    recipes = json.loads(COOKBOOK_DATA.read_text(encoding="utf-8")).get("recipes")
-    if not isinstance(recipes, list) or not recipes:
-        raise ValueError(f"{COOKBOOK_DATA}: recipes must be a non-empty list")
-
-    seen: set[str] = set()
-    for recipe in recipes:
-        required = ("id", "task", "label", "model", "source", "command")
-        missing = {key for key in required if not recipe.get(key)}
-        if missing:
-            raise ValueError(f"Cookbook recipe is missing: {', '.join(sorted(missing))}")
-        if recipe["id"] in seen:
-            raise ValueError(f"Duplicate cookbook recipe id: {recipe['id']}")
-        seen.add(recipe["id"])
-
-        source = (ROOT_DIR / recipe["source"]).resolve()
-        if not any(source.is_relative_to(root.resolve()) for root in COOKBOOK_SOURCE_ROOTS):
-            raise ValueError(f"Cookbook source is outside an approved directory: {recipe['source']}")
-        if not source.is_file():
-            raise ValueError(f"Cookbook source does not exist: {recipe['source']}")
-
-        source_text = source.read_text(encoding="utf-8")
-        if recipe["model"] not in source_text:
-            raise ValueError(f"Cookbook model is not present in {recipe['source']}: {recipe['model']}")
-        if recipe["source"] not in recipe["command"]:
-            raise ValueError(f"Cookbook command does not invoke its source: {recipe['id']}")


 def fix_case(text: str) -> str:
@@ -571,7 +536,6 @@ def on_pre_build(config, **kwargs):
    MkDocs hook to generate examples before building the documentation.
    This function is called automatically by MkDocs' native hook system.
    """
-    validate_cookbook()
    print("Generating example documentation...")
    generate_examples(generate_main_index=True)
    print("Example documentation generated successfully!")
@@ -585,7 +549,6 @@ def on_page_context(context, page, **kwargs):


 if __name__ == "__main__":
-    validate_cookbook()
    print("Generating example documentation...")
    generate_examples(generate_main_index=True)
    print("Example documentation generated successfully!")
@@ -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
@@ -55,17 +54,14 @@ 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)

 ## Next Steps

- [Quick Start](quick_start.md) - Generate your first video
- [Inference Cookbook](../cookbook/index.md) - Choose a maintained recipe
+- [Quick Start Guide](quick_start.md) - Get started with your first video generation
 - [Configuration](../inference/configuration.md) - Learn about configuration options
- [Examples](../inference/examples/examples_inference_index.md) - Explore scripts and notebooks
+- [Examples](../inference/examples/examples_inference_index.md) - Explore example scripts and notebooks
@@ -137,13 +137,6 @@ If you hit other issues, please open an issue on our
 our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)
 for additional support.

-## Next: performance & tuning
-
-Installed and verified? See [DGX Spark: Performance & Tuning](spark_performance.md)
-for which models are practical on the GB10, what makes them faster, and what
-won't help on this hardware (and why) — so you don't spend a night tuning knobs
-that can't move here.
-
 ## Development Environment Setup

 If you're planning to contribute to FastVideo please see the
@@ -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.
@@ -23,21 +23,61 @@ Also optionally install flash-attn:
 uv pip install flash-attn --no-build-isolation -v
 ```

-## Choose a maintained recipe
+## Basic Usage

-The cookbook selects complete, checked-in recipes instead of mixing model,
-parallelism, offload, and attention settings independently.
+### Text-to-Video Generation

-[Open the inference cookbook](../cookbook/index.md){ .md-button .md-button--primary }
+```python
+from fastvideo import VideoGenerator

-!!! tip "Need more control?"
-    Start from a maintained recipe, then use the
-    [configuration](../inference/configuration.md) and
-    [optimization](../inference/optimizations.md) guides for supported changes.
+def main():
+    # Create a video generator with a pre-trained model
+    generator = VideoGenerator.from_pretrained(
+        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+        num_gpus=1,  # Adjust based on your hardware
+    )
+
+    # Define a prompt for your video
+    prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."
+
+    # Generate the video
+    video = generator.generate_video(
+        prompt,
+        output_path="my_videos/",  # Controls where videos are saved
+        save_video=True
+    )
+
+if __name__ == '__main__':
+    main()
+```
+
+### Image-to-Video Generation
+
+```python
+from fastvideo import VideoGenerator, SamplingParam
+
+def main():
+    # Create the generator
+    model_name = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+    generator = VideoGenerator.from_pretrained(model_name, num_gpus=1)
+
+    # Set up parameters with an initial image
+    sampling_param = SamplingParam.from_pretrained(model_name)
+    sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+    sampling_param.num_frames = 107
+
+    # Generate video based on the image
+    prompt = "A photograph coming to life with gentle movement"
+    generator.generate_video(prompt, sampling_param=sampling_param,
+                             output_path="my_videos/",
+                             save_video=True)
+
+if __name__ == '__main__':
+    main()
+```

 ## Next Steps

- [Inference Cookbook](../cookbook/index.md) - Choose a maintained recipe
 - [Installation Guide](installation.md) - Detailed installation instructions
 - [Configuration](../inference/configuration.md) - Learn about configuration options
 - [Examples](../inference/examples/examples_inference_index.md) - Explore more
@@ -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

@@ -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

@@ -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:
@@ -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
@@ -49,7 +49,7 @@ configuration reference.
 Download the published preprocessed dataset:

 ```bash
-bash examples/datasets/mixkit/download_dataset.sh
+bash examples/training/finetune/wan_t2v_1.3B/mixkit/download_mixkit_data.sh
 ```

 ## Stage 1: supervised Attn-QAT fine-tuning
@@ -182,7 +182,7 @@ Ready-to-run training scripts are available for multiple models:

 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
@@ -48,7 +48,7 @@ For the complete two-stage Wan2.1 MixKit quantization-aware workflow, see

 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
@@ -231,10 +231,6 @@ 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
@@ -77,7 +77,7 @@ 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
+  `attn_qat_infer`, AND a consumer-Blackwell sm_120 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
@@ -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"
@@ -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
@@ -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"
@@ -9,7 +9,7 @@ uv pip install vsa

 ### 1. Download dataset:
 ```bash
-bash examples/datasets/wan-syn/download_dataset_480p.sh
+bash examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/download_dataset.sh
 ```

 ### 2. Configure and run distillation:
@@ -1,3 +1,3 @@
 #!/bin/bash
-# Wan-Syn 480P dataset (77x448x832, 600k clips).
+mkdir -p data
 python scripts/huggingface/download_hf.py --repo_id "FastVideo/Wan-Syn_77x448x832_600k" --local_dir "data/Wan-Syn_77x448x832_600k" --repo_type "dataset"
@@ -9,7 +9,7 @@ uv pip install vsa

 ### 1. Download dataset:
 ```bash
-bash examples/datasets/crush-smol/download_dataset.sh
+bash examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/download_dataset.sh
 ```

 ### 2. Configure and run distillation:
@@ -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"
@@ -191,22 +191,6 @@ def main() -> None:
    print(f"Using refine upsampler: {refine_upsampler_path}")

    pipeline_config = PipelineConfig.from_pretrained(model_root)
-    # LTX-2 NVFP4 deploy contract (train==deploy surface):
-    #   * Linears: NVFP4 block-scaled GEMMs (per-16 E2M1 + E4M3 SFs) on every
-    #     arch, via flashinfer.
-    #   * ATTN_QAT_INFER attention differs per arch: sm_120a/sm_121a use the
-    #     fastvideo-kernel CUTLASS (SageAttention3-FP4) scheme that
-    #     ATTN_QAT_TRAIN simulates; sm_100a (GB200) / sm_103a (GB300) use the
-    #     FP4 FA4 kernel (flash-attention-fp4) with per-16 block-scaled NVFP4
-    #     Q/K and BF16 P/V -- a train-sim mismatch that is gated by MS-SSIM
-    #     measurement, not assumed equal. The selection receipt is logged at
-    #     backend resolution ("ATTN_QAT_INFER resolved: ...").
-    # Original-weight retention: the default purges the always-FP4 layers'
-    # bf16 originals after conversion. Refine-only layers (the cross-modal
-    # AV projections) always keep theirs: the base stage profile runs them
-    # dense by deployment contract -- in the two-stage fast profile AND the
-    # distilled single-stage deploy. retain_original_weights=True keeps
-    # everything (debugging).
    pipeline_config.dit_config.quant_config = NVFP4Config()
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    torch_compile_kwargs = {
@@ -1,91 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Generate synchronized video/audio from a first frame with MiniMax H3."""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-from PIL import Image
-
-from fastvideo import VideoGenerator
-from fastvideo.api import (
-    EngineConfig,
-    GenerationRequest,
-    GeneratorConfig,
-    InputConfig,
-    OffloadConfig,
-    OutputConfig,
-    ParallelismConfig,
-    SamplingConfig,
-)
-from fastvideo.pipelines.basic.minimax_h3.packing import resolve_canvas_size
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--model-path", default="MiniMaxAI/MiniMax-H3")
-    parser.add_argument("--image", required=True, help="First-frame image path.")
-    parser.add_argument("--last-image", help="Optional last-frame image path.")
-    parser.add_argument("--output", default="outputs/minimax_h3_fl2va")
-    parser.add_argument("--prompt", required=True)
-    parser.add_argument("--num-frames", type=int, default=192, help="192 frames is exactly 8 seconds at 24 fps.")
-    parser.add_argument("--steps", type=int, default=50)
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--num-gpus", type=int, default=4)
-    return parser.parse_args()
-
-
-def main() -> None:
-    args = parse_args()
-    first_image = Image.open(args.image).convert("RGB")
-    last_image = Image.open(args.last_image).convert("RGB") if args.last_image else None
-    height, width = resolve_canvas_size(*first_image.size)
-    output_dir = Path(args.output)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    generator = VideoGenerator.from_config(
-        GeneratorConfig(
-            model_path=args.model_path,
-            engine=EngineConfig(
-                num_gpus=args.num_gpus,
-                use_fsdp_inference=args.num_gpus > 1,
-                parallelism=ParallelismConfig(tp_size=1, sp_size=args.num_gpus),
-                offload=OffloadConfig(
-                    dit=False,
-                    dit_layerwise=False,
-                    text_encoder=True,
-                    vae=True,
-                    pin_cpu_memory=False,
-                ),
-            ),
-        ))
-    try:
-        result = generator.generate(
-            GenerationRequest(
-                prompt=args.prompt,
-                negative_prompt="",
-                inputs=InputConfig(pil_image=first_image, last_image=last_image),
-                sampling=SamplingConfig(
-                    height=height,
-                    width=width,
-                    num_frames=args.num_frames,
-                    fps=24,
-                    num_inference_steps=args.steps,
-                    guidance_scale=1.0,
-                    batch_cfg=False,
-                    seed=args.seed,
-                ),
-                output=OutputConfig(
-                    output_path=str(output_dir / "minimax_h3_fl2va.mp4"),
-                    save_video=True,
-                    return_frames=False,
-                ),
-            ))
-        print(f"Output written to: {result.video_path}")
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,97 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Generate synchronized video/audio from ordered references with MiniMax H3."""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-from fastvideo import VideoGenerator
-from fastvideo.api import (
-    ComponentConfig,
-    EngineConfig,
-    GenerationRequest,
-    GeneratorConfig,
-    InputConfig,
-    OffloadConfig,
-    OutputConfig,
-    ParallelismConfig,
-    PipelineSelection,
-    SamplingConfig,
-)
-from fastvideo.pipelines.basic.minimax_h3 import MiniMaxH3Reference
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--model-path", default="MiniMaxAI/MiniMax-H3")
-    parser.add_argument("--reference-video", required=True)
-    parser.add_argument("--reference-audio", help="Optional additional audio reference.")
-    parser.add_argument("--output", default="outputs/minimax_h3_ref2va")
-    parser.add_argument("--prompt", required=True)
-    parser.add_argument("--height", type=int, default=768)
-    parser.add_argument("--width", type=int, default=1344)
-    parser.add_argument("--num-frames", type=int, default=124)
-    parser.add_argument("--steps", type=int, default=50)
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--num-gpus", type=int, default=4)
-    return parser.parse_args()
-
-
-def main() -> None:
-    args = parse_args()
-    references = [MiniMaxH3Reference(source=args.reference_video, media_type="video")]
-    if args.reference_audio:
-        references.append(MiniMaxH3Reference(source=args.reference_audio, media_type="audio"))
-    output_dir = Path(args.output)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    generator = VideoGenerator.from_config(
-        GeneratorConfig(
-            model_path=args.model_path,
-            engine=EngineConfig(
-                num_gpus=args.num_gpus,
-                use_fsdp_inference=args.num_gpus > 1,
-                parallelism=ParallelismConfig(tp_size=1, sp_size=args.num_gpus),
-                offload=OffloadConfig(
-                    dit=False,
-                    dit_layerwise=False,
-                    text_encoder=True,
-                    vae=True,
-                    pin_cpu_memory=False,
-                ),
-            ),
-            pipeline=PipelineSelection(
-                workload_type="i2v",
-                components=ComponentConfig(override_pipeline_cls_name="MiniMaxH3Ref2VAModularPipeline"),
-            ),
-        ))
-    try:
-        result = generator.generate(
-            GenerationRequest(
-                prompt=args.prompt,
-                negative_prompt="",
-                inputs=InputConfig(references=references),
-                sampling=SamplingConfig(
-                    height=args.height,
-                    width=args.width,
-                    num_frames=args.num_frames,
-                    fps=24,
-                    num_inference_steps=args.steps,
-                    guidance_scale=1.0,
-                    batch_cfg=False,
-                    seed=args.seed,
-                ),
-                output=OutputConfig(
-                    output_path=str(output_dir / "minimax_h3_ref2va.mp4"),
-                    save_video=True,
-                    return_frames=False,
-                ),
-            ))
-        print(f"Output written to: {result.video_path}")
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,103 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Generate video and audio from text with MiniMax H3."""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-from fastvideo import VideoGenerator
-from fastvideo.api import (
-    CompileConfig,
-    EngineConfig,
-    GenerationRequest,
-    GeneratorConfig,
-    OffloadConfig,
-    OutputConfig,
-    ParallelismConfig,
-    SamplingConfig,
-)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--model-path", default="MiniMaxAI/MiniMax-H3")
-    parser.add_argument("--prompt", required=True)
-    parser.add_argument("--output", default="outputs/minimax_h3_t2v")
-    parser.add_argument("--height", type=int, default=768)
-    parser.add_argument("--width", type=int, default=1344)
-    parser.add_argument("--num-frames", type=int, default=124)
-    parser.add_argument("--steps", type=int, default=50)
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--num-gpus", type=int, default=4)
-    parser.add_argument("--torch-compile", action="store_true",
-                        help="torch.compile the DiT transformer path")
-    parser.add_argument("--compile-mode", default=None,
-                        help='torch.compile mode, e.g. "reduce-overhead" for CUDA graphs')
-    parser.add_argument("--repeats", type=int, default=1,
-                        help="generate N times; with --torch-compile the first run pays "
-                             "compilation, so steady-state is the last repeat")
-    return parser.parse_args()
-
-
-def main() -> None:
-    args = parse_args()
-    output_dir = Path(args.output)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    generator = VideoGenerator.from_config(
-        GeneratorConfig(
-            model_path=args.model_path,
-            engine=EngineConfig(
-                num_gpus=args.num_gpus,
-                use_fsdp_inference=args.num_gpus > 1,
-                parallelism=ParallelismConfig(tp_size=1, sp_size=args.num_gpus),
-                offload=OffloadConfig(
-                    dit=False,
-                    dit_layerwise=False,
-                    text_encoder=True,
-                    vae=True,
-                    pin_cpu_memory=False,
-                ),
-                compile=CompileConfig(
-                    enabled=args.torch_compile,
-                    mode=args.compile_mode,
-                ),
-            ),
-        ))
-    try:
-        request = GenerationRequest(
-                prompt=args.prompt,
-                negative_prompt="",
-                sampling=SamplingConfig(
-                    height=args.height,
-                    width=args.width,
-                    num_frames=args.num_frames,
-                    fps=24,
-                    num_inference_steps=args.steps,
-                    guidance_scale=1.0,
-                    batch_cfg=False,
-                    seed=args.seed,
-                ),
-                output=OutputConfig(
-                    output_path=str(output_dir / "minimax_h3_t2v.mp4"),
-                    save_video=True,
-                    return_frames=False,
-                ),
-            )
-        result = generator.generate(request)
-        print(f"Output written to: {result.video_path}")
-        if result.generation_time is not None:
-            # machine-readable: benchmark harnesses parse this line to separate
-            # generation from model-load time (last occurrence = steady state)
-            print(f"Generation time: {result.generation_time:.2f}s")
-        for _ in range(args.repeats - 1):
-            result = generator.generate(request)
-            if result.generation_time is not None:
-                print(f"Generation time: {result.generation_time:.2f}s")
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -6,6 +6,10 @@ original single-file version to make each concern independently reviewable.
 The folder name matches the sibling `gradio_local_demo*.py` demos in this
 directory so both flat and packaged demos read consistently.

+> **Status: draft.** This package is structurally in place but will not run
+> against the current upstream `fastvideo` package. See *Blocking prereqs*
+> below.
+
 ## Layout

 | File | Purpose |
@@ -34,21 +38,27 @@ python -m gradio_local_demo_ltx2_3 --port 7860
 GPU requirement: a single FP4-capable GPU (B200 or comparable) for the
 "real-time 1080p" speed claim. Lower tiers will still run but slower.

-## Upstream surfaces the demo relies on
+## Blocking prereqs (why this draft PR cannot be merged yet)

-Everything the demo needs ships in the upstream `fastvideo` package:
+The upstream `fastvideo` package is missing three pieces that the demo
+currently depends on verbatim. Each needs its own upstreaming PR before this
+demo can actually boot:

- **`fastvideo.layers.quantization.nvfp4_config.NVFP4Config`** — the demo sets
-  `pipeline_config.dit_config.quant_config = NVFP4Config()` in `app.py` (same
-  pattern as `examples/inference/basic/basic_ltx2_distilled_fast_profile.py`).
- **LTX-2.3 refine kwargs on `VideoGenerator`** — `ltx2_refine_enabled`,
-  `ltx2_refine_upsampler_path`, `ltx2_refine_lora_path`,
-  `ltx2_refine_num_inference_steps`, `ltx2_refine_guidance_scale`,
-  `ltx2_refine_add_noise`, and `ltx2_vae_tiling` are wired in
-  `fastvideo/fastvideo_args.py`, backed by
-  `fastvideo/pipelines/basic/ltx2/stages/ltx2_refine.py`.
- **`SamplingParam`** — imported from the public `fastvideo` package root
-  (`fastvideo.api.sampling_param` under the hood).
+1. **`fastvideo.layers.quantization.fp4_config.FP4Config`** — the demo sets
+   `pipeline_config.dit_config.quant_config = FP4Config()` in `app.py`.
+   Upstream only ships `absmax_fp8.py` and `base_config.py` under
+   `fastvideo/layers/quantization/`.
+2. **LTX-2.3 refine / image-conditioning kwargs on `VideoGenerator`** —
+   `ltx2_refine_enabled`, `ltx2_refine_upsampler_path`, `ltx2_refine_lora_path`,
+   `ltx2_refine_num_inference_steps`, `ltx2_refine_guidance_scale`,
+   `ltx2_refine_add_noise`, `ltx2_images`, `ltx2_image_crf`. Upstream
+   `fastvideo/fastvideo_args.py` currently wires only `ltx2_vae_tiling`.
+   The backing stages (`ltx2_refine.py`, `ltx2_i2v_conditioning.py`) are
+   also missing from `fastvideo/pipelines/stages/`.
+3. **`fastvideo.configs.sample.base.SamplingParam`** — the import path used
+   by this demo. Upstream moved sampling params to
+   `fastvideo.api.sampling_param`. A re-export shim at the old path, or an
+   import update here once the other two prereqs land, will resolve it.

 ## Environment variables

@@ -4,10 +4,10 @@ from pathlib import Path

 import gradio as gr

-from fastvideo import SamplingParam
 from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.sample.base import SamplingParam
 from fastvideo.entrypoints.video_generator import VideoGenerator
-from fastvideo.layers.quantization.nvfp4_config import NVFP4Config
+from fastvideo.layers.quantization.fp4_config import FP4Config
 from fastvideo.utils import maybe_download_model

 from .config import (
@@ -44,7 +44,7 @@ def main():
        resolved_model_path = Path(model_root)

        pipeline_config = PipelineConfig.from_pretrained(str(resolved_model_path))
-        pipeline_config.dit_config.quant_config = NVFP4Config()
+        pipeline_config.dit_config.quant_config = FP4Config()
        refine_upsampler_path = resolve_refine_upsampler_path(resolved_model_path)
        print(f"Using refine upsampler: {refine_upsampler_path}")

@@ -4,7 +4,7 @@ from pathlib import Path
 import torch
 import torch._inductor.config

-from fastvideo import SamplingParam
+from fastvideo.configs.sample.base import SamplingParam

 LOCAL_DEMO_DIR = Path(__file__).resolve().parent
 CLASSIFIER_DIR = Path(
@@ -5,7 +5,7 @@ from copy import deepcopy

 import gradio as gr

-from fastvideo import SamplingParam
+from fastvideo.configs.sample.base import SamplingParam
 from fastvideo.entrypoints.video_generator import VideoGenerator

 from .config import (
@@ -1,176 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""LTX-2.3 distilled text-to-video with the optimized NVFP4 inference stack.
-
-Runs `FastVideo/LTX-2.3-Distilled-Diffusers` on a single GPU with the full
-validated optimization stack:
-
-* NVFP4 block-scaled linear layers (per-16 E2M1 weights + E4M3 scale factors),
-* ATTN_QAT_INFER FP4 attention (arch-resolved kernel, receipt logged),
-* torch.compile (fullgraph) over the DiT, text encoder, and VAE,
-* single-stage 8-step distilled sampling at guidance 1.0.
-
-Quick start
-----------
-    # On GB200-class ARM hosts, unset LD_LIBRARY_PATH (see Hardware notes):
-    env -u LD_LIBRARY_PATH python examples/inference/ltx2_3/optimized_nvfp4_t2v.py
-
-    # Optional overrides:
-    #   export LTX23_MODEL_PATH=/path/to/local/snapshot
-    #   export LTX23_T2V_PROMPT="a red fox running through fresh snow"
-    #   export LTX23_OUTPUT_DIR=outputs_video/ltx2_3_nvfp4_t2v
-
-Hardware notes
--------------
- On GB200 / Blackwell, run with `env -u LD_LIBRARY_PATH ...` to avoid a
-  system-cuBLAS / torch-cuBLAS mismatch that fails every GEMM (some ARM
-  container images ship an LD_LIBRARY_PATH that breaks torch.compile's
-  toolchain discovery). The `_inductor.shape_padding = False` line below
-  also avoids a pad_mm landmine on the same generation of cards.
-"""
-from __future__ import annotations
-
-import os
-import time
-from pathlib import Path
-
-import torch._inductor.config as _inductor
-
-from fastvideo import VideoGenerator
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.layers.quantization.nvfp4_config import NVFP4Config
-from fastvideo.utils import maybe_download_model
-
-# ATTN_QAT_INFER is the FP4 attention half of the NVFP4 deploy contract. It
-# resolves per arch (CUTLASS SageAttention3-FP4 on sm_120a/sm_121a, FP4 FA4
-# on sm_100a/sm_103a) and logs a one-line receipt of what actually bound.
-os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "ATTN_QAT_INFER")
-os.environ.setdefault("FASTVIDEO_STAGE_LOGGING", "1")
-
-# Inductor knobs. The first one (shape_padding=False) is mandatory on
-# Blackwell to avoid a cuBLAS INVALID_VALUE crash inside pad_mm. The rest
-# are the same matmul-friendliness flags the sibling LTX-2 examples use.
-_inductor.shape_padding = False
-_inductor.conv_1x1_as_mm = True  # treat 1x1 convolutions as matrix muls
-_inductor.coordinate_descent_tuning = True
-_inductor.coordinate_descent_check_all_directions = True
-_inductor.epilogue_fusion = False  # do not fuse pointwise ops into matmuls
-
-MODEL_ID = os.path.expandvars(
-    os.path.expanduser(
-        os.getenv("LTX23_MODEL_PATH", "FastVideo/LTX-2.3-Distilled-Diffusers")
-    )
-)
-OUTPUT_DIR = Path(os.getenv("LTX23_OUTPUT_DIR", "outputs_video/ltx2_3_nvfp4_t2v"))
-DEFAULT_PROMPT = (
-    "A fashion model takes a slow step forward and shifts her weight, "
-    "the soft fabric of her clothing swaying and rippling with the "
-    "motion, her hair shifting gently, soft even studio lighting on a "
-    "clean light background, elegant slow-motion runway feel."
-)
-PROMPT = os.getenv("LTX23_T2V_PROMPT", DEFAULT_PROMPT)
-
-
-def main() -> None:
-    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-    model_root = maybe_download_model(MODEL_ID)
-    print(f"Model:      {model_root}")
-    print(f"Output dir: {OUTPUT_DIR.resolve()}")
-
-    # Loading the pipeline config *with model_path* binds model-specific
-    # tuning (notably VAE precision/decoder defaults) into the config.
-    pipeline_config = PipelineConfig.from_pretrained(model_root)
-
-    # NVFP4 linear layers on the DiT. The default purges the original BF16
-    # weights of the always-FP4 linears right after conversion — a large
-    # peak-memory reduction. Refine-only layers (the cross-modal AV
-    # projections) always keep theirs: the base stage profile runs them
-    # dense by deployment contract. retain_original_weights=True keeps
-    # everything (debugging).
-    pipeline_config.dit_config.quant_config = NVFP4Config()
-
-    # fullgraph=True is supported on the NVFP4 path: the FP4 quantize step
-    # is a registered custom op (fastvideo::nvfp4_quantize_fa4), so dynamo
-    # traces through it without graph breaks. mode="default" — the two
-    # CUDAGraph modes ("reduce-overhead" / "max-autotune") are a separate
-    # opt-in, not part of this validated preset.
-    torch_compile_kwargs = {
-        "backend": "inductor",
-        "fullgraph": True,
-        "mode": "default",
-        "dynamic": False,
-    }
-
-    generator = VideoGenerator.from_pretrained(
-        model_root,
-        num_gpus=1,
-        pipeline_config=pipeline_config,
-        # Compile the DiT, text encoder, and VAE — all three stages benefit,
-        # and the VAE's codec submodules compile cleanly under fullgraph.
-        enable_torch_compile=True,
-        enable_torch_compile_text_encoder=True,
-        enable_torch_compile_vae=True,
-        torch_compile_kwargs=torch_compile_kwargs,
-        torch_compile_kwargs_vae=torch_compile_kwargs,
-        # Keep everything resident — no CPU offload for serving-style runs.
-        dit_cpu_offload=False,
-        text_encoder_cpu_offload=False,
-        vae_cpu_offload=False,
-        ltx2_vae_tiling=False,
-    )
-
-    common_kwargs = dict(
-        prompt=PROMPT,
-        negative_prompt="",      # distilled is CFG-free; no negative needed
-        guidance_scale=1.0,      # CFG=1 for distilled
-        height=1280, width=832,  # portrait runway aspect
-        num_frames=121, fps=24,  # ~5s clip
-        # Single-stage 8-step distilled sampling — the validated preset for
-        # this checkpoint (no two-stage refine; the NVFP4 deploy contract
-        # runs the distilled single-stage recipe).
-        num_inference_steps=8,
-        save_video=True,
-    )
-
-    try:
-        # Warmup: pays cold compile + first-shape guard work, untimed.
-        print("\n[warmup] compiling + generating…")
-        generator.generate_video(
-            output_path=str(OUTPUT_DIR / "_warmup.mp4"),
-            seed=7,
-            **common_kwargs,
-        )
-        (OUTPUT_DIR / "_warmup.mp4").unlink(missing_ok=True)
-
-        # Measured run.
-        out_path = OUTPUT_DIR / "output_ltx2_3_nvfp4_t2v.mp4"
-        print(f"\n[measured] generating: {out_path}")
-        t0 = time.perf_counter()
-        result = generator.generate_video(
-            output_path=str(out_path),
-            seed=2002,
-            **common_kwargs,
-        )
-        wall = time.perf_counter() - t0
-        e2e = (result.get("e2e_latency") if isinstance(result, dict) else None) or wall
-        print(f"[measured] e2e={e2e:.2f}s wall={wall:.2f}s")
-    finally:
-        generator.shutdown()
-
-    # Expected receipts — verify these two lines in your own run's log:
-    #
-    # 1. ATTN_QAT_INFER routing receipt (logged at backend resolution; on a
-    #    GB200-class part it reads):
-    #
-    #      ATTN_QAT_INFER resolved: arch=sm_100 kernel=flash-attention-fp4 \
-    #        qk_mode=nvfp4(per-16-e4m3-sf) pv_mode=bf16 train_sim_mismatch=measured
-    #
-    # 2. NVFP4 weight purge receipt (logged after model conversion; N/M/X
-    #    depend on the checkpoint):
-    #
-    #      NVFP4 weight purge receipt: purged N original bf16 weight tensors \
-    #        (X.XX GiB freed); retained M (refine-only dense fallback or \
-    #        retain_original_weights).
-
-
-if __name__ == "__main__":
-    main()
@@ -1,219 +0,0 @@
-"""QAD FP4 quality A/B/C/D harness — Wan2.1-T2V-1.3B on sm_121 (DGX Spark GB10).
-
-Tests whether the quantization-aware-distilled checkpoint
-``FastVideo/FastWan-QAD-1.3B`` recovers FP4 quality on sm_121, using the
-sm_121-enabled ``ATTN_QAT_INFER`` attention kernel. Running FP4 attention on
-*stock* Wan weights gives output below bf16 — expected, because stock weights
-were never trained to tolerate FP4 attention. This harness runs the checkpoint
-that *was* (fake-quant FP4 attention + NVFP4 linear trained into the model).
-
-Attention (bf16 vs FP4) and linear (bf16 vs FP4) are fully decoupled at
-inference, so we can isolate each axis:
-
-    FASTVIDEO_ATTENTION_BACKEND unset          -> bf16 attention (SDPA)
-    FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_INFER -> FP4 attention
-    QAD_LINEAR=0                               -> bf16 linear
-    QAD_LINEAR=1                               -> NVFP4 FP4 linear (flashinfer)
-
-    arm  attention   linear   selects with
-    A    bf16        bf16     QAD_LINEAR=0  (no ATTN env)            reference
-    B    FP4         bf16     QAD_LINEAR=0  ATTN_QAT_INFER           isolate attn
-    C    bf16        FP4      QAD_LINEAR=1  (no ATTN env)            isolate linear
-    D    FP4         FP4      QAD_LINEAR=1  ATTN_QAT_INFER           full 4-bit
-
-All four arms run end-to-end on the GB10 (the full 4-bit path — FP4 linear +
-FP4 attention — works on sm_121). This script still runs exactly ONE arm per
-invocation and dumps a C stack on any hard crash, so a single misbehaving arm
-can never take the others down with it; the runbook loops it four times with
-different env. Quality is the eye/ear on the saved mp4 + a matching-frame still;
-timing is the mean generation_time (full pipeline: text-encode +
-denoise + VAE decode) over the measured runs.
-
-On the GB10, expect FP4 attention ~6% faster end-to-end generation vs bf16 and quality-neutral
-by eye on the QAD checkpoint (both share the 3-step distill's quality ceiling).
-FP4 *linear* is roughly break-even at 1.3B/480p (the per-call quantize overhead
-eats the small-GEMM saving in eager mode); its win shows at higher resolution
-with torch.compile.
-
-Knobs (env): QAD_MODEL, QAD_DISTILLED, QAD_STEPS (3), QAD_GUIDANCE (1.0),
-QAD_HEIGHT (480), QAD_WIDTH (832), QAD_FRAMES (77), QAD_SEED (42),
-QAD_WARMUP (1), QAD_RUNS (3), QAD_STILL (20), QAD_OUT (qad_fp4_samples).
-"""
-from __future__ import annotations
-
-import faulthandler
-import glob
-import os
-import time
-
-import torch
-
-faulthandler.enable()  # dump a C stack if any arm hard-crashes.
-
-
-def _env(name: str, default: str) -> str:
-    return os.environ.get(name, default)
-
-
-def _env_int(name: str, default: int) -> int:
-    return int(os.environ.get(name, str(default)))
-
-
-def _env_float(name: str, default: float) -> float:
-    return float(os.environ.get(name, str(default)))
-
-
-# Distilled QAD transformer, loaded on top of the base Wan2.1-1.3B pipeline.
-DEFAULT_DISTILLED = "FastVideo/FastWan-QAD-1.3B"
-# The repo is a full diffusers pipeline; we overlay only its transformer onto the
-# base Wan pipeline (vae/text_encoder are Wan-identical).
-DISTILLED_WEIGHTS_FILE = "transformer/diffusion_pytorch_model.safetensors"
-
-PROMPT = (
-    "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-    "wide with interest. The playful yet serene atmosphere is complemented by soft "
-    "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
-)
-
-
-def resolve_distilled_weights(hf_id: str) -> str:
-    """Return a local path to the distilled transformer safetensors."""
-    if not hf_id:
-        return ""
-    if os.path.exists(hf_id):
-        return hf_id
-    from huggingface_hub import hf_hub_download
-    return hf_hub_download(repo_id=hf_id, filename=DISTILLED_WEIGHTS_FILE)
-
-
-def build_generator(fp4_linear: bool):
-    from fastvideo import VideoGenerator
-    from fastvideo.configs.pipelines.base import PipelineConfig
-
-    model_id = _env("QAD_MODEL", "Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
-
-    pipeline_config = PipelineConfig.from_pretrained(model_id)
-    pipeline_config.dit_precision = "bf16"
-    pipeline_config.vae_precision = "bf16"
-    pipeline_config.text_encoder_precisions = ("bf16",)
-
-    if fp4_linear:
-        # Wan-style config: matches to_q/k/v/out + ffn (the plain NVFP4 config is
-        # LTX2-specific and would quantize nothing on Wan).
-        from fastvideo.layers.quantization.nvfp4_qat_config import NVFP4QATConfig
-        pipeline_config.dit_config.quant_config = NVFP4QATConfig()
-
-    extra_kwargs = {}
-    distilled = resolve_distilled_weights(_env("QAD_DISTILLED", DEFAULT_DISTILLED))
-    if distilled:
-        print(f"[qad] distilled weights: {distilled}")
-        extra_kwargs["init_weights_from_safetensors"] = distilled
-
-    # Keep everything resident (the 1.3B QAD model + FP4 fits the GB10's unified
-    # memory); real Wan VAE decode for a faithful quality read (no TAEHV).
-    return VideoGenerator.from_pretrained(
-        model_id,
-        pipeline_config=pipeline_config,
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        dit_layerwise_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=False,
-        pin_cpu_memory=False,
-        enable_torch_compile=False,  # eager: isolate the FP4 effect, no compile noise
-        **extra_kwargs,
-    )
-
-
-def main() -> None:
-    if not torch.cuda.is_available():
-        raise SystemExit("CUDA is required.")
-
-    fp4_linear = _env_int("QAD_LINEAR", 0) == 1
-    attn = _env("FASTVIDEO_ATTENTION_BACKEND", "default")
-    steps = _env_int("QAD_STEPS", 3)
-    guidance = _env_float("QAD_GUIDANCE", 1.0)
-    height = _env_int("QAD_HEIGHT", 480)
-    width = _env_int("QAD_WIDTH", 832)
-    frames = _env_int("QAD_FRAMES", 77)
-    seed = _env_int("QAD_SEED", 42)
-    warmup = _env_int("QAD_WARMUP", 1)
-    runs = _env_int("QAD_RUNS", 3)
-    still_idx = _env_int("QAD_STILL", 20)
-    out_dir = _env("QAD_OUT", "qad_fp4_samples")
-    prompt = _env("QAD_PROMPT", PROMPT)
-
-    tag = f"lin-{'fp4' if fp4_linear else 'bf16'}_attn-{attn.lower()}"
-    cap = torch.cuda.get_device_capability()
-    print(f"[qad] GPU {torch.cuda.get_device_name()} (cc {cap[0]}.{cap[1]})")
-    print(f"[qad] ARM {tag}: linear={'FP4' if fp4_linear else 'bf16'}, "
-          f"attention={attn}, {steps} steps, guidance {guidance}, "
-          f"{height}x{width}x{frames}, seed {seed}")
-    print(f"[qad] prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}")
-
-    arm_dir = os.path.join(out_dir, tag)
-    os.makedirs(arm_dir, exist_ok=True)
-    generator = build_generator(fp4_linear)
-
-    def _generate():
-        # seed + frame dims live under `sampling` (SamplingConfig); `output`
-        # only takes output_path/save_video/return_frames (OutputConfig).
-        return generator.generate(request={
-            "prompt": prompt,
-            "sampling": {
-                "seed": seed,
-                "num_inference_steps": steps,
-                "guidance_scale": guidance,
-                "height": height,
-                "width": width,
-                "num_frames": frames,
-            },
-            "output": {"save_video": True, "output_path": arm_dir,
-                       "return_frames": True},
-        })
-
-    for _ in range(warmup):
-        _generate()
-
-    denoise_times: list[float] = []
-    last = None
-    for i in range(runs):
-        torch.cuda.synchronize()
-        t0 = time.perf_counter()
-        last = _generate()
-        torch.cuda.synchronize()
-        wall = time.perf_counter() - t0
-        # generate_video returns a plain dict; attribute access would always
-        # fall back to wall time.
-        gen_t = last.get("generation_time") if isinstance(last, dict) else None
-        denoise_times.append(gen_t if gen_t is not None else wall)
-        print(f"[qad] {tag} run {i + 1}/{runs}: {wall:.2f}s wall "
-              f"(gen {denoise_times[-1]:.2f}s)")
-
-    # The pipeline wrote the mp4 (full known-good encode) into arm_dir; report
-    # it and pull a matching-frame still from the [b,c,t,h,w] samples tensor
-    # using the same recipe the pipeline's frame builder uses.
-    mp4s = sorted(glob.glob(os.path.join(arm_dir, "*.mp4")), key=os.path.getmtime)
-    if mp4s:
-        print(f"[qad] video: {mp4s[-1]}")
-    samples = getattr(last, "samples", None) if last is not None else None
-    if samples is not None and getattr(samples, "ndim", 0) == 5:
-        import imageio
-        f = min(still_idx, samples.shape[2] - 1)  # samples: [b, c, t, h, w]
-        still = (samples[0, :, f].permute(1, 2, 0).clamp(0, 1) * 255)
-        still = still.to(torch.uint8).cpu().numpy()
-        png = os.path.join(arm_dir, f"raccoon_{tag}_f{f}.png")
-        imageio.imwrite(png, still)
-        print(f"[qad] still: {png}")
-    else:
-        print("[qad] note: no 5-D samples tensor; grab a frame from the mp4 above")
-
-    mean = sum(denoise_times) / len(denoise_times)
-    print(f"\n[qad][{tag}] generation mean {mean:.2f}s over {runs} runs "
-          f"({warmup} warmup, {steps} steps)")
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
@@ -1,207 +0,0 @@
-"""DGX Spark (GB10) reproduction benchmark for the performance guide.
-
-Reproduces the two headline claims in
-``docs/getting_started/installation/spark_performance.md`` on your own GB10:
-
-  1. A distilled few-step model is usable (~30 s/video) and **decode-bound** on
-     the Spark's unified LPDDR5X memory.
-  2. bf16 VAE decode is essentially lossless (MS-SSIM ~1.0 vs fp32) and modestly
-     faster — which is why FastVideo already defaults Wan's *decode* to bf16.
-
-Two parts, both in-process so they control for the Spark's run-to-run variance:
-
-  A. **Generation timing** — loads a distilled model once, generates ``--runs``
-     videos back-to-back (after ``--warmup``), reports the *median* generation
-     time. Set ``FASTVIDEO_STAGE_LOGGING=1`` to also see the per-stage
-     (denoise / VAE decode / text-encode) split that shows the decode bottleneck.
-
-  B. **Decode precision A/B** — decodes ONE fixed latent fp32-vs-bf16 in the same
-     process and reports MS-SSIM + speedup. This isolates the decode delta with
-     no denoise non-determinism and no video-codec noise.
-
-**Why median, not a single run:** on the GB10 a 3-step generation is dominated by
-one-time per-process startup (Triton autotune, allocator warmup) that never
-amortizes over so few steps, so single-run totals wobble ~±30%. Always compare
-few-step levers back-to-back / as medians, never as two separate single runs.
-
-Run it safely on a shared box (see the best-practices note in the perf guide):
-
-    FASTVIDEO_STAGE_LOGGING=1 nice -n 19 nohup \
-        python examples/inference/optimizations/spark_benchmark.py > spark_bench.log 2>&1 &
-    tail -f spark_bench.log
-
-Knobs (flags or env): --model, --runs (3), --warmup (1), --steps (3),
--frames (81), --height (448), --width (832), --seed (42), --out, --skip-gen,
--skip-decode.
-"""
-from __future__ import annotations
-
-import argparse
-import os
-import statistics
-import time
-
-import torch
-
-
-def _p(msg: str) -> None:
-    print(f"[spark-bench] {msg}", flush=True)
-
-
-def bench_generation(args) -> None:
-    """Part A: median few-step generation time on a distilled model."""
-    from fastvideo import VideoGenerator
-    from fastvideo.api.sampling_param import SamplingParam
-
-    # VSA auto-routes to the Triton kernel on sm_121; do NOT force TORCH_SDPA on a
-    # VSA checkpoint (the SDPA path builds a model without the gate weights the
-    # checkpoint carries and fails to load).
-    os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "VIDEO_SPARSE_ATTN")
-
-    _p(f"loading {args.model} ...")
-    load_t0 = time.perf_counter()
-    generator = VideoGenerator.from_pretrained(
-        args.model,
-        num_gpus=1,
-        use_fsdp_inference=False,
-        # Leave offload at these defaults: "CPU" offload is the same unified RAM
-        # on the GB10, so the win is tiling + sane resolution, not offloading.
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        VSA_sparsity=0.8,
-    )
-    _p(f"model loaded in {time.perf_counter() - load_t0:.1f}s")
-
-    sampling_param = SamplingParam.from_pretrained(args.model)
-    sampling_param.num_frames = args.frames
-    sampling_param.height = args.height
-    sampling_param.width = args.width
-    sampling_param.num_inference_steps = args.steps
-    sampling_param.seed = args.seed
-
-    prompt = (
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, "
-        "its eyes wide with interest. Soft natural light, warm cheerful tones, "
-        "mid-shot, cinematic.")
-
-    def _gen():
-        torch.cuda.synchronize()
-        t0 = time.perf_counter()
-        video = generator.generate_video(prompt, output_path=args.out,
-                                          save_video=True,
-                                          sampling_param=sampling_param)
-        torch.cuda.synchronize()
-        # generate_video returns a plain dict (legacy result), not an object —
-        # attribute access would silently fall back to wall time / None.
-        dt = video.get("generation_time") if isinstance(video, dict) else None
-        if dt is None:
-            dt = time.perf_counter() - t0
-        # Peak memory is measured *inside the worker process* that runs the
-        # pipeline and surfaced on the result; reading torch's allocator in this
-        # (main) process would report ~0 because the allocations aren't here.
-        peak = video.get("peak_memory_mb") if isinstance(video, dict) else None
-        return dt, peak
-
-    for _ in range(args.warmup):
-        _gen()
-
-    times, peaks = [], []
-    for i in range(args.runs):
-        dt, peak = _gen()
-        times.append(dt)
-        if peak:
-            peaks.append(peak)
-        _p(f"gen run {i + 1}/{args.runs}: {dt:.2f}s")
-
-    med = statistics.median(times)
-    _p(f"median generation time over {args.runs} runs "
-       f"({args.warmup} warmup, {args.steps} steps): {med:.2f}s")
-    if peaks:
-        _p(f"peak GPU memory (worker, reported by pipeline): {max(peaks):.0f} MB "
-           f"= {max(peaks) / 1024:.1f} GB")
-    else:
-        _p("peak GPU memory: not reported by this pipeline build")
-    _p("set FASTVIDEO_STAGE_LOGGING=1 to see the denoise / decode / text split "
-       "(few-step generation is VAE-decode-bound on the GB10).")
-    generator.shutdown()
-
-
-def bench_decode_precision(args) -> None:
-    """Part B: fp32-vs-bf16 VAE decode of one fixed latent (SSIM + speedup)."""
-    try:
-        from diffusers import AutoencoderKLWan
-        from torchmetrics.functional import (
-            multiscale_structural_similarity_index_measure as msssim)
-    except ImportError as e:  # torchmetrics is not a hard FastVideo dep
-        _p(f"skipping decode A/B (missing dependency: {e}); "
-           "`uv pip install torchmetrics` to enable it.")
-        return
-
-    dev = "cuda"
-    vae = AutoencoderKLWan.from_pretrained(
-        args.model, subfolder="vae", torch_dtype=torch.float32).to(dev).eval()
-
-    # Wan latent geometry for height x width x frames, patch (4,8,8):
-    #   T_lat = (frames - 1) // 4 + 1,  H_lat = height // 8,  W_lat = width // 8
-    t_lat = (args.frames - 1) // 4 + 1
-    z = torch.randn(1, 16, t_lat, args.height // 8, args.width // 8,
-                    device=dev, dtype=torch.float32)
-
-    def _decode(autocast: bool):
-        torch.cuda.synchronize()
-        t0 = time.perf_counter()
-        with torch.no_grad(), torch.autocast("cuda", dtype=torch.bfloat16,
-                                             enabled=autocast):
-            out = vae.decode(z, return_dict=False)[0]
-        torch.cuda.synchronize()
-        return out.float(), time.perf_counter() - t0
-
-    def _frames(o):  # (1,3,T,H,W) [-1,1] -> (T,3,H,W) [0,1]
-        return ((o.clamp(-1, 1) + 1) / 2)[0].permute(1, 0, 2, 3).contiguous()
-
-    _decode(False)  # warm both paths (excluded from timing)
-    _decode(True)
-
-    o32, t32 = _decode(autocast=False)          # fp32
-    o16, t16 = _decode(autocast=True)           # bf16 (== vae_decode_precision="bf16")
-    ssim = msssim(_frames(o16), _frames(o32), data_range=1.0).item()
-
-    _p(f"fp32 decode : {t32 * 1000:8.1f} ms")
-    _p(f"bf16 decode : {t16 * 1000:8.1f} ms   ({t32 / t16:.2f}x faster)")
-    _p(f"MS-SSIM(bf16, fp32) on identical latent: {ssim:.4f}  "
-       "(>= ~0.99 -> lossless; this is why Wan decode defaults to bf16)")
-
-
-def main() -> None:
-    ap = argparse.ArgumentParser(description=__doc__)
-    ap.add_argument("--model", default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
-    ap.add_argument("--runs", type=int, default=3)
-    ap.add_argument("--warmup", type=int, default=1)
-    ap.add_argument("--steps", type=int, default=3)
-    ap.add_argument("--frames", type=int, default=81)
-    ap.add_argument("--height", type=int, default=448)
-    ap.add_argument("--width", type=int, default=832)
-    ap.add_argument("--seed", type=int, default=42)
-    ap.add_argument("--out", default="spark_bench_samples")
-    ap.add_argument("--skip-gen", action="store_true",
-                    help="skip Part A (generation timing)")
-    ap.add_argument("--skip-decode", action="store_true",
-                    help="skip Part B (decode precision A/B)")
-    args = ap.parse_args()
-
-    if not torch.cuda.is_available():
-        raise SystemExit("CUDA is required (run this on the GB10).")
-    cap = torch.cuda.get_device_capability()
-    _p(f"{torch.cuda.get_device_name()} (cc {cap[0]}.{cap[1]}), "
-       f"torch {torch.__version__}")
-
-    if not args.skip_gen:
-        bench_generation(args)
-    if not args.skip_decode:
-        bench_decode_precision(args)
-
-
-if __name__ == "__main__":
-    main()
@@ -1,140 +0,0 @@
-# LTX-2 T2V NVFP4 quantization-aware fine-tune (QAT).
-#
-# Same recipe as fine_tuning/ltx2/t2v.yaml, with the deployment-matched
-# NVFP4 quantization from the validated
-# examples/train/configs/overfit_ltx2_t2v_nvfp4_qat.yaml run: the curated
-# attention/FFN linear set used by LTX-2 NVFP4 deployment runs its
-# forward through real NVFP4 GEMMs (flashinfer cutlass) with a
-# straight-through-estimator backward into the dense fp32 master weights.
-# No extra parameters or buffers are added, so FSDP sharding,
-# checkpointing, and export are identical to dense training. Video
-# attention uses ATTN_QAT_TRAIN for its quantized forward and STE
-# backward, then ATTN_QAT_INFER during validation. Head-dim-64 audio
-# attention and masked text attention remain dense.
-#
-# Validation-time ATTN_QAT_INFER is arch-aware:
-#   * sm_120 / sm_121: the bundled fastvideo-kernel CUTLASS extension.
-#   * sm_100 (GB200-class) / sm_103 (GB300-class): the FP4 FA4 kernel —
-#     requires the flash-attention-fp4 fork and its dependency pins; see
-#     docs/inference/optimizations.md.
-#   * any other GPU: falls back to flash attention with a logged receipt
-#     (validation still runs, just not with the quantized kernel).
-#
-# Prerequisites: preprocess your dataset into the trainer's latent format
-# (see docs/training/data_preprocess.md;
-# fastvideo/pipelines/preprocess/preprocess_ltx2_overfit.py is a minimal
-# LTX-2 reference to adapt). Data paths, step counts, and learning rate
-# below are placeholders you must adapt to your workload.
-#
-# Run (multi-GPU; see the validation note above for kernel availability):
-#   NUM_GPUS=4 bash examples/train/run.sh \
-#     examples/train/configs/fine_tuning/ltx2/nvfp4_qat_t2v.yaml
-#
-# `attn_qat_infer: true` below is the deployment-representative choice on
-# every arch with a quantized kernel available (see the validation note
-# above). Flip it off only when your GPU has no ATTN_QAT_INFER kernel and
-# you prefer validating with the train-time backend instead of the flash
-# fallback:
-#   NUM_GPUS=4 bash examples/train/run.sh \
-#     examples/train/configs/fine_tuning/ltx2/nvfp4_qat_t2v.yaml \
-#     --callbacks.validation.attn_qat_infer false
-
-models:
-  student:
-    _target_: fastvideo.train.models.ltx2.LTX2Model
-    # Validation settings below (8 steps, guidance 1.0) assume this
-    # distilled checkpoint.
-    init_from: FastVideo/LTX2-Distilled-Diffusers
-    trainable: true
-    enable_gradient_checkpointing_type: full
-    attention_backend: ATTN_QAT_TRAIN
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
-
-training:
-  distributed:
-    # The 13B video branch needs sharding; 4-way HSDP shard matches the
-    # validated LTX-2 runs. Scale num_gpus/hsdp_shard_dim together.
-    num_gpus: 4
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 4
-
-  data:
-    # REQUIRED: path to your preprocessed dataset.
-    data_path: data/your_dataset_preprocessed
-    dataloader_num_workers: 4
-    train_batch_size: 1
-    # LTX2Model requires 0.0: CFG dropout would zero post-connector
-    # embeddings, which is not the model's unconditional input.
-    training_cfg_rate: 0.0
-    seed: 42
-    # Must match your preprocessed resolution/length.
-    # num_latent_t = (num_frames - 1) / 8 + 1.
-    num_latent_t: 11
-    num_height: 480
-    num_width: 832
-    num_frames: 81
-
-  optimizer:
-    # Carried from the validated LTX-2 QAT overfit run as a starting
-    # point; tune for your dataset size and batch configuration.
-    learning_rate: 5.0e-5
-    betas: [0.9, 0.999]
-    weight_decay: 0.0
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    # Workload-dependent: set from your dataset size and target epochs.
-    max_train_steps: 2000
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/ltx2_t2v_nvfp4_qat_finetune
-    # A full training-state checkpoint is ~150GB for the 13B trainable
-    # video branch — size the interval and total limit to your storage.
-    training_state_checkpointing_steps: 500
-    checkpoints_total_limit: 2
-    resume_from_checkpoint: latest
-
-  tracker:
-    trackers: [wandb]
-    project_name: fastvideo_ltx2
-    run_name: ltx2_t2v_nvfp4_qat_finetune
-
-  model:
-    # LTX2Model.predict_noise converts the DiT's x0 output to velocity,
-    # so the default noise-minus-clean target reproduces the official
-    # unweighted masked-MSE (mask is all-ones for plain T2V).
-    precondition_outputs: false
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
-    max_grad_norm: 1.0
-  validation:
-    _target_: fastvideo.train.callbacks.validation.ValidationCallback
-    pipeline_target: fastvideo.pipelines.basic.ltx2.ltx2_pipeline.LTX2Pipeline
-    # REQUIRED: prompts to sample during training.
-    dataset_file: data/your_dataset_preprocessed/validation_prompts.json
-    every_steps: 250
-    # 8-step single-pass sampling matches the distilled checkpoint
-    # (validated in the LTX-2 overfit runs).
-    sampling_steps: [8]
-    guidance_scale: 1.0
-    num_frames: 81
-    # Validation reuses the live transformer and temporarily replaces its
-    # ATTN_QAT_TRAIN implementations with the sm120 inference kernel.
-    # Set false on GB200 (see header).
-    attn_qat_infer: true
-
-# quant_config selects NVFP4 QAT for the same curated linear prefixes as
-# LTX-2 NVFP4 deployment. They fake-quantize through real FP4 GEMMs with
-# an STE backward. The string resolves to NVFP4QATTrainConfig at parse.
-pipeline:
-  dit_config:
-    quant_config: nvfp4_qat_train
@@ -1,110 +0,0 @@
-# LTX-2 T2V full fine-tune.
-#
-# Starting point for fine-tuning LTX-2 on your own dataset with the
-# modular trainer. Model wiring (checkpoint, CFG rate, output
-# preconditioning, sharding) follows the validated
-# examples/train/configs/overfit_ltx2_t2v.yaml run; data paths, step
-# counts, and learning rate are placeholders you must adapt — the right
-# values depend on your dataset and are marked below.
-#
-# Prerequisites: preprocess your dataset into the trainer's latent format
-# (see docs/training/data_preprocess.md;
-# fastvideo/pipelines/preprocess/preprocess_ltx2_overfit.py is a minimal
-# LTX-2 reference to adapt).
-#
-# Run:
-#   NUM_GPUS=4 bash examples/train/run.sh \
-#     examples/train/configs/fine_tuning/ltx2/t2v.yaml
-
-models:
-  student:
-    _target_: fastvideo.train.models.ltx2.LTX2Model
-    # Validation settings below (8 steps, guidance 1.0) assume this
-    # distilled checkpoint.
-    init_from: FastVideo/LTX2-Distilled-Diffusers
-    trainable: true
-    enable_gradient_checkpointing_type: full
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
-
-training:
-  distributed:
-    # The 13B video branch needs sharding; 4-way HSDP shard matches the
-    # validated LTX-2 runs. Scale num_gpus/hsdp_shard_dim together.
-    num_gpus: 4
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 4
-
-  data:
-    # REQUIRED: path to your preprocessed dataset.
-    data_path: data/your_dataset_preprocessed
-    dataloader_num_workers: 4
-    train_batch_size: 1
-    # LTX2Model requires 0.0: CFG dropout would zero post-connector
-    # embeddings, which is not the model's unconditional input.
-    training_cfg_rate: 0.0
-    seed: 42
-    # Must match your preprocessed resolution/length.
-    # num_latent_t = (num_frames - 1) / 8 + 1.
-    num_latent_t: 11
-    num_height: 480
-    num_width: 832
-    num_frames: 81
-
-  optimizer:
-    # Carried from the validated LTX-2 overfit run as a starting point;
-    # tune for your dataset size and batch configuration.
-    learning_rate: 5.0e-5
-    betas: [0.9, 0.999]
-    weight_decay: 0.0
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    # Workload-dependent: set from your dataset size and target epochs.
-    max_train_steps: 2000
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/ltx2_t2v_finetune
-    # A full training-state checkpoint is ~150GB for the 13B trainable
-    # video branch — size the interval and total limit to your storage.
-    training_state_checkpointing_steps: 500
-    checkpoints_total_limit: 2
-    resume_from_checkpoint: latest
-
-  tracker:
-    trackers: [wandb]
-    project_name: fastvideo_ltx2
-    run_name: ltx2_t2v_finetune
-
-  model:
-    # LTX2Model.predict_noise converts the DiT's x0 output to velocity,
-    # so the default noise-minus-clean target reproduces the official
-    # unweighted masked-MSE (mask is all-ones for plain T2V).
-    precondition_outputs: false
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
-    max_grad_norm: 1.0
-  validation:
-    _target_: fastvideo.train.callbacks.validation.ValidationCallback
-    pipeline_target: fastvideo.pipelines.basic.ltx2.ltx2_pipeline.LTX2Pipeline
-    # REQUIRED: prompts to sample during training.
-    dataset_file: data/your_dataset_preprocessed/validation_prompts.json
-    every_steps: 250
-    # 8-step single-pass sampling matches the distilled checkpoint
-    # (validated in the LTX-2 overfit runs).
-    sampling_steps: [8]
-    guidance_scale: 1.0
-    num_frames: 81
-
-# Required so the LTX2T2VConfig pipeline config is resolved from
-# init_from (without a `pipeline:` key the loader falls back to a
-# generic PipelineConfig and the LTX-2 DiT cannot be constructed).
-pipeline: {}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
leffffandClaude Fable 5	35a283531e	[misc]: refresh Kandinsky5 e2e oracle calibration comment with current reference numbers The calibration table/comment cited stats measured against the reference video that shipped in b6ba7003, deleted in that same commit as part of the generator_update_interval fix -- the currently-committed reference (from the corrected run) has slightly different numbers. Recompute and update the comment and both assertion messages; verified the current reference plus its self-calibrating known-bad regression tests (fastvideo/tests/workflow/test_kandinsky5_e2e_media_oracle.py) still pass 4/4 against the new numbers with the same comfortable margin. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-07-26 02:31:15 -07:00
leffff	3c9e604199	[misc]: add Kandinsky5 QAD e2e golden reference video (recorded bootstrap run)	2026-07-26 02:31:15 -07:00
leffffandClaude Fable 5	2cb832290a	[bugfix]: fit Kandinsky5 e2e stage 2 in one 80GiB GPU; make multi-GPU sharding viable The generator-update fix costs a second full Adam state (~16GiB for the 2B student) on top of the critic's: a recorded stage-2 run peaked at ~76GiB and OOMed allocating the last MiBs with ~1.9GiB lost to fragmentation. Set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True for the training stages to reclaim that headroom. Also make KANDINSKY5_E2E_NUM_GPUS=2+ actually work as the fallback: DP_SP_BatchSampler floor-divides batch count by the number of DP groups with drop_last=True, so the previous 1-row dataset produced an EMPTY dataloader on every rank at 2+ GPUs. Synthesize max(2, NUM_GPUS) manifest rows pointing at the same clip -- identical latents/caption keep the single-sample-overfit semantics. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-07-26 02:31:15 -07:00
leffffandClaude Fable 5	5aae1c093a	[bugfix]: address third-round Kandinsky5 QAD review findings - Never touch the user's documented dataset: preprocess roots are now env-overridable (KANDINSKY5_OVERFIT_DATA_DIR/_OUTPUT_DIR) and the nightly keeps every artifact under a single test-owned data/kandinsky5_e2e/ root, cleaning only that - Override method.generator_update_interval to 1 in the E2E stage-2 run: with the recipe's 5, a 3-step run performed zero student updates and the exported "student" was stage-1 weights - Harden the media oracle: exact frame count/geometry (closes the SSIM shorter-clip truncation hole), per-frame luma spatial std (solid clip at reference mean scored 15.4 on the old global RGB std), consecutive-frame temporal diff (frozen clips), and MS-SSIM floor recalibrated to 0.95 against measured known-bads (solid 0.9189, frozen 0.8597, good 1.0000); known-bad clips are permanent self-calibrating regressions in fastvideo/tests/workflow/test_kandinsky5_e2e_media_oracle.py - Capability-gate ATTN_QAT_INFER: is_attn_qat_infer_available() now requires an sm_120/sm_121 device in addition to the extension import (CUDA 13 wheels can bundle the extension on H100/GB200, where the first kernel call failed instead of falling back); the real platform resolver is covered in fastvideo/tests/api/test_attn_qat_infer_capability_gate.py - Remove the now-stale golden reference: the generator-update fix changes what stage 2 trains, so the reference must be re-recorded from the corrected run (KANDINSKY5_E2E_WRITE_REFERENCE=1 bootstrap + review) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-07-26 02:31:15 -07:00
leffffandClaude Fable 5	068c7ba57e	[misc]: tighten Kandinsky5 e2e MS-SSIM floor to 0.90 from recorded runs An independent retrain-from-scratch validation run scored mean/min/max MS-SSIM 1.0000 against the committed reference (recorded 2026-07-18), proving the chain deterministic on fixed hardware. Replace the 0.5 bootstrap placeholder with 0.90: headroom for GPU/library drift, loud failure on real remap/schedule regressions. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-07-26 02:29:52 -07:00
leffff	9b95acadd8	[misc]: add Kandinsky5 QAD e2e golden reference video (recorded bootstrap run)	2026-07-26 02:29:52 -07:00
leffffandClaude Fable 5	5ee7980fcb	[misc]: un-ignore nightly reference videos in .gitignore The catch-all .mp4 rule blocked committing the new Kandinsky5 QAD e2e golden reference (existing nightly references were force-added). Add the negation under the existing "Reference videos" section so current and future reference_video_.mp4 files stage normally. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-07-26 02:29:52 -07:00
leffffandClaude Fable 5	3e1201c455	[misc]: make Kandinsky5 e2e reference video reviewable and SSIM-stable Bootstrap run on a GPU box produced uniform grey static: the "random noise" caption doubled as the generation prompt, and 3 stage-1 steps at the recipe's full 5e-5 LR toward a pure-noise clip collapsed 4-step no-CFG DMD sampling (50-step T2V sampling of the same export still produced a good video, and the base checkpoint under the 4-step sampler produced structured output -- isolating the damage to the finetune). Grey-static references are indistinguishable from pipeline failure by eye and decorrelate across runs, making the SSIM oracle flaky. Use a semantic caption/prompt (clip content stays noise; irrelevant for plumbing) and a 1e-6 stage-1 LR override so the deterministic reference is blurry-but-structured: human-reviewable and stable under SSIM. Document the review bar in the module docstring. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-07-26 02:29:52 -07:00
leffffandClaude Fable 5	64f60b3d8a	[bugfix]: address second-round Kandinsky5 QAD review findings - Keep only ATTN_QAT_TRAIN strict in the stage backend guard; preserve the documented ATTN_QAT_INFER->FlashAttention fallback (+ regression tests) - Resolve FSDP compute dtype from the recorded MixedPrecisionPolicy instead of scanning parameter storage dtypes (+ FSDP regression tests) - E2E: export/strict-reload the final stage-2 student, instantiate it via the documented Kandinsky5DMDPipeline/Kandinsky5DMDConfig override, generate deterministically, and compare MS-SSIM against a committed reference (fail, not skip, when the reference is missing; KANDINSKY5_E2E_WRITE_REFERENCE=1 bootstraps it) - E2E: delete all input/output roots up front and explicitly disable resume_from_checkpoint for both stages - preprocess: path-specific ValueError on zero decoded frames, non-empty manifest validation, fixture VideoWriter.isOpened() check (+ tests) - Stage-boundary regression test driving Kandinsky5DmdDenoisingStage with a backend spy; fails if the set_forward_context wrapper is removed - distill_dmd_qat.sh now accepts a stage-1 DCP checkpoint, runs dcp_to_diffusers --verify, and injects the export into all three models.*.init_from overrides; README/YAML/finetune docs updated Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-07-26 02:29:52 -07:00
leffffandClaude Sonnet 5	21d751fbb4	[bugfix]: address Kandinsky5 QAD PR review findings Fixes all P1/P2 items from the review of 7c7704da: - E2E test's data_path pointed at a nonexistent combined_parquet_dataset/ subdir; preprocess_kandinsky5_overfit.py writes directly to its output root. - Stage 2 loaded the raw DCP checkpoint-N/ directly, which isn't a diffusers model dir. Added dcp_to_diffusers --verify (strict-reload the export immediately) and wired the conversion into the E2E test, launcher scripts, and README between stage 1 and stage 2. - Kandinsky5DmdDenoisingStage called the transformer without set_forward_context, so LocalAttention's missing-context guard caught the AssertionError and silently fell back to plain SDPA -- Stage 2 validation never actually exercised ATTN_QAT_TRAIN. Wrapped the call to match the parent stage's contract and added a scoped backend assertion (ATTN_QAT_TRAIN/ATTN_QAT_INFER only, since other backends can legitimately fall back to auto-selection). - Exported DMD checkpoints kept the base T2V model_index.json, so VideoGenerator.from_pretrained resolved them to Kandinsky5T2VPipeline instead of the DMD sampler. Added Kandinsky5DMDConfig and documented the required override_pipeline_cls_name + pipeline_config combo. - The FSDP-training dtype-detection special case (scan params, default to bf16) applied unconditionally, so an explicit fp32 pipeline was silently cast to bf16 for plain inference. Extracted _resolve_target_dtype and gated the scan on isinstance(transformer, FSDPModule). - Preprocessing tokenized captions with a 512-token cap that doesn't account for the 129-token system template runtime strips off; now reads text_encoder_max_lengths from pipeline_config to match runtime. Also replaced unconditional item["cap"][0] indexing (silently takes the first character of a string caption) with a get_caption() helper that accepts/validates both the documented string schema and the list-of-variants schema some producers use. - test_kandinsky5_qat_attention_engages.py set FASTVIDEO_ATTENTION_BACKEND and cleared the attention backend selector cache without teardown, leaking ATTN_QAT_TRAIN into later tests in the same pytest process. Now uses monkeypatch + try/finally. Added regression tests: test_kandinsky5_target_dtype.py, test_kandinsky5_dmd_pipeline_resolution.py, test_kandinsky5_overfit_caption.py. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-26 02:29:52 -07:00
leffffandClaude Sonnet 5	2dbb0c94a9	[feat] Add Kandinsky5 QAD training pipeline: data preprocessing, QAT finetune, QAT-aware DMD distillation Adds the training-side counterpart to the existing Kandinsky-5 T2V/I2V inference pipeline (#1471): a full quantization-aware distillation (QAD) recipe for Kandinsky-5.0 Lite T2V at 512x768/121 frames on the new fastvideo/train/ stack. - Data preprocessing: preprocess_kandinsky5_overfit.py encodes raw video + captions (Kandinsky5's shared HunyuanVideo VAE + dual Qwen/Reason1 + CLIP text encoders) into the t2v parquet schema. - Stage 1 (Attn-QAT finetune): fastvideo.train.models.kandinsky5.Kandinsky5Model wires Kandinsky5 into the modular trainer; FASTVIDEO_ATTENTION_BACKEND= ATTN_QAT_TRAIN routes dense/local attention through a fake-quantized straight-through-estimator kernel during finetuning so the DiT learns to absorb quantization error. No weight quantization during training. - Stage 2 (QAT-aware DMD2 distillation): Kandinsky5DmdDenoisingStage + Kandinsky5DMDPipeline distill the stage-1 checkpoint from many-step teacher sampling down to a 4-step student, with teacher/critic masked back to full-precision dense attention regardless of the student's QAT env var (component_loader.py's _loading_teacher_critic_model gate). See examples/train/configs/fine_tuning/kandinsky5/README.md for the full data -> stage-1 -> stage-2 -> inference (NVFP4/FP8) walkthrough. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-26 02:29:52 -07:00