fix

update server
add inference script
2026-08-06 21:13:14 -07:00 · 2026-08-06 20:47:35 -07:00 · 2026-08-06 18:18:36 -07:00 · 2026-08-06 16:36:47 -07:00 · 2026-07-24 20:29:03 -07:00 · 2026-07-24 20:04:31 -07:00
260 changed files with 17634 additions and 10212 deletions
@@ -404,7 +404,7 @@ steps:
                - "pyproject.toml"
                - "docker/Dockerfile"
              config:
-                command: "timeout 25m .buildkite/scripts/pr_test.sh"
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
                label: ":test_tube: Training Tests"
                env:
                  - TEST_TYPE=training
@@ -415,7 +415,7 @@ steps:
                - "pyproject.toml"
                - "docker/Dockerfile"
              config:
-                command: "timeout 25m .buildkite/scripts/pr_test.sh"
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
                label: ":test_tube: Distillation DMD Tests"
                env:
                  - TEST_TYPE=distillation_dmd
@@ -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' }}
@@ -75,7 +75,6 @@ docs/distillation/examples/
 *.pkl

 # Reference videos (negations must come after the catch-all on line below)
-!fastvideo/tests/nightly/reference_video_*.mp4

 # Static images
 !docs/assets/images/**/*.png
@@ -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",
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-			"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"
-			},
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-				"@types/react-dom": "*",
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@@ -2195,494 +2017,6 @@
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-			"license": "MIT",
-			"dependencies": {
-				"@radix-ui/primitive": "1.1.7",
-				"@radix-ui/react-collection": "1.1.15",
-				"@radix-ui/react-compose-refs": "1.1.5",
-				"@radix-ui/react-context": "1.2.2",
-				"@radix-ui/react-direction": "1.1.4",
-				"@radix-ui/react-dismissable-layer": "1.1.19",
-				"@radix-ui/react-focus-guards": "1.1.6",
-				"@radix-ui/react-focus-scope": "1.1.16",
-				"@radix-ui/react-id": "1.1.4",
-				"@radix-ui/react-popper": "1.3.7",
-				"@radix-ui/react-portal": "1.1.17",
-				"@radix-ui/react-presence": "1.1.10",
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-				"@radix-ui/react-roving-focus": "1.1.19",
-				"@radix-ui/react-slot": "1.3.3",
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 	},
 	"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 }),
@@ -1,6 +1,6 @@
 'use client';

-import { ImageOff, Loader2 } from 'lucide-react';
+import { Loader2 } from 'lucide-react';
 import { useEffect, useState } from 'react';

 import { Card } from '@/components/ui/card';
@@ -11,54 +11,6 @@ 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);
@@ -125,7 +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>
  );
 }
@@ -1,50 +0,0 @@
-import { render, screen } from '@testing-library/react';
-import userEvent from '@testing-library/user-event';
-import { describe, expect, it, vi } from 'vitest';
-
-import { downloadBlob } from '@/lib/utils';
-
-import DownloadCaptions from './DownloadCaptions';
-
-vi.mock('@/lib/utils', async (importOriginal) => {
-  const actual = await importOriginal<typeof import('@/lib/utils')>();
-  return { ...actual, downloadBlob: vi.fn() };
-});
-
-const props = {
-  fileNames: ['b.mp4', 'a.mp4'],
-  captions: { 'a.mp4': 'cap a', 'b.mp4': 'cap b' },
-};
-
-describe('DownloadCaptions', () => {
-  it('opens the format menu on click and downloads the selection', async () => {
-    const user = userEvent.setup();
-    render(<DownloadCaptions {...props} />);
-
-    await user.click(
-      screen.getByRole('button', { name: 'Download Captions' }),
-    );
-    await user.click(screen.getByRole('menuitem', { name: 'JSON' }));
-
-    expect(vi.mocked(downloadBlob)).toHaveBeenCalledWith(
-      expect.any(Blob),
-      'videos2caption.json',
-    );
-  });
-
-  it('operates entirely from the keyboard', async () => {
-    const user = userEvent.setup();
-    render(<DownloadCaptions {...props} />);
-
-    screen.getByRole('button', { name: 'Download Captions' }).focus();
-    await user.keyboard('{Enter}');
-    const first = await screen.findByRole('menuitem', { name: 'JSON' });
-    expect(first).toHaveFocus();
-    await user.keyboard('{ArrowDown}{Enter}');
-
-    expect(vi.mocked(downloadBlob)).toHaveBeenCalledWith(
-      expect.any(Blob),
-      'videos.txt',
-    );
-  });
-});
@@ -1,13 +1,12 @@
 'use client';

 import { ChevronDown } from 'lucide-react';
-import * as DropdownMenu from '@radix-ui/react-dropdown-menu';

 import { Button } from '@/components/ui/button';
 import { downloadBlob } from '@/lib/utils';

 const MENU_ITEM =
-  'block min-h-11 w-full cursor-pointer select-none px-4 py-2 text-left text-sm font-medium text-foreground outline-none transition-colors data-[highlighted]:bg-muted';
+  '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,
@@ -63,40 +62,52 @@ export default function DownloadCaptions({
    downloadBlob(new Blob([csv], { type: 'text/csv' }), 'captions.csv');
  }

-  // Same click/keyboard-accessible menu idiom as CreateJobButton — hover-only
-  // menus exclude touch and keyboard users.
  return (
-    <DropdownMenu.Root>
-      <DropdownMenu.Trigger asChild>
-        <Button
-          type="button"
-          variant="outline"
-          size="sm"
-          disabled={disabled}
-          className="gap-1.5"
+    <div className="group relative inline-block">
+      <Button
+        type="button"
+        variant="outline"
+        size="sm"
+        disabled={disabled}
+        aria-haspopup="menu"
+        className="gap-1.5"
+      >
+        Download Captions
+        <ChevronDown className="h-3.5 w-3.5 opacity-85" />
+      </Button>
+      {!disabled && (
+        <div
+          role="menu"
+          className="invisible absolute right-0 top-full z-[200] min-w-full -translate-y-1 pt-1 opacity-0 transition-all group-focus-within:visible group-focus-within:translate-y-0 group-focus-within:opacity-100 group-hover:visible group-hover:translate-y-0 group-hover:opacity-100"
        >
-          Download Captions
-          <ChevronDown className="h-3.5 w-3.5 opacity-85" aria-hidden />
-        </Button>
-      </DropdownMenu.Trigger>
-      <DropdownMenu.Portal>
-        <DropdownMenu.Content
-          align="end"
-          sideOffset={4}
-          collisionPadding={8}
-          className="z-[200] min-w-40 overflow-hidden rounded-lg border border-border bg-popover py-1 text-popover-foreground shadow-lg"
-        >
-          <DropdownMenu.Item className={MENU_ITEM} onSelect={handleDownloadJson}>
-            JSON
-          </DropdownMenu.Item>
-          <DropdownMenu.Item className={MENU_ITEM} onSelect={handleDownloadTxt}>
-            TXT
-          </DropdownMenu.Item>
-          <DropdownMenu.Item className={MENU_ITEM} onSelect={handleDownloadCsv}>
-            CSV
-          </DropdownMenu.Item>
-        </DropdownMenu.Content>
-      </DropdownMenu.Portal>
-    </DropdownMenu.Root>
+          <div className="overflow-hidden rounded-lg border border-border bg-popover py-1 shadow-lg">
+            <button
+              type="button"
+              role="menuitem"
+              className={MENU_ITEM}
+              onClick={handleDownloadJson}
+            >
+              JSON
+            </button>
+            <button
+              type="button"
+              role="menuitem"
+              className={MENU_ITEM}
+              onClick={handleDownloadTxt}
+            >
+              TXT
+            </button>
+            <button
+              type="button"
+              role="menuitem"
+              className={MENU_ITEM}
+              onClick={handleDownloadCsv}
+            >
+              CSV
+            </button>
+          </div>
+        </div>
+      )}
+    </div>
  );
 }
@@ -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;
-}
@@ -0,0 +1,35 @@
+# Causal WanTrack Control
+
+This standalone prototype prepares one image and continuously generates causal
+WanTrack blocks. Handle updates received during block N are committed only at
+the next block boundary, so generated frames and control history are immutable.
+One GPU session may generate at a time. The SF checkpoint uses its fixed
+DMD four-step schedule (`method.dmd_denoising_steps`, default
+`[1000, 750, 500, 250]` with warp) without classifier-free guidance; the
+server does not accept client overrides for steps or guidance.
+
+Set a Diffusers-format causal WanTrack export and its Self-Forcing training
+YAML, then launch:
+
+```bash
+export WANTRACK_MODEL_DIR=/path/to/wantrack-causal-export
+export WANTRACK_YAML_PATH=/path/to/sf/config/run.yaml
+export WANTRACK_TAEHV_CHECKPOINT=/path/to/taew2_1.pth
+python -m apps.wantrack_control
+```
+
+Open `http://127.0.0.1:8010`. FFmpeg with `libx264` is required. Completed
+blocks are preserved under `WANTRACK_OUTPUT_DIR` (or the system temporary
+directory) and concatenated into a downloadable MP4 on Stop, disconnect, or a
+recoverable failure.
+
+The interactive preview uses the official TAEHV `StreamingTAEHV` decoder with
+the Wan 2.1 `taew2_1.pth` weights. The full Wan VAE remains loaded only because
+input preparation still needs its encoder.
+
+The `/ws` endpoint accepts `prepare`, `start`, `control_update`, and `stop`
+JSON messages. It emits phase-specific `progress`, `prepared`,
+`session_started`, `block_started`, `block_encoding`, `control_applied`,
+`media_init`, `media_segment_complete`, `stream_complete`, and terminal
+`error` events. Binary frames carry one fMP4 initialization section followed
+by ordered block fragments.
@@ -0,0 +1,5 @@
+"""Standalone causal WanTrack control prototype."""
+
+from apps.wantrack_control.server import create_app
+
+__all__ = ["create_app"]
@@ -0,0 +1,18 @@
+"""Launch the standalone WanTrack control server."""
+
+import os
+
+import uvicorn
+
+
+def main() -> None:
+    uvicorn.run(
+        "apps.wantrack_control.server:app",
+        host=os.getenv("WANTRACK_HOST", "127.0.0.1"),
+        port=int(os.getenv("WANTRACK_PORT", "8010")),
+        reload=False,
+    )
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,236 @@
+"""Video-only fragmented-MP4 encoding and completed-prefix finalization."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import os
+from pathlib import Path
+import shutil
+import subprocess
+from collections.abc import Iterable
+import uuid
+
+import numpy as np
+
+MEDIA_MIME = 'video/mp4; codecs="avc1.42E01E"'
+
+
+@dataclass(frozen=True, slots=True)
+class EncodedMediaSegment:
+    block_index: int
+    init_bytes: bytes
+    media_bytes: bytes
+    path: Path
+    mime: str = MEDIA_MIME
+
+
+def split_fmp4(data: bytes) -> tuple[bytes, bytes]:
+    """Split top-level fMP4 initialization boxes from moof/mdat media."""
+    offset = 0
+    first_fragment: int | None = None
+    while offset + 8 <= len(data):
+        size = int.from_bytes(data[offset:offset + 4], "big")
+        box_type = data[offset + 4:offset + 8]
+        header = 8
+        if size == 1:
+            if offset + 16 > len(data):
+                break
+            size = int.from_bytes(data[offset + 8:offset + 16], "big")
+            header = 16
+        elif size == 0:
+            size = len(data) - offset
+        if size < header or offset + size > len(data):
+            raise ValueError("invalid top-level MP4 box")
+        if box_type == b"moof":
+            first_fragment = offset
+            break
+        offset += size
+    if first_fragment is None:
+        raise ValueError("ffmpeg output did not contain an fMP4 moof box")
+    init_bytes = data[:first_fragment]
+    media_bytes = data[first_fragment:]
+    if b"ftyp" not in init_bytes or b"moov" not in init_bytes:
+        raise ValueError("ffmpeg output is missing fMP4 initialization boxes")
+    return init_bytes, media_bytes
+
+
+class FMP4BlockWriter:
+
+    def __init__(
+        self,
+        output_root: str | os.PathLike[str],
+        *,
+        fps: float,
+        ffmpeg_bin: str | None = None,
+    ) -> None:
+        self.fps = float(fps)
+        if self.fps <= 0:
+            raise ValueError("fps must be positive")
+        resolved_ffmpeg = ffmpeg_bin or shutil.which(os.getenv("WANTRACK_FFMPEG_BIN", "ffmpeg"))
+        if not resolved_ffmpeg:
+            raise RuntimeError("ffmpeg is required for WanTrack streaming")
+        self.ffmpeg_bin = resolved_ffmpeg
+        root = Path(output_root)
+        root.mkdir(parents=True, exist_ok=True)
+        self.session_id = uuid.uuid4().hex
+        self.session_dir = root / self.session_id
+        self.session_dir.mkdir()
+        self._block_paths: list[Path] = []
+
+    @property
+    def block_paths(self) -> tuple[Path, ...]:
+        return tuple(self._block_paths)
+
+    def encode_block(
+        self,
+        frames: np.ndarray,
+        block_index: int,
+    ) -> EncodedMediaSegment:
+        frames = np.asarray(frames)
+        if frames.ndim != 4 or frames.shape[-1] != 3:
+            raise ValueError("frames must have shape [T, H, W, 3]")
+        if frames.shape[0] <= 0:
+            raise ValueError("cannot encode an empty frame block")
+        frames = np.ascontiguousarray(frames, dtype=np.uint8)
+        height, width = int(frames.shape[1]), int(frames.shape[2])
+        gop = max(1, int(frames.shape[0]))
+        command = [
+            self.ffmpeg_bin,
+            "-hide_banner",
+            "-loglevel",
+            "error",
+            "-y",
+            "-f",
+            "rawvideo",
+            "-pix_fmt",
+            "rgb24",
+            "-s:v",
+            f"{width}x{height}",
+            "-r",
+            f"{self.fps:g}",
+            "-i",
+            "pipe:0",
+            "-an",
+            "-c:v",
+            "libx264",
+            "-preset",
+            "ultrafast",
+            "-tune",
+            "zerolatency",
+            "-profile:v",
+            "baseline",
+            "-pix_fmt",
+            "yuv420p",
+            "-g",
+            str(gop),
+            "-keyint_min",
+            str(gop),
+            "-sc_threshold",
+            "0",
+            "-movflags",
+            "+empty_moov+default_base_moof+frag_keyframe",
+            "-frag_duration",
+            str(max(1, round(1_000_000 * frames.shape[0] / self.fps))),
+            "-f",
+            "mp4",
+            "pipe:1",
+        ]
+        result = subprocess.run(
+            command,
+            input=frames.tobytes(),
+            capture_output=True,
+            check=False,
+        )
+        if result.returncode != 0 or not result.stdout:
+            stderr = result.stderr.decode("utf-8", errors="replace").strip()
+            raise RuntimeError(f"ffmpeg failed to encode WanTrack block {block_index}: "
+                               f"{stderr or f'exit {result.returncode}'}")
+        init_bytes, media_bytes = split_fmp4(result.stdout)
+        path = self.session_dir / f"block_{int(block_index):06d}.mp4"
+        path.write_bytes(result.stdout)
+        self._block_paths.append(path)
+        return EncodedMediaSegment(
+            block_index=int(block_index),
+            init_bytes=init_bytes,
+            media_bytes=media_bytes,
+            path=path,
+        )
+
+    def finalize(self) -> Path | None:
+        if not self._block_paths:
+            return None
+        output_path = self.session_dir / "wantrack_control.mp4"
+        concat_path = self.session_dir / "concat.txt"
+        concat_path.write_text(
+            "".join(f"file '{self._concat_escape(path)}'\n" for path in self._block_paths),
+            encoding="utf-8",
+        )
+        command = [
+            self.ffmpeg_bin,
+            "-hide_banner",
+            "-loglevel",
+            "error",
+            "-y",
+            "-f",
+            "concat",
+            "-safe",
+            "0",
+            "-i",
+            str(concat_path),
+            "-an",
+            "-c",
+            "copy",
+            "-movflags",
+            "+faststart",
+            str(output_path),
+        ]
+        result = subprocess.run(
+            command,
+            capture_output=True,
+            check=False,
+        )
+        if result.returncode != 0 or not output_path.is_file():
+            return self._finalize_reencode(output_path)
+        return output_path if output_path.stat().st_size > 0 else None
+
+    @staticmethod
+    def _concat_escape(path: Path) -> str:
+        return str(path.resolve()).replace("'", "'\\''")
+
+    def _finalize_reencode(self, output_path: Path) -> Path | None:
+        concat_path = self.session_dir / "concat.txt"
+        command = [
+            self.ffmpeg_bin,
+            "-hide_banner",
+            "-loglevel",
+            "error",
+            "-y",
+            "-f",
+            "concat",
+            "-safe",
+            "0",
+            "-i",
+            str(concat_path),
+            "-an",
+            "-c:v",
+            "libx264",
+            "-preset",
+            "fast",
+            "-pix_fmt",
+            "yuv420p",
+            "-movflags",
+            "+faststart",
+            str(output_path),
+        ]
+        result = subprocess.run(
+            command,
+            capture_output=True,
+            check=False,
+        )
+        if (result.returncode == 0 and output_path.is_file() and output_path.stat().st_size > 0):
+            return output_path
+        return None
+
+
+def total_size(paths: Iterable[Path]) -> int:
+    return sum(path.stat().st_size for path in paths if path.is_file())
@@ -0,0 +1,422 @@
+"""FastAPI/WebSocket server for causal WanTrack control."""
+
+from __future__ import annotations
+
+import asyncio
+import base64
+from contextlib import suppress
+import io
+import os
+from pathlib import Path
+import tempfile
+import time
+from typing import Any
+
+from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+from PIL import Image
+
+from apps.wantrack_control.media import FMP4BlockWriter
+
+_STATIC_DIR = Path(__file__).resolve().parent / "static"
+
+
+def _decode_image(value: str) -> bytes:
+    if not isinstance(value, str) or not value.strip():
+        raise ValueError("prepare.image must be a non-empty base64 string")
+    encoded = value.split(",", 1)[1] if value.startswith("data:") else value
+    try:
+        data = base64.b64decode(encoded, validate=True)
+    except Exception as exc:
+        raise ValueError("prepare.image is not valid base64") from exc
+    if not data:
+        raise ValueError("prepare.image decoded to no bytes")
+    return data
+
+
+def _encode_image(image: Image.Image) -> str:
+    output = io.BytesIO()
+    image.save(output, format="PNG")
+    encoded = base64.b64encode(output.getvalue()).decode("ascii")
+    return f"data:image/png;base64,{encoded}"
+
+
+def _block_value(block: Any, key: str, default: Any = None) -> Any:
+    if isinstance(block, dict):
+        return block.get(key, default)
+    return getattr(block, key, default)
+
+
+class _RuntimeProvider:
+
+    def __init__(self, runtime: Any | None) -> None:
+        self._runtime = runtime
+        self._lock = asyncio.Lock()
+
+    async def get(self) -> Any:
+        if self._runtime is not None:
+            return self._runtime
+        async with self._lock:
+            if self._runtime is None:
+                model_dir = os.getenv("WANTRACK_MODEL_DIR", "").strip()
+                yaml_path = os.getenv("WANTRACK_YAML_PATH", "").strip()
+                taehv_checkpoint = os.getenv("WANTRACK_TAEHV_CHECKPOINT", "").strip()
+                if not model_dir or not yaml_path or not taehv_checkpoint:
+                    raise RuntimeError("Set WANTRACK_MODEL_DIR, WANTRACK_YAML_PATH, and "
+                                       "WANTRACK_TAEHV_CHECKPOINT before preparing a session")
+                from fastvideo.train.models.wantrack.runtime import (
+                    WanTrackInferenceRuntime, )
+
+                self._runtime = await asyncio.to_thread(
+                    WanTrackInferenceRuntime.from_export,
+                    model_dir,
+                    yaml_path,
+                    taehv_checkpoint,
+                )
+        return self._runtime
+
+
+def create_app(
+    runtime: Any | None = None,
+    *,
+    output_dir: str | os.PathLike[str] | None = None,
+    writer_factory: Any = FMP4BlockWriter,
+) -> FastAPI:
+    app = FastAPI(title="Causal WanTrack Control")
+    app.state.runtime_provider = _RuntimeProvider(runtime)
+    app.state.active_generation = asyncio.Lock()
+    app.state.downloads = {}
+    if output_dir is None:
+        resolved_output_dir: str | os.PathLike[str] = os.getenv(
+            "WANTRACK_OUTPUT_DIR",
+            str(Path(tempfile.gettempdir()) / "wantrack_control"),
+        )
+    else:
+        resolved_output_dir = output_dir
+    app.state.output_dir = Path(resolved_output_dir)
+    app.state.writer_factory = writer_factory
+
+    app.mount("/static", StaticFiles(directory=_STATIC_DIR), name="static")
+
+    @app.get("/")
+    async def index() -> FileResponse:
+        return FileResponse(_STATIC_DIR / "index.html")
+
+    @app.get("/healthz")
+    async def healthz() -> dict[str, Any]:
+        return {
+            "status": "ok",
+            "active": app.state.active_generation.locked(),
+        }
+
+    @app.get("/downloads/{download_id}")
+    async def download(download_id: str) -> FileResponse:
+        path = app.state.downloads.get(download_id)
+        if path is None or not path.is_file():
+            raise HTTPException(status_code=404, detail="download not found")
+        return FileResponse(
+            path,
+            media_type="video/mp4",
+            filename="wantrack_control.mp4",
+        )
+
+    @app.websocket("/ws")
+    async def websocket_endpoint(websocket: WebSocket) -> None:
+        await websocket.accept()
+        send_lock = asyncio.Lock()
+        prepared: Any | None = None
+        session: Any | None = None
+        generation_task: asyncio.Task[None] | None = None
+        stop_event = asyncio.Event()
+        owns_generation_lock = False
+        connected = True
+
+        async def send_json(payload: dict[str, Any]) -> None:
+            nonlocal connected
+            if not connected:
+                return
+            async with send_lock:
+                try:
+                    await websocket.send_json(payload)
+                except Exception:
+                    connected = False
+                    raise
+
+        async def send_bytes(payload: bytes) -> None:
+            nonlocal connected
+            if not connected:
+                return
+            async with send_lock:
+                try:
+                    await websocket.send_bytes(payload)
+                except Exception:
+                    connected = False
+                    raise
+
+        async def run_generation(runtime_value: Any) -> None:
+            nonlocal owns_generation_lock, connected, generation_task
+            writer = app.state.writer_factory(
+                app.state.output_dir,
+                fps=float(getattr(runtime_value, "fps", 16.0)),
+            )
+            init_sent = False
+            last_applied_revision = 0
+            terminal_error: str | None = None
+            try:
+                while not stop_event.is_set():
+                    block_index = int(getattr(session, "block_index", 0))
+                    block_started_at = time.perf_counter()
+                    await send_json({
+                        "type": "block_started",
+                        "block_index": block_index,
+                        "num_inference_steps": len(
+                            getattr(runtime_value, "dmd_denoising_steps", [1000, 750, 500, 250])),
+                        "dmd_denoising_steps": list(
+                            getattr(runtime_value, "dmd_denoising_steps", [1000, 750, 500, 250])),
+                        "cfg_enabled": False,
+                    })
+                    block = await asyncio.to_thread(session.generate_next_block)
+                    generated_at = time.perf_counter()
+                    applied_revision = int(_block_value(block, "applied_revision", 0))
+                    if applied_revision > last_applied_revision:
+                        last_applied_revision = applied_revision
+                        await send_json({
+                            "type": "control_applied",
+                            "revision": applied_revision,
+                            "block_index": int(_block_value(block, "block_index", block_index)),
+                            "radius": float(_block_value(block, "radius", 0.0)),
+                            "active_handle_ids": list(_block_value(block, "active_handle_ids", ())),
+                        })
+                    frames = _block_value(block, "pixel_frames")
+                    await send_json({
+                        "type": "block_encoding",
+                        "block_index": block_index,
+                    })
+                    encoded = await asyncio.to_thread(
+                        writer.encode_block,
+                        frames,
+                        int(_block_value(block, "block_index", block_index)),
+                    )
+                    encoded_at = time.perf_counter()
+                    if not init_sent:
+                        await send_json({
+                            "type": "media_init",
+                            "mime": encoded.mime,
+                        })
+                        await send_bytes(encoded.init_bytes)
+                        init_sent = True
+                    await send_bytes(encoded.media_bytes)
+                    await send_json({
+                        "type": "media_segment_complete",
+                        "block_index": encoded.block_index,
+                        "bytes": len(encoded.media_bytes),
+                        "generation_ms": round((generated_at - block_started_at) * 1000),
+                        "encoding_ms": round((encoded_at - generated_at) * 1000),
+                    })
+            except Exception as exc:
+                terminal_error = str(exc) or type(exc).__name__
+            finally:
+                if session is not None:
+                    with suppress(Exception):
+                        await asyncio.to_thread(
+                            session.close,
+                            "error" if terminal_error else ("disconnect" if not connected else "stop"),
+                        )
+                final_path = await asyncio.to_thread(writer.finalize)
+                download_url = None
+                if final_path is not None:
+                    app.state.downloads[writer.session_id] = final_path
+                    download_url = f"/downloads/{writer.session_id}"
+                if owns_generation_lock:
+                    app.state.active_generation.release()
+                    owns_generation_lock = False
+                if terminal_error:
+                    if connected:
+                        with suppress(Exception):
+                            await send_json({
+                                "type": "error",
+                                "message": terminal_error,
+                                "download_url": download_url,
+                            })
+                elif connected:
+                    with suppress(Exception):
+                        await send_json({
+                            "type": "stream_complete",
+                            "blocks": len(writer.block_paths),
+                            "download_url": download_url,
+                        })
+                generation_task = None
+
+        async def handle_message(message: dict[str, Any]) -> None:
+            nonlocal prepared, session, generation_task
+            nonlocal owns_generation_lock
+            message_type = str(message.get("type", "")).strip()
+            if message_type == "prepare":
+                if generation_task is not None:
+                    raise ValueError("prepare is unavailable during generation")
+                prepare_started_at = time.perf_counter()
+                await send_json({
+                    "type": "progress",
+                    "phase": "loading_model",
+                    "message": "Loading Track-v0",
+                    "detail": "The first request loads the SF checkpoint onto the GPU.",
+                })
+                runtime_value = await app.state.runtime_provider.get()
+                await send_json({
+                    "type": "progress",
+                    "phase": "preparing_input",
+                    "message": "Preparing image and prompt",
+                    "detail": "Encoding text, the reference image, and its first-frame latent.",
+                })
+                image_bytes = _decode_image(message.get("image", ""))
+                prompt = str(message.get("prompt", "") or "")
+                prepared = await asyncio.to_thread(
+                    runtime_value.prepare,
+                    image_bytes,
+                    prompt,
+                )
+                processed_image = getattr(prepared, "image", None)
+                if not isinstance(processed_image, Image.Image):
+                    processed_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+                await send_json({
+                    "type": "prepared",
+                    "image": _encode_image(processed_image),
+                    "width": processed_image.width,
+                    "height": processed_image.height,
+                    "fps": float(getattr(runtime_value, "fps", 16.0)),
+                    "chunk_size": int(getattr(runtime_value, "chunk_size", 3)),
+                    "causal_recipe": getattr(runtime_value, "causal_recipe", {}),
+                    "decoder": str(getattr(runtime_value, "decoder_name", "unknown")),
+                    "prepare_ms": round((time.perf_counter() - prepare_started_at) * 1000),
+                })
+                return
+
+            if message_type == "start":
+                if prepared is None:
+                    raise ValueError("prepare must complete before start")
+                if generation_task is not None:
+                    raise ValueError("session is already generating")
+                handles = message.get("handles")
+                if not isinstance(handles, list) or not handles:
+                    raise ValueError("start requires at least one handle")
+                if app.state.active_generation.locked():
+                    raise RuntimeError("another WanTrack session is already generating")
+                start_started_at = time.perf_counter()
+                await send_json({
+                    "type": "progress",
+                    "phase": "starting_session",
+                    "message": "Starting causal session",
+                    "detail": "Initializing controls and the causal KV cache.",
+                })
+                await app.state.active_generation.acquire()
+                owns_generation_lock = True
+                runtime_value = await app.state.runtime_provider.get()
+                session = runtime_value.create_session()
+                dmd_steps = list(
+                    getattr(runtime_value, "dmd_denoising_steps", [1000, 750, 500, 250]))
+                sampling = {
+                    "seed": int(message.get("seed", 0)),
+                    "num_inference_steps": len(dmd_steps),
+                    "text_guidance_scale": 1.0,
+                    "motion_guidance_scale": 1.0,
+                    "motion_cfg": False,
+                }
+                try:
+                    await asyncio.to_thread(
+                        session.start,
+                        prepared,
+                        getattr(prepared, "prompt", str(message.get("prompt", "") or "")),
+                        handles,
+                        sampling,
+                        radius=float(message.get("radius", 0.15)),
+                    )
+                except Exception:
+                    app.state.active_generation.release()
+                    owns_generation_lock = False
+                    raise
+                await send_json({
+                    "type": "session_started",
+                    "fps": float(getattr(runtime_value, "fps", 16.0)),
+                    "chunk_size": int(getattr(runtime_value, "chunk_size", 3)),
+                    "num_inference_steps": len(dmd_steps),
+                    "dmd_denoising_steps": dmd_steps,
+                    "cfg_enabled": False,
+                    "causal_recipe": getattr(runtime_value, "causal_recipe", {}),
+                    "decoder": str(getattr(runtime_value, "decoder_name", "unknown")),
+                    "start_ms": round((time.perf_counter() - start_started_at) * 1000),
+                })
+                stop_event.clear()
+                generation_task = asyncio.create_task(run_generation(runtime_value))
+                return
+
+            if message_type == "control_update":
+                if session is None:
+                    raise ValueError("control_update requires a running session")
+                revision = int(message.get("revision", 0))
+                accepted = await asyncio.to_thread(
+                    session.apply_control_revision,
+                    revision,
+                    samples=message.get("samples"),
+                    add=message.get("add"),
+                    remove=message.get("remove"),
+                    handles=message.get("handles"),
+                    radius=message.get("radius"),
+                )
+                if not accepted:
+                    await send_json({
+                        "type": "control_applied",
+                        "revision": revision,
+                        "status": "ignored_stale",
+                    })
+                return
+
+            if message_type == "stop":
+                if generation_task is None:
+                    raise ValueError("stop requires a running session")
+                stop_event.set()
+                return
+            raise ValueError(f"unknown client message type: {message_type!r}")
+
+        try:
+            while True:
+                receive_task = asyncio.create_task(websocket.receive_json())
+                waiters: set[asyncio.Task[Any]] = {receive_task}
+                if generation_task is not None:
+                    waiters.add(generation_task)
+                done, _ = await asyncio.wait(
+                    waiters,
+                    return_when=asyncio.FIRST_COMPLETED,
+                )
+                if generation_task is not None and generation_task in done:
+                    receive_task.cancel()
+                    with suppress(asyncio.CancelledError):
+                        await receive_task
+                    await generation_task
+                    return
+                message = await receive_task
+                try:
+                    await handle_message(message)
+                except Exception as exc:
+                    await send_json({
+                        "type": "error",
+                        "message": str(exc) or type(exc).__name__,
+                    })
+                    if generation_task is not None:
+                        stop_event.set()
+        except WebSocketDisconnect:
+            connected = False
+        except Exception:
+            connected = False
+        finally:
+            stop_event.set()
+            if generation_task is not None:
+                with suppress(Exception):
+                    await generation_task
+            elif owns_generation_lock:
+                app.state.active_generation.release()
+
+    return app
+
+
+app = create_app()
@@ -0,0 +1,308 @@
+(() => {
+  const $ = (id) => document.getElementById(id);
+  const imageInput = $("image");
+  const promptInput = $("prompt");
+  const prepareButton = $("prepare");
+  const canvas = $("canvas");
+  const context = canvas.getContext("2d");
+  const addButton = $("add");
+  const removeButton = $("remove");
+  const gridInput = $("grid");
+  const radiusInput = $("radius");
+  const radiusOutput = document.querySelector(".radius output");
+  const startButton = $("start");
+  const stopButton = $("stop");
+  const video = $("video");
+  const status = $("status");
+  const statusLabel = $("status-label");
+  const statusDetail = $("status-detail");
+  const download = $("download");
+
+  let socket;
+  let preparedImage;
+  let handles = [];
+  let selectedId = null;
+  let addMode = false;
+  let dragging = false;
+  let generating = false;
+  let revision = 0;
+  let sessionStart = 0;
+  let mediaSource;
+  let sourceBuffer;
+  let mediaQueue = [];
+  let streamComplete = false;
+
+  function setStatus(value, detail = "", busy = false) {
+    statusLabel.textContent = value;
+    statusDetail.textContent = detail;
+    status.dataset.busy = String(busy);
+  }
+  function seconds(milliseconds) {
+    return `${(Number(milliseconds || 0) / 1000).toFixed(1)}s`;
+  }
+  function connect() {
+    const scheme = location.protocol === "https:" ? "wss" : "ws";
+    socket = new WebSocket(`${scheme}://${location.host}/ws`);
+    socket.binaryType = "arraybuffer";
+    socket.onopen = () => setStatus("Ready", "Choose an image to begin.");
+    socket.onclose = () => setStatus("Disconnected", "Refresh after the server reconnects.");
+    socket.onerror = () => setStatus("Connection error", "The control server is unreachable.");
+    socket.onmessage = async (event) => {
+      if (typeof event.data !== "string") {
+        mediaQueue.push(event.data);
+        flushMedia();
+        return;
+      }
+      const message = JSON.parse(event.data);
+      if (message.type === "progress") {
+        setStatus(message.message || "Working", message.detail || "", true);
+      } else if (message.type === "prepared") {
+        preparedImage = new Image();
+        preparedImage.onload = () => {
+          canvas.width = message.width;
+          canvas.height = message.height;
+          draw();
+        };
+        preparedImage.src = message.image;
+        handles = [];
+        selectedId = null;
+        addButton.disabled = false;
+        startButton.disabled = true;
+        prepareButton.disabled = false;
+        prepareButton.textContent = "Prepare again";
+        const recipe = message.causal_recipe || {};
+        setStatus(
+          `Prepared in ${seconds(message.prepare_ms)}`,
+          `${message.fps} FPS · ${message.decoder || "unknown decoder"} · ${recipe.rope_cache_policy || "unknown"} RoPE · local ${recipe.local_attn_size ?? "?"} · sink ${recipe.sink_size ?? "?"} · Add a handle, then press Start.`,
+        );
+      } else if (message.type === "session_started") {
+        generating = true;
+        sessionStart = performance.now();
+        startButton.disabled = true;
+        stopButton.disabled = false;
+        setStatus(
+          `Session started in ${seconds(message.start_ms)}`,
+          "4-step SF · CFG off · Building the first causal block.",
+          true,
+        );
+      } else if (message.type === "block_started") {
+        setStatus(
+          `Generating block ${message.block_index}`,
+          "DMD 4-step, single conditional branch. Controls update at the next block.",
+          true,
+        );
+      } else if (message.type === "block_encoding") {
+        setStatus(
+          `Encoding block ${message.block_index}`,
+          "The model is done; packaging frames for immediate playback.",
+          true,
+        );
+      } else if (message.type === "control_applied") {
+        if (message.status === "ignored_stale") {
+          setStatus(`Stale revision ${message.revision} ignored`, "Drag again to send a newer control update.");
+        } else {
+          setStatus(`Control ${message.revision} applied`, "The updated motion is active in this block.");
+        }
+      } else if (message.type === "media_init") {
+        setupMediaSource(message.mime);
+      } else if (message.type === "media_segment_complete") {
+        setStatus(
+          `Playing through block ${message.block_index}`,
+          `Generated in ${seconds(message.generation_ms)} · encoded in ${seconds(message.encoding_ms)} · drag handles for the next block.`,
+        );
+      } else if (message.type === "stream_complete") {
+        generating = false;
+        streamComplete = true;
+        stopButton.disabled = true;
+        startButton.disabled = false;
+        if (message.download_url) {
+          download.href = message.download_url;
+          download.hidden = false;
+        }
+        flushMedia();
+        setStatus(`Complete · ${message.blocks} blocks`, "The final MP4 is ready to download.");
+      } else if (message.type === "error") {
+        generating = false;
+        prepareButton.disabled = false;
+        stopButton.disabled = true;
+        if (message.download_url) {
+          download.href = message.download_url;
+          download.hidden = false;
+        }
+        setStatus("Request failed", message.message || "Unknown error");
+      }
+    };
+  }
+
+  function setupMediaSource(mime) {
+    mediaQueue = [];
+    streamComplete = false;
+    mediaSource = new MediaSource();
+    video.src = URL.createObjectURL(mediaSource);
+    mediaSource.addEventListener("sourceopen", () => {
+      sourceBuffer = mediaSource.addSourceBuffer(mime);
+      sourceBuffer.mode = "sequence";
+      sourceBuffer.addEventListener("updateend", flushMedia);
+      flushMedia();
+    }, { once: true });
+  }
+
+  function flushMedia() {
+    if (!sourceBuffer || sourceBuffer.updating) return;
+    if (mediaQueue.length) {
+      sourceBuffer.appendBuffer(mediaQueue.shift());
+      return;
+    }
+    if (streamComplete && mediaSource && mediaSource.readyState === "open") {
+      mediaSource.endOfStream();
+    }
+    video.play().catch(() => {});
+  }
+
+  function draw() {
+    context.clearRect(0, 0, canvas.width, canvas.height);
+    if (preparedImage) context.drawImage(preparedImage, 0, 0, canvas.width, canvas.height);
+    if (gridInput.checked) {
+      context.strokeStyle = "rgba(255,255,255,.12)";
+      context.lineWidth = 1;
+      for (let index = 0; index < 50; index += 1) {
+        const x = index * canvas.width / 49;
+        const y = index * canvas.height / 49;
+        context.beginPath(); context.moveTo(x, 0); context.lineTo(x, canvas.height); context.stroke();
+        context.beginPath(); context.moveTo(0, y); context.lineTo(canvas.width, y); context.stroke();
+      }
+    }
+    for (const handle of handles) {
+      const x = handle.x * canvas.width;
+      const y = handle.y * canvas.height;
+      context.beginPath();
+      context.arc(x, y, handle.id === selectedId ? 9 : 7, 0, Math.PI * 2);
+      context.fillStyle = handle.id === selectedId ? "#ffcf4a" : "#58c7ff";
+      context.fill();
+      context.strokeStyle = "#111";
+      context.lineWidth = 2;
+      context.stroke();
+    }
+    removeButton.disabled = !selectedId;
+    startButton.disabled = !preparedImage || handles.length === 0 || generating;
+  }
+
+  function canvasPoint(event) {
+    const rect = canvas.getBoundingClientRect();
+    return {
+      x: Math.min(1, Math.max(0, (event.clientX - rect.left) / rect.width)),
+      y: Math.min(1, Math.max(0, (event.clientY - rect.top) / rect.height)),
+    };
+  }
+  function nearest(point) {
+    let match = null;
+    let distance = Infinity;
+    for (const handle of handles) {
+      const value = Math.hypot(handle.x - point.x, handle.y - point.y);
+      if (value < distance && value < 18 / canvas.clientWidth) {
+        match = handle;
+        distance = value;
+      }
+    }
+    return match;
+  }
+  function sendControl(extra = {}) {
+    if (!generating) return;
+    revision += 1;
+    socket.send(JSON.stringify({ type: "control_update", revision, ...extra }));
+  }
+
+  canvas.addEventListener("pointerdown", (event) => {
+    if (!preparedImage) return;
+    const point = canvasPoint(event);
+    if (addMode) {
+      const handle = { id: crypto.randomUUID(), ...point };
+      handles.push(handle);
+      selectedId = handle.id;
+      addMode = false;
+      addButton.textContent = "Add handle";
+      sendControl({ add: [handle] });
+      draw();
+      return;
+    }
+    const handle = nearest(point);
+    selectedId = handle ? handle.id : null;
+    dragging = Boolean(handle);
+    if (dragging) canvas.setPointerCapture(event.pointerId);
+    draw();
+  });
+  canvas.addEventListener("pointermove", (event) => {
+    if (!dragging || !selectedId) return;
+    const point = canvasPoint(event);
+    const handle = handles.find((item) => item.id === selectedId);
+    if (!handle) return;
+    handle.x = point.x; handle.y = point.y;
+    sendControl({ samples: [{ id: handle.id, ...point, timestamp_ms: performance.now() - sessionStart }] });
+    draw();
+  });
+  canvas.addEventListener("pointerup", () => { dragging = false; });
+  addButton.addEventListener("click", () => {
+    addMode = !addMode;
+    addButton.textContent = addMode ? "Click canvas" : "Add handle";
+  });
+  removeButton.addEventListener("click", () => {
+    if (!selectedId) return;
+    const removed = selectedId;
+    handles = handles.filter((item) => item.id !== removed);
+    selectedId = null;
+    sendControl({ remove: [removed] });
+    draw();
+  });
+  gridInput.addEventListener("change", draw);
+  radiusInput.addEventListener("input", () => {
+    radiusOutput.textContent = Number(radiusInput.value).toFixed(2);
+    sendControl({ radius: Number(radiusInput.value) });
+  });
+
+  prepareButton.addEventListener("click", async () => {
+    const file = imageInput.files[0];
+    if (!file) {
+      setStatus("Choose an image", "Prepare needs a reference frame.");
+      return;
+    }
+    prepareButton.disabled = true;
+    prepareButton.textContent = "Preparing…";
+    setStatus("Reading image", "Using the browser's native file reader.", true);
+    try {
+      const image = await new Promise((resolve, reject) => {
+        const reader = new FileReader();
+        reader.onload = () => resolve(reader.result);
+        reader.onerror = () => reject(reader.error || new Error("Failed to read image"));
+        reader.readAsDataURL(file);
+      });
+      setStatus("Sending image", "The server will encode the prompt and reference frame next.", true);
+      socket.send(JSON.stringify({
+        type: "prepare",
+        image,
+        prompt: promptInput.value,
+      }));
+    } catch (error) {
+      prepareButton.disabled = false;
+      prepareButton.textContent = "Prepare";
+      setStatus("Could not read image", error.message || String(error));
+    }
+  });
+  startButton.addEventListener("click", () => {
+    revision = 0;
+    download.hidden = true;
+    startButton.disabled = true;
+    setStatus("Sending controls", "Starting the fixed DMD 4-step, CFG-free SF sampler.", true);
+    socket.send(JSON.stringify({
+      type: "start",
+      handles,
+      radius: Number(radiusInput.value),
+      seed: Number($("seed").value),
+    }));
+  });
+  stopButton.addEventListener("click", () => {
+    socket.send(JSON.stringify({ type: "stop" }));
+    stopButton.disabled = true;
+    setStatus("Finishing current block");
+  });
+  connect();
+})();
@@ -0,0 +1,56 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width,initial-scale=1">
+  <title>WanTrack Control</title>
+  <link rel="stylesheet" href="/static/styles.css">
+</head>
+<body>
+  <main>
+    <header>
+      <div>
+        <h1>WanTrack Control</h1>
+        <p>Drag points to steer the next causal block.</p>
+      </div>
+      <div id="status" role="status" aria-live="polite">
+        <span id="status-label">Disconnected</span>
+        <small id="status-detail">Waiting for the server connection.</small>
+      </div>
+    </header>
+
+    <section class="controls">
+      <label class="file">Image <input id="image" type="file" accept="image/*"></label>
+      <label class="prompt">Prompt <input id="prompt" type="text" placeholder="Optional scene description"></label>
+      <button id="prepare">Prepare</button>
+    </section>
+
+    <section class="workspace">
+      <div class="canvas-panel">
+        <div class="canvas-toolbar">
+          <button id="add" disabled>Add handle</button>
+          <button id="remove" disabled>Delete selected</button>
+          <label><input id="grid" type="checkbox"> Grid</label>
+        </div>
+        <canvas id="canvas" width="832" height="480"></canvas>
+        <label class="radius">Radius <input id="radius" type="range" min="0.03" max="0.45" step="0.01" value="0.15"><output>0.15</output></label>
+      </div>
+      <div class="video-panel">
+        <video id="video" muted autoplay playsinline controls></video>
+        <a id="download" hidden>Download completed MP4</a>
+      </div>
+    </section>
+
+    <section class="sampling">
+      <label>Seed <input id="seed" type="number" value="0" step="1"></label>
+      <div class="recipe">
+        <strong>SF checkpoint</strong>
+        <span>DMD [1000,750,500,250] · CFG off · TAEHV · relativistic RoPE · local 6 · sink 1</span>
+      </div>
+      <button id="start" disabled>Start</button>
+      <button id="stop" disabled>Stop</button>
+    </section>
+  </main>
+  <script src="/static/app.js"></script>
+</body>
+</html>
@@ -0,0 +1,57 @@
+:root {
+  color-scheme: dark;
+  font-family: Inter, ui-sans-serif, system-ui, sans-serif;
+  background: #111315;
+  color: #edf0f2;
+}
+* { box-sizing: border-box; }
+body { margin: 0; }
+main { width: min(1180px, calc(100% - 32px)); margin: 24px auto; }
+header { display: flex; align-items: start; justify-content: space-between; gap: 16px; }
+h1 { margin: 0; font-size: 22px; }
+p { margin: 6px 0 20px; color: #929ba3; }
+#status {
+  display: grid; grid-template-columns: auto 1fr; gap: 2px 9px; min-width: 300px;
+  padding: 9px 12px; border-radius: 10px; background: #23282d; color: #dce2e7;
+}
+#status::before {
+  content: ""; grid-row: 1 / 3; align-self: center; width: 8px; height: 8px;
+  border-radius: 50%; background: #62d58b;
+}
+#status[data-busy="true"]::before {
+  width: 12px; height: 12px; border: 2px solid #65717a; border-top-color: #edf0f2;
+  background: transparent; animation: spin .8s linear infinite;
+}
+#status-label { font-size: 12px; font-weight: 700; }
+#status-detail { color: #9ea8b0; font-size: 11px; }
+@keyframes spin { to { transform: rotate(360deg); } }
+.controls, .sampling, .canvas-toolbar { display: flex; align-items: end; gap: 10px; flex-wrap: wrap; }
+label { color: #aeb6bd; font-size: 12px; }
+input[type="text"], input[type="number"], input[type="file"] {
+  display: block; margin-top: 5px; min-height: 36px; border: 1px solid #353b41;
+  border-radius: 7px; background: #1a1e22; color: inherit; padding: 7px 9px;
+}
+.prompt { flex: 1; }
+.prompt input { width: 100%; }
+button, #download {
+  border: 0; border-radius: 7px; background: #e7ebee; color: #111315;
+  min-height: 36px; padding: 8px 13px; font-weight: 650; cursor: pointer;
+}
+button:disabled { opacity: .38; cursor: default; }
+.workspace { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; margin: 16px 0; }
+.canvas-panel, .video-panel { min-width: 0; border: 1px solid #2e3439; background: #181c20; border-radius: 10px; padding: 10px; }
+.canvas-toolbar { margin-bottom: 8px; }
+canvas, video { display: block; width: 100%; aspect-ratio: 832 / 480; object-fit: contain; background: #090a0b; border-radius: 6px; }
+canvas { touch-action: none; cursor: crosshair; }
+.radius { display: flex; align-items: center; gap: 9px; margin-top: 10px; }
+.radius input { flex: 1; }
+#download { display: inline-block; margin-top: 10px; text-decoration: none; }
+#download[hidden] { display: none; }
+.sampling input { width: 92px; }
+.recipe {
+  display: grid; gap: 2px; min-height: 36px; padding: 6px 10px;
+  border: 1px solid #353b41; border-radius: 7px; background: #1a1e22;
+}
+.recipe strong { color: #edf0f2; font-size: 12px; }
+.recipe span { color: #8f99a1; font-size: 11px; }
+@media (max-width: 800px) { .workspace { grid-template-columns: 1fr; } }
@@ -0,0 +1,267 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+import threading
+
+import numpy as np
+from PIL import Image
+from fastapi.testclient import TestClient
+
+from apps.wantrack_control.media import EncodedMediaSegment
+from apps.wantrack_control.server import create_app
+
+
+@dataclass
+class _Prepared:
+    image: Image.Image
+    prompt: str
+
+
+@dataclass
+class _Block:
+    block_index: int
+    pixel_frames: np.ndarray
+    applied_revision: int
+    radius: float = 0.15
+    active_handle_ids: tuple[str, ...] = ("h", )
+
+
+class _FakeSession:
+    def __init__(
+        self,
+        gates: list[threading.Event],
+        *,
+        fail_at: int | None = None,
+    ) -> None:
+        self.gates = gates
+        self.fail_at = fail_at
+        self.block_index = 0
+        self.pending_revision = 0
+        self.closed_reason = None
+
+    def start(self, image, prompt, handles, sampling, *, radius):
+        assert image.prompt == prompt
+        assert handles
+        assert sampling == {
+            "seed": 0,
+            "num_inference_steps": 4,
+            "text_guidance_scale": 1.0,
+            "motion_guidance_scale": 1.0,
+            "motion_cfg": False,
+        }
+        assert radius > 0
+
+    def apply_control_revision(self, revision, **kwargs):
+        del kwargs
+        if revision <= self.pending_revision:
+            return False
+        self.pending_revision = revision
+        return True
+
+    def generate_next_block(self):
+        index = self.block_index
+        applied = self.pending_revision
+        if index < len(self.gates):
+            assert self.gates[index].wait(timeout=5)
+        if self.fail_at == index:
+            raise RuntimeError("fake generation failure")
+        self.block_index += 1
+        return _Block(
+            block_index=index,
+            pixel_frames=np.zeros((2, 8, 8, 3), dtype=np.uint8),
+            applied_revision=applied,
+        )
+
+    def close(self, reason):
+        self.closed_reason = reason
+
+
+class _FakeRuntime:
+    fps = 16.0
+    chunk_size = 3
+    decoder_name = "TAEHV (taew2_1)"
+    causal_recipe = {
+        "local_attn_size": 6,
+        "sink_size": 1,
+        "rope_cache_policy": "relativistic",
+    }
+
+    def __init__(self, session):
+        self.session = session
+
+    def prepare(self, image_bytes, prompt):
+        assert image_bytes
+        return _Prepared(Image.new("RGB", (16, 12), "blue"), prompt)
+
+    def create_session(self):
+        return self.session
+
+
+class _FakeWriter:
+    def __init__(self, output_root, *, fps):
+        assert fps == 16.0
+        self.session_id = "fake-download"
+        self.session_dir = Path(output_root) / self.session_id
+        self.session_dir.mkdir(parents=True, exist_ok=True)
+        self.block_paths = []
+
+    def encode_block(self, frames, block_index):
+        assert frames.shape[-1] == 3
+        path = self.session_dir / f"block-{block_index}.mp4"
+        path.write_bytes(b"complete-block")
+        self.block_paths.append(path)
+        return EncodedMediaSegment(
+            block_index=block_index,
+            init_bytes=b"init",
+            media_bytes=f"media-{block_index}".encode(),
+            path=path,
+        )
+
+    def finalize(self):
+        if not self.block_paths:
+            return None
+        path = self.session_dir / "wantrack_control.mp4"
+        path.write_bytes(b"".join(item.read_bytes()
+                                 for item in self.block_paths))
+        return path
+
+
+def _json(websocket):
+    message = websocket.receive()
+    assert message["type"] == "websocket.send"
+    assert "text" in message
+    import json
+    return json.loads(message["text"])
+
+
+def _bytes(websocket):
+    message = websocket.receive()
+    assert message["type"] == "websocket.send"
+    return message["bytes"]
+
+
+def _prepare_and_start(websocket):
+    import base64
+    import io
+
+    image = io.BytesIO()
+    Image.new("RGB", (8, 8)).save(image, format="PNG")
+    websocket.send_json({
+        "type": "prepare",
+        "image": base64.b64encode(image.getvalue()).decode(),
+        "prompt": "",
+    })
+    assert _json(websocket)["phase"] == "loading_model"
+    assert _json(websocket)["phase"] == "preparing_input"
+    prepared = _json(websocket)
+    assert prepared["type"] == "prepared"
+    assert prepared["prepare_ms"] >= 0
+    assert prepared["causal_recipe"]["rope_cache_policy"] == "relativistic"
+    assert prepared["decoder"] == "TAEHV (taew2_1)"
+    websocket.send_json({
+        "type": "start",
+        "handles": [{
+            "id": "h",
+            "x": 0.5,
+            "y": 0.5,
+        }],
+        "radius": 0.15,
+        # Client overrides are ignored for the fixed SF recipe.
+        "steps": 99,
+        "text_guidance": 9.0,
+        "motion_guidance": 9.0,
+    })
+    assert _json(websocket)["phase"] == "starting_session"
+    started = _json(websocket)
+    assert started["type"] == "session_started"
+    assert started["num_inference_steps"] == 4
+    assert started["cfg_enabled"] is False
+    assert started["causal_recipe"]["local_attn_size"] == 6
+    assert started["decoder"] == "TAEHV (taew2_1)"
+
+
+def test_two_block_binary_order_future_update_and_stop(tmp_path):
+    gates = [threading.Event(), threading.Event()]
+    session = _FakeSession(gates)
+    app = create_app(
+        _FakeRuntime(session),
+        output_dir=tmp_path,
+        writer_factory=_FakeWriter,
+    )
+    with TestClient(app) as client:
+        with client.websocket_connect("/ws") as websocket:
+            _prepare_and_start(websocket)
+            started = _json(websocket)
+            assert started["type"] == "block_started"
+            assert started["block_index"] == 0
+            assert started["num_inference_steps"] == 4
+            assert started["cfg_enabled"] is False
+            websocket.send_json({
+                "type": "control_update",
+                "revision": 1,
+                "samples": [{
+                    "id": "h",
+                    "x": 0.7,
+                    "y": 0.5,
+                    "timestamp_ms": 10,
+                }],
+            })
+            websocket.send_json({
+                "type": "control_update",
+                "revision": 1,
+                "samples": [],
+            })
+            stale = _json(websocket)
+            assert stale["status"] == "ignored_stale"
+            gates[0].set()
+            assert _json(websocket)["type"] == "block_encoding"
+            assert _json(websocket)["type"] == "media_init"
+            assert _bytes(websocket) == b"init"
+            assert _bytes(websocket) == b"media-0"
+            assert _json(websocket)["type"] == "media_segment_complete"
+
+            assert _json(websocket)["type"] == "block_started"
+            websocket.send_json({"type": "stop"})
+            gates[1].set()
+            applied = _json(websocket)
+            assert applied["type"] == "control_applied"
+            assert applied["revision"] == 1
+            assert _json(websocket)["type"] == "block_encoding"
+            assert _bytes(websocket) == b"media-1"
+            assert _json(websocket)["type"] == "media_segment_complete"
+            complete = _json(websocket)
+            assert complete["type"] == "stream_complete"
+            assert complete["blocks"] == 2
+            assert complete["download_url"]
+        response = client.get(complete["download_url"])
+        assert response.status_code == 200
+        assert response.content
+        assert client.get("/healthz").json()["active"] is False
+
+
+def test_error_releases_lock_and_preserves_completed_prefix(tmp_path):
+    gates = [threading.Event(), threading.Event()]
+    gates[0].set()
+    gates[1].set()
+    session = _FakeSession(gates, fail_at=1)
+    app = create_app(
+        _FakeRuntime(session),
+        output_dir=tmp_path,
+        writer_factory=_FakeWriter,
+    )
+    with TestClient(app) as client:
+        with client.websocket_connect("/ws") as websocket:
+            _prepare_and_start(websocket)
+            assert _json(websocket)["type"] == "block_started"
+            assert _json(websocket)["type"] == "block_encoding"
+            assert _json(websocket)["type"] == "media_init"
+            assert _bytes(websocket) == b"init"
+            assert _bytes(websocket) == b"media-0"
+            assert _json(websocket)["type"] == "media_segment_complete"
+            assert _json(websocket)["type"] == "block_started"
+            error = _json(websocket)
+            assert error["type"] == "error"
+            assert error["download_url"]
+        assert client.get(error["download_url"]).content
+        assert client.get("/healthz").json()["active"] is False
@@ -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,
@@ -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
@@ -134,4 +134,4 @@ If you're planning to contribute to FastVideo please see the following page:

 If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).

-You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.
+You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.
@@ -100,4 +100,4 @@ If you're planning to contribute to FastVideo please see the following page:

 If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).

-You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ) for additional support.
+You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.
@@ -134,7 +134,7 @@ uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/rele

 If you hit other issues, please open an issue on our
 [GitHub repository](https://github.com/hao-ai-lab/FastVideo). You can also join
-our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)
+our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ)
 for additional support.

 ## Development Environment Setup
@@ -11,7 +11,7 @@ This guide explains how to implement a custom diffusion pipeline in FastVideo, l
 4. **Register Your Pipeline** - Make it discoverable by the framework
 5. **Configure Your Pipeline** - (Coming soon)

-Need help? Join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ).
+Need help? Join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).

 ## Step 1: Pipeline Modules

@@ -134,4 +134,5 @@ If the generated video doesn't match your prompt:
 - Learn about [Advanced Inference Configurations](configuration.md)
 - Learn about using [Optimizations](optimizations.md)
 - See [Examples](examples/examples_inference_index.md) for more usage scenarios
- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ).
+- Join our [Community Discord](https://discord.gg/JA7cksDz86).
+- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).
@@ -119,22 +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:
@@ -13,92 +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) |
-| 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 (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
@@ -111,9 +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 {
@@ -145,7 +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 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
@@ -154,14 +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 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
@@ -187,21 +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
@@ -212,29 +212,6 @@ pipeline:
  flow_shift: 8
 ```

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

 ## Training Methods
@@ -0,0 +1,188 @@
+# WanTrack training
+
+WanTrack extends Wan I2V with sparse point tracks. The same model wrapper and
+conditioning path are used for preprocessing, training, validation, and
+standalone inference. Both bidirectional and causal checkpoints are supported.
+
+## Build an initialization checkpoint
+
+WanTrack uses the pretrained control slot from Wan2.1-Fun Control as its track
+slot. Convert a VideoX-Fun control checkpoint together with a diffusers-format
+Wan2.1-Fun InP base:
+
+```bash
+python scripts/checkpoint_conversion/wan_fun_control_to_trackwan.py \
+    --inp-base models/Wan2.1-Fun-1.3B-InP-Diffusers \
+    --control-ckpt models/Wan2.1-Fun-1.3B-Control/diffusion_pytorch_model.safetensors \
+    --out models/wantrack-control-init
+```
+
+For the causal transformer, add `--causal` and use a separate output directory:
+
+```bash
+python scripts/checkpoint_conversion/wan_fun_control_to_trackwan.py \
+    --inp-base models/Wan2.1-Fun-1.3B-InP-Diffusers \
+    --control-ckpt models/Wan2.1-Fun-1.3B-Control/diffusion_pytorch_model.safetensors \
+    --out models/wantrack-control-causal-init \
+    --causal
+```
+
+`--causal` selects `CausalTrackWanTransformer3DModel` in the generated
+transformer config. Without it, the converter selects
+`TrackWanTransformer3DModel`.
+
+The converted patch input has 52 channels:
+
+| Channels | Meaning |
+|----------|---------|
+| `0:16` | Noisy video latent |
+| `16:20` | I2V mask |
+| `20:36` | First-frame latent |
+| `36:52` | Track map |
+
+The first three entries form the usual 36-channel Wan I2V input. A
+`TrackEncoder` rasterizes sparse points on the VAE grid and appends the
+16-channel track map.
+
+## Prepare point-track data
+
+Each video needs an `.npz` sidecar referenced by `points_path` in its metadata.
+The archive must contain:
+
+- `tracks`: floating-point source-pixel coordinates with shape `[T, N, 2]`.
+  The final dimension is `(x, y)`.
+- `visibility`: a boolean or numeric visibility mask with shape `[T, N]`.
+
+`T` is the source-video timeline and `N` is the number of point tracks. The
+preprocessor applies the same temporal sample and center-crop/resize transform
+to the sidecar as it applies to the video. Coordinates should therefore be in
+the original video's pixel space, not normalized or pre-cropped. Points outside
+the retained crop are marked invisible.
+
+For example, a merged-dataset annotation can contain:
+
+```json
+[
+  {
+    "path": "videos/clip_0001.mp4",
+    "points_path": "tracks/clip_0001.npz",
+    "cap": ["A cyclist follows a winding road."],
+    "resolution": {"width": 1920, "height": 1080},
+    "fps": 24.0,
+    "duration": 5.0,
+    "num_frames": 120
+  }
+]
+```
+
+The paths are relative to the dataset root named in the merge file:
+
+```text
+data/wantrack/raw,data/wantrack/metadata.json
+```
+
+All examples combined into one training batch must have a stackable point
+dimension. Keeping `N` fixed across the dataset is the simplest option.
+
+Run the I2V-track preprocessor with matching pixel and latent lengths. Wan's
+temporal compression is four, so 81 pixel frames produce 21 latent frames:
+
+```bash
+torchrun --nproc_per_node=1 fastvideo/pipelines/preprocess/v1_preprocess.py \
+    --model_path models/wantrack-control-init \
+    --data_merge_path data/wantrack/data_merge.txt \
+    --output_dir data/wantrack/preprocessed \
+    --preprocess_task i2v_track \
+    --num_frames 81 \
+    --num_latent_t 21 \
+    --max_height 480 \
+    --max_width 832 \
+    --train_fps 16 \
+    --preprocess_video_batch_size 1
+```
+
+The trainer reads the resulting
+`data/wantrack/preprocessed/combined_parquet_dataset` directory with
+`preprocessed_data_type: i2v_track`.
+
+## Train the bidirectional model
+
+The bidirectional training wrapper is
+`fastvideo.train.models.wantrack.WanTrackModel`; it loads
+`TrackWanTransformer3DModel`.
+
+```bash
+torchrun --nproc_per_node=8 -m fastvideo.train.entrypoint.train \
+    --config examples/train/configs/fine_tuning/wantrack/bidirectional_i2v.yaml
+```
+
+The example optionally subsamples points and applies temporal track masking.
+Track IDs are sampled once for a training sample and then reused by every
+denoising call and its conditional/unconditional branches. Re-sampling IDs per
+call would change the point embeddings even when the coordinates are
+unchanged.
+
+## Train the causal model
+
+The causal wrapper is
+`fastvideo.train.models.wantrack.WanTrackCausalModel`; it loads
+`CausalTrackWanTransformer3DModel`. The example uses
+`TeacherForcingSFTMethod`, so clean history and the noisy current chunk receive
+the same I2V and track conditioning:
+
+```bash
+torchrun --nproc_per_node=8 -m fastvideo.train.entrypoint.train \
+    --config examples/train/configs/fine_tuning/wantrack/causal_i2v.yaml
+```
+
+Track encoding is causal at the VAE temporal boundary. The implementation
+left-pads the complete source track sequence at its global beginning, encodes
+it, and then slices the latent track map at the current latent `start_frame`.
+It must not independently pad and encode each later chunk: doing so would
+reset temporal context and produce a different feature for the same point
+history.
+
+The same stable `track_ids` are reused across all chunks, denoising steps, and
+CFG branches. Causal sampling follows the RobotWM streaming contract:
+`predict_noise_streaming()` owns the KV caches, each denoised block is committed
+once as clean context, and caches are cleared at the sample boundary.
+
+## Validation and inference
+
+Both example configs use
+`fastvideo.train.callbacks.track_validation.TrackValidationCallback`. It loads
+fixed samples from the preprocessed WanTrack parquet, builds conditions through
+the student's normal `prepare_batch()` path, generates videos, overlays the
+active tracks, and logs them to the configured tracker. Set
+`callbacks.track_validation.val_data_path` to use a dedicated validation
+parquet; otherwise it samples from the training parquet.
+
+The callback and standalone callers share two helpers:
+
+```python
+from fastvideo.train.models.wantrack.inference import (
+    prepare_wantrack_batch,
+    sample_wantrack,
+)
+
+batch = prepare_wantrack_batch(
+    model,
+    raw_batch,
+    seed=1000,
+    latents_source="zeros",
+)
+latents = sample_wantrack(
+    model,
+    batch,
+    num_inference_steps=30,
+    seed=1000,
+    text_guidance_scale=3.0,
+    motion_guidance_scale=1.5,
+)
+video = model.decode_latents(latents)
+```
+
+`sample_wantrack()` denoises the complete clip for `WanTrackModel`. For
+`WanTrackCausalModel`, it uses the existing `CausalModelBase` streaming API and
+the transformer's configured block size; it does not modify or fork the common
+Wan causal denoising stage.
@@ -77,9 +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
-  other device the backend reports unavailable (even if a CUDA 13 wheel
-  bundles the extension) and selection falls back to FlashAttention
+  `attn_qat_infer`
 - `ATTN_QAT_TRAIN`: `fastvideo-kernel`; its runtime-JIT Triton implementation
  selects an optimized route on SM100, joins the quantized and STE P@V paths on
  SM120, and retains the previous route for unsupported configurations. See
@@ -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 = {
@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Run causal WanTrack Self-Forcing I2V through FastVideo.
+"""
+import argparse
+from pathlib import Path
+
+from fastvideo import VideoGenerator
+from fastvideo.api import (
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    InputConfig,
+    OffloadConfig,
+    OutputConfig,
+    ParallelismConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
+from fastvideo.models.dits.trackwan.utils import load_tracks
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run causal WanTrack Self-Forcing I2V.")
+    parser.add_argument(
+        "--model-path",
+        default="path/to/ckpt",
+        help="Local diffusers-format Track-v0 weights directory (or HF id).",
+    )
+    parser.add_argument(
+        "--image",
+        default="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
+        help="Condition image path or URL.",
+    )
+    parser.add_argument(
+        "--tracks",
+        default=None,
+        help=("Track package: .pt/.npz dict with track_points+track_visibility "
+              "(optional track_ids), or a directory containing those files."),
+    )
+    parser.add_argument(
+        "--track-points",
+        default=None,
+        help="Optional standalone track_points tensor file (.pt/.npy/.npz).",
+    )
+    parser.add_argument(
+        "--track-visibility",
+        default=None,
+        help="Optional standalone track_visibility tensor file (.pt/.npy/.npz).",
+    )
+    parser.add_argument(
+        "--track-ids",
+        default=None,
+        help="Optional standalone track_ids tensor file (.pt/.npy/.npz).",
+    )
+    parser.add_argument(
+        "--output",
+        default="video_samples_wantrack_causal_sf/output.mp4",
+        help="Output mp4 path.",
+    )
+    parser.add_argument(
+        "--prompt",
+        default=("Summer beach vacation style, a white cat wearing sunglasses "
+                 "sits on a surfboard. The fluffy-furred feline gazes directly "
+                 "at the camera with a relaxed expression."),
+        help="Text prompt.",
+    )
+    parser.add_argument("--height", type=int, default=480)
+    parser.add_argument("--width", type=int, default=832)
+    parser.add_argument("--num-frames", type=int, default=121)
+    parser.add_argument("--fps", type=int, default=16)
+    parser.add_argument("--steps", type=int, default=4)
+    parser.add_argument("--guidance-scale", type=float, default=1.0)
+    parser.add_argument("--seed", type=int, default=1000)
+    parser.add_argument(
+        "--num-tracks",
+        type=int,
+        default=8,
+        help="Demo track count when no track files are provided.",
+    )
+    parser.add_argument("--num-gpus", type=int, default=1)
+    parser.add_argument("--tp-size", type=int, default=None)
+    parser.add_argument("--sp-size", type=int, default=None)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
+    sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
+
+    tracks = load_tracks(
+        tracks_path=args.tracks,
+        track_points_path=args.track_points,
+        track_visibility_path=args.track_visibility,
+        track_ids_path=args.track_ids,
+        num_frames=args.num_frames,
+        num_tracks=args.num_tracks,
+        seed=args.seed,
+    )
+
+    generator_config = GeneratorConfig(
+        model_path=args.model_path,
+        engine=EngineConfig(
+            num_gpus=args.num_gpus,
+            use_fsdp_inference=False,
+            parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
+            offload=OffloadConfig(
+                dit=False,
+                vae=False,
+                text_encoder=True,
+                image_encoder=True,
+                pin_cpu_memory=True,
+            ),
+        ),
+        pipeline=PipelineSelection(workload_type="i2v"),
+    )
+
+    generator = VideoGenerator.from_config(generator_config)
+    try:
+        request = GenerationRequest(
+            prompt=args.prompt,
+            inputs=InputConfig(
+                image_path=args.image,
+                track_points=tracks["track_points"],
+                track_visibility=tracks["track_visibility"],
+                track_ids=tracks["track_ids"],
+            ),
+            sampling=SamplingConfig(
+                height=args.height,
+                width=args.width,
+                num_frames=args.num_frames,
+                fps=args.fps,
+                num_inference_steps=args.steps,
+                guidance_scale=args.guidance_scale,
+                seed=args.seed,
+            ),
+            output=OutputConfig(
+                output_path=str(output.parent),
+                output_video_name=output.stem,
+                save_video=True,
+                return_frames=False,
+            ),
+        )
+        result = generator.generate(request)
+        if isinstance(result, list):
+            result = result[0]
+        print(f"Saved video to {result.video_path}")
+    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()
@@ -0,0 +1,86 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/export/tf/transformer/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/export/tf/transformer/model.safetensors
+  ema:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/export/tf/transformer/model.safetensors
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 48
+  guidance_scale: 3.0
+  ema_decay: 0.99
+  ema_start_step: 200
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 2000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/cd/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 2
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A12_sink1_local6_relativistic_chunk3_cd2k_from_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/cd/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,110 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/export/cd/transformer/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.0
+  dmd_denoising_steps:
+  - 1000
+  - 750
+  - 500
+  - 250
+  warp_denoising_step: true
+  chunk_size: 3
+  student_sample_type: sde
+  context_noise: 0.0
+  same_step_across_blocks: true
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+  fake_score_learning_rate: 4.0e-07
+  fake_score_betas:
+  - 0.0
+  - 0.999
+  fake_score_lr_scheduler: constant
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 1000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/sf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 1
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A12_sink1_local6_relativistic_chunk3_sf1k_from_cd2k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/sf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+    sampling_timesteps:
+    - 1000
+    - 750
+    - 500
+    - 250
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,72 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/tf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A12_sink1_local6_relativistic_chunk3_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 40
+    guidance_scale: 6.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12/tf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,86 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/export/tf/transformer/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/export/tf/transformer/model.safetensors
+  ema:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/export/tf/transformer/model.safetensors
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 48
+  guidance_scale: 3.0
+  ema_decay: 0.99
+  ema_start_step: 200
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 2000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/cd/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 2
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A13_sink0_local6_relativistic_chunk3_cd2k_from_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/cd/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 0
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,110 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/export/cd/transformer/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.0
+  dmd_denoising_steps:
+  - 1000
+  - 750
+  - 500
+  - 250
+  warp_denoising_step: true
+  chunk_size: 3
+  student_sample_type: sde
+  context_noise: 0.0
+  same_step_across_blocks: true
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+  fake_score_learning_rate: 4.0e-07
+  fake_score_betas:
+  - 0.0
+  - 0.999
+  fake_score_lr_scheduler: constant
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 1000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/sf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 1
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A13_sink0_local6_relativistic_chunk3_sf1k_from_cd2k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/sf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+    sampling_timesteps:
+    - 1000
+    - 750
+    - 500
+    - 250
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 0
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,72 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/tf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A13_sink0_local6_relativistic_chunk3_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 40
+    guidance_scale: 6.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A13/tf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 0
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,86 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/export/tf/transformer/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/export/tf/transformer/model.safetensors
+  ema:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/export/tf/transformer/model.safetensors
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 48
+  guidance_scale: 3.0
+  ema_decay: 0.99
+  ema_start_step: 200
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 2000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/cd/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 2
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A14_sink1_local6_absolute_chunk3_cd2k_from_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/cd/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: absolute
+    causal_train_attention: triton
@@ -0,0 +1,110 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/export/cd/transformer/model.safetensors
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.0
+  dmd_denoising_steps:
+  - 1000
+  - 750
+  - 500
+  - 250
+  warp_denoising_step: true
+  chunk_size: 3
+  student_sample_type: sde
+  context_noise: 0.0
+  same_step_across_blocks: true
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+  fake_score_learning_rate: 4.0e-07
+  fake_score_betas:
+  - 0.0
+  - 0.999
+  fake_score_lr_scheduler: constant
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 1000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/sf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 1
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A14_sink1_local6_absolute_chunk3_sf1k_from_cd2k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/sf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+    sampling_timesteps:
+    - 1000
+    - 750
+    - 500
+    - 250
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: absolute
+    causal_train_attention: triton
@@ -0,0 +1,72 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/tf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A14_sink1_local6_absolute_chunk3_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 40
+    guidance_scale: 6.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A14/tf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: absolute
+    causal_train_attention: triton
@@ -0,0 +1,89 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/export/tf/transformer/model.safetensors
+    num_frames_per_block: 1
+  teacher:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/export/tf/transformer/model.safetensors
+    num_frames_per_block: 1
+  ema:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/export/tf/transformer/model.safetensors
+    num_frames_per_block: 1
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 48
+  guidance_scale: 3.0
+  ema_decay: 0.99
+  ema_start_step: 200
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 2000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/cd/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 2
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A15_sink1_local6_relativistic_framewise_cd2k_from_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/cd/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,111 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/export/cd/transformer/model.safetensors
+    num_frames_per_block: 1
+  teacher:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-14B-Diffusers
+    trainable: false
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wan.WanModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    disable_custom_init_weights: true
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.0
+  dmd_denoising_steps:
+  - 1000
+  - 750
+  - 500
+  - 250
+  warp_denoising_step: true
+  chunk_size: 1
+  student_sample_type: sde
+  context_noise: 0.0
+  same_step_across_blocks: true
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+  fake_score_learning_rate: 4.0e-07
+  fake_score_betas:
+  - 0.0
+  - 0.999
+  fake_score_lr_scheduler: constant
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 1000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/sf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 1
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A15_sink1_local6_relativistic_framewise_sf1k_from_cd2k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 4
+    guidance_scale: 3.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/sf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+    sampling_timesteps:
+    - 1000
+    - 750
+    - 500
+    - 250
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,73 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    num_frames_per_block: 1
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 1
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset
+    dataloader_type: streaming
+    streaming_manifest_path: /mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json
+    streaming_read_batch_size: 2
+    streaming_shuffle_row_groups: true
+    dataloader_num_workers: 0
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+  optimizer:
+    learning_rate: 2.0e-06
+    betas:
+    - 0.0
+    - 0.999
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 8
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/tf/checkpoints
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+  tracker:
+    trackers:
+    - wandb
+    project_name: causal_forcing_openvid_a12_a15
+    run_name: A15_sink1_local6_relativistic_framewise_tf3k_openvid_81f21l_gbs64
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: /mnt/nfs/vlm-k1kong/FastVideo-openvid-a12-a15-final-20260717/examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 200
+    sampling_steps:
+    - 40
+    guidance_scale: 6.0
+    num_frames: 81
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A15/tf/validation
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,52 @@
+# OpenVid causal A12-A15 plan (NOT STARTED)
+
+Prepared from commit `30ada30e4c6b05aa68cd1eb8940a34d149457147`. This directory contains configuration only;
+no training command was launched.
+
+Data source: `/mnt/lustre/vlm-s4duan/openvid_1m/combined_parquet_dataset` (4494 parquet files, about 5.5 TiB). It is owned by
+`vlm-s4duan`; filesystem read permission exists, but obtain the owner's consent
+before launching and coordinate I/O. Do not write into that directory.
+
+The opt-in streaming loader projects only the 15 T2V columns, reads each
+assigned row group sequentially, and stores its JSON manifest at
+`/mnt/lustre/vlm-k1kong/dataset-index/openvid/streaming-t2v-v2.json` in user-owned Lustre. It uses zero DataLoader workers and never
+writes a cache or index into the shared source tree.
+
+All stages use 4 GPUs, microbatch 2/rank, gradient accumulation 8, hence global
+batch = 2 * 4 * 8 = 64. `dataloader_num_workers=0` limits shared-memory and
+Lustre prefetch pressure.
+
+All four conditions use exactly 21 latent frames / 81 raw frames in TF, CD,
+SF, and validation. Chunk-3 conditions therefore use seven identical
+three-latent blocks. A15 is length-matched and uses framewise blocks.
+
+A15 "framewise" means `num_frames_per_block=1` on every causal role, plus
+`method.chunk_size=1` in TF and SF. Causal CD has no independent-frame timestep
+option: it samples one t/t_next pair and broadcasts it over T, so A15 CD is
+framewise causal attention but not framewise diffusion-time sampling.
+
+The requested LR/betas apply to each stage's main optimizer: 2e-6 and
+(0.0, 0.999). SF critic keeps the proven DMD value 4e-7 with (0.0, 0.999).
+
+To launch one condition later:
+
+    export WANDB_API_KEY=...
+    bash /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/scripts/run_condition.sh A12 /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12 29820
+
+To run all four sequentially on one 4-GPU node:
+
+    export WANDB_API_KEY=...
+    bash /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/scripts/run_all_sequential.sh /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15
+
+Online W&B is the default. For an intentionally offline launch, omit the key
+and set `WANDB_MODE=offline`. Training checkpoints still resume from `latest`,
+but W&B does not merge separate offline process restarts into one run; sync the
+resulting offline runs individually later.
+
+To validate all three configs for one condition without starting training,
+creating W&B state, or requiring a key:
+
+    PREFLIGHT_ONLY=1 bash /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/scripts/run_condition.sh A12 /mnt/lustre/vlm-k1kong/experiments/openvid_causal_a12_a15/20260717_014659_openvid_a12_a15/A12 29820
+
+`PREFLIGHT_ONLY=1` is also supported by `run_all_sequential.sh`; it validates
+all twelve configs and exits before queue state or checkpoint checks.
@@ -0,0 +1,40 @@
+# MixKit-21 causal-kernel teacher-forcing ablation
+
+All runs use the same training and validation settings. Only the three
+attention axes listed in `experiment_matrix.tsv` vary.
+
+| ID | sink | local | RoPE | lane | primary contrast |
+|---|---:|---:|---|---|---|
+| A01 | 0 | 21 | absolute | node0 | rope policy control |
+| A02 | 0 | 21 | relativistic | node1 | rope policy control |
+| A03 | 1 | 21 | relativistic | node0 | sink at local 21 |
+| A04 | 0 | 6 | relativistic | node0 | local window at sink 0 |
+| A05 | 1 | 6 | relativistic | node0 | sink at local 6 |
+| A06 | 0 | 12 | relativistic | node1 | local window at sink 0 |
+| A07 | 1 | 12 | relativistic | node1 | sink at local 12 |
+| A08 | 3 | 12 | relativistic | node1 | sink size at local 12 |
+
+Fixed invariants:
+
+- MixKit precomputed data at 480x832 with 21 stored latent frames.
+- Teacher forcing for 2,000 steps, batch size 1, chunk size 3.
+- Fused training attention: `causal_train_attention: triton`.
+- Validation at 249 pixel frames, equivalent to 63 Wan latent frames.
+- Validation reuses the training transformer and pipeline config, so sink,
+  local window, and RoPE policy stay identical between training and inference.
+- Four GPUs, full gradient checkpointing, validation/checkpoint every 200/1000
+  steps, and W&B online tracking in a dedicated project.
+
+Validate the matrix and template:
+
+```bash
+python scripts/train/manage_mixkit21_tf_ablation.py validate
+```
+
+Run one lane after supplying `WANDB_API_KEY` at runtime:
+
+```bash
+SEQUENCE_ID=<timestamp> LANE=node0 \
+RUN_CONDITIONS="A01 A03 A04 A05" \
+bash scripts/train/run_mixkit21_tf_ablation_lane.sh
+```
@@ -0,0 +1,9 @@
+id	condition	sink_size	local_attn_size	rope_cache_policy	lane	primary_contrast
+A01	sink0_local21_absolute	0	21	absolute	node0	A01-vs-A02: rope policy control
+A02	sink0_local21_relative	0	21	relativistic	node1	A01-vs-A02: rope policy control
+A03	sink1_local21_relative	1	21	relativistic	node0	A02-vs-A03: sink at local21
+A04	sink0_local6_relative	0	6	relativistic	node0	A02-vs-A04: local window at sink0
+A05	sink1_local6_relative	1	6	relativistic	node0	A04-vs-A05: sink at local6
+A06	sink0_local12_relative	0	12	relativistic	node1	A02-vs-A06: local window at sink0
+A07	sink1_local12_relative	1	12	relativistic	node1	A06-vs-A07: sink at local12
+A08	sink3_local12_relative	3	12	relativistic	node1	A06-vs-A07-vs-A08: sink size at local12
@@ -0,0 +1,74 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+
+training:
+  distributed:
+    num_gpus: 4
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 4
+
+  data:
+    data_path: /mnt/lustre/vlm-k1kong/datasets/HD-Mixkit-Finetune-Wan/combined_parquet_dataset
+    dataloader_num_workers: 0
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 21
+    num_height: 480
+    num_width: 832
+    num_frames: 81
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: __MAX_TRAIN_STEPS__
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: __CHECKPOINT_DIR__
+    training_state_checkpointing_steps: __CHECKPOINT_STEPS__
+    checkpoints_total_limit: 2
+
+  tracker:
+    trackers: [wandb]
+    project_name: __PROJECT_NAME__
+    run_name: __WANDB_RUN_NAME__
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: __VALIDATION_DATASET_FILE__
+    every_steps: __VALIDATION_EVERY_STEPS__
+    sampling_steps: [__VALIDATION_SAMPLING_STEPS__]
+    guidance_scale: 6.0
+    num_frames: 249
+    output_dir: __VALIDATION_DIR__
+    offload_training_state: true
+    unload_pipeline_after_validation: true
+
+pipeline:
+  flow_shift: 5
+  dit_config:
+    local_attn_size: __LOCAL_ATTN_SIZE__
+    sink_size: __SINK_SIZE__
+    rope_cache_policy: __ROPE_CACHE_POLICY__
+    causal_train_attention: triton
@@ -0,0 +1,7 @@
+{
+  "data": [
+    {
+      "caption": "A lone rider guides a horse across an open field at sunset, with steady motion and a slowly changing background."
+    }
+  ]
+}
@@ -0,0 +1,43 @@
+# WanTrack causal synth stage-2 run
+
+This directory archives the exact configuration and launch artifacts used by
+the completed WanTrack causal run on 2026-07-23/24.
+
+- Run root:
+  `/mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32`
+- Initial checkpoint:
+  `/mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias`
+- Dataset:
+  `/mnt/lustre/vlm-s4duan/data/combined_synth_parquets`
+- Validation dataset:
+  `/mnt/lustre/vlm-s4duan/val_examples_mixed/combined_parquet_dataset`
+- Topology: 4 rack-3 nodes, 4 GPUs per node, micro-batch 2, global batch 32
+- Attention: 3 latent frames per block, sink 1, local window 6,
+  relativistic RoPE
+- Schedule: TF 3000, CD 2000, SF 1000 optimizer steps
+- Validation: every 250 steps; TF uses 30 denoising steps while CD and SF use
+  4 denoising steps
+- Checkpoints: every 500 optimizer steps
+
+The TF job resumed from checkpoint 2000 after its validation configuration was
+corrected to multi-step sampling. The YAML files in this directory are the
+final files used by the run, including their absolute cluster paths.
+
+`cluster/run_pipeline_node.sh` launches the resumable TF -> CD -> SF pipeline
+and exports TF `student`, CD `ema`, and SF `student_ema`. It requires
+`WANDB_API_KEY` to be injected through the process environment. The gallery
+upload script similarly requires an in-memory `HF_TOKEN`; no credentials are
+stored here.
+
+The original Kubernetes manifest retains the historical workload label
+`wantrack-causal-framewise-gbs32`. That label is stale metadata: the training
+authority is the three YAML files, all of which use
+`num_frames_per_block: 3`.
+
+The SF EMA export intentionally contains only the trainable checkpoint role.
+For standalone inference, four frozen track-encoder parameters were restored
+from the initialization checkpoint. See `receipts/sf-full-export.json` for the
+full bundle provenance and hashes.
+
+The `gallery/` scripts produced 64 generated examples plus their 64 matching
+ground-truth videos from the full SF EMA bundle.
@@ -0,0 +1,95 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wantrack.WanTrackCausalModel
+    init_from: /mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/export/tf/transformer/model.safetensors
+    trainable: true
+    num_frames_per_block: 3
+    freeze_track_encoder: true
+    track_augmentation:
+      enabled: true
+      sparse_object_sampling: true
+      extra_points: 20
+      extra_point_sampling: random
+      track_dropout_probability: 0.5
+      temporal_mask_probability: 0.2
+      temporal_mask_chunk_size: 8
+      motion_dropout_probability: 0.3
+      text_dropout_probability: 0.0
+  teacher:
+    _target_: fastvideo.train.models.wantrack.WanTrackCausalModel
+    init_from: /mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/export/tf/transformer/model.safetensors
+    trainable: false
+    num_frames_per_block: 3
+    freeze_track_encoder: true
+  ema:
+    _target_: fastvideo.train.models.wantrack.WanTrackCausalModel
+    init_from: /mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/export/tf/transformer/model.safetensors
+    trainable: false
+    num_frames_per_block: 3
+    freeze_track_encoder: true
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 48
+  guidance_scale: 3.0
+  ema_decay: 0.99
+  ema_start_step: 200
+training:
+  distributed:
+    num_gpus: 16
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 4
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/data/combined_synth_parquets
+    preprocessed_data_type: i2v_track
+    dataloader_num_workers: 4
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 31
+    num_height: 480
+    num_width: 832
+    num_frames: 121
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 2000
+    gradient_accumulation_steps: 1
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/cd/checkpoints
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 4
+  tracker:
+    trackers: [wandb]
+    entity: kaiqin_kong_ucsd
+    project_name: wantrack_causal_synth_stage2
+    run_name: wantrack_ckpt600_block3_sink1_local6_relative_cd2k_gbs32
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  wantrack_validation:
+    _target_: fastvideo.train.callbacks.track_validation.TrackValidationCallback
+    every_steps: 250
+    val_data_path: /mnt/lustre/vlm-s4duan/val_examples_mixed/combined_parquet_dataset
+    num_val_samples: 4
+    num_inference_steps: 4
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/cd/validation
+    seed: 1000
+    validate_at_start: false
+pipeline:
+  flow_shift: 6
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,106 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wantrack.WanTrackCausalModel
+    init_from: /mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias
+    transformer_override_safetensor: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/export/cd/transformer/model.safetensors
+    trainable: true
+    num_frames_per_block: 3
+    freeze_track_encoder: true
+    track_augmentation:
+      enabled: true
+      sparse_object_sampling: true
+      extra_points: 20
+      extra_point_sampling: random
+      track_dropout_probability: 0.5
+      temporal_mask_probability: 0.2
+      temporal_mask_chunk_size: 8
+      motion_dropout_probability: 0.3
+      text_dropout_probability: 0.0
+  teacher:
+    _target_: fastvideo.train.models.wantrack.WanTrackModel
+    init_from: /mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias
+    trainable: false
+    freeze_track_encoder: true
+    disable_custom_init_weights: true
+  critic:
+    _target_: fastvideo.train.models.wantrack.WanTrackModel
+    init_from: /mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias
+    trainable: true
+    freeze_track_encoder: true
+    disable_custom_init_weights: true
+method:
+  _target_: fastvideo.train.methods.distribution_matching.self_forcing.SelfForcingMethod
+  rollout_mode: simulate
+  generator_update_interval: 5
+  real_score_guidance_scale: 4.0
+  dmd_denoising_steps: [1000, 750, 500, 250]
+  warp_denoising_step: true
+  chunk_size: 3
+  student_sample_type: sde
+  context_noise: 0.0
+  same_step_across_blocks: true
+  enable_gradient_in_rollout: true
+  start_gradient_frame: 0
+  fake_score_learning_rate: 4.0e-7
+  fake_score_betas: [0.0, 0.999]
+  fake_score_lr_scheduler: constant
+training:
+  distributed:
+    num_gpus: 16
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 4
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/data/combined_synth_parquets
+    preprocessed_data_type: i2v_track
+    dataloader_num_workers: 4
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 31
+    num_height: 480
+    num_width: 832
+    num_frames: 121
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 1000
+    gradient_accumulation_steps: 1
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/sf/checkpoints
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 2
+  tracker:
+    trackers: [wandb]
+    entity: kaiqin_kong_ucsd
+    project_name: wantrack_causal_synth_stage2
+    run_name: wantrack_ckpt600_block3_sink1_local6_relative_sf1k_gbs32
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  ema:
+    decay: 0.99
+    start_iter: 200
+  grad_clip:
+    max_grad_norm: 1.0
+  wantrack_validation:
+    _target_: fastvideo.train.callbacks.track_validation.TrackValidationCallback
+    every_steps: 250
+    val_data_path: /mnt/lustre/vlm-s4duan/val_examples_mixed/combined_parquet_dataset
+    num_val_samples: 4
+    num_inference_steps: 4
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/sf/validation
+    seed: 1000
+    validate_at_start: false
+pipeline:
+  flow_shift: 6
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -0,0 +1,79 @@
+models:
+  student:
+    _target_: fastvideo.train.models.wantrack.WanTrackCausalModel
+    init_from: /mnt/lustre/vlm-k1kong/models/wantrack-synth-stage2-ckpt600-bias
+    trainable: true
+    num_frames_per_block: 3
+    freeze_track_encoder: true
+    track_augmentation:
+      enabled: true
+      sparse_object_sampling: true
+      extra_points: 20
+      extra_point_sampling: random
+      track_dropout_probability: 0.5
+      temporal_mask_probability: 0.2
+      temporal_mask_chunk_size: 8
+      motion_dropout_probability: 0.3
+      text_dropout_probability: 0.0
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+training:
+  distributed:
+    num_gpus: 16
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 4
+    hsdp_shard_dim: 4
+  data:
+    data_path: /mnt/lustre/vlm-s4duan/data/combined_synth_parquets
+    preprocessed_data_type: i2v_track
+    dataloader_num_workers: 4
+    train_batch_size: 2
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 31
+    num_height: 480
+    num_width: 832
+    num_frames: 121
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.0, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 1
+  checkpoint:
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/tf/checkpoints
+    training_state_checkpointing_steps: 500
+    checkpoints_total_limit: 6
+  tracker:
+    trackers: [wandb]
+    entity: kaiqin_kong_ucsd
+    project_name: wantrack_causal_synth_stage2
+    run_name: wantrack_ckpt600_block3_sink1_local6_relative_tf3k_gbs32_resume2000_multistep
+  model:
+    enable_gradient_checkpointing_type: full
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  wantrack_validation:
+    _target_: fastvideo.train.callbacks.track_validation.TrackValidationCallback
+    every_steps: 250
+    val_data_path: /mnt/lustre/vlm-s4duan/val_examples_mixed/combined_parquet_dataset
+    num_val_samples: 4
+    num_inference_steps: 30
+    guidance_scale: 3.0
+    motion_guidance_scale: 1.5
+    output_dir: /mnt/lustre/vlm-k1kong/experiments/wantrack_causal/20260723_070728/wantrack-ckpt600-block3-local6-sink1-relative-gbs32/tf/validation
+    seed: 1000
+    validate_at_start: false
+pipeline:
+  flow_shift: 6
+  dit_config:
+    local_attn_size: 6
+    sink_size: 1
+    rope_cache_policy: relativistic
+    causal_train_attention: triton
@@ -1,70 +0,0 @@
-#!/bin/bash
-# Stage-2 QAT-aware DMD2 distillation for Kandinsky5 T2V (480p).
-#
-# Usage:
-#   bash distill_dmd_qat.sh <stage1-checkpoint> [--dotted.key value ...]
-#
-# <stage1-checkpoint> is either:
-#   - a raw stage-1 DCP checkpoint written by
-#     examples/train/configs/fine_tuning/kandinsky5/finetune_qat.sh
-#     (a checkpoint-<N>/ dir with a dcp/ subdir, or the stage-1 output_dir
-#     itself -- dcp_to_diffusers auto-picks the latest checkpoint). It is
-#     converted to a diffusers export (<checkpoint>-diffusers) with
-#     `dcp_to_diffusers --role student --verify` before launching --
-#     models.*.init_from need a diffusers model directory (model_index.json
-#     + component subfolders), not the raw DCP layout; or
-#   - an already-exported diffusers model dir (model_index.json present),
-#     used as-is with no conversion.
-#
-# The export is passed to all three models.*.init_from overrides: student,
-# teacher, and critic all load the same weights. Teacher/critic are
-# automatically masked back to dense attention and full-precision weights
-# by the _loading_teacher_critic_model gate in
-# fastvideo/models/loader/component_loader.py (family-agnostic, no
-# Kandinsky5-specific handling needed) -- NOT by pointing them at a
-# different export.
-#
-# FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_TRAIN keeps the student's dense/local
-# attention fake-quantized during distillation.
-set -euo pipefail
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(cd "${SCRIPT_DIR}/../../../../.." && pwd)"
-
-CHECKPOINT="${1:?Usage: $0 <stage1-checkpoint (DCP dir or diffusers export)> [extra --dotted.key overrides...]}"
-shift
-
-if [[ "${CHECKPOINT}" != /* ]]; then
-    CHECKPOINT="$(pwd)/${CHECKPOINT}"
-fi
-CHECKPOINT="${CHECKPOINT%/}"
-
-if [[ -f "${CHECKPOINT}/model_index.json" ]]; then
-    # Already a diffusers export -- use as-is.
-    INIT_FROM="${CHECKPOINT}"
-else
-    # Convert the raw DCP checkpoint. Runs BEFORE ATTN_QAT_TRAIN is
-    # exported below: backend selection for that env var refuses to start
-    # (ImportError) when the training kernel isn't built, and the
-    # conversion itself must not depend on it. --overwrite keeps relaunches
-    # from silently reusing a stale export for a newer checkpoint;
-    # --verify strictly reloads the exported transformer so a key-mapping
-    # bug fails here, not deep inside the training launch below.
-    INIT_FROM="${CHECKPOINT}-diffusers"
-    echo "Converting stage-1 DCP checkpoint ${CHECKPOINT} -> ${INIT_FROM}"
-    python -m fastvideo.train.entrypoint.dcp_to_diffusers \
-        --checkpoint "${CHECKPOINT}" \
-        --output-dir "${INIT_FROM}" \
-        --role student \
-        --overwrite \
-        --verify
-fi
-
-export FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_TRAIN
-
-bash "${REPO_ROOT}/examples/train/run.sh" \
-    "${SCRIPT_DIR}/dmd2_t2v_480p_qat.yaml" \
-    --models.student.init_from "${INIT_FROM}" \
-    --models.teacher.init_from "${INIT_FROM}" \
-    --models.critic.init_from "${INIT_FROM}" \
-    "$@"
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
H1yori233	9f94673c34	fix	2026-08-06 21:13:14 -07:00
H1yori233	c150889330	update server	2026-08-06 20:47:35 -07:00
H1yori233	7f783e30b7	add inference script	2026-08-06 18:18:36 -07:00
lpc0220	ec9db97431	[kernel] attention: sm_100a CUDA forward for block-causal-sink (Blackwell) (#1677 )	2026-08-06 16:36:47 -07:00
H1yori233	fd236a06a7	add config	2026-07-24 20:29:03 -07:00
H1yori233	54fe0be488	app test	2026-07-24 20:04:31 -07:00
H1yori233	3a398a2bbd	[bugfix]: load only exported role from DCP	2026-07-24 08:44:33 -07:00
H1yori233	4e76897f1f	[bugfix]: restore lazy optimizer checkpoints	2026-07-23 12:19:14 -07:00
H1yori233	95d8e74d65	[bugfix]: flatten WanTrack validation logging	2026-07-23 11:37:43 -07:00
H1yori233	bcd0ad7dee	[bugfix]: preserve WanTrack sampling dtype	2026-07-23 03:05:32 -07:00
H1yori233	9acbf92455	[bugfix]: support leading I2V frame in WanTrack sampling	2026-07-23 02:59:46 -07:00
H1yori233	ce4fa8ef2d	[feat]: add bidirectional and causal WanTrack support	2026-07-23 02:23:10 -07:00
H1yori233	41dcd065ae	Fix rank-local dataloader checkpoint resume	2026-07-22 21:29:48 -07:00
H1yori233	c0cb2228ac	Fix causal EMA update and export semantics	2026-07-22 21:29:48 -07:00
H1yori233	2fa0f70ee8	Use 21 latent frames for OpenVid causal training	2026-07-22 21:29:47 -07:00
H1yori233	e172215880	Harden OpenVid launcher modes	2026-07-22 21:29:46 -07:00
H1yori233	23e3b325a4	harden-openvid-launch-safety	2026-07-22 21:29:46 -07:00
H1yori233	44d0990199	streaming-config-parser	2026-07-22 21:29:45 -07:00
H1yori233	59b6578906	feat: add OpenVid causal A12-A15 runs	2026-07-22 21:29:45 -07:00
H1yori233	fd8372d172	Add OpenVid causal A12-A15 experiment plan	2026-07-22 21:29:44 -07:00
H1yori233	33eb7ea13c	feat(dataset): harden parquet streaming loader	2026-07-22 21:29:43 -07:00
H1yori233	051d21958d	[feat]:	2026-07-22 21:29:43 -07:00
H1yori233	bf29e20bf5	[bugfix]: fix causal Wan batch and padding dimensions	2026-07-22 21:29:42 -07:00
H1yori233	291fa5d9a6	fix(train): save and export exact EMA checkpoints	2026-07-22 21:29:42 -07:00
H1yori233	954daf7fb3	Add	2026-07-22 21:29:40 -07:00
H1yori233	e05c04a2f6	[feat]: import block causal sink attention kernel	2026-07-22 21:29:05 -07:00