docs: improve skill documentation clarity
- Fix Provider Selection: default to Replicate when multiple keys available (matches code) - Add Replicate column to Quality Presets table (normal→1K, 2k→2K) - Add Replicate aspect ratio behavior (match_input_image when --ref without --ar) - Remove stale Google Imagen reference from Aspect Ratios - Add batch file format example with JSON schema to SKILL.md - Note that batch paths resolve relative to batch file directory - Move batch execution strategy in article-illustrator before numbered steps - Fix translate image-language reminder to use standard markdown syntax with note to match article's own image syntax convention
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Jim Liu 宝玉
parent
5acef7151b
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88b433d565
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@ -118,6 +118,8 @@ Full template: [references/workflow.md](references/workflow.md#step-4-generate-o
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⛔ **BLOCKING: Prompt files MUST be saved before ANY image generation.**
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**Execution strategy**: When multiple illustrations have saved prompt files and the task is now plain generation, prefer `baoyu-image-gen` batch mode (`build-batch.ts` → `--batchfile`) over spawning subagents. Use subagents only when each image still needs separate prompt iteration or creative exploration.
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1. For each illustration, create a prompt file per [references/prompt-construction.md](references/prompt-construction.md)
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2. Save to `prompts/NN-{type}-{slug}.md` with YAML frontmatter
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3. Prompts **MUST** use type-specific templates with structured sections (ZONES / LABELS / COLORS / STYLE / ASPECT)
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@ -315,6 +315,10 @@ Prompt Files:
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**DO NOT** pass ad-hoc inline text to `--prompt` without first saving prompt files. The generation command should either use `--promptfiles prompts/NN-{type}-{slug}.md` or read the saved file content for `--prompt`.
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**Execution choice**:
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- If multiple illustrations already have saved prompt files and the task is now plain generation, prefer `baoyu-image-gen` batch mode (`build-batch.ts` -> `main.ts --batchfile`)
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- Use subagents only when each illustration still needs separate prompt rewriting, style exploration, or other per-image reasoning before generation
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**CRITICAL - References in Frontmatter**:
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- Only add `references` field if files ACTUALLY EXIST in `references/` directory
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- If style/palette was extracted verbally (no file), append info to prompt BODY instead
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@ -1,6 +1,6 @@
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---
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name: baoyu-image-gen
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description: AI image generation with OpenAI, Google, DashScope and Replicate APIs. Supports text-to-image, reference images, aspect ratios. Sequential by default; parallel generation available on request. Use when user asks to generate, create, or draw images.
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description: AI image generation with OpenAI, Google, DashScope and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
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version: 1.56.1
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metadata:
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openclaw:
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@ -99,6 +99,33 @@ ${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json
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${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4 --json
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```
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### Batch File Format
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```json
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{
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"jobs": 4,
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"tasks": [
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{
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"id": "hero",
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"promptFiles": ["prompts/hero.md"],
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"image": "out/hero.png",
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"provider": "replicate",
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"model": "google/nano-banana-pro",
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"ar": "16:9",
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"quality": "2k"
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},
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{
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"id": "diagram",
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"promptFiles": ["prompts/diagram.md"],
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"image": "out/diagram.png",
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"ref": ["references/original.png"]
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}
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]
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}
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```
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Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch file's directory. `jobs` is optional (overridden by CLI `--jobs`). Top-level array format (without `jobs` wrapper) is also accepted.
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## Options
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| Option | Description |
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@ -177,14 +204,14 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider r
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1. `--ref` provided + no `--provider` → auto-select Google first, then OpenAI, then Replicate
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2. `--provider` specified → use it (if `--ref`, must be `google`, `openai`, or `replicate`)
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3. Only one API key available → use that provider
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4. Multiple available → default to Google
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4. Multiple available → default to Replicate (`google/nano-banana-pro`)
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## Quality Presets
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| Preset | Google imageSize | OpenAI Size | Use Case |
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|--------|------------------|-------------|----------|
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| `normal` | 1K | 1024px | Quick previews |
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| `2k` (default) | 2K | 2048px | Covers, illustrations, infographics |
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| Preset | Google imageSize | OpenAI Size | Replicate resolution | Use Case |
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|--------|------------------|-------------|----------------------|----------|
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| `normal` | 1K | 1024px | 1K | Quick previews |
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| `2k` (default) | 2K | 2048px | 2K | Covers, illustrations, infographics |
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**Google imageSize**: Can be overridden with `--imageSize 1K|2K|4K`
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@ -193,8 +220,8 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider r
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Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`
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- Google multimodal: uses `imageConfig.aspectRatio`
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- Google Imagen: uses `aspectRatio` parameter
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- OpenAI: maps to closest supported size
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- Replicate: passes `aspect_ratio` to model; when `--ref` is provided without `--ar`, defaults to `match_input_image`
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## Generation Mode
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@ -207,6 +234,20 @@ Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`
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| Sequential (default) | Normal usage, single images, small batches |
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| Parallel batch | Batch mode with 2+ tasks |
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Execution choice:
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| Situation | Preferred approach | Why |
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|-----------|--------------------|-----|
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| One image, or 1-2 simple images | Sequential | Lower coordination overhead and easier debugging |
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| Multiple images already have saved prompt files | Batch (`--batchfile`) | Reuses finalized prompts, applies shared throttling/retries, and gives predictable throughput |
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| Each image still needs separate reasoning, prompt writing, or style exploration | Subagents | The work is still exploratory, so each image may need independent analysis before generation |
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| Output comes from `baoyu-article-illustrator` with `outline.md` + `prompts/` | Batch (`build-batch.ts` -> `--batchfile`) | That workflow already produces prompt files, so direct batch execution is the intended path |
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Rule of thumb:
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- Prefer batch over subagents once prompt files are already saved and the task is "generate all of these"
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- Use subagents only when generation is coupled with per-image thinking, rewriting, or divergent creative exploration
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Parallel behavior:
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- Default worker count is automatic, capped by config, built-in default 10
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@ -40,6 +40,12 @@ type ProviderRateLimit = {
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startIntervalMs: number;
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};
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type LoadedBatchTasks = {
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tasks: BatchTaskInput[];
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jobs: number | null;
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batchDir: string;
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};
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const MAX_ATTEMPTS = 3;
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const DEFAULT_MAX_WORKERS = 10;
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const POLL_WAIT_MS = 250;
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@ -74,7 +80,9 @@ Options:
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-h, --help Show help
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Batch file format:
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[
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{
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"jobs": 4,
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"tasks": [
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{
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"id": "hero",
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"promptFiles": ["prompts/hero.md"],
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@ -84,6 +92,7 @@ Batch file format:
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"ar": "16:9"
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}
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]
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}
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Behavior:
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- Batch mode automatically runs in parallel when pending tasks >= 2
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@ -433,6 +442,17 @@ function parsePositiveInt(value: string | undefined): number | null {
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return Number.isFinite(parsed) && parsed > 0 ? parsed : null;
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}
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function parsePositiveBatchInt(value: unknown): number | null {
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if (value === null || value === undefined) return null;
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if (typeof value === "number") {
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return Number.isInteger(value) && value > 0 ? value : null;
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}
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if (typeof value === "string") {
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return parsePositiveInt(value);
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}
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return null;
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}
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function getConfiguredMaxWorkers(extendConfig: Partial<ExtendConfig>): number {
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const envValue = parsePositiveInt(process.env.BAOYU_IMAGE_GEN_MAX_WORKERS);
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const configValue = extendConfig.batch?.max_workers ?? null;
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@ -626,27 +646,49 @@ async function prepareSingleTask(args: CliArgs, extendConfig: Partial<ExtendConf
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};
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}
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async function loadBatchTasks(batchFilePath: string): Promise<BatchTaskInput[]> {
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const content = await readFile(path.resolve(batchFilePath), "utf8");
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async function loadBatchTasks(batchFilePath: string): Promise<LoadedBatchTasks> {
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const resolvedBatchFilePath = path.resolve(batchFilePath);
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const content = await readFile(resolvedBatchFilePath, "utf8");
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const parsed = JSON.parse(content.replace(/^\uFEFF/, "")) as BatchFile;
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if (Array.isArray(parsed)) return parsed;
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if (parsed && typeof parsed === "object" && Array.isArray(parsed.tasks)) return parsed.tasks;
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const batchDir = path.dirname(resolvedBatchFilePath);
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if (Array.isArray(parsed)) {
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return {
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tasks: parsed,
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jobs: null,
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batchDir,
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};
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}
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if (parsed && typeof parsed === "object" && Array.isArray(parsed.tasks)) {
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const jobs = parsePositiveBatchInt(parsed.jobs);
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if (parsed.jobs !== undefined && parsed.jobs !== null && jobs === null) {
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throw new Error("Invalid batch file. jobs must be a positive integer when provided.");
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}
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return {
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tasks: parsed.tasks,
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jobs,
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batchDir,
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};
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}
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throw new Error("Invalid batch file. Expected an array of tasks or an object with a tasks array.");
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}
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function createTaskArgs(baseArgs: CliArgs, task: BatchTaskInput): CliArgs {
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function resolveBatchPath(batchDir: string, filePath: string): string {
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return path.isAbsolute(filePath) ? filePath : path.resolve(batchDir, filePath);
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}
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function createTaskArgs(baseArgs: CliArgs, task: BatchTaskInput, batchDir: string): CliArgs {
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return {
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...baseArgs,
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prompt: task.prompt ?? null,
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promptFiles: task.promptFiles ? [...task.promptFiles] : [],
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imagePath: task.image ?? null,
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promptFiles: task.promptFiles ? task.promptFiles.map((filePath) => resolveBatchPath(batchDir, filePath)) : [],
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imagePath: task.image ? resolveBatchPath(batchDir, task.image) : null,
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provider: task.provider ?? baseArgs.provider ?? null,
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model: task.model ?? baseArgs.model ?? null,
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aspectRatio: task.ar ?? baseArgs.aspectRatio ?? null,
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size: task.size ?? baseArgs.size ?? null,
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quality: task.quality ?? baseArgs.quality ?? null,
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imageSize: task.imageSize ?? baseArgs.imageSize ?? null,
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referenceImages: task.ref ? [...task.ref] : [],
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referenceImages: task.ref ? task.ref.map((filePath) => resolveBatchPath(batchDir, filePath)) : [],
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n: task.n ?? baseArgs.n,
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batchFile: null,
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jobs: baseArgs.jobs,
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@ -658,15 +700,15 @@ function createTaskArgs(baseArgs: CliArgs, task: BatchTaskInput): CliArgs {
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async function prepareBatchTasks(
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args: CliArgs,
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extendConfig: Partial<ExtendConfig>
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): Promise<PreparedTask[]> {
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): Promise<{ tasks: PreparedTask[]; jobs: number | null }> {
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if (!args.batchFile) throw new Error("--batchfile is required in batch mode");
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const taskInputs = await loadBatchTasks(args.batchFile);
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const { tasks: taskInputs, jobs: batchJobs, batchDir } = await loadBatchTasks(args.batchFile);
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if (taskInputs.length === 0) throw new Error("Batch file does not contain any tasks.");
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const prepared: PreparedTask[] = [];
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for (let i = 0; i < taskInputs.length; i++) {
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const task = taskInputs[i]!;
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const taskArgs = createTaskArgs(args, task);
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const taskArgs = createTaskArgs(args, task, batchDir);
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const prompt = await loadPromptForArgs(taskArgs);
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if (!prompt) throw new Error(`Task ${i + 1} is missing prompt or promptFiles.`);
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if (!taskArgs.imagePath) throw new Error(`Task ${i + 1} is missing image output path.`);
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@ -686,7 +728,10 @@ async function prepareBatchTasks(
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});
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}
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return prepared;
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return {
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tasks: prepared,
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jobs: args.jobs ?? batchJobs,
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};
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}
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async function writeImage(outputPath: string, imageData: Uint8Array): Promise<void> {
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@ -861,8 +906,8 @@ async function runSingleMode(args: CliArgs, extendConfig: Partial<ExtendConfig>)
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}
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async function runBatchMode(args: CliArgs, extendConfig: Partial<ExtendConfig>): Promise<void> {
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const tasks = await prepareBatchTasks(args, extendConfig);
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const results = await runBatchTasks(tasks, args.jobs, extendConfig);
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const { tasks, jobs } = await prepareBatchTasks(args, extendConfig);
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const results = await runBatchTasks(tasks, jobs, extendConfig);
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printBatchSummary(results);
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if (args.json) {
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@ -34,7 +34,12 @@ export type BatchTaskInput = {
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n?: number;
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};
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export type BatchFile = BatchTaskInput[] | { tasks: BatchTaskInput[] };
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export type BatchFile =
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| BatchTaskInput[]
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| {
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tasks: BatchTaskInput[];
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jobs?: number | null;
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};
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export type ExtendConfig = {
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version: number;
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@ -258,11 +258,11 @@ After the final translation is written, do a lightweight image-language pass:
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3. If any image likely contains a main text language that does not match the translated article language, proactively remind the user
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4. The reminder must be a list only. Do not automatically localize those images unless the user asks
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Reminder format:
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Reminder format (use whatever image syntax the article already uses — standard markdown or wikilink):
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```text
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Possible image localization needed:
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- ![[attachments/example-cover.png]]: likely still contains source-language text while the article is now in target language
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- ![[attachments/example-diagram.png]]: likely text-heavy framework graphic, check whether labels need translation
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- : likely still contains source-language text while the article is now in target language
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- : likely text-heavy framework graphic, check whether labels need translation
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```
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Display summary:
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