From 5acef7151b5abe2eb3e561e3345ed644fed208cd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jim=20Liu=20=E5=AE=9D=E7=8E=89?= Date: Mon, 9 Mar 2026 00:07:45 -0500 Subject: [PATCH] feat: add batch parallel image generation and provider-level throttling MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add --batchfile and --jobs flags for multi-image parallel generation with per-provider concurrency control and rate limiting - Refactor main.ts into prepareSingleTask/prepareBatchTasks/runBatchTasks with worker pool pattern and up to 3 retries per image - Fix Replicate provider: use image_input array (nano-banana-pro schema), add match_input_image aspect ratio, add quality-to-resolution mapping - Improve OpenAI error message for missing API key (Codex auth hint) - Expand non-retryable error detection (4xx codes, disabled models) - Add batch config to EXTEND.md schema (max_workers, provider_limits) - Add build-batch.ts for article-illustrator batch workflow integration - Add image-language awareness pass to baoyu-translate Co-authored-by: 敖氏 --- .../scripts/build-batch.ts | 156 +++++ skills/baoyu-image-gen/SKILL.md | 52 +- .../references/config/preferences-schema.md | 27 +- skills/baoyu-image-gen/scripts/main.ts | 622 +++++++++++++++--- .../scripts/providers/openai.ts | 6 +- .../scripts/providers/replicate.ts | 16 +- skills/baoyu-image-gen/scripts/types.ts | 31 + skills/baoyu-translate/SKILL.md | 17 + 8 files changed, 788 insertions(+), 139 deletions(-) create mode 100644 skills/baoyu-article-illustrator/scripts/build-batch.ts diff --git a/skills/baoyu-article-illustrator/scripts/build-batch.ts b/skills/baoyu-article-illustrator/scripts/build-batch.ts new file mode 100644 index 0000000..5105e98 --- /dev/null +++ b/skills/baoyu-article-illustrator/scripts/build-batch.ts @@ -0,0 +1,156 @@ +import path from "node:path"; +import process from "node:process"; +import { readdir, readFile, writeFile } from "node:fs/promises"; + +type CliArgs = { + outlinePath: string | null; + promptsDir: string | null; + outputPath: string | null; + imagesDir: string | null; + provider: string; + model: string; + aspectRatio: string; + quality: string; + jobs: number | null; + help: boolean; +}; + +type OutlineEntry = { + index: number; + filename: string; +}; + +function printUsage(): void { + console.log(`Usage: + npx -y tsx scripts/build-batch.ts --outline outline.md --prompts prompts --output batch.json --images-dir attachments + +Options: + --outline Path to outline.md + --prompts Path to prompts directory + --output Path to output batch.json + --images-dir Directory for generated images + --provider Provider for baoyu-image-gen batch tasks (default: replicate) + --model Model for baoyu-image-gen batch tasks (default: google/nano-banana-pro) + --ar Aspect ratio for all tasks (default: 16:9) + --quality Quality for all tasks (default: 2k) + --jobs Recommended worker count metadata (optional) + -h, --help Show help`); +} + +function parseArgs(argv: string[]): CliArgs { + const args: CliArgs = { + outlinePath: null, + promptsDir: null, + outputPath: null, + imagesDir: null, + provider: "replicate", + model: "google/nano-banana-pro", + aspectRatio: "16:9", + quality: "2k", + jobs: null, + help: false, + }; + + for (let i = 0; i < argv.length; i++) { + const current = argv[i]!; + if (current === "--outline") args.outlinePath = argv[++i] ?? null; + else if (current === "--prompts") args.promptsDir = argv[++i] ?? null; + else if (current === "--output") args.outputPath = argv[++i] ?? null; + else if (current === "--images-dir") args.imagesDir = argv[++i] ?? null; + else if (current === "--provider") args.provider = argv[++i] ?? args.provider; + else if (current === "--model") args.model = argv[++i] ?? args.model; + else if (current === "--ar") args.aspectRatio = argv[++i] ?? args.aspectRatio; + else if (current === "--quality") args.quality = argv[++i] ?? args.quality; + else if (current === "--jobs") { + const value = argv[++i]; + args.jobs = value ? parseInt(value, 10) : null; + } else if (current === "--help" || current === "-h") { + args.help = true; + } + } + return args; +} + +function parseOutline(content: string): OutlineEntry[] { + const entries: OutlineEntry[] = []; + const blocks = content.split(/^## Illustration\s+/m).slice(1); + + for (const block of blocks) { + const indexMatch = block.match(/^(\d+)/); + const filenameMatch = block.match(/\*\*Filename\*\*:\s*(.+)/); + if (indexMatch && filenameMatch) { + entries.push({ + index: parseInt(indexMatch[1]!, 10), + filename: filenameMatch[1]!.trim(), + }); + } + } + return entries; +} + +async function findPromptFile(promptsDir: string, entry: OutlineEntry): Promise { + const files = await readdir(promptsDir); + const prefix = String(entry.index).padStart(2, "0"); + const match = files.find((f) => f.startsWith(prefix) && f.endsWith(".md")); + return match ? path.join(promptsDir, match) : null; +} + +async function main(): Promise { + const args = parseArgs(process.argv.slice(2)); + if (args.help) { + printUsage(); + return; + } + + if (!args.outlinePath) { + console.error("Error: --outline is required"); + process.exit(1); + } + if (!args.promptsDir) { + console.error("Error: --prompts is required"); + process.exit(1); + } + if (!args.outputPath) { + console.error("Error: --output is required"); + process.exit(1); + } + + const outlineContent = await readFile(args.outlinePath, "utf8"); + const entries = parseOutline(outlineContent); + + if (entries.length === 0) { + console.error("No illustration entries found in outline."); + process.exit(1); + } + + const tasks = []; + for (const entry of entries) { + const promptFile = await findPromptFile(args.promptsDir, entry); + if (!promptFile) { + console.error(`Warning: No prompt file found for illustration ${entry.index}, skipping.`); + continue; + } + + const imageDir = args.imagesDir ?? path.dirname(args.outputPath); + tasks.push({ + id: `illustration-${String(entry.index).padStart(2, "0")}`, + promptFiles: [promptFile], + image: path.join(imageDir, entry.filename), + provider: args.provider, + model: args.model, + ar: args.aspectRatio, + quality: args.quality, + }); + } + + const output: Record = { tasks }; + if (args.jobs) output.jobs = args.jobs; + + await writeFile(args.outputPath, JSON.stringify(output, null, 2) + "\n"); + console.log(`Batch file written: ${args.outputPath} (${tasks.length} tasks)`); +} + +main().catch((error) => { + console.error(error instanceof Error ? error.message : String(error)); + process.exit(1); +}); diff --git a/skills/baoyu-image-gen/SKILL.md b/skills/baoyu-image-gen/SKILL.md index 5086217..85fac82 100644 --- a/skills/baoyu-image-gen/SKILL.md +++ b/skills/baoyu-image-gen/SKILL.md @@ -55,7 +55,7 @@ if (Test-Path "$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md") { "user" } | `.baoyu-skills/baoyu-image-gen/EXTEND.md` | Project directory | | `$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md` | User home | -**EXTEND.md Supports**: Default provider | Default quality | Default aspect ratio | Default image size | Default models +**EXTEND.md Supports**: Default provider | Default quality | Default aspect ratio | Default image size | Default models | Batch worker cap | Provider-specific batch limits Schema: `references/config/preferences-schema.md` @@ -91,6 +91,12 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider r # Replicate with specific model ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana + +# Batch mode with saved prompt files +${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json + +# Batch mode with explicit worker count +${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4 --json ``` ## Options @@ -99,14 +105,16 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider r |--------|-------------| | `--prompt `, `-p` | Prompt text | | `--promptfiles ` | Read prompt from files (concatenated) | -| `--image ` | Output image path (required) | -| `--provider google\|openai\|dashscope\|replicate` | Force provider (default: google) | -| `--model `, `-m` | Model ID (Google: `gemini-3-pro-image-preview`, `gemini-3.1-flash-image-preview`; OpenAI: `gpt-image-1.5`) | +| `--image ` | Output image path (required in single-image mode) | +| `--batchfile ` | JSON batch file for multi-image generation | +| `--jobs ` | Worker count for batch mode (default: auto, max from config, built-in default 10) | +| `--provider google\|openai\|dashscope\|replicate` | Force provider (default: auto-detect) | +| `--model `, `-m` | Model ID (Google: `gemini-3-pro-image-preview`, `gemini-3.1-flash-image-preview`; OpenAI: `gpt-image-1.5`, `gpt-image-1`) | | `--ar ` | Aspect ratio (e.g., `16:9`, `1:1`, `4:3`) | | `--size ` | Size (e.g., `1024x1024`) | -| `--quality normal\|2k` | Quality preset (default: 2k) | +| `--quality normal\|2k` | Quality preset (default: `2k`) | | `--imageSize 1K\|2K\|4K` | Image size for Google (default: from quality) | -| `--ref ` | Reference images. Supported by Google multimodal (`gemini-3-pro-image-preview`, `gemini-3-flash-preview`, `gemini-3.1-flash-image-preview`) and OpenAI edits (GPT Image models). If provider omitted: Google first, then OpenAI | +| `--ref ` | Reference images. Supported by Google multimodal, OpenAI GPT Image edits, and Replicate | | `--n ` | Number of images | | `--json` | JSON output | @@ -126,6 +134,9 @@ ${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider r | `GOOGLE_BASE_URL` | Custom Google endpoint | | `DASHSCOPE_BASE_URL` | Custom DashScope endpoint | | `REPLICATE_BASE_URL` | Custom Replicate endpoint | +| `BAOYU_IMAGE_GEN_MAX_WORKERS` | Override batch worker cap | +| `BAOYU_IMAGE_GEN__CONCURRENCY` | Override provider concurrency, e.g. `BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY` | +| `BAOYU_IMAGE_GEN__START_INTERVAL_MS` | Override provider start gap, e.g. `BAOYU_IMAGE_GEN_REPLICATE_START_INTERVAL_MS` | **Load Priority**: CLI args > EXTEND.md > env vars > `/.baoyu-skills/.env` > `~/.baoyu-skills/.env` @@ -187,36 +198,29 @@ Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1` ## Generation Mode -**Default**: Sequential generation (one image at a time). This ensures stable output and easier debugging. +**Default**: Sequential generation. -**Parallel Generation**: Only use when user explicitly requests parallel/concurrent generation. +**Batch Parallel Generation**: When `--batchfile` contains 2 or more pending tasks, the script automatically enables parallel generation. | Mode | When to Use | |------|-------------| | Sequential (default) | Normal usage, single images, small batches | -| Parallel | User explicitly requests, large batches (10+) | +| Parallel batch | Batch mode with 2+ tasks | -**Parallel Settings** (when requested): +Parallel behavior: -| Setting | Value | -|---------|-------| -| Recommended concurrency | 4 subagents | -| Max concurrency | 8 subagents | -| Use case | Large batch generation when user requests parallel | - -**Agent Implementation** (parallel mode only): -``` -# Launch multiple generations in parallel using Task tool -# Each Task runs as background subagent with run_in_background=true -# Collect results via TaskOutput when all complete -``` +- Default worker count is automatic, capped by config, built-in default 10 +- Provider-specific throttling is applied only in batch mode, and the built-in defaults are tuned for faster throughput while still avoiding obvious RPM bursts +- You can override worker count with `--jobs ` +- Each image retries automatically up to 3 attempts +- Final output includes success count, failure count, and per-image failure reasons ## Error Handling - Missing API key → error with setup instructions -- Generation failure → auto-retry once +- Generation failure → auto-retry up to 3 attempts per image - Invalid aspect ratio → warning, proceed with default -- Reference images with unsupported provider/model → error with fix hint (switch to Google multimodal: `gemini-3-pro-image-preview`, `gemini-3.1-flash-image-preview`; or OpenAI GPT Image edits) +- Reference images with unsupported provider/model → error with fix hint ## Extension Support diff --git a/skills/baoyu-image-gen/references/config/preferences-schema.md b/skills/baoyu-image-gen/references/config/preferences-schema.md index 362c7b9..8c79021 100644 --- a/skills/baoyu-image-gen/references/config/preferences-schema.md +++ b/skills/baoyu-image-gen/references/config/preferences-schema.md @@ -21,9 +21,25 @@ default_image_size: null # 1K|2K|4K|null (Google only, overrides quality) default_model: google: null # e.g., "gemini-3-pro-image-preview", "gemini-3.1-flash-image-preview" - openai: null # e.g., "gpt-image-1.5" + openai: null # e.g., "gpt-image-1.5", "gpt-image-1" dashscope: null # e.g., "z-image-turbo" replicate: null # e.g., "google/nano-banana-pro" + +batch: + max_workers: 10 + provider_limits: + replicate: + concurrency: 5 + start_interval_ms: 700 + google: + concurrency: 3 + start_interval_ms: 1100 + openai: + concurrency: 3 + start_interval_ms: 1100 + dashscope: + concurrency: 3 + start_interval_ms: 1100 --- ``` @@ -40,6 +56,9 @@ default_model: | `default_model.openai` | string\|null | null | OpenAI default model | | `default_model.dashscope` | string\|null | null | DashScope default model | | `default_model.replicate` | string\|null | null | Replicate default model | +| `batch.max_workers` | int\|null | 10 | Batch worker cap | +| `batch.provider_limits..concurrency` | int\|null | provider default | Max simultaneous requests per provider | +| `batch.provider_limits..start_interval_ms` | int\|null | provider default | Minimum gap between request starts per provider | ## Examples @@ -65,5 +84,11 @@ default_model: openai: "gpt-image-1.5" dashscope: "z-image-turbo" replicate: "google/nano-banana-pro" +batch: + max_workers: 10 + provider_limits: + replicate: + concurrency: 5 + start_interval_ms: 700 --- ``` diff --git a/skills/baoyu-image-gen/scripts/main.ts b/skills/baoyu-image-gen/scripts/main.ts index 85b940f..b2ba3d8 100644 --- a/skills/baoyu-image-gen/scripts/main.ts +++ b/skills/baoyu-image-gen/scripts/main.ts @@ -2,34 +2,99 @@ import path from "node:path"; import process from "node:process"; import { homedir } from "node:os"; import { access, mkdir, readFile, writeFile } from "node:fs/promises"; -import type { CliArgs, Provider, ExtendConfig } from "./types"; +import type { + BatchFile, + BatchTaskInput, + CliArgs, + ExtendConfig, + Provider, +} from "./types"; + +type ProviderModule = { + getDefaultModel: () => string; + generateImage: (prompt: string, model: string, args: CliArgs) => Promise; +}; + +type PreparedTask = { + id: string; + prompt: string; + args: CliArgs; + provider: Provider; + model: string; + outputPath: string; + providerModule: ProviderModule; +}; + +type TaskResult = { + id: string; + provider: Provider; + model: string; + outputPath: string; + success: boolean; + attempts: number; + error: string | null; +}; + +type ProviderRateLimit = { + concurrency: number; + startIntervalMs: number; +}; + +const MAX_ATTEMPTS = 3; +const DEFAULT_MAX_WORKERS = 10; +const POLL_WAIT_MS = 250; +const DEFAULT_PROVIDER_RATE_LIMITS: Record = { + replicate: { concurrency: 5, startIntervalMs: 700 }, + google: { concurrency: 3, startIntervalMs: 1100 }, + openai: { concurrency: 3, startIntervalMs: 1100 }, + dashscope: { concurrency: 3, startIntervalMs: 1100 }, +}; function printUsage(): void { console.log(`Usage: npx -y bun scripts/main.ts --prompt "A cat" --image cat.png - npx -y bun scripts/main.ts --prompt "A landscape" --image landscape.png --ar 16:9 npx -y bun scripts/main.ts --promptfiles system.md content.md --image out.png + npx -y bun scripts/main.ts --batchfile batch.json Options: -p, --prompt Prompt text --promptfiles Read prompt from files (concatenated) - --image Output image path (required) + --image Output image path (required in single-image mode) + --batchfile JSON batch file for multi-image generation + --jobs Worker count for batch mode (default: auto, max from config, built-in default 10) --provider google|openai|dashscope|replicate Force provider (auto-detect by default) -m, --model Model ID --ar Aspect ratio (e.g., 16:9, 1:1, 4:3) --size Size (e.g., 1024x1024) --quality normal|2k Quality preset (default: 2k) --imageSize 1K|2K|4K Image size for Google (default: from quality) - --ref Reference images (Google multimodal or OpenAI edits) - --n Number of images (default: 1) + --ref Reference images (Google multimodal, OpenAI GPT Image edits, or Replicate) + --n Number of images for the current task (default: 1) --json JSON output -h, --help Show help +Batch file format: + [ + { + "id": "hero", + "promptFiles": ["prompts/hero.md"], + "image": "out/hero.png", + "provider": "replicate", + "model": "google/nano-banana-pro", + "ar": "16:9" + } + ] + +Behavior: + - Batch mode automatically runs in parallel when pending tasks >= 2 + - Each image retries automatically up to 3 attempts + - Batch summary reports success count, failure count, and per-image errors + Environment variables: OPENAI_API_KEY OpenAI API key GOOGLE_API_KEY Google API key GEMINI_API_KEY Gemini API key (alias for GOOGLE_API_KEY) - DASHSCOPE_API_KEY DashScope API key (阿里云通义万象) + DASHSCOPE_API_KEY DashScope API key REPLICATE_API_TOKEN Replicate API token OPENAI_IMAGE_MODEL Default OpenAI model (gpt-image-1.5) GOOGLE_IMAGE_MODEL Default Google model (gemini-3-pro-image-preview) @@ -40,6 +105,9 @@ Environment variables: GOOGLE_BASE_URL Custom Google endpoint DASHSCOPE_BASE_URL Custom DashScope endpoint REPLICATE_BASE_URL Custom Replicate endpoint + BAOYU_IMAGE_GEN_MAX_WORKERS Override batch worker cap + BAOYU_IMAGE_GEN__CONCURRENCY Override provider concurrency + BAOYU_IMAGE_GEN__START_INTERVAL_MS Override provider start gap in ms Env file load order: CLI args > EXTEND.md > process.env > /.baoyu-skills/.env > ~/.baoyu-skills/.env`); } @@ -57,6 +125,8 @@ function parseArgs(argv: string[]): CliArgs { imageSize: null, referenceImages: [], n: 1, + batchFile: null, + jobs: null, json: false, help: false, }; @@ -110,9 +180,26 @@ function parseArgs(argv: string[]): CliArgs { continue; } + if (a === "--batchfile") { + const v = argv[++i]; + if (!v) throw new Error("Missing value for --batchfile"); + out.batchFile = v; + continue; + } + + if (a === "--jobs") { + const v = argv[++i]; + if (!v) throw new Error("Missing value for --jobs"); + out.jobs = parseInt(v, 10); + if (isNaN(out.jobs) || out.jobs < 1) throw new Error(`Invalid worker count: ${v}`); + continue; + } + if (a === "--provider") { const v = argv[++i]; - if (v !== "google" && v !== "openai" && v !== "dashscope" && v !== "replicate") throw new Error(`Invalid provider: ${v}`); + if (v !== "google" && v !== "openai" && v !== "dashscope" && v !== "replicate") { + throw new Error(`Invalid provider: ${v}`); + } out.provider = v; continue; } @@ -228,9 +315,11 @@ function parseSimpleYaml(yaml: string): Partial { const config: Partial = {}; const lines = yaml.split("\n"); let currentKey: string | null = null; + let currentProvider: Provider | null = null; for (const line of lines) { const trimmed = line.trim(); + const indent = line.match(/^\s*/)?.[0].length ?? 0; if (!trimmed || trimmed.startsWith("#")) continue; if (trimmed.includes(":") && !trimmed.startsWith("-")) { @@ -247,18 +336,57 @@ function parseSimpleYaml(yaml: string): Partial { } else if (key === "default_provider") { config.default_provider = value === "null" ? null : (value as Provider); } else if (key === "default_quality") { - config.default_quality = value === "null" ? null : (value as "normal" | "2k"); + config.default_quality = value === "null" ? null : value as "normal" | "2k"; } else if (key === "default_aspect_ratio") { const cleaned = value.replace(/['"]/g, ""); config.default_aspect_ratio = cleaned === "null" ? null : cleaned; } else if (key === "default_image_size") { - config.default_image_size = value === "null" ? null : (value as "1K" | "2K" | "4K"); + config.default_image_size = value === "null" ? null : value as "1K" | "2K" | "4K"; } else if (key === "default_model") { config.default_model = { google: null, openai: null, dashscope: null, replicate: null }; currentKey = "default_model"; - } else if (currentKey === "default_model" && (key === "google" || key === "openai" || key === "dashscope" || key === "replicate")) { + currentProvider = null; + } else if (key === "batch") { + config.batch = {}; + currentKey = "batch"; + currentProvider = null; + } else if (currentKey === "batch" && indent >= 2 && key === "max_workers") { + config.batch ??= {}; + config.batch.max_workers = value === "null" ? null : parseInt(value, 10); + } else if (currentKey === "batch" && indent >= 2 && key === "provider_limits") { + config.batch ??= {}; + config.batch.provider_limits ??= {}; + currentKey = "provider_limits"; + currentProvider = null; + } else if ( + currentKey === "provider_limits" && + indent >= 4 && + (key === "google" || key === "openai" || key === "dashscope" || key === "replicate") + ) { + config.batch ??= {}; + config.batch.provider_limits ??= {}; + config.batch.provider_limits[key] ??= {}; + currentProvider = key; + } else if ( + currentKey === "default_model" && + (key === "google" || key === "openai" || key === "dashscope" || key === "replicate") + ) { const cleaned = value.replace(/['"]/g, ""); config.default_model![key] = cleaned === "null" ? null : cleaned; + } else if ( + currentKey === "provider_limits" && + currentProvider && + indent >= 6 && + (key === "concurrency" || key === "start_interval_ms") + ) { + config.batch ??= {}; + config.batch.provider_limits ??= {}; + const providerLimit = (config.batch.provider_limits[currentProvider] ??= {}); + if (key === "concurrency") { + providerLimit.concurrency = value === "null" ? null : parseInt(value, 10); + } else { + providerLimit.start_interval_ms = value === "null" ? null : parseInt(value, 10); + } } } } @@ -280,7 +408,6 @@ async function loadExtendConfig(): Promise> { const content = await readFile(p, "utf8"); const yaml = extractYamlFrontMatter(content); if (!yaml) continue; - return parseSimpleYaml(yaml); } catch { continue; @@ -300,6 +427,46 @@ function mergeConfig(args: CliArgs, extend: Partial): CliArgs { }; } +function parsePositiveInt(value: string | undefined): number | null { + if (!value) return null; + const parsed = parseInt(value, 10); + return Number.isFinite(parsed) && parsed > 0 ? parsed : null; +} + +function getConfiguredMaxWorkers(extendConfig: Partial): number { + const envValue = parsePositiveInt(process.env.BAOYU_IMAGE_GEN_MAX_WORKERS); + const configValue = extendConfig.batch?.max_workers ?? null; + return Math.max(1, envValue ?? configValue ?? DEFAULT_MAX_WORKERS); +} + +function getConfiguredProviderRateLimits( + extendConfig: Partial +): Record { + const configured: Record = { + replicate: { ...DEFAULT_PROVIDER_RATE_LIMITS.replicate }, + google: { ...DEFAULT_PROVIDER_RATE_LIMITS.google }, + openai: { ...DEFAULT_PROVIDER_RATE_LIMITS.openai }, + dashscope: { ...DEFAULT_PROVIDER_RATE_LIMITS.dashscope }, + }; + + for (const provider of ["replicate", "google", "openai", "dashscope"] as Provider[]) { + const envPrefix = `BAOYU_IMAGE_GEN_${provider.toUpperCase()}`; + const extendLimit = extendConfig.batch?.provider_limits?.[provider]; + configured[provider] = { + concurrency: + parsePositiveInt(process.env[`${envPrefix}_CONCURRENCY`]) ?? + extendLimit?.concurrency ?? + configured[provider].concurrency, + startIntervalMs: + parsePositiveInt(process.env[`${envPrefix}_START_INTERVAL_MS`]) ?? + extendLimit?.start_interval_ms ?? + configured[provider].startIntervalMs, + }; + } + + return configured; +} + async function readPromptFromFiles(files: string[]): Promise { const parts: string[] = []; for (const f of files) { @@ -311,9 +478,12 @@ async function readPromptFromFiles(files: string[]): Promise { async function readPromptFromStdin(): Promise { if (process.stdin.isTTY) return null; try { - const t = await Bun.stdin.text(); - const v = t.trim(); - return v.length > 0 ? v : null; + const chunks: Buffer[] = []; + for await (const chunk of process.stdin) { + chunks.push(Buffer.isBuffer(chunk) ? chunk : Buffer.from(chunk)); + } + const value = Buffer.concat(chunks).toString("utf8").trim(); + return value.length > 0 ? value : null; } catch { return null; } @@ -327,7 +497,13 @@ function normalizeOutputImagePath(p: string): string { } function detectProvider(args: CliArgs): Provider { - if (args.referenceImages.length > 0 && args.provider && args.provider !== "google" && args.provider !== "openai" && args.provider !== "replicate") { + if ( + args.referenceImages.length > 0 && + args.provider && + args.provider !== "google" && + args.provider !== "openai" && + args.provider !== "replicate" + ) { throw new Error( "Reference images require a ref-capable provider. Use --provider google (Gemini multimodal), --provider openai (GPT Image edits), or --provider replicate." ); @@ -349,13 +525,18 @@ function detectProvider(args: CliArgs): Provider { ); } - const available = [hasGoogle && "google", hasOpenai && "openai", hasDashscope && "dashscope", hasReplicate && "replicate"].filter(Boolean) as Provider[]; + const available = [ + hasReplicate && "replicate", + hasGoogle && "google", + hasOpenai && "openai", + hasDashscope && "dashscope", + ].filter(Boolean) as Provider[]; if (available.length === 1) return available[0]!; if (available.length > 1) return available[0]!; throw new Error( - "No API key found. Set GOOGLE_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY, DASHSCOPE_API_KEY, or REPLICATE_API_TOKEN.\n" + + "No API key found. Set REPLICATE_API_TOKEN, GOOGLE_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY, or DASHSCOPE_API_KEY.\n" + "Create ~/.baoyu-skills/.env or /.baoyu-skills/.env with your keys." ); } @@ -371,11 +552,6 @@ async function validateReferenceImages(referenceImages: string[]): Promise } } -type ProviderModule = { - getDefaultModel: () => string; - generateImage: (prompt: string, model: string, args: CliArgs) => Promise; -}; - function isRetryableGenerationError(error: unknown): boolean { const msg = error instanceof Error ? error.message : String(error); const nonRetryableMarkers = [ @@ -384,26 +560,328 @@ function isRetryableGenerationError(error: unknown): boolean { "only supported", "No API key found", "is required", + "Invalid ", + "Unexpected ", + "API error (400)", + "API error (401)", + "API error (402)", + "API error (403)", + "API error (404)", + "temporarily disabled", ]; return !nonRetryableMarkers.some((marker) => msg.includes(marker)); } async function loadProviderModule(provider: Provider): Promise { - if (provider === "google") { - return (await import("./providers/google")) as ProviderModule; - } - if (provider === "dashscope") { - return (await import("./providers/dashscope")) as ProviderModule; - } - if (provider === "replicate") { - return (await import("./providers/replicate")) as ProviderModule; - } + if (provider === "google") return (await import("./providers/google")) as ProviderModule; + if (provider === "dashscope") return (await import("./providers/dashscope")) as ProviderModule; + if (provider === "replicate") return (await import("./providers/replicate")) as ProviderModule; return (await import("./providers/openai")) as ProviderModule; } +async function loadPromptForArgs(args: CliArgs): Promise { + let prompt: string | null = args.prompt; + if (!prompt && args.promptFiles.length > 0) { + prompt = await readPromptFromFiles(args.promptFiles); + } + return prompt; +} + +function getModelForProvider( + provider: Provider, + requestedModel: string | null, + extendConfig: Partial, + providerModule: ProviderModule +): string { + if (requestedModel) return requestedModel; + if (extendConfig.default_model) { + if (provider === "google" && extendConfig.default_model.google) return extendConfig.default_model.google; + if (provider === "openai" && extendConfig.default_model.openai) return extendConfig.default_model.openai; + if (provider === "dashscope" && extendConfig.default_model.dashscope) return extendConfig.default_model.dashscope; + if (provider === "replicate" && extendConfig.default_model.replicate) return extendConfig.default_model.replicate; + } + return providerModule.getDefaultModel(); +} + +async function prepareSingleTask(args: CliArgs, extendConfig: Partial): Promise { + if (!args.quality) args.quality = "2k"; + + const prompt = (await loadPromptForArgs(args)) ?? (await readPromptFromStdin()); + if (!prompt) throw new Error("Prompt is required"); + if (!args.imagePath) throw new Error("--image is required"); + if (args.referenceImages.length > 0) await validateReferenceImages(args.referenceImages); + + const provider = detectProvider(args); + const providerModule = await loadProviderModule(provider); + const model = getModelForProvider(provider, args.model, extendConfig, providerModule); + + return { + id: "single", + prompt, + args, + provider, + model, + outputPath: normalizeOutputImagePath(args.imagePath), + providerModule, + }; +} + +async function loadBatchTasks(batchFilePath: string): Promise { + const content = await readFile(path.resolve(batchFilePath), "utf8"); + const parsed = JSON.parse(content.replace(/^\uFEFF/, "")) as BatchFile; + if (Array.isArray(parsed)) return parsed; + if (parsed && typeof parsed === "object" && Array.isArray(parsed.tasks)) return parsed.tasks; + throw new Error("Invalid batch file. Expected an array of tasks or an object with a tasks array."); +} + +function createTaskArgs(baseArgs: CliArgs, task: BatchTaskInput): CliArgs { + return { + ...baseArgs, + prompt: task.prompt ?? null, + promptFiles: task.promptFiles ? [...task.promptFiles] : [], + imagePath: task.image ?? null, + provider: task.provider ?? baseArgs.provider ?? null, + model: task.model ?? baseArgs.model ?? null, + aspectRatio: task.ar ?? baseArgs.aspectRatio ?? null, + size: task.size ?? baseArgs.size ?? null, + quality: task.quality ?? baseArgs.quality ?? null, + imageSize: task.imageSize ?? baseArgs.imageSize ?? null, + referenceImages: task.ref ? [...task.ref] : [], + n: task.n ?? baseArgs.n, + batchFile: null, + jobs: baseArgs.jobs, + json: baseArgs.json, + help: false, + }; +} + +async function prepareBatchTasks( + args: CliArgs, + extendConfig: Partial +): Promise { + if (!args.batchFile) throw new Error("--batchfile is required in batch mode"); + const taskInputs = await loadBatchTasks(args.batchFile); + if (taskInputs.length === 0) throw new Error("Batch file does not contain any tasks."); + + const prepared: PreparedTask[] = []; + for (let i = 0; i < taskInputs.length; i++) { + const task = taskInputs[i]!; + const taskArgs = createTaskArgs(args, task); + const prompt = await loadPromptForArgs(taskArgs); + if (!prompt) throw new Error(`Task ${i + 1} is missing prompt or promptFiles.`); + if (!taskArgs.imagePath) throw new Error(`Task ${i + 1} is missing image output path.`); + if (taskArgs.referenceImages.length > 0) await validateReferenceImages(taskArgs.referenceImages); + + const provider = detectProvider(taskArgs); + const providerModule = await loadProviderModule(provider); + const model = getModelForProvider(provider, taskArgs.model, extendConfig, providerModule); + prepared.push({ + id: task.id || `task-${String(i + 1).padStart(2, "0")}`, + prompt, + args: taskArgs, + provider, + model, + outputPath: normalizeOutputImagePath(taskArgs.imagePath), + providerModule, + }); + } + + return prepared; +} + +async function writeImage(outputPath: string, imageData: Uint8Array): Promise { + await mkdir(path.dirname(outputPath), { recursive: true }); + await writeFile(outputPath, imageData); +} + +async function generatePreparedTask(task: PreparedTask): Promise { + console.error(`Using ${task.provider} / ${task.model} for ${task.id}`); + console.error( + `Switch model: --model | EXTEND.md default_model.${task.provider} | env ${task.provider.toUpperCase()}_IMAGE_MODEL` + ); + + let attempts = 0; + while (attempts < MAX_ATTEMPTS) { + attempts += 1; + try { + const imageData = await task.providerModule.generateImage(task.prompt, task.model, task.args); + await writeImage(task.outputPath, imageData); + return { + id: task.id, + provider: task.provider, + model: task.model, + outputPath: task.outputPath, + success: true, + attempts, + error: null, + }; + } catch (error) { + const message = error instanceof Error ? error.message : String(error); + const canRetry = attempts < MAX_ATTEMPTS && isRetryableGenerationError(error); + if (canRetry) { + console.error(`[${task.id}] Attempt ${attempts}/${MAX_ATTEMPTS} failed, retrying...`); + continue; + } + return { + id: task.id, + provider: task.provider, + model: task.model, + outputPath: task.outputPath, + success: false, + attempts, + error: message, + }; + } + } + + return { + id: task.id, + provider: task.provider, + model: task.model, + outputPath: task.outputPath, + success: false, + attempts: MAX_ATTEMPTS, + error: "Unknown failure", + }; +} + +function createProviderGate(providerRateLimits: Record) { + const state = new Map(); + + return async function acquire(provider: Provider): Promise<() => void> { + const limit = providerRateLimits[provider]; + while (true) { + const current = state.get(provider) ?? { active: 0, lastStartedAt: 0 }; + const now = Date.now(); + const enoughCapacity = current.active < limit.concurrency; + const enoughGap = now - current.lastStartedAt >= limit.startIntervalMs; + if (enoughCapacity && enoughGap) { + state.set(provider, { active: current.active + 1, lastStartedAt: now }); + return () => { + const latest = state.get(provider) ?? { active: 1, lastStartedAt: now }; + state.set(provider, { + active: Math.max(0, latest.active - 1), + lastStartedAt: latest.lastStartedAt, + }); + }; + } + await new Promise((resolve) => setTimeout(resolve, POLL_WAIT_MS)); + } + }; +} + +function getWorkerCount(taskCount: number, jobs: number | null, maxWorkers: number): number { + const requested = jobs ?? Math.min(taskCount, maxWorkers); + return Math.max(1, Math.min(requested, taskCount, maxWorkers)); +} + +async function runBatchTasks( + tasks: PreparedTask[], + jobs: number | null, + extendConfig: Partial +): Promise { + if (tasks.length === 1) { + return [await generatePreparedTask(tasks[0]!)]; + } + + const maxWorkers = getConfiguredMaxWorkers(extendConfig); + const providerRateLimits = getConfiguredProviderRateLimits(extendConfig); + const acquireProvider = createProviderGate(providerRateLimits); + const workerCount = getWorkerCount(tasks.length, jobs, maxWorkers); + console.error(`Batch mode: ${tasks.length} tasks, ${workerCount} workers, parallel mode enabled.`); + for (const provider of ["replicate", "google", "openai", "dashscope"] as Provider[]) { + const limit = providerRateLimits[provider]; + console.error(`- ${provider}: concurrency=${limit.concurrency}, startIntervalMs=${limit.startIntervalMs}`); + } + + let nextIndex = 0; + const results: TaskResult[] = new Array(tasks.length); + + const worker = async (): Promise => { + while (true) { + const currentIndex = nextIndex; + nextIndex += 1; + if (currentIndex >= tasks.length) return; + + const task = tasks[currentIndex]!; + const release = await acquireProvider(task.provider); + try { + results[currentIndex] = await generatePreparedTask(task); + } finally { + release(); + } + } + }; + + await Promise.all(Array.from({ length: workerCount }, () => worker())); + return results; +} + +function printBatchSummary(results: TaskResult[]): void { + const successCount = results.filter((result) => result.success).length; + const failureCount = results.length - successCount; + + console.error(""); + console.error("Batch generation summary:"); + console.error(`- Total: ${results.length}`); + console.error(`- Succeeded: ${successCount}`); + console.error(`- Failed: ${failureCount}`); + + if (failureCount > 0) { + console.error("Failure reasons:"); + for (const result of results.filter((item) => !item.success)) { + console.error(`- ${result.id}: ${result.error}`); + } + } +} + +function emitJson(payload: unknown): void { + console.log(JSON.stringify(payload, null, 2)); +} + +async function runSingleMode(args: CliArgs, extendConfig: Partial): Promise { + const task = await prepareSingleTask(args, extendConfig); + const result = await generatePreparedTask(task); + if (!result.success) { + throw new Error(result.error || "Generation failed"); + } + + if (args.json) { + emitJson({ + savedImage: result.outputPath, + provider: result.provider, + model: result.model, + attempts: result.attempts, + prompt: task.prompt.slice(0, 200), + }); + return; + } + + console.log(result.outputPath); +} + +async function runBatchMode(args: CliArgs, extendConfig: Partial): Promise { + const tasks = await prepareBatchTasks(args, extendConfig); + const results = await runBatchTasks(tasks, args.jobs, extendConfig); + printBatchSummary(results); + + if (args.json) { + emitJson({ + mode: "batch", + total: results.length, + succeeded: results.filter((item) => item.success).length, + failed: results.filter((item) => !item.success).length, + results, + }); + } + + if (results.some((item) => !item.success)) { + process.exitCode = 1; + } +} + async function main(): Promise { const args = parseArgs(process.argv.slice(2)); - if (args.help) { printUsage(); return; @@ -412,86 +890,18 @@ async function main(): Promise { await loadEnv(); const extendConfig = await loadExtendConfig(); const mergedArgs = mergeConfig(args, extendConfig); - if (!mergedArgs.quality) mergedArgs.quality = "2k"; - let prompt: string | null = mergedArgs.prompt; - if (!prompt && mergedArgs.promptFiles.length > 0) prompt = await readPromptFromFiles(mergedArgs.promptFiles); - if (!prompt) prompt = await readPromptFromStdin(); - - if (!prompt) { - console.error("Error: Prompt is required"); - printUsage(); - process.exitCode = 1; + if (mergedArgs.batchFile) { + await runBatchMode(mergedArgs, extendConfig); return; } - if (!mergedArgs.imagePath) { - console.error("Error: --image is required"); - printUsage(); - process.exitCode = 1; - return; - } - - if (mergedArgs.referenceImages.length > 0) { - await validateReferenceImages(mergedArgs.referenceImages); - } - - const provider = detectProvider(mergedArgs); - const providerModule = await loadProviderModule(provider); - - let model = mergedArgs.model; - if (!model && extendConfig.default_model) { - if (provider === "google") model = extendConfig.default_model.google ?? null; - if (provider === "openai") model = extendConfig.default_model.openai ?? null; - if (provider === "dashscope") model = extendConfig.default_model.dashscope ?? null; - if (provider === "replicate") model = extendConfig.default_model.replicate ?? null; - } - model = model || providerModule.getDefaultModel(); - - const outputPath = normalizeOutputImagePath(mergedArgs.imagePath); - - let imageData: Uint8Array; - let retried = false; - - while (true) { - try { - imageData = await providerModule.generateImage(prompt, model, mergedArgs); - break; - } catch (e) { - if (!retried && isRetryableGenerationError(e)) { - retried = true; - console.error("Generation failed, retrying..."); - continue; - } - throw e; - } - } - - const dir = path.dirname(outputPath); - await mkdir(dir, { recursive: true }); - await writeFile(outputPath, imageData); - - if (mergedArgs.json) { - console.log( - JSON.stringify( - { - savedImage: outputPath, - provider, - model, - prompt: prompt.slice(0, 200), - }, - null, - 2 - ) - ); - } else { - console.log(outputPath); - } + await runSingleMode(mergedArgs, extendConfig); } -main().catch((e) => { - const msg = e instanceof Error ? e.message : String(e); - console.error(msg); +main().catch((error) => { + const message = error instanceof Error ? error.message : String(error); + console.error(message); process.exit(1); }); diff --git a/skills/baoyu-image-gen/scripts/providers/openai.ts b/skills/baoyu-image-gen/scripts/providers/openai.ts index b9a41ca..73f6eab 100644 --- a/skills/baoyu-image-gen/scripts/providers/openai.ts +++ b/skills/baoyu-image-gen/scripts/providers/openai.ts @@ -68,7 +68,11 @@ export async function generateImage( const baseURL = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1"; const apiKey = process.env.OPENAI_API_KEY; - if (!apiKey) throw new Error("OPENAI_API_KEY is required"); + if (!apiKey) { + throw new Error( + "OPENAI_API_KEY is required. Codex/ChatGPT desktop login does not automatically grant OpenAI Images API access to this script." + ); + } if (process.env.OPENAI_IMAGE_USE_CHAT === "true") { return generateWithChatCompletions(baseURL, apiKey, prompt, model); diff --git a/skills/baoyu-image-gen/scripts/providers/replicate.ts b/skills/baoyu-image-gen/scripts/providers/replicate.ts index 46772de..e829c23 100644 --- a/skills/baoyu-image-gen/scripts/providers/replicate.ts +++ b/skills/baoyu-image-gen/scripts/providers/replicate.ts @@ -36,22 +36,24 @@ function buildInput(prompt: string, args: CliArgs, referenceImages: string[]): R if (args.aspectRatio) { input.aspect_ratio = args.aspectRatio; + } else if (referenceImages.length > 0) { + input.aspect_ratio = "match_input_image"; } if (args.n > 1) { input.number_of_images = args.n; } + if (args.quality === "normal") { + input.resolution = "1K"; + } else if (args.quality === "2k") { + input.resolution = "2K"; + } + input.output_format = "png"; if (referenceImages.length > 0) { - if (referenceImages.length === 1) { - input.image = referenceImages[0]; - } else { - for (let i = 0; i < referenceImages.length; i++) { - input[`image${i > 0 ? i + 1 : ""}`] = referenceImages[i]; - } - } + input.image_input = referenceImages; } return input; diff --git a/skills/baoyu-image-gen/scripts/types.ts b/skills/baoyu-image-gen/scripts/types.ts index 23b3f70..516d3a1 100644 --- a/skills/baoyu-image-gen/scripts/types.ts +++ b/skills/baoyu-image-gen/scripts/types.ts @@ -13,10 +13,29 @@ export type CliArgs = { imageSize: string | null; referenceImages: string[]; n: number; + batchFile: string | null; + jobs: number | null; json: boolean; help: boolean; }; +export type BatchTaskInput = { + id?: string; + prompt?: string | null; + promptFiles?: string[]; + image?: string; + provider?: Provider | null; + model?: string | null; + ar?: string | null; + size?: string | null; + quality?: Quality | null; + imageSize?: "1K" | "2K" | "4K" | null; + ref?: string[]; + n?: number; +}; + +export type BatchFile = BatchTaskInput[] | { tasks: BatchTaskInput[] }; + export type ExtendConfig = { version: number; default_provider: Provider | null; @@ -29,4 +48,16 @@ export type ExtendConfig = { dashscope: string | null; replicate: string | null; }; + batch?: { + max_workers?: number | null; + provider_limits?: Partial< + Record< + Provider, + { + concurrency?: number | null; + start_interval_ms?: number | null; + } + > + >; + }; }; diff --git a/skills/baoyu-translate/SKILL.md b/skills/baoyu-translate/SKILL.md index 2c18508..2adbe84 100644 --- a/skills/baoyu-translate/SKILL.md +++ b/skills/baoyu-translate/SKILL.md @@ -212,6 +212,7 @@ Before translating chunks: - **Natural flow**: Use idiomatic target language word order and sentence patterns; break or restructure sentences freely when the source structure doesn't work naturally in the target language - **Terminology**: Use standard translations; annotate with original term in parentheses on first occurrence - **Preserve format**: Keep all markdown formatting (headings, bold, italic, images, links, code blocks) +- **Image-language awareness**: Preserve image references exactly during translation, but after the translation is complete, review referenced images and check whether their likely main text language still matches the translated article language - **Frontmatter transformation**: If the source has YAML frontmatter, preserve it in the translation with these changes: (1) Rename metadata fields that describe the *source* article — `url`→`sourceUrl`, `title`→`sourceTitle`, `description`→`sourceDescription`, `author`→`sourceAuthor`, `date`→`sourceDate`, and any similar origin-metadata fields — by adding a `source` prefix (camelCase). (2) Translate the values of text fields (title, description, etc.) and add them as new top-level fields. (3) Keep other fields (tags, categories, custom fields) as-is, translating their values where appropriate - **Respect original**: Maintain original meaning and intent; do not add, remove, or editorialize — but sentence structure and imagery may be adapted freely to serve the meaning - **Translator's notes**: For terms, concepts, or cultural references that target readers may not understand — due to jargon, cultural gaps, or domain-specific knowledge — add a concise explanatory note in parentheses immediately after the term. The note should explain *what it means* in plain language, not just provide the English original. Format: `译文（English original，通俗解释）`. Calibrate annotation depth to the target audience: general readers need more notes than technical readers. Only add notes where genuinely needed; do not over-annotate obvious terms. @@ -250,6 +251,20 @@ Each step reads the previous step's file and builds on it. Final translation is always at `translation.md` in the output directory. +After the final translation is written, do a lightweight image-language pass: + +1. Collect image references from the translated article +2. Identify likely text-heavy images such as covers, screenshots, diagrams, charts, frameworks, and infographics +3. If any image likely contains a main text language that does not match the translated article language, proactively remind the user +4. The reminder must be a list only. Do not automatically localize those images unless the user asks + +Reminder format: +```text +Possible image localization needed: +- ![[attachments/example-cover.png]]: likely still contains source-language text while the article is now in target language +- ![[attachments/example-diagram.png]]: likely text-heavy framework graphic, check whether labels need translation +``` + Display summary: ``` **Translation complete** ({mode} mode) @@ -261,6 +276,8 @@ Final: {output-dir}/translation.md Glossary terms applied: {count} ``` +If mismatched image-language candidates were found, append a short note after the summary telling the user that some embedded images may still need image-text localization, followed by the candidate list. + ## Extension Support Custom configurations via EXTEND.md. See **Preferences** section for paths and supported options.