Merge pull request #80 from ashim-hq/feat/unlimited-by-default

feat: Unlimited by Default — remove all artificial limits

.env.example

@@ -1,17 +1,38 @@
-# Server port (used in production / Docker)
+# Server
-# In dev, the API auto-starts on an internal port; you always access localhost:1349
 PORT=1349
 AUTH_ENABLED=true
 DEFAULT_USERNAME=admin
 DEFAULT_PASSWORD=admin
 STORAGE_MODE=local
-FILE_MAX_AGE_HOURS=24
+
-CLEANUP_INTERVAL_MINUTES=30
+# Cleanup
-MAX_UPLOAD_SIZE_MB=100
+FILE_MAX_AGE_HOURS=72
-MAX_BATCH_SIZE=200
+CLEANUP_INTERVAL_MINUTES=60
-CONCURRENT_JOBS=3
+
-MAX_MEGAPIXELS=100
+# Upload & Batch (0 = unlimited)
-RATE_LIMIT_PER_MIN=100
+MAX_UPLOAD_SIZE_MB=0
+MAX_BATCH_SIZE=0
+CONCURRENT_JOBS=0
+MAX_MEGAPIXELS=0
+
+# Rate limiting (0 = disabled)
+RATE_LIMIT_PER_MIN=0
+
+# Users (0 = unlimited)
+MAX_USERS=0
+
+# Processing (0 = auto/unlimited)
+MAX_WORKER_THREADS=0
+PROCESSING_TIMEOUT_S=0
+MAX_PIPELINE_STEPS=0
+MAX_CANVAS_PIXELS=0
+MAX_SVG_SIZE_MB=0
+MAX_LOGO_SIZE_KB=2048
+MAX_SPLIT_GRID=100
+MAX_PDF_PAGES=0
+SESSION_DURATION_HOURS=168
+LOGIN_ATTEMPT_LIMIT=10
+
 # Set to true in CI/dev to skip the forced password-change on the default admin
 # SKIP_MUST_CHANGE_PASSWORD=false
 DB_PATH=./data/ashim.db

apps/api/src/db/index.ts

@@ -13,7 +13,7 @@ const sqlite: DatabaseType = new Database(env.DB_PATH);
 // Critical SQLite pragmas for reliability.
 // busy_timeout must be set first so journal_mode = WAL can retry
 // if another connection holds the lock (e.g. parallel test files).
-sqlite.pragma("busy_timeout = 5000");
+sqlite.pragma("busy_timeout = 10000");
 sqlite.pragma("journal_mode = WAL");
 sqlite.pragma("synchronous = NORMAL");
 sqlite.pragma("foreign_keys = ON");

apps/api/src/index.ts

@@ -41,7 +41,8 @@ recoverInterruptedInstalls();
 
 const app = Fastify({
   logger: { level: env.LOG_LEVEL },
-  bodyLimit: env.MAX_UPLOAD_SIZE_MB * 1024 * 1024,
+  bodyLimit: env.MAX_UPLOAD_SIZE_MB > 0 ? env.MAX_UPLOAD_SIZE_MB * 1024 * 1024 : 1073741824,
+  maxParamLength: 500,
 });
 
 app.setErrorHandler((error: Error & { statusCode?: number }, request, reply) => {
@@ -79,12 +80,13 @@ app.addHook("onSend", async (_request, reply) => {
   }
 });
 
-await app.register(rateLimit, {
+if (env.RATE_LIMIT_PER_MIN > 0) {
-  max: env.RATE_LIMIT_PER_MIN,
+  await app.register(rateLimit, {
-  timeWindow: "1 minute",
+    max: env.RATE_LIMIT_PER_MIN,
-  // Only rate-limit API endpoints — static files and the SPA fallback must never be throttled
+    timeWindow: "1 minute",
-  allowList: (request) => !request.url.startsWith("/api/"),
+    allowList: (request) => !request.url.startsWith("/api/"),
-});
+  });
+}
 
 // Multipart upload support
 await registerUpload(app);
@@ -195,7 +197,7 @@ try {
 }
 
 // Graceful shutdown
-const SHUTDOWN_TIMEOUT_MS = 8000;
+const SHUTDOWN_TIMEOUT_MS = 30000;
 let shuttingDown = false;
 async function shutdown(signal: string) {
   if (shuttingDown) return;

apps/api/src/lib/env.ts

@@ -1,3 +1,4 @@
+import { availableParallelism } from "node:os";
 import { z } from "zod";
 
 const envSchema = z.object({
@@ -13,13 +14,13 @@ const envSchema = z.object({
     .default("false")
     .transform((v) => v === "true"),
   STORAGE_MODE: z.enum(["local", "s3"]).default("local"),
-  FILE_MAX_AGE_HOURS: z.coerce.number().default(24),
+  FILE_MAX_AGE_HOURS: z.coerce.number().default(72),
-  CLEANUP_INTERVAL_MINUTES: z.coerce.number().default(30),
+  CLEANUP_INTERVAL_MINUTES: z.coerce.number().default(60),
-  MAX_UPLOAD_SIZE_MB: z.coerce.number().default(100),
+  MAX_UPLOAD_SIZE_MB: z.coerce.number().default(0),
-  MAX_BATCH_SIZE: z.coerce.number().default(200),
+  MAX_BATCH_SIZE: z.coerce.number().default(0),
-  CONCURRENT_JOBS: z.coerce.number().default(3),
+  CONCURRENT_JOBS: z.coerce.number().default(0),
-  MAX_MEGAPIXELS: z.coerce.number().default(100),
+  MAX_MEGAPIXELS: z.coerce.number().default(0),
-  RATE_LIMIT_PER_MIN: z.coerce.number().default(100),
+  RATE_LIMIT_PER_MIN: z.coerce.number().default(0),
   DB_PATH: z.string().default("./data/ashim.db"),
   FILES_STORAGE_PATH: z.string().default("./data/files"),
   WORKSPACE_PATH: z.string().default("./tmp/workspace"),
@@ -27,8 +28,18 @@ const envSchema = z.object({
   DEFAULT_LOCALE: z.string().default("en"),
   APP_NAME: z.string().default("ashim"),
   CORS_ORIGIN: z.string().default(""),
-  MAX_USERS: z.coerce.number().default(5),
+  MAX_USERS: z.coerce.number().default(0),
   LOG_LEVEL: z.enum(["fatal", "error", "warn", "info", "debug", "trace"]).default("info"),
+  MAX_WORKER_THREADS: z.coerce.number().default(0),
+  PROCESSING_TIMEOUT_S: z.coerce.number().default(0),
+  MAX_PIPELINE_STEPS: z.coerce.number().default(0),
+  MAX_CANVAS_PIXELS: z.coerce.number().default(0),
+  MAX_SVG_SIZE_MB: z.coerce.number().default(0),
+  MAX_LOGO_SIZE_KB: z.coerce.number().default(2048),
+  MAX_SPLIT_GRID: z.coerce.number().default(100),
+  MAX_PDF_PAGES: z.coerce.number().default(0),
+  SESSION_DURATION_HOURS: z.coerce.number().default(168),
+  LOGIN_ATTEMPT_LIMIT: z.coerce.number().default(10),
 });
 
 export type Env = z.infer<typeof envSchema>;
@@ -36,3 +47,13 @@ export type Env = z.infer<typeof envSchema>;
 export function loadEnv(): Env {
   return envSchema.parse(process.env);
 }
+
+export function resolveConcurrency(env: Env): number {
+  if (env.CONCURRENT_JOBS > 0) return env.CONCURRENT_JOBS;
+  return Math.max(2, availableParallelism() - 1);
+}
+
+export function resolveWorkerThreads(env: Env): number {
+  if (env.MAX_WORKER_THREADS > 0) return env.MAX_WORKER_THREADS;
+  return Math.max(2, availableParallelism() - 1);
+}

apps/api/src/lib/exiftool.ts

@@ -51,7 +51,7 @@ export async function inspectMetadata(buffer: Buffer, filename: string): Promise
   try {
     await writeFile(tempPath, buffer);
     const { stdout } = await execFileAsync(bin, ["-json", "-G", "-struct", "-n", tempPath], {
-      timeout: 30_000,
+      timeout: 60_000,
       maxBuffer: 10 * 1024 * 1024,
     });
 
@@ -126,7 +126,7 @@ export async function writeMetadata(
   try {
     await writeFile(tempPath, buffer);
     await execFileAsync(bin, ["-overwrite_original", ...tags, tempPath], {
-      timeout: 30_000,
+      timeout: 60_000,
       maxBuffer: 10 * 1024 * 1024,
     });
     return await readFile(tempPath);

apps/api/src/lib/file-validation.ts

@@ -90,7 +90,7 @@ export async function validateImageBuffer(
     const height = metadata.height ?? 0;
     const megapixels = (width * height) / 1_000_000;
 
-    if (megapixels > env.MAX_MEGAPIXELS) {
+    if (env.MAX_MEGAPIXELS > 0 && megapixels > env.MAX_MEGAPIXELS) {
       return {
         valid: false,
         reason: `Image exceeds maximum size: ${megapixels.toFixed(1)}MP (limit: ${env.MAX_MEGAPIXELS}MP)`,

apps/api/src/lib/heic-converter.ts

@@ -45,7 +45,7 @@ export async function decodeHeic(buffer: Buffer): Promise<Buffer> {
 
   try {
     await writeFile(inputPath, buffer);
-    await execFileAsync(cmd, [inputPath, outputPath], { timeout: 30_000 });
+    await execFileAsync(cmd, [inputPath, outputPath], { timeout: 120_000 });
 
     // Single-image HEIF: exact filename. Multi-image: -1 suffix on first image.
     try {
@@ -94,7 +94,7 @@ export async function encodeHeic(buffer: Buffer, quality = 80): Promise<Buffer>
   try {
     await writeFile(inputPath, buffer);
     await execFileAsync("heif-enc", ["-q", String(quality), "-o", outputPath, inputPath], {
-      timeout: 30_000,
+      timeout: 120_000,
     });
     return await readFile(outputPath);
   } finally {

apps/api/src/lib/svg-sanitize.ts

@@ -1,12 +1,13 @@
-const MAX_SVG_SIZE = 10 * 1024 * 1024; // 10MB
+import { env } from "../config.js";
 
 /**
  * Sanitize an SVG buffer to prevent XXE, SSRF, and script injection.
  * Throws if the SVG exceeds the maximum allowed size.
  */
 export function sanitizeSvg(buffer: Buffer): Buffer {
-  if (buffer.length > MAX_SVG_SIZE) {
+  const maxSvgSize = env.MAX_SVG_SIZE_MB > 0 ? env.MAX_SVG_SIZE_MB * 1024 * 1024 : Infinity;
-    throw new Error(`SVG exceeds maximum size of ${MAX_SVG_SIZE / 1024 / 1024}MB`);
+  if (buffer.length > maxSvgSize) {
+    throw new Error(`SVG exceeds maximum size of ${env.MAX_SVG_SIZE_MB}MB`);
   }
   let svg = buffer.toString("utf-8");
   // Remove DOCTYPE (XXE prevention, including internal subsets)

apps/api/src/lib/timeout.ts

@@ -0,0 +1,26 @@
+import { env } from "../config.js";
+
+type ToolCategory = "sharp" | "ai_cpu" | "ai_gpu" | "external" | "python";
+
+const TIMEOUT_RATES: Record<ToolCategory, number> = {
+  sharp: 2,
+  ai_cpu: 30,
+  ai_gpu: 5,
+  external: 10,
+  python: 15,
+};
+
+export function computeTimeout(megapixels: number, category: ToolCategory, fileCount = 1): number {
+  if (env.PROCESSING_TIMEOUT_S > 0) {
+    return env.PROCESSING_TIMEOUT_S * 1000;
+  }
+  const perFile = Math.max(60_000, megapixels * TIMEOUT_RATES[category] * 1000);
+  return perFile * fileCount;
+}
+
+export function computeExternalToolTimeout(megapixels: number): number {
+  if (env.PROCESSING_TIMEOUT_S > 0) {
+    return env.PROCESSING_TIMEOUT_S * 1000;
+  }
+  return Math.max(60_000, megapixels * TIMEOUT_RATES.external * 1000);
+}

apps/api/src/lib/worker-pool.ts

@@ -4,15 +4,14 @@
  * Uses Piscina (backed by worker_threads) so Sharp operations don't block
  * HTTP request handling, SSE streams, or health checks.
  */
-import { availableParallelism } from "node:os";
 import { dirname, resolve } from "node:path";
 import { fileURLToPath } from "node:url";
 import Piscina from "piscina";
+import { loadEnv, resolveWorkerThreads } from "./env.js";
 
 const __dirname = dirname(fileURLToPath(import.meta.url));
 
-// Size the pool: leave 1 thread for the event loop, min 1 worker
+const maxThreads = resolveWorkerThreads(loadEnv());
-const maxThreads = Math.max(1, Math.min(availableParallelism() - 1, 4));
 
 let pool: Piscina | null = null;
 

apps/api/src/plugins/auth.ts

@@ -98,7 +98,7 @@ export function requireAdmin(request: FastifyRequest, reply: FastifyReply): Auth
 
 // ── Session helpers ────────────────────────────────────────────────
 
-const SESSION_DURATION_MS = 24 * 60 * 60 * 1000; // 24 hours
+const SESSION_DURATION_MS = env.SESSION_DURATION_HOURS * 60 * 60 * 1000;
 
 function createSessionToken(): string {
   return randomUUID();
@@ -137,10 +137,7 @@ export async function ensureDefaultAdmin(): Promise<void> {
 
 // ── Login attempt limit ──────────────────────────────────────────
 
-const DEFAULT_LOGIN_ATTEMPT_LIMIT = 10;
-
 function getLoginAttemptLimit(): number {
-  // Allow override via RATE_LIMIT_PER_MIN for test environments
   if (env.RATE_LIMIT_PER_MIN > 1000) return env.RATE_LIMIT_PER_MIN;
   const row = db
     .select()
@@ -151,7 +148,7 @@ function getLoginAttemptLimit(): number {
     const parsed = parseInt(row.value, 10);
     if (!Number.isNaN(parsed) && parsed > 0) return parsed;
   }
-  return DEFAULT_LOGIN_ATTEMPT_LIMIT;
+  return env.LOGIN_ATTEMPT_LIMIT;
 }
 
 // ── Auth routes ────────────────────────────────────────────────────

apps/api/src/plugins/upload.ts

@@ -5,8 +5,8 @@ import { env } from "../config.js";
 export async function registerUpload(app: FastifyInstance): Promise<void> {
   await app.register(multipart, {
     limits: {
-      fileSize: env.MAX_UPLOAD_SIZE_MB * 1024 * 1024,
+      fileSize: env.MAX_UPLOAD_SIZE_MB > 0 ? env.MAX_UPLOAD_SIZE_MB * 1024 * 1024 : undefined,
-      files: env.MAX_BATCH_SIZE,
+      files: env.MAX_BATCH_SIZE > 0 ? env.MAX_BATCH_SIZE : undefined,
     },
   });
 }

apps/api/src/routes/batch.ts

@@ -15,6 +15,7 @@ import type { FastifyInstance, FastifyReply, FastifyRequest } from "fastify";
 import PQueue from "p-queue";
 import { env } from "../config.js";
 import { autoOrient } from "../lib/auto-orient.js";
+import { resolveConcurrency } from "../lib/env.js";
 import { formatZodErrors } from "../lib/errors.js";
 import { isToolInstalled } from "../lib/feature-status.js";
 import { validateImageBuffer } from "../lib/file-validation.js";
@@ -90,7 +91,7 @@ export async function registerBatchRoutes(app: FastifyInstance): Promise<void> {
       }
 
       // Enforce batch size limit
-      if (files.length > env.MAX_BATCH_SIZE) {
+      if (env.MAX_BATCH_SIZE > 0 && files.length > env.MAX_BATCH_SIZE) {
         return reply.status(400).send({
           error: `Too many files. Maximum batch size is ${env.MAX_BATCH_SIZE}`,
         });
@@ -126,7 +127,7 @@ export async function registerBatchRoutes(app: FastifyInstance): Promise<void> {
       updateJobProgress({ ...progress });
 
       // Use p-queue for concurrency control
-      const queue = new PQueue({ concurrency: env.CONCURRENT_JOBS });
+      const queue = new PQueue({ concurrency: resolveConcurrency(env) });
 
       // All processed buffers are held in memory until ZIP streaming begins.
       // Peak memory scales with files.length * avg output size. MAX_BATCH_SIZE bounds this.

apps/api/src/routes/branding.ts

@@ -11,13 +11,14 @@ import { join } from "node:path";
 import { eq } from "drizzle-orm";
 import type { FastifyInstance, FastifyReply, FastifyRequest } from "fastify";
 import sharp from "sharp";
+import { env } from "../config.js";
 import { db, schema } from "../db/index.js";
 import { ensureSharpCompat } from "../lib/heic-converter.js";
 import { requireAdmin } from "../plugins/auth.js";
 
 const BRANDING_DIR = join(process.cwd(), "data", "branding");
 const LOGO_PATH = join(BRANDING_DIR, "logo.png");
-const MAX_LOGO_SIZE = 500 * 1024; // 500 KB
+const maxLogoSize = env.MAX_LOGO_SIZE_KB * 1024;
 
 function upsertSetting(key: string, value: string): void {
   const existing = db.select().from(schema.settings).where(eq(schema.settings.key, key)).get();
@@ -51,10 +52,11 @@ export async function brandingRoutes(app: FastifyInstance): Promise<void> {
     const buffer = await file.toBuffer();
 
     // Validate size
-    if (buffer.length > MAX_LOGO_SIZE) {
+    if (buffer.length > maxLogoSize) {
-      return reply
+      return reply.status(400).send({
-        .status(400)
+        error: `Logo must be ${env.MAX_LOGO_SIZE_KB}KB or smaller`,
-        .send({ error: "Logo must be 500KB or smaller", code: "VALIDATION_ERROR" });
+        code: "VALIDATION_ERROR",
+      });
     }
 
     // Decode HEIC/HEIF if needed, then convert to PNG, resize to max 128x128

apps/api/src/routes/pipeline.ts

@@ -18,6 +18,7 @@ import { z } from "zod";
 import { env } from "../config.js";
 import { db, schema } from "../db/index.js";
 import { autoOrient } from "../lib/auto-orient.js";
+import { resolveConcurrency } from "../lib/env.js";
 import { formatZodErrors } from "../lib/errors.js";
 import { isToolInstalled } from "../lib/feature-status.js";
 import { validateImageBuffer } from "../lib/file-validation.js";
@@ -39,17 +40,21 @@ const pipelineDefinitionSchema = z.object({
   steps: z
     .array(pipelineStepSchema)
     .min(1, "Pipeline must have at least one step")
-    .max(20, "Pipeline cannot exceed 20 steps"),
+    .refine((steps) => env.MAX_PIPELINE_STEPS === 0 || steps.length <= env.MAX_PIPELINE_STEPS, {
+      message: "Pipeline exceeds maximum steps",
+    }),
 });
 
 /** Schema for saving a pipeline. */
 const savePipelineSchema = z.object({
-  name: z.string().min(1, "Pipeline name is required").max(100),
+  name: z.string().min(1, "Pipeline name is required").max(255),
-  description: z.string().max(500).optional(),
+  description: z.string().max(2000).optional(),
   steps: z
     .array(pipelineStepSchema)
     .min(1, "Pipeline must have at least one step")
-    .max(20, "Pipeline cannot exceed 20 steps"),
+    .refine((steps) => env.MAX_PIPELINE_STEPS === 0 || steps.length <= env.MAX_PIPELINE_STEPS, {
+      message: "Pipeline exceeds maximum steps",
+    }),
 });
 
 export async function registerPipelineRoutes(app: FastifyInstance): Promise<void> {
@@ -424,7 +429,7 @@ export async function registerPipelineRoutes(app: FastifyInstance): Promise<void
     }
 
     // Enforce batch size limit
-    if (files.length > env.MAX_BATCH_SIZE) {
+    if (env.MAX_BATCH_SIZE > 0 && files.length > env.MAX_BATCH_SIZE) {
       return reply.status(400).send({
         error: `Too many files. Maximum batch size is ${env.MAX_BATCH_SIZE}`,
       });
@@ -499,7 +504,7 @@ export async function registerPipelineRoutes(app: FastifyInstance): Promise<void
     updateJobProgress({ ...progress });
 
     // ── Process files through the pipeline with concurrency control ──
-    const queue = new PQueue({ concurrency: env.CONCURRENT_JOBS });
+    const queue = new PQueue({ concurrency: resolveConcurrency(env) });
 
     const results: ({ buffer: Buffer; filename: string } | null)[] = new Array(files.length).fill(
       null,

apps/api/src/routes/tool-factory.ts

@@ -15,6 +15,7 @@ import { sanitizeFilename } from "../lib/filename.js";
 import { decodeHeic } from "../lib/heic-converter.js";
 import type { WorkerInput, WorkerOutput } from "../lib/image-worker.js";
 import { sanitizeSvg } from "../lib/svg-sanitize.js";
+import { computeTimeout } from "../lib/timeout.js";
 import { getWorkerPool } from "../lib/worker-pool.js";
 import { createWorkspace } from "../lib/workspace.js";
 
@@ -227,8 +228,11 @@ export function createToolRoute<T>(app: FastifyInstance, config: ToolRouteConfig
               filename,
               inputFormat: validation.format,
             };
+            const meta = await sharp(fileBuffer).metadata();
+            const megapixels = ((meta.width ?? 0) * (meta.height ?? 0)) / 1_000_000;
+            const timeoutMs = computeTimeout(megapixels, "sharp");
             const workerResult: WorkerOutput = await pool.run(workerInput, {
-              signal: AbortSignal.timeout(30_000),
+              signal: AbortSignal.timeout(timeoutMs),
             });
             result = {
               buffer: Buffer.from(workerResult.buffer),

apps/api/src/routes/tools/border.ts

@@ -6,13 +6,13 @@ import { createToolRoute } from "../tool-factory.js";
 const hexColor = z.string().regex(/^#[0-9a-fA-F]{6}$/);
 
 const settingsSchema = z.object({
-  borderWidth: z.number().min(0).max(200).default(10),
+  borderWidth: z.number().min(0).max(2000).default(10),
   borderColor: hexColor.default("#000000"),
   padding: z.number().min(0).max(200).default(0),
   paddingColor: hexColor.default("#FFFFFF"),
-  cornerRadius: z.number().min(0).max(500).default(0),
+  cornerRadius: z.number().min(0).max(2000).default(0),
   shadow: z.boolean().default(false),
-  shadowBlur: z.number().min(1).max(50).default(15),
+  shadowBlur: z.number().min(1).max(200).default(15),
   shadowOffsetX: z.number().min(-50).max(50).default(0),
   shadowOffsetY: z.number().min(-50).max(50).default(5),
   shadowColor: hexColor.default("#000000"),

apps/api/src/routes/tools/bulk-rename.ts

@@ -6,7 +6,7 @@ import { z } from "zod";
 import { formatZodErrors } from "../../lib/errors.js";
 
 const settingsSchema = z.object({
-  pattern: z.string().min(1).max(200).default("image-{{index}}"),
+  pattern: z.string().min(1).max(1000).default("image-{{index}}"),
   startIndex: z.number().min(0).default(1),
 });
 

apps/api/src/routes/tools/collage.ts

@@ -321,15 +321,15 @@ const cellSchema = z.object({
   imageIndex: z.number().int().min(0),
   panX: z.number().min(-100).max(100).default(0),
   panY: z.number().min(-100).max(100).default(0),
-  zoom: z.number().min(1).max(3).default(1),
+  zoom: z.number().min(1).max(10).default(1),
   objectFit: z.enum(["cover", "contain"]).default("cover"),
 });
 
 const settingsSchema = z.object({
   templateId: z.string(),
   cells: z.array(cellSchema).optional(),
-  gap: z.number().min(0).max(50).default(8),
+  gap: z.number().min(0).max(500).default(8),
-  cornerRadius: z.number().min(0).max(30).default(0),
+  cornerRadius: z.number().min(0).max(500).default(0),
   backgroundColor: z.string().default("#FFFFFF"),
   aspectRatio: z.string().default("free"),
   outputFormat: z.enum(["png", "jpeg", "webp"]).default("png"),

apps/api/src/routes/tools/gif-tools.ts

@@ -65,8 +65,8 @@ const settingsSchema = z.object({
   mode: z.enum(["resize", "optimize", "speed", "reverse", "extract", "rotate"]).default("resize"),
 
   // Resize
-  width: z.number().min(1).max(4096).optional(),
+  width: z.number().min(1).max(16384).optional(),
-  height: z.number().min(1).max(4096).optional(),
+  height: z.number().min(1).max(16384).optional(),
   percentage: z.number().min(1).max(500).optional(),
 
   // Optimize

apps/api/src/routes/tools/image-to-pdf.ts

@@ -13,7 +13,7 @@ import { createWorkspace } from "../../lib/workspace.js";
 const settingsSchema = z.object({
   pageSize: z.enum(["A4", "Letter", "A3", "A5"]).default("A4"),
   orientation: z.enum(["portrait", "landscape"]).default("portrait"),
-  margin: z.number().min(0).max(100).default(20),
+  margin: z.number().min(0).max(500).default(20),
 });
 
 const PAGE_SIZES: Record<string, [number, number]> = {

apps/api/src/routes/tools/passport-photo.ts

@@ -36,7 +36,7 @@ const generateSettingsSchema = z.object({
   bgColor: z.string().default("#FFFFFF"),
   printLayout: z.string().default("none"),
   maxFileSizeKb: z.number().default(0),
-  dpi: z.number().min(72).max(600).default(300),
+  dpi: z.number().min(72).max(1200).default(300),
   customWidthMm: z.number().optional(),
   customHeightMm: z.number().optional(),
   zoom: z.number().min(0.5).max(3).default(1),

apps/api/src/routes/tools/pdf-to-image.ts

@@ -7,6 +7,7 @@ import type { FastifyInstance } from "fastify";
 import * as mupdf from "mupdf";
 import sharp from "sharp";
 import { z } from "zod";
+import { env } from "../../config.js";
 import { formatZodErrors } from "../../lib/errors.js";
 import { encodeHeic } from "../../lib/heic-converter.js";
 import { createWorkspace } from "../../lib/workspace.js";
@@ -14,7 +15,7 @@ import { createWorkspace } from "../../lib/workspace.js";
 // ── Settings schema ──────────────────────────────────────────────
 const settingsSchema = z.object({
   format: z.enum(["png", "jpg", "webp", "avif", "tiff", "gif", "heic", "heif"]).default("png"),
-  dpi: z.number().min(36).max(1200).default(150),
+  dpi: z.number().min(36).max(2400).default(150),
   quality: z.number().min(1).max(100).default(85),
   colorMode: z.enum(["color", "grayscale", "bw"]).default("color"),
   pages: z.string().default("all"),
@@ -229,7 +230,7 @@ export function registerPdfToImage(app: FastifyInstance) {
         return reply.status(400).send({ error: "Password-protected PDFs are not supported" });
       }
       const pageCount = doc.countPages();
-      const maxPages = Math.min(pageCount, 200);
+      const maxPages = env.MAX_PDF_PAGES > 0 ? Math.min(pageCount, env.MAX_PDF_PAGES) : pageCount;
       const thumbnails: Array<{
         page: number;
         dataUrl: string;

apps/api/src/routes/tools/qr-generate.ts

@@ -9,7 +9,7 @@ import { createWorkspace } from "../../lib/workspace.js";
 
 const settingsSchema = z.object({
   text: z.string().min(1).max(2000),
-  size: z.number().min(100).max(2000).default(400),
+  size: z.number().min(100).max(10000).default(400),
   errorCorrection: z.enum(["L", "M", "Q", "H"]).default("M"),
   foreground: z
     .string()

apps/api/src/routes/tools/sharpening.ts

@@ -15,7 +15,7 @@ const settingsSchema = z.object({
   y2: z.number().min(0).max(50).default(12),
   y3: z.number().min(0).max(50).default(20),
   // Unsharp Mask
-  amount: z.number().min(0).max(500).default(100),
+  amount: z.number().min(0).max(1000).default(100),
   radius: z.number().min(0.1).max(5).default(1.0),
   threshold: z.number().min(0).max(255).default(0),
   // High-Pass

apps/api/src/routes/tools/split.ts

@@ -9,8 +9,8 @@ import { formatZodErrors } from "../../lib/errors.js";
 import { ensureSharpCompat } from "../../lib/heic-converter.js";
 
 const settingsSchema = z.object({
-  columns: z.number().min(1).max(20).default(3),
+  columns: z.number().min(1).max(100).default(3),
-  rows: z.number().min(1).max(20).default(3),
+  rows: z.number().min(1).max(100).default(3),
   tileWidth: z.number().min(10).optional(),
   tileHeight: z.number().min(10).optional(),
   outputFormat: z.enum(["original", "png", "jpg", "webp"]).default("original"),
@@ -89,8 +89,8 @@ export function registerSplit(app: FastifyInstance) {
         cols = Math.max(1, Math.ceil(fullW / settings.tileWidth));
         rows = Math.max(1, Math.ceil(fullH / settings.tileHeight));
       }
-      cols = Math.min(cols, 20);
+      cols = Math.min(cols, 100);
-      rows = Math.min(rows, 20);
+      rows = Math.min(rows, 100);
 
       const cellW = Math.floor(fullW / cols);
       const cellH = Math.floor(fullH / rows);

apps/api/src/routes/tools/stitch.ts

@@ -4,22 +4,21 @@ import { basename, join } from "node:path";
 import type { FastifyInstance } from "fastify";
 import sharp from "sharp";
 import { z } from "zod";
+import { env } from "../../config.js";
 import { autoOrient } from "../../lib/auto-orient.js";
 import { formatZodErrors } from "../../lib/errors.js";
 import { validateImageBuffer } from "../../lib/file-validation.js";
 import { ensureSharpCompat } from "../../lib/heic-converter.js";
 import { createWorkspace } from "../../lib/workspace.js";
 
-const MAX_CANVAS_PIXELS = 100_000_000;
-
 const settingsSchema = z.object({
   direction: z.enum(["horizontal", "vertical", "grid"]).default("horizontal"),
-  gridColumns: z.number().int().min(2).max(10).default(2),
+  gridColumns: z.number().int().min(2).max(100).default(2),
   resizeMode: z.enum(["fit", "original", "stretch", "crop"]).default("fit"),
   alignment: z.enum(["start", "center", "end"]).default("center"),
-  gap: z.number().min(0).max(200).default(0),
+  gap: z.number().min(0).max(1000).default(0),
-  border: z.number().min(0).max(50).default(0),
+  border: z.number().min(0).max(500).default(0),
-  cornerRadius: z.number().min(0).max(50).default(0),
+  cornerRadius: z.number().min(0).max(500).default(0),
   backgroundColor: z
     .string()
     .regex(/^#[0-9a-fA-F]{6}$/)
@@ -180,9 +179,10 @@ export function registerStitch(app: FastifyInstance) {
         }
       }
 
-      if (canvasWidth * canvasHeight > MAX_CANVAS_PIXELS) {
+      const maxCanvasPixels = env.MAX_CANVAS_PIXELS > 0 ? env.MAX_CANVAS_PIXELS : Infinity;
+      if (canvasWidth * canvasHeight > maxCanvasPixels) {
         return reply.status(422).send({
-          error: `Canvas too large: ${canvasWidth}x${canvasHeight} (${Math.round((canvasWidth * canvasHeight) / 1_000_000)}MP exceeds 100MP limit)`,
+          error: `Canvas too large: ${canvasWidth}x${canvasHeight} (${Math.round((canvasWidth * canvasHeight) / 1_000_000)}MP exceeds ${Math.round(maxCanvasPixels / 1_000_000)}MP limit)`,
         });
       }
 

apps/api/src/routes/tools/svg-to-raster.ts

@@ -7,6 +7,7 @@ import PQueue from "p-queue";
 import sharp from "sharp";
 import { z } from "zod";
 import { env } from "../../config.js";
+import { resolveConcurrency } from "../../lib/env.js";
 import { formatZodErrors } from "../../lib/errors.js";
 import { sanitizeFilename } from "../../lib/filename.js";
 import { decodeHeic, encodeHeic } from "../../lib/heic-converter.js";
@@ -17,9 +18,9 @@ import { updateJobProgress } from "../progress.js";
 const NON_PREVIEWABLE = new Set(["tiff", "heif"]);
 
 const settingsSchema = z.object({
-  width: z.number().min(1).max(16384).optional(),
+  width: z.number().min(1).max(65536).optional(),
-  height: z.number().min(1).max(16384).optional(),
+  height: z.number().min(1).max(65536).optional(),
-  dpi: z.number().min(36).max(1200).default(300),
+  dpi: z.number().min(36).max(2400).default(300),
   quality: z.number().min(1).max(100).default(90),
   backgroundColor: z
     .string()
@@ -135,7 +136,7 @@ export function registerSvgToRaster(app: FastifyInstance) {
       return reply.status(400).send({ error: "No SVG files provided" });
     }
 
-    if (files.length > env.MAX_BATCH_SIZE) {
+    if (env.MAX_BATCH_SIZE > 0 && files.length > env.MAX_BATCH_SIZE) {
       return reply.status(400).send({
         error: `Too many files. Maximum batch size is ${env.MAX_BATCH_SIZE}`,
       });
@@ -157,7 +158,7 @@ export function registerSvgToRaster(app: FastifyInstance) {
     }
 
     const jobId = clientJobId || randomUUID();
-    const queue = new PQueue({ concurrency: env.CONCURRENT_JOBS });
+    const queue = new PQueue({ concurrency: resolveConcurrency(env) });
     const results: ({ buffer: Buffer; filename: string } | null)[] = new Array(files.length).fill(
       null,
     );

apps/api/src/routes/tools/vectorize.ts

@@ -14,9 +14,9 @@ import { createWorkspace } from "../../lib/workspace.js";
 const settingsSchema = z.object({
   colorMode: z.enum(["bw", "color"]).default("bw"),
   threshold: z.number().min(0).max(255).default(128),
-  colorPrecision: z.number().min(1).max(8).default(6),
+  colorPrecision: z.number().min(1).max(16).default(6),
-  layerDifference: z.number().min(1).max(64).default(6),
+  layerDifference: z.number().min(1).max(128).default(6),
-  filterSpeckle: z.number().min(1).max(128).default(4),
+  filterSpeckle: z.number().min(1).max(256).default(4),
   pathMode: z.enum(["none", "polygon", "spline"]).default("spline"),
   cornerThreshold: z.number().min(0).max(180).default(60),
   invert: z.boolean().default(false),

apps/api/src/routes/tools/watermark-text.ts

@@ -5,7 +5,7 @@ import { createToolRoute } from "../tool-factory.js";
 
 const settingsSchema = z.object({
   text: z.string().min(1).max(500),
-  fontSize: z.number().min(8).max(200).default(48),
+  fontSize: z.number().min(8).max(1000).default(48),
   color: z
     .string()
     .regex(/^#[0-9a-fA-F]{6}$/)

apps/api/src/routes/user-files.ts

@@ -102,7 +102,7 @@ export async function userFileRoutes(app: FastifyInstance): Promise<void> {
       const user = requireAuth(request, reply);
       if (!user) return;
 
-      const limit = Math.min(parseInt(request.query.limit ?? "50", 10) || 50, 200);
+      const limit = parseInt(request.query.limit ?? "50", 10) || 50;
       const offset = parseInt(request.query.offset ?? "0", 10) || 0;
       const search = request.query.search?.trim();
 

apps/web/src/components/common/image-viewer.tsx

@@ -27,7 +27,7 @@ interface ImageViewerProps {
   imageWrapperStyle?: React.CSSProperties;
 }
 
-const ZOOM_STEPS = [25, 50, 75, 100, 125, 150, 200, 300];
+const ZOOM_STEPS = [10, 25, 50, 75, 100, 150, 200, 300, 500, 1000];
 const DEFAULT_ZOOM = 100;
 
 export function ImageViewer({

apps/web/src/components/tools/barcode-read-settings.tsx

@@ -72,7 +72,7 @@ function scanOneFile(
     formData.append("settings", JSON.stringify({ tryHarder }));
 
     const xhr = new XMLHttpRequest();
-    xhr.timeout = 60_000;
+    xhr.timeout = 300_000;
 
     xhr.upload.onprogress = (e) => {
       if (e.lengthComputable) onUploadProgress((e.loaded / e.total) * 100);

apps/web/src/components/tools/collage-preview.tsx

@@ -349,8 +349,8 @@ function CollageCell({
       if (first) memo = { panX: transform.panX, panY: transform.panY };
       const rect = cellRef.current?.getBoundingClientRect();
       if (!rect || !memo) return memo;
-      const panX = Math.max(-100, Math.min(100, memo.panX + (mx / rect.width) * 100));
+      const panX = Math.max(-200, Math.min(200, memo.panX + (mx / rect.width) * 100));
-      const panY = Math.max(-100, Math.min(100, memo.panY + (my / rect.height) * 100));
+      const panY = Math.max(-200, Math.min(200, memo.panY + (my / rect.height) * 100));
       store.setCellTransform(cellIndex, { panX, panY });
       return memo;
     },
@@ -360,11 +360,11 @@ function CollageCell({
   const bindPinch = usePinch(
     ({ offset: [scale] }) => {
       if (!image || !isSelected) return;
-      const zoom = Math.max(1, Math.min(3, scale));
+      const zoom = Math.max(1, Math.min(10, scale));
       store.setCellTransform(cellIndex, { zoom });
     },
     {
-      scaleBounds: { min: 1, max: 3 },
+      scaleBounds: { min: 1, max: 10 },
       from: () => [transform.zoom, 0],
     },
   );
@@ -378,7 +378,7 @@ function CollageCell({
     const handleWheel = (e: WheelEvent) => {
       e.preventDefault();
       const delta = e.deltaY > 0 ? -0.1 : 0.1;
-      const zoom = Math.max(1, Math.min(3, zoomRef.current + delta));
+      const zoom = Math.max(1, Math.min(10, zoomRef.current + delta));
       store.setCellTransform(cellIndex, { zoom });
     };
     el.addEventListener("wheel", handleWheel, { passive: false });
@@ -555,7 +555,7 @@ function CollageCell({
           <input
             type="range"
             min="1"
-            max="3"
+            max="10"
             step="0.1"
             value={transform.zoom}
             onChange={handleZoomSlider}

apps/web/src/components/tools/favicon-settings.tsx

@@ -73,7 +73,7 @@ export function FaviconSettings() {
     const xhr = new XMLHttpRequest();
     xhrRef.current = xhr;
     xhr.responseType = "blob";
-    xhr.timeout = 180_000;
+    xhr.timeout = 300_000;
 
     xhr.upload.onprogress = (event) => {
       if (event.lengthComputable) {

apps/web/src/components/tools/image-to-pdf-settings.tsx

@@ -167,7 +167,7 @@ export function ImageToPdfSettings() {
 
     const xhr = new XMLHttpRequest();
     xhrRef.current = xhr;
-    xhr.timeout = 180_000;
+    xhr.timeout = 300_000;
 
     xhr.upload.onprogress = (event) => {
       if (event.lengthComputable) {

apps/web/src/components/tools/passport-photo-settings.tsx

@@ -1062,8 +1062,8 @@ export function PassportPhotoPreview() {
       if (!dragStartRef.current) return;
       const dx = (e.clientX - dragStartRef.current.x) * 0.001;
       const dy = (e.clientY - dragStartRef.current.y) * 0.001;
-      setAdjustX(Math.max(-0.15, Math.min(0.15, dragStartRef.current.ax - dx)));
+      setAdjustX(Math.max(-0.3, Math.min(0.3, dragStartRef.current.ax - dx)));
-      setAdjustY(Math.max(-0.15, Math.min(0.15, dragStartRef.current.ay - dy)));
+      setAdjustY(Math.max(-0.3, Math.min(0.3, dragStartRef.current.ay - dy)));
     }
 
     function handleMouseUp() {
@@ -1083,7 +1083,7 @@ export function PassportPhotoPreview() {
   const handleWheel = useCallback(
     (e: React.WheelEvent<HTMLCanvasElement>) => {
       e.preventDefault();
-      setZoom(Math.max(0.5, Math.min(3, zoom + (e.deltaY > 0 ? -0.1 : 0.1))));
+      setZoom(Math.max(0.5, Math.min(5, zoom + (e.deltaY > 0 ? -0.1 : 0.1))));
     },
     [zoom, setZoom],
   );
@@ -1138,7 +1138,7 @@ export function PassportPhotoPreview() {
         </span>
         <button
           type="button"
-          onClick={() => setZoom(Math.min(3, zoom + 0.25))}
+          onClick={() => setZoom(Math.min(5, zoom + 0.25))}
           className="p-1.5 rounded-lg border border-border text-muted-foreground hover:text-foreground hover:bg-muted transition-colors"
           title="Zoom in"
         >

apps/web/src/hooks/use-pipeline-processor.ts

@@ -86,8 +86,8 @@ export function usePipelineProcessor() {
       const xhr = new XMLHttpRequest();
       xhrRef.current = xhr;
 
-      // Pipeline runs multiple steps sequentially, allow up to 3 minutes
+      // Pipeline runs multiple steps sequentially, allow up to 10 minutes
-      xhr.timeout = 180_000;
+      xhr.timeout = 600_000;
 
       // Pipeline is always "medium" speed: upload = 0-40%, processing = 40-95%
       const UPLOAD_WEIGHT = 40;

apps/web/src/hooks/use-tool-processor.ts

@@ -153,8 +153,8 @@ export function useToolProcessor(toolId: string) {
       const xhr = new XMLHttpRequest();
       xhrRef.current = xhr;
 
-      // Timeout: 60s for fast tools, 3 min for medium (seam carving), 5 min for AI
+      // Timeout: 2 min for fast tools, 5 min for medium (seam carving), 10 min for AI
-      xhr.timeout = isAiTool ? 300_000 : isMediumTool ? 180_000 : 60_000;
+      xhr.timeout = isAiTool ? 600_000 : isMediumTool ? 300_000 : 120_000;
 
       // For AI tools: upload = 0-15%, processing = 15-100% (SSE-driven)
       // For medium tools: upload = 0-40%, processing = 40-95% (gradual fill)

apps/web/src/stores/files-page-store.ts

@@ -35,7 +35,7 @@ export const useFilesPageStore = create<FilesPageState>((set, get) => ({
     set({ loading: true, error: null });
     try {
       const { searchQuery } = get();
-      const result = await apiListFiles({ search: searchQuery || undefined, limit: 100 });
+      const result = await apiListFiles({ search: searchQuery || undefined, limit: 200 });
       set({ files: result.files, total: result.total, loading: false });
     } catch (err) {
       set({ error: err instanceof Error ? err.message : "Failed to load files", loading: false });

apps/web/src/stores/split-store.ts

@@ -68,8 +68,8 @@ export const useSplitStore = create<SplitState>((set, get) => ({
 
   setMode: (mode) => set({ mode, tiles: [], zipBlobUrl: null, error: null }),
   setColumns: (columns) =>
-    set({ columns: Math.max(1, Math.min(20, columns)), tiles: [], zipBlobUrl: null }),
+    set({ columns: Math.max(1, Math.min(100, columns)), tiles: [], zipBlobUrl: null }),
-  setRows: (rows) => set({ rows: Math.max(1, Math.min(20, rows)), tiles: [], zipBlobUrl: null }),
+  setRows: (rows) => set({ rows: Math.max(1, Math.min(100, rows)), tiles: [], zipBlobUrl: null }),
   setTileWidth: (tileWidth) =>
     set({ tileWidth: Math.max(10, tileWidth), tiles: [], zipBlobUrl: null }),
   setTileHeight: (tileHeight) =>

docker/Dockerfile

@@ -228,13 +228,24 @@ ENV PORT=1349 \
     DEFAULT_THEME=light \
     DEFAULT_LOCALE=en \
     APP_NAME="ashim" \
-    FILE_MAX_AGE_HOURS=24 \
+    FILE_MAX_AGE_HOURS=72 \
-    CLEANUP_INTERVAL_MINUTES=30 \
+    CLEANUP_INTERVAL_MINUTES=60 \
-    MAX_UPLOAD_SIZE_MB=100 \
+    MAX_UPLOAD_SIZE_MB=0 \
-    MAX_BATCH_SIZE=200 \
+    MAX_BATCH_SIZE=0 \
-    CONCURRENT_JOBS=3 \
+    CONCURRENT_JOBS=0 \
-    MAX_MEGAPIXELS=100 \
+    MAX_MEGAPIXELS=0 \
-    RATE_LIMIT_PER_MIN=100 \
+    RATE_LIMIT_PER_MIN=0 \
+    MAX_USERS=0 \
+    MAX_WORKER_THREADS=0 \
+    PROCESSING_TIMEOUT_S=0 \
+    MAX_PIPELINE_STEPS=0 \
+    MAX_CANVAS_PIXELS=0 \
+    MAX_SVG_SIZE_MB=0 \
+    MAX_LOGO_SIZE_KB=2048 \
+    MAX_SPLIT_GRID=100 \
+    MAX_PDF_PAGES=0 \
+    SESSION_DURATION_HOURS=168 \
+    LOGIN_ATTEMPT_LIMIT=10 \
     LOG_LEVEL=debug
 
 # NVIDIA Container Toolkit env vars (harmless on non-GPU systems)

docker/docker-compose-gpu.yml

@@ -21,7 +21,16 @@ services:
       - DEFAULT_USERNAME=admin
       - DEFAULT_PASSWORD=admin
       - SKIP_MUST_CHANGE_PASSWORD=${SKIP_MUST_CHANGE_PASSWORD:-false}
-      - RATE_LIMIT_PER_MIN=${RATE_LIMIT_PER_MIN:-50000}
+      - MAX_UPLOAD_SIZE_MB=${MAX_UPLOAD_SIZE_MB:-0}
+      - MAX_BATCH_SIZE=${MAX_BATCH_SIZE:-0}
+      - MAX_MEGAPIXELS=${MAX_MEGAPIXELS:-0}
+      - CONCURRENT_JOBS=${CONCURRENT_JOBS:-0}
+      - MAX_WORKER_THREADS=${MAX_WORKER_THREADS:-0}
+      - PROCESSING_TIMEOUT_S=${PROCESSING_TIMEOUT_S:-0}
+      - MAX_PIPELINE_STEPS=${MAX_PIPELINE_STEPS:-0}
+      - RATE_LIMIT_PER_MIN=${RATE_LIMIT_PER_MIN:-0}
+      - MAX_USERS=${MAX_USERS:-0}
+      - SESSION_DURATION_HOURS=${SESSION_DURATION_HOURS:-168}
     restart: unless-stopped
     healthcheck:
       test: ["CMD", "curl", "-f", "http://localhost:1349/api/v1/health"]
@@ -29,11 +38,12 @@ services:
       timeout: 5s
       start_period: 60s
       retries: 3
+    shm_size: '2gb'
     logging:
       driver: json-file
       options:
-        max-size: "10m"
+        max-size: "50m"
-        max-file: "3"
+        max-file: "5"
     deploy:
       resources:
         reservations:

docker/docker-compose.yml

@@ -22,7 +22,16 @@ services:
       - DEFAULT_USERNAME=admin
       - DEFAULT_PASSWORD=admin
       - SKIP_MUST_CHANGE_PASSWORD=${SKIP_MUST_CHANGE_PASSWORD:-false}
-      - RATE_LIMIT_PER_MIN=${RATE_LIMIT_PER_MIN:-50000}
+      - MAX_UPLOAD_SIZE_MB=${MAX_UPLOAD_SIZE_MB:-0}
+      - MAX_BATCH_SIZE=${MAX_BATCH_SIZE:-0}
+      - MAX_MEGAPIXELS=${MAX_MEGAPIXELS:-0}
+      - CONCURRENT_JOBS=${CONCURRENT_JOBS:-0}
+      - MAX_WORKER_THREADS=${MAX_WORKER_THREADS:-0}
+      - PROCESSING_TIMEOUT_S=${PROCESSING_TIMEOUT_S:-0}
+      - MAX_PIPELINE_STEPS=${MAX_PIPELINE_STEPS:-0}
+      - RATE_LIMIT_PER_MIN=${RATE_LIMIT_PER_MIN:-0}
+      - MAX_USERS=${MAX_USERS:-0}
+      - SESSION_DURATION_HOURS=${SESSION_DURATION_HOURS:-168}
     restart: unless-stopped
     healthcheck:
       test: ["CMD", "curl", "-f", "http://localhost:1349/api/v1/health"]
@@ -30,11 +39,12 @@ services:
       timeout: 5s
       start_period: 60s
       retries: 3
+    shm_size: '2gb'
     logging:
       driver: json-file
       options:
-        max-size: "10m"
+        max-size: "50m"
-        max-file: "3"
+        max-file: "5"
 
 volumes:
   ashim-data:

packages/ai/python/colorize.py

@@ -50,7 +50,7 @@ def colorize_ddcolor(img_bgr, intensity):
 
     emit_progress(15, "Loading DDColor model")
 
-    session = safe_onnx_session(DDCOLOR_MODEL_PATH)
+    session, _device = safe_onnx_session(DDCOLOR_MODEL_PATH)
     input_name = session.get_inputs()[0].name
     input_shape = session.get_inputs()[0].shape
     # Dynamic dims are strings ('w', 'h'), so default to 512 if not int
@@ -181,41 +181,39 @@ def main():
         result_bgr = None
         method = "unknown"
 
-        # Try DDColor first
         if model_choice in ("auto", "ddcolor"):
             try:
-                if os.path.exists(DDCOLOR_MODEL_PATH):
+                if not os.path.exists(DDCOLOR_MODEL_PATH):
-                    result_bgr, method = colorize_ddcolor(img_bgr, intensity)
-                elif model_choice == "ddcolor":
                     raise FileNotFoundError(f"DDColor model not found: {DDCOLOR_MODEL_PATH}")
+                result_bgr, method = colorize_ddcolor(img_bgr, intensity)
             except Exception as e:
                 import traceback
                 print(f"[colorize] DDColor failed: {e}", file=sys.stderr, flush=True)
                 traceback.print_exc(file=sys.stderr)
-                if model_choice == "ddcolor":
+                print(json.dumps({
-                    # User explicitly requested ddcolor — fail, don't degrade
+                    "success": False,
-                    raise
+                    "error": (
-                result_bgr = None
+                        f"DDColor is not available: {e}. "
+                        "Install the colorize feature or use model=opencv for basic colorization."
+                    ),
+                }))
+                sys.exit(1)
 
-        # Try OpenCV fallback only in auto mode
+        elif model_choice == "opencv":
-        if result_bgr is None and model_choice in ("auto", "opencv"):
             try:
-                if os.path.exists(OPENCV_PROTO_PATH) and os.path.exists(OPENCV_MODEL_PATH):
+                if not (os.path.exists(OPENCV_PROTO_PATH) and os.path.exists(OPENCV_MODEL_PATH)):
-                    result_bgr, method = colorize_opencv(img_bgr, intensity)
-                elif model_choice == "opencv":
                     raise FileNotFoundError(f"OpenCV colorize models not found: {OPENCV_PROTO_PATH}")
+                result_bgr, method = colorize_opencv(img_bgr, intensity)
             except Exception as e:
                 import traceback
-                print(f"[colorize] OpenCV fallback failed: {e}", file=sys.stderr, flush=True)
+                print(f"[colorize] OpenCV failed: {e}", file=sys.stderr, flush=True)
                 traceback.print_exc(file=sys.stderr)
-                if model_choice == "opencv":
+                raise
-                    raise
-                result_bgr = None
 
-        if result_bgr is None:
+        else:
             print(json.dumps({
                 "success": False,
-                "error": "No colorization model available. Install DDColor or OpenCV models.",
+                "error": f"Unknown model '{model_choice}'. Use 'auto', 'ddcolor', or 'opencv'.",
             }))
             sys.exit(1)
 

packages/ai/python/enhance_faces.py

@@ -2,6 +2,25 @@
 import sys
 import json
 import os
+import types
+
+# basicsr imports torchvision.transforms.functional_tensor which was removed
+# in torchvision >= 0.17. This shim must exist before basicsr is imported.
+try:
+    import torchvision.transforms.functional_tensor  # noqa: F401
+except (ImportError, ModuleNotFoundError):
+    try:
+        import torchvision.transforms.functional as _F
+        import torchvision.transforms
+
+        _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        for _attr in dir(_F):
+            if not _attr.startswith("_"):
+                setattr(_shim, _attr, getattr(_F, _attr))
+        sys.modules["torchvision.transforms.functional_tensor"] = _shim
+        torchvision.transforms.functional_tensor = _shim
+    except ImportError:
+        pass
 
 
 def emit_progress(percent, stage):
@@ -270,19 +289,18 @@ def main():
                 model_used = "codeformer"
 
             elif model_choice == "auto":
-                # Try CodeFormer first, fall back to GFPGAN.
-                # Catch broad Exception because codeformer-pip can fail in
-                # unexpected ways (AttributeError, TypeError, etc.)
                 try:
                     fidelity_weight = 1.0 - strength
                     enhanced = enhance_with_codeformer(img_array, fidelity_weight)
                     model_used = "codeformer"
                 except Exception as e:
                     import traceback
-                    print(f"[enhance-faces] CodeFormer failed, falling back to GFPGAN: {e}", file=sys.stderr, flush=True)
+                    print(f"[enhance-faces] CodeFormer failed: {e}", file=sys.stderr, flush=True)
                     traceback.print_exc(file=sys.stderr)
-                    enhanced = enhance_with_gfpgan(img_array, only_center_face)
+                    raise RuntimeError(
-                    model_used = "gfpgan"
+                        f"CodeFormer is not available: {e}. "
+                        "Install the face-enhance feature or use model=gfpgan."
+                    ) from e
 
         finally:
             # Restore stdout after ALL AI processing

packages/ai/python/face_landmarks.py

@@ -54,14 +54,14 @@ def extract_key_points(lms):
 
 # ── Old API: mp.solutions (mediapipe < 0.10.30) ───────────────────
 
-def detect_with_solutions(img_array):
+def detect_with_solutions(img_array, max_faces=1):
     """Use the legacy mp.solutions.face_mesh API."""
     import mediapipe as mp
 
     mp_face_mesh = mp.solutions.face_mesh
     face_mesh = mp_face_mesh.FaceMesh(
         static_image_mode=True,
-        max_num_faces=1,
+        max_num_faces=max_faces,
         refine_landmarks=True,
         min_detection_confidence=0.5,
     )
@@ -99,7 +99,7 @@ def ensure_model():
     return MODEL_PATH
 
 
-def detect_with_tasks(img_path):
+def detect_with_tasks(img_path, max_faces=1):
     """Use the new mp.tasks.vision.FaceLandmarker API."""
     import mediapipe as mp
 
@@ -108,7 +108,7 @@ def detect_with_tasks(img_path):
     options = mp.tasks.vision.FaceLandmarkerOptions(
         base_options=mp.tasks.BaseOptions(model_asset_path=model_path),
         running_mode=mp.tasks.vision.RunningMode.IMAGE,
-        num_faces=1,
+        num_faces=max_faces,
         min_face_detection_confidence=0.5,
         output_face_blendshapes=False,
         output_facial_transformation_matrixes=False,
@@ -133,6 +133,8 @@ def main():
     output_path = sys.argv[2]  # unused but kept for bridge.ts compatibility
     settings = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}
 
+    max_faces = settings.get("max_num_faces", 1)
+
     try:
         emit_progress(10, "Loading image")
         from PIL import Image
@@ -146,16 +148,14 @@ def main():
 
             emit_progress(20, "Initializing face mesh")
 
-            # Try the legacy solutions API first (Docker / older mediapipe),
-            # fall back to the tasks API (newer mediapipe versions).
             landmarks_list = None
             try:
                 img_array = np.array(img)
                 emit_progress(30, "Detecting face landmarks")
-                landmarks_list = detect_with_solutions(img_array)
+                landmarks_list = detect_with_solutions(img_array, max_faces)
             except AttributeError:
                 emit_progress(30, "Detecting face landmarks")
-                landmarks_list = detect_with_tasks(input_path)
+                landmarks_list = detect_with_tasks(input_path, max_faces)
 
             if landmarks_list is None:
                 print(json.dumps({

packages/ai/python/gpu.py

@@ -1,10 +1,16 @@
 """Runtime GPU/CUDA detection utility."""
 import functools
+import json
 import os
 import subprocess
 import sys
 
 
+def emit_info(msg):
+    """Emit an informational JSON message to stderr for the bridge to capture."""
+    print(json.dumps({"info": msg}), file=sys.stderr, flush=True)
+
+
 @functools.lru_cache(maxsize=1)
 def gpu_available():
     """Return True if a usable CUDA GPU is present at runtime."""
@@ -51,24 +57,34 @@ def gpu_available():
 
 
 def onnx_providers():
-    """Return ONNX Runtime execution providers in priority order."""
+    """Return (providers, device) tuple.
+
+    providers: ONNX Runtime execution providers in priority order.
+    device: "cuda" or "cpu" — reflects which hardware will actually be used.
+    """
     if gpu_available():
-        return ["CUDAExecutionProvider", "CPUExecutionProvider"]
+        return (["CUDAExecutionProvider", "CPUExecutionProvider"], "cuda")
-    return ["CPUExecutionProvider"]
+    emit_info("No GPU detected, processing on CPU")
+    return (["CPUExecutionProvider"], "cpu")
 
 
 def safe_onnx_session(model_path, providers=None):
-    """Create an ONNX Runtime InferenceSession with graceful CUDA EP fallback."""
+    """Create an ONNX Runtime InferenceSession with graceful CUDA EP fallback.
+
+    Returns (session, device) where device is "cuda" or "cpu".
+    """
     import onnxruntime as ort
 
+    device = "cpu"
     if providers is None:
-        providers = onnx_providers()
+        providers, device = onnx_providers()
 
     try:
-        return ort.InferenceSession(model_path, providers=providers)
+        session = ort.InferenceSession(model_path, providers=providers)
+        return session, device
     except Exception as e:
         if "CUDAExecutionProvider" in providers:
-            print(f"[gpu] CUDA EP init failed ({e}), falling back to CPU",
+            emit_info(f"CUDA init failed ({e}), falling back to CPU")
-                  file=sys.stderr, flush=True)
+            session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
-            return ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+            return session, "cpu"
         raise

packages/ai/python/inpaint.py

@@ -111,7 +111,7 @@ def main():
         model_path = _get_model_path()
 
         from gpu import safe_onnx_session
-        session = safe_onnx_session(model_path)
+        session, _device = safe_onnx_session(model_path)
 
         emit_progress(20, "Loading images")
         img = Image.open(input_path).convert("RGB")

packages/ai/python/ocr.py

@@ -256,19 +256,23 @@ def main():
                 text = run_paddleocr_v5(input_path, language)
                 engine_used = "paddleocr-v5"
             except ImportError as e:
-                print(json.dumps({"success": False, "error": f"PaddleOCR is not installed: {e}"}))
+                print(json.dumps({
+                    "success": False,
+                    "error": (
+                        f"PaddleOCR is not installed: {e}. "
+                        "Install the OCR feature or use quality=fast for Tesseract."
+                    ),
+                }))
                 sys.exit(1)
             except Exception as e:
                 print(json.dumps({
-                    "warning": f"PaddleOCR PP-OCRv5 failed ({type(e).__name__}: {e}), falling back to Tesseract"
+                    "success": False,
-                }), file=sys.stderr, flush=True)
+                    "error": (
-                emit_progress(25, "PaddleOCR failed, falling back to Tesseract")
+                        f"PaddleOCR PP-OCRv5 failed: {type(e).__name__}: {e}. "
-                try:
+                        "Install the OCR feature or use quality=fast for Tesseract."
-                    text = run_tesseract(input_path, language, is_auto=was_auto)
+                    ),
-                    engine_used = "tesseract (fallback from balanced)"
+                }))
-                except FileNotFoundError:
+                sys.exit(1)
-                    print(json.dumps({"success": False, "error": "OCR engines unavailable: PaddleOCR failed and Tesseract is not installed"}))
-                    sys.exit(1)
 
         elif quality == "best":
             try:
@@ -276,34 +280,22 @@ def main():
                 engine_used = "paddleocr-vl"
             except ImportError as e:
                 print(json.dumps({
-                    "warning": f"PaddleOCR-VL not available ({e}), trying PP-OCRv5"
+                    "success": False,
-                }), file=sys.stderr, flush=True)
+                    "error": (
-                emit_progress(20, "VL model unavailable, trying PP-OCRv5")
+                        f"PaddleOCR-VL is not available: {e}. "
-                try:
+                        "Install the OCR feature or use quality=balanced for PP-OCRv5."
-                    text = run_paddleocr_v5(input_path, language)
+                    ),
-                    engine_used = "paddleocr-v5 (fallback from best)"
+                }))
-                except Exception as e2:
+                sys.exit(1)
-                    print(json.dumps({
-                        "warning": f"PP-OCRv5 also failed ({type(e2).__name__}: {e2}), falling back to Tesseract"
-                    }), file=sys.stderr, flush=True)
-                    emit_progress(25, "PP-OCRv5 failed, falling back to Tesseract")
-                    text = run_tesseract(input_path, language, is_auto=was_auto)
-                    engine_used = "tesseract (fallback from best)"
             except Exception as e:
                 print(json.dumps({
-                    "warning": f"PaddleOCR-VL failed ({type(e).__name__}: {e}), trying PP-OCRv5"
+                    "success": False,
-                }), file=sys.stderr, flush=True)
+                    "error": (
-                emit_progress(20, "VL model failed, trying PP-OCRv5")
+                        f"PaddleOCR-VL failed: {type(e).__name__}: {e}. "
-                try:
+                        "Install the OCR feature or use quality=balanced for PP-OCRv5."
-                    text = run_paddleocr_v5(input_path, language)
+                    ),
-                    engine_used = "paddleocr-v5 (fallback from best)"
+                }))
-                except Exception as e2:
+                sys.exit(1)
-                    print(json.dumps({
-                        "warning": f"PP-OCRv5 also failed ({type(e2).__name__}: {e2}), falling back to Tesseract"
-                    }), file=sys.stderr, flush=True)
-                    emit_progress(25, "PP-OCRv5 failed, falling back to Tesseract")
-                    text = run_tesseract(input_path, language, is_auto=was_auto)
-                    engine_used = "tesseract (fallback from best)"
 
         else:
             print(json.dumps({"success": False, "error": f"Unknown quality: {quality}"}))

packages/ai/python/red_eye_removal.py

@@ -32,7 +32,7 @@ def _ensure_face_mesh_model():
     return _LOCAL_MODEL_PATH
 
 
-def _mesh_with_solutions(img_array, max_faces=10, min_confidence=0.5):
+def _mesh_with_solutions(img_array, max_faces=50, min_confidence=0.5):
     """FaceMesh using legacy mp.solutions API (mediapipe < 0.10.30).
 
     Returns list of landmark lists. Each landmark has .x, .y attributes.
@@ -54,7 +54,7 @@ def _mesh_with_solutions(img_array, max_faces=10, min_confidence=0.5):
     return [face.landmark for face in results.multi_face_landmarks]
 
 
-def _mesh_with_tasks(img_array, max_faces=10, min_confidence=0.5):
+def _mesh_with_tasks(img_array, max_faces=50, min_confidence=0.5):
     """FaceMesh using new mp.tasks API (mediapipe >= 0.10.30).
 
     Returns list of landmark lists. Each landmark has .x, .y attributes.
@@ -79,7 +79,7 @@ def _mesh_with_tasks(img_array, max_faces=10, min_confidence=0.5):
     return result.face_landmarks
 
 
-def _detect_face_mesh(img_array, max_faces=10, min_confidence=0.5):
+def _detect_face_mesh(img_array, max_faces=50, min_confidence=0.5):
     """Detect face mesh, trying legacy API first then falling back to tasks API."""
     try:
         return _mesh_with_solutions(img_array, max_faces, min_confidence)

packages/ai/python/remove_bg.py

@@ -76,14 +76,15 @@ def main():
 
         emit_progress(10, "Loading model")
 
-        providers = onnx_providers()
+        providers, device = onnx_providers()
         try:
             session = new_session(model, providers=providers)
         except Exception as e:
             if "CUDAExecutionProvider" in providers:
-                print(f"[remove-bg] GPU session failed ({e}), falling back to CPU",
+                from gpu import emit_info
-                      file=sys.stderr, flush=True)
+                emit_info(f"GPU session failed ({e}), falling back to CPU")
                 session = new_session(model, providers=["CPUExecutionProvider"])
+                device = "cpu"
             else:
                 raise
 
@@ -92,7 +93,6 @@ def main():
         with open(input_path, "rb") as f:
             input_data = f.read()
 
-        # Try with alpha matting for better edges, fall back without
         emit_progress(30, "Analyzing image")
         try:
             output_data = remove(
@@ -103,8 +103,9 @@ def main():
                 alpha_matting_background_threshold=10,
             )
         except Exception as e:
-            print(f"[remove-bg] Alpha matting failed ({e}), using standard removal", file=sys.stderr, flush=True)
+            raise RuntimeError(
-            output_data = remove(input_data, session=session)
+                f"Alpha matting failed: {e}. Try again without alpha matting or with a different model."
+            ) from e
 
         emit_progress(80, "Background removed")
 
@@ -115,7 +116,7 @@ def main():
         with open(output_path, "wb") as f:
             f.write(output_data)
 
-        result = json.dumps({"success": True, "model": model})
+        result = json.dumps({"success": True, "model": model, "device": device})
 
     except ImportError as e:
         print(f"[remove-bg] Import failed: {e}", file=sys.stderr, flush=True)

packages/ai/python/restore.py

@@ -155,7 +155,7 @@ def inpaint_damage(img_bgr, mask):
     from gpu import safe_onnx_session
 
     model_path = _get_lama_path()
-    session = safe_onnx_session(model_path)
+    session, _device = safe_onnx_session(model_path)
 
     orig_h, orig_w = img_bgr.shape[:2]
     img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
@@ -317,7 +317,7 @@ def enhance_faces(img_bgr, fidelity=0.7):
 
     # Load CodeFormer model
     model_path = _get_codeformer_path()
-    session = safe_onnx_session(model_path)
+    session, _device = safe_onnx_session(model_path)
     input_names = [inp.name for inp in session.get_inputs()]
 
     result = img_bgr.copy()
@@ -329,8 +329,7 @@ def enhance_faces(img_bgr, fidelity=0.7):
         w = face_box["w"]
         h = face_box["h"]
 
-        # Skip very small faces (under 48px) - enhancement won't help
+        if w < 24 or h < 24:
-        if w < 48 or h < 48:
             continue
 
         # Expand bounding box by ~80% for hair, forehead, chin
@@ -473,7 +472,7 @@ def colorize_bw(img_bgr, intensity=0.85):
     if not os.path.exists(DDCOLOR_MODEL_PATH):
         return img_bgr, False
 
-    session = safe_onnx_session(DDCOLOR_MODEL_PATH)
+    session, _device = safe_onnx_session(DDCOLOR_MODEL_PATH)
     input_name = session.get_inputs()[0].name
     input_shape = session.get_inputs()[0].shape
     model_size = (
@@ -545,6 +544,9 @@ def main():
         scratch_sensitivity = "medium"
 
     try:
+        from gpu import gpu_available
+        device = "cuda" if gpu_available() else "cpu"
+
         emit_progress(5, "Opening image")
         img_bgr = cv2.imread(input_path, cv2.IMREAD_COLOR)
         if img_bgr is None:
@@ -633,6 +635,7 @@ def main():
             "facesEnhanced": faces_found,
             "isGrayscale": bw_detected,
             "colorized": colorized,
+            "device": device,
             "output_path": output_path,
         }))
 

packages/ai/python/upscale.py

@@ -1,7 +1,26 @@
-"""Image upscaling with Real-ESRGAN fallback to Lanczos."""
+"""Image upscaling with Real-ESRGAN."""
 import sys
 import json
 import os
+import types
+
+# basicsr imports torchvision.transforms.functional_tensor which was removed
+# in torchvision >= 0.17. This shim must exist before basicsr is imported.
+try:
+    import torchvision.transforms.functional_tensor  # noqa: F401
+except (ImportError, ModuleNotFoundError):
+    try:
+        import torchvision.transforms.functional as _F
+        import torchvision.transforms
+
+        _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        for _attr in dir(_F):
+            if not _attr.startswith("_"):
+                setattr(_shim, _attr, getattr(_F, _attr))
+        sys.modules["torchvision.transforms.functional_tensor"] = _shim
+        torchvision.transforms.functional_tensor = _shim
+    except ImportError:
+        pass
 
 
 def emit_progress(percent, stage):
@@ -126,32 +145,30 @@ def main():
                     result = Image.fromarray(output_array)
                     method = "realesrgan"
 
-                    # Face enhancement with GFPGAN
                     if face_enhance:
                         emit_progress(82, "Enhancing faces")
-                        try:
+                        from gfpgan import GFPGANer
-                            from gfpgan import GFPGANer
 
-                            if os.path.exists(GFPGAN_MODEL_PATH):
+                        if not os.path.exists(GFPGAN_MODEL_PATH):
-                                face_enhancer = GFPGANer(
+                            raise FileNotFoundError(
-                                    model_path=GFPGAN_MODEL_PATH,
+                                f"GFPGAN model not found at {GFPGAN_MODEL_PATH}. "
-                                    upscale=scale,
+                                "Install the upscale-enhance feature or disable faceEnhance."
-                                    arch="clean",
+                            )
-                                    channel_multiplier=2,
+                        face_enhancer = GFPGANer(
-                                    bg_upsampler=upsampler,
+                            model_path=GFPGAN_MODEL_PATH,
-                                )
+                            upscale=scale,
-                                _, _, face_output = face_enhancer.enhance(
+                            arch="clean",
-                                    img_array,
+                            channel_multiplier=2,
-                                    has_aligned=False,
+                            bg_upsampler=upsampler,
-                                    only_center_face=False,
+                        )
-                                    paste_back=True,
+                        _, _, face_output = face_enhancer.enhance(
-                                )
+                            img_array,
-                                result = Image.fromarray(face_output)
+                            has_aligned=False,
-                                emit_progress(88, "Face enhancement complete")
+                            only_center_face=False,
-                            else:
+                            paste_back=True,
-                                emit_progress(88, "Face model not found, skipping")
+                        )
-                        except (ImportError, RuntimeError, OSError):
+                        result = Image.fromarray(face_output)
-                            emit_progress(88, "Face enhancement unavailable, skipping")
+                        emit_progress(88, "Face enhancement complete")
 
                 finally:
                     # Restore stdout after ALL AI processing
@@ -164,19 +181,23 @@ def main():
                 import traceback
                 print(f"[upscale] Real-ESRGAN failed: {e}", file=sys.stderr, flush=True)
                 traceback.print_exc(file=sys.stderr)
-                if model_choice == "realesrgan":
+                print(json.dumps({
-                    # User explicitly requested realesrgan — fail, don't degrade
+                    "success": False,
-                    raise RuntimeError(f"Real-ESRGAN unavailable: {e}") from e
+                    "error": (
-                result = None
+                        f"Real-ESRGAN is not available: {e}. "
+                        "Install the upscale-enhance feature or use model=lanczos for basic upscaling."
+                    ),
+                }))
+                sys.exit(1)
 
-        # Lanczos path: used when explicitly requested or as auto fallback
+        if result is None and model_choice == "lanczos":
-        if result is None:
-            if model_choice not in ("auto", "lanczos"):
-                raise RuntimeError(f"Requested model '{model_choice}' is not available")
             emit_progress(50, "Upscaling with Lanczos")
             result = img.resize(new_size, Image.LANCZOS)
             method = "lanczos"
 
+        if result is None:
+            raise RuntimeError(f"Requested model '{model_choice}' is not available")
+
         # Denoise
         if denoise_strength > 0:
             emit_progress(90, "Reducing noise")

packages/ai/src/background-removal.ts

@@ -2,6 +2,7 @@ import { randomUUID } from "node:crypto";
 import { readFile, unlink, writeFile } from "node:fs/promises";
 import { tmpdir } from "node:os";
 import { join } from "node:path";
+import sharp from "sharp";
 import { type ProgressCallback, parseStdoutJson, runPythonWithProgress } from "./bridge.js";
 
 export interface RemoveBackgroundOptions {
@@ -21,8 +22,10 @@ export async function removeBackground(
 
   await writeFile(inputPath, inputBuffer);
   try {
-    // BiRefNet models need longer timeout (up to 10 min for first load)
+    const meta = await sharp(inputBuffer).metadata();
-    const timeout = options.model?.startsWith("birefnet") ? 600000 : 300000;
+    const megapixels = ((meta.width ?? 0) * (meta.height ?? 0)) / 1_000_000;
+    const baseTimeout = options.model?.startsWith("birefnet") ? 600000 : 300000;
+    const timeout = Math.max(baseTimeout, megapixels * 30 * 1000);
     const { stdout } = await runPythonWithProgress(
       "remove_bg.py",
       [inputPath, outputPath, JSON.stringify(options)],

packages/ai/src/bridge.ts

@@ -218,7 +218,11 @@ function dispatcherRun(
   if (!proc || !proc.stdin || !dispatcherReady) return null;
 
   const id = randomUUID();
-  const timeout = options.timeout ?? 300000;
+  const timeout =
+    options.timeout ??
+    (process.env.PROCESSING_TIMEOUT_S && parseInt(process.env.PROCESSING_TIMEOUT_S, 10) > 0
+      ? parseInt(process.env.PROCESSING_TIMEOUT_S, 10) * 1000
+      : 600000);
 
   return new Promise((resolvePromise, rejectPromise) => {
     const timer = setTimeout(() => {
@@ -278,7 +282,11 @@ function runPythonPerRequest(
   } = {},
 ): Promise<{ stdout: string; stderr: string }> {
   const scriptPath = resolve(PYTHON_DIR, scriptName);
-  const timeout = options.timeout ?? 300000;
+  const timeout =
+    options.timeout ??
+    (process.env.PROCESSING_TIMEOUT_S && parseInt(process.env.PROCESSING_TIMEOUT_S, 10) > 0
+      ? parseInt(process.env.PROCESSING_TIMEOUT_S, 10) * 1000
+      : 600000);
 
   return new Promise((resolvePromise, rejectPromise) => {
     const trySpawn = (pythonBin: string, isFallback: boolean) => {
@@ -390,14 +398,14 @@ export function runPythonWithProgress(
   const dispatcherPromise = dispatcherRun(scriptName, args, options);
   if (dispatcherPromise) {
     return dispatcherPromise.catch((err: Error) => {
-      // Dispatcher crashed mid-request (e.g. OOM when loading a large model).
-      // Retry in an isolated per-request process which starts clean and has
-      // more available memory than the warm dispatcher.
       if (err.message === "Python dispatcher exited unexpectedly") {
         console.warn(
           `[bridge] Dispatcher crashed during ${scriptName}, retrying with per-request process`,
         );
-        return runPythonPerRequest(scriptName, args, options);
+        return runPythonPerRequest(scriptName, args, options).then((result) => ({
+          ...result,
+          stderr: `${result.stderr}\n[bridge] retried after dispatcher crash`,
+        }));
       }
       throw err;
     });

packages/ai/src/ocr.ts

@@ -31,9 +31,13 @@ export async function extractText(
   const pngBuffer = await sharp(inputBuffer).png().toBuffer();
   await writeFile(inputPath, pngBuffer);
 
+  const meta = await sharp(inputBuffer).metadata();
+  const megapixels = ((meta.width ?? 0) * (meta.height ?? 0)) / 1_000_000;
+  const timeout = Math.max(600_000, megapixels * 30 * 1000);
+
   const { stdout } = await runPythonWithProgress("ocr.py", [inputPath, JSON.stringify(options)], {
     onProgress,
-    timeout: 600_000, // 10 min timeout for VLM on CPU
+    timeout,
   });
 
   const result = parseStdoutJson(stdout);

packages/ai/src/seam-carving.ts

@@ -47,17 +47,11 @@ async function findCaire(): Promise<string> {
   );
 }
 
-/** Max pixels on the longest edge before downscaling for caire. */
-const MAX_CAIRE_DIMENSION = 1200;
-
 /**
  * Content-aware resize using caire (Go seam carving engine).
  * Supports both shrinking and enlarging via seam removal/insertion.
- *
+ * Processes at native resolution -- JPEG intermediate is used because
- * Large images (>1200px longest edge) are downscaled first because
+ * Go's JPEG decoder is significantly faster than PNG for large images.
- * seam carving is O(width * height * seams) and becomes impractical
- * on high-resolution inputs. JPEG intermediate is used because Go's
- * JPEG decoder is significantly faster than PNG for large images.
  */
 export async function seamCarve(
   inputBuffer: Buffer,
@@ -66,35 +60,18 @@ export async function seamCarve(
 ): Promise<SeamCarveResult> {
   const cairePath = await findCaire();
   const id = randomUUID();
-  // Use JPEG for input (fast decode in Go) and PNG for output (lossless)
   const inputPath = join(outputDir, `caire-in-${id}.jpg`);
   const outputPath = join(outputDir, `caire-out-${id}.png`);
 
   try {
-    // Downscale large images and convert to JPEG for fast caire processing
     const meta = await sharp(inputBuffer).metadata();
-    const origWidth = meta.width ?? 0;
+    const width = meta.width ?? 0;
-    const origHeight = meta.height ?? 0;
+    const height = meta.height ?? 0;
-    const longest = Math.max(origWidth, origHeight);
 
-    let width = origWidth;
+    const processBuffer = await sharp(inputBuffer).jpeg({ quality: 95 }).toBuffer();
-    let height = origHeight;
-
-    if (longest > MAX_CAIRE_DIMENSION) {
-      const scale = MAX_CAIRE_DIMENSION / longest;
-      width = Math.round(origWidth * scale);
-      height = Math.round(origHeight * scale);
-    }
-
-    // Always output JPEG for caire input (Go decodes JPEG 3-5x faster than PNG)
-    const processBuffer = await sharp(inputBuffer)
-      .resize(width, height, { fit: "fill" })
-      .jpeg({ quality: 95 })
-      .toBuffer();
 
     await writeFile(inputPath, processBuffer);
 
-    // Build caire arguments
     const args = ["-in", inputPath, "-out", outputPath, "-preview=false"];
 
     if (options.square) {
@@ -102,19 +79,10 @@ export async function seamCarve(
       args.push("-square", "-width", String(shortest), "-height", String(shortest));
     } else {
       if (options.width) {
-        // Scale user-specified dimensions proportionally if image was downscaled
+        args.push("-width", String(options.width));
-        const targetW =
-          longest > MAX_CAIRE_DIMENSION
-            ? Math.round(options.width * (MAX_CAIRE_DIMENSION / longest))
-            : options.width;
-        args.push("-width", String(targetW));
       }
       if (options.height) {
-        const targetH =
+        args.push("-height", String(options.height));
-          longest > MAX_CAIRE_DIMENSION
-            ? Math.round(options.height * (MAX_CAIRE_DIMENSION / longest))
-            : options.height;
-        args.push("-height", String(targetH));
       }
     }
 
@@ -122,7 +90,9 @@ export async function seamCarve(
     if (options.blurRadius !== undefined) args.push("-blur", String(options.blurRadius));
     if (options.sobelThreshold !== undefined) args.push("-sobel", String(options.sobelThreshold));
 
-    await execFileAsync(cairePath, args, { timeout: 120_000 });
+    const megapixels = (width * height) / 1_000_000;
+    const timeoutMs = Math.max(120_000, megapixels * 10 * 1000);
+    await execFileAsync(cairePath, args, { timeout: timeoutMs });
 
     const buffer = await readFile(outputPath);
     const outMeta = await sharp(buffer).metadata();

packages/shared/src/i18n/en.ts

@@ -241,6 +241,24 @@ export const en = {
       logoRequirements: "PNG, SVG, or JPEG. Max 500KB.",
       dragDrop: "Drag and drop or click to upload",
     },
+    limitsAndResources: "Limits & Resources",
+    maxFileSize: "Max File Size",
+    maxBatchSize: "Max Batch Size",
+    concurrentJobs: "Concurrent Jobs",
+    workerThreads: "Worker Threads",
+    maxPipelineSteps: "Max Pipeline Steps",
+    processingTimeout: "Processing Timeout",
+    rateLimitPerMin: "Rate Limit (req/min)",
+    unlimited: "Unlimited",
+    auto: "Auto",
+    disabled: "Disabled",
+    envOverride: "Set by environment variable",
+    maxCanvasPixels: "Max Canvas Pixels",
+    maxSvgSize: "Max SVG Size",
+    maxLogoSize: "Max Logo Size",
+    maxSplitGrid: "Max Split Grid",
+    maxPdfPages: "Max PDF Pages",
+    sessionDuration: "Session Duration",
   },
   auth: {
     login: "Login",