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docs: remove hardcoded pipeline/batch limits, default to unlimited

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Pipeline steps and batch size are now unlimited by default. The old
"20 steps" and "200 images" figures had no basis in the actual code
(MAX_BATCH_SIZE already defaulted to 0/unlimited). Both remain
configurable via MAX_PIPELINE_STEPS and MAX_BATCH_SIZE env vars.

Also includes updated hardware requirements and sidebar nav from
prior documentation audit.
This commit is contained in:
ashim-hq
2026-04-23 20:50:38 +08:00
parent 4bfbc51b77
commit 8aea77b4be
7 changed files with 148 additions and 40 deletions
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README.md
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@@ -16,7 +16,7 @@
- **45+ image tools** - Resize, crop, compress, convert, watermark, color adjust, vectorize, create GIFs, find duplicates, generate passport photos, and more
- **Local AI** - Remove backgrounds, upscale images, restore and colorize old photos, erase objects, blur faces, enhance faces, extract text (OCR). All on your hardware - no internet required
- **Pipelines** - Chain tools into reusable workflows. Batch process up to 200 images at once
- **Pipelines** - Chain tools into reusable workflows with unlimited steps. Batch process unlimited images at once
- **REST API** - Every tool available via API with API key auth. Interactive docs at `/api/docs`
- **Single container** - One `docker run`, no Redis, no Postgres, no external services
- **Multi-arch** - Runs on AMD64 and ARM64 (Intel, Apple Silicon, Raspberry Pi)
apps/api/src/routes/pipeline.ts
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@@ -39,23 +39,23 @@ const pipelineStepSchema = z.object({
});
/** Schema for a full pipeline definition. */
const maxSteps = env.MAX_PIPELINE_STEPS > 0 ? env.MAX_PIPELINE_STEPS : 20;
const stepsSchema =
env.MAX_PIPELINE_STEPS > 0
? z
.array(pipelineStepSchema)
.min(1, "Pipeline must have at least one step")
.max(env.MAX_PIPELINE_STEPS, "Pipeline exceeds maximum steps")
: z.array(pipelineStepSchema).min(1, "Pipeline must have at least one step");
const pipelineDefinitionSchema = z.object({
steps: z
.array(pipelineStepSchema)
.min(1, "Pipeline must have at least one step")
.max(maxSteps, "Pipeline exceeds maximum steps"),
steps: stepsSchema,
});
/** Schema for saving a pipeline. */
const savePipelineSchema = z.object({
name: z.string().min(1, "Pipeline name is required").max(100),
description: z.string().max(500).optional(),
steps: z
.array(pipelineStepSchema)
.min(1, "Pipeline must have at least one step")
.max(maxSteps, "Pipeline exceeds maximum steps"),
steps: stepsSchema,
});
export async function registerPipelineRoutes(app: FastifyInstance): Promise<void> {
apps/docs/.vitepress/config.mts
+1
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@@ -73,6 +73,7 @@ export default defineConfig({
{ text: "Configuration", link: "/guide/configuration" },
{ text: "Database", link: "/guide/database" },
{ text: "Deployment", link: "/guide/deployment" },
{ text: "Hardware requirements", link: "/guide/deployment#hardware-requirements" },
{ text: "Docker tags", link: "/guide/docker-tags" },
{ text: "Developer guide", link: "/guide/developer" },
{ text: "Translation guide", link: "/guide/translations" },
apps/docs/api/rest.md
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@@ -229,7 +229,7 @@ curl -X POST http://localhost:1349/api/v1/pipeline/batch \
-F 'pipeline=[{"toolId":"resize","settings":{"width":800}}]'
```
Each step's output is the next step's input. Up to **20 steps** per pipeline.
Each step's output is the next step's input. Unlimited steps per pipeline by default (configurable via `MAX_PIPELINE_STEPS`).
### Save and manage pipelines
apps/docs/guide/deployment.md
+133 -26
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@@ -121,46 +121,153 @@ docker logs ashim 2>&1 | head -20
## Hardware Requirements
### Minimum (basic image tools only)
These numbers come from benchmarks run across four systems (Apple M2 Max, AMD Ryzen 5 7500F + RTX 4070, Intel i7-7600U, Docker Desktop on Windows). See `docs/HARDWARE_RECOMMENDATIONS.md` in the repo for full methodology and raw data.
### Quick Reference
| Tier | Use Case | CPU | RAM | GPU | Storage |
|------|----------|-----|-----|-----|---------|
| Minimum | Core tools, single user | 1 core | 1 GB | None | 5 GB |
| Recommended | All tools + AI on CPU | 4 cores | 4 GB | None | 20 GB |
| Full | All tools + AI on GPU | 4+ cores | 8 GB | NVIDIA 8 GB+ | 30 GB |
### Minimum (core image tools)
| Resource | Requirement |
|---|---|
| CPU | 2 cores |
| CPU | 1 core |
| RAM | 1 GB |
| Disk | 3 GB (image) + 1 GB (data volume) |
| GPU | Not required |
Basic tools (resize, crop, rotate, convert, watermark, border, etc.) work on any hardware. They use Sharp (libvips) and complete in milliseconds.
All 35 non-AI tools (resize, crop, rotate, convert, compress, watermark, collage, etc.) run on any hardware. Most operations complete in under 1 second even on a single core. The exception is AVIF encoding, which takes ~27s on 1 core but drops to ~5s on 4 cores.
### Recommended (AI tools)
```yaml
deploy:
resources:
limits:
cpus: '1'
memory: 1G
```
### Recommended (AI tools on CPU)
| Resource | Requirement |
|---|---|
| CPU | 4 cores |
| RAM | 4 GB |
| Disk | 3 GB (image) + 14 GB (AI models) + workspace |
| GPU | Not required (CPU fallback) |
AI tools work on CPU but are significantly slower. Some tools are practical on CPU, others are not:
| AI Tool | CPU Time | Usable? |
|---|---|---|
| blur-faces, smart-crop, red-eye-removal | 2-5s | Yes |
| remove-background | 37-41s | Marginal (long wait) |
| upscale (small image) | 22s | Marginal |
| upscale (large image) | 241s | No |
| enhance-faces, colorize, noise-removal | 30-90s | Marginal to No |
AI model download sizes:
| Bundle | Disk Size |
|---|---|
| Background removal | 3-4 GB |
| Upscale + Face enhance + Noise removal | 4-5 GB |
| Face detection | 200-300 MB |
| Object eraser + Colorize | 1-2 GB |
| OCR | 3-4 GB |
| Photo restoration | 800 MB - 1 GB |
| **All bundles** | **~14 GB** |
```yaml
deploy:
resources:
limits:
cpus: '4'
memory: 4G
```
### Full (AI tools on GPU)
| Resource | Requirement |
|---|---|
| CPU | 4+ cores |
| RAM | 4 GB minimum, 8 GB recommended |
| Disk | 3 GB (image) + 10-25 GB (AI models, downloaded on first use) |
| GPU | NVIDIA with 4+ GB VRAM (optional but 5-20x faster) |
AI tools (background removal, upscaling, face enhancement, OCR, object erasing) download models on first use. Model sizes:
| Feature | Model Size | VRAM Usage |
|---|---|---|
| Background removal | ~200 MB | ~1 GB |
| Face detection | ~10 MB | ~500 MB |
| Upscale + Face enhance | ~1.5 GB | ~4 GB |
| OCR | ~200 MB | ~1 GB |
| Object eraser + Colorize | ~500 MB | ~2 GB |
### Heavy workloads (upscale + GFPGAN)
| Resource | Requirement |
|---|---|
| CPU | 8+ cores |
| RAM | 16 GB |
| GPU | NVIDIA with 8+ GB VRAM (RTX 3070 or better) |
| RAM | 8 GB |
| GPU | NVIDIA with 8+ GB VRAM (12 GB recommended) |
| Disk | 30 GB total |
Upscaling a 4K image with face enhancement at 4x scale uses ~6 GB VRAM peak. Without a GPU, the same operation takes 5-10 minutes on CPU vs. 10-30 seconds on GPU.
GPU acceleration gives 3-13,000x speedup depending on the operation. Measured on an RTX 4070 vs Intel i7-7600U:
| AI Tool | GPU Time | CPU Time | Speedup |
|---|---|---|---|
| noise-removal (quick) | 17ms | 228s | 13,400x |
| blur-faces | 0.27s | 27s | 100x |
| upscale 2x | 6.3s | >300s (timeout) | 47x+ |
| enhance-faces (GFPGAN) | 2.3s | 28s | 12x |
| remove-background | 5-10s | 21-41s | 3-8x |
| OCR (best) | 70s | 243s | 3.5x |
| restore-photo | 31s | 90s | 2.9x |
| colorize | 10s | 13s | 1.3x |
Peak VRAM usage reaches 7.5 GB during upscale with face enhancement. A 6 GB GPU works for most AI tools individually but will fail on upscale. 8-12 GB VRAM handles everything.
```yaml
deploy:
resources:
limits:
cpus: '4'
memory: 8G
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
```
### Concurrent Users
Benchmarked with parallel resize requests on a large image (Mac M2 Max, 10 Docker CPUs):
| Concurrent Users | Avg Response Time | Errors |
|---|---|---|
| 1 | 0.28s | 0 |
| 5 | 0.54s | 0 |
| 10 | 1.08s | 0 |
| 20 | 2.10s | 0 |
The server scales linearly with no errors or crashes up to 20 concurrent requests.
### Supported Image Formats
| Format | Read | Write | Notes |
|---|---|---|---|
| JPEG | Yes | Yes | |
| PNG | Yes | Yes | |
| WebP | Yes | Yes | |
| AVIF | Yes | Yes | Encode is CPU-intensive (~5s on 4 cores for a large image) |
| GIF | Yes | Yes | Animated GIF supported |
| TIFF | Yes | Yes | Multi-page supported |
| SVG | Yes | No | Rasterized on input, sanitized for security |
| HEIC | Yes | No | Decoded via heif-dec (~0.4s) |
| HEIF | Yes | No | Very slow decode (~15s) |
| DNG (RAW) | Yes (Linux) | No | Decoded via dcraw, not available on macOS |
| PSD | Yes | No | Decoded via ImageMagick |
| HDR | Yes | No | Tone-mapped on decode |
| TGA | Yes | No | Decoded via ImageMagick |
| ICO | Yes | Yes | Via favicon tool |
| PDF | Yes | Yes | Via pdf-to-image / image-to-pdf tools |
Not supported: BMP (V4/V5 headers), JPEG XL (JXL), EXR (missing decode delegate in Docker image).
### Known Limitations
- **Content-aware resize** crashes on large images (>5 MP) due to a limitation in the caire binary. Works fine with smaller images.
- **HEIF decode** takes 13-23 seconds. HEIC (Apple's variant) is much faster at 0.3-0.9 seconds.
- **OCR Japanese** fails on CPU due to a PaddlePaddle MKLDNN bug. Works on GPU.
- **Upscale** times out on CPU for anything beyond small images. GPU required for practical use.
- **CodeFormer** face enhancement is significantly slower than GFPGAN (53s vs 2s on GPU). GFPGAN is recommended for most use cases.
## Volumes
apps/docs/guide/getting-started.md
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@@ -92,10 +92,10 @@ Chain tools into multi-step workflows and apply them to one image or a whole bat
1. Open **Pipelines** in the sidebar.
2. Add steps (any tool, any settings).
3. Run on a single file - or up to 200 files at once.
3. Run on a single file - or an entire batch at once.
4. Save the pipeline for later reuse.
Pipelines can have up to 20 steps.
Pipelines have unlimited steps by default.
### File Library
apps/docs/index.md
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@@ -19,7 +19,7 @@ features:
- title: Local AI
details: 14 AI-powered tools - remove backgrounds, upscale, enhance images, restore and colorize old photos, erase objects, blur faces, enhance faces, extract text (OCR). All on your hardware, no internet required.
- title: Pipelines
details: Chain tools into reusable workflows with up to 20 steps. Batch process up to 200 images at once with a single request.
details: Chain tools into reusable workflows with unlimited steps. Batch process unlimited images at once with a single request.
- title: REST API
details: Every tool available via API with API key auth. Interactive docs at /api/docs, plus /llms.txt and /llms-full.txt for AI agents.
- title: File Library