| name | noisett |
| description | AI Brand Asset Generator with 25 commands for image generation, LoRA training, quality pipeline, and history management. Built with AFD principles.
|
| version | 0.9.1 |
| category | core |
| triggers | ["noisett","asset generator","image generation","LoRA training","quality pipeline","Fireworks","FLUX"] |
Noisett - AI Brand Asset Generator
Generate on-brand illustrations and icons using AI with Agent-First Development patterns.
Capabilities
- Asset Generation — Generate images from text prompts with brand alignment
- LoRA Training — Train custom styles for brand consistency
- Quality Pipeline — Refine, upscale, and post-process images
- History & Favorites — Track and manage generated assets
Routing Logic
Architecture
┌─────────────────────────────────────────────────────────────────────────┐
│ SURFACES (Thin Wrappers) │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────────────┐ │
│ │ VS Code / │ │ Web UI │ │ Figma Plugin │ │
│ │ Cursor (MCP) │ │ (Vanilla JS) │ │ (v2) │ │
│ └────────┬────────┘ └────────┬────────┘ └────────────┬────────────┘ │
│ │ MCP (stdio) │ REST API │ REST API │
│ └────────────────────┼────────────────────────┘ │
│ ▼ │
├─────────────────────────────────────────────────────────────────────────┤
│ COMMAND LAYER (Source of Truth) │
│ Python + FastMCP + Pydantic (25 Commands) │
│ asset.* │ job.* │ model.* │ lora.* │ quality.* │ history.* │ favorites.* │
├─────────────────────────────────────────────────────────────────────────┤
│ ML INFERENCE LAYER │
│ Mock | HuggingFace | Fireworks.ai (FLUX) | Replicate │
└─────────────────────────────────────────────────────────────────────────┘
Command Categories
| Category | Commands | Purpose |
|---|
asset.* | 2 | Image generation |
job.* | 3 | Job management |
model.* | 2 | Model discovery |
lora.* | 7 | Custom style training |
quality.* | 5 | Image refinement |
history.* | 3 | Generation history |
favorites.* | 3 | Saved generations |
Quick CLI Examples
noisett asset.generate '{"prompt": "cloud computing concept", "asset_type": "product"}'
noisett lora.create '{"name": "Xbox Style", "trigger_word": "xboxstyle"}'
noisett lora.upload-images '{"lora_id": "lora_xxx", "images": [...]}'
noisett lora.train '{"lora_id": "lora_xxx"}'
noisett upscale '{"image_url": "...", "scale": 4}'
noisett refine '{"image_url": "...", "strength": 0.3}'
CommandResult Pattern
All commands return structured results:
{
"success": true,
"data": {...},
"reasoning": "Started generation of 4 product illustrations",
"confidence": 0.95,
"suggestions": ["Try 'premium' for marketing-grade quality"]
}
AFD Development Workflow
1. DEFINE → Create command with Pydantic schema
2. VALIDATE → Test via CLI: noisett <command> '<json>'
3. SURFACE → Build UI that calls command
The Honesty Check: If it can't be done via CLI, the architecture is wrong.
Source Locations
src/
├── commands/ # Command definitions
│ ├── asset.py # asset.generate, asset.types
│ ├── job.py # job.status, job.cancel, job.list
│ ├── model.py # model.list, model.info
│ ├── lora.py # lora.* (7 commands)
│ ├── quality.py # quality.* (5 commands)
│ ├── history.py # history.* (3 commands)
│ └── favorites.py # favorites.* (3 commands)
├── core/ # Shared types (CommandResult, errors)
├── ml/ # ML backends
└── server/
├── mcp.py # FastMCP server
└── api.py # FastAPI REST server
When to Escalate
- Custom ML backend integration
- LoRA training infrastructure (GPU requirements)
- Azure deployment troubleshooting
