Imagen

MCP server for generating pixel-art sprites with transparent backgrounds. Bring your own model — any diffusers-compatible text-to-image pipeline works.

The default configuration uses FLUX.2-klein-4B + pixel-art-lora, but you can swap in any model you like.

Features

• Bring your own model — any diffusers-compatible pipeline (FLUX, SDXL, SD3, etc.)
• Text-to-sprite generation — describe any character, get a pixel-art PNG
• Transparent background — automatic background removal via flood-fill
• Pixel-art effect — downscale/upscale with NEAREST interpolation
• Reproducible — optional seed for consistent results
• Batch generation — generate multiple sprites in one call
• MCP integration — works with any MCP-compatible client (opencode, Claude, etc.)
• Feedback loop — rate generated sprites, AI uses high-rated ones as reference

Quick Start

1. Install dependencies

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Download a model

You need a base text-to-image model. Optionally, a LoRA adapter for pixel-art style.

Example: FLUX.2-klein-4B + pixel-art-lora (default)

mkdir -p ~/models

# Base model (~23 GB)
huggingface-cli download black-forest-labs/FLUX.2-klein-4b \
    --local-dir ~/models/flux2-klein-4b

# LoRA adapter (~625 MB) — optional but recommended for pixel-art
huggingface-cli download Limbicnation/pixel-art-lora \
    --local-dir ~/models/pixel-art-lora

Other models that work:

Model	Size	LoRA support	Notes
FLUX.2-klein-4B	~23 GB	Yes	Default, distilled (4 steps)
FLUX.1-dev	~23 GB	Yes	More detail, slower (20+ steps)
SDXL	~7 GB	Yes	Lighter, good for 8 GB VRAM
SD3.5-large	~16 GB	Yes	Good quality/speed balance

Note: You may need to adjust LORA_SCALE and pipeline class in server.py depending on your model. See Configuration.

3. Configure paths

By default, models are expected at ~/models/. Override with environment variables:

export IMAGEGEN_MODEL_DIR=/path/to/your/base-model
export IMAGEGEN_LORA_DIR=/path/to/your/lora        # optional, set empty to disable
export IMAGEGEN_OUTPUT_DIR=/path/to/output

4. Run as MCP server

./venv/bin/python server.py

Or configure in your MCP client:

{
  "mcp": {
    "pixel-art": {
      "type": "local",
      "command": ["./venv/bin/python", "server.py"],
      "enabled": true
    }
  }
}

Tools

Generation

generate_sprite

Generate a single pixel-art sprite. Automatically saved to feedback DB (unrated).

Parameter	Type	Default	Description
prompt	str	required	Sprite description (e.g. "a brave knight in armor")
output_path	str	required	PNG save path (relative to output dir or absolute)
seed	int?	null	Seed for reproducibility
width	int	512	Image width
height	int	512	Image height
steps	int	4	Inference steps (lower = faster, less detail)
remove_bg	bool	true	Remove background, make transparent
pixel_size	int	4	Pixel block size (0 = off, 4 = chunky pixel-art)

Returns: output_path, db_id, generation_time, and other metadata.

batch_generate

Generate multiple sprites in one call. Each is saved to the feedback DB.

Feedback

rate_sprite

Rate a generated sprite 1-5 stars with optional feedback.

Parameter	Type	Default	Description
db_id	str	required	ID returned by generate_sprite / batch_generate
rating	int	required	1-5 stars
feedback	str?	null	Optional text feedback

get_reference_sprites

Get highly-rated reference sprites for a prompt. The AI uses these as examples when generating similar sprites.

Parameter	Type	Default	Description
prompt	str	required	Search query (e.g. "knight")
limit	int	5	Max results
min_rating	int	4	Minimum rating threshold

list_sprites

List sprites in the feedback database.

Parameter	Type	Default	Description
filter	str	"all"	"all", "unrated", or "top"
limit	int	20	Max results

db_stats

Get database statistics: total sprites, rated, unrated, average rating.

Configuration

Environment variables

Variable	Default	Description
IMAGEGEN_MODEL_DIR	~/models/flux2-klein-4b	Path to base model
IMAGEGEN_LORA_DIR	~/models/pixel-art-lora	Path to LoRA adapter
IMAGEGEN_OUTPUT_DIR	./output	Default output directory

Swapping models

The server is configured for FLUX.2-klein by default. To use a different model, edit server.py:

1. Pipeline class — replace Flux2KleinPipeline with your model's pipeline (e.g. StableDiffusionXLPipeline for SDXL)
2. LoRA scale — adjust LORA_SCALE (rsLoRA needs ~0.1, regular LoRA typically 0.7-1.0)
3. Guidance scale — distilled models ignore it; standard models need 5-8
4. Steps — distilled models work at 4; standard models need 20-30

How It Works

1. Generation — text-to-image model generates a 512x512 image
2. Pixelation — downscale with LANCZOS, upscale with NEAREST → chunky pixel-art blocks
3. Background removal — detect border color, normalize to solid fill, flood-fill from edges → transparent PNG

Requirements

• GPU: NVIDIA with >= 8 GB VRAM (uses CPU offload automatically)
• Python: 3.12+
• CUDA: 12.0+

Credits

• Default model: FLUX.2-klein-4B by Black Forest Labs (Apache 2.0)
• Default LoRA: pixel-art-lora by Limbicnation (Apache 2.0)
• MCP SDK: modelcontextprotocol/python-sdk

License

MIT — see LICENSE

Model licenses are separate from this project. Check each model's license card for usage terms.