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Local image generation for Go

Native stable-diffusion.cpp bindings for local image and video generation. Text-to-image, image-to-image, ControlNet, ADetailer, AnimateDiff, and Real-ESRGAN upscaling — with pure-Go PNG and JPEG I/O, no CGo, and no cloud dependency. OpenAI-compatible /v1/images/generations and /v1/images/edits on the Kronk model server.

Full Details View on GitHub
Malina — local image generation for Go
kronk — malina example
$ git clone https://github.com/ardanlabs/kronk.git
$ cd kronk
$ make example-malina

✓ installed stable-diffusion.cpp libraries
✓ downloaded sd-1.5 model bundle
- dimensions       : 512x512
- output           : malina.png

Why Malina

Built for production-minded Go teams

Malina brings local image generation into the Kronk ecosystem so Go apps can create and transform images on infrastructure they control — with pure-Go I/O and no cloud dependency. The public API is experimental and still evolving.

  1. 01

    Generate on your stack

    Run image generation where your application runs — not behind another vendor API.

  2. 02

    No CGo required

    Pure-Go PNG and JPEG I/O plus Motion-JPEG AVI muxing keeps the integration path simpler for Go services and tools.

  3. 03

    Familiar API direction

    OpenAI-compatible /v1/images/generations and /v1/images/edits on the Kronk model server, with advanced workflows available in the Go SDK.

  4. 04

    Fits the Kronk model

    Same local-first philosophy as Kronk and Bucky: own the runtime, control cost, keep data private.

What you can do

Image workflows Malina is built for

Use the Go SDK inside your process for the full surface area, or create and edit images through Kronk’s OpenAI-compatible image APIs.

Text-to-image

Generate images from prompts with curated Stable Diffusion, SDXL, and multi-file diffusion bundles.

Image-to-image

Transform an existing PNG or JPEG with a prompt and strength — through the SDK or /v1/images/edits.

ControlNet & ADetailer

Canny edge conditioning for composition control, plus face detection and refinement for portraits.

Video & upscaling

AnimateDiff frame sequences, Wan2.2 S2V speech-to-video, Real-ESRGAN upscaling, and Motion-JPEG AVI output.

Capabilities

What you get

stable-diffusion.cpp

Native bindings for local diffusion models without standing up a separate generation service first.

Curated model bundles

Pull complete workflows — SD 1.5, ControlNet, AnimateDiff, Real-ESRGAN, ADetailer, SDXL, and more — with validated manifests.

Pure-Go media I/O

PNG and JPEG handling in Go, plus Motion-JPEG AVI muxing so image and video pipelines stay inside your application.

SDK + model server

Use Malina in-process, or create and edit images through Kronk’s OpenAI-compatible image endpoints.

Hardware accelerated

Metal, CUDA, Vulkan, and ROCm backends depending on host — with automatic library detection and install.

Local by default

No cloud round-trips for generation — useful for air-gapped, regulated, or cost-sensitive environments.

Kronk and Malina — local image generation in the Kronk ecosystem

Get started

Run Malina with Kronk

Install Kronk, pull stable-diffusion.cpp libraries and an SD 1.5 bundle, then run the Malina example — or generate images through the model server once it is up.

Install Kronk

brew install ardanlabs/kronk/kronk
# or: go install github.com/ardanlabs/kronk/cmd/kronk@latest

Install SD libraries + a model bundle

kronk malina libs --local
kronk malina model pull --local sd-1.5

Run the example

git clone https://github.com/ardanlabs/kronk.git
cd kronk
make example-malina

Or generate through the API

kronk server start

# OpenAI-compatible image generation on the model server
# POST /v1/images/generations  ·  POST /v1/images/edits

Deeper docs: Malina manual chapter · Kronk on GitHub · Malina SDK repo

Explore Kronk View on GitHub

Platforms

Where Malina runs

Compatible stable-diffusion.cpp library bundles are downloaded per host. Use the CLI as the source of truth for supported combinations on your install.

macOS

CPU arm64 · GPU Metal

Linux

CPU amd64 · GPU Vulkan and ROCm

Windows

CPU amd64 · GPU CUDA, Vulkan, and ROCm

Model bundles

Pull a complete workflow

Curated bundles from the catalog — pull what you need with kronk malina model pull --local.

sd-1.5

~4.3 GB

Stable Diffusion 1.5 checkpoint — the default path for first image generation

controlnet-canny-sd1.5

~2.3 GB

Quantized SD 1.5 plus Canny ControlNet for composition-constrained generation

realesrgan-x4-anime

~18 MB

Compact Real-ESRGAN 4× anime upscaler for enlarging generated frames

adetailer-face-yolov8n

~1.6 GB

Face detection and inpainting refinement for portrait workflows

animatediff-sd1.5

~2.4 GB

AnimateDiff motion module for temporally conditioned video sequences

sdxl-base-1.0

~6.9 GB

SDXL Base 1.0 checkpoint for higher-resolution generation

Related tooling

More in the ecosystem

Kronk

Go With Your Own Intelligence

Bucky

Local speech-to-text for Go

All AI Tooling

Build AI systems you can own

Talk with Ardan Labs about local inference, private AI infrastructure, and production systems beyond API prototypes.

Explore AI Engineering

Leverage our experience. Get what you need.