Scope
Computer software
Consumer‑grade hardware target
Mid-range devices (Laptop/Desktop)
Open source
Generative AI
Local & offline generation
Diffusion models only
Flow‑match schedulers
Multimodal output support
Philosophy
Diffusion is a Journey: Walk your own path
Principally Subjective: Output is complete when the operator declares it
Feedback: Tangible, rapid system feel. Instantaneous creativity demands instant tools
Keep Flow State: Perpetuate the disruptive and unique as long as possible
Transformation & Iteration: Exploration over refinement
The End Game: Tractable chaos equilibrium
Objectives
Step‑wise diffusion: edit parameters while generating (live latent walk)
Generation window: ≤1s inference for all use‑cases
Intermediate Preview = Final Product: always display/save current state
Input: adjust all parameters within the time for Generation window
Corruptions: ≥10 significantly transformative parameter adjustments (one per finger)
Code: abstracted from model frameworks, generalizable to any diffusion modality
Hackable & Branching: Reversible and revisionable output
Create Value: Store and share every edit and the process leading to it
Related readings: https://diffusionflow.github.io/ Diffusion Meets Flow Matching Flow matching and diffusion models are two popular frameworks in generative modeling. Despite seeming similar, there is some confusion in the community about their exact connection. In this post, we aim to clear up this confusion and show that diffusion models and Gaussian flow matching are the same, although different model specification...
Its an ML system that runs models using the fastest library per system type
No-code system, self-arranges to run more models than I even know about
It creates images and generates text
Its totally local and offline (ie private)
As libraries update, it immediately supports them
We are able to recognize any kind of open source AI model in Transformers, Diffusers, and GGUF, and some MFLUX/MLX, ONNX models too
All models supported run right out of the box with no questions asked
We are able to generate several types of media with no configuration required
share workflows certain to work on any computer (impossible w/o docker & even often with)
create optimized, auto-arranged workflows
hand this system to a non-experienced person and they can use it
allow people to explore with guidance from the system with no fear of breaking it
concentrates and distributes legacy model knowledge
future proof due to versatility of upstream channels, always-up-to-date
deeply reduce complexity and learning required to get to peak performance with any model (otherwise impossible), and then automate its construction per system
For this to run at all/have any purpose on most computers, it needs :
memory management - component recognition done, pipe needs to sequentially load
model chaining - automatic chaining of models towards specific tasks
add more libraries and the ability to install them via sdbx
add a fuck ton of upscale/3d/audio/svg/midi models etc (things that arent already recognized by diffusers/transformers/etc)
modularity - tasks need to be dynamically looked up and added to model chain
deep analysis of models
https://darkshapes.org/docs/divisor/divisor/ https://darkshapes.org/docs/zodiac/zodiac/ https://darkshapes.org/docs/mir/mir/ https://darkshapes.org/docs/nnll/nnll/