What will this do?

Convert SD15 to flow matching, similar to how Flux operates.

What will this model need?

Nothing. Just plug it in and rig up whatever shift you want using ComfyUI. It'll operate on shift similar to SD3 but not the same.

Should operate just fine with the David collective completely removed and standard SD15 + CLIP_L system.

Why?

David needs a real bench test, and I figure one of the best ways to showcase David's power would be a collective of distillation agents specifically assigned to distill a model into another noise variation.

Better tools. This will advance the process, enabling a great deal of additionally powerful diffusion tools. A direct diffusion-based accuracy assessment at train time with a guaranteed response other than how much loss you're seeing is my biggest desire.

Properly measuring the chaos drift between images and complexity association between classifications will be possible, so the system can literally measure how accurately something is learned during train time - rather than simply guessing.

Process

Each block will be given a David. Each David will be trained specifically with flow-matching euler discreet noise patterns applied to gathered latents from SD15.

There will be a multitude of checks based on L2 for the diffusion model while the David collective will be operating on a form on contrastive geometric loss with minimal backprop.

Gathering Features

I'll be trying a few processes to see which is fastest. Most likely each block's David will be trained specifically with that block's feature. Meaning, roughly 32,000 classes per block, one per block. I don't remember EXACTLY off the top of my head how many blocks but that isn't so critical.

Each layer's features will be extracted at runtime from a teacher model, and those features specifically fed into the learner model - the learner model learning the flow matching and the teacher model being a completely frozen standard sd15 model.

I can prepare millions of features rapidly and bulk upload shards of block-by-block features to huggingface in a special distillation-typed repo.

I figure I'll need at least 10 prime number seeds worth of features to give the student model a good tuning to the correct noise, but it may need more.

For captions I'll be running a full set of LAION flavors including probably a couple million additional common phrases from my caption synthesizer, which should be more than enough to distill this model.

Training

I don't have the exact process down yet, however there are a multitude of methods to calculate flow-matching - including the direct variation attached to huggingface, which I will likely use for this process.

Each layer's david will be translating directly at train time, ensuring the classification is cohesive and the correct scaled variation of features reach the correct locations within the model.

The multi-scale variances allow for David to provide additional accuracy beyond the standard LORA models that I've tested, by a large margin, as well as having a full classification validation system that is considerably more accurate than anything else I could find by a large margin.

Due to the rapid feature learning capacity and volatile nature of David models, they can be taught topics rapidly and are perfect for reuse in those same topics. They learn incredibly fast and allow generic utility beyond standard expectations of generic utility when it comes to features. This is why they are to be used for this process for the experiment.

This should be a fair bench test for class classification association while enabling full CFG control due to noise-inclusion within the elemental structure as usual.

This full system's layering structure will be the first bench test of interpolation as well as classification, which should be an interesting formula to solve. The result will solidify a few implications.

Training will be rapid

Once the features are extracted and prepared, the David cluster will be more than capable of teaching SD15 within a week or less on a single A100.

https://huggingface.co/AbstractPhil/gated-david

I have a few options for Davids, most likely I'll use gated-david due to the accuracy. If I can improve the geofractal variation it will be far superior but as of today the geofractal variation is indev and requires additional formulas for correction - so Gated it is.

https://huggingface.co/AbstractPhil/geofractal-david

The results will be useful

This will determine a few primary utilities.

1. Can interpolation be applied directly to decoupled Davids?

2. Can interpolative structures utilizing features train noise patterns discreetly?

3. Does geometric association provide assistance or hinder the strategy?

There are a multitude of additional proofs and potentials that spring from this one. Especially since I'm fairly certain it will work pretty effectively.

This will essentially stretch the concept of teacher/student to the utmost limit as I recognize it today, and it will determine how much further I can push it through direct hands-on tests and debugging.

Useful upcoming tools when successful

1. Video interpolation through image diffusion and teacher/student

2. Processes to train variant noise types to other models that simply don't recognize them.

3. Improved lora-based archetypes that provide full-finetune capacity with guaranteed accuracy.