---
license: mit
library_name: sd15-flow-trainer
tags:
  - geometric-deep-learning
  - stable-diffusion
  - ksimplex
  - pentachoron
  - flow-matching
  - cross-attention-prior
base_model: sd-legacy/stable-diffusion-v1-5
pipeline_tag: text-to-image
---

# KSimplex Geometric Attention Prior

Geometric cross-attention prior for SD1.5 using pentachoron (4-simplex) structures.

## Architecture

| Component | Params |
|-----------|--------|
| SD1.5 UNet (frozen) | 859,520,964 |
| **Geo prior (trained)** | **4,845,725** |
| **Geo conditioner (trained)** | **1,613,847** |


## Simplex Configuration

| Parameter | Value |
|-----------|-------|
| k (simplex dim) | 4 |
| Embedding dim | 32 |
| Feature dim | 768 |
| Stacked layers | 4 |
| Attention heads | 8 |
| Base deformation | 0.25 |
| Residual blend | learnable |
| Timestep conditioned | True |

## GeoVocab Conditioning

| Parameter | Value |
|-----------|-------|
| Gate dim | 17 |
| Patch feat dim | 256 |
| Num patches | 64 |
| Cross-attention | enabled |
| Cross-attn heads | 8 |
| Blend mode | learnable |


## Usage

```python
from sd15_trainer_geo.pipeline import load_pipeline

pipe = load_pipeline(geo_repo_id="AbstractPhil/sd15-geovocab-lora-prototype")
```

## Training Info

- **dataset**: AbstractPhil/synthetic-characters (schnell_full_1_512)
- **subdir**: schnell_full_1_5e-5
- **samples**: 50000
- **epochs**: 10
- **steps**: 83330
- **shift**: 2.0
- **base_lr**: 5e-05
- **min_snr_gamma**: 5.0
- **cfg_dropout**: 0.1
- **batch_size**: 6
- **geo_loss_weight**: 0.01
- **geovocab_lr_mult**: 2.0
- **clip_vae**: AbstractPhil/geovae-proto/clip_vae/best_model.pt
- **patch_maker**: AbstractPhil/geovocab-patch-maker
- **loss_final**: 0.3035515168607235

## License

MIT — [AbstractPhil](https://huggingface.co/AbstractPhil)