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Sponsored by
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AbstractPhil / sd15-rectified-geometric-matching
Model's Last Updated: February 08 2026
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Introduction of sd15-rectified-geometric-matching
Model Details of sd15-rectified-geometric-matching
V1 weights test push
https://github.com/AbstractEyes/sd15-flow-trainer
https://huggingface.co/AbstractPhil/sd15-rectified-geometric-matching/blob/main/colab_trainer.py
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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 |
The geometric prior modulates CLIP encoder hidden states through 4-layer stacked k-simplex attention before they reach the 16 cross-attention blocks in the UNet.
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 |
Usage
from sd15_trainer_geo.pipeline import load_pipeline, load_geo_from_hub
# Load base SD1.5 + fresh geo prior
pipe = load_pipeline()
# Load trained geo weights from this repo
load_geo_from_hub(pipe, "AbstractPhil/sd15-rectified-geometric-matching")
# Or one-shot: load base + geo in one call
pipe = load_pipeline(geo_repo_id="AbstractPhil/sd15-rectified-geometric-matching")
Training Info
- dataset : AbstractPhil/imagenet-synthetic (flux_schnell_512)
- samples : 10000
- epochs : 1
- shift : 2.5
- base_lr : 0.0001
- min_snr_gamma : 5.0
- cfg_dropout : 0.1
- batch_size : 6
- loss_final : 0.3784324672818184
License
MIT — AbstractPhil
Runs of AbstractPhil sd15-rectified-geometric-matching on huggingface.co
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Updated:October 09 2026
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