Downloads · 30 days
0
sd15-rectified-geometric-matching
AbstractPhil/sd15-rectified-geometric-matching
sd15-rectified-geometric-matching is a text-to-image model from AbstractPhil. Use it when you need an image from a text prompt. It is set up for sd15-flow-trainer. The card lists the license as mit.
https://github.com/AbstractEyes/sd15-flow-trainer
Access
Public
Updated Feb 7, 2026
Repo size
19.9 MB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.safetensors19.4 MB · 96%
At a glance
- Task
- Text-to-Image
- Library
- sd15-flow-trainer
- License
- mit
- Access
- Public
- Created
- Feb 6, 2026
- Updated
- Feb 7, 2026
- SHA
- 8ed2abca
Base models
Model card
From the Hugging Face model README
V1 weights test push
https://github.com/AbstractEyes/sd15-flow-trainer
https://huggingface.co/AbstractPhil/sd15-rectified-geometric-matching/blob/main/colab_trainer.py
Step 0
Step 500
Step 1000
Step 1500
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
Post Analysis
License
MIT — AbstractPhil
- Task
- Text-to-Image
- Library
- sd15-flow-trainer
- License
- mit
- Created
- Feb 6, 2026
- Updated
- Feb 7, 2026