| | --- |
| | license: mit |
| | --- |
| | [](https://discord.gg/2JhHVh7CGu) |
| |
|
| | This is a severely undertrained research network as a POC for the architecture. It was trained on ~700 example images for 2000 epochs reaching a minimal MSE loss of ~0.06. The generation is unconditioned (No text knowledge yet, simply generates something plauible from the flow objective.) This repo is meant only as a demo of a strong, <100M parameter example model that can achieve strong color balance and achieve low loss on pixel diffusion. The next step is scaling up the data. |
| |
|
| | A semi custom network based on the follow paper [Simpler Diffusion (SiD2)](https://arxiv.org/abs/2410.19324v1) |
| |
|
| | This network uses the optimal transport flow matching objective outlined [Flow Matching for Generative Modeling](https://arxiv.org/abs/2210.02747) |
| |
|
| | xATGLU Layers are used instead of linears for entry into the transformer MLP layer [Expanded Gating Ranges |
| | Improve Activation Functions](https://arxiv.org/pdf/2405.20768) |
| |
|
| | ```python train.py``` will train a new image network on the provided dataset (Currently the dataset is being fully rammed into GPU and is defined in the preload_dataset function) |
| | |
| | ```python test_sample.py step_1799.safetensors``` Where step_1799.safetensors is the desired model to test inference on. This will always generate a sample grid of 16x16 images. |
| |
|
| |  |
| |  |
| |  |
| |  |
