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license: mit
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[](https://discord.gg/2JhHVh7CGu)
A semi custom network trained from scratch for 799 epochs based on [Simpler Diffusion (SiD2)](https://arxiv.org/abs/2410.19324v1)
[Modeling](https://huggingface.co./Blackroot/SimpleDiffusion-MultiHeadAttentionNope/blob/main/models/uvit.py) || [Training](https://huggingface.co./Blackroot/SimpleDiffusion-MultiHeadAttentionNope/blob/main/train.py)
This network uses the optimal transport flow matching objective outlined [Flow Matching for Generative Modeling](https://arxiv.org/abs/2210.02747)
This is using multi head attention with no positional encodings. [The Impact of Positional Encoding on Length Generalization in Transformers](https://arxiv.org/abs/2305.19466)
xATGLU Layers are used in some places [Expanded Gating Ranges Improve Activation Functions](https://arxiv.org/pdf/2405.20768)
This network was optimized via [Distributed Shampoo Github](https://github.com/facebookresearch/optimizers/blob/main/distributed_shampoo/README.md) || [Distributed Shampoo Paper](https://arxiv.org/abs/2309.06497)
```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_799.safetensors``` Where step_799.safetensors is the desired model to test inference on. This will always generate a sample grid of 16x16 images.
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