Instructions to use Blackroot/SimpleDiffusion-TensorProductAttentionRope with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Blackroot/SimpleDiffusion-TensorProductAttentionRope with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Blackroot/SimpleDiffusion-TensorProductAttentionRope", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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license: mit
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license: mit
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[](https://discord.gg/2JhHVh7CGu)
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A semi custom network based on the follow paper [Simpler Diffusion (SiD2)](https://arxiv.org/abs/2410.19324v1)
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This network uses the optimal transport flow matching objective outlined [Flow Matching for Generative Modeling](https://arxiv.org/abs/2210.02747)
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A modified tensor product attention with rope is used instead of regular MHA [Tensor Product Attention is All You Need](https://arxiv.org/abs/2501.06425)
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xATGLU Layers are used in some places [Expanded Gating Ranges Improve Activation Functions](https://arxiv.org/pdf/2405.20768)
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```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)
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```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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