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
Add library name and pipeline tag; link to the paper
Browse filesThis PR improves the model card by adding metadata such as `library_name` and `pipeline_tag`. It adds a description about the model in relation to the paper [Tensor Product Attention Is All You Need](https://huggingface.co/papers/2501.06425).
README.md
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license: mit
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---
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[](https://discord.gg/2JhHVh7CGu)
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A semi custom network trained from scratch for 799 epochs
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[Modeling](https://huggingface.co/Blackroot/SimpleDiffusion-TensorProductAttentionRope/blob/main/models/uvit.py) || [Training](https://huggingface.co/Blackroot/SimpleDiffusion-TensorProductAttentionRope/blob/main/train.py)
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---
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license: mit
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library_name: diffusers
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pipeline_tag: image-to-image
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[](https://discord.gg/2JhHVh7CGu)
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A semi custom network trained from scratch for 799 epochs with tensor product attention. This repository contains the model
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described in [Tensor Product Attention Is All You Need](https://huggingface.co/papers/2501.06425).
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Github repository: https://github.com/tensorgi/T6
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[Modeling](https://huggingface.co/Blackroot/SimpleDiffusion-TensorProductAttentionRope/blob/main/models/uvit.py) || [Training](https://huggingface.co/Blackroot/SimpleDiffusion-TensorProductAttentionRope/blob/main/train.py)
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