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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A semi custom network trained from scratch for 799 epochs based on the follow paper [Simpler Diffusion (SiD2)](https://arxiv.org/abs/2410.19324v1)
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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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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 semi custom network trained from scratch for 799 epochs based on the follow paper [Simpler Diffusion (SiD2)](https://arxiv.org/abs/2410.19324v1)
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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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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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