Instructions to use zelaki/eq-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zelaki/eq-vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zelaki/eq-vae", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, license and link to project page
Browse filesThis PR improves the model card by adding the `image-to-image` pipeline tag, so it can be easily found in the model hub.
It also adds a link to the project page.
README.md
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library_name: diffusers
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## EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
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**EQ-VAE** regularizes the latent space of pretrained autoencoders by enforcing equivariance under scaling and rotation transformations.
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#### Model Description
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This model is a regularized version of [SD-VAE](https://github.com/CompVis/latent-diffusion). We finetune it with EQ-VAE regularization for 5 epochs on OpenImages.
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| **PSNR** | 25.95 |
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| **LPIPS** | 0.141 |
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| **SSIM** | 0.72 |
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library_name: diffusers
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pipeline_tag: image-to-image
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license: mit
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## EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
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**EQ-VAE** regularizes the latent space of pretrained autoencoders by enforcing equivariance under scaling and rotation transformations.
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Project page: https://eq-vae.github.io/.
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#### Model Description
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This model is a regularized version of [SD-VAE](https://github.com/CompVis/latent-diffusion). We finetune it with EQ-VAE regularization for 5 epochs on OpenImages.
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| **PSNR** | 25.95 |
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| **LPIPS** | 0.141 |
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| **SSIM** | 0.72 |
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