Instructions to use RedRocket/furception_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RedRocket/furception_vae 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("RedRocket/furception_vae", 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 pipeline
Browse files
README.md
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license: cc-by-nc-sa-4.0
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datasets:
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- lodestones/e6-dump
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---
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# Furception v1.0, by Project RedRocket.
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This is a VAE decoder finetune, resumed from stabilityai/sd-vae-ft-mse using images from e621. It is trained with a mixture of MAE and MSE loss to maintain an acceptable balance between sharpness and smooth outputs, and loss is calculated in Oklab color space in order to prioritize image reconstruction based on which color channels are more perceptually significant.
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license: cc-by-nc-sa-4.0
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datasets:
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- lodestones/e6-dump
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pipeline_tag: image-to-image
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---
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# Furception v1.0, by Project RedRocket.
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This is a VAE decoder finetune, resumed from stabilityai/sd-vae-ft-mse using images from e621. It is trained with a mixture of MAE and MSE loss to maintain an acceptable balance between sharpness and smooth outputs, and loss is calculated in Oklab color space in order to prioritize image reconstruction based on which color channels are more perceptually significant.
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