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
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README.md
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@@ -7,6 +7,13 @@ This is a VAE decoder finetune, resumed from stabilityai/sd-vae-ft-mse using ima
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Our testing has shown that the VAE is good at eliminating unwanted high-frequency noise when used on models trained on similar data. It may have some generalizability to a broader range of art styles due to the variety of different styles in the dataset.
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#### Licensing:
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You are free to use this model for personal, non-commercial use.
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Our testing has shown that the VAE is good at eliminating unwanted high-frequency noise when used on models trained on similar data. It may have some generalizability to a broader range of art styles due to the variety of different styles in the dataset.
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Default VAE (kl-f8):
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Furception 1.0:
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#### Licensing:
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You are free to use this model for personal, non-commercial use.
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