stabilityai / sdxl-vae

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Model's Last Updated: August 04 2023

Introduction of sdxl-vae

Model Details of sdxl-vae

SDXL - VAE

How to use with 🧨 diffusers

You can integrate this fine-tuned VAE decoder to your existing diffusers workflows, by including a vae argument to the StableDiffusionPipeline

from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionPipeline

model = "stabilityai/your-stable-diffusion-model"
vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)
Model

SDXL is a latent diffusion model , where the diffusion operates in a pretrained, learned (and fixed) latent space of an autoencoder. While the bulk of the semantic composition is done by the latent diffusion model, we can improve local , high-frequency details in generated images by improving the quality of the autoencoder. To this end, we train the same autoencoder architecture used for the original Stable Diffusion at a larger batch-size (256 vs 9) and additionally track the weights with an exponential moving average (EMA). The resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below.

Evaluation

SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE

COCO 2017 (256x256, val, 5000 images)
Model rFID PSNR SSIM PSIM Link Comments
SDXL-VAE 4.42 24.7 +/- 3.9 0.73 +/- 0.13 0.88 +/- 0.27 https://huggingface.co/stabilityai/sdxl-vae/blob/main/sdxl_vae.safetensors as used in SDXL
original 4.99 23.4 +/- 3.8 0.69 +/- 0.14 1.01 +/- 0.28 https://ommer-lab.com/files/latent-diffusion/kl-f8.zip as used in SD
ft-MSE 4.70 24.5 +/- 3.7 0.71 +/- 0.13 0.92 +/- 0.27 https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs

Runs of stabilityai sdxl-vae on huggingface.co

149.2K
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3-day runs
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More Information About sdxl-vae huggingface.co Model

More sdxl-vae license Visit here:

https://choosealicense.com/licenses/mit

sdxl-vae huggingface.co

sdxl-vae huggingface.co is an AI model on huggingface.co that provides sdxl-vae's model effect (), which can be used instantly with this stabilityai sdxl-vae model. huggingface.co supports a free trial of the sdxl-vae model, and also provides paid use of the sdxl-vae. Support call sdxl-vae model through api, including Node.js, Python, http.

stabilityai sdxl-vae online free

sdxl-vae huggingface.co is an online trial and call api platform, which integrates sdxl-vae's modeling effects, including api services, and provides a free online trial of sdxl-vae, you can try sdxl-vae online for free by clicking the link below.

stabilityai sdxl-vae online free url in huggingface.co:

https://huggingface.co/stabilityai/sdxl-vae

sdxl-vae install

sdxl-vae is an open source model from GitHub that offers a free installation service, and any user can find sdxl-vae on GitHub to install. At the same time, huggingface.co provides the effect of sdxl-vae install, users can directly use sdxl-vae installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

sdxl-vae install url in huggingface.co:

https://huggingface.co/stabilityai/sdxl-vae

Url of sdxl-vae

Provider of sdxl-vae huggingface.co

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