MODEL PROFILE

sdxl-vae

Stability AI · sdxl-vae

You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` _SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE_ | 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 |

license:mit
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/10/29 07:24:45
OVERVIEW

Model overview

You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` _SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE_ | 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 |

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。