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SDXL_fixedvae_fp16
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look-num6476

v2

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Details
Category
BASE MODEL
Type
Checkpoint-Merge
Base Model
SDXL 1.0
Support
ControlNet | I2I/T2I
Uploaded
September 11.2023
Added
54
Runs
6476
Security
Verified
Version Info
Improved decoder weights * Further-reduced risk of NaNs * Further-reduced discrepancies with original SDXL-VAE (0.9) decoder Encoder weights are unchanged.
This is merge model for: 1. 100% stable-diffusion-xl-base-1.0 and 100% stable-diffusion-xl-refine-1.0 https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 2. sdxl-vae-fp16-fix https://huggingface.co/madebyollin/sdxl-vae-fp16-fix you can use this directly or finetune. same license on stable-diffusion-xl-base-1.0 same vae license on sdxl-vae-fp16-fix SDXL-VAE-FP16-Fix SDXL-VAE-FP16-Fix is the SDXL VAE, but modified to run in fp16 precision without generating NaNs. VAEDecoding in float32 / bfloat16 precisionDecoding in float16 precisionSDXL-VAE✅⚠️SDXL-VAE-FP16-Fix✅✅ Details SDXL-VAE generates NaNs in fp16 because the internal activation values are too big: SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to: 1. keep the final output the same, but 2. make the internal activation values smaller, by 3. scaling down weights and biases within the network There are slight discrepancies between the output of SDXL-VAE-FP16-Fix and SDXL-VAE, but the decoded images should be close enough for most purposes. Benchmark from here:by Kubuxu https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/discussions/7 Evaluation on COCO val-2017, 256x256, RandomCrop with padding Metrics: LPIPS: https://github.com/richzhang/PerceptualSimilarity/ (lower better) and structural similarity index measure via skimage.metrics (higher better) Metrics given as: mean [79% credibility interval]
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