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  1. pretrained_model/sd-vae-ft-mse +0 -1
  2. pretrained_model/sd-vae-ft-mse/.gitattributes +33 -0
  3. pretrained_model/sd-vae-ft-mse/README.md +83 -0
  4. pretrained_model/sd-vae-ft-mse/config.json +29 -0
  5. pretrained_model/sd-vae-ft-mse/diffusion_pytorch_model.bin +3 -0
  6. pretrained_model/sd-vae-ft-mse/diffusion_pytorch_model.safetensors +3 -0
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pretrained_model/sd-vae-ft-mse/README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ inference: false
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+ ---
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+ # Improved Autoencoders
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+
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+ ## Utilizing
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+ These weights are intended to be used with the [🧨 diffusers library](https://github.com/huggingface/diffusers). If you are looking for the model to use with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion), [come here](https://huggingface.co/stabilityai/sd-vae-ft-mse-original).
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+
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+ #### How to use with 🧨 diffusers
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+ You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline`
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+ ```py
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+ from diffusers.models import AutoencoderKL
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+ from diffusers import StableDiffusionPipeline
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+
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+ model = "CompVis/stable-diffusion-v1-4"
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+ vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
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+ pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)
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+ ```
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+
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+ ## Decoder Finetuning
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+ We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces.
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+ The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS).
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+ The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis
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+ on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU).
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+ To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.
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+
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+ _Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE_
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+
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+ ## Evaluation
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+ ### COCO 2017 (256x256, val, 5000 images)
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+ | Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
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+ |----------|---------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
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+ | | | | | | | | |
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+ | original | 246803 | 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 |
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+ | ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
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+ | ft-MSE | 840001 | 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 |
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+
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+
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+ ### LAION-Aesthetics 5+ (256x256, subset, 10000 images)
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+ | Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
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+ |----------|-----------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
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+ | | | | | | | | |
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+ | original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
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+ | ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
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+ | ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | 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 |
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+
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+
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+ ### Visual
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+ _Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset._
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+
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+ <p align="center">
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+ <br>
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+ <b>
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+ 256x256: ft-EMA (left), ft-MSE (middle), original (right)</b>
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+ </p>
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+
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+ <p align="center">
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+ <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00025_merged.png />
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+ </p>
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+
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+ <p align="center">
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+ <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00011_merged.png />
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+ </p>
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+
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+ <p align="center">
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+ <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00037_merged.png />
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+ </p>
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+
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+ <p align="center">
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+ <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00043_merged.png />
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+ </p>
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+
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+ <p align="center">
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+ <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00053_merged.png />
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+ </p>
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+
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+ <p align="center">
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+ <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00029_merged.png />
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+ </p>
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+ "_class_name": "AutoencoderKL",
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+ "in_channels": 3,
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+ "latent_channels": 4,
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+ "layers_per_block": 2,
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+ "norm_num_groups": 32,
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+ "out_channels": 3,
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+ "sample_size": 256,
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+ "up_block_types": [
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+ "UpDecoderBlock2D",
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+ "UpDecoderBlock2D",
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+ "UpDecoderBlock2D",
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+ "UpDecoderBlock2D"
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+ ]
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+ }
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