Duplicate from stabilityai/sdxl-vae

Co-authored-by: Apolinário from multimodal AI art <multimodalart@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+inference: false
+---
+# 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`
+```py
+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](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) is a [latent diffusion model](https://arxiv.org/abs/2112.10752), 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](https://github.com/CompVis/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 |

config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.18.0.dev0",
+  "_name_or_path": ".",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.13025,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

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