---
license: apache-2.0
pipeline_tag: text-to-image
library_name: diffusers
---

# MicroDecoder

**Micro-VAE** that can be trained on *any* diffusion model in **~15min** and provides low-quality reconstruction from latents to RGB in **~0.01sec**.  
Inference includes noise correction based on current timestep, intentional blurring plus upscale interpolation: all with intention of providing as fast-as-possible reconstruction that is viable and consistent with any noise levels.  
Intended use-case is live-preview during generative model inference.  

Shapes/Channels/etc are inferred from the base VAE, so no configuration changes are needed between different models.

- Model definition and training code [here](https://github.com/vladmandic/sdnext/blob/dev/modules/vae/sd_vae_micro_train.py)
- Example inference code [here](https://github.com/vladmandic/sdnext/blob/dev/modules/vae/sd_vae_micro.py)

Example using **MicroDecoder** with `Flux.2-Klein-9B` and compared with official final VAE processing at the end:

![MicroDecoder](https://huggingface.co/vladmandic/MicroDecoder/resolve/main/MicroDecoder.jpg)

## Example

```shell
sd_vae_micro_train.py \
  --dim 256 \
  --epochs 350 \
  --resolution 512 \
  --scale 4 \
  --lr 0.0003
  --folder ~/generative/Input/vae/ \
  --vae AutoencoderKLQwenImage21 \
  --repo Qwen/Qwen-Image-2.1
  --output MicroVAE-qwen21.safetensors
```

```log
Encoding Latents: 100%
Training MicroDecoder [350 epochs | Channels: 32 | Hidden: 256 | Scale: 4x]
Dataset: 360 train samples | 40 validation samples | EMA decay: 0.999

Epoch  | Train Tot | Tr PSNR | Tr SSIM | Val Tot  | Val PSNR | Val SSIM | Val L1  | Val LAB
--------------------------------------------------------------------------------------------
001/350  | 5.7244    | 10.38   | 0.3431  | 5.9729   | 10.56    | 0.3047   | 0.2676  | 0.4254  *
100/350  | 2.7690    | 25.49   | 0.8030  | 2.2011   | 26.52    | 0.8672   | 0.0376  | 0.0699  *
200/350  | 2.4006    | 27.04   | 0.8400  | 1.1679   | 34.24    | 0.9635   | 0.0132  | 0.0317  *
300/350  | 2.3950    | 28.05   | 0.8472  | 1.0002   | 35.63    | 0.9723   | 0.0113  | 0.0282  *
350/350  | 2.3010    | 28.51   | 0.8511  | 0.9781   | 35.83    | 0.9734   | 0.0111  | 0.0278  *

Training complete! Best validation PSNR: 35.83 dB (Epoch 350)
Saved best EMA model weights to: MicroVAE-qwen21.safetensors
```