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