stable-diffusion.cpp/docs/taesd.md

Using TAESD to faster decoding

You can use TAESD to accelerate the decoding of latent images by following these steps:

• Download the model weights.

Or curl

curl -L -O https://huggingface.co/madebyollin/taesd/resolve/main/diffusion_pytorch_model.safetensors

• Specify the model path using the --taesd PATH parameter. example:

sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --taesd ../models/diffusion_pytorch_model.safetensors

Qwen-Image and wan (TAEHV)

sd.cpp also supports TAEHV (#937), which can be used for Qwen-Image and wan.

• For Qwen-Image and wan2.1 and wan2.2-A14B, download the wan2.1 tae safetensors weights

  Or curl

  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_1.safetensors
  

• For wan2.2-TI2V-5B, use the wan2.2 tae safetensors weights

  Or curl

  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_2.safetensors
  

Then simply replace the --vae xxx.safetensors with --tae xxx.safetensors in the commands. If it still out of VRAM, add --vae-conv-direct to your command though might be slower.