StableDiffusion 1.5 LCM
This document explains how to run the StableDiffusion 1.5 LCM sample application on a host device equipped with the Radxa AICore AX-M1.
Create a virtual environment
Host
python3 -m venv .venv && source .venv/bin/activate
Download the demo repository
Host
pip3 install -U "huggingface_hub"
hf download AXERA-TECH/lcm-lora-sdv1-5 --local-dir ./lcm-lora-sdv1-5
cd lcm-lora-sdv1-5
Example usage
Install Python dependencies
Host
pip3 install -r requirements.txt
pip3 install https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/axengine-0.1.3-py3-none-any.whl
Model inference
Host
python run_txt2img_axe_infer.py
(.venv) rock@rock-5b-plus:~/ssd/axera/lcm-lora-sdv1-5$ python run_txt2img_axe_infer.py
[INFO] Available providers: ['AXCLRTExecutionProvider']
The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
0it [00:00, ?it/s]
prompt: Self-portrait oil painting, a beautiful cyborg with golden hair, 8k
text_tokenizer: ./models/tokenizer
text_encoder: ./models/text_encoder
unet_model: ./models/unet.axmodel
vae_decoder_model: ./models/vae_decoder.axmodel
time_input: ./models/time_input_txt2img.npy
save_dir: ./txt2img_output_axe.png
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.4 9215b7e5
text encoder take 3108.7ms
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.3 972f38ca
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.3 972f38ca
load models take 15541.5ms
unet once take 433.6ms
unet once take 433.6ms
unet once take 433.2ms
unet once take 433.4ms
unet loop take 1738.7ms
vae inference take 920.2ms
save image take 187.5ms
StableDiffusion 1.5 LCM demo output