lcm-lora-sd

Manually download the model and upload it to raspberrypi5, or pull the model repository with the following command.

Tip

If git lfs is not installed, please first refer to git lfs installation instructions for setup.

git clone https://huggingface.co/AXERA-TECH/lcm-lora-sdv1-5

File Description:

m5stack@raspberrypi:~/rsp/lcm-lora-sdv1-5 $ ls -lh
total 100K
drwxrwxr-x 2 m5stack m5stack 4.0K Aug 11 17:10 asserts
-rw-rw-r-- 1 m5stack m5stack    0 Aug 11 17:09 config.json
-rw-rw-r-- 1 m5stack m5stack 1.2K Aug 11 17:09 Disclaimer.md
-rw-rw-r-- 1 m5stack m5stack 1.5K Aug 11 17:09 LICENSE
drwxrwxr-x 4 m5stack m5stack 4.0K Aug 11 17:14 models
-rw-rw-r-- 1 m5stack m5stack 5.7K Aug 11 17:09 README.md
-rw-rw-r-- 1 m5stack m5stack  117 Aug 11 17:09 requirements.txt
-rw-rw-r-- 1 m5stack m5stack  23K Aug 11 17:09 run_img2img_axe_infer.py
-rw-rw-r-- 1 m5stack m5stack  23K Aug 11 17:09 run_img2img_onnx_infer.py
-rw-rw-r-- 1 m5stack m5stack 7.8K Aug 11 17:09 run_txt2img_axe_infer_new.py
-rw-rw-r-- 1 m5stack m5stack 7.3K Aug 11 17:09 run_txt2img_axe_infer.py
-rw-rw-r-- 1 m5stack m5stack 7.4K Aug 11 17:09 run_txt2img_onnx_infer.py

Create a virtual environment

python -m venv sd

Activate the virtual environment

source sd/bin/activate

Install dependencies

sudo apt install cmake -y
pip install -r requirements.txt
pip install https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/axengine-0.1.3-py3-none-any.whl

Text-to-Image

python run_txt2img_axe_infer.py

Running result:

(sd) m5stack@raspberrypi:~/rsp/lcm-lora-sdv1-5$ python run_txt2img_axe_infer.py
[INFO] Available providers:  ['AXCLRTExecutionProvider']
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 4466.2ms
[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 22582.9ms
unet once take 433.6ms
unet once take 433.5ms
unet once take 433.3ms
unet once take 433.5ms
unet loop take 1736.7ms
vae inference take 914.2ms
save image take 254.3ms 

Image-to-Image

python run_txt2img_axe_infer.py

Running result:

(sd) m5stack@raspberrypi:~/rsp/lcm-lora-sdv1-5$ python run_txt2img_axe_infer.py
[INFO] Available providers:  ['AXCLRTExecutionProvider']
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 2962.1ms
[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 14483.6ms
unet once take 433.6ms
unet once take 433.3ms
unet once take 433.2ms
unet once take 433.4ms
unet loop take 1736.4ms
vae inference take 914.0ms
save image take 233.2ms 

Next Overview

Linux PC

CM4Stack

CoreMP135

AI Accelerator Card

LLM-8850 Card

Quick Start

Vision Models

Large Language Models

Multimodal Models

Audio Models

Generative Models

Advanced Usage

LLM

Real-Time AI Voice Assistant

OpenAI Voice Assistant

XiaoZhi Voice Assistant

XiaoLing Voice Assistant

AtomS3R-M12 Volcengine Kit

Offline Voice Recognition

Unit ASR

Home Assistant

Industrial Control

StamPLC

IoT Measuring Instruments

Air Quality

Module13.2 PPS

VAMeter

T-Lite

Ezdata

Ethernet Camera

PoECAM

Wi-Fi Camera

TimerCAM

Unit CamS3/-5MP

AI Camera

UnitV2

M5StickV/UnitV

LoRa & LoRaWAN

TTN (The Things Network)

Meshtastic

Motor Control

Unit Roller485/CAN

Develop Tools

Hobby Kit

Restore Factory Firmware