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OpenVINO Latent Consistency Model C++ image generation pipeline

The pure C++ text-to-image pipeline, driven by the OpenVINO native API for SD v1.5 Latent Consistency Model with LCM Scheduler. It includes advanced features like LoRA integration with safetensors and OpenVINO Tokenizers. Loading openvino_tokenizers to ov::Core enables tokenization. The common folder contains schedulers for image generation and imwrite() for saving bmp images. This demo has been tested for Linux platform only. There is also a Jupyter notebook which provides an example of image generaztion in Python.

Note

This tutorial assumes that the current working directory is <openvino.genai repo>/image_generation/lcm_dreamshaper_v7/cpp/ and all paths are relative to this folder.

Step 1: Prepare build environment

Prerequisites:

C++ Packages:

Prepare a python environment and install dependencies:

conda create -n openvino_lcm_cpp python==3.10
conda activate openvino_lcm_cpp
conda update -c conda-forge --all
conda install -c conda-forge openvino=2024.2.0 c-compiler cxx-compiler git make cmake
# Ensure that Conda standard libraries are used
conda env config vars set LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH

Step 2: Latent Consistency Model and Tokenizer models

Latent Consistency Model model

  1. Install dependencies to import models from HuggingFace:

    git submodule update --init
conda activate openvino_lcm_cpp
python -m pip install -r requirements.txt
python -m pip install ../../../thirdparty/openvino_tokenizers/[transformers]

  2. Download the model from Huggingface and convert it to OpenVINO IR via optimum-intel CLI. Example command for downloading and exporting FP16 model:

    optimum-cli export openvino --model SimianLuo/LCM_Dreamshaper_v7 --weight-format fp16 models/lcm_dreamshaper_v7/FP16

If https://huggingface.co/ is down, the script won't be able to download the model.

Note

Only static model is currently supported for this sample.

LoRA enabling with safetensors

Refer to python pipeline blog. The safetensor model is loaded via safetensors.h. The layer name and weight are modified with Eigen Lib and inserted into the LCM model with ov::pass::MatcherPass in the file common/diffusers/src/lora.cpp.

LCM model lcm_dreamshaper_v7 and Lora soulcard are tested in this pipeline.

Download and put safetensors and model IR into the models folder.

Step 3: Build the LCM application

conda activate openvino_lcm_cpp
cmake -DCMAKE_BUILD_TYPE=Release -S . -B build
cmake --build build --config Release --parallel

Step 4: Run Pipeline

./build/lcm_dreamshaper [-p <posPrompt>] [-s <seed>] [--height <output image>] [--width <output image>] [-d <device>] [-r <readNPLatent>] [-a <alpha>] [-h <help>] [-m <modelPath>] [-t <modelType>]

Usage:
  lcm_dreamshaper [OPTION...]
  • -p, --posPrompt arg Initial positive prompt for LCM (default: a beautiful pink unicorn)
  • -d, --device arg AUTO, CPU, or GPU. Doesn't apply to Tokenizer model, OpenVINO Tokenizers can be inferred on a CPU device only (default: CPU)
  • --step arg Number of diffusion step (default: 4)
  • -s, --seed arg Number of random seed to generate latent (default: 42)
  • --num arg Number of image output (default: 1)
  • --height arg Height of output image (default: 512)
  • --width arg Width of output image (default: 512)
  • -c, --useCache Use model caching
  • -r, --readNPLatent Read numpy generated latents from file, only supported for one output image
  • -m, --modelPath arg Specify path to LCM model IRs (default: ./models/lcm_dreamshaper_v7)
  • -t, --type arg Specify the type of LCM model IRs (e.g., FP32, FP16 or INT8) (default: FP16)
  • --dynamic Specify the model input shape to use dynamic shape
  • -l, --loraPath arg Specify path to LoRA file (*.safetensors) (default: )
  • -a, --alpha arg Specify alpha for LoRA (default: 0.75)
  • -h, --help Print usage

Note

The tokenizer model will always be loaded to CPU: OpenVINO Tokenizers can be inferred on a CPU device only.

Example:

Positive prompt: a beautiful pink unicorn

Read the numpy latent input and noise for scheduler instead of C++ std lib for the alignment with Python pipeline.

  • Generate image with random data generated by Python: ./build/lcm_dreamshaper -r

image

  • Generate image with C++ lib generated latent and noise: ./build/lcm_dreamshaper

image

  • Generate image with soulcard lora and C++ generated latent and noise: ./stable_diffusion -r -l path/to/soulcard.safetensors

image

Benchmark:

For the generation quality, C++ random generation with MT19937 results is differ from numpy.random.randn() and diffusers.utils.randn_tensor. Hence, please use -r, --readNPLatent for the alignment with Python (this latent file is for output image 512X512 only)