算法部署-使用OpenVINO在Intel-CPU上部署StableDiffusion图像生成扩散模型-附项目源码-优质项目实战

# stable_diffusion.openvino Implementation of Text-To-Image generation using Stable Diffusion on Intel CPU or GPU. <p align="center"> <img src="data/title.png"/> </p> ## Requirements * Linux, Windows, MacOS * Python <= 3.9.0 * CPU or GPU compatible with OpenVINO. ## Install requirements * Set up and update PIP to the highest version * Install OpenVINO™ Development Tools 2022.3.0 release with PyPI * Download requirements ```bash python -m pip install --upgrade pip pip install openvino-dev[onnx,pytorch]==2022.3.0 pip install -r requirements.txt ``` ## Generate image from text description ```bash usage: demo.py [-h] [--model MODEL] [--device DEVICE] [--seed SEED] [--beta-start BETA_START] [--beta-end BETA_END] [--beta-schedule BETA_SCHEDULE] [--num-inference-steps NUM_INFERENCE_STEPS] [--guidance-scale GUIDANCE_SCALE] [--eta ETA] [--tokenizer TOKENIZER] [--prompt PROMPT] [--params-from PARAMS_FROM] [--init-image INIT_IMAGE] [--strength STRENGTH] [--mask MASK] [--output OUTPUT] optional arguments: -h, --help show this help message and exit --model MODEL model name --device DEVICE inference device [CPU, GPU] --seed SEED random seed for generating consistent images per prompt --beta-start BETA_START LMSDiscreteScheduler::beta_start --beta-end BETA_END LMSDiscreteScheduler::beta_end --beta-schedule BETA_SCHEDULE LMSDiscreteScheduler::beta_schedule --num-inference-steps NUM_INFERENCE_STEPS num inference steps --guidance-scale GUIDANCE_SCALE guidance scale --eta ETA eta --tokenizer TOKENIZER tokenizer --prompt PROMPT prompt --params-from PARAMS_FROM Extract parameters from a previously generated image. --init-image INIT_IMAGE path to initial image --strength STRENGTH how strong the initial image should be noised [0.0, 1.0] --mask MASK mask of the region to inpaint on the initial image --output OUTPUT output image name ``` ## Examples ### Example Text-To-Image ```bash python demo.py --prompt "Street-art painting of Emilia Clarke in style of Banksy, photorealism" ``` ### Example Image-To-Image ```bash python demo.py --prompt "Photo of Emilia Clarke with a bright red hair" --init-image ./data/input.png --strength 0.5 ``` ### Example Inpainting ```bash python demo.py --prompt "Photo of Emilia Clarke with a bright red hair" --init-image ./data/input.png --mask ./data/mask.png --strength 0.5 ``` ## Performance | CPU | Time per iter | Total time | |----------------------------------------------------|---------------|------------| | AMD Ryzen 7 4800H | 4.8 s/it | 2.58 min | | AMD Ryzen Threadripper 1900X | 5.34 s/it | 2.58 min | | Intel(R) Core(TM) i7-4790K @ 4.00GHz | 10.1 s/it | 5.39 min | | Intel(R) Core(TM) i5-8279U | 7.4 s/it | 3.59 min | | Intel(R) Core(TM) i5-8569U @ 2.8GHz (MBP13-2019) | 6.17 s/it | 3.23 min | | Intel(R) Core(TM) i7-1165G7 @ 2.80GHz | 7.4 s/it | 3.59 min | | Intel(R) Core(TM) i7-11800H @ 2.30GHz (16 threads) | 2.9 s/it | 1.54 min | | Intel(R) Core(TM) i7-1280P @ 1.80GHz (6P/8E) | 5.45 s/it | 2.55 min | | Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz | 1 s/it | 33 s | | Intel Arc A770M | 6.64 it/s | 7.53 s |