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Copy file name to clipboardexpand all lines: image_generation/lcm_dreamshaper_v7/cpp/README.md
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# OpenVINO Latent Consistency Model C++ image generation pipeline
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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](https://github.com/openvinotoolkit/openvino_tokenizers). Loading `openvino_tokenizers` to `ov::Core` enables tokenization. [The common folder](../../common/) 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](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/263-latent-consistency-models-image-generation/263-lcm-lora-controlnet.ipynb) which provides an example of image generaztion in Python.
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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](https://github.com/openvinotoolkit/openvino_tokenizers). Loading `openvino_tokenizers` to `ov::Core` enables tokenization. [The common folder](../../common/) 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](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/latent-consistency-models-image-generation/lcm-lora-controlnet.ipynb) which provides an example of image generaztion in Python.
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> [!NOTE]
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> 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.
Copy file name to clipboardexpand all lines: image_generation/stable_diffusion_1_5/cpp/README.md
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# OpenVINO Stable Diffusion (with LoRA) C++ image generation pipeline
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The pure C++ text-to-image pipeline, driven by the OpenVINO native C++ API for Stable Diffusion v1.5 with LMS Discrete Scheduler, supports both static and dynamic model inference. It includes advanced features like [LoRA](https://huggingface.co/docs/peft/conceptual_guides/lora) integration with [safetensors](https://huggingface.co/docs/safetensors/index#format) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers). Loading `openvino_tokenizers` to `ov::Core` enables tokenization. The sample uses [diffusers](../../common/diffusers) for image generation and [imwrite](../../common/imwrite) for saving `.bmp` images. This demo has been tested on Windows and Unix platforms. There is also a Jupyter [notebook](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/225-stable-diffusion-text-to-image/225-stable-diffusion-text-to-image.ipynb) which provides an example of image generation in Python.
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The pure C++ text-to-image pipeline, driven by the OpenVINO native C++ API for Stable Diffusion v1.5 with LMS Discrete Scheduler, supports both static and dynamic model inference. It includes advanced features like [LoRA](https://huggingface.co/docs/peft/conceptual_guides/lora) integration with [safetensors](https://huggingface.co/docs/safetensors/index#format) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers). Loading `openvino_tokenizers` to `ov::Core` enables tokenization. The sample uses [diffusers](../../common/diffusers) for image generation and [imwrite](../../common/imwrite) for saving `.bmp` images. This demo has been tested on Windows and Unix platforms. There is also a Jupyter [notebook](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-diffusion-text-to-image) which provides an example of image generation in Python.
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> [!NOTE]
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>This tutorial assumes that the current working directory is `<openvino.genai repo>/image_generation/stable_diffusion_1_5/cpp/` and all paths are relative to this folder.
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