README.md · lrzjason/Kolors_depth_lora at 3a83b5349941f1ef35f39f5c50eee08c3fa0eeea

metadata

license: apache-2.0
language:
  - zh
  - en
tags:
  - text-to-image
  - stable-diffusion
  - kolors
  - Realistickolors

📖 Introduction

This lora is aims to generate depth map via kolors directly. It is in alpha version. Prompt with color might lead to inconsistency result.

Depth map dataset made via Depth Anything V2

Civitai Page: https://civitai.com/models/786019?modelVersionId=878987

OpenKolors v2.1 Civitai Page: https://civitai.com/models/602580/kolors-openkolors-v21-multiple-style-general-kolors-model Hugging face: https://huggingface.co/lrzjason/OpenKolors_v2_1

Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team. Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and proprietary models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. For more details, please refer to this technical report.

🚀 Quick Start

Replace the weight to original unet weight.

📜 License&Citation

Kolors License

Kolors are fully open-sourced for academic research. For commercial use, please fill out this [questionnaire](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/可图KOLORS模型商业授权申请书.docx) and sent it to [email protected] for registration.

We open-source Kolors to promote the development of large text-to-image models in collaboration with the open-source community. The code of this project is open-sourced under the Apache-2.0 license. We sincerely urge all developers and users to strictly adhere to the [open-source license](MODEL_LICENSE), avoiding the use of the open-source model, code, and its derivatives for any purposes that may harm the country and society or for any services not evaluated and registered for safety. Note that despite our best efforts to ensure the compliance, accuracy, and safety of the data during training, due to the diversity and combinability of generated content and the probabilistic randomness affecting the model, we cannot guarantee the accuracy and safety of the output content, and the model is susceptible to misleading. This project does not assume any legal responsibility for any data security issues, public opinion risks, or risks and liabilities arising from the model being misled, abused, misused, or improperly utilized due to the use of the open-source model and code.

Citation

@article{kolors,
  title={Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis},
  author={Kolors Team},
  journal={arXiv preprint},
  year={2024}
}

Acknowledgments

Thanks to Kolors for providing the codebase and weight.

Contact Me

Feel free to leave a message. You can also contact us via email ([email protected]).