Model Merging and Training Process:
Preparation Phase: Dataset Curation: The first step involved the meticulous selection of over 10,000 high-quality, realistic images. then refined to a 2,000 high-quality curated images. Each image was curated to represent a broad spectrum of scenarios, subjects, and lighting conditions. Annotation & Captioning: Using GPT-4 Vision, every image underwent ai captioning, resulting in detailed captions that encapsulated the essence of the content. Each caption highlighted various visual aspects like composition, subject, and context.
Model Merging: Architectural Fusion: We began by integrating multiple pre-trained models each with licenses allowing for this, each excelling in specific visual tasks (e.g., realism, anime). The architecture was carefully designed to maximize synergy between these specialized networks.
Training Pipeline: Transfer Learning & Fine-Tuning: Pre-trained models were utilized as base architectures. We then fine-tuned the merged model using the previously captioned dataset.
Evaluation and Optimization: Hyperparameter Optimization: Grid Search was employed to identify optimal hyperparameters, such as learning rate, batch size, and dropout rate.
Thanks to the creators of these models for their work. Without them it would not have been possible to create this model:
Juggernaut XL by KandooAI, LEOSAM's HelloWorld XL by LEOSAM, RealVisXL by SG_161222, NIJI Xl by Stan_Katayama, Jib Mix Realistic XL by J1B, DreamShaper XL1.0 by Lykon,
Support ByteDance's Open Source Technology: Official Repository of the paper: Hyper-SD. Hyper-SD Project Page
@misc{ren2024hypersd, title={Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis}, author={Yuxi Ren and Xin Xia and Yanzuo Lu and Jiacheng Zhang and Jie Wu and Pan Xie and Xing Wang and Xuefeng Xiao}, year={2024}, eprint={2404.13686}, archivePrefix={arXiv}, primaryClass={cs.CV} }
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