We present VisionReward, a general strategy to aligning visual generation models——both image and video generation——with human preferences through a fine-grainedand multi-dimensional framework. We decompose human preferences in images and videos into multiple dimensions,each represented by a series of judgment questions, linearly weighted and summed to an interpretable and accuratescore. To address the challenges of video quality assess-ment, we systematically analyze various dynamic features of videos, which helps VisionReward surpass VideoScore by 17.2% and achieve top performance for video preference prediction. Here, we present the model of VisionReward-Image. Use the following command to merge the split files into a single .tar file and then extract it into the specified directory: You can quickly install the Python package dependencies and run model inference in our github. > This model utilizes bf16 precision parameters and requires the use of the sat (SwissArmyTransformer) library for invocation. For the fp32 version of the model, please refer to the following link: https://huggingface.co/THUDM/VisionReward-Image-bf16
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: zai-org
Теги: chat, cogvlm2, ens
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Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
