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---
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license: other
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license_name: cogvlm2
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license_link: https://huggingface.co/THUDM/cogvlm2-llama3-chat-19B/blob/main/LICENS
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language:
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- ens
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pipeline_tag: text-generation
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tags:
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- chat
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- cogvlm2
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inference: false
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---
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# VisionReward-Image
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## Introduction
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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.
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Here, we present the model of VisionReward-Image.
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## Merging and Extracting Checkpoint Files
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Use the following command to merge the split files into a single `.tar` file and then extract it into the specified directory:
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```sh
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cat ckpts/split_part_* > ckpts/visionreward_image.tar
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tar -xvf ckpts/visionreward_image.tar
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```
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## Using this model
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You can quickly install the Python package dependencies and run model inference in our [github](https://github.com/THUDM/VisionReward).
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> 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](https://huggingface.co/THUDM/VisionReward-Image-bf16) |
