HelloWorld4.0 is a progressive transitional version from tagging with blip+clip to tagging with GPT4V. I initially trained a pure GPT4V tagging model, and then merged it with a large proportion of the HelloWorld3.2 version and 0.05 proportion of Juggernaut XL (to adjust the skin tone). The new version has shown improvements in prompt compliance and concept coverage compared to the 3.2 version.
The new GPT4V tagging training set has doubled from the 4000 images of the helloworld3 series to 8000 images, covering not only portraits but also animals, architecture, nature, food, illustrations, and more. However, the pure GPT4V version encountered an overfitting problem, which is preliminarily attributed to the doubling of the number of training images. One of the next steps in iterative optimization is to find out how to include as many non-portrait concepts as possible while ensuring sufficient training of portraits. At this stage, a fusion of the new and old versions has been used for fine-tuning to ensure a smooth transition between versions, so the expanded concept set and the advantages brought by GPT4V tagging are not very perceptible at the moment. These advantages will become increasingly apparent in the subsequent generations 5 and 6 of the model.
模型文件和权重，请浏览“模型文件”页面获取。
当前模型的贡献者未提供更加详细的模型介绍，但是您可以通过如下git clone命令，或者ModelScope SDK来下载模型。
Clone with HTTP
git clone https://www.modelscope.cn/YorickHe/helloworldSDXL.git
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