Corpus Enhancement Program
- Bilingual tip: mix Chinese and English (e.g. "classical garden, Chinese classical garden")
- Terminology shift: translating culture-specific concepts into standard English expressions
Model Optimization Path
- Incremental Training: Fine-tuning the MoVQGAN Module Using Chinese Image-Text Pairs
- Cross-modal alignment: CLIP-based text encoder for Chinese version adjustment
- Assisted Correction: Integration of Baidu Translation API for real-time terminology standardization
Effectiveness verification method
- Build test set: contains 50 sets of prompt words in English and Chinese
- Quantitative assessment: calculation of graphical similarity using CLIP-Score
- A/B testing: comparing the generation differences between the native model and the optimized version
This answer comes from the articleLumina-mGPT-2.0: an autoregressive image generation model for handling multiple image generation tasksThe
May not be reproduced without permission:AI productivity tools " How to overcome the semantic bias problem of Lumina-mGPT-2.0 when processing Chinese cue words?
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