A novel variable-length tokenizer uses learnable global merging to enable cross-length representation alignment in diffusion models. This data-independent approach overcomes position-dependent semantics and improves the quality-compute trade-off on ImageNet 256×25-6 generation compared to prior methods.
arxiv
arXiv cs.AI
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51d ago
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research
Learnable Global Merging for Variable-Length Tokenization in Diffusion Transformers
from English
Importance 2/3
New feature vs. leaders
arXiv cs.AI
Allen AI
Hugging Face
OpenAI
Evaluation & benchmarks
Image generation
Training methods
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