The Quality vs. Compression Dilemma
Traditional tokenizers are caught in a tug-of-war, forced to sacrifice image fidelity and fine detail to achieve efficient compression for model training and storage.
The High Cost of High Resolution
Generating detailed, high-resolution images remains computationally demanding, creating a bottleneck for speed and accessibility.
Loss of Semantic Coherence
Aggressive compression often strips away context, leading to a loss of semantic meaning and visual coherence in generated outputs.