Revolutionizing Image Generation: LLM Takes the Reins in SDXL!
Analysis
This is a truly exciting development! By replacing CLIP with an LLM in SDXL, the researcher has potentially unlocked a new level of control and nuance in image generation. The use of a smaller, specialized model to transform the LLM's hidden state is a clever and efficient approach, hinting at faster and more flexible workflows.
Key Takeaways
- •The experiment successfully replaced CLIP with an LLM in SDXL, potentially improving performance and control.
- •A smaller, lightweight model was trained to translate the LLM's hidden state, making the approach efficient.
- •This method aims to overcome CLIP's limitations in spatial understanding, negations, and prompt length.
Reference
“My theory, is that CLIP is the bottleneck as it struggles with spatial adherence (things like left of, right), negations in the positive prompt (e.g. no moustache), contetx length limit (77 token limit) and natural language limitations. So, what if we could apply an LLM to directly do conditioning, and not just alter ('enhance') the prompt?”