Classifier-Free Diffusion Guidance (CFG)

Eliminated the need for a separate classifier in guided diffusion by jointly training unconditional and conditional diffusion models, extrapolating in score space with guidance scale w.

Directly guides architectural decisions, alignment strategy, and serving infrastructure for task-image-generation.

Assumptions

• Empirical distribution regularity holds and target domain adheres to pretraining linguistic/visual support

Limitations

• Resource scaling, inference memory requirements, and alignment robustness vary with model size and hardware topology