Unfortunately, unlike in NLP where swapping out decoding techniques frequently leads to far better generation (i.e. novelty sampling like top-p/top-k for text g

Unfortunately, unlike in NLP where swapping out decoding techniques frequently leads to far better generation (i.e. novelty sampling like top-p/top-k for text gen, beam search for seq-seq tasks), this is not necessarily the case for diffusion based image generators. Despite many methods being mathematically "superior" to traditional euler adaptive, it remains the default settings for a reason in all the major UIs (Automatic1111, Comfy). I have a lot of fun playing with other samplers, especially ones which converge and thus allow large sampling steps, but the impact is not as massive here as it is in NLP and we may be over thinking this side of the pipeline and not thinking enough about other important things (i.e. mixing in other losses besides regular diffusion loss for more control similar to controlnet).