Hey folks, I’m looking for advice from anyone who’s worked with diffusion or flow models specifically any tips you wish you knew when you first started training them, and what the experience was like if you’ve used them outside the usual image-generation setting. I’m especially curious about challenges that come up with niche or unconventional data, how the workflow differs from image tasks, whether training stability or hyperparameter sensitivity becomes a bigger issue, how much preprocessing matters, if you ended up tweaking the architecture or noise schedule for non-image data, etc. Thanks!