What is the significance of exposing timesteps in DiffusionPipeline?
Hello everyone, hope you are doing great.During my experiments, I noticedDiffusionPipelinetakes atimestepsargument which is set toNoneby default.Given thatnum_inference_stepsalso exists and is used to initializetimesteps(correct me if Im wrong please) and that the majority of schedulers/samplers seem not to accept timesteps through this argument, is this simply deprecated? or is it me that’s missing something?I’ve triedLMSDiscreteScheduler,DDIMScheduler, and thePNDMSchedulerall to no avail.my snippet in using a custom timesteps tensor:timesteps = torch.cat([torch.linspace(0, 0.5, steps=30), torch.linspace(0.5, 1, steps=10)])
text2image.scheduler = dfs.schedulers.scheduling_ddim.DDIMScheduler.from_config(text2image.scheduler.config)
text2image.scheduler.set_timesteps(timesteps)
# ...Could someone kindly enlighten me here? am I using the wrong schedulers? if so what scheduler can be used in this situation?Thanks a lot in advance