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* initial commit * make UNet stream capturable * try to fix noise_pred value * remove cuda graph and keep NB * non blocking unet with PNDMScheduler * make timesteps np arrays for pndm scheduler because lists don't get formatted to tensors in `self.set_format` * make max async in pndm * use channel last format in unet * avoid moving timesteps device in each unet call * avoid memcpy op in `get_timestep_embedding` * add `channels_last` kwarg to `DiffusionPipeline.from_pretrained` * update TODO * replace `channels_last` kwarg with `memory_format` for more generality * revert the channels_last changes to leave it for another PR * remove non_blocking when moving input ids to device * remove blocking from all .to() operations at beginning of pipeline * fix merging * fix merging * model can run in other precisions without autocast * attn refactoring * Revert "attn refactoring" This reverts commit0c70c0e189. * remove restriction to run conv_norm in fp32 * use `baddbmm` instead of `matmul`for better in attention for better perf * removing all reshapes to test perf * Revert "removing all reshapes to test perf" This reverts commit006ccb8a8c. * add shapes comments * hardcore whats needed for jitting * Revert "hardcore whats needed for jitting" This reverts commit2fa9c698ea. * Revert "remove restriction to run conv_norm in fp32" This reverts commitcec592890c. * revert using baddmm in attention's forward * cleanup comment * remove restriction to run conv_norm in fp32. no quality loss was noticed This reverts commitcc9bc1339c. * add more optimizations techniques to docs * Revert "add shapes comments" This reverts commit31c58eadb8. * apply suggestions * make quality * apply suggestions * styling * `scheduler.timesteps` are now arrays so we dont need .to() * remove useless .type() * use mean instead of max in `test_stable_diffusion_inpaint_pipeline_k_lms` * move scheduler timestamps to correct device if tensors * add device to `set_timesteps` in LMSD scheduler * `self.scheduler.set_timesteps` now uses device arg for schedulers that accept it * quick fix * styling * remove kwargs from schedulers `set_timesteps` * revert to using max in K-LMS inpaint pipeline test * Revert "`self.scheduler.set_timesteps` now uses device arg for schedulers that accept it" This reverts commit00d5a51e5c. * move timesteps to correct device before loop in SD pipeline * apply previous fix to other SD pipelines * UNet now accepts tensor timesteps even on wrong device, to avoid errors - it shouldnt affect performance if timesteps are alrdy on correct device - it does slow down performance if they're on the wrong device * fix pipeline when timesteps are arrays with strides