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  1. 28 Feb, 2024 1 commit
  3. 02 Oct, 2023 1 commit
  5. 11 Apr, 2023 1 commit
    • Patrick von Platen's avatar
      Fix config prints and save, load of pipelines (#2849) · 8b451eb6
      Patrick von Platen authored 
      * [Config] Fix config prints and save, load

* Only use potential nn.Modules for dtype and device

* Correct vae image processor

* make sure in_channels is not accessed directly

* make sure in channels is only accessed via config

* Make sure schedulers only access config attributes

* Make sure to access config in SAG

* Fix vae processor and make style

* add tests

* uP

* make style

* Fix more naming issues

* Final fix with vae config

* change more
      8b451eb6
  7. 23 Mar, 2023 1 commit
  9. 14 Feb, 2023 1 commit
  11. 07 Feb, 2023 1 commit
  13. 06 Feb, 2023 1 commit
  15. 30 Dec, 2022 1 commit
  17. 22 Nov, 2022 1 commit
  19. 07 Nov, 2022 2 commits
  21. 06 Nov, 2022 1 commit
    • Cheng Lu's avatar
      Add multistep DPM-Solver discrete scheduler (#1132) · b4a1ed85
      Cheng Lu authored 
      

* add dpmsolver discrete pytorch scheduler

* fix some typos in dpm-solver pytorch

* add dpm-solver pytorch in stable-diffusion pipeline

* add jax/flax version dpm-solver

* change code style

* change code style

* add docs

* add `add_noise` method for dpmsolver

* add pytorch unit test for dpmsolver

* add dummy object for pytorch dpmsolver

* Update src/diffusers/schedulers/scheduling_dpmsolver_discrete.py
Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>

* Update tests/test_config.py
Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>

* Update tests/test_config.py
Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>

* resolve the code comments

* rename the file

* change class name

* fix code style

* add auto docs for dpmsolver multistep

* add more explanations for the stabilizing trick (for steps < 15)

* delete the dummy file

* change the API name of predict_epsilon, algorithm_type and solver_type

* add compatible lists
Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>
      b4a1ed85
  23. 03 Nov, 2022 1 commit
    • Pedro Cuenca's avatar
      Continuation of #1035 (#1120) · 269109db
      Pedro Cuenca authored 
      

* remove batch size from repeat

* repeat empty string if uncond_tokens is none

* fix inpaint pipes

* return back whitespace to pass code quality

* Apply suggestions from code review

* Fix typos.
Co-authored-by: default avatarHad <had-95@yandex.ru>
      269109db
  25. 02 Nov, 2022 1 commit
    • MatthieuTPHR's avatar
      Up to 2x speedup on GPUs using memory efficient attention (#532) · 98c42134
      MatthieuTPHR authored 
      

* 2x speedup using memory efficient attention

* remove einops dependency

* Swap K, M in op instantiation

* Simplify code, remove unnecessary maybe_init call and function, remove unused self.scale parameter

* make xformers a soft dependency

* remove one-liner functions

* change one letter variable to appropriate names

* Remove Env variable dependency, remove MemoryEfficientCrossAttention class and use enable_xformers_memory_efficient_attention method

* Add memory efficient attention toggle to img2img and inpaint pipelines

* Clearer management of xformers' availability

* update optimizations markdown to add info about memory efficient attention

* add benchmarks for TITAN RTX

* More detailed explanation of how the mem eff benchmark were ran

* Removing autocast from optimization markdown

* import_utils: import torch only if is available
Co-authored-by: default avatarNouamane Tazi <nouamane98@gmail.com>
      98c42134
  27. 31 Oct, 2022 3 commits
  29. 28 Oct, 2022 1 commit
  31. 27 Oct, 2022 2 commits
  33. 26 Oct, 2022 1 commit
    • Pi Esposito's avatar
      minimal stable diffusion GPU memory usage with accelerate hooks (#850) · b2e2d141
      Pi Esposito authored 
      * add method to enable cuda with minimal gpu usage to stable diffusion

* add test to minimal cuda memory usage

* ensure all models but unet are onn torch.float32

* move to cpu_offload along with minor internal changes to make it work

* make it test against accelerate master branch

* coming back, its official: I don't know how to make it test againt the master branch from accelerate

* make it install accelerate from master on tests

* go back to accelerate>=0.11

* undo prettier formatting on yml files

* undo prettier formatting on yml files againn
      b2e2d141
  35. 25 Oct, 2022 1 commit
  37. 24 Oct, 2022 1 commit
  39. 13 Oct, 2022 3 commits
  41. 11 Oct, 2022 2 commits
  43. 06 Oct, 2022 1 commit
    • Suraj Patil's avatar
      allow multiple generations per prompt (#741) · c119dc4c
      Suraj Patil authored 
      * compute text embeds per prompt

* don't repeat uncond prompts

* repeat separatly

* update image2image

* fix repeat uncond embeds

* adapt inpaint pipeline

* ifx uncond tokens in img2img

* add tests and fix ucond embeds in im2img and inpaint pipe
      c119dc4c
  45. 05 Oct, 2022 3 commits
  47. 04 Oct, 2022 1 commit
  49. 03 Oct, 2022 2 commits
  51. 02 Oct, 2022 1 commit
  53. 30 Sep, 2022 1 commit
    • Nouamane Tazi's avatar
      Optimize Stable Diffusion (#371) · 9ebaea54
      Nouamane Tazi authored 
      * 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 commit 0c70c0e189cd2c4d8768274c9fcf5b940ee310fb.

* 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 commit 006ccb8a8c6bc7eb7e512392e692a29d9b1553cd.

* add shapes comments

* hardcore whats needed for jitting

* Revert "hardcore whats needed for jitting"

This reverts commit 2fa9c698eae2890ac5f8e367ca80532ecf94df9a.

* Revert "remove restriction to run conv_norm in fp32"

This reverts commit cec592890c32da3d1b78d38b49e4307aedf459b9.

* revert using baddmm in attention's forward

* cleanup comment

* remove restriction to run conv_norm in fp32. no quality loss was noticed

This reverts commit cc9bc1339c998ebe9e7d733f910c6d72d9792213.

* add more optimizations techniques to docs

* Revert "add shapes comments"

This reverts commit 31c58eadb8892f95478cdf05229adf678678c5f4.

* 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 commit 00d5a51e5c20d8d445c8664407ef29608106d899.

* 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
      9ebaea54
  55. 27 Sep, 2022 3 commits
    • Kashif Rasul's avatar
      [Pytorch] Pytorch only schedulers (#534) · bd8df2da
      Kashif Rasul authored 
      

* pytorch only schedulers

* fix style

* remove match_shape

* pytorch only ddpm

* remove SchedulerMixin

* remove numpy from karras_ve

* fix types

* remove numpy from lms_discrete

* remove numpy from pndm

* fix typo

* remove mixin and numpy from sde_vp and ve

* remove remaining tensor_format

* fix style

* sigmas has to be torch tensor

* removed set_format in readme

* remove set format from docs

* remove set_format from pipelines

* update tests

* fix typo

* continue to use mixin

* fix imports

* removed unsed imports

* match shape instead of assuming image shapes

* remove import typo

* update call to add_noise

* use math instead of numpy

* fix t_index

* removed commented out numpy tests

* timesteps needs to be discrete

* cast timesteps to int in flax scheduler too

* fix device mismatch issue

* small fix

* Update src/diffusers/schedulers/scheduling_pndm.py
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
      bd8df2da
    • Pedro Cuenca's avatar
      Remove deprecated `torch_device` kwarg (#623) · b671cb09
      Pedro Cuenca authored 
      * Remove deprecated `torch_device` kwarg.

* Remove unused imports.
      b671cb09
    • Yuta Hayashibe's avatar
      Warning for too long prompts in DiffusionPipelines (Resolve #447) (#472) · f7ebe569
      Yuta Hayashibe authored 
      * Return encoded texts by DiffusionPipelines

* Updated README to show hot to use enoded_text_input

* Reverted examples in README.md

* Reverted all

* Warning for long prompts

* Fix bugs

* Formatted
      f7ebe569