* Restore unet params back to normal from EMA when validation call is finished
* empty commit
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Allow safety and feature extractor arguments to be passed to convert_from_ckpt
Allows management of safety checker and feature extractor
from outside of the convert ckpt class.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* reduce block sizes for unet1d.
* reduce blocks for unet_2d.
* reduce block size for unet_motion
* increase channels.
* correctly increase channels.
* reduce number of layers in unet2dconditionmodel tests.
* reduce block sizes for unet2dconditionmodel tests
* reduce block sizes for unet3dconditionmodel.
* fix: test_feed_forward_chunking
* fix: test_forward_with_norm_groups
* skip spatiotemporal tests on MPS.
* reduce block size in AutoencoderKL.
* reduce block sizes for vqmodel.
* further reduce block size.
* make style.
* Empty-Commit
* reduce sizes for ConsistencyDecoderVAETests
* further reduction.
* further block reductions in AutoencoderKL and AssymetricAutoencoderKL.
* massively reduce the block size in unet2dcontionmodel.
* reduce sizes for unet3d
* fix tests in unet3d.
* reduce blocks further in motion unet.
* fix: output shape
* add attention_head_dim to the test configuration.
* remove unexpected keyword arg
* up a bit.
* groups.
* up again
* fix
* Skip `test_freeu_enabled ` on MPS
* Small fixes
- import skip_mps correctly
- disable all instances of test_freeu_enabled
* Empty commit to trigger tests
* Empty commit to trigger CI
* increase number of workers for the tests.
* move to beefier runner.
* improve the fast push tests too.
* use a beefy machine for pytorch pipeline tests
* up the number of workers further.
* UniPC Multistep add `rescale_betas_zero_snr`
Same patch as DPM and Euler with the patched final alpha cumprod
BF16 doesn't seem to break down, I think cause UniPC upcasts during some
phases already? We could still force an upcast since it only
loses ≈ 0.005 it/s for me but the difference in output is very small. A
better endeavor might upcasting in step() and removing all the other
upcasts elsewhere?
* UniPC ZSNR UT
* Re-add `rescale_betas_zsnr` doc oops
* UniPC UTs iterate solvers on FP16
It wasn't catching errs on order==3. Might be excessive?
* UniPC Multistep fix tensor dtype/device on order=3
* UniPC UTs Add v_pred to fp16 test iter
For completions sake. Probably overkill?
* 7529 do not disable autocast for cuda devices
* Remove typecasting error check for non-mps platforms, as a correct autocast implementation makes it a non-issue
* add autocast fix to other training examples
* disable native_amp for dreambooth (sdxl)
* disable native_amp for pix2pix (sdxl)
* remove tests from remaining files
* disable native_amp on huggingface accelerator for every training example that uses it
* convert more usages of autocast to nullcontext, make style fixes
* make style fixes
* style.
* Empty-Commit
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* start printing the tensors.
* print full throttle
* set static slices for 7 tests.
* remove printing.
* flatten
* disable test for controlnet
* what happens when things are seeded properly?
* set the right value
* style./
* make pia test fail to check things
* print.
* fix pia.
* checking for animatediff.
* fix: animatediff.
* video synthesis
* final piece.
* style.
* print guess.
* fix: assertion for control guess.
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* Add `final_sigma_zero` to UniPCMultistep
Effectively the same trick as DDIM's `set_alpha_to_one` and
DPM's `final_sigma_type='zero'`.
Currently False by default but maybe this should be True?
* `final_sigma_zero: bool` -> `final_sigmas_type: str`
Should 1:1 match DPM Multistep now.
* Set `final_sigmas_type='sigma_min'` in UniPC UTs
* Initial commit
* Implemented block lora
- implemented block lora
- updated docs
- added tests
* Finishing up
* Reverted unrelated changes made by make style
* Fixed typo
* Fixed bug + Made text_encoder_2 scalable
* Integrated some review feedback
* Incorporated review feedback
* Fix tests
* Made every module configurable
* Adapter to new lora test structure
* Final cleanup
* Some more final fixes
- Included examples in `using_peft_for_inference.md`
- Added hint that only attns are scaled
- Removed NoneTypes
- Added test to check mismatching lens of adapter names / weights raise error
* Update using_peft_for_inference.md
* Update using_peft_for_inference.md
* Make style, quality, fix-copies
* Updated tutorial;Warning if scale/adapter mismatch
* floats are forwarded as-is; changed tutorial scale
* make style, quality, fix-copies
* Fixed typo in tutorial
* Moved some warnings into `lora_loader_utils.py`
* Moved scale/lora mismatch warnings back
* Integrated final review suggestions
* Empty commit to trigger CI
* Reverted emoty commit to trigger CI
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* speed up test_vae_slicing in animatediff
* speed up test_karras_schedulers_shape for attend and excite.
* style.
* get the static slices out.
* specify torch print options.
* modify
* test run with controlnet
* specify kwarg
* fix: things
* not None
* flatten
* controlnet img2img
* complete controlet sd
* finish more
* finish more
* finish more
* finish more
* finish the final batch
* add cpu check for expected_pipe_slice.
* finish the rest
* remove print
* style
* fix ssd1b controlnet test
* checking ssd1b
* disable the test.
* make the test_ip_adapter_single controlnet test more robust
* fix: simple inpaint
* multi
* disable panorama
* enable again
* panorama is shaky so leave it for now
* remove print
* raise tolerance.
* Bug fix for controlnetpipeline check_image
Bug fix for controlnetpipeline check_image when using multicontrolnet and prompt list
* Update test_inference_multiple_prompt_input function
* Update test_controlnet.py
add test for multiple prompts and multiple image conditioning
* Update test_controlnet.py
Fix format error
---------
Co-authored-by: Lvkesheng Shen <45848260+Fantast416@users.noreply.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add remove_all_hooks
* a few more fix and tests
* up
* Update src/diffusers/pipelines/pipeline_utils.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* split tests
* add
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* apple mps: training support for SDXL LoRA
* sdxl: support training lora, dreambooth, t2i, pix2pix, and controlnet on apple mps
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* mps: fix XL pipeline inference at training time due to upstream pytorch bug
* Update src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* apply the safe-guarding logic elsewhere.
---------
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
