GitLab

* Change step_offset scheduler docstrings

* Mention it may be needed by some models

* More docstrings

These ones failed literal S&R because I performed it case-sensitive
which is fun.

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Setup LEdits++ file structure

* Fix import

* LEditsPP Stable Diffusion pipeline

* Include variable image aspect ratios

* Implement LEDITS++ for SDXL

* clean up LEditsPPPipelineStableDiffusion

* Adjust inversion output

* Added docu, more cleanup for LEditsPPPipelineStableDiffusion

* clean up LEditsPPPipelineStableDiffusionXL

* Update documentation

* Fix documentation import

* Add skeleton IF implementation

* Fix documentation typo

* Add LEDTIS docu to toctree

* Add missing title

* Finalize SD documentation

* Finalize SD-XL documentation

* Fix code style and quality

* Fix typo

* Fix return types

* added LEditsPPPipelineIF; minor changes for LEditsPPPipelineStableDiffusion and LEditsPPPipelineStableDiffusionXL

* Fix copy reference

* add documentation for IF

* Add first tests

* Fix batching for SD-XL

* Fix text encoding and perfect reconstruction for SD-XL

* Add tests for SD-XL, minor changes

* move user_mask to correct device, use cross_attention_kwargs also for inversion

* Example docstring

* Fix attention resolution for non-square images

* Refactoring for PR review

* Safely remove ledits_utils.py

* Style fixes

* Replace assertions with ValueError

* Remove LEditsPPPipelineIF

* Remove unecessary input checks

* Refactoring of CrossAttnProcessor

* Revert unecessary changes to scheduler

* Remove first progress-bar in inversion

* Refactor scheduler usage and reset

* Use imageprocessor instead of custom logic

* Fix scheduler init warning

* Fix error when running the pipeline in fp16

* Update documentation wrt perfect inversion

* Update tests

* Fix code quality and copy consistency

* Update LEditsPP import

* Remove enable/disable methods that are now in StableDiffusionMixin

* Change import in docs

* Revert import structure change

* Fix ledits imports

---------
Co-authored-by: Katharina Kornmeier <katharina.kornmeier@stud.tu-darmstadt.de>

switch to logger.warning

* I added a new doc string to the class. This is more flexible to understanding other developers what are doing and where it's using.

* Update src/diffusers/models/unet_2d_blocks.py

This changes suggest by maintener.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/models/unet_2d_blocks.py

Add suggested text
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update unet_2d_blocks.py

I changed the Parameter to Args text.

* Update unet_2d_blocks.py

proper indentation set in this file.

* Update unet_2d_blocks.py

a little bit of change in the act_fun argument line.

* I run the black command to reformat style in the code

* Update unet_2d_blocks.py

similar doc-string add to have in the original diffusion repository.

* Fix bug for mention in this issue section #6901

* Update src/diffusers/schedulers/scheduling_ddim_flax.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fix linter

* Restore empty line

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* add: support TCD scheduler


---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Enable FakeTensorMode for EulerDiscreteScheduler scheduler

PyTorch's FakeTensorMode does not support `.numpy()` or `numpy.array()`
calls.

This PR replaces `sigmas` numpy tensor by a PyTorch tensor equivalent

Repro

```python
with torch._subclasses.FakeTensorMode() as fake_mode, ONNXTorchPatcher():
    fake_model = DiffusionPipeline.from_pretrained(model_name, low_cpu_mem_usage=False)
```

that otherwise would fail with
`RuntimeError: .numpy() is not supported for tensor subclasses.`

* Address comments

* DPMMultistep rescale_betas_zero_snr

* DPM upcast samples in step()

* DPM rescale_betas_zero_snr UT

* DPMSolverMulti move sample upcast after model convert

Avoids having to re-use the dtype.

* Add a newline for Ruff

* add DPM scheduler with EDM formulation

* set sigmas in init

* add _compute_sigmas

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* address some review comments

* up,

* add tests

---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Add EDMEulerScheduler

* address review comments

* fix import

* fix test

* add tests

* add co-author

Co-authored-by:  @dg845 dgu8957@gmail.com

[DPMSolverSinglestepScheduler] correct `get_order_list` for `solver_order=2`and `lower_order_final=True`  (#6953)

* add

* change default

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>

change to 2024

add missing param

* load cumprod tensor to device
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

* fixing ci
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

* make fix-copies
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

---------
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

* add Sa-Solver



---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: scxue <xueshuchen17@mails.ucas.edu.cn>
Co-authored-by: jschen <chenjunsong4@h-partners.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>

* fix

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Fix support for MPS

MPS doesn't support float64

* Remove unused parameters and fixed `FutureWarning`

* Fixed wrong config instance

* update unittest for `DDIMInverseScheduler`

* Add rescale_betas_zero_snr argument to DDPMScheduler.

* Propagate rescale_betas_zero_snr changes to DDPMParallelScheduler.

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

amused

rename

Update docs/source/en/api/pipelines/amused.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

AdaLayerNormContinuous default values

custom micro conditioning

micro conditioning docs

put lookup from codebook in constructor

fix conversion script

remove manual fused flash attn kernel

add training script

temp remove training script

add dummy gradient checkpointing func

clarify temperatures is an instance variable by setting it

remove additional SkipFF block args

hardcode norm args

rename tests folder

fix paths and samples

fix tests

add training script

training readme

lora saving and loading

non-lora saving/loading

some readme fixes

guards

Update docs/source/en/api/pipelines/amused.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

Update examples/amused/README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

Update examples/amused/train_amused.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

vae upcasting

add fp16 integration tests

use tuple for micro cond

copyrights

remove casts

delegate to torch.nn.LayerNorm

move temperature to pipeline call

upsampling/downsampling changes

* EulerAncestral add `rescale_betas_zero_snr`

Uses same infinite sigma fix from EulerDiscrete. Interestingly the
ancestral version had the opposite problem: too much contrast instead of
too little.

* UT for EulerAncestral `rescale_betas_zero_snr`

* EulerAncestral upcast samples during step()

It helps this scheduler too, particularly when the model is using bf16.

While the noise dtype is still the model's it's automatically upcasted
for the add so all it affects is determinism.

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* correct

* Apply suggestions from code review

* make style

* fix

* copies

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>

* EulerDiscreteScheduler add `rescale_betas_zero_snr`

* Harmonize HF environment variables + deprecate use_auth_token

* fix import

* fix

* [Euler Discrete] Fix sigma

* make style

* debug

* from step

* print

* turn sigma a list

* make str

* init_noise_sigma

* comment

* remove prints

* feat: introduce fused projections

* change to a better name

* no grad

* device.

* device

* dtype

* okay

* print

* more print

* fix: unbind -> split

* fix: qkv >-> k

* enable disable

* apply attention processor within the method

* attn processors

* _enable_fused_qkv_projections

* remove print

* add fused projection to vae

* add todos.

* add: documentation and cleanups.

* add: test for qkv projection fusion.

* relax assertions.

* relax further

* fix: docs

* fix-copies

* correct error message.

* Empty-Commit

* better conditioning on disable_fused_qkv_projections

* check

* check processor

* bfloat16 computation.

* check latent dtype

* style

* remove copy temporarily

* cast latent to bfloat16

* fix: vae -> self.vae

* remove print.

* add _change_to_group_norm_32

* comment out stuff that didn't work

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* reflect patrick's suggestions.

* fix imports

* fix: disable call.

* fix more

* fix device and dtype

* fix conditions.

* fix more

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix dpm
* all scheulers

* begin model

* finish blocks

* add_embedding

* addition_time_embed_dim

* use TimestepEmbedding

* fix temporal res block

* fix time_pos_embed

* fix add_embedding

* add conversion script

* fix model

* up

* add new resnet blocks

* make forward work

* return sample in original shape

* fix temb shape in TemporalResnetBlock

* add spatio temporal transformers

* add vae blocks

* fix blocks

* update

* update

* fix shapes in Alphablender and add time activation in res blcok

* use new blocks

* style

* fix temb shape

* fix SpatioTemporalResBlock

* reuse TemporalBasicTransformerBlock

* fix TemporalBasicTransformerBlock

* use TransformerSpatioTemporalModel

* fix TransformerSpatioTemporalModel

* fix time_context dim

* clean up

* make temb optional

* add blocks

* rename model

* update conversion script

* remove UNetMidBlockSpatioTemporal

* add in init

* remove unused arg

* remove unused arg

* remove more unsed args

* up

* up

* check for None

* update vae

* update up/mid blocks for decoder

* begin pipeline

* adapt scheduler

* add guidance scalings

* fix norm eps in temporal transformers

* add temporal autoencoder

* make pipeline run

* fix frame decodig

* decode in float32

* decode n frames at a time

* pass decoding_t to decode_latents

* fix decode_latents

* vae encode/decode in fp32

* fix dtype in TransformerSpatioTemporalModel

* type image_latents same as image_embeddings

* allow using differnt eps in temporal block for video decoder

* fix default values in vae

* pass num frames in decode

* switch spatial to temporal for mixing in VAE

* fix num frames during split decoding

* cast alpha to sample dtype

* fix attention in MidBlockTemporalDecoder

* fix typo

* fix guidance_scales dtype

* fix missing activation in TemporalDecoder

* skip_post_quant_conv

* add vae conversion

* style

* take guidance scale as input

* up

* allow passing PIL to export_video

* accept fps as arg

* add pipeline and vae in init

* remove hack

* use AutoencoderKLTemporalDecoder

* don't scale image latents

* add unet tests

* clean up unet

* clean TransformerSpatioTemporalModel

* add slow svd test

* clean up

* make temb optional in Decoder mid block

* fix norm eps in TransformerSpatioTemporalModel

* clean up temp decoder

* clean up

* clean up

* use c_noise values for timesteps

* use math for log

* update

* fix copies

* doc

* upcast vae

* update forward pass for gradient checkpointing

* make added_time_ids is tensor

* up

* fix upcasting

* remove post quant conv

* add _resize_with_antialiasing

* fix _compute_padding

* cleanup model

* more cleanup

* more cleanup

* more cleanup

* remove freeu

* remove attn slice

* small clean

* up

* up

* remove extra step kwargs

* remove eta

* remove dropout

* remove callback

* remove merge factor args

* clean

* clean up

* move to dedicated folder

* remove attention_head_dim

* docstr and small fix

* update unet doc strings

* rename decoding_t

* correct linting

* store c_skip and c_out

* cleanup

* clean TemporalResnetBlock

* more cleanup

* clean up vae

* clean up

* begin doc

* more cleanup

* up

* up

* doc

* Improve

* better naming

* better naming

* better naming

* better naming

* better naming

* better naming

* better naming

* better naming

* Apply suggestions from code review

* Default chunk size to None

* add example

* Better

* Apply suggestions from code review

* update doc

* Update src/diffusers/pipelines/stable_diffusion_video/pipeline_stable_diffusion_video.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* style

* Get torch compile working

* up

* rename

* fix doc

* add chunking

* torch compile

* torch compile

* add modelling outputs

* torch compile

* Improve chunking

* Apply suggestions from code review

* Update docs/source/en/using-diffusers/svd.md

* Close diff tag

* remove slicing

* resnet docstr

* add docstr in resnet

* rename

* Apply suggestions from code review

* update tests

* Fix output type latents

* fix more

* fix more

* Update docs/source/en/using-diffusers/svd.md

* fix more

* add pipeline tests

* remove unused arg

* clean  up

* make sure get_scaling receives tensors

* fix euler scheduler

* fix get_scalings

* simply euler for now

* remove old test file

* use randn_tensor to create noise

* fix device for rand tensor

* increase expected_max_difference

* fix test_inference_batch_single_identical

* actually fix test_inference_batch_single_identical

* disable test_save_load_float16

* skip test_float16_inference

* skip test_inference_batch_single_identical

* fix test_xformers_attention_forwardGenerator_pass

* Apply suggestions from code review

* update StableVideoDiffusionPipelineSlowTests

* update image

* add diffusers example

* fix more

---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: apolinário <joaopaulo.passos@gmail.com>

* Add custom timesteps support to LCMScheduler.

* Add custom timesteps support to StableDiffusionPipeline.

* Add custom timesteps support to StableDiffusionXLPipeline.

* Add custom timesteps support to remaining Stable Diffusion pipelines which support LCMScheduler (img2img, inpaint).

* Add custom timesteps support to remaining Stable Diffusion XL pipelines which support LCMScheduler (img2img, inpaint).

* Add custom timesteps support to StableDiffusionControlNetPipeline.

* Add custom timesteps support to T21 Stable Diffusion (XL) Adapters.

* Clean up Stable Diffusion inpaint tests.

* Manually add support for custom timesteps to AltDiffusion pipelines since make fix-copies doesn't appear to work correctly (it deletes the whole pipeline).

* make style

* Refactor pipeline timestep handling into the retrieve_timesteps function.

* deprecated: KarrasVeScheduler, ScoreSdeVpScheduler

* delete tests relevant to deprecated schedulers

* chore: run make style

* fix: import error caused due to incorrect _import_structure after deprecation

* fix: ScoreSdeVpScheduler was not importable from diffusers

* remove import added by assumption

* Update src/diffusers/schedulers/__init__.py as suggested by @patrickvonplaten
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make it a part deprecated

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Fix

* fix

* fix doc

* fix doc....again.......

* remove karras_ve test folder
Co-Authored-By: YiYi Xu <yixu310@gmail.com>

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>

* Change LCMScheduler.set_timesteps to pick more evenly spaced inference timesteps.

* Change inference_indices implementation to better match previous behavior.

* Add num_inference_steps=26 test case to test_inference_steps.

* run CI

---------
Co-authored-by: patil-suraj <surajp815@gmail.com>

* ruff format

* not need to use doc-builder's black styling as the doc is styled in ruff

* make fix-copies

* comment

* use run_ruff

* consistency decoder

* rename

* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py

* uP

* Apply suggestions from code review

* uP

* uP

* uP

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Refactor LCMScheduler.step such that prev_sample == denoised at the last timestep in the schedule.

* Make timestep scaling when calculating boundary conditions configurable.

* Reparameterize timestep_scaling to be a multiplicative rather than division scaling.

* make style

* fix dtype conversion

* make style

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* [LCM] Clean up implementations

* Add all

* correct more

* correct more

* finish

* up