GitLab

* feat: support saving a model in sharded checkpoints.

* feat: make loading of sharded checkpoints work.

* add tests

* cleanse the loading logic a bit more.

* more resilience while loading from the Hub.

* parallelize shard downloads by using snapshot_download()/

* default to a shard size.

* more fix

* Empty-Commit

* debug

* fix

* uality

* more debugging

* fix more

* initial comments from Benjamin

* move certain methods to loading_utils

* add test to check if the correct number of shards are present.

* add a test to check if loading of sharded checkpoints from the Hub is okay

* clarify the unit when passed as an int.

* use hf_hub for sharding.

* remove unnecessary code

* remove unnecessary function

* lucain's comments.

* fixes

* address high-level comments.

* fix test

* subfolder shenanigans./

* Update src/diffusers/utils/hub_utils.py
Co-authored-by: Lucain <lucainp@gmail.com>

* Apply suggestions from code review
Co-authored-by: Lucain <lucainp@gmail.com>

* remove _huggingface_hub_version as not needed.

* address more feedback.

* add a test for local_files_only=True/

* need hf hub to be at least 0.23.2

* style

* final comment.

* clean up subfolder.

* deal with suffixes in code.

* _add_variant default.

* use weights_name_pattern

* remove add_suffix_keyword

* clean up downloading of sharded ckpts.

* don't return something special when using index.json

* fix more

* don't use bare except

* remove comments and catch the errors better

* fix a couple of things when using is_file()

* empty

---------
Co-authored-by: Lucain <lucainp@gmail.com>

* Hunyuan Team: add HunyuanDiT related updates


---------
Co-authored-by: XCLiu <liuxc1996@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail.com>

* init for patches

* finish patched model.

* continuous transformer

* vectorized transformer2d.

* style.

* inits.

* fix-copies.

* introduce DiTTransformer2DModel.

* fixes

* use REMAPPING as suggested by @DN6

* better logging.

* add pixart transformer model.

* inits.

* caption_channels.

* attention masking.

* fix use_additional_conditions.

* remove print.

* debug

* flatten

* fix: assertion for sigma

* handle remapping for modeling_utils

* add tests for dit transformer2d

* quality

* placeholder for pixart tests

* pixart tests

* add _no_split_modules

* add docs.

* check

* check

* check

* check

* fix tests

* fix tests

* move Transformer output to modeling_output

* move errors better and bring back use_additional_conditions attribute.

* add unnecessary things from DiT.

* clean up pixart

* fix remapping

* fix device_map things in pixart2d.

* replace Transformer2DModel with appropriate classes in dit, pixart tests

* empty

* legacy mixin classes./

* use a remapping dict for fetching class names.

* change to specifc model types in the pipeline implementations.

* move _fetch_remapped_cls_from_config to modeling_loading_utils.py

* fix dependency problems.

* add deprecation note.

fix

* attempt at fixing onetrainer lora.

* fix

* implement marigold depth and normals pipelines in diffusers core

* remove bibtex

* remove deprecations

* remove save_memory argument

* remove validate_vae

* remove config output

* remove batch_size autodetection

* remove presets logic
move default denoising_steps and processing_resolution into the model config
make default ensemble_size 1

* remove no_grad

* add fp16 to the example usage

* implement is_matplotlib_available
use is_matplotlib_available, is_scipy_available for conditional imports in the marigold depth pipeline

* move colormap, visualize_depth, and visualize_normals into export_utils.py

* make the denoising loop more lucid
fix the outputs to always be 4d tensors or lists of pil images
support a 4d input_image case
attempt to support model_cpu_offload_seq
move check_inputs into a separate function
change default batch_size to 1, remove any logic to make it bigger implicitly

* style

* rename denoising_steps into num_inference_steps

* rename input_image into image

* rename input_latent into latents

* remove decode_image
change decode_prediction to use the AutoencoderKL.decode method

* move clean_latent outside of progress_bar

* refactor marigold-reusable image processing bits into MarigoldImageProcessor class

* clean up the usage example docstring

* make ensemble functions members of the pipelines

* add early checks in check_inputs
rename E into ensemble_size in depth ensembling

* fix vae_scale_factor computation

* better compatibility with torch.compile
better variable naming

* move export_depth_to_png to export_utils

* remove encode_prediction

* improve visualize_depth and visualize_normals to accept multi-dimensional data and lists
remove visualization functions from the pipelines
move exporting depth as 16-bit PNGs functionality from the depth pipeline
update example docstrings

* do not shortcut vae.config variables

* change all asserts to raise ValueError

* rename output_prediction_type to output_type

* better variable names
clean up variable deletion code

* better variable names

* pass desc and leave kwargs into the diffusers progress_bar
implement nested progress bar for images and steps loops

* implement scale_invariant and shift_invariant flags in the ensemble_depth function
add scale_invariant and shift_invariant flags readout from the model config
further refactor ensemble_depth
support ensembling without alignment
add ensemble_depth docstring

* fix generator device placement checks

* move encode_empty_text body into the pipeline call

* minor empty text encoding simplifications

* adjust pipelines' class docstrings to explain the added construction arguments

* improve the scipy failure condition
add comments
improve docstrings
change the default use_full_z_range to True

* make input image values range check configurable in the preprocessor
refactor load_image_canonical in preprocessor to reject unknown types and return the image in the expected 4D format of tensor and on right device
support a list of everything as inputs to the pipeline, change type to PipelineImageInput
implement a check that all input list elements have the same dimensions
improve docstrings of pipeline outputs
remove check_input pipeline argument

* remove forgotten print

* add prediction_type model config

* add uncertainty visualization into export utils
fix NaN values in normals uncertainties

* change default of output_uncertainty to False
better handle the case of an attempt to export or visualize none

* fix `output_uncertainty=False`

* remove kwargs
fix check_inputs according to the new inputs of the pipeline

* rename prepare_latent into prepare_latents as in other pipelines
annotate prepare_latents in normals pipeline with "Copied from"
annotate encode_image in normals pipeline with "Copied from"

* move nested-capable `progress_bar` method into the pipelines
revert the original `progress_bar` method in pipeline_utils

* minor message improvement

* fix cpu offloading

* move colormap, visualize_depth, export_depth_to_16bit_png, visualize_normals, visualize_uncertainty to marigold_image_processing.py
update example docstrings

* fix missing comma

* change torch.FloatTensor to torch.Tensor

* fix importing of MarigoldImageProcessor

* fix vae offloading
fix batched image encoding
remove separate encode_image function and use vae.encode instead

* implement marigold's intial tests
relax generator checks in line with other pipelines
implement return_dict __call__ argument in line with other pipelines

* fix num_images computation

* remove MarigoldImageProcessor and outputs from import structure
update tests

* update docstrings

* update init

* update

* style

* fix

* fix

* up

* up

* up

* add simple test

* up

* update expected np input/output to be channel last

* move expand_tensor_or_array into the MarigoldImageProcessor

* rewrite tests to follow conventions - hardcoded slices instead of image artifacts
write more smoke tests

* add basic docs.

* add anton's contribution statement

* remove todos.

* fix assertion values for marigold depth slow tests

* fix assertion values for depth normals.

* remove print

* support AutoencoderTiny in the pipelines

* update documentation page
add Available Pipelines section
add Available Checkpoints section
add warning about num_inference_steps

* fix missing import in docstring
fix wrong value in visualize_depth docstring

* [doc] add marigold to pipelines overview

* [doc] add section "usage examples"

* fix an issue with latents check in the pipelines

* add "Frame-by-frame Video Processing with Consistency" section

* grammarly

* replace tables with images with css-styled images (blindly)

* style

* print

* fix the assertions.

* take from the github runner.

* take the slices from action artifacts

* style.

* update with the slices from the runner.

* remove unnecessary code blocks.

* Revert "[doc] add marigold to pipelines overview"

This reverts commit a505165150afd8dab23c474d1a054ea505a56a5f.

* remove invitation for new modalities

* split out marigold usage examples

* doc cleanup

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

* Init commit

* Removed einops

* Added default movq config for training

* Update explanation of prompts

* Fixed inheritance of discriminator and init_tracker

* Fixed incompatible api between muse and here

* Fixed output

* Setup init training

* Basic structure done

* Removed attention for quick tests

* Style fixes

* Fixed vae/vqgan styles

* Removed redefinition of wandb

* Fixed log_validation and tqdm

* Nothing commit

* Added commit loss to lookup_from_codebook

* Update src/diffusers/models/vq_model.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Adding perliminary README

* Fixed one typo

* Local changes

* Fixed main issues

* Merging

* Update src/diffusers/models/vq_model.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Testing+Fixed bugs in training script

* Some style fixes

* Added wandb to docs

* Fixed timm test

* get testing suite ready.

* remove return loss

* remove return_loss

* Remove diffs

* Remove diffs

* fix ruff format

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* Expand `diffusers-cli env`

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

* Move `safetensors_version = "not installed"` to `else`

* Update `safetensors_version` checking

* Add GPU detection for Linux, Mac OS, and Windows

* Add accelerator detection to environment command

* Add is_peft_version to import_utils

* Update env.py

* Add `huggingface_hub` reference

* Add `transformers` reference

* Add reference for `huggingface_hub`

* Fix print statement in env.py for unusual OS

* Up

* Fix platform information in env.py

* up

* Fix import order in env.py

* ruff

* make style

* Fix platform system check in env.py

* Fix run method return type in env.py

* 🤗



* No need f-string

* Remove location info

* Remove accelerate config

* Refactor env.py to remove accelerate config

* feat: Add support for `bitsandbytes` library in environment command

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* update conversion script to handle motion adapter sdxl checkpoint

* add animatediff xl

* handle addition_embed_type

* fix output

* update

* add imports

* make fix-copies

* add decode latents

* update docstrings

* add animatediff sdxl to docs

* remove unnecessary lines

* update example

* add test

* revert conv_in conv_out kernel param

* remove unused param addition_embed_type_num_heads

* latest IPAdapter impl

* make fix-copies

* fix return

* add IPAdapterTesterMixin to tests

* fix return

* revert based on suggestion

* add freeinit

* fix test_to_dtype test

* use StableDiffusionMixin instead of different helper methods

* fix progress bar iterations

* apply suggestions from review

* hardcode flip_sin_to_cos and freq_shift

* make fix-copies

* fix ip adapter implementation

* fix last failing test

* make style

* Update docs/source/en/api/pipelines/animatediff.md
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* remove todo

* fix doc-builder errors

---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* return layer weight if not found

* better system and test

* key example and typo

* Deprecate resume_download

* align docstring with transformers

* style

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

* support PixArt-DMD

---------
Co-authored-by: jschen <chenjunsong4@h-partners.com>
Co-authored-by: badayvedat <badayvedat@gmail.com>
Co-authored-by: Vedat Baday <54285744+badayvedat@users.noreply.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>

* CheckIn - created DownSubBlocks

* Added extra channels, implemented subblock fwd

* Fixed connection sizes

* checkin

* Removed iter, next in forward

* Models for SD21 & SDXL run through

* Added back pipelines, cleared up connections

* Cleaned up connection creation

* added debug logs

* updated logs

* logs: added input loading

* Update umer_debug_logger.py

* log: Loading hint

* Update umer_debug_logger.py

* added logs

* Changed debug logging

* debug: added more logs

* Fixed num_norm_groups

* Debug: Logging all of SDXL input

* Update umer_debug_logger.py

* debug: updated logs

* checkim

* Readded tests

* Removed debug logs

* Fixed Slow Tests

* Added value ckecks | Updated model_cpu_offload_seq

* accelerate-offloading works ; fast tests work

* Made unet & addon explicit in controlnet

* Updated slow tests

* Added dtype/device to ControlNetXS

* Filled in test model paths

* Added image_encoder/feature_extractor to XL pipe

* Fixed fast tests

* Added comments and docstrings

* Fixed copies

* Added docs ; Updates slow tests

* Moved changes to UNetMidBlock2DCrossAttn

* tiny cleanups

* Removed stray prints

* Removed ip adapters + freeU

- Removed ip adapters + freeU as they don't make sense for ControlNet-XS
- Fixed imports of UNet components

* Fixed test_save_load_float16

* Make style, quality, fix-copies

* Changed loading/saving API for ControlNetXS

- Changed loading/saving API for ControlNetXS
- other small fixes

* Removed ControlNet-XS from research examples

* Make style, quality, fix-copies

* Small fixes

- deleted ControlNetXSModel.init_original
- added time_embedding_mix to StableDiffusionControlNetXSPipeline .from_pretrained / StableDiffusionXLControlNetXSPipeline.from_pretrained
- fixed copy hints

* checkin May 11 '23

* CheckIn Mar 12 '24

* Fixed tests for SD

* Added tests for UNetControlNetXSModel

* Fixed SDXL tests

* cleanup

* Delete Pipfile

* CheckIn Mar 20

Started replacing sub blocks  by `ControlNetXSCrossAttnDownBlock2D` and `ControlNetXSCrossAttnUplock2D`

* check-in Mar 23

* checkin 24 Mar

* Created init for UNetCnxs and CnxsAddon

* CheckIn

* Made from_modules, from_unet and no_control work

* make style,quality,fix-copies & small changes

* Fixed freezing

* Added gradient ckpt'ing; fixed tests

* Fix slow tests(+compile) ; clear naming confusion

* Don't create UNet in init ; removed class_emb

* Incorporated review feedback

- Deleted get_base_pipeline /  get_controlnet_addon for pipes
- Pipes inherit from StableDiffusionXLPipeline
- Made module dicts for cnxs-addon's down/mid/up classes
- Added support for qkv fusion and freeU

* Make style, quality, fix-copies

* Implemented review feedback

* Removed compatibility check for vae/ctrl embedding

* make style, quality, fix-copies

* Delete Pipfile

* Integrated review feedback

- Importing ControlNetConditioningEmbedding now
- get_down/mid/up_block_addon now outside class
- renamed `do_control` to `apply_control`

* Reduced size of test tensors

For this, added `norm_num_groups` as parameter everywhere

* Renamed cnxs-`Addon` to cnxs-`Adapter`

- `ControlNetXSAddon` -> `ControlNetXSAdapter`
- `ControlNetXSAddonDownBlockComponents` -> `DownBlockControlNetXSAdapter`, and similarly for mid/up
- `get_mid_block_addon` -> `get_mid_block_adapter`, and similarly for mid/up

* Fixed save_pretrained/from_pretrained bug

* Removed redundant code

---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* Skip scaling if scale is identity

* move check for weight one to scale and unscale lora

* fix code style/quality

* Empty-Commit

---------
Co-authored-by: Steven Munn <stevenjmunn@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Steven Munn <5297082+stevenjlm@users.noreply.github.com>

* get device <-> component mapping when using multiple gpus.

* condition the device_map bits.

* relax condition

* device_map progress.

* device_map enhancement

* some cleaning up and debugging

* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* incorporate suggestions from PR.

* remove multi-gpu condition for now.

* guard check the component -> device mapping

* fix: device_memory variable

* dispatching transformers model to have force_hooks=True

* better guarding for transformers device_map

* introduce support balanced_low_memory and balanced_ultra_low_memory.

* remove device_map patch.

* fix: intermediate variable scoping.

* fix: condition in cpu offload.

* fix: flax class restrictions.

* remove modifications from cpu_offload and model_offload

* incorporate changes.

* add a simple forward pass test

* add: torch_device in get_inputs()

* add: tests

* remove print

* safe-guard to(), model offloading and cpu offloading when balanced is used as a device_map.

* style

* remove .

* safeguard device_map with more checks and remove invalid device_mapping strategues.

* make  a class attribute and adjust tests accordingly.

* fix device_map check

* fix test

* adjust comment

* fix: device_map attribute

* fix: dispatching.

* max_memory test for pipeline

* version guard the tests

* fix guard.

* address review feedback.

* reset_device_map method.

* add: test for reset_hf_device_map

* fix a couple things.

* add reset_device_map() in the error message.

* add tests for checking reset_device_map doesn't have unintended consequences.

* fix reset_device_map and offloading tests.

* create _get_final_device_map utility.

* hf_device_map -> _hf_device_map

* add documentation

* add notes suggested by Marc.

* styling.

* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* move updates within gpu condition.

* other docs related things

* note on ignore a device not specified in .

* provide a suggestion if device mapping errors out.

* fix: typo.

* _hf_device_map -> hf_device_map

* Empty-Commit

* add: example hf_device_map.

---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* add: utility to format our docs too 📜

* debugging saga

* fix: message

* checking

* should be fixed.

* revert pipeline_fixture

* remove empty line

* make style

* fix: setup.py

* style.

* 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.

* feat: support dora loras from community

* safe-guard dora operations under peft version.

* pop use_dora when False

* make dora lora from kohya work.

* fix: kohya conversion utils.

* add a fast test for DoRA compatibility..

* add a nightly test.

skip dynamo tests when python is 3.12.

update

* strtobool

* replace Command from setuptools.

* checking to improve pipelines.

* more fixes.

* add: tip to encourage the usage of revision

* Apply suggestions from code review

* retrigger ci

---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* cleanse and refactor lora testing suite.

* more cleanup.

* make check_if_lora_correctly_set a utility function

* fix: typo

* retrigger ci

* style

* 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>

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

* Add npu support

* fix for code quality check

* fix for code quality check

* initial diffNext v3

* move to v3 folder

* imports

* dry up the unets

* no switch_level

* fix init

* add switch_level tp config

* Fixed some things

* Added pooled text embeddings

* Initial work on adding image encoder

* changes from @dome272

* Stuff for the image encoder processing and variable naming in decoder

* fix arg name

* inference fixes

* inference fixes

* default TimestepBlock without conds

* c_skip=0 by default

* fix bfloat16 to cpu

* use config

* undo temp change

* fix gen_c_embeddings args

* change text encoding

* text encoding

* undo print

* undo .gitignore change

* Allow WuerstchenV3PriorPipeline to use the base DDPM & DDIM schedulers

* use WuerstchenV3Unet in both pipelines

* fix imports

* initial failing tests

* cleanup

* use scheduler.timesterps

* some fixes to the tests, still not fully working

* fix tests

* fix prior tests

* add dropout to the model_kwargs

* more tests passing

* update expected_slice

* initial rename

* rename tests

* rename class names

* make fix-copies

* initial docs

* autodocs

* typos

* fix arg docs

* add text_encoder info

* combined pipeline has optional image arg

* fix documentation

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

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

* Update src/diffusers/pipelines/stable_cascade/modeling_stable_cascade_common.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

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

* Update src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* Update src/diffusers/pipelines/stable_cascade/modeling_stable_cascade_common.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* use self.config

* Update src/diffusers/pipelines/stable_cascade/modeling_stable_cascade_common.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* c_in -> in_channels

* removed kwargs from unet's forward

* Update src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

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

* remove older callback api

* removed kwargs and fixed decoder guidance > 1

* decoder takes emeds

* check and use image_embeds

* fixed all but one decoder test

* fix decoder tests

* update callback api

* fix some more combined tests

* push combined pipeline

* initial docs

* fix doc_string

* update combined api

* no test_callback_inputs test for combined pipeline

* add optional components

* fix ordering of components

* fix combined tests

* update convert script

* Update src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* Update src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* Update src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* fix imports

* move effnet out of deniosing loop

* prompt_embeds_pooled only when doing guidance

* Fix repeat shape

* move StableCascadeUnet to models/unets/

* more descriptive names

* converted when numpy()

* StableCascadePriorPipelineOutput docs

* rename StableCascadeUNet

* add slow tests

* fix slow tests

* update

* update

* updated model_path

* add args for weights

* set push_to_hub to false

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

---------
Co-authored-by: Dominic Rampas <d6582533@gmail.com>
Co-authored-by: Pablo Pernias <pablo@pernias.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: 99991 <99991@users.noreply.github.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* add: support TCD scheduler


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

* move model helper function in pipeline to EfficiencyMixin

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Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* 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

Fixed deprecation warning for torch.utils._pytree._register_pytree_node in PyTorch 2.2
Co-authored-by: Yinghua <yzho0423@uni.sydney.edu.au>

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

change to 2024

* feat: standarize model card creation for dreambooth training.

* correct 'inference

* remove comments.

* take component out of kwargs

* style

* add: card template to have a leaner description.

* widget support.

* propagate changes to train_dreambooth_lora

* propagate changes to custom diffusion

* make widget properly type-annotated

update

change path to pathlib as_posix
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update testing_utils.py

* Update testing_utils.py