* refactor to support patching LoRA into T5
instantiate the lora linear layer on the same device as the regular linear layer
get lora rank from state dict
tests
fmt
can create lora layer in float32 even when rest of model is float16
fix loading model hook
remove load_lora_weights_ and T5 dispatching
remove Unet#attn_processors_state_dict
docstrings
* text encoder monkeypatch class method
* fix test
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* refactor prior_transformer
adding conversion script
add pipeline
add step_index from pipeline, + remove permute
add zero pad token
remove copy from statement for betas_for_alpha_bar function
* add
* add
* update conversion script for renderer model
* refactor camera a little bit
* clean up
* style
* fix copies
* Update src/diffusers/schedulers/scheduling_heun_discrete.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/pipelines/shap_e/pipeline_shap_e.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/pipelines/shap_e/pipeline_shap_e.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* alpha_transform_type
* remove step_index argument
* remove get_sigmas_karras
* remove _yiyi_sigma_to_t
* move the rescale prompt_embeds from prior_transformer to pipeline
* replace baddbmm with einsum to match origial repo
* Revert "replace baddbmm with einsum to match origial repo"
This reverts commit 3f6b435d65.
* add step_index to scale_model_input
* Revert "move the rescale prompt_embeds from prior_transformer to pipeline"
This reverts commit 5b5a8e6be9.
* move rescale from prior_transformer to pipeline
* correct step_index in scale_model_input
* remove print lines
* refactor prior - reduce arguments
* make style
* add prior_image
* arg embedding_proj_norm -> norm_embedding_proj
* add pre-norm for proj_embedding
* move rescale prompt from pipeline to _encode_prompt
* add img2img pipeline
* style
* copies
* Update src/diffusers/models/prior_transformer.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
add arg: encoder_hid_proj
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
add new config: norm_in_type
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
add new config: added_emb_type
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
rename out_dim -> clip_embed_dim
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
rename config: out_dim -> clip_embed_dim
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/models/prior_transformer.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* finish refactor prior_tranformer
* make style
* refactor renderer
* fix
* make style
* refactor img2img
* remove params_proj
* add test
* add upcast_softmax to prior_transformer
* enable num_images_per_prompt, add save_gif utility
* add
* add fast test
* make style
* add slow test
* style
* add test for img2img
* refactor
* enable batching
* style
* refactor scheduler
* update test
* style
* attempt to solve batch related tests timeout
* add doc
* Update src/diffusers/pipelines/shap_e/pipeline_shap_e.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* hardcode rendering related config
* update betas_for_alpha_bar on ddpm_scheduler
* fix copies
* fix
* export_to_gif
* style
* second attempt to speed up batching tests
* add doc page to index
* Remove intermediate clipping
* 3rd attempt to speed up batching tests
* Remvoe time index
* simplify scheduler
* Fix more
* Fix more
* fix more
* make style
* fix schedulers
* fix some more tests
* finish
* add one more test
* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* style
* apply feedbacks
* style
* fix copies
* add one example
* style
* add example for img2img
* fix doc
* fix more doc strings
* size -> frame_size
* style
* update doc
* style
* fix on doc
* update repo name
* improve the usage example in shap-e img2img
* add usage examples in the shap-e docs.
* consolidate examples.
* minor fix.
* update doc
* Apply suggestions from code review
* Apply suggestions from code review
* remove upcast
* Make sure background is white
* Update src/diffusers/pipelines/shap_e/pipeline_shap_e.py
* Apply suggestions from code review
* Finish
* Apply suggestions from code review
* Update src/diffusers/pipelines/shap_e/pipeline_shap_e.py
* Make style
---------
Co-authored-by: yiyixuxu <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: Pedro Cuenca <pedro@huggingface.co>
* Kandinsky2_2
* fix init kandinsky2_2
* kandinsky2_2 fix inpainting
* rename pipelines: remove decoder + 2_2 -> V22
* Update scheduling_unclip.py
* remove text_encoder and tokenizer arguments from doc string
* add test for text2img
* add tests for text2img & img2img
* fix
* add test for inpaint
* add prior tests
* style
* copies
* add controlnet test
* style
* add a test for controlnet_img2img
* update prior_emb2emb api to accept image_embedding or image
* add a test for prior_emb2emb
* style
* remove try except
* example
* fix
* add doc string examples to all kandinsky pipelines
* style
* update doc
* style
* add a top about 2.2
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* vae -> movq
* vae -> movq
* style
* fix the #copied from
* remove decoder from file name
* update doc: add a section for kandinsky 2.2
* fix
* fix-copies
* add coped from
* add copies from for prior
* add copies from for prior emb2emb
* copy from for img2img
* copied from for inpaint
* more copied from
* more copies from
* more copies
* remove the yiyi comments
* Apply suggestions from code review
* Self-contained example, pipeline order
* Import prior output instead of redefining.
* Style
* Make VQModel compatible with model offload.
* Fix copies
---------
Co-authored-by: Shahmatov Arseniy <62886550+cene555@users.noreply.github.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Add new text encoder
* add transformers depth
* More
* Correct conversion script
* Fix more
* Fix more
* Correct more
* correct text encoder
* Finish all
* proof that in works in run local xl
* clean up
* Get refiner to work
* Add red castle
* Fix batch size
* Improve pipelines more
* Finish text2image tests
* Add img2img test
* Fix more
* fix import
* Fix embeddings for classic models (#3888)
Fix embeddings for classic SD models.
* Allow multiple prompts to be passed to the refiner (#3895)
* finish more
* Apply suggestions from code review
* add watermarker
* Model offload (#3889)
* Model offload.
* Model offload for refiner / img2img
* Hardcode encoder offload on img2img vae encode
Saves some GPU RAM in img2img / refiner tasks so it remains below 8 GB.
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* correct
* fix
* clean print
* Update install warning for `invisible-watermark`
* add: missing docstrings.
* fix and simplify the usage example in img2img.
* fix setup for watermarking.
* Revert "fix setup for watermarking."
This reverts commit 491bc9f5a6.
* fix: watermarking setup.
* fix: op.
* run make fix-copies.
* make sure tests pass
* improve convert
* make tests pass
* make tests pass
* better error message
* fiinsh
* finish
* Fix final test
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* use sample directly instead of the dataclass.
* more usage of directly samples instead of dataclasses
* more usage of directly samples instead of dataclasses
* use direct sample in the pipeline.
* direct usage of sample in the img2img case.
* add default to unet output to prevent it from being a required arg
* add unit test
* make style
* adjust unit test
* mark as fast test
* adjust assert statement in test
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: root <root@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* initial commit
* Improve consistency models sampling implementation.
* Add CMStochasticIterativeScheduler, which implements the multi-step sampler (stochastic_iterative_sampler) in the original code, and make further improvements to sampling.
* Add Unet blocks for consistency models
* Add conversion script for Unet
* Fix bug in new unet blocks
* Fix attention weight loading
* Make design improvements to ConsistencyModelPipeline and CMStochasticIterativeScheduler and add initial version of tests.
* make style
* Make small random test UNet class conditional and set resnet_time_scale_shift to 'scale_shift' to better match consistency model checkpoints.
* Add support for converting a test UNet and non-class-conditional UNets to the consistency models conversion script.
* make style
* Change num_class_embeds to 1000 to better match the original consistency models implementation.
* Add support for distillation in pipeline_consistency_models.py.
* Improve consistency model tests:
- Get small testing checkpoints from hub
- Modify tests to take into account "distillation" parameter of ConsistencyModelPipeline
- Add onestep, multistep tests for distillation and distillation + class conditional
- Add expected image slices for onestep tests
* make style
* Improve ConsistencyModelPipeline:
- Add initial support for class-conditional generation
- Fix initial sigma for onestep generation
- Fix some sigma shape issues
* make style
* Improve ConsistencyModelPipeline:
- add latents __call__ argument and prepare_latents method
- add check_inputs method
- add initial docstrings for ConsistencyModelPipeline.__call__
* make style
* Fix bug when randomly generating class labels for class-conditional generation.
* Switch CMStochasticIterativeScheduler to configuring a sigma schedule and make related changes to the pipeline and tests.
* Remove some unused code and make style.
* Fix small bug in CMStochasticIterativeScheduler.
* Add expected slices for multistep sampling tests and make them pass.
* Work on consistency model fast tests:
- in pipeline, call self.scheduler.scale_model_input before denoising
- get expected slices for Euler and Heun scheduler tests
- make Euler test pass
- mark Heun test as expected fail because it doesn't support prediction_type "sample" yet
- remove DPM and Euler Ancestral tests because they don't support use_karras_sigmas
* make style
* Refactor conversion script to make it easier to add more model architectures to convert in the future.
* Work on ConsistencyModelPipeline tests:
- Fix device bug when handling class labels in ConsistencyModelPipeline.__call__
- Add slow tests for onestep and multistep sampling and make them pass
- Refactor fast tests
- Refactor ConsistencyModelPipeline.__init__
* make style
* Remove the add_noise and add_noise_to_input methods from CMStochasticIterativeScheduler for now.
* Run python utils/check_copies.py --fix_and_overwrite
python utils/check_dummies.py --fix_and_overwrite to make dummy objects for new pipeline and scheduler.
* Make fast tests from PipelineTesterMixin pass.
* make style
* Refactor consistency models pipeline and scheduler:
- Remove support for Karras schedulers (only support CMStochasticIterativeScheduler)
- Move sigma manipulation, input scaling, denoising from pipeline to scheduler
- Make corresponding changes to tests and ensure they pass
* make style
* Add docstrings and further refactor pipeline and scheduler.
* make style
* Add initial version of the consistency models documentation.
* Refactor custom timesteps logic following DDPMScheduler/IFPipeline and temporarily add torch 2.0 SDPA kernel selection logic for debugging.
* make style
* Convert current slow tests to use fp16 and flash attention.
* make style
* Add slow tests for normal attention on cuda device.
* make style
* Fix attention weights loading
* Update consistency model fast tests for new test checkpoints with attention fix.
* make style
* apply suggestions
* Add add_noise method to CMStochasticIterativeScheduler (copied from EulerDiscreteScheduler).
* Conversion script now outputs pipeline instead of UNet and add support for LSUN-256 models and different schedulers.
* When both timesteps and num_inference_steps are supplied, raise warning instead of error (timesteps take precedence).
* make style
* Add remaining diffusers model checkpoints for models in the original consistency model release and update usage example.
* apply suggestions from review
* make style
* fix attention naming
* Add tests for CMStochasticIterativeScheduler.
* make style
* Make CMStochasticIterativeScheduler tests pass.
* make style
* Override test_step_shape in CMStochasticIterativeSchedulerTest instead of modifying it in SchedulerCommonTest.
* make style
* rename some models
* Improve API
* rename some models
* Remove duplicated block
* Add docstring and make torch compile work
* More fixes
* Fixes
* Apply suggestions from code review
* Apply suggestions from code review
* add more docstring
* update consistency conversion script
---------
Co-authored-by: ayushmangal <ayushmangal@microsoft.com>
Co-authored-by: Ayush Mangal <43698245+ayushtues@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add timestep_spacing to DDPM, LMSDiscrete, PNDM.
* Remove spurious line.
* More easy schedulers.
* Add `linspace` to DDIM
* Noise sigma for `trailing`.
* Add timestep_spacing to DEISMultistepScheduler.
Not sure the range is the way it was intended.
* Fix: remove line used to debug.
* Support timestep_spacing in DPMSolverMultistep, DPMSolverSDE, UniPC
* Fix: convert to numpy.
* Use sched. defaults when instantiating from_config
For params not present in the original configuration.
This makes it possible to switch pipeline schedulers even if they use
different timestep_spacing (or any other param).
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Missing args in DPMSolverMultistep
* Test: default args not in config
* Style
* Fix scheduler name in test
* Remove duplicated entries
* Add test for solver_type
This test currently fails in main. When switching from DEIS to UniPC,
solver_type is "logrho" (the default value from DEIS), which gets
translated to "bh1" by UniPC. This is different to the default value for
UniPC: "bh2". This is where the translation happens: 36d22d0709/src/diffusers/schedulers/scheduling_unipc_multistep.py (L171)
* UniPC: use same default for solver_type
Fixes a bug when switching from UniPC from another scheduler (i.e.,
DEIS) that uses a different solver type. The solver is now the same as
if we had instantiated the scheduler directly.
* do not save use default values
* fix more
* fix all
* fix schedulers
* fix more
* finish for real
* finish for real
* flaky tests
* Update tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py
* Default steps_offset to 0.
* Add missing docstrings
* Apply suggestions from code review
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Improve memory text to video
* Apply suggestions from code review
* add test
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* finish test setup
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* - Added validation parameters
- Changed some parameter descriptions to better explain their use.
- Fixed a few typos.
- Added concept_list parameter for better management of multiple subjects
- changed logic for image validation
* - Fixed bad logic for class data root directories
* Defaulting validation_steps to None for an easier logic
* Fixed multiple validation prompts
* Fixed bug on validation negative prompt
* Changed validation logic for tracker.
* Added uuid for validation image labeling
* Fix error when comparing validation prompts and validation negative prompts
* Improved error message when negative prompts for validation are more than the number of prompts
* - Changed image tracking number from epoch to global_step
- Added Typing for functions
* Added some validations more when using concept_list parameter and the regular ones.
* Fixed error message
* Added more validations for validation parameters
* Improved messaging for errors
* Fixed validation error for parameters with default values
* - Added train step to image name for validation
- reformatted code
* - Added train step to image's name for validation
- reformatted code
* Updated README.md file.
* reverted back original script of train_dreambooth.py
* reverted back original script of train_dreambooth.py
* left one blank line at the eof
* reverted back setup.py
* reverted back setup.py
* added same logic for when parameters for prior preservation are used without enabling the flag while using concept_list parameter.
* Ran black formatter.
* fixed a few strings
* fixed import sort with isort and removed fstrings without placeholder
* fixed import order with ruff (since with isort wasn't ok)
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Correct controlnet out of list error
* Apply suggestions from code review
* correct tests
* correct tests
* fix
* test all
* Apply suggestions from code review
* test all
* test all
* Apply suggestions from code review
* Apply suggestions from code review
* fix more tests
* Fix more
* Apply suggestions from code review
* finish
* Apply suggestions from code review
* Update src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
* finish
