GitLab

docs: Update Imagen Video paper link in scheduler docstrings.

* refactor: enhance type hints and documentation in EulerDiscreteScheduler

Updated type hints for function parameters and return types in the EulerDiscreteScheduler class to improve code clarity and maintainability. Enhanced docstrings for several methods to provide clearer descriptions of their functionality and expected arguments. This includes specifying Literal types for certain parameters and ensuring consistent return type annotations across the class.

* refactor: enhance type hints and documentation across multiple schedulers

Updated type hints and improved docstrings in various scheduler classes, including CMStochasticIterativeScheduler, CosineDPMSolverMultistepScheduler, and others. This includes specifying parameter types, return types, and providing clearer descriptions of method functionalities. Notable changes include the addition of default values in the begin_index argument and enhanced explanations for noise addition methods. These improvements aim to enhance code clarity and maintainability across the scheduling module.

* refactor: update docstrings to clarify noise schedule construction

Revised docstrings across multiple scheduler classes to enhance clarity regarding the construction of noise schedules. Updated references to relevant papers, ensuring accurate citations for the methodologies used. This includes changes in DEISMultistepScheduler, DPMSolverMultistepInverseScheduler, and others, improving documentation consistency and readability.

* Enhance type hints and docstrings in scheduling_ddpm.py

- Added type hints for function parameters and return types across the DDPMScheduler class and related functions.
- Improved docstrings for clarity, including detailed descriptions of parameters and return values.
- Updated the alpha_transform_type and beta_schedule parameters to use Literal types for better type safety.
- Refined the _get_variance and previous_timestep methods with comprehensive documentation.

* Refactor docstrings and type hints in scheduling_ddpm.py

- Cleaned up whitespace in the rescale_zero_terminal_snr function.
- Enhanced the variance_type parameter in the DDPMScheduler class with improved formatting for better readability.
- Updated the docstring for the compute_variance method to maintain consistency and clarity in parameter descriptions and return values.

* Apply `make fix-copies`

* Refactor type hints across multiple scheduler files

- Updated type hints to include `Literal` for improved type safety in various scheduling files.
- Ensured consistency in type hinting for parameters and return types across the affected modules.
- This change enhances code clarity and maintainability.

* Improve docstrings and type hints in scheduling_ddim.py

- Add complete type hints for all function parameters
- Enhance docstrings to follow project conventions
- Add missing parameter descriptions

Fixes #9567

* Enhance docstrings and type hints in scheduling_ddim.py

- Update parameter types and descriptions for clarity
- Improve explanations in method docstrings to align with project standards
- Add optional annotations for parameters where applicable

* Refine type hints and docstrings in scheduling_ddim.py

- Update parameter types to use Literal for specific string options
- Enhance docstring descriptions for clarity and consistency
- Ensure all parameters have appropriate type annotations and defaults

* Apply review feedback on scheduling_ddim.py

- Replace "prevent singularities" with "avoid numerical instability" for better clarity
- Add backticks around `alpha_bar` variable name for consistent formatting
- Convert Imagen Video paper URLs to Hugging Face papers references

* Propagate changes using 'make fix-copies'

* Add missing Literal

update

* Use HF Papers

* Apply style fixes

---------
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* Fix beta and exponential sigmas + add tests

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

* Slight performance improvement to Euler

* Slight performance improvement to EDMEuler

* Slight performance improvement to FlowMatchHeun

* Slight performance improvement to KDPM2Ancestral

* Update KDPM2AncestralDiscreteSchedulerTest

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

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

Add beta sigmas / beta noise schedule

* exponential sigmas

* Apply suggestions from code review
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* make style

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

Fix grammatical error

* Merged isinstance calls to make the code simpler.

* Corrected formatting errors using ruff.

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

* find & replace all FloatTensors to Tensor

* apply formatting

* Update torch.FloatTensor to torch.Tensor in the remaining files

* formatting

* Fix the rest of the places where FloatTensor is used as well as in documentation

* formatting

* Update new file from FloatTensor to Tensor

* support custom sigmas and timesteps, dpm euler

---------
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Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Added get_velocity function to EulerDiscreteScheduler.

* Fix white space on blank lines

* Added copied from statement

* back to the original.

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

* Fix typos

* Fix typo in SVD.md

* fix

* fix

* add a tests

* fix

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

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

* Fix PyTorch's convention for inplace functions

* Fix import structure in __init__.py and update config loading logic in test_config.py

* Update configuration access

* Fix typos

* Trim trailing white spaces

* Fix typo in logger name

* Revert "Fix PyTorch's convention for inplace functions"

This reverts commit f65dc4afcb57ceb43d5d06389229d47bafb10d2d.

* Fix typo in step_index property description

* Revert "Update configuration access"

This reverts commit 8d44e870b8c1ad08802e3e904c34baeca1b598f8.

* Revert "Fix import structure in __init__.py and update config loading logic in test_config.py"

This reverts commit 2ad5e8bca25aede3b912da22bd57285b598fe171.

* Fix typos

* Fix typos

* Fix typos

* Fix a typo: tranform -> transform

* 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>

* 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

change to 2024

* correct

* Apply suggestions from code review

* make style

* EulerDiscreteScheduler add `rescale_betas_zero_snr`

* [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>

* 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>

* 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

* initial commit

* move modules to import struct

* add dummy objects and _LazyModule

* add lazy import to schedulers

* clean up unused imports

* lazy import on models module

* lazy import for schedulers module

* add lazy import to pipelines module

* lazy import altdiffusion

* lazy import audio diffusion

* lazy import audioldm

* lazy import consistency model

* lazy import controlnet

* lazy import dance diffusion ddim ddpm

* lazy import deepfloyd

* lazy import kandinksy

* lazy imports

* lazy import semantic diffusion

* lazy imports

* lazy import stable diffusion

* move sd output to its own module

* clean up

* lazy import t2iadapter

* lazy import unclip

* lazy import versatile and vq diffsuion

* lazy import vq diffusion

* helper to fetch objects from modules

* lazy import sdxl

* lazy import txt2vid

* lazy import stochastic karras

* fix model imports

* fix bug

* lazy import

* clean up

* clean up

* fixes for tests

* fixes for tests

* clean up

* remove import of torch_utils from utils module

* clean up

* clean up

* fix mistake import statement

* dedicated modules for exporting and loading

* remove testing utils from utils module

* fixes from  merge conflicts

* Update src/diffusers/pipelines/kandinsky2_2/__init__.py

* fix docs

* fix alt diffusion copied from

* fix check dummies

* fix more docs

* remove accelerate import from utils module

* add type checking

* make style

* fix check dummies

* remove torch import from xformers check

* clean up error message

* fixes after upstream merges

* dummy objects fix

* fix tests

* remove unused module import

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

add self.step_index

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

* clean scheduler mixin

* up to dpmsolvermultistep

* finish cleaning

* first draft

* fix overview table

* apply feedback

* update reference code

* 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 3f6b435d65dad3e5514cad2f5dd9e4419ca78e0b.

* add step_index to scale_model_input

* Revert "move the rescale prompt_embeds from prior_transformer to pipeline"

This reverts commit 5b5a8e6be918fefd114a2945ed89d8e8fa8be21b.

* 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>

* 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: https://github.com/huggingface/diffusers/blob/36d22d0709dc19776e3016fb3392d0f5578b0ab2/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>

* [2737]: Add Karras DPMSolverMultistepScheduler

* [2737]: Add Karras DPMSolverMultistepScheduler

* Add test

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

* fix: repo consistency.

* remove Copied from statement from the set_timestep method.

* fix: test

* Empty commit.
Co-authored-by: njindal <njindal@adobe.com>

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