GitLab

* 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

* add audioldm2 tts

* change gpt2 max new tokens

* remove unnecessary pipeline and class

* add TTS to AudioLDM2Pipeline

* add TTS docs

* delete unnecessary file

* remove unnecessary import

* add audioldm2 slow testcase

* fix code quality

* remove AudioLDMLearnablePositionalEmbedding

* add variable check vits encoder

* add use_learned_position_embedding

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

* 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.

change to 2024

* move transformer scripts to transformers modules

* move transformer model test

* move prior transformer test to  directory

* fix doc path

* correct doc path

* add: __init__.py

* move unets to  module 🦋

* parameterize unet-level import.

* fix flax unet2dcondition model import

* models __init__

* mildly depcrecating models.unet_2d_blocks in favor of models.unets.unet_2d_blocks.

* noqa

* correct depcrecation behaviour

* inherit from the actual classes.

* Empty-Commit

* backwards compatibility for unet_2d.py

* backward compatibility for unet_2d_condition

* bc for unet_1d

* bc for unet_1d_blocks

* handle rest of the stuff related to deprecated lora stuff.

* fix: copies

* don't modify the uNet in-place.

* fix: temporal autoencoder.

* manually remove lora layers.

* don't copy unet.

* alright

* remove lora attn processors from unet3d

* fix: unet3d.

* styl

* Empty-Commit

* fix xformers lora

* improve

* fix

* proposal for flaky tests

* more precision fixes

* move more tests to use cosine distance

* more test fixes

* clean up

* use default attn

* clean up

* update expected value

* make style

* make style

* Apply suggestions from code review

* Update src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

* make style

* fix failing tests

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

* [LoRA Attn] Refactor LoRA attn

* correct for network alphas

* fix more

* fix more tests

* fix more tests

* Move below

* Finish

* better version

* correct serialization format

* fix

* fix more

* fix more

* fix more

* Apply suggestions from code review

* Update src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

* deprecation

* relax atol for slow test slighly

* Finish tests

* make style

* make style

* [AudioLDM2] Doc fixes

* update docstrings

* fix unet docstring

* 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>

* from audioldm

* unet down + mid

* vae, clap, flan-t5

* start sequence audio mae

* iterate on audioldm encoder

* finish encoder

* finish weight conversion

* text pre-processing

* gpt2 pre-processing

* fix projection model

* working

* unet equivalence

* finish in base

* add unet cond

* finish unet

* finish custom unet

* start clean-up

* revert base unet changes

* refactor pre-processing

* tests: from audioldm

* fix some tests

* more fixes

* iterate on tests

* make fix copies

* harden fast tests

* slow integration tests

* finish tests

* update checkpoint

* update copyright

* docs

* remove outdated method

* add docstring

* make style

* remove decode latents

* enable cpu offload

* (text_encoder_1, tokenizer_1) -> (text_encoder, tokenizer)

* more clean up

* more refactor

* build pr docs

* Update docs/source/en/api/pipelines/audioldm2.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* small clean

* tidy conversion

* update for large checkpoint

* generate -> generate_language_model

* full clap model

* shrink clap-audio in tests

* fix large integration test

* fix fast tests

* use generation config

* make style

* update docs

* finish docs

* finish doc

* update tests

* fix last test

* syntax

* finalise tests

* refactor projection model in prep for TTS

* fix fast tests

* style

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

* Add GLIGEN implementation

* GLIGEN: Fix code quality check failures

* GLIGEN: Fix Import block un-sorted or un-formatted failures

* GLIGEN: Fix check_repository_consistency failures

* GLIGEN: Add 'PositionNet' to versatile_diffusion/modeling_text_unet.py

* GLIGEN: check_repository_consistency: fix 'copy does not match' error

* GLIGEN: Fix review comments (1)

* GLIGEN: Fix E721 Do not compare types, use `isinstance()` failures

* GLIGEN : Ensure _encode_prompt() copy matches to StableDiffusionPipeline

* GLIGEN: Fix ruff E721 failure in unidiffuser/test_unidiffuser.py

* GLIGEN: doc_builder: restyle pipeline_stable_diffusion_gligen.py

* GIGLEN: reset files unrelated to gligen

* GLIGEN: Fix documentation comments (1)

* GLIGEN: Fix review comments (2)

* GLIGEN: Added FastTest

* GLIGEN: Fix review comments (3)

* grad checkpointing

* fix make fix-copies

* fix

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

* correct

* correct blocks

* finish

* finish

* finish

* Apply suggestions from code review

* fix

* up

* up

* up

* Update examples/dreambooth/README_sdxl.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Apply suggestions from code review

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

* add automatic licensing.

* debugging

* debugging

* more debugging

* more debugging.

* run make fix-copies.

* change to default tracker.

* Quick implementation of t2i-adapter

Load adapter module with from_pretrained

Prototyping generalized adapter framework

Writeup doc string for sideload framework(WIP) + some minor update on implementation

Update adapter models

Remove old adapter optional args in UNet

Add StableDiffusionAdapterPipeline unit test

Handle cpu offload in StableDiffusionAdapterPipeline

Auto correct coding style

Update model repo name to "RzZ/sd-v1-4-adapter-pipeline"

Refactor MultiAdapter to better compatible with config system

Export MultiAdapter

Create pipeline document template from controlnet

Create dummy objects

Supproting new AdapterLight model

Fix StableDiffusionAdapterPipeline common pipeline test

[WIP] Update adapter pipeline document

Handle num_inference_steps in StableDiffusionAdapterPipeline

Update definition of Adapter "channels_in"

Update documents

Apply code style

Fix doc typo and merge error

Update doc string and example

Quality of life improvement

Remove redundant code and file from prototyping

Remove unused pageage

Remove comments

Fix title

Fix typo

Add conditioning scale arg

Bring back old implmentation

Offload sideload

Add supply info on document

Update src/diffusers/models/adapter.py
Co-authored-by: Will Berman <wlbberman@gmail.com>

Update MultiAdapter constructor

Swap out custom checkpoint and update pipeline constructor

Update docment

Apply suggestions from code review
Co-authored-by: Will Berman <wlbberman@gmail.com>

Correcting style

Following single-file policy

Update auto size in image preprocess func

Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_adapter.py
Co-authored-by: Will Berman <wlbberman@gmail.com>

fix copies

Update adapter pipeline behavior

Add adapter_conditioning_scale doc string

Add the missing doc string

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

Fix few bugs from suggestion

Handle L-mode PIL image as control image

Rename to differentiate adapter resblock

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

Fix typo

Update adapter parameter name

Update test case and code style

Fix copies

Fix typo

Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_adapter.py
Co-authored-by: Will Berman <wlbberman@gmail.com>

Update Adapter class name

Add checkpoint converting script

Fix style

Fix-copies

Remove dev script

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

Updates for parameter rename

Fix convert_adapter

remove main

fix diff

more

refactoring

more

more

small fixes

refactor

tests

more slow tests

more tests

Update docs/source/en/api/pipelines/overview.mdx
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

add community contributor to docs

Update docs/source/en/api/pipelines/stable_diffusion/adapter.mdx
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Update docs/source/en/api/pipelines/stable_diffusion/adapter.mdx
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Update docs/source/en/api/pipelines/stable_diffusion/adapter.mdx
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Update docs/source/en/api/pipelines/stable_diffusion/adapter.mdx
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Update docs/source/en/api/pipelines/stable_diffusion/adapter.mdx
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

fix

remove from_adapters

license

paper link

docs

more url fixes

more docs

fix

fixes

fix

fix

* fix sample inplace add

* additional_kwargs -> additional_residuals

* move t2i adapter pipeline to own module

* preprocess -> _preprocess_adapter_image

* add TencentArc to license

* fix example code links

* add image converter and fix example doc string

* fix links

* clearer additional residual application

---------
Co-authored-by: HimariO <dsfhe49854@gmail.com>

* disable num attenion heads

* finish

* 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 491bc9f5a640bbf46a97a8e52d6eff7e70eb8e4b.

* 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>

* add modelmixin and unets

* remove old model page

* minor fixes

* fix unet2dcondition

* add vqmodel and autoencoderkl

* add rest of models

* fix autoencoderkl path

* fix toctree

* fix toctree again

* apply feedback

* apply feedback

* fix copies

* fix controlnet copy

* fix copies

* relax tolerance slightly

* correct incorrect naming

* correct namingc

* correct more

* Apply suggestions from code review

* Fix more

* Correct more

* correct incorrect naming

* Update src/diffusers/models/controlnet.py

* Correct flax

* Correct renaming

* Correct blocks

* Fix more

* Correct more

* mkae style

* mkae style

* mkae style

* mkae style

* mkae style

* Fix flax

* mkae style

* rename

* rename

* rename attn head dim to attention_head_dim

* correct flax

* make style

* improve

* Correct more

* make style

* fix more

* mkae style

* Update src/diffusers/models/controlnet_flax.py

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

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

* move activation dispatches into helper function

* tests

Make sure we also change the config when setting `encoder_hid_dim_type=="text_proj"` and allow xformers (#3615)

* fix if

* make style

* make style

* add tests for xformers

* make style

* update

add kandinsky2.1

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Ayush Mangal <43698245+ayushtues@users.noreply.github.com>
Co-authored-by: ayushmangal <ayushmangal@microsoft.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Cross-attention masks

prefer qualified symbol, fix accidental Optional

prefer qualified symbol in AttentionProcessor

prefer qualified symbol in embeddings.py

qualified symbol in transformed_2d

qualify FloatTensor in unet_2d_blocks

move new transformer_2d params attention_mask, encoder_attention_mask to the end of the section which is assumed (e.g. by functions such as checkpoint()) to have a stable positional param interface. regard return_dict as a special-case which is assumed to be injected separately from positional params (e.g. by create_custom_forward()).

move new encoder_attention_mask param to end of CrossAttn block interfaces and Unet2DCondition interface, to maintain positional param interface.

regenerate modeling_text_unet.py

remove unused import

unet_2d_condition encoder_attention_mask docs
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

versatile_diffusion/modeling_text_unet.py encoder_attention_mask docs
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

transformer_2d encoder_attention_mask docs
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

unet_2d_blocks.py: add parameter name comments
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

revert description. bool-to-bias treatment happens in unet_2d_condition only.

comment parameter names

fix copies, style

* encoder_attention_mask for SimpleCrossAttnDownBlock2D, SimpleCrossAttnUpBlock2D

* encoder_attention_mask for UNetMidBlock2DSimpleCrossAttn

* support attention_mask, encoder_attention_mask in KCrossAttnDownBlock2D, KCrossAttnUpBlock2D, KAttentionBlock. fix binding of attention_mask, cross_attention_kwargs params in KCrossAttnDownBlock2D, KCrossAttnUpBlock2D checkpoint invocations.

* fix mistake made during merge conflict resolution

* regenerate versatile_diffusion

* pass time embedding into checkpointed attention invocation

* always assume encoder_attention_mask is a mask (i.e. not a bias).

* style, fix-copies

* add tests for cross-attention masks

* add test for padding of attention mask

* explain mask's query_tokens dim. fix explanation about broadcasting over channels; we actually broadcast over query tokens

* support both masks and biases in Transformer2DModel#forward. document behaviour

* fix-copies

* delete attention_mask docs on the basis I never tested self-attention masking myself. not comfortable explaining it, since I don't actually understand how a self-attn mask can work in its current form: the key length will be different in every ResBlock (we don't downsample the mask when we downsample the image).

* review feedback: the standard Unet blocks shouldn't pass temb to attn (only to resnet). remove from KCrossAttnDownBlock2D,KCrossAttnUpBlock2D#forward.

* remove encoder_attention_mask param from SimpleCrossAttn{Up,Down}Block2D,UNetMidBlock2DSimpleCrossAttn, and mask-choice in those blocks' #forward, on the basis that they only do one type of attention, so the consumer can pass whichever type of attention_mask is appropriate.

* put attention mask padding back to how it was (since the SD use-case it enabled wasn't important, and it breaks the original unclip use-case). disable the test which was added.

* fix-copies

* style

* fix-copies

* put encoder_attention_mask param back into Simple block forward interfaces, to ensure consistency of forward interface.

* restore passing of emb to KAttentionBlock#forward, on the basis that removal caused test failures. restore also the passing of emb to checkpointed calls to KAttentionBlock#forward.

* make simple unet2d blocks use encoder_attention_mask, but only when attention_mask is None. this should fix UnCLIP compatibility.

* fix copies

* gradient checkpointing bug fix

* bug fix; changes for reviews

* reformat

* reformat

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

* Fix more torch compile breaks

* add tests

* Fix all

* fix controlnet

* fix more

* Add Horace He as co-author.
>
>
Co-authored-by: Horace He <horacehe2007@yahoo.com>

* Add Horace He as co-author.
Co-authored-by: Horace He <horacehe2007@yahoo.com>

---------
Co-authored-by: Horace He <horacehe2007@yahoo.com>

* fix more

* Fix more

* fix more

* Apply suggestions from code review

* fix

* make style

* make fix-copies

* fix

* make sure torch compile

* Clean

* fix test

* removed unnecessary parameters from get_up_block and get_down_block functions

* adding resnet_skip_time_act, resnet_out_scale_factor and cross_attention_norm to get_up_block and get_down_block functions

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

* add

* clean

* up

* clean up more

* fix more tests

* Improve docs further

* improve

* more fixes docs

* Improve docs more

* Update src/diffusers/models/unet_2d_condition.py

* fix

* up

* update doc links

* make fix-copies

* add safety checker and watermarker to stage 3 doc page code snippets

* speed optimizations docs

* memory optimization docs

* make style

* add watermarking snippets to doc string examples

* make style

* use pt_to_pil helper functions in doc strings

* skip mps tests

* Improve safety

* make style

* new logic

* fix

* fix bad onnx design

* make new stable diffusion upscale pipeline model arguments optional

* define has_nsfw_concept when non-pil output type

* lowercase linked to notebook name

---------
Co-authored-by: William Berman <WLBberman@gmail.com>

This mimics the dtype cast for the standard time embeddings

Adding act fn config to the unet timestep class embedding and conv
activation.

The custom activation defaults to silu which is the default
activation function for both the conv act and the timestep class
embeddings so default behavior is not changed.

The only unet which use the custom activation is the stable diffusion
latent upscaler https://huggingface.co/stabilityai/sd-x2-latent-upscaler/blob/main/unet/config.json
(I ran a script against the hub to confirm).
The latent upscaler does not use the conv activation nor the timestep
class embeddings so we don't change its behavior.

* Better deprecation message

* Better deprecation message

* Better doc string

* Fixes

* fix more

* fix more

* Improve __getattr__

* correct more

* fix more

* fix

* Improve more

* more improvements

* fix more

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

* make style

* Fix all rest & add tests & remove old deprecation fns

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

add AttnAddedKVProcessor2_0 block

add group norm type to attention processor cross attention norm

This lets the cross attention norm use both a group norm block and a
layer norm block.

The group norm operates along the channels dimension
and requires input shape (batch size, channels, *) where as the layer norm with a single
`normalized_shape` dimension only operates over the least significant
dimension i.e. (*, channels).

The channels we want to normalize are the hidden dimension of the encoder hidden states.

By convention, the encoder hidden states are always passed as (batch size, sequence
length, hidden states).

This means the layer norm can operate on the tensor without modification, but the group
norm requires flipping the last two dimensions to operate on (batch size, hidden states, sequence length).

All existing attention processors will have the same logic and we can
consolidate it in a helper function `prepare_encoder_hidden_states`

prepare_encoder_hidden_states -> norm_encoder_hidden_states re: @patrickvonplaten

move norm_cross defined check to outside norm_encoder_hidden_states

add missing attn.norm_cross check

* unet time embedding activation function

* typo act_fn -> time_embedding_act_fn

* flatten conditional

* add only cross attention to simple attention blocks

* add test for only_cross_attention re: @patrickvonplaten

* mid_block_only_cross_attention better default

allow mid_block_only_cross_attention to default to
`only_cross_attention` when `only_cross_attention` is given
as a single boolean

* [Config] Fix config prints and save, load

* Only use potential nn.Modules for dtype and device

* Correct vae image processor

* make sure in_channels is not accessed directly

* make sure in channels is only accessed via config

* Make sure schedulers only access config attributes

* Make sure to access config in SAG

* Fix vae processor and make style

* add tests

* uP

* make style

* Fix more naming issues

* Final fix with vae config

* change more

`encoder_hid_dim` provides an additional projection for the input `encoder_hidden_states` from `encoder_hidden_dim` to `cross_attention_dim`