GitLab

* feat: explicitly tag to diffusers when using push_to_hub

* remove tags.

* reset repo.

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

* fix: tests

* fix: push_to_hub behaviour for tagging from save_pretrained

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

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

* import fixes.

* add library name to existing model card.

* add: standalone test for generate_model_card

* fix tests for standalone method

* moved library_name to a better place.

* merge create_model_card and generate_model_card.

* fix test

* address lucain's comments

* fix return identation

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

* address further comments.

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

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

* 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

* refactor ip-adapter-imageproj, gligen

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

update

* utils and test modifications to enable device agnostic testing

* device for manual seed in unet1d

* fix generator condition in vae test

* consistency changes to testing

* make style

* add device agnostic testing changes to source and one model test

* make dtype check fns private, log cuda fp16 case

* remove dtype checks from import utils, move to testing_utils

* adding tests for most model classes and one pipeline

* fix vae import

* Support IP-Adapter Plus

* fix format

* restore before black format

* restore before black format

* generic

* Refactor PerceiverAttention

* format

* fix test and refactor PerceiverAttention

* generic encode_image

* keep attention implementation

* merge tests

* encode_image backward compatible

* code quality

* fix controlnet inpaint pipeline

* refactor FFN

* refactor FFN

---------
Co-authored-by: YiYi Xu <yixu310@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>

* add comments to explain the code better

* add comments to explain the code better

* add comments to explain the code better

* add comments to explain the code better

* add comments to explain the code better

* fix more

* fix more

* fix more

* fix more

* fix more

* fix more

* add ip-adapter


---------
Co-authored-by: okotaku <to78314910@gmail.com>
Co-authored-by: sayakpaul <spsayakpaul@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.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

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

* uP

* Update src/diffusers/models/consistency_decoder_vae.py

* uP

* uP

* consistency decoder

* rename

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

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

* uP

* Apply suggestions from code review

* uP

* uP

* uP

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

* fix model xformers test

* update

* draft design

* clean up

* clean up

* clean up

* clean up

* clean up

* clean  up

* clean up

* clean up

* clean up

* update pipeline

* clean up

* clean up

* clean up

* add tests

* change motion block

* clean up

* clean up

* clean up

* update

* update

* update

* update

* update

* update

* update

* update

* clean up

* update

* update

* update model test

* update

* update

* update

* update

* make style

* update

* fix embeddings

* update

* merge upstream

* max fix copies

* fix bug

* fix mistake

* add docs

* update

* clean up

* update

* clean up

* clean up

* fix docstrings

* fix docstrings

* update

* update

* clean  up

* update

* Added args, kwargs to ```U

* Add UNetMidBlock2D as a supported mid block type

* Fix extra init input for UNetMidBlock2D, change allowed types for Mid-block init

* Update unet_2d_condition.py

* Update unet_2d_condition.py

* Update unet_2d_condition.py

* Update unet_2d_condition.py

* Update unet_2d_condition.py

* Update unet_2d_condition.py

* Update unet_2d_condition.py

* Update unet_2d_condition.py

* Update unet_2d_blocks.py

* Update unet_2d_blocks.py

* Update unet_2d_blocks.py

* Update unet_2d_condition.py

* Update unet_2d_blocks.py

* Updated docstring, increased check strictness

Updated the docstring for ```UNet2DConditionModel``` to include ```reverse_transformer_layers_per_block``` and updated checking for nested list type ```transformer_layers_per_block```

* Add basic shape-check test for asymmetrical unets

* Update src/diffusers/models/unet_2d_blocks.py

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

* Update unet_2d_condition.py

Remove blank space

* Update unet_2d_condition.py

Changed docstring for `mid_block_type`

* Fixed docstring and wrong default value

* Reformat with black

* Reformat with necessary commands

* Add UNetMidBlockFlat to versatile_diffusion/modeling_text_unet.py to ensure consistency

* Removed args, kwargs, use on mid-block type

* Make fix-copies

* Update src/diffusers/models/unet_2d_condition.py

Wrap into single line
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* make fix-copies

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

* move xformers to dedicated runner

* fix

* remove ptl from test runner images

* fix other tests

* fix tests

* fix tests

* Update tests/pipelines/shap_e/test_shap_e_img2img.py

* Update tests/pipelines/shap_e/test_shap_e_img2img.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix upstream merge mistake

* fix tests:

* test fix

* Update tests/lora/test_lora_layers_old_backend.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/lora/test_lora_layers_old_backend.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

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

* test fix

* fix tests

* fix report name

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

* [LoRA] Centralize LoRA tests

* [LoRA] Centralize LoRA tests

* [LoRA] Centralize LoRA tests

* [LoRA] Centralize LoRA tests

* [LoRA] Centralize LoRA tests

* Add attn_groups argument to UNet2DMidBlock2D to control theinternal Attention block's GroupNorm.

* Add docstring for attn_norm_num_groups in UNet2DModel.

* Since the test UNet config uses resnet_time_scale_shift == 'scale_shift', also set attn_norm_num_groups to 32.

* Add test for attn_norm_num_groups to UNet2DModelTests.

* Fix expected slices for slow tests.

* Also fix tolerances for slow tests.

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

* fix lora fuse unfuse

* add same changes to loaders.py

* add test

---------
Co-authored-by: multimodalart <joaopaulo.passos+multimodal@gmail.com>

[Tests and Docs] Add a test on serializing pipelines with components containing fused LoRA modules (#4962)

* add: test to ensure pipelines can be saved with fused lora modules.

* add docs about serialization with fused lora.

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

* Empty-Commit

* Update docs/source/en/training/lora.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* 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>

* Revert "Temp Revert "[Core] better support offloading when side loading is enabled… (#4927)"

This reverts commit 2ab17049.

* tests: install accelerate from main

Revert "[Core] better support offloading when side loading is enabled. (#4855)"

This reverts commit e4b8e792.

* [Test] Reduce CPU memory

* [Test] Reduce CPU memory

* better support offloading when side loading is enabled.

* load_textual_inversion

* better messaging for textual inversion.

* fixes

* address PR feedback.

* sdxl support.

* improve messaging

* recursive removal when cpu sequential offloading is enabled.

* add: lora tests

* recruse.

* add: offload tests for textual inversion.

* throw warning when more than one lora is attempted to be fused.

* introduce support of lora scale during fusion.

* change test name

* changes

* change to _lora_scale

* lora_scale to call whenever applicable.

* debugging

* lora_scale additional.

* cross_attention_kwargs

* lora_scale -> scale.

* lora_scale fix

* lora_scale in patched projection.

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* styling.

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* remove unneeded prints.

* remove unneeded prints.

* assign cross_attention_kwargs.

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* clean up.

* refactor scale retrieval logic a bit.

* fix nonetypw

* fix: tests

* add more tests

* more fixes.

* figure out a way to pass lora_scale.

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

* unify the retrieval logic of lora_scale.

* move adjust_lora_scale_text_encoder to lora.py.

* introduce dynamic adjustment lora scale support to sd

* fix up copies

* Empty-Commit

* add: test to check fusion equivalence on different scales.

* handle lora fusion warning.

* make lora smaller

* make lora smaller

* make lora smaller

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

* 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>

* Fix Unfuse Lora

* add tests

* Fix more

* Fix more

* Fix all

* make style

* make style

* Fuse loras

* initial implementation.

* add slow test one.

* styling

* add: test for checking efficiency

* print

* position

* place model offload correctly

* style

* style.

* unfuse test.

* final checks

* remove warning test

* remove warnings altogether

* debugging

* tighten up tests.

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* denugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debuging

* debugging

* debugging

* debugging

* suit up the generator initialization a bit.

* remove print

* update assertion.

* debugging

* remove print.

* fix: assertions.

* style

* can generator be a problem?

* generator

* correct tests.

* support text encoder lora fusion.

* tighten up tests.

---------
Co-authored-by: Sayak Paul <spsayakpaul@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

* Fix last ben sdxl lora

* Correct typo

* make style

use max diff to compare model outputs

* Fix AutoencoderTiny encoder scaling convention

  * Add [-1, 1] -> [0, 1] rescaling to EncoderTiny

  * Move [0, 1] -> [-1, 1] rescaling from AutoencoderTiny.decode to DecoderTiny
    (i.e. immediately after the final conv, as early as possible)

  * Fix missing [0, 255] -> [0, 1] rescaling in AutoencoderTiny.forward

  * Update AutoencoderTinyIntegrationTests to protect against scaling issues.
    The new test constructs a simple image, round-trips it through AutoencoderTiny,
    and confirms the decoded result is approximately equal to the source image.
    This test checks behavior with and without tiling enabled.
    This test will fail if new AutoencoderTiny scaling issues are introduced.

  * Context: Raw TAESD weights expect images in [0, 1], but diffusers'
    convention represents images with zero-centered values in [-1, 1],
    so AutoencoderTiny needs to scale / unscale images at the start of
    encoding and at the end of decoding in order to work with diffusers.

* Re-add existing AutoencoderTiny test, update golden values

* Add comments to AutoencoderTiny.forward

* Impl tae slicing and tiling

* add tae tiling test

* add parameterized test

* formatted code

* fix failed test

* style docs

* make safetensors default

* set default save method as safetensors

* update tests

* update to support saving safetensors

* update test to account for safetensors default

* update example tests to use safetensors

* update example to support safetensors

* update unet tests for safetensors

* fix failing loader tests

* fix qc issues

* fix pipeline tests

* fix example test

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