GitLab

* chore: fix typos

* Update src/diffusers/pipelines/shap_e/renderer.py
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>

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Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>

* initial script

* formatting

* prior trainer wip

* add efficient_net_encoder

* add CLIPTextModel

* add prior ema support

* optimizer

* fix typo

* add dataloader

* prompt_embeds and image_embeds

* intial training loop

* fix output_dir

* fix add_noise

* accelerator check

* make effnet_transforms dynamic

* fix training loop

* add validation logging

* use loaded text_encoder

* use PreTrainedTokenizerFast

* load weigth from pickle

* save_model_card

* remove unused file

* fix typos

* save prior pipeilne in its own folder

* fix imports

* fix pipe_t2i

* scale image_embeds

* remove snr_gamma

* format

* initial lora prior training

* log_validation and save

* initial gradient working

* remove save/load hooks

* set set_attn_processor on prior_prior

* add lora script

* typos

* use LoraLoaderMixin for prior pipeline

* fix usage

* make fix-copies

* yse repo_id

* write_lora_layers is a staitcmethod

* use defualts

* fix defaults

* undo

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

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

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

* Update src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py

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

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

* add graident checkpoint support to prior

* gradient_checkpointing

* formatting

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update src/diffusers/loaders.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/wuerstchen/text_to_image/train_text_to_image_prior.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* use default unet and text_encoder

* fix test

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* fix: unconditional generation example

* fix: float in loss.

* apply styling.

* use loralinear instead of depecrecated lora attn procs.

* fix parameters()

* fix saving

* add back support for add kv proj.

* fix: param accumul,ation.

* propagate the changes.

* Added  mark_step for sdxl to run with pytorch xla. Also updated README with instructions for xla

* adding soft dependency on torch_xla

* fix some styling

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

fix problem of 'accelerator.is_main_process' to run in mutiple GPUs or NPUs
Co-authored-by: jiaqiw <wangjiaqi50@huawei.com>

Update requirements_sdxl.txt

Add missing 'datasets'
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update train_custom_diffusion.py

* make style

* Empty-Commit

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

Fix [core/GLIGEN]: TypeError when iterating over 0-d tensor with In-painting mode when EulerAncestralDiscreteScheduler is used (#5305)

* fix(gligen_inpaint_pipeline): 🐛

 Wrap the timestep() 0-d tensor in a list to convert to 1-d tensor. This avoids the TypeError caused by trying to directly iterate over a 0-dimensional tensor in the denoising stage

* test(gligen/gligen_text_image): unit test using the EulerAncestralDiscreteScheduler

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Co-authored-by: zhen-hao.chu <zhen-hao.chu@vitrox.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Min-SNR Gamma: correct the fix for SNR weighted loss in v-prediction by adding 1 to SNR rather than the resulting loss weights
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* [SDXL Flax] Add research folder

* Add co-author
Co-authored-by: Juan Acevedo <jfacevedo@google.com>

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Co-authored-by: Juan Acevedo <jfacevedo@google.com>

* fix all

* make fix copies

* make fix copies

* fix: how print training resume logs.

* propagate changes to text-to-image scripts.

* propagate changes to instructpix2pix.

* propagate changes to dreambooth

* propagate changes to custom diffusion and instructpix2pix

* propagate changes to kandinsky

* propagate changes to textual inv.

* debug

* fix: checkpointing.

* debug

* debug

* debug

* back to the square

* debug

* debug

* change condition order.

* debug

* debug

* debug

* debug

* revert to original

* clean

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

Update README_sdxl.md

* Update run_onnx_controlnet.py

* Update run_tensorrt_controlnet.py

add compute_snr() to training utils.

* merge with main

* fix flax example

* fix onnx example

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

* Timestep bias for fine-tuning SDXL

* Adjust parameter choices to include "range" and reword the help statements

* Condition our use of weighted timesteps on the value of timestep_bias_strategy

* style

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

SDXL microconditioning documentation should indicate the correct default order of parameters, so that developers know (#5155)

* SDXL microconditioning documentation should indicate the correct default order of parameters, so that developers know

* SDXL microconditioning documentation should indicate the correct default order of parameters, so that developers know

* empty

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

* Fix type annotation on Scheduler.from_pretrained

* Fix type annotation on PIL.Image

* min-SNR gamma for Dreambooth training

* Align the mse_loss_weights style with SDXL training example

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

Co-authored-by: bghira <bghira@users.github.com>

* Resolve v_prediction issue for min-SNR gamma weighted loss function

* Combine MSE loss calculation of epsilon and velocity, with a note about the application of the epsilon code to sample prediction

* style

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

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

Fixed `get_word_inds` mistake/typo in P2P community pipeline

The function `get_word_inds` was taking a string of text and either a word (str) or a word index (int) and returned the indices of token(s) the word would be encoded to.

However, there was a typo, in which in the second `if` branch the word was checked to be a `str` **again**, not `int`, which resulted in an [example code from the docs](https://github.com/huggingface/diffusers/tree/main/examples/community#prompt2prompt-pipeline) to result in an error

* Implement `CustomDiffusionAttnProcessor2_0`

* Doc-strings and type annotations for `CustomDiffusionAttnProcessor2_0`. (#1)

* Update attnprocessor.md

* Update attention_processor.py

* Interops for `CustomDiffusionAttnProcessor2_0`.

* Formatted `attention_processor.py`.

* Formatted doc-string in `attention_processor.py`

* Conditional CustomDiffusion2_0 for training example.

* Remove unnecessary reference impl in comments.

* Fix `save_attn_procs`.

* [LoRA] fix typo in attention_processor.py

fixes #5062

* make style

* make fix-copies, logger comented for torch compile

* convert tensorrt controlnet

* Fix code quality

* Fix code quality

* Fix code quality

* Fix code quality

* Fix code quality

* Fix code quality

* Fix number controlnet condition

* Add convert SD XL to onnx

* Add convert SD XL to tensorrt

* Add convert SD XL to tensorrt

* Add examples in comments

* Add examples in comments

* Add test onnx controlnet

* Add tensorrt test

* Remove copied

* Move file test to examples/community

* Remove script

* Remove script

* Remove text

* Fix import

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Co-authored-by: dotieuthien <thien.do@mservice.com.vn>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Add files via upload
Co-authored-by: Shahmatov Arseniy <62886550+cene555@users.noreply.github.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* [Release 0.21] Bump version

* fix & remove

* fix more

* fix all, upload

* Initial commit P2P

* Replaced CrossAttention, added test skeleton

* bug fixes

* Updated docstring

* Removed unused function

* Created tests

* improved tests

- made fast inference tests faster
- corrected image shape assertions

* Corrected expected output shape in tests

* small fix: test inputs

* Update tests

- used conditional unet2d
- set expected image slices
- edit_kwargs are now not popped, so pipe can be run multiple times

* Fixed bug in int tests

* Fixed tests

* Linting

* Create prompt2prompt.md

* Added to docs toc

* Ran make fix-copies

* Fixed code blocks in docs

* Using same interface as StableDiffusionPipeline

* Fixed small test bug

* Added all options SDPipeline.__call_ has

* Fixed docstring; made __call__ like in SD

* Linting

* Added test for multiple prompts

* Improved docs

* Incorporated feedback

* Reverted formatting on unrelated files

* Moved prompt2prompt to community

- Moved prompt2prompt pipeline from main to community
- Deleted tests
- Moved documentation to community and shorted it

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

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

* examples fix t2i training

* make style

* 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

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

add: tests for t2i adapter training.