GitLab

* add fix

* add fix

* Apply style fixes

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* post release

* update

* fix deprecations

* remaining

* update

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* update

* update

* update

* update

* remove custom scheduler

* update requirements.txt

* log_validation with mixed precision

* add intermediate embeddings saving when checkpointing is enabled

* remove comment

* fix validation

* add unwrap_model for accelerator, torch.no_grad context for validation, fix accelerator.accumulate call in advanced script

* revert unwrap_model change temp

* add .module to address distributed training bug + replace accelerator.unwrap_model with unwrap model

* changes to align advanced script with canonical script

* make changes for distributed training + unify unwrap_model calls in advanced script

* add module.dtype fix to dreambooth script

* unify unwrap_model calls in dreambooth script

* fix condition in validation run

* mixed precision

* Update examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* smol style change

* change autocast

* Apply style fixes

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* initial

* Update examples/dreambooth/train_dreambooth_lora_sdxl.py
Co-authored-by: hlky <hlky@hlky.ac>

* update

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Co-authored-by: hlky <hlky@hlky.ac>

[train_dreambooth_lora.py] Fix the LR Schedulers when `num_train_epochs` is passed in a distributed training env (#10973)

* updated train_dreambooth_lora to fix the LR schedulers for `num_train_epochs` in distributed training env

* fixed formatting

* remove trailing newlines

* fixed style error

* Fix seed initialization to handle args.seed = 0 correctly

* Apply style fixes

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Fix fp16 bug
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* feat: lora support for Lumina2.

* fix-copies.

* updates

* updates

* docs.

* fix

* add: training script.

* tests

* updates

* updates

* major updates.

* updates

* fixes

* docs.

* updates

* updates

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* NPU Adaption for Sanna

* [bugfix]NPU Adaption for Sanna

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* [training] Convert to ImageFolder script

* make

* NPU Adaption for Sanna


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Remove the FP32 Wrapper
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* enable dreambooth_lora on other devices
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* enable xpu
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* check cuda device before empty cache
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix comment
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* import free_memory
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

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change licensing to 2025 from 2024.

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* add ds support to lora sd3.
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* style.

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* post release 0.32.0

* stylew

* sana lora training tests and misc.

* remove push to hub

* Update examples/dreambooth/train_dreambooth_lora_sana.py
Co-authored-by: Aryan <aryan@huggingface.co>

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Update README_sana.md to update the default model

fix: reamde -> readme

* feat: lora support for SANA.

* make fix-copies

* rename test class.

* attention_kwargs -> cross_attention_kwargs.

* Revert "attention_kwargs -> cross_attention_kwargs."

This reverts commit 23433bf9bccc12e0f2f55df26bae58a894e8b43b.

* exhaust 119 max line limit

* sana lora fine-tuning script.

* readme

* add a note about the supported models.

* Apply suggestions from code review
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* style

* docs for attention_kwargs.

* remove lora_scale from pag pipeline.

* copy fix

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* fix min-snr implementation

https://github.com/kohya-ss/sd-scripts/blob/main/library/custom_train_functions.py#L66



* Update train_dreambooth.py

fix variable name mse_loss_weights

* fix divisor

* make style

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Add files via upload

* smol change to fix checkpoint saving & resuming (as done in train_dreambooth_sd3.py)

* style

* modify comment to explain reasoning behind hidden size check

* memory improvement as done here: https://github.com/huggingface/diffusers/pull/9829



* fix bug

* fix bug

* style

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* fix use_dora

* fix style and quality

* fix use_dora with peft version

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* updated encode prompt and clip encod prompt


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* Improve NPU performance

* Improve NPU performance

* Improve NPU performance

* Improve NPU performance

* [bugfix] bugfix for npu free memory

* [bugfix] bugfix for npu free memory

* [bugfix] bugfix for npu free memory

* Reduce memory cost for flux training process

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Co-authored-by: 蒋硕 <jiangshuo9@h-partners.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Handling mixed precision and add unwarp
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

* NPU implementation for FLUX

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* use the lr when using 8bit adam.

* remove lr as we pack it in params_to_optimize.

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* make lora target modules configurable and change the default

* style

* make lora target modules configurable and change the default

* fix bug when using prodigy and training te

* fix mixed precision training as  proposed in https://github.com/huggingface/diffusers/pull/9565

 for full dreambooth as well

* add test and notes

* style

* address sayaks comments

* style

* fix test

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* configurable layers

* configurable layers

* update README

* style

* add test

* style

* add layer test, update readme, add nargs

* readme

* test style

* remove print, change nargs

* test arg change

* style

* revert nargs 2/2

* address sayaks comments

* style

* address sayaks comments

[Fix] remove setting lr for T5 text encoder when using prodigy in flux dreambooth lora script (#9473)

* fix: removed setting of text encoder lr for T5 as it's not being tuned

* fix: removed setting of text encoder lr for T5 as it's not being tuned

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* flux pipline: readability enhancement.

* improve readme

* style

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* post-release

* style

* add latent caching + smol updates

* update license

* replace with free_memory

* add --upcast_before_saving to allow saving transformer weights in lower precision

* fix models to accumulate

* fix mixed precision issue as proposed in https://github.com/huggingface/diffusers/pull/9565



* smol update to readme

* style

* fix caching latents

* style

* add tests for latent caching

* style

* fix latent caching

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* fixed issue #9350, Tensor is deprecated

* ran make style