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* 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
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* smol style change

* change autocast

* Apply style fixes

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

* Apply style fixes

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change licensing to 2025 from 2024.

* post release 0.32.0

* stylew

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



* fix bug

* fix bug

* style

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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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Handling mixed precision and add unwarp
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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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[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.

* post-release

* style

* fixed issue #9350, Tensor is deprecated

* ran make style

* fix: retain memory utility.

* fix

* quality

* free_memory.

* add ostris trainer to README & add cache latents of vae

* add ostris trainer to README & add cache latents of vae

* style

* readme

* add test for latent caching

* add ostris noise scheduler
https://github.com/ostris/ai-toolkit/blob/9ee1ef2a0a2a9a02b92d114a95f21312e5906e54/toolkit/samplers/custom_flowmatch_sampler.py#L95



* style

* fix import

* style

* fix tests

* style

* --change upcasting of transformer?

* update readme according to main

* keep only latent caching

* add configurable param for final saving of trained layers- --upcast_before_saving

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* use clear_objs_and_retain_memory from utilities

* style

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* Fix dtype error

* [bugfix] Fixed the issue on sd3 dreambooth training

* [bugfix] Fixed the issue on sd3 dreambooth training

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fix some fast gpu tests.

* post release

* fix quality

* add requirements + fix link to bghira's guide

* text ecnoder training fixes

* text encoder training fixes

* text encoder training fixes

* text encoder training fixes

* style

* add tests

* fix encode_prompt call

* style

* unpack_latents test

* fix lora saving

* remove default val for max_sequenece_length in encode_prompt

* remove default val for max_sequenece_length in encode_prompt

* style

* testing

* style

* testing

* testing

* style

* fix sizing issue

* style

* revert scaling

* style

* style

* scaling test

* style

* scaling test

* remove model pred operation left from pre-conditioning

* remove model pred operation left from pre-conditioning

* fix trainable params

* remove te2 from casting

* transformer to accelerator

* remove prints

* empty commit

* initial commit - dreambooth for flux

* update transformer to be FluxTransformer2DModel

* update training loop and validation inference

* fix sd3->flux docs

* add guidance handling, not sure if it makes sense(?)

* inital dreambooth lora commit

* fix text_ids in compute_text_embeddings

* fix imports of static methods

* fix pipeline loading in readme, remove auto1111 docs for now

* fix pipeline loading in readme, remove auto1111 docs for now, remove some irrelevant text_encoder_3 refs

* Update examples/dreambooth/train_dreambooth_flux.py
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* fix te2 loading and remove te2 refs from text encoder training

* fix tokenizer_2 initialization

* remove text_encoder training refs from lora script (for now)

* try with vae in bfloat16, fix model hook save

* fix tokenization

* fix static imports

* fix CLIP import

* remove text_encoder training refs (for now) from lora script

* fix minor bug in encode_prompt, add guidance def in lora script, ...

* fix unpack_latents args

* fix license in readme

* add "none" to weighting_scheme options for uniform sampling

* style

* adapt model saving - remove text encoder refs

* adapt model loading - remove text encoder refs

* initial commit for readme

* Update examples/dreambooth/train_dreambooth_lora_flux.py
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* Update examples/dreambooth/train_dreambooth_lora_flux.py
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* fix vae casting

* remove precondition_outputs

* readme

* readme

* style

* readme

* readme

* update weighting scheme default & docs

* style

* add text_encoder training to lora script, change vae_scale_factor value in both

* style

* text encoder training fixes

* style

* update readme

* minor fixes

* fix te params

* fix te params

---------
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* SD3 training fixes
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* rewrite noise addition part to respect the eqn.

* styler

* Update examples/dreambooth/README_sd3.md
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* Improve trainer model cards

* Update train_dreambooth_sd3.py

* Update train_dreambooth_lora_sd3.py

* add link to adapters loading doc

* Update train_dreambooth_lora_sd3.py

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* Fix typos & improve contributing page

* `make style && make quality`

* fix typos

* Fix typo

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* Update train_dreambooth_lora_sd3.py

* Update train_dreambooth_lora_sd3.py

* Update train_dreambooth_sd3.py

---------
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* change to logit_normal as the weighting scheme

* sensible default mote

refactor the density and weighting utilities.

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* initial commit

* default back to 77

* better text

* text correction

---------
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Fix the deletion of SD3 text encoders for Dreambooth/LoRA training if the text encoders are not being trained (#8536)

* Update train_dreambooth_sd3.py to fix TE garbage collection

* Update train_dreambooth_lora_sd3.py to fix TE garbage collection

---------
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* Add lognorm and cosmap weighting

* Implement mode sampling

* Update examples/dreambooth/train_dreambooth_lora_sd3.py

* Update examples/dreambooth/train_dreambooth_lora_sd3.py

* Update examples/dreambooth/train_dreambooth_sd3.py

* Update examples/dreambooth/train_dreambooth_sd3.py

* Update examples/dreambooth/train_dreambooth_sd3.py

* Update examples/dreambooth/train_dreambooth_lora_sd3.py

* Update examples/dreambooth/train_dreambooth_sd3.py

* Update examples/dreambooth/train_dreambooth_sd3.py

* Update examples/dreambooth/train_dreambooth_lora_sd3.py

* keep timestamp sampling fully on cpu

---------
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post release

* up

* add sd3

* update

* update

* add tests

* fix copies

* fix docs

* update

* add dreambooth lora

* add LoRA

* update

* update

* update

* update

* import fix

* update

* Update src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py
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* import fix 2

* update

* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* Update src/diffusers/models/autoencoders/autoencoder_kl.py
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* update

* update

* update

* fix ckpt id

* fix more ids

* update

* missing doc

* Update src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py
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* Update src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py
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* Update docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md
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* Update docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md
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* update'

* fix

* update

* Update src/diffusers/models/autoencoders/autoencoder_kl.py

* Update src/diffusers/models/autoencoders/autoencoder_kl.py

* note on gated access.

* requirements

* licensing

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* post release v0.28.0

* style

Fixed few docstrings according to the Google Style Guide.

* 7529 do not disable autocast for cuda devices

* Remove typecasting error check for non-mps platforms, as a correct autocast implementation makes it a non-issue

* add autocast fix to other training examples

* disable native_amp for dreambooth (sdxl)

* disable native_amp for pix2pix (sdxl)

* remove tests from remaining files

* disable native_amp on huggingface accelerator for every training example that uses it

* convert more usages of autocast to nullcontext, make style fixes

* make style fixes

* style.

* Empty-Commit

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* apple mps: training support for SDXL LoRA

* sdxl: support training lora, dreambooth, t2i, pix2pix, and controlnet on apple mps

---------
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