* Remove JAX/Flax support
Deletes all Flax models, schedulers, pipelines, and loading utilities,
the from_flax loading path, flax/jax packaging extras, Flax CI filters,
Flax training examples, and Flax content in translated docs.
Hub compatibility is preserved: Flax class names in old model_index.json
files are still stripped to their PyTorch equivalents, and *.msgpack
files in repos are still ignored during download.
* Drop Flax class-name remapping from model_index.json loading
Flax-saved checkpoint metadata is no longer recognized: class names like
FlaxStableDiffusionPipeline in model_index.json now fail to resolve
instead of being remapped to their PyTorch equivalents. *.msgpack files
in hub repos are still ignored during download.
* Restore Flax training examples
The example scripts are standalone and still work with diffusers<=0.39.x.
A warning note is added to each Flax README section pointing out the
version requirement now that the library itself no longer ships Flax.
* update
* Repoint scheduler default-config tests off the Flax fixture
* Repoint example training tests off the Flax fixture
* Fix examples not loading LoRA adapter weights from checkpoint
* Updated lora saving logic with accelerate save_model_hook and load_model_hook
* Formatted the changes using ruff
* import and upcasting changed
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Enable VAE hash to be able to change with args change. If not, train_dataset_with_embeddiings may have row number inconsistency with train_dataset_with_vae.
Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>
* Improve the performance and suitable for NPU
* Improve the performance and suitable for NPU computing
* Improve the performance and suitable for NPU
* Improve the performance and suitable for NPU
* Improve the performance and suitable for NPU
* Improve the performance and suitable for NPU
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Co-authored-by: 蒋硕 <jiangshuo9@h-partners.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* fix for lr scheduler in distributed training
* Fixed the recalculation of the total training step section
* Fixed lint error
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Trim all the trailing white space in the whole repo
* Remove unnecessary empty places
* make style && make quality
* Trim trailing white space
* trim
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Add support for _foreach operations and non-blocking to EMAModel
* default foreach to false
* add non-blocking EMA offloading to SD1.5 T2I example script
* fix whitespace
* move foreach to cli argument
* linting
* Update README.md re: EMA weight training
* correct args.foreach_ema
* add tests for foreach ema
* code quality
* add foreach to from_pretrained
* default foreach false
* fix linting
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: drhead <a@a.a>
* Modularized the train_lora_sdxl file
* Modularized the train_lora_sdxl file
* Modularized the train_lora_sdxl file
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Update requirements.txt
If the datasets library is old, it will not read the metadata.jsonl and the label will default to an integer of type int.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* 7879 - adjust documentation to use naruto dataset, since pokemon is now gated
* replace references to pokemon in docs
* more references to pokemon replaced
* Japanese translation update
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Co-authored-by: bghira <bghira@users.github.com>
* Add Ascend NPU support for SDXL fine-tuning and fix the model saving bug when using DeepSpeed.
* fix check code quality
* Decouple the NPU flash attention and make it an independent module.
* add doc and unit tests for npu flash attention.
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Co-authored-by: mhh001 <mahonghao1@huawei.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
A new function compute_dream_and_update_latents has been added to the
training utilities that allows you to do DREAM rectified training in line
with the paper https://arxiv.org/abs/2312.00210. The method can be used
with an extra argument in the train_text_to_image.py script.
Co-authored-by: Jimmy <39@🇺🇸.com>
