GitLab

* Fix tensorboard tracking with `accelerate` @ `main`

* Fix `train_unconditional.py` with accelerate from main.

* Update train_text_to_image_lora.py

* Update train_text_to_image_lora.py

* add xformers 0.0.16 warning message

* fix version check to check whole version string

This reverts commit 024c4376.

* Fix EMA

* up

* update

correctly locate 3rd file; also correct misleading docs

* add total number checkpoints to training scripts

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

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

replace type hints

* Log Unconditional Image Generation Samples to WandB

* Check for wandb installation and parity between onnxruntime script

* Log epoch to wandb

* Check for tensorboard logger early on

* style fixes

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

* UnCLIP Text Interpolation Pipeline

* Formatter fixes

* Changes based on feedback

* Formatting fix

* Formatting fix

* isort formatting fix(?)

* Remove duplicate code

* Formatting fix

* Refactor __call__ and change example in readme.

* Update examples/community/unclip_text_interpolation.py

Refactor to linter formatting
Co-authored-by: Will Berman <wlbberman@gmail.com>

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Co-authored-by: Will Berman <wlbberman@gmail.com>

* [LoRA] Freezing the model weights

Freeze the model weights since we don't need to calculate grads for them.

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

* Apply suggestions from code review

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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

Resolves ValueError: `num_inference_steps`: 1000 cannot be larger than `self.config.train_timesteps`: 50 as the unet model trained with this scheduler can only handle maximal 50 timesteps.

* [Examples] Test all examples on CPU

* add

* correct

* Apply suggestions from code review

* EMA: fix `state_dict()` & add `cur_decay_value`

* EMA: fix a bug in `load_state_dict()`

'float' object (`state_dict["power"]`) has no attribute 'get'.

* del train_unconditional_ort.py

* Quality check and adding tokenizer

* Adapted stable diffusion to mixed precision+finished up style fixes

* Fixed based on patrick's review

* Fixed oom from number of validation images

* Removed unnecessary np.array conversion

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

* before running make style

* remove left overs from flake8

* finish

* make fix-copies

* final fix

* more fixes

* better accelerated saving

* up

* finish

* finish

* uP

* up

* up

* fix

* Apply suggestions from code review

* correct ema

* Remove @

* up

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update docs/source/en/training/dreambooth.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fix torchvision.transforms and transforms function naming clash

* Update unconditional script for onnx

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

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

* Add center crop and horizontal flip to args

* Update command to use center crop and random flip

* Add center crop and horizontal flip to args

* Update command to use center crop and random flip

* [Examples] Remove datasets important that is not needed

* remove from lora tambien

* Create convert_vae_pt_to_diffusers.py

Just a simple script to convert VAE.pt files to diffusers format
Tested with: https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/VAEs/orangemix.vae.pt



* Update convert_vae_pt_to_diffusers.py

Forgot to add the function call

* make style

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

fix colossalai dreambooth

* [LoRA] Make sure validation works in multi GPU setup

* more fixes

* up

* refactor onnxruntime integration

* fix requirements.txt bug

* make style

* add support for textual_inversion

* make style

* add readme

* cleanup README files

* 1/27/2023 update to training scripts

* make style

* 1/30 update to train_unconditional

* style with black-22.8.0

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Co-authored-by: Prathik Rao <prathikrao@microsoft.com>
Co-authored-by: anton- <anton@huggingface.co>

* Create train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

* Update README.md

* Update README.md

* Don't copy when unwrapping model.

Otherwise an exception is raised when using fp16.

* Remove unused import