GitLab

* 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>

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.

* before running make style

* remove left overs from flake8

* finish

* make fix-copies

* final fix

* more fixes

* 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

* 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>

* 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>