GitLab

* 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

Fix

* make scaling factor cnfig arg of vae

* fix

* make flake happy

* fix ldm

* fix upscaler

* qualirty

* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* solve conflicts, addres some comments

* examples

* examples min version

* doc

* fix type

* typo

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* remove duplicate line

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* [textual inversion] Allow validation images.

* Change key to `validation`

* Specify format instead of transposing.

As discussed with @sayakpaul.

* Style
Co-authored-by: isamu-isozaki <isamu.website@gmail.com>

check the dtype before preparing model

* [Bump version] 0.13

* Bump model up

* up

* add: a doc on LoRA support in diffusers.

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

* apply PR suggestions.

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

* remove visually incoherent elements.
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Add `lora` tag to the model tags

For lora training

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

* [docs] [dreambooth] note random crop

documenting default random crop behavior