GitLab

* add outputs for models

* add for pipelines

* finish schedulers

* better naming

* adapt tests as well

* replace dict access with . access

* make schedulers works

* finish

* correct readme

* make  bcp compatible

* up

* small fix

* finish

* more fixes

* more fixes

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

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

* Adapt model outputs

* Apply more suggestions

* finish examples

* correct
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

use add_tokens

* add textual inversion script

* make the loop work

* make coarse_loss optional

* save pipeline after training

* add arg pretrained_model_name_or_path

* fix saving

* fix gradient_accumulation_steps

* style

* fix progress bar steps

* scale lr

* add argument to accept style

* remove unused args

* scale lr using num gpus

* load tokenizer using args

* add checks when converting init token to id

* improve commnets and style

* document args

* more cleanup

* fix default adamw arsg

* TextualInversionWrapper -> CLIPTextualInversionWrapper

* fix tokenizer loading

* Use the CLIPTextModel instead of wrapper

* clean dataset

* remove commented code

* fix accessing grads for multi-gpu

* more cleanup

* fix saving on multi-GPU

* init_placeholder_token_embeds

* add seed

* fix flip

* fix multi-gpu

* add utility methods in wrapper

* remove ipynb

* don't use wrapper

* dont pass vae an dunet to accelerate prepare

* bring back accelerator.accumulate

* scale latents

* use only one progress bar for steps

* push_to_hub at the end of training

* remove unused args

* log some important stats

* store args in tensorboard

* pretty comments

* save the trained embeddings

* mobe the script up

* add requirements file

* more cleanup

* fux typo

* begin readme

* style -> learnable_property

* keep vae and unet in eval mode

* address review comments

* address more comments

* removed unused args

* add train command in readme

* update readme

fix grad accum

* [Examples readme]

* Improve

* more

* save

* save

* save more

* up

* up

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

* up

* make deterministic

* up

* better

* up

* add generator to img2img pipe

* save

* make pipelines deterministic

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* apply all changes

* more correctnios

* finish

* improve table

* more fixes

* up

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* Update src/diffusers/pipelines/README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* add better links

* fix more

* finish
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Support K-LMS in img2img

* Apply review suggestions

adds missing torch import to example

* expand latents before the check, style

* update readme

* add inpainting

* added proper noising of init_latent as reccommened by jackloomen (https://github.com/huggingface/diffusers/pull/241#issuecomment-1226283542)

* move image preprocessing inside pipeline and allow non 512x512 mask

* Accept latents as input for StableDiffusionPipeline.

* Notebook to demonstrate reusable seeds (latents).

* More accurate type annotation
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Review comments: move to device, raise instead of assert.

* Actually commit the test notebook.

I had mistakenly pushed an empty file instead.

* Adapt notebook to Colab.

* Update examples readme.

* Move notebook to personal repo.
Co-authored-by: Suraj Patil <surajp815@gmail.com>

raise error if strength is not in desired range

* boom boom

* reorganise examples

* add image2image in example inference

* add readme

* fix example

* update colab url

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

* fix init_timestep

* update colab url

* update main readme

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

* Support training with an image folder

* style

In https://github.com/huggingface/diffusers/issues/124 I incorrectly suggested that the image set creation process was undocumented.  In reality, I just hadn't located it.  @patrickvonplaten did so for me.

This PR places a hotlink so that people like me can be shoehorned over where they needed to be.

* New unet, gradient accumulation

* Save every n epochs

* Remove find_unused_params, hooray!

* Update examples

* Switch to DDPM completely

* Catch unused params in DDP

* Fix proj_out, add test

ema model stepping done automatically now

* add a metrics logger

* fix LatentDiffusionUncondPipeline

* add VQModel in init

* add image logging to tensorboard

* switch manual templates to the modelcards package

* hide ldm example
Co-authored-by: patil-suraj <surajp815@gmail.com>