GitLab

* add conversion script for vae

* uP

* uP

* more changes

* push

* up

* finish again

* up

* up

* up

* up

* finish

* up

* uP

* up

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

* up

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

* [Scheduler] Move predict epsilon to init

* up

* uP

* uP

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

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

* add dpmsolver discrete pytorch scheduler

* fix some typos in dpm-solver pytorch

* add dpm-solver pytorch in stable-diffusion pipeline

* add jax/flax version dpm-solver

* change code style

* change code style

* add docs

* add `add_noise` method for dpmsolver

* add pytorch unit test for dpmsolver

* add dummy object for pytorch dpmsolver

* Update src/diffusers/schedulers/scheduling_dpmsolver_discrete.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update tests/test_config.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update tests/test_config.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* resolve the code comments

* rename the file

* change class name

* fix code style

* add auto docs for dpmsolver multistep

* add more explanations for the stabilizing trick (for steps < 15)

* delete the dummy file

* change the API name of predict_epsilon, algorithm_type and solver_type

* add compatible lists
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Remove the hub token

* replace repos

* style

* [Better scheduler docs] Improve usage examples of schedulers

* finish

* fix warnings and add test

* finish

* more replacements

* adapt fast tests hf token

* correct more

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

* Integrate compatibility with euler
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* [Tests] Make sure tests are on GPU

* move more models

* speed up tests

* split tests_modeling_utils

* Fix SD tests .to(device)

* fix merge

* Fix style
Co-authored-by: anton-l <anton@huggingface.co>

* Support one-string prompts in LDM

* Add other features from SD too

init pipeline once

* Allow passing non-default modules to pipeline.

Override modules are recognized and replaced in the pipeline. However,
no check is performed about mismatched classes yet. This is because the
override module is already instantiated and we have no library or class
name to compare against.

* up

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

* test LMS with LDM

* test LMS with LDM

* Interchangeable sigma and timestep. Added dummy objects

* Debug

* cuda generator

* Fix derivatives

* Update tests

* Rename Lms->LMS

* [PNDM] Stable diffusino

* finish

* [Half precision] Make sure half-precision is correct

* Update src/diffusers/models/unet_2d.py

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

* correct some tests

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

* finalize

* finish
Co-authored-by: Suraj Patil <surajp815@gmail.com>

add tests for sd pipeline

* add stable diffusion pipeline

* get rid of multiple if/else

* batch_size is unused

* add type hints

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

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

* karras + VE, not flexible yet

* Fix inputs incompatibility with the original unet

* Roll back sigma scaling

* Apply suggestions from code review

* Old comment

* Fix doc

* pass downsample_padding in encoder

* update tests

* [Vae and AutoencoderKL clean]

* save intermediate finished work

* more progress

* more progress

* finish modeling code

* save intermediate

* finish

* Correct tests

disable test_ddpm_ddim_equality_batched

* Add torch_device to the VE pipeline

* Mark the training test with slow

final fixes before release

up

* up

* change model name

* renaming

* more changes

* up

* up

* up

* save checkpoint

* finish api / naming

* finish config renaming

* rename all weights

* finish really

* Add a decorator for register_to_config

* All models and test

* big purge

* more fixes

* finish for now

* upload

* make checkpoint work

* finalize

* DDPM

* Fixes

* Edit tests