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* Support one-string prompts in LDM

* Add other features from SD too

* Add is_<framework>_available, refactor import utils

* deps

* quality

* Revive Make utils

* Add datasets for training too

use default params in __call__

* 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>

* allow custom height width

* raise if height width are not mul of 8

* 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>

fix typo in condirion

use inspect instead of unused kwargs

* 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

make text model generic

Fix arg key for `dataset_name`

The example training script was changed in #152, but not
`create_model_card`.

* pass downsample_padding in encoder

* update tests

add cross_attention_dim as an argument

* Extented the ability of ddpm scheduler
to utilize model that also predict the variance.

* Update src/diffusers/schedulers/scheduling_ddpm.py
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* [Vae and AutoencoderKL clean]

* save intermediate finished work

* more progress

* more progress

* finish modeling code

* save intermediate

* finish

* Correct tests

* Add torch_device to the VE pipeline

* Mark the training test with slow

* Expose schedulers

* Update __init__.py
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

final fixes before release

* Typos

* Credits and roadmap

* Second version