* Add instructions on how to install alongside pyenv (#393)
Like probably many others, I have a lot of different virtualenvs, one for each project. Most of them are handled by `pyenv`.
After installing according to these instructions I had issues with ´pyenv`and `miniconda` fighting over the $PATH of my system.
But then I stumbled upon this nice solution on SO: https://stackoverflow.com/a/73139031 , upon which I have based my suggested changes.
It runs perfectly on my M1 setup, with the anaconda setup as a virtual environment handled by pyenv.
Feel free to incorporate these instructions as you see fit.
Thanks a million for all your hard work.
* .gitignore WebUI temp files
Co-authored-by: Håvard Gulldahl <havard@lurtgjort.no>
* start refactoring -not yet functional
* first phase of refactor done - not sure weighted prompts working
* Second phase of refactoring. Everything mostly working.
* The refactoring has moved all the hard-core inference work into
ldm.dream.generator.*, where there are submodules for txt2img and
img2img. inpaint will go in there as well.
* Some additional refactoring will be done soon, but relatively
minor work.
* fix -save_orig flag to actually work
* add @neonsecret attention.py memory optimization
* remove unneeded imports
* move token logging into conditioning.py
* add placeholder version of inpaint; porting in progress
* fix crash in img2img
* inpainting working; not tested on variations
* fix crashes in img2img
* ported attention.py memory optimization #117 from basujindal branch
* added @torch_no_grad() decorators to img2img, txt2img, inpaint closures
* Final commit prior to PR against development
* fixup crash when generating intermediate images in web UI
* rename ldm.simplet2i to ldm.generate
* add backward-compatibility simplet2i shell with deprecation warning
* add back in mps exception, addresses @vargol comment in #354
* replaced Conditioning class with exported functions
* fix wrong type of with_variations attribute during intialization
* changed "image_iterator()" to "get_make_image()"
* raise NotImplementedError for calling get_make_image() in parent class
* Update ldm/generate.py
better error message
Co-authored-by: Kevin Gibbons <bakkot@gmail.com>
* minor stylistic fixes and assertion checks from code review
* moved get_noise() method into img2img class
* break get_noise() into two methods, one for txt2img and the other for img2img
* inpainting works on non-square images now
* make get_noise() an abstract method in base class
* much improved inpainting
Co-authored-by: Kevin Gibbons <bakkot@gmail.com>
* toil(reqs): split requirements to per-platform
Signed-off-by: Ben Alkov <ben.alkov@gmail.com>
* toil(reqs): fix for Win and Lin...
...allow pip to resolve latest torch, numpy
Signed-off-by: Ben Alkov <ben.alkov@gmail.com>
* toil(install): update reqs in Win install notebook
Signed-off-by: Ben Alkov <ben.alkov@gmail.com>
Signed-off-by: Ben Alkov <ben.alkov@gmail.com>
* Update README.md with new Anaconda install steps (#347)
pip3 version did not work for me and this is the recommended way to install Anaconda now it seems
* fix save_original flag saving to the same filename
Before this, the `--save_orig` flag was not working. The upscaled/GFPGAN would overwrite the original output image.
Co-authored-by: greentext2 <112735219+greentext2@users.noreply.github.com>
* Switch to regular pytorch channel and restore Python 3.10 for Macs.
Although pytorch-nightly should in theory be faster, it is currently
causing increased memory usage and slower iterations:
https://github.com/lstein/stable-diffusion/pull/283#issuecomment-1234784885
This changes the environment-mac.yaml file back to the regular pytorch
channel and moves the `transformers` dep into pip for now (since it
cannot be satisfied until tokenizers>=0.11 is built for Python 3.10).
* Specify versions for Pip packages as well.
This merge adds the following major features:
* Support for image variations.
* Security fix for webGUI (binds to localhost by default, use
--host=0.0.0.0 to allow access from external interface.
* Scalable configs/models.yaml configuration file for adding more
models as they become available.
* More tuning and exception handling for M1 hardware running MPS.
* Various documentation fixes.
