This change makes it so any API clients can show the same error as what
happens in the terminal where you run the API. Useful for various WebUIs
to display more helpful error messages to users.
Co-authored-by: CapableWeb <capableweb@domain.com>
* Update dream.py. k_euler_a and k_dpm_2_a M1 fix
Make results reproducible (so runs with the same seed produce the same result).
Implements fix by @wbowling referenced in https://github.com/lstein/stable-diffusion/issues/397#issuecomment-1240679294
* Update dream.py. Remove import torch from dream.py
* generate.py: k_euler_a and k_dpm_2_a M1 fix#579
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* Refactor generate.py and dream.py
* config file path (models.yaml) is parsed inside Generate() to simplify
API
* Better handling of keyboard interrupts in file loading mode vs
interactive
* Removed oodles of unused variables.
* move nonfunctional inpainting out of the scripts directory
* fix ugly ddim tqdm formatting
* fix embiggen breakage, formatting fixes
* fix web server handling of rel and abs outdir paths
* Can now specify either a relative or absolute path for outdir
* Outdir path does not need to be inside the stable-diffusion directory
* Closes security hole that allowed user to read any file within
stable-diffusion (eek!)
* Closes#536
* revert inadvertent change of conda env name (#528)
* Refactor generate.py and dream.py
* config file path (models.yaml) is parsed inside Generate() to simplify
API
* Better handling of keyboard interrupts in file loading mode vs
interactive
* Removed oodles of unused variables.
* move nonfunctional inpainting out of the scripts directory
* fix ugly ddim tqdm formatting
Code cleanup and attention.py einsum_ops update for M1 16-32GB performance.
Expected: On par with fastest ever from 8 to 128GB for 512x512. Allows large images.
When running on just cpu (intel), a call to torch.layer_norm would error with RuntimeError: expected scalar type BFloat16 but found Float
Fix buggy device handling in model.py.
Tested with scripts/dream.py --full_precision on just cpu on intel laptop. Works but slow at ~10s/it.
* Add Embiggen automation
* Make embiggen_tiles masking more intelligent and count from one (at least for the user), rewrite sections of Embiggen README, fix various typos throughout README
* drop duplicate log message
* This moves the call to half() before model.to(device) to avoid GPU
copy of full model. Improves speed and reduces memory usage dramatically
* This fix contributed by @mh-dm (Mihai)
