The k_samplers come with a "karras" noise schedule which performs
very well at low step counts but becomes noisy at higher ones.
This commit introduces a threshold (currently 30 steps) at which the
k samplers will switch over from using karras to the older model
noise schedule.
- This sets a step switchover point at which the k-samplers stop using the
Karras noise schedule and start using the LatentDiffusion noise schedule.
The advantage of this is that the Karras schedule produces excellent
results at low step counts but starts to become unstable at high
steps.
- A new command argument --karras_max, lets the user set where the
switchover occurs. Default is 29 steps (1-29 steps Karras),
(30 or greater LDM)
- Tildebyte, sorry to do a fast forward three-way merge for this
but rebasing was just too painful due to extensive recent
changes to the diffuser code.
This was a difficult merge because both PR #1108 and #1243 made
changes to obscure parts of the diffusion code.
- prompt weighting, merging and cross-attention working
- cross-attention does not work with runwayML inpainting
model, but weighting and merging are tested and working
- CLI command parsing code rewritten in order to get embedded
quotes right
- --hires now works with runwayML inpainting
- --embiggen does not work with runwayML and will give an error
- Added an --invert option to invert masks applied to inpainting
- Updated documentation
- change default model back to 1.4
- remove --fnformat from canonicalized dream prompt arguments
(not needed for image reproducibility)
- add -tm to canonicalized dream prompt arguments
(definitely needed for image reproducibility)
- The plms sampler now works with custom inpainting model
- Quashed bug that was causing generation on normal models to fail (oops!)
- Can now generate non-square images with custom inpainting model
Credits for advice and assistance during porting:
@any-winter-4079 (http://github.com/any-winter-4079)
@db3000 (Danny Beer http://github.com/db3000)
This is still a work in progress but seems functional. It supports
inpainting, txt2img and img2img on the ddim and k* samplers (plms
still needs work, but I know what to do).
To test this, get the file `sd-v1-5-inpainting.ckpt' from
https://huggingface.co/runwayml/stable-diffusion-inpainting and place it
at `models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt`
Launch invoke.py with --model inpainting-1.5 and proceed as usual.
Caveats:
1. The inpainting model takes about 800 Mb more memory than the standard
1.5 model. This model will not work on 4 GB cards.
2. The inpainting model is temperamental. It wants you to describe the
entire scene and not just the masked area to replace. So if you want
to replace the parrot on a man's shoulder with a crow, the prompt
"crow" may fail. Try "man with a crow on shoulder" instead. The
symptom of a failed inpainting is that the area will be erased and
replaced with background.
3. This has not been tested well. Please report bugs.
- This is a merge of the final version of PR #1218 "Inpainting
Improvements"
Various merge conflicts made it easier to commit directly.
Author: Kyle0654
Co-Author: lstein
