## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
**NOTE!!!** This PR is against `feat/ip-adapter`, not `main`. I created
a PR because I made some pretty significant changes that I thought might
spark discussion.
I don't think it makes sense to do a full in-depth review here. If
possible, let's try to agree on the high-level approach and then merge
this and do an in-depth review on the original PR.
High-level changes:
- Split `IPAdapterField` from the `ControlField` and make them separate
inputs on the `DenoiseLatentsInvocation`
- Create context manager that handles patching/un-patching the UNet with
IP-Adapter attention blocks (`IPAdapter.apply_ip_adapter_attention()`)
- Pass IP-Adapter conditioning via `cross_attention_kwargs` rather than
concatenating it to the text embedding. This helps avoid breaking other
features (like long prompts).
- Remove unused blocks of the IP-Adapter implementation and do some
general tidying.
Out of scope:
- I haven't looked at model management yet. I'd like to get this merged
into `feat/ip-adapter` and then look at model management separately.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
fix(ui): fix non-nodes validation logic being applied to nodes invoke
button
For example, if you had an invalid controlnet setup, it would prevent
you from invoking on nodes, when node validation was disabled.
## Related Tickets & Documents
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- Closes
https://discord.com/channels/1020123559063990373/1028661664519831552/1148431783289966603
## What type of PR is this? (check all applicable)
- [x] Feature
- [x] Optimization
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
# Coherence Mode
A new parameter called Coherence Mode has been added to Coherence Pass
settings. This parameter controls what kind of Coherence Pass is done
after Inpainting and Outpainting.
- Unmasked: This performs a complete unmasked image to image pass on the
entire generation.
- Mask: This performs a masked image to image pass using your input mask
as the coherence mask.
- Mask Edge [DEFAULT] - This performs as masked image to image pass on
the edges of your mask to try and clear out the seams.
# Why The Coherence Masked Modes?
One of the issues with unmasked coherence pass arises when the diffusion
process is trying to align detailed or organic objects. Because Image to
Image tends change the image a little bit even at lower strengths, this
ends up in the paste back process being slightly misaligned. By
providing the mask to the Coherence Pass, we can try to eliminate this
in those cases. While it will be impossible to address this for every
image out there, having these options will allow the user to automate a
lot of this. For everything else there's manual paint over with inpaint.
# Graph Improvements
The graphs have now been refined quite a bit. We no longer do manual
blurring of the masks anymore for outpainting. This is no longer needed
because we now dilate the mask depending on the blur size while pasting
back. As a result we got rid of quite a few nodes that were handling
this in the older graph.
The graphs are also a lot cleaner now because we now tackle Scaled
Dimensions & Coherence Mode completely independently.
Inpainting result seem very promising especially with the Mask Edge
mode.
---
# New Infill Methods [Experimental]
We are currently trying out various new infill methods to see which ones
might perform the best in outpainting. We may keep all of them or keep
none. This will be decided as we test more.
## LaMa Infill
- Renabled LaMA infill in the UI.
- We are trying to get this to work without a memory overhead.
In order to use LaMa, you need to manually download and place the LaMa
JIT model in `models/core/misc/lama/lama.pt`. You can download the JIT
model from Sanster
[here](https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt)
and rename it to `lama.pt` or you can use the script in the original
LaMA repo to convert the base model to a JIT model yourself.
## CV2 Infill
- Added a new infilling method using CV2's Inpaint.
## Patchmatch Rescaling
Patchmatch infill input image is now downscaled and infilled. Patchmatch
can be really slow at large resolutions and this is a pretty decent way
to get around that. Additionally, downscaling might also provide a
better patch match by avoiding larger areas to be infilled with
repeating patches. But that's just the theory. Still testing it out.
## [optional] Are there any post deployment tasks we need to perform?
- If we decide to keep LaMA infill, then we will need to host the model
and update the installer to download it as a core model.
