This PR adds the "control_mode" option to ControlNet implementation.
Possible control_mode options are:
- balanced -- this is the default, same as previous implementation
without control_mode
- more_prompt -- pays more attention to the prompt
- more _control -- pays more attention to the ControlNet (in earlier
implementations this was called "guess_mode")
- unbalanced -- pays even more attention to the ControlNet
balanced, more_prompt, and more_control should be nearly identical to
the equivalent options in the [auto1111 sd-webui-controlnet
extension](https://github.com/Mikubill/sd-webui-controlnet#more-control-modes-previously-called-guess-mode)
The changes to enable balanced, more_prompt, and more_control are
managed deeper in the code by two booleans, "soft_injection" and
"cfg_injection". The three control mode options in sd-webui-controlnet
map to these booleans like:
!soft_injection && !cfg_injection ⇒ BALANCED
soft_injection && cfg_injection ⇒ MORE_CONTROL
soft_injection && !cfg_injection ⇒ MORE_PROMPT
The "unbalanced" option simply exposes the fourth possible combination
of these two booleans:
!soft_injection && cfg_injection ⇒ UNBALANCED
With "unbalanced" mode it is very easy to overdrive the controlnet
inputs. It's recommended to use a cfg_scale between 2 and 4 to mitigate
this, along with lowering controlnet weight and possibly lowering "end
step percent". With those caveats, "unbalanced" can yield interesting
results.
Example of all four modes using Canny edge detection ControlNet with
prompt "old man", identical params except for control_mode:
Top middle: BALANCED
Top right: MORE_CONTROL
Bottom middle: MORE_PROMPT
Bottom right : UNBALANCED
I kind of chose this seed because it shows pretty rough results with
BALANCED (the default), but in my opinion better results with both
MORE_CONTROL and MORE_PROMPT. And you can definitely see how MORE_PROMPT
pays more attention to the prompt, and MORE_CONTROL pays more attention
to the control image. And shows that UNBALANCED with default cfg_scale
etc is unusable.
But here are four examples from same series (same seed etc), all have
control_mode = UNBALANCED but now cfg_scale is set to 3.
And param differences are:
Top middle: prompt="old man", control_weight=0.3, end_step_percent=0.5
Top right: prompt="old man", control_weight=0.4, end_step_percent=1.0
Bottom middle: prompt=None, control_weight=0.3, end_step_percent=0.5
Bottom right: prompt=None, control_weight=0.4, end_step_percent=1.0
So with the right settings UNBALANCED seems useful.
Everything seems to be working.
- Due to a change to `reactflow`, I regenerated `yarn.lock`
- New chakra CLI fixes issue I had made a patch for; removed the patch
- Change to fontsource changed how we import that font
- Change to fontawesome means we lost the txt2img tab icon, just chose a
similar one
Everything seems to be working.
- Due to a change to `reactflow`, I regenerated `yarn.lock`
- New chakra CLI fixes issue I had made a patch for; removed the patch
- Change to fontsource changed how we import that font
- Change to fontawesome means we lost the txt2img tab icon, just chose a similar one
Only "real" conflicts were in:
invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
- Reset and Upload buttons along top of initial image
- Also had to mess around with the control net & DnD image stuff after changing the styles
- Abstract image upload logic into hook - does not handle native HTML drag and drop upload - only the button click upload
`openapi-fetch` does not handle non-JSON `body`s, always stringifying them, and sets the `content-type` to `application/json`.
The patch here does two things:
- Do not stringify `body` if it is one of the types that should not be stringified (https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API/Using_Fetch#body)
- Do not add `content-type: application/json` unless it really is stringified JSON.
Upstream issue: https://github.com/drwpow/openapi-typescript/issues/1123
I'm not a bit lost on fixing the types and adding tests, so not raising a PR upstream.
*migrate from `openapi-typescript-codegen` to `openapi-typescript` and `openapi-fetch`*
`openapi-typescript-codegen` is not very actively maintained - it's been over a year since the last update.
`openapi-typescript` and `openapi-fetch` are part of the actively maintained repo. key differences:
- provides a `fetch` client instead of `axios`, which means we need to be a bit more verbose with typing thunks
- fetch client is created at runtime and has a very nice typescript DX
- generates a single file with all types in it, from which we then extract individual types. i don't like how verbose this is, but i do like how it is more explicit.
- removed npm api generation scripts - now we have a single `typegen` script
overall i have more confidence in this new library.
*use nanostores for api base and token*
very simple reactive store for api base url and token. this was suggested in the `openapi-fetch` docs and i quite like the strategy.
*organise rtk-query api*
split out each endpoint (models, images, boards, boardImages) into their own api extensions. tidy!
Unsure at which moment it broke, but now I can't convert vae(and model
as vae it's part) without this fix.
Need further research - maybe it's breaking change in `transformers`?
Changes:
* Linux `install.sh` now prints the maximum python version to use in
case no installed python version matches
Commits:
fix(linux): installer script prints maximum python version usable
PR for the Model Manager UI work related to 3.0
[DONE]
- Update ModelType Config names to be specific so that the front end can
parse them correctly.
- Rebuild frontend schema to reflect these changes.
- Update Linear UI Text To Image and Image to Image to work with the new
model loader.
- Updated the ModelInput component in the Node Editor to work with the
new changes.
[TODO REMEMBER]
- Add proper types for ModelLoaderType in `ModelSelect.tsx`
[TODO]
- Everything else.
Basically updated all slices to be more descriptive in their names. Did so in order to make sure theres good naming scheme available for secondary models.
