Add `dump_path` arg to the converter function & save the model to disk inside the conversion function. This is the same pattern as in the other conversion functions.
Prefer an early return/continue to reduce the indentation of the processor loop. Easier to read.
There are other ways to improve its structure but at first glance, they seem to involve changing the logic in scarier ways.
This must not have been tested after the processors were unified. Needed to shift the logic around so the resume event is handled correctly. Clear and easy fix.
* pass model config to _load_model
* make conversion work again
* do not write diffusers to disk when convert_cache set to 0
* adding same model to cache twice is a no-op, not an assertion error
* fix issues identified by psychedelicious during pr review
* following conversion, avoid redundant read of cached submodels
* fix error introduced while merging
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
We switched all model paths to be absolute in #5900. In hindsight, this is a mistake, because it makes the `models_dir` non-portable.
This change reverts to the previous model pathing:
- Invoke-managed models (in the `models_dir`) are stored with relative paths
- Non-invoke-managed models (outside the `models_dir`, i.e. in-place installed models) still have absolute paths.
## Why absolute paths make things non-portable
Let's say my `models_dir` is `/media/rhino/invokeai/models/`. In the DB, all model paths will be absolute children of this path, like this:
- `/media/rhino/invokeai/models/sd-1/main/model1.ckpt`
I want to change my `models_dir` to `/home/bat/invokeai/models/`. I update my `invokeai.yaml` file and physically move the files to that directory.
On startup, the app checks for missing models. Because all of my model paths were absolute, they now point to a nonexistent path. All models are broken.
There are a couple options to recover from this situation, neither of which are reasonable:
1. The user must manually update every model's path. Unacceptable UX.
2. On startup, we check for missing models. For each missing model, we compare its path with the last-known models dir. If there is a match, we replace that portion of the path with the new models dir. Then we re-check to see if the path exists. If it does, we update the models DB entry. Brittle and requires a new DB entry for last-known models dir.
It's better to use relative paths for Invoke-managed models.
Setting to 'auto' works only for InvokeAI config and auto detects the SD model but will override if user explicitly sets it. If auto used with checkpoint models, we raise an error. Checkpoints will always need to set to non-auto.
The seamless logic errors when a second GPU is selected. I don't understand why, but a workaround is to skip the model patching when there there are no seamless axes specified.
This is also just a good practice regardless - don't patch the model unless we need to. Probably a negligible perf impact.
Closes#6010
The build workflow was naming the file `InvokeAI-installer-v4.0.0rc6.zip.zip` (note the double ".zip"). This caused some confusion when creating releases on GitHub.
Name the build artifact `installer`. This results in `installer.zip`, which it's clear needs to be extracted first before uploading to the GH release.
`scripts/get_external_contributions.py` gets all commits between two refs and outputs a summary.
Useful for getting all external contributions for release notes.
There's still a few references in `WEB.md` but this doc is very outdated and needs to be totally redone. It's hard to just remove the references without redoing a lot more.
Will need to follow up revising this doc.
These two changes are interrelated.
## Autoimport
The autoimport feature can be easily replicated using the scan folder tab in the model manager. Removing the implicit autoimport reduces surface area and unifies all model installation into the UI.
This functionality is removed, and the `autoimport_dir` config setting is removed.
## Startup model dir scanning
We scanned the invoke-managed models dir on startup and took certain actions:
- Register orphaned model files
- Remove model records from the db when the model path doesn't exist
### Orphaned model files
We should never have orphaned model files during normal use - we manage the models directory, and we only delete files when the user requests it.
During testing or development, when a fresh DB or memory DB is used, we could end up with orphaned models that should be registered.
Instead of always scanning for orphaned models and registering them, we now only do the scan if the new `scan_models_on_startup` config flag is set.
The description for this setting indicates it is intended for use for testing only.
### Remove records for missing model files
This functionality could unexpectedly wipe models from the db.
For example, if your models dir was on external media, and that media was inaccessible during startup, the scan would see all your models as missing and delete them from the db.
The "proactive" scan is removed. Instead, we will scan for missing models and log a warning if we find a model whose path doesn't exist. No possibility for data loss.
