For SSDs, `blake3` is about 10x faster than `blake3_single` - 3 files/second vs 30 files/second.
For spinning HDDs, `blake3` is about 100x slower than `blake3_single` - 300 seconds/file vs 3 seconds/file.
For external drives, `blake3` is always worse, but the difference is highly variable. For external spinning drives, it's probably way worse than internal.
The least offensive algorithm is `blake3_single`, and it's still _much_ faster than any other algorithm.
With the change to model identifiers from v3 to v4, if a user had persisted redux state with the old format, we could get unexpected runtime errors when rehydrating state if we try to access model attributes that no longer exist.
For example, the CLIP Skip component does this:
```ts
CLIP_SKIP_MAP[model.base].maxClip
```
In v3, models had a `base_type` attribute, but it is renamed to `base` in v4. This code therefore causes a runtime error:
- `model.base` is `undefined`
- `CLIP_SKIP_MAP[undefined]` is also undefined
- `undefined.maxClip` is a runtime error!
Resolved by adding a migration for the redux slices that have model identifiers. The migration simply resets the slice or the part of the slice that is affected, when it's simple to do a partial reset.
Closes#6000
* add probe for SDXL controlnet models
* Update invokeai/backend/model_management/model_probe.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Update invokeai/backend/model_manager/probe.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
These all support controlnet processors.
- `pil_to_cv2`
- `cv2_to_pil`
- `pil_to_np`
- `np_to_pil`
- `normalize_image_channel_count` (a readable version of `HWC3` from the controlnet repo)
- `fit_image_to_resolution` (a readable version of `resize_image` from the controlnet repo)
- `non_maximum_suppression` (a readable version of `nms` from the controlnet repo)
- `safe_step` (a readable version of `safe_step` from the controlnet repo)
Some processors, like Canny, didn't use `detect_resolution`. The resultant control images were then resized by the processors from 512x512 to the desired dimensions. The result is that the control images are the right size, but very low quality.
Using detect_resolution fixes this.
- Display a toast on UI launch if the HF token is invalid
- Show form in MM if token is invalid or unable to be verified, let user set the token via this form
This allows users to create simple "profiles" via separate `invokeai.yaml` files.
- Remove `InvokeAIAppConfig.set_root()`, it's extraneous
- Remove `InvokeAIAppConfig.merge_from_file()`, it's extraneous
- Add `--config` to the app arg parser, add `InvokeAIAppConfig._config_file`, and consume in the config singleton getter
- `InvokeAIAppConfig.init_file_path` -> `InvokeAIAppConfig.config_file_path`
