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 valid values for this parameter changed when inpainting changed to gradient denoise. The generation slice's redux migration wasn't updated, resulting in a generation error until you change the setting or reset web UI.
- Add and use more performant `deepClone` method for deep copying throughout the UI.
Benchmarks indicate the Really Fast Deep Clone library (`rfdc`) is the best all-around way to deep-clone large objects.
This is particularly relevant in canvas. When drawing or otherwise manipulating canvas objects, we need to do a lot of deep cloning of the canvas layer state objects.
Previously, we were using lodash's `cloneDeep`.
I did some fairly realistic benchmarks with a handful of deep-cloning algorithms/libraries (including the native `structuredClone`). I used a snapshot of the canvas state as the data to be copied:
On Chromium, `rfdc` is by far the fastest, over an order of magnitude faster than `cloneDeep`.
On FF, `fastest-json-copy` and `recursiveDeepCopy` are even faster, but are rather limited in data types. `rfdc`, while only half as fast as the former 2, is still nearly an order of magnitude faster than `cloneDeep`.
On Safari, `structuredClone` is the fastest, about 2x as fast as `cloneDeep`. `rfdc` is only 30% faster than `cloneDeep`.
`rfdc`'s peak memory usage is about 10% more than `cloneDeep` on Chrome. I couldn't get memory measurements from FF and Safari, but let's just assume the memory usage is similar relative to the other algos.
Overall, `rfdc` is the best choice for a single algo for all browsers. It's definitely the best for Chromium, by far the most popular desktop browser and thus our primary target.
A future enhancement might be to detect the browser and use that to determine which algorithm to use.
There were two ways the canvas history could grow too large (past the `MAX_HISTORY` setting):
- Sometimes, when pushing to history, we didn't `shift` an item out when we exceeded the max history size.
- If the max history size was exceeded by more than one item, we still only `shift`, which removes one item.
These issue could appear after an extended canvas session, resulting in a memory leak and recurring major GCs/browser performance issues.
To fix these issues, a helper function is added for both past and future layer states, which uses slicing to ensure history never grows too large.
Previously, exceptions raised as custom nodes are initialized were fatal errors, causing the app to exit.
With this change, any error on import is caught and the error message printed. App continues to start up without the node.
For example, a custom node that isn't updated for v4.0.0 may raise an error on import if it is attempting to import things that no longer exist.
Currently translated at 98.3% (1106 of 1124 strings)
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Currently translated at 98.3% (1104 of 1122 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
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Translation: InvokeAI/Web UI
Currently translated at 72.4% (813 of 1122 strings)
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Translation: InvokeAI/Web UI
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
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.
I had added this because I mistakenly believed the HF token was required to download HF models.
Turns out this is not the case, and the vast majority of HF models do not need the API token to download.
"Normal" models have 4 in-channels, while "Depth" models have 5 and "Inpaint" models have 9.
We need to explicitly tell diffusers the channel count when converting models.
Closes #6058
It's possible for a model's state dict to have integer keys, though we do not actually support such models.
As part of probing, we call `key.startswith(...)` on the state dict keys. This raises an `AttributeError` for integer keys.
This logic is in `invokeai/backend/model_manager/probe.py:get_model_type_from_checkpoint`
To fix this, we can cast the keys to strings first. The models w/ integer keys will still fail to be probed, but we'll get a `InvalidModelConfigException` instead of `AttributeError`.
Closes#6044
Previously we only handled expected error types. If a different error was raised, the install job would end up in an unexpected state where it has failed and isn't doing anything, but its status is still running.
This indirectly prevents the installer threads from exiting - they are waiting for all jobs to be completed, including the failed-but-still-running job.
We need to handle any error here to prevent this.
Updating should always be done via the installer. We initially planned to only deprecate the updater, but given the scale of changes for v4, there's no point in waiting to remove it entirely.
Loading default workflows sometimes requires we mutate the workflow object in order to change the category or ID of the workflow.
This happens in `invokeai/frontend/web/src/features/nodes/util/workflow/validateWorkflow.ts`
The data we get back from the query hooks is frozen and sealed by redux, because they are part of redux state. We need to clone the workflow before operating on it.
It's not clear how this ever worked in the past, because redux state has always been frozen and sealed.
Add `extra="forbid"` to the default settings models.
Closes#6035.
Pydantic has some quirks related to unions. This affected how the union of default settings was evaluated. See https://github.com/pydantic/pydantic/issues/9095 for a detailed description of the behaviour that this change addresses.
- Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own.
- Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
* Fix minor bugs involving model manager handling of model paths
- Leave models found in the `autoimport` directory there. Do not move them
into the `models` hierarchy.
- If model name, type or base is updated and model is in the `models` directory,
update its path as appropriate.
- On startup during model scanning, if a model's path is a symbolic link, then resolve
to an absolute path before deciding it is a new model that must be hashed and
registered. (This prevents needless hashing at startup time).
* fix issue with dropped suffix
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Currently translated at 98.2% (1102 of 1122 strings)
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Currently translated at 97.9% (1099 of 1122 strings)
translationBot(ui): update translation (Italian)
Currently translated at 97.9% (1099 of 1122 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Add class `DefaultInvokeAIAppConfig`, which inherits from `InvokeAIAppConfig`. When instantiated, this class does not parse environment variables, so it outputs a "clean" default config. That's the only difference.
Then, we can use this new class in the 3 places:
- When creating the example config file (no env vars should be here)
- When migrating a v3 config (we want to instantiate the migrated config without env vars, so that when we write it out, they are not written to disk)
- When creating a fresh config file (i.e. on first run with an uninitialized root or new config file path - no env vars here!)
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.
