Commit Graph

272 Commits

Author SHA1 Message Date
maryhipp
5120a76ce5 cleanup 2024-06-28 10:36:05 +10:00
maryhipp
38a948ac9f feat(api): add archived query param to board list endpoint to include them in the response 2024-06-28 10:36:05 +10:00
maryhipp
c33111468e feat(api): ability to archive boards 2024-06-28 10:36:05 +10:00
Lincoln Stein
3e0fb45dd7
Load single-file checkpoints directly without conversion (#6510)
* use model_class.load_singlefile() instead of converting; works, but performance is poor

* adjust the convert api - not right just yet

* working, needs sql migrator update

* rename migration_11 before conflict merge with main

* Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

* Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

* implement lightweight version-by-version config migration

* simplified config schema migration code

* associate sdxl config with sdxl VAEs

* remove use of original_config_file in load_single_file()

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
2024-06-27 17:31:28 -04:00
psychedelicious
cd70937b7f feat(api): improved model install confirmation page styling & messaging 2024-06-17 10:51:08 +10:00
chainchompa
4029972530 formatting 2024-06-14 17:15:55 -04:00
chainchompa
aae318425d added route for installing huggingface model from model marketplace 2024-06-14 17:08:39 -04:00
psychedelicious
9bd78823a3 refactor(events): use pydantic schemas for events
Our events handling and implementation has a couple pain points:
- Adding or removing data from event payloads requires changes wherever the events are dispatched from.
- We have no type safety for events and need to rely on string matching and dict access when interacting with events.
- Frontend types for socket events must be manually typed. This has caused several bugs.

`fastapi-events` has a neat feature where you can create a pydantic model as an event payload, give it an `__event_name__` attr, and then dispatch the model directly.

This allows us to eliminate a layer of indirection and some unpleasant complexity:
- Event handler callbacks get type hints for their event payloads, and can use `isinstance` on them if needed.
- Event payload construction is now the responsibility of the event itself (a pydantic model), not the service. Every event model has a `build` class method, encapsulating this logic. The build methods are provided as few args as possible. For example, `InvocationStartedEvent.build()` gets the invocation instance and queue item, and can choose the data it wants to include in the event payload.
- Frontend event types may be autogenerated from the OpenAPI schema. We use the payload registry feature of `fastapi-events` to collect all payload models into one place, making it trivial to keep our schema and frontend types in sync.

This commit moves the backend over to this improved event handling setup.
2024-05-27 09:06:02 +10:00
psychedelicious
93e4c3dbc2 feat(app): update queue item's session on session completion
The session is never updated in the queue after it is first enqueued. As a result, the queue detail view in the frontend never never updates and the session itself doesn't show outputs, execution graph, etc.

We need a new method on the queue service to update a queue item's session, then call it before updating the queue item's status.

Queue item status may be updated via a session-type event _or_ queue-type event. Adding the updated session to all these events is a hairy - simpler to just update the session before we do anything that could trigger a queue item status change event:
- Before calling `emit_session_complete` in the processor (handles session error, completed and cancel events and the corresponding queue events)
- Before calling `cancel_queue_item` in the processor (handles another way queue items can be canceled, outside the session execution loop)

When serializing the session, both in the new service method and the `get_queue_item` endpoint, we need to use `exclude_none=True` to prevent unexpected validation errors.
2024-05-24 08:59:49 +10:00
psychedelicious
88025d32c2 feat(api): downgrade metadata parse warnings to debug
I set these to warn during testing and neglected to undo the change.
2024-05-23 22:48:34 +10:00
psychedelicious
ecfff6cb1e feat(api): add metadata to upload route
Canvas images are saved by uploading a blob generated from the HTML canvas element. This means the existing metadata handling, inside the graph execution engine, is not available.

To save metadata to canvas images, we need to provide it when uploading that blob.

The upload route now has a `metadata` body param. If this is provided, we use it over any metadata embedded in the image.
2024-05-20 10:32:59 +10:00
psychedelicious
5928ade5fd feat(app): simplified create image API
Graph, metadata and workflow all take stringified JSON only. This makes the API consistent and means we don't need to do a round-trip of pydantic parsing when handling this data.

It also prevents a failure mode where an uploaded image's metadata, workflow or graph are old and don't match the current schema.

As before, the frontend does strict validation and parsing when loading these values.
2024-05-18 09:04:37 +10:00
psychedelicious
93ebc175c6 fix(app): retain graph in metadata when uploading images 2024-05-18 09:04:37 +10:00
psychedelicious
922716d2ab feat(ui): store graph in image metadata
The previous super-minimal implementation had a major issue - the saved workflow didn't take into account batched field values. When generating with multiple iterations or dynamic prompts, the same workflow with the first prompt, seed, etc was stored in each image.

As a result, when the batch results in multiple queue items, only one of the images has the correct workflow - the others are mismatched.

To work around this, we can store the _graph_ in the image metadata (alongside the workflow, if generated via workflow editor). When loading a workflow from an image, we can choose to load the workflow or the graph, preferring the workflow.

Internally, we need to update images router image-saving services. The changes are minimal.

To avoid pydantic errors deserializing the graph, when we extract it from the image, we will leave it as stringified JSON and let the frontend's more sophisticated and flexible parsing handle it. The worklow is also changed to just return stringified JSON, so the API is consistent.
2024-05-18 09:04:37 +10:00
psychedelicious
9c819f0fd8 fix(nodes): fix nsfw checker model download 2024-05-14 07:23:38 +10:00
psychedelicious
818d37f304 fix(api): retain cover image when converting model to diffusers
We need to retrieve and re-save the image, because a conversion to diffusers creates a new model record, with a new key.

See: https://old.reddit.com/r/StableDiffusion/comments/1cnx40d/invoke_42_control_layers_regional_guidance_w_text/l3bv152/
2024-05-13 08:46:07 +10:00
psychedelicious
9cdb801c1c fix(api): add cover image to update model response
Fixes a bug where the image _appears_ to be reset when editing a model.

See: https://old.reddit.com/r/StableDiffusion/comments/1cnx40d/invoke_42_control_layers_regional_guidance_w_text/l3asdej/
2024-05-13 08:46:07 +10:00
psychedelicious
d6ccd5bc81 feat(nodes): disable mosaic fill
Needs a bit of tweaking, leaving the code in just disabled/commented it out.
2024-04-05 08:49:13 +11:00
blessedcoolant
32a6b758cd wip: Initial Infill Methods Refactor 2024-04-05 08:49:13 +11:00
psychedelicious
3a10062b53 feat(mm): more reliable mm scan folder
Compare the installed paths to determine if the model is already installed. Fixes an issue where installed models showed up as uninstalled or vice-versa. Related to relative vs absolute path handling.
2024-04-04 07:58:11 +11:00
Lincoln Stein
3d6d89feb4
[mm] Do not write diffuser model to disk when convert_cache set to zero (#6072)
* 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>
2024-03-29 16:11:08 -04:00
psychedelicious
73c326680a feat(mm): remove autoimport; revise startup model scanning
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.
2024-03-28 12:35:41 +11:00
psychedelicious
3cf196dbb0 tidy(api): remove commented routes 2024-03-28 12:35:41 +11:00
psychedelicious
b8ac524712 feat(mm): remove hf token handling
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.
2024-03-27 18:59:55 +05:30
psychedelicious
05d6661877 feat(mm): revised list of starter models
- 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.
2024-03-22 14:59:33 +11:00
Lincoln Stein
eb558d72d8
Fix minor bugs involving model manager handling of model paths (#6024)
* 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>
2024-03-22 01:14:45 +00:00
psychedelicious
97fe6e483d fix(mm): do not attempt to reinstall starter model dependencies 2024-03-20 15:05:25 +11:00
psychedelicious
9a5575b46b feat(mm): move HF token helper to route 2024-03-20 15:05:25 +11:00
psychedelicious
5ceaeb234d feat(mm): add starter models route
The models from INITIAL_MODELS.yaml have been recreated as a structured python object. This data is served on a new route. The model sources are compared against currently-installed models to determine if they are already installed or not.
2024-03-20 15:05:25 +11:00
psychedelicious
fbe3afa5e1 fix(config): fix nsfw_checker handling
This setting was hardcoded to True. Rework logic around it to not conditionally check the setting.
2024-03-19 09:24:28 +11:00
Jennifer Player
d0800c4888 ui consistency, moved is_diffusers logic to backend, extended HuggingFaceMetadata, removed logic from service 2024-03-13 21:02:29 +11:00
Jennifer Player
90340a39c7 clean up python errors 2024-03-13 21:02:29 +11:00
Jennifer Player
5ad048a161 fixed error handling 2024-03-13 21:02:29 +11:00
Jennifer Player
f7cd3cf1f4 added hf models import tab and route for getting available hf models 2024-03-13 21:02:29 +11:00
Jennifer Player
347f1fd0b7 fix tests 2024-03-06 21:57:41 -05:00
Jennifer Player
4af5a09a68 cleanup 2024-03-06 21:57:41 -05:00
Jennifer Player
aa88fadc30 use webp images 2024-03-06 21:57:41 -05:00
Jennifer Player
8411029d93 get model image url from model config, added thumbnail formatting for images 2024-03-06 21:57:41 -05:00
Jennifer Player
239b1e8cc7 moved upload image field and added delete image functionality 2024-03-06 21:57:41 -05:00
Jennifer Player
2f6964bfa5 fetching model image, still not working 2024-03-06 21:57:41 -05:00
Jennifer Player
c1cdfd132b moved model image to edit page, added model_images service 2024-03-06 21:57:41 -05:00
psychedelicious
afd9ae7712 tidy(mm): remove convenience methods from high level model manager service
These were added as a hold-me-over for the nodes API changes, no longer needed. A followup commit will fix the nodes API to not rely on these.
2024-03-07 10:56:59 +11:00
psychedelicious
48119d9010 revert(mm): restore convert route 2024-03-05 23:50:19 +11:00
psychedelicious
4f9bb00275 tidy(api): tidy mm routes
Rename MM routes to be consistent:
- "import" -> "install"
- "model_record" -> "model"

Comment several unused routes while I work (may end up removing them?):
- list model summary (we use the search route instead)
- add model record
- convert model
- merge models
2024-03-05 23:50:19 +11:00
psychedelicious
5551cf8ac4 feat(mm): revise update_model to use ModelRecordChanges 2024-03-05 23:50:19 +11:00
psychedelicious
44c40d7d1a refactor(mm): remove unused metadata logic, fix tests
- Metadata is merged with the config. We can simplify the MM substantially and remove the handling for metadata.
- Per discussion, we don't have an ETA for frontend implementation of tags, and with the realization that the tags from CivitAI are largely useless, there's no reason to keep tags in the MM right now. When we are ready to implement tags on the frontend, we can refer back to the implementation here and use it if it supports the design.
- Fix all tests.
2024-03-05 23:50:19 +11:00
psychedelicious
c3aa985c93 refactor(mm): get metadata working 2024-03-05 23:50:19 +11:00
psychedelicious
a8cd3dfc99 refactor(mm): add models table (schema WIP), rename "original_hash" -> "hash" 2024-03-05 23:50:19 +11:00
psychedelicious
0cce582f2f tidy(mm): remove current_hash 2024-03-05 23:50:19 +11:00
psychedelicious
bd4fd9693d tidy(mm): rename ckpt "last_modified" -> "converted_at"
Clarify what this timestamp means
2024-03-05 23:50:19 +11:00