This commit adds invokeai.backend.util.logging, which provides support
for formatted console and logfile messages that follow the status
reporting conventions of earlier InvokeAI versions.
Examples:
### A critical error (logging.CRITICAL)
*** A non-fatal error (logging.ERROR)
** A warning (logging.WARNING)
>> Informational message (logging.INFO)
| Debugging message (logging.DEBUG)
This style logs everything through a single logging object and is
identical to using Python's `logging` module. The commonly-used
module-level logging functions are implemented as simple pass-thrus
to logging:
import invokeai.backend.util.logging as ialog
ialog.debug('this is a debugging message')
ialog.info('this is a informational message')
ialog.log(level=logging.CRITICAL, 'get out of dodge')
ialog.disable(level=logging.INFO)
ialog.basicConfig(filename='/var/log/invokeai.log')
Internally, the invokeai logging module creates a new default logger
named "invokeai" so that its logging does not interfere with other
module's use of the vanilla logging module. So `logging.error("foo")`
will go through the regular logging path and not add the additional
message decorations.
For more control, the logging module's object-oriented logging style
is also supported. The API is identical to the vanilla logging
usage. In fact, the only thing that has changed is that the
getLogger() method adds a custom formatter to the log messages.
import logging
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.getLogger(__name__)
fh = logging.FileHandler('/var/invokeai.log')
logger.addHandler(fh)
logger.critical('this will be logged to both the console and the log file')
This commit adds invokeai.backend.util.logging, which provides support
for formatted console and logfile messages that follow the status
reporting conventions of earlier InvokeAI versions.
Examples:
### A critical error (logging.CRITICAL)
*** A non-fatal error (logging.ERROR)
** A warning (logging.WARNING)
>> Informational message (logging.INFO)
| Debugging message (logging.DEBUG)
- add invocation schema customisation
done via fastapi's `Config` class and `schema_extra`. when using `Config`, inherit from `InvocationConfig` to get type hints.
where it makes sense - like for all math invocations - define a `MathInvocationConfig` class and have all invocations inherit from it.
this customisation can provide any arbitrary additional data to the UI. currently it provides tags and field type hints.
this is necessary for `model` type fields, which are actually string fields. without something like this, we can't reliably differentiate `model` fields from normal `string` fields.
can also be used for future field types.
all invocations now have tags, and all `model` fields have ui type hints.
- fix model handling for invocations
added a helper to fall back to the default model if an invalid model name is chosen. model names in graphs now work.
- fix latents progress callback
noticed this wasn't correct while working on everything else.
When running this app first time in WSL2 environment, which is
notoriously slow when it comes to IO, computing the SHAs of the models
takes an eternity.
Computing shas for sd2.1
```
| Calculating sha256 hash of model files
| sha256 = 1e4ce085102fe6590d41ec1ab6623a18c07127e2eca3e94a34736b36b57b9c5e (49 files hashed in 510.87s)
```
I increased the chunk size to 16MB reduce the number of round trips for
loading the data. New results:
```
| Calculating sha256 hash of model files
| sha256 = 1e4ce085102fe6590d41ec1ab6623a18c07127e2eca3e94a34736b36b57b9c5e (49 files hashed in 59.89s)
```
Higher values don't seem to make an impact.
- add `list_images` endpoint at `GET api/v1/images`
- extend `ImageStorageBase` with `list()` method, implemented it for `DiskImageStorage`
- add `ImageReponse` class to for image responses, which includes urls, metadata
- add `ImageMetadata` class (basically a stub at the moment)
- uploaded images now named `"{uuid}_{timestamp}.png"`
- add `models` modules. besides separating concerns more clearly, this helps to mitigate circular dependencies
- improve thumbnail handling
- the functionality to automatically import and run legacy checkpoint
files in a designated folder has been removed from the backend but there
are vestiges of the code remaining in the frontend that are causing
crashes.
- This fixes the problem.
- Closes#3075
This PR introduces a new set of ModelManager methods that enables you to
retrieve the individual parts of a stable diffusion pipeline model,
including the vae, text_encoder, unet, tokenizer, etc.
To use:
```
from invokeai.backend import ModelManager
manager = ModelManager('/path/to/models.yaml')
# get the VAE
vae = manager.get_model_vae('stable-diffusion-1.5')
# get the unet
unet = manager.get_model_unet('stable-diffusion-1.5')
# get the tokenizer
tokenizer = manager.get_model_tokenizer('stable-diffusion-1.5')
# etc etc
feature_extractor = manager.get_model_feature_extractor('stable-diffusion-1.5')
scheduler = manager.get_model_scheduler('stable-diffusion-1.5')
text_encoder = manager.get_model_text_encoder('stable-diffusion-1.5')
# if no model provided, then defaults to the one currently in GPU, if any
vae = manager.get_model_vae()
```
