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()
```
* Add latents nodes.
* Fix iteration expansion.
* Add collection generator nodes, math nodes.
* Add noise node.
* Add some graph debug commands to the CLI.
* Fix negative id linking in CLI.
* Fix a CLI bug with multiple links per node.
- New method is ModelManager.get_sub_model(model_name:str,model_part:SDModelComponent)
To use:
```
from invokeai.backend import ModelManager, SDModelComponent as sdmc
manager = ModelManager('/path/to/models.yaml')
vae = manager.get_sub_model('stable-diffusion-1.5', sdmc.vae)
```
The typo accidentally did not affect functionality; when `query==""`, it
`search()`ed but found everything due to empty query, then paginated
results, so it worked the same as `list()`.
Still fix it
currently if users input eg `happy (camper:0.3)` it gets parsed
incorrectly, which causes crashes if it's in the negative prompt. bump
to compel 1.0.5 fixes the parser to avoid this (note the weight is
parsed as plain text, it's not converted to proper invoke syntax)
- This PR adds support for embedding files that contain a single key
"emb_params". The only example I know of this format is the
"EasyNegative" embedding on HuggingFace, but there are certainly others.
- This PR also adds support for loading embedding files that have been
saved in safetensors format.
- It also cleans up the code so that the logic of probing for and
selecting the right format parser is clear.
- This is the same as #3045, which is on the 2.3 branch.
- Commands, invocations and their parameters will now autocomplete using
introspection.
- Two types of parameter *arguments* will also autocomplete:
- --sampler_name will autocomplete the scheduler name
- --model will autocomplete the model name
- There don't seem to be commands for reading/writing image files yet,
so path autocompletion is not implemented
