The problem was the same seed was getting used for the seam painting pass, causing the fried look.
Same issue as if you do img2img on a txt2img with the same seed/prompt.
Thanks to @hipsterusername for teaming up to debug this. We got pretty deep into the weeds.
This commit makes InvokeAI 3.0 to be installable via PyPi.org and the
installer script.
Main changes.
1. Move static web pages into `invokeai/frontend/web` and modify the
API to look for them there. This allows pip to copy the files into the
distribution directory so that user no longer has to be in repo root
to launch.
2. Update invoke.sh and invoke.bat to launch the new web application
properly. This also changes the wording for launching the CLI from
"generate images" to "explore the InvokeAI node system," since I would
not recommend using the CLI to generate images routinely.
3. Fix a bug in the checkpoint converter script that was identified
during testing.
4. Better error reporting when checkpoint converter fails.
5. Rebuild front end.
- Make environment variable settings case InSenSiTive:
INVOKEAI_MAX_LOADED_MODELS and InvokeAI_Max_Loaded_Models
environment variables will both set `max_loaded_models`
- Updated realesrgan to use new config system.
- Updated textual_inversion_training to use new config system.
- Discovered a race condition when InvokeAIAppConfig is created
at module load time, which makes it impossible to customize
or replace the help message produced with --help on the command
line. To fix this, moved all instances of get_invokeai_config()
from module load time to object initialization time. Makes code
cleaner, too.
- Added `--from_file` argument to `invokeai-node-cli` and changed
github action to match. CI tests will hopefully work now.
- invokeai-configure updated to work with new config system
- migrate invokeai.init to invokeai.yaml during configure
- replace legacy invokeai with invokeai-node-cli
- add ability to run an invocation directly from invokeai-node-cli command line
- update CI tests to work with new invokeai syntax
1. If an external VAE is specified in config file, then
get_model(submodel=vae) will return the external VAE, not the one
burnt into the parent diffusers pipeline.
2. The mechanism in (1) is generalized such that you can now have
"unet:", "text_encoder:" and similar stanzas in the config file.
Valid formats of these subsections:
unet:
repo_id: foo/bar
unet:
path: /path/to/local/folder
unet:
repo_id: foo/bar
subfolder: unet
In the near future, these will also be used to attach external
parts to the pipeline, generalizing VAE behavior.
3. Accommodate callers (i.e. the WebUI) that are passing the
model key ("diffusers/stable-diffusion-1.5") to get_model()
instead of the tuple of model_name and model_type.
4. Fixed bug in VAE model attaching code.
5. Rebuilt web front end.
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)
- 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)
```
This commit fixes bugs related to the on-the-fly conversion and loading of
legacy checkpoint models built on SD-2.0 base.
- When legacy checkpoints built on SD-2.0 models were converted
on-the-fly using --ckpt_convert, generation would crash with a
precision incompatibility error.
A long-standing issue with importing legacy checkpoints (both ckpt and
safetensors) is that the user has to identify the correct config file,
either by providing its path or by selecting which type of model the
checkpoint is (e.g. "v1 inpainting"). In addition, some users wish to
provide custom VAEs for use with the model. Currently this is done in
the WebUI by importing the model, editing it, and then typing in the
path to the VAE.
To improve the user experience, the model manager's
`heuristic_import()` method has been enhanced as follows:
1. When initially called, the caller can pass a config file path, in
which case it will be used.
2. If no config file provided, the method looks for a .yaml file in the
same directory as the model which bears the same basename. e.g.
```
my-new-model.safetensors
my-new-model.yaml
```
The yaml file is then used as the configuration file for
importation and conversion.
3. If no such file is found, then the method opens up the checkpoint
and probes it to determine whether it is V1, V1-inpaint or V2.
If it is a V1 format, then the appropriate v1-inference.yaml config
file is used. Unfortunately there are two V2 variants that cannot be
distinguished by introspection.
4. If the probe algorithm is unable to determine the model type, then its
last-ditch effort is to execute an optional callback function that can
be provided by the caller. This callback, named `config_file_callback`
receives the path to the legacy checkpoint and returns the path to the
config file to use. The CLI uses to put up a multiple choice prompt to
the user. The WebUI **could** use this to prompt the user to choose
from a radio-button selection.
5. If the config file cannot be determined, then the import is abandoned.
The user can attach a custom VAE to the imported and converted model
by copying the desired VAE into the same directory as the file to be
imported, and giving it the same basename. E.g.:
```
my-new-model.safetensors
my-new-model.vae.pt
```
For this to work, the VAE must end with ".vae.pt", ".vae.ckpt", or
".vae.safetensors". The indicated VAE will be converted into diffusers
format and stored with the converted models file, so the ".pt" file
can be deleted after conversion.
No facility is currently provided to swap a diffusers VAE at import
time, but this can be done after the fact using the WebUI and CLI's
model editing functions.
- 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.
- resolve conflicts with generate.py invocation
- remove unused symbols that pyflakes complains about
- add **untested** code for passing intermediate latent image to the
step callback in the format expected.
This PR fixes#2951 and restores the step_callback argument in the
refactored generate() method. Note that this issue states that
"something is still wrong because steps and step are zero." However,
I think this is confusion over the call signature of the callback, which
since the diffusers merge has been `callback(state:PipelineIntermediateState)`
This is the test script that I used to determine that `step` is being passed
correctly:
```
from pathlib import Path
from invokeai.backend import ModelManager, PipelineIntermediateState
from invokeai.backend.globals import global_config_dir
from invokeai.backend.generator import Txt2Img
def my_callback(state:PipelineIntermediateState, total_steps:int):
print(f'callback(step={state.step}/{total_steps})')
def main():
manager = ModelManager(Path(global_config_dir()) / "models.yaml")
model = manager.get_model('stable-diffusion-1.5')
print ('=== TXT2IMG TEST ===')
steps=30
output = next(Txt2Img(model).generate(prompt='banana sushi',
iterations=None,
steps=steps,
step_callback=lambda x: my_callback(x,steps)
)
)
print(f'image={output.image}, seed={output.seed}, steps={output.params.steps}')
if __name__=='__main__':
main()
```
