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implemented the following fixes:

Browse Source 
Enhancements:
1. Directory-based imports will not attempt to import components of diffusers models.
2. Diffuser directory imports now supported
3. Files that end with .ckpt that are not Stable Diffusion models (such as VAEs) are
   skipped during import.

Bugs identified in Psychedelicious's review:
1. The invokeai-configure form now tracks the current contents of `invokeai.init` correctly.
2. The autoencoders are no longer treated like installable models, but instead are
   mandatory support models. They will no longer appear in `models.yaml`

Bugs identified in Damian's review:
1. If invokeai-model-install is started before the root directory is initialized, it will
   call invokeai-configure to fix the matter.
2. Fix bug that was causing empty `models.yaml` under certain conditions.
3. Made import textbox smaller
4. Hide the "convert to diffusers" options if nothing to import.
This commit is contained in:
Lincoln Stein
2023-02-21 11:47:41 -05:00
parent 55dce6cfdd
commit 3795b40f63
7 changed files with 105 additions and 62 deletions
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39
ldm/invoke/model_manager.py
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@ -136,6 +136,7 @@ class ModelManager(object):
for model_name in self.config:
if self.config[model_name].get("default"):
return model_name
return list(self.config.keys())[0] # first one
def set_default_model(self, model_name: str) -> None:
"""
@ -633,18 +634,21 @@ class ModelManager(object):
models.yaml file.
"""
model_name = model_name or Path(repo_or_path).stem
description = description or f"imported diffusers model {model_name}"
description = model_description or f"imported diffusers model {model_name}"
new_config = dict(
description=description,
description=model_description,
vae=vae,
format="diffusers",
)
print(f'DEBUG: here i am 1')
if isinstance(repo_or_path, Path) and repo_or_path.exists():
new_config.update(path=str(repo_or_path))
else:
new_config.update(repo_id=repo_or_path)
print(f'DEBUG: here i am 2')
self.add_model(model_name, new_config, True)
print(f'DEBUG: config = {self.config}')
if commit_to_conf:
self.commit(commit_to_conf)
return model_name
@ -778,8 +782,12 @@ class ModelManager(object):
model_path = self._resolve_path(thing, 'models/ldm/stable-diffusion-v1') # _resolve_path does a download if needed
elif Path(thing).is_file() and thing.endswith(('.ckpt','.safetensors')):
print(f' | {thing} appears to be a checkpoint file on disk')
model_path = self._resolve_path(thing, 'models/ldm/stable-diffusion-v1')
if Path(thing).stem in ['model','diffusion_pytorch_model']:
print(f' | {Path(thing).name} appears to be part of a diffusers model. Skipping import')
return
else:
print(f' | {thing} appears to be a checkpoint file on disk')
model_path = self._resolve_path(thing, 'models/ldm/stable-diffusion-v1')
elif Path(thing).is_dir() and Path(thing, 'model_index.json').exists():
print(f' | {thing} appears to be a diffusers file on disk')
@ -792,11 +800,16 @@ class ModelManager(object):
)
elif Path(thing).is_dir():
print(f'>> {thing} appears to be a directory. Will scan for models to import')
for m in list(Path(thing).rglob('*.ckpt')) + list(Path(thing).rglob('*.safetensors')):
if model_name := self.heuristic_import(str(m), convert, commit_to_conf=commit_to_conf):
print(f' >> {model_name} successfully imported')
return model_name
if (Path(thing) / 'model_index.json').exists():
print(f'>> {thing} appears to be a diffusers model.')
model_name = self.import_diffuser_model(thing, commit_to_conf=commit_to_conf)
else:
print(f'>> {thing} appears to be a directory. Will scan for models to import')
for m in list(Path(thing).rglob('*.ckpt')) + list(Path(thing).rglob('*.safetensors')):
if model_name := self.heuristic_import(str(m), convert, commit_to_conf=commit_to_conf):
print(f' >> {model_name} successfully imported')
return model_name
elif re.match(r'^[\w.+-]+/[\w.+-]+$', thing):
print(f' | {thing} appears to be a HuggingFace diffusers repo_id')
@ -831,9 +844,9 @@ class ModelManager(object):
model_config_file = Path(Globals.root,'configs/stable-diffusion/v2-inference-v.yaml')
convert = True
else:
print(f'** {thing} is a legacy checkpoint file of unkown format. Will treat as a regular v1.X model')
model_config_file = Path(Globals.root,'configs/stable-diffusion/v1-inference.yaml')
print(f'** {thing} is a legacy checkpoint file but not in a known Stable Diffusion model. Skipping import')
return
if convert:
diffuser_path = Path(Globals.root, 'models',Globals.converted_ckpts_dir, model_path.stem)
model_name = self.convert_and_import(
@ -854,6 +867,8 @@ class ModelManager(object):
vae=str(Path(Globals.root,'models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt')),
commit_to_conf=commit_to_conf,
)
if commit_to_conf:
self.commit(commit_to_conf)
return model_name
def convert_and_import(