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Merge branch 'main' into feat/clip_skip

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blessedcoolant
2023-07-07 06:03:39 +12:00
committed by GitHub
parent ce7803231b d5f90b1a02
commit bc5371eeee
9 changed files with 46 additions and 44 deletions
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17
invokeai/backend/install/invokeai_configure.py
View File

@ -430,13 +430,13 @@ to allow InvokeAI to download restricted styles & subjects from the "Concept Lib
max_height=len(PRECISION_CHOICES) + 1,
scroll_exit=True,
)
self.max_loaded_models = self.add_widget_intelligent(
self.max_cache_size = self.add_widget_intelligent(
IntTitleSlider,
name="Number of models to cache in CPU memory (each will use 2-4 GB!)",
value=old_opts.max_loaded_models,
out_of=10,
lowest=1,
begin_entry_at=4,
name="Size of the RAM cache used for fast model switching (GB)",
value=old_opts.max_cache_size,
out_of=20,
lowest=3,
begin_entry_at=6,
scroll_exit=True,
)
self.nextrely += 1
@ -539,7 +539,7 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
"outdir",
"nsfw_checker",
"free_gpu_mem",
"max_loaded_models",
"max_cache_size",
"xformers_enabled",
"always_use_cpu",
]:
@ -555,9 +555,6 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
new_opts.license_acceptance = self.license_acceptance.value
new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
# widget library workaround to make max_loaded_models an int rather than a float
new_opts.max_loaded_models = int(new_opts.max_loaded_models)
return new_opts

5
invokeai/backend/install/model_install_backend.py
View File

@ -193,7 +193,10 @@ class ModelInstall(object):
models_installed.update(self._install_path(path))
# folders style or similar
elif path.is_dir() and any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
elif path.is_dir() and any([(path/x).exists() for x in \
{'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
]
):
models_installed.update(self._install_path(path))
# recursive scan

14
invokeai/backend/model_management/model_cache.py
View File

@ -8,7 +8,7 @@ The cache returns context manager generators designed to load the
model into the GPU within the context, and unload outside the
context. Use like this:
cache = ModelCache(max_models_cached=6)
cache = ModelCache(max_cache_size=7.5)
with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
cache.get_model('stabilityai/stable-diffusion-2') as SD2:
do_something_in_GPU(SD1,SD2)
@ -91,7 +91,7 @@ class ModelCache(object):
logger: types.ModuleType = logger
):
'''
:param max_models: Maximum number of models to cache in CPU RAM [4]
:param max_cache_size: Maximum size of the RAM cache [6.0 GB]
:param execution_device: Torch device to load active model into [torch.device('cuda')]
:param storage_device: Torch device to save inactive model in [torch.device('cpu')]
:param precision: Precision for loaded models [torch.float16]
@ -126,16 +126,6 @@ class ModelCache(object):
key += f":{submodel_type}"
return key
#def get_model(
# self,
# repo_id_or_path: Union[str, Path],
# model_type: ModelType = ModelType.Diffusers,
# subfolder: Path = None,
# submodel: ModelType = None,
# revision: str = None,
# attach_model_part: Tuple[ModelType, str] = (None, None),
# gpu_load: bool = True,
#) -> ModelLocker: # ?? what does it return
def _get_model_info(
self,
model_path: str,

5
invokeai/backend/model_management/model_manager.py
View File

@ -785,7 +785,7 @@ class ModelManager(object):
if path in known_paths or path.parent in scanned_dirs:
scanned_dirs.add(path)
continue
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
new_models_found.update(installer.heuristic_import(path))
scanned_dirs.add(path)
@ -794,7 +794,8 @@ class ModelManager(object):
if path in known_paths or path.parent in scanned_dirs:
continue
if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
new_models_found.update(installer.heuristic_import(path))
import_result = installer.heuristic_import(path)
new_models_found.update(import_result)
self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
installed.update(new_models_found)

9
invokeai/backend/model_management/model_probe.py
View File

@ -78,7 +78,6 @@ class ModelProbe(object):
format_type = 'diffusers' if model_path.is_dir() else 'checkpoint'
else:
format_type = 'diffusers' if isinstance(model,(ConfigMixin,ModelMixin)) else 'checkpoint'
model_info = None
try:
model_type = cls.get_model_type_from_folder(model_path, model) \
@ -105,7 +104,7 @@ class ModelProbe(object):
) else 512,
)
except Exception:
return None
raise
return model_info
@ -127,6 +126,8 @@ class ModelProbe(object):
return ModelType.Vae
elif any(key.startswith(v) for v in {"lora_te_", "lora_unet_"}):
return ModelType.Lora
elif any(key.endswith(v) for v in {"to_k_lora.up.weight", "to_q_lora.down.weight"}):
return ModelType.Lora
elif any(key.startswith(v) for v in {"control_model", "input_blocks"}):
return ModelType.ControlNet
elif key in {"emb_params", "string_to_param"}:
@ -137,7 +138,7 @@ class ModelProbe(object):
if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
return ModelType.TextualInversion
raise ValueError("Unable to determine model type")
raise ValueError(f"Unable to determine model type for {model_path}")
@classmethod
def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin)->ModelType:
@ -167,7 +168,7 @@ class ModelProbe(object):
return type
# give up
raise ValueError("Unable to determine model type")
raise ValueError("Unable to determine model type for {folder_path}")
@classmethod
def _scan_and_load_checkpoint(cls,model_path: Path)->dict: