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expose max_cache_size to invokeai-configure interface (#3664)

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This PR allows the user to set the model manager cache size from within
the `invokeia-configure` TUI.
This commit is contained in:
blessedcoolant 2023-07-07 01:58:22 +12:00 committed by GitHub
commit 7a0154a7b8
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2 changed files with 9 additions and 22 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

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,