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References to context.services.model_manager.store.get_model can only accept keys, remove invalid assertion

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Brandon Rising 2024-02-14 09:51:11 -05:00 committed by Brandon Rising
parent 5cc73ec5dd
commit aa5d124d70
2 changed files with 13 additions and 13 deletions
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4
invokeai/app/invocations/latent.py
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@ -681,7 +681,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
source_node_id = graph_execution_state.prepared_source_mapping[self.id] source_node_id = graph_execution_state.prepared_source_mapping[self.id]
# get the unet's config so that we can pass the base to dispatch_progress() # get the unet's config so that we can pass the base to dispatch_progress()
unet_config = context.services.model_manager.store.get_model(**self.unet.unet.model_dump()) unet_config = context.services.model_manager.store.get_model(self.unet.unet.key)
def step_callback(state: PipelineIntermediateState) -> None: def step_callback(state: PipelineIntermediateState) -> None:
self.dispatch_progress(context, source_node_id, state, unet_config.base) self.dispatch_progress(context, source_node_id, state, unet_config.base)
@ -709,7 +709,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
# Apply the LoRA after unet has been moved to its target device for faster patching. # Apply the LoRA after unet has been moved to its target device for faster patching.
ModelPatcher.apply_lora_unet(unet, _lora_loader()), ModelPatcher.apply_lora_unet(unet, _lora_loader()),
): ):
assert isinstance(unet, torch.Tensor) assert isinstance(unet, UNet2DConditionModel)
latents = latents.to(device=unet.device, dtype=unet.dtype) latents = latents.to(device=unet.device, dtype=unet.dtype)
if noise is not None: if noise is not None:
noise = noise.to(device=unet.device, dtype=unet.dtype) noise = noise.to(device=unet.device, dtype=unet.dtype)

22
invokeai/backend/model_manager/load/model_cache/model_cache_default.py
View File

@ -303,18 +303,18 @@ class ModelCache(ModelCacheBase[AnyModel]):
in_vram_models = 0 in_vram_models = 0
locked_in_vram_models = 0 locked_in_vram_models = 0
for cache_record in self._cached_models.values(): for cache_record in self._cached_models.values():
assert hasattr(cache_record.model, "device") if hasattr(cache_record.model, "device"):
if cache_record.model.device == self.storage_device: if cache_record.model.device == self.storage_device:
in_ram_models += 1 in_ram_models += 1
else: else:
in_vram_models += 1 in_vram_models += 1
if cache_record.locked: if cache_record.locked:
locked_in_vram_models += 1 locked_in_vram_models += 1
self.logger.debug( self.logger.debug(
f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) =" f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) ="
f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})" f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
) )
def make_room(self, model_size: int) -> None: def make_room(self, model_size: int) -> None:
"""Make enough room in the cache to accommodate a new model of indicated size.""" """Make enough room in the cache to accommodate a new model of indicated size."""