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https://github.com/invoke-ai/InvokeAI
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References to context.services.model_manager.store.get_model can only accept keys, remove invalid assertion
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@ -681,7 +681,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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source_node_id = graph_execution_state.prepared_source_mapping[self.id]
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# get the unet's config so that we can pass the base to dispatch_progress()
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unet_config = context.services.model_manager.store.get_model(**self.unet.unet.model_dump())
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unet_config = context.services.model_manager.store.get_model(self.unet.unet.key)
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def step_callback(state: PipelineIntermediateState) -> None:
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self.dispatch_progress(context, source_node_id, state, unet_config.base)
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@ -709,7 +709,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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# Apply the LoRA after unet has been moved to its target device for faster patching.
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ModelPatcher.apply_lora_unet(unet, _lora_loader()),
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):
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assert isinstance(unet, torch.Tensor)
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assert isinstance(unet, UNet2DConditionModel)
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latents = latents.to(device=unet.device, dtype=unet.dtype)
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if noise is not None:
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noise = noise.to(device=unet.device, dtype=unet.dtype)
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@ -303,18 +303,18 @@ class ModelCache(ModelCacheBase[AnyModel]):
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in_vram_models = 0
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locked_in_vram_models = 0
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for cache_record in self._cached_models.values():
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assert hasattr(cache_record.model, "device")
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if cache_record.model.device == self.storage_device:
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in_ram_models += 1
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else:
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in_vram_models += 1
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if cache_record.locked:
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locked_in_vram_models += 1
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if hasattr(cache_record.model, "device"):
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if cache_record.model.device == self.storage_device:
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in_ram_models += 1
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else:
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in_vram_models += 1
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if cache_record.locked:
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locked_in_vram_models += 1
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self.logger.debug(
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f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) ="
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f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
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)
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self.logger.debug(
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f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) ="
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f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
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)
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def make_room(self, model_size: int) -> None:
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"""Make enough room in the cache to accommodate a new model of indicated size."""
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