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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Remove unused param from ModelCache.
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@ -54,7 +54,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
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max_vram_cache_size: float,
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execution_device: torch.device = torch.device("cuda"),
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storage_device: torch.device = torch.device("cpu"),
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precision: torch.dtype = torch.float16,
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lazy_offloading: bool = True,
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log_memory_usage: bool = False,
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logger: Optional[Logger] = None,
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@ -65,7 +64,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
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:param max_cache_size: Maximum size of the RAM cache [6.0 GB]
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:param execution_device: Torch device to load active model into [torch.device('cuda')]
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:param storage_device: Torch device to save inactive model in [torch.device('cpu')]
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:param precision: Precision for loaded models [torch.float16]
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:param lazy_offloading: Keep model in VRAM until another model needs to be loaded
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:param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
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operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
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@ -74,7 +72,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
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"""
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# allow lazy offloading only when vram cache enabled
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self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
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self._precision: torch.dtype = precision
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self._max_cache_size: float = max_cache_size
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self._max_vram_cache_size: float = max_vram_cache_size
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self._execution_device: torch.device = execution_device
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@ -342,8 +339,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
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def make_room(self, 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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# calculate how much memory this model will require
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# multiplier = 2 if self.precision==torch.float32 else 1
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bytes_needed = size
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maximum_size = self.max_cache_size * GIG # stored in GB, convert to bytes
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current_size = self.cache_size()
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