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https://github.com/invoke-ai/InvokeAI
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civit -> civitai
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@ -285,7 +285,7 @@ class InvokeAIAppConfig(InvokeAISettings):
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node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", json_schema_extra=Categories.Nodes)
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# MODEL IMPORT
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civit_api_key : Optional[str] = Field(default=os.environ.get("CIVIT_API_KEY"), description="API key for Civit", json_schema_extra=Categories.Other)
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civitai_api_key : Optional[str] = Field(default=os.environ.get("CIVITAI_API_KEY"), description="API key for CivitAI", json_schema_extra=Categories.Other)
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# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
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always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
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@ -104,14 +104,14 @@ class ModelInstall(object):
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prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
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model_manager: Optional[ModelManager] = None,
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access_token: Optional[str] = None,
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civit_api_key: Optional[str] = None,
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civitai_api_key: Optional[str] = None,
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):
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self.config = config
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self.mgr = model_manager or ModelManager(config.model_conf_path)
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self.datasets = OmegaConf.load(Dataset_path)
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self.prediction_helper = prediction_type_helper
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self.access_token = access_token or HfFolder.get_token()
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self.civit_api_key = civit_api_key or config.civit_api_key
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self.civitai_api_key = civitai_api_key or config.civitai_api_key
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self.reverse_paths = self._reverse_paths(self.datasets)
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def all_models(self) -> Dict[str, ModelLoadInfo]:
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@ -330,7 +330,7 @@ class ModelInstall(object):
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with TemporaryDirectory(dir=self.config.models_path) as staging:
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CIVITAI_RE = r".*civitai.com.*"
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civit_url = re.match(CIVITAI_RE, url, re.IGNORECASE)
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location = download_with_resume(url, Path(staging), access_token=self.civit_api_key if civit_url else None)
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location = download_with_resume(url, Path(staging), access_token=self.civitai_api_key if civit_url else None)
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if not location:
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logger.error(f"Unable to download {url}. Skipping.")
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info = ModelProbe().heuristic_probe(location, self.prediction_helper)
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