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https://github.com/invoke-ai/InvokeAI
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docs(nodes): update config field descriptions
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psychedelicious
Brandon
parent
8ba4b2a150
commit
3361aec065
@ -232,77 +232,79 @@ class InvokeAIAppConfig(InvokeAISettings):
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type: Literal["InvokeAI"] = "InvokeAI"
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# WEB
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host : str = Field(default="127.0.0.1", description="IP address to bind to", json_schema_extra=Categories.WebServer)
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port : int = Field(default=9090, description="Port to bind to", json_schema_extra=Categories.WebServer)
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allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", json_schema_extra=Categories.WebServer)
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allow_credentials : bool = Field(default=True, description="Allow CORS credentials", json_schema_extra=Categories.WebServer)
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allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", json_schema_extra=Categories.WebServer)
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allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", json_schema_extra=Categories.WebServer)
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host : str = Field(default="127.0.0.1", description="IP address to bind to. Use `0.0.0.0` to serve to your local network.", json_schema_extra=Categories.WebServer)
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port : int = Field(default=9090, description="Port to bind to.", json_schema_extra=Categories.WebServer)
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allow_origins : List[str] = Field(default=[], description="Allowed CORS origins.", json_schema_extra=Categories.WebServer)
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allow_credentials : bool = Field(default=True, description="Allow CORS credentials.", json_schema_extra=Categories.WebServer)
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allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS.", json_schema_extra=Categories.WebServer)
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allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS.", json_schema_extra=Categories.WebServer)
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# SSL options correspond to https://www.uvicorn.org/settings/#https
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ssl_certfile : Optional[Path] = Field(default=None, description="SSL certificate file (for HTTPS)", json_schema_extra=Categories.WebServer)
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ssl_keyfile : Optional[Path] = Field(default=None, description="SSL key file", json_schema_extra=Categories.WebServer)
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ssl_certfile : Optional[Path] = Field(default=None, description="SSL certificate file for HTTPS.", json_schema_extra=Categories.WebServer)
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ssl_keyfile : Optional[Path] = Field(default=None, description="SSL key file for HTTPS.", json_schema_extra=Categories.WebServer)
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# FEATURES
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esrgan : bool = Field(default=True, description="Enable/disable upscaling code", json_schema_extra=Categories.Features)
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internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", json_schema_extra=Categories.Features)
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esrgan : bool = Field(default=True, description="Enables or disables the upscaling code.", json_schema_extra=Categories.Features)
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# TODO(psyche): This is not used anywhere.
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internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models.", json_schema_extra=Categories.Features)
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log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", json_schema_extra=Categories.Features)
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patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", json_schema_extra=Categories.Features)
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ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', json_schema_extra=Categories.Features)
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patchmatch : bool = Field(default=True, description="Enable or disable patchmatch inpaint code.", json_schema_extra=Categories.Features)
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ignore_missing_core_models : bool = Field(default=False, description='Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.', json_schema_extra=Categories.Features)
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# PATHS
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root : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
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root : Optional[Path] = Field(default=None, description='The InvokeAI runtime root directory.', json_schema_extra=Categories.Paths)
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autoimport_dir : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
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models_dir : Path = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
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convert_cache_dir : Path = Field(default=Path('models/.cache'), description='Path to the converted models cache directory', json_schema_extra=Categories.Paths)
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legacy_conf_dir : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
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db_dir : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
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outdir : Path = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
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use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
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custom_nodes_dir : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths)
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from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
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models_dir : Path = Field(default=Path('models'), description='Path to the models directory.', json_schema_extra=Categories.Paths)
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convert_cache_dir : Path = Field(default=Path('models/.cache'), description='Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.', json_schema_extra=Categories.Paths)
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legacy_conf_dir : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files.', json_schema_extra=Categories.Paths)
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db_dir : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory.', json_schema_extra=Categories.Paths)
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outdir : Path = Field(default=Path('outputs'), description='Path to directory for outputs.', json_schema_extra=Categories.Paths)
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use_memory_db : bool = Field(default=False, description='Use in-memory database. Useful for development.', json_schema_extra=Categories.Paths)
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custom_nodes_dir : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes.', json_schema_extra=Categories.Paths)
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# TODO(psyche): This is not used anywhere.
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from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only).', json_schema_extra=Categories.Paths)
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# LOGGING
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log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', json_schema_extra=Categories.Logging)
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log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".', json_schema_extra=Categories.Logging)
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# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
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log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', json_schema_extra=Categories.Logging)
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log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", json_schema_extra=Categories.Logging)
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log_sql : bool = Field(default=False, description="Log SQL queries", json_schema_extra=Categories.Logging)
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log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.', json_schema_extra=Categories.Logging)
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log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher.", json_schema_extra=Categories.Logging)
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log_sql : bool = Field(default=False, description="Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.", json_schema_extra=Categories.Logging)
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# Development
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dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", json_schema_extra=Categories.Development)
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profile_graphs : bool = Field(default=False, description="Enable graph profiling", json_schema_extra=Categories.Development)
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dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed. Does not reload node definitions.", json_schema_extra=Categories.Development)
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profile_graphs : bool = Field(default=False, description="Enable graph profiling using `cProfile`.", json_schema_extra=Categories.Development)
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profile_prefix : Optional[str] = Field(default=None, description="An optional prefix for profile output files.", json_schema_extra=Categories.Development)
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profiles_dir : Path = Field(default=Path('profiles'), description="Directory for graph profiles", json_schema_extra=Categories.Development)
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skip_model_hash : bool = Field(default=False, description="Skip model hashing, instead assigning a UUID to models. Useful when using a memory db to reduce startup time.", json_schema_extra=Categories.Development)
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profiles_dir : Path = Field(default=Path('profiles'), description="Path to profiles output directory.", json_schema_extra=Categories.Development)
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skip_model_hash : bool = Field(default=False, description="Skip model hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models.", json_schema_extra=Categories.Development)
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version : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.CLIArgs)
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version : bool = Field(default=False, description="CLI arg - show InvokeAI version and exit.", json_schema_extra=Categories.CLIArgs)
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# CACHE
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ram : float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
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vram : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
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ram : float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).", json_schema_extra=Categories.ModelCache, )
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vram : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB)", json_schema_extra=Categories.ModelCache, )
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convert_cache : float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
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lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
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lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed.", json_schema_extra=Categories.ModelCache, )
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log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
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# DEVICE
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device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
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precision : Literal["auto", "float16", "bfloat16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
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device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device.", json_schema_extra=Categories.Device)
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precision : Literal["auto", "float16", "bfloat16", "float32", "autocast"] = Field(default="auto", description="Floating point precision.", json_schema_extra=Categories.Device)
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# GENERATION
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sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", json_schema_extra=Categories.Generation)
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attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", json_schema_extra=Categories.Generation)
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attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', json_schema_extra=Categories.Generation)
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force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Generation)
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png_compress_level : int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
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sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.", json_schema_extra=Categories.Generation)
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attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type.", json_schema_extra=Categories.Generation)
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attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced".', json_schema_extra=Categories.Generation)
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force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).", json_schema_extra=Categories.Generation)
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png_compress_level : int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.", json_schema_extra=Categories.Generation)
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# QUEUE
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max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", json_schema_extra=Categories.Queue)
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max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue.", json_schema_extra=Categories.Queue)
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# NODES
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allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", json_schema_extra=Categories.Nodes)
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deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes)
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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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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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remote_api_tokens : Optional[list[URLRegexToken]] = Field(default=None, description="List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.", json_schema_extra=Categories.ModelInstall)
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