revise config but need to migrate old format to new

invokeai/app/services/config.py

@@ -167,7 +167,7 @@ from argparse import ArgumentParser
 from omegaconf import OmegaConf, DictConfig, ListConfig
 from pathlib import Path
 from pydantic import BaseSettings, Field, parse_obj_as
-from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
+from typing import Any, ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
 
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
@@ -321,7 +321,7 @@ class InvokeAISettings(BaseSettings):
             for val in allowed_values:
                 allowed_types.add(type(val))
             allowed_types_list = list(allowed_types)
-            field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
+            field_type = allowed_types_list[0] if len(allowed_types) == 1 else int_or_float_or_str
 
             argparse_group.add_argument(
                 f"--{name}",
@@ -331,6 +331,15 @@ class InvokeAISettings(BaseSettings):
                 choices=allowed_values,
                 help=field.field_info.description,
             )
+            
+        elif get_origin(field_type) == Union:
+            argparse_group.add_argument(
+                f"--{name}",
+                dest=name,
+                type=int_or_float_or_str,
+                default=default,
+                help=field.field_info.description,
+            )
 
         elif get_origin(field_type) == list:
             argparse_group.add_argument(
@@ -390,14 +399,15 @@ class InvokeAIAppConfig(InvokeAISettings):
     log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
     patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
 
-    always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
-    free_gpu_mem        : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
-    max_cache_size      : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
-    max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
-    precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance')
-    sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
-    xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
-    tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
+    ram                 : Union[float,Literal['auto']] = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category='Cache')
+    vram                : Union[float,Literal['auto']] = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage (floating point number or 'auto')", category='Cache')
+    lazy_offload        : bool = Field(default=True, description='Keep models in VRAM until their space is needed', category='Cache')
+    precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Device')
+    device              : Literal[tuple(['cpu','cuda','mps','cuda','cuda:1','auto'])] = Field(default='auto',description='Generation device', category='Device')
+    sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Generation')
+    attention_type      : Literal[tuple(['auto','normal','xformers','sliced','torch-sdp'])] = Field(default='auto', description='Attention type', category='Generation')
+    attention_slice_size: Literal[tuple(['auto','max',1,2,3,4,5,6,7,8])] = Field(default='auto', description='Slice size, valid when attention_type=="sliced"', category='Generation')
+    tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Generation')
 
     root                : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
     autoimport_dir      : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
@@ -541,6 +551,11 @@ class InvokeAIAppConfig(InvokeAISettings):
         """Return true if precision set to float32"""
         return self.precision == "float32"
 
+    @property
+    def xformers_enabled(self) -> bool:
+        """Return true if attention_type=='xformers'."""
+        return self.attention_type=='xformers'
+
     @property
     def disable_xformers(self) -> bool:
         """Return true if xformers_enabled is false"""
@@ -561,6 +576,16 @@ class InvokeAIAppConfig(InvokeAISettings):
         """invisible watermark node is always active and disabled from Web UIe"""
         return True
 
+    @property
+    def max_cache_size(self) -> Union[str, float]:
+        """return value of ram attribute."""
+        return self.ram
+
+    @property
+    def max_vram_cache_size(self) -> Union[str, float]:
+        """return value of vram attribute."""
+        return self.vram
+    
     @staticmethod
     def find_root() -> Path:
         """
@@ -569,6 +594,18 @@ class InvokeAIAppConfig(InvokeAISettings):
         """
         return _find_root()
 
+    # @property
+    # def attention_slice_size(self) -> Union[str, int]:
+    #     """
+    #     Return one of "auto", "max", or 1-8.
+    #     """
+    #     size = self.attention_slice
+    #     try:
+    #         size= int(size)
+    #         assert size > 0
+    #     except ValueError:
+    #         pass
+    #     return size
 
 class PagingArgumentParser(argparse.ArgumentParser):
     """
@@ -586,3 +623,18 @@ def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
     Legacy function which returns InvokeAIAppConfig.get_config()
     """
     return InvokeAIAppConfig.get_config(**kwargs)
+
+def int_or_float_or_str(value:Any) -> Union[int, float, str]:
+    """
+    Workaround for argparse type checking.
+    """
+    try:
+        return int(value)
+    except:
+        pass
+    try:
+        return float(value)
+    except:
+        pass
+    return str(value)
+