Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat(config): dynamic ram cache size

Browse Source 
Use the util function to calculate ram cache size on startup. This way, the `ram` setting will always be optimized for a system, even if they add or remove RAM. In other words, the default value is now dynamic.
This commit is contained in:
psychedelicious 2024-03-19 12:41:42 +11:00
parent fabef8b45b
commit 6e882d3fd6
1 changed files with 20 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
invokeai/app/services/config/config_default.py
View File

@ -10,6 +10,7 @@ from functools import lru_cache
from pathlib import Path
from typing import Any, Literal, Optional
import psutil
import yaml
from pydantic import BaseModel, Field, PrivateAttr, field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict
@ -32,6 +33,24 @@ LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
CONFIG_SCHEMA_VERSION = 4
def get_default_ram_cache_size() -> float:
"""Run a heuristic for the default RAM cache based on installed RAM."""
# On some machines, psutil.virtual_memory().total gives a value that is slightly less than the actual RAM, so the
# limits are set slightly lower than than what we expect the actual RAM to be.
GB = 1024**3
max_ram = psutil.virtual_memory().total / GB
if max_ram >= 60:
return 15.0
if max_ram >= 30:
return 7.5
if max_ram >= 14:
return 4.0
return 2.1 # 2.1 is just large enough for sd 1.5 ;-)
class URLRegexTokenPair(BaseModel):
url_regex: str = Field(description="Regular expression to match against the URL")
token: str = Field(description="Token to use when the URL matches the regex")
@ -147,7 +166,7 @@ class InvokeAIAppConfig(BaseSettings):
profiles_dir: Path = Field(default=Path("profiles"), description="Path to profiles output directory.")
# CACHE
ram: float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
ram: float = Field(default_factory=get_default_ram_cache_size, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
vram: float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB).")
convert_cache: float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB).")
lazy_offload: bool = Field(default=True, description="Keep models in VRAM until their space is needed.")