Instructions to use mhnakif/comfy2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mhnakif/comfy2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mhnakif/comfy2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 1,116 Bytes
2795df7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | from typing import TypeVar
class SingletonMetaclass(type):
T = TypeVar("T", bound="SingletonMetaclass")
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
cls._instances[cls] = super(SingletonMetaclass, cls).__call__(
*args, **kwargs
)
return cls._instances[cls]
def inject_instance(cls: type[T], instance: T) -> None:
assert cls not in SingletonMetaclass._instances, (
"Cannot inject instance after first instantiation"
)
SingletonMetaclass._instances[cls] = instance
def get_instance(cls: type[T], *args, **kwargs) -> T:
"""
Gets the singleton instance of the class, creating it if it doesn't exist.
"""
if cls not in SingletonMetaclass._instances:
SingletonMetaclass._instances[cls] = super(
SingletonMetaclass, cls
).__call__(*args, **kwargs)
return cls._instances[cls]
class ProxiedSingleton(object, metaclass=SingletonMetaclass):
def __init__(self):
super().__init__()
|