Instructions to use Cccccz/HY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cccccz/HY with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cccccz/HY", 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: 2,286 Bytes
74da989 | 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from typing import Literal, get_args
from trainer.layers.quantization.base_config import QuantizationConfig
QuantizationMethods = Literal[None]
QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
# The customized quantization methods which will be added to this dict.
_CUSTOMIZED_METHOD_TO_QUANT_CONFIG = {}
def register_quantization_config(quantization: str):
"""Register a customized vllm quantization config.
When a quantization method is not supported by vllm, you can register a customized
quantization config to support it.
Args:
quantization (str): The quantization method name.
Examples:
>>> from trainer.layers.quantization import register_quantization_config
>>> from trainer.layers.quantization import get_quantization_config
>>> from trainer.layers.quantization.base_config import QuantizationConfig
>>>
>>> @register_quantization_config("my_quant")
... class MyQuantConfig(QuantizationConfig):
... pass
>>>
>>> get_quantization_config("my_quant")
<class 'MyQuantConfig'>
""" # noqa: E501
def _wrapper(quant_config_cls):
if quantization in QUANTIZATION_METHODS:
raise ValueError(
f"The quantization method `{quantization}` is already exists.")
if not issubclass(quant_config_cls, QuantizationConfig):
raise ValueError("The quantization config must be a subclass of "
"`QuantizationConfig`.")
_CUSTOMIZED_METHOD_TO_QUANT_CONFIG[quantization] = quant_config_cls
QUANTIZATION_METHODS.append(quantization)
return quant_config_cls
return _wrapper
def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
if quantization not in QUANTIZATION_METHODS:
raise ValueError(f"Invalid quantization method: {quantization}")
method_to_config: dict[str, type[QuantizationConfig]] = {}
# Update the `method_to_config` with customized quantization methods.
method_to_config.update(_CUSTOMIZED_METHOD_TO_QUANT_CONFIG)
return method_to_config[quantization]
all = [
"QuantizationMethods",
"QuantizationConfig",
"get_quantization_config",
"QUANTIZATION_METHODS",
]
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