Video-to-Video
Diffusers
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video-editing
streaming
quantization
int8
text-stream-stripping
Instructions to use kylinfires/Joy-face_change_optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kylinfires/Joy-face_change_optimized with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kylinfires/Joy-face_change_optimized", 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: 892 Bytes
b9fc164 | 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 | import os
import sys
import loguru
_logger = None
class NullLogger:
def __getattr__(self, name):
return lambda *args, **kwargs: None
def bind(self, **kwargs):
return self
def setup_logger(exp_dir: str | None = None):
global _logger
loguru.logger.remove()
if int(os.getenv("RANK", 0)) > 0:
_logger = NullLogger()
return _logger
_logger = loguru.logger
options = dict(
level="INFO",
backtrace=False,
diagnose=False,
format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}",
)
_logger.add(sys.stderr, **options)
if exp_dir is not None:
_logger.add(os.path.join(exp_dir, "train.log"), colorize=False, encoding="utf-8", **options)
return _logger
def get_logger():
return _logger if _logger is not None else setup_logger()
__all__ = ["setup_logger", "get_logger"]
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