Video-to-Video
Diffusers
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video-editing
streaming
quantization
int8
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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: 628 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 | import importlib
def get_obj_from_str(string):
module, cls = string.rsplit(".", 1)
return getattr(importlib.import_module(module), cls)
def build_from_config(config, **kwargs):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate.")
cls = get_obj_from_str(config["target"])
params = dict(config.get("params", {}))
params.update(kwargs)
pretrained_path = config.get("pretrained", None)
if pretrained_path is not None and hasattr(cls, "from_pretrained"):
return cls.from_pretrained(pretrained_path, **params)
obj = cls(**params)
return obj
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