Video-to-Video
Diffusers
English
Chinese
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
Download training/src/models/mmdit/vae/__init__.py from kylinfires/Joy-face_change_optimized: direct link, hf CLI and curl.
- Browser
- Download file 70 Bytes
-
https://huggingface.co/kylinfires/Joy-face_change_optimized/resolve/main/training/src/models/mmdit/vae/__init__.py
- Command line
-
hf download hf://kylinfires/Joy-face_change_optimized/training/src/models/mmdit/vae/__init__.py
-
curl -L -o __init__.py https://huggingface.co/kylinfires/Joy-face_change_optimized/resolve/main/training/src/models/mmdit/vae/__init__.py
70 Bytes
| from .streaming import XVAEChunkCausal | |
| __all__ = ["XVAEChunkCausal"] | |