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
Download training/src/utils/constants.py from kylinfires/Joy-face_change_optimized: direct link, hf CLI and curl.
- Browser
- Download file 120 Bytes
-
https://huggingface.co/kylinfires/Joy-face_change_optimized/resolve/main/training/src/utils/constants.py
- Command line
-
hf download hf://kylinfires/Joy-face_change_optimized/training/src/utils/constants.py
-
curl -L -o constants.py https://huggingface.co/kylinfires/Joy-face_change_optimized/resolve/main/training/src/utils/constants.py
120 Bytes
| import torch | |
| PRECISION_TO_TYPE = { | |
| "fp32": torch.float32, | |
| "fp16": torch.float16, | |
| "bf16": torch.bfloat16, | |
| } | |