Instructions to use Kry4ta1/Effecteraser-VOR-Inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kry4ta1/Effecteraser-VOR-Inference with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Kry4ta1/Effecteraser-VOR-Inference", 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 src/videox_fun/models/__init__.py from Kry4ta1/Effecteraser-VOR-Inference: direct link, hf CLI and curl.
- Browser
- Download file 612 Bytes
-
https://huggingface.co/Kry4ta1/Effecteraser-VOR-Inference/resolve/main/src/videox_fun/models/__init__.py
- Command line
-
hf download hf://Kry4ta1/Effecteraser-VOR-Inference/src/videox_fun/models/__init__.py
-
curl -L -o __init__.py https://huggingface.co/Kry4ta1/Effecteraser-VOR-Inference/resolve/main/src/videox_fun/models/__init__.py
612 Bytes
| from transformers import AutoTokenizer, T5EncoderModel, T5Tokenizer | |
| from .wan_text_encoder import WanT5EncoderModel | |
| from .wan_transformer3d import WanTransformer3DModel | |
| from .wan_vae import AutoencoderKLWan, load_lightx2v_vae | |
| from .vace_transformer3d import VaceWanModel | |
| from .video_latent_upscaler import VideoLatentUpscaler, build_lightvae_signature | |
| __all__ = [ | |
| "AutoTokenizer", | |
| "T5EncoderModel", | |
| "T5Tokenizer", | |
| "WanT5EncoderModel", | |
| "WanTransformer3DModel", | |
| "AutoencoderKLWan", | |
| "load_lightx2v_vae", | |
| "VaceWanModel", | |
| "VideoLatentUpscaler", | |
| "build_lightvae_signature", | |
| ] | |