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
File size: 612 Bytes
9264c1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | 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",
]
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