Instructions to use ViTeX-Bench/ViTeX-Edit-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ViTeX-Bench/ViTeX-Edit-14B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ViTeX-Bench/ViTeX-Edit-14B", 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
| import torch | |
| import torch.nn as nn | |
| from .wan_video_dit import sinusoidal_embedding_1d | |
| class WanMotionControllerModel(torch.nn.Module): | |
| def __init__(self, freq_dim=256, dim=1536): | |
| super().__init__() | |
| self.freq_dim = freq_dim | |
| self.linear = nn.Sequential( | |
| nn.Linear(freq_dim, dim), | |
| nn.SiLU(), | |
| nn.Linear(dim, dim), | |
| nn.SiLU(), | |
| nn.Linear(dim, dim * 6), | |
| ) | |
| def forward(self, motion_bucket_id): | |
| emb = sinusoidal_embedding_1d(self.freq_dim, motion_bucket_id * 10) | |
| emb = self.linear(emb) | |
| return emb | |
| def init(self): | |
| state_dict = self.linear[-1].state_dict() | |
| state_dict = {i: state_dict[i] * 0 for i in state_dict} | |
| self.linear[-1].load_state_dict(state_dict) | |