Instructions to use Alignment-Lab-AI/Vid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Alignment-Lab-AI/Vid with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Alignment-Lab-AI/Vid", torch_dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
File size: 500 Bytes
430583d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"_class_name": "LTXVideoTransformer3DModel",
"_diffusers_version": "0.32.0.dev0",
"activation_fn": "gelu-approximate",
"attention_bias": true,
"attention_head_dim": 64,
"attention_out_bias": true,
"caption_channels": 4096,
"cross_attention_dim": 2048,
"in_channels": 128,
"norm_elementwise_affine": false,
"norm_eps": 1e-06,
"num_attention_heads": 32,
"num_layers": 28,
"out_channels": 128,
"patch_size": 1,
"patch_size_t": 1,
"qk_norm": "rms_norm_across_heads"
}
|