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", 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
| { | |
| "_class_name": "AutoencoderKLLTXVideo", | |
| "_diffusers_version": "0.32.0.dev0", | |
| "block_out_channels": [ | |
| 128, | |
| 256, | |
| 512, | |
| 512 | |
| ], | |
| "decoder_causal": false, | |
| "encoder_causal": true, | |
| "in_channels": 3, | |
| "latent_channels": 128, | |
| "layers_per_block": [ | |
| 4, | |
| 3, | |
| 3, | |
| 3, | |
| 4 | |
| ], | |
| "out_channels": 3, | |
| "patch_size": 4, | |
| "patch_size_t": 1, | |
| "resnet_norm_eps": 1e-06, | |
| "scaling_factor": 1.0, | |
| "spatio_temporal_scaling": [ | |
| true, | |
| true, | |
| true, | |
| false | |
| ] | |
| } |