Instructions to use vdo/Latte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vdo/Latte with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vdo/Latte", 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 t2v_required_models/vae/config.json from vdo/Latte: direct link, hf CLI and curl.
- Browser
- Download file 759 Bytes
-
https://huggingface.co/vdo/Latte/resolve/main/t2v_required_models/vae/config.json
- Command line
-
hf download hf://vdo/Latte/t2v_required_models/vae/config.json
-
curl -L -o config.json https://huggingface.co/vdo/Latte/resolve/main/t2v_required_models/vae/config.json
759 Bytes
| { | |
| "_class_name": "AutoencoderKL", | |
| "_diffusers_version": "0.22.0.dev0", | |
| "_name_or_path": "/home/patrick/.cache/huggingface/hub/models--diffusers--PixArt-XL-2-512x512/snapshots/877b9075010eaa287c8e761447f8767d36621064/vae", | |
| "act_fn": "silu", | |
| "block_out_channels": [ | |
| 128, | |
| 256, | |
| 512, | |
| 512 | |
| ], | |
| "down_block_types": [ | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D" | |
| ], | |
| "force_upcast": true, | |
| "in_channels": 3, | |
| "latent_channels": 4, | |
| "layers_per_block": 2, | |
| "norm_num_groups": 32, | |
| "out_channels": 3, | |
| "sample_size": 256, | |
| "scaling_factor": 0.18215, | |
| "up_block_types": [ | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D" | |
| ] | |
| } | |