Instructions to use hf-internal-testing/tiny-LTX2Pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-LTX2Pipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-LTX2Pipeline", 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
| { | |
| "_class_name": "AutoencoderKLLTX2Audio", | |
| "_diffusers_version": "0.39.0.dev0", | |
| "attn_resolutions": null, | |
| "base_channels": 4, | |
| "causality_axis": "height", | |
| "ch_mult": [ | |
| 1 | |
| ], | |
| "double_z": true, | |
| "dropout": 0.0, | |
| "in_channels": 2, | |
| "is_causal": true, | |
| "latent_channels": 2, | |
| "mel_bins": 8, | |
| "mel_hop_length": 160, | |
| "mid_block_add_attention": false, | |
| "norm_type": "pixel", | |
| "num_res_blocks": 1, | |
| "output_channels": 2, | |
| "resolution": 32, | |
| "sample_rate": 16000 | |
| } | |