Instructions to use nnndite/ltx2.3Decompression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nnndite/ltx2.3Decompression with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nnndite/ltx2.3Decompression") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - output: | |
| url: images/ComfyUI_00056_.png | |
| text: '-' | |
| base_model: '' | |
| instance_prompt: null | |
| license: afl-3.0 | |
| # LTX-2.3-22b-IC-LoRA-Decompression | |
| <Gallery /> | |
| ## Model description | |
| 视频去伪影 | |
| ## Download model | |
| [Download](/nnndite/ltx2.3Decompression/tree/main) them in the Files & versions tab. | |