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
File size: 390 Bytes
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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.
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