Instructions to use odenroberts/LUCID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use odenroberts/LUCID with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NucleusAI/Nucleus-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("odenroberts/LUCID") prompt = "Screenshot" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - output: | |
| url: images/Screenshot 2026-04-21 at 2.22.17 PM.png | |
| text: Screenshot | |
| base_model: NucleusAI/Nucleus-Image | |
| instance_prompt: null | |
| license: unknown | |
| # LUCID | |
| <Gallery /> | |
| ## Model description | |
| https://cdn-uploads.huggingface.co/production/uploads/690e93c2ae6f634188d1d6dd/B_XzIzgcTY23Rp6AnGnrM.mp4 | |
| ## Download model | |
| [Download](/odenroberts/LUCID/tree/main) them in the Files & versions tab. | |