Instructions to use RobinWZQ/CCLAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RobinWZQ/CCLAP with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RobinWZQ/CCLAP", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- a77c86ebf084d01a6ec26115abbe40f45606304ad19a0fa2f1a4bbc7622b95c1
- Size of remote file:
- 3.44 GB
- SHA256:
- 9d624f995b454e137e2b6a3d2017f248c862c31be0238c24d3705c4e1194a68f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.