Instructions to use WizWhite/c0c4c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WizWhite/c0c4c with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DFloat11/Wan2.1-T2V-14B-Diffusers-DF11", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("WizWhite/c0c4c") prompt = "Make it a w1z_p0pc0r3 style illustration" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
c0c4c
.png)
- Prompt
- Make it a w1z_p0pc0r3 style illustration
Download model
Download them in the Files & versions tab.
- Downloads last month
- 6
Model tree for WizWhite/c0c4c
Base model
Wan-AI/Wan2.1-T2V-14B-Diffusers Quantized
DFloat11/Wan2.1-T2V-14B-Diffusers-DF11