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 Settings
- Draw Things
- DiffusionBee
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- Xet hash:
- c2bc16e58bbf514a0b20d518e60079af8c40568f32650e7436a33c51344e666f
- Size of remote file:
- 1.25 MB
- SHA256:
- c1bf4205205620bed2b2837da70e1ad66433882e4e898d66dfbb247e8297f4f5
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