Instructions to use bbbboiwow/cocccck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bbbboiwow/cocccck with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bbbboiwow/cocccck", 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
| # CheckpointLoaderNF4MultiGPU | |
| `CheckpointLoaderNF4MultiGPU` wraps the NF4 checkpoint loader from `ComfyUI_bitsandbytes_NF4` so you can pick the execution device when working with 4-bit Quantised diffusion checkpoints. | |
| ## Inputs | |
| All base parameters from `CheckpointLoaderNF4` are retained. The MultiGPU wrapper adds one optional field: | |
| | Parameter | Data Type | Description | | |
| | --- | --- | --- | | |
| | `device` | `STRING` | Device that should own the loaded NF4 checkpoint (GPU id or `cpu`). | | |
| ## Outputs | |
| Outputs are identical to the upstream NF4 loader (UNet/CLIP/VAE tuple). The only behavioural change is the explicit device placement. | | |