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
LoadFluxControlNetMultiGPU
LoadFluxControlNetMultiGPU exposes device selection for XLabAI's FLUX ControlNet loader, letting you keep the ControlNet on a secondary GPU or the CPU while the main FLUX UNet stays on your primary compute device.
Inputs
All inputs from the upstream LoadFluxControlNet node remain unchanged. The MultiGPU variant introduces one optional field:
| Parameter | Data Type | Description |
|---|---|---|
device |
STRING |
MultiGPU device that will host the ControlNet during inference. |
Outputs
Outputs match the base FLUX ControlNet loader exactly; only the device placement differs.