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
| # Florence2ModelLoaderMultiGPU | |
| `Florence2ModelLoaderMultiGPU` wraps the Florence2 model loader so you can decide which device handles model inference and which device receives Wan/Comfy offloads. Use it exactly like the original node from `ComfyUI-Florence2`; all native inputs remain available. | |
| ## Inputs | |
| All parameters from `Florence2ModelLoader` are still supported. The MultiGPU variant adds the following optional fields: | |
| | Parameter | Data Type | Description | | |
| | --- | --- | --- | | |
| | `device` | `STRING` | MultiGPU device used for runtime compute (`cuda:0`, `cuda:1`, `cpu`, etc.). | | |
| | `offload_device` | `STRING` | Device that receives automatic model offloads (defaults to `cpu`). | | |
| ## Outputs | |
| The outputs are identical to the upstream Florence2 loader (model tuple, additional metadata). Use them interchangeably in existing workflows; only the device placement behaviour changes. | |