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
| # UNetLoaderLP | |
| `UNetLoaderLP` is a low-precision variant of the standard UNet loader that disables high-precision LoRA tensors for CPU-stored models, freeing additional host memory while remaining compatible with MultiGPU device routing. | |
| ## Inputs | |
| ### Required | |
| | Parameter | Data Type | Description | | |
| | --- | --- | --- | | |
| | `unet_name` | `STRING` | UNet checkpoint filename from `ComfyUI/models/unet`. | | |
| ### Optional | |
| | Parameter | Data Type | Description | | |
| | --- | --- | --- | | |
| | `device` | `STRING` | Device that should serve the UNet after loading. | | |
| ## Outputs | |
| | Output Name | Data Type | Description | | |
| | --- | --- | --- | | |
| | `model` | `MODEL` | Loaded UNet model with low-precision LoRA flag set. | | |