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
| # LoadWanVideoClipTextEncoderMultiGPU | |
| `LoadWanVideoClipTextEncoderMultiGPU` loads WanVideo CLIP vision/text encoders on the device you specify, making it easy to keep encoders off your primary compute GPU when memory is tight. | |
| ## Inputs | |
| ### Required | |
| | Parameter | Data Type | Description | | |
| | --- | --- | --- | | |
| | `model_name` | `STRING` | CLIP vision or text encoder model from `ComfyUI/models/clip_vision` or `ComfyUI/models/text_encoders`. | | |
| | `precision` | `STRING` | Weight precision for the model (`fp16`, `fp32`, or `bf16`). | | |
| ### Optional | |
| | Parameter | Data Type | Description | | |
| | --- | --- | --- | | |
| | `device` | `STRING` | Target MultiGPU device to host the encoder. | | |
| ## Outputs | |
| | Output Name | Data Type | Description | | |
| | --- | --- | --- | | |
| | `wan_clip_vision` | `CLIP_VISION` | Loaded CLIP vision/text module ready for image conditioning. | | |
| | `load_device` | `MULTIGPUDEVICE` | Device that now owns the encoder; feed into `WanVideoClipVisionEncode`. | | |