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
| # DownloadAndLoadWav2VecModelMultiGPU | |
| `DownloadAndLoadWav2VecModelMultiGPU` downloads a preset Wav2Vec2 checkpoint from Hugging Face (if missing) and loads it onto the device you choose, mirroring WanVideo's helper while adding MultiGPU awareness. | |
| ## Inputs | |
| ### Required | |
| | Parameter | Data Type | Description | | |
| | --- | --- | --- | | |
| | `model` | `STRING` | Preset identifier (`TencentGameMate/chinese-wav2vec2-base` or `facebook/wav2vec2-base-960h`). | | |
| | `base_precision` | `STRING` | Weight precision (`fp32`, `bf16`, `fp16`). | | |
| | `load_device` | `STRING` | Wan loader slot (`main_device` or `offload_device`). | | |
| | `device` | `STRING` | MultiGPU device to run the audio model. | | |
| ## Outputs | |
| | Output Name | Data Type | Description | | |
| | --- | --- | --- | | |
| | `wav2vec_model` | `WAV2VECMODEL` | Downloaded and loaded Wav2Vec2 model. | | |