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
MMAudioSamplerMultiGPU
MMAudioSamplerMultiGPU renders audio clips with MMAudio while giving you control over which accelerator runs the diffusion loop and whether frames stay offloaded.
Inputs
Required
| Parameter | Data Type | Description |
|---|---|---|
mmaudio_model |
MMAUDIO_MODEL |
Core MMAudio checkpoint prepared by the loader. |
feature_utils |
MMAUDIO_FEATUREUTILS |
Feature utility bundle containing VAE, Synchformer, CLIP, and optional vocoder. |
duration |
FLOAT |
Target duration for the generated audio in seconds. |
steps |
INT |
Number of sampler iterations to run. |
cfg |
FLOAT |
Classifier-free guidance scale. |
seed |
INT |
Random seed, 0 for deterministic repeatability. |
prompt |
STRING |
Positive conditioning text. |
negative_prompt |
STRING |
Negative conditioning text. |
mask_away_clip |
BOOLEAN |
Hide supplied clip video frames during sampling. |
force_offload |
BOOLEAN |
Force temporary offload of the model after sampling. |
Optional
| Parameter | Data Type | Description |
|---|---|---|
images |
IMAGE |
Reference frames to guide the sampler. |
device |
STRING |
Device that hosts the diffusion pass (cuda:0, cpu, etc.). |
Outputs
| Output Name | Data Type | Description |
|---|---|---|
audio |
AUDIO |
Generated audio waveform tensor. |