Instructions to use vidfom/Wav2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vidfom/Wav2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vidfom/Wav2", 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
| from .sd_image import SDImagePipeline | |
| from .sd_video import SDVideoPipeline | |
| from .sdxl_image import SDXLImagePipeline | |
| from .sdxl_video import SDXLVideoPipeline | |
| from .sd3_image import SD3ImagePipeline | |
| from .hunyuan_image import HunyuanDiTImagePipeline | |
| from .svd_video import SVDVideoPipeline | |
| from .flux_image import FluxImagePipeline | |
| from .cog_video import CogVideoPipeline | |
| from .omnigen_image import OmnigenImagePipeline | |
| from .pipeline_runner import SDVideoPipelineRunner | |
| from .hunyuan_video import HunyuanVideoPipeline | |
| from .step_video import StepVideoPipeline | |
| from .wan_video import WanVideoPipeline | |
| KolorsImagePipeline = SDXLImagePipeline | |