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", torch_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_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter | |
| class SDXLVAEDecoder(SDVAEDecoder): | |
| def __init__(self, upcast_to_float32=True): | |
| super().__init__() | |
| self.scaling_factor = 0.13025 | |
| def state_dict_converter(): | |
| return SDXLVAEDecoderStateDictConverter() | |
| class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter): | |
| def __init__(self): | |
| super().__init__() | |
| def from_diffusers(self, state_dict): | |
| state_dict = super().from_diffusers(state_dict) | |
| return state_dict, {"upcast_to_float32": True} | |
| def from_civitai(self, state_dict): | |
| state_dict = super().from_civitai(state_dict) | |
| return state_dict, {"upcast_to_float32": True} | |