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
File size: 762 Bytes
79dc332 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter
class SDXLVAEDecoder(SDVAEDecoder):
def __init__(self, upcast_to_float32=True):
super().__init__()
self.scaling_factor = 0.13025
@staticmethod
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}
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