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: 855 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 26 27 28 29 | from PIL import ImageEnhance
from .base import VideoProcessor
class ContrastEditor(VideoProcessor):
def __init__(self, rate=1.5):
self.rate = rate
@staticmethod
def from_model_manager(model_manager, **kwargs):
return ContrastEditor(**kwargs)
def __call__(self, rendered_frames, **kwargs):
rendered_frames = [ImageEnhance.Contrast(i).enhance(self.rate) for i in rendered_frames]
return rendered_frames
class SharpnessEditor(VideoProcessor):
def __init__(self, rate=1.5):
self.rate = rate
@staticmethod
def from_model_manager(model_manager, **kwargs):
return SharpnessEditor(**kwargs)
def __call__(self, rendered_frames, **kwargs):
rendered_frames = [ImageEnhance.Sharpness(i).enhance(self.rate) for i in rendered_frames]
return rendered_frames
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