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
| # Set web page format | |
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| # Disable virtual VRAM on windows system | |
| import torch | |
| torch.cuda.set_per_process_memory_fraction(0.999, 0) | |
| st.markdown(""" | |
| # DiffSynth Studio | |
| [Source Code](https://github.com/Artiprocher/DiffSynth-Studio) | |
| Welcome to DiffSynth Studio. | |
| """) | |