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
File size: 322 Bytes
79dc332 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # 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.
""")
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