README.md

metadata

title: Anything2Image
emoji: 🏃
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 3.29.0
app_file: app.py
pinned: false

Anything To Image

Generate image from anything with ImageBind's unified latent space and stable-diffusion-2-1-unclip.

• No training is need.
• Integration with 🤗 Diffusers.
• imagebind is directly copy from official repo with modification.
• Gradio Demo.

Audio to Image

assets/wav/bird_audio.wav	assets/wav/dog_audio.wav	assets/wav/cattle.wav

import imagebind
import torch
from diffusers import StableUnCLIPImg2ImgPipeline

# construct models
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
)
pipe = pipe.to(device)

model = imagebind.imagebind_huge(pretrained=True)
model.eval()
model.to(device)

# generate image
with torch.no_grad():
    audio_paths=["assets/wav/bird_audio.wav"]
    embeddings = model.forward({
        imagebind.ModalityType.AUDIO: imagebind.load_and_transform_audio_data(audio_paths, device),
    })
    embeddings = embeddings[imagebind.ModalityType.AUDIO]
    images = pipe(image_embeds=embeddings.half()).images
    images[0].save("bird_audio.png")

More

Under construction

Citation

Latent Diffusion

@InProceedings{Rombach_2022_CVPR,
    author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
    title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {10684-10695}
}

ImageBind

@inproceedings{girdhar2023imagebind,
  title={ImageBind: One Embedding Space To Bind Them All},
  author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
  booktitle={CVPR},
  year={2023}
}