NeROIC
jukebox
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| NeROIC | jukebox | |
|---|---|---|
| 4 | 128 | |
| 826 | 7,172 | |
| 3.4% | 0.7% | |
| 0.0 | 0.0 | |
| 8 months ago | 6 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NeROIC
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Make-A-Video is a state-of-the-art AI system that generates videos from text
I actually have been trying it out this week, and in fact it's currently trying to process the video generation, like their example shows. While I was able to follow their steps for training using their dataset, and generate the lighting/depth maps for the milkcarton example, the video generation is taking a long time (over 24hours, using a 3070Ti with 8GB VRAM).
From what I understand with NeROIC, it's not particularly meant to be able to generate an 3D model that can be imported into Blender (or other software). It requires more work to take the meshes it generates to do something with it. See https://github.com/snap-research/NeROIC/issues/10
jukebox
- Will AI be able to create similar sounding music based off input?
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Why didn't OpenAI MIT license Jukebox the same way they did CLIP?
As many of you are probably aware CLIP research paper and open source code that's under MIT license underpins the entire generative image model industry. Let's not forget it's only a little over 2 years ago that this image was completely mindblowing, and now it's so basic. People took the research and released code it took off like wildfire. But Jukebox, released a year earlier, never did even though it's just as revolutionary.
I didn't even know about it until I heard Sam Altman casually mention it in an interview, I was expecting some basic tunes generator, but this is so amazing! I mean yeah the voices are not clear, it's muffled, but look at how far have image models progressed, if you applied the same amount of collaborative effort here, the results could be amazing! ElevenLabs showed how good and clear can AI-created voices sound. The only reason I can think of is that the Jukebox code is under view license only.
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[R] [N] Noise2Music - Diffusion models for generating high quality music audio from text prompts, by Google Research
OpenAI had this figured out 3 years ago: https://openai.com/blog/jukebox/ . You could then even define your own text. Model is open source too.
- El éxito continuo de OpenAI: Y como llegaron a crear la IA más avanzada del 2023. ChatGPT.
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Implementation of Google's MusicLM in PyTorch
This model is designed to output raw audio.
However, there are many models which do output midi. That's actually much simpler, and has been done already a few years ago.
I thought OpenAI did this. But then, I might misremember, because their Jukebox actually also seems to produce raw audio (https://openai.com/blog/jukebox/).
However, midi generation is so easy, you even find it in some tutorials: https://www.tensorflow.org/tutorials/audio/music_generation
- FREE AI THINGS
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[D] Is MusicGPT a viable possibility?
There is https://openai.com/blog/jukebox/, which does generate music from prompt, but the prompt is not as detailed ("Hip Hop, in the style of Kanye West") and I don't think there is an easy way to use it online.
- AI (Visual) Art is creating a buzz. Is there AI Music?
What are some alternatives?
lucid-sonic-dreams
spleeter - Deezer source separation library including pretrained models.
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
music-demixing-challenge-starter-kit - Starter kit for getting started in the Music Demixing Challenge.
dalle-mini - DALL·E Mini - Generate images from a text prompt
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
ALAE - [CVPR2020] Adversarial Latent Autoencoders
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
musiclm-pytorch - Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch
tt-vae-gan - Timbre transfer with variational autoencoding and cycle-consistent adversarial networks. Able to transfer the timbre of an audio source to that of another.
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.