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alindumitru/
neural-art-eeg

Name: neural-art-eeg
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Neural Art EEG-to-Image Model

This repository contains a custom PyTorch latent diffusion checkpoint trained with the neural-art repository code. It generates images conditioned on Emotiv Epoch X style EEG input.

Learn more about Neural Art

Github: https://github.com/alinvdu/neural-art Youtube: https://www.youtube.com/watch?v=8v_EB73m6cQ

Files

generative/checkpoints/checkpoint.pth: fine-tuned latent diffusion checkpoint.
generative/checkpoints/checkpoint-eeg.pth: EEG encoder checkpoint required by the current inference code.
config15.yaml: model architecture configuration.
generative/: custom model code copied from src/generation-service/generative.
inference.py: small helper for loading the model from this Hub repository.

Usage

This is custom PyTorch code, not a standard transformers or diffusers pipeline. Install the runtime dependencies, clone this repository, and run inference from the repository root.

pip install -r requirements.txt

python

import torch
from inference import NeuralArtPipeline

pipe = NeuralArtPipeline(device="cuda" if torch.cuda.is_available() else "cpu")

# EEG input should match the training/inference shape used by the original repo:
# a 2D tensor/array with shape [channels, time].
eeg = torch.randn(14, 1024)
images = pipe(eeg, num_samples=2, ddim_steps=100)

for i, image in enumerate(images):
    image.save(f"sample_{i}.png")

Intended Use

Research and experimentation with EEG-conditioned image generation. This model was developed for data compatible with an Emotiv Epoch X 14-channel headset and the preprocessing pipeline in the original repository.

Limitations

The model depends on custom repository code.
Inputs outside the training distribution may produce poor or unstable results.
The current loader expects both the diffusion checkpoint and the EEG encoder checkpoint.

Citation / Credits

Original repository: neural-art

Architecture credit in the source repository: DreamDiffusion.

alindumitru/neural-art-eeg