512x512_diffusion_unconditional_ Image Net

Introduction

The 512x512 Diffusion Unconditional ImageNet model is designed for the generation of images, with or without classifier guidance. It is a fine-tuned version of a class-conditional diffusion model originally trained by OpenAI.

Architecture

This model was originally the OpenAI 512x512 class-conditional ImageNet diffusion model. It has been fine-tuned for 8100 steps to become an unconditional model, allowing for better guidance by models such as CLIP or other non-ImageNet classifiers.

Training

The model uses the ImageNet (ILSVRC 2012 subset) as its training data. No specific metrics or evaluations are listed. The training process has led to certain limitations, such as a propensity to produce unrealistic outputs, particularly in images of human faces, and potential biases related to gender and race due to the nature of the ImageNet dataset.

Guide: Running Locally

1. Installation: Ensure you have Python installed along with necessary libraries such as PyTorch.
2. Clone the Repository: Use Git to clone the model's repository from Hugging Face.
3. Install Requirements: Navigate to the cloned directory and install any required dependencies.
4. Run the Model: Execute the provided scripts to generate images.
5. Suggested Cloud GPUs: For optimal performance, consider using cloud services such as AWS EC2 with NVIDIA GPUs or Google Cloud Platform with TPUs.

License

The model is available under the MIT License. It is important to note the potential for biases and limitations inherent in the training data.