text-to-sinogram-v1

Text-To-Sinogram

is an app for generation of sinogram with stable diffusion.

• Model checkpoint: https://huggingface.co/AshrafAlAodat/text-to-sinogram-v1

This repository contains the model files, including:

• a diffusers formated library
• a compiled checkpoint file
• a traced unet for improved inference speed

Model V1

It is a latent text-to-image diffusion model capable of generating sinogram images given any text input. These sinograms can be reconstructed back to the original image.

The model was created by Ashraf Al-Aodat as a proof of concept.

You can use the model directly.

The model was created by fine-tuning the Stable-Diffusion-v1-5 checkpoint. Read about Stable Diffusion here 🤗's Stable Diffusion blog.

Model Output

Examples of some generated sinograms using the prompt bat..

After image reconstruction..

Model Details

• Developed by: Ashraf Al-Aodat
• Model type: Diffusion-based text-to-image generation model
• Language(s): English
• License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based.
• Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (CLIP ViT-L/14) as suggested in the Imagen paper.

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

• Generation of artworks, and use in creative processes.
• Applications in educational or creative tools.
• Research on generative models.

Datasets

The modeal was trained on the sinograms dataset.

Fine Tuning

Check out the diffusers training examples from Hugging Face. Fine tuning requires a dataset of sinogram images of objects, with associated text describing them. Note that the CLIP encoder is able to understand and connect many words even if they never appear in the dataset. It is also possible to use a dreambooth method to get custom styles.

Citation

If you build on this work, please cite it as follows:

@article{TODO,
  author = {Al-Aodat, Ashraf*},
  title = {{Text-To-Sinogram - Stable diffusion for sinogram generation a proof of concept}},
  year = {2022}
}