In this work we introduce the SingleDigit2Image (SD2I) network -a simple, scalable generative network that can be used for direct conversion of sinogram to image (Figure ). The input of the SD2I is a random constant which preferably has a similar order of magnitude as the reconstructed image's signal. The SD2I acts as a generator network that creates an image based on the single number input; the generated image is then converted into a sinogram by the differentiable forward operator which is the Radon transform. This new sinogram is compared with the sinogram from the experimental dataset using a loss function and the weights of the SD2I network are updated accordingly. Our approach relies on a relatively simple and scalable architecture. We show that our approach can reconstruct a series of different modality tomography images with at least as good an accuracy as the FBP algorithm. We demonstrate the scalability of our method compared to the state-of-the-art methods. We also show that our method is able to deal with a commonly encountered challenge to FBP reconstruction, specifically sinograms exhibiting angular undersampling. We have tested our approach on a Shepp-Logan phantom as well as on experimental X-ray diffraction computed tomography (XRD-CT) and micro-CT sinogram data highlighting the flexibility and applicability of the method.