Accuracy and Fidelity Comparison of Luna and DALL-E 2 Diffusion-Based Image Generation Systems
On the map: Diffusion models → Diffusion foundations and sampling
Abstract (arXiv)
We qualitatively examine the accuracy and fidelity between two diffusion-based image generation systems, namely DALL-E 2 and Luna, which have massive differences in training datasets, algorithmic approaches, prompt resolvement, and output upscaling. The methodology used is a qualitative benchmark created by Saharia et al. and in our research we conclude that DALL-E 2 significantly edges Luna in both alignment and fidelity comparisons.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 13 of its 28 references that are in the map. First posted on arXiv 5 Jan 2023.
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13 of 28 references.
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- 2022Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 22 Oct
- 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
Other references · 11
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- 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
- 2018Pros and Cons of GAN Evaluation Measurescited by 224
- 2021A Survey on Bias in Visual Datasetscited by 84
- 2022A very preliminary analysis of DALL-E 2cited by 82
- 2020Biases in Generative Art -- A Causal Look from the Lens of Art Historycited by 26
- 2022A Challenging Benchmark of Anime Style Recognitioncited by 6
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