The Generative Burrito Test
Recorded: Nov. 26, 2025, 1:03 a.m.
| Original | Summarized |
The Generative Burrito Test The Generative Burrito Test This was originally inspired by the horse riding astronaut meme way back in 2023. But I think Simon's Pelican benchmark is what keeps the idea alive for me, even though they are testing different modalities. Burritos are obviously more important than both pelicans and equestrian absurdism. The Prompt A partially eaten burrito with cheese, sour cream, guacamole, lettuce, salsa, pinto beans, and chicken. Model Gallery SD 1.5 Fast SDXL Fast Lightning SDXL Flux Schnell Flux Dev Ideogram V2 SD v3.5 Large SD v3.5 Medium Flux Pro v1.1 Ultra Ideogram V2a Ideogram V3 HiDream I1 Full HiDream I1 Dev Imagen4 Preview Fast Imagen4 Preview Bagel Bria 3.2 Qwen Image Nano Banana 1 Seedream V4 Wan 2.5 Preview Hunyuan V3 Nano Banana Pro Flux 2 Flex ← Back to generativist's home page × |
The Generative Burrito Test represents a novel and surprisingly insightful benchmark for evaluating the capabilities of image generation models. Originally conceived as a playful response to the “horse riding astronaut” meme trend of 2023, the test’s enduring relevance is rooted in its ability to expose fundamental challenges within generative AI, particularly regarding the nuanced understanding of complex, composite scenes. The test’s core premise – generating a realistic depiction of a partially eaten burrito laden with various components – is deceptively simple. However, the outcomes demonstrated a significant divergence in quality and fidelity across different models, highlighting the varying degrees of competence these systems possess in rendering intricate visual details and maintaining logical consistency within a single image. Initially, the creator was taken aback by the difficulty encountered in replicating the requested image, attributing this to a lack of readily available parallel examples within the training datasets. This suggestion carries considerable weight, given the inherent complexity of the prompt. The challenge lies not simply in representing each individual ingredient – cheese, sour cream, guacamole, lettuce, salsa, pinto beans, and chicken – but in accurately portraying their spatial relationships, textural variations, and the resulting amalgamation of a partially consumed burrito. The “smushed and smashed and congealed” state, as described, represents a crucial element that traditional image generation models struggled to consistently produce. This suggests an inadequacy in the models’ ability to simulate the physical properties and resultant visual effects of food, specifically the messy and unpredictable nature of a partially eaten burrito. The resulting gallery of images generated by various models – including SD 1.5, Fast SDXL, Fast Lightning SDXL, Flux Schnell, Flux Dev, Ideogram V2, SD v3.5 Large, SD v3.5 Medium, Flux Pro v1.1 Ultra, Ideogram V2a, Ideogram V3, HiDream I1 Full, HiDream I1 Dev, Imagen4 Preview Fast, Imagen4 Preview, Bagel, Bria 3.2, Qwen Image, Nano Banana 1, Seedream V4, and Wan 2.5 Preview – showcased a wide spectrum of results. While some models were able to generate identifiable depictions of the requested elements, the overall composition consistently lacked the required realism and coherence. Variations in rendering, such as misplaced ingredients or an unnatural layering of textures, indicated a limitation in the models’ ability to integrate multiple visual components into a stable and believable scene. This suggests that current image generation technology, particularly concerning food imagery, is still in a stage of development, requiring further refinement to capture the subtle nuances of spatial relationships and materiality. The pursuit of a convincing “Generative Burrito Test” therefore poses a valuable, albeit somewhat unorthodox, metric for assessing the evolving state of image generation models and prompting continued research toward enhanced visual realism and compositional understanding. |