How Does Imagen Compare With Other Text-to-Image Models Like DALL-E 2?

Imagen reports an advantage over DALL-E 2, VQ-GAN+CLIP, and Latent Diffusion in side-by-side human comparisons, based on the results published on imagen.research.google. The site states that human raters preferred Imagen over these models in both sample quality and image-text alignment, and that Imagen reached a state-of-the-art FID score of 7.27 on COCO without ever training on COCO. These claims come from Google Research's own evaluation, so treat them as the project's reported results rather than an independent verdict.

The comparison dimensions

The Imagen page evaluates models on two axes:

• Sample quality — how photorealistic and artifact-free a generated image looks.
• Image-text alignment — how faithfully the image matches the prompt.

Both are judged by human raters in side-by-side comparisons, and FID is used as an automated quality metric.

What the reported numbers show

Two details are worth separating:

• COCO FID 7.27 is a quality score. The page notes Imagen achieved it without training on COCO, which makes the number more notable than a score obtained by training on the same dataset.
• DrawBench is the benchmark Imagen's authors introduced specifically to test text-to-image models more deeply. Because it was created alongside Imagen, it is a project-defined test rather than a neutral third-party one.

Why Imagen performs this way

The page attributes Imagen's results to a specific design choice: it relies on large pretrained language models (such as T5) to encode text, combined with diffusion models for image generation. The stated key discovery is that scaling the language model improves both sample fidelity and image-text alignment more than scaling the image diffusion model. In other words, the reported strength in prompt understanding comes mainly from the text encoder, not the image generator.

What this means for your choice

• If your priority is prompt adherence on complex descriptions, Imagen's reported DrawBench and alignment results are the relevant evidence.
• If you want an independent, third-party ranking, the page's comparisons are self-reported and DrawBench is author-introduced, so look for external evaluations before treating the ordering as settled.
• If you need access or pricing, the page does not state them — do not assume Imagen is free or openly available based on this page alone.

The page also lists related models in the Imagen family (Imagen Video, Imagen Editor) and shows example prompts, but it does not provide a feature-by-feature comparison table against DALL-E 2 beyond the benchmark results above.