Qwen-Image Technical Report · HackerPapers discussions

Qwen-Image Technical Report — mentioned by schopra909

Wed, 07 Oct 2026 01:51:20 GMT

The approach is cool; but the results feel ~sus~. GenEval and GenEval2 prompts are very terse (~10 tokens) while these models are primarily trained on long-dense captions. So, it's pretty common to upsample prompts before running them through benchmarks. That way you're testing the model as it was trained, rather than testing it on prompts that are out of distribution. If you look at the Qwen-Image Report before they enhanced it for their 12-25 release, upsampled prompts score 0.87 on GenEval* In this paper they take it from 0.74 to 0.81. To me that reads to me that they're basically getting…

Paper and discussion history. A mention is not an endorsement.

Qwen-Image Technical Report — mentioned by mh-

Fri, 22 Aug 2025 20:50:08 GMT

I see where you're coming from, but if you take a look at the system prompts for these models (some are public, some have partially or fully leaked), you'll see that is no longer a concern. At least not for the kind of models being discussed here. That older generation of image diffusion models (e.g. Stable Diffusion) used text encoders like CLIP [0], which simply don't have the language understanding that even the smaller modern LLMs do. Later image models moved on to using variants of T5 [1], sometimes in addition to CLIP variants (this is how FLUX.1 works). The state of the art for open…

Paper and discussion history. A mention is not an endorsement.