Which flux model is better for AI image generation?

12 Tem 05:39

Looking at the new hype around Flux models among diffusion models, it's more like a startup boom rather than an academic research trend. The latest generation of Flux models, Flux.1 [dev] or Flux-schnell, are indeed fast—they generate images in what feels like 4-5 steps and you're done. But when it comes to speed, I think they're sacrificing quality directly. For example, in complex backgrounds or fine details (like finger count or facial expressions), Flux-schnell only produces "neat but dull" images. However, if you put in some effort with Flux-dev, you can get good results—but of course, resource consumption skyrockets. I think it's silly to optimize later just because I care about fine details. I believe the reason Flux models have become trendy is because people expect to get an image the moment they type a prompt. Without prompt engineering and proper inputs, Flux's results end up being mediocre. For instance, you can use SDXL or Playground v2 more comfortably when it comes to diversity compared to Flux. Also, when you talk about Flux models, they're usually dominated by a single company (Black Forest Labs), so it's worth worrying now—if competitors don't step up, we might end up with a monopoly in pricing and licensing. As an alternative, models like Mochi 1 or Hunyuan-DiT from Chinese sources work stably even without full pipeline optimization. Flux is fast, but it loses its edge in stability and efficiency. So, what do you think—should we invest in Flux's flashy demos, or go for long-term solutions with stable resource consumption?