Open image generation models are closer to closed-source quality than this sub thinks [D]
English summary
The author runs evaluations on generative image models and finds the gap between open and closed-source models is much smaller than assumed. Compositional control and text rendering in open models have reached competitive levels. Inference speed on consumer hardware is also faster than commonly believed. Structured prompting is highlighted as a production advantage rather than a downside. Overall, open models serve as strong baselines without requiring additional optimizations.
Chinese summary
作者对生成式图像模型进行了评估，发现开源模型与闭源模型之间的差距远小于普遍假设。开源模型在组合控制和文本渲染方面已达到竞争水平。在消费级硬件上的推理速度也比通常认为的要快。结构化提示被强调为生产环境的优势而非缺点。总体而言，开源模型无需额外优化即可作为强大的基线。
Key points
The perceived quality gap between open and closed image generation models is smaller than commonly believed.
开源和闭源图像生成模型之间的感知质量差距比普遍认为的要小。
Open models have significantly improved in compositional control and text rendering accuracy.
开源模型在组合控制和文本渲染准确性方面有显著提升。
Inference speed on a single consumer GPU can produce 2MP outputs in under two minutes.
在单个消费级GPU上，推理速度可在两分钟内生成2MP输出。
Structured prompting is an advantage for production pipelines, not a limitation.
结构化提示是生产管线的优势，而非限制。
Open models are competitive as baselines without community optimizations or fine-tuning.
开源模型在没有社区优化或微调的情况下作为基线具有竞争力。