Adobe正在测试AI agents如何能像Photoshop专业那样使用编辑工具
关键点
- Adobe Research and UC San Diego 构建了EditAgent，一种将图像编辑任务破解为子步骤的AI系统，并配对一个通用编辑器，并配有用于磨削或缩放等事务的专用工具。
- 一个标准语言模型评价平行生成的图像变体，使用计量统计和特写作物。 有了这一数据，它的业绩就超过了专门培训的评价工作模式。
- EditAgent比之前的AI agents产生更准确的结果，但需要大约十倍的计算。 之后可使用已核实的输出来训练更快的编辑模型。
Adobe研究者建造了用于图像编辑的AI agent，将通用编辑器与专用工具配对。 一个标准语言模型使用测量数据来比较结果，并选择最好的。
用于图像编辑的AI agents将复杂的请求破解成步骤，一般用相同的工具重试失败步骤，而单独的模型通过眼球判断输出。
Adobe Research和UC San Diego的研究人员希望用EditAgent来改变这个说法，根据一份新论文，这个系统的工作更像是Photoshop的亲信。 它为每项任务选择正确的工具，并将结果与使用硬数字的原始结果进行比较。
通用编辑器失败过多的任务
在对600幅图像编辑的测试中，5位专业人士盲目评分，通用编辑器Flux-2-Klein-9B未能产生可用结果42.7%的时间。 专门工具解决了大约三分之二的失败，特别是磨损、旋转和放大。 更多的重复与同一个编辑几乎没有帮助。
研究人员还测试了agent是否通过一步一步地提炼结果或平行生成多个变体来做得更好。 迭代的完善堆积了小错误，因为每轮都建立在已经改变的图像上。 在78.5%的比较中，专业人员选择了从原始中产生的平行变体。 agent只需要通过一半的评价。 EditAgent基于这两个发现。
硬数字将标准模型转化为更好的法官
Editate Agent按步骤规划一个请求，对于每个步骤，将通用编辑器与匹配的专门工具对齐。 一个标准语言模型然后在单传中比较所有变体。
对于每个变体，模型都获得一个验证记录，并有编辑区域的特写，改变区域的变化图，并测量亮度和颜色的统计数据。 例如，照片应该轻轻地亮亮。 数字显示通用编辑器过度曝光，调换了颜色的暖气。 专门的工具仍然在范围之内。
研究人员在他们600版的基准测试上测试了这个。 法官模式必须从几个候选人中挑选最佳结果，他们计算了它选择专业人员同样被评为可用形象的频率。 作为比较，他们运行了"EditScore"等模型，这些模型被微调了人类对图像编辑的收视率。
没有核查记录，所测试的7种语言模型都无法匹配最佳的微调模型。 用它，所有7击败每一个精细的模型。 Qwen3.8-27B在时间上做了81.3%，而EditScore-72B为74.8%. 对于研究人员来说，这表明法官模型所看到的可能比如何培训更重要。
在每回合之后，EditAgent还检查后期编辑是否不慎未完成早期的。 在报纸上的例子中，指示"让在更多的日光下"使之前被霜冻的窗户再次清晰。 agent抓住冲突并修复冲突。
EditAgent改进了开源编辑器Qwen-Image-Edit-2511和Flux-2-Klein-9B，跨越了三个基准测试，并超越了另外七个AI agents. 在EdiVal 基准测试上，Qwen变体在其第三部编辑的圆边缘上经过了商业模型Seedream 4.0和GPT-Image-1.5. 但是，agent需要的计算量大约是单一编辑器的十倍，其性能取决于插入哪些工具。
agent 为更快的编辑生成培训数据
研究人员还使用EditAgent作为数据源。 由于agent只保留了核实的结果，它产生了约26000个培训实例。 编辑器对数据进行了微调，在所有三个基准测试中表现较好。 另一方面，FLUX直接产生的训练数据实际上使FLUX更糟糕。 这种权衡可以让agent的高计算成本随着时间推移变得值得。
Adobe在6月将AI agents添加到Photoshop, Premiere，以及其他Creative Cloud apps中，开始作为公共β. 用户描述他们想要什么，agent处理单个步骤。 这些agents如何选择正确的工具并检查自己的输出是一个直接适用于Adobe的航运产品的问题。 黑森林实验室此后取代了论文中测试的通用模型：新的Flux 3 Image Klein有56亿参数，比Flux 2 Klein小，然而它比所有其他开放模型甚至商业Flux 2 Pro在黑森林实验室内部测试中的得分都高。
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Adobe is testing how well AI agents can use editing tools like a Photoshop professional would
Key Points
- Adobe Research and UC San Diego built EditAgent, an AI system that breaks image editing tasks into sub-steps and pairs a general-purpose editor with specialized tools for things like sharpening or zooming.
- A standard language model evaluates image variants generated in parallel, using measured statistics and close-up crops. With this data, it outperforms evaluation models that were specifically trained for the job.
- EditAgent produces more accurate results than previous AI agents but needs about ten times the compute. The verified output can then be used to train faster editing models.
Adobe researchers built an AI agent for image editing that pairs a general-purpose editor with specialized tools. A standard language model compares results using measured data and picks the best one.
AI agents for image editing break complex requests into steps and typically retry failed steps with the same tools, while a separate model judges the output by eyeballing it.
Researchers at Adobe Research and UC San Diego want to change that with EditAgent, a system that works more like a pro in Photoshop, according to a new paper. It picks the right tool for each task and compares the result against the original using hard numbers.
General-purpose editors fail at too many tasks
In a test of 600 image edits, blind-rated by five professionals, the general-purpose editor Flux-2-Klein-9B failed to produce a usable result 42.7 percent of the time. Specialized tools solved roughly two-thirds of those failures, especially for sharpening, rotation, and zooming. More retries with the same editor barely helped.
The researchers also tested whether an agent does better by refining results step by step or by generating multiple variants in parallel. Iterative refinement piles up small errors because each round builds on an already altered image. Professionals preferred the parallel variants generated from the original in 78.5 percent of the comparisons where they picked a side. The agent needed only half as many evaluation passes. EditAgent is built on these two findings.
Hard numbers turn standard models into better judges
EditAgent plans a request in steps and, for each step, pits the general-purpose editor against matching specialized tools. A standard language model then compares all variants in a single pass.
For each variant, the model gets a verification record with a close-up of the edited area, a change map of altered regions, and measured statistics on brightness and color. In one example, a photo was supposed to be gently brightened. The numbers showed the general-purpose editor overexposed it and shifted the colors warmer. The specialized tool stayed within spec.
The researchers tested this on their 600-edit benchmark. The judge model had to pick the best result from several candidates, and they counted how often it chose an image the professionals also rated as usable. For comparison, they ran models like EditScore that were fine-tuned on human ratings of image edits.
Without the verification record, none of the seven language models tested could match the best fine-tuned model. With it, all seven beat every fine-tuned model. Qwen3.8-27B made a usable pick 81.3 percent of the time, compared to 74.8 percent for EditScore-72B. For the researchers, this suggests that what a judge model sees can matter more than how it was trained.
After each round, EditAgent also checks whether later edits accidentally undid earlier ones. In the paper's example, the instruction "let in more daylight" makes a previously frosted window clear again. The agent catches the conflict and fixes it.
EditAgent improved the open-source editors Qwen-Image-Edit-2511 and Flux-2-Klein-9B across three benchmarks and outperformed seven other AI agents. On the EdiVal benchmark, the Qwen variant in its third editing round edged past the commercial models Seedream 4.0 and GPT-Image-1.5. But the agent needs about ten times as much compute as a single editor, and its performance depends on which tools are plugged in.
The agent generates training data for faster editors
The researchers also use EditAgent as a data source. Because the agent only keeps verified results, it produced about 26,000 training examples. Editors fine-tuned on that data performed better across all three benchmarks. Training data generated directly by FLUX, on the other hand, actually made FLUX worse. That tradeoff could make the agent's high compute costs worth it over time.
Adobe added AI agents to Photoshop, Premiere, and other Creative Cloud apps in June, starting as a public beta. Users describe what they want, and the agent handles the individual steps. How well these agents pick the right tools and check their own output is a question that applies directly to Adobe's shipping products. Black Forest Labs has since replaced the general-purpose model tested in the paper: the new Flux 3 Image Klein has 5.6 billion parameters, smaller than Flux 2 Klein, yet it scores higher than all other open models and even the commercial Flux 2 Pro in Black Forest Labs' internal tests.
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