[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-nvidia-tool-learns-image-edits-from-examples-not-words":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},2915,"nvidia-tool-learns-image-edits-from-examples-not-words","NVIDIA Tool Learns Image Edits From Examples, Not Words","LoRWeB composes a learned basis of LoRA modules at inference time to match visual transformations shown by example, outperforming single-LoRA approaches.","A new NVIDIA research method lets image-editing models learn transformations from visual examples instead of text prompts.\n\nThe system, called LoRWeB, addresses a specific bottleneck in current image-editing pipelines. Existing approaches bolt a single Low-Rank Adaptation (LoRA) module onto a text-to-image model and ask it to handle the full range of possible visual changes — a task that turns out to be too broad for one module to generalize well. LoRWeB instead pre-trains a \"basis\" of multiple LoRA modules, each specializing in a different class of transformation, then uses a lightweight encoder to blend them on the fly for any given example pair. The whole composition happens in a single inference pass, so there is no fine-tuning at runtime.\n\nThe practical upshot is that a user can show the model a before-and-after image pair and ask it to apply the same change to a new image — no caption required. That matters because many visual edits (lighting shifts, style transfers, structural deformations) are genuinely hard to put into words, and forcing users to describe them in text introduces a translation layer that loses precision. The benchmark results show state-of-the-art performance and stronger generalization to transformations the model has not seen during training.\n\nLoRA stacking as a compositional strategy has been gaining traction across the generative AI space, but applying it to analogy-based editing rather than prompt conditioning is a narrower and more honest framing than the usual \"teach AI to understand images\" press release — the code is public on NVIDIA's research site.","[\"ai\",\"image-editing\",\"lora\",\"nvidia\"]","2026-06-30T04:00:00.000Z","2026-06-30T15:01:42.491Z","2026-06-30T15:01:45.359Z","published",null,[],"ai",[24,26,27,28],"image-editing","lora","nvidia",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.15727",0,{"sections":35},[36,40,45,50,55,60,65,70,75,80,85,89,94,99],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":86,"slug":87,"count":83,"latest_published_at":88},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]