[{"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","2026-09-20T16:32:12.437Z","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},[36,40,44,49,54,59,63,68,73,78,83,88,92,97],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",4663,"2026-09-28T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":39},"Security","security",757,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",399,"2026-09-27T18:39:02.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",261,"2026-09-27T15:30:35.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Hardware","hardware",188,"2026-09-27T20:46:36.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":39},"Science","science",148,{"name":64,"slug":65,"count":66,"latest_published_at":67},"Consumer Tech","consumer-tech",135,"2026-09-26T14:30:00.000Z",{"name":69,"slug":70,"count":71,"latest_published_at":72},"Software","software",91,"2026-09-25T20:55:00.000Z",{"name":74,"slug":75,"count":76,"latest_published_at":77},"Dev Tools","dev-tools",84,"2026-09-26T04:20:58.000Z",{"name":79,"slug":80,"count":81,"latest_published_at":82},"Startups","startups",76,"2026-09-25T18:33:59.000Z",{"name":84,"slug":85,"count":86,"latest_published_at":87},"Gaming","gaming",48,"2026-09-25T18:35:21.000Z",{"name":89,"slug":90,"count":86,"latest_published_at":91},"General","general","2026-09-26T17:02:42.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",30,"2026-09-24T20:07:31.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]