Recent advances in powerful text-to-image generation models have made it increasingly important to develop test-time methods that modify the sampling trajectory to produce images more faithful to complex compositional prompts. We present TILT, a training-free framework for compositional text-to-image generation via test-time reward alignment. We interpret compositional failures as overlap modes between joint and single-concept distributions, and define a reward that favors samples where all concepts are jointly present. This reward is intrinsic to the base model and does not require any external supervision or reward models. This yields a KL-constrained objective with a closed-form tilted target distribution and principled guiding steps for diffusion sampling. The interaction of concept distributions together with the above reward naturally leads to two different guidance strategies while a hybrid approach that balances their respective benefits produces stronger performance. Experiments on prompts from T2ICompBench show that our method improves compositional alignment while preserving image quality compared to previous baselines.</p>\n","updatedAt":"2026-07-28T17:28:17.748Z","author":{"_id":"68be23ac5271b9ac99d6f956","avatarUrl":"/avatars/26360a5957f6ffa6d18f335e18e940dc.svg","fullname":"Debottam Dutta","name":"debottamd07","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9014869332313538},"editors":["debottamd07"],"editorAvatarUrls":["/avatars/26360a5957f6ffa6d18f335e18e940dc.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.21606","authors":[{"_id":"6a68e5b65dad3683ba934988","name":"Debottam Dutta","hidden":false},{"_id":"6a68e5b65dad3683ba934989","name":"Jaehoon Hahm","hidden":false},{"_id":"6a68e5b65dad3683ba93498a","name":"Jianchong Chen","hidden":false},{"_id":"6a68e5b65dad3683ba93498b","name":"Romit Roy Choudhury","hidden":false}],"publishedAt":"2026-05-16T00:00:00.000Z","submittedOnDailyAt":"2026-07-28T00:00:00.000Z","title":"TILT: Improving Compositional Generation in Diffusion Models with a Model-Intrinsic Reward","submittedOnDailyBy":{"_id":"68be23ac5271b9ac99d6f956","avatarUrl":"/avatars/26360a5957f6ffa6d18f335e18e940dc.svg","isPro":false,"fullname":"Debottam Dutta","user":"debottamd07","type":"user","name":"debottamd07"},"summary":"Recent advances in powerful text-to-image generation models have made it increasingly important to develop test-time methods that modify the sampling trajectory to produce images more faithful to complex compositional prompts. We present TILT, a training-free framework for compositional text-to-image generation via test-time reward alignment. We interpret compositional failures as overlap modes between joint and single-concept distributions, and define a reward that favors samples where all concepts are jointly present. This reward is intrinsic to the base model and does not require any external supervision or reward models. This yields a KL-constrained objective with a closed-form tilted target distribution and principled guiding steps for diffusion sampling. The interaction of concept distributions together with the above reward naturally leads to two different guidance strategies while a hybrid approach that balances their respective benefits produces stronger performance. Experiments on prompts from T2ICompBench show that our method improves compositional alignment while preserving image quality compared to previous baselines.","upvotes":0,"discussionId":"6a68e5b65dad3683ba93498c"},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2607/2607.21606.md","query":{}}">

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