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arXiv:2610.33460v1 Announce Type: new Abstract: Controllable image synthesis should modify visual affect without replacing the objects, layout, or relations specified by a prompt. Existing methods ad...
The paper introduces a semantic-first temporal scheduling strategy for flow-matching models in affect-controlled image generation. By using deterministic ODE sampling during the high-noise phase and restricting stochastic SDE exploration and group relative policy optimization (GRPO) to a late window, the method improves prompt faithfulness, affect accuracy, and human-rated semantic preservation. Experiments on FLUX.1-dev and SD3.5-Medium demonstrate reduced subject drift, faster GRPO training, and a clear trade-off between affect-semantic quality and computational cost, establishing denoising time as a practical control dimension for reliable emotional image generation.
arXiv:2609.24215v1 Announce Type: new Abstract: Although text-to-image models can accurately depict subjects and scenes, creators still struggle to specify the fine-grained emotions an image should c...
AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source state and a signed difference vector to encode the desired change, which is then translated into context‑dependent semantic and appearance modifications via a transition encoder and a diffusion backbone. The authors introduce AffectPair‑249K, a dataset of 248,841 source‑target pairs covering both cross‑category and within‑category transitions, and show that AffectDelta outperforms six baselines in affective alignment and content preservation.
AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source image’s affective state and encodes the signed difference to guide a diffusion backbone that applies context‑dependent semantic and appearance changes. The authors created a large AffectPair‑249K dataset of source‑target pairs and show that AffectDelta outperforms six baselines in both affective alignment and content preservation, with ablation studies supporting their design choices.
arXiv:2606. 13247v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved impressive results in synthesizing high-quality images from natural language prompts.