Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling
Existing generative models, such as diffusion and auto-regressive networks, are inherently static, relying on a fixed set of pretrained parameters to handle all inputs. In contrast, humans flexibly adapt their internal generative representations to each perceptual or imaginative context. Inspired by this capability, we introduce Composer, a new paradigm for adaptive generative modeling based on test-time instance-specific parameter composition. Composer generates input-conditioned parameter adaptations at inference time, which are injected into the pretrained model's weights, enabling per-input specialization without fine-tuning or retraining. Adaptation occurs once prior to multi-step ge...
| Authors | Minh-Tuan Tran, Xuan-May Le, Quan Hung Tran et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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