FrameForge：利用视频基础模型先验进行统一图像生成
FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation
FrameForge通过动态时间专家和中间推理路径建模预训练视频模型的先验，实现自适应条件图像生成，在DreamBench、COCO2017和GEdit-Bench上取得优于对比方法的性能。
Published on Oct 27
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Abstract
FrameForge enables adaptive conditional image generation by modeling variable condition-target relationships through dynamic temporal experts and intermediate reasoning pathways in pretrained video models.
Unified conditional image generation requires a single model to preserve different aspects of a visual input while producing a target image that follows textual guidance. Pretrained video models provide a promising foundation by encoding visual transformations across time, yet existing approaches typically assign the condition and target to fixed temporal positions. We observe that condition-target relationships vary substantially across generation tasks, while video models naturally encode visual transformations at different temporal scales. This motivates us to model conditional generation through input-adaptive virtual temporal relations. We propose FrameForge, which captures two complementary aspects of such relations. Mixture of Temporal Experts (MoTE) models how far the target lies from the condition by dynamically selecting temporal RoPE experts with different virtual intervals. Chain of Temporal Reasoning (CoTR) models how the condition is transformed into the target through a latent intermediate state, with a reasoning-bottleneck attention mask encouraging information propagation through this pathway. Experiments on DreamBench, COCO2017, and GEdit-Bench show that FrameForge achieves superior performance in subject-driven generation, canny-to-image generation, and image editing among the compared methods.
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来源: Hugging Face Daily Papers · huggingface.co