FrameForge Reuses Video Model Priors for Unified, Adaptive Conditional Image Generation

FrameXAI · hf · 2026-11-02

• Core idea: FrameForge casts conditional image generation tasks (editing, subject-driven generation, Canny-to-Image) as "input-adaptive virtual temporal relations", reusing the transformation priors learned by pretrained video foundation models to adapt to varying condition-target distances.
• Method: A Mixture of Temporal Experts (MoTE) routes each input to short/medium/long virtual time intervals, while a temporal Chain-of-Thought (CoTR) models intermediate reasoning latents via a reasoning-bottleneck attention mask.
• Efficiency: The video backbone stays frozen; a single shared LoRA is jointly trained across all three task types.

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