title: Temporal In-Context Fine-Tuning for Versatile Control of Video Diffusion
    Models
summary: Recent advances in text-to-video diffusion models have enabled high-quality
  video synthesis, but controllable generation remains challenging, particularly
  under limited data and compute. Existing fine-tuning methods for conditional
  generation often rely on external encoders or architectural modifications,
  which demand large datasets and are typically restricted to spatially aligned
  conditioning, limiting flexibility and scalability. In this work, we introduce
  Temporal In-Context Fine-Tuning (TIC-FT), an efficient and versatile approach
  for adapting pretrained video diffusion models to diverse conditional
  generation tasks. Our key idea is to concatenate condition and target frames
  along the temporal axis and insert intermediate buffer frames with
  progressively increasing noise levels. These buffer frames enable smooth
  transitions, aligning the fine-tuning process with the pretrained model's
  temporal dynamics. TIC-FT requires no architectural changes and achieves strong
  performance with as few as 10-30 training samples. We validate our method
  across a range of tasks, including image-to-video and video-to-video
  generation, using large-scale base models such as CogVideoX-5B and Wan-14B.
  Extensive experiments show that TIC-FT outperforms existing baselines in both
  condition fidelity and visual quality, while remaining highly efficient in both
  training and inference. For additional results, visit
  https://kinam0252.github.io/TIC-FT/
paper_authors._id: 683e7cbf402acb186580d5ec
paper_authors.name: Kinam Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-03T12:32:41.547Z
paper_authors._id: 64797735a68454566356b708
paper_authors.avatarUrl: /avatars/3424d022dd8ad29b56eb41814c5c3dee.svg
paper_authors.fullname: Kinam Kim
paper_authors.type: user
paper_authors.user: kinam0252
paper_authors._id: 683e7cbf402acb186580d5ed
paper_authors.name: Junha Hyung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-03T12:32:50.417Z
paper_authors._id: 6331ed964db0a767bbee4706
paper_authors.avatarUrl: /avatars/2ef3fa6083ca5208f4b87c91345267d9.svg
paper_authors.fullname: Junha Hyung
paper_authors.type: user
paper_authors.user: JunhaH
paper_authors._id: 683e7cbf402acb186580d5ee
paper_authors.name: Jaegul Choo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-03T12:33:03.788Z
paper_authors._id: 64be3127805e5b64572da65c
paper_authors.avatarUrl: /avatars/edd3e94ba9e375827cc75b164602bcac.svg
paper_authors.fullname: Jaegul Choo
paper_authors.type: user
paper_authors.user: joyfull78
paper_title: Temporal In-Context Fine-Tuning for Versatile Control of Video Diffusion
    Models
paper_summary: Recent advances in text-to-video diffusion models have enabled high-quality
  video synthesis, but controllable generation remains challenging, particularly
  under limited data and compute. Existing fine-tuning methods for conditional
  generation often rely on external encoders or architectural modifications,
  which demand large datasets and are typically restricted to spatially aligned
  conditioning, limiting flexibility and scalability. In this work, we introduce
  Temporal In-Context Fine-Tuning (TIC-FT), an efficient and versatile approach
  for adapting pretrained video diffusion models to diverse conditional
  generation tasks. Our key idea is to concatenate condition and target frames
  along the temporal axis and insert intermediate buffer frames with
  progressively increasing noise levels. These buffer frames enable smooth
  transitions, aligning the fine-tuning process with the pretrained model's
  temporal dynamics. TIC-FT requires no architectural changes and achieves strong
  performance with as few as 10-30 training samples. We validate our method
  across a range of tasks, including image-to-video and video-to-video
  generation, using large-scale base models such as CogVideoX-5B and Wan-14B.
  Extensive experiments show that TIC-FT outperforms existing baselines in both
  condition fidelity and visual quality, while remaining highly efficient in both
  training and inference. For additional results, visit
  https://kinam0252.github.io/TIC-FT/
paper_ai_summary: Temporal In-Context Fine-Tuning (TIC-FT) enhances pretrained video diffusion models for diverse conditional generation tasks with minimal data and without architectural changes.
paper_ai_keywords: text-to-video diffusion models fine-tuning external encoders architectural modifications Temporal In-Context Fine-Tuning condition and target frames buffer frames noise levels smooth transitions pretrained video diffusion models image-to-video generation video-to-video generation CogVideoX-5B Wan-14B