cs.CVSep 29, 2026
SaveLongLive-Plug: Once-for-All Distillation for Video Generation
Organizations: NVIDIA
Abstract
Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillation stage, for example to accelerate sampling or to improve long-video generation. This stage is typically repeated for every specialized model. We introduce LongLive-Plug, a once-for-all distillation framework that learns reusable capabilities as LoRAs on a base model for training-free, plug-and-play deployment to compatible downstream models. These capabilities include single-pass classifier-free guidance, few-step sampling, and long-context error correction for autoregressive generation. The adapters remain reusable even when downstream models add conditioning branches, expand output channels. Despite training at a fixed guidance scale, our dedicated CFG LoRA provides text guidance control through its inference weight. Combining it with a few-step LoRA simultaneously preserves few-step generation and CFG controllability on downstream tasks. We verify training-free deployment on 54 downstream models across three backbone families and eight task categories, including world modeling, robotics, editing, and multimodal generation. The approach may support additional compatible models. Each capability can thus be distilled once per backbone family and reused without per-target retraining.
Figures & tables
Figure 1 : Decoupled guidance control through an additional CFG-only LoRA. (a) Scaling a single distilled LoRA changes the entire update, including its learned guidance and few-step behavior. (b) Adding a separately weighted CFG-only LoRA lets us adjust guidance for each target while keeping the few-step adapter weight fixed. Arrow colors and widths schematically illustrate guidance compatibility, with the warmest coupled-transfer arrow at CFG . CFG-only LoRA scaling provides an adjustable guidance control.
Figure 3 : Matched transfer comparisons on SCOPE and Wan2.2-Fun-5B-Control (Depth). Two matched frames per task compare native (30/40 steps), naive four-step, per-target distilled, and LongLive-Plug outputs. Naive four-step sampling produces blurry, low-quality videos, whereas LongLive-Plug maintains high visual quality at four steps. Depth thumbnails condition ControlNet; boxes and strips show matched regions across methods. LongLive-Plug uses an additional base-distilled adapter variant. Appendix B gives full four-frame comparisons and alignment details.
Figure 4 : Guidance control after CFG-only distillation. The milk-splatter prompt compares native CFG references with CFG LoRA weights , , and . All variants use 50 sampling steps; every LoRA variant uses the distilled CFG setting and requires only one conditional forward pass per step. Rows show matched frames at 1 and 4 seconds. Higher LoRA weights follow the trend of stronger CFG, producing a more pronounced splash without exactly matching native CFG scales.
Figure 5 : Independent CFG control on SCOPE. (A) The coupled LoRA alone at weight shows little response to the prompts. Adding a separately weighted CFG LoRA strengthens the boxed prompt attributes while the coupled LoRA weight remains at . Colored lines link each box to its corresponding prompt text. (B) Scaling the entire coupled LoRA instead degrades generation. Frames are matched across weights within each row. All runs use four-step sampling with distilled CFG; (A) and (B) use different coupled checkpoints.
Figure 6 : Rank, data diversity, and long-context transfer. (a–b) Higher rank and more diverse prompts (lower concentration) improve FVD after transfer. (c–d) Mean of seven VBench dimensions after transfer to ReWorld and Matrix-Game 3.0, respectively. Long-context comparisons are within each model. Our transferred long-context LoRA improves quality during long AR rollouts.
Appendix figures & tables16 assets
Supplementary material from the paper’s appendix.
Appendix
| Setting | CFG-only, Wan2.2 | Few-step, Wan2.2 | Few-step, Wan2.1 |
| LoRA training rank | 128 | 128 | 128 |
| Fake-score LoRA | None | Yes | Yes |
| Generator / critic LR | / — | / | / |
| AdamW | |||
| Weight decay | 0 | 0 | 0.01 |
| LoRA dropout | 0 | 0 | 0 |
Table 4 : Wan training rank and reference optimization settings. All branches are trained at rank 128. Batch sizes are per process. For DMD, the generator updates once per five fake-score updates. The CFG column reports the reference CFG-only optimization settings.
Figure 7 : Keyframe comparison on SCOPE. Four matched keyframes from an 81-frame sequence at 20 fps. Rows compare default 30-step inference, naive four-step sampling, SCOPE-specific distillation, and transferred LoRAs, using the same case and seed. Boxes mark identical image coordinates across methods; the strips below each frame magnify these regions. Red highlights the naive four-step row’s ghosted wall and door edges, while the distilled variants retain clearer boundaries and surface detail.
Figure 8 : Keyframe comparison on Wan2.2-Fun-5B-Control. Four matched keyframes compare input depth and four generation methods. Boxes mark identical image coordinates across methods, magnified in the strips below each output. Red highlights the naive four-step row’s smeared rock, foliage, and road details and low contrast. Columns match conditioning frame indices (depth at 30 fps, outputs at 24 fps). The transferred output uses an additional base-distilled adapter variant.
| Backbone | World models | Robotics | ControlNet / structure | Camera / trajectory |
| Wan2.1-14B | FantasyWorld [ 19 ] ; LongVie 2 [ 26 ] ; Micro-World I2W [ 7 ] | DreamZero [ 90 ] ; ABot-PhysWorld [ 13 ] ; VERA DROID Planner [ 47 ] | Fun Control [ 1 ] ; TheDenk Dilated ControlNet [ 40 ] | Fun-V1.1 Control-Camera [ 2 ] ; Wan-Move [ 17 ] ; ATI [ 70 ] ; NeoVerse [ 88 ] ; Vista4D [ 48 ] |
| Wan2.2- TI2V-5B | Matrix-Game 3.0 [ 80 ] ; DreamX-World-5B AR [ 24 ] ; SCOPE [ 66 ] ; ReactiveGWM [ 77 ] ; Spatia [ 99 ] | Boundless World Model [ 9 ] ; Fast-WAM LIBERO [ 94 ] ; Motus Stage-1 VGM [ 8 ] | Fun Control [ 4 ] | Fun Control-Camera [ 3 ] ; FlashMotion [ 46 ] ; FrameINO v1.6 [ 71 ] |
| MiniMax-H3 | H3-World [ 10 ] ; Code World Model [ 12 ] ; SolarWM-H3 [ 32 ] | — | Fun ControlNet-Union [ 6 ] | SolarWM-H3 [ 32 ] |
Table 5 : Recorded transfer coverage across backbone families and tasks. Every listed Wan-model entry is evaluated for four-step and CFG transfer. The two panels share the same backbone axis.
Figure 9 : Additional Wan2.1-14B transfer cases. LongVie 2, ABot-PhysWorld, MagicTryOn, and Wan-Alpha illustrate world modeling, robotics, subject conditioning, and RGBA output. Each row compares two native frames (left) with the same two timestamps after four-step transfer (right). Inputs and task conditions are paired in the source report. The checkerboard is part of the Wan-Alpha preview, not a measurement of alpha-channel accuracy. Changes in appearance and motion remain visible after transfer.
Figure 10 : Additional Wan2.2-TI2V-5B transfer cases. Depth-conditioned Fun Control, FlashMotion, Kiwi-Edit, and Loomis Painter cover structure, trajectory, editing, and style adaptation. The native and transferred columns show identical frame indices for each paired task input. Native inference uses 50 steps for Kiwi-Edit and Loomis Painter. All transferred outputs use four steps with CFG distilled into the adapter; downstream conditioning and task adapters are retained.
Figure 11 : H3 task transfer: action control and line-art coloring. Left: undistilled multi-step inference. Middle: naive four-step sampling. Right: four-step inference with LongLive-Plug. H3-World shows matched frames at 1 and 4 s under a forward-action condition. LineartAnime shows frames at 1 s and the final available frame (3.71 s). S4 preserves a clearer character outline than E4 in these examples, while appearance can differ from D. These static frames do not evaluate audio quality or synchronization.
Figure 12 : H3 camera transfer includes a quality–control trade-off. Left: undistilled multi-step inference. Middle: naive four-step sampling. Right: four-step inference with LongLive-Plug. In the museum-crane example, S4 retains clearer architectural detail and a visible upward-camera response. In the library-yaw example, S4 remains sharp but its change in framing is attenuated relative to D/E4. HUD elements are inherited from the supplied anchor images. These cases have no generated-video pose regression metric and do not establish precise trajectory adherence.
Figure 13 : Additional Wan CFG-only control example. Native CFG references and CFG LoRA weights , , and use 50 sampling steps; LoRA outputs use runtime CFG with one conditional evaluation per step. Matched frames at 1 and 4 s show more pronounced flower opening as the adapter weight increases.
Figure 14 : Native CFG and CFG-only LoRA on Rooftop Martial Arts. Matched frames at 1 and 4 s compare native CFG (top: no CFG and scales – ) with CFG LoRA (bottom: weights , , , , ). The prompt, seed, and native schedule are fixed; LoRA outputs use one conditional forward pass per step. LoRA weights are not calibrated native CFG scales.
Figure 15 : Native CFG and CFG-only LoRA on the two night-village cases. Each case places native CFG above CFG LoRA at matched 1 and 4 s frames, using the sweeps in Fig. 14 . The prompt, seed, and native sampling schedule are fixed within each case.
Figure 16 : CFG-branch control with four-step SCOPE generation. The prompts request snow cover in a mountain valley and autumn vegetation around an ancient temple. Both use 81 frames at 20 fps; rows show 1, 2, and 4 s. Increasing the CFG-branch weight strengthens the snow cover and orange-red foliage, while the few-step weight stays at . An excessively large weight ( ) degrades quality, changing geometry and introducing spurious text.
Figure 17 : CFG-branch control with four-step video continuation. We continue the same watercolor paper-boat input with golden butterflies or pink lotus blossoms. The last 24 input frames condition 81 new frames at 24 fps; displayed times are relative to the generated continuation. Weights – produce more visible and persistent butterflies or more prominent lotus blossoms. At weight , dense generated content comes with fragmented scenery and stronger changes to the boat and islands. The few-step branch remains fixed at weight in every column.
Figure 18 : Long-rollout qualitative comparisons on ReWorld. Matched frames from a modern interior and a country lane. The +Long outputs retain more visible texture and object detail at late times.
Figure 19 : Long-context transfer to Matrix-Game 3.0: animated city. Columns show native frames 132, 528, 924, and 1,056 (zero-based), spanning early, middle, and late stages of the same 62.18 s rollout. The +Long output retains distinct facade edges and street objects at late times.
Figure 20 : Long-context transfer to Matrix-Game 3.0: overgrown temple. The same methods and timestamps as Fig. 19 compare stone architecture and vegetation. The +Long frames preserve visible stone-block boundaries, steps, and foliage late in the rollout, while appearance and layout vary across methods.