Buckets:
| # Training and distillation | |
| [← Meridian](../README.md) · [Inference method](method.md) | |
| Training provenance and design are collected here rather than in the model card or research blog. | |
| ## Training overview | |
| The following is the release's training description; this repository contains inference code and | |
| artifacts, **not the training or distillation pipeline**. | |
| The teacher was trained on | |
| [MultiCamVideo](https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset): 13,600 Unreal Engine | |
| scenes, each filmed by ten synchronized cameras over 81 frames. A sample pairs one camera's clip | |
| with a point-cloud render from a second camera; the second camera's actual clip is the target. | |
| Both directions of camera pairs are used, and geometry is reconstructed from the source clip alone. | |
| Later stages added still-frame references, temporally extended examples made by slowing, reversing, | |
| or holding the 81-frame window, and eased sweeps. The student was distilled at 73, 90, and 124 frames, | |
| with 243-frame holds also reported. Training-time temporal augmentation is not a promise that every | |
| time mapping or reverse-playback path is supported by the released interfaces. | |
| ## Distillation and checkpoint design | |
| The release has two learned components: | |
| | Component | Role | | |
| |---|---| | |
| | `teacher_lora/` | Rank-128 re-camera adapter on the **unmodified** MiniMax-H3 transformer, reported size 2.5 GiB. Earlier releases shipped this teacher as a fused 61.7 GiB `transformer/`, now kept under `legacy/`. | | |
| | `turbo_lora/` | Rank-128 DMD student adapter distilled against that teacher, reported size 2.5 GiB. The two are loaded together at weight 1.0; neither is merged into the base weights. | | |
| | Mode | CLI settings | Transformer evaluations | | |
| |---|---|---| | |
| | Student, default | `--steps 4 --flow-shift 3` with both adapters loaded | 3 | | |
| | Teacher | `--lora teacher_lora --steps 50 --flow-shift 12` | 49 | | |
| The H3 scheduler counts the terminal zero-noise point in `--steps`. That endpoint does not require a | |
| model evaluation, hence four grid points produce three forwards. Reducing the teacher's step count | |
| is not equivalent to using the distilled student. Forward counts also do not directly translate to | |
| end-to-end speedups: reconstruction, warping, VAE work, and file writing still take time. | |
| The source and point-cloud-rendered references share the selected source timeline. Synchronized | |
| multi-camera supervision is followed by temporal and camera-path augmentation and student distillation. | |
| The training footage is synthetic; real-world performance depends on the scene. | |
| For weight provenance and modification notices, see [`MODIFICATIONS.md`](../MODIFICATIONS.md). | |
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