Model overview
Everything else is carried over from v1: `control_in_dim = 49` (latent + masked latent + mask, so the same branch does control and inpaint), `control_apply_audio = false`, guidance-distilled (`guidance_scale = 1.0`), and the same zero-gated skip-add into the main branch. > **Loading a v1 config against this checkpoint is a silent failure.** With `minimax_h3_control.yaml` (5 blocks) the model builds only half the control branch; `load_state_dict(strict=False)` drops `control_blocks.5~9` as unexpected keys and misplaces the rest, producing wrong outputs. Always use `minimax_h3_control_inpaint_post_norm.yaml`. - **Union control over 8 conditions**: one checkpoint handles Canny, Depth, HED, MLSD, Pose, Scribble, Layout and Gray control videos for video-to-video generation — no per-condition checkpoint switching. - **Denser control injection**: the control branch attaches to 10 of the 50 transformer blocks (layers 0, 5, 10, 15, 20, 25, 30, 35, 40, 45); every control skip is added to the main branch through a zero-gated projection. This is roughly 2× the injection points of v1 and gives tighter structural adherence. - **Guidance-distilled**: run with `guidance_scale = 1.0`, one forward pass per step, no classifier-free guidance needed. - **Inpainting is supported, with the `post_norm` recipe**: the control input is widened to `control_in_dim = 49` (latent + masked latent + mask channels). Unlike v1, the masked pixels are zeroed *after* the ImageNet normalization (holes sit at 0 / mid-gray) rather than *before* it (holes landed near −2 / extreme dark), which improves how filled regions blend with kept regions. Use `examples/minimax_h3_fun/predict_v2v_control_inpaint.py`. - `control_context_scale` scales every control skip before it is added to the main branch: `1.0` gives the strongest control (used for all results below), values below `1.0` weaken the guidance of the control video, `0.0` switches the control branch off. - The generation follows the control video: the frame count snaps down to the largest `17 * n + 5` the video VAE can decode (duration capped at 15 seconds), the canvas keeps the control video's own aspect ratio at the `height * width` pixel budget (both multiples of 32), at a fixed 24 fps. - Detailed prompts give better stability; we recommend describing the scene, the subject and the camera in the prompt.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Do open weights permit free commercial use?
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。