MiniMax-H3-Fun-Controlnet-Union-2.0

모델 소개

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.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

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출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

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。

자주 묻는 질문

출처 및 추적 기록

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。