The previous action should create the starting state of the next one, instead of constantly resetting back to a neutral pose. Fight Flow V1 does not mainly teach new moves. It explores whether H3 can learn how two or more characters should actually fight together. Unlike regular LoRA training that mainly relies on captions, D-OPSD allows the Teacher to directly reference the target training video.

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H3-superhuman-combat-lora

JOKER141

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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MiniMax-H3-Combat-Base-V2

JOKER141

Combat Base V2 is a major upgrade over V1, focusing not only on faster combat, but on better action continuity, hit reactions, cumulative damage, spatial movement, and physical cause-and-effect. Combat Base V2 is no longer just a combat LoRA — it now works for both action and dialogue scenes, adding richer body movement, finer sound details, and stronger shot-to-shot continuity to character performance. Combat Base V2 不再只是一个武打 LoRA——它现在可文可武，在文戏中也能增加更丰富的身体动作、更细致的声音表现，以及更强的镜头与状态连续性。 If the LoRA looks muddy or less impactful than expected, check the sampler setup first. Sampler choice matters a lot for this LoRA. Core Physics & Motion Logic / 核心物理与动作逻辑 Combat Base V2 moves away from "attack → reset" loops into a continuous physical chain where missed strikes carry momentum, blocks change direction smoothly, and limbs separate cleanly without sticking. Hit reactions now persist and accumulate—sustained torso bending, lowered guards, and impaired mobility directly build toward convincing heavy finishers.

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MiniMax-H3-Weapon-Combat-LoRA

JOKER141

> These links are provided for convenient cloud deployment and one-click workflow access. Some links contain referral / invite codes that may support the creator at no additional cost to the user. More stable weapon trajectories, large swings, rotational attacks, recovery motions, and weapon-state inheritance. Stronger blocking, parrying, deflection, sliding contact, evasive interaction, and weapon separation.

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MiniMaxH3-GunFu-Gunfight-LORA

JOKER141

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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