<h3 id="bunny-selflift-or-12gb-direct-1080p-or-high-motion-+-lora-sharp-hybrid-workflow">BUNNY SelfLift | 12GB Direct 1080P | High Motion + LoRA Sharp Hybrid Workflow</h3><h3 id="bunny-selflift12gb1080p-+-lora">BUNNY SelfLift｜12GB直出1080P｜高动态 + LoRA不模糊混合工作流</h3>Try the workflow here: 工作流体验地址：<a target="_blank" rel="ugc" href="https://www.runninghub.ai/zh-cn/post/2102271264323829761/?inviteCode=rh-v1679">NEW Workflow name: BUNNY_SelfLift_12GB Direct 1080P_High Motion + LoRA Sharp_Hybrid Workflow</a>国内用户可以使用：<a target="_blank" rel="ugc" href="https://www.runninghub.cn/post/2102283144824184833/?inviteCode=cn-v1093">https://www.runninghub.cn/post/2102283144824184833/?inviteCode=cn-v1093</a>Workflow name: BUNNY_SelfLift_12GB Direct 1080P_High Motion + LoRA Sharp_Hybrid Workflow 工作流名称：BUNNY_SelfLift_12GB直出1080P_高动态+LORA不模糊_混合工作流Tested hardware: RTX 4070 Ti 12GB VRAM. 实测硬件：RTX 4070 Ti 12GB 显存。A 10-second H3 video at around 1080P / 2.0MP takes approximately 15 minutes locally on 12GB VRAM. 10秒 H3 视频在约1080P / 2.0MP下，本地12GB显存实测生成时间约 15分钟。The same 10-second test takes approximately 7 minutes on RunningHub. 同样的10秒测试，在 RunningHub 上大约 7分钟完成<h3 id="highly-recommended-for-complex-action:-pair-this-lora-with-bunny-h3-conditioning-bridge-for-better-multi-character-logic-action-ownership-and-scene-stability.-bunny-h3-conditioning-bridge"><a target="_blank" rel="ugc" href="https://civarchive.com/models/2936635/bunny-h3-conditioning-bridge-or-multi-character-logic-and-scene-stability">Highly recommended for complex action: pair this LoRA with BUNNY H3 Conditioning Bridge for better multi-character logic, action ownership, and scene stability. 复杂动作场景强烈推荐搭配 BUNNY H3 Conditioning Bridge，可进一步改善多人互动逻辑、动作归属和场景稳定性。</a></h3><hr />This workflow is designed for MiniMax H3 users who want to generate high-resolution video on consumer GPUs without sacrificing high-motion performance or LoRA clarity. 这套工作流面向 MiniMax H3 用户，目标是在消费级显卡上直接生成高分辨率视频，同时尽量保留高动态表现与 LoRA 清晰度。The core goal is simple: 12GB VRAM + 1080P / ~2.0MP + High Motion + LoRA without the usual post-upscale blur. 核心目标很简单：12GB显存 + 1080P / 约2.0MP + 高动态 + LoRA放大后不再容易发糊。The workflow combines SelfLift with MiniMax H3 related nodes and a customized hybrid sampling setup. 工作流基于 SelfLift，并结合 MiniMax H3 相关节点与定制的混合采样方案。The core solution was completed with help from a community member, while I tested and adjusted it mainly with my own high-motion LoRAs such as Combat, GUNFU, Weapon and Motion Repair. 核心方案由群友完成，我这边主要使用自己的 Combat、GUNFU、Weapon、Motion Repair 等高动态 LoRA 进行了实际测试与调整。The workflow supports H3 LoRAs and is especially useful for high-motion content such as combat, martial arts, gunfights, parkour and other motion-heavy scenes. 工作流支持 H3 LoRA，尤其适合战斗、武术、枪战、跑酷以及其他高动态场景。The point is not simply to make 1080P “run” on 12GB — the real goal is to keep high resolution, high motion and LoRA performance at the same time. 这套工作流并不只是为了让12GB“勉强跑得动1080P”，真正的目标是同时保住 高分辨率、高动态和 LoRA 表现。<hr /><hr /><h3 id="required-custom-nodes">Required Custom Nodes</h3><h3>相关自定义节点</h3>ComfyUI JR MiniMax H3 Node ComfyUI JR MiniMax H3 综合节点<a target="_blank" rel="ugc" href="https://github.com/Goldlionren/ComfyUI_JR_MiniMaxH3Node">https://github.com/Goldlionren/ComfyUI_JR_MiniMaxH3Node</a>SelfLift — progressive-resolution sampling and the core low-VRAM component of this workflow. SelfLift——渐进式分辨率采样，也是这套工作流低显存方案的核心组件。<a target="_blank" rel="ugc" href="https://github.com/facok/comfyui-SelfLift">https://github.com/facok/comfyui-SelfLift</a>ComfyUI-SolAttn_triton — attention acceleration used by the workflow environment. ComfyUI-SolAttn_triton——工作流环境中使用的注意力加速节点。<a target="_blank" rel="ugc" href="https://github.com/kijai/ComfyUI-SolAttn_triton">https://github.com/kijai/ComfyUI-SolAttn_triton</a>MiniMax H3 Audio T8 — MiniMax H3 video/audio and two-pass related nodes. MiniMax H3 Audio T8——提供 MiniMax H3 视频、音频及双采样相关节点。<a target="_blank" rel="ugc" href="https://github.com/T8mars/comfyui-minimax-h3-audio-T8">https://github.com/T8mars/comfyui-minimax-h3-audio-T8</a><hr />12GB VRAM. 1080P H3. High Motion. LoRA stays sharp. 12GB显存，直出1080P，高动态，LoRA不再一放大就糊。

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