A LoRA that teaches MiniMax H3 (video + audio joint DiT) the mangascreentone rendering style with motion: monochrome ink rendering, propertone shading, and tone that holds up under dynamic camera movement and action.<h2 id="usage">Usage</h2>- Trigger phrase: <code>manga-tone rendering</code>- Recommended strength: 1.0 (usable 0.8 - 1.2)- Standalone: res_multistep / simple, 14 steps- With turbo LoRA: euler / beta, 8 steps (ref2v turbo 8-step) or 4 steps (fl2v turbo 4-step), audio sigma shift 5- Checkpoints: int8 and mixed int4/int8 pruned convrot conversions- Load through any H3 LoRA loader (H3LoraStack / LoraLoaderModelOnly)Example prompt:manga-tone rendering, a samurai draws his sword in a bamboo grove,dramatic low angle, speed lines<h2 id="what-it-does">What it does</h2>Without the LoRA, H3 renders "manga" prompts as grayscale anime with flatshading and little motion. With v2, outputs are monochrome with ink/midtonestatistics inside the trained manga distribution, and motion is preservedunder action prompts (measured +35% inter-frame motion vs v1 on amotion-heavy prompt, tone metrics unchanged).<h2 id="training">Training</h2>All training data is synthetic; no published or third-party artwork was used:- 714 static 5-frame crop clips from 58 AI-generated fictional manga pages, two crop scales- 80 animated clips: still crops animated by H3 itself (v1 @0.7 + turbo), then cleaned frame-by-frame with a deterministic monochrome render pass- 6 windowed clips from two action reference videos produced by feeding the same fictional manga pages to Google Omni (x4 repeat)- LoRA rank 32 / alpha 32 on 208 projections, flow matching on the video stream, lr 5e-5 constant with warmup, 2000 steps, bf16, gradient checkpointing; v2 initialized from v1- Audio stream masked in the loss; audio generation unchanged to first approximation but not guaranteed<h2 id="notes">Notes</h2>- Tone dot frequency is learned relative to page scale (renders ~9 px period at 960 px canvas width).- Abstract motion-graphics tropes (layer decomposition, graphic animation) remain out of distribution for the base model; action/cinematic prompts play to this LoRA's strengths.

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