Manga Tone Rendering LoRA for MiniMax H3 v2.0

MiniMax H3 LORA

A LoRA that teaches MiniMax H3 (video + audio joint DiT) the manga

screentone rendering style with motion: monochrome ink rendering, proper

tone shading, and tone that holds up under dynamic camera movement and action.

Usage

- Trigger phrase: manga-tone rendering

- 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

Without the LoRA, H3 renders "manga" prompts as grayscale anime with flat

shading and little motion. With v2, outputs are monochrome with ink/midtone

statistics inside the trained manga distribution, and motion is preserved

under action prompts (measured +35% inter-frame motion vs v1 on a

motion-heavy prompt, tone metrics unchanged).

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

- 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.