H3 Image
模型

MiniMax-H3-Image · 16MP

文生图 · 16MP多参考图编辑 · <Picture 1>–<Picture 4>

MiniMax 33.1B DiT 统一生图与编辑模型,支持 6 步原生 16MP (4096×4096) 直出与 <Picture 1>–<Picture 4> 多参考图指令编辑。

输入与参数

画面比例
1:116:99:164:33:43:22:321:9
输出尺寸
2K / 4K
参考图
最多 4 张

示例

Character Reference Sheet (3 Views + 6 Expressions)

Character Reference Sheet (3 Views + 6 Expressions)

Character reference sheet of one young woman with short silver hair, an olive green bomber jacket, black cargo pants and white sneakers. Top row: full-body front view, side view and back view in the same neutral pose, evenly spaced on a plain light gray background. Bottom row: six head-and-shoulder close-ups with different expressions labeled "HAPPY", "ANGRY", "CALM", "SURPRISED", "THINKING", "LAUGHING". Clean digital concept illustration, flat even studio lighting, identical proportions in every view, no other text.

来源 · MiniMax H3 Community License · 2026-11-10
Multi-Reference Living Room Composition

Multi-Reference Living Room Composition

Edit <Picture 1>. Furnish the sunlit mid-century living room with the cognac leather sofa from <Picture 2>, the sculpted walnut coffee table from <Picture 3>, and the arched brushed-brass floor lamp from <Picture 4>. Keep the tall factory windows, herringbone oak floor and morning sunlight direction exactly as they are.

来源 · MiniMax H3 Community License · 2026-11-10

Architecture, Native Resolution Table & Hardware Comparison

33.1B Single-Stream Transformer

Built on the multimodal H3 backbone with 50 layers, 56 attention heads and 3D multimodal RoPE. Text prompts are encoded through a 50-layer Qwen3-VL encoder while uploaded reference frames pass through a 24-channel 2D ViT spatial VAE into one unified sequence.

6-Step & 8-Step CFG-Free Sampling

Distilled with per-step PDD output heads at fixed guidance 1.0. Because classifier-free guidance and negative prompts are eliminated, every request completes in 6 to 8 deterministic steps with natural tonal roll-off, authentic skin pores and crisp typography.

Cloud Browser vs Local VRAM

Self-hosting the full BF16 checkpoint requires 66.28 GB for the diffusion transformer, 66.7 GB for the text encoder, and 2.34 GB for the VAE (~135 GB total on H200/B200 accelerators). Even 4-bit ComfyUI quants require 21 GB of VRAM. Here you run both checkpoints instantly in your browser.