---
base_model:
- MiniMax/MiniMax-H3
frameworks:
- ""
license: Apache License 2.0
base_model_relation: quantized
---
# MiniMax-H3-NF4

MiniMax-H3 多模态音视频生成模型的 **NF4 量化版本**（通过 `bitsandbytes` 4-bit 量化），配合 [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 使用，可在显存/内存受限的机器上进行「文本/图像/视频/音频 → 视频 + 音频」的联合生成。

## 文件说明

| 文件 | 大小 | 作用 | 是否共用 |
|---|---|---|---|
| `minimax-h3-fl2va-nf4.safetensors` | ~16 GB | **FL2VA** 任务的 DiT 主干（文本 / 首尾关键帧 → 视频+音频） | FL2VA 专用 |
| `minimax-h3-ref2va-nf4.safetensors` | ~16 GB | **Ref2VA** 任务的 DiT 主干（参考图像/视频/音频 → 视频+音频） | Ref2VA 专用 |
| `minimax-h3-text-encoder-nf4.safetensors` | ~15 GB | Qwen3-VL 文本/视觉编码器 | 两任务共用 |
| `video_vae_nf4.safetensors` | ~1.6 GB | 视频 VAE 解码器 | 两任务共用 |
| `audio_vae_nf4.safetensors` | ~271 MB | 音频 VAE 解码器 | 两任务共用 |

> 说明：DiT 按任务二选一，其余三个（text_encoder / video_vae / audio_vae）在两种任务下通用。加载时框架会根据文件 hash 自动识别组件类型并套用对应的量化配置（含对少数量化敏感层的 bf16 保留），无需手动指定量化参数。

## 环境要求

- CUDA GPU（NF4 反量化依赖 `bitsandbytes` 的 CUDA kernel）
- processor / tokenizer 需从原始仓库 `MiniMax/MiniMax-H3` 获取（下面 `processor_config`）

### 安装 DiffSynth-Studio

从源码安装（推荐，可获得最新的 MiniMax-H3 支持），并直接带上 NF4 量化依赖：

```bash
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e ".[quant]"
```

## 使用（Disk offload，低显存和内存占用）

权重存放磁盘、推理时按层流式加载到 GPU，显存占用最低。**纯文本生成视频+音频（t2v）最低约 6 GB 显存即可运行。**

> `vram_limit`（单位 GB）是显存占用阈值，调小可降低显存占用（代价是更慢）。

> 若 CPU 内存充足，可把 `offload_device` / `offload_dtype` 改为 `"cpu"` / `torch.bfloat16`（即 CPU offload），权重常驻内存、不走磁盘，速度更快；其余代码不变。

### FL2VA — 文本 / 首尾关键帧 → 视频+音频

```python
import torch
from diffsynth.pipelines.minimax_h3_audio_video import MiniMaxH3Pipeline, ModelConfig
from diffsynth.utils.data.audio_video import write_video_audio
from modelscope import dataset_snapshot_download
from PIL import Image

vram_config = {
    "offload_dtype": "disk",
    "offload_device": "disk",
    "onload_dtype": torch.bfloat16,
    "onload_device": "cpu",
    "preparing_dtype": torch.bfloat16,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
}
pipe = MiniMaxH3Pipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="minimax-h3-fl2va-nf4.safetensors", **vram_config),
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="minimax-h3-text-encoder-nf4.safetensors", **vram_config),
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="video_vae_nf4.safetensors", **vram_config),
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="audio_vae_nf4.safetensors", **vram_config),
    ],
    processor_config=ModelConfig(model_id="MiniMax/MiniMax-H3", origin_file_pattern="FL2VA/processor/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
)

# Text -> Video + Audio
prompt = "A girl is very happy, she is speaking in english: “I enjoy working with Diffsynth-Studio, it's a perfect framework.”"
video, audio = pipe(
    prompt=prompt,
    height=480, width=832, num_frames=124, num_inference_steps=50, seed=0,
)
write_video_audio(
    video=video, audio=audio,
    output_path="t2va.mp4", fps=24, audio_sample_rate=32000,
)

# Text + First Frame + Last Frame -> Video + Audio
dataset_snapshot_download(dataset_id="DiffSynth-Studio/diffsynth_example_dataset", local_dir="data/diffsynth_example_dataset", allow_file_pattern="minimax_h3/MiniMax-H3-FL2VA/*")
first_frame = Image.open("data/diffsynth_example_dataset/minimax_h3/MiniMax-H3-FL2VA/first.png")
last_frame = Image.open("data/diffsynth_example_dataset/minimax_h3/MiniMax-H3-FL2VA/last.png")
prompt = "室内家庭争吵短剧场景，竖屏短剧质感，真实真人表演，中式家庭/小饭馆室内环境，暖色灯光，背景有红色装饰和书法字幅，浅景深，情绪强烈，剪辑节奏紧凑。表演要求：真实短剧表演风格，不要夸张舞台腔。男人的语气是愤怒、委屈、急切的反驳，他说“你到底想干什么？”；中老年女性的语气是尖锐、强势、咄咄逼人的质问，她说“你必须赔钱！”。两人之间有强烈对峙感，节奏逐步升级。画面风格：竖屏9:16，手机短剧质感，真人实拍感，浅景深，室内暖光，中近景为主，频繁正反打剪辑，背景保持生活化，不要科幻、不要古装、不要动画感。画面中不要出现任何字幕、文字、平台水印或贴片。 "
video, audio = pipe(
    prompt=prompt,
    height=832, width=480, num_frames=124, num_inference_steps=50, seed=0,
    keyframes=[first_frame, last_frame], keyframe_indices=[0, -1],
)
write_video_audio(
    video=video, audio=audio,
    output_path="fl2va.mp4", fps=24, audio_sample_rate=32000,
)
```

### Ref2VA — 参考图像/视频/音频 → 视频+音频

支持四种参考类型，可在一个列表里组合（`video` 无声，带声视频用 `video_audio`）：

```python
{"type": "image",       "image": PIL.Image}
{"type": "video",       "video": list[PIL.Image]}                            # 无声
{"type": "audio",       "audio": Tensor[C, L], "sample_rate": int}
{"type": "video_audio", "video": list[PIL.Image], "audio": Tensor[C, L], "sample_rate": int}
```

```python
import torch
from PIL import Image
from diffsynth.pipelines.minimax_h3_audio_video import MiniMaxH3Pipeline, ModelConfig
from diffsynth.utils.data.audio_video import write_video_audio
from diffsynth.utils.data.audio import read_audio
from diffsynth.utils.data import VideoData
from modelscope import dataset_snapshot_download

def align_frame_count(frame_count):
    current = max(int(frame_count), 1)
    while current % 17 != 5:
        current += 1
    return current

def read_video_with_fps(path, num_out_frames, height, width, fps=24):
    video = VideoData(path, height=height, width=width)
    frames = video.raw_data()
    src_fps = float(video.data.reader.get_meta_data()["fps"])
    out = []
    for k in range(num_out_frames):
        idx = int(round(k * src_fps / fps))
        if idx >= len(frames):
            break
        out.append(frames[idx])
    return out

vram_config = {
    "offload_dtype": "disk",
    "offload_device": "disk",
    "onload_dtype": torch.bfloat16,
    "onload_device": "cpu",
    "preparing_dtype": torch.bfloat16,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
}
pipe = MiniMaxH3Pipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="minimax-h3-ref2va-nf4.safetensors", **vram_config),
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="minimax-h3-text-encoder-nf4.safetensors", **vram_config),
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="video_vae_nf4.safetensors", **vram_config),
        ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-NF4", origin_file_pattern="audio_vae_nf4.safetensors", **vram_config),
    ],
    processor_config=ModelConfig(model_id="MiniMax/MiniMax-H3", origin_file_pattern="Ref2VA/processor/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 5,
)

# Text + Reference Image -> Video + Audio
dataset_snapshot_download(dataset_id="DiffSynth-Studio/diffsynth_example_dataset", local_dir="data/diffsynth_example_dataset", allow_file_pattern="minimax_h3/MiniMax-H3-Ref2VA/*")
ref_image = Image.open("data/diffsynth_example_dataset/minimax_h3/MiniMax-H3-Ref2VA/0.png").convert("RGB")
prompt = "一个网站页面，网站页面UI设计，网站动效，视频展示了流畅的网页向下滚动效果。一个极具爆发力与动感的产品官网风格产品落地页 UI/UX 演示视频，核心展示主体是该产品图片1。页面采用粗犷有力、倾斜的超大号无衬线字体进行张扬的排版。背景有极具速度感的动态光影、暗色碳纤维或运动透气网眼纹理在交织变换。视频展示了节奏紧凑、充满力量感的网页向下滚动效果，以及鼠标悬停时强烈的视觉放大与颜色反转等 UI 交互动作。"
video, audio = pipe(
    prompt=prompt,
    height=480, width=832, num_frames=124, num_inference_steps=50, seed=42,
    references=[{"type": "image", "image": Image.open("data/diffsynth_example_dataset/minimax_h3/MiniMax-H3-Ref2VA/0.png").convert("RGB")}]
)
write_video_audio(
    video=video, audio=audio,
    output_path="ti2va.mp4", fps=24, audio_sample_rate=32000,
)

# Text + Reference Audio + Reference Video -> Video + Audio
ref_video = read_video_with_fps("data/diffsynth_example_dataset/minimax_h3/MiniMax-H3-Ref2VA/video.mp4", 124, 480, 832)
ref_audio, sample_rate = read_audio("data/diffsynth_example_dataset/minimax_h3/MiniMax-H3-Ref2VA/voice.mp3", duration=len(ref_video) / 24, resample=True, resample_rate=pipe.audio_vae.sample_rate)
prompt = "subject_definitions:\n<Subject 1> is the young man with short wavy blonde hair, wearing a bright pink suit jacket, matching pink trousers, an unbuttoned white shirt, and silver rings, holding a small black lamb in his arms in <Video 1>.\n<Video 1> is the source video for the editing task.\n<Audio 1> is the synchronized audio track of <Video 1>, providing the background music.\n<Audio 2> is the voice timbre reference for <Subject 1>'s voice, containing a spoken male voiceover.\n\nsummary:\n[video editing + audio reference + audio reuse] The target video is an edited version of <Video 1>. <Subject 1>, wearing a bright pink suit and holding a black lamb, stands in a grassy field with other white lambs in the background. The edit animates <Subject 1>'s face to speak the user-provided dialogue. <Audio 1> is partially reused as the continuous background music, while the target references the calm male voice timbre of <Audio 2> for <Subject 1>'s spoken lines.\n\nretention_analysis:\n<Subject 1> (appears in [Shot 1]): fully_preserved - the man retains his identity, wavy blonde hair, pink suit, white shirt, accessories, and the black lamb he holds, with his mouth newly animated to speak.\n<Video 1> (source video editing): fully_preserved - the original camera framing, warm golden hour lighting, grassy hill setting, and background white lambs are maintained while the central character is edited.\n<Audio 1>: partially_copy - the atmospheric background music from <Audio 1> is reused in the target video, mixed beneath the newly added spoken dialogue.\n<Audio 2>: reference - the target audio references the male voice timbre from <Audio 2> to generate <Subject 1>'s spoken dialogue.\n\ndetailed_description:\nThe target video is in realistic photographic style.\n[Shot 1] The shot begins from the source <Video 1>, showing <Subject 1>, a young man with short wavy blonde hair, wearing a bright pink suit jacket, matching pink trousers, and a casually unbuttoned white shirt. He stands confidently in a sunlit green pasture, gently holding a small black lamb securely in his arms. The warm, golden hour lighting casts soft shadows across his face and the bright pink fabric of his suit. Behind him, several white lambs stand and graze on the rolling grassy hill against a clear, pale blue sky. The atmospheric background music from <Audio 1> plays continuously throughout the scene. <Subject 1> physically speaks, his mouth movements naturally syncing to the new dialogue, with his voice timbre referencing the calm male delivery from <Audio 2>. Looking thoughtfully forward, <Subject 1> (S1) speaks softly, <d>[English] Follow the wind, live free.</d> As he delivers the line, he subtly shifts his weight, cradling the resting black lamb while the camera slowly pushes in. <Subject 1> (S1) continues his thought, <d>[English] Leave worries behind, enjoy the moment.</d> Exactly as his voice stops, his lips meet in a relaxed, peaceful smile, and his jaw ceases speaking motion. He then turns his gaze slightly away toward the horizon, gently stroking the black lamb's fleece with his fingers as the camera holds on this tranquil, sunlit state through the end of the video.\n\noverall_soundscape:\nThe soundscape consists of the continuous, atmospheric background music from <Audio 1>, overlaid with the clear, calm male dialogue spoken by the main character, referencing the voice timbre of <Audio 2>.\n\nnon_diegetic_music:\nThe atmospheric, sustained background music from <Audio 1> is reused as the continuous score, playing quietly beneath the spoken dialogue."
video, audio = pipe(
    prompt=prompt,
    height=480, width=832, num_frames=124, num_inference_steps=50, seed=42,
    references=[
        {"type": "video", "video": ref_video},
        {"type": "audio", "audio": ref_audio, "sample_rate": sample_rate},
    ],
)
write_video_audio(
    video=video, audio=audio,
    output_path="tav2va.mp4", fps=24, audio_sample_rate=32000,
)
```

## 常用参数

- `height` / `width`：分辨率，如 `480x832`（横）或 `832x480`（竖）。
- `num_frames`：帧数，需满足 `num_frames % 17 == 5`（如 124）。
- `num_inference_steps`：去噪步数，示例用 50。
- `keyframes` / `keyframe_indices`：FL2VA 首尾帧控制（`[0, -1]` 表示首帧和尾帧）。
- `references`：Ref2VA 参考列表，元素为 `{"type": "image|video|audio|video_audio", ...}`。
- 输出：`write_video_audio(video, audio, output_path, fps=24, audio_sample_rate=32000)`。

## 参考示例脚本

仓库内完整可运行脚本（本 README 的示例即取自这些脚本）：

- `examples/minimax_h3/model_inference_low_vram/MiniMax-H3-NF4-FL2VA.py`
- `examples/minimax_h3/model_inference_low_vram/MiniMax-H3-NF4-Ref2VA.py`
- `examples/minimax_h3/model_inference/MiniMax-H3-NF4-FL2VA.py`（CPU offload）
- `examples/minimax_h3/model_inference/MiniMax-H3-NF4-Ref2VA.py`（CPU offload）