MiniMaxH3-CondBridge-Qwen3.5-4B

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CondBridge: Interface-Distilled Text Conditioning for MiniMax-H3

A DeepSeek-V4-Flash 0731 Assisted Project

A 1.14B adapter that bridges a lightweight Qwen3.5-4B student to the MiniMax-H3 33B text encoder's injection space. It converts the student's hidden states into the exact CLIP-injection representation the H3 DiT expects (post-condition_proj + token_refiner), replacing the 33B teacher encoder end-to-end.

Model Description

MiniMax-H3 video generation conditions the DiT on text embeddings produced by a 33B text encoder (condition_proj + 2-layer token_refiner). Running it locally is heavy. CondBridge distills that interface into a 1.14B adapter that consumes:

and outputs the teacher-equivalent representation [1, S_T, 5376]. Because the output lives in the same space as the teacher's post-refiner embeddings, the DiT consumes it directly

Architecture

H3Adapter — 1.144B params total:

Forward: kv = source_proj(student) → x = cross_attn(query_embed(h3_ids), kv) → token_refiner(x).

Training Data

• 10,032 prompts generated from a 9-skill element-pool Cartesian combos (subjects/actions/scenes/camera/lighting/styles...), deduplicated to 10,027
• Each prompt: H3 tokenizer ids + Qwen3.5-4B hidden states + teacher target (condition_proj + token_refiner output of the official encoder)
• Feature-space dedup + length bucketing (short/medium/long)

Training Details

Two-stage fine-tuning (32GB GPU, bf16):

• Optimizer: Muon (2D weights) + AdamW8bit (1D), WSD schedule, wd 0.02
• Loss v3: huber×1.0 + cos×0.8 + infonce×0.05 + sp×0.1 + mag×0.1 + stat×0.0002 with a curriculum ramp on the contrastive terms
• Scale handled via RMS target-norm alignment (pn = normalize(pred) × target_norm)

Evaluation

Held-out 1,002 prompts (disjoint from train):

Usage

import torch
from transformers import AutoTokenizer
from adapter.model import H3Adapter          # repo code
from safetensors.torch import load_file

adapter = H3Adapter().to(torch.bfloat16)
adapter.load_state_dict(load_file("condbridge.safetensors"))
adapter.eval()

h3_tok = AutoTokenizer.from_pretrained("<h3 tokenizer>")
# student_hidden: Qwen3.5-4B hidden_states[-1]  [S_S, 2560]
h3_ids = h3_tok(prompt, add_special_tokens=False)["input_ids"]
embeds = adapter(torch.tensor([h3_ids]), student_hidden.unsqueeze(0))  # [1, S_T, 5376]

Requires: Qwen3.5-4B (student), MiniMax-H3 tokenizer, the H3 DiT.

ComfyUI Integration

Use with the [ComfyUI-MiniMaxH3-Adapter] node: MiniMaxH3AdapterLoader (student dir + adapter .safetensors) → plug into official MiniMaxH3ImageToVideo. The node exposes the adapter as a duck-typed CLIP.

** I use HauhauCS/Qwen3.5-4B-Uncensored-HauhauCS-Aggressive but it worked for Qwen/Qwen3.5-4B and every Fine-Tune or Quantizations **

Limitations

• Distilled fidelity is bounded by the student (Qwen3.5-4B): complex multi-shot or long cinematic prompts may drift from the 33B teacher.
• Trained on generated prompt combinations (9-skill template), not in-the-wild caption distributions.
• Intended for research / local use; check MiniMax-H3 terms of service.

Training your own CondBridge

• Training Code to be released soon

CondBridge：面向 MiniMax-H3 的接口蒸馏文本条件适配器

本项目在DeepSeek-V4-Flash 0731帮助下完成

一个 1.14B 参数的适配器，为轻量级 Qwen3.5-4B 学生模型搭建通往 MiniMax-H3 33B 文本编码器注入空间的"桥"。 它将学生模型的隐藏状态转换为 H3 DiT 期望的 CLIP 注入表示（condition_proj + token_refiner 之后的结果），端到端替代 33B 教师编码器。

模型描述

MiniMax-H3 视频生成用 33B 文本编码器（condition_proj + 2 层 token_refiner） 产生文本嵌入来条件化 DiT。本地运行它很重。CondBridge 蒸馏了这套接口，用 一个 1.14B 适配器消费：

并输出与教师等价的表示 [1, S_T, 5376]。由于输出与教师的 post-refiner 嵌入 处于同一空间，DiT 可直接消费

模型架构

H3Adapter — 共 1.144B 参数：

前向：kv = source_proj(student) → x = cross_attn(query_embed(h3_ids), kv) → token_refiner(x)。

训练数据

• 从 9 类技能元素池笛卡尔组合生成 10,032 条 prompt （主体/动作/场景/镜头/灯光/风格...），去重后 10,027 条
• 每条 prompt 含：H3 tokenizer ids + Qwen3.5-4B 隐藏状态 + 教师目标 （官方编码器的 condition_proj + token_refiner 输出）
• 特征空间去重 + 长度分桶（短/中/长）

训练细节

两阶段微调（32GB 显卡，bf16）：

• 优化器：Muon（2D 权重）+ AdamW8bit（1D 权重），WSD 调度，wd 0.02
• Loss v3：huber×1.0 + cos×0.8 + infonce×0.05 + sp×0.1 + mag×0.1 + stat×0.0002， 对比项带课程 ramp
• 尺度处理：RMS 目标范数对齐（pn = normalize(pred) × target_norm）

评测结果

Held-out 1,002 条 prompt（与训练集不重叠）：

使用方法

import torch
from transformers import AutoTokenizer
from adapter.model import H3Adapter          # 仓库代码
from safetensors.torch import load_file

adapter = H3Adapter().to(torch.bfloat16)
adapter.load_state_dict(load_file("condbridge.safetensors"))
adapter.eval()

h3_tok = AutoTokenizer.from_pretrained("<h3 tokenizer>")
# student_hidden: Qwen3.5-4B hidden_states[-1]  [S_S, 2560]
h3_ids = h3_tok(prompt, add_special_tokens=False)["input_ids"]
embeds = adapter(torch.tensor([h3_ids]), student_hidden.unsqueeze(0))  # [1, S_T, 5376]

依赖：Qwen3.5-4B（学生）、MiniMax-H3 tokenizer、H3 DiT。

ComfyUI 集成

配合 [ComfyUI-MiniMaxH3-Adapter] 节点使用：MiniMaxH3AdapterLoader （学生目录 + 适配器 .safetensors）→ 接入官方 MiniMaxH3ImageToVideo 节点。

** 我在测试时使用HauhauCS/Qwen3.5-4B-Uncensored-HauhauCS-Aggressive，但理论上原版Qwen/Qwen3.5-4B的GGUF量化以及其任何微调的GGUF量化版本都可用 **

局限性

• 蒸馏保真度受限于学生（Qwen3.5-4B）：复杂多镜头或长电影级 prompt 可能 偏离 33B 教师
• 训练数据是生成的 prompt 组合（9 类技能模板），非真实世界字幕分布
• 面向研究 / 本地使用；请遵守 MiniMax-H3 服务条款

我自己想训练一个CondBridge

• 训练代码整理后发布