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Community quantizations of the pruned FL2VA denoiser (the diffusion transformer, about 20 B parameters) of MiniMax-H3, the model that generates video with a synchronized stereo audio track. Made with stable-diffusion.cpp. Independent work, not made or endorsed by MiniMax or Comfy-Org. This repository holds the **denoiser** and unmodified copies of the two **VAEs**. The Qwen3-VL text encoder is a separate file: an abliterated ("uncensored") build is available as a separate repository, Sakura-MiniMax-H3-Uncensored-GGUF; unchanged encoders in GGUF exist from other community members (for example leejet/MiniMax-H3-GGUF). ComfyUI: not tested by us; the community GGUF loader fork named in leejet's repository is the one to try.

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> For adults only; you are responsible for what you generate and for the laws that apply to you. The language weights come from a model whose refusals were removed and whose text generation breaks down on harmful prompts (see below); what this changes in the video we measured only for six non-explicit prompts (see below). Do not use it to depict minors in any sexual or abusive context, or to create sexual or defamatory images of real persons without their consent. Do not put it into a public service without your own moderation. The **text encoder** of MiniMax-H3 (the first 50 language layers of Qwen3-VL-32B plus its vision tower, passed to the video model with `--llm`) with the language weights of an **abliterated (uncensored)** Qwen3-VL-32B-Instruct, as GGUF for stable-diffusion.cpp. Independent work, not made, reviewed or endorsed by MiniMax, Comfy-Org, Qwen / Alibaba or llmfan46. - The text encoder shipped inside MiniMax-H3 is **the unmodified Qwen3-VL-32B-Instruct**: the sha256 of all 14 `text_encoder` shards of `MiniMaxAI/MiniMax-H3` equal those of `Qwen/Qwen3-VL-32B-Instruct`. MiniMax-H3 uses it truncated to 50 language layers and without the final language-model norm. - We built the encoder from an **abliterated checkpoint of that model made by a community member** (llmfan46's abliterated Qwen3-VL-32B-Instruct, Apache-2.0, revision `c44b949b30d111666a5ed9851c5cd633ed39b070`; sha256 of both shards checked against the Hub). **We did not run the ablation ourselves** for this repository. - Tensors were copied one by one into the layout of the Comfy-Org H3 encoder file (902 tensors; names, dtypes and shapes equal that file; the 14 layers 50 to 63, the final norm and the output head are dropped), then converted with `sd-cli -M convert`.

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**Built with Qwen.** Community quantization of Qwen/Qwen-Image-2.1-Turbo (8-step distilled, 7B DiT) with an **abliterated text encoder**. Independent work, not made or endorsed by Alibaba/Qwen. **Non-commercial use only** (Qwen RESEARCH LICENSE AGREEMENT, copy in `LICENSE`). For adults only; you are responsible for what you generate and for the laws that apply to you. Q8_0 image model and our 8bit uncensored text encoder, stable-diffusion.cpp (Vulkan, Radeon 8060S), 1024x1024, 8 steps, CFG 1.0, `euler`, official Turbo sigmas. One seed per prompt and no re-rolls; the prompts themselves were chosen from a larger set of candidates. About 77 s per image including model loading; the GPU memory in use on our machine peaked at 18.3 GiB while generating (Windows counter for the whole GPU, 1.5 GiB of it were in use before the run). The images were made with stable-diffusion.cpp, not with ComfyUI. The prompts are neutral. These images say nothing about what the ablated text encoder changes for restricted content; they only show that ordinary prompts still work. The same nine prompts and seeds are used in the sibling repository Sakura-Qwen-Image-2.1-Turbo-GGUF with the official encoder, so the two sets can be compared.

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图像生成
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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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