MiniMax H3 with a 4B or 8B text encoder instead of the 32B: update, the voice matches now

MiniMax H3 loads a 32B text encoder, 15.7 GB, just to turn your prompt into a conditioning tensor. I replaced it with a Qwen3-VL 4B or 8B plus a learned map into the same space. Same DiT, same VAEs, same sampler. The video is three clips from the same prompt and seed: 32B, then 8B projected, then 4B projected. First post here if you missed it: https://www.reddit.com/r/StableDiffusion/s/VHBkPhk2Ka Three things improved since the first post. Sound. The projected encoder was putting out speech 7.6 dB below the 32B, which I had not spotted because the picture looked fine. It is 3.5 dB now and the voice matches on my side by side. Prompt following. The linear map was at its ceiling, more calibration data did nothing. A small residual network on top of it takes the 4B from 0.7169 to 0.7944 and the 8B from 0.7528 to 0.7970. It is initialised at zero so it can only add to the matrix, never make it worse. Named people. The corpus named almost nobody, so proper nouns came out wrong. I added the 500 most popular people from TMDB. Name tokens went from 0.8265 to 0.8844 for 0.007 of general cosine. I have tested it and they come out better, but what I can measure is the cosine, not the face, so take that part as testimony. https://www.reddit.com/r/StableDiffusion/s/VHBkPhk2Ka Node: https://github.com/nicolab28/ComfyUI-ClipProj Matrices: https://huggingface.co/NicoLab28/ClipProj-MiniMax-H3 Still a proof of concept, one machine, MIT. submitted by /u/Fit_Ad7343 [link] [comments]

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• It looks like MiniMax H3 uses Qwen3-VL-32B as Text Encoder and has has a split Transformer

Source: r/StableDiffusion | 2026-08-10