MiniMax-H3 Prompt Rewriter

MiniMax-H3 is picky about its prompts. It wants a structured, shot-by-shot audio-video description - [Shot 1] markers, an overall_soundscape line, a non_diegetic_music line - because that's the shape it was trained on. Typing that by hand for every clip is misery. This node is the fix: you type "A red fox walks through a snowy forest at dawn," it returns the full structured description H3 expects, and it never touches the network after the weights are down. The "Rewriter" in the name means a short prompt goes in and a production-ready H3 prompt comes out - not a call to some hosted API.

It's the flagship of the pytraveler/MiniMax-H3-Prompt-Rewriter-ComfyUI pack (same person who writes the "ComfyUI update X.XX" posts on r/comfyui, by the way). What makes it the reference is what's inside: LightX2V's T2VA prompt-rewriter LoRA, trained on Qwen3.6-27B until H3-shaped output came out of it with just a seven-line system prompt. The format isn't instructions the model is trying to follow - it's in the weights.

How it works

Short version: pick a Qwen3.6-27B base, the node attaches the LoRA, generates, and hands you four text outputs. The base can be the official bf16 checkpoint, a bitsandbytes nf4 repack, or a GGUF. The model dropdown (the "model list") holds every option plus anything already on disk, and whatever's missing is downloaded on first use with a progress bar on the node itself.

One thing worth knowing before you go down this road: the MiniMax-H3 text encoder can't be reused for this. It's a different model (Qwen3-VL-32B, different vocab, truncated to 50 of 64 layers, no lm_head) that only produces hidden states for the DiT. This node runs a full 27B language model alongside H3. That's the cost of the quality, and there's no shortcut.

The inputs that matter

• prompt - the short idea. Any language the base reads; the rewrite comes back in English, which is what H3 wants.
• model - your base-model choice. nf4 on the official checkpoint is ~16 GB VRAM; a GGUF Q4_K_M is ~19 GB and needs nothing installed (more below). Start there if you're tight on card.
• resolution / duration - keep these equal to what you feed MiniMax-H3, or the shot pacing won't match. Duration is capped at 4–15 s, the range the LoRA was trained on.
• greedy - on by default. It's deterministic and it's what makes the node cacheable. Leave it on.
• keep_model_loaded - off by default, on purpose: the moment the rewrite finishes you need that VRAM back for H3 video generation.

Outputs

Four strings, all wire into a text viewer or straight into H3's text input:

• rewritten_prompt - the full thing, ready to paste.
• integrated_multimodal_description - just the shot-by-shot visual section.
• overall_soundscape - the diegetic audio section.
• non_diegetic_music - the score section.

Install

Clone into custom_nodes (or grab it from ComfyUI Manager - search "MiniMax-H3") and install requirements into ComfyUI's Python environment:

cd ComfyUI/custom_nodes
git clone https://github.com/pytraveler/MiniMax-H3-Prompt-Rewriter-ComfyUI
pip install -r MiniMax-H3-Prompt-Rewriter-ComfyUI/requirements.txt

The pip deps (transformers, peft, accelerate, bitsandbytes) are for the Transformers route. The GGUF route needs none of them: it uses llama-cpp-python if it's already in your environment, and otherwise fetches the official llama.cpp binaries (~34 MB) and runs them as a subprocess. That's the smallest path - about 10–16 GB total download against 52 GB for the official checkpoint.

Where people get burned

• keep_model_loaded stays off. A 27B holds onto 16 GB the moment you turn it on. Leave it until you're iterating on prompts back-to-back.
• The rewrite invents details. It's a trained distribution, not a transcript. Review it when identity, dialogue, timing or composition must be exact.
• "It produced a good rewrite but I'm on a 9B." If a Qwen3.5-9B GGUF is in your model list, it can't take this LoRA (wrong block count - the node flags it (wrong size for the adapter)), so any plausible output is the plain model running the system prompt without the adapter. Not a bug, but it's not what you're paying for.
• Quantized repacks are third-party. The node verifies the architecture from config.json before downloading, which proves the shape - not the uploader's trustworthiness.