MiniMax-H3 NSFW LoRAs — HearmemanAI

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A mirror of the MiniMax-H3 LoRAs published on CivitAI. CivitAI-matched files have identical SHA-256 checksums (listed below); the HMMisDogV2 exception is noted in the recent uploads table. CivitAI carries the sample videos, the version history and the comment threads; this repository exists so the weights can be pulled with a plain resolve URL, without an account.

One anatomy adapter teaches H3 what a body part looks like. One action adapter teaches it what a body does. They are single-concept and they stack, so put them under whatever character or scene LoRA you are already running.

LoRA names link to their CivitAI pages. Where a file checksum matches a published version, the link points to that exact version.

The adapters

Recent uploads

The older releases below remain available. Their version-specific notes do not describe the recent uploads.

Action adapters — video-trained

Anatomy adapters — stills-trained

Where a strength is blank, no single number was published — start at 1.0 and pull it back.

How to prompt each one

HMNSFW V2.5 — the current motion adapter

Trigger hmmotion. Strength 0.5 – 0.9. Trained on close to 1000 videos, roughly 15x the V2 dataset, over 40 hours of training. I2V is very good here, with coherent anatomy and motion, and it handles most T2V prompts with the occasional bad seed.

Your prompt is everything. A short or vague prompt gives you a bad result. Use the example prompts on the CivitAI page as anchors and match their length and register.

This was going to be V3. It isn't finished, and more versions are coming.

HMNSFW V2 — the previous motion adapter

Trigger hmmotion. Strength 0.5 or below; above that it collapses coherence. Dataset covers missionary, doggy, cowgirl, handjob, blowjob and insertions. I2V works well across most positions, with the occasional deformed genitalia; T2V is hit or miss, which is what the anatomy adapters below are for.

Long descriptive prompts beat short ones by a wide margin. The training captions run 165-269 words with a median of 225, so a keyword list is off-distribution for this checkpoint. Write one flowing paragraph of plain anatomical prose, not tags. The full prompt-writing system prompt is on the CivitAI page.

Generated with the full bf16 model, trained on bf16. dpmpp_2m + Beta scheduler, 20 steps.

HMCumshot V0.5 — earlier release

Trigger cumshot. Strength 0.9. Captioned in plain, blunt language, so prompt the same way. A thorough description of the cum texture helps a lot. Stacks on top of HMPussy and HMPenis.

• T2V / I2V — Turbo LoRA 8-step at 0.20, Euler, ddim_uniform.
• R2V — Turbo LoRA 4-step at 0.85, Euler, beta.

Optionally add the ExtendIntermediateSteps node with 2 extra steps starting at sigma 0.85. V0.5 expanded the dataset to 56 videos with more angles.

HMSquirt — squirting

Trigger hmsquirt. Experimental, trained on a small dataset, and it does work. It is not the best thing here and a better version is in progress. Run HMPussy alongside it at 0.4.

Recommended settings:

• Sampler Euler, scheduler Simple, 12 steps, shift 6.
• Turbo LoRA at strength 0.5.

HMPussy V1 — stills only, current version

Trigger pussy. Strength 1.0. Rebuilt from the ground up on 4x the dataset, covering innies, bushes, shaved, anuses and most everything else between a woman's legs. One file, smaller than the old pair, and no video partner needed unless you want the motion adapter under it.

HMPussy v0.5 — two files, both required

vagassist_e40 is stills-trained: run it at 1.0. It restores legible structure in female genital anatomy, which the base model renders soft and vague. hmpussy_v6_epoch30 is video-trained: run it at 0.35, sitting under the stills file rather than carrying a generation alone. Skip the video file if you do not care about motion.

Trained on fingering, spreading and plain show-off. LoKr adapters on the MiniMax-H3 fl2va bf16 base. V0.5 improved anuses considerably; they are still not all the way there.

HMInnie — shape control

Trigger inniepussy, and it replaces the word "pussy" in your prompt rather than sitting in front of it. Every training caption opens with a camera clause, so include one: inniepussy shown from the front / from behind / from below.

Four axes, written as plain English inside the sentence, not as tags:

• Mound — flat · soft · puffy · very puffy
• Cleft — closing to a smooth seam · with a defined cleft · parted around a visible opening
• Inner lips — tucked away · just visible · slightly protruding
• Labia colour — matching skin · pink · rosy · brown

Sits alongside HMPussy for general genital detail, and under HMNSFW when the anatomy needs to survive motion. Trained with AI-Toolkit on 117 images at rank 32 on the full bf16 base.

HMBreasts V2 — chest control, current version

Versatile across most sizes and shapes, and the prompts can stay short — write the chest in plain prose the way the CivitAI examples do, no leading trigger token. Use the seeds2 sampler with the ddim_uniform scheduler and the 8-step Turbo LoRA.

The four axes from V1 still apply and still read as plain English inside the sentence:

tiny tits with brown areoles
tiny sized tits and pale areoles

HMBreasts V1.0 — chest control, previous version

Trigger HMBreasts, leading. Strength 1.0. Base H3 renders a chest as an approximate shape and ignores anything you say about it; this makes size, areole size and areole colour things you type. Four axes, again as plain English:

• Breast size — tiny · small · medium · large · very large
• Shape — natural · perky · round · perfectly shaped · saggy · sagging · hanging · pressed together
• Areole size — tiny · small · medium · large · very large
• Areole colour — pale · ghost · brown · dark
• Nipples — erect · hard · pierced

Spelling matters: it learnt areoles, so write it that way. Example clauses:

HMBreasts, large, natural breasts, medium sized brown areoles and erect nipples
HMBreasts, small, perky breasts, tiny sized pale areoles and hard nipples
HMBreasts, very large, round breasts, large sized ghost areoles and erect nipples

The axes were trained independently, so tiny breasts with large dark areoles is a combination it can actually give you.

Honest limitation: medium is the weakest of the five sizes. The training material clustered at both ends, so tiny/small and large/very large steer hard while the middle is softer than either. A shape word usually pulls it back.

It owns the chest and nothing below it — genitals are HMPussy, and the two stack. It does not touch faces, bodies or identity. Trained on stills, so it teaches H3 what a chest looks like; how it behaves through motion still comes from the base model.

HMPenis — anatomy

Trigger HMPenis, leading. Works best with medium-to-large sizes. Specify the camera direction in the prompt: front (POV-like), back, or side. Useful adjectives: large, circumcised, glans with a colour adjective (pink / pale / brown). The dataset is thin.

Stacking

Anatomy adapters (HMPussy, HMInnie, HMBreasts, HMPenis) are stills-trained and single-concept. HMPussy V1 is stills only; hmpussy_v6_epoch30 from v0.5 is the video partner if you want it. Action adapters (HMNSFW, HMCumshot, HMSquirt) are video-trained. They compose: put the anatomy adapters underneath, the action adapter on top, and your character or scene LoRA above both.

Pick one HMNSFW, one HMBreasts and one HMPussy stills file. The two versions of each are the same line, not stackable partners.

Verification

The SHA-256 values below identify the files hosted here. The new HMCumshot v1.0 and HMMasturbation v2.0 hashes match their CivitAI file metadata. HMMisDogV2 has no matching published CivitAI hash as of 2026-09-16, so its checksum identifies the Hugging Face file only.

Running MiniMax-H3

These load with a stock LoraLoaderModelOnly in ComfyUI. For wiring references into Reference-to-Video without rebuilding the graph every time, see ComfyUI-MiniMaxRefPack.

Licence

The adapter weights are released as-is. Use of the MiniMax-H3 base model is governed by the MiniMax-H3 Community License. You are responsible for what you generate and for complying with the law where you are.