01
Trained model weights
.safetensors / .ckpt files for SDXL fine-tunes; .pt files for LoRA; full HuggingFace export for LLMs.
SDXL · LoRA · DreamBooth · fine-tuned Llama for NSFW NSFW AI Model Training
NSFW AI Model Training
on Your Own Dataset
Custom SDXL checkpoints, LoRAs, DreamBooth identity packs, fine-tuned Llama / Mistral for NSFW chat. We train on your data, you own the weights, your competitors can’t copy your output. 40+ NSFW model trains shipped on dedicated A100 / H100 clusters.
40+
NSFW model trains shipped
5-21d
Typical training timeline
100%
Weights ownership transferred
H100
Dedicated GPU cluster access
TL;DR
Off-the-shelf NSFW AI models look the same as everyone else’s — same characters, same poses, same tells. Custom-trained models on your dataset look like your brand. We train SDXL checkpoints, Pony / Juggernaut fine-tunes, LoRAs (style + character), DreamBooth identity packs, ControlNet adapters, and NSFW-tuned LLMs (Llama 3 70B, Mistral, Mixtral) on isolated GPU clusters. Your dataset never touches a shared model. You own the weights. Starting at $4,000 for a single LoRA, $25,000 for a full SDXL fine-tune with custom characters.
What this actually is
Training a model means your competitors can’t replicate your output
Most NSFW AI tools you see online run on the same 5-6 base models — SDXL, Pony Diffusion, Juggernaut, Realistic Vision, AnimateDiff. That’s why competitors’ outputs all start looking the same after a few months. Same lighting, same poses, same eye shape, same body proportions. Visible to the trained eye, definitely visible to your most paying users.
A custom-trained model fixes this. We take your dataset — whether that’s your own creator content, licensed adult footage, character-design concept art, persona-specific image sets — and either (a) fine-tune the full base model, (b) train a LoRA that sits on top, or (c) train a DreamBooth identity pack for specific characters. Output looks like yours, not like the base model.
On the LLM side: NSFW chat models. Vanilla Llama 3 will refuse most adult prompts or water them down. A fine-tuned Llama 3 70B on consensual erotica + chat-companion dialogue (we have curated datasets we use as a baseline, plus your own) responds in-character, doesn’t moralise, and holds persona across long conversations. Same idea for Mistral and Mixtral.
Who hires us for this
- AI image / character platforms — OnlyFans / OurDream / Candy AI-style apps that want differentiated visual output
- AI companion app founders — Want their companions to look unmistakable, not generic SDXL output
- Adult creator / cam-model brands — Want AI variants in their own style that look like their content
- Adult game / VR studios — Need character-locked output across thousands of generations
- NSFW chat / roleplay platforms — Want their chat engine to handle adult prompts without policy refusals
Why founders pick NSFW Coders for this
- NSFW data handling experience — 40+ NSFW trains shipped. We know what works, what breaks, what overfits
- Isolated GPU clusters — Your dataset trains on dedicated A100/H100 instances. Never used for shared models
- Weights ownership transfers to you — You get the .safetensors / .ckpt / fine-tune weights. We don’t retain a copy after handoff
- Reproducible training runs — Hyperparameters, dataset versioning, eval prompts — all documented for future re-trains
- Pre-bundled compliance — CSAM screening on every input image, age estimation on every face. Training pipeline refuses bad data
What you get
What you take home when training finishes
You own everything we produce. No vendor lock-in, no per-inference fees forever.
02
Training config + scripts
Hyperparameters, dataset prep scripts, eval scripts. Reproducible from scratch if you want to re-train.
03
Eval report + sample outputs
100+ sample generations across diverse prompts. Quality benchmarks vs. base model. FID / CLIP scores.
04
Inference deployment guide
How to serve the model in production. Recommended GPU specs, batch sizes, latency expectations.
05
Optional: hosted endpoint
We host the trained model as a private REST API for you. Optional add-on, billed per million tokens / images.
06
Dataset audit + CSAM scan log
Every training image scanned for CSAM + age. Audit log retained per legal review needs.
How we train
From dataset to deployed model in 5–21 days
Five phases, each one named and scoped. We share a fixed-quote Gantt before training starts.
01
Discovery + dataset audit
NDA call. Look at your dataset, estimate cleanup needed, pick base model + training approach. 2-3 days.
02
Dataset prep + CSAM scan
Caption every image with BLIP-2 / CLIP. Run CSAM hash matching on every file. Age-estimate every face. 2-5 days.
03
Training run + eval
Train on dedicated GPU cluster. Multiple checkpoints saved. Eval against held-out test set. 3-10 days depending on scope.
04
Tuning + final run
Adjust hyperparameters based on eval. Often 1-2 additional runs. Final weights selected with you on a review call. 2-4 days.
05
Handoff + deployment
Weights + docs delivered. Optional: we deploy as a private API. 1-2 days for handoff.
Stack & methodology
Frameworks & infra we use
Real results from real builds
What clients actually got
Numbers from real model trains. Names changed, ranges real.
+47%
Image gen retention after model swap
AI companion app saw 47% higher repeat-generation rate when they swapped from base SDXL to a custom LoRA on their character.
6 days
Fastest LoRA train + ship
12-image character LoRA, trained, tested, deployed to production in 6 days for an adult cam model.
$25k
Full SDXL fine-tune project
Custom SDXL fine-tune on 8k client images + 30+ generation evals. Shipped in 21 days.
12k
Concurrent inferences served
After we deployed the trained model as a private API on dedicated H100 pool.
0
Datasets shared with other clients
Your data trains your model only. Isolated GPU runs, signed DPAs, zero cross-contamination.
95%
Prompts answered in-character
NSFW LLM fine-tune evaluation on 100-prompt regression set, vs. 18% on base Llama 3.
Transparent pricing
Fixed quote, no surprise invoices
Pick the closest fit. We adjust scope, not invoice.
Single LoRA / Adapter
$4,000
one-off · 5-7 day delivery
- 1 LoRA or DreamBooth identity pack
- Up to 50-image training set
- SDXL or Pony base
- Eval report + sample outputs
- Weights ownership transferred
Full Fine-Tune
$25k
one-off · 14-21 day delivery
- Full SDXL / Pony / Flux fine-tune
- Up to 10k-image training set
- Multiple checkpoints + tuning rounds
- Custom eval suite for your vertical
- Weights + scripts + docs
LLM Custom Train
$40k+
one-off · 21-35 day delivery
- Llama 3 70B / Mixtral fine-tune
- NSFW dataset + your conversation data
- DPO / RLHF for character voice
- vLLM deployment guide
- Weights + tokenizer + serving config
Pair this with
Services that stack with this one
Most founders we work with hire two or three of these together. The handoffs between them are how we hit our timelines.
AI Companion App Development
Pair a custom-trained model with a full companion app build. Same vendor, faster integration.
See the page →NSFW Image Generation API
Host your trained model as a managed API on our infrastructure.
See the page →NSFW Content Generation API
Pair a fine-tuned LLM with our serving + persona layer.
See the page →AI Integration Services
Wire your trained model into your existing platform.
See the page →3D Photorealistic AI Companion
Custom characters drive 3D companion builds — train once, render forever.
See the page →FAQ
Questions we get every week
What do you actually train? SDXL? LoRA? LLM?
All of the above. Most common: a custom LoRA on top of SDXL or Pony for character / style consistency (5-7 days, $4k). Mid-tier: a full SDXL fine-tune for clients who want to own a complete custom base model (14-21 days, $25k). Top tier: an NSFW-tuned Llama 3 70B or Mixtral for chat companion apps (21-35 days, $40k+). We also do DreamBooth identity packs, ControlNet adapters, and Wan-2.1 video model fine-tunes.
Do you provide the dataset or do I?
You provide. Your data is what makes the model differentiated — if we use a generic adult dataset, you get a generic model. We can supplement with curated open datasets we’ve assembled (high-quality, consensual, properly licensed) but the core has to be yours. Typical datasets: your own creator content, licensed adult footage, character concept art, persona-specific image sets, or your historical chat logs (for LLM fine-tunes).
How much training data do I need?
For a LoRA: 12-50 images works (12 minimum for character LoRA, 50 ideal for style LoRA). For DreamBooth: 5-20 images per character. For a full SDXL fine-tune: 2,000-10,000 images. For LLM fine-tune: 10,000-100,000 conversation turns. We’ll tell you on the discovery call if your dataset is enough or if we need to supplement.
How much does AI model training cost?
Single LoRA: $4,000 (5-7 day delivery). Full SDXL fine-tune: $25,000 (14-21 days). LLM custom train (Llama 3 70B / Mixtral): $40,000+ (21-35 days). Custom Wan-2.1 video model fine-tune: starting at $35,000. All prices include GPU compute, dataset prep, training, eval, weights transfer, and deployment guide.
Will my dataset be used to train other clients’ models?
No. We sign a DPA before any data transfer. Your dataset trains in an isolated GPU environment, gets deleted after training (or held in encrypted cold storage if you want re-train capability), and is never used for shared models or our internal benchmarks. We retain only the final trained weights long enough for the handoff, then delete our copy.
Can you train models for NSFW chatbots / LLMs?
Yes — this is one of our most-requested services. We fine-tune Llama 3 70B, Mistral 7B, Mixtral 8x7B, and Qwen 2.5 for adult chat / roleplay use cases. Base models refuse explicit prompts; fine-tuned versions respond in-character without moralising. We use a combination of LoRA + QLoRA for cost efficiency, plus DPO (Direct Preference Optimisation) for character-voice consistency.
What GPUs do you train on?
Mostly H100s for big runs (Lambda Labs, RunPod, Hyperbolic), A100s for cost-sensitive LoRA work. We rent dedicated pools per project — your training doesn’t share a GPU with another client. For very large LLM trains we use CoreWeave for the SXM-class interconnects.
How do you handle CSAM / age compliance in training data?
Every input image runs through PhotoDNA hash matching + our in-house perceptual hash database + age estimation before it enters the training pipeline. Images that fail any check are excluded and logged. The audit log is delivered with the trained model so you have a clean compliance record for your jurisdiction (UK Online Safety Act, US 2257, etc.).
Can you host the trained model as an API?
Yes — optional add-on. We deploy your trained weights as a private REST API on dedicated GPU pool, with autoscaling, monitoring, and a customer dashboard. Billed per million tokens (LLM) or per thousand images (diffusion). You can also self-host using the weights and deployment guide we deliver — that’s included in the base price.
Do you sign NDAs?
Always. NDA before discovery call, DPA before any data transfer. We can also offer model-weights escrow for clients with very high IP sensitivity — weights stored with a third-party escrow service, released to you on milestones.
Get a model trained on your data, owned by you
Free 30-min discovery call. NDA + DPA before you share a single image. Quote within 48 hours.
Train my model