How to Train NSFW LoRA 2026 Step by Step Guide

For Linux or Mac, replace setup.bat with setup.sh. Full documentation at github.com/kohya-ss/sd-scripts.

The dataset is 70% of LoRA success. Garbage in, garbage out. For our example, we will train a LoRA of a fictional female character named Lara. You need:

For automatic captioning, download the WD14 Tagger extension in Kohya. It analyzes each image and generates booru-style tags (1girl, brown hair, brown eyes, etc). Good starting point. Then edit manually: if all your images show the character with dark hair, REMOVE dark hair from the captions. You want the LoRA to learn that dark hair is a trait of the character, not something that needs to be stated in the prompt.

In the Kohya GUI, go to LoRA then Training. Configure like this to train an SDXL/Pony LoRA on RTX 3060:

Total steps calculated automatically. For 30 images times 15 repetitions times 10 epochs divided by batch 2 equals 2250 steps. Estimated time on RTX ai porn free 3060: 75 to 90 minutes. Click the Start training button (orange button). The cmd window shows progress step by step.

Complete pipeline I ran last week successfully. LoRA of a fictional character for NSFW content. 28 source images. Pony V6 XL base. RTX 3060 12GB.

Result: 144MB LoRA that reproduces the character Lara consistently in any scenario, with any outfit, in any pose. It worked in NSFW prompts too (the character appears consistent in adult content, without losing traits). Total time: approximately 4 hours. Cost: zero (electricity). See our ComfyUI tutorial to use the LoRA in advanced pipelines.

CUDA out of memory during training: reduce batch size to 1, enable gradient checkpointing, use AdamW8bit instead of AdamW, reduce resolution to 768 by 768.

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Loss does not drop or keeps oscillating: learning rate too high. Reduce from 1e-4 to 5e-5. Increase min_snr_gamma to 5. Check if captions are coherent (no empty caption or one completely different from the others).

LoRA becomes overfitted (copies dataset): reduce epochs from 10 to 6, or use an intermediate checkpoint (epoch 6 instead of 10). Add regularization images (reg folder with generic images).

LoRA learns wrong trait: your dataset has bias. If all images show the character wearing ai porn gen glasses, the LoRA learns glasses as part of the character. Diversify dataset (some images with, some without). Same logic for outfit, background, lighting.

LoRA generates consistent but poor quality: dataset with low quality. Go back and refine dataset (more resolution, more sharpness, more variety). Garbage in, garbage out.

In 2026, training custom LoRA is what separates people who play with AI from people who produce seriously. Consistent character, authentic style, unique look, all possible with RTX 3060 and 4 hours of dedication. The ecosystem (Kohya, Civitai, Pony, Illustrious) is mature enough for anyone with a mid-range gaming PC to enter the game. The cost is only the time to learn the pipeline, which this guide covers.

Next steps: read our Civitai tutorial to understand the upload and model discovery process, and our Forge tutorial to use your LoRA in generation. If you have not trained any yet, start with a small dataset (15 images) and default parameters. Failing is part of it. No GPU? Use the free generator at the top while saving for a 3060.

There are three main techniques to customize Stable Diffusion: LoRA, DreamBooth, and Textual Inversion. LoRA is the ideal balance in 2026: small file (144MB), trains fast (60-90 min on 3060), applies to any compatible checkpoint, high fidelity. DreamBooth trains the entire model (6GB file) with absolute fidelity but unfeasible on 12GB VRAM. Textual Inversion trains only a new token (50KB file) with low fidelity, useful only for abstract concepts.

The revolution was that LoRA reaches 80% of DreamBooth quality with 1% of the training cost. That is why it became the market standard. In 2026, nobody trains DreamBooth for amateur characters. To understand the original code repository, see github.com/kohya-ss/sd-scripts.

No RTX in your PC? Train in the cloud. Two practical options in 2026. First: RunPod with pre-configured Kohya template. Rent an RTX 4090 for USD 0.40 per hour. Train SDXL LoRA in 30 minutes (faster than on local 3060). Cost: USD 0.20 per LoRA. Pay with international card. See our RunPod tutorial for the step by step.

Second option: Civitai trains LoRA on-site using your Buzz. You upload the dataset, choose base model (Pony, Illustrious, SDXL), configure epochs and rank, click train. Costs about 500 Buzz (USD 2.50). More expensive than RunPod but zero setup, ideal for those who only want to train 1 or 2 LoRAs per month. The raw reference models are at huggingface.co/stabilityai if you want to train against the official base.

To close the pipeline and use your LoRA in production, return to the ComfyUI tutorial or Forge tutorial. And to create prompts that extract the best from your LoRA, see our NSFW prompt guide.

LoRA (Low-Rank Adaptation) is a small file (100MB to 250MB) that teaches the base model a new concept: a specific face, an art style, a pose, an outfit. Instead of re-training the entire model (which weighs 6GB), you train only the LoRA and use it on top. Training your own LoRA allows you to create consistent characters (face that looks the same in every image), capture an artist’s style, or teach the model a specific phenotype it did not know. In 2026, training LoRA has become common: runs on RTX 3060 in 30 to 60 minutes.

Minimum viable: 15 images. Good range: 25 to 40 images. Ideal: 50 to 80 images. Above 100 does not improve much and takes longer. For character (same face), 25 to 30 images in varied angles (front, profile, three quarters, close-up) with high quality. For style (art by an artist), 40 to 60 images. For more abstract concept (a pose, an outfit), 20 to 30 images are enough.

Kohya_ss remains the market standard. Runs locally on Windows with friendly GUI. Install via github.com/bmaltais/kohya_ss (fork with visual interface). Alternatives: OneTrainer (cleaner interface, rising), simpletuner (command line for server). For those who do not want to install anything, Civitai offers on-site training using Buzz (pay about USD 5 to train an SDXL LoRA). Recommendation: start with local Kohya_ss.

Trains SDXL/Pony/Illustrious smoothly (45 to 90 minutes per LoRA). Flux requires aggressive optimization: use fp8 base, rank 16 instead of 32, batch size 1, and gradient checkpointing. Result: trains in 90 to 120 minutes with decent quality. For high quality Flux LoRA, 24GB VRAM is recommended or rent A100 on RunPod for about USD 0.80 per hour (training in 30 to 45 minutes). For beginners with 3060, focus on SDXL or Pony LoRA first.

Steps: (1) collect 25-40 high quality images (1024 by 1024 or larger), (2) resize all to 1024 by 1024 maintaining proportion (use bulk resize tool), (3) generate caption for each image using WD14 Tagger (install via Kohya extension) or BLIP, (4) edit captions manually removing what you want the LoRA to learn as default (for example if the LoRA is of the character, remove tokens describing face), (5) save dataset in folder like dataset/15_charactername (15 equals repetitions per image).

Default configuration that works in 90% of cases: rank 32, alpha 16, learning rate 1e-4, text encoder LR 5e-5, scheduler cosine, optimizer AdamW8bit, batch size 2, epochs 10, total steps around 1500 (calculated by: images times repetitions times epochs divided by batch). Mixed precision fp16, save every N epochs 2 (to have intermediate checkpoints). Total time on RTX 3060: 60 to 90 minutes. Result: 144MB LoRA tested and functional.

Take all saved checkpoints (epoch 6, 8, 10, etc), load in Forge or ComfyUI, generate the same image with each checkpoint using fixed seed. Compare visually. Probably epoch 8 or 10 is the best (epoch 6 is undertrained, epoch 12+ may be overfitting). Test with varied prompts (different scenarios, different poses) to ensure the LoRA generalizes, not just reproduces dataset. If it reproduces dataset, it is overfitted, use previous checkpoint.

After validating the best checkpoint, go to Civitai logged in, click Upload Model. Select type LoRA, base model (SDXL, Pony, Illustrious). Upload the .safetensors. Add metadata: name, description in English, tags, and enable the NSFW toggle if applicable. Generate 4-6 sample images with the LoRA and upload together (Civitai shows them on the card). Set license (CreativeML OpenRAIL-M is the permissive standard). In about 5 minutes your LoRA is public and downloadable by everyone. See our Civitai tutorial for platform details.

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