If you use my models, please take the time to post some images to the pages so I can see how they are being used and make improvements in the future. Also feel free to reach out if you have ideas for future models!<h2 id="what-this-is-for">What this is for</h2>This model is a concept-injection model intended to help you create images with Genital nullification — smooth, featureless skin where genitalia would normally be visible. I am not 100% happy with this one, but it does mostly do what it says on the tin. The main faults are sometimes you get some ghosting or artifacts, and sometimes it decides to remove facial features. Play with the weight. Be careful combining with other loras that teach nudity, as they tend to meet in the middle and produce a sub-par result. <h2 id="how-to-prompt">How to Prompt</h2>Flux.2 Klein 9B prefers prose based prompts.Training used a mix of vague, medium, and detailed captions, so the model should be flexible to levels of detail. In all captions, this concept was woven into the prose using the term “nulled”.I strongly recommend combining this model with a fine-tune style LoRA like SNOFS.<h2 id="what-is-a-concept-injection-model">What is a concept injection model?</h2>There’s a fundamental conflict between broad fine-tune style models and maximizing concept fidelity for a narrow concept. Larger scale fine-tunes always blur the concepts more than you’d like.Broad finetunes can fix composition, house style, and add new capabilities, and maintain flexibility, but don’t maximize creative control over fine-grained topics.Narrow, focused models, what I am calling “concept injections” can reproduce detailed concepts reliably, but can’t effectively target the broader concerns, and reduce flexibility around that concept.I think there is value in training both types of models (and I do), and from a research standpoint, I’ve developed training processes that work for both.The main challenge with concept injection models is “off-target effects”. Whether this is facial biases, glamourization, or compositional inflexibility. Some amount of this is going to come with the territory, especially for concepts for which finding or generating diverse data is difficult–but we do want to minimize it. By separating training and inference work into two categories: a broad base model, and narrow concept-injection models, which are then combined at inference time, we can produce better outputs with less overall compute investment.All of that is a long way of saying: for best results, don’t use this model alone. Pair it with a great fine-tune style LoRA like SNOFS.<h2 id="training-details">Training Details</h2>This model was trained using ai-toolkit with a hand-curated dataset.I first developed a generic approach to concept injection training. Over 100 models were trained in order to sweep hyperparameters, dataset prep approaches, adapter types, prompting/captioning styles, etc. 95% of the compute in this project went to experiments, not the final models, which can are each able to be trained in 60-90mins on a single RTX6000 Pro. This framework was designed to support training and evaluating concept injections as efficiently as possible, while also retaining enough repeatability that concepts can be re-trained predictably as new experimental results become available.An experiment framework was developed where candidate models are evaluated using blind pairwise comparisons and then ELO ranked for both on-target and off-target effects. In each case, the pareto-optimal model was selected. This was applied in aggregate at the start to develop the procedure, and then again at a smaller scale for each concept to narrow in on the highest quality model.The training settings are not exotic. Many oft-recommended techniques were evaluated in isolation including LoKR, adaptive optimizers, LR schedulers, batch size, and regularization regimes. While these can be very useful techniques when doing a broad fine-tune over a long period, they were all measured as statistically detrimental in my experiments. To some extent this makes sense–sharp concept injections are at odds with stability and regularization. Most of the interesting levers pulled related to captioning, trigger word selection, and image curation.<hr />This model was trained exclusively on imagery depicting adult human subjects. It is intended only for generating images of adults. Any use to the contrary violates both the model’s intended purpose and Civitai’s policies.

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