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How to create AI PPV NSFW Content - SDXL PPV Prompting Course
This course provides a structured approach to creating ultra-realistic AI NSFW PPV images. It covers setup, prompting techniques, positive and negative prompts, examples, and useful tags to ensure consistency, quality, and professional results across various categories and scenarios.
Free. You only need a member account to watch.
- Lessons
- 35
- Taught in
- English
- Price
- Free
Overview
About this course
A written prompting course for generating ultra-realistic AI NSFW images to sell as PPV in private messages on Fanvue or Seeit, using the SDXL PPV model on Pykaso.ai. It is aimed at OFM operators running AI models who need content good enough to make a fan buy a second and third time, because LTV is what makes traffic and chatting profitable.
The course starts with why consistency and an amateur look matter more than raw realism, and why the SDXL PPV model is used without LoRA training. It then covers the setup: guidance scale, number of steps, and how a fixed or random seed lets you reproduce an image or explore new poses. The prompting module explains weighting with brackets and custom values, and why linked elements should be grouped in the same brackets.
The core is a block-based prompt structure: Block 0 the shot, Block 1 the subject, Block 2 the body, Block 3 the scene, Block 4 shooting and action, Block 5 the photo rendering, plus negative prompts for image defects, distortions such as extra fingers and unwanted features. It ends with worked examples (varying camera angles on the same prompt to build a PPV set, keeping a model consistent without training), an SFW and an NSFW prompt pack, and tag lists by category.
Outcomes
What you will learn
- Set up the SDXL PPV model on Pykaso with suitable guidance scale and step values, and use the seed to reproduce or vary an image
- Write prompts with the block structure: shot, subject, body, scene, shooting and action, then photo rendering
- Use weighting (brackets and custom values such as :1.3) and group linked elements so the AI treats them as one scene
- Keep the same model consistent across many images without LoRA training by leaving the subject and body blocks unchanged
- Build a PPV set from one prompt by changing only the camera angle tag (mirror selfie, from below, overhead, from behind)
- Use negative prompts to remove image defects, hand and finger distortions or unwanted features without overloading them
- Get an amateur, realistic look with tags such as film grain, skin texture, skin pores and soft shadows
Audience
Who it is for
- OFM agencies and operators running AI models who sell PPV in private messages on Fanvue or Seeit
- People already generating SFW content for social media who now need NSFW PPV content
- No prior prompting knowledge required: seed, steps and guidance scale are explained from scratch; a free Pykaso account is needed
Syllabus
Course content
7 modules · 35 lessons
- 01What is an AI NSFW PPV?Reading lessonExplains what an AI NSFW PPV is and how it differs from the content posted on social networks to attract traffic: PPV images are sold in private messages on Fanvue or Seeit. The guide promises ready-to-sell, ultra-realistic images generated without model training, with sample results.
- 02How to sell NSFW AI images?Reading lessonWays to use the generated images: no-face content showing only the body, images with partially visible faces to crop later, or a face swap in Pykaso to add a real face. It also explains that keeping the same physical traits in the prompt gives consistent results.
- 03Generating consistent AI images (without LoRa) Reading lessonWhy consistency matters from social media traffic to PPV sales: the fan needs to see the same face, skin tone and body throughout the relationship. It explains the problems of training on a few dozen photos (deformed limbs, wasted images) and presents a large model that stays consistent through a detailed prompt alone.
- 04Generate ultra-realistic AI images in amateur styleReading lessonArgues that in a crowded AI OFM market, technical realism is not enough: what sells is the amateur effect of an image. The guide announces that position, camera angle and blur are the levers it will teach to reach that look.
- 05Generate IA NSFW / Uncensored / PPV imagesReading lessonExplains why PPV quality decides whether a fan buys again: a poor first image loses the customer after all the work on traffic, conversion and chatting. Repeat sales raise LTV, which the guide presents as necessary to scale past $10k a month, and it frames lighting, poses and expressions as the skills to learn.
- 06Things to keep in mind before you startReading lessonTwo warnings before starting: a LoRA cannot be trained with the SDXL PPV model, so the course teaches consistency without one, and the prompting style is designed for SDXL and should not be used with Flux models.
- 07SDXL - PPVReading lessonExplains that the guide is built for the SDXL PPV model on Pykaso.ai, and how to access it: create a free Pykaso account, open the AI image generator and select SDXL PPV as the model.
- 08SettingsReading lessonThe recommended settings for the SDXL PPV model on Pykaso: a guidance scale between 1.0 and 1.6, between 6 and 8 steps, and prompts using cool colours or soft atmospheres rather than extreme lighting.
- 09SeedReading lessonWhat a seed is and how to use it: leave it empty to get a different pose, angle and composition at each generation and explore ideas, or fix a seed to reproduce the same base image and change a single detail of the prompt. Illustrated with a simple example and image pairs.
- 10Steps (number of steps):Reading lessonHow the number of steps affects an image: few steps are fast but less detailed, more steps refine textures but too many can overload the result. For this model, 6 to 8 steps give a fast, slightly blurred amateur effect, and 10 to 13 steps a sharper image with more skin and light detail.
- 11Guidance Scale (also known as "CFG")Reading lessonWhat the guidance scale (CFG) controls: how closely the AI follows the prompt, from creative freedom at low values to rigid, forced results at high values. After a general explanation, the lesson recommends 1.5 to 2.0 for this model, warning that too low is incoherent and too high looks artificial.
- 12WeightingReading lessonIntroduces the prompting section on writing structured, effective prompts, and the principle of weighting: giving certain tags more importance so the AI prioritises those aspects of the image. It announces the two ways to do it, brackets and custom numeric weights.
- 13BracketsReading lessonHow brackets raise the importance of a word or group of words: one pair multiplies it by 1.1, two pairs by 1.2 and three pairs by 1.4. The lesson warns against overloading the prompt, since rendering strength comes from consistency rather than volume.
- 14Custom weight (:x.x)Reading lessonUsing custom numeric weights such as :1.5 to increase a tag's importance or :0.8 to reduce it, and combining them with brackets for precision. The advice is to stay between 1.1 and 1.5 to avoid artificial-looking results.
- 15Prioritizing keywordsReading lessonHow to prioritise key elements correctly: put elements that belong together (framing, action, nudity, visual intention) inside the same brackets so the AI builds one coherent scene, rather than bracketing each separately. Two to three keywords per category can be combined.
- 16ExampleReading lessonA short worked example of weighting and scene building: adding a weighted freckles tag to make freckles a focal point, then a sequence of setting tags (dim bedroom, unmade bed, natural lighting, soft shadows) that gives the AI a scene with depth.
- 17 Block 0 - The shotReading lessonBlock 0 of the positive prompt: start with a shot tag that sets framing, style and sometimes who holds the camera, because the AI builds the composition around the first block. Examples include mirror selfie, selfie, candid shot, close up selfie, from behind, from below, overhead and POV.
- 18Block 1 - The subject (the woman you want to create)Reading lessonBlock 1 of the positive prompt: describe the woman you want to create on the same line, right after the shot tag, including nationality or ethnicity, age and complexion, with hair and eye details. An example combines Block 0 and Block 1 into one line.
- 19Block 2 - The body (physical)Reading lessonBlock 2 of the positive prompt: describe the model's body type, breasts, hips and bum, optionally in brackets to weight them. The lesson advises sticking to the essentials, since too many terms make the result harder for the AI to interpret.
- 20 Block 3 - The sceneReading lessonBlock 3 of the positive prompt: the location, action and mood that give meaning to the image, with examples like a living room with a white sofa, a bathroom with a glass shower or a garden in afternoon light, and common settings borrowed from adult content scripts.
- 21Block 4 - Shooting + ActionReading lessonBlock 4 of the positive prompt: repeat the shooting style inside double brackets so the AI respects it, then state the model's action or the body element that must appear. This block reinforces the sexual or suggestive intent of the scene, with a mirror selfie example.
- 22Block 5 - Visual rendering (photo finishing)Reading lessonBlock 5 of the positive prompt: the photo finishing tags that keep an image from looking like basic AI output, such as film grain, skin texture, skin pores, soft shadows and natural lighting, reinforced with brackets to double their importance.
- 23Negative imageReading lessonIntroduces the negative prompt as a filter against defects and bad taste, telling the AI what never to produce. It gives a classic list for image defects (flash, pale skin, signature, text, watermark) and warns that overloading the negative prompt makes the AI lose focus.
- 24Negative distortionReading lessonHow to fix distortions with the negative prompt, from visual bugs and anime-style output to cracked hands and text in the image. It lists terms to add, such as bad teeth, amputated body, hand mutation, extra, fused or missing fingers.
- 25Negative otherReading lessonUsing the negative prompt to personalise the rendering by removing features that do not fit the model's style, such as moles, freckles, tan lines, glasses, tattoos, high contrast or a big nose, while avoiding too many words that would lower final quality.
- 26 Complex camera anglesReading lessonFull prompt examples assembling every block plus the negative prompt, shown with their images. The lesson shows how changing only the camera angle tag in Block 0 (mirror selfie, from below, overhead, POV) and the action in Block 4 produces varied but consistent images to sell together as a PPV set.
- 27Keeping consistency without LoRa trainingReading lessonThe method for a consistent model without training: keep the same influencer description, leaving Block 1 (ethnicity, skin, hair, age) and Block 2 (silhouette) untouched across prompts, so the same woman appears in every image you sell.
- 28Prompt Pack SFWReading lessonThree ready-to-use SFW prompts with their images for social media content: a close-up selfie on a café terrace, a mirror selfie in a living room and a post-workout mirror selfie, each with weighted clothing and accessory tags.
- 29Prompt Pack NSFW for PPVReading lessonA pack of about a dozen NSFW prompts with their images, covering different nationalities, ages, body types, settings (bedroom, hotel, kitchen, garden, LED-lit room) and shots (selfie, mirror selfie, from below, overhead), all built with the block structure and realism tags.
- 30Expressions & emotionsReading lessonA tag list for expressions and emotions to give the model personality or a specific vibe across scenes, from smile, eye contact and shy look to blushing, biting lip, playful, seductive and teasing expressions.
- 31 Body & visible partsReading lessonA tag list of body parts and visible areas used to direct the AI's focus on certain parts of the body in NSFW solo images, from breasts and hips to feet, waist, lower back and back dimples.
- 32Clothing & accessoriesReading lessonA tag list of clothing and accessories (lingerie, panties, bra, thighhighs, skirt, crop top, fishnet, bikini, hoodie, heels, jewellery and more), to be combined with weights so that they actually appear in the image.
- 33 Contexts & moodsReading lessonA tag list of settings and moods that add realism and decor to a scene: bedroom, bathroom, mirror, couch, car, hotel room, shower, office, kitchen, balcony, outdoor, private room with pink LED lights and more.
- 34 Sexy poses (model only)Reading lessonA tag list of solo poses that range from soft to explicit scenes, such as kneeling, sitting on the bed, leaning forward, bent over, arching back, leg raised, lying sideways or hands on hips.
- 35Visual effects & photo ambienceReading lessonA tag list of visual effects and photo ambience for Block 5, such as film grain, soft shadows, wet skin, depth of field, bokeh, bad lighting, backlight, harsh shadows, moody tone, lens flare or foggy effect, to control realism and photo style.
Taught by
Your instructor
David
AI OFM expert
David is an AI expert in OnlyFans management who manages creators through Pykaso and Fanvue. His courses follow that setup: the model is created and trained in Pykaso.ai, then monetised on Fanvue and through pay-by-link chatting. He also shares a prompting method built for Pykaso's SDXL PPV model, aimed at selling AI images as PPV.
More about David
Partner
Featured tool
FAQ
Frequently asked questions
No. The course states that a LoRA cannot be trained on the SDXL PPV model, and that consistency comes from the prompt itself: keep the subject block (ethnicity, age, skin, hair) and the body block identical across images, and only change the shot, scene or action.
It is written for the SDXL PPV model in the AI image generator of Pykaso.ai, which you can access with a free account. The course warns that this prompting style is dedicated to SDXL and should not be used with Flux models.
According to the course, realism alone is not enough to stand out in a crowded AI OFM market: what sells is the amateur effect of an image. It teaches how shot type, camera angle, blur, film grain, skin texture and natural lighting create that effect.
Negative prompts tell the AI what never to produce. The course gives lists for image defects (text, watermark, flash), distortions (extra, fused or missing fingers, bad teeth) and personal style choices (moles, tattoos, tan lines), and warns that overloading them lowers image quality.
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