How to Make AI Porn: The Full Pipeline, Idea to Finished Video

I always start with the full script before touching a generation tool: the dialogue, the scene list, who's in each scene, what happens in each one. Writing this down first is what makes every later stage possible. A scene list tells you how many distinct settings and characters you need, so you're not discovering that mid-generation. Dialogue written in advance is what a voice-generation step (stage 4) needs to produce lines that match, instead of generating filler and patching it later.

How to write a script for an AI-generated video covers the format in full: locking every character and location up front, and planning each scene's exact start frame, end frame, duration, dialogue, camera move, and sound before you generate anything. The short version: write scenes, not vibes. "Two characters, a bedroom, one leads into the next scene" is not a script. A script names who says what, in what order, and where the camera would be if this were shot for real.

Once the script names your characters, build a character sheet for each one before generating a single scene. A character sheet is the fixed description you reuse in every prompt (face, hair, build, any tattoo or marking) so the same person shows up in scene 4 as in scene 1. Without one, every generation is a new roll of the dice on what your character looks like.

Write more than one version of the sheet when the script needs it: one per outfit or state of undress the character appears in, each one locking everything except that one variable. A script that undresses a character partway through needs a clothed version of the sheet and an undressed one, not a single fixed description asked to cover both.

How to build a character reference sheet covers the actual technique: generating every angle of a character (front, both profiles, the back) in one image, then feeding that whole sheet back in as the reference for every later generation instead of a single source photo. This is also where a LoRA earns its cost: once a character sheet is locked, training a LoRA on it is what makes that exact face and body repeatable across dozens of generations instead of drifting a little further each time. The LoRA guide linked below covers when a LoRA is worth the setup and when a well-written character sheet alone is enough.

Same idea as the character sheet, applied to the setting: a scene reference sheet locks the room, the lighting, and the camera framing so a scene you return to later (say, the same bedroom in scenes 2 and 5) looks like the same room both times. Write it once, reuse it as an ingredient in every prompt for that scene. How to build character and scene reference sheets covers both techniques, including the exact prompt for a location turnaround.

GenLovers already has a full walkthrough for the image-generation stage itself, including the RunPod cloud-GPU setup, the ComfyUI workflow file, and real cost numbers: read the local NSFW image model setup guide linked below rather than repeating it here. The one addition specific to a video pipeline: generate at a resolution and aspect ratio your video model accepts, checked before your first run, not after. An image cropped or resized at the video stage reintroduces the artifacts the image stage was supposed to avoid.

Before generating a single clip, decide which video-generation approach the shot needs, because the three main techniques trade off against each other in a way that matters for a multi-scene video, not just a one-off clip.

Reference-to-video follows your text prompt the most closely of the three: it's the one to reach for when the shot is really about a specific described action or camera move, and you're willing to accept more variation in the character's exact appearance from generation to generation.

Image-to-video (first-frame only) locks the starting frame to your exact character sheet and scene sheet, so the shot opens on precisely the character and framing you built, but you don't control where the motion ends up. Good for a shot where the start matters more than a specific ending.

First-frame-last-frame (FFLF) locks both ends: you control exactly how the shot starts and exactly how it ends, which is what makes long, multi-scene sequences hold together without drifting. The tradeoff is the same one every current video model has with this technique: giving it two fixed points to travel between makes the motion in between less free than reference-to-video, and sometimes visibly more constrained than a single first-frame generation. How much of that trade you're willing to make is a real judgment call per shot, not a solved problem. The longer-videos guide below covers FFLF's mechanics in full, including the one detail that decides whether it holds quality indefinitely or inherits chaining's drift: generate each keyframe as its own image, never extract one from an already-rendered clip.

MiniMax H3 is worth calling out specifically here: it's a current open-weight option that generates video and audio together in the same pass, which sidesteps stage 5's sync problem entirely for any shot it handles.

Every separate generation produces its own voice, even for the same character, so once your scenes are stitched together the voice audibly changes from clip to clip. This is the pipeline problem most tutorials skip, and it's the difference between a finished video and a collection of clips.

If every shot in a scene used reference-to-video with the same pre-generated audio track fed in as reference, the voice stays consistent by construction, because it's the same audio file driving every generation in that scene. The catch: that only works when your technique choice for the whole scene is reference-to-video with audio input, which isn't always the right call per stage 4 above.

When that's not the setup, the fix is to unify the voice after the fact: run every clip's generated audio through ElevenLabs Voice Changer, which replaces the voice in an already-recorded track while keeping the original timing and delivery. Point every clip at the same target voice and the whole video reads as one consistent character, regardless of how many separate generations it took to build the scenes. How to use ElevenLabs Voice Changer walks through the tool step by step.

If you'd rather not run every clip through a hosted service, AuK is an open-source alternative that does zero-shot voice cloning and instruction-driven speech editing on a rented GPU you control, no account or per-generation fee once it's running. It's more setup than ElevenLabs and needs a GPU pod, but the tradeoff is no content filter on the instruction text and no usage-based billing.

Voice Changer only handles speech: it does not generate or clean up moans, grunts, or other non-verbal sounds. Describe those directly in the video-generation prompt instead, and a LoRA trained specifically for that kind of sound gets noticeably more precise results than describing it in plain text alone. This is also why sequencing matters: once a scene moves from dialogue into a stretch with no speech, only moans and sound effects, stop running that stretch through Voice Changer entirely. There's no voice left to unify, only sound the video model already generated.

If a scene's video and audio came from the same generation (MiniMax H3's native audio path, or reference-to-video with pre-generated audio), assembly for that scene is close to a formality: check sync, trim, export. Once the voice-unification pass from stage 5 runs on the rest, line up each clip's audio against its own video timing (which rarely matches on the first attempt after a voice swap), then re-encode everything to one final container and resolution.

Background music is optional and comes from one of two places: some video models generate it as part of the clip itself, or you generate a separate music track with a dedicated tool like Suno or Udio and layer it under the finished edit. Neither is required. Review the finished clip with sound on before treating it as final: the reference-to-video and image-to-video guides above both flag that current video models aren't fully reliable on audio when they attempt it natively, which is part of why the voice-unification pass in stage 5 exists as a separate step rather than something you can skip.

Resolution, quality, and total length are all your call, not a fixed rule this pipeline imposes. Decide the render quality per clip at generation time in stage 4, and the finished video's length falls out of the scene count you settled on writing the script in stage 1. That flexibility, chain together whatever the story needs, is the point of building the pipeline this way instead of generating one clip and stopping.