Text-to-Video vs Image-to-Video: Which AI Video Workflow is Best for Adult Feeds?

For an adult feed, where subscribers pay to see the same person in every post, AI video from image is the better default. You approve a still first, then the model adds motion to that exact picture. Text-to-video invents the person, the scene and the motion from words, which makes it good for exploring ideas and poor at keeping one face.

What text-to-video and image-to-video are

Both are diffusion models that build a clip out of noise. The difference is what guides them.

Text-to-video

A text-to-video model works from your prompt alone. A line like “a 22-year-old blonde woman looking into the camera” describes a type of person, not one person, so the model picks a slightly different face each time. Across a series of clips, the character drifts.

That freedom is useful when no specific person needs to appear: trying out a scene, a camera move or a mood before committing to it.

Image-to-video

An image-to-video model starts from a picture. The face, body, outfit, setting and light are already decided, and the model’s job is to move them. The prompt only needs to describe the motion.

Open models offer both modes side by side. Alibaba’s Wan 2.2 ships a text-to-video model, an image-to-video model and a combined text-image-to-video model, and lists 480P and 720P output for the first two.

Why a starting image keeps the person stable

Research on image-to-video shows why the starting picture matters. The Motion-I2V paper (Shi et al.) splits the job in two: first predict how each pixel of the reference image should move, then use that motion to carry the reference image’s features across the generated frames. The person in the clip comes from the picture, and the model spends its effort on the movement.

Text-to-video has no such anchor. Every frame’s version of the face comes from the prompt and the model’s guesses.

Comparing the two workflows

What matters	Text-to-video	Image-to-video
Same face across clips	Weak: the face is re-invented each time	Strong: the face comes from the approved still
Outfit, tattoos, body	Drift between clips	Carried from the still
Control over the scene	Only through words	Set in the still, where you can check and edit it
Prompt	Long: person, scene, light, camera and action	Short: motion and camera only
Retakes	Many, to find a clip that looks like the persona	Fewer; failures are usually motion, not identity
Best use	Ideas, mood tests, shots without a recognisable person	Feed clips, PPV clips and custom requests

The lock-then-animate workflow

A workflow that puts identity first and motion second gives you clips that match your photos.

1. Lock the identity. Train an identity model on photos you own, or design an original persona. This is what keeps the face and body the same across images and video.
2. Make the keyframe. Generate or edit a still with the pose, outfit, setting and light you want. Check hands, edges and light here, where fixing is cheap.
3. Animate the keyframe. Feed the still to an image-to-video model with a short motion prompt: “slow head turn toward the camera, gentle smile, static camera”.
4. Finish and post. Upscale the approved clip, download it, add the AI label your platform needs, and post it yourself.

Motion problems are easier to fix in this order too. If a clip melts or smears, the still is usually fine, and you only change the motion prompt. The guide to prompting adult AI images covers the keyframe side.

When text-to-video is still the right tool

Text-to-video earns its place before production, not in it.

• Testing a scene. See whether a setting, a camera move or a mood works before you build stills for it.
• Shots without a recognisable person. Backgrounds, rooms and details for cutaways.
• Finding a look for a new persona. Explore, then lock what you like into stills and an identity model.

Once a person has to be recognisable, move to image-to-video.

Platform rules for AI video

Whichever workflow you use, the clip is AI content.

OnlyFans. The Terms of Use (October 2026) allow AI content only if “it clearly features the verified Creator”, if fans can tell it is AI “with a signifier such as #ai, or #AIGenerated”, and if it follows the Terms and the Acceptable Use Policy. The Help Centre article Can I post AI content on OnlyFans? says the same. The content moderation page lists among removals content that features “accounts or content that are wholly AI-generated, or features heavily altered images”. Image-to-video from your own photos fits the rule. A clip of an original persona does not.

Fanvue. Fanvue allows fully AI-generated content with a “clear and prominent” disclosure in the bio, caption or as a watermark.

MYM. MYM asks for “Virtual Image” in the description or the hashtags #AI or #AIgenerated, and adds an “AI profile” badge.

Fansly. Fansly says “Photorealistic AI-generated content is not allowed on Fansly”, even when labelled.

AI video from image in FansCreator

Image-to-video is the main workflow in FansCreator’s NSFW AI video generator: pick an approved photo of your model and the clip keeps her face, body and scene while adding motion. Stills come from the NSFW AI image generator with the same identity, so photos and clips match. Video runs on models such as Wan and Seedance, described on FansCreator’s Wan page and Seedance page.

Clips can be upscaled before download. You download each one and post it yourself, with the label your platform asks for.

Which workflow to choose

• You post a recognisable person: image-to-video, every time.
• You are exploring a new scene or persona: text-to-video, then lock the result into stills.
• You sell on OnlyFans: image-to-video from your own photos, labelled #ai.
• You run an original AI persona: post it on Fanvue or MYM with their AI labels.