What AI models does ZeroTwo support?

ZeroTwo provides access to ChatGPT-4, Claude, Gemini, and 30+ other leading AI models in one unified platform.

Is ZeroTwo better than ChatGPT?

ZeroTwo includes ChatGPT-4 plus many other AI models, allowing you to compare responses and choose the best AI for each task.

Can I use ZeroTwo for free?

Yes, ZeroTwo offers a free plan with access to multiple AI models. Pro plans start at $29.99/month.

Can I put my face in an AI image generator?

Yes. The four practical methods are reference-photo prompting, LoRA fine-tuning, IP-Adapter or face-conditioning, and descriptive prompting. Reference-photo prompting is the fastest path: upload a clear photo of yourself to a multimodal model and ask for a portrait in any style. LoRA training gives the highest likeness but takes 20–60 minutes; descriptive prompting protects privacy by never uploading any photo at all.

Do I need to train a model on myself to get a good likeness?

No, but training a LoRA gives the best likeness. Reference-photo prompting and IP-Adapter both work without any training step and produce strong results in seconds. Train a LoRA only if you plan to generate dozens of images of yourself across many scenes — the upfront cost pays back when you reuse the adapter.

Which AI image model is best for face likeness?

FLUX.1 [pro] currently leads on photoreal skin and facial texture. Imagen 3 wins on prompt fidelity and full-body composition. GPT Image 1 is the best at handling reference photographs natively. Gemini 3 Native Image is the best for editing existing portraits via natural-language patches. Run the same prompt through three engines and pick the winner — that is the workflow.

Is it safe to upload photos of myself to an AI image generator?

It depends on the platform's data-retention policy. Read the privacy terms before uploading. Prefer platforms that document a clear retention window, do not train future models on your uploads, and let you delete your data on demand. ZeroTwo lets you generate, save, and delete from a private cloud library so your reference photos do not become part of any training set.

What is the difference between LoRA and IP-Adapter?

A LoRA is a small (30–200 MB) trained adapter that teaches a base model your specific identity. It requires a one-time training run on 8–20 photos. IP-Adapter is an inference-time technique that extracts a facial embedding from a single reference photo and injects it into the generation step — no training required. LoRA gives a slightly higher ceiling on likeness; IP-Adapter is faster and easier to start with.

Is generating AI images of myself legal?

Generating AI images of yourself is legal in most jurisdictions when you give your own consent. Generating AI images of another identifiable person without their explicit consent is restricted or illegal in many places — in the United States, several states have passed laws against non-consensual synthetic intimate imagery, and the EU AI Act requires disclosure for synthetic media. Always self-disclose AI-generated content and never depict others without consent.

How many photos do I need to train a likeness model?

For a LoRA, 8–20 photos is the sweet spot. Fewer than 8 and the model under-fits — features look generic. More than 30 and the model often over-fits to a specific lighting, hairstyle, or expression. Vary angles (front, three-quarter, profile), include both close-ups and shoulders-up, and keep lighting natural. Resolution matters: 1024 × 1024 minimum, 2048 × 2048 ideal.

Can I do all of this inside ZeroTwo?

Yes. ZeroTwo runs FLUX.1, Imagen 3, GPT Image 1, Gemini 3 Native Image, DALL·E 3, and SDXL in one chat. Reference-photo prompting works in any multimodal model, inpainting works on FLUX-edit and Gemini Native Image, and your generations save to a private cloud library. The free tier is enough to follow this entire tutorial; Pro removes daily caps.

A gentle how-to · Updated April 2026

How to incorporate yourself in an AI image generator.

A practical, step-by-step guide on how to incorporate yourself in AI image generator workflows — reference photos, prompt scaffolds, identity-preservation models, and the ethical guardrails that keep the practice respectful. Calm, unhurried, and genuinely useful.

Try free See the ranked image-model leaderboard →

FLUX.1 · Imagen 3 · GPT Image · Gemini NativeReference photo · LoRA · IP-AdapterEthics-first

TL;DR

How to incorporate yourself in an AI image generator: gather 8–20 sharp photos of your face, choose between reference-photo prompting (fastest), LoRA fine-tuning (best likeness), or IP-Adapter (no-training middle ground), write an identity-style-scene prompt scaffold, lock the seed, and refine with inpainting. Always disclose synthetic media and never depict another person without consent.

The six-step workflow

A calm, repeatable workflow — from photos to portrait.

Most people get poor likeness because they skip the photo-gathering step and over-rely on the prompt. The order below is what works in 2026.

01
Gather 8–20 high-quality reference photos of yourself
Likeness in AI image generation begins long before the prompt. Collect 8–20 photographs that show your face from multiple angles — front, three-quarter, profile — under varied but natural lighting. Avoid heavy makeup, sunglasses, hats, or filters. Crop tight to the face and upper shoulders. Higher resolution beats more photos: a dozen sharp 2048-pixel images outperforms fifty blurry phone snaps.
02
Choose your method: reference-photo, LoRA, IP-Adapter, or descriptive prompting
There are four practical methods to put yourself into AI generations. Reference-photo prompting (paste a photo into a multimodal chat) is fastest. LoRA fine-tuning gives the best likeness but takes 20–60 minutes of training. IP-Adapter and face-conditioning models inject your features at inference time — a strong middle ground. Descriptive prompting (no photo) is the privacy-first fallback when you only need a stylized facsimile. For reference, ZeroTwo bundles all four methods inside one chat — see the ranked image-model leaderboard to pick your engine.
03
Write a prompt scaffold that locks identity, style, and scene
A reliable prompt has three parts: identity tag, style block, and scene block. Example: a portrait of [me], 28-year-old, short brown hair, warm hazel eyes, soft smile (identity); shot on 50mm, golden hour, shallow depth of field, photoreal (style); standing in a sunlit linen-curtained room, holding a ceramic mug (scene). Keep identity language consistent across every generation so the model anchors features.
04
Generate with the right model and iterate on a fixed seed
FLUX.1 [pro] excels at texture and skin realism, Imagen 3 leads on prompt fidelity, and GPT Image 1 wins on text-in-image and reference photographs. Lock the random seed for the first image you like, then change one variable at a time — outfit, lighting, location — so the face stays consistent. Generate four samples per prompt and discard the worst before iterating.
05
Refine likeness with inpainting and small descriptive corrections
If the eyes drift or the jawline softens, you do not have to start over. Use inpainting to mask just the face and regenerate that region while preserving the body and background. Small prompt corrections — slightly stronger jawline, eyes a touch closer together, hairline lower — pull the result back toward your real features. Two or three rounds of targeted fixes beat ten full re-rolls.
06
Confirm consent, label synthetic media, and protect your photos
Before sharing, label the result as AI-generated. Never use this workflow to depict another person without their explicit, informed consent — in many jurisdictions doing so is illegal and is broadly classified as harmful synthetic media. Keep your training photos in a private folder, do not publish them alongside the generations, and prefer platforms that document a clear data-retention policy.

Method comparison

Which method should you use?

Reference-photo prompting is best for one-off portraits. LoRA wins when you plan to generate dozens of images of yourself. IP-Adapter is the no-training middle ground. Descriptive prompting is the privacy-first fallback.

Method	Ease	Likeness	Time	Cost	Ethical risk	Notes
Reference-photo prompting	Easiest	Medium	1–2 min	Free / low	Low	Paste a photo into a multimodal model (GPT Image, Gemini Native Image).
LoRA fine-tuning	Moderate	Highest	20–60 min	$2–$10	Medium	Trains a 30–200 MB adapter on 8–20 photos. Best photoreal likeness.
IP-Adapter / face-conditioning	Easy	High	Real-time	Free / low	Medium	Injects facial embedding at inference. No training step.
Descriptive prompting (no photo)	Easiest	Stylized	30 sec	Free	Lowest	Privacy-first: never uploads your photos. Best for avatars, not portraits.

Try every method in one place

ZeroTwo runs FLUX, Imagen, GPT Image, and Gemini Native — in one chat.

Paste a reference photo, lock a seed, refine with inpainting. The free tier covers this entire tutorial.

Start free — no card

The numbers

Why ethics is part of this how-to.

Adding yourself to AI images is a personal creative practice. Adding others without consent is where the harm — and the law — kicks in.

73%

U.S. adults who say AI-generated images of real people without consent should be illegal

Pew Research, 2024 →

~4×

Year-over-year growth in image-generation training compute through 2024

Stanford AI Index, 2025 →

100,000+

Identity-preserving fine-tunes (LoRAs) hosted on Civitai by 2025

Civitai public model library →

Ethics & consent

A short, non-negotiable code of conduct.

Only generate yourself — or a person who has given you explicit, informed, revocable consent in writing.
Disclose synthetic media. Label AI-generated images when sharing publicly. The EU AI Act requires disclosure; many U.S. states are following.^[1]
Never impersonate. No fake identities of real people, no political deepfakes, no non-consensual intimate imagery — these cause documented psychological and reputational harm.^[2]
Protect your training photos. Keep them in a private folder. Prefer platforms with documented data-retention windows.
Follow the framework from the Partnership on AI on responsible synthetic media — it covers consent, disclosure, and redress.^[3]

“Synthetic media is a force multiplier for both creative expression and harm. The difference is consent and disclosure.”
— Hany Farid, Professor, UC Berkeley School of Information^[4]

Which model should you use for your face?

The honest answer: run your prompt through three engines and pick the winner. Each model has a different bias.

FLUX.1 [pro] — sharpest skin texture, best for photoreal portraits. Black Forest Labs published the technical report on the FLUX architecture.^[5]
Imagen 3 — leads on prompt fidelity and full-body composition.
GPT Image 1 — best at handling reference photographs natively and rendering text-in-image.
Gemini 3 Native Image — best for editing existing portraits via natural-language patches. The first-choice tool for inpainting.
SDXL + LoRA — open-weights, custom-trainable. Highest ceiling on likeness when you train your own LoRA on 8–20 photos.

According to the Stanford AI Index 2025, image-generation training compute grew roughly 4× year-over-year through 2024 — the resulting quality jump is exactly why a single-model anonymous tool no longer keeps up. To compare engines side by side, ZeroTwo's multi-model image rack is the simplest way to A/B test on the same prompt.

The prompt scaffold that always works.

Three blocks: identity, style, scene. Keep the identity block byte-for-byte identical across every generation so the model anchors features.

/* IDENTITY (lock this) */
a portrait of [me], 28 years old, short brown hair,
warm hazel eyes, light freckles, soft smile

/* STYLE (vary this) */
shot on 50mm, golden hour, soft rim light,
shallow depth of field, photoreal

/* SCENE (vary this) */
standing in a sunlit linen-curtained room,
holding a ceramic mug, soft greenery on shelf

The same scaffold works for stylized output — swap "photoreal" for "watercolor portrait, soft edges, paper texture" and your identity stays put.

Key takeaways

Likeness starts with photos, not prompts: 8–20 sharp images of your face, varied angles.
Pick a method by stakes: reference-photo for speed, LoRA for max likeness, descriptive prompting for privacy.
Lock the seed once you like a face, then change one variable per generation to keep identity stable.
Iterate with inpainting and small descriptive corrections — re-rolling from scratch is the slow path.
Always label synthetic media and never depict another person without explicit consent.

Common questions