When ‘Remove Dad’ Becomes ‘Replace Everyone’: What a Reddit Thread Reveals About AI Image Editing Limits and How to Get Better Results

A Reddit user asked ChatGPT to remove their father from a wedding photo (see thread: https://reddit.com/r/ChatGPT/comments/1np5noq/asked_chatgpt_to_remove_my_father_from_my_wedding/); the result was a cautionary mini-drama in AI image editing. The outputs (shared in the post), kept the broad composition but altered faces, clothing and even the baby’s features in ways commenters described as “half-elf,” “pod people,” or “skin-walker.” The thread quickly turned into a community comparison of tools, a little comedy of errors, and a short lesson in picking the right tool for the job.

What happened (short version)

• OP asked ChatGPT’s image-editing feature to remove a man (the father) from a group wedding photo. The model attempted to regenerate the image and replaced or altered many people in the frame. Faces lost fidelity (missing teeth, odd facial geometry), glasses vanished, and other details shifted.
• Commenters posted competing edits from Gemini, nano-banana, Qwen Image Edit, Samsung AI, Seedream 4 and traditional Photoshop. Most community votes favored Gemini or dedicated image-inpainting tools; several recommended using Photoshop or a human editor for high-fidelity work.

According to top comments

• “I like the subtle upgrades… like the woman no longer needing her glasses and the child becoming half-elf.”
• “But at what cost? The remaining people’s heads have all gone a bit… skin walker.”

Why did the edit go sideways?

• Many generalist LLM-based image editors regenerate too much of the scene rather than doing tight, local inpainting. Instead of patching the masked area and preserving surrounding pixels, the model samples a new scene consistent with the prompt, which hurts identity preservation and fine detail.
• Face and identity fidelity is a known weak point for many image models. Small shifts in facial geometry, lighting or texture lead to uncanny or distorted faces.
• The mask size, model choice and default face-restoration/postprocessing all matter. OP likely used a tool that prioritized semantic consistency over pixel-wise conservation.

Community-recommended alternatives

• Gemini and nano-banana: multiple commenters favored Gemini variants and nano-banana for cleaner edits and fewer collateral changes.
• nano-banana, Seedream 4, Qwen Image Edit, Samsung AI: suggested as better at local edits; some users claimed superior consistency.
• Photoshop or r/photoshoprequest: still the fastest, most reliable route for precise removals when you need identity and detail preserved.

Practical tips if you need to remove someone from a photo

1. Choose the right tool: use a dedicated inpainting/photo-editing model or classic image editors for high fidelity.
2. Mask tightly: smaller, precise masks reduce the model’s need to reimagine surrounding areas.
3. Supply face references: if preserving identity is critical, provide reference images and explicitly instruct the model to preserve nearby faces and clothing.
4. Run face-restoration as a post-process (GFPGAN, ESRGAN variants) if faces look degraded.
5. If quality matters (wedding pictures, prints), use a skilled human in Photoshop — it’s faster and more predictable.

Context and why this matters

This is a practical snapshot of a larger trend: AI image editing capabilities are improving quickly, but the products exposed to consumers vary widely in how they balance semantic coherence versus pixel-accurate preservation. Generalist LLM-image editors can do surprising things, but for tasks that require identity fidelity — family photos, ID-like images — traditional tools or specialized inpainting models still outperform them. The thread also highlights a common user behavior: trying a single, convenient tool first and then crowd-sourcing alternatives when results disappoint.

Pause for a second

If you’re using AI to edit emotionally sensitive photos, think about whether removal is the right step, and whether the end result will hold up under scrutiny. Reddit’s humor masks a real expectation gap: people expect Photoshop-level edits from conversational models, and those models don’t always promise that.

My takeaways from this post would be:

• AI can remove people, but tool choice and masking technique determine success. Use Gemini, nano-banana, Qwen or real Photoshop for best results.
• Expect collateral changes from models that regenerate the whole image, and plan for iterative edits and face restoration.
• For wedding photos and other keepsakes, budget for a human retoucher if you want guaranteed quality.

Takeaway: AI image editing is useful and fast, but not yet a universal replacement for targeted inpainting or human Photoshop work when fidelity matters. Choose your tool, mask precisely, and don’t be surprised if you need a second pass or a real human to finish the job right.