One cause, three symptoms
Hands have twenty-seven bones and appear in training data at every possible angle, occluded, in motion, half out of frame. Eyes must be symmetrical and matched to each other across a gap. Teeth are a repeating structure where any irregularity reads as wrong.
All three fail for the same underlying reason: in a 1024-pixel frame, a hand might occupy 60 pixels, which in latent space is about 8 × 8: roughly sixty values to encode a structure that needs far more. The model is not being careless; it does not have the resolution.
Which tells you the fix. Give the region its own full resolution. Everything below is a variation on that.
The setting that does it
Every interface has a version of it. A1111 and Forge call it “Inpaint at full resolution” with a padding value; ComfyUI does it with a crop-and-stitch workflow; InvokeAI does it on the canvas by default.
What it does: crops the masked region plus some padding, scales that crop up to the model’s native resolution, denoises it there, scales it back down and stitches it in. Your 60-pixel hand is generated as a 1024-pixel hand and then reduced, which is why it comes back with correct structure.
Without this setting, inpainting a hand is the model trying again at 60 pixels, and it will fail again.
Hands
mask: the whole hand plus the wrist, generously
padding: 32-64 px
denoise: 0.45 - 0.6
prompt: a relaxed open hand, five fingers, natural pose
steps: same as baseNotes that matter:
- Mask generously. A tight mask around bad fingers forces the model to connect to the existing wrong geometry. Include the wrist and some background.
- Prompt for the hand, not the scene. During an inpaint the prompt should describe the region. Leaving the full scene prompt in place tends to introduce scene elements into the patch.
- Simplify the pose. If the hand is gripping something at an awkward angle, a relaxed or partly hidden hand often reads better and generates first time.
- Generate four and pick. Hands are high-variance; batch them rather than iterating one at a time.
If four attempts all fail, the pose is outside what the model does. Composite a photographed hand, or crop it out.
Eyes
mask: both eyes together, never one at a time
denoise: 0.35 - 0.5
prompt: clear symmetrical eyes, natural catchlight, looking at cameraMasking both together is the whole technique. Eyes have to agree with each other (direction of gaze, size, catchlight position) and repairing one in isolation guarantees they will not. It is the single most common eye-repair mistake.
Keep denoise lowish. Eyes carry identity, and above about 0.55 you are generating a different person’s eyes.
Teeth and mouths
mask: the mouth and a margin of lip
denoise: 0.3 - 0.45
prompt: natural teeth, slight smile, soft lip edgeTeeth want less denoise than you would expect, because the failure is usually irregularity rather than absence. High denoise produces the other classic failure: a perfect, unnaturally even set that looks like dentures. Some asymmetry is what makes a mouth read as real.
Automatic detection tools
ADetailer and its equivalents automate this: they detect faces, hands or eyes with a small detector, mask each one, and run a full-resolution inpaint pass on it. It is free, it plugs into most interfaces, and for a batch of twenty images it is the difference between an afternoon and five minutes.
Two cautions. It applies the same denoise to every detection, so a face that was already fine gets regenerated and may change. And on a set, that regeneration can shift a character’s identity slightly between frames, which is exactly what the consistency block is trying to prevent. Use it for volume, check every result, and turn it off when identity matters.
The honest ceiling
Some hands do not come back. Two hands interlocked, fingers gripping a complex object, a hand pressed against a face: these are configurations where even a full-resolution attempt fails repeatedly, because the training data is thin there and the structure is ambiguous.
At that point the professional answer is the unglamorous one: photograph your own hand in that position with a phone, and composite it. It takes ten minutes, it works every time, and it is what retouchers have done for decades. Persisting with a twelfth inpaint attempt is a decision to spend an hour on something a photograph solves, and recognising that moment is worth more than any setting in this lesson.
Every lesson here is open. Your progress is recorded once you pass the Vectors, Logos and Things That Get Printed exam.
Check your understanding
Why does “inpaint at full resolution” fix hands when ordinary inpainting at the same denoise does not?
Pick the one you would defend. Your answer stays private.
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