AI Image Inpaint Bleeds Pixels Outside the Mask
You masked a region for inpaint and the model changed pixels outside the mask too — usually a feathering, padding, or full-image-conditioning issue. Tighten mask handling.
Bad fidelity, broken faces, inconsistent style, jittery video, weak hooks, long prompts.
AI-generation "failures" are usually prompt-structure or parameter issues, not model limits. This hub gathers image / video / music / lyric failure modes and their shortest fixes.
You masked a region for inpaint and the model changed pixels outside the mask too — usually a feathering, padding, or full-image-conditioning issue. Tighten mask handling.
You added 'no text, no watermark, no extra fingers' and the output still has all three — negative prompt is either not wired up, too long, or fighting an overtrained concept.
You uploaded a reference for img2img or style transfer and the output barely resembles it — strength, mode, and model architecture all matter. Diagnose by sweeping strength.
Same seed and prompt produce different images run-to-run — usually a model version drift, sampler change, or hidden pipeline randomness. Pin every variable, not just seed.
Suno or Udio starts at 120 BPM and gradually wanders to 128 or 115 by the end — usually a prompt structure issue, not a model bug. Pin tempo with explicit anchors.
You asked for a slow dolly-in and got a dolly-out. Or 'pan left' became 'pan right'. AI video models map motion vocabulary inconsistently — fix with explicit framing.
Your AI voice clone speaks the words but breathes in the wrong places, takes weird mid-word pauses, or has no breath at all — usually a punctuation and pacing problem.
Cut between two AI clips feels jarring — match motion, color, and pacing.
Six-finger hands are still common. Negative prompts + targeted regeneration.
AI-generated series images drift in style across every shot. Six controllable methods — style header prompts, seed locking, style reference, palette limits, LoRA, and image-to-image chaining.
Start with image A, end with someone else. Motion strength + identity anchors fix it.
Mouth movements don't match the audio you generated separately.
Realistic prompts come out plastic-looking. Cause: lighting and skin/material cues.
Studio shot AI prompts often produce uncanny products. Lighting, lens, and surface cues are the fix.
Models still struggle with text-in-image. Two strategies: short words or post-edit.
Background flickers between styles or shapes. Causes: prompt fight, motion strength, model.
AI video models break motion: extra fingers, faces morphing, objects vanishing. These are universal weaknesses. Seven actionable methods to make Sora, Veo, Kling, and Runway clips far more consistent.
Subjects pop in and out across frames. Causes: too many actions, bad start/end pose, low fps.
Person turns into someone else half way through. Identity anchoring helps.
Your AI video is a still with 5-pixel parallax. Add strong verbs, raise motion strength, and name a camera move — the three fixes for under-motion.
Subject dead-center, no depth, no leading lines. Composition cues fix it.