Can AI Inpainting Restore Faded Textures? A Journalist’s Investigation

When a treasured piece of clothing—faded, torn, and washed beyond recognition—becomes a symbol of memory, the urge to restore it is deeply human. For one Reddit user, known only as /u/dying_animal, that object was an 11-year-old t-shirt bearing the iconic imagery of the Florida Keys. After photographing the garment stretched on cardboard and using ChatGPT to generate a clean digital baseline, he turned to AI inpainting tools in ComfyUI to repair smeared textures around the word "resort," the palm tree motif, and the letter "R" in "Florida Keys." But he faced a fundamental question: Can AI inpainting truly repair what already exists—or does it inevitably reimagine it?

Inpainting, a technique popularized by diffusion models like Stable Diffusion, has long been used to remove unwanted objects, fill gaps, or insert new elements into images. But its application to texture restoration—where the goal is not creation but faithful reconstruction—is less explored in mainstream discourse. According to the original Reddit post, the user’s initial attempt with ChatGPT successfully repaired holes but failed to restore the integrity of the smeared, distorted patterns without introducing artifacts or altering adjacent areas.

AI ethics researcher Dr. Elena Márquez of Stanford’s Center for Digital Humanities notes, "Inpainting models are trained to predict plausible content, not to preserve historical fidelity. Even when prompted with "repair this texture," the model interprets that as "generate a texture that fits this context," not "clone this exact pattern."" This distinction is critical. Unlike professional photo restoration software like Adobe Photoshop’s Content-Aware Fill, which analyzes pixel neighborhoods to extrapolate continuity, diffusion-based inpainting operates probabilistically, often prioritizing aesthetic coherence over pixel-perfect replication.

Yet, as the Reddit thread reveals, skilled users are finding ways to push these models toward restoration. Community members suggest using high-precision masks, low denoising strength (0.2–0.4), and negative prompts such as "blurry," "distorted," or "new design" to constrain outputs. Models fine-tuned for photorealistic restoration—such as Stable Diffusion 1.5 with the "Inpainting" checkpoint or the newer SDXL Inpainting model—are preferred over those optimized for fantasy or artistic generation. Some users recommend blending the original image with the inpainted result using layer masks in GIMP or Photoshop to preserve untouched regions.

Workflow recommendations include: (1) isolating the damaged region with a precise mask, (2) using a reference image of an undamaged part of the same texture as a "prompt image" in ControlNet’s Tile or Reference modules, (3) setting the guidance scale below 7 to reduce over-interpretation, and (4) running multiple iterations with slight variations to select the least altered result. One user in the thread shared a ComfyUI node setup using the "Inpaint Only" workflow with a CLIP vision encoder to match texture gradients, achieving near-seamless results on similar garment patterns.

While the technology is not yet perfect, the implications extend beyond nostalgia. Museums, textile historians, and forensic image analysts are beginning to explore AI-assisted restoration of degraded cultural artifacts—from vintage posters to damaged crime scene photos. The line between preservation and fabrication remains thin, but as Dr. Márquez adds, "If the goal is memory, not authenticity, then AI becomes a tool of emotional restoration, not just visual repair."

For /u/dying_animal, the outcome may not be perfect—but with careful prompting and iterative refinement, his t-shirt’s spirit may yet live on, pixel by pixel.