Anatolian Kilim
Your photo, reborn on the loom in wool, dye, and tradition.
What it does
Every part of the image — face, clothing, light, background — is reborn on the loom in dyed wool, while pose, silhouette, and composition stay intact.
When to use it
- Portrait photos turned into textile heirloom pieces for printing or framing
- Wedding, family, and milestone portraits given a cultural, handwoven finish
- Anatolian heritage gifts, cards, and personalized keepsakes
- Album covers, cultural project posters, and editorial visuals with a craft feel
- Wall art, fine-art prints, and NFT pieces with a tactile material identity
- Memorial and commemorative portraits with a traditional craftsmanship tone
When not to use it
- Photo retouching, beauty editing, or realistic skin clean-up
- Sharp text, logos, or fine typographic detail in the source image
- Documentary, photojournalism, or news-accurate visuals
- Product photography that needs preserved labels or surface accuracy
How it works
The model reimagines the entire image as if it were physically built on a traditional Anatolian loom. Every region — face, hair, clothing, background, even the play of light — is rebuilt from layered wool yarn, with hooked motifs, diamond symbolism, and naturally dyed Central Anatolian tones taking the place of pixels. The original composition, pose, silhouette, and depth structure guide the weaving so the subject stays recognizable through thread density and woven geometric flow rather than photographic likeness. The output fills the full frame as a continuous textile surface — no carpet edges, no gallery framing, no overlay effect.
Updates
- v1May 23, 2026
Initial release
Specifications
- Approach
- Full-frame rebirth of the photograph as a single handwoven textile artifact
- Stylization
- Wool fibers, layered yarn, hooked tribal motifs, diamond symbolism, soft loom asymmetry
- What stays
- Composition, pose, silhouette, facial proportions, hairstyle, depth and scene readability
- What changes
- All photographic material — skin, eyes, hair, fabric, background, lighting — becomes woven wool
- Ideal source
- Front-facing portraits or single-subject scenes with clear silhouette and even, soft lighting
- Maximum input
- Up to 25 MP per image
- Supported formats
- JPEG, PNG, HEIC
- Output format
- PNG — preserves fine fiber detail and woven contrast without lossy compression
- Pricing
- 3 credits per image
Frequently asked questions
Will the kilim version still look like me?
Yes — facial proportions, silhouette, hairstyle, and pose are preserved. Identity emerges through thread direction and yarn density instead of skin and pixel detail, so the portrait still reads as you, only translated into woven material.
How traditional are the patterns?
The motifs are drawn from real Central Anatolian weaving language — hooked motifs, diamond symbolism, tribal geometric rhythm, and naturally dyed wool tones. They flow through the composition as woven structure rather than as decoration pasted on top.
Will it add fringes, borders, or a hanging carpet frame?
No. The textile fills the full frame edge-to-edge as a continuous woven surface. There is no rug edge, no gallery mockup, no floating carpet object, and no white margin around the image.
What kind of photo gives the best result?
A clear front-facing portrait or single-subject scene with a strong silhouette and even lighting works best. The cleaner the composition you start with, the more readable the woven version stays.
Can I use any aspect ratio?
Anatolian Kilim outputs at a 4:5 vertical aspect ratio. If your source is a different shape, the model focuses on the framing that fits this aspect and rebuilds it as one continuous woven surface.
Can I use the result commercially?
Yes — generated images on upsa are yours to use for personal and commercial purposes. We recommend you keep the consent and rights to the source photo as well, since the output preserves the identity in it.
How long are my photos stored?
Source uploads and generated results are stored for 48 and then automatically deleted from upsa's storage. We don't train any model on your photos.
AI results vary
Each generation uses a different random seed, so your result may not exactly match the example shown. If the first try doesn't fit what you had in mind, run it again — output differs noticeably between runs.
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