Case Study · Model Training & Fine-Tuning
AI-Based Textile Pattern Design
A diffusion model fine-tuned on a studio's own design archive, so designers can generate on-brand, print-ready textile patterns in minutes and spend their time refining the best ones.
The problem
Textile design studios work to tight seasonal calendars. For each buyer brief, designers sketch concepts, redraw motifs into seamless repeats, reduce colours to what the print process can handle and send samples for strike-off, often several times over. Each round takes days, and buyers expect more options, faster.
Public image generators don't solve this. They don't know the studio's house style, they rarely produce seamless repeats or print-ready colour counts, and training data of unknown origin creates IP risk for a commercial design business.
Our approach
We treat AI as a design assistant, not a replacement for designers. The goal is a model that has learned the studio's own visual language, from motifs and colour stories to line quality and repeat structures, inside a workflow that ends in a file ready for the printer. Designers stay in charge of every design that leaves the studio.
Model training and fine-tuning
Dataset curation
Collect the studio's approved, owned or licensed designs. Remove duplicates and low-resolution scans, and tag each design by motif (floral, geometric, paisley, abstract, ethnic), technique, colourway, scale and repeat type.
Captioning
Write structured captions for each image so the model learns the studio's own vocabulary, for example "tossed small-scale floral, half-drop repeat, 6-colour indigo palette".
Base model selection
Start from an open, commercially licensable text-to-image diffusion model that can be hosted privately, so neither the training data nor the generated designs leave the client's environment.
LoRA fine-tuning
Train lightweight LoRA adapters on the curated set, one per collection or house style. This is fast, needs modest GPU capacity, and each style can be updated independently as the archive grows.
Structure conditioning
Add ControlNet-style conditioning so designers can guide layout, motif placement and repeat structure from a rough sketch or reference.
Evaluation
Measure image-distribution metrics such as FID against the studio's archive, check that patterns tile seamlessly, and run blind reviews where senior designers score style fidelity and usability. Checkpoints are only promoted when designers prefer them.
Generation workflow
1. Brief
The designer enters a text brief, picks a house style and can add a sketch, mood board or reference swatch.
2. Generate
The model produces a batch of variations in seconds, with controls for scale, density and colour story.
3. Make it repeatable
The chosen design is converted to a seamless tile (straight, half-drop or brick repeat) and upscaled to print resolution.
4. Colour reduction
Colours are reduced to the production palette and screen count, with separations for rotary or flat-bed screen printing, or kept full-colour for digital printing.
5. Designer refinement
Designers finish the design in their usual tools (Photoshop, Illustrator or CAD), so the final artwork is always designer-approved.
6. Approval and archive
Approved designs are exported for strike-off and saved back to the archive with their metadata, ready to improve the next round of fine-tuning.
Guardrails
- Trained only on designs the client owns or has licensed.
- Privately hosted, so briefs, archives and outputs never go to a public service.
- Every generated design is logged with its prompt, style and model version for traceability.
- A designer approves every design before it goes to a buyer or into production.
Business outcomes we design for
Technology
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