jewel-images

💍 Museum-Style Jewellery LoRA — SDXL

This model is a DreamBooth LoRA fine-tuned version of Stable Diffusion XL, trained to generate museum-quality images of historical jewellery. It learns the visual language of catalogue photography: controlled lighting, high-resolution metal textures, gemstone reflectivity, and authentic archival aesthetics.

The LoRA specialises in objects resembling those found in the Victoria & Albert Museum collection, using a synthetic token “TOK” to anchor the learned style.

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✨ Model Details

• Model type: LoRA adapter for SDXL
• Base model: stabilityai/stable-diffusion-xl-base-1.0
• VAE: madebyollin/sdxl-vae-fp16-fix
• Fine-tuning method: DreamBooth LoRA
• Training resolution: 512×512
• Domain: Jewellery, catalogue photography
• Task: Text-to-image generation

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🎯 Intended Use

This model excels at:

• Generating museum-style jewellery photos
• Creating catalogue-ready rings, necklaces, bracelets
• Rendering metals, gems, filigree, and antique forms
• Producing historically coherent designs
• Visualising jewellery concepts for creative or research use

Not recommended for:

• Photorealistic human portraits
• Non-jewellery objects
• Safety-critical or factual applications
• High-risk generative design for real gems/metalwork

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📚 Training Data

Data was collected programmatically from the Victoria & Albert Museum’s IIIF API, querying for “Coronet” and related jewellery classifications.

Each image includes:

• high-resolution IIIF image
• systemNumber
• title / summary
• curated metadata

Synthetic training captions were produced using GPT-4.1 with detailed instructions focusing on materials, craftsmanship, gemstones, condition, and stylistic cues. This produced a consistent metadata_gpt4.jsonl file.

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🔧 Training Configuration

• Steps: 2000
• Checkpoints: ~700 steps
• Batch size: Small with gradient accumulation
• Optimiser: 8-bit Adam
• Precision: FP16
• SNR γ: 5.0
• Prompt token: "TOK"
• Framework: Custom Python training pipeline

The LoRA was trained to reproduce metal shine, stone translucency, engraving, patina, and controlled archival lighting.

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🧠 Model Behavior

The model produces:

• Clean, professional product-style photos
• Sharp metal edges and realistic gemstone reflections
• Neutral, controlled museum-style lighting
• Historically plausible jewellery objects
• Minimal compression artifacts or distortions

Example Characteristics

• Strong adherence to the “TOK” concept token
• Good compositional control
• No watermark artifacts (unlike the base SDXL)
• High material fidelity even at small scales

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⚠️ Limitations and Biases

• May invent non-existent gemstones or designs
• Limited to jewellery-like structures
• Not trained on modern fashion photography
• Depends strongly on correct prompt structure
• Synthetic captions may introduce subtle biases

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📊 Evaluation

Evaluation is qualitative:

• Fine-tuned model removes watermark-like patterns
• Strong improvement in metal realism vs base SDXL
• Gemstone rendering is materially consistent
• Museum-style lighting is preserved
• Produces coherent outputs even with complex prompts

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🔐 License

Released under the MIT License.
Users of the LoRA must also comply with the base model’s SDXL license.

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🙏 Acknowledgements

• Image data from the Victoria & Albert Museum IIIF API
• Captions generated with GPT-4.1
• Base model by Stability AI
• LoRA training enabled by PEFT + Diffusers

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📣 Citation

If you use this model, please cite:

• Stability AI (SDXL)
• Victoria & Albert Museum (image source)
• GPT-4.1 (caption generation)

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