Achieving Custom Image Generation with LoRA
You've spent weeks curating 200 images in a signature style, but full SDXL fine-tuning demands 24 GB VRAM and hours of compute. LoRA (Low-Rank Adaptation) eliminates this barrier: the adapter is just 10–150 MB, trains in 30–120 minutes on an RTX 3090, and can be merged with other LoRAs. Time savings reach 80%; GPU expenses drop tenfold versus full fine-tuning, saving over $1,200 per project. Our service provides LoRA fine-tuning for Stable Diffusion (SD 1.5 and SDXL), covering adapter training, style transfer AI, character generation, and product image customization. We use Kohya training scripts and BLIP captions for automatic captioning. For a detailed DreamBooth comparison, see below.
Note: LoRA employs low-rank decomposition of weight gradients, effectively reducing the number of trainable parameters by over three orders of magnitude while preserving the original model's feature space. This technique, rooted in the original paper on large language models LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2021), yields three perks: tiny file size, fast training, and combinability of up to 5 adapters. DreamBooth, by contrast, saves the entire model (6–7 GB) and forbids merging. SDXL LoRA adapters are our specialty; we also support multiple LoRAs in a single pipeline.
Deliverables
| Deliverable | Details |
|---|---|
| Dataset curation & captioning | 20–200 images, BLIP captions, manual check |
| LoRA training | SD 1.5 / SDXL, Kohya scripts, rank 64 |
| Adapter file | .safetensors, 10–150 MB |
| Testing prompts | 10 custom prompts, 3 variations each |
| Documentation | Training parameters, merging guide, best practices |
| Support | 30 days email support, free consultation |
| Additional | Access to training scripts, custom captioning on request |
Our deliverables package includes everything you need to deploy the adapter: documentation, access to training scripts, and 30 days email support. Image personalization with LoRA is fast and cost-effective—total investment only $299, with cloud GPU costs under $1 for a typical session. Typical full fine-tuning costs $1,500, so you save 70–90%.
Comparison: LoRA vs DreamBooth
| Feature | LoRA | DreamBooth |
|---|---|---|
| File size | 10–150 MB | 6–7 GB |
| Training time | 30–120 min | 1–3 hours |
| VRAM requirement | 10 GB (SDXL) | 24 GB (SDXL) |
| Merging ability | Up to 5 adapters | Not possible |
| Cost | $299 | $1,500 |
How to Prepare Your Dataset?
For best results, follow these steps:
- Collect 30–200 high-quality images (at least 512x512).
- Ensure consistent subject or style across images.
- Avoid watermarks, text overlays, or duplicates.
- We handle auto-captioning with BLIP (no manual captioning needed).
Why Choose Us?
- Over 5 years on the market, 5+ years in AI/ML and 50+ LoRA projects delivered. Company metrics: 5+ years in AI, 50+ LoRA projects, 95% satisfaction rate.
- Over 95% client satisfaction with a 100% satisfaction guarantee.
- We use only legal, licensed base models.
- All adapters are tested for quality before delivery.
Get Started Today
Tell us about your project: style, product, or character. We'll prepare the dataset and produce the adapter in 1–2 days. Contact us to order LoRA training with full documentation and support. We offer a free consultation to estimate your project. The training process involves dataset curation, hyperparameter tuning (learning rate=1e-4, rank=64, optimizer=AdamW, 5000 steps, batch size=4, gradient checkpointing, mixed precision), and validation with held-out prompts. Our technical experts ensure optimal convergence and minimal overfitting using techniques like weight decay and cosine annealing. All workflows are compatible with SDXL and SD 1.5 pipelines.