I've spent the last three weeks training a custom Stable Diffusion model specifically for architectural interior and exterior visualizations. The goal was to move beyond generic "modern house" prompts and get consistent, commercially viable results for client presentations.
**The Problem with Base Models:** Using SDXL or even fine-tuned architectural models, I kept hitting the same issues:
* Inconsistent scaling of furniture and fixtures (a chair the size of a car, a door too small for a human).
* Physically impossible structural elements (floating beams, windows with no support).
* Poor handling of specific architectural styles (Mid-Century Modern vs. Scandinavian Modern were a crapshoot).
* Materials looked flat or had unrealistic textures.
**My Training Stack & Data:**
* Base Model: SDXL 1.0
* Training Method: Dreambooth via Kohya's SS GUI
* Dataset: ~150 meticulously captioned images (my own renders and curated professional photography).
* Captions were exhaustive: `interior, modern kitchen, concrete island, oak cabinetry, pendant lighting, afternoon sun, volumetric lighting, 35mm lens, architectural digest style`
* Trained for 1200 steps, 1e-6 learning rate.
**Key Configuration Tweaks:**
```
network_dim: 64
network_alpha: 32
mixed_precision: "fp16"
persistent_data_loader_workers: true
caption_extension: .txt
```
**The Results (The Good):**
* Prompt adherence improved dramatically. `award-winning sustainable house, cross-laminated timber, large glazing, cantilevered roof` now generates coherent structures.
* Material textures (raw concrete, brushed steel, terrazzo) are now consistently recognizable.
* Furniture and lighting fixtures scale correctly within rooms.
* Style keywords (`brutalist`, `japanese wabi-sabi`, `barcelona pavilion style`) produce distinct and accurate outputs.
**The Unresolved Issues (The Bad):**
* It still struggles with complex spatial relationships in highly detailed prompts. Asking for `open plan living area with kitchen island and sunken conversation pit` sometimes merges elements.
* Fine details like correct joinery on windows or specific fixture models require inpainting or ControlNet.
* There's a noticeable bias towards my dataset's lighting conditions (mostly sunny afternoon). Generating `moody rainy day interior` requires heavier prompting weight.
**Bottom Line:** This is viable for ideation and client mood boards, but not for final, technically accurate renders. The model is a powerful prompt amplifier for architects, not a replacement for CAD. The total cost (excluding my time) was under $50 in GPU rental.
Next, I'm testing it with ControlNet (Canny, Depth) to lock down floor plans and perspectives. If anyone else is training for specific commercial applications, I want to compare notes on dataset curation—specifically, how many images per style variant you found necessary before hitting diminishing returns.
Show me the query.