Instructions to use maria26/Floor_Plan_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use maria26/Floor_Plan_LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sd-legacy/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("maria26/Floor_Plan_LoRA") prompt = "Floor plan of a small apartment, few rooms, one bathroom, big kitchen, many windows." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Floor Plan Genrator

- Prompt
- Floor plan of a small apartment, few rooms, one bathroom, big kitchen, many windows.
Model description
The model generates architectural floor plans in the style of the provided image from text descriptions and it is part of my Bachelor Thesis.
This project explored the use of diffusion models for generating architectural floor plans based on textual descriptions.
GithHub repository:
https://github.com/mariaaoprea/Diffusion-Models-for-floor-plan-drafting.git
Trigger words
You should use prompts following this structure: "Floor plan of a small/big apartment, few/many rooms, one/multiple bathrooms, small/big kitchen, few/many windows"
Download model
Weights for this model are available in Safetensors,PyTorch format.
Download them in the Files & versions tab.
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Model tree for maria26/Floor_Plan_LoRA
Base model
stable-diffusion-v1-5/stable-diffusion-v1-5