Flanora-AI-v1 is a fine-tuned Canny ControlNet designed for controlled floor-plan image generation. It uses bedroom-count information extracted from a text prompt to select an appropriate Canny edge control image, which is then provided to a fine-tuned ControlNet together with Stable Diffusion v1.5 to generate a floor-plan image.
Model Details
Model Description
Developed by: Rana Talukdar (Rana-15), Ansh Gaur, Bindupautra Jyotibrat (BJyotibrat)
Shared by: BJyotibrat
Model type: Fine-tuned Canny ControlNet for floor-plan image generation
Language(s) (NLP): English
License: GPL-3.0
Finetuned from model:lllyasviel/sd-controlnet-canny, based on Stable Diffusion v1.5
Flanora AI can be used to generate floor-plan images from text prompts specifying a desired bedroom count between 0 and 5 bedrooms.
The inference pipeline extracts the requested bedroom count from the prompt, selects a corresponding Canny control image, and uses the selected image with the fine-tuned ControlNet and Stable Diffusion v1.5 to generate the final floor-plan image.
Example generated image:
eg_5
Inference example:
flanora-ai-v1-inferencing
Downstream Use
Flanora AI can be integrated into applications for:
Floor-plan image generation
Generative architectural visualization
Research into controlled image generation
Research and experimentation with ControlNet-based floor-plan generation
Out-of-Scope Use
Flanora AI should not be treated as a replacement for professional architectural design, engineering analysis, building-code verification, or construction documentation.
The generated images should not be interpreted as professionally validated architectural plans.
Bias, Risks, and Limitations
The model has several known limitations:
The training dataset is imbalanced across bedroom categories.
The dataset contains substantially more 3-, 4-, and 5-bedroom images than 0-, 1-, and 2-bedroom images.
The model does not reliably generate the requested bedroom count.
Performance is particularly weak for 4- and 5-bedroom prompts.
Generated floor plans may contain disconnected rooms, invalid walls, or problematic doors.
The model can generate visually plausible floor-plan-like images that are not necessarily architecturally valid.
Automatic image metrics do not fully measure architectural correctness.
In the held-out evaluation set, the model achieved a bedroom-count accuracy of 11.94% and an overall structural validity rate of 35.82%.
Recommendations
Users should treat generated floor plans as experimental outputs rather than construction-ready architectural documents.
Human or domain-expert review is recommended before using generated layouts for any practical architectural purpose.
How to Get Started with the Model
Use the provided inference.py and bedroom_selector.py files to run inference.
The inference pipeline accepts a text prompt containing a bedroom count, such as:
Generate a 3 bedroom floor plan
The bedroom count is extracted from the prompt and used to select the corresponding Canny control image before generation.
Training Details
Training Data
Flanora AI was trained using a custom merged floor-plan dataset combining:
The original datasets remain attributable to their respective authors and are subject to their respective licensing terms.
Dataset Credits
ROBIN
D. Sharma, N. Gupta, C. Chattopadhyay and S. Mehta, "DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans," 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017, pp. 420-425, doi: 10.1109/ICDAR.2017.76.
bibtex
1@INPROCEEDINGS{8270007,
2author={Sharma, Divya and Gupta, Nitin and Chattopadhyay, Chiranjoy and Mehta, Sameep},
3booktitle={2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
4title={DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans},
5year={2017},
6volume={01},
7number={},
8pages={420-425},
9doi={10.1109/ICDAR.2017.76}}
ImagesGT
de las Heras, Lluís-Pere and Terrades, Oriol Ramos and Robles, Sergi and Sánchez, Gemma, "CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool," International Journal on Document Analysis and Recognition, 2015.
bibtex
1@article{Heras15a,
2author={de las Heras, Lluís-Pere and Terrades, Oriol Ramos and Robles, Sergi and Sánchez, Gemma},
3title={CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool},
4journal={International Journal on Document Analysis and Recognition},
5year={2015}}
Training Procedure
The floor-plan images were preprocessed using grayscale conversion followed by Canny edge detection.
The resulting edge images were used as ControlNet conditioning images.
Preprocessing
Input images were converted to grayscale.
Canny edge detection was applied.
Canny thresholds: 100 and 200
Images were resized to 512 × 512 during the training pipeline.
The dataset was organized according to bedroom-count categories.
Training Hyperparameters
Base model: stable-diffusion-v1-5/stable-diffusion-v1-5
