from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
controlnet = ControlNetModel.from_pretrained("saad206121/ai_interior_design_model")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"fill-in-base-model", controlnet=controlnet
)AI Interior Design Model
Model Details
Model Description
This model is a fine-tuned AI interior design generation pipeline built using Stable Diffusion and ControlNet Canny. The model is designed to generate realistic and aesthetically enhanced interior room designs while preserving the original room structure and layout from input images.
The pipeline leverages:
- Stable Diffusion for high-quality image generation and style transformation.
- ControlNet Canny for maintaining room geometry, furniture placement, and architectural structure using edge guidance.
- Fine-tuning on interior design datasets to improve style consistency, realism, lighting, furniture generation, and room aesthetics.
This model can be used for virtual interior redesign, room visualization, architectural inspiration, and AI-assisted home decor generation.
- Developed by: [Your Name or Username]
- Model type: Stable Diffusion + ControlNet fine-tuned pipeline
- License: CreativeML Open RAIL-M
- Finetuned from model: Stable Diffusion + ControlNet Canny
Model Sources
- Repository: [Add your Hugging Face repository link]
- Demo: [Optional demo link]
Uses
Direct Use
his model is designed for AI-powered interior redesign using image-to-image generation.
Users provide:
- An input room image
- A text prompt describing the desired interior style
- Guidance scale values
- Inference steps
The model then generates redesigned interior visuals while preserving the original room structure using ControlNet Canny conditioning.
Example Inputs
- Input Image: Existing room photograph
- Prompt:
"modern luxury bedroom with warm lighting and wooden decor" - Guidance Scale:
7.5 - Inference Steps:
30
The model is suitable for:
- Interior visualization
- Room makeover concepts
- Home decor inspiration
- Architectural styling previews
- AI-assisted design workflows
Downstream Use
The model can be integrated into:
- Interior design applications
- Real estate visualization platforms
- AI home decor tools
- Mobile or web-based room redesign apps
Out-of-Scope Use
This model is not intended for:
- Human face generation
- Deepfake creation
- Illegal or harmful content generation
- Misleading architectural claims
- Structural engineering decisions
- Safety-critical design validation
Generated outputs should not replace professional architectural or engineering consultation.
Bias, Risks, and Limitations
- The model may generate unrealistic furniture arrangements or lighting conditions.
- Performance depends heavily on input image quality.
- Certain room types, styles, or layouts may not generalize well.
- The model may inherit biases from training datasets related to design styles, cultural aesthetics, or room layouts.
- Fine details and object consistency may vary across generations.
Recommendations
- Use high-quality Enhanced prompts for better results.
- Use high-quality room images for better results.
- Use clear canny edge inputs for improved structure preservation.
- Experiment with prompts and guidance scales for optimal interior styles.
- Human review is recommended before using generated designs in real-world projects.
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