adirik / interior-design

Realistic interior design with text and image inputs

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Run with an API
Playground API Examples README Versions

Input

Output

Examples

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Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 11 seconds. The predict time for this model varies significantly based on the inputs.

Readme

RealisticVision for Interior Design

A custom interior design pipeline API that combines Realistic Vision V3.0 inpainting pipeline with segmentation and MLSD ControlNets.

Basic Usage

To use the API, simply upload an image of an empty room and enter a text prompt describing your final design. The API outputs an edited image that (mostly) preserves the original room layout.

The API input arguments are as follows:

  • image: The provided image serves as a base or reference for the generation process.
  • prompt: The input prompt is a text description that guides the image generation process. It should be a detailed and specific description of the desired output image.
  • negative_prompt: This parameter allows specifying negative prompts. Negative prompts are terms or descriptions that should be avoided in the generated image, helping to steer the output away from unwanted elements.
  • num_inference_steps: This parameter defines the number of denoising steps in the image generation process.
  • guidance_scale: The guidance scale parameter adjusts the influence of the classifier-free guidance in the generation process. Higher values will make the model focus more on the prompt.
  • prompt_strength: In inpainting mode, this parameter controls the influence of the input prompt on the final image. A value of 1.0 indicates complete transformation according to the prompt.
  • seed: The seed parameter sets a random seed for image generation. A specific seed can be used to reproduce results, or left blank for random generation.

Model Details

This is a custom pipeline inspired by AICrowd’s Generative Interior Design hackathon that uses Realistic Vision V3.0 as the base model. See the base and ControlNet model pages for their respective licenses. This code base is licensed under the MIT license.