The key to InstructPix2pix is understanding the given instructions. The technique uses a deep learning model called a diffusion model, which takes the instructions as input and generates an edited image that matches the desired vision. The diffusion model is trained on a large dataset of images and their corresponding edits, allowing it to learn the relationships between the instructions and the resulting images.
Naively Incorporating Metadata
One potential approach to incorporating metadata into the text caption is to naively include each numerical metadata item kj, j ∈ {1, . . . , M }, into the text caption with a short description. However, this method has several drawbacks. Firstly, it discretizes continuous-valued covariates, which can result in loss of information and accuracy. Secondly, it suffers from the limitations of text encoders, as mentioned in Radford et al. (2021).
Encoding Metadata
To overcome these challenges, InstructPix2pix chooses to encode the metadata using the same sinusoidal timestep embedding used in diffusion models. This approach allows for more accurate and continuous representation of the numerical metadata items, ensuring that they are properly incorporated into the text caption.
Temporal Generation
Apart from conditioning on k, InstructPix2pix also considers temporal generation. The technique uses a factor of 1000 to normalize the numerical metadata fields, such as longitude and latitude, so that low values map to 0 and high values map to scale. This allows for more accurate representation of the metadata in the text caption.
Conclusion
In conclusion, InstructPix2pix is a groundbreaking technique that enables AI to learn how to edit images based on instructions given by humans. By understanding the provided instructions and incorporating numerical metadata into the text caption, InstructPix2pix produces high-quality edited images that match the desired vision. This innovative approach has the potential to revolutionize the field of image editing and open up new possibilities for creative expression.
