This was my “remodeling my living room” project 🙂 So, I took a picture of our small living room and asked DALL-E to change the accenting wall behind sofa to yellow. What I expected DALL-E, a model that generates images, is to edit the existing photo I uploaded. But is that even possible?
This is the photo of our small living room:
This was DALL-E’s response:
Yes, the DALL-E didn’t edit the existing image, but it generated a new image, similar to the one I uploaded.
When I asked DALL-E to directly edit the image I uploded, it replied
I cannot directly edit the uploaded image, but I can guide you on how to do it. You can use photo editing software like Adobe Photoshop, GIMP, or even online tools like Pixlr to change the color of the accent wall.
Why can’t DALL-E directly edit the uploaded image?
The image editing feature is outside the current design and capability focus of DALL-E, which is optimized for generating new images based on textual descriptions. GPT offered several reasons for that:
- Design Limitation: DALL-E is primarily designed for generating new images based on textual descriptions rather than for image editing. The architecture and underlying algorithms are optimized for creation rather than modification.
- Data Handling: Allowing direct editing of uploaded images would require the model to handle a broader and more complex range of image data, which could complicate the training process and potentially reduce the quality of both image generation and editing tasks.
- Consistency and Quality: Ensuring the consistency and quality of edits across a wide variety of images is challenging. Direct editing would need sophisticated mechanisms to understand and maintain the style, context, and specifics of the original image, which might not align with the generative nature of the current model.
- Computational Complexity: Editing an image directly would involve a different set of computational challenges compared to generating an image from scratch. It would require the model to understand the content, structure, and style of the existing image in a much deeper way, increasing the computational complexity.
- User Control and Flexibility: By focusing on generating images from text prompts, users have more control over specifying exactly what they want without the need to upload and manage existing images. This simplifies the user experience and broadens the applicability of the tool.
In conclusion, my “project” highlighted the capabilities and limitations of DALL-E. While I initially expected the AI to edit the uploaded photo of my living room, it instead generated a new image resembling the original with the requested yellow accent wall. This outcome underscored an important aspect of DALL-E’s functionality: it excels at creating new images from textual descriptions but is not designed for direct image editing.