Lately, I've been going deep down the rabbit hole of AI image models, and my main takeaway is: the landscape is incredibly fragmented and confusing for anyone who isn't living in this space 24/7.
My frustration was that no single model is the best at everything.
I found that nanobanana is fantastic for enhancing or merging multiple images, but it can't handle text editing. qwen is surprisingly good at in-image text manipulation, but its general enhancements are weaker. chatgpt is great for structured things like comics or infographics, and other models like kontext are strong generalists with fewer restrictions.
This is a terrible experience for a normal user. Users don't care about which model to use. They have a task they want to accomplish, like "fix this text" or "make my headshot look professional."
So, I built image2image.ai based on this premise.
My project isn't another single model. It's a user-centric layer that sits on top of this fragmentation. I've tried to identify the best-in-class model for each specific use case.
Instead of just giving you a blank prompt, I've organized the site by what you want to do (e.g., AI Headshot Generator, Object Remover, Background Generator, etc.). Behind the scenes, each of these tools is wired up to the model that I've found performs best for that specific job.
To make it even easier, each tool is loaded with practical examples and copy-paste-ready prompts so you don't have to guess.
To me, this is what a true "Image to Image AI" service should be. It shouldn't be the user's job to figure out the complex model ecosystem. The product should do that for them.
This is very much a V1, and I know this community understands the technical trade-offs here. I’d be incredibly grateful for your feedback. What scenarios are we missing? Do you find the results for a specific case (like text editing) genuinely better than a one-size-fits-all image to image ai tool?
Thanks for checking it out!
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Show HN: I built a "router" for image to image AI models based on use case · HackerTrans
Lately, I've been going deep down the rabbit hole of AI image models, and my main takeaway is: the landscape is incredibly fragmented and confusing for anyone who isn't living in this space 24/7.
My frustration was that no single model is the best at everything.
I found that nanobanana is fantastic for enhancing or merging multiple images, but it can't handle text editing. qwen is surprisingly good at in-image text manipulation, but its general enhancements are weaker. chatgpt is great for structured things like comics or infographics, and other models like kontext are strong generalists with fewer restrictions.
This is a terrible experience for a normal user. Users don't care about which model to use. They have a task they want to accomplish, like "fix this text" or "make my headshot look professional."
So, I built image2image.ai based on this premise.
My project isn't another single model. It's a user-centric layer that sits on top of this fragmentation. I've tried to identify the best-in-class model for each specific use case.
You can see this philosophy on our tools page: https://image2image.ai/ai-tools
Instead of just giving you a blank prompt, I've organized the site by what you want to do (e.g., AI Headshot Generator, Object Remover, Background Generator, etc.). Behind the scenes, each of these tools is wired up to the model that I've found performs best for that specific job.
To make it even easier, each tool is loaded with practical examples and copy-paste-ready prompts so you don't have to guess.
To me, this is what a true "Image to Image AI" service should be. It shouldn't be the user's job to figure out the complex model ecosystem. The product should do that for them.
This is very much a V1, and I know this community understands the technical trade-offs here. I’d be incredibly grateful for your feedback. What scenarios are we missing? Do you find the results for a specific case (like text editing) genuinely better than a one-size-fits-all image to image ai tool?
Thanks for checking it out!