u/wackylenses

I’m probably late and everyone already talked about GPT Images 2.0, but I wanted to test it for one specific thing - YouTube thumbnails.

This is basically a short version of the test I did for my YouTube video, so I’ll try to keep it focused on the practical stuff.

Not just making a nice AI image, but actually trying to use it in a normal creator workflow with faces, text, products, references, edits, style matching and cleanup.

Some of this may be obvious for people who already work with AI images a lot. But maybe it can still help someone who wants to use GPT Images for YT thumbnails.

One thing I liked right away is that voice prompting actually feels useful now. You don’t always need some perfect robotic prompt. You can just explain the idea like a normal person, even in a messy way, and most of the time it understands you pretty well. You still need to check what it heard.

Short prompts usually give you the most generic AI thumbnail look possible. And it doesn’t even matter that much what the topic is. For some reason, the default idea of a “good thumbnail” often becomes the same thing: too many elements, too many details, fake UI, random information on the screen, glow, arrows, panels, and a lot of visual noise.

Maybe for some genres this works. But most of the time it just feels like the model is trying too hard.

Text is much better now. Short thumbnail phrases worked pretty well for me. The problem is not spelling anymore, it’s control. Moving text a little, changing size, fixing margins, outline or glow still means another generation. The result is also unpredictable. So for final text, I’d still rather use Photoshop, Photopea, GIMP, Canva or whatever editor you like.

One useful workaround is to generate text elements separately. For example, a stamp, badge, 3D title or label on a transparent background, and then place it yourself in an editor. Sometimes GPT fakes the transparency and gives you that checkerboard look as part of the image, but if you ask again more clearly, it can do a real transparent PNG. That already makes the workflow much more usable.

Another possible option is Canva. You can connect ChatGPT to Canva and use tool, I think it’s called Magic Layers or something like that. Canva can try to rebuild the image into editable layers, so it becomes easier to move things around instead of regenerating the whole image.

I haven’t tested it deeply, and for export you’ll probably need a Canva subscription, but it can be a useful middle ground if you don’t want to work fully in Photoshop.

Simple ideas work better. The more tiny details you add, the faster things start getting weird. Electronics, camera gear, UI screens, product labels, professional tools, repeated lines and complex textures can look okay from far away, but up close they often fall apart.

Same with lighting. Clear, simple light is safer. Dark low-key scenes with smoke, heavy shadows, gradients and multiple colored lights can look cool, but they are harder to control and can turn into muddy AI haze.

Faces were actually one of the strongest parts. Even a boring selfie near a wall can become a decent thumbnail base. It can improve the background, light, colors and overall thumbnail feel. But changing emotion too much is risky. If you need a shocked face, angry face or smile, better shoot that expression yourself.

References help a lot. If you only describe something, the model invents too much. If you give it a face reference, product reference, lighting reference or examples of your thumbnail style, the result becomes much more usable. That also made me think that a Custom GPT could actually be useful here. You could feed it your thumbnail preferences, your style, your usual layout logic, maybe examples of your older thumbnails, and then you don’t have to explain everything from zero every single time. It probably still won’t be perfect, but for keeping things in a similar direction, it could save time.

There is a limit, though. If you start mixing too many references, asking for too many fixes, or changing too much at once, consistency starts drifting. Every new generation becomes another interpretation.

That was one of the biggest things I noticed. Repeated edits are not really final production. After a few fixes, the image starts drifting. The face gets softer, texture gets worse, sharpness drops, consistency gets messy. So the workflow that made the most sense to me was not one prompt and done. It was more like this: use iterations to find the idea, then do a clean rebuild, and finish manually.

The best version of the workflow for me was generating a base, generating some separate elements, and then assembling and polishing everything in an editor. That way you can move text normally, fix margins, add sharpness, clean artifacts and make small changes without asking AI to regenerate the whole image again.

Stylization is probably where it gets most useful. When an image tries to look realistic, your brain judges it much harder. You know how faces, hands and real objects should look, so if something is almost right but not quite right, you feel it immediately. It gets close to that uncanny valley problem.

But with stylization, visual metaphors the rules are different. The image doesn’t have to pretend to be a perfect photo anymore. It can have its own logic, and people are much more forgiving. That’s where GPT Images starts to feel more interesting, because you can test strange visual ideas that would normally take much more time to build manually.

My final take is pretty simple.

GPT Images 2.0 can make decent thumbnails, but I don’t think it works well as a one-prompt magic button.

If you use it blindly, you get AI slop.

If you control the idea, use references, keep it simple, understand your prompts, rebuild clean, generate separate elements when needed and polish manually, it becomes much more useful.

I’m probably late and everyone already talked about GPT Images 2.0, but I wanted to test it for one specific thing - YouTube thumbnails.

This is basically a short version of the test I did for my YouTube video, so I’ll try to keep it focused on the practical stuff.

Not just making a nice AI image, but actually trying to use it in a normal creator workflow with faces, text, products, references, edits, style matching and cleanup.

Some of this may be obvious for people who already work with AI images a lot. But maybe it can still help someone who wants to use GPT Images for YT thumbnails.

One thing I liked right away is that voice prompting actually feels useful now. You don’t always need some perfect robotic prompt. You can just explain the idea like a normal person, even in a messy way, and most of the time it understands you pretty well. You still need to check what it heard.

Short prompts usually give you the most generic AI thumbnail look possible. And it doesn’t even matter that much what the topic is. For some reason, the default idea of a “good thumbnail” often becomes the same thing: too many elements, too many details, fake UI, random information on the screen, glow, arrows, panels, and a lot of visual noise.

Maybe for some genres this works. But most of the time it just feels like the model is trying too hard.

Text is much better now. Short thumbnail phrases worked pretty well for me. The problem is not spelling anymore, it’s control. Moving text a little, changing size, fixing margins, outline or glow still means another generation. The result is also unpredictable. So for final text, I’d still rather use Photoshop, Photopea, GIMP, Canva or whatever editor you like.

One useful workaround is to generate text elements separately. For example, a stamp, badge, 3D title or label on a transparent background, and then place it yourself in an editor. Sometimes GPT fakes the transparency and gives you that checkerboard look as part of the image, but if you ask again more clearly, it can do a real transparent PNG. That already makes the workflow much more usable.

Another possible option is Canva. You can connect ChatGPT to Canva and use tool, I think it’s called Magic Layers or something like that. Canva can try to rebuild the image into editable layers, so it becomes easier to move things around instead of regenerating the whole image.

I haven’t tested it deeply, and for export you’ll probably need a Canva subscription, but it can be a useful middle ground if you don’t want to work fully in Photoshop.

Simple ideas work better. The more tiny details you add, the faster things start getting weird. Electronics, camera gear, UI screens, product labels, professional tools, repeated lines and complex textures can look okay from far away, but up close they often fall apart.

Same with lighting. Clear, simple light is safer. Dark low-key scenes with smoke, heavy shadows, gradients and multiple colored lights can look cool, but they are harder to control and can turn into muddy AI haze.

Faces were actually one of the strongest parts. Even a boring selfie near a wall can become a decent thumbnail base. It can improve the background, light, colors and overall thumbnail feel. But changing emotion too much is risky. If you need a shocked face, angry face or smile, better shoot that expression yourself.

References help a lot. If you only describe something, the model invents too much. If you give it a face reference, product reference, lighting reference or examples of your thumbnail style, the result becomes much more usable. That also made me think that a Custom GPT could actually be useful here. You could feed it your thumbnail preferences, your style, your usual layout logic, maybe examples of your older thumbnails, and then you don’t have to explain everything from zero every single time. It probably still won’t be perfect, but for keeping things in a similar direction, it could save time.

There is a limit, though. If you start mixing too many references, asking for too many fixes, or changing too much at once, consistency starts drifting. Every new generation becomes another interpretation.

That was one of the biggest things I noticed. Repeated edits are not really final production. After a few fixes, the image starts drifting. The face gets softer, texture gets worse, sharpness drops, consistency gets messy. So the workflow that made the most sense to me was not one prompt and done. It was more like this: use iterations to find the idea, then do a clean rebuild, and finish manually.

The best version of the workflow for me was generating a base, generating some separate elements, and then assembling and polishing everything in an editor. That way you can move text normally, fix margins, add sharpness, clean artifacts and make small changes without asking AI to regenerate the whole image again.

Stylization is probably where it gets most useful. When an image tries to look realistic, your brain judges it much harder. You know how faces, hands and real objects should look, so if something is almost right but not quite right, you feel it immediately. It gets close to that uncanny valley problem.

But with stylization, visual metaphors the rules are different. The image doesn’t have to pretend to be a perfect photo anymore. It can have its own logic, and people are much more forgiving. That’s where GPT Images starts to feel more interesting, because you can test strange visual ideas that would normally take much more time to build manually.

My final take is pretty simple.

GPT Images 2.0 can make decent thumbnails, but I don’t think it works well as a one-prompt magic button.

If you use it blindly, you get AI slop.

If you control the idea, use references, keep it simple, understand your prompts, rebuild clean, generate separate elements when needed and polish manually, it becomes much more useful.

I’m probably late and everyone already talked about GPT Images 2.0, but I wanted to test it for one specific thing - YouTube thumbnails.

This is basically a short version of the test I did for my YouTube video, so I’ll try to keep it focused on the practical stuff.

Not just making a nice AI image, but actually trying to use it in a normal creator workflow with faces, text, products, references, edits, style matching and cleanup.

Some of this may be obvious for people who already work with AI images a lot. But maybe it can still help someone who wants to use GPT Images for real thumbnails and not just random pretty pictures.

One thing I liked right away is that voice prompting actually feels useful now. You don’t always need some perfect robotic prompt. You can just explain the idea like a normal person, even in a messy way, and most of the time it understands you pretty well. You still need to check what it heard.

Short prompts usually give you the most generic AI thumbnail look possible. And it doesn’t even matter that much what the topic is. For some reason, the default idea of a “good thumbnail” often becomes the same thing: too many elements, too many details, fake UI, random information on the screen, glow, arrows, panels, and a lot of visual noise.

Maybe for some genres this works. But most of the time it just feels like the model is trying too hard.

Text is much better now. Short thumbnail phrases worked pretty well for me. The problem is not spelling anymore, it’s control. Moving text a little, changing size, fixing margins, outline or glow still means another generation. The result is also unpredictable. So for final text, I’d still rather use Photoshop, Photopea, GIMP, Canva or whatever editor you like.

One useful workaround is to generate text elements separately. For example, a stamp, badge, 3D title or label on a transparent background, and then place it yourself in an editor. Sometimes GPT fakes the transparency and gives you that checkerboard look as part of the image, but if you ask again more clearly, it can do a real transparent PNG. That already makes the workflow much more usable.

Another possible option is Canva. You can connect ChatGPT to Canva and use tool, I think it’s called Magic Layers or something like that. Canva can try to rebuild the image into editable layers, so it becomes easier to move things around instead of regenerating the whole image.

I haven’t tested it deeply, and for export you’ll probably need a Canva subscription, but it can be a useful middle ground if you don’t want to work fully in Photoshop.

Simple ideas work better. The more tiny details you add, the faster things start getting weird. Electronics, camera gear, UI screens, product labels, professional tools, repeated lines and complex textures can look okay from far away, but up close they often fall apart.

Same with lighting. Clear, simple light is safer. Dark low-key scenes with smoke, heavy shadows, gradients and multiple colored lights can look cool, but they are harder to control and can turn into muddy AI haze.

Faces were actually one of the strongest parts. Even a boring selfie near a wall can become a decent thumbnail base. It can improve the background, light, colors and overall thumbnail feel. But changing emotion too much is risky. If you need a shocked face, angry face or smile, better shoot that expression yourself.

References help a lot. If you only describe something, the model invents too much. If you give it a face reference, product reference, lighting reference or examples of your thumbnail style, the result becomes much more usable. That also made me think that a Custom GPT could actually be useful here. You could feed it your thumbnail preferences, your style, your usual layout logic, maybe examples of your older thumbnails, and then you don’t have to explain everything from zero every single time. It probably still won’t be perfect, but for keeping things in a similar direction, it could save time.

There is a limit, though. If you start mixing too many references, asking for too many fixes, or changing too much at once, consistency starts drifting. Every new generation becomes another interpretation.

That was one of the biggest things I noticed. Repeated edits are not really final production. After a few fixes, the image starts drifting. The face gets softer, texture gets worse, sharpness drops, consistency gets messy. So the workflow that made the most sense to me was not one prompt and done. It was more like this: use iterations to find the idea, then do a clean rebuild, and finish manually.

The best version of the workflow for me was generating a base, generating some separate elements, and then assembling and polishing everything in an editor. That way you can move text normally, fix margins, add sharpness, clean artifacts and make small changes without asking AI to regenerate the whole image again.

Stylization is probably where it gets most useful. When an image tries to look realistic, your brain judges it much harder. You know how faces, hands and real objects should look, so if something is almost right but not quite right, you feel it immediately. It gets close to that uncanny valley problem.

But with stylization, visual metaphors the rules are different. The image doesn’t have to pretend to be a perfect photo anymore. It can have its own logic, and people are much more forgiving. That’s where GPT Images starts to feel more interesting, because you can test strange visual ideas that would normally take much more time to build manually.

My final take is pretty simple.

GPT Images 2.0 can make decent thumbnails, but I don’t think it works well as a one-prompt magic button.

If you use it blindly, you get AI slop.

If you control the idea, use references, keep it simple, understand your prompts, rebuild clean, generate separate elements when needed and polish manually, it becomes much more useful.