Google's Most Powerful Gen AI Tool Just Dropped.But no one noticed
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00:00Google has just rolled out its latest text-to-image AI model,
00:06Image in 3, making it accessible to
00:08all users through their ImageFX platform.
00:11Alongside this release,
00:13they've published an in-depth research paper
00:15that delves into the technology behind it.
00:17This move represents a major step forward,
00:20expanding access to a tool that was
00:22previously available only to a select group of users.
00:25All right, so Image in 3 is a text-to-image model.
00:28It can generate images at a default resolution
00:30of 1024 by 1024 pixels,
00:33which is already pretty high quality,
00:34but what really sets it apart is that
00:36you can upscale those images
00:38up to eight times that resolution.
00:41So, if you're working on something
00:42that needs a huge, detailed image,
00:45like a billboard or a high-res print,
00:47you've got the flexibility to do that
00:49without losing any quality.
00:50That's something that not every model out there can offer,
00:53and it's a big plus for anyone working in designer media.
00:56Now, the secret actually lies in the data it was trained on.
01:00Google didn't just use any old data set.
01:02They went through a multi-stage filtering process
01:04to ensure that only the highest quality images
01:07and captions made it into the training set.
01:10This involved removing unsafe, violent,
01:12or low-quality images, which is crucial,
01:14because you don't want the model learning from bad examples.
01:17They also filtered out any AI-generated images
01:20to avoid the model picking up on the quirks
01:22or biases that might come from those.
01:24They also used something called deduplication pipelines.
01:28This means they removed images
01:30that were too similar to each other.
01:32Why?
01:32Because if the model sees the same kind of image
01:35over and over again, it might start to overfit.
01:38That is, it might get too good at generating
01:41just that kind of image and struggle with others.
01:43By reducing repetition in the training data,
01:46Google ensured that Imogen 3
01:48could generate a wider variety of images,
01:50making it more versatile.
01:51Another interesting aspect is how they handled captions.
01:55Each image in the training set
01:56wasn't just paired with a human-written caption.
01:59They also used synthetic captions
02:01generated by other AI models.
02:03This was done to maximize the variety and diversity
02:05in the language that the model learned.
02:07Different models were used
02:08to generate these synthetic captions,
02:10and various prompts were employed
02:11to make sure the language was as rich
02:13and varied as possible.
02:14This is important because it helps the model
02:16understand different ways people
02:18might describe the same scene.
02:20All right, so how does Imogen 3
02:21stack up against other models out there?
02:23Google didn't just make big claims.
02:25They actually put Imogen 3 head-to-head
02:28with some of the best models out there,
02:30including DALL-E3, Mid-Journey V6, and Stable Diffusion 3.
02:35They ran extensive evaluations,
02:36both with human raters and automated metrics,
02:38to see how Imogen 3 performed.
02:40In the human evaluations, they looked at a few key areas,
02:44overall preference, prompt image alignment,
02:46visual appeal, detailed prompt image alignment,
02:48and numerical reasoning.
02:49Let's break these down a bit.
02:51First, overall preference.
02:52This is where they ask people to look at images
02:54generated by different models
02:56and choose which one they like best.
02:58They did this with a few different sets of prompts,
03:01including one called Gene AI Bench,
03:03which consists of 1,600 prompts
03:05collected from professional designers.
03:07On this benchmark, Imogen 3 was the clear winner.
03:11It wasn't just a little bit better.
03:12It was significantly preferred over the other models.
03:15Then there's prompt image alignment.
03:17This measures how accurately
03:19the image matches the text prompt,
03:21ignoring any flaws or differences in style.
03:23Here again, Imogen 3 came out on top,
03:25especially when the prompts were more detailed or complex.
03:29For example, when they used prompts
03:30from a set called Doe CCI,
03:32which includes very detailed descriptions,
03:34Imogen 3 showed a significant lead over the competition.
03:37It had a gap of plus 114 LO points
03:40and a 63% win rate against the second best model.
03:44That's a pretty big deal
03:45because it shows that Imogen 3
03:47is not just good at generating pretty pictures.
03:49It's also really good at sticking to the specifics
03:52of what you ask for.
03:54Visual appeal is another area where Imogen 3 did well,
03:57though this is where Mid Journey V6
03:59actually edged it out slightly.
04:02Visual appeal is all about how good the image looks,
04:04regardless of whether it matches the prompt perfectly.
04:07So while Imogen 3 was close,
04:09if you're all about that eye candy factor,
04:12Mid Journey might still have a slight edge,
04:14but make no mistake.
04:16Imogen 3 is still right up there.
04:17And for a lot of people,
04:18the difference might not even be noticeable.
04:20Now, let's talk about numerical reasoning.
04:22This is where things get really interesting.
04:23Numerical reasoning involves generating
04:25the correct number of objects when the prompt specifies it.
04:28So if the prompt says five apples,
04:31the model needs to generate exactly five apples.
04:33This might sound simple,
04:34but it's actually pretty challenging for these models.
04:37Imogen 3 performed the best in this area
04:39with an accuracy of 58.6%.
04:42It was especially strong when generating images
04:45with between two and five objects,
04:46which is where a lot of models tend to struggle.
04:48To give you an idea of how challenging this is,
04:51let's look at some more numbers.
04:52Imogen 3 was the most accurate model
04:55when generating images with exactly one object,
04:57but its accuracy dropped a bit
04:59as the number of objects increased
05:01by about 51.6 percentage points
05:03between one and five objects.
05:05Still, it outperformed other models like DALI 3
05:08and Stable Diffusion 3 in this task,
05:10which highlights just how good it is
05:12at handling these tricky prompts.
05:14And it's not just humans
05:15who think Imogen 3 is top-notch.
05:17Google also used automated evaluation metrics
05:20to measure how well the images match the prompts
05:23and how good they looked overall.
05:24They used metrics like CLIP, FIQUIS Score, and FD Dyno,
05:28which are all designed to judge the quality
05:30of the generated images.
05:32Interestingly, CLIP, which is a popular metric,
05:35didn't always agree with the human evaluations,
05:37but VQ-ASCORE did,
05:39and it consistently ranked Imogen 3 at the top,
05:42especially when it came to more complex prompts.
05:44So why should you care about all this?
05:46Well, if you're someone who works with images,
05:48whether you're a designer, a marketer,
05:50or even just someone who likes to create content for fun,
05:53having a tool like Imogen 3 could be a huge asset.
05:56It's not just about getting a nice picture,
05:58it's about getting exactly what you need
06:00down to the smallest detail
06:02without compromising on quality.
06:03Whether you're creating something for a website,
06:06a social media campaign, or even a large print project,
06:08Imogen 3 gives you the flexibility and precision
06:11to get it just right.
06:12But let's not forget,
06:13it's not just about creating high-quality images.
06:16Google has put a lot of effort
06:18into making sure this model is also safe
06:21and responsible to use.
06:22However, they've had their fair share of challenges
06:25with this in the past.
06:26You might remember when one of Google's previous models
06:28caused quite a stir.
06:30Someone asked it to generate an image of the pope,
06:32and it ended up creating an image of a black pope.
06:35Now, this might seem harmless at first glance,
06:37but when you think about it,
06:38there's never been a black pope in history.
06:40That's a pretty big factual inaccuracy.
06:43Another time, someone asked the model
06:45to generate an image of Vikings,
06:47and it produced Vikings who looked African and Asian.
06:50Again, this doesn't align with historical facts.
06:52Vikings were Scandinavian, not African or Asian.
06:55These kinds of errors made it clear
06:56that while trying to be inclusive and politically correct,
06:59the model was pushing an agenda
07:01that sometimes led to results that were simply inaccurate
07:04and historically misleading.
07:06These incidents sparked a lot of debate.
07:08There's a fine line between creating a model
07:11that's inclusive and one that distorts reality.
07:13While it's crucial to avoid harmful or offensive content,
07:16it's just as important
07:18that the model remains factually accurate.
07:20After all, if the images it generates
07:22aren't grounded in reality,
07:23it loses its effectiveness and frankly, its usefulness.
07:26If a model starts producing images
07:28that don't reflect historical facts or cultural realities,
07:31it's not doing anyone any favors.
07:33It ends up being more of a tool for pushing an agenda
07:36rather than a reliable factual generator.
07:38Now, with Imogen 3,
07:40Google seems to be aware of these pitfalls.
07:42They've evaluated how often the model
07:44produces diverse outputs,
07:46especially when the prompts are asking for generic people.
07:49They've used classifiers to measure the perceived gender,
07:52age, and skin tone of the people in the generated images.
07:56The goal here was to ensure that the model
07:58didn't fall into the trap
08:00of producing the same type of person over and over again,
08:03which would indicate a lack of diversity in its outputs.
08:06And from what they've found,
08:08Imogen 3 is more balanced than its predecessors.
08:11It's generating a wider variety of appearances,
08:13reducing the risk of producing homogeneous outputs.
08:16They also did something called red teaming,
08:18which is essentially stress testing the model
08:20to see if it would produce any harmful or biased content
08:23when put under pressure.
08:25This involves deliberately trying to push the model
08:27to see where it might fail,
08:29where it might generate something inappropriate or offensive.
08:32The idea is to find these weaknesses
08:35before the model is released to the public.
08:37The good news is that Imogen 3 passed these tests
08:40without generating anything dangerous
08:42or factually incorrect.
08:43However, recognizing that internal testing
08:45might not catch everything,
08:47Google also brought in external experts from various fields,
08:50academia, civil society, and industry
08:53to put the model through its paces.
08:56These experts were given free reign
08:57to test the model in any way they saw fit.
09:00Their feedback was crucial in making further improvements.
09:03This kind of transparency and willingness
09:05to invite external scrutiny is essential.
09:08It helps build trust in the technology
09:10and ensures that it's not just Google
09:12saying the model is safe and responsible,
09:14but independent voices as well.
09:16In the end, while it's important
09:18that a model like Imogen 3 is safe to use
09:20and doesn't produce harmful content,
09:22it's equally important that it doesn't stray
09:24from factual accuracy.
09:25If it can strike the right balance,
09:27being inclusive without pushing a politically correct agenda
09:31at the expense of truth,
09:32it'll not only be a powerful tool
09:34from a technical perspective,
09:35but also one of the most reliable
09:37and effective image-generating models out there.
09:40All right, if you found this interesting,
09:42make sure to hit that like button,
09:44subscribe, and stay tuned for more AI insights.
09:48Let me know in the comments
09:49what you think about Imogen 3 and how you might use it.
09:52Thanks for watching, and I'll catch you in the next one.