# Ideogram Open Weights Model Drives Enterprise Customization

**Podcast:** AI + a16z
**Published:** 2026-06-15

## Transcript

It's not about how good a model is in the general sense.
It's about how good is this model for my use case.
For a lot of design and marketing use cases, we need editable design, not a single flat image.
It's super impressive, honestly, reaching the level of things like nano banana or GPT image with an open source model.
Why did you think that was important?
We really want our models to have taste.
Every artist, they can really customize this model to the nuances of their style, the texture of their canvas, and really get 2K output, and hopefully make that part of their workflow.
One thing we were always wondering is that this release, the open source model is so small.
It's 9.3 billion parameters.
Like previously, the SOTA is probably like 80 billion parameters.
It's like 9x difference.
How did you do it?
We focused on...
Image generation has improved dramatically over the last few years.
The next challenge is not simply creating images, but giving users more control over what gets created and how.
That includes everything from typography and layouts to editing, customization, and workflows that fit into professional creative processes.
Yoga Lee and Justine Moore speak with Ideogram founder and CEO Mohamed Nooruzi about image generation, open-weight models, design tools, and the future of creative AI.
So today we're excited to have Mohamed, CEO and founder of Ideogram, a Toronto-based generative AI company that just released their first open weights model.
Congrats on the huge release.
Congratulations.
Thanks for having me.
We're really excited to talk through something that everyone has been buzzing about, which is the fact that the model is open weights.
The previous Ideogram models have been closed source, so we'd love to hear how you made the call to make it open this time.
What has happened is there has been a lot of progress in the industry.
And we used to do everything.
Basically, we had our own first party app as well as our own first party API.
And model development itself is a lot of work.
And we decided to focus a little more on the model side.
I think that's where a lot of potential exists.
We still want to continue to own the interaction with the users.
We think there is a lot of important...
feedback we can get from the users directly, but then we want to focus more on building the model.
And by releasing the weights, we're actually extending ourselves and working with inference providers, working more directly with large enterprise.
They have every ability to customize the models or host it on-prem or optimize it for device.
And we would love to work with the best chip makers to really...
optimize the model, the best inference providers.
So this is basically us saying, hey, we are very serious about building the foundation model, and we would like to work with you, wherever you are, whether you're an app developer or a chip maker or an inference provider.
I think you already kind of touched on this.
The new open source model is very exciting in that it unlocked a lot of new use cases.
It's very photorealistic.
I think it can generate up to 2K.
with a smaller model too.
Obviously, there's very precise layout control as well.
Do you want to talk about some of the net new use cases that's unlocked by this model?
So this is actually a foundation based on which we're going to release some more exciting features next.
So this is just the first release.
You're just testing the waters, figuring out how to work with Higging Face and the open source community, Comfy UI, et cetera.
What I'm personally most excited about is something we haven't released yet.
which is editable text and layout control.
And I really believe for a lot of design and marketing use cases, we need editable design, not a single flat image.
And we haven't released that yet.
We cannot show the teaser in our video, but I'm personally most excited about that.
On the technical side, what we've done is we went really detailed on the prompting.
And if you look at our prompts, it's like thousands of words, each element in the image, where it is in the image.
We have layout control, bounding box, and a number of elements.
And that's one of the key innovations here that unlocks a lot of, again, design use cases because you clearly want font control, you want layout control.
And this model is very versatile, allows you to really...
fix certain elements, fix positioning and control the image generation in every detail possible.
Amazing.
And one of the other things I immediately noticed about the model was how you can render super long texts, like paragraphs of texts completely accurately, which you either give the model in the prompt or you ask the model to come up with something and it does it really well.
And it's super impressive, honestly, reaching the level of things like Nano Banana or GPT Image with an open source model.
Was that something you guys really focused on and sort of why did you think that was important?
I don't know if you remember, but the very first model we released three years ago.
And at the time, image generation was synonymous with garbled text.
And there were memes about Dolly 2 generating travel posters with incorrect city names, which is fun to look at.
So I remember at the time, there were just a few people building these models.
And the question was, how can we differentiate?
What's unique about our model?
And we said, okay.
Text generation, accurate text is something we have.
And then we released it and we were really surprised.
It's just so many people were so excited about text generation.
And then we realized, oh, actually, that's the whole graphic design and storytelling industry.
Text is a very important part of image generation.
And that became a very important part of our brand.
So if you search IDOgram, people talk about the quality of typography, the quality of text we are known for.
really stylized typography for logo, T-shirt design, graphic design in general.
And so we continued to push forward.
I think our previous model wasn't really beating the seed of the origin text generation, but we continued to focus on that and we had a bunch of research breakthroughs.
And with this model, despite the fact that it's very tiny, the text generation is very, very accurate.
One of the things that stood out to us, which is what the community has been chatting about, is how there's new ways of processing data as you were training the model, which is like you kind of let the model learn what is a bounding box and how to do the layering and color palettes.
Do you want to talk more about some of the innovations you had during the training process on what made this model so good with these different Shinbin features?
resulted in such an amazing model.
I think a lot of it is focus and evaluation.
Evaluating image models is actually a very difficult thing to do.
There are lots of benchmarks out there, but people look at them and they're like, okay, this doesn't correlate with pixel fidelity that I care about or realism.
You don't really want Novice users to judge the quality of these models because they may be looking at small monitors that aren't really adjusted for...
color accuracy, and we always cared so much about quality, photorealism, and again, text accuracy.
So throughout training, we always measure text accuracy and we appellate very detailed changes to the model and data and see how that results in performance.
So I would say a lot of it is really listing all the possible changes and very carefully tuning each element of the model and see what happens.
Obviously, we try to gather as much data as possible.
One of the standard recipes in the industry is that we take images and we turn them to text using visual language models.
The very first models we were training three, four years ago would be based on the alt text that you can find on the internet.
That is, each image on the internet may have an alt text field.
associated with it, which describes what's in the image.
But the problem is the alt text is often very short or inaccurate.
And what we do now is we train models to go from image to text.
And in this case, image to text with detailed bounding box information, detailed element information.
If we care about text, then we really want to make sure all the text in the image is correctly described.
And then we go from text to image backwards.
So it's kind of interesting.
We gather all the images from the internet.
Some of them may have alt text.
Some of them may not have alt text.
And then we use AI to go from image to text.
And then we train another AI model to go from text to image.
So that's one of the key recipes that results in very good models.
I saw a lot of JSON prompting in your technical blog, which is very unique.
And as I was trying a model, it seems like it was translating the text, the prompt, to a JSON representation with implicit structure.
Do you think JSON is a representation for image models going forward, or do you think there's another representation there?
It's a very good question.
I don't know if you've seen the open source community is a little upset because of the safety image that shows up.
They always are.
Reddit was really lashing out at our engineers and one of our people said, oh, we might fix this.
And they were like, we might.
But the fact is the community needs to also read the documentation and bear with us.
This model is only trained with JSON prompting.
And you have to provide JSON with that particular structure for you to get good quality output.
So I don't know if this is a feature or bug.
We did have some safety built into the model, but that is also detecting incorrect prompts.
So if you just give it a one word prompt, then you get this image is blocked by safety image back.
But that's because your prompt is not a well-specified JSON.
Now, we don't want people to write in JSON.
We don't think that's a natural way of interacting with these models.
But I do strongly believe that we need to use all the AI innovation to build the best image generation and editing models.
And there had been a lot of progress in language models in the text space.
So the question is, if you want to go from some vague idea to an image, what's the exact process?
How much of the thinking happens in the language space and how much of the thinking happens in the actual kind of pixel generation space?
I know you're an artist, so you should probably tell us.
Then, for example, we always had this prompt, meaning of life.
We test our models based on this prompt.
So if you have meaning of life as your prompt, then do you want an image and diffusion model to decide what the meaning of life is?
Or do you want a language model to kind of think and go back and forth and come up with a description of a scene that's explaining the meaning of life?
And that's kind of the context of JSON prompting.
It's the intermediate representation that we think language models can describe images in that format and then image generation can happen.
In general, we see a lot of editing happening in the field and that's...
the new frontier.
So I don't think we should expect the interaction to be only through text or JSON, but it's a combination of JSON and image, if I were to make a guess.
Awesome.
So it sounds like a lot of it is basically taking off in a relatively simple prompt that someone puts into the model and then translating it on the back end with the magic prompt and into JSON so that the model can make something probably more detailed and interesting than the person with the short prompt may have even imagined.
Exactly.
And then I think everybody else does it too.
OpenAI does it, Google does it, but then they don't give you the actual input to the model.
But again, for professional use cases, you don't want to just roll the dice and then get some other completely different image interpretation of your prompt.
We show you the actual input to the model as well in the JSON format, and we think that that will foster more innovation and creativity.
And for people who want control or like consistency, I think that'd be key.
Yeah.
And to Justine's point, like exactly, like what is the implication for the professional use cases?
Like what, like was the JSON prompting?
What can they do more easily now with this capability compared to before?
Maybe zooming out a bit, the world is changing, right?
I'm sure your work has changed a lot.
My work has changed a lot.
I'm actually...
writing PRs now, which is very exciting.
Amazing.
We're in collaboration with AI.
AI does the writing.
And then for creative professionals as well, the world is changing.
They are very excited to, okay, a large number of them are very excited.
I think more and more of creatives are excited about adopting AI and they see the potential.
They think ideation is still the most important part of it.
the creative process and humans are very good at putting context into these models and their understanding of situation, creativity will result in the best ideas.
I'm very excited about the future where every kid will have these models at their fingertips and then they can be much more creative and we're going to experience a much more beautiful world.
Now, as it comes to JSON prompting, again, because the JSON prompt describes every detail in the scene, you can take it and change one element in the scene, and that results in very, very consistent output.
So you could be describing like a tiny detail in the corner of the image.
and then leave everything else the same.
And we think this also has a big implication on editing.
We haven't released our editing models yet, but they will also utilize the same JSON prompting approach.
And it's just more control.
And with layout as well, you can imagine for every brand, you have brand guidelines in terms of, okay, the size of text, the font of text.
And we think this kind of foundation allows us to really get into a lot of the...
enterprise use cases.
Amazing.
And we've been talking about some of the things you've focused on with this model.
Obviously, there's always trade-offs in model training in terms of what you want the model to be really amazing at and what you focus on.
Would love to hear what you focused on for this release.
And also, do you consider more...
capability, like we want to be the top for this specific prompt adherence or something like that?
Or is it more thinking about the end user and holistically where the, what are the different vectors where they want the model to be performant?
Right.
So we care about a couple things.
One is graphic design in general.
Again, text rendering is part of that.
We think basic graphic design is everywhere.
Like we go.
You go in a city, you open your eyes, you see billboards, you see storefronts, they all have text.
And actually, it's much more important than, I guess, photography is part of graphic design.
Graphic design is actually the frontier for a lot of business use cases for storytelling.
So we definitely focused a lot on graphic design since the release of our first model, which was good at text rendering.
And in addition, we think taste is extremely important.
We really want our models to have taste.
And it's very hard to explain it, what exactly taste is.
One element of taste is kind of going outside of the norm a little bit and not conforming to the average opinion, which is a little against being on top of the leaderboard.
Yeah.
Which is kind of interesting.
Your own data course.
Yeah, we just care about it.
Ultimately, we work with all the arenas.
We hope all of the arenas will improve in detecting the nuances of images and image quality.
But we care about our own internal evaluation.
And unfortunately, we see that AI is not very good at doing the actual taste evaluation yet.
So we work with designers.
And we have side-by-side comparisons between different versions of the model as well as other models to really push on the taste.
So we really care about taste.
I think there's still so much more to do, obviously.
Yeah, I was going to add, my follow-up was going to be, do you have like one vibe guy or vibe woman internally who's like the taste arbiter?
Because it can be hard to measure taste.
But it sounds like you have a group of designers, which is probably better.
I think, yeah, we need to find that tastemaker.
Yeah.
Yeah.
And then I guess one thing we were always wondering is that this release, the open source model is so small.
It's 9.3 billion parameters.
You know, like previously, a SOTA is probably like 80 billion parameters.
It's like 9x of a difference.
And then you can't run it on a single GPU instead of having a lot of compute footprint, which really opened up opportunity for people to use it.
So the question is, how did you do it?
the details of the model, and we know we can win on scaling.
I don't think, I used to work for Google.
I don't think even if we raise 10x the amount we've raised so far, we can beat Google in terms of the number of chips that we can dedicate to each model training.
So instead, we focused on innovation.
We think there's still so much more to do to innovate.
We are also focusing on differentiation.
I don't think a lot of labs are focusing on design, graphic design in particular, editable text that I'm talking about.
And then we also decided to go open weight to really partner with a lot of other platforms to be at least another option for people who care about design.
And so, yeah, so we focus on the small model primarily because...
We think there's still so much to do.
We think now is actually a good time for us to scale.
Given the quality of the model at 9.3 billion parameters, you should imagine what if this model is 100x bigger and there are a mixture of experts' architectures that don't make the model necessarily slower, but they make the model a lot more powerful.
So I think that's one new frontier for us to kind of scale this model, 10x, 100x.
I imagine because it's a smaller model, as you mentioned, like it's harder to win on scaling and counting number of chips, but it is possible to win on a specific domain or optimizing for a different, you know, thing in a different domain.
So what was the tradeoff for the research team when training this model to decide what to focus on?
Right.
So one thing that you kind of alluded to is the fact that this can run on consumer GPU now.
And we think there's a new frontier that, you know, you do a lot of editing on your phone, a lot of image generation on your phone.
And it's not only about pushing quality at, you know, 100 billion parameter, 1 trillion parameter range.
We think it's really important to have small models that can run on device.
Obviously, a lot of companies care about privacy.
And we are really excited to partner with the industry to push the...
kind of small model size quality further.
Now in terms of the research team, it's an interesting question whether you can focus on a very small, narrow field in image generation.
I sort of believe that you need a general understanding of the world in order to even be good at logo generation or be good at illustration style.
But once you have a general base, then you can customize the model for certain use cases and it can be the best at that particular use case.
So we're really excited about customization.
We think that's a new frontier.
And again, that's an important reason for releasing the model with open weights.
That is every artist who has at least like...
50 pieces of art or hopefully like a little more, they can really customize this model to the nuances of their style, the texture of their canvas and really get 2K output and hopefully make that part of their workflow and, you know, be augmented with AI and be a lot more productive and creative.
And we actually have worked with some artists in Residen who said to us, okay, This at least made me 3x faster in making this comic book.
So that's one frontier.
And another frontier is enterprise again, because it's not about how good a model is in the general sense.
It's about how good is this model for my use case, right?
At the end of the day, I may not care about many of the general purpose use cases.
I may not care about character consistency, for example, as an enterprise.
But even though the small is small, they think it can be the best model for particular use cases, whether that's a certain artist or whether that's an enterprise.
Were you getting a lot of demand from enterprises when it was a closed model that wanted to fine tune it on their own data?
Were there use cases where you're like, we really need to open source it now because there's so many cool things that people want to do?
Yeah, yeah.
So...
Some of the enterprises we work with are very sensitive.
They don't want to talk about using AI in their visual side of things.
But what we've seen over and over again is companies come to us and say, we tried these generic models and they don't meet our design bar.
They don't follow our style.
They don't follow our brand guideline.
And once we train custom models for them, They're like, wow, this understand my brand DNA now.
We can use this for design ideation or we can use this for marketing.
And we thought with open rates release, we can give a glimpse of customization to, you know, developers within enterprise and kind of scale this side of our business further.
And we're really excited about that.
For enterprises, as you alluded to, I mean, customization is really top of mind, whether that's a brand kit or it's something that is stylistically just done, but hard to encode that style into just a doc.
So I imagine customization on top of an open source model is just the best way to go.
So the question becomes like, what is the ramp up for the customers or the artists to start post-training or fine-tuning on top of the ideal ramp model?
What do they have to do?
So one thing I should say is we will work with the open source community to make it as customizable as possible.
But because we are the model developer, we have some secret sauces that can make it even better.
So what will happen is there will be different ways of customizing.
One is in the open source based on the quantized model that's already released.
The other is we already have a product that allows you to customize by just uploading such a number of images.
to our custom model training app.
And we haven't released the 4.0 version of that yet, but we are hoping to release that as well.
And then the kind of third category is when enterprises work with us and we really...
describe these detailed prompts for every image.
We work with their design team to understand what words they want to use because each team has different set of keywords.
Each company may have certain mascots who have certain names.
And so our annotation team gets involved and spends a lot of time curating and cleaning data.
And we think depending on your size and your budget.
You should still be able to customize the model, maybe use the open source at the low budget, and then you can come and talk to us so that we can build a model for you at a high budget.
But then depends on really the ROI that you have in mind for your models.
One of the things I think people have been talking about a lot, both for enterprise and actually for consumer, is fine-tuning versus image editing.
I actually think...
they don't necessarily have to be competitive.
Like some people use image editing as a way of fine tuning.
Like they say, take this image and put it into this style.
Others think it's much more efficient and consistent to just fine tune a model to generate in that style.
I know you alluded to wanting to release an editing model further down the line.
So would love to hear how you guys are thinking about that.
Yeah, I agree.
I mean, I think editing is very powerful.
We both agree.
We all agree when editing.
launched last year, so many new possibilities opened up.
And the nice thing about editing is it's quick.
You don't have to train a model.
You just take a style or an existing image and you make some changes.
And it's part of your iterative workflow because with every creative that we worked with, it's never one-shot promptings.
Often, okay, you get something and then you're going to go and fix certain details after the first generation.
That's for editing.
But then customization gives you really freedom to not prompt at all, right?
Because sometimes it's very hard for you to say, oh, I want to get inspired by this single image and edit that one.
You may have some general style in mind.
and want to ideate in the context of that general style.
Or you may have a character that has many detailed degrees of freedom or characteristics.
The left side may be different from the right side and like certain outfit.
And it's very hard to really put all of those images as the input to your editing model.
it often fails.
So we think customization can give you a lot more powerful adherence to your characters and allows for an easier iteration on ideation.
So I agree with you.
I don't think they are mutually exclusive, but they are both very powerful.
With the JSON prompting and editing and model fine-tuning, the composability aspect of the model is just huge.
There are so many ways you could customize it.
One hot topic in the industry, in the research community, is the agentic loop for creative tools, right?
So it used to be the creativity tools, the consumption layer is always a UI.
As a human, I look at it and then I make modification.
Now, so much of that may become like an API request, like the agent makes.
How do you see that?
What would the API entail compared to how humans use it?
And your earlier point, I want to say something, which is we seem to compare image generation with language models a lot.
And for example, in the language model space, even though customization exists, it's not that like every company customizes their language models.
But I think that actually misses the point.
When you look at visual representation of a brand.
you immediately recognize the differences between brands.
But if you look at the written communication, can you say, oh, is this Andreessen Horowitz or this is Sequoia?
I mean, you probably can tell.
I hope so.
I get the point.
But most people will not be able to immediately look at the text.
That's true.
And then say, so there is a lot more diversity in the visual world.
And that's very exciting for customization.
And there are a lot of unique...
ways of interacting with the models that kind of goes back to your earlier question, a lot of 3D manipulation.
So for that kind of use case, the input will be some 3D representation of the joints or position of the objects.
Then you may have a completely different, you know, stylistic variation with the style being the input.
So there are a lot of different types of interactions that you want to enable.
And for that reason, It's much different from the language space where the input is always text and you can kind of more or less convert everything to text.
We're so excited about agents.
We have our own MCP.
We use them a lot internally.
What's really exciting is when you want to release a new feature, you can go into your agent and then...
ask it to connect to the API and generate a bunch of images and then you can go and find the best ones and like in a couple hours you have your landing page up and running.
So we are very, very excited about agenting workflows.
We think there's this just at the beginning.
To your point, we need evaluation as part of the loop.
We don't want to be, to have to look at every image and then editing will be part of the...
agenting interaction to how we exactly want to compose these different pieces to accomplish a goal is still to be discussed but we have API and we have MCP and we really want to enable the agentic workflow and we think every company is trying to figure it out as well so we're really excited about that.
I guess for the API business it So much of a design is iteration.
It's a long tail of a design.
It's no longer just you prompt something, you get an image and call it a day, right?
It's so much of a get an image, use the edit model to edit it, see if it works well.
If it doesn't, get another image with the JSON flop, which is easier for control.
What are the net new use cases you have seen after launching a model of how people do compose different API calls on the ideal ground model?
What's interesting is, yes, we have these agents, a lot of them live in a chat bot, and that's not enough for iteration, unfortunately.
So I think what's unique about that, you can really scale creativity, like kind of give it some high-level direction and ask it to go and explore many different approaches and come back with hundreds of thousands of designs that can be easily...
you know, looked at and then you get a better sense of, okay, I want to explore more in this direction.
So the language model interaction allows for kind of very large scale exploration of creative possibilities.
But once you know what you want, You need a UI.
You need a UX to be able to go and edit.
And whether it's regional editing or text-based editing, I think at the end of the day, you want your canvas.
You want to be able to point to things.
And then you want to also talk to it with natural language.
And it's actually very hard work because models are changing.
And now you're also designing the user interface at the same time.
Kudos to the best designers who understand how these models work and are trying to figure this part out.
There's still a lot of work to do.
In terms of the things we've seen from the model, again, design is something that's coming up a lot.
And I saw a tweet before coming here that somebody said, I have no design training.
And I got this design in two minutes.
And it actually looked really, really nice.
That was one example.
And then people are really excited about the art possibilities because this model was trained with very unique style description as part of training.
We actually stripped that from the JSON prompt because it became too much.
But the model has a lot of artistic possibilities and many different styles are embedded into the model.
And if you've seen some of the frontier models actually that score very highly in the leader bars, they don't have a lot of kind of design variation.
They always produce the same exact look.
And I believe that's because they did a lot of reinforcement learning training.
They actually have done very little reinforcement learning.
So this is a very raw model.
Now, with that, the outcome is you need to be much more precise with your prompting, but you can get a lot of different styles from the model.
And people seem to be very excited about that aspect of the model, especially in the art community.
I think when you talk both about the design and the art, it really brings back the taste point you made earlier, because for so many sorts of designs, you're trying to communicate some sort of idea, whether it's like an infographic or an ad or a logo or whatever.
And it kind of needs to stand out and like be distinct to you or have a unique style.
And I've totally noticed what you said.
A lot of the frontier, the like historical frontier image models, like you're scrolling the feed and you just see like, I've now seen this style like 50 times.
I've seen it a hundred times.
It doesn't like catch my eye anymore.
And it feels like.
Now when I prompt ideogram four, I often get something that like makes me stop and be like, wow, this is different than anything I've seen before coming out of an image model.
And like this is doing an amazing job of both communicating what I want to communicate and also holding someone's attention.
Yeah, so we try to be enabling very different styles.
And that was one of our goals.
We still want to.
produce tasteful output but that doesn't mean we have to force a complex output to you if you want a minimalist design you should be able to get that actually our minimalist is too minimalist in my opinion i was saying we should ban the word minimalist from the from the output but you get what i mean it's like the model can do many different things and that's that's by design We know what's the first ideogram model we're going to post-train.
It will be an A16Z marketing brand style art deco.
A16Z art deco.
Yeah, let's do it.
For the new branding.
For the new branding.
Yeah, let's do it.
That's very exciting.
Yeah.
So I guess the question also becomes like, I kind of asked you the representation question before, which is, here's a JSON representation.
As an artist, obviously, if you abstract it out, you know, far enough, all the lines are pixels.
So you could say that the composability is on a pixel level, which is actually different from the diffusion representation.
It's like denoising and, you know, they operate in a different space.
Where does this lead to if you travel down the JSON path granular enough?
Does it lead to pixels?
Does it lead to SVGs?
Does it lead to, like, language or something else?
That's a very good question.
So in general, the recipe for building more powerful models, in my opinion, is making the task as straightforward as possible for the diffusion model.
That is, specify the exact details of the image.
And so now if you kind of make that extreme, then it becomes the pixels themselves.
So the diffusion model doesn't have to do anything.
Now the catch is, what we would like to do is to get the language model to produce that intermediate representation.
And language models, as of now, they aren't very good with continuous output.
They aren't very good with kind of pixel values or very high dimensional vector representations.
So I guess the constraint here is that the representation has to be, you know, tokens of like, I mean, depending on your language model power, maybe it can be a million tokens.
Maybe it can be like that's too extreme for us.
It's about 4,000 tokens.
And that's where we still use natural language because these large language models are trained with natural language, so they are very good with natural language.
But it may become more close to HTML, for example.
That's okay because, again, large language models are trained with HTML and they know the tokens.
Would you have to design your own version of HTML or would you?
allowing to each other.
It's actually kind of alluding to the editable model that I'm talking about and we've had a lot of back and forth.
Are we going to have our own JSON for different, you know, text elements and buttons and stuff or are we going to use HTML?
And it seems like HTML makes more sense just because these large language models have already been trained on HTML as opposed to us introducing a new JSON structure.
But I would say To answer your question, that representation needs to be easy for the language model with the particular design that we have right now, which is a language model does some expansion of the ideas, and then the image model takes those expanded descriptions and turn them into images.
We'd love to hear if there's anyone who's interested in working at Ideagram or working with you guys as a customer to find you in a model.
What's the best way for them to get in touch with you and the team?
First of all, we would love to work with more engineers, like cracked engineers.
We have a very tiny team.
You see what we were able to produce.
It's such a tiny team.
And if you want high agency, you know, if you want your work to matter.
and you want to be part of the academic and open source ecosystem, then this is the perfect time to join us.
Now, in addition, enterprises see the potential and would like to work with the most creative brands out there to help them produce the best designs, produce most provocative ads.
And also, we would like to partner with other startups or other...
companies at different levels of the stack.
This is open rate, so we can make it win-win.
And we would love to offer a different option to companies who want more control and data privacy and sovereignty.
So we would love to work with other enterprises as well across the stack.
And then the best way would be like we have this email.
partnerships at ideogram you can dm me on twitter or linkedin and i'm i'm very active on both platform today if i want to find my own style where should i go on ideogram Is there a call to action where you should tell people?
There is actually a model tab.
If you log into iDogram, there's a model tab and then you can go and upload your images and train your model.
It's a little more expensive.
It's $60 for two model training per month.
But I think for professionals, that's totally worth it.
Absolutely.
And how many images do you need to get started?
I think you need at least 15.
So for an enterprise, if they want to fine-tune their own model on Ideogram, like, what's your guidance on what they should upload on Ideogram to start fine-tuning the model?
I think for enterprise, again, we have some sales forms they can fill and then come talk to us because we see that there are many differences in what...
different companies want.
Some companies care more about editing.
Some companies want more like on the marketing side to automate some of their ads.
And we should talk first and then figure out what's the best solution for them.
Awesome.
Thanks so much for joining us.
Thank you so much.
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