What is fine-tuning FLUX.1 on Replicate?
These big image generation models, like FLUX.1 and Stable Diffusion, are trained on a bunch of images that have had noise added, and they learn the reverse function of “adding noise.” Amazingly, that turns out to be “creating images.”
How do they know which image to create? They build on transformer models, like CLIP and T5, which are themselves trained on tons of image-caption pairs. These are language-to-image encoders: they learn to map an image and its caption to the same shape in high-dimensional space. When you send them a text prompt, like “squirrel reading a newspaper in the park,” they can map that to patterns of pixels in a grid. To the encoder, the picture and the caption are the same thing.
The image generation process looks like this: take some input pixels, move them a little bit away from noise and toward the pattern created by your text input, and repeat until the correct number of steps is reached.
The fine-tuning process, in turn, takes each image/caption pair from your dataset and updates that internal mapping a little bit. You can teach the model anything this way, as long as it can be represented through image-caption pairs: characters, settings, mediums, styles, genres. In training the model will learn to associate your concept with a particular text string. Include this string in your prompt to activate that association.
For example, say you want to fine-tune the model on your comic book superhero. You’ll collect some images of your character as your dataset. A well-rounded batch: different settings, costumes, lighting, maybe even different art styles. That way the model understands that what it’s learning is this one person, not any of these other incidental details.
Pick a short, uncommon word or phrase as your trigger: something unique that won’t conflict with other concepts or fine-tunes. You might choose something like “bad 70s food” or “JELLOMOLD”. Train your model. Now, when you prompt “Establishing shot of bad 70s food at a party in San Francisco,” your model will call up your specific concept. Easy as that.
Could it be as easy as that? Yes, actually. We realized that we could use the Replicate platform to make fine-tuning as simple as uploading images. We can even do the captioning for you.
If you’re not familiar with Replicate, we make it easy to run AI as an API. You don’t have to go looking for a beefy GPU, you don’t have to deal with environments and containers, you don’t have to worry about scaling. You write normal code, with normal APIs, and pay only for what you use.
You can try this right now! It doesn’t take a lot of images. Check out our examples gallery to see the kinds of styles and characters people are creating.
Grab a few photos of your pet, or your favorite houseplant, and let’s get started.
These big image generation models, like FLUX.1 and Stable Diffusion, are trained on a bunch of images that have had noise added, and they learn the reverse function of “adding noise.” Amazingly, that turns out to be “creating images.”
How do they know which image to create? They build on transformer models, like CLIP and T5, which are themselves trained on tons of image-caption pairs. These are language-to-image encoders: they learn to map an image and its caption to the same shape in high-dimensional space. When you send them a text prompt, like “squirrel reading a newspaper in the park,” they can map that to patterns of pixels in a grid. To the encoder, the picture and the caption are the same thing.
The image generation process looks like this: take some input pixels, move them a little bit away from noise and toward the pattern created by your text input, and repeat until the correct number of steps is reached.
The fine-tuning process, in turn, takes each image/caption pair from your dataset and updates that internal mapping a little bit. You can teach the model anything this way, as long as it can be represented through image-caption pairs: characters, settings, mediums, styles, genres. In training the model will learn to associate your concept with a particular text string. Include this string in your prompt to activate that association.
For example, say you want to fine-tune the model on your comic book superhero. You’ll collect some images of your character as your dataset. A well-rounded batch: different settings, costumes, lighting, maybe even different art styles. That way the model understands that what it’s learning is this one person, not any of these other incidental details.
Pick a short, uncommon word or phrase as your trigger: something unique that won’t conflict with other concepts or fine-tunes. You might choose something like “bad 70s food” or “JELLOMOLD”. Train your model. Now, when you prompt “Establishing shot of bad 70s food at a party in San Francisco,” your model will call up your specific concept. Easy as that.
Could it be as easy as that? Yes, actually. We realized that we could use the Replicate platform to make fine-tuning as simple as uploading images. We can even do the captioning for you.
If you’re not familiar with Replicate, we make it easy to run AI as an API. You don’t have to go looking for a beefy GPU, you don’t have to deal with environments and containers, you don’t have to worry about scaling. You write normal code, with normal APIs, and pay only for what you use.
You can try this right now! It doesn’t take a lot of images. Check out our examples gallery to see the kinds of styles and characters people are creating.
Grab a few photos of your pet, or your favorite houseplant, and let’s get started.