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PaperclipPerfector  8mo ago (text post)   33871 thread views

Culture War Roundup for the week of March 10, 2025

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Moderately interesting news in AI image gen:

It's been a good while since we've had AI chat assistants able to generate images on user request. Unfortunately, for about as long, we've had people being peeved at the disconnect between what they asked for, and what they actually got. Particularly annoying was the tendency for the assistants to often claim to have generated what you desired, or that they edited an image to change it, without actually doing that.

This was an unfortunate consequence of the LLM, being the assistant persona you speak to, and the actual image generator that spits out images from prompts, actually being two entirely separate entities. The LLM doesn't have any more control over the image model than you do when running something like Midjourney or Stable Diffusion. It's sending a prompt through a function call, getting an image in response, and then trying to modify prompts to meet user needs. Depending on how lazy the devs are, it might not even be 'looking' at the final output at all.

The image models, on the other hand, are a fundamentally different architecture, usually being diffusion-based (Google a better explanation, but the gist of it is that they hallucinate iteratively from a sample of random noise till it resembles the desired image) whereas LLMs use the Transformer architecture. The image models do have some understanding of semantics, but they're far stupider than LLMs when it comes to understanding finer meaning in prompts.

This has now changed.

Almost half a year back, OpenAI teased the ability of their then unreleased GPT-4o to generate images natively. It was the LLM (more of a misnomer now than ever) actually making the image, in the same manner it could output text or audio.

The LLM doesn’t just “talk” to the image generator - it is the image generator, processing everything as tokens, much like it handles text or audio.

Unfortunately, we had nothing but radio silence since then, barring a few leaks of front-end code suggesting OAI would finally switch from DALLE-3 for image generation to using GPT-4o, as well as Altman's assurances that they hadn't canned the project on the grounds of safety.

Unfortunately for him, Google has beaten them to the punch . Gemini 2.0 Flash Experimental (don't ask) has now been blessed with the ability to directly generate images. I'm not sure if this has rolled out to the consumer Gemini app, but it's readily accessible on their developer preview.

First impressions: It's good.

You can generate an image, and then ask it to edit a feature. It will then edit the original image and present the version modified to your taste, unlike all other competitors, who would basically just re-prompt and hope for better luck on the second roll.

Image generation just got way better, at least in the realm of semantic understanding. Most of the usual give-aways of AI generated imagery, such as butchered text, are largely solved. It isn't perfect, but you're looking at a failure rate of 5-10% as opposed to >80% when using DALLE or Flux. It doesn't beat Midjourney on aesthetics, but we'll get there.

You can imagine the scope for chicanery, especially if you're looking to generate images with large amounts of verbiage or numbers involved. I'd expect the usual censoring in consumer applications, especially since the LLM has finer control over things. But it certainly massively expands the mundane utility of image generation, and is something I've been looking forward to ever since I saw the capabilities demoed.

Flash 2.0 Experimental is also a model that's dirt cheap on the API, and while image gen definitely burns more tokens, it's a trivial expense. I'd strongly expect Google to make this free just to steal OAI's thunder.

I'd say it's another bit of evidence for Google upgrading their product strategy, but nothing unexpected capabilities-wise. Shame they did not release the weights, instead shipping only Gemma 3 with image-in text-out. «Safety» reasoning is obvious enough.

Contra @SkoomaDentist I think it's not fair to describe this as «The LLM is still talking to the image generator», ie that the main LLM is basically just the encoder for some diffusion model or another separate module. The semantic fidelity and surgical precision of successive edits suggest nothing like that, and point instead to a unified architecture with a single context where each token, be it textual or visual, is embedded in its network of relationships with all others (well, that's what these models are – literally, hypotheses about the shape of the training data manifold). Back when OpenAI announced their image-out capabilities with 4o, the teaser generation said «suppose we directly model P(text, image, sounds) with one big autoregressive transformer». Shortly after, Meta (or really Armen Aghajanyan, who has since departed largely in protest over Chameleon's safety-informed nerfing, and his team) published their Chameleon, a parallel work in identical spirit:

This early-fusion approach, where all modalities are projected into a shared representational space from the start, allows for seamless reasoning and generation across modalities. … Chameleon represents images, in addition to text, as a series of discrete tokens and takes advantage of the scaling properties of auto-regressive Transformers … We train a new BPE tokenizer (Sennrich et al., 2016) over a subset of the training data outlined below with a vocabulary size of 65,536, which includes the 8192 image codebook tokens …

Later, DeepSeek, who are probably the best team in the business (if not for resource limits), have been working on Janus, which is also a unified model of a potentially superior design:

Specifically, we introduce two independent visual encoding pathways: one for multimodal understanding and one for multimodal generation, unified by the same transformer architecture … Autoregressive models, influenced by the success in language processing, leverage transformers to predict sequences of discrete visual tokens (codebook IDs) [24, 65, 75]. These models tokenize visual data and employ a prediction approach similar to GPT-style [64] techniques. … Chameleon [77] adopts a VQ Tokenizer to encode images for both multimodal understanding and generation. However, this practice may lead to suboptimal outcomes, as the vision encoder might face a trade-off between the demands of understanding and generation. In contrast, our Janus can explicitly decouple the visual representations for understanding and generation, recognizing that different tasks may require varying levels of information. … for text understanding, we use the built-in tokenizer of the LLM to convert the text into discrete IDs and obtain the feature representations corresponding to each ID. For multimodal understanding, we use the SigLIP [92] encoder to extract high-dimensional semantic features from images. These features are flattened from a 2-D grid into a 1-D sequence, and an understanding adaptor is used to map these image features into the input space of the LLM. For visual generation tasks, we use the VQ tokenizer from [73] to convert images into discrete IDs. After the ID sequence is flattened into 1-D, we use a generation adaptor to map the codebook embeddings corresponding to each ID into the input space of the LLM. We then concatenate these feature sequences to form a multimodal feature sequence, which is subsequently fed into the LLM for processing. The built-in prediction head of the LLM is utilized for text predictions in both the pure text understanding and multimodal understanding tasks, while a randomly initialized prediction head is used for image predictions in the visual generation task.

I expect DeepSeek's next generation large model to be based on some mature form of Janus.

I think Gemini is similar. This may be the first time we get to evaluate the power of modality transfer in a well-trained model – usually you run into the bottleneck of the projection layer, as @self_made_human describes. But here, it can clearly copy an image (up to the effective "resolution" of its codebook and tokenizer) and make isolated transformations, precisely the way transformers can do to a text string. Hopefully this means its pure verbalized understanding of the visual modality (eg spatial relations, say… anatomy…) is upgraded. Gooners from 4chan ought to be reaching the conclusion as I type this.

In the next iteration video and probably 3d meshes are getting similar treatment.

P.S. SkoomaDentist being bizarrely aggressive and insistent that this is whatsoever like inpainting is being very funny. Inpaint this. No, no, these are not vulgar tricks, and I don't see why one could be invested in bitterly arguing against that.

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