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PaperclipPerfector  19d ago (text post)   31983 thread views

Culture War Roundup for the week of March 10, 2025

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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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