What sequential generation means in practice
Take a picture and split it into a grid of discrete codes using a learned tokenizer, the same way a sentence is split into word pieces. Now you can generate an image the way you generate a paragraph: predict code 1, then predict code 2 given code 1, and so on to the end, then decode the whole sequence back into pixels.
That is the shape of an autoregressive model applied to visual output. Nothing about it is new; the earliest neural image models worked this way, lost to GANs and then to diffusion on quality, and came back once the language model stack got good enough to carry the sequence.
The return matters because the two families fail in opposite directions, and knowing which one you are talking to changes how you prompt it.
What sequential order buys you
Instructions are followed, not approximated. A prompt with six constraints is just more context, and context is what this architecture is built to condition on. Constraints that diffusion models tend to average into a vibe ("exactly three figures", "the text on the left, the logo on the right") survive far more often.
Text and symbols come out right. Spelling, numbers, dates, code on a screen, labels on a diagram. This is the single most reliable practical difference, and it is worth reaching for even when you prefer another model's look.
Editing is conversational. Because the model consumes images and words in the same token stream, you can hand it a picture and a change request, then another change request, and it keeps track of what stayed the same. That is how character consistency across a series of images is done without training anything.
Length is not fixed. For video, generating frame by frame conditioned on the past means there is no architectural clip limit, and the frames can be streamed as they are made. Real time and interactive video systems are built on this property.
What it costs you
- Latency scales with output. No step count to lower, no fast sampler to swap in. Bigger output means proportionally more sequential work.
- Quantization softens texture. Going through a discrete codebook throws away micro-detail. Output often reads slightly cleaner and flatter than a diffusion render, which is fine for graphic work and noticeable on skin and film grain.
- Errors accumulate. Nothing revisits an earlier decision. In video this shows up as slow drift in colour, identity and geometry over a long take.
- Less structural control. The adapter ecosystem for depth maps, pose skeletons and edge guidance grew up around diffusion backbones. With a sequential model you steer with words and reference images instead.
- No familiar guidance dial. There is no classifier-free guidance scale to push. Sampling temperature and reference images are the controls, so advice about raising or lowering guidance does not transfer.
Choosing between the two families
The practical split, once you have used both for a while:
Reach for a sequential model when the deliverable has requirements: exact wording in frame, a specific number of objects, a described layout, an edit that must preserve everything else, or a character who has to look the same in the next twelve images. Reach for a diffusion family when the deliverable has a look: photographic texture, a particular film stock, a style adapter you already own, structural control from a depth map, or a batch of twenty cheap variations to pick from.
The line between the families is also blurring. Several current video systems are diffusion backbones distilled into a causal, frame by frame sampler so they can stream, which means they behave like an autoregressive model at inference while carrying diffusion quality. When comparing models on a catalog, judge the behaviour that matters to you rather than the label on the architecture.