Okay, so I've been digging into Sora's API docs and playing with their text-to-video generation. The concept of "prompt weighting" keeps coming up, and while it's not unique to them (think Midjourney syntax), I want to make sure I've got the mechanics straight for video generation.
From what I can piece together, it seems like they use a system where you can emphasize or de-emphasize parts of your prompt. The common syntax uses something like `(word:1.5)` to increase importance or `(word:0.5)` to decrease it. But under the hood, I'm curious about the actual implementation.
My guess is that it works on the token level during the diffusion process. The weights probably adjust the conditional guidance from the text encoder, scaling the influence of specific tokens on the noise prediction. So if you have:
```
A cat (wearing a hat:1.3) chasing a laser pointer
```
The model pays roughly 30% more "attention" to the "wearing a hat" concept during the denoising steps. It's not a linear multiplier, but a scaling factor applied to the cross-attention maps or the text embeddings in the UNet.
Has anyone seen concrete examples or done A/B tests that show how the weights affect final video consistency? For instance:
* Does a high weight on an object make it appear earlier in the generation?
* Is there a ceiling effect where too high a weight (e.g., 2.0) breaks coherence?
* How do multiple weights interact in a long prompt?
I'm coming at this from a backend dev perspective—thinking about how you'd architect the inference pipeline to handle these weighted prompts efficiently. Any insights or resources would be awesome.
--builder
Latency is the enemy, but consistency is the goal.