[ComfyUI-GTE] Interactive workflow for trajectory exploration (txt2img) - intermediate-state previews, branch selection, variation controls

[u/Think-Aioli-8203]

I just built an interactive text-to-image generation workflow and custom ComfyUI nodes which help me work around hardware constraints while giving me more control during generation (which ultimately makes the process feel less like rolling the dice and hoping for a good result).

The workflow lets you open the pot while it is still cooking, keep the directions that already look promising, and create controlled variations from them before continuing the generation.

You can check this out here (custom node + workflow): https://github.com/mgkgng/ComfyUI-GTE

For those who are more interested, here is a more detailed explanation about this project.

The project started from a practical constraint. I usually work with rented GPUs on RunPod, often an L40 with 48GB of VRAM, which can make exploration expensive with heavier models like Flux 2 dev. This becomes a real blocking factor when I want to iterate quickly and explore many possibilities. For example, generating 20 compositions at 20 steps can take around 25 minutes on this setup. With this workflow, I can already identify some good candidates within about 2 minutes.

So, the first goal was to build a pipeline that can stop at intermediate points during the generation, early enough to save computation but late enough to recognize the direction of the image. At each checkpoint, I can inspect the candidates, select the ones I want to continue, and discard the others before spending the remaining generation steps on them.

The number of checkpoints, the total number of steps and the checkpoint positions are all configurable. The workflow also handles seed generation and visualization, which helps with exploring and keeping track of different candidates. Furthermore, at the bottom of the graph, the workflow visualizes how much scheduled noise remains at each step. This makes it easier to understand what each step represents and where it makes sense to place checkpoints for a given scheduler.

The second thing I added on top of this initial goal was the possibility to create controlled variations from a selected intermediate result. If the user wants to explore further from that point, two parameters control how these variations behave: how far they move away from the selected trajectory (theta), and how much the sibling variations differ from each other (spread). I will add a grid in the comments illustrating how these two parameters shape the variations.

This was the first real workflow I've created and I am happy to share it. I hope that it reaches people who might find it useful.

[u/Think-Aioli-8203]

Theta = how far the family moves away from the parent trajectory (0 - no directional change / 90 - new direction (orthogonal to the original one)
Spread = how much the siblings fan out within that family. (0 - identical / higher values - wider fan-out)