Flux Attention Seeker 2

This is the same idea as GR85_FluxAttentionSeeker3 - scaling attention per layer inside Flux's text encoders - with one interface difference that actually matters if you're the one turning the knobs: instead of packing CLIP-L's twelve layer values into one comma-separated string you have to edit carefully, this version exposes them as twelve individual float widgets, clip_l_0 through clip_l_11, each with its own slider (range 0–5, step 0.05, default 1.0). T5-XXL still comes in as a single string, t5xxl_values_str, same as in Seeker3.

Worth being upfront about what this actually is: it's not a documented, established community technique - a targeted check turned up nothing on it outside this pack. Treat it as an exploration tool for a personal idea about text-encoder attention, not a technique with known-good settings to copy.

How it works. clip in, modified clip out. The four booleans - apply_to_query, apply_to_key, apply_to_value, apply_to_out (all default true) - decide which of the four attention projection types inside each transformer layer actually get scaled by your values. Each clip_l_N slider scales that specific layer's attention output: 1.0 leaves it alone, higher amplifies that layer, lower dampens it. t5xxl_values_str does the same job for T5-XXL's 24 layers, but as one string of comma-separated floats rather than individual sliders - presumably because 24 more widgets would make the node unreasonably tall.

Why this version might suit you better than Seeker3. Individual sliders mean you can drag one CLIP-L layer up or down and watch the effect immediately, without editing a string and risking a typo that throws off the comma count. If your exploration is CLIP-L-focused, this is the more comfortable interface; if you're mainly interested in T5-XXL (the larger, more influential encoder for Flux's natural-language understanding) both nodes handle it identically as a string, so the choice comes down to how you want to interact with CLIP-L's values.

Installing it. Search "comfyui_gr85" in ComfyUI Manager, or:

cd ComfyUI/custom_nodes
git clone https://github.com/veighnsche/comfyui_gr85

Restart ComfyUI. No models to download - it operates on a CLIP object already loaded elsewhere in your graph.

Common issues. Same T5-XXL string-length trap as its sibling node: t5xxl_values_str needs exactly 24 comma-separated values to map cleanly onto T5-XXL's 24 layers - add or drop one and the mapping shifts. Beyond that, expect the same subtlety caveat as Seeker3: this changes text-encoder attention, one step removed from the image itself, so effects can range from clearly visible to barely perceptible depending on your prompt and how aggressively you push the values away from 1.0. Start with one slider moved at a time so you can actually attribute what changed.