99% of this project's near 10k lines of code was written by either Gemini or Claude. Sorry if that upsets you, I've been a programmer for 15 years and it's simply a much faster means of developing.
With that said, here is a guide on:
Easyily Get AI To Add Features To This Project:
Gemini Pro & Claude Sonnet 4.5 both work great but they sometimes make mistakes. The key is to really get a very clear prompt built out.
Step 1. Send the AI the ProjectStructure.md and README.md files and your task and ask it to tell you which files need to be edited to get your task completed.
Step 2. Start a new chat and attach the files it mentioned, along with the following prompt or something similar with your task request prompt in the middle:
Prompt Fill-in:
Help me update my ComfyUI Custom Node. When updating files, DO NOT REMOVE ANY CODE. DO NOT REMOVE ANY COMMENTS. ONLY CHANGE WHAT IS NECESSARY.
PUT YOUR TASK HERE
Check ProjectStructure.md to get an idea of file structure and contents, check README.md to get an idea of the project as a whole.
step 3 ?????
step 4 Profit!!!
But seriously, after updating your code see if it works. If it didn't send the error message if there isnt one or your symptoms. If its still not working try a different AI or try breaking down your task in smaller pieces.
!!!! Check Roadmap.md for some tasks and do them. If you think you can do the ones marked as New to-do items, needs more info/explaining/numbering those are good.
There is info on the project in README.md and info on the file structure and notes on each files functions in the ProjectStructure.md !!!! ProjectStructure.md has notes to assist AI in developing this project !!!!
Please update ProjectStructure.md to be better structured and fixed up for having all relevant AI Notes in it that will help and assist AI to develop this project further. Add your findings, file system notes, important considerations and more to it and make sure its up to date and full of helpful info.
Feature label mode labels in dashboard when showing lora labels should also show the model str and clip strength like how they're written in the config_json, as lora:1.0:1.0 etc DONE
Feature: Checkbox for "Run Upscales At End Of Session Instead Of After Each Gen" DONE — deferred upscale queue with step-major model batching, resume support, and dashboard progress
Bug: The duration/time taken to generate metric doesn't work properly if upscale is used DONE — duration now includes all upscale time
Feature: Slider in the Dashboard, in the settings cog modal, at the top of Label Model section between the description and the enable button, that increases/decreases the size of the labels. DONE
Bug: If we omit entries from the Builder UI's config_json output the manifest.json files lose that data and entries that are toggled off will be lost when loading the session from the manifest file. DONE — all fields now always included with defaults
Feature idea, Config Section Presets, Saveable, Nameable & Loadable or Importable/Mergeable presets for each section, Prompts, Models, Text Encoders, Vaes, Loras, Trigger Omit, Upscalers. Would make mixing and matching and setting up new configs easier & more customizable. (Upscale presets DONE — other sections still TODO)
The Dashboard Session Load Option in the cog settings topbar modal needs sort by most recent and session search feature like how the Builder UI Session Load has. DONE
Feature: Add builder UI option to pack pure nodes like REVISE copy as comfy nodes into every image always.
Small bug, centering zoom doesn't perfectly fit the images into the canvas, I think it doesnt account for the topbar and bottom smartjson bar sizes. DONE
Vae settings in the Builder UI passed to sampler gen orchestrator through the config_json need to override the hf_remote setting in the sampler node input selection. All settings should be override from the Builder UI config_json, it should take first priority over any setting in the sampler node as we are phasing out the sampler node settings completely eventually. (Already implemented — needs testing)
Issue: If running a job in one tab and reviewing images from a past session with the Dashboard in another tab, the Dashboard receives updates across all tabs and force-loads the Dashboard to the currently running session. DONE — auto-load only when no session loaded yet
The batch encoding step lost its ability to stop everything during its run loop. (Already implemented — interrupt check before each encoding. Needs testing)
Feature: Drag and drop re-organzie loras, models, text encoders, vaes. This would make changing their run order and lora_tag append order much easier to control. (needs more testing/fixing)
Jobs don't generate prompts in the order they're written in the array, it's mildly inconvenient. Lets fix that and make it run in order from top to bottom of the array. DONE
Batch Encoding could use with implementing the smart, look ahead, caching system we built for lora swapping.
bug fix, the Builder UI is very slow to update when first starting, adding models, adding loras, a few other things, we need to check it and optimize it to load and respond faster. (Needs more improvement)
NEW: Dashboard Upscale Feature — Upscale single images or all favorites directly from the dashboard with preset management. Phase 1 DONE. Phase 2 (inline pipeline editing, cancel) TODO.
Builder UI could have a + button right next to model strength text input (on its right side) to make it easy to add a compare different lora strengths. If activated it should add an additonal slider, or two sliders if 🔒 Lock Model & CLIP Strength Together is deactivated, for each time they click +. If a lora has a multiple strength array, the preview json and py node json_output should duplicate the full config for that run for each entry with the only difference being each lora strength. If multiple loras have multiple arrays it needs to output a cartesian of an each for each of all lora strenth combos.
Bug Fix: Batch Encoding Runs Before Job Skip/Continue/Resume check. (Already implemented — prompts are now filtered to only encode for jobs that need work. Optional input tracking still TODO. Needs testing)
A button in the builder UI to pack short sha256 into config with an explanation that it can be used to share or move an Ultimate Sampler Config Tester workflow and allow for downloading all models and loras in the workflow from civitAI with a few simple easy clicks. lora_utils has calculate civit model has function in it. dropdown configurable options for where to store each file type.
10. Validation Warning (Omit vs Lookup) - Warn user if omits are added but lookup is off (low priority) (Needs testing)
13. Real-Time ETA (Improved — now uses rolling 10-job window for more responsive estimates. Distribution times still TODO)
- Problem:
trigger_words.pylogic runs every loop iteration. - Target Files:
trigger_words.py
- Problem: Can only load full sessions, not merge snippets.
- Problem: Advanced. Running a raw JSON workflow as a sub-node. Inserting any node into any part of the genereation would make this tool have an immense customizability increase. More info needed on how this could work, would like to see a visual interface for it in the builder UI eventaully. (big job, very low priority)
24. Combinatorial Randomization - More Randomization tools - generate x configs from y possibilities and z prompts - (very low priority)
- Problem: Feature. Combinatorial generation logic. Combine random prompts with random loras, fun!
Compute objective quality metrics during generation and store in manifest:
- Laplacian variance (sharpness/blur detection)
- Histogram entropy (color diversity/richness)
- Sobel edge quality (detail preservation) Add as sortable/filterable fields in Dashboard. Enables data-driven parameter selection instead of purely visual comparison. Prior art: SamplerSchedulerMetricsTester (proves the concept, only 9 stars but validates demand).
26. Analytics Panel in Dashboard (transforms tool from "look at pictures" to "data-driven decisions")
- Milestone 1: Aggregate stats bar — avg generation time by sampler/scheduler, favorites rate by model/LoRA
- Milestone 2: Canvas-based charts — bar charts, scatter plots (steps vs duration, CFG vs quality metrics)
- Milestone 3: Export — downloadable reports with charts + top images
Pure client-side using canvas API from existing
activeData. Prior art: W&B dashboards.
- Milestone 1: REST manifest fetch — Dashboard loads manifest via
fetch('/config_tester/get_manifest?session=name')instead of inline__JSON_DATA__ - Milestone 2: Remove html_generator dependency — serve Dashboard as static HTML from
WEB_DIRECTORY - Milestone 3: Hot-reload in dev — JS changes visible on browser refresh without re-running sampler Biggest architectural leverage: eliminates the #1 developer friction point.
- Milestone 1: First-run wizard in Builder UI — walks through: create session → pick model → add LoRA → run → view dashboard (5 interactive steps)
- Milestone 2: Video tutorial (3-minute YouTube walkthrough) README is 2000+ words; most users won't read it. Efficiency Nodes grew via community tutorials.