A simple 3-image Klein 9B edit workflow for ComfyUI. This workflow is built for combining multiple image references into one edited output using a prompt-driven FLUX/Klein setup. Drop in three source images, describe how you want them combined, and let the workflow handle the reference conditioning, edit pass, detail refinement, upscaling, and final sharpening.
The setup uses reference latents from all three input images, a two-stage sampling flow, Detail Daemon refinement, Remacri upscaling, resize to 1920px longest side, and CAS sharpening for a cleaner final image.
Good for quick composites, character/object placement, car edits, scene edits, and testing multi-image reference control without building a huge complicated workflow from scratch.
This is a basic V1 workflow, so treat it as a starting point and tweak CFG, denoise, steps, sampler, and prompt strength depending on your images. This workflow uses res2 sampling.
This workflow is designed to squeeze everything possible out of SDXL using a 5-pass quality refinement process. It’s easy to use, easy to read, and built for users who want the most polished SDXL output possible.
Each phase adds controlled improvements to detail, clarity, and finish using res_2 samplers, Detail Daemon, FaceDetailer, HandDetailer, upscale, sharpening, and desaturation. Step-by-step preview outputs are included so you can compare every phase and see exactly where the quality gains happen.
SDXL may not be the newest model on the block, but with the right workflow it can still produce beautiful, high-quality images and this setup is built to prove it.
Features
5-pass SDXL refinement workflow
Res2 samplers for stronger fidelity
SageAttention support for speed
FaceDetailer
HandDetailer
Detail Daemon sampler + graph sigmas
Foolhardy upscale
Sharpening
Desaturation
Step-by-step phase previews / comparison
Node packs used in …
This workflow automatically generates clean, structured image captions by combining WD14 tagging, Florence-style natural language descriptions, and a custom trigger token for training consistency. The idea behind this workflow is to deliver proven results for easily (one-click) captioning datasets for training. I have made many high quality LORA from the datasets this workflow outputs.
Designed for:
Dataset preparation
LoRA / model training
Caption inspection & QA
Prompt reconstruction workflows
The examples shown are direct outputs from the workflow, displayed exactly as generated. No LLM or API keys needed. If you’re tired of messy captions or inconsistent datasets, this keeps everything clean, readable, and repeatable in one easy simple workflow.
What This Workflow Does
Uses WD14 to extract high-quality tag metadata
Uses Florence to generate a natural-language image description
Injects a custom trigger token at the start of every caption
Ou…
