<h1 id="what-is-this-49j4qbhu4">What is this?</h1><p>This is a small collection of workflows for ComfyUI that is meant to serve a certain function. These "mini-flows", as I call them, are meant to be used separately from a whole, since each one serves a different function and have different outputs.</p><p>Technically, since this is ComfyUI, you could meld all of these workflows together if you want, but I wouldn't personally recommend it.</p><p>Currently, it includes the following workflows:</p><ul><li><p>Watermark removal using GroundingDINO + Anzhc's YOLO model (works with complex watermarks)</p></li><li><p>Add Perpendicular merge re-creation in ComfyUI (for re-creating CyberFix-perp and equivalents)</p></li></ul><h1 id="how-to-use-it-dddlyj3ga">How to use it?</h1><ul><li><p>Step 0: Download ComfyUI and ComfyUI-Manager</p></li><li><p>Step 1: Download your workflow of choice</p></li><li><p>Step 2: Extract the .zip file</p></li><li><p>Step 3: Import either the workflow image or the .json file</p></li><li><p>Step 4: Download all missing nodes from ComfyUI-Manager and restart</p></li><li><p>Step 5: Have fun!</p></li></ul><h1 id="qanda-xq0304smt">Q&amp;A</h1><h2 id="q1.-(watermark-removal)-in-your-txt2img-workflow-you-use-clipseg-instead-to-remove-watermarks.-why-not-use-that-here-as-well-8uj9rbvzh">Q1. (Watermark Removal) In your txt2img workflow, you use CLIPSeg instead to remove watermarks. Why not use that here as well?</h2><p>A: CLIPSeg works for really obvious watermarks, but also have a lot of false positives. In the txt2img workflow, I abuse the false positives to "reroll" certain parts of the image as well. For more accurate work, however, GroundingDINO + SAM is more accurate and can detect complex signatures and watermarks to remove them, and the false positive rate is lower.</p>

[ComfyUI Workflow] Mini Workflows for Mini Tasks

<p>Does not use any generation checkpoint. It's basically plug-and-play for the most part. Instructions for Anzhc's Face YOLO is already included within the workflow.</p>

Files with SHA256: f1fa336407f209d053e20beb7f2813ad17e83f1a11e822d118b41e8b85e1af02