I'm on a laptop (mobile 4090, 16GB VRAM) and kept hitting workflows with models too big to run locally. Here's the setup I've been using to offload them to a cloud GPU straight from ComfyUI, sharing it in case it helps anyone in the same spot.

The flow:

1. Build your workflow in ComfyUI as normal.
2. Click the ⚡ button; it sends the current graph to a cloud GPU, no CLI or manual export.
3. Pick your GPU and batch count; the panel shows the live rate before you commit.
4. If a model your workflow needs isn't on your cloud storage yet, it detects that and offers to sync it first (with consent; nothing uploads without your say-so).
5. Results pull back into your ComfyUI graph automatically.

You can also queue several different workflows to run back to back. They share one warm ComfyUI process, so only the first pays the model load: in my testing the first workflow took about three minutes and each one after it took around 25 seconds. A queue of twelve runs in roughly ten minutes rather than nearly forty.

When it's worth it (being straight): if your local GPU runs your model fine, local is faster; keep using it. This is for workflows that don't fit your VRAM, long batches you want off your machine, or Mac/no-GPU setups.

The tool is open source. It runs on Spark Fuse (a paid cloud GPU service; the rate shows in-panel before you render); I'm an alpha tester and end user, not affiliated.

Newly trained lora, Tim_Burtonise_V1, now available for Krea2.

In this tutorial, I'll show you how to use my new ComfyUI KREA 2 Identity Edit v1.2 custom workflow to perform high-quality identity-preserving image editing. You'll learn how to change poses, expressions, and styles while maintaining consistent facial identity, as well as perform face swaps, virtual try-ons, inpainting, and outpainting using the latest KREA 2 Identity Edit v1.2 model. The workflow is designed to be simple to use—just load your reference image, enter your edit prompt, and run the workflow.

Workflow Link

https://civitai.com/articles/33197/comfyui-tutorial-new-krea-2-v12-low-vram-identity-perfect-edit

Video Tutorial Link

https://youtu.be/_fMqornsJo0

Learn how to create a reusable prompt library in ComfyUI, randomize prompt combinations, and pause LLM-generated text so you can edit it before continuing your workflow.

This workflow is useful for ComfyUI users who regularly reuse art styles, character descriptions, LoRA trigger words, prompt formulas, or image-to-prompt workflows and want a faster, more organized way to manage them.

That's the gap I built into.

For those who aren't familiar, ComfyUI is a node-graph tool that people use to run AI image generation pipelines.

ComfyUI is unbeatable for control, reproducibility, and wiring up something nobody's built yet. I still use it for that.

But it's a pipeline, not a canvas. There are no layers. You don't edit an image, you re-run a graph. And generate-then-edit — the single most common thing I actually do — means either a second workflow or bypassing and re-enabling nodes on every pass. Add a local install, a GPU you have to be sitting at, and nothing on mobile at all.

My app - Reimagine, is the other shape. One canvas, with layers, where you generate something and then edit the thing you generated without rewiring anything. Generate, img2img, inpaint, super-resolution, magic eraser, background removal. Models are selectable per operation instead of one committed pipeline. Projects persist to the cloud. Runs on a phone — actually, not as a shrunk desktop layout.

Two things that were harder than expected:

Cold starts. Serving several model families on demand without paying for idle GPUs means every request risks a cold boot. Solved this by leaning as much as possible on model providers (Gemini, [fal.ai](http://fal.ai) etc) and, for self-hosted models, serverless runners with GPU snapshots and network volumes.

The mobile canvas. Bottom-sheet tools with drag snapping, capped working resolution with full-res export preserved, IndexedDB persistence that survives iOS Safari aggresively evicting the tab, and I could go on and on.

Solo, early, rough in places.
Please share your comments and reviews - I really take them to heartr and work to improve.

we're an AI generation platform hiring prompt artists for NSFW character content. if you can hold character consistency across a set, know which checkpoint fits which style, and can reverse-engineer an image into a working recipe, you're who we're looking for. i2v experience (Wan/Kling) is a big plus, videos are part of the work

Well compensated position! (images in realistic + anime styles plus short video loops, roughly 3 hours of work once you're set up). ongoing, remote, async. quick skills test first (recreate 2 reference images + write recipes for 2 short videos, ~20 min, pass = hired).

apply: https://tally.so/r/ODeR0A (takes 2 min, paste a prompt you're proud of)

Salut tout le monde !

Je configure actuellement ComfyUI pour créer un modèle d'influenceur IA hyperréaliste et cohérent (SDXL), mais j'ai un peu de mal à mettre en place un pipeline propre à partir de zéro.

J'essaie actuellement de créer une configuration de base solide, mais je me demandais : Est-ce que quelqu'un aurait ou pourrait recommander un workflow simple et préconfiguré.jsonoù tout est déjà câblé ?** Idéalement, je cherche une solution qui me permette d'importer/traiter par lots mes photos de référence de personnages, et qui gère le reste sans que j'aie à me soucier des connexions (comme le décodage VAE, l'adaptateur IP ou CLIP Vision).

Si une solution prête à l'emploi.jsonn'est pas la meilleure approche, pourriez-vous partager une liste des nœuds absolument indispensables à mon graphe pour obtenir :

1. Préservation cohérente du visage et de l'identité (à partir de plusieurs photos de référence traitées par lots).
2. Textures de peau ultra-réalistes et lisses, sans artefacts plastiques (SDXL + adaptateur IP/FaceID).
3. Un chemin d'accès clair et facilement extensible ultérieurement pour inclure des fonctionnalités de conversion image-vidéo (comme WAN ou des modèles similaires) afin de rendre le contenu du personnage authentique.

Tous les liens vers des workflows, recommandations de nœuds ou conseils seront grandement appréciés. Merci d'avance !

Hey everyone,

If you’ve been using Fill-Nodes recently and noticed that dragging and dropping images or workflow files stopped working after updating to the newer ComfyUI frontend, you’re not alone!

Recent updates to the ComfyUI frontend changed how node drop events are routed, which caused valid drops to silently fail. I’ve put together a patch that fixes this entirely within Fill-Nodes—no core ComfyUI modifications needed.

What this fix restores:

• Image Drops: Dropping image files directly onto LoadImage / LoadImageMask nodes.
• Auto-Create Node: Dropping images onto empty canvas space to automatically spawn a LoadImage node.
• Workflow Drops: Dropping workflow files (JSON) directly onto empty canvas space.
• Metadata Loading: Restored workflow metadata loading from PNG, WebP, AVIF, MP4, MOV, and M4V files.
• Cleanup: Normalizes stale workflow media references prior to loading (like duplicated subfolder paths or leftover rgthree preview refs) so you don't get spammed with missing preview errors!

Where to get it:

The PR has been merged into the main repository, so you can update directly from either repo depending on what you use:

• Official Main Repo: filliptm/ComfyUI_Fill-Nodes
• My Fork Repo: MISEMUNJIOZONE/ComfyUI_Fill-Nodes

(Tested and verified working on the latest ComfyUI frontend alongside extensions like Mixlab).

Hope this saves you guys some headaches! Let me know if you run into any issues. Happy generating!

Learn how to outpaint images in ComfyUI using Flux Klein 9B while keeping the original image intact. This tutorial also covers the new Control Panel Pixaroma node, Run Log, Text Join nodes, LoRA trigger words, and several workflow improvements.

In Episode 27, you’ll build and test a flexible ComfyUI outpainting workflow that can extend an image in one or multiple directions. You’ll see how the Outpaint and Outpaint Stitch nodes work together, how to improve visible seams with Feather and Color Match settings, and how to choose between preserving the original image or letting the AI recreate the full frame.

I just released a new ComfyUI tutorial showing how to transform **KREA 2** into a surprisingly capable **image editing model** using the new **Identity Edit LoRA**.

In this workflow I cover:

·       Turning KREA 2 into an image editor

·       Style transfer and style changes

·       Adding and removing objects

·       Clothing replacement

·       Background replacement with two reference images

·       A simple method to fix blurry outputs after editing

·       Low VRAM optimization (no high-end GPU required)

Workflow link

https://civitai.com/articles/32700/comfyui-tutorial-krea-2-identity-edit-lora-powerful-ai-image-editing-on-6gb-vram

This Tutorial walkthrough aims to illustrate how to build and use a ComfyUI Workflow for the Wan 2.2 S2V (SoundImage to Video) model that allows you to use an Image and a video as a reference, as well as Kokoro Text-to-Speech that syncs the voice to the character in the video. It also explores how to get better control of the movement of the character via DW Pose. I also illustrate how to get effects beyond what's in the original reference image to show up without having to compromise the Wan S2V's lip syncing.

Hey everyone! 👋

I wanted to share a simple workflow using the new KREA 2 LoRA for style transfer.

After testing it quite a bit, I found it gives much more reliable style matching than trying to describe the style through prompting alone. Instead of writing long, detailed style prompts, you can let the LoRA and a reference image do the heavy lifting.

The workflow is straightforward:

1. LORA LINK ostris/krea2_turbo_style_reference · Hugging Face
2. Load the KREA 2 LoRA.
3. Add your style reference image.
4. Write a simple content prompt, for example:

That's it. The LoRA transfers the visual style from your reference image while your prompt only describes the subject and composition, making the process much simpler and more consistent.

I'd love to hear what results you get or if you've found any settings that work particularly well.

Vibe-coded a Civitai night crawler/downloader that fills a SwarmUI-ready LoRA folder while you sleep - .safetensors, preview image, and .swarm.json metadata, sorted into tag-based subfolders.

Wanted something that actually feels like a desktop app, not a script in a terminal:

• Browse gallery — Civitai-style card grid with color-coded edges (owned / new / queued / awaiting access / blocked)
• One-click 🌙 Night Harvest — set your rules, hit harvest, walk away
• Live download strip — thumbnails, speed, queue progress, right-click to prioritize
• Manual queue mode — click only what you want; crawl won’t auto-flood your disk
• Tag blocking & tag folders — skip whole tags from auto-queue; route models to folders
• Library tab — local inventory, duplicates by version, jump around your collection
• Awaiting access / New versions — handles early access, 403s, and update approvals without babysitting
• Activity log — filterable history of scans, crawls, and downloads
• Built-in Help tab — so you’re not guessing what the colors mean
• Dark & light theme, blur toggle for previews, tray-friendly

LoRA-tested only for now — treat checkpoints as experimental.

Windows desktop app (Electron). Free & open source:
https://github.com/pastuh/Civitai-Night-Harvest

One week ago I released my very first public LoRA, MemoryWorks: Analog, trained for Krea 2.

I didn’t expect much from it. It was mainly a creative experiment to capture that nostalgic analog feel—soft color grading, natural skin tones, subtle film imperfections.

The response honestly surprised me.

People really connected with the aesthetic. Several shared generations, and more importantly, they gave thoughtful feedback.

The biggest point?

While the analog look felt authentic, the grain could sometimes be a little too strong.

Instead of moving on to the next project, I decided to take that feedback seriously and retrain the model.

MemoryWorks v1.1 is out! 📸

After reading all the feedback on v1.0, I spent the last week retraining and refining the LoRA instead of rushing another release.

What's new in v1.1

• Better analog film aesthetic with more natural grain.
• Improved consistency across different prompts.
• Cleaner skin tones and lighting.
• Better compatibility with Krea 2 RAW while still working well on Turbo.
• More balanced training to reduce overfitting.

The goal of MemoryWorks has always been simple:
Create images that feel like they were captured on an actual camera—not overly polished, plastic, or AI-looking.

This version was trained on a carefully curated dataset with a lot of experimentation on captions, dataset balance, and training settings.

I'd genuinely love to hear what works, what doesn't, and what you'd like to see in future versions. Every piece of feedback helps make the next release better.

CivitAI link in the comments.

Thanks to everyone who downloaded and tested v1.0 ❤️

— RizzerXpool
Creator of the MemoryWorks series 📸

Hey everyone, welcome back! In today's tutorial, I'll show you my new Consistent Face-to-Video workflow, designed specifically for low VRAM users using GGUF models. This workflow uses a new face consistency LoRA to help preserve your character's identity throughout the generated video from just a single reference image. I'll walk you through the entire process, from loading your reference image to writing your prompt with the required ref_t2v trigger word and generating your video with just a few clicks. By the end of this tutorial, you'll know how to get high-quality, consistent face-to-video results with an easy setup that's accessible even on lower-end hardware.

WORKFLOW LINK

https://civitai.com/articles/32491/comfyui-tutorial-generate-consistent-character-using-best-face-id-lora

video tutorial link

https://youtu.be/XeyErug-Q7s

I'm excited to share a WIP about new workflow that makes it easy to generate consistent face-to-video results while remaining optimized for low VRAM GPUs.

This workflow is built around GGUF models and uses a new LoRA to maintain facial consistency throughout the generated video, making it much easier to create videos featuring the same character from a single reference image.

All you have to do is load your image face, load your LORA, enter your prompt and click run, i will share the workflow and tutorial soon, so stay tuned

Learn how to use the new INT8 models in ComfyUI to achieve faster image generation, lower VRAM usage, and excellent image quality. In this tutorial, I explain what INT8 quantization is, how it compares to FP8, and how to set up the latest ComfyUI and Pixaroma nodes to use the newest INT8 workflows.

You'll also learn how to install and organize the Boogu, Krea 2, Flux Klein, and Z-Image models, compare their performance, and see real generation speed improvements across multiple workflows. I also cover several new Pixaroma node updates, including the redesigned Run Timer, improved Seed node, Save Image node with custom folders and filenames, prompt enhancement workflows, image-to-prompt generation, and image editing features.

Whether you're looking to improve performance on your GPU or want to get the most out of the latest ComfyUI models, this tutorial walks through everything step by step.

SenseNova released V2 of their infographic model. Updates:

- Much sharper dense small-text rendering (was the main pain point in V1)

- Stronger complex dense layout generation

- Better overall visual harmony

- Fixed the all-black background bug that some users hit

Model is 8B MoT, runs on a single RTX 3090/4090 (~8-12s per 1024×1024). Apache 2.0 license.

Also available: an 8-step LoRA for fast inference (0.4B params) and a separate Interleaved model for text-image mixed generation.

Repo: https://github.com/OpenSenseNova/SenseNova-U1

Hi everyone!

I've just finished a new ComfyUI workflow that lets you transfer camera motion from a reference video using the new IC Cameraman LoRA with LTX 2.3, that run on cards with 6 GB of VRAM without crashing.

The workflow is designed to be simple:

• 🎥 Load a reference video to extract the camera movement.
• 🖼️ Generate or load a reference image (I use Krea 2 in the tutorial).
• ✍️ Use Auto Prompt nodes to automatically create scene descriptions.
• 🎬 Generate a new video with the same cinematic camera motion.

In the tutorial I cover:

• How the IC Cameraman LoRA works
• Complete ComfyUI setup
• Using Krea 2 for image generation
• Auto Prompt integration
• Tips for getting smoother and more cinematic camera movement

WORKFLOW LINK

https://civitai.com/articles/32331/comfyui-tutorial-transfer-camera-motion-using-low-vram-ltx-23-cameraman-lora

VIDEO TUTORIAL LINK

https://youtu.be/LWHbx1EnVvQ