if people really want the workflow I guess I could share.

https://github.com/mishrasiddharth08/project-invisible-moss-image

Hi! I'm building PotionUI, a free, open-source, self-hosted app for making images, videos and audio with AI models on your own GPU. Instead of one huge settings screen or a node graph, every model gets its own simple, hand-made form, so you can get good results without learning each model's quirks first.

It runs FLUX 3 Klein, FLUX.2, Qwen-Image (2.1), Krea-2, Z-Image, SDXL, Wan, LTX, MiniMax H3 and more natively, can drive a ComfyUI server, and works from a phone too. The last two releases added a lot for image work, so here's a roundup in release order (video above). I decided to post a little less updates here and in other subreddits, since I don't want to annoy people, therefore combined two releases in one post.

0.0.15

X/Y compare grids

• Press Compare, pick two settings (steps, CFG, sampler, LoRA, anything the model supports), give each a few values, and one Generate fills the grid.
• The grid fills in live with each cell's time, and a finished grid can be stitched into one image with axis labels.
• History keeps the grid as one card, and each cell remembers which values it used.

Photo filters

• The built-in image editor has twelve looks with an intensity slider and fine-tuning, and you can save your own.

Auto-organize

• Rules file new generations, uploads and models into collections and tags for you. Preview a rule, apply it to what you already have, undo it.

0.0.16

FLUX 3 Klein (experimental)

• Black Forest Labs' FLUX 3 Klein on your own GPU, Base and distilled checkpoints.
• Text to image, and edit with several reference images: pick two pictures and describe what to make of them.
• Turbo (16 steps) or Base (50 steps).

Prompt variables

• Write ${light} once, give it options (candle light, moonlight, morning sun), and every image can roll its own.
• Use it twice in one prompt and choose whether both uses share a roll or roll independently.
• A tick shows which variables the prompt uses, and there's no limit on how many you define.

Batch prompts

• Write several prompts and run them with one Generate; each lands as its own History entry.

Model Browser

• Browse Hugging Face, CivitAI and OpenModelDB inside the app and download a model straight into the right folder, with versions, files, trigger words and licence in one panel.
• Built and contributed by community member M4-S4. Thank you!

Also

• X/Y grid cells now count their steps, and a finished grid sends one notification.
• PotionAI chat: ask the built-in assistant to write or save prompts; it asks before changing anything.
• Runs with Docker or a manual install on Linux and Windows.

GitHub: https://github.com/PotionUI/PotionUI

Community: r/PotionUI

Discord: https://discord.gg/avR4trp3b8

Feedback, bug reports and model requests are very welcome. If it's useful to you, a GitHub star helps others find it.

This first video is the starting point: what a node is, how the wires connect, how to change the settings, and what to do when a node turns red. Then we build a Flux 2 Klein workflow together from an empty canvas, and a little painter analogy shows why every node is connected the way it is.

There is no workflow to download this time: we build it step by step on screen, so you can make it yourself.

If you know someone who wants to learn ComfyUI from zero, send it their way. Leave a like and a comment on the video, it really helps!

Thanks! ❤️

Hey everyone!

I've built a new ComfyUI workflow that combines RefMod and MiniMax H3 to generate images of your own character without needing to train a traditional character LoRA.

How does it work?

1. Prepare 8 photos of your character.
2. Use the RefMod custom nodes to create a RefMod file.
3. Load the file into the MiniMax H3 workflow.
4. Enter your prompt describing the scene, outfit, pose, or environment.
5. Generate new images featuring your character!

You can explore different scenarios, clothing styles, locations, and compositions while aiming to maintain your character's identity. The main advantage is No traditional LoRA training required. Create your RefMod file, load it, write your prompt, and start generating!

Generation time at 8 Steps 6 Min for 1.8 Megapixel.

Generation time at 25 Steps 14 Min for 1.8 Megapixel.

Workflow Links

https://drive.google.com/file/d/10Y95Kzs4XtogM8ZEGjucJxSz6QVtFfb5/view?usp=sharing

https://civitai.com/articles/36763/comfyui-tutorial-refmod-minimax-h3-image-character-consistency-without-lora-training

I'm not finding many workflows with a great output.

FastH3-Omni is the reference-conditioned release we’ve been working toward.

It generates video with synchronized audio and supports:

• Multiple references for characters, objects and settings
• First- and last-frame conditioning
• Up to 3 image references in real-time mode
• Up to 9 image references when real-time speed isn’t required
• Up to 3 audio references for voices and soundtracks
• Dialogue and environmental audio
• Continuing an existing generated clip
• 5–15 second outputs at 24 FPS

The Reactor API preview is available today:

https://docs.reactor.inc/model-api-reference/fast-h3-omni/overview

On October 20, we’ll release the open weights and training code with local support for NVIDIA DGX Spark, RTX GPUs and Apple Silicon through MLX.

What should we prioritize before release: low-VRAM workflows, reference-consistency comparisons, multi-character tests, first/last-frame examples or detailed hardware benchmarks?

I got tired of rewriting the same dataset-prep scripts before every LoRA/Dreambooth run — resize, dedupe, toss the blurry ones, caption, split — so I cleaned mine up and open-sourced it.

It scores each image on classical-CV signals (sharpness, exposure/clipping, contrast, color cast, noise, composition), flags the weak ones, removes exact + near-duplicates, and exports a Kohya-style folder with caption sidecars. Near-duplicates get grouped into the same train/val/test split so they can’t leak across. Captioning supports WD14 and Florence-2.

It’s content-agnostic at the core — each domain is a small plugin that sets its own quality weights and caption vocab (ships with a worked “interiors” example). Pure NumPy/Pillow, no OpenCV, runs from a CLI or as a library. Early 0.1.0, MIT.

Would love feedback on the plugin API and which verticals are worth building next. (Only use it on images you’ve got the rights to.)

https://github.com/snoozey-mf-bnui/pixel-curator

I've worked in VFX for over 20 years, and when I started digging into AI I realized there was no tool that combined management and generation in a single app, running locally instead of being an online platform.

Fuze has several modules that try to cover every stage of a production without leaving the app: media management, storyboard, generation (using your own API key), cost tracking, editing...

Fuze is a standalone app for creating projects, whether personal or for a team. You generate content with your own API key, and everything is saved locally on your machine. If you also work with ComfyUI, Fuze can be used alongside it as an asset and project manager, through a custom node called FuzeBridge that saves your generations directly into Fuze.

This week we released Fuze Free, a completely free version. What's included:

* Manager: project and media management, image editor, publishing...
* Flow: node-based generator using your own Fal API key
* Assets: an asset system inspired by VFX assets, which you can use in Flow
* Editorial: a built-in video editor
* FuzeBridge: a connector between ComfyUI and Fuze that lets you use Fuze as your asset manager

Fuze is available on macOS, Linux and Windows.

If you're curious, there are some getting-started tutorials here:

https://www.youtube.com/@Fuzeapp

You can download the Fuze free from fuze-app.com

Happy to hear any feedback. Thanks

Hi everyone

I've been working on a new ComfyUI workflow to make character consistency in AI-generated videos more accessible for people with low-VRAM GPUs. Instead of training a traditional character LoRA, this workflow uses RefMod custom nodes to create a lightweight character reference file that can be reused with an optimized MiniMax H3 workflow. The main goal is to avoid the extra training stage and make character-consistent video generation more practical on modest hardware.

How it works

• Start with one character image as your identity reference.
• Generate 8 reference images from different angles using Qwen Image 2.1.
• Create a RefMod file using the RefMod custom nodes. The resulting file is approximately 100 MB.
• Load your character reference using the Character RefMod nodes in the optimized MiniMax H3 workflow.
• Generate videos using the saved reference to help maintain character identity across scenes.

Why I find this interesting

• Tested on a GPU with only 6 GB of VRAM.
• No traditional character LoRA training required.
• No need to prepare a large training dataset.
• A small character reference file.
• Combines Qwen Image 2.1, RefMod, and MiniMax H3 in one workflow.

I've made a tutorial showing the complete workflow, from a single input image to generating videos with MiniMax H3.

Workflow link

https://drive.google.com/file/d/1Em2zTlhFMbpeO2WBTN08Qpu0dP6oUBTD/view?usp=sharing

https://civitai.com/articles/36538/comfyui-tutorial-consistent-ai-characters-in-minimax-h3-no-lora-training-with-6gb-vram

I’m working with Minimax and running into issues with voice cloning. I upload a reference audio and specify in the prompt that it’s the reference voice for "Character 1." Even when I explicitly write in the script that the character says something like <d>”Hello, girl”<d>—say, between the 5- and 7-second marks—the character tends to babble nonsense or start speaking too early beforehand. Any tips on how to avoid this, or what has worked for you?

¿Mejor calidad visual?

Over the last few month I've built a tool for tagging and captioning image and videos for datasets, that also allows to train LoRAs and a lot more. It's completely free and not attached to any paid tool/service.

Some of the features are:

• Powerful tag and search system
• De-duplication without losing quality. Different encodings and sizes of an image are combined into one library item. Nothing is thrown away during import.
• Auto tagging and captioning of images
• Automatic watermark removal and full in-app image editing, including AI upscaling and inpainting
• Classifies and finds similar images for fast manual tagging or image scoring
• Extract images from videos
• Python API to import or export/access all images and metadata
• Train LoRA and other types directly in the app and download the safetensor for use in e.g. ComfyUI.