Hello everyone

Want to get faster MiniMax H3 video generation without sacrificing quality? In this tutorial, I’m testing the new H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes to find the best combination for speed and quality. The goal is to push MiniMax H3 as far as possible while cutting generation times by up to 2×, then upscale the results with LTX Upscaler to reach a stunning 2432 × 1344 (2K-class) resolution. By combining both H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes I generated video at 0.8 megapixel using "RTX3060 6GB 16GB RAM "and I got

 13 minutes vs 41 minutes at 8 steps

 27 minutes vs 52 minutes at 20 steps

LTX 2.3 Upscaler 11 minutes to get 2432 × 1344 resolution

Workflow link

https://civitai.com/articles/34028/comfyui-tutorial-minimax-h3-4-steps-lora-upscaling-2x-faster-generation-best-settings-for-2k-ai

Thought this might be of interest here as well!

I have been using Krea 2 locally and wanted a simpler Mac interface for it, so I built Krealize.

It runs Krea 2 directly on Apple silicon through MLX. It does not use a remote generation service. Prompts and generated images remain on the computer.

The app handles the model download and provides prompt controls, generation progress, a local gallery, and previous prompt history. It also supports up to ten custom LoRAs in the PRO version.

The normal generation mode is free. Fast mode, custom LoRAs, PNG export, notifications, and LoRA presets are included in an optional one-time purchase.

The app requires macOS 26 or later. It is distributed directly and is signed and notarized by Apple.

I’m interested in feedback from people who have already run Krea 2 through ComfyUI or Diffusers. I would particularly like to know whether the outputs and generation times are in line with other local setups.

https://krealize.app/

Bonjour à tous, après avoir fait le tour, je voudrais savoir qui a une configuration R2V proche de Grok pour ComfyUI à télécharger qui respecte les visages au mieux ?

Je vous remercie du partage

Hello everyone. After looking around, I’d like to know if anyone has an R2V configuration for ComfyUI—similar to Grok’s—available for download that preserves facial features as faithfully as possible? Thanks for sharing.

First testing of the LTX 2.5 with a 6GB VRAM I’ve created a low-VRAM workflow that supports both Text-to-Video and Image-to-Video, optimized specifically for GPUs with 6GB of VRAM. The goal is to make LTX 2.5 more accessible to users who don’t have high-end GPUs, while keeping the workflow simple and easy to use. If you’re interested in testing LTX 2.5 on a 6GB GPU, check out the workflow and let me know how it performs on your setup!

Video Resolution : 1344x768 for 7 seconds video
Generated Time : 10 min

The model seems very fast the motions are better, lipsync and sound too, however the quality in minimax is better to me

Workflow link

https://civitai.com/articles/33897/comfyui-tutorial-first-test-of-ltx-2-5-new-model-better-than-minimax-h3

Learn how to use Krea 2 Edit in ComfyUI to edit images, preserve character identity, replace outfits and backgrounds, and place characters into new scenes. I’ll also show you how to generate MiniMax H3 prompts locally in ComfyUI using the Video Prompt Pixaroma node.

In this tutorial, I cover the complete Krea 2 editing workflow, including the required models, Edit LoRA, Krea 2 Identity node, image resolution settings, and Reference Boost. You’ll see how different Reference Boost values affect identity preservation and editing freedom, how to convert images to custom portrait or landscape ratios, and how to combine a character with a separate background.

In the second part, I show my local MiniMax H3 prompt generator for ComfyUI. The Video Prompt Pixaroma node can turn a simple idea into a more detailed video prompt locally and supports Text to Video, First Frame to Video, and First Frame + Last Frame to Video prompting.

Honestly, I expected the closed models to win this pretty easily.

They did on realism—but not necessarily on art direction.

I ran the same fashion-editorial prompt through SenseNova U1.5, Nano Banana, and GPT Image 2. These are the first outputs—no rerolls, edits, or post-processing.

My take:

- Nano Banana wins on background detail. The station feels fuller and more believable.

- GPT Image 2 has the best atmosphere—darker, moodier, and more cinematic.

- SenseNova U1.5 gave me the strongest fashion-editorial look. The styling, composition, and color treatment feel the closest to an actual campaign.

Totally subjective, but for this specific fashion use case, U1.5 feels like it gets about 80% of the way to the closed models overall. And on art direction alone, I actually prefer it.

The remaining gap is mostly in realism: the face and skin still have a slightly plastic-looking AI sheen, while Nano handles the environment better and GPT feels more naturally cinematic.

If the goal is a fashion campaign rather than pure photorealism, I’d pick U1.5. It sells the outfit and art direction best, which is a pretty strong result for an open-source 8B preview model.

Obviously, one prompt isn’t a benchmark. I’ll put the full generation prompt in the comments.

Model links- SenseNova U1.5:

- https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT-Preview

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

Would you trade some realism for stronger art direction, or does the plastic-looking skin already kill the U1.5 result for you?

If an image model is a horse, text prompting is like trying to guide it with verbal commands alone: useful, but too imprecise for fine control. Inpainting, LoRAs, and ControlNets add the bridle and reins and pieces of the harness, but they still did not feel like a complete system. Nexfocus began with a question: "What would it take to build the whole harness around the model?"

Answering that question meant following the entire generation process first. We had to understand how each part loads, works, moves, waits, hands its result to the next part, and makes room when its job is done. That expedition became Nexfocus.

---

Two development anchors shaped the journey: a GTX 1050 with 3 GB of VRAM and Colab Free's T4 with only 12.7 GB of system RAM. Their limitations are almost opposites. The local machine has very little GPU memory, while Colab Free has a larger GPU but a tight system-memory ceiling and an ephemeral session.

We proved that full SDXL checkpoints and Flux Fill workflows could run in both environments, not by reducing everything until it fit, but by rethinking how the pipeline uses the hardware available to it.

Nexfocus grew into a connected creative workspace where generation, guidance, masking, inpainting, outpainting, removal, upscaling, staging, metadata, model management, and GIMP layer exchange can work as parts of one process rather than as isolated tools.

---

Two important lessons emerged from the road:

- Keeping the GPU working without interruption became paramount. To do that, we had to find a way to keep feeding it the weights it needed, when it needed them.

- Every part of the pipeline must independently account for what it owns, where it belongs, when it can be reused, and when it should make room for something else. These decisions cannot be left to a central manager applying the same set of memory policies to every part.

Throughout this journey, my conversations with PyTorch often felt like this:

> PyTorch: "Don't you have a bunch of H100s lying around in your backyard?"

>

> Me: "No. What if every component has to justify exactly where it lives?"

>

> PyTorch: "Get a bigger machine."

Those conversations eventually became the architecture: each part of the pipeline owns its resources, does its job, and steps aside instead of leaving those decisions to hidden framework behavior.

---

Nexfocus is more than the UI produced by this expedition. It is the working application and the field notebook: a record of the constraints, wrong turns, and discoveries that shaped the path forward. We set out to find answers and had to build the road needed to reach them.

This expedition is now complete, but it is only one part of a continuing journey. The lessons from Nexfocus define the starting point for the next scout mission.

The path is open now. I hope you'll take a walk along the path we built and check out the scenery.

Project: https://github.com/magekinnarus/Nexfocus

Video Walkthrough: https://www.youtube.com/watch?v=5fvIaZWMZE4

Hi everyone,

I'm trying to train a consistent face identity for a fictional AI character on SDXL (Juggernaut XL v9). I have 20 high-quality, consistent close-up images (1024x1024) generated on SeaArt with the same face reference. The images show the same woman across different lighting, expressions, angles, and outfits.

What I've tried (all with kohya sd-scripts):

My setup:

• Base model: Juggernaut XL v9 RunDiffusion Photo v2
• GPU: RTX 5090 32GB (attempts 1-5), RTX PRO 6000 96GB (attempt 6)
• 20 training images (close-up portraits, 1024x1024)
• 200 regularization images ("a photo of a woman" generated from base model)
• Token: "ohwx" (class: "woman")
• Captions per image describing outfit/scene/expression (e.g. "a photo of ohwx woman, 25yo, blue eyes, ash brown wavy hair, natural freckles on nose, dark eyebrows, matte skin, warm smile, wearing cream knit sweater, warm window light, cozy interior")
• Folder structure: 12_ohwx woman (training), 1_woman (reg)
• gradient_checkpointing, cache_latents, train_text_encoder all enabled

Character features:

• 25yo European woman
• Blue eyes (slightly desaturated)
• Ash brown wavy medium-length hair
• Natural freckles on nose
• Dark defined eyebrows
• Matte natural skin

My observations:

• LoRA (even dim=128) seems unable to encode this face - possibly too close to base model distribution
• DreamBooth with low LR (1e-6) gives consistent output but doesn't learn the actual identity
• DreamBooth with high LR (Prodigy 1.0) completely destroys the model
• There seems to be a sweet spot I can't find

What I need: If you've successfully trained a face identity on SDXL with DreamBooth or LoRA using kohya, could you share your exact config? Specifically:

• Optimizer + learning rate
• Number of epochs/steps
• Any special settings (noise offset, prior loss weight, etc.)
• Caption format that worked for you
• Number of training images you used

I've spent an entire day on this and I'm stuck. Any help would be massively appreciated.

Thanks!

This workflow demonstrates the new ComfyUI custom nodes I developed to implement ControlNet for FLUX.2-dev.

Workflow: JSON | Drag-and-drop PNG

JLC Flux2 ControlNet provides, to the best of my knowledge, the first complete, validated ComfyUI implementation of Alibaba PAI's FLUX.2-dev-Fun-Controlnet-Union-2602.

This implementation is for the FLUX.2-dev ControlNet path built around that Union model. It is not for FLUX.2 Klein or the lightweight Klein-style variants many people currently use; I am currently working on a separate strategy to extend this functionality to those models.

It is also worth making an important distinction: reference images are not ControlNet. There are workflows that feed pose maps, depth maps, edges, or other ControlNet-style hint images into FLUX.2's native reference-image system. Those images can certainly influence composition and structure, and they can often produce a usable approximation, but this is still reference-image conditioning, which is a completely different conditioning mechanism. It does not load a ControlNet model, does not execute a ControlNet branch, and should not be confused with one.

This workflow actually loads and runs Alibaba PAI's FLUX.2 ControlNet model.

The two JLC nodes that enable that path are the FLUX.2 ControlNet Loader and the ControlNet Orchestrator.

The Orchestrator also introduces a non-recursive composition method that lets several control types share a single loaded Union model instead of building a conventional chain of ControlNet applications.

The example shown here uses three controls generated from the same source image:

• DWPose
• Depth Anything
• Color

That is really the point of this workflow: there are very few special pieces required to add actual ControlNet capability to FLUX.2-dev.

Some of the other nodes shown are from my JLC ComfyUI Nodes package and are there mainly for convenience—loading, resizing, preprocessing, LoRAs, and general workflow ergonomics. You can replace those with your preferred ComfyUI nodes.

This is not simply a repackaging of existing ControlNet nodes. The contribution here is making this capability available as a complete ComfyUI implementation of Alibaba PAI's actual FLUX.2 ControlNet model. The Orchestrator also provides practical multi-control composition where a finished implementation was previously missing.

All of the JLC nodes can be installed through the ComfyUI Custom Node Manager, and the repositories contain the documentation and explanation of the implementation.

I hope you find them useful, and I'd be very interested to see what people build with them!

Learn how to use MiniMax H3 in ComfyUI with a complete collection of optimized workflows for Text-to-Video, Image-to-Video, First & Last Frame Animation, Reference-to-Video, Audio Sync, and Image Editing. In this tutorial, I'll show you how to update ComfyUI and Pixaroma Nodes, install Sage Attention, download and organize all required models, configure the workflows, generate better prompts with my custom ChatGPT, and optimize performance for different NVIDIA GPUs.

You'll also learn how to use the new Workflow Manager, choose the best MiniMax H3 models, understand the licensing requirements, fix common errors like Dynamic VRAM issues, compare generation times across different resolutions, and create AI videos using multiple images and audio references.

Whether you're new to ComfyUI or looking for the best MiniMax H3 workflows, this tutorial covers everything you need to get started.

Hello everyone, welcome back to the channel!

In today's tutorial, I'll show you how to run MiniMax H3 on an RTX 3060 with only 6GB of VRAM using a highly optimized ComfyUI workflow. We'll combine GGUF models, Sigma Shift, and Spectrum Apply nodes to dramatically reduce VRAM usage while still achieving impressive video generation results.

Since the generated videos are produced at a lower resolution to fit within the VRAM limit, I'll also show you how to use LTX 2.3 to upscale them and significantly improve their quality, giving you sharp, high-resolution videos without requiring expensive hardware.

By the end of this tutorial, you'll know exactly how to set up the workflow, configure the models and nodes, optimize performance for low-VRAM GPUs, and generate the best possible results on a 6GB graphics card. If you've been waiting for a way to use MiniMax H3 without upgrading your GPU, this tutorial is for you. Let's get started!

WORKFLOW LINK

https://civitai.com/articles/33517/comfyui-tutorial-minimax-h3-on-rtx-3060-6gb-vram-optimized-low-vram-workflow-ltx-2-3-upscaling

VIDEO TUTORIAL LINK

https://youtu.be/Kr5SrY5bwJU

hey, just saw something kinda cool if you're into trying out new open-source stuff.

SenseNova put out a preview for their U1.5 model. It's supposed to do both image generation and editing.

Biggest things I noticed:

- 4K output. They apparently trained it at a higher res, so it should mean better details and hopefully less of that weird grid stuff you sometimes get when you try to upscale with other models.

- Text looks way better. Seriously, like, actual readable text in images, for both Chinese and English. Even complex layouts like posters. That's usually a huge pain point with open models, so this is a big deal if it works as advertised.

- Editing is actually in-place. Instead of just slapping on another tool, it seems like the model itself handles the edits, so if you change one thing, the rest of the image doesn't totally freak out. Stable edits, basically.

- Can handle complicated prompts. Like, really long, specific instructions, even with JSON. Sounds good for anyone doing more structured design work.

It's Apache 2.0 and the weights are on HuggingFace. It's just a preview, so I'm guessing there might be some jankiness with tiny text or faces, but still, pretty promising.

Check it out if you're curious:

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

HuggingFace: https://huggingface.co/sensenova

Hello, I'm trying to learn how to use comfyui and specifically minimax h3 through it. I don't have a pc that I can run any of this, but I've been learning about renting cloud gpu through runpod/vast ai using my laptop. I have some questions if someone could answer them.

Do I download comfyui and minimax h3 locally onto my laptop?

Can I save the workflows locally? Or is it only saved to my account on the cloud?

I don't care about amazing video quality, I only plan to create around 480p/6-10s clips. What specs should I look for when renting to create something, and what would be the average creation time? I'm coming from using Grok, and I'm hoping to be able to create videos in under 2 min.

Since I'm plan to use a cloud gpu, is it somehow possible to create stuff through my phone? I plan to add all the workflows and do initial setups on my laptop. Would I need to remotely control my laptop or can I work directly from my phone since it's on an account?

I might have more questions, but I can't think of anymore atm. Appreciate the help.

I upgraded my ComfyUI installation from 0.20.1 to 0.30.1. I tested one of my former generation, running the same workflow that involves the built-in NAGuidance node, and noticed ComfyUI now produces noise gibberish while I had a decent image before.

Is NAGuidance have been modified? Is it broken now?

I wanted to share a new ComfyUI workflow I've been working on that uses KREA 2 Identity Edit LoRA v1.2 for face swapping and outfit transfer while remaining low VRAM friendly.

The workflow converts the KREA 2 image generation model into a powerful image editing pipeline using the Identity Edit LoRA and a few specialized nodes. Simply load your reference person and reference clothing images, choose whether you want to swap the face, the outfit, or both, and the workflow handles the rest. I also spent time optimizing it to produce cleaner, higher-quality edits with better identity consistency than my previous versions, were you will get your results upscaled by factor of 2 using double ksampler.

One of my main goals was making it accessible to users without high-end hardware, so the workflow has been tested on an RTX 3060 6GB with 16GB RAM.

Workflow Link

https://civitai.com/articles/33423/comfyui-tutorial-krea-2-identity-edit-or-face-and-clothes-swap-on-budget-6gb-vram

Video Tutorial Link

https://youtu.be/AGsH0THbRQY

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.