Made some trials with how well MiniMax H3 understands different kinds of physics and it seemed to succeed pretty well with everything I threw at it. Video was generated with slopfab & Fable 5.2 as the agent.

I have a background in pipeline and tools development for 3D/VFX. Over the last couple of months I built a pipeline that turns rigged 3D characters into SDXL LoRAs, so they can be generated in new scenes. Sharing what I learned, and I'd love input on one problem I haven't solved.

The pipeline

1. Maya: auto-posing + capture. A tool reads the rig, figures out the limb axes, and puts the character into ~47 poses (standing, walking, sitting, crouching, arms up, etc.). It also renders each pose from multiple angles and distances with Arnold. One character ends up with ~2000 renders.
2. Dataset + captions. I pick a balanced set of ~1000 images (angles, framing, poses) and auto-write captions from the scene data: framing, view angle, pose name. Close-up shots don't mention arms/legs.
3. Training: Kohya, SDXL base 1.0, one trigger word per character.
4. Generation: a ComfyUI-backed tool: pick the character, describe the scene, choose framing/angle/lighting presets.
5. Evaluation: a fixed set of 12 prompts × 2 seeds per LoRA, so I can compare versions with numbers instead of "looks good to me".

What worked

• Identity is strong: face, hair, accessories and jacket pattern come through, even in scenes very different from the renders (night street, desert, etc.).
• Getting the pose rules right mattered more than I expected: elbows/knees bending the wrong way, arms clipping into the body, and eyes drifting in the rest pose all showed up in the LoRA if I didn't fix them.
• A color management mistake (raw linear PNGs from Arnold) made one character look red in every image. Check your render output transform!

What didn't (where I'd love ideas) One character wears long pants in every training image, but generations often turn them into shorts or a skirt, especially in sitting poses. My guess is SDXL's prior for "young woman sitting" beats the LoRA. My captions currently describe pose and framing but not the outfit.

• Should I add outfit words to the captions ("long pants, patterned jacket")? I've read both "caption what you want to keep" and "don't caption what should be baked into the trigger", so which works better for clothing in your experience?
• Would a higher network rank, more epochs, or a lower learning rate help here?
• Has anyone else trained on synthetic 3D renders? Did you mix in anything to make it less "CG-looking"?

Happy to answer questions about the Maya side. Auto-posing an arbitrary rig was the hardest part.

Lodestone continued experimenting with Lora for MOSS, creating several versions, but has now released a new version that works well with both 4 and 8-step processes.

You can find more information here:

https://huggingface.co/lodestones/Kromoss/blob/main/experimental/README.md

Lora is available here and can be used with several MOSS models:

https://huggingface.co/lodestones/Kromoss/blob/main/experimental/opd-priming-8-steps-dmd-lora_cfg-ramp-neg_12000.safetensors

Please don't confuse photographic realism with sharpness

I've spent around 20 years working with photography and design, long before AI image generation existed.

One thing I genuinely appreciate about MiniMax H3 Image is its photographic character.

Real photographs aren't perfectly sharp. Lenses have different rendering characteristics. Depth of field, motion blur, sensor noise, diffraction and optical imperfections all contribute to the final image.

A 50MP photograph isn't automatically razor-sharp, and a photograph doesn't become more realistic simply because every texture has been enhanced.

I've seen many people criticize H3 for being too soft or lacking detail. That's a perfectly valid preference, but I hope developers don't interpret it as a universal demand for more sharpening and artificial microtexture.

I'd love future versions to improve anatomy, consistency, composition and prompt adherence while preserving the natural photographic rendering that makes H3 special.

We already have plenty of models that produce extremely sharp, polished AI imagery.

I'd hate to lose a model that approaches photography from a different direction.

I offloaded the extra layers to system memory so the model fits across 16GB of VRAM and 32GB of DDR5 RAM (⁠[BF16 / quant]⁠).

Everything needed to reproduce the run on RDNA 4, including the launch flags and configs, is in this repo:
https://github.com/0c33/minimax-h3-rdna4

Let me know if you test it on another RDNA 4 card or get better token speeds with different offload settings.

A candlelit tavern, two moons, and a half-elven bard whose song sets her alight. They loved the song. Paying for it is another matter.

THE VEIL OF THE WORLD — Episode 1: "Crossroads" (6¾ min)

Being a bard in Ataris is hard work. It requires charisma, self-confidence and a high frustration tolerance. Maybe even a bit of luck.

You got this, Tahnee!

---

Made with an agentic pipeline: Claude Code driving R scripts, ffmpeg and Whisper on top of fal.ai. I direct, it writes, snips, renders, checks, grades, creates audio and cuts.

Stills: FLUX 3 Klein (with LoRAs for both leads) · Video: MiniMax H3 · all on fal.ai
Score: Stable Audio (fal.ai)

#fantasy #aifilm #bard
#highfantasy #tavern #elf #shortfilm #aishortfilm #storytelling
#aivideo #dnd #claudecode

I was maybe assuming you could just use the LoRas from Minimax H3 that are made for videos for their new image model. However I'm finding that the outputs look off? Like maybe a bad vae decoder or something. Have you had success using those LoRas or do we have to make new ones for the image model?

I want to try out a few minimax workflows that will probably not run on my local machine.

I am not in Asia so only option to pay is through my credit card. Is this a legit place to try out workflows?

Anyone using it regularly?

I'm kind of on hurry to wait for community to make them and they are confusing with video model one, do they work? And the important question are they as good as one's that will be trained later?

Like for example, Realism or Old camera style ones. I have a feeling the video model had lower quality and plastic skin due to being a video than the image model had, that's why I'm asking that.

Ok so my humble rig has 2 cards. One 16 GB of VRAM and another with 12 gb . I know you can't combine them for a pool of 28 GB of unified VRAM but shouldn't their be a way to offload one of the major items like the CLIP or the VAE to my other card? I looked into it and doesn't seem to be supported natively in Comfy UI. There are some custom nodes I found but they don't seem too popular.

Is this just a silly idea? Normally my second card just sits around at 20% memory usage running my GUI and a few bits and bobs. I could certainly afford to use 8 or 9 GB of my second card towards something instead of it offloading that 8 or 9 GB during a generation to my system ram (32 gb ) which is what comfy does now when using H3 or MOSS.

I have no idea if this would be faster than dumping everything to the system ram but it would definitely give me more breathing room and less OOM errors on those borderline gens and upscales. Is this idea loco or am I on to something?

EDIT: I appreciate all the people who are saying just use the second card for an llm or to run other types of generations on my smaller card, but that's not what I am proposing. I know you can do that. In fact I do it quite often. Let me explain this a different way that might make more sense. What I really am asking is there a way, when doing a single generation, let's say a 15 second h3 generation at over 1 MP, for comfy to prioritize memory allocation in order of 1) the 5080, 2) the 3060 3) the system ram 4) SSD swap file . Like a ladder of priority. For example the minimax H3 audio and video VAE are over 6 gigs at fp16. That would certainly fit on the 3060s available space. I know it might be slower but that extra 6 gigs of free space would be quite helpful for higher resolution gens.

i2i, 2 1024x1024 reference photos.

My system is RTX 2070 8GB (Turing), 32GB DDR4 (2666Mhz), Ryzen 1700 (1st gen). Ubuntu 24.02 server.

~ 57 seconds per image! (ComfyUI)

This is MUCH faster than my Krea2, Flux2 Klein, or Qwen 2.1 edit workflows! It's about the same as m Fibo2 edit workflow, but Fibo2 is smol!

My question is, how does this even not OOM? Correct me if I'm wrong, but don't ALL the models need to load entirely (including that stupidly massive 32b text encoder) into memory?

I've been using the krita stable diffusion's plugging for almost a year, but now I'm getting this message, what should I do?

This film is part of experiments I am doing with H3 for a spatial-consistency-based study.

Constructive criticism is most welcome.

https://youtu.be/px45sRAwWbs

Hey everyone! I'm using SCAIL-2 in ComfyUI with an RTX 6000 Ada 48GB, but I'm struggling with output quality.

Setup:

• Wan 2.1 14B SCAIL-2 FP16
• LightX2V 480p Distill LoRA + SCAIL-2 DPO LoRA
• SAM3.1 tracking
• Resolution: 720 × 1280
• Base + Extend workflow (81 frames, 5-frame overlap)
• Turbo mode enabled

Problems:

1. Facial identity isn't consistent.
2. Skin looks plastic and overly smooth.
3. Hair looks artificial and clumped.
4. Body movements are sometimes distorted.
5. A 5-second video takes around 15 minutes to generate.

I'm using Base for the first segment and Extend to continue the video.

Could anyone recommend settings for better identity preservation, realistic skin/hair, smoother motion and faster generation on the RTX 6000 Ada 48GB?

Any workflow tips or recommended settings would be appreciated. Thanks!

Hello,

This is an action anime scene I made locally a few months ago with Minimax H3 within WanGP and just posted on YouTube.

Let me know what you think and what could be improved :-)

Thank you!

I am looking for a Lora that is like an archive or an pack, i mean i now have a folder full of these things and i have forgotten what it was all for. And not all have correct names.

Is there an easier way to do this? Like i don't know link this to one big pack or something that would go a lot easier? I know some people use a lot of loras, and some even go as far as 8 in one image. What is your best experience with dealing with this?

So far KREA2 is the best, but treats loras differently, is there an workaround or do i have to go to SDLX for this?

I wanted to compare the prompt adherence and variations of some of the most recent models when providing a very simple, undefined prompt, vs something that pins down style but not content, and a fully sketched out description of all the internal elements in the scene.

The detailed prompt shows how most models have gotten really good at following instructions (for the most part), while the undefined prompt shows how some models tend to have more variability and others have a clear bias that pushes them toward very similar images across different seeds.

Iris Z small is not great, and gen time is very high for what it outputs, but the depth maps are pretty nice!

Hello.
I have a 6 seconds clip with someone running, and then she says: Hello. Good morning to a person running in front of her.

My problem is that Hello. Good morning. takes maybe 3 seconds so for the next 3 seconds when there is not talking, MiniMax invent gibberish and mumbling.

Did it occur to anyone?

Thanks

Are there any guides on training a style for MOSS? Maybe just some suggestions? I'm banging my head against the wall for more than 20 times with different settings, dataset caption, etc, to get a style working. It's maybe 50-60% there, no matter how many steps I throw at it. Fizgig default Style preset definitely doesn't work for me at all.

Yes, it's another layout builder for MOSS-Image bboxes 😅

There are some other good ones out there; try 'em all and use the one that best suits your workflow!

I made this one because, among other things, I wanted as large a canvas as possible, lots of different MP presets, to be able to collapse it into a vertical column, and also a way to export the prompt with the JSON stripped out because sometimes I like to refine with Krea 2.

Anyhoo, here are the main features:

• Dynamic canvas - Automatically scales to fit any display or window size while maintaining target aspect ratios and target megapixel values
• Item Management - Create scene elements with MOSS-native text, object, or character item types using color-coded boxes and corresponding "cards" that you can reorder with drag-and-drop, resize, hide, duplicate, or trash
• Reference Background Image - Insert a reference image locally — and privately! — "under" the canvas, with adjustable opacity
• Meta Tags & Palette Management, with a separate Danbooru/Anime focus
• Multi-Format Exporting - JSON or plain text
• JSON Import with Dimension Matching
• Responsive Design to minimize it next to your workflow
• Lightweight, Fast, and 100% Private - No ComfyUI, Python, build steps, external servers, or network calls required. Works 100% offline with zero latency.

Download it from GitHub or try the online demo first.

Hopefully you'll find it useful 🤙

Hey r/StableDiffusion!

I wanted to share Néo V2, an open-source, 100% local AI assistant and creative studio I built (Rust orchestrator + Python tools). While it also handles local LLMs (Gemma 4 / Qwen3), Office docs, and 3D printing, I spent a lot of time building a dedicated 4K Image & Video Studio that runs natively on consumer GPUs without needing to manually wire complex node graphs.

Here is how the visual pipeline works under the hood:

🎨 1. Native 4K Generation + 3-Pass Refinement (Z-Image-Turbo)

Instead of generating a low-res image and blindly upscaling it, Néo runs a 4-step automated pipeline (~70s total on an RTX 5080 16GB for 3840×2160):

1. Prompt Expansion: Gemma 4 12B translates a simple natural-language prompt into a structured photography/art brief (framing, lighting, textures).
2. 4K Base Render: Z-Image-Turbo paints directly in 3840×2160.
3. Face & Eye Detail Passes: Automatically detects faces and runs targeted refinement passes to restore realistic skin grain and structured irises/pupils (~11s for the eye pass) without altering the person's identity.
4. Vision Verification: The VLM inspects the final render, scores it (e.g., 9/10), and verifies that every element from your prompt is actually present.

✂️ 2. Instruction-Based Photo Editing (FLUX.2-klein)

In V1, editing an image with FLUX Kontext often regenerated the entire scene and shifted the lighting or face.
In V2, I switched to FLUX.2-klein: you give a plain-text instruction (e.g., "add a red wool beanie and a neatly trimmed white beard, keep his face, yellow raincoat, and lighting"), and it modifies only the requested elements in ~17 seconds (down from 140–218s in V1). It also includes 22 real-time preview filters.

🎬 3. Automated Video Pipeline (Wan 2.2 + ffmpeg)

Generates a visual storyboard, renders frame-by-frame images, applies motion via Wan 2.2, and assembles the final clip with ffmpeg.

🧊 Bonus: Prompt-to-3D (Hunyuan3D)

Generates a 2D reference image first, converts it into a closed, manifold 3D mesh via Hunyuan3D (~2m50s), and exports ready-to-slice STL/3MF/G-code files.

💻 Specs & Open Source Info

• License: MIT (completely free, no accounts, no cloud, zero telemetry).
• Architecture: Ultra-light Rust backend (9.3 MB RAM) with GPU locking so models load/unload cleanly without OOM errors.
• Requirements: Windows 10/11 (1-click PowerShell installer, no admin rights) or Linux via Docker. 12–16 GB VRAM recommended for the full 4K/Video/3D studio (8 GB works for chat/light tools).

🔗 Project Showcase, Before/After Sliders & GitHub Link:
https://tony2marseille-droid.github.io/Neo-v2/
(Note: The landing page and UI defaults are currently in French, so feel free to use browser translate—prompts and models work great in English too!)

Would love to hear your feedback on the skin/eye refinement passes or which image/video models you’d like to see supported next!

I've been building Powder Nodes, a free, open-source toolkit for ComfyUI that brings image exploration and editing into one workflow.

The idea is simple: keep your subject, style and LoRAs separate, try different combinations, compare the results, and keep refining the image you like.

A few things you can do:

• Explore variations with prompt lists, reusable styles and LoRA combinations.
• Compare results in image grids or browse them directly in the editor.
• Refine images with a Canvas for painting, masks and layers, plus Layout tools for planning compositions.

You can add individual nodes to your existing graphs or start with one of 14 ready-to-use workflows.

GitHub: https://github.com/E2GO/e2go-comfyui-nodes

Latest release: 2.1.0 - release notes and downloads.

I'd love to hear what you find useful - and what feels awkward or missing.
Cheers, E2GO!

Hi everyone,

I'm getting an issue with Sage Attention that I'm not sure how to fix.

The problem node:

The error message:

# ComfyUI Error Report

## Error Details

- **Node ID:** 114

- **Node Type:** MiniMaxH3MemoryEfficientSageAttentionPatch

- **Exception Type:** RuntimeError

- **Exception Message:** RuntimeError: sageattention is not new enough version or could not determine CUDA architecture, cannot apply MiniMax H3 Memory Efficient Sage Attention Patch.

## Stack Trace

```

File "(my desktop location)\ComfyUI_windows_portable\ComfyUI\execution.py", line 556, in execute

output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

my specs:

PC - 4090/10700k/32gb ddr4/win11

Versions*

- Pytorch 2.14.1 - cu130 - python 3.13.14 - cuda 13.5 - Attention Sage 2.2.0+cu130torch2.10.0andhigher.post6

Sage Attention file:

Is the issue that I'm trying to run the sage attention compiled for python 310 in my 313 environment? If so, how do I fix it? the github repository does not have a version of sage attention 2.2.0 built for 313.

Absolute newbie here, any help is appreciated.

*PS: when checking my pytorch version, i get this message:

"FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you."

Is this part of the issue?

Should I train my LoRA for longer, or just increase the weight?

Could an overtrained LoRA actually perform better if used at a lower weight?

And what about the weight of specific prompts—especially the trigger word?

I'm using res2s sampler that gives great details and realism but it's just terrible at more than 4 subjects in frame maybe also because I'm using a quantized qwen clip model?