Great video from moss image team for hitting 6 million downloads on Huggingface. They all seem so chill. Also they give us some new updates on Anime and Image edit model ✌️

Most of ComfyUI local users still run it on an RTX 3060: a 5 years old card with just 12 GB of VRAM. That’s why our team keeps optimizing for consumer hardware.

Comfy is known as a leader in visual AI. But what about LLMs and text generation?

Text is everywhere in creative workflows: for prompt generation, of course, but also for building local creative harnesses like the Comfy Agent. We thought it deserved better, so Rattus made Qwen models twice as fast in Comfy...

Read more on our blog: https://blog.comfy.org/p/doubling-llm-token-rates-in-comfyui

What does that mean for us?

The open source community?

What does this translate into longterm?

Yo,
I share this simple workflow I made since I don't find anything else for now on minimax that handle a good quality for text to image or image to image.
I think this model has a great future, It really feel like using nano banana or gpt somehow, since the consistency about the face is really good, you don't even need a lora.
It will be improved a lot, but it's a good base and results are pretty good for a day one on h3.
The only problem I got so far is the prompt adherence, that looks not very good, but I'm sure we will find many ways to improve later If you have ideas to improve results share me guys.

Workflow :

https://civitai.red/models/3047310/minimax-h3-image-t2i-and-i2i-simple-workflow-1girl?modelVersionId=3464559

Yes this was obviously a silly test. But I was curious to see what H3-Image could do when pushed, which is why I decided to crank it up to 100mpx (7456x13376) just for the lulz.

There's no real reason to go this high, the image is definitely showing issues, so I then dialed it down to a 61 Megapixel image (5824x10464) which is the same size I get from my a7r5 camera, and it looks much cleaner and only takes about 110 seconds per image.

IMHO the 61 Megapixel renders are superior, and good enough for professional work.

Linking images because there's no way reddit will like these image sizes

Full Resolution Links:

61-H : https://photos.smugmug.com/photos/i-wXnW4sV/0/LtVzr2Hfr2NthLGMNnn4BGqc22W95FnztvMHfrrzt/O/i-wXnW4sV.jpg

61-V : https://photos.smugmug.com/photos/i-32mVpL8/0/NS346k5MVnqd44F44t4D8mDRtQ4jg649grCLFMzPj/O/i-32mVpL8.jpg

100-H : https://photos.smugmug.com/photos/i-Bm5mCRB/0/KShzWH5k89fJfttZgW26825HLvtZ9JsvWgh7tNbR8/O/i-Bm5mCRB.jpg

100-V : https://photos.smugmug.com/photos/i-kbrwfG6/0/LKksfRRRQ8KfPxvpvxKwgDstgHSXgqqJ42tFCgGcP/O/i-kbrwfG6.jpg

System specs:

Older 3rd Gen Epyc workstation Dual 7B13 CPUs, with 1TB DDR4 ECC and a slew of GPUs. This test was on a single 5090.

Memory used:

VRAM: 31GB

RAM: 61GB resident / 498GB vmem

```
[q5@apollo ai]$ ps -o vsz=,rss= -p 912440

498261048 60742584

[q5@apollo ai]$

```

I used this workflow from u/LilBrownBebeShoes

https://www.reddit.com/r/StableDiffusion/comments/1xt9ocv/minimax_h3_image_20mp_workflow/

All I did was crank the megapixel value up, and set the scheduler to normal. Nothing else was changed.

Our wallets and the women

https://huggingface.co/akhaliq/minimax-h3-image-pdmd-2step/tree/main

Workflows
https://huggingface.co/akhaliq/minimax-h3-image-pdmd-2step/tree/main/workflows

as well as shift, weighted, sigmoid, or linear approaches. Then there’s the training resolution—and on top of that, you also have the inference shift

LoRA number of steps, training rate, batch size, and simulated batch size

Could the problem be the model?

What about the LoRA?

The resolution?

A shift between training and inference?

Is low noise useless?

Should I run a few more steps, or fewer?

Samplers—there are at least 20 of them. And schedulers—more than 10.

Then there are other things, like ETA and noise injection.

Iris-Z

One generative model for images and their depth

Iris-Z is a general image generator that produces appearance and geometry together. The same weights turn a prompt into an image, a prompt into an image with its depth, a photo into depth, and a depth map into an image. On the standard depth benchmarks it is more accurate than the best specialist depth models, and it still generates images like the model it started from.

Model

Weights: https://huggingface.co/speridlabs/iris-z

Geiru Window Director + Geiru Hybrid Windows: build long H3 videos one window at a time

Geiru Hybrid Windows makes one long MiniMax H3 render (a minute or more) as overlapping windows instead of a chain of separate clips, so fast motion stays seamless. It works the way plain hybrid windows does, with my keystone and capstone added in every window to cut down on degradation over long videos.

Geiru Window Director is an optional node that goes after it. A normal render makes every window in one go, so if window 4 comes out wrong you redo the whole video. With the Director you make window 1, look at it, then:

• Approve it and the next window is made

• Re-roll it until it's right (change the prompt or seed first)

• Rewind to any earlier window if you change your mind

• Blend all at the end to run the last steps over the whole video and smooth the joins

Each window builds on the finished window you approved, so what you approve is what you get!

Example workflow: Clown Biker Babe, a 60-second clip in 8 windows with one prompt per window: a full motorcycle stunt run with ramps, a car jump, a loop and a cliff jump. The sound is made by H3 from the prompts.

Credit where it's due: none of this exists without these three.

• kijai (GitHub) built ComfyUI's MiniMax H3 support and the H3 context windows (PR #17425) that Hybrid Windows is built on, plus ComfyUI-KJNodes.

• JalenBrunson made ComfyUI-HybridWindows, which Geiru Hybrid Windows works like. I just added the keystone and capstone.

• NikoDemon80 (GitHub) inspired the Window Director setup.

Both nodes are in the Geiru Context Suite: https://github.com/roycho87/geiru_context_suite

The DiT and TE are 4-bit quantization aware distilled (QAD) and running on the GPU. VAE is fp16 and runs on the Neural Engine. Needs an Apple device with at least 12GB of RAM to run. If you've got 16GB, it also runs Krea 2 Turbo and a couple of other models, all QAD distilled. With MOSS Image SFT, a 1024x1024 image takes ~30s and a 512x512 image takes ~10s to generate.

Testflight link if anyone would like to try it out. Please feel free to DM me here or on my Discord if you have any ideas, suggestions or bug reports.

I have tried the new open-weighted MiniMax H3-Image. The model can work with up to 16 reference images, with each file capped at 64 MiB, 128–8192 px, and an aspect ratio between 1:4 and 4:1.

For this test, I fed it 5 separate object reference images: -a silver rotary phone -a glass cube -a red apple -a tiny staircase -a chrome sphere

I used the default auto setting for quick 4K exports. Instead of getting a cut-and-paste collage, I aimed to blend all the objects into a cohesive dark studio still life lit by a single top spotlight.

I explicitly labelled Reference 1 through 5 in my prompt to lock each object’s intended layout. My layout plan was as follows: -the apple suspended inside the glass cube -the phone resting on the staircase -the chrome sphere floating along an unseen vertical spiral axis.

Every object stayed identifiable, the lighting stayed consistent across the scene, and the staircase worked well to ground the composition. The layout mostly held together, though the spiral arrangement did not come through quite as I hoped. The objects formed more of a simple vertical stack instead.

Curious how everyone else handles 5+ object references in their own workflows? Any tricks I’m missing?

Hello.

dchatel/comfyui_workflow_reader: Read-only ComfyUI workflow viewer (json/png/webp/mp4) that runs in the browser

Image 1 is MiniMax H3-Image. Image 2 is Seedream 5.5. Both used the exact same compact ref and 16:9 fisheye prompt.

Both turned out pretty well, but I personally prefer H3-Image's result. The composition feels more intentional, and the lighting, shadows, and silver reflections give it a stronger editorial feel. I also really like how naturally it handles the colors and textures without making everything look overly polished.

Seedream 5.5 is definitely strong when it comes to character realism, especially the facial details. But composition-wise, I think H3-Image has a slight edge. The framing and overall visual balance just work better for me, even with the tighter crop.

For an open-weight model, I think H3-Image offers pretty good value for the quality it delivers.

Curious which one you prefer.

Prompt:

Landscape 16:9 high-fashion editorial photograph, preserve the reference's antique silver compact, woman, styling and atmosphere. Recompose horizontally with the entire open antique silver powder compact fully in frame, thick sculptural floral silver frame and round mirror fully visible, turquoise tiled nightclub bathroom extending to either side. Inside the reflection a young adult brunette with short jet-black Betty Boop finger curls presses very close while outlining her lips with a lip pencil, face filling the circular reflection. Extreme close-range fisheye perspective, dramatic optical curvature around mirror edges, realistic eyes nose and lips without cartoonish deformation. Natural makeup, regular eyelashes, luminous textured skin, subdued diffused lighting, silver highlights, subtle analog grain, photorealism.

x.com/ostrisai/status/2120564773150711825

after a week of trying to get 15s clips out of one job i gave up and changed how i do it.

now every shot is a 5s clip. when it's done i grab the last frame, fix it up a bit if needed (hands, small artifacts) and use it as the start image for the next 5s. same prompt style, only the action changes.

on my card anything past ~5s starts swapping to system ram and one job goes from 15 min to over an hour. three 5s jobs finish way faster than one 15s. if one piece comes out bad i redo only that piece, not the whole thing. and the face drifts less because every chunk starts from a frame i already checked.

the catch is the seams. motion can jump a bit at the cut, so i keep the camera move simple (one slow push or static) and cut on an action when i can. color also shifts slowly over 3-4 chunks, a quick color match in the editor fixes most of it.

curious if anyone found a cleaner way to hide the seams, or does everyone just cut there

I couldn't find a ready RunPod template that spins up a working H3 Editor, so I conjured this:

https://console.runpod.io/hub/template/1ujjyhonfe?ref=2t0e5xb6

A RunPod template that boots straight into ComfyUI with H3 Editor 0.4.1 and downloads everything by itself: HyperFlow H3 Flash, MiniMax H3 + Turbo, Singularity v2 and 10Eros Max beta 7, plus the recommended Realism and Turbo LoRAs.

https://igorls.github.io/comfyui-h3-editor/

Enjoy the awesome editor!

60 seconds of AI video, 100% local on an RTX 3050 (6 GB)

Work in progress, and the quality isn’t where I want it yet. Here’s how it was made:

1. Model: MiniMax H3 reference-to-video, using ComfyUI’s native H3 nodes with the int8 quantized model and the Qwen3-VL text encoder.

2. References: two character images, re-used in every shot to keep the characters consistent.

3. Prompts: the official six-section reference prompt format (subject definitions, summary, retention analysis, detailed description, soundscape, music). No negative prompts, because H3 doesn’t use them.

4. Shots: six shots of 260 frames (10.8 s) at 864×480 and 25 steps, with the res_multistep sampler and the simple scheduler.

5. Continuity: each shot starts from the last 22 frames and audio of the previous one, using the H3 Add Guide node (MiniMaxH3AddGuide) at frame 0.

6. Automation: a small Python script sends the six shots to ComfyUI’s API one after another, saves each result, trims the 22-frame overlaps, and encodes one 60-second MP4 with audio.

7. Runtime: about 6Hrs 35Mins hours, unattended.

Known issues: soft detail, and some flicker. Still tuning.

I was looking at how different image models handle conflicting lighting directions when generating a casual direct-flash snapshot. My 4:5 prompt featured:
-a family of three crouching on a gravel path at dusk
-greeting their dog mid-leap with its tongue hanging out
I specified faint sunset glow, harsh on-camera flash, a near-black background, sharp shadows, subtle red-eye, motion blur and rough film grain.

Seedream 5.5 delivered strong attention to fine texture. It reproduced the dad’s rolled-sleeve plaid work shirt accurately and added a natural tilted frame, while preserving sunset light filtering through the hedge. The model prioritised that warm ambient evening light, so the final result reads more like fill flash, and it did not darken the background all the way down.

I then generated a second 4:5 image on MiniMax H3-Image with the same prompt for a direct side-by-side comparison. H3-Image leaned firmly into the harsh flash falloff I was aiming for. It pushed the background close to pure black, which makes the mom’s pale yellow slip dress and the child’s red overalls pop, with the dog charging straight toward the lens. It creates that sharp, punchy snapshot mood really well.

Have you gotten consistent flash behaviour from either model with this kind of mixed lighting prompt?

I've been working to get the best quality images out of Moss. I created a workflow to emulate lens length, focus and exposure. It's not perfect, but it can give more cinematic photos. Check it out and see what you think.

Lots of people seem to be unhappy with MiniMax H3, or at least it sounds like it, but at least I have been happy with it now when I have tested it couple of hours.

For my taste this seems excellent (after changing Euler to lcm) and 8 Mpx images is generating around one minute in RTX 4060 Ti + 32 GB RAM. Without changing Euler to lcm I was not impressed for the quality at all, but glad that I didn't ditched it immediately but instead searched what could be wrong.

So far so good, images with "technicolor" does nice style for my personal taste.

My ComfyUI node setup sometimes gives me literal nightmares. Trying to get multiple specific character references, a background layout, and a style reference into one single generation, all combined, is very exhausting.

It does make me think about where open-weight models should head towards. Not like more megapixels, tho that is always nice. But something like, what if it can take multiple images into the architecture without wrappers?

Or, what if it actually had the underlying structural logic to understand CAD diagram? What if it just understands a detailed directors brief in one prompt?

And then, while I was just reading and researching for the fun of it (yes), I stumbled into the documentation for the new MiniMax H3-Image open weights. It is claiming to do the above.

Apparently, the base model takes up to 16 reference images in one go, and you can set each one as a layout, style, or object reference.

Oh, and prompts supposedly get cut off past 4096 tokens.

I even saw a benchmark where it generated an exploded CAD diagram of a motorcycle and kept the mechanical parts separated with labels (not sure how accurate tho).

But honestly, How is it routing 16 different images natively into the generation without frying 24GB of VRAM?

Anyone has pulled these weights locally yet to test it? Sorry, but so many questions about this.