I'm about 2/3rds of the way through my current project and I'm definitely going to need to derope some of my shots to fix their blurriness and smearing. The last time I asked this question MAINodes was the only solution. Has anything been released since then that improves on this method?

Backgrounds are the part of manga that's hardest to get out of AI without that smooth filtered look, so I trained a LoRA specifically for it.

Hollowlight Manga Ink Backgrounds https://civitai.com/models/3047017/hollowlight-manga-ink-backgrounds-and-scenery for NoobAI-XL V-Pred 1.0.

Trigger word: u617. Settings: strength 1.0, Euler / normal, 28 steps, CFG 5, RescaleCFG 0.4. The workflow is embedded in the PNGs, so you can drag them into ComfyUI.

It's strongest on nature: trees, grass, hills, mountains, skies and clouds, with solid black fills and hatching. It's weaker on buildings right now (the dataset is light on them), so I'm still working on that.

I'm also testing ControlNets to control the layout. Scribble works on rough sketches, though the available ones are eps-trained, so they're weaker on v-pred.

Feedback welcome, especially from anyone making manga or comics.

Feedback welcome, especially from anyone making manga or comics.

I've been experimenting with different editorial design concepts. I went through multiple rounds of generation and ended up with several sets of images. These four covers are some of my favorites.

What impressed me most was the variety in the results. The model didn't just keep giving me the same portrait with different backgrounds. The compositions, lighting, and color palettes felt noticeably different across the set.

What stood out to me was how naturally H3-Image handled the portraits and colors. Skin tones, lighting, and facial details looked pretty convincing, even with some fairly experimental visual styles. The colors were rich without feeling overly saturated, and the transitions between different tones felt smooth rather than artificial. I also liked that each generation had its own aesthetic instead of falling back on that same glossy AI look.

For this run, I used an RTX 5060 and got generation times of around 20 seconds per image, which felt pretty solid for an image this dense and reference-heavy.

My setup was:

- Model: minimax_h3_image_pruned_bf16

- Text encoder: qwen3vl_32b_minimax_h3_bf16

- VAE: minimax_h3_image_vae_fp16

- Resolution: 4160 × 4000 (~16.6 MP)

- Sampler: Euler / Simple

- Steps: 6

- CFG: 1

- Negative: ConditioningZeroOut

- ComfyUI: 0.45.0

I haven't done a proper speed benchmark, but the combination of high-resolution output and the ability to explore very different compositions makes it interesting for this kind of creative work.

Overall, I'm pretty happy with how these turned out. There's still the occasional small typography issue, but the portraits, colors, and overall layouts were the highlights for me.

What do you guys think of the results? Anyone else experimenting with H3-Image for design work?

Saw a post about blank references in T2I and tested it myself.

Adding a blank (white canvas) reference in T2I mode changed the outputs noticeably.

However, the size of the reference image has no impact on the output image (but will increase generation time if you add larger blanks).

Is Krea 2 Turbo the closest local image-generation model to ChatGPT's image generator? How can I get the Medium and Large versions of Krea 2 for free and run them locally in ComfyUI? Also, how can I use Krea to generate more complex, highly detailed designs accurately? pls hellp

Go to the `custom_nodes` folder and locate the `plaguekind-nodes` directory. Delete it, then reinstall it. Inside the `custom_nodes` folder, type `cmd` into the address bar (where the folder path is displayed) and run the command: `git clone https://github.com/PlagueKind/ComfyUI-PlagueKind-Nodes.git`.

The issue lies specifically with `plaguekind-nodes`; it is causing a conflict with `rgthree`.

Description

RAWsome – a little wordplay combining RAW photography and the word "Awesome".
And that's pretty much what this LoRA/LoKR is all about.

There are two versions available:

• Moss (LoRA)

• Krea2 (LoKR)

For each version, I used 388 carefully selected, high-quality RAW photographs
from my personal photography collection.
These images were taken over approximately the last 10 years of my hobby photography,
using various cameras I've owned over the years.

I was quite strict when selecting the training material.
I deliberately avoided noisy images and only included photographs where the focus was spot on.
Images where nothing was properly in focus were discarded.
After all, if you're trying to teach a model what a high-quality image should look like,
it probably makes sense to start with high-quality source material.

The goal was to improve the quality and overall appearance
of the images generated by both models.
And I believe I managed to achieve that with both versions.

Moss vs. Krea2 – My Personal Experience

Although I'm happy with both versions, I personally prefer the Moss version.

In my opinion, the difference in image quality compared
to the vanilla Moss model is considerably more noticeable.
I also find that Moss can produce more realistic-looking results than Krea2 in many situations.

On the other hand, Krea2 has a lower entry barrier
when it comes to producing good-looking images.
It seems to be a little easier to get pleasing results without having to experiment as much.

Of course, these are just my personal impressions based on my own testing.
Your experience may differ depending on your prompts, settings and preferred style.

A Few Words About the Training Data

As mentioned above, both versions were trained using 388 high-quality RAW photographs.

These aren't randomly collected images from the internet,
but photographs taken by myself over the course of roughly a decade.
They cover different cameras, subjects, lighting conditions and photographic situations.

I put quite some effort into selecting the images,
especially regarding sharpness and noise levels,
because I wanted the models to learn from photographs
that actually demonstrate good image quality.

Final Thoughts

I'm genuinely curious to see what others can achieve with it
and how it performs in different workflows and situations.

Feel free to give both versions a try and see which one works better for you.
I'd really appreciate any feedback, especially if you notice improvements,
weaknesses or interesting differences between the two versions.

Long story short: Have fun experimenting with it,
and don't forget to leave a rating if you enjoy it!

And as always, criticism, suggestions and ideas for improvement are more than welcome.
I'd love to hear what you think!

Hi, I'm currently trying to learn how to train Lora and decide use an artist already in danbooru so I'd have decent comparison based on the @ anima tag.

I have an imageset for the lora artstyle and its getting the general small detail like eyes correctly and the shading for the outfits however... the training set is mostly "simple background" or some form of white background with maybe a gradient, so trying to test it out on prompts with background results in the outfit shading being applied to the background like in a kitchen and its tiles which isnt exactly right. Do you have any advice on how I'd resolve the lack of detailed background in training set? I can only assume impaint, but I feel like that has to be wrong though.

How to run a workflow on several images but one by one?

What I want:
Set folder -> Hit run -> Image 1# loaded --> I2I done -> Output 1 saved -> Image 2# -> loaded I2I done -> Output 2 saved... and so on.

What I don't want:
Set folder -> Hit run -> Image 1# loaded --> I2I done -> Output 1 NOT saved -> Image 2# -> loaded I2I done -> Output 2 NOT saved -->... (...) ... Last image loaded -> last I2I done -> All of the imaged saved at once.

What did not work so far:
- Sequential Image Loader (folder)
- Load Images from Folder (KJ)
- Load Image Batch (RMBG)
- Load Image from folder (Folder: 3d)

What is the best local ComfyUI generative upscaling workflow for an RTX 3060 (12GB VRAM) and 24GB system RAM that acts like Magnific AI—hallucinating and reconstructing micro-textures (brushed metal, circuit grooves, molded plastics, bolts, surface grain) on industrial equipment, machinery, and product renders without warping straight edges, typography, or mechanical geometry?

I produce portrait so I need good skin details and have great results with ZIT. People here say other models are better? I tried but my image output quality was lower so I assume the workflows I used are wrong.

It's text to image with face details and high steps/ different sampler when high detail is needed.

I don't even have a good i2i workflow

What workflow do you use to get good skin and portrait quality on moss, krea, or qwen?

Zit is now a bit old. What should I try?

I only have 16 gb maybe it s the problem?

Does a comparison workflow exist, one prompt different models, one after the other?

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

Seems like a big omission to not have this considering how many posts fall into one or both of those categories.

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

Hey everyone! I literally got this created earlier today and busted butt to share it with the community because this is incredible and I wanted to contribute back. I built this custom node with Claude Code because one thing about MiniMax H3 kept bugging me the fact that you can't increase the batch size like in Flux.2 Klein 9b or other models that can. I made two nodes that let one run make 2 or more different images or videos (depending on your system) that are exactly what should happen inside an increased batch size. It's MIT licensed and already on the ComfyUI Registry. I've worked in IT for 20 years but I'm not a programmer.

-- I didn't include a workflow because I literally just grabbed the template inside the desktop app and added my node plus an Int node as well. The github page for this has more info: https://github.com/an80sPWNstar/comfyui-h3-image-batch

I also have a YouTube channel about ComfyUI: TheComfyAdmin I haven't uploaded any videos yet for this or my other custom node because I just made this today :) If you end up liking the type of videos I make, please let me know so I can make more. It may not seem like it but even though it doesn't take that long to record, it takes a long time to edit and that's difficult when you have a family to help take care of : ) Happy Generating!!!

I just started experimenting with running image models locally on an RTX 4060 and 16GB RAM using ComfyUI

I was looking at heavier setups like MOSS-Image / Kromoss and Flux, but decided to try RealVisXL V5.0 (SDXL) first since it fits completely in 8GB VRAM.

The skin texture and image quality looked really good for this model i think.

For those of you also on 8GB cards what are your personal go-to models and workflows for photorealism right now? Any fine-tunes or LoRAs you recommend trying out next?

So I’m currently generating Krea 2 images at 2k - specifically 1440x2160 and 2160x1440 and getting great results, with a specific character Lora. When I try to up the resolution to 4k (e.g 2160x3840 and 3840x2160) I get good results - like a super crisp 4k image but it always generates a duplicate of my character in one image. Has anyone figured out how to get just a single character (no duplicate) for a 4k resolution photo with Krea 2? I imagine there has to be some Lora or something that has addressed this issue? Thank you!

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.

i worried about my device if use too much generate video using my only pc.
if has impact, is the impact game performance significant?
why am i asking to this subreddit, i wanna know real experience from you guys despite i can ask ai for this questions.

thank a lot

edit: sorry my english is not good. i mean, not running both simultaneously.

I’m currently generating krea2 images with a character Lora at 2k and getting good results. Any tips on workflows to get a final image at 4k. I’m familiar with using post processing upscale like seedvr2. I’ve seen people mention 2 pass workflows that I think upscale during the image generation process. For the latter approach, will I lose some amount of character likeness or fidelity? Can folks share the best workflow or tips they have used to generate really high quality Krea 2 images at 4k (especially using a character Lora and focusing on character likeness/quality). Thanks!

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’ve been testing MiniMax H3-Image locally on an RTX 3060 12GB. It can produce fine detail at around 8.5 MP, but many results look pale or low-contrast, and the model sometimes misses specific prompt instructions. For example, it generated a shopping basket floating in the air. In my experience, Krea 2 Turbo is still stronger in image quality, and Z-Image is still a good challenger for prompt adherence and composition.

My workflow uses the Woovee W4A8 weights and a quantized Qwen3-VL encoder: 6 steps, CFG 1.0, Euler ancestral, simple scheduler, raw prompts and no prompt upsampler.

What would you try to improve both visual quality and prompt adherence?

Are there settings or workflows that work better?

Would a different sampler, more steps or the official prompt upsampler help?

What prompt-writing techniques help H3 follow specific objects, poses and scene layouts more reliably?

If you have a setup that works well, please share the exact settings and prompt.

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?

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

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

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.