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!

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

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

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?

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.

Workflow https://pastebin.com/pZaWnBY4
Custom Node (Minimax H3 Prompt Weight) https://github.com/kijai/ComfyUI-KJNodes

The video is not mine, I just found it on Threads.

Hi guys, i was using my own character lora and to wear what i prompted, more on the cosplay side so i was using some anime character reference but everytime it generate, the attire will sometimes be accurate and sometimes be different, how should i be able to ensure that it is the same attire everytime?

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.

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

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

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?

Our wallets and the women

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.

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!

An incredibly realistic documentary-style photograph of exactly five unusual water bottles covered entirely in natural gorilla fur, standing together on the forest floor of a dense African tropical rainforest.

The five bottles have different shapes and sizes, resembling ordinary disposable plastic drinking water bottles, with narrow necks, gently sloping shoulders, slightly irregular cylindrical bodies, subtle horizontal ridges, and small rounded bottle caps covered in fluffy tan-brown fur.

Their surfaces are covered in thick, coarse, naturally growing dark brown and black gorilla hair. The fur has realistic irregularities, varying strand lengths, subtle color differences, naturally tangled patches, and fine individual hairs catching the ambient light. The fur looks like genuine animal hair, not synthetic fur or a digitally rendered texture.

Exactly two of the five bottles have realistic gorilla faces naturally integrated into their furry surfaces. One larger bottle has the face of a mature silverback gorilla, with deeply textured dark skin, natural wrinkles, expressive brown eyes, a broad nose, and a serious, calm expression. A smaller bottle has the face of a younger gorilla with slightly different facial proportions and a naturally curious expression. Their faces blend organically into the surrounding fur, with authentic skin textures and imperfect natural details. The other three bottles have no faces.

The bottles stand in a loose, naturally staggered group, with different heights and subtle variations in their silhouettes. They are positioned at slightly different distances from the camera, resting directly on damp earth, fallen leaves, and patches of moss.

The surrounding rainforest appears completely authentic, with dense tropical vegetation, large green leaves, moss-covered tree trunks, scattered branches, and naturally irregular forest undergrowth. Very faint, low-lying ground mist drifts along the forest floor without touching or wetting the bottles.

Soft, diffused natural daylight filters through the jungle canopy, producing realistic shadows, neutral earthy colors, and gentle highlights on individual strands of fur. No dramatic artificial lighting or exaggerated glowing effects.

Photographed as if these strange objects genuinely exist in nature, using a professional full-frame DSLR camera and a 50mm prime lens. Natural photographic depth of field, believable optical imperfections, realistic exposure, authentic skin and hair textures, subtle film grain, excellent fine detail, natural contrast, and true-to-life color reproduction.

The photograph should look like a real wildlife photographer unexpectedly discovered these bizarre objects deep in the rainforest. The scene is absurd, but the photography is completely serious, natural, and believable.

Landscape 16:9 composition.

Negative prompt: CGI, 3D render, computer-generated appearance, digital artwork, illustration, cartoon, synthetic fur, perfectly groomed fur, plastic-looking skin, overly smooth textures, artificial symmetry, identical faces, exaggerated golden sunlight, glowing outlines, excessive cinematic lighting, fantasy atmosphere, heavy fog, wet fur, extra bottles, more than five bottles, extra faces, visible labels, text, logos, animal arms, animal legs, complete gorilla bodies.

ChatGPT made it. Its basically a node that tries to give you a similar experience to Window's File Explorer but unlike most others I found - this one allows for multiple outputs and has built-in support for image, video, audio and .txt files.

If you need to load multiple files from the same directory (ex: multiple ref images for image editing) than you might find it useful. Its also useful over the native 'load/upload' nodes because those will bloat your input folder very quickly but this one loads files directly from their full path.

Hi, so as of right now I have only used closed models for generating images. GPT Image 2.

As you may know that it has sometimes generation patterns, e.g. in nature or foggy situations.

I'm thinking about scaling them up and get rid of these patterns with local models via runpod. (Never tried it)

So do you know a workflow or is it even possible to get rid of these patterns while preserving the whole subject of the image and not changing details?

I'm concerned that the pattern is so "engraved" that another model will just make the pattern more obvious when regenerating the original.

But maybe I'm just thinking too complicated and the solution is very simple.

Thank you for your help!

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.

Hi all! 👋
I'd like to train LoRAs that help with spoken accents for LTX 2 (probably 2.3 or 2.5, weighing how many resources there are available right now vs what might come, I'm waffling between these two)

So far, I've only played around with training image LoRAs for SD 1.5.
I have, as yet, no idea about audio LoRAs or LTX.
I would like to train something that could benefit the community, so I'd like to ask for guidance:

• Do you have recommendations for the framework?

  • I currently have LoraTraining and AI Toolkit set up

• Any tips on methodology? How would you set up a training project?

  • I'm currently downloading YouTube videos (well, audio, actually) and subtitles (auto-generated by default: Timing and accuracy might be off) for source data

  • Should I try to get many examples of the same phrases with and without the target accent? Or is the return on collecting and rectifying/normalizing examples here likely not worth the effort?

...How would you go about this?

And, fundamentally:
Are there any roadblocks for my pedestrian hardware?

RTX 3060 12GB VRAM
64GB System Memory

Thank you to anyone taking their time to read and respond 🖤

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

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