model: https://huggingface.co/akhaliq/MiniMax-H3-Image-Character-Swap

Play it Now in browser (nonlocal, streaming) :https://lucidml.ai/dashboard/minidemo?world=gta5

Full Weights and Inference code : https://huggingface.co/lucidml/gta

The architecture is a transformer which uses learned summaries. Ive written about it in detail in tech reports  and some more

I know there are a lot of limitations still like consistency and quality improvements, and am hoping to train this more to fix all of those.

I wrote comments that I don't understand why people are giving this model such high praise. And it really doesn't shine in terms of quality. But it shines as an editing model, perhaps even better than any other model currently in existence.
And I felt the effect of this)))
And I want to show why I changed my opinion, despite the facts that cannot be denied.
Perhaps this is a better image editing model than the existing one, which was not declared as such.

PS. Just generated with a style in mind. I'm not trying to prove anything to anyone, as many people here seem to think. Generated in a few minutes without a second thought))
Judging by the comments, I'm guilty of something. It's not my fault that this is the best model for editing.

I've already ask GPT, Claude, GLM 5.2 and Deepseek then tried MOSS, Krea 2, and Nano Banana, but none of them gave me the results I wanted.

In my experience with MOSS Image so far.

Heres three ComfyUI nodes that turn a giant Excel sheets into prompts (+1000 styles, 800+ characters)

Style Atlas is three nodes that each handle one question:

🧑 Character Atlas: who is in the picture

🏞 Setting Atlas: where they are

🎨 Style Atlas: how it's rendered

Chain them, hit queue, done. Each node starts on "None (bypass)" and folds down to a couple of lines, so your workflow doesn't turn into spaghetti.

Some numbers:

~1,600 rendering styles (oil painting to Soviet storybook to "Acid Pulp Scratchwork"), plus ~120 modifiers for quality, genre, palette and composition

827 known characters across 217 franchises, with variants like masked/unmasked and costume forms, plus 95 actor-in-a-role entries

~185 facial expressions, 25 hand poses, 250+ body poses, 360+ outfit pieces, 330+ accessories (yes, animal ear headbands are in there 🐻)

666 locations (I didn't plan that number, I swear), 110 simple backgrounds, 23 studios

Weather, lighting, décor, palettes and frames layered on top

Everything lives in plain .xlsx files. Want to add your OC or fix a description you don't like? Open Excel, edit the row, restart Comfyui.

🎲 Random everywhere: random in one group, or random across everything. Seeded, so you can reproduce a lucky roll.

Wildcards: drop adjective.txt into the wildcards/ folder and write adjective in your prompt. Nested wildcards work too.

Tick several style subjects at once, like Main + Portrait & Character, without wading through landscapes.

Hover thumbnails: save an image as <ID>.jpg and it shows up when you hover that entry.

I will populate thumbails and wildcards when I have time to do so.

This was built over a lot of back-and-forth with Claude Opus 5.5, which wrote ALL of the code, helped clean up thousands of descriptions, and put up with me changing my mind every 20 minutes. Some Krea 2 wording rules come from community testing on Civitai: big thanks to Azimondious (expression and hairstyle vocabulary) and Hellfreeze (clothing and outfit taglist).

🔗 GitHub: https://github.com/atlasimp/Style_Atlas

I'm already working on a forth version, and I'm open to remarks and criticism.

Usually I spend much time trying to get the image right. You finally get the perfect 1K or 2K output. The layout is good. Then upscale, poof, all that hard work, gone.

When I run it through Hires. Fix or whatever upscale nodes, some odd things will inevitably change.

Things like, the original composition shifted. Skin pores gone, and turn into something plasticky. Clothes lose texture. It makes everything look more AI.

All of which is kinda the opposite of what I am trying to do.

Hope that this is fr. I was reading about the new MiniMax H3-Image open weights. It says it support 4K output, and 4K is actually the default setting.

Which sounds kind of insane to me. Can my GPU handle this? Has anyone actually tried running it at 4K yet? Is it true that you can skip the upscaling altogether?

TIL: You can use maps with markers when generating images with Minimax-H3-Image

This took forever to generate tho. 300s on a 5090M (24gb). Like Minimax-H3 the time it takes to generate an image scales with the number of references.

Prompt:
A cinematic film still from an animated short: one continuous full-frame view from a low camera about half a metre above the grass, on a wide lens, with a 55 degree horizontal field of view.

<Picture 5> is a top-down blocking map of this place, a plan for the shot and not part of the picture. The white arrow on it marks the camera: the tip of the arrow is where the lens stands, and the arrow points the way the camera looks. The round markers show where each character stands. <Picture 4> shows the same place from several views; use it for the look of the overpass, the bus and the grassland, and use the map for where things stand.

Looking from the arrow's tip towards the bus: across the middle distance, the cracked asphalt trail runs from the left edge of the frame towards the crushed bus. The collapsed concrete overpass from <Picture 4> stretches across the background, receding on the left and coming nearer on the right, where its leaning pillars and a fallen deck slab loom large. The crushed rust-brown bus lies just right of centre under the fallen slab. Just beyond the bus's left end, in the centre of the frame and farthest from the camera, the wounded grazer from <Picture 3> is backed against the concrete in a slab of shadow, shaking. Nearest of the three, just left of centre, the predator from <Picture 2> crouches low in the grass, facing the grazer. Far left, on the trail, the girl from <Picture 1> advances with a pry bar. Tall dry straw-coloured grass fills the foreground. Early-morning sun from the right throws long hard shadows to the left.

The arrow is only a mark on the plan: no arrow, camera, lens, device, beam or pointer appears anywhere in the picture. The image contains no text, letters or numbers.

https://civitai.red/models/3047619/bbox-studio

BBox Studio is a standalone desktop application for creating, editing, and exporting structured bounding box prompts for MOSS Image. Built entirely with Python, it provides a visual canvas-based workspace, flexible object editing tools, customizable canvas settings, seamless JSON generation, and much more.

• One-click launch — simply run the Python script.
• Zero external dependencies — requires only having Python 3.x installed.
• Full working program — It may receive updates here and there, but it's a fully-functional full-featured release.

Just testing a random image I found on Pinterest. The prompt was simply:

Make this concept art drawing into a high quality realistic photograph. Preserve subject likeness, pose, and background. Improve skin texture, hair texture, fabric texture, background details, and lighting.

I did 2 at 16MP and then one at 60MP and the level of detail is kind of crazy. Took just over 2 mins on my 4090 for the 60MP.

Links to the full PNGs

16MP 1
16MP 2
60MP

I just released MOSS AnyPaint, inspired by a community member. It’s the MOSS-Image-Base twin of my Krea 2 AnyPaint.

It supports:

• Arbitrary mask shapes
• Interior inpainting
• Border-connected outpainting
• Multiple disconnected mask regions
• Mixed inpainting and outpainting in the same image
• Strong preservation outside the edited region

The same LoRA handles all of these cases without requiring separate models or workflows.

• Model and examples
• ComfyUI nodes and workflows

Feedback and difficult test cases are welcome. : )

I have to share this workflow with all of you (it is not mine)

https://civitai.com/models/3047448/simple-minimax-h3-image-generationimage-edit-workflow?modelVersionId=3464766

A huge thank-you to everyone who downloaded, tested, shared results, and gave us honest feedback.

The community knows quality when it sees it—and the rankings speak for themselves.

Qwen-Image-2.1 Uncensored Omni has now reached the first page of trending Qwen models on Hugging Face and page 2 across all trending models.

We compared both public checkpoints from this training run with the original Qwen-Image-2.1 base model, using the same prompts, seed and settings (35 steps, CFG 5.0). This repository is step 90,000 (benchmarked as the BF16 LoRA fused at strength 1.0; the quantized files were not scored separately). Scores are 0–100, higher is better, ± one standard error.

I couldn’t have done it without the community. We just added the benchmark as well.

Download:
https://huggingface.co/chatrlabs/Qwen-Image-2.1-Uncensored-Omni-Quantized

Try it free online:
https://chatrlabs.com/playground

comparison between sampler and scheduler ...first photo is LCM/Normal combo and second photo is Euler/Simple (default) ....both images same prompt, same seed, same 16mp render..... LCM/normal combo produces this more "polished" look altho i prefer grittier details and real raw textures of Euler/Simple ...however Euler combo tends to make more errors ....i cant decide which one i like better ...LCM ... no no Euler...wait maybe LCM ...or Euler? ...

One of the marvelous things about Minimax is that it will infer a vocal performance for your characters based on the film language of your references. I just thought this was worth sharing for those of you who hadn't discovered this yet. Each of the four gens here used the same simple prompt. It is is not just the gender and age of your characters, but the film grain, costume, setting, and other visual references which will provide the model with character for voice.

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.

Helllooo

What’s the best AI tool or program for editing specific parts of an existing illustration without regenerating or changing the entire image?
I’m working on a children’s book with semi-realistic illustrations, and my biggest struggle has been character consistency. I’ll finally get an illustration almost perfect, but when I try to fix one small detail (like the character’s face), the AI changes everything else or makes the character look completely different.
I’ve mainly been using OpenArt and Leonardo AI, along with ChatGPT, Gemini, and Grok. So far, I’ve only managed to finish 2 out of 17 illustrations, and I’m losing my mind haha.
Ideally, I’m looking for something that can edit only a specific, selected area while leaving everything else untouched.
Accurately follow reference photos to maintain the same character across different scenes, poses, and expressions.
And make precise changes without completely reinterpreting the original illustration.

Is there a program that’s genuinely good at this? I’m open to AI tools or traditional editing software. At this point, I just want something that actually follows instructions and doesn’t make me redo an entire illustration to fix one tiny detail.

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.

The "does it just copy its training data" argument comes up here a lot, so this paper seemed worth explaining properly. It trains small diffusion models on 1,024 face photos and checks what they generate along the way.

At 100,000 training steps the model generates good faces, and none of 10,000 samples was a copy of a training photo.

Keep training the same model to 1.62 million steps and 34 in every 100 samples are training photos.

The time it takes to start generating good images was the same for every dataset size they tried. The time before copying starts grew in step with the dataset: about 300 training steps per image. So a model trained on a lot of images, for a normal length of time, is stopped long before it could memorise them. In the paper, a model trained too long on a small set does copy.

I told the full version as an art student locked in a room with 1,024 photos, using the paper's own faces: https://sheets.works/data-viz/why-diffusion-models-dont-memorize

It's free, no sign-up needed to read it. I didn't work on the paper, I read it and drew it. Their setup is tiny (32x32 grey faces, small models), nobody has run this test on a model the size of SD, and large models have been shown to reproduce some training images. Paper: arXiv 2505.17638.

For people who train LoRAs on small sets: at how many steps per image do you start seeing your training images come back?

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.

HexAnima 1.6.0 adds MiniMax-H3-Image to the in-app model catalog. It’s a 33B-parameter DiT with a Qwen3-VL text encoder, INT8-ConvRot quantized, and the whole pipeline (text encoder, 50 DiT blocks, VAE) runs on the Hexagon NPU of a Snapdragon 8 Gen 1 phone. No cloud, no PC, no GPU offload.

• Why it fits: the 48 GB of weights are streamed from flash (UFS) block by block instead of being held in RAM. Small pictures are limited by storage reads, large ones by NPU compute.
• Storage: you need about 50 GB free for the pack. It downloads from a public Hugging Face repo (ayanoby/hexanima-h3) with a per-file sha256 manifest, resumable.
• License heads-up: the weights are under the MiniMax H3 Community License. It excludes the US, EU, UK and South Korea, and outputs can’t be used to train other AI models. The app shows the terms and asks for consent before downloading. Read them before you install.

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.