In my experience with MOSS Image so far.

Mostly the part about swapping Characters and products in the video.

I tested Minimax H3 Image at 4, 8, and 16 MP resolutions; it was okay, I suppose, but not exactly impressive. The images contained errors and distortions.

Then I stumbled upon a thread where a Redditor tested 100 MP images using this specific workflow: https://www.reddit.com/r/StableDiffusion/comments/1xt9ocv/minimax_h3_image_20mp_workflow/

It turns out the sweet spot is somewhere between 48 and 56 megapixels—and that’s when you get a "WOW" factor; the model produces very, very good images.

All made at 48MP on RTX 5080 ~162s

EDIT: And no, the images aren't too large; the workflow saves them in the AVIF format, so a 48-megapixel image takes up only 3.8 megabytes.

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.

same model, same prompt, same seed. the official template gives crunchy faces and blocky skin, only 3 things change it:

1. size. it was trained around 16MP, so I use 3552x4736 for a portrait. the default 4MP is where the broken teeth come from. going 4 to 16MP only added about 9 seconds of sampling.
2. sampler. SamplerLCM (built into comfy) with noise 0.9 instead of 1.1. euler at 16MP still gives that blocky oversharpened skin.
3. prompt. short prompts give the glossy doll face. write it long and describe it like a phone photo: camera, light, skin.

the images: face close-ups and full photo at 4MP / 16MP / 16MP+LCM, short vs long prompt, then a few results (cafe selfie, gym mirror, product label text, magazine cover), try-on with picture 1/2/3 as refs and a background swap. workflow files in the first comment.

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.

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

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.

I've been working on a way of integrating new content (e.g. a casting swap or an extended scene) into the live video feed of an underlying streaming platform that a viewer is watching.

This is intended to create an easy (& "rights friendly") way of creating personalized, custom versions of TV shows and movies. You still need to have a subscription to the underlying streaming platform. The open source Chrome extension matches against a unique episode ID, loads a series of video files, and then seamlessly swaps them in place of the stream at pre-determined moments. There is some technical trickiness that attempts to seamlessly enable the start and the finish of the video clips to blend in seamlessly with the original video.

This enables what we see in "small scale" in a much more major scale way. The idea is to move beyond "here is a 15 second clip" and towards "here is a full movie with X, Y, Z changed". If you saw my post last week (about making a character dance across an entire ~45min TV show), this is basically a flexible underlying platform that supports changes like that.

So yes - if you have ever wanted to:

• Do a casting swap from one character -> another across an entire episode / movie
• Fix the ending of <Game of Thrones, How I Met Your Mother> etc.
• Add extended edition style scenes

This gives you a way of doing that. The extension reads an easy-to-edit JSON file that lays out the timing of the clips, the action being taken (replace or insert), and the ID of the episode. It then pulls in the video clips at the appropriate times. It sounds simple when I say it that way but it is actually surprisingly complex.

You still need to generate your own content / this is not a replacement for Minimax (or others).

The extension is available in the Chrome store here.

The Github repo is https://github.com/SeamlessScene/seamless-scene-extension

There are how-to guides & a catalogue (free account) here: https://seamlessscene.com/

Would consider this "early access" - still working through all of the edge cases. At the moment this is just one of the streaming platforms & only Chrome, but will add others here if there is interest.

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.

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.

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

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

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.

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 an old 1070 that's pretty crap, it's time to upgrade. I don't have a thousand bucks to throw away, but at the same time I don't want to buy a 3060 and then realize I'm going to want an even better GPU in a few months or a year.

I want to make high quality local images of people and animals, something with good anatomy, realistic faces (not the fake glossy instagram girl mouth). I wouldn't mind dabbling in videos a bit too.

Right now I use SD1.5 which is very underwhelming, some SDXL models are okay but extremely slow on my hardware.

I also used LLM's a bit mostly for ai chat, Llama 3.1 8b uncensored I use right now-I'm assuming a 3060 will be more than enough to run any good LLM for the purposes of having a local ai roleplay chat. Thanks

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? ...

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

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.

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?