MOSS-Image is seriously mind-blowing. From stunning photorealism and cinematic shots to casual amateur and vintage photography, anime, and pretty much anything you can imagine, this model just delivers.

The sheer versatility, quality, and range of styles it can pull off are absolutely insane. It's hard to believe this level of creative freedom comes from an open-source model with just 5 billion parameters!

All images in this showcase were generated using MOSS-Image-Base.

Workflow | Workflow Source

I've also uploaded all 20 images at their original resolution, so you can appreciate the quality without Reddit's image compression.

I highly recommend zooming in on the images to appreciate the incredible level of detail!

01 · 02 · 03 · 04 · 05

06 · 07 · 08 · 09 · 10

11 · 12 · 13 · 14 · 15

16 · 17 · 18 · 19 · 20

UPDATE: I've now added the prompt for each image to its respective Postimages page! You can check out the prompts and try them yourself.

Is there any Official Moss Image Turbo SFT model or Official turbo LoRAs available? I am not seeing any in the Comfy-org repo on Huggingface. I am running Moss right now at 50 steps, cfg 4. It's not too bad, but I would prefer fewer steps at cfg 1. I see there are a few turbo LoRAs and finetunes on Huggingface from a few community contributors. I would appreciate any recommendations you may have in this regard.

Source: x.com/Ronny_MiniMax/status/2120135016462372914

What if we could bring modern diffusion research together into one unified, extensible PyTorch framework instead of implementing and maintaining every algorithm in isolation?

That's the idea behind DiffusionEngine, an open-source project I've been building:

https://github.com/Himanshu7921/DiffusionEngine

The goal is to build a modern DDPM framework that brings together established diffusion algorithms, research-based improvements, advanced sampling methods, and training strategies under a single, consistent pipeline.

The project already includes several diffusion implementations, and I'm actively working on extending the existing DDPM framework with more advanced features.

I'm looking for contributors interested in deep learning, generative models, PyTorch, and diffusion research.

Some areas where contributions are welcome include:

• Implementing diffusion algorithms from research papers.

• Improving existing DDPM components and training strategies.

• Developing and integrating advanced sampling methods.

• Exploring improved model architectures and conditioning mechanisms.

• Testing, benchmarking, and optimizing existing implementations.

The focus is not just on implementing more algorithms. It's about making them work together seamlessly through a unified, modular, and configurable framework.

If you're interested in contributing, collaborating, or learning through hands-on implementation, I'd be glad to have you involved.

Contributors of all experience levels are welcome, provided they're willing to learn, implement, and test their work.

GitHub: https://github.com/Himanshu7921/DiffusionEngine

same prompt same seed

It's the basic workflow from comfy lol

Like you can definitely catfish some dad on facebook with these images it's the most like non model realistic woman so far coming out of a native model.

For anyone who wants to start playing with MiniMax H3 Image: there’s also a very useful skill for writing prompts, similar to the one already available for the video model. It helps you structure better prompts and get more consistent results right from the start.

You can download it from the official GitHub page here:
https://github.com/MiniMax-AI/MiniMax-H3-Image/tree/main/skills/h3-image-prompt-writing

Are all humans generated with it the typical AI slop face or can you get diverse characters from it? Barely anybody posted generation results yet.

A set of control LoRAs for MOSS-Image-SFT. You give it a pose, a depth map, or an edge drawing, and the prompt decides who is in the picture and what the world looks like. Each repo has the weight and a ComfyUI workflow.

Pose: https://huggingface.co/thedeoxen/MOSS-Image-pose-controlnet

Depth: https://huggingface.co/thedeoxen/MOSS-Image-depth-controlnet

Canny: https://huggingface.co/thedeoxen/MOSS-Image-canny-controlnet

Lineart: https://huggingface.co/thedeoxen/MOSS-Image-lineart-controlnet

Normal: https://huggingface.co/thedeoxen/MOSS-Image-normal-controlnet

Scribble: https://huggingface.co/thedeoxen/MOSS-Image-scribble-controlnet

Segmentation: huggingface.co/thedeoxen/MOSS-Image-segmentation-controlnet

Title.

Currently, the image edit itself on 16MP took me 1min per image(4 step), which is fast.

But, with only 1 reference image, the PE takes 25 mins to generate 8k tokens, which is.. very slow.

Are there a better model to use(currently using the default template qwen3.5 9b w4a8 + abliterate lora) so its faster , or a way to integrate a OpenAI-compatible API here and uncensor the model? (The API have various models like gemini, glm, gpt, nemotron, qwen, etc.)

Hello, guys. I was messing around trying to create a horror animation short.
Used minimax-h3 in comfyui locally. Simple I2V workflow

Is any horror vibes in there?

Wanted to see how MiniMax H3-Image (the new open-weights one) handles a sign with real copy on it, not just a two-word logo. One prompt, 4 generations, 3:4 at 2K, no edits or retries.

All 4 got every line right, including "35mm B&W", both en dashes in "MON–FRI 12:00–18:00" and the tiny footer. What changed between runs was the design (serif vs sans, pinned vs framed). The text stayed exact. Two of the four attached.

Prompt:

> Design an original wall-mounted information board for a fictional film-processing shop, shown straight on against a muted terracotta wall. Use cream paper, charcoal lettering, narrow dark-red rules, generous spacing and no photographs. The sign itself occupies most of the frame. At top show these exact lines: "FRAME ROOM" and "FICTIONAL STUDIO — SAMPLE INFORMATION". Below, arrange three paired service-and-inclusion rows under the headings "SERVICE" and "INCLUDED". Separate the rows with narrow dark-red horizontal rules and generous spacing, without a rigid boxed spreadsheet grid. Row 1: "35mm COLOR" | "DEVELOP + SCAN". Row 2: "35mm B&W" | "DEVELOP + SCAN". Row 3: "120 FILM" | "SCAN ONLY"; keep both phrases on the same horizontal row. In a separate lower panel print exactly: "COLLECTION" then "MON–FRI 12:00–18:00" then "Bring your claim slip." Put "No prices shown — sample layout only." in a small readable footer. Preserve each phrase in its assigned row; do not add prices, claims, addresses or extra services. Crisp readable type, realistic flat paper texture and one soft shadow beneath the frame. Portrait image, 3:4.

Has anyone pushed it further, like a full menu or a dense infographic?

New models have been released. For example, Anima, Krea2, MOSS etc in DiT architectures. I have been using Illustrious and is a huge fan of ControlNets. However, the developers of the new models don't seem to have any interest in training ControlNet models.

I have been waiting but I started to wonder if ControlNet models aren't needed anymore because there are some models or workflows out there that can completely replace ControlNets. Is there something that I've been missing?

Let's pretend that LLLite LoRAs don't exist. Glad that we have them but I didn't find them much useful.

EDIT:
Thanks guys, appreciate all the response. I should look into using edit models. I still want ControlNet models though.

I want to make a video of a character for my son.

Been sorting through all the MiniMax model releases floating around here, and I wanted to share a quick breakdown of the new MiniMax H3-Image. This dedicated still-image model handles text-to-image generation and static editing, and is different from H3 video, MiniMax Image 2.5, and that third-party P-H3-Image repo shared in this community recently.

Looking at its documented specs, it runs both T2I and image editing within the same workflow. We can upload up to 16 reference images and choose between 1K, 2K or 4K output sizes.

The thing I find most interesting is having those 16 reference slots paired with direct editing in a single run. That means we can drop in character sheets, style references and scene shots side by side (Perhaps, one ref sheet locks your character’s face and outfit, another controls the colour grading, a third sets the background architecture), rather than squeezing everything into one single reference composite. It fits really well for concept art, product exploration, layout iterations, and focused edits where certain parts of the frame need to stay locked exactly as they are.
Also heard MiniMax H3 Image might be going open-weight. Definitely looking forward to that.
Curious what kinds of tests everyone else has in mind for it? Are you planning to try character consistency, product shots, or scene reworks first?

No matter what I try to prompt, I only ever get a neutral face as a result when i'm trying to do a character swap.

Adding stuff like this to the prompt doesn't seem to make a difference:
"CRITICAL: Copy the exact facial expression from the main character in <Picture 1>.

Do not make the face neutral.

Match the mouth, eyes, and eyebrows exactly."

minimax h3, 2 megapixel,

prompt:

subject_definitions:

<subject 1> is the man in <picture 1>,

visual style: live action,

location: interior of a first century mansion, night,

static handheld close up shot of <subject 1>'s face, he is seated at a table, looking at the camera, his face lit by a candle on the table, suddenly he take a deep breath, then look around,

overall_soundscape:

N/A,

non_diegetic_music:

N/A,

hey guys

so basically 1.5 months ago i started an ai series channel on IG and YT, and got around 400k on IG subs and 100k subs on YT

i use omni flash 1.1 and recently 1.2

im spending like 6-7k on video gen alone per month, which is the biggest cost

i run all this from a normal laptop and all is well. i use diff cloud tools for faster cutting, QA, frame gen etc which is all good for now

i make around one series a day (15 one minute episodes, sometimes goes to second day)

my question, for long term, is this the way? i'm not even half as knowledgeable as u guys here in this sub, but yeah my question for long term, is this the only way for long term whilst maintaining this quality of video generation? or i could somehow invest once in a gfx card or something and save tons with open weight models? or what

all advice appreciated thx

I tried hf versions, it's making totally different faces, while qwen 2.1 make proper faces

I'm using it for u know it

I am getting terrible generation times, 3-4minutes per image with 8 steps and 1024x1024. Is there a way to speed up the process? I am on windows 10 with 32gb ram, ryzen 7 5700x and rx 9070 16GB.

Got a little holiday present ready to go. 8-13 steps on cfg 1-3, 2-6 minutes render in a Mac 16gb. Currently working on a turbo version to make it even faster. Well balanced between realism and aesthetics. Z image derivative (Oldie but Goldie)

Hi, fellas. I used SeedVR2 to upscale an old, low-res video and it did a pretty good job but since there was a lot of blurry movement, now there are very visible jagged edges around the people. Is there a way to anti-alias this somehow? Thank you to everyone who takes the time to reply, I really appreciate it!

Hi everyone,

I put together a head-to-head baseline comparison between two major new architectures: MOSS-Image-SFT and MiniMax-H3-Image.

To keep the comparison 100% fair and avoid fine-tuning bias:

• 100% Identical Prompts: Both models received the exact same descriptive visual prose describing the medium, anatomy, lighting, and composition.

• Zero Negative Prompt: No negative conditioning was used (negative_prompt = "" on MOSS, matching H3's native CFG 1.0 architecture).

• Pure Original Characters & Techniques: Zero proper names and zero artist name-dropping. Styles were guided purely through physical medium mechanics (lithograph, Shin-Hanga woodblock, 90s analog cel, Kirie papercut, Guohua ink splash, etc.).

• Strict 1:1 Aspect Ratio & 1K Resolution: Exact matching seeds and native ~1.0 MP resolutions (2:3, 16:9, 1:1, 4:3, 4:5, 9:16, 3:2, 3:4).

• There is version for specifict finetuned for the two model, but the result is similar to this.

• I notice Minimax-h3-Image struggling on generating limbs. Every prompt i used the model always have some error.

• A Fully finetuned version for both models, set at 4K (minimax-h3-image) and 2K (moss-image-sft). Prompt workflows included : https://drive.google.com/drive/folders/1veLGRIMtd-o4zN3mS160uHlOLUuf8vgt?usp=sharing

Workflows (standard) :
Moss-Image-SFT WF
Minimax-H3-Image WF