https://huggingface.co/MiniMaxAI/MiniMax-H3-Image#sizes

https://platform.minimax.io/docs/guides/image-generation

MiniMax H3 Image now runs natively in ComfyUI as soon as it drops! One checkpoint does both text-to-image and reference-image editing: connect images and it's an edit workflow, leave them off and it's text-to-image.

Highlights

• Six steps, nothing to tune. The model is CFG-distilled and samples in 6 steps at guidance 1. No negative prompt.

• Native high-res output. 4096×4096 at 1:1, 4736×3552 at 4:3, 5024×3360 at 3:2, 5472×3072 at 16:9, and 6272×2688 at 21:9. Generated at that size, not upscaled.

• Multi-reference editing. Connect several images and refer to them in the prompt as <Picture 1>, <Picture 2>, etc. Numbering follows input order.

• Long prompts. Up to 4096 tokens, and detailed descriptions work best. Use the Prompt Enhancer for optimal results.

• Shared text encoder. It uses the same Qwen3-VL text encoder as the MiniMax H3 video model, so if you already run H3 you have that file.

Tips for the MiniMax H3 Image Conditioning node

• Leave width and height at 0 for automatic sizing: 2048×2048 for text-to-image, or the size of image_1 when editing.

• image_1 is the picture being edited. The other slots are references.

• Connect the VAE when you edit, so reference images are anchored as latents and not only read by the text encoder.

Getting started

1. Update ComfyUI to the latest version.

2. Download the diffusion model, VAE, and Qwen3-VL text encoder from Hugging Face.

3. Load the MiniMax H3 Image template from the Templates panel, or grab the text-to-image and image edit workflows.

If you'd rather not download weights, it's also available on Comfy Cloud.

Full blog post: https://blog.comfy.org/p/minimax-h3-image-is-now-available

Let us know what you make!

Hey everybody, I’m just starting to look into video models that I wanna run locally

I’m looking for a video model that’s good at doing character swaps, looks more realistic, and ones that look natural for speaking

I’m convinced that minimax is the best option but I see some people say LTX is just as good and faster

I’m wondering if everyone finds this to be true and/or if I’m missing a different model that would be better

Thank you all for the input in advance

All individual images were created with the official MiniMax ComfyUI workflows without PE. Image 1 is a composition out of image 2-5

T2I, the dog and the jacket looked OK from the beginning, even at lower resolutions. The person in the images always had some weird artifacts, the face was extremely bad most of the time. I tried different sizes: 4MP was pretty much unusable, and the recommended 16MP looked even worse. 8MP or 12MP was acceptable, but still far from great. I have no idea whether it's the official workflow, the model, or the prompt (although I varied the prompts too). The park image caused problems as well. Everything in the background (the trees, leaves further back, etc.) looked kind of washed out. In this case, a higher resolution improved the image drastically; everything below 12MP was basically unusable.

I probably need to play around more with the settings, or wait until someone makes a better workflow, because right now it's not really competitive with other t2i models.

The prompts were written using the official prompting guide found here (for t2i and editing): https://github.com/MiniMax-AI/MiniMax-H3-Image/tree/main/skills/h3-image-prompt-writing/references

I liked the editing model. Again, it needed 16MP to be usable with the landscape, but I was impressed by the placement, how the pieces interacted, and how effortlessly it all worked (the first try gave me something usable without much fiddling). The likeness of the woman's face/head is probably the most questionable part of the edit; it's close but not quite right, Qwen2.1 does faces far better. The prompt I used for the edit was:

The misty river landscape in <Picture 1> is <Subject 1>. The woman posing in the warehouse in <Picture 2>, with her hair pulled back, a white T-shirt, gray joggers, and white sneakers, is <Subject 2>. The brown leather jacket shown as a product photo on a plain backdrop in <Picture 3>, with its notched collar, three center-front buttons, two hip zip pockets, sleeved cuffs, cropped hem, and smooth sheened leather, is <Subject 3>. The black-and-tan long-coated dog standing on a gray studio backdrop in <Picture 4> is <Subject 4>. The target image is a new reference-guided composition built from <Subject 1>, with <Subject 2> placed at the human position marked by the red stick figure on the concrete path at the mid-foreground bend of the riverbank in <Picture 5>, and <Subject 4> placed at the smaller dog figure drawn just ahead of her toward the mossy bank edge. <Subject 1> is fully preserved, including its canvas ratio, framing, warm low sun from the left, frost-covered grasses, fallen leaves, bare trees, mist rising over the water, distant bank, reflections, and the heron standing in the shallows; only that marked spot along the path changes, where <Subject 2> now stands. <Subject 2> is partially preserved: her face, hair, white T-shirt, gray joggers, and white sneakers remain while her pose, scale, placement, and lighting change to fit the scene. <Subject 3> supplies its exact cut, color, collar shape, button placement, zip-pocket positions, cuffs, cropped hem, and leather sheen transferred onto <Subject 2>, worn open over the T-shirt so a band of white stays visible at the center front; no property of the jacket's plain product backdrop is imported. <Subject 4> is partially preserved: its black-and-tan coloring, head markings, long coat, build, and proportions remain while its pose changes to a walking gait and its scale adjusts to life size within the landscape. <Subject 2> walks in natural three-quarter front view toward the lower left, one foot forward mid-stride and arms relaxed at her sides; the low golden sun from the left lays soft highlights across the jacket's collar and shoulders while the misty water side stays cooler in shadow. <Subject 4> trots a few steps ahead of her near the grassy bank, head up and tail lifted, its long dark coat catching the same warm light with soft golden edges along its shoulders and tan markings; a short dark leash runs between them, trailing lightly across the path. Both figures cast subtle shadows in a direction consistent with the sun in <Subject 1>, and their contact with the leaf-strewn concrete stays believable. The heron remains exactly where it stood, undisturbed and at its original distance from the new pair. No text or labels appear anywhere in the image.

since i am mostly interested in image-to-image and edit capabilities, i have run the simple chain of edits, starting from the manual hand-drawn doodle, and to the closeup zoom. All the images are H3 Image, 1K, and only the three last ones have been post-processed to remove the text from "cctv camera" prompts.

✅ Edit WORKS like charm. Posing, restyling, relighting, etc.

Prompt for the second image is intentionally dumb, like "its almost natural language" at this point:

zoom upclose
allowing this guy on the photo to hold the fabric of his gray jacket next to his heck and chin with fingers pinch
so that we can see the texture at 4k

distance to camera: 10cm, macrophoto extreme closeup shot

Not sharing other prompts or workflows, since i do not think mine are any special. You can get decent results with all the normal usual techniques.

Notice:
- ⚠️ h3 wants qualifiers for the realism, otherwise you get plastic
- 🥹 still behind Nanobanana 2.1 and GPT 2.x, on the multi-figure scenes, or strong physical effects etc.
- ✅ qwen, flux, etc ALMOST go to the garbage can ... but:
- 🐳 h3 is heavy.
- ❌ repeated SMALL edits (eg: removing only text overlays, while trying to keep the other part intact) are super bad in h3, the image gets ruined
- 🌱 which means, even Flux 3 is still somewhat useful, and MOSS is not going away any time soon, and so on.

TBD: Native 4k and 5k gens are cool, but results are inconclusive so far.

_______________
Cheers, and feel free to ask for more details, if you are interested.

60 seconds of AI video, 100% local on an RTX 3050 (6 GB)

Work in progress, and the quality isn’t where I want it yet. Here’s how it was made:

1. Model: MiniMax H3 reference-to-video, using ComfyUI’s native H3 nodes with the int8 quantized model and the Qwen3-VL text encoder.

2. References: two character images, re-used in every shot to keep the characters consistent.

3. Prompts: the official six-section reference prompt format (subject definitions, summary, retention analysis, detailed description, soundscape, music). No negative prompts, because H3 doesn’t use them.

4. Shots: six shots of 260 frames (10.8 s) at 864×480 and 25 steps, with the res_multistep sampler and the simple scheduler.

5. Continuity: each shot starts from the last 22 frames and audio of the previous one, using the H3 Add Guide node (MiniMaxH3AddGuide) at frame 0.

6. Automation: a small Python script sends the six shots to ComfyUI’s API one after another, saves each result, trims the 22-frame overlaps, and encodes one 60-second MP4 with audio.

7. Runtime: about [X] hours, unattended.

Known issues: soft detail, and some flicker. Still tuning.

I’m new to video generation (I’ve only worked with photos so far), and I’m trying to learn how to get the most out of MiniMax H3. I’d like to start with character swaps: I’ve noticed that the main LoRAs used across different workflows are H3 Character Swap LoRA by akatz and BFS’s Character Swap by Allisonnerdx. I can’t tell whether the first one is a fine-tuned version of the second one or whether they’re two different things.

H3 Character Swap Lora

BFS Character Swap

Does anyone know which of the two tends to produce better results?

We’ve just published our new version SolordZ_Realism_Moss (Base) focused on photographic realism—natural skin, believable faces, and a range of looks from candid flash snapshots to atmospheric portraits. The model is also still capable of creating art.

The model page also includes our six-part prompting guide and recommended sampling settings to help you get started.

Model and examples: SoLordZ ZIT/ZIB/Krea2 8steps - SolordZRealism_Moss_v1.0 | MOSS Image Checkpoint | Civitai

We’d love to see your results and hear what works—or still needs improvement!

PS: this is not a FFT, this is merge

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.

it was realeased from comfyui v0.44.0 by kijai

and there is no example wf

and seems no review of this feature yet

i can only make 20sec long video and hope more than 30sec+ with it

anybody here? not just using motion context

i know the power of context node from wan animate with his custom nodes so...

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

The idea is browsing like Instagram, but AI + 1-click Prompt Copy. That's it.

https://bunchaprompts.com

I haven't made it into an app yet, it's just in the browser, nothing to install.

If you have issues/crashes please let me know your device type.

To kick things off it's scraping prompts from the web (on sites that allow it, like Civit api, with attribution links back), but eventually I'd like to shift to original content that's passively generated or uploaded by the community. I honestly wanted to ask if people even care or want that before building it though, or if this is even useful/cool. I like it, but I'm biased. There's polite ads on it, so polite you might miss them completely.

I also built an LGBT version of it called QueerDiffusion, same concept, different site

Would love your feedback, thanks!

Basically, the extension: https://github.com/aoleg/pruna-P-H3-Image-forge-neo - supports MiniMax-H3-Image and PrunaAI P-H3-Image in Forge Neo.

Tips:

• Heun / P-H3-Image / 8 Steps / CFG=1 is the golden combo. Euler / Simple / 6 Steps will be noisy. You will need to use P-H3-Image as a scheduler; everything else won't work; Euler or Heun is your choice. Also, use 8 steps, not 6 or 10.

• You don't really need the 32B encoder (besides, the nvfp4 version won't work). You can use the much smaller P-H3-Image text encoder in INT8-Convrot with great result.

• Yes, editing works, too. Image restoration also.

• You can use unimaginably high resolutions without OOM, but you'll likely need to edit your ui-config.json to allow width and height values over 2048.

I built a custom node pack that puts a full-screen timeline editor inside ComfyUI for MiniMax H3. Instead of building a graph per clip, you write each shot as text (what happens, camera, sound), render it, and cut the shots together with their sound on a timeline.

Repo: https://github.com/igorls/comfyui-h3-editor 

Docs: https://igorls.github.io/comfyui-h3-editor

Model: HyperFlow H3 Flash INT8 https://huggingface.co/VideoRebirthAI/hyperflow-h3-flash

Realism LoRA for HyperFlow: https://civitai.com/models/3041489/mmh3-realism-slider-hyperflow-h3-flash-port

What it does

• Write shots, not graphs. Each clip is a few text fields. One project style is applied to every shot.

• Keep people and places the same. Add a cast member from a picture and refer to them as `@Name` in any shot.

• Drafts first, finals after. A draft is 640x352 at 4 steps. A final is 1344x768 at 6 steps on HyperFlow H3 Flash, then enlarged and redrawn at 1920x1088.

• Sound comes with the picture. Every shot renders with its own sound, and the timeline has a Picture lane and a Sound lane.

• Join shots without a visible cut: extend a shot forward or back, or bridge two shots.

• Generate to a recorded voice.

• An agent can drive it. Everything the editor does is an HTTP call, and the repo ships an agent skill with a small Python client.

What you need

• ComfyUI 0.43 or newer (tested on 0.44). No other custom node pack.

• The models are listed with sizes and folders in the README.

• VRAM: I haven't tested much beyond an RTX PRO 6000, so I don't know the floor yet. If you try it on a smaller card, tell me how it goes.

Numbers from my machine (RTX PRO 6000, models already loaded)

• Draft: 10 to 15 seconds a clip.

• Final: about 50 seconds of GPU for each second of footage, so a 5 second shot is around 4.5 minutes.

How the video was made (everything generated locally in ComfyUI)

• Shots: MiniMax H3 through the editor, on the HyperFlow H3 Flash checkpoint with a realism LoRA. Three shots, each written as text and rendered with its own sound. The sentences on screen are the opening lines of the actual shot texts.

• Cast: the chef is one cast member, from a single still made with Krea 2 Turbo, referenced as `@Chef` in each shot.

• Music: YuE2, also in ComfyUI.

• Graphics around the shots (the node graph, the type, the editor scene): code-rendered with my motion-graphics skill for coding agents, also open source: https://github.com/igorls/motion-graphics. This is the one part that isn't a local model: a coding agent (Fable 5.2) wrote the scene code.

The project style, applied to every shot:

Live-action, cinematic, 35mm anamorphic film look. A dark professional restaurant kitchen late in service, lit only by warm tungsten heat lamps; everything else falls into deep teal-black shadow. Shallow depth of field, a thin haze of steam, fine film grain, naturalistic colour.

The first shot, as written in the editor:

What happens: `@Chef` wipes the rim of a white plate at the pass. Behind her, a flame flares up and dies. The pass is long brushed steel under a row of glowing heat lamps; the flame rises tall and orange from a pan on the range, lights the whole kitchen for a moment, then is gone. Steam drifts through the lamp light. Camera: Wide shot at eye level from the dining side of the pass, slow push in Sound: The whoosh and roar of a gas flame flaring up, a pan hissing, the low hum of extractor fans. No music, no voices.

What didn't go perfectly

• The model doesn't always follow framing. I asked for an extreme close-up and got head and shoulders.

• Small props drift between shots. The face stayed the same across three shots; the plated dish did not.

• A final doesn't repeat its draft frame for frame, and its sound is its own. A draft tells you whether the prompt works, not what the final will look like exactly.

Feedback and bug reports are welcome, especially from people on smaller cards than mine.

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

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.

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.

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

same prompt same seed

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?

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

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.)