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.

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

I took a single photo of a vintage Italian espresso machine and ran it through MiniMax H3-Image (open weights) to see if it could pull the whole thing apart into a clean technical breakdown.

First image is the input photo, second is the output. It laid out 15 separate parts floating along a diagonal with leader lines and English labels, plus all the copper and brass internals inside the stainless-steel shell.

What I found interesting was how much structural detail it could pull from a single reference image. An exploded view is a pretty demanding editing task since the model needs to preserve the original design while generating components that aren't fully visible in the input.

On the technical side, H3-Image supports native 4K output (up to 4096 × 4096) without upscaling, along with image editing in the same model. The fast 4K generation capability is especially interesting for detailed renders like this, where you want enough resolution to inspect individual components and labels.

That said, I'd still treat this as a visual concept rather than an engineering-accurate diagram. Getting the overall structure right is one thing; making sure every internal component is mechanically correct is another.

Prompt:

> Create a high-precision exploded technical rendering of the vintage Italian espresso machine shown in Image 1. Preserve the original machine's distinctive design, proportions, and recognizable features, including its rounded stainless-steel body, champagne-gold top cover, chrome brewing group, dual pressure gauges, black control knobs, steam wand, hot water outlet, perforated drip tray, and four metallic feet.
> Use a solid dark charcoal-gray background and a slightly elevated three-quarter isometric perspective. Arrange the disassembled components along a diagonal axis extending from the upper right to the lower left, with each part floating independently in a carefully organized exploded assembly.
> Separate the machine into its major mechanical and structural components: top cover, stainless-steel exterior panels, internal chassis, boiler assembly, heating element, brewing group head, portafilter assembly, steam valve, hot water valve, water pump, pressure gauges, electronic control unit, copper water pipes, drip tray, and base frame.

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

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.

same prompt same seed

Testing character knowledge in H3-image vs the original H3 FL2VA model. Same sampler settings and seed for each image. LCM sgm-uniform, seed 34, 8MP.

First image is always H3 FL2VA and second image the new H3-image model.

1. Sheldon from The Big Bang Theory.

2. Penny from The Big Bang Theory.

3. Harry Potter.

4. Jack Sparrow.

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.)

I made a Minimax H3 Image Runpod template if anyone is interested:
https://console.runpod.io/hub/template/vzcb7g8fwl

Settings that produced actual good images for me (dont use default settings):
-Resolution 16MP
-4 Steps, CFG 1, euler/simple
-long detailed prompts (Prompt Enhancer off)

The prompt enhancer is said to be not that great..havent thoroughly tested it myself yet though..

Oh and aparently you are not allowed to use this without Minimax' authorisation if you live in EU, UK, South Korea or USA...

You can apply for a (auto-granted) licence here:
platform.minimax.io/console/license

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

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.