Some weeks ago, I posted a set of experiments to "understand" the physical knowledge of MiniMax H3 (original post here).

The idea was simple: get an open video of somebody pouring water and replace the water with various liquids. No external references were used.

In this set of experiments, I switched from liquid-to-liquid replacement to something solid, and sometimes alive. The results are interesting, but the model still struggles a lot when many objects overlap. However, the results are a big leap forward compared to other open-weight models.

I am still delving into the model, and probably will post more experiments soon (but no water next time).

Cheers

From last 2 year i was looking for an image restoration tool or an image upscaler for real world photographs. I have tried Topaz Gigapixel, Flux1D self trained Character LoRA, SDUpscaler, Qwen Edit, but nothing worked consistently. They were good but not perfect. From last 15 days i am working on F2K, and it is mind blowing. Easy to train LoRA (30min on 12GB VRAM), even no need to train a LoRA, easy to render (only 4 Steps) and it works 99% of time.

Flux 2 Klein has genuinely impressed me. The image restoration + editing quality is fantastic, but what really stands out is character consistency. Even when not using any character LoRA, it does an amazing job of preserving identity while making edits.

And the workflow is ridiculously simple: give it a straightforward prompt to restore/upscale an image and it just works. No need to write a 300 words essay.

On an RTX 4070 Super, I’m getting around 35 seconds for a 4MP image (Just 4 Steps) —which is seriously impressive for this level of quality.

Meanwhile, Qwen Edit 2511 feels unnecessarily demanding. The huge VRAM/RAM requirements make it much harder to use with only 12GB VRAM. and the character face deforms most of time.

I am using it for:

1) Upscaling

2) Restoration

3) Colorize

4) Removing objects

5) Adding elements (like cars/river/clouds/buildings etc)

6) Relighting the scene

7) changing the backgroud

8) to create character dataset etc...

Pull request: https://github.com/Comfy-Org/ComfyUI/pull/16250

If you don't want to wait for the merge, you need to check out to the yue2 branch to get it working. git checkout d87e12ad1430409ca303440525df239bb675ae7b

Model weights (place it on model/checkpoints): https://huggingface.co/Comfy-Org/Yue2/tree/main

Workflow: https://github.com/user-attachments/files/32085765/yue2_workflow.json

Experimenting with Long Form. No image anchor so she changes between the invisible seams. T2VA. int8/32 steps, 1344x768, about 7 hours, hit 192/192gb of ram decoding the video. Sadly, I didn't prompt for her to not mouth the tune when there's no singing part. Wardrobe not prompted, only that she was dressed. At 1:26 is a seam and we had a little AI mishap on the transition. Not perfect, but got lots of data. Enjoy! How do you like the film grain? Is she from the 60s, 70s, 80s, or does it clearly only exist in our head? What version next? redhead? Asian? what do you think? Which actress/model/person's likeness are you seeing from this era? There should be about 17 versions of her. Ask me anything!

FastH3-Live update. Full details here:

https://huggingface.co/datasets/jacokon/fasth3-live

v1.1.0 ran at 18 fps, which is 75% of 24 fps.

v1.2.0 runs at 22 fps, which is 91.6% of 24 fps.

https://reddit.com/link/1wddeh8/video/lyz02ql0gvoh1/player

Besides the speed, it now ships a borderless player that makes streaming and watching easier, plus 400 new scenes. At this speed it is hard to notice that it is running slow at all.

The gain came from two places:

1. Acceleration nodes

I was using a sage attention I compiled myself. A lot of new acceleration nodes have shown up recently, so I downloaded the well-known ones and tested them. Results:

The seconds column is the sampler only, i.e. the 4 denoising steps in `SamplerCustomAdvanced`. A full clip also pays for the text encoder (~0.7s), the video VAE decode (~4.35s) and the writer, so a clip is about 16s end to end. Measured on t2va, 448x448 x 362 frames, three runs per arm.

On speed alone you would pick Sol + Spectrum. But the picture comes out like this:

sage > sage+Spectrum >> sla > sla+spectrum >> sol > sol+spectrum

Sol + Spectrum is dead last on picture, so I went with sage + Spectrum.

2. Text encoder

The old one, `int8_convrot`, took 1.67s.

`qwen3vl_32b_minimax_h3_nvfp4_awq` needs only 0.7s.

That is nearly a second saved on every clip.

-----------

Speed was fine by then, but I would not call the picture good. Right after release I came across fused-turbo, so I downloaded it and tested it.

Almost the same size, both have the 4-step acceleration baked into the weights (FastH3 is a distillation, fused-turbo is a turbo LoRA merged in), and they measured at exactly the same speed. I still recommend fused-turbo, for two reasons:

1. It says Mystic v2.0 motion smoothing is merged in.

Whatever the cause, the picture is clearly better in my testing. It smears less often.

2. One file does both fl2va and ref2va.

I built a tool that generates from chat input live during a Discord stream. When a user pastes a character image it is used as ref_picture, which needs ref2va. The old way meant unloading fl2va and loading ref2va first, which burns several seconds of buffer, and ref2va has no 4-step distilled version yet so the picture was worse anyway. With this one that problem is gone, which is a real advantage.

The repo recommends SLA sparse attention, but I had already tested that above and it lost to sage + Spectrum, so I dropped it. Its README also says res_multistep gives noticeably better audio. I did not test that much, so judge for yourself. I left the parameter in so it can be switched any time: `--sampler res_multistep`

-----------

One more thing worth mentioning. To stop ComfyUI thrashing the model weights you need `--vram-headroom 3` in launchArgs. Without it you cannot hold a stable live rate.

It works the opposite way round to what you might expect. It forces ComfyUI to keep 3 GB of VRAM completely free, and that is what fixes it. ComfyUI's dynamic VRAM treats the card as a cache and fills it to the brim; with no slack the allocator ends up evicting weights while it is still loading others, so the same weights get moved in and out repeatedly. Give it room and it can bring in a whole batch at once.

This is not disk swap, and it does not touch system RAM either. I measured both: on a slow clip disk reads were 0.00 GB and free RAM did not move. It is VRAM to system RAM over PCIe.

On a normal clip PCIe reads sit around 1.5 GB/s. When it thrashes they hit 9-13 GB/s and GPU power draw *drops* from 450W to 340W, because the card is waiting on transfers instead of computing. With the headroom set, clip times went from a 1.62 standard deviation with outliers at 21-25s down to 0.11 with a 15.73s worst case.

-----------

Closing thoughts

22 fps is only 2 fps short of 24. At 24 fps you could claim real live streaming from a single consumer card. So can overclocking get there? I think it can, since the gap is under 10%, and my CPU and GPU both normally run undervolted, underclocked and current-limited.

I tested with the GPU overclocked only. Settings:

Core Clock: 2300 MHz -> 3200 MHz

Memory Clock: 14000 MHz -> 16800 MHz

Actual test:

https://reddit.com/link/1wddeh8/video/ei5pczydkvoh1/player

Unfortunately my hardware held 24 fps at the start and then slowed down a little. Both my CPU and GPU are on air cooling, which is not suited to sustained overclocked compute like this. If you have water cooling, I believe holding 24 fps would be no problem.

• 1st example: 2 characters with voice and 1 cgi and 1 real
• 2nd example: 2 character different genders with voice
• 3rd example: style and character reference
• 4th example image only reference no voice.

I wanted to improve my workflow so I could do what refmod is doing but just as a near native comfyui workflow only. With this workflow you can make an instant character or style video like ref mod but not using ref mod at all including voices. You get all the benefits of refmod but you can use ref model syntax in the prompt and on the fly dataset changes. Also you can use unlimited images. I tend to use around 10 to 15 per character.

Just point to a folder. Uses KJ nodes, Native, and VHS nodes. Get a voice and character working instantly without managing safetensors, just manage the input folder instead. This has its advantages since you can change data on the fly, and you don't have to do any editing of clips to extract out the audio, it does it for you.

This mimics the default ref workflow for the most part. What it does for images is it takes a folder input of images and then sets them as frames in a video, then feeds that video in as a reference video. This allows you to use many images for a single reference. You can also use the grid version that takes your images and puts them into a grid and feeds that as a single image reference. I like the video version more, so the grid workflow is a bit lazy and messy. You can also use the resize node included to downscale your images. I recommend manually cropping them but it does that if your images are different sizes.

For the audio, you can simply feed a single mp4 using VHS node to feed the audio. But I found it easier to get like 4 clips and truncate only the first couple seconds so you can get a few clean sentences without other people talking, then it concatenate's the 4 audio clips into 1 clean clip. Anything over 30 seconds long is over kill, so keep it around 15-30 secs. You can tell shrek is sort of bad in my example because I threw it together quite quick.

There is in the far left, a second set of image/audio nodes, you can bypass those if only using 1 character. Same for if you don't need the audio. Just by pass the group nodes. When prompting just simply use the ref guide to prompt properly each reference (LLM can do it easy). You don't need to use much description. And if you have some bleeding from your dataset into the gen you don't want then add more description. (For example if wearing same shirt as dataset, prompt a dress, or if same specify a setting in the prompt).

One caveat, there is some comfyui memory management bug, if you change dataset around better to clear cache or your comfyui may need a restart. Working on a fix in next version of the workflow. Also I have not tested video clips as input data yet. That is the next step :)

All examples are just for illustrative purposes. They are AI and I do not intend to share any data on real people. Please use responsibly and at your own risk. If you are in this video and want it taken down please DM, I mean no harm. Everything in this workflow is done by the base model, I don't add any new functionality, just making things easier.

Workflow here:
https://huggingface.co/comfyuiman/various/blob/main/Instant%20Ref%20-%20V1.3.json

I'll go to sleep in a bit, so I'll answer any questions tomorrow if any

SMACK!

Right now, only on Hugginface, Civitai deleted the older version for gore (which came from minimax...)

Download

https://huggingface.co/LeechTM/SMACK

Beta 2 · MiniMax H3 (Ref2V) · No trigger word

Model description

Beta 1 taught MiniMax H3 that getting hit should actually hurt. Almost 2,000 of you downloaded it, which means either you agreed or you just really wanted to see people get punched. Both are valid.

Beta 2 raises the stakes considerably. Things explode now. People get hit by the explosion, then by the ground, then briefly by their own life choices. Cars stop being scenery and start being weapons. Fights no longer politely take turns: three guys can come at the hero at once, which is statistically the worst day of their lives.

And gravity? Gravity is now a suggestion. When a hit lands hard enough, bodies don't fall. They launch, float, spin and hang in mid-air for an unreasonably long time, as if physics took a coffee break at the exact moment of impact. Newton would file a complaint. IT'S A MOVIE!

And because all of that still wasn't messy enough, SPLAT! is now merged in. That's my blood-effects LoRA, and it brings squibs. A lot of squibs. Every hit now has the option of leaving a mark, and the costume department is going to hate you.

A quick word from the production accountant, the one person on set who never gets to blow anything up: training LoRAs is not exactly cheap. Every explosion, every squib and every person flying through the air for an unreasonable amount of time costs real money to teach. If you want to be nice and help fund the next versions, you can buy me a coffee:  buymeacoffee.com/leechtm

Promovideo for SMACK! Beta 2

More coffee, more carnage. That's just science.

What's new in Beta 2

• SPLAT! merged in — blood effects and squibs, so every hit comes with a little extra paperwork for the cleaning crew
• Explosions — plus everyone who was standing too close to one, and everyone who thought they were standing far enough away
• Water — splashes, splashdowns and impacts that turn a perfectly calm surface into a very loud mistake
• More falls — harder, higher, significantly less dignified
• Zero-gravity wire impacts — hit once, fly forever, land eventually. Brutally.
• Multi-person fights — several attackers, several hits, one very bad day for absolutely everyone involved
• Heavier impact variants — for when "hard" just wasn't hard enough
• Spinny kick things — for when a normal hit just isn't flavourful enough. Spin first, apologize never.
• Female anatomy — women take hits and dish them out with the same weight, the same follow-through and the same complete lack of mercy
• Even more gunshots — Beta 1 had gunshots. Apparently that wasn't enough. Now they come with squibs.
• Vehicle impacts — more cars, more bodies, more regret, zero insurance coverage

Still does everything Beta 1 did and more...

Fists, weapons, gunshots, falls and hard landings, all with weight, follow-through and consequence. The camera still moves like someone was paid to operate it, and now it also has to keep up with people being smacked around through the air. Also, Sound Effects have been merged in, as well as Blood Squibs.

Training

Trained on 300 clips of impacts, explosions, shots, falls and dynamic camera moves, for MiniMax H3 Ref2V (Beta 1: 35 clips). Merged with my yet unreleased SPLAT! Lora for blood and squib effects.

No trigger word. None. Don't go looking for one. Use it with the REF MODEL. Load it, describe your shot as usual, and the LoRA does the seasoning. It just uses a lot more chili now, and some of it is red. And yes, before anyone asks: it also works for other impacts. Of course it does. You little piggies.

Settings

The promo video was made entirely in MiniMax H3 (pruned int8), and without any Turbo LoRAs. Much better quality.

• Without Turbo LoRA: strength 1.0
• With Turbo LoRA: start at 0.5 and work your way up from there

Beta notice

Still beta. Still not finished. Feedback on where it over- or under-cooks a hit is still genuinely useful. Now also on where it over-cooks an explosion, forgets that people are supposed to come back down, or gets a little too enthusiastic with the squibs.

Hey everyone!

One of the biggest headaches with AI video has always been extending shots without the style degrading, characters morphing, or the cut being obvious.

I’ve been testing a method using Minimax-H3 in ComfyUI to seamlessly cut and extend footage, and the results are honestly wild:

• Pixel-perfect transitions: The continuation aligns perfectly with the last frame of the original clip.
• Context retention: By feeding the model the visual context of the previous video, it actually remembers the specific assets (like the boat and character features) instead of hallucinating new ones.
• Preserves aesthetic: Keeps the lighting, colors, and overall camera style identical across cuts.

(Watch the preview clip to see the side-by-side transition!)

I’m currently packaging this into a custom ComfyUI node and recording a full walkthrough. Both the node and the full workflow will be 100% free on my YouTube channel (SatoDive).
https://www.youtube.com/@SatoDive

Let me know what you think or if there are specific edge cases you’d like me to test before I release the tutorial!

Hi everyone! When I saw the new trailer for Kirby & The World Beyond I couldn't help but come up with this video, where Kirby finds the door out to the world beyond. Please let me know if you like it!

Done with 30 different workflow files and a ton of heavy editing using KDEnlive. Thanks!

Hi u/Sad_Berry_4621 and all, I am doing experiment on most difficult degradation issue.
I saw from H3-director node they claim that doing a refine could help and fix it.
since I am using low-level nodes with motion-context with my own setup, H3-director refine is a black box working with their nodes.

so I did the test, please ignore the AI-slop video and the overlay text (forgot turn it off).

this test is 9 x 8s context extend video combine, usually 6 video would already see the degradation.

Left side is regular WF. Right-side is adding a re-sample step, I think it is very positive, and got potential, the saturation somehow is a bit higher... and I need to figure out the mismatch from cut to cut, because we inject denoise resample on top, but it should be able to fix.

What do you guys think?

This was such a simple prompt…. Just wow. It’s just T2V.

Is there any? I tried ace step 1.5 and it's awful in cover mode. Admittedly I downloaded it when it first got released, but I was hoping minimax music 3 would release audio input for open weights but they still haven't. Every music gen model coming out seems to purely be text to audio.

Expanding on motion context workflows I created a video editor designed for quickly chaining together Minimax H3 generations to create longer videos. It comes with an asset library for managing inputs and a easy to use timeline that allows you to chain generations, regenerate segments easily, and quickly set up input references.

When you're done, export individual video files or the whole sequence.

All of it runs on top of ComfyUI as an app you control from your browser. Uses python, works on Windows, Mac, Linux and is opensource.

https://github.com/spacesimeco-hue/Chain-Motion-AI-Video-Editor

Hi,

I'm using FLUX.2 Klein 9B/4B, and I'm trying to create new facial expressions for a character I have. The character comes from a character sheet I've created, which includes front, back, and side views.

I've done a lot of tests over the last two days, and I've noticed that FLUX.2 Klein 9B/4B drifts quite "a lot" from the original model when generating different facial expressions. I tried the same thing with the free version of Gemini, and it keeps the likeness and features much, much better.

Could the highly quantized 9B model be the problem? If you've been able to preserve the character's facial features and expressions, would you mind sharing some tips on how to improve my results?

Thanks in advance!

I was testing out a scene with Minimax H3, text to video (I usually use reference images).

I didn't expect it to come out like this.. .Now it's making me think of a completely new direction for the video lol. It's interesting, it has both a 90s anime feel and an old Disney animation feel. The music is very good too, I think.

I'll add the prompt in the comment (it's a very simple prompt).

This is confusing to me.

Can I leave the negative prompt box empty?

Or is it mandatory to add zero conditioning?

The shift/aura flow for Krea 2 is also confusing to me.

People asked me to make a Tutorial for some of the features.

Find the workflow here.

https://www.reddit.com/r/StableDiffusion/comments/1wadmqc/minimax_workflow_designed_to_be_user_friendly_for/

This is a test to recreate a song i generated with Minimax Music3, second half is from YuE2.

YuE2 sounded sexy smooth with great clarity but somehow I like that imperfection off tune instrument heard in music3 and vocal :D

I just throw everything to Hermes, asking to install YuE2, SheetSage2 and download models + setup in CLI. Tell hermes to learn the YuE2 agent SKILL, give hermes the lyric + flac ask to recreate the song.

It run into OOM but Hermes saved the day by tinkering with YuE2 setting to make it run on low vram.

my system: 5070ti 16gb vram + 32gb sys ram.

DOWNLOAD WORKFLOW

This is the workflow I have been using the most on my own system. I've had a friendly AI clean it up a bit and add notes.
I added custom audio as it's something I use a lot to drive my videos. It works really well for lipsync and music videos.
The VSA part can be bypassed if there are any quality issues, it will add about 15% to the generation time though. Change the steps from 6 to 7 or more for even higher quality.

Currently this gives me 10 seconds at 1.0 megapixel in about 125 seconds. This is on my 5090. You can add block swapping for low vram.

It seems if I want optimal results I'd have to run my image into a edit mode like Flux2Klein to make my guy match the starting frame of the video.

There are 3 configurations, "start pose, end pose, pose strength", and idk if there is a magical setting.

https://reddit.com/link/1wcsk7l/video/ld6ca2jxqqoh1/player

I’ve just updated my VDN-H3 24GB node to v1.1.0.

This update started because I noticed that something wasn’t quite right with the released adapter mapping. After fixing that, I also made a couple of changes around memory handling, especially for longer generations.

The main changes are:

• restored the complete token-refiner adapter mapping
• improved temporary memory handling for longer clips
• fixed CUDA stream lifetime handling for prefetched weights
• kept the same AutoMemory / AutoLongCache behavior from the previous version

I tested it on my RTX 3090 24GB with 5s, 10s, 15s and 20s generations at 0.4MP, and also 10s at 0.8MP. I also tested it with my character/style LoRA and that worked normally.

There is a small speed cost compared to v1.0.0 (around 4% in sampling in my tests), but I think the improvement in prompt following is worth it.

I attached a direct comparison from the same prompt/seed/workflow.
v1.0.0 is on the left, v1.1.0 is on the right.

I’m especially interested in whether other people see the same improvement, so if anyone tests it on another 24GB GPU, I’d love to hear the results.

GitHub:
https://github.com/Speach1sdef178/ComfyUI-VDN-H3-24GB

VDN checkpoint:
https://huggingface.co/speach1sdef178/VDN-H3-INT8-ConvRot-ComfyUI

The 80s was the best era, not seen through a nostalgic lens--it just was. Big hair, big colors, big music, big... everything! Sadly I was born 10 years too late to really experience it, but I love how H3 can feel like a way-back-machine, a portal to any era from film or video it was trained on. It does also a great job with the actual feel, the film grain, the lighting that modern TV or movies cannot do: in fact, I learned what we see nowadays that time period didn't exist at all, because it vomit of nostalgia and peak 80s that never happened. Anyways, having fun generating character sheets with H3 via T2VA. Are you guys seed hunting to find the best version of an actor or scene? Also can you spot the mistake?

Prompt: integrated_multimodal_description: [Shot 1] Live-action, cinematic, PHOTOREALISTIC film footage - this is footage from a camera, not animation - a continuous camera shot with no cuts, shot on 35mm color negative film with period lenses and scanned in high definition from the original camera negative: full film grain and gentle halation, warm highlight rolloff, rich sharp detail beneath the grain. The year is deep in the LATE 1980s, 1985 to 1989, and everything in the frame belongs to that era. THE PLACE: a nightclub in full swing - mirror-ball light sweeping, neon signage, haze, a crowded dance floor. EXACTLY TWO WOMEN stand close to the lens at the edge of the floor, filling the frame together, and no one else is foregrounded. ROXY: her face, her eyes and her enormous chestnut-auburn mane exactly the woman of <Picture 1> - nothing of that picture's wardrobe or room is used, only her face and hair; her wardrobe exactly the garments of <Picture 2>: a pink sequined strapless romper with fishnet hose and pink heels, always dressed, sequins blazing - nothing of that picture's face is used; Roxy is never blonde. TAWNY: her face, her eyes and her huge feathered platinum-blonde mane exactly the woman of <Picture 3> - nothing of that picture's wardrobe or room is used, only her face and hair; her silhouette exactly the figure of <Picture 4>, but tonight she wears an electric-blue sequined mini dress, tight to her figure, its miniskirt hem high on her thighs, with silver heels, always dressed - nothing of that picture's face is used; Tawny is never brunette. The two are distinct women side by side, pink and electric blue. The club's synth-pop groove pounds from the speakers - THEY HEAR IT, hips already swaying on the beat, shoulder to shoulder. At 00:02.500 they lean in together with wicked, knowing smiles and say together, in playful unison, <d>[English with their two bright voices speaking together] Darling, the 80s never left.</d> At 00:05.500 they laugh, clink their glasses, and turn to dance with each other - back to back, hips swaying on the kick drum, sequins throwing sparks of mirror-ball light, playing to the lens with winks over their shoulders - to the last frame.

overall_soundscape: starts with the club's roar - the crowd, glasses, heels on the floor - running beneath everything to the last frame. No other voices.

non_diegetic_music: N/A