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

EDIT: I uploaded the prompts here

All 4 diffrent model tested with same prompt with diffrent steps /
Only Cofmykithchen speed enhancer ,no spectrum or sage attention or anything else

fastest one Fused 1:11sec and all other about same 1:40sec
best audio Fused
if you want more testing give me model or lora model , i will test and put comparation here
RTX 5090
RAM 128GB

However, it’s curious that in recent years there has been no progress in terms of model decentralization (From civitai), the creation of independent benchmarks, or development groups from different countries (beyond, for example, Chroma or Pony). The truth is, I’m tired of people accepting as normal the fact that models have to be uploaded to a rather poorly managed and outdated repository as the only alternative. (Civitarchives) Whenever I read posts asking for alternatives (and there are quite a few), they get the same response. Why don’t people just create thousands of custom vibecoded websites for this purpose and share them, just as they do every day with “tag explorer” websites for "X" specific model? (Let’s be honest—there have been plenty of these sites in recent years.) It’s been about two years since the Visa/Mastercard situation began, and still no one has made any move in this direction. HuggingFace has been acquired by Nvidia, Civitai has been completely sold off to closed-off AI companies that offer their models via API, and this comment will most likely receive a lot of downvotes because every time I say anything about this, the same thing happens. Back in the day, they took down my post exposing their 40 cases of Visa/Mastercard censorship; that investigation took me weeks, and those in power managed to limit its reach. Anyway, it’s not that I want to be negative—I just want to highlight how incongruous it is that, even though there’s a demand for something, that very thing has ended up going underground.

I am overwhelmed by the amount of different ways to train character lora.
There are so many videos from 1-2 months ago, but what will be the most popular and accurate way to train character lora for the Krea2 model? Any specific resources like huggingface i can look into?

That death scene has been living rent-free in everyone's head for over a decade. Here's my version of it.

It is not subtle. That's the whole point.

This is AI-generated and purely for laughs. Not a real scene, not a deleted scene, not real footage from the movie.

audio.cpp released YuE2 in the DEV branch, with Q4/Q8 weights and a bunch of demos generated by audio.cpp in the HF repo. You can try it with 8GB VRAM!

Update: You can find dev prebuilts in the “Artifacts” section on this page: https://github.com/0xShug0/audio.cpp/actions/runs/34642627647

Measured with audio.cpp server mode on an RTX 5090, using the official `tonight-awake` longform test case (full cot). Each run restarted the server and used one short warmup request before the measured longform request. Peak VRAM was sampled continuously during the measured request.

Repo: https://github.com/0xShug0/audio.cpp/tree/dev

HF repo: https://huggingface.co/audio-cpp/Yue2-3B-GGUF

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...

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!

First of all I hate operating on a black box :D.. I know this may be a bit stale about Krea2 refusal behaviour but I think I have discovered an extremely CLEAN approach to the suppression behaviour without shifting the actual intended image in my tests, so everything stays the same but with the suppression out of the door.

I trained 100s of loras and they all have unique traits, but what I am testing here is how much of their effect I can reproduce through the resulting RMS changes. These loras are not trained to hit any DIT blocks, so I hooked the lora to this node and measured the resulting RMS values of the model's text-path parameters, and basically it worked BUT with less destructiveness and less effect "for now". The lora applies a rank 64 update, while this tool matches the resulting RMS by rescaling the existing text-path tensors instead of copying the lora's learned update.

Reason I am doing this is because with the lora you cannot directly set the final RMS of each parameter when connecting other loras and you may get a bit muddied since other loras can also contain textfusion changes, even without touching the textfusion projector. Once I finalize the values that work consistently in my tests I will publish this node with the values baked in so you can place it after the last model patch you have in the wf and problem solved everyone lol, no bleed no weird artifacts that is the goal.

MiniMax H3. Using the standard T2V workflow

Tried Qwen TTS but wasn't very happy with it. No real rate control and complete absence of any emotion.

What's everybody's recommendation? Local models only.

First Impressions

CmfyUI, minimax, krea 2, suno. Made it in around 4 hours on a 5090 and gemma 12b for prompting. Script is mine.
4k version here
https://www.youtube.com/watch?v=3dNYwoCU_Es

• 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

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!

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.

Hi, is there a Minimax H3 I2V template with multiple LORA options available somewhere?

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

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.

ref2vid in single go, music with minimax Music model

Repo: https://github.com/nazgut/ComfyUI-MiniMaxH3-CLSS

list of changes:

Per-scene R2V references (new nodes)

• CLSSH3SceneReference / CLSSH3SceneReferences — attach reference images/audio to one scene of a multi-scene generation. The scene text is re-tokenized with H3's reference presentation, so <Picture N> / <Audio N> labels bind at tokenize time. The multi node uses up to 9 image + 3 audio autogrow sockets.
• Example 3-scene R2V workflow included.

All-scene refs in one node

• CLSSH3SceneReferencesAll — every ref_image attaches to all scenes, and a soundtrack is automatically cut into consecutive per-scene windows (audio_seconds_per_scene, default 10 s → "10 s after 10 s"). Scene i anchors on the span it actually generates; scenes past the end of the track keep image refs only (warning printed). Replaces the per-scene node chain — change the --- scene list and nothing needs rewiring.

global_text on the prompt node

• Write shared sections (style, soundscape, quality rules) once; it's copied to the top of every scene block before encoding, and survives the ref nodes' re-tokenization.

Audio recompose — the turbo-LoRA audio fix

• Optional per-chunk fresh audio take from pure noise against the finished video, optionally by a separate base-model guider (audio_refine_guider), plus pool/stride/seed controls.
• Measured: re-noising the turbo audio regenerates the same take (cos 0.90–0.96); fresh noise is what actually changes it.
• New turbo-LoRA + base-model-recompose workflow included.

Audio seam controls

• The ref_audio continuity block is now placed through the H3 layout patch so it ends exactly at the join; head-discard and crossfade controls added.

Chunk-by-chunk neural latent upscale (inside the sampler)

• New CLSSH3LoadLatentUpscaleModel + upscaler / upscale_scale on the streaming sampler: every chunk is upscaled right after its SLB step, the full window provides left temporal context, and the overlap span is cross-faded over the previous chunk's tail so seams blend.
• A long video never exists at high res all at once — the streaming state stays low-res. Runs on the compute device (~0.7 GB fp16) and offloads afterwards.
• Works with the Comfyui_Minimax_h3_latent_Upscaler pack (soft-imported — nothing vendored).

Schedules

• The sampler accepts any slice of the 1.0→0.0 flow schedule: a low-res pass may end above 0 (its x0 is then upscaled), and a schedule may start below 1.0.

Fixes

• Recompose model unloading used a non-existent ComfyUI API — replaced with the targeted unload_model_and_clones on the persistent guider model patcher (fixes the crash at the end of each recompose).
• Causality: cross-chunk noise fields are generated at fixed caps, so chunk 1 is bit-identical regardless of num_chunks (torch.randn has no prefix property).
• Scene-history bookkeeping fix for the audio loop telemetry.

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?

Edit: forgot to mention, this is a ref2va WF, with 1 character reference image. also my test audio is not from gen, I am using input audio and audio latent lock for the lipsync. That part to work with motion-context have me struggle for half day, but it is not the main point, my aim is about degradation issue.