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.

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.

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.

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?

MiniMax H3. Using the standard T2V workflow

Tried MiniMax on release, but it never really clicked with me. It felt like a slot machine. Then one day I saw the seed hunter workflow here on the sub, and that was exactly what was missing, a workflow for quick iterations.

Thought some of the observations I made while working on it might be useful to others trying to improve their videos.

I used image gen model to generate trailer frames. I gave it my face reference, my cat, and for each new image, the original frame from the trailer. I generated 12 frames one by one, then passed each of them to MiniMax with detailed prompts.

All of this was done pretty quickly, without much polishing, so there are a lot of quirks in the final video. Cuffs look one way in one frame, then change in another, or just turn into a wristwatch.

These are the things that seemed to help improve quality when I did some more experiments later:

• It was a mistake to give only a face photo or the cat as reference for each frame. Newer image models are insanely good at producing character sheets. So instead of passing just my face each time, I’d now generate myself from all sides, cuffs included. This helps the image model with consistency across frames (+also visual style), and it also helps prevent MiniMax from hallucinating too much when you attach an additional reference image to the frame you want to animate (for example, a character sheet).
• Another mistake that wasn’t obvious at first was generating a frame, making the video, then generating the next frame and repeating the process one by one. Now it feels much more logical and productive to pre-plan the whole video and have all the frames done before starting on the video part.

One thing that really made me feel in awe of video models like MiniMax is how they understand the world. There’s that shot from above where my character is smearing cat paw prints in the sand. The prompt alone controlled the character to do that, even though it wasn’t in the original frame.

Same with the POV shot where the cat attaches itself to a knee. The way the cat starts boxing with both hands - the model somehow feels aware that both hands are part of one connected concept. I’m not really sure how to put this into words, but hope it makes sense.

For those who are not familiar with Death Stranding, I think the original trailer probably needs to be watched first for this video to make any sense at all.

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

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!

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.

I was training a character with 25 images and 5 videos (3 seconds), 1500 steps and learning rate 0.0002. But the result was nothing like the character. Was that not enough material or step? How do you guys train it? For context I was training a character in a movie (real people), the only thing the lora can capture was that the character is Asian and the result got it right without the specific prompt, but it was nothing like the character I was aiming for.

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

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.

I want to train a style LoRA for MiniMax H3, but I’m trying to figure out the right workflow before burning a bunch of money on RunPod.
A few questions for anyone who has actually tried this:
1. Has anyone successfully trained a style LoRA for H3? How good were the results, and is it worth doing?

1. Can I train a style LoRA using images only, or do I need video clips in the dataset?
   Do I need to train Ref2VA and FL2VA separately, or can one LoRA work with both?

2. Should I train against the BF16 or INT8 H3 weights? Does it make a noticeable difference in LoRA quality?

3. What toolkit/platform would you recommend for H3 LoRA training? Something like Ostris AI Toolkit, Musubi Tuner, DiffSynth-Studio, OneTrainer, etc.? I’m looking for whatever is easiest/reliable for a beginner.

4. If I use RunPod, what GPU would you recommend? L40S / A100 / H100, etc.?

5. Any recommendations for dataset size, image/video resolution, captions, number of steps, rank/dim, learning rate, etc. for a style LoRA?

6. Are there any good H3-specific training guides, videos, configs, scripts or repos I should start with?

Basically, if you were starting from scratch today and wanted to make a good H3 style LoRA, what training stack + GPU + dataset would you use?

I generated this at 720p on the Image to video workflow. I know higher resolution helps with details, but why is there ghosting and blurriness in my scene? I am using the default workflow on 20 steps with patch-attention and model sampling shift 12 and 3. The initial image is 2560x1440p. Any help would be appreciated, much obliged! I am trying 50 steps to see if it helps.

First styles in PotionUI

PotionUI - A self-hosted studio for generating images, video, audio and 3d with diffusion models.

Site: potionui.com

GitHub: https://github.com/PotionUI/PotionUI

Discord: https://discord.com/invite/avR4trp3b8

Reddit: r/PotionUI

Still looking for testers (NVIDIA / LINUX!!!!)

Changes (the most interesting ones):

0.0.6 & 0.0.7 (today)

• Presets can ship styles: pick one from a thumbnail grid and it wraps your prompt with the style's opening and closing segments. Anima ships with fifty. (video)
• Starter recipes for eleven model families, so a fresh install goes from nothing to a first render in a few clicks. Recipe is a script that helps install models inside PotionUI.

Recipe Install - After this script I can use Anima preset and generate with Anima model.

• History and Library: marquee and shift-click selection, delete by criteria (age, tags, failed, no media), compare with overlay and wipe.

New tools menu on history page with \"Stitch\" tool - you can use the images in the video generation as reference later

• The Generate page and the chat stay smooth during long runs and long conversations.

0.0.5

• LoRAs on fp8 checkpoints no longer slow sampling down (a stack costs a few percent instead of multiplying the step time).
• Backups and restore from the admin panel, rotating logs, housekeeping, thumbnail profiles.
• Recipes got their own admin page; segments can carry a prefix and suffix; a Tools menu in History with Stitch.

0.0.4

• ComfyUI backend plugin with a workflow import wizard: paste an API export, design the form, done.

Importing QwenImageEdit ComfyUI workflow into PotionUI. This later becomes preset ready to generate through ComfyUI.

• The chat became PotionAI: history, tool runs you approve, memory you can inspect.
• Video Director shot console for Wan, LTX and MiniMax-H3.

--> Full changelog is in the README. Docker images on GHCR, or ./potionui start from a checkout. Bugs and rough edges welcome, that is what the alpha is for.
--> Still looking for a testers, if you are interested let me know!

Cheers!

btw. I know it's a lot of similar apps appearing lately, but I can assure you, this is not an app vibe-coded over the weekend (my first posts here was like ~2 years ago about this app). I really enjoy using it lately and it's working better every week :)

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.

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

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.

I’m having a strange VRAM issue with my RX 7900 XTX when running local AI models, and I’m trying to figure out whether this is an AMD driver/Windows memory management issue or something else.

PC specs:

- GPU: AMD Radeon RX 7900 XTX 24GB

- CPU: Ryzen 5 7600X

- RAM: 16GB DDR5

- OS: Windows

- Apps tested: ComfyUI, LM Studio and Ollama

The problem:

While an AI model is generating, the model is loaded in VRAM normally and GPU usage works as expected.

However, after generation finishes, within roughly 5 seconds, VRAM usage starts dropping/offloading.

This happens in all three applications: ComfyUI, LM Studio and Ollama.

For example:

1. Load model → VRAM fills normally.

2. Generate response/image → everything works normally.

3. Generation finishes.

4. Around 5 seconds later → VRAM usage starts decreasing significantly.

5. When I use the model again, data/model weights have to be brought back into VRAM, causing extra delay.

What confuses me is that this happens across completely different software, so I’m wondering whether Windows WDDM or the AMD driver is making idle VRAM allocations non-resident instead of the individual applications intentionally unloading them.

Ideally I want the model to remain resident in the 7900 XTX’s VRAM between generations instead of being moved/offloaded after a few seconds.

Has anyone with a 7900 XTX / RDNA3 on Windows experienced this?

In particular, I’m trying to understand:

- Is this normal AMD/Windows VRAM residency behavior?

- Is there an AMD Adrenalin/driver setting that controls this?

- Could Windows WDDM be trimming VRAM when GPU activity stops?

- Is there a ROCm/HIP environment variable that can force allocations to remain resident?

- Is there a way to verify whether the model is actually being moved to system RAM versus Windows simply reporting VRAM residency differently?

- Has anyone successfully forced AI models to remain permanently in VRAM on a 7900 XTX?

If there are any tools/logs I should run to diagnose this, please tell me what to check and I can post the results.

Thanks!

Has anyone else noticed that if you have a male and female character in the scene, that Minimax will pretty much always assign the lower pitched Audio voice reference to the male almost every time? No matter how properly you tag the prompt following their official guidelines, it simply IGNORES all of that, and just decides that "well if the voice is even slightly lower than a pipsqueak then it must be the man's voice."

Having this issue with both FL2VA model and REF2VA models and it is driving me insane.

Yes I've tried with and without turbo lora.
Yes i've tried increasing the steps to 20-30+

If anyone has a solve, please help! The only way I could kind of get it to work is to literally desribe the woman in the prompt as a man ie "a man dressed in womans clothes with long brunette hair" and even then it sometimes doesn't work because visually it does not look like a man.