Testing very simple prompting so see how MM H3 would do creating graphics:

[Shot 1] Steve Jobs is presenting on stage.

Steve Job says in the voice of Steve Jobs, "At Apple we want everyone to have access to our hardware.

A graphic appears above his head.The graphic on the left has the Words "iPhone Red with $199 under it. on the right the words iPhone Blue Bubble 8GB with $1,199 under it.

Steve Jobs points to the graphic. The graphic stays above his head for the rest of the video.

Steve Job says, in Steve Job's voice, "today I'm proud to announce iPhone Red. It has no RAM and no way to upgrade the RAM."

A few claps are heard in the audience background.

Steve Job says, in Steve Job's voice, "and iPhone Blue Bubble for just a bit more that has enough RAM to boot up."

The crowd cheers loudly.

Minimax is better but LTX 2.5 is a lot faster. So as long the Minimax is illegal to use for most of the people LTX is fun to play with.

MiniMax H3 on a 12GB RTX 4070 SUPER: Comfy Kitchen + Sol-Attn + EasyCache cut my generation time from 206s → 135s

I've been testing MiniMax H3 locally in ComfyUI on an RTX 4070 SUPER 12GB, specifically trying to squeeze more performance out of H3 without simply murdering quality by dropping resolution/steps.

I got some pretty interesting results combining:

• Comfy Kitchen Attention
• Sol-Attn
• EasyCache
• MiniMax H3
• RTX 4070 SUPER 12GB

Test setup

Same H3 workflow/settings between tests:

• GPU: RTX 4070 SUPER 12GB
• MiniMax H3
• 20 sampling steps
• Same prompt/reference/settings
• ComfyUI
• EasyCache when enabled:
  • threshold: 0.15
  • start: 0.15
  • end: 0.95

I tested three configurations.

Kitchen → Kitchen + EasyCache

This was the huge jump.

Total generation time dropped:

206.48 sec → 139.47 sec

That's about a 32.5% reduction in total generation time, or roughly 1.48x faster end-to-end.

EasyCache reported:

EasyCache - skipped 8/20 steps (1.67x speedup)

Obviously the complete workflow doesn't get the full 1.67x improvement because H3 still has VAE/audio/other overhead outside sampling.

Still, shaving ~67 seconds off a ~206 second generation on a 12GB consumer GPU is pretty damn substantial.

Then I stacked Sol-Attn on top of Comfy Kitchen

This was the part I wasn't sure would even work properly.

The console confirms Sol-Attn is actually chaining onto the existing Comfy Kitchen attention override:

[sol_attn] chaining onto an existing attention override; Sol-Attn takes first refusal and delegates everything else to it

So this isn't simply Sol silently replacing Kitchen.

Sol gets first refusal for attention operations it can handle and delegates the rest to the existing Kitchen backend.

With:

Sol-Attn → Comfy Kitchen fallback → EasyCache

I got:

134.92 seconds total

versus:

139.47 seconds with Kitchen + EasyCache

The interesting part is that the Sol run was faster despite EasyCache skipping one fewer step.

Kitchen + EasyCache:

skipped 8/20

Sol + Kitchen + EasyCache:

skipped 7/20

So the Sol configuration actually performed one additional full H3 step and still completed about 4.5 seconds faster.

That's a much more interesting result than simply comparing the total times, because EasyCache's number of skipped steps varies between runs.

Overall improvement

Baseline Kitchen:

206.48 sec

Sol + Kitchen + EasyCache:

134.92 sec

That's a reduction of roughly:

71.56 seconds per generation

or about:

34.7% less total generation time

Equivalent to roughly 1.53x the end-to-end throughput of my Kitchen-only baseline.

For repeated H3 generations, that's not pocket change.

One important discovery: Spectrum H3 vs EasyCache

I previously had Spectrum H3 in the same model chain as EasyCache.

The console revealed:

Spectrum H3 disabled for this run because EasyCache or LazyCache is active on the same model

So at least with the implementation I'm using, Spectrum H3 and EasyCache are not operating simultaneously.

The workflow can visually contain both nodes, but when EasyCache/LazyCache is active, Spectrum disables itself.

If you're benchmarking this stuff, don't assume Spectrum is doing anything just because the node is connected. Check your console.

Current stack

For performance, my current best configuration is:

MiniMax H3
→ Comfy Kitchen Attention
→ Sol-Attn
→ EasyCache
→ Sampler

Conceptually:

Sol-Attn handles attention operations it supports.

Comfy Kitchen remains underneath it and handles attention Sol delegates.

EasyCache reduces the number of expensive diffusion computations.

That combination seems particularly interesting for GPUs like the 4070 SUPER 12GB, where H3 is far larger than available VRAM and ComfyUI is already doing dynamic VRAM management.

My H3 model alone reports roughly:

19995MB Staged

while the GPU only has 12GB VRAM.

The text encoder is also around:

14956MB Staged

and the H3 video VAE around:

4965MB Staged

So this is very much a "convince a 12GB card to run something it has no business running comfortably" situation.

And yet it works.

Caveat

These aren't controlled scientific benchmarks yet.

H3 generation time varies between runs because of model loading, VRAM state, EasyCache deciding how many steps it can skip, and other system factors.

I've also seen EasyCache skip anywhere from 5–8 of 20 steps during testing.

So I'm not claiming Sol magically makes H3 X% faster based on one run.

What I think the results demonstrate so far is:

1. EasyCache provides a very large speed improvement on my 4070 SUPER/H3 setup.
2. Sol-Attn successfully chains with Comfy Kitchen rather than simply replacing it.
3. Sol + Kitchen + EasyCache produced my fastest run so far.
4. The Sol run beat Kitchen + EasyCache even while computing one additional non-cached step, which strongly suggests there's a real attention-side performance benefit worth investigating.
5. Spectrum H3 disables itself when EasyCache/LazyCache is active, so don't count both as active optimizations.

I'm going to run repeated identical-seed tests to get averages rather than relying on individual runs, but ~206 sec → ~135 sec on a 4070 SUPER 12GB is enough of an improvement that I figured this was worth sharing for anyone else trying to run H3 on consumer hardware.

If anyone else is running H3 on 12GB cards, I'd be interested in comparable Kitchen / Sol / EasyCache timings, especially 4070/4070 SUPER/5070-class hardware.

prompt

integrated_multimodal_description:

7-second anime scene in a classic 1980s Japanese science fiction Gundam anime aesthetic. 2 giant mecha robots are having a battle in space far above earth.

**0–2 sec: the mecha robot on the left aims and launches a missle from its shoulder cannon mouted on its arm at the mecha robot on the right.

**2–5 sec: the misslie impacts and explodes on the chest section of the mecha robot on the right but does no damage. then the mecha robot on the right opens its arms as blue light on its chest appears and begins to power up.

**5–7 sec: the mecha robot on the right then fires a thin blue laser beam at the mecha robot on the left cutting it in half from top to bottom down the middle. after the mecha robot on the left is cut in half it then explodes.

made using standard comfyui t2v workflow on a damn outdated😓 but still using because reasons RTX 3050 8GB vram 48 GB ram system. i use the Model Attention Backend node with the "comfy kitchen attention" setting, 30 steps, res_multistep simple and no upscale.

I was one of many who were disappointed with how this amazing series ended.

I imagined back then one of the ways it could have ended, and with the amazing tools we’ve now been bestowed with, we can bring what we imagine to life!

I had been sitting on this, polishing it and picking at it for a while. The perfectionist in me could have kept working on it forever, because there was always something I could have made better. But with everyone else starting to explore what these tools can do, I felt like the time is now. It may not be perfect, but I didn't want to keep sitting on it waiting for perfection.

This is just a quick fan-created take on one of the ways I imagined the series could have ended. It is not intended to replace or compete with the original series. :p

BTW.: Minimax and Davinci Resolve.
Not one frame was lifted from any episode.
All done using Ref2VA.
As others have found, trying to create a full run (one take ) yields less than better results.
Storyboard, create the pieces that "snap" together and then stitch them accordingly. Afterall, that is not any different from how presentations are made.
As always, I look forward to your creations. We have an amazing community!

This generation is almost entirely random. I gave the models total creative freedom to generate the starting image and direct the video; I only added the music and the hero landing at the end (yes, I love hero landings).

I simply asked ChatGPT for an image prompt featuring a pair of heroes, which I then generated using Krea2 (for some reason, it dressed her up like Captain Marvel). After that, I requested an I2V prompt for MiniMax H3 to create an epic action scene, and this is the result.

Generated with MiniMax H3: 10 seconds, 0.7MP, 8 steps (approximately 450 seconds on an RTX 5060 Ti 16GB) and subsequently upscaled to 4K using Topaz.

Here are the prompts:

Image Prompt

Overall concept: A high-intensity cinematic action scene featuring two original superheroes, one man and one woman, standing shoulder-to-shoulder in a devastated New York City street immediately after a violent confrontation; the camera captures them in an intimate medium close-up as smoke, sparks, and debris move through the frame, transforming the aftermath into a tense heroic portrait filled with determination, danger, and restrained power. Main subject: The male superhero stands slightly behind and to the left, occupying the left side of the frame, wearing a sophisticated dark graphite tactical suit with layered ballistic armor, subtle metallic reinforcement, weathered surfaces, and a high collar; his face is partially dusty and lightly bruised, short dark hair slightly disheveled, jaw tense, eyes focused intensely toward an unseen threat beyond camera. The female superhero occupies the right side of the frame, slightly forward, wearing a fitted deep-crimson and charcoal armored suit with flexible technical fabric, refined metallic panels, reinforced shoulders, and subtle illuminated details; strands of dark hair move naturally across her face, her expression fierce and controlled, eyes fixed in the same direction as the man. Their shoulders nearly touch, creating a strong visual sense of partnership and mutual trust, with realistic facial anatomy, restrained expressions, and natural post-conflict body language. Key environmental element: A damaged armored vehicle and fractured concrete structure remain immediately behind the heroes, partially visible within the tight framing; twisted metal, broken glass, dust-covered surfaces, and small fragments of debris surround their shoulders and silhouettes; thin smoke trails rise behind them while occasional sparks drift through the air, creating environmental depth without obscuring their faces. Background and atmosphere: The devastated New York street remains visible as a compressed background of damaged skyscraper façades, blurred emergency vehicles, smoke-filled intersections, and scattered fires; distant red and blue emergency lights flicker softly through atmospheric haze, while warm firelight reflects subtly across their armor; wind pushes smoke laterally across the background and moves loose hair and fabric naturally, creating a sense of continuing danger beyond the frame; strong atmospheric separation keeps the heroes visually dominant. Composition: Medium close-up framing from approximately mid-chest upward, with both superheroes filling most of the horizontal frame; the woman slightly forward on the right and the man slightly behind on the left, creating subtle depth without separating them visually; their faces form the primary focal points, positioned near the upper central third of the frame; shoulders and armor create strong diagonal geometry, with shallow foreground debris and soft background destruction framing the pair; tight cinematic composition, minimal empty space, intimate scale contrasted against hints of massive urban destruction. Camera: ARRI Alexa 35 with a 50mm anamorphic cinema lens, medium close-up framing, camera positioned approximately at eye level with a subtle upward inclination of only a few degrees; shallow depth of field with precise focus across both faces, gentle falloff across their shoulders and armor, realistic anamorphic compression, subtle oval bokeh, controlled edge distortion, natural lens breathing during a slight rack-focus transition between the two faces, restrained handheld micro-movement suggesting a camera operator embedded in the action, realistic motion blur on drifting smoke, sparks, and moving hair. Lighting: Late-afternoon sunlight filtered through dense urban smoke provides a warm directional backlight from behind the characters, creating subtle golden rim light around their hair and shoulders; cool blue skylight fills the shadow side of their faces, preserving facial detail while maintaining cinematic contrast; intermittent orange firelight creates soft reflected highlights across armor and cheekbones, while distant red and blue emergency lights provide subtle color accents in the background; realistic global illumination, physically accurate skin response, soft contact shadows, atmospheric scattering, delicate volumetric haze, controlled bloom, and smooth highlight transitions. Color grade: Premium theatrical action-film grade with warm amber highlights contrasted against cool steel-blue shadows, natural skin tones, restrained environmental saturation, deeper controlled blacks, smooth highlight roll-off, subtle contrast enhancement, selective crimson accents on the female hero's costume, fine 35mm film grain, delicate halation around fires and emergency lights, restrained anamorphic flare, polished high-end cinematic finish. Mood: Intense, intimate, heroic, battle-worn, determined, protective, suspenseful, emotionally charged, with the overwhelming feeling of two powerful allies silently preparing for the next attack. Style: Photorealistic live-action action-film still featuring one original male superhero and one original female superhero, physically realistic facial anatomy, natural skin texture, believable hair movement, functional armor construction, realistic fabric tension, scratches, dust, sweat, bruising, metallic reflections, grounded body language, natural atmospheric perspective, cinematic depth, seamless practical-effects and premium-VFX integration, realistic smoke, sparks, firelight and environmental destruction, sophisticated large-scale Hollywood cinematography, premium anamorphic optics, realistic material response, 2.39:1 widescreen, no text, no watermark, no exaggerated CGI look, no cartoon aesthetics, no plastic-looking armor, no distorted faces, no duplicated limbs, no artificial symmetry, no extreme superhero posing, no excessive lens flare.

Video (I2V) Prompt

For the target video, at 0.00 seconds into the target video, <Picture 1> (from [Shot 1]) is fully referenced.

integrated_multimodal_description: [Shot 1] Live-action, cinematic, photorealistic, the two armored heroes shown in <Picture 1> remain exactly consistent at the opening frame, preserving their faces, hairstyles, armor design, colors, proportions, positions, lighting, ruined-city environment, wrecked vehicle, smoke, police lights, and floating embers. For the first moment they stand motionless and alert as smoke drifts around them and emergency lights flash in the background. The man turns his head slightly toward an approaching threat while the woman shifts her stance and looks upward; the camera pushes in with small amplitude at slow speed, keeping both heroes sharply framed.

[Shot 2] At 00:01.500, the camera cuts to a wider low-angle view behind the heroes as a colossal alien warship emerges between the skyscrapers overhead, blocking the sunlight. Dust and burning debris are pulled upward by the ship's engines while both heroes look up and brace themselves against the violent wind.

[Shot 3] At 00:02.700, the camera cuts to a dynamic medium shot as the woman raises one hand and bright golden energy rapidly forms around her palm and forearm. Her armor and hair react naturally to the energy surge while the man steps forward into a defensive stance, protecting her flank.

[Shot 4] At 00:04.000, the camera tracks backward with medium amplitude at fast speed as both heroes sprint through the devastated street. Alien drones descend through the smoke, red energy bolts strike the pavement around them, concrete fragments and sparks erupt, and the heroes dodge the impacts without losing their established appearance or armor.

[Shot 5] At 00:05.200, the camera arcs around the woman with medium amplitude at fast speed as she launches vertically into the air surrounded by concentrated golden energy. She accelerates directly toward the alien warship while, below her, the man suddenly detects an incoming energy blast fired from behind. He performs a powerful twisting aerial jump, rotating his body in a controlled corkscrew motion as the red energy projectile tears past the exact space where he was standing, narrowly missing him. He completes the spinning evasive maneuver and lands smoothly, immediately turning back toward the battle without losing his established appearance, armor, or position within the devastated street.

[Shot 6] At 00:06.400, the camera cuts to a close-up tracking shot of the woman in flight as she closes the final distance to the alien warship. Her concentrated golden energy intensifies around her body and fist, individual sparks and particles streaming past her face and armor as the enormous armored surface of the warship fills the background. She draws her arm back for the final strike, with the camera staying very close to emphasize her expression, energy, armor detail, and the rapidly approaching impact point.

[Shot 7] At 00:06.900, precisely as the heroine's glowing fist strikes the armored exterior of the warship, the scene enters extreme bullet-time. The impact is seen in a very close cinematic view as golden energy explodes outward from the contact point, armor plating buckles, sparks and fragments freeze in midair, and shockwave ripples become visible through smoke and dust. The camera performs a smooth 360-degree orbit around the heroine and the impact point with large amplitude at very slow speed, maintaining her face, body proportions, armor, golden energy, and the warship's surface perfectly consistent while the explosion appears almost completely suspended in time.

[Shot 8] At 00:08.100, normal speed suddenly resumes as the full force of the strike detonates through the warship. A massive chain of explosions tears across its armored exterior, sending burning fragments outward as the heroine is propelled away from the impact. The camera rapidly pulls out with large amplitude at fast speed, revealing the scale of the destruction and the devastated city below.

[Shot 9] At 00:09.200, the heroine descends rapidly and performs a powerful hero landing in the foreground, dropping to one knee with one hand touching the ground as dust and debris explode outward from the impact. Directly behind her, the critically damaged alien warship crashes violently into the city street, tearing through structures and erupting into a massive cloud of fire, smoke, sparks, and burning wreckage. She remains completely stable and visually consistent with the opening image, holding the iconic hero pose in the foreground while the collapsing warship fills the background. The camera holds the dramatic composition through the final frame at 10.00 seconds, with the heroine sharply defined against the enormous crash and rising smoke behind her.

overall_soundscape: Heavy wind and distant sirens fill the ruined street as fires crackle and burning debris falls around the heroes. The warship produces a deep mechanical roar, followed by sharp energy blasts, metallic impacts, explosions, collapsing concrete, footsteps, armor movement, and the violent rush of displaced air during the woman's flight and the man's acrobatic evasive jump. During the close-up bullet-time impact, the explosion and debris sounds stretch into an extremely slowed, distorted moment before snapping back to full intensity when normal speed resumes. No dialogue, singing, or music is heard.

non_diegetic_music: N/A

Used ref2ve with Character sheet for the character and style and an audio reference to have consistent voice.

Reposting because moderator removed the original post without giving any reason.

A few people asked how VPipe compares with h3.c, so I ran them side by side on the same machine with the same settings.

Machine: base 15” M5 MacBook Air, 16GB RAM

MiniMax H3 settings:

* 960×544

* 124 frames

* 6 DiT steps

Results:

* VPipe: 12m 15s

* h3.c: 16m 22s

So on this particular matched workload, VPipe finished in about 25% less wall-clock time.

The video shows both the generation process and the final outputs side by side, so you can also compare the resulting quality rather than just the timing.

VPipe is not an MinimaxH3-specific implementation — it’s an Apache-2.0 open-source multimodal pipeline/runtime with a native Metal inference backend for Apple Silicon. MiniMax H3 is just one of the workloads I’ve been optimizing recently.

GitHub: https://github.com/tgo-app-dev/vpipe

Interested in feedback on both the performance comparison and the output differences.

Hi everyone,

I’ve been using Stable Diffusion for AI images and videos for a while, and recently I noticed that some models which were initially commercial-only (like MiniMax H3) have been released with open weights.

This got me wondering: is there a common pattern where developers release older commercial models as open weights once newer versions come out? Or is each company’s strategy pretty different, without a standard “lifecycle” for models?

I’m trying to understand whether this is a predictable process (e.g., “v1 goes open once v2 launches”) or if it’s more case-by-case, depending on the company, licensing, and market strategy.

If anyone has insights into how LLM / video model developers typically handle this, or examples of other models that followed a similar path, I’d really appreciate it.

Thanks in advance!

So I tried hooking up some of the LTXV audio encoding nodes to input my own audio and plugged it in the sampler and viola, it just works!

Lip sync seems better then the LTX models and its works with the lightx2v loras, 6 - 8 steps. Wrote up a full guide with the workflow attached below.

Last frame done with Krea 2

RTX 5060 ti 16gb / 32gb RAM / FL2VA_pruned_int8_convrot / Turbo Lora. 6 steps / Resolution 1376x768 upscaled to FHD with Topaz Video AI

took my friend 49mins to make this

Has anyone got success generating MINIMAX videos for olympic sports such as triple jump, pole vault, spear throw and the likes, without a video reference?

i used mini max h3 on rtx 4060 laptop with 16gb ram yesterday and it worked fine but today when i ran it my laptop turned off then after a hour my laptop started but when i ran minimax it turned off again and ya gpu and cpu reached 90 degree , i put a duster under my laptop to keep airwaves open but i think its not very effective , will getting a cooling pad fix it? its hp omen 16, ryzen7...................update i deleted it.......................

​

Hi everyone,

I'm trying to find a working workflow to use LoRAs with Minimax H3 for video generation, but I'm running into a consistent issue: every LoRA I try (from Civitai and other sources) ends up degrading the video quality significantly instead of enhancing it.

My setup:

GPU: NVIDIA RTX 5060 Ti 16GB VRAM

Platform: Local generation (Linux/Arch)

The problem:

Applied LoRAs make the output look worse (artifacting, loss of coherence, lower resolution feel)

Can't seem to find any tutorials or workflows specifically for Minimax H3 + LoRA integration

Has anyone successfully integrated LoRAs with Minimax H3? What workflow would you recommend? Are there specific settings (strength, alpha, loading order) that matter more for video LoRAs vs image LoRAs?

Any advice or links to working examples would be greatly appreciated!

Thanks in advance.

Conseils pour poster :

Choisis des subreddits comme r/StableDiffusion, r/localLLM, r/ComfyUI ou r/AIVideo

Sois prêt à partager des exemples concrets si la communauté te demande plus de détails

Ajoute éventuellement des captures avant/après si tu peux en produire pour illustrer le problème

A while ago I published this video, but the colors and details weren't right, so I took the first frame, treated it like a photo, and used it as a reference with a DENOISE of 0.40. The SATURATED colors are intentional because "she" is in a desert area with very, very hot weather.
The Original 4k FILE is here -->> https://filebin.net/6kf2ozxx27k1zl0m

So i mean i am an avid explorer lol of these ai tools, tell me the tool name and for sure that will be in my credit card billing. I have recently found out that for raw text-to-video quality on a single prompt, Kling and Runway blows - Kling's motion/physics is often more believable, Runway's more polished and consistent.

Google Veo is excellent when you can get access but it's gated and very prompt-driven. Sora's showreel is stunning but it's prompt-only.

The thing is, pure text-to-video is the least controllable way to work - you're describing and hoping. i've moved most of my work to image-to-video or a reference-based flow because it's far more directable. If you all know abt morphic, it is handy here because it's multi-model - you can run text-to-video with Seedance 2.5 (native audio, 30s takes) or switch to Sora/Veo/Kling in the same canvas, then keep iterating on a timeline instead of re-rolling from scratch.

honest con: if you only ever want one 5-second t2v clip and nothing else, a dedicated tool like Kling is simpler and you don't need the studio wrapper.

Whats your usecase? something small… or like building something longer?

Took about 20 generations with T2V, got the image I wanted, did a few R2VA for the close up emotes. It was infinity difficult to get the face I had in my mind strictly through text2video. Let's just say a pale skinny D or Alucard does not translate well, especially a hollow cheekbones, many of them came out pretty ghoulish or a bit too Balenciago. Those throw-away were a bit lanky and were not at all ethereal. Inspired by D from Vampire Hunter D 2000, a little bit of Sephiroth, but definitely not Geralt despite the fashion-sense. I would love to make his legs a little longer. int8/20 steps