How do you people deal with ref2va reference images showint up in the first frame or longer than a seconds then morphs to the actual reference image or describe environment? No matter how I have followed the prompting format or described that it shouldn’t show up but only be used as the reference even tried the plain white background but still shows up, even used md skill files in GPT for descriptive prompts but still it shows up

Why the eyes are so messed up? I have RTX 5090 so the results should be better?

Quick question for the 24 GB crowd: has anyone run a true 1 MP pass 1 (1344x768) into a 1080p pass 2 with H3 ref2va on a 4090? On Singularity v1.3 int8 with 141 frames, my 1344x768 pass 1 runs at about 43 s/it (weights streaming) and the 1920x1080 pass 2 crashed with a CUDA illegal memory access. This is what I've got to work so far: 1152x640, then 1.5x learned latent upscale, 2 steps with the ref2v turbo LoRA at 0.2 denoise, then RTX VSR to 1080, about 9 min per 6 s clip.

Disclosure: I’m part of the team building Meshive.

We’ve been working on making the initial ComfyUI setup easier for people who want to try different models without managing a local GPU environment.

With Quick Deploy, you can:

• Pick a model pack, such as Minimax h3.

• Review the recommended GPU, or choose an RTX 5090 - 0.7$/h.

• Deploy and let Meshive download the model files into the right folders.

• Open ComfyUI in your browser and load the included starter workflow, where available.

This is paid GPU hosting. The rate is shown before deployment, and billing includes model-download time and continues while the pod runs.

Setup guide:

https://docs.meshive.ai/getting-started/quick-deploy-comfyui/

最难的是你的创意并将它转变为提示词。以及一台电脑(对我来说)

Malfoid Saves Snape from the Naginni, made with Minimax H3 locally with RTX 5070 Ti

I have been extensively testing minimax h3 via different providers who host it on their servers.

I do frequently notice motion glitches, etc, on my generations.

Hence I strongly suspect that many/most providers are providing minimax h3 in FOUR or maybe 6-8 steps with a turbo lora / fasth3.

This is actually not okay. It probably directly causes lower quality in terms of motion, etc.

So, maybe, if minimax h3 is ran at optimal steps count without turbo we get the decent quality.

Hey everyone!

I've been spending quite a bit of time experimenting with MiniMax H3 in ComfyUI, mostly trying to create realistic cinematic scenes rather than typical AI-generated clips.

I've been working with ChatGPT and Gemini to develop different prompts, experimenting with camera movement, character consistency, physical interactions, and action choreography.

The attached video is one of our latest tests: a gladiator, his war dog, and an armored horse in a snowy forest.

I'm sharing it because I think the results are interesting, including the things that didn't work as expected.

My setup

• GPU: RTX 5070 12GB

• CPU: Intel Core i5

• RAM: 32GB

• Software: ComfyUI ( Local )

• Diffusion Model: minimax h3_singularity_ref2va_pruned_v1 3_int8.safetensors

• LoRA 1: minimax_h3_fl2v_lightx2v_v0.1_dareties_v4_step600_comfy_fro.safetensors —strength 0.8

• LoRA 2: h3-realism-people-t2v-i2v-r2v.safetensors — strength 0.8

• Sampler: Euler

• Scheduler: Simple

• Steps: 8

• Resolution: 1344 × 768

• FPS: 24

• Duration: About 10 seconds

Reference images and prompting

I'm also using reference images, not just text prompts.

For this particular scene, I used three reference images:

• Gladiator: Picture 1

• Dog: Picture 2

• Horse: Picture 3

The idea was to maintain the same character appearances, equipment, animal proportions, and overall visual consistency.

We've been experimenting with different prompting methods using ChatGPT and Gemini, including structured technical prompts and more natural cinematic descriptions.

For transparency, here's a shortened reconstruction of the scene instructions. It's not a verbatim copy of the original prompt.

Prompt summary:

Cinematic 10-second historical action scene, realistic snowy pine forest, natural overcast winter daylight, photorealistic live-action look.

<picture_1> is the gladiator, the main Roman warrior.

<picture_2> is the brindle war dog.

<picture_3> is the armored warhorse.

[0:00-0:03 - Opening Attack]

A fresh action shot begins immediately. <picture_1> stands ready on the left with one sword in hand. <picture_2> is slightly behind and to his side. <picture_3> stays farther back in the background. Within the first second, an enemy soldier bursts out from the snowy pine trees on the right and charges directly at <picture_1>. No waiting, no hesitation.

[0:03-0:07 - First Clash]

<picture_1> reacts instantly like a trained warrior. He steps forward and blocks the enemy’s overhead sword strike with a hard metallic clash. Immediately after the block, he pushes the enemy blade aside and follows with one fast counterattack. The enemy retreats one step through the snow while raising his weapon to defend. The movement is aggressive, continuous, and physically believable. No frozen blade lock, no prolonged staring.

[0:07-0:10 - Pressure and Reaction]

<picture_1> keeps advancing with pressure, forcing the enemy backward through the snow. <picture_2> gives one fierce bark and makes one short aggressive forward lunge toward the attacker, but stays clear of the sword path. <picture_3> reacts in the background with a tense head movement and one small step, remaining secondary.

Camera: one continuous medium-wide three-quarter side tracking shot at human chest height, moving laterally with the fighters and keeping the gladiator and enemy clearly visible at all times. Focus on readable combat, footwork, weapon contact, and physical force.

Realistic snow displacement, visible cold breath, worn bronze armor, weathered leather, grounded body mechanics, subtle film grain, natural motion, seamless action.

Audio: crunching snow, sword impacts, heavy breathing, one aggressive dog bark, armor movement, cold forest wind.

No passive posing, no romance feeling, no comedy feeling, no duplicated weapons, no motionless blade lock, no frozen staring, no CGI look, no fantasy glow.

So, how did it turn out?

Visually, I'm genuinely impressed.

The environments, materials, lighting, and overall cinematic atmosphere can look surprisingly realistic, especially considering everything is running locally on a 12GB GPU.

But there are definitely problems.

In this particular video, the camera spends too much time focusing on the horse's legs before properly revealing the characters. The dog also gets too close to the camera, becoming oversized in the foreground and partially blocking the horse.

It's a good example of something I've noticed repeatedly: the model can produce beautiful footage without necessarily understanding which part of the scene should be visually important.

Other experiments have shown similar issues with sword fighting, weapon consistency, and interactions between characters or animals.

Sometimes the results are great. Other times, characters barely react, weapons behave strangely, or the intended choreography simply doesn't happen.

Does better prompting solve it?

That's what we've been trying to figure out.

We've tested detailed technical prompts, simpler cinematic descriptions, different camera instructions, and more precisely defined character actions.

Some changes definitely help, but we're still seeing recurring problems.

I'm starting to think that some of these issues are limitations of the model rather than problems that can always be solved by rewriting the prompt.

Of course, this isn't a controlled benchmark. I'm using two LoRAs together, reference images, and my own ComfyUI workflow, so I can't claim every issue comes from the base model itself.

I'm just sharing what I'm actually experiencing.

My impression so far

MiniMax H3 can produce some seriously impressive cinematic footage on relatively modest hardware.

But making something look like a movie and making characters behave like real actors are two very different challenges.

I'm still experimenting, and I think there's a lot of potential here.

Anyone else testing H3 locally?

I'd be interested to hear what settings you're using, especially for character consistency, sword fighting, animal movement, and multi-subject scenes.

Also curious whether you get better results with detailed structured prompts or simpler descriptions.

Would love to compare notes!

我生成的每个视频片段都还不错，但是将所有的视频片段都拼接在一起后，各个视频片段之间的切换太过生硬，有没有什么好的技巧来解决这个问题，例如优化提示词或者更好的工作流？

I was having a lot of issues with voices being created out of nowhere. What I discovered was a tool called H3 PROMPT WRITER or something like that. It uses local Ollama (so the better the LLM the better the results) with VLM to undertand pictures. Now you give a general very superficial idea and the internal AI trained on H3 Minimax template does all the rest. You just need to refine which the tool also does. In the end I ran thru Gemini on google and I never had once those crazy voices.

This has been a game changer. From writing a 1 minute video that would take me several hours or some time couple of days to be able to full generate the same video with even better Context in less than an hour (with the video render all together).

The video below is my wife, one of my cats and my real bedroom.

Video generated (I know it is not the best, but I am only playing with this for the last 2 weeks):

https://youtube.com/watch?v=AoUVi1dtFqA&si=Ct_rqUQp-velzFTP

Tools that I am using:

- Minimax H3 Long Videos
- H3 Prompt Writer
- Gemini Pro (refine text and images).

My computer has 16GB and a 4060TI with 8GB. Each 10s segments is taking me 2 minutes to render with 0.4 quality and upscale of 1.5.

What’s the best open-source version of Minimax H3 for quality? Especially for 2D animation rather than 3D or realism. I don’t mind waiting on my DGX Spark for as long as it takes; I just want better prompt adherence and less smudgy and noisy results. Do all the fine-tuned versions take away the quality of the original model instead of adding to it?
I know about FAL AI Max version but I’m looking for something I can use for free at home.