This will always be the most iconic video forever for AI,will smith will be the best test subject for every new tool in market…Workflow:this time I made this on Minimax H3 on Akool and prompt generated using Google Gemini “Will Smith eating Spaghetti”
Short answer: yes, it can be removed.
Long answer: you’ll have to heavily edit your text. Alternatively, use open source models on your own hardware
I’ve been looking through some examples people are making overseas, found one I really liked, and tried recreating the vibe. Generated it in 1080p with T2V with MiniMax H3.
most of the video actually looked pretty good, but the Japanese completely fell apart near the end lol. Definitely need to tweak the prompt more. that said, I don’t think it’s a huge issue since I could just generate the voice with an external TTS tool and add it afterward.
also curious to try this with R2V + a reference image. Feels like this format could work really well with your own characters, models, or specific fashion styles.
one thing I noticed: 1080p takes longer, but the difference is pretty obvious. Small details break way less and the whole thing looks cleaner compared with lower-res generations.
I’ve also seen people generating at lower resolution first and then upscaling, so I want to test that workflow too and see whether it’s actually more efficient.
MiniMax H3 is getting pretty interesting for this kind of fashion/ad content.
Built a local tool that turns reference images into a full MiniMax H3 video prompt storyboard — no cloud, no API keys
https://github.com/lololerigolo60/Minimax-H3-prompt-studio
I've been building H3 Prompt Studio, a desktop app (CustomTkinter) that writes MiniMax H3's rigid structured prompts for you, using a local LLM (Ollama / LM Studio / llama.cpp — pick your poison).
The part I'm most excited about is the Story → Sequences mode:
No more manually writing 6-section H3 prompts by hand for every single shot of a sequence. You just curate references and a premise, and let the model handle the structure/labeling grunt work (subject definitions, retention analysis, camera vocab, dialogue tags, the works).
Everything's local, everything's saveable — you can dump a whole session (refs + story + sequences) to a JSON file and reload it later.
Still very much a personal tool, sharing in case it's useful to anyone else building on H3 locally. Happy to answer questions about the pipeline if anyone's curious.
Scores low, costs much
Posted here a while ago, about if it suck in general or if it’s me, got mixed feedback. Circled back to share that it’s in general. M3 is a low performer and crazy overpriced at the same time
Hey everyone!
A few months ago, I used MiniMax 2.7 to build some small, simple Android apps for personal use. At the time, it felt quite slow and sometimes struggled to understand my prompts. I ended up switching to Claude Pro, which gave me much better performance and comprehension.
I paused my subscription for a bit, but now that MiniMax 3 is out, I'm wondering if it's worth coming back to over Claude. Specifically, I'm curious about the token usage / limits. With MiniMax 2.7, I never came close to hitting the 5-hour limit, but browsing this subreddit, I see a lot of people complaining about quota issues.
How do the actual quotas compare between Claude Pro vs. MiniMax 3 (or MiniMax 3 vs. 2.7)?
I couldn't find clear numbers on their official website. I even asked MiniMax 3 directly, but it couldn't give me a comparison (in fact, it honestly recommended I stick with Claude Pro, haha).
Any insights or real-world experience with the current limits would be appreciated!
Despite my limited experience and modest hardware — an NVIDIA RTX 5060 Ti with only 16 GB of VRAM — I was able to create a complete 3-minute video using MiniMax H3.
The process started with ChatGPT, which I used to develop the prompts and create the necessary reference images. I then rendered the project as 10 seconds individual video clips in MiniMax H3. The soundtrack was created separately using ACE-Step 1.5, and finally, I brought everything together in DaVinci Resolve, and completed the final video.
This project shows that with the right tools, some creativity, and a lot of patience, it is possible to achieve decent results even without high-end hardware or extensive experience.
i’ve been testing that continuous-morph format on different subjects, and this time I wanted to see if it could handle a full human outfit transformation.
the setup was pretty simple: one beautiful girl, standing front-facing in the center of the frame, going through five outfits in a single 10-second shot:
Homewear → office wear → athletic set → black evening gown → silver futuristic fashion.
There are no cuts between them. the output still holds a little "AI smooth", but it can get my idea soonly and comprehensively.
i ran it with MiniMax H3 on Atlas Cloud, and the result was cleaner than I expected.
What interested me most is how usable this format could be for fashion content (honestly, i think fashion creators could run with this directly.)
curious what else you’d put through this kind of one-shot transformation.
When can we expect Minimax M3 Pro 2.7T to drop? Minimax announced that it was expected to be released in Q3 2026 and that it's still in development.
I saw the video that Minimax released of the kpop girls wiht text and graphical elements in the BG, but has anyone tried to make a cool into sequence like the one for HER? or Monty Python? Raised by wolves? I would think the hardest one for it to do would be something like Spider Man No Way Home. Something graphical, extract with amazing transitions.
I’ve been working with Acestep for a long time for almost everything music-related. Over the years I’ve had my share of bugs, happy accidents, and some really unexpected magic. I started training my own loras 6 months ago using my original tracks (I’ve been making music for about 30 years), raw sounds from my hardware synths (Matrixbrute, Korg MS-2000), and piano recordings played by my son. I literally discovered what it was really capable of from that point.
Yesterday I learned about the release of a new music model and I asked OBO, my local AI “agent”, to install the Minimax Music workflow so I could run some tests with some prompts and a few lyrics in several languages, following the way I taught it to create them (some occasional weirdness included). The agent made every decision from the styles of the tracks to the video content (it created images, a few short clips and a script to assemble them with panning), all has been made autonomously and locally while I was sleeping.
I wouldn’t say Minimax is clearly better or worse than Acestep. With my custom loras, Acestep still sounds amazing to me and feels very personal. But some of the results I got from the base Minimax model were surprisingly impressive: very clean sound, some tracks had structural issues, track building could be smarter (SFT version?) but some tracks were definitely sounding good and different than the usual Acestep ones.
If you’re curious to hear what Minimax did for me with my prompts, here’s a video with a few examples (nothing commercial, just tests, some bad, some good, the sound is as it was generated, no postprocessing):
I’m really looking forward to training Minimax on my own material to see how far it can go and how it can be improved further.
Has anyone here spent time with both Acestep and Minimax? I’d love to hear about:
Your impressions on sound quality and “feel” if you could compare.
Any resources for custom training / loras / fine-tuning for minimax?
Any tracks or demos you’ve made that you’re willing to share.
Tried making a 30s sci-fi racing scene with Hailuo.
I used Medeo to work through the shot sequence, then iterated the individual shots in Hailuo.
The Prompt:
A high-speed sci-fi motorcycle race in a dense cyberpunk city, extreme saturated deep purple, neon blue and radioactive orange, 35mm film grain, holographic billboards, ion engine trails.
Start with a slow cinematic push through the starting grid, showing an octopus rider, a glowing mechanical racer, a four-eyed alien in a sleek hovercraft, then reveal the main character on a beat-up motorcycle covered in exposed wires and broken sensors.
Launch into an aggressive FPV chase through pipes, scaffolding and holographic signs. The alien racer pulls alongside and says “Go back to your scrap heap, human.” The main character ignores him and pushes the throttle.
The alien tries to overtake through a narrow industrial pipe, crashes into the structure and explodes into a cloud of glowing metal fragments.
Final lap under three moons. The glowing mech racer takes the inside line. The beat-up motorcycle suddenly activates an overdrive mode and wins the race. End on a wide cinematic shot of the rider under the three moons.
Still tweaking the pacing + action continuity, but the crash shot came out way better than I expected lol.
Wanted to do a quick review of the MiniMax token plan, specifically the Max plan.
I've been using it as my main model provider, along with the base OpenAI plan for checking my logic on larger system design ideas. I don't vibe code, so maybe that's why I haven't run into any of the reported token usage issues or intelligence problems. It's also possible MiniMax changed or fixed how cached tokens are counted on the plan since I started using it.
To be clear, I started on the base plan that states roughly 1B tokens. That was pretty much in line with my usage and what I was previously spending on V4 Flash before the price increase.
While on the base plan, I used roughly 1.4B tokens according to the gateway I have set up. MiniMax's dashboard showed about 1.5B.
At around the two week mark, I started getting within the 80% range of the five hour usage windows when actually using my agents. So I moved to the Max plan and haven't broken 50% of a window since.
My daily token usage averages around 180M - 280M tokens, so the mid-tier plan has been great for my use case.
As long as you spend fiveish minutes writing out a prompt that clearly defines what you want done, MiniMax, in my experience, is extremely capable of following instructions.
If your main use is with PI, OMP, or Prime, I've found that setting a good soul for each agent works well. I also use a comprehensive two or three tier memory system. That gives each agent a source of truth it can compare against when running instructions, includes other systems in the same environment along with past changes. Been using TencentDB with Hindsight as the semantic layer.
Creating a tiered agent system has also been useful for token consumption.
I've got one main point of contact agent that breaks instructions into basic chunks for other specialized agents. That might be a Reddit research bot, a general web scraper, or another agent built around a specific task.
Each agent only gets the tools, context, and instructions it needs to complete that task. This gives it fewer ways to get off the rails.
Obviously this is just my use case, but so far the Max plan has handled a pretty good amount of usage.
It's also all very human in the loop. The agents that aren't are extremely sandboxed and specialized to a task through their tool limits and harness choice. With that kind of setup, I don't think you would run into many issues with daily use.
This is also pretty much all outlined in the M3 usage tips in the MiniMax docs if anyone wants to read more about it.
TLDR: I'm averaging around 260M tokens a day on the Max plan and haven't broken 50% of a five hour window. My use is mostly structured agent workflows with limited tools and good memory systems, not vibe coding. For that use case, I haven't had the token or intelligence issues I've seen other people report.
I’m testing MiniMax H3 locally in ComfyUI. In full-body shots, the initial face looks correct, but it quickly becomes soft, distorted, and unrecognizable when the character starts moving.
I tested:
ref_image_size = maxThe problem remains at native resolution. A paid 2K API test preserved the face correctly, so this appears related to local resolution or precision.
Has anyone achieved a clean, recognizable face in a true full-body shot locally? Which model precision, VAE, sampler, scheduler, or workflow worked for you?
GPU: RTX 4070 Laptop, 8 GB VRAM
Duration: 5 seconds at 24 FPS
Seed: 12370778689767
Sampler: res_multistep
Scheduler: simple
Steps: 12
Resolutions tested:
864×480
1344×768 native
Models:
minimax_h3_fl2va_pruned_int8_convrot.safetensors
minimax_h3_ref2va_pruned_int8_convrot.safetensors
Text encoder:
qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
Video VAE:
minimax_h3_video_vae_fp16.safetensors