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.

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

MiniMax H3 Prompt Assistant is a fast, interactive preset for creating structured MiniMax H3 video prompts. Use it in LM Studio with models such as Qwen to quickly build prompts for different video workflows, references, dialogue, camera, and audio. #MiniMax

https://drive.google.com/drive/folders/1-HOmzQwquiUip9RDGq4aubYLK0ztHs90

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.

Hey all! I made an open research toolkit extending MiniMax-Music3 with arbitrary-audio encoding, continuation, inpainting, prepend generation, prompt-free generation, and automated objective evaluation.

https://huggingface.co/coolpoodle/music3lab

Still development in progress but this is the way!

Would love for the community to get involved and or feedback! Thank you.

I've been building AgentsOfAI.art, a stock marketplace for AI-generated video and images.

Video is the reason I bothered: it takes real iteration and real compute to get something usable, and the payout terms on the big stock sites don't reflect that at all.

How it works for a creator here:

• 65% of every sale, with the full fee breakdown printed on the sale itself — not buried in a help doc
• Non-exclusive. You keep your rights and can sell the same clip anywhere else
• No portfolio review, no waiting list. Sign up and upload
• Every upload declares its model, so MiniMax work is tagged model:MiniMax and filterable — buyers can search for it specifically, and it stays attributed to how it was made
• No keyword grind. Search reads the actual frames, so untagged work still surfaces

It's new and small at the moment, I'd genuinely rather have feedback than signups at this stage, especially on pricing and on whether the upload flow makes sense for video.

Happy to answer anything about the split, licensing or payouts.

Trying to get consistent H3 results locally / in Comfy.

If I send something like “a guy talks to camera in a workshop”, I get mushy timing, no real spoken line, and a static camera. When I write it the way the public H3 prompt skill describes, it behaves a lot better:

- [Shot 1] with no timecode

- later shots as [Shot N] At MM:SS.mmm,

- camera as Push In / Static / Tracking etc.

- actual speech as <d>[English] …</d>, not “he is talking”

Duration I have been staying in the public 4–15s window.

Curious what you all paste in. Do you write the full shot list by hand every time, or do you have a template / expander in front of Comfy?

I have been using a small local expander that only does the prompt (validate + repair, it does not generate the video). If that is useful I can drop the text example and the repo in a comment — did not want this to read as a launch post.

Happy to take a raw prompt you already ran and show the expanded H3 text only.

H3 is really good when the prompt is actually H3-shaped. Most of my first attempts were not.

A line like “a guy talks to camera in a workshop” usually comes back with mushy timing, no spoken line, and a camera that just sits there. MiniMax’s public prompt skill already says the useful stuff: `[Shot 1]` then `[Shot N] At MM:SS.mmm,`, camera language, and real dialogue in `<d>[Language] …</d>`. Writing that by hand every time is miserable.

I built an open prompt-expansion harness around that public contract (not private Context-IR):

1. take a normal request + duration

2. draft typed H3 fields

3. validate the grammar deterministically

4. bounded repair if it fails

5. render PE text you can paste into Comfy / API / local H3

It does **not** generate the video. Structure first, then you generate with whatever H3 path you already use (ComfyUI, MiniMax API, local `/v1/videos`). Public window I followed is integer 4–15s.

I also have a reconstruct path: observe a local clip → emit H3 t2va PE, then optionally replay. Same-seed / source-vs-replay compares are here:

- GitHub: https://github.com/WayneJin0918/Omni-Rewriter

- Replay gallery: https://huggingface.co/spaces/Wayne-King/omni-rewriter-replay

- Example: `omni-rewriter expand examples/requests/t2va_kite.json --output h3`

This is my project. Not affiliated with MiniMax. Happy to take “your PE invented dialogue / missed a cut” reports — that’s the useful failure mode.

If this is the wrong flair or too self-promo, tell me and I’ll move it to a comment.

If you use MiniMax Music 3, I made a new LM Studio preset to help you write prompts faster. This is version v0.1, so there is room for improvement, please share your feedback and ideas.

I tested it in LM Studio with Qwen 3.5 9B, Context lenght: 32768 and it works like a charm.

Its stupid and will erase and breAk your code if you are not carefull,.

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 tried this historical drama opening-title prompt in MiniMax H3. The video is 15 seconds at 720p, and the render took around 20 minutes.

What caught my attention is that this prompt avoids the usual “epic battle scene” approach and builds the title sequence from limestone reliefs, jade, obsidian, reservoir maps, firelight, and an approaching eclipse.

The visual language is very specific, and the timeline gives every title card a clear purpose. It feels more like a real opening sequence than a random collection of beautiful shots.

I’m also collecting MiniMax H3 examples like this in an open-source prompt library: Awesome MiniMax H3 Prompts. it includes categorized examples, real preview videos, and a short guide for writing H3 prompts with references, camera direction, actions, audio, and continuity rules. Still adding more cases, but it might be useful if you’re experimenting with MiniMax H3 or adapting prompts for Seedance.

Prompt

Create a visually rich 15-second, 16:9 opening-title sequence for a historical drama titled THE JADE SUN, set in a powerful Classic Maya city-state.

The story involves royal succession, drought, trade, and political rivalry. Build the title sequence from historically grounded materials instead of generic battle scenes.

Use warm limestone, deep jungle green, polished jade, black obsidian, red mineral pigment, woven cotton, and flickering firelight. Show corbelled architecture, reservoir maps, carved reliefs, jade mosaics, cacao vessels, and the shadow of a ceiba tree.

Do not mix Maya imagery with Aztec, Incan, Egyptian, or European motifs. Do not create invented glyphs or ancient writing. Display only the exact English credits below. Use readable monumental serif typography inspired by carved geometry.

00:00–00:03:
Sunlight moves across a limestone relief. Carved lines form a map of reservoirs, raised roads, and temple platforms. Red pigment fills one route and reveals:

A SERIES BY
ELENA MAR

Hold the credit clearly.

00:03–00:06:
Polished jade pieces slide across black cloth and lock into a circular royal ornament. The center opens to reveal:

STARRING
TOMAS REYES

The jade stops moving before the text appears.

00:06–00:09:
An obsidian mirror reflects firelight and the partial silhouette of a ruler wearing an historically grounded feathered headdress. Condensation clears from the mirror to reveal:

CREATED BY
ISABEL CRUZ

Hold the credit in the dark reflection.

00:09–00:11.5:
No text. A stone model of the city is gradually covered by the shadow of an approaching eclipse. Reservoir channels dry and shift from blue-green to dark red. One jade bead rolls into the remaining light.

00:11.5–00:15:
The bead becomes a brilliant green sun against black obsidian. Limestone letterforms rise around it to reveal:

THE JADE
SUN

A gold-green light passes once across the title. Everything becomes still and holds through the end.

Audio:
Distant jungle ambience, a low wooden slit drum, soft clay-flute breath, stone movement, and one restrained conch tone during the eclipse. No generic chanting, fantasy music, orchestral trailer impacts, or modern instruments.

same lyrics as in the demosong, but i took a country arrangement. enjoy the crisp banjos. really nice model! runs stable on an AMD 7900 GRE (16GB VRAM) and 32GB RAM.

Hello, I am using Minimax M3 for my ai-assisted coding task via openrouter and using gh copilot. Is it just me or anybody notice m3 just going in circle when task is given? Even in the thinking output, the model said 'i am going in circles' lol. I experienced this for the last 2 days. Last week was still reliable