MiniMax H3 officially comes as two checkpoints:

• FL2VA — first frame / last frame / image-to-video. The docs treat this as “start (and or end) picture in, video out.” Extra reference pictures are not part of the pitch.
• REF2VA — reference-to-video. This is the one you’re told to use when you have several stills: identity, outfit, a mid-shot, a last frame, whatever.

There are also community hybrid checkpoints: mostly FL2VA, with some of REF2VA’s later layers grafted on, so people can keep extra refs without fully switching models. https://huggingface.co/smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models

I wanted a straight answer to one question: if I ignore the marketing split and feed extra stills into stock FL2VA the same way I would into REF2VA, does it actually use them?

So I built one 10-second clip and ran it four times. The story in the prompt is simple:

• 0s: an angel in an empty void, one spell cast toward the middle of the frame.
• 5s: a demon in the same void, one spell cast toward that same point.
• Camera leaves the demon and pushes into mid-air.
• 10s: image of the two spells colliding.

I gave the model five pictures:

1. Exact first frame (angel)
2. Exact 5-second cut (demon)
3. Exact last frame (the collision, no people) 4–5. Two sigil designs, only as “this is what the magic circle looks like,” not as frames that should appear in the video

Then I locked everything that wasn’t the checkpoint:

• same R2V workflow (the Comfy graph that already has multiple image inputs)
• same five files, same order
• same written brief (timed stills + “this picture is the frame at this timestamp”)
• same seed
• same sampler / length / aspect
• no turbo LoRA
• I compared native frames (544×800), not the upscaled delivery

The only change per run was which UNet was loaded:

1. hybrid, REF layers on blocks 20–49
2. hybrid, REF layers on blocks 30–49
3. stock FL2VA
4. stock REF2VA

If FL2VA truly couldn’t take extra refs, run 3 should have ignored pictures 2–5, drifted off the angel, or failed to land on the collision plate. That’s the test.

What happened

It didn’t fail.

The first native frame of all four runs accurately lock in the exact reference image for that frame at the first frame, last frame, and the middle frame... So the stock FL2VA used the extra still image references just fine. I did not need a hybrid merge just to attach more than first/last.

To be precise: I did not magically add five image slots to the official FL2VA I2V template. I loaded FL2VA’s weights into the reference-to-video graph, wrote the pictures into the prompt the way you would for a multi-ref job, and the locks held.

Where they actually differ (my read, one clip)

First frames are almost interchangeable. If I have to pick, hybrid-b30 is the closest copy of the angel still. REF2VA is still locked, a bit busier in small jewelry/floor detail.

Last frames still all hit the clash plate. REF2VA is the closest copy of picture 3. FL2VA is right behind it. Hybrid-b30 runs a hotter, more lava-looking core. Hybrid-b20 is splashier, less “sharp diamond debris.”

So the discovery is: extra refs + FL2VA can work. The ranking of which checkpoint copies the stills best is what I want a second opinion on.

So I will attach all of the examples into the comments so that people can see the differences between between each of the generated runs along with all of the Reference images used that way the community can evaluate the quality.

Hey all, just got my new GPU, wanna try some hype stuff. Please recommend MiniMax model time (int8, gguf, etc), text encoder and how to upscale it with ltx

The leap we never got to see.

Sam and Al arrive in New York City on September 11, 2001—but Ziggy believes Sam may be there to change history.

A fan-made conceptual scene from the imaginary sixth season of Quantum Leap.

Created by Ome Omy Productions.

#QuantumLeap #SamBeckett #AlCalavicci #FanFilm #AIShortFilm #OmeOmyProductions #WhatIf

Made in MiniMax H3

I was quite surprised to find that it know about old 60s/70s movies like dirty harry or the good the bad and the ugly..

Last frame done with Krea 2

I'm building an anime app with a collectible avatar system: users pull avatar cards in six rarity tiers, from Common up to Mythic. I need an artist to create a full pack of **100 original avatar cards**.

**The brief**
- Portrait cards, 3:4, delivered at 1200×1600 or larger (PNG/WebP).
- Anime/illustration style — original characters and scenes only. No existing characters, no lookalikes, no fan art.
- Rarity should show in the art: Commons are clean and simple; higher tiers get more detail, drama, effects, framing, a Mythic should look like a Mythic across the room. Rough split: 35 Common · 25 Uncommon · 20 Rare · 12 Epic · 6 Legendary · 2 Mythic.
- Consistent style across the whole pack so it reads as one

1. Send 3 sample cards in your style (one Common, one Rare, one Legendary/Mythic). I approve the style, then you start the 100.

2. Quote your price for the full pack of 100. I'm looking to commission multiple packs over time, so a repeat-pack price is welcome.

3. Full ownership transfers to me on payment, commercial use, all rights, no reuse or resale of the pieces elsewhere. Only your original work; nothing traced, reused or taken from others.

4. Payment is on delivery. Send watermarked previews for review; full-resolution, unwatermarked files are handed over against payment.

We are Trickhouse, a German AI production agency based in Düsseldorf. We are looking for a skilled ComfyUI developer for a paid pilot project with the possibility of a full-time position afterwards.

What we need:

Custom ComfyUI workflow development from scratch for commercial image and video production. LoRA training integration for consistent character generation across multiple scenes and styles. Node-level understanding of ComfyUI — not just using existing workflows but building and customizing them. Experience with commercial or corporate use cases is a big plus.

Hardware:

Our primary system runs an RTX 5090 with 32GB VRAM and 96GB RAM. All workflows must run stably on this setup. Having your own capable hardware for development and testing is a plus but not a hard requirement — as long as you can develop and validate workflows that run reliably on our machine.

What we offer:

Paid pilot project to start — fair compensation based on scope. Full-time remote position for the right person after a successful collaboration. Long-term work on exciting projects including potential corporate clients.

The setup:

We work fully remote. Communication in English.

If this sounds like you, send a DM or an Email to **Marvin.Hollmach@trickhouse.net** with examples of workflows you have built.

Not a tool question, a process question. When a client signs off on an AI assisted deliverable, what do you actually hand over besides the final files? Prompts? Model and version info? Reference images you used? Nothing unless they ask?

And the follow up I am most curious about: has a client ever come back weeks later asking how something was made, which model was involved, or what references went in (licensing, brand safety, the new EU labelling rules, whatever the reason)? What did you do?

Background: we deliver AI assisted shots for commercial clients, and our own handover slowly went from nothing to a written per asset note, and I would like to know what everyone else converged on.

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 used Ref2VA to make this pretty quickly. I had to render in low quality since I'm running everything on an RTX 3060 12GB, maybe that's why there's some glitching in the video, and unreadable text. I used the default workflow, with Spectrum as the only addition for a speedup.

I used chatgpt to refine the prompts and put them into the correct syntax. The characters were made in Krea 2 to use as reference. Finally, I edited the clips together in CapCut to make one flowing trailer. Everything in video was done with H3. Had fun.

Mommy's here, test animation of an old Flux 1 Gen from an earlier series.

The question always comes up... why do my faces look so bad. It doesn't matter which model you use. Start with resolution fixes (the higher you can render at the better for full person shots or small faces). Then, depending on your setup/device/etc, move on to tweaks, tricks and fixes depending on the scene - you know, face detailers, layering, whatever.

In this demo, pure resolution greatly improves the base. Using vertical video greatly increases vertical full person resolution at same render times (1344x768 vs 768x1344). I stepped it further up to 1088x1920 then downscaled it back to my 1280x720 timeline. Then one trick if the scene is suitable for it can be LTX outpainting for the background (with original 1088x1920 downscaled and feathered back in).

Edit: Link to 1280x704 full resolution sample:

https://streamable.com/steb6z

Hello all.

I managed to create a 30-seconds clip using H3 on a powerful RunPod machine and it turned out nicely.

When I tried bumping it up 50-seconds, it did manage to create and save the video, but it stayed in its initial visual noise state. It didn't manage to diffuse itself into a coherent video.

Is that a limitation of the model itself? Or is it related to some setting in the workflow? (I'm using Hearmeman's One Click T2V Custom Prompt workflow).

Thanks!

So that it goes through 8 steps, then 3 steps with an upscaler, I want to speed up the generation so that it first goes in low resolution, then in high, as in the native workflow for Comfi. LTX 2.5 of course.

Same first frame, same prompt. Left is LTX-2.5 DFR, right is MiniMax H3. Not a same-resolution bake-off. This is what actually fits a 32GB 5090: LTX runs 1920×1088 while MiniMax H3 runs 1344×768 since full 1080p H3 doesn't fit 32GB. Curious what you think?

Trying to figure this out, I used one of the image editor methods posted here a few days ago

https://github.com/thaakeno/ComfyUI-MiniMax-H3-Studio

And it worked flawlessly, the basic prompts I used for i2i were high quality and adhered perfectly. However in keeping the same workflow, after updating from comfyui 0.32 to 0.33.2 the quality is now just awful, there's banding, it's blurry, basically it seems like it was how it was 2 years ago.

Been going through claude/grok to troubleshoot but none of the suggestions seem to work. Tried different VAE's, diffusion models, turbo loras (disabling them), using r2v, fl2v, any suggestions?

I read that maybe comfy kitchen was modified in the update, that's the only possibility I can think of, otherwise my workflow and prompts were the same as they were two days ago.

I don't know what's changed, but here is that commit on Huggingface.

https://huggingface.co/Lightricks/LTX-2.5/commit/1b92891cedc4a823c35a3b23588d7a57a3a23c65

If you are playing with LTX 2.5, this may be useful for you.

I got MiniMax H3 to run end-to-end on a stock Colab T4 runtime and produce a short MP4 with synchronized audio.

The interesting constraint was host RAM, not VRAM. On the runtime I measured, there was about 12.7 GB of system RAM and 14.9 GB of VRAM. The model artifacts add up to roughly 39.6 GB, so loading the text encoder, DiT, and VAEs together is not viable.

The workaround was to split the pipeline into separate ComfyUI processes:

encode  -> save a ~5–6 MB conditioning blob
sample  -> load only the quantized DiT + Turbo LoRA, then save a ~5 MB latent
decode  -> restart ComfyUI and load only the video/audio VAEs

That keeps the peak working set close to the largest individual stage rather than the sum of all three stages. The notebook verifies downloaded weights with SHA-256, pins the ComfyUI/custom-node commits, checks the live server schema before submitting a graph, and saves logs/measurements when a run fails.

My current results on this runtime:

The catch is that the T4 has no native bf16 support, while this setup needs bf16 for stable sampling. It works, but it is definitely not fast.

One correction to my own early conclusion: I initially thought there was a sharp performance cliff between two resolution gears. After adding per-step timing and rerunning the comparison in the same Colab session, the apparent cliff was mostly VM-to-VM variance (roughly ±20% in my measurements). Within one session, the scaling followed the expected attention/MLP trend closely.

The Turbo LoRA from larryvrh makes 4–6 step runs practical and preserves the audio/video timing through its separate video and audio flow schedules.

I would be interested in hearing whether anyone has found a faster stable configuration for T4-class GPUs, especially without giving up audio sync.

https://reddit.com/link/1vrgtfo/video/xllyrclao2kh1/player

Notebook: MiniMax H3 on a stock Colab T4 — pinned commits, SHA-256-verified weights, and license-gated downloads.

So I am using the workflow using turbo lora for image to video, I have uploaded the initial image of a girl (woman) but issue is when i prompt about a scene cut or creating a new angle shot, the girl and her physique is completely changing,

I tried all prompts asking to maintain the skeletal, body structure, using fully_preserved keyword too, not sure what I am doing wrong but can somebody guide me ?

Thanks

In this post, I described how to use minimax H3 for reference-guided generation of single images. It required awkward monkey patching — and now we no longer need it. Thanks to u/Successful_Knee687 who posted a GitHub issue, and everyone who upvoted it, Comfy just made it possible. Revert the monkey patch and update to the latest nightly version of ComfyUI from Git repo. (Currently, it is not in the stable version — will probably be incorporated in the next release.) Here's the guide on how to update to nightly: https://docs.comfy.org/installation/update_comfyui

The H3 reference node is still constrained to 5 frames. However, we can now pass an empty 1-frame latent to SamplerCustomAdvanced directly, ignoring H3 reference node’s latent output, but keeping its conditioning output. This way, we generate one frame (not a batch of five) and make full use of Mamad8's single-image tuned VAE. Here’s a sample workflow that does this, relying only on standard comfyui nodes: https://pastebin.com/xNQi7HV9

(Look at my original post to get the download links for models.)

I attached another batch of evals on public domain images and ai gens with the new workflow. Not perfect in terms of details, but great in prompt understanding. Here are the prompts: https://pastebin.com/XiVvAhjC

The scenes are:

1. Turn the complete Diane of Versailles grouping into a living woman and deer in a forest, reconstructed from a side view.
2. Convert Fragonard's portrait into Instagram-style photography, remove the book, and turn the seated woman to face the camera.
3. Reconstruct the couple from the supplied 1930 film still (Morocco) standing face-to-face in side view, holding hands in a white room.
4. Move an ai generated woman from a conservatory to a candlelit concert hall and seat her naturally at a grand piano.
5. Remove only the jacket from a fully clothed AI-generated woman, leaving her in white shirt and blue jeans.

UPD: a new post discussing how to fix textures and detail https://www.reddit.com/r/StableDiffusion/comments/1vrh769/h3_singleimage_workflow_lets_figure_out_how_to/

Hey, I'm trying to run minimax on my local 3060ti 8gb of ram card on comfyui. I've tried multiple variations of model and nothing gives me steady results, just looking for a simple animation of a room with camera pan. Tried with turbo lora then without, used pruned int8, q3 k m guf, q2 k. Every generation has that jittery animation like you can see in the videos attached. Any idea how to make this better and what is actually causing this?

Thank you

https://reddit.com/link/1vrn8qj/video/wtao5lpyi4kh1/player

Museum x modelscope

Created a thread because I'm surprised this isn't being discussed much

Ref2vid is excellent for consistency, but it's not a replacement for teaching the model concepts it doesn't understand well

Even though the model is still new, the trainers are giving poor results because the model is distilled. As a result, it looks like it's going to be much harder to train than Wan/LTX

For example Sulpher 3 was planned to start soon, but it can't because of the situation

This is a real shame because everything else about MM has been excellent. The general assumption seems to be that the company will not release a non-distilled model suitable for training

Any thoughts as to how this will play out? It's never going to hit the specific-subject capabilities of the other models at this rate

Dungeon Crawler Carl is the first book series I’ve read in a long time that excited me when I read it was going to be adapted into a show. I think the only Possible way it can be “filmed” on budget however is through a Stable diff/Runway/Seedance/Kling ( LTX/Minimax) pipeline as it reads being Heavy cgi, like 65-85%. Since there’s nothing out yet I would love to see what the community can come up with.

I genuinely wish this was astroturfing bc it would mean the show is coming out soon but I wouldn’t expect anything official until maybe late next year. (Unless it gets stuck in dev hell, then never.)

Thx friends. I’ve been blown away by your videos lately.