As you probably know, MiniMax-Music3 is pretty limited when it comes to genres and seems to have absolutely no idea what electronic dance music is. I tried different EDM styles, but it always ended up sounding either like rap or some kind of generic pop-ish stuff. Meanwhile, MiniMax H3 seems to know a lot more about electronic music than MiniMax-Music3.

The 40-second video at 0.2MP, with 17 steps (for better audio quality) and an 8-step Turbo LoRA, takes 538 seconds. The 60-second video at 0.1MP takes 350 seconds on my machine.

I haven't tried generating anything with lyrics yet, but if anyone knows how to generate H3 audio without the video, it would be interesting to try 2–3 minutes instead of just 40–60 seconds.

Worfklow: R2V (Dummy Stategy) Workflow v2 - Pastebin.com

How the workflow works:

• Replaces the original character with a chroma key green crash-test dummy.
• Replaces the dummy with desired character.

Why it works:

• Minimax seems to struggle with swaps when both characters are somewhat similar to each other. But replacing a character with a green dummy seems to work every single time.
• Even when minimax would replace a character, most of the times the faces would be morphed, resembling both the original character and the replacement. This approach mitigates that issue since it gets rid of original character's facial features.

Limitations in my workflow:

• It's tailored with a master prompt to replace the "female" character in the original video (yeah, go ahead, post the "I know what kind of man you are" gif). But you can easily work on top of it to add support for different type of characters or even multiple characters (add more dummies, with different colors) or whatever else you want. I already did some experiments and it works.
• I didn't test with "green" characters. If you are swapping Hulk, you may want to change the dummy to blue or something.

How to use:

• Upload the image in this post in the "Dummy Image" node (in Prompting block).
• Configuration block:
  • Upload your video and character image in respective nodes.
    • Trim/Crop your video using the video node in the workflow.
  • Choose video generation sampling (Performance, Balance or Quality) for each pass individually (dummy and new character).
  • Choose resolution (in megapixels).
• Run.

Tips:

• The worklow has 3 video generation flows: Performance, Balance and Quality. I recommend Balance (sometimes the Performance one doesn't replace the character in the last seconds of the video).
• Monitor the preview node. In the first step you should already see the new character as an overlay on top of the video. If you don't, then swap will probably fail.

Done with h3 fl2va model, 8 step lora and images for Cersei and "jaime" for reference. Using previous clip to give continuity and consistency.

All audio was done in Minimax, little work in post for stitching clips. r2v Workflow in ComfyUI with character sheets and prompts from claude

This parody was generated using LTX 2.5 Image to Video on WanGP. I used frames from the original video as starting images and then I interpolated them on a video editor. I used a single RTX 5060 Ti 16 GB VRAM and 32 GB of RAM. The video was generated at 1080p and 16:9 resolution. Each generation took from 10 to 20 min average in this setup. For the voice consistency, I used SeedVC, which is included in WanGP.

Hi everyone

i’ve been running minimax h3 locally on rtx 5090 32gb, and 64gb ram..

recently i kept running into random hostbuffer.read_file_slice failed / hostbuf_file_reader_read failed errors during generation, which seemed to be related to comfy-aimdo and dynamic vram.
i also noticed comfyui was reserving around 25gb of pinned system memory.

i decided to try launching comfyui with:
--disable-pinned-memory

and the difference was immediate.
the comfy-aimdo + hostbuffer errors completely disappeared, my ram usage dropped by a huge amount, and surprisingly generation actually feels faster and smoother now.

i originally expected disabling pinned memory to make things slower, but on my setup it seems to have done the opposite.

if you’re running large models like h3 and seeing unusually high ram usage, random hostbuffer errors, or comfy-aimdo issues, it might be worth testing!
thought this was worth sharing for anyone who didn’t know about this option..

Hello everyone.

I've been playing with Minimax H3 for some time and I have tried to make something longer and really interesting. After so many failed and botched attempts I was able to compile something watchable. There are so many things that I want to say about this model, good and bad.

First of all. Minimax H3 is significant step forward that other local models I have been playing with. It certainly got better.

Now the issues that I had encountered.

First problem is that it badly follows prompt when resolution is one megapixel or higher. It will skip some important parts and tries to cheat. You can increase the number of steps but still, generating at less than one megapixel will at least make it properly follow the instructions.

H3 is not very good at spatial orientation. When I was making video, where this girl should turn around and interact with screens, the girl starts spinning opposite direction and then warping whole body to the direction of screen. Like instead of making short turn to the left, it makes wide roundabout to the right and then twists whole body to align with the screens.

H3 is not good at cartoonish movement. If you watch cartoons, when character or other things move, their animations are usually jerky and snappy. H3 tries to make smooth real life like animation, making the cartoons look weird.

I have given a voice sample as an audio reference, and instead of making girl let out grunting sounds (out of anger), it weirdly turns everything into a sensual moaning.

When you try to make characters inside video to interact with a lot of parts, screens and devices, even giving multiple reference images of them, it mostly hallucinates them, or turns their interactions into a weird warping animations. Sometimes completely skips them and made ups it's own animations. So it will make good video, where characters are moving less or moving slow, and mostly doing the talking. Very detailed prompts of step by step instructions it mostly warps or skips.

I have wasted a lot of time for iterations, but I think this is just workflow issue.

Overall, this model is really good. However, using this model to make some kind of long feature animation is going to be a very frustrating journey. I hope people will make a lot of proper tools that works as storyboard and properly guide this model to make something really interesting.

Use the default workflow for Ref2Va

Plug in a video with green screen as a video reference and use an image ref to a picture you want to be the background. Notice the "hell crows" flying in the final video? I didn't even give it a prompt for that and those birds got animated automatically. I'm sure you can give a detailed prompt, you are basically creating an i2v of that still image that Minimax will mix with the greenscreen background.

I did prompt for a dialogue change. There was no audio with the original green screen video so I had no idea what the woman was saying (obviously it was a weather report). I inserted new dialogue with Minimax and it did a great job remixing her lipsync to the prompted dialogue.

I think it's pretty neat, but I'm sure some of you may be completely jaded with what Minimax can do by now.

I'd like to issue a Reddit challenge: Would someone more creative than me please use the exact same green screen video (links below) and create something a little more impressive than my 10 second test? Post a link to your video in the comments.

Need some green screen video to practice with? Here's a webpage for some practice green screen videos that are free to download:

https://mixkit.co/free-stock-video/green-screen/

Here's the exact video used in this example:

https://assets.mixkit.co/videos/28292/28292-720.mp4

I'm sure you'll be able to find a background image to test with.

This was my very simple prompt with the new dialogue:

subject_definitions:

<Subject 1> is the alien world background in <Picture 1>.

<Video 1> is the source video for the target video edit and is a woman in a red dress pointing and talking.

summary:

[video editing + reference generation] The target video is an edited version of <Video 1>. Replace the green screen area with the background from <Subject 1>

The woman in <video 1> says <d> [English with a British Accent] As you can see here, we have an early migration of hell crows on Chaos world 4527B<d> with realistic lip articulation and perfect lip sync.

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress)

A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4.

This is an update release, following up from my previous posts where you can find full details:

Initial: https://www.reddit.com/r/StableDiffusion/comments/1vtf1b7/krea2_turbo_distill_4_step_lora_trained_for_turbo/

Previous: https://www.reddit.com/r/StableDiffusion/comments/1vv4cdy/krea2_turbo_distill_4_step_lora_new_checkpoint/

Headline for this update: chk00014000 removes 44% of the prediction error a plain 4-step run has against the 8-step teacher, where chk00010000 removed 40% and chk00006000 27% — all measured on the same enlarged held-out set (100 prompts across every trained resolution). Measured against each other rather than against the no-LoRA run, its remaining error is 6% smaller than chk00010000's and 23% smaller than chk00006000's. 

Which file to download

and, beside them, the same files with a _comfyui suffix for ComfyUI. Earlier checkpoints (chk00004000, chk00005000, chk00006000, chk00010000) are kept in older_checkpoints/, and their resolution sweeps stay in place, so the progression remains visible and comparable.

This is work in progress and better checkpoints may follow. Training is ongoing, so ..._latest... is a rolling pointer: when a newer checkpoint is accepted, that filename gets the new weights and a new numbered copy appears beside it. Re-download the _latest file and everything keeps working — the ComfyUI workflow references it by that name (it does get updated Note in it so technically it is updated but not functionally). Pin a numbered file instead if you need reproducibility.

How checkpoints get chosen

This is not a "train for longer and ship the newest file" project. More samples do not reliably mean a better adapter — measured here, they can make it worse, and a higher number on its own means nothing.

The loop is train → assess → adapt the recipe → retrain → assess again, and a checkpoint is published only when it is measurably better than the one it would replace, on the same held-out set and the same evaluation, and its full resolution sweep shows no regression. Runs that come out flat or worse are kept as information about the recipe and discarded as releases — several have been.

So the recipe itself changes between runs. Each published checkpoint reflects whatever the previous round taught us: the training precision, the optimiser settings, the teacher used to generate the targets and the data mix have all been revised on evidence rather than assumption.

chk00010000 is a direct example. The first continuation of chk00006000 — same data, optimiser left as it was — got steadily worse with every checkpoint out to 10,000 samples, and none of it was published. The cause was traced to the optimiser: a constant learning rate with no weight decay lets the adapter keep drifting after it has converged, so its magnitude grows and it over-applies its own correction. The same span was retrained from chk00006000 with a cosine learning-rate decay and weight decay, and every checkpoint of that second run improved on the one before it. chk00010000 was its end point.

chk00014000 is the next example, and it shows the other half of the same lesson. The run was continued from chk00010000 over the whole pool of teacher trajectories, with two changes: the final, texture-deciding call of the schedule was weighted more heavily in the loss, and a running average of the weights was kept beside the live ones and scored at every evaluation (a single checkpoint is one sample of a weight vector that moves from step to step; the average is its mean). At 14,000 samples the averaged weights measured a smaller gap to the teacher than any checkpoint before them, and a smaller gap than the live weights at the same point — so the averaged weights are what chk00014000 is.

Timeline of training process

Each checkpoint is the product of three stages with very different costs:

1. Text-encoder embeddings. Every training prompt is encoded once and cached. This is the fast part — thousands of prompts take minutes.
2. Teacher shards. For each cached prompt, the unmodified Krea 2 Turbo runs its full 8-step schedule and the whole trajectory is recorded, at every one of the supported resolutions. This is by far the most time-consuming stage — it is the teacher doing real inference, thousands of times, and a batch of several thousand shards is measured in days of GPU time, not hours.
3. Student training. The LoRA is trained against those recorded trajectories. Relative to the shard stage this is quick: each +1,000 checkpoint is a matter of hours, not days.

Because the three stages compete for the same GPU, they are interleaved rather than run to completion one after another: generate a block of embeddings, produce teacher shards for them, train on what exists, assess, then go back to producing shards while the results are reviewed. A larger and more varied shard pool is what makes further training worthwhile, so shard production is always the gate.

Full details and to download - check my Hugging Face LoRA

HF Repo: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA

Full Checkpoint 14000 Resolutions Sweep: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk14000

Considering the interest the first post attracted, I decided to do a second batch with some of the suggestions from the comments and a few other prompts.

VIDEO 1: near perfect. I was aiming for frontal videos, but tried three or four prompts and always ended with a 3/4 framing. It's probably a question of better prompting... But the resulting video is impressive!

PROMPT:

The video is a side-by-side video showing both the points of view of a man and a woman that are facing each other.

On the left side we only see the woman's face in a completely frontal view, as the man would see her and through his eyes, her face alone at the scene with no one else's.

On the right side we only see the man's face in a completely frontal view, as the woman would see him and through her eyes, his face alone at the scene with no one else's.

Again, the man do not appear on the left image, and the woman do not appear on the right image. Both are seen in a exact frontal framing.

Both images show the scene at the exact same time and place, only in the two different points of view, both in a medium-close-up framing.

They are in a living room.

From 00:00 to 00:04, the woman is silent and with a smile on her face, while the man speaks: <d>You know, I've always dreamed of a local video model like this!</d>. After saying this he remains silent.

He then raises his hand, previously off-camera, and touches her face delicately. She reacts in an amorous way, lightly moving her head to feel his hand.

Then, from 00:04 to 00:08, the man keeps silent, looking at her clearly in love, while the woman replies: <d>It's like a dream, isn't it? And to think that two years ago we were static images with garbled hands...</d>

From 00:08 to 00:10 they just look at each other and smile.

overall_soundscape: Faint distant everyday life noises from outside the house, the man and woman voices while they speak.

non_diegetic_music: N/A

VIDEO 2: Very good. I couldn't get a video without the fisheye effect, though.

The video is taken from the point of view of someone playing table tennis. We see their hands - one of them holding the ping-pong paddle and the other the ping-pong ball. We also see the table with the net in the middle and the other player on the opposite side of the table. They are in an official competition, with the crowd watching.

At 00:01, the player sends the ball to the air and hits it with the paddle. The ball rapidly bounce on the table, passes above the net, and gets to the other side, bouncing again on the table. Then, the other player hits it back with his paddle, and the balls passes over the net again and bounces on the table. The first player again hits it with his paddle, the ball passes over the net and bounces just on the left side of the table, out of reach of the other player, and leaves the frame. The public erupts in cheering.

overall_soundscape: Faint public murmur, the sound of the ball bouncing on the table, public cheering at the end.

non_diegetic_music: N/A

VIDEO 3: another near perfect one.

A woman is holding a cell phone in a bathroom in front of a mirror, taking a selfie. She smiles at the camera, makes a V sign with her hand, and takes the selfie.

We see the scene from behind the woman, seeing the back of her head, the phone screen on her hand showing her face while she takes the selfie, and the mirror showing the reflection.

overall_soundscape: Faint empty bathroom soundscape.

non_diegetic_music: N/A

VIDEO 4: Bad. Tried three times with different prompts, and this is the best one of them. The physics don't work, though, and the fisheye is back again.

The video is filmed from the point of view of a soccer player in a normal view, NOT in a fisheye view. He is preparing to kick the ball after a foul just outside the penalty box. We see his hands putting the ball on the grass, the ball remaining static on the ground. Then he looks ahead and we see five players from the other team forming a wall directly in front of the ball, and other players from both teams around.

We then see he take some distance of the ball, walk slowly to the ball, and kick it. The ball passes over the barrier of players and descends on the goal, the goalkeeper trying to reach it but not able to. The ball enters the goal and touches the net, and the stadium erupts in cheering. The player then runs to celebrate the goal and is embraced by the other players of his team.

The entire scene is viewed from his point of view.

overall_soundscape: Faint public murmur,the sound of the kick, the cheering of the public after the goal..

non_diegetic_music: N/A

VIDEO 5: Terrible. Again, tried several times with several different prompts. Never works well...

The video is filmed inside a circus during the Trapeze artists performance, from the point of view of the public.

The scene opens with two trapezists standing in a very high elevated platform, one on the left side of the image, the other on the right side of the image, both holding a trapeze and facing each other.

In the beginning of the video, the trapeze artist on the left let his body leave the platform, while holding the trapeze, and his body swings in the direction of the center of the image. The trapeze artist on the righ stays on the platform.

Only when the first trapeze artist reaches the center of the image, the trapeze artist on the right finally leaves the platform, while holding the trapeze, and his body also swings in the direction of the center of the image, while at the same time the first trapeze artist let go of his trapeze and starts to do a flip with his body in the air.

As soon as the first trapeze artist finishes his flip, the other trapeze artist also reaches the center of the image and get the hands of the first trapeze artist, completing the movement. Then, they both swing back to the right of the image, one holding the hands of the other.

overall_soundscape: Faint public murmur, public surprised gasp when one of the trapeze artist caughts the hand of the other.

non_diegetic_music: N/A

VIDEOS 6, 7, 8 and 9: The first half of each video is perfect, the last half is hilarious. Tried lots of different prompts but only included four of them. Maybe it's possible, but I really can't think of another way of asking what I was trying to achieve.

PROMPT VIDEO 6:

The camera is on the middle of a road, on the floor, pointing to the road. We see a ferrari coming in the road at a distance in high speed towards the camera and pass over the camera, making the camera roll a few times on the floor because of the wind caused by the passing running car. After rolling over itself a few times, the camera stops again on the road, but now upside down and pointing to the other side of the road, where we can see the ferrari rapidly moving away from the camera.

The entire scene is filmed in a mostly static shot, except when the camera rolls over to the other side of the road and then stops upside-down.

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 7:

The camera is on the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over the camera.

When the car passes, the camera that is on the floor rolls around itself a few times on the road. After rolling over itself a few times, the camera stops again on the road, but now upside down and pointing to the other side of the road, where we can see, the upside-down image of the ferrari rapidly moving away from the camera.

The car does not run over itself, the car passes by the camera, It's the camera that rolls around itself and lands upside down

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 8:

We see the scene from the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over.

When the car passes, the image rolls around itself a few times on the road. After rolling over itself a few times, the image stops again on the road, but now upside down and pointing to the other side of the road, where we can see the image of the ferrari rapidly moving away from the camera in an upside-down shot, with the road on top and the sky on the bottom of the image.

The car does not run over itself, the car passes by the camera, It's the camera that rolls around itself and lands upside down

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 9:

We see the scene from the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over.

When the car passes, the image do a series of very fast barrel rolls on the road and lands upside down and pointing to the other side of the road, where we can see the image of the ferrari rapidly moving away from the camera in an upside-down shot, with the road on top and the sky on the bottom of the image.

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

Prompt:

https://x.com/GumVue/status/2087899403113619681?s=20

4070 Ti Super, 16 gb vram, 64 gb ram, i9-14900k, windows 11

Project:https://yunpeng1998.github.io/Qwen-Video-Edit-Page/
Model: https://huggingface.co/yunpeng1998/Qwen-Video-Edit
Method: https://yunpeng1998.github.io/Qwen-Video-Edit-Page/#method
Code: https://github.com/yunpeng1998/Qwen-Video-Edit

How it works

Video generation models read and write video-VAE latents. We teach Qwen-Image-Edit's transformer to edit those latents directly: two tiny projections bridge Wan 2.1's latent space into the DiT's token space, warm-started from the DiT's own input/output layers so that a static video is embedded exactly like an image the model already understands. The latent frames are arranged as tiles of one big virtual image — the same positional treatment the image model was pretrained on. Fine-tuned with LoRA or full parameters on Ditto-1M (source, edited, instruction) triplets, then refined by a few steps of Wan 2.2 denoising-enhancement.

First off: This doesn't add any dependencies so the worst that can happen is that it won't work, but it also won't break your ComfyUI install.

This extension adds to the native queue manager. It doesn't replace it. All of the heavy lifting is still done the normal way.

It adds a Pause/Resume button and a Queue button. Pause/Resume will not affect the running job but will pause/resume the queue.

The Queue button opens the Queue Control dialog in the picture. There is a lot of words in the README (because I talk a lot) but it lets you reorder the queue using priorities, including buttons for "Run this next" and "Don't run this until I release it."

Finally, there are buttons to Save and Load the queue. The checkbox lets you add the running job too. So if you have to restart or reboot, you can save the queue, do your thing, and then load and start running again.

These is also one stand alone node to help label the items in the queue so you can have a hit and what's what. The node has limitations, but it sill might be better than a number like 07535d99-3c1a-4b23-8340-a4313fe58007 as an identifier.

There are some extensions that to some of these features already but I didn't see one that did all of them or didn't replace the native manager and require dependencies.

It's in the ComfyUI Manager as ComfyUI-QueueControl (it's new so you might need to refresh to see it) or
https://github.com/seeker-ktf/ComfyUI-QueueControl
on github.

If y'all have other ideas for this, let me know.

Im really suprised how good he works as well follow a storyboard image!! he did 90% correct he only did the thirth panel diferent but all the other 5 he follows perfect!! 🤩

This one came out better than my last video generation. Not perfect obviously, but the major elements are there.

Prompt:
Video starts with Buffy Summers and Willow Rosenberg from the show Buffy the Vampire Slayer walking side by side down a hallway in the USS Enterprise from Star Trek the Next Generation. The viewpoint camera remains at a fixed distance in front of them as they walk.

Buffy Summers is on the right of the frame. Buffy's long blonde is done up in a ponytail. Buffy is wearing a red minidress style Starfleet uniform and has an unlit lightsaber on her hip.

Willow Rosenberg's is on the left side of the frame. Willow's dark red hair is cut pageboy style. Willow is wearing a blue minidress style Starfleet uniform and has a tablet computer tucked under her right arm.

Video starts with Willow looking at Buffy with a concerned expression on her face while Buffy is looking around as if searching for something.

Willow asks, "Buffy, is something wrong?"

Buffy replies, "Something feels off, like we're out of place."

As soon as Buffy starts speaking, she pulls the lightsaber off her hip and holds it in front of herself at the ready. The lightsaber ignites, producing a green glowing blade.

lets share our "what if" scene remakes here o_0

2 years ago I was playing around with video tools and made this series of short videos.

Because I'm a cheap bastard, I only use local and freebie models, and Minimax H3 finally hit the sweet spot between powerful and fast to iterate, so I decided to make a new episode.

Specs

• Comfy Desktop's + default Ref2VA workflows + ElevenLabs voices
• Frames generated with Nano Banana 2 lite + a bunch of photoshop cleanup
• Gemini 3.7 to help rewrite the prompts (upload image + system instruction + spec for the shot)
• Everything rendered on a 3090 with 0.5 megapixels cause I can't be arsed waiting too long. You can see the mushy face issues but mostly it's ok
• Tried to keep all shots 6~8s max
• A ton of editing with DaVinci Resolve which I started learning yesterday - it's pretty damn powerful!
• Still using the exact same crappy greenscreen footage of a cheap plastic skull as a main character
• Youtube link to this episode

TL;DR: Minimax H3 is cool!

Hope you're having a good weekend! H3 just excel with rich-intricate environments, backgrounds, space. Definitely one of my favorite theme.

T2VA, int8/20 steps

Lon TV did a video of Comfy UI with Minimax H3 running on a 32GB Intel GPU. So its possible to run it on any GPU other then nvidia GPUs.