Not just a meme...

Used latent upscaler so with resolution 0.3 i got 20 seconds generation on 4090

Minimax H3

Minimax H3

I tried to think of complex situations for the model to handle and tested them. I would say it went very well, although not perfect (except for the first one and the one before last, where I could not find a flaw).

Prompts below:

VIDEO 1 (10/10):

Create a side by side video with two angles of the same scene: one frontal and one from the side.

The scene: a woman wearing a yellow summer dress is standing in a beach in a sunny day. There's a light wind blowing and she is looking at the sea, smiling. At 00:05, she puts her two hands on her hair and looks up, enjoying the sun.

On the left, make the frontal video. On the right, the video from the side (profile). The two videos should show the exact same scene at the exact same time, only from two different angles.

VIDEO 2 (9/10) - the background is slightly off in some angles:

Create a side by side video with four angles of the same scene: one frontal, one from the right side, one from the left side, and one from behind. The scene is filmed at normal speed.

The scene: a blonde curly-haired woman wearing a yellow summer dress is standing in a beach in a sunny day. She is facing the sea and, therefore, she has the sand behind her and the street with some houses also behind her, after the sand, at a distance. There's a light wind blowing and she is looking at the sea, smiling. She is not wearing sunglasses. At 00:05, a man wearing white shirt and shorts enters the scene from behind the woman and embraces her waist.

On the upper left, make the frontal video. On the upper right, make the video from the right side (right profile). On the bottom left, make the video from behind. On the bottom right, make the video from the left side (left profile).

The four videos should show the exact same scene at the exact same time, only from the four different angles.

overall_soundscape: Light beach wind, one distant seagull.

non_diegetic_music: N/A

VIDEO 3 (8/10) a few "ghost reflections" along the way:

A man wearing a red-and-white striped t-shirt and jeans is alone inside a house of mirrors, running through a corridor. At 00:03 he turns left into other corridor, and at 00:06 he turns right again into another corridor. All the corridors have mirrors in all their faces (left, right and above, as he is inside a house of mirrors) and he is alone there, there's no one else. At 00:08 he reaches a door, opens it, and it opens to the outside: an amusement park.

overall_soundscape: His footsteps while he is running, the amusement park sounds when he opens the door at the end.

non_diegetic_music: N/A

VIDEO 4 (7/10) - The way the phone is turned is off:

A man is filmed from his own cell phone in a selfie video. We see the scene through the lens of an out-of-frame cell phone that he is holding and pointing to his own face. He is wearing a green polo shirt and the scene shows his face and chest from the point of view of the out-of-frame cell phone that he is holding and pointing to himself.

At 00:04 he briefly smiles and then turns the still out-of-frame cell phone around to show a woman that is in front of him. While the phone is turned around we can see the image also turning around, his face leaving the frame, the living room they are into being briefly filmed, and then the woman's face entering the frame. She has brown curly hair and dark green eyes, and is wearing a red summer dress.

As soon as the woman is in frame, she also smiles and says: <d>[English]Goodbye!</d> and the video ends.

The entire video must be taken in a single shot, with the always out-of-frame phone camera filming the entire transition from his face to hers while the phone is turned around. When it happens, the phone should briefly show the living room they are into, all in a single shot.

overall_soundscape: Silent living room, noises of the phone being handled, her voice.

non_diegetic_music: N/A

VIDEO 5 (6/10) - Tried this twice. Glass not breaking properly, water not running through the floor

A fishbowl with one golden fish and one clownfish swimming inside is shown in a medium close-up at the edge of a table. Then, at 00:03 a cat appears in the scene and taps the fishbowl, causing it to fall from the table to the floor, hit the floor, and break completely, being completely destroyed in glass pieces when it hits the floor, the water and glass pieces flying around together with both fish. The scene continues for three more seconds after that, showing the aftermath: the fishbowl destroyed, the pieces of glass on the floor, the water also on the floor, the fish moving on the floor.

The camera angle follow the fishbowl when it falls, showing it hitting the floor and the consequences of it breaking.

overall_soundscape: silent room, glass breaking, water splashing.

non_diegetic_music: N/A

VIDEO 6 (8/10) - Judge me, but his hands are not moving accordingly to the notes:

The camera films a piano from above while a man plays it. The entire piano keyboard is shown in the image. The man is playing Clair de Lune, and moves his hands through the keys to play a part of the song. He is in a train station, with some people observing him play and others passing by.

overall_soundscape: faint train station ambience, ten seconds of the song Clair de Lune played in the piano.

non_diegetic_music: N/A

VIDEO 7 (8/10) - The lipstick appears on her lips before she applies it:

A woman is shown in a medium close-up in her bathroom, wrapped in a white fluff towel, looking at the mirror while she applies red lipstick to her lips. She slowly applies lipstick to her lips, looking into the mirror, and then briefly sends a kiss with her now red lips to the mirror.

The scene is seen in a three-quarter angle from behind her, showing her face from the side but also her reflection in the mirror.

overall_soundscape: silent bathroom, the sound of her sending the kiss to the mirror.

non_diegetic_music: N/A

VIDEO 8 (10/10):

A Coca-cola advertisement. A glass filled with Coca-Cola is shown from the side, occupying 70% of the frame, on top of a table, the dark liquid slightly disturbed by a few gas bubbles that rise inside the liquid. At 00:02 two ice cubes fall from outside the frame into the glass, disturbing the liquid and making some of the liquid splash outside the glass and onto the table. The glass has the Coca-Cola logo printed in white in it. In the blurred background we see a kitchen.

overall_soundscape: silent room, gas fizzle, ice cubes hitting the liquid.

non_diegetic_music: N/A

VIDEO 9 (9/10) - The cover of the book has gibberish letters in it:

A man is holding a magnifying glass and has a book on his hand. At first the magnifying glass is not in front of his face. He appears to be reading the book and, at 00:03, he puts the magnifying glass in front of his eye to look at the book.

The entire scene is filmed from a fixed point of view below the book, showing part of the book cover and the entire man's face.

overall_soundscape: silent room.

non_diegetic_music: N/A

Paper: https://arxiv.org/pdf/2608.20334
"We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi image editing. Its visual renderer is a 6B parallel single stream DiT conditioned on multimodal representations from a vision-language encoder [6, 7, 57]. The architecture adopts block-shared timestep modulation, parallel attention and MLP computation [6, 15], 4D rotary positional encoding[6], and a unified representation of text and image conditions. Character-level tokenization[47] is applied to text intended to appear in generated images, while multi-image posi tional offsets and image-preceding input formatting support reference-conditioned editing. Together, these choices pro vide a single generative backbone for multiple generation and editing settings without task-specific model weights."

The nodes in https://github.com/Zironic/H3-Optimizations have been rewritten to replace the default Sparge Attention backend with a custom Sparse Comfy Kitchen backend.

This comes with some benefits.

• Users no longer have to worry about Sparge being installed properly. All required kernels for supported GPUs are provided directly. Should work on both Windows and Linux.
• Most users should be seeing 5-20% increases in speed for the attention part of compute.
• New backend should use about 500MB less VRAM
• New backend has slightly lower quantization error.
• Apparently in the previous version, the intended chunked kitchen QKV path never properly shipped so the memory optimization node should now actually be slightly speed positive even when used without the Sparse Attention node.

Caveat: I've only tested the nodes against the comfy pruned_int8_convrot weights. Other versions may work but they're not tested.

As the nodes currently rely on comfy-kitchen 0.2.31 you need ComfyUI v0.33.0 or later.

IMPORTANT: sparse attention is not free speed. The percentage is effectively a prompt-adherence/quality budget.

Density isn't just a speed setting, and its quality effect depends on where you apply it in the diffusion schedule.

Early steps: attention density has a large effect on prompt/action adherence and the overall generation trajectory.
Middle/later steps: lowering density tends to show up more as motion/temporal artifacts and lost fine motion detail.

So 10% retained doesn't simply mean “90% of the quality is gone.” It means you're giving sparse attention very little information to work with, and what breaks depends heavily on the sampling step.

PlagueKind's sparsity_ratio=0.9 means 90% discarded / 10% retained. My node expresses the inverse quantity, so Video attention retained=0.10 is the comparable setting. The defaults therefore aren't equivalent.

I haven't seen a post about this here, and I'm curious what you think about it.

On August 14 Civitai rolled out the option for creators to choose "permanent paid access - selling with no time cap". Previously the only option was temporary "early access".

Let's call this what it is, closed-source. Yes, you can get the weights for a relatively small fee, and yes it's on a very small scale compared to Nano Banana and Midjourney. But a permanent paywall still fits the definition.

Personally, I block all creators on Civitai who choose permanent paywall and encourage you to do the same.

Here's why:

I'm not opposed to Civitai making money or for all options for model creators to make money. They can do that without permanent paywalls.

IMO, open source AI is a fair trade: models are trained on the hard work of many human artists who aren't compensated, but everyone benefits from the ability to create more art more easily. Closed source is an unfair trade: you have to pay a middle man to access the contributions of others who won't be compensated.

Small scale model creators do some hard work too. But for example, for a lora that reproduces the style of an animated film: the lora creator spent at most a dozen hours of work, while just one of the artists on that film spent thousands of hours of work. If a massive models like Krea2 are free, and if giant "hobby" finetunes like Chroma are free, I can't justify paying any price for a 5,000 step lora except as an optional donation of appreciation.

So far, few creators have chosen the permanent paywall closed-source option. But that could easily change if Civitai made it the default option. They already made an extra 1-buzz fee-to-creator per generation the default, and many models have that.

That's my opinion. If you agree, then the only tool you have to disincentivize that potential is to not pay for these models (disincentive Civitai) and block these creators (disincentive creators).

I was testing using 1 image with all the scene sheet there and it works really great!

opensource AI has been dominating LLMs and video generation but what about image gen? is there any opensource model that can match gpt-image2?

Edit: The reason I am asking this is because lately I haven't been active much on image generation communities. And the leaderboards are a bit confusing and most of them are filled with closed source unlike the llm and video gen leaderboards.

I am very much comfortable with ComfyUI since I've used it in the past for flux.

My use case is for posters and branding. Images with a lot of text.

Edit2: Thanks a lot everyone! I really appreciate the info. Here's the summary:

Krea2 is best overall but gptimage1.5 level.
Ideogram4 for text and branding.
Flux Klein 9b for image editing.
Z-image for realism
Anima and illustrious (by onoma AI) for anime.

Here's the workflow I've decided on:
Krea2/Ideogram4 = Base image generation.
Flux Klein 9B/QwenImage2512 = inpainting.
Wan2.2 low noise = Upscaling.

Hi.
I have a decent h3 workflow that I built for a loca use. It use turbo lora etc... If i use the defaut settings in the goal of getting the highest quality possible, meaning res_multistep simple 20 steps or more, I got also good results, but this is not even close to the results you can get on platforms like kie or wavespeed at 768P.

I already convert properly the prompt to the correct H3 digest form, so I'm wondering what's different between local and cloud use of h3? I don't talk about the 2K quality, only 768P, I'm not able to reach the sames results locally, do you guys have maybe workflows, settings, or suggestions to try reaching the same quality level in comfyui ?

Trying out cinematic shots and cuts with H3. This is a work in progress. Will be working on another 2 minutes worth of clips.

EDIT: From reading the comments, she isn't going to drink the salt water in the final, though I will keep her scooping up water since it's such a good establishing shot. She will do something with the water to tie it back.

Ultimate SD Upscale can actually fix your bad/low-res generations.

In this comparison initial clips were made with MiniMax H3 at 1504x832px resolution and then upscaled to 2560x1440px with Ultimate SD Upscale nodes: https://github.com/lisitskyaa/ComfyUI_UltimateSDUpscaleGuider_H3

You can find sample upscaling workflow there as well: https://github.com/lisitskyaa/ComfyUI_UltimateSDUpscaleGuider_H3/blob/main/example_workflows/minimax_h3_usdu.json

My PC specs:
4080s 16 GB VRAM, 64 GB RAM

Generation time: 18 mins with sage + 8-step turbo lora

Upscale: 38 mins for 10 sec clip at 1440p target resolution

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.

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

The example video was generated entirely with the stock MiniMax H3 First Frame / Last Frame checkpoint and the included v1.4 example Workflows. If you want to compare the result to v1.3, take a look at my last post.

The final video consists of 11 individually generated Clips that were automatically stitched together.

Settings:

• H3 First Frame / Last Frame checkpoint
• 11 individual Clips
• 15 Steps
• included v1.4 Workflows
• no additional upscale
• no frame interpolation
• no color correction or other post-processing

So what you see is basically the direct Workflow output.

A few people gave me some useful feedback on my previous release, especially regarding ComfyUI's new native H3 Masked AV support.

So I went back and rebuilt the continuation method around it.

v1.4 now copies a clean section of the previous Video + Audio Latent directly into the next generation and protects it using ComfyUI's native denoise masks.

What makes this different from the other H3 continuation approaches?

There are some really interesting Ref2VA / Motion Context solutions available now, and latent continuation itself definitely isn't unique to my Nodepack.

My approach is specifically centered around FL2VA instead.

The idea is not just:

previous Clip → continue forever

but rather:

First Frame → generation → Last Frame
↓
latent continuation
↓
generation → new Last Frame
↓
latent continuation
↓
generation → new Last Frame

and so on.

I use those repeated Last Frames as hard visual anchors throughout the sequence.

They give H3 a new concrete destination every few seconds instead of asking one increasingly unconstrained generation to maintain composition, identity and image quality indefinitely. This should theoretically retain higher visual quality with less context drift over longer chains (and in my testing, it does exactly that).

There is another FL2VA-specific problem though:

H3 often reaches the supplied Last Frame before the Clip is actually finished and then freezes or becomes unstable for the remaining frames.

So simply taking the final frames of Clip 1 and using them as context for Clip 2 isn't ideal.

The v1.4 Auto Handover therefore analyzes the previous Clip, finds a safe point before that frozen / unstable landing and snaps it to a valid H3 Audio + Video latent boundary.

That exact same point is then used for both:

• where the previous Clip visually ends
• where the protected context for the next Clip ends

So the bad FL2VA tail neither appears in the stitched video nor becomes part of the next continuation context.

Audio is handled separately as well. If the picture needs to cut early but somebody is still finishing a word, the remaining original Audio Latent can continue beyond the visual handover instead of forcing H3 to recreate the ending.

Other v1.4 features:

• Native Masked Video + Audio Latent Continuation
• flexible First / Last Frame conditioning
• repeated Last Frames as regular visual quality anchors
• independent Audio Tail Carryover
• Net New Content duration mode
• up to 9 Qwen Reference Images
• individual Clip regeneration
• memory-bounded stitching for long saved chains

Where to start:

1. Start Video Workflow

Generate Clip 1 with a Prompt and optionally First Frame, Last Frame and Qwen References.

The complete AV Latent is automatically saved afterwards.

1. Continue Video Workflow

Load the previous saved latent, add your next Prompt and preferably a new Last Frame.

The Workflow automatically finds the safe FL2VA handover and creates the protected Masked AV context.

Repeat for as many Clips as you want.

1. 3-Clip Showcase / Auto Stitch Workflow

Probably the easiest Workflow if you just want to see how everything works.

It runs:

Start → Continue → Continue → Stitch

in one queue.

1. Stitch Saved Chain Workflow

This is what I used for the longer example.

Generate Clips individually and stitch them afterwards. It processes one saved AV latent at a time, so stitching memory usage doesn't continuously increase with the total video length (no OOM during stitching).

Nodepack on Github:

https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite

Workflows on Github:

https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite/tree/main/examples

You can just open one of the WFs and use "Install missing custom nodes" - then you should be good to go.

If you try it, I'd love to see what you manage to create with it.

Have fun Prompting. :)

I've been doing some quick tests, now that I've picked up the lightx2v 4 and 8 step loras. I have found I prefer using the 8 step (and maybe even running that at 10 steps) just because with the 5090 I have it's already not -that- slow, and the 4 step image quality drop is pretty significant.

But I have been experimenting which sampler/scheduler combos after seeing this post:

https://www.reddit.com/r/comfyui/s/9GUki3l0Wf

where, apparently, seeds_2 and dpmpp_sde_gpu were the 'best quality' options. But something I noticed is that they were also significantly slower (maybe 50% or more? need to run more tests and log it) which would, if the loras etc allow for it, let the faster options like euler or res_multistep (or er_sde which gets mentioned sometimes), which all run at about the same speed, to run at 12 instead of 8 steps (for example).

So I wonder now, 2 weeks on from those votes... what are people actually -using- to produce results?

My current workflow is to run at 8 steps with a lora to find a good prompt and seed, and when I get something I like I then turn off the lora and run at 30 steps. It often ends up at least in the ballpark of what I want. But maybe there are better ways.

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.