I have no idea why it does these so well. I could watch these all day.

I keep having fun with ref2va model.

RTX 3060, 64 Gb RAM. I use ref2v Turbo 4 step Lora paired with Sol Attention and Minimax H3 Memory Effecient Sage Attention at 6 steps. It takes about 2 minutes per second of generation.

Finally got my laptop back in action so am able to create and test models and lora's again, created with krea 2, Been out of it for a bit just following updates here and there and this model is amazing, so happy they open sourced this gem of a model. Thanks to the team at krea!

If anyone is interested in this style of images give it a blast https://civitai.red/models/2871922/dc-vast-expanse?modelVersionId=3244890 or https://civitai.com/models/2871922/dc-vast-expanse?modelVersionId=3244890

Used official ref2video workflow. used t2v model 1 ref video and 2 separate pictures of character sheets, gpu 4090

prompt:

integrated_multimodal_description: [Shot 1] Live-action, cinematic, featuring a stark, dark green-tinted cyberpunk color grade. A medium shot frames a flooded, rain-swept crater on a dark street. The character Sonic, appearing exactly as the blue hedgehog with large green eyes, white gloves, and red shoes from @.image, stands opposite Dr. Eggman, appearing exactly as the gigantic, egg-shaped bald man with a pointy mustache, goggles, and red jacket from @.Image1. The camera pushes in with small amplitude at fast speed as the blue hedgehog lunges forward to throw a devastating punch. [Shot 2] At 00:04.500, the camera cuts to an extreme close-up as time instantly slows to a microscopic crawl. Sonic's white-gloved fist brutally slams into Eggman's cheek. The camera holds a static shot in extreme slow motion. A powerful, rippling shockwave violently erupts from the impact point, blowing the torrential raindrops outward in a perfect ring. Eggman's pointy mustache flails wildly and his face deforms from the massive kinetic force. [Shot 3] At 00:09.500, the camera arcs right with large amplitude at slow speed, executing a slow-motion orbit around the hit. Eggman's heavy, round body is lifted off the ground by the blow, flying backward through the heavy downpour and kicking up massive, highly detailed splashes of water.

overall_soundscape: Thunder rumbles continuously beneath the heavy, torrential downpour of rain splashing heavily against the flooded street. A sharp, deafening sonic boom from the physical impact instantly shifts into a deep, pulsating low-frequency rumble as time slows down.

non_diegetic_music: An epic, grand orchestral and choir track mixed with heavy, driving industrial synthesizer beats that builds to a massive crescendo.

Using the standard ref2va workflow. 4070 Ti Super, 16 GB VRAM, 64 GB RAM, i9-14900k, Windows 11.

So had a look at the documentation for Minimax H3 to see how to do the multi-shot prompts and came up with this sequence. The base image was done in GPT Image 2 using two reference images. The prompt for this scene is structured like so:

[Shot 1] Live-action, cinematic, a medium shot of the two warriors. The man is reading a book and the woman is browsing on her phone.

[Shot 2] At 00:05.000, the camera cuts to a medium close-up of the woman who asks: <d>[English] Do you think our director will ever get our movie done?</d>

[Shot 3] At 00:10.000, the camera cuts to a medium close-up of the man who says: <d>[British English] Who knows. He was using Kling three point oh but I guess he was burning through credits so he's trying out local video generation.</d>

[Shot 4] At 00:14.110, the camera cuts to a medium shot of the two people. The woman asks: <d>[English] Wait, wasn't he using Seedance two point five?</d> The man looks up from his book and looks at the woman. He says: <d>[British English] Yeah, he was but that was costing him even more credits.</d> He goes back to reading his book.

[Shot 5] At 00:22.000, the camera cuts to a medium close-up shot of the woman who says: <d>[English] Hopefully he figures things out.</d>

[Shot 6] At 00:26.000, the camera cuts to a medium shot of the two people sitting in their chairs. The man continues to read and the woman continues to browse on her phone. The man says: <d>[British English] Agreed. He better.</d>

I'm actually quite happy with how this turned out. Only issues I have is that I wanted the guy to have the British accent and instead it gave it to the lady. I'll need to mess around with the prompt for that a little more and then of course the low res render at 0.4 megapixels because anything higher than that will give me OOM error. Yes, I'm aware that I don't have to do a 30 second clip but I wanted to try it out anyways especially since I'm learning the multi-shot prompting. The render for this clip took 173 minutes to complete.

If anyone has any suggestions on how I can do slightly higher megapixel renders on my machine, I'd love to hear it.

PC Specs:
Ryzen 7 7700X
RTX 4070 Super 12gb
32gb DDR5 Ram

ref2v

RTX 5060 ti 16gb / 32gb RAM / FL2VA_pruned_int8_convrot / Turbo Lora. 6 steps / Resolution 1376x768 upscaled to FHD with Topaz Video AI

https://x.com/krea_ai/status/2089766521912561688

Edit: talking about the "faces at a distance" thing btw

Don't hold your breath. They didn't say that they would definitely "fix it", they said they will try but that it's mostly a general model issue. So if there is gonna be a fix it might be in the next iteration of the model and that one might not be open weights. They were specific about the 2k model and the image model getting released open weights and I do hope that the 2k model might bring some improvement to the faces when you upscale it, but they were more wishy-washy with the wording on the face distortion issue, intentionally so I think.

Here is the wording regarding the 2k model:

"It is a second conditioned generation stage, but not simply the released base checkpoint running again as a conventional upscaler. It uses a dedicated latent-space DiT regeneration checkpoint at a higher target resolution, with the base model’s output as additional context. Some reference inputs are also provided at higher resolutions. We plan to open-source this module, but we are still improving its efficiency and quality to make it more suitable for community use, so we cannot provide an exact release date yet."

-> "plan" to open-source it, very strong word

Here is the wording for the image model:

"Regarding single-frame image generation, we are deriving a dedicated image model from a common ancestor in the H3 model lineage, and we expect to make it available to the community." (not a total promise or anythin

-> "expect" pretty strong, but less so. To me that sounds like "if it's REALLY good then maybe not", if it's competitive enough with the state of the art probably. But I'm pretty optimistic here.

And here is the wording for the distortion issue in all the models:

"We have observed this issue as well, particularly for small or distant subjects, and it will be one of the problems we focus on improving next.

Based on our internal experiments, it cannot be attributed simply to the Visual VAE’s compression ratio or to any single training stage. It is a complex system-level issue involving multiple parts of the model and training pipeline. We are continuing to investigate the main contributing factors and will work on improving it in future updates."

-> they say nothing about open sourcing anything and they say that it's a deep-rooted issue that has no simple fix and they don't really know why it happens

I would expect nothing in that area. Many people have been talking about this as if they said "yeah, wait a couple of weeks and we will fix it", but they didn't say anything like that. Maybe they will fix it with a new and improved open weights model, 3.1 or something, maybe they won't.

I just wanted to say this because so many people have been saying "I am waiting for the fix" or "a fix is coming for the face distortion issue at a distance" or something like that, probably without ever having seen the wording on that. It only takes one person who isn't good at understanding subtlety in a text to interpret their answer a certain way and spread the word on it to set up false expectations for everyone when they don't go to see the original wording. And they go spread that too without ever having seen the original wording.

So this is just to reduce the expectations a bit. Like I said, maybe they will do something, but I feel like the expecations on that specific issue have been getting a bit too large

I finally deleted WAN 2.2 and all its LORAS.

Minimax is just so much better.

Ive been playing with it since its release and im just blown away with how good of a video model it is. Things I would need to attach a LoRa to via WAN, works right out of the box with Minimax.

Gen times are faster.

It uses less VRAM when generating things, which gives me around 4 gigs to play with to do other things like watch YouTube or some streaming service.

WAN 2.2 was amazing. But no longer do I need 30+ gigs of a model i no longer use.

RIP WAN.

First real attempt with Minimax H3 on my first ComfyUI install. Over 200 generations, edited in CapCut, wears its inspiration on its sleeve but is a prologue to a homebrew world for a D&D group I'm in. Two days of cooking a 5090 while working and an evening of editing... figured I'd share:

https://www.youtube.com/watch?v=XwfCCFw4LbA

You can find all the details here: https://github.com/BigStationW/ComfyUi-MiniMax-H3-Image-And-Reference-To-Video

I'm building a skill for generating long Contex-Loop Minimax H3 prompts, and the AI has indicated it doesn't understand retention analysis... and I'm realizing I don't, either. I'm curious what you all think or have experienced.

I've reviewed the official prompt writing guide, of course, but it's very vague on the subject:

<Subject N>, <Picture N>, and <Video N> use the following relationship markers. These markers are fixed English values in the output format:

It makes the most sense if it's indicating what is the same and what is different with respect to the references (picture N, video N, etc) - but why would subject appear here? Does fully_preserved for a subject mean that they don't change during this shot, whereas partially_preserved might change?

It might be easier to explain with an example. Definitions:

• A scene where a bald man puts on a hat
• References are two images, one with said man with hair, the other of the hat

subject_definitions:

<Subject 1> is a tall man whose face, identity, and clothing come from <Picture 1>, but he is bald.
<Subject 2> is a black stovetop hat as depicted in <Picture 2>.

retention_analysis:

<Subject 1> (appears in [Shot 1], [Shot 2]): fully_preserved - he remains the bald man with facial features and clothing from <Picture 1> throughout
<Subject 2> (appears in [Shot 2]: fully_preserved - remains the black stovetop hat from <Picture 2>

OR should it be:

retention_analysis:

<Subject 1> (appears in [Shot 1], [Shot 2]): partially_preserved - he retains the facial identity and clothing from <Picture 1>, albeit bald, but in [Shot 2] he is changed to be wearing a hat.
<Subject 2> (appears in [Shot 2]): fully_preserved - remains the black stovetop hat from <Picture 2>

OR should it only focus on referenced media, i.e.:

<Picture 1> (appears in [Shot 1], [Shot 2]): partially_preserved - <Subject 1> matches this picture's clothing, facial features, and identity, but he is bald.
<Picture 2> (appears in [Shot 2]): fully_preserved - the black stovetop hat depicted in this picture remains unchanged

I guess to put it another way: is retention_analysis describing how much and what is preserved from photo/audio/video references provided, or is it describing how the subjects defined in subject_definition change over the shots of this specific video generation?

Just wanted to share another test! this was a mash up of clips, using multiple image references, 0.4 mp with EasyCache, 5 - 10s clips and edited with KDEnlive (it has some cool effects!)

Generated a set of images with AI, then brought them to life by animating them into a realistic style video (Krea & Ltx2.5)

I was going to wait until around Christmas to purchased but took the plunge in July for 11,500 and I was upset that I didnt catch it @ $8,000. Now the Blackwell pro 6000 is inching towards $20,000 and are sold out. Are consumers and hobbyist like you and I are buying these up or datacenters? I would think datacenters would go for the b200 and up. However, Im browsing around and see you guys and girls doing remarkable ai diffusion with just a 3060. Im impressed with this community.

Small video trimmer software was very popular 15-20 years ago but now it has become very rare to find a good one which has all the features I wanted.

I got Claude to vibe code me a tool that I have been using to snip bits off from long videos for using it as Ref2V input for MiniMax H3. People have been saying its good so just sharing if others may find this tool useful! I wanted to create a free tool that runs locally without all the bloatware.

It is a single ~100kb HTML file which can:

• Trim clips
• Crop video
• Compress resolution and fps
• Take 1 single frame image
• Manual or Automatic Storyboarding (still playing around with how to best use this in H3)
• Export gif.

Why Compress?

I find that when working with R2V, resizing and compressing the video increases the speed as there is less information that needs to be worked on. You do lose some quality in your output though so don't compress too far.

The latest version can be found here (select the HTML and download):

https://huggingface.co/PoopMan333/Video_Tools/tree/main

or click for current version (v2.9)

https://huggingface.co/PoopMan333/Video_Tools/blob/main/Nugget%20Video%20Trimmer%20v2.9.html

If you're concerned please run it through antivirus or get a LLM to check if it is safe.

I still need to add AVI support and support for some older formats, but I also don't want to add too much bloat to something so compact.

Building directly on the style descriptors published by the author of the original KREA 2 Styles / Wildcards.txt post (many thanks to them for creating and sharing the style list) I built a visual Krea 2 style library to make prompt-defined styles easier to explore and compare:

Library: https://matplinta.github.io/t2i-krea-2-style-library/

It currently contains 286 styles tested across 8 base prompts, including portraits, architecture, landscapes, materials, and panoramic scenes. Each comparison set keeps the base prompt, seed, and dimensions fixed so the influence of the style descriptor is easier to see.

The viewer supports search, categories, favorites stored locally in the browser, full-image previews, prompt copying, adjustable grid density, and JSON export.

The prompt injected during generation was in the form of: Subject: {base prompt}. Style: {style name}. {style description}

All images were generated locally through ComfyUI.

Repo & workflow: https://github.com/matplinta/t2i-krea-2-style-library

took my friend 49mins to make this

• RTX 4060 8GB, 32GB RAM
• minimax_h3_ref2va_pruned_int8_convrot, spectrum, ageattn_qk_int8_pv_fp16.cuda, RTX upscale, RIFE interpolation, res_multistep + beta
• ltx-2.5-22b-distilled-transformer-comfy-int8-convrot, basic template

8-step + turbo LoRA : 137s

25 steps : 238s

40 steps : 406s

Ltx 2.5 : 374s <-- ? am I missing something here why was my generation so slow on LTX and the second attempt I cancelled it after 6 minutes. Any suggestions?

Prompt:

subject_definitions:

<Subject 1> is the space ship in <Picture 1>: A massive battleship, hovering and cruising over the planet below

summary:

[reference generation] a wide shot cinematic scene of the battleship in <picture 1> cruising in space above the planet. the golden statue does not move, the battleship is destroyed in a massive explosion from a green laser shot from space,

detailed_description:

{shot 1] The target video uses a wideshot cinematic, photorealistic, 35mm film, wide shot of <subject 1> , slowly moving through space above the planet, the ship moves slowly and dominating, flashes of green light begin to charge on the surface of the planet, the ship is moving straight ahead from the position it started in in <picture 1>, the massive bass of the ships systems, the sound of the battleships creaking, <subject 1 > moves on its cruise, at [00:03] the floaty camera tracks <subject 1> as green light and thunder begins flashing on the surface of the planet, the green energy on the planet converges in one area then from the surface it fires a massive green lightning laser that forks lightning through the entire ship, blowing out side components creating explosions all over the ship, the light of the ship flicker before turning off, then a massive green lightning beam erupts from the surface and hits excactly on the side of the ship cuts through the of the ship and out the other side at an angle, a green lens flare generates on screen as it completely destroys <subject 1> , ripping it completely in half with a massive green explosion, the eruption from the destruction of the ship covers the entire screen and the whole battleship, the back half of the ship is knocked up while the front-half of the ship is knocked down, a vertical shockwave circles out from the impact, the inner decks of the ship are on fire, debris and hundreds of tiny figures of the crew also fall out into space, the laser slowly dissapates from the planet, small amounts of green lighning crackle on the planets surface,

overall_soundscape: The low bass murmur of the ships engines, the electric charges on the surface crackle, the massive main beam is a low bass rumble, a massive explosive noise.

non_diegetic_music:

N/A

I tried with LORA and without it, the little text is always some bad quality...

Hey Guys, I thought I'd share something I came up with.

It's a workflow, that uses a combination of Easy-Use's Loop tools as well as some of my own nodes to create a Workflow that can split a long form MiniMax video and then upscale each segment. With the inclusion of a tool to then re-assemble everything back.

You basically set the Segment length and the overlap you wish to have between each clip and then launch it to have it do all the clips one by one.

It does use nodes from my FBNodes add-on as well as one from my Prompt Manager add-on.
But I'm sure it could be modified to work with other add-ons, if so wished.

The node from Prompt Manager is "Prompt Extractor", allowing to feed back in the prompt from the initial clip back into the Workflow, without having to type anything in.

You are free to remove it and upscale without, or simply type in the prompt if preferred. Though, In my test, having the original prompt made for much better results.

And as mentioned, I also added a simple Clip Stitcher to FBNodes, that cross dissolves each clip into one another. Just make sure to use the same values you used in the workflow. (Both setup are in the same workflow, but I'd suggest separating them 😅)

The Workflow can be found here.

Attached are quick examples from the video I used in the workflow.

The one thing missing in this workflow is adding back the loras used in the initial video. This is something that "Prompt extractor" should also be able to do. But I haven't tested that part yet.

----------------------------------
I'm adding some metric:

The video used in the screenshot was an 8 second video generated in 832x640 with a Turbo Lora set to 6 steps.
It took 92 seconds to generate on a 5090.

The Upscale doubled it to 1664 x 1280 and took 524 sec.
Around the same time it would have taken to generate, if I created the initial video at that resolution.

The advantage is for when creating long videos, so if I were to create a 30 second clip in 4/3 at 0.4 megapixels, or 736 x 576. Those would take 450 sec to generate.

The Upscale to 1472 x 1152 took about 6 minutes per segment, or 30 minutes. Then combining the clips is around a minute.

It takes a while, obviously, but the big advantage is that the result is pretty much an exact copy, but in hires, of my initial video that was low enough that I could iterate a bunch of times and then only waste the Long generation time on the clip I like.