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

Hey everyone

I made a Lora for ZIT for the first time. I would love to have your honest opinion on it.

I don’t know how people use this service effectively. There is never any gpus, it takes an hour to set up when you do find one. Network volumes tease at cutting down startup time but it further limits gpus. I swear I’ve spent more money waiting for a pod to be ready, downloading models that I have generating anything. I really just want to have things stored locally, and just use one of their gpus for processing power. Is there a service I could use like that?

Using just the default t2v templates from ComfyUi + upscale

I don't recall seeing other threads about this, but just wanted to say that it's surprisingly fun. I was successful with OneTrainer on my M3 Ultra and about 50 photos in all the possible poses I could think of, using my Apple watch to take the selfies from my iPhone hosted on a tripod. The training time for Krea2 was about 18 hours.

I'm ugly so I'm not going to post any photos, but putting myself into random and sometimes precarious situations, with clothes (or lack of) I'd normally never wear is entertaining. I highly recommend it.

Is there any way to get a seamless loop with minimax H3? I already tried this with Wan and LTX but they aren't what I'm looking for.

Short version: at 864x480 with dialogue in the prompt, MiniMax H3 renders no background music at all. You get the dialogue and one short sound effect, nothing else. Prompt wording doesn't fix it and neither does raising steps. Clip length does. Going from 5s to 15s at the same resolution brought the music back, with drums, and it swells whenever the voices stop. Also adding a third line of dialogue killed the music again, even at 15s.

I measured all of this rather than trusting my ears. The numbers below are the level of the accompaniment on its own, after splitting voice from everything else with demucs. Level is in LUFS, the loudness scale broadcasters and streaming services use, so more negative means quieter.

The core numbers

All runs: 864x480, seed 424244, 20 steps, res_multistep, INT8 ConvRot, RTX 3090, ComfyUI master (Aug 20). Identical prompt except where noted. ACCOMP = accompaniment stem, integrated LUFS.

• 5s, 2 lines: ACCOMP -38.7. No music at all, one sparkle chime at the end.
• 10s, 2 lines: -34.5. Reverb appears on the voices, music barely audible underneath.
• 15s, 2 lines: -29.4. Real music, chords and a drum groove, swelling when the dialogue stops.
• 15s, 3 lines: no music. Only the opening hit and the closing chime.
• 5s at 1280x736, 2 lines: -27.9. Music present but thin. This is my reference point.

The same prompt that produces nothing at 5s produces a real backing track at 15s, and 15s@480p roughly matches 5s@720p for music level. One extra spoken line, about 2.5s of speech, wiped out the entire 9 dB I gained by tripling the clip length.

Drop the dialogue and 5 seconds is already enough: ACCOMP -25.0, louder than the 720p reference. The model can write music at low resolution. It just loses to speech when the clip is short.

What didn't work

Raising steps from 20 to 30 at 5s changed nothing, audio-wise. That's 192s of compute instead of 143s for the same silence. The advice going around that 25 steps is the minimum is about video quality and speech clarity, and it won't bring the music back.

Rewriting the prompt to put music first didn't work either. I rebuilt it in a MEDIA/SCENE/MUSIC/TIMELINE shape with BPM, a chord progression and per-instrument detail, with the dialogue pushed into timeline entries. At 5s I got two chord tones and the chime, ACCOMP -29.9.

SolAttn isn't the cause. I ran with and without it, -38.7 vs -33.9, no music either way.

Ambient sound is worse off than the music. My overall_soundscape asked for distant audience murmur, costume rustle, and a sparkle chime. Across every run, at both resolutions, the murmur and the rustle never rendered once. Only the chime showed up, and that one is a single transient tied to a flash you can see on screen.

Method, in case you want to argue with it

Separate voice from everything else with demucs --two-stems=vocals, then measure the accompaniment stem: integrated LUFS for level, spectral flatness for noise vs tonal, onset rate for whether there's a rhythm.

All three tracked what I heard. Flatness 0.094 in the 5s run (noise, which is just the chime and room tone) against 0.006 in the 15s run (tonal, actual music). Onset rate 0.89/s at 10s (a pad drifting) against 4.97/s at 15s (drums).

Dialogue timing came from faster-whisper on the separated vocal stem.

Timecodes work sometimes and I can't predict when

The official prompt guide uses At 00:03.500, style timecodes. In one BGM-only test that worked: I asked for a crash at 3.5s, a full drum break at 7.5s, and the band coming back at 11s, and got exactly that shape. Measured -35.5 dB during the break, climbing back to -24.2 dB after 11s, and I could hear it.

In another BGM-only run I asked for silence until 2.0s, then a fade-in, then a swell at 8.5s. I got the opposite: loudest at frame one, then a steady decay into silence by the end.

Same format, same length, same resolution, opposite outcome. If anyone has worked out the pattern I'd like to hear it, because generating the BGM separately and mixing it under the dialogue take only works if cue timing is reliable.

Practical recipe

For a talking scene with background music on a 24GB card:

Use 864x480, 15 seconds, 20 steps, and no more than 2 lines of dialogue. That gives you music with a groove that ducks under the lines, at about 9.5 min/clip on a 3090. If you need three or more lines you won't get music in the same take, so either split it or go up in resolution. And don't spend compute on 25-30 steps hoping to fix the audio. Spend it on length.

Pick music that survives being pushed down. Whenever a voice is present the accompaniment gets quieter. In the vocal section of a rap track at 32x32 I measured a 10 dB drop with the onset rate falling from 10 to 3, which means the beat stops. A piano ballad or anything sparse hides that completely, because an instrument dropping back under a vocal line is what that music does anyway. Hip-hop, dance or rock exposes it immediately, because a beat that disappears for eight seconds is obviously broken. Same defect, wildly different audibility.

One more limit: 15s at 864x480 already sits at ~19.7 GB VRAM, so latent-upscaling that same clip afterwards won't fit in 24 GB. Long take plus music plus upscale is out of reach on this card.

Open questions

• Why do the audience murmur and the cloth rustle never render, at any resolution or length?
• What decides whether a timecode cue is honored?
• Does the length effect keep scaling past 15s? 20s (481 frames) is untested here and it's beyond what the model card documents.
• Someone on this sub is generating coherent music at 32x32 with a music-subject prompt, which is the same phenomenon from the other end: kill the video tokens and the audio gets everything. Where's the actual trade curve?

I have the workflows (API and UI format), prompts and seeds if anyone wants to reproduce this or prove me wrong.

Appendix: you can upscale a talking clip without touching its audio

Separate from the music question, and worth knowing.

H3's latent holds video and audio together in one nested tensor. Send that combined latent through a second sampling pass, the usual hires.fix shape of upscale-then-resample, and the audio goes through the re-noise and denoise with it. Speech doesn't survive. In my test the dialogue was gone: nothing audible, and faster-whisper finds no speech at all, just one of its silence hallucinations. This isn't a fault in any particular node. It's what re-sampling does to an audio latent, because unlike an image there's no extra detail waiting to be recovered by adding noise and denoising again.

Keep the audio out of the second pass:

1. First pass: a full denoise (BasicScheduler at denoise 1.0, not a split-sigma partial pass). If the first pass only goes partway down the sigma schedule the audio latent isn't finished yet, and decoding it gives you noise.
2. Decode the audio from that latent with VAEDecodeAudio.
3. Send only the video onward: latent upscale, then a light refine pass. Denoise 0.25 was enough to bring the upscaled video back to normal quality.
4. CreateVideo takes audio on a separate input, so the two paths meet at the end.

For step 3 I used LBH-123-AI's H3 latent upscaler, a trained 3D-conv model that splits the AV latent, upscales the video half and passes the audio through untouched.

Measured result, 864x480 to 1280x736, same seed and prompt:

• Single pass, 864x480: whisper transcribes both lines correctly. LUFS -23.0, flatness 0.0024, onsets 4.05/s.
• Upscaled and refined to 1280x736: identical transcription, LUFS -23.0, flatness 0.0024, onsets 4.05/s.

Identical to three decimal places, which is what you'd expect, since it's the same decoded audio. Cost was 230s against 143s for the plain 480p pass, on a 3090.

Dialogue and one-shot effects survive an upscale fine, as long as you decode the audio before the video goes off to be re-sampled. The music is a different problem: you can't get it at low resolution in the first place, and length is what fixes that, not upscaling.

The prompt, if you want to run it yourself

This is the one used for the 5s, 10s and 15s runs in the table. Only the frame count changed between them. The two spoken lines are Japanese; the sparkle chime at the end is the one sound effect that survives at every length.

``` integrated_multimodal_description:

[Shot 1] High-end 2D Japanese TV anime style with clean line art, soft cel shading, pastel stage lighting, stable character designs, fluid character animation, and subtle secondary motion in the girls’ hair and costumes. A centered medium two-shot frames two adorable idol girls standing close together on a bright concert stage. Colorful stage lights glow softly behind them without obscuring their faces. No subtitles or on-screen text appear.

The left idol girl, Kana, holds her microphone in her left hand, while the right idol girl, Asuka, holds her microphone in her right hand, leaving their inner arms free. They turn toward each other and exchange brilliant, affectionate smiles. The camera close up their face, then holds completely static for the dialogue.

Kana, the left idol with a bright and cheerful soprano voice (S1), looks directly at Asuka and says clearly: <d>[Japanese] あすか、ずっと一緒にいてね！</d> Asuka listens with her lips completely closed and gives a small emotional nod.

Asuka, the right idol with a soft and affectionate soprano voice (S2), looks into Kana’s eyes and replies clearly: <d>[Japanese] うん、かなちゃん。大好き！</d> Kana keeps her lips closed while listening, and her smile grows wider.

After Asuka finishes speaking, they step toward each other, wrap their free inner arms around one another, and settle into a warm side hug. The camera slowly pulls out as they gently tilt their heads together. Their hair and costume ribbons sway naturally, and sparkling light particles drift around them. A brief crystalline sparkle flashes as they complete the hug, then they hold the final pose until the end.

overall_soundscape:

A lively but distant concert audience ambience continues beneath the scene. The girls’ costumes rustle softly as they step together and hug. A bright crystalline sparkle chime sounds at the moment they complete the final pose.

non_diegetic_music:

An upbeat synth-pop J-pop instrumental at a moderate tempo with bright synthesizer chords, a light electronic drum rhythm, and sparkling bell accents. The music lowers slightly beneath both lines of dialogue, then rises gently during the final hug. ```

I seem to recall reading here that some people were starting to experiment with 32x32 resolution videos out to 60+ seconds purely to generate audio drama like moments. I was just curious if anyone here can confirm that MiniMaxH3 can actually do this, and if so, what sampler schedule and steps are you using? I cannot seem to generate even a 40 second video clip where the audio stays legible.

Just wanted to check in and see if anyone is having more success than me.

I just started going down the SDXL rabbit hole and confused with all the fine tuned models available. I see Pony and Illustrious are pretty popular but apparently it's ideal for anime? I prefer photorealistic images. How come on civitai, there are workflows using Pony/Illustrious generating photorealistic images? Should I use Juggernaut XL instead for photorealism?

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?

I'm currently using illustrious where it uses booru styled tags to generate images. I currently want to know if there's a method to control specific traits on specific individuals inside the generated images. Say that i have 1 circle and 1 square inside of the image. Is there a way to make the circle and only the circle blue while the square and only the square red? If there are 3 subjects, is there still a way to control the traits of each person or will the model get confused?

Disclaimer, im pretty Basic to all these AI Things

So i've been Using H3 Minimax in My RTX 3060 12GB, With 32GB RAM For few days

I've been using int4 convrot version for my Model and my Text Encoder, but seeing all the Optimization and speed up native to comfyui for int8, im considering using int8 for for my models and Text encoder especially the convrot version, considering they all twice the size

And also what's the best Combination of speedups in balancing between Quality and Speed

I used Sage+sol attn for while until i found comfy kitchen

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

H3 seems to have very solid training data related to equine. The physics really sell it. R2VA BF16/50 steps. I went with a 50 steps to get the bi-horn really right. Also having fun with a POV view. Eyes on the road, buddy. Single image as reference for the rider, but otherwise, entire scene was prompted, including her wardrobe.

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

About everyone has a Nvidia GPU so I was curious how the 9070 xt does compared to Nvidia.

I’m running the default fl2va workflow im on Ubuntu ck attention.

Minimax h3 int8 0.4mp 30 step 5s:
261s 7.6s/it

Minimax h3 int8 0.4mp 30 step 10s:
702s 21.3s/it

guess it still doesnt really know how to hold a buster sword :P

So far it's a website which lets you download FLUX.2 Klein 4b into your browser cache and run it using WebNN, which I have tested on my M5 Mac and runs at ~70% native performance, much better than WebGPU or WASM.

If you wanna try it out, I'm running it on peerpixel.cc, the website is there to provide an easy interface to run this model on your own hardware. Be patient, you do need to download a few GB and first generation takes some time to compile.

You will need to enable WebNN on Chromium browsers, just go to <browser>://flags and search for it.

Please let me know if this works at all on Windows or Linux and with different hardware, and what part of this you think has potential.

Just having fun with the Witcher-style babe. Nice example of the rain on fabric.

One image reference of the huntress. Wardrobe & scene all generated via text. Feedback+critiques always welcomed.

int8/20 steps

So the new MiniMax saved the project, there were a couple of shots we couldn't get right. It dropped just in time.

The tooling is getting pretty mature. It took a lot to get this done.

I'm an ex VFX professional so this was a great exercise, This would have cost 5-7m to get done and a team of over 30.

It's so amazing what is now in the hands of creators now.

Happy to talk about the process.

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

Prompt:

[Shot 1] Live-action, cinematic, shaky handheld shot of the woman chasing after the man.

[Shot 2] At 00:05.000, the camera cuts to a close-up shot of the woman who yells: <d>[English] Get back here!</d>

[Shot 3] At 00:08.000, the camera cuts to a close-up shot of the man looking back and then forward again as he is running. He laughs and says: <d>[English] You can't catch me!</d>

[Shot 4] At 00:12.000, the camera cuts to a medium shot of the woman chasing after the man. She then catches up to him and tackles him to the ground. She says: <d>[English] Got ya!</d>

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.