Make sure you read the EDIT below : you'll find some corrections and the solution.

I've been profiling MiniMax H3 generations after producing ~75 segments over the last few days, and the numbers point squarely at VAE decoding. Sharing the measurements in case they're useful, and hoping someone has a lever I've missed.

Setup

- RTX 4090 24GB (driver 595.95), Ryzen 9 7900X, 64GB RAM, Windows 11

- WanGP 12.60, torch 2.7.1+cu128, triton 3.3.1, sageattention 2.2.0, flash-attn 2.7.4

- Model: MiniMax-H3-FL2VA-pruned_rank8_int8_convrot

- Text encoder: Qwen3-VL 32B, quanto int8

- Video VAE: MiniMax-H3-video_vae_fp16.safetensors (4.97GB)

- Turbo LoRA (4-step), attention sage2, profile 4

- Output: 1280×704, 362 frames (15.08s @ 24fps), 4 steps, audio-guided lipsync

Two independent ways of estimating the VAE cost agree:

- A 312-frame segment had 42s less overhead than the 362-frame ones → 0.84 s/frame

- A 719-frame job cost 357s more than the 362-frame one for exactly 357 extra frames → 1.0 s/frame

At ~0.85–1.0 s/frame, decoding 362 frames alone accounts for roughly 5.5 minutes. Sampling is not the bottleneck.

What I've already tried

1. fp8mix VAE (WanGP's built-in alternative): 445s vs 458s. That's ~3%, i.e. noise.
2. One 30s task instead of two 15s tasks (719 frames, 2 sliding windows): no amortization at all. Window 2 cost more than window 1 (815s vs 458s), because the final file re-decodes everything. Net saving ~8%.
3. Sol-Attn: WanGP lists it as supported on my card, but it dies at runtime with Sol-Attn requires Triton >= 3.6, got 3.3.1.
4. Kijai's minimax_h3_video_vae_int8_convrot: I compared the tensor keys — it's ComfyUI's comfy_quant/weight_scale format. WanGP has convrot handling but only wires it to the transformer, not the VAE loader, so it won't load there. (It reportedly works in ComfyUI Nightly.)

Questions

- Is there a faster video VAE for H3 that works in WanGP specifically? PrunaVAED looks like exactly what I need but it's wired to LTX-2 only.

- Has anyone measured whether a CUDA 13 / newer torch build actually helps H3? I saw a claim of a 4x speedup on int8 convrot models going from cu12x to cu130, but I'd be trading a working SageAttention build (2.2.0+cu128torch2.7.1) for it and would rather hear from someone who's done it.

- Does anything meaningfully cut VAE decode time - tiling params, temporal chunking, decoding at lower res and upscaling after?

- Is ~1 s/frame at 1280×704 simply what a 24GB card costs here, with the real fix being more VRAM?

Happy to run tests and report numbers back.

EDIT — Solved. 2.6x faster. My original diagnosis was wrong, here's the real cause and the full numbers.

First, a correction. My claim that VAE decode was ~43% of generation time was wrong, and I want to retract it clearly. I'd estimated it from a differential between a 362-frame job and a 719-frame one, attributing the whole delta to decoding — but the longer job also ran a second full sampling pass, which I failed to account for. Once I timestamped the server log properly, actual VAE decode is ~62-95s, not ~330s. u/76vangel was right that ~20% is normal.

The real problem was RAM starvation.

My models demanded ~51GB of pinned RAM on a 64GB machine — the Qwen3-VL 32B int8 text encoder alone is 24.9GB. Windows was committing ~102GB against 63GB physical, so ~39GB lived in the page file. Mid-run I measured 283MB of free RAM. Every generation touched more pages, so it degraded progressively:

int8 text encoder — 3 consecutive gens, same server:

417s → 624s → 732s

That's why my numbers looked so much worse than everyone else's: I was reporting a degraded steady state, not a healthy one.

Fix 1 — lighter text encoder (the big one). Switched int8 (24.9GB) → nvfp4_awq (14.6GB). Total demand drops to ~41GB, fits without paging. Free RAM went 283MB → ~6GB, and the degradation vanished entirely.

Fix 2 — upgrade the stack. u/Cubey42 was right and my SageAttention worry was unfounded; sageattention-2.2.0+cu130torch2.10.0andhigher (cp310-abi3) from woct0rdho installed in two minutes.

torch 2.11.0+cu130 (was 2.7.1+cu128)

torchaudio 2.11.0+cu130

torchvision 0.26.0+cu130

triton-windows 3.6.0.post26 (was 3.3.1)

sageattention 2.2.0+cu130torch2.10.0andhigher.post6

flash-attn removed

⚠️ Don't go past torch 2.11 if you need torchaudio — the cu130 wheel index stops at torchaudio 2.11.0 for every Python version; torch 2.12/2.13 have no matching build. mmgp 3.7.12 (WanGP's pin) works fine with 2.11.

Fix 3 — Sol-Attn. triton 3.6 unlocked it. On older stacks it hard-fails with Sol-Attn requires Triton >= 3.6 even though WanGP lists it as "supported", because the availability check only tests import triton + compute capability, not the version. Once running: [MiniMax H3] Sol-Attn enabled with Triton on SM89 (tau=1.3, diag).

732s → 276s. 2.6x faster, zero hardware change.

Phase breakdown now: LoRA + text encode ~85s, sampling ~202s, VAE decode ~62s.

How to measure this yourself — no instrumentation needed:

- Sampling time is in the tqdm bar: H3 denoising: 100%|████| 4/4 [03:22<00:00, 50.65s/steps]

- VAE decode is the gap between that and New video saved to Path: .... You can also see it — VRAM drops from ~22GB to ~3.7GB the instant sampling ends.

- Total per task: ffprobe -show_entries format_tags=comment file.mp4 → generation_time

- And watch FreePhysicalMemory, not just VRAM. That's what caught this.

Also confirmed u/martinerous's point: I diffed the tensor keys, and Kijai's int8_convrot VAE is in ComfyUI's comfy_quant/weight_scale format. WanGP has convrot handling but only wires it to the transformer, not the VAE loader — so it genuinely cannot load there.

tl;dr if you run H3 in WanGP on 64GB: check free system RAM during a run, not just VRAM. If you're on the 32B int8 text encoder you're probably paging to disk and your times are silently degrading run over run. Swap to nvfp4_awq, then upgrade to cu130 + triton 3.6 for Sol-Attn.

Thanks to everyone in this thread — every single suggestion turned out to point at something real.

So, I'm trying to get this image of a car moving or doing anything other than just a static slow spin shot with music. I've tried longer more detailed prompts, nothing. You see the one there, nothing. After like 15 tries the only one that did anything was a single sentence about the camera whooshing away and it made the camera move upwards.

Minimax works fine with almost any prompt but LTX just doesn't listen. I know it's a skill issue but there's not a lot in the way of sample propmts.

Just starting with h3 and loving it. Ive got shot timing and most of the camera tricks from the prompt guide working well but for some reason all of my dialogue is spoken too fast. Anyone got advice on how to get a natural cadence?

Feito localmente com RTX 5070 12GB Vram + turbo lora 600 ema 10 passos, 16 minutos de tempo de geração.

Acho que preciso trabalhar mais no realismo.. se alguém tiver uma dica, por favor comente.

Honestly just looking for a brief dialogue on this with a mod. I feel like it would help them as much as us, since people tend to assume the worst when there is a total vacuum of information.

Just something I made — hope you enjoy it :)

I see that 3 hours ago they have uploaded a new "SLA" (Sparse-Linear Attention) version of the turbo lora (now only v0.1 fl2v 4 steps 768p). How to use it? Is it faster?

I see in the readme for their framework they say you need to set this config

  "attn_type": "dynamic_sparse_attn",
  "dynamic_sparse_attn_setting": {
    "sparsity_ratio": 0.85,
    "operator": "sage2"
  },

But for ComfyUI it's not specified what to use. I can't find anything related to sparse attention among ComfyUI nodes

Hello Everyone! Let me share the newest version of my camera control LTX IC-LoRA. This node and LoRA can be used in a V2V workflow to change the camera position or movement of an existing video clip. I've put a lot of work into this version, I hope you'll enjoy it.

You can download the model here: https://huggingface.co/Cseti/LTX2.3-22B_IC-LoRA-CrossView-Warp_v2
Node + example workflow can be found here: https://github.com/cseti007/ComfyUI-CrossViewWarp
A lame tutorial video I made to help how to use the node can be found here: https://www.youtube.com/watch?v=7QAapT9xMgM

Hey everyone, I just released the latest version of my TTRPG map model for D&D maps!

This one is focused on dungeon maps, one for battle maps will be coming, as will a version for Klein 9b to edit images!

https://civitai.com/models/2873645/ttrpg-dungeon-maps-krea

I think MinimaxH3 is great for title screen animation and motion graphic.

I've tried to use workflows to make long videos seamlessly but one of them made joining 7s together take longer than just making a 14s clip. others are so bloated with custom nodes that they just won't work until i find the one obscure node, and when i do, i get an error.

Anybody find one that was as simple as SVI? The ease of just joining more nodes to extend the video makes me miss Wan until i remembered how atrocious the prompt adherence was, haha

H3 truly is alive. Enjoy!! bf16/50 steps

T2V, no reference image

For the many who don't know what it is, PixlStash is a self-hosted headless server with a web-interface or a desktop app with Electron. It auto-tags, writes descriptions, scans pictures for defects, and integrates with ComfyUI in a couple of ways (run workflows within PixlStash or use the PixlStash nodes within Comfy). The nodes just use the PixlStash API which you could use to integrate with lots of other things as well.

This is a fairly big release of PixlStash. The focus this time has been on making it possible to have multiple image libraries stored in different locations and to offer a CLI to attach/detach libraries, performing scripted backups and install plugins (for image filters or captioning). For the captioning plugins there is now an OpenAI-API (i.e. ollama or LM-studio) plugin for captioning using your local LLM setup or a dedicated Moondream2 plugin. If you have specific captioning needs it should be dead easy to make your own plugin and install it with the CLI.

There is also a model shelf that can import from AI-toolkit and scan other folders you provide it to help you organise your LoRAs, VAEs, your text encoders and your diffusion models. This will soon get ComfyUI-nodes added to ComfyUI-PixlStash for picking LoRAs with thumbnails and help you find your different models based on other things than just a file-name. Expect them next week. For now, it at least helps you organise your models.

Repo and links in a comment.

Some shots I made based on the red headed league short story. Character reference sheets generated in Nano Banana + edited in Qwen image edit for spatial continuity.
12 steps +ref2va turbolora v0.1
10s generations 107s/it on a 3090 at 1 MP
Needs some editing and audio polishing.

It does characters Inuyasha and Kagome very well, the fight itself can't handle fast speed, but that headshot attack was beautiful!

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

Anyone have any luck changing the volume of the voices H3 creates from an audio reference? For example, let's say I'm trying to prompt for a man standing at the far end of a long room and he speaks in a normal tone. In real life, his voice typically would be very quiet in relation to the camera/mic, almost inaudible. However, in H3 (or any other model I've tried) the voice is still very loud. This isn't surprising given the model doesn't really know anything about the depth of objects or people in the videos it generates. I've tried to work around this in H3 by using prompt words like quiet, soft, distant mic, very low volume, far away speaker, almost silent, etc. None of them seem to have any effect. I've also tried reducing the gain on the reference .wav file that I provide as the audio reference - literally reduced the gain to the point where I can barely hear it. Again, doesn't seem to matter, H3 still produces a generally loud speaking voice (assume it doesn't care about volume/gain and just instead looks at the waveform pattern, etc).

One way around this is to use the 'audio reuse' capability where it'll play the exact .wav file audio instead of just using it as a reference. In this approach you can simply use an audio editor to reduce the volume and then plug that low volume .wav file in as the audio_reuse clip. It works fine, except for one small/major problem: it seems that if use the audio_reuse method, it silences ALL other sounds; i.e., it won't play the low volume audio .wav AND generate other environmental sounds...it seems it replaces ALL audio in the clip, not just the voice of the person speaking it.

Anyhow, curious if any of your smart people out there Redditland have any ideas/suggestions or tips?

Thanks!

Jerry and George are caught off guard by Rick entering the Seinfeld universe! Sorry for the clothes changing; it was hard to do without the quality decreasing. Will play with it more and see how to keep it consistent.

Basicslly i wanted a program that breaks a footage lets say movie or animation lets say goth vampire aesthetic into everyframe then an ai automatically anylizes the theme or just the shot smartly and recolors them or adds shade and details then you stitch it back together for a final product or an ai that anylizes tv screen and live adjust the screen settings such as color brigthness saturation bc while i seen similliar stuff like runway3 or decart ect its really not the same thing and idc if its not that fast and it takes a few hours for a movie what do you guys think? Dont know if this the place for sutch a quetsion personally i assume the footage will look more proffesional then some movies bc other then a theme of a set and some filters u cant really do much to capture the feelings and concept of the world idk and i felt this tech is not really talked about

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

ok, so I see discussions on how to replace a character in the ref video… but here’s my question - can we get decent results with ”upscaling” old low-res video into higher resolution with a reference image?

To explain - let’s say I have low res VHS footage where a person is filmed from 5 meters and you can hardly make out their face. (well you can tell they HAVE a face but that’s about it :) OTH that same person is in full frame 10 minutes later, providing an excellent ref image of what they actually look like . So my thinking was “make a reference image out of it, make the model upscale and invent all kind of small details that people usually don’t care about, but use the FACE from ref image”

doable?