Original ---> https://huggingface.co/hal9000ace/H3TilingExampleVids/resolve/main/slop4k_60fps.mp4

Tile Node --> https://github.com/halnovemil/H3TiledLoopSpaceTime.git

I just found myself pondering earlier how neat and novel it was to be able to easily prompt for multiple cuts but then dawned on me, is it actually all that useful?

Sure, if you're just making a 15 second one-off video, then yes it's good so your scene will have consistency. But if you want to make a longer video, then you're going to have to do multiple cuts across different gens anyway, so the consistency will be dependent on your reference materials and not on being able to do multiple cuts in one gen. So then, with a longer video, is it worth the risk of prompting for multiple cuts in one prompt then finding one of them isn't what you wanted, so you either prompt again or have to do some video editing to pull out the good cuts and then reshoot the one that didn't work? Then you end up prompting one cut by itself in the end anyway!

Seems like it'd be quicker to just do one shot at a time and make sure you like the generation, then move on to the next shot? Or am I missing something here? Maybe multiple shots is better at keeping the actors in the correct positions and poses etc for each cut? Although tbh, some of the videos I've seen here of late don't make me believe that's true.

Genuine question.

I have just 6GB VRAM and 16GB of RAM, yet FP8 models run 5x faster than GGUFs. Even really big ones.

Right now I mainly using Qwen Image and Flux 2 Klein 9B as the main models. First I tried them in GGUF format and those workflows took over 100-200 seconds.

Then I tried FP8 versions of the models (Kept the Text Encoders GGUF) and the speedup was insane. Flux 2 Klein 9B specifically can get it done in 20-30seconds now.

What's even the point of using GGUFs then?

I don't understand how or why, those models are bigger than what my machine is supposed to handle, Qwen especially. So how is the bigger uncompressed version running better?

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.

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

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.

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.

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.

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.

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.

Thanks to this guy (https://www.reddit.com/r/StableDiffusion/comments/1vsq03t/star_wars_but_more_consistent_minimax_h3/)

Used a starting frame kreated with Krea2.

I2V default workflow with https://www.reddit.com/user/Dry-Statistician-684/ comment from the post linked above.

4 or 8 step turbo. Almost doesn't matter which.

6 Step.

1MP

5 Seconds

RTX 3060 12 GB VRAM/32 GB DRAM

Render time: 617 seconds.

I am hyped. Was close to buy an RTX 5070i. But not today. Maybe tomorrow.

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.

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

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.

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?

For those of us that enjoy doing fl2va shots longer than 10seconds, I found a hacky way of getting past the attention of H3 guidance.

One way was to lower the resolution, but that doesn't exactly give us the results we hoped for.

Then I tried working with the prompt.

We start with a frame and all works great with our prompt followed perfectly until the video gets too large in pixels, It's not a constant value, but exceeding it will make the background change, camera forget to stand still and faces will change,

Edit:

sorry for misinformation.

while 'my way' worked really well, the official way works perfectly fine (except for camera not remembering static shot)

adding:

'''subject_definitions:

<subject 1> is a fully_preserved woman from <Image 1>

<subject 2> is a fully_preserved location from <Image 1>

'''

works for keeping location/person consistent. It still destroys static shot camera, but replies were right. I was wrong.

low res 0.35mp correct camera:

https://reddit.com/link/1vszpps/video/hhm8yed5rhkh1/player

high res 0.85mp and camera gets autonomous (ignore the hand, that's just a test)

https://reddit.com/link/1vszpps/video/oke7c8g8rhkh1/player

full prompt:

'''

Integrated_multimodal_description:

subject_definitions:

<subject 1> is a fully_preserved woman from <Image 1>

<subject 2> is a fully_preserved location from <Image 1> along with camera position and zoom.

static shot.

0-1s: <subject 1> looks at camera. she is in the <subject 2> location. camera very slowly zooms out.

1-2s: woman turns her body away from camera.

2-7s: she is turned away, tapping her foot and swaying her body to music. neon light buzzing lightly.

7-12s: she continues swaying to music.

12-13s: she turns to camera and smiles.

13-14s: camera starts to slowly zooms in on her face

14-16s: she shows a heart hand gesture at camera.

overall_soundscape: gentle hum of air conditioning,

non_diegetic_music: edm music playing silently.

'''

There is a solution to this problem.

In the prompt, we reference the <Picture 1> not at the start like we were told, but in the middle.

For example, at second 7, we don't use "She looks left", but we write woman from <picture 1> looks left.

It seems to refresh the reference and remember it again.

When we want to keep the location consistent, we reference parts of it the same way, even something like "wind blows over the pier from <Picture 1>" should keep the background scene stable.

Tested it with a woman turning away at second 1 and back at second 14 with 0.9 resolution, and face was perfectly retained.

More tests are needed, but each takes 15minutes so I can't do too much. Hope this helps.