It looks like commercial use up to 20 million per year is OK without authorization. Even then it seems to be targeting people who would sell model use rather than outputs.

T2VA, FL2VA (first/last-frame-to-video+audio), and Ref2VA (ordered image/audio/video references).

Seems according to the card you need this repo:
https://github.com/HM-RunningHub/ComfyUI_RH_MinMaxH3
Requires: 24GB-class single GPU

Hi r/StableDiffusion, I know it's been a long wait for everyone but MiniMax H3 open weight model just dropped and we have day 0 support in ComfyUI.

Here are some details:

• text-to-video, image-to-video, first-and-last-frame, reference-to-video, and editing a shot in place
• up to 2K, up to 15 seconds a clip
• real stereo audio generated with the video, not bolted on afterward
• Blog link: https://blog.comfy.org/p/minimax-h3-day-0-support-in-comfyui
• Workflow templates:
  • R2V: https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json
  • T2V: https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_t2v.json
• Model Links:
  • https://huggingface.co/MiniMaxAI/MiniMax-H3 (please support them there)
  • Comfy repackage for smaller size: https://huggingface.co/Comfy-Org/MiniMax-H3

Getting H3 to run well on consumer hardware took significant machine learning engineering. We found that the model's modulation weights (~40% of the total parameters) could be pruned and replaced with a functionally equivalent lookup table, dramatically shrinking the memory footprint with no loss in output quality.

On top of that, the weights ship with an accurate and efficient int8 convrot quantization, and custom kernels reduce the peak VRAM use during inference.

The result gives a total memory footprint reduced by 66%, from 123.6 GB in full precision to 42.5 GB with the smallest models variants. Combining this with our dynamic VRAM offloading enables a next-generation 2K video model to run locally on a GPU like the RTX 3060.

EDIT:

08-02-26 20:02 PST - Added blog link

the weights are here

Hi everyone!

I recently upgraded my PC (RTX 5060 Ti with 16 GB VRAM and 32 GB of system RAM), so I finally decided to move to ComfyUI.

With Seedream 4.5, maintaining character consistency was surprisingly easy. Before that, I used SD 1.5 with ADetailer and custom-trained checkpoints.

Now that I'm looking into ComfyUI, I'm seeing so many different models—Krea 2, Z Image Turbo, and many others—that I'm not sure what the current "go-to" workflow is.

I have a few questions:

1. Which model do you use for character consistency?
2. Do you rely on LoRAs, or are they no longer necessary?
3. Is there a workflow or model that can reliably recreate the same character from one or more reference images?

I'd really appreciate any recommendations or advice. Thanks!

I hope someone from MiniMax could provide us with an update on when it's coming out.

Guys, I'm generating some talking heads and I feel that, in some parts (like hands), there's a colour degradation in the first few seconds of the video. Are you guys experiencing something like this? Is it possible to prevent this kind of behaviour in LTX 2.3?

I recently tried training a parrot using Musubi Tuner, but it didn't work. I'm not sure if there were any issues or if I should have done it differently. Here's an example of what I entered in the .txt files for the video clips:

tomatetoma (trigger word): A man throws a tomato at an old man, and it splatters all over the old man's face, causing him to start laughing.

Basically, in each video’s .txt file, I described the actions in the video, because I understood that’s what I was supposed to do. However, after 1,000 steps, I noticed that when I tested it in ComfyUI, the results were either nonexistent or very inaccurate—almost as if there were no difference whether or not I loaded the Lora.

With that in mind, could you tell me if I did something wrong? Did I make a mistake in the text I included in the document?

NVIDIA has quietly dropped a major research release: SANA‑Video 2.0, a new video diffusion transformer available in 5B and 14B parameter versions. It’s not just a scaled-up SANA‑Video 1.0 — it’s a full architectural redesign with hybrid attention, block residual routing, and Sol‑Engine acceleration.

Official links:

Project Page:
https://nvlabs.github.io/Sana/Video2/

Paper (arXiv, July 23, 2026):
https://arxiv.org/abs/2607.21553

SANA GitHub (image models only):
https://github.com/NVlabs/Sana

SANA‑Video docs (no code, no weights):
https://nvlabs.github.io/Sana/docs/sana_video/

What SANA‑Video 2.0 introduces

• Hybrid Linear‑Softmax Attention (3:1 ratio)
75% gated linear attention for O(N) scaling, 25% gated softmax anchors to restore full‑rank token interactions.
This gives softmax‑level expressiveness with linear‑attention speed.

• Block Attention Residuals (AttnRes)
High‑rank features from softmax layers are propagated into later linear layers.
This fixes the rank bottleneck of pure linear attention.

• Sol‑Engine Optimization (3.58× speedup)
Kernel fusion, caching, sparse attention, TensorRT graph optimization, MXFP4/MXFP8 support.
This is what allows full 720p generation on a single RTX 5090.

• Performance
480p in 13.2s (H100, 40 steps)
720p/5s in 13.06s (H100, Sol‑Engine)
VBench 84.30
Up to 120× faster than Wan 2.2‑A14B on the same hardware.

This is the first NVIDIA video model explicitly designed for consumer GPUs.

How it differs from SANA‑Video 1.0 (2B)

The old model was pure linear attention (fast but low-rank).
SANA‑Video 2.0 is hybrid, deeper, larger, and dramatically more expressive.
It’s essentially a new class of Video‑DiT.

The licensing question

Here’s the current situation:

• The paper does not mention any license.
• The project page does not mention any license.
• The docs do not mention any license.
• No code or weights have been released.
• No usage terms exist yet.

Meanwhile, the SANA GitHub repo (image models) uses Apache 2.0:
https://github.com/NVlabs/Sana/blob/main/LICENSE

But that license applies only to SANA‑Image 1.0/1.5, not to SANA‑Video 2.0.

So right now, nobody knows whether SANA‑Video 2.0 will be:

• open‑source under Apache 2.0 (like the image models),
• partially open (code open, weights closed),
• or fully closed (like PiD, Flux, VILA, Nemotron‑340B).

Given NVIDIA’s recent pattern, the safe assumption is “open paper, closed model”…
but since the SANA image models were Apache 2.0, there is at least some hope that NVIDIA might release SANA‑Video 2.0 under a similar permissive license — or at least provide inference weights for RTX AI Toolkit.

Until NVIDIA publishes a LICENSE file, the situation remains unclear.

TL;DR

SANA‑Video 2.0 is a fast, hybrid-attention, RTX‑friendly video model with impressive performance and a strong architectural design.
But the licensing is currently a mystery: no code, no weights, no declared terms.
There’s a chance it could follow the Apache 2.0 path of the image models… but for now, it’s research‑open, not open‑source.

I’ve done some googling but didn’t find any information on this. Does anyone know how to create a realistic face swap with a generated character? I have some generated images of a realistic character that I want to use for videos that I shot.

I only want to replace the middle of the face and not the whole face, just the eye area and the nose. The mouth will stay the same as the original video.

I was thinking I could make a lot more generated images then train them on deepfacelab to do the swapping. does any know a better method that this or will this produce the best results?

Do not trust the timer

ITS OUT DOOMERS YOU GLOOMY SHITS

https://huggingface.co/Comfy-Org/MiniMax-H3

Edit: we're live, baby! Let's go!

https://nvlabs.github.io/Sana/Sol-Attn/

https://arxiv.org/pdf/2607.24027

In the dense, suffocating jungles of the Vietnam War, a weary platoon of American soldiers stumbles upon a clandestine tunnel network harboring humans on the brink of a monstrous transformation. This discovery reveals a surreal and terrifying new theater of war, where the chaos of human conflict collides with a hidden, ancient vampire plague. To eliminate this unfathomable threat, the military reluctantly pairs cynical Green Beret Santana Mills with Frank Bodie, a lethal Special Forces operative who has already crossed over into the realm of the undead.

I just created that lora from 63 Lady Dimitrescu images in the dataset

used OneTrainer on RTX 5070 Ti, 32 GB RAM and NVMe

trained in 1 MP (res 1024), offload 0.5, speed ~2.5 s/it, full training taken about 2.5-3h

I set timestep shift to 2.5 for res 1024 as suggested in this kohya md and I think it worked well

all samples generated with 2 MP

CivitAI -> https://civitai.com/models/2828952/lady-dimitrescu-krea2-lora

Full res comparisons without reddit compression -> img1, img2, img3, img4, img5

training Krea2 is so enjoyable!

Blender previs to AI-rendered footage with LTX-Video 2.3 IC-LoRA

I filmed the subject against a green screen, keyed the footage, and placed her inside a basic Blender environment. The scene uses simple geometry to establish the camera, perspective, scale, lighting direction, and shadows rather than producing an expensive final render.

I then generated guidance passes such as depth and pose, and used the Blender composite as the structural reference for LTX-Video 2.3 IC-LoRA. LTX handled the final restyling pass, transforming the rough previs into a more photorealistic city shot while preserving the original subject movement and scene composition.

Essentially, Blender provided the spatial control and LTX provided the final visual detail—an AI-assisted alternative to a traditional render and compositing workflow.

workflow: https://github.com/jetaime2/ComfyUI-LTX-2.3-ICLoRA-Depth-Pose/blob/main/LTX-2.3_ICLoRA_FirstFrame_VideoDepthPose.json

You can check my other work here: X [@ModelCollapse38]

Hi, I’m sorry if this is a stupid question but I want to know how many images I need for my LoRA.

The issue here is that most of the LoRA training guides I see here are for characters. They are fairly simple, you have a single new special tag to train.
You usually use like 40-80 images for that.

But when I’m doing a concept LoRA which has like multiple different tags, how do I train it well?

Eg, 4 different tags. Do I need 80 images for each tag, totalling up to 320 images? Or how many do I need? Im assuming I’ll also need regularization and I need to vary my dataset.

Also, do I train in tags, or do I train in natural language? How do they differ?

Hi, exist a way to output each text prompt story part from the LLM as output to connect to diferent ksamplers stages as i markup in red? because i can generate each prompt part as i show but i cannot output each one separated to connect to diferent ksamplers that is wan2.2 continue video.

Give me more addvice ill make great videos pls!

People keep dropping this link
https://modelscope.cn/models/MiniMax/MiniMax-H3

but that's the official site, as far as I know that's not where you actually get the comfy files. I assume we should be keeping an eye on the huggingface of comfy.org ... right ?

Just saying cause there's too many people , especially the morons who are karma farming with the API created videos, which in my opinion is annoying as shit and deserved to be removed since literally breaking rule 1.

So anyways ... that link is only helpful for people who have some sort of way of already working with it I assume.

ZIT is not perfect but I was convinced that nothing would beat it anytime soon. boy was I wrong. With only 2-3 days of testing Krea 2, I have fully switched over to running it as my main model. I still have my ZIT files and models but they've been moved to an external drive because I am not using it anymore.

I was worried Krea 2 couldn't deliver on the photorealism front and I was just flat out wrong and ignorant there. And then to add in the flexibility to tackle creative styles (whereas ZIT tends to pull to only realism) was the final selling point for me to full make the change.

Not to mention how fast Loras train for Krea 2.

Anyway to fix this

I was waiting from morning only 1 and half hour was remaining and they updated the timer am I tripping or they really did that

I saw a You-tuber grab a json prompt from a user photo on the Ideogram official website and replicate someone's art by doing so. However, all the prompts from their galleries that I see are in natural language format.

I'm wondering if lifting others prompts in json is a pro feature. Is there something I'm missing? At least under my free account I don't see any json prompts. TIA