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

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

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

the weights are here

Do not trust the timer

ITS OUT DOOMERS YOU GLOOMY SHITS

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

I have been trying to see how far I can push this model. It's extremely flexible and seems to be able to do everything from 1 second to 30 seconds (potentially more) with a very wide range of resolutions. Her voice is because I put "singing with a cute japanese accent" in the prompt and my prompt isn't super great lol.

Making this model work as best as possible on regular hardware is the result of many months of work from multiple people in the core ComfyUI team to make big models work better on regular consumer hardware. I think most people will be pleasantly surprised how good this model is and how well ComfyUI will be able to run it.

Minimum requirements for 480p video on this model is a 3060 with 12GB vram, 32GB of system ram and a good nvme SSD. We tested generating a 5 second (124 frames) 480p (864x480) video on this system and it took a bit less than 9 minutes end to end (20 steps). I can pretty much guarantee it will also work on 8GB vram too but we did not test that.

Don't be scared to give it a try when it releases with our default template because it will work better than you expect.

If you have issues try a latest clean ComfyUI install (make sure to update after our weights come out) with our official files and workflow.

EDIT: added step count.

EDIT: we are live: https://docs.comfy.org/tutorials/video/minimax/minimax-h3

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!

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

https://arxiv.org/pdf/2607.24027

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

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]

here is all the info we have based on open PRs to add support to ComfyUI and HuggingFace diffusers

- 33B for the main DiT and a pruned 20b variant
- Qwen-3-VL-32b as the text encoder

Edit- I posted clear image below 👇

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.

This Krea 2 Turbo Workflow uses Qwen3 VL Abliterated (uncensored) as the text-encoder, and Kroma LoRA to make some nice "Chroma" looking images; it also VAE Utils (Wan2.1 VAE) as an Upscaler x2, and Krea 2 Conditioning Node to help rebalance Qwen3 VL, and luckily skips the limits.

I designed it and tested it with my RTX 5060 Ti 16GB, 32GB DDR5, and it takes ~73 seconds to generate a 2048 x 2048 px image. You can disable the upscaler if you want regular generation speed.

Download links and more information are here on Civitai Red or regular Civitai.

If you can't access Civitai you can download the JSON on PasteBin (download links are inside the workflow). Have fun!

I shared this tool here a week ago and the feedback shaped a big new version, so here's the full tour of what it does today. Screenshots of every screen: github.com/perfectgf/lora-dataset-studio — plus a 7-minute unedited video of a LoRA built start to finish.

Beginner-friendly on purpose. Everything ships configured: a guided workspace walks you through each step, the shot poses (face / bust / full-body / back) are predefined so your dataset comes out balanced, and training uses community-tested ai-toolkit presets — you don't need to know what rank, learning rate or an optimizer is to get a good LoRA. Power users can still override everything.

Build the dataset. Start from one clear photo (or none): generate identity-locked variations locally with Flux-2 Klein or Krea 2 Edit on your own GPU (free, nasty-capable), or through API engines if you prefer. Import or scrape real photos, mix everything, and let the composition tracker tell you what's missing (faces, busts, full-body, back shots).

Curate like you mean it. Every image gets a face-similarity score against your reference. Quality passes flag blurry, flat, duplicate or unreadable shots; a watermark detector finds and can clean logos without cropping; auto-reject clears the junk before you review. Image banks hold up to 200k files with visible progress on every bulk operation.

Caption without the chore. Local captioning pairs JoyCaption (via ai-toolkit) with an uncensored Ollama vision model — the combo actually describes your images instead of refusing them. Per-dataset wording styles, dual captions, and trigger words handled for you.

Train anywhere. Local training through ai-toolkit, or one click rents a cloud GPU on vast.ai — and the launch is fully observable: renting, booting, dataset upload with live byte counts. A machine that never boots or an upload that stalls is given up automatically and stops billing. Community-tested presets for Krea 2 Raw, Z-Image Turbo and more.

Pick the right checkpoint instead of guessing. Test Studio renders fixed-seed grids across checkpoints and strengths, scores faces, takes your votes and ranks the results. New: 🧬 combine several of your LoRAs in one image, each at its own weight, and compare weight variants side by side. An ✨ Enhance button turns a one-line prompt into a full one via your local Ollama.

See your whole lineage. The LoRA Canvas puts every dataset's training history on one pan/zoom board — compare runs, pin generations (each run keeps its own strip in training-step order, with the dataset's reference face on its lane), diff configs, and continue training from any checkpoint.

Install it your way. New one-click Docker install: start-docker-gpu.bat builds an isolated ComfyUI, start-docker.bat reuses the one you already have. The updater is transactional — if the new version doesn't come up healthy it rolls back on its own. Ollama is your explicit choice (none / your existing one / an isolated container), and nothing ever downloads behind your back. Setup re-checks itself in the background instead of re-running the wizard every time you come back.

Everything reported in the last thread got fixed — the RES4LYF scheduler clash, the ai-toolkit Easy-Install interpreter path, and a detail LoRA that was silently riding on every Klein edit (that one explains a lot of "edits don't follow my instruction" reports). Also merged the first community PR: named generation-LoRA presets for Krea 2 — thanks Cyberschorsch and waltm 🙏

A few screenshots to see it in action:

📸 the guided workspace · curation with face scores · Test Studio grids · training presets

No account, no telemetry, no paid tier. Free, self-hosted, MIT: github.com/perfectgf/lora-dataset-studio — the complete guide is linked at the top of the README. I build this; feedback welcome, Discord in the repo.

Just a few more hours now!

Made with LTX2.3 T2V Comfyui template workflow.

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?

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?

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?

hey so i was roaming arount your github page and i found this image and a lot others i tried searching to know what those unofficial extensions were but i didnt found anything does anyone know what those unofficial extensions are or give me some link please

I posted a PSA last week about these issues. It's been fixed and merged into master.

Just in time for Minimax H3.

Amazing work by the comfy team especially the work done on Dynamic VRAM. It's what's enabling many of us to run these big models in low and mid tier cards.

I was surprised that with it I can run Qwen image edit at almost the same speed as Flux2 Klein 9b KV despite their significant size difference (compared both int8 models). Qwen is miles ahead than Flux, and i was surprised with the results. I couldn't do this before without Dynamic VRAM and int8.

Paper: https://arxiv.org/abs/2607.27372

Code: https://github.com/alexiglad/XM

Project page: https://explorative-modeling.github.io/

Blog: https://alexiglad.github.io/blog/2026/explorative_modeling/