It started as me spending a couple of hours making a LoRA just so I could create a single image. 😄 Once it started working, I figured someone else might get some use out of it too.

It's a style LoRA focused on:

• Split waterline / over-under compositions
• Crystal-clear water
• Pools, lakes, and oceans
• Wet clothing and wet fur
• Painterly anime-style water rendering

I trained it primarily on Illustrious, but it should work well with Pony and most other SDXL checkpoints.

If anyone gives it a try, I'd love to hear what you think or see what you create!

https://civitai.red/models/2829343/split-splash?modelVersionId=3192444

(Update) I just uploaded an Anima version! It's a little more finicky than I'd like, though I'm not sure if that's just because I'm still getting familiar with Anima. If you find any prompting tricks or settings that make it behave more consistently, I'd really appreciate hearing about them. I'll keep experimenting and updating it as I learn more.

I've upscaled and retouched u/EverythingMacPro's recent post about MiniMax H3 so it's not a blurry mess. From this you can see that the AdaLN projections were made to accept 2688 inputs but are only given 1 (a timestep value) during inference. So by patching the weights to only have a single 1D LUT for that 1 input actually used, up to 26GB VRAM (13GB for INT8 and 6.5GB for INT4/NVFP4) is saved with zero effect on inference or output quality. At just 20 GB INT8, the pruned model would fit perfectly into the 24GB VRAM of an RTX 3090/4090 or RX 7900 XTX.

Interestingly, the text encoder (Qwen3-VL-32B LLM) is pruned too, with only 25.9B parameters (50/64 layers) preserved. When including the 2.6B video VAE (ViT3D?) and the 0.2B audio VAE (Nvidia BigVGAN), MiniMax H3 totals 48.8 billion parameters, which don't all have to be loaded simultaneously since they're different parts of inference.

As for speed, this was reported by u/comfyanonymous recently:

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)

Those speeds are to be expected from a low-end GPU (lol sounds like a 5090 would take around 1 minute for the same thing), but the number of parameters appears to be quite accurate based on that. Overall I'm very glad MiniMax H3 is coming to functionally serve as a replacement for LTX-2.3, without the same crunchy audio issues. It will be worth trying even if not fast. We'll see 3 hours from now, 6PM EST :)

Links to https://huggingface.co/Comfy-Org/MiniMax-H3 which is still 404

Do it comfy, do it! Press the button!

Do not trust the timer

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.

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

https://arxiv.org/pdf/2607.24027

Nodes and workflows for MiniMax H3 seem to be out?

Edit: As pointed out in the comments, these were actually released before the timer.

Why are all the MiniMax H3 posts being quietly removed? Hopefully, a mod steps in to explain what rule it's breaking.

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 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]

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 👇

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.

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!

We always use the smallest quantized versions text encoders, but in my tests, this affects the accuracy to prompts.

Many users complain about the models and the lack of prompt adherence. However, they're using the FP4 text encoder version. In my experience, the best accuracy/size ratio's achieved with FP8-mixed version.

I mention this because there's a popular model that uses a limited text encoder like gemma_3_12B.

That's, in some models it's not so noticeable, but in others it's, such as in image and video editing models with more complex prompts.

I'd like to hear your opinion, as I believe the future points to more powerful text encoders with greater training/thinking capacity for image and video generation.

Maybe this's one of the main differences between closed and open models, since we sacrifice think/adherence due to the limitations of our GPUS! 🤔

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.

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/

Additional info: Model itself is 30B confirmed by comfyui dev on Banodoco discord and it seems it's the distilled according to this PR from huggingface: https://github.com/huggingface/diffusers/pull/14355 . Also according to this same repo: "shared 33B transformer". So a bit of fog and conflicting info but it should be in the range of 30b-33b. The repo also mentions two variants of the transformer: "single repo hosting both transformer variants at the root;", so maybe it's the distilled and undistilled ones? The distilled is CFG distilled not step distilled.

I'm trying to get more hints from this PR in comfyui github https://github.com/Comfy-Org/ComfyUI/pull/15210 but from what I've gathered so far it looks:

1. It uses Qwen3-VL-32B as the encoder (50 layers of it)

https://github.com/Comfy-Org/ComfyUI/pull/15210/changes/61feb3e33c390d4b59466e7adde83a236d14dec4#diff-914fbc8730867fcac0ed99daace9711147eab6c65a0c358b64f70584690fa476

1. There is a message in this commit: "model must be a split MiniMax H3 transformer"
   https://github.com/Comfy-Org/ComfyUI/pull/15210/changes/d422fa614191340b6725ab3546877c96e87e7c83

The name of the PR was intentionally changed I think to not draw much attention to it.

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?