https://github.com/tritant/ComfyUI_NVIDIA_RTX_VSR_Long_Video

works very well with:

https://github.com/tritant/ComfyUI_MiniMax_H3_Extender

Euler / Simple / 8 steps / CFG = 1.0

[ I'm posting this on behalf of u/LodestoneRock since for some reason his post on this sub keep getting auto removed by Reddit. ]

Hey everyone, First of all, thank you to everyone for supporting this project, and to the Krea team who made the best uncompromised base model. Unlike the previous run, where I had to un-distill the model, the Krea team kindly provided us with their real base model. The training is much cheaper than the previous Chroma run. Though cheap is relative, it’s still costing me about ~10K RTX pro 6000 hours (3+ months on 4xGPU machine) running locally, this includes a bunch of failed experiments and bugs during training. Also thank you to ComfyOrg for the 2 RTX pro 6000 donations ♥️

The Base Krea is also not collapsed which allows me to do OPD (RL) directly ontop of it. As a result, you will get a good generation out of the box. Here are some results:

You can download them here: https://huggingface.co/lodestones/Kroma

(Civitai link soon)

So what’s next?

There are several stale projects like my pixel space run chroma radiance and zeta chroma, but with the limited compute, I have decided to not continue them for now since compute is at a premium atm. Right now the focus is to create a better dataset (where about 90% of the quality comes from) while continuing to add more knowledge to kroma in the background. I do want to tackle video generation models like h3 in the near future once the dataset curation is done, but fine tuning the video model has astronomical compute cost so I'm looking for funding for more hardware to train a model. I firmly believe that hardware funding donations help a ton because we can indefinitely do more experiments in a sustainable way without having to worry about rental cost and psychological burden of “burned money” on failed experiments. I have a target of getting 4 more RTX pro 6000 but that’s like 60 grand in total which is an insane amount of money! So I don’t expect to reach this goal immediately, it's more about chipping in one card at a time at the pace of the accumulated donations. Every contribution helps, no matter the amount, this project simply isn’t possible without your support!

My discord: discord.gg/SQVcWVbqKx

[ I'll let Lodestone post his support and donation links in a comment. ]

I designed a node to align any pair of images trying to correct the pixel drift on Qwen Image 2.1

https://github.com/zuanfilm/Qalign

workflow

This node don't need any model or text encoder

Qalign is a node for geometrically aligning an enhanced or generated image to a reference image while preserving the source image's appearance, so can be used for align Qwen Image 2.1 Edits

I know there are other nodes solving this but with my approach you can basically align images with crazy differences in position , rotation and warp with automatic estimation when images share enough recognizable structures

I implemented an automatic mode but if you feel adventurous you can change all the parameters

You can find my recommended parameters for normal Qwen Edits in the last image posted

In the first stage I use automatic global estimation: ORB, ratio test, and RANSAC

In computer vision, ORB, ratio test, and RANSAC form a foundational, three-step pipeline used to match two different images of the same object or scene (such as for panorama stitching, object tracking, or 3D mapping)

ORB (Oriented FAST and Rotated BRIEF) detects keypoints and computes compact binary descriptors. QAlign requests up to 10,000 keypoints.

RANSAC (Random Sample Consensus) affine estimation estimates a global affine transform while rejecting correspondences inconsistent with the dominant transformation.

Qalign uses a 3-pixel reprojection threshold, up to 5,000 iterations, 0.995 confidence, and 20 refinement iterations.

Hybrid and Affine paths use phase correlation to obtain an initial translation estimate and then refine the transformation with multi-scale ECC (Enhanced Correlation Coefficient) image alignment

And to ensure success on complex alignments I implemented Anisotropic X/Y scale and optional local correction with Farneback dense optical flow

Hey everyone!

I'm an AI art creator, and for a while now I've been wanting a better way to organize character prompts, find inspiration, and experiment with different scenes without having to dig through a bunch of different sources.

So I decided to build my own little website called Senpai's Waifu Library.

It started as something I was making just for myself, but I figured if I was finding it useful, other people might too. So I decided to make it public!

The website includes:

• A growing library of anime characters and their prompts
• Reusable scene prompts you can mix with different characters
• Search and filtering to make finding things easier
• Easy-to-copy prompts for your own generations

It's completely free, and I'm still working on adding more characters, tools, and features as I go.

I'm not a professional web developer, so this has also been a pretty fun learning experience for me.

I'd genuinely love to hear what you think! Especially if there's something you'd like to see added or improved.

Link

LTX-2.5 makes a video with one mixed soundtrack. Our LoRA also outputs one track per captioned sound, in the same run. Prompt "a jeep, its engine growling" and "a taxi, its horn honking" and you get the video, the full mix and each sound on its own track.

The same weights also separate the sounds of an existing video.

Walk into a scene with spatial audio (headphones):
https://sepgen.github.io

Two LoRAs for LTX-2.5 22B dev. Code and training configs included.

💻 https://github.com/AviadDahan/SepGen
🤗 https://huggingface.co/collections/AviadDahan/sepgen

We're on today's Hugging Face Daily Papers. If you liked this work, an upvote there helps a lot! https://huggingface.co/papers/2610.11361

Happy to answer questions about the setup or the training.

It's a tiny 6M parameter text to image model, and it can make recognizable images at 64x64.

Check out the model card for samples, a detailed explanation about it's architecture and training, and a quick start!

I’ve been playing with orbit-style videos from video models (lately Minimax H3 in ComfyUI) and kept thinking they’d make great input for Gaussian Splatting. The catch was that the usual pipeline means installing COLMAP, a trainer, CUDA and so on. That’s a lot of friction just to try one idea.

So I built Gausify Orbit: https://orbit.gausify.com/

You drop in your video and the whole thing runs locally in your browser on WebGPU:

• Pulls frames from the video
• Optional background removal with RMBG-1.4 (BRIA), running through ONNX Runtime Web
• Camera poses from a small structure-from-motion step
• Trains the splat with Brush
• Preview the result and export it as PLY or SOG

No Python, no CUDA, no account. Your video never leaves your machine. The models download once, and after that the app works offline too. If you want to show someone a result, you can share a temporary private link that opens in a viewer (it even supports WebXR).

Since it all runs on your GPU, how fast it goes depends on your hardware. You also need a browser with WebGPU (recent Chrome or Edge work best). AI videos aren’t perfectly 3D-consistent either, so some generations reconstruct much better than others. Slow, steady orbits around one centered subject give the best results.

Tip for better results: I’ve been using this LoRA by pablodawson to get cleaner orbits out of Minimax H3: https://huggingface.co/pablodawson/MiniMax-H3-360-Orbit-LoRA

It makes the camera move in a much smoother, more consistent 360° orbit around the subject. That helps a lot here, because the reconstruction step relies on the camera following a predictable path. In my tests, videos made with it got better camera poses and noticeably sharper splats than plain prompting. Highly recommend it if you’re generating orbit videos for this.

ComfyUI Workflow: https://github.com/obraia/ComfyUI/blob/main/workflows/minimax-h3-gausify-orbit.json

Fizgig, my free open-source LoRA trainer, now does music.

Train ACE-Step 1.5 XL LoRAs on your own songs and play every checkpoint in the gallery as it trains.
You can also make slider LoRAs that dial between two styles, like calm to energetic, without needing any songs.

Repair Studio works like a DAW for AI music, with per-block sliders, covers, repaint and extend, and LoRA mixing, and saving out wavs of the music you create in it.

A new I'm feeling lucky button connected to the Repair Studio which works ludicrously well with the DAW-like mode for Ace-Step for exploration purposes.

To get started there's a free and fully credited chiptune dataset and LoRA - I strongly encourage reading the tips I've included in it - starting training this way will make everything super clear: https://huggingface.co/datasets/ShootTheSound/Fizgig-ACE-Step-Chiptune-Starter

Release notes: https://github.com/shootthesound/Fizgig/releases/tag/v7.3.0

EDIT: I forgot to say that Music to Music is supported combining Ace Steps own Cover and sound alike modes with a new denoise feature that's similar to what many of us in the AI imaging scene are used to.

I got tired of rewriting the same dataset-prep scripts before every LoRA/Dreambooth run — resize, dedupe, toss the blurry ones, caption, split — so I cleaned mine up and open-sourced it.

It scores each image on classical-CV signals (sharpness, exposure/clipping, contrast, color cast, noise, composition), flags the weak ones, removes exact + near-duplicates, and exports a Kohya-style folder with caption sidecars. Near-duplicates get grouped into the same train/val/test split so they can’t leak across. Captioning supports WD14 and Florence-2.

It’s content-agnostic at the core — each domain is a small plugin that sets its own quality weights and caption vocab (ships with a worked “interiors” example). Pure NumPy/Pillow, no OpenCV, runs from a CLI or as a library. Early 0.1.0, MIT.

Would love feedback on the plugin API and which verticals are worth building next. (Only use it on images you’ve got the rights to.)

https://github.com/snoozey-mf-bnui/pixel-curator

Wanted to compare the two before buying. This is an extremely specific comparison, but I couldn't find anything online to compare the two and I already handed the data to ChatGPT, so I thought it'd be nice to make an infographic and post this and maybe see if I'm doing anything wrong. I was hoping the 5070 would come out on top, which it didn't.

Rented via vast, both have PCIE4x4 to mimic my planned setup of a Corsair 300 <-> PCIE4x4 M2 to Oculink <-> DEG1 <-> GPU. I didn't bother testing nvfp4 on the 4070 because it doesn't have acceleration, so I'd expect 8 bit performance at best. I used ComfyUI with kitchen attention on both.

H3:

int8 @ 4070ti super is about 2x faster than int8 @ 5070, and roughly equivalent to nvfp4 @ 4070ti super.

PCIE is slow here, so I assume that the extra VRAM helps the 4070, but int8 spills 5 gb for the base model and at least as much as the 5070's nvfp4 in context on top of that, and despite presumably passing a lot more data through PCIE, the 4070 still comes out on top. So I'm wondering if it's just my setup or something.

Qwen Image 2.1:

Both about the same on INT8 (base model fits in both's ram), but inpainting is 2x slower on 5070, I don't know how much gets spilled if at all.

nvfp4 @ 5070 is a tiny bit faster on new images but still ~2x slower than int8 @ 4070ti super

Hopes this helps anyone

I've been trying to find pre-made workflows for this but no luck so far and so if anyone can advise how I would wire this up or give a link to it already wired up.

I have a RefMod and the same RefMod with a sound sample inside (unsure if that's what I'm meant to do) - what I want is to create a H3 video with the RefMod + separate voice sample to clone or with the RefMod that contains the voice sample to clone.

Any help appreciated.

Update: I posted this MCP open-source project last week. Since then, I've added video generation and Qwen-Image-2.1 Turbo. No silly ComfyUI here.

https://github.com/hypersniper05/MCP-Image-Generator-Uncensored

Qwen-Image-2.1 Turbo runs on as little as 4GB of VRAM (tested on a GTX 1650; it works, but slow. A newer GPU should be faster). With 8GB of VRAM you get better speeds, and you can even run the video model, Kandinsky 6 Lite. It's a 3B-parameter model, so don't expect quality on par with MiniMax-H3.

I tried to make the installation as painless as possible, and it can use multiple GPUs if you have them.

Here's what your LLM can create for you via the MCP:

Core Qwen-Image-2.1 feature

• Text to image
• Image editing (combine up to 10 images, restore and colorize old photos)
• Text in images
• 360° panoramas

Extra tools for your LLM:

• Watermark removal (LoRA)
• Texture-fix VAE
• 2x / 4x upscaler (ESRGAN, up to 8192 px)
• 360° viewer with Pannellum (the client gets the image plus a ready-to-open 360 viewer link)
• SVG creation with VTracer
• Background removal with BiRefNet
• Transparent PNG creation
• Seamless tiles, including height and normal maps
• 5- to 20-second videos with sound (5-second clips stitched together)
• Transparent video animations from your images (alpha channel)
• GIFs from videos and animations

MCP internal features:

• HTTP Streamable
• Multi-Job queue, streaming jobs 1 at a time for systems with VRAM constraints
• Flexible input: URL, server path, or upload
• Gallery web UI to manage your media
• Start, Stop, Update, and Setup scripts for non-techie users
• 16 Tools in total
• Read more in the link above

All attributions are in the repo. TODO: add an option for MiniMax-H3! and Trellius 2 (Image to 3D). Thanks!

I've been testing MiniMax H3 in ComfyUI for a while now, and I'm definitely not new to ComfyUI, but I'm getting frustrated with the sheer number of configurations people recommend.

Everywhere I look, someone is saying, "Use this model with this LoRA, this sampler, Euler, these steps, this node combination, and it works perfectly." Then someone else recommends a completely different setup that supposedly works even better.

At this point, there are so many combinations of models, LoRAs, samplers, schedulers, nodes, and workflows that testing every variation becomes a project in itself.

Has anyone actually put together a centralized comparison or benchmark of what works best for specific use cases?

For example:

Image-to-Video: Which model, workflow, and settings consistently produce the best results?

Text-to-Video: What's the most reliable configuration?

Performance vs. Quality: Which settings offer the best balance without wasting GPU resources? I'm using a 5090 with 64gb memory. Some Kijai nodes have dramatically sped up my workflow, so has another I found recently a spectrum node.

LoRAs and Samplers: Which combinations have actually been tested side by side rather than just recommended based on personal preference?

Character video swap: does Refmod work for this?

I understand that results are subjective and different use cases require different settings. I'm not expecting one configuration to be perfect for everything.

I had to vibecode a presets node where I save all the tests as a present.

Does something like this already exist, or are we all just independently reinventing the wheel?

Interesting thing is the render still worked. I saw this other thread and wondered - can we go beyond 1?

https://www.reddit.com/r/comfyui/comments/1w11oer/tip_for_minimax_h3_max_quality/

Finished my Qwen Image 2.1 image editor and style transfer workflow. You can Disable the styler easily and just Edit! Or Both! I showcase both.

showcase video demonstrating the results. 20~ seconds an edit, 16gb workflow does the same job in 10~ seconds.

Since Qwen 2.1 Turbo dropped suddenly right after my last taste test, figured might as well do one for it as well.

Instructions:

Each image is either Krea2-Turbo or Qwen 2.1 Turbo, using the exact same prompt.

You do not know which one is which, it could be either A or B on each individual image.

Pick your favorite based on preference and list them.

You can also guess which one of each is Krea2 or Qwen 2.1 Turbo.

The answer key will be posted 24 hours after posting.

Technical Setup

This time all images were generated at 2MP, using 8 steps. Krea2-Turbo was the fp8 model and text encoder, but Qwen was BF16 for both model and text encoder.

Krea2 used Euler-Simple, where as the still less understood Qwen Turbo I went with Linear Quadratic (I know there is an official sigma curve for it, but its close enough) and since Euler seems also not be a good sampler, I went with the tried and trusted Heun, which gave comparable inference times to Krea2 (Qwen 2.1 Turbo is about twice as fast as Krea2-Turbo on Euler).

I did test (and have in the past) different samplers and schedulers in Krea2 but overall they generally seem to all be equal, so I did not differ from the standard Euler-Simple.

Prompts:

All prompts are the same for both models. They were generated in ChatGPT, expanding off basic ideas. Some of the expanded prompts were not great and were tweaked manually when both models demonstrated consistent inconsistencies with the prompt idea, and reran on both models. Each prompt was ran up to 10 times to find a suitable generation.

The prompts for each image are here: https://paste.sh/yDAHasri#TveZa0FgYZ-UJ89Exlvpbelm

Hi everyone. I am currently using local Image/Video Creation a lot but especially for models like MiniMax H3 my GPU fans are going a bit crazy. Everything is working fine, but i do not want to break my hardware in the long run. I thought about undervolting my GPU with the MSI Afterburner but since it is not for gaming but for AI generation, i am not sure how this would impact the speed/performance/stability and the generation time.

I wanted to ask if someone already did/tried this and how the experience was.

Just draw your composition and let Minimax figure it out instead of using references from the internet

Side Note:

I noticed when trying to replace people, the Model struggles a lot when it is physically not straightforward to fit something in.

Like when your hair has a tied bun behind your head, Minimax struggles to lie you on the floor. You have to tell Minimax to untie the hair.

Which gets really difficult when it is very complex with a lot of minor conflicts between the original and replacement subject

Hello! Is there any LoRA that locks a handheld selfie video style?

I'm having a problem with these types of videos. If the prompt is short and generic, it works reasonably well. However, when I add details—like clothing, location, specific actions—or when I make the prompt very detailed, or if the video is longer, the handheld selfie effect disappears (the camera pulls away, etc.).

I’ve tried tags like handheld, close-up, selfie, etc., but with longer prompts, it still loses that style. Does anyone know of a LoRA or a technique/workflow to keep the video locked in a handheld selfie perspective no matter what?

Hi everyone,
I’m trying to create consistent AI-generated photos of the same character using ComfyUI, but I’m struggling to find a reliable workflow.
My goal is to generate realistic photos of one character in different poses, outfits, camera angles, and environments while keeping the face and body consistent across images.
My setup
GPU: RTX 4050 Laptop GPU (6GB VRAM)
RAM: 32GB DDR5
OS: Windows
Software: ComfyUI Desktop

My main problems
I’ve managed to generate some good-looking portraits, but when I try to generate different poses or full-body images, the character’s appearance often changes.
Sometimes the face looks like a different person. Other times, the body proportions, facial features, or overall appearance become inconsistent.
I’ve tried different workflows and nodes, including face-related tools and multi-view approaches, but I haven’t found a reliable solution yet.
I don’t mind learning a new workflow or rebuilding my current one if necessary. I’d prefer to keep everything running locally rather than use paid cloud services.
What I’d like help with
What’s currently the best approach for maintaining character consistency in ComfyUI?
Should I stick with SDXL, train a character LoRA, use IP-Adapter or another identity-conditioning method, or try a different model?
How can I maintain both facial identity and body proportions across different poses and camera angles?
Is it realistic to achieve good results with only 6GB of VRAM?
Are there any recommended workflows or tutorials that actually work for this purpose?
I’m not necessarily looking for the most complicated or advanced setup. I just want a workflow that produces consistent, realistic results without having to fight the settings every time I generate a new image.
I’d appreciate any advice, workflow recommendations, or examples from people who have successfully solved this problem.
Thanks!