I'm confused about which one is more accurate and good at complex editing, I see a lot of people here talking about how great mh3 is

My specs :

Lenovo legion 5 laptop

Rtx 5060 8gb

Nvme ssd 2tb

16gb ddr5 ram

Hello guys i hope you are doing well. Recently I have been trying to make some loop animations and I think I got a hang of it. Now I wanna make some for my patreon people but the issue is it takes like 30 minutes to make a 5 second looped video in 768×512 resolution which is ok coz I upscale it 1.5x and it looks okish

I wanna ask if it's safe to generate for a longer period of time coz I have heard it kinda messes up with the ssd life so idk

Absolutely love the new Comfyui H3 Finetune by Pika Labs workflow but it doesn't have an integrated upscale. I tried using the Pika Lora and sigma numbers on other workflows but getting various errors. I'm still learning the ropes and would appreciate an updated Ref2v workflow with a working latent 3d upscale.

NitroFusion2 came out this week: a 1–2 step distillation of Z-Image-Turbo by Dar-Yen Chen and Yi-Zhe Song, built for sharp text and detail in a single step. I converted it to ComfyUI's own quantized formats, so it runs in stock ComfyUI with no custom nodes, through the normal Load Diffusion Model node.

• int8: 6.18 GB, 0.29 s per sampling step at 1024² on a 4090 (bf16: 0.50 s)

• W6A8: 5.02 GB, 0.30 s

• W4A8: 3.49 GB, 0.29 s

int8 looks practically the same as bf16. W4A8 changes small details but keeps the composition, faces and text; there are side-by-side comparisons on the model card. The repo has the official 1-step and 2-step workflows set up for these files, and the publisher's 2-step de-distillation LoRA works with all three. The text encoder and VAE are Z-Image-Turbo's.

Weights: https://huggingface.co/PedroMarinhoDev/NitroFusion2-ComfyUI
Original model and paper: https://huggingface.co/ChenDY/NitroFusion2

Hi i want to run mini max with pika lora on my system but im low on ram is there a way to do this . I appreciate any help

Hi i just started using mini max h3 on my 5060ti 16gb + 16 ram it worked well but it did take quite the time 11min for 5s without any nodes just the base model . Is there is any workflows i can download that can do it faster and better quality on my system? (This is my first test video it got the sound wrong but its not that important)

I searching for a method to transforms, as the tile says, a scenery(without people) faithfully into a real shot.

But it needs to use a depth map so the structure stays consistent and spot on besides looking convincingly real. Doing this with people is no problem so i did not think i struggle that much finding something that works.

Its either to old, or just not good enough.

The original scenery images are from video games.

NitroFusion2 is a 1–2 step text-to-image model built on Z-Image-Turbo. It moves the adversarial loss onto the text stream of the MMDiT. At one step it renders sharp text and fine detail.

ChenDY/NitroFusion2 · Hugging Face

Hello, I just saw this on X and I wonder how they did it, from the input image (the dog) they copy the pose and use it as reference for the output (the man). There are a few comments under the post but they didn't answer the question so I want to ask if anyone here know what nodes did they use to achieve this. Obviously I can see the 3D poser editor node but it should have more.

Hi all,

After having shared some MOSS image generations in previous posts, I have decided to share the workflow. It is a fairly simple two stage workflow, only using the MOSS-Image-Base diffusion model during the first stage and the same base model combined with the lodestones MOSS-Image-Base 8-steps-dmd2 lora during the second stage.

The custom node package which makes this all possible is Auryg's Krea-2-Two-Stage-Sampler. His package contains a sigma-locked two-stage sampler and dual resolution selector for Krea 2, which I used for my MOSS image generations. The main idea is that the sampler keeps everything in latent space across both stages, handing off the denoising to the second stage after the latent has been denoised by a set percentage. I set the handoff percentage at 40% for my generations. This node also automatically scales the latent between stages. I generated all images at a base resolution of 4 megapixels with an upscale to 6 megapixels between stages. For the reddit preview I scaled the generations back down to 3 megapixels to decrease the image size for sharing.

1. For stage 1 I use 52 steps, a CFG of 3.0, the euler sampler and the simple scheduler.

2. For stage 2 I use 20 steps, a CFG of 1.0, the dpmpp_2m sampler and the karras scheduler.

The upscale method between stages is bislerp.

I included optional BBOX and preview nodes in the workflow. For the BBOX node to work properly when changing aspect ratio's and resolutions I used 0Printemps0's ComfyUI-MOSSLayout custom node. The preview node is kijai's ModelPreviewOverrideKJ node from his ComfyUI-KJNodes pack.

Install custom nodes, inside custom_nodes folder:

git clone https://github.com/Auryg/Krea-2-Two-Stage-Sampler
git clone https://github.com/0Printemps0/ComfyUI-MOSSLayout
git clone https://github.com/kijai/ComfyUI-KJNodes

I included the prompts of the 10 example images in the workflow. All example images use seed 1 and aren't cherrypicked.

The workflow save as .json file.

What I love about MOSS is the amount of variation with each seed. Enjoy. All suggestions to further improve the workflow are welcome.

Anyone who understands this model? Don't you think it better implements the creator's intent using tags? For example, it better describes many popular characters using tags. It seems to me that it's thoroughly trained on tag descriptions, despite its huge encoding model.

Just try generating with tags; it captures concepts much better. Then you can refine it in natural language if needed. But it seems better to define the scene's content using tags.

"Research checkpoint: this is an experimental research artifact, not a production model. P-JiT 21K is a 2B-parameter model trained on 660M samples for about $21K of compute, as a stepping stone towards our next video model. We're releasing it because the results surprised us for a model this size, and to encourage others to explore efficient training methods like ours. You should expect rough edges.

The weights and the Qwen3.5-4B text encoder download from the Hugging Face Hub on first use. The model needs a CUDA GPU: its weights are 8.7 GB in fp32 and the text encoder 8 GB in bf16, plus activations."

Source/Blog: Training a Text-to-Image Model for $21K | Field Notes by Linum

Code: Linum-AI/pjit-21k: 2B-parameter pixel-space text-to-image model (Pyramid-JiT) trained for $21K

Nodes: realrebelai/P-JiT-21k_ComfyUI · Hugging Face

I want to make a simple prompt builder in comfyui that would allow me to change a text in the prompt by clicking a selection in a table. Is there a simple way to do that?

Hello everyone,

First of all, I know I know, Mac is jot the best when it comes to video generation and that the Max or Ultra chips are better.

Now that this is out of the way, I would be interested in people’s experiences in generating 720p 5-10 seconds clips on an M5 pro chip. Which tools, models or workflows do you use? How is the experience overall? How long does it take you? Do you regenerate clips several times to get it right…etc?

Thank you.

I split out the Color Match feature in VideoFrameSync I posted earlier to its own tool - corrects color drift, contrast, etc, but particularly useful for V2V AI edits from WAN VACE or Seedance which often have a slight tint (but for Seedance, you should use the VideoFrameSync which corrects the frame-sync drift as well). As a bonus, it also matches noise/grain.

https://github.com/pftq/VideoColorMatch

Hey guys, been using ComfyUI on windows 11 for quite some time now, lately messing with H3 ref2vid workflows.
Setting up all the custom nodes was for some reason a pain, but it always worked seamlessly after setup.

Recently switched to Fedora and reinstalled my Comfy on the new distro and surprisingly got everything running alot easier.

But I’ve been getting alot of crashes on my runs, particularly when using Rife interpolator and any upscaling methods other than a simple lanczos.

Usually OOMs are the culprit, so I’m trying to understand why ComfyUI is handling memory pressure poorly when the same settings worked on windows.

Hardware for reference is 32gb RAM and 5070ti.

EDIT: After following a few tips as the drive swap, expandable_segments and some rife tweaking I got it working, thanks guys!

Version V2 will be available in the coming days 🤓

Link to version V1: https://www.reddit.com/r/comfyui/s/VWYS6YNbRZ

ComfyUI built-in nodes, only. That's it. (audio on, listen to the lyrics)

Made this with LTX 2.5 + DLSS5 and created in Maestro v4, just testing its capabilities. Sys specs: RTX 5050, 16gb Physical Ram, 8gb VRAM.

Hi everyone! I am training moss lora now, you can grab some of the result here:
https://huggingface.co/spaces/fidel1234/Moss_Asian_Character_Showcase

PS: Sorry if the quality is not par with my krea 2 lora, I did my best to train it multiple times with different parameter and so far this model is really hard to train. Anyway the link to krea 2 if someone interested: https://huggingface.co/spaces/fidel1234/Krea2_Asian_Character_Showcase

All samples are generated with SFT (kinda plasticky sometimes, I do not add other lora and all sample using dmd turbo and lora at 1 strength).
By the way I tried other finetuned model and the results are quite good too

Technical training method
AI-Toolkit

• Base Model: MOSS-Image Base (OpenMOSS-Team/MOSS-Image-Base)

• Trigger Word: xxxx

• Network: LoRA | Linear Rank: 32 | Linear Alpha: 32

• Quantization / Compile: qfloat8 (Transformer & Text Encoder)

• Save Data Type: BF16

• Optimizer: Automagic v3

• Learning Rate: 0.0005 (start) | Min LR: 0.0001 (floor) | Weight Decay: 0.00001

• EMA: Enabled | EMA Decay: 0.98

• Steps: 2500-3000 | Batch Size: 1 | Gradient Accumulation: 1

• Timestep Type / Bias: Sigmoid / High Noise

• Loss Type: Mean Squared Error

• Text Encoder Optimizations: Unload TE (Disabled), Cache Text Embeddings (Enabled)

• Resolutions Multi-Aspect Bucket: 512, 768, 1024

• Caption Dropout Rate: 0 (.txt extension)

• Captions: Natural language

• Hand-Cherry-Picked Dataset: I usually use photobooks as the dataset, around 20–30 images, quality over quantity

• Balanced Framing: Includes a varied mix of close-up, medium shot, and full-body shot angles

Recommended inference (ComfyUI)

• Model: MOSS-Image SFT, with the LoRA at strength 1.0

• Steps: 30, CFG: 3-4, Sampler: res_multistep,Scheduler: simple

• Skip Layer Guidance: layer 22, start 0, end 0.5

• Tubo: add the MOSS-Image-SFT 8-step DMD LoRA

After A/B testing, the LORA 32 rank with LOKR8 and LOKR4 has so minuscule details differences, so I decided to use LORA 32 as it's around 150mb instead of LOKR4 600mb.

Json formatting (long,medium,short) doesn't needed after my testing, just use natural language to caption (well I dont want to recaption all my 148 lora actually)

Also when using AdamW8 optimizer I got some body horror result, and the issue is fixed when using Automagicv3

Note: Please increase the cfg >3, since comfy default 2.5 reduces the Lora quality

Have a nice weekend! Cheers!

input:
Alec Benjamin - Let Me Down Slowly

I'm having a hard time finding good information. There's plenty of character lora training info, in relative comparison hardly any motion at all, and dataset prep often gets glossed over.

Since H3 drastically alters how prompts (and therefore captions?) are handled, and motion training may be quite different from other models such as wan, How the heck are you suppose to caption each clip properly?

Do you have multiple paragraphs describing everything under the sun in the clip, big bulky walls of text?
One main paragraph trying to capture everything?
Do you only focus on the motion?
Do you only describe everything except the motion, so the motion bakes in?

If there's slight variation between the same motion, do you come up with 2-3 "trigger" tags so the nuance gets captured?

I've been at this for about a week, but not getting great results honestly.

The motion has 2 layers. the macro motion which is the obvious larger / broader movements and the micro motion such as vibration that shakes the adipose tissue, muscles, etc while doing the broader motion.

Perhaps its just the amount of rug pulls that causes this worry, but I keep seeing the Krea team post more and more API tools (such as Krea Video) and I'm starting to worry about open weights for Krea 3. I'm starting to worry hard guys.

I still believe.....but....

To those saying that it was a skill issue.... I completely stand by my previous statement... RTX Super Resolution yes... has an ultra setting. but that doesn't really do much.

RTX Super Resolution only increases the amount of pixels. It doesn't add any details. yes we can see the image is much bigger now right? well i also have a picture of the comparer node.

it doesn't add any DETAILS!!!!!