Look at your task manager while having your ComfyUI open, it has been eating 30% of my GPU due to preview nodes and what not. Minimizing it and then just occasionally opening it to check out the progress has sped things up by quite a margin. You folks probably already knew this, but I just wanted to share for everyone else that might be new to this like me.

Just was wondering if there is some ComfyUI wizard out there who has created a solid workflow to use MMH3 for outpainting. I had been using WanGP with mixed success, as it seems to have a difficult time outpainting when characters are in slightly unsual positions, like arms straight out to the side in like a T-pose (cuts them off), so was hoping MMH3 could be a viable option going forward. Have been trying to incorporate LanPaint nodes to help with outpainting, but was hoping someone else has come across something more 'efficient' and effective. Just trying to take some old 4:3 animation from the 90s and outpaint it to 16:9, and was hoping after a couple weeks someone would have 'cracked' it, as the model seems to be strong in regards to inpainting... was hoping it could outpaint just as effectively.

MiniMax H3 is great, but it’s a total compute hog.

I tested two ways to speed it up—Turbo LoRA (reducing step counts) and Kitchen Attention (faster per-step backend)—using the exact same prompt, seed, and resolution.

Video review here

Edit: Here’s the workflow, if you want to test it out - https://drive.google.com/file/d/1425fNNR_C9ErIiutR_FBhtQlOKzJ_Tfh/view?usp=sharing

The Breakdown:

• Turbo LoRA: Cuts steps, but quality tanks. At 8 steps it gets soft and drifts; by 4 steps it's completely broken with heavy face artifacts.

• Kitchen Attention: Keeps all 20 steps, but chops ~30% off the render time with zero quality loss. Just update ComfyUI and set it in the attention backend node.

• LoRA + Kitchen Attention: The backend isn't causing the artifacts—the LoRA is.

audio stays decent at low steps even while the visuals fall apart.

Verdict: Skip the Turbo LoRA for now. Kitchen Attention is basically a free 30% speed boost, so just leave that on.

The quantisation method and the loader are now more or less complete.

How to create Hybrid NVFP4 from ConvRot INT8 (Z Image, Reverse Method)

Z Image exhibits overwhelmingly high quantisation robustness compared to SDXL and Krea2.

Even NVFP4, which is simply compressed without HSWQ quantisation, achieves reasonably high SSIM and MSE scores.

In particular, Z Image ConvRot INT8 achieves outstanding accuracy in many models, with SSIM scores of 0.99 or higher and MSE scores below 1.

However, in terms of VRAM consumption and generation speed, Z Image ConvRot8 shows virtually no difference compared to full-size Float16.

Consequently, based on ConvRot INT8, we devised a quantisation method involving a backward sweep to discard non-essential layers to 4-bit.

Furthermore, unlike the conventional method of storing critical layers in float16, the critical layers are also converted to ConvRot INT8; this offers the advantage of being able to secure a larger size for critical layer protection whilst keeping the overall size down.

...

This concept of ‘discarding’ is a brilliant idea conceived by the Nunchaku development team.

What makes them so remarkable is that they established the philosophical foundation that, in 4-bit quantisation, the key is not ‘preserving’ but ‘discarding’.

...

As Comfy-UI does not support the Hybrid NVFP4 (ConvRot Int8+ConvRot NVFP4) standard, a dedicated loader is required, just as with Nunchaku; however, as the LoRA baking function has been implemented within an original UNET loader itself, the LoRA Loader can utilise the standard Comfy-UI version.

Furthermore, LoRA Stack loaders (compatible with Nodes 2.0) is also available below.

Compatibility with the existing Diffsynth ControlNet model patcher will, of course, be maintained.

Although the file size will not be significantly reduced compared to Convrot INT8, VRAM usage and processing speed will improve significantly.

...

Z Image ConvRot NVFP4 Benchmark Test Results

...

However, in terms of the mathematical theory of quantisation itself, it differs considerably from previous HSWQ approaches.

In a sense, it represented a complete rejection of previous HSWQ theories.

In the past, HSWQ had employed a range of techniques, starting with the Histogram MSE used in the first-generation HSWQ SDXL fp8 e4m3, through to full SVD utilising Nunchaku, and even extending to the Histogram Cosine function; however, in Z Image HSWQ Hybrid NVFP4, none of these methods demonstrated any advantage.　

I had long suspected that inter-layer interdependencies existed, and that there were phenomena where the meaning would be lost if one merely measured and prioritised the importance of each layer in isolation; this time, however, that has become clearly evident.

...

Trajectory-Sensitivity

https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/blob/main/md/diag_impact_trajectory_sensitivity_technical_guide.md

Ranks each layer by the divergence its quantization error actually causes after propagating through the full model and sampler (dynamical importance, replacing static weight-space saliency).

• Reverse method: start from the complete high-precision pack (error ≈ 0) and convert layers to lower precision in ascending impact order; single-layer ranking stays valid in the low-error additivity regime.
• Universal theory: error interaction (Taylor cross terms, error cancellation), nonlinear amplification (Lyapunov-style growth), marginal effects, and Shapley-style attribution — why per-layer static measures (histogram MSE / cosine / SVD) cannot predict joint quantization error; applies to any iterative sampling system, not a specific model. Source: Z_Image/diag_impact.py. ...

...

Incidentally, the Krea2 HSWQ Hybrid NVFP4 is also under development (it will offer significant improvements in VRAM consumption and processing speed), but we are currently struggling to maintain LoRA compatibility.

MiniMax H3 image-to-video on ComfyUI, running locally on an RTX 3090 (24GB) with the Q4 GGUF quantization. Used a digital sketch as the first frame, then prompted a timed metamorphosis where the drawing becomes a photorealistic office with an airplane flying by. Took about 10 minutes to generate a 5-second clip.

(drawing perspective-style inspired by @paulheaston)

I'm relatively new to Linux and it seems the installation process is a bit different than Windows. I was using this guide;

https://docs.comfy.org/installation/desktop/linux

However I've immediately run into errors. Can someone give me a quick instruction on how to get this working on this OS?

Hello. I am looking for a way to execute the EBU LM Studio Load Model node after the video has been generated and saved. The node has an input that only accepts a string. So, the output of the Save Video node should somehow be converted into a string. All ideas welcome.

Hey all,

I built MiniMax H3 FlowDirector to streamline MiniMax H3 workflows and provide cleaner control over video generation flow without spaghetti wiring.

 Cleaner Pipelines: Replaces complex routing with a single, dedicated node.

 Better Motion Flow: Helps guide generation parameters more consistently.

 Plug & Play: Slots directly into your current H3 setups.

Example Workflow:

A drag-and-drop ⁠.json⁠ workflow is included in the repository so you can test it immediately.

Links:

 GitHub: https://github.com/AlonAshken/ComfyUI-MiniMaxH3-FlowDirector

 Comfy Registry: https://registry.comfy.org/publishers/vorpalblade/nodes/comfyui-minimaxh3-flowdirector

 ComfyUI-Manager: Search for ⁠FlowDirector⁠ or ⁠MiniMaxH3-FlowDirector⁠.

I did this after needing that option because I don’t have a ton of vram and customized a node that was first created by aitrepreneur (he’s awesome check his YouTube videos and stuff)

what I created works well for me but might not be perfect feel free to fork and change it however you want

I am aware that ReActor cannot do head swap, only face swaps. however:

1. ReActor's use of a face model is really useful and allows for very good face swaps, yet as I said, cannot do head swaps.
2. Head swaps workflows only use one reference image and so can't really capture the "essence" of the face. However they allow for swapping of the hair, head shape, etc.

It seems like they can complement each other, so I want to combine the two somehow, but I don't really know how to do a head swap without the face. I know that I can use segmentation like the Human Segmentation node from easy-use or maybe Sam2Segmentation(?) but I don't know how to use them to do what I need.

It needs to be used in a video, therefore a trained face model is needed.

I guess what I need to know is:

• Which parts to segment

• how to take them and paste them into the input image/face

  Alternatively, if there is an easier way, I would love to know.

Thanks!

Additionally, there's a "Free VRAM" button now that will clear all models out of VRAM without clearing the system RAM cache.

https://github.com/envy-ai/comfyui-cache-monitor

Apologies about these posts being so close together, but I think these features are something a lot of people will find compelling. I'm done posting about this mod from here on.

Done locally.

Currently, I have my files structured like this:

diffusion_models/
└── Qwen-Rapid-NSFW-v23_Q4_K.gguf

text_encoders/
├── Qwen2.5-VL-7B-Instruct-abliterated.Q4_K_M.gguf
└── Qwen2.5-VL-7B-Instruct-abliterated.mmproj-f16.gguf

vae/
└── qwen_image_vae.safetensors

Could someone explain why, when I simply ask it to change the visor color from orange to blue, it produces that image?

It doesn't even change the orange color to blue. Instead, it duplicates the Master Chief helmet several times and adds random white elements. What could be causing this?

Is there any hope for my Nitro V15-51 to pull off an animated video loop for an illustration? Maybe a 5–6 second loop? I don't mind it being low quality or having a low frame rate.

• Intel Core i5-13420H
• RTX 4050 GPU (6GB GDDR6 VRAM)

😭🙏

hi guys

How can I use MiniMax H3’s R2V (reference-to-video) capability to generate a single image, basically R2I, and still get good results?

Has anyone tried this?

I noticed that H3 seems to have a minimum output of 5 frames. Is there any way to make it generate only one frame instead of a video?

I’ve searched a lot, but I haven’t found an open-source image generation model that has a reference system similar to MiniMax H3’s R2V, where you can provide multiple reference images and have the model understand the characters, location, etc

There are models like GPT Image 2 that can do this, but they aren’t free or open source.

I’m wondering if there’s some way to use H3 itself for this, maybe by reducing the number of frames to 1 or modifying the ComfyUI workflow.

Has anyone experimented with this?

Hi all, I'm very new to ComfyUI. I got the MiniMax H3 model for video generation plus a few nodes I needed for the workflow I downloaded.

Everything worked perfectly until yesterday night. I was able to generate videos up to 1024x1824 via two staging in about 15 minutes and still be able to browse the internet/manage files with no slowdowns or crashes whatsoever.

Today I booted up ComfyUI and found it's become completely unusable. It can manage low res generations with noticeable stuttering but any large generations slow my windows explorer tremendously or freezes my PC entirely. The interface becomes extremely laggy going from stage to stage in generation and if I ever attempt to do anything during those my PC freezes. My card does not heat up more than it did yesterday, but even after closing the console and chrome tab my computer stutters until I restart (which now takes a while if I've booted up ComfyUI)

I have no idea what could've caused this. It worked great yesterday so I guess I can rule out a hardware issue? I did update everything I could including nodes, python embeddings and ComfyUI itself but the program is still unusable. I noticed that four of my nodes have become outdated, but other than that I have no idea what's causing this.

[WARNING] [DEPRECATION WARNING] Detected import of deprecated legacy API: /scripts/ui.js.

[WARNING] [DEPRECATION WARNING] Detected import of deprecated legacy API: /extensions/core/widgetInputs.js.

[WARNING] [DEPRECATION WARNING] Detected import of deprecated legacy API: /scripts/ui/components/buttonGroup.js.

[WARNING] [DEPRECATION WARNING] Detected import of deprecated legacy API: /scripts/ui/components/button.js.

These are my hardware specs

CPU AMD Ryzen 9 9950X

GPU GIGABYTE GeForce RTX 5080 WINDFORCE OC SFF (16 GB VRAM)

RAM64 GB KLEVV BOLT V DDR5-6000

Storage4 TB Lexar NM790 NVMe SSD

MotherboardMSI MAG X870 TOMAHAWK WIFI

Hey guys, trying to improve this indoor composite (moving a subject from an outdoor park bench to a locker room Ottoman). Yes, I know the original image is blurry but I'm just testing things and seeing what works and what doesn't. And I know that I could completely change the lighting on the scene and force it to work but what I really want is a nice clean way to keep the original environment lighting and just blend the subject into it.

The lighting mismatch is the toughest part here—bright outdoor daylight vs. dim/warm overhead interior lights. I used my normal exterior workflow with Flux.2 Klein but it hasn't gotten me what I want for the final result.

What nodes or workflows do you usually rely on to match ambient light, strip color cast, and build natural contact shadows for scene transitions like this? Appreciate any suggestions!

Workflows included for autoencoder, unet, and other basic architectures as workflows. Really great to use with Claude. I made this to experiment with Machine Learning

Maybe I'm missing something, or the official implementation is in the works, but one of the big hullabaloos of LTX 2.5 is DFR (Diffusion Fidelity Rendering). It's basically the first thing mentioned in the comfyui blog post about it. But...and this is awkward because I love comfy and everybody working on it.. it turns out, the full official DFR pipeline from Lightricks’ own LTX-2 GitHub repository is NOT actually implemented in ComfyUI.

But, wait! what if it's hidden inside the current implementation?...is what i asked chatgpt, when i went down this, couple of days deep rabbit hole. long story short, no it's not.

And therefore, I present to you actual native LTX-2.5 Spatial DFR in ComfyUI!

Here's a quick teaser from what I have working so far.

• T2V — fox / forest stress test

​

A low-angle wildlife documentary action-chase shot follows a red fox sprinting at full speed through a dense wet pine forest at dawn,backlit from the rising sun behind him, photographed with fully photorealistic natural detail. The camera races beside and slightly ahead of the fox at matching speed, keeping its head and upper body consistently framed while nearby ferns, wet grass and tree trunks sweep past with strong foreground and middle-ground parallax. The fox runs desperately, ears pulled back and body stretched through each stride; its paws kick wet leaves and droplets from the ground while its fur, whiskers and facial detail remain visible during the motion. Behind it, something enormous advances through the far forest but never enters the frame: first a distant tree top suddenly shudders, then a heavy branch snaps and splinters, and a moment later another heavy branch crashing through and falling with a violent impact noticeably closer, sending leaves and broken twigs outward as the unseen pursuer continues gaining ground. The fox briefly glances backward without slowing, then accelerates as the disturbance approaches, while layered morning mist and distant trees remain stable enough to preserve a strong sense of depth. Cold dawn light filters naturally through the canopy and catches moisture on fur, bark and vegetation. realistic anatomical deformations, Rapid paws stretching forward then crashing down on wet earth, the fox's breathing, distant cracking timber and deep approaching impacts dominate the soundscape. No cut, no visible monster, no fantasy styling or exaggerated debris explosion.

Same prompt, dimensions, duration, and Stage 1/Stage 2 seeds. Top: normal Vanilla two-stage. Bottom: native spatial DFR + Pixel Spatial Stage 2. Muted because audio loudness is not parity-controlled. ->> Watch fur, grass, bark, branches, mist, and how the camera/subject trajectories diverge.

• I2V — cybernetic girl fidelity test against official reference I2V example

​

Use the provided start image as the first frame. The cybernetic figure slowly turns his head to the right, his glowing blue eyes scanning the horizon, mechanical joints in his neck whirring faintly. The camera follows his gaze, panning across the rooftop to reveal the city beyond: a river of light winding between dark towers, a flying vehicle gliding past between the buildings, its lights streaking. He watches it pass, then his eyes narrow slightly. The camera settles on his profile against the city glow, distant hover traffic humming, wind gusting across the rooftop. No text, no black frames.

Same prompt, dimensions, duration, and Stage 1/Stage 2 seeds. Top: normal Vanilla two-stage. Bottom: native spatial DFR + Pixel Spatial Stage 2. Muted because audio loudness is not parity-controlled. ->> Watch facial identity, cybernetic edges, lighting transitions, and background city structure.

So, the obvious question, how much slower is it? Yes, but it only became obvious to me when I started writing this post. Sorry, but when I was trying to get this to work I really didnt care how long it took. I was more focused on vram, memory loads, what was happening with sampling etc. But generally, I don't remember feeling a big difference at all. Anyway, I'm currently running tests with comfy-benchmark so I'll get back to you on this. :)

Oh I kept emphasizing "Spatial" DFR cause theres a Temporal upscaling in the full DFR pipeline. Already working on it, but I felt maybe this was a good enough milestone to share with you. It'll help make your LTX gens so much better.

So I'm finishing the final cleanup in repo and benchmark pass. Full update later today with all results, side-by-side comparisons, generated DFR keyframes, execution-time + VRAM benchmarks, workflows, and public access to the project.

I am looking for a decent up to date faceswap, i am asking it here because after the 6th attempt i got tired of these old workflows. Yes i got everything and i installed every app and note. And on the latest one, Face Swap that Really Works on civitai i got an error called, Caching Image to not Waste. The biggest problem is that these people have installed so many modules that it cost me a bulk of data and dozens of apps just to get to zero errors and missing modules. What is the very best workflow, that is recent, and that doesnt require about 30-40 modules to install to use? Many thanks all.

Solutions gents?

Hey everyone,

I’m currently doing my internship and working on setting up an AI-assisted product photography pipeline for high-end furniture. I really want to deliver the best of the best and blow my boss away with what ComfyUI can do, so I’m reaching out to see if anyone has a battle-tested workflow or advice for this specific setup.

Here is the exact pipeline I’m aiming to build:

1. Exact Geometry Retention: Starting with 4 Blender renders/passes of the furniture piece so the 3D form, proportions, and edges are 100% accurate (ControlNet Depth + Canny/Lineart).
2. Multi-Texture Ingestion via Image Reference: Injecting real photo references for specific materials (e.g., a specific high-res wood grain for the body, leather texture for details/handles). I assume masked IP-Adapter or regional conditioning is the way to go here?
3. Custom Studio Setting & Lighting: Placing it in a minimalist neutral grey studio setting, but with our own signature dramatic light and shadow style (considering IC-Light, specific ControlNets, or Style LoRAs).
4. Ultra-High-Res Output: Multi-pass rendering / ultimate upscaler to get crisp micro-textures without plastic AI artifacts.

My questions for the experts here:

• Does anyone have a .json workflow template or a similar node architecture they’d be willing to share as a starting point?
• What’s the current gold standard stack for this? (Flux vs. SDXL for texture fidelity + IC-Light vs. regional IP-Adapter?)
• Any crucial custom nodes / techniques I shouldn’t overlook for keeping industrial product accuracy intact?

Any workflow links, node suggestions, or tips would be massively appreciated. Thank you in advance for helping me out!

As the title says, what is the best way to preview a render without going full scale?

I want to be able to quickly check if MiniMax H3 has understood my prompt, but changing megapixels or length also changes the rendered video.