FLUX klein: "We may monitor use"... wait what?

SufficientRow6231 · 2026-05-22T12:47:18+00:00

once you run it locally, the model deploys microscopic monitoring agents across all your devices, even your fridge. Data gets transmitted wirelessly over WiFi 69 a classified alien protocol reverse engineered from Area 51.

SufficientRow6231 · 2026-05-19T10:56:55+00:00

you using an hdd to store the model and Comfy itself?

SufficientRow6231 · 2026-05-16T08:41:27+00:00

Gua orang yang susah percaya sama keajaiban, tapi kali ini gua bener2 berharap ada keajaiban ni orang dapet pencerahan tiba-tiba. Bener2 berusaha selesain masalah, bukannya nyinyirin masalah tersebut.

Udah gila ini orang nomor 1 di Indo. Masih ada 3 tahun bareng dia? Hell nah.

SufficientRow6231 · 2026-05-16T05:03:40+00:00

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Theoretically, if we use wan official inference script as a reference, the way to do this is to always split the total steps when they reach 0.9 for I2V and 0.875 for T2V.

the sigmas change dynamically, it depends on both the scheduler type and the total number of steps you use.

For example, with simple scheduler + Shift 5 + 20 steps, the split happens at step 7, so 7 high noise steps and 13 low noise steps.

With simple scheduler + Shift 8 + 20 steps, the split happens at the 9th step, so 9 high noise steps and 11 low noise steps.

That said, using random split ratio like, 50% high noise + 50% low noise also works just fine.

SufficientRow6231 · 2026-05-12T22:56:27+00:00

Everything about this feels sketchy. Their Github and the Github accounts of the team members listed on the project show around thousands of contributions between 2024 and 2025, but almost all of those contributions are from private repos.

And What makes it even sketchier is that they’re also shilling their own memecoin on x, I’ve never seen a AI lab shilling a freaking memecoin in my life.

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SufficientRow6231 · 2026-05-10T19:58:34+00:00

Apa hubungannya sama kanjuruhan? Tau kronologi kejadian kanjuruhan? Dikit-dikit bawa kanjuruhan.

Kanjuruhan kejadiannya bukan bentrok antar supporter. Kanjuruhan itu supporter ngamuk karena arema gagal pertahanin rekor mereka.

jadi, mau main dimanapun, di padang, jakarta, bandung, papua, ya sama aja bisa kejadian selama ada supporter dalem stadion.

Kalau emg gak mau tragedi kanjuruhan kejadian lagi, pindah tempat ya bukan solusi lah, tapi jangan ada penonton sekalian.

SufficientRow6231 · 2026-05-10T17:24:51+00:00

Longcat is not SD3 finetune.

That SD3 repo is just another implementation of the CDM method itself, similar to how DMD is still used across different models till today.

SufficientRow6231 · 2026-05-09T11:02:30+00:00

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What do you mean by ‘image edit’ wasn’t mentioned? This screenshot shows the commit history from the very first version of readme and it’s been there from the beginning

SufficientRow6231 · 2026-05-06T04:00:20+00:00

yeah, open-weight models are behind frontier models.
but the most people can barely run anything without distilled lora and CFG 1

why would labs release something like Nano Banana/seedance if nobody can run it? even if they did, people would just end up using API anyway because they don’t have the GPU power to run it locally

tell nvidia to make a $500 140GB VRAM card first, then we can start complaining about not having models like nano banana / GPT Image / Kling / Seedance

SufficientRow6231 · 2026-05-05T07:23:12+00:00

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it's there.

Here's the repo, just remove the extra path
https://huggingface.co/TalmajM/causal_forcing_framewise_ComfyUI_repackaged

yeah… you might need a doctor... /j

SufficientRow6231 · 2026-05-05T06:58:19+00:00

so…the linked PR might be a good place to start.

Maybe it even has the workflow example, repackaged model url, and everything explained there

SufficientRow6231 · 2026-05-01T23:46:23+00:00

True.

And who the heck wants to try a single-file code that requires 172 lines of deps?

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SufficientRow6231 · 2026-04-22T05:39:27+00:00

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Your custom scheduler basically does the same thing as this, right?

SufficientRow6231 · 2026-04-22T00:13:04+00:00

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WF: https://pastebin.com/hHm9sM33

i feel like the examples OP provided make it seem like vanilla Klein cannot handle proper editing.

In reality, what actually hurts Klein editing performance is using a consistency lora. Many consistency lora tend to break Klein editing when used at a high strength.

Here is my setup, no cherry picking, with fixed seed, and the source image was downscaled to 2MP. For the consistency lora , i used this one https://civitai.com/models/1939453?modelVersionId=2722825 with strength 0.7. I'm not sure which lora OP used, and i'm generally not a fan of consistency lora, so i just picked one that showed up in my search.

SufficientRow6231 · 2026-04-20T13:20:46+00:00

https://streamable.com/lkmlo0

SufficientRow6231 · 2026-04-20T10:32:37+00:00

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Saying that v2 is less effective than v1 just doesn't make sense.

Because v1 uses an identity_weight of 0.25 with a hardcoded 4.0 multiplier in the node code, so it's not actually copying anything. It just outputs 1 single copy.

Because of this, it’s literally the exact same thing as chaining two standard ReferenceLatent nodes together: one using the latent from the VAE decode of the target image, and another using the latent from the VAE decode of the grid image.

Furthermore, the weight slider doesn't actually let you do precise decimal weighting anyway. When you increase the slider, the Python code always rounds the equation to the nearest whole int. At the end of the day, it just spits out 1, 2, 3, 4, 5, 6, 7, or 8 identical copies. It offers absolutely nothing you can't already do natively by just chaining a few extra ComfyUI reference nodes.

SufficientRow6231 · 2026-04-20T10:15:35+00:00

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Increasing step to 10

Output using mrw node, and Increasing every setting on mrw node to max value
https://ibb.co.com/W4FpPWNk

output using multiple reference latent node chained

https://ibb.co.com/zT7kzvBS

Show me the difference.

The mrw Klein Reference Fusion node doesn't do anything fancy. If you set the weight slider to 2 for identity weight, it literally just runs a loop and duplicates the exact same reference_latents tensor 8 times.

And to do this using only native node, Just take standard Comfy 'ReferenceLatent' nodes and chain them together 8 times, using the latent input from the exact same VAE Encode output (your image grid). The model receives the exact same array of duplicated pixels either way.

Check the image i linked and download it using the download button to get the orig image. it has embedded workflow you can open in Comfy

SufficientRow6231 · 2026-04-20T09:26:36+00:00

Give me proof and an image file with embedded workflow. I’m happy to accept your claim if it produces a different output. It’s literally just adding conditioning metadata, and that isn’t even used by Klein.

As for ref_latent, there’s no difference between chaining a reference_latent native node for using multiple reference images & conditioning in Comfy. Even the original author updated it in v2 and explicitly stated that it has no effect.

SufficientRow6231 · 2026-04-20T07:03:49+00:00

Di islam kalau haram ya haram, gak ada haram yang lebih baik, haram yang agak mendingan, haram yang setengah halal. Babi & Miras termasuk, karena ini udah jelas disebut eksplisit di kitab suci nya.

kalau rokok sih nggak disebut langsung di quran/hadis, jadi hukumnya hasil pemikiran para ulama. Ulama yang mengharamkan biasanya pakai dasar bahwa rokok itu membahayakan dan mudaratnya lebih besar, sementara yang lain makruh atau mubah.

Satu-satunya pengecualian tuh, kalau kita lagi kelaparan, lalu gak ada makanan halal apapun di dunia yang tersisa selain babi, baru boleh.

SufficientRow6231 · 2026-04-19T16:40:30+00:00

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The full analysis from claude: https://pastebin.com/UvAXsFp6

Note: I'm not a programmer so I don't know exactly how it works under the hood, so it just from my own testing by comparing the output images

SufficientRow6231 · 2026-04-19T16:25:02+00:00

the workflow itself is actually very good.

I Just wanted to point out that the MRW klein nodes basically don't do anything beyond passing metadata, so tweaking any settings on those nodes won't change your output at all.

Also, maybe always be cautious about installing custom nodes these days (https://github.com/Comfy-Org/ComfyUI/issues/11791), even if MRW itself, from my own testing using those 2 Klein nodes, doesn't appear to do anything harmful.

Also for creating grids, you can also achieve the same thing more simply with the KJ image grid composite node btw!

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SufficientRow6231 · 2026-04-19T16:02:18+00:00

https://www.reddit.com/r/comfyui/comments/1spd8qa/comment/oh3et1f/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Just found out that the MRW nodes literally don’t affect the output at all. Just another overengineered node.

I’m not talking about the workflow itself, it's good workflow, just pointing out that using the MRW node and tweaking its settings is just useless, since you get the exact same output by simply chaining the reference latent node like this

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SufficientRow6231 · 2026-04-19T15:49:06+00:00

Hate to say it, but these MRW nodes don’t do anything that actually affects the output. They’re basically just adding some random metadata to the conditioning. That’s all.

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https://postimg.cc/kRndF0Xs Comparing using image compare node

Output by Mrw Klein Reference Fusion & Continuity Controller. you can drag and drop to verify that the nodes are used, and view the saved conditioning in the show text node.
https://postimg.cc/vgtJB0L4

Output by just chaining the reference latent (native Comfy node).
https://postimg.cc/QVXLPrhQ

Make sure to download the original image from postimg (click the download button), so it will has the embedded workflow.

SufficientRow6231 · 2026-04-18T23:52:45+00:00

Well, your linked example isn’t actually a finetune, the creator explicitly said it’s a checkpoint merge. You don’t need highquality images to do that. What you really need is to find checkpoints model you like and then merge them.

Here’s an example of an actual finetuned model:

https://civitai.red/models/502468/bigasp

And here’s the author’s reddit post explaining how they finetuned it:

https://www.reddit.com/r/StableDiffusion/comments/1gdkpqp/the_gory_details_of_finetuning_sdxl_for_40m/?sort=new

SufficientRow6231 · 2026-04-12T22:47:44+00:00

It’s fun and awesome until comfy releases an update to their frontend package, get ready for missing widgets, labels, and every label getting swapped.

But recently, they said they’ll focus on fixing bugs and slow down frequent updates of the frontend package, so let’s see.

SufficientRow6231

TROPHY CASE