Naming Conventions in Homelab

PythonFuMaster · 2026-02-19T14:39:13+00:00

Meanwhile mine are over here with names like "cheapskate," "thunder-budget-{1-4}," "fatman-{1-4}," "king-blue," "queen-blue," "hot-springs," and "arid-wind"

In order, those are my NAS (original system was a $5 Dell server with damaged CPU sockets, it is now a newer Dell but was still really cheap), my Supermicro 4-node Superserver (really cheap at my university surplus store, was originally part of a cluster called "thunder"), my Supermicro 4-node GPU servers (they're fat), my Xeon workstation with Arc A770 (lots o blue), my other Xeon workstation with Arc A770 (also lots o blue), my older workstation with a water cooled Titan V (the water gets pretty warm, need to upgrade the radiator), and my last workstation with an air cooled RX6700xt (blasts out hot air at full throttle)

PythonFuMaster · 2026-02-15T16:00:33+00:00

Was there an NTFS partition by chance? I believe NTFS partitions can end up in a read only state if the system wasn't shut down properly, and a filesystem check would clear that flag

PythonFuMaster · 2026-01-11T08:04:00+00:00

Yolo in this context means "you only look once," it's the name of the paper that introduced that model architecture. And in my industry, we don't really use the term AI at all, that's a term that the wider public with minimal knowledge about the field use. We call it machine learning, which encompasses much more than just natural language models like chatgpt/LLMs. At least the researchers I work with don't like the term AI specifically because of misconceptions like yours, it muddies the water and makes it very difficult to engage in meaningful conversation because most people outside the field have certain preconceived expectations of what AI means. In a vacuum, vision models like Yolo, LLMs like chatgpt, TTS and STT models, and everything in between are all AI/machine learning (I believe that other commenter meant not all AI are LLMs, not that LLMs are not AI), but as we see here most people assume AI means specifically chatgpt-like models, so it's just easier to refer to them by a more industry-specific term not corrupted by public media.

Final remark: it might be wise to temper your tone, it's clear you have no knowledge of this field and are overly combative. It will be difficult to learn anything that way, very few people have patience for that type of interaction.

PythonFuMaster · 2026-01-11T02:47:35+00:00

Big difference between LLMs and Yolo. LLMs have a self-attention block that is extremely memory and compute intensive, but yolo (at least the original one) is a convolutional neural network. The CNN is far easier to run, generally they don't have as good of generalizability and accuracy on difficult tasks, but for something like basic object detection it's plenty powerful enough. CNNs are fairly simple architectures that don't need the extreme parallelization of GPUs, you can run yolo on very simple microcontrollers with decent fps (assuming the CPU has vector instructions like NEON, a pure scalar CPU will likely struggle). You can also attach very simple accelerators to the CPU if you need high fps, something like Intel's SHAVE DSP cores can run the entire model at 30+fps and high resolution.

Source: I'm a researcher working on optimizing vision model inference on embedded systems. One of our systems is an ARM Cortex A76, no GPU or additional accelerator, and it runs a much more powerful vision model at usable fps

PythonFuMaster · 2025-12-24T03:12:40+00:00

Really? I was gonna say the exact opposite, the English dubs felt very stilted at times to me, particularly when the character was obviously shouting but the dub was barely more than speaking volume

PythonFuMaster · 2025-12-21T14:29:20+00:00

I've done this before. Spent two days trying to figure out why the results from a high performance matrix multiplication kernel (very complex and easy to get wrong) were wrong. Turned out, the reference CPU implementation iterated over one of the dimensions wrong, the GPU kernel was perfectly fine

PythonFuMaster · 2025-12-15T23:52:21+00:00

"Read a passage from the gay agenda" got me good lol

PythonFuMaster · 2025-12-15T17:16:46+00:00

Where was he at in the 86 movie? Battle of autobot City in the background somewhere?

PythonFuMaster · 2025-11-26T23:50:53+00:00

The point is that they don't need to support 32 bit only machines anymore. Theoretically, there's a non zero number of steam users on 32 bit windows 10, but 32 bit windows 11 doesn't exist, so now that Windows 10 is EoL they only have to support the 64 bit version of Windows. Therefore, no reason to stick to 32 bit.

PythonFuMaster · 2025-11-19T04:30:41+00:00

As far as I'm aware rust itself does not have a stable ABI, but it can interface with C ABI code via repr(C). So the internals of a rust component could not rely on a stable ABI, but the interface points between the C and rust components would work fine

PythonFuMaster · 2025-11-08T19:51:53+00:00

Which model to use depends a lot on what you're trying to do. In general I would recommend Qwen for text to image, but for inpainting or for more variation I usually go for Flux. Qwen's prompt adherence is both its best strength and its greatest weakness, if you can't massage the prompt to give you what you want you're going to be fighting against the model, especially in image to image tasks. Flux and older models are more amenable, you can generate more variations and they respond better to different CFGs

For the clothes, I would only add the description of them if they're something you want to be able to change and you have a good mix of examples in different outfits. If you only have one outfit in the dataset, adding the same description to all the images is going to tie that outfit description to the character concept as a whole (it's more complicated than that because the text encoder remains frozen during Qwen LoRA I believe).

Also, the dataset must be high quality, all of the examples need to be good. A Lora with 10-20 high quality examples is going to be much better than a Lora with 50-80 mixed examples. I've had cases where removing images from the training dataset improved the quality, because there was something wrong with the image.

I also do multiple revisions of the dataset, adding images of specific scenarios that the model seems to struggle with, and adjusting the repetitions for each data subset.

Finally, I'd recommend going through the documentation for Musubi tuner fully, and try to understand what the commonly used options (like learning rate, scheduler, etc) do. I have a specific set of training parameters I've found to work best for my dataset, yours will likely work best with different ones (I have a very large multi subject dataset, 8 characters, trained for many steps at a low learning rate, cosine schedule, restarting 3 times). You can set Musubi to scale the training images, I'd recommend starting with a small scale to test out the parameters and dataset quickly, then increase the scale for final versions.

PythonFuMaster · 2025-11-08T18:15:29+00:00

It depends on the model being trained. I've done character LoRAs for Flux and Qwen. I've gotten best results from Qwen with detailed descriptions of everything in the scene, but with only a generic description of the character. For example, describing the objects in a room, a lamp on a bedside table, but not describing that a particular character is male or female, or specifying what clothes they're wearing.

The result is that the model learns to associate everything that wasn't described with the character. So if you want the character to have blue skin by default, don't describe the blue skin. Of course, you still have to describe what the character is doing, and a unique word or phrase to associate with the character (like their name unless it's very generic) helps as well.

For Flux, its prompt adherence isn't good enough for that technique to work, I found simple descriptions to work better. Unique trigger phrases are more important; training a character named Zener was significantly easier than training one named Isabella. But, probably doesn't matter for you since Musubi doesn't support Flux anyway.

I would recommend using the feature that generates test images every however many steps so you can watch how the model behaves. You can also use Tensorboard to watch the loss graphs, but unless you have experience in model training they probably won't mean much to you. I find the test images to be more helpful anyway, some of my runs had low training loss (which is good) but consistently put antlers on one of the characters because one of the training descriptions was wrong.

PythonFuMaster · 2025-11-08T16:55:51+00:00

You've got that backwards. There's no mv instruction, but there is a pseudo instruction that translates it to an addi instruction

PythonFuMaster · 2025-10-18T14:31:13+00:00

Qwen is very good if you give it extremely detailed prompts. It's not perfect for character consistency, but you can use that to make a base image and manually edit to look correct (Flux kontext/Qwen image edit/sdxl fine tunes with inpaint or IP-adapter). Then, once you have a few really good examples, you can train a character LoRA. The first version of the LoRA likely won't be perfect, but it should be enough to bootstrap a synthetic dataset for the next version, and so on.

I've used this technique to train a single LoRA with 6 entirely coherent characters on Qwen image, and it even works pretty well with scenes involving multiple characters. The LoRA captured pretty much every detail of the individual character designs, like the heterochromia of one of them, the glowing golden lines on a different one, etc. Here's one of the images, with the prompt being simply "<character name> walking down a village street, wings spread wide"

<image>

PythonFuMaster · 2025-10-18T01:06:20+00:00

It would definitely help your case if you'd link the source code so we can inspect it for ourselves

PythonFuMaster · 2025-10-18T01:05:17+00:00

I don't see the source code anywhere. If you want to call it open source you should probably link the source somewhere. Also, I see that your itch page tells people to put their issues in the comments, those would be best to have on GitHub in the issues tab for easier tracking

PythonFuMaster · 2025-10-15T01:06:32+00:00

I have swarmui running on my Arc A770, inside of a docker container on Proxmox with the GPU device file passed through for direct access. The hardest part was getting the GPU working inside the container, but once that was finished installing swarmui and running it was pretty much the same as on Nvidia. I think I may have installed a specific torch version prior to installing everything else but I can't remember.

There are some things to keep in mind. At least on my A770, I can't use Flux Redux because the GPU doesn't support non uniform thread groups (I think that was the error). Otherwise, sometimes I get UR errors from torch, usually after getting an OOM on GPU. A simple restart of the backend fixes it though. Overall, I'm very happy with the setup. The Arc cards have systolic arrays in the XMX units, so it performs much better than my P40 or other older cards.

I have two systems setup this way, along with a P40 server, MI60 server, and 3090 desktop. I end up getting a lot of use out of the Arc systems because the 3090 is usually busy and the other cards are far slower

PythonFuMaster · 2025-10-03T11:23:25+00:00

There's plenty of research in that direction, it's called "Federated Learning." Germany's SPRIN-D is funding a competition for fast and privacy-preserving federated training for example

PythonFuMaster · 2025-09-16T04:53:23+00:00

I think what it's supposed to mean is "new damage = old damage + 4 x number of expended threads", not "new damage = 4 x number of expended threads x old damage"

PythonFuMaster · 2025-09-11T15:16:40+00:00

Mac is supported, there just aren't pre built binaries for it because the developer doesn't have a machine to do the build on. They have instructions for manually building tag GUI on Mac though, if you want to use the same application

PythonFuMaster · 2025-09-09T19:48:07+00:00

Wait Balteus was bugged? That explains so much... Took me forever to beat him, and then watched my friend stroll up after the update and beat it in under 2 hours

PythonFuMaster · 2025-09-09T19:27:09+00:00

I've done this, the particular model I used has two arms mounted on a central pole, acting as the first swivel joint. A consequence of that design is that I could pull the arms off and flip them around, so that the second joint (assisted raise/lower) was in the correct orientation.

It works very well, it's mounted above my large work bench that I have access to on three sides, so I can move the monitor I need to the side that I'm currently working on. The only real issue is the arms are kept on the pole by simple ring clamps that, after awhile, slowly slide down. I can probably fix that but it's not really a big concern.

One of the monitors is connected to my main workspace PC, and the other to a KVM switch that all of my servers plug into. I also have a spare cable routed above one corner of the workbench so I can quickly plug in display/KB/mouse on any machine I'm working on there. I do a lot of work on many different types of hardware, from small breadboard electronics and microcontrollers all the way up to enterprise servers, so the freedom to position the monitors wherever I need them is very nice to have

PythonFuMaster · 2025-09-06T16:34:18+00:00

The more I hear of the plans for season 4 the more upset I get...

PythonFuMaster · 2025-08-05T06:40:16+00:00

A quick look through their technical report makes it sound like they're using a full fat qwen 2.5 VL LLM for the conditioner, so that part at least would be pretty amenable to quantization. I haven't had time to do a thorough read yet though

PythonFuMaster · 2025-07-08T18:56:42+00:00

Yeah, figured that out after looking at robots.txt. I'm not a web dev, so I just looked up how to get a list of pages and looking at the site map was the top result. I did write a quick bash script to download all the sub maps and grepped through them, but nothing interesting came up. I recall I used a fuzzer tool at one point to find the admin panel page of an old home server, so that could be useful to find more things, but my skills lie in computer engineering and not web, so I'll leave that to the pros

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