2025-07-10 01:41:43
If you're using ComfyUI on a Mac to create awesome AI images, you might have hit a wall when trying to make them really big. Specifically, if you try to generate an image larger than 1920x1920 pixels, you've probably seen your process crash right at the end with a weird error message.
It's a frustrating problem, but don't worry, there's a simple fix!
When ComfyUI is almost done generating your large image, it suddenly stops and you see an error message in your terminal that looks something like this:
/AppleInternal/Library/BuildRoots/.../MPSNDArray.mm:829: failed assertion \`... Error: NDArray dimension length \> INT\_MAX'
In simple terms, this is a bug in a recent version of PyTorch, the library ComfyUI uses for its AI magic on Apple Silicon (M1, M2, M3 chips). The part of PyTorch that handles image data on the Mac's graphics chip (Metal Performance Shaders or MPS) can't handle the dimensions of your super-sized image.
The good news is you can fix this by going back to a slightly older, more stable version of PyTorch. The key is to install version 2.4.0.
Here's how to do it:
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0
This command tells Python's package manager, pip
, to uninstall your current PyTorch and install the specific versions that are known to work without this bug.
After the installation is complete, restart ComfyUI, and you should now be able to generate those huge images without any crashes. 🎉
I've personally tested this fix on an M1 Max with 64GB of RAM, successfully generating 3000x3000 pixel images without any crashes. Before the PyTorch downgrade, ComfyUI would consistently crash when attempting to generate images at this resolution. After installing PyTorch 2.4.0, the same workflows completed successfully.
This bug is related to specific versions of PyTorch. If you need to find other previous versions for any reason, the official PyTorch website has a helpful archive.
For those interested in the technical details, you can follow the bug reports on GitHub:
Happy generating!
2025-07-06 11:21:12
In the world of machine learning, one of the most fundamental decisions a data scientist faces is the choice between a simple and a complex model. It's a classic trade-off: the elegant transparency of a simple model versus the raw power of a complex one. There's no single right answer, and the best choice depends entirely on your specific problem, your data, and your goals.
So, how do you pick your champion? Let's break it down.
Simple machine learning models, like linear regression, logistic regression, and decision trees, are the bedrock of the field for a reason. Their strength lies in their transparency and efficiency.
Complex models, such as deep neural networks, gradient boosting machines, and random forests, are the heavyweights of machine learning. They are designed to tackle intricate patterns in massive datasets.
Here's a quick look at how these two approaches stack up:
Feature | Simple Models | Complex Models |
---|---|---|
Interpretability | High | Low |
Training Speed | Fast | Slow |
Computational Cost | Low | High |
Risk of Overfitting | Low | High (with small data) |
Performance on Complex Problems | Lower | Higher |
Data Requirements | Can work with small data | Require large datasets |
Before you commit to a model, ask yourself these questions:
The "simple vs. complex" debate doesn't have a universal winner. The best data scientists have a deep understanding of both and know when to deploy each. The journey often starts with a simple model to understand the data and establish a baseline. Then, if the problem demands it and the resources allow, you can explore more complex solutions.
So, the next time you're faced with this choice, remember to think critically about your project's unique needs. The right model is the one that best serves your purpose.
2025-06-28 02:29:48
Embarking on a machine learning project can feel like navigating a complex maze. Based on a typical university assignment structure, this guide breaks down the process into a clear, repeatable workflow. Whether you're a student or a budding data scientist, you can use this framework for any supervised learning task.
The foundation of any successful ML project is a thorough understanding of the problem and the data. Don't rush this phase!
First, look at your target variable. Is it a continuous number (like a price) or a distinct category (like a type of flower)?
Get your hands dirty with the dataset.
Split your DataFrame into two distinct entities:
With your data prepared, it's time to start building and training your models.
You need to evaluate your model on data it has never seen before.
Choose a few different algorithms to see which performs best. For a standard supervised learning task, good starting points are:
Train each of these models using the .fit() method on your training data (Xtrain, ytrain).
A trained model is useless until you know how well it performs. This is where you critically assess your work.
Use your trained models to make predictions on the testing data (X_test).
Use standard metrics to score your models.
Analyze the evaluation metrics.
Go beyond the basics to refine your model and understand your data more deeply.
For many models (like Random Forest), you can extract feature importances. This tells you which input variables had the most impact on the prediction. This is incredibly valuable for understanding the underlying problem.
Try to squeeze more performance out of your best-performing model.
By following these four phases, you create a structured and comprehensive approach to any machine learning project, ensuring you cover all the critical steps from start to finish.
2025-06-22 07:27:35
Buying Ridley Scott's definitive version of Kingdom of Heaven in 4K on iTunes isn't as obvious as it should be. The storefront shows the standard theatrical cut in HD by default, and there's no separate listing labeled "Director's Cut 4K." Here's the quick path to the version you actually want.
The Director's Cut restores nearly 45 minutes of material and is the only 4K rendition currently available on iTunes. If you simply hit "Buy" on the main page, you'll end up with the shorter HD theatrical cut. Follow the steps below to unlock the full‑length 4K edition.
Apple's storefront doesn't shout about it, but the Director's Cut tucked under "How to Watch" is the version to own—both for its richer cut and for its gorgeous 4K transfer with HDR and Atmos. Follow the steps above and you'll be crossing swords in high resolution in no time. Enjoy the journey to Jerusalem!
2025-06-17 22:32:11
If your GitLab CI pipeline started throwing 403 Forbidden
errors when trying to access archive packages using the CI_JOB_TOKEN
, you're likely running into a security change that became enforced by default in GitLab 18.
This isn’t a brand-new feature—but now, you’re required to explicitly configure access permissions, or your jobs will fail.
GitLab has supported the concept of scoped job token permissions since GitLab 15.9. This feature allowed project maintainers to restrict which other projects or groups could access their resources using CI_JOB_TOKEN
.
For a while, this behavior was optional or hidden behind feature flags. But as of GitLab 18, the old implicit access is gone. You must now explicitly authorize projects or groups—otherwise, your job token will be denied access by default.
If you're seeing something like this in your CI logs:
error: failed to download package: 403 Forbidden
You're likely trying to fetch a package or archive from another project without having the correct permissions set up under GitLab's updated security model.
To allow a CI job from one project to access another project’s archive packages using CI_JOB_TOKEN
:
That’s it — this will grant the necessary access for your pipelines to function as expected under GitLab 18.
Review your existing pipelines before upgrading to GitLab 18. If your workflows rely on cross-project access via CI_JOB_TOKEN
, make sure those permissions are configured ahead of time to avoid unexpected pipeline failures.
By updating your project permissions, your CI/CD pipelines will be back on track—and more secure than before.
2025-06-16 06:12:55
This document introduces and compares the proxy protocols used by V2Ray, Clash, and Clash Meta. It provides standard configuration examples for different protocols to show their differences and help users set them up correctly.
V2Ray, Clash, and Clash Meta support different protocols. V2Ray provides the basic protocol support. Clash focuses on rules and user-friendliness. Clash Meta is an extension of Clash that supports newer, high-performance protocols.
Protocol | V2Ray | Clash | Clash Meta |
---|---|---|---|
VMess | ✅ | ✅ | ✅ |
VLESS | ✅ | ❌ | ✅ |
Trojan | ✅ | ✅ | ✅ |
Shadowsocks (SS) | ✅ | ✅ | ✅ |
ShadowsocksR (SSR) | ❌ | ✅ | ✅ |
SOCKS / SOCKS5 | ✅ | ✅ | ✅ |
HTTP(S) | ✅ | ✅ | ✅ |
Snell | ❌ | ✅ | ✅ |
MTProto | ✅ | ❌ | ❌ |
Hysteria / Hysteria2 | ❌ | ❌ | ✅ |
TUIC | ❌ | ❌ | ✅ |
Below are standard configuration snippets for each protocol on different platforms. Please note that all placeholders like server.com
, your-uuid
, your-password
, etc., in the examples need to be replaced with your own node information.
Features: Classic, lightweight, and efficient.
Clash / Clash Meta (YAML)
- name: "SS-Server"
type: ss
server: server.com
port: 8388
cipher: aes-256-gcm
password: "your-password"
udp: true
V2Ray (JSON)
json
{
"protocol": "shadowsocks",
"settings": {
"servers": [
{
"address": "server.com",
"port": 8388,
"method": "aes-256-gcm",
"password": "your-password"
}
]
}
}
Features: Mimics HTTPS traffic, providing good obfuscation.
Clash / Clash Meta (YAML)
- name: "Trojan-Server"
type: trojan
server: server.com
port: 443
password: "your-password"
sni: "your-domain.com"
udp: true
V2Ray (JSON)
json
{
"protocol": "trojan",
"settings": {
"servers": [
{
"address": "server.com",
"port": 443,
"password": "your-password"
}
]
},
"streamSettings": {
"security": "tls",
"tlsSettings": {
"serverName": "your-domain.com"
}
}
}
Features: V2Ray's core protocol, powerful with many configurable options.
Clash / Clash Meta (YAML)
- name: "VMess-Server"
type: vmess
server: server.com
port: 10086
uuid: "your-uuid"
alterId: 0
cipher: auto
network: "ws"
tls: true
servername: "your-domain.com"
ws-opts:
path: "/your-path"
headers:
Host: your-domain.com
V2Ray (JSON)
json
{
"protocol": "vmess",
"settings": {
"vnext": [
{
"address": "server.com",
"port": 10086,
"users": [
{ "id": "your-uuid", "alterId": 0, "security": "auto" }
]
}
]
},
"streamSettings": {
"network": "ws",
"security": "tls",
"tlsSettings": { "serverName": "your-domain.com" },
"wsSettings": { "path": "/your-path", "headers": { "Host": "your-domain.com" } }
}
}
Features: A general-purpose network transport protocol that can be used for proxy chaining.
Clash / Clash Meta (YAML)
- name: "SOCKS5-Upstream"
type: socks5
server: proxy.server.com
port: 1080
# username: "user" # optional
# password: "password" # optional
# udp: true # optional
V2Ray (JSON)
json
{
"protocol": "socks",
"settings": {
"servers": [
{
"address": "proxy.server.com",
"port": 1080,
"users": [
{ "user": "user", "pass": "password" }
]
}
]
}
}
Features: A general-purpose HTTP proxy that supports TLS encryption.
Clash / Clash Meta (YAML)
- name: "HTTP-Upstream"
type: http
server: proxy.server.com
port: 8080
# username: "user" # optional
# password: "password" # optional
# tls: true # if it is an HTTPS proxy
V2Ray (JSON)
json
{
"protocol": "http",
"settings": {
"servers": [
{
"address": "proxy.server.com",
"port": 8080,
"users": [
{ "user": "user", "pass": "password" }
]
}
]
}
}
Features: The lightweight successor to VMess, offering better performance, often used with XTLS.
Clash Meta (YAML)
- name: "VLESS-Server"
type: vless
server: server.com
port: 443
uuid: "your-uuid"
network: "ws"
tls: true
servername: "your-domain.com"
client-fingerprint: "chrome"
ws-opts:
path: "/your-path"
V2Ray (JSON)
json
{
"protocol": "vless",
"settings": {
"vnext": [
{
"address": "server.com",
"port": 443,
"users": [
{ "id": "your-uuid", "flow": "xtls-rprx-vision", "encryption": "none" }
]
}
]
},
"streamSettings": {
"security": "xtls",
"xtlsSettings": {
"serverName": "your-domain.com"
}
}
}
json
{
"protocol": "mtproto",
"settings": {
"servers": [
{
"address": "proxy.server.com",
"port": 443,
"users": [
{ "secret": "dd000102030405060708090a0b0c0d0e0f" }
]
}
]
}
}