Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Reddit user u/Alternative_Lab_4441 trained a FLUX Kontext LoRA to turn low-res Google Earth screenshots into high-res drone photos. This LoRA turns basic satellite images into professional-quality aerial shots And yes, it’s free to download!

849,331 Aufrufe • vor 1 Jahr •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

As I promised yesterday, I'll briefly explain LoRA training and share a workflow I made so you can do it quickly. First, let me answer a very common question: 'Why train LoRAs when we have such advanced models?' Even though we have incredibly advanced models now (like NBP), we still can't always get them to do specific things we want. Simplest example: the spritesheet LoRA I made the other day. I generated 1000 images with Nano Banana and only 100 were what I wanted. The LoRA I trained using those 100 images gives me nearly 100% consistent results. Second point is cost and speed. With LoRA, we can cut costs by 4-5x. And while doing that, we're generating 4-5x faster. How many images do you need for a good LoRA? This depends on your LoRA's complexity. For example, when I training the spritesheet LoRA, even though I used 100 images, I didn't include buildings in the training data, so this LoRA doesn't work for buildings. So think about your LoRA's use cases and add examples for as many use cases as possible to improve quality. What are paired images and how to train LoRAs for image-editing? When training LoRAs for image editing on fal, we call each edit example paired images - one with _start suffix, one with _end suffix. For example, if you're training a background remove LoRA, the unedited original photo will be your '_start' image. The image with background removed will be the '_end' image. Simply put: images we want to edit or use as reference get _start, target images we want to achieve get '_end'. Important: save both images with the same name. Like image332_start.jpg and image332_end.jpg. This way the system knows which images pair together. What about training LoRAs for models with multiple image inputs? Same logic. We still use _start and _end suffixes, but with one difference. Since there are multiple input images, we can number them: _start, _start1, _start2. Example: start images, 1st image = Woman portrait (image35_start.jpg) 2nd image = Glasses photo (image35_start1.jpg) 3rd image = Hat photo (image35_start2.jpg) Output image = portrait of woman wearing glasses and hat (image35_end.jpg) Can we do more detailed captioning? Yes. Similarly, you can improve training quality by creating a txt file for each set with the caption inside. Example: create image35.txt and write: 'Recreate the image by putting the glasses from the second image and the hat from the third image on the woman in the first image.' What are Steps? How many should I use? What's Learning Rate? Steps determines how many times the model sees and processes your training data (your images). Each step, the model learns a bit more. But as steps increase, so does the risk of overfitting. So there's no real default. But for a simpler LoRA with 20 paired images, 1000 steps is ideal. Here's a metaphor for the Steps and Learning Rate relationship: Imagine you have a balloon. Our goal is to inflate it to the optimal size. Steps = How many times we blow into the balloon Learning rate = How hard we blow each time If we blow too softly, we need to blow many more times. If we blow too hard, we risk popping it quickly and can't reach optimal size. Of course training won't explode, but it won't work as intended because it wasn't trained optimally. Training's done, now what? Once training's complete, you'll have a safetensors file. Every model you train on fal has a LoRA inference endpoint. In that inference, add your safetensors file link to the LoRA url input, and you can use your LoRA. Thanks for the read! The workflow in the video: If I forgot anything, let me know in the replies.

ilker

15,192 Aufrufe • vor 7 Monaten

🇹🇼 SPACEX JUST LAUNCHED TAIWAN’S FIRST HOME-BUILT SATELLITE SpaceX fired up its Falcon 9 rocket from California and gave Taiwan’s brand-new Formosat-8 satellite a ride to space. This isn’t just some shiny tech toy. It’s the first fully built-in-Taiwan satellite meant for serious business: watching Earth, tracking disasters, and showing the world that Taiwan’s space game is no joke. The satellite, named “Chi Po-lin” after a famous Taiwanese aerial photographer, is the first in a planned constellation of eight. It’ll orbit at 561 kilometers above the Earth and snap high-res images for everything from urban planning to spotting deforestation. And no, it’s not just science class stuff. This thing helps with disaster response, climate monitoring, and yes, national security. About 84% to 86% of the satellite was built using Taiwanese-made tech, a huge leap for a country trying to grow its space independence. Taiwan’s space agency (TASA) plans to launch a new Formosat satellite every year until 2031. When it’s done, Taiwan will have its own sky-eye network scanning Earth like a sci-fi movie come to life. While some countries still rent satellite time or import the tech, Taiwan is doing it DIY-style. This is not only about building cool hardware, it’s about making sure they’re not depending on anyone else when it comes to critical data from space. And props to SpaceX for being the go-to launch partner for countries that actually build their own satellites. Elon’s rocket crew keeps proving that if it fits in the payload bay, they’ll get it to orbit fast, smooth, and with a cloud of fire. Sources: Al Arabiya English, RTI Taiwan, TVBS, Taiwan News

Mario Nawfal

103,643 Aufrufe • vor 9 Monaten

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

Yarchi

106,174 Aufrufe • vor 3 Monaten