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Reddit user trained FLUX LoRA to transform Google Earth screenshots into high-res drone photos. The model converts low-resolution satellite images into professional aerial photography. 100% Free.

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Oliver Prompts

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136,871 次观看 • 6 个月前 •via X (Twitter)

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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!
0:30

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!

Miguel | AP

848,910 次观看 • 1 年前

SUPIR upscaler is outdated. ASASR- turns blurry, low-quality photos into sharp, high-res images. Prevents the fake hallucinated details. - improves OCR - high segmentation accuracy - based on FLUX.1 dev this looks sweet
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SUPIR upscaler is outdated. ASASR- turns blurry, low-quality photos into sharp, high-res images. Prevents the fake hallucinated details. - improves OCR - high segmentation accuracy - based on FLUX.1 dev this looks sweet

Wildminder

41,951 次观看 • 2 个月前

Romans used to flood the Colosseum for naval battles. I wanted to see what that looked like. So I made this Chrome extension to capture anywhere on Google Earth, transform it into any time period or style, and turn it into a drone shot 🌍
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Romans used to flood the Colosseum for naval battles. I wanted to see what that looked like. So I made this Chrome extension to capture anywhere on Google Earth, transform it into any time period or style, and turn it into a drone shot 🌍

Blendi

462,362 次观看 • 8 个月前

In 432 BC the Parthenon was covered in bright painted colors. I wanted to see what that looked like, so I made this fal-powered Chrome extension to capture anywhere on Google Earth, transform it, and turn it into a beautiful drone shot 🏛️
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In 432 BC the Parthenon was covered in bright painted colors. I wanted to see what that looked like, so I made this fal-powered Chrome extension to capture anywhere on Google Earth, transform it, and turn it into a beautiful drone shot 🏛️

Blendi

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A completely local agent that lives right inside your pocket. 📱 Watch Gemma 4 run 100% locally in the Google AI Edge Gallery app. It converts images into JSON schemas, transcribes audio, and uses agent skills to interact with apps, all entirely offline.
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A completely local agent that lives right inside your pocket. 📱 Watch Gemma 4 run 100% locally in the Google AI Edge Gallery app. It converts images into JSON schemas, transcribes audio, and uses agent skills to interact with apps, all entirely offline.

Google Gemma

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How can drone imagery and open source tools on GitHub help build sustainable communities for refugees? This is the story of how UNHCR, the UN Refugee Agency (USA for UNHCR), worked with partners to transform aerial images into vital maps for refugees in Kakuma and Kalobeyei. Learn more about this incredible tech-for-good initiative. 👇
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How can drone imagery and open source tools on GitHub help build sustainable communities for refugees? This is the story of how UNHCR, the UN Refugee Agency (USA for UNHCR), worked with partners to transform aerial images into vital maps for refugees in Kakuma and Kalobeyei. Learn more about this incredible tech-for-good initiative. 👇

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🚨⚡️ BREAKING NEWS 🚀 RUSSIA LAUNCHES A ROCKET CARRYING THE IRANIAN SATELLITE "NAHID-2" AND 18 SATELLITES FOR HIGH-PRECISION MONITORING AND SURVEILLANCE. 🚀 The "Soyuz-2.1b" rocket was successfully launched from Vostochny Cosmodrome, carrying the "Ionosfera-M" instruments and 18 accompanying satellites. Among them is Nahid-2 — a communications satellite developed by the Iranian Space Research Center. Additionally, nine small space devices are planned to be placed into orbit. These satellites will capture images of the Earth, track the positions of aerial and maritime vessels, and study space-related phenomena.
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🚨⚡️ BREAKING NEWS 🚀 RUSSIA LAUNCHES A ROCKET CARRYING THE IRANIAN SATELLITE "NAHID-2" AND 18 SATELLITES FOR HIGH-PRECISION MONITORING AND SURVEILLANCE. 🚀 The "Soyuz-2.1b" rocket was successfully launched from Vostochny Cosmodrome, carrying the "Ionosfera-M" instruments and 18 accompanying satellites. Among them is Nahid-2 — a communications satellite developed by the Iranian Space Research Center. Additionally, nine small space devices are planned to be placed into orbit. These satellites will capture images of the Earth, track the positions of aerial and maritime vessels, and study space-related phenomena.

RussiaNews 🇷🇺

28,795 次观看 • 1 年前

🚀 Today Photoroom is announcing the next generation of AI photo-editing features. 🚀 We’re dropping 5 new AI features, including a text-to-image model, generative fill, and our new-and-improved AI backgrounds, plus launching the world’s first foundation model trained specifically for product photography. 🖼️ AI Images: Use AI to generate high-quality images from scratch. Create anything you can imagine, at the click of a button. 💗 AI Expand: Use generative AI to realistically expand the scene around your image. 🎑 AI Backgrounds: Generate stunningly realistic AI backgrounds for product photos. ⏳ AI Fill: Take total control of your AI images, by customizing an item or area of the picture. 🔄 Auto-rotate: Correct your camera angle with AI for more professional results. 🔎 AI Upscale: Get better quality images by enhancing the resolution of your images. If our 6x feature drop wasn’t enough, we also just closed our series B funding round, to the tune of $43 million 🎤 #Photoroom #generativeAI #AI #productphotography #ecommerce
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🚀 Today Photoroom is announcing the next generation of AI photo-editing features. 🚀 We’re dropping 5 new AI features, including a text-to-image model, generative fill, and our new-and-improved AI backgrounds, plus launching the world’s first foundation model trained specifically for product photography. 🖼️ AI Images: Use AI to generate high-quality images from scratch. Create anything you can imagine, at the click of a button. 💗 AI Expand: Use generative AI to realistically expand the scene around your image. 🎑 AI Backgrounds: Generate stunningly realistic AI backgrounds for product photos. ⏳ AI Fill: Take total control of your AI images, by customizing an item or area of the picture. 🔄 Auto-rotate: Correct your camera angle with AI for more professional results. 🔎 AI Upscale: Get better quality images by enhancing the resolution of your images. If our 6x feature drop wasn’t enough, we also just closed our series B funding round, to the tune of $43 million 🎤 #Photoroom #generativeAI #AI #productphotography #ecommerce

Photoroom

22,236 次观看 • 2 年前

✨ Introducing 3 new ways to build with real-world imagery and AI → From the fine details of a city street to a bird's eye view of the planet, at Cloud Next we revealed new ways that businesses can use our Street View, aerial and satellite imagery, and tap into our comprehensive view of the physical world including: 🏙️ Use Maps Imagery Grounding in Gemini Enterprise Agent Platform to generate location-specific assets at scale, anchored in real-world Street View imagery 🌍 Unlock a new perspective with Aerial and Satellite Insights, part of Earth AI, in BigQuery and Agent Platform to quickly extract detailed info about our changing planet 🛰️ Build custom apps with Aerial and Satellite models in Model Garden from Earth AI that easily identify specific objects or scenes Read the blog at the link above and sign up for early access.
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✨ Introducing 3 new ways to build with real-world imagery and AI → From the fine details of a city street to a bird's eye view of the planet, at Cloud Next we revealed new ways that businesses can use our Street View, aerial and satellite imagery, and tap into our comprehensive view of the physical world including: 🏙️ Use Maps Imagery Grounding in Gemini Enterprise Agent Platform to generate location-specific assets at scale, anchored in real-world Street View imagery 🌍 Unlock a new perspective with Aerial and Satellite Insights, part of Earth AI, in BigQuery and Agent Platform to quickly extract detailed info about our changing planet 🛰️ Build custom apps with Aerial and Satellite models in Model Garden from Earth AI that easily identify specific objects or scenes Read the blog at the link above and sign up for early access.

Google Maps Platform

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Type “building” and the model finds the buildings. This is what deploying Google Earth AI imagery models in advanced spatial intelligence systems looks like in practice. Inside Vantor Sentry, Google Earth AI’s Open Vocabulary Detection (OVD) models allow analysts to identify objects in high-resolution satellite imagery simply by typing what they’re looking for. No pre-training. No custom labeling workflow. Combined with the quality and accuracy of Vantor’s spatial foundation—including the satellite imagery you see here—this provides sovereign operators with an unparalleled capability. Deployed inside a secure, air-gapped environment, this capability accelerates: 🔒 Persistent site monitoring 🔄 Change detection 🏗️ Infrastructure analysis 🗺️ Mission planning And because these models are integrated into Tensorglobe, they don’t operate in isolation. They can be fine-tuned locally using Vantor satellite imagery, third-party commercial data, sovereign sensor feeds, and customer-owned private archives. This is how foundation models become operational tools. Learn more:
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Type “building” and the model finds the buildings. This is what deploying Google Earth AI imagery models in advanced spatial intelligence systems looks like in practice. Inside Vantor Sentry, Google Earth AI’s Open Vocabulary Detection (OVD) models allow analysts to identify objects in high-resolution satellite imagery simply by typing what they’re looking for. No pre-training. No custom labeling workflow. Combined with the quality and accuracy of Vantor’s spatial foundation—including the satellite imagery you see here—this provides sovereign operators with an unparalleled capability. Deployed inside a secure, air-gapped environment, this capability accelerates: 🔒 Persistent site monitoring 🔄 Change detection 🏗️ Infrastructure analysis 🗺️ Mission planning And because these models are integrated into Tensorglobe, they don’t operate in isolation. They can be fine-tuned locally using Vantor satellite imagery, third-party commercial data, sovereign sensor feeds, and customer-owned private archives. This is how foundation models become operational tools. Learn more:

Vantor

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META 🔥: Muse Image, the first image-gen model from MSL, is now available on Meta AI. > It uses advanced reasoning to understand complex prompts, seamlessly blending multiple photos into high-quality creations you can download and share anywhere. Users can also access Presets, mention images in the prompt, and edit images directly. Muse Image is also available on WhatsApp and Instagram and is planned to arrive on Messenger and Facebook later. Testing time 👀

🚨 AI News | TestingCatalog

40,989 次观看 • 1 个月前

We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:
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We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:

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🚀 Our Biggest Satellite Update Yet Atlantic hurricane season starts today, and there's no better way to watch the tropics than from orbit. Today we're adding more imagery, more data, more of the planet, faster and at higher resolution. ✅ And it's all completely FREE - every product below, no subscription required. What's new: GOES (East & West) - New RGB composite products - multi-band views that reveal what single channels can't, from dry-air intrusion to Saharan dust - New Level 2 (L2) products - quantitative, derived layers built on the raw imagery - Improved Full Disk - faster and at higher resolution JPSS - NEW (Joint Polar Satellite System) - VIIRS imagery at 375 m native resolution - exceptional fine-scale detail, sharper than any geostationary view - Worldwide reach - VIIRS images the entire planet, bringing high-res views to anywhere on Earth Himawari - NEW - Full coverage of the Western Pacific and East Asia - track typhoons across the Asia-Pacific - L1 bands, RGB composites, sub-sectors & Full Disk Available now on: iOS: Android: Browser: And to Plus/Pro subscribers using our Windows/Mac/Linux App
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🚀 Our Biggest Satellite Update Yet Atlantic hurricane season starts today, and there's no better way to watch the tropics than from orbit. Today we're adding more imagery, more data, more of the planet, faster and at higher resolution. ✅ And it's all completely FREE - every product below, no subscription required. What's new: GOES (East & West) - New RGB composite products - multi-band views that reveal what single channels can't, from dry-air intrusion to Saharan dust - New Level 2 (L2) products - quantitative, derived layers built on the raw imagery - Improved Full Disk - faster and at higher resolution JPSS - NEW (Joint Polar Satellite System) - VIIRS imagery at 375 m native resolution - exceptional fine-scale detail, sharper than any geostationary view - Worldwide reach - VIIRS images the entire planet, bringing high-res views to anywhere on Earth Himawari - NEW - Full coverage of the Western Pacific and East Asia - track typhoons across the Asia-Pacific - L1 bands, RGB composites, sub-sectors & Full Disk Available now on: iOS: Android: Browser: And to Plus/Pro subscribers using our Windows/Mac/Linux App

WeatherWise.app

36,566 次观看 • 2 个月前

WILD FOOTAGE 🔴 Beautiful footage of a Smash fire control system mounted on an M4 taking down a Hezbollah drone with 2 shots. The SMASH fire control system is an Israeli made smart sight developed by Smart Shooter and mounted on an M4 Carbine that tracks moving targets such as drones and calculates the exact moment to fire. It helps shooters hit small fast aerial targets with high accuracy, turning a standard rifle into an effective low cost counter drone weapon. Footage via: David Lisovtsev
1:02

WILD FOOTAGE 🔴 Beautiful footage of a Smash fire control system mounted on an M4 taking down a Hezbollah drone with 2 shots. The SMASH fire control system is an Israeli made smart sight developed by Smart Shooter and mounted on an M4 Carbine that tracks moving targets such as drones and calculates the exact moment to fire. It helps shooters hit small fast aerial targets with high accuracy, turning a standard rifle into an effective low cost counter drone weapon. Footage via: David Lisovtsev

Open Source Intel

419,167 次观看 • 3 个月前

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
2:06

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 次观看 • 2 个月前

DRONE VIDEOGRAPHERS CHARGE $10K FOR THIS SHOT. HE PULLS IT FROM GOOGLE EARTH AND A PROMPT You never buy a drone, book a pilot, or leave the house. You pick any city on Earth, trace the flight path you want, and let Gemini render it as real-looking FPV footage. Clients pay thousands for this shot. You make it from a screenshot Here is the exact process: 1. Open Google Earth. Find the city or building you want. Frame the angle you'd want a drone to start from and take a screenshot 2. Draw the path. On that screenshot, draw a red line showing exactly where the drone should fly through the scene. This line is what the AI follows 3. Open Gemini and drop in the screenshot. Use the video generation in the Gemini app, the part that animates a still image into motion. Nano Banana handles images, the video engine is what turns your shot into footage 4. Paste the prompt. Tell it to follow the red flight path through the city, fast smooth motion, banking around buildings, golden-hour light, motion blur, 9:16 vertical, real FPV drone look. Full prompt is in the comments 5. Generate and clean it up. One clip is a few seconds. Stitch a couple together for a full flythrough and you have a reel Set the prompt once and you can re-run it for any location on the planet Who pays for this: Real estate agents, hotels, restaurants and event venues all need aerial b-roll and almost none can afford a real drone shoot Pull listings or venues with flat, ground-level photos and zero aerial footage. Send a free sample flythrough of their own location, then charge per clip or a monthly rate for ongoing reels One agent with ten listings is a recurring client, fully online Full prompt in the comments Bookmark this
0:21

DRONE VIDEOGRAPHERS CHARGE $10K FOR THIS SHOT. HE PULLS IT FROM GOOGLE EARTH AND A PROMPT You never buy a drone, book a pilot, or leave the house. You pick any city on Earth, trace the flight path you want, and let Gemini render it as real-looking FPV footage. Clients pay thousands for this shot. You make it from a screenshot Here is the exact process: 1. Open Google Earth. Find the city or building you want. Frame the angle you'd want a drone to start from and take a screenshot 2. Draw the path. On that screenshot, draw a red line showing exactly where the drone should fly through the scene. This line is what the AI follows 3. Open Gemini and drop in the screenshot. Use the video generation in the Gemini app, the part that animates a still image into motion. Nano Banana handles images, the video engine is what turns your shot into footage 4. Paste the prompt. Tell it to follow the red flight path through the city, fast smooth motion, banking around buildings, golden-hour light, motion blur, 9:16 vertical, real FPV drone look. Full prompt is in the comments 5. Generate and clean it up. One clip is a few seconds. Stitch a couple together for a full flythrough and you have a reel Set the prompt once and you can re-run it for any location on the planet Who pays for this: Real estate agents, hotels, restaurants and event venues all need aerial b-roll and almost none can afford a real drone shoot Pull listings or venues with flat, ground-level photos and zero aerial footage. Send a free sample flythrough of their own location, then charge per clip or a monthly rate for ongoing reels One agent with ten listings is a recurring client, fully online Full prompt in the comments Bookmark this

Yarchi

53,401 次观看 • 2 个月前

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.
1:06

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

15,184 次观看 • 6 个月前

How to Create Unlimited AI Videos with Consistent Characters (100% FREE Grok + Google Flow Tutorial) In this video you will learn how to use Grok and Google Flow to create high quality AI videos with consistent characters from start to finish without paying for expensive software. You will learn the exact workflow for writing stories, generating character sheets, creating consistent AI images, animating scenes, adding AI voiceovers, and editing everything into a finished video. This tutorial covers AI character consistency, image generation, scene automation, AI animation, voice generation, and the complete process for creating professional AI videos using free tools. This information is for both new and experienced creators who want to produce high quality AI animations, build faceless YouTube channels, and automate their content creation workflow in 2026. MASTER PROMPT: ( CAMERA DIRECTION PROMPT: ( Like, Retweet and Bookmark. Follow BIG CHRIS Video Credit: BigWiz Media 🤝(
15:18

How to Create Unlimited AI Videos with Consistent Characters (100% FREE Grok + Google Flow Tutorial) In this video you will learn how to use Grok and Google Flow to create high quality AI videos with consistent characters from start to finish without paying for expensive software. You will learn the exact workflow for writing stories, generating character sheets, creating consistent AI images, animating scenes, adding AI voiceovers, and editing everything into a finished video. This tutorial covers AI character consistency, image generation, scene automation, AI animation, voice generation, and the complete process for creating professional AI videos using free tools. This information is for both new and experienced creators who want to produce high quality AI animations, build faceless YouTube channels, and automate their content creation workflow in 2026. MASTER PROMPT: ( CAMERA DIRECTION PROMPT: ( Like, Retweet and Bookmark. Follow BIG CHRIS Video Credit: BigWiz Media 🤝(

BIG CHRIS

72,607 次观看 • 1 个月前

A drone just mapped an entire cemetery and found 9,940 individual grave headstones on its own in 21 minutes A 335 megapixel scan gets fused with 134 million lidar points, building a full 3D model of the terrain down to 5cm resolution An AI model called SAM3 scans the imagery and outlines every single headstone automatically, no human tracing anything by hand The system converts every detected object straight into CAD vectors, ready to export as a working drawing Surveyors used to spend weeks manually outlining thousands of objects on a map like this one This pipeline exported a finished CAD file in 21 minutes
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A drone just mapped an entire cemetery and found 9,940 individual grave headstones on its own in 21 minutes A 335 megapixel scan gets fused with 134 million lidar points, building a full 3D model of the terrain down to 5cm resolution An AI model called SAM3 scans the imagery and outlines every single headstone automatically, no human tracing anything by hand The system converts every detected object straight into CAD vectors, ready to export as a working drawing Surveyors used to spend weeks manually outlining thousands of objects on a map like this one This pipeline exported a finished CAD file in 21 minutes

pulmencr

38,406 次观看 • 18 天前