Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

More LTX IC LoRA experimentation - I’m currently training a LoRA for seamless transitions. I think this one’s close to being ready to ship 🚚💨

Ingi Erlingsson 🪄

14,303 subscribers

18,061 views • 3 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

A look at the video outpainting workflow in ComfyUI using LTX 2.3. 🎥 Instead of cropping your 16:9 footage for vertical, use this IC-LoRA setup to generate the missing space and keep your original frame. Shout out to Mohamed Oumoumad for their open source contribution. Link to workflow below! 👇

A look at the video outpainting workflow in ComfyUI using LTX 2.3. 🎥 Instead of cropping your 16:9 footage for vertical, use this IC-LoRA setup to generate the missing space and keep your original frame. Shout out to Mohamed Oumoumad for their open source contribution. Link to workflow below! 👇

ComfyUI

19,041 views • 4 months ago

SDR to HDR from ComfyUI, I've trained a LoRA over Qwen Edit 2011 based on the principle used in A research I've had the previlige to work on with Naomi Ken Korem for video HDR on LTX. Links for the LoRA and workflow (+ my fun grading node) below.

SDR to HDR from ComfyUI, I've trained a LoRA over Qwen Edit 2011 based on the principle used in A research I've had the previlige to work on with Naomi Ken Korem for video HDR on LTX. Links for the LoRA and workflow (+ my fun grading node) below.

Mohamed Oumoumad

58,774 views • 4 months ago

We just open-sourced 3DREAL ! A new render-to-real IC-LoRA for LTX-2.3 from fal: turn any 3D / CG / game render into high 3d render, photorealistic, film-quality video, keeping your exact composition and camera move using the first frame.

We just open-sourced 3DREAL ! A new render-to-real IC-LoRA for LTX-2.3 from fal: turn any 3D / CG / game render into high 3d render, photorealistic, film-quality video, keeping your exact composition and camera move using the first frame.

fal

215,901 views • 2 months ago

Trained a voice lora on Walken using just 6 clips as a test to see how well VoxCPM handles learning a voice. Then I just did audio + image = video with LTX 2.3. This TTS model feels extremely powerful, fully open sourced (Apache 2.0) More below 👇

Trained a voice lora on Walken using just 6 clips as a test to see how well VoxCPM handles learning a voice. Then I just did audio + image = video with LTX 2.3. This TTS model feels extremely powerful, fully open sourced (Apache 2.0) More below 👇

A.I.Warper

23,851 views • 4 months ago

A little taste of what’s to come… ✨ Think this one’s a keeper? Drop a 🔥 if you’re ready for more!

A little taste of what’s to come… ✨ Think this one’s a keeper? Drop a 🔥 if you’re ready for more!

Cannons

11,476 views • 1 year ago

Testing out this LTX 2.3 LoRA for removing objects blocking a shot. There’s still a bit of jitter when the face goes out of view, but coming from shooting weddings, I definitely would’ve tried using this to save shots when something was blocking my view.

Testing out this LTX 2.3 LoRA for removing objects blocking a shot. There’s still a bit of jitter when the face goes out of view, but coming from shooting weddings, I definitely would’ve tried using this to save shots when something was blocking my view.

enigmatic_e

23,582 views • 3 months ago

Not only do we have seamless transitions from you to your ship, we've also added a sensor mode for all players to indentify threats and nice loot! #gamedev #indiedev

Not only do we have seamless transitions from you to your ship, we've also added a sensor mode for all players to indentify threats and nice loot! #gamedev #indiedev

Filip Coulianos

223,477 views • 3 years ago

“For myself, I expect to be close to perfect. I think I’m at that level at this point.” My full interview with Joe Burrow after a spirited #Bengals practice on Inside Training Camp Live NFL Network:

“For myself, I expect to be close to perfect. I think I’m at that level at this point.” My full interview with Joe Burrow after a spirited #Bengals practice on Inside Training Camp Live NFL Network:

Tom Pelissero

141,412 views • 1 year ago

The Indian Air Force may get the first delivery of LORA missiles by year‑end. India signed a 2023 deal with Israel for this high‑precision, long‑range missile system range up to 400 Kilometres. Using LORA against Pakistan would be exciting.

The Indian Air Force may get the first delivery of LORA missiles by year‑end. India signed a 2023 deal with Israel for this high‑precision, long‑range missile system range up to 400 Kilometres. Using LORA against Pakistan would be exciting.

Baba Banaras™

108,736 views • 10 months ago

Joe Flacco is a Cincinnati Bengal. You don’t think I’m ready to root for him?! I was born for this.

Joe Flacco is a Cincinnati Bengal. You don’t think I’m ready to root for him?! I was born for this.

Michael Turner

108,856 views • 10 months ago

I think i’m ready to fall in love after directing this video. I’m a lover boy. 🥹

I think i’m ready to fall in love after directing this video. I’m a lover boy. 🥹

Perliks

36,687 views • 4 months ago

Found a hardcore LoRA workflow! Generate a PLY point cloud --> Adjust angle in editor -->Refine with Qwen-Image-Edit-2511 Gaussian Splash LoRA. It accurately restores complex perspective shifts. As shown in the demo, it handles 3D rotation and can even restore high-def details from blurry close-ups. Within a 45° range, the consistency is unmatched. Huge thanks to 大雄 for this contribution! 🫡

Found a hardcore LoRA workflow! Generate a PLY point cloud --> Adjust angle in editor -->Refine with Qwen-Image-Edit-2511 Gaussian Splash LoRA. It accurately restores complex perspective shifts. As shown in the demo, it handles 3D rotation and can even restore high-def details from blurry close-ups. Within a 45° range, the consistency is unmatched. Huge thanks to 大雄 for this contribution! 🫡

Tongyi Lab

1,493,527 views • 7 months ago

$"Bo Nix had a follow-up cleanup procedure after a fractured ankle and he’s still expected to be ready for the season.. I would expect Bo Nix to be ready for training camp or very close to training camp" ~ Ian Rapoport #PMSLive$

"Bo Nix had a follow-up cleanup procedure after a fractured ankle and he’s still expected to be ready for the season.. I would expect Bo Nix to be ready for training camp or very close to training camp" ~ Ian Rapoport #PMSLive

Pat McAfee

101,553 views • 4 months ago

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.

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 views • 7 months ago

Hey Y’all💚 Has been a longish day and I’m ready to unwind🙌🏻 Momma skipped the THC so more for me🤗 Let’s share this blunt and a few bongs rips together, if you wanna🥹 Cheers🔥💨💨💨💨💨💨 PPP💨💨🤏🏻😉

Hey Y’all💚 Has been a longish day and I’m ready to unwind🙌🏻 Momma skipped the THC so more for me🤗 Let’s share this blunt and a few bongs rips together, if you wanna🥹 Cheers🔥💨💨💨💨💨💨 PPP💨💨🤏🏻😉

*~♡~Erin~♡~*

14,066 views • 2 months ago

“This is why I love the UK. I’m currently being paid to watch a live football match.” — Nigeria Lady says.

“This is why I love the UK. I’m currently being paid to watch a live football match.” — Nigeria Lady says.

𝐀𝐬𝐚𝐤𝐲𝐆𝐑𝐍

403,660 views • 5 months ago

Was forced to drive a truck with my croaky voice for this one. Really excited to be doing this one with the geng FBG 🚚 🚚🚚🚚 Something big is coming. Are You Ready? #WatchBlackMarket

Was forced to drive a truck with my croaky voice for this one. Really excited to be doing this one with the geng FBG 🚚 🚚🚚🚚 Something big is coming. Are You Ready? #WatchBlackMarket

Oreoluwa Awolowo 🖤 🚚

10,730 views • 1 month ago

Currently up about $18,000 on this trade, I'm still looking for more, but I'm tempted to close.

Currently up about $18,000 on this trade, I'm still looking for more, but I'm tempted to close.

Tori 💜

20,662 views • 1 year ago