LTX-2 Is the NEW #1 Open-Source AI Video Model (Audio + Video, Runs Locally)

AI video looks amazing right now, but it still feels fake. Not because of the visuals, because it's silent, or worse, badly dubbed. LTX2 is one of the first open-source models that fixes that problem at the foundation level. This model doesn't add audio to video. It generates both together inside the same diffusion process. And once you see why that matters, is hard to unsee it. LTX2 is a DITbased diffusion transformer foundation model built for joint audio video generation. Most systems today are either texttovideo models that ignore sound or audio models that react after the video exists. LTX2 treats audio and video as two sides of the same event. A door slam isn't just a sound. Speech isn't just text. Emotion isn't just facial movement. They're all generated as one coherent scene. Let's talk about why most AI video pipelines break. In a sequential setup, the video model never knows what the audio will be. So, it guesses the lip movement, it guesses the timing, and it guesses the emotion. Then, the audio model comes in afterward and tries to match something that was never designed for, the sound. That's why the lip sync feels off. That's why the ambient noise feels disconnected from the actual scene. LTX2 avoids this by modeling the joint probability of audio and video together. During generation, sound influences motion and motion influences sound step by step. On the output side, LTX2 can generate up to 20 seconds of continuous video with synchronized stereo audio. But length isn't the impressive part. The impressive part is stability. Most models fall apart as time increases. LTX2 holds identity, timing, and scene coherence because audio and video are locked together throughout the diffusion process, and they're not stitched in at the end. Motion realism is another quiet strength here. Characters don't just move, they move for reasons. Speech causes facial motion. Camera motion affects sound perspective. Physical actions trigger audio events. This happens because LTX2 allocates most of its capacity to the video stream while still letting audio influence it through the cross attention system. So you get expressive motion without sacrificing synchronization. This looks complicated

but the idea behind LTX2 is actually very simple. It takes audio, video, and text and compresses them into smart representations and then lets audio and video talk to each other while they're being generated. Let's start at the top. This is the audio side. Raw audio is first converted into a MEL spectrogram, which is basically a compact way of representing sound. Then that goes through an audio VAE encoder, which compresses it into something called audio latence. Think of latence as a smart compressed version of sound that the model can reason about efficiently. The key idea here is that audio is not generated as raw waveforms is generated in a compressed latent space for speed and stability. The video side works the same way. Raw video frames go into a video VAE encoder which compresses space and time into video latence. This is how LTX2 can handle motion identity and long sequences without blowing up your compute. And the key idea once again here is that audio and video are separate but comparable. Both live in latent space before generation. Text goes through its own pipeline. Instead of just taking a single text embedding, LTX2 extracts features across multiple layers of a large language model that produces a much richer text representation, especially important for speech, emotion, and timing. These text embeddings are then fed into both the audio and video streams. As we can see here, the text connector sends a stream to the audio over here. And then this text connector sends it to the video stream over here. What you're seeing right now, this box is the heart of LTX2. Instead of one giant model, LTX2 uses two transformer streams, one for audio and one for video. They run in parallel, but at every layer, they exchange information through birectional cross attention. This is the key idea. Audio doesn't get added later. Video doesn't get finalized first. They influence each other at every diffusion step. Cross attention means the audio stream can ask what is happening visually right now. And then the video stream can also ask what sound is happening at this exact moment. This is how you get lip movement lining up with speech, footsteps matching motion, ambient sound matching the environment. This is why timing feels natural because it is learned jointly and not aligned afterwards. Because this is a diffusion model, training works by adding noise to audio and video latencing the model to predict and remove that noise. Audio and video each have their own loss, but they're optimized together. That's how the model learns synchronized generation, not just good audio or good video in isolation. So, if you zoom out, the architecture does three things. First, compresses audio and video into efficient latent spaces. Second, lets them influence each other during generation. Third, uses text as a shared semantic guide. And that's why LTX2 feels coherent instead of stitched together. Once you understand this diagram, it becomes obvious why LTX2 feels different. This isn't a video model with sound. It's a scene generator. All right, I'm in the API

playground where I'm going to test this model out. and we can see the results together. So obviously this is pretty simple. You would put your prompt here. Then you would select your model. So you have two options here actually. So you can select either a fast which is optimized for speed or pro which is balanced for quality and speed. So for our purposes today I'm going to choose the pro one. Then you have the duration. You can keep it at 8 second or you could increase it up till 10 seconds. And the resolution you can do 4K, 144, 1080. I'll keep it at 1080. Frames per second. You have the option to upgrade that to 50. I'll keep it at 25. Audio you can have it on. I want to keep it on because obviously you want to test the audio capabilities too. Then the camera motion I want. You can select you can do static, dolly in, dolly out, dolly left, right, whatever. I'll just do uh let's which one should I do? I'll do dolly in I guess. And then I will have my prompt. So my prompt is pretty much I'm trying to recreate a anime style scene which is Naruto basically eating ramen but instead of ramen I'm doing spaghetti to honor our great Will Smith eating spaghetti. So it's kind of like a detailed explanation. So I'm not going to go through all of it but just to give you guys an idea. Spikyhaired ninja wearing an orange and black outfit sitting at a ramen shop but instead of ramen he's eating spaghetti. And then you know he says like this isn't ramen but wow spaghetti hits different blah blah audio details slurping noodle sounds light chewing subtle clinking of a bowl and chopsticks camera medium close-up I don't think it's going to do all the camera functions because obviously the camera motion option we had was dolly in but I'm just going to give it a try anyways and the style handdrawn anime look vibrant colors soft shading mood light-hearted funny I used chat GPT actually to help me come up with this detailed prompt so let's see what we get. — M. This isn't ramen, but wow, spaghetti hits different. [snorts] Hey everyone watching. Hi. Hope you're having an awesome day. Believe it. I might switch to spaghetti full. — So that generation wasn't too bad. Like it had an idea of what I wanted to create. And it created something that looks like it would be in an anime show. But now what I'm going to do is something very specific. So I've actually given it a picture of Naruto eating ramen. So this is what the full picture looks like. And then what I'm going to ask the model to do is follow that same prompt and I'm going to set it to pro again. Put the duration to 10 seconds. This time I'm going to upgrade this to 50 just to see what difference I can notice. And then I'll put no camera motion for now. And let's generate the video. — M. This isn't ramen. But wow, spaghetti hits different. Hey everyone watching. Hi.

Believe it. I might switch to spaghetti fulltime. — So this is why LTX2 matters. is not just generating a cool anime clip or a funny moment. It's doing audio and video together in sync inside one model with motion, expression, dialogue, and timing all emerging at the same time. That's a big shift from how AI video has worked until now. And the fact that this is open source and designed to run locally makes it even more important. I'll leave a link in the description showing how to run LTX2 locally if you want to try it yourself. Make sure to subscribe to our

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