Let's create an Image to Video of LTX2 with 8GB of VRAM (RTX 4060)

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  Video generated from an existing image using LTX-2 🔻



Following our previous guide on txt2vid, today we will generate a video based on an existing image. Since it's basically just adding an image input to the prompt, there's nothing too difficult about it. First, I’ll clarify my specs: RTX 4060 and 16GB System RAM—a typical low-spec setup. I’ll be using a character I personally trained via LoRA to see if she can wink and pull up a blanket in a 4-second clip.


Opening the Img2vid Workflow

As we always do, open ComfyUI, go to Templates, and search for LTX-2. From there, load the node setup indicated by the red box in the screenshot below.


Analyzing the Results: Movement

I tested it after generating an image with ZiT - LoRA i have created.

Close eyes prompt Image
Dance prompt Image

Efficiency starts with proper inputs. Upload your base image and input the prompt. For this test, I used:

"Lying comfortably in bed, she winks slightly with one eye at the viewer in a quiet atmosphere, then covers herself with the blanket and closes her eyes."

After waiting about 10 minutes (rendering time decreases significantly with better hardware), the result is out. There is a slight blurring of the image quality, which is likely a characteristic of the current LTX-2 version. I plan to cover version 2.3 in a future post to see if this improves. Overall, the facial consistency remained quite stable.

However, this was only possible because the movement was minimal. When I tried a dance prompt with more dynamic motion, the result was different. You can see the hands and body parts slightly "glitching" or jumping around. While LoRAs can help stabilize motion, fixing distortions in facial features during high-intensity movement is still a major challenge.

Conclusion

So, what’s the verdict? Even paid premium services struggle with these kinds of motion artifacts during intense action. For now, the best approach is to keep iterating until you get a usable result. However, with the constant release of new LoRAs and improvements to model architectures and algorithms, it’s only a matter of time before these issues are resolved. Let's be patient and keep an eye out for better models in the future.

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