https://civitai.com/models/2509189/synth--ltx-23?modelVersionId=2820451


As the name implies - This is detailed for LTX 2.3 .

Trained on 5k synthetic image dataset. I generated a wide range of angles, ethnicities, lighting conditions and poses.

This is purely a detail lora to get proper and detail. No motion videos have been trained on. Any motion gained is pure luck.

That being said - it plays well with other existing motion loras found here on Civit. I see potential for training simple motion LoRAs to accompany this LoRA as this will do the detail heavy lifting without conflicting motions.

All example videos should have workflow embedded - if they won't load - there should be a single image uploaded as well that would have the WF embedded. It's nothing fancy but it'll give you a start.

PROS :

• Good and detail, learnt a couple of poses as well.
• No face modification. Works well T2V and i2v.
• Even does realistic breasts.

CONS:

• Too many breasts and ! through shirts! I realized too late that my dataset had too many full in it and thus it very rapidly learnt to draw and exposed breasts. By that time I was already too deep into training and didnt want to fix it.I tried compensating by merging the 'best-breasts' lora found here on civit at a -0.1 value. It didnt fix the issue but it helped a little bit.Avoid prompting thin clothing, prompt thick jackets and wool sweaters if you want to keep the ta-ta's hidden.
• Only does fully shaven right now. Majority of the dataset is clean like that. Might be able to blend it with some other hairy loras here for more fluff if you need.

Training details for those curious:

Trained with Musubi trainer - LR 0.00006 for 71,000 steps.
Some decent detail had emerged around 30k already but I wanted to push it longer so see if it would pick up on some of the subtler spreading details in the dataset.
At 30k I dropped the LR to 0.000045 and let it run . The LR was so low I could afford to let it run for a while without overfitting.

Images train much much faster than full videos and use much less VRAM. I could fit this into 24gb if I wanted to.

I'd suggest always doing a first run of image based training to establish details and then take over with your video motion dataset. This way you build a solid base understanding of the concept quickly allowing the video to come in and finish it off.