BodyPositivity LoRA for LTX-2.3
A LoRA for Lightricks/LTX-2.3 that pushes back against Hollywood's narrow, unrealistic beauty standards. It transforms generated subjects into a fuller, heavier-set body — same person, same scene, same motion — so that the bodies appearing in your AI-generated video work better reflect the actual diversity of human bodies in the real world.
Trigger word: bodypositivity — that's all you need. The LoRA does its thing on the trigger word alone, you don't have to write any other prompt. You can add a regular prompt on top if you want to direct the scene; the trigger just needs to be in there somewhere.
Sample outputs
Strength is your knob
LoRA strength scales the effect — go heavier or lighter to match what your scene calls for. Start at 1.0 and adjust from there in either direction:
- Around 1.0 is the sweet spot. The body shape transforms cleanly and identity is preserved.
- Above 1.1 the effect becomes more aggressive but likeness/identity starts to suffer — faces drift, fine details soften. Use sparingly and only if you want a heavily stylized result.
- Below 0.5 the effect becomes subtle — useful if you want a small nudge rather than a full transformation.
Dataset
I created the entire dataset myself — fully synthetic, and curated and filtered by hand. No real people in the training data.
Usage in ComfyUI
This is just an LTX-2.3 IC-LoRA — load it on top of the standard IC-LoRA workflow (the same one you'd use for any LTX-2.3 IC-LoRA). Reference workflows are in the official LTX-2 repository.
Quick recipe:
- Download
bodypositivity-ltx-2.3-rank32-step02750.safetensorsinto your ComfyUImodels/loras/folder. - In the standard LTX-2.3 IC-LoRA workflow, add a Load LoRA node and point it at this file.
- Set strength to 1.0 to start, then adjust either way to taste — back off if you see identity drift, push higher for a stronger effect.
- Put
bodypositivitysomewhere in your prompt. That alone is enough — extra prompt is optional.
Training details
| Parameter | Value |
|---|---|
| Base model | Lightricks/LTX-2.3 (ltx-2.3-22b-dev) |
| Training type | IC-LoRA (paired video-to-video) |
| LoRA rank | 32 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | self-attn (attn1.{to_k,q,v,out.0}), cross-attn (attn2.{to_k,q,v,out.0}), FFN (ff.net.0.proj, ff.net.2) |
| Trainable parameters | 163,577,856 |
| Optimizer | Prodigy (decouple, safeguard_warmup, bias_correction, weight_decay 0.01) |
| Learning rate | 1.0 (Prodigy auto-scale, constant) |
| Precision | bf16 + int8-quanto on transformer |
| Frames per sample | 65 (≈ 2.7 s @ 24 fps) |
| Resolution | 768 × 1024 (portrait), 1024 × 768 (landscape) |
| Batch size | 1 |
| Steps trained | 2,750 |
| Dataset | 143 paired (thin, heavy) clips, 100% synthetic |
| Hardware | 1 × H100 80 GB |
License
The LoRA weights in this repository are released under the Apache-2.0 license. You're free to use, modify, and redistribute them with attribution.
Note that running inference with this LoRA requires the Lightricks LTX-2.3 base model, whose own license terms still apply at inference time. Please review and comply with the LTX-2 / LTX-Video license for any commercial or downstream redistribution use case.
Acknowledgements
- Lightricks — for releasing the LTX-2.3 base model
oumad/LTX-2— for the Prodigy-optimizer fork ofltx-trainerused to train this LoRA- Google — for the Gemini Flash Image API used to build the paired image dataset