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kenzato/ltx-2.3-bodypositivity-lora
ltx-2.3-bodypositivity-lora is a image-to-video model from kenzato. Use it for the image-to-video task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as apache-2.0.
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 tha…
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.safetensors327 MB · 95%
From the Hugging Face model README
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.
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:
I created the entire dataset myself — fully synthetic, and curated and filtered by hand. No real people in the training data.
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:
bodypositivity-ltx-2.3-rank32-step02750.safetensors into your ComfyUI models/loras/ folder.bodypositivity somewhere in your prompt. That alone is enough — extra prompt is optional.| 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 |
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.
oumad/LTX-2 — for the Prodigy-optimizer fork of ltx-trainer used to train this LoRA