LOCAL CHARACTER CONSISTENCY · LoRA / IC-LoRA / ID-LoRA · DOCUMENTATION-BASED · VERIFIED SEPTEMBER 13, 2026
Character consistency is not one feature. A trained LoRA, reference-conditioned workflow and identity-personalization method solve different problems. This guide separates them so you can choose the right local workflow without assuming every LoRA preserves the same person across shots.
This page is based on official project documentation, repositories and workflow documentation. Unless explicitly marked Hands-on Tested, VideoToolMap has not independently trained or benchmarked the exact workflows described here.
Identity retention depends on the model, training data, reference quality, prompt, motion, shot angle, resolution, LoRA strength, workflow and other factors. No method listed here is presented as a guaranteed identity-preservation solution.
A lightweight adapter trained to teach a base model a concept, style, subject or behavior. It can be used for character learning when the dataset is built for that purpose, but LoRA support alone does not guarantee identity consistency.
In-Context LoRA uses reference-conditioned inputs. In the official LTX trainer, reference video latents can be provided alongside the target so the model learns reference-guided video transformations.
An identity-focused LTX-2.3 workflow that uses a reference image and short reference audio to personalize appearance and voice in synchronized video.
Using a first frame, reference image or reference video can preserve visual information without training a new identity adapter. It is simpler, but may be less robust across large pose, wardrobe or camera changes.
| Model / method | Official support | Best-fit use | Hardware signal | Important caveat |
|---|---|---|---|---|
| LTX-2.x standard LoRA | Official trainer includes T2V, I2V and other LoRA configs. | Teaching a subject, style or behavior to the base model. | Official trainer documents 32GB VRAM as the supported low-VRAM floor and 80GB as recommended for standard training configs. | Training support does not itself prove strong character consistency for every dataset. |
| LTX IC-LoRA | Official trainer provides V2V IC-LoRA config and reference-conditioned pipelines. | Reference-guided V2V, pose/depth/style-like conditioning and transformation workflows. | Training follows the LTX trainer hardware envelope; reference conditioning adds workflow complexity. | IC-LoRA is not simply another name for a character LoRA. |
| LTX-2.3 ID-LoRA | Native ComfyUI workflow is documented for identity personalization. | Appearance and voice personalization from a reference image and short reference audio. | Inference uses the LTX-2.3 stack; exact VRAM depends on checkpoint, precision, quantization and workflow. | Identity preservation can still vary by shot, motion and reference quality. |
| HunyuanVideo 1.5 LoRA | Official training code supports --use_lora with rank, alpha and dropout controls. | General fine-tuning and concept adaptation. | Official example is shown with multi-GPU torchrun; docs do not provide a simple consumer-GPU identity-training minimum. | Official docs provide LoRA training, not a dedicated identity-consistency workflow equivalent to ID-LoRA. |
A model supports LoRA is not the same as this model has a proven identity-personalization system. VideoToolMap classifies standard fine-tune LoRA, reference-conditioned IC-LoRA, identity-specific personalization and reference-only inference separately.
Start with image/reference-conditioned inference before training. It is the lowest-complexity path and shows how much consistency the base model already provides.
An identity-specific method such as ID-LoRA is more directly aligned with that goal than assuming any generic style LoRA will do it.
IC-LoRA or reference-conditioned V2V is the more relevant category because the workflow is built around a reference signal.
Standard LoRA training may be appropriate, but it requires dataset design, captions, validation and enough GPU memory. Treat it as a training workflow, not a one-click identity feature.
LoRA inference and LoRA training are very different hardware tasks. A GPU that can use a LoRA for generation may still be unable to train one. The current official LTX trainer documents 32GB VRAM as its supported low-VRAM training floor and 80GB VRAM as recommended. VideoToolMap does not extrapolate this into sub-32GB reliability claims.
For each adapter or reference workflow, record the base model and exact version, LoRA type, trigger terms, reference files, dataset source/rights, rank/alpha, recommended strength, tested resolutions, intended use, license and Last Verified date.
Local processing can improve privacy because references, training media and inference can remain on hardware you control. It does not remove copyright, publicity/personality-rights, consent, model-license or dataset-permission obligations.
VideoToolMap has not yet trained a character LoRA, IC-LoRA or ID-LoRA benchmark on owned hardware. We therefore do not publish our own identity-retention score, dataset-size recommendation, training time, peak VRAM, usable-shot rate or best-settings claim.
LTX Trainer configuration reference
ComfyUI LTX-2.3 workflow documentation
HunyuanVideo 1.5 official repository
Last verified: September 13, 2026. Source links are references only and do not imply affiliation, sponsorship or endorsement.