LTX2.3-22B_IC-LoRA-Cameraman_v1

qqceqqq/LTX2.3-22B_IC-LoRA-Cameraman_v1

LTX2.3-22B_IC-LoRA-Cameraman_v1 is a machine learning model from qqceqqq. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

A fine-tuned In-Context LoRA (IC-LoRA) adapter for LTX-Video 2.3 (22B), trained to replicate camera movements from a reference video.

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Updated Apr 14, 2026

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At a glance

License: apache-2.0
Access: Public
Created: Apr 14, 2026
Updated: Apr 14, 2026
SHA: 59b8de1e

Base models

Lightricks/LTX-2.3

Model card

From the Hugging Face model README

LTX-Video 2.3 22B — IC-LoRA: Cameraman v1

A fine-tuned In-Context LoRA (IC-LoRA) adapter for LTX-Video 2.3 (22B), trained to replicate camera movements from a reference video.

Example ComfyUI workflow

You can find a ComfyUI workflow example here: https://huggingface.co/datasets/Cseti/ComfyUI-Workflows/blob/main/ltx/2.3/ic-lora-cameraman/README.md

Example outputs

Each video shows the reference (left) and generated output (right) side by side.

How It Works

During inference you provide:

A reference video that carries the desired camera motion
A text prompt describing the scene to generate

The model transfers the camera behavior from the reference into the generated output. No trigger word is required.

Training Details

Parameter	Value
Base model	LTX-Video 2.3 (22B)
Training framework	ltx-trainer (Lightricks)
Training strategy	IC-LoRA (video_to_video)
Best checkpoint	step 10,500
LoRA rank / alpha	32 / 32
Target modules	attn1, attn2 (to_k/q/v/out), ff.net.0.proj, ff.net.2
Learning rate	1e-4 (linear decay)
Mixed precision	bf16
Batch size	1 (gradient checkpointing enabled)
Training dataset	77 video pairs
Resolution buckets	768x512x57; 768x512x89; 768x512x121
First frame conditioning	0.2

Dataset

77 video pairs annotated by camera motion type, balanced to up to 15 samples per motion component. Some compound motions (e.g. zoom_in + tilt_up, orbit_cw + pan_left) are also represented.

Motion	Samples
zoom_in	15
zoom_out	15
tilt_up	15
tilt_down	9
pan_left	15
pan_right	15
orbit_cw	15
orbit_ccw	15

Usage

Requires the ltx-trainer repo and its dependencies.

uv run python -m ltx_pipelines.ic_lora \
    --distilled-checkpoint-path /path/to/ltx-2.3-22b-distilled.safetensors \
    --spatial-upsampler-path /path/to/spatial_upsampler.safetensors \
    --gemma-root /path/to/gemma \
    --lora lora_weights_step_10500.safetensors 0.8 \
    --video-conditioning /path/to/reference.mp4 1.0 \
    --prompt "Your scene description here" \
    --width 768 --height 512 --num-frames 97 \
    --output-path output.mp4

--video-conditioning: reference video carrying the camera motion to replicate, followed by conditioning strength
--lora: path to this LoRA followed by strength (0.7–1.0 recommended)
No trigger word needed

Tips

If the camera motion transfer feels too subtle, explicitly describe the desired movement in the prompt. This can strengthen the effect.

Limitations

First experimental IC-LoRA checkpoint — results may vary
Complex compound motions may not transfer reliably
Only tested with I2V (image-to-video) conditioning — T2V mode is untested

License

Apache 2.0

License: apache-2.0
Created: Apr 14, 2026
Updated: Apr 14, 2026