Black Magic: LTX-2.3 + LTX-2.5 Shadow Reconstruction IC-LoRA
Black Magic is a video-to-video IC-LoRA for
LTX-2.3 and LTX-2.5 that transforms dark or underexposed footage into a plausible, visually rich interpretation of the hidden scene. It doesn’t simply brighten the video. It interprets what might be hidden in the dark.
Black Magic is a generative VFX model, not conventional low-light enhancement. It does not claim to reveal the original signal faithfully. Where the input contains too little information, it creates a temporally coherent interpretation of what could be in the dark.
Intended use: Generative shadow reconstruction for short creative and VFX
shots. The model is designed to preserve visible subjects, composition, motion,
and light placement while imagining plausible detail in crushed shadows. This makes Black Magic especially suited to dark concerts, festivals, animals in the wild at night, nighttime footage, and other shots where a compelling reconstruction matters more than accuracy.
Out of scope: Faithful photographic restoration, scientific enhancement,
surveillance, forensics, or any use that requires hidden details to match the
original scene. Indiscernible content is generated, not
recovered.
Put black-magic-ic-lora-450.safetensors in ComfyUI/models/loras.
Install or update the official Lightricks ComfyUI-LTXVideo nodes.
Load ltx23-black-magic-lora-workflow.json.
Select the dark source in the Load Video node.
Update the positive prompt with a short scene description that guides the
reconstruction (see below).
Queue the workflow.
Prompting
The following is the instruction used during training and is the recommended
prompt prefix:
Restore this underexposed video to a natural, well-exposed version with realistic colors and recovered shadow detail.
Append a short description of what the scene contains, or what Black Magic
should plausibly construct in the shadows:
A tiger in a forest.
The description acts as creative direction. Keep it concise if you want the
visible reference to remain dominant; add more detail when the shadows are
nearly empty and you want to steer the reimagined content. Long or strongly
stylized prompts can intentionally pull the result farther from the source and may produce a painted look.
Recommended Settings
Black Magic strength:1.0–1.25
Distilled LoRA strength:0.5
Phase one: 8 distilled Euler steps at approximately 640×352 for 16:9 video
The included workflow safely decodes up to 129 output frames in one LTX VAE
temporal tile. For a longer valid LTX sequence, set:
minimum temporal_size = output frame count + 7
Use frame counts that are one more than a multiple of eight, such as 81, 121,
or 129. Keep input dimensions divisible by 32.
Tips and Limitations
Visible evidence of subjects, composition, or motion gives the model stronger
anchors. Fully black regions leave more room for invention.
A concise scene description steers what the model imagines in ambiguous
shadows.
Re-running with another seed can produce a different but still plausible
interpretation of the same darkness.
This LoRA reconstructs video appearance; it was not trained as an audio
model.
Dataset
The model was trained on video from
Pexels and
BVI-RLV. Pexels videos were
synthetically darkened to create aligned reference and target pairs and are
subject to the Pexels license.
Real low-light pairs came from BVI-Lowlight: Fully registered datasets for
low-light image and video enhancement by P. Anantrasirichai, A. Malyugina,
R. Lin, and D. R. Bull (2023), available under
CC BY 4.0 from
IEEE DataPort. The clips were cropped,
resized, and temporally sampled for training.
Training
Technique: IC-LoRA (rank 32, alpha 32) on the LTX-2.3 22B video
transformer
The model weights are released under the
LTX-2 Community License.
The source datasets remain subject to their respective licenses described in
the Dataset section above.
Acknowledgments
Lightricks for LTX-2.3, the official ComfyUI
nodes, and the LTX-2 Community Trainer.
The creators whose videos from
Pexels and
Mixkit were used as the reference videos to produce the
model-card examples.
The Pexels creators and the BVI-RLV authors for the source training material.
