mskayacioglu/nolanizer-v1

Nolanizer V1

Nolanizer V1 is a scene-adaptive image-to-image model for cinematic global color grading. It preserves the input composition and predicts a color transformation designed to produce a restrained, large-format cinematic look associated with Christopher Nolan-inspired visual language.

The model analyzes each image, predicts a mixture of eight learned 33³ 3D LUT bases, and applies bounded exposure, contrast, saturation, temperature, and tint adjustments. A single intensity control blends continuously between the original image and the full grade.

Nolanizer is an independent research project. It is not affiliated with, endorsed by, or an official product of Christopher Nolan, any cinematographer, colorist, or film studio. The output is an algorithmic interpretation of a cinematic visual style, not a claim about how a specific person would grade an image.

Model details

Property	Value
Task	Scene-adaptive global image color grading
Framework	PyTorch
Architecture	CNN condition encoder with a learnable 3D LUT mixture
LUT basis	8 learned LUTs, each 33 × 33 × 33
Global controls	Exposure, contrast, saturation, temperature, and tint
Effect range	0.0 to 1.0
Recommended weights	EMA
Release checkpoint	Epoch 7

The transformation is global: every output pixel is produced from its input color and the scene-conditioned grade. Spatial structure, objects, and composition are therefore left unchanged.

Files

File	Purpose
nolanizer_v1.pt	Frozen epoch-7 checkpoint; use EMA weights
config.json	Architecture and inference contract
training_config.yaml	Resolved optimization configuration
checkpoint_manifest.json	Checkpoint checksum and release metrics
requirements.txt	Runtime Python dependencies

Usage

The checkpoint requires the Nolanizer Python package or a Nolanizer source checkout. Install the project, then run:

python -m pip install -e .

python -m nolanizer.inference \
  --checkpoint huggingface/nolanizer-v1/nolanizer_v1.pt \
  --input path/to/input.jpg \
  --output path/to/output.jpg \
  --intensity 0.8 \
  --weights ema

Supported inputs include standard RGB images such as JPEG, PNG, and WebP.

Intensity

• 0.0: exact identity output
• 0.6–0.9: recommended range for most images
• 1.0: full predicted grade

The inference command saves the graded image and can also expose the predicted LUT mixture and global adjustment parameters for inspection.

Evaluation

The release checkpoint passed the project's frozen color, content-preservation, clipping, LUT-basis health, and exact-identity gates.

Metric	Value
CIEDE2000	5.7666
Lab MAE	3.6339
Luminance SSIM	0.8617
Edge correlation	0.9837
Clipped-pixel ratio	0.0017

These are internal release metrics rather than a perceptual preference score. They should not be interpreted as a guarantee of equivalent performance on every image domain.

Intended use

• Interactive grading of photographs and still images
• Research and education in global cinematic color grading
• Analysis of scene-conditioned LUT mixtures
• Non-destructive look exploration through the intensity control

Limitations

• The renderer applies global color and tone transformations only; it does not perform local masking or selective relighting.
• It cannot change geometry, composition, production design, lens characteristics, film grain, bloom, or halation.
• Extremely clipped highlights, near-black images, unusual color spaces, and heavily compressed inputs may produce unstable or subtle results.
• Skin-tone behavior has not been assessed with a dedicated demographic benchmark.
• Output quality is subjective and depends on exposure, white balance, scene content, and the selected intensity.

Responsible use

Use the model only with images you have the right to process. Do not present generated results as an official Christopher Nolan look, endorsement, or creative decision. Keep the original image when provenance or faithful color reproduction matters.