Publish Nolanizer V1 model package

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LICENSE

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README.md

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 ---
+library_name: pytorch
+pipeline_tag: image-to-image
 license: apache-2.0
+tags:
+  - image-to-image
+  - color-grading
+  - 3d-lut
+  - computer-vision
+  - pytorch
 ---
+
+# 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 |
+
+Checkpoint SHA-256:
+
+```text
+481cbc4fc47108f3537056cb7499ee72f90240f5d1aea5aa4de7b6c298abe58b
+```
+
+## Usage
+
+The checkpoint requires the Nolanizer Python package or a Nolanizer source
+checkout. Install the project, then run:
+
+```bash
+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.

checkpoint_manifest.json

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+{
+  "schema_version": 1,
+  "release_name": "nolanizer-v1",
+  "checkpoint_file": "nolanizer_v1.pt",
+  "checkpoint_sha256": "481cbc4fc47108f3537056cb7499ee72f90240f5d1aea5aa4de7b6c298abe58b",
+  "source_checkpoint": "checkpoints/main_v1_clipping_ft/best.pt",
+  "source_epoch": 7,
+  "inference_weights": "ema",
+  "fixture_examples": 96,
+  "fixture_sha256": "82cc5b062f97c1293167beda9377fa5af60b302f7ae078aadc8513810284316f",
+  "metrics": {
+    "ciede2000": 5.766579983135064,
+    "lab_mae": 3.6338841834415994,
+    "ssim_luma": 0.8617139191153246,
+    "edge_correlation": 0.9836641856928215,
+    "clipped_pixel_ratio": 0.0017072359720865886
+  },
+  "all_frozen_validation_gates_passed": true
+}

config.json

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+{
+  "model_type": "nolanizer",
+  "architectures": [
+    "SceneAdaptivePaletteModel"
+  ],
+  "framework": "pytorch",
+  "task": "image-to-image",
+  "model": {
+    "feature_dim": 128,
+    "planner_hidden_dim": 128,
+    "num_palette_bases": 8,
+    "lut_size": 33,
+    "use_global_adjustments": true
+  },
+  "inference": {
+    "checkpoint": "nolanizer_v1.pt",
+    "weights": "ema",
+    "input_color_space": "sRGB",
+    "output_color_space": "sRGB",
+    "intensity_min": 0.0,
+    "intensity_max": 1.0,
+    "recommended_intensity": 0.8
+  }
+}

nolanizer_v1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:481cbc4fc47108f3537056cb7499ee72f90240f5d1aea5aa4de7b6c298abe58b
+size 17368088

requirements.txt

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+numpy>=1.26,<3
+Pillow>=10,<12
+torch>=2.2,<3
+torchvision>=0.17,<1

training_config.yaml

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+seed: 42
+data:
+  mode: on_the_fly_degradation
+  image_size: 512
+  num_workers: 8
+  prefetch_factor: 2
+  verify_paths: true
+  frozen_eval_manifest: dataset/scene_metadata/splits/frozen_eval_degradations.jsonl
+model:
+  feature_dim: 128
+  planner_hidden_dim: 128
+  num_palette_bases: 8
+  lut_size: 33
+training:
+  epochs: 10
+  batch_size: 16
+  learning_rate: 2.0e-05
+  weight_decay: 0.0001
+  amp: true
+  gradient_clip: 5.0
+  checkpoint_every: 1
+  ema:
+    enabled: true
+    decay: 0.995
+    warmup: false
+  scheduler:
+    name: cosine
+    eta_min: 2.0e-06
+  early_stopping:
+    enabled: true
+    patience: 5
+    min_delta: 0.001
+loss:
+  reconstruction: 1.0
+  content: 0.5
+  color_moments: 0.1
+  lab_color: 0.25
+  skin: 0.1
+  lut_smoothness: 0.0001
+  lut_monotonicity: 0.0001
+  lut_diversity: 0.001
+  gamut_clipping: 0.001
+  output_clipping: 0.05
+  output_clipping_margin: 0.00784313725490196
+  palette_balance: 0.01
+  palette_specialization: 0.001
+  adjustment_regularization: 0.001
+evaluation:
+  fixture_manifest: dataset/scene_metadata/evaluation/v1_val_fixture.csv
+  image_size: 256
+  batch_size: 8
+  num_workers: 2
+  primary_metric: ciede2000
+  selection_policy: constrained
+  baseline_method: universal_lut
+  baseline_ciede2000: 7.225568388899167
+  maximum_lab_mae: 4.484619155526161
+  minimum_ssim_luma: 0.7614176272917819
+  minimum_edge_correlation: 0.95
+  maximum_clipped_pixel_ratio: 0.005
+  minimum_active_bases: 4
+  minimum_effective_basis_count: 2.0
+  minimum_pairwise_lut_l1: 0.001
+  minimum_scene_plan_variation: 0.01