Release CSA stage-2 checkpoint at step 97,397

Publish the portable FP32 inference checkpoint with both rough and refinement networks, a model card, real validation previews, example inputs, source snapshot, installable wheel, and SHA256 checksums. Original PatchGAN training with L1 and LPIPS. Full training state is retained separately.

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/expected.png filter=lfs diff=lfs merge=lfs -text
+preview-full.jpg filter=lfs diff=lfs merge=lfs -text
+preview.jpg filter=lfs diff=lfs merge=lfs -text

LICENSE.md

@@ -0,0 +1,15 @@
+# License notice
+
+The upstream repository states:
+
+> CC 4.0 Attribution-NonCommercial International. The software is for educaitonal and academic research purpose only.
+
+Source: [KumapowerLIU/CSA-inpainting](https://github.com/KumapowerLIU/CSA-inpainting),
+commit `f0b0712aa950cc559062dfef04c2f3f884a1f3e2`; preserved in `legacy/README.md`.
+
+This reorganization retains that attribution and noncommercial research-use notice.
+See [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/)
+for the license terms. No broader license grant is asserted.
+
+Paper: Hongyu Liu, Bin Jiang, Yi Xiao, Chao Yang,
+*Coherent Semantic Attention for Image Inpainting*, ICCV 2019.

README.md

@@ -1,3 +1,177 @@
 ---
-license: apache-2.0
+language: en
+license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: image-to-image
+tags:
+  - image-inpainting
+  - coherent-semantic-attention
+  - iccv-2019
+  - pytorch
+  - research
 ---
+
+# CSA · stage 2 · step 97,397
+
+**A modern PyTorch research checkpoint for Coherent Semantic Attention for Image
+Inpainting (ICCV 2019).** This checkpoint combines a separately trained rough
+network with a refinement network trained using the original PatchGAN family,
+L1, LPIPS, and CSA consistency supervision.
+
+This is the selected 2026 training run, rather than the original 2019 paper
+weights. It was selected by visual review, not an automatic best-metric rule.
+Model repository: [KumaPower/CSA](https://huggingface.co/KumaPower/CSA) ·
+[Download the 528.5 MB inference weight](https://huggingface.co/KumaPower/CSA/resolve/main/csa-stage2-step97397.pt) ·
+[Source repository](https://github.com/KumapowerLIU/CSA-inpainting/tree/codex/modern-pytorch).
+
+![Selected stage-2 completions at step 97,397](preview.jpg)
+
+Columns: masked input, restored image, original. See the
+[full eight-case gallery](preview-full.jpg) for additional
+examples, including faces and structures that the model does not reconstruct well.
+
+## Model at a glance
+
+| Property | Value |
+|---|---|
+| Refinement checkpoint | Stage 2, step **97,397** |
+| Frozen rough parent | Stage 1, step **156,181** |
+| Architecture | Two U-Nets with coherent semantic attention; width 64, depth 8 |
+| Refinement output | `tanh`, then composite known pixels from the input |
+| Training resolution | 256 × 256 RGB |
+| Training masks | Random rectangles, strokes, or their union; 10–40% missing area |
+| Stage-2 image discriminator | Original PatchGAN; no Real-ESRGAN initialization |
+| Feature discriminator | Original feature PatchGAN |
+| Stage-2 batch / precision | 192 / BF16 on one H100 |
+| Optimizer | Adam, initial LR 0.0002, betas (0.5, 0.999) |
+
+Stage 2 optimizes full-image raw-refinement **L1 × 0.1**, completed-image
+**LPIPS (VGG) × 1**, **CSA consistency × 0.01**, and image/feature relativistic
+average least-squares GAN terms, **0.002 each**. Stage 1 is frozen throughout
+refinement training. These modern training choices differ from the paper's
+original recipe. The sanitized [training configuration](training-config.yaml)
+records the selected stage-2 settings.
+
+## Training data
+
+The base index contains 296,145 Unsplash records and 901,730 images sampled from
+the 1,803,460-image Places365-Standard training archive. Later stage-2 training
+also includes 30,128 distinct HDTF talking-head frames, repeated across 299,469
+sampling entries. The final index has **1,497,344 entries**, approximately 20%
+face sampling weight. Entry counts are not counts of distinct image contents;
+cross-source content deduplication was not performed.
+
+HDTF validation frames were separated by source-video groups. Identity-disjoint
+splits have not been established. The evaluation below uses the unchanged
+general-image holdout, not a dedicated face benchmark. Training images and
+private dataset indices are not distributed with the checkpoint.
+
+## Evaluation
+
+The recorded check uses 64 fixed records from a 2,000-record Unsplash holdout,
+fixed random masks, FP32 inference, and batch size 1 on an H100. The holdout is
+record-level and is not content-deduplicated. Metrics average per-image values;
+L1 is measured on RGB values in [0, 1].
+
+| Output | Hole PSNR ↑ | Hole L1 ↓ | Full-image SSIM ↑ |
+|---|---:|---:|---:|
+| Frozen stage 1 | 18.4163 | 0.083474 | 0.863315 |
+| Stage 2 · 97,397 | 18.1563 | 0.083444 | 0.867322 |
+
+This is a small internal spot check, not the paper's evaluation protocol or a
+state-of-the-art comparison. Refinement improves SSIM on this sample while hole
+PSNR is lower than the frozen rough model. The machine-readable report is
+[`evaluation.json`](evaluation.json).
+
+Natural backgrounds and smooth regions are useful qualitative examples. Large
+holes over faces, buildings, and missing semantic objects can still produce
+blur, incorrect geometry, or repeated texture. The full gallery includes these
+failure cases; inpainting is a plausible completion, not a recovery guarantee.
+
+## Files and inference
+
+The release inference weight is **`csa-stage2-step97397.pt`**. It contains both
+the rough and refinement networks and the configuration required for inference.
+The file is 528,510,920 bytes (528.5 MB). No separate stage-1 weight,
+discriminator, VGG/LPIPS checkpoint, or training
+dataset is required at inference time. The file is loaded with PyTorch's
+`weights_only=True` and strict model-state matching.
+
+Use **Python 3.10+** with a matching PyTorch/torchvision pair from the
+[official PyTorch installer](https://pytorch.org/get-started/locally/).
+The package supports PyTorch `>=2.6,<3` and torchvision `>=0.21,<1`;
+not every cross-version pairing is compatible.
+
+The repository includes an [installable wheel](https://huggingface.co/KumaPower/CSA/resolve/main/csa_inpainting-0.3.0-py3-none-any.whl),
+[complete source archive](https://huggingface.co/KumaPower/CSA/resolve/main/csa-inpainting-source.tar.gz), and
+[example images](https://huggingface.co/KumaPower/CSA/tree/main/examples). A source checkout is optional.
+After installing the appropriate PyTorch/torchvision pair in your environment:
+
+```bash
+python -m pip install \
+  "https://huggingface.co/KumaPower/CSA/resolve/main/csa_inpainting-0.3.0-py3-none-any.whl"
+
+curl -fL https://huggingface.co/KumaPower/CSA/resolve/main/csa-stage2-step97397.pt \
+  -o csa-stage2-step97397.pt
+mkdir -p examples
+curl -fL https://huggingface.co/KumaPower/CSA/resolve/main/examples/masked.png \
+  -o examples/masked.png
+curl -fL https://huggingface.co/KumaPower/CSA/resolve/main/examples/mask.png \
+  -o examples/mask.png
+
+csa infer --checkpoint csa-stage2-step97397.pt \
+  --image examples/masked.png --mask examples/mask.png \
+  --output output.png --device auto
+```
+
+The [reference example output](https://huggingface.co/KumaPower/CSA/resolve/main/examples/expected.png)
+is provided for comparison. The CLI selects CUDA, then MPS, then CPU when
+`--device auto` is used. No additional pretrained networks are downloaded for
+inference.
+
+White mask pixels mean **missing**, black pixels mean **known**. Use `--device
+cpu` for CPU execution. Images and masks must have matching dimensions. The CLI
+pads to the network divisor and crops back to the original size; this checkpoint
+was trained at 256 × 256, so arbitrary-size quality and memory use are not
+validated. Prefer PNG output to preserve known pixels exactly.
+
+The separate **training checkpoint** retains optimizers, discriminators,
+schedulers, RNG state, and the data cursor for resuming this experiment. It is
+preserved privately and is not needed for inference. SHA256 checksums and the
+source snapshot accompany the assembled upload package.
+
+## Provenance
+
+| Artifact | SHA256 |
+|---|---|
+| Portable inference checkpoint | `605b80c18dcb0856367e6276813df0b54ec42aabea782af1631052aedb7abdfe` |
+| Full stage-2 training checkpoint | `9613d50e7d5cc7e7bf097b13a3987d29d086f82c84596c4a7b35411dcfa92c52` |
+| Frozen stage-1 parent | `0ede01d5945c1bb16fa08553ddff5a0279a9db219601729420e277e78748b041` |
+
+The export preserves the trained network parameters without quantization or
+fine-tuning. All generator tensors have identical checksums before and after
+export. Eight full-versus-export forward comparisons are bitwise equal under
+deterministic GPU algorithms on PyTorch 2.6.0 / H100; see
+[`export-verification.json`](export-verification.json). Backend choices can
+change numerical results, particularly the discrete nearest-neighbor selection
+in CSA; cross-device or default-kernel bitwise identity is not promised.
+Packaging and inference checks do not change or resume training.
+
+## Citation and license
+
+Hongyu Liu, Bin Jiang, Yi Xiao, and Chao Yang. **Coherent Semantic Attention for
+Image Inpainting.** ICCV 2019.
+
+```bibtex
+@InProceedings{Liu_2019_ICCV,
+  author = {Liu, Hongyu and Jiang, Bin and Xiao, Yi and Yang, Chao},
+  title = {Coherent Semantic Attention for Image Inpainting},
+  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  month = {October},
+  year = {2019}
+}
+```
+
+The release retains the repository's **CC BY-NC 4.0 / noncommercial academic
+research** notice. Dataset and example-image rights remain with their respective
+owners; this release does not grant additional dataset rights.

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examples/README.md

@@ -0,0 +1,17 @@
+# One-image example
+
+`masked.png`, `mask.png`, and `expected.png` are a fixed validation example from
+the stage-2 checkpoint at step **97,397**. White mask pixels mark the region to
+inpaint; black pixels mark the known image. The input already hides the target
+pixels. `expected.png` is the model output, not the original photograph.
+
+```bash
+csa infer --checkpoint checkpoints/csa-stage2-step97397.pt \
+  --image examples/masked.png --mask examples/mask.png \
+  --output output.png --device cpu
+```
+
+The reference was generated on an H100 with FP32 inference. Floating-point
+differences across devices or PyTorch versions can affect final quantization.
+Known pixels are composited from the input. Evaluation details and all eight
+displayed cases are in [the release notes](../README.md).

export-verification.json

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+{
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+  "python": "3.10.14",
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+  "device": "NVIDIA H100 80GB HBM3",
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+      "full_vs_export_tensor_equal": true,
+      "frozen_run_png_max_abs_error": 1,
+      "known_pixels_equal": true
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+      "frozen_run_png_max_abs_error": 16,
+      "known_pixels_equal": true
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+      "full_vs_export_tensor_equal": true,
+      "frozen_run_png_max_abs_error": 1,
+      "known_pixels_equal": true
+    },
+    {
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+      "full_vs_export_tensor_equal": true,
+      "frozen_run_png_max_abs_error": 5,
+      "known_pixels_equal": true
+    },
+    {
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+      "full_vs_export_tensor_equal": true,
+      "frozen_run_png_max_abs_error": 13,
+      "known_pixels_equal": true
+    }
+  ],
+  "cli_example_png_max_abs_error": 1,
+  "pretrained_downloads": 0,
+  "note": "Bitwise forward equality verified with deterministic GPU algorithms. Default backend kernels and different devices may round differently; frozen-run PNG differences are recorded as observations, not export equality checks."
+}

release-manifest.json

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+{
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+  "checkpoint_step": 97397,
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+  "repository": "KumaPower/CSA",
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+}

source-compatibility.json

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+      "scope": "entire_module",
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+    {
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+      "scope": "Stage2InpaintingModel",
+      "ast_equal_to_training_snapshot": true
+    }
+  ]
+}

source-manifest.json

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+    "src/csa_inpainting/cli.py": "24f23b7956a224342036c737efbbdfad5e6b6004f426592375e6f136570724eb",
+    "src/csa_inpainting/config.py": "e306bd7477eb0bc76f9d42d1103c85a80f419848528b0bf25b0e70aa499c308d",
+    "src/csa_inpainting/data.py": "0122343e05da94d2009389ab24882ca808a75db6029b8551ace4406e900b83e5",
+    "src/csa_inpainting/diagnostics.py": "8c6c28460fd38d5dfdeb33b2e0d83e2019e775f4f3b25c4f2c4171f6281557ed",
+    "src/csa_inpainting/engine.py": "7f85a8fbaea602d9f29398340bf70b8aeaacb7d1ebf71e882c16fd3a98748b2f",
+    "src/csa_inpainting/evaluation.py": "51bd0541d3b040979b2dcaceaf08b886c8ae44e31a8193af84de48664da6c8f1",
+    "src/csa_inpainting/experiment.py": "c5cacf564402d7449d922343cdec0017c10b3d332a0e7d91a12f878aed653e9f",
+    "src/csa_inpainting/losses.py": "4bcf84bd52e93c200eacbd0f446dba312b7c944a5ea028e3df9471aa1684feb8",
+    "src/csa_inpainting/masks.py": "5099bf99d5210246976820e4af0961c849c2c6f227654933001943e04435c977",
+    "src/csa_inpainting/networks.py": "754f16d043c5d6794fc590b4f3acbbe168d9b63f33c034c0f0d3a56bcb455d90"
+  },
+  "source_archive_sha256": "3b5241738138ce9960464b09b275808039e48475022fa7eb4a23963c0d09859f",
+  "source_archive_files": 111
+}

training-config.yaml

@@ -0,0 +1,40 @@
+# Documentary configuration for the indexed stage-2 runner; not csa train.
+model:
+  width: 64
+  depth: 8
+  attention_chunk_size: 256
+  mask_threshold: 0.3125
+data:
+  train_images: data/train.jsonl
+  val_images: data/validation.jsonl
+  train_masks: ''
+  val_masks: ''
+  mask_mode: random
+  random_min_fraction: 0.1
+  random_max_fraction: 0.4
+  mask_white_is_hole: true
+  image_size: 256
+  center_overlap: 4
+  horizontal_flip: true
+  batch_size: 192
+  workers: 32
+train:
+  output: runs/stage2/run
+  device: cuda:0
+  seed: 2019
+  epochs: 120
+  decay_start: 20
+  lr: 0.0002
+  beta1: 0.5
+  beta2: 0.999
+  reconstruction_weight: 0.1
+  lpips_weight: 1.0
+  lpips_net: vgg
+  lpips_batch_size: 8
+  consistency_weight: 0.01
+  adversarial_weight: 0.002
+  discriminator_width: 64
+  feature_discriminator_width: 512
+  amp: bf16
+  log_every: 1
+  image_discriminator: patch

validation.json

@@ -0,0 +1,32 @@
+{
+  "date": "2026-10-29",
+  "package_version": "0.3.0",
+  "development": {
+    "python": "3.12.14",
+    "torch": "2.14.1",
+    "device": "cpu",
+    "pytest": {
+      "passed": 313,
+      "skipped": 16,
+      "subtests_passed": 12,
+      "seconds": 174.52,
+      "skip_reason": "CUDA GPU unavailable"
+    },
+    "ruff_check": "PASS",
+    "ruff_format": "PASS",
+    "uv_lock_check": "PASS",
+    "wheel_build": "PASS",
+    "cpu_smoke": "PASS",
+    "local_full_checkpoint_cli": "PASS"
+  },
+  "production": {
+    "python": "3.10.14",
+    "torch": "2.6.0+cu124",
+    "release_tests_passed": 21,
+    "real_image_export_equivalence_cases": 8,
+    "device": "NVIDIA H100 80GB HBM3",
+    "deterministic_algorithms": true,
+    "generator_tensors_equal": true
+  },
+  "resume_test_contract": "All immediately restored states exact; future losses and functional outputs checked within float32 tolerance; no cross-backend bitwise Adam-update claim."
+}