Publish MEDFE model card, architecture and inference examples

.gitattributes

@@ -33,3 +33,5 @@ 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
+medfe-architecture.png filter=lfs diff=lfs merge=lfs -text
+medfe-preview.png filter=lfs diff=lfs merge=lfs -text

LICENSE.md

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README.md

@@ -1,3 +1,179 @@
 ---
-license: apache-2.0
+library_name: pytorch
+pipeline_tag: image-to-image
+license: cc-by-nc-sa-4.0
+language:
+- en
+tags:
+- image-inpainting
+- medfe
+- partial-convolution
+- computer-vision
 ---
+
+<div align="center">
+
+# MEDFE
+### Mutual Encoder–Decoder with Feature Equalizations
+
+**ECCV 2020 · Modern PyTorch implementation**
+
+[Paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470715.pdf) · [Weights](https://huggingface.co/KumaPower/MEDFE) · [Quick start](#quick-start) · [Training](#training) · [Datasets](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/data.md) · [Architecture](#architecture)
+
+Hongyu Liu · Bin Jiang · Yibing Song · Wei Huang · Chao Yang
+
+</div>
+
+![Masked input, MEDFE completion, and ground truth](medfe-preview.png)
+
+*MEDFE results. Each row shows masked input → completion → ground truth. [More examples and checkpoint details](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/checkpoint.md).*
+
+This repository is a new PyTorch implementation of **MEDFE**, which jointly recovers image structure and texture through a mutual encoder–decoder and feature equalization. It provides batched training, reproducible random masks, explicit configuration, resumable checkpoints, and offline inference.
+
+The network combines a **PartialConv residual encoder** with a **GroupNorm/SiLU residual decoder**, using nearest-neighbor upsampling and ordinary convolutions. Structure and texture branches fill missing features through multi-scale partial convolutions, followed by channel equalization and bilateral propagation. A single forward pass produces the completed RGB image.
+
+## Quick start
+
+Python **3.10+** is required. The training environment used PyTorch **2.6.0**, torchvision **0.21.0** and CUDA **12.4**. For Linux/NVIDIA:
+
+```bash
+git clone https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE.git
+cd Rethinking-Inpainting-MEDFE
+python3 -m venv .venv
+source .venv/bin/activate
+python -m pip install torch==2.6.0 torchvision==0.21.0 \
+  --index-url https://download.pytorch.org/whl/cu124
+python -m pip install -e .
+medfe --help
+```
+
+For macOS or CPU, install the matching [PyTorch wheels](https://pytorch.org/get-started/previous-versions/#v260) before installing this package.
+
+Download the **step 120,994** generator from [Hugging Face](https://huggingface.co/KumaPower/MEDFE), then run:
+
+```bash
+mkdir -p checkpoints
+curl -L --fail \
+  https://huggingface.co/KumaPower/MEDFE/resolve/main/medfe-sd-partial-step120994.pt \
+  -o checkpoints/medfe-sd-partial.pt
+
+medfe infer --checkpoint checkpoints/medfe-sd-partial.pt \
+  --image examples/masked.png --mask examples/mask.png \
+  --output outputs/completed.png --device cuda:0
+```
+
+**White mask pixels are holes; black pixels are known.** Image and mask must have matching dimensions. Inference preserves the original size through padding/cropping, copies known pixels exactly, and writes a lossless PNG. `--device cpu` is supported. The download is a **579.6 MB FP32 inference bundle**; [checkpoint details](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/checkpoint.md) include its checksum and export instructions.
+
+## Architecture
+
+![MEDFE architecture: PartialConv encoder, structure and texture filling, feature equalization, refined skips, and residual decoder](medfe-architecture.png)
+
+*Architecture overview, with a layout inspired by [Figure 2 of the paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470715.pdf#page=5). Scale labels are spatial side lengths for a 256 × 256 input. The image tiles are schematic; model results are shown above.*
+
+The generator is trained jointly. Equalized features are projected to each encoder scale and added to the encoder features; the resulting feature pyramid supplies the decoder input and skip connections. See the [architecture notes](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/sd-partial-backbone.md) for feature scales, mask propagation and normalization.
+
+## Training
+
+The current recipe is [`configs/sd-partial-csa-mix-large-batch.yaml`](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/configs/sd-partial-csa-mix-large-batch.yaml).
+
+| Setting | Current experiment |
+| --- | --- |
+| GPU | **1 × NVIDIA H100 80 GB HBM3** |
+| Image size / batch | **256 × 256 / 128**, one GPU |
+| Precision | BF16 training; FP32 perceptual computation and validation |
+| Memory | About **33 GiB** process usage observed; **38 GiB** allocator budget |
+| Measured short-test peak | 25.925 GiB allocated / 30.551 GiB reserved; synthetic full D/G test |
+| Optimizer | Adam, lr `0.0002`, betas `(0.5, 0.999)` |
+| Masks | Rectangles, brush strokes, or mixtures; 10%–40% missing area |
+| Stop point | **120,994 steps**, paused with a complete training checkpoint |
+
+The 80 GB card capacity is not the measured requirement. Memory varies with inputs, backends and image size; reduce batch size for smaller GPUs. [Training details](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/training.md) include the loss definitions, warmup, resume rules and hardware measurements.
+
+### 1. Prepare your images
+
+Use separate training and validation folders. The indexer reads existing files; it does not download or extract a dataset.
+
+```bash
+python scripts/build_image_index.py --images-root /path/to/train \
+  --manifest data/train.jsonl
+python scripts/build_image_index.py --images-root /path/to/validation \
+  --manifest data/validation.jsonl
+```
+
+The indexed reader resizes RGB to 256², computes a true RTV structure target, and caches it on first use. See [data preparation](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/data.md) for storage requirements, archive formats, face sampling and the alternative paired-data protocol.
+
+### 2. Prepare LPIPS weights
+
+```bash
+export TORCH_HOME="$PWD/weights/torch"
+medfe prepare-weights --lpips-only --output weights
+```
+
+This explicitly fetches missing VGG-LPIPS assets. The current discriminators are randomly initialized PatchGANs and need no pretrained D. Training itself defaults to **no implicit downloads**.
+
+### 3. Train and resume
+
+```bash
+medfe train --config configs/sd-partial-csa-mix-large-batch.yaml \
+  --set train.device=cuda:0 --max-steps 20
+
+medfe train --config configs/sd-partial-csa-mix-large-batch.yaml \
+  --set train.device=cuda:0 \
+  --resume runs/medfe-sd-partial-csa-mix-large-batch/latest.pt
+```
+
+The first command is a short startup check; `--max-steps` is an **absolute** step limit. The second continues the same run. Checkpoints contain both discriminators, optimizer states, perceptual weights, RNG state and the data cursor. SIGINT/SIGTERM finish the current update and save a complete checkpoint. Source/config/data checks protect resume compatibility; archived runs use their frozen source for exact continuation.
+
+| Generator loss | Weight |
+| --- | ---: |
+| Full-image raw-prediction L1 | 1 |
+| Structure auxiliary L1 | 1 |
+| Texture auxiliary L1 | 1 |
+| VGG-LPIPS on the completed image | 1 |
+| Global + local RaLSGAN | Ramps to **0.002** |
+
+GAN is disabled for the first 5,000 updates, then ramps linearly over 10,000 updates. Separate VGG perceptual/style losses are disabled in this recipe.
+
+## Data used for this checkpoint
+
+The run uses the same source mix and split as the modern CSA experiment:
+
+| Source | Training entries | Obtain the source data |
+| --- | ---: | --- |
+| Unsplash-derived photographs | 296,145 | [Unsplash Dataset](https://unsplash.com/data) · [access/schema](https://github.com/unsplash/datasets) |
+| Places365-Standard | 901,730, half of the training archive | [CSAIL dataset repository](https://github.com/CSAILVision/places365) |
+| HDTF talking-head frames | 299,469 entries from 30,128 unique frames | [HDTF dataset and preprocessing](https://github.com/MRzzm/HDTF) |
+
+**1,497,344 sampling entries; 1,228,003 distinct source records.** Face frames repeat 9 or 10 times for approximately 20% sampling weight. Validation uses 2,000 held-out Unsplash records. The training masks are generated online. The local selection/manifests are not a public benchmark split; source access and reproducibility details are in [datasets](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/data.md).
+
+## Repository guide
+
+| Location | Purpose |
+| --- | --- |
+| `src/medfe/model.py`, `blocks.py`, `sd_backbone.py` | Generator, feature equalization, PartialConv and decoder |
+| `src/medfe/trainer.py`, `losses.py`, `discriminators.py` | Joint training, loss definitions and PatchGANs |
+| `src/medfe/indexed_data.py`, `rtv.py`, `masks.py` | Data decoding, cached structure targets and random masks |
+| `src/medfe/evaluation.py`, `inference.py` | Image-preserving inference, evaluation and portable export |
+| `configs/` | Training configurations |
+| `legacy/` | Archived source files |
+
+```bash
+python -m pip install -e '.[dev]'
+ruff check src tests scripts
+python -m pytest tests -q
+```
+
+Large holes can still lead to repeated textures, implausible faces and missing body parts. See the [validation records](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/docs/validation.md) for implementation checks and image-quality observations.
+
+## Citation and license
+
+```bibtex
+@inproceedings{liu2020rethinking,
+  title={Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations},
+  author={Liu, Hongyu and Jiang, Bin and Song, Yibing and Huang, Wei and Yang, Chao},
+  booktitle={European Conference on Computer Vision},
+  year={2020}
+}
+```
+
+[CC BY-NC-SA 4.0](https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE/blob/master/LICENSE.md). Dataset and pretrained-weight terms remain those of their respective providers.

SHA256SUMS

@@ -0,0 +1,10 @@
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+57b24f85f16e861201a00355bf17e2f7c89ae32c808a32bb1f073aa0bfb82f9d  medfe-architecture.png
+1487661ecb423e891441eba88e05b78315b5a8aa4b2b0f816dee67d6cdaa7e0c  medfe-preview.png
+269f94964262d6f2f3df50a92e16d95ff2f1e99b7cf24f51a152bc18486c5e48  release.json
+29dfc517150714442b1549c7990e079d3e1efbb59a0c026d9f60c5af66cc15ea  training-config.yaml

release.json

@@ -0,0 +1,66 @@
+{
+  "model": "MEDFE sd_partial",
+  "step": 120994,
+  "status": "paused",
+  "date": "2026-11-01",
+  "inference": {
+    "step": 120994,
+    "bytes": 579558450,
+    "sha256": "ca98198ef4cd9fad732d5709a0f3a5f9f9646743528dacde744bfb95ddb71e50",
+    "generator_sha256": "e1f0c8e34374523d1b1f1e5ef715b5e7722189dec2d4f12bf39d2128c2f2ebc8",
+    "source_checkpoint_sha256": "bd2b516ba3d7dd19e444feec35279a483e33835b5148d82b1f9c21fd7d7dc975"
+  },
+  "training_checkpoint": {
+    "filename": "medfe-training-step120994.pt",
+    "bytes": 1864094437,
+    "sha256": "bd2b516ba3d7dd19e444feec35279a483e33835b5148d82b1f9c21fd7d7dc975"
+  },
+  "training_hardware": {
+    "gpu": "NVIDIA H100 80GB HBM3",
+    "gpus": 1,
+    "batch": 128,
+    "image_size": 256,
+    "amp": "bf16",
+    "allocator_budget_gib": 38
+  },
+  "environment": {
+    "python": "3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0]",
+    "torch": "2.6.0+cu124",
+    "torchvision": "0.21.0+cu124"
+  },
+  "verification": {
+    "full_vs_export": {
+      "cases": 8,
+      "bitwise_equal": true,
+      "precision": "FP32"
+    },
+    "model_source_matches_training": {
+      "model.py": true,
+      "blocks.py": true,
+      "sd_backbone.py": true
+    },
+    "cli": {
+      "cli_example_max_byte_difference": 0
+    },
+    "cpu_tests": {
+      "passed": 350,
+      "skipped_cuda": 5
+    }
+  },
+  "training_data": {
+    "entries": 1497344,
+    "distinct_source_records": 1228003,
+    "unsplash_entries": 296145,
+    "places365_entries": 901730,
+    "face_entries": 299469,
+    "unique_face_frames": 30128,
+    "validation_entries": 2000
+  },
+  "evaluation": {
+    "images": 32,
+    "hole_psnr": 18.356236154308785,
+    "hole_l1": 0.0759446257571164
+  },
+  "public_download_url": "https://huggingface.co/KumaPower/MEDFE/resolve/main/medfe-sd-partial-step120994.pt",
+  "note": "Final safely saved checkpoint, not validation-best. Fixed32 local validation is not a paper benchmark."
+}

training-config.yaml

@@ -0,0 +1,57 @@
+# Use byte-identical CSA train/validation manifests; retain intentional face repeats.
+model:
+  backbone: sd_partial
+  blocks_per_scale: 2
+  base_channels: 64
+  residual_blocks: 4
+  pconv_layers: 5
+  gaussian_sigma: 1.5
+  init_gain: 0.02
+  activation_checkpointing: true
+data:
+  format: indexed_rtv
+  rtv_cache: data/indexed-rtv-cache
+  train_manifest: data/train.jsonl
+  val_manifest: data/validation.jsonl
+  image_size: 256
+  batch_size: 128
+  workers: 8
+  mask_mode: random
+  min_fraction: 0.1
+  max_fraction: 0.4
+  allow_repeated_records: true
+loss:
+  reconstruction: 1.0
+  structure: 1.0
+  texture: 1.0
+  lpips: 1.0
+  perceptual: 0.0
+  style: 0.0
+  adversarial: 0.002
+  lpips_net: vgg
+  lpips_chunk_size: 8
+discriminator:
+  global_kind: patch
+  global_width: 64
+  local_width: 64
+  local_crop_size: 64
+  chunk_size: 8
+  pretrained_path: ''
+train:
+  output: runs/medfe-sd-partial-csa-mix-large-batch
+  amp: bf16
+  seed: 2019
+  epochs: 60
+  schedule: constant
+  lr: 0.0002
+  beta1: 0.5
+  beta2: 0.999
+  adversarial_warmup_steps: 5000
+  adversarial_ramp_steps: 10000
+  save_every: 100
+  evaluate_every: 100
+  log_every: 10
+  evaluation_images: 32
+  evaluation_batch_size: 4
+  memory_budget_gib: 38
+  allow_weight_download: false