Add model card for FE2E

README.md

@@ -1,3 +1,73 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: depth-estimation
+---
+
+# FE2E: From Editor to Dense Geometry Estimator
+
+FE2E is a Diffusion Transformer (DiT)-based foundation model for monocular dense geometry prediction. It adapts an advanced image editing model to dense geometry tasks, achieving strong zero-shot performance on both monocular depth and normal estimation.
+
+[[Project Page](https://amap-ml.github.io/FE2E/)] [[Paper](https://huggingface.co/papers/2509.04338)] [[GitHub](https://github.com/AMAP-ML/FE2E)]
+
+![teaser](https://github.com/AMAP-ML/FE2E/raw/main/assets/demo.png)
+
+## Introduction
+FE2E (From Editor to Dense Geometry Estimator) adapts an advanced image editing model based on Diffusion Transformer (DiT) architecture for dense geometry prediction. Key features include:
+- **Consistent Velocity Objective**: Reformulates the editor's original flow matching loss for deterministic tasks.
+- **Logarithmic Quantization**: Resolves precision conflicts between the editor's native BFloat16 format and the high precision demands of geometry tasks.
+- **Joint Estimation**: Leverages DiT's global attention for joint estimation of depth and normals in a single forward pass.
+
+FE2E achieves impressive performance improvements in zero-shot monocular depth and normal estimation, notably achieving over 35% gains on the ETH3D dataset and outperforming models trained on significantly more data.
+
+## 🕹️ Inference
+
+### 1. Setup
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Prepare Model Weights
+1. Download the base weights from the official [Step1X-Edit](https://github.com/stepfun-ai/Step1X-Edit) release.
+2. Download the FE2E LoRA [checkpoint](https://huggingface.co/exander/FE2E/blob/main/LDRN.safetensors) from this repository.
+
+### 3. Run Evaluation
+
+To run evaluation for **depth estimation**:
+```bash
+python -u evaluation.py \
+  --model_path ./pretrain \
+  --eval_data_root ./infer \
+  --output_dir ./infer/eval_results \
+  --num_gpus 8 \
+  --lora ./lora/LDRN.safetensors \
+  --single_denoise \
+  --prompt_type empty \
+  --norm_type ln \
+  --task_name depth \
+  --depth_eval_datasets [dataset]
+```
+
+To run evaluation for **normal estimation**:
+```bash
+python -u evaluation.py \
+  --model_path ./pretrain \
+  --eval_data_root ./infer \
+  --output_dir ./infer/eval_results \
+  --num_gpus 8 \
+  --lora ./lora/LDRN.safetensors \
+  --single_denoise \
+  --prompt_type empty \
+  --norm_type ln \
+  --task_name normal \
+  --normal_eval_datasets [dataset]
+```
+
+## Citation
+```bibtex
+@article{wang2025editor,
+  title={From Editor to Dense Geometry Estimator},
+  author={Wang, JiYuan and Lin, Chunyu and Sun, Lei and Liu, Rongying and Nie, Lang and Li, Mingxing and Liao, Kang and Chu, Xiangxiang and Zhao, Yao},
+  journal={arXiv preprint arXiv:2509.04338},
+  year={2025}
+}
+```