Update README.md

README.md

@@ -1,31 +1,12 @@
----
-license: apache-2.0
-language:
-- en
-metrics:
-- accuracy
-base_model:
-- prs-eth/marigold-depth-v1-0
-tags:
-- code
----
 # ApDepth: Aiming for Precise Monocular Depth Estimation Based on Diffusion Models
 
 This repository is based on [Marigold](https://marigoldmonodepth.github.io), CVPR 2024 Best Paper: [**Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation**](https://arxiv.org/abs/2312.02145)
 
-<!-- [![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io)
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-[![Hugging Face (LCM) Model](https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Model-green)](https://huggingface.co/prs-eth/marigold-lcm-v1-0)
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 [![Website](doc/badges/badge-website.svg)](https://haruko386.github.io/research)
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-<!-- [![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co/prs-eth/marigold-v1-0) -->
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+[![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co/developy/ApDepth)
+[![Hugging Face Demo](https://img.shields.io/badge/🤗%20Hugging%20Face-Demo-purple)](https://huggingface.co/spaces/developy/ApDepth)
 
 [Haruko386](https://haruko386.github.io/),
 [Shuai Yuan](https://syjz.teacher.360eol.com/teacherBasic/preview?teacherId=23776)
@@ -35,7 +16,8 @@ This repository is based on [Marigold](https://marigoldmonodepth.github.io), CVP
 >We present **ApDepth**, a diffusion model, and associated fine-tuning protocol for monocular depth estimation. Based on Marigold. Its core innovation lies in addressing the deficiency of diffusion models in feature representation capability. Our model followed Marigold, derived from Stable Diffusion and fine-tuned with synthetic data: Hypersim and VKitti, achieved ideal results in object edge refinement.
 
 ## 📢 News
-- 2025-09-23: We change Marigold from `Stochastic multi-step generation` to `Deterministic one-step perception`
+- 2025-10-09: We propose a novel diffusion-based deep estimation framework guided by pre-trained models.
+- 2025-09-23: We change Marigold from **Stochastic multi-step generation** to **Deterministic one-step perception**
 - 2025-08-10: Trying to make some optimizations in Feature Expression<br>
 - 2025-05-08: Clone Marigold to local.<br>
 
@@ -43,7 +25,7 @@ This repository is based on [Marigold](https://marigoldmonodepth.github.io), CVP
 
 **We offer several ways to interact with Marigold**:
 
-1. A free online interactive demo is available here: <a href="https://huggingface.co/spaces/prs-eth/marigold-lcm"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Space-yellow" height="16"></a> (kudos to the HF team for the GPU grant)
+1. A free online interactive demo is available here: <a href="https://huggingface.co/spaces/developy/ApDepth"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Demo-purple" height="18"></a>
 
 2. If you just want to see the examples, visit our gallery: <a href="https://haruko386.github.io/research"><img src="doc/badges/badge-website.svg" height="16"></a>
 
@@ -77,8 +59,8 @@ cd ApDepth
  **Using Conda:** 
     Alternatively, create a Python native virtual environment and install dependencies into it:
 
-    conda create -n marigold python==3.12.9
-    conda activate marigold
+    conda create -n apdepth python==3.12.9
+    conda activate apdepth
     pip install -r requirements.txt
 
 Keep the environment activated before running the inference script. 
@@ -88,25 +70,11 @@ Activate the environment again after restarting the terminal session.
 
 ### 📷 Prepare images
 
-1. Use selected images from our paper:
+1. Use selected images under `input`
 
-    ```bash
-    bash script/download_sample_data.sh
-    ```
+1. Or place your images in a directory, for example, under `input/test-image`, and run the following inference command.
 
-1. Or place your images in a directory, for example, under `input/in-the-wild_example`, and run the following inference command.
-
-### 🚀 Run inference with LCM (faster)
-
-The [LCM checkpoint](https://huggingface.co/prs-eth/marigold-lcm-v1-0) is distilled from our original checkpoint towards faster inference speed (by reducing inference steps). The inference steps can be as few as 1 (default) to 4. Run with default LCM setting:
-
-```bash
- python run.py \
-     --input_rgb_dir input/in-the-wild_example \
-     --output_dir output/in-the-wild_example_lcm
- ```
-
-### 🎮 Run inference with DDIM (paper setting)
+### 🎮 Run inference with paper setting
 
 This setting corresponds to our paper. For academic comparison, please run with this setting.