diff --git a/.gitignore b/.gitignore
index 331500d..612930b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -20,3 +20,9 @@
*.mexa64
*.pdf
*.tar
+
+# ignore files and dirs with temp in the name
+*temp*
+
+
+
diff --git a/LICENSE-MODEL.txt b/LICENSE-MODEL.txt
new file mode 100644
index 0000000..4a649da
--- /dev/null
+++ b/LICENSE-MODEL.txt
@@ -0,0 +1,69 @@
+OpenRAIL++-M License
+
+Copyright (c) 2023-2025 PRS, ETH Zürich
+
+Open RAIL++-M
+dated November 22, 2024
+
+(This license is based on the CreativeML Open RAIL-M license for stable diffusion.)
+
+Section I: PREAMBLE
+
+[describe reasoning for the license]
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+1. Definitions
+
+- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
+- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
+- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
+- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
+- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
+- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
+- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
+- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
+- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
+- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
+- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
+
+Section II: INTELLECTUAL PROPERTY RIGHTS
+
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
+3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
+Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
+You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
+You must cause any modified files to carry prominent notices stating that You changed the files;
+You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
+6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS
+
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License.
+8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
+9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
+10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+
+END OF TERMS AND CONDITIONS
+
+
+
+
+Attachment A
+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+
+[insert use restrictions]
\ No newline at end of file
diff --git a/README.md b/README.md
index 70d95ca..f9a3814 100644
--- a/README.md
+++ b/README.md
@@ -1,59 +1,99 @@
-# Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
+# Marigold Computer Vision
+
+This project implements Marigold, a Computer Vision method for estimating image characteristics. Initially proposed for
+extracting high-resolution depth maps in our CVPR 2024 paper **"Repurposing Diffusion-Based Image Generators for Monocular
+Depth Estimation"**, we extended the method to other modalities as described in our follow-up paper **"Marigold: Affordable
+Adaptation of Diffusion-Based Image Generators for Image Analysis"**.
+
+## Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
+
+[](https://marigoldcomputervision.github.io)
+[](https://arxiv.org/abs/2505.09358)
+[](https://huggingface.co/spaces/prs-eth/marigold)
+[](https://huggingface.co/spaces/prs-eth/marigold-normals)
+[](https://huggingface.co/spaces/prs-eth/marigold-iid)
+[](https://huggingface.co/prs-eth/marigold-depth-v1-1)
+[](https://huggingface.co/prs-eth/marigold-normals-v1-1)
+[](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1)
+[](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1)
+[](https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage)
+
+Team:
+[Bingxin Ke](http://www.kebingxin.com/),
+[Kevin Qu](https://www.linkedin.com/in/kevin-qu-b3417621b/),
+[Tianfu Wang](https://tianfwang.github.io/)
+[Nando Metzger](https://nandometzger.github.io/),
+[Shengyu Huang](https://shengyuh.github.io/),
+[Bo Li](https://www.linkedin.com/in/bobboli0202/),
+[Anton Obukhov](https://www.obukhov.ai/),
+[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ)
-**CVPR 2024 (Oral, Best Paper Award Candidate)**
+We present Marigold, a family of conditional generative models and a fine-tuning protocol that extracts the knowledge
+from pretrained latent diffusion models like Stable Diffusion and adapts them for dense image analysis tasks, including
+monocular depth estimation, surface normal prediction, and intrinsic decomposition. Marigold requires minimal
+modification of the pre-trained latent diffusion model's architecture, trains with small synthetic datasets on a single
+GPU over a few days, and demonstrates state-of-the-art zero-shot generalization.
-This repository represents the official implementation of the paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation".
+
+
+## Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
[](https://marigoldmonodepth.github.io)
-[](https://arxiv.org/abs/2312.02145)
-[-Space-yellow)](https://huggingface.co/spaces/prs-eth/marigold-lcm)
-[-Model-green)](https://huggingface.co/prs-eth/marigold-lcm-v1-0)
+[](https://arxiv.org/abs/2312.02145)
+[](https://huggingface.co/spaces/prs-eth/marigold)
+[](https://huggingface.co/prs-eth/marigold-depth-v1-1)
[](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing)
-[](https://www.apache.org/licenses/LICENSE-2.0)
-
-
-
-
-
+In **CVPR 2024 (Oral, Best Paper Award Candidate)**
+Team:
[Bingxin Ke](http://www.kebingxin.com/),
[Anton Obukhov](https://www.obukhov.ai/),
[Shengyu Huang](https://shengyuh.github.io/),
[Nando Metzger](https://nandometzger.github.io/),
[Rodrigo Caye Daudt](https://rcdaudt.github.io/),
-[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en )
+[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ)
-We present Marigold, a diffusion model, and associated fine-tuning protocol for monocular depth estimation. Its core principle is to leverage the rich visual knowledge stored in modern generative image models. Our model, derived from Stable Diffusion and fine-tuned with synthetic data, can zero-shot transfer to unseen data, offering state-of-the-art monocular depth estimation results.
+We present Marigold, a diffusion model, and an associated fine-tuning protocol for monocular depth estimation. Its core
+principle is to leverage the rich visual knowledge stored in modern generative image models. Our model, derived from
+Stable Diffusion and fine-tuned with synthetic data, can zero-shot transfer to unseen data, offering state-of-the-art
+monocular depth estimation results.
-
+
## 📢 News
+2025-05-15: Released code and a [checkpoint](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1) of Marigold Intrinsic Image Decomposition predicting Albedo, diffuse Shading, and non-diffuse Residual (Marigold-IID-Lighting v1.1).
+2025-05-15: Released code and a [checkpoint](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1) of Marigold Intrinsic Image Decomposition predicting Albedo, Roughness, and Metallicity (Marigold-IID-Appearance v1.1).
+2025-05-15: Released code and a [checkpoint](https://huggingface.co/prs-eth/marigold-normals-v1-1) of Marigold Surface Normals Estimation (v1.1).
+2025-05-15: Released an updated [checkpoint](https://huggingface.co/prs-eth/marigold-depth-v1-1) of Marigold Depth (v1.1), trained with updated noise scheduler settings (zero-SNR and trailing timestamps), and augmentations.
2024-05-28: Training code is released.
-2024-03-23: Added [LCM v1.0](https://huggingface.co/prs-eth/marigold-lcm-v1-0) for faster inference - try it out at -Space-yellow)
-2024-03-04: Accepted to CVPR 2024.
-2023-12-22: Contributed to Diffusers [community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation).
+2024-05-27: Marigold pipelines are merged into the `diffusers` core starting v0.28.0 [release](https://github.com/huggingface/diffusers/releases/tag/v0.28.0)!
+2024-03-23: Added a Latent Consistency Model (LCM) [checkpoint](https://huggingface.co/prs-eth/marigold-depth-lcm-v1-0).
+2024-03-04: The paper is accepted at CVPR 2024.
+2023-12-22: Contributed to Diffusers [community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation).
2023-12-19: Updated [license](LICENSE.txt) to Apache License, Version 2.0.
-2023-12-08: Added
-
- try it out with your images for free!
-2023-12-05: Added 
- dive deeper into our inference pipeline!
-2023-12-04: Added 
-paper and inference code (this repository).
+2023-12-08: Added the first interactive [Hugging Face Space Demo](https://huggingface.co/spaces/prs-eth/marigold) of depth estimation.
+2023-12-05: Added a [Google Colab](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing)
+2023-12-04: Added an [arXiv paper](https://arxiv.org/abs/2312.02145) and inference code (this repository).
## 🚀 Usage
**We offer several ways to interact with Marigold**:
-1. We integrated [Marigold Pipelines into diffusers 🧨](https://huggingface.co/docs/diffusers/api/pipelines/marigold). Check out many exciting usage scenarios in [this diffusers tutorial](https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage).
+1. A family of free online interactive demos:
+
+
+
+(kudos to the HF team for the GPU grants)
-1. A free online interactive demo is available here: (kudos to the HF team for the GPU grant)
+1. Marigold pipelines are part of
+
- a one-stop shop for diffusion 🧨!
1. Run the demo locally (requires a GPU and an `nvidia-docker2`, see [Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)):
- 1. Paper version: `docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/toshas-marigold:latest python app.py`
- 1. LCM version: `docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/prs-eth-marigold-lcm:latest python app.py`
+`docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/prs-eth-marigold:latest python app.py`
1. Extended demo on a Google Colab:
-1. If you just want to see the examples, visit our gallery: 
+1. If you just want to see the examples, visit our gallery:
1. Finally, local development instructions with this codebase are given below.
@@ -82,29 +122,13 @@ cd Marigold
### 💻 Dependencies
-We provide several ways to install the dependencies.
-
-1. **Using [Mamba](https://github.com/mamba-org/mamba)**, which can installed together with [Miniforge3](https://github.com/conda-forge/miniforge?tab=readme-ov-file#miniforge3).
-
- Windows users: Install the Linux version into the WSL.
-
- After the installation, Miniforge needs to be activated first: `source /home/$USER/miniforge3/bin/activate`.
+Install the dependencies:
- Create the environment and install dependencies into it:
-
- ```bash
- mamba env create -n marigold --file environment.yaml
- conda activate marigold
- ```
-
-2. **Using pip:**
- Alternatively, create a Python native virtual environment and install dependencies into it:
-
- ```bash
- python -m venv venv/marigold
- source venv/marigold/bin/activate
- pip install -r requirements.txt
- ```
+```bash
+python -m venv venv/marigold
+source venv/marigold/bin/activate
+pip install -r requirements.txt
+```
Keep the environment activated before running the inference script.
Activate the environment again after restarting the terminal session.
@@ -113,62 +137,130 @@ Activate the environment again after restarting the terminal session.
### 📷 Prepare images
-1. Use selected images from our paper:
-
- ```bash
- bash script/download_sample_data.sh
- ```
+Use selected images from our paper:
-1. Or place your images in a directory, for example, under `input/in-the-wild_example`, and run the following inference command.
+```bash
+bash script/download_sample_data.sh
+```
-### 🚀 Run inference with LCM (faster)
+Or place your images in a directory, for example, under `input/in-the-wild_example`, and run the following inference command.
-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:
+### 🚀 Run inference (for practical usage)
```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)
-
-This setting corresponds to our paper. For academic comparison, please run with this setting.
+# Depth
+python script/depth/run.py \
+ --checkpoint prs-eth/marigold-depth-v1-1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example \
+ --fp16
+```
```bash
-python run.py \
- --checkpoint prs-eth/marigold-v1-0 \
- --denoise_steps 50 \
- --ensemble_size 10 \
+# Normals
+python script/normals/run.py \
+ --checkpoint prs-eth/marigold-normals-v1-1 \
--input_rgb_dir input/in-the-wild_example \
- --output_dir output/in-the-wild_example
+ --output_dir output/in-the-wild_example \
+ --fp16
```
-You can find all results in `output/in-the-wild_example`. Enjoy!
+```bash
+# IID (appearance model)
+python script/iid/run.py \
+ --checkpoint prs-eth/marigold-iid-appearance-v1-1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example \
+ --fp16
+
+# IID (lighting model)
+python script/iid/run.py \
+ --checkpoint prs-eth/marigold-iid-lighting-v1-1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example \
+ --fp16
+```
### ⚙️ Inference settings
-The default settings are optimized for the best result. However, the behavior of the code can be customized:
+The default settings are optimized for the best results. However, the behavior of the code can be customized:
+
+- `--half_precision` or `--fp16`: Run with half-precision (16-bit float) to have faster speed and reduced VRAM usage, but might lead to suboptimal results.
+
+- `--ensemble_size`: Number of inference passes in the ensemble. Larger values tend to give better results in evaluations at the cost of slower inference; for most cases 1 is enough. Default: 1.
-- Trade-offs between the **accuracy** and **speed** (for both options, larger values result in better accuracy at the cost of slower inference.)
- - `--ensemble_size`: Number of inference passes in the ensemble. For LCM `ensemble_size` is more important than `denoise_steps`. Default: ~~10~~ 5 (for LCM).
- - `--denoise_steps`: Number of denoising steps of each inference pass. For the original (DDIM) version, it's recommended to use 10-50 steps, while for LCM 1-4 steps. When unassigned (`None`), will read default setting from model config. Default: ~~10 4 (for LCM)~~ `None`.
+- `--denoise_steps`: Number of denoising diffusion steps. Default settings are defined in the model checkpoints and are sufficient for most cases.
- By default, the inference script resizes input images to the *processing resolution*, and then resizes the prediction back to the original resolution. This gives the best quality, as Stable Diffusion, from which Marigold is derived, performs best at 768x768 resolution.
- - `--processing_res`: the processing resolution; set as 0 to process the input resolution directly. When unassigned (`None`), will read default setting from model config. Default: ~~768~~ `None`.
+ - `--processing_res`: the processing resolution; set as 0 to process the input resolution directly. When unassigned (`None`), will read default setting from model config. Default: `None`.
- `--output_processing_res`: produce output at the processing resolution instead of upsampling it to the input resolution. Default: False.
- `--resample_method`: the resampling method used to resize images and depth predictions. This can be one of `bilinear`, `bicubic`, or `nearest`. Default: `bilinear`.
-- `--half_precision` or `--fp16`: Run with half-precision (16-bit float) to have faster speed and reduced VRAM usage, but might lead to suboptimal results.
- `--seed`: Random seed can be set to ensure additional reproducibility. Default: None (unseeded). Note: forcing `--batch_size 1` helps to increase reproducibility. To ensure full reproducibility, [deterministic mode](https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms) needs to be used.
- `--batch_size`: Batch size of repeated inference. Default: 0 (best value determined automatically).
- `--color_map`: [Colormap](https://matplotlib.org/stable/users/explain/colors/colormaps.html) used to colorize the depth prediction. Default: Spectral. Set to `None` to skip colored depth map generation.
- `--apple_silicon`: Use Apple Silicon MPS acceleration.
+
+### 🎮 Run inference (for academic comparisons)
+
+These settings correspond to our paper. For academic comparison, please run with the settings below (if you only want to do fast inference on your own images, you can set `--ensemble_size 1`).
+
+```bash
+# Depth
+python script/depth/run.py \
+ --checkpoint prs-eth/marigold-depth-v1-1 \
+ --denoise_steps 1 \
+ --ensemble_size 10 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+```
+
+```bash
+# Normals
+python script/normals/run.py \
+ --checkpoint prs-eth/marigold-normals-v1-1 \
+ --denoise_steps 4 \
+ --ensemble_size 10 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+```
+
+```bash
+# IID (appearance model)
+python script/iid/run.py \
+ --checkpoint prs-eth/marigold-iid-appearance-v1-1 \
+ --denoise_steps 4 \
+ --ensemble_size 1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+
+# IID (lighting model)
+python script/iid/run.py \
+ --checkpoint prs-eth/marigold-iid-lighting-v1-1 \
+ --denoise_steps 4 \
+ --ensemble_size 1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+```
+
+```bash
+# Depth (the original CVPR version)
+python script/depth/run.py \
+ --checkpoint prs-eth/marigold-depth-v1-0 \
+ --denoise_steps 50 \
+ --ensemble_size 10 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+```
+
+You can find all results in the `output` directory. Enjoy!
+
+
### ⬇ Checkpoint cache
-By default, the [checkpoint](https://huggingface.co/prs-eth/marigold-v1-0) is stored in the Hugging Face cache.
+By default, the checkpoint ([depth](https://huggingface.co/prs-eth/marigold-depth-v1-1), [normals](https://huggingface.co/prs-eth/marigold-normals-v1-1), [iid](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1)) is stored in the Hugging Face cache.
The `HF_HOME` environment variable defines its location and can be overridden, e.g.:
```bash
@@ -178,46 +270,102 @@ export HF_HOME=$(pwd)/cache
Alternatively, use the following script to download the checkpoint weights locally:
```bash
-bash script/download_weights.sh marigold-v1-0
-# or LCM checkpoint
-bash script/download_weights.sh marigold-lcm-v1-0
+bash script/download_weights.sh marigold-depth-v1-1 # depth checkpoint
+bash script/download_weights.sh marigold-normals-v1-1 # normals checkpoint
+bash script/download_weights.sh marigold-iid-appearance-v1-1 # iid appearance checkpoint
+bash script/download_weights.sh marigold-iid-lighting-v1-1 # iid lighting checkpoint
+# bash script/download_weights.sh marigold-depth-v1-0 # CVPR depth checkpoint
```
At inference, specify the checkpoint path:
```bash
-python run.py \
- --checkpoint checkpoint/marigold-v1-0 \
- --denoise_steps 50 \
- --ensemble_size 10 \
- --input_rgb_dir input/in-the-wild_example\
+# Depth
+python script/depth/run.py \
+ --checkpoint checkpoint/marigold-depth-v1-1 \
+ --denoise_steps 4 \
+ --ensemble_size 1 \
+ --input_rgb_dir input/in-the-wild_example \
--output_dir output/in-the-wild_example
```
-## 🦿 Evaluation on test datasets
+```bash
+# Normals
+python script/normals/run.py \
+ --checkpoint checkpoint/marigold-normals-v1-1 \
+ --denoise_steps 4 \
+ --ensemble_size 1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+```
+```bash
+# IID (appearance model)
+python script/iid/run.py \
+ --checkpoint checkpoint/marigold-iid-appearance-v1-1 \
+ --denoise_steps 4 \
+ --ensemble_size 1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+
+# IID (lighting model)
+python script/iid/run.py \
+ --checkpoint checkpoint/marigold-iid-lighting-v1-1 \
+ --denoise_steps 4 \
+ --ensemble_size 1 \
+ --input_rgb_dir input/in-the-wild_example \
+ --output_dir output/in-the-wild_example
+```
+
+## 🦿 Evaluation on test datasets
Install additional dependencies:
```bash
pip install -r requirements+.txt -r requirements.txt
-```
+```
-Set data directory variable (also needed in evaluation scripts) and download [evaluation datasets](https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset) into corresponding subfolders:
+Set data directory variable (also needed in evaluation scripts) and download the evaluation datasets ([depth](https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset), [normals](https://share.phys.ethz.ch/~pf/bingkedata/marigold/marigold_normals/evaluation_dataset)) into the corresponding subfolders:
```bash
export BASE_DATA_DIR= # Set target data directory
+# Depth
wget -r -np -nH --cut-dirs=4 -R "index.html*" -P ${BASE_DATA_DIR} https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset/
+
+# Normals
+wget -r -np -nH --cut-dirs=4 -R "index.html*" -P ${BASE_DATA_DIR} https://share.phys.ethz.ch/~pf/bingkedata/marigold/marigold_normals/evaluation_dataset.zip
+unzip ${BASE_DATA_DIR}/evaluation_dataset.zip -d ${BASE_DATA_DIR}/
+rm -f ${BASE_DATA_DIR}/evaluation_dataset.zip
```
+For download instructions of the intrinsic image decomposition test data, please refer to [iid-appearance instructions](script/iid/dataset_preprocess/interiorverse_appearance/README.md) and [iid-lighting instructions](script/iid/dataset_preprocess/hypersim_lighting/README.md).
Run inference and evaluation scripts, for example:
```bash
-# Run inference
-bash script/eval/11_infer_nyu.sh
+# Depth
+bash script/depth/eval/11_infer_nyu.sh # Run inference
+bash script/depth/eval/12_eval_nyu.sh # Evaluate predictions
+```
-# Evaluate predictions
-bash script/eval/12_eval_nyu.sh
+```bash
+# Normals
+bash script/normals/eval/11_infer_scannet.sh # Run inference
+bash script/normals/eval/12_eval_scannet.sh # Evaluate predictions
+```
+
+```bash
+# IID
+bash script/iid/eval/11_infer_appearance_interiorverse.sh # Run inference
+bash script/iid/eval/12_eval_appearance_interiorverse.sh # Evaluate predictions
+
+bash script/iid/eval/21_infer_lighting_hypersim.sh # Run inference
+bash script/iid/eval/22_eval_lighting_hypersim.sh # Evaluate predictions
+```
+
+```bash
+# Depth (the original CVPR version)
+bash script/depth/eval_old/11_infer_nyu.sh # Run inference
+bash script/depth/eval_old/12_eval_nyu.sh # Evaluate predictions
```
Note: although the seed has been set, the results might still be slightly different on different hardware.
@@ -233,29 +381,73 @@ pip install -r requirements++.txt -r requirements+.txt -r requirements.txt
Set environment parameters for the data directory:
```bash
-export BASE_DATA_DIR=YOUR_DATA_DIR # directory of training data
+export BASE_DATA_DIR=YOUR_DATA_DIR # directory of training data
export BASE_CKPT_DIR=YOUR_CHECKPOINT_DIR # directory of pretrained checkpoint
```
Download Stable Diffusion v2 [checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2) into `${BASE_CKPT_DIR}`
-Prepare for [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets and save into `${BASE_DATA_DIR}`. Please refer to [this README](script/dataset_preprocess/hypersim/README.md) for Hypersim preprocessing.
+### Prepare for training data
+**Depth**
+
+Prepare for [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets and save into `${BASE_DATA_DIR}`. Please refer to [this README](script/depth/dataset_preprocess/hypersim/README.md) for Hypersim preprocessing.
+
+**Normals**
+
+Prepare for [Hypersim](https://github.com/apple/ml-hypersim), [Interiorverse](https://interiorverse.github.io/) and [Sintel](http://sintel.is.tue.mpg.de/) datasets and save into `${BASE_DATA_DIR}`. Please refer to [this README](script/normals/dataset_preprocess/hypersim/README.md) for Hypersim preprocessing, [this README](script/normals/dataset_preprocess/interiorverse/README.md) for Interiorverse and [this README](script/normals/dataset_preprocess/sintel/README.md) for Sintel.
+
+**Intrinsic Image Decomposition**
+
+*Appearance model*: Prepare for [Interiorverse](https://interiorverse.github.io/) dataset and save into `${BASE_DATA_DIR}`. Please refer to [this README](script/iid/dataset_preprocess/interiorverse_appearance/README.md) for Interiorverse preprocessing.
+
+*Lighting model*: Prepare for [Hypersim](https://github.com/apple/ml-hypersim) dataset and save into `${BASE_DATA_DIR}`. Please refer to [this README](script/iid/dataset_preprocess/hypersim_lighting/README.md) for Hypersim preprocessing.
+
+
+### Run training script
+
+```bash
+# Depth
+python script/depth/train.py --config config/train_marigold_depth.yaml
+```
-Run training script
+```bash
+# Normals
+python script/normals/train.py --config config/train_marigold_normals.yaml
+```
```bash
-python train.py --config config/train_marigold.yaml
+# IID (appearance model)
+python script/iid/train.py --config config/train_marigold_iid_appearance.yaml
+
+# IID (lighting model)
+python script/iid/train.py --config config/train_marigold_iid_lighting.yaml
```
-Resume from a checkpoint, e.g.
+Resume from a checkpoint, e.g.:
+
+```bash
+# Depth
+python script/depth/train.py --resume_run output/marigold_base/checkpoint/latest
+```
```bash
-python train.py --resume_run output/marigold_base/checkpoint/latest
+# Normals
+python script/normals/train.py --resume_run output/train_marigold_normals/checkpoint/latest
```
-Evaluating results
+```bash
+# IID (appearance model)
+python script/iid/train.py --resume_run output/train_marigold_iid_appearance/checkpoint/latest
+
+# IID (lighting model)
+python script/iid/train.py --resume_run output/train_marigold_iid_lighting/checkpoint/latest
+```
-Only the U-Net is updated and saved during training. To use the inference pipeline with your training result, replace `unet` folder in Marigold checkpoints with that in the `checkpoint` output folder. Then refer to [this section](#evaluation) for evaluation.
+### Compose checkpoint:
+Only the U-Net and scheduler config are updated during training. They are saved in the training directory. To use the inference pipeline with your training result:
+- replace `unet` folder in Marigold checkpoints with that in the `checkpoint` output folder.
+- replace the `scheduler/scheduler_config.json` file in Marigold checkpoints with `checkpoint/scheduler_config.json` generated during training.
+Then refer to [this section](#evaluation) for evaluation.
**Note**: Although random seeds have been set, the training result might be slightly different on different hardwares. It's recommended to train without interruption.
@@ -269,25 +461,36 @@ Please refer to [this](CONTRIBUTING.md) instruction.
|----------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------|
| (Windows) Invalid DOS bash script on WSL | Run `dos2unix