[ADD] Marigold checkpoint v1

LICENSE.txt

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README.md

@@ -1,3 +1,99 @@
----
-license: cc-by-sa-4.0
----
+# Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
+
+This model represents the official checkpoint of the paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation".
+
+[![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io)
+[![Paper](doc/badges/badge-pdf.svg)](https://arxiv.org/abs/2312.02145)
+[![License](doc/badges/badge-license.svg)](LICENSE)
+[![GitHub](https://img.shields.io/github/stars/prs-eth/Marigold?style=default&label=GitHub%20★&logo=github)](https://github.com/prs-eth/Marigold)
+<!-- [![HF Space](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-blue)]() -->
+<!-- [![Open In Colab](doc/badges/badge-colab.svg)]() -->
+<!-- [![Docker](doc/badges/badge-docker.svg)]() -->
+<!-- ### [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation]() -->
+
+[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 )
+
+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.
+
+![teaser](doc/teaser_collage_transparant.png)
+
+## 📢 News
+
+2023-12-04: Upload [arxiv](https://arxiv.org/abs/2312.02145) paper.
+
+2023-12-04: Release inference code.
+
+## 🛠️ Setup
+
+This code has been tested on:
+
+- Python 3.10.12, PyTorch 2.0.1, CUDA 11.7, GeForce RTX 3090
+- python 3.10.4, Pytorch 2.0.1, CUDA 11.7, GeForce RTX 4090
+
+
+💻 Dependencies
+
+```bash
+python -m venv venv/marigold
+source venv/marigold/bin/activate
+pip install -r requirements.txt
+```
+
+🚩 Checkpoint
+
+```bash
+bash script/download_weights.sh
+```
+
+## 🚀 Inference on in-the-wild images
+
+📷 Sample images
+
+```bash
+bash script/download_sample_data.sh
+```
+
+🎮 Inference
+
+```bash
+python run.py \
+    --input_rgb_dir data/in-the-wild_example\
+    --output_dir output/in-the-wild_example
+```
+
+⚙️ Inference settings
+
+- The inference script by default will resize the input images and resize back to the original resolution.
+  
+  - `--resize_to_max_res`: The maximum edge length of resized input image. Default: 768.
+  - `--not_resize_input`: If given, will not resize the input image.
+  - `--not_resize_output`: If given, will not resize the output image back to the original resolution. Only valid without `--not_resize_input` option.
+
+- Trade-offs between **accuracy** and **speed** (for both options, larger value results in more accurate results at the cost of slower inference speed.)
+
+  - `--n_infer`: Number of inference passes to be ensembled. Default: 10.
+  - `--denoise_steps`: Number of diffusion denoising steps of each inference pass. Default: 10.
+
+- `--seed`: Random seed, can be set to ensure reproducibility. Default: None (using current time as random seed).
+- `--depth_cmap`: Colormap used to colorize the depth prediction. Default: Spectral.
+
+## 🎓 Citation
+
+```bibtex
+@misc{ke2023marigold,
+  author    = {Ke, Bingxin and Obukhov, Anton and Huang, Shengyu and Metzger, Nando and Daudt, Rodrigo Caye and Schindler, Konrad},
+  title     = {Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
+  year      = {2023},
+}
+```
+
+## License
+
+This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
+
+[<img src="doc/badges/badge-license.svg" height="20"/>](http://creativecommons.org/licenses/by-nc-sa/4.0/)

feature_extractor/preprocessor_config.json

@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.8.0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "trained_betas": null
+}

text_encoder/config.json

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+{
+  "_name_or_path": "hf-models/stable-diffusion-v2-768x768/text_encoder",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.0.dev0",
+  "vocab_size": 49408
+}

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text_encoder/pytorch_model.bin

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tokenizer/special_tokens_map.json

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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+}

tokenizer/tokenizer_config.json

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