+
+
+
+This repository contains the official implementation of the paper: [DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation](https://arxiv.org/abs/2501.16764), which is accepted to ICLR 2025.
+DiffSplat is a generative framework to synthesize 3D Gaussian Splats from text prompts & single-view images in 1~2 seconds. It is fine-tuned directly from a pretrained text-to-image diffusion model.
+
+Feel free to contact me (chenguolin@stu.pku.edu.cn) or open an issue if you have any questions or suggestions.
+
+
+## 📢 News
+
+- **2025-02-02**: Inference instructions (text-conditioned & image-conditioned & controlnet) are provided.
+- **2025-01-29**: The source code and pretrained models are released. Happy 🐍 Chinese New Year 🎆!
+- **2025-01-22**: InstructScene is accepted to ICLR 2025.
+
+
+## 📋 TODO
+
+- [x] Provide detailed instructions for inference.
+- [ ] Provide detailed instructions for training.
+- [ ] Implement a Gradio demo.
+
+
+## 🔧 Installation
+
+You may need to modify the specific version of `torch` in `settings/setup.sh` according to your CUDA version.
+There are not restrictions on the `torch` version, feel free to use your preferred one.
+```bash
+git clone https://github.com/chenguolin/DiffSplat.git
+cd DiffSplat
+bash settings/setup.sh
+```
+
+
+## 📊 Dataset
+
+- We use [G-Objaverse](https://github.com/modelscope/richdreamer/tree/main/dataset/gobjaverse) with about 265K 3D objects and 10.6M rendered images (265K x 40 views, including RGB, normal and depth maps) for `GSRecon` and `GSVAE` training. [Its subset](https://github.com/ashawkey/objaverse_filter) with about 83K 3D objects provided by [LGM](https://me.kiui.moe/lgm) is used for `DiffSplat` training. Their text descriptions are provided by the latest version of [Cap3D](https://huggingface.co/datasets/tiange/Cap3D) (i.e., refined by [DiffuRank](https://arxiv.org/abs/2404.07984)).
+- We find the filtering is crucial for the generation quality of `DiffSplat`, and a larger dataset is beneficial for the performance of `GSRecon` and `GSVAE`.
+- We store the dataset in an internal HDFS cluster in this project. Thus, the training code can NOT be directly run on your local machine. Please implement your own dataloading logic referring to our provided dataset & dataloader code.
+
+
+## 🚀 Usage
+
+### 📷 Camera Conventions
+
+The camera and world coordinate systems in this project are both defined in the `OpenGL` convention, i.e., X: right, Y: up, Z: backward. The camera is located at `(0, 0, 1.4)` in the world coordinate system, and the camera looks at the origin `(0, 0, 0)`.
+Please refer to [kiuikit camera doc](https://kit.kiui.moe/camera) for visualizations of the camera and world coordinate systems.
+
+### 🤗 Pretrained Models
+
+All pretrained models are available at [HuggingFace🤗](https://huggingface.co/chenguolin/DiffSplat).
+
+| **Model Name** | **Fine-tined From** | **#Param.** | **Link** | **Note** |
+|-------------------------------|---------------------|-------------|----------|----------|
+| **GSRecon** | From scratch | 42M | [gsrecon_gobj265k_cnp_even4](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsrecon_gobj265k_cnp_even4) | Feed-forward reconstruct per-pixel 3DGS from (RGB, normal, point) maps |
+| **GSVAE (SD)** | [SD1.5 VAE](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) | 84M | [gsvae_gobj265k_sd](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsvae_gobj265k_sd) | |
+| **GSVAE (SDXL)** | [SDXL fp16 VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) | 84M | [gsvae_gobj265k_sdxl_fp16](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsvae_gobj265k_sdxl_fp16) | fp16-fixed SDXL VAE is more robust |
+| **GSVAE (SD3)** | [SD3 VAE](https://huggingface.co/stabilityai/stable-diffusion-3-medium) | 84M | [gsvae_gobj265k_sd3](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsvae_gobj265k_sd3) | |
+| **DiffSplat (SD1.5)** | [SD1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) | 0.86B | Text-cond: [gsdiff_gobj83k_sd15__render](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_sd15__render) Image-cond: [gsdiff_gobj83k_sd15_image__render](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_sd15_image__render) | Best efficiency |
+| **DiffSplat (PixArt-Sigma)** | [PixArt-Sigma](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-512-MS) | 0.61B | Text-cond: [gsdiff_gobj83k_pas_fp16__render](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_pas_fp16__render) Image-cond: [gsdiff_gobj83k_pas_fp16_image__render](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_pas_fp16_image__render) | Best Trade-off |
+| **DiffSplat (SD3.5m)** | [SD3.5 median](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) | 2.24B | Text-cond: [gsdiff_gobj83k_sd35m__render](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_sd35m__render) Image-cond: [gsdiff_gobj83k_sd35m_image__render](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_sd35m_image__render) | Best performance |
+| **DiffSplat ControlNet (SD1.5)** | From scratch | 361M | Depth: [gsdiff_gobj83k_sd15__render__depth](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_sd15__render__depth) Normal: [gsdiff_gobj83k_sd15__render__normal](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_sd15__render__normal) Canny: [gsdiff_gobj83k_sd15__render__canny](https://huggingface.co/chenguolin/DiffSplat/tree/main/gsdiff_gobj83k_sd15__render__canny) | |
+| **(Optional) ElevEst** | [dinov2_vitb14_reg](https://github.com/facebookresearch/dinov2) | 86 M | [elevest_gobj265k_b_C25](https://huggingface.co/chenguolin/DiffSplat/tree/main/elevest_gobj265k_b_C25) | (Optional) Single-view image elevation estimation |
+
+
+### ⚡ Inference
+
+#### 0. Download Pretrained Models
+
+Note that:
+- Pretrained weights will download from HuggingFace and stored in `./out`.
+- Other pretrained models (such as CLIP, T5, image VAE, etc.) will be downloaded automatically and stored in your HuggingFace cache directory.
+- If you face problems in visiting HuggingFace Hub, you can try to set the environment variable `export HF_ENDPOINT=https://hf-mirror.com`.
+
+```bash
+python3 download_ckpt.py --model_type [MODEL_TYPE] [--image_cond]
+
+# `MODEL_TYPE`: choose from "sd15", "pas", "sd35m", "depth", "normal", "canny", "elevest".
+# `--image_cond`: add this flag for downloading image-conditioned models
+```
+
+For example, to download the `text-cond SD1.5-based DiffSplat`:
+```bash
+python3 download_ckpt.py --model_type sd15
+```
+To download the `image-cond PixArt-Sigma-based DiffSplat`:
+```bash
+python3 download_ckpt.py --model_type pas --image_cond
+```
+
+#### 1. Text-conditioned 3D Object Generation
+
+Note that:
+- Model differences may not be significant for simple text prompts. We recommend using `DiffSplat (SD1.5)` for better efficiency, `DiffSplat (SD3.5m)` for better performance, and `DiffSplat (PixArt-Sigma)` for a better trade-off.
+- By default, `export HF_HOME=~/.cache/huggingface`, `export TORCH_HOME=~/.cache/torch`. You can change theses paths in `scripts/infer.sh`. SD3-related models require HuggingFace token for downloading, which is expected to be stored in `HF_HOME`.
+- Outputs will be stored in `./out//inference`.
+- Prompt is specified by `--prompt` (e.g., `a_toy_robot`). Please seperate words by `_` and it will be replaced by space in the code automatically.
+- If `"gif"` is in `--output_video_type`, the output will be a `.gif` file. Otherwise, it will be a `.mp4` file. If `"fancy"` is in `--output_video_type`, the output video will be in a fancy style that 3DGS scales gradually increase while rotating.
+- `--seed` is used for random seed setting. `--gpu_id` is used for specifying the GPU device.
+- Use `--half_precision` for `BF16` half-precision inference. It will reduce the memory usage but may slightly affect the quality.
+
+```bash
+# DiffSplat (SD1.5)
+bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15__render \
+--prompt a_toy_robot --output_video_type gif \
+--gpu_id 0 --seed 0 [--half_precision]
+
+# DiffSplat (PixArt-Sigma)
+bash scripts/infer.sh src/infer_gsdiff_pas.py configs/gsdiff_pas.yaml gsdiff_gobj83k_pas_fp16__render \
+--prompt a_toy_robot --output_video_type gif \
+--gpu_id 0 --seed 0 [--half_precision]
+
+# DiffSplat (SD3.5m)
+bash scripts/infer.sh src/infer_gsdiff_sd3.py configs/gsdiff_sd35m_80g.yaml gsdiff_gobj83k_sd35m__render \
+--prompt a_toy_robot --output_video_type gif \
+--gpu_id 0 --seed 0 [--half_precision]
+```
+
+You will get:
+| DiffSplat (SD1.5) | DiffSplat (PixArt-Sigma) | DiffSplat (SD3.5m) |
+|-------------------------|-------------------------------|-------------------------|
+|  |  |  |
+
+
+**More Advanced Arguments**:
+- `--prompt_file`: instead of using `--prompt`, `--prompt_file` will read prompts from a `.txt` file line by line.
+- Diffusion configurations:
+ - `--scheduler_type`: choose from `ddim`, `dpmsolver++`, `sde-dpmsolver++`, etc.
+ - `--num_inference_timesteps`: the number of diffusion steps.
+ - `--guidance_scale`: classifier-free guidance (CFG) scale; `1.0` means no CFG.
+ - `--eta`: specified for `DDIM` scheduler; the weight of noise for added noise in diffusion steps.
+- [Instant3D](https://instant-3d.github.io) tricks:
+ - `--init_std`, `--init_noise_strength`, `--init_bg`: initial noise settings, cf. [Instant3D Sec. 3.1](https://arxiv.org/pdf/2311.06214); NOT used by default, as we found it's not that helpful in our case.
+- Others:
+ - `--elevation`: elevation for viewing and rendering; not necessary for text-conditioned generation; set to `10` by default (from xz-plane (`0`) to +y axis (`90`)).
+ - `--negative_prompt`: empty prompt (`""`) by default; used with CFG for better visual quality (e.g., more vibrant colors), but we found it causes lower metric values (such as [ImageReward](https://github.com/THUDM/ImageReward)).
+ - `--save_ply`: save the generated 3DGS as a `.ply` file; used with `--opacity_threshold_ply` to filter out low-opacity splats for much smaller `.ply` file size.
+ - `--eval_text_cond`: evaluate text-conditioned generation automatically.
+ - ...
+
+Please refer to [infer_gsdiff_sd.py](./src/infer_gsdiff_sd.py), [infer_gsdiff_pas.py](./src/infer_gsdiff_pas.py), and [infer_gsdiff_sd3.py](./src/infer_gsdiff_sd3.py) for more argument details.
+
+#### 2. Image-conditioned 3D Object Generation
+
+Note that:
+- Most of the arguments are the same as text-conditioned generation. Our method support **text and image as conditions simultaneously**.
+- Elevation is necessary for image-conditioned generation. You can specify the elevation angle by `--elevation` for viewing and rendering (from xz-plane (`0`) to +y axis (`90`)) or estimate it from the input image by `--use_elevest` (download the pretrained `ElevEst` model by `python3 download_ckpt.py --model_type elevest`) first. But we found that the **estimated elevation is not always accurate**, so it's better to set it manually.
+- Text prompt is **optional** for image-conditioned generation. If you want to use text prompt, you can specify it by `--prompt` (e.g., `a_frog`), otherwise, empty prompt (`""`) will be used. Note that **DiffSplat (SD3.5m)** is sensitive to text prompts, and it may generate bad results without a proper prompt.
+- Remember to set a smaller `--guidance_scale` for image-conditioned generation, as the default value is set for text-conditioned generation. `2.0` is recommended for most cases.
+- `--triangle_cfg_scaling` is a trick that set larger CFG values for far-away views from the input image, while smaller CFG values for close-up views, cf. [SV3D Sec. 3](https://arxiv.org/pdf/2403.12008).
+- `--rembg_and_center` will remove the background and center the object in the image. It can be used with `--rembg_model_name` (by default `u2net`) and `--border_ratio` (by default `0.2`).
+- Image-conditioned generation is more sensitive to arguments, and you may need to tune them for better results.
+
+```bash
+# DiffSplat (SD1.5)
+bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15_image__render \
+--rembg_and_center --triangle_cfg_scaling --output_video_type gif --guidance_scale 2 \
+--image_path assets/grm/frog.png --elevation 20 --prompt a_frog
+
+# DiffSplat (PixArt-Sigma)
+bash scripts/infer.sh src/infer_gsdiff_pas.py configs/gsdiff_pas.yaml gsdiff_gobj83k_pas_fp16_image__render \
+--rembg_and_center --triangle_cfg_scaling --output_video_type gif --guidance_scale 2 \
+--image_path assets/grm/frog.png --elevation 20 --prompt a_frog
+
+# DiffSplat (SD3.5m)
+bash scripts/infer.sh src/infer_gsdiff_sd3.py configs/gsdiff_sd35m_80g.yaml gsdiff_gobj83k_sd35m_image__render \
+--rembg_and_center --triangle_cfg_scaling --output_video_type gif --guidance_scale 2 \
+--image_path assets/grm/frog.png --elevation 20 --prompt a_frog
+```
+
+You will get
+| Arguments | DiffSplat (SD1.5) | DiffSplat (PixArt-Sigma) | DiffSplat (SD3.5m) |
+|---------|-------------------------|-------------------------------|-------------------------|
+| `--elevation 20 --prompt a_frog` |  |  |  |
+| `--use_elevest --prompt a_frog` (estimated elevation: -0.78 deg) |  |  |  |
+| `--elevation 20` (prompt is `""`) |  |  |  |
+
+**More Advanced Arguments**:
+- `--image_dir`: instead of using `--image_path`, `--image_dir` will read images from a directory.
+
+Please refer to [infer_gsdiff_sd.py](./src/infer_gsdiff_sd.py), [infer_gsdiff_pas.py](./src/infer_gsdiff_pas.py), and [infer_gsdiff_sd3.py](./src/infer_gsdiff_sd3.py) for more argument details.
+
+#### 3. ControlNet for 3D Object Generation
+
+Note that:
+- After downloading pretrained **DiffSplat (SD1.5)**, you shoule download the controlnet weights by `python3 download_ckpt.py --model_type [depth | normal | canny]`.
+- For **depth-controlnet**, values in depth maps are normalized to `[0, 1]` and larger values (white) mean closer to the camera (smaller depth). Please refer to [GObjaverse Dataset](./src/data/gobjaverse_parquet_dataset.py) for more details.
+- For **normal-controlnet**, input camera is normalized to locate at `(0, 0, 1.4)` and look at `(0, 0, 0)`, thus the input normal maps are transformed accordingly. Please refer to [GObjaverse Dataset](./src/data/gobjaverse_parquet_dataset.py) for more details.
+- For **canny-controlnet**, canny edges are extracted from the input RGB images automatically by `cv2.Canny`. Please refer to [GObjaverse Dataset](./src/data/gobjaverse_parquet_dataset.py) for more details.
+
+```bash
+# ControlNet (depth)
+bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15__render \
+--load_pretrained_controlnet gsdiff_gobj83k_sd15__render__depth \
+--output_video_type gif --image_path assets/diffsplat/controlnet/toy_depth.png \
+--prompt teddy_bear --elevation 10
+
+# ControlNet (normal)
+bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15__render \
+--load_pretrained_controlnet gsdiff_gobj83k_sd15__render__normal \
+--output_video_type gif --image_path assets/diffsplat/controlnet/robot_normal.png \
+--prompt iron_robot --elevation 10
+
+# ControlNet (canny)
+bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15__render \
+--load_pretrained_controlnet gsdiff_gobj83k_sd15__render__canny \
+--output_video_type gif --image_path assets/diffsplat/controlnet/cookie_canny.png \
+--prompt book --elevation 10
+```
+
+You will get:
+| Original Image | Input Control | `--prompt teddy_bear` | `--prompt panda` |
+|----------------|---------------|-----------------------|--------------------|
+|  |  |  |  |
+
+| Original Image | Input Control | `--prompt iron_robot` | `--prompt plush_dog_toy` |
+|----------------|---------------|-----------------------|--------------------|
+|  |  |  |  |
+
+| Original Image | Input Control | `--prompt book` | `--prompt cookie` |
+|----------------|---------------|-----------------|---------------------|
+|  |  |  |  |
+
+**More Advanced Arguments**:
+- `--guess_mode`: ControlNet encoder tries to recognize the content of the input image even if you remove all prompts, cf. [the original ControlNet repo](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) and [HF ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet#guess-mode).
+- `--controlnet_scale`: determines how much weight to assign to the conditioning inputs; outputs of the ControlNet are multiplied by `controlnet_scale` before they are added to the residual in the original UNet.
+
+Please refer to [infer_gsdiff_sd.py](./src/infer_gsdiff_sd.py) for more argument details.
+
+
+### 🦾 Training
+
+#### 1. GSRecon
+
+Please refer to [train_gsrecon.py](./src/train_gsrecon.py).
+
+Instructions for `GSRecon` training will be provided soon.
+
+#### 2. GSVAE
+
+Please refer to [train_gsvae.py](./src/train_gsvae.py).
+
+Instructions for `GSVAE` training will be provided soon.
+
+#### 3. DiffSplat
+
+Please refer to [train_gsdiff_sd.py](./src/train_gsdiff_sd.py), [train_gsdiff_pas.py](./src/train_gsdiff_pas.py), and [train_gsdiff_sd3.py](./src/train_gsdiff_sd3.py).
+
+Instructions for `DiffSplat` training will be provided soon.
+
+#### 4. ControlNet
+
+Please refer to [train_gsdiff_sd_controlnet.py](./src/train_gsdiff_sd_controlnet.py).
+
+Instructions for `ControlNet` training and inference will be provided soon.
+
+
+## 😊 Acknowledgement
+We would like to thank the authors of [LGM](https://me.kiui.moe/lgm), [GRM](https://justimyhxu.github.io/projects/grm), and [Wonder3D](https://www.xxlong.site/Wonder3D) for their great work and generously providing source codes, which inspired our work and helped us a lot in the implementation.
+
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 📚 Citation
+If you find our work helpful, please consider citing:
+```bibtex
+@inproceedings{lin2025diffsplat,
+ title={DiffSplat: Repurposing Image Diffusion Models for Scalable 3D Gaussian Splat Generation},
+ author={Lin, Chenguo and Pan, Panwang and Yang, Bangbang and Li, Zeming and Mu, Yadong},
+ booktitle={International Conference on Learning Representations (ICLR)},
+ year={2025}
+}
+```
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7866b950312a8e6042c82e0692fb82595792429
--- /dev/null
+++ b/app.py
@@ -0,0 +1,165 @@
+import os
+import shlex
+import subprocess
+import imageio
+import numpy as np
+
+import gradio as gr
+import spaces
+import sys
+from loguru import logger
+current_path = os.path.dirname(os.path.abspath(__file__))
+
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = os.path.join(current_path, 'out')
+os.makedirs(TMP_DIR, exist_ok=True)
+TAG = "gsdiff_gobj83k_sd15__render"
+
+# download checkpoints
+# subprocess.run(shlex.split("python3 download_ckpt.py --model_type pas")) # for txt condition
+# subprocess.run(shlex.split("python3 download_ckpt.py --model_type pas --image_cond")) # for img condition
+
+# img_commands = "PYTHONPATH=./ bash scripts/infer.sh src/infer_gsdiff_pas.py configs/gsdiff_pas.yaml {} \
+# --rembg_and_center --triangle_cfg_scaling --save_ply --output_video_type mp4 --guidance_scale {} \
+# --image_path {} --elevation {} --prompt {} --seed {}"
+
+# txt_commands = "PYTHONPATH=./ bash scripts/infer.sh src/infer_gsdiff_pas.py configs/gsdiff_pas.yaml \
+# --rembg_and_center --triangle_cfg_scaling --save_ply --output_video_type mp4 --guidance_scale {} \
+# --image_path {} --elevation {} --prompt {} --seed {}"
+
+# SD1.5
+subprocess.run(shlex.split("python3 download_ckpt.py --model_type sd15")) # for txt condition
+# subprocess.run(shlex.split("python3 download_ckpt.py --model_type sd15 --image_cond")) # for img condition
+img_commands = "PYTHONPATH=./ bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml \
+--rembg_and_center --triangle_cfg_scaling --save_ply --output_video_type mp4 --guidance_scale {} \
+--image_path {} --elevation {} --prompt {} --seed {}"
+
+txt_commands = "PYTHONPATH=./ bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml {} \
+--rembg_and_center --save_ply --output_video_type mp4 --guidance_scale {} \
+ --elevation {} --prompt {} --seed {}"
+
+
+
+# process function
+@spaces.GPU
+def process(input_image, prompt='a_high_quality_3D_asset', prompt_neg='ugly, blurry, pixelated obscure, unnatural colors, poor lighting, dull, unclear, cropped, lowres, low quality, artifacts, duplicate', input_elevation=20, guidance_scale=2., input_seed=0):
+
+ if input_image is not None:
+ image_path = os.path.join(TMP_DIR, "input_image.png")
+ image_name = image_path.split('/')[-1].split('.')[0]
+ input_image.save(image_path)
+ full_command = img_commands.format(TAG, guidance_scale, image_path, input_elevation, prompt, input_seed)
+ else:
+ full_command = txt_commands.format(TAG, guidance_scale, input_elevation, prompt, input_seed)
+ image_name = ""
+
+ os.system(full_command)
+
+ # save video and ply files
+ ckpt_dir = os.path.join(TMP_DIR, TAG, "checkpoints")
+ infer_from_iter = int(sorted(os.listdir(ckpt_dir))[-1])
+ MAX_NAME_LEN = 20 # TODO: make `20` configurable
+ prompt = prompt.replace("_", " ")
+ prompt_name = prompt[:MAX_NAME_LEN] + "..." if prompt[:MAX_NAME_LEN] != "" else prompt
+ name = f"[{image_name}]_[{prompt_name}]_{infer_from_iter:06d}"
+ output_video_path = os.path.join(TMP_DIR, TAG, "inference", name + ".mp4")
+ output_ply_path = os.path.join(TMP_DIR, TAG, "inference", name + ".ply")
+ output_img_path = os.path.join(TMP_DIR, TAG, "inference", name + "_gs.png")
+
+ logger.info(full_command, output_video_path, output_ply_path)
+
+ output_image = imageio.imread(output_img_path)
+ return output_image, output_video_path, output_ply_path
+
+
+# gradio UI
+_TITLE = '''DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation'''
+
+_DESCRIPTION = '''
+### If you find our work helpful, please consider citing our paper 📚 or giving the repo a star 🌟
+
+
+
+
+
+
+
+* Input can be only text, only image, or both image and text.
+* If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
+* Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
+'''
+
+block = gr.Blocks(title=_TITLE).queue()
+with block:
+ with gr.Row():
+ with gr.Column(scale=1):
+ gr.Markdown('# ' + _TITLE)
+ gr.Markdown(_DESCRIPTION)
+
+ with gr.Row(variant='panel'):
+ with gr.Column(scale=1):
+ # input image
+ input_image = gr.Image(label="image", type='pil')
+
+ # input prompt
+ input_text = gr.Textbox(label="prompt",value="a_high_quality_3D_asset")
+
+ # negative prompt
+ input_neg_text = gr.Textbox(label="negative prompt", value="")
+
+ # guidance_scale
+ guidance_scale = gr.Slider(label="guidance scale", minimum=1., maximum=7.5, step=0.5, value=2.0)
+
+ # elevation
+ input_elevation = gr.Slider(label="elevation", minimum=-90, maximum=90, step=1, value=20)
+ # # inference steps
+ # input_num_steps = gr.Slider(label="inference steps", minimum=1, maximum=100, step=1, value=30)
+ # random seed
+ input_seed = gr.Slider(label="random seed", minimum=0, maximum=100000, step=1, value=0)
+ # gen button
+ button_gen = gr.Button("Generate")
+
+
+ with gr.Column(scale=1):
+ with gr.Tab("Video"):
+ # final video results
+ output_video = gr.Video(label="video")
+ # ply file
+ output_file = gr.File(label="3D Gaussians (ply format)")
+ with gr.Tab("Splatter Images"):
+ output_image = gr.Image(interactive=False, show_label=False)
+
+
+ button_gen.click(process, inputs=[input_image, input_text, input_neg_text, input_elevation, guidance_scale, input_seed], outputs=[output_image, output_video, output_file])
+
+ gr.Examples(
+ examples=[
+ f'assets/diffsplat/{image}'
+ for image in os.listdir("assets/diffsplat") if image.endswith('.png')
+ ],
+ inputs=[input_image],
+ outputs=[output_image, output_video, output_file],
+ fn=lambda x: process(input_image=x),
+ # cache_examples=True,
+ run_on_click=True,
+ label='Image-to-3D Examples'
+ )
+
+ gr.Examples(
+ examples=[
+ "a_toy_robot",
+ "a_cute_panda",
+ "a_book"
+ ],
+ inputs=[input_text],
+ outputs=[output_image, output_video, output_file],
+ fn=lambda x: process(input_image=None, prompt=x),
+ # cache_examples=True,
+ run_on_click=True,
+ label='Text-to-3D Examples'
+ )
+
+
+# Launch the Gradio app
+if __name__ == "__main__":
+ block.launch(share=True)
\ No newline at end of file
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+An assortment of vintage, fragrant perfumes on display
+A group of vibrant, chattering parrots perched together
+Swan with feathers resembling soft, white origami folds
+A quaint, little house nestled at the end of a winding path
+Flamingo balancing on a sphere instead of standing in water
+A bright yellow rubber duck gently floats in a sudsy bathtub
+A collection of fresh vegetables arranged in a wicker basket
+An ancient, weathered statue, now covered in a blanket of moss
+A mesmerizing dance performed by a kaleidoscope of butterflies
+A lone, ancient tree stands tall in the middle of a quiet field
+A plush teddy bear, sitting alone with a slight tear in its seam
+A pair of hiking boots caked with mud at the doorstep of a cabin
+A worn leather recliner with a knitted throw draped over the back
+A small, intricately carved antique wooden box filled with mystery
+Frog with a translucent skin displaying a mechanical heart beating
+A small, rustic cabin sits alone in a peaceful, snow-covered forest
+Jellyfish with bioluminescent tentacles shaped like lightning bolts
+A floating teapot, pouring a stream of endless, steaming jasmine tea
+A smoldering campfire under a clear starry night, embers glowing softly
+A smartphone with a cracked screen lying on a coffee-stained office desk
+An ice cream scoop that serves up scoops of cloud fluff instead of ice cream
+A velvet-lined violin case, which opens to reveal a garden of miniature roses
+A hammock strung between two skyscrapers, swaying high above a neon cityscape
+A delicate porcelain teacup, painted with intricate flowers, rests on a saucer
+A sleek red sports car with chrome finishes parked by a bustling city sidewalk
+An ensemble of jellyfish-like hanging lamps, pulsing with soft bioluminescence
+A cluster of tents pitched near a forest, campfire smoke curling into the evening sky
+A colorful kite tangled in the branches of an oak tree, fabric fluttering in the wind
+A dragon-shaped kite, with scales that shimmer in the sunlight as it dances in the wind
+A stone bridge arching over a babbling brook, encrusted with moss and echoing with stories
+A sequence of street lamps, casting pools of light on cobblestone paths as twilight descends
+A bouquet of metallic flowers that bloom in response to moonlight, shimmering silver at night
+An array of small, solid, symmetrical, pastel-colored eggs, each revealing a miniature, enchanted forest scene when cracked open
+A heavy, layered, asymmetrical winter quilt, with a patchwork of plaid fabrics in reds and greens, folded at the foot of a well-made bed
+Various hollow, asymmetrical, textured seashells, collected in a sand-filled, clear glass jar with a twine-tied neck, displayed on a windowsill
+An old, solid, asymmetrical bronze bell, its contours irregular from centuries of use, with a green patina, sitting silent in an abandoned temple
+A small, hollow, asymmetrical birdhouse, painted in cheerful colors, with a round entrance and a tiny perch, swaying gently in a backyard apple tree
+An ensemble of hollow, irregularly shaped musical instruments, including a saxophone, a violin, and a drum, resting on a stage before a jazz concert
+An assortment of solid, symmetrical, smooth marbles, each one a different color with a unique swirl pattern, scattered playfully across a hardwood floor
+A medium-sized, layered, radially symmetrical conch shell, with a rough texture on the outside, fading from pink to cream, sitting alone on a sandy beach
+Several large, solid, symmetrical hay bales, with a rough, golden texture, scattered across a rural, open field, with the setting sun casting long shadows
+An old, layered, asymmetrical lantern, with a patina copper finish and translucent panes that flicker with bioluminescent light from cultured algae within
+A collection of solid, irregularly shaped hand tools, with wooden handles and metal ends, well-used and slightly rusty, hanging on a pegboard in a workshop
+Several solid, spherical, weathered cannonballs, with a rough cast-iron texture, lying beside a rusted cannon in a historical fort overlooking a serene bay
+A small, solid, geometrically spherical, metallic orb, with a glossy ruby finish, nestled in a nest of black velvet, untouched and gleaming under a spotlight
+An intricately carved, solid, wooden figurine, with jagged contours depicting an ancient deity, the wood grain visible under a matte finish, on a stone altar
+A large, multi-layered, symmetrical wedding cake, with smooth fondant, delicate piping, and lifelike sugar flowers in full bloom, displayed on a silver stand
+A large, hollow, asymmetrically shaped amphitheater, with jagged stone seating, nestled in a natural landscape, a classical play being performed as the sun sets
+A medium-sized, hollow, asymmetrical teapot, crafted to look like a slumbering dragon, with a scaly, rough texture and smoke gently wafting from its snout-spout
+A solid, smooth, symmetrical porcelain teapot, with a cobalt blue dragon design, steam rising from the spout, suggesting it's just been filled with boiling water
+A compact, cylindrical, vintage pepper mill, with a polished, ornate brass body, slightly worn from use, placed beside a porcelain plate on a checkered tablecloth
+Several large, solid, cube-shaped parcels, wrapped in brown paper and tied with string, each labeled with a different destination, awaiting dispatch in a post office
+An oversized, porous, sphere-shaped birdcage, made of woven golden wires, with a matte finish, housing a small, mechanical, singing bird that flutters in a lifelike manner
+A small, solid, radially symmetrical, iridescent abalone shell, with jagged contours, hosting a miniature, tranquil Zen garden complete with tiny, raked sand and micro bonsai
+A solid, symmetrical, smooth stone fountain, with water cascading over its edges into a clear, circular pond surrounded by blooming lilies, in the center of a sunlit courtyard
diff --git a/assets/prompts/instant3d.txt b/assets/prompts/instant3d.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e2812ee60d1012fee3e5c5baed16cf78c13f84b4
--- /dev/null
+++ b/assets/prompts/instant3d.txt
@@ -0,0 +1,112 @@
+a bear dancing ballet
+a woolly mammoth standing on ice
+a rotary telephone carved out of wood
+the titanic, aerial view
+a barbecue grill cooking sausages and burger patties
+a yorkie dog wearing extremely cool sneakers
+a yorkie dog eating a donut
+a beautiful rainbow fish
+a tiger waiter at a fancy restaurant
+an origami hippo in a river
+a frog wearing a sweater
+a covered wagon
+Michelangelo style statue of an astronaut
+a humanoid robot lying on a couch using a laptop
+a yorkie dog dressed as a maid
+a kangaroo sitting on a bench playing the accordion
+a red-eyed tree frog
+a robot tiger
+a blue jay standing on a large basket of rainbow macarons
+a shiba inu playing golf wearing tartan golf clothes and hat
+a dog made out of salad
+a cocker spaniel wearing a crown
+a vintage record player
+a chimpanzee dressed as a football player
+a baby dragon drinking boba
+a ghost eating a hamburger
+a hippo wearing a sweater
+a panda rowing a boat in a pond
+a plush dragon toy
+a squirrel dressed like Henry VIII King of England
+a tiger karate master
+a train engine made out of clay
+a mountain goat standing on a boulder
+two gummy bears playing dominoes
+a baby monkey riding on a pig
+an eggshell broken in two with an adorable chick standing next to it
+a train engine made out of clay
+an amigurumi bulldozer
+an old vintage car
+a cat wearing a lion costume
+a plate of delicious tacos
+a snail on a leaf
+an extravagant mansion, aerial view
+a drum set made of cheese
+a bulldozer clearing away a pile of snow
+an old car overgrown by vines and weeds
+a capybara wearing a top hat, low poly
+a ceramic upside down yellow octopus holding a blue green ceramic cup
+an opulent couch from the palace of Versailles
+a hippo biting through a watermelon
+a tiger karate master
+baby elephant jumping on a trampoline
+a colorful camping tent in a patch of grass
+a fox playing the cello
+an origami bulldozer sitting on the ground
+a group of squirrels rowing crew
+a squirrel gesturing in front of an easel showing colorful pie charts
+a chihuahua lying in a pool ring
+a lobster playing the saxophone
+a construction excavator
+a pig playing a drum set
+an orc forging a hammer on an anvil
+a marble bust of a fox head
+Michelangelo style statue of dog reading news on a cellphone
+a hippo wearing a sweater
+a porcelain dragon
+a baby dragon hatching out of a stone egg
+a human skeleton relaxing in a lounge chair
+a chimpanzee dressed like Napoleon Bonaparte
+a nest with a few white eggs and one golden egg
+a bald eagle
+a baby dragon drinking boba
+a ghost eating a hamburger
+a dachsund dressed up in a hotdog costume
+a robot made out of vegetables
+an octopus playing the piano
+a steaming basket full of dumplings
+a llama wearing a suit
+a chimpanzee dressed like Henry VIII king of England
+a panda rowing a boat in a pond
+a tiger dressed as a doctor
+a chihuahua wearing a tutu
+a plush toy of a corgi nurse
+a lionfish
+a panda wearing a necktie and sitting in an office chair
+an origami motorcycle
+a plush triceratops toy, studio lighting, high resolution
+two macaw parrots playing chess
+a teddy bear pushing a shopping cart full of fruits and vegetables
+a dachsund wearing a boater hat
+a pig playing the saxophone
+a lemur taking notes in a journal
+a group of dogs playing poker
+a blue motorcycle
+a pigeon reading a book
+a gummy bear playing the saxophone
+an amigurumi motorcycle
+a mouse holding a candlestick
+an ice cream sundae
+a bear dressed as a lumberjack
+a squirrel dressed like Henry VIII king of England
+a bumblebee sitting on a pink flower
+a pug wearing a bee costume
+a crab, low poly
+a wizard raccoon casting a spell
+two macaw parrots sharing a milkshake with two straws
+an orangutan making a clay bowl on a throwing wheel
+an orchid flower planted in a clay pot
+a tray of Sushi containing pugs
+an egg cracked open with a newborn chick hatching out of it
+a wide angle DSLR photo of a colorful rooster
+a pirate collie dog, high resolution
diff --git a/assets/prompts/instant3d_subset.txt b/assets/prompts/instant3d_subset.txt
new file mode 100644
index 0000000000000000000000000000000000000000..547c6ab7271cccdea64dc8b8e54214ace09038d5
--- /dev/null
+++ b/assets/prompts/instant3d_subset.txt
@@ -0,0 +1,16 @@
+a hippo wearing a sweater
+a plush dragon toy
+an old car overgrown by vines and weeds
+an origami bulldozer sitting on the ground
+a chimpanzee dressed as a football player
+a red-eye tree frog
+a ghost eating a hamburger
+a squirrel dressed like Henry VIII King of England
+a tiger karate master
+a train engine made out of clay
+a dog made out of salad
+baby dragon drinking boba
+a bear dancing ballet
+a robot tiger
+a yorkie dog wearing extremely cool sneakers
+a pigeon reading a book
diff --git a/assets/prompts/t3bench.txt b/assets/prompts/t3bench.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f9baf2ded049a1600ae43f00306ebe7299c3baf
--- /dev/null
+++ b/assets/prompts/t3bench.txt
@@ -0,0 +1,300 @@
+A cactus with pink flowers
+A rainbow-colored umbrella
+An antique wooden rocking horse
+A golden retriever with a blue bowtie
+An ivory candlestick holder
+A pair of polka-dotted sneakers
+A steaming mug of hot chocolate with whipped cream
+A bright red fire hydrant
+A gleaming silver saxophone
+A leather-bound book with gold details
+A vibrant sunflower with green leaves
+A castle-shaped sandcastle
+A neon green skateboard with black wheels
+A pirate flag with skull and crossbones
+A plush teddy bear with a satin bow
+A ripe watermelon sliced in half
+A sparkling diamond ring in a velvet box
+A vintage porcelain doll with a frilly dress
+A chameleon perched on a tree branch
+A tarnished brass pocket watch
+A ceramic teapot with floral patterns
+An antique ruby-studded brooch
+A simple burgundy colored feather quill
+A vintage iron-cast typewriter
+A shiny emerald green beetle
+A crystal glass paperweight with abstract design
+A velvet cushion stitched with golden threads
+A small porcelain white rabbit figurine
+A left-handed electric guitar painted black
+A bright blue plastic swimming goggles
+A partly broken shell of a tortoise
+A long woolen scarf, striped red and black
+A tattered old explorer’s map
+A well-used black iron frying pan
+A crumpled silver aluminum soda can
+A thick, green-spined book with yellowed pages
+A shimmering emerald pendant necklace
+A well-worn straw sun hat
+A tarnished silver letter opener
+An antique glass perfume bottle
+A polished mahogany grand piano
+A dented brass trumpet
+A pristine white wedding gown
+A chipped porcelain teacup
+A rustic wrought-iron candle holder
+A vibrant, handmade patchwork quilt
+A plush velvet armchair
+A sleek, black top hat
+A paint-splattered easel
+A bent steel crowbar
+A crisp paper airplane
+A worn-out rubber tire swing
+An intricately-carved wooden chess set
+A bright red kite with a frayed tail
+A smooth, round opal stone
+A rusty, vintage metal key
+A delicate, handmade lace doily
+A sturdy mahogany walking cane
+A sparkling crystal chandelier
+A worn-out red flannel shirt
+A cracked porcelain doll's face
+A dusty classic typewriter
+A glossy grand black piano
+A faux-fur leopard print hat
+A futuristic, sleek electric car model
+A cherry red vintage lipstick tube
+A cobweb-covered old wooden chest
+A gold glittery carnival mask
+A tattered world map with stained edges
+A shiny red apple
+A worn-out leather briefcase
+An antique gold pocket watch
+A sleek, slim smartphone
+A wet, vibrant beach ball
+A rusty, abandoned bicycle
+A fluffy, orange cat
+Crisp, folded origami paper
+A shiny, new electric guitar
+A weather-beaten wooden bat
+A delicate crystal champagne flute
+An old, frayed straw hat
+A scuffed up soccer ball
+A pair of worn-in blue jeans
+A well-loved stuffed teddy bear
+A chipped, white coffee mug
+A bright, yellow rubber duck
+A sleek stainless steel teapot
+A water-streaked glass window pane
+An intricate ceramic vase with peonies painted on it
+A fuzzy pink flamingo lawn ornament
+A blooming potted orchid with purple flowers
+An old bronze ship's wheel
+A sparkling diamond tiara
+A vintage plaid woolen blanket
+A pair of shiny black patent leather shoes
+An elegant feather-quill ink pen
+A fragrant pine Christmas wreath
+A silver mirror with ornate detailing
+A green enameled watering can
+A classic leatherette radio with dials
+A white seashell on a sandy beach
+A vintage clock hanging on a brick wall
+A pair of glasses on an open notebook
+A green cactus in a clay pot
+A colorful kite flying in a clear sky
+A wooden rocking chair on a porch
+A silver spoon in a bowl of soup
+A black cat sleeping on a windowsill
+A bluebird perched on a tree branch
+A pair of sneakers hanging from a power line
+A yellow umbrella on a rainy day
+A pink bicycle leaning against a fence
+A snowman wearing a scarf in a winter landscape
+A leather-bound book on a wooden table
+A red rose in a crystal vase
+A jar of homemade jam on a kitchen counter
+A pair of ballet shoes on a dance floor
+A green turtle swimming in a clear pond
+A rainbow-colored kite soaring in the sky
+A chocolate cake on a white plate
+A pair of binoculars on a mountain peak
+A teddy bear on a child's bed
+A pair of blue jeans hanging on a clothesline
+A bright sunflower in a field
+A colorful parrot on a jungle tree
+A lighthouse on a rocky shore
+A pair of hiking boots on a trail
+A red and white lighthouse on a cliff
+A yellow school bus on a city street
+A pair of lovebirds in a golden cage
+A white sailboat on a calm sea
+A vintage typewriter on a desk
+A ripe watermelon on a picnic table
+A stack of pancakes on a breakfast table
+A green frog on a lily pad
+A pair of mittens on a snowy day
+A blue butterfly on a pink flower
+A red fire hydrant on a sidewalk
+A pair of cowboy boots in a barn
+A black and white photo in a silver frame
+A rainbow over a waterfall
+A white dove flying in a blue sky
+A pink piggy bank on a shelf
+A pair of scissors on a craft table
+A red barn in a green field
+A pair of flip flops on a beach
+A hot air balloon in a clear sky
+A green apple on a teacher's desk
+A yellow rubber duck in a bathtub
+A white swan on a tranquil lake
+A red cardinal on a snowy branch
+A black and white soccer ball on a field
+A silver teapot on a dining table
+A red and white candy cane on a Christmas tree
+A pair of red boxing gloves hanging on a wall
+A white wedding dress on a hanger
+A brown leather suitcase on a train platform
+A pink flamingo in a zoo
+A pair of green wellies in a puddle
+A black grand piano in a concert hall
+A white picket fence around a garden
+A blue whale in the deep ocean
+A brown horse in a green pasture
+A brown teddy bear in a toy shop
+A brown leather suitcase at an airport
+A green wellington boot in mud
+A blue and white china cup on a saucer
+A silver laptop sitting on a wooden desk
+A blue ceramic mug filled with steaming coffee
+A vintage pocket watch with a golden chain
+A green leather-bound book on an antique shelf
+A pair of white sneakers on a black mat
+A golden locket with a delicate chain
+A white porcelain teapot on a lace tablecloth
+A silver telescope pointing towards the starry sky
+A purple umbrella left on a park bench
+A wooden rocking horse in a child's playroom
+A red apple resting on a white ceramic plate
+A green paperback novel lying on a beige couch
+A white cotton t-shirt hanging on a wooden hanger
+A silver wristwatch ticking on a glass bedside table
+A pink ceramic vase filled with fresh white lilies
+A gold necklace with a diamond pendant displayed in a velvet box
+A black and white photograph framed in dark mahogany
+A pair of red high-heeled shoes sitting in a shoebox
+A blue denim jacket draped over a wooden chair
+A purple yoga mat rolled up in a gym bag
+A silver toaster with two slices of bread browning
+A red ceramic coffee mug sitting on a wooden table
+A green plastic watering can filled with fresh water
+A shiny gold pocket watch ticking away in a velvet box
+A blue denim jacket hanging on a metal coat rack
+A pair of brown leather boots standing at the front door
+A crystal chandelier sparkling in the grand foyer
+A fluffy white pillow resting on a red velvet couch
+A vibrant orange pumpkin sitting on a hay bale
+A striped beach umbrella standing tall on a sandy beach
+A stainless steel toaster sitting on a marble countertop
+A delicate china teacup resting on a lace doily
+A pair of neon running shoes waiting by the treadmill
+An artist is painting on a blank canvas
+A student is typing on his laptop
+A young gymnast trains with a balance beam
+A chef is making pizza dough in the kitchen
+A footballer is kicking a soccer ball
+A man is holding an umbrella against rain
+A girl is reading a hardcover book in her room
+A woman putting lipstick on in front of a mirror
+A worker is climbing a ladder to repair a roof
+A florist is making a bouquet with fresh flowers
+A boy is flying a colorful kite in the sky
+A gardener is watering plants with a hose
+A photographer is capturing a beautiful butterfly with his camera
+A scientist is examining a specimen under a microscope
+A drummer is beating the drumsticks on a drum
+A fisherman is throwing the fishing rod in the sea
+A baby is reaching for a teddy bear on the bed
+A candle burns beside an ancient, leather-bound book
+Colorful beads are scattered around a small, yellow sewing box
+A skateboard leans casually against a brightly-marked graffiti wall
+An apple lays nestled next to a vintage, brass pocket watch
+Two seashells gleam under the soft light of the moon
+A pair of worn ballet shoes rest next to a glossy violin
+A quill pen lies across a stack of unmarked parchment paper
+A baseball glove is forgotten next to a half-eaten hot dog
+A wine bottle and two empty glasses glisten under a chandelier
+A magnifying glass sits on top of a mysterious, printed map
+A black cat sleeps peacefully beside a carved pumpkin
+A weathered straw hat hangs beside a freshly picked sunflower
+An open diary lays flat, a single dried rose on its pages
+A twinkling star ornament hangs closely with a snow globe
+A chessboard is set up, the king and queen standing in opposition
+An elegant masquerade mask sits besides a velvet-handled brush
+A vintage typewriter shares space with a half-full bottle of whisky
+A teddy bear cuddles against a softly worn out children's book
+A sandy hourglass and a rugged compass lay side by side
+A pair of spectacles lies open on a dog-eared paperback
+A tarnished key lies next to a fading photograph
+Ripe apples cluster next to a gleaming knife
+An empty notebook rests beside a rustling quill
+An antique clock sits next to a fleeting hourglass
+Weathered boots stand next to a worn hiking stick
+A stack of vinyl records leans against an old gramophone
+A half-eaten sandwich sits next to a lukewarm thermos
+A flickering candle casts a dim light on a dusty mirror
+Luxurious perfume bottles sit next to a dainty jewelry box
+Burnt-out lightbulbs lie beside a discarded screwdriver
+Sandwiched between two hardbound novels lies a bookmark
+A broken tablespoon lies next to an empty sugar bowl
+A polished flute gleams next to hastily-scrawled sheet music
+A wine bottle rests besides a tall glass on a glossy surface
+A novel is sprawled open next to a pair of reading glasses
+An untouched slice of pizza is cooling beside a half-drunk can of soda
+A tangle of yarn rests beside a pair of silver knitting needles
+A violin reclines on a chair next to a music sheet filled with notes
+An exploded bottle of ink is smeared across a pile of papers
+A child's teddy bear is left abandoned near a frequently-read bedtime story book
+A brightly lit birthday cake is set near a closed, wrapped gift
+A dripping paintbrush stands poised above a half-finished canvas
+A silent alarm clock is glaring at a tousled pillow
+Two worn paddles rest next to an overturned canoe
+A crumpled sheet of paper lies beside a blue ink pen
+Two worn-out gym shoes are placed by the front door
+An abandoned teddy bear leans against a discarded toy car
+A bottle of red wine stands alongside an empty wine glass
+An old brass key sits next to an intricate, dust-covered lock
+Two silken, colorful scarves are knotted together
+A flickering candle stands beside a vintage photograph in a wooden frame
+A worn violin and a broken bow rest casually on a chair
+A chewed pencil rests alongside a frustration-filled sudoku puzzle
+A steaming teapot is standing next to a china teacup with roses
+A sprouting potted plant sits comfortably next to a watering can filled with water
+A camera with a telephoto lens rests atop a folded map of a city
+A novel rests atop a worn-out diary
+The golden trophy shines brightly next to a ruffled blue ribbon
+An antique clock and a retro radio compete for attention on the dusty shelf
+An artist's brush is soaked inwater, awaiting its turn beside a vibrant color palette
+A candle, half-burnt, emits an inviting scent near a generous plate of cookies
+Two wine glasses, one filled with red, the other white, touch in a graceful toast
+An old-fashioned typewriter aspires to outshine a sleek laptop
+A well-worn skateboard rests against a gleaming silver bike
+A ceramic elephant stands guard over an intricate puzzle yet to be completed
+The fragrance of an open perfume bottle mingles with the smell of a single red rose
+A pyramid of stacked poker chips dwarfs a pair of playing cards
+An oversharpened pencil argues with a ballpoint pen over a stash of blank paper
+A colorful array of spices in tiny jars sits next to an unused cooking book
+A warm, glowing lantern rests beside a well-read paperback book
+A treasure map, crumped and faded, cocooned beside an ornate compass
+Two vinyl records rest idly on the sputtering gramophone
+An envelope sealed with love stands demurely beside an ancient inkwell and a feather quill
+An open book sits beside a vintage brass spectacles
+Hot popcorn jump out from the red striped popcorn maker
+A rustic cowboy hat hangs on the rugged saddle
+A half-eaten slice of pizza forgotten beside a remote control
+A dripping paintbrush strokes a vibrant palette of colors
+A shimmering diamond necklace lays elegantly on a black velvet box
+A thick novel is accompanied by a steaming cup of cocoa
+A woolen knitted scarf is wrapped around a carved pumpkin
+A scratched surfboard lies in the sandy shadow of a worn-out straw hat
+An old brass key longs for the dusted antique chest it could unlock
diff --git a/assets/prompts/t3bench_multi.txt b/assets/prompts/t3bench_multi.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aac03071ec49e3fe95e4f559ddcf4c9f51ea36b7
--- /dev/null
+++ b/assets/prompts/t3bench_multi.txt
@@ -0,0 +1,100 @@
+An artist is painting on a blank canvas
+A student is typing on his laptop
+A young gymnast trains with a balance beam
+A chef is making pizza dough in the kitchen
+A footballer is kicking a soccer ball
+A man is holding an umbrella against rain
+A girl is reading a hardcover book in her room
+A woman putting lipstick on in front of a mirror
+A worker is climbing a ladder to repair a roof
+A florist is making a bouquet with fresh flowers
+A boy is flying a colorful kite in the sky
+A gardener is watering plants with a hose
+A photographer is capturing a beautiful butterfly with his camera
+A scientist is examining a specimen under a microscope
+A drummer is beating the drumsticks on a drum
+A fisherman is throwing the fishing rod in the sea
+A baby is reaching for a teddy bear on the bed
+A candle burns beside an ancient, leather-bound book
+Colorful beads are scattered around a small, yellow sewing box
+A skateboard leans casually against a brightly-marked graffiti wall
+An apple lays nestled next to a vintage, brass pocket watch
+Two seashells gleam under the soft light of the moon
+A pair of worn ballet shoes rest next to a glossy violin
+A quill pen lies across a stack of unmarked parchment paper
+A baseball glove is forgotten next to a half-eaten hot dog
+A wine bottle and two empty glasses glisten under a chandelier
+A magnifying glass sits on top of a mysterious, printed map
+A black cat sleeps peacefully beside a carved pumpkin
+A weathered straw hat hangs beside a freshly picked sunflower
+An open diary lays flat, a single dried rose on its pages
+A twinkling star ornament hangs closely with a snow globe
+A chessboard is set up, the king and queen standing in opposition
+An elegant masquerade mask sits besides a velvet-handled brush
+A vintage typewriter shares space with a half-full bottle of whisky
+A teddy bear cuddles against a softly worn out children's book
+A sandy hourglass and a rugged compass lay side by side
+A pair of spectacles lies open on a dog-eared paperback
+A tarnished key lies next to a fading photograph
+Ripe apples cluster next to a gleaming knife
+An empty notebook rests beside a rustling quill
+An antique clock sits next to a fleeting hourglass
+Weathered boots stand next to a worn hiking stick
+A stack of vinyl records leans against an old gramophone
+A half-eaten sandwich sits next to a lukewarm thermos
+A flickering candle casts a dim light on a dusty mirror
+Luxurious perfume bottles sit next to a dainty jewelry box
+Burnt-out lightbulbs lie beside a discarded screwdriver
+Sandwiched between two hardbound novels lies a bookmark
+A broken tablespoon lies next to an empty sugar bowl
+A polished flute gleams next to hastily-scrawled sheet music
+A wine bottle rests besides a tall glass on a glossy surface
+A novel is sprawled open next to a pair of reading glasses
+An untouched slice of pizza is cooling beside a half-drunk can of soda
+A tangle of yarn rests beside a pair of silver knitting needles
+A violin reclines on a chair next to a music sheet filled with notes
+An exploded bottle of ink is smeared across a pile of papers
+A child's teddy bear is left abandoned near a frequently-read bedtime story book
+A brightly lit birthday cake is set near a closed, wrapped gift
+A dripping paintbrush stands poised above a half-finished canvas
+A silent alarm clock is glaring at a tousled pillow
+Two worn paddles rest next to an overturned canoe
+A crumpled sheet of paper lies beside a blue ink pen
+Two worn-out gym shoes are placed by the front door
+An abandoned teddy bear leans against a discarded toy car
+A bottle of red wine stands alongside an empty wine glass
+An old brass key sits next to an intricate, dust-covered lock
+Two silken, colorful scarves are knotted together
+A flickering candle stands beside a vintage photograph in a wooden frame
+A worn violin and a broken bow rest casually on a chair
+A chewed pencil rests alongside a frustration-filled sudoku puzzle
+A steaming teapot is standing next to a china teacup with roses
+A sprouting potted plant sits comfortably next to a watering can filled with water
+A camera with a telephoto lens rests atop a folded map of a city
+A novel rests atop a worn-out diary
+The golden trophy shines brightly next to a ruffled blue ribbon
+An antique clock and a retro radio compete for attention on the dusty shelf
+An artist's brush is soaked inwater, awaiting its turn beside a vibrant color palette
+A candle, half-burnt, emits an inviting scent near a generous plate of cookies
+Two wine glasses, one filled with red, the other white, touch in a graceful toast
+An old-fashioned typewriter aspires to outshine a sleek laptop
+A well-worn skateboard rests against a gleaming silver bike
+A ceramic elephant stands guard over an intricate puzzle yet to be completed
+The fragrance of an open perfume bottle mingles with the smell of a single red rose
+A pyramid of stacked poker chips dwarfs a pair of playing cards
+An oversharpened pencil argues with a ballpoint pen over a stash of blank paper
+A colorful array of spices in tiny jars sits next to an unused cooking book
+A warm, glowing lantern rests beside a well-read paperback book
+A treasure map, crumped and faded, cocooned beside an ornate compass
+Two vinyl records rest idly on the sputtering gramophone
+An envelope sealed with love stands demurely beside an ancient inkwell and a feather quill
+An open book sits beside a vintage brass spectacles
+Hot popcorn jump out from the red striped popcorn maker
+A rustic cowboy hat hangs on the rugged saddle
+A half-eaten slice of pizza forgotten beside a remote control
+A dripping paintbrush strokes a vibrant palette of colors
+A shimmering diamond necklace lays elegantly on a black velvet box
+A thick novel is accompanied by a steaming cup of cocoa
+A woolen knitted scarf is wrapped around a carved pumpkin
+A scratched surfboard lies in the sandy shadow of a worn-out straw hat
+An old brass key longs for the dusted antique chest it could unlock
diff --git a/assets/prompts/t3bench_single.txt b/assets/prompts/t3bench_single.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00ee393d6334414d71d61e31d69ad5ac46b9b1f4
--- /dev/null
+++ b/assets/prompts/t3bench_single.txt
@@ -0,0 +1,100 @@
+A cactus with pink flowers
+A rainbow-colored umbrella
+An antique wooden rocking horse
+A golden retriever with a blue bowtie
+An ivory candlestick holder
+A pair of polka-dotted sneakers
+A steaming mug of hot chocolate with whipped cream
+A bright red fire hydrant
+A gleaming silver saxophone
+A leather-bound book with gold details
+A vibrant sunflower with green leaves
+A castle-shaped sandcastle
+A neon green skateboard with black wheels
+A pirate flag with skull and crossbones
+A plush teddy bear with a satin bow
+A ripe watermelon sliced in half
+A sparkling diamond ring in a velvet box
+A vintage porcelain doll with a frilly dress
+A chameleon perched on a tree branch
+A tarnished brass pocket watch
+A ceramic teapot with floral patterns
+An antique ruby-studded brooch
+A simple burgundy colored feather quill
+A vintage iron-cast typewriter
+A shiny emerald green beetle
+A crystal glass paperweight with abstract design
+A velvet cushion stitched with golden threads
+A small porcelain white rabbit figurine
+A left-handed electric guitar painted black
+A bright blue plastic swimming goggles
+A partly broken shell of a tortoise
+A long woolen scarf, striped red and black
+A tattered old explorer’s map
+A well-used black iron frying pan
+A crumpled silver aluminum soda can
+A thick, green-spined book with yellowed pages
+A shimmering emerald pendant necklace
+A well-worn straw sun hat
+A tarnished silver letter opener
+An antique glass perfume bottle
+A polished mahogany grand piano
+A dented brass trumpet
+A pristine white wedding gown
+A chipped porcelain teacup
+A rustic wrought-iron candle holder
+A vibrant, handmade patchwork quilt
+A plush velvet armchair
+A sleek, black top hat
+A paint-splattered easel
+A bent steel crowbar
+A crisp paper airplane
+A worn-out rubber tire swing
+An intricately-carved wooden chess set
+A bright red kite with a frayed tail
+A smooth, round opal stone
+A rusty, vintage metal key
+A delicate, handmade lace doily
+A sturdy mahogany walking cane
+A sparkling crystal chandelier
+A worn-out red flannel shirt
+A cracked porcelain doll's face
+A dusty classic typewriter
+A glossy grand black piano
+A faux-fur leopard print hat
+A futuristic, sleek electric car model
+A cherry red vintage lipstick tube
+A cobweb-covered old wooden chest
+A gold glittery carnival mask
+A tattered world map with stained edges
+A shiny red apple
+A worn-out leather briefcase
+An antique gold pocket watch
+A sleek, slim smartphone
+A wet, vibrant beach ball
+A rusty, abandoned bicycle
+A fluffy, orange cat
+Crisp, folded origami paper
+A shiny, new electric guitar
+A weather-beaten wooden bat
+A delicate crystal champagne flute
+An old, frayed straw hat
+A scuffed up soccer ball
+A pair of worn-in blue jeans
+A well-loved stuffed teddy bear
+A chipped, white coffee mug
+A bright, yellow rubber duck
+A sleek stainless steel teapot
+A water-streaked glass window pane
+An intricate ceramic vase with peonies painted on it
+A fuzzy pink flamingo lawn ornament
+A blooming potted orchid with purple flowers
+An old bronze ship's wheel
+A sparkling diamond tiara
+A vintage plaid woolen blanket
+A pair of shiny black patent leather shoes
+An elegant feather-quill ink pen
+A fragrant pine Christmas wreath
+A silver mirror with ornate detailing
+A green enameled watering can
+A classic leatherette radio with dials
diff --git a/assets/prompts/t3bench_surr.txt b/assets/prompts/t3bench_surr.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0163c022cdfcd665018b16f475da20d59316c6bb
--- /dev/null
+++ b/assets/prompts/t3bench_surr.txt
@@ -0,0 +1,100 @@
+A white seashell on a sandy beach
+A vintage clock hanging on a brick wall
+A pair of glasses on an open notebook
+A green cactus in a clay pot
+A colorful kite flying in a clear sky
+A wooden rocking chair on a porch
+A silver spoon in a bowl of soup
+A black cat sleeping on a windowsill
+A bluebird perched on a tree branch
+A pair of sneakers hanging from a power line
+A yellow umbrella on a rainy day
+A pink bicycle leaning against a fence
+A snowman wearing a scarf in a winter landscape
+A leather-bound book on a wooden table
+A red rose in a crystal vase
+A jar of homemade jam on a kitchen counter
+A pair of ballet shoes on a dance floor
+A green turtle swimming in a clear pond
+A rainbow-colored kite soaring in the sky
+A chocolate cake on a white plate
+A pair of binoculars on a mountain peak
+A teddy bear on a child's bed
+A pair of blue jeans hanging on a clothesline
+A bright sunflower in a field
+A colorful parrot on a jungle tree
+A lighthouse on a rocky shore
+A pair of hiking boots on a trail
+A red and white lighthouse on a cliff
+A yellow school bus on a city street
+A pair of lovebirds in a golden cage
+A white sailboat on a calm sea
+A vintage typewriter on a desk
+A ripe watermelon on a picnic table
+A stack of pancakes on a breakfast table
+A green frog on a lily pad
+A pair of mittens on a snowy day
+A blue butterfly on a pink flower
+A red fire hydrant on a sidewalk
+A pair of cowboy boots in a barn
+A black and white photo in a silver frame
+A rainbow over a waterfall
+A white dove flying in a blue sky
+A pink piggy bank on a shelf
+A pair of scissors on a craft table
+A red barn in a green field
+A pair of flip flops on a beach
+A hot air balloon in a clear sky
+A green apple on a teacher's desk
+A yellow rubber duck in a bathtub
+A white swan on a tranquil lake
+A red cardinal on a snowy branch
+A black and white soccer ball on a field
+A silver teapot on a dining table
+A red and white candy cane on a Christmas tree
+A pair of red boxing gloves hanging on a wall
+A white wedding dress on a hanger
+A brown leather suitcase on a train platform
+A pink flamingo in a zoo
+A pair of green wellies in a puddle
+A black grand piano in a concert hall
+A white picket fence around a garden
+A blue whale in the deep ocean
+A brown horse in a green pasture
+A brown teddy bear in a toy shop
+A brown leather suitcase at an airport
+A green wellington boot in mud
+A blue and white china cup on a saucer
+A silver laptop sitting on a wooden desk
+A blue ceramic mug filled with steaming coffee
+A vintage pocket watch with a golden chain
+A green leather-bound book on an antique shelf
+A pair of white sneakers on a black mat
+A golden locket with a delicate chain
+A white porcelain teapot on a lace tablecloth
+A silver telescope pointing towards the starry sky
+A purple umbrella left on a park bench
+A wooden rocking horse in a child's playroom
+A red apple resting on a white ceramic plate
+A green paperback novel lying on a beige couch
+A white cotton t-shirt hanging on a wooden hanger
+A silver wristwatch ticking on a glass bedside table
+A pink ceramic vase filled with fresh white lilies
+A gold necklace with a diamond pendant displayed in a velvet box
+A black and white photograph framed in dark mahogany
+A pair of red high-heeled shoes sitting in a shoebox
+A blue denim jacket draped over a wooden chair
+A purple yoga mat rolled up in a gym bag
+A silver toaster with two slices of bread browning
+A red ceramic coffee mug sitting on a wooden table
+A green plastic watering can filled with fresh water
+A shiny gold pocket watch ticking away in a velvet box
+A blue denim jacket hanging on a metal coat rack
+A pair of brown leather boots standing at the front door
+A crystal chandelier sparkling in the grand foyer
+A fluffy white pillow resting on a red velvet couch
+A vibrant orange pumpkin sitting on a hay bale
+A striped beach umbrella standing tall on a sandy beach
+A stainless steel toaster sitting on a marble countertop
+A delicate china teacup resting on a lace doily
+A pair of neon running shoes waiting by the treadmill
diff --git a/configs/elevest.yaml b/configs/elevest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..74893f7b0a88a30c38d93abad665d3d1109aaf45
--- /dev/null
+++ b/configs/elevest.yaml
@@ -0,0 +1,31 @@
+opt_type: "elevest"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0001
+ betas:
+ - 0.9
+ - 0.95
+ weight_decay: 0.05
+
+lr_scheduler:
+ name: "cosine_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 16
+ epochs: 30
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsdiff_paa.yaml b/configs/gsdiff_paa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9aa996dcea85c21ebb7c1842218cec414c5b0bfc
--- /dev/null
+++ b/configs/gsdiff_paa.yaml
@@ -0,0 +1,32 @@
+opt_type: "gsdiff_paa"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0001
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.01
+ eps: 1.e-8
+
+lr_scheduler:
+ name: "constant_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 8
+ epochs: 100
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsdiff_pas.yaml b/configs/gsdiff_pas.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e477837cdfdcff1a7e25459374ccf9daa7565258
--- /dev/null
+++ b/configs/gsdiff_pas.yaml
@@ -0,0 +1,32 @@
+opt_type: "gsdiff_pas"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0001
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.01
+ eps: 1.e-8
+
+lr_scheduler:
+ name: "constant_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 8
+ epochs: 100
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsdiff_sd15.yaml b/configs/gsdiff_sd15.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8f8ef81e46d48bd9c1797336aa30dbff402d1c34
--- /dev/null
+++ b/configs/gsdiff_sd15.yaml
@@ -0,0 +1,32 @@
+opt_type: "gsdiff_sd15"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0001
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.01
+ eps: 1.e-8
+
+lr_scheduler:
+ name: "constant_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 8
+ epochs: 100
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsdiff_sd35m_80g.yaml b/configs/gsdiff_sd35m_80g.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8dbd8b1d6430ec823a68451039b7672acff19a52
--- /dev/null
+++ b/configs/gsdiff_sd35m_80g.yaml
@@ -0,0 +1,32 @@
+opt_type: "gsdiff_sd35m"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0001
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.01
+ eps: 1.e-8
+
+lr_scheduler:
+ name: "constant_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 2
+ epochs: 100
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsdiff_sd3m_80g.yaml b/configs/gsdiff_sd3m_80g.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9fbfa914489da5130550c6e93796e594fff34f35
--- /dev/null
+++ b/configs/gsdiff_sd3m_80g.yaml
@@ -0,0 +1,32 @@
+opt_type: "gsdiff_sd3m"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0001
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.01
+ eps: 1.e-8
+
+lr_scheduler:
+ name: "constant_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 2
+ epochs: 100
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsdiff_sdxl_80g.yaml b/configs/gsdiff_sdxl_80g.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..22ba7f5a641fabaf49a052044ed0ee5f232d2054
--- /dev/null
+++ b/configs/gsdiff_sdxl_80g.yaml
@@ -0,0 +1,32 @@
+opt_type: "gsdiff_sdxl"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0001
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.01
+ eps: 1.e-8
+
+lr_scheduler:
+ name: "constant_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 2
+ epochs: 100
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsrecon.yaml b/configs/gsrecon.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..497905fd9a5dad8cfa9379e2ed0b039777c01f0c
--- /dev/null
+++ b/configs/gsrecon.yaml
@@ -0,0 +1,31 @@
+opt_type: "gsrecon"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0004
+ betas:
+ - 0.9
+ - 0.95
+ weight_decay: 0.05
+
+lr_scheduler:
+ name: "cosine_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 8
+ epochs: 30
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/configs/gsvae.yaml b/configs/gsvae.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cc3dfe38d498584c12b9e8854c41e76b88cc9f4d
--- /dev/null
+++ b/configs/gsvae.yaml
@@ -0,0 +1,31 @@
+opt_type: "gsvae"
+
+optimizer:
+ name: "adamw"
+ lr: 0.0004
+ betas:
+ - 0.9
+ - 0.95
+ weight_decay: 0.05
+
+lr_scheduler:
+ name: "cosine_warmup"
+ num_warmup_steps: 1000
+
+train:
+ batch_size_per_gpu: 2
+ epochs: 30
+ log_freq: 1
+ early_eval_freq: 100
+ eval_eval: 1000
+ save_freq: 2000
+ eval_freq_epoch: 5
+ save_freq_epoch: 10
+ ema_kwargs:
+ decay: 0.9999
+ use_ema_warmup: true
+ inv_gamma: 1.
+ power: 0.75
+
+val:
+ batch_size_per_gpu: 4
diff --git a/download_ckpt.py b/download_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6ad417c844ed52e0d8df265bde3dedc23a5d03d
--- /dev/null
+++ b/download_ckpt.py
@@ -0,0 +1,91 @@
+import os
+import argparse
+from huggingface_hub import snapshot_download
+
+
+def download_ckpt():
+ parser = argparse.ArgumentParser(description="Download checkpoints from HuggingFace Hub")
+ parser.add_argument(
+ "--local_dir",
+ type=str,
+ default="./out",
+ help="Local directory to save the checkpoints"
+ )
+ parser.add_argument(
+ "--model_type",
+ type=str,
+ default="sd15",
+ choices=["sd15", "pas", "sd35m", "depth", "normal", "canny", "elevest"],
+ help="Model type to download"
+ )
+ parser.add_argument(
+ "--image_cond",
+ action="store_true",
+ help="Whether to download image-conditioned models"
+ )
+
+ args = parser.parse_args()
+
+ repo_id, local_dir = "chenguolin/DiffSplat", args.local_dir
+ os.makedirs(local_dir, exist_ok=True)
+
+ model_type, image_cond = args.model_type, args.image_cond
+ suffix = "_image" if image_cond else ""
+
+ # DiffSplat (SD1.5)
+ if model_type == "sd15":
+ snapshot_download(
+ repo_id=repo_id,
+ local_dir=local_dir,
+ allow_patterns=[
+ "gsrecon_gobj265k_cnp_even4/*", # `GSRecon`
+ "gsvae_gobj265k_sd/*", # `GSVAE (SD)`
+ f"gsdiff_gobj83k_sd15{suffix}__render/*", # `DiffSplat (SD)`
+ ]
+ )
+ # DiffSplat (PixArt-Sigma)
+ elif model_type == "pas":
+ snapshot_download(
+ repo_id=repo_id,
+ local_dir=local_dir,
+ allow_patterns=[
+ "gsrecon_gobj265k_cnp_even4/*", # `GSRecon`
+ "gsvae_gobj265k_sdxl_fp16/*", # `GSVAE (SDXL)`
+ f"gsdiff_gobj83k_pas_fp16{suffix}__render/*", # `DiffSplat (PixArt-Sigma)`
+ ]
+ )
+ # DiffSplat (SD3.5m)
+ elif model_type == "sd35m":
+ snapshot_download(
+ repo_id=repo_id,
+ local_dir=local_dir,
+ allow_patterns=[
+ "gsrecon_gobj265k_cnp_even4/*", # `GSRecon`
+ "gsvae_gobj265k_sd3/*", # `GSVAE (SD3)`
+ f"gsdiff_gobj83k_sd35m{suffix}__render/*", # `DiffSplat (SD3.5m)`
+ ]
+ )
+ # DiffSplat ControlNet (SD1.5)
+ elif model_type in ["depth", "normal", "canny"]:
+ snapshot_download(
+ repo_id=repo_id,
+ local_dir=local_dir,
+ allow_patterns=[
+ f"gsdiff_gobj83k_sd15__render__{model_type}/*", # `DiffSplat ControlNet (SD1.5)`
+ ]
+ )
+ # Elevation Estimation
+ elif model_type == "elevest":
+ snapshot_download(
+ repo_id=repo_id,
+ local_dir=local_dir,
+ allow_patterns=[
+ "elevest_gobj265k_b_C25/*",
+ ]
+ )
+ else:
+ raise ValueError(f"Choose from ['sd15', 'pas', 'sd35m', 'depth', 'normal', 'canny', 'elevest'], but got [{model_type}]")
+
+
+if __name__ == "__main__":
+ download_ckpt()
diff --git a/extensions/RaDe-GS/.gitignore b/extensions/RaDe-GS/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..046f52ad7e7a6c9784971e0e42797d27b0f4cea0
--- /dev/null
+++ b/extensions/RaDe-GS/.gitignore
@@ -0,0 +1,19 @@
+*.pyc
+.vscode
+output*/
+build
+__pycache__
+tensorboard_3d
+screenshots
+debug/
+gaussian_renderer/__pycache__
+scene/__pycache__
+utils/__pycache__
+*.egg-info
+debug*
+dtu_eval/Offical_DTU_Dataset
+*.sh
+submodules/diff-gaussian-rasterization-org
+snapshot_fw.dump
+snapshot_bw.dump
+train_test_depth.py
\ No newline at end of file
diff --git a/extensions/RaDe-GS/.gitmodules b/extensions/RaDe-GS/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..ec0586f58c4961ad5079e2a26c83284590519d4e
--- /dev/null
+++ b/extensions/RaDe-GS/.gitmodules
@@ -0,0 +1,7 @@
+[submodule "submodules/simple-knn"]
+ path = submodules/simple-knn
+ url = https://gitlab.inria.fr/bkerbl/simple-knn.git
+
+[submodule "submodules/diff-gaussian-rasterization/third_party/glm"]
+ path = submodules/diff-gaussian-rasterization/third_party/glm
+ url = https://github.com/g-truc/glm.git
diff --git a/extensions/RaDe-GS/LICENSE.md b/extensions/RaDe-GS/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..c869e695fa63bfde6f887d63a24a2a71f03480ac
--- /dev/null
+++ b/extensions/RaDe-GS/LICENSE.md
@@ -0,0 +1,83 @@
+Gaussian-Splatting License
+===========================
+
+**Inria** and **the Max Planck Institut for Informatik (MPII)** hold all the ownership rights on the *Software* named **gaussian-splatting**.
+The *Software* is in the process of being registered with the Agence pour la Protection des
+Programmes (APP).
+
+The *Software* is still being developed by the *Licensor*.
+
+*Licensor*'s goal is to allow the research community to use, test and evaluate
+the *Software*.
+
+## 1. Definitions
+
+*Licensee* means any person or entity that uses the *Software* and distributes
+its *Work*.
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diff --git a/extensions/RaDe-GS/README.md b/extensions/RaDe-GS/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..31afd29bdf7390bfe2a70e9072cd213d531a3782
--- /dev/null
+++ b/extensions/RaDe-GS/README.md
@@ -0,0 +1,118 @@
+# RaDe-GS: Rasterizing Depth in Gaussian Splatting
+
+### RaDe-GS: Rasterizing Depth in Gaussian Splatting
+Baowen Zhang, Chuan Fang, Rakesh Shrestha, Yixun Liang, Xiaoxiao Long, Ping Tan
+
+[Project page](https://baowenz.github.io/radegs/)
+
+# News!
+### 1. We have updated the formluation of RaDe-GS (as shown in the 'Modifications'). It achieves better performance on TNT dataset.
+### 2. Now, we release the updated code of Marching Tetrahedra, based on [GOF](https://github.com/autonomousvision/gaussian-opacity-fields/blob/main/eval_tnt/run.py)'s orginal proposal. In our version, opacities are calculated in ray space, which better fits our needs.
+
+# Modifications
+
+1. We change to calculate depth using per-pixel cosine values (Eq. 14: $d=cos\theta\ t^*$). Additionally, an option is provided to directly render the camera space coordinate map. We utilize the inverse of affine approximation to transform intersections from ray space to camera space, and these transformed points are then used in computing normal consistency loss.
+2. The depth distortion loss has been eliminated from our training process. Currently, we rely solely on normal consistency loss for geometry regularization. We believe future techniques will enhance performance even further.
+
+
+# 1. Installation
+## Clone this repository.
+```
+git clone https://github.com/BaowenZ/RaDe-GS.git --recursive
+```
+
+## Install dependencies.
+1. create an environment
+```
+conda create -n radegs python=3.9
+conda activate radegs
+```
+
+2. install pytorch and other dependencies.
+```
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+pip install -r requirements.txt
+```
+
+3. install submodules
+```
+pip install submodules/diff-gaussian-rasterization
+pip install submodules/simple-knn/
+
+# tetra-nerf for Marching Tetrahedra
+cd submodules/tetra_triangulation
+conda install cmake
+conda install conda-forge::gmp
+conda install conda-forge::cgal
+cmake .
+# you can specify your own cuda path
+# export CPATH=/usr/local/cuda-11.3/targets/x86_64-linux/include:$CPATH
+make
+pip install -e .
+```
+
+# 2. Preparation
+We use preprocessed DTU dataset from [2DGS](https://surfsplatting.github.io/) for training. And we follow GOF to evaluate the geometry. Point clouds from the [DTU dataset](https://roboimagedata.compute.dtu.dk/?page_id=36) need saved to dtu_eval/Offical_DTU_Dataset for the geometry evaluation.
+We use preprocessed Tanks and Temples dataset from [GOF](https://huggingface.co/datasets/ZehaoYu/gaussian-opacity-fields/tree/main). For evalution, please download ground truth point cloud, camera poses, alignments and cropfiles from [Tanks and Temples dataset](https://www.tanksandtemples.org/download/). The ground truth dataset should be organized as:
+```
+GT_TNT_dataset
+│
+└─── Barn
+│ │
+| └─── Barn.json
+│ │
+| └─── Barn.ply
+│ │
+| └─── Barn_COLMAP_SfM.log
+│ │
+| └─── Barn_trans.txt
+│
+└─── Caterpillar
+│ │
+......
+```
+
+# 3. Training and Evalution
+## DTU Dataset
+```
+# training
+python train.py -s -m
+ 
+ 
+ 
+ 
+
+ 
+ 
+ 
+ 
+
+
+
+
+
+