Add pipeline tag and library name (#1)

- Add pipeline tag and library name (89a0b5d6a1daf2d933427039550a1d569f89ce04)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,7 +1,260 @@
 ---
-license: apache-2.0
 base_model:
 - stabilityai/stable-diffusion-2-1-base
+license: apache-2.0
+pipeline_tag: text-to-3d
+library_name: diffusers
 paper:
-- arxiv.org/abs/2503.2169
----
+- arxiv.org/abs/2503.21694
+---
+
+## File information
+
+The repository contains the following file information:
+
+
+
+Note: file information is just provided as context for you, do not add it to the model card.
+
+# Project page
+
+The project page URL we found has the following URL: 
+
+# Github README
+
+The Github README we found contains the following content:
+
+<img src="assets/Showcase_v4.drawio.png" width="100%" align="center">
+<div align="center">
+<h1>Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data</h1>
+<div>
+    <a href='https://scholar.google.com/citations?user=F15mLDYAAAAJ&hl=en' target='_blank'>Zhiyuan Ma</a>&emsp;
+    <a href='https://scholar.google.com/citations?user=R9PlnKgAAAAJ&hl=en' target='_blank'>Xinyue Liang</a>&emsp;
+    <a href='https://scholar.google.com/citations?user=A-U8zE8AAAAJ&hl=en' target='_blank'>Rongyuan Wu</a>&emsp;
+    <a href='https://scholar.google.com/citations?user=1rbNk5oAAAAJ&hl=zh-CN' target='_blank'>Xiangyu Zhu</a>&emsp;
+    <a href='https://scholar.google.com/citations?user=cuJ3QG8AAAAJ&hl=en' target='_blank'>Zhen Lei</a>&emsp;
+    <a href='https://scholar.google.com/citations?user=tAK5l1IAAAAJ&hl=en' target='_blank'>Lei Zhang</a>
+</div>
+
+<div>
+<a href="https://arxiv.org/abs/2503.21694"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
+<a href='https://theericma.github.io/TriplaneTurbo/'><img src='https://img.shields.io/badge/Project_Page-Website-green?logo=googlechrome&logoColor=white' alt='Project Page'></a>
+<a href='https://huggingface.co/spaces/ZhiyuanthePony/TriplaneTurbo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Live_Demo-blue'></a>
+<a href='https://theericma.github.io/TriplaneTurbo/static/pdf/main.pdf'><img src='https://img.shields.io/badge/Slides-Presentation-orange?logo=microsoftpowerpoint&logoColor=white' alt='Presentation Slides'></a>
+</div>
+
+
+---
+
+</div>
+
+<!-- Updates -->
+## ⏩ Updates
+
+- **2025-04-01**: Presentation slides are now available for download.
+- **2025-03-27**: The paper is now available on Arxiv.
+- **2025-03-03**: Gradio and HuggingFace Demos are available.
+- **2025-02-27**: TriplaneTurbo is accepted to CVPR 2025.
+
+<!-- Features -->
+## 🌟 Features
+- **Fast Inference 🚀**: Our code excels in inference efficiency, capable of outputting textured mesh in around 1 second.
+- **Text Comprehension 🆙**: It demonstrates strong understanding capabilities for complex text prompts, ensuring accurate generation according to the input.
+- **3D-Data-Free Training 🙅‍♂️**: The entire training process doesn't rely on any 3D datasets, making it more resource-friendly and adaptable.
+
+
+## 🤖 Start local inference in 3 minutes
+If you only wish to set up the demo locally, use the following code for the inference. Otherwise, for training and evaluation, use the next section of instructions for environment setup.
+
+```python
+python -m venv venv
+source venv/bin/activate
+bash setup.sh
+python gradio_app.py
+```
+
+## 🛠️ Official Installation
+
+Create a virtual environment:
+```sh
+conda create -n triplaneturbo python=3.10
+conda activate triplaneturbo
+conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
+```
+(Optional, Recommended) Install xFormers for attention acceleration:
+```sh
+conda install xFormers -c xFormers
+```
+(Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions
+```sh
+pip install ninja
+```
+Install major dependencies
+```sh
+pip install -r requirements.txt
+```
+Install iNGP
+```sh
+export PATH="/usr/local/cuda/bin:$PATH"
+export LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
+pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
+```
+If you encounter errors while installing iNGP, it is recommended to check your gcc version. Follow these steps to change the gcc version within your -cconda environment. After that, return to the project directory and reinstall iNGP and NerfAcc:
+```sh
+conda install -c conda-forge gxx=9.5.0
+cd  $CONDA_PREFIX/lib
+ln -s  /usr/lib/x86_64-linux-gnu/libcuda.so ./
+cd <your project directory>
+```
+
+## 📊 Evaluation
+
+If you only want to run the evaluation without training, follow these steps:
+
+```sh
+# Download the model from HuggingFace
+huggingface-cli download --resume-download ZhiyuanthePony/TriplaneTurbo \
+    --include "triplane_turbo_sd_v1.pth" \
+    --local-dir ./pretrained \
+    --local-dir-use-symlinks False
+
+# Download evaluation assets
+python scripts/prepare/download_eval_only.py
+
+# Run evaluation script
+bash scripts/eval/dreamfusion.sh --gpu 0,1 # You can use more GPUs (e.g. 0,1,2,3,4,5,6,7). For single GPU usage, please check the script for required modifications
+```
+
+Our evaluation metrics include:
+- CLIP Similarity Score
+- CLIP Recall@1
+
+For detailed evaluation results, please refer to our paper.
+
+If you want to evaluate your own model, use the following script:
+```sh
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python launch.py \
+    --config <path_to_your_exp_config> \
+    --export \
+    system.exporter_type="multiprompt-mesh-exporter" \
+    resume=<path_to_your_ckpt> \
+    data.prompt_library="dreamfusion_415_prompt_library" \
+    system.exporter.fmt=obj
+```
+
+After running the script, you will find generated OBJ files in `outputs/<your_exp>/dreamfusion_415_prompt_library/save/<itXXXXX-export>`. Set this path as `<OBJ_DIR>`, and set `outputs/<your_exp>/dreamfusion_415_prompt_library/save/<itXXXXX-4views>` as `<VIEW_DIR>`. Then run:
+
+```sh
+SAVE_DIR=<VIEW_DIR>
+python evaluation/mesh_visualize.py \
+    <OBJ_DIR> \
+    --save_dir $SAVE_DIR \
+    --gpu 0,1,2,3,4,5,6,7
+
+python evaluation/clipscore/compute.py \
+    --result_dir $SAVE_DIR
+```
+The evaluation results will be displayed in your terminal once the computation is complete.
+
+## 🚀 Training Options
+
+### 1. Download Required Pretrained Models and Datasets
+Use the provided download script to get all necessary files:
+```sh
+python scripts/prepare/download_full.py
+```
+
+This will download:
+- Stable Diffusion 2.1 Base
+- Stable Diffusion 1.5
+- MVDream 4-view checkpoint
+- RichDreamer checkpoint
+- Text prompt datasets (3DTopia and DALLE+Midjourney)
+
+### 2. Training Options
+
+#### Option 1: Train with 3DTopia Text Prompts
+```sh
+# Single GPU
+CUDA_VISIBLE_DEVICES=0 python launch.py \
+    --config configs/TriplaneTurbo_v0_acc-2.yaml \
+    --train \
+    data.prompt_library="3DTopia_prompt_library" \
+    data.condition_processor.cache_dir=".threestudio_cache/text_embeddings_3DTopia" \
+    data.guidance_processor.cache_dir=".threestudio_cache/text_embeddings_3DTopia"
+```
+
+For multi-GPU training:
+```sh
+# 8 GPUs with 48GB+ memory each
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python launch.py \
+    --config configs/TriplaneTurbo_v1_acc-2.yaml \
+    --train \
+    data.prompt_library="3DTopia_361k_prompt_library" \
+    data.condition_processor.cache_dir=".threestudio_cache/text_embeddings_3DTopia" \
+    data.guidance_processor.cache_dir=".threestudio_cache/text_embeddings_3DTopia"
+```
+
+#### Option 2: Train with DALLE+Midjourney Text Prompts
+Choose the appropriate command based on your GPU configuration:
+
+```sh
+# Single GPU
+CUDA_VISIBLE_DEVICES=0 python launch.py \
+    --config configs/TriplaneTurbo_v0_acc-2.yaml \
+    --train \
+    data.prompt_library="DALLE_Midjourney_prompt_library" \
+    data.condition_processor.cache_dir=".threestudio_cache/text_embeddings_DE+MJ" \
+    data.guidance_processor.cache_dir=".threestudio_cache/text_embeddings_DE+MJ"
+```
+
+For multi-GPU training (higher performance):
+```sh
+# 8 GPUs with 48GB+ memory each
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python launch.py \
+    --config configs/TriplaneTurbo_v1_acc-2.yaml \
+    --train \
+    data.prompt_library="DALLE_Midjourney_prompt_library" \
+    data.condition_processor.cache_dir=".threestudio_cache/text_embeddings_DE+MJ" \
+    data.guidance_processor.cache_dir=".threestudio_cache/text_embeddings_DE+MJ"
+```
+
+### 3. Configuration Notes
+- **Memory Requirements**: 
+  - v1 configuration: Requires GPUs with 48GB+ memory
+  - v0 configuration: Works with GPUs that have less memory (46GB+) but with reduced performance
+  
+- **Acceleration Options**:
+  - Use `_acc-2.yaml` configs for gradient accumulation to reduce memory usage
+
+- **Advanced Options**:
+  - For highest quality, use `configs/TriplaneTurbo_v1.yaml` with `system.parallel_guidance=true` (requires 98GB+ memory GPUs)
+  - To disable certain guidance components: add `guidance.rd_weight=0 guidance.sd_weight=0` to the command
+
+
+
+
+
+<!-- Citation -->
+## 📜 Citation
+
+If you find this work helpful, please consider citing our paper:
+```
+@article{ma2025progressive,
+  title={Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data},
+  author={Ma, Zhiyuan and Liang, Xinyue and Wu, Rongyuan and Zhu, Xiangyu and Lei, Zhen and Zhang, Lei},
+  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
+  year={2025}
+}
+```
+
+
+<!-- Acknowledgement -->
+## 🙏 Acknowledgement
+Our code is heavily based on the following works
+- [ThreeStudio](https://github.com/threestudio-project/threestudio): A clean and extensible codebase for 3D generation via Score Distillation.
+- [MVDream](https://github.com/bytedance/MVDream): Used as one of our multi - view teachers.
+- [RichDreamer](https://github.com/bytedance/MVDream): Serves as another multi - view teacher for normal and depth supervision
+- [3DTopia](https://github.com/3DTopia/3DTopia): Its text caption dataset is applied in our training and comparison.
+- [DiffMC](https://github.com/SarahWeiii/diso): Our solution uses its differentiable marching cube for mesh rasterization.
+- [NeuS](https://github.com/Totoro97/NeuS): We implement its SDF - based volume rendering for dual rendering in our solution