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base on Official repo for paper "Structured 3D Latents for Scalable and Versatile 3D Generation" (CVPR'25 Spotlight). <img src="assets/logo.webp" width="100%" align="center">
<h1 align="center">Structured 3D Latents<br>for Scalable and Versatile 3D Generation</h1>
<p align="center"><a href="https://arxiv.org/abs/2412.01506"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
<a href='https://trellis3d.github.io'><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/JeffreyXiang/TRELLIS'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Live_Demo-blue'></a>
</p>
<p align="center"><img src="assets/teaser.png" width="100%"></p>
<span style="font-size: 16px; font-weight: 600;">T</span><span style="font-size: 12px; font-weight: 700;">RELLIS</span> is a large 3D asset generation model. It takes in text or image prompts and generates high-quality 3D assets in various formats, such as Radiance Fields, 3D Gaussians, and meshes. The cornerstone of <span style="font-size: 16px; font-weight: 600;">T</span><span style="font-size: 12px; font-weight: 700;">RELLIS</span> is a unified Structured LATent (<span style="font-size: 16px; font-weight: 600;">SL</span><span style="font-size: 12px; font-weight: 700;">AT</span>) representation that allows decoding to different output formats and Rectified Flow Transformers tailored for <span style="font-size: 16px; font-weight: 600;">SL</span><span style="font-size: 12px; font-weight: 700;">AT</span> as the powerful backbones. We provide large-scale pre-trained models with up to 2 billion parameters on a large 3D asset dataset of 500K diverse objects. <span style="font-size: 16px; font-weight: 600;">T</span><span style="font-size: 12px; font-weight: 700;">RELLIS</span> significantly surpasses existing methods, including recent ones at similar scales, and showcases flexible output format selection and local 3D editing capabilities which were not offered by previous models.
***Check out our [Project Page](https://trellis3d.github.io) for more videos and interactive demos!***
<!-- Features -->
## π Features
- **High Quality**: It produces diverse 3D assets at high quality with intricate shape and texture details.
- **Versatility**: It takes text or image prompts and can generate various final 3D representations including but not limited to *Radiance Fields*, *3D Gaussians*, and *meshes*, accommodating diverse downstream requirements.
- **Flexible Editing**: It allows for easy editings of generated 3D assets, such as generating variants of the same object or local editing of the 3D asset.
<!-- Updates -->
## β© Updates
**03/25/2025**
- Release training code.
- Release **TRELLIS-text** models and asset variants generation.
- Examples are provided as [example_text.py](example_text.py) and [example_variant.py](example_variant.py).
- Gradio demo is provided as [app_text.py](app_text.py).
- *Note: It is always recommended to do text to 3D generation by first generating images using text-to-image models and then using TRELLIS-image models for 3D generation. Text-conditioned models are less creative and detailed due to data limitations.*
**12/26/2024**
- Release [**TRELLIS-500K**](https://github.com/microsoft/TRELLIS#-dataset) dataset and toolkits for data preparation.
**12/18/2024**
- Implementation of multi-image conditioning for **TRELLIS-image** model. ([#7](https://github.com/microsoft/TRELLIS/issues/7)). This is based on tuning-free algorithm without training a specialized model, so it may not give the best results for all input images.
- Add Gaussian export in `app.py` and `example.py`. ([#40](https://github.com/microsoft/TRELLIS/issues/40))
<!-- Installation -->
## π¦ Installation
### Prerequisites
- **System**: The code is currently tested only on **Linux**. For windows setup, you may refer to [#3](https://github.com/microsoft/TRELLIS/issues/3) (not fully tested).
- **Hardware**: An NVIDIA GPU with at least 16GB of memory is necessary. The code has been verified on NVIDIA A100 and A6000 GPUs.
- **Software**:
- The [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive) is needed to compile certain submodules. The code has been tested with CUDA versions 11.8 and 12.2.
- [Conda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) is recommended for managing dependencies.
- Python version 3.8 or higher is required.
### Installation Steps
1. Clone the repo:
```sh
git clone --recurse-submodules https://github.com/microsoft/TRELLIS.git
cd TRELLIS
```
2. Install the dependencies:
**Before running the following command there are somethings to note:**
- By adding `--new-env`, a new conda environment named `trellis` will be created. If you want to use an existing conda environment, please remove this flag.
- By default the `trellis` environment will use pytorch 2.4.0 with CUDA 11.8. If you want to use a different version of CUDA (e.g., if you have CUDA Toolkit 12.2 installed and do not want to install another 11.8 version for submodule compilation), you can remove the `--new-env` flag and manually install the required dependencies. Refer to [PyTorch](https://pytorch.org/get-started/previous-versions/) for the installation command.
- If you have multiple CUDA Toolkit versions installed, `PATH` should be set to the correct version before running the command. For example, if you have CUDA Toolkit 11.8 and 12.2 installed, you should run `export PATH=/usr/local/cuda-11.8/bin:$PATH` before running the command.
- By default, the code uses the `flash-attn` backend for attention. For GPUs do not support `flash-attn` (e.g., NVIDIA V100), you can remove the `--flash-attn` flag to install `xformers` only and set the `ATTN_BACKEND` environment variable to `xformers` before running the code. See the [Minimal Example](#minimal-example) for more details.
- The installation may take a while due to the large number of dependencies. Please be patient. If you encounter any issues, you can try to install the dependencies one by one, specifying one flag at a time.
- If you encounter any issues during the installation, feel free to open an issue or contact us.
Create a new conda environment named `trellis` and install the dependencies:
```sh
. ./setup.sh --new-env --basic --xformers --flash-attn --diffoctreerast --spconv --mipgaussian --kaolin --nvdiffrast
```
The detailed usage of `setup.sh` can be found by running `. ./setup.sh --help`.
```sh
Usage: setup.sh [OPTIONS]
Options:
-h, --help Display this help message
--new-env Create a new conda environment
--basic Install basic dependencies
--train Install training dependencies
--xformers Install xformers
--flash-attn Install flash-attn
--diffoctreerast Install diffoctreerast
--vox2seq Install vox2seq
--spconv Install spconv
--mipgaussian Install mip-splatting
--kaolin Install kaolin
--nvdiffrast Install nvdiffrast
--demo Install all dependencies for demo
```
<!-- Pretrained Models -->
## π€ Pretrained Models
We provide the following pretrained models:
| Model | Description | #Params | Download |
| --- | --- | --- | --- |
| TRELLIS-image-large | Large image-to-3D model | 1.2B | [Download](https://huggingface.co/JeffreyXiang/TRELLIS-image-large) |
| TRELLIS-text-base | Base text-to-3D model | 342M | [Download](https://huggingface.co/JeffreyXiang/TRELLIS-text-base) |
| TRELLIS-text-large | Large text-to-3D model | 1.1B | [Download](https://huggingface.co/JeffreyXiang/TRELLIS-text-large) |
| TRELLIS-text-xlarge | Extra-large text-to-3D model | 2.0B | [Download](https://huggingface.co/JeffreyXiang/TRELLIS-text-xlarge) |
*Note: It is always recommended to use the image conditioned version of the models for better performance.*
*Note: All VAEs are included in **TRELLIS-image-large** model repo.*
The models are hosted on Hugging Face. You can directly load the models with their repository names in the code:
```python
TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
```
If you prefer loading the model from local, you can download the model files from the links above and load the model with the folder path (folder structure should be maintained):
```python
TrellisImageTo3DPipeline.from_pretrained("/path/to/TRELLIS-image-large")
```
<!-- Usage -->
## π‘ Usage
### Minimal Example
Here is an [example](example.py) of how to use the pretrained models for 3D asset generation.
```python
import os
# os.environ['ATTN_BACKEND'] = 'xformers' # Can be 'flash-attn' or 'xformers', default is 'flash-attn'
os.environ['SPCONV_ALGO'] = 'native' # Can be 'native' or 'auto', default is 'auto'.
# 'auto' is faster but will do benchmarking at the beginning.
# Recommended to set to 'native' if run only once.
import imageio
from PIL import Image
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.utils import render_utils, postprocessing_utils
# Load a pipeline from a model folder or a Hugging Face model hub.
pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
pipeline.cuda()
# Load an image
image = Image.open("assets/example_image/T.png")
# Run the pipeline
outputs = pipeline.run(
image,
seed=1,
# Optional parameters
# sparse_structure_sampler_params={
# "steps": 12,
# "cfg_strength": 7.5,
# },
# slat_sampler_params={
# "steps": 12,
# "cfg_strength": 3,
# },
)
# outputs is a dictionary containing generated 3D assets in different formats:
# - outputs['gaussian']: a list of 3D Gaussians
# - outputs['radiance_field']: a list of radiance fields
# - outputs['mesh']: a list of meshes
# Render the outputs
video = render_utils.render_video(outputs['gaussian'][0])['color']
imageio.mimsave("sample_gs.mp4", video, fps=30)
video = render_utils.render_video(outputs['radiance_field'][0])['color']
imageio.mimsave("sample_rf.mp4", video, fps=30)
video = render_utils.render_video(outputs['mesh'][0])['normal']
imageio.mimsave("sample_mesh.mp4", video, fps=30)
# GLB files can be extracted from the outputs
glb = postprocessing_utils.to_glb(
outputs['gaussian'][0],
outputs['mesh'][0],
# Optional parameters
simplify=0.95, # Ratio of triangles to remove in the simplification process
texture_size=1024, # Size of the texture used for the GLB
)
glb.export("sample.glb")
# Save Gaussians as PLY files
outputs['gaussian'][0].save_ply("sample.ply")
```
After running the code, you will get the following files:
- `sample_gs.mp4`: a video showing the 3D Gaussian representation
- `sample_rf.mp4`: a video showing the Radiance Field representation
- `sample_mesh.mp4`: a video showing the mesh representation
- `sample.glb`: a GLB file containing the extracted textured mesh
- `sample.ply`: a PLY file containing the 3D Gaussian representation
### Web Demo
[app.py](app.py) provides a simple web demo for 3D asset generation. Since this demo is based on [Gradio](https://gradio.app/), additional dependencies are required:
```sh
. ./setup.sh --demo
```
After installing the dependencies, you can run the demo with the following command:
```sh
python app.py
```
Then, you can access the demo at the address shown in the terminal.
***The web demo is also available on [Hugging Face Spaces](https://huggingface.co/spaces/JeffreyXiang/TRELLIS)!***
<!-- Dataset -->
## π Dataset
We provide **TRELLIS-500K**, a large-scale dataset containing 500K 3D assets curated from [Objaverse(XL)](https://objaverse.allenai.org/), [ABO](https://amazon-berkeley-objects.s3.amazonaws.com/index.html), [3D-FUTURE](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-future), [HSSD](https://huggingface.co/datasets/hssd/hssd-models), and [Toys4k](https://github.com/rehg-lab/lowshot-shapebias/tree/main/toys4k), filtered based on aesthetic scores. Please refer to the [dataset README](DATASET.md) for more details.
<!-- Training -->
## ποΈββοΈ Training
TRELLISβs training framework is organized to provide a flexible and modular approach to building and fine-tuning large-scale 3D generation models. The training code is centered around `train.py` and is structured into several directories to clearly separate dataset handling, model components, training logic, and visualization utilities.
### Code Structure
- **train.py**: Main entry point for training.
- **trellis/datasets**: Dataset loading and preprocessing.
- **trellis/models**: Different models and their components.
- **trellis/modules**: Custom modules for various models.
- **trellis/pipelines**: Inference pipelines for different models.
- **trellis/renderers**: Renderers for different 3D representations.
- **trellis/representations**: Different 3D representations.
- **trellis/trainers**: Training logic for different models.
- **trellis/utils**: Utility functions for training and visualization.
### Training Setup
1. **Prepare the Environment:**
- Ensure all training dependencies are installed.
- Use a Linux system with an NVIDIA GPU (The models are trained on NVIDIA A100 GPUs).
- For distributed training, verify that your nodes can communicate through the designated master address and port.
2. **Dataset Preparation:**
- Organize your dataset similar to TRELLIS-500K. Specify your dataset path using the `--data_dir` argument when launching training.
3. **Configuration Files:**
- Training hyperparameters and model architectures are defined in configuration files under the `configs/` directory.
- Example configuration files include:
| Config | Pretained Model | Description |
| --- | --- | --- |
| [`vae/ss_vae_conv3d_16l8_fp16.json`](configs/vae/ss_vae_conv3d_16l8_fp16.json) | [Encoder](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/ss_enc_conv3d_16l8_fp16.safetensors) [Decoder](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/ss_dec_conv3d_16l8_fp16.safetensors) | Sparse structure VAE |
| [`vae/slat_vae_enc_dec_gs_swin8_B_64l8_fp16.json`](configs/vae/slat_vae_enc_dec_gs_swin8_B_64l8_fp16.json) | [Encoder](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/slat_enc_swin8_B_64l8_fp16.safetensors) [Decoder](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/slat_dec_gs_swin8_B_64l8gs32_fp16.safetensors) | SLat VAE with Gaussian Decoder |
| [`vae/slat_vae_dec_rf_swin8_B_64l8_fp16.json`](configs/vae/slat_vae_dec_rf_swin8_B_64l8_fp16.json) | [Decoder](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/slat_dec_rf_swin8_B_64l8r16_fp16.safetensors) | SLat Radiance Field Decoder |
| [`vae/slat_vae_dec_mesh_swin8_B_64l8_fp16.json`](configs/vae/slat_vae_dec_mesh_swin8_B_64l8_fp16.json) | [Decoder](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16.safetensors) | SLat Mesh Decoder |
| [`generation/ss_flow_img_dit_L_16l8_fp16.json`](configs/generation/ss_flow_img_dit_L_16l8_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/ss_flow_img_dit_L_16l8_fp16.safetensors) | Image conditioned sparse structure Flow Model |
| [`generation/slat_flow_img_dit_L_64l8p2_fp16.json`](configs/generation/slat_flow_img_dit_L_64l8p2_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-image-large/blob/main/ckpts/slat_flow_img_dit_L_64l8p2_fp16.safetensors) | Image conditioned SLat Flow Model |
| [`generation/ss_flow_txt_dit_B_16l8_fp16.json`](configs/generation/ss_flow_txt_dit_B_16l8_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-text-base/blob/main/ckpts/ss_flow_txt_dit_B_16l8_fp16.safetensors) | Base text-conditioned sparse structure Flow Model |
| [`generation/slat_flow_txt_dit_B_64l8p2_fp16.json`](configs/generation/slat_flow_txt_dit_B_64l8p2_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-text-base/blob/main/ckpts/slat_flow_txt_dit_B_64l8p2_fp16.safetensors) | Base text-conditioned SLat Flow Model |
| [`generation/ss_flow_txt_dit_L_16l8_fp16.json`](configs/generation/ss_flow_txt_dit_L_16l8_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-text-large/blob/main/ckpts/ss_flow_txt_dit_L_16l8_fp16.safetensors) | Large text-conditioned sparse structure Flow Model |
| [`generation/slat_flow_txt_dit_L_64l8p2_fp16.json`](configs/generation/slat_flow_txt_dit_L_64l8p2_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-text-large/blob/main/ckpts/slat_flow_txt_dit_L_64l8p2_fp16.safetensors) | Large text-conditioned SLat Flow Model |
| [`generation/ss_flow_txt_dit_XL_16l8_fp16.json`](configs/generation/ss_flow_txt_dit_XL_16l8_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-text-xlarge/blob/main/ckpts/ss_flow_txt_dit_XL_16l8_fp16.safetensors) | Extra-large text-conditioned sparse structure Flow Model |
| [`generation/slat_flow_txt_dit_XL_64l8p2_fp16.json`](configs/generation/slat_flow_txt_dit_XL_64l8p2_fp16.json) | [Denoiser](https://huggingface.co/JeffreyXiang/TRELLIS-text-xlarge/blob/main/ckpts/slat_flow_txt_dit_XL_64l8p2_fp16.safetensors) | Extra-large text-conditioned SLat Flow Model |
### Command-Line Options
The training script can be run as follows:
```sh
usage: train.py [-h] --config CONFIG --output_dir OUTPUT_DIR [--load_dir LOAD_DIR] [--ckpt CKPT] [--data_dir DATA_DIR] [--auto_retry AUTO_RETRY] [--tryrun] [--profile] [--num_nodes NUM_NODES] [--node_rank NODE_RANK] [--num_gpus NUM_GPUS] [--master_addr MASTER_ADDR] [--master_port MASTER_PORT]
options:
-h, --help show this help message and exit
--config CONFIG Experiment config file
--output_dir OUTPUT_DIR Output directory
--load_dir LOAD_DIR Load directory, default to output_dir
--ckpt CKPT Checkpoint step to resume training, default to latest
--data_dir DATA_DIR Data directory
--auto_retry AUTO_RETRY Number of retries on error
--tryrun Try run without training
--profile Profile training
--num_nodes NUM_NODES Number of nodes
--node_rank NODE_RANK Node rank
--num_gpus NUM_GPUS Number of GPUs per node, default to all
--master_addr MASTER_ADDR Master address for distributed training
--master_port MASTER_PORT Port for distributed training
```
### Example Training Commands
#### Single-node Training
To train a image-to-3D stage 2 model with a single machine.
```sh
python train.py \
--config configs/vae/slat_vae_dec_mesh_swin8_B_64l8_fp16.json \
--output_dir outputs/slat_vae_dec_mesh_swin8_B_64l8_fp16_1node \
--data_dir /path/to/your/dataset1,/path/to/your/dataset2 \
```
The script will automatically distribute the training across all available GPUs. Specify the number of GPUs with the `--num_gpus` flag if you want to limit the number of GPUs used.
#### Multi-node Training
To train a image-to-3D stage 2 model with multiple GPUs across nodes (e.g., 2 nodes):
```sh
python train.py \
--config configs/generation/slat_flow_img_dit_L_64l8p2_fp16.json \
--output_dir outputs/slat_flow_img_dit_L_64l8p2_fp16_2nodes \
--data_dir /path/to/your/dataset1,/path/to/your/dataset2 \
--num_nodes 2 \
--node_rank 0 \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
```
Be sure to adjust `node_rank`, `master_addr`, and `master_port` for each node accordingly.
#### Resuming Training
By default, training will resume from the latest saved checkpoint in the same output directory. To specify a specific checkpoint to resume from, use the `--load_dir` and `--ckpt` flags:
```sh
python train.py \
--config configs/generation/slat_flow_img_dit_L_64l8p2_fp16.json \
--output_dir outputs/slat_flow_img_dit_L_64l8p2_fp16_resume \
--data_dir /path/to/your/dataset1,/path/to/your/dataset2 \
--load_dir /path/to/your/checkpoint \
--ckpt [step]
```
### Additional Options
- **Auto Retry:** Use the `--auto_retry` flag to specify the number of retries in case of intermittent errors.
- **Dry Run:** The `--tryrun` flag allows you to check your configuration and environment without launching full training.
- **Profiling:** Enable profiling with the `--profile` flag to gain insights into training performance and diagnose bottlenecks.
Adjust the file paths and parameters to match your experimental setup.
<!-- License -->
## βοΈ License
TRELLIS models and the majority of the code are licensed under the [MIT License](LICENSE). The following submodules may have different licenses:
- [**diffoctreerast**](https://github.com/JeffreyXiang/diffoctreerast): We developed a CUDA-based real-time differentiable octree renderer for rendering radiance fields as part of this project. This renderer is derived from the [diff-gaussian-rasterization](https://github.com/graphdeco-inria/diff-gaussian-rasterization) project and is available under the [LICENSE](https://github.com/JeffreyXiang/diffoctreerast/blob/master/LICENSE).
- [**Modified Flexicubes**](https://github.com/MaxtirError/FlexiCubes): In this project, we used a modified version of [Flexicubes](https://github.com/nv-tlabs/FlexiCubes) to support vertex attributes. This modified version is licensed under the [LICENSE](https://github.com/nv-tlabs/FlexiCubes/blob/main/LICENSE.txt).
<!-- Citation -->
## π Citation
If you find this work helpful, please consider citing our paper:
```bibtex
@article{xiang2024structured,
title = {Structured 3D Latents for Scalable and Versatile 3D Generation},
author = {Xiang, Jianfeng and Lv, Zelong and Xu, Sicheng and Deng, Yu and Wang, Ruicheng and Zhang, Bowen and Chen, Dong and Tong, Xin and Yang, Jiaolong},
journal = {arXiv preprint arXiv:2412.01506},
year = {2024}
}
```
", Assign "at most 3 tags" to the expected json: {"id":"13144","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"