Add model card and metadata

README.md

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 license: mit
+pipeline_tag: text-to-3d
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+
+# Garment Particles: A 2D–3D Symmetric Garment Representation for Generation and Editing
+
+Official checkpoints for **Garment Particles**, a framework for garment design spanning intuitive creation from high-level intent (text, image, sketch) to complex low-level editing across 2D sewing patterns and 3D draped geometry.
+
+- **Paper**: [Garment Particles: A 2D--3D Symmetric Garment Representation for Generation and Editing](https://huggingface.co/papers/2605.26391)
+- **Project Page**: [https://garment-particles.github.io](https://garment-particles.github.io)
+- **Code**: [https://github.com/garment-particles/GarmentParticles](https://github.com/garment-particles/GarmentParticles)
+
+## Overview
+Garment Particles uses a 5D point-cloud representation to jointly encode 2D sewing patterns and 3D geometry. This representation enables Garment Particles Flow (GPF), a rectified flow framework that supports intuitive generation from high-level inputs (text, images, sketches) and various editing operations on 2D sewing patterns and 3D geometries.
+
+## Installation
+To use these checkpoints, clone the [official repository](https://github.com/garment-particles/GarmentParticles) and install the dependencies:
+
+```bash
+conda create -n interact_garment python=3.10
+conda activate interact_garment
+pip install torch torchvision
+pip install -r src/requirements.txt
+export PYTHONPATH=$PWD/src:$PYTHONPATH
+```
+
+## Sample Usage (Inference)
+
+Download the checkpoints into `src/checkpoints/` and run the inference script from the `src` directory.
+
+### Text / Unconditional Generation
+```bash
+torchrun --standalone --nproc_per_node=1 inference/infer_twostage.py \
+  eval.sample_per_batch=1 eval.n_samples=0 eval.evaluate=False \
+  train.exp_name=uncond_samples sample.num_sampling_steps=100 \
+  gpf_ckpt=null \
+  dataset.front_only=True dataset.use_all_captions=True \
+  dataset.img_drop_prob=1 dataset.text_drop_prob=1 \
+  model.use_qknorm=True \
+  edge_model.use_qknorm=True \
+  edge_model_ckpt=checkpoints/edge \
+  model=sparse_lightningdit_v3_xl1_w_text_fsdp2 \
+  pgf_weight_init=checkpoints/pgf_text \
+  --config-name sparselightningdit_xl_garment_particle_inference
+```
+
+### Image-Conditioned Generation
+```bash
+torchrun --standalone --nproc_per_node=1 inference/infer_twostage.py \
+  eval.sample_per_batch=1 eval.n_samples=0 eval.evaluate=False \
+  train.exp_name=img_cond_samples sample.num_sampling_steps=100 \
+  gpf_ckpt=null \
+  dataset.front_only=True dataset.use_all_captions=True \
+  dataset.img_drop_prob=0 dataset.text_drop_prob=1 \
+  model.use_qknorm=True model.use_rope=False model.in_channels=6 model.freeze_everything=False \
+  edge_model.use_qknorm=True \
+  edge_model_ckpt=checkpoints/edge \
+  model=sparse_lightningdit_v3_xl1_w_img_text_v2 \
+  pgf_weight_init=checkpoints/pgf_image \
+  --config-name sparselightningdit_xl_garment_particle_inference
+```
+
+## Citation
+```bibtex
+@inproceedings{garmentparticles2026,
+  title={Garment Particles: A 2D--3D Symmetric Garment Representation for Generation and Editing},
+  author={George Nakayama and others},
+  booktitle={SIGGRAPH Conference Papers},
+  year={2026}
+}
+```