image2garment/GarmentParticles-Realistic

Garment Particles (Realistic Image Fine-Tuned Checkpoints)

Official fine-tuned checkpoints for Garment Particles, adapted for realistic and rendered image conditioning.

• Paper: Garment Particles: A 2D--3D Symmetric Garment Representation for Generation and Editing
• Project Page: https://garment-particles.github.io
• Code: https://github.com/garment-particles/GarmentParticles
• Base Models: georgeNakayama/GarmentParticles

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Overview

This repository hosts fine-tuned Stage 1 (PGF) checkpoints across multiple training paradigms:

1. from_text_baseline/: Trained directly from the pgf_text checkpoint on realistic images without prior synthetic image cross-attention.
2. pgf_image_realistic_step*/: Fine-tuned from the pretrained pgf_image baseline.
3. edge/: Pretrained Stage 2 Edge Model for 2D sewing pattern reconstruction.
4. test_images/: 200 evaluation garment test images (eval_set_40_prompt5).

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Checkpoints Summary

1. From-Text Baseline (from_text_baseline/)

Trained from pgf_text using 46,119 realistic GPT Image 2 complete outfit renders across 16 × H100 GPUs.

Checkpoint Directory	Steps	Training Description	Size
from_text_baseline/pgf_image_realistic_step1000/	1,000	Early vision-text cross-attention alignment from text base	15 GB
from_text_baseline/pgf_image_realistic_step2000/	2,000	Intermediate alignment & geometry adaptation from text base	15 GB

2. Fine-Tuned Checkpoints (From pgf_image)

Checkpoint Directory	Steps	Val Loss	Training Phase & Recommended Usage	Size
pgf_image_realistic_step5000/	5,000	0.8781	Early Stage: Initial domain adaptation from synthetic renders	15 GB
pgf_image_realistic_step10000/	10,000	0.7472	Early-Mid: Rapid feature alignment & general silhouette formation	15 GB
pgf_image_realistic_step15000/	15,000	0.6867	Mid Stage: Balanced generation before fine pattern specialization	15 GB
pgf_image_realistic_step20000/	20,000	0.6466	High Diversity: Strong realistic feature capture, diverse variations	15 GB
pgf_image_realistic_step25000/	25,000	0.6255	Late Stage: High geometric consistency and detailed seams	15 GB
pgf_image_realistic_step30000/	30,000	0.6150	Near-Convergence: Crisp geometric shapes and panel alignments	15 GB
pgf_image_realistic_step35000/	35,000	0.6131	Fully Converged: Final plateaued checkpoint (-30.2% loss reduction)	15 GB
edge/	-	-	Stage 2: Pretrained Edge Model for 2D pattern reconstruction	8.8 GB

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Evaluation Test Images

• test_images/: 200 real-world & diverse evaluation garment images (eval_set_40_prompt5) spanning multiple fabric textures, silhouettes, and draping behaviors.

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Quickstart & Inference

Download a specific checkpoint:

# Example: Download step 1000 from from_text_baseline along with edge model and test images
hf download image2garment/GarmentParticles-Realistic   --include "from_text_baseline/pgf_image_realistic_step1000/*" "edge/*" "test_images/*"   --local-dir checkpoints_hub/realistic

Run two-stage image-conditioned inference:

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=realistic_img_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_hub/realistic/edge \
  model=sparse_lightningdit_v3_xl1_w_img_text_v2 \
  pgf_weight_init=checkpoints_hub/realistic/from_text_baseline/pgf_image_realistic_step1000 \
  --config-name sparselightningdit_xl_garment_particle_inference

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Citation

@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}
}