pasqualedem/weeddiffusion

WeedDiffusion — Model Checkpoints

Model checkpoints for WeedDiffusion: A Dual-Branch Synthetic Augmentation Framework for Weed Mapping.

Code: https://github.com/pasqualedem/WeedDiffusion

This repository hosts the trained checkpoints that are not shipped with the GitHub repository due to their size:

1. 1.Two class-specific DreamBooth Stable Diffusion v1.5 models used for synthetic data augmentation.
2. 2.The best semantic-segmentation checkpoints used to produce the paper's result tables.

Repository layout

crops/           # DreamBooth SD-1.5 pipeline (diffusers format) — crop inpainting branch
weeds/           # DreamBooth SD-1.5 pipeline (diffusers format) — weed generation branch
segmentation/    # Best-mIoU segmentation checkpoints, grouped by dataset/experiment
  ├── images_37/     # real-only baseline experiments
  └── weeddiff_37/   # experiments trained with WeedDiffusion-augmented data

DreamBooth generative models

Both are Stable Diffusion v1.5 fine-tuned with DreamBooth (resolution=512, learning_rate=1e-6, max_train_steps=4000, text encoder trained).

Only inference weights are included (the full diffusers pipeline: unet, vae, text_encoder, safety_checker, tokenizer, scheduler, feature_extractor). Optimizer / training-resume state is omitted.

Usage

from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline
from huggingface_hub import snapshot_download

# Download a single branch
crops_dir = snapshot_download("pasqualedem/weeddiffusion", allow_patterns="crops/*")
weeds_dir = snapshot_download("pasqualedem/weeddiffusion", allow_patterns="weeds/*")

# Weed generation branch
pipe = StableDiffusionPipeline.from_pretrained(f"{weeds_dir}/weeds").to("cuda")
image = pipe("sks weed").images[0]

# Crop inpainting branch
inpaint = StableDiffusionInpaintPipeline.from_pretrained(f"{crops_dir}/crops").to("cuda")

In the GitHub pipeline these correspond to the local paths out/model_crops and out/model_weeds.

Segmentation checkpoints

For each experiment, we provide the single checkpoint selected by the evaluation code (main.py): the highest-train_mIoU checkpoint of the final training run. Filenames encode the epoch and the training mIoU, e.g. phenobench_epoch=185_train_mIoU=0.8644.ckpt.

• —segmentation/images_37/<arch>_<variant> — models trained on real data only.
• —segmentation/weeddiff_37/<arch>_<variant> — models trained with WeedDiffusion augmentation.

Architectures: deeplab, unet, erfnet. Variants: base, color, geo, geocolor (and numbered re-runs). These are PyTorch-Lightning checkpoints; load them with the matching config via main.py test (see the GitHub README).

License

MIT — see the code repository.

Citation

If you use these models, please cite WeedDiffusion (see the GitHub repository).