VGG-Flow

Official implementation of Value Gradient Guidance for Flow Matching Alignment (VGG-Flow), NeurIPS 2025.

Overview

VGG-Flow is an efficient and robust RL finetuning method for flow-matching models.

This repository currently provides:

• SD3 LoRA finetuning with VGG-Flow.
• Multiple reward models (aesthetic_score, pickscore, imagereward, hpscore).
• Config-driven training with command-line overrides.

Project Structure

• train_vggflow.py: main training entrypoint.
• config/default_config.py: base config values.
• config/*.py: reward-specific experiment configs.
• lib/: model, flow matching, reward, and training modules.
• run.sh: example launch command with many overrides.

Setup

Requirements

• Python >= 3.8
• CUDA (tested with 12.4)
• PyTorch + TorchVision

Install dependencies:

pip install -r requirements.txt

Model Access

By default, training loads stabilityai/stable-diffusion-3-medium-diffusers via Diffusers. Make sure your environment has access to required Hugging Face model weights.

Configuration

Before training, review config/default_config.py and update values as needed.

Important fields:

• config.logging.wandb_key: replace "PLACEHOLDER" if using Weights & Biases.
• config.logging.use_wandb: set to False to disable W&B logging.
• config.logging.wandb_dir: local W&B output directory.
• config.saving.output_dir: checkpoint/output directory.

Reward presets:

• config/aesthetic.py
• config/pickscore.py
• config/imagereward.py
• config/hpsv2.py

Quick Start

Example: 2-GPU single-node training with the aesthetic reward preset:

torchrun --standalone --nproc_per_node=2 train_vggflow.py \
  --config=config/aesthetic.py \
  --seed=1 \
  --exp_name=exp_aesthetic

Single-GPU run:

torchrun --standalone --nproc_per_node=1 train_vggflow.py \
  --config=config/aesthetic.py \
  --seed=1 \
  --exp_name=exp_aesthetic

Override any config value from the command line, for example:

torchrun --standalone --nproc_per_node=2 train_vggflow.py \
  --config=config/aesthetic.py \
  --config.model.reward_scale=1e4 \
  --config.sampling.num_steps=20 \
  --config.training.lr=1e-3 \
  --seed=1 \
  --exp_name=exp_custom

Key Hyperparameters

• config.model.reward_scale: reward strength; larger values push harder toward the reward objective.
• config.model.timestep_fraction: fraction of trajectory transitions used for updates.
• config.sampling.num_steps: number of Euler sampling steps.
• config.training.lr: optimizer learning rate.
• config.training.batch_size + config.training.gradient_accumulation_steps: effective optimization batch size.
• config.model.unet_reg_scale: regularization strength for preserving base behavior.

Outputs

• Checkpoints are saved under:
  • config.saving.output_dir/<reward>_vggflow_<exp_name>_seed<seed>/checkpoint_epoch*
• Training stats are written to:
  • .../result.json (compressed pickle format)
• If enabled, metrics and sample images are also logged to W&B.

Citation

If you find this work useful, please cite:

@inproceedings{liu2025vggflow,
  title={Value Gradient Guidance for Flow Matching Alignment},
  author={Liu, Zhen and Xiao, Tim Z. and Liu, Weiyang and Domingo-Enrich, Carles and Zhang, Dinghuai},
  booktitle={NeurIPS},
  year={2025},
}