LoRA weights for UniDG (Universal Defect Generation), trained via Consistency-RFT with Flow-GRPO and dual reward models on the UDG dataset (300K quadruplets).

[Paper] [Code] [UniDG-SFT-LoRA]

Overview

UniDG is a universal defect generation foundation model that transfers defects from a reference image to a target region via Defect-Context Editing and MM-DiT multimodal attention, without per-category fine-tuning. This checkpoint is the Consistency-RFT variant, further refined from UniDG-SFT using Flow-GRPO with dual reward models (Defect-Und-Reward & Defect-Recog-Reward) for improved defect fidelity and consistency.

Variant	Training	Focus
UniDG-SFT	Diversity-SFT with complementary sampling	Diverse defect patterns
UniDG-RFT (this)	Consistency-RFT with Flow-GRPO + dual rewards	Consistent & faithful defects

Important: Usage Difference from UniDG-SFT-LoRA

The UniDG-RFT-LoRA weights are stored in PEFT format (adapter_model.safetensors + adapter_config.json), which is different from UniDG-SFT-LoRA (which uses pytorch_lora_weights.safetensors). This means:

UniDG-SFT-LoRA can be directly loaded via the lora_weights_path parameter in ImageUniDG.
UniDG-RFT-LoRA must first be merged into the base SFT model using the provided combine_peft_weights.py script. After merging, the resulting model can be loaded directly without any additional LoRA loading step.

Repository Contents

File	Description
`adapter_model.safetensors`	PEFT LoRA weights (Consistency-RFT)
`adapter_config.json`	LoRA configuration (rank=64, alpha=128)
`combine_peft_weights.py`	Script to merge RFT LoRA into the base SFT model

Step-by-Step Usage

Prerequisites

FLUX.1-Fill-dev (inpainting backbone)
FLUX.1-Redux-dev (reference conditioning)
UniDG-SFT-LoRA (base SFT model — the RFT LoRA is fine-tuned on top of this)
UniDG code (inference framework)
Python dependencies: diffusers, peft, torch

Step 1: Prepare the Base SFT Model

First, you need a base FLUX.1-Fill-dev model with UniDG-SFT-LoRA weights already merged in. If you haven't done this, you can prepare it by loading the SFT model and saving the merged weights:

python

from diffusers import FluxFillPipeline
import torch

# Load base FLUX.1-Fill-dev
pipe = FluxFillPipeline.from_pretrained(
    "path/to/FLUX.1-Fill-dev",
    torch_dtype=torch.bfloat16,
)

# Load SFT LoRA weights
pipe.load_lora_weights("path/to/UniDG-SFT-LoRA-Release/pytorch_lora_weights.safetensors")

# Save the merged SFT model as the base for RFT merging
pipe.save_pretrained("path/to/FLUX.1-Fill-dev-UDG-SFT", safe_serialization=True, max_shard_size="5GB")

Step 2: Merge RFT LoRA into the Base SFT Model

Use the provided combine_peft_weights.py to merge the RFT LoRA weights into the base SFT model:

bash

python combine_peft_weights.py \
    --base_model_path path/to/FLUX.1-Fill-dev-UDG-SFT \
    --lora_weights_path path/to/UniDG-RFT-LoRA-Release \
    --output_path path/to/FLUX.1-Fill-dev-UDG-RFT \
    --save_full_pipeline

Parameters:

--base_model_path: Path to the base SFT model (from Step 1)
--lora_weights_path: Path to this RFT LoRA repository (containing adapter_model.safetensors and adapter_config.json)
--output_path: Output path for the merged model
--save_full_pipeline: Save the full pipeline (including VAE, text encoder, etc.) so you can load it directly later
--dtype: Data type, default bfloat16
--device: Device for loading, default cpu (recommended to avoid OOM)

Tip: Use --device cpu (default) to save GPU memory during the merge process. The merge only needs to run once.

Step 3: Use the Merged Model with UniDG

After merging, the model can be used directly with the UniDG inference code — no additional LoRA loading is needed:

python

from unidg import ImageUniDG
from PIL import Image
import torch

# Load the merged RFT model — set lora_weights_path="" since LoRA is already merged
model = ImageUniDG(
    flux_model_path="path/to/FLUX.1-Fill-dev-UDG-RFT",
    redux_model_path="path/to/FLUX.1-Redux-dev",
    lora_weights_path="",  # No additional LoRA needed!
    device="cuda:0",
    dtype=torch.bfloat16,
)

result, mask = model.process_images(
    target_image=Image.open("target.jpg"),
    reference_image=Image.open("reference.jpg"),
    reference_mask=Image.open("reference_mask.png"),
    target_mask=Image.open("target_mask.png"),
    num_inference_steps=28,
    guidance_scale=3.5,
    seed=42,
)
result.save("result.png")

Quick Reference: SFT vs RFT Usage

	UniDG-SFT	UniDG-RFT
Weight format	`pytorch_lora_weights.safetensors`	`adapter_model.safetensors` + `adapter_config.json`
Merge required?	No	Yes (with SFT base model)
`lora_weights_path`	Path to SFT weights	`""` (empty, after merge)
`flux_model_path`	`path/to/FLUX.1-Fill-dev`	`path/to/merged-RFT-model`
Load time	LoRA loaded on-the-fly	Pre-merged, no LoRA overhead

LoRA Configuration

Parameter	Value
PEFT type	LORA
Rank (r)	64
Alpha	128
Dropout	0.0
Target modules	`ff.net.0.proj`, `ff.net.2`, `ff_context.net.0.proj`, `proj_mlp`, `attn.to_q`, `attn.to_v`, `attn.to_add_out`, `attn.add_k_proj`, `attn.add_v_proj`, `ff_context.net.2`, `attn.add_q_proj`, `attn.to_out.0`, `attn.to_k`
Base model	FLUX.1-Fill-dev + UniDG-SFT-LoRA

Citation

bibtex

@article{fan2026unidg,
  title={Large-Scale Universal Defect Generation: Foundation Models and Datasets},
  author={Fan, Yuanting and Liu, Jun and Gao, Bin-Bin and Chen, Xiaochen and Lin, Yuhuan and Dai, Zhewei and Zhan, Jiawei and Wang, Chengjie},
  journal={arXiv preprint arXiv:2604.08915},
  year={2026}
}

retofan23333/UniDG-RFT-LoRA-Release

UniDG-RFT-LoRA