showlab/OmniConsistency

OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data
Yiren Song, Cheng Liu, and Mike Zheng Shou
Show Lab, National University of Singapore

[Official Code] [Paper] [Dataset]

Installation

We recommend using Python 3.10 and PyTorch with CUDA support. To set up the environment:

# Create a new conda environment
conda create -n omniconsistency python=3.10
conda activate omniconsistency

# Install other dependencies
pip install -r requirements.txt

Download

You can download the OmniConsistency model and trained LoRAs directly from Hugging Face. Or download using Python script:

Trained LoRAs

from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/3D_Chibi_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/American_Cartoon_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Chinese_Ink_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Clay_Toy_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Fabric_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Ghibli_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Irasutoya_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Jojo_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/LEGO_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Line_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Macaron_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Oil_Painting_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Origami_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Paper_Cutting_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Picasso_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Pixel_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Poly_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Pop_Art_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Rick_Morty_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Snoopy_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Van_Gogh_rank128_bf16.safetensors", local_dir="./LoRAs")
hf_hub_download(repo_id="showlab/OmniConsistency", filename="LoRAs/Vector_rank128_bf16.safetensors", local_dir="./LoRAs")

OmniConsistency Model

from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="showlab/OmniConsistency", filename="OmniConsistency.safetensors", local_dir="./Model")

Usage

Here's a basic example of using OmniConsistency:

Model Initialization

import time
import torch
from PIL import Image
from src_inference.pipeline import FluxPipeline
from src_inference.lora_helper import set_single_lora

def clear_cache(transformer):
    for name, attn_processor in transformer.attn_processors.items():
        attn_processor.bank_kv.clear()

# Initialize model
device = "cuda"
base_path = "/path/to/black-forest-labs/FLUX.1-dev"
pipe = FluxPipeline.from_pretrained(base_path, torch_dtype=torch.bfloat16).to("cuda")

# Load OmniConsistency model
set_single_lora(pipe.transformer, 
                "/path/to/OmniConsistency.safetensors", 
                lora_weights=[1], cond_size=512)

# Load external LoRA
pipe.unload_lora_weights()
pipe.load_lora_weights("/path/to/lora_folder", 
                       weight_name="lora_name.safetensors")

Style Inference

image_path1 = "figure/test.png"
prompt = "3D Chibi style, Three individuals standing together in the office."

subject_images = []
spatial_image = [Image.open(image_path1).convert("RGB")]

width, height = 1024, 1024

start_time = time.time()

image = pipe(
    prompt,
    height=height,
    width=width,
    guidance_scale=3.5,
    num_inference_steps=25,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(5),
    spatial_images=spatial_image,
    subject_images=subject_images,
    cond_size=512,
).images[0]

end_time = time.time()
elapsed_time = end_time - start_time
print(f"code running time: {elapsed_time} s")

# Clear cache after generation
clear_cache(pipe.transformer)

image.save("results/output.png")

Datasets

Our datasets have been uploaded to the Hugging Face. and is available for direct use via the datasets library.

You can easily load any of the 22 style subsets like this:

from datasets import load_dataset

# Load a single style (e.g., Ghibli)
ds = load_dataset("showlab/OmniConsistency", split="Ghibli")
print(ds[0])

Citation

@inproceedings{Song2025OmniConsistencyLS,
  title={OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data},
  author={Yiren Song and Cheng Liu and Mike Zheng Shou},
  year={2025},
  url={https://api.semanticscholar.org/CorpusID:278905729}
}