metadata
base_model:
- black-forest-labs/FLUX.1-dev
license: mit
pipeline_tag: image-to-image
title: OmniConsistency
emoji: 🚀
colorFrom: gray
colorTo: pink
sdk: gradio
sdk_version: 5.31.0
app_file: app.py
pinned: false
short_description: Generate styled image from reference image and external LoRA
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 pretrained LoRAs directly from Hugging Face. Or download using Python script:
OmniConsistency Model
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")
Pretrained LoRAs
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}
}