InstantX/InstantIR

InstantIR Model Card

<div style="display: flex; gap: 10px; align-items: center; justify-content: center; height: auto;"> <a href='https://arxiv.org/abs/2410.06551'><img src='https://img.shields.io/badge/paper-arXiv-b31b1b.svg' style="height: 24px;"></a> <a href='https://jy-joy.github.io/InstantIR'><img src='https://img.shields.io/badge/project-Website-green' style="height: 24px;"></a> <a href='https://github.com/instantX-research/InstantIR'><img src='https://img.shields.io/badge/code-Github-informational' style="height: 24px;"></a> <a href='https://huggingface.co/spaces/JOY-Huang/InstantIR'><img src='https://img.shields.io/badge/🤗-Demo-orange' style="height: 24px;"></a> </div>

InstantIR is a novel single-image restoration model designed to resurrect your damaged images, delivering extrem-quality yet realistic details. You can further boost InstantIR performance with additional text prompts, even achieve customized editing!

<div align="center"> <img src='assets/teaser_figure.png'> </div>

Usage

1. Clone the github repo

git clone https://github.com/JY-Joy/InstantIR.git
cd InstantIR

2. Download model weights

You can directly download InstantIR weights in this repository, or you can download them using python script:

from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="InstantX/InstantIR", filename="models/adapter.pt", local_dir=".")
hf_hub_download(repo_id="InstantX/InstantIR", filename="models/aggregator.pt", local_dir=".")
hf_hub_download(repo_id="InstantX/InstantIR", filename="models/previewer_lora_weights.bin", local_dir=".")

3. Load InstantIR with 🧨 diffusers

# !pip install diffusers opencv-python transformers accelerate
import torch
from PIL import Image

from diffusers import DDPMScheduler
from schedulers.lcm_single_step_scheduler import LCMSingleStepScheduler

from module.ip_adapter.utils import load_adapter_to_pipe
from pipelines.sdxl_instantir import InstantIRPipeline

# prepare models under ./models
instantir_path = f'./models'

# load pretrained models
pipe = InstantIRPipeline.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16)

# load adapter
load_adapter_to_pipe(
    pipe,
    f"{instantir_path}/adapter.pt",
    image_encoder_or_path = 'facebook/dinov2-large',
)

# load previewer lora
pipe.prepare_previewers(instantir_path)
pipe.scheduler = DDPMScheduler.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', subfolder="scheduler")
lcm_scheduler = LCMSingleStepScheduler.from_config(pipe.scheduler.config)

# load aggregator weights
pretrained_state_dict = torch.load(f"{instantir_path}/aggregator.pt")
pipe.aggregator.load_state_dict(pretrained_state_dict)

# send to GPU and fp16
pipe.to(device='cuda', dtype=torch.float16)
pipe.aggregator.to(device='cuda', dtype=torch.float16)

Then, you can restore your broken images with:

# load a broken image
low_quality_image = Image.open('path/to/your-image').convert("RGB")

# InstantIR restoration
image = pipe(
    image=low_quality_image,
    previewer_scheduler=lcm_scheduler,
).images[0]

For more details including text-guided enhancement/editing, please refer to our GitHub repository.

<!-- ## Usage Tips

1. 1.If you're not satisfied with the similarity, try to increase the weight of "IdentityNet Strength" and "Adapter Strength".
2. 2.If you feel that the saturation is too high, first decrease the Adapter strength. If it is still too high, then decrease the IdentityNet strength.
3. 3.If you find that text control is not as expected, decrease Adapter strength.
4. 4.If you find that realistic style is not good enough, go for our Github repo and use a more realistic base model. -->

Examples

<div align="center"> <img src='assets/qualitative_real.png'> </div>

<div align="center"> <img src='assets/outdomain_preview.png'> </div>

Disclaimer

This project is released under Apache License and aims to positively impact the field of AI-driven image generation. Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users.

Acknowledgment

Our work is sponsored by HuggingFace and fal.ai.

Citation

If InstantIR helps your research or project, please cite us via

@article{huang2024instantir,
  title={InstantIR: Blind Image Restoration with Instant Generative Reference},
  author={Huang, Jen-Yuan and Wang, Haofan and Wang, Qixun and Bai, Xu and Ai, Hao and Xing, Peng and Huang, Jen-Tse},
  journal={arXiv preprint arXiv:2410.06551},
  year={2024}
}