VisualCloze/VisualClozePipeline-384

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning (Implementation with Diffusers)

Note: You still need to install our modified version of diffusers.

[Paper]   [Project Page]   [Github]

[🤗 Diffusers Implementation]

[🤗 Online Demo]   [🤗 Dataset Card]

🌠 Key Features

An in-context learning based universal image generation framework.

1. Support various in-domain tasks.
2. Generalize to unseen tasks through in-context learning.
3. Unify multiple tasks into one step and generate both target image and intermediate results.
4. Support reverse-engineering a set of conditions from a target image.

🔥 Examples are shown in the project page.

🔧 Installation

Install diffusers from our modified repository.

git clone https://github.com/lzyhha/diffusers

cd diffusers
pip install -v -e .

💻 Diffusers Usage

Example with Depth-to-Image:

import torch
from diffusers import VisualClozePipeline
from diffusers.utils import load_image


# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/93bc1c43af2d6c91ac2fc966bf7725a2/93bc1c43af2d6c91ac2fc966bf7725a2_depth-anything-v2_Large.jpg'),
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/93bc1c43af2d6c91ac2fc966bf7725a2/93bc1c43af2d6c91ac2fc966bf7725a2.jpg'),
    ],
    # query with the target image
    [
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/79f2ee632f1be3ad64210a641c4e201b/79f2ee632f1be3ad64210a641c4e201b_depth-anything-v2_Large.jpg'),
        None,  # No image needed for the query in this case
    ],
]

# Task and content prompt
task_prompt = "Each row outlines a logical process, starting from [IMAGE1] gray-based depth map with detailed object contours, to achieve [IMAGE2] an image with flawless clarity."
content_prompt = """A serene portrait of a young woman with long dark hair, wearing a beige dress with intricate 
gold embroidery, standing in a softly lit room. She holds a large bouquet of pale pink roses in a black box, 
positioned in the center of the frame. The background features a tall green plant to the left and a framed artwork 
on the wall to the right. A window on the left allows natural light to gently illuminate the scene. 
The woman gazes down at the bouquet with a calm expression. Soft natural lighting, warm color palette, 
high contrast, photorealistic, intimate, elegant, visually balanced, serene atmosphere."""

# Load the VisualClozePipeline
pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()  # Save some VRAM by offloading the model to CPU

# Run the pipeline
image_result = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    upsampling_width=1024,
    upsampling_height=1024,
    upsampling_strength=0.4,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0][0]

# Save the resulting image
image_result.save("visualcloze.png")

Example with Virtual Try-On:

import torch
from diffusers import VisualClozePipeline
from diffusers.utils import load_image


# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/tryon/00700_00.jpg'),
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/tryon/03673_00.jpg'),
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/tryon/00700_00_tryon_catvton_0.jpg'),
    ],
    # query with the target image
    [
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/tryon/00555_00.jpg'),
        load_image('https://github.com/lzyhha/VisualCloze/raw/main/examples/examples/tryon/12265_00.jpg'),
        None
    ],
]

# Task and content prompt
task_prompt = "Each row shows a virtual try-on process that aims to put [IMAGE2] the clothing onto [IMAGE1] the person, producing [IMAGE3] the person wearing the new clothing."
content_prompt = None

# Load the VisualClozePipeline
pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()  # Save some VRAM by offloading the model to CPU

# Run the pipeline
image_result = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    upsampling_height=1632,
    upsampling_width=1232,
    upsampling_strength=0.3,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0][0]

# Save the resulting image
image_result.save("visualcloze.png")

Citation

If you find VisualCloze useful for your research and applications, please cite using this BibTeX:

@article{li2025visualcloze,
  title={VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning},
  author={Li, Zhong-Yu and Du, Ruoyi and Yan, Juncheng and Zhuo, Le and Li, Zhen and Gao, Peng and Ma, Zhanyu and Cheng, Ming-Ming},
  journal={arXiv preprint arXiv:2504.07960},
  year={2025}
}