diffusers_repo/docs/source/en/api/pipelines/visualcloze.md

VisualCloze

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning is an innovative in-context learning based universal image generation framework that offers key capabilities:

1. Support for various in-domain tasks
2. Generalization 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 conditions from target images

Overview

The abstract from the paper is:

Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models, which have limited efficiency when supporting a wide range of different needs. While universal models attempt to address this limitation, they face critical challenges, including generalizable task instruction, appropriate task distributions, and unified architectural design. To tackle these challenges, we propose VisualCloze, a universal image generation framework, which supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation. Unlike existing methods that rely on language-based task instruction, leading to task ambiguity and weak generalization, we integrate visual in-context learning, allowing models to identify tasks from visual demonstrations. Meanwhile, the inherent sparsity of visual task distributions hampers the learning of transferable knowledge across tasks. To this end, we introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and transferable knowledge. Furthermore, we uncover that our unified image generation formulation shared a consistent objective with image infilling, enabling us to leverage the strong generative priors of pre-trained infilling models without modifying the architectures. The codes, dataset, and models are available at https://visualcloze.github.io.

Inference

Model loading

VisualCloze is a two-stage cascade pipeline, containing VisualClozeGenerationPipeline and VisualClozeUpsamplingPipeline.

• In VisualClozeGenerationPipeline, each image is downsampled before concatenating images into a grid layout, avoiding excessively high resolutions. VisualCloze releases two models suitable for diffusers, i.e., VisualClozePipeline-384 and VisualClozePipeline-512, which downsample images to resolutions of 384 and 512, respectively.
• VisualClozeUpsamplingPipeline uses SDEdit to enable high-resolution image synthesis.

The VisualClozePipeline integrates both stages to support convenient end-to-end sampling, while also allowing users to utilize each pipeline independently as needed.

Input Specifications

Task and Content Prompts

• Task prompt: Required to describe the generation task intention
• Content prompt: Optional description or caption of the target image
• When content prompt is not needed, pass None
• For batch inference, pass List[str|None]

Image Input Format

• Format: List[List[Image|None]]
• Structure:
  • All rows except the last represent in-context examples
  • Last row represents the current query (target image set to None)
• For batch inference, pass List[List[List[Image|None]]]

Resolution Control

• Default behavior:
  • Initial generation in the first stage: area of ${pipe.resolution}^2$
  • Upsampling in the second stage: 3x factor
• Custom resolution: Adjust using upsampling_height and upsampling_width parameters

Examples

For comprehensive examples covering a wide range of tasks, please refer to the Online Demo and GitHub Repository. Below are simple examples for three cases: mask-to-image conversion, edge detection, and subject-driven generation.

Example for mask2image

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

pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg'),
    ],
    # query with the target image
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg'),
        None, # No image needed for the target image
    ],
]

# Task and content prompt
task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
content_prompt = """Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. 
The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. 
Its plumage is a mix of dark brown and golden hues, with intricate feather details. 
The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. 
The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, 
soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, 
tranquil, majestic, wildlife photography."""

# Run the pipeline
image_result = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    upsampling_width=1344,
    upsampling_height=768,
    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 for edge-detection

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

pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_image.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_edge.jpg'),
    ],
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_image.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_edge.jpg'),
    ],
    # query with the target image
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_query_image.jpg'),
        None, # No image needed for the target image
    ],
]

# Task and content prompt
task_prompt = "Each row illustrates a pathway from [IMAGE1] a sharp and beautifully composed photograph to [IMAGE2] edge map with natural well-connected outlines using a clear logical task."
content_prompt = ""

# Run the pipeline
image_result = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    upsampling_width=864,
    upsampling_height=1152,
    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 for subject-driven generation

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

pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
    # in-context examples
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_reference.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_depth.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_image.jpg'),
    ],
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_reference.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_depth.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_image.jpg'),
    ],
    # query with the target image
    [
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_reference.jpg'),
        load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_depth.jpg'),
        None, # No image needed for the target image
    ],
]

# Task and content prompt
task_prompt = """Each row describes a process that begins with [IMAGE1] an image containing the key object, 
[IMAGE2] depth map revealing gray-toned spatial layers and results in 
[IMAGE3] an image with artistic qualitya high-quality image with exceptional detail."""
content_prompt = """A vintage porcelain collector's item. Beneath a blossoming cherry tree in early spring, 
this treasure is photographed up close, with soft pink petals drifting through the air and vibrant blossoms framing the scene."""

# 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=0.2,
    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")

Utilize each pipeline independently

import torch
from diffusers import VisualClozeGenerationPipeline, FluxFillPipeline as VisualClozeUpsamplingPipeline
from diffusers.utils import load_image
from PIL import Image

pipe = VisualClozeGenerationPipeline.from_pretrained(
    "VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16
)
pipe.to("cuda")

image_paths = [
    # in-context examples
    [
        load_image(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg"
        ),
        load_image(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg"
        ),
    ],
    # query with the target image
    [
        load_image(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg"
        ),
        None,  # No image needed for the target image
    ],
]
task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
content_prompt = "Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. Its plumage is a mix of dark brown and golden hues, with intricate feather details. The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, tranquil, majestic, wildlife photography."

# Stage 1: Generate initial image
image = pipe(
    task_prompt=task_prompt,
    content_prompt=content_prompt,
    image=image_paths,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0),
).images[0][0]

# Stage 2 (optional): Upsample the generated image
pipe_upsample = VisualClozeUpsamplingPipeline.from_pipe(pipe)
pipe_upsample.to("cuda")

mask_image = Image.new("RGB", image.size, (255, 255, 255))

image = pipe_upsample(
    image=image,
    mask_image=mask_image,
    prompt=content_prompt,
    width=1344,
    height=768,
    strength=0.4,
    guidance_scale=30,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0),
).images[0]

image.save("visualcloze.png")

VisualClozePipeline

[[autodoc]] VisualClozePipeline

• all
• call

VisualClozeGenerationPipeline

[[autodoc]] VisualClozeGenerationPipeline

• all
• call