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ICE

This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and Kai Han
Visual AI Lab, The University of Hong Kong
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv

Installation

The environment can be installed through conda and pip. After cloning this repository, run the following command:

$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future

*After setting up the environment, it’s recommended to restart the kernel.

Data & Setup

Please ensure that:

  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,

thus the image will be located at data/$folder_name/img.jpg

Run concept extraction

The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.

→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)

$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh

→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)

$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh

Inference

(please refer to README-infer.md for more details)

$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh

Supporting Files

We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.

License

This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.

Acknowledgements

Our code is developed based on Break-A-Scene.

Citation

@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}

About

[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models

visual-ai.github.io/ice/

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19 stars

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3 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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ICE
This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and
Kai Han
Visual AI Lab, The University of Hong Kong

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv
Installation
The environment can be installed through conda and pip. After cloning this repository, run the following command:
$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future
*After setting up the environment, it’s recommended to restart the kernel.
Data & Setup
Please ensure that:
  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,
thus the image will be located at data/$folder_name/img.jpg
Run concept extraction
The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.
→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh
→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh
Inference
(please refer to README-infer.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh
Supporting Files
We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.
License
This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.
Acknowledgements
Our code is developed based on Break-A-Scene.
Citation
@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
About
[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
visual-ai.github.io/ice/
Resources
Readme
Activity
Custom properties
Stars
19 stars
Watchers
3 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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ICE
This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and
Kai Han
Visual AI Lab, The University of Hong Kong

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv
Installation
The environment can be installed through conda and pip. After cloning this repository, run the following command:
$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future
*After setting up the environment, it’s recommended to restart the kernel.
Data & Setup
Please ensure that:
  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,
thus the image will be located at data/$folder_name/img.jpg
Run concept extraction
The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.
→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh
→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh
Inference
(please refer to README-infer.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh
Supporting Files
We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.
License
This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.
Acknowledgements
Our code is developed based on Break-A-Scene.
Citation
@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
About
[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
visual-ai.github.io/ice/
Resources
Readme
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19 stars
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3 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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ICE
This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and
Kai Han
Visual AI Lab, The University of Hong Kong

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv
Installation
The environment can be installed through conda and pip. After cloning this repository, run the following command:
$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future
*After setting up the environment, it’s recommended to restart the kernel.
Data & Setup
Please ensure that:
  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,
thus the image will be located at data/$folder_name/img.jpg
Run concept extraction
The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.
→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh
→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh
Inference
(please refer to README-infer.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh
Supporting Files
We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.
License
This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.
Acknowledgements
Our code is developed based on Break-A-Scene.
Citation
@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
About
[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
visual-ai.github.io/ice/
Resources
Readme
Activity
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19 stars
Watchers
3 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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ICE
This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and
Kai Han
Visual AI Lab, The University of Hong Kong

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv
Installation
The environment can be installed through conda and pip. After cloning this repository, run the following command:
$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future
*After setting up the environment, it’s recommended to restart the kernel.
Data & Setup
Please ensure that:
  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,
thus the image will be located at data/$folder_name/img.jpg
Run concept extraction
The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.
→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh
→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh
Inference
(please refer to README-infer.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh
Supporting Files
We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.
License
This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.
Acknowledgements
Our code is developed based on Break-A-Scene.
Citation
@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
About
[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
visual-ai.github.io/ice/
Resources
Readme
Activity
Custom properties
Stars
19 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
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Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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ICE
This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and
Kai Han
Visual AI Lab, The University of Hong Kong

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv
Installation
The environment can be installed through conda and pip. After cloning this repository, run the following command:
$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future
*After setting up the environment, it’s recommended to restart the kernel.
Data & Setup
Please ensure that:
  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,
thus the image will be located at data/$folder_name/img.jpg
Run concept extraction
The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.
→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh
→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh
Inference
(please refer to README-infer.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh
Supporting Files
We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.
License
This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.
Acknowledgements
Our code is developed based on Break-A-Scene.
Citation
@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
About
[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
visual-ai.github.io/ice/
Resources
Readme
Activity
Custom properties
Stars
19 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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ICE
This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and
Kai Han
Visual AI Lab, The University of Hong Kong

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv
Installation
The environment can be installed through conda and pip. After cloning this repository, run the following command:
$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future
*After setting up the environment, it’s recommended to restart the kernel.
Data & Setup
Please ensure that:
  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,
thus the image will be located at data/$folder_name/img.jpg
Run concept extraction
The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.
→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh
→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh
Inference
(please refer to README-infer.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh
Supporting Files
We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.
License
This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.
Acknowledgements
Our code is developed based on Break-A-Scene.
Citation
@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
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[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
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ICE
This is the official PyTorch codes for the paper:
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra and
Kai Han
Visual AI Lab, The University of Hong Kong

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
pagearXiv
Installation
The environment can be installed through conda and pip. After cloning this repository, run the following command:
$ conda create -n ice python=3.11.11
$ conda activate ice
$ pip install -r requirements.txt
$ conda install pytorch==2.5.1 torchvision==0.20.1 pytorch-cuda=12.4 -c pytorch -c nvidia
$ pip install --upgrade keras-cv==0.6.4 tensorflow==2.14.0 numpy==1.23.5
$ pip uninstall tensorboard # TODO: need to fix this in the future
*After setting up the environment, it’s recommended to restart the kernel.
Data & Setup
Please ensure that:
  1. You create a folder ($folder_name) under the data/ directory.
  2. Your input image is renamed to img.jpg before running this script,
thus the image will be located at data/$folder_name/img.jpg
Run concept extraction
The ICE framework operates through a two-stage process, i.e, Stage One: Automatic Concept Localization and Stage Two: Structured Concept Learning.
→ Stage One: Automatic Concept Localization (please refer to README-stage-one.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_one.sh
→ Stage Two: Structured Concept Learning (please refer to README-stage-two.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/run_stage_two.sh
Inference
(please refer to README-infer.md for more details)
$ CUDA_VISIBLE_DEVICES=0 bash scripts/infer.sh
Supporting Files
We have released the ICE Stage One D1 results, the D1 ground-truth masks, and the ICBench annotations. You can access all of these resources through the attached link in this repository.
License
This project is under the CC BY-NC-SA 4.0 license. See LICENSE for details.
Acknowledgements
Our code is developed based on Break-A-Scene.
Citation
@inproceedings{cendra2025ICE,
author = {Fernando Julio Cendra and Kai Han},
title = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025}
}
About
[CVPR2025 Highlight] ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
visual-ai.github.io/ice/
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