Instructions to use bigshanedogg/ImgEdit-Judge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bigshanedogg/ImgEdit-Judge with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="bigshanedogg/ImgEdit-Judge")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("bigshanedogg/ImgEdit-Judge")
model = AutoModelForMultimodalLM.from_pretrained("bigshanedogg/ImgEdit-Judge", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bigshanedogg/ImgEdit-Judge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bigshanedogg/ImgEdit-Judge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bigshanedogg/ImgEdit-Judge",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/bigshanedogg/ImgEdit-Judge

SGLang

How to use bigshanedogg/ImgEdit-Judge with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bigshanedogg/ImgEdit-Judge" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bigshanedogg/ImgEdit-Judge",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bigshanedogg/ImgEdit-Judge" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bigshanedogg/ImgEdit-Judge",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use bigshanedogg/ImgEdit-Judge with Docker Model Runner:
```
docker model run hf.co/bigshanedogg/ImgEdit-Judge
```

ImgEdit-Judge (re-host)

Unmodified re-host of ImgEdit_Judge, the LLM-as-judge model for ImgEdit-Bench image-editing evaluation. Published for users who cannot access the original local checkpoint. The weights here are byte-identical to the authors' release — this repository only mirrors them for accessibility.

What it is

Given a source image, an edited image, and an edit instruction + rubric, the model rates the edit on a 1–5 scale across per-category rubric dimensions (used to compute the ImgEdit-Bench "Add / Adjust / Extract / Replace / Remove / Background / Style / Hybrid / Action" scores). It is a fine-tune of Qwen2.5-VL-7B.

Source & attribution

Original work: PKU-YuanGroup — ImgEdit: A Unified Image Editing Dataset and Benchmark (arXiv:2505.20275, GitHub). The checkpoint is published inside the dataset repo sysuyy/ImgEdit under the ImgEdit_Judge/ subfolder (there is no standalone model repo from the authors).
Base model: Qwen/Qwen2.5-VL-7B-Instruct (Apache-2.0).
License: Apache-2.0, inherited from the ImgEdit release and the Qwen2.5-VL-7B base.
Changes: none — weights are re-hosted unmodified.

All credit belongs to the original ImgEdit authors (PKU-YuanGroup) and the Qwen team.

Citation

Please cite the original work (author list on the paper page):

@article{imgedit2025,
  title  = {ImgEdit: A Unified Image Editing Dataset and Benchmark},
  journal= {arXiv preprint arXiv:2505.20275},
  year   = {2025},
  url    = {https://arxiv.org/abs/2505.20275}
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for bigshanedogg/ImgEdit-Judge

Base model

Qwen/Qwen2.5-VL-7B-Instruct

Finetuned

(1186)

this model

Paper for bigshanedogg/ImgEdit-Judge

ImgEdit: A Unified Image Editing Dataset and Benchmark

Paper • 2505.20275 • Published May 26, 2025 • 20