Instructions to use Allenda/VIEScore2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Allenda/VIEScore2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Allenda/VIEScore2")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Allenda/VIEScore2")
model = AutoModelForMultimodalLM.from_pretrained("Allenda/VIEScore2", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Allenda/VIEScore2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Allenda/VIEScore2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Allenda/VIEScore2",
		"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/Allenda/VIEScore2

SGLang

How to use Allenda/VIEScore2 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 "Allenda/VIEScore2" \
    --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": "Allenda/VIEScore2",
		"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 "Allenda/VIEScore2" \
        --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": "Allenda/VIEScore2",
		"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 Allenda/VIEScore2 with Docker Model Runner:
```
docker model run hf.co/Allenda/VIEScore2
```

VIEScore2 / README.md

Allenda

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metadata

license: apache-2.0
base_model: Qwen/Qwen3-VL-8B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - image-evaluation
  - image-quality-assessment
  - defect-localization
  - grounding
  - grpo
  - reinforcement-learning

VIEScore2

A unified evaluator for generated and edited images. In a single autoregressive pass the model emits:

a quality score — dual-axis perceptual quality (pq:) and semantic consistency (sc:),
a defect grid — sparse cells on a text-native 16×16 grid localizing problems, optionally split into artifact: / misalign: channels, and
a verifiable explanation, rendered deterministically from the score and grid so the text can never contradict the spatial evidence.

Because every output element is a discrete text token set, predictions are exactly checkable against ground truth. Training is supervised fine-tuning followed by GRPO with a verifiable cell-level F_β reward on the defect grid.

Repository contents

Naming convention: VIEScore2 denotes the full model after GRPO; ablated variants are marked by what they lack, never by added suffixes.

path	contents
`/` (root)	VIEScore2 — the full post-GRPO checkpoint (drop-in `Qwen3VLForConditionalGeneration`)
`wo-grpo/`	VIEScore2 (w/o GRPO) — the SFT-stage LoRA adapter on `Qwen/Qwen3-VL-8B-Instruct`

Usage

from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Allenda/VIEScore2", dtype="bfloat16", device_map="auto")
processor = AutoProcessor.from_pretrained("Allenda/VIEScore2")

Evaluation prompts, the frozen protocol, the deterministic explanation renderer, and all benchmark converters and baseline harnesses live in the accompanying code release (see the paper).

Evaluation

On a 1,300-example multi-source suite (RichHF, PAL4VST, EvalMuse, ImagenWorld, COCO; frozen protocol v3.1): localization cell-F1 0.506, problem-sample grid IoU 0.324, overall-score SRCC 0.601. After mapping all methods to a shared 16×16 grid, VIEScore2 is the only evaluator in the top two of cell-F1 or grid-IoU on all six external localization benchmarks (RichHF, AbHuman, HAD, SynthScars, PAL4VST, SDG-30K). See the paper and code repository for the full tables and protocol details.

Citation

@misc{du2026viescore2unifiedimageevaluation,
      title={VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations}, 
      author={Xianda Du and Max Ku and Weiming Ren and Zhi Rui Tam and Chunlin Ren and Ping Nie and Min-Hung Chen and Wenhu Chen},
      year={2026},
      eprint={2610.00994},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2610.00994}, 
}