Instructions to use MeiGen-AI/GenEvolve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MeiGen-AI/GenEvolve with Transformers:

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

pipe = pipeline("image-text-to-text", model="MeiGen-AI/GenEvolve")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("MeiGen-AI/GenEvolve")
model = AutoModelForImageTextToText.from_pretrained("MeiGen-AI/GenEvolve")
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

vLLM

How to use MeiGen-AI/GenEvolve with vLLM:

Install from pip and serve model

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

SGLang

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

Ephemeral182 commited on 1 day ago

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@@ -199,10 +199,13 @@ The agent's final `<answer>` is a JSON object:
 ## 📑 Citation
 ```bibtex
-@article{chen2026genevolve,
-  title  = {GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation},
-  author = {Chen, Sixiang and Xing, Zhaohu and Ye, Tian and Geng, Xinyu and Lin, Yunlong
-            and Lai, Jianyu and He, Xuanhua and Zhai, Fuxiang and Gao, Jialin and Zhu, Lei},
-  year   = {2026}
 }
 ```

 ## 📑 Citation
 ```bibtex
+@misc{chen2026genevolveselfevolvingimagegeneration,
+      title={GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation},
+      author={Sixiang Chen and Zhaohu Xing and Tian Ye and Xinyu Geng and Yunlong Lin and Jianyu Lai and Xuanhua He and Fuxiang Zhai and Jialin Gao and Lei Zhu},
+      year={2026},
+      eprint={2605.21605},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2605.21605},
 }
 ```