Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
chart
infographic
scene-graph
image2scenegraph
conversational
Instructions to use ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph") 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("ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph") model = AutoModelForMultimodalLM.from_pretrained("ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph", 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 ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph", "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/ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph
- SGLang
How to use ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph 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 "ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph" \ --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": "ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph", "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 "ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph" \ --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": "ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph", "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 ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph with Docker Model Runner:
docker model run hf.co/ChartGalaxyPP/ChartGalaxyPlusPlus-Image2SceneGraph
File size: 1,595 Bytes
1f05665 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"system_prompt": "You produce a complete structured design description from an image. Return only valid JSON in the requested full schema.",
"user_prompt": "Generate the complete JSON containing high_level_description, style_description, elements.background, and elements.layout. Every layout item must contain type, label, parent, bbox in xyxy_1000 coordinates, and desc. When exact visible text is available for an item of type \"text\", include it in the text field. Also include style_description.color_palette and preserve the full annotation schema: layout items include node_kind, color_palette, and role or role_family when applicable. The type value is the group_type or element_type of the node. Use null for unavailable style descriptions and [] for empty color palettes.",
"enable_thinking": false,
"max_model_len": 32768,
"max_new_tokens": 20000,
"max_pixels": 2359296,
"gpu_memory_utilization": 0.82,
"seed": 42,
"temperature": 0.0,
"top_p": 1.0,
"top_k": 0,
"repetition_penalty": 1.0,
"frequency_penalty": 0.0,
"dtype": "bfloat16",
"tensor_parallel_size": 1,
"max_num_seqs": 1,
"gdn_prefill_backend": "triton",
"compilation_config": {
"mode": 0,
"cudagraph_mode": 2,
"cudagraph_capture_sizes": [
128,
192,
256
]
},
"environment": {
"VLLM_USE_DEEP_GEMM": "0",
"VLLM_MOE_USE_DEEP_GEMM": "0",
"VLLM_USE_FLASHINFER_SAMPLER": "0"
},
"upstream_validated_runtime": {
"python": "3.11",
"vllm": "0.20.2"
},
"output_bbox_format": "xyxy_1000",
"output_nodes_path": "elements.layout"
}
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