license: mit
from transformers import AutoModelForCausalLM, AutoTokenizer from PIL import Image
model = AutoModelForCausalLM.from_pretrained( "sanctumoferrors/smallvisoionmodel", trust_remote_code=True, device_map={"": "cuda"} )
Optional, but recommended when running inference on a large number of
images since it has upfront compilation cost but significantly speeds
up inference:
model.model.compile()
Captioning
print("Short caption:") print(model.caption(image, length="short")["caption"])
print("\nNormal caption:") for t in model.caption(image, length="normal", stream=True)["caption"]: # Streaming generation example, supported for caption() and detect() print(t, end="", flush=True) print(model.caption(image, length="normal"))
Visual Querying
print("\nVisual query: 'How many people are in the image?'") print(model.query(image, "How many people are in the image?")["answer"])
Object Detection
print("\nObject detection: 'face'") objects = model.detect(image, "face")["objects"] print(f"Found {len(objects)} face(s)")
Pointing
print("\nPointing: 'person'") points = model.point(image, "person")["points"] print(f"Found {len(points)} person(s)")