Instructions to use Artiron/Florence-2-SD3-Captioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Artiron/Florence-2-SD3-Captioner with Transformers:

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

pipe = pipeline("image-text-to-text", model="Artiron/Florence-2-SD3-Captioner", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Artiron/Florence-2-SD3-Captioner", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("Artiron/Florence-2-SD3-Captioner", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Artiron/Florence-2-SD3-Captioner with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Artiron/Florence-2-SD3-Captioner"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Artiron/Florence-2-SD3-Captioner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Artiron/Florence-2-SD3-Captioner

SGLang

How to use Artiron/Florence-2-SD3-Captioner 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 "Artiron/Florence-2-SD3-Captioner" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Artiron/Florence-2-SD3-Captioner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Artiron/Florence-2-SD3-Captioner" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Artiron/Florence-2-SD3-Captioner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Artiron/Florence-2-SD3-Captioner with Docker Model Runner:
```
docker model run hf.co/Artiron/Florence-2-SD3-Captioner
```

Florence-2-SD3-Captioner / README.md

Artiron

Duplicate from gokaygokay/Florence-2-SD3-Captioner

325295c 3 months ago

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metadata

license: apache-2.0
datasets:
  - google/docci
  - google/imageinwords
  - ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions
language:
  - en
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - art

pip install -q datasets flash_attn timm einops

from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained("gokaygokay/Florence-2-SD3-Captioner", trust_remote_code=True).to(device).eval()
processor = AutoProcessor.from_pretrained("gokaygokay/Florence-2-SD3-Captioner", trust_remote_code=True)

# Function to run the model on an example
def run_example(task_prompt, text_input, image):
    prompt = task_prompt + text_input

    # Ensure the image is in RGB mode
    if image.mode != "RGB":
        image = image.convert("RGB")

    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
    generated_ids = model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024,
        num_beams=3
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
    return parsed_answer

from PIL import Image
import requests
import copy

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw)
answer = run_example("<DESCRIPTION>", "Describe this image in great detail.", image)
final_answer = answer['<DESCRIPTION>']
print(final_answer)

# 'Captured at eye-level on a sunny day, a light blue Volkswagen Beetle is parked on a cobblestone street. The beetle is parked in front of a yellow building with two brown doors. The door on the right side of the frame is white, while the left side is a darker shade of blue. The car is facing the camera, and the car is positioned in the middle of the street.'