Fine-tuned version of PaliGemma 224x224 on google/docci and google/imageinwords datasets.
pip install git+https://github.com/huggingface/transformers
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from PIL import Image
import requests
import torch
model_id = "gokaygokay/sd3-long-captioner-v2"
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)
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval()
processor = AutoProcessor.from_pretrained(model_id)
## prefix
prompt = "caption en"
model_inputs = processor(text=prompt, images=image, return_tensors="pt")
input_len = model_inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**model_inputs, repetition_penalty=1.10, max_new_tokens=256, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
Citation and attribution
This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.
@software{aydogan2024sd3_long_captioner_v2,
author = {Aydoğan, Gökay},
title = {{sd3-long-captioner-v2}},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/gokaygokay/sd3-long-captioner-v2},
note = {Model repository; cite the base model and upstream datasets as required.}
}