|
--- |
|
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 |
|
``` |
|
|
|
```python |
|
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.' |
|
``` |
