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import spaces
from transformers import AutoProcessor, AutoModelForCausalLM
import re
from PIL import Image
import torch
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
try:
fl_model = AutoModelForCausalLM.from_pretrained('gokaygokay/Florence-2-SD3-Captioner', trust_remote_code=True).to("cpu").eval()
fl_processor = AutoProcessor.from_pretrained('gokaygokay/Florence-2-SD3-Captioner', trust_remote_code=True)
except Exception as e:
print(e)
fl_model = fl_processor = None
def fl_modify_caption(caption: str) -> str:
"""
Removes specific prefixes from captions if present, otherwise returns the original caption.
Args:
caption (str): A string containing a caption.
Returns:
str: The caption with the prefix removed if it was present, or the original caption.
"""
# Define the prefixes to remove
prefix_substrings = [
('captured from ', ''),
('captured at ', '')
]
# Create a regex pattern to match any of the prefixes
pattern = '|'.join([re.escape(opening) for opening, _ in prefix_substrings])
replacers = {opening.lower(): replacer for opening, replacer in prefix_substrings}
# Function to replace matched prefix with its corresponding replacement
def replace_fn(match):
return replacers[match.group(0).lower()]
# Apply the regex to the caption
modified_caption = re.sub(pattern, replace_fn, caption, count=1, flags=re.IGNORECASE)
# If the caption was modified, return the modified version; otherwise, return the original
return modified_caption if modified_caption != caption else caption
@spaces.GPU(duration=30)
def fl_run_example(image):
task_prompt = "<DESCRIPTION>"
prompt = task_prompt + "Describe this image in great detail."
# Ensure the image is in RGB mode
if image.mode != "RGB":
image = image.convert("RGB")
fl_model.to(device)
inputs = fl_processor(text=prompt, images=image, return_tensors="pt").to(device)
generated_ids = fl_model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
num_beams=3
)
fl_model.to("cpu")
generated_text = fl_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = fl_processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
return fl_modify_caption(parsed_answer["<DESCRIPTION>"])
def predict_tags_fl2_sd3(image: Image.Image, input_tags: str, algo: list[str]):
def to_list(s):
return [x.strip() for x in s.split(",") if not s == ""]
def list_uniq(l):
return sorted(set(l), key=l.index)
if not "Use Florence-2-SD3-Long-Captioner" in algo:
return input_tags
tag_list = list_uniq(to_list(input_tags) + to_list(fl_run_example(image) + ", "))
tag_list.remove("")
return ", ".join(tag_list)
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