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from transformers import AutoProcessor, AutoModelForCausalLM
import spaces
from PIL import Image
import torch
import re
import numpy as np
device = "cuda" if torch.cuda.is_available() else "cpu"
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"
fl_model = AutoModelForCausalLM.from_pretrained('thwri/CogFlorence-2.1-Large', trust_remote_code=True).eval().to("cpu").eval()
fl_processor = AutoProcessor.from_pretrained('thwri/CogFlorence-2.1-Large', trust_remote_code=True)
def modify_caption(caption: str) -> str:
special_patterns = [
(r'the image is ', ''),
(r'the image captures ', ''),
(r'the image showcases ', ''),
(r'the image shows ', ''),
(r'the image ', ''),
]
for pattern, replacement in special_patterns:
caption = re.sub(pattern, replacement, caption, flags=re.IGNORECASE)
caption = caption.replace('\n', '').replace('\r', '')
caption = re.sub(r'(?<=[.,?!])(?=[^\s])', r' ', caption)
caption = ' '.join(caption.strip().splitlines())
return caption
@spaces.GPU(duration=30)
def process_image(image):
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
elif isinstance(image, str):
image = Image.open(image)
if image.mode != "RGB":
image = image.convert("RGB")
prompt = "<MORE_DETAILED_CAPTION>"
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,
do_sample=True
)
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=prompt, image_size=(image.width, image.height))
return modify_caption(parsed_answer["<MORE_DETAILED_CAPTION>"])
def predict_tags_fl2_cog(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 CogFlorence-2.1-Large" in algo:
return input_tags
tag_list = list_uniq(to_list(input_tags) + to_list(process_image(image) + ", "))
tag_list.remove("")
return ", ".join(tag_list)
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