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| from transformers import AutoProcessor, AutoModelForCausalLM | |
| import spaces | |
| 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" | |
| fl_model = AutoModelForCausalLM.from_pretrained('gokaygokay/Florence-2-SD3-Captioner', trust_remote_code=True).to(device).eval() | |
| fl_processor = AutoProcessor.from_pretrained('gokaygokay/Florence-2-SD3-Captioner', trust_remote_code=True) | |
| 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 | |
| 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") | |
| 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 | |
| ) | |
| 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) | |