import gradio as gr from transformers import AutoProcessor, AutoModelForCausalLM import re from PIL import Image import os import numpy as np #local use delete 9~11、36 line import spaces import subprocess subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to("cuda").eval() processor = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True) TITLE = "# [Florence-2 SD3 Long Captioner](https://huggingface.co/gokaygokay/Florence-2-SD3-Captioner/)" DESCRIPTION = "[Florence-2 Base](https://huggingface.co/microsoft/Florence-2-base-ft) fine-tuned on Long SD3 Prompt and Image pairs. Check above link for datasets that are used for fine-tuning." def modify_caption(caption: str) -> str: special_patterns = [ (r'The image shows ', ''), # 匹配 "The image shows " 并替换为空字符串 (r'The image is .*? of ', ''), # 匹配 "The image is .*? of" 并替换为空字符串 (r'of the .*? is', 'is') # 匹配 "of the .*? is" 并替换为 "is" ] # 对每个特殊模式进行替换 for pattern, replacement in special_patterns: caption = re.sub(pattern, replacement, caption, flags=re.IGNORECASE) no_blank_lines = re.sub(r'\n\s*\n', '\n', caption) # 合并内容 merged_content = ' '.join(no_blank_lines.strip().splitlines()) return merged_content if merged_content != caption else caption @spaces.GPU 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 = "" inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda") 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=prompt, image_size=(image.width, image.height)) return modify_caption(parsed_answer[""]) def extract_frames(image_path, output_folder): with Image.open(image_path) as img: base_name = os.path.splitext(os.path.basename(image_path))[0] frame_paths = [] try: for i in range(0, img.n_frames): img.seek(i) frame_path = os.path.join(output_folder, f"{base_name}_frame_{i:03d}.png") img.save(frame_path) frame_paths.append(frame_path) except EOFError: pass # We've reached the end of the sequence return frame_paths def process_folder(folder_path): if not os.path.isdir(folder_path): return "Invalid folder path." processed_files = [] skipped_files = [] for filename in os.listdir(folder_path): if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp', '.webp', '.heic')): image_path = os.path.join(folder_path, filename) txt_filename = os.path.splitext(filename)[0] + '.txt' txt_path = os.path.join(folder_path, txt_filename) # Check if the corresponding text file already exists if os.path.exists(txt_path): skipped_files.append(f"Skipped {filename} (text file already exists)") continue # Check if the image has multiple frames with Image.open(image_path) as img: if getattr(img, "is_animated", False) and img.n_frames > 1: # Extract frames frames = extract_frames(image_path, folder_path) for frame_path in frames: frame_txt_filename = os.path.splitext(os.path.basename(frame_path))[0] + '.txt' frame_txt_path = os.path.join(folder_path, frame_txt_filename) # Check if the corresponding text file for the frame already exists if os.path.exists(frame_txt_path): skipped_files.append(f"Skipped {os.path.basename(frame_path)} (text file already exists)") continue caption = process_image(frame_path) with open(frame_txt_path, 'w', encoding='utf-8') as f: f.write(caption) processed_files.append(f"Processed {os.path.basename(frame_path)} -> {frame_txt_filename}") else: # Process single image caption = process_image(image_path) with open(txt_path, 'w', encoding='utf-8') as f: f.write(caption) processed_files.append(f"Processed {filename} -> {txt_filename}") result = "\n".join(processed_files + skipped_files) return result if result else "No image files found or all files were skipped in the specified folder." css = """ #output { height: 500px; overflow: auto; border: 1px solid #ccc; } """ with gr.Blocks(css=css) as demo: gr.Markdown(TITLE) gr.Markdown(DESCRIPTION) with gr.Tab(label="Single Image Processing"): with gr.Row(): with gr.Column(): input_img = gr.Image(label="Input Picture") submit_btn = gr.Button(value="Submit") with gr.Column(): output_text = gr.Textbox(label="Output Text") gr.Examples( [["image1.jpg"], ["image2.jpg"], ["image3.png"], ["image4.jpg"], ["image5.jpg"], ["image6.PNG"]], inputs=[input_img], outputs=[output_text], fn=process_image, label='Try captioning on below examples' ) submit_btn.click(process_image, [input_img], [output_text]) with gr.Tab(label="Batch Processing"): with gr.Row(): folder_input = gr.Textbox(label="Input Folder Path") batch_submit_btn = gr.Button(value="Process Folder") batch_output = gr.Textbox(label="Batch Processing Results", lines=10) batch_submit_btn.click(process_folder, [folder_input], [batch_output]) demo.launch(debug=True)