import spaces import torch from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM, Qwen2VLForConditionalGeneration from qwen_vl_utils import process_vision_info import numpy as np import os from datetime import datetime 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" # Initialize Florence model florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to(device).eval() florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True) # Initialize Qwen2-VL-2B model qwen_model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype="auto").to(device).eval() qwen_processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True) @spaces.GPU def florence_caption(image): if not isinstance(image, Image.Image): image = Image.fromarray(image) inputs = florence_processor(text="", images=image, return_tensors="pt").to(device) generated_ids = florence_model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, early_stopping=False, do_sample=False, num_beams=3, ) generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = florence_processor.post_process_generation( generated_text, task="", image_size=(image.width, image.height) ) return parsed_answer[""] def array_to_image_path(image_array): img = Image.fromarray(np.uint8(image_array)) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"image_{timestamp}.png" img.save(filename) full_path = os.path.abspath(filename) return full_path @spaces.GPU def qwen_caption(image): if not isinstance(image, Image.Image): image = Image.fromarray(np.uint8(image)) image_path = array_to_image_path(np.array(image)) messages = [ { "role": "user", "content": [ { "type": "image", "image": image_path, }, {"type": "text", "text": "Describe this image in great detail."}, ], } ] text = qwen_processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = qwen_processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to(device) generated_ids = qwen_model.generate(**inputs, max_new_tokens=256) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = qwen_processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) return output_text[0]