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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- Qwen/Qwen2-VL-2B-Instruct |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- caption |
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- text-generation-inference |
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- flux |
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--- |
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# **JSONify-Flux** |
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The **JSONify-Flux** model is a fine-tuned version of Qwen2-VL, specifically tailored for **Flux-generated image analysis**, **caption extraction**, and **structured JSON formatting**. This model is optimized for tasks involving **image-to-text conversion**, **Optical Character Recognition (OCR)**, and **context-aware structured data extraction**. |
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#### Key Enhancements: |
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* **Advanced Image Understanding**: JSONify-Flux has been trained using **30 million trainable parameters** on **Flux-generated images and their captions**, ensuring precise image comprehension. |
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* **Optimized for JSON Output**: The model is designed to output structured JSON data, making it suitable for integration with databases, APIs, and automation pipelines. |
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* **Enhanced OCR Capabilities**: JSONify-Flux excels in recognizing and extracting text from images with a high degree of accuracy. |
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* **Multimodal Processing**: Supports both image and text inputs while generating structured JSON-formatted outputs. |
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* **Multilingual Support**: Trained to recognize text inside images in multiple languages, including English, Chinese, European languages, Japanese, Korean, Arabic, and more. |
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### How to Use |
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```python |
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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# Load the model with optimized parameters |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/JSONify-Flux", torch_dtype="auto", device_map="auto" |
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) |
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# Recommended acceleration for performance optimization |
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# model = Qwen2VLForConditionalGeneration.from_pretrained( |
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# "prithivMLmods/JSONify-Flux", |
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# torch_dtype=torch.bfloat16, |
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# attn_implementation="flash_attention_2", |
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# device_map="auto", |
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# ) |
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# Default processor |
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processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://flux-generated.com/sample_image.jpeg", |
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}, |
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{"type": "text", "text": "Extract structured information from this image in JSON format."}, |
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], |
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} |
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] |
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# Prepare for inference |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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# Generate output |
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generated_ids = model.generate(**inputs, max_new_tokens=256) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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### JSON Output Example: |
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```json |
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{ |
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"image_id": "sample_image.jpeg", |
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"captions": [ |
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"A futuristic cityscape with neon lights.", |
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"A digital artwork featuring an abstract environment." |
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], |
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"recognized_text": "Welcome to Flux City!", |
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"metadata": { |
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"color_palette": ["#FF5733", "#33FF57", "#3357FF"], |
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"detected_objects": ["building", "sign", "street light"] |
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} |
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} |
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``` |
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### **Key Features** |
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1. **Flux-Based Training Data** |
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- Trained using **Flux-generated images** and captions to ensure high-quality structured output. |
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2. **Optical Character Recognition (OCR)** |
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- Extracts and processes textual content within images. |
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3. **Structured JSON Output** |
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- Outputs information in **JSON format** for easy integration with various applications. |
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4. **Conversational Capabilities** |
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- Handles **multi-turn interactions** with structured responses. |
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5. **Image & Text Processing** |
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- Inputs can include **images, text, or both**, with JSON-formatted results. |
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6. **Secure and Optimized Model Weights** |
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- Uses **Safetensors** for enhanced security and efficient model loading. |
