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