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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

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:

{
  "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.
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