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
Flux-Based Training Data
- Trained using Flux-generated images and captions to ensure high-quality structured output.
Optical Character Recognition (OCR)
- Extracts and processes textual content within images.
Structured JSON Output
- Outputs information in JSON format for easy integration with various applications.
Conversational Capabilities
- Handles multi-turn interactions with structured responses.
Image & Text Processing
- Inputs can include images, text, or both, with JSON-formatted results.
Secure and Optimized Model Weights
- Uses Safetensors for enhanced security and efficient model loading.
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