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Model: prithivMLmods/JSONify-Flux Source: Original Platform
2026-05-21 01:24:13 +08:00
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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
+---
+![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/3L4AaG9QB2A6fnoIQavt_.png)
+
+# **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.