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README.md

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-title: Joycaption Reliable
-emoji: 📈
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 5.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: JoyCaption Reliable
+emoji: 🔍
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: reliable_joycaption.py
+pinned: false
+license: apache-2.0
+---
+
+# 🔍 JoyCaption Reliable
+
+**Ultra-optimized JoyCaption for ZeroGPU - No more stuck generations!**
+
+This is a streamlined version of JoyCaption designed specifically for reliable performance on Hugging Face's ZeroGPU infrastructure. It prioritizes **consistency and speed** over advanced features.
+
+## ✅ Key Optimizations
+
+- **45-second GPU limit** - Prevents ZeroGPU timeouts
+- **Aggressive memory cleanup** - Immediate model deletion after each generation
+- **Fast loading** - Optimized with `low_cpu_mem_usage=True`
+- **Progress tracking** - Timestamps show exactly where processing is at
+- **Emergency cleanup** - Graceful error handling with memory clearing
+
+## 🎯 Features
+
+- **Multiple Styles**: Engaging, Descriptive, SEO-Friendly, Creative
+- **Length Control**: Short (100 tokens), Medium (200 tokens), Long (300 tokens)
+- **Fast Processing**: Typically completes in 15-25 seconds
+- **No Freezing**: Designed to avoid the common ZeroGPU stuck generation issue
+
+## 🚀 Performance
+
+- **Loading**: 5-10 seconds
+- **Generation**: 10-20 seconds  
+- **Total Time**: 15-30 seconds
+- **Memory Usage**: Aggressively cleaned after each request
+
+## 💡 Why This Version is More Reliable
+
+Unlike complex dual-model setups that can timeout or freeze, this version:
+
+1. Uses only the JoyCaption model (no secondary Venice model)
+2. Limits GPU duration to prevent ZeroGPU timeouts
+3. Performs immediate cleanup to prevent memory issues
+4. Has simplified prompts for faster processing
+5. Includes progress timestamps to track performance
+
+## 🔧 Technical Details
+
+- **Model**: `fancyfeast/llama-joycaption-beta-one-hf-llava`
+- **Framework**: Transformers + PyTorch
+- **Optimization**: `torch.bfloat16`, `device_map="auto"`
+- **GPU Duration**: 45 seconds maximum
+- **Token Limits**: 100-300 based on length setting
+
+## 📊 Trade-offs
+
+**Gained**:
+- ✅ Consistent, reliable performance
+- ✅ Fast loading and generation
+- ✅ No stuck generations or timeouts
+- ✅ Predictable timing
+
+**Sacrificed**:
+- ❌ No secondary Venice model integration  
+- ❌ No advanced keyword injection
+- ❌ No complex correction systems
+- ❌ Reduced maximum output length
+
+This version is perfect if you want **reliable, fast captions** without the complexity and potential issues of multi-model systems.
+
+## 🎨 Caption Styles
+
+- **Engaging**: Creative, captivating descriptions that avoid "A photo of"
+- **Descriptive**: Focused on people, poses, clothing, and setting details  
+- **SEO-Friendly**: Optimized for search with engaging language
+- **Creative**: Witty captions with interesting, unique language
+
+Perfect for content creators, social media managers, and anyone who needs consistent, quality image captions without waiting or worrying about system freezes!