Flux-Vision-Upscaler-SeedVR2-FlashPack

Official release of SeedVR2 for ComfyUI that enables high-quality video and image upscaling.

Can run as Multi-GPU standalone CLI too, see 🖥️ Run as Standalone section.

📋 Quick Access

• 🆙 Future Releases
• 🚀 Updates
• 🎯 Features
• 🔧 Requirements
• 📦 Installation
• 📖 Usage
• 🖥️ Run as Standalone
• ⚠️ Limitations
• 🤝 Contributing
• 🙏 Credits
• 📜 License

🆙 Future Releases

We're actively working on improvements and new features. To stay informed:

• 📌 Track Active Development: Visit Issues to see active development, report bugs, and request new features
• 💬 Join the Community: Learn from others, share your workflows, and get help in the Discussions
• 🔮 Next Model Survey: We're looking for community input on the next open-source super-powerful generic restoration model. Share your suggestions in Issue #164

🚀 Updates

2025.11.09 - Version 2.5.5

• 💾 Memory: Fixed RAM leak for long videos - On-demand reconstruction with lightweight batch indices instead of storing full transformed videos, fixed release_tensor_memory to handle CPU/CUDA/MPS consistently, and refactored batch processing helpers

2025.11.08 - Version 2.5.4

• 🎨 Fix: AdaIN color correction - Replace .view() with .reshape() to handle non-contiguous tensors after spatial padding, resolving "view size is not compatible with input tensor's size and stride" error
• 🔴 Fix: AMD ROCm compatibility - Add cuDNN availability check in Conv3d workaround to prevent "ATen not compiled with cuDNN support" error on ROCm systems (AMD GPUs on Windows/Linux)

2025.11.08 - Version 2.5.3

• 🍎 Fix: Apple Silicon MPS device handling - Corrected MPS device enumeration to use "mps" instead of "mps:0", resolving invalid device errors on M-series Macs
• 🪟 Fix: torch.mps AttributeError on Windows - Add defensive checks for torch.mps.is_available() to handle PyTorch versions where the method doesn't exist on non-Mac platforms

2025.11.07 - Version 2.5.0 🎉

⚠️ BREAKING CHANGE: This is a major update requiring workflow recreation. All nodes and CLI parameters have been redesigned for better usability and consistency. Watch the latest video from AInVFX for a deep dive and check out the usage section.

📦 Official Release: Now available on main branch with ComfyUI Manager support for easy installation and automatic version tracking. Updated dependencies and local imports prevent conflicts with other ComfyUI custom nodes.

🎨 ComfyUI Improvements

• Four-Node Modular Architecture: Split into dedicated nodes for DiT model, VAE model, torch.compile settings, and main upscaler for granular control
• Global Model Cache: Models now shared across multiple upscaler instances with automatic config updates - no more redundant loading
• ComfyUI V3 Migration: Full compatibility with ComfyUI V3 stateless node design
• RGBA Support: Native alpha channel processing with edge-guided upscaling for clean transparency
• Improved Memory Management: Streaming architecture prevents VRAM spikes regardless of video length
• Flexible Resolution Support: Upscale to any resolution divisible by 2 with lossless padding approach (replaced restrictive cropping)
• Enhanced Parameters: Added uniform_batch_size, temporal_overlap, prepend_frames, and max_resolution for better control

🖥️ CLI Enhancements

• Batch Directory Processing: Process entire folders of videos/images with model caching for efficiency
• Single Image Support: Direct image upscaling without video conversion
• Smart Output Detection: Auto-detects output format (MP4/PNG) based on input type
• Enhanced Multi-GPU: Improved workload distribution with temporal overlap blending
• Unified Parameters: CLI and ComfyUI now use identical parameter names for consistency
• Better UX: Auto-display help, validation improvements, progress tracking, and cleaner output

⚡ Performance & Optimization

• torch.compile Support: 20-40% DiT speedup and 15-25% VAE speedup with full graph compilation
• Optimized BlockSwap: Adaptive memory clearing (5% threshold), separate I/O component handling, reduced overhead
• Enhanced VAE Tiling: Tensor offload support for accumulation buffers, separate encode/decode configuration
• Native Dtype Pipeline: Eliminated unnecessary conversions, maintains bfloat16 precision throughout for speed and quality
• Optimized Tensor Operations: Replaced einops rearrange with native PyTorch ops for 2-5x faster transforms

🎯 Quality Improvements

• LAB Color Correction: New perceptual color transfer method with superior color accuracy (now default)
• Additional Color Methods: HSV saturation matching, wavelet adaptive, and hybrid approaches
• Deterministic Generation: Seed-based reproducibility with phase-specific seeding strategy
• Better Temporal Consistency: Hann window blending for smooth transitions between batches

💾 Memory Management

• Smarter Offloading: Independent device configuration for DiT, VAE, and tensors (CPU/GPU/none)
• Four-Phase Pipeline: Completes each phase (encode→upscale→decode→postprocess) for all batches before moving to next, minimizing model swaps
• Better Cleanup: Phase-specific resource management with proper tensor memory release
• Peak VRAM Tracking: Per-phase memory monitoring with summary display

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