- Standardize tooltip format with multi-line descriptions and bullet points
- Add comprehensive output tooltips for all nodes (DiT, VAE, torch.compile, upscaler)
- Enhance node descriptions with detailed capability summaries
- Simplify CLI tile size arguments to single integers (converted internally to tuples)
- Remove OneOrTwoValues argparse class for cleaner implementation
- Fix encode_tiled tooltip (was incorrectly referencing decoding)
- Clarify color correction purpose (corrects upscaling color shifts)
- Add multi-GPU offloading information to all offload_device tooltips
- Improve torch.compile parameter descriptions with use cases
- Ensure CLI and ComfyUI tooltips are consistent in terminology and structure
Core Changes:
- Implement deterministic generation with phase-specific seeding strategy
* VAE encoding: seed+1M for deterministic sampling without quality loss
* DiT upscaling: base seed for reproducible noise generation
- Add cfg_scale parameter for user control of upscaling strength (WIP)
- Fix ComfyUI V3 unique_id extraction using get_executing_context().node_id
Improvements:
- Standardize Optional['Debug'] type hints across codebase
- Make debug parameter required where it's essential (generate, infer)
- Remove legacy get_unique_id() stack inspection approach
- Add seed and cfg_scale logging for transparency
- Fix FP8CompatibleDiT parameter order consistency
- Refine input/latent noise scale steps (0.01 → 0.001 for finer control)
Major improvements to device handling, model offloading, and code clarity:
Device Management:
- Replace string-based devices with torch.device objects throughout
- Add explicit offload device parameters: dit_offload_device, vae_offload_device, tensor_offload_device
- Remove preserve_vram in favor of explicit offload control
- Improve get_device() to return torch.device objects consistently
- Enhance get_device_list() with smart MPS-only system handling
Context & Pipeline:
- Merge setup_device_environment and prepare_generation_context into single setup_generation_context
- Simplify LOCAL_RANK handling (set to '0' for single-GPU mode)
- Store device configuration on runner for submodule access
Tensor & Model Management:
- Add manage_tensor_device() for consistent tensor movement with logging
- Update manage_model_device() to use torch.device objects
- Add validation for BlockSwap and caching configurations
- Rename cache_in_ram to cache_model (more accurate naming)
CLI & Interface:
- Remove --preserve_vram flag (breaking change)
- Add --vae_offload_device and --tensor_offload_device flags
- Update ComfyUI node parameters with validation and better tooltips
- Improve device selection UI with offload_device options
Major refactor introducing:
- GlobalModelCache for cross-node model sharing with dynamic config updates
* Models cached by node ID, reused across different upscaler instances
* Config changes (torch.compile, BlockSwap, tiling) handled dynamically
* Runner templates cached when both models present
- Complete alpha channel processing rewrite
* New edge-guided alpha upscaling in src/core/alpha_upscaling.py
* Remove broken VAE RGBA adapter approach
* Alpha properly extracted, upscaled, and merged with RGB
- BlockSwap enhancements
* Support I/O-only swapping without transformer block offloading
* Separate memory reporting for I/O components vs transformer blocks
* Clearer logging of GPU vs CPU placement
* New is_blockswap_enabled() utility
- Memory management improvements
* Add release_tensor_collection() for batch tensor cleanup
* Proper tensor memory release throughout pipeline
* Better cleanup in postprocess phase
- Code quality and refactoring
* Extensive docstrings with type hints across all modules
* Generic _update_model_config() reduces code duplication
* Better function signatures and parameter documentation
* Improved separation of concerns in model_manager.py
* Clearer debug logging categories and messages
Core Changes:
- Split monolithic comfyui_node.py into modular per-node files:
* video_upscaler.py - main upscaler node
* dit_model_loader.py - DiT model configuration
* vae_model_loader.py - VAE model configuration
* torch_compile_settings.py - torch.compile settings
* __init__.py - centralized node registry
BlockSwap Enhancements:
- Fixed swap_io_components to work independently of blocks_to_swap
- Both features can now be enabled separately or together
- Updated validation logic throughout (model_manager, blockswap, generation)
- Improved config description to reflect combined capabilities
Logging Improvements:
- Standardized indentation and improved debug messages clarity
- Added footer with support links (YouTube, GitHub) for community engagement
Adrien Toupet