- Rename new_resolution to resolution for consistency
- Change --input to positional input argument
- Rename --model to --dit_model for clarity
- Simplify VAE tiling flags: --vae_encode_tiled and --vae_decode_tiled
- Update all documentation and example workflows
- Maintain consistent naming convention across entire codebase
- Eliminate graph breaks in na.py and attention.py for full torch.compile support
* Replace cumsum-based tensor slicing with _tensor_split to avoid .item() calls
* Use torch.tensor_split with .long().cpu() for PyTorch API requirements
* Replace reshape-based averaging with split-stack-mean pattern
- Fix phase peak VRAM tracking to capture peaks during OOM retry cycles
- Standardize phase4 naming to 'postprocessing' for consistency
- Update RoPE docstrings for accuracy
- Update example workflows with icon
- Add uniform_batch_size parameter to eliminate temporal artifacts in final batch
- Unify temporal padding: single pad_video_temporal() replaces cut_videos() and prepend_video_frames()
- Improve logging: separate messages for uniform vs 4n+1 padding
- Enhance CLI: Improved dynamic examples and use actual invocation path
- README.md: standardize folder references, use seedvr2_videoupscaler folder name consistently, improve parameter documentation
- 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
- Fix validation cache location to respect --model_dir parameter
- Fix output path handling for directories without extensions
- Remove spurious directory creation in get_base_cache_dir
- Enhanced CLI help with usage examples and argument grouping
- Auto-display help when script invoked without arguments
- Correct type hints (device_id: str, debug: Debug)
- Remove redundant type conversions and makedirs calls
- Reorganize imports to module top for clarity
- Improved docstrings & tooltip
- Change default batch_size from 1 to 5 to match ComfyUI integration
- Add support for seedvr2_ema_3b-Q8_0.gguf model
- Validate --cuda_device arguments early in pre-parsing phase
- Check device IDs exist and are within available GPU range
- Fail fast with clear error messages showing available devices
- Auto-detect output format per file type (mp4 for videos, png for images)
- Add visual separators between processed files for better readability
- Simplify FPS calculation to use wall-clock time for real-world throughput
- Consolidate banner/footer into shared Debug methods
- Update offload device args to support multi-GPU (cpu/cuda:N)
- Standardize terminology: 'upscaling' instead of 'video upscaling'
- Remove code duplication between CLI and ComfyUI implementations
- Consistent quote style (double quotes) throughout CLI
Major Features:
- Renamed --video_path to --input supporting video files, images, and directories
- Added batch processing for directories (iterates all media files)
- Added single image upscaling with extract_frames_from_image()
- Auto-detect output format: images→PNG, videos→MP4 (overridable)
- Smart output path generation (single PNG vs frame sequences)
Critical Bug Fixes:
- Fixed 'str' object has no attribute 'type' by normalizing devices to torch.device
- Fixed 'Got unsupported ScalarType BFloat16' by converting ML dtypes to float32
- Fixed prepare_runner() signature mismatch (returned 2 values, claimed 3)
- Fixed KeyError 'cache_context' by storing cache_context in ctx
- Fixed duplicate optimization logging (3x imports) using environment variable
Performance Improvements:
- Removed mp.Manager() overhead
- Using direct mp.Queue(maxsize=0) for better throughput
- Improved multiprocessing reliability
Consistency & Quality:
- Unified model directory between CLI & ComfyUI to models/SEEDVR2 using constants
- Default CLI output folder to use ./output/
- Add max_resolution parameter (default: 0 = no limit) to both CLI and ComfyUI
- After new_resolution scales shortest edge, max_resolution ensures no edge exceeds limit
- Scales down proportionally if constraint violated
- Maintains backward compatibility with default value of 0
BlockSwap now uses minimal, pressure-based cache clearing instead of forced deep cleans:
- Clear only when VRAM < 5% free (was 15%, more aggressive threshold)
- Use minimal GPU cache clear (deep=False) for reduced overhead
- Applied after each block/IO swap when pressure detected
Also fixes peft dependency: Add explicit peft>=0.17.0 in requirements for diffusers>=0.33.1 compatibility in ComfyUI ecosystem where other plugins may install outdated peft versions.
- VAE: Add tensor_offload_device support for tiled encode/decode accumulation buffers to reduce VRAM usage
- BlockSwap: Re-enable adaptive memory clearing under pressure to prevent OOM at high resolutions
- Memory: Fix offload_target device type error when set to 'none'
- Interface: Rename 'pixels' input to 'image' for ComfyUI convention alignment
- Interface: Fix singular/plural frame text display
- Add dit_offload_device parameter for proper blockSwap configuration
- Ensure consistent dtype management throughout CLI and ComfyUI (float32 input with bfloat16 pipeline)
- Translate all French comments to English
- Add comprehensive docstrings and section headers
- Remove obsolete use_non_blocking and enable_debug parameters
- Add error handling and validation
- Implement prepend_video_frames() for artifact reduction at video start
- Add blend_overlapping_frames() with Hann window for smooth transitions
- Expose temporal_overlap (0-16) and prepend_frames (0-32) in ComfyUI node
- Unify CLI and ComfyUI to use shared prepend/overlap functions
- Add comprehensive logging for frame adjustments (prepend/overlap/padding)
- Optimize color operations for VRAM efficiency with safe precision handling
- Limit clear_memory calls to OOM retry and final cleanup only
- Fix model cleanup regression ensuring proper GPU memory release
Resolves ModuleNotFoundError on PyTorch ROCm 7 nightly builds by:
- Adding conditional imports for torch.distributed.fsdp in advanced.py
- Removing unused _is_fsdp_flattened import from meta_init_utils.py
- Removing unused log_runtime decorator from decorators.py
FSDP (Fully Sharded Data Parallel) features are only needed for multi-GPU
training and are not available in some PyTorch builds including AMD ROCm 7.
This change makes FSDP optional while preserving all inference functionality.
CFG (Classifier-Free Guidance) does not work with SeedVR2's distilled
one-step diffusion model. The model was trained to produce final results
in a single step without iterative guidance.
- Remove cfg_scale parameter from ComfyUI node and CLI interface
- Force internal cfg_scale to 1.0 in upscale_all_batches()
This avoids artifacts introduced when users changed cfg_scale away from 1.0.
- Remove forced bfloat16 conversion for DiT, use native model dtype
- Fixed materialization logic to use offload_device when configured
- Fix BlockSwap VRAM leak by moving IO components to offload during BlockSwap cleanup
These changes restore performance and fix memory regressions
introduced in previous commits.
- Switch to deterministic VAE encoding (mode vs sample) to eliminate high-resolution noise artifacts
- Make VAE encode tiling optional (disabled by default) since deterministic encoding resolves artifacts
- Add tile debug visualization feature with adaptive scaling and color-coded boundaries
- Remove redundant dtype conversions in VAE code for better performance
- Minor code cleanup and documentation update
VAE Changes:
- Enable encode tiling by default (prevents noise artifacts at high resolution)
- Increase tile size to 1024px (down from 512px) for optimal quality
- Increase tile overlap to 128px for better blending
Dtype Pipeline:
- Hardcode compute_dtype to bfloat16 for consistent quality/performance/VRAM balance
- Ensure all pipeline steps are using compute_dtype when relevant
- Refactor code for improved performance and memory management
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)
- Add indent_level param to debug.log() and manage_tensor()
- Replace all hardcoded spaces with indent_level (0/1/2)
- Fix encode_all_batches memory flow: encode before storage
- Minor whitespace cleanup
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
- Phase 3: Move decoded samples to CPU immediately after decoding to prevent VRAM accumulation across batches
- Phase 4: Load samples back to GPU at processing start for fast color correction (consistent with other phases)
- Implement streaming architecture: pre-allocate final_video tensor and write directly without accumulation
- Add progressive memory release: free batch_samples and transformed_videos as they're processed
- Store total_frames in context early for accurate pre-allocation
- Ensure consistent VRAM usage regardless of batch count and eliminates memory spikes for long videos
- Move ensure_float32_precision() from alpha_upscaling to common/half_precision_fixes
- Update color_fix.py to use ensure_float32_precision
- Add type hints and docstrings to all half_precision_fixes functions
- Clarify batch size tips: emphasize avoiding padding waste over maximizing batch size
- Update default seed to 42 (the answer to life, the universe, and everything)
- Add LAB color transfer as new default method for superior perceptual color matching
- Add HSV hue-conditional saturation histogram matching for targeted oversaturation correction
- Add wavelet_adaptive hybrid method combining wavelet base with selective HSV correction
- Change default color correction from 'wavelet' to 'lab' for better color accuracy
- Implement full CIELAB color space conversion with D65 illuminant and histogram matching
- Add comprehensive documentation and method descriptions to color_fix.py
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
Adrien Toupet