- LTX Video VAE detection (128 channels, 32x spatial compression, 8x temporal)
- z-image / z-image-turbo pixel-space VAE passthrough (no decode needed)
- fp16 default on all AMD GPUs (Strix Halo, RDNA 2/3, CDNA)
- Per-architecture tuning with automatic detection (gfx1151/gfx1100/gfx1030/CDNA)
- Temporal tiling for long videos: overlapping chunks with linear blend,
zero frame loss, progress bar in UI (disabled by default)
- Chunked IO bypass for LTX — fixes OOM with max_split_size_mb caps
- Fixed memory estimation: correct for all dims (including 5D temporal) and dtypes
- APU-aware memory management (psutil for unified memory on Strix Halo)
- Tile size floor for high-compression VAEs (min 64 latent pixels)
- 25 unit tests covering all new features
- Updated version to 2.0.11 in pyproject.toml and comfyui_manager.json for consistency.
- Removed video chunking from ROCMOptimizedVAEDecode to ensure full video decoding for temporal/causal VAEs, preventing artifacts.
- Added new ROCm Text Generate LTX2 Prompt node, enhancing generative AI capabilities.
- Updated README to reflect new features and improvements in v2.0.11.
- Enhanced changelog with details on changes and fixes in this release.
- Added optional in-memory cache to ROCmOptimizedCheckpointLoader to avoid redundant disk loads during repeated API runs.
- Implemented cache eviction strategy to manage memory efficiently on gfx1151 architecture.
- Updated INPUT_TYPES to include parameters for cache usage and forced reload.
- Added integration and unit tests to verify cache behavior and functionality.
- Documented caching mechanism in CHECKPOINT_CACHE_API.md and updated LOADER_NODES_GUIDE.md for clarity.
- Added full on-the-fly dequantization for GGUF models (Q8_0, Q4_0, Q4_1, Q5_0, Q5_1, BF16)
- Implemented get_weight() and cast_bias_weight() methods for proper tensor handling
- Fixed 'Multiple dispatch failed for torch.nn.linear' error by converting all ROCmGGMLTensor objects to regular tensors
- Added forward_comfy_cast_weights() routing for all ROCmGGMLTensor objects (quantized and non-quantized)
- Based on City96's ComfyUI-GGUF implementation, optimized for ROCm/gfx1151
- Added LoRA patch support and VRAM estimation
- All GGUF models now work correctly for loading and sampling
- Fixed file listing issue: now checks multiple folder names (diffusion_models, unet, unet_gguf)
- Fixed PyTorch 2.0+ GGUF loading: patches torch.load to set weights_only=False for GGUF files
- Removed all Unicode emojis: replaced with ASCII prefixes [INFO], [WARNING], [ERROR], etc.
- Improved cross-platform compatibility for Windows console
Issues reported by u/x5nder on Reddit - thank you for the bug reports!
Version bump: 2.0.5 -> 2.0.6
- Fixed UnicodeEncodeError when printing emoji characters on Windows
- Windows console (cp1252) cannot display emoji characters like ✅, ❌, ⚠️
- Added safe_print() function with automatic ASCII fallbacks for Windows
- Emojis are replaced with [OK], [ERROR], [WARNING] on incompatible encodings
- All print statements in install.py now use safe_print() for cross-platform compatibility
Issue reported by u/x5nder on Reddit - thank you for finding this bug!
Version bump: 2.0.4 -> 2.0.5
- Fixed ROCm Diffusion Model loader to accept .gguf, .ckpt, .pt, .pth, .bin, and .onnx files
- Previously only accepted .safetensors files due to ComfyUI's default filter
- Added custom file scanner that supports all common model formats
- Falls back to ComfyUI's default if custom scanning fails
Issue reported by u/x5nder on Reddit - thank you for finding this bug!
Version bump: 2.0.3 -> 2.0.4
- Added reliable WAN VAE detection using isinstance() with imported classes
- Added fallback detection using latent_channels (16 for WAN 2.1, 48 for WAN 2.2)
- Disabled chunking for WAN VAEs to preserve causal decoding chain with feature caching
- Matched native ComfyUI behavior exactly for WAN VAE video processing
- Fixed detection logic to use correct file (rocm_nodes/core/vae.py)
- Applied ROCm backend optimizations to video processing
- Added async memory cleanup for WAN VAE videos
- Added gfx1151 architecture detection and logging
- Updated version to 2.0.1 across all files
- Created custom LoRA loader node to handle memory fragmentation during LoRA operations
- Implements aggressive_memory_cleanup() before, during, and after LoRA loading
- Added detailed memory state logging for monitoring fragmentation
- Registered node in NODE_CLASS_MAPPINGS and NODE_DISPLAY_NAME_MAPPINGS
- Addresses OOM errors that occur during LoRA weight patching in comfy.float.stochastic_rounding
- Version 1.0.26 with enhanced memory management for ROCm gfx1151
- Fixed free memory calculation to use reserved_memory instead of allocated_memory
- Added accurate memory monitoring with both allocated and reserved memory display
- Implemented dynamic memory fraction calculation based on actual available memory
- Added helper functions for consistent memory management across all samplers
- Improved OOM prevention with more accurate memory thresholds
- Enhanced memory cleanup with aggressive cache clearing and garbage collection
- Fixed memory calculation consistency between regular and advanced KSamplers
- Add progress indicators for Windows users experiencing 6-minute hang
- Add conservative memory allocation for Windows stability
- Add completion messages for better user feedback
- Maintain all ROCm optimizations for AMD GPU performance
- Fix WAN VAE frame duplication issue
- Ready for publication
- Fixed 'Input type (float) and bias type (c10::BFloat16) should be the same' error
- Added smart precision detection to match VAE model dtype
- Added explicit BFloat16 to Float32 conversion when needed
- Enhanced model conversion logic with proper logging
- Updated version to 1.0.16
This fix ensures VAE model and input tensors always have matching dtypes,
preventing PyTorch type mismatch errors while maintaining ROCm optimizations.
- Restored v1.0.13 with all 9 working nodes (ROCMOptimizedCheckpointLoader, ROCMOptimizedKSampler, etc.)
- Implemented comprehensive debug flag system (ROCM_NINODES_DEBUG=1)
- Created complete test suite with performance and correctness tests
- Added data capture infrastructure for real workflow testing
- Built standalone test runner with mock data support
- Created ComfyUI integration tests for Flux and WAN workflows
- Added comprehensive documentation (ARCHITECTURE.md, RULES.md, TESTING_GUIDE.md)
- Updated README with testing section and performance targets
- All tests passing with proper error handling and data validation
Performance Targets:
- Flux: 78% improvement (≤110s total)
- WAN: 5.6% improvement (≤93s sampling)
- VAE Decode: <10s for any resolution
- Checkpoint Load: <30s for Flux models
Ready for production use with comprehensive testing infrastructure!
- Fixed AttributeError: 'dict' object has no attribute 'shape'
- Fixed IndexError: tuple index out of range in WAN VAE memory calculation
- Fixed ValueError: Expected numpy array with ndim 3 but got 4 in video output
- Added comprehensive test suite with 9 test cases covering all error scenarios
- Added debug data collection and optimization testing framework
- Updated documentation with testing section and performance benchmarks
- Cleaned up unused files and improved code quality
- WAN video workflows now work end-to-end without errors
- Fixed blessed workflow using diffusion-model-only file instead of full checkpoint
- Resolved 'clip input is invalid: None' error caused by flux1-dev-fp8.safetensors
- Enhanced error handling with comprehensive validation for model, CLIP, and VAE outputs
- Updated troubleshooting guide to explain diffusion-model-only files issue
- Added Flux model guidance for separate file structure
- Blessed workflow now uses user-selectable checkpoint for maximum compatibility
- Fixed critical ROCMOptimizedCheckpointLoader errors causing noise output
- Resolved torch.pickle and comfy.model_management.ModelPatcher errors
- Streamlined checkpoint loader implementation for maximum reliability
- Enhanced with lazy_loading, optimize_for_flux, and precision_mode parameters
- Performance within 2-3% of standard loader (28.32s vs 29.07s)
- Verified proper 512x512 PNG image generation with both standard and ROCM nodes
- Updated blessed workflow with enhanced checkpoint loader parameters
- Comprehensive testing confirms stable end-to-end workflow execution
- Increment version in pyproject.toml and comfyui_manager.json from 1.0.7 to 1.0.9
- Add new nodes: ROCMOptimizedCheckpointLoader, ROCMFluxBenchmark, and ROCMMemoryOptimizer
- Implement Flux-specific optimizations and adaptive tile sizing in VAE decode
- Introduce memory management improvements and performance benchmarks
- Update README with new features and installation methods for enhanced user experience
- Increment version in pyproject.toml from 1.0.6 to 1.0.7
- Increment version in package.json from 1.0.6 to 1.0.7
- Update comfyui_manager.json version to 1.0.7
- Prepare for release with version bump
- Add comfyui_manager.json with node definitions
- Update package.json with node list and requirements
- Update pyproject.toml with ComfyUI Manager metadata
- This should fix ComfyUI Manager's 'Check Missing' functionality
- Introduced a new node.zip file for plugin distribution
- Updated README to include new installation methods for the plugin via ComfyUI CLI and manual installation
- Renumbered installation methods for clarity
This enhances the installation experience for users by providing clear and updated instructions.
- Changed icon URL to use branch reference for better stability
- Removed outdated icon URL to ensure proper loading in various environments
This update enhances the reliability of the icon display in the project.
- Introduced a new icon for the project
- Updated project version from 1.0.0 to 1.0.4
- Adjusted pyproject.toml to include the icon URL
This enhances the project's visual identity and ensures the versioning reflects recent changes.
- Add complete project metadata with description, authors, and maintainers
- Include proper classifiers for OS support, Python versions, and GPU support
- Configure ComfyUI-specific settings with PublisherId and includes
- Add build system configuration with setuptools
- Include optional dependencies for development and testing
- Set up package data inclusion for JSON, JS, MD, TXT, and TOML files
- Ready for PyPI publishing and ComfyUI registry registration
iGavroche