- Introduce MGPU_MM_LOG flag and logger.mgpu_mm_log(...) to gate and
prefix MultiGPU Model Management logs (disabled by default)
- Replace ad-hoc logger.info("[MultiGPU ...]") calls with mgpu_mm_log
in DisTorch2 cache-clearing and delegation paths to reduce noise
- In load_models_gpu, parse safetensor allocation strings to infer
incoming_compute_device and incoming_compute_planned_bytes (supports
hash#device;GB and expert fraction syntax); track required bytes
- Remove coarse large-model threshold heuristic in favor of allocation-
informed planning
Why: centralize and quiet verbose MGPU logs by default, and enable
smarter, data-driven device selection and memory planning for multi-GPU
model loading.
- Add comfyui_memory_load and create_model_identifier utilities (device_utils)
- Log GPU memory before/after UNet, VAE, and CLIP construction and after UNet weight load
- Include model identifiers in logs to correlate memory to specific patchers
- Guard logging calls with try/except to avoid impacting load flow
- Improves observability of memory usage for multi-GPU checkpoints and aids OOM/debugging
On a 4x PCIe bus, swapping a normal CLIP-sized number of layers once/twice (for neg) into compute should be the optimal solution: Reside on `cpu`, use the optimized cuda kernals for computation JiT on `compute`, discard layers once used (residing permenantly on `cpu`), then move efficently to the main UNet computation.
- Added preemptive model unloading and cache clearing in register_patched_safetensor_modelpatcher() to resolve potential memory issues when allocations are unavailable, prompting the usage of the standard loaders.
Add comprehensive memory cache clearing aligned with ComfyUI patterns to improve stability and reduce OOM incidents in multi-device scenarios.
**Addresses Memory/Garbage Collection Issues:**
- Created `soft_empty_cache_multigpu()` function in device_utils.py
- Replicates ComfyUI's cache clearing for all devices (CUDA, MPS, XPU, NPU, MLU)
- Includes CUDA IPC collect optimization like ComfyUI
- Strategically placed calls before major memory allocations
**Addresses CLIP loading issues:**
- Fixed DisTorch2 device device varibale management before text encoder operations
**`soft_empty_cache_multigpu()` implementation Aligned with ComfyUI's Patterns:**
- Called after GC operations
- Placed before major memory allocations
- Matches ComfyUI's proven memory management strategy
- Same device clearing logic for multi-device scenarios
Refactor device detection into dedicated utility module
- Extract device enumeration and compatibility checks to device_utils.py
- Add support for additional device types (NPU, MLU, DirectML, CoreX)
- Update all modules to use centralized device utilities
- Implement caching for device list to improve performance
- Reduce code duplication across distorch, nodes, and wanvideo modules
Fix compute device inclusion in expert mode allocations
Include compute device in vram_string when expert_mode_allocations
is set but virtual_vram_gb is 0. This ensures the compute device is
properly specified in the full allocation string for expert mode
configurations without virtual VRAM.
Bump version to 2.2.1
This commit refactors the DisTorch safetensor loading and allocation logic for improved performance and correctness.
The main changes to the block assignment are:
- The primary compute device is now included in the pool of "donor" devices, allowing for more holistic memory quota calculation across all available GPUs.
- Unassigned "orphan" blocks are now allocated to the compute device instead of the CPU. This keeps more of the model in VRAM, reducing potential bottlenecks.
Additionally, this commit:
- Fixes a bug in the byte expert string parser where the wildcard `*` was incorrectly checked in the device name instead of the value.
- Standardizes variable names like `allocations_string` for better code clarity and consistency.
Change the fallback assignment for tensor blocks that do not fit within any donor device's memory quota. These blocks are now assigned to the CPU instead of the primary compute device.
This prevents potential VRAM Out-Of-Memory errors on the main GPU, improving the stability of the model loading process, especially under tight memory constraints.
Additionally, the allocation log is now updated to include all available system devices, even those with zero allocation, to provide a more comprehensive report.
This commit refactors several aspects of the DisTorch2 device allocation logic to make it more robust, predictable, and easier to debug.
Key changes:
- Rework the byte-based allocation string parser (`calculate_fraction_from_byte_expert_string`). The new implementation correctly respects the user-defined device order and more cleanly handles the wildcard (`*`) for assigning remaining model parts.
-revert the "improvements" to the analyze safetensor loading routine causing it to catestrophically fail
This commit enhances the device handling logic within `analyze_safetensor_loading` for greater robustness and better user feedback.
Key changes:
- Dynamically discovers all available devices using `get_device_list` instead of only using devices from the allocation string. This prevents potential `KeyError` crashes when analyzing devices that are not part of the distribution plan.
- Changes the fallback device for unallocated model blocks from the primary compute device to the CPU. This is a safer default that prevents unexpectedly overloading the main GPU.
- Adds a warning log when a block falls back to the CPU, alerting the user to a possible misconfiguration in their allocation string.
Introduces a new expert allocation mode allowing users to define model distribution using absolute memory values (e.g., "8g", "512m"). This provides more direct and predictable control over how a model is split across devices compared to the percentage-based method.
The new allocation string format is `device,size;device,size;...`, for example: `"cuda:0,8g;cuda:1,4g;cpu*,2g"`.
Key features:
- A wildcard `*` designates a device to receive any remaining unallocated model parts.
- If requested allocations exceed the model size, they are pro-rated down.
- A new `parse_memory_string` utility handles flexible memory unit parsing (g, m, k, b).
Additionally, the device allocation summary table has been improved to be more descriptive, now showing total VRAM, percentage of device VRAM used, absolute model GB allocated, and the model distribution percentage.
- Remove redundant logging and counters in safetensor model patcher
- Add model original dtype detection for better precision handling
- Streamline FP8 casting conditions and remove verbose debug logs
- Improve static allocation parsing and device assignment flow
- Remove redundant references to GGUF patterns in comments for clarity
- Update logging prefixes from '[MULTIGPU_DISTORCHV2]' to '[MultiGPU_DisTorch2]' for consistency
- Reorganize code in `register_patched_safetensor_modelpatcher` to streamline allocation checks and device assignments
- Add force_patch_weights parameter to new_partially_load signature for better control
- Implement check for _distorch_high_precision_loras with fallback to original loading behavior
- Include cleanup for _distorch_block_assignments attribute
- Comment out debug logging statements to minimize noise during execution
Add logic to detect and unpatch weights for modules with comfy_cast_weights, introduce memory and patch counters, and integrate static device assignments from analyze_safetensor_loading to improve distributed safetensor loading efficiency.
Remove redundant comments and simplify compute device determination by importing and using `current_device` directly, improving code readability and streamlining the analysis logic for better efficiency in model allocation handling.
Simplify the analyze_safetensor_loading function by replacing manual model module iteration with ComfyUI's built-in _load_list() for calculating total memory and building block lists. This improves efficiency, reduces redundant code, and enhances compatibility with ComfyUI's internal mechanisms while maintaining accurate memory reporting and threshold filtering.
This commit introduces DisTorch v2.0.0, a major overhaul that extends multi-device model distribution to standard `.safetensors` models.
Key changes include:
- **Universal `.safetensors` Support:** The core distribution logic is no longer limited to GGUF models. It now fully supports `.safetensors`, allowing any UNet supported by native Comfy loaders to have its layers distributed across multiple devices (GPUs and CPU/RAM).
This commit refactors the model loading logic to properly integrate with ComfyUI's caching system.
- Implemented the `IS_CHANGED` class method, which creates a hash of the DisTorch-specific settings (e.g., `compute_device`, `virtual_vram_gb`).
- This allows ComfyUI to automatically detect when settings have changed and trigger a model reload, invalidating the cache correctly.
- Removed the previous manual and less reliable logic for unloading and reloading the model from within the `override` function.
- Set the default log level to "Engineering" to provide more detailed output during development.
This commit introduces a major architectural refactoring, laying the groundwork for DisTorch V2. The changes focus on improving modularity, memory management, and diagnostics.
Key changes include:
- Renaming `distorch_safetensor.py` to `distorch_2.py` to house the new core logic.
- Deleting the legacy `block_swap.py` module.
- Adding `device_memory_audit.py` for more sophisticated analysis of GPU memory usage.
- Implementing a centralized and configurable logging system in `__init__.py` to provide standardized and level-controlled (DEBUG/INFO) output for better debugging.
John Pollock