Remove ineffective allow_vram_overflow setting
- PyTorch's set_per_process_memory_fraction cannot prevent WDDM paging on Windows - Keep overflow detection and warning when VRAM exceeds physical limit - Simplify peak memory formatting - Remove setting from CLI, ComfyUI node, and memory_manager
This commit is contained in:
Adrien Toupet
@@ -418,12 +418,6 @@ Configure the DiT (Diffusion Transformer) model for video upscaling.
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- `sdpa`: PyTorch scaled_dot_product_attention (default, stable, always available)
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- `flash_attn`: Flash Attention 2 (faster on supported hardware, requires flash-attn package)
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- **allow_vram_overflow**: Windows only - allow VRAM to overflow to system RAM
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- `False` (default): Strict VRAM limit - faster when within limits
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- `True`: Allow overflow - prevents OOM but causes severe slowdown
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- Last resort when other optimizations are insufficient
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- Requires ComfyUI restart to change
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- **torch_compile_args**: Connect to SeedVR2 Torch Compile Settings node for 20-40% speedup
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**BlockSwap Explained:**
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+1
-9
@@ -79,7 +79,6 @@ else:
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# Pre-parse arguments that must be handled before torch import
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_pre_parser = argparse.ArgumentParser(add_help=False)
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_pre_parser.add_argument("--cuda_device", type=str, default=None)
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_pre_parser.add_argument("--allow_vram_overflow", action="store_true")
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_pre_args, _ = _pre_parser.parse_known_args()
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if _pre_args.cuda_device is not None:
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@@ -128,13 +127,9 @@ from src.core.generation_phases import (
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postprocess_all_batches
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)
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from src.utils.debug import Debug
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from src.optimization.memory_manager import clear_memory, configure_vram_limit, get_gpu_backend, is_cuda_available
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from src.optimization.memory_manager import clear_memory, get_gpu_backend, is_cuda_available
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debug = Debug(enabled=False) # Will be enabled via --debug CLI flag
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# Configure VRAM limit (must be before any CUDA allocations)
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if platform.system() != "Darwin":
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configure_vram_limit(allow_overflow=_pre_args.allow_vram_overflow)
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# =============================================================================
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# Device Management Helpers
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# =============================================================================
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@@ -1335,9 +1330,6 @@ Examples:
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"Requires --dit_offload_device. Default: 0 (disabled)")
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blockswap_group.add_argument("--swap_io_components", action="store_true",
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help="Offload DiT I/O layers for extra VRAM savings. Requires --dit_offload_device")
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blockswap_group.add_argument("--allow_vram_overflow", action="store_true",
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help="Windows only: Allow VRAM overflow to system RAM. Prevents OOM but causes severe slowdown. "
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"Last resort when other optimizations are insufficient.")
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# VAE Tiling
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vae_group = parser.add_argument_group('VAE tiling (for high resolution upscale)')
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@@ -7,7 +7,7 @@ from comfy_api.latest import io
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from comfy_execution.utils import get_executing_context
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from typing import Dict, Any, Tuple
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from ..utils.model_registry import get_available_dit_models, DEFAULT_DIT
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from ..optimization.memory_manager import get_device_list, configure_vram_limit
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from ..optimization.memory_manager import get_device_list
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class SeedVR2LoadDiTModel(io.ComfyNode):
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@@ -112,18 +112,6 @@ class SeedVR2LoadDiTModel(io.ComfyNode):
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"Flash Attention provides speedup through optimized CUDA kernels on compatible GPUs."
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)
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),
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io.Boolean.Input("allow_vram_overflow",
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default=False,
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optional=True,
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tooltip=(
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"Windows only: Allow VRAM to overflow to system RAM.\n"
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"• False (default): Strict VRAM limit - faster when within limits\n"
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"• True: Allow overflow - prevents OOM but may cause severe slowdown\n"
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"\n"
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"Last resort when other optimizations are insufficient.\n"
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"Requires ComfyUI restart to change."
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)
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),
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io.Custom("TORCH_COMPILE_ARGS").Input("torch_compile_args",
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optional=True,
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tooltip=(
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@@ -143,7 +131,6 @@ class SeedVR2LoadDiTModel(io.ComfyNode):
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def execute(cls, model: str, device: str, offload_device: str = "none",
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cache_model: bool = False, blocks_to_swap: int = 0,
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swap_io_components: bool = False, attention_mode: str = "sdpa",
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allow_vram_overflow: bool = False,
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torch_compile_args: Dict[str, Any] = None) -> io.NodeOutput:
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"""
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Create DiT model configuration for SeedVR2 main node
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@@ -156,7 +143,6 @@ class SeedVR2LoadDiTModel(io.ComfyNode):
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blocks_to_swap: Number of transformer blocks to swap (requires offload_device != device)
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swap_io_components: Whether to offload I/O components (requires offload_device != device)
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attention_mode: Attention computation backend ('sdpa' or 'flash_attn')
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allow_vram_overflow: Allow VRAM overflow to system RAM (prevents OOM but slower)
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torch_compile_args: Optional torch.compile configuration from settings node
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Returns:
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@@ -182,9 +168,6 @@ class SeedVR2LoadDiTModel(io.ComfyNode):
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"(e.g., 'cpu' or another device). Set cache_model=False if you don't want to cache the model."
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)
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# Configure VRAM limit enforcement (once per session, first call wins)
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configure_vram_limit(allow_overflow=allow_vram_overflow)
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config = {
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"model": model,
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"device": device,
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@@ -138,62 +138,6 @@ else:
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print(f"⚠️ Memory check failed: {vram_info['error']} - No available backend!")
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# VRAM overflow configuration state
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_vram_overflow_allowed: bool = True
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_vram_limit_configured: bool = False
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_vram_limit_change_attempted: bool = False
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def configure_vram_limit(allow_overflow: bool = False) -> bool:
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"""
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Configure VRAM limit enforcement. Call early before heavy CUDA usage.
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Args:
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allow_overflow: If True, allow VRAM overflow to system RAM (prevents OOM but may be slow).
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If False (default), enforce strict physical VRAM limit.
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Returns:
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True if configuration applied successfully, False otherwise
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Note:
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Can only be configured once per session. Restart required to change.
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"""
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global _vram_overflow_allowed, _vram_limit_configured, _vram_limit_change_attempted
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# Already configured this session - track if user tried to change
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if _vram_limit_configured:
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if _vram_overflow_allowed != allow_overflow:
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_vram_limit_change_attempted = True
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return _vram_overflow_allowed == allow_overflow
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_vram_limit_configured = True
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_vram_overflow_allowed = allow_overflow
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if allow_overflow:
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return True
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if not is_cuda_available():
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return True
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try:
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for i in range(torch.cuda.device_count()):
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torch.cuda.set_per_process_memory_fraction(1.0, i)
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return True
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except RuntimeError:
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_vram_overflow_allowed = True
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return False
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def is_vram_overflow_allowed() -> bool:
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"""Check if VRAM overflow to system RAM is allowed."""
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return _vram_overflow_allowed
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def was_vram_limit_change_attempted() -> bool:
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"""Check if user tried to change VRAM limit setting after initial configuration."""
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return _vram_limit_change_attempted
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def get_vram_usage(device: Optional[torch.device] = None, debug: Optional['Debug'] = None) -> Tuple[float, float, float, float]:
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"""
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Get current VRAM usage metrics for monitoring.
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+13
-35
@@ -15,30 +15,28 @@ from ..optimization.memory_manager import (
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get_vram_usage,
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get_basic_vram_info,
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get_ram_usage,
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reset_vram_peak,
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is_vram_overflow_allowed,
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was_vram_limit_change_attempted,
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reset_vram_peak,
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is_mps_available,
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is_cuda_available
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)
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from ..utils.constants import __version__
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def _format_peak_with_swap(peak_gb: float, total_vram_gb: float) -> str:
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"""Format peak memory, showing overflow breakdown on Windows.
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def _format_peak_with_overflow(peak_gb: float, total_vram_gb: float) -> str:
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"""Format peak reserved memory, showing overflow breakdown on Windows.
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Args:
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peak_gb: Peak reserved memory from PyTorch
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total_vram_gb: Physical GPU VRAM capacity
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"""
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if total_vram_gb <= 0:
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return f"{peak_gb:.2f}GB"
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return f"{peak_gb:.2f}GB reserved"
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overflow_gb = peak_gb - total_vram_gb
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if overflow_gb <= 0 or platform.system() != 'Windows':
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return f"{peak_gb:.2f}GB"
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return f"{peak_gb:.2f}GB reserved"
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return f"{peak_gb:.2f}GB ({total_vram_gb:.0f}GB GPU + {overflow_gb:.2f}GB system RAM)"
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return f"{peak_gb:.2f}GB reserved ({total_vram_gb:.0f}GB GPU + {overflow_gb:.2f}GB overflow)"
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class Debug:
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@@ -176,11 +174,6 @@ class Debug:
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# Environment info - only in debug mode
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if self.enabled:
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self._print_environment_info(cli)
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# VRAM overflow status - warnings always shown
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vram_warning_shown = self._print_vram_overflow_status()
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self.log("", category="none", force=vram_warning_shown)
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def _print_environment_info(self, cli: bool = False) -> None:
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"""Print concise environment info for bug reports - zero cost when debug disabled"""
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@@ -242,21 +235,7 @@ class Debug:
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self.log(f"Python: {py_ver} | PyTorch: {torch_ver} | Flash Attn: {flash_str} | Triton: {triton_str}", category="info")
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cuda_line = f"CUDA: {cuda_ver} | cuDNN: {cudnn_ver}"
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self.log(f"{cuda_line} | ComfyUI: {comfy_str}" if comfy_str else cuda_line, category="info")
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def _print_vram_overflow_status(self) -> bool:
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"""Print VRAM overflow status (Windows only). Returns True if warning was printed."""
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if platform.system() != 'Windows':
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return False
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if was_vram_limit_change_attempted():
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self.log("allow_vram_overflow setting changed - restart ComfyUI to apply", level="WARNING", category="memory", force=True)
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return True
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elif is_vram_overflow_allowed():
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self.log("allow_vram_overflow: enabled - may cause severe slowdown if physical VRAM exceeded", level="WARNING", category="memory", force=True)
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return True
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else:
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self.log("allow_vram_overflow: disabled (recommended)", category="success")
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return False
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self.log("", category="none")
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def print_footer(self) -> None:
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"""Print the footer with links - always displayed"""
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@@ -430,7 +409,7 @@ class Debug:
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# Overflow warning (Windows only - WDDM can page to system RAM)
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overflow = memory_info.get('vram_overflow', 0.0)
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if overflow > 0 and platform.system() == 'Windows' and not is_vram_overflow_allowed():
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if overflow > 0 and platform.system() == 'Windows':
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self.log(f"VRAM overflow: {overflow:.2f}GB paged to system RAM - severe slowdown expected. "
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"Consider optimizing (e.g., reduce resolution, batch size, enable BlockSwap, VAE tiling...).",
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level="WARNING", category="memory", force=True)
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@@ -494,11 +473,10 @@ class Debug:
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metrics['vram_overflow'] = max(0.0, metrics['vram_peak_rsv'] - metrics['vram_total'])
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backend = "Unified Memory" if is_mps else "VRAM"
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peak_alloc_str = _format_peak_with_swap(metrics['vram_peak_alloc'], metrics['vram_total'])
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metrics['summary_vram'] = (
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f" [{backend}] {metrics['vram_allocated']:.2f}GB allocated / "
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f"{metrics['vram_reserved']:.2f}GB reserved / "
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f"Peak: {peak_alloc_str} / "
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f"Peak: {metrics['vram_peak_alloc']:.2f}GB / "
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f"{metrics['vram_free']:.2f}GB free / "
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f"{metrics['vram_total']:.2f}GB total"
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)
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@@ -677,8 +655,8 @@ class Debug:
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if is_mps:
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self.log(f"{phase_num}. {phase_name}: {alloc:.2f}GB", category="memory", indent_level=1, force=force)
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else:
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rsv_str = _format_peak_with_swap(rsv, total_vram_gb)
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self.log(f"{phase_num}. {phase_name}: VRAM {alloc:.2f}GB allocated, {rsv_str} reserved | RAM {ram:.2f}GB", category="memory", indent_level=1, force=force)
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rsv_str = _format_peak_with_overflow(rsv, total_vram_gb)
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self.log(f"{phase_num}. {phase_name}: VRAM {alloc:.2f}GB allocated, {rsv_str} | RAM {ram:.2f}GB", category="memory", indent_level=1, force=force)
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overall_alloc = max(self.phase_vram_peaks_alloc.values()) if self.phase_vram_peaks_alloc else 0
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overall_rsv = max(self.phase_vram_peaks_rsv.values()) if self.phase_vram_peaks_rsv else 0
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@@ -687,8 +665,8 @@ class Debug:
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if is_mps:
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self.log(f"Overall peak: {overall_alloc:.2f}GB", category="memory", force=force)
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else:
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overall_rsv_str = _format_peak_with_swap(overall_rsv, total_vram_gb)
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self.log(f"Overall peak: VRAM {overall_alloc:.2f}GB allocated, {overall_rsv_str} reserved | RAM {overall_ram:.2f}GB", category="memory", force=force)
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overall_rsv_str = _format_peak_with_overflow(overall_rsv, total_vram_gb)
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self.log(f"Overall peak: VRAM {overall_alloc:.2f}GB allocated, {overall_rsv_str} | RAM {overall_ram:.2f}GB", category="memory", force=force)
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@torch._dynamo.disable # Skip tracing to avoid time.time() warnings
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def _store_checkpoint(self, label: str, metrics: Dict[str, Any]) -> None:
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