ComfyUI/comfy/cli_args.py
loxotron b9f5145f4d Add fast_sampler.py with optimized sampling and VAE decoding, enhance PreviewImage
This commit introduces `fast_sampler.py`, a new module designed to enhance the performance of sampling and VAE decoding in ComfyUI. It replaces or augments functionality previously handled in `model_management.py`, providing better VRAM management, FP16 support, and tiled decoding for low-memory scenarios. Additionally, it improves the `PreviewImage` node in `nodes.py` for faster and more efficient preview generation. These changes improve efficiency, stability, and usability, particularly for GPU-based workflows.

**Key Changes:**
- Implemented `fast_ksampler` for optimized sampling with improved memory management, FP16 support via `torch.amp.autocast`, and `channels_last` memory format for better GPU performance.
- Added `fast_vae_decode` for efficient VAE decoding, incorporating FP16 support, `channels_last`, and selective VRAM clearing to prevent out-of-memory errors.
- Introduced `fast_vae_tiled_decode` for tiled VAE decoding, enabling processing of large latents on GPUs with limited VRAM by using configurable tile sizes and overlaps.
- Added profiling and debugging utilities (`profile_section`, `profile_cuda_sync`) to track execution times and VRAM usage when `--profile` or `--debug` flags are enabled.
- Improved VRAM management with `clear_vram`, ensuring sufficient free memory before loading models or VAE, with configurable thresholds and minimum free memory requirements.
- Implemented `is_fp16_safe` to check GPU compatibility for FP16 operations, disabling them on unsupported hardware (e.g., GTX 1660/Turing).
- Optimized tensor transfers with `optimized_transfer` and `optimized_conditioning` for synchronous device placement and dtype casting.
- Enhanced model preloading with `preload_model`, which unloads VAE before loading U-Net to conserve VRAM and checks for already-loaded VAE to avoid redundant transfers.
- Integrated `cudnn.benchmark` for for tests, disabled by default.
- VRAM should now be managed efficiently.
- Updated `PreviewImage` node in `nodes.py` to support adaptive resizing of preview images to a maximum dimension of ~512 pixels while preserving aspect ratio, using `Image.LANCZOS` for quality. Increased `compress_level` from 1 to 4 for faster PNG compression, optimizing preview generation.

**Impact:**
- Significantly reduces VRAM usage during sampling and VAE decoding, making ComfyUI more stable on GPUs with limited memory.
- Improves performance for large-scale image generation through tiled decoding and FP16 optimizations.
- Enhances debugging capabilities with detailed profiling and logging, aiding development and optimization.

**Dependencies:**
- Relies on `nodes.py` for integration with `KSampler`, `VAEDecode`, `VAEDecodeTiled`, and `PreviewImage` nodes.
- Assumes compatibility with existing `ModelPatcher` functionality for model patching (e.g., in `LoraLoader`).

**Notes:**
- Users should enable `--profile` or `--debug` flags to access detailed performance logs.
- FP16 support requires compatible GPU hardware (compute capability ≥ 8 or > 7).
- Tiled decoding parameters (`tile_size`, `overlap`, etc.) may need tuning for specific workflows.
- Preview images are now smaller and faster to generate, but users can adjust `max_size` in `PreviewImage` if higher resolution previews are needed.

This is a foundational change to improve ComfyUI's performance and scalability, particularly for resource-constrained environments.

Thanks to Grok @ xAI for help.
2025-05-15 06:54:33 +03:00

236 lines
15 KiB
Python

"""
This file is part of ComfyUI.
Copyright (C) 2024 Comfy
"""
import argparse
import enum
import os
import comfy.options
class EnumAction(argparse.Action):
"""
Argparse action for handling Enums
"""
def __init__(self, **kwargs):
# Pop off the type value
enum_type = kwargs.pop("type", None)
# Ensure an Enum subclass is provided
if enum_type is None:
raise ValueError("type must be assigned an Enum when using EnumAction")
if not issubclass(enum_type, enum.Enum):
raise TypeError("type must be an Enum when using EnumAction")
# Generate choices from the Enum
choices = tuple(e.value for e in enum_type)
kwargs.setdefault("choices", choices)
kwargs.setdefault("metavar", f"[{','.join(list(choices))}]")
super(EnumAction, self).__init__(**kwargs)
self._enum = enum_type
def __call__(self, parser, namespace, values, option_string=None):
# Convert value back into an Enum
value = self._enum(values)
setattr(namespace, self.dest, value)
parser = argparse.ArgumentParser()
parser.add_argument("--debug", action="store_true", help="Enable debug logging.")
parser.add_argument("--profile", action="store_true", help="Enable profiling.")
parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)")
parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.")
parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.")
parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.")
parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
cm_group = parser.add_mutually_exclusive_group()
cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
cm_group.add_argument("--model-dtype", type=str, choices=["fp16", "bf16", "fp32"], help="Force model data type (fp16, bf16, fp32)")
fp_group = parser.add_mutually_exclusive_group()
fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
fp_group.add_argument("--force-fp32-vae", action="store_true", help="Force VAE to use FP32 precision")
fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
fp_group.add_argument("--force-fp16-vae", action="store_true", help="Force VAE to use FP16 precision")
fpunet_group = parser.add_mutually_exclusive_group()
fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.")
fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64 (not recommended).")
fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16 (requires SM >= 8.0).")
fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16 (may cause black images on VRAM < 6 GB).")
fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Run UNet in fp8 e4m3fn (requires SM >= 9.0 or SM 8.9 with PyTorch >= 2.3).")
fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Run UNet in fp8 e5m2 (requires SM >= 9.0 or SM 8.9 with PyTorch >= 2.3).")
fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Run UNet in fp8 e8m0fnu (requires SM >= 9.0 or SM 8.9 with PyTorch >= 2.3).") # UPDATED: Clarified requirements
fpvae_group = parser.add_mutually_exclusive_group()
fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16 (risks black images on VRAM < 6 GB).")
fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in fp32 (recommended for GPUs with VRAM < 6 GB).")
fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16 (requires SM >= 8.0).")
parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU (slower, but safe for low VRAM).")
fpte_group = parser.add_mutually_exclusive_group()
fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Run text encoder in fp8 e4m3fn (requires SM >= 9.0 or SM 8.9 with PyTorch >= 2.3).")
fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Run text encoder in fp8 e5m2 (requires SM >= 9.0 or SM 8.9 with PyTorch >= 2.3).")
fpte_group.add_argument("--fp8_e8m0fnu-text-enc", action="store_true", help="Run text encoder in fp8 e8m0fnu (requires SM >= 9.0 or SM 8.9 with PyTorch >= 2.3).") # NEW
fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Run text encoder in fp16 (may cause issues on VRAM < 6 GB).")
fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Run text encoder in fp32 (recommended for GPUs with VRAM < 6 GB).")
fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Run text encoder in bf16 (requires SM >= 8.0).")
parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
class LatentPreviewMethod(enum.Enum):
NoPreviews = "none"
Auto = "auto"
Latent2RGB = "latent2rgb"
TAESD = "taesd"
parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
cache_group = parser.add_mutually_exclusive_group()
cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
attn_group = parser.add_mutually_exclusive_group()
attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")
attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.")
attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.")
parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
upcast = parser.add_mutually_exclusive_group()
upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.")
upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
vram_group = parser.add_mutually_exclusive_group()
vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.")
parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
class PerformanceFeature(enum.Enum):
Fp16Accumulation = "fp16_accumulation"
Fp8MatrixMultiplication = "fp8_matrix_mult"
CublasOps = "cublas_ops"
parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.")
parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
# The default built-in provider hosted under web/
DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
parser.add_argument(
"--front-end-version",
type=str,
default=DEFAULT_VERSION_STRING,
help="""
Specifies the version of the frontend to be used. This command needs internet connectivity to query and
download available frontend implementations from GitHub releases.
The version string should be in the format of:
[repoOwner]/[repoName]@[version]
where version is one of: "latest" or a valid version number (e.g. "1.0.0")
""",
)
def is_valid_directory(path: str) -> str:
"""Validate if the given path is a directory, and check permissions."""
if not os.path.exists(path):
raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.")
if not os.path.isdir(path):
raise argparse.ArgumentTypeError(f"'{path}' is not a directory.")
if not os.access(path, os.R_OK):
raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.")
return path
parser.add_argument(
"--front-end-root",
type=is_valid_directory,
default=None,
help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
)
parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
parser.add_argument(
"--comfy-api-base",
type=str,
default="https://api.comfy.org",
help="Set the base URL for the ComfyUI API (default: https://api.comfy.org).",
)
if comfy.options.args_parsing:
args = parser.parse_args()
else:
args = parser.parse_args([])
if args.windows_standalone_build:
args.auto_launch = True
if args.disable_auto_launch:
args.auto_launch = False
if args.force_fp16:
args.fp16_unet = True
args.fp16_vae = True
args.fp16_text_enc = True
# '--fast' is not provided, use an empty set
if args.fast is None:
args.fast = set()
# '--fast' is provided with an empty list, enable all optimizations
elif args.fast == []:
args.fast = set(PerformanceFeature)
# '--fast' is provided with a list of performance features, use that list
else:
args.fast = set(args.fast)