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Merge branch 'comfyanonymous:master' into master

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patientx 2025-11-06 15:35:41 +03:00 committed by GitHub
commit 3ab45ae725
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3 changed files with 12 additions and 15 deletions
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3
comfy/cli_args.py
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@ -145,10 +145,11 @@ class PerformanceFeature(enum.Enum):
Fp8MatrixMultiplication = "fp8_matrix_mult"
CublasOps = "cublas_ops"
AutoTune = "autotune"
PinnedMem = "pinned_memory"
parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.")
parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")

2
comfy/ldm/qwen_image/controlnet.py
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@ -44,7 +44,7 @@ class QwenImageControlNetModel(QwenImageTransformer2DModel):
txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
ids = torch.cat((txt_ids, img_ids), dim=1)
image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
del ids, txt_ids, img_ids
hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint)

22
comfy/model_management.py
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@ -1086,22 +1086,21 @@ def cast_to_device(tensor, device, dtype, copy=False):
PINNED_MEMORY = {}
TOTAL_PINNED_MEMORY = 0
if PerformanceFeature.PinnedMem in args.fast:
if WINDOWS:
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45 # Windows limit is apparently 50%
else:
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
else:
MAX_PINNED_MEMORY = -1
MAX_PINNED_MEMORY = -1
if not args.disable_pinned_memory:
if is_nvidia() or is_amd():
if WINDOWS:
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45 # Windows limit is apparently 50%
else:
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
def pin_memory(tensor):
global TOTAL_PINNED_MEMORY
if MAX_PINNED_MEMORY <= 0:
return False
if not is_nvidia():
return False
if not is_device_cpu(tensor.device):
return False
@ -1122,9 +1121,6 @@ def unpin_memory(tensor):
if MAX_PINNED_MEMORY <= 0:
return False
if not is_nvidia():
return False
if not is_device_cpu(tensor.device):
return False