65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
import importlib.metadata
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import torch
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import logging
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from contextlib import contextmanager
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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log = logging.getLogger(__name__)
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def check_diffusers_version():
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try:
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version = importlib.metadata.version('diffusers')
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required_version = '0.31.0'
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if version < required_version:
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raise AssertionError(f"diffusers version {version} is installed, but version {required_version} or higher is required.")
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except importlib.metadata.PackageNotFoundError:
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raise AssertionError("diffusers is not installed.")
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def print_memory(device):
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memory = torch.cuda.memory_allocated(device) / 1024**3
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max_memory = torch.cuda.max_memory_allocated(device) / 1024**3
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max_reserved = torch.cuda.max_memory_reserved(device) / 1024**3
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log.info(f"Allocated memory: {memory=:.3f} GB")
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log.info(f"Max allocated memory: {max_memory=:.3f} GB")
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log.info(f"Max reserved memory: {max_reserved=:.3f} GB")
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#memory_summary = torch.cuda.memory_summary(device=device, abbreviated=False)
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#log.info(f"Memory Summary:\n{memory_summary}")
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def get_module_memory_mb(module):
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memory = 0
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for param in module.parameters():
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if param.data is not None:
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memory += param.nelement() * param.element_size()
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return memory / (1024 * 1024) # Convert to MB
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def apply_lora(model, device_to=None):
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to_load = []
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for n, m in model.model.named_modules():
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params = []
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skip = False
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for name, param in m.named_parameters(recurse=False):
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params.append(name)
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for name, param in m.named_parameters(recurse=True):
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if name not in params:
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skip = True # skip random weights in non leaf modules
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break
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if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
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to_load.append((n, m, params))
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to_load.sort(reverse=True)
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for x in to_load:
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n = x[0]
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m = x[1]
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params = x[2]
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if hasattr(m, "comfy_patched_weights"):
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if m.comfy_patched_weights == True:
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continue
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for param in params:
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model.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to)
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m.comfy_patched_weights = True
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model.current_weight_patches_uuid = model.patches_uuid
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return model
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