601 lines
24 KiB
Python
601 lines
24 KiB
Python
"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Comfy
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import torch
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import copy
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import inspect
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import logging
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import uuid
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import collections
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import math
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import comfy.utils
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import comfy.float
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import comfy.model_management
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import comfy.lora
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from comfy.comfy_types import UnetWrapperFunction
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def string_to_seed(data):
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crc = 0xFFFFFFFF
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for byte in data:
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if isinstance(byte, str):
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byte = ord(byte)
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crc ^= byte
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for _ in range(8):
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if crc & 1:
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crc = (crc >> 1) ^ 0xEDB88320
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else:
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crc >>= 1
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return crc ^ 0xFFFFFFFF
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def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
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to = model_options["transformer_options"].copy()
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if "patches_replace" not in to:
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to["patches_replace"] = {}
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else:
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to["patches_replace"] = to["patches_replace"].copy()
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if name not in to["patches_replace"]:
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to["patches_replace"][name] = {}
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else:
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to["patches_replace"][name] = to["patches_replace"][name].copy()
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if transformer_index is not None:
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block = (block_name, number, transformer_index)
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else:
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block = (block_name, number)
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to["patches_replace"][name][block] = patch
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model_options["transformer_options"] = to
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return model_options
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def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False):
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model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
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if disable_cfg1_optimization:
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model_options["disable_cfg1_optimization"] = True
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return model_options
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def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False):
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model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function]
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if disable_cfg1_optimization:
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model_options["disable_cfg1_optimization"] = True
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return model_options
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def wipe_lowvram_weight(m):
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if hasattr(m, "prev_comfy_cast_weights"):
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m.comfy_cast_weights = m.prev_comfy_cast_weights
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del m.prev_comfy_cast_weights
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m.weight_function = None
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m.bias_function = None
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class LowVramPatch:
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def __init__(self, key, patches):
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self.key = key
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self.patches = patches
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def __call__(self, weight):
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intermediate_dtype = weight.dtype
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if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
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intermediate_dtype = torch.float32
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return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
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return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
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def get_key_weight(model, key):
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set_func = None
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convert_func = None
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op_keys = key.rsplit('.', 1)
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if len(op_keys) < 2:
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weight = comfy.utils.get_attr(model, key)
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else:
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op = comfy.utils.get_attr(model, op_keys[0])
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try:
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set_func = getattr(op, "set_{}".format(op_keys[1]))
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except AttributeError:
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pass
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try:
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convert_func = getattr(op, "convert_{}".format(op_keys[1]))
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except AttributeError:
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pass
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weight = getattr(op, op_keys[1])
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if convert_func is not None:
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weight = comfy.utils.get_attr(model, key)
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return weight, set_func, convert_func
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class ModelPatcher:
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def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
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self.size = size
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self.model = model
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if not hasattr(self.model, 'device'):
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logging.debug("Model doesn't have a device attribute.")
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self.model.device = offload_device
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elif self.model.device is None:
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self.model.device = offload_device
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self.patches = {}
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self.backup = {}
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self.object_patches = {}
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self.object_patches_backup = {}
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self.model_options = {"transformer_options":{}}
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self.model_size()
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self.load_device = load_device
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self.offload_device = offload_device
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self.weight_inplace_update = weight_inplace_update
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self.patches_uuid = uuid.uuid4()
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self.parent = None
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if not hasattr(self.model, 'model_loaded_weight_memory'):
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self.model.model_loaded_weight_memory = 0
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if not hasattr(self.model, 'lowvram_patch_counter'):
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self.model.lowvram_patch_counter = 0
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if not hasattr(self.model, 'model_lowvram'):
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self.model.model_lowvram = False
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if not hasattr(self.model, 'current_weight_patches_uuid'):
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self.model.current_weight_patches_uuid = None
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def model_size(self):
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if self.size > 0:
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return self.size
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self.size = comfy.model_management.module_size(self.model)
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return self.size
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def loaded_size(self):
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return self.model.model_loaded_weight_memory
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def lowvram_patch_counter(self):
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return self.model.lowvram_patch_counter
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def clone(self):
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n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
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n.patches = {}
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for k in self.patches:
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n.patches[k] = self.patches[k][:]
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n.patches_uuid = self.patches_uuid
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n.object_patches = self.object_patches.copy()
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n.model_options = copy.deepcopy(self.model_options)
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n.backup = self.backup
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n.object_patches_backup = self.object_patches_backup
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n.parent = self
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return n
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def is_clone(self, other):
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if hasattr(other, 'model') and self.model is other.model:
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return True
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return False
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def clone_has_same_weights(self, clone):
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if not self.is_clone(clone):
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return False
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if len(self.patches) == 0 and len(clone.patches) == 0:
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return True
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if self.patches_uuid == clone.patches_uuid:
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if len(self.patches) != len(clone.patches):
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logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.")
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else:
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return True
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def memory_required(self, input_shape):
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return self.model.memory_required(input_shape=input_shape)
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def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False):
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if len(inspect.signature(sampler_cfg_function).parameters) == 3:
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self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
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else:
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self.model_options["sampler_cfg_function"] = sampler_cfg_function
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if disable_cfg1_optimization:
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self.model_options["disable_cfg1_optimization"] = True
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def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
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self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)
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def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False):
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self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization)
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def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
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self.model_options["model_function_wrapper"] = unet_wrapper_function
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def set_model_denoise_mask_function(self, denoise_mask_function):
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self.model_options["denoise_mask_function"] = denoise_mask_function
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def set_model_patch(self, patch, name):
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to = self.model_options["transformer_options"]
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if "patches" not in to:
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to["patches"] = {}
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to["patches"][name] = to["patches"].get(name, []) + [patch]
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def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None):
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self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index)
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def set_model_attn1_patch(self, patch):
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self.set_model_patch(patch, "attn1_patch")
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def set_model_attn2_patch(self, patch):
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self.set_model_patch(patch, "attn2_patch")
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def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None):
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self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index)
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def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None):
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self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index)
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def set_model_attn1_output_patch(self, patch):
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self.set_model_patch(patch, "attn1_output_patch")
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def set_model_attn2_output_patch(self, patch):
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self.set_model_patch(patch, "attn2_output_patch")
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def set_model_input_block_patch(self, patch):
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self.set_model_patch(patch, "input_block_patch")
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def set_model_input_block_patch_after_skip(self, patch):
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self.set_model_patch(patch, "input_block_patch_after_skip")
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def set_model_output_block_patch(self, patch):
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self.set_model_patch(patch, "output_block_patch")
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def add_object_patch(self, name, obj):
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self.object_patches[name] = obj
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def get_model_object(self, name):
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if name in self.object_patches:
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return self.object_patches[name]
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else:
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if name in self.object_patches_backup:
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return self.object_patches_backup[name]
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else:
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return comfy.utils.get_attr(self.model, name)
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def model_patches_to(self, device):
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to = self.model_options["transformer_options"]
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if "patches" in to:
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patches = to["patches"]
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for name in patches:
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patch_list = patches[name]
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for i in range(len(patch_list)):
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if hasattr(patch_list[i], "to"):
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patch_list[i] = patch_list[i].to(device)
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if "patches_replace" in to:
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patches = to["patches_replace"]
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for name in patches:
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patch_list = patches[name]
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for k in patch_list:
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if hasattr(patch_list[k], "to"):
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patch_list[k] = patch_list[k].to(device)
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if "model_function_wrapper" in self.model_options:
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wrap_func = self.model_options["model_function_wrapper"]
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if hasattr(wrap_func, "to"):
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self.model_options["model_function_wrapper"] = wrap_func.to(device)
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def model_dtype(self):
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if hasattr(self.model, "get_dtype"):
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return self.model.get_dtype()
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def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
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p = set()
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model_sd = self.model.state_dict()
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for k in patches:
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offset = None
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function = None
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if isinstance(k, str):
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key = k
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else:
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offset = k[1]
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key = k[0]
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if len(k) > 2:
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function = k[2]
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if key in model_sd:
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p.add(k)
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current_patches = self.patches.get(key, [])
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current_patches.append((strength_patch, patches[k], strength_model, offset, function))
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self.patches[key] = current_patches
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self.patches_uuid = uuid.uuid4()
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return list(p)
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def get_key_patches(self, filter_prefix=None):
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model_sd = self.model_state_dict()
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p = {}
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for k in model_sd:
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if filter_prefix is not None:
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if not k.startswith(filter_prefix):
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continue
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bk = self.backup.get(k, None)
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weight, set_func, convert_func = get_key_weight(self.model, k)
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if bk is not None:
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weight = bk.weight
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if convert_func is None:
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convert_func = lambda a, **kwargs: a
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if k in self.patches:
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p[k] = [(weight, convert_func)] + self.patches[k]
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else:
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p[k] = [(weight, convert_func)]
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return p
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def model_state_dict(self, filter_prefix=None):
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sd = self.model.state_dict()
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keys = list(sd.keys())
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if filter_prefix is not None:
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for k in keys:
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if not k.startswith(filter_prefix):
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sd.pop(k)
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return sd
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def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
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if key not in self.patches:
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return
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weight, set_func, convert_func = get_key_weight(self.model, key)
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inplace_update = self.weight_inplace_update or inplace_update
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if key not in self.backup:
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self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
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if device_to is not None:
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temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
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else:
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temp_weight = weight.to(torch.float32, copy=True)
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if convert_func is not None:
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temp_weight = convert_func(temp_weight, inplace=True)
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out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
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if set_func is None:
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out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key))
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if inplace_update:
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comfy.utils.copy_to_param(self.model, key, out_weight)
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else:
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comfy.utils.set_attr_param(self.model, key, out_weight)
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else:
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set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
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def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
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mem_counter = 0
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patch_counter = 0
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lowvram_counter = 0
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loading = []
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for n, m in self.model.named_modules():
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if hasattr(m, "comfy_cast_weights") or hasattr(m, "weight"):
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loading.append((comfy.model_management.module_size(m), n, m))
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load_completely = []
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loading.sort(reverse=True)
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for x in loading:
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n = x[1]
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m = x[2]
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module_mem = x[0]
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lowvram_weight = False
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if not full_load and hasattr(m, "comfy_cast_weights"):
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if mem_counter + module_mem >= lowvram_model_memory:
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lowvram_weight = True
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lowvram_counter += 1
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if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
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continue
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weight_key = "{}.weight".format(n)
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bias_key = "{}.bias".format(n)
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if lowvram_weight:
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if weight_key in self.patches:
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if force_patch_weights:
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self.patch_weight_to_device(weight_key)
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else:
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m.weight_function = LowVramPatch(weight_key, self.patches)
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patch_counter += 1
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if bias_key in self.patches:
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if force_patch_weights:
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self.patch_weight_to_device(bias_key)
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else:
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m.bias_function = LowVramPatch(bias_key, self.patches)
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patch_counter += 1
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m.prev_comfy_cast_weights = m.comfy_cast_weights
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m.comfy_cast_weights = True
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else:
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if hasattr(m, "comfy_cast_weights"):
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if m.comfy_cast_weights:
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wipe_lowvram_weight(m)
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if hasattr(m, "weight"):
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mem_counter += module_mem
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load_completely.append((module_mem, n, m))
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load_completely.sort(reverse=True)
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for x in load_completely:
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n = x[1]
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m = x[2]
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weight_key = "{}.weight".format(n)
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bias_key = "{}.bias".format(n)
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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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self.patch_weight_to_device(weight_key, device_to=device_to)
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self.patch_weight_to_device(bias_key, device_to=device_to)
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logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
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m.comfy_patched_weights = True
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for x in load_completely:
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x[2].to(device_to)
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if lowvram_counter > 0:
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logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
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self.model.model_lowvram = True
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else:
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logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
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self.model.model_lowvram = False
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if full_load:
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self.model.to(device_to)
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mem_counter = self.model_size()
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self.model.lowvram_patch_counter += patch_counter
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self.model.device = device_to
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self.model.model_loaded_weight_memory = mem_counter
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self.model.current_weight_patches_uuid = self.patches_uuid
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|
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def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False):
|
|
for k in self.object_patches:
|
|
old = comfy.utils.set_attr(self.model, k, self.object_patches[k])
|
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if k not in self.object_patches_backup:
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self.object_patches_backup[k] = old
|
|
|
|
if lowvram_model_memory == 0:
|
|
full_load = True
|
|
else:
|
|
full_load = False
|
|
|
|
if load_weights:
|
|
self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load)
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return self.model
|
|
|
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def unpatch_model(self, device_to=None, unpatch_weights=True):
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|
if unpatch_weights:
|
|
if self.model.model_lowvram:
|
|
for m in self.model.modules():
|
|
wipe_lowvram_weight(m)
|
|
|
|
self.model.model_lowvram = False
|
|
self.model.lowvram_patch_counter = 0
|
|
|
|
keys = list(self.backup.keys())
|
|
|
|
for k in keys:
|
|
bk = self.backup[k]
|
|
if bk.inplace_update:
|
|
comfy.utils.copy_to_param(self.model, k, bk.weight)
|
|
else:
|
|
comfy.utils.set_attr_param(self.model, k, bk.weight)
|
|
|
|
self.model.current_weight_patches_uuid = None
|
|
self.backup.clear()
|
|
|
|
if device_to is not None:
|
|
self.model.to(device_to)
|
|
self.model.device = device_to
|
|
self.model.model_loaded_weight_memory = 0
|
|
|
|
for m in self.model.modules():
|
|
if hasattr(m, "comfy_patched_weights"):
|
|
del m.comfy_patched_weights
|
|
|
|
keys = list(self.object_patches_backup.keys())
|
|
for k in keys:
|
|
comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
|
|
|
|
self.object_patches_backup.clear()
|
|
|
|
def partially_unload(self, device_to, memory_to_free=0):
|
|
memory_freed = 0
|
|
patch_counter = 0
|
|
unload_list = []
|
|
|
|
for n, m in self.model.named_modules():
|
|
shift_lowvram = False
|
|
if hasattr(m, "comfy_cast_weights"):
|
|
module_mem = comfy.model_management.module_size(m)
|
|
unload_list.append((module_mem, n, m))
|
|
|
|
unload_list.sort()
|
|
for unload in unload_list:
|
|
if memory_to_free < memory_freed:
|
|
break
|
|
module_mem = unload[0]
|
|
n = unload[1]
|
|
m = unload[2]
|
|
weight_key = "{}.weight".format(n)
|
|
bias_key = "{}.bias".format(n)
|
|
|
|
if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
|
|
for key in [weight_key, bias_key]:
|
|
bk = self.backup.get(key, None)
|
|
if bk is not None:
|
|
if bk.inplace_update:
|
|
comfy.utils.copy_to_param(self.model, key, bk.weight)
|
|
else:
|
|
comfy.utils.set_attr_param(self.model, key, bk.weight)
|
|
self.backup.pop(key)
|
|
|
|
m.to(device_to)
|
|
if weight_key in self.patches:
|
|
m.weight_function = LowVramPatch(weight_key, self.patches)
|
|
patch_counter += 1
|
|
if bias_key in self.patches:
|
|
m.bias_function = LowVramPatch(bias_key, self.patches)
|
|
patch_counter += 1
|
|
|
|
m.prev_comfy_cast_weights = m.comfy_cast_weights
|
|
m.comfy_cast_weights = True
|
|
m.comfy_patched_weights = False
|
|
memory_freed += module_mem
|
|
logging.debug("freed {}".format(n))
|
|
|
|
self.model.model_lowvram = True
|
|
self.model.lowvram_patch_counter += patch_counter
|
|
self.model.model_loaded_weight_memory -= memory_freed
|
|
return memory_freed
|
|
|
|
def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
|
|
unpatch_weights = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid or force_patch_weights)
|
|
# TODO: force_patch_weights should not unload + reload full model
|
|
used = self.model.model_loaded_weight_memory
|
|
self.unpatch_model(self.offload_device, unpatch_weights=unpatch_weights)
|
|
if unpatch_weights:
|
|
extra_memory += (used - self.model.model_loaded_weight_memory)
|
|
|
|
self.patch_model(load_weights=False)
|
|
full_load = False
|
|
if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0:
|
|
return 0
|
|
if self.model.model_loaded_weight_memory + extra_memory > self.model_size():
|
|
full_load = True
|
|
current_used = self.model.model_loaded_weight_memory
|
|
try:
|
|
self.load(device_to, lowvram_model_memory=current_used + extra_memory, force_patch_weights=force_patch_weights, full_load=full_load)
|
|
except Exception as e:
|
|
self.detach()
|
|
raise e
|
|
|
|
return self.model.model_loaded_weight_memory - current_used
|
|
|
|
def detach(self, unpatch_all=True):
|
|
self.model_patches_to(self.offload_device)
|
|
if unpatch_all:
|
|
self.unpatch_model(self.offload_device, unpatch_weights=unpatch_all)
|
|
return self.model
|
|
|
|
def current_loaded_device(self):
|
|
return self.model.device
|
|
|
|
def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
|
|
print("WARNING the ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
|
|
return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
|
|
|
|
def __del__(self):
|
|
self.detach(unpatch_all=False)
|
|
|