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