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| """ | |
| 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): | |
| intermediate_dtype = weight.dtype | |
| if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops | |
| intermediate_dtype = torch.float32 | |
| 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)) | |
| return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype) | |
| def get_key_weight(model, key): | |
| set_func = None | |
| convert_func = None | |
| op_keys = key.rsplit('.', 1) | |
| if len(op_keys) < 2: | |
| weight = comfy.utils.get_attr(model, key) | |
| else: | |
| op = comfy.utils.get_attr(model, op_keys[0]) | |
| try: | |
| set_func = getattr(op, "set_{}".format(op_keys[1])) | |
| except AttributeError: | |
| pass | |
| try: | |
| convert_func = getattr(op, "convert_{}".format(op_keys[1])) | |
| except AttributeError: | |
| pass | |
| weight = getattr(op, op_keys[1]) | |
| if convert_func is not None: | |
| weight = comfy.utils.get_attr(model, key) | |
| return weight, set_func, convert_func | |
| 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) | |
| weight, set_func, convert_func = get_key_weight(self.model, k) | |
| if bk is not None: | |
| weight = bk.weight | |
| if convert_func is None: | |
| convert_func = lambda a, **kwargs: a | |
| if k in self.patches: | |
| p[k] = [(weight, convert_func)] + self.patches[k] | |
| else: | |
| p[k] = [(weight, convert_func)] | |
| 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, set_func, convert_func = get_key_weight(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) | |
| if convert_func is not None: | |
| temp_weight = convert_func(temp_weight, inplace=True) | |
| out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) | |
| if set_func is None: | |
| 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) | |
| else: | |
| set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key)) | |
| def _load_list(self): | |
| loading = [] | |
| for n, m in self.model.named_modules(): | |
| params = [] | |
| skip = False | |
| for name, param in m.named_parameters(recurse=False): | |
| params.append(name) | |
| for name, param in m.named_parameters(recurse=True): | |
| if name not in params: | |
| skip = True # skip random weights in non leaf modules | |
| break | |
| if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0): | |
| loading.append((comfy.model_management.module_size(m), n, m, params)) | |
| return loading | |
| 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 = self._load_list() | |
| load_completely = [] | |
| loading.sort(reverse=True) | |
| for x in loading: | |
| n = x[1] | |
| m = x[2] | |
| params = x[3] | |
| 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 full_load or mem_counter + module_mem < lowvram_model_memory: | |
| mem_counter += module_mem | |
| load_completely.append((module_mem, n, m, params)) | |
| load_completely.sort(reverse=True) | |
| for x in load_completely: | |
| n = x[1] | |
| m = x[2] | |
| params = x[3] | |
| if hasattr(m, "comfy_patched_weights"): | |
| if m.comfy_patched_weights == True: | |
| continue | |
| for param in params: | |
| self.patch_weight_to_device("{}.{}".format(n, param), 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 = self._load_list() | |
| 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] | |
| params = unload[3] | |
| lowvram_possible = hasattr(m, "comfy_cast_weights") | |
| if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True: | |
| move_weight = True | |
| for param in params: | |
| key = "{}.{}".format(n, param) | |
| bk = self.backup.get(key, None) | |
| if bk is not None: | |
| if not lowvram_possible: | |
| move_weight = False | |
| break | |
| 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) | |
| weight_key = "{}.weight".format(n) | |
| bias_key = "{}.bias".format(n) | |
| if move_weight: | |
| m.to(device_to) | |
| if lowvram_possible: | |
| 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) | |