""" 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 . """ 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(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)