import torch import copy import inspect import logging import comfy.utils import comfy.model_management class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): self.size = size self.model = model 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 if current_device is None: self.current_device = self.offload_device else: self.current_device = current_device self.weight_inplace_update = weight_inplace_update self.model_lowvram = False def model_size(self): if self.size > 0: return self.size model_sd = self.model.state_dict() self.size = comfy.model_management.module_size(self.model) self.model_keys = set(model_sd.keys()) return self.size def clone(self): n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update) n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] n.object_patches = self.object_patches.copy() n.model_options = copy.deepcopy(self.model_options) n.model_keys = self.model_keys return n def is_clone(self, other): if hasattr(other, 'model') and self.model is other.model: return True return False 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["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] if disable_cfg1_optimization: self.model_options["disable_cfg1_optimization"] = True def set_model_unet_function_wrapper(self, unet_wrapper_function): 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): to = self.model_options["transformer_options"] if "patches_replace" not in to: to["patches_replace"] = {} if name not in to["patches_replace"]: to["patches_replace"][name] = {} if transformer_index is not None: block = (block_name, number, transformer_index) else: block = (block_name, number) to["patches_replace"][name][block] = patch 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 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() for k in patches: if k in self.model_keys: p.add(k) current_patches = self.patches.get(k, []) current_patches.append((strength_patch, patches[k], strength_model)) self.patches[k] = current_patches return list(p) def get_key_patches(self, filter_prefix=None): comfy.model_management.unload_model_clones(self) 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 if k in self.patches: p[k] = [model_sd[k]] + self.patches[k] else: p[k] = (model_sd[k],) 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): if key not in self.patches: return weight = comfy.utils.get_attr(self.model, key) inplace_update = self.weight_inplace_update if key not in self.backup: self.backup[key] = weight.to(device=self.offload_device, copy=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 = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) 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 patch_model(self, device_to=None, patch_weights=True): 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 patch_weights: model_sd = self.model_state_dict() for key in self.patches: if key not in model_sd: logging.warning("could not patch. key doesn't exist in model: {}".format(key)) continue self.patch_weight_to_device(key, device_to) if device_to is not None: self.model.to(device_to) self.current_device = device_to return self.model def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0): self.patch_model(device_to, patch_weights=False) logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024))) class LowVramPatch: def __init__(self, key, model_patcher): self.key = key self.model_patcher = model_patcher def __call__(self, weight): return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key) mem_counter = 0 for n, m in self.model.named_modules(): lowvram_weight = False if hasattr(m, "comfy_cast_weights"): module_mem = comfy.model_management.module_size(m) if mem_counter + module_mem >= lowvram_model_memory: lowvram_weight = True weight_key = "{}.weight".format(n) bias_key = "{}.bias".format(n) if lowvram_weight: if weight_key in self.patches: m.weight_function = LowVramPatch(weight_key, self) if bias_key in self.patches: m.bias_function = LowVramPatch(weight_key, self) m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True else: if hasattr(m, "weight"): self.patch_weight_to_device(weight_key, device_to) self.patch_weight_to_device(bias_key, device_to) m.to(device_to) mem_counter += comfy.model_management.module_size(m) logging.debug("lowvram: loaded module regularly {}".format(m)) self.model_lowvram = True return self.model def calculate_weight(self, patches, weight, key): for p in patches: alpha = p[0] v = p[1] strength_model = p[2] if strength_model != 1.0: weight *= strength_model if isinstance(v, list): v = (self.calculate_weight(v[1:], v[0].clone(), key), ) if len(v) == 1: patch_type = "diff" elif len(v) == 2: patch_type = v[0] v = v[1] if patch_type == "diff": w1 = v[0] if alpha != 0.0: if w1.shape != weight.shape: logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype) elif patch_type == "lora": #lora/locon mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) if v[2] is not None: alpha *= v[2] / mat2.shape[0] if v[3] is not None: #locon mid weights, hopefully the math is fine because I didn't properly test it mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32) final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) try: weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "lokr": w1 = v[0] w2 = v[1] w1_a = v[3] w1_b = v[4] w2_a = v[5] w2_b = v[6] t2 = v[7] dim = None if w1 is None: dim = w1_b.shape[0] w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, torch.float32), comfy.model_management.cast_to_device(w1_b, weight.device, torch.float32)) else: w1 = comfy.model_management.cast_to_device(w1, weight.device, torch.float32) if w2 is None: dim = w2_b.shape[0] if t2 is None: w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32), comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32)) else: w2 = torch.einsum('i j k l, j r, i p -> p r k l', comfy.model_management.cast_to_device(t2, weight.device, torch.float32), comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32), comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32)) else: w2 = comfy.model_management.cast_to_device(w2, weight.device, torch.float32) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) if v[2] is not None and dim is not None: alpha *= v[2] / dim try: weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "loha": w1a = v[0] w1b = v[1] if v[2] is not None: alpha *= v[2] / w1b.shape[0] w2a = v[3] w2b = v[4] if v[5] is not None: #cp decomposition t1 = v[5] t2 = v[6] m1 = torch.einsum('i j k l, j r, i p -> p r k l', comfy.model_management.cast_to_device(t1, weight.device, torch.float32), comfy.model_management.cast_to_device(w1b, weight.device, torch.float32), comfy.model_management.cast_to_device(w1a, weight.device, torch.float32)) m2 = torch.einsum('i j k l, j r, i p -> p r k l', comfy.model_management.cast_to_device(t2, weight.device, torch.float32), comfy.model_management.cast_to_device(w2b, weight.device, torch.float32), comfy.model_management.cast_to_device(w2a, weight.device, torch.float32)) else: m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, torch.float32), comfy.model_management.cast_to_device(w1b, weight.device, torch.float32)) m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, torch.float32), comfy.model_management.cast_to_device(w2b, weight.device, torch.float32)) try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) elif patch_type == "glora": if v[4] is not None: alpha *= v[4] / v[0].shape[0] a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) try: weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) except Exception as e: logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: logging.warning("patch type not recognized {} {}".format(patch_type, key)) return weight def unpatch_model(self, device_to=None): if self.model_lowvram: for m in self.model.modules(): 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 self.model_lowvram = False keys = list(self.backup.keys()) if self.weight_inplace_update: for k in keys: comfy.utils.copy_to_param(self.model, k, self.backup[k]) else: for k in keys: comfy.utils.set_attr_param(self.model, k, self.backup[k]) self.backup = {} if device_to is not None: self.model.to(device_to) self.current_device = device_to 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 = {}