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| import torch | |
| import copy | |
| import inspect | |
| import ldm_patched.modules.utils | |
| import ldm_patched.modules.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 | |
| def model_size(self): | |
| if self.size > 0: | |
| return self.size | |
| model_sd = self.model.state_dict() | |
| self.size = ldm_patched.modules.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_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): | |
| ldm_patched.modules.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_model(self, device_to=None, patch_weights=True): | |
| for k in self.object_patches: | |
| old = getattr(self.model, k) | |
| if k not in self.object_patches_backup: | |
| self.object_patches_backup[k] = old | |
| setattr(self.model, k, self.object_patches[k]) | |
| if patch_weights: | |
| model_sd = self.model_state_dict() | |
| for key in self.patches: | |
| if key not in model_sd: | |
| print("could not patch. key doesn't exist in model:", key) | |
| continue | |
| weight = model_sd[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 = ldm_patched.modules.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: | |
| ldm_patched.modules.utils.copy_to_param(self.model, key, out_weight) | |
| else: | |
| ldm_patched.modules.utils.set_attr(self.model, key, out_weight) | |
| del temp_weight | |
| if device_to is not None: | |
| self.model.to(device_to) | |
| self.current_device = device_to | |
| 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: | |
| print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) | |
| else: | |
| weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) | |
| elif patch_type == "lora": #lora/locon | |
| mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) | |
| mat2 = ldm_patched.modules.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 = ldm_patched.modules.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: | |
| print("ERROR", 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(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), | |
| ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) | |
| else: | |
| w1 = ldm_patched.modules.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(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), | |
| ldm_patched.modules.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', | |
| ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), | |
| ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), | |
| ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) | |
| else: | |
| w2 = ldm_patched.modules.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: | |
| print("ERROR", 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', | |
| ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), | |
| ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), | |
| ldm_patched.modules.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', | |
| ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), | |
| ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), | |
| ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) | |
| else: | |
| m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), | |
| ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) | |
| m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), | |
| ldm_patched.modules.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: | |
| print("ERROR", key, e) | |
| elif patch_type == "glora": | |
| if v[4] is not None: | |
| alpha *= v[4] / v[0].shape[0] | |
| a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) | |
| a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) | |
| b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) | |
| b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) | |
| weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) | |
| else: | |
| print("patch type not recognized", patch_type, key) | |
| return weight | |
| def unpatch_model(self, device_to=None): | |
| keys = list(self.backup.keys()) | |
| if self.weight_inplace_update: | |
| for k in keys: | |
| ldm_patched.modules.utils.copy_to_param(self.model, k, self.backup[k]) | |
| else: | |
| for k in keys: | |
| ldm_patched.modules.utils.set_attr(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: | |
| setattr(self.model, k, self.object_patches_backup[k]) | |
| self.object_patches_backup = {} | |