diff --git a/networks/lora_flux.py b/networks/lora_flux.py index 91e9cd77..98cf8c55 100644 --- a/networks/lora_flux.py +++ b/networks/lora_flux.py @@ -9,6 +9,7 @@ import math import os +from contextlib import contextmanager from typing import Dict, List, Optional, Tuple, Type, Union from diffusers import AutoencoderKL from transformers import CLIPTextModel @@ -27,6 +28,42 @@ logger = logging.getLogger(__name__) NUM_DOUBLE_BLOCKS = 19 NUM_SINGLE_BLOCKS = 38 +@contextmanager +def temp_random_seed(seed, device=None): + """ + Context manager that temporarily sets a specific random seed and then + restores the original RNG state afterward. + + Args: + seed (int): The random seed to set temporarily + device (torch.device, optional): The device to set the seed for. + If None, will detect from the current context. + """ + # Save original RNG states + original_cpu_rng_state = torch.get_rng_state() + original_cuda_rng_states = None + if torch.cuda.is_available(): + original_cuda_rng_states = torch.cuda.get_rng_state_all() + + # Determine if we need to set CUDA seed + set_cuda = False + if device is not None: + set_cuda = device.type == 'cuda' + elif torch.cuda.is_available(): + set_cuda = True + + try: + # Set the temporary seed + torch.manual_seed(seed) + if set_cuda: + torch.cuda.manual_seed_all(seed) + yield + finally: + # Restore original RNG states + torch.set_rng_state(original_cpu_rng_state) + if torch.cuda.is_available() and original_cuda_rng_states is not None: + torch.cuda.set_rng_state_all(original_cuda_rng_states) + class LoRAModule(torch.nn.Module): """ @@ -44,6 +81,8 @@ class LoRAModule(torch.nn.Module): rank_dropout=None, module_dropout=None, split_dims: Optional[List[int]] = None, + ggpo_beta: Optional[float] = None, + ggpo_sigma: Optional[float] = None, ): """ if alpha == 0 or None, alpha is rank (no scaling). @@ -103,9 +142,16 @@ class LoRAModule(torch.nn.Module): self.rank_dropout = rank_dropout self.module_dropout = module_dropout + self.ggpo_sigma = ggpo_sigma + self.ggpo_beta = ggpo_beta + + self.perturbation_norm_factor = 1.0 / math.sqrt(org_module.weight.shape[0]) + self._org_module_weight = self.org_module.weight.detach() + def apply_to(self): self.org_forward = self.org_module.forward self.org_module.forward = self.forward + del self.org_module def forward(self, x): @@ -140,7 +186,15 @@ class LoRAModule(torch.nn.Module): lx = self.lora_up(lx) - return org_forwarded + lx * self.multiplier * scale + # LoRA Gradient-Guided Perturbation Optimization + if self.training and hasattr(self, 'perturbation_seed') and self.ggpo_sigma is not None and self.ggpo_beta is not None: + with torch.no_grad(), torch.autocast(self.device.type), temp_random_seed(self.perturbation_seed): + perturbation = torch.randn_like(self._org_module_weight, dtype=self.dtype, device=self.device) + perturbation.mul_(self.perturbation_scale_factor) + perturbation_output = x @ perturbation.T # Result: (batch × n) + return org_forwarded + (self.multiplier * scale * lx) + perturbation_output + else: + return org_forwarded + lx * self.multiplier * scale else: lxs = [lora_down(x) for lora_down in self.lora_down] @@ -167,6 +221,58 @@ class LoRAModule(torch.nn.Module): return org_forwarded + torch.cat(lxs, dim=-1) * self.multiplier * scale + @torch.no_grad() + def update_norms(self): + # Not running GGPO so not currently running update norms + if self.ggpo_beta is None or self.ggpo_sigma is None: + return + + # only update norms when we are training + if self.lora_down.weight.requires_grad is not True: + print(f"skipping update_norms for {self.lora_name}") + return + + lora_down_grad = None + lora_up_grad = None + + for name, param in self.named_parameters(): + if name == "lora_down.weight": + lora_down_grad = param.grad + elif name == "lora_up.weight": + lora_up_grad = param.grad + + with torch.autocast(self.device.type): + module_weights = self.scale * (self.lora_up.weight @ self.lora_down.weight) + org_device = self._org_module_weight.device + org_dtype = self._org_module_weight.dtype + org_weight = self._org_module_weight.to(device=self.device, dtype=self.dtype) + combined_weight = org_weight + module_weights + + self.combined_weight_norms = torch.norm(combined_weight, dim=1, keepdim=True) + + self._org_module_weight.to(device=org_device, dtype=org_dtype) + + + # Calculate gradient norms if we have both gradients + if lora_down_grad is not None and lora_up_grad is not None: + with torch.autocast(self.device.type): + approx_grad = self.scale * ((self.lora_up.weight @ lora_down_grad) + (lora_up_grad @ self.lora_down.weight)) + self.grad_norms = torch.norm(approx_grad, dim=1, keepdim=True) + + self.perturbation_scale = (self.ggpo_sigma * torch.sqrt(self.combined_weight_norms ** 2)) + (self.ggpo_beta * (self.grad_norms ** 2)) + self.perturbation_scale_factor = (self.perturbation_scale * self.perturbation_norm_factor).to(self.device) + + # LoRA Gradient-Guided Perturbation Optimization + self.perturbation_seed = torch.randint(0, 2**32 - 1, (1,)).detach().item() + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + class LoRAInfModule(LoRAModule): def __init__( @@ -420,6 +526,16 @@ def create_network( if split_qkv is not None: split_qkv = True if split_qkv == "True" else False + ggpo_beta = kwargs.get("ggpo_beta", None) + ggpo_sigma = kwargs.get("ggpo_sigma", None) + + if ggpo_beta is not None: + ggpo_beta = float(ggpo_beta) + + if ggpo_sigma is not None: + ggpo_sigma = float(ggpo_sigma) + + # train T5XXL train_t5xxl = kwargs.get("train_t5xxl", False) if train_t5xxl is not None: @@ -449,6 +565,8 @@ def create_network( in_dims=in_dims, train_double_block_indices=train_double_block_indices, train_single_block_indices=train_single_block_indices, + ggpo_beta=ggpo_beta, + ggpo_sigma=ggpo_sigma, verbose=verbose, ) @@ -561,6 +679,8 @@ class LoRANetwork(torch.nn.Module): in_dims: Optional[List[int]] = None, train_double_block_indices: Optional[List[bool]] = None, train_single_block_indices: Optional[List[bool]] = None, + ggpo_beta: Optional[float] = None, + ggpo_sigma: Optional[float] = None, verbose: Optional[bool] = False, ) -> None: super().__init__() @@ -599,10 +719,16 @@ class LoRANetwork(torch.nn.Module): # logger.info( # f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}" # ) + + if ggpo_beta is not None and ggpo_sigma is not None: + logger.info(f"LoRA-GGPO training sigma: {ggpo_sigma} beta: {ggpo_beta}") + if self.split_qkv: logger.info(f"split qkv for LoRA") if self.train_blocks is not None: logger.info(f"train {self.train_blocks} blocks only") + + if train_t5xxl: logger.info(f"train T5XXL as well") @@ -722,6 +848,8 @@ class LoRANetwork(torch.nn.Module): rank_dropout=rank_dropout, module_dropout=module_dropout, split_dims=split_dims, + ggpo_beta=ggpo_beta, + ggpo_sigma=ggpo_sigma, ) loras.append(lora) @@ -790,6 +918,10 @@ class LoRANetwork(torch.nn.Module): for lora in self.text_encoder_loras + self.unet_loras: lora.enabled = is_enabled + def update_norms(self): + for lora in self.text_encoder_loras + self.unet_loras: + lora.update_norms() + def load_weights(self, file): if os.path.splitext(file)[1] == ".safetensors": from safetensors.torch import load_file diff --git a/train_network.py b/train_network.py index 2d279b3b..9db335b0 100644 --- a/train_network.py +++ b/train_network.py @@ -1400,6 +1400,10 @@ class NetworkTrainer: params_to_clip = accelerator.unwrap_model(network).get_trainable_params() accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) + if global_step % 5 == 0: + if hasattr(network, "update_norms"): + network.update_norms() + optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=True)