import torch import torch.nn as nn import os import json import torch.nn.functional as F import random from torch.utils.data import Dataset from transformers import AutoTokenizer from glob import glob import math from PIL import Image device = torch.device('cuda') import numpy as np from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn from diffusers.utils import logging from diffusers.models.embeddings import PatchEmbed from diffusers.models.modeling_outputs import Transformer2DModelOutput from diffusers.models.attention import BasicTransformerBlock from diffusers.models.normalization import AdaLayerNormContinuous from torchvision import transforms def add_hook_to_module(model, module_name): outputs = [] def hook(module, input, output): outputs.append(output) module = dict(model.named_modules()).get(module_name) if module is None: raise ValueError(f"can't find module {module_name}") hook_handle = module.register_forward_hook(hook) return hook_handle, outputs class PromptSD35Net(nn.Module): def __init__(self, sample_size: int = 128, patch_size: int = 2, in_channels: int = 16, num_layers: int = 8, attention_head_dim: int = 64, num_attention_heads: int = 24, out_channels: int = 16, pos_embed_max_size: int = 192 ): super().__init__() self.sample_size = sample_size self.patch_size = patch_size self.in_channels = in_channels self.num_layers = num_layers self.attention_head_dim = attention_head_dim self.num_attention_heads = num_attention_heads self.out_channels = out_channels self.pos_embed_max_size = pos_embed_max_size self.inner_dim = self.num_attention_heads * self.attention_head_dim self.pos_embed = PatchEmbed( height=self.sample_size, width=self.sample_size, patch_size=self.patch_size, in_channels=self.in_channels, embed_dim=self.inner_dim, pos_embed_max_size=pos_embed_max_size ) self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( dim=self.inner_dim, num_attention_heads=self.num_attention_heads, attention_head_dim=self.attention_head_dim, ff_inner_dim=2*self.inner_dim # mult should be 4 by default ) for i in range(self.num_layers) ] ) self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True) self.noise_shape = (1, 16, 128, 128) # (667, 4096) self.pre8_linear = nn.Sequential(nn.Linear(4096, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre16_linear = nn.Sequential(nn.Linear(4096, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre24_linear = nn.Sequential(nn.Linear(4096, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre8_linear2 = nn.Sequential(nn.Linear(4096, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre16_linear2 = nn.Sequential(nn.Linear(4096, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre24_linear2 = nn.Sequential(nn.Linear(4096, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.last_linear = nn.Sequential(nn.Linear(4096, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) # self.last_linear2 = nn.Sequential(nn.Linear(667, 32)) self.skip_connection2 = nn.Linear(4096, 1, bias=False) self.skip_connection = nn.Linear(667, 32, bias=False) self.trans_linear = nn.Linear(666+1+4096, 1536, bias=False) nn.init.constant_(self.skip_connection.weight.data, 0) nn.init.constant_(self.trans_linear.weight.data, 0) nn.init.constant_(self.trans_linear.weight.data, 0) nn.init.constant_(self.pre8_linear[-1].weight.data, 0) nn.init.constant_(self.pre16_linear[-1].weight.data, 0) nn.init.constant_(self.pre24_linear[-1].weight.data, 0) nn.init.constant_(self.pre8_linear2[-1].weight.data, 0) nn.init.constant_(self.pre16_linear2[-1].weight.data, 0) nn.init.constant_(self.pre24_linear2[-1].weight.data, 0) def forward(self, noise: torch.Tensor, _s, _v, _d, _pool_embedding) -> torch.Tensor: assert noise is not None _ori_v = _v.clone() _v = torch.stack([torch.diag(_v[jj]) for jj in range(_v.shape[0])], dim=0) positive_embedding = _s.permute(0, 2, 1) @ _v @ _d # [2, 64, 666] [2, 64] [2, 64, 4096] pool_embedding = _pool_embedding[:, None, :] embedding = torch.cat([positive_embedding, pool_embedding], dim=1) bs = noise.shape[0] height, width = noise.shape[-2:] embed_8 = embedding embed_16 = embedding embed_24 = embedding scale_8 = self.pre8_linear2(embed_8).mean(1) scale_16 = self.pre16_linear2(embed_16).mean(1) scale_24 = self.pre24_linear2(embed_24).mean(1) embed_8 = self.pre8_linear(embed_8).mean(1) embed_16 = self.pre16_linear(embed_16).mean(1) embed_24 = self.pre24_linear(embed_24).mean(1) embed_last = self.last_linear(embedding).mean(1) embed_trans = self.trans_linear(torch.cat([_s, _ori_v[...,None], _d], dim=2)).mean(1) skip_embedding = self.skip_connection(self.skip_connection2(embedding).permute(0,2,1)).permute(0,2,1) scale_skip, embed_skip = skip_embedding.chunk(2,dim=1) ori_noise = noise * (scale_skip[...,None]) + embed_skip[...,None] noise = self.pos_embed(noise) noise = noise * (1 + scale_8[:, None, :] + embed_trans[:, None, :]) + embed_8[:, None, :] scale_list = [scale_16, scale_24] embed_list = [embed_16, embed_24] for _ii, block in enumerate(self.transformer_blocks): noise = block(noise) if len(scale_list)!=0 and len(embed_list)!=0: noise = noise * (1 + scale_list[int(_ii//4)][:, None, :] + embed_trans[:, None, :]) + embed_list[int(_ii//4)][:, None, :] hidden_states = noise hidden_states = self.norm_out(hidden_states, embed_last) hidden_states = self.proj_out(hidden_states) # unpatchify patch_size = self.patch_size height = height // patch_size width = width // patch_size hidden_states = hidden_states.reshape( shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size) ) return output + ori_noise def weak_load_state_dict(self, state_dict: os.Mapping[str, torch.any], strict: bool = True, assign: bool = False): return load_filtered_state_dict(self, state_dict) class PromptSDXLNet(nn.Module): def __init__(self, sample_size: int = 128, patch_size: int = 2, in_channels: int = 4, num_layers: int = 4, attention_head_dim: int = 64, num_attention_heads: int = 24, out_channels: int = 4, pos_embed_max_size: int = 192 ): super().__init__() self.sample_size = sample_size self.patch_size = patch_size self.in_channels = in_channels self.num_layers = num_layers self.attention_head_dim = attention_head_dim self.num_attention_heads = num_attention_heads self.out_channels = out_channels self.pos_embed_max_size = pos_embed_max_size self.inner_dim = self.num_attention_heads * self.attention_head_dim self.pos_embed = PatchEmbed( height=self.sample_size, width=self.sample_size, patch_size=self.patch_size, in_channels=self.in_channels, embed_dim=self.inner_dim, pos_embed_max_size=pos_embed_max_size ) self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( dim=self.inner_dim, num_attention_heads=self.num_attention_heads, attention_head_dim=self.attention_head_dim, ff_inner_dim=2*self.inner_dim # mult should be 4 by default ) for i in range(self.num_layers) ] ) self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True) self.noise_shape = (1, 4, 128, 128) self.pre8_linear = nn.Sequential(nn.Linear(2048, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre16_linear = nn.Sequential(nn.Linear(2048, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre24_linear = nn.Sequential(nn.Linear(2048, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre8_linear2 = nn.Sequential(nn.Linear(2048, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre16_linear2 = nn.Sequential(nn.Linear(2048, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.pre24_linear2 = nn.Sequential(nn.Linear(2048, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) self.last_linear = nn.Sequential(nn.Linear(2048, 128), nn.SiLU(), nn.LayerNorm(128), nn.Linear(128, 1536)) # self.last_linear2 = nn.Sequential(nn.Linear(667, 32)) self.skip_connection2 = nn.Linear(2048, 1, bias=False) self.skip_connection = nn.Linear(154+1, 8, bias=False) self.trans_linear = nn.Linear(154+1+2048, 1536, bias=False) self.pool_prompt_linear = nn.Linear(2560, 2048, bias=False) nn.init.constant_(self.skip_connection.weight.data, 0) nn.init.constant_(self.trans_linear.weight.data, 0) nn.init.constant_(self.trans_linear.weight.data, 0) nn.init.constant_(self.pre8_linear[-1].weight.data, 0) nn.init.constant_(self.pre16_linear[-1].weight.data, 0) nn.init.constant_(self.pre24_linear[-1].weight.data, 0) nn.init.constant_(self.pre8_linear2[-1].weight.data, 0) nn.init.constant_(self.pre16_linear2[-1].weight.data, 0) nn.init.constant_(self.pre24_linear2[-1].weight.data, 0) def forward(self, noise: torch.Tensor, _s, _v, _d, _pool_embedding) -> torch.Tensor: assert noise is not None _ori_v = _v.clone() _v = torch.stack([torch.diag(_v[jj]) for jj in range(_v.shape[0])], dim=0) positive_embedding = _s.permute(0, 2, 1) @ _v @ _d # [2, 64, 154] [2, 64] [2, 64, 2048] pool_embedding = self.pool_prompt_linear(_pool_embedding[:, None, :]) embedding = torch.cat([positive_embedding, pool_embedding], dim=1) bs = noise.shape[0] height, width = noise.shape[-2:] embed_8 = embedding embed_16 = embedding embed_24 = embedding scale_8 = self.pre8_linear2(embed_8).mean(1) scale_16 = self.pre16_linear2(embed_16).mean(1) scale_24 = self.pre24_linear2(embed_24).mean(1) embed_8 = self.pre8_linear(embed_8).mean(1) embed_16 = self.pre16_linear(embed_16).mean(1) embed_24 = self.pre24_linear(embed_24).mean(1) embed_last = self.last_linear(embedding).mean(1) embed_trans = self.trans_linear(torch.cat([_s, _ori_v[...,None], _d], dim=2)).mean(1) skip_embedding = self.skip_connection(self.skip_connection2(embedding).permute(0,2,1)).permute(0,2,1) scale_skip, embed_skip = skip_embedding.chunk(2,dim=1) ori_noise = noise * (scale_skip[...,None]) + embed_skip[...,None] noise = self.pos_embed(noise) noise = noise * (1 + scale_8[:, None, :] + embed_trans[:, None, :]) + embed_8[:, None, :] scale_list = [scale_16, scale_24] embed_list = [embed_16, embed_24] for _ii, block in enumerate(self.transformer_blocks): noise = block(noise) if len(scale_list)!=0 and len(embed_list)!=0: noise = noise * (1 + scale_list[int(_ii//4)][:, None, :] + embed_trans[:, None, :]) + embed_list[int(_ii//4)][:, None, :] hidden_states = noise hidden_states = self.norm_out(hidden_states, embed_last) hidden_states = self.proj_out(hidden_states) # unpatchify patch_size = self.patch_size height = height // patch_size width = width // patch_size hidden_states = hidden_states.reshape( shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size) ) return output + ori_noise def weak_load_state_dict(self, state_dict: os.Mapping[str, torch.any], strict: bool = True, assign: bool = False): return load_filtered_state_dict(self, state_dict) def load_filtered_state_dict(model, state_dict): model_state_dict = model.state_dict() filtered_state_dict = {} for k, v in state_dict.items(): if k in model_state_dict: if model_state_dict[k].size() == v.size(): filtered_state_dict[k] = v else: print(f"Skipping {k}: shape mismatch ({model_state_dict[k].size()} vs {v.size()})") else: print(f"Skipping {k}: not found in model's state_dict.") model.load_state_dict(filtered_state_dict, strict=False) return model def custom_collate_fn_2_0(batch): noise_pred_texts, prompts, noise_preds, max_scores = zip(*batch) noise_pred_texts = torch.stack(noise_pred_texts) noise_preds = torch.stack(noise_preds) max_scores = torch.stack(max_scores) return noise_pred_texts, prompts, noise_preds, max_scores