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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
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