# Copyright (c) 2023 Dominic Rampas MIT License # Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import numpy as np import torch import torch.nn as nn from ...configuration_utils import ConfigMixin, register_to_config from ...models.modeling_utils import ModelMixin from .modeling_wuerstchen_common import AttnBlock, GlobalResponseNorm, TimestepBlock, WuerstchenLayerNorm class WuerstchenDiffNeXt(ModelMixin, ConfigMixin): @register_to_config def __init__( self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1024, c_hidden=[320, 640, 1280, 1280], nhead=[-1, 10, 20, 20], blocks=[4, 4, 14, 4], level_config=["CT", "CTA", "CTA", "CTA"], inject_effnet=[False, True, True, True], effnet_embd=16, clip_embd=1024, kernel_size=3, dropout=0.1, ): super().__init__() self.c_r = c_r self.c_cond = c_cond if not isinstance(dropout, list): dropout = [dropout] * len(c_hidden) # CONDITIONING self.clip_mapper = nn.Linear(clip_embd, c_cond) self.effnet_mappers = nn.ModuleList( [ nn.Conv2d(effnet_embd, c_cond, kernel_size=1) if inject else None for inject in inject_effnet + list(reversed(inject_effnet)) ] ) self.seq_norm = nn.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6) self.embedding = nn.Sequential( nn.PixelUnshuffle(patch_size), nn.Conv2d(c_in * (patch_size**2), c_hidden[0], kernel_size=1), WuerstchenLayerNorm(c_hidden[0], elementwise_affine=False, eps=1e-6), ) def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0): if block_type == "C": return ResBlockStageB(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout) elif block_type == "A": return AttnBlock(c_hidden, c_cond, nhead, self_attn=True, dropout=dropout) elif block_type == "T": return TimestepBlock(c_hidden, c_r) else: raise ValueError(f"Block type {block_type} not supported") # BLOCKS # -- down blocks self.down_blocks = nn.ModuleList() for i in range(len(c_hidden)): down_block = nn.ModuleList() if i > 0: down_block.append( nn.Sequential( WuerstchenLayerNorm(c_hidden[i - 1], elementwise_affine=False, eps=1e-6), nn.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2), ) ) for _ in range(blocks[i]): for block_type in level_config[i]: c_skip = c_cond if inject_effnet[i] else 0 down_block.append(get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i])) self.down_blocks.append(down_block) # -- up blocks self.up_blocks = nn.ModuleList() for i in reversed(range(len(c_hidden))): up_block = nn.ModuleList() for j in range(blocks[i]): for k, block_type in enumerate(level_config[i]): c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 c_skip += c_cond if inject_effnet[i] else 0 up_block.append(get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i])) if i > 0: up_block.append( nn.Sequential( WuerstchenLayerNorm(c_hidden[i], elementwise_affine=False, eps=1e-6), nn.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2), ) ) self.up_blocks.append(up_block) # OUTPUT self.clf = nn.Sequential( WuerstchenLayerNorm(c_hidden[0], elementwise_affine=False, eps=1e-6), nn.Conv2d(c_hidden[0], 2 * c_out * (patch_size**2), kernel_size=1), nn.PixelShuffle(patch_size), ) # --- WEIGHT INIT --- self.apply(self._init_weights) def _init_weights(self, m): # General init if isinstance(m, (nn.Conv2d, nn.Linear)): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) for mapper in self.effnet_mappers: if mapper is not None: nn.init.normal_(mapper.weight, std=0.02) # conditionings nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs nn.init.constant_(self.clf[1].weight, 0) # outputs # blocks for level_block in self.down_blocks + self.up_blocks: for block in level_block: if isinstance(block, ResBlockStageB): block.channelwise[-1].weight.data *= np.sqrt(1 / sum(self.config.blocks)) elif isinstance(block, TimestepBlock): nn.init.constant_(block.mapper.weight, 0) def gen_r_embedding(self, r, max_positions=10000): r = r * max_positions half_dim = self.c_r // 2 emb = math.log(max_positions) / (half_dim - 1) emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() emb = r[:, None] * emb[None, :] emb = torch.cat([emb.sin(), emb.cos()], dim=1) if self.c_r % 2 == 1: # zero pad emb = nn.functional.pad(emb, (0, 1), mode="constant") return emb.to(dtype=r.dtype) def gen_c_embeddings(self, clip): clip = self.clip_mapper(clip) clip = self.seq_norm(clip) return clip def _down_encode(self, x, r_embed, effnet, clip=None): level_outputs = [] for i, down_block in enumerate(self.down_blocks): effnet_c = None for block in down_block: if isinstance(block, ResBlockStageB): if effnet_c is None and self.effnet_mappers[i] is not None: dtype = effnet.dtype effnet_c = self.effnet_mappers[i]( nn.functional.interpolate( effnet.float(), size=x.shape[-2:], mode="bicubic", antialias=True, align_corners=True ).to(dtype) ) skip = effnet_c if self.effnet_mappers[i] is not None else None x = block(x, skip) elif isinstance(block, AttnBlock): x = block(x, clip) elif isinstance(block, TimestepBlock): x = block(x, r_embed) else: x = block(x) level_outputs.insert(0, x) return level_outputs def _up_decode(self, level_outputs, r_embed, effnet, clip=None): x = level_outputs[0] for i, up_block in enumerate(self.up_blocks): effnet_c = None for j, block in enumerate(up_block): if isinstance(block, ResBlockStageB): if effnet_c is None and self.effnet_mappers[len(self.down_blocks) + i] is not None: dtype = effnet.dtype effnet_c = self.effnet_mappers[len(self.down_blocks) + i]( nn.functional.interpolate( effnet.float(), size=x.shape[-2:], mode="bicubic", antialias=True, align_corners=True ).to(dtype) ) skip = level_outputs[i] if j == 0 and i > 0 else None if effnet_c is not None: if skip is not None: skip = torch.cat([skip, effnet_c], dim=1) else: skip = effnet_c x = block(x, skip) elif isinstance(block, AttnBlock): x = block(x, clip) elif isinstance(block, TimestepBlock): x = block(x, r_embed) else: x = block(x) return x def forward(self, x, r, effnet, clip=None, x_cat=None, eps=1e-3, return_noise=True): if x_cat is not None: x = torch.cat([x, x_cat], dim=1) # Process the conditioning embeddings r_embed = self.gen_r_embedding(r) if clip is not None: clip = self.gen_c_embeddings(clip) # Model Blocks x_in = x x = self.embedding(x) level_outputs = self._down_encode(x, r_embed, effnet, clip) x = self._up_decode(level_outputs, r_embed, effnet, clip) a, b = self.clf(x).chunk(2, dim=1) b = b.sigmoid() * (1 - eps * 2) + eps if return_noise: return (x_in - a) / b else: return a, b class ResBlockStageB(nn.Module): def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0): super().__init__() self.depthwise = nn.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c) self.norm = WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6) self.channelwise = nn.Sequential( nn.Linear(c + c_skip, c * 4), nn.GELU(), GlobalResponseNorm(c * 4), nn.Dropout(dropout), nn.Linear(c * 4, c), ) def forward(self, x, x_skip=None): x_res = x x = self.norm(self.depthwise(x)) if x_skip is not None: x = torch.cat([x, x_skip], dim=1) x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return x + x_res