407 lines
16 KiB
Python
407 lines
16 KiB
Python
from typing import Mapping, Any
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import copy
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from collections import OrderedDict
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import einops
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import torch
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import torch as th
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import torch.nn as nn
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from ..ldm.modules.diffusionmodules.util import (
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conv_nd,
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linear,
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zero_module,
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timestep_embedding,
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)
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from ..ldm.modules.attention import SpatialTransformer
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from ..ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock, UNetModel
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from ..ldm.models.diffusion.ddpm_ccsr_stage2 import LatentDiffusion
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from ..ldm.util import log_txt_as_img, exists, instantiate_from_config
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from ..ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from ..utils.common import frozen_module
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from .spaced_sampler import SpacedSampler
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class ControlledUnetModel(UNetModel):
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def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs):
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hs = []
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with torch.no_grad():
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
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emb = self.time_embed(t_emb)
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h = x.type(self.dtype)
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for module in self.input_blocks:
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h = module(h, emb, context)
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hs.append(h)
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h = self.middle_block(h, emb, context)
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if control is not None:
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h += control.pop()
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for i, module in enumerate(self.output_blocks):
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if only_mid_control or control is None:
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h = torch.cat([h, hs.pop()], dim=1)
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else:
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h = torch.cat([h, hs.pop() + control.pop()], dim=1)
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h = module(h, emb, context)
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h = h.type(x.dtype)
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return self.out(h)
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class ControlNet(nn.Module):
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def __init__(
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self,
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image_size,
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in_channels,
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model_channels,
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hint_channels,
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num_res_blocks,
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attention_resolutions,
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dropout=0,
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channel_mult=(1, 2, 4, 8),
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conv_resample=True,
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dims=2,
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use_checkpoint=False,
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use_fp16=False,
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num_heads=-1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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resblock_updown=False,
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use_new_attention_order=False,
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use_spatial_transformer=False, # custom transformer support
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transformer_depth=1, # custom transformer support
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context_dim=None, # custom transformer support
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n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
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legacy=True,
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disable_self_attentions=None,
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num_attention_blocks=None,
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disable_middle_self_attn=False,
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use_linear_in_transformer=False,
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):
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super().__init__()
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if use_spatial_transformer:
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assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
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if context_dim is not None:
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assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
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from omegaconf.listconfig import ListConfig
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if type(context_dim) == ListConfig:
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context_dim = list(context_dim)
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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if num_heads == -1:
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assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
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if num_head_channels == -1:
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assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
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self.dims = dims
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self.image_size = image_size
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self.in_channels = in_channels
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self.model_channels = model_channels
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if isinstance(num_res_blocks, int):
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self.num_res_blocks = len(channel_mult) * [num_res_blocks]
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else:
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if len(num_res_blocks) != len(channel_mult):
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raise ValueError("provide num_res_blocks either as an int (globally constant) or "
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"as a list/tuple (per-level) with the same length as channel_mult")
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self.num_res_blocks = num_res_blocks
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if disable_self_attentions is not None:
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# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
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assert len(disable_self_attentions) == len(channel_mult)
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if num_attention_blocks is not None:
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assert len(num_attention_blocks) == len(self.num_res_blocks)
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assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
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print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
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f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
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f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
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f"attention will still not be set.")
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.use_checkpoint = use_checkpoint
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self.dtype = th.float16 if use_fp16 else th.float32
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.predict_codebook_ids = n_embed is not None
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels + hint_channels, model_channels, 3, padding=1)
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)
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]
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)
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self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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for level, mult in enumerate(channel_mult):
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for nr in range(self.num_res_blocks[level]):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=mult * model_channels,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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if legacy:
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# num_heads = 1
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dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
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if exists(disable_self_attentions):
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disabled_sa = disable_self_attentions[level]
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else:
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disabled_sa = False
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if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
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layers.append(
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AttentionBlock(
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ch,
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use_checkpoint=use_checkpoint,
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num_heads=num_heads,
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num_head_channels=dim_head,
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use_new_attention_order=use_new_attention_order,
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) if not use_spatial_transformer else SpatialTransformer(
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ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
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disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
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use_checkpoint=use_checkpoint
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self.zero_convs.append(self.make_zero_conv(ch))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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)
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if resblock_updown
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else Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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self.zero_convs.append(self.make_zero_conv(ch))
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ds *= 2
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self._feature_size += ch
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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if legacy:
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# num_heads = 1
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dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
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self.middle_block = TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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AttentionBlock(
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ch,
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use_checkpoint=use_checkpoint,
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num_heads=num_heads,
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num_head_channels=dim_head,
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use_new_attention_order=use_new_attention_order,
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) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
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ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
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disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
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use_checkpoint=use_checkpoint
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),
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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)
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self.middle_block_out = self.make_zero_conv(ch)
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self._feature_size += ch
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def make_zero_conv(self, channels):
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return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
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def forward(self, x, hint, timesteps, context, **kwargs):
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
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emb = self.time_embed(t_emb)
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x = torch.cat((x, hint), dim=1)
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outs = []
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h = x.type(self.dtype)
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for module, zero_conv in zip(self.input_blocks, self.zero_convs):
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h = module(h, emb, context)
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outs.append(zero_conv(h, emb, context))
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h = self.middle_block(h, emb, context)
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outs.append(self.middle_block_out(h, emb, context))
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return outs
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class ControlLDM(LatentDiffusion):
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def __init__(
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self,
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control_stage_config: Mapping[str, Any],
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control_key: str,
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sd_locked: bool,
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only_mid_control: bool,
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learning_rate: float,
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*args,
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**kwargs
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) -> "ControlLDM":
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super().__init__(*args, **kwargs)
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# instantiate control module
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self.control_model: ControlNet = instantiate_from_config(control_stage_config)
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self.control_key = control_key
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self.sd_locked = sd_locked
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self.only_mid_control = only_mid_control
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self.learning_rate = learning_rate
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self.control_scales = [1.0] * 13
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# instantiate preprocess module (SwinIR)
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# self.preprocess_model = instantiate_from_config(preprocess_config)
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# frozen_module(self.preprocess_model)
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# instantiate condition encoder, since our condition encoder has the same
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# structure with AE encoder, we just make a copy of AE encoder. please
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# note that AE encoder's parameters has not been initialized here.
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self.cond_encoder = nn.Sequential(OrderedDict([
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("encoder", copy.deepcopy(self.first_stage_model.encoder)), # cond_encoder.encoder
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("quant_conv", copy.deepcopy(self.first_stage_model.quant_conv)) # cond_encoder.quant_conv
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]))
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frozen_module(self.cond_encoder)
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def apply_condition_encoder(self, control):
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c_latent_meanvar = self.cond_encoder(control * 2 - 1)
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c_latent = DiagonalGaussianDistribution(c_latent_meanvar).mode() # only use mode
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c_latent = c_latent * self.scale_factor
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return c_latent
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@torch.no_grad()
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def get_input(self, batch, k, bs=None, *args, **kwargs):
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gt, x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs)
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control = batch[self.control_key]
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if bs is not None:
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control = control[:bs]
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control = control.to(self.device)
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control = einops.rearrange(control, 'b h w c -> b c h w')
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control = control.to(memory_format=torch.contiguous_format).float()
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lq = control
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# apply preprocess model
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# control = self.preprocess_model(control)
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# apply condition encoder
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c_latent = self.apply_condition_encoder(control)
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return gt, x, dict(c_crossattn=[c], c_latent=[c_latent], lq=[lq], c_concat=[control])
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def apply_model(self, x_noisy, t, cond, *args, **kwargs):
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assert isinstance(cond, dict)
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diffusion_model = self.model.diffusion_model
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cond_txt = torch.cat(cond['c_crossattn'], 1)
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if cond['c_latent'] is None:
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eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control)
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else:
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control = self.control_model(
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x=x_noisy, hint=torch.cat(cond['c_latent'], 1),
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timesteps=t, context=cond_txt
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)
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control = [c * scale for c, scale in zip(control, self.control_scales)]
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eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control)
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return eps
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@torch.no_grad()
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def get_unconditional_conditioning(self, N):
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return self.get_learned_conditioning([""] * N)
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@torch.no_grad()
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def log_images(self, batch, sample_steps=50):
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log = dict()
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gt, z, c = self.get_input(batch, self.first_stage_key)
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c_lq = c["lq"][0]
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c_latent = c["c_latent"][0]
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c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
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log["hq"] = (self.decode_first_stage(z) + 1) / 2
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log["control"] = c_cat
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log["decoded_control"] = (self.decode_first_stage(c_latent) + 1) / 2
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log["lq"] = c_lq
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log["text"] = (log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16) + 1) / 2
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log["samples"] = self.sample_log(
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cond_img=c_cat,
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steps=sample_steps
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)
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return log
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@torch.no_grad()
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def sample_log(self, cond_img, steps):
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sampler = SpacedSampler(self)
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b, c, h, w = cond_img.shape
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shape = (b, self.channels, h // 8, w // 8)
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samples = sampler.sample(
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steps, shape, cond_img, positive_prompt="", negative_prompt="",
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cfg_scale=1.0, color_fix_type="wavelet"
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)
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return samples
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def configure_optimizers(self):
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lr = self.learning_rate
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params_decode = list(self.first_stage_model.post_quant_conv.parameters()) + list(self.first_stage_model.decoder.parameters())
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# discriminator
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params_discriminator = list(self.decoder_loss.discriminator.parameters())
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if not self.sd_locked:
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params += list(self.model.diffusion_model.output_blocks.parameters())
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params += list(self.model.diffusion_model.out.parameters())
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opt = torch.optim.Adam(params_decode, lr=lr, betas=(0.5, 0.9))
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opt2 = torch.optim.Adam(params_discriminator, lr=lr, betas=(0.5, 0.9))
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return opt, opt2
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def validation_step(self, batch, batch_idx):
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# TODO:
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pass |