mirror of
https://github.com/lllyasviel/ControlNet.git
synced 2026-01-12 15:38:18 -05:00
436 lines
18 KiB
Python
436 lines
18 KiB
Python
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 einops import rearrange, repeat
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from torchvision.utils import make_grid
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from ldm.modules.attention import SpatialTransformer
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from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
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from ldm.models.diffusion.ddpm import LatentDiffusion
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from ldm.util import log_txt_as_img, exists, instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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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, 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.input_hint_block = TimestepEmbedSequential(
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conv_nd(dims, hint_channels, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 32, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 32, 32, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 32, 96, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 96, 96, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 96, 256, 3, padding=1, stride=2),
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nn.SiLU(),
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zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
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)
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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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guided_hint = self.input_hint_block(hint, emb, context)
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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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if guided_hint is not None:
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h = module(h, emb, context)
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h += guided_hint
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guided_hint = None
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else:
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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__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.control_model = instantiate_from_config(control_stage_config)
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self.control_key = control_key
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self.only_mid_control = only_mid_control
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self.control_scales = [1.0] * 13
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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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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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return x, dict(c_crossattn=[c], 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_concat'] 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(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt)
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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, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None,
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quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
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plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None,
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use_ema_scope=True,
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**kwargs):
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use_ddim = ddim_steps is not None
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log = dict()
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z, c = self.get_input(batch, self.first_stage_key, bs=N)
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c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N]
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N = min(z.shape[0], N)
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n_row = min(z.shape[0], n_row)
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log["reconstruction"] = self.decode_first_stage(z)
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log["control"] = c_cat * 2.0 - 1.0
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log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16)
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if plot_diffusion_rows:
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# get diffusion row
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diffusion_row = list()
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z_start = z[:n_row]
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for t in range(self.num_timesteps):
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if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
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t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
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t = t.to(self.device).long()
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noise = torch.randn_like(z_start)
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z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
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diffusion_row.append(self.decode_first_stage(z_noisy))
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diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
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diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
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diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
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diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
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log["diffusion_row"] = diffusion_grid
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if sample:
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# get denoise row
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samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
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batch_size=N, ddim=use_ddim,
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ddim_steps=ddim_steps, eta=ddim_eta)
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x_samples = self.decode_first_stage(samples)
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log["samples"] = x_samples
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if plot_denoise_rows:
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denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
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log["denoise_row"] = denoise_grid
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if unconditional_guidance_scale > 1.0:
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uc_cross = self.get_unconditional_conditioning(N)
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uc_cat = c_cat # torch.zeros_like(c_cat)
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uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
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samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
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batch_size=N, ddim=use_ddim,
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ddim_steps=ddim_steps, eta=ddim_eta,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=uc_full,
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)
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x_samples_cfg = self.decode_first_stage(samples_cfg)
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log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
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return log
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@torch.no_grad()
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def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
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ddim_sampler = DDIMSampler(self)
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b, c, h, w = cond["c_concat"][0].shape
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shape = (self.channels, h // 8, w // 8)
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samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs)
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return samples, intermediates
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def configure_optimizers(self):
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lr = self.learning_rate
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params = list(self.control_model.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.AdamW(params, lr=lr)
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return opt
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def low_vram_shift(self, is_diffusing):
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if is_diffusing:
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self.model = self.model.cuda()
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self.control_model = self.control_model.cuda()
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self.first_stage_model = self.first_stage_model.cpu()
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self.cond_stage_model = self.cond_stage_model.cpu()
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else:
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self.model = self.model.cpu()
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self.control_model = self.control_model.cpu()
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self.first_stage_model = self.first_stage_model.cuda()
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self.cond_stage_model = self.cond_stage_model.cuda()
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