mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-02-24 08:43:57 +00:00
323 lines
12 KiB
Python
323 lines
12 KiB
Python
import torch
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import torch.nn as nn
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from modules import devices
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try:
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from sgm.modules.diffusionmodules.openaimodel import conv_nd, linear, zero_module, timestep_embedding, \
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TimestepEmbedSequential, ResBlock, Downsample, SpatialTransformer, exists
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using_sgm = True
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except:
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from ldm.modules.diffusionmodules.openaimodel import conv_nd, linear, zero_module, timestep_embedding, \
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TimestepEmbedSequential, ResBlock, Downsample, SpatialTransformer, exists
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using_sgm = False
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class PlugableControlModel(nn.Module):
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def __init__(self, config, state_dict=None):
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super().__init__()
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self.config = config
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self.control_model = ControlNet(**self.config).cpu()
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if state_dict is not None:
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self.control_model.load_state_dict(state_dict, strict=False)
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self.gpu_component = None
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self.is_control_lora = False
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def reset(self):
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pass
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def forward(self, *args, **kwargs):
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return self.control_model(*args, **kwargs)
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def aggressive_lowvram(self):
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self.to('cpu')
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def send_me_to_gpu(module, _):
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if self.gpu_component == module:
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return
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if self.gpu_component is not None:
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self.gpu_component.to('cpu')
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module.to(devices.get_device_for("controlnet"))
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self.gpu_component = module
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self.control_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
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self.control_model.input_hint_block.register_forward_pre_hook(send_me_to_gpu)
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self.control_model.label_emb.register_forward_pre_hook(send_me_to_gpu)
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for m in self.control_model.input_blocks:
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m.register_forward_pre_hook(send_me_to_gpu)
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for m in self.control_model.zero_convs:
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m.register_forward_pre_hook(send_me_to_gpu)
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self.control_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
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self.control_model.middle_block_out.register_forward_pre_hook(send_me_to_gpu)
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return
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def fullvram(self):
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self.to(devices.get_device_for("controlnet"))
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return
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class ControlNet(nn.Module):
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def __init__(
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self,
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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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num_classes=None,
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use_checkpoint=False,
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use_fp16=True,
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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_spatial_transformer=True,
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transformer_depth=1,
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context_dim=None,
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n_embed=None,
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legacy=False,
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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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adm_in_channels=None,
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transformer_depth_middle=None,
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device=None,
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global_average_pooling=False,
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):
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super().__init__()
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self.global_average_pooling = global_average_pooling
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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self.dims = dims
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self.in_channels = in_channels
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self.model_channels = model_channels
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if isinstance(transformer_depth, int):
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transformer_depth = len(channel_mult) * [transformer_depth]
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if transformer_depth_middle is None:
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transformer_depth_middle = transformer_depth[-1]
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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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self.num_res_blocks = num_res_blocks
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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.num_classes = num_classes
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self.use_checkpoint = use_checkpoint
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self.dtype = torch.float16 if use_fp16 else torch.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, dtype=self.dtype, device=device),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
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)
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if self.num_classes is not None:
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if isinstance(self.num_classes, int):
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self.label_emb = nn.Embedding(num_classes, time_embed_dim)
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elif self.num_classes == "continuous":
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print("setting up linear c_adm embedding layer")
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self.label_emb = nn.Linear(1, time_embed_dim)
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elif self.num_classes == "sequential":
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assert adm_in_channels is not None
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self.label_emb = nn.Sequential(
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nn.Sequential(
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linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
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)
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)
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else:
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raise ValueError()
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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, dtype=self.dtype, device=device)
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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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SpatialTransformer(
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ch, num_heads, dim_head, depth=transformer_depth[level], 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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SpatialTransformer( # always uses a self-attn
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ch, num_heads, dim_head, depth=transformer_depth_middle, 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, y=None, **kwargs):
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original_type = x.dtype
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x = x.to(self.dtype)
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hint = hint.to(self.dtype)
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timesteps = timesteps.to(self.dtype)
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context = context.to(self.dtype)
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if y is not None:
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y = y.to(self.dtype)
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
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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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if self.num_classes is not None:
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assert y.shape[0] == x.shape[0]
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emb = emb + self.label_emb(y)
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h = x
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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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outs = [o.to(original_type) for o in outs]
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return outs
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