374 lines
15 KiB
Python
374 lines
15 KiB
Python
import torch
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import torch.nn as nn
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from omegaconf import OmegaConf
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from modules import devices, shared
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cond_cast_unet = getattr(devices, 'cond_cast_unet', lambda x: x)
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from ldm.util import exists
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from ldm.modules.attention import SpatialTransformer
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from ldm.modules.diffusionmodules.util import conv_nd, linear, zero_module, timestep_embedding
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from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
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class TorchHijackForUnet:
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"""
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This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
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this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
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"""
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def __getattr__(self, item):
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if item == 'cat':
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return self.cat
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if hasattr(torch, item):
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return getattr(torch, item)
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raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
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def cat(self, tensors, *args, **kwargs):
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if len(tensors) == 2:
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a, b = tensors
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if a.shape[-2:] != b.shape[-2:]:
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a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
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tensors = (a, b)
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return torch.cat(tensors, *args, **kwargs)
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th = TorchHijackForUnet()
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def align(hint, size):
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b, c, h1, w1 = hint.shape
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h, w = size
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if h != h1 or w != w1:
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hint = th.nn.functional.interpolate(hint, size=size, mode="nearest")
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return hint
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def get_node_name(name, parent_name):
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if len(name) <= len(parent_name):
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return False, ''
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p = name[:len(parent_name)]
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if p != parent_name:
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return False, ''
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return True, name[len(parent_name):]
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class PlugableControlModel(nn.Module):
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def __init__(self, state_dict, config_path, lowvram=False, base_model=None) -> None:
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super().__init__()
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self.config = OmegaConf.load(config_path)
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self.control_model = ControlNet(**self.config.model.params.control_stage_config.params)
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if any([k.startswith("control_model.") for k, v in state_dict.items()]):
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if 'difference' in state_dict and base_model is not None:
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print('We will stop supporting diff models soon because of its lack of robustness.')
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print('Please begin to use official models as soon as possible.')
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unet_state_dict = base_model.state_dict()
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unet_state_dict_keys = unet_state_dict.keys()
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final_state_dict = {}
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counter = 0
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for key in state_dict.keys():
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if not key.startswith("control_model."):
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continue
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p = state_dict[key]
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is_control, node_name = get_node_name(key, 'control_')
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key_name = node_name.replace("model.", "") if is_control else key
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if key_name in unet_state_dict_keys:
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p_new = p + unet_state_dict[key_name].clone().cpu()
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counter += 1
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else:
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p_new = p
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final_state_dict[key] = p_new
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print(f'Diff model cloned: {counter} values')
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state_dict = final_state_dict
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state_dict = {k.replace("control_model.", ""): v for k, v in state_dict.items() if k.startswith("control_model.")}
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self.control_model.load_state_dict(state_dict)
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if not lowvram:
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self.control_model.to(devices.get_device_for("controlnet"))
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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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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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# custom support for prediction of discrete ids into codebook of first stage vq model
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n_embed=None,
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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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use_fp16 = getattr(devices, 'dtype_unet', devices.dtype) == th.float16 and not getattr(shared.cmd_opts, "no_half_controlnet", False)
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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(
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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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# always uses a self-attn
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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=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 align(self, hint, h, w):
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c, h1, w1 = hint.shape
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if h != h1 or w != w1:
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hint = align(hint.unsqueeze(0), (h, w))
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return hint.squeeze(0)
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return hint
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def forward(self, x, hint, timesteps, context, **kwargs):
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t_emb = cond_cast_unet(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(cond_cast_unet(hint), emb, context)
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outs = []
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h1, w1 = x.shape[-2:]
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guided_hint = self.align(guided_hint, h1, w1)
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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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