206 lines
7.3 KiB
Python
206 lines
7.3 KiB
Python
"""
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Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
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Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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"""
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import re
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import torch
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import torch.nn as nn
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from ldm.modules.diffusionmodules.util import normalization, checkpoint
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from ldm.modules.diffusionmodules.openaimodel import ResBlock, UNetModel
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class SPADE(nn.Module):
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def __init__(self, norm_nc, label_nc=256, config_text='spadeinstance3x3'):
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super().__init__()
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assert config_text.startswith('spade')
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parsed = re.search('spade(\D+)(\d)x\d', config_text)
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ks = int(parsed.group(2))
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self.param_free_norm = normalization(norm_nc)
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# The dimension of the intermediate embedding space. Yes, hardcoded.
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nhidden = 128
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pw = ks // 2
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self.mlp_shared = nn.Sequential(
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nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw),
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nn.ReLU()
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)
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self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)
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self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)
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def forward(self, x_dic, segmap_dic):
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return checkpoint(
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self._forward, (x_dic, segmap_dic), self.parameters(), True
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)
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def _forward(self, x_dic, segmap_dic):
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segmap = segmap_dic[str(x_dic.size(-1))]
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x = x_dic
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# Part 1. generate parameter-free normalized activations
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normalized = self.param_free_norm(x)
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# Part 2. produce scaling and bias conditioned on semantic map
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# segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
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actv = self.mlp_shared(segmap)
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repeat_factor = normalized.shape[0]//segmap.shape[0]
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if repeat_factor > 1:
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out = normalized
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out *= (1 + self.mlp_gamma(actv).repeat_interleave(repeat_factor, dim=0))
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out += self.mlp_beta(actv).repeat_interleave(repeat_factor, dim=0)
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else:
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out = normalized
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out *= (1 + self.mlp_gamma(actv))
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out += self.mlp_beta(actv)
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return out
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def dual_resblock_forward(self: ResBlock, x, emb, spade: SPADE, get_struct_cond):
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if self.updown:
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in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
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h = in_rest(x)
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h = self.h_upd(h)
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x = self.x_upd(x)
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h = in_conv(h)
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else:
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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h = h + emb_out
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h = self.out_layers(h)
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h = spade(h, get_struct_cond())
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return self.skip_connection(x) + h
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class SPADELayers(nn.Module):
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def __init__(self):
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'''
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A container class for fast SPADE layer loading.
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params inferred from the official checkpoint
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'''
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super().__init__()
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self.input_blocks = nn.ModuleList([
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nn.Identity(),
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SPADE(320),
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SPADE(320),
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nn.Identity(),
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SPADE(640),
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SPADE(640),
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nn.Identity(),
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SPADE(1280),
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SPADE(1280),
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nn.Identity(),
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SPADE(1280),
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SPADE(1280),
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])
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self.middle_block = nn.ModuleList([
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SPADE(1280),
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nn.Identity(),
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SPADE(1280),
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])
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self.output_blocks = nn.ModuleList([
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SPADE(1280),
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SPADE(1280),
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SPADE(1280),
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SPADE(1280),
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SPADE(1280),
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SPADE(1280),
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SPADE(640),
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SPADE(640),
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SPADE(640),
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SPADE(320),
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SPADE(320),
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SPADE(320),
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])
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self.input_ids = [1,2,4,5,7,8,10,11]
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self.output_ids = list(range(12))
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self.mid_ids = [0,2]
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self.forward_cache_name = 'org_forward_stablesr'
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self.unet = None
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def hook(self, unet: UNetModel, get_struct_cond):
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# hook all resblocks
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self.unet = unet
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resblock: ResBlock = None
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for i in self.input_ids:
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resblock = unet.input_blocks[i][0]
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# debug
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# assert isinstance(resblock, ResBlock)
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if not hasattr(resblock, self.forward_cache_name):
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setattr(resblock, self.forward_cache_name, resblock._forward)
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resblock._forward = lambda x, timesteps, resblock=resblock, spade=self.input_blocks[i]: dual_resblock_forward(resblock, x, timesteps, spade, get_struct_cond)
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for i in self.output_ids:
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resblock = unet.output_blocks[i][0]
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# debug
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# assert isinstance(resblock, ResBlock)
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if not hasattr(resblock, self.forward_cache_name):
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setattr(resblock, self.forward_cache_name, resblock._forward)
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resblock._forward = lambda x, timesteps, resblock=resblock, spade=self.output_blocks[i]: dual_resblock_forward(resblock, x, timesteps, spade, get_struct_cond)
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for i in self.mid_ids:
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resblock = unet.middle_block[i]
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# debug
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# assert isinstance(resblock, ResBlock)
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if not hasattr(resblock, self.forward_cache_name):
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setattr(resblock, self.forward_cache_name, resblock._forward)
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resblock._forward = lambda x, timesteps, resblock=resblock, spade=self.middle_block[i]: dual_resblock_forward(resblock, x, timesteps, spade, get_struct_cond)
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def unhook(self):
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unet = self.unet
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if unet is None: return
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resblock: ResBlock = None
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for i in self.input_ids:
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resblock = unet.input_blocks[i][0]
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if hasattr(resblock, self.forward_cache_name):
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resblock._forward = getattr(resblock, self.forward_cache_name)
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delattr(resblock, self.forward_cache_name)
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for i in self.output_ids:
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resblock = unet.output_blocks[i][0]
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if hasattr(resblock, self.forward_cache_name):
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resblock._forward = getattr(resblock, self.forward_cache_name)
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delattr(resblock, self.forward_cache_name)
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for i in self.mid_ids:
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resblock = unet.middle_block[i]
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if hasattr(resblock, self.forward_cache_name):
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resblock._forward = getattr(resblock, self.forward_cache_name)
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delattr(resblock, self.forward_cache_name)
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self.unet = None
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def load_from_dict(self, state_dict):
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"""
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Load model weights from a dictionary.
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:param state_dict: a dict of parameters.
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"""
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filtered_dict = {}
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for k, v in state_dict.items():
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if k.startswith("model.diffusion_model."):
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key = k[len("model.diffusion_model.") :]
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# remove the '.0.spade' within the key
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if 'middle_block' not in key:
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key = key.replace('.0.spade', '')
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else:
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key = key.replace('.spade', '')
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filtered_dict[key] = v
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self.load_state_dict(filtered_dict)
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if __name__ == '__main__':
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path = '../models/stablesr_sd21.ckpt'
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state_dict = torch.load(path)
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model = SPADELayers()
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model.load_from_dict(state_dict)
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print(model) |