mirror of
https://github.com/lucidrains/DALLE2-pytorch.git
synced 2025-12-19 17:54:20 +01:00
276 lines
8.2 KiB
Python
276 lines
8.2 KiB
Python
import copy
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from functools import partial
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import torch
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from torch import nn
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from torch.cuda.amp import autocast, GradScaler
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from dalle2_pytorch.dalle2_pytorch import Decoder, DiffusionPrior
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from dalle2_pytorch.optimizer import get_optimizer
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# helper functions
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def exists(val):
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return val is not None
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def cast_tuple(val, length = 1):
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return val if isinstance(val, tuple) else ((val,) * length)
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def pick_and_pop(keys, d):
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values = list(map(lambda key: d.pop(key), keys))
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return dict(zip(keys, values))
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def group_dict_by_key(cond, d):
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return_val = [dict(),dict()]
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for key in d.keys():
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match = bool(cond(key))
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ind = int(not match)
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return_val[ind][key] = d[key]
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return (*return_val,)
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def string_begins_with(prefix, str):
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return str.startswith(prefix)
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def group_by_key_prefix(prefix, d):
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return group_dict_by_key(partial(string_begins_with, prefix), d)
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def groupby_prefix_and_trim(prefix, d):
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kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
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kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
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return kwargs_without_prefix, kwargs
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# exponential moving average wrapper
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class EMA(nn.Module):
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def __init__(
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self,
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model,
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beta = 0.99,
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update_after_step = 1000,
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update_every = 10,
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):
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super().__init__()
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self.beta = beta
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self.online_model = model
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self.ema_model = copy.deepcopy(model)
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self.update_after_step = update_after_step # only start EMA after this step number, starting at 0
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self.update_every = update_every
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self.register_buffer('initted', torch.Tensor([False]))
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self.register_buffer('step', torch.tensor([0.]))
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def update(self):
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self.step += 1
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if self.step <= self.update_after_step or (self.step % self.update_every) != 0:
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return
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if not self.initted:
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self.ema_model.state_dict(self.online_model.state_dict())
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self.initted.data.copy_(torch.Tensor([True]))
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self.update_moving_average(self.ema_model, self.online_model)
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def update_moving_average(self, ma_model, current_model):
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def calculate_ema(beta, old, new):
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if not exists(old):
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return new
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return old * beta + (1 - beta) * new
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for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
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old_weight, up_weight = ma_params.data, current_params.data
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ma_params.data = calculate_ema(self.beta, old_weight, up_weight)
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for current_buffer, ma_buffer in zip(current_model.buffers(), ma_model.buffers()):
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new_buffer_value = calculate_ema(self.beta, ma_buffer, current_buffer)
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ma_buffer.copy_(new_buffer_value)
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def __call__(self, *args, **kwargs):
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return self.ema_model(*args, **kwargs)
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# diffusion prior trainer
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class DiffusionPriorTrainer(nn.Module):
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def __init__(
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self,
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diffusion_prior,
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use_ema = True,
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lr = 3e-4,
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wd = 1e-2,
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max_grad_norm = None,
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amp = False,
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**kwargs
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):
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super().__init__()
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assert isinstance(diffusion_prior, DiffusionPrior)
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ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
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self.diffusion_prior = diffusion_prior
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# exponential moving average
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self.use_ema = use_ema
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if self.use_ema:
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self.ema_diffusion_prior = EMA(diffusion_prior, **ema_kwargs)
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# optimizer and mixed precision stuff
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self.amp = amp
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self.scaler = GradScaler(enabled = amp)
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self.optimizer = get_optimizer(
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diffusion_prior.parameters(),
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lr = lr,
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wd = wd,
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**kwargs
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)
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# gradient clipping if needed
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self.max_grad_norm = max_grad_norm
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def update(self):
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if exists(self.max_grad_norm):
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self.scaler.unscale_(self.optimizer)
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nn.utils.clip_grad_norm_(self.diffusion_prior.parameters(), self.max_grad_norm)
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self.scaler.step(self.optimizer)
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self.scaler.update()
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self.optimizer.zero_grad()
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if self.use_ema:
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self.ema_diffusion_prior.update()
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@torch.inference_mode()
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def p_sample_loop(self, *args, **kwargs):
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return self.ema_diffusion_prior.ema_model.p_sample_loop(*args, **kwargs)
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@torch.inference_mode()
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def sample(self, *args, **kwargs):
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return self.ema_diffusion_prior.ema_model.sample(*args, **kwargs)
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@torch.inference_mode()
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def sample_batch_size(self, *args, **kwargs):
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return self.ema_diffusion_prior.ema_model.sample_batch_size(*args, **kwargs)
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def forward(
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self,
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*args,
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divisor = 1,
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**kwargs
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):
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with autocast(enabled = self.amp):
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loss = self.diffusion_prior(*args, **kwargs)
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return self.scaler.scale(loss / divisor)
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# decoder trainer
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class DecoderTrainer(nn.Module):
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def __init__(
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self,
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decoder,
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use_ema = True,
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lr = 3e-4,
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wd = 1e-2,
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max_grad_norm = None,
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amp = False,
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**kwargs
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):
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super().__init__()
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assert isinstance(decoder, Decoder)
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ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
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self.decoder = decoder
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self.num_unets = len(self.decoder.unets)
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self.use_ema = use_ema
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if use_ema:
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has_lazy_linear = any([type(module) == nn.LazyLinear for module in decoder.modules()])
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assert not has_lazy_linear, 'you must set the text_embed_dim on your u-nets if you plan on doing automatic exponential moving average'
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self.ema_unets = nn.ModuleList([])
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self.amp = amp
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# be able to finely customize learning rate, weight decay
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# per unet
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lr, wd = map(partial(cast_tuple, length = self.num_unets), (lr, wd))
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for ind, (unet, unet_lr, unet_wd) in enumerate(zip(self.decoder.unets, lr, wd)):
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optimizer = get_optimizer(
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unet.parameters(),
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lr = unet_lr,
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wd = unet_wd,
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**kwargs
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)
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setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
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if self.use_ema:
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self.ema_unets.append(EMA(unet, **ema_kwargs))
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scaler = GradScaler(enabled = amp)
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setattr(self, f'scaler{ind}', scaler)
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# gradient clipping if needed
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self.max_grad_norm = max_grad_norm
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@property
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def unets(self):
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return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
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def scale(self, loss, *, unet_number):
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assert 1 <= unet_number <= self.num_unets
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index = unet_number - 1
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scaler = getattr(self, f'scaler{index}')
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return scaler.scale(loss)
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def update(self, unet_number):
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assert 1 <= unet_number <= self.num_unets
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index = unet_number - 1
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unet = self.decoder.unets[index]
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optimizer = getattr(self, f'optim{index}')
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scaler = getattr(self, f'scaler{index}')
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if exists(self.max_grad_norm):
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scaler.unscale_(optimizer)
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nn.utils.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad()
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if self.use_ema:
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ema_unet = self.ema_unets[index]
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ema_unet.update()
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@torch.no_grad()
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def sample(self, *args, **kwargs):
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if self.use_ema:
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trainable_unets = self.decoder.unets
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self.decoder.unets = self.unets # swap in exponential moving averaged unets for sampling
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output = self.decoder.sample(*args, **kwargs)
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if self.use_ema:
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self.decoder.unets = trainable_unets # restore original training unets
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return output
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def forward(
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self,
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x,
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*,
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unet_number,
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divisor = 1,
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**kwargs
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):
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with autocast(enabled = self.amp):
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loss = self.decoder(x, unet_number = unet_number, **kwargs)
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return self.scale(loss / divisor, unet_number = unet_number)
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