Merge remote-tracking branch 'origin/main'
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pesser
commit
2c744c9e85
2 changed files with 128 additions and 7 deletions
113
ldm/util.py
113
ldm/util.py
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@ -1,6 +1,7 @@
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import importlib
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import torch
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from torch import optim
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import numpy as np
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from inspect import isfunction
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@ -83,4 +84,114 @@ def get_obj_from_str(string, reload=False):
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if reload:
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module_imp = importlib.import_module(module)
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importlib.reload(module_imp)
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return getattr(importlib.import_module(module, package=None), cls)
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return getattr(importlib.import_module(module, package=None), cls)
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class AdamWwithEMAandWings(optim.Optimizer):
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# credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
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def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
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weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
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ema_power=1., param_names=()):
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"""AdamW that saves EMA versions of the parameters."""
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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if not 0.0 <= ema_decay <= 1.0:
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raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
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ema_power=ema_power, param_names=param_names)
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super().__init__(params, defaults)
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group.setdefault('amsgrad', False)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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exp_avgs = []
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exp_avg_sqs = []
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ema_params_with_grad = []
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state_sums = []
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max_exp_avg_sqs = []
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state_steps = []
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amsgrad = group['amsgrad']
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beta1, beta2 = group['betas']
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ema_decay = group['ema_decay']
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ema_power = group['ema_power']
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for p in group['params']:
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if p.grad is None:
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continue
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('AdamW does not support sparse gradients')
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grads.append(p.grad)
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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if amsgrad:
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# Maintains max of all exp. moving avg. of sq. grad. values
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state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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# Exponential moving average of parameter values
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state['param_exp_avg'] = p.detach().float().clone()
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exp_avgs.append(state['exp_avg'])
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exp_avg_sqs.append(state['exp_avg_sq'])
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ema_params_with_grad.append(state['param_exp_avg'])
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if amsgrad:
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max_exp_avg_sqs.append(state['max_exp_avg_sq'])
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# update the steps for each param group update
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state['step'] += 1
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# record the step after step update
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state_steps.append(state['step'])
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optim._functional.adamw(params_with_grad,
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grads,
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exp_avgs,
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exp_avg_sqs,
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max_exp_avg_sqs,
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state_steps,
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amsgrad=amsgrad,
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beta1=beta1,
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beta2=beta2,
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lr=group['lr'],
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weight_decay=group['weight_decay'],
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eps=group['eps'],
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maximize=False)
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cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
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for param, ema_param in zip(params_with_grad, ema_params_with_grad):
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ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
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return loss
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@ -83,6 +83,11 @@ def main():
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action='store_true',
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help="use plms sampling",
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)
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parser.add_argument(
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"--fixed_code",
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action='store_true',
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help="if enabled, uses the same starting code across all samples ",
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)
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parser.add_argument(
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"--ddim_eta",
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@ -155,7 +160,6 @@ def main():
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type=str,
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help="if specified, load prompts from this file",
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)
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parser.add_argument(
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"--config",
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type=str,
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@ -209,6 +213,10 @@ def main():
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(outpath)) - 1
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start_code = None
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if opt.fixed_code:
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start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
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with torch.no_grad():
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with model.ema_scope():
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tic = time.time()
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@ -221,7 +229,7 @@ def main():
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [opt.C, opt.H//opt.f, opt.W//opt.f]
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shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
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samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
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conditioning=c,
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batch_size=opt.n_samples,
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@ -230,15 +238,17 @@ def main():
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unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,
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eta=opt.ddim_eta,
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dynamic_threshold=opt.dyn)
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dynamic_threshold=opt.dyn,
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x_T=start_code)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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if not opt.skip_save:
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:05}.png"))
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(sample_path, f"{base_count:05}.png"))
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base_count += 1
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all_samples.append(x_samples_ddim)
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@ -256,7 +266,7 @@ def main():
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toc = time.time()
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
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f"Sampling took {toc-tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
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f"Sampling took {toc - tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
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f" \nEnjoy.")
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