194 lines
6.7 KiB
Python
194 lines
6.7 KiB
Python
import torch
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import numpy as np
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from tqdm import tqdm
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from .utils import make_ddim_timesteps, make_ddim_sampling_parameters, noise_like
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from loguru import logger
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class DDIMSampler(object):
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def __init__(self, model, schedule="linear"):
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super().__init__()
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self.model = model
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self.ddpm_num_timesteps = model.num_timesteps
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self.schedule = schedule
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def register_buffer(self, name, attr):
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setattr(self, name, attr)
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def make_schedule(
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self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
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):
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self.ddim_timesteps = make_ddim_timesteps(
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ddim_discr_method=ddim_discretize,
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num_ddim_timesteps=ddim_num_steps,
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# array([1])
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num_ddpm_timesteps=self.ddpm_num_timesteps,
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verbose=verbose,
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)
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alphas_cumprod = self.model.alphas_cumprod # torch.Size([1000])
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assert (
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alphas_cumprod.shape[0] == self.ddpm_num_timesteps
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), "alphas have to be defined for each timestep"
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to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
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self.register_buffer("betas", to_torch(self.model.betas))
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self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
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self.register_buffer(
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"alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
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)
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer(
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"sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
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)
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self.register_buffer(
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"sqrt_one_minus_alphas_cumprod",
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to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
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)
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self.register_buffer(
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"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
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)
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self.register_buffer(
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"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
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)
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self.register_buffer(
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"sqrt_recipm1_alphas_cumprod",
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to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
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)
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# ddim sampling parameters
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ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
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alphacums=alphas_cumprod.cpu(),
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ddim_timesteps=self.ddim_timesteps,
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eta=ddim_eta,
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verbose=verbose,
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)
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self.register_buffer("ddim_sigmas", ddim_sigmas)
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self.register_buffer("ddim_alphas", ddim_alphas)
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self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
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self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
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sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
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(1 - self.alphas_cumprod_prev)
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/ (1 - self.alphas_cumprod)
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* (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
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)
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self.register_buffer(
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"ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
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)
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@torch.no_grad()
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def sample(self, steps, conditioning, batch_size, shape):
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self.make_schedule(ddim_num_steps=steps, ddim_eta=0, verbose=False)
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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# samples: 1,3,128,128
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return self.ddim_sampling(
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conditioning,
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size,
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quantize_denoised=False,
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ddim_use_original_steps=False,
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noise_dropout=0,
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temperature=1.0,
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)
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@torch.no_grad()
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def ddim_sampling(
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self,
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cond,
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shape,
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ddim_use_original_steps=False,
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quantize_denoised=False,
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temperature=1.0,
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noise_dropout=0.0,
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):
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device = self.model.betas.device
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b = shape[0]
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img = torch.randn(shape, device=device, dtype=cond.dtype)
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timesteps = (
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self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
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)
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time_range = (
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reversed(range(0, timesteps))
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if ddim_use_original_steps
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else np.flip(timesteps)
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)
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total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
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logger.info(f"Running DDIM Sampling with {total_steps} timesteps")
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iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps)
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for i, step in enumerate(iterator):
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index = total_steps - i - 1
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ts = torch.full((b,), step, device=device, dtype=torch.long)
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outs = self.p_sample_ddim(
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img,
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cond,
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ts,
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index=index,
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use_original_steps=ddim_use_original_steps,
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quantize_denoised=quantize_denoised,
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temperature=temperature,
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noise_dropout=noise_dropout,
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)
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img, _ = outs
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return img
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@torch.no_grad()
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def p_sample_ddim(
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self,
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x,
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c,
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t,
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index,
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repeat_noise=False,
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use_original_steps=False,
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quantize_denoised=False,
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temperature=1.0,
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noise_dropout=0.0,
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):
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b, *_, device = *x.shape, x.device
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e_t = self.model.apply_model(x, t, c)
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alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
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alphas_prev = (
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self.model.alphas_cumprod_prev
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if use_original_steps
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else self.ddim_alphas_prev
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)
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sqrt_one_minus_alphas = (
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self.model.sqrt_one_minus_alphas_cumprod
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if use_original_steps
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else self.ddim_sqrt_one_minus_alphas
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)
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sigmas = (
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self.model.ddim_sigmas_for_original_num_steps
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if use_original_steps
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else self.ddim_sigmas
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)
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# select parameters corresponding to the currently considered timestep
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
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sqrt_one_minus_at = torch.full(
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(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
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)
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# current prediction for x_0
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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if quantize_denoised: # 没用
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pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
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# direction pointing to x_t
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dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t
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noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
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if noise_dropout > 0.0: # 没用
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noise = torch.nn.functional.dropout(noise, p=noise_dropout)
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x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
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return x_prev, pred_x0
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