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447 lines
16 KiB
Python
447 lines
16 KiB
Python
"""SAMPLING ONLY."""
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import logging
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import numpy as np
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import torch
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from tqdm import tqdm
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from imaginairy.img_log import log_latent
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from imaginairy.modules.diffusion.util import (
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extract_into_tensor,
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make_ddim_sampling_parameters,
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make_ddim_timesteps,
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noise_like,
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)
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from imaginairy.utils import get_device
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logger = logging.getLogger(__name__)
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class PLMSSampler:
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"""probabilistic least-mean-squares"""
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def __init__(self, model, **kwargs):
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self.model = model
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self.ddpm_num_timesteps = model.num_timesteps
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self.device_available = get_device()
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def register_buffer(self, name, attr):
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if type(attr) == torch.Tensor:
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if attr.device != torch.device(self.device_available):
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attr = attr.to(torch.float32).to(torch.device(self.device_available))
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setattr(self, name, attr)
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def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0):
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if ddim_eta != 0:
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raise ValueError("ddim_eta must be 0 for PLMS")
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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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num_ddpm_timesteps=self.ddpm_num_timesteps,
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)
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alphas_cumprod = self.model.alphas_cumprod
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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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)
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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(
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self,
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num_steps,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.0,
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mask=None,
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x0=None,
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temperature=1.0,
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noise_dropout=0.0,
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score_corrector=None,
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corrector_kwargs=None,
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x_T=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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**kwargs,
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):
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if conditioning is not None:
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if isinstance(conditioning, dict):
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cbs = conditioning[list(conditioning.keys())[0]].shape[0]
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if cbs != batch_size:
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logger.warning(
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f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
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)
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else:
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if conditioning.shape[0] != batch_size:
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logger.warning(
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f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
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)
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self.make_schedule(ddim_num_steps=num_steps, ddim_eta=eta)
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samples = self.plms_sampling(
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conditioning,
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(batch_size, *shape),
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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mask=mask,
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x0=x0,
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ddim_use_original_steps=False,
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noise_dropout=noise_dropout,
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temperature=temperature,
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score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs,
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x_T=x_T,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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)
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return samples
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@torch.no_grad()
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def plms_sampling(
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self,
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cond,
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shape,
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x_T=None,
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ddim_use_original_steps=False,
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callback=None,
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timesteps=None,
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quantize_denoised=False,
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mask=None,
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x0=None,
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img_callback=None,
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temperature=1.0,
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noise_dropout=0.0,
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score_corrector=None,
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corrector_kwargs=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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):
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device = self.model.betas.device
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b = shape[0]
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if x_T is None:
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img = torch.randn(shape, device="cpu").to(device)
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else:
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img = x_T
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log_latent(img, "initial img")
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if timesteps is None:
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timesteps = (
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self.ddpm_num_timesteps
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if ddim_use_original_steps
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else self.ddim_timesteps
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)
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elif timesteps is not None and not ddim_use_original_steps:
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subset_end = (
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int(
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min(timesteps / self.ddim_timesteps.shape[0], 1)
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* self.ddim_timesteps.shape[0]
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)
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- 1
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)
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timesteps = self.ddim_timesteps[:subset_end]
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time_range = (
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list(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.debug(f"Running PLMS Sampling with {total_steps} timesteps")
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iterator = tqdm(time_range, desc=" PLMS Sampler", total=total_steps)
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old_eps = []
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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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ts_next = torch.full(
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(b,),
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time_range[min(i + 1, len(time_range) - 1)],
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device=device,
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dtype=torch.long,
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)
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if mask is not None:
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assert x0 is not None
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img_orig = self.model.q_sample(
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x0, ts
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) # TODO: deterministic forward pass?
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img = img_orig * mask + (1.0 - mask) * img
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img, pred_x0, e_t = self.p_sample_plms(
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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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score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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old_eps=old_eps,
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t_next=ts_next,
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)
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old_eps.append(e_t)
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if len(old_eps) >= 4:
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old_eps.pop(0)
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if callback:
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callback(i)
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if img_callback:
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img_callback(img, "img")
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img_callback(pred_x0, "pred_x0")
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return img
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@torch.no_grad()
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def p_sample_plms(
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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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score_corrector=None,
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corrector_kwargs=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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old_eps=None,
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t_next=None,
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):
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b, *_, device = *x.shape, x.device
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def get_model_output(x, t):
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if (
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unconditional_conditioning is None
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or unconditional_guidance_scale == 1.0
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):
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e_t = self.model.apply_model(x, t, c)
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else:
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x_in = torch.cat([x] * 2)
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t_in = torch.cat([t] * 2)
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c_in = torch.cat([unconditional_conditioning, c])
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e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
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log_latent(e_t_uncond, "noise pred uncond")
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log_latent(e_t, "noise pred cond")
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e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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log_latent(e_t, "noise pred combined")
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if score_corrector is not None:
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assert self.model.parameterization == "eps"
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e_t = score_corrector.modify_score(
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self.model, e_t, x, t, c, **corrector_kwargs
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)
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return e_t
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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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def get_x_prev_and_pred_x0(e_t, index):
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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:
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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:
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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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e_t = get_model_output(x, t)
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if len(old_eps) == 0:
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# Pseudo Improved Euler (2nd order)
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x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
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e_t_next = get_model_output(x_prev, t_next)
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e_t_prime = (e_t + e_t_next) / 2
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elif len(old_eps) == 1:
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# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
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e_t_prime = (3 * e_t - old_eps[-1]) / 2
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elif len(old_eps) == 2:
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# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
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e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
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elif len(old_eps) >= 3:
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# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
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e_t_prime = (
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55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]
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) / 24
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x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
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log_latent(x_prev, "x_prev")
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log_latent(pred_x0, "pred_x0")
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return x_prev, pred_x0, e_t
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@torch.no_grad()
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def stochastic_encode(self, init_latent, t, noise=None):
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# fast, but does not allow for exact reconstruction
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# t serves as an index to gather the correct alphas
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sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
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sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
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if noise is None:
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noise = torch.randn_like(init_latent, device="cpu").to(get_device())
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return (
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extract_into_tensor(sqrt_alphas_cumprod, t, init_latent.shape) * init_latent
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+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, init_latent.shape)
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* noise
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)
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@torch.no_grad()
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def decode(
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self,
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x_latent,
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cond,
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t_start,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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img_callback=None,
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score_corrector=None,
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temperature=1.0,
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mask=None,
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orig_latent=None,
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):
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timesteps = self.ddim_timesteps[:t_start]
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time_range = np.flip(timesteps)
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total_steps = timesteps.shape[0]
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iterator = tqdm(time_range, desc="PLMS altering image", total=total_steps)
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x_dec = x_latent
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old_eps = []
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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(
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(x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long
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)
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ts_next = torch.full(
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(x_latent.shape[0],),
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time_range[min(i + 1, len(time_range) - 1)],
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device=x_latent.device,
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dtype=torch.long,
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)
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if mask is not None:
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assert orig_latent is not None
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xdec_orig = self.model.q_sample(orig_latent, ts)
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log_latent(xdec_orig, "xdec_orig")
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log_latent(xdec_orig * mask, "masked_xdec_orig")
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x_dec = xdec_orig * mask + (1.0 - mask) * x_dec
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log_latent(x_dec, "x_dec")
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x_dec, pred_x0, e_t = self.p_sample_plms(
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x_dec,
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cond,
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ts,
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index=index,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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temperature=temperature,
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old_eps=old_eps,
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t_next=ts_next,
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)
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# original_loss = ((x_dec - x_latent).abs().mean()*70)
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# sigma_t = torch.full((1, 1, 1, 1), self.ddim_sigmas[index], device=get_device())
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# x_dec = x_dec.detach() + (original_loss * 0.1) ** 2
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# cond_grad = -torch.autograd.grad(original_loss, x_dec)[0]
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# x_dec = x_dec.detach() + cond_grad * sigma_t ** 2
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## x_dec_alt = x_dec + (original_loss * 0.1) ** 2
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old_eps.append(e_t)
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if len(old_eps) >= 4:
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old_eps.pop(0)
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if img_callback:
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img_callback(x_dec, "x_dec")
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img_callback(pred_x0, "pred_x0")
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log_latent(x_dec, f"x_dec {i}")
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log_latent(pred_x0, f"pred_x0 {i}")
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return x_dec
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