mirror of
https://github.com/Stability-AI/stablediffusion.git
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245 lines
13 KiB
Python
245 lines
13 KiB
Python
"""SAMPLING ONLY."""
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import torch
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import numpy as np
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from tqdm import tqdm
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from functools import partial
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from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
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from ldm.models.diffusion.sampling_util import norm_thresholding
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class PLMSSampler(object):
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def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
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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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self.device = 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 != self.device:
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attr = attr.to(self.device)
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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., verbose=True):
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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(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
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num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
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alphas_cumprod = self.model.alphas_cumprod
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assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, '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('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
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self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
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# ddim sampling parameters
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ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
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ddim_timesteps=self.ddim_timesteps,
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eta=ddim_eta,verbose=verbose)
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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. - ddim_alphas))
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sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
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(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
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1 - self.alphas_cumprod / self.alphas_cumprod_prev))
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self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
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@torch.no_grad()
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def sample(self,
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S,
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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.,
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mask=None,
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x0=None,
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temperature=1.,
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noise_dropout=0.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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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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dynamic_threshold=None,
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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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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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else:
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if conditioning.shape[0] != batch_size:
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
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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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print(f'Data shape for PLMS sampling is {size}')
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samples, intermediates = self.plms_sampling(conditioning, size,
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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, 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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log_every_t=log_every_t,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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dynamic_threshold=dynamic_threshold,
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)
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return samples, intermediates
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@torch.no_grad()
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def plms_sampling(self, cond, shape,
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x_T=None, ddim_use_original_steps=False,
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callback=None, timesteps=None, quantize_denoised=False,
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mask=None, x0=None, img_callback=None, log_every_t=100,
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temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
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unconditional_guidance_scale=1., unconditional_conditioning=None,
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dynamic_threshold=None):
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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=device)
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else:
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img = x_T
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if timesteps is None:
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timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
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elif timesteps is not None and not ddim_use_original_steps:
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subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
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timesteps = self.ddim_timesteps[:subset_end]
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intermediates = {'x_inter': [img], 'pred_x0': [img]}
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time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
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total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
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print(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((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
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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(x0, ts) # TODO: deterministic forward pass?
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img = img_orig * mask + (1. - mask) * img
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outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
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quantize_denoised=quantize_denoised, temperature=temperature,
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noise_dropout=noise_dropout, 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, t_next=ts_next,
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dynamic_threshold=dynamic_threshold)
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img, pred_x0, e_t = outs
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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: callback(i)
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if img_callback: img_callback(pred_x0, i)
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if index % log_every_t == 0 or index == total_steps - 1:
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intermediates['x_inter'].append(img)
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intermediates['pred_x0'].append(pred_x0)
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return img, intermediates
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@torch.no_grad()
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def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
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temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
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unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
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dynamic_threshold=None):
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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 unconditional_conditioning is None or unconditional_guidance_scale == 1.:
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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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e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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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(self.model, e_t, x, t, c, **corrector_kwargs)
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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 = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
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sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
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sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
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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((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
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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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if dynamic_threshold is not None:
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pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
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# direction pointing to x_t
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dir_xt = (1. - 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.:
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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 = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
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x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
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return x_prev, pred_x0, e_t
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