mirror of
https://github.com/Stability-AI/stablediffusion.git
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336 lines
17 KiB
Python
336 lines
17 KiB
Python
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"""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 ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
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class DDIMSampler(object):
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def __init__(self, model, schedule="linear", **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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def register_buffer(self, name, attr):
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if type(attr) == torch.Tensor:
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if attr.device != torch.device("cuda"):
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attr = attr.to(torch.device("cuda"))
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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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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, # 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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ucg_schedule=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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ctmp = conditioning[list(conditioning.keys())[0]]
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while isinstance(ctmp, list): ctmp = ctmp[0]
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cbs = ctmp.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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elif isinstance(conditioning, list):
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for ctmp in conditioning:
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if ctmp.shape[0] != 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 DDIM sampling is {size}, eta {eta}')
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samples, intermediates = self.ddim_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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ucg_schedule=ucg_schedule
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)
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return samples, intermediates
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@torch.no_grad()
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def ddim_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, dynamic_threshold=None,
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ucg_schedule=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 = 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 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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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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if ucg_schedule is not None:
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assert len(ucg_schedule) == len(time_range)
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unconditional_guidance_scale = ucg_schedule[i]
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outs = self.p_sample_ddim(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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dynamic_threshold=dynamic_threshold)
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img, pred_x0 = outs
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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_ddim(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,
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dynamic_threshold=None):
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b, *_, device = *x.shape, x.device
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if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
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model_output = 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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if isinstance(c, dict):
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assert isinstance(unconditional_conditioning, dict)
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c_in = dict()
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for k in c:
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if isinstance(c[k], list):
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c_in[k] = [torch.cat([
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unconditional_conditioning[k][i],
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c[k][i]]) for i in range(len(c[k]))]
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else:
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c_in[k] = torch.cat([
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unconditional_conditioning[k],
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c[k]])
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elif isinstance(c, list):
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c_in = list()
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assert isinstance(unconditional_conditioning, list)
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for i in range(len(c)):
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c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
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else:
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c_in = torch.cat([unconditional_conditioning, c])
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model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
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model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
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if self.model.parameterization == "v":
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e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
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else:
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e_t = model_output
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if score_corrector is not None:
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assert self.model.parameterization == "eps", 'not implemented'
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e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
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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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# 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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if self.model.parameterization != "v":
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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else:
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pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
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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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raise NotImplementedError()
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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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@torch.no_grad()
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def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
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unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
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num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
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assert t_enc <= num_reference_steps
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num_steps = t_enc
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if use_original_steps:
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alphas_next = self.alphas_cumprod[:num_steps]
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alphas = self.alphas_cumprod_prev[:num_steps]
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else:
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alphas_next = self.ddim_alphas[:num_steps]
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alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
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x_next = x0
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intermediates = []
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inter_steps = []
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for i in tqdm(range(num_steps), desc='Encoding Image'):
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t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
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if unconditional_guidance_scale == 1.:
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noise_pred = self.model.apply_model(x_next, t, c)
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else:
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assert unconditional_conditioning is not None
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e_t_uncond, noise_pred = torch.chunk(
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self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
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torch.cat((unconditional_conditioning, c))), 2)
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noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
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xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
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weighted_noise_pred = alphas_next[i].sqrt() * (
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(1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
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x_next = xt_weighted + weighted_noise_pred
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if return_intermediates and i % (
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num_steps // return_intermediates) == 0 and i < num_steps - 1:
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intermediates.append(x_next)
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inter_steps.append(i)
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elif return_intermediates and i >= num_steps - 2:
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intermediates.append(x_next)
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inter_steps.append(i)
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if callback: callback(i)
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out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
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if return_intermediates:
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out.update({'intermediates': intermediates})
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return x_next, out
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@torch.no_grad()
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def stochastic_encode(self, x0, t, use_original_steps=False, 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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if use_original_steps:
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sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
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|
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
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||
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|
else:
|
||
|
|
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
|
||
|
|
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
|
||
|
|
|
||
|
|
if noise is None:
|
||
|
|
noise = torch.randn_like(x0)
|
||
|
|
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
|
||
|
|
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
|
||
|
|
|
||
|
|
@torch.no_grad()
|
||
|
|
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
|
||
|
|
use_original_steps=False, callback=None):
|
||
|
|
|
||
|
|
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
|
||
|
|
timesteps = timesteps[:t_start]
|
||
|
|
|
||
|
|
time_range = np.flip(timesteps)
|
||
|
|
total_steps = timesteps.shape[0]
|
||
|
|
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
||
|
|
|
||
|
|
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
|
||
|
|
x_dec = x_latent
|
||
|
|
for i, step in enumerate(iterator):
|
||
|
|
index = total_steps - i - 1
|
||
|
|
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
|
||
|
|
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
|
||
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
||
|
|
unconditional_conditioning=unconditional_conditioning)
|
||
|
|
if callback: callback(i)
|
||
|
|
return x_dec
|