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410 lines
21 KiB
410 lines
21 KiB
"""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", 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.float().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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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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self.make_schedule_timesteps(ddim_timesteps, ddim_eta=ddim_eta, verbose=verbose) |
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def make_schedule_timesteps(self, ddim_timesteps, ddim_eta=0., verbose=True): |
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self.ddim_timesteps = torch.tensor(ddim_timesteps) |
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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.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_custom(self, |
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ddim_timesteps, |
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conditioning, |
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callback=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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denoise_function=None, |
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cond_concat=None, |
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to_zero=True, |
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end_step=None, |
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**kwargs |
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): |
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self.make_schedule_timesteps(ddim_timesteps=ddim_timesteps, ddim_eta=eta, verbose=verbose) |
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samples, intermediates = self.ddim_sampling(conditioning, x_T.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, 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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denoise_function=denoise_function, |
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cond_concat=cond_concat, |
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to_zero=to_zero, |
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end_step=end_step |
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) |
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return samples, intermediates |
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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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denoise_function=None, |
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cond_concat=None |
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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, denoise_function=None, cond_concat=None, to_zero=True, end_step=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 timesteps.flip(0) |
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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[:end_step], desc='DDIM Sampler', total=end_step) |
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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, denoise_function=denoise_function, cond_concat=cond_concat) |
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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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if to_zero: |
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img = pred_x0 |
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else: |
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if ddim_use_original_steps: |
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sqrt_alphas_cumprod = self.sqrt_alphas_cumprod |
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else: |
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sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) |
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img /= sqrt_alphas_cumprod[index - 1] |
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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, denoise_function=None, cond_concat=None): |
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b, *_, device = *x.shape, x.device |
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if denoise_function is not None: |
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model_output = denoise_function(self.model.apply_model, x, t, unconditional_conditioning, c, unconditional_guidance_scale, cond_concat) |
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elif 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, max_denoise=False): |
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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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else: |
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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(x0) |
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if max_denoise: |
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noise_multiplier = 1.0 |
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else: |
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noise_multiplier = extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) |
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return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + noise_multiplier * noise) |
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@torch.no_grad() |
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def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, |
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use_original_steps=False, callback=None): |
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timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps |
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timesteps = 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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print(f"Running DDIM Sampling with {total_steps} timesteps") |
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iterator = tqdm(time_range, desc='Decoding image', total=total_steps) |
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x_dec = x_latent |
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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((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) |
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x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, |
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unconditional_guidance_scale=unconditional_guidance_scale, |
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unconditional_conditioning=unconditional_conditioning) |
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if callback: callback(i) |
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return x_dec |