mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
418 lines
14 KiB
Python
418 lines
14 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 (
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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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extract_into_tensor,
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)
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class DDIMSampler(object):
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def __init__(self, model, schedule='linear', 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 != torch.device(self.device):
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attr = attr.to(torch.device(self.device))
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setattr(self, name, attr)
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def make_schedule(
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self,
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ddim_num_steps,
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ddim_discretize='uniform',
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ddim_eta=0.0,
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verbose=True,
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):
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self.ddim_timesteps = make_ddim_timesteps(
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ddim_discr_method=ddim_discretize,
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num_ddim_timesteps=ddim_num_steps,
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num_ddpm_timesteps=self.ddpm_num_timesteps,
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verbose=verbose,
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)
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alphas_cumprod = self.model.alphas_cumprod
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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 = (
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lambda x: x.clone()
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.detach()
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.to(torch.float32)
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.to(self.model.device)
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)
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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',
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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',
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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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'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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(
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ddim_sigmas,
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ddim_alphas,
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ddim_alphas_prev,
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) = make_ddim_sampling_parameters(
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alphacums=alphas_cumprod.cpu(),
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ddim_timesteps=self.ddim_timesteps,
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eta=ddim_eta,
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verbose=verbose,
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)
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self.register_buffer('ddim_sigmas', ddim_sigmas)
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self.register_buffer('ddim_alphas', ddim_alphas)
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self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
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self.register_buffer(
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'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
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)
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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',
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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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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=0.0,
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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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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.0,
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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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print(
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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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print(
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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=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(
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conditioning,
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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,
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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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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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)
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return samples, intermediates
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@torch.no_grad()
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def ddim_sampling(
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self,
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cond,
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shape,
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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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log_every_t=100,
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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=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 = (
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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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intermediates = {'x_inter': [img], 'pred_x0': [img]}
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time_range = (
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reversed(range(0, timesteps))
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if ddim_use_original_steps
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else np.flip(timesteps)
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)
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total_steps = (
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timesteps if ddim_use_original_steps else timesteps.shape[0]
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)
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print(f'Running DDIM Sampling with {total_steps} timesteps')
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iterator = tqdm(
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time_range,
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desc='DDIM Sampler',
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total=total_steps,
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dynamic_ncols=True,
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)
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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(
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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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outs = self.p_sample_ddim(
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img,
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cond,
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ts,
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index=index,
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use_original_steps=ddim_use_original_steps,
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quantize_denoised=quantize_denoised,
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temperature=temperature,
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noise_dropout=noise_dropout,
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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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)
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img, pred_x0 = outs
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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(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(
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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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):
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b, *_, device = *x.shape, x.device
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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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e_t = e_t_uncond + unconditional_guidance_scale * (
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e_t - e_t_uncond
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)
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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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alphas = (
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self.model.alphas_cumprod
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if use_original_steps
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else self.ddim_alphas
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)
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alphas_prev = (
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self.model.alphas_cumprod_prev
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if use_original_steps
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else self.ddim_alphas_prev
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)
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sqrt_one_minus_alphas = (
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self.model.sqrt_one_minus_alphas_cumprod
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if use_original_steps
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else self.ddim_sqrt_one_minus_alphas
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)
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sigmas = (
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self.model.ddim_sigmas_for_original_num_steps
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if use_original_steps
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else self.ddim_sigmas
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)
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# select parameters corresponding to the currently considered timestep
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
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sqrt_one_minus_at = torch.full(
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(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
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)
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# current prediction for x_0
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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if quantize_denoised:
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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 = (
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sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
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)
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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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@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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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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return (
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extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
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+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.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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img_callback=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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use_original_steps=False,
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):
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timesteps = (
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np.arange(self.ddpm_num_timesteps)
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if use_original_steps
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else self.ddim_timesteps
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)
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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(
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(x_latent.shape[0],),
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step,
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device=x_latent.device,
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dtype=torch.long,
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)
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x_dec, _ = self.p_sample_ddim(
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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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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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)
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if img_callback:
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img_callback(x_dec, i)
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return x_dec
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