mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
403 lines
13 KiB
Python
403 lines
13 KiB
Python
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'''
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ldm.models.diffusion.sampler
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Base class for ldm.models.diffusion.ddim, ldm.models.diffusion.ksampler, etc
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'''
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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.dream.devices import choose_torch_device
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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 Sampler(object):
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def __init__(self, model, schedule='linear', steps=None, device=None, **kwargs):
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self.model = model
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self.ddpm_num_timesteps = steps
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self.schedule = schedule
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self.device = device or choose_torch_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.float32).to(torch.device(self.device))
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setattr(self, name, attr)
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# This method was copied over from ddim.py and probably does stuff that is
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# ddim-specific. Disentangle at some point.
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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=False,
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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 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 sample(
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self,
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S, # S is steps
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.0,
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mask=None,
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x0=None,
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temperature=1.0,
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noise_dropout=0.0,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=False,
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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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ts = self.get_timesteps(S)
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# sampling
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C, H, W = shape
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shape = (batch_size, C, H, W)
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samples, intermediates = self.do_sampling(
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conditioning,
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shape,
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timesteps=ts,
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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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steps=S,
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)
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return samples, intermediates
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#torch.no_grad()
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def do_sampling(
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self,
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cond,
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shape,
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timesteps=None,
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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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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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steps=None,
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):
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b = shape[0]
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time_range = (
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list(reversed(range(0, timesteps)))
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if ddim_use_original_steps
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else np.flip(timesteps)
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)
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total_steps=steps
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iterator = tqdm(
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time_range,
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desc=f'{self.__class__.__name__}',
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total=total_steps,
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dynamic_ncols=True,
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)
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old_eps = []
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self.prepare_to_sample(t_enc=total_steps)
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img = self.get_initial_image(x_T,shape,total_steps)
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# probably don't need this at all
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intermediates = {'x_inter': [img], 'pred_x0': [img]}
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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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(b,),
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step,
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device=self.device,
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dtype=torch.long
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)
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ts_next = torch.full(
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(b,),
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time_range[min(i + 1, len(time_range) - 1)],
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device=self.device,
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dtype=torch.long,
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)
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if mask is not None:
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assert x0 is not None
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img_orig = self.model.q_sample(
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x0, ts
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) # TODO: deterministic forward pass?
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img = img_orig * mask + (1.0 - mask) * img
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outs = self.p_sample(
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img,
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cond,
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ts,
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index=index,
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use_original_steps=ddim_use_original_steps,
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quantize_denoised=quantize_denoised,
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temperature=temperature,
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noise_dropout=noise_dropout,
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score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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old_eps=old_eps,
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t_next=ts_next,
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)
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img, pred_x0, e_t = outs
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old_eps.append(e_t)
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if len(old_eps) >= 4:
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old_eps.pop(0)
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if callback:
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callback(i)
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if img_callback:
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img_callback(img)
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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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# NOTE that decode() and sample() are almost the same code, and do the same thing.
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# The variable names are changed in order to be confusing.
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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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init_latent = None,
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mask = None,
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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 {self.__class__.__name__} 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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x0 = init_latent
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self.prepare_to_sample(t_enc=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(
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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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ts_next = torch.full(
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(x_latent.shape[0],),
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time_range[min(i + 1, len(time_range) - 1)],
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device=self.device,
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dtype=torch.long,
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)
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if mask is not None:
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assert x0 is not None
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xdec_orig = self.q_sample(x0, ts) # TODO: deterministic forward pass?
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x_dec = xdec_orig * mask + (1.0 - mask) * x_dec
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outs = self.p_sample(
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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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t_next = ts_next,
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)
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x_dec, pred_x0, e_t = outs
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if img_callback:
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img_callback(img)
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return x_dec
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def get_initial_image(self,x_T,shape,timesteps=None):
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if x_T is None:
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return torch.randn(shape, device=self.device)
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else:
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return x_T
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def p_sample(
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self,
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img,
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cond,
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ts,
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index,
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repeat_noise=False,
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use_original_steps=False,
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quantize_denoised=False,
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temperature=1.0,
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noise_dropout=0.0,
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score_corrector=None,
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corrector_kwargs=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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old_eps=None,
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t_next=None,
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||
|
|
steps=None,
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||
|
|
):
|
||
|
|
raise NotImplementedError("p_sample() must be implemented in a descendent class")
|
||
|
|
|
||
|
|
def prepare_to_sample(self,t_enc,**kwargs):
|
||
|
|
'''
|
||
|
|
Hook that will be called right before the very first invocation of p_sample()
|
||
|
|
to allow subclass to do additional initialization. t_enc corresponds to the actual
|
||
|
|
number of steps that will be run, and may be less than total steps if img2img is
|
||
|
|
active.
|
||
|
|
'''
|
||
|
|
pass
|
||
|
|
|
||
|
|
def get_timesteps(self,ddim_steps):
|
||
|
|
'''
|
||
|
|
The ddim and plms samplers work on timesteps. This method is called after
|
||
|
|
ddim_timesteps are created in make_schedule(), and selects the portion of
|
||
|
|
timesteps that will be used for sampling, depending on the t_enc in img2img.
|
||
|
|
'''
|
||
|
|
return self.ddim_timesteps[:ddim_steps]
|
||
|
|
|
||
|
|
def q_sample(self,x0,ts):
|
||
|
|
'''
|
||
|
|
Returns self.model.q_sample(x0,ts). Is overridden in the k* samplers to
|
||
|
|
return self.model.inner_model.q_sample(x0,ts)
|
||
|
|
'''
|
||
|
|
return self.model.q_sample(x0,ts)
|