mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-18 00:33:15 +00:00
salt
589 lines
22 KiB
Python
589 lines
22 KiB
Python
"""SAMPLING ONLY."""
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# CrossAttn precision handling
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import os
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import sys
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import einops
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import numpy as np
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import torch
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from tqdm import tqdm
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from deps.ControlNet.ldm.modules.diffusionmodules.util import (
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extract_into_tensor, make_ddim_sampling_parameters, make_ddim_timesteps,
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noise_like)
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_ATTN_PRECISION = os.environ.get('ATTN_PRECISION', 'fp32')
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def register_attention_control(model, controller=None):
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def ca_forward(self, place_in_unet):
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def forward(x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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is_cross = context is not None
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context = context if is_cross else x
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context = controller(context, is_cross, place_in_unet)
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k = self.to_k(context)
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v = self.to_v(context)
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q, k, v = map(
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lambda t: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h),
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(q, k, v))
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# force cast to fp32 to avoid overflowing
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if _ATTN_PRECISION == 'fp32':
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with torch.autocast(enabled=False, device_type='cuda'):
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q, k = q.float(), k.float()
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sim = torch.einsum('b i d, b j d -> b i j', q,
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k) * self.scale
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else:
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sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
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del q, k
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if mask is not None:
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mask = einops.rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = einops.repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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out = torch.einsum('b i j, b j d -> b i d', sim, v)
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out = einops.rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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return forward
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class DummyController:
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def __call__(self, *args):
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return args[0]
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def __init__(self):
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self.cur_step = 0
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if controller is None:
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controller = DummyController()
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def register_recr(net_, place_in_unet):
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if net_.__class__.__name__ == 'CrossAttention':
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net_.forward = ca_forward(net_, place_in_unet)
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elif hasattr(net_, 'children'):
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for net__ in net_.children():
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register_recr(net__, place_in_unet)
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sub_nets = model.named_children()
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for net in sub_nets:
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if 'input_blocks' in net[0]:
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register_recr(net[1], 'down')
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elif 'output_blocks' in net[0]:
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register_recr(net[1], 'up')
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elif 'middle_block' in net[0]:
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register_recr(net[1], 'mid')
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class DDIMVSampler(object):
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def __init__(self, model, schedule='linear', **kwargs):
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super().__init__()
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self.model = model
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self.ddpm_num_timesteps = model.num_timesteps
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self.schedule = schedule
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def register_buffer(self, name, attr):
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if type(attr) == torch.Tensor:
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if attr.device != torch.device('cuda'):
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attr = attr.to(torch.device('cuda'))
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setattr(self, name, attr)
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def make_schedule(self,
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ddim_num_steps,
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ddim_discretize='uniform',
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ddim_eta=0.,
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verbose=True):
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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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alphas_cumprod = self.model.alphas_cumprod
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assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, \
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'alphas have to be defined for each timestep'
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def to_torch(x):
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return x.clone().detach().to(torch.float32).to(self.model.device)
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self.register_buffer('betas', to_torch(self.model.betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev',
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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',
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to_torch(np.sqrt(alphas_cumprod.cpu())))
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self.register_buffer('sqrt_one_minus_alphas_cumprod',
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to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
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self.register_buffer('log_one_minus_alphas_cumprod',
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to_torch(np.log(1. - alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recip_alphas_cumprod',
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to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recipm1_alphas_cumprod',
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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 = \
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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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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',
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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',
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sigmas_for_original_sampling_steps)
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@torch.no_grad()
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def sample(self,
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S,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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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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xtrg=None,
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noise_rescale=None,
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temperature=1.,
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noise_dropout=0.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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unconditional_conditioning=None,
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dynamic_threshold=None,
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ucg_schedule=None,
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controller=None,
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strength=0.0,
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**kwargs):
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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):
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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'
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f'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'
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f'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]}'
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f'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(
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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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xtrg=xtrg,
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noise_rescale=noise_rescale,
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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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controller=controller,
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strength=strength,
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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,
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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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xtrg=None,
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noise_rescale=None,
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img_callback=None,
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log_every_t=100,
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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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unconditional_guidance_scale=1.,
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unconditional_conditioning=None,
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dynamic_threshold=None,
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ucg_schedule=None,
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controller=None,
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strength=0.0):
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if strength == 1 and x0 is not None:
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return x0, None
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register_attention_control(self.model.model.diffusion_model,
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controller)
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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 \
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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(
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min(timesteps / self.ddim_timesteps.shape[0], 1) *
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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(
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0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)
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total_steps = timesteps if ddim_use_original_steps \
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else timesteps.shape[0]
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print(f'Running DDIM Sampling with {total_steps} timesteps')
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iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps, file=sys.stdout)
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if controller is not None:
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controller.set_total_step(total_steps)
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if mask is None:
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mask = [None] * total_steps
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dir_xt = 0
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for i, step in enumerate(iterator):
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if controller is not None:
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controller.set_step(i)
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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 strength >= 0 and i == int(
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total_steps * strength) and x0 is not None:
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img = self.model.q_sample(x0, ts)
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if mask is not None and xtrg is not None:
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# TODO: deterministic forward pass?
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if type(mask) == list:
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weight = mask[i]
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else:
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weight = mask
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if weight is not None:
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rescale = torch.maximum(1. - weight, (1 - weight**2)**0.5 *
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controller.inner_strength)
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if noise_rescale is not None:
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rescale = (1. - weight) * (
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1 - noise_rescale) + rescale * noise_rescale
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img_ref = self.model.q_sample(xtrg, ts)
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img = img_ref * weight + (1. - weight) * (
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img - dir_xt) + rescale * dir_xt
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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(
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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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dynamic_threshold=dynamic_threshold,
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controller=controller,
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return_dir=True)
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img, pred_x0, dir_xt = 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(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.,
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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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unconditional_guidance_scale=1.,
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unconditional_conditioning=None,
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dynamic_threshold=None,
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controller=None,
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return_dir=False):
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b, *_, device = *x.shape, x.device
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if unconditional_conditioning is None or \
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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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model_t = self.model.apply_model(x, t, c)
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model_uncond = self.model.apply_model(x, t,
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unconditional_conditioning)
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model_output = model_uncond + unconditional_guidance_scale * (
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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,
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**corrector_kwargs)
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if use_original_steps:
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alphas = self.model.alphas_cumprod
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alphas_prev = self.model.alphas_cumprod_prev
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sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod
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sigmas = self.model.ddim_sigmas_for_original_num_steps
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else:
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alphas = self.ddim_alphas
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alphas_prev = self.ddim_alphas_prev
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sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas
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sigmas = 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),
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sqrt_one_minus_alphas[index],
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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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'''
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if mask is not None and xtrg is not None:
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pred_x0 = xtrg * mask + (1. - mask) * pred_x0
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'''
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if controller is not None:
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pred_x0 = controller.update_x0(pred_x0)
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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,
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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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if return_dir:
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return x_prev, pred_x0, dir_xt
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return x_prev, pred_x0
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@torch.no_grad()
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def encode(self,
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x0,
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c,
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t_enc,
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use_original_steps=False,
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return_intermediates=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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callback=None):
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timesteps = np.arange(self.ddpm_num_timesteps
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) if use_original_steps else self.ddim_timesteps
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num_reference_steps = 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], ),
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timesteps[i],
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device=self.model.device,
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dtype=torch.long)
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if unconditional_guidance_scale == 1.:
|
|
noise_pred = self.model.apply_model(x_next, t, c)
|
|
else:
|
|
assert unconditional_conditioning is not None
|
|
e_t_uncond, noise_pred = torch.chunk(
|
|
self.model.apply_model(
|
|
torch.cat((x_next, x_next)), torch.cat((t, t)),
|
|
torch.cat((unconditional_conditioning, c))), 2)
|
|
noise_pred = e_t_uncond + unconditional_guidance_scale * (
|
|
noise_pred - e_t_uncond)
|
|
xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
|
|
weighted_noise_pred = alphas_next[i].sqrt() * (
|
|
(1 / alphas_next[i] - 1).sqrt() -
|
|
(1 / alphas[i] - 1).sqrt()) * noise_pred
|
|
x_next = xt_weighted + weighted_noise_pred
|
|
if return_intermediates and i % (num_steps // return_intermediates
|
|
) == 0 and i < num_steps - 1:
|
|
intermediates.append(x_next)
|
|
inter_steps.append(i)
|
|
elif return_intermediates and i >= num_steps - 2:
|
|
intermediates.append(x_next)
|
|
inter_steps.append(i)
|
|
if callback:
|
|
callback(i)
|
|
|
|
out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
|
|
if return_intermediates:
|
|
out.update({'intermediates': intermediates})
|
|
return x_next, out
|
|
|
|
@torch.no_grad()
|
|
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
|
|
# fast, but does not allow for exact reconstruction
|
|
# t serves as an index to gather the correct alphas
|
|
if use_original_steps:
|
|
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
|
|
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
|
|
else:
|
|
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
|
|
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
|
|
|
|
if noise is None:
|
|
noise = torch.randn_like(x0)
|
|
if t >= len(sqrt_alphas_cumprod):
|
|
return noise
|
|
return (
|
|
extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
|
|
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) *
|
|
noise)
|
|
|
|
@torch.no_grad()
|
|
def decode(self,
|
|
x_latent,
|
|
cond,
|
|
t_start,
|
|
unconditional_guidance_scale=1.0,
|
|
unconditional_conditioning=None,
|
|
use_original_steps=False,
|
|
callback=None):
|
|
|
|
timesteps = np.arange(self.ddpm_num_timesteps
|
|
) if use_original_steps else self.ddim_timesteps
|
|
timesteps = timesteps[:t_start]
|
|
|
|
time_range = np.flip(timesteps)
|
|
total_steps = timesteps.shape[0]
|
|
print(f'Running DDIM Sampling with {total_steps} timesteps')
|
|
|
|
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
|
|
x_dec = x_latent
|
|
for i, step in enumerate(iterator):
|
|
index = total_steps - i - 1
|
|
ts = torch.full((x_latent.shape[0], ),
|
|
step,
|
|
device=x_latent.device,
|
|
dtype=torch.long)
|
|
x_dec, _ = self.p_sample_ddim(
|
|
x_dec,
|
|
cond,
|
|
ts,
|
|
index=index,
|
|
use_original_steps=use_original_steps,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=unconditional_conditioning)
|
|
if callback:
|
|
callback(i)
|
|
return x_dec
|
|
|
|
|
|
def calc_mean_std(feat, eps=1e-5):
|
|
# eps is a small value added to the variance to avoid divide-by-zero.
|
|
size = feat.size()
|
|
assert (len(size) == 4)
|
|
N, C = size[:2]
|
|
feat_var = feat.view(N, C, -1).var(dim=2) + eps
|
|
feat_std = feat_var.sqrt().view(N, C, 1, 1)
|
|
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
|
|
return feat_mean, feat_std
|
|
|
|
|
|
def adaptive_instance_normalization(content_feat, style_feat):
|
|
assert (content_feat.size()[:2] == style_feat.size()[:2])
|
|
size = content_feat.size()
|
|
style_mean, style_std = calc_mean_std(style_feat)
|
|
content_mean, content_std = calc_mean_std(content_feat)
|
|
|
|
normalized_feat = (content_feat -
|
|
content_mean.expand(size)) / content_std.expand(size)
|
|
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
|