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import logging
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Tuple, Union
import torch
from einops import rearrange, repeat
from ...util import append_dims, default
logpy = logging.getLogger(__name__)
class Guider(ABC):
@abstractmethod
def __call__(self, x: torch.Tensor, sigma: float) -> torch.Tensor:
pass
def prepare_inputs(
self, x: torch.Tensor, s: float, c: Dict, uc: Dict
) -> Tuple[torch.Tensor, float, Dict]:
pass
class VanillaCFG(Guider):
def __init__(self, scale: float):
self.scale = scale
def __call__(self, x: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
x_u, x_c = x.chunk(2)
x_pred = x_u + self.scale * (x_c - x_u)
return x_pred
def prepare_inputs(self, x, s, c, uc):
c_out = dict()
for k in c:
if k in ["vector", "crossattn", "concat"]:
c_out[k] = torch.cat((uc[k], c[k]), 0)
else:
assert c[k] == uc[k]
c_out[k] = c[k]
return torch.cat([x] * 2), torch.cat([s] * 2), c_out
class IdentityGuider(Guider):
def __call__(self, x: torch.Tensor, sigma: float) -> torch.Tensor:
return x
def prepare_inputs(
self, x: torch.Tensor, s: float, c: Dict, uc: Dict
) -> Tuple[torch.Tensor, float, Dict]:
c_out = dict()
for k in c:
c_out[k] = c[k]
return x, s, c_out
class LinearPredictionGuider(Guider):
def __init__(
self,
max_scale: float,
num_frames: int,
min_scale: float = 1.0,
additional_cond_keys: Optional[Union[List[str], str]] = None,
):
self.min_scale = min_scale
self.max_scale = max_scale
self.num_frames = num_frames
self.scale = torch.linspace(min_scale, max_scale, num_frames).unsqueeze(0)
additional_cond_keys = default(additional_cond_keys, [])
if isinstance(additional_cond_keys, str):
additional_cond_keys = [additional_cond_keys]
self.additional_cond_keys = additional_cond_keys
def __call__(self, x: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
x_u, x_c = x.chunk(2)
x_u = rearrange(x_u, "(b t) ... -> b t ...", t=self.num_frames)
x_c = rearrange(x_c, "(b t) ... -> b t ...", t=self.num_frames)
scale = repeat(self.scale, "1 t -> b t", b=x_u.shape[0])
scale = append_dims(scale, x_u.ndim).to(x_u.device)
return rearrange(x_u + scale * (x_c - x_u), "b t ... -> (b t) ...")
def prepare_inputs(
self, x: torch.Tensor, s: torch.Tensor, c: dict, uc: dict
) -> Tuple[torch.Tensor, torch.Tensor, dict]:
c_out = dict()
for k in c:
if k in ["vector", "crossattn", "concat"] + self.additional_cond_keys:
c_out[k] = torch.cat((uc[k], c[k]), 0)
else:
assert c[k] == uc[k]
c_out[k] = c[k]
return torch.cat([x] * 2), torch.cat([s] * 2), c_out
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