|
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 |
|
|
