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from typing import Literal, Optional, Union, List
import yaml
from pathlib import Path
from pydantic import BaseModel, root_validator
import torch
import copy
ACTION_TYPES = Literal[
"erase",
"enhance",
]
# XL ใฏไบ็จฎ้กๅฟ
่ฆใชใฎใง
class PromptEmbedsXL:
text_embeds: torch.FloatTensor
pooled_embeds: torch.FloatTensor
def __init__(self, *args) -> None:
self.text_embeds = args[0]
self.pooled_embeds = args[1]
# SDv1.x, SDv2.x ใฏ FloatTensorใXL ใฏ PromptEmbedsXL
PROMPT_EMBEDDING = Union[torch.FloatTensor, PromptEmbedsXL]
class PromptEmbedsCache: # ไฝฟใใพใใใใใฎใง
prompts: dict[str, PROMPT_EMBEDDING] = {}
def __setitem__(self, __name: str, __value: PROMPT_EMBEDDING) -> None:
self.prompts[__name] = __value
def __getitem__(self, __name: str) -> Optional[PROMPT_EMBEDDING]:
if __name in self.prompts:
return self.prompts[__name]
else:
return None
class PromptSettings(BaseModel): # yaml ใฎใใค
target: str
positive: str = None # if None, target will be used
unconditional: str = "" # default is ""
neutral: str = None # if None, unconditional will be used
action: ACTION_TYPES = "erase" # default is "erase"
guidance_scale: float = 1.0 # default is 1.0
resolution: int = 512 # default is 512
dynamic_resolution: bool = False # default is False
batch_size: int = 1 # default is 1
dynamic_crops: bool = False # default is False. only used when model is XL
@root_validator(pre=True)
def fill_prompts(cls, values):
keys = values.keys()
if "target" not in keys:
raise ValueError("target must be specified")
if "positive" not in keys:
values["positive"] = values["target"]
if "unconditional" not in keys:
values["unconditional"] = ""
if "neutral" not in keys:
values["neutral"] = values["unconditional"]
return values
class PromptEmbedsPair:
target: PROMPT_EMBEDDING # not want to generate the concept
positive: PROMPT_EMBEDDING # generate the concept
unconditional: PROMPT_EMBEDDING # uncondition (default should be empty)
neutral: PROMPT_EMBEDDING # base condition (default should be empty)
guidance_scale: float
resolution: int
dynamic_resolution: bool
batch_size: int
dynamic_crops: bool
loss_fn: torch.nn.Module
action: ACTION_TYPES
def __init__(
self,
loss_fn: torch.nn.Module,
target: PROMPT_EMBEDDING,
positive: PROMPT_EMBEDDING,
unconditional: PROMPT_EMBEDDING,
neutral: PROMPT_EMBEDDING,
settings: PromptSettings,
) -> None:
self.loss_fn = loss_fn
self.target = target
self.positive = positive
self.unconditional = unconditional
self.neutral = neutral
self.guidance_scale = settings.guidance_scale
self.resolution = settings.resolution
self.dynamic_resolution = settings.dynamic_resolution
self.batch_size = settings.batch_size
self.dynamic_crops = settings.dynamic_crops
self.action = settings.action
def _erase(
self,
target_latents: torch.FloatTensor, # "van gogh"
positive_latents: torch.FloatTensor, # "van gogh"
unconditional_latents: torch.FloatTensor, # ""
neutral_latents: torch.FloatTensor, # ""
) -> torch.FloatTensor:
"""Target latents are going not to have the positive concept."""
return self.loss_fn(
target_latents,
neutral_latents
- self.guidance_scale * (positive_latents - unconditional_latents)
)
def _enhance(
self,
target_latents: torch.FloatTensor, # "van gogh"
positive_latents: torch.FloatTensor, # "van gogh"
unconditional_latents: torch.FloatTensor, # ""
neutral_latents: torch.FloatTensor, # ""
):
"""Target latents are going to have the positive concept."""
return self.loss_fn(
target_latents,
neutral_latents
+ self.guidance_scale * (positive_latents - unconditional_latents)
)
def loss(
self,
**kwargs,
):
if self.action == "erase":
return self._erase(**kwargs)
elif self.action == "enhance":
return self._enhance(**kwargs)
else:
raise ValueError("action must be erase or enhance")
def load_prompts_from_yaml(path, attributes = []):
with open(path, "r") as f:
prompts = yaml.safe_load(f)
print(prompts)
if len(prompts) == 0:
raise ValueError("prompts file is empty")
if len(attributes)!=0:
newprompts = []
for i in range(len(prompts)):
for att in attributes:
copy_ = copy.deepcopy(prompts[i])
copy_['target'] = att + ' ' + copy_['target']
copy_['positive'] = att + ' ' + copy_['positive']
copy_['neutral'] = att + ' ' + copy_['neutral']
copy_['unconditional'] = att + ' ' + copy_['unconditional']
newprompts.append(copy_)
else:
newprompts = copy.deepcopy(prompts)
print(newprompts)
print(len(prompts), len(newprompts))
prompt_settings = [PromptSettings(**prompt) for prompt in newprompts]
return prompt_settings
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