mirror of https://github.com/Filexor/Clip_IO.git
Add Directive mode to Clip Input
File_xor
parent
1dcc9a116a
commit
4e815714d1
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@ -1,4 +1,7 @@
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import os, csv, warnings, datetime
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import math as math
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from collections import namedtuple
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from enum import IntEnum
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import gradio
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import torch
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@ -37,8 +40,8 @@ class Clip_IO(scripts.Script):
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with gradio.Accordion("Clip input", open = False):
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with gradio.Row():
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enabled = gradio.Checkbox(label = "Enable")
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mode_positive = gradio.Dropdown(["Disabled", "Simple"], value = "Disabled", max_choices = 1, label = "Positive prompt mode")
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mode_negative = gradio.Dropdown(["Disabled", "Simple"], value = "Disabled", max_choices = 1, label = "Positive prompt mode")
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mode_positive = gradio.Dropdown(["Disabled", "Simple", "Directive"], value = "Disabled", max_choices = 1, label = "Positive prompt mode")
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mode_negative = gradio.Dropdown(["Disabled", "Simple", "Directive"], value = "Disabled", max_choices = 1, label = "Positive prompt mode")
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pass
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pass
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if not is_img2img:
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@ -132,6 +135,188 @@ class Clip_IO(scripts.Script):
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pass
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pass
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syntax_directive = r"""
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start: (FILE | PROMPT | directive | SPACE)*
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FILE: /"(?!"").+?"/ | /'(?!"").+?'/ | /[^?"'\s]+/
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PROMPT: /"{3}.*?"{3}|'{3}.*?'{3}/
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directive: "?" DIRECTIVE ("_" DIRECTIVE_ORDER)? "(" directive_inner ")"
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DIRECTIVE: (/[0-9a-zA-Z]+/ | /_(?![0-9]+\()+/)+
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DIRECTIVE_ORDER: /[0-9]+/
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directive_inner: (DIRECTIVE_PLAIN | directive_parentheses)*
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!directive_parentheses: "(" (DIRECTIVE_PLAIN | directive_parentheses)* ")"
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DIRECTIVE_PLAIN: /[^()]+/
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SPACE: /\s+/
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"""
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class Directive:
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class Names(IntEnum):
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eval
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pass
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def __init__(self, name: str, order: int, inner: str):
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self.name = name.lower()
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self.order = order
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self.inner = inner
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pass
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def __lt__(self, other) -> bool:
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if type(self) != type(other):
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raise TypeError()
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pass
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if self.order == other.order:
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if self.Names[self.name] == self.Names[other.name]:
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return True
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pass
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else:
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return self.Names[self.name] < self.Names[other.name]
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pass
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pass
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else:
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return self.order < other.order
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pass
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pass
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pass
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def get_cond_directive(model, input: str, is_negative: bool) -> torch.Tensor | None:
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conds: list[torch.tensor] = []
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dirs: list[Clip_IO.Directive] = []
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class Process(lark.Transformer):
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def FILE(self, token: lark.Token):
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cond: torch.Tensor | None = None
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filename_original = token.value
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if filename_original.startswith('"') and filename_original.endswith('"') or filename_original.startswith("'") and filename_original.endswith("'"):
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filename_original = filename_original[1:-1]
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if filename_original in Clip_IO.conditioning_cache:
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cond = Clip_IO.conditioning_cache[filename_original]
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if cond is not None:
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conds.append(cond)
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pass
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return
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pass
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filename = os.path.join(os.path.dirname(__file__), "../conditioning", filename_original)
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filename = os.path.realpath(filename)
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if filename.endswith(".csv"):
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cond = Clip_IO.load_csv_conditioning(filename)
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pass
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elif filename.endswith(".pt"):
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try:
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cond = torch.load(filename)
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pass
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except Exception:
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cond = None
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pass
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pass
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else:
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if os.path.exists(filename) and not os.path.isdir(filename):
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cond = Clip_IO.load_csv_conditioning(filename)
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if cond is None:
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try:
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cond = torch.load(filename)
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pass
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except Exception:
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cond = None
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pass
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pass
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pass
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if cond is None and os.path.exists(filename + ".csv"):
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cond = Clip_IO.load_csv_conditioning(filename + ".csv")
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pass
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if cond is None and not os.path.exists(filename + ".csv") and os.path.exists(filename + ".pt"):
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try:
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cond = torch.load(filename + ".pt")
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pass
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except Exception:
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cond = None
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pass
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pass
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pass
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if cond is not None:
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conds.append(cond.to(devices.device))
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pass
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Clip_IO.conditioning_cache[filename_original] = cond
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pass
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def PROMPT(self, token: lark.Token):
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string = token.value
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if string.startswith('"""') and string.endswith('"""') or string.startswith("'''") and string.endswith("'''"):
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string = string[3:-3]
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pass
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conds.append(model.get_learned_conditioning([string])[0].to(devices.device))
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pass
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def directive(self, args: list[lark.Token | lark.Tree]):
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def flatten(arg: lark.Token | lark.Tree | list[lark.Token | lark.Tree]) -> str | lark.Token:
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if type(arg) == lark.Token:
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return arg.value
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pass
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elif type(arg) == lark.Tree:
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array = ""
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for child in arg.children:
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array += flatten(child)
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pass
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return array
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pass
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elif type(arg) == list:
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array = ""
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for component in arg:
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array += flatten(component)
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pass
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return array
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pass
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else:
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return arg
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pass
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pass
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dirs.append(Clip_IO.Directive(args[0], args[1] if len(args) == 3 else 0, flatten(args[-1])))
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pass
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pass
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Process().transform(lark.Lark(Clip_IO.syntax_directive).parse(input))
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i = torch.vstack(conds)
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o = i.clone()
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dirs.sort()
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for dir in dirs:
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if dir.name == "eval":
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try:
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for t in range(i.shape[0]):
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for d in range(i.shape[1]):
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local = {"i": i, "o": o, "t": t, "d": d, "torch": torch.__dict__} | math.__dict__
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o[t, d] = eval(dir.inner, None, local)
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pass
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pass
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pass
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except Exception as e:
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print(repr(e))
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o = i
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pass
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finally:
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i = o.clone()
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pass
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elif dir.name == "exec":
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try:
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local = {"i": i, "o": o, "torch": torch.__dict__} | math.__dict__
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exec(dir.inner, None, local)
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except Exception as e:
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print(repr(e))
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o = i
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pass
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finally:
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i = o.clone()
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pass
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else:
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warnings.warn(f'Directive "{dir.name}" does not exist.')
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pass
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pass
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cond = o
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if cond is not None and cond.shape[0] > 0 and (cond.shape[1] == 768 or cond.shape[1] == 1024):
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return cond.to(devices.device)
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pass
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else:
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warnings.warn(f"{'Negative prompt' if is_negative else 'Positive prompt'} is empty. Retrieving conditioning for empty string.")
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return model.get_learned_conditioning([""])[0]
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pass
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pass
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def my_get_learned_conditioning(model, prompts, steps, is_negative = True):
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"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
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and the sampling step at which this condition is to be replaced by the next one.
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@ -150,11 +335,17 @@ class Clip_IO(scripts.Script):
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]
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]
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"""
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if Clip_IO.enabled and (Clip_IO.mode_positive == "Directive" and not is_negative or Clip_IO.mode_negative == "Directive" and is_negative):
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# TODO: Implement own parser
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prompt_schedules = [[[steps, prompt]] for prompt in prompts]
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pass
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else:
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prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompts, steps)
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pass
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res = []
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prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompts, steps)
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cache = {}
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for prompt, prompt_schedule in zip(prompts, prompt_schedules):
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cached = cache.get(prompt, None)
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@ -163,10 +354,17 @@ class Clip_IO(scripts.Script):
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continue
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texts: list[str] = [x[1] for x in prompt_schedule]
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if Clip_IO.mode_positive == "Simple" and not is_negative or Clip_IO.mode_negative == "Simple" and is_negative:
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if Clip_IO.enabled and (Clip_IO.mode_positive == "Simple" and not is_negative or Clip_IO.mode_negative == "Simple" and is_negative):
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conds = []
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for text in texts:
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conds.append(Clip_IO.get_cond_simple(model, text, is_negative))
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pass
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pass
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elif Clip_IO.enabled and (Clip_IO.mode_positive == "Directive" and not is_negative or Clip_IO.mode_negative == "Directive" and is_negative):
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conds = []
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for text in texts:
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conds.append(Clip_IO.get_cond_directive(model, text, is_negative))
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pass
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pass
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else:
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conds = model.get_learned_conditioning(texts)
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