diff options
Diffstat (limited to 'modules/prompt_parser.py')
| -rw-r--r-- | modules/prompt_parser.py | 52 |
1 files changed, 36 insertions, 16 deletions
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 32d214e3..cba13455 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -2,10 +2,9 @@ from __future__ import annotations import re
from collections import namedtuple
-from typing import List
import lark
-# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
+# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][: in background:0.25] [shoddy:masterful:0.5]"
# will be represented with prompt_schedule like this (assuming steps=100):
# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
@@ -26,7 +25,7 @@ plain: /([^\\\[\]():|]|\\.)+/ %import common.SIGNED_NUMBER -> NUMBER
""")
-def get_learned_conditioning_prompt_schedules(prompts, steps):
+def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False):
"""
>>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
>>> g("test")
@@ -57,18 +56,39 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
>>> g("[fe|||]male")
[[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
+ >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]
+ >>> g("a [b:.5] c")
+ [[10, 'a b c']]
+ >>> g("a [b:1.5] c")
+ [[5, 'a c'], [10, 'a b c']]
"""
+ if hires_steps is None or use_old_scheduling:
+ int_offset = 0
+ flt_offset = 0
+ steps = base_steps
+ else:
+ int_offset = base_steps
+ flt_offset = 1.0
+ steps = hires_steps
+
def collect_steps(steps, tree):
res = [steps]
class CollectSteps(lark.Visitor):
def scheduled(self, tree):
- tree.children[-2] = float(tree.children[-2])
- if tree.children[-2] < 1:
- tree.children[-2] *= steps
- tree.children[-2] = min(steps, int(tree.children[-2]))
- res.append(tree.children[-2])
+ s = tree.children[-2]
+ v = float(s)
+ if use_old_scheduling:
+ v = v*steps if v<1 else v
+ else:
+ if "." in s:
+ v = (v - flt_offset) * steps
+ else:
+ v = (v - int_offset)
+ tree.children[-2] = min(steps, int(v))
+ if tree.children[-2] >= 1:
+ res.append(tree.children[-2])
def alternate(self, tree):
res.extend(range(1, steps+1))
@@ -86,7 +106,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): yield args[(step - 1) % len(args)]
def start(self, args):
def flatten(x):
- if type(x) == str:
+ if isinstance(x, str):
yield x
else:
for gen in x:
@@ -134,7 +154,7 @@ class SdConditioning(list): -def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
+def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):
"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
and the sampling step at which this condition is to be replaced by the next one.
@@ -154,7 +174,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps): """
res = []
- prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
+ prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)
cache = {}
for prompt, prompt_schedule in zip(prompts, prompt_schedules):
@@ -219,17 +239,17 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]): class ComposableScheduledPromptConditioning:
def __init__(self, schedules, weight=1.0):
- self.schedules: List[ScheduledPromptConditioning] = schedules
+ self.schedules: list[ScheduledPromptConditioning] = schedules
self.weight: float = weight
class MulticondLearnedConditioning:
def __init__(self, shape, batch):
self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
- self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
+ self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
-def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
+def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
For each prompt, the list is obtained by splitting the prompt using the AND separator.
@@ -238,7 +258,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
- learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
+ learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling)
res = []
for indexes in res_indexes:
@@ -257,7 +277,7 @@ class DictWithShape(dict): return self["crossattn"].shape
-def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
param = c[0][0].cond
is_dict = isinstance(param, dict)
|