allow first pass and hires pass to use a single prompt to do different prompt editing, hires is 1.0..2.0:

  relative time range is [1..2]
  absolute time range is [steps+1..steps+hire_steps], e.g. with 30 steps and 20 hires steps, '20' is 2/3rds through first pass, and 40 is halfway through hires pass

1 files changed, 31 insertions, 10 deletions

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 32d214e3..e8c41f38 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -26,7 +26,7 @@ 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 +57,39 @@ 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))

@@ -134,7 +155,7 @@ 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 +175,7 @@ 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):

@@ -229,7 +250,7 @@ class MulticondLearnedConditioning:
         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 +259,7 @@ 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: