Merge pull request #12457 from rubberbaron/shared-hires-prompt-test

prompt editing timeline has separate range for first pass and hires-fix pass

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: