Merge branch 'shared-hires-prompt-raw' into shared-hires-prompt-test

modules/processing.py

@@ -326,12 +326,14 @@ class StableDiffusionProcessing:
         self.main_prompt = self.all_prompts[0]
         self.main_negative_prompt = self.all_negative_prompts[0]
 
-    def cached_params(self, required_prompts, steps, extra_network_data):
+    def cached_params(self, required_prompts, steps, hires_steps, extra_network_data, use_old_scheduling):
         """Returns parameters that invalidate the cond cache if changed"""
 
         return (
             required_prompts,
             steps,
+            hires_steps,
+            use_old_scheduling,
             opts.CLIP_stop_at_last_layers,
             shared.sd_model.sd_checkpoint_info,
             extra_network_data,
@@ -341,7 +343,7 @@ class StableDiffusionProcessing:
             self.height,
         )
 
-    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
+    def get_conds_with_caching(self, function, required_prompts, steps, hires_steps, caches, extra_network_data):
         """
         Returns the result of calling function(shared.sd_model, required_prompts, steps)
         using a cache to store the result if the same arguments have been used before.
@@ -354,7 +356,7 @@ class StableDiffusionProcessing:
         caches is a list with items described above.
         """
 
-        cached_params = self.cached_params(required_prompts, steps, extra_network_data)
+        cached_params = self.cached_params(required_prompts, steps, hires_steps, extra_network_data, shared.opts.use_old_scheduling)
 
         for cache in caches:
             if cache[0] is not None and cached_params == cache[0]:
@@ -363,7 +365,7 @@ class StableDiffusionProcessing:
         cache = caches[0]
 
         with devices.autocast():
-            cache[1] = function(shared.sd_model, required_prompts, steps)
+            cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
 
         cache[0] = cached_params
         return cache[1]
@@ -374,8 +376,9 @@ class StableDiffusionProcessing:
 
         sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
         self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
-        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
+        self.firstpass_steps = self.steps * self.step_multiplier
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.firstpass_steps, None, [self.cached_uc], self.extra_network_data)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.firstpass_steps, None, [self.cached_c], self.extra_network_data)
 
     def get_conds(self):
         return self.c, self.uc
@@ -1188,8 +1191,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
         hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
 
-        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
-        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+        hires_steps = (self.hr_second_pass_steps or self.steps) * self.step_multiplier
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
 
     def setup_conds(self):
         super().setup_conds()

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:

modules/shared_options.py

@@ -197,6 +197,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
     "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
     "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
     "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
+    "use_old_scheduling": OptionInfo(False, "Use old prompt where first pass and hires both used the same timeline, and < 1 meant relative and >= 1 meant absolute"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate"), {