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

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

3 files changed, 42 insertions, 16 deletions

diff --git a/modules/processing.py b/modules/processing.py
index e60cc92b..066351c1 100755..100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -407,12 +407,14 @@ 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, extra_network_data, hires_steps=None, use_old_scheduling=False):

         """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,

@@ -422,7 +424,7 @@ 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, caches, extra_network_data, hires_steps=None):

         """

         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.

@@ -435,7 +437,7 @@ 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, extra_network_data, hires_steps, shared.opts.use_old_scheduling)

 

         for cache in caches:

             if cache[0] is not None and cached_params == cache[0]:

@@ -444,7 +446,7 @@ 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]

@@ -456,6 +458,8 @@ class StableDiffusionProcessing:
         sampler_config = sd_samplers.find_sampler_config(self.sampler_name)

         total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps

         self.step_multiplier = total_steps // self.steps

+        self.firstpass_steps = total_steps

+

         self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)

         self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)

 

@@ -1292,8 +1296,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         steps = self.hr_second_pass_steps or self.steps

         total_steps = sampler_config.total_steps(steps) if sampler_config else steps

 

-        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, total_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, total_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)

+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)

+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)

 

     def setup_conds(self):

         if self.is_hr_pass:

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index e811ae99..334efeef 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:

diff --git a/modules/shared_options.py b/modules/shared_options.py
index 88f6b334..d1389838 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -203,6 +203,7 @@ 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"), {