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

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

1 files changed, 10 insertions, 6 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: