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, 12 insertions, 8 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 31745006..0750b299 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -319,12 +319,14 @@ class StableDiffusionProcessing:
         self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]

         self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]

 

-    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,

@@ -334,7 +336,7 @@ 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.

@@ -347,7 +349,7 @@ 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]:

@@ -356,7 +358,7 @@ 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]

@@ -367,8 +369,9 @@ 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 parse_extra_network_prompts(self):

         self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)

@@ -1225,8 +1228,9 @@ 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()