fix conds caching with extra network

modules/extra_networks.py

@@ -32,6 +32,9 @@ class ExtraNetworkParams:
             else:
                 self.positional.append(item)
 
+    def __eq__(self, other):
+        return self.items == other.items and self.positional == other.positional and self.named == other.named
+
 
 class ExtraNetwork:
     def __init__(self, name):

modules/processing.py

@@ -171,6 +171,7 @@ class StableDiffusionProcessing:
 
         self.prompts = None
         self.negative_prompts = None
+        self.extra_network_data = None
         self.seeds = None
         self.subseeds = None
 
@@ -311,7 +312,7 @@ 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 get_conds_with_caching(self, function, required_prompts, steps, cache):
+    def get_conds_with_caching(self, function, required_prompts, steps, cache, 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.
@@ -321,21 +322,21 @@ class StableDiffusionProcessing:
         have been used before. The second element is where the previously
         computed result is stored.
         """
-        if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info) == cache[0]:
+        if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
             return cache[1]
 
         with devices.autocast():
             cache[1] = function(shared.sd_model, required_prompts, steps)
 
-        cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
+        cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
         return cache[1]
 
     def setup_conds(self):
         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, self.negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, self.cached_c)
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.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, self.prompts, self.steps * self.step_multiplier, self.cached_c, self.extra_network_data)
 
     def parse_extra_network_prompts(self):
         self.prompts, extra_network_data = extra_networks.parse_prompts(self.prompts)
@@ -681,7 +682,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         if state.job_count == -1:
             state.job_count = p.n_iter
 
-        extra_network_data = None
         for n in range(p.n_iter):
             p.iteration = n
 
@@ -702,11 +702,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(p.prompts) == 0:
                 break
 
-            extra_network_data = p.parse_extra_network_prompts()
+            p.extra_network_data = p.parse_extra_network_prompts()
 
             if not p.disable_extra_networks:
                 with devices.autocast():
-                    extra_networks.activate(p, extra_network_data)
+                    extra_networks.activate(p, p.extra_network_data)
 
             if p.scripts is not None:
                 p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
@@ -828,8 +828,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
-    if not p.disable_extra_networks and extra_network_data:
-        extra_networks.deactivate(p, extra_network_data)
+    if not p.disable_extra_networks and p.extra_network_data:
+        extra_networks.deactivate(p, p.extra_network_data)
 
     devices.torch_gc()
 
@@ -1101,8 +1101,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         super().setup_conds()
 
         if self.enable_hr:
-            self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
-            self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_c)
+            self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_uc, self.hr_extra_network_data)
+            self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_c, self.hr_extra_network_data)
 
     def parse_extra_network_prompts(self):
         res = super().parse_extra_network_prompts()