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prevent calculating cons for second pass of hires fix when they are the same as for the first pass

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AUTOMATIC 2023-06-04 16:29:02 +03:00
parent 1ca5e76f7b
commit fbf88343de
1 changed files with 13 additions and 7 deletions
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modules/processing.py
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@ -312,7 +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_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] 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, extra_network_data): def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
""" """
Returns the result of calling function(shared.sd_model, required_prompts, steps) 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. using a cache to store the result if the same arguments have been used before.
@ -321,9 +321,15 @@ class StableDiffusionProcessing:
representing the previously used arguments, or None if no arguments representing the previously used arguments, or None if no arguments
have been used before. The second element is where the previously have been used before. The second element is where the previously
computed result is stored. computed result is stored.
caches is a list with items described above.
""" """
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] for cache in caches:
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]
cache = caches[0]
with devices.autocast(): with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps) cache[1] = function(shared.sd_model, required_prompts, steps)
@ -335,8 +341,8 @@ class StableDiffusionProcessing:
sampler_config = sd_samplers.find_sampler_config(self.sampler_name) 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.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.extra_network_data) 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) 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): def parse_extra_network_prompts(self):
self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts) self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
@ -1106,8 +1112,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.hr_c is not None: if self.hr_c is not None:
return return
self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_hr_uc, self.hr_extra_network_data) self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.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, self.hr_prompts, self.steps * self.step_multiplier, self.cached_hr_c, 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_hr_c, self.cached_c], self.hr_extra_network_data)
def setup_conds(self): def setup_conds(self):
super().setup_conds() super().setup_conds()