put refiner into main UI, into the new accordions section

add VAE from main model into infotext, not from refiner model
option to make scripts UI without gr.Group
fix inconsistencies with refiner when usings samplers that do more denoising than steps

1 files changed, 15 insertions, 7 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 131c4c3c..5996cbac 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -373,9 +373,10 @@ class StableDiffusionProcessing:
         negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)

 

         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)

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

+        self.step_multiplier = total_steps // self.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)

 

     def get_conds(self):

         return self.c, self.uc

@@ -579,8 +580,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Size": f"{p.width}x{p.height}",

         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),

         "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),

-        "VAE hash": sd_vae.get_loaded_vae_hash() if opts.add_model_hash_to_info else None,

-        "VAE": sd_vae.get_loaded_vae_name() if opts.add_model_name_to_info else None,

+        "VAE hash": p.loaded_vae_hash if opts.add_model_hash_to_info else None,

+        "VAE": p.loaded_vae_name if opts.add_model_name_to_info else None,

         "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),

         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

         "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

@@ -669,6 +670,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.tiling is None:

         p.tiling = opts.tiling

 

+    p.loaded_vae_name = sd_vae.get_loaded_vae_name()

+    p.loaded_vae_hash = sd_vae.get_loaded_vae_hash()

+

     modules.sd_hijack.model_hijack.apply_circular(p.tiling)

     modules.sd_hijack.model_hijack.clear_comments()

 

@@ -1188,8 +1192,12 @@ 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)

+        sampler_config = sd_samplers.find_sampler_config(self.hr_sampler_name or self.sampler_name)

+        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)

 

     def setup_conds(self):

         super().setup_conds()