hr conditioning

modules/processing.py

@@ -235,7 +235,7 @@ class StableDiffusionProcessing:
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+    def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         raise NotImplementedError()
 
     def close(self):
@@ -516,25 +516,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
 
-    # if type(p) == StableDiffusionProcessingTxt2Img:
-    #     if p.enable_hr and p.is_hr_pass:
-    #         logging.info("Running hr pass with custom prompt")
-    #         if p.hr_prompt:
-    #             if type(p.prompt) == list:
-    #                 p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
-    #             else:
-    #                 p.all_prompts = p.batch_size * p.n_iter * [
-    #                     shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
-    #             logging.info(p.all_prompts)
-    #
-    #         if p.hr_negative_prompt:
-    #             if type(p.negative_prompt) == list:
-    #                 p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
-    #                                           p.hr_negative_prompt]
-    #             else:
-    #                 p.all_negative_prompts = p.batch_size * p.n_iter * [
-    #                     shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
-    #             logging.info(p.all_negative_prompts)
+    if type(p) == StableDiffusionProcessingTxt2Img:
+        if p.enable_hr and p.is_hr_pass:
+            logging.info("Running hr pass with custom prompt")
+            if p.hr_prompt:
+                if type(p.prompt) == list:
+                    p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
+                else:
+                    p.all_hr_prompts = p.batch_size * p.n_iter * [
+                        shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
+                logging.info(p.all_prompts)
+
+            if p.hr_negative_prompt:
+                if type(p.negative_prompt) == list:
+                    p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
+                                              p.hr_negative_prompt]
+                else:
+                    p.all_hr_negative_prompts = p.batch_size * p.n_iter * [
+                        shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
+                logging.info(p.all_negative_prompts)
 
     if type(seed) == list:
         p.all_seeds = seed
@@ -607,6 +607,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
             negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+
+            if type(p) == StableDiffusionProcessingTxt2Img:
+                if p.enable_hr:
+                    hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                    hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
@@ -620,6 +626,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+            if type(p) == StableDiffusionProcessingTxt2Img:
+                if p.enable_hr:
+                    hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps,
+                                                cached_uc)
+                    hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps,
+                                               cached_c)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -629,7 +641,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
+                if type(p) == StableDiffusionProcessingTxt2Img:
+                    if p.enable_hr:
+                        samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_uconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
+                                            subseeds=subseeds,
+                                            subseed_strength=p.subseed_strength, prompts=prompts)
+                else:
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
+                                            subseeds=subseeds,
+                                            subseed_strength=p.subseed_strength, prompts=prompts)
 
             x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
             for x in x_samples_ddim:
@@ -744,6 +765,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_sampler = hr_sampler
         self.hr_prompt = hr_prompt if hr_prompt != '' else self.prompt
         self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else self.negative_prompt
+        self.all_hr_prompts = None
+        self.all_hr_negative_prompts = None
 
         if firstphase_width != 0 or firstphase_height != 0:
             self.hr_upscale_to_x = self.width
@@ -817,7 +840,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             if self.hr_upscaler is not None:
                 self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+    def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
         latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
@@ -830,9 +853,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if not self.enable_hr:
             return samples
 
-        self.prompt = self.hr_prompt
-        self.negative_prompt = self.hr_negative_prompt
-
         target_width = self.hr_upscale_to_x
         target_height = self.hr_upscale_to_y
 
@@ -904,7 +924,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+        samples = self.sampler.sample_img2img(self, samples, noise, hr_conditioning, hr_unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
 
         return samples