i

extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py

@@ -29,9 +29,7 @@ class ControlNetExampleForge(scripts.Script):
 
         return input_image, funny_slider
 
-    def process_batch(self, p, *script_args, **kwargs):
-        # This function will be called every batch. Use your own way to cache.
-
+    def process(self, p, *script_args, **kwargs):
         input_image, funny_slider = script_args
 
         # This slider does nothing. It just shows you how to transfer parameters.
@@ -40,8 +38,6 @@ class ControlNetExampleForge(scripts.Script):
         if input_image is None:
             return
 
-        print('Input image is read.')
-
         model_dir = os.path.join(models_path, 'ControlNet')
         os.makedirs(model_dir, exist_ok=True)
         controlnet_canny_path = load_file_from_url(
@@ -54,10 +50,25 @@ class ControlNetExampleForge(scripts.Script):
         controlnet = load_controlnet(controlnet_canny_path)
         print('Controlnet loaded.')
 
-        input_image = cv2.resize(input_image, (p.height, p.width))
+        return
+
+    def process_before_every_sampling(self, p, *script_args, **kwargs):
+        # This will be called before every sampling.
+        # If you use highres fix, this will be called twice.
+
+        input_image, funny_slider = script_args
+
+        if input_image is None:
+            return
+
+        B, C, H, W = kwargs['noise'].shape  # latent_shape
+        height = H * 8
+        width = W * 8
+
+        input_image = cv2.resize(input_image, (height, width))
         canny_image = cv2.Canny(input_image, 100, 200)
 
-        # Display preprocessor result. Called every batch. Cache in your own way.
+        # Display preprocessor result. Called every sampling. Cache in your own way.
         p.extra_result_images.append(canny_image)
 
         print('Preprocessor Canny finished.')

modules/processing.py

@@ -1247,6 +1247,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             # here we generate an image normally
 
             x = self.rng.next()
+
+            if self.scripts is not None:
+                self.scripts.process_before_every_sampling(self,
+                                                           x=x,
+                                                           noise=x,
+                                                           c=conditioning,
+                                                           uc=unconditional_conditioning)
+
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             del x
 
@@ -1348,6 +1356,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if self.scripts is not None:
             self.scripts.before_hr(self)
 
+        if self.scripts is not None:
+            self.scripts.process_before_every_sampling(self,
+                                                       x=samples,
+                                                       noise=noise,
+                                                       c=self.hr_c,
+                                                       uc=self.hr_uc)
+
         samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
 
         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
@@ -1651,6 +1666,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
             x *= self.initial_noise_multiplier
 
+        if self.scripts is not None:
+            self.scripts.process_before_every_sampling(self,
+                                                       x=self.init_latent,
+                                                       noise=x,
+                                                       c=conditioning,
+                                                       uc=unconditional_conditioning)
+
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
         if self.mask is not None:

modules/scripts.py

@@ -186,6 +186,14 @@ class Script:
         """
         pass
 
+    def process_before_every_sampling(self, p, *args, **kwargs):
+        """
+        Similar to process(), called before every sampling.
+        If you use high-res fix, this will be called two times.
+        """
+
+        pass
+
     def process_batch(self, p, *args, **kwargs):
         """
         Same as process(), but called for every batch.
@@ -809,6 +817,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running process_batch: {script.filename}", exc_info=True)
 
+    def process_before_every_sampling(self, p, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.process_before_every_sampling(p, *script_args, **kwargs)
+            except Exception:
+                errors.report(f"Error running process_before_every_sampling: {script.filename}", exc_info=True)
+
     def postprocess(self, p, processed):
         for script in self.alwayson_scripts:
             try: