do not unnecessarily run VAE one more time when saving intermediate image with hires fix

Author: AUTOMATIC1111

modules/processing.py

@@ -199,7 +199,7 @@ def img2img_image_conditioning(self, source_image, latent_image, image_mask = No
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         raise NotImplementedError()
 
     def close(self):
@@ -521,11 +521,7 @@ def infotext(iteration=0, position_in_batch=0):
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. 
-                if isinstance(p, StableDiffusionProcessingTxt2Img):
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n)
-                else:
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
@@ -653,7 +649,7 @@ def init(self, all_prompts, all_seeds, all_subseeds):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
@@ -666,9 +662,21 @@ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subs
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
+        """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+        def save_intermediate(image, index):
+            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
+                return
+
+            if not isinstance(image, Image.Image):
+                image = sd_samplers.sample_to_image(image, index)
+
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -678,6 +686,9 @@ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subs
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
                 image = Image.fromarray(x_sample)
+
+                save_intermediate(image, i)
+
                 image = images.resize_image(0, image, self.width, self.height)
                 image = np.array(image).astype(np.float32) / 255.0
                 image = np.moveaxis(image, 2, 0)
@@ -689,15 +700,6 @@ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subs
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
-        # Save a copy of the image/s before doing highres fix, if applicable.
-        if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix:
-            for i in range(self.batch_size):
-                # This batch's ith image.
-                img = sd_samplers.sample_to_image(samples, i)
-                # Index that accounts for both batch size and batch count.
-                ind = i + self.batch_size*n
-                images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix")
-
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -844,8 +846,7 @@ def init(self, all_prompts, all_seeds, all_subseeds):
 
         self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
@@ -856,4 +857,4 @@ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subs
         del x
         devices.torch_gc()
 
-        return samples
+        return samples

modules/sd_samplers.py

@@ -96,6 +96,7 @@ def single_sample_to_image(sample):
 def sample_to_image(samples, index=0):
     return single_sample_to_image(samples[index])
 
+
 def samples_to_image_grid(samples):
     return images.image_grid([single_sample_to_image(sample) for sample in samples])
 

modules/shared.py

@@ -256,6 +256,7 @@ def options_section(section_identifier, options_dict):
     "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
     "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
+    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
@@ -322,7 +323,6 @@ def options_section(section_identifier, options_dict):
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),

scripts/img2imgalt.py

@@ -166,8 +166,7 @@ def run(self, p, _, override_sampler, override_prompt, original_prompt, original
         if override_strength:
             p.denoising_strength = 1.0
 
-
-        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
             same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \