diff --git a/javascript/black-orange.css b/javascript/black-orange.css
index 51ff3298a..705221c9b 100644
--- a/javascript/black-orange.css
+++ b/javascript/black-orange.css
@@ -81,7 +81,7 @@ svg.feather.feather-image, .feather .feather-image { display: none }
 #quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-left: -20px; margin-top: -2px; height: 2.4em; }
 #quicksettings > div, #quicksettings > fieldset { min-width: 26em; max-width: 26em; line-height: 2em; }
 #refresh_sd_model_checkpoint { height: 48px; margin-left: -14px; background: #333333; box-shadow: none; }
-#refresh_txt2img_styles, #refresh_img2img_styles, #open_folder_txt2img, #open_folder_img2img, #open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_res_switch_btn, #img2img_res_switch_btn, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h, #txt2img_tiling { display: none; }
+#refresh_txt2img_styles, #refresh_img2img_styles, #open_folder_txt2img, #open_folder_img2img, #open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_res_switch_btn, #img2img_res_switch_btn, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
 #save-animation { border-radius: 0 !important; margin-bottom: 16px; background-color: #111111; }
 #script_list { padding: 4px; margin-top: 20px; margin-bottom: 20px; }
 #settings > div.flex-wrap { width: 15em; }
diff --git a/modules/extras.py b/modules/extras.py
index cdfb78410..7be84ce38 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -15,18 +15,14 @@ from modules import shared, images, sd_models, sd_vae, sd_models_config
 def run_pnginfo(image):
     if image is None:
         return '', '', ''
-
     geninfo, items = images.read_info_from_image(image)
     items = {**{'parameters': geninfo}, **items}
-
     info = ''
     for key, text in items.items():
         info += f"<div><b>{html.escape(str(key))}</b>: {html.escape(str(text))}</div>"
-
     if len(info) == 0:
         message = "Nothing found in the image."
         info = f"<div><p>{message}<p></div>"
-
     return '', geninfo, info
 
 
@@ -43,13 +39,10 @@ def create_config(ckpt_result, config_source, a, b, c):
         cfg = config(c)
     else:
         cfg = None
-
     if cfg is None:
         return
-
     filename, _ = os.path.splitext(ckpt_result)
     checkpoint_filename = filename + ".yaml"
-
     shared.log.info("Copying config: {cfg} -> {checkpoint_filename}")
     shutil.copyfile(cfg, checkpoint_filename)
 
@@ -60,7 +53,6 @@ checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embedd
 def to_half(tensor, enable):
     if enable and tensor.dtype == torch.float:
         return tensor.half()
-
     return tensor
 
 
diff --git a/modules/img2img.py b/modules/img2img.py
index 893f7cabb..cceef1e66 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -11,6 +11,7 @@ from modules.memstats import memory_stats
 
 
 def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
+    shared.log.debug(f'batch: {input_dir}|{output_dir}|{inpaint_mask_dir}')
     processing.fix_seed(p)
     images = shared.listfiles(input_dir)
     is_inpaint_batch = False
@@ -68,6 +69,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     if shared.sd_model is None:
         shared.log.warning('Model not loaded')
         return
+    shared.log.debug(f'img2img: {id_task}|{mode}|{prompt}|{negative_prompt}|{prompt_styles}|{init_img}|{sketch}|{init_img_with_mask}|{inpaint_color_sketch}|{inpaint_color_sketch_orig}|{init_img_inpaint}|{init_mask_inpaint}|{steps}|{sampler_index}|{mask_blur}|{mask_alpha}|{inpainting_fill}|{restore_faces}|{tiling}|{n_iter}|{batch_size}|{cfg_scale}|{image_cfg_scale}|{denoising_strength}|{seed}|{subseed}|{subseed_strength}|{seed_resize_from_h}|{seed_resize_from_w}|{seed_enable_extras}|{selected_scale_tab}|{height}|{width}|{scale_by}|{resize_mode}|{inpaint_full_res}|{inpaint_full_res_padding}|{inpainting_mask_invert}|{img2img_batch_input_dir}|{img2img_batch_output_dir}|{img2img_batch_inpaint_mask_dir}|{override_settings_texts}')
 
     override_settings = create_override_settings_dict(override_settings_texts)
 
diff --git a/modules/processing.py b/modules/processing.py
index c37d71ccb..0f5411787 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -449,7 +449,6 @@ def fix_seed(p):
 
 def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0): # pylint: disable=unused-argument
     index = position_in_batch + iteration * p.batch_size
-
     generation_params = {
         "Steps": p.steps,
         "Sampler": p.sampler_name,
@@ -479,11 +478,8 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
         "Token merging stride y": None if opts.token_merging_stride_y == 2 else opts.token_merging_stride_y
     }
     generation_params.update(p.extra_generation_params)
-
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
-
     negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
-
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
@@ -542,17 +538,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         assert len(p.prompt) > 0
     else:
         assert p.prompt is not None
-
     devices.torch_gc()
-
     seed = get_fixed_seed(p.seed)
     subseed = get_fixed_seed(p.subseed)
-
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
-
     comments = {}
-
     if type(p.prompt) == list:
         p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
     else:
@@ -562,12 +553,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
     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(seed) == list:
         p.all_seeds = seed
     else:
         p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
-
     if type(subseed) == list:
         p.all_subseeds = subseed
     else:
@@ -578,13 +567,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     if os.path.exists(opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
-
     if p.scripts is not None:
         p.scripts.process(p)
-
     infotexts = []
     output_images = []
-
     cached_uc = [None, None]
     cached_c = [None, None]
 
@@ -598,13 +584,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         have been used before. The second element is where the previously
         computed result is stored.
         """
-
         if cache[0] is not None and (required_prompts, steps) == cache[0]:
             return cache[1]
-
         with devices.autocast():
             cache[1] = function(shared.sd_model, required_prompts, steps)
-
         cache[0] = (required_prompts, steps)
         return cache[1]
 
@@ -613,49 +596,33 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
             if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
                 sd_vae_approx.model()
-
         if state.job_count == -1:
             state.job_count = p.n_iter
-
         extra_network_data = None
         for n in range(p.n_iter):
             p.iteration = n
-
             if state.skipped:
                 state.skipped = False
-
             if state.interrupted:
                 break
-
             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]
             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]
-
             if p.scripts is not None:
                 p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
-
             if len(prompts) == 0:
                 break
-
             prompts, extra_network_data = extra_networks.parse_prompts(prompts)
-
             if not p.disable_extra_networks:
                 with devices.autocast():
                     extra_networks.activate(p, extra_network_data)
-
             if p.scripts is not None:
                 p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
-
-            # params.txt should be saved after scripts.process_batch, since the
-            # infotext could be modified by that callback
-            # Example: a wildcard processed by process_batch sets an extra model
-            # strength, which is saved as "Model Strength: 1.0" in the infotext
             if n == 0:
                 with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
                     processed = Processed(p, [], p.seed, "")
                     file.write(processed.infotext(p, 0))
-
             step_multiplier = 1
             if not shared.opts.dont_fix_second_order_samplers_schedule:
                 try:
@@ -664,17 +631,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     pass
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
-
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
                     comments[comment] = 1
-
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
-
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 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))]
             try:
                 for x in x_samples_ddim:
@@ -690,45 +653,41 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                         devices.test_for_nans(x, "vae")
                 else:
                     raise e
-
             x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-
             del samples_ddim
-
             if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
                 lowvram.send_everything_to_cpu()
-
             devices.torch_gc()
-
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
-
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
-
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
-
+                        orig = p.restore_faces
+                        p.restore_faces = False
+                        info=infotext(n, i)
+                        p.restore_faces = orig
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=info, p=p, suffix="-before-face-restoration")
                     devices.torch_gc()
-
                     x_sample = modules.face_restoration.restore_faces(x_sample)
                     devices.torch_gc()
-
                 image = Image.fromarray(x_sample)
-
                 if p.scripts is not None:
                     pp = scripts.PostprocessImageArgs(image)
                     p.scripts.postprocess_image(p, pp)
                     image = pp.image
-
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
+                        orig = p.color_corrections
+                        p.color_corrections = None
+                        info=infotext(n, i)
+                        p.color_corrections = orig
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
-                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=info, p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
                 if opts.samples_save and not p.do_not_save_samples:
@@ -878,7 +837,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 return
             if not isinstance(image, Image.Image):
                 image = sd_samplers.sample_to_image(image, index, approximation=0)
+            orig1 = self.extra_generation_params
+            orig2 = self.restore_faces
+            self.extra_generation_params = {}
+            self.restore_faces = False
             info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
+            self.extra_generation_params = orig1
+            self.restore_faces = orig2
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
 
         if latent_scale_mode is not None:
diff --git a/modules/txt2img.py b/modules/txt2img.py
index d16a0f011..73dc5698b 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -8,9 +8,12 @@ from modules.memstats import memory_stats
 
 
 def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args): # pylint: disable=unused-argument
+
     if shared.sd_model is None:
         shared.log.warning('Model not loaded')
         return
+    shared.log.debug(f'txt2img: {id_task}|{prompt}|{negative_prompt}|{prompt_styles}|{steps}|{sampler_index}|{restore_faces}|{tiling}|{n_iter}|{batch_size}|{cfg_scale}|{seed}|{subseed}|{subseed_strength}|{seed_resize_from_h}|{seed_resize_from_w}|{seed_enable_extras}|{height}|{width}|{enable_hr}|{denoising_strength}|{hr_scale}|{hr_upscaler}|{hr_second_pass_steps}|{hr_resize_x}|{hr_resize_y}|{override_settings_texts}')
+
     override_settings = create_override_settings_dict(override_settings_texts)
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
diff --git a/modules/upscaler.py b/modules/upscaler.py
index a71c22024..6ce2d17b8 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -50,6 +50,7 @@ class Upscaler:
         return img
 
     def upscale(self, img: PIL.Image, scale, selected_model: str = None):
+        shared.log.debug(f'upscale: {img}|{scale}|{selected_model}')
         self.scale = scale
         dest_w = int(img.width * scale)
         dest_h = int(img.height * scale)