diff --git a/javascript/ui.js b/javascript/ui.js
index f3860d2e..4944800b 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -35,13 +35,36 @@ function extract_image_from_gallery_extras(gallery){
     return extract_image_from_gallery(gallery);
 }
 
-function submit(){
-    // this calls a function from progressbar.js
-    requestProgress()
+function get_tab_index(tabId){
+    var res = 0
 
+    gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){
+        if(button.className.indexOf('bg-white') != -1)
+            res = i
+    })
+
+    return res
+}
+
+function create_tab_index_args(tabId, args){
+    var res = []
+    for(var i=0; i<args.length; i++){
+        res.push(args[i])
+    }
+
+    res[0] = get_tab_index(tabId)
+
+    return res
+}
+
+function get_extras_tab_index(){
+    return create_tab_index_args('mode_extras', arguments)
+}
+
+function create_submit_args(args){
     res = []
-    for(var i=0;i<arguments.length;i++){
-        res.push(arguments[i])
+    for(var i=0;i<args.length;i++){
+        res.push(args[i])
     }
 
     // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
@@ -55,11 +78,30 @@ function submit(){
     return res
 }
 
+function submit(){
+    requestProgress()
+
+    return create_submit_args(arguments)
+}
+
+function submit_img2img(){
+    requestProgress()
+
+    res = create_submit_args(arguments)
+
+    res[0] = get_tab_index('mode_img2img')
+
+    return res
+}
+
+
 function ask_for_style_name(_, prompt_text, negative_prompt_text) {
     name_ = prompt('Style name:')
     return name_ === null ? [null, null, null]: [name_, prompt_text, negative_prompt_text]
 }
 
+
+
 opts = {}
 function apply_settings(jsdata){
     console.log(jsdata)
diff --git a/modules/extras.py b/modules/extras.py
index 96bd2118..92569cff 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -15,30 +15,22 @@ import piexif.helper
 cached_images = {}
 
 
-def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
 
-    if image_folder is not None:
-        if image is not None:
-            print("Batch detected and single image detected, please only use one of the two. Aborting.")
-            return None
+    if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
             image = Image.fromarray(np.array(Image.open(img)))
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
-
-    elif image is not None:
-        if image_folder is not None:
-            print("Batch detected and single image detected, please only use one of the two. Aborting.")
-            return None
-        else:
-            imageArr.append(image)
-            imageNameArr.append(None)
+    else:
+        imageArr.append(image)
+        imageNameArr.append(None)
 
     outpath = opts.outdir_samples or opts.outdir_extras_samples
 
diff --git a/modules/img2img.py b/modules/img2img.py
index 6328f9d6..91689232 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -1,4 +1,8 @@
 import math
+import os
+import sys
+import traceback
+
 import numpy as np
 from PIL import Image, ImageOps, ImageChops
 
@@ -11,9 +15,45 @@ from modules.ui import plaintext_to_html
 import modules.images as images
 import modules.scripts
 
-def img2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_mask, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
+
+def process_batch(p, input_dir, output_dir, args):
+    processing.fix_seed(p)
+
+    images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+
+    print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
+
+    p.do_not_save_grid = True
+    p.do_not_save_samples = True
+
+    state.job_count = len(images) * p.n_iter
+
+    for i, image in enumerate(images):
+        state.job = f"{i+1} out of {len(images)}"
+
+        if state.interrupted:
+            break
+
+        img = Image.open(image)
+        p.init_images = [img] * p.batch_size
+
+        proc = modules.scripts.scripts_img2img.run(p, *args)
+        if proc is None:
+            proc = process_images(p)
+
+        for n, processed_image in enumerate(proc.images):
+            filename = os.path.basename(image)
+
+            if n > 0:
+                left, right = os.path.splitext(filename)
+                filename = f"{left}-{n}{right}"
+
+            processed_image.save(os.path.join(output_dir, filename))
+
+
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
-    is_upscale = mode == 2
+    is_batch = mode == 2
 
     if is_inpaint:
         if mask_mode == 0:
@@ -23,8 +63,8 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
             mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
             image = image.convert('RGB')
         else:
-            image = init_img
-            mask = init_mask
+            image = init_img_inpaint
+            mask = init_mask_inpaint
     else:
         image = init_img
         mask = None
@@ -60,79 +100,19 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         resize_mode=resize_mode,
         denoising_strength=denoising_strength,
         inpaint_full_res=inpaint_full_res,
+        inpaint_full_res_padding=inpaint_full_res_padding,
         inpainting_mask_invert=inpainting_mask_invert,
     )
     print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
     p.extra_generation_params["Mask blur"] = mask_blur
 
-    if is_upscale:
-        initial_info = None
-
-        processing.fix_seed(p)
-        seed = p.seed
-
-        upscaler = shared.sd_upscalers[upscaler_index]
-        img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
-
-        devices.torch_gc()
-
-        grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
-
-        batch_size = p.batch_size
-        upscale_count = p.n_iter
-        p.n_iter = 1
-        p.do_not_save_grid = True
-        p.do_not_save_samples = True
-
-        work = []
-
-        for y, h, row in grid.tiles:
-            for tiledata in row:
-                work.append(tiledata[2])
-
-        batch_count = math.ceil(len(work) / batch_size)
-        state.job_count = batch_count * upscale_count
-
-        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
-
-        result_images = []
-        for n in range(upscale_count):
-            start_seed = seed + n
-            p.seed = start_seed
-
-            work_results = []
-            for i in range(batch_count):
-                p.batch_size = batch_size
-                p.init_images = work[i*batch_size:(i+1)*batch_size]
-
-                state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
-                processed = process_images(p)
-
-                if initial_info is None:
-                    initial_info = processed.info
-
-                p.seed = processed.seed + 1
-                work_results += processed.images
-
-            image_index = 0
-            for y, h, row in grid.tiles:
-                for tiledata in row:
-                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
-                    image_index += 1
-
-            combined_image = images.combine_grid(grid)
-            result_images.append(combined_image)
-
-            if opts.samples_save:
-                images.save_image(combined_image, p.outpath_samples, "", start_seed, prompt, opts.samples_format, info=initial_info, p=p)
-
-        processed = Processed(p, result_images, seed, initial_info)
+    if is_batch:
+        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, args)
 
+        processed = Processed(p, [], p.seed, "")
     else:
-
         processed = modules.scripts.scripts_img2img.run(p, *args)
-
         if processed is None:
             processed = process_images(p)
 
diff --git a/modules/processing.py b/modules/processing.py
index e844b7e2..f44c3f26 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -491,7 +491,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpainting_mask_invert=0, **kwargs):
+    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -505,6 +505,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.mask_blur = mask_blur
         self.inpainting_fill = inpainting_fill
         self.inpaint_full_res = inpaint_full_res
+        self.inpaint_full_res_padding = inpaint_full_res_padding
         self.inpainting_mask_invert = inpainting_mask_invert
         self.mask = None
         self.nmask = None
@@ -527,7 +528,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.inpaint_full_res:
                 self.mask_for_overlay = self.image_mask
                 mask = self.image_mask.convert('L')
-                crop_region = masking.get_crop_region(np.array(mask), opts.upscale_at_full_resolution_padding)
+                crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
                 crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
                 x1, y1, x2, y2 = crop_region
 
diff --git a/modules/scripts.py b/modules/scripts.py
index 0e0b949b..9ab207dc 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -143,7 +143,6 @@ class ScriptRunner:
 
         return inputs
 
-
     def run(self, p: StableDiffusionProcessing, *args):
         script_index = args[0]
 
diff --git a/modules/shared.py b/modules/shared.py
index 0a852838..841a9877 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -147,14 +147,13 @@ class Options:
         "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
         "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
         "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-        "realesrgan_enabled_models": OptionInfo(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"],"Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
+        "realesrgan_enabled_models": OptionInfo(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"], "Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
         "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
         "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
         "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
         "ldsr_pre_down":OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
         "ldsr_post_down":OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
         "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-        "upscale_at_full_resolution_padding": OptionInfo(16, "Inpainting at full resolution: padding, in pixels, for the masked region.", gr.Slider, {"minimum": 0, "maximum": 128, "step": 4}),
         "upscaler_for_hires_fix": OptionInfo(None, "Upscaler for highres. fix", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
         "show_progressbar": OptionInfo(True, "Show progressbar"),
         "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
diff --git a/modules/ui.py b/modules/ui.py
index 572dcfb9..3c898bd2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -527,36 +527,47 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 progressbar = gr.HTML(elem_id="progressbar")
                 img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                 setup_progressbar(progressbar, img2img_preview)
-        
+
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-                with gr.Group():
-                    switch_mode = gr.Radio(label='Mode', elem_id="img2img_mode", choices=['Redraw whole image', 'Inpaint a part of image', 'SD upscale'], value='Redraw whole image', type="index", show_label=False)
-                    init_img = gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil")
-                    init_img_with_mask = gr.Image(label="Image for inpainting with mask", elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", visible=False, image_mode="RGBA")
-                    init_mask = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False)
-                    init_img_with_mask_comment = gr.HTML(elem_id="mask_bug_info", value="<small>if the editor shows ERROR, switch to another tab and back, then to another img2img mode above and back</small>", visible=False)
 
-                    with gr.Row():
-                        resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-                        mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask")
+                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
+                    with gr.TabItem('img2img'):
+                        init_img = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil")
+
+                    with gr.TabItem('Inpaint'):
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
+                        init_img_with_mask_comment = gr.HTML(elem_id="mask_bug_info", value="<small>if the editor shows ERROR, switch to another tab and back, then to \"Upload mask\" mode above and back</small>")
+
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False)
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False)
+
+                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+
+                        with gr.Row():
+                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask")
+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
+
+                        with gr.Row():
+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+
+                    with gr.TabItem('Batch img2img'):
+                        gr.HTML("<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.</p>")
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory")
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory")
+
+                with gr.Row():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
 
                 steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
-                inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
-
-                with gr.Row():
-                    inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False, visible=False)
-                    inpainting_mask_invert = gr.Radio(label='Masking mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", visible=False)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
                     tiling = gr.Checkbox(label='Tiling', value=False)
-                    sd_upscale_overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
-
-                with gr.Row():
-                    sd_upscale_upscaler_name = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", visible=False)
 
                 with gr.Row():
                     batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
@@ -589,7 +600,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                         img2img_send_to_extras = gr.Button('Send to extras')
                         img2img_save_style = gr.Button('Save prompt as style')
 
-
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
@@ -597,70 +607,36 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
-            def apply_mode(mode, uploadmask):
-                is_classic = mode == 0
-                is_inpaint = mode == 1
-                is_upscale = mode == 2
-
-                return {
-                    init_img: gr_show(not is_inpaint or (is_inpaint and uploadmask == 1)),
-                    init_img_with_mask: gr_show(is_inpaint and uploadmask == 0),
-                    init_img_with_mask_comment: gr_show(is_inpaint and uploadmask == 0),
-                    init_mask: gr_show(is_inpaint and uploadmask == 1),
-                    mask_mode: gr_show(is_inpaint),
-                    mask_blur: gr_show(is_inpaint),
-                    inpainting_fill: gr_show(is_inpaint),
-                    sd_upscale_upscaler_name: gr_show(is_upscale),
-                    sd_upscale_overlap: gr_show(is_upscale),
-                    inpaint_full_res: gr_show(is_inpaint),
-                    inpainting_mask_invert: gr_show(is_inpaint),
-                    img2img_interrogate: gr_show(not is_inpaint),
-                }
-
-            switch_mode.change(
-                apply_mode,
-                inputs=[switch_mode, mask_mode],
+            mask_mode.change(
+                lambda mode, img: {
+                    #init_img_with_mask: gr.Image.update(visible=mode == 0, value=img["image"]),
+                    init_img_with_mask: gr_show(mode == 0),
+                    init_img_with_mask_comment: gr_show(mode == 0),
+                    init_img_inpaint: gr_show(mode == 1),
+                    init_mask_inpaint: gr_show(mode == 1),
+                },
+                inputs=[mask_mode, init_img_with_mask],
                 outputs=[
-                    init_img,
                     init_img_with_mask,
                     init_img_with_mask_comment,
-                    init_mask,
-                    mask_mode,
-                    mask_blur,
-                    inpainting_fill,
-                    sd_upscale_upscaler_name,
-                    sd_upscale_overlap,
-                    inpaint_full_res,
-                    inpainting_mask_invert,
-                    img2img_interrogate,
-                ]
-            )
-
-            mask_mode.change(
-                lambda mode: {
-                    init_img: gr_show(mode == 1),
-                    init_img_with_mask: gr_show(mode == 0),
-                    init_mask: gr_show(mode == 1),
-                },
-                inputs=[mask_mode],
-                outputs=[
-                    init_img,
-                    init_img_with_mask,
-                    init_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
                 ],
             )
 
             img2img_args = dict(
                 fn=img2img,
-                _js="submit",
+                _js="submit_img2img",
                 inputs=[
+                    dummy_component,
                     img2img_prompt,
                     img2img_negative_prompt,
                     img2img_prompt_style,
                     img2img_prompt_style2,
                     init_img,
                     init_img_with_mask,
-                    init_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
                     mask_mode,
                     steps,
                     sampler_index,
@@ -668,7 +644,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     inpainting_fill,
                     restore_faces,
                     tiling,
-                    switch_mode,
                     batch_count,
                     batch_size,
                     cfg_scale,
@@ -678,10 +653,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     height,
                     width,
                     resize_mode,
-                    sd_upscale_upscaler_name,
-                    sd_upscale_overlap,
                     inpaint_full_res,
+                    inpaint_full_res_padding,
                     inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
                 ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
@@ -748,7 +724,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-                with gr.Tabs():
+                with gr.Tabs(elem_id="mode_extras"):
                     with gr.TabItem('Single Image'):
                         image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
 
@@ -778,9 +754,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 html_info_x = gr.HTML()
                 html_info = gr.HTML()
 
-        extras_args = dict(
+        submit.click(
             fn=run_extras,
+            _js="get_extras_tab_index",
             inputs=[
+                dummy_component,
                 image,
                 image_batch,
                 gfpgan_visibility,
@@ -798,8 +776,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             ]
         )
 
-        submit.click(**extras_args)
-
     pnginfo_interface = gr.Interface(
         wrap_gradio_call(run_pnginfo),
         inputs=[
@@ -929,6 +905,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             outputs=[init_img_with_mask],
         )
 
+        tabs_img2img_mode.change(
+            fn=lambda x: x,
+            inputs=[init_img_with_mask],
+            outputs=[init_img_with_mask],
+        )
+
         send_to_img2img.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_img2img",
diff --git a/scripts/batch.py b/scripts/batch.py
deleted file mode 100644
index 1af4a7bc..00000000
--- a/scripts/batch.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import math
-import os
-import sys
-import traceback
-
-import modules.scripts as scripts
-import gradio as gr
-
-from modules.processing import Processed, process_images
-from PIL import Image
-from modules.shared import opts, cmd_opts, state
-
-
-class Script(scripts.Script):
-    def title(self):
-        return "Batch processing"
-
-    def show(self, is_img2img):
-        return is_img2img
-
-    def ui(self, is_img2img):
-        input_dir = gr.Textbox(label="Input directory", lines=1)
-        output_dir = gr.Textbox(label="Output directory", lines=1)
-
-        return [input_dir, output_dir]
-
-    def run(self, p, input_dir, output_dir):
-        images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
-
-        batch_count = math.ceil(len(images) / p.batch_size)
-        print(f"Will process {len(images)} images in {batch_count} batches.")
-
-        p.batch_count = 1
-        p.do_not_save_grid = True
-        p.do_not_save_samples = True
-
-        state.job_count = batch_count
-
-        for batch_no in range(batch_count):
-            batch_images = []
-            for path in images[batch_no*p.batch_size:(batch_no+1)*p.batch_size]:
-                try:
-                    img = Image.open(path)
-                    batch_images.append((img, path))
-                except:
-                    print(f"Error processing {path}:", file=sys.stderr)
-                    print(traceback.format_exc(), file=sys.stderr)
-
-            if len(batch_images) == 0:
-                continue
-
-            state.job = f"{batch_no} out of {batch_count}: {batch_images[0][1]}"
-            p.init_images = [x[0] for x in batch_images]
-            proc = process_images(p)
-            for image, (_, path) in zip(proc.images, batch_images):
-                filename = os.path.basename(path)
-                image.save(os.path.join(output_dir, filename))
-
-        return Processed(p, [], p.seed, "")
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
new file mode 100644
index 00000000..b87a145b
--- /dev/null
+++ b/scripts/sd_upscale.py
@@ -0,0 +1,93 @@
+import math
+
+import modules.scripts as scripts
+import gradio as gr
+from PIL import Image
+
+from modules import processing, shared, sd_samplers, images, devices
+from modules.processing import Processed
+from modules.shared import opts, cmd_opts, state
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "SD upscale"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")
+        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
+        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", visible=False)
+
+        return [info, overlap, upscaler_index]
+
+    def run(self, p, _, overlap, upscaler_index):
+        processing.fix_seed(p)
+        upscaler = shared.sd_upscalers[upscaler_index]
+
+        p.extra_generation_params["SD upscale overlap"] = overlap
+        p.extra_generation_params["SD upscale upscaler"] = upscaler.name
+
+        initial_info = None
+        seed = p.seed
+
+        init_img = p.init_images[0]
+        img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
+
+        devices.torch_gc()
+
+        grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)
+
+        batch_size = p.batch_size
+        upscale_count = p.n_iter
+        p.n_iter = 1
+        p.do_not_save_grid = True
+        p.do_not_save_samples = True
+
+        work = []
+
+        for y, h, row in grid.tiles:
+            for tiledata in row:
+                work.append(tiledata[2])
+
+        batch_count = math.ceil(len(work) / batch_size)
+        state.job_count = batch_count * upscale_count
+
+        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
+
+        result_images = []
+        for n in range(upscale_count):
+            start_seed = seed + n
+            p.seed = start_seed
+
+            work_results = []
+            for i in range(batch_count):
+                p.batch_size = batch_size
+                p.init_images = work[i*batch_size:(i+1)*batch_size]
+
+                state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
+                processed = processing.process_images(p)
+
+                if initial_info is None:
+                    initial_info = processed.info
+
+                p.seed = processed.seed + 1
+                work_results += processed.images
+
+            image_index = 0
+            for y, h, row in grid.tiles:
+                for tiledata in row:
+                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
+                    image_index += 1
+
+            combined_image = images.combine_grid(grid)
+            result_images.append(combined_image)
+
+            if opts.samples_save:
+                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
+
+        processed = Processed(p, result_images, seed, initial_info)
+
+        return processed
diff --git a/style.css b/style.css
index 655a00e5..de5f94a6 100644
--- a/style.css
+++ b/style.css
@@ -97,6 +97,11 @@
     background: transparent;
 }
 
+.my-4{
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
 #toprow div{
     border: none;
     gap: 0;
@@ -198,7 +203,8 @@ input[type="range"]{
 #mask_bug_info {
   text-align: center;
   display: block;
-  margin-bottom: 0.5em;
+  margin-top: -0.75em;
+  margin-bottom: -0.75em;
 }
 
 #txt2img_negative_prompt, #img2img_negative_prompt{