A big rework, just what you were secretly hoping for!

SD upscale moved to scripts
Batch processing script removed
Batch processing added to main img2img and now works with scripts
img2img page UI reworked to use tabs

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)