Implemented batch_size logic in outpainting_mk2

scripts/outpainting_mk_2.py

@@ -176,50 +176,53 @@ class Script(scripts.Script):
 
         state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)
 
-        def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
+        def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
             is_horiz = is_left or is_right
             is_vert = is_top or is_bottom
             pixels_horiz = expand_pixels if is_horiz else 0
             pixels_vert = expand_pixels if is_vert else 0
 
-            res_w = init.width + pixels_horiz
-            res_h = init.height + pixels_vert
-            process_res_w = math.ceil(res_w / 64) * 64
-            process_res_h = math.ceil(res_h / 64) * 64
-
-            img = Image.new("RGB", (process_res_w, process_res_h))
-            img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
-            mask = Image.new("RGB", (process_res_w, process_res_h), "white")
-            draw = ImageDraw.Draw(mask)
-            draw.rectangle((
-                expand_pixels + mask_blur if is_left else 0,
-                expand_pixels + mask_blur if is_top else 0,
-                mask.width - expand_pixels - mask_blur if is_right else res_w,
-                mask.height - expand_pixels - mask_blur if is_bottom else res_h,
-            ), fill="black")
-
-            np_image = (np.asarray(img) / 255.0).astype(np.float64)
-            np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
-            noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
-            out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
-
-            target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width
-            target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height
-
-            crop_region = (
-                0 if is_left else out.width - target_width,
-                0 if is_top else out.height - target_height,
-                target_width if is_left else out.width,
-                target_height if is_top else out.height,
-            )
-
-            image_to_process = out.crop(crop_region)
-            mask = mask.crop(crop_region)
-
-            p.width = target_width if is_horiz else img.width
-            p.height = target_height if is_vert else img.height
-            p.init_images = [image_to_process]
-            p.image_mask = mask
+            images_to_process = []
+            for n in range(count):
+                res_w = init[n].width + pixels_horiz
+                res_h = init[n].height + pixels_vert
+                process_res_w = math.ceil(res_w / 64) * 64
+                process_res_h = math.ceil(res_h / 64) * 64
+
+                img = Image.new("RGB", (process_res_w, process_res_h))
+                img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
+                mask = Image.new("RGB", (process_res_w, process_res_h), "white")
+                draw = ImageDraw.Draw(mask)
+                draw.rectangle((
+                    expand_pixels + mask_blur if is_left else 0,
+                    expand_pixels + mask_blur if is_top else 0,
+                    mask.width - expand_pixels - mask_blur if is_right else res_w,
+                    mask.height - expand_pixels - mask_blur if is_bottom else res_h,
+                ), fill="black")
+
+                np_image = (np.asarray(img) / 255.0).astype(np.float64)
+                np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
+                noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
+                out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
+
+                target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
+                target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
+                p.width = target_width if is_horiz else img.width
+                p.height = target_height if is_vert else img.height
+
+                crop_region = (
+                    0 if is_left else out.width - target_width,
+                    0 if is_top else out.height - target_height,
+                    target_width if is_left else out.width,
+                    target_height if is_top else out.height,
+                )
+                mask = mask.crop(crop_region)
+                p.image_mask = mask
+
+                image_to_process = out.crop(crop_region)
+                images_to_process.append(image_to_process)
+
+            p.init_images = images_to_process
 
             latent_mask = Image.new("RGB", (p.width, p.height), "white")
             draw = ImageDraw.Draw(latent_mask)
@@ -232,44 +235,49 @@ class Script(scripts.Script):
             p.latent_mask = latent_mask
 
             proc = process_images(p)
-            proc_img = proc.images[0]
 
             if initial_seed_and_info[0] is None:
                 initial_seed_and_info[0] = proc.seed
                 initial_seed_and_info[1] = proc.info
 
-            out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
-            out = out.crop((0, 0, res_w, res_h))
-            return out
+            for proc_img in proc.images:
+                out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
+                out = out.crop((0, 0, res_w, res_h))
+
+            return proc.images
 
         batch_count = p.n_iter
+        batch_size = p.batch_size
         p.n_iter = 1
         state.job_count = batch_count
-        all_images = []
+        all_processed_images = []
 
         for i in range(batch_count):
-            img = init_image
-            state.job = f"Batch {i + 1} out of {state.job_count}"
+            imgs = [init_img] * batch_size
+            state.job = f"Batch {i + 1} out of {batch_count}"
 
             if left > 0:
-                img = expand(img, left, is_left=True)
+                imgs = expand(imgs, batch_size, left, is_left=True)
             if right > 0:
-                img = expand(img, right, is_right=True)
+                imgs = expand(imgs, batch_size, right, is_right=True)
             if up > 0:
-                img = expand(img, up, is_top=True)
+                imgs = expand(imgs, batch_size, up, is_top=True)
             if down > 0:
-                img = expand(img, down, is_bottom=True)
+                imgs = expand(imgs, batch_size, down, is_bottom=True)
 
-            all_images.append(img)
+            all_processed_images += imgs
+
+        combined_grid_image = images.image_grid(all_processed_images)
+        all_images = all_processed_images
 
-        combined_grid_image = images.image_grid(all_images)
         if opts.return_grid:
-            all_images = [combined_grid_image] + all_images
-        
+            all_images = [combined_grid_image] + all_processed_images
+
         res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
 
         if opts.samples_save:
-            images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+            for img in all_processed_images:
+                images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
 
         if opts.grid_save:
             images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)