diff --git a/scripts/ddetailer.py b/scripts/ddetailer.py
index a58c736..fd42705 100644
--- a/scripts/ddetailer.py
+++ b/scripts/ddetailer.py
@@ -6,6 +6,7 @@
 import gradio as gr
 import shutil
 
+from copy import copy
 from modules import processing, images
 from modules import scripts, script_callbacks, shared, devices, modelloader
 from modules.processing import Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
@@ -95,100 +96,132 @@ def gr_show(visible=True):
     return {"visible": visible, "__type__": "update"}
 
 class DetectionDetailerScript(scripts.Script):
+    def __init__(self):
+        super().__init__()
+
     def title(self):
         return "Detection Detailer"
     
     def show(self, is_img2img):
-        return True
+        return scripts.AlwaysVisible
 
     def ui(self, is_img2img):
         import modules.ui
 
-        model_list = list_models(dd_models_path)
-        model_list.insert(0, "None")
-        if is_img2img:
-            info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Use from inpaint tab, inpaint at full res ON, denoise <0.5</p>")
-        else:
-            info = gr.HTML("")
-        with gr.Group():
+        with gr.Accordion("Detection Detailer", open=False):
             with gr.Row():
-                dd_model_a = gr.Dropdown(label="Primary detection model (A)", choices=model_list,value = "None", visible=True, type="value")
-            
-            with gr.Row():
-                dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=False)
-                dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=False)
+                enabled = gr.Checkbox(label="Enable", value=False, visible=True)
 
-            with gr.Row():
-                dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
-                dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
-            
-            with gr.Row():
-                dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=False)
-                dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=False)  
-        
-        br = gr.HTML("<br>")
+            model_list = list_models(dd_models_path)
+            model_list.insert(0, "None")
+            if is_img2img:
+                info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Use from inpaint tab, inpaint at full res ON, denoise <0.5</p>")
+            else:
+                info = gr.HTML("")
+            with gr.Group():
+                with gr.Row():
+                    dd_model_a = gr.Dropdown(label="Primary detection model (A)", choices=model_list,value = "None", visible=True, type="value")
+
+                with gr.Row():
+                    dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=False)
+                    dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=False)
+
+                with gr.Row():
+                    dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
+                    dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
+
+                with gr.Row():
+                    dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=False)
+                    dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=False)
+
+            br = gr.HTML("<br>")
+
+            with gr.Group():
+                with gr.Row():
+                    dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =False, type="value")
+
+                with gr.Row():
+                    dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=False)
+                    dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=False)
+
+                with gr.Row():
+                    dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
+                    dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
+
+            with gr.Group():
+                with gr.Row():
+                    dd_mask_blur = gr.Slider(label='Mask blur ', minimum=0, maximum=64, step=1, value=4, visible=(not is_img2img))
+                    dd_denoising_strength = gr.Slider(label='Denoising strength (Inpaint)', minimum=0.0, maximum=1.0, step=0.01, value=0.4, visible=(not is_img2img))
+
+                with gr.Row():
+                    dd_inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution ', value=True, visible = (not is_img2img))
+                    dd_inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels ', minimum=0, maximum=256, step=4, value=32, visible=(not is_img2img))
+
+            dd_model_a.change(
+                lambda modelname: {
+                    dd_model_b:gr_show( modelname != "None" ),
+                    dd_conf_a:gr_show( modelname != "None" ),
+                    dd_dilation_factor_a:gr_show( modelname != "None"),
+                    dd_offset_x_a:gr_show( modelname != "None" ),
+                    dd_offset_y_a:gr_show( modelname != "None" )
+
+                },
+                inputs= [dd_model_a],
+                outputs =[dd_model_b, dd_conf_a, dd_dilation_factor_a, dd_offset_x_a, dd_offset_y_a]
+            )
+
+            dd_model_b.change(
+                lambda modelname: {
+                    dd_preprocess_b:gr_show( modelname != "None" ),
+                    dd_bitwise_op:gr_show( modelname != "None" ),
+                    dd_conf_b:gr_show( modelname != "None" ),
+                    dd_dilation_factor_b:gr_show( modelname != "None"),
+                    dd_offset_x_b:gr_show( modelname != "None" ),
+                    dd_offset_y_b:gr_show( modelname != "None" )
+                },
+                inputs= [dd_model_b],
+                outputs =[dd_preprocess_b, dd_bitwise_op, dd_conf_b, dd_dilation_factor_b, dd_offset_x_b, dd_offset_y_b]
+            )
+
+            return [info, enabled,
+                    dd_model_a,
+                    dd_conf_a, dd_dilation_factor_a,
+                    dd_offset_x_a, dd_offset_y_a,
+                    dd_preprocess_b, dd_bitwise_op,
+                    br,
+                    dd_model_b,
+                    dd_conf_b, dd_dilation_factor_b,
+                    dd_offset_x_b, dd_offset_y_b,
+                    dd_mask_blur, dd_denoising_strength,
+                    dd_inpaint_full_res, dd_inpaint_full_res_padding
+            ]
+
+    def get_seed(self, p) -> tuple[int, int]:
+        i = p.iteration
+
+        if not p.all_seeds:
+            seed = p.seed
+        elif i < len(p.all_seeds):
+            seed = p.all_seeds[i]
+        else:
+            j = i % len(p.all_seeds)
+            seed = p.all_seeds[j]
 
-        with gr.Group():
-            with gr.Row():
-                dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =False, type="value")
+        if not p.all_subseeds:
+            subseed = p.subseed
+        elif i < len(p.all_subseeds):
+            subseed = p.all_subseeds[i]
+        else:
+            j = i % len(p.all_subseeds)
+            subseed = p.all_subseeds[j]
 
-            with gr.Row():
-                dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=False)
-                dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=False)
-            
-            with gr.Row():
-                dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
-                dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
-        
-        with gr.Group():
-            with gr.Row():
-                dd_mask_blur = gr.Slider(label='Mask blur ', minimum=0, maximum=64, step=1, value=4, visible=(not is_img2img))
-                dd_denoising_strength = gr.Slider(label='Denoising strength (Inpaint)', minimum=0.0, maximum=1.0, step=0.01, value=0.4, visible=(not is_img2img))
-            
-            with gr.Row():
-                dd_inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution ', value=True, visible = (not is_img2img))
-                dd_inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels ', minimum=0, maximum=256, step=4, value=32, visible=(not is_img2img))
-
-        dd_model_a.change(
-            lambda modelname: {
-                dd_model_b:gr_show( modelname != "None" ),
-                dd_conf_a:gr_show( modelname != "None" ),
-                dd_dilation_factor_a:gr_show( modelname != "None"),
-                dd_offset_x_a:gr_show( modelname != "None" ),
-                dd_offset_y_a:gr_show( modelname != "None" )
-
-            },
-            inputs= [dd_model_a],
-            outputs =[dd_model_b, dd_conf_a, dd_dilation_factor_a, dd_offset_x_a, dd_offset_y_a]
-        )
-
-        dd_model_b.change(
-            lambda modelname: {
-                dd_preprocess_b:gr_show( modelname != "None" ),
-                dd_bitwise_op:gr_show( modelname != "None" ),
-                dd_conf_b:gr_show( modelname != "None" ),
-                dd_dilation_factor_b:gr_show( modelname != "None"),
-                dd_offset_x_b:gr_show( modelname != "None" ),
-                dd_offset_y_b:gr_show( modelname != "None" )
-            },
-            inputs= [dd_model_b],
-            outputs =[dd_preprocess_b, dd_bitwise_op, dd_conf_b, dd_dilation_factor_b, dd_offset_x_b, dd_offset_y_b]
-        )
-        
-        return [info,
-                dd_model_a, 
-                dd_conf_a, dd_dilation_factor_a,
-                dd_offset_x_a, dd_offset_y_a,
-                dd_preprocess_b, dd_bitwise_op, 
-                br,
-                dd_model_b,
-                dd_conf_b, dd_dilation_factor_b,
-                dd_offset_x_b, dd_offset_y_b,  
-                dd_mask_blur, dd_denoising_strength,
-                dd_inpaint_full_res, dd_inpaint_full_res_padding
-        ]
+        return seed, subseed
 
-    def run(self, p, info,
+    def process(self, p, *args):
+        if getattr(p, "_disable_ddetailer", False):
+            return
+
+    def postprocess_image(self, p, pp, info, enabled,
                      dd_model_a, 
                      dd_conf_a, dd_dilation_factor_a,
                      dd_offset_x_a, dd_offset_y_a,
@@ -200,21 +233,36 @@ def run(self, p, info,
                      dd_mask_blur, dd_denoising_strength,
                      dd_inpaint_full_res, dd_inpaint_full_res_padding):
 
-        processing.fix_seed(p)
+        if getattr(p, "_disable_ddetailer", False):
+            return
+
+        if not enabled:
+            return
+
+        script_mode = False
+
         initial_info = None
-        seed = p.seed
-        p.batch_size = 1
-        ddetail_count = p.n_iter
-        p.n_iter = 1
-        p.do_not_save_grid = True
-        p.do_not_save_samples = True
+        seed, subseed = self.get_seed(p)
+        p.seed = seed
+        p.subseed = subseed
+
+        info = ""
+        if not script_mode:
+            ddetail_count = 1
+        else:
+            ddetail_count = p.n_iter
+            p.batch_size = 1
+            p.n_iter = 1
+            p.do_not_save_grid = True
+            p.do_not_save_samples = True
+
         is_txt2img = isinstance(p, StableDiffusionProcessingTxt2Img)
+        p_txt = copy(p)
         if (not is_txt2img):
             orig_image = p.init_images[0]
         else:
-            p_txt = p
             p = StableDiffusionProcessingImg2Img(
-                    init_images = None,
+                    init_images = [pp.image],
                     resize_mode = 0,
                     denoising_strength = dd_denoising_strength,
                     mask = None,
@@ -235,7 +283,8 @@ def run(self, p, info,
                     seed_resize_from_h=p_txt.seed_resize_from_h,
                     seed_resize_from_w=p_txt.seed_resize_from_w,
                     sampler_name=p_txt.sampler_name,
-                    n_iter=p_txt.n_iter,
+                    batch_size=1,
+                    n_iter=1,
                     steps=p_txt.steps,
                     cfg_scale=p_txt.cfg_scale,
                     width=p_txt.width,
@@ -244,16 +293,18 @@ def run(self, p, info,
                 )
             p.do_not_save_grid = True
             p.do_not_save_samples = True
+
+        p._disable_ddetailer = True
+
         output_images = []
         state.job_count = ddetail_count
         for n in range(ddetail_count):
             devices.torch_gc()
             start_seed = seed + n
             if ( is_txt2img ):
-                print(f"Processing initial image for output generation {n + 1}.")
-                p_txt.seed = start_seed
-                processed = processing.process_images(p_txt)
-                init_image = processed.images[0]   
+                print(f"Prepare initial image for output generation {p_txt.iteration + 1}.")
+                init_image = copy(pp.image)
+                info = processing.create_infotext(p_txt, p_txt.all_prompts, p_txt.all_seeds, p_txt.all_subseeds, None, 0, 0)
             else: 
                 init_image = orig_image
             
@@ -276,7 +327,7 @@ def run(self, p, info,
                         images.save_image(segmask_preview_b, opts.outdir_ddetailer_previews, "", start_seed, p.prompt, opts.samples_format, p=p)
                     gen_count = len(masks_b_pre)
                     state.job_count += gen_count
-                    print(f"Processing {gen_count} model {label_b_pre} detections for output generation {n + 1}.")
+                    print(f"Processing {gen_count} model {label_b_pre} detections for output generation {p_txt.iteration + 1}.")
                     p.seed = start_seed
                     p.init_images = [init_image]
 
@@ -285,7 +336,12 @@ def run(self, p, info,
                         if ( opts.dd_save_masks):
                             images.save_image(masks_b_pre[i], opts.outdir_ddetailer_masks, "", start_seed, p.prompt, opts.samples_format, p=p)
                         processed = processing.process_images(p)
+
+                        if not is_txt2img:
+                            p.prompt = processed.all_prompts[0]
+                            info = processed.info
                         p.seed = processed.seed + 1
+                        p.subseed = processed.subseed + 1
                         p.init_images = processed.images
 
                     if (gen_count > 0):
@@ -293,7 +349,7 @@ def run(self, p, info,
                         init_image = processed.images[0]
 
                 else:
-                    print(f"No model B detections for output generation {n} with current settings.")
+                    print(f"No model B detections for output generation {p_txt.iteration + 1} with current settings.")
 
             # Primary run
             if (dd_model_a != "None"):
@@ -335,7 +391,7 @@ def run(self, p, info,
                         images.save_image(segmask_preview_a, opts.outdir_ddetailer_previews, "", start_seed, p.prompt, opts.samples_format, p=p)
                     gen_count = len(masks_a)
                     state.job_count += gen_count
-                    print(f"Processing {gen_count} model {label_a} detections for output generation {n + 1}.")
+                    print(f"Processing {gen_count} model {label_a} detections for output generation {p_txt.iteration + 1}.")
                     p.seed = start_seed
                     p.init_images = [init_image]
 
@@ -347,21 +403,23 @@ def run(self, p, info,
                         processed = processing.process_images(p)
                         if initial_info is None:
                             initial_info = processed.info
+                        info = processed.info
                         p.seed = processed.seed + 1
+                        p.subseed = processed.subseed + 1
                         p.init_images = processed.images
                     
                     if (gen_count > 0):
                         output_images[n] = processed.images[0]
-                        if ( opts.samples_save ):
-                            images.save_image(processed.images[0], p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
+                        if ( script_mode and opts.samples_save ):
+                            images.save_image(processed.images[0], p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=info, p=p)
   
                 else: 
-                    print(f"No model {label_a} detections for output generation {n} with current settings.")
-            state.job = f"Generation {n + 1} out of {state.job_count}"
+                    print(f"No model {label_a} detections for output generation {p_txt.iteration + 1} with current settings.")
+            state.job = f"Generation {p_txt.iteration + 1} out of {state.job_count}"
         if (initial_info is None):
             initial_info = "No detections found."
 
-        return Processed(p, output_images, seed, initial_info)
+        pp.image = output_images[0]
 
 def modeldataset(model_shortname):
     path = modelpath(model_shortname)