diff --git a/args_manager.py b/args_manager.py
index d063a5e1..e5e76753 100644
--- a/args_manager.py
+++ b/args_manager.py
@@ -34,5 +34,7 @@ args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload
 if args_parser.args.disable_analytics:
     import os
     os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+if args_parser.args.disable_in_browser:
+    args_parser.args.in_browser = False
 
 args = args_parser.args
diff --git a/css/style.css b/css/style.css
index b87b20a7..010c8e7f 100644
--- a/css/style.css
+++ b/css/style.css
@@ -196,3 +196,25 @@
     pointer-events: none;
     display: none;
 }
+
+#stylePreviewOverlay {
+    opacity: 0;
+    pointer-events: none;
+    width: 128px;
+    height: 128px;
+    position: fixed;
+    top: 0px;
+    left: 0px;
+    border: solid 1px lightgrey;
+    transform: translate(-140px, 20px);
+    background-size: cover;
+    background-position: center;
+    background-color: rgba(0, 0, 0, 0.3);
+    border-radius: 5px;
+    z-index: 100;
+    transition: transform 0.1s ease, opacity 0.3s ease;
+}
+
+#stylePreviewOverlay.lower-half {
+    transform: translate(-140px, -140px);
+}
diff --git a/extras/preprocessors.py b/extras/preprocessors.py
index 798fe15d..0aa83109 100644
--- a/extras/preprocessors.py
+++ b/extras/preprocessors.py
@@ -1,27 +1,26 @@
 import cv2
 import numpy as np
-import modules.advanced_parameters as advanced_parameters
 
 
-def centered_canny(x: np.ndarray):
+def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 2 and x.dtype == np.uint8
 
-    y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
+    y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
     y = y.astype(np.float32) / 255.0
     return y
 
 
-def centered_canny_color(x: np.ndarray):
+def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
-    result = [centered_canny(x[..., i]) for i in range(3)]
+    result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
     result = np.stack(result, axis=2)
     return result
 
 
-def pyramid_canny_color(x: np.ndarray):
+def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
@@ -31,7 +30,7 @@ def pyramid_canny_color(x: np.ndarray):
     for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
         Hs, Ws = int(H * k), int(W * k)
         small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
-        edge = centered_canny_color(small)
+        edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
         if acc_edge is None:
             acc_edge = edge
         else:
@@ -54,11 +53,11 @@ def norm255(x, low=4, high=96):
     return x * 255.0
 
 
-def canny_pyramid(x):
+def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
     # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
     # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
 
-    color_canny = pyramid_canny_color(x)
+    color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
     result = np.sum(color_canny, axis=2)
 
     return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)
diff --git a/fooocus_version.py b/fooocus_version.py
index 482cc12c..efe3b925 100644
--- a/fooocus_version.py
+++ b/fooocus_version.py
@@ -1 +1 @@
-version = '2.1.860'
+version = '2.1.862'
diff --git a/javascript/localization.js b/javascript/localization.js
index 8fda68e2..0a8394ca 100644
--- a/javascript/localization.js
+++ b/javascript/localization.js
@@ -45,6 +45,9 @@ function processTextNode(node) {
     var tl = getTranslation(text);
     if (tl !== undefined) {
         node.textContent = tl;
+        if (text && node.parentElement) {
+          node.parentElement.setAttribute("data-original-text", text);
+        }
     }
 }
 
diff --git a/javascript/script.js b/javascript/script.js
index 593d8c36..8f4cac58 100644
--- a/javascript/script.js
+++ b/javascript/script.js
@@ -119,6 +119,7 @@ document.addEventListener("DOMContentLoaded", function() {
         }
     });
     mutationObserver.observe(gradioApp(), {childList: true, subtree: true});
+    initStylePreviewOverlay();
 });
 
 /**
@@ -145,6 +146,40 @@ document.addEventListener('keydown', function(e) {
     }
 });
 
+function initStylePreviewOverlay() {
+    let overlayVisible = false;
+    const samplesPath = document.querySelector("meta[name='samples-path']").getAttribute("content")
+    const overlay = document.createElement('div');
+    overlay.id = 'stylePreviewOverlay';
+    document.body.appendChild(overlay);
+    document.addEventListener('mouseover', function(e) {
+        const label = e.target.closest('.style_selections label');
+        if (!label) return;
+        label.removeEventListener("mouseout", onMouseLeave);
+        label.addEventListener("mouseout", onMouseLeave);
+        overlayVisible = true;
+        overlay.style.opacity = "1";
+        const originalText = label.querySelector("span").getAttribute("data-original-text");
+        const name = originalText || label.querySelector("span").textContent;
+        overlay.style.backgroundImage = `url("${samplesPath.replace(
+          "fooocus_v2",
+          name.toLowerCase().replaceAll(" ", "_")
+        ).replaceAll("\\", "\\\\")}")`;
+        function onMouseLeave() {
+            overlayVisible = false;
+            overlay.style.opacity = "0";
+            overlay.style.backgroundImage = "";
+            label.removeEventListener("mouseout", onMouseLeave);
+        }
+    });
+    document.addEventListener('mousemove', function(e) {
+        if(!overlayVisible) return;
+        overlay.style.left = `${e.clientX}px`;
+        overlay.style.top = `${e.clientY}px`;
+        overlay.className = e.clientY > window.innerHeight / 2 ? "lower-half" : "upper-half";
+    });
+}
+
 /**
  * checks that a UI element is not in another hidden element or tab content
  */
diff --git a/modules/advanced_parameters.py b/modules/advanced_parameters.py
deleted file mode 100644
index 0caa3eec..00000000
--- a/modules/advanced_parameters.py
+++ /dev/null
@@ -1,33 +0,0 @@
-disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,  \
-    scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
-    overwrite_vary_strength, overwrite_upscale_strength, \
-    mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
-    debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
-    refiner_swap_method, \
-    freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
-    debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
-    inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = [None] * 35
-
-
-def set_all_advanced_parameters(*args):
-    global disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
-        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
-        overwrite_vary_strength, overwrite_upscale_strength, \
-        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
-        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
-        refiner_swap_method, \
-        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
-        debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
-        inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate
-
-    disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
-        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
-        overwrite_vary_strength, overwrite_upscale_strength, \
-        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
-        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
-        refiner_swap_method, \
-        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
-        debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
-        inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = args
-
-    return
diff --git a/modules/async_worker.py b/modules/async_worker.py
index bf21efa5..9eec49fb 100644
--- a/modules/async_worker.py
+++ b/modules/async_worker.py
@@ -1,4 +1,8 @@
 import threading
+import os
+from modules.patch import PatchSettings, patch_settings, patch_all
+
+patch_all()
 
 
 class AsyncTask:
@@ -33,7 +37,6 @@ def worker():
     import extras.preprocessors as preprocessors
     import modules.inpaint_worker as inpaint_worker
     import modules.constants as constants
-    import modules.advanced_parameters as advanced_parameters
     import extras.ip_adapter as ip_adapter
     import extras.face_crop
     import fooocus_version
@@ -45,6 +48,9 @@ def worker():
         get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate
     from modules.upscaler import perform_upscale
 
+    pid = os.getpid()
+    print(f'Started worker with PID {pid}')
+
     try:
         async_gradio_app = shared.gradio_root
         flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}'''
@@ -71,9 +77,6 @@ def worker():
         return
 
     def build_image_wall(async_task):
-        if not advanced_parameters.generate_image_grid:
-            return
-
         results = async_task.results
 
         if len(results) < 2:
@@ -143,6 +146,40 @@ def worker():
         inpaint_input_image = args.pop()
         inpaint_additional_prompt = args.pop()
         inpaint_mask_image_upload = args.pop()
+        disable_preview = args.pop()
+        adm_scaler_positive = args.pop()
+        adm_scaler_negative = args.pop()
+        adm_scaler_end = args.pop()
+        adaptive_cfg = args.pop()
+        sampler_name = args.pop()
+        scheduler_name = args.pop()
+        overwrite_step = args.pop()
+        overwrite_switch = args.pop()
+        overwrite_width = args.pop()
+        overwrite_height = args.pop()
+        overwrite_vary_strength = args.pop()
+        overwrite_upscale_strength = args.pop()
+        mixing_image_prompt_and_vary_upscale = args.pop()
+        mixing_image_prompt_and_inpaint = args.pop()
+        debugging_cn_preprocessor = args.pop()
+        skipping_cn_preprocessor = args.pop()
+        canny_low_threshold = args.pop()
+        canny_high_threshold = args.pop()
+        refiner_swap_method = args.pop()
+        controlnet_softness = args.pop()
+        freeu_enabled = args.pop()
+        freeu_b1 = args.pop()
+        freeu_b2 = args.pop()
+        freeu_s1 = args.pop()
+        freeu_s2 = args.pop()
+        debugging_inpaint_preprocessor = args.pop()
+        inpaint_disable_initial_latent = args.pop()
+        inpaint_engine = args.pop()
+        inpaint_strength = args.pop()
+        inpaint_respective_field = args.pop()
+        inpaint_mask_upload_checkbox = args.pop()
+        invert_mask_checkbox = args.pop()
+        inpaint_erode_or_dilate = args.pop()
 
         cn_tasks = {x: [] for x in flags.ip_list}
         for _ in range(4):
@@ -189,30 +226,33 @@ def worker():
                 print(f'Refiner disabled in LCM mode.')
 
             refiner_model_name = 'None'
-            sampler_name = advanced_parameters.sampler_name = 'lcm'
-            scheduler_name = advanced_parameters.scheduler_name = 'lcm'
-            modules.patch.sharpness = sharpness = 0.0
-            cfg_scale = guidance_scale = 1.0
-            modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
+            sampler_name = 'lcm'
+            scheduler_name = 'lcm'
+            sharpness = 0.0
+            guidance_scale = 1.0
+            adaptive_cfg = 1.0
             refiner_switch = 1.0
-            modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
-            modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
-            modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
+            adm_scaler_positive = 1.0
+            adm_scaler_negative = 1.0
+            adm_scaler_end = 0.0
             steps = 8
 
-        modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
-        print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
-
-        modules.patch.sharpness = sharpness
-        print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
-
-        modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
-        modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
-        modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
+        print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
+        print(f'[Parameters] Sharpness = {sharpness}')
+        print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
         print(f'[Parameters] ADM Scale = '
-              f'{modules.patch.positive_adm_scale} : '
-              f'{modules.patch.negative_adm_scale} : '
-              f'{modules.patch.adm_scaler_end}')
+              f'{adm_scaler_positive} : '
+              f'{adm_scaler_negative} : '
+              f'{adm_scaler_end}')
+
+        patch_settings[pid] = PatchSettings(
+            sharpness,
+            adm_scaler_end,
+            adm_scaler_positive,
+            adm_scaler_negative,
+            controlnet_softness,
+            adaptive_cfg
+        )
 
         cfg_scale = float(guidance_scale)
         print(f'[Parameters] CFG = {cfg_scale}')
@@ -225,10 +265,9 @@ def worker():
         width, height = int(width), int(height)
 
         skip_prompt_processing = False
-        refiner_swap_method = advanced_parameters.refiner_swap_method
 
         inpaint_worker.current_task = None
-        inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
+        inpaint_parameterized = inpaint_engine != 'None'
         inpaint_image = None
         inpaint_mask = None
         inpaint_head_model_path = None
@@ -242,15 +281,12 @@ def worker():
         seed = int(image_seed)
         print(f'[Parameters] Seed = {seed}')
 
-        sampler_name = advanced_parameters.sampler_name
-        scheduler_name = advanced_parameters.scheduler_name
-
         goals = []
         tasks = []
 
         if input_image_checkbox:
             if (current_tab == 'uov' or (
-                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
+                    current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \
                     and uov_method != flags.disabled and uov_input_image is not None:
                 uov_input_image = HWC3(uov_input_image)
                 if 'vary' in uov_method:
@@ -274,12 +310,12 @@ def worker():
                     progressbar(async_task, 1, 'Downloading upscale models ...')
                     modules.config.downloading_upscale_model()
             if (current_tab == 'inpaint' or (
-                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
+                    current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \
                     and isinstance(inpaint_input_image, dict):
                 inpaint_image = inpaint_input_image['image']
                 inpaint_mask = inpaint_input_image['mask'][:, :, 0]
-                
-                if advanced_parameters.inpaint_mask_upload_checkbox:
+
+                if inpaint_mask_upload_checkbox:
                     if isinstance(inpaint_mask_image_upload, np.ndarray):
                         if inpaint_mask_image_upload.ndim == 3:
                             H, W, C = inpaint_image.shape
@@ -288,10 +324,10 @@ def worker():
                             inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
                             inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
 
-                if int(advanced_parameters.inpaint_erode_or_dilate) != 0:
-                    inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate)
+                if int(inpaint_erode_or_dilate) != 0:
+                    inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
 
-                if advanced_parameters.invert_mask_checkbox:
+                if invert_mask_checkbox:
                     inpaint_mask = 255 - inpaint_mask
 
                 inpaint_image = HWC3(inpaint_image)
@@ -302,7 +338,7 @@ def worker():
                     if inpaint_parameterized:
                         progressbar(async_task, 1, 'Downloading inpainter ...')
                         inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
-                            advanced_parameters.inpaint_engine)
+                            inpaint_engine)
                         base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
                         print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
                         if refiner_model_name == 'None':
@@ -318,8 +354,8 @@ def worker():
                             prompt = inpaint_additional_prompt + '\n' + prompt
                     goals.append('inpaint')
             if current_tab == 'ip' or \
-                    advanced_parameters.mixing_image_prompt_and_inpaint or \
-                    advanced_parameters.mixing_image_prompt_and_vary_upscale:
+                    mixing_image_prompt_and_vary_upscale or \
+                    mixing_image_prompt_and_inpaint:
                 goals.append('cn')
                 progressbar(async_task, 1, 'Downloading control models ...')
                 if len(cn_tasks[flags.cn_canny]) > 0:
@@ -340,17 +376,17 @@ def worker():
 
         switch = int(round(steps * refiner_switch))
 
-        if advanced_parameters.overwrite_step > 0:
-            steps = advanced_parameters.overwrite_step
+        if overwrite_step > 0:
+            steps = overwrite_step
 
-        if advanced_parameters.overwrite_switch > 0:
-            switch = advanced_parameters.overwrite_switch
+        if overwrite_switch > 0:
+            switch = overwrite_switch
 
-        if advanced_parameters.overwrite_width > 0:
-            width = advanced_parameters.overwrite_width
+        if overwrite_width > 0:
+            width = overwrite_width
 
-        if advanced_parameters.overwrite_height > 0:
-            height = advanced_parameters.overwrite_height
+        if overwrite_height > 0:
+            height = overwrite_height
 
         print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
         print(f'[Parameters] Steps = {steps} - {switch}')
@@ -449,8 +485,8 @@ def worker():
                 denoising_strength = 0.5
             if 'strong' in uov_method:
                 denoising_strength = 0.85
-            if advanced_parameters.overwrite_vary_strength > 0:
-                denoising_strength = advanced_parameters.overwrite_vary_strength
+            if overwrite_vary_strength > 0:
+                denoising_strength = overwrite_vary_strength
 
             shape_ceil = get_image_shape_ceil(uov_input_image)
             if shape_ceil < 1024:
@@ -521,8 +557,8 @@ def worker():
             tiled = True
             denoising_strength = 0.382
 
-            if advanced_parameters.overwrite_upscale_strength > 0:
-                denoising_strength = advanced_parameters.overwrite_upscale_strength
+            if overwrite_upscale_strength > 0:
+                denoising_strength = overwrite_upscale_strength
 
             initial_pixels = core.numpy_to_pytorch(uov_input_image)
             progressbar(async_task, 13, 'VAE encoding ...')
@@ -566,19 +602,19 @@ def worker():
 
                 inpaint_image = np.ascontiguousarray(inpaint_image.copy())
                 inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
-                advanced_parameters.inpaint_strength = 1.0
-                advanced_parameters.inpaint_respective_field = 1.0
+                inpaint_strength = 1.0
+                inpaint_respective_field = 1.0
 
-            denoising_strength = advanced_parameters.inpaint_strength
+            denoising_strength = inpaint_strength
 
             inpaint_worker.current_task = inpaint_worker.InpaintWorker(
                 image=inpaint_image,
                 mask=inpaint_mask,
                 use_fill=denoising_strength > 0.99,
-                k=advanced_parameters.inpaint_respective_field
+                k=inpaint_respective_field
             )
 
-            if advanced_parameters.debugging_inpaint_preprocessor:
+            if debugging_inpaint_preprocessor:
                 yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
                              do_not_show_finished_images=True)
                 return
@@ -624,7 +660,7 @@ def worker():
                     model=pipeline.final_unet
                 )
 
-            if not advanced_parameters.inpaint_disable_initial_latent:
+            if not inpaint_disable_initial_latent:
                 initial_latent = {'samples': latent_fill}
 
             B, C, H, W = latent_fill.shape
@@ -637,24 +673,24 @@ def worker():
                 cn_img, cn_stop, cn_weight = task
                 cn_img = resize_image(HWC3(cn_img), width=width, height=height)
 
-                if not advanced_parameters.skipping_cn_preprocessor:
-                    cn_img = preprocessors.canny_pyramid(cn_img)
+                if not skipping_cn_preprocessor:
+                    cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
 
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_cpds]:
                 cn_img, cn_stop, cn_weight = task
                 cn_img = resize_image(HWC3(cn_img), width=width, height=height)
 
-                if not advanced_parameters.skipping_cn_preprocessor:
+                if not skipping_cn_preprocessor:
                     cn_img = preprocessors.cpds(cn_img)
 
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip]:
@@ -665,21 +701,21 @@ def worker():
                 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
             for task in cn_tasks[flags.cn_ip_face]:
                 cn_img, cn_stop, cn_weight = task
                 cn_img = HWC3(cn_img)
 
-                if not advanced_parameters.skipping_cn_preprocessor:
+                if not skipping_cn_preprocessor:
                     cn_img = extras.face_crop.crop_image(cn_img)
 
                 # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
                 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
 
                 task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
-                if advanced_parameters.debugging_cn_preprocessor:
+                if debugging_cn_preprocessor:
                     yield_result(async_task, cn_img, do_not_show_finished_images=True)
                     return
 
@@ -688,14 +724,14 @@ def worker():
             if len(all_ip_tasks) > 0:
                 pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
 
-        if advanced_parameters.freeu_enabled:
+        if freeu_enabled:
             print(f'FreeU is enabled!')
             pipeline.final_unet = core.apply_freeu(
                 pipeline.final_unet,
-                advanced_parameters.freeu_b1,
-                advanced_parameters.freeu_b2,
-                advanced_parameters.freeu_s1,
-                advanced_parameters.freeu_s2
+                freeu_b1,
+                freeu_b2,
+                freeu_s1,
+                freeu_s2
             )
 
         all_steps = steps * image_number
@@ -771,7 +807,8 @@ def worker():
                     denoise=denoising_strength,
                     tiled=tiled,
                     cfg_scale=cfg_scale,
-                    refiner_swap_method=refiner_swap_method
+                    refiner_swap_method=refiner_swap_method,
+                    disable_preview=disable_preview
                 )
 
                 del task['c'], task['uc'], positive_cond, negative_cond  # Save memory
@@ -790,9 +827,9 @@ def worker():
                         ('Sharpness', sharpness),
                         ('Guidance Scale', guidance_scale),
                         ('ADM Guidance', str((
-                            modules.patch.positive_adm_scale,
-                            modules.patch.negative_adm_scale,
-                            modules.patch.adm_scaler_end))),
+                            modules.patch.patch_settings[pid].positive_adm_scale,
+                            modules.patch.patch_settings[pid].negative_adm_scale,
+                            modules.patch.patch_settings[pid].adm_scaler_end))),
                         ('Base Model', base_model_name),
                         ('Refiner Model', refiner_model_name),
                         ('Refiner Switch', refiner_switch),
@@ -825,14 +862,20 @@ def worker():
         time.sleep(0.01)
         if len(async_tasks) > 0:
             task = async_tasks.pop(0)
+            generate_image_grid = task.args.pop(0)
+
             try:
                 handler(task)
-                build_image_wall(task)
+                if generate_image_grid:
+                    build_image_wall(task)
                 task.yields.append(['finish', task.results])
                 pipeline.prepare_text_encoder(async_call=True)
             except:
                 traceback.print_exc()
                 task.yields.append(['finish', task.results])
+            finally:
+                if pid in modules.patch.patch_settings:
+                    del modules.patch.patch_settings[pid]
     pass
 
 
diff --git a/modules/core.py b/modules/core.py
index 989b8e32..7a29d988 100644
--- a/modules/core.py
+++ b/modules/core.py
@@ -1,8 +1,3 @@
-from modules.patch import patch_all
-
-patch_all()
-
-
 import os
 import einops
 import torch
@@ -16,7 +11,6 @@ import ldm_patched.modules.controlnet
 import modules.sample_hijack
 import ldm_patched.modules.samplers
 import ldm_patched.modules.latent_formats
-import modules.advanced_parameters
 
 from ldm_patched.modules.sd import load_checkpoint_guess_config
 from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \
@@ -268,7 +262,7 @@ def get_previewer(model):
 def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
              scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
              force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
-             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None):
+             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):
 
     if sigmas is not None:
         sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())
@@ -299,7 +293,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
     def callback(step, x0, x, total_steps):
         ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
         y = None
-        if previewer is not None and not modules.advanced_parameters.disable_preview:
+        if previewer is not None and not disable_preview:
             y = previewer(x0, previewer_start + step, previewer_end)
         if callback_function is not None:
             callback_function(previewer_start + step, x0, x, previewer_end, y)
diff --git a/modules/default_pipeline.py b/modules/default_pipeline.py
index 6001d97f..2f45667c 100644
--- a/modules/default_pipeline.py
+++ b/modules/default_pipeline.py
@@ -315,7 +315,7 @@ def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):
 
 @torch.no_grad()
 @torch.inference_mode()
-def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
+def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):
     target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \
         = final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip
 
@@ -374,6 +374,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             refiner_switch=switch,
             previewer_start=0,
             previewer_end=steps,
+            disable_preview=disable_preview
         )
         decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)
 
@@ -392,6 +393,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             scheduler=scheduler_name,
             previewer_start=0,
             previewer_end=steps,
+            disable_preview=disable_preview
         )
         print('Refiner swapped by changing ksampler. Noise preserved.')
 
@@ -414,6 +416,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             scheduler=scheduler_name,
             previewer_start=switch,
             previewer_end=steps,
+            disable_preview=disable_preview
         )
 
         target_model = target_refiner_vae
@@ -422,7 +425,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     if refiner_swap_method == 'vae':
-        modules.patch.eps_record = 'vae'
+        modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
 
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.unswap()
@@ -440,7 +443,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             sampler_name=sampler_name,
             scheduler=scheduler_name,
             previewer_start=0,
-            previewer_end=steps
+            previewer_end=steps,
+            disable_preview=disable_preview
         )
         print('Fooocus VAE-based swap.')
 
@@ -459,7 +463,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
                                   denoise=denoise)[switch:] * k_sigmas
         len_sigmas = len(sigmas) - 1
 
-        noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
+        noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
 
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.swap()
@@ -479,7 +483,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             previewer_start=switch,
             previewer_end=steps,
             sigmas=sigmas,
-            noise_mean=noise_mean
+            noise_mean=noise_mean,
+            disable_preview=disable_preview
         )
 
         target_model = target_refiner_vae
@@ -488,5 +493,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     images = core.pytorch_to_numpy(decoded_latent)
-    modules.patch.eps_record = None
+    modules.patch.patch_settings[os.getpid()].eps_record = None
     return images
diff --git a/modules/patch.py b/modules/patch.py
index 2e2409c5..3c2dd8f4 100644
--- a/modules/patch.py
+++ b/modules/patch.py
@@ -17,7 +17,6 @@ import ldm_patched.controlnet.cldm
 import ldm_patched.modules.model_patcher
 import ldm_patched.modules.samplers
 import ldm_patched.modules.args_parser
-import modules.advanced_parameters as advanced_parameters
 import warnings
 import safetensors.torch
 import modules.constants as constants
@@ -29,15 +28,25 @@ from modules.patch_precision import patch_all_precision
 from modules.patch_clip import patch_all_clip
 
 
-sharpness = 2.0
+class PatchSettings:
+    def __init__(self,
+                 sharpness=2.0,
+                 adm_scaler_end=0.3,
+                 positive_adm_scale=1.5,
+                 negative_adm_scale=0.8,
+                 controlnet_softness=0.25,
+                 adaptive_cfg=7.0):
+        self.sharpness = sharpness
+        self.adm_scaler_end = adm_scaler_end
+        self.positive_adm_scale = positive_adm_scale
+        self.negative_adm_scale = negative_adm_scale
+        self.controlnet_softness = controlnet_softness
+        self.adaptive_cfg = adaptive_cfg
+        self.global_diffusion_progress = 0
+        self.eps_record = None
 
-adm_scaler_end = 0.3
-positive_adm_scale = 1.5
-negative_adm_scale = 0.8
 
-adaptive_cfg = 7.0
-global_diffusion_progress = 0
-eps_record = None
+patch_settings = {}
 
 
 def calculate_weight_patched(self, patches, weight, key):
@@ -201,14 +210,13 @@ class BrownianTreeNoiseSamplerPatched:
 
 
 def compute_cfg(uncond, cond, cfg_scale, t):
-    global adaptive_cfg
-
-    mimic_cfg = float(adaptive_cfg)
+    pid = os.getpid()
+    mimic_cfg = float(patch_settings[pid].adaptive_cfg)
     real_cfg = float(cfg_scale)
 
     real_eps = uncond + real_cfg * (cond - uncond)
 
-    if cfg_scale > adaptive_cfg:
+    if cfg_scale > patch_settings[pid].adaptive_cfg:
         mimicked_eps = uncond + mimic_cfg * (cond - uncond)
         return real_eps * t + mimicked_eps * (1 - t)
     else:
@@ -216,13 +224,13 @@ def compute_cfg(uncond, cond, cfg_scale, t):
 
 
 def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
-    global eps_record
+    pid = os.getpid()
 
     if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
         final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
 
-        if eps_record is not None:
-            eps_record = ((x - final_x0) / timestep).cpu()
+        if patch_settings[pid].eps_record is not None:
+            patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()
 
         return final_x0
 
@@ -231,16 +239,16 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
     positive_eps = x - positive_x0
     negative_eps = x - negative_x0
 
-    alpha = 0.001 * sharpness * global_diffusion_progress
+    alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress
 
     positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
     positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
 
     final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
-                            cfg_scale=cond_scale, t=global_diffusion_progress)
+                            cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)
 
-    if eps_record is not None:
-        eps_record = (final_eps / timestep).cpu()
+    if patch_settings[pid].eps_record is not None:
+        patch_settings[pid].eps_record = (final_eps / timestep).cpu()
 
     return x - final_eps
 
@@ -255,20 +263,19 @@ def round_to_64(x):
 
 
 def sdxl_encode_adm_patched(self, **kwargs):
-    global positive_adm_scale, negative_adm_scale
-
     clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
     width = kwargs.get("width", 1024)
     height = kwargs.get("height", 1024)
     target_width = width
     target_height = height
+    pid = os.getpid()
 
     if kwargs.get("prompt_type", "") == "negative":
-        width = float(width) * negative_adm_scale
-        height = float(height) * negative_adm_scale
+        width = float(width) * patch_settings[pid].negative_adm_scale
+        height = float(height) * patch_settings[pid].negative_adm_scale
     elif kwargs.get("prompt_type", "") == "positive":
-        width = float(width) * positive_adm_scale
-        height = float(height) * positive_adm_scale
+        width = float(width) * patch_settings[pid].positive_adm_scale
+        height = float(height) * patch_settings[pid].positive_adm_scale
 
     def embedder(number_list):
         h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
@@ -322,7 +329,7 @@ def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale,
 
 def timed_adm(y, timesteps):
     if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
-        y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
+        y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
         y_with_adm = y[..., :2816].clone()
         y_without_adm = y[..., 2816:].clone()
         return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
@@ -332,6 +339,7 @@ def timed_adm(y, timesteps):
 def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
     t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
     emb = self.time_embed(t_emb)
+    pid = os.getpid()
 
     guided_hint = self.input_hint_block(hint, emb, context)
 
@@ -357,19 +365,17 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
     h = self.middle_block(h, emb, context)
     outs.append(self.middle_block_out(h, emb, context))
 
-    if advanced_parameters.controlnet_softness > 0:
+    if patch_settings[pid].controlnet_softness > 0:
         for i in range(10):
             k = 1.0 - float(i) / 9.0
-            outs[i] = outs[i] * (1.0 - advanced_parameters.controlnet_softness * k)
+            outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)
 
     return outs
 
 
 def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
-    global global_diffusion_progress
-
     self.current_step = 1.0 - timesteps.to(x) / 999.0
-    global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
+    patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
 
     y = timed_adm(y, timesteps)
 
@@ -483,7 +489,7 @@ def patch_all():
     if ldm_patched.modules.model_management.directml_enabled:
         ldm_patched.modules.model_management.lowvram_available = True
         ldm_patched.modules.model_management.OOM_EXCEPTION = Exception
-    
+
     patch_all_precision()
     patch_all_clip()
 
diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py
index e59b151b..bebf9f8c 100644
--- a/modules/ui_gradio_extensions.py
+++ b/modules/ui_gradio_extensions.py
@@ -30,6 +30,7 @@ def javascript_html():
     edit_attention_js_path = webpath('javascript/edit-attention.js')
     viewer_js_path = webpath('javascript/viewer.js')
     image_viewer_js_path = webpath('javascript/imageviewer.js')
+    samples_path = webpath(os.path.abspath('./sdxl_styles/samples/fooocus_v2.jpg'))
     head = f'<script type="text/javascript">{localization_js(args_manager.args.language)}</script>\n'
     head += f'<script type="text/javascript" src="{script_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{context_menus_js_path}"></script>\n'
@@ -38,6 +39,7 @@ def javascript_html():
     head += f'<script type="text/javascript" src="{edit_attention_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{viewer_js_path}"></script>\n'
     head += f'<script type="text/javascript" src="{image_viewer_js_path}"></script>\n'
+    head += f'<meta name="samples-path" content="{samples_path}"></meta>\n'
 
     if args_manager.args.theme:
         head += f'<script type="text/javascript">set_theme(\"{args_manager.args.theme}\");</script>\n'
diff --git a/sdxl_styles/samples/abstract_expressionism.jpg b/sdxl_styles/samples/abstract_expressionism.jpg
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index 00000000..226b8fa7
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diff --git a/sdxl_styles/samples/academia.jpg b/sdxl_styles/samples/academia.jpg
new file mode 100644
index 00000000..26a700d0
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diff --git a/sdxl_styles/samples/action_figure.jpg b/sdxl_styles/samples/action_figure.jpg
new file mode 100644
index 00000000..fcd1c092
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diff --git a/sdxl_styles/samples/adorable_3d_character.jpg b/sdxl_styles/samples/adorable_3d_character.jpg
new file mode 100644
index 00000000..493bfb8f
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diff --git a/sdxl_styles/samples/adorable_kawaii.jpg b/sdxl_styles/samples/adorable_kawaii.jpg
new file mode 100644
index 00000000..52bc7733
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diff --git a/sdxl_styles/samples/ads_advertising.jpg b/sdxl_styles/samples/ads_advertising.jpg
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index 00000000..40631d4c
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diff --git a/sdxl_styles/samples/ads_automotive.jpg b/sdxl_styles/samples/ads_automotive.jpg
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index 00000000..ceea6c4d
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diff --git a/sdxl_styles/samples/ads_corporate.jpg b/sdxl_styles/samples/ads_corporate.jpg
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index 00000000..1d590743
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diff --git a/sdxl_styles/samples/ads_fashion_editorial.jpg b/sdxl_styles/samples/ads_fashion_editorial.jpg
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index 00000000..22fdd232
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diff --git a/sdxl_styles/samples/ads_food_photography.jpg b/sdxl_styles/samples/ads_food_photography.jpg
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diff --git a/sdxl_styles/samples/ads_gourmet_food_photography.jpg b/sdxl_styles/samples/ads_gourmet_food_photography.jpg
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index 00000000..305770b0
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diff --git a/sdxl_styles/samples/ads_luxury.jpg b/sdxl_styles/samples/ads_luxury.jpg
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index 00000000..54248568
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diff --git a/sdxl_styles/samples/ads_real_estate.jpg b/sdxl_styles/samples/ads_real_estate.jpg
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index 00000000..438b9fd1
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diff --git a/sdxl_styles/samples/ads_retail.jpg b/sdxl_styles/samples/ads_retail.jpg
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index 00000000..93aea1e7
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diff --git a/sdxl_styles/samples/art_deco.jpg b/sdxl_styles/samples/art_deco.jpg
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index 00000000..7a37c722
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diff --git a/sdxl_styles/samples/art_nouveau.jpg b/sdxl_styles/samples/art_nouveau.jpg
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index 00000000..e318db83
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diff --git a/sdxl_styles/samples/artstyle_abstract.jpg b/sdxl_styles/samples/artstyle_abstract.jpg
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index 00000000..d1c3223b
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diff --git a/sdxl_styles/samples/artstyle_abstract_expressionism.jpg b/sdxl_styles/samples/artstyle_abstract_expressionism.jpg
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diff --git a/sdxl_styles/samples/artstyle_constructivist.jpg b/sdxl_styles/samples/artstyle_constructivist.jpg
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diff --git a/sdxl_styles/samples/artstyle_expressionist.jpg b/sdxl_styles/samples/artstyle_expressionist.jpg
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diff --git a/sdxl_styles/samples/artstyle_hyperrealism.jpg b/sdxl_styles/samples/artstyle_hyperrealism.jpg
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diff --git a/sdxl_styles/samples/watercolor_2.jpg b/sdxl_styles/samples/watercolor_2.jpg
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diff --git a/sdxl_styles/samples/whimsical_and_playful.jpg b/sdxl_styles/samples/whimsical_and_playful.jpg
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diff --git a/update_log.md b/update_log.md
index e9a829be..1e8914d1 100644
--- a/update_log.md
+++ b/update_log.md
@@ -1,5 +1,9 @@
 **(2023 Dec 21) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid or late Jan. However, you may still see updates if other collaborators are fixing bugs or solving problems.**
 
+# 2.1.861 (requested update)
+
+* Show image preview in Style when mouse hover.
+
 # 2.1.860 (requested update)
 
 * Allow upload inpaint mask in developer mode.
diff --git a/webui.py b/webui.py
index 86940981..9ab497f1 100644
--- a/webui.py
+++ b/webui.py
@@ -11,7 +11,6 @@ import modules.async_worker as worker
 import modules.constants as constants
 import modules.flags as flags
 import modules.gradio_hijack as grh
-import modules.advanced_parameters as advanced_parameters
 import modules.style_sorter as style_sorter
 import modules.meta_parser
 import args_manager
@@ -24,9 +23,9 @@ from modules.auth import auth_enabled, check_auth
 
 def get_task(*args):
     args = list(args)
-    currentTask = args.pop(0)
-    currentTask = worker.AsyncTask(args=args)
-    return currentTask
+    args.pop(0)
+
+    return worker.AsyncTask(args=args)
 
 def generate_clicked(task):
     import ldm_patched.modules.model_management as model_management
@@ -260,7 +259,7 @@ with shared.gradio_root:
                                    queue=False, show_progress=False)
 
                 if not args_manager.args.disable_image_log:
-                    gr.HTML(f'<a href="/file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')
+                    gr.HTML(f'<a href="file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')
 
             with gr.Tab(label='Style'):
                 style_sorter.try_load_sorted_styles(
@@ -433,7 +432,7 @@ with shared.gradio_root:
                                                                  '(default is 0, always process before any mask invert)')
                         inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
                         invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)
-                        
+
                         inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine,
                                          inpaint_strength, inpaint_respective_field,
                                          inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate]
@@ -450,15 +449,6 @@ with shared.gradio_root:
                         freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
                         freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2]
 
-                adps = [disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,
-                        scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height,
-                        overwrite_vary_strength, overwrite_upscale_strength,
-                        mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint,
-                        debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness,
-                        canny_low_threshold, canny_high_threshold, refiner_swap_method]
-                adps += freeu_ctrls
-                adps += inpaint_ctrls
-
                 def dev_mode_checked(r):
                     return gr.update(visible=r)
 
@@ -523,8 +513,9 @@ with shared.gradio_root:
             inpaint_strength, inpaint_respective_field
         ], show_progress=False, queue=False)
 
-        ctrls = [
-            currentTask, prompt, negative_prompt, style_selections,
+        ctrls = [currentTask, generate_image_grid]
+        ctrls += [
+            prompt, negative_prompt, style_selections,
             performance_selection, aspect_ratios_selection, image_number, image_seed, sharpness, guidance_scale
         ]
 
@@ -532,6 +523,14 @@ with shared.gradio_root:
         ctrls += [input_image_checkbox, current_tab]
         ctrls += [uov_method, uov_input_image]
         ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
+        ctrls += [disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
+        ctrls += [sampler_name, scheduler_name]
+        ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]
+        ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]
+        ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]
+        ctrls += [refiner_swap_method, controlnet_softness]
+        ctrls += freeu_ctrls
+        ctrls += inpaint_ctrls
         ctrls += ip_ctrls
 
         state_is_generating = gr.State(False)
@@ -586,7 +585,6 @@ with shared.gradio_root:
         generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True),
                               outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \
             .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \
-            .then(advanced_parameters.set_all_advanced_parameters, inputs=adps) \
             .then(fn=get_task, inputs=ctrls, outputs=currentTask) \
             .then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \
             .then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False),