24 files changed, 1360 insertions, 354 deletions

diff --git a/modules/devices.py b/modules/devices.py
index eb422583..dc1f3cdd 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,7 +1,6 @@
+import sys, os, shlex
 import contextlib
-
 import torch
-
 from modules import errors
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
@@ -9,10 +8,22 @@ has_mps = getattr(torch, 'has_mps', False)
 
 cpu = torch.device("cpu")
 
+def extract_device_id(args, name):
+    for x in range(len(args)):
+        if name in args[x]: return args[x+1]
+    return None
 
 def get_optimal_device():
     if torch.cuda.is_available():
-        return torch.device("cuda")
+        from modules import shared
+
+        device_id = shared.cmd_opts.device_id
+
+        if device_id is not None:
+            cuda_device = f"cuda:{device_id}"
+            return torch.device(cuda_device)
+        else:
+            return torch.device("cuda")
 
     if has_mps:
         return torch.device("mps")
@@ -34,7 +45,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/extras.py b/modules/extras.py
index b853fa5b..22c5a1c1 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -39,9 +39,12 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 

         if input_dir == '':

             return outputs, "Please select an input directory.", ''

-        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]

+        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]

         for img in image_list:

-            image = Image.open(img)

+            try:

+                image = Image.open(img)

+            except Exception:

+                continue

             imageArr.append(image)

             imageNameArr.append(img)

     else:

@@ -118,10 +121,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 

         while len(cached_images) > 2:

             del cached_images[next(iter(cached_images.keys()))]

-

-        images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,

-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,

-                          forced_filename=image_name if opts.use_original_name_batch else None)

+        

+        if opts.use_original_name_batch and image_name != None:

+            basename = os.path.splitext(os.path.basename(image_name))[0]

+        else:

+            basename = ''

+

+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,

+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)

 

         if opts.enable_pnginfo:

             image.info = existing_pnginfo

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 0f041449..f73647da 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -4,13 +4,22 @@ import gradio as gr
 from modules.shared import script_path

 from modules import shared

 

-re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"

+re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'

 re_param = re.compile(re_param_code)

 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")

 re_imagesize = re.compile(r"^(\d+)x(\d+)$")

 type_of_gr_update = type(gr.update())

 

 

+def quote(text):

+    if ',' not in str(text):

+        return text

+

+    text = str(text)

+    text = text.replace('\\', '\\\\')

+    text = text.replace('"', '\\"')

+    return f'"{text}"'

+

 def parse_generation_parameters(x: str):

     """parses generation parameters string, the one you see in text field under the picture in UI:

 ```

@@ -83,7 +92,12 @@ def connect_paste(button, paste_fields, input_comp, js=None):
             else:

                 try:

                     valtype = type(output.value)

-                    val = valtype(v)

+

+                    if valtype == bool and v == "False":

+                        val = False

+                    else:

+                        val = valtype(v)

+

                     res.append(gr.update(value=val))

                 except Exception:

                     res.append(gr.update())

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index e493f366..b7a04038 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -41,12 +41,12 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))

 

             # Add an activation func

-            if activation_func == "linear":

+            if activation_func == "linear" or activation_func is None:

                 pass

             elif activation_func in self.activation_dict:

                 linears.append(self.activation_dict[activation_func]())

             else:

-                raise NotImplementedError(

+                raise RuntimeError(

                     "Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"

                 )

 

@@ -65,7 +65,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)

         else:

             for layer in self.linear:

-                if isinstance(layer, torch.nn.Linear):

+                if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:

                     layer.weight.data.normal_(mean=0.0, std=0.01)

                     layer.bias.data.zero_()

 

@@ -93,7 +93,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):

         layer_structure = []

         for layer in self.linear:

-            if isinstance(layer, torch.nn.Linear):

+            if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:

                 layer_structure += [layer.weight, layer.bias]

         return layer_structure

 

@@ -272,6 +272,9 @@ def stack_conds(conds):
 

 

 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

+    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.

+    from modules import images

+

     assert hypernetwork_name, 'hypernetwork not selected'

 

     path = shared.hypernetworks.get(hypernetwork_name, None)

@@ -314,6 +317,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 

     last_saved_file = "<none>"

     last_saved_image = "<none>"

+    forced_filename = "<none>"

 

     ititial_step = hypernetwork.step or 0

     if ititial_step > steps:

@@ -353,7 +357,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         pbar.set_description(f"loss: {mean_loss:.7f}")

 

         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:

-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')

+            # Before saving, change name to match current checkpoint.

+            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'

+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')

             hypernetwork.save(last_saved_file)

 

         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {

@@ -362,7 +368,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         })

 

         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:

-            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')

+            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'

+            last_saved_image = os.path.join(images_dir, forced_filename)

 

             optimizer.zero_grad()

             shared.sd_model.cond_stage_model.to(devices.device)

@@ -398,7 +405,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 

             if image is not None:

                 shared.state.current_image = image

-                image.save(last_saved_image)

+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)

                 last_saved_image += f", prompt: {preview_text}"

 

         shared.state.job_no = hypernetwork.step

@@ -408,7 +415,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 Loss: {mean_loss:.7f}<br/>

 Step: {hypernetwork.step}<br/>

 Last prompt: {html.escape(entries[0].cond_text)}<br/>

-Last saved embedding: {html.escape(last_saved_file)}<br/>

+Last saved hypernetwork: {html.escape(last_saved_file)}<br/>

 Last saved image: {html.escape(last_saved_image)}<br/>

 </p>

 """

@@ -417,6 +424,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 

     hypernetwork.sd_checkpoint = checkpoint.hash

     hypernetwork.sd_checkpoint_name = checkpoint.model_name

+    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).

+    hypernetwork.name = hypernetwork_name

+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')

     hypernetwork.save(filename)

 

     return hypernetwork, filename

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 5f6f17b6..2b472d87 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,9 +9,13 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork

 

 

-def create_hypernetwork(name, enable_sizes, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):

+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):

+    # Remove illegal characters from name.

+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))

+

     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")

-    assert not os.path.exists(fn), f"file {fn} already exists"

+    if not overwrite_old:

+        assert not os.path.exists(fn), f"file {fn} already exists"

 

     if type(layer_structure) == str:

         layer_structure = [float(x.strip()) for x in layer_structure.split(",")]

diff --git a/modules/images_history.py b/modules/images_history.py
index 46b23e56..bc5cf11f 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,183 +1,424 @@
 import os
 import shutil
-import sys
+import time
+import hashlib
+import gradio
+system_bak_path = "webui_log_and_bak"
+custom_tab_name = "custom fold"
+faverate_tab_name = "favorites"
+tabs_list = ["txt2img", "img2img", "extras", faverate_tab_name]
+def is_valid_date(date):
+    try:
+        time.strptime(date, "%Y%m%d")
+        return True
+    except:
+        return False
+
+def reduplicative_file_move(src, dst):
+    def same_name_file(basename, path):
+        name, ext = os.path.splitext(basename)
+        f_list = os.listdir(path)
+        max_num = 0
+        for f in f_list:
+            if len(f) <= len(basename):
+                continue
+            f_ext = f[-len(ext):] if len(ext) > 0 else ""
+            if f[:len(name)] == name and f_ext == ext:                
+                if f[len(name)] == "(" and f[-len(ext)-1] == ")":
+                    number = f[len(name)+1:-len(ext)-1]
+                    if number.isdigit():
+                        if int(number) > max_num:
+                            max_num = int(number)
+        return f"{name}({max_num + 1}){ext}"
+    name = os.path.basename(src)
+    save_name = os.path.join(dst, name)
+    if not os.path.exists(save_name):
+        shutil.move(src, dst)
+    else:
+        name = same_name_file(name, dst)
+        shutil.move(src, os.path.join(dst, name))
 
-def traverse_all_files(output_dir, image_list, curr_dir=None):
-    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
+def traverse_all_files(curr_path, image_list, all_type=False):
     try:
         f_list = os.listdir(curr_path)
     except:
-        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
-            image_list.append(curr_dir)
+        if all_type or (curr_path[-10:].rfind(".") > 0 and curr_path[-4:] != ".txt" and curr_path[-4:] != ".csv"):
+            image_list.append(curr_path)
         return image_list
     for file in f_list:
-        file = file if curr_dir is None else os.path.join(curr_dir, file)
-        file_path = os.path.join(curr_path, file)
-        if file[-4:] == ".txt":
+        file = os.path.join(curr_path, file)
+        if (not all_type) and (file[-4:] == ".txt" or file[-4:] == ".csv"):
             pass
-        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
+        elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
             image_list.append(file)
         else:
-            image_list = traverse_all_files(output_dir, image_list, file)
+            image_list = traverse_all_files(file, image_list)
     return image_list
 
+def auto_sorting(dir_name):    
+    bak_path = os.path.join(dir_name, system_bak_path)
+    if not os.path.exists(bak_path):
+        os.mkdir(bak_path)
+    log_file = None 
+    files_list = []    
+    f_list = os.listdir(dir_name)
+    for file in f_list:   
+        if file == system_bak_path:
+            continue     
+        file_path = os.path.join(dir_name, file)
+        if not is_valid_date(file):
+            if file[-10:].rfind(".") > 0:
+                files_list.append(file_path)
+            else:
+                files_list = traverse_all_files(file_path, files_list, all_type=True)
 
-def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    page_index = int(page_index)
-    image_list = []
-    if not os.path.exists(dir_name):
-        pass
-    elif os.path.isdir(dir_name):
-        image_list = traverse_all_files(dir_name, image_list)
-        image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-    else:
-        print(f'ERROR: "{dir_name}" is not a directory. Check the path in the settings.', file=sys.stderr)
-    num = 48 if tabname != "extras" else 12
-    max_page_index = len(image_list) // num + 1
-    page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index
-    page_index = max_page_index if page_index > max_page_index else page_index
-    idx_frm = (page_index - 1) * num
-    image_list = image_list[idx_frm:idx_frm + num]
-    image_index = int(image_index)
-    if image_index < 0 or image_index > len(image_list) - 1:
-        current_file = None
-        hidden = None
-    else:
-        current_file = image_list[int(image_index)]
-        hidden = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
-
-
-def first_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, 1, 0, image_index, tabname)
-
-
-def end_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, -1, 0, image_index, tabname)
-
-
-def prev_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
-
-
-def next_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
-
-
-def page_index_change(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
-
-
-def show_image_info(num, image_path, filenames):
-    # print(f"select image {num}")
-    file = filenames[int(num)]
-    return file, num, os.path.join(image_path, file)
+    for file in files_list:        
+        date_str = time.strftime("%Y%m%d",time.localtime(os.path.getmtime(file)))
+        file_path = os.path.dirname(file)
+        hash_path = hashlib.md5(file_path.encode()).hexdigest()
+        path = os.path.join(dir_name, date_str, hash_path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+        if log_file is None:
+            log_file = open(os.path.join(bak_path,"path_mapping.csv"),"a") 
+        log_file.write(f"{hash_path},{file_path}\n")
+        reduplicative_file_move(file, path)
+       
+    date_list = []
+    f_list = os.listdir(dir_name)
+    for f in f_list:
+        if is_valid_date(f):
+            date_list.append(f)
+        elif f == system_bak_path:
+            continue
+        else:
+            try:
+                reduplicative_file_move(os.path.join(dir_name, f), bak_path)  
+            except:
+                pass         
+            
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    if today not in date_list:
+        date_list.append(today)
+    return sorted(date_list, reverse=True)
 
+def archive_images(dir_name, date_to):    
+    filenames = []   
+    batch_size =int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    if batch_size <= 0:
+        batch_size = opts.images_history_num_per_page * 6
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    date_to = today if date_to is None  or date_to == "" else date_to
+    date_to_bak = date_to 
+    if False: #opts.images_history_reconstruct_directory:        
+        date_list = auto_sorting(dir_name)               
+        for date in date_list:
+            if date <= date_to:
+                path = os.path.join(dir_name, date)
+                if date == today and not os.path.exists(path):
+                    continue
+                filenames = traverse_all_files(path, filenames)
+            if len(filenames) > batch_size:            
+                break
+        filenames = sorted(filenames, key=lambda file: -os.path.getmtime(file))
+    else:
+        filenames = traverse_all_files(dir_name, filenames)  
+        total_num = len(filenames) 
+        tmparray = [(os.path.getmtime(file), file) for file in filenames ]
+        date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
+        filenames = []
+        date_list = {date_to:None}
+        date = time.strftime("%Y%m%d",time.localtime(time.time()))
+        for t, f in tmparray:
+            date = time.strftime("%Y%m%d",time.localtime(t))
+            date_list[date] = None
+            if t <= date_stamp:
+                filenames.append((t, f ,date))
+        date_list = sorted(list(date_list.keys()), reverse=True)
+        sort_array = sorted(filenames, key=lambda x:-x[0])
+        if len(sort_array) > batch_size:
+            date = sort_array[batch_size][2]
+            filenames = [x[1] for x in sort_array]
+        else:
+            date =  date_to if len(sort_array) == 0 else sort_array[-1][2]
+            filenames = [x[1] for x in sort_array]
+        filenames = [x[1] for x in sort_array if x[2]>= date]   
+    num = len(filenames)  
+    last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
+    date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
+    date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
+    load_info = "<div style='color:#999' align='center'>"
+    load_info += f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
+    load_info += "</div>"
+    _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
+    return (
+        date_to,
+        load_info,
+        filenames,        
+        1,
+        image_list,
+        "",
+        "",
+        visible_num, 
+        last_date_from, 
+        gradio.update(visible=total_num > num)
+    )
 
-def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
+def delete_image(delete_num, name, filenames, image_index, visible_num):
     if name == "":
         return filenames, delete_num
     else:
         delete_num = int(delete_num)
+        visible_num = int(visible_num)
+        image_index = int(image_index)
         index = list(filenames).index(name)
         i = 0
         new_file_list = []
         for name in filenames:
             if i >= index and i < index + delete_num:
-                path = os.path.join(dir_name, name)
-                if os.path.exists(path):
-                    print(f"Delete file {path}")
-                    os.remove(path)
-                    txt_file = os.path.splitext(path)[0] + ".txt"
+                if os.path.exists(name):
+                    if visible_num == image_index:
+                        new_file_list.append(name)
+                        i += 1
+                        continue                    
+                    print(f"Delete file {name}")
+                    os.remove(name)
+                    visible_num -= 1
+                    txt_file = os.path.splitext(name)[0] + ".txt"
                     if os.path.exists(txt_file):
                         os.remove(txt_file)
                 else:
-                    print(f"Not exists file {path}")
+                    print(f"Not exists file {name}")
             else:
                 new_file_list.append(name)
             i += 1
-    return new_file_list, 1
+    return new_file_list, 1, visible_num
+
+def save_image(file_name):
+    if file_name is not None and os.path.exists(file_name):
+        shutil.copy(file_name, opts.outdir_save)
+
+def get_recent_images(page_index, step, filenames):
+    page_index = int(page_index)
+    num_of_imgs_per_page = int(opts.images_history_num_per_page)
+    max_page_index = len(filenames) // num_of_imgs_per_page + 1
+    page_index = max_page_index if page_index == -1 else page_index + step
+    page_index = 1 if page_index < 1 else page_index
+    page_index = max_page_index if page_index > max_page_index else page_index
+    idx_frm = (page_index - 1) * num_of_imgs_per_page
+    image_list = filenames[idx_frm:idx_frm + num_of_imgs_per_page]
+    length = len(filenames)
+    visible_num = num_of_imgs_per_page if  idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page 
+    visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
+    return page_index, image_list,  "", "",  visible_num
+
+def loac_batch_click(date_to):
+    if date_to is None:
+        return time.strftime("%Y%m%d",time.localtime(time.time())), []
+    else:
+        return None, []
+def forward_click(last_date_from, date_to_recorder):
+    if len(date_to_recorder) == 0:
+        return None, []
+    if last_date_from == date_to_recorder[-1]:
+        date_to_recorder = date_to_recorder[:-1]
+    if len(date_to_recorder) == 0:
+        return None, []
+    return date_to_recorder[-1], date_to_recorder[:-1]
+
+def backward_click(last_date_from, date_to_recorder):
+    if last_date_from is None or last_date_from == "":
+        return time.strftime("%Y%m%d",time.localtime(time.time())), []
+    if len(date_to_recorder) == 0 or last_date_from != date_to_recorder[-1]:
+        date_to_recorder.append(last_date_from)    
+    return last_date_from, date_to_recorder
+
+
+def first_page_click(page_index, filenames):
+    return get_recent_images(1, 0, filenames)
 
+def end_page_click(page_index, filenames):
+    return get_recent_images(-1, 0, filenames)
+
+def prev_page_click(page_index, filenames):
+    return get_recent_images(page_index, -1, filenames)
+
+def next_page_click(page_index, filenames):
+    return get_recent_images(page_index, 1, filenames)
+
+def page_index_change(page_index, filenames):
+    return get_recent_images(page_index, 0, filenames)
+
+def show_image_info(tabname_box, num, page_index, filenames):
+    file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]   
+    tm =   "<div style='color:#999' align='right'>" + time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + "</div>"
+    return file, tm, num, file
+
+def enable_page_buttons():
+    return gradio.update(visible=True)
+
+def change_dir(img_dir, date_to):
+    warning = None
+    try:
+        if os.path.exists(img_dir):
+            try:
+                f = os.listdir(img_dir)                
+            except:
+                warning = f"'{img_dir} is not a directory"
+        else:
+            warning = "The directory is not exist"
+    except:
+        warning = "The format of the directory is incorrect"
+    if warning is None:        
+        today = time.strftime("%Y%m%d",time.localtime(time.time()))
+        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today, gradio.update(visible=True), gradio.update(visible=True)
+    else:
+        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to, gradio.update(visible=False), gradio.update(visible=False)
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    if opts.outdir_samples != "":
-        dir_name = opts.outdir_samples
-    elif tabname == "txt2img":
+    custom_dir = False
+    if tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
+    elif tabname == faverate_tab_name:
+        dir_name = opts.outdir_save
     else:
-        return
-    with gr.Row():
-        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
-        first_page = gr.Button('First Page')
-        prev_page = gr.Button('Prev Page')
-        page_index = gr.Number(value=1, label="Page Index")
-        next_page = gr.Button('Next Page')
-        end_page = gr.Button('End Page')
-    with gr.Row(elem_id=tabname + "_images_history"):
-        with gr.Row():
-            with gr.Column(scale=2):
-                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                with gr.Row():
-                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
-                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
-            with gr.Column():
-                with gr.Row():
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                with gr.Row():
-                    with gr.Column():
-                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                        img_file_name = gr.Textbox(label="File Name", interactive=False)
-                with gr.Row():
+        custom_dir = True
+        dir_name = None
+
+    if not custom_dir:
+        d = dir_name.split("/")
+        dir_name = d[0]
+        for p in d[1:]:
+            dir_name = os.path.join(dir_name, p)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name)
+
+    with gr.Column() as page_panel:            
+            with gr.Row():                
+                with gr.Column(scale=1, visible=not custom_dir) as load_batch_box: 
+                    load_batch = gr.Button('Load', elem_id=tabname + "_images_history_start", full_width=True)    
+                with gr.Column(scale=4): 
+                    with gr.Row():                        
+                        img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir)  
+            with gr.Row():
+                with gr.Column(visible=False, scale=1) as batch_panel: 
+                    with gr.Row():
+                        forward = gr.Button('Prev batch')
+                        backward = gr.Button('Next batch')
+                with gr.Column(scale=3):
+                    load_info =     gr.HTML(visible=not custom_dir)
+            with gr.Row(visible=False) as warning:                 
+                warning_box = gr.Textbox("Message", interactive=False)                        
+
+            with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
+                with gr.Column(scale=2):                     
+                    with gr.Row(visible=True) as turn_page_buttons:                        
+                        #date_to = gr.Dropdown(label="Date to")                          
+                        first_page = gr.Button('First Page')
+                        prev_page = gr.Button('Prev Page')
+                        page_index = gr.Number(value=1, label="Page Index")
+                        next_page = gr.Button('Next Page')
+                        end_page = gr.Button('End Page')                 
+
+                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=opts.images_history_grid_num)
+                    with gr.Row():
+                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
+                        delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
+                        
+                with gr.Column():                    
+                    with gr.Row():
+                        with gr.Column():
+                            img_file_info = gr.Textbox(label="Generate Info", interactive=False, lines=6)
+                            gr.HTML("<hr>")                           
+                            img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
+                            img_file_time= gr.HTML()
+                    with gr.Row():
+                        if tabname != faverate_tab_name:
+                            save_btn = gr.Button('Collect')
+                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                           
+                            
                     # hiden items
+                    with gr.Row(visible=False): 
+                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
+                        batch_date_to = gr.Textbox(label="Date to")  
+                        visible_img_num = gr.Number()                     
+                        date_to_recorder = gr.State([])
+                        last_date_from = gr.Textbox()
+                        tabname_box = gr.Textbox(tabname)
+                        image_index = gr.Textbox(value=-1)
+                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
+                        filenames = gr.State()
+                        all_images_list = gr.State()
+                        hidden = gr.Image(type="pil")
+                        info1 = gr.Textbox()
+                        info2 = gr.Textbox()
 
-                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
-                    tabname_box = gr.Textbox(tabname, visible=False)
-                    image_index = gr.Textbox(value=-1, visible=False)
-                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
-                    filenames = gr.State()
-                    hidden = gr.Image(type="pil", visible=False)
-                    info1 = gr.Textbox(visible=False)
-                    info2 = gr.Textbox(visible=False)
-
-    # turn pages
-    gallery_inputs = [img_path, page_index, image_index, tabname_box]
-    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
-
-    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
+    img_path.submit(change_dir, inputs=[img_path, batch_date_to], outputs=[warning, main_panel, warning_box, batch_date_to, load_batch_box, load_info])
+
+    #change batch
+    change_date_output = [batch_date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from,  batch_panel]  
+   
+    batch_date_to.change(archive_images, inputs=[img_path, batch_date_to], outputs=change_date_output)   
+    batch_date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
+    batch_date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
+
+    load_batch.click(loac_batch_click, inputs=[batch_date_to], outputs=[batch_date_to, date_to_recorder])
+    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
+    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
+
+
+    #delete
+    delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
+    delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None) 
+    if tabname != faverate_tab_name: 
+        save_btn.click(save_image, inputs=[img_file_name], outputs=None)     
+
+    #turn page
+    gallery_inputs = [page_index, filenames]
+    gallery_outputs = [page_index, history_gallery, img_file_name, img_file_time, visible_img_num]
+    first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
+    renew_page.click(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
+
+    first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
 
     # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
-    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, img_file_time, image_index, hidden])
+    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)   
     hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-
-    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
     switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
     switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
 
+   
 
-def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
+def create_history_tabs(gr, sys_opts, cmp_ops, run_pnginfo, switch_dict):
+    global opts;
+    opts = sys_opts
+    loads_files_num = int(opts.images_history_num_per_page)
+    num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    if cmp_ops.browse_all_images:
+        tabs_list.append(custom_tab_name)
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
-            with gr.Tab("txt2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
-                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
-            with gr.Tab("img2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
-            with gr.Tab("extras history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
+            for tab in tabs_list:
+                with gr.Tab(tab):
+                    with gr.Blocks(analytics_enabled=False) :
+                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict)
+        gradio.Checkbox(opts.images_history_preload, elem_id="images_history_preload", visible=False)         
+        gradio.Textbox(",".join(tabs_list), elem_id="images_history_tabnames_list", visible=False)    
+        
     return images_history
diff --git a/modules/img2img.py b/modules/img2img.py
index 24126774..8d9f7cf9 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -109,6 +109,9 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,

     )

 

+    p.scripts = modules.scripts.scripts_txt2img

+    p.script_args = args

+

     if shared.cmd_opts.enable_console_prompts:

         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

 

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 64b91eb4..65b05d34 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -28,9 +28,11 @@ class InterrogateModels:
     clip_preprocess = None

     categories = None

     dtype = None

+    running_on_cpu = None

 

     def __init__(self, content_dir):

         self.categories = []

+        self.running_on_cpu = devices.device_interrogate == torch.device("cpu")

 

         if os.path.exists(content_dir):

             for filename in os.listdir(content_dir):

@@ -53,7 +55,11 @@ class InterrogateModels:
     def load_clip_model(self):

         import clip

 

-        model, preprocess = clip.load(clip_model_name)

+        if self.running_on_cpu:

+            model, preprocess = clip.load(clip_model_name, device="cpu")

+        else:

+            model, preprocess = clip.load(clip_model_name)

+

         model.eval()

         model = model.to(devices.device_interrogate)

 

@@ -62,14 +68,14 @@ class InterrogateModels:
     def load(self):

         if self.blip_model is None:

             self.blip_model = self.load_blip_model()

-            if not shared.cmd_opts.no_half:

+            if not shared.cmd_opts.no_half and not self.running_on_cpu:

                 self.blip_model = self.blip_model.half()

 

         self.blip_model = self.blip_model.to(devices.device_interrogate)

 

         if self.clip_model is None:

             self.clip_model, self.clip_preprocess = self.load_clip_model()

-            if not shared.cmd_opts.no_half:

+            if not shared.cmd_opts.no_half and not self.running_on_cpu:

                 self.clip_model = self.clip_model.half()

 

         self.clip_model = self.clip_model.to(devices.device_interrogate)

diff --git a/modules/lowvram.py b/modules/lowvram.py
index 7eba1349..f327c3df 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -1,9 +1,8 @@
 import torch

-from modules.devices import get_optimal_device

+from modules import devices

 

 module_in_gpu = None

 cpu = torch.device("cpu")

-device = gpu = get_optimal_device()

 

 

 def send_everything_to_cpu():

@@ -33,7 +32,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         if module_in_gpu is not None:

             module_in_gpu.to(cpu)

 

-        module.to(gpu)

+        module.to(devices.device)

         module_in_gpu = module

 

     # see below for register_forward_pre_hook;

@@ -51,7 +50,7 @@ def setup_for_low_vram(sd_model, use_medvram):
     # send the model to GPU. Then put modules back. the modules will be in CPU.

     stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model

     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None

-    sd_model.to(device)

+    sd_model.to(devices.device)

     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored

 

     # register hooks for those the first two models

@@ -70,7 +69,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         # so that only one of them is in GPU at a time

         stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed

         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None

-        sd_model.model.to(device)

+        sd_model.model.to(devices.device)

         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored

 

         # install hooks for bits of third model

diff --git a/modules/processing.py b/modules/processing.py
index bcb0c32c..372489f7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional

 

 import modules.sd_hijack

-from modules import devices, prompt_parser, masking, sd_samplers, lowvram

+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste

 from modules.sd_hijack import model_hijack

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

@@ -104,6 +104,12 @@ class StableDiffusionProcessing():
             self.seed_resize_from_h = 0

             self.seed_resize_from_w = 0

 

+        self.scripts = None

+        self.script_args = None

+        self.all_prompts = None

+        self.all_seeds = None

+        self.all_subseeds = None

+

 

     def init(self, all_prompts, all_seeds, all_subseeds):

         pass

@@ -304,7 +310,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",

         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),

         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),

-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]),

+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),

         "Batch size": (None if p.batch_size < 2 else p.batch_size),

         "Batch pos": (None if p.batch_size < 2 else position_in_batch),

         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),

@@ -318,7 +324,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 

     generation_params.update(p.extra_generation_params)

 

-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])

+    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])

 

     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""

 

@@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     shared.prompt_styles.apply_styles(p)

 

     if type(p.prompt) == list:

-        all_prompts = p.prompt

+        p.all_prompts = p.prompt

     else:

-        all_prompts = p.batch_size * p.n_iter * [p.prompt]

+        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]

 

     if type(seed) == list:

-        all_seeds = seed

+        p.all_seeds = seed

     else:

-        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]

+        p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]

 

     if type(subseed) == list:

-        all_subseeds = subseed

+        p.all_subseeds = subseed

     else:

-        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]

+        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]

 

     def infotext(iteration=0, position_in_batch=0):

-        return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)

+        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)

 

     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:

         model_hijack.embedding_db.load_textual_inversion_embeddings()

 

+    if p.scripts is not None:

+        p.scripts.run_alwayson_scripts(p)

+

     infotexts = []

     output_images = []

 

     with torch.no_grad(), p.sd_model.ema_scope():

         with devices.autocast():

-            p.init(all_prompts, all_seeds, all_subseeds)

+            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)

 

         if state.job_count == -1:

             state.job_count = p.n_iter

@@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:

                 break

 

-            prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]

-            seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]

-            subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

+            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]

+            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]

+            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

 

             if (len(prompts) == 0):

                 break

@@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1

 

             if opts.grid_save:

-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

 

     devices.torch_gc()

-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)

+    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)

 

 

 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

@@ -540,17 +549,37 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f

             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f

 

+    def create_dummy_mask(self, x, width=None, height=None):

+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:

+            height = height or self.height

+            width = width or self.width

+

+            # The "masked-image" in this case will just be all zeros since the entire image is masked.

+            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)

+            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 

+

+            # Add the fake full 1s mask to the first dimension.

+            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)

+            image_conditioning = image_conditioning.to(x.dtype)

+

+        else:

+            # Dummy zero conditioning if we're not using inpainting model.

+            # Still takes up a bit of memory, but no encoder call.

+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)

+

+        return image_conditioning

 

     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

 

         if not self.enable_hr:

             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))

             return samples

 

         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))

 

         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]

 

@@ -587,7 +616,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None

         devices.torch_gc()

 

-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)

+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))

 

         return samples

 

@@ -613,6 +642,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpainting_mask_invert = inpainting_mask_invert

         self.mask = None

         self.nmask = None

+        self.image_conditioning = None

 

     def init(self, all_prompts, all_seeds, all_subseeds):

         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)

@@ -714,10 +744,39 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:

                 self.init_latent = self.init_latent * self.mask

 

+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:

+            if self.image_mask is not None:

+                conditioning_mask = np.array(self.image_mask.convert("L"))

+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0

+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])

+

+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0

+                conditioning_mask = torch.round(conditioning_mask)

+            else:

+                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])

+

+            # Create another latent image, this time with a masked version of the original input.

+            conditioning_mask = conditioning_mask.to(image.device)

+            conditioning_image = image * (1.0 - conditioning_mask)

+            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))

+

+            # Create the concatenated conditioning tensor to be fed to `c_concat`

+            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])

+            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)

+            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)

+            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)

+        else:

+            self.image_conditioning = torch.zeros(

+                self.init_latent.shape[0], 5, 1, 1,

+                dtype=self.init_latent.dtype,

+                device=self.init_latent.device

+            )

+

+

     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

 

-        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)

+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

 

         if self.mask is not None:

             samples = samples * self.nmask + self.init_latent * self.mask

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
new file mode 100644
index 00000000..866b7acd
--- /dev/null
+++ b/modules/script_callbacks.py
@@ -0,0 +1,42 @@
+

+callbacks_model_loaded = []

+callbacks_ui_tabs = []

+

+

+def clear_callbacks():

+    callbacks_model_loaded.clear()

+    callbacks_ui_tabs.clear()

+

+

+def model_loaded_callback(sd_model):

+    for callback in callbacks_model_loaded:

+        callback(sd_model)

+

+

+def ui_tabs_callback():

+    res = []

+

+    for callback in callbacks_ui_tabs:

+        res += callback() or []

+

+    return res

+

+

+def on_model_loaded(callback):

+    """register a function to be called when the stable diffusion model is created; the model is

+    passed as an argument"""

+    callbacks_model_loaded.append(callback)

+

+

+def on_ui_tabs(callback):

+    """register a function to be called when the UI is creating new tabs.

+    The function must either return a None, which means no new tabs to be added, or a list, where

+    each element is a tuple:

+        (gradio_component, title, elem_id)

+

+    gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)

+    title is tab text displayed to user in the UI

+    elem_id is HTML id for the tab

+    """

+    callbacks_ui_tabs.append(callback)

+

diff --git a/modules/scripts.py b/modules/scripts.py
index 1039fa9c..9323af3e 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,86 +1,175 @@
 import os

 import sys

 import traceback

+from collections import namedtuple

 

 import modules.ui as ui

 import gradio as gr

 

 from modules.processing import StableDiffusionProcessing

-from modules import shared

+from modules import shared, paths, script_callbacks

+

+AlwaysVisible = object()

+

 

 class Script:

     filename = None

     args_from = None

     args_to = None

+    alwayson = False

+

+    infotext_fields = None

+    """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when

+    parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example

+    """

 

-    # The title of the script. This is what will be displayed in the dropdown menu.

     def title(self):

+        """this function should return the title of the script. This is what will be displayed in the dropdown menu."""

+

         raise NotImplementedError()

 

-    # How the script is displayed in the UI. See https://gradio.app/docs/#components

-    # for the different UI components you can use and how to create them.

-    # Most UI components can return a value, such as a boolean for a checkbox.

-    # The returned values are passed to the run method as parameters.

     def ui(self, is_img2img):

+        """this function should create gradio UI elements. See https://gradio.app/docs/#components

+        The return value should be an array of all components that are used in processing.

+        Values of those returned componenbts will be passed to run() and process() functions.

+        """

+

         pass

 

-    # Determines when the script should be shown in the dropdown menu via the 

-    # returned value. As an example:

-    # is_img2img is True if the current tab is img2img, and False if it is txt2img.

-    # Thus, return is_img2img to only show the script on the img2img tab.

     def show(self, is_img2img):

+        """

+        is_img2img is True if this function is called for the img2img interface, and Fasle otherwise

+

+        This function should return:

+         - False if the script should not be shown in UI at all

+         - True if the script should be shown in UI if it's scelected in the scripts drowpdown

+         - script.AlwaysVisible if the script should be shown in UI at all times

+         """

+

         return True

 

-    # This is where the additional processing is implemented. The parameters include

-    # self, the model object "p" (a StableDiffusionProcessing class, see

-    # processing.py), and the parameters returned by the ui method.

-    # Custom functions can be defined here, and additional libraries can be imported 

-    # to be used in processing. The return value should be a Processed object, which is

-    # what is returned by the process_images method.

-    def run(self, *args):

+    def run(self, p, *args):

+        """

+        This function is called if the script has been selected in the script dropdown.

+        It must do all processing and return the Processed object with results, same as

+        one returned by processing.process_images.

+

+        Usually the processing is done by calling the processing.process_images function.

+

+        args contains all values returned by components from ui()

+        """

+

         raise NotImplementedError()

 

-    # The description method is currently unused.

-    # To add a description that appears when hovering over the title, amend the "titles" 

-    # dict in script.js to include the script title (returned by title) as a key, and 

-    # your description as the value.

+    def process(self, p, *args):

+        """

+        This function is called before processing begins for AlwaysVisible scripts.

+        scripts. You can modify the processing object (p) here, inject hooks, etc.

+        """

+

+        pass

+

     def describe(self):

+        """unused"""

         return ""

 

 

+current_basedir = paths.script_path

+

+

+def basedir():

+    """returns the base directory for the current script. For scripts in the main scripts directory,

+    this is the main directory (where webui.py resides), and for scripts in extensions directory

+    (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)

+    """

+    return current_basedir

+

+

 scripts_data = []

+ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])

+ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"])

 

 

-def load_scripts(basedir):

-    if not os.path.exists(basedir):

-        return

+def list_scripts(scriptdirname, extension):

+    scripts_list = []

 

-    for filename in sorted(os.listdir(basedir)):

-        path = os.path.join(basedir, filename)

+    basedir = os.path.join(paths.script_path, scriptdirname)

+    if os.path.exists(basedir):

+        for filename in sorted(os.listdir(basedir)):

+            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))

 

-        if os.path.splitext(path)[1].lower() != '.py':

-            continue

+    extdir = os.path.join(paths.script_path, "extensions")

+    if os.path.exists(extdir):

+        for dirname in sorted(os.listdir(extdir)):

+            dirpath = os.path.join(extdir, dirname)

+            scriptdirpath = os.path.join(dirpath, scriptdirname)

+

+            if not os.path.isdir(scriptdirpath):

+                continue

+

+            for filename in sorted(os.listdir(scriptdirpath)):

+                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))

+

+    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]

+

+    return scripts_list

+

+

+def list_files_with_name(filename):

+    res = []

+

+    dirs = [paths.script_path]

+

+    extdir = os.path.join(paths.script_path, "extensions")

+    if os.path.exists(extdir):

+        dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]

 

-        if not os.path.isfile(path):

+    for dirpath in dirs:

+        if not os.path.isdir(dirpath):

             continue

 

+        path = os.path.join(dirpath, filename)

+        if os.path.isfile(filename):

+            res.append(path)

+

+    return res

+

+

+def load_scripts():

+    global current_basedir

+    scripts_data.clear()

+    script_callbacks.clear_callbacks()

+

+    scripts_list = list_scripts("scripts", ".py")

+

+    syspath = sys.path

+

+    for scriptfile in sorted(scripts_list):

         try:

-            with open(path, "r", encoding="utf8") as file:

+            if scriptfile.basedir != paths.script_path:

+                sys.path = [scriptfile.basedir] + sys.path

+            current_basedir = scriptfile.basedir

+

+            with open(scriptfile.path, "r", encoding="utf8") as file:

                 text = file.read()

 

             from types import ModuleType

-            compiled = compile(text, path, 'exec')

-            module = ModuleType(filename)

+            compiled = compile(text, scriptfile.path, 'exec')

+            module = ModuleType(scriptfile.filename)

             exec(compiled, module.__dict__)

 

             for key, script_class in module.__dict__.items():

                 if type(script_class) == type and issubclass(script_class, Script):

-                    scripts_data.append((script_class, path))

+                    scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir))

 

         except Exception:

-            print(f"Error loading script: {filename}", file=sys.stderr)

+            print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)

             print(traceback.format_exc(), file=sys.stderr)

 

+        finally:

+            sys.path = syspath

+            current_basedir = paths.script_path

+

 

 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):

     try:

@@ -96,56 +185,80 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
 class ScriptRunner:

     def __init__(self):

         self.scripts = []

+        self.selectable_scripts = []

+        self.alwayson_scripts = []

         self.titles = []

+        self.infotext_fields = []

 

     def setup_ui(self, is_img2img):

-        for script_class, path in scripts_data:

+        for script_class, path, basedir in scripts_data:

             script = script_class()

             script.filename = path

 

-            if not script.show(is_img2img):

-                continue

+            visibility = script.show(is_img2img)

 

-            self.scripts.append(script)

+            if visibility == AlwaysVisible:

+                self.scripts.append(script)

+                self.alwayson_scripts.append(script)

+                script.alwayson = True

 

-        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]

+            elif visibility:

+                self.scripts.append(script)

+                self.selectable_scripts.append(script)

 

-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")

-        dropdown.save_to_config = True

-        inputs = [dropdown]

+        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]

+

+        inputs = [None]

+        inputs_alwayson = [True]

 

-        for script in self.scripts:

+        def create_script_ui(script, inputs, inputs_alwayson):

             script.args_from = len(inputs)

             script.args_to = len(inputs)

 

             controls = wrap_call(script.ui, script.filename, "ui", is_img2img)

 

             if controls is None:

-                continue

+                return

 

             for control in controls:

                 control.custom_script_source = os.path.basename(script.filename)

-                control.visible = False

+                if not script.alwayson:

+                    control.visible = False

+

+            if script.infotext_fields is not None:

+                self.infotext_fields += script.infotext_fields

 

             inputs += controls

+            inputs_alwayson += [script.alwayson for _ in controls]

             script.args_to = len(inputs)

 

+        for script in self.alwayson_scripts:

+            with gr.Group():

+                create_script_ui(script, inputs, inputs_alwayson)

+

+        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")

+        dropdown.save_to_config = True

+        inputs[0] = dropdown

+

+        for script in self.selectable_scripts:

+            create_script_ui(script, inputs, inputs_alwayson)

+

         def select_script(script_index):

-            if 0 < script_index <= len(self.scripts):

-                script = self.scripts[script_index-1]

+            if 0 < script_index <= len(self.selectable_scripts):

+                script = self.selectable_scripts[script_index-1]

                 args_from = script.args_from

                 args_to = script.args_to

             else:

                 args_from = 0

                 args_to = 0

 

-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]

+            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]

 

         def init_field(title):

             if title == 'None':

                 return

             script_index = self.titles.index(title)

-            script = self.scripts[script_index]

+            script = self.selectable_scripts[script_index]

             for i in range(script.args_from, script.args_to):

                 inputs[i].visible = True

 

@@ -164,7 +277,7 @@ class ScriptRunner:
         if script_index == 0:

             return None

 

-        script = self.scripts[script_index-1]

+        script = self.selectable_scripts[script_index-1]

 

         if script is None:

             return None

@@ -176,7 +289,16 @@ class ScriptRunner:
 

         return processed

 

-    def reload_sources(self):

+    def run_alwayson_scripts(self, p):

+        for script in self.alwayson_scripts:

+            try:

+                script_args = p.script_args[script.args_from:script.args_to]

+                script.process(p, *script_args)

+            except Exception:

+                print(f"Error running alwayson script: {script.filename}", file=sys.stderr)

+                print(traceback.format_exc(), file=sys.stderr)

+

+    def reload_sources(self, cache):

         for si, script in list(enumerate(self.scripts)):

             with open(script.filename, "r", encoding="utf8") as file:

                 args_from = script.args_from

@@ -186,9 +308,12 @@ class ScriptRunner:
 

                 from types import ModuleType

 

-                compiled = compile(text, filename, 'exec')

-                module = ModuleType(script.filename)

-                exec(compiled, module.__dict__)

+                module = cache.get(filename, None)

+                if module is None:

+                    compiled = compile(text, filename, 'exec')

+                    module = ModuleType(script.filename)

+                    exec(compiled, module.__dict__)

+                    cache[filename] = module

 

                 for key, script_class in module.__dict__.items():

                     if type(script_class) == type and issubclass(script_class, Script):

@@ -197,19 +322,22 @@ class ScriptRunner:
                         self.scripts[si].args_from = args_from

                         self.scripts[si].args_to = args_to

 

+

 scripts_txt2img = ScriptRunner()

 scripts_img2img = ScriptRunner()

 

+

 def reload_script_body_only():

-    scripts_txt2img.reload_sources()

-    scripts_img2img.reload_sources()

+    cache = {}

+    scripts_txt2img.reload_sources(cache)

+    scripts_img2img.reload_sources(cache)

 

 

-def reload_scripts(basedir):

+def reload_scripts():

     global scripts_txt2img, scripts_img2img

 

-    scripts_data.clear()

-    load_scripts(basedir)

+    load_scripts()

 

     scripts_txt2img = ScriptRunner()

     scripts_img2img = ScriptRunner()

+

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 984b35c4..0f10828e 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -19,6 +19,7 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity

 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward

 

+

 def apply_optimizations():

     undo_optimizations()

 

@@ -167,11 +168,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

                     remade_tokens = remade_tokens[:last_comma]

                     length = len(remade_tokens)

-                    

+

                     rem = int(math.ceil(length / 75)) * 75 - length

                     remade_tokens += [id_end] * rem + reloc_tokens

                     multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults

-                

+

                 if embedding is None:

                     remade_tokens.append(token)

                     multipliers.append(weight)

@@ -223,7 +224,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

 

-

     def process_text_old(self, text):

         id_start = self.wrapped.tokenizer.bos_token_id

         id_end = self.wrapped.tokenizer.eos_token_id

@@ -280,7 +280,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

                 token_count = len(remade_tokens)

                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))

-                remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]

+                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]

                 cache[tuple_tokens] = (remade_tokens, fixes, multipliers)

 

             multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))

@@ -290,7 +290,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             hijack_fixes.append(fixes)

             batch_multipliers.append(multipliers)

         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

-    

+

     def forward(self, text):

         use_old = opts.use_old_emphasis_implementation

         if use_old:

@@ -302,11 +302,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

         if len(used_custom_terms) > 0:

             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))

-        

+

         if use_old:

             self.hijack.fixes = hijack_fixes

             return self.process_tokens(remade_batch_tokens, batch_multipliers)

-        

+

         z = None

         i = 0

         while max(map(len, remade_batch_tokens)) != 0:

@@ -320,7 +320,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     if fix[0] == i:

                         fixes.append(fix[1])

                 self.hijack.fixes.append(fixes)

-            

+

             tokens = []

             multipliers = []

             for j in range(len(remade_batch_tokens)):

@@ -333,19 +333,18 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

             z1 = self.process_tokens(tokens, multipliers)

             z = z1 if z is None else torch.cat((z, z1), axis=-2)

-            

+

             remade_batch_tokens = rem_tokens

             batch_multipliers = rem_multipliers

             i += 1

-        

+

         return z

-        

-    

+

     def process_tokens(self, remade_batch_tokens, batch_multipliers):

         if not opts.use_old_emphasis_implementation:

             remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]

             batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]

-        

+

         tokens = torch.asarray(remade_batch_tokens).to(device)

         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)

 

@@ -385,8 +384,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
         for fixes, tensor in zip(batch_fixes, inputs_embeds):

             for offset, embedding in fixes:

                 emb = embedding.vec

-                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])

-                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])

+                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])

+                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])

 

             vecs.append(tensor)

 

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
new file mode 100644
index 00000000..fd92a335
--- /dev/null
+++ b/modules/sd_hijack_inpainting.py
@@ -0,0 +1,331 @@
+import torch
+
+from einops import repeat
+from omegaconf import ListConfig
+
+import ldm.models.diffusion.ddpm
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.ddim import DDIMSampler, noise_like
+
+# =================================================================================================
+# Monkey patch DDIMSampler methods from RunwayML repo directly.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
+# =================================================================================================
+@torch.no_grad()
+def sample_ddim(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+    samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+@torch.no_grad()
+def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None):
+    b, *_, device = *x.shape, x.device
+
+    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+        e_t = self.model.apply_model(x, t, c)
+    else:
+        x_in = torch.cat([x] * 2)
+        t_in = torch.cat([t] * 2)
+        if isinstance(c, dict):
+            assert isinstance(unconditional_conditioning, dict)
+            c_in = dict()
+            for k in c:
+                if isinstance(c[k], list):
+                    c_in[k] = [
+                        torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                        for i in range(len(c[k]))
+                    ]
+                else:
+                    c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+        else:
+            c_in = torch.cat([unconditional_conditioning, c])
+        e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+        e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+    if score_corrector is not None:
+        assert self.model.parameterization == "eps"
+        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+    # select parameters corresponding to the currently considered timestep
+    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+    # current prediction for x_0
+    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+    if quantize_denoised:
+        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+    # direction pointing to x_t
+    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+    if noise_dropout > 0.:
+        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+    return x_prev, pred_x0
+
+
+# =================================================================================================
+# Monkey patch PLMSSampler methods.
+# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
+# Adapted from:
+# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
+# =================================================================================================
+@torch.no_grad()
+def sample_plms(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for PLMS sampling is {size}')
+
+    samples, intermediates = self.plms_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+
+@torch.no_grad()
+def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+    b, *_, device = *x.shape, x.device
+
+    def get_model_output(x, t):
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            
+            if isinstance(c, dict):
+                assert isinstance(unconditional_conditioning, dict)
+                c_in = dict()
+                for k in c:
+                    if isinstance(c[k], list):
+                        c_in[k] = [
+                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                            for i in range(len(c[k]))
+                        ]
+                    else:
+                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+            else:
+                c_in = torch.cat([unconditional_conditioning, c])
+
+            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps"
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        return e_t
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    e_t = get_model_output(x, t)
+    if len(old_eps) == 0:
+        # Pseudo Improved Euler (2nd order)
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+        e_t_next = get_model_output(x_prev, t_next)
+        e_t_prime = (e_t + e_t_next) / 2
+    elif len(old_eps) == 1:
+        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (3 * e_t - old_eps[-1]) / 2
+    elif len(old_eps) == 2:
+        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+    elif len(old_eps) >= 3:
+        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+    return x_prev, pred_x0, e_t
+    
+# =================================================================================================
+# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
+# =================================================================================================
+
+@torch.no_grad()
+def get_unconditional_conditioning(self, batch_size, null_label=None):
+    if null_label is not None:
+        xc = null_label
+        if isinstance(xc, ListConfig):
+            xc = list(xc)
+        if isinstance(xc, dict) or isinstance(xc, list):
+            c = self.get_learned_conditioning(xc)
+        else:
+            if hasattr(xc, "to"):
+                xc = xc.to(self.device)
+            c = self.get_learned_conditioning(xc)
+    else:
+        # todo: get null label from cond_stage_model
+        raise NotImplementedError()
+    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
+    return c
+
+
+class LatentInpaintDiffusion(LatentDiffusion):
+    def __init__(
+        self,
+        concat_keys=("mask", "masked_image"),
+        masked_image_key="masked_image",
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+        self.concat_keys = concat_keys
+
+
+def should_hijack_inpainting(checkpoint_info):
+    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
+
+
+def do_inpainting_hijack():
+    ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
+    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+
+    ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
+    ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+
+    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
+    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
\ No newline at end of file
diff --git a/modules/sd_models.py b/modules/sd_models.py
index eae22e87..f9b3063d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,8 +7,9 @@ from omegaconf import OmegaConf
 

 from ldm.util import instantiate_from_config

 

-from modules import shared, modelloader, devices

+from modules import shared, modelloader, devices, script_callbacks

 from modules.paths import models_path

+from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting

 

 model_dir = "Stable-diffusion"

 model_path = os.path.abspath(os.path.join(models_path, model_dir))

@@ -20,7 +21,7 @@ checkpoints_loaded = collections.OrderedDict()
 try:

     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.

 

-    from transformers import logging

+    from transformers import logging, CLIPModel

 

     logging.set_verbosity_error()

 except Exception:

@@ -154,6 +155,9 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd

 

 

+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}

+

+

 def load_model_weights(model, checkpoint_info):

     checkpoint_file = checkpoint_info.filename

     sd_model_hash = checkpoint_info.hash

@@ -185,7 +189,7 @@ def load_model_weights(model, checkpoint_info):
         if os.path.exists(vae_file):

             print(f"Loading VAE weights from: {vae_file}")

             vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)

-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}

+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}

             model.first_stage_model.load_state_dict(vae_dict)

 

         model.first_stage_model.to(devices.dtype_vae)

@@ -203,14 +207,26 @@ def load_model_weights(model, checkpoint_info):
     model.sd_checkpoint_info = checkpoint_info

 

 

-def load_model():

+def load_model(checkpoint_info=None):

     from modules import lowvram, sd_hijack

-    checkpoint_info = select_checkpoint()

+    checkpoint_info = checkpoint_info or select_checkpoint()

 

     if checkpoint_info.config != shared.cmd_opts.config:

         print(f"Loading config from: {checkpoint_info.config}")

 

     sd_config = OmegaConf.load(checkpoint_info.config)

+    

+    if should_hijack_inpainting(checkpoint_info):

+        # Hardcoded config for now...

+        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"

+        sd_config.model.params.use_ema = False

+        sd_config.model.params.conditioning_key = "hybrid"

+        sd_config.model.params.unet_config.params.in_channels = 9

+

+        # Create a "fake" config with a different name so that we know to unload it when switching models.

+        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))

+

+    do_inpainting_hijack()

     sd_model = instantiate_from_config(sd_config.model)

     load_model_weights(sd_model, checkpoint_info)

 

@@ -222,6 +238,9 @@ def load_model():
     sd_hijack.model_hijack.hijack(sd_model)

 

     sd_model.eval()

+    shared.sd_model = sd_model

+

+    script_callbacks.model_loaded_callback(sd_model)

 

     print(f"Model loaded.")

     return sd_model

@@ -234,9 +253,9 @@ def reload_model_weights(sd_model, info=None):
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:

         return

 

-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:

+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):

         checkpoints_loaded.clear()

-        shared.sd_model = load_model()

+        load_model(checkpoint_info)

         return shared.sd_model

 

     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..f58a29b9 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -117,6 +117,8 @@ class VanillaStableDiffusionSampler:
         self.config = None

         self.last_latent = None

 

+        self.conditioning_key = sd_model.model.conditioning_key

+

     def number_of_needed_noises(self, p):

         return 0

 

@@ -136,6 +138,12 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:

             raise InterruptedException

 

+        # Have to unwrap the inpainting conditioning here to perform pre-processing

+        image_conditioning = None

+        if isinstance(cond, dict):

+            image_conditioning = cond["c_concat"][0]

+            cond = cond["c_crossattn"][0]

+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]

 

         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)

@@ -157,6 +165,12 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)

             x_dec = img_orig * self.mask + self.nmask * x_dec

 

+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.

+        # Note that they need to be lists because it just concatenates them later.

+        if image_conditioning is not None:

+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}

+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

+

         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)

 

         if self.mask is not None:

@@ -182,7 +196,7 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None

         self.nmask = p.nmask if hasattr(p, 'nmask') else None

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):

+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

         steps, t_enc = setup_img2img_steps(p, steps)

 

         self.initialize(p)

@@ -196,20 +210,33 @@ class VanillaStableDiffusionSampler:
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)

 

         self.init_latent = x

+        self.last_latent = x

         self.step = 0

 

+        # Wrap the conditioning models with additional image conditioning for inpainting model

+        if image_conditioning is not None:

+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

+            

+            

         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))

 

         return samples

 

-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

         self.initialize(p)

 

         self.init_latent = None

+        self.last_latent = x

         self.step = 0

 

         steps = steps or p.steps

 

+        # Wrap the conditioning models with additional image conditioning for inpainting model

+        if image_conditioning is not None:

+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

+

         # existing code fails with certain step counts, like 9

         try:

             samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])

@@ -228,7 +255,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None

         self.step = 0

 

-    def forward(self, x, sigma, uncond, cond, cond_scale):

+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):

         if state.interrupted or state.skipped:

             raise InterruptedException

 

@@ -239,28 +266,29 @@ class CFGDenoiser(torch.nn.Module):
         repeats = [len(conds_list[i]) for i in range(batch_size)]

 

         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])

+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])

         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])

 

         if tensor.shape[1] == uncond.shape[1]:

             cond_in = torch.cat([tensor, uncond])

 

             if shared.batch_cond_uncond:

-                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)

+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})

             else:

                 x_out = torch.zeros_like(x_in)

                 for batch_offset in range(0, x_out.shape[0], batch_size):

                     a = batch_offset

                     b = a + batch_size

-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])

+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})

         else:

             x_out = torch.zeros_like(x_in)

             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size

             for batch_offset in range(0, tensor.shape[0], batch_size):

                 a = batch_offset

                 b = min(a + batch_size, tensor.shape[0])

-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])

+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})

 

-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)

+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})

 

         denoised_uncond = x_out[-uncond.shape[0]:]

         denoised = torch.clone(denoised_uncond)

@@ -306,6 +334,8 @@ class KDiffusionSampler:
         self.config = None

         self.last_latent = None

 

+        self.conditioning_key = sd_model.model.conditioning_key

+

     def callback_state(self, d):

         step = d['i']

         latent = d["denoised"]

@@ -361,7 +391,7 @@ class KDiffusionSampler:
 

         return extra_params_kwargs

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):

+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

         steps, t_enc = setup_img2img_steps(p, steps)

 

         if p.sampler_noise_scheduler_override:

@@ -388,12 +418,18 @@ class KDiffusionSampler:
             extra_params_kwargs['sigmas'] = sigma_sched

 

         self.model_wrap_cfg.init_latent = x

+        self.last_latent = x

 

-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={

+            'cond': conditioning, 

+            'image_cond': image_conditioning, 

+            'uncond': unconditional_conditioning, 

+            'cond_scale': p.cfg_scale

+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         return samples

 

-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):

         steps = steps or p.steps

 

         if p.sampler_noise_scheduler_override:

@@ -414,7 +450,13 @@ class KDiffusionSampler:
         else:

             extra_params_kwargs['sigmas'] = sigmas

 

-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        self.last_latent = x

+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={

+            'cond': conditioning, 

+            'image_cond': image_conditioning, 

+            'uncond': unconditional_conditioning, 

+            'cond_scale': p.cfg_scale

+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         return samples

 

diff --git a/modules/shared.py b/modules/shared.py
index faede821..5d83971e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -3,6 +3,7 @@ import datetime
 import json

 import os

 import sys

+from collections import OrderedDict

 

 import gradio as gr

 import tqdm

@@ -78,6 +79,8 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)

 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")

 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")

+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)

+parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False)

 

 cmd_opts = parser.parse_args()

 restricted_opts = [

@@ -249,7 +252,7 @@ options_templates.update(options_section(('system', "System"), {
 }))

 

 options_templates.update(options_section(('training', "Training"), {

-    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),

+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),

     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),

     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

@@ -315,6 +318,14 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),

 }))

 

+options_templates.update(options_section(('images-history', "Images Browser"), {

+    #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),

+    "images_history_preload": OptionInfo(False, "Preload images at startup"),

+    "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),

+    "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   

+    "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   

+

+}))

 

 class Options:

     data = None

@@ -387,6 +398,8 @@ sd_upscalers = []
 

 sd_model = None

 

+clip_model = None

+

 progress_print_out = sys.stdout

 

 

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 23bb4b6a..5b1c5002 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -83,7 +83,7 @@ class PersonalizedBase(Dataset):
 

             self.dataset.append(entry)

 

-        assert len(self.dataset) > 1, "No images have been found in the dataset."

+        assert len(self.dataset) > 0, "No images have been found in the dataset."

         self.length = len(self.dataset) * repeats // batch_size

 

         self.initial_indexes = np.arange(len(self.dataset))

@@ -91,7 +91,7 @@ class PersonalizedBase(Dataset):
         self.shuffle()

 

     def shuffle(self):

-        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]

+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]

 

     def create_text(self, filename_text):

         text = random.choice(self.lines)

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 898ce3b3..ea653806 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -5,6 +5,7 @@ import zlib
 from PIL import Image, PngImagePlugin, ImageDraw, ImageFont

 from fonts.ttf import Roboto

 import torch

+from modules.shared import opts

 

 

 class EmbeddingEncoder(json.JSONEncoder):

@@ -133,7 +134,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     from math import cos

 

     image = srcimage.copy()

-

+    fontsize = 32

     if textfont is None:

         try:

             textfont = ImageFont.truetype(opts.font or Roboto, fontsize)

@@ -150,7 +151,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))

 

     draw = ImageDraw.Draw(image)

-    fontsize = 32

+

     font = ImageFont.truetype(textfont, fontsize)

     padding = 10

 

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..33eaddb6 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,6 @@
 import os

 from PIL import Image, ImageOps

+import math

 import platform

 import sys

 import tqdm

@@ -11,7 +12,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru

 

 

-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):

+def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):

     try:

         if process_caption:

             shared.interrogator.load()

@@ -21,7 +22,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False

             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)

 

-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)

+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio)

 

     finally:

 

@@ -33,11 +34,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 

 

 

-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):

+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):

     width = process_width

     height = process_height

     src = os.path.abspath(process_src)

     dst = os.path.abspath(process_dst)

+    split_threshold = max(0.0, min(1.0, split_threshold))

+    overlap_ratio = max(0.0, min(0.9, overlap_ratio))

 

     assert src != dst, 'same directory specified as source and destination'

 

@@ -48,7 +51,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     shared.state.textinfo = "Preprocessing..."

     shared.state.job_count = len(files)

 

-    def save_pic_with_caption(image, index):

+    def save_pic_with_caption(image, index, existing_caption=None):

         caption = ""

 

         if process_caption:

@@ -66,17 +69,49 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         basename = f"{index:05}-{subindex[0]}-{filename_part}"

         image.save(os.path.join(dst, f"{basename}.png"))

 

+        if preprocess_txt_action == 'prepend' and existing_caption:

+            caption = existing_caption + ' ' + caption

+        elif preprocess_txt_action == 'append' and existing_caption:

+            caption = caption + ' ' + existing_caption

+        elif preprocess_txt_action == 'copy' and existing_caption:

+            caption = existing_caption

+

+        caption = caption.strip()

+        

         if len(caption) > 0:

             with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:

                 file.write(caption)

 

         subindex[0] += 1

 

-    def save_pic(image, index):

-        save_pic_with_caption(image, index)

+    def save_pic(image, index, existing_caption=None):

+        save_pic_with_caption(image, index, existing_caption=existing_caption)

 

         if process_flip:

-            save_pic_with_caption(ImageOps.mirror(image), index)

+            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)

+

+    def split_pic(image, inverse_xy):

+        if inverse_xy:

+            from_w, from_h = image.height, image.width

+            to_w, to_h = height, width

+        else:

+            from_w, from_h = image.width, image.height

+            to_w, to_h = width, height

+        h = from_h * to_w // from_w

+        if inverse_xy:

+            image = image.resize((h, to_w))

+        else:

+            image = image.resize((to_w, h))

+

+        split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))

+        y_step = (h - to_h) / (split_count - 1)

+        for i in range(split_count):

+            y = int(y_step * i)

+            if inverse_xy:

+                splitted = image.crop((y, 0, y + to_h, to_w))

+            else:

+                splitted = image.crop((0, y, to_w, y + to_h))

+            yield splitted

 

     for index, imagefile in enumerate(tqdm.tqdm(files)):

         subindex = [0]

@@ -86,31 +121,27 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         except Exception:

             continue

 

+        existing_caption = None

+        existing_caption_filename = os.path.splitext(filename)[0] + '.txt'

+        if os.path.exists(existing_caption_filename):

+            with open(existing_caption_filename, 'r', encoding="utf8") as file:

+                existing_caption = file.read()

+

         if shared.state.interrupted:

             break

 

-        ratio = img.height / img.width

-        is_tall = ratio > 1.35

-        is_wide = ratio < 1 / 1.35

-

-        if process_split and is_tall:

-            img = img.resize((width, height * img.height // img.width))

-

-            top = img.crop((0, 0, width, height))

-            save_pic(top, index)

-

-            bot = img.crop((0, img.height - height, width, img.height))

-            save_pic(bot, index)

-        elif process_split and is_wide:

-            img = img.resize((width * img.width // img.height, height))

-

-            left = img.crop((0, 0, width, height))

-            save_pic(left, index)

+        if img.height > img.width:

+            ratio = (img.width * height) / (img.height * width)

+            inverse_xy = False

+        else:

+            ratio = (img.height * width) / (img.width * height)

+            inverse_xy = True

 

-            right = img.crop((img.width - width, 0, img.width, height))

-            save_pic(right, index)

+        if process_split and ratio < 1.0 and ratio <= split_threshold:

+            for splitted in split_pic(img, inverse_xy):

+                save_pic(splitted, index, existing_caption=existing_caption)

         else:

             img = images.resize_image(1, img, width, height)

-            save_pic(img, index)

+            save_pic(img, index, existing_caption=existing_caption)

 

         shared.state.nextjob()

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3be69562..529ed3e2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -153,7 +153,7 @@ class EmbeddingDatabase:
         return None, None

 

 

-def create_embedding(name, num_vectors_per_token, init_text='*'):

+def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):

     cond_model = shared.sd_model.cond_stage_model

     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings

 

@@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
         vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]

 

     fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")

-    assert not os.path.exists(fn), f"file {fn} already exists"

+    if not overwrite_old:

+        assert not os.path.exists(fn), f"file {fn} already exists"

 

     embedding = Embedding(vec, name)

     embedding.step = 0

@@ -275,6 +276,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             loss.backward()

             optimizer.step()

 

+

         epoch_num = embedding.step // len(ds)

         epoch_step = embedding.step - (epoch_num * len(ds)) + 1

 

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 36881e7a..e712284d 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -7,8 +7,8 @@ import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared

 

 

-def create_embedding(name, initialization_text, nvpt):

-    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)

+def create_embedding(name, initialization_text, nvpt, overwrite_old):

+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)

 

     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

 

diff --git a/modules/txt2img.py b/modules/txt2img.py
index 2381347f..c9d5a090 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,5 +1,6 @@
 import modules.scripts

-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images

+from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \

+    StableDiffusionProcessingImg2Img, process_images

 from modules.shared import opts, cmd_opts

 import modules.shared as shared

 import modules.processing as processing

@@ -35,6 +36,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,

     )

 

+    p.scripts = modules.scripts.scripts_txt2img

+    p.script_args = args

+

     if cmd_opts.enable_console_prompts:

         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)

 

@@ -53,4 +57,3 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         processed.images = []

 

     return processed.images, generation_info_js, plaintext_to_html(processed.info)

-

diff --git a/modules/ui.py b/modules/ui.py
index d4b32c05..cd118552 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -22,9 +22,14 @@ import piexif
 import torch

 from PIL import Image, PngImagePlugin

 

-from modules import localization, sd_hijack, sd_models

+import gradio as gr

+import gradio.utils

+import gradio.routes

+

+from modules import sd_hijack, sd_models, localization, script_callbacks

 from modules.paths import script_path

-from modules.shared import cmd_opts, opts, restricted_opts

+

+from modules.shared import opts, cmd_opts, restricted_opts

 

 if cmd_opts.deepdanbooru:

     from modules.deepbooru import get_deepbooru_tags

@@ -43,6 +48,11 @@ from modules import prompt_parser
 from modules.images import save_image

 from modules.sd_hijack import model_hijack

 from modules.sd_samplers import samplers, samplers_for_img2img

+import modules.textual_inversion.ui

+import modules.hypernetworks.ui

+

+import modules.images_history as img_his

+

 

 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI

 mimetypes.init()

@@ -593,27 +603,29 @@ def apply_setting(key, value):
     return value

 

 

-def create_ui(wrap_gradio_gpu_call):

-    import modules.img2img

-    import modules.txt2img

+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):

+    def refresh():

+        refresh_method()

+        args = refreshed_args() if callable(refreshed_args) else refreshed_args

 

-    def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):

-        def refresh():

-            refresh_method()

-            args = refreshed_args() if callable(refreshed_args) else refreshed_args

+        for k, v in args.items():

+            setattr(refresh_component, k, v)

 

-            for k, v in args.items():

-                setattr(refresh_component, k, v)

+        return gr.update(**(args or {}))

 

-            return gr.update(**(args or {}))

+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)

+    refresh_button.click(

+        fn=refresh,

+        inputs=[],

+        outputs=[refresh_component]

+    )

+    return refresh_button

+

+

+def create_ui(wrap_gradio_gpu_call):

+    import modules.img2img

+    import modules.txt2img

 

-        refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)

-        refresh_button.click(

-            fn = refresh,

-            inputs = [],

-            outputs = [refresh_component]

-        )

-        return refresh_button

 

     with gr.Blocks(analytics_enabled=False) as txt2img_interface:

         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)

@@ -711,6 +723,7 @@ def create_ui(wrap_gradio_gpu_call):
                     firstphase_width,

                     firstphase_height,

                 ] + custom_inputs,

+

                 outputs=[

                     txt2img_gallery,

                     generation_info,

@@ -787,6 +800,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),

                 (firstphase_width, "First pass size-1"),

                 (firstphase_height, "First pass size-2"),

+                *modules.scripts.scripts_txt2img.infotext_fields

             ]

 

             txt2img_preview_params = [

@@ -854,8 +868,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")

 

                 with gr.Group():

-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")

+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")

 

                 with gr.Row():

                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)

@@ -1052,6 +1066,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),

                 (seed_resize_from_h, "Seed resize from-2"),

                 (denoising_strength, "Denoising strength"),

+                *modules.scripts.scripts_img2img.infotext_fields

             ]

             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])

 

@@ -1174,12 +1189,12 @@ def create_ui(wrap_gradio_gpu_call):
         )

     #images history

     images_history_switch_dict = {

-        "fn":modules.generation_parameters_copypaste.connect_paste,

-        "t2i":txt2img_paste_fields,

-        "i2i":img2img_paste_fields

+        "fn": modules.generation_parameters_copypaste.connect_paste,

+        "t2i": txt2img_paste_fields,

+        "i2i": img2img_paste_fields

     }

 

-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)

+    images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)

 

     with gr.Blocks() as modelmerger_interface:

         with gr.Row().style(equal_height=False):

@@ -1212,6 +1227,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_embedding_name = gr.Textbox(label="Name")

                     initialization_text = gr.Textbox(label="Initialization text", value="*")

                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)

+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")

 

                     with gr.Row():

                         with gr.Column(scale=3):

@@ -1227,6 +1243,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"])

                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")

                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")

+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")

+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])

 

                     with gr.Row():

                         with gr.Column(scale=3):

@@ -1240,13 +1258,18 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')

                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])

 

                     with gr.Row():

                         process_flip = gr.Checkbox(label='Create flipped copies')

-                        process_split = gr.Checkbox(label='Split oversized images into two')

+                        process_split = gr.Checkbox(label='Split oversized images')

                         process_caption = gr.Checkbox(label='Use BLIP for caption')

                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)

 

+                    with gr.Row(visible=False) as process_split_extra_row:

+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)

+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)

+

                     with gr.Row():

                         with gr.Column(scale=3):

                             gr.HTML(value="")

@@ -1254,15 +1277,24 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():

                             run_preprocess = gr.Button(value="Preprocess", variant='primary')

 

+                    process_split.change(

+                        fn=lambda show: gr_show(show),

+                        inputs=[process_split],

+                        outputs=[process_split_extra_row],

+                    )

+

                 with gr.Tab(label="Train"):

-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")

                     with gr.Row():

                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))

                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")

                     with gr.Row():

                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])

                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")

-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")

+                    with gr.Row():

+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")

+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")

+

                     batch_size = gr.Number(label='Batch size', value=1, precision=0)

                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")

                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")

@@ -1296,6 +1328,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_embedding_name,

                 initialization_text,

                 nvpt,

+                overwrite_old_embedding,

             ],

             outputs=[

                 train_embedding_name,

@@ -1309,6 +1342,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[

                 new_hypernetwork_name,

                 new_hypernetwork_sizes,

+                overwrite_old_hypernetwork,

                 new_hypernetwork_layer_structure,

                 new_hypernetwork_activation_func,

                 new_hypernetwork_add_layer_norm,

@@ -1329,10 +1363,13 @@ def create_ui(wrap_gradio_gpu_call):
                 process_dst,

                 process_width,

                 process_height,

+                preprocess_txt_action,

                 process_flip,

                 process_split,

                 process_caption,

-                process_caption_deepbooru

+                process_caption_deepbooru,

+                process_split_threshold,

+                process_overlap_ratio,

             ],

             outputs=[

                 ti_output,

@@ -1345,7 +1382,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_embedding_name,

-                learn_rate,

+                embedding_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,

@@ -1370,7 +1407,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_hypernetwork_name,

-                learn_rate,

+                hypernetwork_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,

@@ -1491,10 +1528,10 @@ Requested path was: {f}
         if not opts.same_type(value, opts.data_labels[key].default):

             return gr.update(visible=True), opts.dumpjson()

 

+        oldval = opts.data.get(key, None)

         if cmd_opts.hide_ui_dir_config and key in restricted_opts:

             return gr.update(value=oldval), opts.dumpjson()

 

-        oldval = opts.data.get(key, None)

         opts.data[key] = value

 

         if oldval != value:

@@ -1600,19 +1637,24 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),

         (extras_interface, "Extras", "extras"),

         (pnginfo_interface, "PNG Info", "pnginfo"),

-        (images_history, "History", "images_history"),

+        (images_history, "Image Browser", "images_history"),

         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),

         (train_interface, "Train", "ti"),

-        (settings_interface, "Settings", "settings"),

     ]

 

-    with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:

-        css = file.read()

+    interfaces += script_callbacks.ui_tabs_callback()

+

+    interfaces += [(settings_interface, "Settings", "settings")]

+

+    css = ""

+

+    for cssfile in modules.scripts.list_files_with_name("style.css"):

+        with open(cssfile, "r", encoding="utf8") as file:

+            css += file.read() + "\n"

 

     if os.path.exists(os.path.join(script_path, "user.css")):

         with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:

-            usercss = file.read()

-            css += usercss

+            css += file.read() + "\n"

 

     if not cmd_opts.no_progressbar_hiding:

         css += css_hide_progressbar

@@ -1835,9 +1877,9 @@ def load_javascript(raw_response):
     with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:

         javascript = f'<script>{jsfile.read()}</script>'

 

-    jsdir = os.path.join(script_path, "javascript")

-    for filename in sorted(os.listdir(jsdir)):

-        with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:

+    scripts_list = modules.scripts.list_scripts("javascript", ".js")

+    for basedir, filename, path in scripts_list:

+        with open(path, "r", encoding="utf8") as jsfile:

             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"

 

     if cmd_opts.theme is not None:

@@ -1855,6 +1897,5 @@ def load_javascript(raw_response):
     gradio.routes.templates.TemplateResponse = template_response

 

 

-reload_javascript = partial(load_javascript,

-                            gradio.routes.templates.TemplateResponse)

+reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)

 reload_javascript()