removed aesthetic gradients as built-in

added support for extensions

14 files changed, 249 insertions, 410 deletions

diff --git a/.gitignore b/.gitignore
index f9c3357c..2f1e08ed 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,4 +27,4 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
-.vscode
\ No newline at end of file
+.vscode
diff --git a/extensions/put extension here.txt b/extensions/put extension here.txt
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/extensions/put extension here.txt
diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
deleted file mode 100644
index 8c828541..00000000
--- a/modules/aesthetic_clip.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import copy
-import itertools
-import os
-from pathlib import Path
-import html
-import gc
-
-import gradio as gr
-import torch
-from PIL import Image
-from torch import optim
-
-from modules import shared
-from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
-from tqdm.auto import tqdm, trange
-from modules.shared import opts, device
-
-
-def get_all_images_in_folder(folder):
-    return [os.path.join(folder, f) for f in os.listdir(folder) if
-            os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
-
-
-def check_is_valid_image_file(filename):
-    return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
-
-
-def batched(dataset, total, n=1):
-    for ndx in range(0, total, n):
-        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
-
-
-def iter_to_batched(iterable, n=1):
-    it = iter(iterable)
-    while True:
-        chunk = tuple(itertools.islice(it, n))
-        if not chunk:
-            return
-        yield chunk
-
-
-def create_ui():
-    import modules.ui
-
-    with gr.Group():
-        with gr.Accordion("Open for Clip Aesthetic!", open=False):
-            with gr.Row():
-                aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
-                                             value=0.9)
-                aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
-
-            with gr.Row():
-                aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
-                                          placeholder="Aesthetic learning rate", value="0.0001")
-                aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
-                aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
-                                             label="Aesthetic imgs embedding",
-                                             value="None")
-
-                modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
-
-            with gr.Row():
-                aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
-                                                 placeholder="This text is used to rotate the feature space of the imgs embs",
-                                                 value="")
-                aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
-                                                  value=0.1)
-                aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
-
-    return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
-
-
-aesthetic_clip_model = None
-
-
-def aesthetic_clip():
-    global aesthetic_clip_model
-
-    if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
-        aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
-        aesthetic_clip_model.cpu()
-
-    return aesthetic_clip_model
-
-
-def generate_imgs_embd(name, folder, batch_size):
-    model = aesthetic_clip().to(device)
-    processor = CLIPProcessor.from_pretrained(model.name_or_path)
-
-    with torch.no_grad():
-        embs = []
-        for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
-                          desc=f"Generating embeddings for {name}"):
-            if shared.state.interrupted:
-                break
-            inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
-            outputs = model.get_image_features(**inputs).cpu()
-            embs.append(torch.clone(outputs))
-            inputs.to("cpu")
-            del inputs, outputs
-
-        embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
-
-        # The generated embedding will be located here
-        path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
-        torch.save(embs, path)
-
-        model.cpu()
-        del processor
-        del embs
-        gc.collect()
-        torch.cuda.empty_cache()
-        res = f"""
-        Done generating embedding for {name}!
-        Aesthetic embedding saved to {html.escape(path)}
-        """
-        shared.update_aesthetic_embeddings()
-        return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
-                                  value="None"), \
-               gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
-                                  label="Imgs embedding",
-                                  value="None"), res, ""
-
-
-def slerp(low, high, val):
-    low_norm = low / torch.norm(low, dim=1, keepdim=True)
-    high_norm = high / torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm * high_norm).sum(1))
-    so = torch.sin(omega)
-    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
-    return res
-
-
-class AestheticCLIP:
-    def __init__(self):
-        self.skip = False
-        self.aesthetic_steps = 0
-        self.aesthetic_weight = 0
-        self.aesthetic_lr = 0
-        self.slerp = False
-        self.aesthetic_text_negative = ""
-        self.aesthetic_slerp_angle = 0
-        self.aesthetic_imgs_text = ""
-
-        self.image_embs_name = None
-        self.image_embs = None
-        self.load_image_embs(None)
-
-    def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
-                             aesthetic_slerp=True, aesthetic_imgs_text="",
-                             aesthetic_slerp_angle=0.15,
-                             aesthetic_text_negative=False):
-        self.aesthetic_imgs_text = aesthetic_imgs_text
-        self.aesthetic_slerp_angle = aesthetic_slerp_angle
-        self.aesthetic_text_negative = aesthetic_text_negative
-        self.slerp = aesthetic_slerp
-        self.aesthetic_lr = aesthetic_lr
-        self.aesthetic_weight = aesthetic_weight
-        self.aesthetic_steps = aesthetic_steps
-        self.load_image_embs(image_embs_name)
-
-        if self.image_embs_name is not None:
-            p.extra_generation_params.update({
-                "Aesthetic LR": aesthetic_lr,
-                "Aesthetic weight": aesthetic_weight,
-                "Aesthetic steps": aesthetic_steps,
-                "Aesthetic embedding": self.image_embs_name,
-                "Aesthetic slerp": aesthetic_slerp,
-                "Aesthetic text": aesthetic_imgs_text,
-                "Aesthetic text negative": aesthetic_text_negative,
-                "Aesthetic slerp angle": aesthetic_slerp_angle,
-            })
-
-    def set_skip(self, skip):
-        self.skip = skip
-
-    def load_image_embs(self, image_embs_name):
-        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
-            image_embs_name = None
-            self.image_embs_name = None
-        if image_embs_name is not None and self.image_embs_name != image_embs_name:
-            self.image_embs_name = image_embs_name
-            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
-            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
-            self.image_embs.requires_grad_(False)
-
-    def __call__(self, z, remade_batch_tokens):
-        if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
-            tokenizer = shared.sd_model.cond_stage_model.tokenizer
-            if not opts.use_old_emphasis_implementation:
-                remade_batch_tokens = [
-                    [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
-                    remade_batch_tokens]
-
-            tokens = torch.asarray(remade_batch_tokens).to(device)
-
-            model = copy.deepcopy(aesthetic_clip()).to(device)
-            model.requires_grad_(True)
-            if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
-                text_embs_2 = model.get_text_features(
-                    **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
-                if self.aesthetic_text_negative:
-                    text_embs_2 = self.image_embs - text_embs_2
-                    text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
-                img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
-            else:
-                img_embs = self.image_embs
-
-            with torch.enable_grad():
-
-                # We optimize the model to maximize the similarity
-                optimizer = optim.Adam(
-                    model.text_model.parameters(), lr=self.aesthetic_lr
-                )
-
-                for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
-                    text_embs = model.get_text_features(input_ids=tokens)
-                    text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                    sim = text_embs @ img_embs.T
-                    loss = -sim
-                    optimizer.zero_grad()
-                    loss.mean().backward()
-                    optimizer.step()
-
-                zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
-                if opts.CLIP_stop_at_last_layers > 1:
-                    zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
-                    zn = model.text_model.final_layer_norm(zn)
-                else:
-                    zn = zn.last_hidden_state
-                model.cpu()
-                del model
-                gc.collect()
-                torch.cuda.empty_cache()
-            zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
-            if self.slerp:
-                z = slerp(z, zn, self.aesthetic_weight)
-            else:
-                z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
-
-        return z
diff --git a/modules/images_history.py b/modules/images_history.py
index 78fd0543..bc5cf11f 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -310,7 +310,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                         forward = gr.Button('Prev batch')
                         backward = gr.Button('Next batch')
                 with gr.Column(scale=3):
-                    load_info =  gr.HTML(visible=not custom_dir)   
+                    load_info =     gr.HTML(visible=not custom_dir)
             with gr.Row(visible=False) as warning:                 
                 warning_box = gr.Textbox("Message", interactive=False)                        
 
diff --git a/modules/img2img.py b/modules/img2img.py
index eea5199b..8d9f7cf9 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -56,7 +56,7 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))

 

 

-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):

+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):

     is_inpaint = mode == 1

     is_batch = mode == 2

 

@@ -109,7 +109,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,

     )

 

-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)

+    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/processing.py b/modules/processing.py
index ff1ec4c9..372489f7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -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

@@ -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):

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..65f25f49 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,86 +1,153 @@
 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 list_scripts(scriptdirname, extension):

+    scripts_list = []

+

+    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)))

+

+    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)

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

+                continue

 

+            for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))):

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

 

-def load_scripts(basedir):

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

-        return

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

 

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

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

+    return scripts_list

 

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

-            continue

 

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

-            continue

+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 +163,80 @@ 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 +255,7 @@ 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

@@ -176,6 +267,15 @@ class ScriptRunner:
 

         return processed

 

+    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):

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

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

@@ -197,19 +297,21 @@ 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()

 

 

-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 1f8587d1..0f10828e 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -332,7 +332,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     multipliers.append([1.0] * 75)

 

             z1 = self.process_tokens(tokens, multipliers)

-            z1 = shared.aesthetic_clip(z1, remade_batch_tokens)

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

 

             remade_batch_tokens = rem_tokens

diff --git a/modules/sd_models.py b/modules/sd_models.py
index d99dbce8..f9b3063d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,7 +7,7 @@ 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

 

@@ -238,6 +238,9 @@ def load_model(checkpoint_info=None):
     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

@@ -252,7 +255,7 @@ def reload_model_weights(sd_model, info=None):
 

     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(checkpoint_info)

+        load_model(checkpoint_info)

         return shared.sd_model

 

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

diff --git a/modules/shared.py b/modules/shared.py
index 0dbe360d..7d786f07 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -31,7 +31,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")

 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")

 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")

-parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(models_path, 'aesthetic_embeddings'), help="aesthetic_embeddings directory(default: aesthetic_embeddings)")

 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")

 parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")

 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")

@@ -109,21 +108,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)

 loaded_hypernetwork = None

 

-

-os.makedirs(cmd_opts.aesthetic_embeddings_dir, exist_ok=True)

-aesthetic_embeddings = {}

-

-

-def update_aesthetic_embeddings():

-    global aesthetic_embeddings

-    aesthetic_embeddings = {f.replace(".pt", ""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in

-                            os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}

-    aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)

-

-

-update_aesthetic_embeddings()

-

-

 def reload_hypernetworks():

     global hypernetworks

 

@@ -415,9 +399,6 @@ sd_model = None
 

 clip_model = None

 

-from modules.aesthetic_clip import AestheticCLIP

-aesthetic_clip = AestheticCLIP()

-

 progress_print_out = sys.stdout

 

 

diff --git a/modules/txt2img.py b/modules/txt2img.py
index 1761cfa2..c9d5a090 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -7,7 +7,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html

 

 

-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):

+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):

     p = StableDiffusionProcessingTxt2Img(

         sd_model=shared.sd_model,

         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,

@@ -36,7 +36,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,

     )

 

-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)

+    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)

diff --git a/modules/ui.py b/modules/ui.py
index 70a9cf10..c977482c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,10 +23,10 @@ import gradio as gr
 import gradio.utils

 import gradio.routes

 

-from modules import sd_hijack, sd_models, localization

+from modules import sd_hijack, sd_models, localization, script_callbacks

 from modules.paths import script_path

 

-from modules.shared import opts, cmd_opts, restricted_opts, aesthetic_embeddings

+from modules.shared import opts, cmd_opts, restricted_opts

 

 if cmd_opts.deepdanbooru:

     from modules.deepbooru import get_deepbooru_tags

@@ -44,7 +44,6 @@ from modules.images import save_image
 import modules.textual_inversion.ui

 import modules.hypernetworks.ui

 

-import modules.aesthetic_clip as aesthetic_clip

 import modules.images_history as img_his

 

 

@@ -662,8 +661,6 @@ def create_ui(wrap_gradio_gpu_call):
 

                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()

 

-                aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui()

-

                 with gr.Group():

                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)

 

@@ -718,14 +715,6 @@ def create_ui(wrap_gradio_gpu_call):
                     denoising_strength,

                     firstphase_width,

                     firstphase_height,

-                    aesthetic_lr,

-                    aesthetic_weight,

-                    aesthetic_steps,

-                    aesthetic_imgs,

-                    aesthetic_slerp,

-                    aesthetic_imgs_text,

-                    aesthetic_slerp_angle,

-                    aesthetic_text_negative

                 ] + custom_inputs,

 

                 outputs=[

@@ -804,14 +793,7 @@ 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"),

-                (aesthetic_lr, "Aesthetic LR"),

-                (aesthetic_weight, "Aesthetic weight"),

-                (aesthetic_steps, "Aesthetic steps"),

-                (aesthetic_imgs, "Aesthetic embedding"),

-                (aesthetic_slerp, "Aesthetic slerp"),

-                (aesthetic_imgs_text, "Aesthetic text"),

-                (aesthetic_text_negative, "Aesthetic text negative"),

-                (aesthetic_slerp_angle, "Aesthetic slerp angle"),

+                *modules.scripts.scripts_txt2img.infotext_fields

             ]

 

             txt2img_preview_params = [

@@ -896,8 +878,6 @@ def create_ui(wrap_gradio_gpu_call):
 

                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()

 

-                aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui()

-

                 with gr.Group():

                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)

 

@@ -988,14 +968,6 @@ def create_ui(wrap_gradio_gpu_call):
                     inpainting_mask_invert,

                     img2img_batch_input_dir,

                     img2img_batch_output_dir,

-                    aesthetic_lr_im,

-                    aesthetic_weight_im,

-                    aesthetic_steps_im,

-                    aesthetic_imgs_im,

-                    aesthetic_slerp_im,

-                    aesthetic_imgs_text_im,

-                    aesthetic_slerp_angle_im,

-                    aesthetic_text_negative_im,

                 ] + custom_inputs,

                 outputs=[

                     img2img_gallery,

@@ -1087,14 +1059,7 @@ 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"),

-                (aesthetic_lr_im, "Aesthetic LR"),

-                (aesthetic_weight_im, "Aesthetic weight"),

-                (aesthetic_steps_im, "Aesthetic steps"),

-                (aesthetic_imgs_im, "Aesthetic embedding"),

-                (aesthetic_slerp_im, "Aesthetic slerp"),

-                (aesthetic_imgs_text_im, "Aesthetic text"),

-                (aesthetic_text_negative_im, "Aesthetic text negative"),

-                (aesthetic_slerp_angle_im, "Aesthetic slerp angle"),

+                *modules.scripts.scripts_img2img.infotext_fields

             ]

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

 

@@ -1217,9 +1182,9 @@ 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, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)

@@ -1264,18 +1229,6 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():

                             create_embedding = gr.Button(value="Create embedding", variant='primary')

 

-                with gr.Tab(label="Create aesthetic images embedding"):

-

-                    new_embedding_name_ae = gr.Textbox(label="Name")

-                    process_src_ae = gr.Textbox(label='Source directory')

-                    batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)

-                    with gr.Row():

-                        with gr.Column(scale=3):

-                            gr.HTML(value="")

-

-                        with gr.Column():

-                            create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')

-

                 with gr.Tab(label="Create hypernetwork"):

                     new_hypernetwork_name = gr.Textbox(label="Name")

                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

@@ -1375,21 +1328,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]

         )

 

-        create_embedding_ae.click(

-            fn=aesthetic_clip.generate_imgs_embd,

-            inputs=[

-                new_embedding_name_ae,

-                process_src_ae,

-                batch_ae

-            ],

-            outputs=[

-                aesthetic_imgs,

-                aesthetic_imgs_im,

-                ti_output,

-                ti_outcome,

-            ]

-        )

-

         create_hypernetwork.click(

             fn=modules.hypernetworks.ui.create_hypernetwork,

             inputs=[

@@ -1580,10 +1518,10 @@ 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:

@@ -1692,9 +1630,12 @@ Requested path was: {f}
         (images_history, "Image Browser", "images_history"),

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

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

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

     ]

 

+    interfaces += script_callbacks.ui_tabs_callback()

+

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

+

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

         css = file.read()

 

diff --git a/webui.py b/webui.py
index 87589064..b1deca1b 100644
--- a/webui.py
+++ b/webui.py
@@ -71,6 +71,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 

     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)

 

+

 def initialize():

     modelloader.cleanup_models()

     modules.sd_models.setup_model()

@@ -79,9 +80,9 @@ def initialize():
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())

     modelloader.load_upscalers()

 

-    modules.scripts.load_scripts(os.path.join(script_path, "scripts"))

+    modules.scripts.load_scripts()

 

-    shared.sd_model = modules.sd_models.load_model()

+    modules.sd_models.load_model()

     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))

     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))

     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)

@@ -145,7 +146,7 @@ def webui():
         sd_samplers.set_samplers()

 

         print('Reloading Custom Scripts')

-        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))

+        modules.scripts.reload_scripts()

         print('Reloading modules: modules.ui')

         importlib.reload(modules.ui)

         print('Refreshing Model List')