Merge branch 'dev' into extra-networks-performance-updates

modules/codeformer_model.py

@@ -50,7 +50,7 @@ class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration):
 
         def restore_face(cropped_face_t):
             assert self.net is not None
-            return self.net(cropped_face_t, w=w, adain=True)[0]
+            return self.net(cropped_face_t, weight=w, adain=True)[0]
 
         return self.restore_with_helper(np_image, restore_face)
 

modules/infotext_utils.py

@@ -8,7 +8,7 @@ import sys
 
 import gradio as gr
 from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions, images, prompt_parser
+from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions, images, prompt_parser, errors
 from PIL import Image
 
 sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__]  # alias for old name
@@ -488,7 +488,11 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
 
         for output, key in paste_fields:
             if callable(key):
-                v = key(params)
+                try:
+                    v = key(params)
+                except Exception:
+                    errors.report(f"Error executing {key}", exc_info=True)
+                    v = None
             else:
                 v = params.get(key, None)
 

modules/postprocessing.py

@@ -131,13 +131,15 @@ def run_postprocessing_webui(id_task, *args, **kwargs):
     return run_postprocessing(*args, **kwargs)
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True, max_side_length: int = 0):
     """old handler for API"""
 
     args = scripts.scripts_postproc.create_args_for_run({
         "Upscale": {
+            "upscale_enabled": True,
             "upscale_mode": resize_mode,
             "upscale_by": upscaling_resize,
+            "max_side_length": max_side_length,
             "upscale_to_width": upscaling_resize_w,
             "upscale_to_height": upscaling_resize_h,
             "upscale_crop": upscaling_crop,

modules/processing.py

@@ -608,7 +608,7 @@ class Processed:
             "version": self.version,
         }
 
-        return json.dumps(obj)
+        return json.dumps(obj, default=lambda o: None)
 
     def infotext(self, p: StableDiffusionProcessing, index):
         return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
@@ -703,8 +703,54 @@ def program_version():
     return res
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None, all_hr_prompts=None, all_hr_negative_prompts=None):
-    if index is None:
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+    """
+    this function is used to generate the infotext that is stored in the generated images, it's contains the parameters that are required to generate the imagee
+    Args:
+        p: StableDiffusionProcessing
+        all_prompts: list[str]
+        all_seeds: list[int]
+        all_subseeds: list[int]
+        comments: list[str]
+        iteration: int
+        position_in_batch: int
+        use_main_prompt: bool
+        index: int
+        all_negative_prompts: list[str]
+
+    Returns: str
+
+    Extra generation params
+    p.extra_generation_params dictionary allows for additional parameters to be added to the infotext
+    this can be use by the base webui or extensions.
+    To add a new entry, add a new key value pair, the dictionary key will be used as the key of the parameter in the infotext
+    the value generation_params can be defined as:
+        - str | None
+        - List[str|None]
+        - callable func(**kwargs) -> str | None
+
+    When defined as a string, it will be used as without extra processing; this is this most common use case.
+
+    Defining as a list allows for parameter that changes across images in the job, for example, the 'Seed' parameter.
+    The list should have the same length as the total number of images in the entire job.
+
+    Defining as a callable function allows parameter cannot be generated earlier or when extra logic is required.
+    For example 'Hires prompt', due to reasons the hr_prompt might be changed by process in the pipeline or extensions
+    and may vary across different images, defining as a static string or list would not work.
+
+    The function takes locals() as **kwargs, as such will have access to variables like 'p' and 'index'.
+    the base signature of the function should be:
+        func(**kwargs) -> str | None
+    optionally it can have additional arguments that will be used in the function:
+        func(p, index, **kwargs) -> str | None
+    note: for better future compatibility even though this function will have access to all variables in the locals(),
+        it is recommended to only use the arguments present in the function signature of create_infotext.
+    For actual implementation examples, see StableDiffusionProcessingTxt2Img.init > get_hr_prompt.
+    """
+
+    if use_main_prompt:
+        index = 0
+    elif index is None:
         index = position_in_batch + iteration * p.batch_size
 
     if all_negative_prompts is None:
@@ -715,6 +761,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     token_merging_ratio = p.get_token_merging_ratio()
     token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
 
+    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
+    negative_prompt = p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]
+
     uses_ensd = opts.eta_noise_seed_delta != 0
     if uses_ensd:
         uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
@@ -747,22 +796,24 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
         "Tiling": "True" if p.tiling else None,
-        "Hires prompt": None,  # This is set later, insert here to keep order
-        "Hires negative prompt": None,  # This is set later, insert here to keep order
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
         "User": p.user if opts.add_user_name_to_info else None,
     }
 
-    if all_hr_prompts := all_hr_prompts or getattr(p, 'all_hr_prompts', None):
-        generation_params['Hires prompt'] = all_hr_prompts[index] if all_hr_prompts[index] != all_prompts[index] else None
-    if all_hr_negative_prompts := all_hr_negative_prompts or getattr(p, 'all_hr_negative_prompts', None):
-        generation_params['Hires negative prompt'] = all_hr_negative_prompts[index] if all_hr_negative_prompts[index] != all_negative_prompts[index] else None
+    for key, value in generation_params.items():
+        try:
+            if isinstance(value, list):
+                generation_params[key] = value[index]
+            elif callable(value):
+                generation_params[key] = value(**locals())
+        except Exception:
+            errors.report(f'Error creating infotext for key "{key}"', exc_info=True)
+            generation_params[key] = None
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
-    negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {negative_prompt}" if negative_prompt else ""
 
     return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -1204,6 +1255,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
                 self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
 
+            def get_hr_prompt(p, index, prompt_text, **kwargs):
+                hr_prompt = p.all_hr_prompts[index]
+                return hr_prompt if hr_prompt != prompt_text else None
+
+            def get_hr_negative_prompt(p, index, negative_prompt, **kwargs):
+                hr_negative_prompt = p.all_hr_negative_prompts[index]
+                return hr_negative_prompt if hr_negative_prompt != negative_prompt else None
+
+            self.extra_generation_params["Hires prompt"] = get_hr_prompt
+            self.extra_generation_params["Hires negative prompt"] = get_hr_negative_prompt
+
             self.extra_generation_params["Hires schedule type"] = None  # to be set in sd_samplers_kdiffusion.py
 
             if self.hr_scheduler is None:

modules/script_callbacks.py

@@ -439,6 +439,9 @@ def remove_current_script_callbacks():
     for callback_list in callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
             callback_list.remove(callback_to_remove)
+    for ordered_callbacks_list in ordered_callbacks_map.values():
+        for callback_to_remove in [cb for cb in ordered_callbacks_list if cb.script == filename]:
+            ordered_callbacks_list.remove(callback_to_remove)
 
 
 def remove_callbacks_for_function(callback_func):

modules/script_loading.py

@@ -2,6 +2,10 @@ import os
 import importlib.util
 
 from modules import errors
+import sys
+
+
+loaded_scripts = {}
 
 
 def load_module(path):
@@ -9,6 +13,11 @@ def load_module(path):
     module = importlib.util.module_from_spec(module_spec)
     module_spec.loader.exec_module(module)
 
+    loaded_scripts[path] = module
+
+    module_name, _ = os.path.splitext(os.path.basename(path))
+    sys.modules["scripts." + module_name] = module
+
     return module
 
 

modules/scripts.py

@@ -739,12 +739,17 @@ class ScriptRunner:
         def onload_script_visibility(params):
             title = params.get('Script', None)
             if title:
-                title_index = self.titles.index(title)
-                visibility = title_index == self.script_load_ctr
-                self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
-                return gr.update(visible=visibility)
-            else:
-                return gr.update(visible=False)
+                try:
+                    title_index = self.titles.index(title)
+                    visibility = title_index == self.script_load_ctr
+                    self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
+                    return gr.update(visible=visibility)
+                except ValueError:
+                    params['Script'] = None
+                    massage = f'Cannot find Script: "{title}"'
+                    print(massage)
+                    gr.Warning(massage)
+            return gr.update(visible=False)
 
         self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
         self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])

modules/scripts_postprocessing.py

@@ -143,6 +143,7 @@ class ScriptPostprocessingRunner:
             self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
 
         scripts_order = shared.opts.postprocessing_operation_order
+        scripts_filter_out = set(shared.opts.postprocessing_disable_in_extras)
 
         def script_score(name):
             for i, possible_match in enumerate(scripts_order):
@@ -151,9 +152,10 @@ class ScriptPostprocessingRunner:
 
             return len(self.scripts)
 
-        script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
+        filtered_scripts = [script for script in self.scripts if script.name not in scripts_filter_out]
+        script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(filtered_scripts)}
 
-        return sorted(self.scripts, key=lambda x: script_scores[x.name])
+        return sorted(filtered_scripts, key=lambda x: script_scores[x.name])
 
     def setup_ui(self):
         inputs = []

modules/sd_models.py

@@ -1,5 +1,5 @@
 import collections
-import os.path
+import os
 import sys
 import threading
 
@@ -7,7 +7,6 @@ import torch
 import re
 import safetensors.torch
 from omegaconf import OmegaConf, ListConfig
-from os import mkdir
 from urllib import request
 import ldm.modules.midas as midas
 
@@ -151,7 +150,7 @@ def list_models():
     if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
         model_url = None
     else:
-        model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+        model_url = f"{shared.hf_endpoint}/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
 
     model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
 
@@ -508,7 +507,7 @@ def enable_midas_autodownload():
         path = midas.api.ISL_PATHS[model_type]
         if not os.path.exists(path):
             if not os.path.exists(midas_path):
-                mkdir(midas_path)
+                os.mkdir(midas_path)
 
             print(f"Downloading midas model weights for {model_type} to {path}")
             request.urlretrieve(midas_urls[model_type], path)
@@ -787,6 +786,13 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
     Additionally deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
     """
 
+    if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+        return sd_model
+
+    if shared.opts.sd_checkpoints_keep_in_cpu:
+        send_model_to_cpu(sd_model)
+        timer.record("send model to cpu")
+
     already_loaded = None
     for i in reversed(range(len(model_data.loaded_sd_models))):
         loaded_model = model_data.loaded_sd_models[i]
@@ -796,14 +802,10 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
 
         if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
             print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
-            model_data.loaded_sd_models.pop()
+            del model_data.loaded_sd_models[i]
             send_model_to_trash(loaded_model)
             timer.record("send model to trash")
 
-        if shared.opts.sd_checkpoints_keep_in_cpu:
-            send_model_to_cpu(sd_model)
-            timer.record("send model to cpu")
-
     if already_loaded is not None:
         send_model_to_device(already_loaded)
         timer.record("send model to device")

modules/shared.py

@@ -90,3 +90,5 @@ list_checkpoint_tiles = shared_items.list_checkpoint_tiles
 refresh_checkpoints = shared_items.refresh_checkpoints
 list_samplers = shared_items.list_samplers
 reload_hypernetworks = shared_items.reload_hypernetworks
+
+hf_endpoint = os.getenv('HF_ENDPOINT', 'https://huggingface.co')

modules/shared_options.py

@@ -19,7 +19,9 @@ restricted_opts = {
     "outdir_grids",
     "outdir_txt2img_grids",
     "outdir_save",
-    "outdir_init_images"
+    "outdir_init_images",
+    "temp_dir",
+    "clean_temp_dir_at_start",
 }
 
 categories.register_category("saving", "Saving images")
@@ -384,6 +386,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
 
 options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
     'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+    'postprocessing_disable_in_extras': OptionInfo([], "Disable postprocessing operations in extras tab", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),

modules/ui_common.py

@@ -3,13 +3,10 @@ import dataclasses
 import json
 import html
 import os
-import platform
-import sys
 
 import gradio as gr
-import subprocess as sp
 
-from modules import call_queue, shared, ui_tempdir
+from modules import call_queue, shared, ui_tempdir, util
 from modules.infotext_utils import image_from_url_text
 import modules.images
 from modules.ui_components import ToolButton
@@ -176,31 +173,7 @@ def create_output_panel(tabname, outdir, toprow=None):
         except Exception:
             pass
 
-        if not os.path.exists(f):
-            msg = f'Folder "{f}" does not exist. After you create an image, the folder will be created.'
-            print(msg)
-            gr.Info(msg)
-            return
-        elif not os.path.isdir(f):
-            msg = f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-"""
-            print(msg, file=sys.stderr)
-            gr.Warning(msg)
-            return
-
-        path = os.path.normpath(f)
-        if platform.system() == "Windows":
-            os.startfile(path)
-        elif platform.system() == "Darwin":
-            sp.Popen(["open", path])
-        elif "microsoft-standard-WSL2" in platform.uname().release:
-            sp.Popen(["wsl-open", path])
-        else:
-            sp.Popen(["xdg-open", path])
+        util.open_folder(f)
 
     with gr.Column(elem_id=f"{tabname}_results"):
         if toprow:

modules/ui_extensions.py

@@ -58,8 +58,9 @@ def apply_and_restart(disable_list, update_list, disable_all):
 
 def save_config_state(name):
     current_config_state = config_states.get_config()
-    if not name:
-        name = "Config"
+
+    name = os.path.basename(name or "Config")
+
     current_config_state["name"] = name
     timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S')
     filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")

modules/upscaler.py

@@ -60,6 +60,9 @@ class Upscaler:
             if img.width >= dest_w and img.height >= dest_h:
                 break
 
+            if shared.state.interrupted:
+                break
+
             shape = (img.width, img.height)
 
             img = self.do_upscale(img, selected_model)

modules/upscaler_utils.py

@@ -69,6 +69,8 @@ def upscale_with_model(
         for y, h, row in grid.tiles:
             newrow = []
             for x, w, tile in row:
+                if shared.state.interrupted:
+                    return img
                 output = upscale_pil_patch(model, tile)
                 scale_factor = output.width // tile.width
                 newrow.append([x * scale_factor, w * scale_factor, output])

modules/util.py

@@ -148,6 +148,11 @@ class MassFileLister:
         """Clear the cache of all directories."""
         self.cached_dirs.clear()
 
+    def update_file_entry(self, path):
+        """Update the cache for a specific directory."""
+        dirname, filename = os.path.split(path)
+        if cached_dir := self.cached_dirs.get(dirname):
+            cached_dir.update_entry(filename)
 
 def topological_sort(dependencies):
     """Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
@@ -171,3 +176,38 @@ def topological_sort(dependencies):
             inner(depname)
 
     return result
+
+
+def open_folder(path):
+    """Open a folder in the file manager of the respect OS."""
+    # import at function level to avoid potential issues
+    import gradio as gr
+    import platform
+    import sys
+    import subprocess
+
+    if not os.path.exists(path):
+        msg = f'Folder "{path}" does not exist. after you save an image, the folder will be created.'
+        print(msg)
+        gr.Info(msg)
+        return
+    elif not os.path.isdir(path):
+        msg = f"""
+WARNING
+An open_folder request was made with an path that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {path}
+"""
+        print(msg, file=sys.stderr)
+        gr.Warning(msg)
+        return
+
+    path = os.path.normpath(path)
+    if platform.system() == "Windows":
+        os.startfile(path)
+    elif platform.system() == "Darwin":
+        subprocess.Popen(["open", path])
+    elif "microsoft-standard-WSL2" in platform.uname().release:
+        subprocess.Popen(["wsl-open", path])
+    else:
+        subprocess.Popen(["xdg-open", path])