Upload extensions using SD-Hub extension

.gitattributes

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extensions/1-sd-dynamic-thresholding/.github/FUNDING.yml

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+github: mcmonkey4eva

extensions/1-sd-dynamic-thresholding/.github/workflows/publish.yml

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+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - master
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

extensions/1-sd-dynamic-thresholding/.gitignore

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+__pycache__/

extensions/1-sd-dynamic-thresholding/LICENSE.txt

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+The MIT License (MIT)
+
+Copyright (c) 2023 Alex "mcmonkey" Goodwin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

extensions/1-sd-dynamic-thresholding/README.md

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+# Stable Diffusion Dynamic Thresholding (CFG Scale Fix)
+
+### Concept
+
+Extension for [SwarmUI](https://github.com/mcmonkeyprojects/SwarmUI), [ComfyUI](https://github.com/comfyanonymous/ComfyUI), and [AUTOMATIC1111 Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) that enables a way to use higher CFG Scales without color issues.
+
+This works by clamping latents between steps. You can read more [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/3962) or [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/3268) or [this tweet](https://twitter.com/Birchlabs/status/1582165379832348672).
+
+--------------
+
+### Credit
+
+The core functionality of this PR was originally developed by [Birch-san](https://github.com/Birch-san) and ported to the WebUI by [dtan3847](https://github.com/dtan3847), then converted to an Auto WebUI extension and given a UI by [mcmonkey4eva](https://github.com/mcmonkey4eva), further development and research done by [mcmonkey4eva](https://github.com/mcmonkey4eva) and JDMLeverton. Ported by ComfyUI by [TwoDukes](https://github.com/TwoDukes) and [mcmonkey4eva](https://github.com/mcmonkey4eva). Ported to SwarmUI by [mcmonkey4eva](https://github.com/mcmonkey4eva).
+
+--------------
+
+### Examples
+
+![img](github/cat_demo_1.jpg)
+
+![img](github/ui.png)
+
+
+--------------
+
+### Demo Grid
+
+View at <https://sd.mcmonkey.org/dynthresh/>.
+
+![img](github/grid_preview.png)
+
+(Was generated via [this YAML config](https://gist.github.com/mcmonkey4eva/fccd29172f44424dfc0217a482c824f6) for the [Infinite Grid Generator](https://github.com/mcmonkeyprojects/sd-infinity-grid-generator-script))
+
+--------------
+
+### Installation and Usage
+
+#### SwarmUI
+
+- Supported out-of-the-box on default installations.
+    - If using a custom installation, just make sure the backend you use has this repo installed per the instructions specific to the backend as written below.
+- It's under the "Display Advanced Options" parameter checkbox.
+
+#### Auto WebUI
+
+- You must have the [AUTOMATIC1111 Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) already installed and working. Refer to that project's readme for help with that.
+- Open the WebUI, go to the `Extensions` tab
+- -EITHER- Option **A**:
+    - go to the `Available` tab with
+    - click `Load from` (with the default list)
+    - Scroll down to find `Dynamic Thresholding (CFG Scale Fix)`, or use `CTRL+F` to find it
+- -OR- Option **B**:
+    - Click on `Install from URL`
+    - Copy/paste this project's URL into the `URL for extension's git repository` textbox: `https://github.com/mcmonkeyprojects/sd-dynamic-thresholding`
+- Click `Install`
+- Restart or reload the WebUI
+- Go to txt2img or img2img
+- Check the `Enable Dynamic Thresholding (CFG Scale Fix)` box
+- Read the info on-page and set the sliders where you want em.
+- Click generate.
+
+
+#### ComfyUI
+
+- Must have [ComfyUI](https://github.com/comfyanonymous/ComfyUI) already installed and working. Refer to that project's readme for help with that.
+- -EITHER- Option **A**: (TODO: Manager install)
+- -OR- Option **B**:
+    - `cd ComfyUI/custom_nodes`
+    - `git clone https://github.com/mcmonkeyprojects/sd-dynamic-thresholding`
+    - restart ComfyUI
+    - Add node `advanced/mcmonkey/DynamicThresholdingSimple` (or `Full`)
+    - Link your model to the input, and then link the output model to your KSampler's input
+
+![img](github/comfy_node.png)
+
+--------------
+
+### Supported Auto WebUI Extensions
+
+- This can be configured within the [Infinity Grid Generator](https://github.com/mcmonkeyprojects/sd-infinity-grid-generator-script#supported-extensions) extension, see the readme of that project for details.
+
+### ComfyUI Compatibility
+
+- This would work with any variant of the `KSampler` node, including custom ones, so long as they do not totally override the internal sampling function (most don't).
+
+----------------------
+
+### Licensing pre-note:
+
+This is an open source project, provided entirely freely, for everyone to use and contribute to.
+
+If you make any changes that could benefit the community as a whole, please contribute upstream.
+
+### The short of the license is:
+
+You can do basically whatever you want, except you may not hold any developer liable for what you do with the software.
+
+### The long version of the license follows:
+
+The MIT License (MIT)
+
+Copyright (c) 2023 Alex "mcmonkey" Goodwin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

extensions/1-sd-dynamic-thresholding/__init__.py

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+from . import dynthres_comfyui
+
+NODE_CLASS_MAPPINGS = {
+    "DynamicThresholdingSimple": dynthres_comfyui.DynamicThresholdingSimpleComfyNode,
+    "DynamicThresholdingFull": dynthres_comfyui.DynamicThresholdingComfyNode,
+}

extensions/1-sd-dynamic-thresholding/dynthres_comfyui.py

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+from .dynthres_core import DynThresh
+
+class DynamicThresholdingComfyNode:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "mimic_scale": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.5}),
+                "threshold_percentile": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "mimic_mode": (DynThresh.Modes, ),
+                "mimic_scale_min": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.5}),
+                "cfg_mode": (DynThresh.Modes, ),
+                "cfg_scale_min": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.5}),
+                "sched_val": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                "separate_feature_channels": (["enable", "disable"], ),
+                "scaling_startpoint": (DynThresh.Startpoints, ),
+                "variability_measure": (DynThresh.Variabilities, ),
+                "interpolate_phi": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "advanced/mcmonkey"
+
+    def patch(self, model, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi):
+
+        dynamic_thresh = DynThresh(mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, 0, 999, separate_feature_channels == "enable", scaling_startpoint, variability_measure, interpolate_phi)
+        
+        def sampler_dyn_thresh(args):
+            input = args["input"]
+            cond = input - args["cond"]
+            uncond = input - args["uncond"]
+            cond_scale = args["cond_scale"]
+            time_step = model.model.model_sampling.timestep(args["sigma"])
+            time_step = time_step[0].item()
+            dynamic_thresh.step = 999 - time_step
+
+            if cond_scale == mimic_scale:
+                return input - (uncond + (cond - uncond) * cond_scale)
+            else:
+                return input - dynamic_thresh.dynthresh(cond, uncond, cond_scale, None)
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(sampler_dyn_thresh)
+        return (m, )
+
+class DynamicThresholdingSimpleComfyNode:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "mimic_scale": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.5}),
+                "threshold_percentile": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "advanced/mcmonkey"
+
+    def patch(self, model, mimic_scale, threshold_percentile):
+
+        dynamic_thresh = DynThresh(mimic_scale, threshold_percentile, "CONSTANT", 0, "CONSTANT", 0, 0, 0, 999, False, "MEAN", "AD", 1)
+        
+        def sampler_dyn_thresh(args):
+            input = args["input"]
+            cond = input - args["cond"]
+            uncond = input - args["uncond"]
+            cond_scale = args["cond_scale"]
+            time_step = model.model.model_sampling.timestep(args["sigma"])
+            time_step = time_step[0].item()
+            dynamic_thresh.step = 999 - time_step
+
+            if cond_scale == mimic_scale:
+                return input - (uncond + (cond - uncond) * cond_scale)
+            else:
+                return input - dynamic_thresh.dynthresh(cond, uncond, cond_scale, None)
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(sampler_dyn_thresh)
+        return (m, )

extensions/1-sd-dynamic-thresholding/dynthres_core.py

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+import torch, math
+
+######################### DynThresh Core #########################
+
+class DynThresh:
+
+    Modes = ["Constant", "Linear Down", "Cosine Down", "Half Cosine Down", "Linear Up", "Cosine Up", "Half Cosine Up", "Power Up", "Power Down", "Linear Repeating", "Cosine Repeating", "Sawtooth"]
+    Startpoints = ["MEAN", "ZERO"]
+    Variabilities = ["AD", "STD"]
+
+    def __init__(self, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, experiment_mode, max_steps, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi):
+        self.mimic_scale = mimic_scale
+        self.threshold_percentile = threshold_percentile
+        self.mimic_mode = mimic_mode
+        self.cfg_mode = cfg_mode
+        self.max_steps = max_steps
+        self.cfg_scale_min = cfg_scale_min
+        self.mimic_scale_min = mimic_scale_min
+        self.experiment_mode = experiment_mode
+        self.sched_val = sched_val
+        self.sep_feat_channels = separate_feature_channels
+        self.scaling_startpoint = scaling_startpoint
+        self.variability_measure = variability_measure
+        self.interpolate_phi = interpolate_phi
+
+    def interpret_scale(self, scale, mode, min):
+        scale -= min
+        max = self.max_steps - 1
+        frac = self.step / max
+        if mode == "Constant":
+            pass
+        elif mode == "Linear Down":
+            scale *= 1.0 - frac
+        elif mode == "Half Cosine Down":
+            scale *= math.cos(frac)
+        elif mode == "Cosine Down":
+            scale *= math.cos(frac * 1.5707)
+        elif mode == "Linear Up":
+            scale *= frac
+        elif mode == "Half Cosine Up":
+            scale *= 1.0 - math.cos(frac)
+        elif mode == "Cosine Up":
+            scale *= 1.0 - math.cos(frac * 1.5707)
+        elif mode == "Power Up":
+            scale *= math.pow(frac, self.sched_val)
+        elif mode == "Power Down":
+            scale *= 1.0 - math.pow(frac, self.sched_val)
+        elif mode == "Linear Repeating":
+            portion = (frac * self.sched_val) % 1.0
+            scale *= (0.5 - portion) * 2 if portion < 0.5 else (portion - 0.5) * 2
+        elif mode == "Cosine Repeating":
+            scale *= math.cos(frac * 6.28318 * self.sched_val) * 0.5 + 0.5
+        elif mode == "Sawtooth":
+            scale *= (frac * self.sched_val) % 1.0
+        scale += min
+        return scale
+
+    def dynthresh(self, cond, uncond, cfg_scale, weights):
+        mimic_scale = self.interpret_scale(self.mimic_scale, self.mimic_mode, self.mimic_scale_min)
+        cfg_scale = self.interpret_scale(cfg_scale, self.cfg_mode, self.cfg_scale_min)
+        # uncond shape is (batch, 4, height, width)
+        conds_per_batch = cond.shape[0] / uncond.shape[0]
+        assert conds_per_batch == int(conds_per_batch), "Expected # of conds per batch to be constant across batches"
+        cond_stacked = cond.reshape((-1, int(conds_per_batch)) + uncond.shape[1:])
+
+        ### Normal first part of the CFG Scale logic, basically
+        diff = cond_stacked - uncond.unsqueeze(1)
+        if weights is not None:
+            diff = diff * weights
+        relative = diff.sum(1)
+
+        ### Get the normal result for both mimic and normal scale
+        mim_target = uncond + relative * mimic_scale
+        cfg_target = uncond + relative * cfg_scale
+        ### If we weren't doing mimic scale, we'd just return cfg_target here
+
+        ### Now recenter the values relative to their average rather than absolute, to allow scaling from average
+        mim_flattened = mim_target.flatten(2)
+        cfg_flattened = cfg_target.flatten(2)
+        mim_means = mim_flattened.mean(dim=2).unsqueeze(2)
+        cfg_means = cfg_flattened.mean(dim=2).unsqueeze(2)
+        mim_centered = mim_flattened - mim_means
+        cfg_centered = cfg_flattened - cfg_means
+
+        if self.sep_feat_channels:
+            if self.variability_measure == 'STD':
+                mim_scaleref = mim_centered.std(dim=2).unsqueeze(2)
+                cfg_scaleref = cfg_centered.std(dim=2).unsqueeze(2)
+            else: # 'AD'
+                mim_scaleref = mim_centered.abs().max(dim=2).values.unsqueeze(2)
+                cfg_scaleref = torch.quantile(cfg_centered.abs(), self.threshold_percentile, dim=2).unsqueeze(2)
+
+        else:
+            if self.variability_measure == 'STD':
+                mim_scaleref = mim_centered.std()
+                cfg_scaleref = cfg_centered.std()
+            else: # 'AD'
+                mim_scaleref = mim_centered.abs().max()
+                cfg_scaleref = torch.quantile(cfg_centered.abs(), self.threshold_percentile)
+
+        if self.scaling_startpoint == 'ZERO':
+            scaling_factor = mim_scaleref / cfg_scaleref
+            result = cfg_flattened * scaling_factor
+
+        else: # 'MEAN'
+            if self.variability_measure == 'STD':
+                cfg_renormalized = (cfg_centered / cfg_scaleref) * mim_scaleref
+            else: # 'AD'
+                ### Get the maximum value of all datapoints (with an optional threshold percentile on the uncond)
+                max_scaleref = torch.maximum(mim_scaleref, cfg_scaleref)
+                ### Clamp to the max
+                cfg_clamped = cfg_centered.clamp(-max_scaleref, max_scaleref)
+                ### Now shrink from the max to normalize and grow to the mimic scale (instead of the CFG scale)
+                cfg_renormalized = (cfg_clamped / max_scaleref) * mim_scaleref
+
+            ### Now add it back onto the averages to get into real scale again and return
+            result = cfg_renormalized + cfg_means
+
+        actual_res = result.unflatten(2, mim_target.shape[2:])
+
+        if self.interpolate_phi != 1.0:
+            actual_res = actual_res * self.interpolate_phi + cfg_target * (1.0 - self.interpolate_phi)
+
+        if self.experiment_mode == 1:
+            num = actual_res.cpu().numpy()
+            for y in range(0, 64):
+                for x in range (0, 64):
+                    if num[0][0][y][x] > 1.0:
+                        num[0][1][y][x] *= 0.5
+                    if num[0][1][y][x] > 1.0:
+                        num[0][1][y][x] *= 0.5
+                    if num[0][2][y][x] > 1.5:
+                        num[0][2][y][x] *= 0.5
+            actual_res = torch.from_numpy(num).to(device=uncond.device)
+        elif self.experiment_mode == 2:
+            num = actual_res.cpu().numpy()
+            for y in range(0, 64):
+                for x in range (0, 64):
+                    over_scale = False
+                    for z in range(0, 4):
+                        if abs(num[0][z][y][x]) > 1.5:
+                            over_scale = True
+                    if over_scale:
+                        for z in range(0, 4):
+                            num[0][z][y][x] *= 0.7
+            actual_res = torch.from_numpy(num).to(device=uncond.device)
+        elif self.experiment_mode == 3:
+            coefs = torch.tensor([
+                #  R       G        B      W
+                [0.298,   0.207,  0.208, 0.0], # L1
+                [0.187,   0.286,  0.173, 0.0], # L2
+                [-0.158,  0.189,  0.264, 0.0], # L3
+                [-0.184, -0.271, -0.473, 1.0], # L4
+            ], device=uncond.device)
+            res_rgb = torch.einsum("laxy,ab -> lbxy", actual_res, coefs)
+            max_r, max_g, max_b, max_w = res_rgb[0][0].max(), res_rgb[0][1].max(), res_rgb[0][2].max(), res_rgb[0][3].max()
+            max_rgb = max(max_r, max_g, max_b)
+            print(f"test max = r={max_r}, g={max_g}, b={max_b}, w={max_w}, rgb={max_rgb}")
+            if self.step / (self.max_steps - 1) > 0.2:
+                if max_rgb < 2.0 and max_w < 3.0:
+                    res_rgb /= max_rgb / 2.4
+            else:
+                if max_rgb > 2.4 and max_w > 3.0:
+                    res_rgb /= max_rgb / 2.4
+            actual_res = torch.einsum("laxy,ab -> lbxy", res_rgb, coefs.inverse())
+
+        return actual_res

extensions/1-sd-dynamic-thresholding/dynthres_unipc.py

@@ -0,0 +1,111 @@
+import gradio as gr
+import torch
+import math
+import traceback
+from modules import shared
+try:
+    from modules.models.diffusion import uni_pc
+except Exception as e:
+    from modules import unipc as uni_pc
+
+######################### UniPC Implementation logic #########################
+
+# The majority of this is straight from modules.models/diffusion/uni_pc/sampler.py
+# Unfortunately that's not an easy middle-injection point, so, just copypasta'd it all
+# It's like they designed it to intentionally be as difficult to inject into as possible :(
+# (It has hooks but not in useful locations)
+# I stripped the original comments for brevity.
+# Some never-used code (scheduler modes, noise modes, guidance modes) have been removed as well for brevity.
+# The actual impl comes down to just the last line in particular, and the `before_sample` insert to track step count.
+
+class CustomUniPCSampler(uni_pc.sampler.UniPCSampler):
+    def __init__(self, model, **kwargs):
+        super().__init__(model, *kwargs)
+    @torch.no_grad()
+    def sample(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, **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}")
+
+            elif isinstance(conditioning, list):
+                for ctmp in conditioning:
+                    if ctmp.shape[0] != 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}")
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        device = self.model.betas.device
+        if x_T is None:
+            img = torch.randn(size, device=device)
+        else:
+            img = x_T
+        ns = uni_pc.uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+        model_type = "v" if self.model.parameterization == "v" else "noise"
+        model_fn = CustomUniPC_model_wrapper(lambda x, t, c: self.model.apply_model(x, t, c), ns, model_type=model_type, guidance_scale=unconditional_guidance_scale, dt_data=self.main_class)
+        self.main_class.step = 0
+        def before_sample(x, t, cond, uncond):
+            self.main_class.step += 1
+            return self.before_sample(x, t, cond, uncond)
+        uni_pc_inst = uni_pc.uni_pc.UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=before_sample, after_sample=self.after_sample, after_update=self.after_update)
+        x = uni_pc_inst.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+        return x.to(device), None
+
+def CustomUniPC_model_wrapper(model, noise_schedule, model_type="noise", model_kwargs={}, guidance_scale=1.0, dt_data=None):
+    def expand_dims(v, dims):
+        return v[(...,) + (None,)*(dims - 1)]
+    def get_model_input_time(t_continuous):
+        return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+    def noise_pred_fn(x, t_continuous, cond=None):
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        t_input = get_model_input_time(t_continuous)
+        if cond is None:
+            output = model(x, t_input, None, **model_kwargs)
+        else:
+            output = model(x, t_input, cond, **model_kwargs)
+        if model_type == "noise":
+            return output
+        elif model_type == "v":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+    def model_fn(x, t_continuous, condition, unconditional_condition):
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        if guidance_scale == 1. or unconditional_condition is None:
+            return noise_pred_fn(x, t_continuous, cond=condition)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t_continuous] * 2)
+            if isinstance(condition, dict):
+                assert isinstance(unconditional_condition, dict)
+                c_in = dict()
+                for k in condition:
+                    if isinstance(condition[k], list):
+                        c_in[k] = [torch.cat([
+                            unconditional_condition[k][i],
+                            condition[k][i]]) for i in range(len(condition[k]))]
+                    else:
+                        c_in[k] = torch.cat([
+                            unconditional_condition[k],
+                            condition[k]])
+            elif isinstance(condition, list):
+                c_in = list()
+                assert isinstance(unconditional_condition, list)
+                for i in range(len(condition)):
+                    c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
+            else:
+                c_in = torch.cat([unconditional_condition, condition])
+            noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+            #return noise_uncond + guidance_scale * (noise - noise_uncond)
+            return dt_data.dynthresh(noise, noise_uncond, guidance_scale, None)
+    return model_fn

extensions/1-sd-dynamic-thresholding/javascript/active.js

@@ -0,0 +1,68 @@
+let dynthres_update_enabled = function() {
+    return Array.from(arguments);
+};
+
+(function(){
+    let accordions = {};
+    let enabled = {};
+    onUiUpdate(() => {
+        let accordion_id_prefix = "#dynthres_";
+        let extension_checkbox_class = ".dynthres-enabled";
+
+        dynthres_update_enabled = function() {
+            let res = Array.from(arguments);
+            let tabname = res[1] ? "img2img" : "txt2img";
+
+            let checkbox = accordions[tabname]?.querySelector(extension_checkbox_class + ' input');
+            checkbox?.dispatchEvent(new Event('change'));
+
+            return res;
+        };
+
+        function attachEnabledButtonListener(checkbox, accordion) {
+            let span = accordion.querySelector('.label-wrap span');
+            let badge = document.createElement('input');
+            badge.type = "checkbox";
+            badge.checked = checkbox.checked;
+            badge.addEventListener('click', (e) => {
+                checkbox.checked = !checkbox.checked;
+                badge.checked = checkbox.checked;
+                checkbox.dispatchEvent(new Event('change'));
+                e.stopPropagation();
+            });
+
+            badge.className = checkbox.className;
+            badge.classList.add('primary');
+            span.insertBefore(badge, span.firstChild);
+            let space = document.createElement('span');
+            space.innerHTML = " ";
+            span.insertBefore(space, badge.nextSibling);
+
+            checkbox.addEventListener('change', () => {
+                let badge = accordion.querySelector('.label-wrap span input');
+                badge.checked = checkbox.checked;
+            });
+            checkbox.parentNode.style.display = "none";
+        }
+
+        if (Object.keys(accordions).length < 2) {
+            let accordion = gradioApp().querySelector(accordion_id_prefix + 'txt2img');
+            if (accordion) {
+                accordions.txt2img = accordion;
+            }
+            accordion = gradioApp().querySelector(accordion_id_prefix + 'img2img');
+            if (accordion) {
+                accordions.img2img = accordion;
+            }
+        }
+
+        if (Object.keys(accordions).length > 0 && accordions.txt2img && !enabled.txt2img) {
+            enabled.txt2img = accordions.txt2img.querySelector(extension_checkbox_class + ' input');
+            attachEnabledButtonListener(enabled.txt2img, accordions.txt2img);
+        }
+        if (Object.keys(accordions).length > 0 && accordions.img2img && !enabled.img2img) {
+            enabled.img2img = accordions.img2img.querySelector(extension_checkbox_class + ' input');
+            attachEnabledButtonListener(enabled.img2img, accordions.img2img);
+        }
+    });
+})();

extensions/1-sd-dynamic-thresholding/pyproject.toml

@@ -0,0 +1,13 @@
+[project]
+name = "sd-dynamic-thresholding"
+description = "Adds nodes for Dynamic Thresholding, CFG scheduling, and related techniques"
+version = "1.0.1"
+license = { file = "LICENSE.txt" }
+
+[project.urls]
+Repository = "https://github.com/mcmonkeyprojects/sd-dynamic-thresholding"
+
+[tool.comfy]
+PublisherId = "mcmonkey"
+DisplayName = "Dynamic Thresholding"
+Icon = ""

extensions/1-sd-dynamic-thresholding/scripts/dynamic_thresholding.py

@@ -0,0 +1,270 @@
+##################
+# Stable Diffusion Dynamic Thresholding (CFG Scale Fix)
+#
+# Author: Alex 'mcmonkey' Goodwin
+# GitHub URL: https://github.com/mcmonkeyprojects/sd-dynamic-thresholding
+# Created: 2022/01/26
+# Last updated: 2023/01/30
+#
+# For usage help, view the README.md file in the extension root, or via the GitHub page.
+#
+##################
+
+import gradio as gr
+import torch, traceback
+import dynthres_core
+from modules import scripts, script_callbacks, sd_samplers, sd_samplers_compvis, sd_samplers_common
+try:
+    import dynthres_unipc
+except Exception as e:
+    print(f"\n\n======\nError! UniPC sampler support failed to load! Is your WebUI up to date?\n(Error: {e})\n======")
+try:
+    from modules.sd_samplers_kdiffusion import CFGDenoiserKDiffusion as cfgdenoisekdiff
+    IS_AUTO_16 = True
+except Exception as e:
+    print(f"\n\n======\nWarning! Using legacy KDiff version! Is your WebUI up to date?\n======")
+    from modules.sd_samplers_kdiffusion import CFGDenoiser as cfgdenoisekdiff
+    IS_AUTO_16 = False
+
+DISABLE_VISIBILITY = True
+
+######################### Data values #########################
+MODES_WITH_VALUE = ["Power Up", "Power Down", "Linear Repeating", "Cosine Repeating", "Sawtooth"]
+
+######################### Script class entrypoint #########################
+class Script(scripts.Script):
+
+    def title(self):
+        return "Dynamic Thresholding (CFG Scale Fix)"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        def vis_change(is_vis):
+            return {"visible": is_vis, "__type__": "update"}
+        # "Dynamic Thresholding (CFG Scale Fix)"
+        dtrue = gr.Checkbox(value=True, visible=False)
+        dfalse = gr.Checkbox(value=False, visible=False)
+        with gr.Accordion("Dynamic Thresholding (CFG Scale Fix)", open=False, elem_id="dynthres_" + ("img2img" if is_img2img else "txt2img")):
+            with gr.Row():
+                enabled = gr.Checkbox(value=False, label="Enable Dynamic Thresholding (CFG Scale Fix)", elem_classes=["dynthres-enabled"], elem_id='dynthres_enabled')
+            with gr.Group():
+                gr.HTML(value=f"View <a style=\"border-bottom: 1px #00ffff dotted;\" href=\"https://github.com/mcmonkeyprojects/sd-dynamic-thresholding/wiki/Usage-Tips\">the wiki for usage tips.</a><br><br>", elem_id='dynthres_wiki_link')
+                mimic_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Mimic CFG Scale', value=7.0, elem_id='dynthres_mimic_scale')
+                with gr.Accordion("Advanced Options", open=False, elem_id='dynthres_advanced_opts'):
+                    with gr.Row():
+                        threshold_percentile = gr.Slider(minimum=90.0, value=100.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp', elem_id='dynthres_threshold_percentile')
+                        interpolate_phi = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Interpolate Phi", value=1.0, elem_id='dynthres_interpolate_phi')
+                    with gr.Row():
+                        mimic_mode = gr.Dropdown(dynthres_core.DynThresh.Modes, value="Constant", label="Mimic Scale Scheduler", elem_id='dynthres_mimic_mode')
+                        cfg_mode = gr.Dropdown(dynthres_core.DynThresh.Modes, value="Constant", label="CFG Scale Scheduler", elem_id='dynthres_cfg_mode')
+                    mimic_scale_min = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, visible=DISABLE_VISIBILITY, label="Minimum value of the Mimic Scale Scheduler", elem_id='dynthres_mimic_scale_min')
+                    cfg_scale_min = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, visible=DISABLE_VISIBILITY, label="Minimum value of the CFG Scale Scheduler", elem_id='dynthres_cfg_scale_min')
+                    sched_val = gr.Slider(minimum=0.0, maximum=40.0, step=0.5, value=4.0, visible=DISABLE_VISIBILITY, label="Scheduler Value", info="Value unique to the scheduler mode - for Power Up/Down, this is the power. For Linear/Cosine Repeating, this is the number of repeats per image.", elem_id='dynthres_sched_val')
+                    with gr.Row():
+                        separate_feature_channels = gr.Checkbox(value=True, label="Separate Feature Channels", elem_id='dynthres_separate_feature_channels')
+                        scaling_startpoint = gr.Radio(["ZERO", "MEAN"], value="MEAN", label="Scaling Startpoint")
+                        variability_measure = gr.Radio(["STD", "AD"], value="AD", label="Variability Measure")
+        def should_show_scheduler_value(cfg_mode, mimic_mode):
+            sched_vis = cfg_mode in MODES_WITH_VALUE or mimic_mode in MODES_WITH_VALUE or DISABLE_VISIBILITY
+            return vis_change(sched_vis), vis_change(mimic_mode != "Constant" or DISABLE_VISIBILITY), vis_change(cfg_mode != "Constant" or DISABLE_VISIBILITY)
+        cfg_mode.change(should_show_scheduler_value, inputs=[cfg_mode, mimic_mode], outputs=[sched_val, mimic_scale_min, cfg_scale_min])
+        mimic_mode.change(should_show_scheduler_value, inputs=[cfg_mode, mimic_mode], outputs=[sched_val, mimic_scale_min, cfg_scale_min])
+        enabled.change(
+            _js="dynthres_update_enabled",
+            fn=None,
+            inputs=[enabled, dtrue if is_img2img else dfalse],
+            show_progress = False)
+        self.infotext_fields = (
+            (enabled, lambda d: gr.Checkbox.update(value="Dynamic thresholding enabled" in d)),
+            (mimic_scale, "Mimic scale"),
+            (separate_feature_channels, "Separate Feature Channels"),
+            (scaling_startpoint, lambda d: gr.Radio.update(value=d.get("Scaling Startpoint", "MEAN"))),
+            (variability_measure, lambda d: gr.Radio.update(value=d.get("Variability Measure", "AD"))),
+            (interpolate_phi, "Interpolate Phi"),
+            (threshold_percentile, "Threshold percentile"),
+            (mimic_scale_min, "Mimic scale minimum"),
+            (mimic_mode, lambda d: gr.Dropdown.update(value=d.get("Mimic mode", "Constant"))),
+            (cfg_mode, lambda d: gr.Dropdown.update(value=d.get("CFG mode", "Constant"))),
+            (cfg_scale_min, "CFG scale minimum"),
+            (sched_val, "Scheduler value"))
+        return [enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi]
+
+    last_id = 0
+
+    def process_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi, batch_number, prompts, seeds, subseeds):
+        enabled = getattr(p, 'dynthres_enabled', enabled)
+        if not enabled:
+            return
+        orig_sampler_name = p.sampler_name
+        orig_latent_sampler_name = getattr(p, 'latent_sampler', None)
+        if orig_sampler_name in ["DDIM", "PLMS"]:
+            raise RuntimeError(f"Cannot use sampler {orig_sampler_name} with Dynamic Thresholding")
+        if orig_latent_sampler_name in ["DDIM", "PLMS"]:
+            raise RuntimeError(f"Cannot use secondary sampler {orig_latent_sampler_name} with Dynamic Thresholding")
+        if 'UniPC' in (orig_sampler_name, orig_latent_sampler_name) and p.enable_hr:
+            raise RuntimeError(f"UniPC does not support Hires Fix. Auto WebUI silently swaps to DDIM for this, which DynThresh does not support. Please swap to a sampler capable of img2img processing for HR Fix to work.")
+        mimic_scale = getattr(p, 'dynthres_mimic_scale', mimic_scale)
+        separate_feature_channels = getattr(p, 'dynthres_separate_feature_channels', separate_feature_channels)
+        scaling_startpoint = getattr(p, 'dynthres_scaling_startpoint', scaling_startpoint)
+        variability_measure = getattr(p, 'dynthres_variability_measure', variability_measure)
+        interpolate_phi = getattr(p, 'dynthres_interpolate_phi', interpolate_phi)
+        threshold_percentile = getattr(p, 'dynthres_threshold_percentile', threshold_percentile)
+        mimic_mode = getattr(p, 'dynthres_mimic_mode', mimic_mode)
+        mimic_scale_min = getattr(p, 'dynthres_mimic_scale_min', mimic_scale_min)
+        cfg_mode = getattr(p, 'dynthres_cfg_mode', cfg_mode)
+        cfg_scale_min = getattr(p, 'dynthres_cfg_scale_min', cfg_scale_min)
+        experiment_mode = getattr(p, 'dynthres_experiment_mode', 0)
+        sched_val = getattr(p, 'dynthres_scheduler_val', sched_val)
+        p.extra_generation_params["Dynamic thresholding enabled"] = True
+        p.extra_generation_params["Mimic scale"] = mimic_scale
+        p.extra_generation_params["Separate Feature Channels"] = separate_feature_channels
+        p.extra_generation_params["Scaling Startpoint"] = scaling_startpoint
+        p.extra_generation_params["Variability Measure"] = variability_measure
+        p.extra_generation_params["Interpolate Phi"] = interpolate_phi
+        p.extra_generation_params["Threshold percentile"] = threshold_percentile
+        p.extra_generation_params["Sampler"] = orig_sampler_name
+        if mimic_mode != "Constant":
+            p.extra_generation_params["Mimic mode"] = mimic_mode
+            p.extra_generation_params["Mimic scale minimum"] = mimic_scale_min
+        if cfg_mode != "Constant":
+            p.extra_generation_params["CFG mode"] = cfg_mode
+            p.extra_generation_params["CFG scale minimum"] = cfg_scale_min
+        if cfg_mode in MODES_WITH_VALUE or mimic_mode in MODES_WITH_VALUE:
+            p.extra_generation_params["Scheduler value"] = sched_val
+        # Note: the ID number is to protect the edge case of multiple simultaneous runs with different settings
+        Script.last_id += 1
+        # Percentage to portion
+        threshold_percentile *= 0.01
+
+        def make_sampler(orig_sampler_name):
+            fixed_sampler_name = f"{orig_sampler_name}_dynthres{Script.last_id}"
+
+            # Make a placeholder sampler
+            sampler = sd_samplers.all_samplers_map[orig_sampler_name]
+            dt_data = dynthres_core.DynThresh(mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, experiment_mode, p.steps, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi)
+            if orig_sampler_name == "UniPC":
+                def unipc_constructor(model):
+                    return CustomVanillaSDSampler(dynthres_unipc.CustomUniPCSampler, model, dt_data)
+                new_sampler = sd_samplers_common.SamplerData(fixed_sampler_name, unipc_constructor, sampler.aliases, sampler.options)
+            else:
+                def new_constructor(model):
+                    result = sampler.constructor(model)
+                    cfg = CustomCFGDenoiser(result if IS_AUTO_16 else result.model_wrap_cfg.inner_model, dt_data)
+                    result.model_wrap_cfg = cfg
+                    return result
+                new_sampler = sd_samplers_common.SamplerData(fixed_sampler_name, new_constructor, sampler.aliases, sampler.options)
+            return fixed_sampler_name, new_sampler
+
+        # Apply for usage
+        p.orig_sampler_name = orig_sampler_name
+        p.orig_latent_sampler_name = orig_latent_sampler_name
+        p.fixed_samplers = []
+
+        if orig_latent_sampler_name:
+            latent_sampler_name, latent_sampler = make_sampler(orig_latent_sampler_name)
+            sd_samplers.all_samplers_map[latent_sampler_name] = latent_sampler
+            p.fixed_samplers.append(latent_sampler_name)
+            p.latent_sampler = latent_sampler_name
+
+        if orig_sampler_name != orig_latent_sampler_name:
+            p.sampler_name, new_sampler = make_sampler(orig_sampler_name)
+            sd_samplers.all_samplers_map[p.sampler_name] = new_sampler
+            p.fixed_samplers.append(p.sampler_name)
+        else:
+            p.sampler_name = p.latent_sampler
+
+        if p.sampler is not None:
+            p.sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
+
+    def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi, batch_number, images):
+        if not enabled or not hasattr(p, 'orig_sampler_name'):
+            return
+        p.sampler_name = p.orig_sampler_name
+        if p.orig_latent_sampler_name:
+            p.latent_sampler = p.orig_latent_sampler_name
+        for added_sampler in p.fixed_samplers:
+            del sd_samplers.all_samplers_map[added_sampler]
+        del p.fixed_samplers
+        del p.orig_sampler_name
+        del p.orig_latent_sampler_name
+
+######################### CompVis Implementation logic #########################
+
+class CustomVanillaSDSampler(sd_samplers_compvis.VanillaStableDiffusionSampler):
+    def __init__(self, constructor, sd_model, dt_data):
+        super().__init__(constructor, sd_model)
+        self.sampler.main_class = dt_data
+
+######################### K-Diffusion Implementation logic #########################
+
+class CustomCFGDenoiser(cfgdenoisekdiff):
+    def __init__(self, model, dt_data):
+        super().__init__(model)
+        self.main_class = dt_data
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        if isinstance(uncond, dict) and 'crossattn' in uncond:
+            uncond = uncond['crossattn']
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        # conds_list shape is (batch, cond, 2)
+        weights = torch.tensor(conds_list, device=uncond.device).select(2, 1)
+        weights = weights.reshape(*weights.shape, 1, 1, 1)
+        self.main_class.step = self.step
+        if hasattr(self, 'total_steps'):
+            self.main_class.max_steps = self.total_steps
+
+        if self.main_class.experiment_mode >= 4 and self.main_class.experiment_mode <= 5:
+            # https://arxiv.org/pdf/2305.08891.pdf "Rescale CFG". It's not good, but if you want to test it, just set experiment_mode = 4 + phi.
+            denoised = torch.clone(denoised_uncond)
+            fi = self.main_class.experiment_mode - 4.0
+            for i, conds in enumerate(conds_list):
+                for cond_index, weight in conds:
+                    xcfg = (denoised_uncond[i] + (x_out[cond_index] - denoised_uncond[i]) * (cond_scale * weight))
+                    xrescaled = xcfg * (torch.std(x_out[cond_index]) / torch.std(xcfg))
+                    xfinal = fi * xrescaled + (1.0 - fi) * xcfg
+                    denoised[i] = xfinal
+            return denoised
+
+        return self.main_class.dynthresh(x_out[:-uncond.shape[0]], denoised_uncond, cond_scale, weights)
+
+######################### XYZ Plot Script Support logic #########################
+
+def make_axis_options():
+    xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ in ("xyz_grid.py", "scripts.xyz_grid")][0].module
+    def apply_mimic_scale(p, x, xs):
+        if x != 0:
+            setattr(p, "dynthres_enabled", True)
+            setattr(p, "dynthres_mimic_scale", x)
+        else:
+            setattr(p, "dynthres_enabled", False)
+    def confirm_scheduler(p, xs):
+        for x in xs:
+            if x not in dynthres_core.DynThresh.Modes:
+                raise RuntimeError(f"Unknown Scheduler: {x}")
+    extra_axis_options = [
+        xyz_grid.AxisOption("[DynThres] Mimic Scale", float, apply_mimic_scale),
+        xyz_grid.AxisOption("[DynThres] Separate Feature Channels", int,
+                            xyz_grid.apply_field("dynthres_separate_feature_channels")),
+        xyz_grid.AxisOption("[DynThres] Scaling Startpoint", str, xyz_grid.apply_field("dynthres_scaling_startpoint"), choices=lambda:['ZERO', 'MEAN']),
+        xyz_grid.AxisOption("[DynThres] Variability Measure", str, xyz_grid.apply_field("dynthres_variability_measure"), choices=lambda:['STD', 'AD']),
+        xyz_grid.AxisOption("[DynThres] Interpolate Phi", float, xyz_grid.apply_field("dynthres_interpolate_phi")),
+        xyz_grid.AxisOption("[DynThres] Threshold Percentile", float, xyz_grid.apply_field("dynthres_threshold_percentile")),
+        xyz_grid.AxisOption("[DynThres] Mimic Scheduler", str, xyz_grid.apply_field("dynthres_mimic_mode"), confirm=confirm_scheduler, choices=lambda: dynthres_core.DynThresh.Modes),
+        xyz_grid.AxisOption("[DynThres] Mimic minimum", float, xyz_grid.apply_field("dynthres_mimic_scale_min")),
+        xyz_grid.AxisOption("[DynThres] CFG Scheduler", str, xyz_grid.apply_field("dynthres_cfg_mode"), confirm=confirm_scheduler, choices=lambda: dynthres_core.DynThresh.Modes),
+        xyz_grid.AxisOption("[DynThres] CFG minimum", float, xyz_grid.apply_field("dynthres_cfg_scale_min")),
+        xyz_grid.AxisOption("[DynThres] Scheduler value", float, xyz_grid.apply_field("dynthres_scheduler_val"))
+    ]
+    if not any("[DynThres]" in x.label for x in xyz_grid.axis_options):
+        xyz_grid.axis_options.extend(extra_axis_options)
+
+def callback_before_ui():
+    try:
+        make_axis_options()
+    except Exception as e:
+        traceback.print_exc()
+        print(f"Failed to add support for X/Y/Z Plot Script because: {e}")
+
+script_callbacks.on_before_ui(callback_before_ui)

extensions/4-adetailer/.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -0,0 +1,63 @@
+name: Bug report
+description: Create a report
+title: "[Bug]: "
+labels:
+  - bug
+
+body:
+  - type: textarea
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+      placeholder: |
+        Any language accepted
+        아무 언어 사용가능
+        すべての言語に対応
+        接受所有语言
+        Se aceptan todos los idiomas
+        Alle Sprachen werden akzeptiert
+        Toutes les langues sont acceptées
+        Принимаются все языки
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Steps to reproduce
+      description: |
+        Description of how we can reproduce this issue.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Screenshots
+      description: Screenshots related to the issue.
+
+  - type: textarea
+    attributes:
+      label: Console logs, from start to end.
+      description: |
+        The full console log of your terminal.
+      placeholder: |
+        Python ...
+        Version: ...
+        Commit hash: ...
+        Installing requirements
+        ...
+
+        Launching Web UI with arguments: ...
+        [-] ADetailer initialized. version: ...
+        ...
+        ...
+
+        Traceback (most recent call last):
+        ...
+        ...
+      render: Shell
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: List of installed extensions

extensions/4-adetailer/.github/ISSUE_TEMPLATE/feature_request.yaml

@@ -0,0 +1,26 @@
+name: Feature request
+description: Suggest an idea for this project
+title: "[Feature Request]: "
+labels:
+  - enhancement
+
+body:
+  - type: textarea
+    attributes:
+      label: Is your feature request related to a problem? Please describe.
+      description: A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+  - type: textarea
+    attributes:
+      label: Describe the solution you'd like
+      description: A clear and concise description of what you want to happen.
+
+  - type: textarea
+    attributes:
+      label: Describe alternatives you've considered
+      description: A clear and concise description of any alternative solutions or features you've considered.
+
+  - type: textarea
+    attributes:
+      label: Additional context
+      description: Add any other context or screenshots about the feature request here.

extensions/4-adetailer/.github/workflows/lgtm.yml

@@ -0,0 +1,23 @@
+name: Empirical Implementation of JDD
+
+on:
+  pull_request:
+    types:
+      - opened
+
+jobs:
+  lint:
+    permissions:
+      issues: write
+      pull-requests: write
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: peter-evans/create-or-update-comment@v4
+        with:
+          issue-number: ${{ github.event.pull_request.number }}
+          body: |
+            ![Imgur](https://i.imgur.com/ESow3BL.png)
+
+            LGTM
+          reactions: hooray

extensions/4-adetailer/.github/workflows/pypi.yml

@@ -0,0 +1,49 @@
+name: Publish to PyPI
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  test:
+    name: test
+    runs-on: macos-14
+    strategy:
+      matrix:
+        python-version:
+          - "3.10"
+          - "3.11"
+          - "3.12"
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - uses: yezz123/setup-uv@v4
+
+      - name: Install dependencies
+        run: |
+          uv pip install --system . pytest
+
+      - name: Run tests
+        run: pytest -v
+
+  build:
+    name: build
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+    needs: [test]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Build wheel
+        run: pipx run build
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1

extensions/4-adetailer/.github/workflows/stale.yml

@@ -0,0 +1,13 @@
+name: Close stale issues and PRs
+on:
+  schedule:
+    - cron: "30 1 * * *"
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v9
+        with:
+          days-before-stale: 17
+          days-before-close: 3

extensions/4-adetailer/.gitignore

@@ -0,0 +1,197 @@
+# Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,visualstudiocode
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
+*.ipynb
+node_modules

extensions/4-adetailer/.pre-commit-config.yaml

@@ -0,0 +1,29 @@
+ci:
+  autoupdate_branch: "dev"
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: check-added-large-files
+        args: [--maxkb=100]
+      - id: check-merge-conflict
+      - id: check-case-conflict
+      - id: check-ast
+      - id: check-yaml
+      - id: trailing-whitespace
+        args: [--markdown-linebreak-ext=md]
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+
+  - repo: https://github.com/rbubley/mirrors-prettier
+    rev: v3.3.3
+    hooks:
+      - id: prettier
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.5.6
+    hooks:
+      - id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
+      - id: ruff-format

extensions/4-adetailer/.vscode/extensions.json

@@ -0,0 +1,9 @@
+{
+  "recommendations": [
+    "ms-python.vscode-pylance",
+    "ms-python.black-formatter",
+    "kevinrose.vsc-python-indent",
+    "charliermarsh.ruff",
+    "shardulm94.trailing-spaces"
+  ]
+}

extensions/4-adetailer/.vscode/settings.json

@@ -0,0 +1,8 @@
+{
+  "explorer.fileNesting.enabled": true,
+  "explorer.fileNesting.patterns": {
+    "pyproject.toml": ".env, .gitignore, .pre-commit-config.yaml, Taskfile.yml",
+    "README.md": "LICENSE.md, CHANGELOG.md",
+    "install.py": "preload.py"
+  }
+}

extensions/4-adetailer/CHANGELOG.md

@@ -0,0 +1,472 @@
+# Changelog
+
+## 2024-08-03
+
+- v24.8.0
+- 샘플러 선택칸에 Use same sampler 옵션 추가
+- 컨트롤넷 유니온 모델을 선택할 수 있게 함
+
+- webui 1.9.0이상에서 기본 스케줄러가 설정되지 않던 문제 수정
+- issus #656의 문제 해결을 위해 v24.4.0에 적용되었던 프롬프트 표시 기능을 되돌림
+- mediapipe에서 에러가 발생하면 추론이 실패한 것으로 처리하고 조용히 넘어감
+
+## 2024-06-16
+
+- v24.6.0
+- webui 1.6.0 미만 버전을 위한 기능들을 제거하고, 최소 버전을 1.6.0으로 올림
+- 허깅페이스 연결을 체크하는데 1초만 소요되도록 함
+  - 허깅페이스 미러 (hf-mirror.com)도 체크함 (합쳐서 2초)
+- InputAccordion을 적용함
+
+## 2024-05-20
+
+- v24.5.1
+- uv를 사용하지 않게 함
+- 모든 허깅페이스 모델을 동시에 다운로드 시도함
+- 기본 탭 수를 2에서 4로 변경
+
+## 2024-05-19
+
+- v24.5.0
+- 개별 탭 활성화/비활성화 체크박스 추가
+- ad_extra_model_dir 옵션에 |로 구분된 여러 디렉토리를 추가할 수 있게 함 (PR #596)
+- `hypertile` 빌트인 확장이 지원되도록 함
+- 항상 cond 캐시를 비움
+- 설치 스크립트에 uv를 사용함
+- mediapipe 최소 버전을 올려 protobuf 버전 4를 사용하게 함
+
+## 2024-04-17
+
+- v24.4.2
+- `params.txt` 파일이 없을 때 에러가 발생하지 않도록 수정
+- 파이썬 3.9 이하에서 유니온 타입 에러 방지
+
+## 2024-04-14
+
+- v24.4.1
+- webui 1.9.0에서 발생한 에러 수정
+  - extra generation params에 callable이 들어와서 생긴 문제
+  - assign_current_image에 None이 들어갈 수 있던 문제
+- webui 1.9.0에서 변경된 scheduler 지원
+- 컨트롤넷 모델을 찾을 때, 대소문자 구분을 하지 않음 (PR #577)
+- 몇몇 기능을 스크립트에서 분리하여 별도 파일로 빼냄
+
+## 2024-04-10
+
+- v24.4.0
+- txt2img에서 hires를 설정했을 때, 이미지의 exif에서 Denoising Strength가 adetailer의 denoisiog stregnth로 덮어 쓰이는 문제 수정
+- ad prompt, ad negative prompt에 프롬프트를 변경하는 기능을 적용했을 때(와일드카드 등), 적용된 프롬프트가 이미지의 exif에 제대로 표시됨
+
+## 2024-03-29
+
+- v24.3.5
+- 알 수 없는 이유로 인페인팅을 확인하는 과정에서 Txt2Img 인스턴스가 들어오는 문제에 대한 임시 해결
+
+## 2024-03-28
+
+- v24.3.4
+- 인페인트에서, 이미지 해상도가 16의 배수가 아닐 때 사이즈 불일치로 인한 opencv 에러 방지
+
+## 2024-03-25
+
+- v24.3.3
+- webui 1.6.0 미만 버전에서 create_binary_mask 함수에 대해 ImportError가 발생하는 것 수정
+
+## 2024-03-21
+
+- v24.3.2
+- UI를 거치지 않은 입력에 대해, image_mask를 입력했을 때 opencv 에러가 발생하는 것 수정
+- img2img inpaint에서 skip img2img 옵션을 활성화할 경우, adetailer를 비활성화함
+  - 마스크 크기에 대해 해결하기 힘든 문제가 있음
+
+## 2024-03-16
+
+- v24.3.1
+- YOLO World v2, YOLO9 지원가능한 버전으로 ultralytics 업데이트
+- inpaint full res인 경우 인페인트 모드에서 동작하게 변경
+- inpaint full res가 아닌 경우, 사용자가 입력한 마스크와 교차점이 있는 마스크만 선택하여 사용함
+
+## 2024-03-01
+
+- v24.3.0
+- YOLO World 모델 추가: 가장 큰 yolov8x-world.pt 모델만 기본적으로 선택할 수 있게 함.
+- lllyasviel/stable-diffusion-webui-forge에서 컨트롤넷을 사용가능하게 함 (PR #517)
+- 기본 스크립트 목록에 soft_inpainting 추가 (https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14208)
+
+  - 기존에 설치한 사람에게 소급적용되지는 않음
+
+- 감지모델에 대한 간단한 pytest 추가함
+- xyz grid 컨트롤넷 모델 옵션에 `Passthrough` 추가함
+
+## 2024-01-23
+
+- v24.1.2
+- controlnet 모델에 `Passthrough` 옵션 추가. 입력으로 들어온 컨트롤넷 옵션을 그대로 사용
+- fastapi 엔드포인트 추가
+
+## 2024-01-10
+
+- v24.1.1
+- SDNext 호환 업데이트 (issue #466)
+  - 설정 값 state에 초기값 추가
+  - 위젯 값을 변경할 때마다 state도 변경되게 함 (기존에는 생성 버튼을 누를 때 적용되었음)
+- `inpaint_depth_hand` 컨트롤넷 모델이 depth 모델로 인식되게 함 (issue #463)
+
+## 2024-01-04
+
+- v24.1.0
+- `depth_hand_refiner` ControlNet 추가 (PR #460)
+
+## 2023-12-30
+
+- v23.12.0
+- 파일을 인자로 추가하는 몇몇 스크립트에 대해 deepcopy의 에러를 피하기 위해 script_args 복사 방법을 변경함
+- skip img2img 기능을 사용할 때 너비, 높이를 128로 고정하여 스킵 과정이 조금 더 나아짐
+- img2img inpainting 모드에서 adetailer 자동 비활성화
+- 처음 생성된 params.txt 파일을 항상 유지하도록 변경함
+
+## 2023-11-19
+
+- v23.11.1
+- 기본 스크립트 목록에 negpip 추가
+  - 기존에 설치한 사람에게 소급적용되지는 않��
+- skip img2img 옵션이 2스텝 이상일 때, 제대로 적용되지 않는 문제 수정
+- SD.Next에서 이미지가 np.ndarray로 입력되는 경우 수정
+- 컨트롤넷 경로를 sys.path에 추가하여 --data-dir등을 지정한 경우에도 임포트 에러가 일어나지 않게 함.
+
+## 2023-10-30
+
+- v23.11.0
+- 이미지의 인덱스 계산방법 변경
+  - webui 1.1.0 미만에서 adetailer 실행 불가능하게 함
+- 컨트롤넷 preprocessor 선택지 늘림
+- 추가 yolo 모델 디렉터리를 설정할 수 있는 옵션 추가
+- infotext에 `/`가 있는 항목이 exif에서 복원되지 않는 문제 수정
+  - 이전 버전에 생성된 이미지는 여전히 복원안됨
+- 같은 탭에서 항상 같은 시드를 적용하게 하는 옵션 추가
+- 컨트롤넷 1.1.411 (f2aafcf2beb99a03cbdf7db73852228ccd6bd1d6) 버전을 사용중일 경우,
+  webui 버전 1.6.0 미만에서 사용할 수 없다는 메세지 출력
+
+## 2023-10-15
+
+- v23.10.1
+- xyz grid에 prompt S/R 추가
+- img2img에서 steps가 1일때 에러가 발생하는 샘플러의 처리를 위해 샘플러 이름도 변경하게 수정
+
+## 2023-10-07
+
+- v23.10.0
+- 허깅페이스 모델을 다운로드 실패했을 때, 계속 다운로드를 시도하지 않음
+- img2img에서 img2img단계를 건너뛰는 기능 추가
+- live preview에서 감지 단계를 보여줌 (PR #352)
+
+## 2023-09-20
+
+- v23.9.3
+- ultralytics 버전 8.0.181로 업데이트 (https://github.com/ultralytics/ultralytics/pull/4891)
+- mediapipe와 ultralytics의 lazy import
+
+## 2023-09-10
+
+- v23.9.2
+- (실험적) VAE 선택 기능
+
+## 2023-09-01
+
+- v23.9.1
+- webui 1.6.0에 추가된 인자를 사용해서 생긴 하위 호환 문제 수정
+
+## 2023-08-31
+
+- v23.9.0
+- (실험적) 체크포인트 선택기능
+  - 버그가 있어 리프레시 버튼은 구현에서 빠짐
+- 1.6.0 업데이트에 따라 img2img에서 사용불가능한 샘플러를 선택했을 때 더이상 Euler로 변경하지 않음
+- 유효하지 않은 인자가 전달되었을 때, 에러를 일으키지 않고 대신 adetailer를 비활성화함
+
+## 2023-08-25
+
+- v23.8.1
+- xyz grid에서 model을 `None`으로 설정한 이후에 adetailer가 비활성화 되는 문제 수정
+- skip을 눌렀을 때 진행을 멈춤
+- `--medvram-sdxl`을 설정했을 때에도 cpu를 사용하게 함
+
+## 2023-08-14
+
+- v23.8.0
+- `[PROMPT]` 키워드 추가. `ad_prompt` 또는 `ad_negative_prompt`에 사용하면 입력 프롬프트로 대체됨 (PR #243)
+- Only top k largest 옵션 추가 (PR #264)
+- ultralytics 버전 업데이트
+
+## 2023-07-31
+
+- v23.7.11
+- separate clip skip 옵션 추가
+- install requirements 정리 (ultralytics 새 버전, mediapipe~=3.20)
+
+## 2023-07-28
+
+- v23.7.10
+- ultralytics, mediapipe import문 정리
+- traceback에서 컬러를 없앰 (api 때문), 라이브러리 버전도 보여주게 설정.
+- huggingface_hub, pydantic을 install.py에서 없앰
+- 안쓰는 컨트롤넷 관련 코드 삭제
+
+## 2023-07-23
+
+- v23.7.9
+- `ultralytics.utils` ModuleNotFoundError 해결 (https://github.com/ultralytics/ultralytics/issues/3856)
+- `pydantic` 2.0 이상 버전 설치안되도록 함
+- `controlnet_dir` cmd args 문제 수정 (PR #107)
+
+## 2023-07-20
+
+- v23.7.8
+- `paste_field_names` 추가했던 것을 되돌림
+
+## 2023-07-19
+
+- v23.7.7
+- 인페인팅 단계에서 별도의 샘플러를 선택할 수 있게 옵션을 추가함 (xyz그리드에도 추가)
+- webui 1.0.0-pre 이하 버전에서 batch index 문제 수정
+- 스크립트에 `paste_field_names`을 추가함. 사용되는지는 모르겠음
+
+## 2023-07-16
+
+- v23.7.6
+- `ultralytics 8.0.135`에 추가된 cpuinfo 기능을 위해 `py-cpuinfo`를 미리 설치하게 함. (미리 설치 안하면 cpu나 mps사용할 때 재시작해야함)
+- init_image가 RGB 모드가 아닐 때 RGB로 변경.
+
+## 2023-07-07
+
+- v23.7.4
+- batch count > 1일때 프롬프트의 인덱스 문제 수정
+
+- v23.7.5
+- i2i의 `cached_uc`와 `cached_c`가 p의 `cached_uc`와 `cached_c`가 다른 인스턴스가 되도록 수정
+
+## 2023-07-05
+
+- v23.7.3
+- 버그 수정
+  - `object()`가 json 직렬화 안되는 문제
+  - `process`를 호출함에 따라 배치 카운트가 2이상일 때, all_prompts가 고정되는 문제
+  - `ad-before`와 `ad-preview` 이미지 파일명이 실제 파일명과 다른 문제
+  - pydantic 2.0 호환성 문제
+
+## 2023-07-04
+
+- v23.7.2
+- `mediapipe_face_mesh_eyes_only` 모델 추가: `mediapipe_face_mesh`로 감지한 뒤 눈만 사용함.
+- 매 배치 시작 전에 `scripts.postprocess`를, 후에 `scripts.process`를 호출함.
+  - 컨트롤넷을 사용하면 소요 시간이 조금 늘어나지만 몇몇 문제 해결에 도움이 됨.
+- `lora_block_weight`를 스크립트 화이트리스트에 추가함.
+  - 한번이라도 ADetailer를 사용한 사람은 수동으로 추가해야함.
+
+## 2023-07-03
+
+- v23.7.1
+- `process_images`를 진행한 뒤 `StableDiffusionProcessing` 오브젝트의 close를 호출함
+- api 호출로 사용했는지 확인하는 속성 추가
+- `NansException`이 발생했을 때 중지하지 않고 남은 과정 계속 진행함
+
+## 2023-07-02
+
+- v23.7.0
+- `NansException`이 발생하면 로그에 표시하고 원본 이미지를 반환하게 설정
+- `rich`를 사용한 에러 트레이싱
+  - install.py에 `rich` 추가
+- 생성 중에 컴포넌트의 값을 변경하면 args의 값도 함께 변경되는 문제 수정 (issue #180)
+- 터미널 로그로 ad_prompt와 ad_negative_prompt에 적용된 실제 프롬프트 확인할 수 있음 (입력과 다를 경우에만)
+
+## 2023-06-28
+
+- v23.6.4
+- 최대 모델 수 5 -> 10개
+- ad_prompt와 ad_negative_prompt에 빈칸으로 놔두면 입력 프롬프트가 사용된다는 문구 추가
+- huggingface 모델 다운로드 실패시 로깅
+- 1st 모델이 `None`일 경우 나머지 입력을 무시하던 문제 수정
+- `--use-cpu` 에 `adetailer` 입력 시 cpu로 yolo모델을 사용함
+
+## 2023-06-20
+
+- v23.6.3
+- 컨트롤넷 inpaint 모델에 대해, 3가지 모듈을 사용할 수 있도록 함
+- Noise Multiplier 옵션 추가 (PR #149)
+- pydantic 최소 버전 1.10.8로 설정 (Issue #146)
+
+## 2023-06-05
+
+- v23.6.2
+- xyz_grid에서 ADetailer를 사용할 수 있게함.
+  - 8가지 옵션만 1st 탭에 적용되도록 함.
+
+## 2023-06-01
+
+- v23.6.1
+- `inpaint, scribble, lineart, openpose, tile` 5가지 컨트롤넷 모델 지원 (PR #107)
+- controlnet guidance start, end 인자 추가 (PR #107)
+- `modules.extensions`를 사용하여 컨트롤넷 확장을 불러오고 경로를 알아내로록 변경
+- ui에서 컨트롤넷을 별도 함수로 분리
+
+## 2023-05-30
+
+- v23.6.0
+- 스크립트의 이름을 `After Detailer`에서 `ADetailer`로 변경
+  - API 사용자는 변경 필요함
+- 몇몇 설정 변경
+  - `ad_conf` → `ad_confidence`. 0~100 사이의 int → 0.0~1.0 사이의 float
+  - `ad_inpaint_full_res` → `ad_inpaint_only_masked`
+  - `ad_inpaint_full_res_padding` → `ad_inpaint_only_masked_padding`
+- mediapipe face mesh 모델 추가
+
+  - mediapipe 최소 버전 `0.10.0`
+
+- rich traceback 제거함
+- huggingface 다운로드 실패할 때 에러가 나지 않게 하고 해당 모델을 제거함
+
+## 2023-05-26
+
+- v23.5.19
+- 1번째 탭에도 `None` 옵션을 추가함
+- api로 ad controlnet model에 inpaint가 아닌 다른 컨트롤넷 모델을 사용하지 못하도록 막음
+- adetailer 진행중에 total tqdm 진행바 업데이트를 멈춤
+- state.inturrupted 상태에서 adetailer 과정을 중지함
+- 컨트롤넷 process를 각 batch가 끝난 순간에만 호출하도록 변경
+
+### 2023-05-25
+
+- v23.5.18
+- 컨트롤넷 관련 수정
+  - unit의 `input_mode`를 `SIMPLE`로 모두 변경
+  - 컨트롤넷 유넷 훅과 하이잭 함수들을 adetailer를 실행할 때에만 되돌리는 기능 추가
+  - adetailer 처리가 끝난 뒤 컨트롤넷 스크립트의 process를 다시 진행함. (batch count 2 이상일때의 문제 해결)
+- 기본 활성 스크립트 목록에서 컨트롤넷을 뺌
+
+### 2023-05-22
+
+- v23.5.17
+- 컨트롤넷 확장이 있으면 컨트롤넷 스크립트를 활성화함. (컨트롤넷 관련 문제 해결)
+- 모든 컴포넌트에 elem_id 설정
+- ui에 버전을 표시함
+
+### 2023-05-19
+
+- v23.5.16
+- 추가한 옵션
+  - Mask min/max ratio
+  - Mask merge mode
+  - Restore faces after ADetailer
+- 옵션들을 Accordion으로 묶음
+
+### 2023-05-18
+
+- v23.5.15
+- 필요한 것만 임포트하도록 변경 (vae 로딩 오류 없어짐. 로딩 속도 빨라짐)
+
+### 2023-05-17
+
+- v23.5.14
+- `[SKIP]`으로 ad prompt 일부를 건너뛰는 기능 추가
+- bbox 정렬 옵션 추가
+- sd_webui 타입힌트를 만들어냄
+- enable checker와 관련된 api 오류 수정?
+
+### 2023-05-15
+
+- v23.5.13
+- `[SEP]`으로 ad prompt를 분리하여 적용하는 기능 추가
+- enable checker를 다시 pydantic으로 변경함
+- ui 관련 함수를 adetailer.ui 폴더로 분리함
+- controlnet을 사용할 때 모든 controlnet unit 비활성화
+- adetailer 폴더가 없으면 만들게 함
+
+### 2023-05-13
+
+- v23.5.12
+- `ad_enable`을 제외한 입력이 dict타입으로 들어오도록 변경
+  - web api로 사용할 때에 특히 사용하기 쉬움
+  - web api breaking change
+- `mask_preprocess` 인자를 넣지 않았던 오류 수정 (PR #47)
+- huggingface에서 모델을 다운로드하지 않는 옵션 추가 `--ad-no-huggingface`
+
+### 2023-05-12
+
+- v23.5.11
+- `ultralytics` 알람 제거
+- 필요없는 exif 인자 더 제거함
+- `use separate steps` 옵션 추가
+- ui 배치를 조정함
+
+### 2023-05-09
+
+- v23.5.10
+- 선택한 스크립트만 ADetailer에 적용하는 옵션 추가, 기본값 `True`. 설정 탭에서 지정가능.
+  - 기본값: `dynamic_prompting,dynamic_thresholding,wildcards,wildcard_recursive`
+- `person_yolov8s-seg.pt` 모델 추가
+- `ultralytics`의 최소 버전을 `8.0.97`로 설정 (C:\\ 문제 해결된 버전)
+
+### 2023-05-08
+
+- v23.5.9
+- 2가지 이상의 모델을 사용할 수 있음. 기본값: 2, 최대: 5
+- segment 모델을 사용할 수 있게 함. `person_yolov8n-seg.pt` 추가
+
+### 2023-05-07
+
+- v23.5.8
+- 프롬프트와 네거티브 프롬프트에 방향키 지원 (PR #24)
+- `mask_preprocess`를 추가함. 이전 버전과 시드값이 달라질 가능성 있음!
+- 이미지 처리가 일어났을 때에만 before이미지를 저장함
+- 설정창의 레이블을 ADetailer 대신 더 적절하게 수정함
+
+### 2023-05-06
+
+- v23.5.7
+- `ad_use_cfg_scale` 옵션 추가. cfg 스케일을 따로 사용할지 말지 결정함.
+- `ad_enable` 기본값을 `True`에서 `False`로 변경
+- `ad_model`의 기본값을 `None`에서 첫번째 모델로 변경
+- 최소 2개의 입력(ad_enable, ad_model)만 들어오면 작동하게 변경.
+
+- v23.5.7.post0
+- `init_controlnet_ext`을 controlnet_exists == True일때에만 실행
+- webui를 C드라이브 바로 밑에 설치한 사람들에게 `ultralytics` 경고 표시
+
+### 2023-05-05 (어린이날)
+
+- v23.5.5
+- `Save images before ADetailer` 옵션 추가
+- 입력으로 들어온 인자와 ALL_ARGS의 길이가 다르면 에러메세지
+- README.md에 설치방법 추가
+
+- v23.5.6
+- get_args에서 IndexError가 발생하면 자세한 에러메세지를 볼 수 있음
+- AdetailerArgs에 extra_params 내장
+- scripts_args를 딥카피함
+- postprocess_image를 약간 분리함
+
+- v23.5.6.post0
+- `init_controlnet_ext`에서 에러메세지를 자세히 볼 수 있음
+
+### 2023-05-04
+
+- v23.5.4
+- use pydantic for arguments validation
+- revert: ad_model to `None` as default
+- revert: `__future__` imports
+- lazily import yolo and mediapipe
+
+### 2023-05-03
+
+- v23.5.3.post0
+- remove `__future__` imports
+- change to copy scripts and scripts args
+
+- v23.5.3.post1
+- change default ad_model from `None`
+
+### 2023-05-02
+
+- v23.5.3
+- Remove `None` from model list and add `Enable ADetailer` checkbox.
+- install.py `skip_install` fix.

extensions/4-adetailer/LICENSE.md

@@ -0,0 +1,661 @@
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+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+16. Limitation of Liability.
+
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+17. Interpretation of Sections 15 and 16.
+
+If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source. For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code. There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<http://www.gnu.org/licenses/>.

extensions/4-adetailer/README.md

@@ -0,0 +1,118 @@
+# ADetailer
+
+ADetailer is an extension for the stable diffusion webui that does automatic masking and inpainting. It is similar to the Detection Detailer.
+
+## Install
+
+You can install it directly from the Extensions tab.
+
+![image](https://i.imgur.com/qaXtoI6.png)
+
+Or
+
+(from Mikubill/sd-webui-controlnet)
+
+1. Open "Extensions" tab.
+2. Open "Install from URL" tab in the tab.
+3. Enter `https://github.com/Bing-su/adetailer.git` to "URL for extension's git repository".
+4. Press "Install" button.
+5. Wait 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\adetailer. Use Installed tab to restart".
+6. Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use this method to update extensions.)
+7. Completely restart A1111 webui including your terminal. (If you do not know what is a "terminal", you can reboot your computer: turn your computer off and turn it on again.)
+
+## Options
+
+| Model, Prompts                    |                                                                                    |                                                                                                                                                        |
+| --------------------------------- | ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| ADetailer model                   | Determine what to detect.                                                          | `None` = disable                                                                                                                                       |
+| ADetailer model classes           | Comma separated class names to detect. only available when using YOLO World models | If blank, use default values.<br/>default = [COCO 80 classes](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) |
+| ADetailer prompt, negative prompt | Prompts and negative prompts to apply                                              | If left blank, it will use the same as the input.                                                                                                      |
+| Skip img2img                      | Skip img2img. In practice, this works by changing the step count of img2img to 1.  | img2img only                                                                                                                                           |
+
+| Detection                            |                                                                                              |              |
+| ------------------------------------ | -------------------------------------------------------------------------------------------- | ------------ |
+| Detection model confidence threshold | Only objects with a detection model confidence above this threshold are used for inpainting. |              |
+| Mask min/max ratio                   | Only use masks whose area is between those ratios for the area of the entire image.          |              |
+| Mask only the top k largest          | Only use the k objects with the largest area of the bbox.                                    | 0 to disable |
+
+If you want to exclude objects in the background, try setting the min ratio to around `0.01`.
+
+| Mask Preprocessing              |                                                                                                                                     |                                                                                         |
+| ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
+| Mask x, y offset                | Moves the mask horizontally and vertically by                                                                                       |                                                                                         |
+| Mask erosion (-) / dilation (+) | Enlarge or reduce the detected mask.                                                                                                | [opencv example](https://docs.opencv.org/4.7.0/db/df6/tutorial_erosion_dilatation.html) |
+| Mask merge mode                 | `None`: Inpaint each mask<br/>`Merge`: Merge all masks and inpaint<br/>`Merge and Invert`: Merge all masks and Invert, then inpaint |                                                                                         |
+
+Applied in this order: x, y offset → erosion/dilation → merge/invert.
+
+#### Inpainting
+
+Each option corresponds to a corresponding option on the inpaint tab. Therefore, please refer to the inpaint tab for usage details on how to use each option.
+
+## ControlNet Inpainting
+
+You can use the ControlNet extension if you have ControlNet installed and ControlNet models.
+
+Support `inpaint, scribble, lineart, openpose, tile, depth` controlnet models. Once you choose a model, the preprocessor is set automatically. It works separately from the model set by the Controlnet extension.
+
+If you select `Passthrough`, the controlnet settings you set outside of ADetailer will be used.
+
+## Advanced Options
+
+API request example: [wiki/REST-API](https://github.com/Bing-su/adetailer/wiki/REST-API)
+
+`[SEP], [SKIP], [PROMPT]` tokens: [wiki/Advanced](https://github.com/Bing-su/adetailer/wiki/Advanced)
+
+## Media
+
+- 🎥 [どこよりも詳しい After Detailer (adetailer)の使い方 ① 【Stable Diffusion】](https://youtu.be/sF3POwPUWCE)
+- 🎥 [どこよりも詳しい After Detailer (adetailer)の使い方 ② 【Stable Diffusion】](https://youtu.be/urNISRdbIEg)
+
+- 📜 [ADetailer Installation and 5 Usage Methods](https://kindanai.com/en/manual-adetailer/)
+
+## Model
+
+| Model                 | Target                | mAP 50                        | mAP 50-95                     |
+| --------------------- | --------------------- | ----------------------------- | ----------------------------- |
+| face_yolov8n.pt       | 2D / realistic face   | 0.660                         | 0.366                         |
+| face_yolov8s.pt       | 2D / realistic face   | 0.713                         | 0.404                         |
+| hand_yolov8n.pt       | 2D / realistic hand   | 0.767                         | 0.505                         |
+| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox)<br/>0.761 (mask) | 0.555 (bbox)<br/>0.460 (mask) |
+| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox)<br/>0.809 (mask) | 0.605 (bbox)<br/>0.508 (mask) |
+| mediapipe_face_full   | realistic face        | -                             | -                             |
+| mediapipe_face_short  | realistic face        | -                             | -                             |
+| mediapipe_face_mesh   | realistic face        | -                             | -                             |
+
+The YOLO models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer).
+
+For a detailed description of the YOLO8 model, see: https://docs.ultralytics.com/models/yolov8/#overview
+
+YOLO World model: https://docs.ultralytics.com/models/yolo-world/
+
+### Additional Model
+
+Put your [ultralytics](https://github.com/ultralytics/ultralytics) yolo model in `models/adetailer`. The model name should end with `.pt`.
+
+It must be a bbox detection or segment model and use all label.
+
+## How it works
+
+ADetailer works in three simple steps.
+
+1. Create an image.
+2. Detect object with a detection model and create a mask image.
+3. Inpaint using the image from 1 and the mask from 2.
+
+## Development
+
+ADetailer is developed and tested using the stable-diffusion 1.5 model, for the latest version of [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) repository only.
+
+## License
+
+ADetailer is a derivative work that uses two AGPL-licensed works (stable-diffusion-webui, ultralytics) and is therefore distributed under the AGPL license.
+
+## See Also
+
+- https://github.com/ototadana/sd-face-editor
+- https://github.com/continue-revolution/sd-webui-segment-anything
+- https://github.com/portu-sim/sd-webui-bmab

extensions/4-adetailer/Taskfile.yml

@@ -0,0 +1,32 @@
+# https://taskfile.dev
+
+version: "3"
+
+dotenv:
+  - .env
+
+tasks:
+  default:
+    cmds:
+      - echo "$PYTHON"
+      - echo "$WEBUI"
+      - echo "$UV_PYTHON"
+    silent: true
+
+  launch:
+    dir: "{{.WEBUI}}"
+    cmds:
+      - "{{.PYTHON}} launch.py --xformers --api"
+    silent: true
+
+  lint:
+    cmds:
+      - pre-commit run -a
+
+  update:
+    cmds:
+      - "{{.PYTHON}} -m uv pip install -U ultralytics mediapipe ruff pre-commit black devtools pytest"
+
+  update-torch:
+    cmds:
+      - "{{.PYTHON}} -m uv pip install -U torch torchvision torchaudio -f https://download.pytorch.org/whl/torch_stable.html"

extensions/4-adetailer/aaaaaa/conditional.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+msg = "[-] ADetailer: WebUI versions below 1.6.0 are not supported."
+
+try:
+    from modules.processing import create_binary_mask  # noqa: F401
+except ImportError as e:
+    raise RuntimeError(msg) from e
+
+
+try:
+    from modules.ui_components import InputAccordion  # noqa: F401
+except ImportError as e:
+    raise RuntimeError(msg) from e
+
+
+try:
+    from modules.sd_schedulers import schedulers
+except ImportError:
+    # webui < 1.9.0
+    schedulers = []

extensions/4-adetailer/aaaaaa/helper.py

@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from copy import copy
+from typing import TYPE_CHECKING, Any, Union
+
+import torch
+
+from modules import safe
+from modules.shared import opts
+
+if TYPE_CHECKING:
+    # 타입 체커가 빨간 줄을 긋지 않게 하는 편법
+    from types import SimpleNamespace
+
+    StableDiffusionProcessingTxt2Img = SimpleNamespace
+    StableDiffusionProcessingImg2Img = SimpleNamespace
+else:
+    from modules.processing import (
+        StableDiffusionProcessingImg2Img,
+        StableDiffusionProcessingTxt2Img,
+    )
+
+PT = Union[StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img]
+
+
+@contextmanager
+def change_torch_load():
+    orig = torch.load
+    try:
+        torch.load = safe.unsafe_torch_load
+        yield
+    finally:
+        torch.load = orig
+
+
+@contextmanager
+def pause_total_tqdm():
+    orig = opts.data.get("multiple_tqdm", True)
+    try:
+        opts.data["multiple_tqdm"] = False
+        yield
+    finally:
+        opts.data["multiple_tqdm"] = orig
+
+
+@contextmanager
+def preserve_prompts(p: PT):
+    all_pt = copy(p.all_prompts)
+    all_ng = copy(p.all_negative_prompts)
+    try:
+        yield
+    finally:
+        p.all_prompts = all_pt
+        p.all_negative_prompts = all_ng
+
+
+def copy_extra_params(extra_params: dict[str, Any]) -> dict[str, Any]:
+    return {k: v for k, v in extra_params.items() if not callable(v)}

extensions/4-adetailer/aaaaaa/p_method.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+
+def need_call_process(p) -> bool:
+    if p.scripts is None:
+        return False
+    i = p.batch_index
+    bs = p.batch_size
+    return i == bs - 1
+
+
+def need_call_postprocess(p) -> bool:
+    if p.scripts is None:
+        return False
+    return p.batch_index == 0
+
+
+def is_img2img_inpaint(p) -> bool:
+    return hasattr(p, "image_mask") and p.image_mask is not None
+
+
+def is_inpaint_only_masked(p) -> bool:
+    return hasattr(p, "inpaint_full_res") and p.inpaint_full_res
+
+
+def get_i(p) -> int:
+    it = p.iteration
+    bs = p.batch_size
+    i = p.batch_index
+    return it * bs + i
+
+
+def is_skip_img2img(p) -> bool:
+    return getattr(p, "_ad_skip_img2img", False)

extensions/4-adetailer/aaaaaa/traceback.py

@@ -0,0 +1,175 @@
+from __future__ import annotations
+
+import io
+import platform
+import sys
+from collections.abc import Callable
+from importlib.metadata import version
+from typing import Any, TypeVar
+
+from rich.console import Console, Group
+from rich.panel import Panel
+from rich.table import Table
+from rich.traceback import Traceback
+from typing_extensions import ParamSpec
+
+from adetailer.__version__ import __version__
+from adetailer.args import ADetailerArgs
+
+
+def processing(*args: Any) -> dict[str, Any]:
+    try:
+        from modules.processing import (
+            StableDiffusionProcessingImg2Img,
+            StableDiffusionProcessingTxt2Img,
+        )
+    except ImportError:
+        return {}
+
+    p = None
+    for arg in args:
+        if isinstance(
+            arg, (StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img)
+        ):
+            p = arg
+            break
+
+    if p is None:
+        return {}
+
+    info = {
+        "prompt": p.prompt,
+        "negative_prompt": p.negative_prompt,
+        "n_iter": p.n_iter,
+        "batch_size": p.batch_size,
+        "width": p.width,
+        "height": p.height,
+        "sampler_name": p.sampler_name,
+        "enable_hr": getattr(p, "enable_hr", False),
+        "hr_upscaler": getattr(p, "hr_upscaler", ""),
+    }
+
+    info.update(sd_models())
+    return info
+
+
+def sd_models() -> dict[str, str]:
+    try:
+        from modules import shared
+
+        opts = shared.opts
+    except Exception:
+        return {}
+
+    return {
+        "checkpoint": getattr(opts, "sd_model_checkpoint", "------"),
+        "vae": getattr(opts, "sd_vae", "------"),
+        "unet": getattr(opts, "sd_unet", "------"),
+    }
+
+
+def ad_args(*args: Any) -> dict[str, Any]:
+    ad_args = []
+    for arg in args:
+        if not isinstance(arg, dict):
+            continue
+
+        try:
+            a = ADetailerArgs(**arg)
+        except ValueError:
+            continue
+
+        if not a.need_skip():
+            ad_args.append(a)
+
+    if not ad_args:
+        return {}
+
+    arg0 = ad_args[0]
+    return {
+        "version": __version__,
+        "ad_model": arg0.ad_model,
+        "ad_prompt": arg0.ad_prompt,
+        "ad_negative_prompt": arg0.ad_negative_prompt,
+        "ad_controlnet_model": arg0.ad_controlnet_model,
+        "is_api": arg0.is_api,
+    }
+
+
+def library_version():
+    libraries = ["torch", "torchvision", "ultralytics", "mediapipe"]
+    d = {}
+    for lib in libraries:
+        try:
+            d[lib] = version(lib)
+        except Exception:  # noqa: PERF203
+            d[lib] = "Unknown"
+    return d
+
+
+def sys_info() -> dict[str, Any]:
+    try:
+        import launch
+
+        version = launch.git_tag()
+        commit = launch.commit_hash()
+    except Exception:
+        version = "Unknown (too old or vladmandic)"
+        commit = "Unknown"
+
+    return {
+        "Platform": platform.platform(),
+        "Python": sys.version,
+        "Version": version,
+        "Commit": commit,
+        "Commandline": sys.argv,
+        "Libraries": library_version(),
+    }
+
+
+def get_table(title: str, data: dict[str, Any]) -> Table:
+    table = Table(title=title, highlight=True)
+    table.add_column(" ", justify="right", style="dim")
+    table.add_column("Value")
+    for key, value in data.items():
+        if not isinstance(value, str):
+            value = repr(value)
+        table.add_row(key, value)
+
+    return table
+
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+
+def rich_traceback(func: Callable[P, T]) -> Callable[P, T]:
+    def wrapper(*args, **kwargs):
+        string = io.StringIO()
+        width = Console().width
+        width = width - 4 if width > 4 else None
+        console = Console(file=string, width=width)
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            tables = [
+                get_table(title, data)
+                for title, data in [
+                    ("System info", sys_info()),
+                    ("Inputs", processing(*args)),
+                    ("ADetailer", ad_args(*args)),
+                ]
+                if data
+            ]
+            tables.append(Traceback(extra_lines=1))
+
+            console.print(Panel(Group(*tables)))
+            output = "\n" + string.getvalue()
+
+            try:
+                error = e.__class__(output)
+            except Exception:
+                error = RuntimeError(output)
+            raise error from None
+
+    return wrapper

extensions/4-adetailer/aaaaaa/ui.py

@@ -0,0 +1,710 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from functools import partial
+from itertools import chain
+from types import SimpleNamespace
+from typing import Any
+
+import gradio as gr
+
+from aaaaaa.conditional import InputAccordion
+from adetailer import ADETAILER, __version__
+from adetailer.args import ALL_ARGS, MASK_MERGE_INVERT
+from controlnet_ext import controlnet_exists, controlnet_type, get_cn_models
+
+if controlnet_type == "forge":
+    from lib_controlnet import global_state
+
+    cn_module_choices = {
+        "inpaint": list(global_state.get_filtered_preprocessors("Inpaint")),
+        "lineart": list(global_state.get_filtered_preprocessors("Lineart")),
+        "openpose": list(global_state.get_filtered_preprocessors("OpenPose")),
+        "tile": list(global_state.get_filtered_preprocessors("Tile")),
+        "scribble": list(global_state.get_filtered_preprocessors("Scribble")),
+        "depth": list(global_state.get_filtered_preprocessors("Depth")),
+    }
+else:
+    cn_module_choices = {
+        "inpaint": [
+            "inpaint_global_harmonious",
+            "inpaint_only",
+            "inpaint_only+lama",
+        ],
+        "lineart": [
+            "lineart_coarse",
+            "lineart_realistic",
+            "lineart_anime",
+            "lineart_anime_denoise",
+        ],
+        "openpose": ["openpose_full", "dw_openpose_full"],
+        "tile": ["tile_resample", "tile_colorfix", "tile_colorfix+sharp"],
+        "scribble": ["t2ia_sketch_pidi"],
+        "depth": ["depth_midas", "depth_hand_refiner"],
+    }
+
+union = list(chain.from_iterable(cn_module_choices.values()))
+cn_module_choices["union"] = union
+
+
+class Widgets(SimpleNamespace):
+    def tolist(self):
+        return [getattr(self, attr) for attr in ALL_ARGS.attrs]
+
+
+@dataclass
+class WebuiInfo:
+    ad_model_list: list[str]
+    sampler_names: list[str]
+    scheduler_names: list[str]
+    t2i_button: gr.Button
+    i2i_button: gr.Button
+    checkpoints_list: list[str]
+    vae_list: list[str]
+
+
+def gr_interactive(value: bool = True):
+    return gr.update(interactive=value)
+
+
+def ordinal(n: int) -> str:
+    d = {1: "st", 2: "nd", 3: "rd"}
+    return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th"))
+
+
+def suffix(n: int, c: str = " ") -> str:
+    return "" if n == 0 else c + ordinal(n + 1)
+
+
+def on_widget_change(state: dict, value: Any, *, attr: str):
+    if "is_api" in state:
+        state = state.copy()
+        state.pop("is_api")
+    state[attr] = value
+    return state
+
+
+def on_generate_click(state: dict, *values: Any):
+    for attr, value in zip(ALL_ARGS.attrs, values):
+        state[attr] = value  # noqa: PERF403
+    state["is_api"] = ()
+    return state
+
+
+def on_ad_model_update(model: str):
+    if "-world" in model:
+        return gr.update(
+            visible=True,
+            placeholder="Comma separated class names to detect, ex: 'person,cat'. default: COCO 80 classes",
+        )
+    return gr.update(visible=False, placeholder="")
+
+
+def on_cn_model_update(cn_model_name: str):
+    cn_model_name = cn_model_name.replace("inpaint_depth", "depth")
+    for t in cn_module_choices:
+        if t in cn_model_name:
+            choices = cn_module_choices[t]
+            return gr.update(visible=True, choices=choices, value=choices[0])
+    return gr.update(visible=False, choices=["None"], value="None")
+
+
+def elem_id(item_id: str, n: int, is_img2img: bool) -> str:
+    tab = "img2img" if is_img2img else "txt2img"
+    suf = suffix(n, "_")
+    return f"script_{tab}_adetailer_{item_id}{suf}"
+
+
+def state_init(w: Widgets) -> dict[str, Any]:
+    return {attr: getattr(w, attr).value for attr in ALL_ARGS.attrs}
+
+
+def adui(
+    num_models: int,
+    is_img2img: bool,
+    webui_info: WebuiInfo,
+):
+    states = []
+    infotext_fields = []
+    eid = partial(elem_id, n=0, is_img2img=is_img2img)
+
+    with InputAccordion(
+        value=False,
+        elem_id=eid("ad_main_accordion"),
+        label=ADETAILER,
+        visible=True,
+    ) as ad_enable:
+        with gr.Row():
+            with gr.Column(scale=8):
+                ad_skip_img2img = gr.Checkbox(
+                    label="Skip img2img",
+                    value=False,
+                    visible=is_img2img,
+                    elem_id=eid("ad_skip_img2img"),
+                )
+
+            with gr.Column(scale=1, min_width=180):
+                gr.Markdown(
+                    f"v{__version__}",
+                    elem_id=eid("ad_version"),
+                )
+
+        infotext_fields.append((ad_enable, "ADetailer enable"))
+        infotext_fields.append((ad_skip_img2img, "ADetailer skip img2img"))
+
+        with gr.Group(), gr.Tabs():
+            for n in range(num_models):
+                with gr.Tab(ordinal(n + 1)):
+                    state, infofields = one_ui_group(
+                        n=n,
+                        is_img2img=is_img2img,
+                        webui_info=webui_info,
+                    )
+
+                states.append(state)
+                infotext_fields.extend(infofields)
+
+    # components: [bool, bool, dict, dict, ...]
+    components = [ad_enable, ad_skip_img2img, *states]
+    return components, infotext_fields
+
+
+def one_ui_group(n: int, is_img2img: bool, webui_info: WebuiInfo):
+    w = Widgets()
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    model_choices = (
+        [*webui_info.ad_model_list, "None"]
+        if n == 0
+        else ["None", *webui_info.ad_model_list]
+    )
+
+    with gr.Group():
+        with gr.Row(variant="compact"):
+            w.ad_tab_enable = gr.Checkbox(
+                label=f"Enable this tab ({ordinal(n + 1)})",
+                value=True,
+                visible=True,
+                elem_id=eid("ad_tab_enable"),
+            )
+
+        with gr.Row():
+            w.ad_model = gr.Dropdown(
+                label="ADetailer detector" + suffix(n),
+                choices=model_choices,
+                value=model_choices[0],
+                visible=True,
+                type="value",
+                elem_id=eid("ad_model"),
+                info="Select a model to use for detection.",
+            )
+
+        with gr.Row():
+            w.ad_model_classes = gr.Textbox(
+                label="ADetailer detector classes" + suffix(n),
+                value="",
+                visible=False,
+                elem_id=eid("ad_classes"),
+            )
+
+            w.ad_model.change(
+                on_ad_model_update,
+                inputs=w.ad_model,
+                outputs=w.ad_model_classes,
+                queue=False,
+            )
+
+    gr.HTML("<br>")
+
+    with gr.Group():
+        with gr.Row(elem_id=eid("ad_toprow_prompt")):
+            w.ad_prompt = gr.Textbox(
+                label="ad_prompt" + suffix(n),
+                show_label=False,
+                lines=3,
+                placeholder="ADetailer prompt"
+                + suffix(n)
+                + "\nIf blank, the main prompt is used.",
+                elem_id=eid("ad_prompt"),
+            )
+
+        with gr.Row(elem_id=eid("ad_toprow_negative_prompt")):
+            w.ad_negative_prompt = gr.Textbox(
+                label="ad_negative_prompt" + suffix(n),
+                show_label=False,
+                lines=2,
+                placeholder="ADetailer negative prompt"
+                + suffix(n)
+                + "\nIf blank, the main negative prompt is used.",
+                elem_id=eid("ad_negative_prompt"),
+            )
+
+    with gr.Group():
+        with gr.Accordion(
+            "Detection", open=False, elem_id=eid("ad_detection_accordion")
+        ):
+            detection(w, n, is_img2img)
+
+        with gr.Accordion(
+            "Mask Preprocessing",
+            open=False,
+            elem_id=eid("ad_mask_preprocessing_accordion"),
+        ):
+            mask_preprocessing(w, n, is_img2img)
+
+        with gr.Accordion(
+            "Inpainting", open=False, elem_id=eid("ad_inpainting_accordion")
+        ):
+            inpainting(w, n, is_img2img, webui_info)
+
+    with gr.Group():
+        controlnet(w, n, is_img2img)
+
+    state = gr.State(lambda: state_init(w))
+
+    for attr in ALL_ARGS.attrs:
+        widget = getattr(w, attr)
+        on_change = partial(on_widget_change, attr=attr)
+        widget.change(fn=on_change, inputs=[state, widget], outputs=state, queue=False)
+
+    all_inputs = [state, *w.tolist()]
+    target_button = webui_info.i2i_button if is_img2img else webui_info.t2i_button
+    target_button.click(
+        fn=on_generate_click, inputs=all_inputs, outputs=state, queue=False
+    )
+
+    infotext_fields = [(getattr(w, attr), name + suffix(n)) for attr, name in ALL_ARGS]
+
+    return state, infotext_fields
+
+
+def detection(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Row():
+        with gr.Column(variant="compact"):
+            w.ad_confidence = gr.Slider(
+                label="Detection model confidence threshold" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.3,
+                visible=True,
+                elem_id=eid("ad_confidence"),
+            )
+            w.ad_mask_k_largest = gr.Slider(
+                label="Mask only the top k largest (0 to disable)" + suffix(n),
+                minimum=0,
+                maximum=10,
+                step=1,
+                value=0,
+                visible=True,
+                elem_id=eid("ad_mask_k_largest"),
+            )
+
+        with gr.Column(variant="compact"):
+            w.ad_mask_min_ratio = gr.Slider(
+                label="Mask min area ratio" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.001,
+                value=0.0,
+                visible=True,
+                elem_id=eid("ad_mask_min_ratio"),
+            )
+            w.ad_mask_max_ratio = gr.Slider(
+                label="Mask max area ratio" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.001,
+                value=1.0,
+                visible=True,
+                elem_id=eid("ad_mask_max_ratio"),
+            )
+
+
+def mask_preprocessing(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Group():
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_x_offset = gr.Slider(
+                    label="Mask x(→) offset" + suffix(n),
+                    minimum=-200,
+                    maximum=200,
+                    step=1,
+                    value=0,
+                    visible=True,
+                    elem_id=eid("ad_x_offset"),
+                )
+                w.ad_y_offset = gr.Slider(
+                    label="Mask y(↑) offset" + suffix(n),
+                    minimum=-200,
+                    maximum=200,
+                    step=1,
+                    value=0,
+                    visible=True,
+                    elem_id=eid("ad_y_offset"),
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_dilate_erode = gr.Slider(
+                    label="Mask erosion (-) / dilation (+)" + suffix(n),
+                    minimum=-128,
+                    maximum=128,
+                    step=4,
+                    value=4,
+                    visible=True,
+                    elem_id=eid("ad_dilate_erode"),
+                )
+
+        with gr.Row():
+            w.ad_mask_merge_invert = gr.Radio(
+                label="Mask merge mode" + suffix(n),
+                choices=MASK_MERGE_INVERT,
+                value="None",
+                elem_id=eid("ad_mask_merge_invert"),
+                info="None: do nothing, Merge: merge masks, Merge and Invert: merge all masks and invert",
+            )
+
+
+def inpainting(w: Widgets, n: int, is_img2img: bool, webui_info: WebuiInfo):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Group():
+        with gr.Row():
+            w.ad_mask_blur = gr.Slider(
+                label="Inpaint mask blur" + suffix(n),
+                minimum=0,
+                maximum=64,
+                step=1,
+                value=4,
+                visible=True,
+                elem_id=eid("ad_mask_blur"),
+            )
+
+            w.ad_denoising_strength = gr.Slider(
+                label="Inpaint denoising strength" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.4,
+                visible=True,
+                elem_id=eid("ad_denoising_strength"),
+            )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_inpaint_only_masked = gr.Checkbox(
+                    label="Inpaint only masked" + suffix(n),
+                    value=True,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_only_masked"),
+                )
+                w.ad_inpaint_only_masked_padding = gr.Slider(
+                    label="Inpaint only masked padding, pixels" + suffix(n),
+                    minimum=0,
+                    maximum=256,
+                    step=4,
+                    value=32,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_only_masked_padding"),
+                )
+
+                w.ad_inpaint_only_masked.change(
+                    gr_interactive,
+                    inputs=w.ad_inpaint_only_masked,
+                    outputs=w.ad_inpaint_only_masked_padding,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_inpaint_width_height = gr.Checkbox(
+                    label="Use separate width/height" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_inpaint_width_height"),
+                )
+
+                w.ad_inpaint_width = gr.Slider(
+                    label="inpaint width" + suffix(n),
+                    minimum=64,
+                    maximum=2048,
+                    step=4,
+                    value=512,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_width"),
+                )
+
+                w.ad_inpaint_height = gr.Slider(
+                    label="inpaint height" + suffix(n),
+                    minimum=64,
+                    maximum=2048,
+                    step=4,
+                    value=512,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_height"),
+                )
+
+                w.ad_use_inpaint_width_height.change(
+                    lambda value: (gr_interactive(value), gr_interactive(value)),
+                    inputs=w.ad_use_inpaint_width_height,
+                    outputs=[w.ad_inpaint_width, w.ad_inpaint_height],
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_steps = gr.Checkbox(
+                    label="Use separate steps" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_steps"),
+                )
+
+                w.ad_steps = gr.Slider(
+                    label="ADetailer steps" + suffix(n),
+                    minimum=1,
+                    maximum=150,
+                    step=1,
+                    value=28,
+                    visible=True,
+                    elem_id=eid("ad_steps"),
+                )
+
+                w.ad_use_steps.change(
+                    gr_interactive,
+                    inputs=w.ad_use_steps,
+                    outputs=w.ad_steps,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_cfg_scale = gr.Checkbox(
+                    label="Use separate CFG scale" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_cfg_scale"),
+                )
+
+                w.ad_cfg_scale = gr.Slider(
+                    label="ADetailer CFG scale" + suffix(n),
+                    minimum=0.0,
+                    maximum=30.0,
+                    step=0.5,
+                    value=7.0,
+                    visible=True,
+                    elem_id=eid("ad_cfg_scale"),
+                )
+
+                w.ad_use_cfg_scale.change(
+                    gr_interactive,
+                    inputs=w.ad_use_cfg_scale,
+                    outputs=w.ad_cfg_scale,
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_checkpoint = gr.Checkbox(
+                    label="Use separate checkpoint" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_checkpoint"),
+                )
+
+                ckpts = ["Use same checkpoint", *webui_info.checkpoints_list]
+
+                w.ad_checkpoint = gr.Dropdown(
+                    label="ADetailer checkpoint" + suffix(n),
+                    choices=ckpts,
+                    value=ckpts[0],
+                    visible=True,
+                    elem_id=eid("ad_checkpoint"),
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_vae = gr.Checkbox(
+                    label="Use separate VAE" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_vae"),
+                )
+
+                vaes = ["Use same VAE", *webui_info.vae_list]
+
+                w.ad_vae = gr.Dropdown(
+                    label="ADetailer VAE" + suffix(n),
+                    choices=vaes,
+                    value=vaes[0],
+                    visible=True,
+                    elem_id=eid("ad_vae"),
+                )
+
+        with gr.Row(), gr.Column(variant="compact"):
+            w.ad_use_sampler = gr.Checkbox(
+                label="Use separate sampler" + suffix(n),
+                value=False,
+                visible=True,
+                elem_id=eid("ad_use_sampler"),
+            )
+
+            sampler_names = [
+                "Use same sampler",
+                *webui_info.sampler_names,
+            ]
+
+            with gr.Row():
+                w.ad_sampler = gr.Dropdown(
+                    label="ADetailer sampler" + suffix(n),
+                    choices=sampler_names,
+                    value=sampler_names[1],
+                    visible=True,
+                    elem_id=eid("ad_sampler"),
+                )
+
+                scheduler_names = [
+                    "Use same scheduler",
+                    *webui_info.scheduler_names,
+                ]
+                w.ad_scheduler = gr.Dropdown(
+                    label="ADetailer scheduler" + suffix(n),
+                    choices=scheduler_names,
+                    value=scheduler_names[0],
+                    visible=len(scheduler_names) > 1,
+                    elem_id=eid("ad_scheduler"),
+                )
+
+                w.ad_use_sampler.change(
+                    lambda value: (gr_interactive(value), gr_interactive(value)),
+                    inputs=w.ad_use_sampler,
+                    outputs=[w.ad_sampler, w.ad_scheduler],
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_noise_multiplier = gr.Checkbox(
+                    label="Use separate noise multiplier" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_noise_multiplier"),
+                )
+
+                w.ad_noise_multiplier = gr.Slider(
+                    label="Noise multiplier for img2img" + suffix(n),
+                    minimum=0.5,
+                    maximum=1.5,
+                    step=0.01,
+                    value=1.0,
+                    visible=True,
+                    elem_id=eid("ad_noise_multiplier"),
+                )
+
+                w.ad_use_noise_multiplier.change(
+                    gr_interactive,
+                    inputs=w.ad_use_noise_multiplier,
+                    outputs=w.ad_noise_multiplier,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_clip_skip = gr.Checkbox(
+                    label="Use separate CLIP skip" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_clip_skip"),
+                )
+
+                w.ad_clip_skip = gr.Slider(
+                    label="ADetailer CLIP skip" + suffix(n),
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=1,
+                    visible=True,
+                    elem_id=eid("ad_clip_skip"),
+                )
+
+                w.ad_use_clip_skip.change(
+                    gr_interactive,
+                    inputs=w.ad_use_clip_skip,
+                    outputs=w.ad_clip_skip,
+                    queue=False,
+                )
+
+        with gr.Row(), gr.Column(variant="compact"):
+            w.ad_restore_face = gr.Checkbox(
+                label="Restore faces after ADetailer" + suffix(n),
+                value=False,
+                elem_id=eid("ad_restore_face"),
+            )
+
+
+def controlnet(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+    cn_models = ["None", "Passthrough", *get_cn_models()]
+
+    with gr.Row(variant="panel"):
+        with gr.Column(variant="compact"):
+            w.ad_controlnet_model = gr.Dropdown(
+                label="ControlNet model" + suffix(n),
+                choices=cn_models,
+                value="None",
+                visible=True,
+                type="value",
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_model"),
+            )
+
+            w.ad_controlnet_module = gr.Dropdown(
+                label="ControlNet module" + suffix(n),
+                choices=["None"],
+                value="None",
+                visible=False,
+                type="value",
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_module"),
+            )
+
+            w.ad_controlnet_weight = gr.Slider(
+                label="ControlNet weight" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=1.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_weight"),
+            )
+
+            w.ad_controlnet_model.change(
+                on_cn_model_update,
+                inputs=w.ad_controlnet_model,
+                outputs=w.ad_controlnet_module,
+                queue=False,
+            )
+
+        with gr.Column(variant="compact"):
+            w.ad_controlnet_guidance_start = gr.Slider(
+                label="ControlNet guidance start" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_guidance_start"),
+            )
+
+            w.ad_controlnet_guidance_end = gr.Slider(
+                label="ControlNet guidance end" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=1.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_guidance_end"),
+            )

extensions/4-adetailer/adetailer/__init__.py

@@ -0,0 +1,18 @@
+from .__version__ import __version__
+from .args import ALL_ARGS, ADetailerArgs
+from .common import PredictOutput, get_models
+from .mediapipe import mediapipe_predict
+from .ultralytics import ultralytics_predict
+
+ADETAILER = "ADetailer"
+
+__all__ = [
+    "__version__",
+    "ADetailerArgs",
+    "ADETAILER",
+    "ALL_ARGS",
+    "PredictOutput",
+    "get_models",
+    "mediapipe_predict",
+    "ultralytics_predict",
+]