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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:
+      - main
+    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 [StableSwarmUI](https://github.com/Stability-AI/StableSwarmUI), [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 StableSwarmUI 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
+
+#### StableSwarmUI
+
+- 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 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
+
+            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
+
+            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 = "1.0.0"
+license = "LICENSE"
+
+[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/ABG_extension/.gitignore

@@ -0,0 +1 @@
+scripts/__pycache__

extensions/ABG_extension/README.md

@@ -0,0 +1,32 @@
+<h3 align="center">
+<b>ABG extension</b>
+</h3>
+
+<p align="center">
+	<a href="https://github.com/KutsuyaYuki/ABG_extension/stargazers"><img src="https://img.shields.io/github/stars/KutsuyaYuki/ABG_extension?style=for-the-badge"></a>
+	<a href="https://github.com/KutsuyaYuki/ABG_extension/issues"><img src="https://img.shields.io/github/issues/KutsuyaYuki/ABG_extension?style=for-the-badge"></a>
+	<a href="https://github.com/KutsuyaYuki/ABG_extension/commits/main"><img src="https://img.shields.io/github/last-commit/KutsuyaYuki/ABG_extension?style=for-the-badge"></a>
+</p>
+
+## Installation
+
+ 1. Install extension by going to Extensions tab -> Install from URL -> Paste github URL and click Install.
+ 2. After it's installed, go back to the Installed tab in Extensions and press Apply and restart UI.
+ 3. Installation finished.
+ 4. If the script does not show up or work, please restart the WebUI.
+
+## Usage
+
+### txt2img
+
+ 1. In the bottom of the WebUI in Script, select **ABG Remover**.
+ 2. Select the desired options: **Only save background free pictures** or **Do not auto save**.
+ 3. Generate an image and you will see the result in the output area.
+
+### img2img
+
+ 1. In the bottom of the WebUI in Script, select **ABG Remover**.
+ 2. Select the desired options: **Only save background free pictures** or **Do not auto save**.
+ 3. **IMPORTANT**: Set **Denoising strength** to a low value, like **0.01**
+
+Based on https://huggingface.co/spaces/skytnt/anime-remove-background

extensions/ABG_extension/install.py

@@ -0,0 +1,11 @@
+import launch
+
+for dep in ['onnx', 'onnxruntime', 'numpy']:
+    if not launch.is_installed(dep):
+        launch.run_pip(f"install {dep}", f"{dep} for ABG_extension")
+
+if not launch.is_installed("cv2"):
+    launch.run_pip("install opencv-python", "opencv-python")
+
+if not launch.is_installed("PIL"):
+    launch.run_pip("install Pillow", "Pillow")

extensions/ABG_extension/scripts/app.py

@@ -0,0 +1,183 @@
+import random
+import modules.scripts as scripts
+from modules import images
+from modules.processing import process_images, Processed
+from modules.processing import Processed
+from modules.shared import opts, cmd_opts, state
+
+import gradio as gr
+import huggingface_hub
+import onnxruntime as rt
+import copy
+import numpy as np
+import cv2
+from PIL import Image as im, ImageDraw
+
+
+# Declare Execution Providers
+providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+
+# Download and host the model
+model_path = huggingface_hub.hf_hub_download(
+    "skytnt/anime-seg", "isnetis.onnx")
+rmbg_model = rt.InferenceSession(model_path, providers=providers)
+
+# Function to get mask
+def get_mask(img, s=1024):
+    # Resize the img to a square shape with dimension s
+    # Convert img pixel values from integers 0-255 to float 0-1
+    img = (img / 255).astype(np.float32)
+    # get the amount of dimensions of img
+    dim = img.shape[2]
+    # Convert the input image to RGB format if it has an alpha channel
+    if dim == 4:
+        img = img[..., :3]
+        dim = 3
+    # Get height and width of the image
+    h, w = h0, w0 = img.shape[:-1]
+    # IF height is greater than width, set h as s and w as s*width/height
+    # ELSE, set w as s and h as s*height/width
+    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
+    # Calculate padding for height and width
+    ph, pw = s - h, s - w
+    # Create a 1024x1024x3 array of 0's
+    img_input = np.zeros([s, s, dim], dtype=np.float32)
+    # Resize the original image to (w,h) and then pad with the calculated ph,pw
+    img_input[ph // 2:ph // 2 + h, pw //
+              2:pw // 2 + w] = cv2.resize(img, (w, h))
+    # Change the axes
+    img_input = np.transpose(img_input, (2, 0, 1))
+    # Add an extra axis (1,0)
+    img_input = img_input[np.newaxis, :]
+    # Run the model to get the mask
+    mask = rmbg_model.run(None, {'img': img_input})[0][0]
+    # Transpose axis
+    mask = np.transpose(mask, (1, 2, 0))
+    # Crop it to the images original dimensions (h0,w0)
+    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
+    # Resize the mask to original image size (h0,w0)
+    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
+    return mask
+
+### Function to remove background
+def rmbg_fn(img):
+    # Call get_mask() to get the mask
+    mask = get_mask(img)
+    # Multiply the image and the mask together to get the output image
+    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
+    # Convert mask value back to int 0-255
+    mask = (mask * 255).astype(np.uint8)
+    # Concatenate the output image and mask
+    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
+    # Stacking 3 identical copies of the mask for displaying
+    mask = mask.repeat(3, axis=2)
+    return mask, img
+
+
+class Script(scripts.Script):
+    is_txt2img = False
+
+    # Function to set title
+    def title(self):
+        return "ABG Remover"
+
+    def ui(self, is_img2img):
+
+        with gr.Column():
+            only_save_background_free_pictures = gr.Checkbox(
+                label='Only save background free pictures')
+            do_not_auto_save = gr.Checkbox(label='Do not auto save')
+            with gr.Row():
+                custom_background = gr.Checkbox(label='Custom Background')
+                custom_background_color = gr.ColorPicker(
+                    label='Background Color', default='#ff0000')
+            custom_background_random = gr.Checkbox(
+                label='Random Custom Background')
+
+        return [only_save_background_free_pictures, do_not_auto_save, custom_background, custom_background_color, custom_background_random]
+
+    # Function to show the script
+    def show(self, is_img2img):
+        return True
+
+    # Function to run the script
+    def run(self, p, only_save_background_free_pictures, do_not_auto_save, custom_background, custom_background_color, custom_background_random):
+        # If only_save_background_free_pictures is true, set do_not_save_samples to true
+        if only_save_background_free_pictures:
+            p.do_not_save_samples = True
+
+        # Create a process_images object
+        proc = process_images(p)
+
+        has_grid = False
+
+        unwanted_grid_because_of_img_count = len(
+            proc.images) < 2 and opts.grid_only_if_multiple
+        if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
+            has_grid = True
+
+        # Loop through all the images in proc
+        for i in range(len(proc.images)):
+            # Separate the background from the foreground
+            nmask, nimg = rmbg_fn(np.array(proc.images[i]))
+
+            # Check the number of channels in the nimg array, select only the first 3 or 4 channels
+            num_channels = nimg.shape[2]
+            if num_channels > 4:
+                nimg = nimg[:, :, :4]
+                
+            # Ensure the data type is uint8 and convert the image back to a format that can be saved
+            nimg = nimg.astype(np.uint8)
+            img = im.fromarray(nimg)
+
+            # If only_save_background_free_pictures is true, check if the image has a background
+            if custom_background or custom_background_random:
+                # If custom_background_random is true, set the background color to a random color
+                if custom_background_random:
+                    custom_background_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+
+                # Create a new image with the same size as the original image
+                background = im.new('RGBA', img.size, custom_background_color)
+
+                # Draw a colored rectangle onto the new image
+                draw = ImageDraw.Draw(background)
+                draw.rectangle([(0, 0), img.size],
+                               fill=custom_background_color)
+
+                # Merge the two images
+                img = im.alpha_composite(background, img)
+
+            # determine output path
+            outpath = p.outpath_grids if has_grid and i == 0 else p.outpath_samples
+
+            # If we are saving all images, save the mask and the image
+            if not only_save_background_free_pictures:
+                mask = im.fromarray(nmask)
+                # Dot not save the new images if checkbox is checked
+                if not do_not_auto_save:
+                    # Save the new images
+                    images.save_image(
+                        mask, outpath, "mask_", proc.seed + i, proc.prompt, "png", info=proc.info, p=p)
+                    images.save_image(
+                        img, outpath, "img_", proc.seed + i, proc.prompt, "png", info=proc.info, p=p)
+                # Add the images to the proc object
+                proc.images.append(mask)
+                proc.images.append(img)
+            # If we are only saving background-free images, save the image and replace it in the proc object
+            else:
+                proc.images[i] = img
+
+            # Check if automatic saving is enabled
+            if not do_not_auto_save:
+                # Check if the image is the first one and has a grid
+                if has_grid and i == 0:
+                    # Save the image
+                    images.save_image(img, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0],
+                                      opts.grid_format, info=proc.info, short_filename=not opts.grid_extended_filename, p=p)
+                else:
+                    # Save the image
+                    images.save_image(img, outpath, "", proc.seed,
+                                      proc.prompt, "png", info=proc.info, p=p)
+
+        # Return the proc object
+        return proc

extensions/AdverseCleanerExtension/.gitignore

@@ -0,0 +1,2 @@
+**scripts/__pycache__/
+**dist

extensions/AdverseCleanerExtension/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
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+
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+
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+   2. Grant of Copyright License. Subject to the terms and conditions of
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+
+   4. Redistribution. You may reproduce and distribute copies of the
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+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
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extensions/AdverseCleanerExtension/README.md

@@ -0,0 +1,8 @@
+# AdverseCleaner Extension for AUTOMATIC1111/stable-diffusion-webui
+This extension provides a simple and easy-to-use way to denoise images using the cv2 bilateral filter and guided filter.
+Original script by: https://github.com/lllyasviel/AdverseCleaner
+## Usage
+Select AdverseCleaner as your active script
+![image](https://user-images.githubusercontent.com/2740936/226215666-17d876fa-b314-4457-9a9f-9bd1d8cf7561.png)
+You will then have access to all the parameters for both filter steps
+![image](https://user-images.githubusercontent.com/2740936/226215696-962efbb5-7ce8-4ea8-a94d-d1c32561796b.png)

extensions/AdverseCleanerExtension/install.py

@@ -0,0 +1,12 @@
+import launch
+
+if not launch.is_installed("opencv-contrib-python"):
+    if launch.is_installed("opencv-python"):
+        launch.run_pip("uninstall opencv-python")    
+        print("Uninstalling opencv-python...")    
+    launch.run_pip("install opencv-contrib-python")
+    print("Installing opencv-contrib-python...")
+
+if not launch.is_installed("numpy"):
+    launch.run_pip("install numpy")
+    print("Installing numpy...")

extensions/AdverseCleanerExtension/scripts/denoise.py

@@ -0,0 +1,74 @@
+
+import gradio as gr
+import numpy as np
+import cv2
+import modules.scripts as scripts
+
+from modules import images
+from modules.shared import opts
+
+from cv2.ximgproc import guidedFilter
+from modules.processing import process_images
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "AdverseCleaner"
+
+    def show(self, is_img2img):
+        return True
+
+    def ui(self, is_img2img):
+        info = gr.Markdown('''
+        ### Bilateral Filter
+        ''')
+        bilateral_steps = gr.Slider(minimum=1, maximum=128, step=1,
+                                    value=64, label="Bilateral Steps")
+        diameter = gr.Slider(minimum=1, maximum=30, step=1,
+                             value=5, label="Diameter")
+        sigma_color = gr.Slider(minimum=1, maximum=30,
+                                step=1, value=8, label="SigmaColor")
+        sigma_space = gr.Slider(minimum=1, maximum=30,
+                                step=1, value=8, label="SigmaSpace")
+        info2 = gr.Markdown('''
+        ### Guided Filter
+        ''')
+        guided_steps = gr.Slider(minimum=1, maximum=64, step=1,
+                                 value=4, label="Guided Steps")
+        radius = gr.Slider(minimum=1, maximum=30, step=1,
+                           value=4, label="Radius")
+        eps = gr.Slider(minimum=1, maximum=30, step=1,
+                        value=16, label="Accuracy")
+        return [info, bilateral_steps, diameter, sigma_color, sigma_space, info2, guided_steps, radius, eps]
+
+    def run(self, p, _, bilateral_steps, diameter, sigma_color, sigma_space, __, guided_steps, radius, eps):
+        from PIL import Image
+        has_grid = False
+
+        proc = process_images(p)
+        unwanted_grid_because_of_img_count = len(
+            proc.images) < 2 and opts.grid_only_if_multiple
+        if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
+            has_grid = True
+
+
+        def process(im,i):
+            outpath = p.outpath_grids if has_grid and i == 0 else p.outpath_samples
+            img = cv2.cvtColor(
+                np.array(im), cv2.COLOR_RGB2BGR).astype(np.float32)
+            y = img.copy()
+            for _ in range(bilateral_steps):
+                y = cv2.bilateralFilter(y, diameter, sigma_color, sigma_space)
+
+            for _ in range(guided_steps):
+                y = guidedFilter(img, y, radius, eps)
+
+            out_image = Image.fromarray(cv2.cvtColor(
+                y.clip(0, 255).astype(np.uint8), cv2.COLOR_BGR2RGB))
+            images.save_image(out_image, outpath, "img_", proc.seed +
+                              i, proc.prompt, "png", info=proc.info, p=p)
+            return out_image
+
+        for i in range(len(proc.images)):
+            proc.images.append(process(proc.images[i], i))
+        return proc

extensions/CFG-Schedule-for-Automatic1111-SD/Examples

@@ -0,0 +1,62 @@
+Copy paste following to use their effect. CFG slider will still work
+
+CFG is how much to stick to the prompt
+ETA is how much of shift to cause ... maybe
+
+
+
+Example 1
+About: Gradually reduce CFG
+CFG
+0: cfg * e
+ETA
+0: e
+
+Example 3
+About: Half the way, use CFG while reducing its strong effect
+0: cfg * (0.5+  (t<T/2) *0.5)
+0: 1.2-e
+
+
+
+Example 3
+About: The higher the CFG, the more frequent details added from prompt
+CFG:
+0: 5-( cfg/30*5 ) + (  (t% math.floor((35-cfg)/2))==0)*(cfg*e*0.5+30)
+ETA:
+0:  pow(e,1/(1/pow(2,  (cfg-15)/30*3  )   ))  
+
+
+Example 4
+About: Focus on adding details midway
+CFG:
+0:( 1-pow(2*e-1 ,2))*cfg*0.75 * (t%4<1 and t<T-4)+(31-cfg)*0.25
+ETA:
+0:  pow(2*e-1 ,2)*cfg
+
+
+
+
+Example 5
+About: Pay more attention to details at beginning, then relax
+CFG:
+0:pow(e,0.1)*cfg*( t%4<1)+5*(1-e)
+ETA:
+0:pow(e,0.3)
+
+
+Example 6
+About: This can help you compare results of this script with results without it.
+        It will try to create the same image as without it, but enhance it.
+CFG:
+0: cfg*(e>0.7 or e<0.1)+ (15-cfg*0.2)*(e<0.4 and (t%3<1))
+ETA:
+0:e+(e<0.4)*(e*3)
+
+
+Example 7
+About: Conditional. Does some clean up at the end.
+CFG:
+0:abs(0.3-e)*(3.5+cfg*2)
+ETA:
+0:abs( (1.5-2*e)*3)*(pow(1-e,2) if e>.2 or cfg>20 else e)

extensions/CFG-Schedule-for-Automatic1111-SD/Experiments

@@ -0,0 +1,108 @@
+Other experimental ideas I was trying. CFG value might be useless in some:
+
+0: cfg*(e>0.5)+cfg*    (3*(e%2<=0.5 and t%5<1) - 1*(t%5==2)) * (t<T-3)
+0:e+e*(e<0.5)+(e%2<=0.5 and t%5<2 and t<T-3)*0.5
+
+
+
+0: 5+ (  (t% math.floor((30+cfg)/2))==0)*10
+0:  1/(1+cfg*e*e)
+
+
+
+0:    40*(    ((t)%math.floor(5+cfg/8)  )==0) *2*e*(t<T-5)+ (31-cfg)*0.1
+0: (    ((t)%math.floor(5+cfg/8)  )>x(e*2,0,1)   )*e*2+e*0.1 
+
+
+
+0:    40*(    ((t)%math.floor(5+cfg/8)  )==0) *2*e*(t<T-5)+ (31-cfg)*0.1
+0: (    ((t)%math.floor(5+cfg/8)  )>0)*e*2+e*0.1 
+
+
+
+0:pow(cfg,0.5)+  5*(cfg/30)* (1-((t*0.2)%1))*(t%6<2)*(  (t%6==1)*3) + (31-cfg)*.05
+0:5*((t%6)>0)*pow(e,2)+((t%6)<20)*2
+
+0:2;3:1;5:=10-(t/T)*10; 25:=10+20*e
+0:=60*d;3:0.5;  10:1;23:=2.4-(t/T)
+
+
+
+0:1+(t%5 ==0 )*(15-d*5) ;13:10 ;20:5+e*5-d*2
+0:20-min(0,t*3);13:0.5*e           ;20:1+e; 30:e*2
+
+
+
+0:1+(t%4<1 )*95*e*e ;50:5+(t%4<1 )*5*e
+0:(15-(t%4<1 )*14)*(1+e)
+
+
+
+0:1+((t%4<1)*20+(t%5<1)*190)*e*e ;50:5+(t%5<1 )*4*e
+0:(15-(t%5<1 )*14)*(1+e)
+
+
+
+=2+50*(t%4<1)*e
+=1/(1+t/T)
+
+
+0:15; 2:(5*e+30*(t%3<1 and t<25)*e*e)   /(1+d)
+0:5*e; 10:5*e*e
+
+
+# changes show on all denoising strength
+0: 6-( cfg/30*5 ) + (  (t% math.floor((32-cfg+d*4)/2))==0)*( pow(200,1-d/2)*e*e)
+0:  e*0.2 if  (t% math.floor((36-cfg)/2))==0 else pow(e,1/(1/pow(2,  (cfg-15)/30*3  )   ))  
+
+# Trying to force modification
+0:( 1-pow(2*e-1 ,2))*cfg*(0.75+(t<T/5)) * (t%4<(1+(t<T/5)) and t<T-4)+(31-cfg)*0.25
+0:pow(e,0.3)*(1+e*6*(t%8>3 and t%8<5 and t>T*.6 and t<T*.6+6)   )
+
+#or
+0:( 1-pow(2*e-1 ,2))*(cfg +pow((1-d)*6,2) )*(0.75+(t<T/5)) * (t%4<(1+(t<T/5)) and t<T-4)+(31-cfg)*0.25
+0:pow(e,0.3)*(1+e*6*(t%8>3 and t%8<5+0*pow( (1-d)*10,2) and t>T*.6 and t<T*.6+6)   )
+
+
+#The more CFG value, the stronger it drops.
+0:(cfg+1)*(pow(cfg+1,e-1)-1/(cfg+1))/(cfg)*cfg+3
+0:e
+
+
+
+
+#Test for better img2img consistancy. Keep denoising at low level and try different CFG values
+0:(1-e*.9)*cfg   + ( ((t%(2+(e>0.7)) <1)*1.1*cfg*(e+(e>0.3 and e<0.6)*0.2))    )*     (      2*( 1+pow(d,pow(2,1/(1+d))))-1     )
+0:e+ (e>0.7 and t%2==0)*3  + (e<0.3 and t%2==1 and t<T-3)*1
+
+
+#random idea
+0:cfg if (t%2==0) else cfg*pow(e,pow(1-e,2))
+0:(1-pow(e,pow(1-e,2)))*2
+--
+0:cfg/(8**(2*(1-e))) if t%2==0 else 0
+--
+
+Update DEC 2022
+#Very foreceful. Needs low deniosing
+0:200/(t*0.4+1); 25:0.07;     37:cfg
+0:0.3;           25:3;        37:max((3-(t-37)/10 ),e)
+
+#Super forceful
+0:(1100-d*1050)/(t*0.4+1);            25:0.07;           37:cfg+cfg*d*3
+0:0.8-d*0.5;                              25:30-d*29;                      37:max((3-(t-37)/(10-10*d) ),e)
+
+#Forceful using CFG and Denoising as parameters
+0:(200-d*150)/(t*(30-cfg)/30+1);            25:0.07;          37:5
+0:0.8-d*0.5;                              25:30 if (t<37-d*11) else 0.2;                     37:1+e
+
+#idea 2
+0:( 50* (4-d*3.75))/(t*(30-cfg)/30+1);            25:0.07;          37:5
+0:0.8-d*0.5;                              25:30 if (t<37-d*11) else 0.2;                     37:1+e
+
+#idea 3
+0:(100*(1-d)+d*50 )/(t*(30-cfg)/30+1);            25:1;          37:5
+0:0.5-min(t*0.1,0.49);                              25:2 if (t<29) else 0.2;                     37:1+e
+
+0:(120*(1-d)+d*50 )/(t*(30-cfg)/30+1);        25:1;          37:5
+0:0.8+1.5-min(t*0.1,     0.8+1.5     -0.01);  25:2 if (t<29) else 0.2;    37:1+e

extensions/CFG-Schedule-for-Automatic1111-SD/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+
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extensions/CFG-Schedule-for-Automatic1111-SD/README.md

@@ -0,0 +1,57 @@
+# How to install
+Drop the .py files in your automatic1111 scripts folder.
+
+# CFG-Schedule-for-Automatic1111-SD
+Note: Works with Eular a. Might not work with the rest of the samplers.
+
+
+## CFG Auto.py
+A simplified version. Contains tools to make the small denoising effective.
+Its main use is to fix img2img drastic change to images and makes it preserve original image features.
+
+Following was set on denising: 0.1
+Batch size is used to loop. Batch size:4
+##
+Input:![index](https://user-images.githubusercontent.com/4189008/211174581-c115bfde-970a-4a41-a9f7-306138e71462.jpg) output ![00150-2661151044-k-pop star  Red Necktie  Black suit and a white shirt under it](https://user-images.githubusercontent.com/4189008/211174606-1d540377-3e5e-48de-82a4-890a4b4287be.jpg)
+
+
+## CFG Schedule.py
+A script for scheduling CFG scale and ETA to change during the denoising steps
+<pre>
+Modified code of https://github.com/tkalayci71/attenuate-cfg-scale
+To use: Place the .py file in AUTOMATIC1111\scripts folder
+Update: 11/14/2022
+  -Now works with all samplers
+  -new function x. Example use: x(t,60,70,2) which will map t to be 0 at 60 and 1 and 70, then multipy by 2
+  -edit: Indecies can now have more than one index. Example: 0,5:=1 ; 1:10
+Update: 11/13/2022
+
+You can now use functions like: 
+0:=math.sin(t) , 10:=math.cos(t)
+or simply just:
+=math.sin(t)
+
+Available variables:
+t: current step
+T: total steps
+e: (1-t/T) which is current ETA
+d: denoising strength
+pi:pi
+cfg: initial CFG
+math: The python math module
+
+Available functions:
+-From python: min, max, pow, abs
+-x(value,min,max,multiply)
+  This will take value and make it 0 when <=min, and make it 1 when >=max. Then, multiply result by last argumant.
+  If multiply is less than 0, the range becomes 1 to 0 instead
+
+
+
+I've tried the following function on 58 steps and it is giving good results:
+CFG:
+0:=math.fabs( (e*1.4-2)*15);30:5
+ETA:
+0:=0.01; 20:=e*1.5;10:=e
+</pre>
+![00031-336559885-car](https://user-images.githubusercontent.com/4189008/201653592-f719533e-573a-4a59-807a-085fb7e320d0.jpg)

extensions/CFG-Schedule-for-Automatic1111-SD/scripts/CFG Auto.py

@@ -0,0 +1,368 @@
+#CFG Scheduler for Automatic1111 Stable Diffusion web-ui
+#Author: https://github.com/guzuligo/
+#Based on: https://github.com/tkalayci71/attenuate-cfg-scale
+#Version: 1.81
+
+from logging import PlaceHolder
+import math
+import os
+import sys
+import traceback
+import copy
+import numpy as np
+import modules.scripts as scripts
+import gradio as gr
+
+#from modules.processing import Processed, process_images
+from modules import images,processing
+from modules.processing import process_images, Processed
+from modules.processing import Processed
+from modules.shared import opts, cmd_opts, state
+class Script(scripts.Script):
+    def run(self,p,n0,dns,ns1,ns2,nr1,nr2   ,loops,nSingle):
+        return self.runBasic(p,n0,dns,ns1,ns2,nr1,nr2   ,loops,nSingle)
+
+    #def run(self,p,cfg,eta,dns ,loops,nSingle):
+    #    return self.runAdvanced(p,cfg,eta,dns ,loops,nSingle)
+
+    def show(self, is_img2img):
+        self.isAdvanced=False
+        return True
+    def title(self):
+        return "CFG Scheduling" if (self.isAdvanced) else "CFG Auto"
+
+    def uiAdvanced(self, is_img2img):
+
+        placeholder="The steps on which to modify, in format step:value - example: 0:10 ; 10:15"
+        n0 = gr.Textbox(label="CFG",placeholder=placeholder)
+        placeholder="You can also use functions like: 0: math.fabs(-t) ; 1: (1-t/T) ; 2:=e ;3:t*d"
+        n1 = gr.Textbox(label="ETA",placeholder=placeholder)
+        #loops
+        #n2 = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=1)
+        n2 = gr.Slider(minimum=0, maximum=1, step=0.01, label='Target Denoising : Decay per Batch', value=0.5)
+        with gr.Row():
+            loops=gr.Number(value=1,precision=0,label="loops")
+            nSingle= gr.Checkbox(label="Loop returns one")
+            
+        return [n0,n1,n2    ,loops,nSingle]
+    #uiBasic
+    def uiAuto(self, is_img2img):
+        self.autoOptions={"b1":"Blur First V1","b2":"Blur Last","f1":"Force at Start V1","f2":"Force Allover"}
+        with gr.Row():
+            dns = gr.Slider(minimum=0, maximum=1, step=0.01, label='Target Denoising : Decay per Batch', value=0.25)
+            n0=gr.Dropdown(list(self.autoOptions.values()),value=self.autoOptions["b1"],label="Scheduler")
+        with gr.Row():
+            n1 = gr.Slider(minimum=0, maximum=100, step=1, label='Main Strength', value=10)
+            n2 = gr.Slider(minimum=0, maximum=100, step=1, label='Sub- Strength', value=10)
+        with gr.Row():
+            n3 = gr.Slider(minimum=0, maximum=100, step=1, label='Main Range', value=10)
+            n4 = gr.Slider(minimum=0, maximum=100, step=1, label='Sub- Range', value=10)
+        with gr.Row():
+            loops=gr.Number(value=1,precision=0,label="loops")
+            nSingle= gr.Checkbox(label="Loop returns one")
+        return [n0,dns,   n1,n2,n3,n4   ,loops,nSingle]
+
+    def ui(self, is_img2img):
+        return self.uiAdvanced(is_img2img) if (self.isAdvanced) else self.uiAuto(is_img2img)
+
+    def prepare(self,p,cfg,eta):
+        sampler_name=p.sampler_name
+        if not sampler_name:
+            print("Warning: sampler not specified. Using Euler a")
+            sampler_name="Euler a"
+        #if p.sampler_index in (0,1,2,7,8,10,14):
+        if sampler_name in ('Euler a','Euler','LMS','DPM++ 2M','DPM fast','LMS Karras','DPM++ 2M Karras'):
+            max_mul_count = p.steps * p.batch_size
+            steps_per_mul = p.batch_size
+        #elif p.sampler_index in (3,4,5,6,11,12,13):
+        elif sampler_name in ('Heun','DPM2','DPM2 a','DPM++ 2S a','DPM2 Karras','DPM2 a Karras','DPM++ 2S a Karras'):
+            max_mul_count = ((p.steps*2)-1) * p.batch_size
+            steps_per_mul = 2 * p.batch_size
+        #elif p.sampler_index==15: # ddim
+        elif sampler_name=='DDIM': # ddim
+            max_mul_count = fix_ddim_step_count(p.steps)
+            steps_per_mul = 1
+        #elif p.sampler_index==16: # plms
+        elif sampler_name=='PLMS': # plms
+            max_mul_count = fix_ddim_step_count(p.steps)+1
+            steps_per_mul = 1
+        else:
+            print("Not supported sampler", p.sampler_name, p.sampler_index)
+            return # 9=dpm adaptive
+
+        
+
+        #print("it is:",n0t)
+        #for x in range(int(n)):
+        self.p=p
+        cfg=cfg.strip()
+        eta=eta.strip()
+        if cfg:
+            p.cfg_scale=Fake_float(p.cfg_scale,self.split(cfg,str(p.cfg_scale))  , max_mul_count, steps_per_mul)
+            #p.cfg_scale.p=p
+        if eta:
+            if (eta.find("@")==-1):
+                p.s_churn=p.eta    =Fake_float(p.eta     or 1,self.split(eta,str(p.eta))    , max_mul_count, steps_per_mul)
+                #print(p.s_noise)
+            
+                #Fake_float(p.s_churn or 1,self.split(eta,str(p.s_churn)), max_mul_count, steps_per_mul)
+            else:
+                eta=eta.split("@")
+                if eta[0].strip()!="":
+                    p.s_churn=Fake_float(p.s_churn or 1,self.split(eta[0],str(p.s_churn)), max_mul_count, steps_per_mul)
+                if len(eta)>1 and eta[1].strip()!="":
+                    p.s_noise=Fake_float(p.s_noise or 1,self.split(eta[1],str(p.s_noise)), max_mul_count, steps_per_mul)
+                if len(eta)>2 and eta[2].strip()!="":
+                    p.s_tmin=Fake_float(p.s_tmin or 1,self.split(eta[2],str(p.s_tmin)), max_mul_count, steps_per_mul)
+                if len(eta)>3 and eta[3].strip()!="":
+                    p.s_tmax=Fake_float(p.s_tmax or 1,self.split(eta[2],str(p.s_tmax)), max_mul_count, steps_per_mul)
+
+
+            #p.cfg_scale.p=p
+        #
+
+
+
+    def runBasic(self,p,n0,dns,ns1,ns2,nr1,nr2  ,loops,nSingle):
+        if(n0==self.autoOptions["b1"]):
+            cfg=f"""0:{ns2}/2 if (t<T* (({nr1}/100)**2)) else cfg"""
+            eta=f"""0:{ns1}+1 if (t<T*(({nr1}/100)**2)  ) else e*({nr2}/50)"""
+        elif(n0==self.autoOptions["f1"]):
+            cfg=f"""0:({ns1}*4)*((1-d**0.5)**1.5)/(t*(30-cfg)/30+1)/(l*2+1) 	if (t<T*{nr1}/100) else 0.1 if (t<T*({nr1}+{nr2}-{nr1}*{nr2})/100) else 7-d*7"""
+            eta=f"""0:0.8+{ns2}/25-min(t*0.1, 0.8+{ns2}/25 -0.01)			if (t<T*{nr1}/100) else {ns2}/(10*(1+l*0.5)) if (t<T*({nr1}+{nr2}-{nr1}*{nr2})/100) else 1+e"""
+        elif(n0==self.autoOptions["b2"]):
+            cfg=f"""0:cfg if (e>{nr1}/100 or e<(1-({nr1}+{nr2}*(100-{nr1})/100)/100)) else {ns2}/10"""
+            eta=f"""0:e   if (e>{nr1}/100 or e<(1-({nr1}+{nr2}*(100-{nr1})/100)/100)) else {ns1}/10"""
+        elif(n0==self.autoOptions["f2"]):
+            cfg=f"""= min(40,max(0,cfg+x(t)*({ns2}-50)/2 )) """
+            eta=f"""0:(1-(t%(2+  10-.1*{nr1}  ))/ (2+10-.1*{nr1}) )*{ns1}*.1  * (e*(100-{nr2})+{nr2})*.01 """
+        self.cfgsib={"Scheduler":n0,'Main Strength':ns1,'Sub- Strength':ns2,'Main Range':nr1,'Sub- Range':nr2}
+        return self.runAdvanced(p,cfg,eta,dns   ,loops,nSingle)
+
+
+    def runAdvanced(self, p, cfg,eta,dns    ,loops,nSingle):
+        self.initSeed=p.seed
+        #loops=p.batch_size
+        loops = loops if (loops>0) else 1
+
+        batch_count=p.n_iter
+        state.job_count = loops*p.n_iter
+        p.denoising_strength=p.denoising_strength or (1 if (self.isAdvanced) else 0.2)
+        initial_denoising_strength=p.denoising_strength
+        p.do_not_save_grid = True
+        if hasattr(p,"init_images"):
+            original_init_image = p.init_images
+            initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
+        else:
+            original_init_image=None
+        
+        all_images = []
+        cfgsi=" loops:"+str(loops)+" terget denoising: "+str(dns)+"\nCFG: "+cfg+"\nETA: "+eta+"\n"
+        
+        p.extra_generation_params = {
+                "CFG Scheduler Info":cfgsi,
+            } 
+        
+
+        #if basic, add basic info as well
+        if (self.isAdvanced==False):
+            self.cfgsib.update(p.extra_generation_params)
+            p.extra_generation_params=self.cfgsib
+
+        if loops>1:
+            processing.fix_seed(p)
+            #self.initDenoise=p.denoising_strength
+            
+        for n in range(batch_count):
+            proc=None
+            history = []
+            p.denoising_strength=initial_denoising_strength
+            if (original_init_image!=None):
+                p.init_images=original_init_image
+            for loop in range(loops):
+                if opts.img2img_color_correction and original_init_image!=None:
+                        p.color_corrections = initial_color_corrections
+
+                p.batch_size = 1
+                p.n_iter = 1
+                self.loop=loop
+                self.prepare(p, cfg,eta)
+                proc = process_images(p)
+                if loop==0:
+                    self.initInfo=proc.info
+                    self.initSeed=proc.seed
+                if len(proc.images)>0:
+                    history.append(proc.images[0])
+                    p.seed+=1
+                    p.init_images=[proc.images[0]]
+                    #p.denoising_strength=min(max(p.denoising_strength * dns, 0.05), 1)
+                    p.denoising_strength=initial_denoising_strength+(dns-initial_denoising_strength)*(loop+1)/(loops)
+                else:#interrupted
+                    break
+                #print("New denoising:"+str(p.denoising_strength)+"\n" )
+            all_images += history
+        if loops>0:#TODO:maybe this is not needed
+            p.seed=self.initSeed
+        #return proc if (loops==1 and p.batch_size==1) else Processed(p, all_images, self.initSeed, self.initInfo)
+        return proc if(nSingle) else Processed(p, all_images, self.initSeed, self.initInfo)
+
+
+
+
+
+
+
+    def peek(self,val):
+        print(val)
+        return val
+
+    def split(self,src,default='0'):
+        p=self.p
+        self.P=copy.copy({
+            'cfg':float(str(p.cfg_scale)),
+            'd':p.denoising_strength or 1,
+            'l':self.loop,
+            'min':min,
+            'max':max,
+            'abs':abs,
+            'pow':pow,
+            'pi':math.pi,
+            'x':self._interpolate,
+            'int':int,
+            'floor':math.floor,
+            'peek':self.peek,
+        })
+
+        if src[0:4]=="eval":
+            src="0:"+src[4:]
+        if src[0]=="=":
+            src="0:"+src[1:]
+
+        #clean up
+        while src[len(src)-1] in [";"," "]:
+            src=src[0:len(src)-1]
+        while src[0] in [";"," "]:
+            src=src[1:]
+
+        arr0 = src.split(';')##2
+
+        #resort array accounting for commas in indecies
+        arr=[]
+        for j in arr0:
+            #print(j)
+            v=j.split(":")
+            q=v[0].split(",")
+                
+            for i in q:
+                arr.append(i+":"+v[1])
+
+                
+
+
+        arr.sort(key=self._sort)
+        s=[]
+        val=default
+        for j in range(p.steps+1):
+          i=0
+          while i<len(arr) and i<=j:
+              v=arr[i].split(":")
+              #s=proc[j].n_iter
+              if math.floor(int(v[0]) if v[0].isnumeric() else float(v[0])*p.steps)==j:
+                 val=v[1].strip()
+                 break
+              i=i+1
+              
+          #lets just evaluate all        
+          if val[0]=="=":
+            val=val[1:]
+          
+          _eta=1-j/p.steps
+          params={'t':j,'T':p.steps,'math':math,'p':p,'e':float(str(_eta))}
+          
+          params.update(copy.copy(self.P))
+          #print(params)
+          s.append(float(eval(val,params)))
+          #end while loop
+          #else:    
+            #s.append(float(val))
+        print(np.round(s,1),"\n")
+        return s
+    #limits a range of a value
+    def _interpolate(self,v,start=0,end=None,m=1):
+        end=end or self.p.steps
+        v=min(max(v,start),end)-start
+        return v*m/(end-start)+(1 if m<0 else 0)
+
+    def _sort(self,a):
+        _=a.split(":")[0]#splitter tester
+        return math.floor(int(_) if (_.isnumeric()) else float(_)*self.p.steps)
+
+    def evaluate (self,src):
+        s=[]
+        p=self.p
+        T=self.p.steps
+        for j in range(T+1):
+            _eta=1-j/p.steps
+            params={'t':j,'T':p.steps,'math':math,'p':p,'e':_eta}
+            params.update(self.P)
+            s.append(float(eval(src,params)))
+        return s
+
+class Fake_float(float):
+    def __new__(self, value, arr, max_mul_count, steps_per_mul):
+        return float.__new__(self, value)
+
+    def __init__(self, value, arr, max_mul_count, steps_per_mul):
+        float.__init__(value)
+        self.arr = arr
+        self.curstep = 0
+        #self.p=p
+
+        #self.orig_value = orig_value
+        #self.target_value = target_value
+        self.max_mul_count = max_mul_count
+        self.current_mul = 0
+        self.steps_per_mul = steps_per_mul
+        self.current_step = 0 #fake
+        self.max_step_count = (max_mul_count // steps_per_mul) + (max_mul_count % steps_per_mul > 0)
+
+
+    def __mul__(self,other):
+        return self.fake_mul(other)
+
+    def __rmul__(self,other):
+        return self.fake_mul(other)
+
+    #def __add__(self,other):
+        #print("ADD!")
+    #    return self.get_fake_value(other)+other
+    #def __sub__(self,other):
+        #print("SUB!")
+    #    return self.get_fake_value(other)-other
+
+
+
+    def fake_mul(self,other):
+        #print("MUL!")
+        return self.get_fake_value(other) * other
+
+
+    def get_fake_value(self,other):
+        if (self.max_step_count==1):
+            fake_value = self.arr[0]
+        else:
+           
+            fake_value = self.arr[self.curstep]
+        self.current_mul = (self.current_mul+1) % self.max_mul_count
+        self.curstep = (self.current_mul) // self.steps_per_mul
+        self.current_step+=1#FAKE STEP
+        return fake_value
+
+    
+
+
+def fix_ddim_step_count(steps):
+    valid_step = 999 / (1000 // steps)
+    if valid_step == int(valid_step): steps=int(valid_step)+1
+    if ((1000 % steps)!=0): steps +=1
+    return steps

extensions/CFG-Schedule-for-Automatic1111-SD/scripts/CFG Schedule.py

@@ -0,0 +1,369 @@
+#CFG Scheduler for Automatic1111 Stable Diffusion web-ui
+#Author: https://github.com/guzuligo/
+#Based on: https://github.com/tkalayci71/attenuate-cfg-scale
+#Version: 1.81
+
+from logging import PlaceHolder
+import math
+import os
+import sys
+import traceback
+import copy
+import numpy as np
+import modules.scripts as scripts
+import gradio as gr
+
+#from modules.processing import Processed, process_images
+from modules import images,processing
+from modules.processing import process_images, Processed
+from modules.processing import Processed
+from modules.shared import opts, cmd_opts, state
+class Script(scripts.Script):
+    #def run(self,p,n0,dns,ns1,ns2,nr1,nr2   ,loops,nSingle):
+    #    return self.runBasic(p,n0,dns,ns1,ns2,nr1,nr2   ,loops,nSingle)
+
+    def run(self,p,cfg,eta,dns ,loops,nSingle):
+        return self.runAdvanced(p,cfg,eta,dns ,loops,nSingle)
+
+    def show(self, is_img2img):
+        self.isAdvanced=True
+        return True
+    def title(self):
+        return "CFG Scheduling" if (self.isAdvanced) else "CFG Auto"
+
+    def uiAdvanced(self, is_img2img):
+
+        placeholder="The steps on which to modify, in format step:value - example: 0:10 ; 10:15"
+        n0 = gr.Textbox(label="CFG",placeholder=placeholder)
+        placeholder="You can also use functions like: 0: math.fabs(-t) ; 1: (1-t/T) ; 2:=e ;3:t*d"
+        n1 = gr.Textbox(label="ETA",placeholder=placeholder)
+        #loops
+        #n2 = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=1)
+        n2 = gr.Slider(minimum=0, maximum=1, step=0.01, label='Target Denoising : Decay per Batch', value=0.5)
+        with gr.Row():
+            loops=gr.Number(value=1,precision=0,label="loops")
+            nSingle= gr.Checkbox(label="Loop returns one")
+            
+        return [n0,n1,n2    ,loops,nSingle]
+    #uiBasic
+    def uiAuto(self, is_img2img):
+        self.autoOptions={"b1":"Blur First V1","b2":"Blur Last","f1":"Force at Start V1","f2":"Force Allover"}
+        with gr.Row():
+            dns = gr.Slider(minimum=0, maximum=1, step=0.01, label='Target Denoising : Decay per Batch', value=0.25)
+            n0=gr.Dropdown(list(self.autoOptions.values()),value=self.autoOptions["b1"],label="Scheduler")
+        with gr.Row():
+            n1 = gr.Slider(minimum=0, maximum=100, step=1, label='Main Strength', value=10)
+            n2 = gr.Slider(minimum=0, maximum=100, step=1, label='Sub- Strength', value=10)
+        with gr.Row():
+            n3 = gr.Slider(minimum=0, maximum=100, step=1, label='Main Range', value=10)
+            n4 = gr.Slider(minimum=0, maximum=100, step=1, label='Sub- Range', value=10)
+        with gr.Row():
+            loops=gr.Number(value=1,precision=0,label="loops")
+            nSingle= gr.Checkbox(label="Loop returns one")
+        return [n0,dns,   n1,n2,n3,n4   ,loops,nSingle]
+
+    def ui(self, is_img2img):
+        return self.uiAdvanced(is_img2img) if (self.isAdvanced) else self.uiAuto(is_img2img)
+
+    def prepare(self,p,cfg,eta):
+        sampler_name=p.sampler_name
+        if not sampler_name:
+            print("Warning: sampler not specified. Using Euler a")
+            sampler_name="Euler a"
+        #if p.sampler_index in (0,1,2,7,8,10,14):
+        if sampler_name in ('Euler a','Euler','LMS','DPM++ 2M','DPM fast','LMS Karras','DPM++ 2M Karras'):
+            max_mul_count = p.steps * p.batch_size
+            steps_per_mul = p.batch_size
+        #elif p.sampler_index in (3,4,5,6,11,12,13):
+        elif sampler_name in ('Heun','DPM2','DPM2 a','DPM++ 2S a','DPM2 Karras','DPM2 a Karras','DPM++ 2S a Karras'):
+            max_mul_count = ((p.steps*2)-1) * p.batch_size
+            steps_per_mul = 2 * p.batch_size
+        #elif p.sampler_index==15: # ddim
+        elif sampler_name=='DDIM': # ddim
+            max_mul_count = fix_ddim_step_count(p.steps)
+            steps_per_mul = 1
+        #elif p.sampler_index==16: # plms
+        elif sampler_name=='PLMS': # plms
+            max_mul_count = fix_ddim_step_count(p.steps)+1
+            steps_per_mul = 1
+        else:
+            print("Not supported sampler", p.sampler_name, p.sampler_index)
+            return # 9=dpm adaptive
+
+        
+
+        #print("it is:",n0t)
+        #for x in range(int(n)):
+        self.p=p
+        cfg=cfg.strip()
+        eta=eta.strip()
+        if cfg:
+            p.cfg_scale=Fake_float(p.cfg_scale,self.split(cfg,str(p.cfg_scale))  , max_mul_count, steps_per_mul)
+            #p.cfg_scale.p=p
+        if eta:
+            if (eta.find("@")==-1):
+                p.s_churn=p.eta    =Fake_float(p.eta     or 1,self.split(eta,str(p.eta))    , max_mul_count, steps_per_mul)
+                #print(p.s_noise)
+            
+                #Fake_float(p.s_churn or 1,self.split(eta,str(p.s_churn)), max_mul_count, steps_per_mul)
+            else:
+                eta=eta.split("@")
+                if eta[0].strip()!="":
+                    p.s_churn=Fake_float(p.s_churn or 1,self.split(eta[0],str(p.s_churn)), max_mul_count, steps_per_mul)
+                if len(eta)>1 and eta[1].strip()!="":
+                    p.s_noise=Fake_float(p.s_noise or 1,self.split(eta[1],str(p.s_noise)), max_mul_count, steps_per_mul)
+                if len(eta)>2 and eta[2].strip()!="":
+                    p.s_tmin=Fake_float(p.s_tmin or 1,self.split(eta[2],str(p.s_tmin)), max_mul_count, steps_per_mul)
+                if len(eta)>3 and eta[3].strip()!="":
+                    p.s_tmax=Fake_float(p.s_tmax or 1,self.split(eta[2],str(p.s_tmax)), max_mul_count, steps_per_mul)
+
+
+            #p.cfg_scale.p=p
+        #
+
+
+
+    def runBasic(self,p,n0,dns,ns1,ns2,nr1,nr2  ,loops,nSingle):
+        if(n0==self.autoOptions["b1"]):
+            cfg=f"""0:{ns2}/2 if (t<T* (({nr1}/100)**2)) else cfg"""
+            eta=f"""0:{ns1}+1 if (t<T*(({nr1}/100)**2)  ) else e*({nr2}/50)"""
+        elif(n0==self.autoOptions["f1"]):
+            cfg=f"""0:({ns1}*4)*((1-d**0.5)**1.5)/(t*(30-cfg)/30+1)/(l*2+1) 	if (t<T*{nr1}/100) else 0.1 if (t<T*({nr1}+{nr2}-{nr1}*{nr2})/100) else 7-d*7"""
+            eta=f"""0:0.8+{ns2}/25-min(t*0.1, 0.8+{ns2}/25 -0.01)			if (t<T*{nr1}/100) else {ns2}/(10*(1+l*0.5)) if (t<T*({nr1}+{nr2}-{nr1}*{nr2})/100) else 1+e"""
+        elif(n0==self.autoOptions["b2"]):
+            cfg=f"""0:cfg if (e>{nr1}/100 or e<(1-({nr1}+{nr2}*(100-{nr1})/100)/100)) else {ns2}/10"""
+            eta=f"""0:e   if (e>{nr1}/100 or e<(1-({nr1}+{nr2}*(100-{nr1})/100)/100)) else {ns1}/10"""
+        elif(n0==self.autoOptions["f2"]):
+            cfg=f"""= min(40,max(0,cfg+x(t)*({ns2}-50)/2 )) """
+            eta=f"""0:(1-(t%(2+  10-.1*{nr1}  ))/ (2+10-.1*{nr1}) )*{ns1}*.1  * (e*(100-{nr2})+{nr2})*.01 """
+        self.cfgsib={"Scheduler":n0,'Main Strength':ns1,'Sub- Strength':ns2,'Main Range':nr1,'Sub- Range':nr2}
+        self.cfgsib={"Scheduler":n0,'Main Strength':ns1,'Sub- Strength':ns2,'Main Range':nr1,'Sub- Range':nr2}
+        return self.runAdvanced(p,cfg,eta,dns   ,loops,nSingle)
+
+
+    def runAdvanced(self, p, cfg,eta,dns    ,loops,nSingle):
+        self.initSeed=p.seed
+        #loops=p.batch_size
+        loops = loops if (loops>0) else 1
+
+        batch_count=p.n_iter
+        state.job_count = loops*p.n_iter
+        p.denoising_strength=p.denoising_strength or (1 if (self.isAdvanced) else 0.2)
+        initial_denoising_strength=p.denoising_strength
+        p.do_not_save_grid = True
+        if hasattr(p,"init_images"):
+            original_init_image = p.init_images
+            initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
+        else:
+            original_init_image=None
+        
+        all_images = []
+        cfgsi=" loops:"+str(loops)+" terget denoising: "+str(dns)+"\nCFG: "+cfg+"\nETA: "+eta+"\n"
+        
+        p.extra_generation_params = {
+                "CFG Scheduler Info":cfgsi,
+            } 
+        
+
+        #if basic, add basic info as well
+        if (self.isAdvanced==False):
+            self.cfgsib.update(p.extra_generation_params)
+            p.extra_generation_params=self.cfgsib
+
+        if loops>1:
+            processing.fix_seed(p)
+            #self.initDenoise=p.denoising_strength
+            
+        for n in range(batch_count):
+            proc=None
+            history = []
+            p.denoising_strength=initial_denoising_strength
+            if (original_init_image!=None):
+                p.init_images=original_init_image
+            for loop in range(loops):
+                if opts.img2img_color_correction and original_init_image!=None:
+                        p.color_corrections = initial_color_corrections
+
+                p.batch_size = 1
+                p.n_iter = 1
+                self.loop=loop
+                self.prepare(p, cfg,eta)
+                proc = process_images(p)
+                if loop==0:
+                    self.initInfo=proc.info
+                    self.initSeed=proc.seed
+                if len(proc.images)>0:
+                    history.append(proc.images[0])
+                    p.seed+=1
+                    p.init_images=[proc.images[0]]
+                    #p.denoising_strength=min(max(p.denoising_strength * dns, 0.05), 1)
+                    p.denoising_strength=initial_denoising_strength+(dns-initial_denoising_strength)*(loop+1)/(loops)
+                else:#interrupted
+                    break
+                #print("New denoising:"+str(p.denoising_strength)+"\n" )
+            all_images += history
+        if loops>0:#TODO:maybe this is not needed
+            p.seed=self.initSeed
+        #return proc if (loops==1 and p.batch_size==1) else Processed(p, all_images, self.initSeed, self.initInfo)
+        return proc if(nSingle) else Processed(p, all_images, self.initSeed, self.initInfo)
+
+
+
+
+
+
+
+    def peek(self,val):
+        print(val)
+        return val
+
+    def split(self,src,default='0'):
+        p=self.p
+        self.P=copy.copy({
+            'cfg':float(str(p.cfg_scale)),
+            'd':p.denoising_strength or 1,
+            'l':self.loop,
+            'min':min,
+            'max':max,
+            'abs':abs,
+            'pow':pow,
+            'pi':math.pi,
+            'x':self._interpolate,
+            'int':int,
+            'floor':math.floor,
+            'peek':self.peek,
+        })
+
+        if src[0:4]=="eval":
+            src="0:"+src[4:]
+        if src[0]=="=":
+            src="0:"+src[1:]
+
+        #clean up
+        while src[len(src)-1] in [";"," "]:
+            src=src[0:len(src)-1]
+        while src[0] in [";"," "]:
+            src=src[1:]
+
+        arr0 = src.split(';')##2
+
+        #resort array accounting for commas in indecies
+        arr=[]
+        for j in arr0:
+            #print(j)
+            v=j.split(":")
+            q=v[0].split(",")
+                
+            for i in q:
+                arr.append(i+":"+v[1])
+
+                
+
+
+        arr.sort(key=self._sort)
+        s=[]
+        val=default
+        for j in range(p.steps+1):
+          i=0
+          while i<len(arr) and i<=j:
+              v=arr[i].split(":")
+              #s=proc[j].n_iter
+              if math.floor(int(v[0]) if v[0].isnumeric() else float(v[0])*p.steps)==j:
+                 val=v[1].strip()
+                 break
+              i=i+1
+              
+          #lets just evaluate all        
+          if val[0]=="=":
+            val=val[1:]
+          
+          _eta=1-j/p.steps
+          params={'t':j,'T':p.steps,'math':math,'p':p,'e':float(str(_eta))}
+          
+          params.update(copy.copy(self.P))
+          #print(params)
+          s.append(float(eval(val,params)))
+          #end while loop
+          #else:    
+            #s.append(float(val))
+        print(np.round(s,1),"\n")
+        return s
+    #limits a range of a value
+    def _interpolate(self,v,start=0,end=None,m=1):
+        end=end or self.p.steps
+        v=min(max(v,start),end)-start
+        return v*m/(end-start)+(1 if m<0 else 0)
+
+    def _sort(self,a):
+        _=a.split(":")[0]#splitter tester
+        return math.floor(int(_) if (_.isnumeric()) else float(_)*self.p.steps)
+
+    def evaluate (self,src):
+        s=[]
+        p=self.p
+        T=self.p.steps
+        for j in range(T+1):
+            _eta=1-j/p.steps
+            params={'t':j,'T':p.steps,'math':math,'p':p,'e':_eta}
+            params.update(self.P)
+            s.append(float(eval(src,params)))
+        return s
+
+class Fake_float(float):
+    def __new__(self, value, arr, max_mul_count, steps_per_mul):
+        return float.__new__(self, value)
+
+    def __init__(self, value, arr, max_mul_count, steps_per_mul):
+        float.__init__(value)
+        self.arr = arr
+        self.curstep = 0
+        #self.p=p
+
+        #self.orig_value = orig_value
+        #self.target_value = target_value
+        self.max_mul_count = max_mul_count
+        self.current_mul = 0
+        self.steps_per_mul = steps_per_mul
+        self.current_step = 0 #fake
+        self.max_step_count = (max_mul_count // steps_per_mul) + (max_mul_count % steps_per_mul > 0)
+
+
+    def __mul__(self,other):
+        return self.fake_mul(other)
+
+    def __rmul__(self,other):
+        return self.fake_mul(other)
+
+    #def __add__(self,other):
+        #print("ADD!")
+    #    return self.get_fake_value(other)+other
+    #def __sub__(self,other):
+        #print("SUB!")
+    #    return self.get_fake_value(other)-other
+
+
+
+    def fake_mul(self,other):
+        #print("MUL!")
+        return self.get_fake_value(other) * other
+
+
+    def get_fake_value(self,other):
+        if (self.max_step_count==1):
+            fake_value = self.arr[0]
+        else:
+           
+            fake_value = self.arr[self.curstep]
+        self.current_mul = (self.current_mul+1) % self.max_mul_count
+        self.curstep = (self.current_mul) // self.steps_per_mul
+        self.current_step+=1#FAKE STEP
+        return fake_value
+
+    
+
+
+def fix_ddim_step_count(steps):
+    valid_step = 999 / (1000 // steps)
+    if valid_step == int(valid_step): steps=int(valid_step)+1
+    if ((1000 % steps)!=0): steps +=1
+    return steps

extensions/CFG_Rescale_webui/.gitignore

@@ -0,0 +1,160 @@
+# 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/

extensions/CFG_Rescale_webui/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 CartUniverse
+
+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/CFG_Rescale_webui/README.md

@@ -0,0 +1,10 @@
+# CFG_Rescale_webui
+Adds a CFG rescale option to A1111 webui for vpred models. Implements features described in https://arxiv.org/pdf/2305.08891.pdf
+
+When installed, you will see a CFG slider below appear below the seed selection.
+
+The recommended settings from the paper is 0.7 CFG rescale. The paper recommends using it at 7.5 CFG, but I find that the default 7.0 CFG works just fine as well.
+If you notice your images are dull or muddy, try setting CFG Rescale to 0.5 and/or turning on color fix.
+
+# Extra Features
+Auto color fix: CFG rescale can result in the color palette of the image becoming smaller, resulting in muddy or brown images. Turning this on attempts to return the image colors to full range again, making it appear more vibrant and natural.

extensions/CFG_Rescale_webui/scripts/CFGRescale.py

@@ -0,0 +1,225 @@
+import math
+import torch
+import re
+import gradio as gr
+import numpy as np
+import modules.scripts as scripts
+import modules.images as saving
+from modules import devices, processing, shared, sd_samplers_kdiffusion, sd_samplers_compvis, script_callbacks
+from modules.processing import Processed
+from modules.shared import opts, state
+from ldm.models.diffusion import ddim
+from PIL import Image
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, noise_like
+
+re_prompt_cfgr = re.compile(r"<cfg_rescale:([^>]+)>")
+
+class Script(scripts.Script):
+
+    def __init__(self):
+        self.old_denoising = sd_samplers_kdiffusion.CFGDenoiser.combine_denoised
+        self.old_schedule = ddim.DDIMSampler.make_schedule
+        self.old_sample = ddim.DDIMSampler.p_sample_ddim
+        globals()['enable_furry_cocks'] = True
+
+        def find_module(module_names):
+            if isinstance(module_names, str):
+                module_names = [s.strip() for s in module_names.split(",")]
+            for data in scripts.scripts_data:
+                if data.script_class.__module__ in module_names and hasattr(data, "module"):
+                    return data.module
+            return None
+
+        def rescale_opt(p, x, xs):
+            globals()['cfg_rescale_fi'] = x
+            globals()['enable_furry_cocks'] = False
+
+        xyz_grid = find_module("xyz_grid.py, xy_grid.py")
+        if xyz_grid:
+            extra_axis_options = [xyz_grid.AxisOption("Rescale CFG", float, rescale_opt)]
+            xyz_grid.axis_options.extend(extra_axis_options)
+
+    def title(self):
+        return "CFG Rescale Extension"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        with gr.Accordion("CFG Rescale", open=True, elem_id="cfg_rescale"):
+            rescale = gr.Slider(label="CFG Rescale", show_label=False, minimum=0.0, maximum=1.0, step=0.01, value=0.0)
+            with gr.Row():
+                recolor = gr.Checkbox(label="Auto Color Fix", default=False)
+                rec_strength = gr.Slider(label="Fix Strength", interactive=True, visible=False,
+                                         elem_id=self.elem_id("rec_strength"), minimum=0.1, maximum=10.0, step=0.1,
+                                         value=1.0)
+                show_original = gr.Checkbox(label="Keep Original Images", elem_id=self.elem_id("show_original"), visible=False, default=False)
+
+            def show_recolor_strength(rec_checked):
+                return [gr.update(visible=rec_checked), gr.update(visible=rec_checked)]
+
+            recolor.change(
+                fn=show_recolor_strength,
+                inputs=recolor,
+                outputs=[rec_strength, show_original]
+            )
+
+        self.infotext_fields = [
+            (rescale, "CFG Rescale"),
+            (recolor, "Auto Color Fix")
+        ]
+        self.paste_field_names = []
+        for _, field_name in self.infotext_fields:
+            self.paste_field_names.append(field_name)
+        return [rescale, recolor, rec_strength, show_original]
+
+    def cfg_replace(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+        fi = globals()['cfg_rescale_fi']
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                if fi == 0:
+                    denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+                else:
+                    xcfg = (denoised_uncond[i] + (x_out[cond_index] - denoised_uncond[i]) * (cond_scale * weight))
+                    xrescaled = (torch.std(x_out[cond_index]) / torch.std(xcfg))
+                    xfinal = fi * xrescaled + (1.0 - fi)
+                    denoised[i] = xfinal * xcfg
+
+        return denoised
+
+    def process(self, p, rescale, recolor, rec_strength, show_original):
+
+        if globals()['enable_furry_cocks']:
+            # parse <cfg_rescale:[number]> from prompt for override
+            rescale_override = None
+            def found(m):
+                nonlocal rescale_override
+                try:
+                    rescale_override = float(m.group(1))
+                except ValueError:
+                    rescale_override = None
+                return ""
+            p.prompt = re.sub(re_prompt_cfgr, found, p.prompt)
+            if rescale_override is not None:
+                rescale = rescale_override
+            
+            globals()['cfg_rescale_fi'] = rescale
+        else:
+            # rescale value is being set from xyz_grid
+            rescale = globals()['cfg_rescale_fi']
+        globals()['enable_furry_cocks'] = True
+
+        sd_samplers_kdiffusion.CFGDenoiser.combine_denoised = self.cfg_replace
+
+        if rescale > 0:
+            p.extra_generation_params["CFG Rescale"] = rescale
+
+        if recolor:
+            p.extra_generation_params["Auto Color Fix Strength"] = rec_strength
+            p.do_not_save_samples = True
+
+    def postprocess_batch_list(self, p, pp, rescale, recolor, rec_strength, show_original, batch_number):
+        if recolor and show_original:
+            num = len(pp.images)
+            for i in range(num):
+                pp.images.append(pp.images[i])
+                p.prompts.append(p.prompts[i])
+                p.negative_prompts.append(p.negative_prompts[i])
+                p.seeds.append(p.seeds[i])
+                p.subseeds.append(p.subseeds[i])
+
+    def postprocess(self, p, processed, rescale, recolor, rec_strength, show_original):
+        sd_samplers_kdiffusion.CFGDenoiser.combine_denoised = self.old_denoising
+
+        def postfix(img, rec_strength):
+            prec = 0.0005 * rec_strength
+            r, g, b = img.split()
+
+            # softer effect
+            # r_min, r_max = np.percentile(r, p), np.percentile(r, 100.0 - p)
+            # g_min, g_max = np.percentile(g, p), np.percentile(g, 100.0 - p)
+            # b_min, b_max = np.percentile(b, p), np.percentile(b, 100.0 - p)
+
+            rh, rbins = np.histogram(r, 256, (0, 256))
+            tmp = np.where(rh > rh.sum() * prec)[0]
+            r_min = tmp.min()
+            r_max = tmp.max()
+
+            gh, gbins = np.histogram(g, 256, (0, 256))
+            tmp = np.where(gh > gh.sum() * prec)[0]
+            g_min = tmp.min()
+            g_max = tmp.max()
+
+            bh, bbins = np.histogram(b, 256, (0, 256))
+            tmp = np.where(bh > bh.sum() * prec)[0]
+            b_min = tmp.min()
+            b_max = tmp.max()
+
+            r = r.point(lambda i: int(255 * (min(max(i, r_min), r_max) - r_min) / (r_max - r_min)))
+            g = g.point(lambda i: int(255 * (min(max(i, g_min), g_max) - g_min) / (g_max - g_min)))
+            b = b.point(lambda i: int(255 * (min(max(i, b_min), b_max) - b_min) / (b_max - b_min)))
+
+            new_img = Image.merge("RGB", (r, g, b))
+
+            return new_img
+
+        if recolor:
+            grab = 0
+            n_img = len(processed.images)
+            for i in range(n_img):
+                doit = False
+
+                if show_original:
+                    check = i
+                    if opts.return_grid:
+                        if i == 0:
+                            continue
+                        else:
+                            check = check - 1
+                    doit = check % (p.batch_size * 2) >= p.batch_size
+                else:
+                    if n_img > 1 and i != 0:
+                        doit = True
+                    elif n_img == 1 or not opts.return_grid:
+                        doit = True
+
+                if doit:
+                    res_img = postfix(processed.images[i], rec_strength)
+                    if opts.samples_save:
+                        ind = grab
+                        grab += 1
+                        prompt_infotext = processing.create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds,
+                                                                     index=ind)
+                        # Save images to disk
+                        if opts.samples_save:
+                                saving.save_image(processed.images[i], p.outpath_samples, "", seed=p.all_seeds[ind],
+                                                  prompt=p.all_prompts[ind],
+                                                  info=prompt_infotext, p=p, suffix="colorfix")
+                                saving.save_image(res_img, p.outpath_samples, "", seed=p.all_seeds[ind],
+                                                  prompt=p.all_prompts[ind],
+                                                  info=prompt_infotext, p=p, suffix="colorfix")
+
+                    processed.images[i] = res_img
+
+
+def on_infotext_pasted(infotext, params):
+    if "CFG Rescale" not in params:
+        params["CFG Rescale"] = 0
+
+        if "CFG Rescale φ" in params:
+            params["CFG Rescale"] = params["CFG Rescale φ"]
+            del params["CFG Rescale φ"]
+
+        if "CFG Rescale phi" in params and scripts.scripts_txt2img.script("Neutral Prompt") is None:
+            params["CFG Rescale"] = params["CFG Rescale phi"]
+            del params["CFG Rescale phi"]
+
+    if "DDIM Trailing" not in params:
+        params["DDIM Trailing"] = False
+
+
+script_callbacks.on_infotext_pasted(on_infotext_pasted)

extensions/DeepShrinkHires.fix/.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+settings.json

extensions/DeepShrinkHires.fix/scripts/DeepShrinkHires.fix.py

@@ -0,0 +1,215 @@
+import gradio
+import torch
+
+import modules.devices as devices
+import modules.scripts as scripts
+import modules.script_callbacks as script_callbacks
+import modules.sd_unet as sd_unet
+import modules.shared as shared
+
+from ldm.modules.diffusionmodules.util import timestep_embedding as timestep_embedding
+
+class DeepShrinkHiresFixAction():
+    def __init__(self, enable: bool, timestep: float, depth: int, scale: float):
+        self.enable = enable
+        self.timestep = timestep
+        self.depth = depth
+        self.scale = scale
+        pass
+    pass
+
+class DeepShrinkHiresFix(scripts.Script):
+    deepShrinkHiresFixActions: list[DeepShrinkHiresFixAction] = []
+    enableExperimental: bool = False
+    experimentalTimestep: float = 900
+    experimentalScales: list[float] = []
+
+    def __init__(self):
+        pass
+
+    def title(self):
+        return "Deep Shrink Hires.fix"
+        pass
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+        pass
+
+    def ui(self, is_img2img):
+        with gradio.Accordion(label="Deep Shrink Hires.fix", open=False):
+            with gradio.Row():
+                Enable_1 = gradio.Checkbox(value=True, label="Enable 1")
+                Timestep_1 = gradio.Number(value=900, label="Timestep 1")
+                Depth_1 = gradio.Number(value=3, label="Block Depth 1", precision=0)
+                Scale_1 = gradio.Number(value=2, label="Scale factor 1")
+                pass
+            with gradio.Row():
+                Enable_2 = gradio.Checkbox(value=True, label="Enable 2")
+                Timestep_2 = gradio.Number(value=650, label="Timestep 2")
+                Depth_2 = gradio.Number(value=3, label="Block Depth 2", precision=0)
+                Scale_2 = gradio.Number(value=2, label="Scale factor 2")
+                pass
+            with gradio.Accordion(label="Advanced Settings", open=False):
+                with gradio.Row():
+                    Enable_3 = gradio.Checkbox(value=False, label="Enable 3")
+                    Timestep_3 = gradio.Number(value=900, label="Timestep 3")
+                    Depth_3 = gradio.Number(value=3, label="Block Depth 3", precision=0)
+                    Scale_3 = gradio.Number(value=2, label="Scale factor 3")
+                    pass
+                with gradio.Row():
+                    Enable_4 = gradio.Checkbox(value=False, label="Enable 4")
+                    Timestep_4 = gradio.Number(value=650, label="Timestep 4")
+                    Depth_4 = gradio.Number(value=3, label="Block Depth 4", precision=0)
+                    Scale_4 = gradio.Number(value=2, label="Scale factor 4")
+                    pass
+                with gradio.Row():
+                    Enable_5 = gradio.Checkbox(value=False, label="Enable 5")
+                    Timestep_5 = gradio.Number(value=900, label="Timestep 5")
+                    Depth_5 = gradio.Number(value=3, label="Block Depth 5", precision=0)
+                    Scale_5 = gradio.Number(value=2, label="Scale factor 5")
+                    pass
+                with gradio.Row():
+                    Enable_6 = gradio.Checkbox(value=False, label="Enable 6")
+                    Timestep_6 = gradio.Number(value=650, label="Timestep 6")
+                    Depth_6 = gradio.Number(value=3, label="Block Depth 6", precision=0)
+                    Scale_6 = gradio.Number(value=2, label="Scale factor 6")
+                    pass
+                with gradio.Row():
+                    Enable_7 = gradio.Checkbox(value=False, label="Enable 7")
+                    Timestep_7 = gradio.Number(value=900, label="Timestep 7")
+                    Depth_7 = gradio.Number(value=3, label="Block Depth 7", precision=0)
+                    Scale_7 = gradio.Number(value=2, label="Scale factor 7")
+                    pass
+                with gradio.Row():
+                    Enable_8 = gradio.Checkbox(value=False, label="Enable 8")
+                    Timestep_8 = gradio.Number(value=650, label="Timestep 8")
+                    Depth_8 = gradio.Number(value=3, label="Block Depth 8", precision=0)
+                    Scale_8 = gradio.Number(value=2, label="Scale factor 8")
+                    pass
+                pass
+            with gradio.Accordion(label="Experimental Settings", open=False):
+                with gradio.Row():
+                    Enable_Experimental = gradio.Checkbox(value=False, label="Enable Experimental Mode")
+                    Timestep_Experimental = gradio.Number(value=900, label="Timestep")
+                    Scale_Experimental = gradio.Textbox(value="1,1,1, 1,1,1, 1,1,1, 1,1,1, 2, 1,1,1, 1,1,1, 1,1,1, 1,1,1", label="Scale Factor List")
+                    pass
+                pass
+            pass
+        return [Enable_1, Timestep_1, Depth_1, Scale_1, Enable_2, Timestep_2, Depth_2, Scale_2, Enable_3, Timestep_3, Depth_3, Scale_3, Enable_4, Timestep_4, Depth_4, Scale_4,
+                Enable_5, Timestep_5, Depth_5, Scale_5, Enable_6, Timestep_6, Depth_6, Scale_6, Enable_7, Timestep_7, Depth_7, Scale_7, Enable_8, Timestep_8, Depth_8, Scale_8,
+                Enable_Experimental, Timestep_Experimental, Scale_Experimental]
+        pass
+
+    def process(self, p, *args):
+        del DeepShrinkHiresFix.deepShrinkHiresFixActions[:]
+        for i in range(8):
+            DeepShrinkHiresFix.deepShrinkHiresFixActions.append(DeepShrinkHiresFixAction(args[i*4], args[i*4+1], args[i*4+2], args[i*4+3]))
+            pass
+        del DeepShrinkHiresFix.experimentalScales[:]
+        DeepShrinkHiresFix.enableExperimental = args[8*4]
+        DeepShrinkHiresFix.experimentalTimestep = args[8*4+1]
+        scaleFactorsTexts: str = args[8*4+2]
+        scaleFactorsTextsList = scaleFactorsTexts.split(",")
+        for scaleFactorsText in scaleFactorsTextsList:
+            DeepShrinkHiresFix.experimentalScales.append(float(scaleFactorsText))
+            pass
+        pass
+
+    class DeepShrinkHiresFixUNet(sd_unet.SdUnet):
+        def __init__(self, _model):
+            super().__init__()
+            self.model = _model.to(devices.device)
+            pass
+        def forward(self, x, timesteps, context, y=None, **kwargs):
+            assert (y is not None) == (
+                self.model.num_classes is not None
+            ), "must specify y if and only if the model is class-conditional"
+            hs = []
+            ss = []
+            t_emb = timestep_embedding(timesteps, self.model.model_channels, repeat_only=False)
+            emb = self.model.time_embed(t_emb)
+
+            if self.model.num_classes is not None:
+                assert y.shape[0] == x.shape[0]
+                emb = emb + self.model.label_emb(y)
+
+            h = x.type(self.model.dtype)
+            depth = 0
+            block = 0
+            scale = 1
+            for module in self.model.input_blocks:
+                for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
+                    if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
+                        h = torch.nn.functional.interpolate(h.float(), scale_factor=1/action.scale, mode="bicubic", align_corners=False).to(h.dtype)
+                        break
+                        pass
+                    pass
+                if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
+                    h = torch.nn.functional.interpolate(h.float(), scale_factor=scale/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype)
+                    scale = DeepShrinkHiresFix.experimentalScales[block]
+                    ss.append(scale)
+                    pass
+                h = module(h, emb, context)
+                hs.append(h)
+                depth += 1
+                block += 1
+                pass
+
+            for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
+                if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
+                    h = torch.nn.functional.interpolate(h.float(), scale_factor=1/action.scale, mode="bicubic", align_corners=False).to(h.dtype)
+                    break
+                    pass
+                pass
+            if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
+                h = torch.nn.functional.interpolate(h.float(), scale_factor=scale/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype)
+                scale = DeepShrinkHiresFix.experimentalScales[block]
+                pass
+
+            h = self.model.middle_block(h, emb, context)
+
+            for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
+                if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
+                    h = torch.nn.functional.interpolate(h.float(), scale_factor=action.scale, mode="bicubic", align_corners=False).to(h.dtype)
+                    break
+                    pass
+                pass
+            block += 1
+
+            for module in self.model.output_blocks:
+                depth -= 1
+                if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
+                    h = torch.cat([torch.nn.functional.interpolate(h.float(), scale_factor=scale/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype), 
+                                   torch.nn.functional.interpolate(hs.pop().float(), scale_factor=ss.pop()/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype)], dim=1)
+                    scale = DeepShrinkHiresFix.experimentalScales[block]
+                    pass
+                else:
+                    h = torch.cat([h, hs.pop()], dim=1)
+                    pass
+                h = module(h, emb, context)
+                for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
+                    if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
+                        h = torch.nn.functional.interpolate(h.float(), scale_factor=action.scale, mode="bicubic", align_corners=False).to(h.dtype)
+                        break
+                        pass
+                    pass
+                block += 1
+                pass
+            if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
+                h = torch.nn.functional.interpolate(h.float(), scale_factor=scale, mode="bicubic", align_corners=False).to(h.dtype)
+                pass
+            h = h.type(x.dtype)
+            if self.model.predict_codebook_ids:
+                return self.model.id_predictor(h)
+            else:
+                return self.model.out(h)
+            pass
+        pass
+
+    DeepShrinkHiresFixUNetOption = sd_unet.SdUnetOption()
+    DeepShrinkHiresFixUNetOption.label = "Deep Shrink Hires.fix"
+    DeepShrinkHiresFixUNetOption.create_unet = lambda: DeepShrinkHiresFix.DeepShrinkHiresFixUNet(shared.sd_model.model.diffusion_model)
+
+    pass
+
+script_callbacks.on_list_unets(lambda unets: unets.append(DeepShrinkHiresFix.DeepShrinkHiresFixUNetOption))

extensions/Euler-Smea-Dyn-Sampler/.gitignore

@@ -0,0 +1 @@
+__pycache__

extensions/Euler-Smea-Dyn-Sampler/LICENSE

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extensions/Euler-Smea-Dyn-Sampler/README.md

@@ -0,0 +1,271 @@
+| Catalog |
+| ---- |
+| [2024.05.09 Add Euler Negative And Euler dy Negtive 添加两个新采样器](#section1) |
+| [2024.04.24](#section2) |
+| [2024.04.18 Stage Technical Report 阶段性技术报告](#section3) |
+| [2024.04.15 Compatible with Stable Cascade Models 适配Sc模型](#section4) |
+| [2024.04.13 Fix Bug In ComfyUI 修复ComfyUI中的Bug](#section5) |
+| [2024.04.11 Important! This repo can be use as a extension! 重大！现在此仓库可作为插件使用](#section6) |
+| [2024.04.10](#section7) |
+| [2024.04.09](#section8) |
+| [Euler Smea Dyn Sampler](#section9) |
+| [Effect 效果](#section10) |
+| [how to use(This has become outdated, but it will be retained) 如何使用（已经过期，依然保留，仅做参考）](#section11) |
+| [The technical principles 技术报告](#section12) |
+| [Contact the author 联系作者](#section13) |
+
+
+<a id="section1"></a>
+## 2024.05.09 Add Euler Negative And Euler dy Negtive
+
+新增两个采样器，Euler Negative 和 Euler dy Negtive。我不会说它们效果比别的好，因为没有理论依据。不过在实践中我很喜欢它们。
+
+在SDXL表现更好一些，但在SD1.5使用效果也不差
+
+我得去稍微进修一下关于AI的知识，目前这种纯粹依靠灵感和实践的方案过于自由。
+
+以下是关于它们的测试：
+
+Two new samplers have been added, Euler Negative and Euler dy Negative. I won't claim they perform better than others because there's no theoretical basis for it. However, in practice, I quite like them.
+
+They perform slightly better in SDXL, but their performance in SD1.5 is also decent.
+
+I need to brush up on my professional knowledge of AI. Currently, relying solely on intuition and practice feels too unrestricted.
+
+Below are the test results for them:
+
+**768x768， model meinaMixV11**
+![xyz_grid-0005-1234-1girl,heart hands,river,cherry blossoms,hair flower,hair ribbon,cat ears,animal ear fluff,blue eyes,grey hair,short hair,bangs,h](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/04131413-0173-4afc-91dc-d5ac42d0560c)
+
+**832x1216，model kohaku-xl-epsilon**
+![xyz_grid-0007-4286407380-1girl,_(midori _(blue archive_)_1 1),blue archive,_ciloranko,lobelia _(saclia_),(konya karasue_0 9),wanke,(jiu ye sang_1 1),(rum](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/c237d930-cb00-4142-9b46-faeb59c4faba)
+![xyz_grid-0008-3096426698-1girl,_mayano top gun _(umamusume_),umamusume,_shiro9jira,ciloranko,ask _(askzy_),(tianliang duohe fangdongye_0 8),_(solo_1 2),(](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/6d47de71-ceca-4091-8dac-3c59bb1aee1e)
+![xyz_grid-0016-789654357-1girl,_gotoh hitori,bocchi the rock!,_ciloranko,maccha _(mochancc_),lobelia _(saclia_),migolu,ask _(askzy_),wanke,(jiu ye sang_1](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/f46be012-2b07-4408-9449-b5a20769ebb4)
+
+**832x1216，model animegineV30**
+![xyz_grid-0014-3532334-1girl,_vivlos _(umamusume_),umamusume,_ciloranko,maccha _(mochancc_),lobelia _(saclia_),migolu,ask _(askzy_),wanke,(jiu ye sang_](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/92bd48bf-fe30-45b6-9014-5f98bd3622d7)
+
+**Please Note**
+
+目前插件有一些小bug，会使得人物在画面中的占比变小，就像这样：
+
+The current plugin has a few minor bugs that cause the characters to shrink in the frame, like this:
+
+![xyz_grid-0011-3096426698-1girl,_mayano top gun _(umamusume_),umamusume,_shiro9jira,ciloranko,ask _(askzy_),(tianliang duohe fangdongye_0 8),_(solo_1 2),(](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/41d067ec-7e49-413c-bc4a-9e8e8b173140)
+![xyz_grid-0013-3096426698-1girl,_mayano top gun _(umamusume_),umamusume,_shiro9jira,ciloranko,ask _(askzy_),(tianliang duohe fangdongye_0 8),_(solo_1 2),(](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/80c1481a-d5a6-40c1-96a6-665f215c7b39)
+
+所以你可以采用其他方案，例如修改源码的方案去添加这两个采样器，请参考：[How to use](#section11)
+
+So, you can consider alternative solutions, such as modifying the source code to add these two samplers. Please refer to:[How to use](#section11)
+
+<a id="section2"></a>
+## 2024.04.24
+
+简单分析Dy Step的原理
+
+最近我尝试在https://civitai.com/models/399873/kohaku-xl-epsilon上测试了Euler Dy，效果不尽人意（但在ang3以及pony系列效果不错）。因此我咨询了作者，得到的回复是“模型没有使用任何低分辨率的图片进行训练”。我想这就是原因所在，Euler Dy将图片放在一个小的尺度上，让去噪工作来到模型的舒适区，并给予一个参考。尤其在SD1.5，Euler Dy确保图像始终处于模型的舒适区域。
+
+而在本次的SDXL模型中，它几乎遗忘了如何在小尺度上生成图片。所以，Dy Step的改进方向已经变得很明显：寻找到SDXL模型的舒适区域，并让采样器在SDXL的舒适区工作。
+
+同时我也写了几个其他��采样器，效果平平，达不到Dy Step的效果。如果有人想试试它们，请在评论区留言。
+
+Recently, I attempted to analyze the principle of Dy Step on https://civitai.com/models/399873/kohaku-xl-epsilon using Euler Dy. The results were unsatisfactory (although they performed well on ang3 and pony series). Therefore, I consulted the author and received the response that "the model did not use any low-resolution images for training." I believe this is the reason why. Euler Dy places images on a small scale, allowing denoising to operate within the model's comfort zone and providing a reference. Especially in SD1.5, Euler Dy ensures that the image always remains within the model's comfort zone.
+
+However, in the current SDXL model, it has almost forgotten how to generate images on a small scale. Therefore, the direction for improving Dy Step has become apparent: to find the comfort zone of the SDXL model and enable the sampler to work within the comfort zone of SDXL.
+
+I've also written a few other samplers, but their performance is mediocre and doesn't match up to Dy Step's effectiveness. If anyone wants to try them out, please leave a comment in the discussions.
+
+<a id="section3"></a>
+## 2024.04.18 Stage Technical Report
+
+阶段性技术报告报告。
+
+这些日子里，我尝试了超过二十种策略，但采样器的质量总是优于euler a却差于euler dy，所以暂时还不能发布euler dy a。我必须承认这和nai3的dyn是不同的东西。我依旧会长期维护这个项目，并为了新的采样方法努力，同时尽可能降低ai的算力需求。
+
+Stage Technical Report
+
+In these days, I have attempted over twenty strategies, but the quality of the sampler is always better than Euler A yet worse than Euler DY. Therefore, I cannot release Euler DY A for the time being. I must acknowledge that this is different from NAI3's DYN. I will continue to maintain this project in the long term and work towards developing new sampling methods while trying to minimize the AI's computing power requirements as much as possible.
+
+![xyz_grid-0036-1234-1girl,heart hands,river,cherry blossoms,hair flower,hair ribbon,cat ears,animal ear fluff,blue eyes,grey hair,short hair,bangs,h](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/32618333-0228-472e-bfbe-6c1a2e84ae96)
+
+
+<a id="section4"></a>
+## 2024.04.15 Compatible with Stable Cascade Models
+
+Makes dy_step respect original channel count, making it compatible with Stable Cascade models.
+
+使dy_step遵循原本的通道数，使其与Stable Cascade模型相适应。
+
+<a id="section5"></a>
+## 2024.04.13 Fix Bug In ComfyUI
+
+Change code for ComfyUI import. This will fix the overwrite error that occurs in ComfyUI when other extensions use `scripts` as the import folder (I really hope ComfyUI will standardize its interfaces and version dependencies).
+
+P.S.You may find some commits with no means, that because I am not familiar with Github, and try times. So don't care.
+
+更改代码，用于ComfyUI导入。这将修复在ComfyUI中存在其他插件时，若其他插件将`scripts`作为导入文件夹时引起的覆盖错误。（真希望ComfyUI能规范一下它的接口和版本依赖）。
+
+P.S.你可能会发现一些无意义的提交，这是因为我不熟悉Github的使用，并且尝试了几次。别在意。
+
+<a id="section6"></a>
+## 2024.04.11 Important! This repo can be use as a extension!
+
+Thanks for @pamparamm, his selfless work has been a great help.
+
+Now this sampler can be use as a extension for **ComfyUI** and **WebUI from Automatic1111**.
+
+The inpainting bug will be fixed.(**At least doesn't throw any exceptions.**)
+
+Thanks again.
+
+Another extension from @licyk , in repo: https://github.com/licyk/advanced_euler_sampler_extension **suitable for 1.8 version**
+
+It's also useful, and thanks hard efforts from licky, too.
+
+In the future, I will work on making dy step compatible with more samplers (such as the DPM series).
+
+感谢 @pamparamm，他的无私工作帮助很大。
+
+现在，这个采样器可以作为 **ComfyUI** 和 **Automatic1111 的 WebUI** 的扩展来使用。
+
+修复了inpainting的bug。（**至少不再抛出异常。**）
+
+再次感谢。
+
+另一个拓展来自@licyk，位于： https://github.com/licyk/advanced_euler_sampler_extension **适用于1.8**
+
+也同样很好用， 同样感谢licyk的辛勤努力。
+
+之后我会想办法让dy step适配更多采样器（例如dpm系列）。
+
+<a id="section7"></a>
+## 2024.04.10
+
+![image](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/aa3dd88a-5760-4589-857c-5717a3253ea0)
+
+Find a way to avoid errors during inpaint and extensions. 
+
+**Please note that this is just a temporary solution and doesn't actually resolve the issue.It will try to use Euler method if error occurs.**
+
+P.S.I trying to fix it……but all methods seems doesn't work.I've working for it over 36 hours.
+
+**Suggestions from anyone are welcome**. 
+
+I need to take a short break and prepare for my other project.*<== A mobile phone app base on flutter, using for TRPG.(No worries, I don't mean I will give up this project,also not about diverting traffic either. LOL.)*
+
+想了个办法避免在局部重绘以及拓展中的报错。
+
+**请注意，这只是一个临时解决方案，实际上并没有解决问题。如果出现错误，它将尝试使用欧拉方法。**
+
+我努力尝试修复……但所有方案都不起作用。我已经连续工作了36小时以上。**欢迎任何人提出建议。**
+，
+我需要稍微休息一下，并且为我的其他项目做准备。*<== 一个基于Flutter的手机应用，用于TRPG。（别担心，我不是说我要放弃这个项目，也不是引流。）*
+
+<a id="section8"></a>
+## 2024.04.09
+
+Add `__init__.py` for ComfyUI. Thanks for CapsAdmin. I don't use ComfyUI so I can't tell you how to add it, sorry.
+
+为ComfyUI增加`__init__.py`  感谢CapsAdmin  我不用ComfyUI所以我没法告诉你怎么添加它，抱歉
+
+<a id="section9"></a>
+## Euler Smea Dyn Sampler
+
+A sampling method based on Euler's approach, designed to generate superior imagery.
+
+The SMEA sampler can significantly mitigate the structural and limb collapse that occurs when generating large images, and to a great extent, it can produce superior hand depictions (not perfect, but better than existing sampling methods).
+
+The SMEA sampler is designed to accommodate the majority of image sizes, with particularly outstanding performance on larger images. It also supports the generation of images in unconventional sizes that lack sufficient training data (for example, running 512x512 in SDXL, 823x1216 in SD1.5, as well as 640x960, etc.).
+
+The SMEA sampler performs very well in SD1.5, but the effects are not as pronounced in SDXL.
+
+In terms of computational resource consumption, the Euler dy is approximately equivalent to the Euler a, while the Euler SMEA Dy sampler will consume more computational resources, approximately 1.25 times more.
+
+一种基于Euler的采样方法，旨在生成更好的图片
+
+Dyn采样器可以很大程度上避免出大图时的结构、肢体崩坏,能很大程度得到更优秀的手部（不完美但比已有采样方法更好）
+
+Smea采样器理论上将增加图片的细节（**无法达到Nai3让图片闪闪发光的效果**）
+
+适配绝大多数图片尺寸，在大图的效果尤其优秀，支持缺乏训练的异种尺寸（例如在sdxl跑512x512,在sd1.5跑823x1216,以及640x960等）
+
+在SD1.5效果很好，在SDXL效果不明显。
+
+计算资源消耗：Euler dy将约等于euler a, 而euler smea dy将消耗更多计算资源（约1.25倍）
+
+<a id="section10"></a>
+## Effect
+**SD1.5，测试模型AnythingV5-Prt-RE，测试姿势Heart Hand,一个容易出坏手的姿势**
+
+**SD1.5: Testing the AnythingV5-Prt-RE model with the Heart Hand pose often results in distorted hand positions.**
+
+768x768,without Lora：
+![xyz_grid-0049-1234-masterpiece,best quality,highres,1girl,heart hands,river,cherry blossoms,hair flower,hair ribbon,cat ears,animal ear fluff,blue](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/c13dcf8b-b9da-4624-9a04-a1488e850647)
+768x768,with Lora：
+![xyz_grid-0048-1234-_lora_manhattan_cafe_loha-000008_0 75_,manhattan cafe _(umamusume_),black choker,long sleeves,collared shirt,yellow necktie,blac](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/a60d7395-9ebd-4505-9ad7-3f0de0944bb8)
+832x1216,without lora:
+![xyz_grid-0046-1234-1girl,heart hands,river,cherry blossoms,hair flower,hair ribbon,cat ears,animal ear fluff,blue eyes,grey hair,short hair,bangs,h](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/9d6024f1-e9fa-42a0-841b-5a5b18089fd8)
+832x1216,with Lora:
+![xyz_grid-0047-1234-_lora_manhattan_cafe_loha-000008_0 75_,manhattan cafe _(umamusume_),black choker,long sleeves,collared shirt,yellow necktie,blac](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/3432dafb-4ebb-43ae-8c62-a4c4c168ce63)
+
+**SDXL,测试模型animagineXLV31,测试姿势也是手部姿势**
+
+**SDXL: Testing animagineXLV31 model with hand poses.**
+
+768x768:
+![xyz_grid-0019-114-1girl,manhattan cafe _(umamusume_),umamusume,heart hands,masterpiece,best quality,highres,](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/6883ace1-3712-4cd1-b974-4f0d1ed41bc2)
+832x1216:
+![xyz_grid-0018-114514-1girl,manhattan cafe _(umamusume_),umamusume,heart hands,masterpiece,best quality,highres,](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/9424ef70-a54f-454c-bf03-e543562367fc)
+![xyz_grid-0019-114515-1girl,manhattan cafe _(umamusume_),umamusume,finger_on_trigger,upper body,masterpiece,best quality,highres,](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/2f9d1a68-3a28-47e5-a1a8-ce14e2ad4563)
+
+<a id="section11"></a>
+## how to use(This has become outdated, but it will be retained)
+
+**step.1:** 打开`sd-webui-aki-v4.6\repositories\k-diffusion\k_diffusion`文件夹，打开其中的`sampling.py`文件（可以用记事本打开，称为文件1）
+
+**Step 1:** Navigate to the `k_diffusion` folder within the `sd-webui-aki-v4.6\repositories\k-diffusion` directory and open the `sampling.py` file within it (this can be done using a text editor like Notepad, which will be referred to as File 1).
+![QQ截图20240408193751](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/482d9647-f6d7-458e-a680-3c5a0c5fad9c)
+
+**step.2:** 复制本仓库中的`sampling.py`中的所有内容并粘贴到文件1末尾
+
+**Step 2:** Copy the entire content from the `sampling.py` file in the current repository and paste it at the end of File 1.
+![image](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/2b48db77-2d44-410e-afe7-81f04ede34ce)
+(To present the complete picture, I have utilized PyTorch's abbreviation feature.)
+
+**Step 3:** Open the `sd_samplers_kdiffusion.py` file located in the `sd-webui-aki-v4.6\modules` directory (refer to this as File 2).
+![image](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/e014a51f-7d8a-49ee-ba3d-ec5d338f51d1)
+
+**Step 4:** Copy the following two lines from this repository:
+![QQ截图20240408192923](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/440f522a-1acb-4d78-bebe-b75c7b969adb)
+
+Paste them into File 2:
+![image](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/782722bf-713c-41dc-a7ec-669419423ae5)
+
+**Step 5:** Restart the webui, and you will see:
+![image](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler/assets/66173435/15abbf9e-4d4d-4325-9223-1506f08f25cc)
+
+现在你就可以使用它们了。在图生图中可能有一些bug，欢迎向我汇报（请带上截图/报错声明）
+
+Now you can start using them. There may be some bugs in the image generation process, and I welcome you to report any issues to me (please provide screenshots or error statements).
+
+<a id="section12"></a>
+## The technical principles
+
+简单地讲，dyn方法有规律地取出图片中的一部分，去噪后加回原图。在理论上这应当等同于euler a，但其加噪环节被替代为有引导的噪声。
+
+而smea方法将图片潜空间放大再压缩回原本的大小，这增加了图片的可能性。很抱歉我没能实现Nai3中smea让图片微微发光的效果。
+
+一点忠告：不要相信pytorch的插值放大和缩小方法，不会对改善图像带来任何帮助。同时用有条件引导取代随机噪声也是有希望的道路。
+
+In simple terms, the dyn method regularly extracts a portion of the image, denoises it, and then adds it back to the original image. Theoretically, this should be equivalent to the Euler A method, but its noise addition step is replaced with guided noise.
+
+The SMEA method enlarges the image's latent space and then compresses it back to its original dimensions, thereby increasing the range of possible image variations. I apologize that I was unable to achieve the subtle glowing effect in Nai3 with the SMEA method.
+
+A piece of advice: Do not trust PyTorch's interpolation methods for enlarging and shrinking images; they will not contribute to improving image quality. Additionally, replacing random noise with conditional guidance is also a promising path forward.
+
+<a id="section13"></a>
+## Contact the author
+
+Email:872324454@qq.com
+
+Bilibili:星河主炮发射