initial checkin

KY_SaveImageToPath.py

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+import os
+import folder_paths
+import json
+import torch
+from PIL import Image
+import numpy as np
+import json
+
+from nodes import SaveImage
+
+
+class KY_SaveImageToPath(SaveImage):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"IMG": ("IMAGE",),
+                     "path": ("STRING", {"default": "ComfyUI.png"})},
+                "hidden":
+                    {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}
+                }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "save_image_to_path"
+    OUTPUT_NODE = True
+    CATEGORY = "KY Nodes/Image"
+
+    def save_image_to_path(self, IMG, path="ComfyUI.png", prompt=None, extra_pnginfo=None):
+        #results = self.save_images(images, filename_prefix, prompt, extra_pnginfo)
+        saved_paths = []
+        #folder_structure = []
+        #folder_structure = json.loads(folder_structure)
+        base_directory = path
+        full_file_path = path
+        images = IMG
+
+        # Ensure base directory exists
+        #os.makedirs(base_directory, exist_ok=True)
+
+        for i, image in enumerate(images):
+            # Convert the image tensor to a PIL Image
+            img = Image.fromarray((image.cpu().numpy() * 255).astype(np.uint8))
+
+            # Create the full folder path based on the folder structure
+            #full_folder_path = self.create_folder_path(base_directory, [])
+            #os.makedirs(full_folder_path, exist_ok=True)
+
+            # Create the file name and ensure it doesn't overwrite existing files
+            #index = 0 + i
+            #while True:
+                #full_file_name = file_name_template.format(index=index)
+            #    full_file_path = path #os.path.join(full_folder_path, full_file_name)
+            #    if not os.path.exists(full_file_path):
+            #        break
+            #    index += 1
+
+            # Save the image
+            img.save(full_file_path)
+
+            # Save metadata if provided
+            #if metadata:
+            #    metadata_file_name = f"{os.path.splitext(full_file_name)[0]}_metadata.txt"
+            #    metadata_file_path = os.path.join(full_folder_path, metadata_file_name)
+            #    with open(metadata_file_path, 'w') as f:
+            #        f.write(metadata)
+
+            saved_paths.append(path)
+            break
+        #return (", ".join(saved_paths),)
+        #results = (", ".join(saved_paths))
+        return {
+            "ui": {
+                #"images": results['ui']['IMG']
+                "images": path
+            },
+            "result": (IMG,)
+        }
+    def create_folder_path(self, base_directory, folder_structure):
+        path = base_directory
+        for folder in folder_structure:
+            path = os.path.join(path, folder['name'])
+            for child in folder['children']:
+                path = self.create_folder_path(path, [child])
+        return path
+
+

__init__.py

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+from .advanced_lying_sigma_sampler import AdvancedLyingSigmaSamplerNode
+from .KY_SaveImageToPath import KY_SaveImageToPath
+
+# 将节点映射到NODE_CLASS_MAPPINGS和NODE_DISPLAY_NAME_MAPPINGS
+NODE_CLASS_MAPPINGS = {
+    "AdvancedLyingSigmaSampler": AdvancedLyingSigmaSamplerNode,
+    "KY_SaveImageToPath": KY_SaveImageToPath
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AdvancedLyingSigmaSampler": "Advanced Lying Sigma Sampler",
+    "KY_SaveImageToPath": "Save Image To target Path"
+}

advanced_lying_sigma_sampler.py

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+# Based on the concept from https://github.com/muerrilla/sd-webui-detail-daemon
+# Original code courtesy from https://github.com/blepping/
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+import inspect  
+
+from comfy.samplers import KSAMPLER
+import numpy as np
+
+if TYPE_CHECKING:
+    import torch
+
+def advanced_lying_sigma_sampler(
+    model: object,
+    x: torch.Tensor,
+    sigmas: torch.Tensor,
+    *,
+    sampler: object,
+    dishonesty_factor: float,
+    start_percent: float,
+    end_percent: float,
+    smooth_factor: float = 0.5,
+    **kwargs: dict,
+) -> torch.Tensor:
+    start_sigma = round(model.inner_model.inner_model.model_sampling.percent_to_sigma(start_percent), 4)
+    end_sigma = round(model.inner_model.inner_model.model_sampling.percent_to_sigma(end_percent), 4)
+
+    def model_wrapper(x: torch.Tensor, sigma: torch.Tensor, **extra_args: dict):
+        sigma_float = float(sigma.max().detach().cpu())
+        if end_sigma <= sigma_float <= start_sigma:
+            adjustment = dishonesty_factor * (0.5 * (1 - np.cos(smooth_factor * np.pi)))
+            sigma = sigma * (1.0 + adjustment)
+        return model(x, sigma, **extra_args)
+
+    for k in (
+        "inner_model",
+        "sigmas",
+    ):
+        if hasattr(model, k):
+            setattr(model_wrapper, k, getattr(model, k))
+
+    # Check if denoise_mask is supported by the sampler function
+    if 'denoise_mask' in inspect.signature(sampler.sampler_function).parameters:
+        return sampler.sampler_function(
+            model_wrapper,
+            x,
+            sigmas,
+            denoise_mask=kwargs.get('denoise_mask'),
+            **kwargs,
+            **sampler.extra_options,
+        )
+    else:
+        return sampler.sampler_function(
+            model_wrapper,
+            x,
+            sigmas,
+            **kwargs,
+            **sampler.extra_options,
+        )
+
+class AdvancedLyingSigmaSamplerNode:
+    DESCRIPTION = "高级sigma控制器，使用前最好关闭float rounding并重启comfui,以达到最佳控制效果|For advanced sigma controllers, it is best to turn off float rounding and restart comfui before use for optimal control."
+    CATEGORY = "KY Nodes/advanced_samplers"
+    RETURN_TYPES = ("SAMPLER",)
+    FUNCTION = "initialize"
+
+    @classmethod
+    def INPUT_TYPES(cls) -> dict:
+        return {
+            "required": {
+                "sampler": ("SAMPLER",),
+                "dishonesty_factor": (
+                    "FLOAT",
+                    {
+                        "default": -0.1,
+                        "min": -1.0,
+                        "max": 1.0,
+                        "step": 0.001,
+                        "tooltip": "默认值-0.1，一般来说这是一个较大值|Default value -0.1, which is generally a larger value."
+                    }
+                ),
+                "start_percent": (
+                    "FLOAT",
+                    {
+                        "default": 0.1,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.01,
+                        "tooltip": "bilibili@深深蓝hana."
+                    }
+                ),
+                "end_percent": (
+                    "FLOAT",
+                    {
+                        "default": 0.9,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.01,
+                        "tooltip": "bilibili@深深蓝hana."
+                    }
+                ),
+                "smooth_factor": (
+                    "FLOAT",
+                    {
+                        "default": 0.5,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.01,
+                        "tooltip": "bilibili@深深蓝hana."
+                    }
+                ),
+            }
+        }
+
+    @classmethod
+    def initialize(
+        cls,
+        sampler: object,
+        dishonesty_factor: float,
+        start_percent: float,
+        end_percent: float,
+        smooth_factor: float,
+    ) -> tuple:
+        return (
+            KSAMPLER(
+                advanced_lying_sigma_sampler,
+                extra_options={
+                    "sampler": sampler,
+                    "dishonesty_factor": dishonesty_factor,
+                    "start_percent": start_percent,
+                    "end_percent": end_percent,
+                    "smooth_factor": smooth_factor,
+                },
+            ),
+        )
+
+NODE_CLASS_MAPPINGS = {
+    "AdvancedLyingSigmaSampler": AdvancedLyingSigmaSamplerNode,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AdvancedLyingSigmaSampler": "Advanced Lying Sigma Sampler"
+}