Merge branch 'sipherxyz:main' into main

2025-07-06 20:10:11 +05:00
parent efaa2dcd6c 0d7bcc5e23
commit 71722e4c7f
3 changed files with 80 additions and 78 deletions
@@ -9,12 +9,13 @@ import comfy.utils

 from ...utils import ensure_package, tensor2pil, pil2tensor

-folder_paths.folder_names_and_paths["sams"] = (
-    [
-        os.path.join(folder_paths.models_dir, "sams"),
-    ],
-    folder_paths.supported_pt_extensions,
-)
+if "sams" not in folder_paths.folder_names_and_paths:
+    folder_paths.folder_names_and_paths["sams"] = (
+        [
+            os.path.join(folder_paths.models_dir, "sams"),
+        ],
+        folder_paths.supported_pt_extensions,
+    )

 gpu = model_management.get_torch_device()
 cpu = torch.device("cpu")
@@ -5,7 +5,7 @@ import torch
 import base64
 import random
 import requests
-from typing import List, Dict, Tuple
+from typing import List, Dict, Tuple, Optional

 from PIL import Image, ImageOps, ImageFilter
 import numpy as np
@@ -80,7 +80,7 @@ def prepare_image_for_preview(image: Image.Image, output_dir: str, prefix=None):

 def load_images_from_url(urls: List[str], keep_alpha_channel=False):
    images: List[Image.Image] = []
-    masks: List[Image.Image] = []
+    masks: List[Optional[Image.Image]] = []

    for url in urls:
        if url.startswith("data:image/"):
@@ -158,7 +158,7 @@ class UtilLoadImageFromUrl:
        self.filename_prefix = "TempImageFromUrl"

    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("STRING", {
@@ -188,58 +188,59 @@ class UtilLoadImageFromUrl:

    def load_image(self, image: str, keep_alpha_channel=False, output_mode=False):
        urls = image.strip().split("\n")
-        images, masks = load_images_from_url(urls, keep_alpha_channel)
-        if len(images) == 0:
-            image = torch.zeros((1, 64, 64, 3), dtype=torch.float32, device="cpu")
-            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
-            images = [tensor2pil(image)]
-            masks = [tensor2pil(mask, mode="L")]
+        pil_images, pil_masks = load_images_from_url(urls, keep_alpha_channel)
+        has_image = len(pil_images) > 0
+        if not has_image:
+            i = torch.zeros((1, 64, 64, 3), dtype=torch.float32, device="cpu")
+            m = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            pil_images = [tensor2pil(i)]
+            pil_masks = [tensor2pil(m, mode="L")]

        previews = []
-        np_images = []
-        np_masks = []
+        np_images: list[torch.Tensor] = []
+        np_masks: list[torch.Tensor] = []

-        for image, mask in zip(images, masks):
-            if mask is not None:
-                preview_image = Image.new("RGB", image.size)
-                preview_image.paste(image, (0, 0))
-                preview_image.putalpha(mask)
+        for pil_image, pil_mask in zip(pil_images, pil_masks):
+            if pil_mask is not None:
+                preview_image = Image.new("RGB", pil_image.size)
+                preview_image.paste(pil_image, (0, 0))
+                preview_image.putalpha(pil_mask)
            else:
-                preview_image = image
+                preview_image = pil_image

            previews.append(prepare_image_for_preview(preview_image, self.output_dir, self.filename_prefix))

-            image = pil2tensor(image)
-            if mask:
-                mask = np.array(mask).astype(np.float32) / 255.0
-                mask = 1.0 - torch.from_numpy(mask)
+            np_image = pil2tensor(pil_image)
+            if pil_mask:
+                np_mask = np.array(pil_mask).astype(np.float32) / 255.0
+                np_mask = 1.0 - torch.from_numpy(np_mask)
            else:
-                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+                np_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")

-            np_images.append(image)
-            np_masks.append(mask.unsqueeze(0))
+            np_images.append(np_image)
+            np_masks.append(np_mask.unsqueeze(0))

        if output_mode:
-            result = (np_images, np_masks, True)
+            result = (np_images, np_masks, has_image)
        else:
            has_size_mismatch = False
            if len(np_images) > 1:
-                for image in np_images[1:]:
-                    if image.shape[1] != np_images[0].shape[1] or image.shape[2] != np_images[0].shape[2]:
+                for np_image in np_images[1:]:
+                    if np_image.shape[1] != np_images[0].shape[1] or np_image.shape[2] != np_images[0].shape[2]:
                        has_size_mismatch = True
                        break

            if has_size_mismatch:
                raise Exception("To output as batch, images must have the same size. Use list output mode instead.")

-            result = ([torch.cat(np_images)], [torch.cat(np_masks)], True)
+            result = ([torch.cat(np_images)], [torch.cat(np_masks)], has_image)

        return {"ui": {"images": previews}, "result": result}


 class UtilLoadImageAsMaskFromUrl(UtilLoadImageFromUrl):
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("STRING", {
@@ -267,11 +268,11 @@ class UtilLoadImageAsMaskFromUrl(UtilLoadImageFromUrl):
            image = url

        urls = image.strip().split("\n")
-        images, alphas = load_images_from_url(urls, True)
+        pil_images, pil_alphas = load_images_from_url(urls, True)

        masks: List[torch.Tensor] = []

-        for img, alpha in zip(images, alphas):
+        for img, alpha in zip(pil_images, pil_alphas):
            if channel == "alpha":
                mask = alpha
            elif channel == "red":
@@ -304,7 +305,7 @@ class UtilLoadImageAsMaskFromUrl(UtilLoadImageFromUrl):

 class UtilLoadJsonFromText:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "data": (
@@ -324,7 +325,7 @@ class UtilLoadJsonFromText:

 class UtilLoadJsonFromUrl:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "url": ("STRING", {"default": ""}),
@@ -352,7 +353,7 @@ class UtilLoadJsonFromUrl:

 class UtilGetObjectFromJson:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "json": ("JSON",),
@@ -371,7 +372,7 @@ class UtilGetObjectFromJson:

 class UtilGetTextFromJson:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "json": ("JSON",),
@@ -390,7 +391,7 @@ class UtilGetTextFromJson:

 class UtilGetFloatFromJson:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "json": ("JSON",),
@@ -409,7 +410,7 @@ class UtilGetFloatFromJson:

 class UtilGetIntFromJson:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "json": ("JSON",),
@@ -428,7 +429,7 @@ class UtilGetIntFromJson:

 class UtilGetBoolFromJson:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "json": ("JSON",),
@@ -447,7 +448,7 @@ class UtilGetBoolFromJson:

 class UtilRandomInt:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "min": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
@@ -460,7 +461,7 @@ class UtilRandomInt:
    FUNCTION = "random_int"

    @classmethod
-    def IS_CHANGED(s, *args, **kwargs):
+    def IS_CHANGED(cls, *args, **kwargs):
        return torch.rand(1).item()

    def random_int(self, min: int, max: int):
@@ -470,7 +471,7 @@ class UtilRandomInt:

 class UtilRandomFloat:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "min": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0xFFFFFFFFFFFFFFFF}),
@@ -483,7 +484,7 @@ class UtilRandomFloat:
    FUNCTION = "random_float"

    @classmethod
-    def IS_CHANGED(s, *args, **kwargs):
+    def IS_CHANGED(cls, *args, **kwargs):
        return torch.rand(1).item()

    def random_float(self, min: float, max: float):
@@ -493,7 +494,7 @@ class UtilRandomFloat:

 class UtilStringToInt:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {"string": ("STRING", {"default": "0"})},
        }
@@ -508,7 +509,7 @@ class UtilStringToInt:

 class UtilStringToNumber:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "string": ("STRING", {"default": "0"}),
@@ -533,7 +534,7 @@ class UtilStringToNumber:

 class UtilNumberScaler:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "min": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 0xFFFFFFFFFFFFFFFF}),
@@ -555,7 +556,7 @@ class UtilNumberScaler:

 class UtilBooleanPrimitive:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "value": ("BOOLEAN", {"default": False}),
@@ -576,7 +577,7 @@ class UtilBooleanPrimitive:

 class UtilTextSwitchCase:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "switch_cases": (
@@ -625,7 +626,7 @@ class UtilTextSwitchCase:

 class UtilImageMuxer:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "image_1": ("IMAGE",),
@@ -646,7 +647,7 @@ class UtilImageMuxer:

 class UtilSDXLAspectRatioSelector:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "aspect_ratio": (
@@ -709,7 +710,7 @@ class UtilSDXLAspectRatioSelector:

 class UtilAspectRatioSelector(UtilSDXLAspectRatioSelector):
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "aspect_ratio": (
@@ -739,7 +740,7 @@ class UtilAspectRatioSelector(UtilSDXLAspectRatioSelector):

 class UtilDependenciesEdit:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "dependencies": ("DEPENDENCIES",),
@@ -828,7 +829,7 @@ class UtilImageScaleDown:
    crop_methods = ["disabled", "center"]

    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -840,7 +841,7 @@ class UtilImageScaleDown:
                    "INT",
                    {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1},
                ),
-                "crop": (s.crop_methods,),
+                "crop": (cls.crop_methods,),
            }
        }

@@ -867,7 +868,7 @@ class UtilImageScaleDown:
        results = []
        for image in s:
            img = tensor2pil(image).convert("RGB")
-            img = img.resize((width, height), Image.LANCZOS)
+            img = img.resize((width, height), Image.Resampling.LANCZOS)
            results.append(pil2tensor(img))

        return (torch.cat(results, dim=0),)
@@ -875,7 +876,7 @@ class UtilImageScaleDown:

 class UtilImageScaleDownBy(UtilImageScaleDown):
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -900,7 +901,7 @@ class UtilImageScaleDownBy(UtilImageScaleDown):

 class UtilImageScaleDownToSize(UtilImageScaleDownBy):
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -928,7 +929,7 @@ class UtilImageScaleDownToSize(UtilImageScaleDownBy):

 class UtilImageScaleToTotalPixels(UtilImageScaleDownBy, ImageUpscaleWithModel):
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -969,7 +970,7 @@ class UtilImageScaleToTotalPixels(UtilImageScaleDownBy, ImageUpscaleWithModel):

 class UtilImageAlphaComposite:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "image_1": ("IMAGE",),
@@ -1001,7 +1002,7 @@ class UtilImageAlphaComposite:

 class UtilImageGaussianBlur:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -1025,7 +1026,7 @@ class UtilImageGaussianBlur:

 class UtilImageExtractChannel:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -1055,7 +1056,7 @@ class UtilImageExtractChannel:

 class UtilImageApplyChannel:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -1114,13 +1115,13 @@ class UtillQRCodeGenerator:
        import qrcode

        if error_correction == "L":
-            error_level = qrcode.constants.ERROR_CORRECT_L
+            error_level = qrcode.ERROR_CORRECT_L
        elif error_correction == "M":
-            error_level = qrcode.constants.ERROR_CORRECT_M
+            error_level = qrcode.ERROR_CORRECT_M
        elif error_correction == "Q":
-            error_level = qrcode.constants.ERROR_CORRECT_Q
+            error_level = qrcode.ERROR_CORRECT_Q
        else:
-            error_level = qrcode.constants.ERROR_CORRECT_H
+            error_level = qrcode.ERROR_CORRECT_H

        qr = qrcode.QRCode(version=qr_version, error_correction=error_level, box_size=box_size, border=border)
        qr.add_data(text)
@@ -1133,7 +1134,7 @@ class UtillQRCodeGenerator:

 class UtilRepeatImages:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
@@ -1151,7 +1152,7 @@ class UtilRepeatImages:

 class UtilSeedSelector:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "mode": ("BOOLEAN", {"default": True, "label_on": "random", "label_off": "fixed"}),
@@ -1174,7 +1175,7 @@ class UtilSeedSelector:

 class UtilCheckpointSelector:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
@@ -1187,7 +1188,7 @@ class UtilCheckpointSelector:
    FUNCTION = "get_ckpt_name"

    @classmethod
-    def IS_CHANGED(s, *args, **kwargs):
+    def IS_CHANGED(cls, *args, **kwargs):
        return torch.rand(1).item()

    def get_ckpt_name(self, ckpt_name):
@@ -1196,7 +1197,7 @@ class UtilCheckpointSelector:

 class UtilModelMerge:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "model1": ("MODEL",),
@@ -1228,7 +1229,7 @@ class UtilModelMerge:

 class UtilTextRandomMultiline:
    @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
        return {
            "required": {
                "text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-art-venture"
 description = "A comprehensive set of custom nodes for ComfyUI, focusing on utilities for image processing, JSON manipulation, model operations and working with object via URLs"
-version = "1.0.7"
+version = "1.0.8"
 license = "LICENSE"
 dependencies = ["timm==0.6.13", "transformers", "fairscale", "pycocoevalcap", "opencv-python", "qrcode[pil]", "pytorch_lightning", "kornia", "pydantic", "segment_anything", "boto3>=1.34.101"]