Removed as now part of LoadImage node

upload-image/notes.txt

@@ -1,35 +0,0 @@
-This will embed the selected image into the generated images which probably isnt desired
-Havent checked if there is a better way to filter these out.
-Current fix for this is to add this to execution.py:
-
-def prune_prompt(prompt):
-    pruned_prompt = {}
-    for n in prompt:
-        class_type = prompt[n]['class_type']
-        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-        valid_inputs = class_def.INPUT_TYPES()
-        pruned_prompt[n] = copy.deepcopy(prompt[n])
-        
-        if "inputs" in pruned_prompt[n]:
-            for x in pruned_prompt[n]["inputs"]:
-                input_type = None
-                if ("required" in valid_inputs and x in valid_inputs["required"]):
-                    input_type = valid_inputs["required"][x]
-                elif ("optional" in valid_inputs and x in valid_inputs["optional"]):
-                    input_type = valid_inputs["optional"][x]
-
-                if input_type is not None and input_type[0] == "B64IMAGE":
-                    pruned_prompt[n]["inputs"][x] = None
-
-    return pruned_prompt
-
-
-And call that from 
-            if h[x] == "PROMPT":
-                input_data_all[x] = prune_prompt(prompt)
-                
-                
-It also requires increasing the maximum request size on the API in server.py 
-    self.app = web.Application(client_max_size=20971520)
-    
-Or some other sensible limit

upload-image/uploadImage.js

@@ -1,54 +0,0 @@
-import { app } from "/scripts/app.js";
-
-// Adds a new UploadImage node
-
-const toBase64 = (file) =>
-	new Promise((resolve, reject) => {
-		const reader = new FileReader();
-		reader.readAsDataURL(file);
-		reader.onload = () => resolve(reader.result);
-		reader.onerror = (error) => reject(error);
-	});
-
-app.registerExtension({
-	name: "Comfy.UploadImage",
-	async getCustomWidgets() {
-		return {
-			B64IMAGE(node) {
-				let uploadWidget;
-				const fileInput = document.createElement("input");
-
-				Object.assign(fileInput, {
-					type: "file",
-					accept: "image/jpeg,image/png",
-					style: "display: none",
-					onchange: () => {
-						if (fileInput.files.length) {
-							const img = new Image();
-							img.onload = () => {
-								node.imgs = [img];
-							};
-							toBase64(fileInput.files[0]).then((d) => {
-								img.src = d;
-							});
-						}
-					},
-				});
-				document.body.append(fileInput);
-
-				uploadWidget = node.addWidget("button", "image", "image", () => {
-					fileInput.click();
-				});
-
-				uploadWidget.serializeValue = () => {
-					if(node.imgs && node.imgs.length) {
-						return node.imgs[0].src;
-					}
-					return null;
-				};
-
-				return { widget: uploadWidget };
-			},
-		};
-	},
-});

upload-image/upload_image.py

@@ -1,35 +0,0 @@
-from PIL import Image
-import numpy as np
-import torch
-
-
-class UploadImage:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                {"image": ("B64IMAGE",)},
-                }
-
-    CATEGORY = "image"
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "load_image"
-
-    def load_image(self, image):
-        from io import BytesIO
-        import re
-        import base64
-        if image.startswith("data:image/"):
-            image_data = re.sub('^data:image/.+;base64,', '', image)
-            i = Image.open(BytesIO(base64.b64decode(image_data)))
-        else:
-            raise Exception("Invalid image data")
-
-        image = i.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        return (image,)
-
-NODE_CLASS_MAPPINGS = {
-    "UploadImage": UploadImage,
-}