Added Rainbow Scripts

color_output.txt

@@ -0,0 +1,5 @@
+MC1: #d1a443 - peru, MC2: #16b3d8 - darkturquoise, MC3: #c0e5ed - powderblue, MC4: #5f870f - olivedrab
+AN1: #b7d143 - yellowgreen, AN2: #d15d43 - indianred
+T1: #43d1a4 - mediumaquamarine, T2: #a443d1 - darkorchid
+C1: #4370d1 - royalblue, C2: #d83b16 - orangered
+MO1: #d1c4a6 - tan, MO2: #9ecdd8 - lightsteelblue, MO3: #e0ebed - lavender, MO4: #7b8763 - gray

comfy_runpod.py

@@ -10,7 +10,7 @@ from PIL import Image
 import base64
 import io
 
-
+from custom_scripts_for_nodes.Rainbow import extract_rainbow
 
 import runpod
 
@@ -64,22 +64,40 @@ def get_images(ws, prompt):
 
 
 def run_prompt(job):
-
+    
+    data = {'images':[],'rainbow':"None"}
+    
+    #Inferring from the Rainbow Script
+    image_string = job['input']['image_string']
     prompt_text = job["input"]["prompt"]
-    prompt = prompt_text
+    
-    ws = websocket.WebSocket()
+    if image_string != 'None': 
-    ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+        # Decode the base64 string into bytes
-    images = get_images(ws, prompt)
+        decoded_bytes = base64.b64decode(image_string)
-    data = {'images':[]}
+        # Convert the bytes to an in-memory file-like object using io.BytesIO
-    for node_id in images:
+        image_data = io.BytesIO(decoded_bytes)
-        for image_data in images[node_id]:
+        image = Image.open(image_data)
-            image = Image.open(io.BytesIO(image_data))
+        rnbw = extract_rainbow()
-            im_file = io.BytesIO()
+        rnbw_values = rnbw.main(image)
-            image.save(im_file, format="JPEG")
+        
-            im_bytes = im_file.getvalue()  # im_bytes: image in binary format.
+        data['rainbow'] = rnbw_values
-            im_b64 = base64.b64encode(im_bytes)
+    
-            im_b64 = str(im_b64) 
+    if prompt_text != "None":
-            data['images'].append(im_b64)
+        prompt = prompt_text
+        ws = websocket.WebSocket()
+        ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+        images = get_images(ws, prompt)
+        
+        
+        for node_id in images:
+            for image_data in images[node_id]:
+                image = Image.open(io.BytesIO(image_data))
+                im_file = io.BytesIO()
+                image.save(im_file, format="JPEG")
+                im_bytes = im_file.getvalue()  # im_bytes: image in binary format.
+                im_b64 = base64.b64encode(im_bytes)
+                im_b64 = str(im_b64) 
+                data['images'].append(im_b64)
     return data
     
 

custom_scripts_for_nodes/Rainbow.py

@@ -0,0 +1,135 @@
+from PIL import Image
+from sklearn.cluster import KMeans
+import numpy as np
+from scipy.spatial import KDTree
+import colorsys
+import webcolors
+
+
+
+class extract_rainbow():
+
+    def __init__(self):
+        pass
+
+    # Utility Functions
+    def rgb_to_hsv(self,rgb):
+        return colorsys.rgb_to_hsv(rgb[0]/255, rgb[1]/255, rgb[2]/255)
+
+    def hsv_to_rgb(self,hsv):
+        return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(hsv[0], hsv[1], hsv[2]))
+
+    def rgb_to_hex(self,rgb):
+        return '#{:02x}{:02x}{:02x}'.format(int(rgb[0]), int(rgb[1]), int(rgb[2]))
+
+    def hex_to_rgb(self,hex_color):
+        return webcolors.hex_to_rgb(hex_color)
+
+    def get_most_common_colors(self,image, n_colors=4):
+        image = image.resize((50, 50))    # Reduce the size
+        ar = np.asarray(image)
+        ar = ar.reshape(-1, 3)
+
+        kmeans = KMeans(n_clusters=n_colors)
+        kmeans.fit(ar)
+        
+        return [self.rgb_to_hex(tuple(map(int, center))) for center in kmeans.cluster_centers_]
+
+    def closest_color_name(self,requested_color):
+        min_colors = {}
+        for key, name in webcolors.CSS3_HEX_TO_NAMES.items():
+            r_c, g_c, b_c = webcolors.hex_to_rgb(key)
+            rd = (r_c - requested_color[0]) ** 2
+            gd = (g_c - requested_color[1]) ** 2
+            bd = (b_c - requested_color[2]) ** 2
+            min_colors[(rd + gd + bd)] = name
+        return min_colors[min(min_colors.keys())]
+
+    def get_color_name(self,hex_color):
+        try:
+            closest_name = actual_name = webcolors.hex_to_name(hex_color)
+        except ValueError:
+            closest_name = self.closest_color_name(self.hex_to_rgb(hex_color))
+            actual_name = None
+        return closest_name
+
+    def get_analogous_colors(self,hsv, angle=30):
+        analogous1_hue = (hsv[0] + angle/360) % 1
+        analogous2_hue = (hsv[0] - angle/360) % 1
+        analogous1 = self.rgb_to_hex(self.hsv_to_rgb((analogous1_hue, hsv[1], hsv[2])))
+        analogous2 = self.rgb_to_hex(self.hsv_to_rgb((analogous2_hue, hsv[1], hsv[2])))
+        return analogous1, analogous2
+
+    def get_triadic_colors(self,hsv):
+        triadic1_hue = (hsv[0] + 1/3) % 1
+        triadic2_hue = (hsv[0] + 2/3) % 1
+        triadic1 = self.rgb_to_hex(self.hsv_to_rgb((triadic1_hue, hsv[1], hsv[2])))
+        triadic2 = self.rgb_to_hex(self.hsv_to_rgb((triadic2_hue, hsv[1], hsv[2])))
+        return triadic1, triadic2
+
+    def get_complementary_color(self,hsv):
+        complementary_hue = (hsv[0] + 0.5) % 1
+        complementary = self.rgb_to_hex(self.hsv_to_rgb((complementary_hue, hsv[1], hsv[2])))
+        return complementary
+
+    def get_monochromatic_color(self,hsv):
+        # Here we are taking 3 saturation levels for a given color
+        monochromatic1 = self.rgb_to_hex(self.hsv_to_rgb((hsv[0], hsv[1]*0.3, hsv[2])))
+        monochromatic2 = self.rgb_to_hex(self.hsv_to_rgb((hsv[0], hsv[1]*0.5, hsv[2])))
+        monochromatic3 = self.rgb_to_hex(self.hsv_to_rgb((hsv[0], hsv[1]*0.7, hsv[2])))
+        return monochromatic1, monochromatic2, monochromatic3
+
+    def main(self,tensor):
+        # Load image and get most common colors
+        hex_colors = self.get_most_common_colors(image)
+
+        # Get color names and HSV
+        MC_hex = []
+        MC_names = []
+        MC_hsv = []
+
+        for hex_color in hex_colors:
+            MC_hex.append(hex_color)
+            MC_names.append(self.get_color_name(hex_color))
+            MC_hsv.append(self.rgb_to_hsv(self.hex_to_rgb(hex_color)))
+
+        # If MC1 color is black or very dark, use MC2 for calculations
+        dark_colors = ['black', 'darkslategrey', 'indigo', 'midnightblue', 'darkred']
+        if MC_names[0].lower() in dark_colors:
+            MC = MC_hex[1]
+            MC_name = MC_names[1]
+            MC_hsv = MC_hsv[1]
+        else:
+            MC = MC_hex[0]
+            MC_name = MC_names[0]
+            MC_hsv = MC_hsv[0]
+
+        # Calculate color schemes
+        AN1, AN2 = self.get_analogous_colors(MC_hsv)
+        T1, T2 = self.get_triadic_colors(MC_hsv)
+        C1 = self.get_complementary_color(MC_hsv)
+        C2 = self.get_complementary_color(self.rgb_to_hsv(self.hex_to_rgb(MC_hex[1])))  # Use MC2 for second complementary color
+
+        # Get monochromatic colors for each MC
+        MO = []
+        for color in MC_hex:
+            hsv = self.rgb_to_hsv(self.hex_to_rgb(color))
+            MO.append(self.get_monochromatic_color(hsv))
+
+        # Generate Output
+        output = f"MC1: {MC_hex[0]} - {MC_names[0]}, MC2: {MC_hex[1]} - {MC_names[1]}, MC3: {MC_hex[2]} - {MC_names[2]}, MC4: {MC_hex[3]} - {MC_names[3]}\n"
+        output += f"AN1: {AN1} - {self.get_color_name(AN1)}, AN2: {AN2} - {self.get_color_name(AN2)}\n"
+        output += f"T1: {T1} - {self.get_color_name(T1)}, T2: {T2} - {self.get_color_name(T2)}\n"
+        output += f"C1: {C1} - {self.get_color_name(C1)}, C2: {C2} - {self.get_color_name(C2)}\n"
+        output += f"MO1: {MO[0][0]} - {self.get_color_name(MO[0][0])}, MO2: {MO[1][0]} - {self.get_color_name(MO[1][0])}, MO3: {MO[2][0]} - {self.get_color_name(MO[2][0])}, MO4: {MO[3][0]} - {self.get_color_name(MO[3][0])}\n"
+
+        # Save to a text file
+        with open('color_output.txt', 'w') as f:
+            f.write(output)
+
+        return output
+        
+        
+if __name__ == '__main__':
+    rnbw = extract_rainbow()
+    rnbw.main()

custom_scripts_for_nodes/color_output.txt

@@ -0,0 +1,5 @@
+MC1: #9a5938 - sienna, MC2: #656771 - dimgray, MC3: #1e1413 - black, MC4: #d1ad91 - tan
+AN1: #9a8a38 - olivedrab, AN2: #9a3848 - brown
+T1: #389a59 - seagreen, T2: #59389a - darkslateblue
+C1: #38799a - steelblue, C2: #716f65 - dimgray
+MO1: #9a867d - gray, MO2: #6d6e71 - dimgray, MO3: #1e1b1b - black, MO4: #d1c6be - silver

custom_scripts_for_nodes/color_output_class.txt

@@ -0,0 +1,5 @@
+MC1: #1d1413 - black, MC2: #656771 - dimgray, MC3: #995838 - sienna, MC4: #d0ad90 - tan
+AN1: #696571 - dimgray, AN2: #656d71 - dimgray
+T1: #716567 - dimgray, T2: #677165 - dimgray
+C1: #716f65 - dimgray, C2: #716f65 - dimgray
+MO1: #1d1a1a - black, MO2: #6d6e71 - dimgray, MO3: #99867c - gray, MO4: #d0c5bd - silver

custom_scripts_for_nodes/color_output_orig.txt

@@ -0,0 +1,5 @@
+MC1: #d1ad91 - tan, MC2: #9a5938 - sienna, MC3: #1e1413 - black, MC4: #656771 - dimgray
+AN1: #d1cd91 - tan, AN2: #d19195 - rosybrown
+T1: #91d1ad - darkseagreen, T2: #ad91d1 - mediumpurple
+C1: #91b5d1 - lightsteelblue, C2: #38799a - steelblue
+MO1: #d1c6be - silver, MO2: #9a867d - gray, MO3: #1e1b1b - black, MO4: #6d6e71 - dimgray

requirement_our.txt

@@ -0,0 +1 @@
+webcolors==1.13

test_input.json

@@ -0,0 +1,6 @@
+{
+    "input": {
+        "prompt": {"3": {"inputs": {"seed": 156680208700286, "steps": 20, "cfg": 8.0, "sampler_name": "euler", "scheduler": "normal", "denoise": 1.0, "model": ["4", 0], "positive": ["6", 0], "negative": ["7", 0], "latent_image": ["5", 0]}, "class_type": "KSampler"}, "4": {"inputs": {"ckpt_name": "sd-v1-4.ckpt"}, "class_type": "CheckpointLoaderSimple"}, "5": {"inputs": {"width": 512, "height": 512, "batch_size": 1}, "class_type": "EmptyLatentImage"}, "6": {"inputs": {"text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,", "clip": ["4", 1]}, "class_type": "CLIPTextEncode"}, "7": {"inputs": {"text": "text, watermark", "clip": ["4", 1]}, "class_type": "CLIPTextEncode"}, "8": {"inputs": {"samples": ["3", 0], "vae": ["4", 2]}, "class_type": "VAEDecode"}, "9": {"inputs": {"filename_prefix": "ComfyUI", "images": ["8", 0]}, "class_type": "SaveImage"}},
+        "image_string" : "None"
+    }
+}