rework commit

.env

@@ -1 +0,0 @@
-DEEPINFRA_API_KEY = "YOUR API KEY HERE"

.gitignore

@@ -1 +1,2 @@
-venv/*
+venv/*
+config.cfg

.vscode/launch.json

@@ -11,14 +11,10 @@
             "program": "${file}",
             "console": "integratedTerminal",
             "args": [
-                "--n_per_country",
-                "2",
-                "--width",
-                "1024",
-                "--height",
-                "1024",
                 "--num_inference_steps",
-                "2"
+                "6",
+                "--rtf_file",
+                "./2030New.rtf"
             ]
         }
     ]

async_image_generator.py

@@ -1,190 +0,0 @@
-"""
-Author: Emil Mirzayev
-
-This script generates images based on prompts for different country groups using the DeepInfra API.
-It allows for customization of image parameters and provides cost estimation before execution.
-
-Features:
-- Asynchronous image generation for improved performance
-- Configurable number of images per country group
-- Customizable image dimensions and inference steps
-- Option to resize generated images
-- Cost estimation and user confirmation before execution
-- Saves generated images in separate folders for each country group
-
-Usage:
-python async_image_generator.py [--n_per_country N] [--width W] [--height H] 
-                                [--num_inference_steps S] [--model MODEL] [--resize]
-
-Requirements:
-- Python 3.7+
-- Required packages: aiohttp, Pillow, python-dotenv
-- DeepInfra API key (set in .env file)
-- config.json file with country groups, prompts, and other configuration details
-
-Note: Ensure you have sufficient API credits before running large batches.
-"""
-
-import aiohttp
-import asyncio
-import json
-import base64
-from PIL import Image
-from io import BytesIO
-from dotenv import load_dotenv
-import os
-import random
-import argparse
-import sys
-
-# Load the .env file from the current directory
-load_dotenv()
-
-DEEPINFRA_API_KEY = os.getenv('DEEPINFRA_API_KEY')
-
-async def generate_image(session, api_key, prompt, width, height, num_inference_steps, model):
-    if model == "schnell":
-        url = "https://api.deepinfra.com/v1/inference/black-forest-labs/FLUX-1-schnell"
-    elif model == "dev":
-        url ="https://api.deepinfra.com/v1/inference/black-forest-labs/FLUX-1-dev"
-    else:
-        return None
-    headers = {'Authorization': f'Bearer {api_key}'}
-    data = {"prompt": prompt, "width": width, "height": height, "num_inference_steps": num_inference_steps}
-    json_data = json.dumps(data)
-
-    async with session.post(url, headers=headers, data=json_data) as response:
-        if response.status == 200:
-            response_data = await response.json()
-            image_data = response_data['images'][0].split(',')[1]
-            image_bytes = base64.b64decode(image_data)
-            return image_bytes
-        else:
-            return None
-
-def save_image(image_bytes, folder, filename, resize=False):
-    if image_bytes:
-        os.makedirs(folder, exist_ok=True)
-        full_path = os.path.join(folder, filename)
-        
-        # Open the image using PIL
-        image = Image.open(BytesIO(image_bytes))
-        
-        # Resize the image if the resize option is True
-        if resize:
-            image = image.resize((256, 256), Image.LANCZOS)
-        
-        # Save the image
-        image.save(full_path)
-        print(f"Image saved as {full_path}")
-    else:
-        print("Failed to generate or save the image.")
-
-def get_next_image_number(folder):
-    os.makedirs(folder, exist_ok=True)
-    existing_files = [f for f in os.listdir(folder) if f.endswith('.png')]
-    return len(existing_files) + 1
-
-
-def calculate_total_images(config, n_per_country):
-    return len(config['countries']) * n_per_country
-
-def ask_user_confirmation(total_images, total_cost):
-    print(f"\nPotential cost of this run: ${total_cost:.4f} for {total_images} images")
-    while True:
-        response = input("Do you want to proceed? (yes/no): ").lower().strip()
-        if response in ['yes', 'y']:
-            return True
-        elif response in ['no', 'n']:
-            return False
-        else:
-            print("Please answer with 'yes' or 'no'.")
-
-def calculate_cost(width, height, num_inference_steps):
-    return 0.0005 * (width / 1024) * (height / 1024) * num_inference_steps
-
-async def generate_images_for_country_group(session, country_group, config, n_per_country, width, height, num_inference_steps, model, resize):
-    print(f"\nGenerating images for {country_group}")
-    folder_name = f"generated_images/{country_group}"
-    os.makedirs(folder_name, exist_ok=True)
-
-    tasks = []
-    for i in range(n_per_country):
-        country = random.choice(config['countries'][country_group])
-        facial_characteristics = random.choice(config['facial_characteristics'])
-        hair = random.choice(config['hair'])
-
-        prompt = config['prompt'].format(
-            country=country,
-            facial_characteristics=facial_characteristics if facial_characteristics else "no facial hair",
-            hair=hair
-        )
-
-        print(f"Generated prompt: {prompt}")
-
-        task = asyncio.create_task(generate_image(
-            session=session,
-            api_key=DEEPINFRA_API_KEY,
-            prompt=prompt,
-            width=width,
-            height=height,
-            num_inference_steps=num_inference_steps,
-            model=model
-        ))
-        tasks.append(task)
-
-    image_bytes_list = await asyncio.gather(*tasks)
-
-    for i, image_bytes in enumerate(image_bytes_list):
-            if image_bytes:
-                next_number = get_next_image_number(folder_name)
-                file_name = f"{country_group}{next_number}.png"
-                save_image(image_bytes, folder_name, file_name, resize)
-
-
-    return len([img for img in image_bytes_list if img is not None])
-
-async def main():
-    parser = argparse.ArgumentParser(description="Generate images for country groups")
-    parser.add_argument("--n_per_country", type=int, default=1, help="Number of images to generate per country group. Defaults to 1")
-    parser.add_argument("--width", type=int, default=512, help="Width of the generated images. Defaults to 512")
-    parser.add_argument("--height", type=int, default=512, help="Height of the generated images. Defaults to 512")
-    parser.add_argument("--num_inference_steps", type=int, default=1, help="Number of inference steps. Defaults to 1")
-    parser.add_argument("--model", type= str, default= "schnell", help= "The model to be used. Must be one of `schnell` or `dev`. Schnell is cheaper and faster")
-    parser.add_argument("--resize", action="store_true", help="Resize images to 256x256 if set")
-
-    args = parser.parse_args()
-
-    with open('config.json', 'r') as f:
-        config = json.load(f)
-
-    total_images = calculate_total_images(config, args.n_per_country)
-    total_cost = total_images * calculate_cost(args.width, args.height, args.num_inference_steps)
-
-    if not ask_user_confirmation(total_images, total_cost):
-        print("Operation cancelled by user.")
-        sys.exit(0)
-
-    generated_images = 0
-    async with aiohttp.ClientSession() as session:
-        tasks = []
-        for country_group in config['countries'].keys():
-            task = asyncio.create_task(generate_images_for_country_group(
-                session, country_group, config, args.n_per_country,
-                args.width, args.height, args.num_inference_steps,
-                args.model, args.resize
-            ))
-            tasks.append(task)
-
-        results = await asyncio.gather(*tasks)
-        generated_images = sum(results)
-
-    actual_cost = generated_images * calculate_cost(args.width, args.height, args.num_inference_steps)
-
-    print("\nImage generation complete for all country groups.")
-    print(f"Total images generated: {generated_images}")
-    print(f"Actual cost of this run: ${actual_cost:.4f}")
-
-if __name__ == "__main__":
-    asyncio.run(main())
-

comfy_fm_newgen.py

@@ -0,0 +1,223 @@
+"""
+"""
+from lib.rtf_parser import RTF_Parser
+import aiohttp
+import asyncio
+import json
+from PIL import Image
+from io import BytesIO
+import os
+import random
+import json
+import pycountry
+import inflect
+import configparser
+import argparse
+
+user_config = configparser.ConfigParser()
+user_config.read('config.cfg')
+
+rtf = RTF_Parser()
+p = inflect.engine()
+
+
+async def generate_image(session, prompt, num_inference_steps, model):
+    import random
+
+    payload = json.dumps(
+        {
+            "prompt": {
+                "3": {
+                    "class_type": "KSampler",
+                    "inputs": {
+                        "cfg": 1.5,
+                        "denoise": 1,
+                        "latent_image": ["5", 0],
+                        "model": ["4", 0],
+                        "negative": ["7", 0],
+                        "positive": ["6", 0],
+                        "sampler_name": "dpmpp_2m_sde",
+                        "scheduler": "karras",
+                        "seed": random.getrandbits(32),
+                        "steps": num_inference_steps,
+                    },
+                },
+                "4": {
+                    "class_type": "CheckpointLoaderSimple",
+                    "inputs": {"ckpt_name": model},
+                },
+                "5": {
+                    "class_type": "EmptyLatentImage",
+                    "inputs": {"batch_size": 1, "height": 512, "width": 512},
+                },
+                "6": {
+                    "class_type": "CLIPTextEncode",
+                    "inputs": {
+                        "clip": ["4", 1],
+                        "text": prompt,
+                    },
+                },
+                "7": {
+                    "class_type": "CLIPTextEncode",
+                    "inputs": {
+                        "clip": ["4", 1],
+                        "text": "(nsfw, naked, nude, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation",
+                    },
+                },
+                "8": {
+                    "class_type": "VAEDecode",
+                    "inputs": {"samples": ["3", 0], "vae": ["4", 2]},
+                },
+                "9": {
+                    "class_type": "SaveImage",
+                    "inputs": {"filename_prefix": "FM", "images": ["8", 0]},
+                },
+            }
+        }
+    )
+    url = user_config["general"]["url"]
+    url = f"{url}prompt"
+    headers = {"Content-Type": "application/json"}
+    async with session.post(url, headers=headers, data=payload) as response:
+        if response.status == 200:
+            # response_data = await response.json()
+            # image_data = response_data["images"][0].split(",")[1]
+            # image_bytes = base64.b64decode(image_data)
+            # return image_bytes
+            pass
+        else:
+            return None
+
+
+# def save_image(image_bytes, folder, filename, resize=False):
+#     if image_bytes:
+#         os.makedirs(folder, exist_ok=True)
+#         full_path = os.path.join(folder, filename)
+
+#         # Open the image using PIL
+#         image = Image.open(BytesIO(image_bytes))
+
+#         # Resize the image if the resize option is True
+#         if resize:
+#             image = image.resize((256, 256), Image.LANCZOS)
+
+#         # Save the image
+#         image.save(full_path)
+#         print(f"Image saved as {full_path}")
+#     else:
+#         print("Failed to generate or save the image.")
+
+
+def get_country_name_from_code(code):
+    try:
+        country = pycountry.countries.get(alpha_3=code.upper())
+        return country.name if country else "Unknown country code"
+    except KeyError:
+        return "Invalid country code"
+    
+
+async def generate_images_for_country_group(
+    session,
+    player,
+    config,
+    model,
+):
+    print(f"\nGenerating image for {player[0]} - {player[8]}")
+    folder_name = f"generated_images/"
+    os.makedirs(folder_name, exist_ok=True)
+
+    tasks = []
+    for i in range(1):
+        country = get_country_name_from_code(player[1])
+        facial_characteristics = random.choice(config["facial_characteristics"])
+        hair_length = config["hair_length"][player[5]]
+        hair_colour = config["hair_color"][player[6]]
+        skin_tone = config["skin_tone_map"][player[7]]
+        player_age = p.number_to_words(player[3])
+        hair = random.choice(config["hair"])
+
+        prompt = config["prompt"].format(
+            skin_tone=skin_tone,
+            age=player_age,
+            country=country,
+            facial_characteristics=(
+                facial_characteristics if facial_characteristics else "no facial hair"
+            ),
+            hair=f"{hair_length} {hair_colour} {hair}",
+        )
+
+        print(f"Generated prompt: {prompt}")
+
+        task = asyncio.create_task(
+            generate_image(
+                session=session,
+                prompt=prompt,
+                num_inference_steps=6,
+                model=model,
+            )
+        )
+        tasks.append(task)
+
+    image_bytes_list = await asyncio.gather(*tasks)
+
+    # for i, image_bytes in enumerate(image_bytes_list):
+    #     if image_bytes:
+    #         next_number = get_next_image_number(folder_name)
+    #         file_name = f"{country_group}{next_number}.png"
+    #         save_image(image_bytes, folder_name, file_name, resize)
+
+    # return len([img for img in image_bytes_list if img is not None])
+
+
+async def main():
+    parser = argparse.ArgumentParser(description="Generate images for country groups")
+    parser.add_argument("--rtf_file", type= str, default=None, help= "Path to the rtf file to be processed")
+    parser.add_argument("--num_inference_steps", type=int, default=6, help="Number of inference steps. Defaults to 1")
+
+    args = parser.parse_args()
+    if not args.rtf_file:
+        raise Exception("Please pass in a RTF file as --rtf_file")
+    rtf_file = rtf.parse_rtf(args.rtf_file)
+    
+    # Extract unique values from positions 5, 6, and 7
+    hair_length = list(
+        set(item[pos] for item in rtf_file for pos in [5])
+    )
+    hair_colour = list(
+            set(item[pos] for item in rtf_file for pos in [6])
+        )
+    skin_tone = list(
+            set(item[pos] for item in rtf_file for pos in [7])
+        )
+
+    with open("config.json", "r") as f:
+        app_config = json.load(f)
+
+    total_images = len(rtf_file)
+    # if not ask_user_confirmation(total_images, total_cost):
+    #     print("Operation cancelled by user.")
+    #     sys.exit(0)
+
+    generated_images = 0
+    async with aiohttp.ClientSession() as session:
+        tasks = []
+        for player in rtf_file[:500]:
+            task = asyncio.create_task(
+                generate_images_for_country_group(
+                    session,
+                    player,
+                    app_config,
+                    user_config["general"]["model"],
+                )
+            )
+            tasks.append(task)
+
+        results = await asyncio.gather(*tasks)
+        # generated_images = sum(results)
+
+    print("\nImage generation complete for all country groups.")
+    print(f"Total images generated: {generated_images}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

config.cfg.sample

@@ -0,0 +1,3 @@
+[general]
+url = 
+model = realisticVisionV60B1_v51HyperVAE.safetensors

config.json

@@ -1,41 +1,195 @@
-{"countries":{
-  "African": ["Nigeria", "Kenya", "South Africa", "Egypt", "Ghana"],
-  "Asian": ["China", "Japan", "India", "South Korea", "Vietnam"],
-  "Caucasian": ["United States", "Canada", "Australia", "New Zealand", "United Kingdom"],
-  "Central European": ["Germany", "Poland", "Czech Republic", "Austria", "Hungary"],
-  "EECA": ["Ukraine", "Kazakhstan", "Belarus", "Georgia", "Azerbaijan"],
-  "Italmed": ["Italy", "Greece", "Croatia", "Malta", "Cyprus"],
-  "MENA": ["Saudi Arabia", "UAE", "Iran", "Israel", "Morocco"],
-  "MESA": ["Pakistan", "Afghanistan", "Bangladesh", "Nepal", "Sri Lanka"],
-  "SAMed": ["Tunisia", "Algeria", "Libya", "Lebanon", "Jordan"],
-  "Scandinavian": ["Sweden", "Norway", "Denmark", "Finland", "Iceland"],
-  "Seasian": ["Indonesia", "Malaysia", "Thailand", "Philippines", "Singapore"],
-  "South American": ["Brazil", "Argentina", "Colombia", "Peru", "Chile"],
-  "SpanMed": ["Spain", "Portugal", "Andorra", "Gibraltar", "Monaco"],
-  "YugoGreek": ["Serbia", "Greece", "North Macedonia", "Montenegro", "Albania"]
-},
-"facial_characteristics": [
-  "beard",
-  "moustache",
-  "clean-shaven",
-  "stubble",
-  "goatee",
-  "sideburns",
-  null
-],
-"hair": [
-  "short",
-  "long",
-  "bald",
-  "buzz cut",
-  "medium-length",
-  "curly",
-  "wavy",
-  "spiky",
-  "afro",
-  "dreadlocks",
-  "mohawk",
-  "ponytail"
-],
-  "prompt": "Ultra realistic headshot with transparent background of a male soccer player looking at the camera being twenty five years old from {country} with {facial_characteristics}, and {hair} hair"
-}
+{
+  "countries": {
+    "African": [
+      "Nigeria",
+      "Kenya",
+      "South Africa",
+      "Egypt",
+      "Ghana"
+    ],
+    "Asian": [
+      "China",
+      "Japan",
+      "India",
+      "South Korea",
+      "Vietnam"
+    ],
+    "Caucasian": [
+      "United States",
+      "Canada",
+      "Australia",
+      "New Zealand",
+      "United Kingdom"
+    ],
+    "Central European": [
+      "Germany",
+      "Poland",
+      "Czech Republic",
+      "Austria",
+      "Hungary"
+    ],
+    "EECA": [
+      "Ukraine",
+      "Kazakhstan",
+      "Belarus",
+      "Georgia",
+      "Azerbaijan"
+    ],
+    "Italmed": [
+      "Italy",
+      "Greece",
+      "Croatia",
+      "Malta",
+      "Cyprus"
+    ],
+    "MENA": [
+      "Saudi Arabia",
+      "UAE",
+      "Iran",
+      "Israel",
+      "Morocco"
+    ],
+    "MESA": [
+      "Pakistan",
+      "Afghanistan",
+      "Bangladesh",
+      "Nepal",
+      "Sri Lanka"
+    ],
+    "SAMed": [
+      "Tunisia",
+      "Algeria",
+      "Libya",
+      "Lebanon",
+      "Jordan"
+    ],
+    "Scandinavian": [
+      "Sweden",
+      "Norway",
+      "Denmark",
+      "Finland",
+      "Iceland"
+    ],
+    "Seasian": [
+      "Indonesia",
+      "Malaysia",
+      "Thailand",
+      "Philippines",
+      "Singapore"
+    ],
+    "South American": [
+      "Brazil",
+      "Argentina",
+      "Colombia",
+      "Peru",
+      "Chile"
+    ],
+    "SpanMed": [
+      "Spain",
+      "Portugal",
+      "Andorra",
+      "Gibraltar",
+      "Monaco"
+    ],
+    "YugoGreek": [
+      "Serbia",
+      "Greece",
+      "North Macedonia",
+      "Montenegro",
+      "Albania"
+    ]
+  },
+  "facial_characteristics": [
+    "beard",
+    "moustache",
+    "clean-shaven",
+    "stubble",
+    "goatee",
+    "sideburns",
+    null
+  ],
+  "hair": [
+    "short",
+    "long",
+    "bald",
+    "buzz cut",
+    "medium-length",
+    "curly",
+    "wavy",
+    "spiky",
+    "afro",
+    "dreadlocks",
+    "mohawk",
+    "ponytail"
+  ],
+  "skin_tone_map": {
+    "0": "Lightest",
+    "1": "Lightest",
+    "2": "Very Light",
+    "3": "Light",
+    "4": "Light with Beige Undertones",
+    "5": "Beige",
+    "6": "Light Tan",
+    "7": "Tan",
+    "8": "Medium Tan",
+    "9": "Olive",
+    "10": "Light Brown",
+    "11": "Brown",
+    "12": "Medium Brown",
+    "13": "Dark Brown",
+    "14": "Very Dark Brown",
+    "15": "Deep Brown",
+    "16": "Light Ebony",
+    "17": "Ebony",
+    "18": "Dark Ebony",
+    "19": "Deep Ebony",
+    "20": "Darkest"
+  },
+  "hair_color": {
+    "0": "Jet Black",
+    "1": "Jet Black",    
+    "2": "Dark Black",
+    "3": "Very Dark Brown",
+    "4": "Dark Brown",
+    "5": "Medium Brown",
+    "6": "Light Brown",
+    "7": "Ash Brown",
+    "8": "Brown with Red Tint",
+    "9": "Auburn",
+    "10": "Chestnut",
+    "11": "Dark Blonde",
+    "12": "Medium Blonde",
+    "13": "Ash Blonde",
+    "14": "Light Blonde",
+    "15": "Platinum Blonde",
+    "16": "Strawberry Blonde",
+    "17": "Light Ginger",
+    "18": "Ginger",
+    "19": "Bright Red",
+    "20": "Copper Red"
+  },
+  "hair_length": {
+    "0": "Bald",
+    "1": "Bald",
+    "2": "Very Short (buzz cut)",
+    "3": "Short (crew cut)",
+    "4": "Short with fade",
+    "5": "Classic Short",
+    "6": "Medium-Short",
+    "7": "Medium (textured)",
+    "8": "Medium (layered)",
+    "9": "Medium with fringe",
+    "10": "Medium-Long (shaggy)",
+    "11": "Long (shoulder-length)",
+    "12": "Long with parting",
+    "13": "Long (tied back)",
+    "14": "Very Long (untied)",
+    "15": "Curly, Short",
+    "16": "Curly, Medium",
+    "17": "Curly, Long",
+    "18": "Afro-Short",
+    "19": "Afro-Medium",
+    "20": "Afro-Long"
+  },
+  "prompt": "Ultra realistic headshot with transparent background of a {skin_tone} skinned male soccer player looking at the camera being {age} years old from {country} with {facial_characteristics}, and {hair} hair"
+}

examples/read.txt

@@ -1 +0,0 @@
-Here are some examples of the script runs

lib/rtf_parser.py

@@ -0,0 +1,34 @@
+import re
+
+
+class RTF_Parser:
+    def __init__(self):
+        pass
+
+    def parse_rtf(self, path):
+        UID_regex = re.compile('([0-9]){4,}')
+        result_data = []
+        rtf = open(path, 'r', encoding="UTF-8")
+        # self.logger.info(rtf)
+        rtf_data = []
+        for line in rtf:
+            if UID_regex.search(line):
+                # self.logger.info(line.strip())
+                rtf_data.append(line.strip())
+        for newgen in rtf_data:
+            data_fields = newgen.split('|')
+            sec_nat = data_fields[3].strip()
+            if sec_nat == '':
+                sec_nat = None
+            result_data.append([data_fields[1].strip(), data_fields[2].strip(), sec_nat, data_fields[5].strip(),data_fields[6].strip(),data_fields[7].strip(),data_fields[8].strip(),data_fields[9].strip(), data_fields[4].strip()])
+        rtf.close()
+        return result_data
+
+    def is_rtf_valid(self, path):
+        rtf_regex = re.compile('(\|\s*[0-9]{8,}\s*)(\|\s*([A-Z]{3})*\s*)+(\|[\s*\w*\.*\-*]+)(\|[\s*\d+]+){3}\|')
+        rtf = open(path, 'r', encoding="UTF-8")
+        rtf_data = rtf.read()
+        rtf.close()
+        if rtf_regex.search(rtf_data):
+            return True
+        return False

workflow.json

@@ -0,0 +1,107 @@
+{
+    "3": {
+        "inputs": {
+            "seed": 513041955012511,
+            "steps": 6,
+            "cfg": 1.5,
+            "sampler_name": "dpmpp_2m_sde",
+            "scheduler": "karras",
+            "denoise": 1,
+            "model": [
+                "4",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "latent_image": [
+                "5",
+                0
+            ]
+        },
+        "class_type": "KSampler",
+        "_meta": {
+            "title": "KSampler"
+        }
+    },
+    "4": {
+        "inputs": {
+            "ckpt_name": "realisticVisionV60B1_v51HyperVAE.safetensors"
+        },
+        "class_type": "CheckpointLoaderSimple",
+        "_meta": {
+            "title": "Load Checkpoint"
+        }
+    },
+    "5": {
+        "inputs": {
+            "width": 512,
+            "height": 512,
+            "batch_size": 1
+        },
+        "class_type": "EmptyLatentImage",
+        "_meta": {
+            "title": "Empty Latent Image"
+        }
+    },
+    "6": {
+        "inputs": {
+            "text": "Ultra realistic headshot with transparent background of a male soccer player looking at the camera being sixteen years old from Serbia with no facial hair, and dreadlocks hair",
+            "clip": [
+                "4",
+                1
+            ]
+        },
+        "class_type": "CLIPTextEncode",
+        "_meta": {
+            "title": "CLIP Text Encode (Prompt)"
+        }
+    },
+    "7": {
+        "inputs": {
+            "text": "(nsfw, naked, nude, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation",
+            "clip": [
+                "4",
+                1
+            ]
+        },
+        "class_type": "CLIPTextEncode",
+        "_meta": {
+            "title": "CLIP Text Encode (Prompt)"
+        }
+    },
+    "8": {
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        },
+        "class_type": "VAEDecode",
+        "_meta": {
+            "title": "VAE Decode"
+        }
+    },
+    "9": {
+        "inputs": {
+            "filename_prefix": "ComfyUI",
+            "images": [
+                "8",
+                0
+            ]
+        },
+        "class_type": "SaveImage",
+        "_meta": {
+            "title": "Save Image"
+        }
+    }
+}