From 424cd100e4e104ead74cc37fcc12fbbda3d4803e Mon Sep 17 00:00:00 2001
From: NeuralSamurAI <164533106+NeuralSamurAI@users.noreply.github.com>
Date: Sun, 11 Aug 2024 00:22:37 -0400
Subject: [PATCH] Add files via upload
---
PromptJSON.py | 209 ++++++++++++++++++++
__init__.py | 5 +-
prompt_templates.py | 453 ++++++++++++++++++++++++++++++++++++++++++++
3 files changed, 663 insertions(+), 4 deletions(-)
create mode 100644 PromptJSON.py
create mode 100644 prompt_templates.py
diff --git a/PromptJSON.py b/PromptJSON.py
new file mode 100644
index 0000000..728ea69
--- /dev/null
+++ b/PromptJSON.py
@@ -0,0 +1,209 @@
+import json
+import logging
+from .prompt_templates import PromptTemplates
+
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+
+class PromptJSON:
+ def __init__(self):
+ logging.info("Initializing PromptJSON")
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "prompt": ("STRING", {"multiline": True}),
+ "negative_prompt": ("STRING", {"multiline": True}),
+ "complexity": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1}),
+ "llm_prompt_type": (["One Shot", "Few Shot"],),
+ "schema_type": (["JSON", "HTML", "Key", "Attribute-Based", "Visual Layer Breakdown", "Compositional Grid", "Artistic Reference"],),
+ "enhance_prompt": ("BOOLEAN", {"default": False}),
+ },
+ "optional": {
+ "custom_schema": ("STRING", {"multiline": True}),
+ }
+ }
+
+ RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
+ RETURN_NAMES = ("system_prompt", "user_prompt", "negative_passthru", "schema")
+ FUNCTION = "process"
+ CATEGORY = "prompt_converters"
+
+ def process(self, prompt, negative_prompt, complexity, llm_prompt_type, schema_type, enhance_prompt, custom_schema=""):
+ logging.info(f"Starting process method with schema_type: {schema_type}, enhance_prompt: {enhance_prompt}")
+
+ system_prompt = self.generate_system_prompt(llm_prompt_type, schema_type, enhance_prompt)
+ schema = self.parse_custom_schema(custom_schema, schema_type)
+ user_prompt = self.generate_user_prompt(prompt, negative_prompt, schema, complexity, schema_type, enhance_prompt)
+
+ return (system_prompt, user_prompt, negative_prompt, self.format_schema_for_llm(schema, schema_type))
+
+ def generate_system_prompt(self, llm_prompt_type, schema_type, enhance_prompt):
+ base_prompt = PromptTemplates.get_system_prompt(schema_type)
+
+ if enhance_prompt:
+ base_prompt += "\nYou are encouraged to create additional details not explicitly mentioned in the original prompt, while maintaining consistency with the given information. Use the complexity value to determine the level of detail and elaboration in your response."
+ else:
+ base_prompt += "\nStrictly use only the information provided in the original prompt. Do not add any details or elements not explicitly mentioned."
+
+ if llm_prompt_type == "One Shot":
+ example = PromptTemplates.get_example_prompt(schema_type, "medium")
+ return f"{base_prompt}\n\nHere's an example of how to structure your response:\n\n{example}"
+ else: # Few Shot
+ examples = [
+ PromptTemplates.get_example_prompt(schema_type, "low"),
+ PromptTemplates.get_example_prompt(schema_type, "medium"),
+ PromptTemplates.get_example_prompt(schema_type, "high")
+ ]
+ return f"{base_prompt}\n\nHere are a few examples of how to structure your response for different complexity levels:\n\n" + "\n\n".join(examples)
+
+ def parse_custom_schema(self, custom_schema, schema_type):
+ if not custom_schema:
+ return PromptTemplates.get_default_schema(schema_type)
+ try:
+ if schema_type in ["JSON", "Visual Layer Breakdown", "Compositional Grid", "Artistic Reference"]:
+ schema = json.loads(custom_schema)
+ if not isinstance(schema, dict):
+ raise ValueError(f"Custom {schema_type} schema must be a JSON object")
+ elif schema_type == "HTML":
+ schema = self.parse_html_schema(custom_schema)
+ elif schema_type == "Key":
+ schema = self.parse_key_schema(custom_schema)
+ elif schema_type == "Attribute-Based":
+ schema = custom_schema.strip().split('\n')
+ else:
+ raise ValueError(f"Unsupported schema type: {schema_type}")
+ return schema
+ except Exception as e:
+ logging.error(f"Invalid custom schema: {str(e)}")
+ return PromptTemplates.get_default_schema(schema_type)
+
+ def parse_html_schema(self, custom_schema):
+ if isinstance(custom_schema, str):
+ return custom_schema.strip()
+ return PromptTemplates.get_default_schema("HTML")
+
+ def parse_key_schema(self, custom_schema):
+ if isinstance(custom_schema, str):
+ return custom_schema.strip()
+ return PromptTemplates.get_default_schema("Key")
+
+ def generate_user_prompt(self, prompt, negative_prompt, schema, complexity, schema_type, enhance_prompt):
+ formatted_schema = self.format_schema_for_llm(schema, schema_type)
+
+ enhancement_instruction = ""
+ if enhance_prompt:
+ enhancement_instruction = f"""
+Enhance the prompt with additional details not explicitly mentioned, while maintaining consistency with the given information.
+Use the complexity value of {complexity} to determine the level of detail and elaboration in your response.
+Higher complexity should result in more detailed and nuanced descriptions.
+"""
+ else:
+ enhancement_instruction = "Strictly use only the information provided in the original prompt. Do not add any details or elements not explicitly mentioned."
+
+ return f"""Generate a detailed image description based on the following prompt: "{prompt}"
+
+Negative prompt (elements to avoid): "{negative_prompt}"
+
+{enhancement_instruction}
+
+Use the following {schema_type} schema to structure your response. Replace the placeholders with appropriate, detailed content:
+{formatted_schema}
+
+Ensure that your response adheres strictly to this schema, providing detailed and creative content for each field. Make sure to avoid including any elements mentioned in the negative prompt.
+
+Your response should be a valid {schema_type} structure that follows the provided schema."""
+
+ def format_schema_for_llm(self, schema, schema_type):
+ if schema_type == "JSON":
+ return json.dumps(schema, indent=2)
+ elif schema_type == "HTML":
+ return self.format_html_schema_for_llm(schema)
+ elif schema_type == "Key":
+ return self.format_key_schema_for_llm(schema)
+ elif schema_type == "Attribute-Based":
+ return "\n".join(schema)
+ elif schema_type in ["Visual Layer Breakdown", "Compositional Grid", "Artistic Reference"]:
+ return json.dumps(schema, indent=2)
+ else:
+ raise ValueError(f"Unsupported schema type: {schema_type}")
+
+ def format_html_schema_for_llm(self, schema):
+ if isinstance(schema, str):
+ return schema
+ # If it's not a string, assume it's a dictionary and format it
+ return self._format_html_dict(schema)
+
+ def _format_html_dict(self, schema, indent=""):
+ result = []
+ for key, value in schema.items():
+ if isinstance(value, dict):
+ result.append(f"{indent}<{key}>")
+ result.append(self._format_html_dict(value, indent + " "))
+ result.append(f"{indent}")
+ elif isinstance(value, list):
+ for item in value:
+ if isinstance(item, dict):
+ result.append(f"{indent}<{key}>")
+ result.append(self._format_html_dict(item, indent + " "))
+ result.append(f"{indent}")
+ else:
+ result.append(f"{indent}<{key}>[appropriate {key}]")
+ else:
+ result.append(f"{indent}<{key}>[appropriate {key}]")
+ return "\n".join(result)
+
+ def format_key_schema_for_llm(self, schema):
+ if isinstance(schema, str):
+ return schema
+ # If it's not a string, assume it's a dictionary and format it
+ return self._format_key_dict(schema)
+
+ def _format_key_dict(self, schema, prefix=""):
+ result = []
+ for key, value in schema.items():
+ if isinstance(value, dict):
+ result.append(self._format_key_dict(value, f"{prefix}{key}."))
+ elif isinstance(value, list):
+ for i, item in enumerate(value):
+ if isinstance(item, dict):
+ result.append(self._format_key_dict(item, f"{prefix}{key}[{i}]."))
+ else:
+ result.append(f"{prefix}{key}[{i}]: [appropriate {key}]")
+ else:
+ result.append(f"{prefix}{key}: [appropriate {key}]")
+ return "\n".join(result)
+
+ def format_visual_layer_breakdown_for_llm(self, schema):
+ result = []
+ for layer, description in schema.items():
+ result.append(f"{layer.capitalize()}:")
+ result.append(f" [Describe the {layer} elements. {description}]")
+ return "\n".join(result)
+
+ def format_compositional_grid_for_llm(self, schema):
+ result = [
+ "Compositional Grid:",
+ "┌─────────────┬─────────────┬─────────────┐",
+ "│ Top Left │ Top Center │ Top Right │",
+ "│ [Describe] │ [Describe] │ [Describe] │",
+ "├─────────────┼─────────────┼─────────────┤",
+ "│ Middle Left │ Center │ Middle Right│",
+ "│ [Describe] │ [Describe] │ [Describe] │",
+ "├─────────────┼─────────────┼─────────────┤",
+ "│ Bottom Left │Bottom Center│ Bottom Right│",
+ "│ [Describe] │ [Describe] │ [Describe] │",
+ "└─────────────┴─────────────┴─────────────┘"
+ ]
+ for position, description in schema.items():
+ formatted_position = position.replace('_', ' ').title()
+ result.append(f"\n{formatted_position}:")
+ result.append(f" [Describe this section. {description}]")
+ return "\n".join(result)
+
+ def format_artistic_reference_for_llm(self, schema):
+ result = ["Artistic Reference:"]
+ for aspect, description in schema.items():
+ result.append(f"\n{aspect.capitalize()}:")
+ result.append(f" [Describe the {aspect}. {description}]")
+ return "\n".join(result)
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
index 2ccfb1b..a45cd0b 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,14 +1,11 @@
-from .prompt_json import PromptJSON
-from .token_counter import TokenCounter
+from .PromptJSON import PromptJSON
NODE_CLASS_MAPPINGS = {
"PromptJSON": PromptJSON,
- "TokenCounter": TokenCounter
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PromptJSON": "Prompt JSON",
- "TokenCounter": "Token Counter"
}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
\ No newline at end of file
diff --git a/prompt_templates.py b/prompt_templates.py
new file mode 100644
index 0000000..2285a7f
--- /dev/null
+++ b/prompt_templates.py
@@ -0,0 +1,453 @@
+import json
+
+class PromptTemplates:
+ @staticmethod
+ def get_system_prompt(schema_type):
+ return f"You are an AI assistant specialized in generating detailed image descriptions based on user prompts. " \
+ f"Your task is to take the user's input and create a structured, detailed description that can be used for image generation. " \
+ f"Use the provided {schema_type} schema to organize the information, and adjust the level of detail based on the complexity value given. " \
+ f"The user will provide both a positive prompt describing what should be in the image and a negative prompt describing what should NOT be in the image. " \
+ f"Ensure that your description does not include any elements mentioned in the negative prompt. Lastly you will reply ONLY with the answer, no preamble or summary is needed."
+
+ @staticmethod
+ def get_default_schema(schema_type):
+ if schema_type == "JSON":
+ return {
+ "title": "[Brief title for the image]",
+ "subjects": [{"type": "[Subject type]", "description": "[Detailed description of the subject]"}],
+ "objects": [{"type": "[Object type]", "description": "[Detailed description of the object]"}],
+ "setting": {"location": "[Where the scene takes place]", "time": "[Time of day or era]"},
+ "style": {"artistic_movement": "[Art style or movement]", "mood": "[Overall mood or atmosphere]"},
+ "color_scheme": ["[Main colors used in the image]"]
+ }
+ elif schema_type == "HTML":
+ return (
+ "[Brief title for the image]\n"
+ "\n"
+ " [Subject type]\n"
+ " [Detailed description of the subject]\n"
+ "\n"
+ "\n"
+ "\n"
+ " [Where the scene takes place]\n"
+ " \n"
+ "\n"
+ "\n"
+ "\n"
+ " [Main color used in the image]\n"
+ ""
+ )
+ elif schema_type == "Key":
+ return (
+ "title: [Brief title for the image]\n"
+ "subject.type: [Subject type]\n"
+ "subject.description: [Detailed description of the subject]\n"
+ "object.type: [Object type]\n"
+ "object.description: [Detailed description of the object]\n"
+ "setting.location: [Where the scene takes place]\n"
+ "setting.time: [Time of day or era]\n"
+ "style.artistic_movement: [Art style or movement]\n"
+ "style.mood: [Overall mood or atmosphere]\n"
+ "color_scheme[0]: [Main color used in the image]"
+ )
+ elif schema_type == "Attribute-Based":
+ return [
+ "[Color: description of main colors]",
+ "[Lighting: description of lighting conditions]",
+ "[Mood: overall atmosphere or feeling]",
+ "[Composition: layout and arrangement of elements]",
+ "[Style: artistic style or technique]"
+ ]
+ elif schema_type == "Visual Layer Breakdown":
+ return {
+ "background": "[Description of the furthest elements]",
+ "midground": "[Description of the middle-distance elements]",
+ "foreground": "[Description of the closest elements]",
+ "focus": "[Main point of interest in the image]"
+ }
+ elif schema_type == "Compositional Grid":
+ return {
+ "top_left": "[Description of top-left section]",
+ "top_center": "[Description of top-center section]",
+ "top_right": "[Description of top-right section]",
+ "middle_left": "[Description of middle-left section]",
+ "middle_center": "[Description of middle-center section]",
+ "middle_right": "[Description of middle-right section]",
+ "bottom_left": "[Description of bottom-left section]",
+ "bottom_center": "[Description of bottom-center section]",
+ "bottom_right": "[Description of bottom-right section]"
+ }
+ elif schema_type == "Artistic Reference":
+ return {
+ "subject": "[Main focus or theme of the image]",
+ "composition": "[Layout and arrangement of elements]",
+ "color_palette": "[Main colors and their relationships]",
+ "lighting": "[Type and quality of light in the scene]",
+ "texture": "[Surface qualities of objects in the image]",
+ "artistic_style": "[Overall artistic approach or technique]",
+ "mood": "[Emotional tone or atmosphere of the image]"
+ }
+ else:
+ raise ValueError(f"Unsupported schema type: {schema_type}")
+
+ @staticmethod
+ def get_example_prompt(schema_type, complexity):
+ if complexity == "low":
+ prompt = "A cat"
+ negative_prompt = "dogs, human elements"
+ complexity_value = 0.1
+ elif complexity == "medium":
+ prompt = "A bustling city street at night"
+ negative_prompt = "daytime, rural elements"
+ complexity_value = 0.5
+ else: # high
+ prompt = "A fantastical underwater civilization with merpeople and futuristic technology"
+ negative_prompt = "land animals, surface world elements"
+ complexity_value = 1.0
+
+ if schema_type == "JSON":
+ return PromptTemplates._get_json_example(prompt, negative_prompt, complexity_value)
+ elif schema_type == "HTML":
+ return PromptTemplates._get_html_example(prompt, negative_prompt, complexity_value)
+ elif schema_type == "Key":
+ return PromptTemplates._get_key_example(prompt, negative_prompt, complexity_value)
+ elif schema_type == "Attribute-Based":
+ return PromptTemplates._get_attribute_based_example(prompt, negative_prompt, complexity_value)
+ elif schema_type == "Visual Layer Breakdown":
+ return PromptTemplates._get_visual_layer_breakdown_example(prompt, negative_prompt, complexity_value)
+ elif schema_type == "Compositional Grid":
+ return PromptTemplates._get_compositional_grid_example(prompt, negative_prompt, complexity_value)
+ elif schema_type == "Artistic Reference":
+ return PromptTemplates._get_artistic_reference_example(prompt, negative_prompt, complexity_value)
+ else:
+ raise ValueError(f"Unsupported schema type: {schema_type}")
+
+ @staticmethod
+ def _get_json_example(prompt, negative_prompt, complexity):
+ if complexity == 0.1:
+ output = (
+ "{\n"
+ ' "title": "Simple Cat Portrait",\n'
+ ' "subjects": [{"type": "animal", "description": "cat: furry: domesticated: sitting"}],\n'
+ ' "setting": {"location": "indoors: unspecified", "time": "unspecified"},\n'
+ ' "style": {"artistic_movement": "Realism", "mood": "calm: neutral"},\n'
+ ' "color_scheme": ["gray: soft", "white: clean"]\n'
+ "}"
+ )
+ elif complexity == 0.5:
+ output = (
+ "{\n"
+ ' "title": "Nocturnal Urban Scene",\n'
+ ' "subjects": [{"type": "urban", "description": "city street: busy: crowded"}],\n'
+ ' "objects": [\n'
+ ' {"type": "vehicle", "description": "cars: moving: headlights on"},\n'
+ ' {"type": "building", "description": "skyscrapers: tall: illuminated"}\n'
+ ' ],\n'
+ ' "setting": {"location": "city center: downtown", "time": "night: late"},\n'
+ ' "style": {"artistic_movement": "Urban Realism", "mood": "energetic: lively"},\n'
+ ' "color_scheme": ["black: deep", "yellow: bright: neon", "blue: cool: distant"]\n'
+ "}"
+ )
+ else:
+ output = (
+ "{\n"
+ ' "title": "Futuristic Aquatic Metropolis",\n'
+ ' "subjects": [\n'
+ ' {"type": "mythical", "description": "merpeople: diverse: swimming"},\n'
+ ' {"type": "technology", "description": "underwater gadgets: advanced: floating"}\n'
+ ' ],\n'
+ ' "objects": [\n'
+ ' {"type": "architecture", "description": "buildings: coral-shaped: bioluminescent"},\n'
+ ' {"type": "flora", "description": "seaweed: genetically modified: glowing"},\n'
+ ' {"type": "fauna", "description": "fish: robotic: schooling"}\n'
+ ' ],\n'
+ ' "setting": {"location": "deep ocean: underwater city", "time": "timeless: eternal"},\n'
+ ' "style": {"artistic_movement": "Bio-futurism", "mood": "awe-inspiring: mysterious"},\n'
+ ' "color_scheme": ["blue: bioluminescent", "green: neon", "purple: deep", "silver: metallic"]\n'
+ "}"
+ )
+ return f"Prompt: {prompt}\nNegative prompt: {negative_prompt}\nComplexity: {complexity}\n\nOutput:\n{output}"
+
+ @staticmethod
+ def _get_html_example(prompt, negative_prompt, complexity):
+ if complexity == 0.1:
+ output = (
+ "Simple Cat Portrait\n"
+ "\n"
+ " animal\n"
+ " cat: furry: domesticated: sitting\n"
+ "\n"
+ "\n"
+ " indoors: unspecified\n"
+ " \n"
+ "\n"
+ "\n"
+ "\n"
+ " gray: soft\n"
+ " white: clean\n"
+ ""
+ )
+ elif complexity == 0.5:
+ output = (
+ "Nocturnal Urban Scene\n"
+ "\n"
+ " urban\n"
+ " city street: busy: crowded\n"
+ "\n"
+ "\n"
+ "\n"
+ "\n"
+ " city center: downtown\n"
+ " \n"
+ "\n"
+ "\n"
+ "\n"
+ " black: deep\n"
+ " yellow: bright: neon\n"
+ " blue: cool: distant\n"
+ ""
+ )
+ else:
+ output = (
+ "Futuristic Aquatic Metropolis\n"
+ "\n"
+ " mythical\n"
+ " merpeople: diverse: swimming\n"
+ "\n"
+ "\n"
+ " technology\n"
+ " underwater gadgets: advanced: floating\n"
+ "\n"
+ "\n"
+ "\n"
+ "\n"
+ "\n"
+ " deep ocean: underwater city\n"
+ " \n"
+ "\n"
+ "\n"
+ "\n"
+ " blue: bioluminescent\n"
+ " green: neon\n"
+ " purple: deep\n"
+ " silver: metallic\n"
+ ""
+ )
+ return f"Prompt: {prompt}\nNegative prompt: {negative_prompt}\nComplexity: {complexity}\n\nOutput:\n{output}"
+
+ @staticmethod
+ def _get_key_example(prompt, negative_prompt, complexity):
+ if complexity == 0.1:
+ output = (
+ "title: Simple Cat Portrait\n"
+ "subject.type: animal\n"
+ "subject.description: cat: furry: domesticated: sitting\n"
+ "setting.location: indoors: unspecified\n"
+ "setting.time: unspecified\n"
+ "style.artistic_movement: Realism\n"
+ "style.mood: calm: neutral\n"
+ "color_scheme[0]: gray: soft\n"
+ "color_scheme[1]: white: clean"
+ )
+ elif complexity == 0.5:
+ output = (
+ "title: Nocturnal Urban Scene\n"
+ "subject.type: urban\n"
+ "subject.description: city street: busy: crowded\n"
+ "object[0].type: vehicle\n"
+ "object[0].description: cars: moving: headlights on\n"
+ "object[1].type: building\n"
+ "object[1].description: skyscrapers: tall: illuminated\n"
+ "setting.location: city center: downtown\n"
+ "setting.time: night: late\n"
+ "style.artistic_movement: Urban Realism\n"
+ "style.mood: energetic: lively\n"
+ "color_scheme[0]: black: deep\n"
+ "color_scheme[1]: yellow: bright: neon\n"
+ "color_scheme[2]: blue: cool: distant"
+ )
+ else:
+ output = (
+ "title: Futuristic Aquatic Metropolis\n"
+ "subject[0].type: mythical\n"
+ "subject[0].description: merpeople: diverse: swimming\n"
+ "subject[1].type: technology\n"
+ "subject[1].description: underwater gadgets: advanced: floating\n"
+ "object[0].type: architecture\n"
+ "object[0].description: buildings: coral-shaped: bioluminescent\n"
+ "object[1].type: flora\n"
+ "object[1].description: seaweed: genetically modified: glowing\n"
+ "object[2].type: fauna\n"
+ "object[2].description: fish: robotic: schooling\n"
+ "setting.location: deep ocean: underwater city\n"
+ "setting.time: timeless: eternal\n"
+ "style.artistic_movement: Bio-futurism\n"
+ "style.mood: awe-inspiring: mysterious\n"
+ "color_scheme[0]: blue: bioluminescent\n"
+ "color_scheme[1]: green: neon\n"
+ "color_scheme[2]: purple: deep\n"
+ "color_scheme[3]: silver: metallic"
+ )
+ return f"Prompt: {prompt}\nNegative prompt: {negative_prompt}\nComplexity: {complexity}\n\nOutput:\n{output}"
+
+ @staticmethod
+ def _get_attribute_based_example(prompt, negative_prompt, complexity):
+ if complexity == 0.1:
+ output = (
+ "[Color: gray: soft: white: clean]\n"
+ "[Lighting: neutral: even: soft]\n"
+ "[Mood: calm: serene: peaceful]\n"
+ "[Composition: centered: simple: portrait]\n"
+ "[Style: realistic: straightforward: minimalistic]"
+ )
+ elif complexity == 0.5:
+ output = (
+ "[Color: black: deep: yellow: bright: neon: blue: cool: distant]\n"
+ "[Lighting: high contrast: artificial: street lamps: neon signs: car headlights]\n"
+ "[Mood: energetic: vibrant: bustling: slightly mysterious]\n"
+ "[Composition: dynamic: diagonal: multiple layers: foreground to background]\n"
+ "[Style: urban realism: noir-inspired: contemporary]"
+ )
+ else:
+ output = (
+ "[Color: blue: bioluminescent: green: neon: purple: deep: silver: metallic]\n"
+ "[Lighting: ethereal: glowing: bioluminescent: multiple sources: complex shadows]\n"
+ "[Mood: awe-inspiring: mysterious: futuristic: dreamlike]\n"
+ "[Composition: multi-layered: intricate: vast scale: foreground to background depth]\n"
+ "[Style: bio-futurism: surrealistic: blend of organic and technological]\n"
+ "[Texture: smooth: metallic: organic: coral-like: flowing]\n"
+ "[Motion: fluid: graceful: swirling currents: schools of fish]\n"
+ "[Scale: monumental: sprawling: dwarfing inhabitants]"
+ )
+ return f"Prompt: {prompt}\nNegative prompt: {negative_prompt}\nComplexity: {complexity}\n\nOutput:\n{output}"
+
+ @staticmethod
+ def _get_visual_layer_breakdown_example(prompt, negative_prompt, complexity):
+ if complexity == 0.1:
+ output = (
+ "background: plain: neutral tone: slightly out of focus\n"
+ "midground: none specified: focus entirely on subject\n"
+ "foreground: single cat: sitting: centered: detailed fur texture\n"
+ "focus: cat's face: eyes: whiskers: defining features clearly visible"
+ )
+ elif complexity == 0.5:
+ output = (
+ "background: night sky: dark: few visible stars: silhouettes of distant skyscrapers\n"
+ "midground: illuminated building facades: neon signs: streetlights: pools of light\n"
+ "foreground: busy street: moving cars: headlights creating light streaks: pedestrians\n"
+ "focus: intersection: traffic flow: interplay of light and shadow"
+ )
+ else:
+ output = (
+ "background: vast ocean depths: dark water: bioluminescent particles: starry effect\n"
+ "midground: coral-shaped skyscrapers: blue-green glow: interconnected transparent tubes\n"
+ "foreground: diverse merpeople: advanced gadgets: schools of robotic fish\n"
+ "focus: central plaza: gathering merpeople: swirling information displays: holographic projections"
+ )
+ return f"Prompt: {prompt}\nNegative prompt: {negative_prompt}\nComplexity: {complexity}\n\nOutput:\n{output}"
+
+ @staticmethod
+ def _get_compositional_grid_example(prompt, negative_prompt, complexity):
+ if complexity == 0.1:
+ output = (
+ "top_left: empty: neutral background\n"
+ "top_center: cat's ears: top of head\n"
+ "top_right: empty: neutral background\n"
+ "middle_left: cat's body: side view\n"
+ "middle_center: cat's face: main focus: detailed features\n"
+ "middle_right: cat's body: opposite side view\n"
+ "bottom_left: empty: neutral background\n"
+ "bottom_center: cat's lower body: paws\n"
+ "bottom_right: empty: neutral background"
+ )
+ elif complexity == 0.5:
+ output = (
+ "top_left: skyscraper: lit windows: partial view\n"
+ "top_center: night sky: few stars: maybe moon\n"
+ "top_right: another skyscraper: neon sign\n"
+ "middle_left: street side: walking pedestrians\n"
+ "middle_center: main intersection: traffic lights: car headlights\n"
+ "middle_right: storefront: bright display windows\n"
+ "bottom_left: parked car: partial view: reflective surface\n"
+ "bottom_center: wet street: reflections of lights\n"
+ "bottom_right: street corner: newspaper stand: fire hydrant"
+ )
+ else:
+ output = (
+ "top_left: bioluminescent coral skyscraper: reaching upwards\n"
+ "top_center: school of silver robotic fish: perfect formation\n"
+ "top_right: floating holographic displays: underwater charts: data\n"
+ "middle_left: merpeople: operating advanced underwater vehicles\n"
+ "middle_center: central plaza: giant pulsating energy core\n"
+ "middle_right: seaweed farm: neon green glow: genetically modified\n"
+ "bottom_left: network of transparent tubes: transportation system\n"
+ "bottom_center: diverse merpeople: engaged in discussion: trade\n"
+ "bottom_right: underwater laboratory: visible experiments: large windows"
+ )
+ return f"Prompt: {prompt}\nNegative prompt: {negative_prompt}\nComplexity: {complexity}\n\nOutput:\n{output}"
+
+ @staticmethod
+ def _get_artistic_reference_example(prompt, negative_prompt, complexity):
+ if complexity == 0.1:
+ output = (
+ "subject: single cat: domestic: sitting pose\n"
+ "composition: centered: simple portrait view: minimal background\n"
+ "color_palette: grayscale: soft grays: clean whites: subtle tonal variations\n"
+ "lighting: even: soft: highlights on fur: gentle shadows\n"
+ "texture: soft fur: visible but not overly detailed\n"
+ "artistic_style: basic realism: minimal embellishment: focus on form\n"
+ "mood: calm: serene: domestic tranquility"
+ )
+ elif complexity == 0.5:
+ output = (
+ "subject: nocturnal urban street scene: busy city life\n"
+ "composition: dynamic diagonal layout: streets and buildings create depth\n"
+ "color_palette: rich blacks: bright yellows: cool blues: pops of neon\n"
+ "lighting: high contrast: dark shadows: bright artificial lights: multiple sources\n"
+ "texture: smooth reflective surfaces: cars and wet streets: rough building facades\n"
+ "artistic_style: urban realism: elements of noir: capturing night city energy\n"
+ "mood: energetic: slightly mysterious: sense of constant motion: urban vitality"
+ )
+ else:
+ output = (
+ "subject: futuristic underwater metropolis: merpeople civilization\n"
+ "composition: multi-layered: overlapping structures: foreground to background depth\n"
+ "color_palette: bioluminescent blues: neon greens: deep purples: metallic silvers\n"
+ "lighting: multiple bioluminescent sources: complex interplay of shadows and glowing elements\n"
+ "texture: contrasting smooth metallic surfaces: organic flowing forms: coral-like structures\n"
+ "artistic_style: bio-futurism: blend of art nouveau organic forms: sci-fi technology: surrealist dreamlike quality\n"
+ "mood: awe-inspiring: mysterious: sense of wonder and endless possibility\n"
+ "motion: implied through swirling water currents: graceful merpeople movements: schools of robotic fish\n"
+ "scale: vast and intricate: monumental structures dwarfing inhabitants: sense of grandeur and exploration"
+ )
+ return f"Prompt: {prompt}\nNegative prompt: {negative_prompt}\nComplexity: {complexity}\n\nOutput:\n{output}"
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