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# -*- coding: utf-8 -*-
"""
Prompt generation utilities for different inference types
"""
from typing import Dict, List, Tuple, Optional
def create_prompt_templates():
"""Create prompt templates for various tasks"""
templates = {
"text_understanding": "You are a multimodal model that can process both text and images. Answer the following question based on the provided images. Analyze each image and combine relevant details to answer.",
"image_generation": "Generate an image according to the text prompt.",
"image_editing": "Generate an image applying the following editing instruction based on the original image.",
"dense_prediction": "Perform dense prediction on the given images.",
"control_generation": "Generate an image according to the text prompt and the given control image.",
"subject_generation": "Generate an image according to the text prompt and the given object image.",
"multi_view": "Generate a view-image based on the given image.",
"style_transfer": "Transform the current image into the style of the provided image."
}
return templates
def generate_text_to_image_prompt(prompt_text: str, templates: Optional[Dict] = None) -> Tuple[str, str]:
"""
Generate prompt for text-to-image generation
Args:
prompt_text: User input text prompt
templates: Optional prompt templates dict
Returns:
Tuple of (input_prompt, unconditional_prompt)
"""
if templates is None:
templates = create_prompt_templates()
system_prompt = templates["image_generation"]
input_prompt = "<system>" + system_prompt + "</system>" + "<user>" + prompt_text + "</user>"
uncon_prompt = "<system>" + system_prompt + "</system>" + "<user>" + "<uncondition>" + "</user>"
return input_prompt, uncon_prompt
def generate_image_to_image_prompt(
prompt_text: str,
edit_type: str,
templates: Optional[Dict] = None,
**kwargs
) -> Tuple[str, str, str]:
"""
Generate prompt for image-to-image generation
Args:
prompt_text: User input text prompt
edit_type: Type of editing operation
templates: Optional prompt templates dict
**kwargs: Additional parameters for specific edit types
Returns:
Tuple of (input_prompt, unconditional_prompt, system_prompt)
"""
if templates is None:
templates = create_prompt_templates()
# Determine system prompt and processed prompt text based on edit type
if 'dense' in edit_type:
des = {
"canny": "canny edge map",
"hed": "hed edge map",
"normal": "normal map",
"sam2mask": "sam2 mask",
"depth": "depth map",
"openpose": "pose estimation map"
}
system_prompt = templates["dense_prediction"]
prompt_text_used = f"Generate a {des.get(edit_type.split('_')[0], 'dense map')} according to the image."
elif 'control' in edit_type:
system_prompt = templates["control_generation"]
prompt_text_used = prompt_text
elif 'subject' in edit_type:
system_prompt = templates["subject_generation"]
prompt_text_used = prompt_text
elif 'edit' in edit_type:
system_prompt = templates["image_editing"]
prompt_text_used = prompt_text
elif "ref_transfer" in edit_type:
system_prompt = templates["style_transfer"]
prompt_text_used = "Transform the current image into the style of the provided image."
elif 'multi_view' in edit_type:
system_prompt = templates["multi_view"]
prompt_text_used = f"Generate the {edit_type.split('_')[-1]} view based on the provided front view."
else:
system_prompt = "Generate an image according to the prompt and image."
prompt_text_used = prompt_text
# Build final prompts
input_prompt = "<system>" + system_prompt + "</system>" + "<user>" + prompt_text_used + "</user>"
uncon_prompt = "<system>" + system_prompt + "</system>" + "<user>" + "<uncondition>" + "</user>"
return input_prompt, uncon_prompt, system_prompt
def generate_multimodal_understanding_prompt(question: str, templates: Optional[Dict] = None) -> str:
"""
Generate prompt for multimodal understanding (MMU)
Args:
question: User question about the image
templates: Optional prompt templates dict
Returns:
Formatted input prompt
"""
if templates is None:
templates = create_prompt_templates()
system_prompt = "You are a multimodal model that can process both text and images. Answer the following question based on the provided images. Analyze each image and combine relevant details to answer."
input_prompt = "<system>" + system_prompt + "</system>" + "<user>" + question + "</user>"
return input_prompt
def get_edit_type_specific_prompt(edit_type: str, prompt_text: str, templates: Optional[Dict] = None) -> str:
"""
Get edit type specific prompt text
Args:
edit_type: Type of editing operation
prompt_text: Original prompt text
templates: Optional prompt templates dict
Returns:
Processed prompt text for the specific edit type
"""
if templates is None:
templates = create_prompt_templates()
if 'dense' in edit_type:
des = {
"canny": "canny edge map",
"hed": "hed edge map",
"normal": "normal map",
"sam2mask": "sam2 mask",
"depth": "depth map",
"openpose": "pose estimation map"
}
return f"Generate a {des.get(edit_type.split('_')[0], 'dense map')} according to the image."
elif 'control' in edit_type:
return prompt_text
elif 'subject' in edit_type:
return prompt_text
elif 'edit' in edit_type:
if "multiturn" in edit_type:
ids = int(edit_type.split("_")[-1])
if ids == 0:
return prompt_text[0] if isinstance(prompt_text, list) else prompt_text
else:
return prompt_text[ids][0] if isinstance(prompt_text[ids], list) else prompt_text[ids]
else:
return prompt_text
elif "ref_transfer" in edit_type:
return "Transform the current image into the style of the provided image."
elif 'multi_view' in edit_type:
return f"Generate the {edit_type.split('_')[-1]} view based on the provided front view."
else:
return prompt_text
def get_system_prompt_for_edit_type(edit_type: str, templates: Optional[Dict] = None) -> str:
"""
Get system prompt for specific edit type
Args:
edit_type: Type of editing operation
templates: Optional prompt templates dict
Returns:
System prompt for the edit type
"""
if templates is None:
templates = create_prompt_templates()
if 'dense' in edit_type:
return templates["dense_prediction"]
elif 'control' in edit_type:
return templates["control_generation"]
elif 'subject' in edit_type:
return templates["subject_generation"]
elif 'edit' in edit_type:
return templates["image_editing"]
elif "ref_transfer" in edit_type:
return templates["style_transfer"]
elif 'multi_view' in edit_type:
return templates["multi_view"]
else:
return "Generate an image according to the prompt and image."
def generate_text_image_to_text_image_prompt(prompt_text, system_prompt):
"""
Generate prompts for TI2TI tasks
Args:
prompt_text: User's editing instruction
system_prompt: System prompt for the task
Returns:
input_prompt: Conditional prompt
uncon_text: Unconditional prompt
"""
# Conditional prompt
input_prompt = (
f"<system>{system_prompt}</system>"
f"<user>{prompt_text}</user>"
)
# Unconditional prompt (for CFG)
uncon_text = (
f"<system>{system_prompt}</system>"
f"<user><uncondition></user>"
)
return input_prompt, uncon_text |