# Import the necessary libraries import torch # Define the prompt and configura
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# Import the necessary libraries import torch # Define the prompt and configuration settings prompt = ( "A close-up portrait of Victoria, a bold and resourceful female agent with a hint of a smirk, showing her confidence. " "She is dressed in a dark purple tango dance outfit with tactical accents, exuding elegance and strength. " "Her eyes reflect a mix of cunning and bravery. The background is a dimly lit, high-tech room with flickering screens. " "The lighting is dramatic, highlighting her facial structure and expressions. The image should have a gritty, filmic look, " "as if shot with a Canon XF605 UHD 4K HDR Pro Camcorder, capturing the fine details and atmosphere." ) # Configuration settings clip_guidance_scale = 25 # Stronger guidance for focused detail hr_scale = 4.0 # High-resolution scaling for fine details hr_upscaler = "R-ESRGAN" # High-quality upscaling latent_shift = torch.randn(1, 4, 64, 64) * 0.03 # Subtle variation for dynamic effect cross_attention_strength = 2.8 # Emphasis on facial features and details shallow_depth_of_field = True # Shallow depth of field to focus on Victoria lighting_style = "dramatic" # Dramatic lighting to highlight facial features color_match_strength = 1.9 # Enhanced color matching for vivid tones perlin_noise_scale = 0.02 # Subtle noise for added texture fractal_noise = True # Adds fractal noise for enhanced texture depth_map = None # No depth map needed parallax_effect = False # No parallax effect # Size configuration size = (1024, 1024) # Square image dimensions for a close-up portrait # Assume `pipe` is your image generation pipeline image = pipe( prompt=prompt, clip_guidance_scale=clip_guidance_scale, hr_scale=hr_scale, hr_upscaler=hr_upscaler, latent_shift=latent_shift, cross_attention_strength=cross_attention_strength, shallow_depth_of_field=shallow_depth_of_field, lighting_style=lighting_style, color_match_strength=color_match_strength, nois
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# Import the necessary libraries
import torch
# Define the prompt and configuration settings
prompt = (
"A close-up portrait of Victoria, a bold and resourceful female agent with a hint of a smirk, showing her confidence. "
"She is dressed in a dark purple tango dance outfit with tactical accents, exuding elegance and strength. "
"Her eyes reflect a mix of cunning and bravery. The background is a dimly lit, high-tech room with flickering screens. "
"The lighting is dramatic, highlighting her facial structure and expressions. The image should have a gritty, filmic look, "
"as if shot with a Canon XF605 UHD 4K HDR Pro Camcorder, capturing the fine details and atmosphere."
)
# Configuration settings
clip_guidance_scale = 25 # Stronger guidance for focused detail
hr_scale = 4.0 # High-resolution scaling for fine details
hr_upscaler = "R-ESRGAN" # High-quality upscaling
latent_shift = torch.randn(1, 4, 64, 64) * 0.03 # Subtle variation for dynamic effect
cross_attention_strength = 2.8 # Emphasis on facial features and details
shallow_depth_of_field = True # Shallow depth of field to focus on Victoria
lighting_style = "dramatic" # Dramatic lighting to highlight facial features
color_match_strength = 1.9 # Enhanced color matching for vivid tones
perlin_noise_scale = 0.02 # Subtle noise for added texture
fractal_noise = True # Adds fractal noise for enhanced texture
depth_map = None # No depth map needed
parallax_effect = False # No parallax effect
# Size configuration
size = (1024, 1024) # Square image dimensions for a close-up portrait
# Assume `pipe` is your image generation pipeline
image = pipe(
prompt=prompt,
clip_guidance_scale=clip_guidance_scale,
hr_scale=hr_scale,
hr_upscaler=hr_upscaler,
latent_shift=latent_shift,
cross_attention_strength=cross_attention_strength,
shallow_depth_of_field=shallow_depth_of_field,
lighting_style=lighting_style,
color_match_strength=color_match_strength,
nois
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