# Import the necessary libraries import torch # Define the prompt and configura

# 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