# Import necessary libraries from stable_diffusion import StableDiffusionPipelin

# Import necessary libraries from stable_diffusion import StableDiffusionPipeline import torch from PIL import Image # Load the Stable Diffusion model pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-4") # Define the prompt with a vibrant and playful interaction prompt = ( "A lively scene in a luxury airplane cabin with a go-go dancer performing a vibrant and playful dance for a Bitcoin trader. The dancer wears a colorful, eye-catching outfit with playful Bitcoin motifs, including a Bitcoin emblem on her belt and flashy accessories. Her hair is styled in a playful manner with Bitcoin-themed clips. She is dancing energetically, making eye contact with the trader and smiling flirtatiously. The trader, seated in a high-end chair, watches her with a delighted and engaged expression. The cabin is decorated with vibrant colors and dynamic lighting in shades of red, pink, and gold, creating an exciting and engaging atmosphere. The scene captures the lively interaction and playful energy between the dancer and the trader." ) # Configuration settings clip_guidance_scale = 18 # Moderate guidance for a lively and playful feel hr_scale = 3.2 # Balanced resolution hr_upscaler = "R-ESRGAN" # High-quality upscaling latent_shift = torch.randn(1, 4, 64, 64) * 0.025 # Slight variation for dynamic feel prompt_weighting = ( "Go-Go Dancer::4.0 | Playful Dance::4.5 | Bitcoin Motifs::3.5 | Delighted Trader::4.0 | Luxury Cabin::3.5 | Vibrant Lighting::4.5" ) # Emphasizes playful interaction and vibrant energy cross_attention_strength = 2.0 # Balanced focus on Bitcoin and vibrant details shallow_depth_of_field = True # Shallow depth of field to focus on interaction lighting_style = "dynamic and colorful" # Bright, vibrant lighting effects color_match_strength = 1.8 # Enhanced color matching perlin_noise_scale = 0.02 # Subtle noise for texture fractal_noise = False # No fractal noise depth_map = None # No depth map needed parallax_effect = False # N