From stable_diffusion import StableDiffusionPipeline import torch from PIL impor

$from stable_diffusion import StableDiffusionPipeline import torch from PIL import Image # Load the Stable Diffusion model pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-4") # Define the prompt for the airplane scene prompt = ( "Inside a luxurious airplane cabin, the go-go dancer performs a hilariously energetic dance for a Bitcoin trader. She’s wearing a vibrant outfit with exaggerated Bitcoin designs and accessories. Her dance moves are exaggerated and playful, and she makes comedic facial expressions while making eye contact with the trader. The trader, seated comfortably, is laughing and clapping, clearly entertained. The cabin is decorated with bright reds, pinks, and golds, and the lighting is vibrant and warm, emphasizing the fun and cheeky interaction." ) # Configuration settings clip_guidance_scale = 16 # Slightly lower to add a playful feel hr_scale = 3.0 # Balanced resolution hr_upscaler = "R-ESRGAN" latent_shift = torch.randn(1, 4, 64, 64) * 0.03 # Slight variation for humor cross_attention_strength = 1.5 # Focus on playful details shallow_depth_of_field = True # Emphasize interaction lighting_style = "bright and colorful" # Vibrant lighting effects color_match_strength = 1.5 # Enhanced color matching perlin_noise_scale = 0.03 # Subtle noise for texture fractal_noise = False # Generate the image image = pipe( prompt=prompt, clip_guidance_scale=clip_guidance_scale, hr_scale=hr_scale, hr_upscaler=hr_upscaler, latent_shift=latent_shift, cross_attention_strength=cross_attention_strength, shallow_depth_of_field=shallow_depth_of_field, lighting_style=lighting_style, color_match_strength=color_match_strength, noise="perlin", noise_scale=perlin_noise_scale, fractal_noise=fractal_noise ).images[0] # Save the generated image image.save("airplane_dancer_performance.png")$