From stable_diffusion import StableDiffusionPipeline import torch from PIL impor

$from stable_diffusion import StableDiffusionPipeline import torch from PIL import Image # Load the Stable Diffusion model pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-4") # Define the prompt for the airport scene prompt = ( "In a bustling airport terminal, a cheeky go-go dancer in a flashy outfit stands near the departure gate. Her outfit is adorned with playful Bitcoin symbols and exaggerated Japanese motifs. She winks at the camera while holding a sign that says 'Welcome, Bitcoin Trader!' The setting is vibrant with travelers and amusing digital billboards showcasing funny cryptocurrency memes. The lighting is bright and colorful, adding a comical flair to the scene. The dancer’s playful pose and animated expression create a fun and engaging atmosphere." ) # Configuration settings clip_guidance_scale = 16 # Slightly lower to add a playful feel hr_scale = 3.0 # Balanced resolution hr_upscaler = "R-ESRGAN" latent_shift = torch.randn(1, 4, 64, 64) * 0.03 # Slight variation for humor cross_attention_strength = 1.5 # Focus on playful details shallow_depth_of_field = True # Emphasize interaction lighting_style = "bright and colorful" # Vibrant lighting effects color_match_strength = 1.5 # Enhanced color matching perlin_noise_scale = 0.03 # Subtle noise for texture fractal_noise = False # Generate the image image = pipe( prompt=prompt, clip_guidance_scale=clip_guidance_scale, hr_scale=hr_scale, hr_upscaler=hr_upscaler, latent_shift=latent_shift, cross_attention_strength=cross_attention_strength, shallow_depth_of_field=shallow_depth_of_field, lighting_style=lighting_style, color_match_strength=color_match_strength, noise="perlin", noise_scale=perlin_noise_scale, fractal_noise=fractal_noise ).images[0] # Save the generated image image.save("airport_dancer_welcome.png")$