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 Japanese city scene prompt = ( "In a lively Japanese city street, the go-go dancer and Bitcoin trader are having a humorous adventure. The dancer, now in a quirky Japanese-themed outfit with Bitcoin accents, playfully interacts with street performers and vendors. They are surrounded by colorful neon signs and amusing cultural elements. The scene is vibrant and whimsical, with exaggerated expressions and playful poses. The lighting reflects the bustling city vibe, creating a humorous and lively 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("japanese_city_adventure.png")