# Import necessary libraries from stable_diffusion import StableDiffusionPipelin

$# Import necessary libraries from stable_diffusion import StableDiffusionPipeline import torch from PIL import Image # Load the Stable Diffusion model pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-4") # Define the prompt prompt = ( "Elegant scene in a Spanish office: A woman in a white oversized men's shirt, barely covering her hips, with one shoulder exposed. " "Her hair is braided into a single braid, with a Bitcoin-shaped hairpin. She is barefoot, relaxed, with a slightly worn appearance, " "playing bowling with a bowling ball featuring a Bitcoin symbol. Her eyes are blue, her nose is large, and her lips are narrow. " "Her nipples are visible through the shirt. On the table, there is spilled milk, and some milk is left on her lips. " "The scene combines elements of Vatican secretaries with a relaxed, sensual atmosphere in a bowling setting." ) # Configuration settings clip_guidance_scale = 18 # Strength of adherence to the description hr_scale = 2.5 # High resolution for detailed output hr_upscaler = "R-ESRGAN" # High-quality upscaling method latent_shift = torch.randn(1, 4, 64, 64) * 0.01 # Slight adjustments to the latent space for natural variations prompt_weighting = ( "Woman in oversized shirt::2.0 | Bitcoin hairpin::1.8 | Relaxed atmosphere::1.7 | Bowling with Bitcoin symbol::1.8 | " "Blue eyes, large nose, narrow lips::1.6 | Spilled milk::1.5" ) # Emphasizing key elements of the scene cross_attention_strength = 1.0 # Strong focus on the main elements of the scene symmetry = "none" # No symmetry for a natural composition symmetry_loss_weight = 0.1 # Minimal influence of symmetry color_match_strength = 0.85 # Harmonious color blending perlin_noise_scale = 0.03 # Low noise level for smooth textures fractal_noise = False # Disable fractal noise depth_map = None # No depth map parallax_effect = False # No parallax effect # Generate the image image = pipe( prompt=prompt_weighting, clip_gu$

# 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 prompt = ( "Elegant scene in a Spanish office: A woman in a white oversized men's shirt, barely covering her hips, with one shoulder exposed. " "Her hair is braided into a single braid, with a Bitcoin-shaped hairpin. She is barefoot, relaxed, with a slightly worn appearance, " "playing bowling with a bowling ball featuring a Bitcoin symbol. Her eyes are blue, her nose is large, and her lips are narrow. " "Her nipples are visible through the shirt. On the table, there is spilled milk, and some milk is left on her lips. " "The scene combines elements of Vatican secretaries with a relaxed, sensual atmosphere in a bowling setting." ) # Configuration settings clip_guidance_scale = 18 # Strength of adherence to the description hr_scale = 2.5 # High resolution for detailed output hr_upscaler = "R-ESRGAN" # High-quality upscaling method latent_shift = torch.randn(1, 4, 64, 64) * 0.01 # Slight adjustments to the latent space for natural variations prompt_weighting = ( "Woman in oversized shirt::2.0 | Bitcoin hairpin::1.8 | Relaxed atmosphere::1.7 | Bowling with Bitcoin symbol::1.8 | " "Blue eyes, large nose, narrow lips::1.6 | Spilled milk::1.5" ) # Emphasizing key elements of the scene cross_attention_strength = 1.0 # Strong focus on the main elements of the scene symmetry = "none" # No symmetry for a natural composition symmetry_loss_weight = 0.1 # Minimal influence of symmetry color_match_strength = 0.85 # Harmonious color blending perlin_noise_scale = 0.03 # Low noise level for smooth textures fractal_noise = False # Disable fractal noise depth_map = None # No depth map parallax_effect = False # No parallax effect # Generate the image image = pipe( prompt=prompt_weighting, clip_gu

Generation Data

Enregistrements

# 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 prompt = ( "Elegant scene in a Spanish office: A woman in a white oversized men's shirt , barely covering her hips , with one shoulder exposed . " "Her hair is braided into a single braid , with a Bitcoin-shaped hairpin . She is barefoot , relaxed , with a slightly worn appearance , " "playing bowling with a bowling ball featuring a Bitcoin symbol . Her eyes are blue , her nose is large , and her lips are narrow . " "Her nipples are visible through the shirt . On the table , there is spilled milk , and some milk is left on her lips . " "The scene combines elements of Vatican secretaries with a relaxed , sensual atmosphere in a bowling setting . " ) # Configuration settings clip_guidance_scale = 18 # Strength of adherence to the description hr_scale = 2 . 5 # High resolution for detailed output hr_upscaler = "R-ESRGAN" # High-quality upscaling method latent_shift = torch . randn(1 , 4 , 64 , 64) * 0 . 01 # Slight adjustments to the latent space for natural variations prompt_weighting = ( "Woman in oversized shirt::2 . 0 | Bitcoin hairpin::1 . 8 | Relaxed atmosphere::1 . 7 | Bowling with Bitcoin symbol::1 . 8 | " "Blue eyes , large nose , narrow lips::1 . 6 | Spilled milk::1 . 5" ) # Emphasizing key elements of the scene cross_attention_strength = 1 . 0 # Strong focus on the main elements of the scene symmetry = "none" # No symmetry for a natural composition symmetry_loss_weight = 0 . 1 # Minimal influence of symmetry color_match_strength = 0 . 85 # Harmonious color blending perlin_noise_scale = 0 . 03 # Low noise level for smooth textures fractal_noise = False # Disable fractal noise depth_map = None # No depth map parallax_effect = False # No parallax effect # Generate the image image = pipe( prompt=prompt_weighting , clip_gu

Info

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688X1024

Date

Aug 24, 2024

Mode

Studio

Type

cell

Checkpoint & LoRA

SeaArt Infinity

SeaArt Infinity

#SeaArt Infinity

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