# 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 = ( "A dynamic and sensitive scene in a cozy home: 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 at home 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 setting is warm and intimate, with soft natural light coming through the windows, reflecting the emotional and physical warmth of the moment. " "The overall atmosphere is lively, with a touch of playful spontaneity." ) # Configuration settings clip_guidance_scale = 20 # Stronger adherence to the description for dynamic and emotional elements 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) * 0.015 # Slightly more variation in the latent space for a natural, lively look prompt_weighting = ( "Woman in oversized shirt::2.2 | Bitcoin hairpin::1.9 | Relaxed and lively atmosphere::2.0 | Bowling at home with Bitcoin symbol::1.9 | " "Blue eyes, large nose, narrow lips::1.7 | Spilled milk::1.6 | Cozy home with natural light::2.1" ) # Enhanced focus on key elements to create a dynamic and sensitive scene cross_attention_strength = 1.2 # Enhanced focus on dynamic elements symmetry = "none" # No symmetry for a natural composition symmetry_loss_weight = 0.05 # Minimal influence of symmetry color_match_strength = 0.9 # Harmonious color blending to enhance war
Prompts
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# 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 = (
"A dynamic and sensitive scene in a cozy home: 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 at home 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 setting is warm and intimate
,
with soft natural light coming through the windows
,
reflecting the emotional and physical warmth of the moment
.
"
"The overall atmosphere is lively
,
with a touch of playful spontaneity
.
"
)
# Configuration settings
clip_guidance_scale = 20 # Stronger adherence to the description for dynamic and emotional elements
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
.
015 # Slightly more variation in the latent space for a natural
,
lively look
prompt_weighting = (
"Woman in oversized shirt::2
.
2 | Bitcoin hairpin::1
.
9 | Relaxed and lively atmosphere::2
.
0 | Bowling at home with Bitcoin symbol::1
.
9 | "
"Blue eyes
,
large nose
,
narrow lips::1
.
7 | Spilled milk::1
.
6 | Cozy home with natural light::2
.
1"
) # Enhanced focus on key elements to create a dynamic and sensitive scene
cross_attention_strength = 1
.
2 # Enhanced focus on dynamic elements
symmetry = "none" # No symmetry for a natural composition
symmetry_loss_weight = 0
.
05 # Minimal influence of symmetry
color_match_strength = 0
.
9 # Harmonious color blending to enhance war
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