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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")
pipe = pipe.to("cuda")  # Use GPU if available

# Define the prompt for Mezo Pepe
prompt_pepe = (
    "A playful meme featuring Pepe the Frog in the context of Mezo. Pepe is depicted as a Bitcoin superhero, complete with a mask and cape, "
    "fighting off market FUD. The background is filled with Bitcoin symbols and meme culture references, combining humor and resilience."
)

# Configuration settings
clip_guidance_scale = 20  # Strong guidance for focus
hr_scale = 4.0  # High-resolution scaling
hr_upscaler = "R-ESRGAN"  # High-quality upscaling
latent_shift = torch.randn(1, 4, 64, 64) * 0.02  # Subtle variation for dynamic effect
shallow_depth_of_field = True  # Shallow depth of field for subject focus
lighting_style = "modern and bright"  # Modern lighting effects
color_match_strength = 1.5  # Balanced color matching
noise_scale = 0.02  # Noise level for texture
fractal_noise = True  # Adds fractal noise for enhanced texture

# Generate the image for Mezo Pepe
image_pepe = pipe(
    prompt=prompt_pepe,
    clip_guidance_scale=clip_guidance_scale,
    hr_scale=hr_scale,
    hr_upscaler=hr_upscaler,
    latent_shift=latent_shift,
    shallow_depth_of_field=shallow_depth_of_field,
    lighting_style=lighting_style,
    color_match_strength=color_match_strength,
    noise="perlin",
    noise_scale=noise_scale,
    fractal_noise=fractal_noise
).images[0]

# Save the generated image
filename_pepe = "mezo_pepe.png"
image_pepe.save(filename_pepe)
print(f"Saved image: {filename_pepe}")
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from stable_diffusion import StableDiffusionPipeline import torch from PIL import Image # Load the Stable Diffusion model pipe = StableDiffusionPipeline . from_pretrained("stable-diffusion-v1-4") pipe = pipe . to("cuda") # Use GPU if available # Define the prompt for Mezo Pepe prompt_pepe = ( "A playful meme featuring Pepe the Frog in the context of Mezo . Pepe is depicted as a Bitcoin superhero , complete with a mask and cape , " "fighting off market FUD . The background is filled with Bitcoin symbols and meme culture references , combining humor and resilience . " ) # Configuration settings clip_guidance_scale = 20 # Strong guidance for focus hr_scale = 4 . 0 # High-resolution scaling hr_upscaler = "R-ESRGAN" # High-quality upscaling latent_shift = torch . randn(1 , 4 , 64 , 64) * 0 . 02 # Subtle variation for dynamic effect shallow_depth_of_field = True # Shallow depth of field for subject focus lighting_style = "modern and bright" # Modern lighting effects color_match_strength = 1 . 5 # Balanced color matching noise_scale = 0 . 02 # Noise level for texture fractal_noise = True # Adds fractal noise for enhanced texture # Generate the image for Mezo Pepe image_pepe = pipe( prompt=prompt_pepe , clip_guidance_scale=clip_guidance_scale , hr_scale=hr_scale , hr_upscaler=hr_upscaler , latent_shift=latent_shift , shallow_depth_of_field=shallow_depth_of_field , lighting_style=lighting_style , color_match_strength=color_match_strength , noise="perlin" , noise_scale=noise_scale , fractal_noise=fractal_noise ) . images[0] # Save the generated image filename_pepe = "mezo_pepe . png" image_pepe . save(filename_pepe) print(f"Saved image: {filename_pepe}")
INFO
Prompts
from stable_diffusion import StableDiffusionPipeline import torch from PIL import Image # Load the Stable Diffusion model pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-4") pipe = pipe.to("cuda") # Use GPU if available # Define the prompt for Mezo Pepe prompt_pepe = ( "A playful meme featuring Pepe the Frog in the context of Mezo. Pepe is depicted as a Bitcoin superhero, complete with a mask and cape, " "fighting off market FUD. The background is filled with Bitcoin symbols and meme culture references, combining humor and resilience." ) # Configuration settings clip_guidance_scale = 20 # Strong guidance for focus hr_scale = 4.0 # High-resolution scaling hr_upscaler = "R-ESRGAN" # High-quality upscaling latent_shift = torch.randn(1, 4, 64, 64) * 0.02 # Subtle variation for dynamic effect shallow_depth_of_field = True # Shallow depth of field for subject focus lighting_style = "modern and bright" # Modern lighting effects color_match_strength = 1.5 # Balanced color matching noise_scale = 0.02 # Noise level for texture fractal_noise = True # Adds fractal noise for enhanced texture # Generate the image for Mezo Pepe image_pepe = pipe( prompt=prompt_pepe, clip_guidance_scale=clip_guidance_scale, hr_scale=hr_scale, hr_upscaler=hr_upscaler, latent_shift=latent_shift, shallow_depth_of_field=shallow_depth_of_field, lighting_style=lighting_style, color_match_strength=color_match_strength, noise="perlin", noise_scale=noise_scale, fractal_noise=fractal_noise ).images[0] # Save the generated image filename_pepe = "mezo_pepe.png" image_pepe.save(filename_pepe) print(f"Saved image: {filename_pepe}")
CFG Scale
7
Steps
25
Sampler
euler
Seed
1618229668
Scheduler
karras
Image Size
688 X 1024
Model
SeaArt Infinity
Generate
Size
688X1024
Date
Aug 30, 2024
Mode
Studio
Type
cell
Checkpoint & LoRA
SeaArt Infinity
Checkpoint
SeaArt Infinity
#Cartoon
#Furry
#SeaArt Infinity
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