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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 detailed third-person action game-style prompt
prompts = {
    "cooking_for_bitcoin_whale_action_game": (
        "A dynamic, third-person action game scene where a powerful, muscular whale, adorned with a glowing Bitcoin chain, is the central character. "
        "The whale is engaged in an intense standoff in a futuristic, dystopian kitchen that doubles as a high-tech battle arena. "
        "A beautiful woman in tactical gear, with a sleek and agile build, is preparing to serve the whale food, but her posture and expression indicate "
        "she's ready to spring into action at any moment. Her eyes are locked on the whale, who is smirking confidently, seemingly unaware of the impending danger. "
        "Behind the whale, the environment is filled with destructible objects, flickering lights, and a sense of tension, as if a battle is about to break out. "
        "The camera angle is over-the-shoulder, focusing on the woman as she moves stealthily, ready to either serve or strike, depending on the player's choice. "
        "The scene is packed with action elements, including explosions, debris, and other characters who are either allies or enemies, adding to the chaos and intensity. "
        "The overall atmosphere is gritty and suspenseful, capturing the high stakes and adrenaline-pumping action typical of a third-person shooter."
    )
}

# Generate the image for the prompt
for name, prompt in prompts.items():
    # Generate the image using the prompt
    image = pipe(prompt).images[0]
    
    # Save the generated image
    filename = f"{name.lower().replace(' ', '_')}.png"
    image.save(filename)
    print(f"Saved image: {filename}")

    # Optionally, show the generated image
    image.show()
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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 detailed third-person action game-style prompt prompts = { "cooking_for_bitcoin_whale_action_game": ( "A dynamic , third-person action game scene where a powerful , muscular whale , adorned with a glowing Bitcoin chain , is the central character . " "The whale is engaged in an intense standoff in a futuristic , dystopian kitchen that doubles as a high-tech battle arena . " "A beautiful woman in tactical gear , with a sleek and agile build , is preparing to serve the whale food , but her posture and expression indicate " "she's ready to spring into action at any moment . Her eyes are locked on the whale , who is smirking confidently , seemingly unaware of the impending danger . " "Behind the whale , the environment is filled with destructible objects , flickering lights , and a sense of tension , as if a battle is about to break out . " "The camera angle is over-the-shoulder , focusing on the woman as she moves stealthily , ready to either serve or strike , depending on the player's choice . " "The scene is packed with action elements , including explosions , debris , and other characters who are either allies or enemies , adding to the chaos and intensity . " "The overall atmosphere is gritty and suspenseful , capturing the high stakes and adrenaline-pumping action typical of a third-person shooter . " ) } # Generate the image for the prompt for name , prompt in prompts . items(): # Generate the image using the prompt image = pipe(prompt) . images[0] # Save the generated image filename = f"{name . lower() . replace(' ' , '_')} . png" image . save(filename) print(f"Saved image: {filename}") # Optionally , show the generated image image . show()
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 detailed third-person action game-style prompt prompts = { "cooking_for_bitcoin_whale_action_game": ( "A dynamic, third-person action game scene where a powerful, muscular whale, adorned with a glowing Bitcoin chain, is the central character. " "The whale is engaged in an intense standoff in a futuristic, dystopian kitchen that doubles as a high-tech battle arena. " "A beautiful woman in tactical gear, with a sleek and agile build, is preparing to serve the whale food, but her posture and expression indicate " "she's ready to spring into action at any moment. Her eyes are locked on the whale, who is smirking confidently, seemingly unaware of the impending danger. " "Behind the whale, the environment is filled with destructible objects, flickering lights, and a sense of tension, as if a battle is about to break out. " "The camera angle is over-the-shoulder, focusing on the woman as she moves stealthily, ready to either serve or strike, depending on the player's choice. " "The scene is packed with action elements, including explosions, debris, and other characters who are either allies or enemies, adding to the chaos and intensity. " "The overall atmosphere is gritty and suspenseful, capturing the high stakes and adrenaline-pumping action typical of a third-person shooter." ) } # Generate the image for the prompt for name, prompt in prompts.items(): # Generate the image using the prompt image = pipe(prompt).images[0] # Save the generated image filename = f"{name.lower().replace(' ', '_')}.png" image.save(filename) print(f"Saved image: {filename}") # Optionally, show the generated image image.show()
CFG Scale
7
Steps
25
Sampler
euler
Seed
474230914
Scheduler
karras
Image Size
688 X 1024
Model
SeaArt Infinity
Generate
Size
688X1024
Date
Aug 31, 2024
Mode
Studio
Type
cell
Checkpoint & LoRA
SeaArt Infinity
Checkpoint
SeaArt Infinity
#SeaArt Infinity
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