license: mit language:

en pipeline_tag: text-to-image tags:
stablediffusion
uncensored
nsfw
image
generation widget:
- text: >- TEST output: url: samples/nsfw1.webp
- text: >- TEST output: url: samples/nsfw2.webp
- text: >- TEST output: url: samples/nsfw3.webp
- text: >- TEST output: url: samples/nsfw4.webp

NSFW-Uncensored

Uncensored Image Generation Model

Model Description

This model is a playground that minimizes censorship restrictions, allowing exploration of the technical possibilities of AI-based image generation. Through various prompts, you can test censorship boundaries and verify the actual performance of image generation AI.

Example code

# Basic usage example
from diffusers import DiffusionPipeline
import torch

# Load the model (with float16 precision for GPU)
pipe = DiffusionPipeline.from_pretrained(
    "Heartsync/NSFW-Uncensored",
    torch_dtype=torch.float16
)
pipe.to("cuda")  # Move to GPU

# Generate an image with a simple prompt
prompt = "Woman in an elegant dress standing by a window, detailed lighting, 8k"
negative_prompt = "low quality, blurry, deformed"

# Create the image
image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]

# Save the image
image.save("generated_image.png")

# Advanced example - fixed seed and additional parameters
import numpy as np

# Set seed for reproducible results
seed = 42
generator = torch.Generator("cuda").manual_seed(seed)

# Advanced parameter settings
prompt = "A dramatic scene with explicit details, cinematic lighting, high resolution"
image = pipe(
    prompt=prompt,
    negative_prompt="ugly, deformed, disfigured, poor quality, low resolution",
    num_inference_steps=50,  # More steps for higher quality
    guidance_scale=8.0,     # Increase prompt fidelity
    width=768,              # Adjust image width
    height=768,             # Adjust image height
    generator=generator     # Fixed seed
).images[0]

image.save("high_quality_image.png")

Example code

# Basic usage example from diffusers import DiffusionPipeline import torch # Load the model (with float16 precision for GPU) pipe = DiffusionPipeline.from_pretrained( "Heartsync/NSFW-Uncensored", torch_dtype=torch.float16 ) pipe.to("cuda") # Move to GPU # Generate an image with a simple prompt prompt = "Woman in an elegant dress standing by a window, detailed lighting, 8k" negative_prompt = "low quality, blurry, deformed" # Create the image image = pipe( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=30, guidance_scale=7.5 ).images[0] # Save the image image.save("generated_image.png") # Advanced example - fixed seed and additional parameters import numpy as np # Set seed for reproducible results seed = 42 generator = torch.Generator("cuda").manual_seed(seed) # Advanced parameter settings prompt = "A dramatic scene with explicit details, cinematic lighting, high resolution" image = pipe( prompt=prompt, negative_prompt="ugly, deformed, disfigured, poor quality, low resolution", num_inference_steps=50, # More steps for higher quality guidance_scale=8.0, # Increase prompt fidelity width=768, # Adjust image width height=768, # Adjust image height generator=generator # Fixed seed ).images[0] image.save("high_quality_image.png")