| # 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") | |
Xet Storage Details
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