Editing Diffusion Models (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Generating images with Stable Diffusion using diffusers:'''

<syntaxhighlight lang="python">
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch

# Load Stable Diffusion XL
pipe = StableDiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16"
)

# Use fast DPM-Solver++ scheduler (25 steps instead of 50+)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

# Generate image
image = pipe(
    prompt="A futuristic library with glowing holographic books, cinematic lighting, 8K",
    negative_prompt="blurry, low quality, distorted, watermark",
    num_inference_steps=25,
    guidance_scale=7.5,
    width=1024,
    height=1024,
    generator=torch.Generator("cuda").manual_seed(42)
).images[0]

image.save("output.png")
</syntaxhighlight>

; Key diffusion model parameters
: '''num''inference''steps''' → More steps = higher quality but slower. 20-50 is typical; use DDIM or DPM-Solver for efficiency.
: '''guidance_scale''' → 7–12 for good prompt adherence. Too high → oversaturation and artifacts. Too low → prompt ignored.
: '''negative_prompt''' → Tells the model what to avoid. Useful for removing common quality issues.
: '''seed''' → Set for reproducibility; vary for diversity.
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