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Python / Python Modern Generative AI and Agents Interview Questions

How do you generate and manipulate images using Hugging Face's Diffusers library?

The diffusers library provides a unified API for diffusion models including Stable Diffusion, SDXL, Flux, and ControlNet. Diffusion models generate images by progressively denoising random Gaussian noise, guided by a text prompt encoded by a text encoder (typically CLIP or T5). The DiffusionPipeline wraps the full pipeline — scheduler, UNet/DiT, VAE, and text encoder — into a single callable.

# pip install diffusers accelerate
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch

# ── Text-to-image with Stable Diffusion 2.1
pipe = StableDiffusionPipeline.from_pretrained(
    'stabilityai/stable-diffusion-2-1',
    torch_dtype=torch.float16,
)
# Faster scheduler (20 steps instead of default 50)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to('cuda')
pipe.enable_attention_slicing()  # reduce VRAM usage

image = pipe(
    prompt='A serene mountain lake at sunset, photorealistic, 8k',
    negative_prompt='blurry, low quality, distorted, ugly',  # what to avoid
    num_inference_steps=20,
    guidance_scale=7.5,       # higher = more prompt-adherent, less diverse
    height=768, width=768,
    generator=torch.Generator('cuda').manual_seed(42),  # reproducible
).images[0]
image.save('landscape.png')

# ── Image-to-image (modify an existing image)
from diffusers import StableDiffusionImg2ImgPipeline
from PIL import Image

img2img_pipe = StableDiffusionImg2ImgPipeline(**pipe.components)
input_image  = Image.open('sketch.png').resize((512, 512))
output = img2img_pipe(
    prompt='oil painting style, masterpiece',
    image=input_image,
    strength=0.75,  # 0=no change, 1=ignore input entirely
).images[0]

# ── FLUX.1 (2024 state-of-the-art)
from diffusers import FluxPipeline
flux_pipe = FluxPipeline.from_pretrained(
    'black-forest-labs/FLUX.1-schnell', torch_dtype=torch.bfloat16
).to('cuda')
img = flux_pipe('A futuristic city at night', num_inference_steps=4).images[0]

What does the guidance_scale parameter control in Stable Diffusion generation?
What does the negative_prompt parameter do in Stable Diffusion?

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More Related questions...

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