Stable Diffusion Inference with Diffusers (high-level)

deep learning
python
english
Author

Mingyu Jeon

Published

September 9, 2023

Stable Diffusion Inference (high-level)

Create an image using Diffusers library.

Install and Import Libraries

!pip install -qq diffusers transformers scipy ftfy accelerate
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
device
'cuda'
prompt = ["a photograph of an astronaut riding a horse"]

height = 512  # default height of Stable Diffusion
width = 512  # default width of Stable Diffusion

num_inference_steps = 50  # Number of denoising steps

guidance_scale = 7.5  # Scale for classifier-free guidance

generator = torch.manual_seed(256)  # Seed generator to create the inital latent noise

batch_size = 2
from diffusers import LMSDiscreteScheduler, StableDiffusionPipeline

scheduler = LMSDiscreteScheduler.from_pretrained(
    "CompVis/stable-diffusion-v1-4", subfolder="scheduler"
)
pipe = StableDiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4", scheduler=scheduler
)
pipe
StableDiffusionPipeline {
  "_class_name": "StableDiffusionPipeline",
  "_diffusers_version": "0.20.2",
  "_name_or_path": "CompVis/stable-diffusion-v1-4",
  "feature_extractor": [
    "transformers",
    "CLIPImageProcessor"
  ],
  "requires_safety_checker": true,
  "safety_checker": [
    "stable_diffusion",
    "StableDiffusionSafetyChecker"
  ],
  "scheduler": [
    "diffusers",
    "LMSDiscreteScheduler"
  ],
  "text_encoder": [
    "transformers",
    "CLIPTextModel"
  ],
  "tokenizer": [
    "transformers",
    "CLIPTokenizer"
  ],
  "unet": [
    "diffusers",
    "UNet2DConditionModel"
  ],
  "vae": [
    "diffusers",
    "AutoencoderKL"
  ]
}
pipe = pipe.to(device)

High-level

pil_images = pipe(
    prompt=prompt * batch_size,
    height=height,
    width=width,
    num_inference_steps=num_inference_steps,
    guidance_scale=guidance_scale,
    generator=generator,
).images
for pil_image in pil_images:
    display(pil_image)

References