Diffusers documentation

Text-guided depth-to-image generation

Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

Text-guided depth-to-image generation

The StableDiffusionDepth2ImgPipeline lets you pass a text prompt and an initial image to condition the generation of new images. In addition, you can also pass a depth_map to preserve the image structure. If no depth_map is provided, the pipeline automatically predicts the depth via an integrated depth-estimation model.

Start by creating an instance of the StableDiffusionDepth2ImgPipeline:

import torch
from diffusers import StableDiffusionDepth2ImgPipeline
from diffusers.utils import load_image, make_image_grid

pipeline = StableDiffusionDepth2ImgPipeline.from_pretrained(

Now pass your prompt to the pipeline. You can also pass a negative_prompt to prevent certain words from guiding how an image is generated:

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
init_image = load_image(url)
prompt = "two tigers"
negative_prompt = "bad, deformed, ugly, bad anatomy"
image = pipeline(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
Input Output
< > Update on GitHub