[[open-in-colab]]
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
import requests
from PIL import Image
from diffusers import StableDiffusionDepth2ImgPipeline
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth",
torch_dtype=torch.float16,
).to("cuda")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 = Image.open(requests.get(url, stream=True).raw)
prompt = "two tigers"
n_prompt = "bad, deformed, ugly, bad anatomy"
image = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.7).images[0]
image| Input | Output |
|---|---|
 |
 |
Play around with the Spaces below and see if you notice a difference between generated images with and without a depth map!
<iframe src="https://radames-stable-diffusion-depth2img.hf.space" frameborder="0" width="850" height="500" ></iframe>