Diffusers documentation
Text-guided depth-to-image generation
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( "stabilityai/stable-diffusion-2-depth", torch_dtype=torch.float16, use_safetensors=True, ).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 = 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 |
|---|---|
 |  |
