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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 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 image
Play around with the Spaces below and see if you notice a difference between generated images with and without a depth map!