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* Fix typos, improve, update; kandinsky doesn't want fp16 due to deprecation; ogkalu and kohbanye don't have safetensor; add make_image_grid for better visualization * Update inpaint.md * Remove erronous Space * Update docs/source/en/using-diffusers/conditional_image_generation.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update img2img.md * load_image() already converts to RGB * Update depth2img.md * Update img2img.md * Update inpaint.md --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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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
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)
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