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47 lines
2.5 KiB
Markdown
47 lines
2.5 KiB
Markdown
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# Text-guided depth-to-image generation
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[[open-in-colab]]
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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](https://github.com/isl-org/MiDaS).
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Start by creating an instance of the [`StableDiffusionDepth2ImgPipeline`]:
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```python
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import torch
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from diffusers import StableDiffusionDepth2ImgPipeline
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from diffusers.utils import load_image, make_image_grid
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pipeline = StableDiffusionDepth2ImgPipeline.from_pretrained(
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"stabilityai/stable-diffusion-2-depth",
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torch_dtype=torch.float16,
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use_safetensors=True,
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).to("cuda")
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```
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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:
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```python
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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init_image = load_image(url)
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prompt = "two tigers"
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negative_prompt = "bad, deformed, ugly, bad anatomy"
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image = pipeline(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0]
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make_image_grid([init_image, image], rows=1, cols=2)
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```
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| Input | Output |
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|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
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| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/coco-cats.png" width="500"/> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/depth2img-tigers.png" width="500"/> |
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