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* Remove bogus file * [Docs] Remove mentioning of gated access since no longer exsits * add docs to index * Apply suggestions from code review Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
36 lines
1.5 KiB
Plaintext
36 lines
1.5 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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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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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Text-Guided Image-to-Image Generation
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The [`StableDiffusionDepth2ImgPipeline`] lets you pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the images' structure. If no `depth_map` is provided, the pipeline will automatically predict the depth via an integrated depth-estimation model.
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```python
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import torch
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import requests
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from PIL import Image
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from diffusers import StableDiffusionDepth2ImgPipeline
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pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
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"stabilityai/stable-diffusion-2-depth",
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torch_dtype=torch.float16,
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).to("cuda")
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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init_image = Image.open(requests.get(url, stream=True).raw)
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prompt = "two tigers"
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n_prompt = "bad, deformed, ugly, bad anatomy"
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image = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.7).images[0]
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```
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