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* Docs kr update 3 controlnet, reproducibility 업로드 generator 그대로 사용 seamless multi-GPU 그대로 사용 create_dataset 번역 1차 stable_diffusion_jax new translation Add coreml, tome kr docs minor fix translate training/instructpix2pix fix training/instructpix2pix.mdx using-diffusers/weighting_prompts 번역 1차 add SDXL docs Translate using-diffuers/loading_overview.md translate using-diffusers/textual_inversion_inference.md Conditional image generation (#37) * stable_diffusion_jax * index_update * index_update * condition_image_generation --------- Co-authored-by: Seongsu Park <tjdtnsu@gmail.com> jihwan/stable_diffusion.mdx custom_diffusion 작업 완료 quicktour 작업 완료 distributed inference & control brightness (#40) * distributed_inference.mdx * control_brightness --------- Co-authored-by: idra79haza <idra79haza@github.com> Co-authored-by: Seongsu Park <tjdtnsu@gmail.com> using_safetensors (#41) * distributed_inference.mdx * control_brightness * using_safetensors.mdx --------- Co-authored-by: idra79haza <idra79haza@github.com> Co-authored-by: Seongsu Park <tjdtnsu@gmail.com> delete safetensor short * Repace mdx to md * toctree update * Add controlling_generation * toctree fix * colab link, minor fix * docs name typo fix * frontmatter fix * translation fix
76 lines
3.5 KiB
Markdown
76 lines
3.5 KiB
Markdown
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# Text-guided 이미지 인페인팅(inpainting)
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[[open-in-colab]]
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[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.
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먼저 [`StableDiffusionInpaintPipeline`] 인스턴스를 불러옵니다:
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```python
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import PIL
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import requests
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import torch
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from io import BytesIO
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from diffusers import StableDiffusionInpaintPipeline
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pipeline = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting",
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torch_dtype=torch.float16,
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)
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pipeline = pipeline.to("cuda")
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```
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나중에 교체할 강아지 이미지와 마스크를 다운로드하세요:
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```python
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def download_image(url):
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response = requests.get(url)
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return PIL.Image.open(BytesIO(response.content)).convert("RGB")
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img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
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mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
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init_image = download_image(img_url).resize((512, 512))
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mask_image = download_image(mask_url).resize((512, 512))
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```
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이제 마스크를 다른 것으로 교체하라는 프롬프트를 만들 수 있습니다:
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```python
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prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
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image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
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```
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`image` | `mask_image` | `prompt` | output |
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:-------------------------:|:-------------------------:|:-------------------------:|-------------------------:|
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<img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="250"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="250"/> | ***Face of a yellow cat, high resolution, sitting on a park bench*** | <img src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint/yellow_cat_sitting_on_a_park_bench.png" alt="drawing" width="250"/> |
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<Tip warning={true}>
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이전의 실험적인 인페인팅 구현에서는 품질이 낮은 다른 프로세스를 사용했습니다. 이전 버전과의 호환성을 보장하기 위해 새 모델이 포함되지 않은 사전학습된 파이프라인을 불러오면 이전 인페인팅 방법이 계속 적용됩니다.
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</Tip>
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아래 Space에서 이미지 인페인팅을 직접 해보세요!
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<iframe
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src="https://runwayml-stable-diffusion-inpainting.hf.space"
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frameborder="0"
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width="850"
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height="500"
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></iframe>
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