Instructions to use CompVis/stable-diffusion-v1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CompVis/stable-diffusion-v1-2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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Parent(s): 6d1b34d
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README.md
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@@ -148,6 +148,8 @@ Using the model to generate content that is cruel to individuals is a misuse of
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[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
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and is not fit for product use without additional safety mechanisms and
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considerations.
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### Bias
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[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
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and is not fit for product use without additional safety mechanisms and
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considerations.
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- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
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The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
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### Bias
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