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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README.md
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@@ -29,6 +29,8 @@ checkpoint and subsequently fine-tuned on 515,000 steps at resolution `512x512`
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filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`.
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For more information, please refer to [Training](#training).
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## Model Details
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- **Developed by:** Robin Rombach, Patrick Esser
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- **Model type:** Diffusion-based text-to-image generation model
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filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`.
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For more information, please refer to [Training](#training).
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This weights here are intended to be used with the D🧨iffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original)
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## Model Details
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- **Developed by:** Robin Rombach, Patrick Esser
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- **Model type:** Diffusion-based text-to-image generation model
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