Instructions to use joachimsallstrom/Double-Exposure-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use joachimsallstrom/Double-Exposure-Diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("joachimsallstrom/Double-Exposure-Diffusion", 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 Settings
- Draw Things
- DiffusionBee
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**Double Exposure Diffusion**
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This is version 2 of the
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The model file (Double_Exposure_v2.ckpt) can be downloaded on the
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**Example 1:**
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This model was trained using Shivam's DreamBooth model on Google Colab @ 2000 steps.
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The previous version 1 of Double Exposure Diffusion is also available in the
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## License
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**Double Exposure Diffusion**
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This is version 2 of the <i>Double Exposure Diffusion</i> model, trained specifically on images of people and a few animals.
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The model file (Double_Exposure_v2.ckpt) can be downloaded on the **Files** page. You trigger double exposure style images using token: **_dublex style_** or just **_dublex_**.
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**Example 1:**
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This model was trained using Shivam's DreamBooth model on Google Colab @ 2000 steps.
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</p>
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The previous version 1 of Double Exposure Diffusion is also available in the **Files** section.
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## License
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