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 the Double Exposure Diffusion model, trained on personally generated images off of MidJourney version 4.
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You trigger double exposure style images using token: **_dbl_ex_**.
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**Cars and Landscapes rendered with the model:**
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#### Prompt and settings
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**modern disney (baby lion) Negative prompt: person human**
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_Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512_
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This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 9.000 steps.
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## License
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---
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**Double Exposure Diffusion**
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This is version 1 of the Double Exposure Diffusion model, trained on personally generated images off of MidJourney version 4.
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You trigger double exposure style images using token: **_dbl_ex_**.
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**Example:**
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#### Prompt and settings:
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**dbl_ex, white background**<br>
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**Negative prompt: contrast**<br>
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_Steps: 30, Sampler: Euler a, CFG scale: 5, Seed: 3908678340, Size: 512x512_
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This model was trained using TheLastBen’s fast DreamBooth model @ 1500 steps.
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## License
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