Instructions to use nitrosocke/archer-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/archer-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("nitrosocke/archer-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
- Draw Things
- DiffusionBee
Commit ·
616aa52
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Parent(s): 27fa72e
updated prompt information
Browse files
README.md
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@@ -19,4 +19,11 @@ If you enjoy this model, please check out my other models on [Huggingface](https
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**Sample images used for training:**
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This model was trained using the diffusers based dreambooth training and prior-preservation loss in 4.000 steps and using the _train-text-encoder_ feature.
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**Sample images used for training:**
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#### Prompt and settings for portraits:
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**archer style beautiful portrait of ariana grande**
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_Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 946707299, Size: 512x704_
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#### Prompt and settings for landscapes:
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**archer style suburban street night blue indoor lighting Negative prompt: grey cars**
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_Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2915669764, Size: 1024x576_
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This model was trained using the diffusers based dreambooth training and prior-preservation loss in 4.000 steps and using the _train-text-encoder_ feature.
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