Instructions to use nitrosocke/Ghibli-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/Ghibli-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/Ghibli-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 ·
358bba0
1
Parent(s): 3d426d1
minor typo (#6)
Browse files- minor typo (4bcc73654a7df01354e42a2a17f18485b4dc5ae3)
Co-authored-by: Fabio Barth <barthfab@users.noreply.huggingface.co>
README.md
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@@ -54,7 +54,7 @@ You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/op
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from diffusers import StableDiffusionPipeline
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import torch
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model_id = "nitrosocke/
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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from diffusers import StableDiffusionPipeline
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import torch
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model_id = "nitrosocke/Ghibli-Diffusion"
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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