Instructions to use Falah/islamicdiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Falah/islamicdiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Falah/islamicdiffusion", 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
update
Browse files
README.md
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@@ -28,6 +28,7 @@ any prompt and add word islamidiffusion style :
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"Bearded old man, handsome, rugged, sadness + crying, Arabic, award-winning photography, nikon d750 islamicdiffusion"
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<img src="https://huggingface.co/Falah/islamicdiffusion/resolve/main/00725-913347336-islamicdiffusion%20%20Bearded%20old%20man%2C%20handsome%2C%20rugged%2C%20sadness%20%2B%20crying%2C%20Arabic%2C%20award-winning%20photography%2C%20nikon%20d750.png" style="max-width: 800px;" width="100%"/>
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### 🧨 Diffusers
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This model can be used just like any other Stable Diffusion model. For more information,
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model_id = "Falah/islamicdiffusion"
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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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prompt = "
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image = pipe(prompt).images[0]
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image.save("./result.jpg")
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```
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"Bearded old man, handsome, rugged, sadness + crying, Arabic, award-winning photography, nikon d750 islamicdiffusion"
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<img src="https://huggingface.co/Falah/islamicdiffusion/resolve/main/00725-913347336-islamicdiffusion%20%20Bearded%20old%20man%2C%20handsome%2C%20rugged%2C%20sadness%20%2B%20crying%2C%20Arabic%2C%20award-winning%20photography%2C%20nikon%20d750.png" style="max-width: 800px;" width="100%"/>
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+
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### 🧨 Diffusers
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This model can be used just like any other Stable Diffusion model. For more information,
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model_id = "Falah/islamicdiffusion"
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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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prompt = ""Bearded old man, handsome, rugged, sadness + crying, Arabic, award-winning photography, nikon d750 islamicdiffusion"
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image = pipe(prompt).images[0]
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image.save("./result.jpg")
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```
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