Instructions to use Ketansomewhere/FER_2013_Conditional_Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ketansomewhere/FER_2013_Conditional_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("Ketansomewhere/FER_2013_Conditional_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
Update README.md
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README.md
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@@ -145,7 +145,7 @@ def create_image_grid(images, grid_size, save_path):
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return grid_img
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if __name__ == "__main__":
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repo_id = "Ketansomewhere/
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num_classes = 7 # Adjust to your number of classes
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batch_size = 64
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num_inference_steps = 1000 # Can be as low as 50 for faster generation
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return grid_img
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if __name__ == "__main__":
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repo_id = "Ketansomewhere/FER_2013_Conditional_Diffusion"
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num_classes = 7 # Adjust to your number of classes
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batch_size = 64
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num_inference_steps = 1000 # Can be as low as 50 for faster generation
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