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README.md
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@@ -38,7 +38,7 @@ pip install diffusers accelerate transformers
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from PIL import Image
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from diffusers import DiffusionPipeline
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pipeline = DiffusionPipeline.from_pretrained("
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output = pipeline("a flamingo", "a candy cane", seed=1, num_inference_steps=200, batch_size=1)
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image = Image.fromarray(output[0])
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@@ -53,7 +53,7 @@ Arguments that can be set by user in `pipeline()`:
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- `num_inference_steps`[optional: default=200]: number of denoising steps
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- `batch_size` [optional: default=1]: batch size
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- `guidance_scale` [optional: default=7.5]: scale for classifier-free guidance
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- `height`, `width` [optional: default=
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## Citation
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from PIL import Image
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from diffusers import DiffusionPipeline
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pipeline = DiffusionPipeline.from_pretrained("superdiff/superdiff-sdxl-v1-0", custom_pipeline='pipeline', trust_remote_code=True)
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output = pipeline("a flamingo", "a candy cane", seed=1, num_inference_steps=200, batch_size=1)
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image = Image.fromarray(output[0])
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- `num_inference_steps`[optional: default=200]: number of denoising steps
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- `batch_size` [optional: default=1]: batch size
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- `guidance_scale` [optional: default=7.5]: scale for classifier-free guidance
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- `height`, `width` [optional: default=1024]: height and width of generated images (we recommend leaving it at 1024!)
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## Citation
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