Steven Liu
a69754bb87
* start with stable diffusion * fix * finish stable diffusion pipelines * fix path to pipeline output * fix flax paths * fix copies * add up to score sde ve * finish first pass of pipelines * fix copies * second review * align doc titles * more review fixes * final review
38 lines
1.7 KiB
Plaintext
38 lines
1.7 KiB
Plaintext
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# Image variation
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The Stable Diffusion model can also generate variations from an input image. It uses a fine-tuned version of a Stable Diffusion model by [Justin Pinkney](https://www.justinpinkney.com/) from [Lambda](https://lambdalabs.com/).
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The original codebase can be found at [LambdaLabsML/lambda-diffusers](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) and additional official checkpoints for image variation can be found at [lambdalabs/sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers).
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<Tip>
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Make sure to check out the Stable Diffusion [Tips](./overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
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</Tip>
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## StableDiffusionImageVariationPipeline
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[[autodoc]] StableDiffusionImageVariationPipeline
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- all
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- __call__
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- enable_attention_slicing
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- disable_attention_slicing
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- enable_xformers_memory_efficient_attention
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- disable_xformers_memory_efficient_attention
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## StableDiffusionPipelineOutput
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[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
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