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@@ -28,7 +28,7 @@ or created independently from each other.
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To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API.
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More specifically, we strive to provide pipelines that
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- 1. can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (*e.g.* [LatentDiffusionPipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_diffusion), uses the officially released weights of [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)),
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- 1. can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (*e.g.* [LDMTextToImagePipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_diffusion), uses the officially released weights of [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)),
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- 2. have a simple user interface to run the model in inference (see the [Pipelines API](#pipelines-api) section),
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- 3. are easy to understand with code that is self-explanatory and can be read along-side the official paper (see [Pipelines summary](#pipelines-summary)),
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- 4. can easily be contributed by the community (see the [Contribution](#contribution) section).
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@@ -16,7 +16,7 @@ or created independently from each other.
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To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API.
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More specifically, we strive to provide pipelines that
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- 1. can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (*e.g.* [LatentDiffusionPipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_diffusion), uses the officially released weights of [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)),
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- 1. can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (*e.g.* [LDMTextToImagePipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_diffusion), uses the officially released weights of [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)),
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- 2. have a simple user interface to run the model in inference (see the [Pipelines API](#pipelines-api) section),
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- 3. are easy to understand with code that is self-explanatory and can be read along-side the official paper (see [Pipelines summary](#pipelines-summary)),
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- 4. can easily be contributed by the community (see the [Contribution](#contribution) section).
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