Custom pipelines and components in Diffusers 🎸
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Custom pipelines and components in Diffusers 🎸
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Wanted to use customized pipelines and other components (schedulers, unets, text encoders, etc.) in Diffusers?
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Wanted to use customized pipelines and other components (schedulers, unets, text encoders, etc.) in Diffusers?
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Found it inflexible? 
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Found it inflexible? 
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Since the first dawn on earth, we have supported loading custom pipelines via a 
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Since the first dawn on earth, we have supported loading custom pipelines via a 
custom_pipeline
`custom_pipeline`
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 argument 🌄
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 argument 🌄
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These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation.  
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These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation.  
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We have many cool pipelines, implemented that way. They all share the same benefits available to a 
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We have many cool pipelines, implemented that way. They all share the same benefits available to a 
DiffusionPipeline
`DiffusionPipeline`
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, no compromise there 🤗
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, no compromise there 🤗
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Check them here:
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Check them here:
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https://github.com/huggingface/diffusers/tree/main/examples/community
https://github.com/huggingface/diffusers/tree/main/examples/community
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Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible. 
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Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible. 
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All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.
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All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.
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SDXL Japanese was implemented like this 🔥
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SDXL Japanese was implemented like this 🔥
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https://huggingface.co/stabilityai/japanese-stable-diffusion-xl
stabilityai/japanese-stable-diffusion-xl
model
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https://huggingface.co/stabilityai/japanese-stable-diffusion-xl
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Full guide is available here ⬇️
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Full guide is available here ⬇️
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https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview
https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview
link
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And, of course, these share all the benefits that come with 
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And, of course, these share all the benefits that come with 
DiffusionPipeline
`DiffusionPipeline`
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. 
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. 
Custom pipelines and components in Diffusers 🎸

Wanted to use customized pipelines and other components (schedulers, unets, text encoders, etc.) in Diffusers?

Found it inflexible? 

Since the first dawn on earth, we have supported loading custom pipelines via a `custom_pipeline` argument 🌄

These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation.  

We have many cool pipelines, implemented that way. They all share the same benefits available to a `DiffusionPipeline`, no compromise there 🤗

Check them here:
https://github.com/huggingface/diffusers/tree/main/examples/community

Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible. 

All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.

SDXL Japanese was implemented like this 🔥
https://huggingface.co/stabilityai/japanese-stable-diffusion-xl

Full guide is available here ⬇️
https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview

And, of course, these share all the benefits that come with `DiffusionPipeline`. 
🔥
radames