It's been a while we shipped native quantization support in 
text
It's been a while we shipped native quantization support in 
diffusers
`diffusers`
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 🧨
text
 🧨
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new_line
We currently support 
text
We currently support 
bistandbytes
`bistandbytes`
inline_code
 as the official backend but using others like 
text
 as the official backend but using others like 
torchao
`torchao`
inline_code
 is already very simple. 
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 is already very simple. 
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new_line
This post is just a reminder of what's possible:
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This post is just a reminder of what's possible:
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1. Loading a model with a quantization config
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1. Loading a model with a quantization config
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2. Saving a model with quantization config
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2. Saving a model with quantization config
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3. Loading a pre-quantized model
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3. Loading a pre-quantized model
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4. 
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4. 
enable_model_cpu_offload()
`enable_model_cpu_offload()`
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5. Training and loading LoRAs into quantized checkpoints
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5. Training and loading LoRAs into quantized checkpoints
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Docs:
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Docs:
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https://huggingface.co/docs/diffusers/main/en/quantization/bitsandbytes
https://huggingface.co/docs/diffusers/main/en/quantization/bitsandbytes
link
It's been a while we shipped native quantization support in `diffusers` 🧨

We currently support `bistandbytes` as the official backend but using others like `torchao` is already very simple. 

This post is just a reminder of what's possible:

1. Loading a model with a quantization config
2. Saving a model with quantization config
3. Loading a pre-quantized model
4. `enable_model_cpu_offload()`
5. Training and loading LoRAs into quantized checkpoints

Docs:
https://huggingface.co/docs/diffusers/main/en/quantization/bitsandbytes
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