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