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Sayak Paul

sayakpaul
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https://sayak.dev

AI & ML interests

Diffusion models, representation learning

Recent Activity

updated a model 28 minutes ago
sayakpaul/FLUX.1-Canny-dev-nf4
updated a model 33 minutes ago
sayakpaul/FLUX.1-Depth-dev-nf4
updated a model 34 minutes ago
sayakpaul/FLUX.1-Fill-dev-nf4
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Posts 13

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2181
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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2596
Did some little experimentation to resize pre-trained LoRAs on Flux. I explored two themes:

* Decrease the rank of a LoRA
* Increase the rank of a LoRA

The first one is helpful in reducing memory requirements if the LoRA is of a high rank, while the second one is merely an experiment. Another implication of this study is in the unification of LoRA ranks when you would like to torch.compile() them.

Check it out here:
sayakpaul/flux-lora-resizing
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Collections 2

Optimizing diffusion models
Provides a list of papers focusing on optimizing T2I diffusion models, targeting fewer timesteps, architecture optimization, and more.

Papers 11