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sayakpaulย 
posted an update 2 days ago
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2913
Commits speak louder than words ๐Ÿคช

* 4 new video models
* Multiple image models, including SANA & Flux Control
* New quantizers -> GGUF & TorchAO
* New training scripts

Enjoy this holiday-special Diffusers release ๐Ÿค—
Notes: https://github.com/huggingface/diffusers/releases/tag/v0.32.0
sayakpaulย 
posted an update 8 days ago
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1558
In the past seven days, the Diffusers team has shipped:

1. Two new video models
2. One new image model
3. Two new quantization backends
4. Three new fine-tuning scripts
5. Multiple fixes and library QoL improvements

Coffee on me if someone can guess 1 - 4 correctly.
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sayakpaulย 
posted an update 16 days ago
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2041
Introducing a high-quality open-preference dataset to further this line of research for image generation.

Despite being such an inseparable component for modern image generation, open preference datasets are a rarity!

So, we decided to work on one with the community!

Check it out here:
https://huggingface.co/blog/image-preferences
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sayakpaulย 
posted an update 17 days ago
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2096
The Control family of Flux from @black-forest-labs should be discussed more!

It enables structural controls like ControlNets while being significantly less expensive to run!

So, we're working on a Control LoRA training script ๐Ÿค—

It's still WIP, so go easy:
https://github.com/huggingface/diffusers/pull/10130
sayakpaulย 
posted an update 27 days ago
sayakpaulย 
posted an update about 1 month ago
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2602
It's been a while we shipped native quantization support in diffusers ๐Ÿงจ

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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sayakpaulย 
posted an update 3 months ago
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2752
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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sayakpaulย 
posted an update 4 months ago
sayakpaulย 
posted an update 5 months ago
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4478
Flux.1-Dev like images but in fewer steps.

Merging code (very simple), inference code, merged params: sayakpaul/FLUX.1-merged

Enjoy the Monday ๐Ÿค—
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sayakpaulย 
posted an update 5 months ago
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3793
With larger and larger diffusion transformers coming up, it's becoming increasingly important to have some good quantization tools for them.

We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers.

We demonstrate excellent memory savings with a bit of sacrifice on inference latency which is expected to improve in the coming days.

Diffusers ๐Ÿค Quanto โค๏ธ

This was a juicy collaboration between @dacorvo and myself.

Check out the post to learn all about it
https://huggingface.co/blog/quanto-diffusers
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sayakpaulย 
posted an update 6 months ago
sayakpaulย 
posted an update 6 months ago
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3131
What is your favorite part of our Diffusers integration of Stable Diffusion 3?

My personal favorite is the ability to run it on a variety of different GPUs with minimal code changes.

Learn more about them here:
https://huggingface.co/blog/sd3
sayakpaulย 
posted an update 7 months ago
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1866
๐Ÿงจ Diffusers 0.28.0 is out ๐Ÿ”ฅ

It features the first non-generative pipeline of the library -- Marigold ๐Ÿฅ

Marigold shines at performing Depth Estimation and Surface Normal Estimation. It was contributed by @toshas , one of the authors of Marigold.

This release also features a massive refactor (led by @DN6 ) of the from_single_file() method, highlighting our efforts for making our library more amenable to community features ๐Ÿค—

Check out the release notes here:
https://github.com/huggingface/diffusers/releases/tag/v0.28.0
sayakpaulย 
posted an update 8 months ago
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2022
Custom pipelines and components in Diffusers ๐ŸŽธ

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 ๐ŸŒ„

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 ๐Ÿค—

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 ๐Ÿ”ฅ
stabilityai/japanese-stable-diffusion-xl

Full guide is available here โฌ‡๏ธ
https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview

And, of course, these share all the benefits that come with DiffusionPipeline.
sayakpaulย 
posted an update 9 months ago