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donaldssh  authored a paper 14 days ago
LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation
orhir  authored a paper 29 days ago
CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation
orhir  authored a paper 29 days ago
Edge Weight Prediction For Category-Agnostic Pose Estimation
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HuggingPartyParis's activity

sayakpaul 
posted an update 1 day ago
sayakpaul 
posted an update 7 days ago
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1550
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.
  • 1 reply
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sayakpaul 
posted an update 16 days ago
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2040
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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sayakpaul 
posted an update 16 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 🤗

It's still WIP, so go easy:
https://github.com/huggingface/diffusers/pull/10130
sayakpaul 
authored a paper 19 days ago
sayakpaul 
posted an update 26 days ago
sayakpaul 
posted an update about 1 month ago
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2601
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
  • 1 reply
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Borchmann 
authored 4 papers about 2 months ago
ymxlzgy 
authored 2 papers 3 months ago
sayakpaul 
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
  • 1 reply
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robin-courant 
authored a paper 3 months ago
sayakpaul 
authored a paper 4 months ago
sayakpaul 
posted an update 4 months ago
JoseRFJunior 
posted an update 5 months ago
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1692
JoseRFJunior/TransNAR
https://github.com/JoseRFJuniorLLMs/TransNAR
https://arxiv.org/html/2406.09308v1
TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language model’s internal knowledge at the beginning of training.
sayakpaul 
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 🤗
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sayakpaul 
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 🤝 Quanto ❤️

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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