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Fusing diffusion models

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sayakpaul  authored a paper about 1 month ago
A Noise is Worth Diffusion Guidance
abhishek  authored a paper 3 months ago
AutoTrain: No-code training for state-of-the-art models
sayakpaul  authored a paper 4 months ago
LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs
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fusing's activity

sayakpaul 
posted an update 14 days ago
anton-l 
posted an update 18 days ago
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2143
Introducing 📐𝐅𝐢𝐧𝐞𝐌𝐚𝐭𝐡: the best public math pre-training dataset with 50B+ tokens!
HuggingFaceTB/finemath

Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.

We build the dataset by:
🛠️ carefully extracting math data from Common Crawl;
🔎 iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.

We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.

We hope this helps advance the performance of LLMs on math and reasoning! 🚀
We’re also releasing all the ablation models as well as the evaluation code.

HuggingFaceTB/finemath-6763fb8f71b6439b653482c2
sayakpaul 
posted an update 20 days ago
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1758
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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sayakpaul 
posted an update 28 days ago
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2072
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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thomwolf 
posted an update 29 days ago
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4572
We are proud to announce HuggingFaceFW/fineweb-2: A sparkling update to HuggingFaceFW/fineweb with 1000s of 🗣️languages.

We applied the same data-driven approach that led to SOTA English performance in🍷 FineWeb to thousands of languages.

🥂 FineWeb2 has 8TB of compressed text data and outperforms other multilingual datasets in our experiments.

The dataset is released under the permissive 📜 ODC-By 1.0 license, and the 💻 code to reproduce it and our evaluations is public.

We will very soon announce a big community project, and are working on a 📝 blogpost walking you through the entire dataset creation process. Stay tuned!

In the mean time come ask us question on our chat place: HuggingFaceFW/discussion

H/t @guipenedo @hynky @lvwerra as well as @vsabolcec Bettina Messmer @negar-foroutan and @mjaggi
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sayakpaul 
posted an update 29 days ago
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2119
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 about 1 month ago
thomwolf 
posted an update about 1 month ago
thomwolf 
posted an update about 1 month ago
abhishek 
posted an update about 1 month ago
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1712
🎉 SUPER BLACK FRIDAY DEAL 🎉

Train almost any model on a variety of tasks such as llm finetuning, text classification/regression, summarization, question answering, image classification/regression, object detection, tabular data, etc for FREE using AutoTrain locally. 🔥
https://github.com/huggingface/autotrain-advanced
sayakpaul 
posted an update about 1 month ago
thomwolf 
posted an update about 1 month ago
thomwolf 
posted an update about 2 months ago
sayakpaul 
posted an update about 2 months ago
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2626
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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abhishek 
posted an update about 2 months ago
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5534
INTRODUCING Hugging Face AutoTrain Client 🔥
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗

To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.

"pip install autotrain-advanced"

Github repo: https://github.com/huggingface/autotrain-advanced
  • 6 replies
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thomwolf 
posted an update 2 months ago
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4135
Parents in the 1990: Teach the kids to code
Parents now: Teach the kids to fix the code when it starts walking around 🤖✨
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abhishek 
authored a paper 3 months ago
abhishek 
posted an update 3 months ago
sayakpaul 
posted an update 3 months ago
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2757
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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dn6 
posted an update 4 months ago
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2666
Sharing for anyone using Diffusers from_single_file loading and affected by the Runway SD 1.5 issue.

If you have runwayml/stable-diffusion-v1-5 saved locally in your HF cache then loading single file checkpoints in the following way should still work. 

from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_single_file("<url or path to single file checkpoint>")


If you do not have the model repo saved in your cache, then automatically inferring the pipeline config will not work since the reference repo runwayml/stable-diffusion-v1-5 doesn't exist anymore.

You can use an alternative SD1.5 repo id to still configure your pipeline.

from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_single_file("<url or path to single file checkpoint>", config="Lykon/DreamShaper")


We're working on resolving the issue ASAP.
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