notebooks
diffusers
notebooks | diffusers | |
---|---|---|
17 | 266 | |
3,293 | 22,543 | |
2.7% | 2.3% | |
8.4 | 9.9 | |
4 days ago | 7 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
notebooks
- Training multiple models like ResNet50 or ViT on the same dataset [P]
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Sagemaker Model deployment and Integration
đź““ Open the notebook for an example of how to run a batch transform job for inference.
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Your own Stable Diffusion endpoint with AWS SageMaker
In order to overwrite it, the package readme has some general information about it, and also there is an example in this jupyter notebook. We are doing what is necessary via the files inside sagemaker/code, which has the inference code following SageMaker requirements, and a requirements.txt, that has the necessary dependencies that will be installed when the endpoint gets created
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Is there a huggingface model that does free response QA?
You still haven’t explained your use-case for the model. You can look up “Open Domain QA” models. There are a lot of them, but they’re often restricted in how well they generalize and benefit from fine tuning. E.g., https://github.com/huggingface/notebooks/blob/main/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb
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List of Stable Diffusion systems - Part 3
(Updated Aug. 27, 2022) Colab notebook Stable Diffusion with diffusers by huggingface. GitHub repo. Video tutorial. Official Colab notebook. txt2img. Uses HuggingFace diffusers repo.
- anyone having issues with the textual inversion colab?
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Training textual inversion of Stable Diffusion on your own dataset
Looks like they updated the notebook 15 minutes ago. Hopefully it works now.
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Ask HN: What kind of data do I need to build a language model?
Basically, you can then do similar things using HuggingFace, as indeed many have (you can explore the models in their hub)[2]
[1] https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQju...
[2] https://github.com/huggingface/notebooks/blob/main/examples/...
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[D] NLP has HuggingFace, what does Computer Vision have?
image classification: ViT, DeiT, BEiT, Swin Transformer, PoolFormer, ResNet, RegNet, ConvNeXT, Perceiver, ImageGPT, VAN. Check out the official example scripts, example notebooks.
- Need help in extracting a binary label from a text corpus
diffusers
- StableDiffusionSafetyChecker
- 🧨 diffusers 0.24.0 is out with Kandinsky 3.0, IP Adapters, and others
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What am I missing here? wheres the RND coming from?
I'm missing something about the random factor, from the sample code from https://github.com/huggingface/diffusers/blob/main/README.md
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T2IAdapter+ControlNet at the same time
Hey people, I noticed that combining these two methods in a single forward pass increases the controllability of the generation quite a bit. I was kind of puzzled that sometimes ControlNet yielded better results than T2IAdapter for some cases, and sometimes it was the other way around, so I decided to test both at the same time, and results were quite nice. Some visuals and more motivation here: https://github.com/huggingface/diffusers/issues/5847 And it was already merged here: https://github.com/huggingface/diffusers/pull/5869
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Won't you benchmark me?
Open Parti Prompts: The better way to evaluate diffusion models (repo)
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kohya_ss error. How do I solve this?
You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
- Making a ControlNet inpaint for sdxl
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Stable Diffusion Gets a Major Boost with RTX Acceleration
For developers, TensorRT support also exists for the diffusers library via community pipelines. [1] It's limited, but if you're only supporting a subset of features, it can help.
In general, these insane speed boosts comes at the cost of bleeding edge features.
[1] https://github.com/huggingface/diffusers/blob/28e8d1f6ec82a6...
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Mysterious weights when training UNET
I was training sdxl UNET base model, with the diffusers library, which was going great until around step 210k when the weights suddenly turned back to their original values and stayed that way. I also tried with the ema version, which didn't change at all. I also looked at the tensor's weight values directly which confirmed my suspicions.
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I Made Stable Diffusion XL Smarter by Finetuning It on Bad AI-Generated Images
Merging LoRAs is essentially taking a weighted average of the LoRA adapter weights. It's more common in other UIs.
diffusers is working on a PR for it: https://github.com/huggingface/diffusers/pull/4473
What are some alternatives?
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
stable-diffusion-webui - Stable Diffusion web UI
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion - k_diffusion wrapper included for k_lms sampling. fixed for notebook.
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
easydiffusion - Easy Diffusion is an advanced Stable Diffusion Notebook with a feature rich image processing suite.
invisible-watermark - python library for invisible image watermark (blind image watermark)
stable-diffusion-colab - Adapdet for google colab
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
HidamariDiffusionColab - colab for stable diffusion
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.