diffusers-interpret
diffusers
diffusers-interpret | diffusers | |
---|---|---|
15 | 266 | |
259 | 22,763 | |
- | 3.3% | |
10.0 | 9.9 | |
over 1 year ago | 4 days ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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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.
diffusers-interpret
- Stable Diffusion links from around September 29, 2022 that I collected for further processing
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Diffusers-Interpret π€π§¨π΅οΈββοΈ - Model explainability for π€ Diffusers
Check the project at https://github.com/JoaoLages/diffusers-interpret
- Diffusers-Interpret v0.4.0 is out! Explainability for Stable Diffusion
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Can we please make a general update on all the "most important" news/repos available?
For those who want to explore what the denoising process looks like, check out the [diffusers-interpret package](https://github.com/JoaoLages/diffusers-interpret)! You can generate a GIF like [this one](https://github.com/TomPham97/diffuser/blob/main/diffusion-process.gif?raw=true).
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Commas, How do they work?!
If you have lots of RAM the diffusers-interpreter is an explainability tool that can show exactly how much each token is beings weighted and which part of the image it is influencing.
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[D] Senior research scientist at GoogleAI, Negar Rostamzadeh: βCan't believe Stable Diffusion is out there for public use and that's considered as βokβ!!!β
github.com/JoaoLages/diffusers-interpret
- Model explainability for π€ Diffusers. Get explanations for your generated images with the latest stable diffusion model!
- [P] Model explainability for π€ Diffusers. Get explanations for your generated images with the latest stable diffusion model!
diffusers
- StableDiffusionSafetyChecker
- 𧨠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?
stable-diffusion-webui - Stable Diffusion web UI
diffusionbee-stable-diffusion-ui - Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
stable-diffusion - A latent text-to-image diffusion model
diffusion-ui - Frontend for deeplearning Image generation
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
stable-diffusion-webui-feature-showcase - Feature showcase for stable-diffusion-webui
invisible-watermark - python library for invisible image watermark (blind image watermark)
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
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.