IOPaint
diffusers
IOPaint | diffusers | |
---|---|---|
48 | 266 | |
17,288 | 22,763 | |
- | 3.3% | |
9.4 | 9.9 | |
7 days ago | 3 days ago | |
Python | 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.
IOPaint
- IOPaint: Image inpainting tool powered by SOTA. Erase and replace objects/people
- FLaNK Stack Weekly 12 February 2024
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ADetailer signature model
„Inpaint anything“ and „cleaner“ extension can remove text or anything you mark, if you are on A1111 webui, you can install them from extension tab. Standalone , there is lama cleaner , https://github.com/Sanster/lama-cleaner
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OptiClean: A native macOS inpainting app that helps you quickly clean up your images in seconds
This is an app developed from my other open source side project Lama Cleaner.
- Show HN: Open-source background removal in the browser
- ITAP of a bull and a bee
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Few questions about LoRA (face training)
When you're training, the model learns things you don't prompt for, like the face you want, but it also learns things that don't change much, even if prompted. So if there is a necklace there, it'll learn that something goes in the neck area. You might look into Lama Cleaner to remove the necklaces without messing anything else up.
- Câți dintre voi deja folosiți A.I? Si pentru ce?
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Image watermark remover?
I prefer Lamar Cleaner: https://github.com/Sanster/lama-cleaner to anything built into A1111.
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UnpromptedControl: Noprompt ControlNet Image Restoration/Object removal, GitHub link in comments
I use Lama Cleaner for stuff like this. Works great and supports all the SD models + Segment Anything, etc etc etc : https://github.com/Sanster/lama-cleaner
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?
lama - 🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
stable-diffusion-webui - Stable Diffusion web UI
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]
stable-diffusion - A latent text-to-image diffusion model
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
cleanup.pictures - Code for https://cleanup.pictures
invisible-watermark - python library for invisible image watermark (blind image watermark)
OnnxDiffusersUI - UI for ONNX based diffusers
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
auto-sd-paint-ext - Extension for AUTOMATIC1111 to add custom backend API for Krita Plugin & more
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.