g-diffuser-lib
Discord bot and utilities for the diffusers library (stable-diffusion) [Moved to: https://github.com/parlance-zz/g-diffuser-bot] (by parlance-zz)
diffusers
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX. (by huggingface)
g-diffuser-lib | diffusers | |
---|---|---|
7 | 266 | |
156 | 22,646 | |
- | 2.8% | |
9.8 | 9.9 | |
over 1 year ago | 2 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
g-diffuser-lib
Posts with mentions or reviews of g-diffuser-lib.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-30.
- Will Stable Diffusion ever gain a better inpainting feature on par with Dalle, or is this a fundamental difference?
- Ultra-high resolution (4900x800) generation in 1 step, 3GB memory, no manual editing, pure stable-diffusion
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Playing with Unreal Engine integration for players to create content in-game
Unreal is good too, but for those who might prefer python: https://github.com/parlance-zz/g-diffuser-lib/discussions/46 https://github.com/parlance-zz/g-diffuser-lib
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New g-diffuser-lib for easy-to-use diffusers
Installation: 1) clone this repository to a folder of your choice (or click the green "code" button up top and click "download zip") 2) download / install miniconda (https://docs.conda.io/en/latest/miniconda.html) 3) open a conda prompt (click on the start menu and look for "anaconda prompt"), then navigate to the folder where you cloned / downloaded this repository. 4) run "conda env create -f environment.yaml" 5) place any pre-downloaded models into the models folder, if you want to use a hugging-face token instead, enter it in g_diffuser_config.py for specific instructions on model download / installation please see models/README.md (https://github.com/parlance-zz/g-diffuser-lib/tree/g-diffuser-bot-beta2/models) 6) If you are running Windows 10 you may need to turn on "developer mode". Look for "developer settings" in the start menu.
diffusers
Posts with mentions or reviews of diffusers.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-10-27.
- 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