diffusers-interpret
stable-diffusion
diffusers-interpret | stable-diffusion | |
---|---|---|
15 | 383 | |
259 | 65,624 | |
- | 1.3% | |
10.0 | 0.0 | |
over 1 year ago | about 1 month ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
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.
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!
stable-diffusion
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Top 7 Text-to-Image Generative AI Models
Stable Diffusion: It is based on a kind of diffusion model called a latent diffusion model, which is trained to remove noise from images in an iterative process. It is one of the first text-to-image models that can run on consumer hardware and has its code and model weights publicly available.
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Go is bigger than crab!
Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
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Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
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How to create an Image generating AI?
It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
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Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems Β· Issue #87 Β· CompVis/stable-diffusion Β· GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
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Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
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Is SDXL really open-source?
stable diffusion Β· CompVis/stable-diffusion@2ff270f Β· GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
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.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
diffusion-ui - Frontend for deeplearning Image generation
diffusers-uncensored - Uncensored fork of diffusers
stable-diffusion-webui-feature-showcase - Feature showcase for stable-diffusion-webui
diffusers - π€ Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
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]
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
stable-diffusion
onnx - Open standard for machine learning interoperability