stable-diffusion-webui VS CLIP

[https://github.com/sd-webui/stable-diffusion-webui/wiki/installation de numéro(https://github.com/sd-webui/stable-diffusion-webui/wiki/installation)

https://github.com/sd-webui/stable-diffusion-webui (prend beaucoup à installer)

you can "animate" with stable diffusion usining text to video https://github.com/nateraw/stable-diffusion-videos or https://github.com/sd-webui/stable-diffusion-webui

I mentioned Automatic1111 on SD-WEBUI and they deleted the comment. I guess this is why. My installation failed on SD-WEBUI and there was no solution for me. I suspect that's why Automatic1111's fork is so popular. He went above and beyond to make sure people with 1660ti's could run SD flawlessly with all the different tools available.

Any way to convert a .pt model to a .ckpt model? Stable-diffusion-webui only seems to support the second type of file but just renaming them does not work:

This is Stable-Diffusion. Here is a UI version https://github.com/sd-webui/stable-diffusion-webui

I dunno about the app. I use this https://github.com/sd-webui/stable-diffusion-webui it's very resource hungry though.

Haven't tried this GUI yet. Can anyone chime in about how it compares to Automatic1111's and sd-webui/HLKY's? There are so many good repos out there that it's getting hard to keep track of them all

Maybe those who joined were not aware that they joined the horde :-)

We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:

Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.

In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:

(Github Repo | Most Popular Model | Paper | Project Page)

the first thing that comes to mind is CLIP: https://github.com/openai/CLIP

Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.

in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).

Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary

CLIP might work for your needs.

pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade