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Unstable Diffusion AI: A Powerful Tool for Image Generation

Step 3: The AI will generate four(ish) different images based on your prompt. If one of the images was just what you were looking for, you can select it for download and copying. You can also continue to generate images until you find something that you like.

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However, it is a third-party app that, at this time, is still very new. That means it may introduce bugs and cause other difficulties. Make sure you have the latest version of Photoshop before you try to download it, and be prepared to disable the plugin if problems appear.

then click on top and type cmd and hit enterafter that in the console window type the first command that i pointed then it turned out that the pip needs to be updated ,look at the second command i pointed and type it, it should update the pip after it finishes close the window and now you can run the webui-user.bat folder and the download will continue without any problems

"Unstable Dissusion was created in response to Stability AI's decision to neuter the 2.0 model."This model is based on the official unstable_diffusion . Credit to the original developers and donors.The data was collected by volunteers in the Unstable Diffusion community, and the model was trained by the Unstable Diffusion development team. You can join the community here:

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The module for face restoration is called GFPGAN. Follow its installation instructions here, clone the GFPGAN directory alongside the stable-diffusion directory. And be sure to download the pre-trained model as shown. You can then use the -G flag as shown in the Dream Script Stable Diffusion repo.

50 Mb/s isn't really needed for video. Even Netflix [] says you only need about 15 mbps for 4k video, and for 1080p you only need 5 mbps. The only real use I can think of for higher speed downloads for the average person would be things like video game downloads and updates, which seem to have reached ridiculous sizes in recent years.

Diffusion comes into the picture by analogy of diffusion in liquids. Imagine you have a drop of dye in a glass of water that's been diffusing, and you want to roll back time to the original droplet, you could train a neural net to try to figure that out - though the more diffused the drop has gotten, the more that its guessed endstate - while a perfectly plausible starting drop - won't match the original.

Diffusion models do the same with images. Add gaussian noise as a bunch of "diffusion" cycles, then backpropagate based on how well it removed the noise and restored the original image (reverse diffusion).

The problem you get is that images are really big and contain tons of data; doing reverse diffusion on images as a whole is thus impractical. So you first train an encoder and decoder for a latent space. That is, you connect the input and output images, but pinch down the network in the middle. So what passes through that bottleneck must be a conceptual representation of the image, rather than actual image data. So then you can reverse diffuse the latent image, and then decode that.

The concept of latent spaces applies to text as well. And early experiments with textual latent spaces showed that they have interesting properties - they respond to mathematical operations. For example, the latent space for "woman", plus the latent space for "king", minus the latent space for "man", will resemble the latent space for "queen". And it's this sort of property that becomes useful for pairing reverse diffusion of images with text inputs. During training, you encode both the image and text to latents, and then you dot product the two together to create a unified latent that represents both the image and the text. So reverse diffusion has to keep the paired combination of both the image and the text coherent.

So when a user runs such an application, their text is being encoded into a textual latent, but then it's just being dotproducted with random noise. Repeated cycles of reverse diffusion then make the latent coherent again. And then the decoder from latent space to image space is run, and that's your output.

Otherwise, go to the conda website and download and run the appropriate Miniconda installer for your version of Python and operating system. For Python3.8, you can download and run the installer with the following commands:

Now that we are working in the appropriate environment to use Stable Diffusion, we need to download the weights we'll need to run it. If you haven't already read and accepted the Stable Diffusion license, make sure to do so now. Several Stable Diffusion checkpoint versions have been released. Higher version numbers have been trained on more data and are, in general, better performing than lower version numbers. We will be using checkpoint v1.4. Download the weights with the following command:

It appears that the number of steps in the diffusion process does not affect results much beyond a certain threshold of about 50 timesteps. The below images were generated using the same random seed and prompt "A red sports car". It can be seen that a greater number of timesteps consistently improves the quality of the generated images, but past 50 timesteps improvements are only manifested in a slight change to the incidental environment of the object of interest. The details of the car are in fact almost fully consistent from 25 timesteps onward, and it is the environment that is improving to become more appropriate for the car in greater timesteps.

To avoid having to suppling the checkpoint with --ckpt sd-v1-4.ckpt each time you generate an image, you can create a symbolic link between the checkpoint and the default value of --ckpt. In the terminal, navigate to the stable-diffusion directory and execute the following commands:

Follow the instructions here for more detailed instructions and to make sure you do everything properly: used to following links to other online guides and tutorials to get everything setup and to learn new things).

After downloading and installing Python and Git, and then "cloning" Auto1111 WebUI, the next step is to place the "v1-5-pruned-emaonly.ckpt" checkpoint you previously downloaded into the sub-folder \stable-diffusion-webui\models\Stable-diffusion

The exact full path will depend where you had the git clone into. For example, the full path for me (NOT you) is M:\Digital Art\AI Art\Stable Diffusion New\stable-diffusion-webui\models\Stable-diffusion

Remember the VAE we also downloaded? The file name should be vae-ft-mse-840000-ema-pruned.ckpt, but we need to change the file extension to For example, it will now be

You will need to do this for any VAE you download. Most checkpoints typically use the but other models will specify if they need a different VAE and where to get it. Some other checkpoints will have this embedded in the model itself and won't need either VAE.

The name of the game here is PATIENCE as this will be pretty slow and depends on your Internet speeds and whether you're using an SSD/NVME or mechanical hard drive. A lot of stuff needs to be downloaded and at some points it might look like there's no progress.

In simple terms, it's possible for these to contain malware and virus that your antivirus might be unable to detect. Whenever possible, opt for downloading checkpoints with the safetensors format as these are unlikely to contain any additional code.

CivitaiThis is currently the best place to find and download new checkpoints and other useful stuff. Models shared here are generally going to be safe (they have been scanned for viruses).

This website hosts checkpoints in both ckpt and safetensors format. Both of these work in Automatic1111 Stable Diffusion WebUI and should be placed in the \stable-diffusion-webui\models\Stable-diffusion folder.

Be careful where you download these from, especially checkpoints that end in cpkt as these may contain malicious code known as malicious pickles.If offered, it's best to download checkpoints with the .safetensors file extension as these are currently known to be unable to store any malicious code.

Deep learning Text-to-Image AI models are becoming increasingly popular due to their ability to translate text accurately into images. This model is free to use and can be found on Hugging Face Spaces and DreamStudio. The model weights can also be downloaded and used locally.

A criterion of the instability of a flow of a thermal plasma and cosmic rays in front of an oblique MHD shock wave with respect to short-wavelength magnetosonic disturbances is derived. The dependence of a cosmic-ray diffusion tensor on a plasma density and a large-scale magnetic field is taken into account. The most unstable disturbances propagate at an angle to the magnetic field if diffusion is strongly anisotropic. In some cases the most strong instability connects with the off-diagonal terms of the diffusion tensor.

In essence, Miniconda3 is a tool for convenience. It enables you to manage all of the libraries needed for Stable Diffusion to run without requiring a lot of manual labor. It will also be how we apply stable diffusion in practice.

Once it has been downloaded, double-click the executable to launch the installation. Installation with Miniconda3 requires fewer page clicks than with Git. However, you should be cautious with this choice:


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