MultiDiffusion VS ControlNet

For composing this approach works pretty well

https://multidiffusion.github.io/

So I've continued to experiment with how many papers I can fit into a single pipe and have them play nicely together. The images below were created by combining the panorama code from omerbt/MultiDiffusion with the ideas from albarji/mixture-of-diffusers. Also turns out nateraw/stable-diffusion-videos can be seen as a special case of a panorama (in latent space rather than prompt space).

Essentially, I fine-tuned Stable Diffusion 2.1 base (the 512x512) model on the ESA Hubble Deep Space Images & Captions dataset I collected from public Hubble images & captions. After around 33,000 training steps, I saved the model and was really impressed by the results. But I really wanted to be able to generate wallpaper-level quality space images, so I stumbled upon MultiDiffusion: a new project for generating massive panorama images using stable diffusion models. I then used hubble-diffusion-2 along with MultiDiffusion to generate each one of these amazing 2560x1536 images. Each image took a little over an hour to generate on a Google Colab T4 GPU. I used the following prompts for each of these images:

If you need generated and not upscaled 4k for some reason, try something like https://github.com/omerbt/MultiDiffusion

Project: https://multidiffusion.github.io/ Paper: https://arxiv.org/abs/2302.08113 GitHub: https://github.com/omerbt/MultiDiffusion

Quick Read: https://www.marktechpost.com/2023/02/24/meet-multidiffusion-a-unified-ai-framework-that-enables-versatile-and-controllable-image-generation-using-a-pre-trained-text-to-image-diffusion-model/ Paper: https://arxiv.org/abs/2302.08113 Github: https://github.com/omerbt/MultiDiffusion Project: https://multidiffusion.github.io/

So I haven't made many images with Stable Diffusion despite using it heavily. The reason is I've been messing with the internals of the diffusion pipe, to interfere with the diffusion process in different ways. Todays fun result is based on omerbt/MultiDiffusion for making panoramas.

If we combine Mixture of Diffusers + MultiDiffusion+ Composer+ cross-domain-compositing and probably some more I'm not thinking of.

It`s all possible. Have a look into Automatic1111`s Web UI, ControlNet, OpenPose and, if you don`t have a dedicated GPU with at least 8GB of VRAM, or at least 16GB of RAM to use the CPU, you can also use Stable Horde to use the webUI with a peer-to-peer connection, where you`ll only use a fraction of your resources, but you`ll be able to use local AI models with all the bells and whistles that you won`t get from "state-of-the-art" paid services.

Next up is ControlNet. Controlnet, as Illyasviel--creator of controlnet--describes it, "let's us control diffusion models!." ControlNet is a neural network structure to control diffusion models by adding extra connections. [8]. There is more to that than what I described, but the big take-away is that ControlNet takes a preprocessed image that you provide (or is generated) and uses that as a way of constraining the output the sampler's noise generates, allowing you to have a bit more control of the output. ControlNet is typically used for character or scene "artwork", which previously would have been a challenge with just prompting alone (at least with this current architecture).

For your first part of the comment, I can simply refer you to technologies like ControlNet, LoRA and prompt embedding: https://github.com/lllyasviel/ControlNet https://github.com/microsoft/LoRA

Here you can see what every controlnet preprocessor and model do, to give you an idea of ​​how to use

Ya, it takes awhile to really start getting comfortable with the wonkiness. If you are trying to do something specific, look for a LoRA, but in general I'd recommend you get controlnet so you can feed it a reference image. Another simple trick is to edit the image a bit in GIMP or a photo editor to get the color scheme you like and then feed it back to img2img at low denoising (0.1-0.2) to refine it. You can also add just garishly bad cartoon drawing or photoshop in assets and img2img will usually make something of them and blend them into your image, I find this easier than using img2img scribble.

If there are no errors, go to the "Extensions" tab, then "Install from URL". There, enter "https://github.com/lllyasviel/ControlNet" then click "Install".

This is a good place to start reading. Given the open-source nature of SD, there are setups of various difficulty available. A1111 is the "standard" people enjoy because it's easy to plug in new stuff (ControlNet, new models, etc.), but it's not inherently easy to set up and get going. There is an installer for it, but I haven't tried it.