Dreambooth-Stable-Diffusion VS stablediffusion

I did search online for an answer, but I am a PC noob, I didn't know what to do when I found this solution in this link: https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/113

Do you know some projects or guided tutorials that could help me? How many drawings with the desired style I should then have to give to train the AI model? I found Dreambooth on Stable Diffusion but it seams to be for another use case.

This seems to be an issue with the code and or the environment itself. There is an open bug for this where some suggestions are p provided by others on how to fix. https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/47

Could you provide some more detail on how this works? Did you just use this GitHub repository or did you put together your own implementation?

I think the best way to do this would be to train Dreambooth on a number of photos of Ray Liotta first, and use Stable Diffusion instead. https://github.com/XavierXiao/Dreambooth-Stable-Diffusion

Dreambooth. There are other ways, but that is the gold standard. It takes even more Vram than regular stable diffusion, so if you don't have a very beefy card (e.g. 4090 with 25 GB VRAM) various websites let you do it onlin for a small fee. You then download a new model that has all the old stuff (e.g.the 4 gigabyte SD 1.5 file) plus your new images. Like I said, there are other ways that are easier, but when people show great results they are usually talking about Dreambooth.

THE CODE (unofficial implementation, for the exact wording stating how little images you need read the paper) is designed with extremely little data in mind. I don't know how else to phrase it dude, do you think the training is a magic black box that runs with snail neurons? If you train a dreambooth model the jupyter ide makes calls to python files, those are the files. That is the code

With this tutorial's help, you can generate images with AI on your own computer with Stable Diffusion.

If your PC has a GPU(Nvidia RTX 30series+ recommended) of VRAM more than 4GB then try training your own Stable Diffusion model.

Command: "git" clone "https://github.com/Stability-AI/stablediffusion.git" "C:\Users\Naveed\Documents\A1111 Web UI Autoinstaller\stable-diffusion-webui\repositories\stable-diffusion-stability-ai"

Automatic11112 and sygil-webui aren't "distributions" of Stable Diffusion. This is a repository with some distributions of Stable Diffusion.

New stable diffusion finetune (Stable unCLIP 2.1, Hugging Face) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. Comes in two variants: Stable unCLIP-L and Stable unCLIP-H, which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available here.

This model will soon be open-sourced in StabilityAI’s GitHub.

Another question, if that's ok? Stable Diffusion 2.0 - https://github.com/Stability-AI/stablediffusion - if I wanted to use that, do I follow along their instructions and it will work on the M1 still, or you advise against it?

Stable Diffusion (2D Image Generation and Animation) https://github.com/CompVis/stable-diffusion (Stable Diffusion V1) https://huggingface.co/CompVis/stable-diffusion (Stable Diffusion Checkpoints 1.1-1.4) https://huggingface.co/runwayml/stable-diffusion-v1-5 (Stable Diffusion Checkpoint 1.5) https://github.com/Stability-AI/stablediffusion (Stable Difusion V2) https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main (Stable Diffusion Checkpoint 2.1) Stable Diffusion Automatic 1111 Webui and Extensions https://github.com/AUTOMATIC1111/stable-diffusion-webui (WebUI - Easier to use) PLEASE NOTE, MANY EXTENSIONS CAN BE INSTALLED FROM THE WEBUI BY CLICK "AVAILABLE" OR "INSTALL FROM URL" BUT YOU MAY STILL NEED TO DOWNLOAD THE MODEL CHECKPOINTS! https://github.com/Mikubill/sd-webui-controlnet (Control Net Extension - Use various models to control your image generation, useful for animation and temporal consistency) https://huggingface.co/lllyasviel/ControlNet/tree/main/models (Control Net Checkpoints -Canny, Normal, OpenPose, Depth, ect.) https://github.com/thygate/stable-diffusion-webui-depthmap-script (Depth Map Extension - Generate high-resolution depthmaps and animated videos or export to 3d modeling programs) https://github.com/graemeniedermayer/stable-diffusion-webui-normalmap-script (Normal Map Extension - Generate high-resolution normal maps for use in 3d programs) https://github.com/d8ahazard/sd_dreambooth_extension (Dream Booth Extension - Train your own objects, people, or styles into Stable Diffusion) https://github.com/deforum-art/sd-webui-deforum (Deforum - Generate Weird 2D animations) https://github.com/deforum-art/sd-webui-text2video (Deforum Text2Video - Generate videos from texts prompts using ModelScope or VideoCrafter)

Stable Diffusion's code : https://github.com/Stability-AI/stablediffusion

But if you want to run a 2.x-based model, you'll need to download the corresponding YAML file (either the standard one – v2-inference-v.yaml – from Github or the one that is distributed with the model, if it requires a special one), rename it to have the same name as the model, and place it in the models folder alongside the model.