stablediffusion VS MiDaS

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.

The checkpoint below uses MiDaS, which returns the inverse depth map, so we have to invert it back to get a comparable depth map.

Hi, I have made use of the KITTI dataset for this, and yes it depends on objects of know sizes. Here I have defined the following classes: Car, Van, Truck, Pedestrian, Person_sitting, Cyclist, Tram, Misc, or DontCare and the predictions are pretty accurate for those classes. Even if it's not the same class, it still recognizes the object since I have made use of the coco names dataset here and that is used along with YOLO for object detection. And there are several already implemented projects that make use of deep learning models trained on 2D datasets to predict 3D distance. This was one of my inspirations for this project: https://blogs.nvidia.com/blog/2019/06/19/drive-labs-distance-to-object-detection/ Furthermore, there are well-documented and researched papers like DistYOLO or MiDaS that makes use of deep learning for depth estimation

-Added AI image depth generator Create your own depth map image at a click of a button. Using the awesome MIDAS3.1 https://github.com/isl-org/MiDaS as the backend and the model "dpt_beit_large_512" for the highest quality depth map. Video and GIF depth map generators coming out next together with the Depth movie player feature.

It uses MiDaS. That extension may be the most accessible way to use it at home. IDK.

Here's the project homepage https://github.com/isl-org/MiDaS

The problem came up after MiDaS updated to version V3_1 on Dec 24th. Although the fix works fine, with the new version there are many changes, which for me produces slightly different results. I would like to able to produce results like before. I still clone the MiDaS repo, but then set it back to the last commit before the changes in december, which is 66882994a432727317267145dc3c2e47ec78c38a.

try: from midas.dpt_depth import DPTDepthModel except: if not os.path.exists('MiDaS'): gitclone("https://github.com/isl-org/MiDaS.git") gitclone("https://github.com/bytedance/Next-ViT.git", f'{PROJECT_DIR}/externals/Next_ViT') if not os.path.exists('MiDaS/midas_utils.py'): shutil.move('MiDaS/utils.py', 'MiDaS/midas_utils.py') if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'): wget("https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", model_path) sys.path.append(f'{PROJECT_DIR}/MiDaS')

Cool. The repo for MiDaS is here. https://github.com/isl-org/MiDaS You can see that they partially trained the model on 3D movies Here's a list of the movies that were used to train it. I wonder if they'll be training a MiDaS v 4.0 as things have moved on quite a bit since it was released in Apr 2021?

We present a stand-alone implementation of our Merging Operator. This new repo allows using any pair of monocular depth estimations in our double estimation. This includes using separate networks for base and high-res estimations, using networks not supported by this repo (such as Midas-v3), or using manually edited depth maps for artistic use. This will also be useful for scientists developing CNN-based MDE as a way to quickly apply double estimation to their own network. For more details please take a look here.

I'll make the feature to export only the depthmap .png file. If you need the depthmap .png right now you can use the MiDaS python script.