Graphite VS MiDaS

If you're interested in either computational geometry, layout/packing/constraints, or functional programming language concepts, those are all the math-related concepts that we're currently interacting with for Graphite, a 2D vector graphics editor that's aiming to become the next Blender (but for 2D instead of 3D). If that sounds interesting, I'd love to help get you started if you want to join our Discord and I can explain the math-related work that we need to get done. Cheers!

Not sure which web-based spreadsheet app you're talking about, because there are many that do use these frameworks. Here's a PS/AI clone built with a Svelte frontend: https://graphite.rs

Wanted to contribute to a good Rust-based project last week, started searching and found a good Reddit thread featuring several great projects. Looked at and found Graphite. I liked the concept though I know almost nothing about graphic design.

If you're interested in helping us build a 2D graphics editing suite for designers and artists, consider contributing to Graphite. Getting started instructions are here. We code review PRs closely and give feedback to help you improve, and offer advice and mentorship via our Discord while you're learning and coding.

If graphical apps suit your fancy, the Graphite tries hard to make new contributors feel at home.

Yesterday I just submitted my first contribution to open source. https://github.com/GraphiteEditor/Graphite

I run an open source 2D graphics editor project and our license is Apache 2.0 (which is basically the same as MIT) which provides much more freedom than the GPL does, since it's not copyleft. We have a Stable Diffusion feature built in, and we want to provide a hosted component so users can utilize that feature without self-hosting. A1111 being AGPL likely means we have to find an alternate backend. I'm looking into other options like SHARK (and would love some ideas if anyone else has suggestions).

Graphite is an in-development 2D creative tool for vector and raster graphics editing (basically, the goal is to make a better Inkscape and Gimp, plus way more). If that's interesting to you, we try really hard to have an inviting community that makes it approachable to get up and running with contributing to the project. Come say hi on our Discord and I can help get you set up. Or read our quick contributing tutorial/intro.

For example, here is MiDaS from Intel Research that does monocular depth estimation.

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.

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.

The latter. It uses a pretrained network called MiDaS to estimate image depth. If I'm not mistaken this will produce a depth estimation of the same resolution as the image, and then downsample that to the latent space's dimension (64x64 for a 512x512 image I think) and pass it in as extra conditioning.

C:\PROGRAMME2\stable_diffusion_automatic1111\stable-diffusion-webui>git clone https://github.com/isl-org/MiDaS.git repositories/midas