nvdiffrec VS stable-diffusion-webui

It depends on which CV research you’re in. In NeRF view synthesis, it’s pretty common to use test sets as validation sets. This has been done in several papers, including oral papers.

I think there came a paper recently nerf2mesh, which I still have to evaluate (but haven't found time yet). There's also https://github.com/NVlabs/nvdiffrec/. And there's cool easy-to-use research software like nerfstudio (at least if you compare it to a lot of the raw code releases from research papers).

For your use case, why is your model devoid of texture? You can try 3D scanning your desired object so that it comes with texture. Either that or use Nvidia's MoMA here to get your object from images.

Have you seen this project: https://github.com/NVlabs/nvdiffrec (I haven't tried it). Also videos tend to have compression. If you can get images you'll get higher quality results with most photogrammetry software. Projects like meshroom are probably better for this if you have high quality pictures. There's a few articles that cover high quality scans that can help also.

You can generate an accurate mesh from a NeRF: https://github.com/NVlabs/nvdiffrec, and measure from that.

NERF specifically generates a radiance field, but there are research codes for turning that into a mesh (https://github.com/NVlabs/nvdiffrec) (not easy to use yet)

Hi, I'm not sure if this is the right place, but I was looking into seeing what the latest photo to model reconstruction looks like from here with NVIDIA (ArXiV paper is included there). There's a couple of neat examples, and after one dumb mistake setup was pretty easy. However, the meshes are not converging except very loosely when using the examples from the paper.

Hi everyone! I'm not sure this is the right place to ask, but I've been drooling over these cool ml and deep learning techniques showcased in videos. I was wondering if anyone could help me out in getting something like nvdiffrec to work with my own sample. https://github.com/NVlabs/nvdiffrec

* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.

- https://diffusionbee.com/

- https://github.com/comfyanonymous/ComfyUI

- https://github.com/AUTOMATIC1111/stable-diffusion-webui

I would love to be able to have a native stable diffusion experience, my rx 580 takes 30s to generate a single image. But it does work after following https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...

I got this up and running on my windows machine in short order and I don't even know what stable diffusion is.

But again, it would be nice to have first class support to locally participate in the fun.

In Auto1111, that just uses Image.blend. :)

https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob...

I used SDXL in automatic1111 webui for both images. Now that I think about it, the procedure I described was how I made this one, but the one that looks like an illustration was done in two steps. I used the canny ControlNet as I said for the outer part of the logo to preserve the shape of the fonts, but I had to turn it off for the boot to give SDXL leeway to add detail and make it look more like a boot.

That said, please do learn coding and don't get discouraged when somebody says to learn PyTorch or recommends using a Jupiter notebook with no further information on how to translate the skill into images. I would highly recommend some short term goals. Get your feet wet by taking apart the UIs. The comfy API documentation is here and the A1111 API documentation is here. There is a difference in completeness, welcome to programming. Writing nodes or plugins is also a good way to jump into this world. Custom wildcard logic might be very attractive to you if you aren't the type that want to deal with a nested file structure to simulate logic.

Possibly Unprompted? https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8094

version: v1.6.1  •  python: 3.10.13  •  torch: 2.0.1+cu118  •  xformers: 0.0.20  •  gradio: 3.41.2  •  checkpoint: 6e8d4871f8