Comments (2)
@iiiCpu even i use that Stable-Zero123 it will be again resized to 320 image size , pre trained weights are trained on this
images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) # (6, 3, 320, 320)
are there any parameters which i can use?
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InstantMesh uses Zero123++. And Zero123++, as far as I know, is 256x256 model. Giving better images with higher resolution will do nothing as it is forcefully downscaled to 256x256. So, there's no way to increase quality without switching to Stable-Zero123, which is likely to be 512x512.
Well, you can try train Stable Diffusion to upscale textures based on source image and generated texture. Might even work out somehow.
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Related Issues (20)
- Training time?
- RuntimeError: handle_0 INTERNAL ASSERT FAILED at "../c10/cuda/driver_api.cpp":15, please report a bug to PyTorch.` HOT 2
- not all arguments converted during string formatting log HOT 2
- How to Improve Texture Quality and is there a way to tweak the model generation part by providing the normal maps?
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- Command '['ninja', '-v']' returned non-zero exit status 1 HOT 1
- Loading diffusion model ... Error HOT 1
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- Using other multi view models? HOT 1
- Getting errors when running the demo.
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- Train on nerf of stage 1 with custom dataset HOT 8
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- Fine-Tuning Zero123++ pre-defined range of query image. HOT 2
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- 8GB of models downloading every time I launch app.py
- INTERNAL ASSERT FAILED after generating multiviews
- Training CUDA Out Of Memory error HOT 5
- How to get the quantitative results as reported in the paper HOT 1
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