Marigold
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[CVPR 2024 - Oral, Best Paper Award Candidate] Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Thank you for your outstanding work! It is very impressive to deploy a diffusion pipeline into monocular depth estimation. As stated in the paper, the model works for affine-invariance depth...
pass invariance flag through model_index.json support scale-invariant mode
I've been studying the train.py code and I can't figure out this: If we continue resume training from a checkpoint, then we need to retrieve wandb_id from the WAND_ID file...
Hi! I'm trying to train the LCM model. However, the model I trained is worse than the LCM model you released. Could you give more training details about LCM distillation...
Dear Authors, Thank you for presenting this model. It is indeed a good model for downstream tasks. I have used your model for 3D reconstruction on my own data, and...
I am getting the following error in the validation step: AssertionError: Wrong input shape torch.Size([3, 768, 1024]), expected [1, rgb, H, W] In marigold_trainer.py function validate_single_dataset(), the input image is...
I just used the vkitti dataset, RGB data path for autodl - TMP/Marigold - main/vkitti/vkitti tar/vkitti/RGB, The depth data path for autodl - TMP/Marigold - main/vkitti/vkitti tar/vkitti/the depth.When I run...
How to train this code on multi-GPUs?
 My system is Ubantu 20.04.1, and the training environment is GeForce RTX3090, python 3.10, CUDA=11.7. I used the command ```python train.py--config config/train_marigold.yaml --no_wandb ```to start the project and it...