Comments (2)
I've noticed this as well - curious as to why the depth was clipped too!
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During the fine-tuning process, Stable Diffusion quickly adapts its latents to be within the range of [-1,1] after decoding. One can plot a histogram of the depth values of the decoded generated depth maps and see that the overwhelming majority of the depth map distribution is bound between [-1,1]. The few depth values outside of these ranges can be considered outliers. If not clipped, extreme outliers may lead to a squishing of the objects within the [-1,1] range to accommodate them. I presume that with more training time, the number of outliers will converge to 0.
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Related Issues (20)
- Is code from the Bas-relief available
- Why set NaN depth values to zero on preprocessing? HOT 1
- Request for vkitti_val.tar and vkitti_vis.tar files HOT 1
- How to Manage the Large Hypersim Dataset for Reproduction? HOT 3
- ask for LCM distillation code HOT 2
- Low-Rank(LoRA) training of Marigold
- Multi-GPU Training HOT 1
- Clarification Needed: Training and Inference Pipeline HOT 4
- Any reason for not using vae.std to generate RGB latent? HOT 1
- do you plan to release better and more accurate models for this original marigold? HOT 1
- The purpose of using v_prediction as the target? HOT 1
- where can I get the "output/marigold_base/checkpoint/latest" HOT 1
- The demo 3D looks ok but not match with the predicted depth image HOT 1
- How to organize the vkitti data HOT 1
- train the model on my custom dataset HOT 1
- Unusual slow training speed HOT 6
- About some problems that arise during training HOT 9
- issue with reproducing results in the paper HOT 1
- Is it possible to train the model by feeding it rgb images + 8-bit relative depth maps ? HOT 1
- Muti-GPUs training
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