Comments (5)
As far as i understand, you can just pass the empty prompts.
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I cannot understand the fine-tuning, i think it is a process of pre-training for getting the CKPT, and also not find the part corresponding to fine-tuning in the code. I wanna ask for your help, pls.
Fine-tuning means training the pre-trained stable diffusion model on your specific dataset. This repository doesn't contain the training code yet; only the testing/inference code is available.
If you want to train or fine-tune the model on your dataset or any other dataset, I have written a simple training code for Marigold. You can find it on my GitHub here: training code for Marigold.
from marigold.
I cannot understand the fine-tuning, i think it is a process of pre-training for getting the CKPT, and also not find the part corresponding to fine-tuning in the code. I wanna ask for your help, pls.
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Thx, :)
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Training code has been released.
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Related Issues (20)
- Validation Error
- Fine-tune the base model
- What does the consistent value of 721.5377 represent in eigen_test_files_with_gt.txt? HOT 1
- Questions about LCM model training 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 8
- 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
- Data cannot be read during training HOT 1
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