Model Card for ControlNet - Hand Depth

Finetuned ControlNet - depth from HandRefiner.

Model Details

Model converted from checkpoint using the following command.

python3 /workspace/diffusers/scripts/convert_original_controlnet_to_diffusers.py \
    --checkpoint_path inpaint_depth_control.ckpt \
    --original_config_file control_depth_inpaint.yaml \
    --num_in_channels 9 \
    --to_safetensors \
    --dump_path controlnet-depth-hand-converted

Model Description

Developed by: Wenquan Lu et al.
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Model type: ControlNet
Language(s) (NLP): [More Information Needed]
License: MIT
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: https://github.com/wenquanlu/HandRefiner
Paper: https://arxiv.org/abs/2311.17957
Demo [optional]: [More Information Needed]

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

RHD
Static Gesture Dataset

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

1x NVIDIA A100

Hardware

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Software

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Citation [optional]

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