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.
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- 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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Glossary [optional]
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