---
license: mit
library_name: diffusers
---

# Model Card for ControlNet - Hand Depth

<!-- Provide a quick summary of what the model is/does. -->

Finetuned [ControlNet](https://github.com/lllyasviel/ControlNet) - depth from [HandRefiner](https://github.com/wenquanlu/HandRefiner).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642f57bdaa1dd0ebdf3e4aeb/oCGqoHs-2PKvWX6hAUXgI.png)

## Model Details

Model converted from [checkpoint](https://drive.google.com/file/d/1eD2Lnfk0KZols68mVahcVfNx3GnYdHxo/view?usp=sharing) using the following command.
```bash
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

<!-- Provide a longer summary of what this model is. -->



- **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]

<!-- Provide the basic links for the model. -->

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

## Uses

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### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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.

[More Information Needed]

## Training Details

### Training Data

- RHD
- Static Gesture Dataset
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

### Training Procedure

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#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

- 1x NVIDIA A100

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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