Certainly! Below are two model card templates for your models: **Stable Diffusion Finetuned** and **PRNet 3D Face Reconstruction**. These model cards can be published on Hugging Face or similar platforms to provide useful information about each model, including usage, limitations, and training details.

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### Model Card: **Stable Diffusion Finetuned**

**Model Name**: `stable-diffusion-finetuned`

#### Model Description:
This is a fine-tuned version of the Stable Diffusion model, a state-of-the-art generative model capable of producing high-quality images from textual descriptions. The model has been fine-tuned on a custom dataset for improved performance in a specific domain.

- **Architecture**: Stable Diffusion
- **Base Model**: Stable Diffusion 1.x (before fine-tuning)
- **Training Data**: Custom dataset of images and corresponding textual descriptions.
- **Purpose**: This model is intended for generating images based on specific domain-related text descriptions (e.g., architecture, landscapes, characters).

#### Model Details:
- **Training**: Fine-tuned using Google Colab with the Stable Diffusion base model. The training used the free quota on Colab and was optimized for generating images based on domain-specific prompts.
- **Optimizations**: The model was fine-tuned for a reduced number of epochs to prevent overfitting and to ensure generalizability across different prompts.

#### Usage:
This model is intended for generating images from text inputs. The quality of generated images may vary based on the input prompt and the specificity of the fine-tuning dataset.

##### Example:
```python
from transformers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained("your-hf-username/stable-diffusion-finetuned")
prompt = "A scenic view of mountains during sunset"
image = pipe(prompt).images[0]
image.show()
```

#### Intended Use:
- **Domain-Specific Image Generation**: Designed to generate images for specific scenarios (e.g., concept art, landscape images, etc.).
- **Text-to-Image**: Works by taking text prompts and producing visually coherent images.

#### Limitations and Risks:
- **Bias in Generation**: Since the model was fine-tuned on a specific dataset, it may produce biased outputs, and its applicability outside the fine-tuned domain may be limited.
- **Sensitive Content**: The model may inadvertently generate inappropriate or unintended imagery depending on the prompt.
- **Performance**: Since the model was trained on limited resources (free Colab), generation may not be as fast or optimized for large-scale use cases.

#### How to Cite:
If you use this model, please cite the original Stable Diffusion authors and mention that this version is fine-tuned for specific tasks:
```
@misc{stable-diffusion-finetuned,
  title={Stable Diffusion Finetuned Model},
  author={Mostafa Aly},
  year={2024},
  howpublished={\url{https://huggingface.co./your-hf-username/stable-diffusion-finetuned}},
}
```