stable diffusion finetuned

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:

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}},
}