| | --- |
| | license: creativeml-openrail-m |
| | tags: |
| | - text-to-image |
| | - stable-diffusion |
| | language: |
| | - en |
| | library_name: diffusers |
| | pipeline_tag: text-to-image |
| | --- |
| | |
| | # Model Card: Stable-Cats-Generator |
| |
|
| | ## Model Information |
| |
|
| | - **Model Name:** Stable-Cats-Generator |
| | - **Model Version:** v1 |
| | - **Model Type:** Image Generation |
| | - **Based on:** Stable Diffusion v2 |
| |
|
| |  |
| |
|
| | ## Model Description |
| |
|
| | Stable-Cats-Generator is an image generation model fine-tuned for generating white cat images based on text prompts. |
| | It is built upon **Stable Diffusion v2** and utilizes a pretrained text encoder (OpenCLIP-ViT/H) for text-to-image generation. |
| | **Stable Diffusion v2** is the latest version of the Stable Diffusion text-to-image diffusion model. |
| | It was released in 2023 and is based on the same core principles as the original Stable Diffusion model, but it has a number of improvements |
| |
|
| |
|
| | ## Intended Use |
| |
|
| | - Safe content generation |
| | - Artistic and creative processes |
| | - Bias and limitation exploration |
| | - Educational and creative tools |
| |
|
| | ## Potential Use Cases |
| |
|
| | - Generating cat images for artistic purposes |
| | - Investigating biases and limitations of generative models |
| | - Creating safe and customizable content |
| | - Enhancing educational or creative tools |
| |
|
| | ## Model Capabilities |
| |
|
| | - High-quality white cat image generation |
| | - Quick image generation, even on single GPUs |
| | - Customizable for specific needs and datasets |
| |
|
| | ## Limitations |
| |
|
| | - May not always produce realistic images |
| | - Limited to generating white cat images based on text prompts |
| | - Ethical considerations when using generated content |
| |
|
| | ## Ethical Considerations |
| |
|
| | - Ensure generated content is safe and non-harmful |
| | - Monitor and mitigate potential biases in generated content |
| | - Respect copyright and licensing when using generated images |
| |
|
| |
|
| | ## Responsible AI |
| |
|
| | - Ongoing monitoring and evaluation of model outputs |
| | - Regular updates to address limitations and improve safety |
| | - Compliance with ethical guidelines and legal regulations |
| |
|
| |
|
| | ## Disclaimer |
| |
|
| | This model card serves as a documentation tool and does not constitute legal or ethical guidance. Users of the model are responsible for adhering to ethical and legal standards in their use of the model. |
| |
|
| | ## Usage |
| |
|
| | ``` |
| | pip install diffusers==0.11.1 |
| | pip install transformers scipy ftfy accelerate |
| | ``` |
| | ```python |
| | import torch |
| | from diffusers import StableDiffusionPipeline |
| | |
| | pipe = StableDiffusionPipeline.from_pretrained("ayoubkirouane/Stable-Cats-Generator", torch_dtype=torch.float16) |
| | pipe = pipe.to("cuda") |
| | |
| | prompt = "A photo of a picture-perfect white cat." |
| | image = pipe(prompt).images[0] # image here is in [PIL format](https://pillow.readthedocs.io/en/stable/) |
| | |
| | # Now to display an image you can either save it such as: |
| | image.save(f"cat.png") |
| | |
| | # or if you're in a google colab you can directly display it with |
| | image |
| | |
| | ``` |
| |
|
