All Star Stable Codes That Work

Stable Diffusion v1, a general text-to-image diffusion model, has gained significant attention in recent times due to its impressive capabilities. The model was designed to generate high-quality images from text prompts and has been trained on a massive dataset of 170M examples from LAION-5B with resolution >= 1024x1024.

In this article, we will delve into the details of Stable Diffusion v1, including its architecture, training procedure, and available checkpoints. We will also discuss the limitations and biases of the model and provide guidance on how to use it responsibly.

Architecture

Stable Diffusion v1 is based on a downsampling-factor 8 autoencoder with an 860M UNet text encoder for the diffusion model. The architecture is designed to handle large-scale text-to-image generation tasks and has been optimized for performance on high-resolution images.

Training Procedure

The model was trained using a combination of image-text pairs from LAION2B-en and laion-high-resolution datasets. The training procedure involved generating images at resolutions 256x256 and 512x512, with the latter being used to fine-tune the model on more challenging tasks.

Available Checkpoints

We currently provide several checkpoints for Stable Diffusion v1, including sd-v1-1.ckpt, which has been trained for 237k steps at resolution 256x256 and 94k steps at resolution 512x512. These checkpoints can be used to generate images from text prompts and are available via the CompVis organization at Hugging Face.

License

Stable Diffusion v1 is licensed under the CreativeML OpenRAIL M license, which is an adaptation of the BLOOM Open RAIL license. The license allows for commercial use of the model, but recommends that users implement additional safety mechanisms and considerations to prevent misuse and harm.

Limitations and Biases

While Stable Diffusion v1 has shown impressive capabilities, it is important to note that the model is not perfect and can be biased by its training data. The model may reflect societal biases present in the training data and may not always generate images that are safe or appropriate for all audiences. Therefore, we recommend that users use the model responsibly and with caution.

Stable Diffusion v1 is a powerful text-to-image diffusion model that has been designed to handle large-scale generation tasks. While it has shown impressive capabilities, it is important to note its limitations and biases and use it responsibly. We hope that this article has provided you with a better understanding of the model and its available checkpoints.

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All star stable codes thats work