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RealVisXL Stable Diffusion XL fine-tuned model for realistic AI art

If you are looking to create fantastically realistic AI art using Stable Diffusion created by Stability AI you would be pleased to know that Realistic Vision has released the SDXL of its find tuned stable Diffusion model, which is still in the testing phase but already producing impressive results.

The base model of Stable Diffusion Excel, even without fine-tuning, has been producing impressive results. This has been made possible by the integration of Stable Diffusion Excel and the new version of Realistic Vision into Diffusion Hub, a platform that embeds Stable Diffusion Automatic 1111. The beauty of Diffusion Hub is that it doesn’t require a powerful computer or advanced coding skills, making it accessible to anyone that would like to create realistic AI art for a wide variety of different reasons.

“The RealVisXL V1.0 model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.”

How to generate realistic AI art using RealVisXL

The SDXL version of the model has been fine-tuned using a checkpoint merge and recommends the use of a variational autoencoder. This autoencoder can be conveniently downloaded from Hacking Face and integrated into the stable diffusion web UI. For those using Diffusion Hub, the SDXL variational encoder is already included, further simplifying the process.

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The quality of the images produced by the SDXL version is noteworthy. The images are impressive in their detail and realism, even without the need for upscaling. The model performs consistently well with different seeds and batch counts, indicating a reliable level of consistency in the results. This is a significant achievement, as the model includes aspect ratio conditioning, which ensures that changing the dimensions doesn’t impact the quality of the output.

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RealVisXL Stable Diffusion XL fine-tuned model for realistic AI art

The SDXL version has shown its prowess in producing detailed and realistic images, including landscapes and portraits. The results are comparable to those of Mid Journey, a testament to the model’s capabilities. However, like any technology in its testing phase, there are still some issues that need to be addressed. For instance, the details of teeth in portraits could be improved.

RealVisXL V1.0

To enhance the details of images, users can utilize the ‘a detailer’ feature in Diffusion Hub. Realistic Vision has shown to perform best with close-up portraits and landscapes, providing users with a range of options for their AI art generation.

It’s important to note that to terminate a session in Diffusion Hub, users must press “terminate” to avoid using up credits. This is a simple yet crucial step to ensure efficient use of the platform.

The SDXL version of Realistic Vision, while still in the beta phase, has shown immense promise. Further improvements are expected in the next version, which will undoubtedly enhance the capabilities of this fine-tuned Stable Diffusion model. This development marks an exciting time in the field of AI art generation, as the technology continues to evolve and push the boundaries of what is possible.

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John Smith

My John Smith is a seasoned technology writer with a passion for unraveling the complexities of the digital world. With a background in computer science and a keen interest in emerging trends, John has become a sought-after voice in translating intricate technological concepts into accessible and engaging articles.

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